{"text": "/*********************************************************************\n * Software License Agreement (BSD License)\n *\n * Copyright (c) 2015, Rice University\n * All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions\n * are met:\n *\n * * Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n * * Redistributions in binary form must reproduce the above\n * copyright notice, this list of conditions and the following\n * disclaimer in the documentation and/or other materials provided\n * with the distribution.\n * * Neither the name of Rice University nor the names of its\n * contributors may be used to endorse or promote products derived\n * from this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n * \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\n * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n * POSSIBILITY OF SUCH DAMAGE.\n *********************************************************************/\n\n/* Author: Ryan Luna */\n\n#include \n#include \"PlanarManipulator.h\"\n#include \n#include \n\n#define PI boost::math::constants::pi()\n#define TWOPI boost::math::constants::two_pi()\n\nPlanarManipulator::PlanarManipulator(unsigned int numLinks, double linkLength, const std::pair &origin)\n : numLinks_(numLinks), linkLengths_(numLinks_, linkLength)\n{\n baseFrame_ = Eigen::Affine2d::Identity();\n baseFrame_.translation()[0] = origin.first;\n baseFrame_.translation()[1] = origin.second;\n\n hasBounds_.assign(numLinks_, false);\n lowerBounds_.assign(numLinks_, std::numeric_limits::min());\n upperBounds_.assign(numLinks_, std::numeric_limits::max());\n}\n\nPlanarManipulator::PlanarManipulator(unsigned int numLinks, const std::vector &linkLengths,\n const std::pair &origin)\n : numLinks_(numLinks), linkLengths_(linkLengths)\n{\n if (linkLengths_.size() != numLinks)\n {\n std::cerr << \"Length of linkLengths (\" << linkLengths.size() << \") is not equal to the number of links (\"\n << numLinks << \")\" << std::endl;\n throw;\n }\n\n baseFrame_ = Eigen::Affine2d::Identity();\n baseFrame_.translation()[0] = origin.first;\n baseFrame_.translation()[1] = origin.second;\n\n hasBounds_.assign(numLinks_, false);\n lowerBounds_.assign(numLinks_, std::numeric_limits::min());\n upperBounds_.assign(numLinks_, std::numeric_limits::max());\n}\n\nPlanarManipulator::~PlanarManipulator()\n{\n}\n\n// Return the number of links\nunsigned int PlanarManipulator::getNumLinks() const\n{\n return numLinks_;\n}\n\nconst Eigen::Affine2d &PlanarManipulator::getBaseFrame() const\n{\n return baseFrame_;\n}\n\nvoid PlanarManipulator::setBaseFrame(const Eigen::Affine2d &frame)\n{\n baseFrame_ = frame;\n}\n\n// Return the length of each link\nconst std::vector &PlanarManipulator::getLinkLengths() const\n{\n return linkLengths_;\n}\n\n// Set the bounds for link # link\nvoid PlanarManipulator::setBounds(unsigned int link, double low, double high)\n{\n assert(link < hasBounds_.size());\n\n hasBounds_[link] = true;\n lowerBounds_[link] = low;\n upperBounds_[link] = high;\n}\n\n// Set the bounds for ALL links\nvoid PlanarManipulator::setBounds(const std::vector &low, const std::vector &high)\n{\n assert(low.size() == high.size());\n assert(low.size() == numLinks_);\n\n hasBounds_.assign(numLinks_, true);\n lowerBounds_ = low;\n upperBounds_ = high;\n}\n\nbool PlanarManipulator::hasBounds(unsigned int link) const\n{\n return hasBounds_[link];\n}\n\nvoid PlanarManipulator::getBounds(unsigned int link, double &low, double &high) const\n{\n low = lowerBounds_[link];\n high = upperBounds_[link];\n}\nconst std::vector &PlanarManipulator::lowerBounds() const\n{\n return lowerBounds_;\n}\n\nconst std::vector &PlanarManipulator::upperBounds() const\n{\n return upperBounds_;\n}\n\n// Forward kinematics for the given joint configuration. The frames for links\n// 1 through the end-effector are returned.\nvoid PlanarManipulator::FK(const std::vector &joints, std::vector &frames) const\n{\n FK(&joints[0], frames);\n}\n\nvoid PlanarManipulator::FK(const Eigen::VectorXd &joints, std::vector &frames) const\n{\n FK(&joints(0), frames);\n}\n\nvoid PlanarManipulator::FK(const double *joints, std::vector &frames) const\n{\n frames.clear();\n Eigen::Affine2d frame(baseFrame_);\n\n for (unsigned int i = 0; i < numLinks_; ++i)\n {\n // Rotate, then translate. Just like the old gypsy woman said.\n Eigen::Affine2d offset(Eigen::Rotation2Dd(joints[i]) * Eigen::Translation2d(linkLengths_[i], 0));\n frame = frame * offset;\n frames.push_back(frame);\n }\n}\n\nvoid PlanarManipulator::FK(const std::vector &joints, Eigen::Affine2d &eeFrame) const\n{\n FK(&joints[0], eeFrame);\n}\n\nvoid PlanarManipulator::FK(const Eigen::VectorXd &joints, Eigen::Affine2d &eeFrame) const\n{\n FK(&joints(0), eeFrame);\n}\n\nvoid PlanarManipulator::FK(const double *joints, Eigen::Affine2d &eeFrame) const\n{\n eeFrame = baseFrame_;\n\n for (unsigned int i = 0; i < numLinks_; ++i)\n {\n // Rotate, then translate. Just like the old gypsy woman said.\n Eigen::Affine2d offset(Eigen::Rotation2Dd(joints[i]) * Eigen::Translation2d(linkLengths_[i], 0));\n eeFrame = eeFrame * offset;\n }\n}\n\n// Inverse kinematics for the given end effector frame. Only one solution is returned.\n// Returns false if no solution exists to the given pose.\nbool PlanarManipulator::IK(std::vector &solution, const Eigen::Affine2d &eeFrame) const\n{\n std::vector seed(numLinks_, M_PI / 2.0);\n return IK(solution, seed, eeFrame);\n}\n\nbool PlanarManipulator::IK(std::vector &solution, const std::vector &seed,\n const Eigen::Affine2d &desiredFrame) const\n{\n // desired frame is clearly impossible to achieve\n if (infeasible(desiredFrame))\n return false;\n\n // This is the orientation for the end effector\n double angle = acos(desiredFrame.rotation()(0, 0));\n // Due to numerical instability, sometimes acos will return nan if\n // the value is slightly larger than one. Check for this and try the\n // asin of the next value\n if (angle != angle) // a nan is never equal to itself\n angle = asin(desiredFrame.rotation()(0, 1));\n\n // If still nan, return false\n if (angle != angle)\n return false;\n\n // Get the current pose\n Eigen::Affine2d frame;\n FK(seed, frame);\n Eigen::VectorXd current;\n frameToPose(frame, current);\n\n // Get the desired pose\n Eigen::VectorXd desired;\n frameToPose(desiredFrame, desired);\n\n // Compute the error\n Eigen::VectorXd e(desired - current);\n\n Eigen::VectorXd joints(seed.size());\n for (size_t i = 0; i < seed.size(); ++i)\n joints(i) = seed[i];\n\n double alpha = 0.1; // step size\n\n unsigned int iter = 1;\n double eps = 1e-6;\n\n Eigen::MatrixXd jac = Eigen::MatrixXd::Zero(3, numLinks_);\n Eigen::JacobiSVD svd(3, numLinks_, Eigen::ComputeFullU | Eigen::ComputeFullV);\n Eigen::MatrixXd D = Eigen::MatrixXd::Zero(numLinks_, 3);\n\n // Iterate the jacobian\n while (e.norm() > eps)\n {\n // Get jacobian for this joint configuration\n Jacobian(joints, jac);\n\n // Compute inverse of the jacobian\n // Really unstable\n // Eigen::MatrixXd jac_inv = ((jac.transpose() * jac).inverse()) * jac.transpose();\n\n // Moore-Penrose Pseudoinverse\n svd.compute(jac);\n const Eigen::JacobiSVD::SingularValuesType &d = svd.singularValues();\n\n // \"Invert\" the singular value matrix\n for (int i = 0; i < d.size(); ++i)\n if (d(i) > eps)\n D(i, i) = 1.0 / d(i);\n else\n D(i, i) = 0.0;\n\n // Inverse is V*D^-1*U^t\n Eigen::MatrixXd jac_inv = svd.matrixV() * D * svd.matrixU().transpose();\n\n // Get the joint difference\n Eigen::VectorXd delta_theta = jac_inv * e;\n\n // Check for failure\n if (delta_theta(0) != delta_theta(0)) // nan\n {\n // std::cout << jac_inv.matrix() << std::endl;\n return false;\n }\n\n // Increment the current joints by a (small) multiple of the difference\n joints = joints + (alpha * delta_theta);\n\n // Figure out the current EE pose and update e.\n FK(joints, frame);\n frameToPose(frame, current);\n // double n = e.norm();\n e = desired - current;\n iter++;\n\n if (iter > 5000)\n return false; // diverge\n }\n\n // Store the solution. Make sure angles are in range [-pi, pi]\n solution.resize(numLinks_);\n for (size_t i = 0; i < solution.size(); ++i)\n {\n double angle = joints(i);\n while (angle > M_PI)\n angle -= 2.0 * M_PI;\n while (angle < -M_PI)\n angle += 2.0 * M_PI;\n solution[i] = angle;\n }\n\n return true;\n}\n\nbool PlanarManipulator::FABRIK(std::vector &solution, const Eigen::Affine2d &eeFrame, double xyTol,\n double thetaTol) const\n{\n std::vector seed(numLinks_, M_PI / 2.0);\n return FABRIK(solution, seed, eeFrame, xyTol, thetaTol);\n}\n\nbool PlanarManipulator::FABRIK(std::vector &solution, const std::vector &seed,\n const Eigen::Affine2d &desiredFrame, double xyTol, double thetaTol) const\n{\n unsigned int numLinks = getNumLinks(); // have to use this local variable. Compiler error (bug?) in\n // Eigen::Translation2d when I use numLinks_\n\n if (seed.size() != numLinks)\n {\n std::cerr << \"Seed has length \" << seed.size() << \" but there are \" << numLinks << \" links\" << std::endl;\n return false;\n }\n\n // desired frame is clearly impossible to achieve\n if (infeasible(desiredFrame))\n return false;\n\n // copy the seed into the solution\n solution.assign(seed.begin(), seed.end());\n\n // Compute the location of the origin of the last link with the correct rotation\n // This location will not move\n Eigen::Vector2d loc(-linkLengths_.back(), 0);\n loc = desiredFrame * loc;\n\n // The desired orientation of the end effector\n double v = desiredFrame.matrix()(0, 0);\n // Go Go Gadget Numerical Stabilizer!\n if (v < -1.0)\n v = -1.0;\n if (v > 1.0)\n v = 1.0;\n double eeTheta = acos(v);\n if (desiredFrame.matrix()(1, 0) < 0) // sin is negative, so the angle is negative\n eeTheta = -eeTheta;\n\n // The location of each joint, starting with the origin and ending with the end effector\n // For an n-joint chain, jointPositions with have n+1 entries.\n std::vector jointPositions;\n jointPositions.push_back(baseFrame_.translation()); // root position\n\n // Compute the initial locations of each joint\n Eigen::Affine2d frame(baseFrame_);\n for (unsigned int i = 0; i < seed.size(); ++i)\n {\n Eigen::Affine2d offset(Eigen::Rotation2Dd(seed[i]) * Eigen::Translation2d(linkLengths_[i], 0));\n frame = frame * offset;\n jointPositions.push_back(frame.translation());\n }\n\n // Store the errors here\n double xyError = std::numeric_limits::max();\n double thetaError = std::numeric_limits::max();\n\n int numIterations = 0;\n int maxIter = 250; // after this many iterations, declare failure. This is probably WAY too many - tends to\n // converge very fast, after 1-3 iterations\n while ((xyError > xyTol || thetaError > thetaTol) && numIterations++ < maxIter)\n {\n xyError = thetaError = 0.0;\n\n // Placing last link where it must go\n jointPositions[numLinks] = desiredFrame.translation(); // goal position, nailed it.\n jointPositions[numLinks - 1] = loc; // goal orientation, nailed it.\n\n // A reverse coordinate frame, working backward along the chain. Used to resolve joint limits in backward phase\n // VERY IMPORTANT to translate first, then rotate. This NOT like the old gypsy woman said. I'm getting my\n // money back.\n Eigen::Affine2d backwardFrame(Eigen::Translation2d(jointPositions[numLinks - 1]) *\n Eigen::Rotation2Dd(PI + eeTheta)); // end of chain, looking down the chain\n\n // Move backward from the end effector toward the base\n for (int i = jointPositions.size() - 3; i >= 0;\n --i) // -1 is end effector position. Skip -2 to preserve the end effector orientation.\n {\n // draw a line between position i+1 and i, and place pt i at the kinematically feasible place along the line\n double t = linkLengths_[i] / (jointPositions[i + 1] - jointPositions[i]).norm();\n jointPositions[i] = (1 - t) * jointPositions[i + 1] + t * jointPositions[i];\n\n // Ensure the angle for joint i is within bounds. If not, move the joint position\n Eigen::Vector2d vec = backwardFrame.inverse() * jointPositions[i];\n double angle = -atan2(vec(1), vec(0)); // The angle computed is the negative of the real angle (because we\n // worked backward).\n\n // If not in bounds, the angle will be one of the joint limits\n if (hasBounds_[i] && (angle < lowerBounds_[i] || angle > upperBounds_[i]))\n {\n double dlow = fabs(angle - lowerBounds_[i]);\n double dhigh = fabs(angle - upperBounds_[i]);\n angle = (dlow < dhigh ? lowerBounds_[i] : upperBounds_[i]);\n }\n\n Eigen::Affine2d offset(Eigen::Rotation2Dd(-angle) * Eigen::Translation2d(linkLengths_[i], 0));\n backwardFrame = backwardFrame * offset;\n jointPositions[i] = backwardFrame.translation();\n }\n\n jointPositions[0] = baseFrame_.translation(); // move base back to where it is supposed to be\n Eigen::Affine2d forwardFrame(baseFrame_);\n\n // Move forward toward the end effector\n for (size_t i = 0; i < jointPositions.size() - 1;\n ++i) // This pass moves everything except end effector. Orientation at the end may be violated, but we\n // check for this\n {\n // draw a line between position i+1 and i and place i+1 at the kinematically feasible place along the line\n double t = linkLengths_[i] / (jointPositions[i + 1] - jointPositions[i]).norm();\n jointPositions[i + 1] = (1 - t) * jointPositions[i] + t * jointPositions[i + 1];\n\n // Figure out the joint angle required for this\n Eigen::Vector2d vec = forwardFrame.inverse() * jointPositions[i + 1];\n double angle = atan2(vec(1), vec(0));\n\n // If not in bounds, the angle will be one of the joint limits\n if (hasBounds_[i] && (angle < lowerBounds_[i] || angle > upperBounds_[i]))\n {\n double dlow = fabs(angle - lowerBounds_[i]);\n double dhigh = fabs(angle - upperBounds_[i]);\n angle = (dlow < dhigh ? lowerBounds_[i] : upperBounds_[i]);\n }\n solution[i] = angle;\n\n // Update frame\n Eigen::Affine2d offset(Eigen::Rotation2Dd(angle) * Eigen::Translation2d(linkLengths_[i], 0));\n forwardFrame = forwardFrame * offset;\n jointPositions[i + 1] = forwardFrame.translation(); // joint location is the translation of the frame. Do\n // this here in case joint limits changed the angle\n }\n\n // Compute translation and orientation error\n xyError = (jointPositions.back() - desiredFrame.translation()).norm();\n\n v = forwardFrame.matrix()(0, 0);\n // Go Go Gadget Numerical Stabilizer!\n if (v > 1.0)\n v = 1.0;\n if (v < -1.0)\n v = -1.0;\n\n // The real angle\n double thetaActual = acos(v);\n if (forwardFrame.matrix()(1, 0) < 0) // sin is negative, so the angle is negative\n thetaActual = -thetaActual;\n thetaError = fabs(eeTheta - thetaActual);\n }\n\n // Winning. Extract joint angles. Oh wait. Already done BOOM.\n if (xyError < xyTol && thetaError < thetaTol)\n return true;\n\n return false;\n}\n\nvoid PlanarManipulator::Jacobian(const double *joints, Eigen::MatrixXd &jac) const\n{\n if (jac.rows() != 3 || jac.cols() != numLinks_)\n jac = Eigen::MatrixXd::Zero(3, numLinks_);\n\n std::vector sins(numLinks_);\n std::vector coss(numLinks_);\n double theta = 0.0;\n for (size_t i = 0; i < numLinks_; ++i)\n {\n theta += joints[i];\n sins[i] = sin(theta);\n coss[i] = cos(theta);\n }\n\n for (size_t i = 0; i < numLinks_; ++i)\n {\n double entry1 = 0.0;\n double entry2 = 0.0;\n for (size_t j = numLinks_; j > i; --j)\n {\n entry1 += -(linkLengths_[j - 1] * sins[j - 1]);\n entry2 += linkLengths_[j - 1] * coss[j - 1];\n }\n jac(0, i) = entry1;\n jac(1, i) = entry2;\n jac(2, i) = 1.0;\n }\n}\n\n// Return the Jacobian for the manipulator at the given joint state.\nvoid PlanarManipulator::Jacobian(const std::vector &joints, Eigen::MatrixXd &jac) const\n{\n Jacobian(&joints[0], jac);\n}\n\nvoid PlanarManipulator::Jacobian(const Eigen::VectorXd &joints, Eigen::MatrixXd &jac) const\n{\n Jacobian(&joints(0), jac); // TODO: This is untested. I dunno if an eigen::vector is contiguous. It probably is.\n}\n\nvoid PlanarManipulator::frameToPose(const Eigen::Affine2d &frame, Eigen::VectorXd &pose)\n{\n pose = Eigen::VectorXd(3);\n pose(0) = frame.translation()(0);\n pose(1) = frame.translation()(1);\n pose(2) = acos(frame.matrix()(0, 0));\n}\n\nbool PlanarManipulator::infeasible(const Eigen::Affine2d &frame) const\n{\n // Check for impossible query\n // Compute the location of the origin of the last link with the correct rotation\n Eigen::Vector2d loc(-linkLengths_[numLinks_ - 1], 0);\n loc = frame * loc;\n\n // Length of the chain, except for the last link\n double len = 0.0;\n for (size_t i = 0; i < numLinks_ - 1; ++i)\n len += linkLengths_[i];\n\n // Make sure the chain (without the last link) is long enough to reach the\n // origin of the last link\n Eigen::Vector2d org(baseFrame_.translation());\n if ((org - loc).norm() > len) // infeasible IK request\n return true;\n\n return false;\n}\n", "meta": {"hexsha": "9b5207bb47396cdaafa3e7659de1d89c11c1bb3c", "size": 19601, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "demos/PlanarManipulator/PlanarManipulator.cpp", "max_stars_repo_name": "ericpairet/ompl", "max_stars_repo_head_hexsha": "25c76431cef25f0100ed74d09dd88944ecca5ee1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 837.0, "max_stars_repo_stars_event_min_datetime": "2015-01-07T12:01:20.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T08:42:42.000Z", "max_issues_repo_path": "demos/PlanarManipulator/PlanarManipulator.cpp", "max_issues_repo_name": "ericpairet/ompl", "max_issues_repo_head_hexsha": "25c76431cef25f0100ed74d09dd88944ecca5ee1", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 271.0, "max_issues_repo_issues_event_min_datetime": "2015-01-12T22:05:06.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T22:16:01.000Z", "max_forks_repo_path": "demos/PlanarManipulator/PlanarManipulator.cpp", "max_forks_repo_name": "ericpairet/ompl", "max_forks_repo_head_hexsha": "25c76431cef25f0100ed74d09dd88944ecca5ee1", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 452.0, "max_forks_repo_forks_event_min_datetime": "2015-02-10T08:48:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-23T06:53:33.000Z", "avg_line_length": 36.5690298507, "max_line_length": 120, "alphanum_fraction": 0.6251211673, "num_tokens": 5010, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.24999510770195077}} {"text": "//transforms_ex.cpp\n/*\n* the transforms from object to shadow and vice versa\n* .implementation.\n* (C) 2005 olegabr. All rights reserved.\n*/\n#include \"transforms_ex.h\"\n#include \"object3d.h\"\n#include \"shadow2d.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \nusing std::cout; using std::endl;\n\nshadow2d& create_mask(const shadow2d& sh, shadow2d& mask)\n{\n\tshadow2d::const_iterator s = sh.begin(), send = sh.end();\n\tshadow2d::iterator m = mask.begin();\n\tfor (; s != send; ++s, ++m)\n\t{\n\t\tsize2d_t sz = m->get_size();\n\t\tprojection2d::const_iterator ps = s->begin(), psend = s->end();\n\t\tint i1(0), i2(0), j1(0), j2(0);\n\t\t{\n\t\t\tprojection1d::const_iterator pps = ps->begin(), ppsend = ps->end();\n\t\t\tfor (; pps != ppsend; ++pps)\n\t\t\t\tif (*pps > 0.) break; else ++i1;\n\t\t}\n\n\t\tfor (; ps != psend; ++ps)\n\t\t\tif (*(ps->end()-1) > 0.) break; else ++i2;\n\n\t\tfor (ps = s->begin(); ps != psend; ++ps, ++j1)\n\t\t\tif (*(ps->begin()) > 0.) break;\n\t\tfor (; ps != psend; ++ps, ++j1)\n\t\t\tif (*(ps->begin()) == 0.) break;\n\n\t\t{\n\t\t\tps = psend-1;\n\t\t\tprojection1d::const_iterator pps = ps->begin(), ppsend = ps->end();\n\t\t\tfor (; pps != ppsend; ++pps, ++j2)\n\t\t\t\tif (*pps > 0.) break;\n\t\t\tfor (; pps != ppsend; ++pps, ++j2)\n\t\t\t\tif (*pps == 0.) break;\n\t\t}\n\n\t\t{\n\t\t\tint offset = 2;\n\t\t\tprojection2d::iterator pm = m->begin(), pmend = m->end();\n\t\t\tpixel2d px(0,0); //i,j\n\t\t\tfor (; pm != pmend; ++pm, ++px.j)\n\t\t\t{\n\t\t\t\tprojection1d::iterator ppm = pm->begin(), ppmend = pm->end();\n\t\t\t\tfor (px.i = 0; ppm != ppmend; ++ppm, ++px.i)\n\t\t\t\t{\n\t\t\t\t\tif\n\t\t\t\t\t(\n\t\t\t\t\t\t(px.i == 0)||\n\t\t\t\t\t\t(px.i == (sz.height - 1))||\n\t\t\t\t\t\t((px.i+px.j) <= i1+offset)||\n\t\t\t\t\t\t((int(sz.height)+px.j-px.i-1) <= i2+offset)||\n\t\t\t\t\t\t((sz.width - 1 + px.i-px.j) <= sz.width - 1 - (j1-offset))||\n\t\t\t\t\t\t((sz.width - 1 + sz.height - 1 - px.i-px.j) <= sz.height - 1 - (j2-offset))\n\t\t\t\t\t) *ppm = 0.;\n\t\t\t\t\telse *ppm = 1.;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn mask;\n}\n\nshadow2d& apply_mask(const shadow2d& mask, shadow2d& sh)\n{\n\t//shadow2d::iterator s = sh.begin(), send = sh.end();\n\t//shadow2d::const_iterator m = mask.begin();\n\t//for (; s != send; ++s, ++m)\n\t//{\n\t//\tprojection2d::iterator ps = s->begin(), psend = s->end();\n\t//\tprojection2d::const_iterator pm = m->begin();\n\t//\tfor (; ps != psend; ++ps, ++pm)\n\t//\t{\n\t//\t\tprojection1d::iterator pps = ps->begin(), ppsend = ps->end();\n\t//\t\tprojection1d::const_iterator ppm = pm->begin();\n\t//\t\tfor (; pps != ppsend; ++pps, ++ppm)\n\t//\t\t\t//if (*ppm < 0.5) *pps = 0.;\n\t//\t\t\t*pps *= *ppm;\n\t//\t}\n\t//}\n\treturn sh;\n}\n\nnamespace {\n\tobject3d::value_type calc_mean(const object3d& o, const pixel3d& px)\n\t{\n\t\tdouble k(1.1);\n\t\tobject3d::value_type value(0);\n\t\tfor (pixel3d d(-1,-1,-1); d.i <= 1; ++d.i)\n\t\t\tfor (d.j = -1; d.j <= 1; ++d.j)\n\t\t\t\tfor (d.k = -1; d.k <= 1; ++d.k)\n\t\t\t\t\tvalue += o(px+d);\n\n\t\treturn k*( value )/27.;\n\t}\n\n\tbool is_hole(const object3d& o, const pixel3d& px)\n\t{\n\t\tconst object3d::value_type z = 0.001;\n\t\tint n(0);\n\t\tfor (pixel3d d(-1,-1,-1); d.i <= 1; ++d.i)\n\t\t\tfor (d.j = -1; d.j <= 1; ++d.j)\n\t\t\t\tfor (d.k = -1; d.k <= 1; ++d.k)\n\t\t\t\t\tn += ((o(px+d) > z) ? 1 : 0);\n\t\treturn\n\t\t(\n\t\t\to(px) < z && /* == 0.*/\n\t\t\tn >= (26 - 23)\n\t\t);\n\t}\n\n\tstruct filled_area\n\t{\n\t\ttypedef std::size_t hash_t;\n\t\ttypedef std::set data_type;\n\n\t\tfilled_area(const size3d_t& sz) : size_(sz)\n\t\t{\n\t\t\t//use size_.depth to store the multiplication for optimization purpose\n\t\t\tsize_.depth = size_.width*size_.height;\n\t\t}\n\t\tfilled_area& add_pixel(const pixel3d& px)\n\t\t{\n\t\t\tdata_.insert(pixel2hash_(px));\n\t\t\treturn *this;\n\t\t}\n\t\tfilled_area& add_area(filled_area& ar)\n\t\t{\n\t\t\tassert(ar.size_ == size_);\n\t\t\tdata_.insert(ar.data_.begin(), ar.data_.end());\n\t\t\tar.data_.clear();\n\t\t\treturn *this;\n\t\t}\n\t\tbool is_adjacent(const pixel3d& px)\n\t\t{\n\t\t\tfor (pixel3d d(-1,-1,-1); d.i <= 1; ++d.i)\n\t\t\t\tfor (d.j = -1; d.j <= 1; ++d.j)\n\t\t\t\t\tfor (d.k = -1; d.k <= 1; ++d.k)\n\t\t\t\t\t{\n\t\t\t\t\t\tif (d == pixel3d()) continue;\n\t\t\t\t\t\tif ( find_pixel_(px+d) ) return true;\n\t\t\t\t\t}\n\t\t\treturn false;\n\t\t}\n\t\tstd::size_t size() const { return data_.size(); }\n\t\tvoid clear(object3d& o, object3d::value_type replace_val)\n\t\t{\n\t\t\tfor (data_type::iterator i(data_.begin()); i != data_.end(); ++i)\n\t\t\t\to(hash2pixel_(*i)) = replace_val;\n\t\t\t//data_.clear();\n\t\t}\n\tprivate :\n\t\thash_t pixel2hash_(const pixel3d& px)\n\t\t{\n\t\t\treturn (px.k*size_.height + px.i)*size_.width + px.j;//ind;\n\t\t}\n\t\tpixel3d hash2pixel_(hash_t ind)\n\t\t{\n\t\t\tpixel3d px;\n\t\t\tpx.k = ind/size_.depth;\n\t\t\tpx.i = (ind - px.k*size_.depth)/size_.width;\n\t\t\tpx.j = ind - px.k*size_.depth - px.i*size_.width;\n\t\t\treturn px;\n\t\t}\n\t\tbool find_pixel_(const pixel3d& px)\n\t\t{\n\t\t\treturn data_.find(pixel2hash_(px)) != data_.end();\n\t\t}\n\t\tdata_type data_;\n\t\tsize3d_t size_;\n\t};\n\n\tbool operator < (const filled_area& lh, const filled_area& rh)\n\t{\n\t\treturn lh.size() < rh.size();\n\t}\n\n\tvoid remove_small_areas(object3d& o, object3d::value_type search_val, object3d::value_type replace_val)\n\t{\n\t\ttypedef std::list areas_type;\n\t\ttypedef std::vector areas_ptrs_type;\n\t\tareas_type areas;\n\t\tsize3d_t sz(o.get_size());\n\t\tlong count(0), COUNT(norm(sz));\n\t\tfor (pixel3d p(0, 0, 0); p.i < sz.height; ++p.i)\n\t\t{\n\t\t\tfor (p.j = 0; p.j < sz.width; ++p.j)\n\t\t\t{\n\t\t\t\tfor (p.k = 0; p.k < sz.depth; ++p.k)\n\t\t\t\t{ ++count;\n\t\t\t\t\tobject3d::value_type v(o(p) - search_val);\n\t\t\t\t\tobject3d::value_type absv(v>0 ? v : -v);\n\t\t\t\t\tif (absv < 0.0001)\n\t\t\t\t\t{\n\t\t\t\t\t\tareas_ptrs_type arp;\n\t\t\t\t\t\tfor (areas_type::iterator a(areas.begin()); a != areas.end(); ++a)\n\t\t\t\t\t\t\tif ( a->is_adjacent(p) ) arp.push_back(a);\n\n\t\t\t\t\t\tif (arp.size() > 1)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\t//cout << \"arp.size = \" << arp.size() << \"\\n\";\n\t\t\t\t\t\t\t//cout << count << '/' << COUNT << \"\\tareas.size = \" << areas.size() << '\\n';\n\t\t\t\t\t\t\tstd::sort(arp.begin(), arp.end(), *boost::lambda::_1 < *boost::lambda::_2);\n\t\t\t\t\t\t\tareas_ptrs_type::iterator a(arp.begin()), big(arp.end()-1), aend(arp.end()-1);\n\t\t\t\t\t\t\tfor (; a != aend; ++a)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t(*big)->add_area(**a);\n\t\t\t\t\t\t\t\tareas.erase(*a);\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t(*big)->add_pixel(p);\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse if (arp.empty())\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\t//cout << \"arp.empty\" << \"\\n\\n\";\n\t\t\t\t\t\t\tfilled_area a(sz);\n\t\t\t\t\t\t\ta.add_pixel(p);\n\t\t\t\t\t\t\tareas.push_back(a);\n\t\t\t\t\t\t\tcout << count << '/' << COUNT << \"\\tareas.size = \" << areas.size() << '\\n';\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse /*arp.size() == 1*/\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\t//cout << \"add_pixel\" << \"\\n\\n\";\n\t\t\t\t\t\t\tarp.front()->add_pixel(p);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tcout << \"areas.size() = \" << areas.size() << endl;\n\t\tareas.sort();\n\t\tareas.reverse();\n\t\tareas.erase(areas.begin());\n\t\tfor (areas_type::iterator a(areas.begin()); a != areas.end(); ++a)\n\t\t\ta->clear(o, replace_val);\n\n\t}\n\tvoid add_mass(object3d& o, object3d::value_type search_val, long V)\n\t{\n\t\t//filled_area area;\n\t\t//size3d_t sz(o.get_size());\n\t\t//long rV(0);\n\t\t//for (pixel3d p(0, 0, 0); p.i < sz.height; ++p.i)\n\t\t//\tfor (p.j = 0; p.j < sz.width; ++p.j)\n\t\t//\t\tfor (p.k = 0; p.k < sz.depth; ++p.k)\n\t\t//\t\t{\n\t\t//\t\t\t++rV;\n\t\t//\t\t\tobject3d::value_type v(o(p) - search_val);\n\t\t//\t\t\tobject3d::value_type absv(v>0 ? v : -v);\n\t\t//\t\t\tif (absv < 0.0001) area.add_pixel(p);\n\t\t//\t\t}\n\t\t//for(; rV < V; ++rV)\n\t\t//{\n\t\t//\tarea.add_pixel_to_border();\n\t\t//}\n\t}\n} //unnamed namespace\n\n/*\n * V = 427437 pixels\n * density = 1.85\n */\nobject3d& apply_filter(const object3d& obj, const point3d& center, object3d& result)\n{\n\tassert(obj.get_size() == result.get_size());\n\tconst pixel3d pc = obj.point2pixel(center);\n\tconst long V = 216600;\n\tconst double d = 1.85;\n\tdouble rd(d); // real density\n\tlong rN(1); // number of pixels in count\n\tresult(pc) = d;\n\tlong rV(1); // number of pixels in new object\n\tconst int rmax(38);\n\tfor (int r(1); r <= rmax; ++r)\n\t{\n\t\t/*\n\t\t * calc main volume\n\t\t */\n\t\t{pixel3d dp(-r, -r, -r);\n\t\t\tfor (; dp.j <= r; ++dp.j)\n\t\t\t{\n\t\t\t\tfor (dp.k = -r; dp.k <= r; ++dp.k)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\trd += obj(px);\n\t\t\t\t\t++rN;\n\t\t\t\t\tif (calc_mean(obj, px) >= (rd/rN))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(r, -r, -r);\n\t\t\tfor (; dp.j <= r; ++dp.j)\n\t\t\t{\n\t\t\t\tfor (dp.k = -r; dp.k <= r; ++dp.k)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\trd += obj(px);\n\t\t\t\t\t++rN;\n\t\t\t\t\tif (calc_mean(obj, px) >= (rd/rN))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(1-r, -r, -r);\n\t\t\tfor (; dp.i < r; ++dp.i)\n\t\t\t{\n\t\t\t\tfor (dp.k = -r; dp.k <= r; ++dp.k)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\trd += obj(px);\n\t\t\t\t\t++rN;\n\t\t\t\t\tif (calc_mean(obj, px) >= (rd/rN))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(1-r, r, -r);\n\t\t\tfor (; dp.i < r; ++dp.i)\n\t\t\t{\n\t\t\t\tfor (dp.k = -r; dp.k <= r; ++dp.k)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\trd += obj(px);\n\t\t\t\t\t++rN;\n\t\t\t\t\tif (calc_mean(obj, px) >= (rd/rN))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(1-r, 1-r, -r);\n\t\t\tfor (; dp.j < r; ++dp.j)\n\t\t\t{\n\t\t\t\tfor (dp.i = 1-r; dp.i < r; ++dp.i)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\trd += obj(px);\n\t\t\t\t\t++rN;\n\t\t\t\t\tif (calc_mean(obj, px) >= (rd/rN))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(1-r, 1-r, r);\n\t\t\tfor (; dp.j < r; ++dp.j)\n\t\t\t{\n\t\t\t\tfor (dp.i = 1-r; dp.i < r; ++dp.i)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\trd += obj(px);\n\t\t\t\t\t++rN;\n\t\t\t\t\tif (calc_mean(obj, px) >= (rd/rN))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t/*\n\t\t * fill holes\n\t\t */\n\t\t--r;\n\t\t{pixel3d dp(-r, -r, -r);\n\t\t\tfor (; dp.j <= r; ++dp.j)\n\t\t\t{\n\t\t\t\tfor (dp.k = 0; dp.k <= r; ++dp.k)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\tif (is_hole(obj, px))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(r, -r, -r);\n\t\t\tfor (; dp.j <= r; ++dp.j)\n\t\t\t{\n\t\t\t\tfor (dp.k = 0; dp.k <= r; ++dp.k)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\tif (is_hole(obj, px))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(1-r, -r, -r);\n\t\t\tfor (; dp.i < r; ++dp.i)\n\t\t\t{\n\t\t\t\tfor (dp.k = 0; dp.k <= r; ++dp.k)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\tif (is_hole(obj, px))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(1-r, r, -r);\n\t\t\tfor (; dp.i < r; ++dp.i)\n\t\t\t{\n\t\t\t\tfor (dp.k = 0; dp.k <= r; ++dp.k)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\tif (is_hole(obj, px))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(1-r, 1-r, -r);\n\t\t\tfor (; dp.j < r; ++dp.j)\n\t\t\t{\n\t\t\t\tfor (dp.i = 0; dp.i < r; ++dp.i)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\tif (is_hole(obj, px))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t{pixel3d dp(1-r, 1-r, r);\n\t\t\tfor (; dp.j < r; ++dp.j)\n\t\t\t{\n\t\t\t\tfor (dp.i = 0; dp.i < r; ++dp.i)\n\t\t\t\t{\n\t\t\t\t\tpixel3d px(pc);\n\t\t\t\t\tpx += dp;\n\t\t\t\t\tif (is_hole(obj, px))\n\t\t\t\t\t{\n\t\t\t\t\t\t++rV;\n\t\t\t\t\t\tresult(px) = d;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t++r;\n\t\t/*\n\t\t * end of fill holes\n\t\t */\n\t\tif (rV >= V) break;\n\t}\n\n\tremove_small_areas(result, d, 0.);\n\tremove_small_areas(result, 0., d);\n\t//add_mass(result, d, V);\n\t//cout << \"new Volume = \" << rV << endl;\n\n\treturn result;\n}\n\n\npoint3d calc_center(const shadow2d& sh)\n{\n\tpoint3d p;\n\tshadow2d::const_iterator pr = sh.begin(), epr = sh.end();\n\tfor (; pr != epr; ++pr)\n\t\tif (abs(pr->get_angle()) <= angle_t(43)) break;\n\tpoint2d p1 = pr->get_mass_center();\n\tangle_t a1 = pr->get_angle();\n\tfor (; pr != epr; ++pr)\n\t\tif (pr->get_angle() == angle_t(0)) break;\n\tpoint2d p2 = pr->get_mass_center();\n\tangle_t a2 = pr->get_angle();\n\n\tp.z = .5*(p1.x + p2.x);\n\tp.y = (p1.y*cos(a2) - p2.y*cos(a1))/(sin(a1)*cos(a2) - sin(a2)*cos(a1));\n\tp.x = (p1.y - p.y*sin(a1))/cos(a1);\n\n\treturn p;\n}\n\nobject3d& filter_object(object3d& object)\n{\n\tconst int mean = 167;\n\tobject3d::iterator o2d(object.begin()), end1(object.end());\n\tfor (; o2d != end1; ++o2d)\n\t{\n\t\tobject2d::iterator o(o2d->begin()), end2(o2d->end());\n\t\tfor (; o != end2; ++o)\n\t\t{\n\t\t\tdouble d(*o-mean);\n\t\t\t*o = std::pow((d>0?d:0.), 10.);\n\t\t}\n\t}\n\treturn object;\n}\n\nvoid black_threshold(shadow2d& sh, double threshold)\n{\n\tshadow2d::iterator s = sh.begin(), send = sh.end();\n\tfor (; s != send; ++s)\n\t{\n\t\tprojection2d::iterator ps = s->begin(), psend = s->end();\n\t\tfor (; ps != psend; ++ps)\n\t\t{\n\t\t\tprojection1d::iterator pps = ps->begin(), ppsend = ps->end();\n\t\t\tfor (; pps != ppsend; ++pps)\n\t\t\t{\n\t\t\t\tprojection1d::value_type val = *pps;\n\t\t\t\t*pps = (val >= threshold) ? val : 0.;\n\t\t\t}\n\t\t}\n\t}\n}\n", "meta": {"hexsha": "703d04cbd588de68b6f73debc988edb2650ae471", "size": 12295, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3doptim/transforms_ex.cpp", "max_stars_repo_name": "olegabr/tomo3d", "max_stars_repo_head_hexsha": "36ffca69aba8556170ec7330271eb58ebaf7459b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5.0, "max_stars_repo_stars_event_min_datetime": "2016-01-07T12:27:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-03T06:58:42.000Z", "max_issues_repo_path": "3doptim/transforms_ex.cpp", "max_issues_repo_name": "olegabr/tomo3d", "max_issues_repo_head_hexsha": "36ffca69aba8556170ec7330271eb58ebaf7459b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "3doptim/transforms_ex.cpp", "max_forks_repo_name": "olegabr/tomo3d", "max_forks_repo_head_hexsha": "36ffca69aba8556170ec7330271eb58ebaf7459b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2020-12-10T10:22:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-10T10:22:13.000Z", "avg_line_length": 21.8772241993, "max_line_length": 104, "alphanum_fraction": 0.4978446523, "num_tokens": 4704, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.49218813572079556, "lm_q1q2_score": 0.2499389747764833}} {"text": "#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \"maybe_omp.h\"\n#include \n\n#include \"model.h\"\n#include \"propagator.h\"\n#include \"param.h\"\n#include \"neuralClasses.h\"\n#include \"graphClasses.h\"\n#include \"util.h\"\n#include \"multinomial.h\"\n//#include \"gradientCheck.h\"\n\n//#define EIGEN_DONT_PARALLELIZE\n\nusing namespace std;\nusing namespace TCLAP;\nusing namespace Eigen;\nusing namespace boost;\nusing namespace boost::random;\n\nusing namespace nplm;\n\ntypedef unordered_map, double> vector_map;\n\ntypedef long long int data_size_t; // training data can easily exceed 2G instances\n\nint main(int argc, char** argv)\n{ \n param myParam;\n try {\n // program options //\n CmdLine cmd(\"Trains a two-layer neural probabilistic language model.\", ' ' , \"0.1\");\n\n // The options are printed in reverse order\n\n ValueArg unigram_probs_file(\"\", \"unigram_probs_file\", \"Unigram model (deprecated and ignored).\" , false, \"\", \"string\", cmd);\n\n ValueArg num_threads(\"\", \"num_threads\", \"Number of threads. Default: maximum.\", false, 0, \"int\", cmd);\n\n ValueArg final_momentum(\"\", \"final_momentum\", \"Final value of momentum. Default: 0.9.\", false, 0.9, \"double\", cmd);\n ValueArg initial_momentum(\"\", \"initial_momentum\", \"Initial value of momentum. Default: 0.9.\", false, 0.9, \"double\", cmd);\n ValueArg use_momentum(\"\", \"use_momentum\", \"Use momentum (hidden layer weights only). 1 = yes, 0 = no. Default: 0.\", false, 0, \"bool\", cmd);\n\n ValueArg normalization_init(\"\", \"normalization_init\", \"Initial normalization parameter. Default: 0.\", false, 0.0, \"double\", cmd);\n ValueArg normalization(\"\", \"normalization\", \"Learn individual normalization factors during training. 1 = yes, 0 = no. Default: 0.\", false, 0, \"bool\", cmd);\n\n ValueArg num_noise_samples(\"\", \"num_noise_samples\", \"Number of noise samples for noise-contrastive estimation. Default: 25.\", false, 25, \"int\", cmd);\n\n ValueArg L2_reg(\"\", \"L2_reg\", \"L2 regularization strength (hidden layer weights only). Default: 0.\", false, 0.0, \"double\", cmd);\n\n ValueArg learning_rate(\"\", \"learning_rate\", \"Learning rate for stochastic gradient ascent. Default: 0.01.\", false, 0.01, \"double\", cmd);\n\n ValueArg validation_minibatch_size(\"\", \"validation_minibatch_size\", \"Minibatch size for validation. Default: 64.\", false, 64, \"int\", cmd);\n ValueArg minibatch_size(\"\", \"minibatch_size\", \"Minibatch size (for training). Default: 64.\", false, 64, \"int\", cmd);\n\n ValueArg num_epochs(\"\", \"num_epochs\", \"Number of epochs. Default: 10.\", false, 10, \"int\", cmd);\n\n ValueArg init_range(\"\", \"init_range\", \"Maximum (of uniform) or standard deviation (of normal) for initialization. Default: 0.01\", false, 0.01, \"double\", cmd);\n ValueArg init_normal(\"\", \"init_normal\", \"Initialize parameters from a normal distribution. 1 = normal, 0 = uniform. Default: 0.\", false, 0, \"bool\", cmd);\n\n ValueArg loss_function(\"\", \"loss_function\", \"Loss function (log, nce). Default: nce.\", false, \"nce\", \"string\", cmd);\n ValueArg activation_function(\"\", \"activation_function\", \"Activation function (identity, rectifier, tanh, hardtanh). Default: rectifier.\", false, \"rectifier\", \"string\", cmd);\n ValueArg num_hidden(\"\", \"num_hidden\", \"Number of hidden nodes. Default: 100.\", false, 100, \"int\", cmd);\n\n ValueArg share_embeddings(\"\", \"share_embeddings\", \"Share input and output embeddings. 1 = yes, 0 = no. Default: 0.\", false, 0, \"bool\", cmd);\n ValueArg output_embedding_dimension(\"\", \"output_embedding_dimension\", \"Number of output embedding dimensions. Default: 50.\", false, 50, \"int\", cmd);\n ValueArg input_embedding_dimension(\"\", \"input_embedding_dimension\", \"Number of input embedding dimensions. Default: 50.\", false, 50, \"int\", cmd);\n ValueArg embedding_dimension(\"\", \"embedding_dimension\", \"Number of input and output embedding dimensions. Default: none.\", false, -1, \"int\", cmd);\n\n ValueArg vocab_size(\"\", \"vocab_size\", \"Vocabulary size. Default: auto.\", false, 0, \"int\", cmd);\n ValueArg input_vocab_size(\"\", \"input_vocab_size\", \"Vocabulary size. Default: auto.\", false, 0, \"int\", cmd);\n ValueArg output_vocab_size(\"\", \"output_vocab_size\", \"Vocabulary size. Default: auto.\", false, 0, \"int\", cmd);\n ValueArg ngram_size(\"\", \"ngram_size\", \"Size of n-grams. Default: auto.\", false, 0, \"int\", cmd);\n\n ValueArg model_prefix(\"\", \"model_prefix\", \"Prefix for output model files.\" , false, \"\", \"string\", cmd);\n ValueArg words_file(\"\", \"words_file\", \"Vocabulary.\" , false, \"\", \"string\", cmd);\n ValueArg input_words_file(\"\", \"input_words_file\", \"Vocabulary.\" , false, \"\", \"string\", cmd);\n ValueArg output_words_file(\"\", \"output_words_file\", \"Vocabulary.\" , false, \"\", \"string\", cmd);\n ValueArg validation_file(\"\", \"validation_file\", \"Validation data (one numberized example per line).\" , false, \"\", \"string\", cmd);\n ValueArg train_file(\"\", \"train_file\", \"Training data (one numberized example per line).\" , true, \"\", \"string\", cmd);\n\n cmd.parse(argc, argv);\n\n // define program parameters //\n myParam.train_file = train_file.getValue();\n myParam.validation_file = validation_file.getValue();\n myParam.input_words_file = input_words_file.getValue();\n myParam.output_words_file = output_words_file.getValue();\n if (words_file.getValue() != \"\")\n\t myParam.input_words_file = myParam.output_words_file = words_file.getValue();\n\n myParam.model_prefix = model_prefix.getValue();\n\n myParam.ngram_size = ngram_size.getValue();\n myParam.vocab_size = vocab_size.getValue();\n myParam.input_vocab_size = input_vocab_size.getValue();\n myParam.output_vocab_size = output_vocab_size.getValue();\n if (vocab_size.getValue() >= 0)\n\t myParam.input_vocab_size = myParam.output_vocab_size = vocab_size.getValue();\n\n myParam.num_hidden = num_hidden.getValue();\n myParam.activation_function = activation_function.getValue();\n myParam.loss_function = loss_function.getValue();\n\n myParam.num_threads = num_threads.getValue();\n\n myParam.num_noise_samples = num_noise_samples.getValue();\n\n myParam.input_embedding_dimension = input_embedding_dimension.getValue();\n myParam.output_embedding_dimension = output_embedding_dimension.getValue();\n if (embedding_dimension.getValue() >= 0)\n\t myParam.input_embedding_dimension = myParam.output_embedding_dimension = embedding_dimension.getValue();\n\n myParam.minibatch_size = minibatch_size.getValue();\n myParam.validation_minibatch_size = validation_minibatch_size.getValue();\n myParam.num_epochs= num_epochs.getValue();\n myParam.learning_rate = learning_rate.getValue();\n myParam.use_momentum = use_momentum.getValue();\n myParam.share_embeddings = share_embeddings.getValue();\n myParam.normalization = normalization.getValue();\n myParam.initial_momentum = initial_momentum.getValue();\n myParam.final_momentum = final_momentum.getValue();\n myParam.L2_reg = L2_reg.getValue();\n myParam.init_normal= init_normal.getValue();\n myParam.init_range = init_range.getValue();\n myParam.normalization_init = normalization_init.getValue();\n\n cerr << \"Command line: \" << endl;\n cerr << boost::algorithm::join(vector(argv, argv+argc), \" \") << endl;\n\n const string sep(\" Value: \");\n cerr << train_file.getDescription() << sep << train_file.getValue() << endl;\n cerr << validation_file.getDescription() << sep << validation_file.getValue() << endl;\n cerr << input_words_file.getDescription() << sep << input_words_file.getValue() << endl;\n cerr << output_words_file.getDescription() << sep << output_words_file.getValue() << endl;\n cerr << model_prefix.getDescription() << sep << model_prefix.getValue() << endl;\n\n cerr << ngram_size.getDescription() << sep << ngram_size.getValue() << endl;\n cerr << input_vocab_size.getDescription() << sep << input_vocab_size.getValue() << endl;\n cerr << output_vocab_size.getDescription() << sep << output_vocab_size.getValue() << endl;\n\n if (embedding_dimension.getValue() >= 0)\n {\n\t cerr << embedding_dimension.getDescription() << sep << embedding_dimension.getValue() << endl;\n }\n else\n {\n\t cerr << input_embedding_dimension.getDescription() << sep << input_embedding_dimension.getValue() << endl;\n\t cerr << output_embedding_dimension.getDescription() << sep << output_embedding_dimension.getValue() << endl;\n }\n cerr << share_embeddings.getDescription() << sep << share_embeddings.getValue() << endl;\n if (share_embeddings.getValue() && input_embedding_dimension.getValue() != output_embedding_dimension.getValue())\n {\n\t cerr << \"error: sharing input and output embeddings requires that input and output embeddings have same dimension\" << endl;\n\t exit(1);\n }\n\n cerr << num_hidden.getDescription() << sep << num_hidden.getValue() << endl;\n\n if (string_to_activation_function(activation_function.getValue()) == InvalidFunction)\n {\n\t cerr << \"error: invalid activation function: \" << activation_function.getValue() << endl;\n\t exit(1);\n }\n cerr << activation_function.getDescription() << sep << activation_function.getValue() << endl;\n\n if (string_to_loss_function(loss_function.getValue()) == InvalidLoss)\n {\n\t cerr << \"error: invalid loss function: \" << loss_function.getValue() << endl;\n\t exit(1);\n }\n cerr << loss_function.getDescription() << sep << loss_function.getValue() << endl;\n\n cerr << init_normal.getDescription() << sep << init_normal.getValue() << endl;\n cerr << init_range.getDescription() << sep << init_range.getValue() << endl;\n\n cerr << num_epochs.getDescription() << sep << num_epochs.getValue() << endl;\n cerr << minibatch_size.getDescription() << sep << minibatch_size.getValue() << endl;\n if (myParam.validation_file != \"\")\n\t cerr << validation_minibatch_size.getDescription() << sep << validation_minibatch_size.getValue() << endl;\n cerr << learning_rate.getDescription() << sep << learning_rate.getValue() << endl;\n cerr << L2_reg.getDescription() << sep << L2_reg.getValue() << endl;\n\n cerr << num_noise_samples.getDescription() << sep << num_noise_samples.getValue() << endl;\n\n cerr << normalization.getDescription() << sep << normalization.getValue() << endl;\n if (myParam.normalization)\n\t cerr << normalization_init.getDescription() << sep << normalization_init.getValue() << endl;\n\n cerr << use_momentum.getDescription() << sep << use_momentum.getValue() << endl;\n if (myParam.use_momentum)\n {\n\t cerr << initial_momentum.getDescription() << sep << initial_momentum.getValue() << endl;\n\t cerr << final_momentum.getDescription() << sep << final_momentum.getValue() << endl;\n }\n\n cerr << num_threads.getDescription() << sep << num_threads.getValue() << endl;\n\n if (unigram_probs_file.getValue() != \"\")\n {\n\t cerr << \"Note: --unigram_probs_file is deprecated and ignored.\" << endl;\n }\n }\n catch (TCLAP::ArgException &e)\n {\n cerr << \"error: \" << e.error() << \" for arg \" << e.argId() << endl;\n exit(1);\n }\n\n myParam.num_threads = setup_threads(myParam.num_threads);\n int save_threads;\n\n //unsigned seed = std::time(0);\n unsigned seed = 1234; //for testing only\n mt19937 rng(seed);\n\n /////////////////////////READING IN THE TRAINING AND VALIDATION DATA///////////////////\n /////////////////////////////////////////////////////////////////////////////////////\n\n // Read training data\n vector training_data_flat;\n readDataFile(myParam.train_file, myParam.ngram_size, training_data_flat, myParam.minibatch_size);\n data_size_t training_data_size = training_data_flat.size() / myParam.ngram_size;\n cerr << \"Number of training instances: \"<< training_data_size << endl;\n\n Map< Matrix > training_data(training_data_flat.data(), myParam.ngram_size, training_data_size);\n\n // If neither --input_vocab_size nor --input_words_file is given, set input_vocab_size to the maximum word index\n if (myParam.input_vocab_size == 0 and myParam.input_words_file == \"\")\n {\n myParam.input_vocab_size = training_data.topRows(myParam.ngram_size-1).maxCoeff()+1;\n }\n\n // If neither --output_vocab_size nor --output_words_file is given, set output_vocab_size to the maximum word index\n if (myParam.output_vocab_size == 0 and myParam.words_file == \"\")\n {\n myParam.output_vocab_size = training_data.row(myParam.ngram_size-1).maxCoeff()+1;\n }\n\n // Randomly shuffle training data to improve learning\n for (data_size_t i=training_data_size-1; i>0; i--)\n {\n data_size_t j = uniform_int_distribution(0, i-1)(rng);\n\ttraining_data.col(i).swap(training_data.col(j));\n }\n\n // Read validation data\n vector validation_data_flat;\n int validation_data_size = 0;\n \n if (myParam.validation_file != \"\")\n {\n\treadDataFile(myParam.validation_file, myParam.ngram_size, validation_data_flat);\n\tvalidation_data_size = validation_data_flat.size() / myParam.ngram_size;\n\tcerr << \"Number of validation instances: \" << validation_data_size << endl;\n }\n\n Map< Matrix > validation_data(validation_data_flat.data(), myParam.ngram_size, validation_data_size);\n\n ///// Read in vocabulary file. We don't actually use it; it just gets reproduced in the output file\n\n vector input_words;\n if (myParam.input_words_file != \"\")\n {\n readWordsFile(myParam.input_words_file, input_words);\n\tif (myParam.input_vocab_size == 0)\n\t myParam.input_vocab_size = input_words.size();\n }\n\n vector output_words;\n if (myParam.output_words_file != \"\")\n {\n readWordsFile(myParam.output_words_file, output_words);\n\tif (myParam.output_vocab_size == 0)\n\t myParam.output_vocab_size = output_words.size();\n }\n\n ///// Construct unigram model and sampler that will be used for NCE\n\n vector unigram_counts(myParam.output_vocab_size);\n for (data_size_t train_id=0; train_id < training_data_size; train_id++)\n {\n int output_word = training_data(myParam.ngram_size-1, train_id);\n\tunigram_counts[output_word] += 1;\n }\n multinomial unigram (unigram_counts);\n\n ///// Create and initialize the neural network and associated propagators.\n\n model nn(myParam.ngram_size,\n myParam.input_vocab_size,\n myParam.output_vocab_size,\n myParam.input_embedding_dimension,\n\t myParam.num_hidden,\n myParam.output_embedding_dimension,\n myParam.share_embeddings);\n\n nn.initialize(rng, myParam.init_normal, myParam.init_range, -log(myParam.output_vocab_size));\n nn.set_activation_function(string_to_activation_function(myParam.activation_function));\n loss_function_type loss_function = string_to_loss_function(myParam.loss_function);\n\n propagator prop(nn, myParam.minibatch_size);\n propagator prop_validation(nn, myParam.validation_minibatch_size);\n SoftmaxNCELoss > softmax_loss(unigram);\n // normalization parameters\n vector_map c_h, c_h_running_gradient;\n \n ///////////////////////TRAINING THE NEURAL NETWORK////////////////////////////////////\n /////////////////////////////////////////////////////////////////////////////////////\n\n data_size_t num_batches = (training_data_size-1)/myParam.minibatch_size + 1;\n cerr<<\"Number of training minibatches: \"< 0)\n {\n num_validation_batches = (validation_data_size-1)/myParam.validation_minibatch_size+1;\n\tcerr<<\"Number of validation minibatches: \"< context = training_data.block(0,i,ngram_size-1,1);\n\t if (c_h.find(context) == c_h.end())\n\t {\n\t c_h[context] = -myParam.normalization_init;\n\t }\n\t}\n }\n\n for (int epoch=0; epoch minibatch_weights(num_samples, minibatch_size);\n\tMatrix minibatch_samples(num_samples, minibatch_size);\n\tMatrix scores(num_samples, minibatch_size);\n\tMatrix probs(num_samples, minibatch_size);\n\n for(data_size_t batch=0;batch 0 && batch % 10000 == 0)\n {\n\t cerr << batch <<\"...\";\n } \n\n data_size_t minibatch_start_index = minibatch_size * batch;\n int current_minibatch_size = min(static_cast(minibatch_size), training_data_size - minibatch_start_index);\n\t Matrix minibatch = training_data.middleCols(minibatch_start_index, current_minibatch_size);\n\n double adjusted_learning_rate = current_learning_rate/current_minibatch_size;\n //cerr<<\"Adjusted learning rate: \"<fProp(prop.second_hidden_activation_node.fProp_matrix,\n\t\t\t\t\t\t minibatch_samples, scores);\n\t\tstop_timer(4);\n\n\t\t// Apply normalization parameters\n\t\tif (myParam.normalization)\n\t\t{\n\t\t for (int train_id = 0;train_id < current_minibatch_size;train_id++)\n\t\t {\n\t\t\tMatrix context = minibatch.block(0, train_id, ngram_size-1, 1);\n\t\t\tscores.col(train_id).array() += c_h[context];\n\t\t }\n\t\t}\n\n\t\tdouble minibatch_log_likelihood;\n\t\tstart_timer(5);\n\t\tsoftmax_loss.fProp(scores.leftCols(current_minibatch_size), \n\t\t\t\t minibatch_samples,\n\t\t\t\t probs, minibatch_log_likelihood);\n\t\tstop_timer(5);\n\t\tlog_likelihood += minibatch_log_likelihood;\n\n\t\t///// Backward propagation\n\n\t\tstart_timer(6);\n\t\tsoftmax_loss.bProp(probs, minibatch_weights);\n\t\tstop_timer(6);\n\t\t\n\t\t// Update the normalization parameters\n\t\t\n\t\tif (myParam.normalization)\n\t\t{\n\t\t for (int train_id = 0;train_id < current_minibatch_size;train_id++)\n\t\t {\n\t\t\tMatrix context = minibatch.block(0, train_id, ngram_size-1, 1);\n\t\t\tc_h[context] += adjusted_learning_rate * minibatch_weights.col(train_id).sum();\n\t\t }\n\t\t}\n\n\t\t// Be careful of short minibatch\n\t\tprop.bProp(minibatch.topRows(ngram_size-1),\n\t\t\t minibatch_samples.leftCols(current_minibatch_size), \n\t\t\t minibatch_weights.leftCols(current_minibatch_size),\n\t\t\t adjusted_learning_rate, current_momentum, myParam.L2_reg);\n\t }\n\t else if (loss_function == LogLoss)\n\t {\n\t ///// Standard log-likelihood\n\t start_timer(4);\n\t\tprop.output_layer_node.param->fProp(prop.second_hidden_activation_node.fProp_matrix, scores);\n\t\tstop_timer(4);\n\n\t\tdouble minibatch_log_likelihood;\n\t\tstart_timer(5);\n\t\tSoftmaxLogLoss().fProp(scores.leftCols(current_minibatch_size), \n\t\t\t\t minibatch.row(ngram_size-1), \n\t\t\t\t probs, \n\t\t\t\t minibatch_log_likelihood);\n\t\tstop_timer(5);\n\t\tlog_likelihood += minibatch_log_likelihood;\n\n\t\t///// Backward propagation\n\t\t\n\t\tstart_timer(6);\n\t\tSoftmaxLogLoss().bProp(minibatch.row(ngram_size-1).leftCols(current_minibatch_size), \n\t\t\t\t probs.leftCols(current_minibatch_size), \n\t\t\t\t minibatch_weights);\n\t\tstop_timer(6);\n\t\t\n\t\tprop.bProp(minibatch.topRows(ngram_size-1).leftCols(current_minibatch_size),\n\t\t\t minibatch_weights,\n\t\t\t adjusted_learning_rate, current_momentum, myParam.L2_reg);\n\t }\n }\n\tcerr << \"done.\" << endl;\n\n\tif (loss_function == LogLoss)\n\t{\n\t cerr << \"Training log-likelihood: \" << log_likelihood << endl;\n cerr << \" perplexity: \"<< exp(-log_likelihood/training_data_size) << endl;\n\t}\n\telse if (loss_function == NCELoss)\n\t cerr << \"Training NCE log-likelihood: \" << log_likelihood << endl;\n\n current_momentum += momentum_delta;\n\n\t#ifdef USE_CHRONO\n\tcerr << \"Propagation times:\";\n\tfor (int i=0; i(epoch+1), input_words, output_words);\n\t else\n\t nn.write(myParam.model_prefix + \".\" + lexical_cast(epoch+1));\n\t}\n\n if (epoch % 1 == 0 && validation_data_size > 0)\n {\n //////COMPUTING VALIDATION SET PERPLEXITY///////////////////////\n ////////////////////////////////////////////////////////////////\n\n double log_likelihood = 0.0;\n\n\t Matrix scores(output_vocab_size, validation_minibatch_size);\n\t Matrix output_probs(output_vocab_size, validation_minibatch_size);\n\t Matrix minibatch(ngram_size, validation_minibatch_size);\n\n for (int validation_batch =0;validation_batch < num_validation_batches;validation_batch++)\n {\n int validation_minibatch_start_index = validation_minibatch_size * validation_batch;\n\t\tint current_minibatch_size = min(validation_minibatch_size,\n\t\t\t\t\t\t validation_data_size - validation_minibatch_start_index);\n\t\tminibatch.leftCols(current_minibatch_size) = validation_data.middleCols(validation_minibatch_start_index, \n\t\t\t\t\t\t\t\t\t\t\tcurrent_minibatch_size);\n\t\tprop_validation.fProp(minibatch.topRows(ngram_size-1));\n\n\t\t// Do full forward prop through output word embedding layer\n\t\tstart_timer(4);\n\t\tprop_validation.output_layer_node.param->fProp(prop_validation.second_hidden_activation_node.fProp_matrix, scores);\n\t\tstop_timer(4);\n\n\t\t// And softmax and loss. Be careful of short minibatch\n\t\tdouble minibatch_log_likelihood;\n\t\tstart_timer(5);\n\t\tSoftmaxLogLoss().fProp(scores.leftCols(current_minibatch_size), \n\t\t\t\t minibatch.row(ngram_size-1),\n\t\t\t\t output_probs,\n\t\t\t\t minibatch_log_likelihood);\n\t\tstop_timer(5);\n\t\tlog_likelihood += minibatch_log_likelihood;\n\t }\n\n cerr << \"Validation log-likelihood: \"<< log_likelihood << endl;\n cerr << \" perplexity: \"<< exp(-log_likelihood/validation_data_size) << endl;\n\n\t // If the validation perplexity decreases, halve the learning rate.\n if (epoch > 0 && log_likelihood < current_validation_ll)\n { \n current_learning_rate /= 2;\n }\n current_validation_ll = log_likelihood;\n\t}\n\n }\n return 0;\n}\n", "meta": {"hexsha": "f45bc7141e7a5313a6a77960e8d4a64d8cb1aba5", "size": 25394, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/trainNeuralNetwork.cpp", "max_stars_repo_name": "gonzaloiglesiasiglesias/nplm", "max_stars_repo_head_hexsha": "a4a69b83e6ed03e031625a5d3b2e1ab3ad2909ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2015-02-21T10:44:02.000Z", "max_stars_repo_stars_event_max_datetime": "2015-02-21T10:44:02.000Z", "max_issues_repo_path": "nplm/src/trainNeuralNetwork.cpp", "max_issues_repo_name": "zaycev/nnsmt", "max_issues_repo_head_hexsha": "a030e51a6679a22b7fdcd03bf2161ee0d08281a7", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "nplm/src/trainNeuralNetwork.cpp", "max_forks_repo_name": "zaycev/nnsmt", "max_forks_repo_head_hexsha": "a030e51a6679a22b7fdcd03bf2161ee0d08281a7", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2015-09-04T13:20:40.000Z", "max_forks_repo_forks_event_max_datetime": "2015-09-04T13:20:40.000Z", "avg_line_length": 43.4085470085, "max_line_length": 187, "alphanum_fraction": 0.6791761834, "num_tokens": 5862, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.37754066879814546, "lm_q1q2_score": 0.24990280524119862}} {"text": "#include \n#include \n#include \n#include \n#include \n\n#include \"structures.h\"\n#include \"transformations.h\"\n#include \"relative_pose_tait_bryan_wc_jacobian.h\"\n#include \"relative_pose_rodrigues_wc_jacobian.h\"\n#include \"relative_pose_quaternion_wc_jacobian.h\"\n#include \"quaternion_constraint_jacobian.h\"\n#include \"relative_pose_wc_jacobian.h\"\n#include \"relative_pose_2_tait_bryan_wc_jacobian.h\"\n#include \"relative_pose_2_rodrigues_wc_jacobian.h\"\n#include \"relative_pose_2_quaternion_wc_jacobian.h\"\n\nconst unsigned int window_width = 1920;\nconst unsigned int window_height = 1080;\nint mouse_old_x, mouse_old_y;\nint mouse_buttons = 0;\nfloat rotate_x = 0.0, rotate_y = 0.0;\nfloat translate_z = -10.0;\nfloat translate_x, translate_y = 0.0;\n\nbool initGL(int *argc, char **argv);\nvoid display();\nvoid keyboard(unsigned char key, int x, int y);\nvoid mouse(int button, int state, int x, int y);\nvoid motion(int x, int y);\nvoid reshape(int w, int h);\nvoid printHelp();\n\nstd::vector m_poses;\nstd::vector m_poses_desired;\n\nstd::vector> odo_edges;\nstd::vector> loop_edges;\n\nint main(int argc, char *argv[]){\n\n\tfor(size_t i = 0 ; i < 100; i++){\n\t\tTaitBryanPose p;\n\t\tp.px = i;\n\t\tp.py = -1;\n\t\tp.pz = 0.0;\n\t\tp.om = ((float(rand()%1000000))/1000000.0f - 0.5) * 0.01;\n\t\tp.fi = ((float(rand()%1000000))/1000000.0f - 0.5) * 0.01;\n\t\tp.ka = ((float(rand()%1000000))/1000000.0f - 0.5) * 0.01;\n\n\t\tEigen::Affine3d m = affine_matrix_from_pose_tait_bryan(p);\n\t\tm_poses.push_back(m);\n\t}\n\tfor(size_t i = 0 ; i < 100; i++){\n\t\tTaitBryanPose p;\n\t\tp.px = i;\n\t\tp.py = 1;\n\t\tp.pz = 0.0;\n\t\tp.om = ((float(rand()%1000000))/1000000.0f - 0.5) * 0.01;\n\t\tp.fi = ((float(rand()%1000000))/1000000.0f - 0.5) * 0.01;\n\t\tp.ka = ((float(rand()%1000000))/1000000.0f - 0.5) * 0.01;\n\n\t\tEigen::Affine3d m = affine_matrix_from_pose_tait_bryan(p);\n\t\tm_poses.push_back(m);\n\t}\n\tm_poses_desired = m_poses;\n\n\tfor(size_t i = 1; i < 100; i++){\n\t\todo_edges.emplace_back(i-1,i);\n\t}\n\n\tfor(size_t i = 101; i < 200; i++){\n\t\todo_edges.emplace_back(i-1,i);\n\t}\n\n\tfor(size_t i = 0; i < 100; i+=10){\n\t\tloop_edges.emplace_back(i,i+100);\n\t}\n\n\tif (false == initGL(&argc, argv)) {\n\t\treturn 4;\n\t}\n\n\tprintHelp();\n\tglutDisplayFunc(display);\n\tglutKeyboardFunc(keyboard);\n\tglutMouseFunc(mouse);\n\tglutMotionFunc(motion);\n\tglutMainLoop();\n\n\treturn 0;\n}\n\n\n\nbool initGL(int *argc, char **argv) {\n\tglutInit(argc, argv);\n\tglutInitDisplayMode(GLUT_RGBA | GLUT_DOUBLE);\n\tglutInitWindowSize(window_width, window_height);\n\tglutCreateWindow(\"relative_pose\");\n\tglutDisplayFunc(display);\n\tglutKeyboardFunc(keyboard);\n\tglutMotionFunc(motion);\n\n\t// default initialization\n\tglClearColor(1.0, 1.0, 1.0, 1.0);\n\tglEnable(GL_DEPTH_TEST);\n\n\t// viewport\n\tglViewport(0, 0, window_width, window_height);\n\n\t// projection\n\tglMatrixMode(GL_PROJECTION);\n\tglLoadIdentity();\n\tgluPerspective(60.0, (GLfloat) window_width / (GLfloat) window_height, 0.01,\n\t\t\t10000.0);\n\tglutReshapeFunc(reshape);\n\n\treturn true;\n}\n\nvoid display() {\n\tglClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);\n\n\tglMatrixMode(GL_MODELVIEW);\n\tglLoadIdentity();\n\tglTranslatef(translate_x, translate_y, translate_z);\n\tglRotatef(rotate_x, 1.0, 0.0, 0.0);\n\tglRotatef(rotate_y, 0.0, 0.0, 1.0);\n\n\tglBegin(GL_LINES);\n\tglColor3f(1.0f, 0.0f, 0.0f);\n\tglVertex3f(0.0f, 0.0f, 0.0f);\n\tglVertex3f(1.0f, 0.0f, 0.0f);\n\n\tglColor3f(0.0f, 1.0f, 0.0f);\n\tglVertex3f(0.0f, 0.0f, 0.0f);\n\tglVertex3f(0.0f, 1.0f, 0.0f);\n\n\tglColor3f(0.0f, 0.0f, 1.0f);\n\tglVertex3f(0.0f, 0.0f, 0.0f);\n\tglVertex3f(0.0f, 0.0f, 1.0f);\n\tglEnd();\n\n\tglColor3f(1,0,0);\n\tglBegin(GL_LINES);\n\tfor(size_t i = 0; i < odo_edges.size(); i++){\n\t\tglVertex3f(m_poses[odo_edges[i].first](0,3), m_poses[odo_edges[i].first](1,3), m_poses[odo_edges[i].first](2,3) );\n\t\tglVertex3f(m_poses[odo_edges[i].second](0,3), m_poses[odo_edges[i].second](1,3), m_poses[odo_edges[i].second](2,3) );\n\t}\n\tglEnd();\n\n\tglColor3f(0,1,0);\n\tglBegin(GL_LINES);\n\tfor(size_t i = 0; i < loop_edges.size(); i++){\n\t\tglVertex3f(m_poses[loop_edges[i].first](0,3), m_poses[loop_edges[i].first](1,3), m_poses[loop_edges[i].first](2,3) );\n\t\tglVertex3f(m_poses[loop_edges[i].second](0,3), m_poses[loop_edges[i].second](1,3), m_poses[loop_edges[i].second](2,3) );\n\t}\n\tglEnd();\n\n\n\tglutSwapBuffers();\n}\n\nvoid keyboard(unsigned char key, int /*x*/, int /*y*/) {\n\tswitch (key) {\n\t\tcase (27): {\n\t\t\tglutDestroyWindow(glutGetWindow());\n\t\t\treturn;\n\t\t}\n\t\tcase 'n':{\n\t\t\tfor(size_t i = 0 ; i < m_poses.size(); i++){\n\t\t\t\tTaitBryanPose pose = pose_tait_bryan_from_affine_matrix(m_poses[i]);\n\t\t\t\tpose.px += ((float(rand()%1000000))/1000000.0f - 0.5) * 0.1;\n\t\t\t\tpose.py += ((float(rand()%1000000))/1000000.0f - 0.5) * 0.1;\n\t\t\t\tpose.pz += ((float(rand()%1000000))/1000000.0f - 0.5) * 0.1;\n\t\t\t\tpose.om += ((float(rand()%1000000))/1000000.0f - 0.5) * 0.01;\n\t\t\t\tpose.fi += ((float(rand()%1000000))/1000000.0f - 0.5) * 0.01;\n\t\t\t\tpose.ka += ((float(rand()%1000000))/1000000.0f - 0.5) * 0.01;\n\t\t\t\tm_poses[i] = affine_matrix_from_pose_tait_bryan(pose);\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n\t\tcase 't':{\n\t\t\tstd::vector> tripletListA;\n\t\t\tstd::vector> tripletListP;\n\t\t\tstd::vector> tripletListB;\n\n\t\t\tstd::vector poses;\n\t\t\tstd::vector poses_desired;\n\n\t\t\tfor(size_t i = 0 ; i < m_poses.size(); i++){\n\t\t\t\tposes.push_back(pose_tait_bryan_from_affine_matrix(m_poses[i]));\n\t\t\t}\n\t\t\tfor(size_t i = 0 ; i < m_poses_desired.size(); i++){\n\t\t\t\tposes_desired.push_back(pose_tait_bryan_from_affine_matrix(m_poses_desired[i]));\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < odo_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_odo;\n\t\t\t\trelative_pose_tait_bryan_wc_case1(relative_pose_measurement_odo,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].om,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].fi,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].ka,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].om,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].fi,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].ka);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_tait_bryan_wc_case1(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].om,\n\t\t\t\t\t\tposes[odo_edges[i].first].fi,\n\t\t\t\t\t\tposes[odo_edges[i].first].ka,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].om,\n\t\t\t\t\t\tposes[odo_edges[i].second].fi,\n\t\t\t\t\t\tposes[odo_edges[i].second].ka,\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_tait_bryan_wc_case1_jacobian(jacobian,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].om,\n\t\t\t\t\t\tposes[odo_edges[i].first].fi,\n\t\t\t\t\t\tposes[odo_edges[i].first].ka,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].om,\n\t\t\t\t\t\tposes[odo_edges[i].second].fi,\n\t\t\t\t\t\tposes[odo_edges[i].second].ka);\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = odo_edges[i].first * 6;\n\t\t\t\tint ic_2 = odo_edges[i].second * 6;\n\n\t\t\t\tfor(size_t row = 0 ; row < 6; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,11));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < loop_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_loop;\n\t\t\t\trelative_pose_tait_bryan_wc_case1(relative_pose_measurement_loop,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].om,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].fi,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].ka,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].om,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].fi,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].ka);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_tait_bryan_wc_case1(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].om,\n\t\t\t\t\t\tposes[loop_edges[i].first].fi,\n\t\t\t\t\t\tposes[loop_edges[i].first].ka,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].om,\n\t\t\t\t\t\tposes[loop_edges[i].second].fi,\n\t\t\t\t\t\tposes[loop_edges[i].second].ka,\n\t\t\t\t\t\trelative_pose_measurement_loop(0,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(1,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(2,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(3,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(4,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(5,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_tait_bryan_wc_case1_jacobian(jacobian,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].om,\n\t\t\t\t\t\tposes[loop_edges[i].first].fi,\n\t\t\t\t\t\tposes[loop_edges[i].first].ka,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].om,\n\t\t\t\t\t\tposes[loop_edges[i].second].fi,\n\t\t\t\t\t\tposes[loop_edges[i].second].ka);\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = loop_edges[i].first * 6;\n\t\t\t\tint ic_2 = loop_edges[i].second * 6;\n\n\t\t\t\tfor(size_t row = 0 ; row < 6; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,11));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t}\n\n\t\t\tint ir = tripletListB.size();\n\t\t\ttripletListA.emplace_back(ir , 0, 1);\n\t\t\ttripletListA.emplace_back(ir + 1 , 1, 1);\n\t\t\ttripletListA.emplace_back(ir + 2 , 2, 1);\n\t\t\ttripletListA.emplace_back(ir + 3 , 3, 1);\n\t\t\ttripletListA.emplace_back(ir + 4 , 4, 1);\n\t\t\ttripletListA.emplace_back(ir + 5 , 5, 1);\n\n\t\t\ttripletListP.emplace_back(ir , ir, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 1 , ir + 1, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 2 , ir + 2, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 3 , ir + 3, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 4 , ir + 4, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 5 , ir + 5, 10000000000000);\n\n\t\t\ttripletListB.emplace_back(ir , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 1 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 2 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 3 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 4 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 5 , 0, 0);\n\n\n\t\t\tEigen::SparseMatrix matA(tripletListB.size(), m_poses.size() * 6);\n\t\t\tEigen::SparseMatrix matP(tripletListB.size(), tripletListB.size());\n\t\t\tEigen::SparseMatrix matB(tripletListB.size(), 1);\n\n\t\t\tmatA.setFromTriplets(tripletListA.begin(), tripletListA.end());\n\t\t\tmatP.setFromTriplets(tripletListP.begin(), tripletListP.end());\n\t\t\tmatB.setFromTriplets(tripletListB.begin(), tripletListB.end());\n\n\n\t\t\tEigen::SparseMatrix AtPA(m_poses.size() * 6 , m_poses.size() * 6);\n\t\t\tEigen::SparseMatrix AtPB(m_poses.size() * 6 , 1);\n\n\t\t\t{\n\t\t\tEigen::SparseMatrix AtP = matA.transpose() * matP;\n\t\t\tAtPA = (AtP) * matA;\n\t\t\tAtPB = (AtP) * matB;\n\t\t\t}\n\n\t\t\ttripletListA.clear();\n\t\t\ttripletListP.clear();\n\t\t\ttripletListB.clear();\n\n\t\t\tstd::cout << \"AtPA.size: \" << AtPA.size() << std::endl;\n\t\t\tstd::cout << \"AtPB.size: \" << AtPB.size() << std::endl;\n\n\t\t\tstd::cout << \"start solving AtPA=AtPB\" << std::endl;\n\t\t\tEigen::SimplicialCholesky> solver(AtPA);\n\n\t\t\tstd::cout << \"x = solver.solve(AtPB)\" << std::endl;\n\t\t\tEigen::SparseMatrix x = solver.solve(AtPB);\n\n\t\t\tstd::vector h_x;\n\n\t\t\tfor (int k=0; k::InnerIterator it(x,k); it; ++it){\n\t\t\t\t\th_x.push_back(it.value());\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tstd::cout << \"h_x.size(): \" << h_x.size() << std::endl;\n\n\t\t\tstd::cout << \"AtPA=AtPB SOLVED\" << std::endl;\n\n\t\t\tfor(size_t i = 0 ; i < h_x.size(); i++){\n\t\t\t\tstd::cout << h_x[i] << std::endl;\n\t\t\t}\n\n\t\t\tif(h_x.size() == 6 * m_poses.size()){\n\t\t\t\tint counter = 0;\n\n\t\t\t\tfor(size_t i = 0; i < m_poses.size(); i++){\n\t\t\t\t\tTaitBryanPose pose = pose_tait_bryan_from_affine_matrix(m_poses[i]);\n\t\t\t\t\tpose.px += h_x[counter++];\n\t\t\t\t\tpose.py += h_x[counter++];\n\t\t\t\t\tpose.pz += h_x[counter++];\n\t\t\t\t\tpose.om += h_x[counter++];\n\t\t\t\t\tpose.fi += h_x[counter++];\n\t\t\t\t\tpose.ka += h_x[counter++];\n\t\t\t\t\tm_poses[i] = affine_matrix_from_pose_tait_bryan(pose);\n\t\t\t\t}\n\t\t\t\tstd::cout << \"optimizing with tait bryan finished\" << std::endl;\n\t\t\t}else{\n\t\t\t\tstd::cout << \"optimizing with tait bryan FAILED\" << std::endl;\n\t\t\t}\n\n\t\t\tbreak;\n\t\t}\n\n\t\tcase 'r':{\n\t\t\tstd::vector> tripletListA;\n\t\t\tstd::vector> tripletListP;\n\t\t\tstd::vector> tripletListB;\n\n\t\t\tstd::vector poses;\n\t\t\tstd::vector poses_desired;\n\n\t\t\tfor(size_t i = 0 ; i < m_poses.size(); i++){\n\t\t\t\tposes.push_back(pose_rodrigues_from_affine_matrix(m_poses[i]));\n\t\t\t}\n\t\t\tfor(size_t i = 0 ; i < m_poses_desired.size(); i++){\n\t\t\t\tposes_desired.push_back(pose_rodrigues_from_affine_matrix(m_poses_desired[i]));\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < odo_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_odo;\n\t\t\t\trelative_pose_rodrigues_wc(relative_pose_measurement_odo,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].sx,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].sy,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].sz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].sx,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].sy,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].sz);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_rodrigues_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].sx,\n\t\t\t\t\t\tposes[odo_edges[i].first].sy,\n\t\t\t\t\t\tposes[odo_edges[i].first].sz,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].sx,\n\t\t\t\t\t\tposes[odo_edges[i].second].sy,\n\t\t\t\t\t\tposes[odo_edges[i].second].sz,\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_rodrigues_wc_jacobian(jacobian,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].sx,\n\t\t\t\t\t\tposes[odo_edges[i].first].sy,\n\t\t\t\t\t\tposes[odo_edges[i].first].sz,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].sx,\n\t\t\t\t\t\tposes[odo_edges[i].second].sy,\n\t\t\t\t\t\tposes[odo_edges[i].second].sz);\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = odo_edges[i].first * 6;\n\t\t\t\tint ic_2 = odo_edges[i].second * 6;\n\n\t\t\t\tfor(size_t row = 0 ; row < 6; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,11));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < loop_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_loop;\n\t\t\t\trelative_pose_rodrigues_wc(relative_pose_measurement_loop,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].sx,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].sy,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].sz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].sx,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].sy,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].sz);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_rodrigues_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].sx,\n\t\t\t\t\t\tposes[loop_edges[i].first].sy,\n\t\t\t\t\t\tposes[loop_edges[i].first].sz,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].sx,\n\t\t\t\t\t\tposes[loop_edges[i].second].sy,\n\t\t\t\t\t\tposes[loop_edges[i].second].sz,\n\t\t\t\t\t\trelative_pose_measurement_loop(0,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(1,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(2,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(3,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(4,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(5,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_rodrigues_wc_jacobian(jacobian,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].sx,\n\t\t\t\t\t\tposes[loop_edges[i].first].sy,\n\t\t\t\t\t\tposes[loop_edges[i].first].sz,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].sx,\n\t\t\t\t\t\tposes[loop_edges[i].second].sy,\n\t\t\t\t\t\tposes[loop_edges[i].second].sz);\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = loop_edges[i].first * 6;\n\t\t\t\tint ic_2 = loop_edges[i].second * 6;\n\n\t\t\t\tfor(size_t row = 0 ; row < 6; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,11));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t}\n\n\t\t\tint ir = tripletListB.size();\n\t\t\ttripletListA.emplace_back(ir , 0, 1);\n\t\t\ttripletListA.emplace_back(ir + 1 , 1, 1);\n\t\t\ttripletListA.emplace_back(ir + 2 , 2, 1);\n\t\t\ttripletListA.emplace_back(ir + 3 , 3, 1);\n\t\t\ttripletListA.emplace_back(ir + 4 , 4, 1);\n\t\t\ttripletListA.emplace_back(ir + 5 , 5, 1);\n\n\t\t\ttripletListP.emplace_back(ir , ir, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 1 , ir + 1, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 2 , ir + 2, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 3 , ir + 3, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 4 , ir + 4, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 5 , ir + 5, 10000000000000);\n\n\t\t\ttripletListB.emplace_back(ir , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 1 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 2 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 3 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 4 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 5 , 0, 0);\n\n\n\t\t\tEigen::SparseMatrix matA(tripletListB.size(), m_poses.size() * 6);\n\t\t\tEigen::SparseMatrix matP(tripletListB.size(), tripletListB.size());\n\t\t\tEigen::SparseMatrix matB(tripletListB.size(), 1);\n\n\t\t\tmatA.setFromTriplets(tripletListA.begin(), tripletListA.end());\n\t\t\tmatP.setFromTriplets(tripletListP.begin(), tripletListP.end());\n\t\t\tmatB.setFromTriplets(tripletListB.begin(), tripletListB.end());\n\n\n\t\t\tEigen::SparseMatrix AtPA(m_poses.size() * 6 , m_poses.size() * 6);\n\t\t\tEigen::SparseMatrix AtPB(m_poses.size() * 6 , 1);\n\n\t\t\t{\n\t\t\tEigen::SparseMatrix AtP = matA.transpose() * matP;\n\t\t\tAtPA = (AtP) * matA;\n\t\t\tAtPB = (AtP) * matB;\n\t\t\t}\n\n\t\t\ttripletListA.clear();\n\t\t\ttripletListP.clear();\n\t\t\ttripletListB.clear();\n\n\t\t\tstd::cout << \"AtPA.size: \" << AtPA.size() << std::endl;\n\t\t\tstd::cout << \"AtPB.size: \" << AtPB.size() << std::endl;\n\n\t\t\tstd::cout << \"start solving AtPA=AtPB\" << std::endl;\n\t\t\tEigen::SimplicialCholesky> solver(AtPA);\n\n\t\t\tstd::cout << \"x = solver.solve(AtPB)\" << std::endl;\n\t\t\tEigen::SparseMatrix x = solver.solve(AtPB);\n\n\t\t\tstd::vector h_x;\n\n\t\t\tfor (int k=0; k::InnerIterator it(x,k); it; ++it){\n\t\t\t\t\th_x.push_back(it.value());\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tstd::cout << \"h_x.size(): \" << h_x.size() << std::endl;\n\n\t\t\tstd::cout << \"AtPA=AtPB SOLVED\" << std::endl;\n\n\t\t\tfor(size_t i = 0 ; i < h_x.size(); i++){\n\t\t\t\tstd::cout << h_x[i] << std::endl;\n\t\t\t}\n\n\t\t\tif(h_x.size() == 6 * m_poses.size()){\n\t\t\t\tint counter = 0;\n\n\t\t\t\tfor(size_t i = 0; i < m_poses.size(); i++){\n\t\t\t\t\tRodriguesPose pose = pose_rodrigues_from_affine_matrix(m_poses[i]);\n\t\t\t\t\tpose.px += h_x[counter++];\n\t\t\t\t\tpose.py += h_x[counter++];\n\t\t\t\t\tpose.pz += h_x[counter++];\n\t\t\t\t\tpose.sx += h_x[counter++];\n\t\t\t\t\tpose.sy += h_x[counter++];\n\t\t\t\t\tpose.sz += h_x[counter++];\n\n\t\t\t\t\tm_poses[i] = affine_matrix_from_pose_rodrigues(pose);\n\n\t\t\t\t\tEigen::Vector3d vx(m_poses[i](0,0), m_poses[i](1,0), m_poses[i](2,0));\n\t\t\t\t\tEigen::Vector3d vy(m_poses[i](0,1), m_poses[i](1,1), m_poses[i](2,1));\n\t\t\t\t\tEigen::Vector3d vz(m_poses[i](0,2), m_poses[i](1,2), m_poses[i](2,2));\n\n\t\t\t\t\tstd::cout << std::setprecision(15);\n\t\t\t\t\tstd::cout << \"norm: \"<< vx.norm() << \" \" << vy.norm() << \" \" << vz.norm() << \" \" <<\n\t\t\t\t\t\t\tvx.dot(vy) << \" \" << vy.dot(vz) << \" \" << vx.dot(vz) << std::endl;\n\t\t\t\t}\n\t\t\t\tstd::cout << \"optimizing with rodrigues finished\" << std::endl;\n\t\t\t}else{\n\t\t\t\tstd::cout << \"optimizing with rodrigues FAILED\" << std::endl;\n\t\t\t}\n\n\t\t\tbreak;\n\t\t}\n\t\tcase 'q':{\n\t\t\tstd::vector> tripletListA;\n\t\t\tstd::vector> tripletListP;\n\t\t\tstd::vector> tripletListB;\n\n\t\t\tstd::vector poses;\n\t\t\tstd::vector poses_desired;\n\n\t\t\tfor(size_t i = 0 ; i < m_poses.size(); i++){\n\t\t\t\tposes.push_back(pose_quaternion_from_affine_matrix(m_poses[i]));\n\t\t\t}\n\t\t\tfor(size_t i = 0 ; i < m_poses_desired.size(); i++){\n\t\t\t\tposes_desired.push_back(pose_quaternion_from_affine_matrix(m_poses_desired[i]));\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < odo_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_odo;\n\t\t\t\trelative_pose_quaternion_wc(relative_pose_measurement_odo,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].q0,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].q1,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].q2,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].q3,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].q0,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].q1,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].q2,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].q3);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_quaternion_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].q0,\n\t\t\t\t\t\tposes[odo_edges[i].first].q1,\n\t\t\t\t\t\tposes[odo_edges[i].first].q2,\n\t\t\t\t\t\tposes[odo_edges[i].first].q3,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].q0,\n\t\t\t\t\t\tposes[odo_edges[i].second].q1,\n\t\t\t\t\t\tposes[odo_edges[i].second].q2,\n\t\t\t\t\t\tposes[odo_edges[i].second].q3,\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(6,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_quaternion_wc_jacobian(jacobian,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].q0,\n\t\t\t\t\t\tposes[odo_edges[i].first].q1,\n\t\t\t\t\t\tposes[odo_edges[i].first].q2,\n\t\t\t\t\t\tposes[odo_edges[i].first].q3,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].q0,\n\t\t\t\t\t\tposes[odo_edges[i].second].q1,\n\t\t\t\t\t\tposes[odo_edges[i].second].q2,\n\t\t\t\t\t\tposes[odo_edges[i].second].q3);\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = odo_edges[i].first * 7;\n\t\t\t\tint ic_2 = odo_edges[i].second * 7;\n\n\t\t\t\tfor(size_t row = 0 ; row < 7; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 6, -jacobian(row,6));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,11));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,12));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 6, -jacobian(row,13));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\t\t\t\ttripletListB.emplace_back(ir + 6, 0, delta(6,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 6, ir + 6, 1);\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < loop_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_loop;\n\t\t\t\trelative_pose_quaternion_wc(relative_pose_measurement_loop,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].q0,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].q1,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].q2,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].q3,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].q0,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].q1,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].q2,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].q3);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_quaternion_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].q0,\n\t\t\t\t\t\tposes[loop_edges[i].first].q1,\n\t\t\t\t\t\tposes[loop_edges[i].first].q2,\n\t\t\t\t\t\tposes[loop_edges[i].first].q3,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].q0,\n\t\t\t\t\t\tposes[loop_edges[i].second].q1,\n\t\t\t\t\t\tposes[loop_edges[i].second].q2,\n\t\t\t\t\t\tposes[loop_edges[i].second].q3,\n\t\t\t\t\t\trelative_pose_measurement_loop(0,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(1,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(2,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(3,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(4,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(5,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(6,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_quaternion_wc_jacobian(jacobian,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].q0,\n\t\t\t\t\t\tposes[loop_edges[i].first].q1,\n\t\t\t\t\t\tposes[loop_edges[i].first].q2,\n\t\t\t\t\t\tposes[loop_edges[i].first].q3,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].q0,\n\t\t\t\t\t\tposes[loop_edges[i].second].q1,\n\t\t\t\t\t\tposes[loop_edges[i].second].q2,\n\t\t\t\t\t\tposes[loop_edges[i].second].q3);\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = loop_edges[i].first * 7;\n\t\t\t\tint ic_2 = loop_edges[i].second * 7;\n\n\t\t\t\tfor(size_t row = 0 ; row < 7; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 6, -jacobian(row,6));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,11));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,12));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 6, -jacobian(row,13));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\t\t\t\ttripletListB.emplace_back(ir + 6, 0, delta(6,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 6, ir + 6, 1);\n\t\t\t}\n\n\t\t\tint ir = tripletListB.size();\n\t\t\ttripletListA.emplace_back(ir , 0, 1);\n\t\t\ttripletListA.emplace_back(ir + 1 , 1, 1);\n\t\t\ttripletListA.emplace_back(ir + 2 , 2, 1);\n\t\t\ttripletListA.emplace_back(ir + 3 , 3, 1);\n\t\t\ttripletListA.emplace_back(ir + 4 , 4, 1);\n\t\t\ttripletListA.emplace_back(ir + 5 , 5, 1);\n\t\t\ttripletListA.emplace_back(ir + 6 , 6, 1);\n\n\t\t\ttripletListP.emplace_back(ir , ir, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 1 , ir + 1, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 2 , ir + 2, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 3 , ir + 3, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 4 , ir + 4, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 5 , ir + 5, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 6 , ir + 6, 10000000000000);\n\n\t\t\ttripletListB.emplace_back(ir , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 1 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 2 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 3 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 4 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 5 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 6 , 0, 0);\n\n\t\t\tfor(size_t i = 0 ; i < m_poses.size(); i++){\n\t\t\t\tint ic = i * 7;\n\t\t\t\tir = tripletListB.size();\n\t\t\t\tQuaternionPose pose = pose_quaternion_from_affine_matrix(m_poses[i]);\n\n\t\t\t\tdouble delta;\n\t\t\t\tquaternion_constraint(delta, pose.q0, pose.q1, pose.q2, pose.q3);\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\tquaternion_constraint_jacobian(jacobian, pose.q0, pose.q1, pose.q2, pose.q3);\n\n\t\t\t\ttripletListA.emplace_back(ir, ic + 3 , -jacobian(0,0));\n\t\t\t\ttripletListA.emplace_back(ir, ic + 4 , -jacobian(0,1));\n\t\t\t\ttripletListA.emplace_back(ir, ic + 5 , -jacobian(0,2));\n\t\t\t\ttripletListA.emplace_back(ir, ic + 6 , -jacobian(0,3));\n\n\t\t\t\ttripletListP.emplace_back(ir, ir, 1000000.0);\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta);\n\t\t\t}\n\n\n\t\t\tEigen::SparseMatrix matA(tripletListB.size(), m_poses.size() * 7);\n\t\t\tEigen::SparseMatrix matP(tripletListB.size(), tripletListB.size());\n\t\t\tEigen::SparseMatrix matB(tripletListB.size(), 1);\n\n\t\t\tmatA.setFromTriplets(tripletListA.begin(), tripletListA.end());\n\t\t\tmatP.setFromTriplets(tripletListP.begin(), tripletListP.end());\n\t\t\tmatB.setFromTriplets(tripletListB.begin(), tripletListB.end());\n\n\n\t\t\tEigen::SparseMatrix AtPA(m_poses.size() * 7 , m_poses.size() * 7);\n\t\t\tEigen::SparseMatrix AtPB(m_poses.size() * 7 , 1);\n\n\t\t\t{\n\t\t\tEigen::SparseMatrix AtP = matA.transpose() * matP;\n\t\t\tAtPA = (AtP) * matA;\n\t\t\tAtPB = (AtP) * matB;\n\t\t\t}\n\n\t\t\ttripletListA.clear();\n\t\t\ttripletListP.clear();\n\t\t\ttripletListB.clear();\n\n\t\t\tstd::cout << \"AtPA.size: \" << AtPA.size() << std::endl;\n\t\t\tstd::cout << \"AtPB.size: \" << AtPB.size() << std::endl;\n\n\t\t\tstd::cout << \"start solving AtPA=AtPB\" << std::endl;\n\t\t\tEigen::SimplicialCholesky> solver(AtPA);\n\n\t\t\tstd::cout << \"x = solver.solve(AtPB)\" << std::endl;\n\t\t\tEigen::SparseMatrix x = solver.solve(AtPB);\n\n\t\t\tstd::vector h_x;\n\n\t\t\tfor (int k=0; k::InnerIterator it(x,k); it; ++it){\n\t\t\t\t\th_x.push_back(it.value());\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tstd::cout << \"h_x.size(): \" << h_x.size() << std::endl;\n\n\t\t\tstd::cout << \"AtPA=AtPB SOLVED\" << std::endl;\n\n\t\t\tfor(size_t i = 0 ; i < h_x.size(); i++){\n\t\t\t\tstd::cout << h_x[i] << std::endl;\n\t\t\t}\n\n\t\t\tif(h_x.size() == 7 * m_poses.size()){\n\t\t\t\tint counter = 0;\n\n\t\t\t\tfor(size_t i = 0; i < m_poses.size(); i++){\n\t\t\t\t\tQuaternionPose pose = pose_quaternion_from_affine_matrix(m_poses[i]);\n\t\t\t\t\tpose.px += h_x[counter++];\n\t\t\t\t\tpose.py += h_x[counter++];\n\t\t\t\t\tpose.pz += h_x[counter++];\n\t\t\t\t\tpose.q0 += h_x[counter++];\n\t\t\t\t\tpose.q1 += h_x[counter++];\n\t\t\t\t\tpose.q2 += h_x[counter++];\n\t\t\t\t\tpose.q3 += h_x[counter++];\n\t\t\t\t\tm_poses[i] = affine_matrix_from_pose_quaternion(pose);\n\t\t\t\t}\n\t\t\t\tstd::cout << \"optimizing with quaternions finished\" << std::endl;\n\t\t\t}else{\n\t\t\t\tstd::cout << \"optimizing with quaternions FAILED\" << std::endl;\n\t\t\t}\n\n\t\t\tbreak;\n\t\t}\n\t\tcase 'x':{\n\t\t\tstd::vector> tripletListA;\n\t\t\tstd::vector> tripletListP;\n\t\t\tstd::vector> tripletListB;\n\n\t\t\tfor(size_t i = 0 ; i < odo_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_odo;\n\t\t\t\trelative_pose_wc(relative_pose_measurement_odo,\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](0,3),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](1,3),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](2,3),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](0,0),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](0,1),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](0,2),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](1,0),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](1,1),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](1,2),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](2,0),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](2,1),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].first](2,2),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](0,3),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](1,3),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](2,3),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](0,0),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](0,1),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](0,2),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](1,0),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](1,1),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](1,2),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](2,0),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](2,1),\n\t\t\t\t\t\tm_poses_desired[odo_edges[i].second](2,2));\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tm_poses[odo_edges[i].first](0,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](1,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](2,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](0,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](0,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](0,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](1,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](1,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](1,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](2,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](2,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](2,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](0,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](1,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](2,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](0,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](0,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](0,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](1,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](1,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](1,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](2,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](2,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](2,2),\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(6,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(7,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(8,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(9,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(10,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(11,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_wc_jacobian(jacobian,\n\t\t\t\t\t\tm_poses[odo_edges[i].first](0,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](1,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](2,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](0,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](0,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](0,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](1,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](1,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](1,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](2,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](2,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].first](2,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](0,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](1,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](2,3),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](0,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](0,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](0,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](1,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](1,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](1,2),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](2,0),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](2,1),\n\t\t\t\t\t\tm_poses[odo_edges[i].second](2,2));\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = odo_edges[i].first * 12;\n\t\t\t\tint ic_2 = odo_edges[i].second * 12;\n\n\t\t\t\tfor(size_t row = 0 ; row < 12; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 6, -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 7, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 8, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 9, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 10, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 11, -jacobian(row,11));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,12));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,13));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,14));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,15));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,16));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,17));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 6, -jacobian(row,18));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 7, -jacobian(row,19));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 8, -jacobian(row,20));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 9, -jacobian(row,21));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 10, -jacobian(row,22));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 11, -jacobian(row,23));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\t\t\t\ttripletListB.emplace_back(ir + 6, 0, delta(6,0));\n\t\t\t\ttripletListB.emplace_back(ir + 7, 0, delta(7,0));\n\t\t\t\ttripletListB.emplace_back(ir + 8, 0, delta(8,0));\n\t\t\t\ttripletListB.emplace_back(ir + 9, 0, delta(9,0));\n\t\t\t\ttripletListB.emplace_back(ir + 10, 0, delta(10,0));\n\t\t\t\ttripletListB.emplace_back(ir + 11, 0, delta(11,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 6, ir + 6, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 7, ir + 7, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 8, ir + 8, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 9, ir + 9, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 10, ir + 10, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 11, ir + 11, 1);\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < loop_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_loop;\n\t\t\t\trelative_pose_wc(relative_pose_measurement_loop,\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](0,3),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](1,3),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](2,3),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](0,0),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](0,1),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](0,2),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](1,0),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](1,1),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](1,2),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](2,0),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](2,1),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].first](2,2),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](0,3),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](1,3),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](2,3),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](0,0),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](0,1),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](0,2),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](1,0),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](1,1),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](1,2),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](2,0),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](2,1),\n\t\t\t\t\t\tm_poses_desired[loop_edges[i].second](2,2));\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tm_poses[loop_edges[i].first](0,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](1,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](2,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](0,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](0,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](0,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](1,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](1,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](1,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](2,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](2,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](2,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](0,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](1,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](2,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](0,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](0,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](0,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](1,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](1,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](1,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](2,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](2,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](2,2),\n\t\t\t\t\t\trelative_pose_measurement_loop(0,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(1,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(2,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(3,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(4,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(5,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(6,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(7,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(8,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(9,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(10,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(11,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_wc_jacobian(jacobian,\n\t\t\t\t\t\tm_poses[loop_edges[i].first](0,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](1,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](2,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](0,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](0,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](0,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](1,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](1,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](1,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](2,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](2,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].first](2,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](0,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](1,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](2,3),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](0,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](0,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](0,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](1,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](1,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](1,2),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](2,0),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](2,1),\n\t\t\t\t\t\tm_poses[loop_edges[i].second](2,2));\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = loop_edges[i].first * 12;\n\t\t\t\tint ic_2 = loop_edges[i].second * 12;\n\n\t\t\t\tfor(size_t row = 0 ; row < 12; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 6, -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 7, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 8, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 9, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 10, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 11, -jacobian(row,11));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,12));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,13));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,14));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,15));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,16));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,17));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 6, -jacobian(row,18));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 7, -jacobian(row,19));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 8, -jacobian(row,20));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 9, -jacobian(row,21));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 10, -jacobian(row,22));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 11, -jacobian(row,23));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\t\t\t\ttripletListB.emplace_back(ir + 6, 0, delta(6,0));\n\t\t\t\ttripletListB.emplace_back(ir + 7, 0, delta(7,0));\n\t\t\t\ttripletListB.emplace_back(ir + 8, 0, delta(8,0));\n\t\t\t\ttripletListB.emplace_back(ir + 9, 0, delta(9,0));\n\t\t\t\ttripletListB.emplace_back(ir + 10, 0, delta(10,0));\n\t\t\t\ttripletListB.emplace_back(ir + 11, 0, delta(11,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 6, ir + 6, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 7, ir + 7, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 8, ir + 8, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 9, ir + 9, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 10, ir + 10, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 11, ir + 11, 1);\n\t\t\t}\n\n\t\t\tint ir = tripletListB.size();\n\t\t\ttripletListA.emplace_back(ir , 0, 1);\n\t\t\ttripletListA.emplace_back(ir + 1 , 1, 1);\n\t\t\ttripletListA.emplace_back(ir + 2 , 2, 1);\n\t\t\ttripletListA.emplace_back(ir + 3 , 3, 1);\n\t\t\ttripletListA.emplace_back(ir + 4 , 4, 1);\n\t\t\ttripletListA.emplace_back(ir + 5 , 5, 1);\n\t\t\ttripletListA.emplace_back(ir + 6 , 6, 1);\n\t\t\ttripletListA.emplace_back(ir + 7 , 7, 1);\n\t\t\ttripletListA.emplace_back(ir + 8 , 8, 1);\n\t\t\ttripletListA.emplace_back(ir + 9 , 9, 1);\n\t\t\ttripletListA.emplace_back(ir + 10 , 10, 1);\n\t\t\ttripletListA.emplace_back(ir + 11 , 11, 1);\n\n\t\t\ttripletListP.emplace_back(ir , ir, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 1 , ir + 1, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 2 , ir + 2, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 3 , ir + 3, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 4 , ir + 4, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 5 , ir + 5, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 6 , ir + 6, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 7 , ir + 7, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 8 , ir + 8, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 9 , ir + 9, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 10 , ir + 10, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 11 , ir + 11, 10000000000000);\n\n\n\t\t\ttripletListB.emplace_back(ir , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 1 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 2 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 3 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 4 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 5 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 6 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 7 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 8 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 9 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 10 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 11 , 0, 0);\n\n\n\t\t\tEigen::SparseMatrix matA(tripletListB.size(), m_poses.size() * 12);\n\t\t\tEigen::SparseMatrix matP(tripletListB.size(), tripletListB.size());\n\t\t\tEigen::SparseMatrix matB(tripletListB.size(), 1);\n\n\t\t\tmatA.setFromTriplets(tripletListA.begin(), tripletListA.end());\n\t\t\tmatP.setFromTriplets(tripletListP.begin(), tripletListP.end());\n\t\t\tmatB.setFromTriplets(tripletListB.begin(), tripletListB.end());\n\n\n\t\t\tEigen::SparseMatrix AtPA(m_poses.size() * 12 , m_poses.size() * 12);\n\t\t\tEigen::SparseMatrix AtPB(m_poses.size() * 12 , 1);\n\n\t\t\t{\n\t\t\tEigen::SparseMatrix AtP = matA.transpose() * matP;\n\t\t\tAtPA = (AtP) * matA;\n\t\t\tAtPB = (AtP) * matB;\n\t\t\t}\n\n\t\t\ttripletListA.clear();\n\t\t\ttripletListP.clear();\n\t\t\ttripletListB.clear();\n\n\t\t\tstd::cout << \"AtPA.size: \" << AtPA.size() << std::endl;\n\t\t\tstd::cout << \"AtPB.size: \" << AtPB.size() << std::endl;\n\n\t\t\tstd::cout << \"start solving AtPA=AtPB\" << std::endl;\n\t\t\tEigen::SimplicialCholesky> solver(AtPA);\n\n\t\t\tstd::cout << \"x = solver.solve(AtPB)\" << std::endl;\n\t\t\tEigen::SparseMatrix x = solver.solve(AtPB);\n\n\t\t\tstd::vector h_x;\n\n\t\t\tfor (int k=0; k::InnerIterator it(x,k); it; ++it){\n\t\t\t\t\th_x.push_back(it.value());\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tstd::cout << \"h_x.size(): \" << h_x.size() << std::endl;\n\t\t\tstd::cout << \"AtPA=AtPB SOLVED\" << std::endl;\n\n\t\t\tfor(size_t i = 0 ; i < h_x.size(); i++){\n\t\t\t\tstd::cout << h_x[i] << std::endl;\n\t\t\t}\n\n\t\t\tif(h_x.size() == 12 * m_poses.size()){\n\t\t\t\tint counter = 0;\n\n\t\t\t\tfor(size_t i = 0; i < m_poses.size(); i++){\n\t\t\t\t\tm_poses[i](0,3) += h_x[counter++];\n\t\t\t\t\tm_poses[i](1,3) += h_x[counter++];\n\t\t\t\t\tm_poses[i](2,3) += h_x[counter++];\n\t\t\t\t\tm_poses[i](0,0) += h_x[counter++];\n\t\t\t\t\tm_poses[i](0,1) += h_x[counter++];\n\t\t\t\t\tm_poses[i](0,2) += h_x[counter++];\n\t\t\t\t\tm_poses[i](1,0) += h_x[counter++];\n\t\t\t\t\tm_poses[i](1,1) += h_x[counter++];\n\t\t\t\t\tm_poses[i](1,2) += h_x[counter++];\n\t\t\t\t\tm_poses[i](2,0) += h_x[counter++];\n\t\t\t\t\tm_poses[i](2,1) += h_x[counter++];\n\t\t\t\t\tm_poses[i](2,2) += h_x[counter++];\n\n\t\t\t\t\torthogonalize_rotation(m_poses[i]);\n\t\t\t\t}\n\t\t\t\tstd::cout << \"optimizing without rotation matrix parametrization finished\" << std::endl;\n\t\t\t}else{\n\t\t\t\tstd::cout << \"optimizing without rotation matrix parametrization FAILED\" << std::endl;\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n\t\tcase 'a':{\n\t\t\tstd::vector> tripletListA;\n\t\t\tstd::vector> tripletListP;\n\t\t\tstd::vector> tripletListB;\n\n\t\t\tstd::vector poses;\n\t\t\tstd::vector poses_desired;\n\n\t\t\tfor(size_t i = 0 ; i < m_poses.size(); i++){\n\t\t\t\tposes.push_back(pose_tait_bryan_from_affine_matrix(m_poses[i]));\n\t\t\t}\n\t\t\tfor(size_t i = 0 ; i < m_poses_desired.size(); i++){\n\t\t\t\tposes_desired.push_back(pose_tait_bryan_from_affine_matrix(m_poses_desired[i]));\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < odo_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_odo;\n\t\t\t\trelative_pose_tait_bryan_wc_case1(relative_pose_measurement_odo,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].om,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].fi,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].ka,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].om,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].fi,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].ka);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_2_obs_eq_tait_bryan_wc_case1(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].om,\n\t\t\t\t\t\tposes[odo_edges[i].first].fi,\n\t\t\t\t\t\tposes[odo_edges[i].first].ka,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].om,\n\t\t\t\t\t\tposes[odo_edges[i].second].fi,\n\t\t\t\t\t\tposes[odo_edges[i].second].ka,\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_2_obs_eq_tait_bryan_wc_case1_jacobian(jacobian,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].om,\n\t\t\t\t\t\tposes[odo_edges[i].first].fi,\n\t\t\t\t\t\tposes[odo_edges[i].first].ka,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].om,\n\t\t\t\t\t\tposes[odo_edges[i].second].fi,\n\t\t\t\t\t\tposes[odo_edges[i].second].ka,\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0));\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = odo_edges[i].first * 6;\n\t\t\t\tint ic_2 = odo_edges[i].second * 6;\n\n\t\t\t\tfor(size_t row = 0 ; row < 6; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,11));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < loop_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_loop;\n\t\t\t\trelative_pose_tait_bryan_wc_case1(relative_pose_measurement_loop,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].om,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].fi,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].ka,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].om,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].fi,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].ka);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_tait_bryan_wc_case1(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].om,\n\t\t\t\t\t\tposes[loop_edges[i].first].fi,\n\t\t\t\t\t\tposes[loop_edges[i].first].ka,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].om,\n\t\t\t\t\t\tposes[loop_edges[i].second].fi,\n\t\t\t\t\t\tposes[loop_edges[i].second].ka,\n\t\t\t\t\t\trelative_pose_measurement_loop(0,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(1,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(2,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(3,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(4,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(5,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_tait_bryan_wc_case1_jacobian(jacobian,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].om,\n\t\t\t\t\t\tposes[loop_edges[i].first].fi,\n\t\t\t\t\t\tposes[loop_edges[i].first].ka,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].om,\n\t\t\t\t\t\tposes[loop_edges[i].second].fi,\n\t\t\t\t\t\tposes[loop_edges[i].second].ka);\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = loop_edges[i].first * 6;\n\t\t\t\tint ic_2 = loop_edges[i].second * 6;\n\n\t\t\t\tfor(size_t row = 0 ; row < 6; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,11));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t}\n\n\t\t\tint ir = tripletListB.size();\n\t\t\ttripletListA.emplace_back(ir , 0, 1);\n\t\t\ttripletListA.emplace_back(ir + 1 , 1, 1);\n\t\t\ttripletListA.emplace_back(ir + 2 , 2, 1);\n\t\t\ttripletListA.emplace_back(ir + 3 , 3, 1);\n\t\t\ttripletListA.emplace_back(ir + 4 , 4, 1);\n\t\t\ttripletListA.emplace_back(ir + 5 , 5, 1);\n\n\t\t\ttripletListP.emplace_back(ir , ir, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 1 , ir + 1, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 2 , ir + 2, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 3 , ir + 3, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 4 , ir + 4, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 5 , ir + 5, 10000000000000);\n\n\t\t\ttripletListB.emplace_back(ir , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 1 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 2 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 3 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 4 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 5 , 0, 0);\n\n\n\t\t\tEigen::SparseMatrix matA(tripletListB.size(), m_poses.size() * 6);\n\t\t\tEigen::SparseMatrix matP(tripletListB.size(), tripletListB.size());\n\t\t\tEigen::SparseMatrix matB(tripletListB.size(), 1);\n\n\t\t\tmatA.setFromTriplets(tripletListA.begin(), tripletListA.end());\n\t\t\tmatP.setFromTriplets(tripletListP.begin(), tripletListP.end());\n\t\t\tmatB.setFromTriplets(tripletListB.begin(), tripletListB.end());\n\n\n\t\t\tEigen::SparseMatrix AtPA(m_poses.size() * 6 , m_poses.size() * 6);\n\t\t\tEigen::SparseMatrix AtPB(m_poses.size() * 6 , 1);\n\n\t\t\t{\n\t\t\tEigen::SparseMatrix AtP = matA.transpose() * matP;\n\t\t\tAtPA = (AtP) * matA;\n\t\t\tAtPB = (AtP) * matB;\n\t\t\t}\n\n\t\t\ttripletListA.clear();\n\t\t\ttripletListP.clear();\n\t\t\ttripletListB.clear();\n\n\t\t\tstd::cout << \"AtPA.size: \" << AtPA.size() << std::endl;\n\t\t\tstd::cout << \"AtPB.size: \" << AtPB.size() << std::endl;\n\n\t\t\tstd::cout << \"start solving AtPA=AtPB\" << std::endl;\n\t\t\tEigen::SimplicialCholesky> solver(AtPA);\n\n\t\t\tstd::cout << \"x = solver.solve(AtPB)\" << std::endl;\n\t\t\tEigen::SparseMatrix x = solver.solve(AtPB);\n\n\t\t\tstd::vector h_x;\n\n\t\t\tfor (int k=0; k::InnerIterator it(x,k); it; ++it){\n\t\t\t\t\th_x.push_back(it.value());\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tstd::cout << \"h_x.size(): \" << h_x.size() << std::endl;\n\n\t\t\tstd::cout << \"AtPA=AtPB SOLVED\" << std::endl;\n\n\t\t\tfor(size_t i = 0 ; i < h_x.size(); i++){\n\t\t\t\tstd::cout << h_x[i] << std::endl;\n\t\t\t}\n\n\t\t\tif(h_x.size() == 6 * m_poses.size()){\n\t\t\t\tint counter = 0;\n\n\t\t\t\tfor(size_t i = 0; i < m_poses.size(); i++){\n\t\t\t\t\tTaitBryanPose pose = pose_tait_bryan_from_affine_matrix(m_poses[i]);\n\t\t\t\t\tpose.px += h_x[counter++];\n\t\t\t\t\tpose.py += h_x[counter++];\n\t\t\t\t\tpose.pz += h_x[counter++];\n\t\t\t\t\tpose.om += h_x[counter++];\n\t\t\t\t\tpose.fi += h_x[counter++];\n\t\t\t\t\tpose.ka += h_x[counter++];\n\t\t\t\t\tm_poses[i] = affine_matrix_from_pose_tait_bryan(pose);\n\t\t\t\t}\n\t\t\t\tstd::cout << \"optimizing with tait bryan finished\" << std::endl;\n\t\t\t}else{\n\t\t\t\tstd::cout << \"optimizing with tait bryan FAILED\" << std::endl;\n\t\t\t}\n\n\t\t\tbreak;\n\t\t}\n\t\tcase 's':{\n\t\t\tstd::vector> tripletListA;\n\t\t\tstd::vector> tripletListP;\n\t\t\tstd::vector> tripletListB;\n\n\t\t\tstd::vector poses;\n\t\t\tstd::vector poses_desired;\n\n\t\t\tfor(size_t i = 0 ; i < m_poses.size(); i++){\n\t\t\t\tposes.push_back(pose_rodrigues_from_affine_matrix(m_poses[i]));\n\t\t\t}\n\t\t\tfor(size_t i = 0 ; i < m_poses_desired.size(); i++){\n\t\t\t\tposes_desired.push_back(pose_rodrigues_from_affine_matrix(m_poses_desired[i]));\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < odo_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_odo;\n\t\t\t\trelative_pose_rodrigues_wc(relative_pose_measurement_odo,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].sx,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].sy,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].sz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].sx,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].sy,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].sz);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_2_obs_eq_rodrigues_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].sx,\n\t\t\t\t\t\tposes[odo_edges[i].first].sy,\n\t\t\t\t\t\tposes[odo_edges[i].first].sz,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].sx,\n\t\t\t\t\t\tposes[odo_edges[i].second].sy,\n\t\t\t\t\t\tposes[odo_edges[i].second].sz,\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_2_obs_eq_rodrigues_wc_jacobian(jacobian,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].sx,\n\t\t\t\t\t\tposes[odo_edges[i].first].sy,\n\t\t\t\t\t\tposes[odo_edges[i].first].sz,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].sx,\n\t\t\t\t\t\tposes[odo_edges[i].second].sy,\n\t\t\t\t\t\tposes[odo_edges[i].second].sz,\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0));\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = odo_edges[i].first * 6;\n\t\t\t\tint ic_2 = odo_edges[i].second * 6;\n\n\t\t\t\tfor(size_t row = 0 ; row < 6; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,11));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < loop_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_loop;\n\t\t\t\trelative_pose_rodrigues_wc(relative_pose_measurement_loop,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].sx,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].sy,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].sz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].sx,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].sy,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].sz);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_rodrigues_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].sx,\n\t\t\t\t\t\tposes[loop_edges[i].first].sy,\n\t\t\t\t\t\tposes[loop_edges[i].first].sz,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].sx,\n\t\t\t\t\t\tposes[loop_edges[i].second].sy,\n\t\t\t\t\t\tposes[loop_edges[i].second].sz,\n\t\t\t\t\t\trelative_pose_measurement_loop(0,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(1,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(2,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(3,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(4,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(5,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_rodrigues_wc_jacobian(jacobian,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].sx,\n\t\t\t\t\t\tposes[loop_edges[i].first].sy,\n\t\t\t\t\t\tposes[loop_edges[i].first].sz,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].sx,\n\t\t\t\t\t\tposes[loop_edges[i].second].sy,\n\t\t\t\t\t\tposes[loop_edges[i].second].sz);\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = loop_edges[i].first * 6;\n\t\t\t\tint ic_2 = loop_edges[i].second * 6;\n\n\t\t\t\tfor(size_t row = 0 ; row < 6; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,6));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,11));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t}\n\n\t\t\tint ir = tripletListB.size();\n\t\t\ttripletListA.emplace_back(ir , 0, 1);\n\t\t\ttripletListA.emplace_back(ir + 1 , 1, 1);\n\t\t\ttripletListA.emplace_back(ir + 2 , 2, 1);\n\t\t\ttripletListA.emplace_back(ir + 3 , 3, 1);\n\t\t\ttripletListA.emplace_back(ir + 4 , 4, 1);\n\t\t\ttripletListA.emplace_back(ir + 5 , 5, 1);\n\n\t\t\ttripletListP.emplace_back(ir , ir, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 1 , ir + 1, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 2 , ir + 2, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 3 , ir + 3, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 4 , ir + 4, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 5 , ir + 5, 10000000000000);\n\n\t\t\ttripletListB.emplace_back(ir , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 1 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 2 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 3 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 4 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 5 , 0, 0);\n\n\n\t\t\tEigen::SparseMatrix matA(tripletListB.size(), m_poses.size() * 6);\n\t\t\tEigen::SparseMatrix matP(tripletListB.size(), tripletListB.size());\n\t\t\tEigen::SparseMatrix matB(tripletListB.size(), 1);\n\n\t\t\tmatA.setFromTriplets(tripletListA.begin(), tripletListA.end());\n\t\t\tmatP.setFromTriplets(tripletListP.begin(), tripletListP.end());\n\t\t\tmatB.setFromTriplets(tripletListB.begin(), tripletListB.end());\n\n\n\t\t\tEigen::SparseMatrix AtPA(m_poses.size() * 6 , m_poses.size() * 6);\n\t\t\tEigen::SparseMatrix AtPB(m_poses.size() * 6 , 1);\n\n\t\t\t{\n\t\t\tEigen::SparseMatrix AtP = matA.transpose() * matP;\n\t\t\tAtPA = (AtP) * matA;\n\t\t\tAtPB = (AtP) * matB;\n\t\t\t}\n\n\t\t\ttripletListA.clear();\n\t\t\ttripletListP.clear();\n\t\t\ttripletListB.clear();\n\n\t\t\tstd::cout << \"AtPA.size: \" << AtPA.size() << std::endl;\n\t\t\tstd::cout << \"AtPB.size: \" << AtPB.size() << std::endl;\n\n\t\t\tstd::cout << \"start solving AtPA=AtPB\" << std::endl;\n\t\t\tEigen::SimplicialCholesky> solver(AtPA);\n\n\t\t\tstd::cout << \"x = solver.solve(AtPB)\" << std::endl;\n\t\t\tEigen::SparseMatrix x = solver.solve(AtPB);\n\n\t\t\tstd::vector h_x;\n\n\t\t\tfor (int k=0; k::InnerIterator it(x,k); it; ++it){\n\t\t\t\t\th_x.push_back(it.value());\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tstd::cout << \"h_x.size(): \" << h_x.size() << std::endl;\n\n\t\t\tstd::cout << \"AtPA=AtPB SOLVED\" << std::endl;\n\n\t\t\tfor(size_t i = 0 ; i < h_x.size(); i++){\n\t\t\t\tstd::cout << h_x[i] << std::endl;\n\t\t\t}\n\n\t\t\tif(h_x.size() == 6 * m_poses.size()){\n\t\t\t\tint counter = 0;\n\n\t\t\t\tfor(size_t i = 0; i < m_poses.size(); i++){\n\t\t\t\t\tRodriguesPose pose = pose_rodrigues_from_affine_matrix(m_poses[i]);\n\t\t\t\t\tpose.px += h_x[counter++];\n\t\t\t\t\tpose.py += h_x[counter++];\n\t\t\t\t\tpose.pz += h_x[counter++];\n\t\t\t\t\tpose.sx += h_x[counter++];\n\t\t\t\t\tpose.sy += h_x[counter++];\n\t\t\t\t\tpose.sz += h_x[counter++];\n\n\t\t\t\t\tm_poses[i] = affine_matrix_from_pose_rodrigues(pose);\n\n\t\t\t\t\tEigen::Vector3d vx(m_poses[i](0,0), m_poses[i](1,0), m_poses[i](2,0));\n\t\t\t\t\tEigen::Vector3d vy(m_poses[i](0,1), m_poses[i](1,1), m_poses[i](2,1));\n\t\t\t\t\tEigen::Vector3d vz(m_poses[i](0,2), m_poses[i](1,2), m_poses[i](2,2));\n\n\t\t\t\t\tstd::cout << std::setprecision(15);\n\t\t\t\t\tstd::cout << \"norm: \"<< vx.norm() << \" \" << vy.norm() << \" \" << vz.norm() << \" \" <<\n\t\t\t\t\t\t\tvx.dot(vy) << \" \" << vy.dot(vz) << \" \" << vx.dot(vz) << std::endl;\n\t\t\t\t}\n\t\t\t\tstd::cout << \"optimizing with rodrigues finished\" << std::endl;\n\t\t\t}else{\n\t\t\t\tstd::cout << \"optimizing with rodrigues FAILED\" << std::endl;\n\t\t\t}\n\n\t\t\tbreak;\n\t\t}\n\t\tcase 'd':{\n\t\t\tstd::vector> tripletListA;\n\t\t\tstd::vector> tripletListP;\n\t\t\tstd::vector> tripletListB;\n\n\t\t\tstd::vector poses;\n\t\t\tstd::vector poses_desired;\n\n\t\t\tfor(size_t i = 0 ; i < m_poses.size(); i++){\n\t\t\t\tposes.push_back(pose_quaternion_from_affine_matrix(m_poses[i]));\n\t\t\t}\n\t\t\tfor(size_t i = 0 ; i < m_poses_desired.size(); i++){\n\t\t\t\tposes_desired.push_back(pose_quaternion_from_affine_matrix(m_poses_desired[i]));\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < odo_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_odo;\n\t\t\t\trelative_pose_quaternion_wc(relative_pose_measurement_odo,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].q0,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].q1,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].q2,\n\t\t\t\t\t\tposes_desired[odo_edges[i].first].q3,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].q0,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].q1,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].q2,\n\t\t\t\t\t\tposes_desired[odo_edges[i].second].q3);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_2_obs_eq_quaternion_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].q0,\n\t\t\t\t\t\tposes[odo_edges[i].first].q1,\n\t\t\t\t\t\tposes[odo_edges[i].first].q2,\n\t\t\t\t\t\tposes[odo_edges[i].first].q3,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].q0,\n\t\t\t\t\t\tposes[odo_edges[i].second].q1,\n\t\t\t\t\t\tposes[odo_edges[i].second].q2,\n\t\t\t\t\t\tposes[odo_edges[i].second].q3,\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(6,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_2_obs_eq_quaternion_wc_jacobian(jacobian,\n\t\t\t\t\t\tposes[odo_edges[i].first].px,\n\t\t\t\t\t\tposes[odo_edges[i].first].py,\n\t\t\t\t\t\tposes[odo_edges[i].first].pz,\n\t\t\t\t\t\tposes[odo_edges[i].first].q0,\n\t\t\t\t\t\tposes[odo_edges[i].first].q1,\n\t\t\t\t\t\tposes[odo_edges[i].first].q2,\n\t\t\t\t\t\tposes[odo_edges[i].first].q3,\n\t\t\t\t\t\tposes[odo_edges[i].second].px,\n\t\t\t\t\t\tposes[odo_edges[i].second].py,\n\t\t\t\t\t\tposes[odo_edges[i].second].pz,\n\t\t\t\t\t\tposes[odo_edges[i].second].q0,\n\t\t\t\t\t\tposes[odo_edges[i].second].q1,\n\t\t\t\t\t\tposes[odo_edges[i].second].q2,\n\t\t\t\t\t\tposes[odo_edges[i].second].q3,\n\t\t\t\t\t\trelative_pose_measurement_odo(0,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(1,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(2,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(3,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(4,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(5,0),\n\t\t\t\t\t\trelative_pose_measurement_odo(6,0));\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = odo_edges[i].first * 7;\n\t\t\t\tint ic_2 = odo_edges[i].second * 7;\n\n\t\t\t\tfor(size_t row = 0 ; row < 7; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 6, -jacobian(row,6));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,11));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,12));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 6, -jacobian(row,13));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\t\t\t\ttripletListB.emplace_back(ir + 6, 0, delta(6,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 6, ir + 6, 1);\n\t\t\t}\n\n\t\t\tfor(size_t i = 0 ; i < loop_edges.size(); i++){\n\t\t\t\tEigen::Matrix relative_pose_measurement_loop;\n\t\t\t\trelative_pose_quaternion_wc(relative_pose_measurement_loop,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].q0,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].q1,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].q2,\n\t\t\t\t\t\tposes_desired[loop_edges[i].first].q3,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].px,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].py,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].q0,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].q1,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].q2,\n\t\t\t\t\t\tposes_desired[loop_edges[i].second].q3);\n\n\t\t\t\tEigen::Matrix delta;\n\t\t\t\trelative_pose_obs_eq_quaternion_wc(\n\t\t\t\t\t\tdelta,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].q0,\n\t\t\t\t\t\tposes[loop_edges[i].first].q1,\n\t\t\t\t\t\tposes[loop_edges[i].first].q2,\n\t\t\t\t\t\tposes[loop_edges[i].first].q3,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].q0,\n\t\t\t\t\t\tposes[loop_edges[i].second].q1,\n\t\t\t\t\t\tposes[loop_edges[i].second].q2,\n\t\t\t\t\t\tposes[loop_edges[i].second].q3,\n\t\t\t\t\t\trelative_pose_measurement_loop(0,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(1,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(2,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(3,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(4,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(5,0),\n\t\t\t\t\t\trelative_pose_measurement_loop(6,0));\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\trelative_pose_obs_eq_quaternion_wc_jacobian(jacobian,\n\t\t\t\t\t\tposes[loop_edges[i].first].px,\n\t\t\t\t\t\tposes[loop_edges[i].first].py,\n\t\t\t\t\t\tposes[loop_edges[i].first].pz,\n\t\t\t\t\t\tposes[loop_edges[i].first].q0,\n\t\t\t\t\t\tposes[loop_edges[i].first].q1,\n\t\t\t\t\t\tposes[loop_edges[i].first].q2,\n\t\t\t\t\t\tposes[loop_edges[i].first].q3,\n\t\t\t\t\t\tposes[loop_edges[i].second].px,\n\t\t\t\t\t\tposes[loop_edges[i].second].py,\n\t\t\t\t\t\tposes[loop_edges[i].second].pz,\n\t\t\t\t\t\tposes[loop_edges[i].second].q0,\n\t\t\t\t\t\tposes[loop_edges[i].second].q1,\n\t\t\t\t\t\tposes[loop_edges[i].second].q2,\n\t\t\t\t\t\tposes[loop_edges[i].second].q3);\n\n\t\t\t\tint ir = tripletListB.size();\n\n\t\t\t\tint ic_1 = loop_edges[i].first * 7;\n\t\t\t\tint ic_2 = loop_edges[i].second * 7;\n\n\t\t\t\tfor(size_t row = 0 ; row < 7; row ++){\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 , -jacobian(row,0));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 1, -jacobian(row,1));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 2, -jacobian(row,2));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 3, -jacobian(row,3));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 4, -jacobian(row,4));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 5, -jacobian(row,5));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_1 + 6, -jacobian(row,6));\n\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 , -jacobian(row,7));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 1, -jacobian(row,8));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 2, -jacobian(row,9));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 3, -jacobian(row,10));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 4, -jacobian(row,11));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 5, -jacobian(row,12));\n\t\t\t\t\ttripletListA.emplace_back(ir + row, ic_2 + 6, -jacobian(row,13));\n\t\t\t\t}\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta(0,0));\n\t\t\t\ttripletListB.emplace_back(ir + 1, 0, delta(1,0));\n\t\t\t\ttripletListB.emplace_back(ir + 2, 0, delta(2,0));\n\t\t\t\ttripletListB.emplace_back(ir + 3, 0, delta(3,0));\n\t\t\t\ttripletListB.emplace_back(ir + 4, 0, delta(4,0));\n\t\t\t\ttripletListB.emplace_back(ir + 5, 0, delta(5,0));\n\t\t\t\ttripletListB.emplace_back(ir + 6, 0, delta(6,0));\n\n\t\t\t\ttripletListP.emplace_back(ir , ir, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 1, ir + 1, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 2, ir + 2, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 3, ir + 3, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 4, ir + 4, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 5, ir + 5, 1);\n\t\t\t\ttripletListP.emplace_back(ir + 6, ir + 6, 1);\n\t\t\t}\n\n\t\t\tint ir = tripletListB.size();\n\t\t\ttripletListA.emplace_back(ir , 0, 1);\n\t\t\ttripletListA.emplace_back(ir + 1 , 1, 1);\n\t\t\ttripletListA.emplace_back(ir + 2 , 2, 1);\n\t\t\ttripletListA.emplace_back(ir + 3 , 3, 1);\n\t\t\ttripletListA.emplace_back(ir + 4 , 4, 1);\n\t\t\ttripletListA.emplace_back(ir + 5 , 5, 1);\n\t\t\ttripletListA.emplace_back(ir + 6 , 6, 1);\n\n\t\t\ttripletListP.emplace_back(ir , ir, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 1 , ir + 1, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 2 , ir + 2, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 3 , ir + 3, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 4 , ir + 4, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 5 , ir + 5, 10000000000000);\n\t\t\ttripletListP.emplace_back(ir + 6 , ir + 6, 10000000000000);\n\n\t\t\ttripletListB.emplace_back(ir , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 1 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 2 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 3 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 4 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 5 , 0, 0);\n\t\t\ttripletListB.emplace_back(ir + 6 , 0, 0);\n\n\t\t\tfor(size_t i = 0 ; i < m_poses.size(); i++){\n\t\t\t\tint ic = i * 7;\n\t\t\t\tir = tripletListB.size();\n\t\t\t\tQuaternionPose pose = pose_quaternion_from_affine_matrix(m_poses[i]);\n\n\t\t\t\tdouble delta;\n\t\t\t\tquaternion_constraint(delta, pose.q0, pose.q1, pose.q2, pose.q3);\n\n\t\t\t\tEigen::Matrix jacobian;\n\t\t\t\tquaternion_constraint_jacobian(jacobian, pose.q0, pose.q1, pose.q2, pose.q3);\n\n\t\t\t\ttripletListA.emplace_back(ir, ic + 3 , -jacobian(0,0));\n\t\t\t\ttripletListA.emplace_back(ir, ic + 4 , -jacobian(0,1));\n\t\t\t\ttripletListA.emplace_back(ir, ic + 5 , -jacobian(0,2));\n\t\t\t\ttripletListA.emplace_back(ir, ic + 6 , -jacobian(0,3));\n\n\t\t\t\ttripletListP.emplace_back(ir, ir, 1000000.0);\n\n\t\t\t\ttripletListB.emplace_back(ir, 0, delta);\n\t\t\t}\n\n\n\t\t\tEigen::SparseMatrix matA(tripletListB.size(), m_poses.size() * 7);\n\t\t\tEigen::SparseMatrix matP(tripletListB.size(), tripletListB.size());\n\t\t\tEigen::SparseMatrix matB(tripletListB.size(), 1);\n\n\t\t\tmatA.setFromTriplets(tripletListA.begin(), tripletListA.end());\n\t\t\tmatP.setFromTriplets(tripletListP.begin(), tripletListP.end());\n\t\t\tmatB.setFromTriplets(tripletListB.begin(), tripletListB.end());\n\n\n\t\t\tEigen::SparseMatrix AtPA(m_poses.size() * 7 , m_poses.size() * 7);\n\t\t\tEigen::SparseMatrix AtPB(m_poses.size() * 7 , 1);\n\n\t\t\t{\n\t\t\tEigen::SparseMatrix AtP = matA.transpose() * matP;\n\t\t\tAtPA = (AtP) * matA;\n\t\t\tAtPB = (AtP) * matB;\n\t\t\t}\n\n\t\t\ttripletListA.clear();\n\t\t\ttripletListP.clear();\n\t\t\ttripletListB.clear();\n\n\t\t\tstd::cout << \"AtPA.size: \" << AtPA.size() << std::endl;\n\t\t\tstd::cout << \"AtPB.size: \" << AtPB.size() << std::endl;\n\n\t\t\tstd::cout << \"start solving AtPA=AtPB\" << std::endl;\n\t\t\tEigen::SimplicialCholesky> solver(AtPA);\n\n\t\t\tstd::cout << \"x = solver.solve(AtPB)\" << std::endl;\n\t\t\tEigen::SparseMatrix x = solver.solve(AtPB);\n\n\t\t\tstd::vector h_x;\n\n\t\t\tfor (int k=0; k::InnerIterator it(x,k); it; ++it){\n\t\t\t\t\th_x.push_back(it.value());\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tstd::cout << \"h_x.size(): \" << h_x.size() << std::endl;\n\n\t\t\tstd::cout << \"AtPA=AtPB SOLVED\" << std::endl;\n\n\t\t\tfor(size_t i = 0 ; i < h_x.size(); i++){\n\t\t\t\tstd::cout << h_x[i] << std::endl;\n\t\t\t}\n\n\t\t\tif(h_x.size() == 7 * m_poses.size()){\n\t\t\t\tint counter = 0;\n\n\t\t\t\tfor(size_t i = 0; i < m_poses.size(); i++){\n\t\t\t\t\tQuaternionPose pose = pose_quaternion_from_affine_matrix(m_poses[i]);\n\t\t\t\t\tpose.px += h_x[counter++];\n\t\t\t\t\tpose.py += h_x[counter++];\n\t\t\t\t\tpose.pz += h_x[counter++];\n\t\t\t\t\tpose.q0 += h_x[counter++];\n\t\t\t\t\tpose.q1 += h_x[counter++];\n\t\t\t\t\tpose.q2 += h_x[counter++];\n\t\t\t\t\tpose.q3 += h_x[counter++];\n\t\t\t\t\tm_poses[i] = affine_matrix_from_pose_quaternion(pose);\n\t\t\t\t}\n\t\t\t\tstd::cout << \"optimizing with quaternions finished\" << std::endl;\n\t\t\t}else{\n\t\t\t\tstd::cout << \"optimizing with quaternions FAILED\" << std::endl;\n\t\t\t}\n\n\t\t\tbreak;\n\t\t}\n\t}\n\tprintHelp();\n\tglutPostRedisplay();\n}\n\n\nvoid mouse(int button, int state, int x, int y) {\n\tif (state == GLUT_DOWN) {\n\t\tmouse_buttons |= 1 << button;\n\t} else if (state == GLUT_UP) {\n\t\tmouse_buttons = 0;\n\t}\n\n\tmouse_old_x = x;\n\tmouse_old_y = y;\n}\n\nvoid motion(int x, int y) {\n\tfloat dx, dy;\n\tdx = (float) (x - mouse_old_x);\n\tdy = (float) (y - mouse_old_y);\n\n\tif (mouse_buttons & 1) {\n\t\trotate_x += dy * 0.2f;\n\t\trotate_y += dx * 0.2f;\n\n\t} else if (mouse_buttons & 4) {\n\t\ttranslate_z += dy * 0.05f;\n\t} else if (mouse_buttons & 3) {\n\t\ttranslate_x += dx * 0.05f;\n\t\ttranslate_y -= dy * 0.05f;\n\t}\n\n\tmouse_old_x = x;\n\tmouse_old_y = y;\n\n\tglutPostRedisplay();\n}\n\nvoid reshape(int w, int h) {\n\tglViewport(0, 0, (GLsizei) w, (GLsizei) h);\n\tglMatrixMode(GL_PROJECTION);\n\tglLoadIdentity();\n\tgluPerspective(60.0, (GLfloat) w / (GLfloat) h, 0.01, 10000.0);\n\tglMatrixMode(GL_MODELVIEW);\n\tglLoadIdentity();\n}\n\nvoid printHelp() {\n\tstd::cout << \"-------help-------\" << std::endl;\n\tstd::cout << \"n: add noise to poses\" << std::endl;\n\tstd::cout << \"t: optimize (Tait-Bryan)\" << std::endl;\n\tstd::cout << \"r: optimize (Rodriguez)\" << std::endl;\n\tstd::cout << \"q: optimize (Quaternion)\" << std::endl;\n\tstd::cout << \"x: optimize (Without rotation matrix parametrization)\" << std::endl;\n\tstd::cout << \"a: optimize (Tait-Bryan 2)\" << std::endl;\n\tstd::cout << \"s: optimize (Rodriguez 2)\" << std::endl;\n\tstd::cout << \"d: optimize (Quaternion 2)\" << std::endl;\n\n}\n\n\n\n\n\n\n\n", "meta": {"hexsha": "bba3b74594ebc98daa98ddaac93c17564e20ec0f", "size": 97001, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "codes/c++Examples/src/relative_pose.cpp", "max_stars_repo_name": "karolmajek/observation_equations", "max_stars_repo_head_hexsha": "ae4c84f4488c3f9187c03620a03e55575ce2342c", "max_stars_repo_licenses": 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"alphanum_fraction": 0.6487974351, "num_tokens": 33704, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.48828339529583464, "lm_q1q2_score": 0.24986272117420633}} {"text": "// Copyright 2021 Apex.AI, Inc.\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n//    http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n//\n// Co-developed by Tier IV, Inc. and Apex.AI, Inc.\n\n/// \\copyright Copyright 2021 Apex.AI, Inc.\n/// All rights reserved.\n\n#ifndef STATE_ESTIMATION_NODES__HISTORY_HPP_\n#define STATE_ESTIMATION_NODES__HISTORY_HPP_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace autoware\n{\nnamespace common\n{\nnamespace state_estimation\n{\n\nnamespace detail\n{\n\n///\n/// Check if the sample passes the Mahalanobis gate.\n///\n/// @param[in] sample The input sample\n/// @param[in] mean The mean to be compared against\n/// @param[in] covariance Covariance around the given mean\n/// @param[in] mahalanobis_threshold The mahalanobis threshold factor\n///\n/// @tparam kNumOfStates Number of states in the state vector.\n///\n/// @return True if the sample passes the gate and false otherwise.\n///\ntemplate\nbool passes_mahalanobis_gate(\n const Eigen::Matrix & sample,\n const Eigen::Matrix & mean,\n const Eigen::Matrix & covariance,\n const common::types::float32_t mahalanobis_threshold)\n{\n using Matrix = Eigen::Matrix;\n const Matrix L = covariance.llt().matrixL();\n const auto squared_mahalanobis_distance =\n autoware::common::helper_functions::calculate_squared_mahalanobis_distance(sample, mean, L);\n const auto squared_threshold = mahalanobis_threshold * mahalanobis_threshold;\n return squared_mahalanobis_distance < squared_threshold;\n}\n\n} // namespace detail\n\n/// @brief An event to indicate a prediction step.\nstruct PredictionEvent {};\n\n///\n/// @brief An event to reset the state of the filter.\n///\n/// @tparam FilterT Type of the EKF filter used to infer vector and matrix types.\n///\ntemplate\nstruct ResetEvent\n{\n EIGEN_MAKE_ALIGNED_OPERATOR_NEW\n typename FilterT::State state;\n typename FilterT::State::Matrix covariance;\n};\n\n///\n/// @brief This class encapsulates a history of events used with EKF.\n///\n/// The class handles adding events to a history of a specified size. It models the\n/// behavior of a circular buffer, meaning that as new events come in, the oldest ones\n/// are removed. The events can be either measurement types or specific events like\n/// reset or prediction. Whenever an event is added to the middle of the history all the\n/// following events get rolled on top of this event to produce a new state.\n///\n/// @tparam FilterT Type of EKF filter used.\n/// @tparam kNumOfStates Dimensionality of the state in the filter.\n/// @tparam EventT A variadic template of all possible events.\n///\ntemplate\nclass History\n{\n ///\n /// @brief A single entry in the history. Holds state and covariance and a variant of events.\n ///\n class HistoryEntry;\n ///\n /// @brief A functor that handles updating the state. It is used with the variant stored\n /// within the HistoryEntry.\n ///\n class EkfStateUpdater;\n\n /// Typedef for timestamps.\n using Timestamp = std::chrono::system_clock::time_point;\n /// Typedef for history map type.\n using HistoryMap = std::multimap;\n\npublic:\n ///\n /// @brief Construct history from a filter pointer with a specific size.\n ///\n /// @param filter The filter pointer to be used internally.\n /// @param[in] max_history_size The maximum history size.\n /// @param[in] mahalanobis_threshold The mahalanobis threshold\n ///\n explicit History(\n FilterT & filter,\n const std::size_t max_history_size,\n const common::types::float32_t mahalanobis_threshold)\n : m_filter{filter},\n m_max_history_size{max_history_size},\n m_mahalanobis_threshold{mahalanobis_threshold} {}\n\n ///\n /// @brief Add an event to history. If it is added to the middle the following ones are\n /// automatically replayed on top of it.\n ///\n /// @param[in] timestamp The timestamp of the event.\n /// @param[in] entry The entry to be added to history.\n ///\n void emplace_event(const Timestamp & timestamp, const HistoryEntry & entry);\n /// @brief Check if the history is empty.\n inline bool empty() const noexcept {return m_history.empty();}\n /// @brief Get size of history.\n inline std::size_t size() const noexcept {return m_history.size();}\n /// @brief Get last timestamp in history.\n inline const Timestamp & get_last_timestamp() const noexcept {return m_history.rbegin()->first;}\n /// @brief Get last event in history.\n inline const HistoryEntry & get_last_event() const noexcept {return m_history.rbegin()->second;}\n /// @brief Get the filter as a const ref.\n const FilterT & get_filter() const noexcept {return m_filter;}\n /// @brief Get the filter.\n FilterT & get_filter() noexcept {return m_filter;}\n\nprivate:\n ///\n /// @brief If the history is too large, drop the oldest event from it.\n ///\n inline void drop_oldest_event_if_needed()\n {\n if ((m_history.size() >= m_max_history_size) && (m_max_history_size > 0U)) {\n (void) m_history.erase(m_history.begin());\n }\n }\n\n ///\n /// @brief Update all the following events as their state is based on the current one.\n ///\n /// @param[in] start_iter The current iterator with the new state.\n ///\n void update_impacted_events(const typename HistoryMap::iterator & start_iter);\n\n HistoryMap m_history{}; ///< history of events.\n FilterT & m_filter{}; ///< pointer to the filter implementation.\n std::size_t m_max_history_size{}; ///< Maximum number of events in history.\n common::types::float32_t m_mahalanobis_threshold{}; ///< Mahalanobis distance threshold.\n};\n\ntemplate\nclass History::HistoryEntry\n{\npublic:\n // cppcheck-suppress unknownMacro // cppcheck seems to be confused due to lots of templates.\n EIGEN_MAKE_ALIGNED_OPERATOR_NEW\n\n template\n // cppcheck-suppress noExplicitConstructor; Conversion to the variant type takes place.\n HistoryEntry(const SingleEventT & event) : m_event {event} {}\n\n /// @brief Update the stored state.\n void update_stored_state(const typename FilterT::State & state) noexcept\n {\n m_stored_state = state;\n }\n /// @brief Get the stored state.\n const typename FilterT::State & stored_state() const noexcept {return m_stored_state;}\n /// @brief Set the stored covariance.\n void update_stored_covariance(const typename FilterT::State::Matrix & covariance) noexcept\n {\n m_stored_covariance = covariance;\n }\n /// @brief Get the stored covariance.\n const typename FilterT::State::Matrix & stored_covariance() const noexcept\n {\n return m_stored_covariance;\n }\n /// @brief Get the event stored in this history entry.\n const mpark::variant & event() const {return m_event;}\n\nprivate:\n /// State stored in this entry.\n typename FilterT::State m_stored_state{};\n /// Covariance stored in this entry.\n typename FilterT::State::Matrix m_stored_covariance{FilterT::State::Matrix::Zero()};\n /// Event stored in this history entry.\n mpark::variant m_event;\n};\n\ntemplate\nclass History::EkfStateUpdater\n{\npublic:\n explicit EkfStateUpdater(\n FilterT & filter,\n const common::types::float32_t mahalanobis_threshold,\n const std::chrono::system_clock::duration & dt = std::chrono::milliseconds{0})\n : m_filter {filter}, m_mahalanobis_threshold{mahalanobis_threshold}, m_dt{dt}\n {}\n\n ///\n /// @brief An operator that passes a measurement event to the filter implementation.\n ///\n /// @param[in] event A measurement event.\n ///\n /// @tparam MeasurementT Type of measurement event. Can be any measurement type.\n ///\n template\n void operator()(const MeasurementT & event)\n {\n m_filter.predict(m_dt);\n // TODO(#887): I see a couple of ways to check mahalanobis distance in case the measurement does\n // not cover the full state. Here I upscale it to the full state, copying the values of the\n // current state for ones missing in the observation. We can alternatively apply the H matrix\n // and only compute the distance in the measurement world. Don't really know which one is best\n // here.\n const auto measurement_as_state = event.map_into(m_filter.state());\n if (!detail::passes_mahalanobis_gate(\n measurement_as_state.vector(),\n m_filter.state().vector(),\n m_filter.covariance(),\n m_mahalanobis_threshold)) {return;}\n m_filter.correct(event);\n }\n\n /// @brief An operator that resets the state of the filter implementation.\n void operator()(const ResetEvent & event)\n {\n m_filter.reset(event.state, event.covariance);\n }\n\n /// @brief An operator that applies the prediction event to the filter implementation.\n void operator()(const PredictionEvent &)\n {\n m_filter.predict(m_dt);\n }\n\nprivate:\n FilterT & m_filter{}; ///< A pointer to the filter implementation.\n common::types::float32_t m_mahalanobis_threshold{}; ///< Mahalanobis distance threshold.\n std::chrono::system_clock::duration m_dt{}; ///< Current time step.\n};\n\ntemplate\nvoid History::emplace_event(\n const Timestamp & timestamp, const HistoryEntry & entry)\n{\n drop_oldest_event_if_needed();\n const auto iterator_to_inserted_position = m_history.emplace(timestamp, entry);\n update_impacted_events(iterator_to_inserted_position);\n}\n\ntemplate\nvoid History::update_impacted_events(\n const typename HistoryMap::iterator & start_iter)\n{\n Timestamp previous_timestamp{};\n if (start_iter == m_history.begin()) {\n if (!mpark::holds_alternative>(start_iter->second.event())) {\n (void) m_history.erase(start_iter);\n throw std::runtime_error(\n \"Non-reset event inserted to the beginning of history. This might \"\n \"happen if a very old event is inserted into the queue. Consider \"\n \"increasing the queue size or debug program latencies.\");\n }\n } else {\n const auto prev_iter = std::prev(start_iter);\n previous_timestamp = prev_iter->first;\n const auto & prev_entry = prev_iter->second;\n m_filter.reset(\n typename FilterT::State{prev_entry.stored_state()},\n prev_entry.stored_covariance());\n }\n for (auto iter = start_iter; iter != m_history.end(); ++iter) {\n const auto current_timestamp = iter->first;\n auto & entry = iter->second;\n mpark::visit(\n EkfStateUpdater{m_filter, m_mahalanobis_threshold, current_timestamp - previous_timestamp},\n entry.event());\n entry.update_stored_state(m_filter.state());\n entry.update_stored_covariance(m_filter.covariance());\n previous_timestamp = iter->first;\n }\n}\n\n\n} // namespace state_estimation\n} // namespace common\n} // namespace autoware\n\n\n#endif // STATE_ESTIMATION_NODES__HISTORY_HPP_\n", "meta": {"hexsha": "64779ecef3d5a8defbe0324d99c17fb5d89f0d4b", "size": 12139, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/common/state_estimation_nodes/include/state_estimation_nodes/history.hpp", "max_stars_repo_name": "ruvus/auto", "max_stars_repo_head_hexsha": "25ae62d6e575cae40212356eed43ec3e76e9a13e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 19.0, "max_stars_repo_stars_event_min_datetime": "2021-05-28T06:14:21.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-10T10:03:08.000Z", "max_issues_repo_path": "src/common/state_estimation_nodes/include/state_estimation_nodes/history.hpp", "max_issues_repo_name": "ruvus/auto", "max_issues_repo_head_hexsha": "25ae62d6e575cae40212356eed43ec3e76e9a13e", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 222.0, "max_issues_repo_issues_event_min_datetime": "2021-10-29T22:00:27.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T20:56:34.000Z", "max_forks_repo_path": "src/common/state_estimation_nodes/include/state_estimation_nodes/history.hpp", "max_forks_repo_name": "ruvus/auto", "max_forks_repo_head_hexsha": "25ae62d6e575cae40212356eed43ec3e76e9a13e", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 14.0, "max_forks_repo_forks_event_min_datetime": "2021-05-29T14:59:17.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-10T10:03:09.000Z", "avg_line_length": 37.236196319, "max_line_length": 100, "alphanum_fraction": 0.7032704506, "num_tokens": 2877, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.24981693979236788}} {"text": "#include \"step_crawling.h\"\n\n#include \n#include // For srand() and rand()\n\n#include \n#include \n#include \n\n#include \n\n#define PI 3.14159265\n\nusing namespace RUT;\nusing std::cout;\nusing std::cerr;\nusing std::endl;\nusing std::string;\nusing Eigen::Vector2d;\nusing Eigen::Matrix2d;\n\nbool StepCrawlingTaskServer::initStepCrawlingTaskServer() {\n ROS_INFO_STREAM(\"Step crawling server is starting\");\n if (_ros_handle_p == nullptr) {\n ROS_ERROR_STREAM(\"[StepCrawlingTaskServer] You must call .init() before .initStepCrawlingTaskServer().\");\n exit(1);\n }\n\n _ros_handle_p->param(string(\"/task/v_singular_value_threshold\"),\n _v_singular_value_threshold, 0.1);\n _ros_handle_p->param(string(\"/task/f_singular_value_threshold\"),\n _f_singular_value_threshold, 0.1);\n if (!_ros_handle_p->hasParam(\"/task/v_singular_value_threshold\"))\n ROS_WARN_STREAM(\"Parameter [/task/v_singular_value_threshold] not found\");\n if (!_ros_handle_p->hasParam(\"/task/f_singular_value_threshold\"))\n ROS_WARN_STREAM(\"Parameter [/task/f_singular_value_threshold] not found\");\n\n _ros_handle_p->param(std::string(\"/task/time_step\"), _kTimeStepSec, 0.1);\n if (!_ros_handle_p->hasParam(\"/task/time_step\"))\n ROS_WARN_STREAM(\"Parameter [/task/time_step] not found\");\n _ros_handle_p->param(std::string(\"/task/number_of_time_steps\"), _kNumOfTimeSteps, 30);\n if (!_ros_handle_p->hasParam(\"/task/number_of_time_steps\"))\n ROS_WARN_STREAM(\"Parameter [/task/number_of_time_steps] not found\");\n _ros_handle_p->param(std::string(\"/task/goal_veloicty_meter\"), _kGoalVelocityM, 0.01);\n if (!_ros_handle_p->hasParam(\"/task/goal_veloicty_meter\"))\n ROS_WARN_STREAM(\"Parameter [/task/goal_veloicty_meter] not found\");\n _ros_handle_p->param(std::string(\"/task/engage_step_length\"), _kEngageStepLength, 0.001);\n if (!_ros_handle_p->hasParam(\"/task/engage_step_length\"))\n ROS_WARN_STREAM(\"Parameter [/task/engage_step_length] not found\");\n _ros_handle_p->param(std::string(\"/task/min_normal_force\"), _kNormalForceMin, 4.0);\n if (!_ros_handle_p->hasParam(\"/task/min_normal_force\"))\n ROS_WARN_STREAM(\"Parameter [/task/min_normal_force] not found\");\n\n _F_W(0) = 0;\n _F_W(1) = 1;\n _F_W(2) = 0;\n\n return true;\n}\n\nbool StepCrawlingTaskServer::hostServices() {\n // --------------------------------------------------------\n // Establish Services\n // --------------------------------------------------------\n ros::ServiceServer reset_service = _ros_handle_p->advertiseService(\"reset\", &StepCrawlingTaskServer::SrvReset, (RobotBridge*)this);\n ros::ServiceServer move_tool_service = _ros_handle_p->advertiseService(\"move_tool\", &StepCrawlingTaskServer::SrvMoveTool, (RobotBridge*)this);\n ros::ServiceServer move_until_touch_service = _ros_handle_p->advertiseService(\"move_until_touch\", &StepCrawlingTaskServer::SrvMoveUntilTouch, (RobotBridge*)this);\n ros::ServiceServer get_pose_service = _ros_handle_p->advertiseService(\"get_pose\", &StepCrawlingTaskServer::SrvGetPose, (RobotBridge*)this);\n ros::ServiceServer execute_task_service = _ros_handle_p->advertiseService(\"execute_task\", &StepCrawlingTaskServer::SrvExecuteTask, this);\n\n cout << endl << \"[StepCrawlingTaskServer] Service servers are listening..\" << endl;\n ros::spin();\n\n ROS_INFO_STREAM(endl << \"[StepCrawlingTaskServer] Service servers stopped.\" << endl);\n return true;\n}\n\nbool StepCrawlingTaskServer::SrvExecuteTask(std_srvs::Empty::Request &req,\n std_srvs::Empty::Response &res) {\n _controller.reset();\n\n if (_controller._f_queue.size() < 50) {\n cout << \"Run update() for \" << _main_loop_rate << \" frames:\" << endl;\n // first, run update for 1s to populate the data deques\n ros::Rate pub_rate(_main_loop_rate);\n for (int i = 0; i < _main_loop_rate; ++i) {\n bool b_is_safe = _controller.update();\n // if(!b_is_safe) break;\n pub_rate.sleep();\n }\n cout << \"Done.\" << endl;\n }\n\n Timer timer;\n std::srand(std::time(0));\n MatrixXd f_data, v_data;\n for (int fr = 0; fr < _kNumOfTimeSteps; ++fr) {\n timer.tic();\n /**\n * Estimate Natural Constraints\n */\n // get the weighted data\n f_data = MatrixXd::Zero(3, _controller._f_queue.size());\n v_data = MatrixXd::Zero(3, _controller._v_queue.size());\n for (int i = 0; i < _controller._f_queue.size(); ++i)\n f_data.col(i) = _controller._f_queue[i].head(3) * _controller._f_weights[i];\n for (int i = 0; i < _controller._v_queue.size(); ++i)\n v_data.col(i) = _controller._v_queue[i].head(3) * _controller._v_weights[i];\n\n // SVD on velocity data\n Eigen::JacobiSVD svd_v(v_data.transpose(), Eigen::ComputeThinV);\n VectorXd sigma_v = svd_v.singularValues();\n int DimV = 0;\n for (int i = 0; i < 3; ++i)\n if (sigma_v(i) > _v_singular_value_threshold) DimV ++;\n\n // get a basis for row space of velocity data\n MatrixXd rowspace_v = svd_v.matrixV().leftCols(DimV);\n\n // filter out force data that:\n // 1. has a small weight\n std::vector f_id;\n for (int i = 0; i < f_data.cols(); ++i) {\n double length = f_data.col(i).norm();\n if (length > 1.5) { // weighted length in newton\n f_id.push_back(i);\n }\n }\n\n double pose_fb[7], pose_set[7];\n _robot.getPose(pose_fb);\n Matrix3d R_WT = quat2SO3(pose_fb[3], pose_fb[4], pose_fb[5], pose_fb[6]);\n\n int f_data_length = f_id.size();\n if (f_data_length < 5) {\n ROS_WARN_STREAM(\"[StepCrawlingTaskServer] lacks significant force data\");\n // stabilize onto the surface.\n Vector3d F_T = R_WT.transpose() * _F_W.normalized() * _kNormalForceMin;\n double force_temp[6] = {- F_T(0), - F_T(1), - F_T(2), 0, 0, 0};\n _controller.ExecuteHFVC(6, 0, Matrix6d::Identity(), pose_fb, force_temp,\n HS_CONTINUOUS, _main_loop_rate, 10*_kTimeStepSec);\n continue;\n }\n int DimF = 1;\n\n MatrixXd f_data_filtered = MatrixXd::Zero(3, f_data_length);\n for (int i = 0; i < f_data_length; ++i)\n f_data_filtered.col(i) = f_data.col(f_id[i]);\n\n MatrixXd f_data_selected;\n // SVD on filtered force data, get the prime direction\n Eigen::JacobiSVD svd_f(f_data_filtered.transpose(), Eigen::ComputeThinV);\n Vector3d F_T = svd_f.matrixV().col(0).normalized();\n // check sign\n MatrixXd check_sum = F_T.transpose() * f_data_filtered * VectorXd::Ones(f_data_length);\n if (check_sum(0,0) < 0) F_T = -F_T;\n MatrixXd Nf = F_T.transpose();\n\n /**\n * Infer Goal\n */\n // Transform Nf to world frame\n _F_W = R_WT * F_T;\n cout << \"F_W: \" << _F_W.transpose().format(MatlabFmt) << endl;\n Vector3d goal_direction_W = _F_W.cross(-Vector3d::UnitX());\n cout << \"goal_direction_W: \" << goal_direction_W.transpose().format(MatlabFmt) << endl;\n\n MatrixXd G = MatrixXd::Zero(1, 3);\n VectorXd b_G = VectorXd::Zero(1);\n G = (R_WT.transpose() * goal_direction_W).transpose();\n b_G(0) = _kGoalVelocityM;\n\n /**\n * Do Hybrid Servoing (3D)\n */\n HFVC action;\n int kDimActualized = 3;\n int kDimUnActualized = 0;\n int kDimSlidingFriction = 0;\n int kNumSeeds = 3;\n int kDimLambda = 1;\n int kPrintLevel = 2;\n\n VectorXd F = VectorXd::Zero(3);\n MatrixXd Aeq(0, 1+3); // dummy\n VectorXd beq(0); // dummy\n MatrixXd A = MatrixXd::Zero(1, 1 + 3);\n VectorXd b_A = VectorXd::Zero(1);\n A(0, 0) = -1;\n b_A(0) = -_kNormalForceMin;\n\n cout << \"Nf: \" << Nf.format(MatlabFmt) << endl;\n cout << \"G: \" << G.format(MatlabFmt) << endl;\n cout << \"b_G: \" << b_G.format(MatlabFmt) << endl;\n cout << \"F: \" << F.format(MatlabFmt) << endl;\n cout << \"Aeq: \" << Aeq.format(MatlabFmt) << endl;\n cout << \"beq: \" << beq.format(MatlabFmt) << endl;\n cout << \"A: \" << A.format(MatlabFmt) << endl;\n cout << \"b_A: \" << b_A.format(MatlabFmt) << endl;\n\n solvehfvc(Nf, G, b_G, F, Aeq, beq, A, b_A,\n kDimActualized, kDimUnActualized,\n kDimSlidingFriction, kDimLambda,\n kNumSeeds, kPrintLevel,\n &action);\n\n double computation_time_ms = timer.toc();\n\n /**\n * Execute the hybrid action (6D)\n */\n Vector6d v_Tr = Vector6d::Zero();\n for (int i = 0; i < action.n_av; ++i) v_Tr(i+action.n_af) = action.w_av(i);\n\n // Tool frame velocity\n Matrix6d R_a;\n R_a << action.R_a, Matrix3d::Zero(), Matrix3d::Zero(), Matrix3d::Identity();\n Matrix6d R_a_inv = R_a.inverse();\n Vector6d v_T = R_a_inv*v_Tr;\n\n // World frame velocity, set pose\n _robot.getPose(pose_fb);\n Matrix4d SE3_WT_fb = posemm2SE3(pose_fb);\n Matrix6d Adj_WT = SE32Adj(SE3_WT_fb);\n Vector6d v_W = Adj_WT*v_T;\n Matrix4d SE3_WT_command;\n SE3_WT_command = SE3_WT_fb + wedge6(v_W)*SE3_WT_fb*_kTimeStepSec;\n SE32Posemm(SE3_WT_command, pose_set);\n\n // Tool frame force\n Vector6d force_Tr_set = Vector6d::Zero();\n for (int i = 0; i < action.n_af; ++i) force_Tr_set[i] = action.eta_af(i);\n Vector6d force_T = R_a_inv*force_Tr_set;\n Matrix6d Adj_TW = SE32Adj(SE3Inv(SE3_WT_fb));\n\n Vector6d force_W = Adj_TW.transpose() * force_T;\n cout << \"force_Tr_set: \" << force_Tr_set.transpose().format(MatlabFmt) << endl;\n cout << \"force_T: \" << force_T.transpose().format(MatlabFmt) << endl;\n printf(\"V in world: %.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\n\", v_W[0], v_W[1],\n v_W[2],v_W[3],v_W[4],v_W[5]);\n printf(\"F in world: %.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\t%.3f\\n\", force_W[0],\n force_W[1], force_W[2],force_W[3],force_W[4],force_W[5]);\n cout << \"Computation Time: \" << computation_time_ms << endl << endl;\n\n if (fr == _kNumOfTimeSteps-1) {\n // print to file\n std::ofstream fp;\n // f_queue\n fp.open(_task_data_file_path + \"f_queue.txt\");\n if (!fp) {\n cerr << \"Unable to open file to write.\";\n return false;\n }\n for (int i = 0; i < _controller._f_queue.size(); ++i) {\n stream_array_in(fp, _controller._f_queue[i].data(), 3);\n fp << endl;\n }\n fp.close();\n // v_queue\n fp.open(_task_data_file_path + \"v_queue.txt\");\n if (!fp) {\n cerr << \"Unable to open file to write.\";\n return false;\n }\n for (int i = 0; i < _controller._v_queue.size(); ++i) {\n stream_array_in(fp, _controller._v_queue[i].data(), 3);\n fp << endl;\n }\n fp.close();\n // f_weights\n fp.open(_task_data_file_path + \"f_weights.txt\");\n if (!fp) {\n cerr << \"Unable to open file to write.\";\n return false;\n }\n for (int i = 0; i < _controller._f_weights.size(); ++i)\n fp << _controller._f_weights[i] << endl;\n fp.close();\n\n // v_weights\n fp.open(_task_data_file_path + \"v_weights.txt\");\n if (!fp) {\n cerr << \"Unable to open file to write.\";\n return false;\n }\n for (int i = 0; i < _controller._v_weights.size(); ++i)\n fp << _controller._v_weights[i] << endl;\n fp.close();\n\n // f_data_filtered\n fp.open(_task_data_file_path + \"f_data_filtered.txt\");\n if (!fp) {\n cerr << \"Unable to open file to write.\";\n return false;\n }\n fp << f_data_filtered.transpose() << endl;\n fp.close();\n // f_data_selected\n fp.open(_task_data_file_path + \"f_data_selected.txt\");\n if (!fp) {\n cerr << \"Unable to open file to write.\";\n return false;\n }\n fp << f_data_selected.transpose() << endl;\n fp.close();\n // others\n fp.open(_task_data_file_path + \"process.txt\");\n if (!fp) {\n cerr << \"Unable to open file to write.\";\n return false;\n }\n fp << DimV << endl << DimF << endl;\n stream_array_in(fp, v_T.data(), 3);\n fp << endl;\n stream_array_in(fp, force_T.data(), 3);\n fp.close();\n }\n\n if (std::isnan(force_Tr_set[0])) {\n cout << \"================== NaN =====================\" << endl;\n cout << \"Press Enter to continue..\" << endl;\n getchar();\n }\n\n cout << \"Current pose: \" << pose_fb[0] << \", \" << pose_fb[1] << \", \" << pose_fb[2] << endl;\n cout << \"Set pose: \" << pose_set[0] << \", \" << pose_set[1] << \", \" << pose_set[2] << endl;\n cout << \"Press Enter to begin motion: \\n\";\n // getchar();\n // cout << \"motion begins:\" << endl;\n _controller.ExecuteHFVC(action.n_af, action.n_av+3,\n R_a, pose_set, force_Tr_set.data(),\n HS_CONTINUOUS, _main_loop_rate, _kTimeStepSec);\n\n } // end for\n return true;\n}", "meta": {"hexsha": "5ddf9ace8e9073f28dcb7557fb8a4e61865ed058", "size": 12494, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "experiments/step_crawling/cpp_robot_server/step_crawling.cpp", "max_stars_repo_name": "yifan-hou/hybrid_servoing", "max_stars_repo_head_hexsha": "4d3a2cc047a3bad5ded19934247b5ab5e598f911", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16.0, "max_stars_repo_stars_event_min_datetime": "2020-04-15T04:45:02.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-13T03:28:46.000Z", "max_issues_repo_path": "experiments/step_crawling/cpp_robot_server/step_crawling.cpp", "max_issues_repo_name": "yifan-hou/hybrid_servoing", "max_issues_repo_head_hexsha": "4d3a2cc047a3bad5ded19934247b5ab5e598f911", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "experiments/step_crawling/cpp_robot_server/step_crawling.cpp", "max_forks_repo_name": "yifan-hou/hybrid_servoing", "max_forks_repo_head_hexsha": "4d3a2cc047a3bad5ded19934247b5ab5e598f911", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.1845238095, "max_line_length": 164, "alphanum_fraction": 0.6246198175, "num_tokens": 3730, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631698328917, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.24981693395037463}} {"text": "#include \n#include \n#include \n#include \"bhtsne.h\"\n\nusing namespace std;\nnamespace po = boost::program_options;\nnamespace fsys = boost::filesystem;\n\nint main(int argc, char **argv) {\n // Declare the supported options\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"help\", \"produce help message\")\n (\"input-P\", po::value()->value_name(\"FILE\")->default_value(\"P.dat\"), \"name of binary input file containing P matrix (see ComputeP)\")\n (\"out-dir\", po::value()->value_name(\"DIR\")->default_value(\"out\"), \"where to create output files; directory will be created if it does not exist\")\n (\"out-dim\", po::value()->value_name(\"NUM\")->default_value(2), \"number of output dimensions\")\n (\"max-iter\", po::value()->value_name(\"NUM\")->default_value(1000), \"maximum number of iterations\")\n (\"rand-seed\", po::value()->value_name(\"NUM\")->default_value(-1), \"seed for random number generator; to use current time as seed set it to -1\")\n (\"theta\", po::value()->value_name(\"NUM\")->default_value(0.5, \"0.5\"), \"a value between 0 and 1 that controls the accuracy-efficiency tradeoff in SPTree for gradient computation; 0 means exact\")\n (\"learn-rate\", po::value()->value_name(\"NUM\")->default_value(200, \"200\"), \"learning rate for gradient steps\")\n (\"mom-init\", po::value()->value_name(\"NUM\")->default_value(0.5, \"0.5\"), \"initial momentum between 0 and 1\")\n (\"mom-final\", po::value()->value_name(\"NUM\")->default_value(0.8, \"0.8\"), \"final momentum between 0 and 1 (switch point controlled by --mom-switch-iter)\")\n (\"mom-switch-iter\", po::value()->value_name(\"NUM\")->default_value(250), \"duration (number of iterations) of initial momentum\")\n (\"early-exag-iter\", po::value()->value_name(\"NUM\")->default_value(250), \"duration (number of iterations) of early exaggeration\")\n (\"skip-random-init\", po::bool_switch()->default_value(false), \"skip random initialization\")\n (\"batch-frac\", po::value()->value_name(\"NUM\"), \"what fraction of points to update for each iteration\")\n (\"cache-iter\", po::value()->value_name(\"NUM\")->default_value(INT_MAX, \"INT_MAX\"), \"After every NUM iterations, write intermediary embeddings to disk. Final embedding is always reported.\")\n ;\n\n po::variables_map vm;\n po::store(po::command_line_parser(argc, argv).\n options(desc).run(), vm);\n po::notify(vm); \n \n if (vm.count(\"help\")) {\n cout << \"Usage: RunBhtsne [options]\" << endl;\n cout << desc << \"\\n\";\n return 1;\n }\n\n string infile = vm[\"input-P\"].as();\n string outdir = vm[\"out-dir\"].as();\n\n fsys::path dir(outdir);\n if (fsys::is_directory(dir)) {\n cout << \"Error: Output directory already exists\" << endl;\n return 1;\n }\n if (fsys::create_directory(dir)) {\n cout << \"Output directory created: \" << outdir << endl; \n }\n\n TSNE *tsne = new TSNE();\n\n fsys::path paramfile = dir;\n paramfile /= \"param.txt\";\n ofstream ofs(paramfile.string().c_str());\n ofs << \"input-P: \" << infile << endl;\n ofs << \"out-dir: \" << fsys::canonical(dir).string() << endl;\n\n int no_dims = vm[\"out-dim\"].as(); ofs << \"out-dim: \" << no_dims << endl;\n double theta = vm[\"theta\"].as(); ofs << \"theta: \" << theta << endl;\n int rand_seed = vm[\"rand-seed\"].as(); ofs << \"rand-seed: \" << rand_seed << endl;\n bool skip_random_init = vm[\"skip-random-init\"].as(); ofs << \"skip-random-init: \" << skip_random_init << endl;\n int max_iter = vm[\"max-iter\"].as(); ofs << \"max-iter: \" << max_iter << endl;\n int stop_lying_iter = vm[\"early-exag-iter\"].as(); ofs << \"early-exag-iter: \" << stop_lying_iter << endl;\n int mom_switch_iter = vm[\"mom-switch-iter\"].as(); ofs << \"mom-switch-iter: \" << mom_switch_iter << endl;\n double momentum = vm[\"mom-init\"].as(); ofs << \"mom-init: \" << momentum << endl;\n double final_momentum = vm[\"mom-final\"].as(); ofs << \"mom-final: \" << final_momentum << endl;\n double eta = vm[\"learn-rate\"].as(); ofs << \"learn-rate: \" << eta << endl;\n tsne->CACHE_ITER = vm[\"cache-iter\"].as(); ofs << \"cache-iter: \" << tsne->CACHE_ITER << endl;\n\n if (vm.count(\"batch-frac\")) {\n double batch_frac = vm[\"batch-frac\"].as(); ofs << \"batch-frac: \" << batch_frac << endl;\n tsne->BATCH_FLAG = true;\n tsne->BATCH_FRAC = batch_frac;\n }\n\n ofs.close();\n\n printf(\"Loading input similarities...\\n\");\n int N;\n unsigned int *row_P;\n unsigned int *col_P;\n double *val_P;\n if (!tsne->load_P(infile, N, &row_P, &col_P, &val_P)) {\n cout << \"Error: failed to load P from \" << infile << endl;\n return 1;\n }\n\n double *Y = (double *)malloc(N * no_dims * sizeof(double));\n if (Y == NULL) {\n cout << \"Error: Memory allocation for the output failed\" << endl;\n return 1;\n }\n\n if (!tsne->run(N, row_P, col_P, val_P, Y, no_dims, theta, rand_seed,\n skip_random_init, max_iter, stop_lying_iter, mom_switch_iter,\n momentum, final_momentum, eta, dir)) {\n return 1;\n }\n\n free(row_P);\n free(col_P);\n free(val_P);\n free(Y);\n delete(tsne);\n\n cout << \"Done\" << endl;\n\n return 0;\n}\n", "meta": {"hexsha": "b9962d1a4d9527ff4b1d0a8e58aac0237beed46e", "size": 5201, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "RunBhtsne.cpp", "max_stars_repo_name": "hhcho/netsne", "max_stars_repo_head_hexsha": "d928b7f514efe68e91984ba0a51428a30beed647", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19.0, "max_stars_repo_stars_event_min_datetime": "2018-04-15T18:25:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-05T13:30:37.000Z", "max_issues_repo_path": "RunBhtsne.cpp", "max_issues_repo_name": "hhcho/netsne", "max_issues_repo_head_hexsha": "d928b7f514efe68e91984ba0a51428a30beed647", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2020-03-24T01:38:10.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-06T00:48:47.000Z", "max_forks_repo_path": "RunBhtsne.cpp", "max_forks_repo_name": "hhcho/netsne", "max_forks_repo_head_hexsha": "d928b7f514efe68e91984ba0a51428a30beed647", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2018-04-23T13:10:43.000Z", "max_forks_repo_forks_event_max_datetime": "2018-08-28T18:00:59.000Z", "avg_line_length": 45.6228070175, "max_line_length": 204, "alphanum_fraction": 0.6408383003, "num_tokens": 1442, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.2497560182328059}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\r\n\r\n// Copyright (c) 2007-2012 Barend Gehrels, Amsterdam, the Netherlands.\r\n// Copyright (c) 2008-2012 Bruno Lalande, Paris, France.\r\n// Copyright (c) 2009-2012 Mateusz Loskot, London, UK.\r\n\r\n// Parts of Boost.Geometry are redesigned from Geodan's Geographic Library\r\n// (geolib/GGL), copyright (c) 1995-2010 Geodan, Amsterdam, the Netherlands.\r\n\r\n// Use, modification and distribution is subject to the Boost Software License,\r\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\r\n// http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n#ifndef BOOST_GEOMETRY_STRATEGIES_STRATEGY_TRANSFORM_HPP\r\n#define BOOST_GEOMETRY_STRATEGIES_STRATEGY_TRANSFORM_HPP\r\n\r\n#include \r\n#include \r\n#include \r\n\r\n#include \r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n#include \r\n#include \r\n\r\nnamespace boost { namespace geometry\r\n{\r\n\r\nnamespace strategy { namespace transform\r\n{\r\n\r\n#ifndef DOXYGEN_NO_DETAIL\r\nnamespace detail\r\n{\r\n\r\ntemplate\r\n<\r\n typename Src, typename Dst,\r\n std::size_t D, std::size_t N,\r\n template class F\r\n>\r\nstruct transform_coordinates\r\n{\r\n template \r\n static inline void transform(Src const& source, Dst& dest, T value)\r\n {\r\n typedef typename select_coordinate_type::type coordinate_type;\r\n\r\n F function;\r\n set(dest, boost::numeric_cast(function(get(source), value)));\r\n transform_coordinates::transform(source, dest, value);\r\n }\r\n};\r\n\r\ntemplate\r\n<\r\n typename Src, typename Dst,\r\n std::size_t N,\r\n template class F\r\n>\r\nstruct transform_coordinates\r\n{\r\n template \r\n static inline void transform(Src const& , Dst& , T )\r\n {\r\n }\r\n};\r\n\r\n} // namespace detail\r\n#endif // DOXYGEN_NO_DETAIL\r\n\r\n\r\n/*!\r\n \\brief Transformation strategy to copy one point to another using assignment operator\r\n \\ingroup transform\r\n \\tparam P point type\r\n */\r\ntemplate \r\nstruct copy_direct\r\n{\r\n inline bool apply(P const& p1, P& p2) const\r\n {\r\n p2 = p1;\r\n return true;\r\n }\r\n};\r\n\r\n/*!\r\n \\brief Transformation strategy to do copy a point, copying per coordinate.\r\n \\ingroup transform\r\n \\tparam P1 first point type\r\n \\tparam P2 second point type\r\n */\r\ntemplate \r\nstruct copy_per_coordinate\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n // Defensive check, dimensions are equal, selected by specialization\r\n assert_dimension_equal();\r\n\r\n geometry::convert(p1, p2);\r\n return true;\r\n }\r\n};\r\n\r\n\r\n/*!\r\n \\brief Transformation strategy to go from degree to radian and back\r\n \\ingroup transform\r\n \\tparam P1 first point type\r\n \\tparam P2 second point type\r\n \\tparam F additional functor to divide or multiply with d2r\r\n */\r\ntemplate class F>\r\nstruct degree_radian_vv\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n // Spherical coordinates always have 2 coordinates measured in angles\r\n // The optional third one is distance/height, provided in another strategy\r\n // Polar coordinates having one angle, will be also in another strategy\r\n assert_dimension();\r\n assert_dimension();\r\n\r\n detail::transform_coordinates::transform(p1, p2, math::d2r);\r\n return true;\r\n }\r\n};\r\n\r\ntemplate class F>\r\nstruct degree_radian_vv_3\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n assert_dimension();\r\n assert_dimension();\r\n\r\n detail::transform_coordinates::transform(p1, p2, math::d2r);\r\n // Copy height or other third dimension\r\n set<2>(p2, get<2>(p1));\r\n return true;\r\n }\r\n};\r\n\r\n\r\n#ifndef DOXYGEN_NO_DETAIL\r\nnamespace detail\r\n{\r\n\r\n /// Helper function for conversion, phi/theta are in radians\r\n template \r\n inline void spherical_polar_to_cartesian(T phi, T theta, R r, P& p)\r\n {\r\n assert_dimension();\r\n\r\n // http://en.wikipedia.org/wiki/List_of_canonical_coordinate_transformations#From_spherical_coordinates\r\n // http://www.vias.org/comp_geometry/math_coord_convert_3d.htm\r\n // https://moodle.polymtl.ca/file.php/1183/Autres_Documents/Derivation_for_Spherical_Co-ordinates.pdf\r\n // http://en.citizendium.org/wiki/Spherical_polar_coordinates\r\n \r\n // Phi = first, theta is second, r is third, see documentation on cs::spherical\r\n\r\n // (calculations are splitted to implement ttmath)\r\n\r\n T r_sin_theta = r;\r\n T r_cos_theta = r;\r\n r_sin_theta *= sin(theta);\r\n r_cos_theta *= cos(theta);\r\n\r\n set<0>(p, r_sin_theta * cos(phi));\r\n set<1>(p, r_sin_theta * sin(phi));\r\n set<2>(p, r_cos_theta);\r\n }\r\n \r\n /// Helper function for conversion, lambda/delta (lon lat) are in radians\r\n template \r\n inline void spherical_equatorial_to_cartesian(T lambda, T delta, R r, P& p)\r\n {\r\n assert_dimension();\r\n\r\n // http://mathworld.wolfram.com/GreatCircle.html\r\n // http://www.spenvis.oma.be/help/background/coortran/coortran.html WRONG\r\n \r\n T r_cos_delta = r;\r\n T r_sin_delta = r;\r\n r_cos_delta *= cos(delta);\r\n r_sin_delta *= sin(delta);\r\n\r\n set<0>(p, r_cos_delta * cos(lambda));\r\n set<1>(p, r_cos_delta * sin(lambda));\r\n set<2>(p, r_sin_delta);\r\n }\r\n \r\n\r\n /// Helper function for conversion\r\n template \r\n inline bool cartesian_to_spherical2(T x, T y, T z, P& p)\r\n {\r\n assert_dimension();\r\n\r\n // http://en.wikipedia.org/wiki/List_of_canonical_coordinate_transformations#From_Cartesian_coordinates\r\n\r\n#if defined(BOOST_GEOMETRY_TRANSFORM_CHECK_UNIT_SPHERE)\r\n // TODO: MAYBE ONLY IF TO BE CHECKED?\r\n T const r = /*sqrt not necessary, sqrt(1)=1*/ (x * x + y * y + z * z);\r\n\r\n // Unit sphere, so r should be 1\r\n if (geometry::math::abs(r - 1.0) > T(1e-6))\r\n {\r\n return false;\r\n }\r\n // end todo\r\n#endif\r\n\r\n set_from_radian<0>(p, atan2(y, x));\r\n set_from_radian<1>(p, acos(z));\r\n return true;\r\n }\r\n \r\n template \r\n inline bool cartesian_to_spherical_equatorial2(T x, T y, T z, P& p)\r\n {\r\n assert_dimension();\r\n\r\n set_from_radian<0>(p, atan2(y, x));\r\n set_from_radian<1>(p, asin(z));\r\n return true;\r\n }\r\n \r\n\r\n template \r\n inline bool cartesian_to_spherical3(T x, T y, T z, P& p)\r\n {\r\n assert_dimension();\r\n\r\n // http://en.wikipedia.org/wiki/List_of_canonical_coordinate_transformations#From_Cartesian_coordinates\r\n T const r = sqrt(x * x + y * y + z * z);\r\n set<2>(p, r);\r\n set_from_radian<0>(p, atan2(y, x));\r\n if (r > 0.0)\r\n {\r\n set_from_radian<1>(p, acos(z / r));\r\n return true;\r\n }\r\n return false;\r\n }\r\n\r\n\ttemplate \r\n\tinline bool cartesian_to_spherical_equatorial3(T x, T y, T z, P& p)\r\n\t{\r\n\t\tassert_dimension();\r\n\r\n\t\t// http://en.wikipedia.org/wiki/List_of_canonical_coordinate_transformations#From_Cartesian_coordinates\r\n\t\tT const r = sqrt(x * x + y * y + z * z);\r\n\t\tset<2>(p, r);\r\n\t\tset_from_radian<0>(p, atan2(y, x));\r\n\t\tif (r > 0.0)\r\n\t\t{\r\n\t\t\tset_from_radian<1>(p, asin(z / r));\r\n\t\t\treturn true;\r\n\t\t}\r\n\t\treturn false;\r\n\t}\r\n\r\n} // namespace detail\r\n#endif // DOXYGEN_NO_DETAIL\r\n\r\n\r\n/*!\r\n \\brief Transformation strategy for 2D spherical (phi,theta) to 3D cartesian (x,y,z)\r\n \\details on Unit sphere\r\n \\ingroup transform\r\n \\tparam P1 first point type\r\n \\tparam P2 second point type\r\n */\r\ntemplate \r\nstruct from_spherical_polar_2_to_cartesian_3\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n assert_dimension();\r\n detail::spherical_polar_to_cartesian(get_as_radian<0>(p1), get_as_radian<1>(p1), 1.0, p2);\r\n return true;\r\n }\r\n};\r\n\r\ntemplate \r\nstruct from_spherical_equatorial_2_to_cartesian_3\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n assert_dimension();\r\n detail::spherical_equatorial_to_cartesian(get_as_radian<0>(p1), get_as_radian<1>(p1), 1.0, p2);\r\n return true;\r\n }\r\n};\r\n\r\n\r\n/*!\r\n \\brief Transformation strategy for 3D spherical (phi,theta,r) to 3D cartesian (x,y,z)\r\n \\ingroup transform\r\n \\tparam P1 first point type\r\n \\tparam P2 second point type\r\n */\r\ntemplate \r\nstruct from_spherical_polar_3_to_cartesian_3\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n assert_dimension();\r\n detail::spherical_polar_to_cartesian(\r\n get_as_radian<0>(p1), get_as_radian<1>(p1), get<2>(p1), p2);\r\n return true;\r\n }\r\n};\r\n\r\ntemplate \r\nstruct from_spherical_equatorial_3_to_cartesian_3\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n assert_dimension();\r\n detail::spherical_equatorial_to_cartesian(\r\n get_as_radian<0>(p1), get_as_radian<1>(p1), get<2>(p1), p2);\r\n return true;\r\n }\r\n};\r\n\r\n\r\n/*!\r\n \\brief Transformation strategy for 3D cartesian (x,y,z) to 2D spherical (phi,theta)\r\n \\details on Unit sphere\r\n \\ingroup transform\r\n \\tparam P1 first point type\r\n \\tparam P2 second point type\r\n \\note If x,y,z point is not lying on unit sphere, transformation will return false\r\n */\r\ntemplate \r\nstruct from_cartesian_3_to_spherical_polar_2\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n assert_dimension();\r\n return detail::cartesian_to_spherical2(get<0>(p1), get<1>(p1), get<2>(p1), p2);\r\n }\r\n};\r\n\r\ntemplate \r\nstruct from_cartesian_3_to_spherical_equatorial_2\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n assert_dimension();\r\n return detail::cartesian_to_spherical_equatorial2(get<0>(p1), get<1>(p1), get<2>(p1), p2);\r\n }\r\n};\r\n\r\n\r\n/*!\r\n \\brief Transformation strategy for 3D cartesian (x,y,z) to 3D spherical (phi,theta,r)\r\n \\ingroup transform\r\n \\tparam P1 first point type\r\n \\tparam P2 second point type\r\n */\r\ntemplate \r\nstruct from_cartesian_3_to_spherical_polar_3\r\n{\r\n inline bool apply(P1 const& p1, P2& p2) const\r\n {\r\n assert_dimension();\r\n return detail::cartesian_to_spherical3(get<0>(p1), get<1>(p1), get<2>(p1), p2);\r\n }\r\n};\r\n\r\ntemplate \r\nstruct from_cartesian_3_to_spherical_equatorial_3\r\n{\r\n\tinline bool apply(P1 const& p1, P2& p2) const\r\n\t{\r\n\t\tassert_dimension();\r\n\t\treturn detail::cartesian_to_spherical_equatorial3(get<0>(p1), get<1>(p1), get<2>(p1), p2);\r\n\t}\r\n};\r\n\r\n#ifndef DOXYGEN_NO_STRATEGY_SPECIALIZATIONS\r\n\r\nnamespace services\r\n{\r\n\r\n/// Specialization for same coordinate system family, same system, same dimension, same point type, can be copied\r\ntemplate \r\nstruct default_strategy\r\n{\r\n typedef copy_direct

type;\r\n};\r\n\r\n/// Specialization for same coordinate system family and system, same dimension, different point type, copy per coordinate\r\ntemplate \r\nstruct default_strategy\r\n{\r\n typedef copy_per_coordinate type;\r\n};\r\n\r\n/// Specialization to transform from degree to radian for any coordinate system / point type combination\r\ntemplate class CoordSys, typename P1, typename P2>\r\nstruct default_strategy, CoordSys, 2, 2, P1, P2>\r\n{\r\n typedef degree_radian_vv type;\r\n};\r\n\r\n/// Specialization to transform from radian to degree for any coordinate system / point type combination\r\ntemplate class CoordSys, typename P1, typename P2>\r\nstruct default_strategy, CoordSys, 2, 2, P1, P2>\r\n{\r\n typedef degree_radian_vv type;\r\n};\r\n\r\n\r\n/// Specialization degree->radian in 3D\r\ntemplate class CoordSys, typename P1, typename P2>\r\nstruct default_strategy, CoordSys, 3, 3, P1, P2>\r\n{\r\n typedef degree_radian_vv_3 type;\r\n};\r\n\r\n/// Specialization radian->degree in 3D\r\ntemplate class CoordSys, typename P1, typename P2>\r\nstruct default_strategy, CoordSys, 3, 3, P1, P2>\r\n{\r\n typedef degree_radian_vv_3 type;\r\n};\r\n\r\n/// Specialization to transform from unit sphere(phi,theta) to XYZ\r\ntemplate \r\nstruct default_strategy\r\n{\r\n typedef from_spherical_polar_2_to_cartesian_3 type;\r\n};\r\n\r\n/// Specialization to transform from sphere(phi,theta,r) to XYZ\r\ntemplate \r\nstruct default_strategy\r\n{\r\n typedef from_spherical_polar_3_to_cartesian_3 type;\r\n};\r\n\r\ntemplate \r\nstruct default_strategy\r\n{\r\n typedef from_spherical_equatorial_2_to_cartesian_3 type;\r\n};\r\n\r\ntemplate \r\nstruct default_strategy\r\n{\r\n typedef from_spherical_equatorial_3_to_cartesian_3 type;\r\n};\r\n\r\n/// Specialization to transform from XYZ to unit sphere(phi,theta)\r\ntemplate \r\nstruct default_strategy\r\n{\r\n typedef from_cartesian_3_to_spherical_polar_2 type;\r\n};\r\n\r\ntemplate \r\nstruct default_strategy\r\n{\r\n typedef from_cartesian_3_to_spherical_equatorial_2 type;\r\n};\r\n\r\n/// Specialization to transform from XYZ to sphere(phi,theta,r)\r\ntemplate \r\nstruct default_strategy\r\n{\r\n typedef from_cartesian_3_to_spherical_polar_3 type;\r\n};\r\ntemplate \r\nstruct default_strategy\r\n{\r\n\ttypedef from_cartesian_3_to_spherical_equatorial_3 type;\r\n};\r\n\r\n\r\n} // namespace services\r\n\r\n\r\n#endif // DOXYGEN_NO_STRATEGY_SPECIALIZATIONS\r\n\r\n\r\n}} // namespace strategy::transform\r\n\r\n\r\n}} // namespace boost::geometry\r\n\r\n#endif // BOOST_GEOMETRY_STRATEGIES_STRATEGY_TRANSFORM_HPP\r\n", "meta": {"hexsha": "d51b5e8bf9c9e050ad381d022173c6b8fc38e09c", "size": 16187, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "master/core/third/boost/geometry/strategies/strategy_transform.hpp", "max_stars_repo_name": "importlib/klib", "max_stars_repo_head_hexsha": "a59837857689d0e60d3df6d2ebd12c3160efa794", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 198.0, "max_stars_repo_stars_event_min_datetime": "2015-01-13T05:47:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T04:46:46.000Z", "max_issues_repo_path": "master/core/third/boost/geometry/strategies/strategy_transform.hpp", "max_issues_repo_name": "isuhao/klib", "max_issues_repo_head_hexsha": "a59837857689d0e60d3df6d2ebd12c3160efa794", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 197.0, "max_issues_repo_issues_event_min_datetime": "2017-07-06T16:53:59.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-31T17:57:51.000Z", "max_forks_repo_path": "master/core/third/boost/geometry/strategies/strategy_transform.hpp", "max_forks_repo_name": "isuhao/klib", "max_forks_repo_head_hexsha": "a59837857689d0e60d3df6d2ebd12c3160efa794", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 139.0, "max_forks_repo_forks_event_min_datetime": "2015-01-15T20:09:31.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-31T15:21:16.000Z", "avg_line_length": 32.0534653465, "max_line_length": 123, "alphanum_fraction": 0.6738740965, "num_tokens": 4397, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.48438008427698437, "lm_q1q2_score": 0.2497560182328059}} {"text": "//---------------------------------------------------------------------------//\n// Copyright (c) 2018-2020 Mikhail Komarov \n// Copyright (c) 2020 Ilias Khairullin \n//\n// MIT License\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n//---------------------------------------------------------------------------//\n\n#ifndef CRYPTO3_PUBKEY_MODES_CREATE_KEY_HPP\n#define CRYPTO3_PUBKEY_MODES_CREATE_KEY_HPP\n\n#include \n#include \n\n#include \n#include \n\n#include \n\n#include \n#include \n\n#include \n\n#include \n#include \n#include \n\nnamespace nil {\n namespace crypto3 {\n //\n // CoeffsIterator - coefficients of polynomial\n //\n template::sss_public_key_group_type,\n typename ValueType = typename std::iterator_traits::value_type,\n typename SecretSharingScheme::template check_coeff_type = true>\n inline typename std::enable_if<\n std::is_same, SecretSharingScheme>::value ||\n std::is_same, SecretSharingScheme>::value,\n std::pair, std::vector>>>::type\n create_key(CoeffsIterator first, CoeffsIterator last, Number n) {\n BOOST_CONCEPT_ASSERT((boost::InputIteratorConcept));\n\n using privkeys_type = std::vector>;\n using sss_no_key_ops_type = typename pubkey::private_key::sss_public_key_no_key_ops_type;\n\n typename sss_no_key_ops_type::shares_type shares =\n nil::crypto3::deal_shares(first, last, n);\n privkeys_type privkeys;\n for (const auto &s : shares) {\n privkeys.emplace_back(s);\n }\n auto PK = pubkey::public_key(sss_no_key_ops_type::get_public_coeffs(first, last).front());\n return std::make_pair(PK, privkeys);\n }\n\n //\n // CoeffsRange - coefficients of polynomial\n //\n template::sss_public_key_group_type,\n typename ValueType = typename std::iterator_traits::value_type,\n typename SecretSharingScheme::template check_coeff_type = true>\n inline typename std::enable_if<\n std::is_same, SecretSharingScheme>::value ||\n std::is_same, SecretSharingScheme>::value,\n std::pair, std::vector>>>::type\n create_key(const CoeffsRange &r, Number n) {\n BOOST_RANGE_CONCEPT_ASSERT((boost::SinglePassRangeConcept));\n return create_key(r.begin(), r.end(), n);\n }\n\n //\n // InputIterator - public representation values of polynomial's coefficients\n //\n template::sss_public_key_group_type,\n typename ValueType = typename std::iterator_traits::value_type,\n typename SecretSharingScheme::template check_public_coeff_type = true>\n inline typename std::enable_if<\n std::is_same, SecretSharingScheme>::value,\n pubkey::private_key>::type\n create_key(CoeffsIterator first,\n CoeffsIterator last,\n typename SecretSharingScheme::share_type share,\n Number n) {\n BOOST_CONCEPT_ASSERT((boost::InputIteratorConcept));\n\n using privkey_type = pubkey::private_key;\n\n assert(static_cast(nil::crypto3::verify_share(first, last, share)));\n return privkey_type(share);\n }\n\n //\n // PublicCoeffsRange - public representation values of polynomial's coefficients\n //\n template::sss_public_key_group_type,\n typename ValueType = typename std::iterator_traits::value_type,\n typename SecretSharingScheme::template check_public_coeff_type = true>\n inline typename std::enable_if<\n std::is_same, SecretSharingScheme>::value,\n pubkey::private_key>::type\n create_key(const PublicCoeffsRange &r, typename SecretSharingScheme::share_type share, Number n) {\n BOOST_RANGE_CONCEPT_ASSERT((boost::SinglePassRangeConcept));\n return create_key(r.begin(), r.end(), share, n);\n }\n\n //\n // CoeffsIterator - coefficients of polynomial\n // InputIterator2 - participants' weights\n //\n template::sss_public_key_group_type,\n typename ValueType1 = typename std::iterator_traits::value_type,\n typename ValueType2 = typename std::iterator_traits::value_type,\n typename SecretSharingScheme::template check_coeff_type = true,\n typename SecretSharingScheme::template check_weight_type = true>\n inline typename std::enable_if<\n std::is_same,\n SecretSharingScheme>::value,\n std::pair, std::vector>>>::type\n create_key(CoeffsIterator first1, CoeffsIterator last1, WeightsIterator first2, WeightsIterator last2) {\n BOOST_CONCEPT_ASSERT((boost::InputIteratorConcept));\n BOOST_CONCEPT_ASSERT((boost::InputIteratorConcept));\n\n using privkeys_type = std::vector>;\n using sss_no_key_ops_type = typename pubkey::private_key::sss_public_key_no_key_ops_type;\n\n typename sss_no_key_ops_type::shares_type shares = nil::crypto3::deal_shares(\n first1, last1, first2, last2, std::distance(first2, last2));\n privkeys_type privkeys;\n for (const auto &s : shares) {\n privkeys.emplace_back(s, std::distance(first1, last1));\n }\n auto PK = pubkey::public_key(sss_no_key_ops_type::get_public_coeffs(first1, last1).front(),\n std::distance(first2, last2));\n return std::make_pair(PK, privkeys);\n }\n\n //\n // CoeffsRange - coefficients of polynomial\n // WeightsRange - participants' weights\n //\n template::sss_public_key_group_type,\n typename ValueType1 = typename std::iterator_traits::value_type,\n typename ValueType2 = typename std::iterator_traits::value_type,\n typename SecretSharingScheme::template check_coeff_type = true,\n typename SecretSharingScheme::template check_weight_type = true>\n inline typename std::enable_if<\n std::is_same,\n SecretSharingScheme>::value,\n std::pair, std::vector>>>::type\n create_key(const CoeffsRange &r1, const WeightsRange &r2) {\n BOOST_RANGE_CONCEPT_ASSERT((boost::SinglePassRangeConcept));\n BOOST_RANGE_CONCEPT_ASSERT((boost::SinglePassRangeConcept));\n return create_key(r1.begin(), r1.end(), r2.begin(), r2.end());\n }\n\n //\n // PublicCoeffsIterators - public representation values of polynomials' coefficients of other participants\n // SharesIterator - shares generated by other participants\n //\n template::sss_public_key_group_type,\n typename ValueType1 = typename std::iterator_traits<\n typename std::iterator_traits::value_type::iterator>::value_type,\n typename ValueType2 = typename std::iterator_traits::value_type,\n typename SecretSharingScheme::template check_public_coeff_type = true,\n typename SecretSharingScheme::template check_share_type = true>\n inline typename std::enable_if<\n std::is_same, SecretSharingScheme>::value,\n std::pair, pubkey::private_key>>::type\n create_key(PublicCoeffsIterators first1, PublicCoeffsIterators last1, SharesIterator first2,\n SharesIterator last2, Number n) {\n BOOST_CONCEPT_ASSERT((boost::InputIteratorConcept));\n BOOST_RANGE_CONCEPT_ASSERT((\n boost::SinglePassRangeConcept::value_type>));\n BOOST_CONCEPT_ASSERT((boost::InputIteratorConcept));\n assert(n == std::distance(first1, last1));\n assert(n == std::distance(first2, last2));\n\n using privkey_type = pubkey::private_key;\n\n auto it_coeffs = first1;\n auto it_share = first2;\n typename SecretSharingScheme::public_element_type PK = SecretSharingScheme::public_element_type::zero();\n while (it_coeffs != last1 && it_share != last2) {\n assert(static_cast(nil::crypto3::verify_share(*it_coeffs, *it_share)));\n PK = PK + *((*it_coeffs).begin());\n it_coeffs++;\n it_share++;\n }\n return std::make_pair(pubkey::public_key(PK),\n privkey_type(static_cast(\n nil::crypto3::deal_share(first2, last2))));\n }\n\n //\n // PublicCoeffsRanges - public representation values of polynomials' coefficients of other participants\n // SharesRange - shares generated by other participants\n //\n template::sss_public_key_group_type,\n typename ValueType1 = typename std::iterator_traits::value_type::iterator>::value_type,\n typename ValueType2 = typename std::iterator_traits::value_type,\n typename SecretSharingScheme::template check_public_coeff_type = true,\n typename SecretSharingScheme::template check_share_type = true>\n inline typename std::enable_if<\n std::is_same, SecretSharingScheme>::value,\n std::pair, pubkey::private_key>>::type\n create_key(const PublicCoeffsRanges &r, const SharesRange &shares, Number n) {\n BOOST_RANGE_CONCEPT_ASSERT((boost::SinglePassRangeConcept));\n BOOST_RANGE_CONCEPT_ASSERT(\n (boost::SinglePassRangeConcept<\n const typename std::iterator_traits::value_type>));\n BOOST_RANGE_CONCEPT_ASSERT((boost::SinglePassRangeConcept));\n return create_key(r.begin(), r.end(), shares.begin(), shares.end(), n);\n }\n } // namespace crypto3\n} // namespace nil\n\n#endif // include guard", "meta": {"hexsha": "d263bb84c815c4258438eab36b38aa8651551d8b", "size": 14703, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/nil/crypto3/pubkey/modes/algorithm/create_key.hpp", "max_stars_repo_name": "JasonCoombs/crypto3-pkmodes", "max_stars_repo_head_hexsha": "0dc8defe1a2199cb798df943b8665ec1a53c295b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/nil/crypto3/pubkey/modes/algorithm/create_key.hpp", "max_issues_repo_name": "JasonCoombs/crypto3-pkmodes", "max_issues_repo_head_hexsha": "0dc8defe1a2199cb798df943b8665ec1a53c295b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4.0, "max_issues_repo_issues_event_min_datetime": "2021-04-01T15:38:20.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-20T03:45:24.000Z", "max_forks_repo_path": "include/nil/crypto3/pubkey/modes/algorithm/create_key.hpp", "max_forks_repo_name": "JasonCoombs/crypto3-pkmodes", "max_forks_repo_head_hexsha": "0dc8defe1a2199cb798df943b8665ec1a53c295b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2022-02-13T21:40:59.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-20T03:13:02.000Z", "avg_line_length": 60.2581967213, "max_line_length": 120, "alphanum_fraction": 0.6729919064, "num_tokens": 3005, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.41869690935568665, "lm_q1q2_score": 0.24972471497828685}} {"text": "// This file is part of snark, a generic and flexible library for robotics research\n// Copyright (c) 2011 The University of Sydney\n// All rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are met:\n// 1. Redistributions of source code must retain the above copyright\n// notice, this list of conditions and the following disclaimer.\n// 2. Redistributions in binary form must reproduce the above copyright\n// notice, this list of conditions and the following disclaimer in the\n// documentation and/or other materials provided with the distribution.\n// 3. Neither the name of the University of Sydney nor the\n// names of its contributors may be used to endorse or promote products\n// derived from this software without specific prior written permission.\n//\n// NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE\n// GRANTED BY THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT\n// HOLDERS AND CONTRIBUTORS \\\"AS IS\\\" AND ANY EXPRESS OR IMPLIED\n// WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF\n// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n// DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR\n// BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\n// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE\n// OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN\n// IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n#include \"convert.h\"\n#include \"traits.h\"\n\n#include \n#include \n\nnamespace {\n\n using namespace snark::imaging;\n\n typedef std::vector< double > channels;\n\n // conversion does not have to be a linear operation, but many are; these function simplify setting up such conversions\n /*\n commented-out to remove a compiler warning (-Wunused-function)\n channels linear_combination( const channels & i, const Eigen::Vector3d & before, const Eigen::Matrix3d & m, const Eigen::Vector3d & after )\n {\n channels t = { i[0] + before(0), i[1] + before(1), i[2] + before(2) };\n return channels( { m(0,0) * t[0] + m(0,1) * t[1] + m(0,2) * t[2] + after(0)\n , m(1,0) * t[0] + m(1,1) * t[1] + m(1,2) * t[2] + after(1)\n , m(2,0) * t[0] + m(2,1) * t[1] + m(2,2) * t[2] + after(2) } );\n }\n\n channels linear_combination( const channels & i, const Eigen::Vector3d & before, const Eigen::Matrix3d & m )\n {\n channels t = { i[0] + before(0), i[1] + before(1), i[2] + before(2) };\n return channels( { m(0,0) * t[0] + m(0,1) * t[1] + m(0,2) * t[2]\n , m(1,0) * t[0] + m(1,1) * t[1] + m(1,2) * t[2]\n , m(2,0) * t[0] + m(2,1) * t[1] + m(2,2) * t[2] } );\n }\n */\n channels linear_combination( const channels & i, const Eigen::Matrix3d & m, const Eigen::Vector3d & after )\n {\n return channels( { m(0,0) * i[0] + m(0,1) * i[1] + m(0,2) * i[2] + after(0)\n , m(1,0) * i[0] + m(1,1) * i[1] + m(1,2) * i[2] + after(1)\n , m(2,0) * i[0] + m(2,1) * i[1] + m(2,2) * i[2] + after(2) } );\n }\n\n channels linear_combination( const channels & i, const Eigen::Matrix3d & m )\n {\n return channels( { m(0,0) * i[0] + m(0,1) * i[1] + m(0,2) * i[2]\n , m(1,0) * i[0] + m(1,1) * i[1] + m(1,2) * i[2]\n , m(2,0) * i[0] + m(2,1) * i[1] + m(2,2) * i[2] } );\n }\n\n typedef std::function< channels ( const channels & p ) > C;\n // TODO:\n // - outr can be duplicate to the explicit convert call in convert (or the other way around); sort out\n typedef std::pair< colorspace::cspace, range > half_key_t;\n typedef std::pair< half_key_t, half_key_t > conversion_key_t;\n typedef std::map< conversion_key_t, C > conversion_map_t;\n\n C scale( range inr, range outr )\n {\n double uin, lin, uout, lout;\n switch ( inr ) {\n case ub : uin = limits< ub >::upper(); lin = limits< ub >::lower(); break;\n case uw : uin = limits< uw >::upper(); lin = limits< uw >::lower(); break;\n case ui : uin = limits< ui >::upper(); lin = limits< ui >::lower(); break;\n case f : uin = limits< f >::upper(); lin = limits< f >::lower(); break;\n case d : uin = limits< d >::upper(); lin = limits< d >::lower(); break;\n default:\n COMMA_THROW( comma::exception, \"unknown input range '\" << stringify::from( inr ) << \"'\" );\n }\n\n switch ( outr ) {\n case ub : uout = limits< ub >::upper(); lout = limits< ub >::lower(); break;\n case uw : uout = limits< uw >::upper(); lout = limits< uw >::lower(); break;\n case ui : uout = limits< ui >::upper(); lout = limits< ui >::lower(); break;\n case f : uout = limits< f >::upper(); lout = limits< f >::lower(); break;\n case d : uout = limits< d >::upper(); lout = limits< d >::lower(); break;\n default:\n COMMA_THROW( comma::exception, \"unknown output range '\" << stringify::from( outr ) << \"'\" );\n }\n\n double factor = ( uout - lout ) / ( uin - lin );\n return [ factor, lin, lout ]( const channels & rhs ){\n return channels( { factor * ( rhs[0] - lin ) + lout\n , factor * ( rhs[1] - lin ) + lout\n , factor * ( rhs[2] - lin ) + lout } ); };\n }\n\n const conversion_map_t & conversions()\n {\n static conversion_map_t m;\n m[ std::make_pair( std::make_pair( colorspace::rgb, ub ), std::make_pair( colorspace::rgb, ub ) ) ] =\n []( const channels & i ){ return linear_combination( i, (Eigen::Matrix3d() << 1, 0, 0, 0, 1, 0, 0, 0, 1).finished() ); };\n m[ std::make_pair( std::make_pair( colorspace::rgb, f ), std::make_pair( colorspace::ypbpr, f ) ) ] =\n []( const channels & i ){ return linear_combination( i, (Eigen::Matrix3d() << 0.299, 0.587, 0.114, -0.168736, -0.331264, 0.5, 0.5, -0.418688, -0.081312).finished(), (Eigen::Vector3d() << 0, 0.5, 0.5).finished() ); };\n m[ std::make_pair( std::make_pair( colorspace::rgb, d ), std::make_pair( colorspace::ypbpr, d ) ) ] = m[ std::make_pair( std::make_pair( colorspace::rgb, f ), std::make_pair( colorspace::ypbpr, f ) ) ];\n m[ std::make_pair( std::make_pair( colorspace::rgb, f ), std::make_pair( colorspace::ypbpr, d ) ) ] = m[ std::make_pair( std::make_pair( colorspace::rgb, f ), std::make_pair( colorspace::ypbpr, f ) ) ];\n m[ std::make_pair( std::make_pair( colorspace::rgb, d ), std::make_pair( colorspace::ypbpr, f ) ) ] = m[ std::make_pair( std::make_pair( colorspace::rgb, f ), std::make_pair( colorspace::ypbpr, f ) ) ];\n m[ std::make_pair( std::make_pair( colorspace::rgb, f ), std::make_pair( colorspace::ycbcr, ub ) ) ] =\n []( const channels & i ){ return linear_combination( i, (Eigen::Matrix3d() << 65.481, 128.553, 24.966, -37.797, -74.203, 112.0, 112.0, -93.786, -18.214).finished(), (Eigen::Vector3d() << 16, 128, 128).finished() ); };\n m[ std::make_pair( std::make_pair( colorspace::rgb, d ), std::make_pair( colorspace::ycbcr, ub ) ) ] = m[ std::make_pair( std::make_pair( colorspace::rgb, f ), std::make_pair( colorspace::ycbcr, ub ) ) ];\n // this differs from commonly presented values, e.g., wiki@YCbCr page, that rounds numbers to 3 decimal digits\n m[ std::make_pair( std::make_pair( colorspace::rgb, ub ), std::make_pair( colorspace::ycbcr, ub ) ) ] =\n []( const channels & i ){ return linear_combination( i, (Eigen::Matrix3d() << 65.481, 128.553, 24.966, -37.797, -74.203, 112.0, 112.0, -93.786, -18.214).finished() / 255., (Eigen::Vector3d() << 16, 128, 128).finished() ); };\n {\n m[ std::make_pair( std::make_pair( colorspace::ycbcr, ub ), std::make_pair( colorspace::rgb, ub ) ) ] =\n []( const channels & i) {\n return channels( { 255/219. * ( i[0] - 16 ) + 255/112.*0.701 * ( i[2] - 128 )\n , 255/219. * ( i[0] - 16 ) - 255/112.*0.886*0.114/0.587 * ( i[1] - 128 ) - 255/112.*0.701*0.299/0.587 * ( i[2] - 128 )\n , 255/219. * ( i[0] - 16 ) + 255/112.*0.886 * ( i[1] - 128 ) } );\n };\n }\n return m;\n }\n\n C select( const conversion_map_t & m, const colorspace & inc, range inr, const colorspace & outc, range outr, bool recurse = true )\n {\n // the logic for searching for the conversion method:\n // 0. check for non-conversions: same colorspace, different (or even same) range; handle explicitly\n if ( inc.value == outc.value )\n {\n if ( inr == outr ) { return []( const channels & c ) { return c; }; }\n return scale( inr, outr );\n }\n // 1. if there is a direct conversion from (inc, inr) to (outc, outr) use it (data are stored as doubles on both in and out)\n {\n const conversion_key_t & key = std::make_pair( std::make_pair( inc.value, inr ), std::make_pair( outc.value, outr ) );\n conversion_map_t::const_iterator i = m.find( key );\n if ( i != m.end() ) { return i->second; }\n }\n // 2. if there is a conversion from (inc, inr) to (outc, default-range-of-outc) use it, then change range of outc (chain conversions)\n {\n range outdef = colorspace::default_range( outc.value );\n const conversion_key_t & key = std::make_pair( std::make_pair( inc.value, inr ), std::make_pair( outc.value, outdef ) );\n conversion_map_t::const_iterator i = m.find( key );\n if ( i != m.end() ) { return [ i, outdef, outr ]( const channels & c ){ return scale( outdef, outr )( i->second( c ) ); }; }\n }\n // 3. if there is a conversion from (inc, default-range-of-inc) to (outc, outr), use it after changing range of inc\n {\n range indef = colorspace::default_range( inc.value );\n const conversion_key_t & key = std::make_pair( std::make_pair( inc.value, indef ), std::make_pair( outc.value, outr ) );\n conversion_map_t::const_iterator i = m.find( key );\n if ( i != m.end() ) { return [ i, inr, indef ]( const channels & c ){ return i->second( scale( inr, indef )( c ) ); }; }\n }\n // 4. if there is a conversion from (inc, default-range-of-inc) to (outc, default-range-of-outc), change range of inc, apply conversion, then change range of outc\n {\n range indef = colorspace::default_range( inc.value );\n range outdef = colorspace::default_range( outc.value );\n const conversion_key_t & key = std::make_pair( std::make_pair( inc.value, indef ), std::make_pair( outc.value, outdef ) );\n conversion_map_t::const_iterator i = m.find( key );\n if ( i != m.end() ) { return [ i, inr, indef, outdef, outr ]( const channels & c ){ return scale( outdef, outr )( i->second( scale( inr, indef )( c ) ) ); }; }\n }\n // 5. if there is a conversion from (inc, inr ) to (rgb, some-range-of-rgb) and also conversion from (rgb, some-range-of-rgb) to (outc, outr)\n // ( covering all possible intermediate conversions through a recursive call), convert through the intermediate rgb\n {\n if ( recurse )\n {\n for ( range r : { ub, f } )\n {\n try {\n // do not stuck in infinite recursion\n C c0 = select( m, inc, inr, colorspace::rgb, r, false );\n C c1 = select( m, colorspace::rgb, r, outc, outr, false );\n return [ c0, c1 ]( const channels & c ){ return c1( c0( c ) ); };\n }\n catch ( comma::exception & )\n {}\n }\n }\n }\n // 6. if all the above fails, throw\n COMMA_THROW( comma::exception, \"conversion from colorspace \" << inc << \", range \" << stringify::from( inr ) << \" to colorspace \" << outc << \", range \" << stringify::from( outr ) << \" is not known\" );\n }\n\n typedef std::function< void ( const comma::csv::options & csv, const C & c ) > F;\n\n template< colorspace::cspace inc, range inr, colorspace::cspace outc, range outr, typename outt >\n void convert( const comma::csv::options & csv, const C & c )\n {\n pixel< double, inr > sample_in;\n comma::csv::input_stream< pixel< double, inr > > is( std::cin, csv, sample_in );\n comma::csv::options output_csv;\n output_csv.full_xpath = false;\n output_csv.flush = csv.flush;\n if( csv.binary() ) { output_csv.format( comma::csv::format::value< pixel< outt, outr > >() ); }\n pixel< outt, outr > sample_out;\n comma::csv::output_stream< pixel< outt, outr > > os( std::cout, output_csv, sample_out );\n comma::csv::tied< pixel< double, inr >, pixel< outt, outr > > tied( is, os );\n pixel< double, outr > op;\n while( is.ready() || std::cin.good() )\n {\n const pixel< double, inr > * ip = is.read();\n if( !ip ) { break; }\n c( ip->channel ).swap( op.channel );\n tied.append( pixel< outt, outr >( op ) );\n if ( output_csv.flush ) { std::cout.flush(); }\n }\n }\n\n template< colorspace::cspace inc, range inr, colorspace::cspace outc, range outr, range outt >\n F resolve_( typename std::enable_if< std::is_integral< typename range_traits< outr >::value_t >::value, typename range_traits< outr >::value_t >::type outr_v\n , typename std::enable_if< std::is_integral< typename range_traits< outt >::value_t >::value, typename range_traits< outt >::value_t >::type outt_v )\n {\n if ( outr_v == ub && outt_v == ub ) { return convert< inc, inr, outc, ub, typename range_traits< ub >::value_t >; }\n if ( outr_v == ub && outt_v == uw ) { return convert< inc, inr, outc, ub, typename range_traits< uw >::value_t >; }\n if ( outr_v == ub && outt_v == ui ) { return convert< inc, inr, outc, ub, typename range_traits< ui >::value_t >; }\n if ( outr_v == uw && outt_v == uw ) { return convert< inc, inr, outc, uw, typename range_traits< uw >::value_t >; }\n if ( outr_v == uw && outt_v == ui ) { return convert< inc, inr, outc, uw, typename range_traits< ui >::value_t >; }\n if ( outr_v == ui && outt_v == ui ) { return convert< inc, inr, outc, ui, typename range_traits< ui >::value_t >; }\n COMMA_THROW( comma::exception, \"unsupported combination of output range \" << stringify::from( outr ) << \" and type \" << stringify::from( outt ) );\n }\n\n template< colorspace::cspace inc, range inr, colorspace::cspace outc, range outr, range outt >\n F resolve_( typename std::enable_if< std::is_floating_point< typename range_traits< outr >::value_t >::value, typename range_traits< outr >::value_t >::type outr_v\n , typename std::enable_if< std::is_integral< typename range_traits< outt >::value_t >::value, typename range_traits< outt >::value_t >::type outt_v )\n {\n COMMA_THROW( comma::exception, \"cannot use integer output type \" << stringify::from( outt ) << \" for \" << stringify::from( outr ) << \" output range\" );\n }\n\n template< colorspace::cspace inc, range inr, colorspace::cspace outc, range outr, range outt >\n F resolve_( typename std::enable_if< std::is_integral< typename range_traits< outr >::value_t >::value, typename range_traits< outr >::value_t >::type outr_v\n , typename std::enable_if< std::is_floating_point< typename range_traits< outt >::value_t >::value, typename range_traits< outt >::value_t >::type outt_v )\n {\n return convert< inc, inr, outc, outr, typename range_traits< outt >::value_t >;\n }\n\n template< colorspace::cspace inc, range inr, colorspace::cspace outc, range outr, range outt >\n F resolve_( typename std::enable_if< std::is_floating_point< typename range_traits< outr >::value_t >::value, typename range_traits< outr >::value_t >::type outr_v\n , typename std::enable_if< std::is_floating_point< typename range_traits< outt >::value_t >::value, typename range_traits< outt >::value_t >::type outt_v )\n {\n return convert< inc, inr, outc, outr, typename range_traits< outt >::value_t >;\n }\n\n template< colorspace::cspace inc, range inr, colorspace::cspace outc, range outr, range outt >\n struct outt_\n {\n static F dispatch()\n {\n // shall not attempt instantiating if:\n // - outr is integer, outt is integer and outr > outt\n // - outr is float, outt is integer\n typename range_traits< outr >::value_t outr_v( outr );\n typename range_traits< outt >::value_t outt_v( outt );\n return resolve_< inc, inr, outc, outr, outt >( outr_v, outt_v );\n }\n };\n\n template< colorspace::cspace inc, range inr, colorspace::cspace outc, range outr >\n struct outr_\n {\n static F dispatch( range outt )\n {\n switch ( outt )\n {\n case ub: return outt_< inc, inr, outc, outr, ub >::dispatch( ); break;\n case uw: return outt_< inc, inr, outc, outr, uw >::dispatch( ); break;\n case ui: return outt_< inc, inr, outc, outr, ui >::dispatch( ); break;\n case f: return outt_< inc, inr, outc, outr, f >::dispatch( ); break;\n case d: return outt_< inc, inr, outc, outr, d >::dispatch( ); break;\n default:\n COMMA_THROW( comma::exception, \"unknown output storage format '\" << stringify::from( outt ) << \"'\" );\n }\n }\n };\n\n template< colorspace::cspace inc, range inr, colorspace::cspace outc >\n struct outc_\n {\n static F dispatch( range outr, range outt )\n {\n switch ( outr )\n {\n case ub: return outr_< inc, inr, outc, ub >::dispatch( outt ); break;\n case uw: return outr_< inc, inr, outc, uw >::dispatch( outt ); break;\n case ui: return outr_< inc, inr, outc, ui >::dispatch( outt ); break;\n case f: return outr_< inc, inr, outc, f >::dispatch( outt ); break;\n case d: return outr_< inc, inr, outc, d >::dispatch( outt ); break;\n default:\n COMMA_THROW( comma::exception, \"unknown output range '\" << stringify::from( outr ) << \"'\" );\n }\n }\n };\n\n template< colorspace::cspace inc, range inr >\n struct inr_\n {\n static F dispatch( const colorspace & outc, range outr, range outt )\n {\n switch ( outc.value )\n {\n case colorspace::rgb: return outc_< inc, inr, colorspace::rgb >::dispatch( outr, outt ); break;\n case colorspace::ypbpr: return outc_< inc, inr, colorspace::ypbpr >::dispatch( outr, outt ); break;\n case colorspace::ycbcr: return outc_< inc, inr, colorspace::ycbcr >::dispatch( outr, outt ); break;\n default:\n COMMA_THROW( comma::exception, \"unknown colorspace to '\" << std::string( outc ) << \"'\" );\n }\n }\n };\n\n template< colorspace::cspace inc >\n struct inc_\n {\n static F dispatch( range inr, const colorspace & outc, range outr, range outt )\n {\n switch ( inr )\n {\n case ub: return inr_< inc, ub >::dispatch( outc, outr, outt ); break;\n case uw: return inr_< inc, uw >::dispatch( outc, outr, outt ); break;\n case ui: return inr_< inc, ui >::dispatch( outc, outr, outt ); break;\n case f: return inr_< inc, f >::dispatch( outc, outr, outt ); break;\n case d: return inr_< inc, d >::dispatch( outc, outr, outt ); break;\n default:\n COMMA_THROW( comma::exception, \"unknown input range '\" << stringify::from( inr ) << \"'\" );\n }\n }\n };\n\n} // anonymous\n\nnamespace snark { namespace imaging {\n\n converter::F converter::dispatch( const colorspace & inc, range inr, const colorspace & outc, range outr, range outt )\n {\n const C & conv = select( conversions(), inc, inr, outc, outr );\n\n switch ( inc.value )\n {\n case colorspace::rgb: return std::bind( inc_< colorspace::rgb >::dispatch( inr, outc, outr, outt ), std::placeholders::_1, conv ); break;\n case colorspace::ypbpr: return std::bind( inc_< colorspace::ypbpr >::dispatch( inr, outc, outr, outt ), std::placeholders::_1, conv ); break;\n case colorspace::ycbcr: return std::bind( inc_< colorspace::ycbcr >::dispatch( inr, outc, outr, outt ), std::placeholders::_1, conv ); break;\n default:\n COMMA_THROW( comma::exception, \"unknown colorspace from '\" << std::string( inc ) << \"'\" );\n }\n }\n\n void converter::list( std::ostream & os )\n {\n const auto & allc = { colorspace( colorspace::rgb ), colorspace( colorspace::ycbcr ), colorspace( colorspace::ypbpr ) };\n const auto & allr = { ub, uw, ui, f, d };\n for ( const auto & inc : allc )\n {\n for ( auto inr : allr )\n {\n for ( const auto & outc : allc )\n {\n for ( auto outr : allr )\n {\n try\n {\n select( conversions(), inc, inr, outc, outr );\n os << inc << ',' << stringify::from( inr ) << ',' << outc << ',' << stringify::from( outr ) << std::endl;\n }\n catch ( comma::exception & )\n {}\n }\n }\n }\n }\n }\n\n} } // namespace snark { namespace imaging {\n", "meta": {"hexsha": "ff936e18879f148eef12f6bb1ebcdcbd35052a42", "size": 22384, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "imaging/color/convert.cpp", "max_stars_repo_name": "mission-systems-pty-ltd/snark", "max_stars_repo_head_hexsha": "2bc8a20292ee3684d3a9897ba6fee43fed8d89ae", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2022-03-27T00:24:37.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-27T00:24:37.000Z", "max_issues_repo_path": "imaging/color/convert.cpp", "max_issues_repo_name": "NEU-LC/snark", "max_issues_repo_head_hexsha": "db890f73f4c4bbe679405f3a607fd9ea373deb2c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "imaging/color/convert.cpp", "max_forks_repo_name": "NEU-LC/snark", "max_forks_repo_head_hexsha": "db890f73f4c4bbe679405f3a607fd9ea373deb2c", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2020-10-30T02:11:55.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-30T02:11:55.000Z", "avg_line_length": 58.1402597403, "max_line_length": 236, "alphanum_fraction": 0.5585686204, "num_tokens": 6225, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804478040616, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.2497192784732513}} {"text": "/* Copyright (c) 2010-2018, Delft University of Technology\n * All rigths reserved\n *\n * This file is part of the Tudat. Redistribution and use in source and\n * binary forms, with or without modification, are permitted exclusively\n * under the terms of the Modified BSD license. You should have received\n * a copy of the license with this file. If not, please or visit:\n * http://tudat.tudelft.nl/LICENSE.\n */\n\n#include \n#include \n#include \n\n#include \"Tudat/Astrodynamics/Aerodynamics/aerodynamics.h\"\n#include \"Tudat/Astrodynamics/Aerodynamics/flightConditions.h\"\n#include \"Tudat/Astrodynamics/Aerodynamics/standardAtmosphere.h\"\n#include \"Tudat/Astrodynamics/BasicAstrodynamics/oblateSpheroidBodyShapeModel.h\"\n#include \"Tudat/Mathematics/BasicMathematics/mathematicalConstants.h\"\n\nnamespace tudat\n{\n\nnamespace aerodynamics\n{\n\n\n//! Constructor, sets objects and functions from which relevant environment and state variables are retrieved.\nFlightConditions::FlightConditions( const std::shared_ptr< basic_astrodynamics::BodyShapeModel > shapeModel,\n const std::shared_ptr< reference_frames::AerodynamicAngleCalculator >\n aerodynamicAngleCalculator ):\n shapeModel_( shapeModel ),\n aerodynamicAngleCalculator_( aerodynamicAngleCalculator ),\n currentTime_( TUDAT_NAN )\n{\n // Link body-state function.\n bodyCenteredPseudoBodyFixedStateFunction_ = std::bind(\n &reference_frames::AerodynamicAngleCalculator::getCurrentAirspeedBasedBodyFixedState, aerodynamicAngleCalculator_ );\n\n // Check if given body shape is an oblate spheroid and set geodetic latitude function if so\n if( std::dynamic_pointer_cast< basic_astrodynamics::OblateSpheroidBodyShapeModel >( shapeModel ) != nullptr )\n {\n geodeticLatitudeFunction_ = std::bind(\n &basic_astrodynamics::OblateSpheroidBodyShapeModel::getGeodeticLatitude,\n std::dynamic_pointer_cast< basic_astrodynamics::OblateSpheroidBodyShapeModel >( shapeModel ),\n std::placeholders::_1, 1.0E-4 );\n }\n}\n\n//! Function to update all flight conditions.\nvoid FlightConditions::updateConditions( const double currentTime )\n{\n if( !( currentTime == currentTime_ ) )\n {\n currentTime_ = currentTime;\n\n // Update aerodynamic angles (but not angles w.r.t. body-fixed frame).\n if( aerodynamicAngleCalculator_!= nullptr )\n {\n aerodynamicAngleCalculator_->update( currentTime, false );\n }\n\n // Calculate state of vehicle in global frame and corotating frame.\n currentBodyCenteredAirspeedBasedBodyFixedState_ = bodyCenteredPseudoBodyFixedStateFunction_( );\n }\n}\n\n//! Constructor, sets objects and functions from which relevant environment and state variables are retrieved.\nAtmosphericFlightConditions::AtmosphericFlightConditions(\n const std::shared_ptr< aerodynamics::AtmosphereModel > atmosphereModel,\n const std::shared_ptr< basic_astrodynamics::BodyShapeModel > shapeModel,\n const std::shared_ptr< AerodynamicCoefficientInterface > aerodynamicCoefficientInterface,\n const std::shared_ptr< reference_frames::AerodynamicAngleCalculator > aerodynamicAngleCalculator,\n const std::function< double( const std::string& ) > controlSurfaceDeflectionFunction ):\n FlightConditions( shapeModel, aerodynamicAngleCalculator ),\n atmosphereModel_( atmosphereModel ),\n aerodynamicCoefficientInterface_( aerodynamicCoefficientInterface ),\n controlSurfaceDeflectionFunction_( controlSurfaceDeflectionFunction )\n{\n // Check if atmosphere requires latitude and longitude update.\n if( std::dynamic_pointer_cast< aerodynamics::StandardAtmosphere >( atmosphereModel_ ) == nullptr )\n {\n updateLatitudeAndLongitudeForAtmosphere_ = 1;\n }\n else\n {\n updateLatitudeAndLongitudeForAtmosphere_ = 0;\n }\n isLatitudeAndLongitudeSet_ = 0;\n\n if( updateLatitudeAndLongitudeForAtmosphere_ && aerodynamicAngleCalculator_== nullptr )\n {\n throw std::runtime_error( \"Error when making flight conditions, angles are to be updated, but no calculator is set\" );\n }\n}\n\n//! Function to set custom dependency of aerodynamic coefficients\nvoid AtmosphericFlightConditions::setAerodynamicCoefficientsIndependentVariableFunction(\n const AerodynamicCoefficientsIndependentVariables independentVariable,\n const std::function< double( ) > coefficientDependency )\n{\n if( ( independentVariable == mach_number_dependent ) ||\n ( independentVariable == angle_of_attack_dependent ) ||\n ( independentVariable == angle_of_sideslip_dependent )||\n ( independentVariable == altitude_dependent ) )\n {\n throw std::runtime_error(\n std::string( \"Error when setting aerodynamic coefficient function dependency, value of parameter \" ) +\n std::to_string( independentVariable ) +\n std::string(\", will not be used.\" ) );\n }\n else\n {\n customCoefficientDependencies_[ independentVariable ] = coefficientDependency;\n }\n}\n\n//! Function to update all flight conditions.\nvoid AtmosphericFlightConditions::updateConditions( const double currentTime )\n{\n if( !( currentTime == currentTime_ ) )\n {\n currentTime_ = currentTime;\n\n // Update aerodynamic angles (but not angles w.r.t. body-fixed frame).\n if( aerodynamicAngleCalculator_!= nullptr )\n {\n aerodynamicAngleCalculator_->update( currentTime, false );\n }\n\n // Calculate state of vehicle in global frame and corotating frame.\n currentBodyCenteredAirspeedBasedBodyFixedState_ = bodyCenteredPseudoBodyFixedStateFunction_( );\n\n updateAerodynamicCoefficientInput( );\n\n // Update angles from aerodynamic to body-fixed frame (if relevant).\n if( aerodynamicAngleCalculator_!= nullptr )\n {\n aerodynamicAngleCalculator_->update( currentTime, true );\n updateAerodynamicCoefficientInput( );\n }\n\n // Update aerodynamic coefficients.\n aerodynamicCoefficientInterface_->updateFullCurrentCoefficients(\n aerodynamicCoefficientIndependentVariables_, controlSurfaceAerodynamicCoefficientIndependentVariables_,\n currentTime_ );\n }\n}\n\n//! Function to (compute and) retrieve the value of an independent variable of aerodynamic coefficients\ndouble AtmosphericFlightConditions::getAerodynamicCoefficientIndependentVariable(\n const AerodynamicCoefficientsIndependentVariables independentVariableType,\n const std::string& secondaryIdentifier )\n{\n double currentIndependentVariable;\n switch( independentVariableType )\n {\n //Calculate Mach number if needed.\n case mach_number_dependent:\n currentIndependentVariable = getCurrentMachNumber( );\n break;\n //Get angle of attack if needed.\n case angle_of_attack_dependent:\n if( aerodynamicAngleCalculator_== nullptr )\n {\n throw std::runtime_error( \"Error, aerodynamic angle calculator is nullptr, but require angle of attack\" );\n }\n currentIndependentVariable = aerodynamicAngleCalculator_->getAerodynamicAngle(\n reference_frames::angle_of_attack );\n break;\n //Get angle of sideslip if needed.\n case angle_of_sideslip_dependent:\n if( aerodynamicAngleCalculator_== nullptr )\n {\n throw std::runtime_error( \"Error, aerodynamic angle calculator is nullptr, but require angle of sideslip\" );\n }\n currentIndependentVariable = aerodynamicAngleCalculator_->getAerodynamicAngle(\n reference_frames::angle_of_sideslip );\n break;\n case altitude_dependent:\n currentIndependentVariable = getCurrentAltitude( );\n break;\n case control_surface_deflection_dependent:\n {\n try\n {\n currentIndependentVariable = controlSurfaceDeflectionFunction_( secondaryIdentifier );\n }\n catch( std::runtime_error )\n {\n throw std::runtime_error( \"Error, control surface \" + secondaryIdentifier + \"not recognized when updating coefficients\" );\n }\n break;\n }\n default:\n if( customCoefficientDependencies_.count( independentVariableType ) == 0 )\n {\n throw std::runtime_error( \"Error, did not recognize aerodynamic coefficient dependency \"\n + std::to_string( independentVariableType ) );\n }\n else\n {\n currentIndependentVariable = customCoefficientDependencies_.at( independentVariableType )( );\n }\n }\n\n return currentIndependentVariable;\n}\n\n//! Function to update the independent variables of the aerodynamic coefficient interface\nvoid AtmosphericFlightConditions::updateAerodynamicCoefficientInput( )\n{\n aerodynamicCoefficientIndependentVariables_.clear( );\n // Calculate independent variables for aerodynamic coefficients.\n for( unsigned int i = 0; i < aerodynamicCoefficientInterface_->getNumberOfIndependentVariables( ); i++ )\n {\n aerodynamicCoefficientIndependentVariables_.push_back(\n getAerodynamicCoefficientIndependentVariable(\n aerodynamicCoefficientInterface_->getIndependentVariableName( i ) ) );\n }\n\n controlSurfaceAerodynamicCoefficientIndependentVariables_.clear( );\n for( unsigned int i = 0; i < aerodynamicCoefficientInterface_->getNumberOfControlSurfaces( ); i++ )\n {\n std::string currentControlSurface = aerodynamicCoefficientInterface_->getControlSurfaceName( i );\n for( unsigned int j = 0; j < aerodynamicCoefficientInterface_->getNumberOfControlSurfaceIndependentVariables( currentControlSurface ); j++ )\n {\n controlSurfaceAerodynamicCoefficientIndependentVariables_[ currentControlSurface ].push_back(\n getAerodynamicCoefficientIndependentVariable(\n aerodynamicCoefficientInterface_->getControlSurfaceIndependentVariableName(\n currentControlSurface, j ), currentControlSurface ) );\n }\n }\n}\n\n//! Function to set the angle of attack to trimmed conditions.\nstd::shared_ptr< TrimOrientationCalculator > setTrimmedConditions(\n const std::shared_ptr< AtmosphericFlightConditions > flightConditions )\n{\n // Create trim object.\n std::shared_ptr< TrimOrientationCalculator > trimOrientation =\n std::make_shared< TrimOrientationCalculator >(\n flightConditions->getAerodynamicCoefficientInterface( ) );\n\n // Create angle-of-attack function from trim object.\n std::function< std::vector< double >( ) > untrimmedIndependentVariablesFunction =\n std::bind( &AtmosphericFlightConditions::getAerodynamicCoefficientIndependentVariables,\n flightConditions );\n std::function< std::map< std::string, std::vector< double > >( ) > untrimmedControlSurfaceIndependentVariablesFunction =\n std::bind( &AtmosphericFlightConditions::getControlSurfaceAerodynamicCoefficientIndependentVariables,\n flightConditions );\n flightConditions->getAerodynamicAngleCalculator( )->setOrientationAngleFunctions(\n std::bind( &TrimOrientationCalculator::findTrimAngleOfAttackFromFunction, trimOrientation,\n untrimmedIndependentVariablesFunction, untrimmedControlSurfaceIndependentVariablesFunction ) );\n\n return trimOrientation;\n}\n\n} // namespace aerodynamics\n\n} // namespace tudat\n", "meta": {"hexsha": "a9a9e31ee9ae6443c4c4fd4158d20fddde1001c3", "size": 11677, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Tudat/Astrodynamics/Aerodynamics/flightConditions.cpp", "max_stars_repo_name": "J-Westin/tudat", "max_stars_repo_head_hexsha": "82ebe9e6e2dd51d0688b77960e62e980e6b8bcb8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Tudat/Astrodynamics/Aerodynamics/flightConditions.cpp", "max_issues_repo_name": "J-Westin/tudat", "max_issues_repo_head_hexsha": "82ebe9e6e2dd51d0688b77960e62e980e6b8bcb8", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Tudat/Astrodynamics/Aerodynamics/flightConditions.cpp", "max_forks_repo_name": "J-Westin/tudat", "max_forks_repo_head_hexsha": "82ebe9e6e2dd51d0688b77960e62e980e6b8bcb8", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.5687022901, "max_line_length": 148, "alphanum_fraction": 0.7112271988, "num_tokens": 2324, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.2496586845488787}} {"text": "/*\n * Adapt3DInterpolator.cpp\n *\n * Created on: Oct 29, 2010\n * Author: dberrios\n */\n\n#include \"Adapt3DInterpolator.h\"\n#include \"Adapt3D.h\" //include the smart values structure\n#include \"StringConstants.h\"\n#include \"MathHelper.h\"\n#include \n#include \n//#include \n#define MIN_RANGE -1e9\n#define MAX_RANGE +1e9\n#define NNODE_ADAPT3D 4\n#define LINEAR_INTERPOL\n\n//#define DEBUG\n//#define DEBUGS\nnamespace ccmc\n{\n\t/**\n\t * @param modelReader Pointer to the Model object containing the appropriate variable maps. Adapt3DInterpolator\n\t * should be returned by a Adapt3D::createNewInterpolator() call.\n\t */\n\tAdapt3DInterpolator::Adapt3DInterpolator(Model * modelReader)\n\t{\n\t\t// TODO Auto-generated constructor stub\n\t\tthis->modelReader = modelReader;\n\t\t/*** Open should have failed previosly, so they should exist! ***/\n\n\t\tthis->grid_reg_no = (modelReader->getGlobalAttribute(ccmc::strings::variables::grid_reg_no_)).getAttributeInt();\n\t\tthis->ndimn = (modelReader->getGlobalAttribute(ccmc::strings::variables::ndimn_)).getAttributeInt();\n\t\tthis->npoin = (modelReader->getGlobalAttribute(ccmc::strings::variables::npoin_)).getAttributeInt();\n\t\tthis->nelem = (modelReader->getGlobalAttribute(ccmc::strings::variables::nelem_)).getAttributeInt();\n\t\t//->nboun = (modelReader->getGlobalAttribute(ccmc::strings::variables::nboun_)).getAttributeInt();\n\t\t//this->nconi = (modelReader->getGlobalAttribute(ccmc::strings::variables::nconi_)).getAttributeInt();\n\t\tthis->coord = modelReader->getVariableFromMap(ccmc::strings::variables::coord_);\n\t\tthis->intmat = modelReader->getIntVariableFromMap(ccmc::strings::variables::intmat_);\n\t\t//this->unkno = (modelReader->getVariableFromMap(ccmc::strings::variables::unkno_));\n\n\t\tthis->smartSearchValues = ((Adapt3D*)(modelReader))->getSmartGridSearchValues();\n\t\t//indx = new int[nelem];\n\t\t//esup1 = new int[nelem*4];\n\t\t//esup2 = new int[npoin+1];\n\t\tthis->nnode = NNODE_ADAPT3D;\n\t\t//this->unstructured_grid_setup_done = this->setupUnstructuredGridSearch();\n\t\t//this->smartSearchSetup();\n\t\tthis->smartSearchValues->last_element_found = -1;\n//\t\tstd::cout << \"created Adapt3DInterpolator object\" << std::endl;\n\n\t}\n\n\t/**\n\t * @param variable\n\t * @param c0 X component of the position\n\t * @param c1 Y component of the position\n\t * @param c2 Z component of the position\n\t * @return\n\t */\n\tfloat Adapt3DInterpolator::interpolate(const std::string& variable, const float& c0, const float& c1,\n\t\t\tconst float& c2)\n\t{\n\n\t\tfloat dc0, dc1, dc2;\n\t\t//long variable_id = modelReader->getVariableID(variable);\n\t\treturn interpolate(variable, c0, c1, c2, dc0, dc1, dc2);\n\n\t}\n\n\t/**\n\t * @param variable_id\n\t * @param c0 X component of the position\n\t * @param c1 Y component of the position\n\t * @param c2 Z component of the position\n\t * @return\n\t */\n\tfloat Adapt3DInterpolator::interpolate(const long& variable_id, const float& c0, const float& c1, const float& c2)\n\t{\n\n\t\tfloat dc0, dc1, dc2;\n\t\treturn interpolate(variable_id, c0, c1, c2, dc0, dc1, dc2);\n\n\t}\n\n\t/**\n\t * Interpolation method. Note that using the variable ID is significantly faster than using the variable string.\n\t * @param variable_id A long representing the variable ID.\n\t * @param c0 X component of the position\n\t * @param c1 Y component of the position\n\t * @param c2 Z component of the position\n\t * @param dc0 Reference to a variable to store the delta for component 0\n\t * @param dc1 Reference to a variable to store the delta for component 1\n\t * @param dc2 Reference to a variable to store the delta for component 2\n\t * @return The interpolated value at position (c0,c1,c2) with deltas (dc0,dc1,dc2)\n\t */\n\tfloat Adapt3DInterpolator::interpolate(const long& variable_id, const float& c0, const float& c1, const float& c2,\n\t\t\tfloat& dc0, float& dc1, float& dc2)\n\t{\n\n\t\tstd::string variable = this->modelReader->getVariableName(variable_id);\n\t\treturn interpolate(variable, c0,c1,c2,dc0,dc1,dc2);\n\t}\n\n\t/**\n\t * Interpolation method. Note that using the variable ID is significantly faster than using the variable string.\n\t * @param variable The input variable.\n\t * @param c0 X component of the position\n\t * @param c1 Y component of the position\n\t * @param c2 Z component of the position\n\t * @param dc0 Reference to a variable to store the delta for component 0\n\t * @param dc1 Reference to a variable to store the delta for component 1\n\t * @param dc2 Reference to a variable to store the delta for component 2\n\t * @return The interpolated value at position (c0,c1,c2) with deltas (dc0,dc1,dc2)\n\t */\n\tfloat Adapt3DInterpolator::interpolate(const std::string& variable, const float& c0, const float& c1,\n\t\t\tconst float& c2, float& dc0, float& dc1, float& dc2)\n\t{\n\t\tfloat missingValue = this->modelReader->getMissingValue();\n\t\t//this->last_element_found = -1;\n\t\tif (!point_within_grid(c0,c1,c2))\n\t\t\treturn missingValue;\n\t\t//float rsun_in_meters = 7.0e8;\n\t\t//float unkno_local[9];\n\n\t\tlong counts[1] = { 0 };\n\t\tlong intervals[1] = { 1 };\n\n\t\tfloat interpolated_value;\n\n\t\tint clear_cache;\n\n\n\n\n\n\t\tint array_size;\n\t\tint istatus;\n\t\tint ielem;\n\n\t\t//char variable_name0[] = \"intmat\";\n\t\t//char variable_name1[] = \"coord\";\n\n\t\t/**\n\t\t* TODO: figure out what to do about the dc0,dc1,dc2 values\n\t\t*/\n\t\tdc0=dc1=dc2=.02f;\n\n\n\n\n\n\t\t/** lets see if required variables are in memory **/\n\n\n\t\t/**** since the cdf data is stored in r[meters], radians, radians or r, phi theta\n\t\t* but we are accepting input as r[AU], lon, lat - we must do some coordiante transformations\n\t\t*/\n\n\t\t/* convert rsun to meters */\n\t\t/*\n\t\t coord1[0] = X * rsun_in_meters;\n\t\t coord1[1] = Y * rsun_in_meters;\n\t\t coord1[2] = Z * rsun_in_meters;\n\t\t*/\n\n\n\n\n\t\tcounts[0] = 0; /*reset values after once through */\n\t\tintervals[0] = 1;\n\n\n\t\t/* for field line tracing,etc..., select appropriate variable number ie. bx_cdfNum|by_cdfNum|bz_cdfNum based on *variable_string */\n\t\t/* also select appropriate position arrays x, y , z for bx1, by1,bz1 */\n\n\t\t/* set default grid arrays and change if neccessary */\n\n\n\t\t//char *filename=\"adapt3d_kameleon_soln.cdf\";\n\n\n\n\n\t\tif ( variable == ccmc::strings::variables::bx_ || variable == ccmc::strings::variables::b1_)\n\t\t{\n\t\t //unkno_index=5;\n\t\t}\n\t\telse if ( variable == \"by\" || variable == \"b2\")\n\t\t{\n\t\t // unkno_index=6;\n\t\t}\n\t\telse if ( variable == \"bz\" || variable == \"b3\")\n\t\t{\n\t\t // unkno_index=7;\n\t\t}\n\t\telse if ( variable == ccmc::strings::variables::ux_ || variable == \"u1\")\n\t\t{\n\t\t //unkno_index=1;\n\t\t}\n\t\telse if ( variable == ccmc::strings::variables::uy_ || variable == \"u2\")\n\t\t{\n\t\t //unkno_index=2;\n\t\t}\n\t\telse if ( variable == ccmc::strings::variables::uz_ || variable == \"u3\")\n\t\t{\n\t\t //unkno_index=3;\n\t\t}\n\t\telse if (variable == ccmc::strings::variables::rho_)\n\t\t{\n\t\t //unkno_index=0;\n\t\t}\n\t\telse if (variable == ccmc::strings::variables::p_ || variable == ccmc::strings::variables::e_)\n\t\t{\n\t\t //unkno_index=4;\n\t\t}\n\t\telse if ( variable == ccmc::strings::variables::temp_ )\n\t\t{\n\t\t //unkno_index=8;\n\t\t}\n\t\telse\n\t\t{\n\t\t printf(\n\t\t\t\t\"ERROR:\\tcould not find cdf variable number for %s\\n\",\n\t\t\t\tvariable.c_str());\n\t\t printf(\n\t\t\t\t\"Valid Variable Names for ADAPT3D :\\n bx OR b1 \\n by OR b2 \\n bz OR b3\\n ux OR u1 \\n uy OR u2 \\n uz OR u3 \\n p OR e \\n OR temp\\n----------------------------------------------\\n\"\n\t\t\t\t);\n\t\t return 0;\n\t\t}\n\n\n\t\tclear_cache = 0;\n\t\t\t/* locate the grid element that contains the point coord1 */\n\t\t//ielem = smartSearch(c0,c1,c2);\n\t\tielem = findElement(c0,c1,c2, clear_cache);\n\t\t//Element * element = this->smartSearchValues->parent->findElement(c0, c1, c2);\n\t\t//if (element != NULL)\n\t\t//\tielem = this->smartSearchValues->parent->findElement(c0, c1, c2)->getIndex();\n\t\t//else ielem = -1;\n#ifdef DEBUG\n\t std::cerr << \"ielem: \" << ielem << \" for position \" << c0 << \",\" << c1 << \",\" << c2 << std::endl;\n#endif\n\t\tinterpolated_value = missingValue; /* test value */\n\n\t\tif(ielem > -1)\n\t\t{\n\t\t interpolated_value = interpolate_adapt3d_solution(c0, c1, c2, ielem, variable);\n#ifdef DEBUG\n\t\t std::cout << \"MIN_RANGE: \" << MIN_RANGE << \" this->missingValue: \" << this->missingValue << \" interpolated_value: \" << interpolated_value << std::endl;\n#endif\n\t\t this->smartSearchValues->last_element_found = ielem;\n\t\t} else {\n\t\t\t//printf(\"Failed to find point in grid\\n\");\n\t\t\tthis->smartSearchValues->last_element_found = -1;\n\t\t}\n\n\n\t\t/* return interpolated_value */\n\t\tif (interpolated_value >= MIN_RANGE && interpolated_value <= MAX_RANGE && interpolated_value != missingValue)\n\t\t{\n\n\t\t //std::cerr << \"position: \" << c0 << \",\" << c1 << \",\" << c2 << \": \" << (float)interpolated_value << std::endl;\n\t\t return (float)interpolated_value;\n\n\t\t}\n\t\telse\n\t\t{\n\t\t //std::cerr << \"position: \" << c0 << \",\" << c1 << \",\" << c2 << \": \" << this->missingValue << std::endl;\n\t\t return missingValue;\n\t\t}\n\t}\n\n\n\n\n\n\n\n\tint Adapt3DInterpolator::smartSearch(const float& c0, const float& c1, const float& c2)\n\t{\n//#define DEBUGS\n\t\tint lfound, mask[NNODE_ADAPT3D], try_grid_search;\n\n\t\tint i,j,k,ielem,inode,jnode ;\n\t\tint ifound, jelem, kelem;\n\t\tint node_order[NNODE_ADAPT3D];\n\t\tint i_node, j_node, k_node, k_node_hi;\n\t\tint next_node, i_order;\n\n\t\tint nelems_checked;\n\t\tint clear_cache;\n\n\t\tfloat shapex[NNODE_ADAPT3D];\n\t\tfloat radius;\n\n\t\tfloat distance[NNODE_ADAPT3D];\n\n\t\tfloat size_of_last_element;\n\n#ifdef DELAUNEY_SEARCH\n int iteration, iteration_max;\n int next_element, next_element0;\n int in0,in1,in2,in3,iselect;\n int jnext,jk,jrand;\n int i_s,j_s,k_s;\n float x_last_element,y_last_element,z_last_element,distance0;\n float x,y,z,r,t,p;\n#endif\n\n//std::cout << \"smart search\" << std::endl;\n/*----------------------------------------------------------------\n!\n! Step A\n!\n! First check the last_element_found to see if the new point is still\n! inside it. If yes, then set ifound=.true'\n*/\n\t\tifound = -1;\n\t\tif( this->smartSearchValues->last_element_found >= 0 )\n\t\t{\n#ifdef DEBUGS\n\t\t\tprintf(\"Checkin if still in last element \\n\");\n#endif\n\n\t\t\tifound = chkineln(c0,c1,c2, this->smartSearchValues->last_element_found ,shapex);\n\t\t\tnelems_checked = 1;\n\t\t}\n//std::cout << \"ifound: \" << ifound << std::endl;\n/*--------*/\n\t\tif( ifound == 0 )\n\t\t{\n/*--------*/\n\n\t\t\t#ifdef DEBUGS\n\t\t\tprintf(\"Point is still in starting element! \\n\");\n\t\t\tstd::cerr << \"this->smartSearchValues->last_element_found: \" << this->smartSearchValues->last_element_found << std::endl;\n\t\t\t#endif\n\t\t\tkelem = this->smartSearchValues->last_element_found;\n\t\t\t//this->smartSearchValues->still_in_same_element++;\n\n\n/*--------*/\n\t\t} else\n\t\t{\n/*--------*/\n\n\n/* If we have a starting_element number set to begin the search */\n\t\t\tif( this->smartSearchValues->last_element_found >= 0 )\n\t\t\t{\n\n//std::cout << \"starting search\" << std::endl;\n#ifdef DELAUNEY_SEARCH\n/* Delauney algorithm */\n ifound=1;\n iteration=0;\n next_element=this->smartSearchValues->last_element_found;\n iteration_max=DELAUNEY_ITER_MAX;\n while ((ifound != 0) && (iteration < iteration_max)) {\n kelem = chkineln(c0,c1,c2,next_element ,shapex);\n\n in0=(*intmat)[ index_2d_to_1d(next_element,0,4) ];\n in1=(*intmat)[ index_2d_to_1d(next_element,1,4) ];\n in2=(*intmat)[ index_2d_to_1d(next_element,2,4) ];\n in3=(*intmat)[ index_2d_to_1d(next_element,3,4) ];\n x_last_element = 0.25*(\n (*coord)[ index_2d_to_1d(in0,0,3) ]\n +(*coord)[ index_2d_to_1d(in1,0,3) ]\n +(*coord)[ index_2d_to_1d(in2,0,3) ]\n +(*coord)[ index_2d_to_1d(in3,0,3) ] );\n y_last_element = 0.25*(\n (*coord)[ index_2d_to_1d(in0,1,3) ]\n +(*coord)[ index_2d_to_1d(in1,1,3) ]\n +(*coord)[ index_2d_to_1d(in2,1,3) ]\n +(*coord)[ index_2d_to_1d(in3,1,3) ] );\n z_last_element = 0.25*(\n (*coord)[ index_2d_to_1d(in0,2,3) ]\n +(*coord)[ index_2d_to_1d(in1,2,3) ]\n +(*coord)[ index_2d_to_1d(in2,2,3) ]\n +(*coord)[ index_2d_to_1d(in3,2,3) ] );\n distance0=std::sqrt( (x_last_element-c0)*(x_last_element-c0)\n + (y_last_element-c1)*(y_last_element-c1)\n + (z_last_element-c2)*(z_last_element-c2) );\n\n/* If the distance from last element found is too great compared with the element size then force use of the structured grid */\n\n#ifdef DEBUGS\n printf(\"Delauney iteration no %d\\n\",iteration);\n radius=sqrt(x_last_element*x_last_element+y_last_element*y_last_element+z_last_element*z_last_element);\n printf(\"Center of last element %d in search : radius %e\\n\",next_element,radius);\n printf(\"Center of last element %d in search %e %e %e\\n\",next_element,x_last_element,y_last_element,z_last_element);\n x=x_last_element;\n y=y_last_element;\n z=z_last_element;\n Math::convert_xyz_to_rthetaphi(x,y,z,&r,&t,&p);\n printf(\"Center of last element (rtp) %d in search %e %e %e\\n\",next_element,r,t,p);\n\n i_s = (int)( (r-this->smartSearchValues->xl_sg)/this->smartSearchValues->dx_sg );\n j_s = (int)( (t-this->smartSearchValues->yl_sg)/this->smartSearchValues->dy_sg );\n k_s = (int)( (p-this->smartSearchValues->zl_sg)/this->smartSearchValues->dz_sg );\n printf(\"Located in structured cell %d %d %d\\n\",i_s,j_s,k_s);\n\n\n printf(\"Distance from last element to search point is %e\\n\",distance0);\n printf(\"Search pt coords %e %e %e \\n\",c0,c1,c2);\n printf(\"Search pt radius %e\\n\",std::sqrt(c0*c0+c1*c1+c2*c2));\n fflush(stdout);\n#endif\n next_element0=next_element;\n jrand=(int)(3.0001 *(float)rand() / (float)RAND_MAX );\n jk=0;\n iselect=-1;\n while ((iselect == -1) && (jk < 4)) {\n jnext=(jk+jrand)%4;\n if(shapex[ jnext ] < 0.) {\n next_element=(*this->smartSearchValues->facing_elements)[ index_2d_to_1d(next_element0,jnext,4) ];\n if(next_element > -1) {\n iselect=jnext;\n#ifdef DEBUGS\n } else {\n printf(\"Loop %d: Face %d is a boundary with next_element=%d. Skip to next face\\n\",jk,jnext,next_element);\n printf(\"shapex %e %e %e %e\\n\",shapex[0],shapex[1],shapex[2],shapex[3]);\n fflush(stdout);\n#endif\n }\n#ifdef DEBUGS\n printf(\"jrand=%d\\n\",jrand);\n printf(\"iselect=%d\\n\",iselect);\n fflush(stdout);\n#endif\n }\n jk=jk+1;\n }\n\n\n iteration++;\n\n/* If only face in direction of search point is a boundary then end search and reset last-element_found */\n if(next_element == -999) {\n iteration=iteration_max;\n this->smartSearchValues->last_element_found=-1;\n kelem=-1;\n // this->smartSearchValues->outside_grid += 1;\n }\n\n if(kelem == 0) {\n ifound=0;\n#ifdef DEBUGS\n printf(\"Delauney search successful : found in element %d \\n\",next_element);\n // fflush(stdout);\n#endif\n if(iteration-1 < DELAUNEY_ITER_MAX) (*this->smartSearchValues->delauney_search_iteration_profile)[iteration-1] += 1;\n }\n }\n if(ifound == 0) kelem=next_element;\n\n/* End of Delauney algorithm */\n#endif /* ifdef DELAUNEY_SEARCH */\n\n\n } /* if( last_element_found .ge. 0 ) */\n\n/*--------*/\n } /* if( ifound .eq. 0) */\n/*--------*/\n\n\n#ifdef DEBUGS\n if( ifound != 0) {\n printf(\"Smart search failed! \\n\");\n printf(\"search_point_coords %e %e %e \\n\",c0,c1,c2);\n fflush(stdout);\n\n if(kelem > 0) {\n printf(\"Found in element %i \\n\",kelem);\n fflush(stdout);\n } else {\n printf(\"Failed to locate element in grid \\n\");\n fflush(stdout);\n }\n }\n#endif\n\n\n\t\treturn kelem;\n\n\t}\n\n\tint Adapt3DInterpolator::findElement(const float& c0, const float& c1, const float& c2, int clear_cache)\n\t{\n\n//#define DEBUGS\n\n\t\t//std::cout << \"entered findElement\" << std::endl;\n\n\t\tint\t\t\tielem,kelem, inode;\n\t\tint i_s,j_s,k_s,i,j,k,indx_start,indx_end;\n\t\tint indx1,ifound,just_found,jelem;\n\t\tfloat x,y,z;\n //float* shapex = new float[nnode];\n\t\tint\t\t\tnext_element, next_element0, iselect;\n\n\t\tint in0,in1,in2,in3,iteration;\n\t\tint delta_i;\n\t\tfloat x_last_element,y_last_element,z_last_element;\n\t\tfloat distance;\n\t\tfloat ddx,ddy,ddz,ss,xx,yy,zz,radius;\n\t\tint new_del,i_new,j_new,k_new;\n\t\tfloat r, t, p;\n\t\tfloat distance0,size_of_last_element;\n\n\t\tint i_min,i_max;\n\t\tint j_min,j_max;\n\t\tint k_min,k_max;\n\t\tint j0,k0;\n\n\t\tkelem=-1;\n\t\tielem=-1;\n\t\tifound=-1;\n\t\tif ( point_within_grid(c0, c1, c2) == 1)\n\t\t{\n\n\t if(clear_cache == 1) this->smartSearchValues->last_element_found=-1;\n\t#ifdef DEBUG\n\t printf(\"0find_element: (*coord)[0][0-2] : %e %e %e \\n\",(*coord)[ index_2d_to_1d(0,0,npoin) ],(*coord)[ index_2d_to_1d(0,1,npoin) ],(*coord)[ index_2d_to_1d(0,2,npoin) ]);\n\t#endif\n\n\t /* If available, use the last element found to begin the search */\n\t if(this->smartSearchValues->last_element_found != -1) {\n\t \t in0=(*intmat)[ index_2d_to_1d(this->smartSearchValues->last_element_found,0,4) ];\n\t \t in1=(*intmat)[ index_2d_to_1d(this->smartSearchValues->last_element_found,1,4) ];\n\t \t x_last_element=(*coord)[ index_2d_to_1d(in0,0,3) ];\n\t \t y_last_element=(*coord)[ index_2d_to_1d(in0,1,3) ];\n\t \t z_last_element=(*coord)[ index_2d_to_1d(in0,2,3) ];\n\t \t distance0=std::sqrt( (x_last_element-c0)*(x_last_element-c0)\n\t \t\t\t \t \t + (y_last_element-c1)*(y_last_element-c1)\n\t \t\t\t \t \t + (z_last_element-c2)*(z_last_element-c2));\n\t \t size_of_last_element=std::sqrt(\n\t \t\t\t ( (*coord)[ index_2d_to_1d(in0,0,3) ] -(*coord)[ index_2d_to_1d(in1,0,3) ] )*\n\t \t\t\t ( (*coord)[ index_2d_to_1d(in0,0,3) ] -(*coord)[ index_2d_to_1d(in1,0,3) ] )\n\t \t\t\t +( (*coord)[ index_2d_to_1d(in0,1,3) ] -(*coord)[ index_2d_to_1d(in1,1,3) ] )*\n\t \t\t\t ( (*coord)[ index_2d_to_1d(in0,1,3) ] -(*coord)[ index_2d_to_1d(in1,1,3) ] )\n\t \t\t\t +( (*coord)[ index_2d_to_1d(in0,2,3) ] -(*coord)[ index_2d_to_1d(in1,2,3) ] )*\n\t \t\t\t ( (*coord)[ index_2d_to_1d(in0,2,3) ] -(*coord)[ index_2d_to_1d(in1,2,3) ] ) );\n\n#ifdef DEBUGS\n\t \t printf(\"coords %e %e %e %e %e %e\\n\", (*coord)[ index_2d_to_1d(in0,0,3) ],(*coord)[ index_2d_to_1d(in1,0,3) ],\n\t \t\t\t (*coord)[ index_2d_to_1d(in0,1,3) ] ,(*coord)[ index_2d_to_1d(in1,1,3) ],\n\t \t\t\t (*coord)[ index_2d_to_1d(in0,2,3) ] ,(*coord)[ index_2d_to_1d(in1,2,3) ] );\n\t \t printf(\"distance to element : %e\\n\",distance0);\n\t \t printf(\"element size : %e\\n\",size_of_last_element);\n\t \t printf(\"Ratio of distance to element size : %e\\n\",distance0/size_of_last_element);\n#endif\n\n\n\t \t /* If the distance from last element found is too great compared with the element size then force use of the structured grid */\n\t \t if (distance0/size_of_last_element > 50.) {\n\t \t\t this->smartSearchValues->last_element_found = -1;\n#ifdef DEBUGS\n\t \t\t printf(\"Force new structured grid search\\n\");\n#endif\n\t \t }\n\n\t }\n\t /* If necessary generate a first guess element to begin search */\n\t if(this->smartSearchValues->last_element_found == -1) {\n\n\n#ifdef CARTESIAN_S_GRID\n\t \t x = cintp[0];\n\t \t y = cintp[1];\n\t \t z = cintp[2];\n#endif /* CARTESIAN_S_GRID */\n#ifdef SPHERICAL_S_GRID\n\t \t Math::convert_xyz_to_rthetaphi(c0,c1,c2,&r,&t,&p);\n\t \t x=r;\n\t \t y=t;\n\t \t z=p;\n#endif /* SPHERICAL_S_GRID */\n\n#ifdef DEBUGS\n\t \t printf(\"find_element: Searching for point x y z = %e %e %e\\n\",x,y,z);\n#endif\n\t \t i_s = (int)( (x-this->smartSearchValues->xl_sg)/this->smartSearchValues->dx_sg );\n\t \t j_s = (int)( (y-this->smartSearchValues->yl_sg)/this->smartSearchValues->dy_sg );\n\t \t k_s = (int)( (z-this->smartSearchValues->zl_sg)/this->smartSearchValues->dz_sg );\n\t \t /* pmn fix section */\n\t \t i_s = std::min(i_s,nx_sg-1);\n\t \t i_s = std::max(i_s,0);\n\t \t j_s = std::min(j_s,ny_sg-1);\n\t \t j_s = std::max(j_s,0);\n\t \t k_s = std::min(k_s,nz_sg-1);\n\t \t k_s = std::max(k_s,0);\n\t \t /* end pmn fix section */\n#ifdef DEBUGS\n\t \t printf(\"Located in structured cell %d %d %d\\n\",i_s,j_s,k_s);\n#endif\n\t \t if(this->smartSearchValues->nelems_in_cell[k_s][j_s][i_s] > 0) {\n\t \t\t indx_start = this->smartSearchValues->start_index[k_s][j_s][i_s];\n\t \t\t indx_end = this->smartSearchValues->end_index[k_s][j_s][i_s];\n\t \t\t delta_i=(int)( (float)(indx_end-indx_start) * ( (float)rand() / (float)RAND_MAX ) );\n\t \t\t this->smartSearchValues->last_element_found = (*this->smartSearchValues->indx)[indx_start + delta_i];\n#ifdef DEBUGS\n\t \t\t printf(\"Reseting last_element_found from structured grid to %d delta_i %d\\n\",this->smartSearchValues->last_element_found,indx_end-indx_start);\n#endif\n\t \t } else {\n\t \t\t this->smartSearchValues->last_element_found = -1;\n#ifdef DEBUGS\n\t \t\t printf(\"Structured cell has no elements - Trying the node list now\\n\");\n\t \t\t printf(\"nnodes_in_cell %d %d %d is %d \\n\",i_s,j_s,k_s,this->smartSearchValues->nnodes_in_cell[k_s][j_s][i_s]);\n\t \t\t fflush(stdout);\n#endif\n\t \t\t if(this->smartSearchValues->nnodes_in_cell[k_s][j_s][i_s] > 0) {\n\t \t\t\t indx_start = this->smartSearchValues->start_index_nodes[k_s][j_s][i_s];\n\t \t\t\t indx_end = this->smartSearchValues->end_index_nodes[k_s][j_s][i_s];\n#ifdef DEBUGS\n\t \t\t\t printf(\"nnodes_in_cell %d %d %d is %d \\n\",i_s,j_s,k_s,this->smartSearchValues->nnodes_in_cell[k_s][j_s][i_s]);\n\t \t\t\t printf(\"indx_start indx_end %d %d\\n\",indx_start,indx_end);\n\t \t\t\t fflush(stdout);\n#endif\n\t \t\t\t delta_i=(int)( (float)(indx_end-indx_start) * ( (float)rand() / (float)RAND_MAX ) );\n\t \t\t\t last_node_found = (*this->smartSearchValues->indx_nodes)[indx_start + delta_i];\n#ifdef DEBUGS\n\t \t\t\t printf(\"last_node_found=%d\\n\",last_node_found);\n\t \t\t\t printf(\"coords of last node are %e %e %e\\n\",(*coord)[last_node_found*3],\n\t \t\t\t\t\t (*coord)[last_node_found*3+1],(*coord)[last_node_found*3+2]);\n\t \t\t\t fflush(stdout);\n#endif\n\t \t\t\t this->smartSearchValues->last_element_found = this->smartSearchValues->esup1->at( this->smartSearchValues->esup2->at(last_node_found) );\n#ifdef DEBUGS\n\t \t\t\t printf(\"Node search found element %d as starting point for search\\n\", this->smartSearchValues->last_element_found);\n\t \t\t\t //fflush(stdout);\n#endif\n\t \t\t } else {\n\n#ifdef DEBUGS\n\t \t\t\t printf(\"Structured cell is empty - Node search also failed\\n\");\n\t \t\t\t printf(\"Start scanning neighbor cells using elements\\n\");\n#endif\n\t \t\t\t /* search immediate neighbor cells for search starting point */\n\n\t \t\t\t /* Locate a neighboring cell of the structured grid that contains a node */\n\t \t\t\t iselect = 1 ;\n\n\t \t\t\t i_min = std::max(0,i_s-1);\n\t \t\t\t i_max = std::min(nx_sg-1,i_s+1);\n#ifdef CARTESIAN_S_GRID\n\t \t\t\t j_min = max(0,j_s-1);\n\t \t\t\t j_max = min(ny_sg-1,j_s+1);\n\t \t\t\t k_min = max(0,k_s-1);\n\t \t\t\t k_max = min(nz_sg-1,k_s+1);\n#endif /* CARTESIAN_S_GRID */\n#ifdef SPHERICAL_S_GRID\n\t \t\t\t j_min = std::max(j_s-1, 0);\t\t\t/* pmn fix */\n\t \t\t\t j_max = std::min(j_s+1, ny_sg-1); \t/* pmn fix */\n\t \t\t\t k_min = k_s-1;\n\t \t\t\t k_max = k_s+1;\n#endif /* SPHERICAL_S_GRID */\n\n\t \t\t\t /* if using elements for search */\n\t \t\t\t for(k=k_min;ksmartSearchValues->nelems_in_cell[k0][j0][i]);\n#endif\n\t \t\t\t\t\t\t if(this->smartSearchValues->nelems_in_cell[k0][j0][i]>0) {\n\t \t\t\t\t\t\t\t iselect=0;\n\t \t\t\t\t\t\t\t i_s=i;\n\t \t\t\t\t\t\t\t j_s=j0;\n\t \t\t\t\t\t\t\t k_s=k0;\n\t \t\t\t\t\t\t }\n\t \t\t\t\t\t }}}\n\t \t\t\t if(iselect==0) {\n#ifdef DEBUGS\n\t \t\t\t\t printf(\"Found a starting element in neighbor %d %d %d\\n\",i_s,j_s,k_s);\n#endif\n\t \t\t\t\t indx_start = this->smartSearchValues->start_index[k_s][j_s][i_s];\n\t \t\t\t\t indx_end = this->smartSearchValues->end_index[k_s][j_s][i_s];\n\t \t\t\t\t delta_i=(int)( (float)(indx_end-indx_start) * ( (float)rand() / (float)RAND_MAX ) );\n\t \t\t\t\t this->smartSearchValues->last_element_found = (*this->smartSearchValues->indx)[indx_start + delta_i];\n#ifdef DEBUGS\n\t \t\t\t\t printf(\"Element is %d\\n\",this->smartSearchValues->last_element_found);\n#endif\n\t \t\t\t }\n\n\t \t\t\t /* if using nodes for search */\n\t \t\t\t if(iselect == 1) {\n#ifdef DEBUGS\n\t \t\t\t\t printf(\"Neighbor search using elements failed also\\n\");\n\t \t\t\t\t printf(\"Start scanning neighbor cells using nodes\\n\");\n#endif\n\t \t\t\t\t for(k=k_min;ksmartSearchValues->nnodes_in_cell[k0][j0][i]);\n#endif\n\t \t\t\t\t\t\t\t if(this->smartSearchValues->nnodes_in_cell[k0][j0][i]>0) {\n\t \t\t\t\t\t\t\t\t iselect=0;\n\t \t\t\t\t\t\t\t\t i_s=i;\n\t \t\t\t\t\t\t\t\t j_s=j0;\n\t \t\t\t\t\t\t\t\t k_s=k0;\n\t \t\t\t\t\t\t\t }\n\t \t\t\t\t\t\t }}}\n\t \t\t\t\t if(iselect==0) {\n#ifdef DEBUGS\n\t \t\t\t\t\t printf(\"Found a starting node in neighbor %d %d %d\\n\",i_s,j_s,k_s);\n#endif\n\t \t\t\t\t\t indx_start = this->smartSearchValues->start_index_nodes[k_s][j_s][i_s];\n\t \t\t\t\t\t indx_end = this->smartSearchValues->end_index_nodes[k_s][j_s][i_s];\n\t \t\t\t\t\t delta_i=(int)( (float)(indx_end-indx_start) * ( (float)rand() / (float)RAND_MAX ) );\n\t \t\t\t\t\t last_node_found = (*this->smartSearchValues->indx_nodes)[indx_start + delta_i];\n#ifdef DEBUGS\n\t \t\t\t\t\t printf(\"Node is %d\\n\",last_node_found);\n#endif\n\t \t\t\t\t\t this->smartSearchValues->last_element_found = this->smartSearchValues->esup1->at( this->smartSearchValues->esup2->at(last_node_found) );\n#ifdef DEBUGS\n\t \t\t\t\t\t printf(\"Element is %d\\n\",this->smartSearchValues->last_element_found);\n\t \t\t\t\t } else {\n\t \t\t\t\t\t printf(\"\\n\\n Final part of search failed\\n\\n\\n\");\n#endif\n\t \t\t\t\t }\n\n\t \t\t\t\t /* end of immediate neighbor search */\n\n\t \t\t\t\t /* If still not found search up to 2 neighbors away */\n\t \t\t\t\t if(iselect == 1) {\n\n\t \t\t\t\t\t i_min = std::max(0,i_s-2);\n\t \t\t\t\t\t i_max = std::min(nx_sg-1,i_s+2);\n#ifdef CARTESIAN_S_GRID\n\t \t\t\t\t\t j_min = max(0,j_s-2);\n\t \t\t\t\t\t j_max = min(ny_sg-1,j_s+2);\n\t \t\t\t\t\t k_min = max(0,k_s-2);\n\t \t\t\t\t\t k_max = min(nz_sg-1,k_s+2);\n#endif /* CARTESIAN_S_GRID */\n#ifdef SPHERICAL_S_GRID\n\t \t\t\t\t\t j_min = std::max(j_s-2, 0);\n\t \t\t\t\t\t j_max = std::min(j_s+2, ny_sg-1);\n\t \t\t\t\t\t k_min = k_s-2;\n\t \t\t\t\t\t k_max = k_s+2;\n#endif /* SPHERICAL_S_GRID */\n\n\t \t\t\t\t\t /* if using elements for search */\n\t \t\t\t\t\t for(k=k_min;ksmartSearchValues->nelems_in_cell[k0][j0][i]);\n#endif\n\t \t\t\t\t\t\t\t\t if(this->smartSearchValues->nelems_in_cell[k0][j0][i]>0) {\n\t \t\t\t\t\t\t\t\t\t iselect=0;\n\t \t\t\t\t\t\t\t\t\t i_s=i;\n\t \t\t\t\t\t\t\t\t\t j_s=j0;\n\t \t\t\t\t\t\t\t\t\t k_s=k0;\n\t \t\t\t\t\t\t\t\t }\n\t \t\t\t\t\t\t\t }}}\n\t \t\t\t\t\t if(iselect==0) {\n#ifdef DEBUGS\n\t \t\t\t\t\t\t printf(\"Found a starting element in neighbor %d %d %d\\n\",i_s,j_s,k_s);\n#endif\n\t \t\t\t\t\t\t indx_start = this->smartSearchValues->start_index[k_s][j_s][i_s];\n\t \t\t\t\t\t\t indx_end = this->smartSearchValues->end_index[k_s][j_s][i_s];\n\t \t\t\t\t\t\t delta_i=(int)( (float)(indx_end-indx_start) * ( (float)rand() / (float)RAND_MAX ) );\n\t \t\t\t\t\t\t this->smartSearchValues->last_element_found = (*this->smartSearchValues->indx)[indx_start + delta_i];\n#ifdef DEBUGS\n\t \t\t\t\t\t\t printf(\"Element is %d\\n\",this->smartSearchValues->last_element_found);\n#endif\n\t \t\t\t\t\t }\n\n\t \t\t\t\t }\n\t \t\t\t\t /* end of 2 neighbor search */\n\n\n\t \t\t\t }\n\t \t\t\t /* next cell to pick from should be i_s,j_s,k_s */\n\n\n\t \t\t }\n\t \t }\n\n\n\t }\n//\t std::cout << \"last_element_found = \" << this->smartSearchValues->last_element_found << std::endl;\n\t /* If available, use the last element found to begin the search */\n\t if(this->smartSearchValues->last_element_found != -1) kelem = smartSearch(c0,c1,c2);\n\n\n\t this->smartSearchValues->last_element_found=kelem;\n\t previous_c0=c0;\n\t previous_c1=c1;\n\t previous_c2=c2;\n\n#ifdef DEBUGS\n\t printf(\"Exiting find element with last_element_found=%d\\n\",this->smartSearchValues->last_element_found);\n#endif\n\n\n\n\t\t} else\n\t\t{\n#ifdef DEBUGS\n\t\t\t//this->smartSearchValues->outside_grid += 1;\n\t\t\tthis->smartSearchValues->last_element_found = -1;\n\t\t\tprevious_c0 = 0.f;\n\t\t\tprevious_c1 = 0.f;\n\t\t\tprevious_c2 = 0.f;\n#endif\n\t\t}\n\t return kelem;\n\n\t/* end subroutine find_element */\n\t}\n\n\tint Adapt3DInterpolator::index_2d_to_1d( int i1, int i2, int n2)\n\t{\n\t/* converts a 2D array index into a flat 1D index */\n\t int idx = n2*i1 + i2;\n\n\t return idx;\n\t}\n\n\tint Adapt3DInterpolator::point_within_grid( const float& c0, const float& c1, const float& c2)\n\t{\n\t\t/*\n\t\t!\n\t\t! This function test to see if the given point (coord) is inside\n\t\t! the grid bounds. This function requires specific knowledge of the\n\t\t! grid type and range. It will need a customized function for each model\n\t\t! used with this search.\n\t\t*/\n\n\n\t float radius;\n\t int within_bounds = 1;\n\n\t radius=std::sqrt(c0*c0+c1*c1+c2*c2);\n\t if(c0 < this->smartSearchValues->xl_gr)\n\t {\n\t \t return 0;\n\n\t } else if(c0 > this->smartSearchValues->xr_gr)\n\t {\n\t \t return 0;\n\n\t } else if (c1 < this->smartSearchValues->yl_gr)\n\t {\n\t \t return 0;\n\n\t } else if (c1 > this->smartSearchValues->yr_gr)\n\t {\n\t \t return 0;\n\n\n\t } else if(c2 < this->smartSearchValues->zl_gr)\n\t {\n\t \t return 0;\n\n\n\t } else if(c2 > this->smartSearchValues->zr_gr)\n\t {\n\t \t return 0;\n\n\n\t }\n\t if(radius < INNER_RADIUS) return 0;\n\t if(radius > OUTER_RADIUS) return 0;\n\n\n\t return within_bounds;\n\n\n\t}\n\n\tint Adapt3DInterpolator::point_within_grid( const float * scoord )\n\t{\n\t\t/*\n\t\t!\n\t\t! This function test to see if the given point (coord) is inside\n\t\t! the grid bounds. This function requires specific knowledge of the\n\t\t! grid type and range. It will need a customized function for each model\n\t\t! used with this search.\n\t\t*/\n\n\n\t float radius;\n\t int within_bounds = 1;\n\n\t radius=std::sqrt(scoord[0]*scoord[0]+scoord[1]*scoord[1]+scoord[2]*scoord[2]);\n\t if(scoord[0] < this->smartSearchValues->xl_gr)\n\t {\n\t \t within_bounds = 0;\n\n\t \t std::cerr << \"scoord[0]: \" << scoord[0] << \" < \" << this->smartSearchValues->xl_gr << std::endl;\n\t } else if(scoord[0] > this->smartSearchValues->xr_gr)\n\t {\n\t \t within_bounds = 0;\n\t \t std::cerr << \"scoord[0]: \" << scoord[0] << \" > \" << this->smartSearchValues->xr_gr << std::endl;\n\t } else if (scoord[1] < this->smartSearchValues->yl_gr)\n\t {\n\t \t within_bounds = 0;\n\t \t std::cerr << \"scoord[1]: \" << scoord[1] << \" < \" << this->smartSearchValues->yl_gr << std::endl;\n\t } else if (scoord[1] > this->smartSearchValues->yr_gr)\n\t {\n\t \t within_bounds = 0;\n\t \t std::cerr << \"scoord[1]: \" << scoord[1] << \" > \" << this->smartSearchValues->yr_gr << std::endl;\n\n\t } else if(scoord[2] < this->smartSearchValues->zl_gr)\n\t {\n\t \t within_bounds = 0;\n\t \t std::cerr << \"scoord[2]: \" << scoord[2] << \" < \" << this->smartSearchValues->zl_gr << std::endl;\n\n\t } else if(scoord[2] > this->smartSearchValues->zr_gr)\n\t {\n\t \t within_bounds = 0;\n\t \t std::cerr << \"scoord[2]: \" << scoord[2] << \" > \" << this->smartSearchValues->zr_sg << std::endl;\n\n\t }\n\t if(radius < INNER_RADIUS) within_bounds = 0;\n\t if(radius > OUTER_RADIUS) within_bounds = 0;\n\n\n\t return within_bounds;\n\n\t}\n\n\tvoid Adapt3DInterpolator::calculation1(const float& a, const float& b, const float& c, const float& d, const float& e, float& result)\n\t{\n\t\tresult = a*(b*c-d*e);\n\n\t}\n\n\n\n int Adapt3DInterpolator::chkineln( const float& c0, const float& c1, const float& c2, int ielem , float * shapex)\n\t{\n\n \t//\n//#define DEBUG\n\t/*\n\t!... mesh arrays\n\t*/\n\t/*\n\t\tinteger,intent(in) :: ndimn,npoin,nnode,nelem\n\t\tinteger,intent(in) :: intmat(nnode,nelem)\n\t\tinteger,intent(in) :: ielem\n\t\treal*8,intent(in) :: coord(ndimn,npoin),cintp(ndimn)\n\n\t\treal*8,intent(out) :: shapex(nnode)\n\t*/\n\n\t\tint ipa,ipb,ipc,ipd;\n\t\tint ierro;\n\t\tfloat xa,ya,za,xba,yba,zba,xca,yca,zca,xda,yda,zda;\n\t\tfloat xpa,ypa,zpa;\n\t\tfloat deter,detin,shmin,shmax;\n\t\tfloat rin11,rin12,rin13;\n\t\tfloat rin21,rin22,rin23;\n\t\tfloat rin31,rin32,rin33;\n\n\n\t/*\n\t! data tolow/ -0.005 /\n\t! data tolhi/ 1.005 /\n\t! data c00/ 0.0 /\n\t! data c10/ 1.0 /\n\t!\n\t!... this sub sees if element ielem contains point cintp,\n\t! writing the shape-function values into ==> shape\n\t!\n\t!... find the local coordinates\n\t!\n\t*/\n\t\tipa = intmat->at(index_2d_to_1d(ielem,0,4));\n\t\tipb = intmat->at(index_2d_to_1d(ielem,1,4));\n\t\tipc = intmat->at(index_2d_to_1d(ielem,2,4));\n\t\tipd = intmat->at(index_2d_to_1d(ielem,3,4));\n\n\t\t//std::cerr << \"npoin: \" << npoin << \" ndimn: \" << ndimn << \" ipa: \" << ipa << \" ipb: \" << ipb << \" ipc: \" << ipc << \" ipd: \" << ipd << std::endl;\n\t\txa = (*coord)[index_2d_to_1d(ipa,0,3)];\n\t\tya = (*coord)[index_2d_to_1d(ipa,1,3)];\n\t\tza = (*coord)[index_2d_to_1d(ipa,2,3)];\n\t\txba = (*coord)[index_2d_to_1d(ipb,0,3)] - xa;\n\t\tyba = (*coord)[index_2d_to_1d(ipb,1,3)] - ya;\n\t\tzba = (*coord)[index_2d_to_1d(ipb,2,3)] - za;\n\t\txca = (*coord)[index_2d_to_1d(ipc,0,3)] - xa;\n\t\tyca = (*coord)[index_2d_to_1d(ipc,1,3)] - ya;\n\t\tzca = (*coord)[index_2d_to_1d(ipc,2,3)] - za;\n\t\txda = (*coord)[index_2d_to_1d(ipd,0,3)] - xa;\n\t\tyda = (*coord)[index_2d_to_1d(ipd,1,3)] - ya;\n\t\tzda = (*coord)[index_2d_to_1d(ipd,2,3)] - za;\n\n\t\tfloat t1 = 0.f;\n\t\tfloat t2 = 0.f;\n\t\tfloat t3 = 0.f;\n\t\tt1 = xba*(yca*zda - zca*yda);\n\t\tt2 = yba*(xca*zda - zca*xda);\n\t\tt3 = zba*(xca*yda - yca*xda);\n\t\t//a*(b*c-d*e);\n//\t\tboost::thread thread1(&Adapt3DInterpolator::calculation1,boost::ref(xba),boost::ref(yca),boost::ref(zda),boost::ref(zca),boost::ref(yda), boost::ref(t1));\n//\t\tboost::thread thread2(&Adapt3DInterpolator::calculation1,boost::ref(yba),boost::ref(xca),boost::ref(zda),boost::ref(zca),boost::ref(xda), boost::ref(t2));\n//\t\tboost::thread thread3(&Adapt3DInterpolator::calculation1,boost::ref(zba),boost::ref(xca),boost::ref(yda),boost::ref(yca),boost::ref(xda), boost::ref(t3));\n//\t\tthread1.join();\n//\t\tthread2.join();\n//\t\tthread3.join();\n\t\t//calculation1(xba,yca,zda,zca,yda, t1);\n\t\t//calculation1(yba,xca,zda,zca,xda, t2);\n\t\t//calculation1(zba,xca,yda,yca,xda, t3);\n\t\t//float deter = xba*(yca*zda-zca*yda) - yba*(xca*zda-zca*xda) + zba*(xca*yda-yca*xda);\n\t\tdeter = t1 - t2 + t3;\n\n\n\t#ifdef DEBUG\n//\t\tstd::cerr << \"xa: \" << xa << \" ya: \" << ya << \" za: \" << za << \" xba: \" << xba;\n//\t\tstd::cerr << \" yba: \" << yba << \" zba: \" << zba << \" xca: \" << xca << \" yca: \" << yca;\n//\t\tstd::cerr << \" zca: \" << zca << \" xda: \" << xda << \" yda: \" << yda << \" zda: \" << zda << std::endl;\n\t\t printf(\"(*coord)[ipa]= %d %e %e %e \\n\",ipa,(*coord)[index_2d_to_1d(ipa,0, 3)],(*coord)[index_2d_to_1d(ipa,1,3)],(*coord)[index_2d_to_1d(ipa,2,3)]);\n\t\t std::cerr << \"deter= \" << deter << std::endl;\n\t#endif\n\t/* detin = c10/deter */\n\t\tdetin = 1.0/deter;\n\n\t\trin11 = detin*(yca*zda-zca*yda);\n\t\trin12 =-detin*(xca*zda-zca*xda);\n\t\trin13 = detin*(xca*yda-yca*xda);\n\t\trin21 =-detin*(yba*zda-zba*yda);\n\t\trin22 = detin*(xba*zda-zba*xda);\n\t\trin23 =-detin*(xba*yda-yba*xda);\n\t\trin31 = detin*(yba*zca-zba*yca);\n\t\trin32 =-detin*(xba*zca-zba*xca);\n\t\trin33 = detin*(xba*yca-yba*xca);\n\n\t\txpa = c0-xa;\n\t\typa = c1-ya;\n\t\tzpa = c2-za;\n\t/*\n\t!... local coordinates & shape-function values\n\t*/\n\t\tshapex[1] = rin11*xpa + rin12*ypa + rin13*zpa;\n\t\tshapex[2] = rin21*xpa + rin22*ypa + rin23*zpa;\n\t\tshapex[3] = rin31*xpa + rin32*ypa + rin33*zpa;\n\t\tshapex[0] = 1.0 - shapex[1] - shapex[2] - shapex[3];\n\t#ifdef DEBUG\n//\t\t printf(\"cintp= %e %e %e \\n\",cintp[0],cintp[1],cintp[2]);\n//\t\t printf(\"xa-za= %e %e %e \\n\",xa,ya,za);\n\t\t //std::cerr << \"rin11: \" << rin11 << \" xpa: \" << xpa << \" rin12: \" << rin12 << \" ypa: \" << ypa << \" rin13: \" << rin13 << \" zpa: \" << zpa << std::endl;\n\t\tstd::cerr << \"cintp: \" << c0 << \",\" << c1 << \",\" << c2 << std::endl;\n\t\tstd::cerr << \"ielem: \" << ielem << \" shapex = \" << shapex[0] << \" \" << shapex[1] << \" \" << shapex[2] << \" \" << shapex[3] << std::endl;\n\t#endif\n\t/* shape(1) = c10 - shape(2) - shape(3) - shape(4)\n\t!\n\t!... max/min of these shape-functions\n\t!\n\t*/\n\t\t//float t = shapex[0] + shapex[1] + shapex[2] + shapex[3];\n\t\tshmin = ccmc::Math::ffindmin(shapex,4);\n\t\tshmax = ccmc::Math::ffindmax(shapex,4);\n\t/*\n\t!... see if in the element\n\t!\n\t! if(shmin .ge. tolow .and. shmax .le. tolhi) then\n\t*/\n\t\tif ( (shmin > 0.) && (shmax <= 1.0) && !std::isnan(shapex[0])) {\n\t\t\t ierro = 0;\n\t\t} else {\n\t\t\t ierro = 1;\n\t\t}\n\n\t/*\n\t!... control output\n\t! write(*,*)' ielem,shmin,shmax,ierro=',ielem,shmin,shmax,ierro\n\t*/\n\n\t#ifdef DEBUG\n\t\tif( ierro == 0) {\n\t\t printf(\"shmin= %e \\n\",shmin);\n\t\t printf(\"shmax= %e \\n\",shmax);\n\t\t printf(\"ierro= %d \\n\",ierro);\n\t\t printf(\"cintp= %e %e %e \\n\",c0,c1,c2);\n\t\t printf(\"node 1 = %e %e %e %d \\n\",(*coord)[index_2d_to_1d(ipa,0,3)],(*coord)[index_2d_to_1d(ipa,1,3)],(*coord)[index_2d_to_1d(ipa,2,3)],ipa);\n\t\t printf(\"node 2 = %e %e %e %d \\n\",(*coord)[index_2d_to_1d(ipb,0,3)],(*coord)[index_2d_to_1d(ipb,1,3)],(*coord)[index_2d_to_1d(ipb,2,3)],ipb);\n\t\t printf(\"node 3 = %e %e %e %d \\n\",(*coord)[index_2d_to_1d(ipc,0,3)],(*coord)[index_2d_to_1d(ipc,1,3)],(*coord)[index_2d_to_1d(ipc,2,3)],ipc);\n\t\t printf(\"node 4 = %e %e %e %d \\n\",(*coord)[index_2d_to_1d(ipd,0,3)],(*coord)[index_2d_to_1d(ipd,1,3)],(*coord)[index_2d_to_1d(ipd,2,3)],ipd);\n\t\t }\n\t#endif\n\n\t\t return ierro;\n\n\t/* end subroutine chkineln */\n\t}\n\n float Adapt3DInterpolator::interpolate_adapt3d_solution(const float& x, const float& y, const float& z, int ielem, const std::string& variable)\n {\n \tfloat missingValue = this->modelReader->getMissingValue();\n /*\n * Interpolate values of unkno to position coord in element ielem\n */\n\n\n int ipa,ipb,ipc,ipd;\n int iv;\n float x1,y1,z1;\n float x2,y2,z2;\n float x3,y3,z3;\n float x4,y4,z4;\n float vol,vol6;\n float a1,b1,c1,d1;\n float a2,b2,c2,d2;\n float a3,b3,c3,d3;\n float a4,b4,c4,d4;\n float f1,f2,f3,f4;\n\n\n ipa = intmat->at(index_2d_to_1d(ielem,0,4));\n ipb = intmat->at(index_2d_to_1d(ielem,1,4));\n ipc = intmat->at(index_2d_to_1d(ielem,2,4));\n ipd = intmat->at(index_2d_to_1d(ielem,3,4));\n x1 = (*coord)[index_2d_to_1d(ipa,0,3)];\n y1 = (*coord)[index_2d_to_1d(ipa,1,3)];\n z1 = (*coord)[index_2d_to_1d(ipa,2,3)];\n x2 = (*coord)[index_2d_to_1d(ipb,0,3)];\n y2 = (*coord)[index_2d_to_1d(ipb,1,3)];\n z2 = (*coord)[index_2d_to_1d(ipb,2,3)];\n x3 = (*coord)[index_2d_to_1d(ipc,0,3)];\n y3 = (*coord)[index_2d_to_1d(ipc,1,3)];\n z3 = (*coord)[index_2d_to_1d(ipc,2,3)];\n x4 = (*coord)[index_2d_to_1d(ipd,0,3)];\n y4 = (*coord)[index_2d_to_1d(ipd,1,3)];\n z4 = (*coord)[index_2d_to_1d(ipd,2,3)];\n\n\n\n #ifdef TEST_CASE1\n x1=0.;\n y1=0.;\n z1=0.;\n x2=1.;\n y2=0.;\n z2=0.;\n x3=0.;\n y3=2.;\n z3=0.;\n x4=0.;\n y4=0.;\n z4=3.;\n #endif\n\n// #ifdef LINEAR_INTERPOL\n a1 = x2*(y3*z4-z3*y4)+y2*(z3*x4-z4*x3)+z2*(x3*y4-x4*y3);\n b1 = - ( y3*z4-z3*y4 + y2*(z3-z4) + z2*(y4-y3) );\n c1 = - ( x2*(z4-z3) + (z3*x4-z4*x3) + z2*(x3-x4) );\n d1 = - ( x2*(y3-y4) + y2*(x4-x3) + (x3*y4-y3*x4) );\n\n a2 = x3*(y4*z1-z4*y1)+y3*(z4*x1-z1*x4)+z3*(x4*y1-x1*y4);\n b2 = - ( y4*z1-z4*y1 + y3*(z4-z1) + z3*(y1-y4) );\n c2 = - ( x3*(z1-z4) + (z4*x1-z1*x4) + z3*(x4-x1) );\n d2 = - ( x3*(y4-y1) + y3*(x1-x4) + (x4*y1-y4*x1) );\n\n a3 = x4*(y1*z2-z1*y2)+y4*(z1*x2-z2*x1)+z4*(x1*y2-x2*y1);\n b3 = - ( y1*z2-z1*y2 + y4*(z1-z2) + z4*(y2-y1) );\n c3 = - ( x4*(z2-z1) + (z1*x2-z2*x1) + z4*(x1-x2) );\n d3 = - ( x4*(y1-y2) + y4*(x2-x1) + (x1*y2-y1*x2) );\n\n a4 = x1*(y2*z3-z2*y3)+y1*(z2*x3-z3*x2)+z1*(x2*y3-x3*y2);\n b4 = - ( y2*z3-z2*y3 + y1*(z2-z3) + z1*(y3-y2) );\n c4 = - ( x1*(z3-z2) + (z2*x3-z3*x2) + z1*(x2-x3) );\n d4 = - ( x1*(y2-y3) + y1*(x3-x2) + (x2*y3-y2*x3) );\n\n vol6 = a1 + x1*b1 + y1*c1 + z1*d1;\n vol = vol6/6.;\n\n #ifdef TEST_CASE1\n printf(\"Volume = %e\\n\",vol);\n printf(\"Correct Volume should be 1.0\\n\");\n #endif\n\n const std::vector * vData = modelReader->getVariableFromMap(variable);\n if (vData == NULL || vData->size() == 0)\n {\n \t std::cerr << \"missing value\" << std::endl;\n \t return missingValue;\n }\n /* for ( iv=0; iv<9; iv++) {\n\n unkno_local[iv] = 0.;\n f1 = (a1 + b1*x + c1*y + d1*z)/vol6;\n f2 = -(a2 + b2*x + c2*y + d2*z)/vol6;\n f3 = (a3 + b3*x + c3*y + d3*z)/vol6;\n f4 = -(a4 + b4*x + c4*y + d4*z)/vol6;\n\n unkno_local[iv] = f1*(*unkno)[ index_2d_to_1d(ipa,iv,npoin,NVARS_ADAPT3D))+f2*(*unkno)[ index_2d_to_1d(ipb,iv,npoin,NVARS_ADAPT3D) ]\n +f3*(*unkno)[ index_2d_to_1d(ipc,iv,npoin,NVARS_ADAPT3D) ]+f4*(*unkno)[ index_2d_to_1d(ipd,iv,npoin,NVARS_ADAPT3D) ] ;\n\n }*/\n\n /*\n * int Adapt3DInterpolator::index_2d_to_1d( int i1, int i2, int n1, int n2)\n\t\t\t{\n\n\t\t\t\t int idx = n2*i1 + i2;\n\n\t\t\t\t return idx;\n\t\t\t}\n */\n f1 = (a1 + b1*x + c1*y + d1*z)/vol6;\n f2 = -(a2 + b2*x + c2*y + d2*z)/vol6;\n f3 = (a3 + b3*x + c3*y + d3*z)/vol6;\n f4 = -(a4 + b4*x + c4*y + d4*z)/vol6;\n// std::cout << \"a1: \" << a1 << \" a2: \" << a2 << \" a3: \" << a3 << \" a4: \" << a4 << std::endl;\n// std::cout << \"b1: \" << b1 << \" b2: \" << b2 << \" b3: \" << b3 << \" b4: \" << b4 << std::endl;\n// std::cout << \"c1: \" << c1 << \" c2: \" << c2 << \" c3: \" << c3 << \" c4: \" << c4 << std::endl;\n// std::cout << \"d1: \" << d1 << \" d2: \" << d2 << \" d3: \" << d3 << \" d4: \" << d4 << std::endl;\n#ifdef DEBUG\n std::cout << \"vol6: \" << vol6 << \" vol: \" << vol << std::endl;\n std::cout << \"f1: \" << f1 << \" f2: \" << f2 << \" f3: \" << f3 << \" f4: \" << f4 << std::endl;\n std::cout << \"ipa: \" << ipa << \" ipb: \" << ipb << \" ipc: \" << ipc << \" ipd: \" << ipd << std::endl;\n std::cout << \"(*vData)[\" << ipa << \"] \" << (*vData)[ ipa ] << std::endl;\n std::cout << \"(*vData)[\" << ipb << \"] \" << (*vData)[ ipb ] << std::endl;\n std::cout << \"(*vData)[\" << ipc << \"] \" << (*vData)[ ipc ] << std::endl;\n std::cout << \"(*vData)[\" << ipd << \"] \" << (*vData)[ ipd ] << std::endl;\n#endif\n\n return f1*(*vData)[ ipa ]+f2*(*vData)[ ipb ]\n\t\t\t\t +f3*(*vData)[ ipc ]+f4*(*vData)[ ipd ] ;\n\n\n\n\n /* end subroutine interpolate_solution */\n }\n\n\t/**\n\t * Destructor\n\t */\n\tAdapt3DInterpolator::~Adapt3DInterpolator()\n\t{\n\t\t// TODO Auto-generated destructor stub\n\n\t}\n}\n", "meta": {"hexsha": "337c164634c96d1d0cdbea247a162b0a719891a7", "size": 45452, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ext/kameleon/src/ccmc/Adapt3DInterpolator.cpp", "max_stars_repo_name": "alexanderbock/Kameleon-Converter", "max_stars_repo_head_hexsha": "6c2e66bfea60b17a369a3615bc1a623bba100a6f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ext/kameleon/src/ccmc/Adapt3DInterpolator.cpp", "max_issues_repo_name": "alexanderbock/Kameleon-Converter", "max_issues_repo_head_hexsha": "6c2e66bfea60b17a369a3615bc1a623bba100a6f", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ext/kameleon/src/ccmc/Adapt3DInterpolator.cpp", "max_forks_repo_name": "alexanderbock/Kameleon-Converter", "max_forks_repo_head_hexsha": "6c2e66bfea60b17a369a3615bc1a623bba100a6f", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.8825786646, "max_line_length": 181, "alphanum_fraction": 0.5681378157, "num_tokens": 15163, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.24961891798924274}} {"text": "#include \n#include \n#include \n#include \"fouriertransform.h\"\n\nPyObject* ArminForwardFourierTransform(PyObject *py_ftau, const double beta, \n PyObject *py_ftail, const int num_matsubara) {\n Eigen::VectorXd ftau, ftail;\n numpy::from_numpy(py_ftau, ftau);\n numpy::from_numpy(py_ftail, ftail);\n\n Eigen::VectorXcd fmat;\n FourierTransformer fourier(beta, ftail);\n fourier.Armin_forward_ft(ftau, fmat, num_matsubara);\n \n return numpy::to_numpy(fmat);\n}\n\nPyObject* ForwardFourierTransform(PyObject *py_ftau, const double beta, \n PyObject *py_ftail, const int num_matsubara) {\n Eigen::VectorXd ftau, ftail;\n numpy::from_numpy(py_ftau, ftau);\n numpy::from_numpy(py_ftail, ftail);\n\n Eigen::VectorXcd fmat;\n FourierTransformer fourier(beta, ftail);\n fourier.forward_ft(ftau, fmat, num_matsubara);\n \n return numpy::to_numpy(fmat);\n}\n\nPyObject* BackwardFourierTransform(PyObject *py_fmat, const double beta, \n PyObject *py_ftail, const int num_tau) {\n Eigen::VectorXcd fmat;\n Eigen::VectorXd ftail;\n numpy::from_numpy(py_fmat, fmat);\n numpy::from_numpy(py_ftail, ftail);\n\n Eigen::VectorXd ftau;\n FourierTransformer fourier(beta, ftail);\n fourier.backward_ft(fmat, ftau, num_tau);\n return numpy::to_numpy(ftau);\n}\n\nBOOST_PYTHON_MODULE(fourier)\n{\n boost::python::def(\"Armin_forward_ft\", ArminForwardFourierTransform);\n boost::python::def(\"forward_ft\", ForwardFourierTransform);\n boost::python::def(\"backward_ft\", BackwardFourierTransform);\n}\n", "meta": {"hexsha": "cd3b31e14074deba3cdbe92875b28e847380479f", "size": 1606, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cppext/cubic_spline_ft/main.cpp", "max_stars_repo_name": "hungdt/scf_dmft", "max_stars_repo_head_hexsha": "845a2e144268350af0340927bba0044d538c34db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8.0, "max_stars_repo_stars_event_min_datetime": "2015-06-05T17:44:10.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-10T11:55:13.000Z", "max_issues_repo_path": "cppext/cubic_spline_ft/main.cpp", "max_issues_repo_name": "hungdt/scf_dmft", "max_issues_repo_head_hexsha": "845a2e144268350af0340927bba0044d538c34db", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cppext/cubic_spline_ft/main.cpp", "max_forks_repo_name": "hungdt/scf_dmft", "max_forks_repo_head_hexsha": "845a2e144268350af0340927bba0044d538c34db", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4901960784, "max_line_length": 80, "alphanum_fraction": 0.7117061021, "num_tokens": 431, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5234203489363239, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.24945148725570077}} {"text": "/* Copyright (c) 2010-2019, Delft University of Technology\n * All rigths reserved\n *\n * This file is part of the Tudat. Redistribution and use in source and\n * binary forms, with or without modification, are permitted exclusively\n * under the terms of the Modified BSD license. You should have received\n * a copy of the license with this file. If not, please or visit:\n * http://tudat.tudelft.nl/LICENSE.\n *\n */\n\n#include \n\n#include \"Tudat/Astrodynamics/BasicAstrodynamics/physicalConstants.h\"\n#include \"Tudat/Astrodynamics/BasicAstrodynamics/timeConversions.h\"\n#include \"Tudat/Basics/timeType.h\"\n\nnamespace tudat\n{\nnamespace basic_astrodynamics\n{\n\n//! Function to get the Julian day on J2000, in double precision.\ntemplate< >\ndouble getJulianDayOnJ2000< double >( )\n{\n return JULIAN_DAY_ON_J2000;\n}\n\n//! Function to get the Julian day on J2000, in long double precision.\ntemplate< >\nlong double getJulianDayOnJ2000< long double >( )\n{\n return JULIAN_DAY_ON_J2000_LONG;\n}\n\n//! Function to get the Julian day on zero modified Julian day, in double precision.\ntemplate< >\ndouble getJulianDayOnMjd0< double >( )\n{\n return JULIAN_DAY_AT_0_MJD;\n}\n\n//! Function to get the Julian day on zero modified Julian day, in long double precision.\ntemplate< >\nlong double getJulianDayOnMjd0< long double >( )\n{\n return JULIAN_DAY_AT_0_MJD_LONG;\n}\n\n//! Function to get the synchronization Julian day of TT, TCG, and TCB, in double precision.\ntemplate< >\ndouble getTimeOfTaiSynchronizationJulianDay< double >( )\n{\n return TAI_JULIAN_DAY_AT_TIME_SYNCHRONIZATION;\n}\n\n//! Function to get the synchronization Julian day of TT, TCG, and TCB, in long double precision.\ntemplate< >\nlong double getTimeOfTaiSynchronizationJulianDay< long double >( )\n{\n return TAI_JULIAN_DAY_AT_TIME_SYNCHRONIZATION_LONG;\n}\n\n//! Function to get the difference between TDB and (TT, TCB and TCB) at TAI_JULIAN_DAY_AT_TIME_SYNCHRONIZATION,\n//! in double precision.\ntemplate< >\ndouble getTdbSecondsOffsetAtSynchronization< double >( )\n{\n return TDB_SECONDS_OFFSET_AT_SYNCHRONIZATION;\n}\n\n//! Function to get the difference between TDB and (TT, TCB and TCB) at TAI_JULIAN_DAY_AT_TIME_SYNCHRONIZATION,\n//! in long double precision.\ntemplate< >\nlong double getTdbSecondsOffsetAtSynchronization< long double >( )\n{\n return TDB_SECONDS_OFFSET_AT_SYNCHRONIZATION_LONG;\n}\n\n//! Function to get the offset of TT from TAI (constant by definition), in double precision.\ntemplate< >\ndouble getTTMinusTai< double >( )\n{\n return TT_MINUS_TAI;\n}\n\n//! Function to get the offset of TT from TAI (constant by definition), in long double precision.\ntemplate< >\nlong double getTTMinusTai< long double >( )\n{\n return TT_MINUS_TAI_LONG;\n}\n\n//! Function to get the offset of TT from TAI (constant by definition), in Time format.\ntemplate< >\nTime getTTMinusTai< Time>( )\n{\n return Time( TT_MINUS_TAI_LONG );\n}\n\n\n//! Function to convert julian day to gregorian calendar date.\nboost::gregorian::date convertJulianDayToCalendarDate( const double julianDay )\n{\n // Declare temporary variables.\n int L, M, N, P, Q;\n\n // Declare date variables.\n int day, month, year;\n\n // Execute algorithm.\n double shiftedJulianDay = julianDay + 0.5;\n if( shiftedJulianDay > 2299160 ) // after Oct 4, 1582\n {\n L = shiftedJulianDay + 68569;\n M = (4 * L) / 146097;\n L = L - ((146097 * M + 3) / 4);\n N = (4000 * (L + 1)) / 1461001;\n L = L - ((1461 * N) / 4) + 31;\n P = (80 * L) / 2447;\n day = int(L - (2447 * P) / 80);\n L = P / 11;\n month = int(P + 2 - 12 * L);\n year = int(100 * (M - 49) + N + L);\n }\n else\n {\n P = shiftedJulianDay + 1402;\n Q = (P - 1) / 1461;\n L = P - 1461 * Q;\n M = (L - 1) / 365 - L / 1461;\n N = L - 365 * M + 30;\n P = (80 * N) / 2447;\n day = int(N - (2447 * P) / 80);\n N = P / 11;\n month = int( P + 2 - 12 * N );\n year = int( 4 * Q + M + N - 4716 );\n if(year <= 0)\n {\n --year;\n }\n }\n // catch century/non-400 non-leap years\n if( year > 1599 && !( year % 100 )\n && ( year % 400 ) && month == 2 && day == 29 )\n {\n month = 3;\n day = 1;\n }\n\n // Create and return date object\n\n return boost::gregorian::date( year, month, static_cast< double >( day ) );\n}\n\n\n//! Function to determine whether the given year is a leap year (i.e. has 366 days)\nbool isLeapYear( const int year )\n{\n bool isLeapYear = 0;\n if( ( year % 4 == 0 ) && !( ( year % 100 == 0 ) && !( year % 400 == 0 ) ) )\n {\n isLeapYear = 1;\n }\n return isLeapYear;\n}\n\n//! Function that returns number of days in given month number\nint getDaysInMonth( const int month,\n const int year )\n{\n // Declare number of days per month\n static const int daysPerMonth[ 12 ] =\n { 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31 };\n int numberOfDays = 0;\n\n // Check input consistency\n if( month < 1 || month > 12 )\n {\n throw std::runtime_error( \"Error, month number \" + std::to_string( month ) +\n \" does not exist, value must be gretaer than 0 and smaller than 13\" );\n }\n else\n {\n numberOfDays = daysPerMonth[ month - 1 ];\n\n // Check for leap year.\n if( month == 2 && isLeapYear( year ) )\n {\n numberOfDays++;\n }\n }\n return numberOfDays;\n}\n\n//! Determine number of full days that have passed in current year\nint convertDayMonthYearToDayOfYear( const int day,\n const int month,\n const int year )\n{\n return convertDayMonthYearToDayOfYear( boost::gregorian::date( year, month, day ) );\n}\n\n//! Determine number of full days that have passed in current year\nint convertDayMonthYearToDayOfYear( const boost::gregorian::date calendarDate )\n{\n // Calculate and return result (taking into account that this function should return 0 for first day, not 1)\n return ( calendarDate.day_of_year( ) - 1 );\n}\n\n//! Determine number of seconds into current day of given time\ndouble calculateSecondsInCurrentJulianDay( const double julianDay )\n{\n // Calculate and return result, taking into accoun the fact that julian day 0.0 is at 12:00 and result starts counting at 00:00\n return ( julianDay + 0.5 - std::floor( julianDay + 0.5 ) ) * physical_constants::JULIAN_DAY;\n}\n\n//! Function to create the calendar date from the year and the number of days in the year\nboost::gregorian::date convertYearAndDaysInYearToDate( const int year, const int daysInYear )\n{\n // Go to day 1 at 01-01 convention\n int daysLeft = daysInYear + 1;\n\n // Loop over all months (starting at January until month in which daysinYear is situated is found)\n int currentMonth = 1;\n int daysInCurrentMonth;\n bool isConverged = 0;\n while( !isConverged )\n {\n // Determine days in current month\n daysInCurrentMonth = getDaysInMonth( currentMonth, year );\n if( daysInCurrentMonth < daysLeft )\n {\n // Subtract days in current month from days left.\n daysLeft -= daysInCurrentMonth;\n\n // Update month and check consistency\n currentMonth++;\n if( currentMonth > 12 )\n {\n throw std::runtime_error(\n \"Error when converting year and days in year to date, month number has exceeded 12\" );\n }\n }\n else\n {\n isConverged = 1;\n }\n }\n\n // Create date and return\n boost::gregorian::date date( year, currentMonth, daysLeft );\n\n if( date.day_of_year( ) != daysInYear + 1 )\n {\n throw std::runtime_error(\n \"Error when converting year and days in year to date, inconsistent output\" );\n }\n\n return date;\n}\n\n//! Perform apprixmate conversion of TT to TDB\ndouble approximateConvertTTtoTDB( const double ttSecondsSinceJ2000 )\n{\n double ttCenturiesSinceJ2000 = ttSecondsSinceJ2000 / ( 100.0 * physical_constants::JULIAN_YEAR );\n return ttSecondsSinceJ2000 + 0.001657 * std::sin( 628.3076 * ttCenturiesSinceJ2000 + 6.2401 );\n}\n\n\n} // namespace basic_astrodynamics\n} // namespace tudat\n", "meta": {"hexsha": "5767c64f96cbeaa4b300385adcf1baf015e12f2e", "size": 8324, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Tudat/Astrodynamics/BasicAstrodynamics/timeConversions.cpp", "max_stars_repo_name": "sebranchett/tudat", "max_stars_repo_head_hexsha": "24e5f3cc85c250fcbed0aac37f026c1dd7fd6c44", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Tudat/Astrodynamics/BasicAstrodynamics/timeConversions.cpp", "max_issues_repo_name": "sebranchett/tudat", "max_issues_repo_head_hexsha": "24e5f3cc85c250fcbed0aac37f026c1dd7fd6c44", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Tudat/Astrodynamics/BasicAstrodynamics/timeConversions.cpp", "max_forks_repo_name": "sebranchett/tudat", "max_forks_repo_head_hexsha": "24e5f3cc85c250fcbed0aac37f026c1dd7fd6c44", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.3795620438, "max_line_length": 131, "alphanum_fraction": 0.6370735223, "num_tokens": 2273, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.24943366841632944}} {"text": "//=============================================================================================================\n/**\n * @file icp.cpp\n * @author Ruben Dörfel \n * @since 0.1.5\n * @date July, 2020\n *\n * @section LICENSE\n *\n * Copyright (C) 2020, Ruben Dörfel. All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without modification, are permitted provided that\n * the following conditions are met:\n * * Redistributions of source code must retain the above copyright notice, this list of conditions and the\n * following disclaimer.\n * * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and\n * the following disclaimer in the documentation and/or other materials provided with the distribution.\n * * Neither the name of MNE-CPP authors nor the names of its contributors may be used\n * to endorse or promote products derived from this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED\n * WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\n * PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,\n * INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\n * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n * POSSIBILITY OF SUCH DAMAGE.\n *\n *\n * @brief ICP class definition.\n *\n */\n\n//=============================================================================================================\n// INCLUDES\n//=============================================================================================================\n\n#include \"icp.h\"\n#include \n\n#include \"fiff/fiff_coord_trans.h\"\n#include \"mne/mne_project_to_surface.h\"\n\n//=============================================================================================================\n// QT INCLUDES\n//=============================================================================================================\n\n#include \n#include \n\n//=============================================================================================================\n// EIGEN INCLUDES\n//=============================================================================================================\n\n#include \n#include \n#include \n#include \n\n//=============================================================================================================\n// USED NAMESPACES\n//=============================================================================================================\n\nusing namespace MNELIB;\nusing namespace RTPROCESSINGLIB;\nusing namespace Eigen;\nusing namespace FIFFLIB;\n\n//=============================================================================================================\n// DEFINE GLOBAL METHODS\n//=============================================================================================================\n\nbool RTPROCESSINGLIB::performIcp(const MNEProjectToSurface::SPtr mneSurfacePoints,\n const Eigen::MatrixXf& matPointCloud,\n FiffCoordTrans& transFromTo,\n float& fRMSE,\n bool bScale,\n int iMaxIter,\n float fTol,\n const VectorXf& vecWeitgths)\n/**\n * Follow notation of P.J. Besl and N.D. McKay, A Method for\n * Registration of 3-D Shapes, IEEE Trans. Patt. Anal. Machine Intell., 14,\n * 239 - 255, 1992.\n */\n{\n if(matPointCloud.rows() == 0){\n qWarning() << \"[RTPROCESSINGLIB::icp] Passed point cloud is empty.\";\n return false;\n }\n\n // Initialization\n int iNP = matPointCloud.rows(); // The number of points\n float fMSEPrev,fMSE = 0.0; // The mean square error\n float fScale = 1.0;\n MatrixXf matP0 = matPointCloud; // Initial Set of points\n MatrixXf matPk = matP0; // Transformed Set of points\n MatrixXf matYk(matPk.rows(),matPk.cols()); // Iterative closest points on the surface\n MatrixXf matDiff = matYk;\n VectorXf vecSE(matDiff.rows());\n Matrix4f matTrans; // the transformation matrix\n VectorXi vecNearest; // Triangle of the new point\n VectorXf vecDist; // The Distance between matX and matP\n\n // Initial transformation - From point cloud To surface\n FiffCoordTrans transICP = transFromTo;\n matPk = transICP.apply_trans(matPk);\n\n // Icp algorithm:\n for(int iIter = 0; iIter < iMaxIter; ++iIter) {\n\n // Step a: compute the closest point on the surface; eq 29\n if(!mneSurfacePoints->mne_find_closest_on_surface(matPk, iNP, matYk, vecNearest, vecDist)) {\n qWarning() << \"[RTPROCESSINGLIB::icp] mne_find_closest_on_surface was not sucessfull.\";\n return false;\n }\n\n // Step b: compute the registration; eq 30\n if(!fitMatchedPoints(matP0, matYk, matTrans, fScale, bScale, vecWeitgths)) {\n qWarning() << \"[RTPROCESSINGLIB::icp] point cloud registration not succesfull\";\n }\n\n // Step c: apply registration\n transICP.trans = matTrans;\n matPk = transICP.apply_trans(matP0);\n\n // step d: compute mean-square-error and terminate if below fTol\n vecDist = vecDist.cwiseProduct(vecDist);\n fMSE = vecDist.sum() / iNP;\n fRMSE = std::sqrt(fMSE);\n\n if(std::sqrt(std::fabs(fMSE - fMSEPrev)) < fTol) {\n transFromTo = transICP;\n qInfo() << \"[RTPROCESSINGLIB::icp] ICP was succesfull and exceeded after \" << iIter +1 << \" Iterations with RMSE dist: \" << fRMSE * 1000 << \" mm.\";\n return true;\n }\n fMSEPrev = fMSE;\n qInfo() << \"[RTPROCESSINGLIB::icp] ICP iteration \" << iIter + 1 << \" with RMSE: \" << fRMSE * 1000 << \" mm.\";\n }\n transFromTo = transICP;\n\n qWarning() << \"[RTPROCESSINGLIB::icp] Maximum number of \" << iMaxIter << \" Iterations exceeded with RMSE: \" << fRMSE * 1000 << \" mm.\";\n return true;\n}\n\n//=============================================================================================================\n\nbool RTPROCESSINGLIB::fitMatchedPoints(const MatrixXf& matSrcPoint,\n const MatrixXf& matDstPoint,\n Eigen::Matrix4f& matTrans,\n float fScale,\n bool bScale,\n const VectorXf& vecWeitgths)\n/**\n * Follow notation of P.J. Besl and N.D. McKay, A Method for\n * Registration of 3-D Shapes, IEEE Trans. Patt. Anal. Machine Intell., 14,\n * 239 - 255, 1992.\n *\n * The code is further adapted from MNE Python function _fitMatched_points(...).\n */\n{\n // init values\n MatrixXf matP = matSrcPoint;\n MatrixXf matX = matDstPoint;\n VectorXf vecW = vecWeitgths;\n VectorXf vecMuP; // column wise mean - center of mass\n VectorXf vecMuX; // column wise mean - center of mass\n MatrixXf matDot;\n MatrixXf matSigmaPX; // cross-covariance\n MatrixXf matAij; // Anti-Symmetric matrix\n Vector3f vecDelta; // column vector, elements of matAij\n Matrix4f matQ = Matrix4f::Identity(4,4);\n Matrix3f matScale = Matrix3f::Identity(3,3); // scaling matrix\n Matrix3f matRot = Matrix3f::Identity(3,3);\n Vector3f vecTrans;\n float fTrace = 0.0;\n fScale = 1.0;\n\n // test size of point clouds\n if(matSrcPoint.size() != matDstPoint.size()) {\n qWarning() << \"[RTPROCESSINGLIB::fitMatched] Point clouds do not match.\";\n return false;\n }\n\n // get center of mass\n if(vecWeitgths.isZero()) {\n vecMuP = matP.colwise().mean(); // eq 23\n vecMuX = matX.colwise().mean();\n matDot = matP.transpose() * matX;\n matDot = matDot / matP.rows();\n } else {\n vecW = vecWeitgths / vecWeitgths.sum();\n vecMuP = vecW.transpose() * matP;\n vecMuX = vecW.transpose() * matX;\n\n MatrixXf matXWeighted = matX;\n for(int i = 0; i < (vecW.size()); ++i) {\n matXWeighted.row(i) = matXWeighted.row(i) * vecW(i);\n }\n matDot = matP.transpose() * (matXWeighted);\n }\n\n // get cross-covariance\n matSigmaPX = matDot - (vecMuP * vecMuX.transpose()); // eq 24\n matAij = matSigmaPX - matSigmaPX.transpose();\n vecDelta(0) = matAij(1,2); vecDelta(1) = matAij(2,0); vecDelta(2) = matAij(0,1);\n fTrace = matSigmaPX.trace();\n matQ(0,0) = fTrace; // eq 25\n matQ.block(0,1,1,3) = vecDelta.transpose();\n matQ.block(1,0,3,1) = vecDelta;\n matQ.block(1,1,3,3) = matSigmaPX + matSigmaPX.transpose() - fTrace * MatrixXf::Identity(3,3);\n\n // unit eigenvector coresponding to maximum eigenvalue of matQ is selected as optimal rotation quaterions q0,q1,q2,q3\n SelfAdjointEigenSolver es(matQ);\n Vector4f vecEigVec = es.eigenvectors().col(matQ.cols()-1); // only take last Eigen-Vector since this corresponds to the maximum Eigenvalue\n\n // quatRot(w,x,y,z)\n Quaternionf quatRot(vecEigVec(0),vecEigVec(1),vecEigVec(2),vecEigVec(3));\n quatRot.normalize();\n matRot = quatRot.matrix();\n\n // get scaling factor and matrix\n if(bScale) {\n MatrixXf matDevX = matX.rowwise() - vecMuX.transpose();\n MatrixXf matDevP = matP.rowwise() - vecMuP.transpose();\n matDevX = matDevX.cwiseProduct(matDevX);\n matDevP = matDevP.cwiseProduct(matDevP);\n\n if(!vecWeitgths.isZero()) {\n for(int i = 0; i < (vecW.size()); ++i) {\n matDevX.row(i) = matDevX.row(i) * vecW(i);\n matDevP.row(i) = matDevP.row(i) * vecW(i);\n }\n }\n // get scaling factor and set scaling matrix\n fScale = std::sqrt(matDevX.sum() / matDevP.sum());\n matScale *= fScale;\n }\n\n // get translation and Rotation\n vecTrans = vecMuX - fScale * matRot * vecMuP;\n matRot *= matScale;\n\n matTrans.block<3,3>(0,0) = matRot;\n matTrans.block<3,1>(0,3) = vecTrans;\n matTrans(3,3) = 1.0f;\n matTrans.block<1,3>(3,0) = MatrixXf::Zero(1,3);\n return true;\n}\n\n//=========================================================================================================\n\nbool RTPROCESSINGLIB::discard3DPointOutliers(const QSharedPointer mneSurfacePoints,\n const MatrixXf& matPointCloud,\n const FiffCoordTrans& transFromTo,\n VectorXi& vecTake,\n MatrixXf& matTakePoint,\n float fMaxDist)\n{\n // Initialization\n int iNP = matPointCloud.rows(); // The number of points\n MatrixXf matP = matPointCloud; // Initial Set of points\n MatrixXf matYk(matPointCloud.rows(),matPointCloud.cols()); // Iterative losest points on the surface\n VectorXi vecNearest; // Triangle of the new point\n VectorXf vecDist; // The Distance between matX and matP\n\n // Initial transformation - From point cloud To surface\n matP = transFromTo.apply_trans(matP);\n\n int iDiscarded = 0;\n\n // discard outliers if necessary\n if(fMaxDist > 0.0) {\n if(!mneSurfacePoints->mne_find_closest_on_surface(matP, iNP, matYk, vecNearest, vecDist)) {\n qWarning() << \"[RTPROCESSINGLIB::icp] mne_find_closest_on_surface was not sucessfull.\";\n return false;\n }\n\n for(int i = 0; i < vecDist.size(); ++i) {\n if(std::fabs(vecDist(i)) < fMaxDist) {\n vecTake.conservativeResize(vecTake.size()+1);\n vecTake(vecTake.size()-1) = i;\n matTakePoint.conservativeResize(matTakePoint.rows()+1,3);\n matTakePoint.row(matTakePoint.rows()-1) = matPointCloud.row(i);\n } else {\n iDiscarded++;\n }\n }\n }\n qInfo() << \"[RTPROCESSINGLIB::discardOutliers] \" << iDiscarded << \"digitizers discarded.\";\n return true;\n}\n\n//=============================================================================================================\n// DEFINE MEMBER METHODS\n//=============================================================================================================\n", "meta": {"hexsha": "1be4bf932cf43c81baa0e150ee8b70e7e56a2e2e", "size": 13115, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libraries/rtprocessing/icp.cpp", "max_stars_repo_name": "ultrapre/mne-cpp", "max_stars_repo_head_hexsha": "7036b112a5573bd9ab9c54d9fe22c3eea2c03725", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "libraries/rtprocessing/icp.cpp", "max_issues_repo_name": "ultrapre/mne-cpp", "max_issues_repo_head_hexsha": "7036b112a5573bd9ab9c54d9fe22c3eea2c03725", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "libraries/rtprocessing/icp.cpp", "max_forks_repo_name": "ultrapre/mne-cpp", "max_forks_repo_head_hexsha": "7036b112a5573bd9ab9c54d9fe22c3eea2c03725", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.0100671141, "max_line_length": 159, "alphanum_fraction": 0.5245139154, "num_tokens": 3058, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.24925380755045357}} {"text": "/*\n Copyright (C) 2017 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\n\nnamespace QuantExt {\n\nstruct CloseEnoughComparator {\n explicit CloseEnoughComparator(const Real v) : v_(v) {}\n bool operator()(const Real w) const { return close_enough(v_, w); }\n Real v_;\n};\n\nBlackVarianceSurfaceSparse::BlackVarianceSurfaceSparse(const QuantLib::Date& referenceDate, const Calendar& cal,\n const std::vector& dates, const std::vector& strikes,\n const std::vector& volatilities,\n const DayCounter& dayCounter)\n : BlackVarianceTermStructure(referenceDate, cal), dayCounter_(dayCounter) {\n\n QL_REQUIRE((strikes.size() == dates.size()) && (dates.size() == volatilities.size()),\n \"dates, strikes and volatilities vectors not of equal size.\");\n\n // first get uniques dates and sort\n set datesSet(dates.begin(), dates.end());\n datesSet.insert(this->referenceDate());\n expiries_ = vector(datesSet.begin(), datesSet.end());\n vector dateDone(expiries_.size(), false);\n times_ = vector

  1. 'UX' Assembly: UO2\n // fuel assembly with 24 guide tubes and a central Moveable Fission\n // Chamber
  2. 'UA' Assembly: UO2 fuel assembly with 24 AIC and a central\n // Moveable Fission Chamber
  3. 'PX' Assembly: MOX fuel assembly with 24\n // guide tubes and a central Moveable Fission Chamber
  4. 'R' Assembly: a\n // reflector.
\n //\n // Note that the numbers listed here and taken from the benchmark\n // description are, in good old Fortran fashion, one-based. We will later\n // subtract one from each number when assigning materials to individual\n // cells to convert things into the C-style zero-based indexing.\n const unsigned int n_assemblies=4;\n const unsigned int\n assembly_materials[n_assemblies][rods_per_assembly_x][rods_per_assembly_y]\n =\n {\n {\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 5, 1, 1, 5, 1, 1, 5, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 5, 1, 1, 5, 1, 1, 5, 1, 1, 5, 1, 1, 5, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 5, 1, 1, 5, 1, 1, 7, 1, 1, 5, 1, 1, 5, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 5, 1, 1, 5, 1, 1, 5, 1, 1, 5, 1, 1, 5, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 5, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 5, 1, 1, 5, 1, 1, 5, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }\n },\n {\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 8, 1, 1, 8, 1, 1, 7, 1, 1, 8, 1, 1, 8, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 8, 1, 1, 8, 1, 1, 8, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 },\n { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 }\n },\n {\n { 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 },\n { 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 },\n { 2, 3, 3, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 3, 3, 2 },\n { 2, 3, 3, 5, 3, 4, 4, 4, 4, 4, 4, 4, 3, 5, 3, 3, 2 },\n { 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 2 },\n { 2, 3, 5, 4, 4, 5, 4, 4, 5, 4, 4, 5, 4, 4, 5, 3, 2 },\n { 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 2 },\n { 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 2 },\n { 2, 3, 5, 4, 4, 5, 4, 4, 7, 4, 4, 5, 4, 4, 5, 3, 2 },\n { 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 2 },\n { 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 2 },\n { 2, 3, 5, 4, 4, 5, 4, 4, 5, 4, 4, 5, 4, 4, 5, 3, 2 },\n { 2, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 3, 3, 2 },\n { 2, 3, 3, 5, 3, 4, 4, 4, 4, 4, 4, 4, 3, 5, 3, 3, 2 },\n { 2, 3, 3, 3, 3, 5, 3, 3, 5, 3, 3, 5, 3, 3, 3, 3, 2 },\n { 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2 },\n { 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2 }\n },\n {\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 },\n { 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 }\n }\n };\n\n // After the description of the materials that make up an assembly, we\n // have to specify the arrangement of assemblies within the core. We use a\n // symmetric pattern that in fact only uses the 'UX' and 'PX' assemblies:\n const unsigned int core[assemblies_x][assemblies_y][assemblies_z]\n = {{{0}, {2}}, {{2}, {0}}};\n\n // We are now in a position to actually set material IDs for each cell. To\n // this end, we loop over all cells, look at the location of the cell's\n // center, and determine which assembly and fuel rod this would be in. (We\n // add a few checks to see that the locations we compute are within the\n // bounds of the arrays in which we have to look up materials.) At the end\n // of the loop, we set material identifiers accordingly:\n for (typename Triangulation::active_cell_iterator\n cell = coarse_grid.begin_active();\n cell!=coarse_grid.end();\n ++cell)\n {\n const Point cell_center = cell->center();\n\n const unsigned int tmp_x = int(cell_center[0]/pin_pitch_x);\n const unsigned int ax = tmp_x/rods_per_assembly_x;\n const unsigned int cx = tmp_x - ax * rods_per_assembly_x;\n\n const unsigned tmp_y = int(cell_center[1]/pin_pitch_y);\n const unsigned int ay = tmp_y/rods_per_assembly_y;\n const unsigned int cy = tmp_y - ay * rods_per_assembly_y;\n\n const unsigned int az = (dim == 2\n ?\n 0\n :\n int (cell_center[dim-1]/assembly_height));\n\n Assert (ax < assemblies_x, ExcInternalError());\n Assert (ay < assemblies_y, ExcInternalError());\n Assert (az < assemblies_z, ExcInternalError());\n\n Assert (core[ax][ay][az] < n_assemblies, ExcInternalError());\n\n Assert (cx < rods_per_assembly_x, ExcInternalError());\n Assert (cy < rods_per_assembly_y, ExcInternalError());\n\n cell->set_material_id(assembly_materials[core[ax][ay][az]][cx][cy] - 1);\n }\n\n // With the coarse mesh so initialized, we create the appropriate number\n // of energy group objects and let them initialize their individual meshes\n // with the coarse mesh generated above:\n energy_groups.resize (parameters.n_groups);\n for (unsigned int group=0; group (group, material_data,\n coarse_grid, fe);\n }\n\n\n // @sect5{NeutronDiffusionProblem::get_total_fission_source}\n //\n // In the eigenvalue computation, we need to calculate total fission neutron\n // source after each power iteration. The total power then is used to renew\n // k-effective.\n //\n // Since the total fission source is a sum over all the energy groups, and\n // since each of these sums can be computed independently, we actually do\n // this in parallel. One of the problems is that the function in the\n // EnergyGroup class that computes the fission source returns a\n // value. If we now simply spin off a new thread, we have to later capture\n // the return value of the function run on that thread. The way this can be\n // done is to use the return value of the Threads::new_thread function,\n // which returns an object of type Threads::Thread@ if the function\n // spawned returns a double. We can then later ask this object for the\n // returned value (when doing so, the Threads::Thread::return_value function\n // first waits for the thread to finish if it hasn't done so already).\n //\n // The way this function then works is to first spawn one thread for each\n // energy group we work with, then one-by-one collecting the returned values\n // of each thread and return the sum.\n template \n double NeutronDiffusionProblem::get_total_fission_source () const\n {\n std::vector > threads;\n for (unsigned int group=0; group::get_fission_source,\n *energy_groups[group]));\n\n double fission_source = 0;\n for (unsigned int group=0; groupNeutronDiffusionProblem::refine_grid}\n //\n // The next function lets the individual energy group objects refine their\n // meshes. Much of this, again, is a task that can be done independently in\n // parallel: first, let all the energy group objects calculate their error\n // indicators in parallel, then compute the maximum error indicator over all\n // energy groups and determine thresholds for refinement and coarsening of\n // cells, and then ask all the energy groups to refine their meshes\n // accordingly, again in parallel.\n template \n void NeutronDiffusionProblem::refine_grid ()\n {\n std::vector n_cells (parameters.n_groups);\n for (unsigned int group=0; groupn_active_cells();\n\n BlockVector group_error_indicators(n_cells);\n\n {\n Threads::ThreadGroup<> threads;\n for (unsigned int group=0; group::estimate_errors,\n *energy_groups[group],\n group_error_indicators.block(group));\n threads.join_all ();\n }\n\n const float max_error = group_error_indicators.linfty_norm();\n const float refine_threshold = 0.3*max_error;\n const float coarsen_threshold = 0.01*max_error;\n\n {\n Threads::ThreadGroup<> threads;\n for (unsigned int group=0; group::refine_grid,\n *energy_groups[group],\n group_error_indicators.block(group),\n refine_threshold,\n coarsen_threshold);\n threads.join_all ();\n }\n }\n\n\n // @sect5{NeutronDiffusionProblem::run}\n //\n // Finally, this is the function where the meat is: iterate on a sequence of\n // meshes, and on each of them do a power iteration to compute the\n // eigenvalue.\n //\n // Given the description of the algorithm in the introduction, there is\n // actually not much to comment on:\n template \n void NeutronDiffusionProblem::run ()\n {\n std::cout << std::setprecision (12) << std::fixed;\n\n double k_eff_old = k_eff;\n\n Timer timer;\n timer.start ();\n\n for (unsigned int cycle=0; cyclesolution *= k_eff;\n }\n\n for (unsigned int group=0; groupsetup_linear_system ();\n\n std::cout << \" Numbers of active cells: \";\n for (unsigned int group=0; groupn_active_cells()\n << ' ';\n std::cout << std::endl;\n std::cout << \" Numbers of degrees of freedom: \";\n for (unsigned int group=0; groupn_dofs()\n << ' ';\n std::cout << std::endl << std::endl;\n\n\n Threads::ThreadGroup<> threads;\n for (unsigned int group=0; group::assemble_system_matrix,\n *energy_groups[group]);\n threads.join_all ();\n\n double error;\n unsigned int iteration = 1;\n do\n {\n for (unsigned int group=0; groupassemble_ingroup_rhs (ZeroFunction());\n\n for (unsigned int bgroup=0; bgroupassemble_cross_group_rhs (*energy_groups[bgroup]);\n\n energy_groups[group]->solve ();\n }\n\n k_eff = get_total_fission_source();\n error = fabs(k_eff-k_eff_old)/fabs(k_eff);\n std::cout << \" Iteration \" << iteration\n << \": k_eff=\" << k_eff\n << std::endl;\n k_eff_old=k_eff;\n\n for (unsigned int group=0; groupsolution_old = energy_groups[group]->solution;\n energy_groups[group]->solution_old /= k_eff;\n }\n\n ++iteration;\n }\n while ((error > parameters.convergence_tolerance)\n &&\n (iteration < 500));\n\n for (unsigned int group=0; groupoutput_results (cycle);\n\n std::cout << std::endl;\n std::cout << \" Cycle=\" << cycle\n << \", n_dofs=\" << energy_groups[0]->n_dofs() + energy_groups[1]->n_dofs()\n << \", k_eff=\" << k_eff\n << \", time=\" << timer()\n << std::endl;\n\n\n std::cout << std::endl << std::endl;\n }\n }\n}\n\n\n\n// @sect3{The main() function}\n//\n// The last thing in the program in the main() function. The\n// structure is as in most other tutorial programs, with the only exception\n// that we here handle a parameter file. To this end, we first look at the\n// command line arguments passed to this function: if no input file is\n// specified on the command line, then use \"project.prm\", otherwise take the\n// filename given as the first argument on the command line.\n//\n// With this, we create a ParameterHandler object, let the\n// NeutronDiffusionProblem::Parameters class declare all the\n// parameters it wants to see in the input file (or, take the default values,\n// if nothing is listed in the parameter file), then read the input file, ask\n// the parameters object to extract the values, and finally hand everything\n// off to an object of type NeutronDiffusionProblem for\n// computation of the eigenvalue:\nint main (int argc, char **argv)\n{\n try\n {\n using namespace dealii;\n using namespace Step28;\n\n deallog.depth_console (0);\n\n std::string filename;\n if (argc < 2)\n filename = \"project.prm\";\n else\n filename = argv[1];\n\n\n const unsigned int dim = 2;\n\n ParameterHandler parameter_handler;\n\n NeutronDiffusionProblem::Parameters parameters;\n parameters.declare_parameters (parameter_handler);\n\n parameter_handler.read_input (filename);\n\n parameters.get_parameters (parameter_handler);\n\n\n NeutronDiffusionProblem neutron_diffusion_problem (parameters);\n neutron_diffusion_problem.run ();\n }\n catch (std::exception &exc)\n {\n std::cerr << std::endl << std::endl\n << \"----------------------------------------------------\"\n << std::endl;\n std::cerr << \"Exception on processing: \" << std::endl\n << exc.what() << std::endl\n << \"Aborting!\" << std::endl\n << \"----------------------------------------------------\"\n << std::endl;\n\n return 1;\n }\n catch (...)\n {\n std::cerr << std::endl << std::endl\n << \"----------------------------------------------------\"\n << std::endl;\n std::cerr << \"Unknown exception!\" << std::endl\n << \"Aborting!\" << std::endl\n << \"----------------------------------------------------\"\n << std::endl;\n return 1;\n }\n\n return 0;\n}\n", "meta": {"hexsha": "f6edf971443d0cd24afebf59e69586e9ca6d2a2c", "size": 75904, "ext": "cc", "lang": "C++", "max_stars_repo_path": "MHD/examples/step-28/step-28.cc", "max_stars_repo_name": "wathen/PhD", "max_stars_repo_head_hexsha": "35524f40028541a4d611d8c78574e4cf9ddc3278", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2020-10-25T13:30:20.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-10T21:27:30.000Z", "max_issues_repo_path": "MHD/examples/step-28/step-28.cc", "max_issues_repo_name": "wathen/PhD", "max_issues_repo_head_hexsha": "35524f40028541a4d611d8c78574e4cf9ddc3278", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "MHD/examples/step-28/step-28.cc", "max_forks_repo_name": "wathen/PhD", "max_forks_repo_head_hexsha": "35524f40028541a4d611d8c78574e4cf9ddc3278", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2019-10-28T16:12:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-13T13:59:44.000Z", "avg_line_length": 42.3335192415, "max_line_length": 108, "alphanum_fraction": 0.6156197302, "num_tokens": 20101, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.46490157137338844, "lm_q1q2_score": 0.2451502803303129}} {"text": "/*\n For more information, please see: http://software.sci.utah.edu\n\n The MIT License\n\n Copyright (c) 2015 Scientific Computing and Imaging Institute,\n University of Utah.\n\n License for the specific language governing rights and limitations under\n Permission is hereby granted, free of charge, to any person obtaining a\n copy of this software and associated documentation files (the \"Software\"),\n to deal in the Software without restriction, including without limitation\n the rights to use, copy, modify, merge, publish, distribute, sublicense,\n and/or sell copies of the Software, and to permit persons to whom the\n Software is furnished to do so, subject to the following conditions:\n\n The above copyright notice and this permission notice shall be included\n in all copies or substantial portions of the Software.\n../../src//Core/Algorithms/Math/ConvertMatrixType.cc:\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n DEALINGS IN THE SOFTWARE.\n */\n \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace SCIRun::Core::Algorithms;\nusing namespace SCIRun::Core::Algorithms::BrainStimulator;\nusing namespace SCIRun::Core::Datatypes;\nusing namespace SCIRun::Core::Thread;\nusing namespace SCIRun::Core::Geometry;\nusing namespace SCIRun::Core::Logging;\nusing namespace SCIRun;\n\nALGORITHM_PARAMETER_DEF(BrainStimulator, Mesh);\nALGORITHM_PARAMETER_DEF(BrainStimulator, Coil);\nALGORITHM_PARAMETER_DEF(BrainStimulator, VectorBField);\nALGORITHM_PARAMETER_DEF(BrainStimulator, VectorAField);\nALGORITHM_PARAMETER_DEF(BrainStimulator, OutType);\n\n\tclass KernelBase\n\t\t{\n\n\t\tpublic:\n\t\t\tKernelBase(const AlgorithmBase* algo, int t) :\n\t\t\t ref_cnt(0),\n\t\t\t algo_(algo),\n\t\t\t numprocessors_(Parallel::NumCores()),\n\t\t\t barrier_(\"BSV KernelBase Barrier\", numprocessors_),\n\t\t\t typeOut(t),\n\t\t\t matOut(0)\n\t\t\t{\n\t\t\t}\n\t\t\t\n\t\t\tvirtual ~KernelBase()\n\t\t\t{\n\t\t\t}\n\t\t\t\n\t\t\t//! Local entry function, must be implemented by each specific kernel\n\t\t\tvirtual bool Integrate(FieldHandle& mesh, FieldHandle& coil, MatrixHandle& outdata) = 0;\n\n\t\n\t\t\t//! Global reference counting\n\t\t\tint ref_cnt;\n\t\t\t\n\t\tprotected:\n\n\t\t\t//! ref to the executing algorithm context\n\t\t\tconst AlgorithmBase* algo_;\n\t\t\tunsigned int numprocessors_;\n\t\t\t\n\t\t\t//! model miscs.\n\t\t\tVMesh* vmesh;\n\t\t\tVField* vfield;\n\t\t\tsize_type modelSize;\n\n\t\t\t//! coil miscs.\n\t\t\tVMesh* vcoil;\n\t\t\tVField* vcoilField;\n\t\t\tsize_type coilSize;\n\n\t\t\t//! parallel essential primitives \n\t\t\tBarrier barrier_;\n\t\t\tstd::vector success;\n\t\t\t\n\t\t\t//! output Field\n\t\t\tint typeOut;\n\t\t\tDenseMatrix *matOut;\n\t\t\tMatrixHandle matOutHandle;\n\n\t\t\tbool PreIntegration( FieldHandle& mesh, FieldHandle& coil )\n\t\t\t{\n\t\t\t\t\tthis->vmesh = mesh->vmesh();\n\t\t\t\t\tassert(vmesh);\n\n\t\t\t\t\tthis->vcoil = coil->vmesh();\n\t\t\t\t\tassert(vcoil);\n\n\t\t\t\t\tthis->vfield = mesh->vfield();\n\t\t\t\t\tassert(vfield);\n\n\t\t\t\t\tthis->vcoilField = coil->vfield();\n\t\t\t\t\tassert(vcoilField);\n\n\n\t\t\t\t\tthis->numprocessors_ = Parallel::NumCores();\n\n int numproc = Parallel::NumCores();\n\n\t\t\t\t\tif (numproc > 0) \n\t\t\t\t\t{ \n\t\t\t\t\t\tnumprocessors_ = numproc; \n\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t\t#ifdef _DEBUG\n\t\t\t\t\t\t//! DEBUG when we want to test with one CPU only\n\t\t\t\t\t\tnumprocessors_ = 1;\n\t\t\t\t\t#endif\n\t\t\t\t\t\n\t\t\t\t\talgo_->remark(\"number of processors: \" + boost::lexical_cast(this->numprocessors_));\n\t\t\t\t\t\n\t\t\t\t\tsuccess.resize(numprocessors_,true);\n\t\t\t\t\t\n\t\t\t\t\t//! get number of nodes for the model\n\t\t\t\t\tmodelSize = vmesh->num_nodes();\n\t\t\t\t\tassert(modelSize > 0);\n\t\t\t\t\t\n\t\t\t\t\ttry\n\t\t\t\t\t{\t\t\t\n\t\t\t\t\t\tmatOut = new DenseMatrix(static_cast(modelSize),3);\n\t\t\t\t\t\tmatOutHandle = static_cast(matOut);\n\t\t\t\t\t}\n\t\t\t\t\tcatch (...)\n\t\t\t\t\t{\n\t\t\t\t\t\talgo_->error(\"Error alocating output matrix\");\n\t\t\t\t\t\treturn (false);\n\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t\treturn (true);\n\t\t\t}\n\t\t\t\n\t\t\tbool PostIntegration( MatrixHandle& outdata )\n\t\t\t{\n\t\t\t\t//! check for error\n\t\t\t\tfor (size_t j=0; jsynchronize(Mesh::NODES_E | Mesh::EDGES_E);\n\t\t\t\t\t\n\t\t\t\t\tVMesh::Node::array_type enodes;\n\t\t\t\t\tPoint enode1;\n\t\t\t\t\tPoint enode2;\n\t\t\t\t\t\n\t\t\t\t\t//! get numbder of nodes for the coil\n\t\t\t\t\tcoilSize = vcoil->num_nodes();\n\n\t\t\t\t\t//! basic assumption\n\t\t\t\t\tassert(modelSize > 0 && coilSize > 1);\n\t\t\t\t\t\n\t\t\t\t\tcoilNodes.clear();\n\t\t\t\t\tcoilNodes.reserve(coilSize);\n\t\t\t\t\t\n\n\t\t\t\t\tfor(VMesh::Edge::index_type i = 0; i < vcoil->num_edges(); i++)\n\t\t\t\t\t{\n\t\t\t\t\t\tvcoil->get_nodes(enodes,i);\n\t\t\t\t\t\tvcoil->get_point(enode1,enodes[0]);\n\t\t\t\t\t\tvcoil->get_point(enode2,enodes[1]);\n\t\t\t\t\t\tcoilNodes.push_back(Vector(enode1));\n\t\t\t\t\t\tcoilNodes.push_back(Vector(enode2));\n\t\t\t\t\t}\n\n\t\t\t\t\t//! Start the multi threaded\n\t\t\t\t\tParallel::RunTasks([this](int i) { ParallelKernel(i); }, numprocessors_);\n\t\t\t\t\t\n\t\t\t\t\treturn PostIntegration(outdata);\n\t\t\t\t}\n\n\t\t\t\tvoid SetIntegrationStep(double step)\n\t\t\t\t{\n\t\t\t\t\tassert(step >= 0.0);\n\t\t\t\t\textstep = step;\n\t\t\t\t}\n \n\t\t\t\tdouble GetIntegrationStep() const\n\t\t\t\t{\n\t\t\t\t\treturn extstep;\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\t\n\t\t\tprivate:\n\n\t\t\t\t//! integration step, will auto adapt\n\t\t\t\tdouble autostep;\n\n\t\t\t\t//! integration step, externally provided\n\t\t\t\tdouble extstep;\n\n\t\t\t\t//! keep nodes on the coil cached\n\t\t\t\tstd::vector coilNodes;\n\t\t\t\t\n\t\t\t\t//! execute in parallel\n\t\t\t\tvoid ParallelKernel(int proc_num)\n\t\t\t\t{\n\n\t\t\t\t\tassert(proc_num >= 0);\n\n\t\t\t\t\tint cnt = 0;\n\t\t\t\t\tdouble current = 1.0; \n\t\t\t\t\tPoint modelNode;\n\n\t\t\t\t\tconst index_type begins = (modelSize * proc_num) / numprocessors_;\n\t\t\t\t\tconst index_type ends = (modelSize * (proc_num+1)) / numprocessors_;\n\n\t\t\t\t\tassert( begins <= ends );\n\n\t\t\t\t\t//! buffer of points used for integration\n\t\t\t\t\tstd::vector integrPoints;\n\t\t\t\t\tintegrPoints.reserve(256);\n\n\t\t\t\t\t//! keep previous step length\n\t\t\t\t\t//! used for optimization purpose\n\t\t\t\t\tdouble prevSegLen = 123456789.12345678;\n\n\t\t\t\t\t//! number of integration points\n\t\t\t\t\tint nips = 0;\n\t\t\t\t\tindex_type helpme=0;\n\n\t\t\t\t\ttry{\n\n\t\t\t\t\t\tfor(index_type iM = begins; \n\t\t\t\t\t\t\tiM < ends; \n\t\t\t\t\t\t\tiM++)\n\t\t\t\t\t\t{ \n\t\t\t\t\t\t helpme++;\n\t\t\t\t\t\t\tvmesh->get_node(modelNode,iM); \n\n\t\t\t\t\t\t\t// result\n\t\t\t\t\t\t\tVector F;\n\n\t\t\t\t\t\t\tfor( size_t iC0 = 0, iC1 =1, iCV = 0; \n\t\t\t\t\t\t\t\tiC0 < coilNodes.size(); \n\t\t\t\t\t\t\t\tiC0+=2, iC1+=2, iCV++)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tvcoilField->get_value(current,iCV);\n\n\t\t\t\t\t\t\t\tcurrent = current == 0.0 ? 1.0 : current;\n\n\t\t\t\t\t\t\t\tVector coilNodeThis;\n\t\t\t\t\t\t\t\tVector coilNodeNext;\n\n\t\t\t\t\t\t\t\tif(current >= 0.0)\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tcoilNodeThis = coilNodes[iC0];\n\t\t\t\t\t\t\t\t\tcoilNodeNext = coilNodes[iC1];\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\telse\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tcoilNodeThis = coilNodes[iC1];\n\t\t\t\t\t\t\t\t\tcoilNodeNext = coilNodes[iC0];\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t//! Length of the curve element\n\t\t\t\t\t\t\t\tVector diffNodes = coilNodeNext - coilNodeThis;\n\t\t\t\t\t\t\t\tdouble newSegLen = diffNodes.length();\n\n\t\t\t\t\t\t\t\t//first check if externally suplied integration step is available and use it\n\t\t\t\t\t\t\t\tif(extstep > 0)\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tnips = newSegLen / extstep;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\telse\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t//! optimization\n\t\t\t\t\t\t\t\t\t//! only rexompute integration step only if segment length changes\n\t\t\t\t\t\t\t\t\tif( Abs(prevSegLen - newSegLen ) > 0.00000001 )\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\tprevSegLen = newSegLen;\n\n\t\t\t\t\t\t\t\t\t\t//auto adaptive integration step calculation\n\t\t\t\t\t\t\t\t\t\tnips = AdjustNumberOfIntegrationPoints(newSegLen);\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\tif( nips < 3 )\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\talgo_->warning(\"integration step too big\");\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tintegrPoints.clear();\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t//! curve segment discretization\n\t\t\t\t\t\t\t\tfor(int iip = 0; iip < nips; iip++)\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\tdouble interpolant = static_cast(iip) / static_cast(nips);\n\t\t\t\t\t\t\t\t\tVector v = Interpolate( coilNodeThis, coilNodeNext, interpolant );\n\t\t\t\t\t\t\t\t\tintegrPoints.push_back( v );\n\t\t\t\t\t\t\t\t}\n\n\n\t\t\t\t\t\t\t\t//! integration step over line segment\t\t\t\t\n\t\t\t\t\t\t\t\tfor(int iip = 0; iip < nips -1; iip++)\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t//! Vector connecting the infinitesimal curve-element\t\t\t\n\t\t\t\t\t\t\t\t\tVector Rxyz = (integrPoints[iip] + integrPoints[iip+1] ) / 2 - Vector(modelNode);\n\n\t\t\t\t\t\t\t\t\t//! Infinitesimal curve-element components\n\t\t\t\t\t\t\t\t\tVector dLxyz = integrPoints[iip+1] - integrPoints[iip];\n\n\t\t\t\t\t\t\t\t\tdouble Rn = Rxyz.length();\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t//! check for distance between coil and model close to zero\n\t\t\t\t\t\t\t\t\t//! it might cause numerical stability issues with respect to the cross-product\n\t\t\t\t\t\t\t\t\tif(Rn < 0.00001)\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\talgo_->warning(\"coil<->model distance approaching zero!\");\n\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\tif(typeOut == 1)\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t//! Biot-Savart Magnetic Field\n\t\t\t\t\t\t\t\t\t\tF += 1.0e-7 * Cross( Rxyz, dLxyz ) * ( Abs(current) / (Rn*Rn*Rn) );\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t}\t\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tif(typeOut == 2)\n\t\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t\t//! Biot-Savart Magnetic Vector Potential Field\n\t\t\t\t\t\t\t\t\t\tF += 1.0e-7 * dLxyz * ( Abs(current) / (Rn) );\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tmatOut->put(iM,0, F[0]);\n\t\t\t\t\t\t\tmatOut->put(iM,1, F[1]);\n\t\t\t\t\t\t\tmatOut->put(iM,2, F[2]);\n\n\t\t\t\t\t\t\t//! progress reporter\n\t\t\t\t\t\t\tif (proc_num == 0) \n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tcnt++;\n\t\t\t\t\t\t\t\tif (cnt == 200) \n\t\t\t\t\t\t\t\t{ \n\t\t\t\t\t\t\t\t\tcnt = 0; \n\t\t\t\t\t\t\t\t\talgo_->update_progress(iM/(ends-begins)); \n\t\t\t\t\t\t /// The progress bar update does not work ... and it also counts to iM/2 in other classes strange!\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} \n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tsuccess[proc_num] = true;\n\t\t\t\t\t}\n\t\t\t\t\tcatch (...)\n\t\t\t\t\t{\n\t\t\t\t\t\talgo_->error(std::string(\"PieceWiseKernel crashed while integrating\"));\n\t\t\t\t\t\tsuccess[proc_num] = false;\n\t\t\t\t\t}\n\t\t\t \n\t\t\t\t\t//! check point\n\t\t\t\t\tbarrier_.wait();\n\n\t\t\t\t\t// Bail out if one of the processes failed\n\t\t\t\t\tfor (size_t q=0; q maxNP ? true : false; \n\n\t\t\t\t\t\tif(under) autostep *= 0.5;\n\t\t\t\t\t\tif(over) autostep *= 1.5;\n\n\t\t\t\t\t}while( under || over );\n\n\t\t\t\t\treturn NP;\n\t\t\t\t}\n\t\t\t\n\t\t};\n\n//! TODO\n\t\tclass VolumetricKernel : public KernelBase\n\t\t{\n\t\t\tpublic:\n\t\t\t\n\t\t\t\tVolumetricKernel(const AlgorithmBase* algo, int t) : KernelBase(algo,t)\n\t\t\t\t{\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\t~VolumetricKernel()\n\t\t\t\t{\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\tvirtual bool Integrate(FieldHandle& mesh, FieldHandle& coil, MatrixHandle& outdata)\n\t\t\t\t{\n\t\t\t\t\tif(!PreIntegration(mesh,coil))\n\t\t\t\t\t{\n\t\t\t\t\t\treturn (false);\n\t\t\t\t\t}\n\t\t\t\t\t\n\n\t\t\t\t\t//! get numbder of nodes for the coil\n\t\t\t\t\tcoilSize = vcoil->num_elems();\n\n\t\t\t\t\t//! basic assumption\n\t\t\t\t\tassert(modelSize > 0 && coilSize > 1);\n\t\t\t\t\t\n\t\t\t\t\tvmesh->synchronize(Mesh::NODES_E | Mesh::EDGES_E);\t\t\t\t\t\n\n\t\t\t\t\t//! Start the multi threaded\n\t\t\t\t\tParallel::RunTasks([this](int i) { ParallelKernel(i); }, numprocessors_);\n\t\t\t\t\t\n\t\t\t\t\treturn PostIntegration(outdata);\n\t\t\t\t}\n\t\t\t\t\n\t\t\tprivate:\n\t\t\t\t\n\t\t\t\t//! execute in parallel\n\t\t\t\tvoid ParallelKernel(int proc_num)\n\t\t\t\t{\n\t\t\t\t\tassert(proc_num >= 0);\n\n\t\t\t\t\tint cnt = 0;\n\t\t\t\t\tPoint modelNode;\n\t\t\t\t\tPoint coilCenter;\n\t\t\t\t\tVector current;\n\t\t\t\t\t\n\t\t\t\t\tconst VMesh::Node::index_type begins = (modelSize * proc_num) / numprocessors_;\n\t\t\t\t\tconst VMesh::Node::index_type ends = (modelSize * (proc_num+1)) / numprocessors_;\n\n\t\t\t\t\tassert( begins <= ends );\n\n\t\t\t\t\ttry{\n\n\t\t\t\t\t\tfor(VMesh::Node::index_type iM = begins; iM < ends;\tiM++)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tvmesh->get_node(modelNode,iM); \n\n\t\t\t\t\t\t\t//! accumulatedresult\n\t\t\t\t\t\t\tVector F;\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tVector R;\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tdouble evol = 0.0;\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tdouble Rl;\n\n\t\t\t\t\t\t\tfor(VMesh::Elem::index_type iC = 0; iC < coilSize; iC++)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tvcoilField->get_value(current,iC);\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tvcoilField->get_center(coilCenter, iC);//auto resolve based on basis_order\n\n\t\t\t\t\t\t\t\tevol = vcoil->get_volume(iC);\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tR = coilCenter - modelNode;\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tRl = R.length();\n\n\t\t\t\t\t\t\t\tif(typeOut == 1)\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t//! Biot-Savart Magnetic Field\t\n\t\t\t\t\t\t\t\t\tF += Cross ( current , R ) * ( evol / (4.0 * M_PI * Rl) );\n\t\t\t\t\t\t\t\t}\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tif(typeOut == 2)\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t//! Biot-Savart Magnetic Vector Potential Field\n\t\t\t\t\t\t\t\t\tF += current * ( evol / (4.0 * M_PI * Rl) );\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tmatOut->put(iM,0, F[0]);\n\t\t\t\t\t\t\tmatOut->put(iM,1, F[1]);\n\t\t\t\t\t\t\tmatOut->put(iM,2, F[2]);\n\n\t\t\t\t\t\t\t//! progress reporter\n\t\t\t\t\t\t\tif (proc_num == 0) \n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tcnt++;\n\t\t\t\t\t\t\t\tif (cnt == 200) \n\t\t\t\t\t\t\t\t{ \n\t\t\t\t\t\t\t\t\tcnt = 0; \n\t\t\t\t\t\t\t\t\talgo_->update_progress(iM/2*(begins-ends)); \n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} \n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tsuccess[proc_num] = true;\n\t\t\t\t\t}\n\t\t\t\t\tcatch (...)\n\t\t\t\t\t{\n\t\t\t\t\t\talgo_->error(std::string(\"VolumetricKernel crashed while integrating\"));\n\t\t\t\t\t\tsuccess[proc_num] = false;\n\t\t\t\t\t}\n\t\t\t \n\t\t\t\t\t//! check point\n\t\t\t\t\tbarrier_.wait();\n\n\t\t\t\t\t// Bail out if one of the processes failed\n\t\t\t\t\tfor (size_t q=0; qnum_elems();\n\n\t\t\t\t\t//! basic assumption\n\t\t\t\t\tassert(modelSize > 0 && coilSize > 1);\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t//needed?\n\t\t\t\t\tvmesh->synchronize(Mesh::NODES_E | Mesh::EDGES_E);\n\t\t\t\t\t\t\t\t\t\t\n\n\t\t\t\t\t//! Start the multi threaded\n\t\t\t\t\tParallel::RunTasks([this](int i) { ParallelKernel(i); }, numprocessors_);\n\t\t\t\t\t\n\t\t\t\t\treturn PostIntegration(outdata);\n\t\t\t\t}\n\t\t\t\t\n\t\t\tprivate:\n\t\t\t\t\n\t\t\t\t//! execute in parallel\n\t\t\t\tvoid ParallelKernel(int proc_num)\n\t\t\t\t{\n\t\t\t\t\tassert(proc_num >= 0);\n\n\t\t\t\t\tint cnt = 0;\n\t\t\t\t\tPoint modelNode;\n\t\t\t\t\tPoint dipoleLocation;\n\t\t\t\t\tVector dipoleMoment;\n\t\t\t\t\t\n\t\t\t\t\tconst VMesh::Node::index_type begins = (modelSize * proc_num) / numprocessors_;\n\t\t\t\t\tconst VMesh::Node::index_type ends = (modelSize * (proc_num+1)) / numprocessors_;\n\n\t\t\t\t\tassert( begins <= ends );\n\n\t\t\t\t\ttry{\n\n\n\t\t\t\t\t\tfor(VMesh::Node::index_type iM = begins; iM < ends;\tiM++)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tvmesh->get_node(modelNode,iM); \n\n\t\t\t\t\t\t\t//! accumulated result\n\t\t\t\t\t\t\tVector F;\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tVector R;\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tdouble Rl;\n\n\t\t\t\t\t\t\tfor(VMesh::Elem::index_type iC = 0; iC < coilSize; iC++)\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tvcoilField->get_value(dipoleMoment,iC);\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tvcoilField->get_center(dipoleLocation, iC);//auto resolve based on basis_order\n\n\t\t\t\t\t\t\t\tR = dipoleLocation - modelNode;\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tRl = R.length();\n\n\t\t\t\t\t\t\t\tif(typeOut == 1)\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t//! Biot-Savart Magnetic Field\n\t\t\t\t\t\t\t\t\tF += 1.0e-7 * ( 3 * R * Dot ( dipoleMoment, R ) / (Rl*Rl*Rl*Rl*Rl) - dipoleMoment / (Rl*Rl*Rl) ) ; \n\t\t\t\t\t\t\t\t}\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tif(typeOut == 2)\n\t\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\t\t//! Biot-Savart Magnetic Vector Potential Field\n\t\t\t\t\t\t\t\t\tF += 1.0e-7 * Cross ( dipoleMoment , R ) / (Rl*Rl*Rl) ;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tmatOut->put(iM,0, F[0]);\n\t\t\t\t\t\t\tmatOut->put(iM,1, F[1]);\n\t\t\t\t\t\t\tmatOut->put(iM,2, F[2]);\n\n\t\t\t\t\t\t\t//! progress reporter\n\t\t\t\t\t\t\tif (proc_num == 0) \n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tcnt++;\n\t\t\t\t\t\t\t\tif (cnt == 200) \n\t\t\t\t\t\t\t\t{ \n\t\t\t\t\t\t\t\t\tcnt = 0; \n\t\t\t\t\t\t\t\t\talgo_->update_progress(iM/2*(begins-ends)); \n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} \n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tsuccess[proc_num] = true;\n\t\t\t\t\t}\n\t\t\t\t\tcatch (...)\n\t\t\t\t\t{\n\t\t\t\t\t\talgo_->error(std::string(\"DipoleKernel crashed while integrating\"));\n\t\t\t\t\t\tsuccess[proc_num] = false;\n\t\t\t\t\t}\n\t\t\t \n\t\t\t\t\t//! check point\n\t\t\t\t\tbarrier_.wait();\n\n\t\t\t\t\t// Bail out if one of the processes failed\n\t\t\t\t\tfor (size_t q=0; qvfield()->basis_order() == -1)\n {\n error(\"Need data on coil mesh.\");\n return (false);\n }\n\t \n if( coil->vmesh()->is_curvemesh() )\n {\n if(coil->vfield()->is_constantdata() && coil->vfield()->is_scalar())\n {\n auto pwk = std::unique_ptr(new PieceWiseKernel(this, outtype));\n //pwk->SetIntegrationStep(this->istep);\n if( !pwk->Integrate(mesh,coil,outdata) )\n {\n error(\"Aborted during integration\");\n return (false);\n }\n }\n else\n {\n error(\"Curve mesh expected with constant scalar data.\");\n return (false); \n }\n }\n else if(coil->vmesh()->is_pointcloudmesh())\n {\n if((coil->vfield()->is_lineardata() || coil->vfield()->is_constantdata() ) && coil->vfield()->is_vector())\n {\n auto dp = std::unique_ptr(new DipolesKernel(this, outtype));\n if( !dp->Integrate(mesh,coil,outdata) )\n {\n error(\"Aborted during integration\");\n return (false);\n }\n }\n else\n {\n error(\"Pointcloud expected with linear vector data.\");\n return (false);\n }\n }\n else if( coil->vmesh()->is_volume() )\n {\n if( coil->vfield()->is_constantdata() && coil->vfield()->is_vector() )\n {\n auto vp = std::unique_ptr(new VolumetricKernel(this, outtype));\n if( !vp->Integrate(mesh,coil,outdata) )\n {\n error(\"Aborted during integration\");\n return (false);\n }\n }\n else\n { \n error(\"Volumetric mesh expected with constant vector data.\");\n return (false);\n }\n }\n else\n {\n error(\"Unsupported mesh type! Only curve or volumetric.\");\n return (false);\n }\n \n return (true);\n}\n\nAlgorithmOutput BiotSavartSolverAlgorithm::run(const AlgorithmInput& input) const\n{\n AlgorithmOutput output;\n \n auto mesh = input.get(Parameters::Mesh);\n auto coil = input.get(Parameters::Coil);\n\n MatrixHandle outdata1,outdata2;\n \n auto oports = get(Parameters::OutType).toInt();\n \n if (oports==3) //both are output ports\n {\n if(!run(mesh, coil, outdata1, 1))\n {\n error(\"Error: Algorithm of BiotSavartSolver failed.\");\n }\n output[Parameters::VectorBField] = outdata1;\n \n if(!run(mesh, coil, outdata2, 2))\n {\n error(\"Error: Algorithm of BiotSavartSolver failed.\");\n }\n output[Parameters::VectorAField] = outdata2;\n \n return output;\n } else\n {\n if(oports==1)\n {\n if(!run(mesh, coil, outdata1, oports))\n {\n error(\"Error: Algorithm of BiotSavartSolver failed.\"); \n }\n output[Parameters::VectorBField] = outdata1;\n } else\n if (oports==2)\n {\n if(!run(mesh, coil, outdata2, oports))\n {\n error(\"Error: Algorithm of BiotSavartSolver failed.\"); \n }\n output[Parameters::VectorAField] = outdata2;\n }\n }\n\n return output;\n}\n", "meta": {"hexsha": "9ee6dd2c569291718ec2f39859f2077007b5c82c", "size": 20402, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/Core/Algorithms/BrainStimulator/BiotSavartSolverAlgorithm.cc", "max_stars_repo_name": "Nahusa/SCIRun", "max_stars_repo_head_hexsha": "c54e714d4c7e956d053597cf194e07616e28a498", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-05-30T06:00:15.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-30T06:00:15.000Z", "max_issues_repo_path": "src/Core/Algorithms/BrainStimulator/BiotSavartSolverAlgorithm.cc", "max_issues_repo_name": "manual123/SCIRun", "max_issues_repo_head_hexsha": "3816b1dc4ebd0c5bd4539b7e50e08592acdac903", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Core/Algorithms/BrainStimulator/BiotSavartSolverAlgorithm.cc", "max_forks_repo_name": "manual123/SCIRun", "max_forks_repo_head_hexsha": "3816b1dc4ebd0c5bd4539b7e50e08592acdac903", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.4628297362, "max_line_length": 122, "alphanum_fraction": 0.5865111264, "num_tokens": 5648, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526368038304, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.24512060080581896}} {"text": "#include \n\n//[[Rcpp::depends(RcppEigen)]]\n#include \n\n#include \"experimentalSetup.hpp\"\n#include \"individual.hpp\"\n#include \"encodingScheme.hpp\"\n#include \"logLikelihoods.hpp\"\n#include \"estimatePoissonGammaParameters.hpp\"\n\n#include \"AuxiliaryFunctions.hpp\"\n#include \n#include \n\nIndividual::Individual()\n{\n Fitness = -HUGE_VAL;\n}\n\nIndividual::Individual(const ExperimentalSetup & ES)\n{\n if ((ES.NumberOfContributors - ES.NumberOfKnownContributors) == 0)\n {\n Eigen::MatrixXd decodedProfile = decoding(EncodedProfile, ES.NumberOfAlleles, ES.NumberOfMarkers, ES.NumberOfContributors - ES.NumberOfKnownContributors);\n\n CreateReducedElements(ES, decodedProfile);\n EstimateParameters(ES);\n CalculateFitness(ES, decodedProfile);\n }\n else\n {\n Rcpp::stop(\"The only use of this contructor is when the number of contributors equals the number of known contributors under the proposed hypothesis.\");\n }\n}\n\nIndividual::Individual(const Eigen::VectorXd & encodedGenotype, const ExperimentalSetup & ES)\n{\n EncodedProfile = encodedGenotype;\n\n Eigen::MatrixXd decodedProfile = decoding(EncodedProfile, ES.NumberOfAlleles, ES.NumberOfMarkers, ES.NumberOfContributors - ES.NumberOfKnownContributors);\n CreateReducedElements(ES, decodedProfile);\n\n EstimateParameters(ES);\n CalculateFitness(ES, decodedProfile);\n}\n\nIndividual::Individual(const Eigen::VectorXd & encodedGenotype, const Eigen::VectorXd & sampleParameters, const Eigen::VectorXd & noiseParameters,\n const Eigen::VectorXd & mixtureParameters, const Eigen::VectorXd & markerParameters, const ExperimentalSetup & ES)\n{\n EncodedProfile = encodedGenotype;\n\n SampleParameters = sampleParameters;\n MixtureParameters = mixtureParameters;\n MarkerImbalanceParameters = markerParameters;\n NoiseParameters = noiseParameters;\n\n Eigen::MatrixXd decodedProfile = decoding(EncodedProfile, ES.NumberOfAlleles, ES.NumberOfMarkers, ES.NumberOfContributors - ES.NumberOfKnownContributors);\n\n CreateReducedElements(ES, decodedProfile);\n CalculateFitness(ES, decodedProfile);\n}\n\nIndividual::Individual(const Eigen::VectorXd & encodedGenotype, const Eigen::VectorXd & sampleParameters, const Eigen::VectorXd & noiseParameters,\n const Eigen::VectorXd & mixtureParameters, const Eigen::VectorXd & markerParameters, const double & fitness, const ExperimentalSetup & ES)\n{\n EncodedProfile = encodedGenotype;\n\n SampleParameters = sampleParameters;\n MixtureParameters = mixtureParameters;\n MarkerImbalanceParameters = markerParameters;\n NoiseParameters = noiseParameters;\n\n Eigen::MatrixXd decodedProfile = decoding(EncodedProfile, ES.NumberOfAlleles, ES.NumberOfMarkers, ES.NumberOfContributors - ES.NumberOfKnownContributors);\n CreateReducedElements(ES, decodedProfile);\n\n CalculateFitness(ES, decodedProfile);\n}\n\nIndividual::Individual(const Eigen::VectorXd & encodedGenotype, const std::vector reducedExpectedContributionMatrix,\n const std::vector reducedAlleleIndex, const std::vector reducedNoiseIndex,\n const ExperimentalSetup & ES)\n{\n EncodedProfile = encodedGenotype;\n\n ReducedExpectedContributionMatrix = reducedExpectedContributionMatrix;\n ReducedAlleleIndex = reducedAlleleIndex;\n ReducedNoiseIndex = reducedNoiseIndex;\n\n Eigen::MatrixXd decodedProfile = decoding(EncodedProfile, ES.NumberOfAlleles, ES.NumberOfMarkers, ES.NumberOfContributors - ES.NumberOfKnownContributors);\n EstimateParameters(ES);\n CalculateFitness(ES, decodedProfile);\n}\n\ndouble ParentStutterContribution(const std::size_t & currentAllele, const std::size_t & stutterRecursion, const double & levelOfRecursion,\n const Eigen::VectorXd & decodedProfile_mu, const std::vector & potentialParents_m, const double & numberOfAlleles_m)\n{\n if (stutterRecursion == 0)\n {\n return 0.0;\n }\n\n Eigen::MatrixXd potentialParents_ma = potentialParents_m[currentAllele];\n Eigen::VectorXd potentialParentIndex = potentialParents_ma.col(1);\n Eigen::VectorXd stutterContribution = potentialParents_ma.col(2);\n\n Eigen::VectorXd potentialParentContribution = Eigen::VectorXd::Zero(potentialParentIndex.size());\n if (potentialParentIndex[0] != -1)\n {\n for (std::size_t i = 0; i < potentialParentIndex.size(); i++)\n {\n double parentContribution = ParentStutterContribution(potentialParentIndex[i] - 1, stutterRecursion - 1, levelOfRecursion + 1, decodedProfile_mu, potentialParents_m, numberOfAlleles_m);\n potentialParentContribution[i] = stutterContribution[i] * (decodedProfile_mu[potentialParentIndex[i] - 1] + parentContribution) / levelOfRecursion; // ;\n }\n }\n\n double totalParentContribition = potentialParentContribution.sum();\n return totalParentContribition;\n}\n\n\nEigen::MatrixXd Individual::GenerateExpectedContributionProfile(const ExperimentalSetup & ES, const Eigen::MatrixXd & decodedProfile)\n{\n std::size_t numberOfUnknownContributors = ES.NumberOfContributors - ES.NumberOfKnownContributors;\n Eigen::VectorXd partialSumAlleles = partialSumEigen(ES.NumberOfAlleles);\n\n Eigen::MatrixXd profile(ES.Coverage.size(), ES.NumberOfContributors);\n if (ES.NumberOfKnownContributors > 0)\n {\n profile = bindColumns(ES.KnownProfiles, decodedProfile);\n }\n else\n {\n profile = decodedProfile;\n }\n\n Eigen::MatrixXd expectedContributionProfile = Eigen::MatrixXd::Zero(decodedProfile.rows(), ES.NumberOfContributors);\n for (std::size_t m = 0; m < ES.NumberOfMarkers; m++)\n {\n std::vector potentialParents_m = ES.PotentialParents[m];\n Eigen::MatrixXd expectedContributionProfile_m = Eigen::MatrixXd::Zero(ES.NumberOfAlleles[m], ES.NumberOfContributors);\n Eigen::MatrixXd decodedProfile_m = profile.block(partialSumAlleles[m], 0, ES.NumberOfAlleles[m], ES.NumberOfContributors);\n for (std::size_t u = 0; u < ES.NumberOfContributors; u++)\n {\n Eigen::VectorXd decodedProfile_mu = decodedProfile_m.col(u);\n Eigen::VectorXd stutterContribution = Eigen::VectorXd::Zero(ES.NumberOfAlleles[m]);\n for (std::size_t a = 0; a < ES.NumberOfAlleles[m]; a++)\n {\n stutterContribution[a] = ParentStutterContribution(a, ES.LevelsOfStutterRecursion, 1, decodedProfile_mu, potentialParents_m, ES.NumberOfAlleles[m]);\n }\n\n expectedContributionProfile_m.col(u) = decodedProfile_mu + stutterContribution;\n }\n\n expectedContributionProfile.block(partialSumAlleles[m], 0, ES.NumberOfAlleles[m], ES.NumberOfContributors) = expectedContributionProfile_m;\n }\n\n return expectedContributionProfile;\n}\n\n\nEigen::VectorXd Individual::GenerateNoiseProfile(const ExperimentalSetup & ES, const Eigen::MatrixXd & expectedContributionProfile)\n{\n Eigen::VectorXd Ones = Eigen::VectorXd::Ones(expectedContributionProfile.cols());\n Eigen::VectorXd ExpectedContributionProfileRowSum = expectedContributionProfile * Ones;\n\n std::size_t N = ExpectedContributionProfileRowSum.size();\n Eigen::VectorXd identifiedNoise = Eigen::VectorXd::Zero(N);\n for (std::size_t n = 0; n < N; n++)\n {\n if (!(ExpectedContributionProfileRowSum[n] > 0.0) & (ES.Coverage[n] > 0.0))\n {\n identifiedNoise[n] = 1.0;\n }\n }\n\n return identifiedNoise;\n}\n\nvoid Individual::CreateReducedElements(const ExperimentalSetup & ES, const Eigen::MatrixXd & decodedProfile)\n{\n Eigen::MatrixXd expectedContributionProfile = GenerateExpectedContributionProfile(ES, decodedProfile);\n Eigen::VectorXd noiseProfile = GenerateNoiseProfile(ES, expectedContributionProfile);\n\n std::vector reducedExpectedContributionMatrix(ES.NumberOfMarkers);\n std::vector reducedAlleleIndex(ES.NumberOfMarkers);\n std::vector reducedNoiseIndex(ES.NumberOfMarkers);\n\n std::size_t n = 0;\n for (std::size_t m = 0; m < ES.NumberOfMarkers; m++)\n {\n Eigen::VectorXd noiseProfile_m = noiseProfile.segment(ES.PartialSumAlleles[m], ES.NumberOfAlleles[m]);\n std::size_t noiseProfileSize_m = noiseProfile_m.size();\n std::size_t noiseProfileSum_m = noiseProfile_m.sum();\n\n Eigen::MatrixXd reducedExpectedContributionMatrix_m = Eigen::MatrixXd::Zero(noiseProfileSize_m - noiseProfileSum_m, ES.NumberOfContributors);\n Eigen::VectorXd reducedAlleleIndex_m = Eigen::VectorXd::Zero(noiseProfileSize_m - noiseProfileSum_m);\n Eigen::VectorXd reducedNoiseIndex_m = Eigen::VectorXd::Zero(noiseProfileSum_m);\n\n std::size_t i = 0, j = 0;\n for (std::size_t a = 0; a < ES.NumberOfAlleles[m]; a++)\n {\n if (noiseProfile[n] == 0)\n {\n reducedExpectedContributionMatrix_m.row(i) = expectedContributionProfile.row(n);\n reducedAlleleIndex_m[i] = a;\n i++;\n }\n else\n {\n reducedNoiseIndex_m[j] = a;\n j++;\n }\n\n n++;\n }\n\n reducedExpectedContributionMatrix[m] = reducedExpectedContributionMatrix_m;\n reducedAlleleIndex[m] = reducedAlleleIndex_m;\n reducedNoiseIndex[m] = reducedNoiseIndex_m;\n }\n\n ReducedExpectedContributionMatrix = reducedExpectedContributionMatrix;\n ReducedAlleleIndex = reducedAlleleIndex;\n ReducedNoiseIndex = reducedNoiseIndex;\n}\n\nvoid Individual::EstimateParameters(const ExperimentalSetup & ES)\n{\n //// Estimating parameters\n // Allele parameters\n EstimatePoissonGammaAlleleParameters EPGA(ES.Coverage, ReducedExpectedContributionMatrix, ReducedAlleleIndex,\n ES.MarkerImbalances, ES.PartialSumAlleles,\n ES.ConvexMarkerImbalanceInterpolation, ES.Tolerance);\n\n estimateParametersAlleleCoverage(EPGA);\n SampleParameters = EPGA.SampleParameters;\n MixtureParameters = EPGA.MixtureParameters;\n MarkerImbalanceParameters = EPGA.MarkerImbalancesParameters;\n\n // Noise parameters\n if (ES.DualEstimation) {\n const double varianceUpperLimit = SampleParameters[1];\n EstimatePoissonGammaNoiseParameters EPGN(ES.Coverage, ReducedNoiseIndex, ES.PartialSumAlleles, ES.Tolerance, varianceUpperLimit);\n estimateParametersNoiseCoverage(EPGN);\n\n NoiseParameters = EPGN.NoiseParameters;\n }\n else {\n NoiseParameters = ES.NoiseParameters;\n }\n}\n\nvoid Individual::CalculateFitness(const ExperimentalSetup & ES, const Eigen::MatrixXd & decodedProfile)\n{\n LogLikelihoodAlleleMarker = logLikelihoodAlleleCoverage(ES.Coverage, ReducedExpectedContributionMatrix, ReducedAlleleIndex,\n ES.PartialSumAlleles, SampleParameters, MixtureParameters,\n MarkerImbalanceParameters);\n LogLikelihoodAllele = LogLikelihoodAlleleMarker.sum();\n\n LogLikelihoodNoiseMarker = logLikelihoodNoiseCoverage(ES.Coverage, ReducedNoiseIndex, ES.PartialSumAlleles, NoiseParameters[0], NoiseParameters[1], NoiseParameters[2]);\n LogLikelihoodNoise = LogLikelihoodNoiseMarker.sum();\n\n if ((ES.Theta < 0.0) | (ES.AlleleFrequencies.sum() == 0))\n {\n LogPriorGenotypeProbabilityMarker = Eigen::VectorXd::Zero(ES.NumberOfMarkers);\n LogPriorGenotypeProbability = 0.0;\n }\n else\n {\n LogPriorGenotypeProbabilityMarker = logPriorGenotypeProbability(ES.AlleleFrequencies, ES.Theta, decodedProfile, ES.AllKnownProfiles, ES.NumberOfMarkers, ES.NumberOfAlleles);\n LogPriorGenotypeProbability = LogPriorGenotypeProbabilityMarker.sum();\n }\n\n Fitness = LogLikelihoodAllele + LogLikelihoodNoise + LogPriorGenotypeProbability;\n}\n\nEigen::VectorXd Individual::CalculateResiduals(const ExperimentalSetup & ES, const Eigen::MatrixXd & expectedContributionProfile,\n const Eigen::VectorXd & noiseProfile)\n{\n const double & referenceMarkerAverage = SampleParameters[0];\n double dispersion;\n const Eigen::VectorXd & Coverage = ES.Coverage;\n const Eigen::VectorXd & MarkerImbalance = ES.MarkerImbalances;\n const Eigen::VectorXd EC = expectedContributionProfile * MixtureParameters;\n\n std::size_t N = Coverage.size();\n Eigen::VectorXd devianceResiduals = Eigen::VectorXd::Zero(N);\n for (std::size_t n = 0; n < N; n++)\n {\n double mu_ma;\n if (noiseProfile[n] == 0)\n {\n mu_ma = referenceMarkerAverage * MarkerImbalance[n] * EC[n];\n dispersion = SampleParameters[1];\n }\n else\n {\n mu_ma = NoiseParameters[0];\n dispersion = NoiseParameters[1];\n }\n\n double deviance_ma = (Coverage[n] + dispersion) * (std::log(mu_ma + dispersion) - std::log(Coverage[n] + dispersion));\n if (Coverage[n] > 0)\n {\n deviance_ma += Coverage[n] * (std::log(Coverage[n]) - std::log(mu_ma));\n }\n\n devianceResiduals[n] = boost::math::sign(Coverage[n] - mu_ma) * std::pow(deviance_ma, 0.5);\n }\n\n return devianceResiduals;\n}\n\nRcpp::List Individual::ReturnRcppList(const ExperimentalSetup & ES)\n{\n Eigen::MatrixXd decodedProfile = decoding(EncodedProfile, ES.NumberOfAlleles, ES.NumberOfMarkers, ES.NumberOfContributors - ES.NumberOfKnownContributors);\n Eigen::MatrixXd expectedContributionProfile = GenerateExpectedContributionProfile(ES, decodedProfile);\n\n Eigen::VectorXd noiseProfile = GenerateNoiseProfile(ES, expectedContributionProfile);\n\n CalculateFitness(ES, decodedProfile);\n return Rcpp::List::create(Rcpp::Named(\"EncodedUnknownProfiles\") = EncodedProfile,\n Rcpp::Named(\"DecodedUnknownProfiles\") = decodedProfile,\n Rcpp::Named(\"ExpectedContributionMatrix\") = expectedContributionProfile,\n Rcpp::Named(\"NoiseVector\") = noiseProfile,\n Rcpp::Named(\"Parameters\") = Rcpp::List::create(\n Rcpp::Named(\"SampleParameters\") = SampleParameters,\n Rcpp::Named(\"MixtureParameters\") = MixtureParameters,\n Rcpp::Named(\"MarkerImbalanceParameters\") = MarkerImbalanceParameters,\n Rcpp::Named(\"NoiseParameters\") = NoiseParameters),\n Rcpp::Named(\"LogLikelihoodAlleleCoverage\") = LogLikelihoodAlleleMarker,\n Rcpp::Named(\"LogLikelihoods\") = Rcpp::NumericVector::create(LogLikelihoodAllele, LogLikelihoodNoise, LogPriorGenotypeProbability),\n Rcpp::Named(\"Fitness\") = Fitness);\n}\n\nRcpp::List Individual::ReturnRcppListSimplified()\n{\n return Rcpp::List::create(Rcpp::Named(\"SampleParameters\") = SampleParameters,\n Rcpp::Named(\"MarkerImbalanceParameters\") = MarkerImbalanceParameters,\n Rcpp::Named(\"MixtureParameters\") = MixtureParameters,\n Rcpp::Named(\"NoiseParameters\") = NoiseParameters,\n Rcpp::Named(\"Fitness\") = Fitness);\n}\n\n//[[Rcpp::export(.setupIndividual)]]\nRcpp::List setupIndividual(const std::size_t & numberOfMarkers,\n const Eigen::VectorXd & numberOfAlleles,\n const std::size_t & numberOfContributors,\n const std::size_t & numberOfKnownContributors,\n const Eigen::MatrixXd & knownProfiles,\n const Eigen::VectorXd & coverage,\n const std::vector< std::vector < Eigen::MatrixXd > > & potentialParents,\n const Eigen::VectorXd & markerImbalances,\n const double & convexMarkerImbalanceInterpolation,\n const Eigen::VectorXd & noiseParameters,\n const Eigen::VectorXd & tolerance,\n const double & theta,\n const Eigen::VectorXd & alleleFrequencies,\n const std::size_t & levelsOfStutterRecursion,\n const bool & dualEstimation)\n{\n const Eigen::MatrixXd allProfilesEmpty;\n const ExperimentalSetup ES(numberOfMarkers, numberOfAlleles, numberOfContributors, numberOfKnownContributors,\n knownProfiles, allProfilesEmpty, coverage, potentialParents, markerImbalances,\n convexMarkerImbalanceInterpolation, noiseParameters, tolerance, theta, alleleFrequencies,\n levelsOfStutterRecursion, dualEstimation);\n Individual I(ES);\n\n Eigen::MatrixXd decodedProfile = decoding(I.EncodedProfile, ES.NumberOfAlleles, ES.NumberOfMarkers, ES.NumberOfContributors - ES.NumberOfKnownContributors);\n Eigen::MatrixXd expectedContributionProfile = I.GenerateExpectedContributionProfile(ES, decodedProfile);\n Eigen::VectorXd noiseProfile = I.GenerateNoiseProfile(ES, expectedContributionProfile);\n\n return Rcpp::List::create(Rcpp::Named(\"EncodedUnknownProfiles\") = I.EncodedProfile,\n Rcpp::Named(\"DecodedUnknownProfiles\") = decodedProfile,\n Rcpp::Named(\"ExpectedContributionMatrix\") = expectedContributionProfile,\n Rcpp::Named(\"NoiseVector\") = noiseProfile,\n Rcpp::Named(\"Parameters\") = Rcpp::List::create(\n Rcpp::Named(\"SampleParameters\") = I.SampleParameters,\n Rcpp::Named(\"MixtureParameters\") = I.MixtureParameters,\n Rcpp::Named(\"MarkerImbalanceParameters\") = I.MarkerImbalanceParameters,\n Rcpp::Named(\"NoiseParameters\") = I.NoiseParameters),\n Rcpp::Named(\"LogLikelihoodAlleleCoverage\") = I.LogLikelihoodAlleleMarker,\n Rcpp::Named(\"LogLikelihoods\") = Rcpp::NumericVector::create(I.LogLikelihoodAllele, I.LogLikelihoodNoise, I.LogPriorGenotypeProbability),\n Rcpp::Named(\"Fitness\") = I.Fitness);\n}\n", "meta": {"hexsha": "ae0026ffd086dc3f7582de0bfded12a60083cd8e", "size": 18530, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/individual.cpp", "max_stars_repo_name": "svilsen/MPSMixtures", "max_stars_repo_head_hexsha": "07b21c593bee795162f6282446ff370985ceec38", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/individual.cpp", "max_issues_repo_name": "svilsen/MPSMixtures", "max_issues_repo_head_hexsha": "07b21c593bee795162f6282446ff370985ceec38", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/individual.cpp", "max_forks_repo_name": "svilsen/MPSMixtures", "max_forks_repo_head_hexsha": "07b21c593bee795162f6282446ff370985ceec38", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.8811369509, "max_line_length": 197, "alphanum_fraction": 0.676848354, "num_tokens": 3993, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.41869690935568665, "lm_q1q2_score": 0.24498004492533793}} {"text": "/*\n * Copyright (c) 2013, Christian Gehring, Hannes Sommer, Paul Furgale, Remo Diethelm\n * All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions are met:\n * * Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n * * Redistributions in binary form must reproduce the above copyright\n * notice, this list of conditions and the following disclaimer in the\n * documentation and/or other materials provided with the distribution.\n * * Neither the name of the Autonomous Systems Lab, ETH Zurich nor the\n * names of its contributors may be used to endorse or promote products\n * derived from this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\n * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n * DISCLAIMED. IN NO EVENT SHALL Christian Gehring, Hannes Sommer, Paul Furgale,\n * Remo Diethelm BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,\n * OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE\n * GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\n * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n *\n*/\n\n#ifndef KINDR_ROTATIONS_EIGEN_ROTATIONMATRIX_HPP_\n#define KINDR_ROTATIONS_EIGEN_ROTATIONMATRIX_HPP_\n\n#include \n\n#include \n\n#include \"kindr/common/common.hpp\"\n#include \"kindr/common/assert_macros_eigen.hpp\"\n#include \"kindr/rotations/RotationBase.hpp\"\n#include \"kindr/rotations/eigen/RotationEigenFunctions.hpp\"\n\nnamespace kindr {\nnamespace rotations {\nnamespace eigen_impl {\n\n\n/*! \\class RotationMatrix\n * \\brief Implementation of matrix rotation based on Eigen::Matrix\n *\n * The following four typedefs are provided for convenience:\n * - \\ref eigen_impl::RotationMatrixAD \"RotationMatrixAD\" for active rotation and double primitive type\n * - \\ref eigen_impl::RotationMatrixAF \"RotationMatrixAF\" for active rotation and float primitive type\n * - \\ref eigen_impl::RotationMatrixPD \"RotationMatrixPD\" for passive rotation and double primitive type\n * - \\ref eigen_impl::RotationMatrixPF \"RotationMatrixPF\" for passive rotation and float primitive type\n *\n * \\tparam PrimType_ the primitive type of the data (double or float)\n * \\tparam Usage_ the rotation usage which is either active or passive\n *\n * \\ingroup rotations\n */\ntemplate\nclass RotationMatrix : public RotationMatrixBase, Usage_>, private Eigen::Matrix {\n private:\n /*! \\brief The base type.\n */\n typedef Eigen::Matrix Base;\n public:\n /*! \\brief The implementation type.\n * The implementation type is always an Eigen object.\n */\n typedef Base Implementation;\n /*! \\brief The primitive type.\n * Float/Double\n */\n typedef PrimType_ Scalar;\n\n /*! \\brief Default constructor using identity rotation.\n */\n RotationMatrix()\n : Base(Base::Identity()) {\n }\n\n /*! \\brief Constructor using nine scalars.\n * In debug mode, an assertion is thrown if the matrix is not a rotation matrix.\n * \\param r11 entry in row 1, col 1\n * \\param r12 entry in row 1, col 2\n * \\param r13 entry in row 1, col 3\n * \\param r21 entry in row 2, col 1\n * \\param r22 entry in row 2, col 2\n * \\param r23 entry in row 2, col 3\n * \\param r31 entry in row 3, col 1\n * \\param r32 entry in row 3, col 2\n * \\param r33 entry in row 3, col 3\n */\n RotationMatrix(Scalar r11, Scalar r12, Scalar r13,\n Scalar r21, Scalar r22, Scalar r23,\n Scalar r31, Scalar r32, Scalar r33) {\n if(Usage_ == RotationUsage::ACTIVE)\n {\n *this << r11,r12,r13,r21,r22,r23,r31,r32,r33;\n } else {\n *this << r11,r21,r31,r12,r22,r32,r13,r23,r33;\n }\n KINDR_ASSERT_MATRIX_NEAR_DBG(std::runtime_error, this->toImplementation() * this->toImplementation().transpose(), Base::Identity(), static_cast(1e-4), \"Input matrix is not orthogonal.\");\n KINDR_ASSERT_SCALAR_NEAR_DBG(std::runtime_error, this->determinant(), static_cast(1), static_cast(1e-4), \"Input matrix determinant is not 1.\");\n }\n\n /*! \\brief Constructor using Eigen::Matrix.\n * In debug mode, an assertion is thrown if the rotation vector has not unit length.\n * \\param other Eigen::Matrix\n */\n explicit RotationMatrix(const Base& other)\n // : Base(other)\n {\n if(Usage_ == RotationUsage::ACTIVE)\n {\n this->toImplementation() = other;\n } else {\n this->toImplementation() = other.transpose();\n }\n KINDR_ASSERT_MATRIX_NEAR_DBG(std::runtime_error, other * other.transpose(), Base::Identity(), static_cast(1e-4), \"Input matrix is not orthogonal.\");\n KINDR_ASSERT_SCALAR_NEAR_DBG(std::runtime_error, other.determinant(), static_cast(1), static_cast(1e-4), \"Input matrix determinant is not 1.\");\n }\n\n /*! \\brief Constructor using another rotation.\n * \\param other other rotation\n */\n template\n inline explicit RotationMatrix(const RotationBase& other)\n // : Base(internal::ConversionTraits::convert(other.derived()).toImplementation())\n {\n this->toImplementation() = internal::ConversionTraits::convert(other.derived()).toImplementation();\n }\n\n /*! \\brief Assignment operator using another rotation.\n * \\param other other rotation\n * \\returns referece\n */\n template\n RotationMatrix& operator =(const RotationBase& other) {\n this->toImplementation() = internal::ConversionTraits::convert(other.derived()).toImplementation();\n return *this;\n }\n\n /*! \\brief Parenthesis operator to convert from another rotation.\n * \\param other other rotation\n * \\returns reference\n */\n template\n RotationMatrix& operator ()(const RotationBase& other) {\n this->toImplementation() = internal::ConversionTraits::convert(other.derived()).toImplementation();\n return *this;\n }\n\n /*! \\brief Returns the inverse of the rotation.\n * \\returns the inverse of the rotation\n */\n RotationMatrix inverted() const {\n RotationMatrix matrix;\n matrix.toImplementation() = this->toImplementation().transpose();\n return matrix;\n }\n\n /*! \\brief Inverts the rotation.\n * \\returns reference\n */\n RotationMatrix& invert() {\n *this = this->inverted();\n return *this;\n }\n\n /*! \\brief Returns the transpose of the rotation matrix.\n * \\returns the inverse of the rotation\n */\n RotationMatrix transposed() const {\n RotationMatrix matrix;\n matrix.toImplementation() = this->toImplementation().transpose();\n return matrix;\n }\n\n /*! \\brief Transposes the rotation matrix.\n * \\returns reference\n */\n RotationMatrix& transpose() {\n *this = this->transposed();\n return *this;\n }\n\n /*! \\brief Returns the determinant of the rotation matrix.\n * \\returns determinant of the rotation matrix\n */\n Scalar determinant() const {\n return toImplementation().determinant();\n }\n\n /*! \\brief Cast to the implementation type.\n * \\returns the implementation for direct manipulation (recommended only for advanced users)\n */\n inline Implementation& toImplementation() {\n return static_cast(*this);\n }\n\n /*! \\brief Cast to the implementation type.\n * \\returns the implementation for direct manipulation (recommended only for advanced users)\n */\n inline const Implementation& toImplementation() const {\n return static_cast(*this);\n }\n\n /*! \\brief Reading access to the rotation matrix.\n * \\returns rotation matrix (matrix) with reading access\n */\n inline Implementation matrix() const {\n if(Usage_ == RotationUsage::ACTIVE)\n {\n return this->toImplementation();\n } else {\n return this->toImplementation().transpose();\n }\n }\n\n /*! \\brief Writing access to the rotation matrix.\n */\n inline void setMatrix(const Implementation & input) {\n if(Usage_ == RotationUsage::ACTIVE)\n {\n this->toImplementation() = input;\n } else {\n this->toImplementation() = input.transpose();\n }\n }\n\n /*! \\brief Writing access to the rotation matrix.\n */\n inline void setMatrix(Scalar r11, Scalar r12, Scalar r13,\n Scalar r21, Scalar r22, Scalar r23,\n Scalar r31, Scalar r32, Scalar r33) {\n if(Usage_ == RotationUsage::ACTIVE)\n {\n *this << r11,r12,r13,r21,r22,r23,r31,r32,r33;\n } else {\n *this << r11,r21,r31,r12,r22,r32,r13,r23,r33;\n }\n }\n\n /*! \\brief Sets the rotation to identity.\n * \\returns reference\n */\n RotationMatrix& setIdentity() {\n this->Implementation::setIdentity();\n return *this;\n }\n\n /*! \\brief Returns a unique matrix rotation.\n * A rotation matrix is always unique.\n * This function is used to compare different rotations.\n * \\returns copy of the matrix rotation which is unique\n */\n RotationMatrix getUnique() const {\n return *this;\n }\n\n /*! \\brief Modifies the matrix rotation such that it becomes unique.\n * A rotation matrix is always unique.\n * \\returns reference\n */\n RotationMatrix& setUnique() {\n return *this;\n }\n\n /*! \\brief Concenation operator.\n * This is explicitly specified, because Eigen::Matrix provides also an operator*.\n * \\returns the concenation of two rotations\n */\n using RotationMatrixBase, Usage_>::operator*; // otherwise ambiguous RotationBase and Eigen\n\n /*! \\brief Equivalence operator.\n * This is explicitly specified, because Eigen::Matrix provides also an operator==.\n * \\returns true if two rotations are similar.\n */\n using RotationMatrixBase, Usage_>::operator==; // otherwise ambiguous RotationBase and Eigen\n\n /*! \\brief Used for printing the object with std::cout.\n * \\returns std::stream object\n */\n friend std::ostream& operator << (std::ostream& out, const RotationMatrix& rotationMatrix) {\n if(Usage_ == RotationUsage::ACTIVE) {\n out << rotationMatrix.toImplementation();\n } else {\n out << rotationMatrix.inverted().toImplementation();\n }\n return out;\n }\n};\n\n//! \\brief Active matrix rotation with double primitive type\ntypedef RotationMatrix RotationMatrixAD;\n//! \\brief Active matrix rotation with float primitive type\ntypedef RotationMatrix RotationMatrixAF;\n//! \\brief Passive matrix rotation with double primitive type\ntypedef RotationMatrix RotationMatrixPD;\n//! \\brief Passive matrix rotation with float primitive type\ntypedef RotationMatrix RotationMatrixPF;\n\n} // namespace eigen_impl\n\n\nnamespace internal {\n\ntemplate\nclass get_scalar> {\n public:\n typedef PrimType_ Scalar;\n};\n\ntemplate\nclass get_matrix3X>{\n public:\n typedef int IndexType;\n\n template \n using Matrix3X = Eigen::Matrix;\n};\n\ntemplate\nclass get_other_usage> {\n public:\n typedef eigen_impl::RotationMatrix OtherUsage;\n};\n\ntemplate\nclass get_other_usage> {\n public:\n typedef eigen_impl::RotationMatrix OtherUsage;\n};\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Conversion Traits\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\ntemplate\nclass ConversionTraits, eigen_impl::AngleAxis> {\n public:\n inline static eigen_impl::RotationMatrix convert(const eigen_impl::AngleAxis& aa) {\n eigen_impl::RotationMatrix matrix;\n matrix.toImplementation() = eigen_impl::eigen_internal::getRotationMatrixFromAngleAxis(aa.toImplementation());\n return matrix;\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::RotationVector> {\n public:\n inline static eigen_impl::RotationMatrix convert(const eigen_impl::RotationVector& rotationVector) {\n typename eigen_impl::RotationMatrix::Implementation matrixdata;\n typedef typename eigen_impl::RotationVector::Scalar Scalar;\n const typename eigen_impl::RotationVector::Implementation rv = rotationVector.toImplementation().template cast();\n const SourcePrimType_ v1 = rv.x();\n const SourcePrimType_ v2 = rv.y();\n const SourcePrimType_ v3 = rv.z();\n const SourcePrimType_ v = rv.norm();\n\n if (v < common::internal::NumTraits::dummy_precision()) {\n\n // active to passive\n// matrixdata << 1.0, v3, -v2,\n// -v3, 1.0, v1,\n// v2, -v1, 1.0;\n // active to active\n matrixdata << 1.0, -v3, v2,\n v3, 1.0, -v1,\n -v2, v1, 1.0;\n } else {\n // active rotation vector to active matrix // not matlab code (is transposed)\n const DestPrimType_ t3 = v*(1.0/2.0);\n const DestPrimType_ t2 = sin(t3);\n const DestPrimType_ t4 = cos(t3);\n const DestPrimType_ t5 = 1.0/(v*v);\n const DestPrimType_ t6 = t4*v*v3;\n const DestPrimType_ t7 = t2*v1*v2;\n const DestPrimType_ t8 = t2*t2;\n const DestPrimType_ t9 = v1*v1;\n const DestPrimType_ t10 = v2*v2;\n const DestPrimType_ t11 = v3*v3;\n const DestPrimType_ t12 = v*v;\n const DestPrimType_ t13 = t4*t4;\n const DestPrimType_ t14 = t12*t13;\n const DestPrimType_ t15 = t2*v1*v3;\n const DestPrimType_ t16 = t4*v*v1;\n const DestPrimType_ t17 = t2*v2*v3;\n matrixdata(0,0) = t5*(t14-t8*(-t9+t10+t11));\n matrixdata(1,0) = t2*t5*(t6+t7)*2.0;\n matrixdata(2,0) = t2*t5*(t15-t4*v*v2)*2.0;\n matrixdata(0,1) = t2*t5*(t6-t7)*-2.0;\n matrixdata(1,1) = t5*(t14-t8*(t9-t10+t11));\n matrixdata(2,1) = t2*t5*(t16+t17)*2.0;\n matrixdata(0,2) = t2*t5*(t15+t4*v*v2)*2.0;\n matrixdata(1,2) = t2*t5*(t16-t17)*-2.0;\n matrixdata(2,2) = t5*(t14-t8*(t9+t10-t11));\n\n\n\n }\n\n eigen_impl::RotationMatrix matrix;\n matrix.toImplementation() = matrixdata;\n return matrix;\n\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::RotationQuaternion> {\n public:\n inline static eigen_impl::RotationMatrix convert(const eigen_impl::RotationQuaternion& q) {\n eigen_impl::RotationMatrix matrix;\n matrix.toImplementation() = eigen_impl::eigen_internal::getRotationMatrixFromQuaternion(q.toImplementation());\n return matrix;\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::RotationMatrix> {\n public:\n inline static eigen_impl::RotationMatrix convert(const eigen_impl::RotationMatrix& R) {\n eigen_impl::RotationMatrix matrix;\n matrix.toImplementation() = R.toImplementation().template cast();\n return matrix;\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::EulerAnglesXyz> {\n public:\n inline static eigen_impl::RotationMatrix convert(const eigen_impl::EulerAnglesXyz& xyz) {\n eigen_impl::RotationMatrix matrix;\n matrix.toImplementation() = eigen_impl::eigen_internal::getRotationMatrixFromRpy(xyz.toImplementation());\n return matrix;\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::EulerAnglesZyx> {\n public:\n inline static eigen_impl::RotationMatrix convert(const eigen_impl::EulerAnglesZyx& zyx) {\n eigen_impl::RotationMatrix matrix;\n matrix.toImplementation() = eigen_impl::eigen_internal::getRotationMatrixFromYpr(zyx.toImplementation());\n return matrix;\n }\n};\n\n\n\n/*! \\brief Multiplication of two rotation matrices\n */\ntemplate\nclass MultiplicationTraits, Usage_>, RotationBase, Usage_>> {\n public:\n inline static eigen_impl::RotationMatrix mult(const eigen_impl::RotationMatrix& lhs, const eigen_impl::RotationMatrix& rhs) {\n if(Usage_ == RotationUsage::ACTIVE) {\n eigen_impl::RotationMatrix result;\n result.toImplementation() = lhs.toImplementation() * rhs.toImplementation();\n return result;\n } else {\n eigen_impl::RotationMatrix result;\n result.toImplementation() = rhs.toImplementation() * lhs.toImplementation();\n return result;\n }\n }\n};\n\n\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Rotation Traits\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\n//template\n//class RotationTraits> {\n// public:\n// template>::IndexType Cols>\n// inline static typename get_matrix3X>::template Matrix3X rotate(const eigen_impl::RotationMatrix& R, const typename get_matrix3X>::template Matrix3X& m){\n// return R.toImplementation() * m;\n// }\n//};\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Comparison Traits\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\n\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Usage Conversion Traits - required?\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\n\ntemplate\nclass UsageConversionTraits,RotationUsage::PASSIVE> {\n public:\n inline static typename get_other_usage>::OtherUsage getActive(const eigen_impl::RotationMatrix& in) {\n return typename get_other_usage>::OtherUsage(in.inverted().toImplementation());\n }\n\n // getPassive() does not exist (on purpose)\n};\n\ntemplate\nclass UsageConversionTraits,RotationUsage::ACTIVE> {\n public:\n inline static typename get_other_usage>::OtherUsage getPassive(const eigen_impl::RotationMatrix& in) {\n return typename get_other_usage>::OtherUsage(in.toImplementation());\n }\n\n // getActive() does not exist (on purpose)\n};\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Box Operations - required?\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\n\n//template\n//class BoxOperationTraits, Usage_>, RotationBase, Usage_>> {\n// public:\n// inline static typename internal::get_matrix3X>::template Matrix3X<1> box_minus(const eigen_impl::RotationMatrix& lhs, const eigen_impl::RotationMatrix& rhs) {\n// return (lhs*rhs.inverted()).getLogarithmicMap();\n// }\n//\n// inline static eigen_impl::RotationMatrix box_plus(const eigen_impl::RotationMatrix& rotation, const typename internal::get_matrix3X>::template Matrix3X<1>& vector) {\n// return eigen_impl::RotationMatrix((MapTraits, Usage_>>::set_exponential_map(vector)).toImplementation()*rotation.toImplementation());\n// }\n//};\n\n\n//template\n//class MapTraits> {\n// public:\n//\n// inline static eigen_impl::RotationMatrix set_exponential_map(const typename internal::get_matrix3X>::template Matrix3X<1>& vector) {\n// typedef typename get_scalar>::Scalar Scalar;\n// if (Usage_ == RotationUsage::ACTIVE) {\n// return eigen_impl::RotationMatrix(eigen_impl::RotationVector(vector));\n// }\n// if (Usage_ == RotationUsage::PASSIVE) {\n// return eigen_impl::RotationMatrix(eigen_impl::RotationVector(vector));\n// }\n// }\n//\n//// inline static typename internal::get_matrix3X::template Matrix3X<1> get_logarithmic_map(const Rotation_& rotation) {\n//// typedef typename get_scalar::Scalar Scalar;\n//// eigen_impl::RotationVector rotationVector(rotation);\n//// return rotationVector.getUnique().toImplementation();\n//// }\n//\n// inline static typename internal::get_matrix3X>::template Matrix3X<1> get_logarithmic_map(const eigen_impl::RotationMatrix& rotation) {\n// typedef typename get_scalar>::Scalar Scalar;\n//\n//\n//\n// if (Usage_ == RotationUsage::ACTIVE) {\n// return eigen_impl::RotationVector(rotation).toImplementation();\n// }\n// if (Usage_ == RotationUsage::PASSIVE) {\n// return eigen_impl::RotationVector(rotation).toImplementation();\n// }\n// }\n//\n//};\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Fixing Traits\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\ntemplate\nclass FixingTraits> {\n public:\n inline static void fix(eigen_impl::RotationMatrix& R) {\n const PrimType_ factor = 1/pow(R.determinant(), 1.0/3.0);\n R.setMatrix(factor*R.matrix()(0,0),\n factor*R.matrix()(0,1),\n factor*R.matrix()(0,2),\n factor*R.matrix()(1,0),\n factor*R.matrix()(1,1),\n factor*R.matrix()(1,2),\n factor*R.matrix()(2,0),\n factor*R.matrix()(2,1),\n factor*R.matrix()(2,2));\n }\n};\n\n\n} // namespace internal\n} // namespace rotations\n} // namespace kindr\n\n\n#endif /* KINDR_ROTATIONS_EIGEN_ROTATIONMATRIX_HPP_ */\n", "meta": {"hexsha": "f7e274a7e60de6530918c6132e1ec15942ab488a", "size": 26708, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "IHMCPerception/third-party/kindr/include/kindr/rotations/eigen/RotationMatrix.hpp", "max_stars_repo_name": "wxmerkt/ihmc-open-robotics-software", "max_stars_repo_head_hexsha": "2c47c9a9bd999e7811038e99c3888683f9973a2a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 170.0, "max_stars_repo_stars_event_min_datetime": 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"2022-03-28T03:11:24.000Z", "avg_line_length": 45.1148648649, "max_line_length": 279, "alphanum_fraction": 0.6528381009, "num_tokens": 6078, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.2448964344532826}} {"text": "/* \n * Copyright (c) 2009 Carnegie Mellon University. \n * All rights reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an \"AS\n * IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\n * express or implied. See the License for the specific language\n * governing permissions and limitations under the License.\n *\n * For more about this software visit:\n *\n * http://www.graphlab.ml.cmu.edu\n *\n */\n\n\n/**\n *\n * \\brief This application performs MAP inference on Markov Nets \n * provided in standard UAI file format via Dual-Decomposition. \n *\n *\n * \\authors Dhruv Batra, André Martins, Aroma Mahendru\n */\n\n\n\n#ifndef _AD3_QP_HPP_\n#define _AD3_QP_HPP_\n\n\n#include \n#include \n#include \n#include \"dd_grlab.hpp\"\n\n\n#define NEARLY_ZERO_TOL(a,tol) (((a)<=(tol)) && ((a)>=(-(tol))))\n#define NEARLY_EQ_TOL(a,b,tol) (((a)-(b))*((a)-(b))<=(tol))\n#define num_max_iterations_QP_ 10\n\n\n/**\n * \\brief ADMM vertex program general implements Alternating direction dual \n * decomposition for general dense factors.\n */\n\nstruct admm_vertex_program_general:public admm_vertex_program {\n\n/**\n * \\brief Maximize returns the maximum value and configuration with reference to \n * input additional and variable log potentials. addtional log potential corresponds \n * factor potentials and variable potential corresponds to sum of lagrange \n * multipliers and unary potentials divided by degree of the unary vertex.\n */\n\n\nvoid Maximize(vertex_type& vertex, vec additional_log_potentials, vec variable_log_potentials,\n Configuration &configuration,\n double *value) {\n \n vector states(vertex.data().nvars,-1);\n int best = -1;\n *value = -1e12;\n for (int index = 0;\n index < additional_log_potentials.size();\n ++index) {\n double score = additional_log_potentials[index];\n get_configuration_states(vertex,index, &states);\n int offset = 0;\n for (int i = 0; i < vertex.data().nvars; ++i) {\n score += variable_log_potentials[offset+states[i]];\n offset = vertex.data().cards[i];\n \n }\n \n if (configuration < 0 || score > *value) {\n configuration = index;\n *value = score;\n }\n }\n assert(configuration >= 0);\n \n }\n\n \n void DeleteConfiguration(Configuration &configuration) {\n configuration = -1;\n }\n\n/**\n * \\brief InvertAfterInsertion function is used to invert the matrix. Used in solveQP\n */\n\n bool InvertAfterInsertion(vertex_type& vertex, vector & inverse_A_,\n const vector &active_set, const Configuration &inserted_element) {\n\n vector inverse_A = inverse_A_;\n int size_A = active_set.size() + 1;\n vector r(size_A);\n\n r[0] = 1.0;\n for (int i = 0; i < active_set.size(); ++i) {\n // Count how many variable values the new assignment\n // have in common with the i-th assignment.\n int num_common_values = CountCommonValues(vertex, active_set[i], inserted_element);\n r[i+1] = static_cast(num_common_values);\n }\n\n double r0 = static_cast(CountCommonValues(vertex,\n inserted_element, inserted_element));\n double s = r0;\n for (int i = 0; i < size_A; ++i) {\n if (r[i] == 0.0) continue;\n s -= r[i] * r[i] * inverse_A[i * size_A + i];\n for (int j = i+1; j < size_A; ++j) {\n if (r[j] == 0.0) continue;\n s -= 2 * r[i] * r[j] * inverse_A[i * size_A + j];\n }\n }\n\n if (NEARLY_ZERO_TOL(s, 1e-9)) {\n if (opts.verbose> 2) {\n cout << \"Warning: updated matrix will become singular after insertion.\"\n << endl;\n }\n return false;\n }\n\n double invs = 1.0 / s;\n vector d(size_A, 0.0);\n for (int i = 0; i < size_A; ++i) {\n if (r[i] == 0.0) continue;\n for (int j = 0; j < size_A; ++j) {\n d[j] += inverse_A[i * size_A + j] * r[i];\n }\n }\n\n int size_A_after = size_A + 1;\n inverse_A_.resize(size_A_after * size_A_after);\n for (int i = 0; i < size_A; ++i) {\n for (int j = 0; j < size_A; ++j) {\n inverse_A_[i * size_A_after + j] = inverse_A[i * size_A + j] +\n invs * d[i] * d[j];\n }\n inverse_A_[i * size_A_after + size_A] = -invs * d[i];\n inverse_A_[size_A * size_A_after + i] = -invs * d[i];\n }\n inverse_A_[size_A * size_A_after + size_A] = invs;\n\n return true;\n}\n\n/**\n * \\brief InvertAfterRemoval function is used to invert the matrix. Used in solveQP\n */\nvoid InvertAfterRemoval(vector &inverse_A_,const vector &active_set,\n int removed_index) {\n vector inverse_A = inverse_A_;\n int size_A = active_set.size() + 1;\n vector r(size_A);\n\n ++removed_index; // Index in A has an offset of 1.\n double invs = inverse_A[removed_index * size_A + removed_index];\n assert(!NEARLY_ZERO_TOL(invs, 1e-12));\n double s = 1.0 / invs;\n vector d(size_A - 1, 0.0);\n int k = 0;\n for (int i = 0; i < size_A; ++i) {\n if (i == removed_index) continue;\n d[k] = -s * inverse_A[removed_index * size_A + i];\n ++k;\n }\n\n int size_A_after = size_A - 1;\n inverse_A_.resize(size_A_after * size_A_after);\n k = 0;\n for (int i = 0; i < size_A; ++i) {\n if (i == removed_index) continue;\n int l = 0;\n for (int j = 0; j < size_A; ++j) {\n if (j == removed_index) continue;\n inverse_A_[k * size_A_after + l] = inverse_A[i * size_A + j] -\n invs * d[k] * d[l];\n ++l;\n }\n ++k;\n }\n}\n\n/**\n * \\brief ComputeActiveSetSimilarities computes Mnz'*Mnz. Used in solveQP\n */\nvoid ComputeActiveSetSimilarities(vertex_type& vertex,\n const vector &active_set,\n vector *similarities) {\n int size = active_set.size();\n\n // Compute similarity matrix.\n similarities->resize(size * size);\n (*similarities)[0] = 0.0;\n for (int i = 0; i < active_set.size(); ++i) {\n (*similarities)[i*size + i] = static_cast(\n CountCommonValues(vertex,active_set[i], active_set[i]) );\n for (int j = i+1; j < active_set.size(); ++j) {\n // Count how many variable values the i-th and j-th \n // assignments have in common.\n int num_common_values = CountCommonValues(vertex,active_set[i], active_set[j]);\n (*similarities)[i*size + j] = num_common_values;\n (*similarities)[j*size + i] = num_common_values;\n }\n }\n}\n\n/**\n * \\brief ComputeMarginalsFromSparseDistribution computes marginalvalues for unary \n * factor from given factor distribution.\n */\n \nvoid ComputeMarginalsFromSparseDistribution( vertex_type& vertex, \n const vector &active_set,\n const vector &distribution,\n vec &variable_posteriors,\n vec &additional_posteriors) {\n variable_posteriors.setZero(); \n additional_posteriors.setZero(); \n for (int i = 0; i < active_set.size(); ++i) {\n UpdateMarginalsFromConfiguration(vertex,active_set[i],\n distribution[i],\n variable_posteriors,\n additional_posteriors);\n }\n }\n \n \n // Given a configuration with a probability (weight), \n // increment the vectors of variable and additional posteriors.\n void UpdateMarginalsFromConfiguration(vertex_type& vertex,\n const Configuration &configuration,\n double weight,\n vec &variable_posteriors,\n vec &additional_posteriors) {\n \n vector states(vertex.data().nvars, -1);\n get_configuration_states(vertex, configuration, &states);\n \n int offset = 0;\n \n for (int k = 0; k < vertex.data().nvars; ++k) \n { variable_posteriors[offset + states[k]] += weight;\n offset += vertex.data().cards[k];\n }\n additional_posteriors[configuration] += weight;\n \n }\n // Count how many common values two configurations have.\n int CountCommonValues(vertex_type& vertex,Configuration configuration1,\n Configuration configuration2) {\n \n //assert(states1->size() == states2->size());\n int count = 0;\n vector states1(vertex.data().nvars, -1); \n vector states2(vertex.data().nvars, -1);\n get_configuration_states(vertex, configuration1, &states1);\n get_configuration_states(vertex, configuration2, &states2);\n for(int i = 0; i< vertex.data().nvars; i++)\n { if (states1[i] == states2[i])\n { count++;} }\n return count;\n }\n \n\n/**\n * \\brief Evaluate returns the maximum value with reference to \n * input additional and variable log potentials and configuration. addtional \n * log potential corresponds factor potentials and variable potential corresponds \n * to sum of lagrange * multipliers and unary potentials divided by degree of \n * the unary vertex.\n */\n \n \nvoid Evaluate(vertex_type& vertex, vec additional_log_potentials, vec variable_log_potentials,\n const Configuration configuration,\n double *value) {\n \n vector states(vertex.data().nvars, -1);\n get_configuration_states(vertex, configuration, &states);\n *value = 0.0;\n int offset = 0;\n for (int i = 0;i *similarities,\n vector *eigenvalues) {\n\n int size = sqrt(similarities->size());\n\n Eigen::SelfAdjointEigenSolver es;\n Eigen::MatrixXd sim(size, size);\n int t = 0;\n for (int i = 0; i < size; ++i) {\n for (int j = 0; j < size; ++j) {\n sim(i, j) = (*similarities)[t];\n ++t;\n }\n }\n es.compute(sim);\n const Eigen::VectorXd &eigvals = es.eigenvalues(); \n eigenvalues->resize(size);\n for (int i = 0; i < size; ++i) {\n (*eigenvalues)[i] = eigvals[i];\n }\n const Eigen::MatrixXd &eigvectors = es.eigenvectors().transpose();\n t = 0;\n for (int i = 0; i < size; ++i) {\n for (int j = 0; j < size; ++j) {\n (*similarities)[t] = eigvectors(i, j);\n ++t;\n }\n }\n\n}\n\n\n// Function to solve each quadratic sub problem for dense factors. \n// It uses active set method. Caching is deactivated\n// TODO: Activate caching feature\n\nvoid SolveQP_dense(vertex_type& vertex,const gather_type& total,\n vec& variable_posteriors, vec& additional_posteriors) {\n vertex_data& vdata = vertex.data(); \n //cout<<\"loop 1 in solveQP ..\"< active_set_;\n vector distribution_;\n vector inverse_A_;\n // Initialize the active set.\n \n if (active_set_.size() == 0) {\n variable_posteriors.resize(variable_log_potentials.size()); \n additional_posteriors.resize(additional_log_potentials.size()); \n distribution_.clear();\n // Initialize by solving the LP, discarding the quadratic\n // term.\n Configuration configuration = -1;\n double value;\n Maximize(vertex, additional_log_potentials, variable_log_potentials,\n configuration,\n &value);\n active_set_.push_back(configuration);\n distribution_.push_back(1.0);\n\n // Initialize inv(A) as [-M,1;1,0].\n inverse_A_.resize(4);\n inverse_A_[0] = static_cast(\n -CountCommonValues(vertex,configuration, configuration));\n inverse_A_[1] = 1;\n inverse_A_[2] = 1;\n inverse_A_[3] = 0;\n }\n\n bool changed_active_set = true;\n vector z;\n int num_max_iterations = num_max_iterations_QP_;\n double tau = 0;\n for (int iter = 0; iter < num_max_iterations; ++iter) {\n bool same_as_before = true;\n bool unbounded = false;\n if (changed_active_set) {\n // Recompute vector b.\n vector b(active_set_.size() + 1, 0.0);\n b[0] = 1.0;\n for (int i = 0; i < active_set_.size(); ++i) {\n const Configuration &configuration = active_set_[i];\n double score;\n Evaluate(vertex, additional_log_potentials, variable_log_potentials,\n configuration,\n &score);\n b[i+1] = score;\n }\n // Solve the system Az = b.\n z.resize(active_set_.size());\n int size_A = active_set_.size() + 1;\n for (int i = 0; i < active_set_.size(); ++i) {\n z[i] = 0.0;\n for (int j = 0; j < size_A; ++j) {\n z[i] += inverse_A_[(i+1) * size_A + j] * b[j];\n }\n }\n tau = 0.0;\n for (int j = 0; j < size_A; ++j) {\n tau += inverse_A_[j] * b[j];\n }\n\n same_as_before = false;\n }\n\n if (same_as_before) {\n // Compute the variable marginals from the full distribution\n // stored in z.\n ComputeMarginalsFromSparseDistribution(vertex, active_set_,\n z,\n variable_posteriors,\n additional_posteriors);\n // Get the most violated constraint\n // (by calling the black box that computes the MAP).\n vec scores = variable_log_potentials; \n for (int i = 0; i < scores.size(); ++i) {\n scores[i] -= variable_posteriors[i];\n }\n Configuration configuration = -1;\n double value = 0.0;\n \n Maximize(vertex,\n additional_log_potentials, scores,\n configuration,\n &value);\n double very_small_threshold = 1e-9;\n if (value <= tau + very_small_threshold) { // value <= tau.\n // We have found the solution;\n // the distribution, active set, and inv(A) are cached for the next round.\n DeleteConfiguration(configuration);\n return;\n } else {\n for (int k = 0; k < active_set_.size(); ++k) {\n // This is expensive and should just be a sanity check.\n // However, in practice, numerical issues force an already existing\n // configuration to try to be added. Therefore, we always check\n // if a configuration already exists before inserting it.\n // If it does, that means the active set method converged to a\n // solution (but numerical issues had prevented us to see it.)\n if (active_set_[k] == configuration) { \n if (opts.verbose > 2) {\n cout << \"Warning: value - tau = \"\n << value - tau << \" \" << value << \" \" << tau\n << endl;\n }\n // We have found the solution;\n // the distribution, active set, and inv(A)\n // are cached for the next round.\n DeleteConfiguration(configuration);\n\n // Just in case, clean the cache.\n // This may prevent eventual numerical problems in the future.\n for (int j = 0; j < active_set_.size(); ++j) {\n if (j == k) continue; // This configuration was deleted already.\n DeleteConfiguration(active_set_[j]);\n }\n active_set_.clear();\n inverse_A_.clear();\n distribution_.clear();\n\n // Return.\n return;\n }\n }\n z.push_back(0.0);\n distribution_ = z;\n\n // Update inv(A).\n bool singular = !InvertAfterInsertion(vertex, inverse_A_, active_set_, configuration);\n if (singular) {\n // If adding a new configuration causes the matrix to be singular,\n // don't just add it. Instead, look for a configuration in the null\n // space and remove it before inserting the new one.\n // Right now, if more than one such configuration exists, we just\n // remove the first one we find. There's a chance this could cause\n // some cyclic behaviour. If that is the case, we should randomize\n // this choice.\n // Note: This step is expensive and requires an eigendecomposition.\n // TODO: I think there is a graph interpretation for this problem.\n // Maybe some specialized graph algorithm is cheaper than doing\n // the eigendecomposition.\n vector similarities(active_set_.size() * active_set_.size());\n ComputeActiveSetSimilarities(vertex, active_set_, &similarities);\n \n //cout<<\"compute active similarities in solveQP ..\"< padded_similarities((active_set_.size()+2) * \n (active_set_.size()+2), 1.0);\n for (int i = 0; i < active_set_.size(); ++i) {\n for (int j = 0; j < active_set_.size(); ++j) {\n padded_similarities[(i+1)*(active_set_.size()+2) + (j+1)] =\n similarities[i*active_set_.size() + j];\n }\n }\n padded_similarities[0] = 0.0;\n for (int i = 0; i < active_set_.size(); ++i) {\n double value = static_cast(\n CountCommonValues(vertex, configuration, active_set_[i]));\n padded_similarities[(i+1)*(active_set_.size()+2) +\n (active_set_.size()+1)] = value;\n padded_similarities[(active_set_.size()+1)*(active_set_.size()+2) +\n (i+1)] = value;\n }\n double value = static_cast(\n CountCommonValues(vertex, configuration, configuration));\n padded_similarities[(active_set_.size()+1)*(active_set_.size()+2) +\n (active_set_.size()+1)] = value;\n\n vector eigenvalues(active_set_.size()+2);\n EigenDecompose(&padded_similarities, &eigenvalues);\n int zero_eigenvalue = -1;\n for (int i = 0; i < active_set_.size()+2; ++i) {\n if (NEARLY_EQ_TOL(eigenvalues[i], 0.0, 1e-9)) {\n if (zero_eigenvalue >= 0) {\n // If this happens, something failed. Maybe a numerical problem\n // may cause this. In that case, just give up, clean the cache\n // and return. Hopefully the next iteration will fix it.\n cout << \"Multiple zero eigenvalues: \"\n << eigenvalues[zero_eigenvalue] << \" and \"\n << eigenvalues[i] << endl;\n cout << \"Warning: Giving up.\" << endl;\n // Clean the cache.\n for (int j = 0; j < active_set_.size(); ++j) {\n DeleteConfiguration(active_set_[j]);\n }\n active_set_.clear();\n inverse_A_.clear();\n distribution_.clear();\n return;\n }\n zero_eigenvalue = i;\n }\n }\n assert(zero_eigenvalue >= 0);\n vector configurations_to_remove;\n for (int j = 1; j < active_set_.size()+1; ++j) {\n double value = padded_similarities[zero_eigenvalue*(active_set_.size()+2) + j];\n if (!NEARLY_EQ_TOL(value, 0.0, 1e-9)) {\n configurations_to_remove.push_back(j-1);\n }\n }\n if (opts.verbose > 2) {\n cout << \"Pick a configuration to remove (\" << configurations_to_remove.size()\n << \" out of \" << active_set_.size() << \").\" << endl;\n }\n\n assert(configurations_to_remove.size() >= 1);\n int j = configurations_to_remove[0];\n\n // Update inv(A).\n InvertAfterRemoval(inverse_A_, active_set_, j);\n\n // Remove blocking constraint from the active set.\n DeleteConfiguration(active_set_[j]); // Delete configutation.\n active_set_.erase(active_set_.begin() + j);\n\n singular = !InvertAfterInsertion(vertex, inverse_A_, active_set_, configuration);\n assert(!singular);\n }\n\n // Insert configuration to active set.\n if (opts.verbose > 2) {\n cout << \"Inserted one element to the active set (iteration \"\n << iter << \").\" << endl;\n }\n active_set_.push_back(configuration);\n changed_active_set = true;\n } \n } else {\n // Solution has changed from the previous iteration.\n // Look for blocking constraints.\n int blocking = -1;\n bool exist_blocking = false;\n double alpha = 1.0;\n for (int i = 0; i < active_set_.size(); ++i) {\n assert(distribution_[i] >= -1e-12);\n if (z[i] >= distribution_[i]) continue;\n if (z[i] < 0) exist_blocking = true;\n double tmp = distribution_[i] / (distribution_[i] - z[i]);\n if (blocking < 0 || tmp < alpha) {\n alpha = tmp;\n blocking = i;\n }\n }\n\n if (!exist_blocking) {\n // No blocking constraints.\n assert(!unbounded);\n distribution_ = z;\n alpha = 1.0;\n changed_active_set = false;\n } else {\n if (alpha > 1.0 && !unbounded) alpha = 1.0;\n // Interpolate between factor_posteriors_[i] and z.\n if (alpha == 1.0) {\n distribution_ = z;\n } else {\n for (int i = 0; i < active_set_.size(); ++i) {\n z[i] = (1 - alpha) * distribution_[i] + alpha * z[i];\n distribution_[i] = z[i];\n }\n }\n\n // Update inv(A).\n InvertAfterRemoval(inverse_A_, active_set_, blocking);\n\n // Remove blocking constraint from the active set.\n if (opts.verbose > 2) {\n cout << \"Removed one element to the active set (iteration \"\n << iter << \").\" << endl;\n }\n\n DeleteConfiguration(active_set_[blocking]); // Delete configutation.\n active_set_.erase(active_set_.begin() + blocking);\n\n z.erase(z.begin() + blocking);\n distribution_.erase(distribution_.begin() + blocking);\n changed_active_set = true;\n for (int i = 0; i < distribution_.size(); ++i) {\n assert(distribution_[i] > -1e-16);\n }\n }\n }\n }\n\n // Maximum number of iterations reached.\n // Return the best existing solution by computing the variable marginals \n // from the full distribution stored in z.\n //assert(false);\n ComputeMarginalsFromSparseDistribution(vertex, active_set_,\n z,\n variable_posteriors,\n additional_posteriors); \n }\n \n \n void InsertionSort(pair arr[], int length) {\n int i, j;\n pair tmp;\n\n for (i = 1; i < length; i++) {\n j = i;\n while (j > 0 && arr[j - 1].first > arr[j].first) {\n tmp = arr[j];\n arr[j] = arr[j - 1];\n arr[j - 1] = tmp;\n j--;\n }\n }\n}\n\n \n int project_onto_budget_constraint_cached(vec& x,\n int d,\n double budget, \n vector >& y) {\n int j, k, l, level;\n double s = 0.0;\n double tau = 0.0, tightsum;\n double left, right = -std::numeric_limits::infinity();\n\n // Load x into a reordered y (the reordering is cached).\n if (y.size() != d) {\n y.resize(d);\n for (j = 0; j < d; j++) {\n s -= x[j];\n y[j].first = -x[j];\n y[j].second = j;\n }\n sort(y.begin(), y.end());\n } else {\n for (j = 0; j < d; j++) {\n s -= x[j];\n y[j].first = -x[y[j].second];\n }\n // If reordering is cached, use a sorting algorithm \n // which is fast when the vector is almost sorted.\n InsertionSort(&y[0], d);\n }\n\n tightsum = s;\n s += budget;\n \n k = l = level = 0;\n bool found = false;\n double val_a, val_b;\n while (k < d && l < d) {\n if (level != 0) {\n tau = (s - tightsum) / static_cast(level);\n }\n if (k < d) val_a = y[k].first;\n val_b = 1.0 + y[l].first;\n left = right;\n if (k == d || val_b <= val_a) {\n right = val_b;\n } else {\n right = val_a;\n }\n if ((level == 0 && s == tightsum) || (level != 0 && tau <= right)) {\n // Found the right split-point!\n found = true;\n break;\n }\n if (k == d || val_b <= val_a) {\n tightsum += val_b;\n --level;\n ++l;\n } else {\n tightsum -= val_a;\n ++level;\n ++k;\n }\n }\n\n if (!found) {\n left = right;\n right = std::numeric_limits::infinity();\n }\n \n for (j = 0; j < d; j++) {\n if (-x[j] >= right) {\n x[j] = 0.0;\n } else if (1.0 - x[j] <= left) {\n x[j] = 1.0;\n } else {\n x[j] += tau;\n }\n }\n\n return 0;\n}\n\n \n \n \n // Solve the QP subproblem for budget factor.\n // TODO Enable caching\nvoid SolveQP_budget(vertex_type& vertex,const gather_type& total,\n vec& variable_posteriors, vec& additional_posteriors){\n \n vertex_data& vdata = vertex.data();\n vec variable_log_potentials = total.neighbor_distribution + total.messages;\n vector > last_sort_;\n for (int f = 0; f < variable_log_potentials.size(); ++f) {\n variable_posteriors[f] = variable_log_potentials[f];\n if (variable_posteriors[f] < 0.0) {\n variable_posteriors[f] = 0.0;\n } else if (variable_posteriors[f] > 1.0) {\n variable_posteriors[f] = 1.0;\n }\n }\n\n double s = 0.0;\n for (int f = 0; f < vdata.nvars; ++f) {\n s += variable_posteriors[f];\n }\n\n if (s > static_cast(vdata.budget)) {\n for (int f = 0; f < variable_log_potentials.size(); ++f) {\n variable_posteriors[f] = variable_log_potentials[f];\n }\n project_onto_budget_constraint_cached(variable_posteriors, \n variable_log_potentials.size(), \n static_cast(vdata.budget), \n last_sort_);\n }\n\n}\n\n// Finds best configuration ofr budget factors\nvoid SolveMAP_budget(vertex_type& vertex,const gather_type& total,\n vec& variable_posteriors, vec& additional_posteriors, double& value) {\n \n vertex_data& vdata = vertex.data();\n // Create a local copy of the log potentials.\n vec log_potentials(total.messages); \n double valaux;\n \n value = 0.0;\n \n int num_active = 0;\n double sum = 0.0;\n\n for (int f = 0; f < vdata.nvars; ++f) {\n valaux = log_potentials[f];\n if (valaux < 0.0) {\n variable_posteriors[f] = 0.0;\n } else {\n sum += valaux;\n variable_posteriors[f] = 1.0;\n }\n ++num_active;\n }\n if (num_active > vdata.budget) {\n vector > scores(vdata.nvars);\n for (int f = 0; f < vdata.nvars; ++f) {\n scores[f].first = -log_potentials[f];\n scores[f].second = f;\n }\n\n sort(scores.begin(), scores.end());\n num_active = 0;\n sum = 0.0;\n for (int k = 0; k < vdata.budget; ++k) {\n valaux = -scores[k].first;\n if (valaux < 0.0) break;\n int f = scores[k].second;\n variable_posteriors[f] = 1.0;\n sum += valaux;\n ++num_active; \n }\n\n for (int k = num_active; k < vdata.nvars; ++k) {\n int f = scores[k].second;\n variable_posteriors[f] = 0.0;\n } \n // cout<<\"third inner loop ...\"< states(vdata.nvars, -1);\n get_configuration_states(vertex, index_configuration, &states);\n int offset = 0;\n for (int k = 0; k < vdata.nvars; ++k) {\n beliefs[index_configuration] += total.messages[offset + states[k]];\n offset += vdata.cards[k];}\n } \n \n value = beliefs.maxCoeff();\n \n }\n\n// Finds beliefs using dense and budget factors\nvoid compute_beliefs(vertex_type& vertex,const gather_type& total,\n vec& variable_posteriors, vec& additional_posteriors){\nswitch(vertex.data().factor_type){\n\ncase 0: SolveQP_dense(vertex,total, variable_posteriors, additional_posteriors);\n break;\ncase 1: SolveQP_budget(vertex,total, variable_posteriors, additional_posteriors);\n\n}\n};\n\n// General solvMAP function\nvoid SolveMAP(vertex_type& vertex,const gather_type& total,\n vec& variable_posteriors, vec& additional_posteriors, double& value){\nswitch(vertex.data().factor_type){\n\ncase 0: SolveMAP_dense(vertex,total, variable_posteriors, additional_posteriors, value);\n break;\ncase 1: SolveMAP_budget(vertex,total, variable_posteriors, additional_posteriors, value);\n }\n };\n\n};\n\n#endif\n", "meta": {"hexsha": "86eef04981f7e7ec3305b456428320bb947c37d9", "size": 29117, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "toolkits/graphical_models/ad3_qp.hpp", "max_stars_repo_name": "xcgoner/powerlore", "max_stars_repo_head_hexsha": "c95ab1ca5a3636eaf5fb9c4feeaddcb96bb2d6ee", "max_stars_repo_licenses": ["ECL-2.0", "Apache-2.0"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2019-02-20T07:41:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-29T00:52:29.000Z", "max_issues_repo_path": "toolkits/graphical_models/ad3_qp.hpp", "max_issues_repo_name": "kesinger/graphlab", "max_issues_repo_head_hexsha": "5acb39d816f33e59433e88a9d3621eb4cf7cb05e", "max_issues_repo_licenses": ["ECL-2.0", "Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "toolkits/graphical_models/ad3_qp.hpp", "max_forks_repo_name": "kesinger/graphlab", "max_forks_repo_head_hexsha": "5acb39d816f33e59433e88a9d3621eb4cf7cb05e", "max_forks_repo_licenses": ["ECL-2.0", "Apache-2.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-05-27T12:40:52.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-27T12:40:52.000Z", "avg_line_length": 33.2006841505, "max_line_length": 94, "alphanum_fraction": 0.5816533297, "num_tokens": 7401, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.2448946368113344}} {"text": "//\n// Copyright (c) 2009, Markus Rickert\n// All rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are met:\n//\n// * Redistributions of source code must retain the above copyright notice,\n// this list of conditions and the following disclaimer.\n// * Redistributions in binary form must reproduce the above copyright notice,\n// this list of conditions and the following disclaimer in the documentation\n// and/or other materials provided with the distribution.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\n// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n// POSSIBILITY OF SUCH DAMAGE.\n//\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nstd::shared_ptr\ncreatePlanar2()\n{\n\tstd::shared_ptr dynamic = std::make_shared();\n\tdynamic->setName(\"planar2\");\n\t\n\t// frames\n\t\n\tstd::shared_ptr world = std::make_shared();\n\tworld->setName(\"world\");\n\tworld->setGravity(rl::math::Vector3(0, 0, rl::math::constants::gravity));\n\tdynamic->add(world);\n\t\n\tstd::shared_ptr link0 = std::make_shared();\n\tlink0->setName(\"link0\");\n\tdynamic->add(link0);\n\t\n\tstd::shared_ptr link1 = std::make_shared();\n\tlink1->setCenterOfMass(static_cast(0.5), 0, 0);\n\tlink1->setInertia(static_cast(0.01), static_cast(0.083333333), static_cast(0.083333333), 0, 0, 0);\n\tlink1->setMass(1);\n\tlink1->setName(\"link1\");\n\tdynamic->add(link1);\n\t\n\tstd::shared_ptr frame0 = std::make_shared();\n\tframe0->setName(\"frame0\");\n\tdynamic->add(frame0);\n\t\n\tstd::shared_ptr link2 = std::make_shared();\n\tlink2->setCenterOfMass(static_cast(0.5), 0, 0);\n\tlink2->setInertia(static_cast(0.01), static_cast(0.083333333), static_cast(0.083333333), 0, 0, 0);\n\tlink2->setMass(1);\n\tlink2->setName(\"link2\");\n\tdynamic->add(link2);\n\t\n\tstd::shared_ptr frame1 = std::make_shared();\n\tframe1->setName(\"frame1\");\n\tdynamic->add(frame1);\n\t\n\t// selfcollision\n\t\n\tlink0->setCollision(false);\n\tlink1->setCollision(link0.get(), false);\n\tlink1->setCollision(link2.get(), false);\n\tlink2->setCollision(link1.get(), false);\n\t\n\t// transforms\n\t\n\tstd::shared_ptr fixed0 = std::make_shared();\n\tfixed0->setName(\"fixed0\");\n\tdynamic->add(fixed0, world.get(), link0.get());\n\t\n\tstd::shared_ptr joint0 = std::make_shared();\n\tjoint0->setMaximum(rl::math::Vector::Constant(1, 360 * rl::math::constants::deg2rad));\n\tjoint0->setMinimum(rl::math::Vector::Constant(1, -360 * rl::math::constants::deg2rad));\n\tjoint0->setName(\"joint0\");\n\tdynamic->add(joint0, link0.get(), link1.get());\n\t\n\tstd::shared_ptr fixed1 = std::make_shared();\n\tfixed1->setName(\"fixed1\");\n\tfixed1->setTransform(rl::math::Transform(rl::math::Translation(rl::math::Vector3(1, 0, 0))));\n\tdynamic->add(fixed1, link1.get(), frame0.get());\n\t\n\tstd::shared_ptr joint1 = std::make_shared();\n\tjoint1->setMaximum(rl::math::Vector::Constant(1, 360 * rl::math::constants::deg2rad));\n\tjoint1->setMinimum(rl::math::Vector::Constant(1, -360 * rl::math::constants::deg2rad));\n\tjoint1->setName(\"joint1\");\n\tdynamic->add(joint1, frame0.get(), link2.get());\n\t\n\tstd::shared_ptr fixed2 = std::make_shared();\n\tfixed2->setName(\"fixed2\");\n\tfixed2->setTransform(rl::math::Transform(rl::math::Translation(rl::math::Vector3(1, 0, 0))));\n\tdynamic->add(fixed2, link2.get(), frame1.get());\n\t\n\t// initialize\n\t\n\tdynamic->update();\n\t\n\treturn dynamic;\n}\n\nint\nmain(int argc, char** argv)\n{\n\tif (argc < 7)\n\t{\n\t\tstd::cout << \"Usage: rlDynamics1Planar2Demo Q1 Q2 QD1 QD2 QDD1 QDD2\" << std::endl;\n\t\treturn EXIT_FAILURE;\n\t}\n\t\n\ttry\n\t{\n\t\tstd::shared_ptr dynamic = createPlanar2();\n\t\t\n\t\trl::math::Vector q(dynamic->getDofPosition());\n\t\trl::math::Vector qd(dynamic->getDof());\n\t\trl::math::Vector qdd(dynamic->getDof());\n\t\t\n\t\tfor (std::size_t i = 0; i < dynamic->getDofPosition(); ++i)\n\t\t{\n\t\t\tq(i) = boost::lexical_cast(argv[i + 1]);\n\t\t}\n\t\t\n\t\tfor (std::size_t i = 0; i < dynamic->getDof(); ++i)\n\t\t{\n\t\t\tqd(i) = boost::lexical_cast(argv[i + 1 + dynamic->getDofPosition()]);\n\t\t\tqdd(i) = boost::lexical_cast(argv[i + 1 + dynamic->getDofPosition() + dynamic->getDof()]);\n\t\t}\n\t\t\n\t\tdynamic->setPosition(q);\n\t\tdynamic->setVelocity(qd);\n\t\tdynamic->setAcceleration(qdd);\n\t\t\n\t\tdynamic->inverseDynamics();\n\t\tstd::cout << \"tau = \" << dynamic->getTorque().transpose() << std::endl;\n\t\t\n\t\tdynamic->forwardDynamics();\n\t\tstd::cout << \"qdd = \" << dynamic->getAcceleration().transpose() << std::endl;\n\t\t\n\t\trl::mdl::RungeKuttaNystromIntegrator integrator(dynamic.get());\n\t\tintegrator.integrate(1);\n\t\tstd::cout << \"q = \" << dynamic->getPosition().transpose() << std::endl;\n\t\tstd::cout << \"qd = \" << dynamic->getVelocity().transpose() << std::endl;\n\t}\n\tcatch (const std::exception& e)\n\t{\n\t\tstd::cout << e.what() << std::endl;\n\t\treturn EXIT_FAILURE;\n\t}\n\t\n\treturn EXIT_SUCCESS;\n}\n", "meta": {"hexsha": "5fc8bf67cdc9ad155dd7dd6dfb5734f339a884e4", "size": 6234, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "demos/rlDynamics1Demo/rlDynamics1Planar2Demo.cpp", "max_stars_repo_name": "mcx/rl", "max_stars_repo_head_hexsha": "aa6eb334b5279ece8523258957b70e3b8c0c42ce", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 568.0, "max_stars_repo_stars_event_min_datetime": "2015-01-23T03:38:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T16:12:56.000Z", "max_issues_repo_path": "demos/rlDynamics1Demo/rlDynamics1Planar2Demo.cpp", "max_issues_repo_name": "mcx/rl", "max_issues_repo_head_hexsha": "aa6eb334b5279ece8523258957b70e3b8c0c42ce", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 53.0, "max_issues_repo_issues_event_min_datetime": "2016-03-23T13:16:47.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-17T05:58:06.000Z", "max_forks_repo_path": "demos/rlDynamics1Demo/rlDynamics1Planar2Demo.cpp", "max_forks_repo_name": "mcx/rl", "max_forks_repo_head_hexsha": "aa6eb334b5279ece8523258957b70e3b8c0c42ce", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 169.0, "max_forks_repo_forks_event_min_datetime": "2015-01-26T12:59:41.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T13:44:54.000Z", "avg_line_length": 36.2441860465, "max_line_length": 147, "alphanum_fraction": 0.6883221046, "num_tokens": 1875, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.388618026705849, "lm_q1q2_score": 0.24474175979819987}} {"text": "/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */\n/* */\n/* This file is part of the tsppd program and library for solving */\n/* Traveling Salesman Problems with Pickup and Delivery. tsppd requires */\n/* other commercial and open source software to build. tsppd is decribed */\n/* in the paper \"Exact Methods for Solving Traveling Salesman Problems */\n/* with Pickup and Delivery in Real Time\". */\n/* */\n/* Copyright (C) 2017 Ryan J. O'Neil */\n/* */\n/* tsppd is distributed under the terms of the ZIB Academic License. */\n/* You should have received a copy of the ZIB Academic License along with */\n/* tsppd. See the file LICENSE. If not, email roneil1@gmu.edu. */\n/* */\n/* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * */\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \n\nusing namespace Gecode;\nusing namespace TSPPD::Data;\nusing namespace TSPPD::Solver;\nusing namespace std;\n\nOneTree::OneTree(\n ViewArray& next,\n const TSPPDProblem& problem,\n const unsigned int max_iterations) :\n OneTree(next, problem, max_iterations, nullptr) { }\n\nOneTree::OneTree(\n ViewArray& next,\n const TSPPDProblem& problem,\n const unsigned int max_iterations,\n TSPPD::AP::PrimalDualAPSolver* ap) :\n next(next),\n problem(problem),\n max_iterations(max_iterations),\n ap(ap),\n graph(next.size() - 2),\n potentials(next.size(), 0),\n edges(next.size(), set()),\n start_index(problem.index(\"+0\")),\n end_index(problem.index(\"-0\")),\n iteration(1),\n w(0) {\n\n weights = boost::get(boost::edge_weight, graph);\n initialize_one_tree();\n}\n\n\ndouble OneTree::bound() {\n double best_w = 0;\n vector> best_edges;\n\n while (!done) {\n improve();\n \n if (iteration == 1 || w > best_w) {\n best_w = w;\n best_edges = edges;\n }\n\n if (done)\n break;\n }\n \n w = best_w;\n edges = best_edges;\n return w;\n}\n\nvoid OneTree::improve() {\n // Find the max min 1-tree by updating node potentials based on violation of degree constraints.\n bool is_tour = true;\n\n // Compute optimal 1-tree.\n edges = vector>(next.size(), set());\n auto new_w = minimize_one_tree();\n\n // Update step size\n auto M = max_iterations;\n auto m = iteration;\n if (m == 1) {\n t1 = new_w / (2.0 * next.size());\n ti = t1;\n } else {\n ti = t1*(m - 1)*(2*M - 5)/(2*(M-1)) - t1*(m-2) + t1*(m-1)*(m-2)/(2*(M-1)*(M-2));\n }\n\n // Remove node potentials from tour.\n for (auto pi : potentials)\n new_w -= 2 * pi;\n\n // Update node potentials.\n for (int node = 0; node < next.size(); ++node) {\n if (node != start_index && node != end_index && edges[node].size() != 2)\n is_tour = false;\n potentials[node] += (((int) edges[node].size()) - 2) * ti;\n }\n\n if (is_tour || iteration++ > max_iterations)\n done = true;\n else\n update_one_tree();\n\n w = new_w;\n}\n\nbool OneTree::has_edge(int from, int to) {\n return edges[from].find(to) != edges[from].end();\n}\n\nint OneTree::marginal_cost(int from, int to) {\n vector seen(edges.size(), false);\n seen[to] = true;\n return marginal_cost(from, to, seen, to, 0);\n}\n\nvoid OneTree::initialize_one_tree() {\n for (int i = 0; i < next.size(); ++i) {\n // Arcs are undirected for MST.\n if (i == start_index || i == end_index)\n continue;\n\n for (int j = i + 1; j < next.size(); ++j) {\n if (j == start_index || j == end_index)\n continue;\n\n if (!(next[i].in(j) || next[j].in(i)))\n continue;\n\n OneTreeEdge e;\n bool inserted;\n boost::tie(e, inserted) = boost::add_edge(i, j, graph);\n weights[e] = undirected_cost(i, j);\n }\n }\n}\n\nvoid OneTree::update_one_tree() {\n auto es = boost::edges(graph);\n for (auto eit = es.first; eit != es.second; ++eit) {\n auto e = *eit;\n weights[e] = transformed_cost(e.m_source, e.m_target);\n }\n}\n\ndouble OneTree::minimize_one_tree() {\n vector mst;\n boost::kruskal_minimum_spanning_tree(graph, back_inserter(mst));\n\n // Calculate cost of the MST and pull out edges.\n double z = 0;\n for (auto e : mst) {\n edges[e.m_source].insert(e.m_target);\n edges[e.m_target].insert(e.m_source);\n z += weights[e];\n }\n\n // Add cheapest arc connecting +0.\n int min_p0_idx = -1;\n double min_p0 = numeric_limits::max();\n for (auto to = next[start_index].min(); to <= next[start_index].max(); ++to) {\n if (!next[start_index].in(to))\n continue;\n\n auto c = transformed_cost(start_index, to);\n if (c < min_p0) {\n min_p0_idx = to;\n min_p0 = c;\n }\n }\n edges[start_index].insert(min_p0_idx);\n edges[min_p0_idx].insert(start_index);\n\n // Add cheapest arc connecting -0.\n int min_d0_idx = -1;\n double min_d0 = numeric_limits::max();\n for (int from = 0; from < next.size(); ++from) {\n if (!next[from].in(end_index))\n continue;\n\n auto c = transformed_cost(from, end_index);\n if (c < min_d0) {\n min_d0_idx = from;\n min_d0 = c;\n }\n }\n edges[end_index].insert(min_d0_idx);\n edges[min_d0_idx].insert(end_index);\n\n edges[start_index].insert(end_index);\n edges[end_index].insert(start_index);\n\n return z + min_p0 + min_d0;\n}\n\nint OneTree::undirected_cost(int i, int j) {\n auto c_ij = next[i].in(j) ? cost(i, j) : numeric_limits::max();\n auto c_ji = next[j].in(i) ? cost(j, i) : numeric_limits::max();\n return min(c_ij, c_ji);\n}\n\ndouble OneTree::transformed_cost(int i, int j) {\n return undirected_cost(i, j) + potentials[i] + potentials[j];\n}\n\nint OneTree::marginal_cost(\n int from,\n int to,\n vector seen,\n int node,\n int max_edge_cost) {\n\n // Introducing a nonbasic arc into the basis would create a cycle.\n // The marginal cost of this operation is the cost of the new arc\n // minus the max cost in the cycle. This can be found using DFS.\n for (auto next : edges[node]) {\n if ((node == start_index && next == end_index) || (node == end_index && next == start_index))\n continue;\n\n if (seen[next])\n continue;\n\n // If we loop back to the from node, then compute marginal cost.\n if (next == from && node != to)\n return cost(from, to) - max_edge_cost;\n\n vector new_seen(seen);\n new_seen[next] = true;\n\n int new_max = max(max_edge_cost, undirected_cost(node, next));\n auto cost = marginal_cost(from, to, new_seen, next, new_max);\n if (cost > -1)\n return cost;\n }\n\n return -1;\n}\n\nint OneTree::cost(int i, int j) {\n if (ap == nullptr)\n return problem.cost(i, j);\n return ap->get_rc({i, j});\n}\n", "meta": {"hexsha": "6a2e307d0c01b508050f8d8464aba95f5f9804c2", "size": 7585, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/tsppd/solver/focacci/filter/one_tree/focacci_tsp_one_tree.cpp", "max_stars_repo_name": "ryanjoneil/tsppd", "max_stars_repo_head_hexsha": "f0e1e5e867e13c8fa0dcddf4d2ffa2aae7f46da4", "max_stars_repo_licenses": ["AFL-1.1"], "max_stars_count": 4.0, "max_stars_repo_stars_event_min_datetime": "2018-03-30T20:58:57.000Z", "max_stars_repo_stars_event_max_datetime": "2018-04-25T01:48:09.000Z", "max_issues_repo_path": "src/tsppd/solver/focacci/filter/one_tree/focacci_tsp_one_tree.cpp", "max_issues_repo_name": "ryanjoneil/tsppd", "max_issues_repo_head_hexsha": "f0e1e5e867e13c8fa0dcddf4d2ffa2aae7f46da4", "max_issues_repo_licenses": ["AFL-1.1"], "max_issues_count": 7.0, "max_issues_repo_issues_event_min_datetime": "2019-04-21T13:42:09.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-09T15:49:56.000Z", "max_forks_repo_path": "src/tsppd/solver/focacci/filter/one_tree/focacci_tsp_one_tree.cpp", "max_forks_repo_name": "ryanjoneil/tsppd-hybrid", "max_forks_repo_head_hexsha": "f0e1e5e867e13c8fa0dcddf4d2ffa2aae7f46da4", "max_forks_repo_licenses": ["AFL-1.1"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2020-03-01T16:19:18.000Z", "max_forks_repo_forks_event_max_datetime": "2020-03-01T16:19:18.000Z", "avg_line_length": 29.7450980392, "max_line_length": 101, "alphanum_fraction": 0.5450230719, "num_tokens": 1991, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984434543458, "lm_q2_score": 0.45713671682749485, "lm_q1q2_score": 0.24461314562022252}} {"text": "/*Copyright (c) 2021 James Gayvert\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n*/\n\n#include \"TrajectoryAnalysis.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\n#include \"utils.h\"\n\nvoid CAPHamiltonian::set_eta_list(std::map params)\n{\n\tdouble eta_step;\n\tsize_t nsteps;\n\tstd::stringstream stepss(params[\"eta_step\"]);\n\tstd::stringstream nstepss(params[\"nsteps\"]);\n\tstd::istringstream save_traj(params[\"save_trajectory\"]);\n\tstepss >> eta_step;\n\tnstepss >> nsteps;\n\tsave_traj >> std::boolalpha >> do_output;\n\teta_step*=1E-5;\n\tfor(size_t i=0;i<=nsteps;i++)\n\t\teta_list.push_back(i*eta_step);\n}\n\nCAPHamiltonian::CAPHamiltonian(Eigen::MatrixXd h0,Eigen::MatrixXd cap,std::map params,std::string filename)\n{\n\tCAP_MAT = cap;\n\tZERO_ORDER_H = h0;\n\tnstates = h0.cols();\n\tset_eta_list(params);\n\tfiname = filename;\n}\n\nvoid CAPHamiltonian::track_state(size_t state_idx)\n{\n\tstd::cout << \"Results for state:\" << state_idx << std::endl;\n\tstd::cout << \"----------------------\" << std::endl;\n\tEigenvalueTrajectory traj(all_roots[0],state_idx);\n\tfor(size_t i=1;i roots;\n\t\tEigen::MatrixXcd CAPH(nstates,nstates);\n\t\tCAPH.real() = ZERO_ORDER_H;\n\t\tCAPH.imag() = eta_list[i]*CAP_MAT;\n\t\tEigen::ComplexEigenSolver ces;\n\t\tces.compute(CAPH);\n\t\tfor(size_t j=0;j energy = ces.eigenvalues()[j];\n\t\t\tEigen::VectorXcd vec = ces.eigenvectors().col(j);\n\t\t\troot r1(eta_list[i],energy,vec);\n\t\t\troots.push_back(r1);\n\t\t}\n\t\tall_roots.push_back(roots);\n\t}\n\ttrack_states();\n\tif(do_output)\n\t\tsave_trajectory();\n}\n\nEigenvalueTrajectory::EigenvalueTrajectory(std::vector initial_states, size_t state_idx)\n{\n\tprev = initial_states[state_idx];\n\tstates.push_back(prev);\n\tuncorrected_energies.push_back(prev.energy);\n\tcorrected_energies.push_back(prev.energy);\n}\n\nvoid EigenvalueTrajectory::add_state(std::vector new_states)\n{\n\tdouble max_overlap=0.0;\n\troot best=new_states[0];\n\tfor(auto cur: new_states)\n\t{\n\t\tdouble overlap = abs(prev.eigv.dot(cur.eigv));\n\t\tif(overlap>max_overlap)\n\t\t{\n\t\t\tbest = cur;\n\t\t\tmax_overlap = overlap;\n\t\t}\n\t}\n\tstates.push_back(best);\n\tprev = best;\n\tuncorrected_energies.push_back(best.energy);\n}\n\nvoid EigenvalueTrajectory::analyze()\n{\n\t// get corrections, find minimum of logarithmic velocity of uncorrected trajectory\n\n\tsize_t num_points = uncorrected_energies.size();\n\tstd::vector grad;\n\tstd::vector corr_grad;\n\tdouble step = states[1].eta;\n\n\tgrad.push_back(0.0);\n\tcorrected_energies.push_back(uncorrected_energies[0]);\n\tfor (int i=1;i log_velo = states[i].eta*(uncorrected_energies[i+1]-uncorrected_energies[i-1])/(2.0*step);\n\t grad.push_back(std::abs(log_velo));\n\t corrected_energies.push_back(uncorrected_energies[i]-log_velo);\n\t}\n\t// last point\n\tstd::complex last_deriv = states[num_points-1].eta*(uncorrected_energies[num_points-1]-uncorrected_energies[num_points-2])/step;\n\tgrad.push_back(std::abs(last_deriv));\n\tcorrected_energies.push_back(uncorrected_energies[num_points-1]-last_deriv);\n\n\tcorr_grad.push_back(0.0);\n\tfor (int i=1;i\n#include \n#include \n\n#ifdef WIN32\ninline double round(double x)\n{\n\treturn (x-floor(x))>0.5 ? ceil(x) : floor(x);\n}\n#endif\nusing namespace std;\n\nNodeHyperPlane::NodeHyperPlane(const HyperParameters &hp, int depth) : Node(hp, depth), m_bestThreshold( 0.0 )\n{\n}\n\nNodeHyperPlane::NodeHyperPlane(const HyperParameters &hp, int depth, int reset) : Node(hp, depth, reset), m_bestThreshold( 0.0 )\n{\n}\n\nNodeHyperPlane::NodeHyperPlane(const HyperParameters &hp, int reset, const xmlNodePtr nodeNode) : Node(hp,0,reset)\n{\n\tm_isLeaf = (readStringProp(nodeNode,\"isLeaf\") == \"true\") ? true : false;\n\n\tif (m_isLeaf)\n\t{\n\t\tm_nodeLabel = readIntProp( nodeNode, \"label\", 0 );\n\t\txmlNodePtr cur = nodeNode->xmlChildrenNode;\n\t\twhile ( cur != 0 )\n\t\t{\n\t\t\tif ( xmlStrcmp( cur->name, reinterpret_cast( \"confidence\" ) ) == 0 )\n\t\t\t{\n\t\t\t\tm_nodeConf.push_back( static_cast(readDoubleProp( cur, \"conf\", 0 )) );\n\t\t\t}\n\t\t\tcur = cur->next;\n\t\t}\n\t}\n\telse\n\t{\n\t\txmlNodePtr cur = nodeNode->xmlChildrenNode;\n\t\twhile ( cur != 0 )\n\t\t{\n\t\t\tif ( xmlStrcmp( cur->name, reinterpret_cast( \"feature\" ) ) == 0 )\n\t\t\t{\n for (int i=0;i( i );\n int tmp = readIntProp( cur, s.c_str(), 0 );\n m_bestFeatures[i]=tmp;\n string s2 = \"weight\"+boost::lexical_cast( i );\n double tmpW=(float)readDoubleProp(cur,s2.c_str(), 0);\n m_bestWeights[i]=tmpW;\n }\n\t\t\t\t\n\t\t\t\tm_bestThreshold = (float)readDoubleProp( cur, \"threshold\", 0 );\n\t\t\t}\n\t\t\telse if ( xmlStrcmp( cur->name, reinterpret_cast( \"node\" ) ) == 0 )\n\t\t\t{\n\t\t\t\tconst std::string childNode = readStringProp(cur,\"child\");\n\t\t\t\tif ( childNode == \"left\" )\n\t\t\t\t{\n\t\t\t\t\tconst std::string type = readStringProp(cur,\"type\");\n\t\t\t\t\tif (type == NODE_GINI)\n\t\t\t\t\t{\n\t\t\t\t\t\tm_leftChildNode = NodeGini::Ptr(new NodeGini(m_hp,-1,cur));\n\t\t\t\t\t}\n\t\t\t\t\telse if (type == NODE_INFO_GAIN)\n\t\t\t\t\t{\n\t\t\t\t\t\tm_leftChildNode = NodeInfoGain::Ptr(new NodeInfoGain(m_hp,-1, cur));\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tconst std::string type = readStringProp(nodeNode,\"type\");\n\t\t\t\t\tif (type == NODE_GINI)\n\t\t\t\t\t{\n\t\t\t\t\t\tm_rightChildNode = NodeGini::Ptr(new NodeGini(m_hp,-1,cur));\n\t\t\t\t\t}\n\t\t\t\t\telse if (type == NODE_INFO_GAIN)\n\t\t\t\t\t{\n\t\t\t\t\t\tm_rightChildNode = NodeInfoGain::Ptr(new NodeInfoGain(m_hp,-1,cur));\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tcur = cur->next;\n\t\t}\n\t}\n}\n\nxmlNodePtr NodeHyperPlane::saveFeature() const\n{\n\txmlNodePtr node = xmlNewNode( NULL, reinterpret_cast( \"feature\" ) );\n //std::cout<( i );\n addIntProp(node, s.c_str(), m_bestFeatures[i]);\n string s2 = \"weight\"+boost::lexical_cast( i );\n addDoubleProp(node, s2.c_str(), m_bestWeights[i]);\n }\n\taddDoubleProp(node, \"threshold\", m_bestThreshold);\n\n\treturn node;\n}\n\n\nxmlNodePtr NodeHyperPlane::save() const\n{\n\txmlNodePtr node = xmlNewNode( NULL, reinterpret_cast( \"node\" ) );\n\txmlNewProp( node, reinterpret_cast( \"type\" ),\n\t\t\treinterpret_cast( NODE_GINI ) );\n\tconst char* isLeaf = (m_isLeaf) ? \"true\" : \"false\";\n\txmlNewProp( node, reinterpret_cast( \"isLeaf\" ),\n\t\t\treinterpret_cast( isLeaf ) );\n\tif (!m_isLeaf)\n\t{\n\t\txmlAddChild(node, saveFeature());\n\t\txmlNodePtr leftChildNode = m_leftChildNode->save();\n\t\txmlNewProp( leftChildNode, reinterpret_cast( \"child\" ),\n\t\t\t\treinterpret_cast( LEFT_CHILD_NODE ) );\n\t\txmlAddChild( node, leftChildNode );\n\n\t\txmlNodePtr rightChildNode = m_rightChildNode->save();\n\t\txmlNewProp( rightChildNode, reinterpret_cast( \"child\" ),\n\t\t\t\treinterpret_cast( RIGHT_CHILD_NODE ) );\n\t\txmlAddChild( node, rightChildNode );\n\t}\n\telse\n\t{\n\t\taddIntProp( node, \"label\", m_nodeLabel);\n\t\tstd::vector::const_iterator it(m_nodeConf.begin()),end(m_nodeConf.end());\n\t\tint idx = 0;\n\t\tfor (;it != end;it++,idx++)\n\t\t{\n\t\t\txmlAddChild(node,saveConfidence(idx,*it));\n\t\t}\n\t}\n\n\treturn node;\n}\n\nstd::pair NodeHyperPlane::calcGiniAndThreshold(const std::vector& labels,\n\t\tconst std::vector >& responses)\n{\n\t// Initialize the counters: left takes all at the begining\n\tdouble DGini, LGini, RGini, LTotal, RTotal, bestW0 = 0, bestDGini = 1e10;\n\tstd::vector LCount(m_hp.numClasses, 0.0), RCount(m_hp.numClasses, 0.0);\n\n\tif (m_hp.isExtreme)\n\t{\n\t\tstd::vector >::const_iterator resIt(responses.begin()), resEnd(responses.end());\n\t\tbestW0 = 2.0 * randomDouble( 1.0 ) - 1.0;\n\t\tRTotal = 0;\n\t\tLTotal = 0;\n\t\tfor (; resIt != resEnd; resIt++)\n\t\t{\n\t\t\tif (resIt->first > bestW0)\n\t\t\t{\n\t\t\t\tRTotal++;\n\t\t\t\tRCount[labels[resIt->second]]++;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tLTotal++;\n\t\t\t\tLCount[labels[resIt->second]]++;\n\t\t\t}\n\t\t}\n\n\t\tLGini = 0;\n\t\tRGini = 0;\n\t\tstd::vector::iterator LIt = LCount.begin(), RIt = RCount.begin(), end = LCount.end(), REnd = RCount.end();\n\t\tfor (; LIt != end; LIt++, RIt++) // Calculate Gini index\n\t\t\t\t{\n\t\t\tif (LTotal)\n\t\t\t{\n\t\t\t\tLGini += (*LIt/LTotal)*(1 - *LIt/LTotal);\n\t\t\t}\n\t\t\tif (RTotal)\n\t\t\t{\n\t\t\t\tRGini += (*RIt/RTotal)*(1 - *RIt/RTotal);\n\t\t\t}\n\t\t\t\t}\n\n\t\tbestDGini = (LTotal*LGini + RTotal*RGini)/responses.size();\n\t}\n\telse\n\t{\n\t\tRTotal = responses.size();\n\t\tLTotal = 0;\n\n\t\t// Count the number of samples in each class\n\t\tstd::vector >::const_iterator resIt(responses.begin()), resEnd(responses.end()), tmpResIt;\n\t\tfor (; resIt != resEnd; resIt++)\n\t\t{\n\t\t\tRCount[labels[resIt->second]]++;\n\t\t}\n\n\t\t// Loop over the sorted values and find the min DGini\n\t\tstd::vector::iterator LIt = LCount.begin(), RIt = RCount.begin(), end = LCount.end(), REnd = RCount.end();\n\t\tresIt = responses.begin();\n\t\t++resIt;\n\t\tfor (; resIt != resEnd; resIt++)\n\t\t{\n\t\t\ttmpResIt = resIt;\n\t\t\t--tmpResIt;\n\n\t\t\tRTotal--;\n\t\t\tLTotal++;\n\t\t\tRCount[labels[tmpResIt->second]]--;\n\t\t\tLCount[labels[tmpResIt->second]]++;\n\n\t\t\tif (resIt->first != tmpResIt->first)\n\t\t\t{\n\t\t\t\tLGini = 0;\n\t\t\t\tRGini = 0;\n\t\t\t\tLIt = LCount.begin();\n\t\t\t\tRIt = RCount.begin();\n\t\t\t\tfor (; LIt != end; LIt++, RIt++) // Calculate Gini index\n\t\t\t\t\t\t{\n\t\t\t\t\tLGini += (*LIt/LTotal)*(1 - *LIt/LTotal);\n\t\t\t\t\tRGini += (*RIt/RTotal)*(1 - *RIt/RTotal);\n\t\t\t\t\t\t}\n\n\t\t\t\tDGini = (LTotal*LGini + RTotal*RGini)/responses.size();\n\t\t\t\tif (DGini < bestDGini)\n\t\t\t\t{\n\t\t\t\t\tbestDGini = DGini;\n\t\t\t\t\tbestW0 = (resIt->first + tmpResIt->first)*0.5;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn std::pair((float)bestDGini,(float)bestW0);\n}\n\nstd::pair NodeHyperPlane::calcInfoGainAndThreshold(const std::vector& labels,\n\t\tconst std::vector >& responses)\n{\n\t// Initialize the counters: left takes all at the begining\n\tdouble DInfo, LInfo, RInfo, LTotal, RTotal, bestW0 = 0.0, bestDInfo = 1e10;\n\tstd::vector LCount(m_hp.numClasses, 0.0), RCount(m_hp.numClasses, 0.0);\n\n\tRTotal = responses.size();\n\tLTotal = 0.0;\n\t// Count the number of samples in each class\n\tstd::vector >::const_iterator resIt(responses.begin()), resEnd(responses.end()), tmpResIt;\n\tfor (; resIt != resEnd; resIt++)\n\t{\n\t\tRCount[labels[resIt->second]]++;\n\t}\n\n\t// Loop over the sorted values and find the max DInfo\n\tstd::vector::iterator LIt = LCount.begin(), RIt = RCount.begin(), end = LCount.end(), REnd = RCount.end();\n\tresIt = responses.begin();\n\t++resIt;\n\tfor (; resIt != resEnd; resIt++)\n\t{\n\t\ttmpResIt = resIt;\n\t\t--tmpResIt;\n\n\t\tRTotal--;\n\t\tLTotal++;\n\t\tRCount[labels[tmpResIt->second]]--;\n\t\tLCount[labels[tmpResIt->second]]++;\n\n\t\tif (resIt->first != tmpResIt->first)\n\t\t{\n\t\t\tLInfo = 0.0;\n\t\t\tRInfo = 0.0;\n\t\t\tLIt = LCount.begin();\n\t\t\tRIt = RCount.begin();\n\t\t\tfor (; LIt != end; LIt++, RIt++) // Calculate Info index\n\t\t\t\t\t{\n\t\t\t\tif (*LIt)\n\t\t\t\t{\n\t\t\t\t\tLInfo -= (*LIt/LTotal)*log(*LIt/LTotal);\n\t\t\t\t}\n\t\t\t\tif (*RIt)\n\t\t\t\t{\n\t\t\t\t\tRInfo -= (*RIt/RTotal)*log(*RIt/RTotal);\n\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\tDInfo = (LTotal*LInfo + RTotal*RInfo)/responses.size();\n\t\t\tif (DInfo < bestDInfo)\n\t\t\t{\n\t\t\t\tbestDInfo = DInfo;\n\t\t\t\tbestW0 = (resIt->first + tmpResIt->first)*0.5;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn std::pair((float)bestDInfo,(float)bestW0);\n}\n\nstd::pair NodeHyperPlane::calcInfoGainAndThreshold(const std::vector& labels, const std::vector& weights,\n\t\tconst std::vector >& responses)\n{\n\t// Initialize the counters: left takes all at the begining\n\tdouble DInfo, LInfo, RInfo, LTotal, RTotal, bestW0 = 0.0, bestDInfo = 1e10;\n\tstd::vector LCount(m_hp.numClasses, 0.0), RCount(m_hp.numClasses, 0.0);\n\n\tRTotal = 0.0;\n\tLTotal = 0.0;\n\t// Count the number of samples in each class\n\tstd::vector >::const_iterator resIt(responses.begin()), resEnd(responses.end()), tmpResIt;\n\tfor (; resIt != resEnd; resIt++)\n\t{\n\t\tRCount[labels[resIt->second]] += weights[resIt->second];\n\t\tRTotal += weights[resIt->second];\n\t}\n\n\t// Loop over the sorted values and find the max DInfo\n\tstd::vector::iterator LIt = LCount.begin(), RIt = RCount.begin(), end = LCount.end(), REnd = RCount.end();\n\tresIt = responses.begin();\n\t++resIt;\n\tfor (; resIt != resEnd; resIt++)\n\t{\n\t\ttmpResIt = resIt;\n\t\t--tmpResIt;\n\n\t\tRTotal -= weights[tmpResIt->second];\n\t\tLTotal += weights[tmpResIt->second];\n\t\tRCount[labels[tmpResIt->second]] -= weights[tmpResIt->second];\n\t\tLCount[labels[tmpResIt->second]] += weights[tmpResIt->second];\n\n\t\tif (resIt->first != tmpResIt->first)\n\t\t{\n\t\t\tLInfo = 0.0;\n\t\t\tRInfo = 0.0;\n\t\t\tLIt = LCount.begin();\n\t\t\tRIt = RCount.begin();\n\t\t\tfor (; LIt != end; LIt++, RIt++) // Calculate Info index\n\t\t\t\t\t{\n\t\t\t\tif (*LIt)\n\t\t\t\t{\n\t\t\t\t\tLInfo -= (*LIt/LTotal)*log(*LIt/LTotal);\n\t\t\t\t}\n\t\t\t\tif (*RIt)\n\t\t\t\t{\n\t\t\t\t\tRInfo -= (*RIt/RTotal)*log(*RIt/RTotal);\n\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\tDInfo = (LTotal*LInfo + RTotal*RInfo)/(LTotal + RTotal);\n\t\t\tif (DInfo < bestDInfo)\n\t\t\t{\n\t\t\t\tbestDInfo = DInfo;\n\t\t\t\tbestW0 = (resIt->first + tmpResIt->first)*0.5;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn std::pair((float)bestDInfo,(float)bestW0);\n}\n\n\nstd::pair NodeHyperPlane::calcGiniAndThreshold(const std::vector& labels, const std::vector& weights,\n\t\tconst std::vector >& responses, const bool useUnlabeledData)\n{\n\t// Initialize the counters: left takes all at the begining\n\tdouble DGini, LGini, RGini, LTotal, RTotal, bestW0 = 0, bestDGini = 1e10;\n\tstd::vector LCount(m_hp.numClasses, 0.0), RCount(m_hp.numClasses, 0.0);\n\n\tRTotal = 0;\n\tLTotal = 0;\n\n\t// Count the number of samples in each class\n\tstd::vector >::const_iterator resIt(responses.begin()), resEnd(responses.end()), tmpResIt;\n\tfor (; resIt != resEnd; resIt++)\n\t{\n\t\tif (useUnlabeledData || resIt->second < m_hp.numLabeled)\n\t\t{\n\t\t\tRCount[labels[resIt->second]] += weights[resIt->second];\n\t\t\tRTotal += weights[resIt->second];\n\t\t}\n\t}\n\n\t// Loop over the sorted values and find the min DGini\n\tstd::vector::iterator LIt = LCount.begin(), RIt = RCount.begin(), end = LCount.end(), REnd = RCount.end();\n\tresIt = responses.begin();\n\t++resIt;\n\tfor (; resIt != resEnd; resIt++)\n\t{\n\t\tif (useUnlabeledData || resIt->second < m_hp.numLabeled)\n\t\t{\n\t\t\ttmpResIt = resIt;\n\t\t\t--tmpResIt;\n\n\t\t\tRTotal -= weights[tmpResIt->second];\n\t\t\tLTotal += weights[tmpResIt->second];\n\t\t\tRCount[labels[tmpResIt->second]] -= weights[tmpResIt->second];\n\t\t\tLCount[labels[tmpResIt->second]] += weights[tmpResIt->second];\n\n\t\t\tif (resIt->first != tmpResIt->first)\n\t\t\t{\n\t\t\t\tLGini = 0;\n\t\t\t\tRGini = 0;\n\t\t\t\tLIt = LCount.begin();\n\t\t\t\tRIt = RCount.begin();\n\t\t\t\tfor (; LIt != end; LIt++, RIt++) // Calculate Gini index\n\t\t\t\t\t\t{\n\t\t\t\t\tLGini += (*LIt/LTotal)*(1 - *LIt/LTotal);\n\t\t\t\t\tRGini += (*RIt/RTotal)*(1 - *RIt/RTotal);\n\t\t\t\t\t\t}\n\n\t\t\t\tDGini = (LTotal*LGini + RTotal*RGini)/(LTotal + RTotal);\n\t\t\t\tif (DGini < bestDGini)\n\t\t\t\t{\n\t\t\t\t\tbestDGini = DGini;\n\t\t\t\t\tbestW0 = (resIt->first + tmpResIt->first)*0.5;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn std::pair((float)bestDGini,(float)bestW0);\n}\n\n\nvoid NodeHyperPlane::findHypotheses(const matrix& data, const std::vector& labels,\n\t\tconst std::vector& inBagSamples, const std::vector& randFeatures, int numTries)\n{\n\tstd::vector gini(m_hp.numRandomFeatures), thresholds(m_hp.numRandomFeatures);\n\tstd::vector::const_iterator it(randFeatures.begin());\n\tstd::vector::const_iterator end(randFeatures.end());\n\n\tstd::vector::const_iterator bagIt;\n\tstd::vector::const_iterator bagEnd(inBagSamples.end());\n\n\tdouble bestDGini = 1e10, bestThreshold = 0;\n\tstd::pair curGiniThresh;\n\tstd::vector > responses;\n\tstd::vector bestWeights(randFeatures.size(),0.0);\n\tstd::vector tmpWeights(randFeatures.size(),0.0);\n\tfloat tmp = 0.0;\n\tfor ( int i = 0; i < numTries; i++)\n\t{\n\t\tfillWithRandomNumbers(tmpWeights);\n\t\tresponses.clear();\n\t\tresponses.reserve(inBagSamples.size());\n\t\tbagIt = inBagSamples.begin();\n\t\twhile ( bagIt != bagEnd )\n\t\t{\n\t\t\ttmp = 0.0;\n\t\t\tint counter = 0;\n\t\t\tBOOST_FOREACH(int feat, randFeatures)\n\t\t\t{\n\t\t\t\ttmp += data(*bagIt,feat)*tmpWeights[counter];\n\t\t\t\tcounter++;\n\t\t\t}\n\n\t\t\tresponses.push_back(std::pair(tmp,*bagIt));\n\t\t\t++bagIt;\n\t\t}\n\n\t\tsort(responses.begin(), responses.end());\n\n\t\tif (m_hp.useInfoGain)\n\t\t{\n\t\t\tcurGiniThresh = calcInfoGainAndThreshold(labels, responses);\n\t\t}\n\t\telse\n\t\t{\n\t\t\tcurGiniThresh = calcGiniAndThreshold(labels, responses);\n\t\t}\n\n\t\tif (curGiniThresh.first < bestDGini)\n\t\t{\n\t\t\tbestDGini = curGiniThresh.first;\n\t\t\tbestThreshold = curGiniThresh.second;\n\t\t\tbestWeights = tmpWeights;\n\t\t}\n\t}\n\n\tm_bestWeights = bestWeights;\n\tm_bestFeatures = randFeatures;\n\tm_bestThreshold = (float) bestThreshold;\n}\n\nvoid NodeHyperPlane::findHypothesesLU(const matrix& data, const std::vector& labels,\n\t\tconst std::vector& inBagSamples, const std::vector& randFeatures, int numTries)\n{\n\tstd::vector gini(m_hp.numRandomFeatures), thresholds(m_hp.numRandomFeatures);\n\tstd::vector::const_iterator it(randFeatures.begin());\n\tstd::vector::const_iterator end(randFeatures.end());\n\n\tstd::vector::const_iterator bagIt;\n\tstd::vector::const_iterator bagEnd(inBagSamples.end());\n\n\tdouble bestDGini = 1e10, bestThreshold = 0;\n\tstd::pair curGiniThresh;\n\tstd::vector > responses;\n\tstd::vector bestWeights(randFeatures.size(),0.0);\n\tstd::vector tmpWeights(randFeatures.size(),0.0);\n\tfloat tmp = 0.0;\n\tfor ( int i = 0; i < numTries; i++)\n\t{\n\t\tfillWithRandomNumbers(tmpWeights);\n\t\tresponses.clear();\n\t\tresponses.reserve(inBagSamples.size());\n\t\tbagIt = inBagSamples.begin();\n\t\twhile ( bagIt != bagEnd )\n\t\t{\n\t\t\ttmp = 0.0;\n\t\t\tint counter = 0;\n\t\t\tBOOST_FOREACH(int feat, randFeatures)\n\t\t\t{\n\t\t\t\ttmp += data(*bagIt,feat)*tmpWeights[counter];\n\t\t\t\tcounter++;\n\t\t\t}\n\n\t\t\tresponses.push_back(std::pair(tmp,*bagIt));\n\t\t\t++bagIt;\n\t\t}\n\n\t\tsort(responses.begin(), responses.end());\n\n\t\tif (inBagSamples[0] < m_hp.numLabeled)\n\t\t{\n\t\t\tcurGiniThresh = calcGiniAndThreshold(labels, responses);\n\t\t}\n\t\telse\n\t\t{\n\t\t\tcurGiniThresh = calcClusterScoreAndThreshold(data, inBagSamples, responses);\n\t\t}\n\t\tif (curGiniThresh.first < bestDGini)\n\t\t{\n\t\t\tbestDGini = curGiniThresh.first;\n\t\t\tbestThreshold = curGiniThresh.second;\n\t\t\tbestWeights = tmpWeights;\n\t\t}\n\t}\n\n\tm_bestWeights = bestWeights;\n\tm_bestFeatures = randFeatures;\n\tm_bestThreshold = (float) bestThreshold;\n}\n\nstd::pair NodeHyperPlane::calcClusterScoreAndThreshold(const matrix& data, const std::vector& inBagSamples,\n\t\tconst std::vector& weights,\n\t\tconst std::vector >& responses)\n{\n\t// Find the mid-point using responsess\n\t\t\tint numResponse = responses.size();\n\tint midPointIndex = (int) round(numResponse/2 - 1);\n\tfloat threshold = (responses[midPointIndex].first + responses[midPointIndex + 1].first)/2;\n\n\t// Calculate the weighted cluster center for left and right splits\n\tdouble LWeight = 0, RWeight = 0;\n\tstd::vector LCenter(data.size2(), 0.0), RCenter(data.size2(), 0.0);\n\tfor (int m = 0; m < (int) data.size2(); m++)\n\t{\n\t\tfor (int n = 0; n < midPointIndex; n++)\n\t\t{\n\t\t\tLCenter[m] += (float)weights[responses[n].second]*data(responses[n].second, m);\n\t\t\tif (m == 0)\n\t\t\t{\n\t\t\t\tLWeight += weights[responses[n].second];\n\t\t\t}\n\t\t}\n\t\tLCenter[m] /= (float)LWeight;\n\n\t\tfor (int n = midPointIndex; n < (int) responses.size(); n++)\n\t\t{\n\t\t\tRCenter[m] += (float)weights[responses[n].second]*data(responses[n].second, m);\n\t\t\tif (m == 0)\n\t\t\t{\n\t\t\t\tRWeight += weights[responses[n].second];\n\t\t\t}\n\t\t}\n\t\tRCenter[m] /= (float)RWeight;\n\t}\n\n\t// Calculate the weighted distance from each point to the centers\n\tfloat LScore = 0, RScore = 0;\n\tfor (int m = 0; m < (int) data.size2(); m++)\n\t{\n\t\tfor (int n = 0; n < midPointIndex; n++)\n\t\t{\n\t\t\tLScore += weights[responses[n].second]*pow((double) (data(responses[n].second, m) - LCenter[m]), 2.0);\n\t\t}\n\t\tLScore /= (float)LWeight;\n\t\tfor (int n = midPointIndex; n < (int) responses.size(); n++)\n\t\t{\n\t\t\tRScore += (float)weights[responses[n].second]*pow((double) (data(responses[n].second, m) - RCenter[m]), 2.0);\n\t\t}\n\t\tRScore /= (float)RWeight;\n\t}\n\n\treturn std::pair(0.5f*(LScore + RScore), threshold);\n}\n\nstd::pair NodeHyperPlane::calcClusterScoreAndThreshold(const matrix& data, const std::vector& inBagSamples,\n\t\tconst std::vector >& responses)\n{\n\t// Find the mid-point using responsess\n\tint numResponse = responses.size();\n\tint midPointIndex = (int) round(numResponse/2 - 1);\n\tfloat threshold = (responses[midPointIndex].first + responses[midPointIndex + 1].first)/2;\n\n\t// Calculate the weighted cluster center for left and right splits\n\tdouble LWeight = 0, RWeight = 0;\n\tstd::vector LCenter(data.size2(), 0.0), RCenter(data.size2(), 0.0);\n\tfor (int m = 0; m < (int) data.size2(); m++)\n\t{\n\t\tfor (int n = 0; n < midPointIndex; n++)\n\t\t{\n\t\t\tLCenter[m] += data(responses[n].second, m);\n\t\t\tif (m == 0)\n\t\t\t{\n\t\t\t\tLWeight++;\n\t\t\t}\n\t\t}\n\t\tLCenter[m] /= LWeight;\n\n\t\tfor (int n = midPointIndex; n < (int) responses.size(); n++)\n\t\t{\n\t\t\tRCenter[m] += data(responses[n].second, m);\n\t\t\tif (m == 0)\n\t\t\t{\n\t\t\t\tRWeight++;\n\t\t\t}\n\t\t}\n\t\tRCenter[m] /= RWeight;\n\t}\n\n\t// Calculate the weighted distance from each point to the centers\n\tfloat LScore = 0, RScore = 0;\n\tfor (int m = 0; m < (int) data.size2(); m++)\n\t{\n\t\tfor (int n = 0; n < midPointIndex; n++)\n\t\t{\n\t\t\tLScore += pow((double) (data(responses[n].second, m) - LCenter[m]), 2.0);\n\t\t}\n\t\tLScore /= LWeight;\n\t\tfor (int n = midPointIndex; n < (int) responses.size(); n++)\n\t\t{\n\t\t\tRScore += pow((double) (data(responses[n].second, m) - RCenter[m]), 2.0);\n\t\t}\n\t\tRScore /= RWeight;\n\t}\n\n\treturn std::pair(0.5*(LScore + RScore), threshold);\n}\n\nvoid NodeHyperPlane::findHypotheses(const matrix& data, const std::vector& labels,\n\t\tconst std::vector& weights,\n\t\tconst std::vector& inBagSamples, const std::vector& randFeatures, int numTries)\n{\n\tstd::vector gini(m_hp.numRandomFeatures), thresholds(m_hp.numRandomFeatures);\n\tstd::vector::const_iterator it(randFeatures.begin());\n\tstd::vector::const_iterator end(randFeatures.end());\n\tstd::vector::const_iterator bagIt;\n\tstd::vector::const_iterator bagEnd(inBagSamples.end());\n\n\tdouble bestDGini = 1e10, bestThreshold = 0.0;\n\tstd::pair curGiniThresh;\n\tstd::vector > responses;\n\n\tstd::vector bestWeights(randFeatures.size(),0.0);\n\tstd::vector tmpWeights(randFeatures.size(),0.0);\n\tfloat tmp = 0.0;\n\tbool doClustering = clusterOrGini(), useUnlabeledData = true;\n\tfor ( int i = 0; i < numTries; i++)\n\t{\n\t\tfillWithRandomNumbers(tmpWeights);\n\t\tresponses.clear();\n\t\tresponses.reserve(inBagSamples.size());\n\t\tbagIt = inBagSamples.begin();\n\t\twhile ( bagIt != bagEnd )\n\t\t{\n\t\t\ttmp = 0.0;\n\t\t\tint counter = 0;\n\t\t\tBOOST_FOREACH(int feat, randFeatures)\n\t\t\t{\n\t\t\t\ttmp += data(*bagIt,feat)*tmpWeights[counter];\n\t\t\t\tcounter++;\n\t\t\t}\n\n\t\t\tresponses.push_back(std::pair(tmp,*bagIt));\n\t\t\t++bagIt;\n\t\t}\n\t\tsort(responses.begin(), responses.end());\n\n\t\tif (!doClustering)\n\t\t{\n\t\t\tif (m_hp.useInfoGain)\n\t\t\t{\n\t\t\t\tcurGiniThresh = calcInfoGainAndThreshold(labels, weights, responses);\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tcurGiniThresh = calcGiniAndThreshold(labels, weights, responses, useUnlabeledData);\n\t\t\t}\n\t\t}\n\t\telse\n\t\t{\n\t\t\tcurGiniThresh = calcClusterScoreAndThreshold(data, inBagSamples, weights, responses);\n\t\t}\n\n\t\tif (curGiniThresh.first < bestDGini)\n\t\t{\n\t\t\tbestDGini = curGiniThresh.first;\n\t\t\tbestThreshold = curGiniThresh.second;\n\t\t\tbestWeights = tmpWeights;\n\t\t}\n\t}\n\n\tm_bestFeatures = randFeatures;\n\tm_bestWeights = bestWeights;\n\tm_bestThreshold = (float) bestThreshold;\n}\n\nNODE_TRAIN_STATUS NodeHyperPlane::trainLU(const matrix& data, const std::vector& labels,\n\t\tstd::vector& inBagSamples, matrix& confidences, std::vector& predictions)\n{\n\tbool doSplit = shouldISplitLU(labels,inBagSamples);\n\tNODE_TRAIN_STATUS myTrainingStatus = IS_NOT_LEAF;\n\n\tif ( doSplit )\n\t{\n\t\tm_isLeaf = false;\n\n\t\t//train here the node: Select random features and evaluate them\n\t\tstd::vector randFeatures = randPerm(data.size2(), m_hp.numProjFeatures );\n\t\tint numTries = m_hp.numRandomFeatures;// * (m_depth+1);\n\t\tfindHypothesesLU(data, labels, inBagSamples, randFeatures, numTries);\n\t\tif (m_hp.verbose)\n\t\t{\n\t\t\tcout << \"Node #: \" << m_nodeIndex;\n\t\t\tcout << \" and the threshold is: \" << m_bestThreshold << \" at depth \" << m_depth << endl;\n\t\t}\n\n\t\t// split the data\n\t\tstd::vector leftNodeSamples, rightNodeSamples;\n\t\tevalNode(data,inBagSamples,leftNodeSamples,rightNodeSamples);\n\n\t\t// pass them to the left and right child, respectively\n\t\tm_leftChildNode = Ptr(new NodeHyperPlane(m_hp,m_depth + 1));\n\t\tm_rightChildNode = Ptr(new NodeHyperPlane(m_hp,m_depth + 1));\n\n\t\tm_leftChildNode->train(data,labels,leftNodeSamples,confidences,predictions);\n\t\tm_rightChildNode->train(data,labels,rightNodeSamples,confidences,predictions);\n\t}\n\telse\n\t{\n\t\tif (m_hp.verbose)\n\t\t{\n\t\t\tcout << \"Node #: \" << m_nodeIndex << \" is terminal, at depth \" << m_depth << endl;\n\t\t}\n\n\t\t// calc confidence, labels, etc\n\t\tm_isLeaf = true;\n\t\tmyTrainingStatus = IS_LEAF;\n\t\tm_nodeConf.resize(m_hp.numClasses, 0.0);\n\t\tint numNodeLabeled = 0;\n\t\tBOOST_FOREACH(int n, inBagSamples)\n\t\t{\n\t\t\tif (n < m_hp.numLabeled)\n\t\t\t{\n\t\t\t\tm_nodeConf[labels[n]]++;\n\t\t\t\tnumNodeLabeled++;\n\t\t\t}\n\t\t}\n\n\t\tint bestClass = 0, tmpN = 0;\n\t\tfloat bestConf = 0;\n\t\tstd::vector::iterator confItr = m_nodeConf.begin(), confEnd = m_nodeConf.end();\n\t\tfor (; confItr != confEnd; confItr++)\n\t\t{\n\t\t\tif (numNodeLabeled)\n\t\t\t{\n\t\t\t\t*confItr /= numNodeLabeled;\n\t\t\t\tif (*confItr > bestConf)\n\t\t\t\t{\n\t\t\t\t\tbestConf = *confItr;\n\t\t\t\t\tbestClass = tmpN;\n\t\t\t\t}\n\t\t\t\ttmpN++;\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\t*confItr = 1.0/m_hp.numClasses;\n\t\t\t}\n\t\t}\n\t\tm_nodeLabel = bestClass;\n\n\t\tBOOST_FOREACH(int n, inBagSamples)\n\t\t{\n\t\t\tpredictions[n] = m_nodeLabel;\n\t\t\ttmpN = 0;\n\t\t\tBOOST_FOREACH(float conf, m_nodeConf)\n\t\t\t{\n\t\t\t\tconfidences(n, tmpN) = conf;\n\t\t\t\ttmpN++;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn myTrainingStatus;\n}\n\nbool NodeHyperPlane::clusterOrGini()\n{\n\treturn false;\n}\n\nvoid NodeHyperPlane::evalNode(const matrix& data, const std::vector& inBagSamples,\n\t\tstd::vector& leftNodeSamples, std::vector& rightNodeSamples)\n{\n\tfloat tmp;\n\tBOOST_FOREACH(int n, inBagSamples)\n\t{\n\t\ttmp = 0.0;\n\t\tint counter = 0;\n\t\tBOOST_FOREACH(int feat, m_bestFeatures)\n\t\t{\n\t\t\ttmp += data(n,feat)*m_bestWeights[counter];\n\t\t\tcounter++;\n\t\t}\n\n\t\tif (tmp > m_bestThreshold)\n\t\t{\n\t\t\trightNodeSamples.push_back(n);\n\t\t}\n\t\telse\n\t\t{\n\t\t\tleftNodeSamples.push_back(n);\n\t\t}\n\t}\n}\n\nNODE_TRAIN_STATUS NodeHyperPlane::train(const matrix& data, const std::vector& labels,\n\t\tstd::vector& inBagSamples, matrix& confidences, std::vector& predictions)\n{\n\tbool doSplit = shouldISplit(labels,inBagSamples);\n\tNODE_TRAIN_STATUS myTrainingStatus = IS_NOT_LEAF;\n\n\tif ( doSplit )\n\t{\n\t\tm_isLeaf = false;\n\n\t\t//train here the node: Select random features and evaluate them\n\t\tstd::vector randFeatures = randPerm(data.size2(), m_hp.numProjFeatures );\n\t\tint numTries = m_hp.numRandomFeatures;// * (m_depth+1);\n\t\tfindHypotheses(data, labels, inBagSamples, randFeatures, numTries);\n\t\tif (m_hp.verbose)\n\t\t{\n\t\t\tcout << \"Node #: \" << m_nodeIndex;\n\t\t\tcout << \" and the threshold is: \" << m_bestThreshold << \" at depth \" << m_depth << endl;\n\t\t}\n\n\t\t// split the data\n\t\tstd::vector leftNodeSamples, rightNodeSamples;\n\t\tevalNode(data,inBagSamples,leftNodeSamples,rightNodeSamples);\n\n\t\t// pass them to the left and right child, respectively\n\t\tm_leftChildNode = Ptr(new NodeHyperPlane(m_hp,m_depth + 1));\n\t\tm_rightChildNode = Ptr(new NodeHyperPlane(m_hp,m_depth + 1));\n\n\t\tNODE_TRAIN_STATUS leftChildStatus = m_leftChildNode->train(data,labels,leftNodeSamples,confidences,predictions);\n\t\tNODE_TRAIN_STATUS rightChildStatus= m_rightChildNode->train(data,labels,rightNodeSamples,confidences,predictions);\n\n\t}\n\telse\n\t{\n\t\tif (m_hp.verbose)\n\t\t{\n\t\t\tcout << \"Node #: \" << m_nodeIndex << \" is terminal, at depth \" << m_depth << endl;\n\t\t}\n\n\t\t// calc confidence, labels, etc\n\t\tm_isLeaf = true;\n\t\tmyTrainingStatus = IS_LEAF;\n\t\tm_nodeConf.resize(m_hp.numClasses, 0.0);\n\n\t\tBOOST_FOREACH(int n, inBagSamples)\n\t\t{\n\t\t\tm_nodeConf[labels[n]]++;\n\t\t}\n\n\t\tint bestClass = 0, tmpN = 0;\n\t\tfloat bestConf = 0;\n\t\tstd::vector::iterator confItr = m_nodeConf.begin(), confEnd = m_nodeConf.end();\n\t\tfor (; confItr != confEnd; confItr++)\n\t\t{\n\t\t\t*confItr /= inBagSamples.size();\n\t\t\tif (*confItr > bestConf)\n\t\t\t{\n\t\t\t\tbestConf = *confItr;\n\t\t\t\tbestClass = tmpN;\n\t\t\t}\n\t\t\ttmpN++;\n\t\t}\n\t\tm_nodeLabel = bestClass;\n\n\t\tBOOST_FOREACH(int n, inBagSamples)\n\t\t{\n\t\t\tpredictions[n] = m_nodeLabel;\n\t\t\ttmpN = 0;\n\t\t\tBOOST_FOREACH(float conf, m_nodeConf)\n\t\t\t{\n\t\t\t\tconfidences(n, tmpN) = conf;\n\t\t\t\ttmpN++;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn myTrainingStatus;\n}\n\nNODE_TRAIN_STATUS NodeHyperPlane::train(const matrix& data, const std::vector& labels, const std::vector& weights,\n\t\tstd::vector& inBagSamples, matrix& confidences, std::vector& predictions)\n{\n\tbool doSplit = shouldISplit(labels,inBagSamples);\n\tNODE_TRAIN_STATUS myTrainingStatus = IS_NOT_LEAF;\n\n\tif ( doSplit )\n\t{\n\t\tm_isLeaf = false;\n\n\t\t//train here the node: Select random features and evaluate them\n\t\tstd::vector randFeatures = randPerm(data.size2(),m_hp.numProjFeatures );\n\t\tint numTries = m_hp.numRandomFeatures * (m_depth+1);\n\t\tfindHypotheses(data, labels, weights, inBagSamples, randFeatures,numTries);\n\t\tif (m_hp.verbose)\n\t\t{\n\t\t\tcout << \"Node #: \" << m_nodeIndex;\n\t\t\tcout << \" and the threshold is: \" << m_bestThreshold << \" at depth \" << m_depth << endl;\n\t\t}\n\n\t\t// split the data\n\t\tstd::vector leftNodeSamples, rightNodeSamples;\n\t\tevalNode(data,inBagSamples,leftNodeSamples,rightNodeSamples);\n\n\t\t// pass them to the left and right child, respectively\n\t\tm_leftChildNode = Ptr(new NodeHyperPlane(m_hp,m_depth + 1));\n\t\tm_rightChildNode = Ptr(new NodeHyperPlane(m_hp,m_depth + 1));\n\n\t\tNODE_TRAIN_STATUS leftChildStatus = m_leftChildNode->train(data,labels,weights,leftNodeSamples,confidences,predictions);\n\t\tNODE_TRAIN_STATUS rightChildStatus= m_rightChildNode->train(data,labels,weights,rightNodeSamples,confidences,predictions);\n\n\t}\n\telse\n\t{\n\t\tif (m_hp.verbose)\n\t\t{\n\t\t\tcout << \"Node #: \" << m_nodeIndex << \" is terminal, at depth \" << m_depth << endl;\n\t\t}\n\n\t\t// calc confidence, labels, etc\n\t\tm_isLeaf = true;\n\t\tmyTrainingStatus = IS_LEAF;\n\t\tm_nodeConf.resize(m_hp.numClasses, 0.0);\n\n\t\tdouble totalW = 0;\n\t\tBOOST_FOREACH(int n, inBagSamples)\n\t\t{\n\t\t\tm_nodeConf[labels[n]] += weights[n];\n\t\t\ttotalW += weights[n];\n\t\t}\n\n\t\tint bestClass = 0, tmpN = 0;\n\t\tfloat bestConf = 0;\n\t\tstd::vector::iterator confItr = m_nodeConf.begin(), confEnd = m_nodeConf.end();\n\t\tfor (; confItr != confEnd; confItr++)\n\t\t{\n\t\t\t*confItr /= (totalW + 1e-10);\n\t\t\tif (*confItr > bestConf)\n\t\t\t{\n\t\t\t\tbestConf = *confItr;\n\t\t\t\tbestClass = tmpN;\n\t\t\t}\n\t\t\ttmpN++;\n\t\t}\n\t\tm_nodeLabel = bestClass;\n\n\t\tBOOST_FOREACH(int n, inBagSamples)\n\t\t{\n\t\t\tpredictions[n] = m_nodeLabel;\n\t\t\ttmpN = 0;\n\t\t\tBOOST_FOREACH(float conf, m_nodeConf)\n\t\t\t{\n\t\t\t\tconfidences(n, tmpN) = conf;\n\t\t\t\ttmpN++;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn myTrainingStatus;\n}\n\n\nvoid NodeHyperPlane::eval(const matrix& data, const std::vector& sampleIndeces,\n\t\tmatrix& confidences, std::vector& predictions)\n{\n\tif (m_isLeaf)\n\t{\n\t\t// Make predictions and confidences\n\t\tint tmpN;\n\t\tBOOST_FOREACH( int n, sampleIndeces)\n\t\t{\n\t\t\tpredictions[n] = m_nodeLabel;\n\t\t\ttmpN = 0;\n\t\t\tBOOST_FOREACH(float conf, m_nodeConf)\n\t\t\t{\n\t\t\t\tconfidences(n, tmpN) = conf;\n\t\t\t\ttmpN++;\n\t\t\t}\n\t\t}\n\t}\n\telse\n\t{\n\t\t// split the data\n\t\tstd::vector leftNodeSamples, rightNodeSamples;\n\t\tevalNode(data,sampleIndeces,leftNodeSamples,rightNodeSamples);\n\n\t\tm_leftChildNode->eval(data,leftNodeSamples,confidences,predictions);\n\t\tm_rightChildNode->eval(data,rightNodeSamples,confidences,predictions);\n\t}\n}\n\nvoid NodeHyperPlane::getPath(const matrix& data, const std::vector& sampleIndeces, std::vector >& path)\n{\n\tBOOST_FOREACH(int n, sampleIndeces)\n \t\t{\n\t\tpath[n].push_back(m_nodeIndex);\n \t\t}\n\n\tif (!m_isLeaf)\n\t{\n\t\t// split the data\n\t\tstd::vector leftNodeSamples, rightNodeSamples;\n\t\tevalNode(data,sampleIndeces,leftNodeSamples,rightNodeSamples);\n\n\t\tm_leftChildNode->getPath(data,leftNodeSamples,path);\n\t\tm_rightChildNode->getPath(data,rightNodeSamples,path);\n\t}\n}\n\nvoid NodeHyperPlane::refine(const matrix& data, const std::vector& labels,\n\t\tstd::vector& samples, matrix& confidences, std::vector& predictions)\n{\n\tif ( !m_isLeaf )\n\t{\n\t\t// split the data\n\t\tstd::vector leftNodeSamples, rightNodeSamples;\n\t\tevalNode(data,samples,leftNodeSamples,rightNodeSamples);\n\n\t\tm_leftChildNode->refine(data,labels,leftNodeSamples,confidences,predictions);\n\t\tm_rightChildNode->refine(data,labels,rightNodeSamples,confidences,predictions);\n\t}\n\telse\n\t{\n\t\t// calc confidence, labels, etc\n\t\tm_nodeConf.resize(m_hp.numClasses,0.0);\n\t\tBOOST_FOREACH(int n, samples)\n\t\t{\n\t\t\tm_nodeConf[labels[n]]++;\n\t\t}\n\n\t\tint bestClass = 0, tmpN = 0;\n\t\tfloat bestConf = 0;\n\t\tstd::vector::iterator confItr = m_nodeConf.begin(), confEnd = m_nodeConf.end();\n\t\tfor (; confItr != confEnd; confItr++)\n\t\t{\n\t\t\t*confItr /= samples.size();\n\t\t\tif (*confItr > bestConf)\n\t\t\t{\n\t\t\t\tbestConf = *confItr;\n\t\t\t\tbestClass = tmpN;\n\t\t\t}\n\t\t\ttmpN++;\n\t\t}\n\t\tm_nodeLabel = bestClass;\n\n\t\tBOOST_FOREACH(int n, samples)\n\t\t{\n\t\t\tpredictions[n] = m_nodeLabel;\n\t\t\ttmpN = 0;\n\t\t\tBOOST_FOREACH(float conf, m_nodeConf)\n\t\t\t{\n\t\t\t\tconfidences(n, tmpN) = conf;\n\t\t\t\ttmpN++;\n\t\t\t}\n\t\t}\n\t}\n}\n\nvoid NodeHyperPlane::refine(const matrix& data, const std::vector& labels, const std::vector& weights,\n\t\tstd::vector& samples, matrix& confidences, std::vector& predictions)\n{\n\t// calc confidence, labels, etc\n\tif (!m_isLeaf)\n\t{\n\t\t// split the data\n\t\tstd::vector leftNodeSamples, rightNodeSamples;\n\t\tevalNode(data,samples,leftNodeSamples,rightNodeSamples);\n\n\t\tm_leftChildNode->refine(data,labels,weights,leftNodeSamples,confidences,predictions);\n\t\tm_rightChildNode->refine(data,labels,weights,rightNodeSamples,confidences,predictions);\n\t}\n\telse\n\t{\n\t\tm_nodeConf.resize(m_hp.numClasses,0.0);\n\t\tstd::vector::iterator confItr2 = m_nodeConf.begin(), confEnd2 = m_nodeConf.end();\n\t\tfor (; confItr2 != confEnd2; confItr2++)\n\t\t{\n\t\t\t*confItr2=0;\n//\t\t\tstd::cout<<*confItr2<::iterator confItr = m_nodeConf.begin(), confEnd = m_nodeConf.end();\n\t\tfor (; confItr != confEnd; confItr++)\n\t\t{\n\t\t\t*confItr /= (totalW + 1e-10);\n\t\t\tif (*confItr > bestConf)\n\t\t\t{\n\t\t\t\tbestConf = *confItr;\n\t\t\t\tbestClass = tmpN;\n\t\t\t}\n\t\t\ttmpN++;\n\t\t}\n\t\tm_nodeLabel = bestClass;\n\n\t\tBOOST_FOREACH(int n, samples)\n\t\t{\n\t\t\tpredictions[n] = m_nodeLabel;\n\t\t\ttmpN = 0;\n\t\t\tBOOST_FOREACH(float conf, m_nodeConf)\n\t\t\t{\n\t\t\t\tconfidences(n, tmpN) = conf;\n\t\t\t\ttmpN++;\n\t\t\t}\n\t\t}\n\t}\n}\n", "meta": {"hexsha": "5b0673486680e5e4be9face468bea88dcf1eeac2", "size": 32398, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "source/External/RF/nodehyperplane.cpp", "max_stars_repo_name": "tschuls/ETH-SegReg-DLL", "max_stars_repo_head_hexsha": "34bd10464dc5e8b3c7bf3371ca1e190d692385b7", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBSD"], "max_stars_count": 15.0, "max_stars_repo_stars_event_min_datetime": "2015-04-15T06:49:06.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-01T09:09:50.000Z", "max_issues_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.4263215925474903, "lm_q1q2_score": 0.24457149227526195}} {"text": "/* This file is part of libDAI - http://www.libdai.org/\n *\n * libDAI is licensed under the terms of the GNU General Public License version\n * 2, or (at your option) any later version. libDAI is distributed without any\n * warranty. See the file COPYING for more details.\n *\n * Copyright (C) 2010 Joris Mooij [joris dot mooij at libdai dot org]\n */\n\n#include \n#include \n#include \n#include \n#include \"dai/emrun.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include // std::setprecision\n#include \n#include \n#include \n#include \n#include \n#include \n#include\n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \nusing namespace std;\nusing namespace dai;\nusing namespace boost;\n// Define a random number generator and initialize it with a reproducible\n// seed.\nmt19937 generator(42);\n\n// Define a uniform random number distribution which produces \"double\"\n// values between 0 and 1 (0 inclusive, 1 exclusive).\nboost::uniform_real<> uni_dist(0,1);\nboost::variate_generator > uni(generator, uni_dist);\n\nboost::normal_distribution<> nd(0.0, 1.0);\nboost::variate_generator > normal(generator, nd);\n\n// GA - initialization\nsize_t max_gen = 10; // no. of generations\nsize_t population_size =4; // population size\nReal pc;// = 0.8; // crossover probability - between 0.6 an 0.9\nReal pm;// = 0.3; // mutation probability - between 1/pop.size and 1/chromosome_length\nint population_no=0; // current population id\n\n// 1. Creating initial population of EM runs \n\t\nchar* gibbsfile;// = \"/home/priya/libDAI-0.3.1/alarm/500/5.tab\";\n//const char* gibbsfile = argv[1];\nstring alarmfgloc;// = \"/home/priya/libDAI-0.3.1/alarm_learned_factor_graphs/\"; \nstring alarmlearnedfgloc;// = \"/home/priya/libDAI-0.3.1/alarm_learned_factor_graphs/\";\nchar* networkname ; //= \"alarm\";\nsize_t maxIters =1000; // maximum number of iterations\nchar* emfile;\nofstream out,out1;\nEMRun* runname;\nvector layer; \nvector layer_curr;\nvector layer_prev;\nint noVars;\nint *arrval;\n//\n// Generate a random number between 0 and 1\n// return a uniform number in [0,1].\ndouble unifRand()\n{\n return rand()/double(RAND_MAX);\n}\n// Reset the random number generator with the system clock.\nvoid seed()\n{\n\t/*Declare variable to hold seconds on clock.*/\n\t//time_t seconds;\n\t/*Get value from system clock and place in seconds variable.*/\n//\ttime(&seconds);\n\t/*Convert seconds to a unsigned integer.*/\n// struct timeval time;\n// gettimeofday(&time,NULL);\n\n // microsecond has 1 000 000\n //srand((time.tv_sec * 1000) + (time.tv_usec / 10));\n //srand((unsigned)time(0));\n}\nstd::string& trim(std::string& s, const char* t = \" \\t\\n\\r\\f\\v\")\n{\n\ts.erase(0, s.find_first_not_of(t));\n\ts.erase(s.find_last_not_of(t) + 1);\n\treturn s;\n}\ndouble euclideanDist(double v1[],double v2[],int length)\n{\n double distance=0;\n for(int i=0;ipm)\n {\n //c1<>no_factors;\n //cout<<\"no. of factors \"< fields;\n fields = tokenizeString( line1, true,\" \");\n for( size_t i = 0; i < fields.size(); ++i )\n {\n stringstream n;\n n << fields[i];\n string s = n.str();\n if( s.find_first_not_of(\" \")!= std::string::npos)\n {\n //c1<> factor_names[i];\n if(factor_names[i] == (factor-1))\n factor_pos =i;\n }\n }\n //c1< fields1;\n fields1 = tokenizeString( line1, true,\" \");\n for( size_t j = 0; j < fields1.size(); ++j )\n {\n stringstream n1;\n n1 << fields1[j];\n string state = n1.str();\n if( state.find_first_not_of(\" \")!= std::string::npos)\n {\n //c1<> states_arr[j];\n no_states = no_states * states_arr[j];\n if(j < factor_pos)\n arr_size = arr_size * states_arr[j];\n if(j == factor_pos)\n {\n factor_states = states_arr[j];\n arr_set_states = arr_size*states_arr[j];\n }\n }\n }\n //c1<= 5)\n {\n int k=0;\n while(k Arrays, Arrays1;\n for(int l=0; l::iterator it2,it21;\n it2=Arrays.begin();\n it21=Arrays1.begin();\n //get the current cpt values and copy them in the array\n int l=0;\n while(l values,values1;\n // values = tokenizeString( line1, true,\" +\");\n // values1 = tokenizeString( line2, true,\" +\");\n// split(values, line1, is_any_of(\" +\")); // here it is\n// split(values1, line2, is_any_of(\" +\")); // here it is\n char_separator sep(\" \");\n tokenizer> tokens(line1, sep);\n for (const auto& t : tokens) {\n //cout<> tokens1(line2, sep);\n for (const auto& t : tokens1) {\n //cout<> arr[m];\n n2 >> arr1[m];\n //cout<::iterator it,it1;\n double sumarr[arr_size];\n for(int m=0; m=5\n \n } // end of if pmut::iterator it; \n\t for (it=layer.begin(); it != layer.end(); ++it){ \n\t //cout<<\"layer size \"<hasTerminated()) { //if it has terminated, set the run aside\n// ////////////\n// if(layer_prev.size() !=0 )\n// {\n// boost::thread thread6(boost::bind(&EMRun::stopEM, boost::ref(*it)));\n// \n// cout<<\" iters = \"<<(*it)->getIters()<getIters();\n// double val = (*it)->getRun();\n// cout<<\" em run stopped \"<getLL();\n// vector::iterator itprev;\n// std::vector llTraceprev;\n// // get the pervious run\n// \n// for (itprev=layer_prev.begin(); itprev != layer_prev.end(); ++itprev)\n// {\n// double runid = (*itprev)->getRun();\n// if(val==runid)\n// {\n// //out1<lastlog();\n// cout<getRun()<<\" \"<<\" \"<<(*itprev)->getIters()<iters)\n// llprev = llTraceprev.at(iters);\n// else\n// llprev = llTraceprev.at(llTraceprev.size()-1);\n// cout<<\" LL for prev run = \"<=llprev)\n// {\n// cout<<\" current LL \"<forceStopped();\n// cout<<\" em run not resumed\"<getLL();\n\t\tdouble iters = (*it)->getIters();\n\t\tdouble runid = (*it)->getRun();\n //cout<<\"em run \"< llTrace = (*it)->lastlog();\n\t\tstring number; \n\t\tstringstream num; \n\t\tnum << runid; \n\t\tnumber = num.str();\n\t \tstring fglearnedfileloc = alarmlearnedfgloc + \"learned/\"+dirname+\"/\"+networkname+\"_\"+number+\".fg\";\n const char* fglearnedfile = &fglearnedfileloc[0];\n\t\tcout<<\"Run number \"<getInf();\n\t\tofstream learnedstream;\n\t\tcout<<\"learned file = \"<fg(); \n\t\tlearnedstream.flush();\n\t\tlearnedstream.close();\n\t\t//delete inf;\n\t\tsleep(1);\n\t\tcout<<\"Run number \"<::iterator logval = llTrace.begin(); logval != llTrace.end(); ++logval)\n// {\n// out1 << *logval<<\" \";\n// }\n// out1<<\" \"< mutateFactors;\n mutateFactors = (*it)->getmutateFactors();\n cout<<\" Run id \"<getIters()) == maxIters){\n\t\tdouble ll = (*it)->getLL();\n\t\tdouble iters = (*it)->getIters();\n\t\tdouble runid = (*it)->getRun();\n\t\tstring number; \n\t\tstringstream num; \n\t\tnum << runid; \n\t\tnumber = num.str();\n\t \tstring fglearnedfileloc = alarmlearnedfgloc + \"learned/\"+dirname+\"/\"+networkname+\"_\"+number+\".fg\";\n\t \tconst char* fglearnedfile = &fglearnedfileloc[0];\n\t\t//cout<<\"Run number \"<getRun()<<\" has reached maximum iterations, so stopping the run\"<getInf();\n\t \tofstream learnedstream;\n\t \t//cout<<\"learned file = \"<fg(); \n\t \tlearnedstream.close();\n\t\t//delete inf;\n\t\tsleep(1);\t\t\n\t\t//cout<<\"Run number \"<isForceStop())\n {\n boost::thread thread5(boost::bind(&EMRun::stopEM, boost::ref(*it)));\n \n }\n layer.erase(it);\n\t//\tif (layer.size() == 0){break;}\n\t//\telse { it = layer.begin(); }\n\t }\n else \n {\n //*it=compareRuns(it);\n if(layer_prev.size() !=0 )\n {\n boost::thread thread6(boost::bind(&EMRun::stopEM, boost::ref(*it)));\n \n cout<<\" iters = \"<<(*it)->getIters()<getIters();\n double val = (*it)->getRun();\n cout<<\" em run stopped \"<getLL();\n vector::iterator itprev;\n std::vector llTraceprev;\n // get the pervious run\n \n for (itprev=layer_prev.begin(); itprev != layer_prev.end(); ++itprev)\n {\n double runid = (*itprev)->getRun();\n if(val==runid)\n {\n //out1<lastlog();\n cout<getRun()<<\" \"<<\" \"<<(*itprev)->getIters()<iters)\n llprev = llTraceprev.at(iters);\n else\n llprev = llTraceprev.at(llTraceprev.size()-1);\n cout<<\" LL for prev run = \"<=llprev)\n {\n cout<<\" current LL \"<forceStopped();\n cout<<\" em run not resumed\"<::iterator it = layer_curr.begin();\n \n for( size_t n = 0; n < population_size; n++ )\n {\n string number1;\n stringstream num1;\n num1 << n;//arrval[n];\n number1 = num1.str();\n string child = childloc+ networkname+\"C_\"+number1+\".fg\";\n string child1 = childloc+ networkname+\"_\"+number1+\".fg\";\n const char* childfgfile = &child[0]; \n const char* childfgfile1 = &child1[0]; \n //cout<<\"child file \"<< childfgfile< hashmap = (*it)->getParams();\n double runid = (*it)->getRun();\n std::vector mutateFactors;\n mutateFactors.clear();\n mutateFactors = (*it)->getmutateFactors();\n cout<<\" Run id \"<::iterator it;\n \n it = find (mutateFactors.begin(), mutateFactors.end(), factor);\n if (it != mutateFactors.end())\n {\n //std::cout << \"Element found in myvector: \" << *it << '\\n';\n pmut=1;\n }\n else\n {\n // std::cout << \"Element not found in myvector\\n\";\n pmut=0;\n }\n // cout<pm) //do mutation handling the constraint that the states of the random variable sum to 1.\n {\n \n if(p_c == 0)\n {\n c1<>no_factors;\n //cout<<\"no. of factors \"< fields;\n fields = tokenizeString( line1, true,\" \"); \n for( size_t i = 0; i < fields.size(); ++i )\n {\n stringstream n;\n n << fields[i];\n string s = n.str(); \n if( s.find_first_not_of(\" \")!= std::string::npos) \n {\n c1<> factor_names[i];\n if(factor_names[i] == (factor-1))\n factor_pos =i;\n }\n }\n c1< fields1;\n fields1 = tokenizeString( line1, true,\" \"); \n for( size_t j = 0; j < fields1.size(); ++j )\n {\n stringstream n1;\n n1 << fields1[j];\n string state = n1.str(); \n if( state.find_first_not_of(\" \")!= std::string::npos) \n {\n c1<> states_arr[j];\n no_states = no_states * states_arr[j];\n if(j < factor_pos)\n arr_size = arr_size * states_arr[j];\n if(j == factor_pos)\n {\n factor_states = states_arr[j];\n arr_set_states = arr_size*states_arr[j]; \n }\n }\n }\n c1< Arrays1;\n// for(int l=0; l::iterator it;\n// for(int m=0; m vec;\n// for(int i=0; i Arrays2;\n for(int lll=0; lll::iterator it2;\n it2=Arrays2.begin();\n //get the current cpt values and copy them in the array\n int ll=0;\n while(ll sep(\" \");\n tokenizer> tokens(line1, sep);\n std::vector valuesl;\n for (const auto& t : tokens) {\n // cout<> arrl[ml];\n ml++;\n if(ml Arrays1;\n vector::iterator a_it,nor_it;\n for(int l=0; l vec;\n for(int i=0; i Arrays3;\n for(int l=0; l::iterator it3;\n for(int m=0; m vec2;\n double pmut1 = uni();\n for(int i=0; ipm)\n vec2.push_back(1);\n else\n vec2.push_back(0);\n }\n for (double& value: vec2)\n {\n double* arr_rd = *it3;\n arr_rd[m] = value;\n ++it3;\n //cout<::iterator it11,it22,it33;\n it11=Arrays1.begin();\n it22=Arrays2.begin();\n it33=Arrays3.begin();\n for(int l=0; l myset;\n\tset::iterator it;\n\tsize_t k =0,p=0;\n\tarrval = new int[population_size];\n\tstring dirname; \n\tstringstream p_id; \n\tp_id << population_no; \n\tdirname = p_id.str();\n\tstring dirname2; \n\tstringstream p_id2; \n\tp_id2 << population_no+1; \n\tdirname2 = p_id2.str();\n\tstring childloc = alarmlearnedfgloc + dirname2+\"/\";\n\tconst char* childfgloc = &childloc[0];\n\tmkdir(childfgloc,0755);\n\tfor( size_t n = 0; n < population_size; n++ )\n\t{ \n\t int val = rand() % (HIGH - LOW + 1) + LOW;\n\t it=myset.find(val);\n\t arrval[n] = val;\n\t if(it==myset.end()) // if the value is not in the set\n\t {\n\t\tmyset.insert(val);\n\t\tp++;\n\t\tif(p == 2)\n\t\t{\n\t\t cout <<\" Random value is\"<::iterator it, it1; \n\tint i=0;\n\tfor (it=layer_prev.begin(); it != layer_prev.end(); ++it)\n { \n\t // cout<<\"layer size \"<getLL();\n\t double iters = (*it)->getIters();\n\t double runid = (*it)->getRun();\n\t stringstream sll;\n\t stringstream siters;\t \n stringstream srunid;\t \n\t sll<getLL();\n\t double iters1 = (*it1)->getIters();\n\t double runid1 = (*it1)->getRun();\n stringstream sll1;\n stringstream siters1;\t \n stringstream srunid1;\n\t sll1< llTrace = (*it)->lastlog();\n// \n// for(std::vector::iterator logval = llTrace.begin(); logval != llTrace.end(); ++logval)\n// {\n// out1 << *logval<<\" \";\n// }\n // out1<<\" \"< llTrace = (*it1)->lastlog();\n// for(std::vector::iterator logval = llTrace.begin(); logval != llTrace.end(); ++logval)\n// {\n// out1 << *logval<<\" \";\n// }\n// out1<<\" \"<(std::time(0)));\nconst char* fname =argv[2];\nconst char* fname1 =argv[6];\ngibbsfile = argv[1];\nemfile = argv[8];\nstringstream ss9( argv[9] );\nss9 << noVars;\nistringstream ss( argv[3] );\nss >> pm;\nstringstream ss1( argv[4] );\nss1 >> pc;\nstringstream ss2(argv[5]);\nalarmfgloc = ss2.str();\nalarmlearnedfgloc = ss2.str();\nnetworkname = argv[7];\nout.open(fname);\nout1.open(fname1);\ntime_t timer1, timer2;\ntime(&timer1);\nclock_t tStart = clock();\nwhile(population_no 0)\n\t{\n\t replacementStrategy();\n\t}\n\tdoCrossover();\n\tdoMutation();\n\t//cout<<\"Next population\"<=nocompare_gens)\n// {\n// \n// for (int j=0; j\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"formic/utils/openmp.h\"\n\n#include \n#include \n\n#include \"formic/utils/matrix.h\"\n#include \"formic/utils/mpi_interface.h\"\n#include \"block_mat.h\"\n#include \"formic/utils/lmyengine/block_detail.h\"\n\n//////////////////////////////////////////////////////////////////////////////////////////////////\n/// \\brief Function that contracts each block's previous update components with derivative ratios\n///\n/// \\param[in] dr derivative ratio(bare or energy) for this sample\n/// \\param[in] ou_mat matrix holds old updates, size num_old_update * num_variables\n/// \\param[out] cont_vec contracted results\n///\n//////////////////////////////////////////////////////////////////////////////////////////////////\nvoid cqmc::engine::LMBlockerMatData::prep_block_ou_contractions(const std::vector & dr, \n const formic::Matrix & ou_mat, \n std::vector > & cont_vec) {\n \n // loop over blocks\n for (int b = 0; b < this->nb(); b++) {\n \n const int ibeg = 1 + m_block_beg.at(b);\n const int len = m_block_len.at(b);\n\n formic::ColVec & cont = cont_vec.at(b);\n for (int i = 0; i < cont.size(); i++)\n cont.at(i) = 0.0;\n\n for (int k = 0; k < m_nou; k++) {\n for (int j = ibeg; j < ibeg+len; j++) {\n cont.at(k) += dr.at(j) * ou_mat.at(j-1,k);\n }\n }\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////////////////////\n/// \\brief Function that create a matrix in the basis of a block of variables and older updates \n/// from a different block\n///\n/// \\param[in] b block index\n/// \\param[in] x block index\n/// \\param[out] mat output matrix\n///\n////////////////////////////////////////////////////////////////////////////////////////////////\nvoid cqmc::engine::LMBlockerMatData::prep_lm_block_plus_other_ou_matrix(const int b, const int x, formic::Matrix & mat) {\n\n // first check that if x and b is the same block\n if ( x == b ) \n throw formic::Exception(\"b and x must be different in prep_lm_block_plus_other_ou_matrix\");\n\n // get the length of this block\n const int len = this->bl(b);\n\n // get the dimension of this matrix \n const int dim = 1 + len + m_nou;\n\n const int y = x - ( x > b ? 1 : 0 ); \n \n // size the output matrix correctly\n mat.reset(dim, dim);\n\n // element\n mat.at(0,0) = m_ww[0]; \n //std::cout << \"in prep_lm_block_plus_other_ou_matrix mat(0,0) is \" << m_ww << std::endl;\n\n // \n for (int i = 0; i < len; i++) \n mat.at(0, 1+i) = m_wv[0].at(b).at(i);\n\n // \n for (int i = 0; i < len; i++)\n mat.at(1+i, 0) = m_vw[0].at(b).at(i);\n\n // \n for (int i = 0; i < len; i++) {\n for (int j = 0; j < len; j++) {\n mat.at(1+i,1+j) = m_vv[0].at(b).at(i,j);\n }\n }\n\n // \n for (int k = 0; k < m_nou; k++) \n mat.at(0, 1+len+k) = m_wo[0].at(x).at(k);\n\n // \n for (int k = 0; k < m_nou; k++)\n mat.at(1+len+k, 0) = m_ow[0].at(x).at(k);\n\n // \n for (int i = 0; i < len; i++) {\n for (int k = 0; k < m_nou; k++) {\n mat.at(1+i, 1+len+k) = m_vo[0].at(b).at(i, y*m_nou+k);\n }\n }\n\n // \n for (int k = 0; k < m_nou; k++) {\n for (int i = 0; i < len; i++) {\n mat.at(1+len+k, 1+i) = m_ov[0].at(b).at(y*m_nou+k, i);\n }\n }\n\n // \n for (int k = 0; k < m_nou; k++) {\n for (int l = 0; l < m_nou; l++) {\n mat.at(1+len+k, 1+len+l) = m_oo[0].at(x).at(k,l);\n }\n }\n}\n\n\n////////////////////////////////////////////////////////////////////////////////////////////////\n/// \\brief Function that accumulates contribution to block matrices for this sample\n///\n/// \\param[in] d weight for this sample\n/// \\param[in] lr left vector for outer product\n/// \\param[in] rr right vector for outer product\n/// \\param[in] ou_mat matrix storing the old update coefficients\n///\n////////////////////////////////////////////////////////////////////////////////////////////////\nvoid cqmc::engine::LMBlockerMatData::acc(const double d, const std::vector & lr, const std::vector &rr, const formic::Matrix & ou_mat) {\n \n // get the thread number \n int myThread = omp_get_thread_num();\n\n // check to see if the input vector is of the correct size\n if ( lr.size() != 1 + this->tot_rows(m_vv[myThread]) )\n throw formic::Exception(\"lr vector length was %i but should be %i LMBlockerMatData::acc\") % lr.size() % ( 1 + this->tot_rows(m_vv[myThread]) );\n if ( rr.size() != 1 + this->tot_rows(m_vv[myThread]) )\n throw formic::Exception(\"rr vector length was %i but should be %i LMBlockerMatData::acc\") % rr.size() % ( 1 + this->tot_rows(m_vv[myThread]) );\n\n // prep intermediates for old updates\n this->prep_block_ou_contractions(lr, ou_mat, m_boulr[myThread]);\n this->prep_block_ou_contractions(rr, ou_mat, m_bourr[myThread]);\n\n // \n m_ww[myThread] += d * lr.at(0) * rr.at(0);\n\n // loop over blocks\n for (int b = 0; b < this->nb(); b++) {\n \n // beginning index\n const int ibeg = 1 + m_block_beg.at(b);\n\n // block length\n const int len = m_block_len.at(b);\n\n for (int i = 0; i < len; i++) {\n\n // \n m_wv[myThread].at(b).at(i) += d * lr.at(0) * rr.at(ibeg + i);\n\n // \n m_vw[myThread].at(b).at(i) += d * lr.at(ibeg + i) * rr.at(0);\n\n }\n\n for (int k = 0; k < m_nou; k++) {\n\n // \n m_wo[myThread].at(b).at(k) += d * lr.at(0) * m_bourr[myThread].at(b).at(k);\n \n // \n m_ow[myThread].at(b).at(k) += d * m_boulr[myThread].at(b).at(k) * rr.at(0);\n }\n\n // \n { formic::Matrix & vv_mat = m_vv[myThread].at(b);\n for (int i = 0; i < len; i++) {\n for (int j = 0; j < len; j++) {\n vv_mat.at(i,j) += d * lr.at(ibeg+i) * rr.at(ibeg+j);\n }\n }\n }\n\n // note this is for var in the current block and old-update in all other blocks\n { formic::Matrix & vo_mat = m_vo[myThread].at(b);\n for (int x = 0, y = 0; x < this->nb(); x++) {\n if ( x == b )\n continue;\n const formic::ColVec & rr_vec = m_bourr[myThread].at(x);\n for (int o = 0; o < m_nou; o++) {\n for (int i = 0; i < len; i++) {\n vo_mat.at(i,y*m_nou+o) += d * lr.at(ibeg+i) * rr_vec.at(o);\n }\n } \n y++;\n } \n } \n\n // note this is for var in the current block and old-update in all other blocks\n { formic::Matrix & ov_mat = m_ov[myThread].at(b);\n for (int i = 0; i < len; i++) {\n for (int x = 0, y = 0; x < this->nb(); x++) {\n if ( x == b ) \n continue;\n const formic::ColVec & lr_vec = m_boulr[myThread].at(x);\n for (int k = 0; k < m_nou; k++) {\n ov_mat.at(m_nou*y+k, i) += d * lr_vec.at(k) * rr.at(ibeg+i);\n }\n y++;\n }\n }\n }\n\n // \n { formic::Matrix & oo_mat = m_oo[myThread].at(b);\n for (int k = 0; k < m_nou; k++) {\n for (int l = 0; l < m_nou; l++) {\n oo_mat.at(k,l) += d * m_boulr[myThread].at(b).at(k) * m_bourr[myThread].at(b).at(l);\n }\n }\n }\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////////////////////\n/// \\brief Function that finalizes the accumulation by dividing matrices total weight\n///\n////////////////////////////////////////////////////////////////////////////////////////////////\nvoid cqmc::engine::LMBlockerMatData::finalize(const double total_weight) {\n \n // get the number of threads\n int NumThreads = omp_get_max_threads();\n\n // sum over threads\n for (int ip = 1; ip < NumThreads; ip++) {\n m_ww[0] += m_ww[ip];\n for (int b = 0; b < this->nb(); b++) {\n m_wv[0].at(b) += m_wv[ip].at(b);\n m_vw[0].at(b) += m_vw[ip].at(b);\n m_wo[0].at(b) += m_wo[ip].at(b);\n m_ow[0].at(b) += m_ow[ip].at(b);\n m_ov[0].at(b) += m_ov[ip].at(b);\n m_vo[0].at(b) += m_vo[ip].at(b);\n m_oo[0].at(b) += m_oo[ip].at(b);\n }\n }\n \n // \n m_ww[0] /= total_weight;\n\n // loop over blocks\n for (int b = 0; b < this->nb(); b++) {\n m_wv[0].at(b) /= total_weight;\n m_vw[0].at(b) /= total_weight;\n m_vv[0].at(b) /= total_weight;\n m_wo[0].at(b) /= total_weight;\n m_ow[0].at(b) /= total_weight;\n m_ov[0].at(b) /= total_weight;\n m_vo[0].at(b) /= total_weight;\n m_oo[0].at(b) /= total_weight;\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////////////////////\n/// \\brief Function that finalizes the accumulation by dividing matrices total weight \n/// across the whole processes\n///\n////////////////////////////////////////////////////////////////////////////////////////////////\nvoid cqmc::engine::LMBlockerMatData::mpi_finalize(const double total_weight) {\n \n // get rank number and number of ranks\n int my_rank = formic::mpi::rank();\n\n // get the number of threads \n int NumThreads = omp_get_max_threads();\n\n // loop over blocks\n //for (int b = 0; b < this->nb(); b++) {\n // // print out to debug\n // for (int i = 0; i < m_ow.at(b).size(); i++) {\n // std::cout << m_ow.at(b).at(i) << \" \";\n // }\n // std::cout << std::endl;\n //}\n //std::cout << std::endl;\n\n // sum over threads\n for (int ip = 1; ip < NumThreads; ip++) {\n m_ww[0] += m_ww[ip];\n for (int b = 0; b < this->nb(); b++) {\n m_wv[0].at(b) += m_wv[ip].at(b);\n m_vw[0].at(b) += m_vw[ip].at(b);\n m_vv[0].at(b) += m_vv[ip].at(b);\n m_wo[0].at(b) += m_wo[ip].at(b);\n m_ow[0].at(b) += m_ow[ip].at(b);\n m_ov[0].at(b) += m_ov[ip].at(b);\n m_vo[0].at(b) += m_vo[ip].at(b);\n m_oo[0].at(b) += m_oo[ip].at(b);\n }\n }\n\n // \n double m_ww_tot = 0.0;\n formic::mpi::reduce(&m_ww[0], &m_ww_tot, 1, MPI_SUM);\n //MPI_Reduce(&m_ww, &m_ww_tot, 1, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD);\n m_ww[0] = m_ww_tot / total_weight;\n\n // get space for MPI reduce \n std::vector > m_wv_tot, m_vw_tot, m_wo_tot, m_ow_tot;\n std::vector > m_vv_tot, m_vo_tot, m_ov_tot, m_oo_tot;\n for (int i = 0; i < this->nb(); i++) {\n m_wv_tot.push_back(formic::ColVec(this->bl(i), 0.0));\n m_vw_tot.push_back(formic::ColVec(this->bl(i), 0.0));\n m_vv_tot.push_back(formic::Matrix(this->bl(i), this->bl(i), 0.0));\n m_wo_tot.push_back(formic::ColVec(m_nou, 0.0));\n m_ow_tot.push_back(formic::ColVec(m_nou, 0.0));\n m_ov_tot.push_back(formic::Matrix((this->nb()-1)*m_nou, this->bl(i), 0.0));\n m_vo_tot.push_back(formic::Matrix(this->bl(i), (this->nb()-1)*m_nou, 0.0));\n m_oo_tot.push_back(formic::Matrix(m_nou, m_nou, 0.0));\n }\n\n // do MPI reduce\n for (int i = 0; i < this->nb(); i++) {\n formic::mpi::reduce(&m_wv[0].at(i).at(0), &m_wv_tot.at(i).at(0), this->bl(i), MPI_SUM);\n formic::mpi::reduce(&m_vw[0].at(i).at(0), &m_vw_tot.at(i).at(0), this->bl(i), MPI_SUM);\n formic::mpi::reduce(&m_vv[0].at(i).at(0,0), &m_vv_tot.at(i).at(0,0), m_vv[0].at(i).size(), MPI_SUM);\n formic::mpi::reduce(&m_wo[0].at(i).at(0), &m_wo_tot.at(i).at(0), m_nou, MPI_SUM);\n formic::mpi::reduce(&m_ow[0].at(i).at(0), &m_ow_tot.at(i).at(0), m_nou, MPI_SUM);\n formic::mpi::reduce(&m_ov[0].at(i).at(0,0), &m_ov_tot.at(i).at(0,0), m_ov[0].at(i).size(), MPI_SUM);\n formic::mpi::reduce(&m_vo[0].at(i).at(0,0), &m_vo_tot.at(i).at(0,0), m_vo[0].at(i).size(), MPI_SUM);\n formic::mpi::reduce(&m_oo[0].at(i).at(0,0), &m_oo_tot.at(i).at(0,0), m_oo[0].at(i).size(), MPI_SUM);\n }\n\n // evaluate the average across all processors\n if ( my_rank == 0 ) {\n \n // loop over blocks\n for (int b = 0; b < this->nb(); b++) {\n m_vw[0].at(b) = m_vw_tot.at(b) / total_weight;\n m_wv[0].at(b) = m_wv_tot.at(b) / total_weight;\n m_vv[0].at(b) = m_vv_tot.at(b) / total_weight;\n m_ow[0].at(b) = m_ow_tot.at(b) / total_weight;\n m_wo[0].at(b) = m_wo_tot.at(b) / total_weight;\n m_vo[0].at(b) = m_vo_tot.at(b) / total_weight;\n m_ov[0].at(b) = m_ov_tot.at(b) / total_weight;\n m_oo[0].at(b) = m_oo_tot.at(b) / total_weight;\n // print out to debug\n //for (int i = 0; i < m_ow.at(b).size(); i++) {\n // std::cout << m_ow.at(b).at(i) << \" \";\n //}\n //std::cout << std::endl;\n }\n }\n}\n\n/////////////////////////////////////////////////////////////////////////////////////////////////\n/// \\brief Function that resets the matrices\n///\n/////////////////////////////////////////////////////////////////////////////////////////////////\nvoid cqmc::engine::LMBlockerMatData::reset(const int nv, const int nblock, const int nou) {\n \n m_nou = nou;\n\n // get block information\n cqmc::engine::brlm_get_block_info(nv, nblock, m_block_beg, m_block_end, m_block_len);\n\n // get the maximum number of threads \n int NumThreads = omp_get_max_threads();\n \n m_ww.assign(NumThreads, 0.0);\n\n m_wv.clear();\n m_wv.resize(NumThreads);\n for (int ip = 0; ip < NumThreads; ip++) {\n for (int i = 0; i < this->nb(); i++) {\n m_wv[ip].push_back(formic::ColVec(this->bl(i), 0.0));\n }\n }\n\n m_vw.clear();\n m_vw.resize(NumThreads);\n for (int ip = 0; ip < NumThreads; ip++) {\n for (int i = 0; i < this->nb(); i++) {\n m_vw[ip].push_back(formic::ColVec(this->bl(i), 0.0));\n }\n }\n\n m_vv.clear();\n m_vv.resize(NumThreads);\n for (int ip = 0; ip < NumThreads; ip++) {\n for (int i = 0; i < this->nb(); i++) {\n m_vv[ip].push_back(formic::Matrix(this->bl(i), this->bl(i), 0.0));\n }\n }\n\n m_wo.clear();\n m_wo.resize(NumThreads);\n for (int ip = 0; ip < NumThreads; ip++) {\n for (int i = 0; i < this->nb(); i++) {\n m_wo[ip].push_back(formic::ColVec(m_nou, 0.0));\n }\n }\n\n m_ow.clear();\n m_ow.resize(NumThreads);\n for (int ip = 0; ip < NumThreads; ip++) {\n for (int i = 0; i < this->nb(); i++) {\n m_ow[ip].push_back(formic::ColVec(m_nou, 0.0));\n }\n }\n\n m_vo.clear();\n m_vo.resize(NumThreads);\n for (int ip = 0; ip < NumThreads; ip++) {\n for (int i = 0; i < this->nb(); i++) {\n m_vo[ip].push_back(formic::Matrix(this->bl(i), (this->nb()-1)*m_nou, 0.0));\n }\n }\n\n m_ov.clear();\n m_ov.resize(NumThreads);\n for (int ip = 0; ip < NumThreads; ip++) {\n for (int i = 0; i < this->nb(); i++) {\n m_ov[ip].push_back(formic::Matrix((this->nb()-1)*m_nou, this->bl(i), 0.0));\n }\n }\n\n m_oo.clear();\n m_oo.resize(NumThreads);\n for (int ip = 0; ip < NumThreads; ip++) {\n for (int i = 0; i < this->nb(); i++) {\n m_oo[ip].push_back(formic::Matrix(m_nou, m_nou, 0.0));\n }\n }\n\n m_boulr.clear();\n m_bourr.clear();\n m_boulr.resize(NumThreads);\n m_bourr.resize(NumThreads);\n for (int ip = 0; ip < NumThreads; ip++) {\n for (int i = 0; i < this->nb(); i++) {\n m_boulr[ip].push_back(formic::ColVec(m_nou, 0.0));\n m_bourr[ip].push_back(formic::ColVec(m_nou, 0.0));\n }\n }\n}\n\n", "meta": {"hexsha": "c28bdeaee153bf73d131a6d73fbec0ac8c843544", "size": 15715, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/formic/utils/lmyengine/block_mat.cpp", "max_stars_repo_name": "eugeneswalker/qmcpack", "max_stars_repo_head_hexsha": "352ff27f163bb92e0c232c48bec8ae7951ed9d8c", "max_stars_repo_licenses": ["NCSA"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/formic/utils/lmyengine/block_mat.cpp", "max_issues_repo_name": "eugeneswalker/qmcpack", "max_issues_repo_head_hexsha": "352ff27f163bb92e0c232c48bec8ae7951ed9d8c", "max_issues_repo_licenses": ["NCSA"], "max_issues_count": 11.0, "max_issues_repo_issues_event_min_datetime": "2020-05-09T20:57:21.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-10T00:00:17.000Z", "max_forks_repo_path": "src/formic/utils/lmyengine/block_mat.cpp", "max_forks_repo_name": "williamfgc/qmcpack", "max_forks_repo_head_hexsha": "732b473841e7823a21ab55ff397eed059f0f2e96", "max_forks_repo_licenses": ["NCSA"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.1630434783, "max_line_length": 160, "alphanum_fraction": 0.5101495387, "num_tokens": 5159, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.43782349911420193, "lm_q1q2_score": 0.24444867830982886}} {"text": "/*!\n * @file swp_surfvol_cubic_primary.cpp\n * @author Robert Patterson\n *\n Project: sweepc (population balance solver)\n Sourceforge: http://sourceforge.net/projects/mopssuite\n \n Copyright (C) 2013 Robert I A Patterson\n\n File purpose:\n Implementation of a particle the approximates cuboidal crystals by\n storing their surface area and volume\n @brief Implementation of cuboidal crystals described by volume and surface area.\n\n Licence:\n This file is part of \"sweepc\".\n\n sweepc is free software; you can redistribute it and/or\n modify it under the terms of the GNU General Public License\n as published by the Free Software Foundation; either version 2\n of the License, or (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program; if not, write to the Free Software\n Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.\n\n Contact:\n Prof Markus Kraft\n Dept of Chemical Engineering\n University of Cambridge\n New Museums Site\n Pembroke Street\n Cambridge\n CB2 3RA\n UK\n\n Email: mk306@cam.ac.uk\n Website: http://como.cheng.cam.ac.uk\n*/\n\n#include \"swp_surfvol_cubic_primary.h\"\n\n#include \"swp_aggmodel_type.h\"\n#include \"swp_model_factory.h\"\n\n#include \n#include \n\nusing namespace Sweep;\nusing namespace Sweep::AggModels;\n\n// CONSTRUCTORS AND DESTRUCTORS.\n\n// Default constructor (protected).\nSurfVolCubicPrimary::SurfVolCubicPrimary(void)\n{\n}\n\n// Initialising constructor.\nSurfVolCubicPrimary::SurfVolCubicPrimary(double time, const Sweep::ParticleModel &model)\n: Primary(time, model)\n{\n}\n\n// Copy constructor.\nSurfVolCubicPrimary::SurfVolCubicPrimary(const SurfVolCubicPrimary ©)\n{\n *this = copy;\n}\n\n// Stream-reading constructor.\nSurfVolCubicPrimary::SurfVolCubicPrimary(std::istream &in, const Sweep::ParticleModel &model)\n{\n Deserialize(in, model);\n}\n\n// Default destructor.\nSurfVolCubicPrimary::~SurfVolCubicPrimary()\n{\n releaseMem();\n}\n\n\n// OPERATOR OVERLOADS.\n\n// Assignment operator (Primary RHS).\nSurfVolCubicPrimary &SurfVolCubicPrimary::operator=(const Primary &rhs)\n{\n operator=(dynamic_cast(rhs));\n return *this;\n}\n\n// AGGREGATION MODEL.\n\n// Returns the aggregation model which this primary describes.\nAggModels::AggModelType SurfVolCubicPrimary::AggID(void) const {return AggModels::SurfVolCubic_ID;}\n\n// BASIC DERIVED PARTICLE PROPERTIES.\n\n// Calculates the derived properties from the unique properties. This\n// function is broadly similar to the version in the spherical Primary\n// class except that it uses the surface-volume model. Therefore the\n// surface area is not altered and the collision diameter is calculated\n// using the arithmetic mean function.\n//\n// Mobility diameter:\n// This calculation is based on the work of Rogak et al., 1993 Aer. Sci.\n// Tech. 18:25-47, who give the calculation of dmob in the FM, SF and\n// transition regime.\nvoid SurfVolCubicPrimary::UpdateCache(void)\n{\n // Store the old surface area, this value may be 0 if the particle is in\n // the process of initialisation\n double s = m_surf;\n\n // Pretend that the primary is spherical and set the cache\n // accordingly.\n Primary::UpdateCache();\n\n m_surf = std::max(s, m_surf);\n\n \n // Calculate diameters.\n m_dcol = (m_diam + sqrt(m_surf / PI)) * 0.5;\n\n // Calculate mobility diameter\n m_dmob = PP_Diameter();\n if (false) {\n // SF regime mobility diameter\n m_dmob *= 0.9 * sqrt(m_pmodel->GetFractDim() / (m_pmodel->GetFractDim() + 2));\n m_dmob *= pow(PP_Count(), (1.0/m_pmodel->GetFractDim()));\n } else {\n // FM regime mobility diameter\n m_dmob *= sqrt(0.802*(PP_Count()-1) + 1);\n }\n\n if (m_dmob < m_diam) m_dmob = m_diam;\n}\n\n// Returns the number of primary particles if the aggregate is assumed\n// to consist of mono-sized primaries.\nunsigned int SurfVolCubicPrimary::PP_Count(void) const\n{\n // Note the minimum number of primary particles must be 1.\n return std::max(1u, (unsigned int)((m_surf * m_surf * m_surf) /\n (36.0 * PI * m_vol * m_vol)));\n}\n\n// Returns the primary particle diameter if the aggregate is assumed\n// to consist of mono-sized primaries.\ndouble SurfVolCubicPrimary::PP_Diameter(void) const\n{\n // This should always be <= equiv. sphere diameter.\n return 6.0 * m_vol / m_surf;\n}\n\n\n// OPERATIONS.\n\n// Adjusts the primary with the given composition and \n// tracker values changes n times. If the particle cannot be adjust\n// n times, then this function returns the number of times\n// it was adjusted.\nunsigned int SurfVolCubicPrimary::Adjust(const fvector &dcomp, const fvector &dvalues, rng_type &rng,\n unsigned int n)\n{\n // Store initial surface and volume\n double surfOld = m_surf;\n double volOld = m_vol;\n\n // Adjust the particle assuming that it is spherical.\n n = Primary::Adjust(dcomp, dvalues, rng, n);\n\n\n // Calculate change in volume.\n double dvol = 0.0;\n for (unsigned int i=0; i!=dcomp.size(); ++i) {\n dvol += dcomp[i] * m_pmodel->Components(i)->MolWt() / \n m_pmodel->Components(i)->Density();\n }\n dvol *= (double)n / NA;\n\n // Calculate change in surface area.\n double invRadius = 0.0;\n if (dvol > 0.0) {\n // Inverse growth radius.\n invRadius = sqrt(4.0 * PI / surfOld);\n } else {\n // Inverse oxidation radius. \n invRadius = surfOld / (3.0 * volOld);\n }\n\n // Save new surface area.\n double s = surfOld + (2.0 * dvol * invRadius);\n\n // Set correct surface area, which was incorrectly set by\n // Primary::Adjust.\n m_surf = s;\n\n // This has a knock-on affect of changing the collision diameter.\n // Note, we can avoid recalling UpdateCache() here, because only\n // a couple of cached values will have changed.\n m_dcol = (m_diam + sqrt(m_surf / PI)) * 0.5;\n m_dmob = m_dcol;\n\n return n;\n}\n\n/*!\n * Combines this primary with another.\n *\n * \\param[in] rhs Particle to add to current instance\n * \\param[in,out] rng Random number generator\n *\n * \\return Reference to the current instance after rhs has been added\n */\nSurfVolCubicPrimary &SurfVolCubicPrimary::Coagulate(const Primary &rhs, rng_type &rng)\n\n{\n // Store the resultant surface area.\n double s = m_surf + rhs.SurfaceArea();\n\n // Perform the coagulation.\n Primary::Coagulate(rhs, rng);\n\n // One sixth of the surface area is assumed to be lost as a result of two faces, one\n // from each incoming particle covering each other. More sophisticated formulae\n // could be implemented here.\n m_surf = s * 0.8333333333333333333;\n\n // This has a knock-on affect of changing the collision diameter.\n // Note, we can avoid recalling UpdateCache() here, because only\n // a couple of cached values will have changed.\n m_dcol = (m_diam + sqrt(m_surf / PI)) * 0.5;\n m_dmob = m_dcol;\n\n return *this;\n}\n\n/*!\n * Combines this primary with another.\n *\n * \\param[in] rhs Particle to add to current instance\n * \\param[in,out] rng Random number generator\n *\n * \\return Reference to the current instance after rhs has been added\n */\nSurfVolCubicPrimary &SurfVolCubicPrimary::Fragment(const Primary &rhs, rng_type &rng)\n\n{\n // Store the resultant surface area.\n double s = m_surf + rhs.SurfaceArea();\n\n // Perform the coagulation.\n Primary::Fragment(rhs, rng);\n\n // One sixth of the surface area is assumed to be lost as a result of two faces, one\n // from each incoming particle covering each other. More sophisticated formulae\n // could be implemented here.\n m_surf = s * 0.8333333333333333333;\n\n // This has a knock-on affect of changing the collision diameter.\n // Note, we can avoid recalling UpdateCache() here, because only\n // a couple of cached values will have changed.\n m_dcol = (m_diam + sqrt(m_surf / PI)) * 0.5;\n m_dmob = m_dcol;\n\n return *this;\n}\n\n// READ/WRITE/COPY.\n\n//! Returns a copy of the model data.\nSurfVolCubicPrimary *const SurfVolCubicPrimary::Clone(void) const\n{\n return new SurfVolCubicPrimary(*this);\n}\n\n// Writes the object to a binary stream.\nvoid SurfVolCubicPrimary::Serialize(std::ostream &out) const\n{\n if (out.good()) {\n // Output base class.\n Primary::Serialize(out);\n } else {\n throw std::invalid_argument(\"Output stream not ready \"\n \"(Sweep, SurfVolCubicPrimary::Serialize).\");\n }\n}\n\n// Reads the object from a binary stream.\nvoid SurfVolCubicPrimary::Deserialize(std::istream &in, const Sweep::ParticleModel &model)\n{\n if (in.good()) {\n // Read base class.\n Primary::Deserialize(in, model);\n } else {\n throw std::invalid_argument(\"Input stream not ready \"\n \"(Sweep, SurfVolCubicPrimary::Deserialize).\");\n }\n}\n", "meta": {"hexsha": "32b9feb24bc440649f537695a59b1afdef8901f7", "size": 9308, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/sweepc/source/swp_surfvol_cubic_primary.cpp", "max_stars_repo_name": "sm453/MOpS", "max_stars_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2020-09-08T14:06:33.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-04T07:52:19.000Z", "max_issues_repo_path": "src/sweepc/source/swp_surfvol_cubic_primary.cpp", "max_issues_repo_name": "sm453/MOpS", "max_issues_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/sweepc/source/swp_surfvol_cubic_primary.cpp", "max_forks_repo_name": "sm453/MOpS", "max_forks_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2021-11-15T05:18:26.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-01T13:51:20.000Z", "avg_line_length": 30.319218241, "max_line_length": 101, "alphanum_fraction": 0.6773743017, "num_tokens": 2348, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5583269943353744, "lm_q2_score": 0.43782349911420193, "lm_q1q2_score": 0.2444486783098288}} {"text": "#include \n#include \n#include /* srand, rand */\n#include \n\n#include \n#include \n\n#include \"kmergraphwithcoverage.h\"\n#include \"localPRG.h\"\n\nusing namespace prg;\n\nvoid KmerGraphWithCoverage::set_exp_depth_covg(const uint32_t edp)\n{\n const bool exp_depth_covg_parameter_is_valid = edp > 0;\n if (!exp_depth_covg_parameter_is_valid) {\n fatal_error(\n \"Error setting exp_depth_covg: exp_depth_covg is invalid, must be > 0, is \",\n edp);\n }\n exp_depth_covg = edp;\n}\n\nvoid KmerGraphWithCoverage::set_binomial_parameter_p(const float e_rate)\n{\n BOOST_LOG_TRIVIAL(debug) << \"Set p in kmergraph\";\n\n const bool valid_parameters_to_set_p\n = (kmer_prg->k != 0) && (0 < e_rate and e_rate < 1);\n if (!valid_parameters_to_set_p) {\n fatal_error(\"Error setting binomial parameter p, invalid parameters: \",\n \"kmer_prg->k = \", kmer_prg->k, \", e_rate = \", e_rate);\n }\n\n binomial_parameter_p = 1 / exp(e_rate * kmer_prg->k);\n}\n\nvoid KmerGraphWithCoverage::increment_covg(\n uint32_t node_id, pandora::Strand strand, uint32_t sample_id)\n{\n const bool sample_is_valid\n = this->node_index_to_sample_coverage[node_id].size() > sample_id;\n if (!sample_is_valid) {\n fatal_error(\n \"Error incrementing coverage: sample_id is invalid (\", sample_id, \")\");\n }\n\n // get a pointer to the value we want to increment\n uint16_t* coverage_ptr = nullptr;\n if (strand == pandora::Strand::Forward) {\n coverage_ptr = &(this->node_index_to_sample_coverage[node_id][sample_id].first);\n } else {\n coverage_ptr\n = &(this->node_index_to_sample_coverage[node_id][sample_id].second);\n }\n\n const bool safe_to_increase_covg { (*coverage_ptr) < UINT16_MAX };\n if (safe_to_increase_covg) {\n ++(*coverage_ptr);\n }\n}\n\nuint32_t KmerGraphWithCoverage::get_covg(\n uint32_t node_id, pandora::Strand strand, uint32_t sample_id) const\n{\n\n if (this->node_index_to_sample_coverage[node_id].size() <= sample_id)\n return 0;\n\n if (strand == pandora::Strand::Forward) {\n return (uint32_t)(\n this->node_index_to_sample_coverage[node_id][sample_id].first);\n } else {\n return (uint32_t)(\n this->node_index_to_sample_coverage[node_id][sample_id].second);\n }\n}\n\nvoid KmerGraphWithCoverage::set_covg(\n uint32_t node_id, uint16_t value, pandora::Strand strand, uint32_t sample_id)\n{\n const bool sample_is_valid\n = this->node_index_to_sample_coverage[node_id].size() > sample_id;\n if (!sample_is_valid) {\n fatal_error(\"Error setting coverage: sample_id is invalid (\", sample_id, \")\");\n }\n\n if (strand == pandora::Strand::Forward) {\n this->node_index_to_sample_coverage[node_id][sample_id].first = value;\n } else {\n this->node_index_to_sample_coverage[node_id][sample_id].second = value;\n }\n}\n\nvoid KmerGraphWithCoverage::set_negative_binomial_parameters(\n const float& nbin_prob, const float& nb_fail)\n{\n if (nbin_prob == 0 and nb_fail == 0)\n return;\n\n const bool negative_binomial_parameters_were_previously_set\n = (negative_binomial_parameter_p > 0 and negative_binomial_parameter_p < 1)\n && (negative_binomial_parameter_r > 0);\n if (!(negative_binomial_parameters_were_previously_set)) {\n fatal_error(\"Error setting negative_binomial_parameters: \"\n \"negative_binomial_parameter_p (\",\n negative_binomial_parameter_p, \")\", \" or negative_binomial_parameter_r (\",\n negative_binomial_parameter_r, \") \", \"were not correctly set\");\n }\n\n negative_binomial_parameter_p += nbin_prob;\n negative_binomial_parameter_r += nb_fail;\n}\n\nfloat KmerGraphWithCoverage::nbin_prob(uint32_t node_id, const uint32_t& sample_id)\n{\n auto k = this->get_forward_covg(node_id, sample_id)\n + this->get_reverse_covg(node_id, sample_id);\n float return_prob\n = log(pdf(boost::math::negative_binomial(\n negative_binomial_parameter_r, negative_binomial_parameter_p),\n k));\n return_prob = std::max(return_prob, std::numeric_limits::lowest() / 1000);\n return return_prob;\n}\n\nfloat KmerGraphWithCoverage::lin_prob(uint32_t node_id, const uint32_t& sample_id)\n{\n const bool reads_were_mapped_to_this_kmer_graph = num_reads != 0;\n if (!reads_were_mapped_to_this_kmer_graph) {\n fatal_error(\n \"Impossible to compute lin_prob, no reads were mapped to this kmer graph\");\n }\n auto k = this->get_forward_covg(node_id, sample_id)\n + this->get_reverse_covg(node_id, sample_id);\n return log(float(k) / num_reads);\n}\n\nfloat KmerGraphWithCoverage::bin_prob(uint32_t node_id, const uint32_t& sample_id)\n{\n const bool reads_were_mapped_to_this_kmer_graph = num_reads != 0;\n if (!reads_were_mapped_to_this_kmer_graph) {\n fatal_error(\n \"Impossible to compute bin_prob, no reads were mapped to this kmer graph\");\n }\n return bin_prob(node_id, num_reads, sample_id);\n}\n\nfloat KmerGraphWithCoverage::bin_prob(\n const uint32_t& node_id, const uint32_t& num, const uint32_t& sample_id)\n{\n const bool binomial_parameter_p_is_set_correctly = binomial_parameter_p != 1;\n if (!binomial_parameter_p_is_set_correctly) {\n fatal_error(\"Error when computing bin_prob: binomial_parameter_p (\",\n binomial_parameter_p, \") is not correctly set\");\n }\n\n const bool node_exists = node_id < kmer_prg->nodes.size();\n if (!node_exists) {\n fatal_error(\"Error when computing bin_prob: attempt to access inexistent node \",\n node_id);\n }\n\n uint32_t sum_coverages = this->get_forward_covg(node_id, sample_id)\n + this->get_reverse_covg(node_id, sample_id);\n\n float return_prob;\n if (node_id == (*(kmer_prg->sorted_nodes.begin()))->id\n or node_id == (*(kmer_prg->sorted_nodes.rbegin()))->id) {\n return_prob = 0; // is really undefined\n } else if (sum_coverages > num) {\n // under model assumptions this can't happen, but it inevitably will, so bodge\n return_prob\n = lognchoosek2(sum_coverages, this->get_forward_covg(node_id, sample_id),\n this->get_reverse_covg(node_id, sample_id))\n + sum_coverages * log(binomial_parameter_p / 2);\n // note this may give disadvantage to repeat kmers\n } else {\n return_prob = lognchoosek2(num, this->get_forward_covg(node_id, sample_id),\n this->get_reverse_covg(node_id, sample_id))\n + sum_coverages * log(binomial_parameter_p / 2)\n + (num - sum_coverages) * log(1 - binomial_parameter_p);\n }\n return return_prob;\n}\n\nfloat KmerGraphWithCoverage::get_prob(\n const std::string& prob_model, const uint32_t& node_id, const uint32_t& sample_id)\n{\n if (prob_model == \"nbin\") {\n // is there no parameter check here?\n return nbin_prob(node_id, sample_id);\n } else if (prob_model == \"bin\") {\n const bool binomial_parameters_are_ok\n = (binomial_parameter_p < 1) && (num_reads > 0);\n if (!binomial_parameters_are_ok) {\n fatal_error(\"Error when computing kmer prob: binomial parameters are not \"\n \"ok (binomial_parameter_p = \",\n binomial_parameter_p, \", \", \"num_reads = \", num_reads);\n }\n return bin_prob(node_id, sample_id);\n } else if (prob_model == \"lin\") {\n // is there no parameter check here?\n return lin_prob(node_id, sample_id);\n } else {\n fatal_error(\"Invalid probability model for kmer coverage distribution: \",\n prob_model, \". Should be nbin, bin or lin\");\n }\n}\n\nbool KmerGraphWithCoverage::coverage_is_zeroes(const uint32_t& sample_id)\n{\n bool all_zero = true;\n for (const auto& node_ptr : kmer_prg->nodes) {\n const auto covg { this->get_forward_covg(node_ptr->id, sample_id)\n + this->get_reverse_covg(node_ptr->id, sample_id) };\n if (covg > 0) {\n BOOST_LOG_TRIVIAL(debug) << \"Found non-zero coverage in kmer graph\";\n all_zero = false;\n break;\n }\n }\n if (all_zero) {\n BOOST_LOG_TRIVIAL(debug) << \"ALL ZEROES in kmer graph coverages\";\n }\n return all_zero;\n}\n\nfloat KmerGraphWithCoverage::find_max_path(std::vector& maxpath,\n const std::string& prob_model, const uint32_t& max_num_kmers_to_average,\n const uint32_t& sample_id)\n{\n // TODO: FIX THIS INNEFICIENCY I INTRODUCED\n const std::vector sorted_nodes(\n this->kmer_prg->sorted_nodes.begin(), this->kmer_prg->sorted_nodes.end());\n this->kmer_prg->check();\n\n // also check not all 0 covgs\n auto coverages_all_zero = coverage_is_zeroes(sample_id);\n if (coverages_all_zero)\n return std::numeric_limits::lowest();\n\n // create vectors to hold the intermediate values\n std::vector max_sum_of_log_probs_from_node(sorted_nodes.size(), 0);\n std::vector length_of_maxpath_from_node(sorted_nodes.size(), 0);\n std::vector prev_node_along_maxpath(\n sorted_nodes.size(), sorted_nodes.size() - 1);\n float max_mean;\n int max_length;\n const float tolerance = 0.000001;\n\n for (uint32_t j = sorted_nodes.size() - 1; j != 0; --j) {\n max_mean = std::numeric_limits::lowest();\n max_length = 0; // tie break with longest kmer path\n const auto& current_node = sorted_nodes[j - 1];\n for (uint32_t i = 0; i != current_node->out_nodes.size(); ++i) {\n const auto& considered_outnode = current_node->out_nodes[i].lock();\n const bool is_terminus_and_most_likely\n = considered_outnode->id == sorted_nodes.back()->id\n and thresh > max_mean + tolerance;\n const bool avg_log_likelihood_is_most_likely\n = max_sum_of_log_probs_from_node[considered_outnode->id]\n / length_of_maxpath_from_node[considered_outnode->id]\n > max_mean + tolerance;\n const bool avg_log_likelihood_is_close_to_most_likely = max_mean\n - max_sum_of_log_probs_from_node[considered_outnode->id]\n / length_of_maxpath_from_node[considered_outnode->id]\n <= tolerance;\n const bool is_longer_path\n = length_of_maxpath_from_node[considered_outnode->id]\n > (uint)max_length;\n\n if (is_terminus_and_most_likely or avg_log_likelihood_is_most_likely\n or (avg_log_likelihood_is_close_to_most_likely and is_longer_path)) {\n max_sum_of_log_probs_from_node[current_node->id]\n = get_prob(prob_model, current_node->id, sample_id)\n + max_sum_of_log_probs_from_node[considered_outnode->id];\n length_of_maxpath_from_node[current_node->id]\n = 1 + length_of_maxpath_from_node[considered_outnode->id];\n prev_node_along_maxpath[current_node->id] = considered_outnode->id;\n\n if (length_of_maxpath_from_node[current_node->id]\n > max_num_kmers_to_average) {\n uint32_t prev_node = prev_node_along_maxpath[current_node->id];\n for (uint step = 0; step < max_num_kmers_to_average; step++) {\n prev_node = prev_node_along_maxpath[prev_node];\n }\n max_sum_of_log_probs_from_node[current_node->id]\n -= get_prob(prob_model, sorted_nodes[prev_node]->id, sample_id);\n length_of_maxpath_from_node[current_node->id] -= 1;\n\n // this remains as an assert, as it is a code check\n // Note: I think we might even be able to remove this\n assert(length_of_maxpath_from_node[current_node->id]\n == max_num_kmers_to_average);\n }\n\n if (considered_outnode->id != sorted_nodes.back()->id) {\n max_mean = max_sum_of_log_probs_from_node[considered_outnode->id]\n / length_of_maxpath_from_node[considered_outnode->id];\n max_length = length_of_maxpath_from_node[considered_outnode->id];\n } else {\n max_mean = thresh;\n }\n }\n }\n }\n\n // extract path\n uint32_t prev_node = prev_node_along_maxpath[sorted_nodes[0]->id];\n while (prev_node < sorted_nodes.size() - 1) {\n maxpath.push_back(this->kmer_prg->nodes[prev_node]);\n prev_node = prev_node_along_maxpath[prev_node];\n\n if (maxpath.size() > 1000000) {\n fatal_error(\"I think I've found an infinite loop - is \"\n \"something wrong with this kmergraph?\");\n }\n }\n\n const bool path_was_found_through_the_kmer_PRG = length_of_maxpath_from_node[0] > 0;\n if (!path_was_found_through_the_kmer_PRG) {\n fatal_error(\"Error when finding max path: found no path through kmer prg\");\n }\n\n return prob_path(maxpath, sample_id, prob_model);\n}\n\nstd::vector> KmerGraphWithCoverage::get_random_paths(\n uint32_t num_paths)\n{\n // find a random path through kmergraph picking ~uniformly from the outnodes at each\n // point\n std::vector> rpaths;\n std::vector rpath;\n uint32_t i;\n\n time_t now;\n now = time(nullptr);\n srand((unsigned int)now);\n\n if (!kmer_prg->nodes.empty()) {\n for (uint32_t j = 0; j != num_paths; ++j) {\n i = rand() % kmer_prg->nodes[0]->out_nodes.size();\n rpath.push_back(kmer_prg->nodes[0]->out_nodes[i].lock());\n while (rpath.back() != kmer_prg->nodes[kmer_prg->nodes.size() - 1]) {\n if (rpath.back()->out_nodes.size() == 1) {\n rpath.push_back(rpath.back()->out_nodes[0].lock());\n } else {\n i = rand() % rpath.back()->out_nodes.size();\n rpath.push_back(rpath.back()->out_nodes[i].lock());\n }\n }\n rpath.pop_back();\n rpaths.push_back(rpath);\n rpath.clear();\n }\n }\n return rpaths;\n}\n\nfloat KmerGraphWithCoverage::prob_path(const std::vector& kpath,\n const uint32_t& sample_id, const std::string& prob_model)\n{\n float return_prob_path = 0;\n for (uint32_t i = 0; i != kpath.size(); ++i) {\n return_prob_path += get_prob(prob_model, kpath[i]->id, sample_id);\n }\n uint32_t len = kpath.size();\n if (kpath[0]->path.length() == 0) {\n len -= 1;\n }\n if (kpath.back()->path.length() == 0) {\n len -= 1;\n }\n if (len == 0) {\n len = 1;\n }\n return return_prob_path / len;\n}\n\nvoid KmerGraphWithCoverage::save_covg_dist(const std::string& filepath)\n{\n std::ofstream handle;\n handle.open(filepath);\n\n for (const auto& kmer_node_ptr : kmer_prg->nodes) {\n const KmerNode& kmer_node = *kmer_node_ptr;\n\n uint32_t sample_id = 0;\n for (const auto& sample_coverage :\n node_index_to_sample_coverage[kmer_node.id]) {\n handle << kmer_node.id << \" \" << sample_id << \" \" << sample_coverage.first\n << \" \" << sample_coverage.second;\n\n sample_id++;\n }\n }\n handle.close();\n}\n\n// save the KmerGraph as gfa\n// TODO: THIS SHOULD BE RECODED, WE ARE DUPLICATING CODE HERE (SEE KmerGraph::save())!!!\nvoid KmerGraphWithCoverage::save(\n const fs::path& filepath, const std::shared_ptr localprg) const\n{\n uint32_t sample_id = 0;\n\n fs::ofstream handle(filepath);\n if (handle.is_open()) {\n handle << \"H\\tVN:Z:1.0\\tbn:Z:--linear --singlearr\" << std::endl;\n for (const auto& c : kmer_prg->nodes) {\n handle << \"S\\t\" << c->id << \"\\t\";\n\n if (localprg != nullptr) {\n handle << localprg->string_along_path(c->path);\n } else {\n handle << c->path;\n }\n\n handle << \"\\tFC:i:\" << this->get_forward_covg(c->id, sample_id)\n << \"\\tRC:i:\" << this->get_reverse_covg(c->id, sample_id)\n << std::endl;\n\n for (uint32_t j = 0; j < c->out_nodes.size(); ++j) {\n handle << \"L\\t\" << c->id << \"\\t+\\t\" << c->out_nodes[j].lock()->id\n << \"\\t+\\t0M\" << std::endl;\n }\n }\n handle.close();\n } else {\n fatal_error(\"Unable to open kmergraph file \", filepath);\n }\n}\n\n// TODO: THIS SHOULD BE RECODED, WE ARE DUPLICATING CODE HERE (SEE KmerGraph::load())!!!\n// TODO: remove this method?\nvoid KmerGraphWithCoverage::load(const std::string& filepath)\n{\n // TODO: this might be dangerous, recode this?\n auto kmer_prg = const_cast(this->kmer_prg);\n kmer_prg->clear();\n uint32_t sample_id = 0;\n\n std::string line;\n std::vector split_line;\n std::stringstream ss;\n uint32_t id = 0, from, to;\n uint16_t covg;\n prg::Path p;\n uint32_t num_nodes = 0;\n\n std::ifstream myfile(filepath);\n if (myfile.is_open()) {\n\n while (getline(myfile, line).good()) {\n if (line[0] == 'S') {\n split_line = split(line, \"\\t\");\n\n const bool line_is_consistent = split_line.size() >= 4;\n if (!line_is_consistent) {\n fatal_error(\"Error reading GFA. Offending line: \", line);\n }\n\n id = std::stoi(split_line[1]);\n num_nodes = std::max(num_nodes, id);\n }\n }\n myfile.clear();\n myfile.seekg(0, myfile.beg);\n kmer_prg->nodes.reserve(num_nodes);\n std::vector outnode_counts(num_nodes + 1, 0),\n innode_counts(num_nodes + 1, 0);\n\n while (getline(myfile, line).good()) {\n if (line[0] == 'S') {\n split_line = split(line, \"\\t\");\n\n const bool line_is_consistent = split_line.size() >= 4;\n if (!line_is_consistent) {\n fatal_error(\"Error reading GFA. Offending line: \", line);\n }\n\n id = stoi(split_line[1]);\n ss << split_line[2];\n char c = ss.peek();\n\n if (!isdigit(c)) {\n fatal_error(\"Error reading GFA: cannot read in this sort of \"\n \"kmergraph GFA as it \",\n \"does not label nodes with their PRG path. \",\n \"Offending line: \", line);\n }\n\n ss >> p;\n ss.clear();\n // add_node(p);\n KmerNodePtr n = std::make_shared(id, p);\n\n const bool id_is_consistent = (id == kmer_prg->nodes.size()\n or num_nodes - id == kmer_prg->nodes.size());\n if (!id_is_consistent) {\n fatal_error(\"Error reading GFA: node ID is inconsistent.\",\n \"id = \", id, \", \", \"nodes.size() = \", kmer_prg->nodes.size(),\n \", \", \"num_nodes = \", num_nodes);\n }\n\n kmer_prg->nodes.push_back(n);\n kmer_prg->sorted_nodes.insert(n);\n if (kmer_prg->k == 0 and p.length() > 0) {\n kmer_prg->k = p.length();\n }\n covg = (uint16_t)(stoul(split(split_line[3], \"FC:i:\")[0]));\n this->set_forward_covg(n->id, covg, sample_id);\n covg = (uint16_t)(stoul(split(split_line[4], \"RC:i:\")[0]));\n this->set_reverse_covg(n->id, covg, sample_id);\n if (split_line.size() >= 6) {\n n->num_AT = std::stoi(split_line[5]);\n }\n } else if (line[0] == 'L') {\n split_line = split(line, \"\\t\");\n\n const bool line_is_consistent = split_line.size() >= 5;\n if (!line_is_consistent) {\n fatal_error(\"Error reading GFA. Offending line: \", line);\n }\n\n const int from_node = stoi(split_line[1]);\n const int to_node = stoi(split_line[3]);\n const bool from_node_in_range = from_node < (int)outnode_counts.size();\n const bool to_node_in_range = to_node < (int)innode_counts.size();\n if (!from_node_in_range) {\n fatal_error(\n \"Error reading GFA: from_node out of range: \", from_node,\n \">=\", outnode_counts.size(), \". Offending line: \", line);\n }\n if (!to_node_in_range) {\n fatal_error(\"Error reading GFA: to_node out of range: \", to_node,\n \">=\", innode_counts.size(), \". Offending line: \", line);\n }\n\n outnode_counts[stoi(split_line[1])] += 1;\n innode_counts[stoi(split_line[3])] += 1;\n }\n }\n\n if (id == 0) {\n reverse(kmer_prg->nodes.begin(), kmer_prg->nodes.end());\n }\n\n id = 0;\n for (const auto& n : kmer_prg->nodes) {\n const bool id_is_consistent = (kmer_prg->nodes[id]->id == id)\n && (n->id < outnode_counts.size()) && (n->id < innode_counts.size());\n if (!id_is_consistent) {\n fatal_error(\"Error reading GFA: node: \", n,\n \" has inconsistent id, should be \", id);\n }\n id++;\n n->out_nodes.reserve(outnode_counts[n->id]);\n n->in_nodes.reserve(innode_counts[n->id]);\n }\n\n myfile.clear();\n myfile.seekg(0, myfile.beg);\n\n while (getline(myfile, line).good()) {\n if (line[0] == 'L') {\n split_line = split(line, \"\\t\");\n\n const bool line_is_consistent = split_line.size() >= 5;\n if (!line_is_consistent) {\n fatal_error(\"Error reading GFA. Offending line: \", line);\n }\n\n if (split_line[2] == split_line[4]) {\n from = std::stoi(split_line[1]);\n to = std::stoi(split_line[3]);\n } else {\n // never happens\n from = std::stoi(split_line[3]);\n to = std::stoi(split_line[1]);\n }\n kmer_prg->add_edge(kmer_prg->nodes[from], kmer_prg->nodes[to]);\n }\n }\n } else {\n fatal_error(\"Error reading GFA: unable to open kmergraph file: \", filepath);\n }\n}", "meta": {"hexsha": "b09e0568cf51d6928bf227af6eb81ba7f78d0f93", "size": 22761, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/kmergraphwithcoverage.cpp", "max_stars_repo_name": "rmcolq/pandora", "max_stars_repo_head_hexsha": "93c541017a5c1ea45f999f5eabdb58be061711e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 77.0, "max_stars_repo_stars_event_min_datetime": "2018-07-06T00:13:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-19T03:58:20.000Z", "max_issues_repo_path": "src/kmergraphwithcoverage.cpp", "max_issues_repo_name": "rmcolq/pandora", "max_issues_repo_head_hexsha": "93c541017a5c1ea45f999f5eabdb58be061711e0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 215.0, "max_issues_repo_issues_event_min_datetime": "2018-07-09T16:41:55.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T22:44:36.000Z", "max_forks_repo_path": "src/kmergraphwithcoverage.cpp", "max_forks_repo_name": "mbhall88/pandora", "max_forks_repo_head_hexsha": "16de553795267a55af223748b24510a904ca5ef9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 16.0, "max_forks_repo_forks_event_min_datetime": "2018-07-06T13:09:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-05T15:01:09.000Z", "avg_line_length": 38.5126903553, "max_line_length": 88, "alphanum_fraction": 0.5884627213, "num_tokens": 5617, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.24432941467075914}} {"text": "///////////////////////////////////////////////////////////////////////////////\r\n// tail_variate_means.hpp\r\n//\r\n// Copyright 2006 Daniel Egloff, Olivier Gygi. Distributed under the Boost\r\n// Software License, Version 1.0. (See accompanying file\r\n// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n#ifndef BOOST_ACCUMULATORS_STATISTICS_TAIL_VARIATE_MEANS_HPP_DE_01_01_2006\r\n#define BOOST_ACCUMULATORS_STATISTICS_TAIL_VARIATE_MEANS_HPP_DE_01_01_2006\r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n#ifdef _MSC_VER\r\n# pragma warning(push)\r\n# pragma warning(disable: 4127) // conditional expression is constant\r\n#endif\r\n\r\nnamespace boost { namespace accumulators\r\n{\r\n\r\nnamespace impl\r\n{\r\n /**\r\n @brief Estimation of the absolute and relative tail variate means (for both left and right tails)\r\n\r\n For all \\f$j\\f$-th variates associated to the \\f$\\lceil n(1-\\alpha)\\rceil\\f$ largest samples (or the\r\n \\f$\\lceil n(1-\\alpha)\\rceil\\f$ smallest samples in case of the left tail), the absolute tail means\r\n \\f$\\widehat{ATM}_{n,\\alpha}(X, j)\\f$ are computed and returned as an iterator range. Alternatively,\r\n the relative tail means \\f$\\widehat{RTM}_{n,\\alpha}(X, j)\\f$ are returned, which are the absolute\r\n tail means normalized with the (non-coherent) sample tail mean \\f$\\widehat{NCTM}_{n,\\alpha}(X)\\f$.\r\n\r\n \\f[\r\n \\widehat{ATM}_{n,\\alpha}^{\\mathrm{right}}(X, j) =\r\n \\frac{1}{\\lceil n(1-\\alpha) \\rceil}\r\n \\sum_{i=\\lceil \\alpha n \\rceil}^n \\xi_{j,i}\r\n \\f]\r\n\r\n \\f[\r\n \\widehat{ATM}_{n,\\alpha}^{\\mathrm{left}}(X, j) =\r\n \\frac{1}{\\lceil n\\alpha \\rceil}\r\n \\sum_{i=1}^{\\lceil n\\alpha \\rceil} \\xi_{j,i}\r\n \\f]\r\n\r\n \\f[\r\n \\widehat{RTM}_{n,\\alpha}^{\\mathrm{right}}(X, j) =\r\n \\frac{\\sum_{i=\\lceil n\\alpha \\rceil}^n \\xi_{j,i}}\r\n {\\lceil n(1-\\alpha)\\rceil\\widehat{NCTM}_{n,\\alpha}^{\\mathrm{right}}(X)}\r\n \\f]\r\n\r\n \\f[\r\n \\widehat{RTM}_{n,\\alpha}^{\\mathrm{left}}(X, j) =\r\n \\frac{\\sum_{i=1}^{\\lceil n\\alpha \\rceil} \\xi_{j,i}}\r\n {\\lceil n\\alpha\\rceil\\widehat{NCTM}_{n,\\alpha}^{\\mathrm{left}}(X)}\r\n \\f]\r\n */\r\n\r\n ///////////////////////////////////////////////////////////////////////////////\r\n // tail_variate_means_impl\r\n // by default: absolute tail_variate_means\r\n template\r\n struct tail_variate_means_impl\r\n : accumulator_base\r\n {\r\n typedef typename numeric::functional::fdiv::result_type float_type;\r\n typedef std::vector array_type;\r\n // for boost::result_of\r\n typedef iterator_range result_type;\r\n\r\n tail_variate_means_impl(dont_care) {}\r\n\r\n template\r\n result_type result(Args const &args) const\r\n {\r\n std::size_t cnt = count(args);\r\n\r\n std::size_t n = static_cast(\r\n std::ceil(\r\n cnt * ( ( is_same::value ) ? args[quantile_probability] : 1. - args[quantile_probability] )\r\n )\r\n );\r\n\r\n std::size_t num_variates = tail_variate(args).begin()->size();\r\n\r\n this->tail_means_.clear();\r\n this->tail_means_.resize(num_variates, Sample(0));\r\n\r\n // If n is in a valid range, return result, otherwise return NaN or throw exception\r\n if (n < static_cast(tail(args).size()))\r\n {\r\n this->tail_means_ = std::accumulate(\r\n tail_variate(args).begin()\r\n , tail_variate(args).begin() + n\r\n , this->tail_means_\r\n , numeric::plus\r\n );\r\n\r\n float_type factor = n * ( (is_same::value) ? non_coherent_tail_mean(args) : 1. );\r\n\r\n std::transform(\r\n this->tail_means_.begin()\r\n , this->tail_means_.end()\r\n , this->tail_means_.begin()\r\n#ifdef BOOST_NO_CXX98_BINDERS\r\n , std::bind(std::divides(), std::placeholders::_1, factor)\r\n#else\r\n , std::bind2nd(std::divides(), factor)\r\n#endif\r\n );\r\n }\r\n else\r\n {\r\n if (std::numeric_limits::has_quiet_NaN)\r\n {\r\n std::fill(\r\n this->tail_means_.begin()\r\n , this->tail_means_.end()\r\n , std::numeric_limits::quiet_NaN()\r\n );\r\n }\r\n else\r\n {\r\n std::ostringstream msg;\r\n msg << \"index n = \" << n << \" is not in valid range [0, \" << tail(args).size() << \")\";\r\n boost::throw_exception(std::runtime_error(msg.str()));\r\n }\r\n }\r\n return make_iterator_range(this->tail_means_);\r\n }\r\n\r\n // make this accumulator serializeable\r\n template\r\n void serialize(Archive & ar, const unsigned int file_version)\r\n { \r\n ar & tail_means_;\r\n }\r\n\r\n private:\r\n\r\n mutable array_type tail_means_;\r\n\r\n };\r\n\r\n} // namespace impl\r\n\r\n///////////////////////////////////////////////////////////////////////////////\r\n// tag::absolute_tail_variate_means\r\n// tag::relative_tail_variate_means\r\n//\r\nnamespace tag\r\n{\r\n template\r\n struct absolute_tail_variate_means\r\n : depends_on, tail_variate >\r\n {\r\n typedef accumulators::impl::tail_variate_means_impl impl;\r\n };\r\n template\r\n struct relative_tail_variate_means\r\n : depends_on, tail_variate >\r\n {\r\n typedef accumulators::impl::tail_variate_means_impl impl;\r\n };\r\n struct abstract_absolute_tail_variate_means\r\n : depends_on<>\r\n {\r\n };\r\n struct abstract_relative_tail_variate_means\r\n : depends_on<>\r\n {\r\n };\r\n}\r\n\r\n///////////////////////////////////////////////////////////////////////////////\r\n// extract::tail_variate_means\r\n// extract::relative_tail_variate_means\r\n//\r\nnamespace extract\r\n{\r\n extractor const tail_variate_means = {};\r\n extractor const relative_tail_variate_means = {};\r\n\r\n BOOST_ACCUMULATORS_IGNORE_GLOBAL(tail_variate_means)\r\n BOOST_ACCUMULATORS_IGNORE_GLOBAL(relative_tail_variate_means)\r\n}\r\n\r\nusing extract::tail_variate_means;\r\nusing extract::relative_tail_variate_means;\r\n\r\n// tail_variate_means(absolute) -> absolute_tail_variate_means\r\ntemplate\r\nstruct as_feature(absolute)>\r\n{\r\n typedef tag::absolute_tail_variate_means type;\r\n};\r\n\r\n// tail_variate_means(relative) ->relative_tail_variate_means\r\ntemplate\r\nstruct as_feature(relative)>\r\n{\r\n typedef tag::relative_tail_variate_means type;\r\n};\r\n\r\n// Provides non-templatized extractor\r\ntemplate\r\nstruct feature_of >\r\n : feature_of\r\n{\r\n};\r\n\r\n// Provides non-templatized extractor\r\ntemplate\r\nstruct feature_of >\r\n : feature_of\r\n{\r\n};\r\n\r\n// So that absolute_tail_means can be automatically substituted\r\n// with absolute_weighted_tail_means when the weight parameter is non-void.\r\ntemplate\r\nstruct as_weighted_feature >\r\n{\r\n typedef tag::absolute_weighted_tail_variate_means type;\r\n};\r\n\r\ntemplate\r\nstruct feature_of >\r\n : feature_of >\r\n{\r\n};\r\n\r\n// So that relative_tail_means can be automatically substituted\r\n// with relative_weighted_tail_means when the weight parameter is non-void.\r\ntemplate\r\nstruct as_weighted_feature >\r\n{\r\n typedef tag::relative_weighted_tail_variate_means type;\r\n};\r\n\r\ntemplate\r\nstruct feature_of >\r\n : feature_of >\r\n{\r\n};\r\n\r\n}} // namespace boost::accumulators\r\n\r\n#ifdef _MSC_VER\r\n# pragma warning(pop)\r\n#endif\r\n\r\n#endif\r\n", "meta": {"hexsha": "d525df2ce1f34ef52a8bc4f25153d9ad2dba62f5", "size": 10733, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/boost/include/boost/accumulators/statistics/tail_variate_means.hpp", "max_stars_repo_name": "kindlychung/mediasoup-sfu-cpp", "max_stars_repo_head_hexsha": "f69d2f48f7edbf4f0c57244280a47bea985f39cf", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 80.0, "max_stars_repo_stars_event_min_datetime": "2021-09-07T12:44:32.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T01:22:19.000Z", "max_issues_repo_path": "deps/boost/include/boost/accumulators/statistics/tail_variate_means.hpp", "max_issues_repo_name": "kindlychung/mediasoup-sfu-cpp", "max_issues_repo_head_hexsha": "f69d2f48f7edbf4f0c57244280a47bea985f39cf", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2021-12-23T02:49:42.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-15T05:28:24.000Z", "max_forks_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.2443005306581844}} {"text": "/*******************************************************************************\n\n Copyright (c) 2017, Honda Research Institute Europe GmbH\n\n Redistribution and use in source and binary forms, with or without\n modification, are permitted provided that the following conditions are\n met:\n\n 1. Redistributions of source code must retain the above copyright notice,\n this list of conditions and the following disclaimer.\n\n 2. Redistributions in binary form must reproduce the above copyright\n notice, this list of conditions and the following disclaimer in the\n documentation and/or other materials provided with the distribution.\n\n 3. Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\n THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS\n IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,\n THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\n CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\n LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n*******************************************************************************/\n\n/******************************************************************************\n\n http://eigen.tuxfamily.org/dox/TopicPreprocessorDirectives.html:\n\n EIGEN_NO_MALLOC - if defined, any request from inside the Eigen to allocate\n memory from the heap results in an assertion failure. This\n is useful to check that some routine does not allocate\n memory dynamically. Not defined by default.\n\n EIGEN_RUNTIME_NO_MALLOC - if defined, a new switch is introduced which can be\n turned on and off by calling set_is_malloc_allowed\n (bool). If malloc is not allowed and Eigen tries to\n allocate memory dynamically anyway, an assertion\n failure results. Not defined by default.\n\n Mapping C-Arrays:\n http://eigen.tuxfamily.org/dox/group__TutorialMapClass.html#\n TutorialMapPlacementNew\n\n Malloc checking:\n Eigen::internal::set_is_malloc_allowed(true, false) will create a fatal\n error if set to true, and disable checking for false. However, this is\n not thread-safe, and therefore should only be used for testing.\n\n******************************************************************************/\n\n#include \n#include \n\n#define EIGEN_RUNTIME_NO_MALLOC\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \n#include \n\nusing namespace Eigen;\n\n#if defined (MATND_MAX_STACK_VECTOR_SIZE)\n#undef MATND_MAX_STACK_VECTOR_SIZE\n#endif\n\n#if defined (MATND_MAX_STACK_MATRIX_SIZE)\n#undef MATND_MAX_STACK_MATRIX_SIZE\n#endif\n\n#define MATND_MAX_STACK_VECTOR_SIZE (25)\n#define MATND_MAX_STACK_MATRIX_SIZE (25*25)\n\n\n// These types have an unknown size at compile time, and therefore are\n// allocated on the heap. The storage order of the matrices is set to row\n// major, so that the Eigen's Map class can be used to cast them over a MatNd\n// type without any additional memory allocation.\ntypedef Matrix HeapMat;\ntypedef Matrix HeapVec;\n\n\n\n// These types have a known size at compile time, and therefore are allocated\n// on the stack. The storage order of the matrices is set to row major, so that\n// the Eigen's Map class can be used to cast them over a MatNd type without any\n// additional memory allocation.\ntypedef Matrix StackMat;\ntypedef Matrix StackVec;\n\n\n\ntemplate \nstatic inline void MatNd_fromEigen3(MatNd* dst, const T& src,\n bool reshape=false)\n{\n if (reshape==false)\n {\n RCHECK_EQ((int)dst->m, src.rows());\n RCHECK_EQ((int)dst->n, src.cols());\n }\n else\n {\n MatNd_reshape(dst, src.rows(), src.cols());\n }\n\n for (size_t m=0; mm; m++)\n {\n for (size_t n=0; nn; n++)\n {\n MatNd_set2(dst, m, n, src(m,n));\n }\n }\n}\n\ntemplate \nstatic inline double CholDet(const T& L)\n{\n double det = 1.0;\n\n for (int i=0; i\nstatic inline double CholDC(MatNd* L_, const MatNd* A_)\n{\n Map A(A_->ele, A_->m, A_->n);\n LLT llt;\n llt.compute(A);\n\n if (llt.info() != Eigen::Success)\n {\n RLOG(1, \"Error on LLT!\");\n MatNd_setZero(L_);\n return 0.0;\n }\n\n MatT L(llt.matrixL());\n double det = CholDet(L);\n MatNd_fromEigen3(L_, L);\n\n return det;\n}\n\ntemplate \nstatic inline double CholSolve(MatNd* x_, const MatNd* A_, const MatNd* b_)\n{\n Map A(A_->ele, A_->m, A_->n);\n LLT llt;\n llt.compute(A);\n\n if (llt.info() != Eigen::Success)\n {\n RLOG(1, \"Error on LLT!\");\n return 0.0;\n }\n\n Map x(x_->ele, x_->m, x_->n);\n Map b(b_->ele, b_->m, b_->n);\n x = llt.solve(b);\n\n // Compute determinant\n MatT L(llt.matrixL());\n\n return CholDet(L);\n}\n\ntemplate \nstatic inline double CholInv(MatNd* A_inv_, const MatNd* A_)\n{\n size_t n = A_->m;\n Map A(A_->ele, n, n);\n\n LLT llt;\n llt.compute(A);\n\n if (llt.info() != Eigen::Success)\n {\n RLOG(1, \"Error on LLT!\");\n MatNd_setZero(A_inv_);\n return 0.0;\n }\n\n Map invA(A_inv_->ele, n, n);\n invA = llt.solve(MatT::Identity(n,n));\n\n MatT L(llt.matrixL());\n\n return CholDet(L);\n}\n\ntemplate \nstatic inline int SvdDC(MatNd* U, MatNd* S, MatNd* V, const MatNd* J,\n double eps)\n{\n Map mapJ(J->ele, J->m, J->n);\n\n // We need to compute the full SVD, since otherwise there will dynamic\n // memory allocation for the StackMat types due to the QR preconditioning\n // (on Linux)\n JacobiSVD svd(mapJ, ComputeFullU | ComputeFullV);\n\n // Cut off singular values smaller than eps\n int rank = svd.nonzeroSingularValues();\n\n for (int i=0; i(S, svd.singularValues(), true);\n S->m = rank;\n }\n\n if (U != NULL)\n {\n MatT svdU = svd.matrixU();\n MatNd_reshape(U, svdU.rows(), rank);\n\n for (unsigned int m=0; mm; m++)\n {\n for (unsigned int n=0; nn; n++)\n {\n MatNd_set2(U, m, n, svdU(m,n));\n }\n }\n }\n\n if (V != NULL)\n {\n MatT svdV = svd.matrixV();\n MatNd_reshape(V, svdV.rows(), rank);\n\n for (unsigned int m=0; mm; m++)\n {\n for (unsigned int n=0; nn; n++)\n {\n MatNd_set2(V, m, n, svdV(m,n));\n }\n }\n }\n\n return rank;\n}\n\ntemplate \nstatic inline double SvdSolve(MatNd* x_, const MatNd* A_, const MatNd* b_)\n{\n Map x(x_->ele, x_->m, x_->n);\n Map A(A_->ele, A_->m, A_->n);\n Map b(b_->ele, b_->m, b_->n);\n\n // We need to compute the full SVD, since otherwise there will dynamic\n // memory allocation for the StackMat types due to the QR preconditioning\n // (on Linux)\n JacobiSVD svd(A, ComputeFullU | ComputeFullV);\n x = svd.solve(b);\n\n // Compute determinant\n double det = 1.0;\n MatT S = svd.singularValues();\n\n // Cut off singular values smaller than eps\n for (int i=0; i\nstatic inline size_t SvdInverse(MatNd* A_, double eps=1.0e-12)\n{\n Map A(A_->ele, A_->m, A_->n);\n\n // We need to compute the full SVD, since otherwise there will dynamic\n // memory allocation for the StackMat types due to the QR preconditioning\n // (on Linux)\n JacobiSVD svd;\n // svd.setThreshold(eps);\n svd.compute(A, ComputeThinU | ComputeThinV);\n\n MatT invS = svd.singularValues();\n\n // Cut off singular values smaller than eps\n size_t rank = 0;\n for (int i=0; i eps)\n {\n invS(i) = 1.0/invS(i);\n rank++;\n }\n }\n\n // inv = V diag(S^-1) U^T\n A.noalias() = svd.matrixV()*invS.asDiagonal()*svd.matrixU().transpose();\n\n return rank;\n}\n\ntemplate \nstatic inline void QRDC(MatNd* Q_, MatNd* R_, const MatNd* A_)\n{\n Map A(A_->ele, A_->m, A_->n);\n Map Q(Q_->ele, Q_->m, Q_->n);\n Map R(R_->ele, R_->m, R_->n);\n\n HouseholderQR qr(A);\n\n Q = qr.householderQ();\n R = qr.matrixQR().template triangularView();\n}\n\ntemplate \nstatic inline bool EigenVectors(MatNd* V_, double* d_, const MatNd* A_)\n{\n Map A(A_->ele, A_->m, A_->n);\n\n Eigen::SelfAdjointEigenSolver eigensolver(A);\n\n if (eigensolver.info() != Eigen::Success)\n {\n return false;\n }\n\n Map V(V_->ele, V_->m, V_->n);\n Map d(d_, A_->m, 1);\n V = eigensolver.eigenvectors();\n d = eigensolver.eigenvalues();\n\n return true;\n}\n\ntemplate \nstatic inline void ColPivHhQR(MatNd* X_, const MatNd* A_, const MatNd* B_)\n{\n Map A(A_->ele, A_->m, A_->n);\n Map B(B_->ele, B_->m, B_->n);\n Map X(X_->ele, X_->m, X_->n);\n\n // A X = B\n X = A.colPivHouseholderQr().solve(B);\n}\n\n\n\n\n\n\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n\ndouble MatNd_choleskyDecomposition_E3(MatNd* L, const MatNd* A)\n{\n RCHECK_EQ(A->m, A->n);\n RCHECK_EQ(L->m, L->n);\n RCHECK_EQ(A->m, L->m);\n\n if (A->m > MATND_MAX_STACK_VECTOR_SIZE)\n {\n return CholDC(L, A);\n }\n else\n {\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(false);\n#endif\n double det = CholDC(L, A);\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(true);\n#endif\n return det;\n }\n}\n\ndouble MatNd_choleskySolve_E3(MatNd* x, const MatNd* A, const MatNd* b)\n{\n RCHECK_EQ(A->m, A->n);\n RCHECK_EQ(x->m, A->m);\n RCHECK_EQ(b->m, x->m);\n RCHECK_EQ(b->n, x->n);\n\n if (A->m > MATND_MAX_STACK_VECTOR_SIZE)\n {\n return CholSolve(x, A, b);\n }\n else\n {\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(false);\n#endif\n double det = CholSolve(x, A, b);\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(true);\n#endif\n return det;\n }\n}\n\ndouble MatNd_choleskyInverse_E3(MatNd* invA, const MatNd* A)\n{\n RCHECK_EQ(A->m, A->n);\n RCHECK_EQ(invA->m, invA->n);\n RCHECK_EQ(A->m, invA->m);\n\n if (A->m > MATND_MAX_STACK_VECTOR_SIZE)\n {\n return CholInv(invA,A);\n }\n else\n {\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(false);\n#endif\n double det = CholInv(invA,A);\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(true);\n#endif\n return det;\n }\n}\n\nvoid MatNd_mul_E3(MatNd* C_, const MatNd* A_, const MatNd* B_)\n{\n if ((A_->m > MATND_MAX_STACK_VECTOR_SIZE) ||\n (B_->n > MATND_MAX_STACK_VECTOR_SIZE))\n {\n Map C(C_->ele, C_->m, C_->n);\n Map A(A_->ele, A_->m, A_->n);\n Map B(B_->ele, B_->m, B_->n);\n\n // The noalias function inhibits the creation of a temporary object which\n // would lead to dynamic memory alllocation.\n C.noalias() = A*B;\n }\n else\n {\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(false);\n#endif\n\n Map C(C_->ele, C_->m, C_->n);\n Map A(A_->ele, A_->m, A_->n);\n Map B(B_->ele, B_->m, B_->n);\n\n C.noalias() = A*B;\n\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(true);\n#endif\n }\n}\n\n\n\n\n\n\n\nunsigned int MatNd_SVD(MatNd* U, MatNd* S, MatNd* V, const MatNd* J,\n double eps)\n{\n if ((J->m > MATND_MAX_STACK_VECTOR_SIZE) ||\n (J->n > MATND_MAX_STACK_VECTOR_SIZE))\n {\n return SvdDC(U, S, V, J, eps);\n }\n else\n {\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(false);\n#endif\n int rank = SvdDC(U, S, V, J, eps);\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(true);\n#endif\n return rank;\n }\n}\n\ndouble MatNd_SVDSolve(MatNd* x, const MatNd* A, const MatNd* b)\n{\n if ((A->m > MATND_MAX_STACK_VECTOR_SIZE) ||\n (A->n > MATND_MAX_STACK_VECTOR_SIZE))\n {\n return SvdSolve(x, A, b);\n }\n else\n {\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(false);\n#endif\n double det = SvdSolve(x, A, b);\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(true);\n#endif\n return det;\n }\n}\n\nunsigned int MatNd_SVDInverse(MatNd* inv, const MatNd* src)\n{\n MatNd_copy(inv, src);\n return MatNd_SVDInverseSelf(inv);\n}\n\nunsigned int MatNd_SVDInverseSelf(MatNd* A)\n{\n if ((A->m > MATND_MAX_STACK_VECTOR_SIZE) ||\n (A->n > MATND_MAX_STACK_VECTOR_SIZE))\n {\n return SvdInverse(A);\n }\n else\n {\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(false);\n#endif\n int rank = SvdInverse(A);\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(true);\n#endif\n return rank;\n }\n}\n\nunsigned int MatNd_rank(const MatNd* J, double eps)\n{\n return MatNd_SVD(NULL, NULL, NULL, J, eps);\n}\n\nvoid MatNd_QRDecomposition(MatNd* Q, MatNd* R, const MatNd* A)\n{\n if ((A->m > MATND_MAX_STACK_VECTOR_SIZE) ||\n (A->n > MATND_MAX_STACK_VECTOR_SIZE) ||\n (A->n > A->m)) // That's a bug in Eigen3\n {\n QRDC(Q, R, A);\n }\n else\n {\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(false);\n#endif\n QRDC(Q, R, A);\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(true);\n#endif\n }\n}\n\nbool MatNd_getEigenVectors(MatNd* V, double* d, const MatNd* A)\n{\n RCHECK_EQ(A->m, A->n);\n RCHECK_EQ(A->n, V->m);\n RCHECK_EQ(V->m, V->n);\n\n bool success;\n\n if (A->m > MATND_MAX_STACK_VECTOR_SIZE)\n {\n success = EigenVectors(V, d, A);\n }\n else\n {\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(false);\n#endif\n success = EigenVectors(V, d, A);\n#ifdef EIGEN_RUNTIME_NO_MALLOC\n Eigen::internal::set_is_malloc_allowed(true);\n#endif\n }\n\n return success;\n}\n\nvoid MatNd_HouseholderQR(MatNd* X, const MatNd* A, const MatNd* B)\n{\n if ((A->m > MATND_MAX_STACK_VECTOR_SIZE) ||\n (A->n > MATND_MAX_STACK_VECTOR_SIZE))\n {\n return ColPivHhQR(X, A, B);\n }\n else\n {\n#ifdef 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YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.24430053065818438}} {"text": "/*\n * Copyright 2008-2016 Jan Gasthaus\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n#include \"libplump/hpyp_model.h\"\n\n#include \n#include \n#include \n\n#include \"libplump/utils.h\"\n#include \"libplump/subseq.h\"\n#include \"libplump/stirling.h\"\n#include \"libplump/random.h\"\n#include \"libplump/hpyp_restaurants.h\"\n\n\nnamespace gatsby { namespace libplump {\n\nHPYPModel::HPYPModel(seq_type& seq,\n INodeManager& nodeManager,\n const IAddRemoveRestaurant& restaurant,\n IParameters& parameters,\n int numTypes) \n : seq(seq), \n contextTree_(new ContextTree(nodeManager, seq)), \n contextTree(*contextTree_), \n restaurant(restaurant),\n parameters(parameters), \n numTypes(numTypes) {\n baseProb = 1./((double) numTypes);\n }\n \n\nvoid HPYPModel::insertRoot(e_type obs) {\n WrappedNodeList root_path = this->contextTree.findLongestSuffix(0,0);\n d_vec discount_path = this->parameters.getDiscounts(root_path);\n d_vec concentration_path = this->parameters.getConcentrations(root_path,\n discount_path);\n d_vec prob_path = this->computeProbabilityPath(root_path,\n discount_path,\n concentration_path,\n obs);\n this->updatePath(root_path,\n prob_path,\n discount_path,\n concentration_path,\n obs);\n}\n\n\n/**\n * Insert a context into the tree and handle a potentially occuring \n * split.\n */\nWrappedNodeList HPYPModel::insertContext(l_type start, l_type stop) {\n typedef ContextTree::InsertionResult InsertionResult;\n\n // insert context\n InsertionResult insertionResult = contextTree.insert(start, stop);\n\n // handle split if one occurred\n if (insertionResult.action != InsertionResult::INSERT_ACTION_NO_SPLIT) { \n WrappedNode nodeA, nodeC;\n WrappedNodeList::iterator i = insertionResult.path.end();\n\n // move iterator to point to the split node which is either the \n // last or second to last element on the path, depending on \n // whether the inserted node was a suffix\n switch (insertionResult.action) {\n case InsertionResult::INSERT_ACTION_SPLIT :\n // split where inserted node is not a suffix of a node in \n // the tree\n i--; i--; // split node is above the newly inserted node\n break;\n case InsertionResult::INSERT_ACTION_SPLIT_SUFFIX :\n // split where inserted node is a suffix -> inserted node\n // is the shorter node\n i--; // split node is the newly inserted node\n break;\n case InsertionResult::INSERT_ACTION_NO_SPLIT :\n break;\n }\n nodeC = *i;\n i--; // move iterator to parent\n nodeA = *i;\n this->handleSplit(nodeA, \n insertionResult.splitChild,\n nodeC); \n }\n return insertionResult.path; \n}\n\n\nd_vec HPYPModel::insertContextAndObservation(l_type start, \n l_type stop,\n e_type obs) {\n // insert context (and handle a potential split)\n WrappedNodeList path = insertContext(start, stop);\n\n // insert observation\n d_vec discountPath = this->parameters.getDiscounts(path);\n d_vec concentrationPath = this->parameters.getConcentrations(path,\n discountPath);\n d_vec probabilityPath = this->computeProbabilityPath(path,\n discountPath,\n concentrationPath,\n obs);\n this->parameters.accumulateParameterGradient(this->restaurant, \n path, probabilityPath, discountPath, concentrationPath, obs);\n\n this->updatePath(path, probabilityPath, discountPath,\n concentrationPath, obs);\n\n // static int j = 0;\n //if (j == 1) {\n this->parameters.stepParameterGradient(10e-4);\n // j = 0;\n //}\n \n\n return probabilityPath; \n}\n\n\nd_vec HPYPModel::insertObservation(l_type start, l_type stop, e_type obs, \n WrappedNodeList* cached_path) {\n tracer << \"HPYPModel::insertObservation(\" << start << \", \" << stop \n << \", \" << obs << \")\" << std::endl;\n\n WrappedNodeList path;\n if (cached_path != NULL) {\n path = *cached_path;\n } else {\n path = this->contextTree.findLongestSuffix(start,stop);\n }\n \n tracer << \" HPYPModel::insertObservation: longest suffix path: \" \n << std::endl << this->contextTree.pathToString(path) << std::endl;\n \n d_vec discount_path = this->parameters.getDiscounts(path);\n d_vec concentration_path = this->parameters.getConcentrations(path,\n discount_path);\n d_vec prob_path = this->computeProbabilityPath(path,\n discount_path,\n concentration_path,\n obs);\n this->updatePath(path, prob_path, discount_path,\n concentration_path, obs);\n return prob_path; \n}\n\n\nvoid HPYPModel::removeObservation(\n l_type start, l_type stop, e_type obs, \n const HPYPModel::PayloadDataPath& payloadDataPath,\n WrappedNodeList* cached_path) {\n tracer << \"HPYPModel::removeObservation(\" << start << \", \" << stop \n << \", \" << obs << \")\" << std::endl;\n\n WrappedNodeList path;\n if (cached_path != NULL) {\n path = *cached_path;\n } else {\n path = this->contextTree.findLongestSuffix(start,stop);\n }\n assert(path.back().end == (*cached_path).back().end);\n \n tracer << \" HPYPModel::removeObservation: longest suffix path: \" \n << std::endl << contextTree.pathToString(path) << std::endl;\n \n d_vec discountPath = this->parameters.getDiscounts(path);\n \n this->removeObservationFromPath(path, \n discountPath,\n obs,\n payloadDataPath);\n}\n\n\nvoid HPYPModel::removeAddSweep(l_type start, l_type stop) {\n // start timer\n clock_t start_t,end_t;\n start_t = clock();\n for (int i = start; i < stop; ++i) {\n HPYPModel::PayloadDataPath payloadDataPath;\n WrappedNodeList path = this->contextTree.findNode(start, i);\n //WrappedNodeList p2 = this->contextTree.findLongestSuffix(start, i);\n //std::cout << path.back().end << \", \" << p2.back().end\n // << \", \" << path.size() << \", \" << p2.size() <removeObservation(start, i, this->seq[i], payloadDataPath, &path);\n this->insertObservation(start, i, this->seq[i], &path);\n\n if (i%10000==0) {\n end_t = clock();\n std::cerr << makeProgressBarString(i/(double)stop) << \" \" \n << ((double)i*CLOCKS_PER_SEC)/(end_t-start_t) << \" chars/sec\" \n << \"\\r\";\n }\n }\n}\n\n\nd_vec HPYPModel::computeLosses(l_type start, l_type stop) {\n d_vec losses;\n\n // deal with first symbol: add loss and insert customer\n losses.push_back(log2((double) this->numTypes));\n insertRoot(this->seq[start]);\n\n // start timer\n clock_t start_t,end_t;\n start_t = clock();\n\n for (l_type i=start+1; i < stop; i++) {\n d_vec prob_path = this->insertContextAndObservation(start,i,this->seq[i]);\n double prob = prob_path[prob_path.size()-2];\n losses.push_back(-log2(prob));\n \n if (i%10000==0) {\n end_t = clock();\n std::cerr << makeProgressBarString(i/(double)stop) << \" \" \n << ((double)i*CLOCKS_PER_SEC)/(end_t-start_t) << \" chars/sec\" \n << \"\\r\";\n }\n\n }\n end_t = clock();\n\n std::cerr << makeProgressBarString(1) << \" \" \n << ((double)stop*CLOCKS_PER_SEC)/(end_t-start_t) << \" chars/sec\" \n << std::endl;\n \n return losses;\n}\n\n\nd_vec HPYPModel::computeLossesWithDeletion(l_type start, l_type stop, l_type lag) {\n d_vec losses;\n\n // deal with first symbol: add loss and insert customer\n losses.push_back(log2((double) this->numTypes));\n insertRoot(this->seq[start]);\n\n // start timer\n clock_t start_t,end_t;\n start_t = clock();\n\n for (l_type i=start+1; i < stop; i++) {\n d_vec prob_path = this->insertContextAndObservation(start,i,this->seq[i]);\n double prob = prob_path[prob_path.size()-2];\n losses.push_back(-log2(prob));\n if (i - lag >= start) {\n HPYPModel::PayloadDataPath payloadDataPath;\n WrappedNodeList path = this->contextTree.findNode(start, i - lag);\n this->removeObservation(start, i - lag, this->seq[i - lag], payloadDataPath, &path);\n }\n\n if (i%10000==0) {\n end_t = clock();\n std::cerr << makeProgressBarString(i/(double)stop) << \" \" \n << ((double)i*CLOCKS_PER_SEC)/(end_t-start_t) << \" chars/sec\" \n << \"\\r\";\n }\n\n }\n end_t = clock();\n\n std::cerr << makeProgressBarString(1) << \" \" \n << ((double)stop*CLOCKS_PER_SEC)/(end_t-start_t) << \" chars/sec\" \n << std::endl;\n\n return losses;\n}\n\n\nd_vec HPYPModel::predictSequence(l_type start, l_type stop, PredictMode mode) {\n d_vec probs;\n for (l_type i = start; i < stop; i++) {\n switch(mode) {\n case ABOVE:\n probs.push_back(this->predict(start, i, this->seq[i]));\n break;\n case FRAGMENT:\n probs.push_back(this->predictWithFragmentation(start, i, this->seq[i]));\n break;\n case BELOW:\n probs.push_back(this->predictBelow(start, i, this->seq[i]));\n break;\n }\n }\n return probs;\n}\n\n\nvoid HPYPModel::buildTree(l_type stop) {\n this->insertRoot(this->seq[0]);\n for (l_type i=1; i < stop; ++i) {\n this->insertContextAndObservation(0, i, this->seq[i]);\n }\n}\n\n\nvoid HPYPModel::updateTree(l_type start, l_type stop) {\n for (l_type i = start; i < stop; ++i) {\n this->insertContextAndObservation(0, i, this->seq[i]);\n }\n}\n\n\ndouble HPYPModel::predict(l_type start, l_type stop, e_type obs) {\n WrappedNodeList path = this->contextTree.findLongestSuffix(start,stop);\n d_vec discount_path = this->parameters.getDiscounts(path);\n d_vec concentration_path = this->parameters.getConcentrations(path,\n discount_path);\n\n d_vec prob_path = this->computeProbabilityPath(path,\n discount_path,\n concentration_path,\n obs);\n return prob_path.back();\n}\n\n\n/**\n * Predict prob; in the case of required fragmentation, predict form \n * _below_ the split point!\n */\ndouble HPYPModel::predictBelow(l_type start, l_type stop, e_type obs) {\n WrappedNodeList path = \n this->contextTree.findLongestSuffixVirtual(start,stop).second;\n d_vec discount_path = this->parameters.getDiscounts(path);\n d_vec concentration_path = this->parameters.getConcentrations(path,\n discount_path);\n\n d_vec prob_path = this->computeProbabilityPath(path,\n discount_path,\n concentration_path,\n obs);\n return prob_path.back();\n}\n\n\ndouble HPYPModel::predictWithFragmentation(l_type start, \n l_type stop,\n e_type obs) {\n std::pair path = contextTree.findLongestSuffixVirtual(\n start, stop);\n\n d_vec discountPath = parameters.getDiscounts(path.second);\n d_vec concentrationPath = parameters.getConcentrations(path.second,\n discountPath);\n d_vec probabilityPath = this->computeProbabilityPath(path.second,\n discountPath,\n concentrationPath,\n obs);\n\n double probability = 0;\n if (path.first != 0) {\n // create a new payload for the node we are predicting from\n // fragmentation -- last probability on the path needs to be recomputed\n // by creating a new, split node of length path.second. \n void* splitNode = this->restaurant.getFactory().make();\n WrappedNodeList::iterator it = path.second.end();\n it--; it--; // one before last; parent of node we need to split\n int parentLength = it->end - it->start; \n // path.first is length of parent after split\n double discountAfter = parameters.getDiscount(path.first, it->end - it->start);\n it++; // last node\n double discountFragmented = this->parameters.getDiscount(parentLength,\n path.first);\n this->restaurant.updateAfterSplit(\n it->payload,\n splitNode,\n discountPath.back(),\n discountFragmented,\n true); // update splitNode only\n double concentrationFragmented = this->parameters.getConcentration(\n discountFragmented, parentLength, path.first);\n probability = this->restaurant.computeProbability(\n splitNode, obs, probabilityPath[probabilityPath.size()-2],\n discountFragmented, concentrationFragmented);\n this->restaurant.getFactory().recycle(splitNode);\n } else {\n probability = probabilityPath.back();\n }\n return probability;\n}\n\n\nd_vec HPYPModel::predictiveDistribution(l_type start, l_type stop) {\n d_vec predictive;\n predictive.reserve(this->numTypes);\n WrappedNodeList path = this->contextTree.findLongestSuffix(start,stop);\n d_vec discount_path = this->parameters.getDiscounts(path);\n d_vec concentration_path = this->parameters.getConcentrations(path,\n discount_path);\n\n for(int i = 0; i < this->numTypes; ++i) {\n d_vec prob_path = this->computeProbabilityPath(path,\n discount_path,\n concentration_path,\n i);\n predictive.push_back(prob_path.back());\n }\n\n return predictive;\n}\n\n\nd_vec HPYPModel::predictiveDistributionWithMixing(l_type start, \n l_type stop, \n d_vec& mixingWeights) {\n d_vec predictive;\n predictive.reserve(this->numTypes);\n const WrappedNodeList path = this->contextTree.findLongestSuffix(start, stop);\n const d_vec discount_path = this->parameters.getDiscounts(path);\n const d_vec concentration_path \n = this->parameters.getConcentrations(path, discount_path);\n\n for(int i = 0; i < this->numTypes; ++i) {\n d_vec prob_path = this->computeProbabilityPath(path,\n discount_path,\n concentration_path,\n i);\n double prob = 0;\n double sum = 0;\n for (int j = 0; \n j < std::min(mixingWeights.size(), prob_path.size());\n ++j) {\n prob += mixingWeights[j]*prob_path[j];\n sum += mixingWeights[j];\n }\n predictive.push_back(prob + (1 - sum)*prob_path.back());\n }\n\n return predictive;\n}\n \n\n/**\n * Given a path from the root to some node (not a leaf), \n * perform add/remove Gibbs sampling of the last node by repeatedly \n * removing and adding customers, cus times for each type s.\n */\nvoid HPYPModel::addRemoveSamplePath(\n const WrappedNodeList& path, \n const d_vec& discountPath, \n const d_vec& concentrationPath, \n const HPYPModel::PayloadDataPath& payloadDataPath,\n double baseProb) {\n assert(path.size() > 0);\n assert(path.size() == discountPath.size());\n assert(path.size() == concentrationPath.size());\n\n bool useAdditionalData = payloadDataPath.size() == path.size();\n void* main = path.back().payload;\n const IAddRemoveRestaurant& r = this->restaurant; // shortcut\n \n IHPYPBaseRestaurant::TypeVector types = r.getTypeVector(main);\n for(IHPYPBaseRestaurant::TypeVectorIterator it = types.begin(); \n it != types.end(); ++it) { // for each type of customer \n\n e_type type = *it;\n l_type cw = r.getC(main, type);\n \n if (cw == 1) {\n continue; // no point in reseating in a 1 customer restaurant\n }\n\n d_vec probabilityPath = this->computeProbabilityPath(path,\n discountPath,\n concentrationPath,\n type);\n WrappedNodeList::const_iterator current;\n for (l_type i = 0; i < cw; ++i) { // for each customer of this type\n current = --path.end(); // set current to last restaurant in path\n \n // index into d/alpha vectors for current restaurant\n int j = discountPath.size() - 1;\n \n bool goUp = true;\n while(goUp && j != -1) {\n void* additionalData = NULL;\n if (useAdditionalData) {\n additionalData = payloadDataPath[j].get();\n }\n bool removed = r.removeCustomer(current->payload,\n type,\n discountPath[j],\n additionalData);\n if (removed) {\n --current;\n --j;\n } else {\n goUp = false;\n }\n }\n\n // recompute probabilities back down; need not recompute last probability\n while(j < (int)probabilityPath.size() - 1) {\n if (j == -1) {\n // can't recompute base distribution probabilities at prob_path[0]\n j = 0; // \n ++current;\n }\n probabilityPath[j+1] = r.computeProbability(current->payload, \n type,\n probabilityPath[j],\n discountPath[j],\n concentrationPath[j]);\n ++j; \n ++current;\n }\n\n // set current and j to the last restaurant on path\n current = --path.end(); \n j = discountPath.size() - 1; \n goUp = true;\n while(goUp && j != -1) {\n void* additionalData = NULL;\n if (useAdditionalData) {\n additionalData = payloadDataPath[j].get();\n }\n bool inserted = r.addCustomer(current->payload, \n type,\n probabilityPath[j],\n discountPath[j],\n concentrationPath[j], \n additionalData);\n if (inserted) {\n current--;\n j--;\n } else {\n goUp = false;\n }\n }\n\n }\n\n }\n\n}\n\n\n/**\n * Given a path from the root to some node (not a leaf), \n * perform add/remove Gibbs sampling of the last node by repeatedly \n * removing and adding customers, cus times for each type s.\n */\nvoid HPYPModel::directGibbsSamplePath(\n const WrappedNodeList& path, \n const d_vec& discountPath, \n const d_vec& concentrationPath, \n const HPYPModel::PayloadDataPath& payloadDataPath,\n double baseProb) {\n assert(path.size() > 0);\n assert(path.size() == discountPath.size());\n assert(path.size() == concentrationPath.size());\n\n bool useAdditionalData = payloadDataPath.size() == path.size();\n // XXX: HACK! We assume this is the type of addData for the used restaurant\n stirling_generator_full_log *stirlingGenCurrent = NULL;\n stirling_generator_full_log *stirlingGenParent = NULL;\n void* main = path.back().payload;\n const BaseCompactRestaurant& r = (BaseCompactRestaurant&)this->restaurant; // shortcut\n \n IHPYPBaseRestaurant::TypeVector types = r.getTypeVector(main);\n for(IHPYPBaseRestaurant::TypeVectorIterator it = types.begin(); \n it != types.end(); ++it) { // for each type of customer \n\n e_type type = *it;\n l_type cw = r.getC(main, type);\n \n if (cw == 1) {\n continue; // no point in reseating in a 1 customer restaurant\n }\n\n WrappedNodeList::const_iterator current;\n current = --path.end(); // set current to last restaurant in path\n\n // index into d/alpha vectors for current restaurant\n int j = discountPath.size() - 1;\n\n bool goUp = true;\n while(goUp && j != -1) {\n goUp = false;\n stirlingGenCurrent = (stirling_generator_full_log*)payloadDataPath[j].get();\n void* currentPayload = (*current).payload;\n void* parentPayload = NULL; // initialized below\n int currentCw = r.getC(currentPayload, type);\n int currentTw = r.getT(currentPayload, type);\n int otherT = r.getT(currentPayload) - currentTw;\n std::vector logProbs(currentCw, 0);\n std::vector logProbs1(currentCw, 0);\n std::vector logProbs2(currentCw, 0);\n std::vector logProbs3(currentCw, 0);\n std::vector logProbs4(currentCw, 0);\n if (j > 0) { // not at the top, so we have a CRP parent\n stirlingGenParent = (stirling_generator_full_log*)payloadDataPath[j-1].get();\n current--; // move to parent\n parentPayload = (*current).payload;\n current++;\n \n int parentTw = r.getT(parentPayload, type);\n int parentCw = r.getC(parentPayload, type);\n int parentOtherC = r.getC(parentPayload) - currentTw;\n for (int tw = 1; tw <= currentCw; ++tw) {\n int newParentCw = parentCw - currentTw + tw;\n if (newParentCw < parentTw) {\n logProbs4[tw-1] = -INFINITY;\n } else {\n logProbs1[tw-1] = logKramp(concentrationPath[j] + discountPath[j], discountPath[j], otherT + tw - 1);\n logProbs2[tw-1] = - logKramp(concentrationPath[j-1] + 1, 1, parentOtherC + tw - 1);\n logProbs3[tw-1] = stirlingGenCurrent->getLog(currentCw, tw);\n logProbs4[tw-1] = stirlingGenParent->getLog(newParentCw, parentTw);\n //std::cerr << logKramp(concentrationPath[j] + discountPath[j], discountPath[j], otherT + tw - 1) << std::endl;\n //std::cerr << logKramp(concentrationPath[j-1], 1, parentOtherC + tw - 1)<< std::endl;\n //std::cerr << stirlingGenCurrent->getLog(cw, tw)<< std::endl;\n //std::cerr << cw << \", \" << tw << std::endl;\n //std::cerr << stirlingGenParent->getLog(newParentCw, parentTw)<< std::endl;\n //std::cerr << newParentCw << \", \" << parentTw << std::endl;\n }\n }\n //std::cerr << parentCw << \", \" << parentTw << \", \" << parentOtherC << \", \" << currentTw << \", \" << otherT << std::endl;\n } else { // at the root, take base prob into account\n for (int tw = 1; tw <= currentCw; ++tw) {\n logProbs1[tw-1] = logKramp(concentrationPath[j] + discountPath[j], discountPath[j], otherT + tw - 1);\n logProbs2[tw-1] = stirlingGenCurrent->getLog(currentCw, tw);\n logProbs3[tw-1] = tw * log(baseProb);\n }\n }\n // subtract max for stability\n //std::cerr << iterableToString(logProbs1) << std::endl;\n //std::cerr << iterableToString(logProbs2) << std::endl;\n //std::cerr << iterableToString(logProbs3) << std::endl;\n //std::cerr << iterableToString(logProbs4) << std::endl;\n subMax_vec(logProbs1);\n subMax_vec(logProbs2);\n subMax_vec(logProbs3);\n subMax_vec(logProbs4);\n add_vec(logProbs, logProbs1);\n add_vec(logProbs, logProbs2);\n add_vec(logProbs, logProbs3);\n add_vec(logProbs, logProbs4);\n subMax_vec(logProbs);\n\n\n //std::cerr << iterableToString(logProbs1) << std::endl;\n //std::cerr << iterableToString(logProbs2) << std::endl;\n //std::cerr << iterableToString(logProbs3) << std::endl;\n //std::cerr << iterableToString(logProbs4) << std::endl;\n //std::cerr << iterableToString(logProbs) << std::endl;\n exp_vec(logProbs);\n //std::cerr << iterableToString(logProbs) << std::endl;\n // if (max == -INFINITY) { // if all choices are improbable, choose uniformly\n // // we can do better by normalizing the component individually\n // for (int ii = 0; ii < logProbs.size(); ++ii)\n // logProbs[ii] = 1.0;\n // }\n //std::cerr << iterableToString(logProbs) << std::endl;\n int sampledTw = sample_unnormalized_pdf(logProbs, 0) + 1;\n //std::cerr << \"Old tw: \" << currentTw << \", newTw: \" << sampledTw << std::endl;\n\n\n r.setT(currentPayload, type, sampledTw);\n if (j > 0) { \n int newCw = r.getC(parentPayload, type) - currentTw + sampledTw;\n assert(newCw >= r.getT(parentPayload, type));\n r.setC(parentPayload, type, newCw);\n }\n\n if (sampledTw != currentTw) {\n --current;\n --j;\n } else {\n goUp = false;\n }\n }\n\n }\n\n}\n\n\nboost::shared_ptr HPYPModel::makeAdditionalDataPtr(void* payload, \n double discount, \n double concentration) const {\n return boost::shared_ptr(\n this->restaurant.createAdditionalData(payload,\n discount,\n concentration),\n boost::bind(&IAddRemoveRestaurant::freeAdditionalData, \n &(this->restaurant),\n _1));\n}\n\n\nvoid HPYPModel::runGibbsSampler(bool directGibbs) {\n ContextTree::DFSPathIterator pathIterator = contextTree.getDFSPathIterator();\n d_vec discountPath = parameters.getDiscounts(*pathIterator);\n d_vec concentrationPath = parameters.getConcentrations(*pathIterator, \n discountPath);\n\n // initialize payloadDataPath; by using shared_ptr with the proper\n // destruction function, all clean-up should be automatic.\n HPYPModel::PayloadDataPath payloadDataPath;\n int j = 0;\n for (WrappedNodeList::const_iterator it = (*pathIterator).begin();\n it != (*pathIterator).end(); ++it) {\n payloadDataPath.push_back(this->makeAdditionalDataPtr(\n it->payload, discountPath[j], concentrationPath[j]));\n j++;\n }\n\n if (directGibbs) {\n this->directGibbsSamplePath(*pathIterator, discountPath, concentrationPath,\n payloadDataPath, baseProb);\n } else {\n this->addRemoveSamplePath(*pathIterator, discountPath, concentrationPath,\n payloadDataPath, baseProb);\n }\n\n size_t pathLength = (*pathIterator).size();\n \n while(pathIterator.hasMore()) { // loop over all paths in the tree\n ++pathIterator;\n if ((*pathIterator).size() == 0) {\n break;\n }\n\n if ((*pathIterator).size() == pathLength) {\n // sibling\n discountPath.pop_back();\n parameters.extendDiscounts(*pathIterator, discountPath);\n concentrationPath.pop_back();\n parameters.extendConcentrations(*pathIterator,\n discountPath, \n concentrationPath);\n payloadDataPath.pop_back();\n payloadDataPath.push_back(\n this->makeAdditionalDataPtr((*pathIterator).back().payload, \n discountPath.back(), \n concentrationPath.back()));\n\n } else {\n if ((*pathIterator).size() == pathLength - 1) {\n // we went up -- just drop the last term\n discountPath.pop_back();\n concentrationPath.pop_back();\n payloadDataPath.pop_back();\n } else {\n // we went up one and then down some number of levels -- recompute\n discountPath.pop_back();\n concentrationPath.pop_back();\n parameters.extendDiscounts(*pathIterator, discountPath);\n parameters.extendConcentrations(*pathIterator,\n discountPath,\n concentrationPath);\n payloadDataPath.pop_back();\n WrappedNodeList::const_iterator it = (*pathIterator).begin();\n // move it to the first item not covered by the payloadDataPath\n for (size_t i = 0; i < payloadDataPath.size(); ++i) {\n ++it;\n }\n for (size_t i = payloadDataPath.size(); i < discountPath.size(); ++i) {\n payloadDataPath.push_back(\n this->makeAdditionalDataPtr(it->payload, \n discountPath[i], \n concentrationPath[i]));\n\n ++it;\n }\n assert(it == (*pathIterator).end());\n }\n }\n pathLength = (*pathIterator).size();\n \n tracer << (*pathIterator).size() << \" \" << discountPath.size() << \" \" \n << concentrationPath.size() << \" \" << payloadDataPath.size() \n << std::endl;\n \n \n if (directGibbs) {\n this->directGibbsSamplePath(*pathIterator, discountPath, concentrationPath,\n payloadDataPath, baseProb);\n } else {\n this->addRemoveSamplePath(*pathIterator, discountPath, concentrationPath,\n payloadDataPath, baseProb);\n }\n }\n}\n\n\nd_vec HPYPModel::computeProbabilityPath(const WrappedNodeList& path, \n const d_vec& discount_path, \n const d_vec& concentration_path,\n e_type obs) {\n d_vec out;\n out.reserve(path.size() + 1);\n \n double prob = this->baseProb; // base distribution\n out.push_back(prob);\n \n int j = 0;\n for(WrappedNodeList::const_iterator it = path.begin();\n it!= path.end();\n ++it) {\n prob = this->restaurant.computeProbability(it->payload, \n obs,\n prob,\n discount_path[j],\n concentration_path[j]);\n out.push_back(prob);\n j++;\n } \n return out;\n}\n\n\nvoid HPYPModel::updatePath(const WrappedNodeList& path, \n const d_vec& prob_path, \n const d_vec& discount_path, \n const d_vec& concentration_path, \n e_type obs) {\n\n unsigned int j=path.size()-1;\n double newTable = 1;\n for(WrappedNodeList::const_reverse_iterator it = path.rbegin(); \n it != path.rend();\n ++it) {\n newTable = this->restaurant.addCustomer(it->payload,\n obs,\n prob_path[j],\n discount_path[j],\n concentration_path[j],\n NULL,\n newTable);\n if (newTable==0) {\n break;\n }\n j--;\n }\n}\n \n\nvoid HPYPModel::removeObservationFromPath(\n const WrappedNodeList& path,\n const d_vec& discountPath,\n e_type obs,\n const HPYPModel::PayloadDataPath& payloadDataPath) {\n int j = path.size()-1;\n\n\n double frac_t = 1;\n\n for(WrappedNodeList::const_reverse_iterator it = path.rbegin();\n it != path.rend(); it++) {\n\n void* payloadData = NULL;\n if (payloadDataPath.size() == path.size()) {\n payloadData = payloadDataPath[j].get();\n }\n\n frac_t = this->restaurant.removeCustomer(it->payload,\n obs,\n discountPath[j],\n payloadData, frac_t);\n if (frac_t == 0.)\n break;\n j--;\n }\n}\n \n\nvoid HPYPModel::handleSplit(const WrappedNode& nodeA,\n const WrappedNode& nodeB, \n const WrappedNode& nodeC) {\n int lengthA = nodeA.end - nodeA.start;\n int lengthB = nodeB.end - nodeB.start;\n int lengthC = nodeC.end - nodeC.start;\n \n // parent context should be shorter than both its children \n assert(lengthA < lengthB && lengthA < lengthC);\n // length of node that required splitting should be longer than the result\n assert(lengthC < lengthB);\n\n double discBBeforeSplit = this->parameters.getDiscount(lengthA, lengthB);\n double discBAfterSplit = this->parameters.getDiscount(lengthC, lengthB);\n this->restaurant.updateAfterSplit(nodeB.payload, \n nodeC.payload, \n discBBeforeSplit,\n discBAfterSplit);\n}\n\n\nbool HPYPModel::checkConsistency(const WrappedNode& node, \n const std::list& children) const {\n bool consistent = this->restaurant.checkConsistency(node.payload);\n std::map table_counts;\n for(std::list::const_iterator it = children.begin();\n it != children.end(); ++it) {\n IHPYPBaseRestaurant::TypeVector keys = \n this->restaurant.getTypeVector(it->payload);\n for(IHPYPBaseRestaurant::TypeVectorIterator key_it = keys.begin();\n key_it != keys.end(); ++key_it) {\n table_counts[*key_it] += this->restaurant.getT(it->payload, *key_it);\n }\n }\n\n for(std::map::iterator it = table_counts.begin();\n it != table_counts.end(); ++it) {\n consistent = (this->restaurant.getC(node.payload, (*it).first) \n >= (*it).second) && consistent;\n if (!consistent) {\n std::cerr << \"Child table sum is: \" << (*it).second \n << \", parent customers is: \" \n << this->restaurant.getC(node.payload, (*it).first) \n << std::endl;\n }\n }\n return consistent;\n}\n\n\nvoid HPYPModel::prunePath(WrappedNodeList& path) {\n\n}\n\n\nbool HPYPModel::checkConsistency() const {\n CheckConsistencyVisitor v(*this);\n this->contextTree.visitDFSWithChildren(v);\n return v.consistent;\n}\n \n\ndouble HPYPModel::computeLogRestaurantProb(\n const WrappedNodeList& path, \n const d_vec& discountPath, \n const d_vec& concentrationPath, \n const HPYPModel::PayloadDataPath& payloadDataPath,\n double baseProb) const {\n assert(path.size() > 0);\n assert(path.size() == discountPath.size());\n assert(path.size() == concentrationPath.size());\n\n void* payload = path.back().payload;\n const BaseCompactRestaurant& r = (BaseCompactRestaurant&)this->restaurant; // shortcut\n double logProb = 0;\n int j = discountPath.size() - 1;\n l_type c = r.getC(payload); \n if (c == 1) {\n // deterministic restaurant\n return 0;\n }\n l_type t = r.getT(payload);\n //std::cerr << \"c: \" << c << \", t: \" << t << std::endl;\n //std::cerr << \"a: \" << concentrationPath[j] << \", d: \" << discountPath[j] << std::endl;\n logProb += logKramp(concentrationPath[j] + discountPath[j], discountPath[j], t - 1);\n //std::cerr << logProb << std::endl;\n logProb -= logKramp(concentrationPath[j] + 1, 1, c - 1);\n //std::cerr << logProb << std::endl;\n\n \n stirling_generator_full_log *stirlingGen = (stirling_generator_full_log*)payloadDataPath[j].get();\n IHPYPBaseRestaurant::TypeVector types = r.getTypeVector(payload);\n for(IHPYPBaseRestaurant::TypeVectorIterator it = types.begin(); \n it != types.end(); ++it) { // for each type of customer \n\n e_type type = *it;\n l_type cw = r.getC(payload, type);\n l_type tw = r.getT(payload, type);\n \n logProb += stirlingGen->getLog(cw, tw);\n\n if (j == 0) { // at the root, take base prob into account\n logProb += tw * log(baseProb);\n }\n }\n //std::cerr << logProb << std::endl;\n return logProb;\n}\n\ndouble HPYPModel::computeLogJoint() const {\n double logJoint = 0;\n ContextTree::DFSPathIterator pathIterator = contextTree.getDFSPathIterator();\n d_vec discountPath = parameters.getDiscounts(*pathIterator);\n d_vec concentrationPath = parameters.getConcentrations(*pathIterator, \n discountPath);\n\n // initialize payloadDataPath; by using shared_ptr with the proper\n // destruction function, all clean-up should be automatic.\n HPYPModel::PayloadDataPath payloadDataPath;\n int j = 0;\n for (WrappedNodeList::const_iterator it = (*pathIterator).begin();\n it != (*pathIterator).end(); ++it) {\n payloadDataPath.push_back(this->makeAdditionalDataPtr(\n it->payload, discountPath[j], concentrationPath[j]));\n j++;\n }\n\n logJoint += computeLogRestaurantProb(*pathIterator, discountPath, concentrationPath, payloadDataPath, baseProb);\n\n size_t pathLength = (*pathIterator).size();\n \n while(pathIterator.hasMore()) { // loop over all paths in the tree\n ++pathIterator;\n if ((*pathIterator).size() == 0) {\n break;\n }\n\n if ((*pathIterator).size() == pathLength) {\n // sibling\n discountPath.pop_back();\n parameters.extendDiscounts(*pathIterator, discountPath);\n concentrationPath.pop_back();\n parameters.extendConcentrations(*pathIterator,\n discountPath, \n concentrationPath);\n payloadDataPath.pop_back();\n payloadDataPath.push_back(\n this->makeAdditionalDataPtr((*pathIterator).back().payload, \n discountPath.back(), \n concentrationPath.back()));\n\n } else {\n if ((*pathIterator).size() == pathLength - 1) {\n // we went up -- just drop the last term\n discountPath.pop_back();\n concentrationPath.pop_back();\n payloadDataPath.pop_back();\n } else {\n // we went up one and then down some number of levels -- recompute\n discountPath.pop_back();\n concentrationPath.pop_back();\n parameters.extendDiscounts(*pathIterator, discountPath);\n parameters.extendConcentrations(*pathIterator,\n discountPath,\n concentrationPath);\n payloadDataPath.pop_back();\n WrappedNodeList::const_iterator it = (*pathIterator).begin();\n // move it to the first item not covered by the payloadDataPath\n for (size_t i = 0; i < payloadDataPath.size(); ++i) {\n ++it;\n }\n for (size_t i = payloadDataPath.size(); i < discountPath.size(); ++i) {\n payloadDataPath.push_back(\n this->makeAdditionalDataPtr(it->payload, \n discountPath[i], \n concentrationPath[i]));\n\n ++it;\n }\n assert(it == (*pathIterator).end());\n }\n }\n pathLength = (*pathIterator).size();\n\n logJoint += computeLogRestaurantProb(*pathIterator, discountPath, concentrationPath, payloadDataPath, baseProb);\n \n }\n return logJoint;\n}\n\n\nHPYPModel::ToStringVisitor::ToStringVisitor(seq_type& seq, \n const IHPYPBaseRestaurant& restaurant) \n : outstream(), seq(seq), restaurant(restaurant) {}\n\n\nvoid HPYPModel::ToStringVisitor::operator()(const WrappedNode& n) {\n for (l_type i = 0; i < n.depth; ++i) {\n this->outstream << \" \"; \n }\n this->outstream << SubSeq::toString(n.start, n.end, seq);\n this->outstream << \" \" << this->restaurant.toString(n.payload);\n this->outstream << std::endl;\n}\n\n\nHPYPModel::CheckConsistencyVisitor::CheckConsistencyVisitor(\n const HPYPModel& model) : consistent(true), model(model) {}\n\n\nvoid HPYPModel::CheckConsistencyVisitor::operator()(\n WrappedNode& n, std::list& children) {\n bool nodeConsistent = this->model.checkConsistency(n, children);\n if (!nodeConsistent) {\n std::cerr << \"Node \" << n.toString() << \" not consistent!\" << std::endl;\n }\n consistent = consistent && nodeConsistent;\n}\n\n\nHPYPModel::LogJointVisitor::LogJointVisitor(\n const HPYPModel& model) : logJoint(0), model(model) {}\n\n\nvoid HPYPModel::LogJointVisitor::operator()(\n // FIXME\n WrappedNode& n, std::list& children) {\n int c = model.restaurant.getC(n.payload);\n int t = model.restaurant.getT(n.payload);\n IHPYPBaseRestaurant::TypeVector keys = \n model.restaurant.getTypeVector(n.payload);\n for(IHPYPBaseRestaurant::TypeVectorIterator key_it = keys.begin();\n key_it != keys.end(); ++key_it) {\n int cw = model.restaurant.getC(n.payload, *key_it);\n int tw = model.restaurant.getT(n.payload, *key_it);\n }\n}\n\nstd::string HPYPModel::toString() {\n HPYPModel::ToStringVisitor visitor(this->seq, this->restaurant);\n this->contextTree.visitDFS(visitor);\n return visitor.outstream.str();\n}\n\n\n}} // namespace gatsby::libplump\n", "meta": {"hexsha": "7df68b97e1263dbed69fbebf1ccd0cbc8fc20f13", "size": 41222, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/libplump/hpyp_model.cc", "max_stars_repo_name": "jgasthaus/libPLUMP", "max_stars_repo_head_hexsha": "18e5911575e3c9a054482b08d637dc91b0cd05b9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 13.0, "max_stars_repo_stars_event_min_datetime": "2015-02-02T21:46:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-02-16T03:50:37.000Z", "max_issues_repo_path": "src/libplump/hpyp_model.cc", "max_issues_repo_name": "jgasthaus/libPLUMP", "max_issues_repo_head_hexsha": "18e5911575e3c9a054482b08d637dc91b0cd05b9", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/libplump/hpyp_model.cc", "max_forks_repo_name": "jgasthaus/libPLUMP", "max_forks_repo_head_hexsha": "18e5911575e3c9a054482b08d637dc91b0cd05b9", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2016-11-17T19:19:37.000Z", "max_forks_repo_forks_event_max_datetime": "2016-11-20T00:56:17.000Z", "avg_line_length": 36.3189427313, "max_line_length": 128, "alphanum_fraction": 0.5781378875, "num_tokens": 9418, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.24430053065818438}} {"text": "/// Causal Dynamical Triangulations in C++ using CGAL\n///\n/// Copyright © 2016-2017 Adam Getchell\n///\n/// Data structures for operations on simplicial manifolds\n///\n/// \\done Classify cells as (3,1), (2,2), or (1,3) based on their foliation.\n/// A tuple of vectors contain cell handles to the simplices of type (3,1),\n/// (2,2), and (1,3) respectively.\n/// \\done Classify edges as timelike or spacelike so that action can be\n/// calculated.\n/// \\done SimplicialManifold data structure holding a std::unique_ptr to\n/// the Delaunay triangulation and a std::tuple of geometry information.\n/// \\done Move constructor recalculates geometry.\n///\n/// @file SimplicialManifold.hpp\n/// @brief Data structures for simplicial manifolds\n/// @author Adam Getchell\n/// @todo Deprecated in favor of Manifold.hpp\n\n#ifndef INCLUDE_SIMPLICIALMANIFOLD_HPP_\n#define INCLUDE_SIMPLICIALMANIFOLD_HPP_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing Facet = Delaunay3::Facet;\n\n/// @brief A tuple of the geometric values of the Simplicial Manifold\n///\n/// The first element is the vector of (3,1) simplices\n/// The second element is the vector of (2,2) simplices\n/// The third element is the vector of (1,3) simplices\n/// The fourth element is the vector of timelike edges\n/// The fifth element is the vector of spacelike edges\n/// The sixth element is the vector of vertices\n///\n/// Useful for constructing GeometryInfo, which contains this and other\n/// information, and in comparing the results of moves, which will change one or\n/// more elements of the Geometry_tuple.\nusing Geometry_tuple =\n std::tuple, std::vector,\n std::vector, std::vector,\n std::vector, std::vector>;\n\n// Non-member non-friend functions\n\n/// @brief Classifies edges\n///\n/// This function iterates over all edges in the triangulation\n/// and classifies them as timelike or spacelike.\n/// Timelike edges are stored in the **timelike_edges** vector as an Edge_handle\n/// (tuple of Cell_handle, std::size_t, std::size_t) for later use by\n/// ergodic moves on timelike edges. Spacelike edges are also stored as a\n/// vector of Edge_handle **spacelike_edges**, for use by (4,4) moves as\n/// well as the distance-finding algorithms.\n/// @param[in] universe_ptr A std::unique_ptr to the triangulation\n/// @returns A std::pair, std::vector> of\n/// timelike edges and spacelike edges\ntemplate \n[[deprecated]] auto classify_edges(T&& universe_ptr)\n{\n#ifndef NDEBUG\n std::cout << \"Classifying edges....\\n\";\n#endif\n Delaunay3::Finite_edges_iterator eit;\n std::vector timelike_edges;\n std::vector spacelike_edges;\n\n // Iterate over all edges in the Delaunay triangulation\n for (eit = universe_ptr->finite_edges_begin();\n eit != universe_ptr->finite_edges_end(); ++eit)\n {\n Cell_handle ch = eit->first;\n // Get timevalues of vertices at the edge ends\n auto time1 = ch->vertex(eit->second)->info();\n auto time2 = ch->vertex(eit->third)->info();\n\n // Make Edge_handle\n Edge_handle thisEdge{\n ch, static_cast(ch->index(ch->vertex(eit->second))),\n static_cast(ch->index(ch->vertex(eit->third)))};\n\n if (time1 != time2)\n { // We have a timelike edge\n timelike_edges.emplace_back(thisEdge);\n\n#ifdef DETAILED_DEBUGGING\n std::cout << \"First vertex of edge is \" << std::get<1>(thisEdge)\n << \" and second vertex of edge is \" << std::get<2>(thisEdge)\n << std::endl;\n#endif\n }\n else\n { // We have a spacelike edge\n spacelike_edges.emplace_back(thisEdge);\n } // endif\n } // Finish iterating over edges\n\n// Display results if debugging\n#ifndef NDEBUG\n std::cout << \"There are \" << timelike_edges.size() << \" timelike edges and \"\n << spacelike_edges.size() << \" spacelike edges.\\n\";\n#endif\n return std::make_pair(timelike_edges, spacelike_edges);\n} // classify_edges()\n\n/// @brief Classify simplices as (3,1), (2,2), or (1,3)\n///\n/// This function iterates over all cells in the triangulation\n/// and classifies them as:\n/// \\f{eqnarray*}{\n/// 31 &=& (3, 1) \\\\\n/// 22 &=& (2, 2) \\\\\n/// 13 &=& (1, 3)\n/// \\f}\n/// The vectors **three_one**, **two_two**, and **one_three** contain\n/// Cell_handles to all the simplices in the triangulation of that corresponding\n/// type.\n///\n/// @param[in] universe_ptr A std::unique_ptr to the triangulation\n/// @returns A std::tuple of\n/// **three_one**, **two_two**, and **one_three**\ntemplate \n[[deprecated]] auto classify_simplices(T&& universe_ptr)\n{\n#ifndef NDEBUG\n std::cout << \"Classifying simplices....\\n\";\n#endif\n Delaunay3::Finite_cells_iterator cit;\n std::vector three_one;\n std::vector two_two;\n std::vector one_three;\n\n // Iterate over all cells in the Delaunay triangulation\n for (cit = universe_ptr->finite_cells_begin();\n cit != universe_ptr->finite_cells_end(); ++cit)\n {\n int max_values{0};\n int min_values{0};\n // Push every time value of every vertex into a list\n int timevalues[4] = {\n cit->vertex(0)->info(),\n cit->vertex(1)->info(),\n cit->vertex(2)->info(),\n cit->vertex(3)->info(),\n };\n int max_time =\n *std::max_element(std::begin(timevalues), std::end(timevalues));\n for (auto elt : timevalues)\n {\n if (elt == max_time) { ++max_values; }\n else\n {\n ++min_values;\n }\n }\n\n // Classify simplex using max_values and write to cit->info()\n if (min_values == 1 && max_values == 3)\n {\n cit->info() = 13;\n one_three.emplace_back(cit);\n }\n else if (min_values == 2 && max_values == 2)\n {\n cit->info() = 22;\n two_two.emplace_back(cit);\n }\n else if (min_values == 3 && max_values == 1)\n {\n cit->info() = 31;\n three_one.emplace_back(cit);\n }\n else\n {\n throw std::runtime_error(\"Invalid simplex in classify_simplices()!\");\n } // endif\n } // Finish iterating over cells\n\n// Display results if debugging\n#ifndef NDEBUG\n std::cout << \"There are \" << three_one.size() << \" (3,1) simplices and \"\n << two_two.size() << \" (2,2) simplices\\n\";\n std::cout << \"and \" << one_three.size() << \" (1,3) simplices.\\n\";\n#endif\n return std::make_tuple(three_one, two_two, one_three);\n} // classify_simplices()\n\ntemplate \n[[deprecated]] auto classify_all_simplices(T&& universe_ptr)\n{\n#ifndef NDEBUG\n std::cout << \"Classifying all simplices....\\n\";\n#endif\n\n auto cells = classify_simplices(universe_ptr);\n auto edges = classify_edges(universe_ptr);\n std::vector vertices;\n for (auto vit = universe_ptr->finite_vertices_begin();\n vit != universe_ptr->finite_vertices_end(); ++vit)\n { vertices.emplace_back(vit); }\n return std::make_tuple(std::get<0>(cells), std::get<1>(cells),\n std::get<2>(cells), edges.first, edges.second,\n vertices);\n}\n\n/// @struct\n/// @brief A struct containing detailed geometry information\n///\n/// GeometryInfo contains information about the geometry of\n/// a triangulation. In addition, it defines convenient functions to\n/// retrieve commonly used values. This is to save the expense of\n/// calculating manually from the triangulation. GeometryInfo() is\n/// recalculated using the move assignment operator anytime a\n/// SimplicialManifold() is move constructed.\n/// The default constructor, destructor, move constructor, copy\n/// constructor, and copy assignment operator are explicitly defaulted.\n/// See http://en.cppreference.com/w/cpp/language/rule_of_three\nstruct [[deprecated]] GeometryInfo\n{\n private:\n /// @brief (3,1) cells in the foliation\n std::vector three_one;\n\n /// @brief (2,2) cells in the foliation\n std::vector two_two;\n\n /// @brief (1,3) cells in the foliation\n std::vector one_three;\n\n /// @brief Edges spanning two adjacent time slices in the foliation\n std::vector timelike_edges;\n\n /// @brief Non-spanning edges in the foliation\n std::vector spacelike_edges;\n\n /// @brief Vertices of the foliation\n std::vector vertices;\n\n /// @brief Spacelike facets for each timeslice\n boost::optional> spacelike_facets;\n\n /// @brief Actual timevalues of simulation\n boost::optional> timevalues;\n\n public:\n /// @brief Getter for spacelike facets\n /// @return The multimap of facets\n const boost::optional>&\n getSpacelike_facets() const\n {\n return spacelike_facets;\n }\n\n /// @brief Setter for spacelike facets\n /// @param spacelike_facets The multimap of facets\n void setSpacelike_facets(\n const boost::optional>&\n spacelike_facets)\n {\n GeometryInfo::spacelike_facets = spacelike_facets;\n }\n\n /// @brief Getter for timevalues\n /// @return The set of timevalues\n const boost::optional>& getTimevalues() const\n {\n return timevalues;\n }\n\n /// @brief Setter for timevalues\n /// @param timevalues The set of timevalues\n void setTimevalues(const boost::optional>& timevalues)\n {\n GeometryInfo::timevalues = timevalues;\n }\n\n /// @brief Default constructor\n GeometryInfo() = default;\n\n /// @brief Constructor from Geometry_tuple\n ///\n /// This is usually called as a result of classify_all_simplices(),\n /// which itself takes a std::unique_ptr\n /// @param geometry Geometry_tuple initializing values\n /// @return A populated GeometryInfo{}\n explicit GeometryInfo(const Geometry_tuple&& geometry) noexcept\n : three_one{std::get<0>(geometry)}\n , two_two{std::get<1>(geometry)}\n , one_three{std::get<2>(geometry)}\n , timelike_edges{std::get<3>(geometry)}\n , spacelike_edges{std::get<4>(geometry)}\n , vertices{std::get<5>(geometry)}\n {}\n\n /// @brief Default destructor\n ~GeometryInfo() = default;\n\n /// @brief Default move constructor\n GeometryInfo(GeometryInfo&&) = default;\n\n /// @brief Default move assignment operator\n // GeometryInfo& operator=(GeometryInfo&&) = default;\n GeometryInfo& operator=(Geometry_tuple&& other)\n {\n#ifndef NDEBUG\n std::cout << \"GeometryInfo move assignment operator.\" << std::endl;\n#endif\n three_one = std::move(std::get<0>(other));\n two_two = std::move(std::get<1>(other));\n one_three = std::move(std::get<2>(other));\n timelike_edges = std::move(std::get<3>(other));\n spacelike_edges = std::move(std::get<4>(other));\n vertices = std::move(std::get<5>(other));\n return *this;\n }\n\n /// @brief Default copy constructor\n GeometryInfo(const GeometryInfo&) = default;\n\n /// @brief Default copy assignment operator\n GeometryInfo& operator=(const GeometryInfo&) = default;\n\n /// @brief Timelike edges\n /// @return The number of edges spanning timeslices\n auto N1_TL() { return static_cast(timelike_edges.size()); }\n\n /// @brief Spacelike edges\n /// @return The number of edges on same timeslice\n auto N1_SL() { return static_cast(spacelike_edges.size()); }\n\n /// @brief (3,1) simplices\n /// @return The total number of simplices with 3 vertices on the t\n /// timeslice and 1 vertex on the t+1 timeslice\n auto N3_31() { return static_cast(three_one.size()); }\n\n /// @brief (1,3) simplices\n /// @return The total number of simplices with 1 vertex on the t timeslice\n /// and 3 vertices on the t+1 timeslice\n auto N3_13() { return static_cast(one_three.size()); }\n\n /// @brief (3,1) and (1,3) simplices\n /// @return The total number of simplices with 3 vertices on one\n /// timeslice and 1 vertex on the adjacent timeslice. Used to\n /// calculate the change in action.\n auto N3_31_13() { return N3_31() + N3_13(); }\n\n /// @brief (2,2) simplices\n /// @return The total number of simplices with 2 vertices on one\n /// timeslice and 2 vertices on the adjacent timeslice. Used to\n /// calculate the change in action.\n auto N3_22() { return static_cast(two_two.size()); }\n\n /// @brief Number of cells\n ///\n /// This should be the equivalent of\n /// SimplicialManifold::triangulation->number_of_finite_cells(),\n /// and is used as a check to ensure that GeometryInfo{} matches.\n /// @return The number of cells in GeometryInfo{}\n auto number_of_cells() { return N3_31() + N3_22() + N3_13(); }\n\n /// @brief Number of edges\n ///\n /// This should be the equivalent of\n /// SimplicialManifold::triangulation->number_of_finite_edges(),\n /// and is used as a check to ensure that GeometryInfo{} matches.\n /// @return The number of edges in the triangulation\n auto number_of_edges() { return N1_TL() + N1_SL(); }\n\n // auto max_timevalue() { return *timevalues.crbegin();}\n boost::optional max_timevalue()\n {\n return timevalues ? *timevalues->crbegin() : 0;\n }\n\n boost::optional min_timevalue()\n {\n return timevalues ? *timevalues->begin() : 0;\n }\n /// @brief Number of vertices\n /// @return The number of vertices in the triangulation\n auto N0() { return static_cast(vertices.size()); }\n\n template \n friend auto make_23_move(T1&& universe, T2&& attempted_moves)\n -> decltype(universe);\n\n template \n friend auto make_32_move(T1&& universe, T2&& attempted_moves)\n -> decltype(universe);\n\n template \n friend auto make_26_move(T1&& universe, T2&& attempted_moves)\n -> decltype(universe);\n\n template \n friend auto make_62_move(T1&& universe, T2&& attempted_moves)\n -> decltype(universe);\n\n template \n friend auto make_44_move(T1&& universe, T2&& attempted_moves)\n -> decltype(universe);\n\n template \n friend auto VolumePerTimeslice(T&& manifold) -> decltype(manifold);\n};\n\n/// @struct\n/// @brief A struct to hold triangulation and geometry information\n///\n/// SimplicialManifold contains information about the triangulation and\n/// its geometry. In addition, it defines convenient constructors.\nstruct [[deprecated]] SimplicialManifold\n{\n /// @brief Owning pointer to the Delaunay triangulation\n std::unique_ptr triangulation;\n\n /// @brief Owning pointer to GeometryInfo\n std::unique_ptr geometry;\n\n /// @brief Default constructor\n SimplicialManifold()\n : triangulation{std::make_unique()}\n , geometry{std::make_unique()}\n {\n#ifndef NDEBUG\n std::cout << \"SimplicialManifold default ctor.\\n\";\n#endif\n }\n\n /// @brief Constructor with std::unique_ptr\n ///\n /// Constructor taking a std::unique_ptr which should be created\n /// using make_triangulation(). If you wish to default initialize a\n /// SimplicialManifold with no values, use the default\n /// constructor SimplicialManifold::SimplicialManifold() instead.\n /// Non-static data members are initialized in the order they are declared,\n /// (see http://open-std.org/JTC1/SC22/WG21/docs/papers/2016/n4594.pdf,\n /// \\f$\\S\\f$ 12.6.2.13.3), so **geometry** depending upon **triangulation**\n /// is fine.\n /// @param manifold A std::unique_ptr\n /// @return A SimplicialManifold{}\n explicit SimplicialManifold(std::unique_ptr&& manifold)\n : triangulation{std::move(manifold)}\n , geometry{std::make_unique(\n classify_all_simplices(triangulation))}\n {\n#ifndef NDEBUG\n std::cout << \"SimplicialManifold std::unique_ptr ctor.\\n\";\n#endif\n }\n\n /// @brief make_triangulation constructor\n ///\n /// Constructor that initializes **triangulation** by calling\n /// make_triangulation() and **geometry** by calling\n /// classify_all_simplices().\n /// @param simplices The number of desired simplices in the triangulation\n /// @param timeslices The number of timeslices in the triangulation\n /// @return A populated SimplicialManifold{}\n SimplicialManifold(std::size_t simplices, std::size_t timeslices)\n : triangulation{make_triangulation(simplices, timeslices)}\n , geometry{std::make_unique(\n classify_all_simplices(triangulation))}\n {\n#ifndef NDEBUG\n std::cout << \"SimplicialManifold make_triangulation ctor.\\n\";\n#endif\n }\n\n /// @brief Destructor\n ~SimplicialManifold()\n {\n#ifndef NDEBUG\n std::cout << \"SimplicialManifold dtor.\\n\";\n#endif\n this->triangulation = nullptr;\n this->geometry = nullptr;\n }\n\n /// @brief Move constructor\n /// @param other The SimplicialManifold to be move-constructed from\n /// @return A moved-to SimplicialManifold{}\n SimplicialManifold(SimplicialManifold&& other)\n : triangulation{std::move(other.triangulation)}\n , geometry{std::make_unique(\n classify_all_simplices(triangulation))}\n // , geometry{std::move(other.geometry)}\n {\n#ifndef NDEBUG\n std::cout << \"SimplicialManifold move ctor.\\n\";\n#endif\n }\n // SimplicialManifold(SimplicialManifold&&) = default;\n\n /// @brief Move assignment operator\n /// @param other The SimplicialManifold to be moved from\n /// @return A moved-assigned SimplicialManifold{}\n SimplicialManifold& operator=(SimplicialManifold&& other) noexcept\n {\n#ifndef NDEBUG\n std::cout << \"SimplicialManifold move assignment operator.\\n\";\n#endif\n triangulation = std::move(other.triangulation);\n geometry = std::make_unique(\n classify_all_simplices(std::move(triangulation)));\n // geometry = std::move(other.geometry);\n return *this;\n }\n // SimplicialManifold& operator=(SimplicialManifold&&) = default;\n\n /// @brief SimplicialManifold copy constructor\n /// @param other The SimplicialManifold to copy\n /// @return A copied SimplicialManifold{}\n SimplicialManifold(const SimplicialManifold& other)\n : triangulation{std::make_unique(*(other.triangulation))}\n , geometry{std::make_unique(*(other.geometry))}\n {\n#ifndef NDEBUG\n std::cout << \"SimplicialManifold copy ctor.\\n\";\n#endif\n }\n\n /// @brief Copy assignment operator\n /// @return A copy-assigned SimplicialManifold{}\n // SimplicialManifold& operator=(const SimplicialManifold&) = default;\n SimplicialManifold& operator=(const SimplicialManifold& other) noexcept\n {\n#ifndef NDEBUG\n std::cout << \"SimplicialManifold copy assignment operator.\\n\";\n#endif\n SimplicialManifold temp(other);\n swap(triangulation, temp.triangulation);\n swap(geometry, temp.geometry);\n return *this;\n }\n\n /// @brief Exception-safe swap\n /// @param first The first SimplicialManifold to be swapped\n /// @param second The second SimplicialManifold to be swapped with.\n friend void swap(SimplicialManifold& first,\n SimplicialManifold& second) noexcept\n {\n#ifndef NDEBUG\n std::cout << \"SimplicialManifold swapperator.\\n\";\n#endif\n using std::swap;\n swap(first.triangulation, second.triangulation);\n swap(first.geometry, second.geometry);\n }\n\n bool reconcile()\n {\n return (this->triangulation->number_of_vertices() == this->geometry->N0() &&\n this->triangulation->number_of_finite_edges() ==\n this->geometry->number_of_edges() &&\n this->triangulation->number_of_finite_cells() ==\n this->geometry->number_of_cells());\n }\n\n void update()\n {\n geometry = std::make_unique(\n classify_all_simplices(std::move(triangulation)));\n }\n};\n\n#endif // INCLUDE_SIMPLICIALMANIFOLD_HPP_\n", "meta": {"hexsha": "c08c3f3f2724cb78262cf3314e15e406721b2ae8", "size": 19992, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/SimplicialManifold.hpp", "max_stars_repo_name": "jefftrull/CDT-plusplus", "max_stars_repo_head_hexsha": "d66e82d2fcc459151c3bbcaa8eab74253e40c3cf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, 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YES\n2. NO", "lm_q1_score": 0.538983220687684, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.24430053065818436}} {"text": "/*\n * Copyright 2010 CNRS\n *\n * Florent Lamiraux\n */\n\n#include \n#include \n\n#include \n#include \n#include \n#include \"dynamic-graph/tutorial/inverted-pendulum.hh\"\n#include \"command-increment.hh\"\n#include \"constant.hh\"\n\nusing namespace dynamicgraph;\nusing namespace dynamicgraph::tutorial;\n\nconst double Constant::gravity = 9.81;\n\n// Register new Entity type in the factory\n// Note that the second argument is the type name of the python class\n// that will be created when importing the python module.\nDYNAMICGRAPH_FACTORY_ENTITY_PLUGIN(InvertedPendulum, \"InvertedPendulum\");\n\nInvertedPendulum::InvertedPendulum(const std::string& inName)\n : Entity(inName),\n forceSIN(NULL, \"InvertedPendulum(\" + inName + \")::input(double)::force\"),\n stateSOUT(\"InvertedPendulum(\" + inName + \")::output(vector)::state\"),\n cartMass_(1.0),\n pendulumMass_(1.0),\n pendulumLength_(1.0),\n viscosity_(0.1) {\n // Register signals into the entity.\n signalRegistration(forceSIN);\n signalRegistration(stateSOUT);\n\n // Set signals as constant to size them\n Vector state(4);\n state.fill(0.);\n double input = 0.;\n stateSOUT.setConstant(state);\n forceSIN.setConstant(input);\n\n // Commands\n std::string docstring;\n\n // Incr\n docstring =\n \"\\n\"\n \" Integrate dynamics for time step provided as input\\n\"\n \"\\n\"\n \" take one floating point number as input\\n\"\n \"\\n\";\n addCommand(std::string(\"incr\"), new command::Increment(*this, docstring));\n\n // setCartMass\n docstring =\n \"\\n\"\n \" Set cart mass\\n\"\n \"\\n\";\n addCommand(std::string(\"setCartMass\"), new ::dynamicgraph::command::Setter(\n *this, &InvertedPendulum::setCartMass, docstring));\n\n // getCartMass\n docstring =\n \"\\n\"\n \" Get cart mass\\n\"\n \"\\n\";\n addCommand(std::string(\"getCartMass\"), new ::dynamicgraph::command::Getter(\n *this, &InvertedPendulum::getCartMass, docstring));\n\n // setPendulumMass\n docstring =\n \"\\n\"\n \" Set pendulum mass\\n\"\n \"\\n\";\n addCommand(std::string(\"setPendulumMass\"), new ::dynamicgraph::command::Setter(\n *this, &InvertedPendulum::setPendulumMass, docstring));\n\n // getPendulumMass\n docstring =\n \"\\n\"\n \" Get pendulum mass\\n\"\n \"\\n\";\n addCommand(std::string(\"getPendulumMass\"), new ::dynamicgraph::command::Getter(\n *this, &InvertedPendulum::getPendulumMass, docstring));\n\n // setPendulumLength\n docstring =\n \"\\n\"\n \" Set pendulum length\\n\"\n \"\\n\";\n addCommand(std::string(\"setPendulumLength\"), new ::dynamicgraph::command::Setter(\n *this, &InvertedPendulum::setPendulumLength, docstring));\n\n // getPendulumLength\n docstring =\n \"\\n\"\n \" Get pendulum length\\n\"\n \"\\n\";\n addCommand(std::string(\"getPendulumLength\"), new ::dynamicgraph::command::Getter(\n *this, &InvertedPendulum::getPendulumLength, docstring));\n}\n\nInvertedPendulum::~InvertedPendulum() {}\n\nVector InvertedPendulum::computeDynamics(const Vector& inState, const double& inControl, double inTimeStep) {\n if (inState.size() != 4)\n throw dynamicgraph::ExceptionSignal(dynamicgraph::ExceptionSignal::GENERIC, \"state signal size is \",\n \"%d, should be 4.\", inState.size());\n\n double dt = inTimeStep;\n double dt2 = dt * dt;\n double g = Constant::gravity;\n double x = inState(0);\n double th = inState(1);\n double dx = inState(2);\n double dth = inState(3);\n double F = inControl;\n double m = pendulumMass_;\n double M = cartMass_;\n double l = pendulumLength_;\n double lambda = viscosity_;\n double l2 = l * l;\n double dth2 = dth * dth;\n double sth = sin(th);\n double cth = cos(th);\n double sth2 = sth * sth;\n\n double b1 = F - m * l * dth2 * sth - lambda * dx;\n double b2 = m * l * g * sth - lambda * dth;\n\n double det = m * l2 * (M + m * sth2);\n\n double ddx = (b1 * m * l2 + b2 * m * l * cth) / det;\n double ddth = ((M + m) * b2 + m * l * cth * b1) / det;\n\n Vector nextState(4);\n nextState(0) = x + dx * dt + .5 * ddx * dt2;\n nextState(1) = th + dth * dt + .5 * ddth * dt2;\n nextState(2) = dx + dt * ddx;\n nextState(3) = dth + dt * ddth;\n\n return nextState;\n}\n\nvoid InvertedPendulum::incr(double inTimeStep) {\n int t = stateSOUT.getTime();\n Vector nextState = computeDynamics(stateSOUT(t), forceSIN(t), inTimeStep);\n stateSOUT.setConstant(nextState);\n stateSOUT.setTime(t + 1);\n forceSIN(t + 1);\n}\n", "meta": {"hexsha": "40031b8b40104b1a6fccf54f5ff2a548c2029d52", "size": 4935, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/inverted-pendulum.cpp", "max_stars_repo_name": "Rascof/dynamic-graph-tutorial", "max_stars_repo_head_hexsha": "8b3df91f8ba86e41841c8b14bb300e4444e240cd", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-06-12T15:37:40.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-23T12:59:08.000Z", "max_issues_repo_path": "src/inverted-pendulum.cpp", "max_issues_repo_name": "Rascof/dynamic-graph-tutorial", "max_issues_repo_head_hexsha": "8b3df91f8ba86e41841c8b14bb300e4444e240cd", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2020-08-24T20:55:04.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-24T20:55:04.000Z", "max_forks_repo_path": "src/inverted-pendulum.cpp", "max_forks_repo_name": "Rascof/dynamic-graph-tutorial", "max_forks_repo_head_hexsha": "8b3df91f8ba86e41841c8b14bb300e4444e240cd", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2017-12-14T14:57:13.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-22T08:40:33.000Z", "avg_line_length": 31.6346153846, "max_line_length": 109, "alphanum_fraction": 0.6216818642, "num_tokens": 1373, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.538983220687684, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.24430053065818436}} {"text": "#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\n\n#include \"Simulator.hpp\"\n#include \"QFRSimulator.hpp\"\n#include \"GroverSimulator.hpp\"\n#include \"ShorFastSimulator.hpp\"\n#include \"ShorSimulator.hpp\"\n\n\nint main(int argc, char** argv) {\n namespace po = boost::program_options;\n unsigned long long seed;\n\n po::options_description description(\"JKQ DDSIM by https://iic.jku.at/eda/ -- Allowed options\");\n description.add_options()\n (\"help,h\", \"produce help message\")\n (\"seed\", po::value(&seed)->default_value(0), \"seed for random number generator (default zero is possibly directly used as seed!)\")\n (\"shots\", po::value()->default_value(0), \"number of measurements (if the algorithm does not contain non-unitary gates, weak simulation is used)\")\n (\"display_vector\", \"display the state vector\")\n (\"ps\", \"print simulation stats (applied gates, sim. time, and maximal size of the DD)\")\n (\"verbose\", \"Causes some simulators to print additional information to STDERR\")\n (\"benchmark\", \"print simulation stats in a single CSV style line (overrides --ps and suppresses most other output, please don't rely on the format across versions)\")\n\n (\"simulate_file\", po::value(), \"simulate a quantum circuit given by file (detection by the file extension)\")\n (\"simulate_qft\", po::value(), \"simulate Quantum Fourier Transform for given number of qubits\")\n (\"simulate_ghz\", po::value(), \"simulate state preparation of GHZ state for given number of qubits\")\n (\"step_fidelity\", po::value()->default_value(1.0), \"target fidelity for each approximation run (>=1 = disable approximation)\")\n (\"steps\", po::value()->default_value(1), \"number of approximation steps\")\n (\"initial_reorder\", po::value()->default_value(0), \"Try to find a good initial variable order (0=None, 1=Most affected qubits to the top, 2=Most affected targets to the top)\")\n (\"dynamic_reorder\", po::value()->default_value(0), \"Apply reordering strategy during simulation (0=None, 1=Sifting, 2=Move2Top)\")\n (\"post_reorder\", po::value()->default_value(0), \"Apply a reordering strategy after simulation (0=None, 1=Sifting)\")\n\n (\"simulate_grover\", po::value(), \"simulate Grover's search for given number of qubits with random oracle\")\n (\"simulate_grover_emulated\", po::value(), \"simulate Grover's search for given number of qubits with random oracle and emulation\")\n (\"simulate_grover_oracle_emulated\", po::value(), \"simulate Grover's search for given number of qubits with given oracle and emulation\")\n\n (\"simulate_shor\", po::value(), \"simulate Shor's algorithm factoring this number\")\n (\"simulate_shor_coprime\", po::value()->default_value(0), \"coprime number to use with Shor's algorithm (zero randomly generates a coprime)\")\n (\"simulate_shor_no_emulation\", \"Force Shor simulator to do modular exponentiation instead of using emulation (you'll usually want emulation)\")\n\n (\"simulate_fast_shor\", po::value(), \"simulate Shor's algorithm factoring this number with intermediate measurements\")\n (\"simulate_fast_shor_coprime\", po::value()->default_value(0), \"coprime number to use with Shor's algorithm (zero randomly generates a coprime)\")\n (\"noise_effects\", po::value(),\n \"Noise effects (A (=amplitude damping),D (=depolarization),P (=phase flip)) in the form of a character string describing the noise effects (default=\\\" \\\")\")\n (\"noise_prob\", po::value(), \"Probability for applying noise (default=0.001)\")\n (\"confidence\", po::value(),\n \"Confidence in the error bound of the stochastic simulation (default= 0.05)\")\n (\"error_bound\", po::value(), \"Error bound of the stochastic simulation (default=0.1)\")\n (\"stoch_runs\", po::value()->default_value(0), \"Number of stochastic runs. When the value is 0 the value is calculated using the confidence, error_bound and number of tracked properties. (default = 0)\")\n (\"properties\", po::value(), R\"(Comma separated list of tracked properties. Note that -1 is the fidelity and \"-\" can be used to specify a range. (default=\"0-1000\"))\")\n ;\n po::variables_map vm;\n try {\n po::store(po::parse_command_line(argc, argv, description), vm);\n\n if (vm.count(\"help\")) {\n std::cout << description;\n return 0;\n }\n po::notify(vm);\n } catch (const po::error &e) {\n std::cerr << \"[ERROR] \" << e.what() << \"! Try option '--help' for available commandline options.\\n\";\n std::exit(1);\n }\n\n std::unique_ptr quantumComputation;\n std::unique_ptr ddsim{nullptr};\n\n if (vm.count(\"simulate_file\")) {\n const std::string fname = vm[\"simulate_file\"].as();\n \tquantumComputation = std::make_unique(fname);\n ddsim = std::make_unique(quantumComputation,\n vm[\"steps\"].as(), vm[\"step_fidelity\"].as(),\n vm[\"initial_reorder\"].as(), vm[\"dynamic_reorder\"].as(), vm[\"post_reorder\"].as(),\n seed);\n } else {\n std::cerr << \"Did not find anything to simulate. See help below.\\n\"\n << description << \"\\n\";\n return 1;\n }\n\n if (quantumComputation && quantumComputation->getNqubits() > dd::MAXN) {\n std::cerr << \"Quantum computation contains to many qubits (limit is set to \" << dd::MAXN << \"). See documentation for details.\\n\";\n std::exit(1);\n }\n if (vm.count(\"noise_effects\")) {\n ddsim->setNoiseEffects((char *) &vm[\"noise_effects\"].as()[0]);\n } else {\n ddsim->setNoiseEffects((char *) &std::string(\"0\")[0]);\n }\n if (vm.count(\"noise_prob\")) {\n ddsim->setAmplitudeDampingProbability(vm[\"noise_prob\"].as());\n } else {\n ddsim->setAmplitudeDampingProbability(0.001);\n }\n\n if (vm.count(\"confidence\")) {\n ddsim->stoch_confidence = vm[\"confidence\"].as();\n }\n\n if (vm.count(\"properties\")) {\n ddsim->setRecordedProperties((char *) &vm[\"properties\"].as()[0]);\n } else {\n ddsim->setRecordedProperties(\"0-1000\");\n }\n\n if (vm.count(\"error_bound\")) {\n ddsim->stoch_error_margin = vm[\"error_bound\"].as();\n } else {\n ddsim->stoch_error_margin = 0.01;\n }\n ddsim->stochastic_runs = vm[\"stoch_runs\"].as();\n\n auto t1 = std::chrono::high_resolution_clock::now();\n ddsim->StochSimulate();\n auto t2 = std::chrono::high_resolution_clock::now();\n\n std::chrono::duration duration_simulation = t2-t1;\n\n if (vm.count(\"benchmark\")) {\n auto more_info = ddsim->AdditionalStatistics();\n std::cout << ddsim->getName() << \", \"\n << ddsim->getNumberOfQubits() << \", \"\n //<< vm[\"approximate\"].as() << \", \"\n << std::fixed << duration_simulation.count() << std::defaultfloat << \", \"\n //<< more_info[\"approximation_runs\"] << \",\"\n //<< more_info[\"final_fidelity\"] << \", \"\n << more_info[\"coprime_a\"] << \", \"\n << more_info[\"sim_result\"] << \", \"\n << more_info[\"polr_result\"] << \", \"\n << ddsim->getSeed() << \", \"\n << ddsim->getNumberOfOps() << \", \"\n << ddsim->getMaxNodeCount()\n << \"\\n\";\n return 0;\n }\n\n std::cout << \"{\\n\";\n\n if (vm.count(\"ps\")) {\n std::cout << \" \\\"statistics\\\": {\\n\"\n << \" \\\"simulation_time\\\": \" << std::fixed << duration_simulation.count() << std::defaultfloat << \",\\n\"\n << \" \\\"benchmark\\\": \\\"\" << ddsim->getName() << \"\\\",\\n\"\n << \" \\\"shots\\\": \" << vm[\"shots\"].as() << \",\\n\"\n// << \" \\\"distinct_results\\\": \" << m.size() << \",\\n\"\n << \" \\\"n_qubits\\\": \" << ddsim->getNumberOfQubits() << \",\\n\"\n << \" \\\"applied_gates\\\": \" << ddsim->getNumberOfOps() << \",\\n\"\n << \" \\\"max_nodes\\\": \" << ddsim->getMaxNodeCount() << \",\\n\"\n ;\n for(const auto& item : ddsim->AdditionalStatistics()) {\n std::cout << \" \\\"\" << item.first << \"\\\": \\\"\" << item.second << \"\\\",\\n\";\n }\n std::cout << \" \\\"seed\\\": \" << ddsim->getSeed() << \"\\n\"\n << \" },\\n\";\n }\n std::cout << \" \\\"dummy\\\": 0\\n}\\n\"; // trailing element to make json printout easier\n}\n", "meta": {"hexsha": "7cb54f47c1098cc7dd094fc5e41c8b8f03fe03a5", "size": 9205, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "apps/noise_aware.cpp", "max_stars_repo_name": "Tonanguyxiro/ddsim", "max_stars_repo_head_hexsha": "ff6d8afba6e72f4795394688872a9081f871d320", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-03-05T09:17:39.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-05T09:17:39.000Z", "max_issues_repo_path": "apps/noise_aware.cpp", "max_issues_repo_name": "Tonanguyxiro/ddsim", "max_issues_repo_head_hexsha": "ff6d8afba6e72f4795394688872a9081f871d320", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "apps/noise_aware.cpp", "max_forks_repo_name": "Tonanguyxiro/ddsim", "max_forks_repo_head_hexsha": "ff6d8afba6e72f4795394688872a9081f871d320", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 55.119760479, "max_line_length": 219, "alphanum_fraction": 0.5900054318, "num_tokens": 2217, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792046, "lm_q2_score": 0.48047867804790706, "lm_q1q2_score": 0.2439927732459154}} {"text": "/*\n * Copyright (c) 2011, Mattia Penati \n * All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without modification,\n * are permitted provided that the following conditions are met:\n *\n * * Redistributions of source code must retain the above copyright notice,\n * this list of conditions and the following disclaimer.\n * * Redistributions in binary form must reproduce the above copyright notice,\n * this list of conditions and the following disclaimer in the documentation\n * and/or other materials provided with the distribution.\n * * Neither the name of the Politecnico di Milano nor the names of its\n * contributors may be used to endorse or promote products derived from\n * this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\n * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR\n * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON\n * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n */\n\n#ifndef AMA_TENSOR_DETAIL_TENSOR_OUTER_HPP\n#define AMA_TENSOR_DETAIL_TENSOR_OUTER_HPP 1\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace ama\n{\n namespace tensor_\n {\n\n /* forward declaration*/\n template class tensor_outer;\n\n\n /* traits definition */\n template \n struct tensor_traits< tensor_outer >\n {\n typedef typename LEFT::value_type value_type;\n\n typedef typename LEFT::dimension_type dimension_type;\n\n typedef typename ::boost::mpl::plus<\n typename LEFT::controvariant_type\n , typename RIGHT::controvariant_type\n >::type controvariant_type;\n typedef typename ::boost::mpl::plus<\n typename LEFT::covariant_type\n , typename RIGHT::covariant_type\n >::type covariant_type;\n\n typedef ::boost::mpl::false_ is_assignable;\n typedef ::boost::mpl::true_ is_temporary;\n };\n\n\n /* class declaration */\n template \n class tensor_outer:\n public tensor_base< tensor_outer >\n {\n protected:\n typedef tensor_base< tensor_outer > base_type;\n typedef tensor_outer derived_type;\n\n protected:\n typedef LEFT left_operand_type;\n typedef RIGHT right_operand_type;\n\n public:\n typedef typename base_type::value_type value_type;\n\n public:\n /* costructor */\n tensor_outer(left_operand_type const & left,\n right_operand_type const & right)\n : m_left(left),\n m_right(right) { }\n\n public:\n /* retrieve the value */\n template \n value_type at() const\n {\n namespace mpl = ::boost::mpl;\n\n /* iterator */\n typedef typename mpl::begin::type begin1;\n typedef typename mpl::advance::type begin2;\n typedef typename mpl::advance::type begin3;\n typedef typename mpl::advance::type begin4;\n typedef typename mpl::advance::type begin5;\n typedef typename mpl::end::type end;\n\n /* check for no errors in the code */\n BOOST_MPL_ASSERT((mpl::equal_to,mpl::size_t<0> >));\n\n /* split multi-index */\n typedef typename mpl::fold<\n mpl::iterator_range\n , mpl::vector0_c\n , mpl::push_back\n >::type controvariant_left;\n\n typedef typename mpl::fold<\n mpl::iterator_range\n , mpl::vector0_c\n , mpl::push_back\n >::type controvariant_right;\n\n typedef typename mpl::fold<\n mpl::iterator_range\n , mpl::vector0_c\n , mpl::push_back\n >::type covariant_left;\n\n typedef typename mpl::fold<\n mpl::iterator_range\n , mpl::vector0_c\n , mpl::push_back\n >::type covariant_right;\n\n /* append the splittend multi-index */\n typedef typename mpl::fold<\n covariant_left\n , controvariant_left\n , mpl::push_back\n >::type left_ilist;\n\n typedef typename mpl::fold<\n covariant_right\n , controvariant_right\n , mpl::push_back\n >::type right_ilist;\n\n return (m_left.template at()) *\n (m_right.template at());\n }\n\n protected:\n /* if the operand is temporary we save a copy, otherwise a reference */\n typedef typename LEFT::is_temporary left_operand_is_temporary;\n typedef typename ::boost::mpl::if_<\n left_operand_is_temporary\n , left_operand_type const\n , left_operand_type const &\n >::type const_left_operand_type;\n\n /* if the operand is temporary we save a copy, otherwise a reference */\n typedef typename RIGHT::is_temporary right_operand_is_temporary;\n typedef typename ::boost::mpl::if_<\n right_operand_is_temporary\n , right_operand_type const\n , right_operand_type const &\n >::type const_right_operand_type;\n\n /* members */\n const_left_operand_type m_left;\n const_right_operand_type m_right;\n };\n\n }\n}\n\n#endif /* AMA_TENSOR_DETAIL_TENSOR_OUTER_HPP */\n", "meta": {"hexsha": "c2ea500f37bd476a634ce845d45bb46be48aca23", "size": 6707, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/ama/tensor/detail/tensor_outer.hpp", "max_stars_repo_name": "mattiapenati/amanita", "max_stars_repo_head_hexsha": "c5c16d1f17e71151ce1d8e6972ddff6cec3c7305", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/ama/tensor/detail/tensor_outer.hpp", "max_issues_repo_name": "mattiapenati/amanita", "max_issues_repo_head_hexsha": "c5c16d1f17e71151ce1d8e6972ddff6cec3c7305", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/ama/tensor/detail/tensor_outer.hpp", "max_forks_repo_name": "mattiapenati/amanita", "max_forks_repo_head_hexsha": "c5c16d1f17e71151ce1d8e6972ddff6cec3c7305", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.650273224, "max_line_length": 95, "alphanum_fraction": 0.6536454451, "num_tokens": 1447, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118493816807, "lm_q2_score": 0.480478678047907, "lm_q1q2_score": 0.2439927660879728}} {"text": "////////////////////////////////////////////////////////////////////////////////\n// Copyright 2019 FZI Research Center for Information Technology\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are met:\n//\n// 1. Redistributions of source code must retain the above copyright notice,\n// this list of conditions and the following disclaimer.\n//\n// 2. Redistributions in binary form must reproduce the above copyright notice,\n// this list of conditions and the following disclaimer in the documentation\n// and/or other materials provided with the distribution.\n//\n// 3. Neither the name of the copyright holder nor the names of its\n// contributors may be used to endorse or promote products derived from this\n// software without specific prior written permission.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\n// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n// POSSIBILITY OF SUCH DAMAGE.\n////////////////////////////////////////////////////////////////////////////////\n\n//-----------------------------------------------------------------------------\n/*!\\file SelectivelyDampedLeastSquaresSolver.cpp\n *\n * \\author Stefan Scherzinger \n * \\date 2020/03/27\n *\n */\n//-----------------------------------------------------------------------------\n\n// this package\n#include \n\n// Pluginlib\n#include \n\n// other\n#include \n\n/**\n * \\class cartesian_controller_base::SelectivelyDampedLeastSquaresSolver \n *\n * Users may explicitly specify this solver with \\a \"selectively_damped_least_squares\" as \\a\n * ik_solver in their controllers.yaml configuration file for each controller:\n *\n * \\code{.yaml}\n * :\n * type: \"\"\n * ik_solver: \"selectively_damped_least_squares\"\n * ...\n * \\endcode\n *\n */\nPLUGINLIB_EXPORT_CLASS(cartesian_controller_base::SelectivelyDampedLeastSquaresSolver, cartesian_controller_base::IKSolver)\n\n\n\n\n\nnamespace cartesian_controller_base{\n\n SelectivelyDampedLeastSquaresSolver::SelectivelyDampedLeastSquaresSolver()\n {\n }\n\n SelectivelyDampedLeastSquaresSolver::~SelectivelyDampedLeastSquaresSolver(){}\n\n trajectory_msgs::JointTrajectoryPoint SelectivelyDampedLeastSquaresSolver::getJointControlCmds(\n ros::Duration period,\n const ctrl::Vector6D& net_force)\n {\n // Compute joint Jacobian\n m_jnt_jacobian_solver->JntToJac(m_current_positions,m_jnt_jacobian);\n\n Eigen::JacobiSVD > JSVD(\n m_jnt_jacobian.data, Eigen::ComputeFullU | Eigen::ComputeFullV);\n Eigen::Matrix U = JSVD.matrixU();\n Eigen::Matrix V = JSVD.matrixV();\n Eigen::Matrix s = JSVD.singularValues();\n\n // Default recommendation by Buss and Kim.\n const double gamma_max = 3.141592653 / 4;\n\n Eigen::Matrix sum_phi =\n Eigen::Matrix::Zero(m_number_joints);\n\n // Compute each joint velocity with the SDLS method. This implements the\n // algorithm as described in the paper (but for only one end-effector).\n // Also see Buss' own implementation:\n // https://www.math.ucsd.edu/~sbuss/ResearchWeb/ikmethods/index.html\n\n for (int i = 0; i < m_number_joints; ++i)\n {\n double alpha = U.col(i).transpose() * net_force;\n\n double N = U.col(i).head(3).norm();\n double M = 0;\n for (int j = 0; j < m_number_joints; ++j)\n {\n double rho = m_jnt_jacobian.data.col(j).head(3).norm();\n M += std::abs(V.col(i)[j]) * rho;\n }\n M *= 1.0 / s[i];\n\n double gamma = std::min(1.0, N / M) * gamma_max;\n\n Eigen::Matrix phi = clampMaxAbs(1.0 / s[i] * alpha * V.col(i), gamma);\n sum_phi += phi;\n }\n\n m_current_velocities.data = clampMaxAbs(sum_phi, gamma_max);\n\n // Integrate once, starting with zero motion\n m_current_positions.data = m_last_positions.data + 0.5 * m_current_velocities.data * period.toSec();\n\n // Make sure positions stay in allowed margins\n applyJointLimits();\n\n // Apply results\n trajectory_msgs::JointTrajectoryPoint control_cmd;\n for (int i = 0; i < m_number_joints; ++i)\n {\n control_cmd.positions.push_back(m_current_positions(i));\n control_cmd.velocities.push_back(m_current_velocities(i));\n\n // Accelerations should be left empty. Those values will be interpreted\n // by most hardware joint drivers as max. tolerated values. As a\n // consequence, the robot will move very slowly.\n }\n control_cmd.time_from_start = period; // valid for this duration\n\n return control_cmd;\n }\n\n bool SelectivelyDampedLeastSquaresSolver::init(ros::NodeHandle& nh,\n const KDL::Chain& chain,\n const KDL::JntArray& upper_pos_limits,\n const KDL::JntArray& lower_pos_limits)\n {\n IKSolver::init(nh, chain, upper_pos_limits, lower_pos_limits);\n\n m_jnt_jacobian_solver.reset(new KDL::ChainJntToJacSolver(m_chain));\n m_jnt_jacobian.resize(m_number_joints);\n\n return true;\n }\n\n Eigen::Matrix SelectivelyDampedLeastSquaresSolver::clampMaxAbs(\n const Eigen::Matrix& w, double d)\n {\n if (w.cwiseAbs().maxCoeff() <= d)\n {\n return w;\n }\n else\n {\n return d * w / w.cwiseAbs().maxCoeff();\n }\n }\n\n} // namespace\n", "meta": {"hexsha": "7394e7346c0a607ff5d8de32a0432ee387b19243", "size": 6320, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cartesian_controller_base/src/SelectivelyDampedLeastSquaresSolver.cpp", "max_stars_repo_name": "graziegrazie/cartesian_controllers", "max_stars_repo_head_hexsha": "40156bbbd45de17f0e03e9007863d087295c318f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 137.0, "max_stars_repo_stars_event_min_datetime": "2019-11-01T07:14:53.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-23T08:14:52.000Z", "max_issues_repo_path": "cartesian_controller_base/src/SelectivelyDampedLeastSquaresSolver.cpp", "max_issues_repo_name": "graziegrazie/cartesian_controllers", "max_issues_repo_head_hexsha": "40156bbbd45de17f0e03e9007863d087295c318f", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 39.0, "max_issues_repo_issues_event_min_datetime": "2020-05-14T20:40:58.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T14:17:50.000Z", "max_forks_repo_path": "cartesian_controller_base/src/SelectivelyDampedLeastSquaresSolver.cpp", "max_forks_repo_name": "graziegrazie/cartesian_controllers", "max_forks_repo_head_hexsha": "40156bbbd45de17f0e03e9007863d087295c318f", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 37.0, "max_forks_repo_forks_event_min_datetime": "2019-11-01T07:05:03.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-14T08:26:35.000Z", "avg_line_length": 36.9590643275, "max_line_length": 123, "alphanum_fraction": 0.6693037975, "num_tokens": 1478, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.2439741813327488}} {"text": "#include \"BootstrapRunner.h\"\n#include \n#include \nusing namespace std;\n\nBootstrapRunner::BootstrapRunner(BoostrapConfig* bootstrapConfig, CftConfig cftConfig)\n{\n this->bootstrapConfig = bootstrapConfig;\n this->cftData = new CftData(cftConfig);\n this->correlatorSet = new CorrelatorSet(cftData, bootstrapConfig->NumberOfScalarsToBootstrap);\n}\n\nBootstrapRunner::~BootstrapRunner()\n{\n if (this->cftData != 0) {\n delete cftData;\n }\n\n if (this->correlatorSet != 0) {\n delete this->correlatorSet;\n }\n}\n\nfloat_type BootstrapRunner::ConstraintCost(CftData* cftData)\n{\n float_type ret = .0;\n float_type lastDim = .0, dim;\n int lastSpin = -1;\n\n for (uint i = 1; i <= cftData->MaxPrimaryId(); i++) {\n dim = cftData->GetPrimaryDim(i);\n if (cftData->GetPrimarySpin(i) != lastSpin) {\n lastSpin = cftData->GetPrimarySpin(i);\n if (lastSpin == 0) {\n if (dim < cftData->D / 2.0 - 1) ret += (cftData->D / 2.0 - 1 - dim);\n } else {\n if (dim < lastSpin + cftData->D - 2) ret += (lastSpin + cftData->D - 2 - dim);\n }\n } else if (dim < lastDim) {\n ret += (lastDim - dim);\n }\n\n lastDim = dim;\n }\n\n return ret * bootstrapConfig->ConstraintFactor;\n}\n\nvoid BootstrapRunner::ConstraintCostDerivative(CftData* cftData, vector& ops, vector& opsDerivatives)\n{\n assert(ops.size() == opsDerivatives.size());\n\n float_type dim, lastDim, nextDim;\n\n for (uint i = 0; i < ops.size(); i++) {\n int l = cftData->GetPrimarySpin(ops[i]);\n dim = cftData->GetPrimaryDim(ops[i]);\n\n if (ops[i] > 1 && cftData->GetPrimarySpin(ops[i] - 1) == l) {\n lastDim = cftData->GetPrimaryDim(ops[i] - 1);\n if (dim < lastDim) {\n opsDerivatives[i] -= bootstrapConfig->ConstraintFactor;\n }\n }\n\n if (ops[i] < cftData->MaxPrimaryId() && cftData->GetPrimarySpin(ops[i] + 1) == l) {\n nextDim = cftData->GetPrimaryDim(ops[i] + 1);\n if (dim > nextDim) {\n opsDerivatives[i] += bootstrapConfig->ConstraintFactor;\n }\n }\n\n if (ops[i] == 1 || cftData->GetPrimarySpin(ops[i] - 1) != l) {\n if (l == 0 && dim <= cftData->D / 2.0 - 1) {\n opsDerivatives[i] -= bootstrapConfig->ConstraintFactor;\n }\n\n if (l > 0 && dim <= cftData->D + l - 2.0) {\n opsDerivatives[i] -= bootstrapConfig->ConstraintFactor;\n }\n }\n }\n}\n\nvoid BootstrapRunner::Run()\n{\n vector coefs;\n vector ops;\n\n for (uint op = 1; op <= cftData->MaxPrimaryId(); op++) {\n if (op != cftData->StressTensorId) {\n ops.push_back(op);\n }\n }\n\n for (uint op1 = 1; op1 <= bootstrapConfig->NumberOfScalarsToBootstrap; op1++) {\n for (uint op2 = op1; op2 <= bootstrapConfig->NumberOfScalarsToBootstrap; op2++) {\n for (uint op3 = op2; op3 <= cftData->MaxPrimaryId(); op3++) {\n if (op3 != cftData->StressTensorId) {\n coefs.push_back(OpeCoefficientKey(op1, op2, op3));\n }\n }\n }\n }\n\n vector zs;\n zs.reserve(bootstrapConfig->SamplesEachStep);\n for (uint i = 0; i < bootstrapConfig->SamplesEachStep; i++) {\n float_type dr = random(0.0, 0.2);\n float_type theta = random(0.0, 2 * acos(-1.0));\n zs.push_back(cpx_t(dr * cos(theta), dr * sin(theta)) + 0.5);\n }\n\n vector opsDerivatives(ops.size(), .0);\n vector opsDerivativeTot(ops.size(), .0);\n vector coefsDerivatives(coefs.size(), .0);\n vector coefsDerivativeTot(coefs.size(), .0);\n\n float_type lastCost = std::numeric_limits::max();\n vector scalarIds;\n for (uint i = 1; i <= cftData->MaxScalarId(); i++) scalarIds.push_back(i);\n\n boost::timer stopwatch;\n\n while (true) {\n std::cout << \"Time elapsed: \" << stopwatch.elapsed() << \" seconds.\" << endl;\n cftData->Output(scalarIds);\n\n opsDerivativeTot.resize(ops.size(), .0);\n coefsDerivativeTot.resize(coefs.size(), .0);\n\n float_type cost = .0;\n\n for (uint i = 0; i < zs.size(); i++) {\n this->correlatorSet->CostDerivatives(zs[i], ops, coefs, opsDerivatives, coefsDerivatives);\n cost += this->correlatorSet->Cost(zs[i]);\n\n for (uint j = 0; j < opsDerivatives.size(); j++) {\n opsDerivativeTot[j] += opsDerivatives[j];\n }\n\n for (uint j = 0; j < coefsDerivatives.size(); j++) {\n coefsDerivativeTot[j] += coefsDerivatives[j];\n }\n }\n\n cost /= zs.size();\n std::cout << \"pure cost = \" << cost << \"\\t\";\n cost += ConstraintCost(cftData);\n std::cout << \"total cost = \" << cost << std::endl;\n \n if (cost < bootstrapConfig->Accuracy) break;\n\n float_type gradNorm = .0;\n for (uint j = 0; j < coefsDerivativeTot.size(); j++) {\n coefsDerivativeTot[j] /= zs.size();\n gradNorm += coefsDerivativeTot[j] * coefsDerivativeTot[j];\n }\n\n std::cout << \"Coefs Derivative: \" << coefsDerivativeTot << std::endl;\n\n for (uint j = 0; j < opsDerivativeTot.size(); j++) {\n opsDerivativeTot[j] /= zs.size();\n }\n\n std::cout << \"Ops Derivative: \" << opsDerivativeTot << std::endl;\n\n ConstraintCostDerivative(cftData, ops, opsDerivativeTot);\n\n std::cout << \"Ops Derivative with constraints: \" << opsDerivativeTot << std::endl;\n\n for (uint j = 0; j < opsDerivativeTot.size(); j++) {\n gradNorm += opsDerivativeTot[j] * opsDerivativeTot[j];\n }\n\n float_type factor = gradNorm / ((ops.size() + coefs.size()));\n factor = sqrt(factor);\n\n for (uint j = 0; j < coefsDerivativeTot.size(); j++) {\n coefsDerivativeTot[j] /= factor;\n }\n\n for (uint j = 0; j < opsDerivativeTot.size(); j++) {\n opsDerivativeTot[j] /= factor;\n }\n\n std::cout << \"Coefs Derivative normalized: \" << coefsDerivativeTot << std::endl;\n std::cout << \"Ops Derivative normalized: \" << opsDerivativeTot << std::endl;\n\n for (uint j = 0; j < opsDerivativeTot.size(); j++) {\n float_type dim = cftData->GetPrimaryDim(ops[j]);\n cftData->SetPrimaryDim(ops[j], dim - opsDerivativeTot[j] * bootstrapConfig->Step);\n }\n\n for (uint j = 0; j < coefsDerivativeTot.size(); j++) {\n float_type coef = cftData->GetOpeCoefficient(coefs[j]);\n cftData->SetOpeCoefficient(coefs[j], coef - coefsDerivativeTot[j] * bootstrapConfig->Step);\n }\n\n this->correlatorSet->UpdateCftData(cftData);\n lastCost = cost;\n }\n}\n\n\n", "meta": {"hexsha": "667247bb1ee2a35171e5d9ac89da415cb24d5d5d", "size": 6879, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/BootstrapRunner.cpp", "max_stars_repo_name": "gaolichen/cftbtsp", "max_stars_repo_head_hexsha": "e764b6ca339d6d68a5c6b6acd9f58ef64c628d47", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/BootstrapRunner.cpp", "max_issues_repo_name": "gaolichen/cftbtsp", "max_issues_repo_head_hexsha": "e764b6ca339d6d68a5c6b6acd9f58ef64c628d47", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/BootstrapRunner.cpp", "max_forks_repo_name": "gaolichen/cftbtsp", "max_forks_repo_head_hexsha": "e764b6ca339d6d68a5c6b6acd9f58ef64c628d47", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.3932038835, "max_line_length": 118, "alphanum_fraction": 0.5596743713, "num_tokens": 1948, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.2439741813327488}} {"text": "#include \"../is_valid_char.hxx\"\n#include \"../parse_vector.hxx\"\n#include \"../parse_number.hxx\"\n#include \"../../../../Damped_Rational.hxx\"\n\n#include \n\n#include \n#include \n#include \n\nconst char *parse_damped_rational(const char *begin, const char *end,\n Damped_Rational &damped_rational)\n{\n const std::string damped_literal(\"DampedRational[\");\n\n // Parse constant numbers\n for(auto current = begin; current != end; ++current)\n {\n if(*current == damped_literal.front())\n {\n break;\n }\n if(is_valid_char(*current))\n {\n damped_rational.base = 1;\n auto comma(std::find(current, end, ','));\n if(comma == end)\n {\n throw std::runtime_error(\n \"Missing a comma when parsing a constant as a DampedRational. \"\n \" The parsed string is\\n\\t'\"\n + std::string(current, end) + \"'\");\n }\n damped_rational.constant = Boost_Float(parse_number(current, comma));\n return comma;\n }\n }\n\n auto damped_start(\n std::search(begin, end, damped_literal.begin(), damped_literal.end()));\n if(damped_start == end)\n {\n throw std::runtime_error(\"Could not find '\" + damped_literal + \"'\");\n }\n\n auto comma(std::find(damped_start, end, ','));\n if(comma == end)\n {\n throw std::runtime_error(\"Missing comma after DampedRational.constant\");\n }\n auto constant_start(std::next(damped_start, damped_literal.size()));\n damped_rational.constant = Boost_Float(parse_number(constant_start, comma));\n\n auto start_poles(std::next(comma));\n auto end_poles(parse_vector(start_poles, end, damped_rational.poles));\n\n comma = std::find(end_poles, end, ',');\n if(comma == end)\n {\n throw std::runtime_error(\"Missing comma after DampedRational.poles\");\n }\n\n auto start_base(std::next(comma));\n comma = std::find(start_base, end, ',');\n if(comma == end)\n {\n throw std::runtime_error(\"Missing comma after DampedRational.base\");\n }\n damped_rational.base = Boost_Float(parse_number(start_base, comma));\n\n auto start_variable(std::next(comma));\n const auto close_bracket(std::find(start_variable, end, ']'));\n if(close_bracket == end)\n {\n throw std::runtime_error(\"Missing ']' at end of DampedRational\");\n }\n\n return std::next(close_bracket);\n}\n", "meta": {"hexsha": "69ccf86aa2d6aa2bfb90cad62b7acdafd4ab357e", "size": 2425, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "src/sdp_read/read_input/read_mathematica/parse_SDP/parse_matrix/parse_damped_rational.cxx", "max_stars_repo_name": "ChrisPattison/sdpb", "max_stars_repo_head_hexsha": "4668f72c935e7feba705dd8247d9aacb23185f1c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 45.0, "max_stars_repo_stars_event_min_datetime": "2015-02-10T15:45:22.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-24T07:45:01.000Z", "max_issues_repo_path": "src/sdp_read/read_input/read_mathematica/parse_SDP/parse_matrix/parse_damped_rational.cxx", "max_issues_repo_name": "ChrisPattison/sdpb", "max_issues_repo_head_hexsha": "4668f72c935e7feba705dd8247d9aacb23185f1c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 58.0, "max_issues_repo_issues_event_min_datetime": "2015-02-27T10:03:18.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-10T04:21:42.000Z", "max_forks_repo_path": "src/sdp_read/read_input/read_mathematica/parse_SDP/parse_matrix/parse_damped_rational.cxx", "max_forks_repo_name": "ChrisPattison/sdpb", "max_forks_repo_head_hexsha": "4668f72c935e7feba705dd8247d9aacb23185f1c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 38.0, "max_forks_repo_forks_event_min_datetime": "2015-02-10T11:11:27.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-11T20:59:42.000Z", "avg_line_length": 29.9382716049, "max_line_length": 79, "alphanum_fraction": 0.6239175258, "num_tokens": 573, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.24395876097908092}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nfloat gen_gaussian() {\n static std::random_device r;\n static std::default_random_engine generator(r());\n static std::normal_distribution distribution;\n return distribution(generator);\n}\n\ntemplate \nint sgn(T val) {\n return (T(0) < val) - (val < T(0));\n}\n\nenum class Optimizer { Sgd, Sgdm, PowerSign };\n\nstruct Dims {\n int w;\n int h;\n int c;\n\n int sz() const { return w * h * c; }\n};\n\nstruct Settings {\n float lr;\n int batch_size;\n int epochs;\n float l2;\n float grad_max;\n float lr_decay;\n Optimizer optimizer;\n\n Settings()\n : lr(0.1),\n batch_size(8),\n epochs(10),\n l2(0.0001),\n grad_max(5),\n lr_decay(1),\n optimizer(Optimizer::Sgdm) {}\n};\n\ntemplate \nclass Pool {\n public:\n class PoolAlloc {\n std::unique_ptr x_;\n int sz_;\n int owners_;\n\n public:\n PoolAlloc(std::unique_ptr&& x, int sz)\n : x_(std::move(x)), sz_(sz), owners_(0) {}\n\n bool is_free() const { return owners_ == 0; }\n\n void add_owner() { ++owners_; }\n void remove_owner() {\n#if _GLIBCXX_DEBUG\n if (owners_ == 0)\n throw std::runtime_error(\n \"trying to deown something that was said to have zero \"\n \"wner\");\n#endif\n --owners_;\n }\n\n auto* get() { return x_.get(); }\n const auto* get() const { return x_.get(); }\n\n int sz() const { return sz_; }\n };\n\n class Recyclable {\n public:\n Recyclable(PoolAlloc* alloc) : alloc_(alloc) { alloc_->add_owner(); }\n\n Recyclable(Recyclable&& o) {\n alloc_ = o.alloc_;\n o.alloc_ = nullptr;\n }\n\n Recyclable() : alloc_(nullptr) {}\n\n Recyclable(const Recyclable& o) {\n alloc_ = o.alloc_;\n if (alloc_) {\n alloc_->add_owner();\n }\n }\n\n ~Recyclable() {\n if (alloc_) {\n alloc_->remove_owner();\n }\n }\n\n Recyclable& operator=(Recyclable&& o) {\n if (alloc_) {\n alloc_->remove_owner();\n }\n alloc_ = o.alloc_;\n o.alloc_ = nullptr;\n return *this;\n }\n\n Recyclable& operator=(const Recyclable& o) {\n if (alloc_) {\n alloc_->remove_owner();\n }\n alloc_ = o.alloc_;\n if (alloc_) {\n alloc_->add_owner();\n }\n return *this;\n }\n\n auto& operator*() { return *alloc_->get(); }\n const auto& operator*() const { return *alloc_->get(); }\n\n auto& operator-> () { return alloc_->get().operator->(); }\n const auto& operator-> () const { return alloc_->get().operator->(); }\n\n auto& operator[](int i) { return alloc_->get()[i]; }\n const auto& operator[](int i) const { return alloc_->get()[i]; }\n\n int sz() const { return alloc_->sz_; }\n\n private:\n PoolAlloc* alloc_;\n };\n\n Recyclable alloc(int sz) {\n auto found = std::find_if(pool_.begin(), pool_.end(), [=](auto& r) {\n return r.sz() == sz && r.is_free();\n });\n\n if (found != pool_.end()) {\n PoolAlloc* p = &*found;\n return Recyclable(p);\n }\n\n pool_.emplace_back(std::make_unique(sz), sz);\n return Recyclable(&pool_.back());\n }\n\n private:\n std::list pool_;\n};\n\nstatic Pool float_pool_;\n\nclass Volume {\n public:\n Volume(int w, int h, int c)\n : w_(w), h_(h), c_(c), sz_(h * w * c), res_(float_pool_.alloc(sz_)) {}\n Volume(const Dims& sz) : Volume(sz.w, sz.h, sz.c) {}\n\n Volume() = default;\n Volume(Volume&& o) = default;\n\n Volume from_shape() const { return Volume(w_, h_, c_); }\n void zero() {\n for (int i = 0; i < sz_; ++i) {\n res_[i] = 0;\n }\n }\n\n Volume& operator=(Volume&& o) = default;\n\n float& operator[](int i) {\n#if _GLIBCXX_DEBUG\n if (i >= sz_) {\n throw std::runtime_error(\"volume bound checks fail\");\n }\n#endif\n return res_[i];\n }\n float operator[](int i) const {\n#if _GLIBCXX_DEBUG\n if (i >= sz_) {\n throw std::runtime_error(\"volume bound checks fail\");\n }\n#endif\n return res_[i];\n }\n\n int w() const { return w_; }\n int h() const { return h_; }\n int c() const { return c_; }\n int sz() const { return sz_; }\n\n int row_idx(int i) const { return i * w_; };\n int cha_idx(int i) const { return i * w_ * h_; }\n\n void show() const {\n std::cout << \"[\";\n for (int c = 0; c < c_; ++c) {\n std::cout << \"[\\n\";\n for (int h = 0; h < h_; ++h) {\n std::cout << \" [\";\n std::cout << res_[h_ * c * w_ + h * w_];\n for (int w = 1; w < w_; ++w) {\n std::cout << \", \" << res_[w + w_ * h + w_ * h_ * c];\n }\n std::cout << \"],\\n\";\n }\n std::cout << \"]\";\n }\n std::cout << \"]\\n\";\n }\n\n void share_with(Volume& v) const {\n v.res_ = res_;\n v.w_ = w_;\n v.h_ = h_;\n v.c_ = c_;\n v.sz_ = sz_;\n }\n\n void gaussian() {\n for (int i = 0; i < sz_; ++i) {\n res_[i] = gen_gaussian();\n }\n }\n\n std::pair mean_std() const {\n float m = 0;\n for (int i = 0; i < sz_; ++i) {\n m += res_[i];\n }\n m = m / sz_;\n\n float s = 0;\n for (int i = 0; i < sz_; ++i) {\n s += (res_[i] - m) * (res_[i] - m);\n }\n s = std::sqrt(s / (sz_ - 1));\n return std::make_pair(m, s);\n }\n\n float min() const {\n float m = res_[0];\n for (int i = 1; i < sz_; ++i) {\n m = m < res_[i] ? m : res_[i];\n }\n return m;\n }\n\n float max() const {\n float m = res_[0];\n for (int i = 1; i < sz_; ++i) {\n m = m > res_[i] ? m : res_[i];\n }\n return m;\n }\n\n int nonzero() const {\n int count = 0;\n for (int i = 1; i < sz_; ++i) {\n count += res_[i] == 0 ? 0 : 1;\n }\n return count;\n }\n\n float norm() const {\n float len = 0;\n for (int i = 0; i < sz_; ++i) {\n len += res_[i] * res_[i];\n }\n len = std::sqrt(len + 1e-6);\n return len;\n }\n\n private:\n int w_;\n int h_;\n int c_;\n int sz_;\n Pool::Recyclable res_;\n};\n\nclass Param {\n public:\n Param() = default;\n Param(Param&&) = default;\n Param(Dims d) : Param(d.w, d.h, d.c) {}\n Param(int w, int h, int c) : val_(w, h, c), grad_(w, h, c), mem_(w, h, c) {\n val_.gaussian();\n grad_.zero();\n mem_.zero();\n }\n\n float& operator[](int i) { return val_[i]; }\n float operator[](int i) const { return val_[i]; }\n\n void reset_grad() { grad_.zero(); }\n Volume& grad() { return grad_; }\n const Volume& grad() const { return grad_; }\n\n int sz() const { return val_.sz(); }\n Volume& vol() { return val_; }\n const Volume& vol() const { return val_; }\n\n Param& operator=(Param&& o) = default;\n\n void reinit(Volume& v) {\n v.share_with(val_);\n grad_ = v.from_shape();\n grad_.zero();\n mem_ = v.from_shape();\n mem_.zero();\n }\n\n int w() const { return val_.w(); }\n int h() const { return val_.h(); }\n int c() const { return val_.c(); }\n int cha_idx(int c) const { return val_.cha_idx(c); }\n\n void descend(const Settings& s) {\n switch (s.optimizer) {\n case Optimizer::Sgdm:\n sgdm(s);\n break;\n case Optimizer::Sgd:\n sgd(s);\n break;\n case Optimizer::PowerSign:\n power_sign(s);\n break;\n }\n }\n\n private:\n void sgd(const Settings& s) {\n float len = grad_.norm();\n for (int i = 0; i < val_.sz(); ++i) {\n val_[i] = val_[i] - s.lr * grad_[i] - s.l2 * 2 * val_[i];\n }\n grad_.zero();\n }\n\n void sgdm(const Settings& s) {\n float len = grad_.norm();\n for (int i = 0; i < val_.sz(); ++i) {\n grad_[i] =\n len > s.grad_max ? grad_[i] / len * s.grad_max : grad_[i];\n mem_[i] = 0.9 * mem_[i] + 0.1 * grad_[i];\n val_[i] = val_[i] - s.lr * mem_[i] - s.l2 * 2 * val_[i];\n }\n grad_.zero();\n }\n\n void power_sign(const Settings& s) {\n float len = grad_.norm();\n for (int i = 0; i < val_.sz(); ++i) {\n grad_[i] =\n len > s.grad_max ? grad_[i] / len * s.grad_max : grad_[i];\n mem_[i] = 0.9 * mem_[i] + 0.1 * grad_[i];\n val_[i] -= s.lr * exp(sgn(grad_[i]) * sgn(mem_[i])) * grad_[i];\n }\n grad_.zero();\n }\n\n Volume val_;\n Volume grad_;\n Volume mem_;\n};\n\nclass Layer {\n public:\n virtual Volume& forward(const Volume&) = 0;\n virtual Volume backward(const Volume& grad) = 0;\n virtual void update(const Settings&) {}\n virtual Dims out_shape() const = 0;\n virtual void set_train_mode(bool train) {}\n};\n\nclass Tanh : public Layer {\n public:\n Tanh(Dims d) : out_shape_(d) {}\n\n virtual Volume& forward(const Volume& input) {\n res_ = input.from_shape();\n for (int i = 0; i < res_.sz(); ++i) {\n res_[i] = std::tanh(input[i]);\n }\n return res_;\n }\n virtual Volume backward(const Volume& pgrad) {\n Volume grad = pgrad.from_shape();\n for (int i = 0; i < res_.sz(); ++i) {\n grad[i] = pgrad[i] * (1 - res_[i] * res_[i]);\n }\n return grad;\n }\n virtual Dims out_shape() const override { return out_shape_; }\n\n private:\n Volume res_;\n Dims out_shape_;\n};\n\nclass Verbose : public Layer {\n public:\n Verbose(Dims d, std::string name)\n : out_shape_(d), name_(name), train_(false) {}\n\n virtual Volume& forward(const Volume& x) override {\n if (train_) {\n fwd_.emplace_back();\n x.share_with(fwd_.back());\n return fwd_.back();\n } else {\n x.share_with(test_thru_);\n return test_thru_;\n }\n }\n\n virtual Volume backward(const Volume& x) override {\n bwd_.emplace_back();\n x.share_with(bwd_.back());\n Volume v;\n bwd_.back().share_with(v);\n return v;\n }\n\n virtual void update(const Settings&) override {\n float statsfwd[] = {0, 0, 0, 0, 0, 0};\n for (auto& v : fwd_) {\n auto local = v.mean_std();\n statsfwd[0] += local.first;\n statsfwd[1] += local.second;\n statsfwd[2] += v.max();\n statsfwd[3] += v.min();\n statsfwd[4] += float(v.nonzero()) / v.sz();\n statsfwd[5] += v.norm();\n }\n float statsbwd[] = {0, 0, 0, 0, 0, 0};\n for (auto& v : bwd_) {\n auto local = v.mean_std();\n statsbwd[0] += local.first;\n statsbwd[1] += local.second;\n statsbwd[2] += v.max();\n statsbwd[3] += v.min();\n statsbwd[4] += float(v.nonzero()) / v.sz();\n statsbwd[5] += v.norm();\n }\n\n std::cout << \" Statistics for \" << name_ << \":\\n\";\n std::cout << \" Batch size: \" << fwd_.size() << \"\\n\";\n std::cout << \" Forward pass: Backward Pass:\\n\";\n std::cout << \" Shape: (\" << std::setw(3) << fwd_[0].w() << \", \"\n << std::setw(3) << fwd_[0].h() << \", \" << std::setw(3)\n << fwd_[0].c() << \") \"\n << \" Shape: (\" << std::setw(3) << bwd_[0].w() << \", \"\n << std::setw(3) << bwd_[0].h() << \", \" << std::setw(3)\n << bwd_[0].c() << \")\\n\";\n\n std::cout << \" Mean: \" << std::setw(8)\n << statsfwd[0] / fwd_.size()\n << \" Mean: \" << statsbwd[0] / bwd_.size()\n << \"\\n\";\n std::cout << \" Std: \" << std::setw(8)\n << statsfwd[1] / fwd_.size()\n << \" Std: \" << statsbwd[1] / bwd_.size()\n << \"\\n\";\n std::cout << \" Max: \" << std::setw(8)\n << statsfwd[2] / fwd_.size()\n << \" Max: \" << statsbwd[2] / bwd_.size()\n << \"\\n\";\n std::cout << \" Min: \" << std::setw(8)\n << statsfwd[3] / fwd_.size()\n << \" Min: \" << statsbwd[3] / bwd_.size()\n << \"\\n\";\n std::cout << \" Non-Zero: \" << std::setw(8)\n << statsfwd[4] / fwd_.size()\n << \" Non-Zero: \" << statsbwd[4] / fwd_.size()\n << \"\\n\";\n std::cout << \" Norm: \" << statsfwd[5] / fwd_.size()\n << \" Norm: \" << statsbwd[5] / bwd_.size()\n << \"\\n\";\n\n fwd_.clear();\n bwd_.clear();\n }\n\n virtual Dims out_shape() const override { return out_shape_; }\n virtual void set_train_mode(bool b) override { train_ = b; }\n\n private:\n std::vector fwd_;\n std::vector bwd_;\n Dims out_shape_;\n std::string name_;\n Volume test_thru_;\n bool train_;\n};\n\nclass BatchNorm : public Layer {\n public:\n BatchNorm(Dims d)\n : mu_(d), sigma_(d), out_shape_(d), beta_(d), gamma_(d), train_(false) {\n beta_.vol().zero();\n mu_.zero();\n for (int i = 0; i < gamma_.sz(); ++i) {\n gamma_.vol()[i] = 1;\n sigma_[i] = 1;\n }\n }\n\n virtual Volume& forward(const Volume& input) {\n if (train_) {\n memory_.emplace_back();\n input.share_with(memory_.back());\n }\n scaled_ = input.from_shape();\n centered_ = input.from_shape();\n for (int i = 0; i < scaled_.sz(); ++i) {\n centered_[i] = (input[i] - mu_[i]) / sigma_[i];\n scaled_[i] = gamma_[i] * centered_[i] + beta_[i];\n }\n return scaled_;\n }\n\n virtual Volume backward(const Volume& pgrad) {\n Volume out = pgrad.from_shape();\n for (int i = 0; i < out.sz(); ++i) {\n gamma_.grad()[i] += pgrad[i] * centered_[i];\n beta_.grad()[i] += pgrad[i];\n out[i] = pgrad[i] * gamma_[i] / sigma_[i];\n }\n return out;\n }\n\n virtual void update(const Settings& s) {\n beta_.descend(s);\n gamma_.descend(s);\n Volume avg = mu_.from_shape();\n avg.zero();\n // compute average\n for (auto& m : memory_) {\n for (int i = 0; i < m.sz(); ++i) {\n avg[i] += m[i];\n }\n }\n for (int i = 0; i < mu_.sz(); ++i) {\n avg[i] /= memory_.size();\n }\n\n // compute std\n Volume std = mu_.from_shape();\n std.zero();\n for (auto& m : memory_) {\n for (int i = 0; i < m.sz(); ++i) {\n float x = m[i] - avg[i];\n std[i] += x * x;\n }\n }\n for (int i = 0; i < mu_.sz(); ++i) {\n std[i] /= memory_.size() - 1;\n }\n\n // update\n for (int i = 0; i < mu_.sz(); ++i) {\n mu_[i] = 0.9 * mu_[i] + 0.1 * avg[i];\n sigma_[i] = 0.9 * sigma_[i] + 0.1 * std[i];\n }\n\n memory_.clear();\n }\n\n virtual Dims out_shape() const { return out_shape_; }\n virtual void set_train_mode(bool train) override { train_ = train; }\n\n private:\n Volume mu_;\n Volume sigma_;\n Param beta_;\n Param gamma_;\n std::vector memory_;\n Dims out_shape_;\n Volume centered_;\n Volume scaled_;\n bool train_;\n};\n\nclass Input : public Layer {\n public:\n Input(Dims d) : out_shape_(d) {}\n virtual Volume& forward(const Volume& x) override {\n x.share_with(out_);\n return out_;\n }\n virtual Volume backward(const Volume& grad) override {\n Volume out;\n grad.share_with(out);\n return out;\n }\n virtual void update(const Settings&) {}\n virtual Dims out_shape() const { return out_shape_; }\n virtual void set_train_mode(bool train) override {}\n\n private:\n Dims out_shape_;\n Volume out_;\n};\n\nclass Relu : public Layer {\n public:\n Relu() = default;\n Relu(Dims d) : out_shape_(d){};\n virtual Volume& forward(const Volume& input) override {\n auto res = input.from_shape();\n for (int i = 0; i < input.sz(); ++i) {\n float x = input[i];\n res[i] = x < 0 ? 0 : x;\n }\n res_ = std::move(res);\n return res_;\n }\n\n virtual Volume backward(const Volume& pgrad) override {\n Volume grad = pgrad.from_shape();\n for (int i = 0; i < grad.sz(); ++i) {\n grad[i] = res_[i] > 0 ? pgrad[i] : 0;\n }\n return std::move(grad);\n }\n\n virtual Dims out_shape() const { return out_shape_; }\n\n private:\n Volume res_;\n Dims out_shape_;\n};\n\nclass FullyConn : public Layer {\n public:\n FullyConn() = default;\n FullyConn(Dims in_sz, int c) : w_(in_sz.sz(), c, 1), b_(1, c, 1) {\n b_.vol().zero();\n }\n\n virtual Volume& forward(const Volume& input) override {\n input.share_with(x_);\n res_ = Volume(b_.sz(), 1, 1);\n\n for (int c = 0, wptr = 0; c < b_.sz(); ++c, wptr += input.sz()) {\n float sum = b_[c];\n for (int i = 0; i < input.sz(); ++i) {\n sum += input[i] * w_[wptr + i];\n }\n res_[c] = sum;\n }\n return res_;\n }\n\n virtual Volume backward(const Volume& pgrad) override {\n Volume x_grad = x_.from_shape();\n x_grad.zero();\n\n for (int c = 0, wptr = 0; c < b_.sz(); ++c, wptr += x_.sz()) {\n float d = pgrad[c];\n auto& dw = w_.grad();\n\n b_.grad()[c] += d;\n for (int i = 0; i < x_.sz(); ++i) {\n dw[wptr + i] += d * x_[i];\n }\n\n for (int i = 0; i < x_.sz(); ++i) {\n x_grad[i] += d * w_[wptr + i];\n }\n }\n return x_grad;\n }\n\n virtual void update(const Settings& s) override {\n w_.descend(s);\n b_.descend(s);\n }\n\n void set_weights(std::vector&& w, Volume b) {\n Volume v(w[0].sz(), w.size(), 1);\n for (int i = 0; i < w.size(); ++i) {\n for (int j = 0; j < w[i].sz(); ++j) {\n v[v.w() * i + j] = w[i][j];\n }\n }\n w_.reinit(v);\n b_.reinit(b);\n }\n\n std::vector grads() const {\n std::vector gs;\n for (int i = 0; i < b_.sz(); ++i) {\n gs.emplace_back(x_.w(), x_.h(), x_.c());\n for (int j = 0; j < w_.w(); ++j) {\n gs.back()[j] = w_.grad()[w_.w() * i + j];\n }\n }\n return gs;\n }\n float bias() const { return b_.vol()[0]; }\n std::vector weights() const {\n std::vector gs;\n for (int i = 0; i < b_.sz(); ++i) {\n gs.emplace_back(x_.w(), x_.h(), x_.c());\n for (int j = 0; j < w_.w(); ++j) {\n gs.back()[j] = w_[w_.w() * i + j];\n }\n }\n return gs;\n }\n\n virtual void set_train_mode(bool train) override {}\n virtual Dims out_shape() const override { return Dims{b_.h(), 1, 1}; }\n\n private:\n Param w_;\n Param b_;\n Volume res_;\n Volume x_;\n};\n\nclass MaxPool : public Layer {\n public:\n MaxPool(Dims d) : out_shape_(Dims{d.w / 2, d.h / 2, d.c}) {}\n\n virtual Volume& forward(const Volume& input) override {\n input.share_with(x_);\n res_ = Volume(input.w() / 2, input.h() / 2, input.c());\n if (!cache_) {\n cache_ = std::make_unique(res_.sz());\n std::fill(cache_.get(), cache_.get() + res_.sz(), 0);\n }\n\n for (int c = 0; c < input.c(); ++c) {\n int src_row_idx = input.cha_idx(c);\n int dst_row_idx = res_.cha_idx(c);\n for (int h = 0; h < res_.h(); ++h) {\n for (int w = 0; w < res_.w(); ++w) {\n int dst_idx = dst_row_idx + w;\n int src_idx = src_row_idx + 2 * w;\n int max_pos = std::max(\n {src_idx,\n src_idx + 1,\n src_idx + input.w(),\n src_idx + input.w() + 1},\n [&](int a, int b) { return input[a] < input[b]; });\n cache_[dst_idx] = max_pos;\n res_[dst_idx] = input[max_pos];\n }\n src_row_idx += 2 * input.w();\n dst_row_idx += res_.w();\n }\n }\n\n return res_;\n }\n\n virtual Volume backward(const Volume& pgrad) override {\n Volume grad = x_.from_shape();\n grad.zero();\n for (int i = 0; i < res_.sz(); ++i) {\n grad[cache_[i]] = pgrad[i];\n }\n return grad;\n }\n\n virtual void set_train_mode(bool train) override {}\n virtual void update(const Settings&) override {}\n\n virtual Dims out_shape() const override { return out_shape_; }\n\n private:\n Volume x_;\n std::unique_ptr cache_;\n Volume res_;\n Dims out_shape_;\n};\n\nclass MSE : public Layer {\n public:\n void set_target(const Volume& target) { target.share_with(target_); }\n\n virtual Volume& forward(const Volume& input) override {\n input.share_with(x_);\n res_ = Volume(1, 1, 1);\n\n float total = 0;\n\n for (int i = 0; i < input.sz(); ++i) {\n float err = input[i] - target_[i];\n total += err * err;\n }\n\n res_[0] = total / (2 * input.sz());\n\n return res_;\n }\n\n virtual Volume backward(const Volume& pgrad) override {\n Volume grad = x_.from_shape();\n for (int i = 0; i < grad.sz(); ++i) {\n grad[i] = pgrad[0] * (x_[i] - target_[i]);\n }\n return grad;\n }\n\n virtual void set_train_mode(bool train) override {}\n virtual void update(const Settings&) override {}\n virtual Dims out_shape() const override { return Dims{1, 1, 1}; }\n\n private:\n Volume target_;\n Volume x_;\n Volume res_;\n};\n\nclass Conv : public Layer {\n public:\n Conv() = default;\n Conv(Dims in, Dims kerns) : in_sz_(in), kern_sz_(kerns) {\n for (int i = 0; i < kern_sz_.c; ++i) {\n filters_.emplace_back(kern_sz_.w, kern_sz_.h, in.c);\n biases_.emplace_back(1, 1, 1);\n biases_.back().vol().zero();\n }\n }\n\n virtual Volume& forward(const Volume& input) override {\n input.share_with(x_);\n res_ = Volume(input.w(), input.h(), filters_.size());\n\n for (int filter = 0; filter < int(filters_.size()); ++filter) {\n for (int i = res_.cha_idx(filter), end = res_.cha_idx(filter + 1);\n i < end;\n ++i) {\n res_[i] = biases_[filter][0];\n }\n for (int wptr = 0; wptr < filters_[0].sz(); ++wptr) {\n auto& f = filters_[filter].vol();\n\n float weight = f[wptr];\n\n int channel = wptr / (f.w() * f.h());\n int hoffset = (wptr - f.cha_idx(channel)) / f.w() - f.h() / 2;\n int woffset = (wptr - f.cha_idx(channel)) % f.w() - f.w() / 2;\n\n int dst_row_ptr = res_.cha_idx(filter) +\n std::max(0, hoffset) * res_.w() +\n std::max(0, woffset);\n int src_row_ptr = input.cha_idx(channel) +\n std::max(0, -hoffset) * input.w() +\n std::max(0, -woffset);\n for (int h = 0; h < input.h() - std::abs(hoffset); ++h) {\n for (int w = 0; w < input.w() - std::abs(woffset); ++w) {\n int dst_ptr = dst_row_ptr + w;\n int src_ptr = src_row_ptr + w;\n\n res_[dst_ptr] += weight * input[src_ptr];\n }\n dst_row_ptr += res_.w();\n src_row_ptr += input.w();\n }\n }\n }\n\n return res_;\n }\n\n virtual Volume backward(const Volume& pgrad) override {\n Volume dx = x_.from_shape();\n dx.zero();\n\n for (int filter = 0; filter < int(filters_.size()); ++filter) {\n for (int i = pgrad.cha_idx(filter), end = pgrad.cha_idx(filter + 1);\n i < end;\n ++i) {\n biases_[filter].grad()[0] += pgrad[i];\n }\n auto& f = filters_[filter].vol();\n auto& df = filters_[filter].grad();\n for (int wptr = 0; wptr < filters_[0].sz(); ++wptr) {\n float weight = f[wptr];\n\n int channel = wptr / (f.w() * f.h());\n int hoffset = (wptr - f.cha_idx(channel)) / f.w() - f.h() / 2;\n int woffset = (wptr - f.cha_idx(channel)) % f.w() - f.w() / 2;\n\n int dst_row_ptr = res_.cha_idx(filter) +\n std::max(0, -hoffset) * res_.w() +\n std::max(0, -woffset);\n int src_row_ptr = dx.cha_idx(channel) +\n std::max(0, hoffset) * dx.w() +\n std::max(0, woffset);\n for (int h = 0; h < dx.h() - std::abs(hoffset); ++h) {\n for (int w = 0; w < dx.w() - std::abs(woffset); ++w) {\n int dst_ptr = dst_row_ptr + w;\n int src_ptr = src_row_ptr + w;\n\n dx[src_ptr] += pgrad[dst_ptr] * weight;\n df[wptr] += pgrad[dst_ptr] * x_[src_ptr];\n }\n dst_row_ptr += dx.w();\n src_row_ptr += dx.w();\n }\n }\n }\n return dx;\n }\n\n void set_filters(std::vector&& fs, std::vector&& bs) {\n if (fs.size() != bs.size()) {\n throw std::runtime_error(\"filters and biases of a different size\");\n }\n\n filters_.clear();\n for (auto& v : fs) {\n filters_.emplace_back();\n filters_.back().reinit(v);\n }\n\n biases_.clear();\n for (auto& v : bs) {\n biases_.emplace_back(1, 1, 1);\n biases_.back()[0] = v;\n }\n }\n\n std::vector filters_grad() const {\n std::vector vs;\n for (auto& f : filters_) {\n vs.emplace_back();\n f.grad().share_with(vs.back());\n }\n return vs;\n }\n\n virtual void update(const Settings& s) override {\n for (int i = 0; i < int(filters_.size()); ++i) {\n filters_[i].descend(s);\n biases_[i].descend(s);\n }\n }\n\n virtual Dims out_shape() const override {\n return Dims{in_sz_.w, in_sz_.h, kern_sz_.c};\n }\n virtual void set_train_mode(bool train) override {}\n\n private:\n int nb_f_;\n std::vector filters_;\n std::vector biases_;\n Volume res_;\n Volume x_;\n Dims in_sz_;\n Dims kern_sz_;\n};\n\nclass Net {\n public:\n void set_lr(float x) { settings_.lr = x; }\n void set_batch_size(int x) { settings_.batch_size = x; }\n void set_epochs(int e) { settings_.epochs = e; }\n void set_l2(float x) { settings_.l2 = x; }\n void set_lr_decay(float x) { settings_.lr_decay = x; }\n void set_grad_max(float x) { settings_.grad_max = x; }\n void set_optimizer(Optimizer o) { settings_.optimizer = o; }\n void set_train_mode(bool train) {\n for (auto& l : layers_) {\n l->set_train_mode(train);\n }\n }\n\n void train(std::vector&& xs, std::vector&& ys) {\n xs_ = std::move(xs);\n ys_ = std::move(ys);\n\n set_train_mode(true);\n std::vector errs(xs_.size() / settings_.batch_size + 1);\n for (int e = 0; e < settings_.epochs; ++e) {\n settings_.lr *= settings_.lr_decay;\n std::cout << \"Epoch \" << e << \"\\n\";\n for (int i = 0; i < int(xs_.size()); i += settings_.batch_size) {\n errs.clear();\n for (int j = i;\n j < std::min(int(xs_.size()), i + settings_.batch_size);\n ++j) {\n mse_.set_target(ys_[j]);\n errs.push_back(forward(xs_[j])[0]);\n backward();\n }\n update();\n float total = 0;\n for (float x : errs) {\n total += x;\n }\n total /= errs.size();\n std::cout << \"loss: \" << total << \"\\n\";\n }\n }\n }\n\n void conv(int kw, int kh, int nb) {\n if (layers_.empty()) {\n throw std::invalid_argument(\"input() must be the first layer\");\n }\n auto c = std::make_unique(layers_.back()->out_shape(),\n Dims{kw, kh, nb});\n layers_.emplace_back(std::move(c));\n }\n\n void input(int w, int h, int c) {\n if (!layers_.empty()) {\n throw std::invalid_argument(\"input() must be the first layer\");\n }\n layers_.emplace_back(std::make_unique(Dims{w, h, c}));\n }\n\n void relu() {\n layers_.emplace_back(\n std::make_unique(layers_.back()->out_shape()));\n }\n\n void maxpool() {\n layers_.emplace_back(\n std::make_unique(layers_.back()->out_shape()));\n }\n\n void fc(int out) {\n layers_.emplace_back(\n std::make_unique(layers_.back()->out_shape(), out));\n }\n\n void verbose(std::string name) {\n layers_.emplace_back(\n std::make_unique(layers_.back()->out_shape(), name));\n }\n\n void batch_norm() {\n layers_.emplace_back(\n std::make_unique(layers_.back()->out_shape()));\n }\n\n void tanh() {\n layers_.emplace_back(\n std::make_unique(layers_.back()->out_shape()));\n }\n\n const Volume predict(const Volume& x) {\n set_train_mode(false);\n Volume it;\n x.share_with(it);\n for (int i = 0; i < int(layers_.size()); ++i) {\n layers_[i]->forward(it).share_with(it);\n }\n return std::move(it);\n }\n const Volume& forward(const Volume& x) {\n Volume it;\n x.share_with(it);\n for (int i = 0; i < int(layers_.size()); ++i) {\n layers_[i]->forward(it).share_with(it);\n }\n return mse_.forward(it);\n }\n\n Volume backward() {\n Volume one(1, 1, 1);\n one[0] = 1;\n Volume it = mse_.backward(one);\n for (int i = layers_.size(); i-- > 0;) {\n layers_[i]->backward(it).share_with(it);\n }\n return it;\n }\n\n void update() {\n for (auto& l : layers_) {\n l->update(settings_);\n }\n }\n\n private:\n std::vector> layers_;\n std::vector xs_;\n std::vector ys_;\n MSE mse_;\n Settings settings_;\n};\n\nnamespace p = boost::python;\nnamespace np = boost::python::numpy;\n\nVolume from_array(np::ndarray arr) {\n if (arr.get_nd() != 3) {\n throw std::runtime_error(\"volume doesn't have 3 dimensions\");\n }\n auto as_f = arr.astype(np::dtype::get_builtin());\n Volume v(arr.shape(1), arr.shape(0), arr.shape(2));\n const int channel_size = v.cha_idx(1);\n const int img_size = v.w() * v.h();\n\n for (int dst = 0, src = 0; dst < img_size; ++dst, src += arr.shape(2)) {\n int channel_dst = dst;\n for (int j = 0; j < arr.shape(2); ++j) {\n float x = reinterpret_cast(as_f.get_data())[src + j];\n v[channel_dst] = x;\n channel_dst += channel_size;\n }\n }\n return v;\n}\n\nnp::ndarray to_array(const Volume& v) {\n auto res = np::empty(p::make_tuple(v.h(), v.w(), v.c()),\n np::dtype::get_builtin());\n float* dat = reinterpret_cast(res.get_data());\n\n int img_sz = v.w() * v.h();\n for (int src = 0, dst = 0; src < img_sz; ++src, dst += v.c()) {\n for (int c = 0, src_c = 0; c < v.c(); ++c, src_c += v.cha_idx(1)) {\n dat[dst + c] = v[src + src_c];\n }\n }\n\n return res;\n}\n\nBOOST_PYTHON_MODULE(miniconv) {\n using namespace boost::python;\n class_(\"Conv\")\n .def(\"forward\",\n +[](Conv* conv, np::ndarray arr) {\n return to_array(conv->forward(from_array(arr)));\n })\n .def(\"backward\",\n +[](Conv* conv, np::ndarray arr) {\n return to_array(conv->backward(from_array(arr)));\n })\n .def(\"set_filters\",\n +[](Conv* conv, p::list kerns, p::list biases) {\n std::vector ks;\n std::vector bs;\n for (int i = 0; i < p::len(kerns); ++i) {\n ks.emplace_back(\n from_array(p::extract(kerns[i])));\n bs.push_back(p::extract(biases[i]));\n }\n conv->set_filters(std::move(ks), std::move(bs));\n })\n .def(\"filters_grad\",\n +[](Conv* conv) {\n p::list res;\n for (auto& vol : conv->filters_grad()) {\n res.append(to_array(vol));\n }\n return res;\n })\n .def(\"update\", &Conv::update);\n\n class_(\"MSE\")\n .def(\"forward\",\n +[](MSE* mse, np::ndarray arr) {\n return to_array(mse->forward(from_array(arr)));\n })\n .def(\"backward\",\n +[](MSE* mse, np::ndarray arr) {\n return to_array(mse->backward(from_array(arr)));\n })\n .def(\"set_target\",\n +[](MSE* mse, np::ndarray arr) {\n return mse->set_target(from_array(arr));\n })\n .def(\"update\", &MSE::update);\n\n class_(\"Relu\")\n .def(\"forward\",\n +[](Relu* relu, np::ndarray arr) {\n return to_array(relu->forward(from_array(arr)));\n })\n .def(\"backward\",\n +[](Relu* relu, np::ndarray arr) {\n return to_array(relu->backward(from_array(arr)));\n })\n .def(\"update\", &Relu::update);\n\n class_(\"FullyConn\")\n .def(\"forward\",\n +[](FullyConn* relu, np::ndarray arr) {\n return to_array(relu->forward(from_array(arr)));\n })\n .def(\"backward\",\n +[](FullyConn* fc, np::ndarray arr) {\n return to_array(fc->backward(from_array(arr)));\n })\n .def(\"set_weights\",\n +[](FullyConn* fc, p::list weights, np::ndarray b) {\n std::vector ws;\n for (int i = 0; i < p::len(weights); ++i) {\n ws.emplace_back(\n from_array(p::extract(weights[i])));\n }\n fc->set_weights(std::move(ws), from_array(b));\n })\n .def(\"grads\",\n +[](FullyConn* fc) {\n p::list ws;\n auto ws2 = fc->grads();\n for (auto& w : ws2) {\n ws.append(to_array(w));\n }\n return ws;\n })\n .def(\"bias\", +[](FullyConn* fc) { return fc->bias(); })\n .def(\"weights\",\n +[](FullyConn* fc) {\n p::list ws;\n auto ws2 = fc->weights();\n for (auto& w : ws2) {\n ws.append(to_array(w));\n }\n return ws;\n })\n .def(\"update\", &FullyConn::update);\n class_(\"Net\")\n .def(\"conv\", &Net::conv)\n .def(\"fc\", &Net::fc)\n .def(\"backward\", +[](Net* net) { return to_array(net->backward()); })\n .def(\"forward\",\n +[](Net* net, np::ndarray arr) {\n return to_array(net->forward(from_array(arr)));\n })\n .def(\"predict\",\n +[](Net* net, np::ndarray arr) {\n return to_array(net->predict(from_array(arr)));\n })\n .def(\"input\", &Net::input)\n .def(\"maxpool\", &Net::maxpool)\n .def(\"relu\", &Net::relu)\n .def(\"verbose\", &Net::verbose)\n .def(\"tanh\", &Net::tanh)\n .def(\"batch_norm\", &Net::batch_norm)\n .def(\"set_batch_size\", &Net::set_batch_size)\n .def(\"set_l2\", &Net::set_l2)\n .def(\"set_epochs\", &Net::set_epochs)\n .def(\"set_lr\", &Net::set_lr)\n .def(\"set_lr_decay\", &Net::set_lr_decay)\n .def(\"set_optimizer\", &Net::set_optimizer)\n .def(\"set_grad_max\", &Net::set_grad_max)\n .def(\"set_train_mode\", &Net::set_train_mode)\n .def(\"train\",\n +[](Net* net, p::list xs, p::list ys) {\n std::vector vxs;\n std::vector vys;\n if (p::len(xs) != p::len(ys)) {\n throw std::invalid_argument(\n \"ys and xs must be of equal size\");\n }\n for (int i = 0; i < p::len(xs); ++i) {\n vxs.emplace_back(\n from_array(p::extract(xs[i])));\n vys.emplace_back(\n from_array(p::extract(ys[i])));\n }\n net->train(std::move(vxs), std::move(vys));\n })\n .def(\"update\", &Net::update);\n\n class_(\"Settings\")\n .def_readwrite(\"lr\", &Settings::lr)\n .def_readwrite(\"lr_decay\", &Settings::lr_decay)\n .def_readwrite(\"l2\", &Settings::l2)\n .def_readwrite(\"epochs\", &Settings::epochs)\n .def_readwrite(\"grad_max\", &Settings::grad_max)\n .def_readwrite(\"batch_size\", &Settings::batch_size);\n\n enum_(\"Optimizer\")\n .value(\"sgd\", Optimizer::Sgd)\n .value(\"sgdm\", Optimizer::Sgdm)\n .value(\"powersign\", Optimizer::PowerSign);\n\n np::initialize();\n // feenableexcept(FE_ALL_EXCEPT & ~FE_INEXACT);\n}\n", "meta": {"hexsha": "171669077bc1ba28f67da917105ba785aa1f9b26", "size": 38845, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "conv.cpp", "max_stars_repo_name": "Vermeille/miniconv", "max_stars_repo_head_hexsha": "24ab1f8aa276acff22356c4d4af0f3e87bec5f19", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "conv.cpp", "max_issues_repo_name": "Vermeille/miniconv", "max_issues_repo_head_hexsha": "24ab1f8aa276acff22356c4d4af0f3e87bec5f19", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "conv.cpp", "max_forks_repo_name": "Vermeille/miniconv", "max_forks_repo_head_hexsha": "24ab1f8aa276acff22356c4d4af0f3e87bec5f19", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.8807692308, "max_line_length": 80, "alphanum_fraction": 0.4603938731, "num_tokens": 10406, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.24395876097908092}} {"text": "/*******************************************************************************\n*\n* Filename : RooFit_MCTemplate.cc\n* Description : Plotting to file for template methods.cc\n* Author : Yi-Mu \"Enoch\" Chen [ ensc@hep1.phys.ntu.edu.tw ]\n*\n*******************************************************************************/\n#include \"TstarAnalysis/LimitCalc/interface/Common.hpp\"\n#include \"TstarAnalysis/LimitCalc/interface/MakeKeysPdf.hpp\"\n#include \"TstarAnalysis/LimitCalc/interface/SampleRooFitMgr.hpp\"\n#include \"TstarAnalysis/LimitCalc/interface/SimFit.hpp\"\n#include \"TstarAnalysis/LimitCalc/interface/Template.hpp\"\n\n#include \"ManagerUtils/Maths/interface/RooFitExt.hpp\"\n#include \"ManagerUtils/PlotUtils/interface/Common.hpp\"\n#include \"ManagerUtils/PlotUtils/interface/RooFitUtils.hpp\"\n#include \"TstarAnalysis/Common/interface/PlotStyle.hpp\"\n\n#include \n#include \n#include \n\n#include \"RooDataSet.h\"\n#include \"RooNLLVar.h\"\n#include \"RooMinimizer.h\"\n\nusing namespace std;\nusing namespace mgr;\n\n/*******************************************************************************\n* Object naming convention\n*******************************************************************************/\nstring\nTemplatePdfName( const std::string& datasetname )\n{\n return datasetname + \"template\";\n}\n\nextern const string StitchTemplatePdfName = \"templatemaster\";\n\n/*******************************************************************************\n* Main control flow to be caleed by main function\n*******************************************************************************/\nvoid\nMakeTemplate(\n SampleRooFitMgr* data,\n SampleRooFitMgr* bg,\n vector& signal_list\n )\n{\n vector pdflist;\n vector funclist;\n\n for( auto& sig : signal_list ){\n pdflist.push_back( MakeFullKeysPdf( sig ) );\n // funclist.push_back( sig->Func(StitchKeyNormName));\n }\n\n auto fitresultlist = MakeFullTemplate( bg );\n MakeTemplatePlot( data, bg, signal_list.front(), fitresultlist.front(), true );\n MakeTemplatePlot( data, bg, signal_list.front(), fitresultlist.front(), false );\n pdflist.push_back( bg->Pdf( StitchTemplatePdfName ) );\n\n SaveRooWorkSpace(\n data->DataSet( \"\" ),\n pdflist,\n {}\n );\n\n for( auto& signal : signal_list ){\n MakeTemplateCardFile( data, bg, signal );\n }\n}\n\n\n/*******************************************************************************\n* Fitting function implementations\n*******************************************************************************/\nRooFitResult*\nFitBackgroundTemplate( SampleRooFitMgr* bg, const string& datatag )\n{\n const string bgpdfname = TemplatePdfName( datatag );\n\n RooAbsPdf* bgpdf = bg->NewPdf( bgpdfname, limnamer.GetInput( \"fitfunc\" ) );\n // Manually calling NNL minizer functions\n static RooCmdArg min = RooFit::Minimizer( \"Minuit\", \"Migrad\" );\n static RooCmdArg sumerr = RooFit::SumW2Error( kTRUE );\n static RooCmdArg hesse = RooFit::Minos( kTRUE );\n static RooCmdArg save = RooFit::Save();\n //static RooCmdArg ncpu = RooFit::NumCPU( 6 );\n static RooCmdArg verb = RooFit::Verbose( kFALSE );\n static RooCmdArg printl = RooFit::PrintLevel( -1 );\n static RooCmdArg printe = RooFit::PrintEvalErrors( -1 );\n static RooCmdArg printw = RooFit::Warnings( kFALSE );\n\n RooLinkedList fitopt;\n fitopt.Add( &min );\n fitopt.Add( &sumerr );\n fitopt.Add( &hesse );\n fitopt.Add( &save );\n //fitopt.Add( &ncpu );\n fitopt.Add( &verb );\n fitopt.Add( &printl );\n fitopt.Add( &printe );\n fitopt.Add( &printw );\n\n const unsigned maxiter = 30;\n unsigned iter = 0 ;\n RooFitResult* ans = NULL ;\n while( !ans ){\n ans = bgpdf->fitTo( *( bg->DataSet(datatag) ), fitopt );\n if( ans->status() ){ // Not properly converged\n iter++;\n if( iter > maxiter ) { break; }\n delete ans;\n ans = NULL;\n }\n }\n cout << \">>>> BKG FIT iteration: \" << iter << endl;\n bg->SetConstant( kTRUE );// Freezing all constants\n\n return ans;\n}\n\n/******************************************************************************/\n\nvector\nMakeFullTemplate( SampleRooFitMgr* bg )\n{\n vector bgpdflist;\n vector ans;\n\n for( const auto& datasetname : bg->SetNameList() ){\n ans.push_back( FitBackgroundTemplate( bg, datasetname ) );\n bgpdflist.push_back( TemplatePdfName( datasetname ) );\n }\n\n MakeSimpleStitchPdf( bg, StitchTemplatePdfName, bgpdflist );\n return ans;\n}\n\n/******************************************************************************/\n\nvoid\nMakeTemplateCardFile( SampleRooFitMgr* data, SampleRooFitMgr* bg, SampleRooFitMgr* sig )\n{\n RooAbsData* dataobs = data->DataSet( \"\" );\n RooAbsPdf* bgpdf = bg->Pdf( StitchTemplatePdfName );\n RooAbsPdf* sigpdf = sig->Pdf( StitchKeyPdfName );\n\n ofstream cardfile( limnamer.TextFileName( \"card\", {sig->Name()} ) );\n\n MakeCardCommon( cardfile, dataobs, bgpdf, sigpdf );\n\n cardfile << boost::format( \"%12s %15lf %15lf\" )\n % \"rate\"\n % sig->DataSet()->sumEntries()\n % bg->DataSet()->sumEntries()\n << endl;\n\n cardfile << \"----------------------------------------\" << endl;\n\n const Parameter null( 0, 0, 0 );\n const Parameter lumi( 1, 0.062, 0.062 );\n const Parameter lepunc( 1, 0.03, 0.03 );\n const Parameter sigstatunc = sig->Sample().SelectionEfficiency();\n const Parameter bkgstatunc( 1, 0.03, 0.03 );// includes uncertaintly from cross section and selection effiency\n\n const Parameter sigjecunc = GetMCNormError( sig, \"jecUp\", \"jecDown\" );\n const Parameter sigjerunc = GetMCNormError( sig, \"jetresUp\", \"jetresDown\" );\n const Parameter siglepunc = GetMCNormError( sig, \"lepUp\", \"lepDown\" );\n const Parameter sigbtagunc = GetMCNormError( sig, \"btagUp\", \"btagDown\" );\n const Parameter sigpuunc = GetMCNormError( sig, \"puUp\", \"puDown\" );\n const Parameter sigpdfunc = GetMCNormError( sig, \"pdfUp\", \"pdfDown\" );\n\n const Parameter bkgjecunc = GetMCNormError( bg, \"jecUp\", \"jecDown\" );\n const Parameter bkgjerunc = GetMCNormError( bg, \"jetresUp\", \"jetresDown\" );\n const Parameter bkglepunc = GetMCNormError( bg, \"lepUp\", \"lepDown\" );\n const Parameter bkgbtagunc = GetMCNormError( bg, \"btagUp\", \"btagDown\" );\n const Parameter bkgpuunc = GetMCNormError( bg, \"puUp\", \"puDown\" );\n const Parameter bkgpdfunc = GetMCNormError( bg, \"pdfUp\", \"pdfDown\" );\n\n PrintNuisanceFloats( cardfile, \"Lumi\", \"lnN\", lumi, lumi );\n PrintNuisanceFloats( cardfile, \"lepsys\", \"lnN\", lepunc, lepunc );\n PrintNuisanceFloats( cardfile, \"sigstat\", \"lnN\", sigstatunc, null );\n PrintNuisanceFloats( cardfile, \"bkgstat\", \"lnN\", null, bkgstatunc );\n PrintNuisanceFloats( cardfile, \"jec\", \"lnN\", sigjecunc, bkgjecunc );\n PrintNuisanceFloats( cardfile, \"jer\", \"lnN\", sigjerunc, bkgjerunc );\n PrintNuisanceFloats( cardfile, \"lep\", \"lnN\", siglepunc, bkglepunc );\n PrintNuisanceFloats( cardfile, \"btag\", \"lnN\", sigbtagunc, bkgbtagunc );\n PrintNuisanceFloats( cardfile, \"pileup\", \"lnN\", sigpuunc, bkgpuunc );\n PrintNuisanceFloats( cardfile, \"pdf\", \"lnN\", sigpdfunc, bkgpdfunc );\n\n // Getting fitting parameters\n for( const auto& var : bg->VarContains( \"template\" ) ){\n const string varname = var->GetName();\n if( varname.find( \"coeff\" ) == string::npos ){\n PrintFloatParam( cardfile, var );\n }\n }\n\n // Getting stitching co-efficiencts\n for( const auto& var : bg->VarContains( \"coeff\" ) ){\n PrintFlatParam( cardfile, var );\n }\n\n for( const auto& var : sig->VarContains( \"coeff\" ) ){\n PrintFlatParam( cardfile, var );\n }\n\n cardfile.close();\n}\n\n\n/*******************************************************************************\n* Plotting fit results\n*******************************************************************************/\nvoid\nMakeTemplatePlot(\n SampleRooFitMgr* data,\n SampleRooFitMgr* mc,\n SampleRooFitMgr* signal,\n RooFitResult* fitresult,\n const bool use_data )\n{\n // First plot against MC\n const double TotalLuminosity = mgr::SampleMgr::TotalLuminosity();\n const double xmin = SampleRooFitMgr::x().getMin();\n const double xmax = SampleRooFitMgr::x().getMax();\n\n TCanvas* c = mgr::NewCanvas();\n TPad* toppad = mgr::NewTopPad();\n TPad* botpad = mgr::NewBottomPad();\n RooPlot* frame = SampleRooFitMgr::x().frame();\n\n // Objects to draw\n RooDataSet* set = (RooDataSet*)( use_data ? data->DataSet() : mc->DataSet() );\n RooAbsPdf* bkgpdf = mc->Pdf( TemplatePdfName( \"\" ) );// Taking central value only\n RooAbsPdf* sigpdf = signal->Pdf( StitchKeyPdfName );\n\n /*******************************************************************************\n * Objects for drawing top pad\n *******************************************************************************/\n toppad->Draw();\n toppad->cd();\n\n TGraph* setplot = mgr::PlotOn(\n frame,\n set,\n RooFit::DrawOption( PGS_DATA )\n );\n\n TGraph* pdfplot = mgr::PlotFitErrorOn(\n frame, bkgpdf, fitresult,\n RooFit::Normalization( set->sumEntries(), RooAbsReal::NumEvent )\n );\n\n TGraph* sigplot = mgr::PlotOn(\n frame,\n sigpdf,\n RooFit::DrawOption( PGS_SIGNAL ),\n RooFit::Normalization( signal->ExpectedYield(), RooAbsReal::NumEvent )\n );\n tstar::RemoveDataXBar( (TGraphAsymmErrors*)setplot );\n\n // Typical styling options\n frame->Draw();\n frame->SetMinimum( 0.3 );\n mgr::SetTopPlotAxis( frame );\n frame->SetTitle(\"\");\n\n c->cd();\n /*******************************************************************************\n * Objects for drawing bottom pad\n *******************************************************************************/\n botpad->Draw();\n botpad->cd();\n\n TGraphAsymmErrors* bgrelplot = mgr::DividedGraph(\n (TGraphAsymmErrors*)pdfplot,\n pdfplot\n );\n TGraphAsymmErrors* datarelplot = mgr::DividedGraph(\n (TGraphAsymmErrors*)setplot,\n pdfplot\n );\n tstar::RemoveDataXBar( datarelplot );\n bgrelplot->Draw( \"AL3\" );\n datarelplot->Draw( PGS_DATA );\n\n TLine cen( xmin, 1, xmax, 1 );\n TLine lineup( xmin, 1.5, xmax, 1.5 );\n TLine linedown( xmin, 0.5, xmax, 0.5 );\n cen.Draw();\n cen.SetLineColor( KBLUE );\n cen.SetLineWidth( 2 );\n lineup.Draw();\n lineup.SetLineColor( kBlack );\n lineup.SetLineStyle( 3 );\n linedown.Draw();\n linedown.SetLineColor( kBlack );\n linedown.SetLineStyle( 3 );\n\n // Title setting\n bgrelplot->GetXaxis()->SetTitle( frame->GetXaxis()->GetTitle() );\n bgrelplot->GetXaxis()->SetRangeUser( xmin, xmax );\n bgrelplot->GetYaxis()->SetTitle( \"Data/Bkg.fit\" );\n bgrelplot->SetMaximum( 1.6 );\n bgrelplot->SetMinimum( 0.4 );\n mgr::SetBottomPlotAxis( bgrelplot );\n\n c->cd();\n /*******************************************************************************\n * Common styling and additiona objects\n *******************************************************************************/\n tstar::SetSignalStyle( sigplot );\n tstar::SetFitBGStyle( pdfplot );\n tstar::SetSignalStyle( sigplot );\n\n tstar::SetFitBGStyle( bgrelplot );\n tstar::SetDataStyle( datarelplot );\n tstar::SetDataStyle( setplot );\n\n // Legend entries\n const double legend_x_min = 0.65;\n const double legend_y_min = 0.70;\n TLegend* l = mgr::NewLegend( legend_x_min, legend_y_min );\n boost::format sigfmt( \"%s\" );\n const string sigentry = str( sigfmt % signal->RootName() );\n const string dataentry = ( use_data ) ? \"Data\" : \"M.C. Bkg.\";\n const string fitentry = string( \"Bkg. fit to MC\" ) + ( use_data ? \"(Norm.)\" : \"\" );\n\n\n l->AddEntry( setplot, dataentry.c_str(), \"pe\" );\n l->AddEntry( pdfplot, fitentry.c_str(), \"l\" );\n l->AddEntry( sigplot, sigentry.c_str(), \"l\" );\n l->Draw();\n\n // Additional information plotting\n boost::format goffmt( \"K = %.3lf\" );\n const double ksprob = KSTest( *( set ), *( bkgpdf ), SampleRooFitMgr::x() );\n const string gofentry = str( goffmt % ksprob );\n\n mgr::DrawCMSLabel();\n mgr::DrawLuminosity( TotalLuminosity );\n\n LatexMgr latex;\n latex.SetOrigin( PLOT_X_MIN, PLOT_Y_MAX + TEXT_MARGIN/2, TOP_LEFT )\n .WriteLine( limnamer.GetChannelEXT( \"Root Name\" ) )\n .SetOrigin( PLOT_X_TEXT_MAX, legend_y_min-TEXT_MARGIN, TOP_RIGHT )\n .WriteLine( gofentry );\n\n // Range setting and saving\n const double ymax = mgr::GetYmax( pdfplot, setplot, sigplot );\n frame->SetMaximum( ymax * 1.5 );\n\n const string rootfile = limnamer.PlotRootFile();\n const string lastag = use_data ? \"fitmc-vs-data\" : \"fitmc-vs-mc\";\n mgr::SaveToPDF( c, limnamer.PlotFileName( \"\", \"fitplot\", signal->Name(), lastag ) );\n mgr::SaveToROOT( c, rootfile, limnamer.PlotFileName( \"fitplot\", signal->Name(), lastag ) );\n frame->SetMaximum( ymax * 300 );\n toppad->SetLogy( kTRUE );\n mgr::SaveToPDF( c, limnamer.PlotFileName( \"fitplot\", signal->Name(), lastag, \"log\" ) );\n\n // Cleaning up\n delete frame;\n delete c;\n delete l;\n}\n", "meta": {"hexsha": "df9cc0448607443a4e72992352d4f2aae9fe0805", "size": 12910, "ext": "cc", "lang": "C++", "max_stars_repo_path": "LimitCalc/src/RooFit_MCTemplate.cc", "max_stars_repo_name": "NTUHEP-Tstar/TstarAnalysis", "max_stars_repo_head_hexsha": "d3bcdf6f0fd19b9a34bbacb1052143856917bea7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "LimitCalc/src/RooFit_MCTemplate.cc", "max_issues_repo_name": "NTUHEP-Tstar/TstarAnalysis", "max_issues_repo_head_hexsha": "d3bcdf6f0fd19b9a34bbacb1052143856917bea7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "LimitCalc/src/RooFit_MCTemplate.cc", "max_forks_repo_name": "NTUHEP-Tstar/TstarAnalysis", "max_forks_repo_head_hexsha": "d3bcdf6f0fd19b9a34bbacb1052143856917bea7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.8918918919, "max_line_length": 112, "alphanum_fraction": 0.5899302866, "num_tokens": 3588, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6584175139669997, "lm_q2_score": 0.37022539259558657, "lm_q1q2_score": 0.24376288260024254}} {"text": "/*\n * Copyright (C) 2022 Agtonomy\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\n#ifndef TRELLIS_CONTAINERS_TRANSFORMS_HPP\n#define TRELLIS_CONTAINERS_TRANSFORMS_HPP\n\n#include \n#include \n#include \n#include \n\n#include \"trellis/core/time.hpp\"\n\nnamespace trellis {\nnamespace containers {\n\n/**\n * Transforms a container to hold rigid coordinate frame transformations\n *\n * This class holds transforms with associated timestamps. Lookups are done based on a timestamp. The algorithm will\n * find the transform with the timestamp nearest the requested one.\n *\n * Future improvements:\n * - Support a graph structure to allow lookups that are transitive. Say we know A -> B -> C, we should support a lookup\n * of A -> C\n */\nclass Transforms {\n public:\n /**\n * Tranlsation represents a translation in 3D space\n */\n using Translation = Eigen::Vector3d;\n\n /**\n * Rotation represents a rotation in 3D space represented as a quaternion\n */\n using Rotation = Eigen::Vector4d;\n\n /**\n * AffineTransform3D an Eigen 3-dimensional affine transform representation\n */\n using AffineTransform3D = Eigen::Transform;\n\n /**\n * RigidTransform represents a transformation comprised of a translation and rotation\n *\n * NOTE: The convention is such that the translation is performed before the rotation\n */\n struct RigidTransform {\n RigidTransform() = default;\n\n /**\n * RigidTransform construct a rigid transfrom from an Eigen affine transform representation\n *\n * @param transform the Eigen affine transform\n */\n RigidTransform(const AffineTransform3D& transform)\n : translation{GetTranslationFromAffineTransform(transform)},\n rotation{GetRotationFromAffineTransform(transform)} {}\n\n /**\n * GetAffineRepresentation return an Eigen affine transform representation of this rigid transform\n *\n * @return the Eigen affine transform\n */\n AffineTransform3D GetAffineRepresentation() const {\n return AffineTransform3D(Eigen::Translation(translation.x(), translation.y(), translation.z()) *\n Eigen::Quaterniond(rotation.w(), rotation.x(), rotation.y(), rotation.z()));\n }\n\n /**\n * Inverse retrieve the inverse transform\n *\n * @return the inverse transform\n */\n RigidTransform Inverse() const { return Transforms::RigidTransform(GetAffineRepresentation().inverse()); }\n\n /**\n * Exact quality operator.\n *\n * Note: for approximations use Eigen's GetAffineRepresentation().isApprox() instead\n *\n * @returns true if both operands are exact copies of each other\n */\n bool operator==(const RigidTransform& other) const {\n return this->translation.x() == other.translation.x() && this->translation.y() == other.translation.y() &&\n this->translation.z() == other.translation.z() && this->rotation.w() == other.rotation.w() &&\n this->rotation.x() == other.rotation.x() && this->rotation.y() == other.rotation.y() &&\n this->rotation.z() == other.rotation.z();\n }\n\n bool operator!=(const RigidTransform& other) const { return !(*this == other); }\n\n Translation translation;\n Rotation rotation;\n\n private:\n static Translation GetTranslationFromAffineTransform(const AffineTransform3D& transform) {\n return Translation{transform.translation().x(), transform.translation().y(), transform.translation().z()};\n }\n static Rotation GetRotationFromAffineTransform(const AffineTransform3D& transform) {\n const Eigen::Quaterniond q(transform.rotation());\n return Rotation{q.x(), q.y(), q.z(), q.w()};\n }\n };\n\n struct Sample {\n const trellis::core::time::TimePoint& timestamp;\n const RigidTransform& transform;\n };\n\n static constexpr std::size_t kMaxTransformLengthDefault = 100U;\n\n Transforms(std::size_t max_transform_length = kMaxTransformLengthDefault)\n : max_transform_length_{max_transform_length} {}\n\n /**\n * UpdateTransform update a transform associated with the current time\n *\n * @param from the starting reference frame for the transform\n * @param to the ending reference frame for the transform\n * @param transform the actual transformation in terms of a translation and a rotation\n */\n void UpdateTransform(const std::string& from, const std::string& to, const RigidTransform& transform);\n\n /**\n * UpdateTransform update a transform associated with the given time\n *\n * @param from the starting reference frame for the transform\n * @param to the ending reference frame for the transform\n * @param transform the actual transformation in terms of a translation and a rotation\n * @param when the time point to associate with the transform\n */\n void UpdateTransform(const std::string& from, const std::string& to, const RigidTransform& transform,\n const trellis::core::time::TimePoint& when);\n\n /**\n * HasTransform determine if a transform for a given pair of reference frames exists and is within the valid time\n * window\n *\n * @param from the starting reference frame for the transform\n * @param to the ending reference frame for the transform\n * @return true if the transform exists and is valid\n *\n */\n bool HasTransform(const std::string& from, const std::string& to) const;\n\n /**\n * HasTransform determine if a transform for a given pair of reference frames exists and is within the valid time\n * window relative to the given time\n *\n * @param from the starting reference frame for the transform\n * @param to the ending reference frame for the transform\n * @param when the time point to associate with the transform\n * @return true if the transform exists and is valid\n *\n */\n bool HasTransform(const std::string& from, const std::string& to, const trellis::core::time::TimePoint& when) const;\n\n /**\n * GetTransform retrieve the most recent transform for a given pair of reference frames\n *\n * @param from the starting reference frame for the transform\n * @param to the ending reference frame for the transform\n * @return the rigid transformation between the two reference frames with associated timestamp\n * @throws std::runtime_error if no valid transform exists\n */\n Sample GetTransform(const std::string& from, const std::string& to) const;\n\n /**\n * GetTransform retrieve the transform for a given pair of reference frames nearest the given time\n *\n * @param from the starting reference frame for the transform\n * @param to the ending reference frame for the transform\n * @param when the time point with which to find the nearest transform\n * @return the rigid transformation between the two reference frames with associated timestamp\n * @throws std::runtime_error if no valid transform exists\n */\n Sample GetTransform(const std::string& from, const std::string& to, const trellis::core::time::TimePoint& when) const;\n\n private:\n std::optional FindNearestTransformTimestamp(\n const std::string& from, const std::string& to, const trellis::core::time::TimePoint& when) const;\n\n void Insert(const std::string& from, const std::string& to, const RigidTransform& transform,\n const trellis::core::time::TimePoint& when);\n\n static void ValidateFrameName(const std::string& frame);\n\n using KeyType = std::string;\n\n struct FrameNames {\n const std::string from;\n const std::string to;\n };\n\n static KeyType CalculateKeyFromFrames(const std::string& from, const std::string& to);\n static FrameNames GetFrameNamesFromKey(const KeyType& key);\n\n static constexpr char kDelimiter = '|';\n\n using TransformHistoryContainer = std::map;\n std::unordered_map transforms_;\n const std::size_t max_transform_length_;\n};\n} // namespace containers\n} // namespace trellis\n\n#endif // TRELLIS_CONTAINERS_TRANSFORMS_HPP\n", "meta": {"hexsha": "34c44d08e74f2941c3b696f0a094ea0c9b1449c5", "size": 8513, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "trellis/containers/transforms.hpp", "max_stars_repo_name": "agtonomy/trellis", "max_stars_repo_head_hexsha": "ef50dc85d11af5badae9ca1487df100817c2b97f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 13.0, "max_stars_repo_stars_event_min_datetime": "2021-10-18T00:48:31.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-06T06:23:47.000Z", "max_issues_repo_path": "trellis/containers/transforms.hpp", "max_issues_repo_name": "agtonomy/trellis", "max_issues_repo_head_hexsha": "ef50dc85d11af5badae9ca1487df100817c2b97f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 6.0, "max_issues_repo_issues_event_min_datetime": "2021-11-24T18:26:54.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-09T00:08:18.000Z", "max_forks_repo_path": "trellis/containers/transforms.hpp", "max_forks_repo_name": "agtonomy/trellis", "max_forks_repo_head_hexsha": "ef50dc85d11af5badae9ca1487df100817c2b97f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2022-01-05T16:39:59.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-05T16:39:59.000Z", "avg_line_length": 38.0044642857, "max_line_length": 120, "alphanum_fraction": 0.714201809, "num_tokens": 1886, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6039318337259583, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.24372687000742033}} {"text": "//\n// Created by philipp on 26.12.19.\n//\n\n#ifndef FUNNELS_CPP_DYNAMICS_HH\n#define FUNNELS_CPP_DYNAMICS_HH\n\n#include \n#include \n#include \n#include \n\n\nusing namespace Eigen;\n\nnamespace dynamics {\n \n class kinematic_2d_sys_t {\n public:\n constexpr static const size_t dimx = 4, dimp = 2, dimv = 2, dimu = 2;\n \n using matrix_t = Eigen::Matrix;\n using vector_x_t = Eigen::Matrix;\n using vector_u_t = Eigen::Matrix;\n using vector_t_t = Eigen::Matrix;\n using matrix_ptr_t = std::shared_ptr;\n using vector_x_ptr_t = std::shared_ptr;\n using vector_u_ptr_t = std::shared_ptr;\n using vector_t_ptr_t = std::shared_ptr;\n \n static void\n compute(matrix_t &x, const vector_u_t &u, const vector_t_t &t);\n };\n}\n#endif //FUNNELS_CPP_DYNAMICS_HH\n", "meta": {"hexsha": "e5be840fd0087e03b27521d1e36cdcb2a8fdbc12", "size": 939, "ext": "hh", "lang": "C++", "max_stars_repo_path": "include/funnels/dynamics.hh", "max_stars_repo_name": "schlepil/funnels_cpp_2", "max_stars_repo_head_hexsha": "1ed746a90019f7f6aff7a54fd4b63bfbd58fa2ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/funnels/dynamics.hh", "max_issues_repo_name": "schlepil/funnels_cpp_2", "max_issues_repo_head_hexsha": "1ed746a90019f7f6aff7a54fd4b63bfbd58fa2ac", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/funnels/dynamics.hh", "max_forks_repo_name": "schlepil/funnels_cpp_2", "max_forks_repo_head_hexsha": "1ed746a90019f7f6aff7a54fd4b63bfbd58fa2ac", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 26.0833333333, "max_line_length": 73, "alphanum_fraction": 0.7060702875, "num_tokens": 275, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5926665999540697, "lm_q2_score": 0.41111086923216805, "lm_q1q2_score": 0.2436516810719912}} {"text": "#pragma once\n/** @file KDTree\n * @brief A binary tree constructed by splitting each box by a median.\n */\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n\n#include \"fmmtl/tree/util/CountedProxyIterator.hpp\"\n\n#include \"fmmtl/util/Logger.hpp\"\n#include \"fmmtl/numeric/Vec.hpp\"\n#include \"fmmtl/tree/BoundingSphere.hpp\"\n\n#include \"fmmtl/numeric/bits.hpp\"\n\nnamespace fmmtl {\nusing boost::has_range_iterator;\n\n\n//! Class for tree structure\ntemplate \nclass BallTree {\n // Predeclarations\n struct Box;\n struct Body;\n struct BoxData;\n\n // The type of this tree\n typedef BallTree tree_type;\n\n public:\n //! The type of indices and integers in this tree\n typedef unsigned size_type;\n\n //! The spacial point type used for centers and extents\n typedef Vec point_type;\n\n //! Public type declarations\n typedef Box box_type;\n typedef Body body_type;\n using box_iterator = CountedProxyIterator;\n using body_iterator = CountedProxyIterator;\n\n private:\n // Tree representation\n\n // Permutation: permute_[i] is the current idx of originally ith point\n std::vector permute_;\n // Vector of data describing a box\n std::vector box_data_;\n\n struct BoxData {\n typedef BoundingSphere bounding_sphere_type;\n\n // Index of the first body in this box\n size_type body_begin_;\n // Index of one-past-last body in this box\n size_type body_end_;\n // Bounding sphere\n bounding_sphere_type bounding_sphere_;\n\n // Constructor\n BoxData(size_type bb, size_type be, const bounding_sphere_type& bs)\n : body_begin_(bb), body_end_(be), bounding_sphere_(bs) {}\n };\n\n struct Body {\n /** Construct an invalid Body */\n Body() {}\n //! The original order this body was seen\n size_type number() const {\n return tree_->permute_[idx_];\n }\n //! The current order of this body\n size_type index() const {\n return idx_;\n }\n private:\n size_type idx_;\n tree_type* tree_;\n friend body_iterator;\n Body(size_type idx, const tree_type* tree)\n : idx_(idx), tree_(const_cast(tree)) {\n FMMTL_ASSERT(idx_ < tree_->size());\n }\n friend class BallTree;\n };\n\n // A box of the tree\n struct Box {\n typedef typename tree_type::box_iterator box_iterator;\n typedef typename tree_type::body_iterator body_iterator;\n\n //! Construct an invalid Box\n Box() {}\n //! The index of this box\n size_type index() const {\n return idx_;\n }\n //! The level of this box (root level is 0)\n size_type level() const {\n return std::log2(idx_+1); // XXX: Slow? do this with bits\n }\n\n //! The dimension of each side of this box\n point_type extents() const {\n point_type e;\n e.fill(2*std::sqrt(radius_sq()));\n return e;\n }\n //! The squared radius of this box\n double radius_sq() const {\n return data().bounding_sphere_.radius_sq();\n }\n //! The center of this box\n point_type center() const {\n return data().bounding_sphere_.center();\n }\n\n //! The parent box of this box\n Box parent() const {\n FMMTL_ASSERT(!(*this == tree_->root()));\n return Box((idx_-1)/2, tree_);\n }\n\n //! True if this box is a leaf and has no children\n bool is_leaf() const {\n return 2*idx_+1 >= tree_->box_data_.size();\n }\n //! The begin iterator to the child boxes contained in this box\n box_iterator child_begin() const {\n FMMTL_ASSERT(!is_leaf());\n return box_iterator(2*idx_+1, tree_);\n }\n //! The end iterator to the child boxes contained in this box\n box_iterator child_end() const {\n FMMTL_ASSERT(!is_leaf());\n return box_iterator(2*idx_+3, tree_);\n }\n //! The number of children this box has\n static constexpr size_type num_children() {\n return 2;\n }\n\n //! The begin iterator to the bodies contained in this box\n body_iterator body_begin() const {\n return body_iterator(data().body_begin_, tree_);\n }\n //! The end iterator to the bodies contained in this box\n body_iterator body_end() const {\n return body_iterator(data().body_end_, tree_);\n }\n //! The number of bodies this box contains\n size_type num_bodies() const {\n return std::distance(body_begin(), body_end());\n }\n\n //! Equality comparison operator\n bool operator==(const Box& b) const {\n FMMTL_ASSERT(tree_ == b.tree_);\n return idx_ == b.idx_;\n }\n //! Comparison operator for std:: containers and algorithms\n bool operator<(const Box& b) const {\n FMMTL_ASSERT(tree_ == b.tree_);\n return idx_ < b.idx_;\n }\n\n //! Write a Box to an output stream\n friend std::ostream& operator<<(std::ostream& s, const box_type& b) {\n size_type num_bodies = b.num_bodies();\n size_type first_body = b.body_begin()->index();\n size_type last_body = first_body + num_bodies - 1;\n size_type parent_idx = b.index()==0 ? 0 : b.parent().index();\n\n return s << \"Box \" << b.index()\n << \" (L\" << b.level() << \", P\" << parent_idx\n << \", \" << num_bodies << (num_bodies == 1 ? \" body\" : \" bodies\")\n << \" \" << first_body << \"-\" << last_body\n << \"): Center: \" << b.center()\n << \"; Radius: \" << std::sqrt(b.radius_sq());\n }\n\n private:\n size_type idx_;\n tree_type* tree_;\n friend box_iterator;\n Box(size_type idx, const tree_type* tree)\n : idx_(idx), tree_(const_cast(tree)) {\n FMMTL_ASSERT(idx_ < tree_->boxes());\n }\n inline BoxData& data() const {\n return tree_->box_data_[idx_];\n }\n friend class BallTree;\n };\n\n public:\n\n /** Construct a tree encompassing a bounding box\n * and insert a range of points */\n template \n BallTree(const Range& rng, size_type n_crit = 256,\n typename std::enable_if::value>::type* = 0)\n : BallTree(rng.begin(), rng.end(), n_crit) {\n }\n\n /** Construct an tree encompassing a bounding box\n * and insert a range of points */\n template \n BallTree(PointIter first, PointIter last, size_type n_crit = 256) {\n insert(first, last, n_crit);\n }\n\n /** Return the Bounding Box that this BallTree encompasses */\n BoundingSphere bounding_sphere() const {\n return box_data_[0].bounding_sphere_;\n }\n\n /** Return the center of this BallTree */\n point_type center() const {\n return root().center();\n }\n\n /** The number of bodies contained in this tree */\n inline size_type size() const {\n return permute_.size();\n }\n /** The number of bodies contained in this tree */\n inline size_type bodies() const {\n return size();\n }\n\n /** The number of boxes contained in this tree */\n inline size_type boxes() const {\n return box_data_.size();\n }\n\n /** The number of boxes contained in level L of this tree */\n inline size_type boxes(size_type L) const {\n return (1 << L);\n }\n\n /** The maximum level of any box in this tree */\n inline size_type levels() const {\n return std::log2(boxes()); // XXX: Slow?\n }\n\n /** Returns true if the box is contained in this tree, false otherwise */\n inline bool contains(const box_type& box) const {\n return this == box.tree_;\n }\n /** Returns true if the body is contained in this tree, false otherwise */\n inline bool contains(const body_type& body) const {\n return this == body.tree_;\n }\n\n /** Return the root box of this tree */\n box_type root() const {\n return Box(0, this);\n }\n /** Return a box given its index */\n box_type box(const size_type idx) const {\n FMMTL_ASSERT(idx < box_data_.size());\n return Box(idx, this);\n }\n /** Return a body given its index */\n body_type body(const size_type idx) const {\n FMMTL_ASSERT(idx < size());\n return Body(idx, this);\n }\n /** Return an iterator to the first body in this tree */\n body_iterator body_begin() const {\n return body_iterator(0, this);\n }\n /** Return an iterator one past the last body in this tree */\n body_iterator body_end() const {\n return body_iterator(bodies(), this);\n }\n /** Return an iterator to the first box in this tree */\n box_iterator box_begin() const {\n return box_iterator(0, this);\n }\n /** Return an iterator one past the last box in this tree */\n box_iterator box_end() const {\n return box_iterator(boxes(), this);\n }\n /** Return an iterator to the first box at level L in this tree\n * @pre L < levels()\n */\n box_iterator box_begin(size_type L) const {\n FMMTL_ASSERT(L < levels());\n return box_iterator((1 << L) - 1, this);\n }\n /** Return an iterator one past the last box at level L in this tree\n * @pre L < levels()\n */\n box_iterator box_end(size_type L) const {\n FMMTL_ASSERT(L < levels());\n return box_iterator((1 << (L+1)) - 1, this);\n }\n\n template \n struct body_permuted_iterator {\n typedef typename std::vector::const_iterator permute_iter;\n typedef boost::permutation_iterator type;\n };\n\n /** Tranform (permute) an iterator so its traversal follows the same order as\n * the bodies contained in this tree\n */\n template \n typename body_permuted_iterator::type\n body_permute(RandomAccessIter it, const body_iterator& bi) const {\n return boost::make_permutation_iterator(it, permute_.cbegin() + bi.index());\n }\n\n /** Tranform (permute) an iterator so its traversal follows the same order as\n * the bodies contained in this tree\n *\n * Specialized for bi = body_begin().\n */\n template \n typename body_permuted_iterator::type\n body_permute(RandomAccessIter it) const {\n return body_permute(it, body_begin());\n }\n\n /** Write a BallTree to an output stream */\n friend std::ostream& operator<<(std::ostream& s, const BallTree& t) {\n struct {\n std::ostream& print(std::ostream& s, const box_type& box) {\n s << std::string(2*box.level(), ' ') << box;\n if (!box.is_leaf())\n for (auto ci = box.child_begin(); ci != box.child_end(); ++ci)\n print(s << \"\\n\", *ci);\n return s;\n }\n } recursive_box;\n\n return recursive_box.print(s, t.root());\n }\n\n private:\n //! TODO: Make dynamic and public?\n template \n void insert(PointIter p_first, PointIter p_last, size_type NCRIT) {\n FMMTL_LOG(\"BallTree Insert\");\n\n assert(p_first != p_last);\n\n // Create a point-idx pair vector\n typedef typename std::iterator_traits::value_type point_i_type;\n\n // XXX: Generalize?\n static_assert(std::is_same::value,\n \"PointIter value_type must be point_type\");\n\n typedef std::pair point_t;\n\n std::vector point;\n // If iterators are random access, we can reserve space efficiently\n if (std::is_same::iterator_category,\n std::random_access_iterator_tag>::value)\n point.reserve(std::distance(p_first, p_last));\n\n // Copy to point-idx pair\n unsigned idx = 0;\n for (PointIter pi = p_first; pi != p_last; ++pi, ++idx) {\n point.emplace_back(*pi, idx);\n }\n permute_.reserve(point.size());\n\n // Helper std::pair projection operator\n struct pair2point {\n point_type& operator()(point_t& p) const { return p.first; }\n };\n using proj = boost::transform_iterator;\n\n // The number of leaf boxes that will be created\n // (Smallest power of two greater than or equal to ceil(N/NCRIT))\n unsigned leaves = ceil_pow_2((point.size() + NCRIT - 1) / NCRIT);\n unsigned levels = std::log2(leaves);\n\n // Reserve the number of boxes that will be added\n box_data_.reserve(2*leaves - 1);\n\n // Push the root sphere which contains all points\n box_data_.emplace_back(0, size_type(point.size()),\n approx_bounding_sphere(proj(point.begin()),\n proj(point.end())));\n\n // For every box that is created\n unsigned end_k = (1 << levels) - 1;\n for (unsigned k = 0; k < end_k; ++k) {\n // The range of points in this box\n auto p_begin = point.begin() + box_data_[k].body_begin_;\n auto p_end = point.begin() + box_data_[k].body_end_;\n\n // Invariant of helper functions\n assert(p_begin != p_end);\n\n // Compute the furthest point from the center\n const point_type& p1 = furthest_point_from(proj(p_begin), proj(p_end),\n box(k).center());\n // Compute the furthest point from point1\n const point_type& p2 = furthest_point_from(proj(p_begin), proj(p_end),\n p1);\n\n // Partition on the median: balanced tree, but worse spheres\n auto p_mid = p_begin + (p_end - p_begin) / 2;\n point_type r = p2 - p1;\n std::nth_element(p_begin, p_mid, p_end,\n [&](const point_t& a, const point_t& b) {\n return inner_prod(a.first-p1, r) < inner_prod(b.first-p1, r);\n });\n\n /*\n // Partition on proximity: better spheres, potentially unbalanced\n // Would need to account for the possibility of empty boxes\n auto p_mid = std::partition(p_begin, p_end, [&] (const point_t& a) {\n return norm_2_sq(a.first-p1) < norm_2_sq(a.first-p2);\n });\n */\n\n // Record the child boxes\n size_type mid = p_mid - point.begin();\n box_data_.emplace_back(box_data_[k].body_begin_, mid,\n approx_bounding_sphere(proj(p_begin), proj(p_mid)));\n\n box_data_.emplace_back(mid, box_data_[k].body_end_,\n approx_bounding_sphere(proj(p_mid), proj(p_end)));\n }\n\n // Assert no re-allocation\n assert(box_data_.size() <= 2*leaves-1);\n\n // Extract the permutation idx\n for (const point_t& p : point)\n permute_.push_back(p.second);\n }\n\n /** Calculate the approximate bounding sphere of a set of points\n * using the Points Closest to Furthest Pair approach.\n */\n template \n BoundingSphere approx_bounding_sphere(PointIter first,\n PointIter last) {\n auto n = std::distance(first, last);\n point_type center = std::accumulate(first, last, point_type{}) / n;\n\n double r_sq = std::accumulate(first, last, double{},\n [&] (double r_sq, point_type& p) {\n return std::max(r_sq, norm_2_sq(center-p));\n });\n\n return {center, r_sq};\n }\n\n /** Find the point in a set of points that is furthest away from a target point\n */\n template \n const point_type& furthest_point_from(PointIter first,\n PointIter last,\n const point_type& target) {\n using pair = std::pair;\n return *std::accumulate(first, last, pair{},\n [&] (const pair& r, const point_type& p) {\n return std::max(r, pair{norm_2_sq(target-p), &p});\n }).second;\n }\n\n // Just making sure for now\n BallTree(const BallTree&) {};\n void operator=(const BallTree&) {};\n};\n\n\n} // end namespace fmmtl\n", "meta": {"hexsha": "1c8992e9110511071932ac05acb9908ef1ebd89f", "size": 15686, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "source/fmmtl/fmmtl/tree/BallTree.hpp", "max_stars_repo_name": "sergeneren/BubbleH", "max_stars_repo_head_hexsha": "e018e5a008e101221a4f8a3bbb25e7ec03fc9662", "max_stars_repo_licenses": ["BSD-3-Clause-Clear"], "max_stars_count": 52.0, "max_stars_repo_stars_event_min_datetime": "2019-10-06T17:25:39.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-20T23:01:13.000Z", "max_issues_repo_path": "source/fmmtl/fmmtl/tree/BallTree.hpp", "max_issues_repo_name": "sergeneren/BubbleH", "max_issues_repo_head_hexsha": "e018e5a008e101221a4f8a3bbb25e7ec03fc9662", "max_issues_repo_licenses": ["BSD-3-Clause-Clear"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2019-10-08T18:44:41.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-09T07:48:31.000Z", "max_forks_repo_path": "source/fmmtl/fmmtl/tree/BallTree.hpp", "max_forks_repo_name": "sergeneren/BubbleH", "max_forks_repo_head_hexsha": "e018e5a008e101221a4f8a3bbb25e7ec03fc9662", "max_forks_repo_licenses": ["BSD-3-Clause-Clear"], "max_forks_count": 10.0, "max_forks_repo_forks_event_min_datetime": "2019-10-07T16:33:24.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-21T01:09:47.000Z", "avg_line_length": 32.0777096115, "max_line_length": 81, "alphanum_fraction": 0.6310085426, "num_tokens": 3816, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185498374789, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.24347137846299532}} {"text": "///////////////////////////////////////////////////////////////\n// Copyright 2012 John Maddock. Distributed under the Boost\n// Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_\n\n#ifndef BOOST_MP_INTEGER_HPP\n#define BOOST_MP_INTEGER_HPP\n\n#include \n#include \n\nnamespace boost{\nnamespace multiprecision{\n\ntemplate \ntypename enable_if_c::value && is_integral::value, Integer&>::type\n multiply(Integer& result, const I2& a, const I2& b)\n{\n return result = static_cast(a) * static_cast(b);\n}\ntemplate \ntypename enable_if_c::value && is_integral::value, Integer&>::type\n add(Integer& result, const I2& a, const I2& b)\n{\n return result = static_cast(a) + static_cast(b);\n}\ntemplate \ntypename enable_if_c::value && is_integral::value, Integer&>::type\n subtract(Integer& result, const I2& a, const I2& b)\n{\n return result = static_cast(a) - static_cast(b);\n}\n\ntemplate \ntypename enable_if_c::value>::type divide_qr(const Integer& x, const Integer& y, Integer& q, Integer& r)\n{\n q = x / y;\n r = x % y;\n}\n\ntemplate \ntypename enable_if_c::value && is_integral::value, I2>::type integer_modulus(const I1& x, I2 val)\n{\n return static_cast(x % val);\n}\n\nnamespace detail{\n//\n// Figure out the kind of integer that has twice as many bits as some builtin\n// integer type I. Use a native type if we can (including types which may not\n// be recognised by boost::int_t because they're larger than boost::long_long_type),\n// otherwise synthesize a cpp_int to do the job.\n//\ntemplate \nstruct double_integer\n{\n static const unsigned int_t_digits =\n 2 * sizeof(I) <= sizeof(boost::long_long_type) ? std::numeric_limits::digits * 2 : 1;\n\n typedef typename mpl::if_c<\n 2 * sizeof(I) <= sizeof(boost::long_long_type),\n typename mpl::if_c<\n is_signed::value,\n typename boost::int_t::least,\n typename boost::uint_t::least\n >::type,\n typename mpl::if_c<\n 2 * sizeof(I) <= sizeof(double_limb_type),\n typename mpl::if_c<\n is_signed::value,\n signed_double_limb_type,\n double_limb_type\n >::type,\n number::value ? signed_magnitude : unsigned_magnitude), unchecked, void> >\n >::type\n >::type type;\n};\n\n}\n\ntemplate \ntypename enable_if_c::value && is_unsigned::value && is_integral::value, I1>::type\n powm(const I1& a, I2 b, I3 c)\n{\n typedef typename detail::double_integer::type double_type;\n\n I1 x(1), y(a);\n double_type result;\n\n while(b > 0)\n {\n if(b & 1)\n {\n multiply(result, x, y);\n x = integer_modulus(result, c);\n }\n multiply(result, y, y);\n y = integer_modulus(result, c);\n b >>= 1;\n }\n return x % c;\n}\n\ntemplate \ninline typename enable_if_c::value && is_signed::value && is_integral::value, I1>::type\n powm(const I1& a, I2 b, I3 c)\n{\n if(b < 0)\n {\n BOOST_THROW_EXCEPTION(std::runtime_error(\"powm requires a positive exponent.\"));\n }\n return powm(a, static_cast::type>(b), c);\n}\n\ntemplate \ntypename enable_if_c::value, unsigned>::type lsb(const Integer& val)\n{\n if(val <= 0)\n {\n if(val == 0)\n {\n BOOST_THROW_EXCEPTION(std::range_error(\"No bits were set in the operand.\"));\n }\n else\n {\n BOOST_THROW_EXCEPTION(std::range_error(\"Testing individual bits in negative values is not supported - results are undefined.\"));\n }\n }\n return detail::find_lsb(val);\n}\n\ntemplate \ntypename enable_if_c::value, unsigned>::type msb(Integer val)\n{\n if(val <= 0)\n {\n if(val == 0)\n {\n BOOST_THROW_EXCEPTION(std::range_error(\"No bits were set in the operand.\"));\n }\n else\n {\n BOOST_THROW_EXCEPTION(std::range_error(\"Testing individual bits in negative values is not supported - results are undefined.\"));\n }\n }\n return detail::find_msb(val);\n}\n\ntemplate \ntypename enable_if_c::value, bool>::type bit_test(const Integer& val, unsigned index)\n{\n Integer mask = 1;\n if(index >= sizeof(Integer) * CHAR_BIT)\n return 0;\n if(index)\n mask <<= index;\n return val & mask ? true : false;\n}\n\ntemplate \ntypename enable_if_c::value, Integer&>::type bit_set(Integer& val, unsigned index)\n{\n Integer mask = 1;\n if(index >= sizeof(Integer) * CHAR_BIT)\n return val;\n if(index)\n mask <<= index;\n val |= mask;\n return val;\n}\n\ntemplate \ntypename enable_if_c::value, Integer&>::type bit_unset(Integer& val, unsigned index)\n{\n Integer mask = 1;\n if(index >= sizeof(Integer) * CHAR_BIT)\n return val;\n if(index)\n mask <<= index;\n val &= ~mask;\n return val;\n}\n\ntemplate \ntypename enable_if_c::value, Integer&>::type bit_flip(Integer& val, unsigned index)\n{\n Integer mask = 1;\n if(index >= sizeof(Integer) * CHAR_BIT)\n return val;\n if(index)\n mask <<= index;\n val ^= mask;\n return val;\n}\n\ntemplate \ntypename enable_if_c::value, Integer>::type sqrt(const Integer& x, Integer& r)\n{\n //\n // This is slow bit-by-bit integer square root, see for example\n // http://en.wikipedia.org/wiki/Methods_of_computing_square_roots#Binary_numeral_system_.28base_2.29\n // There are better methods such as http://hal.inria.fr/docs/00/07/28/54/PDF/RR-3805.pdf\n // and http://hal.inria.fr/docs/00/07/21/13/PDF/RR-4475.pdf which should be implemented\n // at some point.\n //\n Integer s = 0;\n if(x == 0)\n {\n r = 0;\n return s;\n }\n int g = msb(x);\n if(g == 0)\n {\n r = 1;\n return s;\n }\n\n Integer t = 0;\n r = x;\n g /= 2;\n bit_set(s, g);\n bit_set(t, 2 * g);\n r = x - t;\n --g;\n do\n {\n t = s;\n t <<= g + 1;\n bit_set(t, 2 * g);\n if(t <= r)\n {\n bit_set(s, g);\n r -= t;\n }\n --g;\n }\n while(g >= 0);\n return s;\n}\n\ntemplate \ntypename enable_if_c::value, Integer>::type sqrt(const Integer& x)\n{\n Integer r;\n return sqrt(x, r);\n}\n\n}} // namespaces\n\n#endif\n", "meta": {"hexsha": "29699d1b2411aec2e5f723d8cc729aaf32f17f0a", "size": 6849, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/src/boost_1_65_1/boost/multiprecision/integer.hpp", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 11356.0, "max_stars_repo_stars_event_min_datetime": "2017-12-08T19:42:32.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T16:55:25.000Z", "max_issues_repo_path": "deps/src/boost_1_65_1/boost/multiprecision/integer.hpp", "max_issues_repo_name": "shreyasvj25/turicreate", "max_issues_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 2402.0, "max_issues_repo_issues_event_min_datetime": "2017-12-08T22:31:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-28T19:25:52.000Z", "max_forks_repo_path": "deps/src/boost_1_65_1/boost/multiprecision/integer.hpp", "max_forks_repo_name": "shreyasvj25/turicreate", "max_forks_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1343.0, "max_forks_repo_forks_event_min_datetime": "2017-12-08T19:47:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-26T11:31:36.000Z", "avg_line_length": 27.1785714286, "max_line_length": 157, "alphanum_fraction": 0.6403854577, "num_tokens": 1842, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.2431884100770308}} {"text": "//==============================================================================\n// Copyright 2003 - 2012 LASMEA UMR 6602 CNRS/Univ. Clermont II\n// Copyright 2009 - 2012 LRI UMR 8623 CNRS/Univ Paris Sud XI\n//\n// Distributed under the Boost Software License, Version 1.0.\n// See accompanying file LICENSE.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt\n//==============================================================================\n#ifndef NT2_INTEGRATION_FUNCTIONS_COMMON_QUADGK_HPP_INCLUDED\n#define NT2_INTEGRATION_FUNCTIONS_COMMON_QUADGK_HPP_INCLUDED\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\nnamespace nt2 { namespace details\n{\n template class quadgk_impl\n {\n public :\n typedef T input_t;\n typedef typename meta::as_logical::type bi_t;\n typedef V value_t;\n typedef typename meta::is_complex::type v_is_cplx_t;\n typedef typename boost::mpl::if_::type result_t;\n typedef typename meta::as_real::type real_t;\n typedef details::integration_settings o_t;\n typedef container::table vtab_t;\n typedef container::table itab_t;\n typedef container::table rtab_t;\n typedef container::table restab_t;\n typedef container::table bitab_t;\n\n quadgk_impl() : errbnd_(Nan()),\n fcnt_(0),\n maxfcnt_(Valmax()),\n warn_(0),\n res_(){}\n ~quadgk_impl() {}\n\n size_t nbeval() const { return fcnt_; }\n real_t lasterror() const { return errbnd_; }\n bool ok() const { return warn_ == 0; }\n const restab_t & result() const { return res_; }\n void setwarn(size_t w) { if(w > warn_) warn_ = w; }\n size_t warn() const { return warn_; }\n\n template < class FUNC, class X>\n void compute( const FUNC& f, const X & x, const o_t & o,\n const boost::mpl::false_ & choice) // cplx path integration\n {\n init(o, x, choice);\n interval_ = tinterval_;\n vadapt(transform(f, a_, b_, interval_));\n }\n\n template < class FUNC, class X>\n void compute( const FUNC& f, const X & x, const o_t & o,\n const boost::mpl::true_ & choice) // real interval integration\n {\n init(o, x, choice);\n if (a_ == b_) //quick return\n {\n result_t r = nt2::multiplies(f(a_), Zero());\n //result_t r = details::midparea(f, tinterval_(begin_), tinterval_(end_));\n res_ = r;\n errbnd_ = nt2::abs(r);\n return;\n }\n interval_ = tinterval_;\n adjust_and_call(f);\n if(reversedir_) res_ = -res_;\n }\n private :\n real_t errbnd_;\n size_t fcnt_;\n size_t maxfcnt_;\n size_t maxintvcnt_;\n size_t warn_;\n real_t abstol_;\n real_t reltol_;\n restab_t res_;\n real_t hmin_;\n bool reversedir_;\n itab_t tinterval_; //original points\n itab_t interval_; //transformed points, to reduce integration to a subinterval of [-1, 1]\n input_t a_, b_;\n bool firstfunceval_;\n size_t nb_;\n static const size_t minintervalcount_ = 10; // Minimum number subintervals to start.\n\n// Gauss-Kronrod (7,15) pair. Use symmetry in defining nodes and weights.\n static const itab_t& nodes()\n {\n static const input_t gk[7] = {\n input_t(0.2077849550078985), input_t(0.4058451513773972), input_t(0.5860872354676911),\n input_t(0.7415311855993944), input_t(0.8648644233597691), input_t(0.9491079123427585),\n input_t(0.9914553711208126)};\n static const itab_t k(of_size(7, 1), &gk[0], &gk[7]);\n static const itab_t k15 = nt2::catv(nt2::catv(-flipud(k), nt2::Zero()),k);\n return k15;\n }\n\n static const vtab_t& wt()\n {\n static const value_t pwt[7] = {\n value_t(0.2044329400752989), value_t(0.1903505780647854), value_t(0.1690047266392679),\n value_t(0.1406532597155259), value_t(0.1047900103222502), value_t(0.06309209262997855),\n value_t(0.02293532201052922) };\n static const vtab_t wt(of_size(1, 7), &pwt[0], &pwt[7]);\n static const vtab_t wt15 = nt2::cath(nt2::cath(fliplr(wt),value_t(0.2094821410847278)),wt);\n return wt15;\n }\n\n static const vtab_t& ewt()\n {\n static const value_t pwt7[7] = {\n value_t(0),value_t(0.3818300505051189),value_t(0),value_t(0.2797053914892767),\n value_t(0),value_t(0.1294849661688697),value_t(0)\n };\n static const vtab_t wt7(of_size(1, 7), &pwt7[0], &pwt7[7]);\n static const vtab_t ewt15 = wt()-nt2::cath(nt2::cath(fliplr(wt7),value_t(0.4179591836734694)),wt7);\n return ewt15;\n }\n\n template < class X >\n void init( const o_t & o, const X& x, const boost::mpl::false_ & )\n {\n details::prepare_waypoints(o, x, tinterval_);\n a_ = tinterval_(begin_);\n b_ = tinterval_(end_);\n abstol_ = o.abstol;\n reltol_ = o.reltol;\n warn_ = 0;\n fcnt_ = 0;\n maxfcnt_ = Valmax();\n maxintvcnt_ = o.maxintvcnt;\n errbnd_ = nt2::Inf();\n }\n\n template < class X >\n void init( const o_t & o, const X& x, const boost::mpl::true_ & )//real path integration\n {\n details::prepare_waypoints(o, x, tinterval_);\n reversedir_ = details::order_points(tinterval_, true);\n a_ = tinterval_(begin_);\n b_ = tinterval_(end_);\n abstol_ = o.abstol;\n reltol_ = o.reltol;\n warn_ = 0;\n fcnt_ = 0;\n maxfcnt_ = Valmax();\n maxintvcnt_ = o.maxintvcnt;\n errbnd_ = nt2::Inf();\n firstfunceval_ = true;\n }\n\n template < class FUNC >\n inline bool check(FUNC f, bool t, size_t w, const result_t& q)\n {\n if (t)\n {\n fcnt_ = f.fcnt();\n res_ = q;\n setwarn(w);\n }\n return t;\n }\n template < class FUNC >\n void vadapt(FUNC f)\n {\n real_t pathlen;\n itab_t tmp0;\n details::split(f.interval_, minintervalcount_, tmp0, pathlen);\n interval_ = tmp0;\n if (pathlen == 0)\n {\n input_t tmp = nt2::multiplies(f.interval_(end_), nt2::Half());\n input_t xx = nt2::fma(f.interval_(begin_), nt2::Half(), tmp);\n input_t d = (f.interval_(end_)-f.interval_(begin_));\n\n result_t r = nt2::multiplies(f(xx), d);\n res_ = r;\n errbnd_ = nt2::abs(r);\n return;\n }\n // Initialize array of subintervals of [a,b].\n itab_t subs = nt2::catv(interval_(nt2::_(begin_, end_-1)),\n interval_(nt2::_(begin_+1, end_)));\n // Initialize partial sums.\n result_t q_ok = nt2::Zero();\n result_t err_ok = nt2::Zero();\n // Initialize main loop\n nb_ = 0;\n while (true)\n {\n ++nb_;\n // subs contains subintervals of [a,b] where the integral is not\n // sufficiently accurate. The first row of SUBS holds the left end\n // points and the second row, the corresponding right endpoints.\n itab_t midpt = nt2::mean(subs); // midpoints of the subintervals\n itab_t halfh = diff(subs)*Half(); // half the lengths of the subintervals\n\n itab_t x = nt2::rowvect(nt2::sx(nt2::tag::plus_(),nt2::mtimes(nodes(), halfh),midpt));\n // BOOST_AUTO_TPL(fx, f(x));\n vtab_t fx = f(x);\n // Quit if mesh points are too close.\n fx.resize(nt2::of_size(nt2::numel(wt()), numel(fx)/numel(wt())));\n // Quantities for subintervals.\n restab_t qsubs = nt2::mtimes(wt(), fx)*halfh;\n restab_t errsubs = nt2::mtimes(ewt(), fx)* halfh;\n // Calculate current values of q and tol.\n result_t q = nt2::globalsum(qsubs) + q_ok;\n if (check(f, f.tooclose(), 6, q)) break;\n real_t tol = max(abstol_,reltol_*nt2::abs(q));\n // Locate subintervals where the approximate integrals are\n // sufficiently accurate and use them to update the partial\n // error sum.\n bitab_t ff= nt2::le(nt2::abs(errsubs), (Two()*tol/pathlen)*nt2::abs(halfh));\n bitab_t notff = nt2::logical_not(ff);\n err_ok += nt2::globalsum(errsubs(ff));\n // Remove errsubs entries for subintervals with accurate\n // approximations.\n restab_t errsubs1 = nt2::rowvect(errsubs(notff)); errsubs = errsubs1; //ALIASING CAN BE AVOIDED PERHAPS\n // The approximate error bound is constructed by adding the\n // approximate error bounds for the subintervals with accurate\n // approximations to the 1-norm of the approximate error bounds\n // for the remaining subintervals. This guards against\n // excessive cancellation of the errors of the remaining\n // subintervals.\n errbnd_ = nt2::abs(err_ok) + nt2::globalasum1(errsubs);\n // Check for nonfinites.\n if (check(f, is_not_finite(q) && is_finite(errbnd_), 3, q)) break; // Infinite or Not-a-Number value encountered.\n if (check(f, errbnd_ <= tol, 0, q)) break; // tolerance reached: convergence\n // if (check(f, f.fcnt() > maxfcnt_, 5, q)) break; // Max evaluation number reached\n // Remove subintervals with accurate approximations.\n itab_t subs_tmp = subs(nt2::_, notff); subs = subs_tmp;\n if (check(f, nt2::isempty(subs), 0, q)) break; // All subs got required precision\n // Update the partial sum for the integral.\n q_ok += nt2::globalsum(qsubs(ff));\n // Split the remaining subintervals in half. Quit if splitting\n // results in too many subintervals.\n size_t nsubs = 2*nt2::size(subs,2);\n if (check(f, nsubs > maxintvcnt_, 2, q)) break; //Reached the limit on the maximum number of intervals in use.\n itab_t midpt1 = nt2::rowvect(midpt(notff)); midpt = midpt1;\n itab_t z = catv(catv(catv(subs(begin_,nt2::_),midpt),midpt),subs(end_,nt2::_));\n subs = reshape(z,2,numel(z)/2);\n }\n }\n\n template < class F >\n inline void adjust_and_call(const F &f)\n {\n bool fina = nt2::is_finite(a_);\n bool finb = nt2::is_finite(b_);\n if(fina && finb)\n {\n vadapt(transform(f, a_, b_, interval_));\n }\n else\n {\n bool infb = nt2::is_inf(b_);\n if (fina && infb)\n {\n vadapt(transform(f, a_, b_, interval_));\n }\n else\n {\n bool infa = nt2::is_inf(a_);\n if (infa && finb)\n {\n vadapt(transform(f, a_, b_, interval_));\n }\n else if (infa && infb)\n {\n vadapt(transform(f, a_, b_, interval_));\n }\n else //is_nan(a) || is_nan(b)\n {\n result_t r = Nan();\n errbnd_ = nt2::abs(r);\n fcnt_ = 1;\n }\n }\n }\n }\n };\n} }\n\n\nnamespace nt2 { namespace ext\n {\n BOOST_DISPATCH_IMPLEMENT ( quadgk_, tag::cpu_\n , (F)(X)(O)\n , (unspecified_< F >)\n ((ast_))\n (unspecified_)\n )\n {\n typedef typename O::value_t value_t;\n typedef typename O::input_t input_t;\n typedef typename O::real_t real_t;\n typedef typename O::result_t result_t;\n typedef typename O::restab_t restab_t;\n typedef typename boost::is_same::type input_is_real_t;\n typedef nt2::integration::output result_type;\n\n result_type operator()(F f, X const& x, O const& o)\n {\n details::quadgk_impl q;\n q.compute(f, x, o, input_is_real_t());\n result_type that = {q.result(), q.lasterror(),q.nbeval(),q.ok(),q.warn()};\n return that;\n }\n };\n} }\n#endif\n", "meta": {"hexsha": "8809dcf21bdfcfce59b4c87b5a6d4d4394dc4fd9", "size": 14750, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "modules/core/integration/include/nt2/integration/functions/common/quadgk.hpp", "max_stars_repo_name": "psiha/nt2", "max_stars_repo_head_hexsha": "5e829807f6b57b339ca1be918a6b60a2507c54d0", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 34.0, "max_stars_repo_stars_event_min_datetime": "2017-05-19T18:10:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-04T02:18:13.000Z", "max_issues_repo_path": "modules/core/integration/include/nt2/integration/functions/common/quadgk.hpp", "max_issues_repo_name": "psiha/nt2", "max_issues_repo_head_hexsha": "5e829807f6b57b339ca1be918a6b60a2507c54d0", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "modules/core/integration/include/nt2/integration/functions/common/quadgk.hpp", "max_forks_repo_name": "psiha/nt2", "max_forks_repo_head_hexsha": "5e829807f6b57b339ca1be918a6b60a2507c54d0", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 7.0, "max_forks_repo_forks_event_min_datetime": "2017-12-02T12:59:17.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-31T12:46:14.000Z", "avg_line_length": 40.9722222222, "max_line_length": 137, "alphanum_fraction": 0.573220339, "num_tokens": 3964, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073655352403, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.24318841007703074}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n\n#if HAVE_NEW_CXX\n# include \n# include \n#else\n# include \n# include \nnamespace std { using std::tr1::unordered_map; using std::tr1::unordered_set; }\n#endif\n\n#include \n\n#include \n#include \n\n#include \"json_feature_map_lexer.h\"\n#include \"fdict.h\"\n#include \"feature_map.h\"\n#include \"prob.h\"\n#include \"filelib.h\"\n\n#include \n#include \n\nusing adept::adouble;\n\n#define INPUT_DIM 20\ntemplate using FVector = Eigen::Matrix;\n\nusing namespace std;\nnamespace po = boost::program_options;\n\nvolatile bool* requested_stop = NULL;\n\nvoid InitCommandLine(int argc, char** argv, po::variables_map* conf) {\n po::options_description opts(\"Configuration options\");\n opts.add_options()\n (\"x,x\", po::value >(), \"Files containing training instance features\")\n (\"y,y\", po::value(), \"File containing training instance responses (if unspecified, do prediction only)\")\n (\"tx\", po::value >(), \"Files containing training instance features\")\n (\"ty\", po::value(), \"File containing training instance responses (if unspecified, do prediction only)\")\n (\"z,z\", po::value(), \"Write learned weights to this file (optional)\")\n (\"l1\",po::value()->default_value(0.0), \"l_1 regularization strength\")\n (\"l2\",po::value()->default_value(1e-10), \"l_2 regularization strength\")\n (\"help,h\", \"Help\");\n po::options_description dcmdline_options;\n dcmdline_options.add(opts);\n po::store(parse_command_line(argc, argv, dcmdline_options), *conf);\n if (conf->count(\"help\")) {\n cerr << dcmdline_options << endl;\n exit(1);\n }\n}\n\nenum RegressionType { kLINEAR, kLOGISTIC };\n\nstruct TrainingInstance {\n FrozenFeatureMap x;\n union {\n unsigned label; // for categorical & ordinal predictions\n float value; // for continuous predictions\n } y;\n};\n\nstruct ReaderHelper {\n explicit ReaderHelper(vector* xyp,\n bool h,\n vector* iids) : xy_pairs(xyp), lc(), flag(), has_labels(h), ids(iids), merge() {}\n unordered_map id2ind;\n FeatureMapStorage* fms;\n vector* xy_pairs;\n int lc;\n bool flag;\n bool has_labels;\n vector* ids;\n vector merged;\n bool merge;\n};\n\nvoid ReaderCB(const string& id,\n const std::pair* begin,\n const std::pair* end,\n void* extra) {\n ReaderHelper& rh = *reinterpret_cast(extra);\n ++rh.lc;\n if (rh.lc % 1000 == 0) { cerr << '.'; rh.flag = true; }\n if (rh.lc % 50000 == 0) { cerr << \" [\" << rh.lc << \"]\\n\"; rh.flag = false; }\n if (rh.ids && !rh.merge) rh.ids->push_back(id);\n if (rh.has_labels) {\n const unordered_map::iterator it = rh.id2ind.find(id);\n if (it == rh.id2ind.end()) {\n cerr << \"\\nUnlabeled example in line \" << rh.lc << \" (key=\" << id << ')' << endl;\n abort();\n } else {\n if (rh.merge) {\n rh.merged[it->second - 1] = true;\n const FrozenFeatureMap& prev = (*rh.xy_pairs)[it->second - 1].x;\n (*rh.xy_pairs)[it->second - 1].x = rh.fms->AddFeatureMap(begin,end,prev);\n } else {\n (*rh.xy_pairs)[it->second - 1].x = rh.fms->AddFeatureMap(begin,end);\n }\n }\n } else {\n TrainingInstance x_no_y;\n if (rh.merge) {\n if (!rh.ids) {\n cerr << \"Missing IDs\\n\";\n abort();\n }\n unsigned& ind = rh.id2ind[id];\n rh.merged[ind - 1] = true;\n const FrozenFeatureMap& prev = (*rh.xy_pairs)[ind - 1].x;\n (*rh.xy_pairs)[ind - 1].x = rh.fms->AddFeatureMap(begin,end,prev);\n } else {\n x_no_y.x = rh.fms->AddFeatureMap(begin,end);\n unsigned& ind = rh.id2ind[id];\n assert(ind == 0);\n rh.xy_pairs->push_back(x_no_y);\n ind = rh.xy_pairs->size();\n }\n }\n}\n\nvoid ReadLabeledInstances(const vector& ffeats,\n const string& fresp,\n const RegressionType resptype,\n FeatureMapStorage* fms,\n vector* xy_pairs,\n vector* labels,\n vector* instance_ids = NULL) {\n bool flag = false;\n xy_pairs->clear();\n int lc = 0;\n ReaderHelper rh(xy_pairs, fresp.size() > 0, instance_ids);\n rh.merge = false;\n unordered_map label2id;\n if (fresp.size() == 0) {\n cerr << \"No gold standard responses provided to learn from!\" << endl;\n } else {\n cerr << \"Reading responses from \" << fresp << \" ...\" << endl;\n ReadFile fr(fresp);\n for (unsigned i = 0; i < labels->size(); ++i)\n label2id[(*labels)[i]] = i;\n istream& in = *fr.stream();\n string line;\n while(getline(in, line)) {\n ++lc;\n if (lc % 1000 == 0) { cerr << '.'; flag = true; }\n if (lc % 40000 == 0) { cerr << \" [\" << lc << \"]\\n\"; flag = false; }\n if (line.size() == 0) continue;\n if (line[0] == '#') {\n if (line.size() > 1 && line[1] == '#') {\n if (lc != 1) {\n cerr << \"[WARNING] Line \" << lc << \" appears to be label declaration ... ignoring\\n\";\n } else {\n istringstream is(line);\n string label;\n is >> label;\n while(is >> label) {\n unordered_map::iterator it = label2id.find(label);\n if (it == label2id.end()) {\n it = label2id.insert(make_pair(label, labels->size())).first;\n labels->push_back(label);\n }\n }\n }\n }\n continue;\n }\n unsigned p = 0;\n while (p < line.size() && line[p] != ' ' && line[p] != '\\t') { ++p; }\n unsigned& ind = rh.id2ind[line.substr(0, p)];\n if (ind != 0) { cerr << \"ID \" << line.substr(0, p) << \" duplicated in line \" << lc << endl; abort(); }\n while (p < line.size() && (line[p] == ' ' || line[p] == '\\t')) { ++p; }\n assert(p < line.size());\n xy_pairs->push_back(TrainingInstance());\n ind = xy_pairs->size();\n switch (resptype) {\n case kLINEAR:\n xy_pairs->back().y.value = strtof(&line[p], 0);\n break;\n case kLOGISTIC:\n {\n unordered_map::iterator it = label2id.find(line.substr(p));\n if (it == label2id.end()) {\n const string label = line.substr(p);\n it = label2id.insert(make_pair(label, labels->size())).first;\n labels->push_back(label);\n }\n xy_pairs->back().y.label = it->second; // label id\n }\n break;\n }\n }\n if (flag) cerr << endl;\n if (resptype == kLOGISTIC) {\n cerr << \"LABELS:\";\n for (unsigned j = 0; j < labels->size(); ++j)\n cerr << \" \" << (*labels)[j];\n cerr << endl;\n }\n }\n FeatureMapStorage* pfms = NULL;\n FeatureMapStorage* tfms = NULL;\n for (unsigned i = 0; i < ffeats.size(); ++i) {\n if (i == ffeats.size() - 1) {\n pfms = tfms;\n rh.fms = fms;\n } else {\n delete pfms;\n pfms = tfms;\n tfms = new FeatureMapStorage;\n rh.fms = tfms;\n }\n if (pfms) {\n rh.merge = true;\n rh.merged.clear();\n rh.merged.resize(rh.xy_pairs->size(), false);\n }\n const string& ffeat = ffeats[i];\n cerr << \"Reading features from \" << ffeat << \" ...\" << endl;\n ReadFile ff(ffeat);\n JSONFeatureMapLexer::ReadRules(ff.stream(), ReaderCB, &rh);\n if (pfms) {\n for (unsigned j = 0; j < rh.xy_pairs->size(); ++j)\n if (!rh.merged[j])\n (*rh.xy_pairs)[j].x = rh.fms->AddFeatureMap(NULL, NULL, (*rh.xy_pairs)[j].x);\n }\n if (rh.flag) cerr << endl;\n }\n delete pfms;\n}\n\nvoid signal_callback_handler(int /* signum */) {\n if (!requested_stop || *requested_stop) {\n cerr << \"\\nReceived SIGINT again, quitting.\\n\";\n _exit(1);\n }\n cerr << \"\\nReceived SIGINT terminating optimization early.\\n\";\n *requested_stop = true;\n}\n\ntemplate \nstruct Model {\n Model(unsigned feats, unsigned labels) :\n input_reps(feats, FVector::Zero()), output_reps(labels, FVector::Zero()) {}\n\n // number of parameters\n size_t size() const {\n return input_reps.size() * input_reps[0].size() + output_reps.size() * output_reps[0].size();\n }\n\n void Randomize() {\n for (auto& iv: input_reps)\n iv = FVector::Random() / 5.;\n for (auto& ov: output_reps)\n ov = FVector::Random() / 2.;\n }\n\n template \n void copyfrom(const Model& o) {\n for (unsigned i = 0; i < input_reps.size(); ++i)\n for (unsigned j = 0; j < INPUT_DIM; ++j)\n input_reps[i](j,0) = o.input_reps[i](j,0);\n for (unsigned i = 0; i < output_reps.size(); ++i)\n for (unsigned j = 0; j < INPUT_DIM; ++j)\n output_reps[i](j,0) = o.output_reps[i](j,0);\n }\n\n void update(const Model& g, Model& h) {\n double eta = 0.1;\n for (unsigned i = 0; i < input_reps.size(); ++i)\n for (unsigned j = 0; j < INPUT_DIM; ++j) {\n double d = g.input_reps[i](j,0).get_gradient();\n if (d) {\n double s = h.input_reps[i](j,0) += d * d;\n input_reps[i](j,0) -= eta * d / sqrt(s);\n }\n }\n for (unsigned i = 0; i < output_reps.size(); ++i)\n for (unsigned j = 0; j < INPUT_DIM; ++j) {\n double d = g.output_reps[i](j,0).get_gradient();\n if (d) {\n double s = h.output_reps[i](j,0) += d * d;\n output_reps[i](j,0) -= eta * d / sqrt(s);\n }\n }\n }\n\n inline static void nonlinearity(FVector& v) {\n for (unsigned i = 0; i < v.size(); ++i)\n v(i,0) = tanh(v(i,0));\n }\n\n F log_prob(const FrozenFeatureMap& x, unsigned y) const {\n FVector h = FVector::Zero();\n for (auto& it : x)\n h += input_reps[it.first] * it.second;\n nonlinearity(h);\n F z = 0;\n F res = 0;\n unsigned c = 0;\n for (auto& ov : output_reps) {\n F s = h.dot(ov);\n if (c == y) res = s;\n z += exp(s);\n ++c;\n }\n return res - log(z);\n }\n\n unsigned predict(const FrozenFeatureMap& x) const {\n FVector h = FVector::Zero();\n for (auto& it : x)\n h += input_reps[it.first] * it.second;\n nonlinearity(h);\n unsigned c = 0;\n F cur_best = 0.0;\n unsigned cur_best_i = output_reps.size();\n for (auto& ov : output_reps) {\n F s = h.dot(ov);\n if (s > cur_best || cur_best_i == output_reps.size()) {\n cur_best = s; cur_best_i = c;\n }\n ++c;\n }\n return cur_best_i;\n }\n\n vector> input_reps;\n vector> output_reps;\n};\n\nint main(int argc, char** argv) {\n po::variables_map conf;\n InitCommandLine(argc, argv, &conf);\n string line;\n double l1 = conf[\"l1\"].as();\n double l2 = conf[\"l2\"].as();\n if (l1 < 0.0) {\n cerr << \"L1 strength must be >= 0\\n\";\n return 1;\n }\n if (l2 < 0.0) {\n cerr << \"L2 strength must be >= 0\\n\";\n return 2;\n }\n bool stop = false;\n requested_stop = &stop;\n RegressionType resptype = kLOGISTIC;\n if (conf.count(\"linear\")) {\n resptype = kLINEAR;\n }\n if (!(conf.count(\"x\") && conf.count(\"y\"))) {\n cerr << \"You must specify both training_features (-x) and training_responses (-y)!\\n\";\n return 1;\n }\n vector labels; // only populated for non-continuous models\n vector training, test;\n FeatureMapStorage fms;\n vector xfile = conf[\"x\"].as >();\n string yfile = conf[\"y\"].as();\n ReadLabeledInstances(xfile, yfile, resptype, &fms, &training, &labels);\n if (conf.count(\"tx\") && conf.count(\"ty\")) {\n vector xfile = conf[\"tx\"].as >();\n string yfile = conf[\"ty\"].as();\n ReadLabeledInstances(xfile, yfile, resptype, &fms, &test, &labels);\n }\n\n adept::Stack s;\n cerr << \" Number of features: \" << FD::NumFeats() << endl;\n cerr << \"Number of training examples: \" << training.size() << endl;\n const unsigned p = FD::NumFeats();\n\n Model dmodel(p,labels.size());\n dmodel.Randomize();\n Model hmodel(p,labels.size());\n Model amodel(p,labels.size());\n cout.precision(15);\n ostream* out = &cout;\n cerr << \"Model size: \" << dmodel.size() << \" parameters\\n\";\n // set up signal handler to catch SIGINT\n signal(SIGINT, signal_callback_handler);\n\n if (conf.count(\"linear\")) { // linear regression\n cerr << \"Not implemented!\\n\";\n return 1;\n } else { // logistic regression\n for (unsigned iter = 0; !stop && iter < 100000; ++iter) {\n amodel.copyfrom(dmodel);\n s.new_recording();\n adouble llh = 0;\n double right = 0;\n for (auto& xy: training) {\n adouble lp = amodel.log_prob(xy.x, xy.y.label);\n unsigned pred_y = dmodel.predict(xy.x);\n if (pred_y == xy.y.label) right++;\n //cerr << xy.y.label << \" ||| \" << lp << endl;\n llh -= lp;\n }\n llh.set_gradient(1.0);\n s.compute_adjoint();\n double hright = 0.0;\n double hllh = 0;\n unsigned lc = 0;\n for (auto& xy: test) {\n ++lc;\n double lp = dmodel.log_prob(xy.x, xy.y.label);\n unsigned pred_y = dmodel.predict(xy.x);\n if (pred_y == xy.y.label) hright++;\n else if (lc < 100) {\n cerr << \"line \" << lc << \" which is in \" << labels[xy.y.label] << \" was confused for \" << labels[pred_y] << endl;\n }\n //cerr << xy.y.label << \" ||| \" << lp << endl;\n hllh -= lp;\n }\n double perp = exp(llh.value() / training.size());\n double perpho = exp(hllh / test.size());\n cerr << \"i=\" << iter << \"\\tPerplexity: \" << perp << \"\\ttrain-acc: \" << (right / training.size());\n if (test.size()) cerr << \"\\td-perp: \" << perpho << \"\\ttd-acc: \" << (hright / test.size());\n cerr << endl;\n dmodel.update(amodel, hmodel);\n }\n }\n for (unsigned i = 0; 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YES\n2. NO", "lm_q1_score": 0.5273165382362518, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.24310177213579648}} {"text": "// Author: Francesco Regazzoni - MOX, Politecnico di Milano\n// Email: francesco.regazzoni@polimi.it\n// Date: 2020\n\n#include \n\n#include \n#include \n\n#include \"model_RDQ18.hpp\"\n\nmodel_RDQ18::model_RDQ18(std::string parameters_file) : sarcomere(\"RDQ18\") {\n // Read JSON options file\n boost::property_tree::ptree root;\n boost::property_tree::read_json(parameters_file, root);\n\n prm_n_RU = root.get_child(\"geometry\")\n .get_child(\"n_RU\")\n .get_value(); // [-]\n prm_LA = root.get_child(\"geometry\")\n .get_child(\"LA\")\n .get_value(); // [micro m]\n prm_LM = root.get_child(\"geometry\")\n .get_child(\"LM\")\n .get_value(); // [micro m]\n prm_LB = root.get_child(\"geometry\")\n .get_child(\"LB\")\n .get_value(); // [micro m]\n prm_Lsmooth = root.get_child(\"geometry\")\n .get_child(\"Lsmooth\")\n .get_value(); // [micro m]\n prm_Q0 = root.get_child(\"RU_steady_state\")\n .get_child(\"Q0\")\n .get_value(); // [-]\n prm_SLQ = root.get_child(\"RU_steady_state\")\n .get_child(\"SLQ\")\n .get_value(); // [micro m]\n prm_alphaQ = root.get_child(\"RU_steady_state\")\n .get_child(\"alphaQ\")\n .get_value(); // [1 / micro m]\n prm_mu = root.get_child(\"RU_steady_state\")\n .get_child(\"mu\")\n .get_value(); // [-]\n prm_gamma = root.get_child(\"RU_steady_state\")\n .get_child(\"gamma\")\n .get_value(); // [-]\n prm_Kon = root.get_child(\"RU_kinetics\")\n .get_child(\"Kon\")\n .get_value(); // [micro M^-1 * s^-1]\n prm_Koff = root.get_child(\"RU_kinetics\")\n .get_child(\"Koff\")\n .get_value(); // [s^-1]\n prm_Kbasic = root.get_child(\"RU_kinetics\")\n .get_child(\"Kbasic\")\n .get_value(); // [s^-1]\n prm_TaMax = root.get_child(\"upscaling\")\n .get_child(\"TaMax\")\n .get_value(); // [kPa]\n\n allocate_variables();\n\n initialize_rates();\n}\n\nvoid model_RDQ18::allocate_variables() {\n // Variable numbers\n n_variables = (prm_n_RU - 2) * 4 * 4 * 4;\n\n // Allocation of state_RU\n std::array, 4>, 4> base_RU_state;\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n base_RU_state[RU_L][RU_C][RU_R] = 0.0;\n base_RU_state[0][0][0] = 1.0;\n\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n state_RU.push_back(base_RU_state);\n\n // Allocation of initial_state\n for (unsigned int i = 0; i < n_variables; ++i)\n initial_state.push_back(0.0);\n serialize_state(initial_state);\n\n // Allocation of rates_RU\n std::array, 4>, 4>, 4>\n base_RU_rates_or_flux;\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new)\n base_RU_rates_or_flux[RU_L][RU_C][RU_R][RU_new] = 0.0;\n\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU) {\n rates_RU.push_back(base_RU_rates_or_flux);\n }\n\n // Allocation of flux_RU_*\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU) {\n flux_RU_L.push_back(base_RU_rates_or_flux);\n flux_RU_C.push_back(base_RU_rates_or_flux);\n flux_RU_R.push_back(base_RU_rates_or_flux);\n }\n}\n\nvoid model_RDQ18::solve_time_step(std::vector &state,\n const double &calcium,\n const double &sarcomere_length,\n const double & /*dSL_dt*/, const double &dt) {\n // Deserialize state\n deserialize_state(state);\n\n // Update RU transition rates\n RU_update_rates(calcium, sarcomere_length);\n\n // Advance RU state\n double RU_dt = 0.0;\n double time_advanced = 0.0;\n while (time_advanced <=\n dt - 1e-10) // Cover the time-step up to a given tolerance\n {\n RU_dt = std::min(prm_time_step_update_RU_state, dt - time_advanced);\n RU_update_state(RU_dt);\n time_advanced += RU_dt;\n }\n\n // Re-serialize state\n serialize_state(state);\n}\n\nvoid model_RDQ18::deserialize_state(const std::vector &state) {\n unsigned int i_current = 0;\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n state_RU[i_RU][RU_L][RU_C][RU_R] = state[i_current];\n i_current++;\n }\n}\n\nvoid model_RDQ18::serialize_state(std::vector &state) {\n unsigned int i_current = 0;\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n state[i_current] = state_RU[i_RU][RU_L][RU_C][RU_R];\n i_current++;\n }\n}\n\ndouble model_RDQ18::get_active_tension(const std::vector &state,\n const double & /*sarcomere_length*/) {\n return prm_TaMax * get_permissivity(state);\n}\n\ndouble model_RDQ18::get_permissivity(const std::vector &state) {\n deserialize_state(state);\n\n double permissivity = 0;\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU) {\n if (i_RU == 0) // First RU\n {\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n permissivity +=\n state_RU[0][2][RU_C][RU_R] + state_RU[0][3][RU_C][RU_R];\n } else if (i_RU == prm_n_RU - 1) // Last RU\n {\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n permissivity += state_RU[prm_n_RU - 3][RU_L][RU_C][2] +\n state_RU[prm_n_RU - 3][RU_L][RU_C][3];\n } else // Intermediate RUs\n {\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n permissivity += state_RU[i_RU - 1][RU_L][2][RU_R] +\n state_RU[i_RU - 1][RU_L][3][RU_R];\n }\n }\n return permissivity / prm_n_RU;\n}\n\nvoid model_RDQ18::initialize_rates() {\n int permissive_neighbors;\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n permissive_neighbors =\n permissivity_of_state[RU_L] + permissivity_of_state[RU_R];\n rates_RU[i_RU][RU_L][1][RU_R][0] = prm_Koff;\n rates_RU[i_RU][RU_L][2][RU_R][3] = prm_Koff / prm_mu;\n rates_RU[i_RU][RU_L][3][RU_R][0] =\n prm_Kbasic * std::pow(prm_gamma, 2 - permissive_neighbors);\n rates_RU[i_RU][RU_L][2][RU_R][1] =\n prm_Kbasic * std::pow(prm_gamma, 2 - permissive_neighbors);\n }\n}\n\nvoid model_RDQ18::RU_update_rates(const double &calcium,\n const double &sarcomere_length) {\n int permissive_neighbors;\n double ChiRA_i_RU, ChiLA_i_RU;\n double Q_SL = Q(sarcomere_length);\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU) {\n ChiRA_i_RU = ChiRA(sarcomere_length, i_RU);\n ChiLA_i_RU = ChiLA(sarcomere_length, i_RU);\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n permissive_neighbors =\n permissivity_of_state[RU_L] + permissivity_of_state[RU_R];\n rates_RU[i_RU][RU_L][0][RU_R][1] = prm_Kon * calcium * ChiRA_i_RU;\n rates_RU[i_RU][RU_L][1][RU_R][2] =\n ChiRA_i_RU * ChiLA_i_RU *\n std::pow(prm_gamma, permissive_neighbors) * Q_SL * prm_Kbasic;\n rates_RU[i_RU][RU_L][3][RU_R][2] = prm_Kon * calcium * ChiRA_i_RU;\n rates_RU[i_RU][RU_L][0][RU_R][3] =\n ChiRA_i_RU * ChiLA_i_RU *\n std::pow(prm_gamma, permissive_neighbors) * Q_SL * prm_Kbasic /\n prm_mu;\n }\n }\n}\n\nvoid model_RDQ18::RU_update_state(const double &dt) {\n // Compute fluxes associated with center units\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n flux_RU_C[i_RU][RU_L][RU_C][RU_R][RU_new] =\n rates_RU[i_RU + 1][RU_L][RU_C][RU_R][RU_new] *\n state_RU[i_RU][RU_L][RU_C][RU_R];\n }\n\n double prob_tot;\n double rate_tot;\n\n // Compute fluxes associated with left units\n // --- Most left-ward triplet\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n flux_RU_L[0][RU_L][RU_C][RU_R][RU_new] =\n rates_RU[0][0][RU_L][RU_C][RU_new] *\n state_RU[0][RU_L][RU_C][RU_R];\n }\n\n // --- Other triplets\n for (i_RU = 1; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C) {\n prob_tot = 0.0;\n for (RU_dummy = 0; RU_dummy < 4; ++RU_dummy)\n prob_tot += state_RU[i_RU - 1][RU_dummy][RU_L][RU_C];\n\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n rate_tot = 0.0;\n if (prob_tot > 1e-12) {\n for (RU_dummy = 0; RU_dummy < 4; ++RU_dummy)\n rate_tot += flux_RU_C[i_RU - 1][RU_dummy][RU_L][RU_C][RU_new];\n\n rate_tot /= prob_tot;\n }\n\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n flux_RU_L[i_RU][RU_L][RU_C][RU_R][RU_new] =\n rate_tot * state_RU[i_RU][RU_L][RU_C][RU_R];\n }\n }\n }\n\n // Compute fluxes associated with right units\n // --- Most right-ward triplet\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n flux_RU_R[prm_n_RU - 3][RU_L][RU_C][RU_R][RU_new] =\n rates_RU[prm_n_RU - 1][RU_C][RU_R][0][RU_new] *\n state_RU[prm_n_RU - 3][RU_L][RU_C][RU_R];\n }\n\n // --- Other triplets\n for (i_RU = 0; i_RU < prm_n_RU - 3; ++i_RU)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_C = 0; RU_C < 4; ++RU_C) {\n prob_tot = 0.0;\n for (RU_dummy = 0; RU_dummy < 4; ++RU_dummy)\n prob_tot += state_RU[i_RU + 1][RU_C][RU_R][RU_dummy];\n\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n rate_tot = 0.0;\n if (prob_tot > 1e-12) {\n for (RU_dummy = 0; RU_dummy < 4; ++RU_dummy)\n rate_tot += flux_RU_C[i_RU + 1][RU_C][RU_R][RU_dummy][RU_new];\n\n rate_tot /= prob_tot;\n }\n\n for (RU_L = 0; RU_L < 4; ++RU_L) {\n flux_RU_R[i_RU][RU_L][RU_C][RU_R][RU_new] =\n rate_tot * state_RU[i_RU][RU_L][RU_C][RU_R];\n }\n }\n }\n\n // Forward Euler advance\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n state_RU[i_RU][RU_L][RU_C][RU_R] +=\n dt * (flux_RU_L[i_RU][RU_new][RU_C][RU_R][RU_L] +\n flux_RU_C[i_RU][RU_L][RU_new][RU_R][RU_C] +\n flux_RU_R[i_RU][RU_L][RU_C][RU_new][RU_R] -\n flux_RU_L[i_RU][RU_L][RU_C][RU_R][RU_new] -\n flux_RU_C[i_RU][RU_L][RU_C][RU_R][RU_new] -\n flux_RU_R[i_RU][RU_L][RU_C][RU_R][RU_new]);\n }\n}", "meta": {"hexsha": "d792272619c67c7dd59f945404abdb28f897d8f0", "size": 11540, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "models_cpp/model_RDQ18.cpp", "max_stars_repo_name": "FrancescoRegazzoni/cardiac-activation", "max_stars_repo_head_hexsha": "26f05df28891df7b3c69f16bb136cdced6b63c4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-12-17T00:26:28.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-17T00:26:28.000Z", "max_issues_repo_path": "models_cpp/model_RDQ18.cpp", "max_issues_repo_name": "FrancescoRegazzoni/cardiac-activation", "max_issues_repo_head_hexsha": "26f05df28891df7b3c69f16bb136cdced6b63c4d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "models_cpp/model_RDQ18.cpp", "max_forks_repo_name": "FrancescoRegazzoni/cardiac-activation", "max_forks_repo_head_hexsha": "26f05df28891df7b3c69f16bb136cdced6b63c4d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.3987730061, "max_line_length": 80, "alphanum_fraction": 0.5491334489, "num_tokens": 3934, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.3998116407397951, "lm_q1q2_score": 0.24295080894803314}} {"text": "// Copyright 2019 Victor Hugo Schulz\n\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n\n// http://www.apache.org/licenses/LICENSE-2.0\n\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \"structures.h\"\n\nusing namespace std;\nusing namespace cv;\nusing namespace boost::qvm;\n\n// Function declarations:\nvoid load_config(string filename_common, string filename_dut, catalog_parameters &cp, grid_parameters &gp, optical_parameters &op, database_parameters &db);\nvector initialize_database(catalog_parameters &cp);\nvector visible_stars(vector &database, optical_parameters &op);\nvoid next_neighbor(vector &database, database_parameters &db);\nvector reference_stars(vector &vstars);\nvoid calculate_descriptors (vector &vstars, vector &rstars, database_parameters &db, grid_parameters &gp);\ndouble fov(double size, optical_parameters op);\ndouble vangle (vec p1, vec p2);\nquat rot_quat (vec v1, vec v2);\nvoid proper_motion_correction (hip2 entry, int time_y, db *output);\nvector rotate3d(vector visible_stars, vector reference_stars, uint reference_star_id);\ndouble angle_to_origin (db &star);\nvector relevant_fov(vector &lf_list, uint reference_star_id, uint reference_nn_id, database_parameters &db);\ndouble angle2d (db &star);\nvoid rotate2d (vector &star_list, double &theta);\nvoid z_norm (vector &star_list, database_parameters &db, grid_parameters &gp);\nvector cells (vector &star_list, grid_parameters &gp);\nuint bucket_size(int g);\nvector bit_descriptor (vector &cells, grid_parameters &gp);\nvoid sort_nn (vector &stars);\nvoid create_ref(string &ref_db_filename, vector &stars, FileStorage &fs);\nvoid create_index(string &index_filename, vector &stars, FileStorage &fs);\nvoid create_lut(string &lut_db_filename, string &lut_nn_db_filename, vector &stars, grid_parameters &gp, FileStorage &fs);\nvoid create_bin(string &bin_db_filename, vector &stars, grid_parameters &gp, FileStorage &fs);\n\nint main (/*int argc, char **argv*/)\n{// Main function.\n catalog_parameters cp; grid_parameters gp; optical_parameters op; database_parameters dp;\n load_config(\"config/common.yml\",\"config/dut.yml\",cp,gp,op,dp);\n\n vector database = initialize_database(cp);\n vector vstars = visible_stars(database,op);\n next_neighbor(vstars,dp);\n vector rstars = reference_stars(vstars);\n\n calculate_descriptors(vstars,rstars,dp,gp);\n sort_nn(rstars);\n\n mkdir(\"config\", S_IRWXU|S_IRWXG|S_IROTH|S_IXOTH);\n mkdir(\"config/database/\", S_IRWXU|S_IRWXG|S_IROTH|S_IXOTH);\n string ref_db_filename = \"config/database/ref_db.bin\";\n string lut_db_filename = \"config/database/lut_db.bin\";\n string lut_nn_db_filename = \"config/database/lut_nn_db.bin\";\n string bin_db_filename = \"config/database/bin_db.bin\";\n string index_filename = \"config/database/index.bin\";\n\n FileStorage fs(\"config/database.yml\", FileStorage::WRITE);\n\n fs << \"ref_db_filename\" << ref_db_filename;\n create_ref(ref_db_filename,rstars,fs);\n\n fs << \"lut_db_filename\" << lut_db_filename;\n fs << \"lut_nn_db_filename\" << lut_nn_db_filename;\n create_lut(lut_db_filename,lut_nn_db_filename,rstars,gp,fs);\n\n fs << \"bin_db_filename\" << bin_db_filename;\n create_bin(bin_db_filename,rstars,gp,fs);\n\n fs << \"index_filename\" << index_filename;\n create_index(index_filename,rstars,fs);\n fs.release();\n return 0;\n}\n\nvoid load_config(string filename_common, string filename_dut, catalog_parameters &cp, grid_parameters &gp, optical_parameters &op, database_parameters &dp)\n{// Load configuration from file.\n double dtmp;\n Mat mtmp;\n FileStorage fs(filename_common, FileStorage::READ);\n fs[\"cp_hipparcos_filename\"] >> cp.hipparcos;\n fs[\"cp_hipparcos2_filename\"] >> cp.hipparcos2;\n fs[\"cp_hipparcos_size\"] >> dtmp; cp.hipparcos_size = (int)dtmp;\n fs[\"cp_epoch\"] >> dtmp; cp.epoch = (int)dtmp;\n fs[\"op_resolution\"] >> mtmp; op.resolution = Point2i(mtmp);\n fs[\"op_pixel_size\"] >> mtmp; op.pixel_size = Point2d(mtmp);\n fs[\"op_sensor_size\"] >> mtmp; op.sensor_size = Point2d(mtmp);\n fs[\"op_focus\"] >> op.focus;\n fs[\"op_center\"] >> mtmp; op.center = Point2i(mtmp);\n fs[\"op_max_mag\"] >> op.max_mag;\n fs.release();\n FileStorage fs2(filename_dut, FileStorage::READ);\n fs2[\"gp_g\"] >> dtmp; gp.g = (int)dtmp;\n fs2[\"gp_pattern_radius\"] >> dtmp; gp.pattern_radius = (int)dtmp;\n fs2[\"gp_buffer_radius\"] >> gp.buffer_radius;\n fs2[\"gp_confidence_factor\"] >> dtmp; gp.confidence_factor = (int)dtmp;\n fs2[\"gp_expercted_false_stars\"] >> dtmp; gp.expected_false_stars = (int)dtmp;\n fs2[\"gp_minimim_match\"] >> dtmp; gp.minimum_match = (int)dtmp;\n fs2[\"gp_nn_error_angle\"] >> gp.error;\n fs2.release();\n dp.pattern_radius = fov(gp.pattern_radius*op.pixel_size.y, op);\n dp.buffer_radius = fov(gp.buffer_radius*op.pixel_size.y, op);\n dp.min_dist = fov(norm(op.pixel_size), op);\n dp.relevance_radius = fov((norm(op.sensor_size)),op);\n}\n\nvector initialize_database(catalog_parameters &cp)\n{// Read information from Hipparcos and Hipparcos 2 catalogs for initializing the database.\n hip *hipparcos = new hip [cp.hipparcos_size];\n hip initial = {0,'\\0',0};\n for(int i=0;i hipparcos2;\n ifstream myfile2 (cp.hipparcos2);\n if (myfile2.is_open()) {\n while ( getline (myfile2,line) ) {\n hip2 data;\n data.HIP = stoi(line.substr(0,6));\n data.RArad = stod(line.substr(15,13));\n data.DErad = stod(line.substr(29,13));\n data.pmRA = stod(line.substr(51,8));\n data.pmDE = stod(line.substr(60,8));\n hipparcos2.push_back(data);\n }\n myfile2.close();\n }\n else {\n cout << \"Unable to open file \\\"\" << cp.hipparcos2 << \"\\\".\" << endl;\n exit(1);\n }\n\n // Database\n vector database;\n for(uint i=0;i visible_stars(vector &database, optical_parameters &op)\n{// Extract only the visible stars from the database.\n vector output;\n for(uint i=0;i &database, database_parameters &db)\n{// Calculate the next neighbor pointer and distance, populating the database entries.\n for(uint i=0;i reference_stars(vector &vstars)\n{// Remove variable stars and stars with very close neighbors (double, triple...).\n vector output;\n for(uint i=0;i= 3) || vstars[i].proxy_flag))\n output.push_back(vstars[i]);\n }\n return output;\n}\n\nvoid calculate_descriptors (vector &vstars, vector &rstars, database_parameters &dp, grid_parameters &gp)\n{// Populate the descriptor entries of the reference stars in the database.\n for(uint ref_star_id=0; ref_star_id rotated = rotate3d(vstars,rstars,ref_star_id);\n vector cropped = relevant_fov(rotated,ref_star_id,ref_nn_id,dp);\n double theta = angle2d(rotated[rstars[ref_star_id].nn]);\n rotate2d(cropped,theta);\n z_norm(cropped,dp,gp);\n rstars[ref_star_id].angle_2d = theta;\n rstars[ref_star_id].cells_descriptor = cells(cropped,gp);\n rstars[ref_star_id].bit_descriptor = bit_descriptor(rstars[ref_star_id].cells_descriptor,gp);\n }\n}\n\ndouble vangle (vec p1, vec p2)\n{// Angle between two vectors.\n return atan2(mag(cross(p1,p2)), dot(p1,p2));\n}\n\nquat rot_quat (vec v1, vec v2)\n{// Rotation quaternion between two unit vectors. V1 must be different than V2.\n double theta = vangle(v1,v2);\n double angle = cos(theta/2);\n vec v = cross(v1,v2);\n normalize(v);\n v *= sin(theta/2);\n quat output;\n output = {angle,v.a[0],v.a[1],v.a[2]};\n return output;\n}\n\ndouble fov(double size, optical_parameters op)\n{// Field of view calculation.\n return 2*atan2(size,2*op.focus);\n}\n\nvoid proper_motion_correction(hip2 entry, int time_y, db *output)\n{// Proper motion conversion, with output as an unit vector.\n double mu_alpha_rad = (entry.pmRA * M_PI) / (3600 * 1000 * 180 * cos(entry.DErad));\n double mu_delta_rad = (entry.pmDE * M_PI) / (3600 * 1000 * 180);\n double alpha = entry.RArad + mu_alpha_rad * time_y;\n double delta = entry.DErad + mu_delta_rad * time_y;\n output->uv = {cos(alpha) * cos(delta),\n sin(alpha) * cos(delta),\n sin(delta)};\n}\n\nvector rotate3d(vector visible_stars, vector reference_stars, uint reference_star_id)\n{// Rotate the database so that the reference star will appear on the origin.\n vec origin = {0,0,1};\n quat q = rot_quat(reference_stars[reference_star_id].uv, origin);\n for(uint i = 0; i p_ = visible_stars[i].uv;\n quat p = {0,p_.a[0],p_.a[1],p_.a[2]};\n quat rot_uv = q * p * inverse(q);\n visible_stars[i].uv = V(rot_uv);\n }\n return visible_stars;\n}\n\ndouble angle_to_origin (db &star)\n{// Angle between the star in local frame and the coordinate system origin.\n vec origin = {0,0,1};\n return vangle(star.uv, origin);\n}\n\nvector relevant_fov(vector &lf_list, uint reference_star_id, uint reference_nn_id, database_parameters &dp)\n{// Remove stars which are outside of the field of view.\n vector fov;\n for(uint i = 0; i &star_list, double &theta)\n{// In-place rotation of a list of stars by a given angle.\n quat q = rotz_quat(theta);\n for(uint i=0; i p = {0,\n star_list[i].uv.a[0],\n star_list[i].uv.a[1],\n star_list[i].uv.a[2]};\n star_list[i].uv = V(q*p*inverse(q));\n }\n}\n\nvoid z_norm (vector &star_list, database_parameters &dp, grid_parameters &gp)\n{// In-place linear transformation so that points fall within x=[0,g] and y=[0,g].\n // Since pr =! rr, out of bound values must be filtered later.\n // The z coordinate remains unchanged.\n for(uint i=0; i cells (vector &star_list, grid_parameters &gp)\n{// Returns a list with the cells present on the descriptor.\n vector cells;\n for(uint i=0; i= 0) &&\n (star_list[i].uv.a[0] < gp.g) &&\n (star_list[i].uv.a[1] >= 0) &&\n (star_list[i].uv.a[1] < gp.g)){\n cells.push_back(gp.g*star_list[i].uv.a[1] + star_list[i].uv.a[0]);\n }\n }\n // Duplicates are removed (next 2 lines).\n sort(cells.begin(),cells.end());\n cells.erase(unique(cells.begin(),cells.end()), cells.end());\n return cells;\n}\n\nuint bucket_size(int g)\n{// Calculates the number of buckets for the descriptor calculation from the grid size g; ex. ceil(g*g/64) for 64 bits.\n int g2 = g*g;\n int divisor = sizeof(long) << 3;\n return g2/divisor + (g2%divisor ? 1 : 0);\n}\n\nvector bit_descriptor (vector &cells, grid_parameters &gp)\n{// Calculates a bit descriptor from the on cells.\n static unsigned int bit_size = sizeof(long) * 8; // Architecture dependent result\n // Since the needed bits usually exceed the architecture word lenghts, multiple 'buckets' hold the values.\n uint bsize = bucket_size(gp.g);\n unsigned long bucket[bsize];\n unsigned int i;\n for(i=0;i output(bucket, bucket + sizeof bucket / sizeof bucket[0]);\n return output;\n}\n\nvoid sort_nn (vector &stars)\n{// Sorts the database by the next neighbor angle.\n qsort(stars.data(),stars.size(),sizeof(db),[](const void *a_, const void *b_){\n db *a = (db*) a_;\n db *b = (db*) b_;\n return (int)(a->angle_nn > b->angle_nn);\n });\n}\n\nvoid create_ref(string &ref_db_filename, vector &stars, FileStorage &fs)\n{// Produces the reference database and writes it to a file. Format: {id, angle_nn, uv_x, uv_y, uv_z}\n int rows = stars.size();\n int cols = 5;\n double ref_db[rows][cols];\n for(uint i=0;i what)\n high = mid - 1;\n else if (data[mid] < what)\n low = mid + 1;\n else\n return mid;\n }\n return mid;\n}\n\nvoid create_index(string &index_filename, vector &stars, FileStorage &fs)\n{// Creates an index to speed up the lookup of elements by their nn distance.\n double last = stars[stars.size()-1].angle_nn;\n double data[stars.size()];\n for(uint i=0; i stars.size()) break;\n }\n int real_size = (int)(last+1);\n short index[real_size];\n for(int i=0; i &stars, grid_parameters &gp, FileStorage &fs)\n{// Produces the (inverted) lookup table and writes it to a file.\n uint rows = (uint)gp.g*gp.g;\n vector > lut(rows, vector());\n vector > lut_nn(rows, vector());\n for(uint i=0;i cols){\n cols = lut[i].size();\n }\n }\n// short lut_array[rows][cols];\n// for(uint i=0;i &stars, grid_parameters &gp, FileStorage &fs)\n{// Produces the bit descriptor and writes it to a file.\n uint rows = stars.size();\n uint cols = bucket_size(gp.g);\n unsigned long bin_db[rows][cols];\n for(uint i=0;i\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"snark.hpp\"\n#include \"lib_zero_knowledge.h\"\n\n#define VK_PATH \"./vk\"\n#define PK_PATH \"./pk\"\n\n\n\n#define CHECK_STR_LEN(p, max, ret) if (strlen(p) > max) return ret;\n\n\n\n\nusing namespace libsnark;\nusing namespace std;\n\nstatic r1cs_ppzksnark_proving_key proving_key;\nstatic r1cs_ppzksnark_verification_key verifycation_key;\n\n\nstatic void write_debug_vector(ofstream& out, vector &v, const char *prefix)\n{\n\tif (strlen(prefix)) {\n\t\tout << prefix << \"\\n\";\n\t}\n\n\tfor(int i=0; i &h1_bv, vector &h2_bv, vector &h3_bv, \n\tvector &r1_bv, vector &r2_bv, vector &r3_bv, \n\tint *jw, const char *file_name)\n{\n\tofstream out;\n\tout.open(file_name);\n\t\n write_debug_vector(out, h1_bv, \"h1\");\n\twrite_debug_vector(out, h2_bv, \"h2\");\n\twrite_debug_vector(out, h3_bv, \"h3\");\n\twrite_debug_vector(out, r1_bv, \"r1\");\n\twrite_debug_vector(out, r2_bv, \"r2\");\n\twrite_debug_vector(out, r3_bv, \"r3\");\n\n\twrite_debug_array(out, jw, \"jw\");\n\t\n out.close();\n}\n\nstatic bool file_exist (const std::string& name) {\n\tifstream f(name.c_str());\n\treturn f.good();\n}\n\n\n\n//called once\nvoid init_setup()\n{\n\tint jw_neg[2][32];\n\n\t// Initialize the curve parameters.\n default_r1cs_ppzksnark_pp::init_public_params();\n\n\tbool need_gen = true;\t\n\tif (file_exist(PK_PATH) && file_exist(VK_PATH)) { //only read \n\t\tneed_gen = false;\n\t} \n\t\n\tif (need_gen) {\n\t\t//generate key\n\t\tauto keypair = generate_keypair_neg(jw_neg);\n\t\t//pk\n\t\tstringstream provingKey;\n\t\tprovingKey << keypair.pk;\n\t\tofstream pkOf;\n\t\tpkOf.open(PK_PATH);\n\t\tpkOf << provingKey.rdbuf();\n\t\tpkOf.close();\n\t\t//vk\n\t\tofstream vkOf;\n\t\tstringstream verificationKey;\n \tverificationKey << keypair.vk;\n \tvkOf.open(VK_PATH);\n \tvkOf << verificationKey.rdbuf();\n \tvkOf.close();\n\t}\n\t\n\t//read\n\tifstream vkIf, pkIf; \n\tpkIf.open(PK_PATH); \n stringstream provingKeyFromFile;\n\tprovingKeyFromFile << pkIf.rdbuf();\n\tpkIf.close();\n\tprovingKeyFromFile >> proving_key;\n\n vkIf.open(VK_PATH); \n stringstream verifycationKeyFromFile;\n verifycationKeyFromFile << vkIf.rdbuf();\n vkIf.close();\n verifycationKeyFromFile >> verifycation_key;\n}\n\n\nstatic void hex_str_to_bytes(const char* source, unsigned char* dest, int source_len)\n{\n unsigned char high, low;\n\n for (int i = 0; i < source_len; i += 2)\n {\n high = toupper(source[i]);\n low = toupper(source[i + 1]);\n\n if (high > 0x39)\n high -= 0x37;\n else\n high -= 0x30;\n\n if (low > 0x39)\n low -= 0x37;\n else\n low -= 0x30;\n\n dest[i / 2] = (high << 4) | low;\n }\n return;\n}\n\nstatic void bytes_to_hex_str(const unsigned char* source, unsigned char* dest, int source_len)\n{\n unsigned char high, low;\n\n for (int i = 0; i < source_len; i++)\n {\n high = source[i] >> 4;\n low = source[i] & 0x0f ;\n\n high += 0x30;\n\n if (high > 0x39)\n dest[i * 2] = high + 0x07;\n else\n dest[i * 2] = high;\n\n low += 0x30;\n if (low > 0x39)\n dest[i * 2 + 1] = low + 0x07;\n else\n dest[i * 2 + 1] = low;\n }\n\tdest[(source_len-1)*2+1+1] = 0;\n return ;\n}\n\n\n\nstatic void hex_str_to_vector(const char *source, std::vector& dest)\n{\n\tunsigned char bytes[32];\n\tmemset(bytes, 0, sizeof(bytes));\n\thex_str_to_bytes(source, bytes, strlen(source));\n\tsize_t wordsize = 8;\n\tfor (size_t i = 0; i < sizeof(bytes); ++i)\n\t{\n\t\tfor (size_t j = 0; j &list)\n{\n\tchar *dest = (char *)malloc( list.size()*2 + 1);\n\tfor (size_t i = 0; i < list.size(); ++i)\n {\n\t\tunsigned char c = *(list.begin()+i);\n\t\tsprintf(dest+i*2, \"%02x\", c);\n }\n\tdest[ list.size()*2 ] = 0;\n\treturn dest;\n\n}\n\nstatic void set_flag(char *r1, char *r2, int *jw)\n{\n\tunsigned char bytes_r1[32];\n\tmemset(bytes_r1, 0, sizeof(bytes_r1));\n\thex_str_to_bytes(r1, bytes_r1, strlen(r1));\n\t\n\tunsigned char bytes_r2[32];\n\tmemset(bytes_r2, 0, sizeof(bytes_r2));\n\thex_str_to_bytes(r2, bytes_r2, strlen(r2));\n\n\tint fv=0;\n for(int i=31;i>0;i--){\n \tfv=((int)bytes_r1[i]+(int)bytes_r2[i]+fv)/256;\n \tjw[i-1]=fv;\n }\n\n}\n\n//r1 + x = r2 + r3\nchar *get_prove_data(char *r1, char *r2, char *r3, char *h1, char *h2, char *h3, char *x)\n{\n\tstd::vector h1_bv(256);\n\tstd::vector h2_bv(256);\n\tstd::vector h3_bv(256);\n\tstd::vector r1_bv(256);\n\tstd::vector r2_bv(256);\n\tstd::vector r3_bv(256);\n\tstd::vector x_bv(256);\n\n\tconst int max_len = (256 / 8) * 2;\n\tCHECK_STR_LEN(r1, max_len, NULL);\n\tCHECK_STR_LEN(r2, max_len, NULL);\n\tCHECK_STR_LEN(r3, max_len, NULL);\n\tCHECK_STR_LEN(h1, max_len, NULL);\n\tCHECK_STR_LEN(h2, max_len, NULL);\n\tCHECK_STR_LEN(h3, max_len, NULL);\n\tCHECK_STR_LEN(x, max_len, NULL);\n\n\thex_str_to_vector(r1, r1_bv);\n\thex_str_to_vector(r2, r2_bv);\n\thex_str_to_vector(r3, r3_bv);\n\thex_str_to_vector(h1, h1_bv);\n\thex_str_to_vector(h2, h2_bv);\n\thex_str_to_vector(h3, h3_bv);\n\thex_str_to_vector(x, x_bv);\n\n int jw_neg[2][32];\n\t//memset((char *)jw, 0, sizeof(jw));\n\t//set_flag(r1, r2, jw);\n\n\t//write_debug(h1_bv, h2_bv, h3_bv, r1_bv, r2_bv, r3_bv, (int *)jw, \"debug_lib.txt\");\n\n\tauto proof_neg = generate_proof_neg(proving_key, h1_bv, h2_bv, h3_bv, r1_bv, r2_bv, r3_bv,x_bv,jw_neg);\n\tif (!proof_neg) {\n\t\treturn NULL;\n\t}\n\t\n\tcout << \"Proof_neg: \" << proof_neg << endl;\n\tstringstream proofStream;\n\tproofStream << *proof_neg;\n\n\tstd::string s = proofStream.str();\n\tunsigned char *dest = (unsigned char *)malloc( s.length() * 2 + 1 );\n\tbytes_to_hex_str((unsigned char *)s.c_str(), dest, s.length());\n\treturn (char *)dest;\n}\n\n\n//if right, return 1. else return 0\nint is_prove_right(char *h1, char *h2, char *h3, char *x, char *prove_data)\n{\n\tstd::vector h1_bv(256);\n\tstd::vector h2_bv(256);\n\tstd::vector h3_bv(256);\n\tstd::vector x_bv(256);\n \n\tconst int max_len = (256 / 8) * 2;\n\tCHECK_STR_LEN(h1, max_len, 0);\n\tCHECK_STR_LEN(h2, max_len, 0);\n \tCHECK_STR_LEN(h3, max_len, 0);\n \tCHECK_STR_LEN(x, max_len, 0);\n\n\thex_str_to_vector(h1, h1_bv);\n\thex_str_to_vector(h2, h2_bv);\n\thex_str_to_vector(h3, h3_bv);\n\thex_str_to_vector(x, x_bv);\n\n\tr1cs_ppzksnark_proof prove;\n\tunsigned char *bytes = (unsigned char *)malloc(strlen(prove_data) / 2);\n\thex_str_to_bytes(prove_data, bytes, strlen(prove_data));\n\n\tstring s((const char *)bytes, strlen(prove_data)/2);\n\tstringstream proofStream;\n\tproofStream << s;\n\tproofStream >> prove;\n\t\n\n\tint ret;\n\t if(verify_proof(verifycation_key, prove, h1_bv, h2_bv, h3_bv,x_bv)){\n\t \tret = 1;\n \tcout<<\"verify succ neg\"<\n// Copyright (c) 2020-2021 Nikita Kaskov \n\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n//---------------------------------------------------------------------------//\n\n#ifndef FILECOIN_STORAGE_PROOFS_POST_ELECTION_VANILLA_HPP\n#define FILECOIN_STORAGE_PROOFS_POST_ELECTION_VANILLA_HPP\n\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\nnamespace nil {\n namespace filecoin {\n namespace post {\n namespace election {\n struct SetupParams {\n /// Size of the sector in bytes.\n std::uint64_t sector_size;\n std::size_t challenge_count;\n std::size_t challenged_nodes;\n };\n\n struct PublicParams : public parameter_set_metadata {\n virtual std::string identifier() const override {\n return \"ElectionPoSt::PublicParams{{sector_size: \" + sector_size +\n \", count: \" + challenge_count + \", nodes: \" + challenged_nodes + \"}}\";\n }\n\n /// Size of the sector in bytes.\n std::uint64_t sector_size;\n std::size_t challenge_count;\n std::size_t challenged_nodes;\n };\n\n template\n struct PublicInputs {\n Domain randomness;\n sector_id_type sector_id;\n Domain prover_id;\n Domain comm_r;\n Fr partial_ticket;\n std::uint64_t sector_challenge_index;\n };\n\n template\n struct PrivateInputs {\n MerkleTreeWrapper\n tree;\n typename MerkleTreeType::hash_type::digest_type comm_c;\n typename MerkleTreeType::hash_type::digest_type comm_r_last;\n };\n\n struct Candidate {\n sector_id_type sector_id;\n Fr partial_ticket;\n std::array ticket;\n std::uint64_t sector_challenge_index;\n };\n\n template\n struct Proof {\n std::vector leafs() {\n std::vector result;\n for (const auto &proof : inclusion_proofs) {\n result.emplace_back(proof.leaf());\n }\n return result;\n }\n\n typename BasicMerkleProof::hash_type::digest_type comm_r_last() {\n return inclusion_proofs[0].root();\n }\n\n std::vector commitments() {\n std::vector result;\n for (const auto &proof : inclusion_proofs) {\n result.emplace_back(proof.root());\n }\n return result;\n }\n\n std::vector, std::size_t>>>\n paths() {\n std::vector, std::size_t>>>\n result;\n for (const auto &proof : inclusion_proofs) {\n result.emplace_back(proof.path());\n }\n return result;\n }\n\n std::vector>\n inclusion_proofs;\n\n std::array ticket;\n typename BasicMerkleProof::hash_type::digest_type comm_c;\n };\n\n template\n class ElectionPoSt\n : public proof_scheme<\n PublicParams, SetupParams, PublicInputs,\n PrivateInputs, Proof, no_requirements> {\n typedef proof_scheme<\n PublicParams, SetupParams, PublicInputs,\n PrivateInputs, Proof, no_requirements>\n policy_type;\n\n public:\n typedef typename policy_type::public_params_type public_params_type;\n typedef typename policy_type::setup_params setup_params_type;\n typedef typename policy_type::public_inputs public_inputs_type;\n typedef typename policy_type::private_inputs private_inputs_type;\n typedef typename policy_type::proof_type proof_type;\n typedef typename policy_type::requirements_type requirements_type;\n\n virtual public_params_type setup(const setup_params_type &p) override {\n return {p.sector_size, p.challenge_count, p.challenged_nodes};\n }\n virtual proof_type prove(const public_params_type ¶ms, const public_inputs_type &inputs,\n const private_inputs_type &pinputs) override {\n // 1. Inclusions proofs of all challenged leafs in all challenged ranges\n const auto tree = pinputs.tree;\n std::size_t tree_leafs = tree.leafs();\n\n BOOST_LOG_TRIVIAL(trace)\n << std::format(\"Generating proof for tree of len {} with leafs {}\", tree.len(), tree_leafs);\n\n const auto inclusion_proofs =\n (0..pub_params.challenge_count)\n .into_par_iter()\n .flat_map(\n | n |\n {\n // TODO: replace unwrap with proper error handling\n const auto challenged_leaf_start = generate_leaf_challenge(\n pub_params, pub_inputs.randomness, pub_inputs.sector_challenge_index,\n std::uint64_t(n));\n (0..pub_params.challenged_nodes)\n .into_par_iter()\n .map(move | i |\n {tree.gen_cached_proof(std::uint(challenged_leaf_start) + i, None)})\n })\n .collect::>>();\n\n // 2. correct generation of the ticket from the partial_ticket (add this to the candidate)\n const auto ticket = finalize_ticket(inputs.partial_ticket);\n\n return {inclusion_proofs, ticket, pinputs.comm_c};\n }\n virtual bool verify(const public_params_type &pub_params, const public_inputs_type &pub_inputs,\n const proof_type &pr) override {\n // verify that H(Comm_c || Comm_r_last) == Comm_R\n // comm_r_last is the root of the proof\n const auto comm_r_last = pr.inclusion_proofs[0].root();\n const auto comm_c = pr.comm_c;\n const auto comm_r = &pub_inputs.comm_r;\n\n if (AsRef ::<[u8]>::as_ref(&::Function::hash2(\n &comm_c, &comm_r_last, )) != AsRef::<[u8]>::as_ref(comm_r)) {\n return false;\n }\n\n for (int n = 0; n < pub_params.challenge_count; n++) {\n const auto challenged_leaf_start = generate_leaf_challenge(\n pub_params, pub_inputs.randomness, pub_inputs.sector_challenge_index, n);\n for (int i = 0; i < pub_params.challenged_nodes; i++) {\n const auto merkle_proof = &proof.inclusion_proofs[n * pub_params.challenged_nodes + i];\n\n // validate all comm_r_lasts match\n if (merkle_proof.root() != comm_r_last) {\n return false;\n }\n\n // validate the path length\n const auto expected_path_length =\n merkle_proof.expected_len(pub_params.sector_size / NODE_SIZE);\n\n if (expected_path_length != merkle_proof.path().size()) {\n return false;\n }\n\n if (!merkle_proof.validate(challenged_leaf_start + i)) {\n return false;\n }\n }\n }\n\n return true;\n }\n };\n\n template\n std::vector generate_candidates(\n const PublicParams &pub_params, const std::vector &challenged_sectors,\n const btree::map>\n &trees,\n const typename MerkleTreeType::hash_type::digest_type &prover_id,\n const typename MerkleTreeType::hash_type::digest_type &randomness) {\n challenged_sectors.par_iter()\n .enumerate()\n .map(| (sector_challenge_index, sector_id) |\n {\n auto tree;\n switch (trees.get(sector_id)) {\n case Some(tree):\n tree = tree;\n break;\n case None:\n tree = bail !(Error::MissingPrivateInput(\"tree\", (*sector_id).into()));\n break;\n };\n\n generate_candidate::(pub_params, tree, prover_id, *sector_id, randomness,\n std::uint64_t(sector_challenge_index), )\n })\n .collect()\n }\n\n template\n Candidate generate_candidate(\n const PublicParams &pub_params,\n const MerkleTreeWrapper &tree,\n const typename MerkleTreeType::hash_type::digest_type &prover_id, sector_id_type sector_id,\n const typename MerkleTreeType::hash_type::digest_type &randomness,\n std::uint64_t sector_challenge_index) {\n Fr randomness_fr = randomness.into();\n Fr prover_id_fr = prover_id.into();\n std::vector data = {\n randomness_fr.into(), prover_id_fr.into(), Fr::from(sector_id).into()};\n\n for (int n = 0; n < pub_params.challenge_count; n++) {\n const auto challenge =\n generate_leaf_challenge(pub_params, randomness, sector_challenge_index, n);\n\n Fr val = tree.read_at(challenge as usize).into();\n data.push_back(val.into());\n }\n\n // pad for md\n std::size_t arity = PoseidonMDArity;\n while (data.size() % arity) {\n data.push(MerkleTreeType::hash_type::digest_type::default());\n }\n\n Fr partial_ticket = PoseidonFunction::hash_md(&data).into();\n\n // ticket = sha256(partial_ticket)\n std::array ticket = finalize_ticket(&partial_ticket);\n\n return {sector_challenge_index, sector_id, partial_ticket, ticket};\n }\n\n template>\n std::array finalize_ticket(const Fr &partial_ticket) {\n const auto bytes = fr_into_bytes(partial_ticket);\n const auto ticket_hash = Sha256::digest(&bytes);\n std::array ticket;\n ticket.fill(0);\n ticket.copy_from_slice(&ticket_hash[..]);\n return ticket;\n }\n\n bool is_valid_sector_challenge_index(std::uint64_t challenge_count, std::uint64_t index) {\n return index < challenge_count;\n }\n\n template>\n sector_id_type generate_sector_challenge(const Domain &randomness, std::size_t n,\n const ordered_sector_set §ors) {\n using namespace crypto3::hashes;\n\n accumulator_set acc;\n hash(randomness, acc);\n hash(n, acc);\n const auto hash = accumulators::extract(acc);\n\n const auto sector_challenge = LittleEndian::read_u64(&hash[..8]);\n std::uint sector_index = (std::uint64_t(sector_challenge % sectors.size()));\n const auto sector = *sectors.iter().nth(sector_index).context(\"invalid challenge generated\");\n\n return sector;\n }\n\n template\n std::vector generate_sector_challenges(const Domain &randomness,\n std::uint64_t challenge_count,\n const ordered_sector_set §ors) {\n std::vector result(challenge_count);\n for (int i = 0; i < challenge_count; i++) {\n result[i] = generate_sector_challenge(randomness, i, sectors);\n }\n return result;\n }\n\n /// Generate all challenged leaf ranges for a single sector, such that the range fits into the sector.\n template\n std::vector\n generate_leaf_challenges(const PublicParams &pub_params, const Domain &randomness,\n std::uint64_t sector_challenge_index, std::size_t challenge_count) {\n std::vector challenges(challenge_count);\n\n for (int leaf_challenge_index = 0; leaf_challenge_index < challenge_count; leaf_challenge_index++) {\n challenges.emplace_back(generate_leaf_challenge(pub_params, randomness, sector_challenge_index,\n leaf_challenge_index));\n }\n\n return challenges;\n }\n\n /// Generates challenge, such that the range fits into the sector.\n template>\n std::uint64_t generate_leaf_challenge(const PublicParams &pub_params, const Domain &randomness,\n std::uint64_t sector_challenge_index,\n std::uint64_t leaf_challenge_index) {\n BOOST_ASSERT_MSG(pub_params.sector_size > pub_params.challenged_nodes * NODE_SIZE,\n \"sector size is too small\");\n\n auto hasher = Sha256();\n hasher.input(AsRef::<[u8]>::as_ref(&randomness));\n hasher.input(§or_challenge_index.to_le_bytes()[..]);\n hasher.input(&leaf_challenge_index.to_le_bytes()[..]);\n const auto hash = hasher.result();\n\n const auto leaf_challenge = LittleEndian::read_u64(&hash[..8]);\n\n std::uint64_t challenged_range_index =\n leaf_challenge % (pub_params.sector_size / (pub_params.challenged_nodes * NODE_SIZE));\n\n return challenged_range_index * pub_params.challenged_nodes;\n }\n } // namespace election\n } // namespace post\n } // namespace filecoin\n} // namespace nil\n\n#endif\n", "meta": {"hexsha": "1110c4184c0878954bb71f63e6e347e440e36cea", "size": 20088, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/storage/include/nil/filecoin/storage/proofs/post/election/vanilla.hpp", "max_stars_repo_name": "NilFoundation/crypto3-fil-proofs", "max_stars_repo_head_hexsha": "1fd78ad608278a1ed62fb29b0a077347b74a55f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "libs/storage/include/nil/filecoin/storage/proofs/post/election/vanilla.hpp", "max_issues_repo_name": "NilFoundation/crypto3-fil-proofs", "max_issues_repo_head_hexsha": "1fd78ad608278a1ed62fb29b0a077347b74a55f1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4.0, "max_issues_repo_issues_event_min_datetime": "2021-09-06T13:07:46.000Z", "max_issues_repo_issues_event_max_datetime": "2021-11-16T12:38:09.000Z", "max_forks_repo_path": "libs/storage/include/nil/filecoin/storage/proofs/post/election/vanilla.hpp", "max_forks_repo_name": "NilFoundation/crypto3-fil-proofs", "max_forks_repo_head_hexsha": "1fd78ad608278a1ed62fb29b0a077347b74a55f1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 53.4255319149, "max_line_length": 120, "alphanum_fraction": 0.5056252489, "num_tokens": 3450, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6825737344123242, "lm_q2_score": 0.3557749071749625, "lm_q1q2_score": 0.24284260700061214}} {"text": "//\n// Copyright (c) 2018 CNRS\n//\n\n#ifndef __pinocchio_lie_group_collection_hpp__\n#define __pinocchio_lie_group_collection_hpp__\n\n#include \"pinocchio/multibody/liegroup/vector-space.hpp\"\n#include \"pinocchio/multibody/liegroup/cartesian-product.hpp\"\n#include \"pinocchio/multibody/liegroup/special-orthogonal.hpp\"\n#include \"pinocchio/multibody/liegroup/special-euclidean.hpp\"\n\n#include \n\nnamespace pinocchio\n{\n template\n struct LieGroupCollectionDefaultTpl\n {\n typedef _Scalar Scalar;\n enum { Options = _Options };\n \n typedef boost::variant<\n SpecialOrthogonalOperationTpl<2,Scalar,Options>\n ,SpecialOrthogonalOperationTpl<3,Scalar,Options>\n ,SpecialEuclideanOperationTpl<2,Scalar,Options>\n ,SpecialEuclideanOperationTpl<3,Scalar,Options>\n ,VectorSpaceOperationTpl<1,Scalar,Options>\n ,VectorSpaceOperationTpl<2,Scalar,Options>\n ,VectorSpaceOperationTpl<3,Scalar,Options>\n ,VectorSpaceOperationTpl\n > LieGroupVariant;\n \n };\n \n typedef LieGroupCollectionDefaultTpl LieGroupCollectionDefault;\n \n}\n\n#endif // ifndef __pinocchio_lie_group_collection_hpp__\n\n", "meta": {"hexsha": "21362a8b530e8bb89fdd3d21ceeba4d988370ba4", "size": 1193, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/multibody/liegroup/liegroup-collection.hpp", "max_stars_repo_name": "thanhndv212/pinocchio", "max_stars_repo_head_hexsha": "3b4d272bf4e8a231954b71201ee7e0963c944aef", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBSD"], "max_stars_count": 716.0, "max_stars_repo_stars_event_min_datetime": "2015-03-30T16:26:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T12:26:58.000Z", "max_issues_repo_path": "src/multibody/liegroup/liegroup-collection.hpp", "max_issues_repo_name": "thanhndv212/pinocchio", "max_issues_repo_head_hexsha": "3b4d272bf4e8a231954b71201ee7e0963c944aef", "max_issues_repo_licenses": ["BSD-2-Clause-FreeBSD"], "max_issues_count": 1130.0, "max_issues_repo_issues_event_min_datetime": "2015-02-21T17:30:44.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-30T09:06:22.000Z", "max_forks_repo_path": "src/multibody/liegroup/liegroup-collection.hpp", "max_forks_repo_name": "thanhndv212/pinocchio", "max_forks_repo_head_hexsha": "3b4d272bf4e8a231954b71201ee7e0963c944aef", "max_forks_repo_licenses": ["BSD-2-Clause-FreeBSD"], "max_forks_count": 239.0, "max_forks_repo_forks_event_min_datetime": "2015-02-05T14:15:14.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-14T23:51:47.000Z", "avg_line_length": 28.4047619048, "max_line_length": 73, "alphanum_fraction": 0.7845766974, "num_tokens": 311, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.39233683016710835, "lm_q1q2_score": 0.24277035394449054}} {"text": "#ifndef BOOST_GEOMETRY_PROJECTIONS_SCONICS_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_SCONICS_HPP\n\n// Boost.Geometry - extensions-gis-projections (based on PROJ4)\n// This file is automatically generated. DO NOT EDIT.\n\n// Copyright (c) 2008-2012 Barend Gehrels, Amsterdam, the Netherlands.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\n\n// Original copyright notice:\n \n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace boost { namespace geometry { namespace projections\n{\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail { namespace sconics{ \n static const int EULER = 0;\n static const int MURD1 = 1;\n static const int MURD2 = 2;\n static const int MURD3 = 3;\n static const int PCONIC = 4;\n static const int TISSOT = 5;\n static const int VITK1 = 6;\n static const double EPS10 = 1.e-10;\n static const double EPS = 1e-10;\n\n struct par_sconics\n {\n double n;\n double rho_c;\n double rho_0;\n double sig;\n double c1, c2;\n int type;\n };\n /* get common factors for simple conics */\n template \n inline int\n phi12(Parameters& par, par_sconics& proj_parm, double *del) {\n double p1, p2;\n int err = 0;\n \n if (!pj_param(par.params, \"tlat_1\").i ||\n !pj_param(par.params, \"tlat_2\").i) {\n err = -41;\n } else {\n p1 = pj_param(par.params, \"rlat_1\").f;\n p2 = pj_param(par.params, \"rlat_2\").f;\n *del = 0.5 * (p2 - p1);\n proj_parm.sig = 0.5 * (p2 + p1);\n err = (fabs(*del) < EPS || fabs(proj_parm.sig) < EPS) ? -42 : 0;\n *del = *del;\n }\n return err;\n }\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_sconics_spheroid : public base_t_fi,\n Geographic, Cartesian, Parameters>\n {\n\n typedef double geographic_type;\n typedef double cartesian_type;\n\n par_sconics m_proj_parm;\n\n inline base_sconics_spheroid(const Parameters& par)\n : base_t_fi,\n Geographic, Cartesian, Parameters>(*this, par) {}\n\n inline void fwd(geographic_type& lp_lon, geographic_type& lp_lat, cartesian_type& xy_x, cartesian_type& xy_y) const\n {\n double rho;\n \n switch (this->m_proj_parm.type) {\n case MURD2:\n rho = this->m_proj_parm.rho_c + tan(this->m_proj_parm.sig - lp_lat);\n break;\n case PCONIC:\n rho = this->m_proj_parm.c2 * (this->m_proj_parm.c1 - tan(lp_lat - this->m_proj_parm.sig));\n // rho = this->m_proj_parm.c2 * (this->m_proj_parm.c1 - tan(lp_lat)); BUG STILL IN proj (reported 2012-03-03)\n break;\n default:\n rho = this->m_proj_parm.rho_c - lp_lat;\n break;\n }\n xy_x = rho * sin( lp_lon *= this->m_proj_parm.n );\n xy_y = this->m_proj_parm.rho_0 - rho * cos(lp_lon);\n }\n\n inline void inv(cartesian_type& xy_x, cartesian_type& xy_y, geographic_type& lp_lon, geographic_type& lp_lat) const\n {\n double rho;\n \n rho = boost::math::hypot(xy_x, xy_y = this->m_proj_parm.rho_0 - xy_y);\n if (this->m_proj_parm.n < 0.) {\n rho = - rho;\n xy_x = - xy_x;\n xy_y = - xy_y;\n }\n lp_lon = atan2(xy_x, xy_y) / this->m_proj_parm.n;\n switch (this->m_proj_parm.type) {\n case PCONIC:\n lp_lat = atan(this->m_proj_parm.c1 - rho / this->m_proj_parm.c2) + this->m_proj_parm.sig;\n break;\n case MURD2:\n lp_lat = this->m_proj_parm.sig - atan(rho - this->m_proj_parm.rho_c);\n break;\n default:\n lp_lat = this->m_proj_parm.rho_c - rho;\n }\n }\n };\n\n template \n void setup(Parameters& par, par_sconics& proj_parm) \n {\n boost::ignore_unused_variable_warning(par);\n boost::ignore_unused_variable_warning(proj_parm);\n double del, cs;\n int i;\n if( (i = phi12(par, proj_parm, &del)) )\n throw proj_exception(i);\n switch (proj_parm.type) {\n case TISSOT:\n proj_parm.n = sin(proj_parm.sig);\n cs = cos(del);\n proj_parm.rho_c = proj_parm.n / cs + cs / proj_parm.n;\n proj_parm.rho_0 = sqrt((proj_parm.rho_c - 2 * sin(par.phi0))/proj_parm.n);\n break;\n case MURD1:\n proj_parm.rho_c = sin(del)/(del * tan(proj_parm.sig)) + proj_parm.sig;\n proj_parm.rho_0 = proj_parm.rho_c - par.phi0;\n proj_parm.n = sin(proj_parm.sig);\n break;\n case MURD2:\n proj_parm.rho_c = (cs = sqrt(cos(del))) / tan(proj_parm.sig);\n proj_parm.rho_0 = proj_parm.rho_c + tan(proj_parm.sig - par.phi0);\n proj_parm.n = sin(proj_parm.sig) * cs;\n break;\n case MURD3:\n proj_parm.rho_c = del / (tan(proj_parm.sig) * tan(del)) + proj_parm.sig;\n proj_parm.rho_0 = proj_parm.rho_c - par.phi0;\n proj_parm.n = sin(proj_parm.sig) * sin(del) * tan(del) / (del * del);\n break;\n case EULER:\n proj_parm.n = sin(proj_parm.sig) * sin(del) / del;\n del *= 0.5;\n proj_parm.rho_c = del / (tan(del) * tan(proj_parm.sig)) + proj_parm.sig;\n \n proj_parm.rho_0 = proj_parm.rho_c - par.phi0;\n break;\n case PCONIC:\n proj_parm.n = sin(proj_parm.sig);\n proj_parm.c2 = cos(del);\n proj_parm.c1 = 1./tan(proj_parm.sig);\n if (fabs(del = par.phi0 - proj_parm.sig) - EPS10 >= HALFPI)\n throw proj_exception(-43);\n proj_parm.rho_0 = proj_parm.c2 * (proj_parm.c1 - tan(del));\n break;\n case VITK1:\n proj_parm.n = (cs = tan(del)) * sin(proj_parm.sig) / del;\n proj_parm.rho_c = del / (cs * tan(proj_parm.sig)) + proj_parm.sig;\n proj_parm.rho_0 = proj_parm.rho_c - par.phi0;\n break;\n }\n // par.inv = s_inverse;\n // par.fwd = s_forward;\n par.es = 0;\n }\n\n\n // Tissot\n template \n void setup_tissot(Parameters& par, par_sconics& proj_parm)\n {\n proj_parm.type = TISSOT;\n setup(par, proj_parm);\n }\n\n // Murdoch I\n template \n void setup_murd1(Parameters& par, par_sconics& proj_parm)\n {\n proj_parm.type = MURD1;\n setup(par, proj_parm);\n }\n\n // Murdoch II\n template \n void setup_murd2(Parameters& par, par_sconics& proj_parm)\n {\n proj_parm.type = MURD2;\n setup(par, proj_parm);\n }\n\n // Murdoch III\n template \n void setup_murd3(Parameters& par, par_sconics& proj_parm)\n {\n proj_parm.type = MURD3;\n setup(par, proj_parm);\n }\n\n // Euler\n template \n void setup_euler(Parameters& par, par_sconics& proj_parm)\n {\n proj_parm.type = EULER;\n setup(par, proj_parm);\n }\n\n // Perspective Conic\n template \n void setup_pconic(Parameters& par, par_sconics& proj_parm)\n {\n proj_parm.type = PCONIC;\n setup(par, proj_parm);\n }\n\n // Vitkovsky I\n template \n void setup_vitk1(Parameters& par, par_sconics& proj_parm)\n {\n proj_parm.type = VITK1;\n setup(par, proj_parm);\n }\n\n }} // namespace detail::sconics\n #endif // doxygen \n\n /*!\n \\brief Tissot projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Conic\n - Spheroid\n - lat_1= and lat_2=\n \\par Example\n \\image html ex_tissot.gif\n */\n template \n struct tissot_spheroid : public detail::sconics::base_sconics_spheroid\n {\n inline tissot_spheroid(const Parameters& par) : detail::sconics::base_sconics_spheroid(par)\n {\n detail::sconics::setup_tissot(this->m_par, this->m_proj_parm);\n }\n };\n\n /*!\n \\brief Murdoch I projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Conic\n - Spheroid\n - lat_1= and lat_2=\n \\par Example\n \\image html ex_murd1.gif\n */\n template \n struct murd1_spheroid : public detail::sconics::base_sconics_spheroid\n {\n inline murd1_spheroid(const Parameters& par) : detail::sconics::base_sconics_spheroid(par)\n {\n detail::sconics::setup_murd1(this->m_par, this->m_proj_parm);\n }\n };\n\n /*!\n \\brief Murdoch II projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Conic\n - Spheroid\n - lat_1= and lat_2=\n \\par Example\n \\image html ex_murd2.gif\n */\n template \n struct murd2_spheroid : public detail::sconics::base_sconics_spheroid\n {\n inline murd2_spheroid(const Parameters& par) : detail::sconics::base_sconics_spheroid(par)\n {\n detail::sconics::setup_murd2(this->m_par, this->m_proj_parm);\n }\n };\n\n /*!\n \\brief Murdoch III projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Conic\n - Spheroid\n - lat_1= and lat_2=\n \\par Example\n \\image html ex_murd3.gif\n */\n template \n struct murd3_spheroid : public detail::sconics::base_sconics_spheroid\n {\n inline murd3_spheroid(const Parameters& par) : detail::sconics::base_sconics_spheroid(par)\n {\n detail::sconics::setup_murd3(this->m_par, this->m_proj_parm);\n }\n };\n\n /*!\n \\brief Euler projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Conic\n - Spheroid\n - lat_1= and lat_2=\n \\par Example\n \\image html ex_euler.gif\n */\n template \n struct euler_spheroid : public detail::sconics::base_sconics_spheroid\n {\n inline euler_spheroid(const Parameters& par) : detail::sconics::base_sconics_spheroid(par)\n {\n detail::sconics::setup_euler(this->m_par, this->m_proj_parm);\n }\n };\n\n /*!\n \\brief Perspective Conic projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Conic\n - Spheroid\n - lat_1= and lat_2=\n \\par Example\n \\image html ex_pconic.gif\n */\n template \n struct pconic_spheroid : public detail::sconics::base_sconics_spheroid\n {\n inline pconic_spheroid(const Parameters& par) : detail::sconics::base_sconics_spheroid(par)\n {\n detail::sconics::setup_pconic(this->m_par, this->m_proj_parm);\n }\n };\n\n /*!\n \\brief Vitkovsky I projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Conic\n - Spheroid\n - lat_1= and lat_2=\n \\par Example\n \\image html ex_vitk1.gif\n */\n template \n struct vitk1_spheroid : public detail::sconics::base_sconics_spheroid\n {\n inline vitk1_spheroid(const Parameters& par) : detail::sconics::base_sconics_spheroid(par)\n {\n detail::sconics::setup_vitk1(this->m_par, this->m_proj_parm);\n }\n };\n\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail\n {\n\n // Factory entry(s)\n template \n class tissot_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n class murd1_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n class murd2_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n class murd3_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n class euler_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n class pconic_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n class vitk1_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n inline void sconics_init(detail::base_factory& factory)\n {\n factory.add_to_factory(\"tissot\", new tissot_entry);\n factory.add_to_factory(\"murd1\", new murd1_entry);\n factory.add_to_factory(\"murd2\", new murd2_entry);\n factory.add_to_factory(\"murd3\", new murd3_entry);\n factory.add_to_factory(\"euler\", new euler_entry);\n factory.add_to_factory(\"pconic\", new pconic_entry);\n factory.add_to_factory(\"vitk1\", new vitk1_entry);\n }\n\n } // namespace detail \n #endif // doxygen\n\n}}} // namespace boost::geometry::projections\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_SCONICS_HPP\n\n", "meta": {"hexsha": "f5a536af00c02abc5e1b4cbb693c753f37fbf291", "size": 21391, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/geometry/extensions/gis/projections/proj/sconics.hpp", "max_stars_repo_name": "ballisticwhisper/boost", "max_stars_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2015-01-02T14:24:56.000Z", "max_stars_repo_stars_event_max_datetime": "2015-01-02T14:25:17.000Z", "max_issues_repo_path": "boost/geometry/extensions/gis/projections/proj/sconics.hpp", "max_issues_repo_name": "ballisticwhisper/boost", "max_issues_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2019-01-13T23:45:51.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-03T08:13:26.000Z", "max_forks_repo_path": "boost/geometry/extensions/gis/projections/proj/sconics.hpp", "max_forks_repo_name": "ballisticwhisper/boost", "max_forks_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2016-05-29T13:41:15.000Z", "max_forks_repo_forks_event_max_datetime": "2016-05-29T13:41:15.000Z", "avg_line_length": 41.6167315175, "max_line_length": 134, "alphanum_fraction": 0.5808985087, "num_tokens": 4716, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.39233683016710835, "lm_q1q2_score": 0.24277035394449054}} {"text": "/* Copyright (c) 2016 - 2020, the adamantine authors.\n *\n * This file is subject to the Modified BSD License and may not be distributed\n * without copyright and license information. Please refer to the file LICENSE\n * for the text and further information on this license.\n */\n\n#ifndef ADAMANTINE_HH\n#define ADAMANTINE_HH\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \n#include \n\ntemplate ::value,\n int> = 0>\nvoid output_pvtu(\n adamantine::PostProcessor &post_processor, unsigned int cycle,\n unsigned int n_time_step, double time,\n dealii::AffineConstraints const &affine_constraints,\n dealii::LinearAlgebra::distributed::Vector\n &solution)\n{\n affine_constraints.distribute(solution);\n post_processor.output_pvtu(cycle, n_time_step, time, solution);\n}\n\n#ifdef ADAMANTINE_HAVE_CUDA\ntemplate ::value,\n int> = 0>\nvoid output_pvtu(\n adamantine::PostProcessor &post_processor, unsigned int cycle,\n unsigned int n_time_step, double time,\n dealii::AffineConstraints const &affine_constraints,\n dealii::LinearAlgebra::distributed::Vector\n &solution)\n{\n dealii::LinearAlgebra::distributed::Vector\n solution_host(solution.get_partitioner());\n solution_host.import(solution, dealii::VectorOperation::insert);\n affine_constraints.distribute(solution_host);\n post_processor.output_pvtu(cycle, n_time_step, time, solution_host);\n}\n#endif\n\ntemplate ::value,\n int> = 0>\ndealii::Vector estimate_error(\n dealii::parallel::distributed::Triangulation const &triangulation,\n dealii::DoFHandler const &dof_handler, int fe_degree,\n dealii::LA::distributed::Vector const &solution)\n{\n dealii::Vector estimated_error_per_cell(\n triangulation.n_active_cells());\n dealii::KellyErrorEstimator::estimate(\n dof_handler, dealii::QGauss(fe_degree + 1),\n std::map *>(),\n solution, estimated_error_per_cell, dealii::ComponentMask(), nullptr, 0,\n triangulation.locally_owned_subdomain());\n\n return estimated_error_per_cell;\n}\n\n#ifdef ADAMANTINE_HAVE_CUDA\ntemplate ::value,\n int> = 0>\ndealii::Vector estimate_error(\n dealii::parallel::distributed::Triangulation const &triangulation,\n dealii::DoFHandler const &dof_handler, int fe_degree,\n dealii::LA::distributed::Vector const &solution)\n{\n dealii::LA::distributed::Vector\n solution_host(solution.get_partitioner());\n solution_host.import(solution, dealii::VectorOperation::insert);\n dealii::Vector estimated_error_per_cell(\n triangulation.n_active_cells());\n dealii::KellyErrorEstimator::estimate(\n dof_handler, dealii::QGauss(fe_degree + 1),\n std::map *>(),\n solution_host, estimated_error_per_cell, dealii::ComponentMask(), nullptr,\n 0, triangulation.locally_owned_subdomain());\n\n return estimated_error_per_cell;\n}\n#endif\n\n// inlining this function so we can have in the header\ninline void initialize_timers(MPI_Comm const &communicator,\n std::vector &timers)\n{\n timers.push_back(adamantine::Timer(communicator, \"Main\"));\n timers.push_back(adamantine::Timer(communicator, \"Refinement\"));\n timers.push_back(adamantine::Timer(communicator, \"Evolve One Time Step\"));\n timers.push_back(adamantine::Timer(\n communicator, \"Evolve One Time Step: evaluate_thermal_physics\"));\n timers.push_back(adamantine::Timer(\n communicator, \"Evolve One Time Step: id_minus_tau_J_inverse\"));\n timers.push_back(adamantine::Timer(\n communicator, \"Evolve One Time Step: evaluate_material_properties\"));\n}\n\ntemplate \nstd::vector>> &initialize(\n MPI_Comm const &communicator, boost::property_tree::ptree const &database,\n adamantine::Geometry &geometry,\n std::unique_ptr> &thermal_physics)\n{\n thermal_physics.reset(\n new adamantine::ThermalPhysics(communicator, database,\n geometry));\n return static_cast *>(\n thermal_physics.get())\n ->get_electron_beams();\n}\n\ntemplate \nstd::vector>> &\ninitialize_quadrature(\n std::string const &quadrature_type, MPI_Comm const &communicator,\n boost::property_tree::ptree const &database,\n adamantine::Geometry &geometry,\n std::unique_ptr> &thermal_physics)\n{\n if (quadrature_type.compare(\"gauss\") == 0)\n return initialize>(\n communicator, database, geometry, thermal_physics);\n else\n {\n adamantine::ASSERT_THROW(quadrature_type.compare(\"lobatto\") == 0,\n \"quadrature should be Gauss or Lobatto.\");\n return initialize>(communicator, database,\n geometry, thermal_physics);\n }\n}\n\ntemplate \nstd::vector>> &\ninitialize_thermal_physics(\n unsigned int fe_degree, std::string const &quadrature_type,\n MPI_Comm const &communicator, boost::property_tree::ptree const &database,\n adamantine::Geometry &geometry,\n std::unique_ptr> &thermal_physics)\n{\n switch (fe_degree)\n {\n case 1:\n {\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n case 2:\n {\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n case 3:\n {\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n case 4:\n {\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n case 5:\n {\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n case 6:\n {\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n case 7:\n {\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n case 8:\n {\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n case 9:\n {\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n default:\n {\n adamantine::ASSERT_THROW(fe_degree == 10,\n \"fe_degree should be between 1 and 10.\");\n return initialize_quadrature(\n quadrature_type, communicator, database, geometry, thermal_physics);\n }\n }\n}\n\ntemplate ::value,\n int> = 0>\nvoid refine_and_transfer(\n std::unique_ptr> &thermal_physics,\n dealii::DoFHandler &dof_handler,\n dealii::LA::distributed::Vector &solution)\n{\n dealii::parallel::distributed::Triangulation &triangulation =\n dynamic_cast &>(\n const_cast &>(\n dof_handler.get_triangulation()));\n\n std::shared_ptr> material_property =\n thermal_physics->get_material_property();\n\n dealii::parallel::distributed::SolutionTransfer<\n dim, dealii::LA::distributed::Vector>\n solution_transfer(dof_handler);\n std::vector>>\n material_state(\n static_cast(adamantine::MaterialState::SIZE),\n dealii::parallel::distributed::SolutionTransfer<\n dim, dealii::LA::distributed::Vector>(\n material_property->get_dof_handler()));\n\n // We need to update the ghost values before we can do the interpolation on\n // the new mesh.\n solution.update_ghost_values();\n\n // Prepare the Triangulation and the diffent SolutionTransfers for refinement\n triangulation.prepare_coarsening_and_refinement();\n solution_transfer.prepare_for_coarsening_and_refinement(solution);\n for (unsigned int i = 0;\n i < static_cast(adamantine::MaterialState::SIZE); ++i)\n material_state[i].prepare_for_coarsening_and_refinement(\n material_property->get_state()[i]);\n\n // Execute the refinement\n triangulation.execute_coarsening_and_refinement();\n\n // Update the AffineConstraints and resize the solution\n thermal_physics->setup_dofs();\n thermal_physics->initialize_dof_vector(solution);\n\n // Update MaterialProperty DoFHandler and resize the state vectors\n material_property->reinit_dofs();\n\n // Interpolate the solution and the state onto the new mesh\n solution_transfer.interpolate(solution);\n for (unsigned int i = 0;\n i < static_cast(adamantine::MaterialState::SIZE); ++i)\n material_state[i].interpolate(material_property->get_state()[i]);\n\n#if ADAMANTINE_DEBUG\n // Check that we are not losing material\n std::array,\n static_cast(adamantine::MaterialState::SIZE)>\n state = material_property->get_state();\n unsigned int const local_size = state[0].local_size();\n unsigned int constexpr n_material_states =\n static_cast(adamantine::MaterialState::SIZE);\n for (unsigned int i = 0; i < local_size; ++i)\n {\n double material_ratio = 0.;\n for (unsigned int j = 0; j < n_material_states; ++j)\n material_ratio += state[j].local_element(i);\n adamantine::ASSERT(std::abs(material_ratio - 1.) < 1e-14,\n \"Material is lost.\");\n }\n#endif\n}\n\n#ifdef ADAMANTINE_HAVE_CUDA\ntemplate ::value,\n int> = 0>\nvoid refine_and_transfer(\n std::unique_ptr> &thermal_physics,\n dealii::DoFHandler &dof_handler,\n dealii::LA::distributed::Vector &solution)\n{\n dealii::parallel::distributed::Triangulation &triangulation =\n dynamic_cast &>(\n const_cast &>(\n dof_handler.get_triangulation()));\n\n std::shared_ptr> material_property =\n thermal_physics->get_material_property();\n\n dealii::parallel::distributed::SolutionTransfer<\n dim, dealii::LA::distributed::Vector>\n solution_transfer(dof_handler);\n std::vector>>\n material_state(\n static_cast(adamantine::MaterialState::SIZE),\n dealii::parallel::distributed::SolutionTransfer<\n dim,\n dealii::LA::distributed::Vector>(\n material_property->get_dof_handler()));\n\n // We need to update the ghost values before we can do the interpolation on\n // the new mesh.\n solution.update_ghost_values();\n\n // Prepare the Triangulation and the diffent SolutionTransfers for refinement\n triangulation.prepare_coarsening_and_refinement();\n dealii::LA::distributed::Vector\n solution_host(solution.get_partitioner());\n solution_host.import(solution, dealii::VectorOperation::insert);\n solution_transfer.prepare_for_coarsening_and_refinement(solution_host);\n for (unsigned int i = 0;\n i < static_cast(adamantine::MaterialState::SIZE); ++i)\n material_state[i].prepare_for_coarsening_and_refinement(\n material_property->get_state()[i]);\n\n // Execute the refinement\n triangulation.execute_coarsening_and_refinement();\n\n // Update MaterialProperty DoFHandler and resize the state vectors\n material_property->reinit_dofs();\n\n // Update the AffineConstraints and resize the solution\n thermal_physics->setup_dofs();\n thermal_physics->initialize_dof_vector(solution);\n solution_host.reinit(solution.get_partitioner());\n\n // Interpolate the solution and the state onto the new mesh\n solution_transfer.interpolate(solution_host);\n solution.import(solution_host, dealii::VectorOperation::insert);\n for (unsigned int i = 0;\n i < static_cast(adamantine::MaterialState::SIZE); ++i)\n material_state[i].interpolate(material_property->get_state()[i]);\n\n#if ADAMANTINE_DEBUG\n // Check that we are not losing material\n std::array,\n static_cast(adamantine::MaterialState::SIZE)>\n state = material_property->get_state();\n unsigned int const local_size = state[0].local_size();\n unsigned int constexpr n_material_states =\n static_cast(adamantine::MaterialState::SIZE);\n for (unsigned int i = 0; i < local_size; ++i)\n {\n double material_ratio = 0.;\n for (unsigned int j = 0; j < n_material_states; ++j)\n material_ratio += state[j].local_element(i);\n adamantine::ASSERT(std::abs(material_ratio - 1.) < 1e-14,\n \"Material is lost.\");\n }\n#endif\n}\n#endif\n\ntemplate \nstd::vector::active_cell_iterator>\ncompute_cells_to_refine(\n dealii::parallel::distributed::Triangulation &triangulation,\n double const time, double const next_refinement_time,\n unsigned int const n_time_steps,\n std::vector>> &electron_beams)\n{\n // The save_time()/rewind_time() functions are used to save the current time\n // and row in the list of beam positions. It does nothing when the position is\n // given by an analytic formula.\n for (auto &beam : electron_beams)\n beam->save_time();\n\n // Compute the position of the beams between time and next_refinement_time and\n // refine the mesh where the source is greater than 1e-15. This cut-off is due\n // to the fact that the source is gaussian and thus never strictly zero. If\n // the beams intersect, some cells will appear twice in the vector. This is\n // not a problem.\n std::vector::active_cell_iterator>\n cells_to_refine;\n for (unsigned int i = 0; i < n_time_steps; ++i)\n {\n double const current_time = time + static_cast(i) /\n static_cast(n_time_steps) *\n (next_refinement_time - time);\n for (auto &beam : electron_beams)\n {\n beam->set_time(current_time);\n for (auto cell : dealii::filter_iterators(\n triangulation.active_cell_iterators(),\n dealii::IteratorFilters::LocallyOwnedCell()))\n if (beam->value(cell->center()) > 1e-15)\n cells_to_refine.push_back(cell);\n }\n }\n\n for (auto &beam : electron_beams)\n beam->rewind_time();\n\n return cells_to_refine;\n}\n\ntemplate \nvoid refine_mesh(\n std::unique_ptr> &thermal_physics,\n dealii::LA::distributed::Vector &solution,\n std::vector>> &electron_beams,\n double const time, double const next_refinement_time,\n unsigned int const time_steps_refinement,\n boost::property_tree::ptree const &refinement_database)\n{\n dealii::DoFHandler &dof_handler = thermal_physics->get_dof_handler();\n // Use the Kelly error estimator to refine the mesh. This is done so that the\n // part of the domain that were heated stay refined.\n unsigned int const n_kelly_refinements =\n refinement_database.get(\"n_heat_refinements\", 2);\n double coarsening_fraction = 0.3;\n double refining_fraction = 0.6;\n double cells_fraction = refinement_database.get(\"heat_cell_ratio\", 1.);\n dealii::parallel::distributed::Triangulation &triangulation =\n dynamic_cast &>(\n const_cast &>(\n dof_handler.get_triangulation()));\n // Number of times the mesh on the beam paths will be refined and maximum\n // number of time a cell can be refined.\n unsigned int const n_beam_refinements =\n refinement_database.get(\"n_beam_refinements\", 2);\n int max_level = refinement_database.get(\"max_level\");\n for (unsigned int i = 0; i < n_kelly_refinements; ++i)\n {\n // Estimate the error. For simplicity, always use dealii::QGauss\n dealii::Vector estimated_error_per_cell =\n estimate_error(triangulation, dof_handler, fe_degree, solution);\n\n // Flag the cells for refinement.\n unsigned int new_n_cells = static_cast(\n cells_fraction *\n static_cast(triangulation.n_global_active_cells()));\n dealii::GridRefinement::refine_and_coarsen_fixed_fraction(\n triangulation, estimated_error_per_cell, refining_fraction,\n coarsening_fraction, new_n_cells);\n\n // Don't refine cells that are already as much refined as it is allowed.\n for (auto cell :\n dealii::filter_iterators(triangulation.active_cell_iterators(),\n dealii::IteratorFilters::LocallyOwnedCell()))\n if (cell->level() >= max_level)\n cell->clear_refine_flag();\n\n // Execute the refinement and transfer the solution onto the new mesh.\n refine_and_transfer(thermal_physics, dof_handler, solution);\n }\n\n // Refine the mesh along the trajectory of the sources.\n for (unsigned int i = 0; i < n_beam_refinements; ++i)\n {\n // Compute the cells to be refined.\n std::vector::active_cell_iterator>\n cells_to_refine =\n compute_cells_to_refine(triangulation, time, next_refinement_time,\n time_steps_refinement, electron_beams);\n\n // Flag the cells for refinement.\n for (auto &cell : cells_to_refine)\n if (cell->level() < max_level)\n cell->set_refine_flag();\n\n // Execute the refinement and transfer the solution onto the new mesh.\n refine_and_transfer(thermal_physics, dof_handler, solution);\n }\n\n // Recompute the inverse of the mass matrix\n thermal_physics->compute_inverse_mass_matrix();\n}\n\ntemplate \nvoid refine_mesh(\n std::unique_ptr> &thermal_physics,\n dealii::LA::distributed::Vector &solution,\n std::vector>> &electron_beams,\n double const time, double const next_refinement_time,\n unsigned int const time_steps_refinement,\n boost::property_tree::ptree const &refinement_database,\n unsigned int const fe_degree)\n{\n switch (fe_degree)\n {\n case 1:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n case 2:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n case 3:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n case 4:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n case 5:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n case 6:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n case 7:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n case 8:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n case 9:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n case 10:\n {\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database);\n break;\n }\n default:\n {\n adamantine::ASSERT_THROW(false, \"fe_degree should be between 1 and 10.\");\n }\n }\n}\n\ntemplate \ndealii::LinearAlgebra::distributed::Vector\nrun(MPI_Comm const &communicator, boost::property_tree::ptree const &database,\n std::vector &timers)\n{\n // Extract property tree children\n boost::property_tree::ptree geometry_database =\n database.get_child(\"geometry\");\n boost::property_tree::ptree discretization_database =\n database.get_child(\"discretization\");\n boost::property_tree::ptree time_stepping_database =\n database.get_child(\"time_stepping\");\n boost::property_tree::ptree post_processor_database =\n database.get_child(\"post_processor\");\n boost::property_tree::ptree refinement_database =\n database.get_child(\"refinement\");\n\n unsigned int const fe_degree =\n discretization_database.get(\"fe_degree\");\n std::string quadrature_type =\n discretization_database.get(\"quadrature\", \"gauss\");\n std::transform(quadrature_type.begin(), quadrature_type.end(),\n quadrature_type.begin(),\n [](unsigned char c) { return std::tolower(c); });\n double const initial_temperature =\n database.get(\"materials.initial_temperature\", 300.);\n\n adamantine::Geometry geometry(communicator, geometry_database);\n std::unique_ptr> thermal_physics;\n std::vector>> &electron_beams =\n initialize_thermal_physics(fe_degree, quadrature_type, communicator,\n database, geometry, thermal_physics);\n adamantine::PostProcessor post_processor(\n communicator, post_processor_database, thermal_physics->get_dof_handler(),\n thermal_physics->get_material_property());\n\n thermal_physics->setup_dofs();\n thermal_physics->compute_inverse_mass_matrix();\n dealii::LA::distributed::Vector solution;\n thermal_physics->initialize_dof_vector(initial_temperature, solution);\n unsigned int progress = 0;\n unsigned int cycle = 0;\n unsigned int n_time_step = 0;\n double time = 0.;\n // Output the initial solution\n dealii::AffineConstraints &affine_constraints =\n thermal_physics->get_affine_constraints();\n output_pvtu(post_processor, cycle, n_time_step, time, affine_constraints,\n solution);\n ++n_time_step;\n\n bool const verbose_refinement = refinement_database.get(\"verbose\", false);\n unsigned int const time_steps_refinement =\n refinement_database.get(\"time_steps_between_refinement\", 10);\n double next_refinement_time = time;\n double time_step = time_stepping_database.get(\"time_step\");\n double const duration = time_stepping_database.get(\"duration\");\n while (time < duration)\n {\n if ((time + time_step) > duration)\n time_step = duration - time;\n unsigned int rank = dealii::Utilities::MPI::this_mpi_process(communicator);\n\n // Refine the mesh after time_steps_refinement time steps or when time is\n // greater or equal than the next predicted time for refinement. This is\n // necessary when using an embedded method.\n if (((n_time_step % time_steps_refinement) == 0) ||\n (time >= next_refinement_time))\n {\n next_refinement_time = time + time_steps_refinement * time_step;\n timers[adamantine::refine].start();\n refine_mesh(thermal_physics, solution, electron_beams, time,\n next_refinement_time, time_steps_refinement,\n refinement_database, fe_degree);\n timers[adamantine::refine].stop();\n if ((rank == 0) && (verbose_refinement == true))\n std::cout << \"n_dofs: \" << thermal_physics->get_dof_handler().n_dofs()\n << std::endl;\n }\n\n // time can be different than time + time_step if an embedded scheme is\n // used.\n timers[adamantine::evol_time].start();\n time = thermal_physics->evolve_one_time_step(time, time_step, solution,\n timers);\n timers[adamantine::evol_time].stop();\n\n // Get the new time step\n time_step = thermal_physics->get_delta_t_guess();\n\n // Output progress on screen\n if (rank == 0)\n {\n double adim_time = time / (duration / 10.);\n double int_part = 0;\n std::modf(adim_time, &int_part);\n if (int_part > progress)\n {\n std::cout << int_part * 10 << '%' << \" completed\" << std::endl;\n ++progress;\n }\n }\n\n // Output the solution\n output_pvtu(post_processor, cycle, n_time_step, time, affine_constraints,\n solution);\n ++n_time_step;\n }\n post_processor.output_pvd();\n\n // This is only used for integration test\n return solution;\n}\n#endif\n", "meta": {"hexsha": "7604278c53c266b4cd0d58bf5d643687c680f3dc", "size": 28378, "ext": "hh", "lang": "C++", "max_stars_repo_path": "application/adamantine.hh", "max_stars_repo_name": "stvdwtt/adamantine", "max_stars_repo_head_hexsha": "af396f02089a488a35146ab83234974ae465ada2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "application/adamantine.hh", "max_issues_repo_name": "stvdwtt/adamantine", "max_issues_repo_head_hexsha": "af396f02089a488a35146ab83234974ae465ada2", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "application/adamantine.hh", "max_forks_repo_name": "stvdwtt/adamantine", "max_forks_repo_head_hexsha": "af396f02089a488a35146ab83234974ae465ada2", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.9127988748, "max_line_length": 80, "alphanum_fraction": 0.6986045528, "num_tokens": 6650, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704796847396, "lm_q2_score": 0.43782349911420193, "lm_q1q2_score": 0.24276020557110273}} {"text": "/********************************************************************\n\tcreated:\t2013/01/10\n\tcreated:\t10:1:2013 9:15\n\tfilename: \tSimpleFinder.cpp\n\tfile path:\tGAG\\src\\GAGPL\\SPECTRUM\n\tfile base:\tSimpleFinder\n\tfile ext:\tcpp\n\tauthor:\t\tHan Hu\n\t\n\tpurpose:\t\n*********************************************************************/\n\n#include \n#include \"GAGPL/SPECTRUM/SimpleFinder.h\"\n#include \n#include \n\nnamespace gag\n{\n\tSimpleFinder::SimpleFinder(RichList& spec)\n\t\t: spectrum(spec), param(Param::Instance()), pt(PeriodicTable::Instance())\n\t{\n\t\tint precursor_charge = param.getParameter(\"precursor_charge\").first;\n\n\t\tdouble pre_mz = param.getParameter(\"precursor_mz\").first;\n\t\tprecursor_mass = calculateMass(pre_mz, precursor_charge);\n\n\t\t/* \n\t\tGuess the range of average formula. Usually we assume the difference between composition estimated from the base peak and the one estimated from monoisotopic peak can be safely ignored.\n\t\t\tNo Sulfur (100D)\t\tHigh Sulfur (100D) \n\t\t\tC: 3.7238523 1.9961864\n\t\t\tH: 5.4425534 2.9942796\n\t\t\tO: 2.8645018 3.1938983\n\t\t\tN: 0.2864502 0.1996186\n\t\t\tS: 0 0.5988559\n\t\t*/\n\t\tno_sulfur_ave.insert(std::make_pair(\"C\", 3.7238523));\n\t\tno_sulfur_ave.insert(std::make_pair(\"H\", 5.4425534));\n\t\tno_sulfur_ave.insert(std::make_pair(\"O\", 2.8645018));\n\t\tno_sulfur_ave.insert(std::make_pair(\"N\", 0.2864502));\n\t\tno_sulfur_ave.insert(std::make_pair(\"S\", 0.0));\n\n\t\tmax_sulfur_ave.insert(std::make_pair(\"C\", 1.9961864));\n\t\tmax_sulfur_ave.insert(std::make_pair(\"H\", 2.9942796));\n\t\tmax_sulfur_ave.insert(std::make_pair(\"O\", 3.1938983));\n\t\tmax_sulfur_ave.insert(std::make_pair(\"N\", 0.1996186));\n\t\tmax_sulfur_ave.insert(std::make_pair(\"S\", 0.5988559));\n\t\trun();\n\t}\n\t\n\tvoid SimpleFinder::run()\n\t{\n\t\t// There is a need to divide the spectrum into separate islands at this moment.\n\t\tRichPeakListBySignalOverNoise& pks_sn = spectrum.getPeakListByType();\n\n\t\t// The S/N threshold for monoisotopic peak.\n\t\tdouble mono_sn_threshold = param.getParameter(\"signal_noise\").first;\n\n\t\tRichPeakListBySignalOverNoise::iterator end_sn = pks_sn.upper_bound(mono_sn_threshold);\t\t\n\t\tRichPeakListBySignalOverNoise::iterator iter_sn = pks_sn.begin();\n\t\t//end_iter--;\n\n\t\t// Start from the peak with highest s/n\n\t\tfor(; iter_sn != end_sn; iter_sn++) {\n\t\t\tthis->findNextEnvelop(spectrum, *iter_sn);\n\t\t}\n\n\t\tstd::cout << \"Before optimizing, output all the envelop information...\" << std::endl;\n\t\tBOOST_FOREACH(EnvelopPtr env, env_pool)\n\t\t\tthis->printEnvelop(env);\n\n\t\tstd::cout << std::endl;\n\n\t\t//std::cout << \"After optimizing...\" << std::endl;\n\t\t//Estimate parameters for each envelop and update the peak information.\n\t\tthis->optimizeEnvelopSet(env_pool);\n\t\tthis->optimizeSuspiciousPeaks();\n\n\t}\n\n\tvoid SimpleFinder::findNextEnvelop(RichList& pk_list, RichPeakPtr base_pk, bool raw_spec /* = true */)\n\t{\n\t\t// Remove the neighbor peak.\n\t\tstd::cout << \"Check if peak \" << base_pk->mz << \" is in the black list\" << std::endl;\n\t\n\t\tif(!base_pk->pk_status) {\n\t\t\tstd::cout << \"Peak \" << base_pk->mz << \" is a noise peak.\" << std::endl;\n\t\t\treturn;\n\t\t}\n\n\t\tdouble mz = base_pk->mz;\n\n\t\t// Process the apodization problem.\n\t\tthis->cleanNeighborNoise(pk_list, base_pk);\n\n\t\tint precursor_charge = param.getParameter(\"precursor_charge\").first;\n\t\t\n\t\tdouble signal_noise = param.getParameter(\"signal_noise\").first;\n\n\t\tint sign = (precursor_charge > 0 ? 1 : -1);\n\t\tbool suspicious_pk = true;\n\n\t\tfor(int z = abs(precursor_charge); z>=1; z--) {\n\t\n\t\t\t// The actual mass is allowed to be larger than precursor mass.\n\t\t\t//double mass = calculateMass((*iter_intensity)->mz, sign * z);\n\t\t\t//if(mass > precursor_mass) continue;\n\n\t\t\t// Not a qualified peak due to its poor intensity.\n\t\t\tif(base_pk->signal_noise < signal_noise) continue;\n\t\t\t\n\t\t\tstd::cout << \"Identify base peak: \" << base_pk->mz << \" Charge: \" << z;\n\t\t\tstd::cout << (precursor_charge > 0 ? \"+\" : \"-\") << std::endl;\n\n\t\t\tbool status = this->extendEnvelop(pk_list, base_pk, z, raw_spec);\n\t\t\t\n\t\t\tif(status) { \n\t\t\t\tsuspicious_pk = false;\n\t\t\t\t// If the S/N of the base peak is higher than 200.\n\t\t\t\tif(base_pk->signal_noise > 200)\n\t\t\t\t\tthis->detectHarmonicCluster(base_pk, sign * z);\n\t\t\t}\n\n\t\t}\n\n\t\tif(suspicious_pk)\n\t\t\tsetPeakType(base_pk, \"ISO\");\n\t}\n\n\tvoid SimpleFinder::optimizeEnvelopSet(std::set& env_set)\n\t{\n\t\t// Start from low m/z to high m/z.\n\t\tRichList pk_list = env_ref.getRichPeakList(env_set);\n\t\tRichPeakListByMZ& pk_mz = pk_list.getPeakListByType();\n\n\t\t// Guess the number of sulfate.\n\t\tBOOST_FOREACH(EnvelopPtr env, env_set)\n\t\t\tthis->updateEnvelopParameter(env);\n\n\t\tfor(RichPeakListByMZ::iterator mz_iter = pk_mz.begin(); \n\t\t\tmz_iter != pk_mz.end(); mz_iter++)\n\t\t{\n\t\t\tRichPeakPtr pk = *mz_iter;\n\t\t\tdouble mz = pk->mz;\n\t\t\tstd::cout << \"Check peak \" << pk->mz << std::endl;\n\n\t\t\t// 1. For each peak, check if there is any new envelop occurred at this peak.\n\t\t\tstd::vector new_env_entries = env_ref.getOccurredEnvelopEntries(pk, NEW);\n\t\t\tif(new_env_entries.size() == 0) \n\t\t\t\tcontinue;\n\n\t\t\t// Update the fitting score for all the new envelop set.\n\t\t\tBOOST_FOREACH(EnvEntry& env_entry, new_env_entries) {\n\t\t\t\tthis->calculateFittingScore(env_entry.env);\n\t\t\t}\n\n\t\t\t// Check if this is a peak from previously identified envelop.\n\t\t\tstd::vector old_env_entries = env_ref.getOccurredEnvelopEntries(pk, OLD);\n\t\t\t\t\n\t\t\t// 1. Sort the envelop vector by their fitting scores.\n\t\t\t// Since the value of the fitting score will be frequently updated,\t\t\t\t// it might not be a good idea to keep it in a multi-index container\n\t\t\tstd::sort(new_env_entries.begin(), new_env_entries.end(), EnvEntry::scoreLarger);\n\n\t\t\t// Check if the new envelops are actually just subset of the old \n\t\t\t// envelops.\n\t\t\tif(old_env_entries.size() != 0) {\n\t\t\t\t// a. Declared as abnormal by old_env_entry.\n\t\t\t\t// b. Identified new peak.\n\t\t\t\t// c. Meet the threshold of the envelop.\n\t\t\t\tBOOST_FOREACH(EnvEntry& new_entry, new_env_entries)\n\t\t\t\t{\n\t\t\t\t\t\tbool fake_envelop = false;\n\t\t\t\t\t\tBOOST_FOREACH(EnvEntry old_entry, old_env_entries)\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tbool false_positive = this->isRedundantEnvelop(new_entry.env, old_entry.env, old_entry.getShift());\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t// If this peak is considered by any of the old envelops as\t\t\t\t\t\t\t\t// a true one, move to the next step. \n\t\t\t\t\t\t\tif(false_positive) {\n\t\t\t\t\t\t\t\tfake_envelop = true;\n\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tif(fake_envelop) new_entry.info->entry_status = FALSE;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t/* Rule of thumb */\n\t\t\t// 1. If there is only one new envelop, just accept the new envelop.\n\t\t\tif(new_env_entries.size()==1) {\n\t\t\t\t// No longer new envelop.\n\t\t\t\tif(new_env_entries[0].info->entry_status != FALSE) {\n\t\t\t\t\t// Identify if this is a noise envelop.\n\t\t\t\t\tif(!isNoiseEnvelop(new_env_entries[0].env))\n\t\t\t\t\t\tthis->acceptEnvelop(new_env_entries[0].env);\n\t\t\t\t\t\n\t\t\t\t}\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\t// 2. If there is more than one new envelop, which means multiple charge states, select the one with higher fitting score as the basis, sequentially merge the other into it, and see if the overall fitting score will be improved.\n\n\t\t\t// Upgrade the score of the system.\n\t\t\t// Calculate the previous system score.\n\t\t\tdouble system_score = 0.0;\n\t\t\t\n\t\t\t// 2. Sequentially merge one into the basis and decide if the performance is improved.\n\t\t\tstd::vector added_entries;\n\t\t\tBOOST_FOREACH(EnvEntry& new_entry, new_env_entries)\n\t\t\t{\n\t\t\t\tif(new_entry.info->entry_status == FALSE)\n\t\t\t\t\tcontinue;\n\n\t\t\t\t// Assign the fitting score to the system score.\n\t\t\t\tif(system_score == 0.0) {\n\t\t\t\t\tsystem_score = new_entry.env->fitting_score;\n\t\t\t\t\tthis->acceptEnvelop(new_entry.env);\n\n\t\t\t\t\tadded_entries.push_back(new_entry);\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\n\t\t\t\t// 2.a Find the corresponding fitting score. Only considering the situation where there are multiple charge states for the same mono-isotopic peak. \n\t\t\t\tdouble new_score = updateEnvelopTree(old_env_entries, added_entries, new_entry, pk);\t\n\n\t\t\t\t// Decide if the envelop is proper to be added into the \n\t\t\t\t// envelop set.\n\t\t\t\tif(new_score <= system_score) {\n\t\t\t\t\tnew_entry.info->entry_status = FALSE;\n\t\t\t\t}else {\n\t\t\t\t\t/* Accept the candidate envelop.*/\n\t\t\t\t\t// No longer new envelop.\n\t\t\t\t\tthis->acceptEnvelop(new_entry.env);\n\t\t\t\t\tadded_entries.push_back(new_entry);\n\t\t\t\t\tsystem_score = new_score;\n\t\t\t\t}\n\t\t\t}\n\n\t\t}\n\t}\n\n\tbool SimpleFinder::extendEnvelop( RichList& pk_list, RichPeakPtr base_pk, int charge, bool raw_spec)\n\t{\n\t\t// a. Using A+1 and A+2 peak to identify the most likely composition.\n\t\t// b. Try to extend the peak using the estimated composition.\n\t\t// c. Notice that sometimes, the A+2 peak might have missing information due to the defective peak picking algorithm.\n\t\t// d. This algorithm requires that A peak has to present.\n\t\t\n\t\t/* A + 1 peak */\n\t\t// Assume it follows the no sulfate model, get the boundary.\n\t\t// Guess the number of sulfate. Notice that there might be overlapping.\n\t\t// Calculate the no sulfate model. Notice that the information has been corrected by charge.\n\n\t\tAggregatedIsotopicVariants higher_theo = this->estimateDistribution(base_pk->mz, charge);\n\t\t//AggregatedIsotopicVariants lower_theo = this->estimateDistribution(base_pk->mz, charge, ave2);\n\n\t\t// Get the isotope information.\n\t\tconst Isotope s32 = pt.getIsotopeByNominalMass(\"S\", 32);\n\t\tconst Isotope s34 = pt.getIsotopeByNominalMass(\"S\", 34);\n\t\tconst Isotope c12 = pt.getIsotopeByNominalMass(\"C\", 12);\n\t\tconst Isotope c13 = pt.getIsotopeByNominalMass(\"C\", 13);\n\t\tconst Isotope h = pt.getIsotopeByNominalMass(\"H\",1);\n\t\tdouble electron_mass = param.getParameter(\"electron_mass\").first;\n\t\tdouble low_sn_threshold = param.getParameter(\"lower_bound_sn\").first;\n\n\t\t// The difference between mass of H and peak(A -> A+1). This variable is for re-use convinience.\n\t\tdouble mz_diff = (h.mass + electron_mass)/(double)charge - higher_theo.getMassDifferenceByShift(0, 1);\n\n\t\t// Temporary variable for storing the peak information.\n\t\t// 1. Shift;\n\t\t// 2. Peak;\n\t\t// 3. Status.\n\t\tstd::multimap > pk_map;\n\t\tpk_map.insert(std::make_pair(0, std::make_pair(base_pk, \"Normal\")));\n\n\t\tint shift = 1;\n\n\t\twhile(1) {\n\t\t\t// Theoretically, the (A+n)' should always be present.\n\t\t\tdouble mz0 = base_pk->mz + higher_theo.getMassDifferenceByShift(0,shift);\n\n\t\t\tstd::string status = \"Normal\";\n\t\t\tif(shift == 1) {\t\t\t\n\t\t\t\tstd::set pks_set = this->getClosestRichPeaks(pk_list, mz0, raw_spec, low_sn_threshold);\n\t\t\t\t\n\t\t\t\tif(pks_set.size() == 0) { // C+H peak for different shift.\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\tdouble mz1 = mz0 + mz_diff;\n\t\t\t\t\t\n\t\t\t\t\tpks_set = this->getClosestRichPeaks(pk_list, mz1, raw_spec, low_sn_threshold);\n\t\t\t\t\tstatus = \"C+H\";\n\n\t\t\t\t\tif(pks_set.size() == 0 && shift > 1) { // C+2H\n\t\t\t\t\t\tdouble mz2 = mz0 + 2.0 * mz_diff;\n\t\t\t\t\t\tpks_set = this->getClosestRichPeaks(pk_list, mz2, raw_spec, low_sn_threshold);\n\t\t\t\t\t\tstatus = \"C+2H\";\n\t\t\t\t\t\t\n\t\t\t\t\t\tif(pks_set.size() == 0)\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t} else if(pks_set.size() == 0 && shift == 1) {\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\tBOOST_FOREACH(RichPeakPtr pk, pks_set) {\n\t\t\t\t\tpk_map.insert(std::make_pair(shift, std::make_pair(pk, status)));\n\t\t\t\t}\n\n\t\t\t} else if(shift == 2) {\n\t\t\t\t// If 34S peak is present.\n\t\t\t\tdouble mz1 = base_pk->mz + (s34.mass - s32.mass)/(double)charge;\n\t\t\t\tstd::set pks_set1 = this->getClosestRichPeaks(pk_list, mz1, raw_spec, low_sn_threshold);\n\n\t\t\t\tBOOST_FOREACH(RichPeakPtr pk, pks_set1) {\n\t\t\t\t\tpk_map.insert(std::make_pair(shift, std::make_pair(pk, \"Sulfur\")));\n\t\t\t\t}\n\n\t\t\t\t// (A+n)' peak.\n\t\t\t\tstd::set pks_set2 = this->getClosestRichPeaks(pk_list, mz0, raw_spec, low_sn_threshold);\n\n\t\t\t\tBOOST_FOREACH(RichPeakPtr pk, pks_set2) {\n\t\t\t\t\tpk_map.insert(std::make_pair(shift, std::make_pair(pk, \"Normal\")));\n\t\t\t\t}\t\t\n\n\t\t\t\tif(pks_set1.size() == 0 && pks_set2.size() == 0) {\n\t\t\t\t\t// C+H peak.\n\t\t\t\t\tdouble mz2 = mz0 + mz_diff;\n\n\t\t\t\t\tstd::set pks_set3 = this->getClosestRichPeaks(pk_list, mz2, raw_spec, low_sn_threshold);\n\t\t\t\t\tBOOST_FOREACH(RichPeakPtr pk, pks_set3) {\n\t\t\t\t\t\tpk_map.insert(std::make_pair(shift, std::make_pair(pk, \"C+H\")));\n\t\t\t\t\t}\n\n\t\t\t\t\tif(pks_set3.size() == 0) {\n\t\t\t\t\t\t// C + 2H\n\t\t\t\t\t\tdouble mz3 = mz0 + 2.0 * mz_diff;\n\t\t\t\t\t\tstd::set pks_set4 = this->getClosestRichPeaks(pk_list, mz3, raw_spec, low_sn_threshold);\n\t\t\t\t\t\tBOOST_FOREACH(RichPeakPtr pk, pks_set4) {\n\t\t\t\t\t\t\tpk_map.insert(std::make_pair(shift, std::make_pair(pk, \"C+2H\")));\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif(pks_set4.size() == 0) \n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\n\t\t\t\t\t}\n\t\t\t\t}\t\t\n\n\t\t\t} else { // Shift >= 2.\n\t\t\t\t// Left boundary.\n\t\t\t\tint sulfur_multiplier = shift / 2;\n\t\t\t\tsize_t count = 0;\n\t\t\t\tfor(int i = sulfur_multiplier; i>=0; i--) {\n\t\t\t\t\tint carbon_multiplier = shift - i * 2;\n\t\t\t\t\tdouble mzx = base_pk->mz + i * (s34.mass - s32.mass)/(double)charge + carbon_multiplier * (c13.mass - c12.mass)/(double)charge;\n\t\t\t\t\tstd::set pks_set = this->getClosestRichPeaks(pk_list, mzx, raw_spec, low_sn_threshold);\n\t\t\t\t\tcount += pks_set.size();\n\t\t\t\t\tBOOST_FOREACH(RichPeakPtr pk, pks_set) {\n\t\t\t\t\t\tpk_map.insert(std::make_pair(shift, std::make_pair(pk, \"C+S\")));\n\t\t\t\t\t}\n \n // Also consider the H shift.\n double mz1 = mzx + mz_diff;\n pks_set = this->getClosestRichPeaks(pk_list, mz1, raw_spec, low_sn_threshold);\n if(pks_set.size() != 0) {\n status = \"C+H\";\n BOOST_FOREACH(RichPeakPtr pk, pks_set) {\n pk_map.insert(std::make_pair(shift, std::make_pair(pk, status)));\n }\n //pk_map.insert(std::make_pair(shift, std::make_pair(pk, \"C+S\")));\n } \n\t\t\t\t}\n\t\t\t\t// No peak has been found in this range.\n\t\t\t\tif(count == 0) {\n break;\n }\n\t\t\t\t\t\n\t\t\t}\n\n\t\t\tshift++;\n\t\t}\n\n\t\t// No additional peak has been found.\n\t\tif(pk_map.size() == 1)\n\t\t\treturn false;\n\t\t\n\t\tEnvelopPtr env_ptr = createEnvelop(charge);\n\n\t\tdouble param_confidence = 0.0;\n\n\t\tstd::multimap >::iterator map_iter = pk_map.begin();\n\t\tfor(; map_iter != pk_map.end(); map_iter++)\n\t\t{\n\t\t\tint pk_pos = map_iter->first; \n\t\t\tRichPeakPtr pk_ptr = map_iter->second.first;\n\t\t\tthis->cleanNeighborNoise(pk_list, pk_ptr);\n\t\t\tstd::string pk_status = map_iter->second.second;\n\n\t\t\tEntryStatus entry_status = (pk_pos == 0 ? NEW : OLD);\n\t\t\t\n\t\t\tInfoPeakPtr pk_infor = boost::make_shared(pk_ptr->intensity, pk_status, entry_status);\n\n\t\t\tenv_ref.addDictionaryReference(pk_ptr, env_ptr, pk_pos, pk_infor);\n\t\t}\n\n\t\tint max_shift = shift - 1; \n\t\tenv_ptr->max_shift = max_shift;\n\n\t\tstd::cout << \"Found candidate envelop -- mono peak: \" << base_pk->mz << \" charge: \" << env_ptr->charge_state << std::endl;\n\t\tenv_pool.insert(env_ptr);\n\n\t\t//calculateFittingScore(env_ptr);\n\n\t\treturn true;\n\n\t}\n\n\tAggregatedIsotopicVariants SimpleFinder::estimateDistribution( double mz, int charge, bool sulfur)\n\t{\n\t\tdouble mass = calculateMass(mz, charge);\n\t\tdouble coef = mass / 100.0;\n\t\tComposition compo;\n\n\t\tif(sulfur) {\n\t\t\t// Correct sulfur number. \n\t\t\tAveragineFormulae::iterator sulfur_iter = max_sulfur_ave.find(\"S\");\n\t\t\tint num = (int)ceil(sulfur_iter->second * coef);\n\n\t\t\tdouble new_mass = mass - (double)num * Composition(\"SO3\").getMass();\n\t\t\t\n\t\t\tdouble new_coef = new_mass / 100.0;\n\t\t\t\n\t\t\tAveragineFormulae::iterator iter = max_sulfur_ave.begin();\n\t\t\tfor(; iter != max_sulfur_ave.end(); iter++) {\n\t\t\t\tif(iter->first != \"S\") {\n\t\t\t\t\tcompo.addElement(iter->first, (int)floor(iter->second * new_coef + 0.5));\n\t\t\t\t} else {\n\t\t\t\t\tcompo.addElement(iter->first, num);\n\t\t\t\t}\n\t\t\t}\n\t\t\tIsotopicDistribution iso(compo);\n\t\t\treturn iso.getAggregatedIsotopicVariants(charge);\n\t\t} else {\n\t\t\tAveragineFormulae::iterator iter = no_sulfur_ave.begin();\n\t\t\tfor(; iter != no_sulfur_ave.end(); iter++)\n\t\t\t\tcompo.addElement(iter->first, (int)floor(iter->second * coef + 0.5));\n\n\t\t\tIsotopicDistribution iso(compo);\n\t\t\treturn iso.getAggregatedIsotopicVariants(charge);\n\t\t}\n\n\n\t}\n\n\tAggregatedIsotopicVariants SimpleFinder::estimateDistribution( double mz, int charge, int sulfur_num )\n\t{\n\t\tdouble mass = calculateMass(mz, charge);\n\t\tdouble new_mass = mass - (double)sulfur_num * Composition(\"SO3\").getMass();\n\n\t\tdouble new_coef = new_mass / 100.0;\n\n\t\tAveragineFormulae::iterator iter = no_sulfur_ave.begin();\n\t\tComposition compo;\n\t\tfor(; iter != no_sulfur_ave.end(); iter++) {\n\t\t\tif(iter->first == \"S\") {\n\t\t\t\tcompo.addElement(iter->first, sulfur_num);\n\t\t\t} else if(iter->first == \"O\") {\n\t\t\t\tcompo.addElement(iter->first, sulfur_num * 3);\n\t\t\t} else {\n\t\t\t\tcompo.addElement(iter->first, (int)floor(iter->second * new_coef + 0.5));\n\t\t\t}\n\t\t}\n\t\tIsotopicDistribution iso(compo);\n //AggregatedIsotopicVariants agr_var = iso.getAggregatedIsotopicVariants(charge);\n\t\treturn iso.getAggregatedIsotopicVariants(charge);\n\t}\n\n\n\tstd::set SimpleFinder::getClosestRichPeaks(RichList& pk_list, double expected_mz, bool raw_spec, double sn_threshold, double error)\n\t{\n\t\tRichPeakListByMZ& pks_mz = pk_list.getPeakListByType();\n\n\t\tRichPeakListByMZ::iterator mz_increase_iter, mz_decrease_iter;\n\t\tmz_increase_iter = pks_mz.lower_bound(expected_mz);\n\t\tmz_decrease_iter = mz_increase_iter;\n\n\t\tstd::set pk_set;\n\t\tstd::map pk_map;\n\n\t\t// Examine the peak towards two directions.\n\t\twhile(1) {\n\t\t\tif(mz_increase_iter == pks_mz.end())\n\t\t\t\tbreak;\n\n\t\t\tRichPeakPtr pk = *mz_increase_iter;\n\n\t\t\tbool meet_threshold = raw_spec ? true : (pk->signal_noise > sn_threshold); \n\n\t\t\tdouble win_error = this->getWindowError(pk, error);\n\t\t\t\n\t\t\tif(abs(pk->mz - expected_mz) < win_error) {\n\t\t\t\tif(meet_threshold)\n\t\t\t\t\tpk_map.insert(std::make_pair(pk->intensity, pk));\n\t\t\t} else {\n\t\t\t\tbreak;\n\t\t\t}\n\n\t\t\tmz_increase_iter++;\n\t\t\tif(mz_increase_iter == pks_mz.end())\n\t\t\t\tbreak;\n\t\t}\n\n\t\tmz_decrease_iter--;\n\t\twhile(1) {\n\t\t\tRichPeakPtr pk = *mz_decrease_iter;\n\n\t\t\tdouble win_error = this->getWindowError(pk, error);\n\t\t\t\n\t\t\tbool meet_threshold = raw_spec ? true : (pk->signal_noise > sn_threshold);\n\n\t\t\tif(abs(pk->mz - expected_mz) < win_error)\t{\t\t\n\t\t\t\tif(meet_threshold)\n\t\t\t\t\tpk_map.insert(std::make_pair(pk->intensity, pk));\n\t\t\t} else {\n\t\t\t\tbreak;\n\t\t\t}\n\n\t\t\tif(mz_decrease_iter == pks_mz.begin())\n\t\t\t\tbreak;\n\t\t\telse\n\t\t\t\tmz_decrease_iter--;\n\t\t}\n\n\t\t//pk_set.insert(selected_pk);\n\t\tif(pk_map.size()>0) {\n\t\t\tRichPeakPtr selected_pk = pk_map.rbegin()->second;\n\t\t\tpk_set.insert(selected_pk);\n\t\t}\n\t\t\n\t\treturn pk_set;\n\t}\n\n\n\tvoid SimpleFinder::updateEnvelopParameter( EnvelopPtr env )\n\n\t{\n\t\t// 0. Check env id.\n\t\tunsigned int env_id = env->id;\n\t\tstd::cout << \"Check env #\" << env_id << std::endl;\n\n\t\t// 1. Get all the peaks.\n\t\tEnvEntry base_entry = env_ref.getEntryByShift(env, 0);\n\t\t\n\t\tstd::vector entry_vec = env_ref.getEntryByEnvelop(env);\n\n\t\tAggregatedIsotopicVariants higher_theo = this->estimateDistribution(base_entry.pk->mz, env->charge_state);\n\t\tAggregatedIsotopicVariants lower_theo = this->estimateDistribution(base_entry.pk->mz, env->charge_state, true);\n\n\t\tint max_sulfur = this->guessMaxSulfurNumber(base_entry.pk->mz, env->charge_state);\n\t\tint max_num = param.getParameter(\"max_sulfur_num\").first;\n\n\t\tconst Isotope s34 = pt.getIsotopeByNominalMass(\"S\", 34);\n\t \n\t\tstd::pair sulfur_range = std::make_pair(0,max_sulfur);\n\n\t\t// Gradually estimate the sulfur number. The default value is 0.\n\t\tBOOST_FOREACH(EnvEntry entry, entry_vec)\n\t\t{\n\t\t\t// For given shift, estimate the range of the intensities.\n\t\t\tint pk_pos = entry.getShift();\n\t\t\t\n\t\t\tif(pk_pos == 0) continue;\n\n\t\t\t// Expected abundance.\n\t\t\tdouble non_sulfur_model_abundance = base_entry.pk->intensity * higher_theo.getPeakByShift(pk_pos)->intensity;\n\t\t\tdouble sulfur_model_abundance = base_entry.pk->intensity * lower_theo.getPeakByShift(pk_pos)->intensity;\n\n\t\t\tdouble max_abundance = non_sulfur_model_abundance;\n\t\t\tdouble min_abundance = sulfur_model_abundance;\n\t\t\tif(min_abundance > max_abundance)\n\t\t\t\tswap(max_abundance, min_abundance);\n\n\t\t\t// Narrow down the range of sulfur_range.\n\t\t\t// For tandem ms analysis, only use A+1 and A+2 peak.\n\t\t\t// The information from A+1 peak might help increase the lower limit \n\t\t\tif(pk_pos == 1) { // odd peak, increase the lower limit of sulfur number.\n\t\t\t\t\n\t\t\t\tif(entry.info->status != \"Normal\") continue;\n\n\t\t\t\tif((entry.info->adjusted_abundance-max_abundance)/max_abundance >0.2 ) {\n\t\t\t\t\t// Significantly higher than upper boundary.\n\t\t\t\t\t// Nothing to do with the range.\n\t\t\t\t} else if((entry.info->adjusted_abundance-min_abundance)/min_abundance < -0.2) {\n\t\t\t\t\t// Significantly lower than expected.\n\t\t\t\t\t// env->suspicious = true;\n\t\t\t\t} else {\n\t\t\t\t\t// Normal situation. Estimate sulfur_num from the abundance.\n\t\t\t\t\tdouble lambda = (entry.info->adjusted_abundance-min_abundance)/(max_abundance - min_abundance);\n\n\t\t\t\t\t// Convert lambda to sulfur_num.\n\t\t\t\t\tif(sulfur_model_abundance == min_abundance)\n\t\t\t\t\t\tlambda = 1.0 - lambda;\n\n\t\t\t\t\tint sulfur_num = (int)floor(lambda * max_sulfur + 0.5);\n\n\t\t\t\t\t//entry.env->sulfur_num = sulfur_num > max_num ? max_num : sulfur_num;\n\t\t\t\t\tif(sulfur_num < sulfur_range.second)\n\t\t\t\t\t\tsulfur_range.second = sulfur_num;\n\t\t\t\t\t\n\t\t\t\t}\n\t\t\t} else if(pk_pos == 2) { // even peak, decrease the upper limit of sulfur number. Notice that one of the peak might be missing.\n\t\t\t\tif(entry.info->status == \"Sulfur\") {\n\t\t\t\t\t// TBD: decide if the sulfur peak and non-sulfur peak will separate.\n\t\t\t\t\t// If present, estimate sulfur using this information.\n\t\t\t\t\tint sulfur_num = (int)floor(entry.info->adjusted_abundance/base_entry.info->adjusted_abundance / pow(s34.abundance, pk_pos/2) + 0.5);\n\t\t\t\t\n\t\t\t\t\tif(sulfur_num > sulfur_range.first && sulfur_num < max_num)\n\t\t\t\t\t\tsulfur_range.first = sulfur_num;\n\t\t\t\t\t//entry.env->sulfur_num = sulfur_num > max_num ? max_num : sulfur_num;\n\t\t\t\t\t// The information from sulfur peak have enough confidence.\n\t\t\t\t\tbreak;\n\n\t\t\t\t} else if(entry.info->status == \"Normal\") {\n\t\t\t\t\t// Assume the peak and 34S peak are able to be resolved.\n\t\t\t\t\t// Define the intensity range for current peak.\n\n\n\t\t\t\t\t// Expected abundance of the non-sulfur-peak.\n\t\t\t\t\tdouble non_sulfur_peak_abundance = sulfur_model_abundance - base_entry.info->adjusted_abundance * pow(s34.abundance, pk_pos);\n\t\t\t\t\t// Do the same thing for A+1 peak.\n\t\t\t\t\tmax_abundance = non_sulfur_model_abundance;\n\t\t\t\t\tmin_abundance = non_sulfur_peak_abundance;\n\n\t\t\t\t\tif(max_abundance < min_abundance) \n\t\t\t\t\t\tswap(max_abundance, min_abundance);\n\t\t\t\t\t\n\t\t\t\t\tif((entry.info->adjusted_abundance - max_abundance)/max_abundance > 0.2) {\n\t\t\t\t\t\t// Significantly higher than expected.\n\t\t\t\t\t\t// Do nothing.\n\t\t\t\t\t} else if((entry.info->adjusted_abundance - min_abundance)/min_abundance < -0.2) {\n\t\t\t\t\t\t// Significantly lower than expected.\n\t\t\t\t\t\t// Do nothing.\n\t\t\t\t\t} else {\n\n\t\t\t\t\t\tdouble lambda = (entry.info->adjusted_abundance-min_abundance)/(max_abundance - min_abundance);\n\n\t\t\t\t\t\t// Sometimes, the distance between max_abundance and min_abundance is too small, which will cause a lot of troubles.\n\t\t\t\t\t\tif(lambda < 0.0) continue;\n\n\t\t\t\t\t\tif(non_sulfur_peak_abundance == min_abundance)\n\t\t\t\t\t\t\tlambda = 1.0 - lambda;\n\n\t\t\t\t\t\t// Convert lambda to sulfur_num.\n\t\t\t\t\t\tint sulfur_num = (int)floor(lambda * max_sulfur + 0.5);\n\t\t\t\t\t\tif(sulfur_num < sulfur_range.second)\n\t\t\t\t\t\t\tsulfur_range.second = sulfur_num;\n\t\t\t\t\t\t//entry.env->sulfur_num = sulfur_num > max_num ? max_num : sulfur_num;\n\t\t\t\t\t}\n\n\t\t\t\t} else {\n\t\t\t\t\t// C+H peak. Nothing to do with the sulfur number.\n\t\t\t\t}\n\t\t\t} \n\t\t}\n\t\t\n\t\t// Get the sulfur number estimation by averaing all possible \n\t\tenv->sulfur_num = (sulfur_range.first + sulfur_range.second)/2;\n\n\t\t// Unlikely situation.\n\t\tif(sulfur_range.first > sulfur_range.second) \n\t\t\tenv->env_status = FP;\n\n\t\t// Update the theoretical distribution for current envelop.\n\t\tenv->theo_dist = this->estimateDistribution(base_entry.pk->mz, env->charge_state, env->sulfur_num);\n\n\t\tstd::map pk_map = env->getTheoreticalPeaks();\n\n\t\tfor(std::map::iterator iter = pk_map.begin(); \n\t\t\titer != pk_map.end(); iter++)\n\t\t{\n\t\t\t// Theo peak information.\n\t\t\tint shift = iter->first; PeakPtr theo_pk = iter->second;\n\t\t\tdouble theo_abundance = base_entry.info->adjusted_abundance * theo_pk->intensity;\n\t\t\t\n\t\t\t// No need to estimate base peak.\n\t\t\tif(shift == 0) continue;\n\n\t\t\t// Be careful of the situation that the shift for the envelop cannot be observed.\n\t\t\tstd::vector exp_pks = env_ref.getPeaksByShift(env, shift);\n\n\t\t\t// Depending on the status of the peak, the theoretical information will be recalculated. It might be possible that some of the peaks are missing, it is better to calculate the mean of the shift for each position.\n\t\t\t// If the sulfur number is 0, we assume there is no peak split.\n\t\t\tif(env->sulfur_num == 0) {\n\t\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, exp_pks) {\n\t\t\t\t\tif(pk_entry.info->status == \"Normal\") {\n\t\t\t\t\t\tpk_entry.info->relative_shift = (pk_entry.info->adjusted_abundance - theo_abundance) / theo_abundance;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tif(shift == 1 || shift == 2) {\n\t\t\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, exp_pks) {\n\t\t\t\t\t\tif(shift == 1) {\n\t\t\t\t\t\t\tif(pk_entry.info->status == \"Normal\") {\n\t\t\t\t\t\t\t\tpk_entry.info->relative_shift = (pk_entry.info->adjusted_abundance - theo_abundance)/ theo_abundance;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} else if(shift == 2){\n\t\t\t\t\t\t\t// Here we assume the peak will split.\n\t\t\t\t\t\t\tif(pk_entry.info->status == \"Sulfur\") {\n\t\t\t\t\t\t\t\t// Calculate the theoretical abundance and update the relative shift.\n\t\t\t\t\t\t\t\ttheo_abundance = base_entry.info->adjusted_abundance * pow(s34.abundance, env->sulfur_num);\n\t\t\t\t\t\t\t\tpk_entry.info->relative_shift = (pk_entry.info->adjusted_abundance - theo_abundance) / theo_abundance;\n\t\t\t\t\t\t\t} else if(pk_entry.info->status == \"Normal\"){\n\t\t\t\t\t\t\t\t// Notice that theo_abundance should be larger than 0.\n\t\t\t\t\t\t\t\ttheo_abundance = base_entry.info->adjusted_abundance * (theo_pk->intensity - pow(s34.abundance, env->sulfur_num));\n\t\t\t\t\t\t\t\tpk_entry.info->relative_shift = (pk_entry.info->adjusted_abundance - theo_abundance) / theo_abundance;\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t// C+H peak. Nothing to do with the relative shift.\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t} \n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\t// A + n peak\n\t\t\t\t\tdouble total_shift_abundance = 0.0;\n\t\t\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, exp_pks) {\n\t\t\t\t\t\tif(pk_entry.info->status == \"Normal\" \n\t\t\t\t\t\t\t|| pk_entry.info->status == \"Sulfur\") {\n\t\t\t\t\t\t\t\ttotal_shift_abundance += pk_entry.info->adjusted_abundance; \n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, exp_pks) {\n\t\t\t\t\t\tpk_entry.info->relative_shift = (pk_entry.info->adjusted_abundance - theo_abundance)/ theo_abundance;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t}\n\n\n\t}\n\n\tbool SimpleFinder::isRedundantEnvelop( EnvelopPtr new_env, EnvelopPtr old_env, int old_shift)\n\t{\n\t\tbool state = false;\n\n std::set old_pks = env_ref.getPeaksByEnvelop(old_env);\n std::set new_pks = env_ref.getPeaksByEnvelop(new_env);\n\n if(std::includes(old_pks.begin(), old_pks.end(), new_pks.begin(), new_pks.end())) {\n // Calculate the sub fitting score of the subset from old_pks.\n if(new_env->fitting_score < old_env->fitting_score)\n state = true;\n\n } else {\n if(new_env->fitting_score < old_env->fitting_score && (old_env->charge_state % new_env->charge_state == 0))\n state = true;\n\n EnvEntry base_entry = env_ref.getEntryByShift(old_env, 0);\n // //EnvEntry new_base = env_ref.getEntryByShift(new_env, 0);\n EnvEntry current_entry = env_ref.getEntryByShift(old_env, old_shift);\n // // Check if it is different from theoretical abundance.\n AggregatedIsotopicVariants higher_theo = this->estimateDistribution(base_entry.pk->mz, old_env->charge_state);\n AggregatedIsotopicVariants lower_theo = this->estimateDistribution(base_entry.pk->mz, old_env->charge_state, true);\n\n double non_sulfur_abundance = higher_theo.getPeakByShift(old_shift)->intensity * base_entry.info->adjusted_abundance;\n double sulfur_abundance = lower_theo.getPeakByShift(old_shift)->intensity * base_entry.info->adjusted_abundance;\n\n double expected_abundance(0.0);\n if(current_entry.info->status == \"Normal\") {\n expected_abundance = std::max(non_sulfur_abundance,sulfur_abundance);\n } else {\n if(current_entry.info->status == \"Sulfur\")\n state = true;\n\n return state;\n }\n\n double upper_shift = (current_entry.pk->intensity - expected_abundance)/(expected_abundance);\n\n double max_intensity_shift = param.getParameter(\"max_intensity_shift\").first;\n \n if(upper_shift < max_intensity_shift)\n \tstate = true;\n }\n\n\t\treturn state;\n\t}\n\n\tvoid SimpleFinder::printEnvelop( EnvelopPtr env )\n\t{\n\t\tstd::cout << \"Envelop ID: \" << env->id << std::endl;\n\t\tstd::cout << \"Charge: \" << env->charge_state << std::endl;\n std::cout << \"Fitting score: \" << env->fitting_score << std::endl;\n\t\tstd::cout << std::endl;\n\t\t// Peak information.\n\t\tstd::cout << \"Covered peaks:\" << std::endl;\n\t\tstd::cout << \"Shift\\tMZ\\tEXP_ABD\\tSTATUS\\tSTATUS2\" << std::endl;\n \n\t\t// Get all peaks.\n\t\tstd::vector pk_entries = env_ref.getEntryByEnvelop(env);\n\t\tstd::cout.precision(5);\n\t\tBOOST_FOREACH(EnvEntry& pk_entry, pk_entries)\n\t\t{\n\t\t\tstd::cout << pk_entry.getShift() << \"\\t\" ;\n\t\t\tstd::cout << std::fixed << pk_entry.pk->mz << \"\\t\";\n\t\t\tstd::cout\t<< pk_entry.pk->intensity << \"\\t\" << pk_entry.info->status << std::endl;\n\t\t}\n\t\tstd::cout << std::endl;\n\t}\n\n\tstd::multimap SimpleFinder::getEnvelops(EntryStatus status)\n\t{\n\t\tstd::vector entry_vec = env_ref.getBaseEntriesByStatus();\n\t\t\n\t\t//EnvDictByEnvID& env_by_eid = env_\n\t\tstd::multimap env_map;\n\t\tBOOST_FOREACH(EnvEntry& env_entry, entry_vec)\n\t\t{\n\t\t\t// Get the base peak for the envelop.\n\t\t\tRichPeakPtr base_pk = env_entry.pk;\n\t\t\tenv_map.insert(std::make_pair(base_pk, env_entry.env));\n\t\t}\n\t\treturn env_map;\n\t}\n\n\tdouble SimpleFinder::getWindowError( RichPeakPtr pk, double error )\n\t{\n\t\t// If error is specified.\n\t\tif(error != -1.0) return error * pk->mz;\n\n\t\tdouble internal_accuracy = param.getParameter(\"internal_accuracy\").first;\n\t\t\n\t\t//double resolving_power = pk->resolution;\n\t\t//double coef = pk->mz / resolving_power;\n\t\tdouble coef = (pk->signal_noise > 15 && pk->mz > 400.0) ? 1.2 : 1.5;\n //double coef = 1.5;\n\t\treturn 2e-6 * internal_accuracy * pk->mz * coef;\n\t}\n\n\tbool SimpleFinder::isNoiseEnvelop( EnvelopPtr env )\n\t{\t\n\t\tunsigned int env_id = env->id;\n\t\t// Identification of noise envelop.\n\t\t// Situation 1. Weired cluster shape. envelop size has to be larger than 3. This is not noise envelop.\n\t\tstd::set shift_set = env_ref.getShiftSet(env);\n\t\tRichPeakPtr base_pk = env_ref.getBasePeakForEnvelop(env);\n\n\t\tif(shift_set.size() > 2) {\n\t\t\tBOOST_FOREACH(int shift, shift_set)\n\t\t\t{\n\t\t\t\tstd::vector pk_entry_vec = env_ref.getPeaksByShift(env, shift);\n\t\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, pk_entry_vec)\n\t\t\t\t{\n\t\t\t\t\tif(pk_entry.info->relative_shift < -0.3) {\n\t\t\t\t\t\tenv->env_status = MISC;\n\t\t\t\t\t\t// Misc envelop is not noise envelop.\n\t\t\t\t\t\treturn false;\n\t\t\t\t\t}\n\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// Situation 2. Matching to noise peak.\n\t\t// 2.a size == 2\n\t\tif(shift_set.size() == 2) {\n\t\t\t// Get A+1 peak.\n\t\t\tstd::vector pk_entry_vec = env_ref.getPeaksByShift(env, 1);\n\t\t\tif(pk_entry_vec.size() == 1 && pk_entry_vec[0].info->status == \"C+H\") {\n\t\t\t\t// Get theoretical abundance.\n\t\t\t\tdouble theo_abundance = env_ref.getTheoreticalPeak(env, 1)->intensity;\n\t\t\t\tif(theo_abundance - pk_entry_vec[0].info->adjusted_abundance > 0)\n\t\t\t\t\treturn true;\n\t\t\t}\n\n\t\t\tRichPeakPtr base_pk = env_ref.getBasePeakForEnvelop(env);\n\t\t\tstd::vector old_env_entries = env_ref.getOccurredEnvelopEntries(base_pk, OLD);\n\t\t\t// 2.b A + 1 peak is significantly lower than expected.\n\t\t\tif(pk_entry_vec[0].info->relative_shift < -0.2 || old_env_entries.size() > 0)\t\t{\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\n\t\treturn false;\n\n\t}\n\n\tvoid SimpleFinder::cleanNeighborNoise( RichList& pk_list, RichPeakPtr pk/*, std::set& blacklist*/ )\n\t{\n\t\tRichPeakListByMZ& pks_mz = pk_list.getPeakListByType();\n\n\t\tRichPeakListByMZ::iterator mz_increase_iter, mz_decrease_iter;\n\t\tmz_increase_iter = pks_mz.lower_bound(pk->mz);\n\t\tmz_decrease_iter = mz_increase_iter;\n\n\t\tmz_increase_iter++;\n\n\t\tdouble win_error = 0.012;\n\n\t\twhile(1) {\n\t\t\tif(mz_increase_iter == pks_mz.end())\n\t\t\t\tbreak;\n\n\t\t\tRichPeakPtr neighbor_pk = *mz_increase_iter;\n\n\t\t\tif(abs(neighbor_pk->mz - pk->mz) < win_error && (neighbor_pk->intensity / pk->intensity < 0.1)) {\n\t\t\t\t//blacklist.insert(neighbor_pk);\n\t\t\t\t//neighbor_pk->pk_status = false;\n\t\t\t\tthis->setNoisePeak(neighbor_pk);\n\t\t\t} else if(abs(neighbor_pk->mz - pk->mz) >= win_error)\n\t\t\t\tbreak;\n\n\t\t\tmz_increase_iter++;\n\t\t\tif(mz_increase_iter == pks_mz.end())\n\t\t\t\tbreak;\n\t\t}\n\n\t\t// This is the begin of the peak list.\n\t\tif(mz_decrease_iter == pks_mz.begin())\n\t\t\treturn;\n\n\t\t\tmz_decrease_iter--;\n\t\twhile(1) {\n\t\t\tRichPeakPtr neighbor_pk = *mz_decrease_iter;\n\n\t\t\tif(abs(neighbor_pk->mz - pk->mz) < win_error && (neighbor_pk->intensity / pk->intensity < 0.1)) {\n\t\t\t\t\n\t\t\t\t//neighbor_pk->pk_status = false;\n\t\t\t\tthis->setNoisePeak(neighbor_pk);\n\t\t\t} else if(abs(neighbor_pk->mz - pk->mz) >= win_error)\n\t\t\t\tbreak;\n\n\t\t\tif(mz_decrease_iter == pks_mz.begin())\n\t\t\t\tbreak;\n\t\t\telse\n\t\t\t\tmz_decrease_iter--;\n\t\t}\n\t}\n\n\tdouble SimpleFinder::updateEnvelopTree( std::vector& old_entries, std::vector& added_entries, EnvEntry& test_entry, RichPeakPtr pk )\n\t{\n\t\tdouble rest_intensity = 0.0;\n\t\tBOOST_FOREACH(EnvEntry& entry, old_entries)\n\t\t\trest_intensity += entry.info->adjusted_abundance;\n\n\t\trest_intensity = test_entry.pk->intensity - rest_intensity;\n\n\t\t// Construct the vector using theoretical isotopic distributions. Scale the second one and get the best fit.\n\t\tdouble scaling_factor = 0.0; \n\t\tdouble scaling_range[2] = {0.0, 1.0};\n\n\t\tdouble total_score = this->calculateLinearAssociationScore(added_entries);\n\t\t\n\t\tstd::vector temp_entries = *(&added_entries);\n\t\ttemp_entries.push_back(test_entry);\n\n\t\twhile(1)\n\t\t{\n\t\t\t// Based on scaling factor, update the peak abundance.\n\t\t\tstd::vector pk_entry_vec = env_ref.getEntryByEnvelop(test_entry.env);\n\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, pk_entry_vec)\n\t\t\t{\n\t\t\t\tpk_entry.info->adjusted_abundance *= scaling_factor;\n\t\t\t}\n\n\t\t\tBOOST_FOREACH(EnvEntry& env_entry, added_entries)\n\t\t\t{\n\t\t\t\tstd::vector pk_entry_vec2 = env_ref.getEntryByEnvelop(env_entry.env);\n\t\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, pk_entry_vec2)\n\t\t\t\t{\n\t\t\t\t\tpk_entry.info->adjusted_abundance *= (1-scaling_factor);\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Update scaling factor. It depends on the peaks associated with the second test_entry.\n\t\t\tdouble new_score = this->calculateLinearAssociationScore(temp_entries);\n\n\t\t\tdouble ratio0 = this->calculateScalingRatio(temp_entries);\n\t\t\tdouble ratio1 = this->calculateScalingRatio(test_entry);\n\t\t\t\n\t\t\tint scale_index = ratio0 > ratio1 ? 1 : 0;\n\n\t\t\t// Keep a copy of the current scaling factor.\n\t\t\tdouble old_scaling_factor = scaling_factor;\n\n\t\t\t// Update the scaling factor of the new envelop.\n\t\t\tscaling_factor = (scaling_range[scale_index] + scaling_factor)/2.0;\n\t\t\t\n\t\t\t// Update the boundary of the scale range.\n\t\t\tscaling_range[1-scale_index] = old_scaling_factor;\n\n\t\t\tif(abs(new_score - total_score) < 1e-6) \n\t\t\t\tbreak;\n\t\t\telse \n\t\t\t\ttotal_score = new_score;\n\t\t}\n\n\t\tdouble total_fitting_score = 0.0;\n\t\tBOOST_FOREACH(EnvEntry& env_entry, temp_entries) {\n\t\t\t// Reset the scaling factor to 1.\n\t\t\tenv_entry.env->scaling_factor = 1.0;\n\t\t\ttotal_fitting_score += this->calculateFittingScore(env_entry.env);\n\t\t}\n\t\treturn total_fitting_score;\n\t\t\n\t}\n\n\tdouble SimpleFinder::calculateFittingScore( EnvelopPtr env )\n\t{\t\n\t\t// Get base entry.\n\t\tdouble fitting_score = 0.0;\n\n\t\tstd::set shift_set = env_ref.getShiftSet(env);\n\n\t\t// If sulfur peak is present, re-estimate the abundance.\n\n\t\tBOOST_FOREACH(int shift, shift_set)\n\t\t{\n\t\t\tdouble expected_abundance = env_ref.getTheoreticalPeak(env, shift)->intensity;\n\t\t\tdouble experimental_abundance = env_ref.getExperimentalAbundanceByShift(env, shift);\n\n\t\t\t// The fitting score comes from 3 parts: \n\t\t\t//1. the shift of intensity: abs(E-T)/T.\n\t\t\tdouble intensity_shift = (expected_abundance - experimental_abundance)/ expected_abundance;\n\t\t\tif(intensity_shift > 0.0 && intensity_shift <= 1.0)\n\t\t\t\tintensity_shift = 1.0 - intensity_shift;\n\t\t\telse if(intensity_shift <= 0.0 && intensity_shift >= -1.0)\n\t\t\t\tintensity_shift = sqrt(1.0 + intensity_shift);\n\t\t\telse if(intensity_shift > 1)\n\t\t\t\tintensity_shift = 1.0;\n\t\t\telse\n\t\t\t\tintensity_shift = 0.0;\n\n\t\t\t// 2. the shift of m/z. Since this is for high resolution data, the shift of m/z should be ignored.\n\n\t\t\t// 3. the square root of expected_abundance.\n\t\t\tdouble weight = sqrt(expected_abundance);\n\n\t\t\tfitting_score += weight * intensity_shift;\n\t\t}\n\n\t\tenv->fitting_score = fitting_score;\n\t\treturn fitting_score;\n\n\t}\n\n\tdouble SimpleFinder::calculateFittingScore(std::set& env_set)\n\t{\n\t\tdouble total_fitting_score = 0.0;\n\t\tBOOST_FOREACH(EnvelopPtr env, env_set)\n\t\t\ttotal_fitting_score += this->calculateFittingScore(env);\n\t\treturn total_fitting_score;\n\t}\n\n\tdouble SimpleFinder::calculateLinearAssociationScore( std::vector& entries )\n\t{\n\t\t// Calculate the value of cos theta.\n\t\tdouble inner_prod = 0.0; double norm_term[2] = {0.0,0.0};\n\t\tBOOST_FOREACH(EnvEntry& env_entry, entries)\n\t\t{\n\t\t\tstd::set shift_set = env_ref.getShiftSet(env_entry.env);\n\n\t\t\tBOOST_FOREACH(int shift, shift_set)\n\t\t\t{\n\t\t\t\t// Calculate the accumulated abundance for multiple peaks mapped into the same shift position.\n\t\t\t\tstd::vector pk_entry_vec = env_ref.getPeaksByShift(env_entry.env, shift);\n\t\t\t\tdouble accumulated_abundance = 0.0;\n\t\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, pk_entry_vec)\n\t\t\t\t\taccumulated_abundance += pk_entry.info->adjusted_abundance;\n\n\t\t\t\tdouble theo_abundance = env_entry.env->getTheoreticalPeak(shift)->intensity;\n\t\t\t\tinner_prod += theo_abundance * accumulated_abundance;\n\n\t\t\t\tnorm_term[0] += pow(theo_abundance, 2);\n\t\t\t\tnorm_term[1] += pow(accumulated_abundance, 2);\n\n\t\t\t}\n\t\t}\n\n\t\treturn inner_prod / (sqrt(norm_term[0]) * sqrt(norm_term[1]));\n\n\t}\n\n\tdouble SimpleFinder::calculateScalingRatio( std::vector& entries )\n\t{\n\t\tdouble adjusted_total = 0.0; double theo_total = 0.0;\n\n\t\tBOOST_FOREACH(EnvEntry& env_entry, entries)\n\t\t{\n\t\t\tstd::set shift_set = env_ref.getShiftSet(env_entry.env);\n\t\t\tBOOST_FOREACH(int shift, shift_set) {\n\t\t\t\t// Get the acutal peaks for given position. There might be more than 1 which can be mapped into one peak.\n\t\t\t\tstd::vector pk_entry_vec = env_ref.getPeaksByShift(env_entry.env, shift);\n\n\t\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, pk_entry_vec)\n\t\t\t\t\tadjusted_total += pk_entry.info->adjusted_abundance;\n\n\t\t\t\t// There will be only one theoretical peak for each shift position.\n\t\t\t\ttheo_total += env_entry.env->getTheoreticalPeak(shift)->intensity;\n\t\t\t}\n\t\t}\n\n\t\treturn adjusted_total / theo_total;\n\t}\n\n\tdouble SimpleFinder::calculateScalingRatio( EnvEntry& env_entry )\n\t{\n\t\t// Compare the total intensity of the suggested envleop over the theoretical intensity.\n\t\tdouble adjusted_total = 0.0; double theo_total = 0.0;\n\t\t\n\t\tstd::set shift_set = env_ref.getShiftSet(env_entry.env);\n\t\tBOOST_FOREACH(int shift, shift_set) {\n\t\t\t// Get the acutal peaks for given position. There might be more \n\t\t\t// than 1 which can be mapped into one peak.\n\t\t\tstd::vector pk_entry_vec = env_ref.getPeaksByShift(env_entry.env, shift);\n\n\t\t\tBOOST_FOREACH(EnvEntry& pk_entry, pk_entry_vec)\n\t\t\t\tadjusted_total += pk_entry.info->adjusted_abundance;\n\t\t\t\n\t\t\t// There will be only one theoretical peak for each shift position.\n\t\t\ttheo_total += env_entry.env->getTheoreticalPeak(shift)->intensity;\n\t\t}\n\n\t\treturn adjusted_total/theo_total;\n\n\t}\n\n\tint SimpleFinder::guessMaxSulfurNumber( double mz, int charge_state )\n\t{\n\t\tdouble mass = calculateMass(mz, charge_state);\n\t\tdouble coef = mass / 100.0;\n\n\t\tAveragineFormulae::iterator sulfur_iter = max_sulfur_ave.find(\"S\");\n\t\treturn (int)ceil(sulfur_iter->second * coef);\n\t}\n\n\tvoid SimpleFinder::acceptEnvelop( EnvelopPtr env )\n\t{\n\t\t// 1. Set the status of the envelop as true positive.\n\t\tenv->env_status = TP;\n\t\t// 2. Set all the peaks associated to the envelop as OLD and the peak type as ENV.\n\t\tstd::vector pk_entry_vec = env_ref.getEntryByEnvelop(env);\n\t\t\n\t\t\n\t\tBOOST_FOREACH(EnvEntry& pk_entry, pk_entry_vec) {\n\t\t\tpk_entry.info->entry_status = OLD;\n\t\t\tsetPeakType(pk_entry.pk, \"ENV\");\n\t\t\t//pk_entry.pk->pk_type = \"ENV\";\n\t\t}\n\t\t\n\t}\n\n\tvoid SimpleFinder::setNoisePeak( RichPeakPtr pk )\n\t{\n\t\tpk->pk_status = false;\n\t\tsetPeakType(pk, \"NOISE\");\n\t}\n\n\tvoid SimpleFinder::optimizeSuspiciousPeaks()\n\t{\n\t\t// 1. For each suspicious peak, iterate over all charge state, try to decide if there is any peak that can follow into the range.\n\t\tRichPeakListBySignalOverNoise& pks_sn = spectrum.getPeakListByType();\n\n\t\t// The S/N threshold for monoisotopic peawk.\n\t\tdouble mono_sn_threshold = param.getParameter(\"signal_noise\").first;\n\n\t\tRichPeakListBySignalOverNoise::iterator end_sn = pks_sn.upper_bound(mono_sn_threshold);\t\t\n\t\tRichPeakListBySignalOverNoise::iterator iter = pks_sn.begin();\n\t\t//RichPeakListBySignalOverNoise::iterator iter;\n\n\n\t\t/*RichPeakListByType& pks_type = spectrum.getPeakListByType();\n\n\t\tstd::pair p = pks_type.equal_range(\"ISO\");*/\n\n\t\tint precursor_z = param.getParameter(\"precursor_charge\").first;\n\t\tint charge_sign = precursor_z > 0 ? 1 : -1;\n\n\t\t//for(RichPeakListByType::iterator iter = p.first; iter != p.second; iter++)\n\t\tfor(; iter != end_sn; iter++ )\n\t\t{\n\t\t\tRichPeakPtr base_pk = *iter;\n\t\t\tif(base_pk->pk_type != \"ISO\")\n\t\t\t{\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tint count = 0;\n\t\t\tfor(int z = 1; z < abs(precursor_z); z++)\n\t\t\t{\n\t\t\t\t// Consider only A+1 peak.\n\t\t\t\tstd::pair pk_pair = getPeakRangeByShift(base_pk, charge_sign*z, 1, -1);\n\t\t\t\tdouble mz0 = pk_pair.second->mz;\n\t\t\t\tstd::set pk_set = getClosestRichPeaks(spectrum, mz0, true, 0.0);\n\n\t\t\t\tif(pk_set.size() == 0)\n\t\t\t\t\tcontinue;\n\n\t\t\t\t// Estimate the intensity shift.\n\t\t\t\tRichPeakPtr pk0 = *(pk_set.begin());\n\n\t\t\t\tdouble intensity_shift = (pk0->intensity - pk_pair.first->intensity) / pk_pair.first->intensity;\n\n\t\t\t\tif(intensity_shift > -0.2) {\n\t\t\t\t\tcount++;\n\t\t\t\t\t// A candidate envelop.\n\t\t\t\t\tund_pk.insert(std::make_pair(base_pk, z));\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t\tif(count == 0)\n\t\t\t\tsetPeakType(base_pk, \"NOISE\");\n\n\t\t}\n\t}\n\n\tstd::pair SimpleFinder::getPeakRangeByShift( RichPeakPtr base_pk, int charge, int shift, int sulfur /*= -1*/ )\n\t{\n\t\tdouble pre_mass = calculateMass(base_pk->mz, charge);\n\n\t\tAggregatedIsotopicVariants iso_no_sulfur = this->estimateDistribution(base_pk->mz, charge, 0);\n\t\tint max_sulfur = (sulfur == -1 ? guessMaxSulfurNumber(base_pk->mz, charge) : sulfur);\n\t\tAggregatedIsotopicVariants iso_max_sulfur = this->estimateDistribution(base_pk->mz, charge, max_sulfur);\n\n\t\tdouble mz_max_sulfur = base_pk->mz + iso_max_sulfur.getMassDifferenceByShift(0, shift)/(float)abs(charge);\n\t\tdouble mz_no_sulfur = base_pk->mz + iso_no_sulfur.getMassDifferenceByShift(0, shift)/(float)abs(charge);\n\n\t\tdouble int_max_sulfur = base_pk->intensity * iso_max_sulfur.getPeakByShift(shift)->intensity;\n\t\tdouble int_no_sulfur = base_pk->intensity * iso_no_sulfur.getPeakByShift(shift)->intensity;\n\n\t\tPeakPtr pk_max_sulfur = boost::make_shared(mz_max_sulfur, int_max_sulfur);\n\t\tPeakPtr pk_no_sulfur = boost::make_shared(mz_no_sulfur, int_no_sulfur);\n\n\t\treturn std::make_pair(pk_max_sulfur, pk_no_sulfur);\n\n\t}\n\n\tvoid SimpleFinder::setPeakType( RichPeakPtr pk, const std::string& new_type )\n\t{\n\t\t// 1. Find the iterator of the peak.\n\t\tRichPeakListByID& pks_id = spectrum.getPeakListByType();\n\t\tRichPeakListByID::iterator id_iter = pks_id.find(pk->id);\n\n\t\t// 2. Project the iterator to type.\n\t\tRichPeakListByType::iterator type_iter = spectrum.getPeakContainer().project(id_iter);\n\n\t\t// 2. Modify the type of the element referred by the iterator.\n\t\tspectrum.modifyType(type_iter, new_type);\n\t}\n\n\tvoid SimpleFinder::detectHarmonicCluster(RichPeakPtr base_pk, int charge)\n\t{\n\t\t//std::set harmonic_cluster;\n\n\t\t// The S/N threshold for monoisotopic peawk.\n\t\tdouble mono_sn_threshold = param.getParameter(\"signal_noise\").first;\n\n\t\t/*double cur_mz = param.getParameter(\"precursor_mz\").first;\n\t\tint cur_charge = param.getParameter(\"precursor_charge\").first;*/\n\n\t\tdouble cur_mass = calculateMass(base_pk->mz, charge);\n\n\t\tint coef = 2;\n\t\t// Get the minimum m/z.\n\t\tRichPeakListByMZ& pks_mz = spectrum.getPeakListByType();\n\t\tdouble min_mz = (*(pks_mz.begin()))->mz;\n\n\t\twhile(1)\n\t\t{\n\t\t\t// Get new m/z based on adjusted charge state.\n\t\t\t\n\t\t\tint new_charge = coef * charge;\n\t\t\tdouble new_mz = calculateMZ(cur_mass, new_charge, charge);\n\n\t\t\tcoef++;\n\n\t\t\tif(new_mz < min_mz)\n\t\t\t\tbreak;\n\n\t\t\t// Find the base peak.\n\t\t\tstd::set pk_set = getClosestRichPeaks(spectrum, new_mz, false, mono_sn_threshold, 3e-5);\n\n\t\t\tif(pk_set.size() == 0)\n\t\t\t\tcontinue;\n\t\t\t\n\t\t\tRichPeakPtr base_pk = *(pk_set.begin());\n\t\t\tthis->cleanNeighborNoise(spectrum, base_pk);\n\n\t\t\tif(base_pk->signal_noise < mono_sn_threshold)\n\t\t\t\tcontinue;\n\n\t\t\tbool status = extendEnvelop(spectrum, base_pk, abs(new_charge), false);\n\n\t\t\tif(status) {\n\t\t\t\t//harmonic_cluster.insert(base_pk);\n\t\t\t\tstd::cout << \"Find harmonic cluster: \" << base_pk->mz << \" \" << new_charge << std::endl;\n\t\t\t}\n\t\t}\n\t\t\n\t\t//return harmonic_cluster;\n\t}\n\n}", "meta": {"hexsha": "1a999b4e1a431189541f2528525349947f275f30", "size": 45390, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "GAG/src/GAGPL/SPECTRUM/SimpleFinder.cpp", "max_stars_repo_name": "hh1985/multi_hs_seq", "max_stars_repo_head_hexsha": "9cf4e70fb59283da30339499952c43a0684f7e77", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2015-04-03T14:44:45.000Z", "max_stars_repo_stars_event_max_datetime": "2015-04-15T13:38:39.000Z", "max_issues_repo_path": "GAG/src/GAGPL/SPECTRUM/SimpleFinder.cpp", "max_issues_repo_name": "hh1985/multi_hs_seq", "max_issues_repo_head_hexsha": "9cf4e70fb59283da30339499952c43a0684f7e77", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "GAG/src/GAGPL/SPECTRUM/SimpleFinder.cpp", "max_forks_repo_name": "hh1985/multi_hs_seq", "max_forks_repo_head_hexsha": "9cf4e70fb59283da30339499952c43a0684f7e77", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.8617511521, "max_line_length": 231, "alphanum_fraction": 0.6893148271, "num_tokens": 13005, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.24275779355327787}} {"text": "#ifndef SPLX_PIECEWISECURVE_QP_GENERATOR_HPP\n#define SPLX_PIECEWISECURVE_QP_GENERATOR_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace splx {\n\ntemplate\nclass PiecewiseCurveQPGenerator {\npublic:\n using _BezierQPOperations = BezierQPOperations;\n using _Problem = QPWrappers::Problem;\n using Row = splx::Row;\n using VectorDIM = splx::VectorDIM;\n using Vector = splx::Vector;\n using Hyperplane = splx::Hyperplane;\n using AlignedBox = splx::AlignedBox;\n using Index = splx::Index;\n using Matrix = splx::Matrix;\n using _QPOperations = QPOperations;\n using _PiecewiseCurve = PiecewiseCurve;\n using StdVectorVectorDIM\n = std::vector>;\n using Constraint = splx::Constraint;\n\n PiecewiseCurveQPGenerator() : m_problem(0) {}\n\n void addPiece(std::shared_ptr<_QPOperations> opt_ptr) {\n m_operations.push_back(opt_ptr);\n \n if(m_cumulativeMaxParameters.empty()) {\n m_cumulativeMaxParameters.push_back(opt_ptr->maxParameter());\n } else {\n m_cumulativeMaxParameters.push_back(\n m_cumulativeMaxParameters.back() + opt_ptr->maxParameter()\n );\n }\n\n if(m_cumulativeDecisionVars.empty()) {\n m_cumulativeDecisionVars.push_back(opt_ptr->numDecisionVariables());\n } else {\n m_cumulativeDecisionVars.push_back(\n m_cumulativeDecisionVars.back() \n + opt_ptr->numDecisionVariables()\n );\n }\n\n m_problem = QPWrappers::Problem(this->numDecisionVariables());\n }\n\n void setPiece(std::size_t idx, std::shared_ptr<_QPOperations> opt_ptr) {\n m_operations[idx] = opt_ptr;\n this->fixCumulativeStructures(idx);\n m_problem = QPWrappers::Problem(this->numDecisionVariables());\n }\n\n void removePiece(std::size_t idx) {\n m_operations.erase(m_operations.begin() + idx);\n m_cumulativeDecisionVars.erase(\n m_cumulativeDecisionVars.begin() + idx\n );\n m_cumulativeMaxParameters.erase(\n m_cumulativeMaxParameters.begin() + idx\n );\n this->fixCumulativeStructures(idx);\n m_problem = QPWrappers::Problem(this->numDecisionVariables());\n }\n\n void removeAllPieces() {\n m_operations.clear();\n m_cumulativeMaxParameters.clear();\n m_cumulativeDecisionVars.clear();\n m_problem = QPWrappers::Problem(0);\n }\n\n void addBezier(Index ncpts, T a) {\n auto bezptr = std::make_shared<_BezierQPOperations>(ncpts, a);\n auto optptr = std::static_pointer_cast<_QPOperations>(bezptr);\n this->addPiece(optptr);\n }\n\n void setBezier(std::size_t idx, Index ncpts, T a) {\n auto bezptr = std::make_shared<_BezierQPOperations>(ncpts, a);\n auto optptr = std::static_pointer_cast<_QPOperations>(bezptr);\n this->setPiece(idx, optptr);\n }\n\n std::size_t numPieces() const {\n return m_cumulativeDecisionVars.size();\n }\n\n std::vector pieceMaxParameters() const {\n std::vector piece_max_params;\n for(const auto piece_opt : m_operations) {\n piece_max_params.push_back(piece_opt->maxParameter());\n }\n return piece_max_params;\n }\n\n T maxParameter() const {\n if(m_operations.empty()) {\n throw std::domain_error(\n absl::StrCat(\n \"piecewise curve has no pieces\"\n )\n );\n }\n\n return m_cumulativeMaxParameters.back();\n }\n\n void setPieceMaxParameters(const std::vector& new_max_params) {\n if(new_max_params.size() != m_operations.size()) {\n throw std::domain_error(\n absl::StrCat(\n \"new max parameters size does not match number of pieces\",\n \", number of pieces: \",\n m_operations.size(),\n \", given number of max parameters: \",\n new_max_params.size()\n )\n );\n }\n\n for(std::size_t i = 0; i < m_operations.size(); i++) {\n m_operations[i]->maxParameter(new_max_params[i]);\n }\n\n this->fixCumulativeStructures(0);\n m_problem = QPWrappers::Problem(this->numDecisionVariables());\n }\n\n Index numDecisionVariables() const {\n if(m_cumulativeDecisionVars.empty()) return 0;\n\n return m_cumulativeDecisionVars.back();\n }\n \n void addIntegratedSquaredDerivativeCost(unsigned int k, T lambda) {\n Matrix Q(this->numDecisionVariables(), this->numDecisionVariables());\n Vector c(this->numDecisionVariables());\n Q.setZero();\n c.setZero();\n\n for(std::size_t i = 0; i < this->numPieces(); i++) {\n Index dvar_start_idx = (i == 0 ? 0 : m_cumulativeDecisionVars[i-1]);\n Index dvar_count = m_operations[i]->numDecisionVariables();\n\n auto [Qs, cs]\n = m_operations[i]->integratedSquaredDerivativeCost(k, lambda);\n \n Q.block(dvar_start_idx, dvar_start_idx, dvar_count, dvar_count) = Qs;\n c.block(dvar_start_idx, 0, dvar_count, 1) = cs;\n }\n\n m_problem.add_Q(Q);\n m_problem.add_c(c);\n }\n\n void addEvalCost(T u, unsigned int k, const VectorDIM& target, T lambda) {\n Matrix Q(this->numDecisionVariables(), this->numDecisionVariables());\n Vector c(this->numDecisionVariables());\n Q.setZero();\n c.setZero();\n\n auto [idx, param, first_dvar_index, dvar_count] = pieceInfo(u);\n auto [Qs, cs] = m_operations[idx]->evalCost(param, k, target, lambda);\n Q.block(first_dvar_index, first_dvar_index, \n dvar_count, dvar_count) = Qs;\n c.block(first_dvar_index, 0, dvar_count, 1) = cs;\n\n m_problem.add_Q(Q);\n m_problem.add_c(c);\n }\n\n void addEvalConstraint(T u, unsigned int k, const VectorDIM& target,\n bool soft_convertible = false,\n T soft_convertible_weight = T(1)) {\n auto [idx, param, first_dvar_index, dvar_count] = pieceInfo(u);\n auto constraints = m_operations[idx]->evalConstraint(param, k, target,\n soft_convertible, soft_convertible_weight);\n\n this->addConstraints(constraints, first_dvar_index, dvar_count);\n }\n\n void addHyperplaneConstraintForPiece(std::size_t idx, const Hyperplane& hp,\n bool soft_convertible = false,\n T soft_convertible_weight = T(1)) {\n Index first_dvar_index = \n (idx == 0 ? 0 : m_cumulativeDecisionVars[idx-1]);\n Index dvar_count = m_operations[idx]->numDecisionVariables();\n\n auto constraints = m_operations[idx]->hyperplaneConstraintAll(hp,\n soft_convertible, soft_convertible_weight);\n\n this->addConstraints(constraints, first_dvar_index, dvar_count);\n }\n\n void addHyperplaneConstraintAll(const Hyperplane& hp,\n bool soft_convertible = false,\n T soft_convertible_weight = T(1)) {\n for(std::size_t i = 0; i < this->numPieces(); i++) {\n this->addHyperplaneConstraintForPiece(i, hp,\n soft_convertible, soft_convertible_weight);\n }\n }\n\n void addHyperplaneConstraintAt(T u, const Hyperplane& hp,\n bool soft_convertible = false,\n T soft_convertible_weight = T(1)) {\n auto [idx, param, first_dvar_index, dvar_count] = pieceInfo(u);\n auto constraints = m_operations[idx]->hyperplaneConstraintAt(param, hp,\n soft_convertible, soft_convertible_weight);\n this->addConstraints(constraints, first_dvar_index, dvar_count);\n }\n\n void addBoundingBoxConstraint(const AlignedBox& bbox) {\n for(std::size_t i = 0; i < this->numPieces(); i++) {\n auto [lbx, ubx] = m_operations[i]->boundingBoxConstraint(bbox);\n Index first_dvar_index = \n (i == 0 ? 0 : m_cumulativeDecisionVars[i-1]);\n Index dvar_count = m_operations[i]->numDecisionVariables();\n\n assert(lbx.rows() == dvar_count);\n\n for(Index j = 0; j < dvar_count; j++) {\n m_problem.set_var_limits(first_dvar_index + j, lbx(j), ubx(j));\n }\n }\n }\n\n // adds continueity constraint between piece idx and piece idx + 1\n // in their kth derivatives\n void addContinuityConstraint(std::size_t idx, unsigned int k,\n bool soft_convertible = false,\n T soft_convertible_weight = T(1)) {\n Index first_piece_numdvars = m_operations[idx]->numDecisionVariables();\n Index second_piece_numdvars = \n m_operations[idx+1]->numDecisionVariables();\n Index first_piece_dvars_start \n = (idx == 0 ? 0 : m_cumulativeDecisionVars[idx-1]);\n Index second_piece_dvars_start = m_cumulativeDecisionVars[idx];\n\n for(unsigned int d = 0; d < DIM; d++) {\n Row coeff1 = m_operations[idx]->evalBasisRow(\n d, m_operations[idx]->maxParameter(), k\n );\n Row coeff2 = m_operations[idx+1]->evalBasisRow(\n d, 0, k\n );\n\n Row coeff(this->numDecisionVariables());\n coeff.setZero();\n coeff.block(0, first_piece_dvars_start, 1, first_piece_numdvars) \n = coeff1;\n coeff.block(0, second_piece_dvars_start, 1, second_piece_numdvars)\n = -coeff2;\n\n m_problem.add_constraint(coeff, 0, 0, soft_convertible, soft_convertible_weight);\n }\n }\n\n Vector getDVarsForSegments(const StdVectorVectorDIM& segments) const {\n if(segments.size() != this->numPieces() + 1) {\n throw std::domain_error\n (\n absl::StrCat\n (\n \"number of segments is not equal to the number of pieces\",\n \", segment count: \",\n segments.size() - 1,\n \", piece count: \",\n this->numPieces()\n )\n );\n }\n\n Vector res(this->numDecisionVariables());\n\n for(std::size_t i = 0; i < segments.size() - 1; i++) {\n Index piece_dvars_start\n = (i == 0 ? 0: m_cumulativeDecisionVars[i-1]);\n Index piece_numdvars = m_operations[i]->numDecisionVariables();\n res.block(piece_dvars_start, 0, piece_numdvars, 1)\n = m_operations[i]->getDVarsForSegment\n (\n segments[i],\n segments[i+1]\n );\n }\n\n return res;\n }\n _PiecewiseCurve extractCurve(const Vector& soln) {\n if(soln.rows() != this->numDecisionVariables()) {\n throw std::domain_error(\n absl::StrCat(\n \"number of decision variables does not match. given: \",\n soln.rows(),\n \", required: \",\n this->numDecisionVariables()\n )\n );\n }\n\n _PiecewiseCurve piecewise;\n for(std::size_t i = 0; i < this->numPieces(); i++) {\n Index piece_numdvars = m_operations[i]->numDecisionVariables();\n Index piece_dvars_start = \n (i==0 ? 0 : m_cumulativeDecisionVars[i-1]);\n \n Vector dvars = soln.block(piece_dvars_start, 0, piece_numdvars, 1);\n piecewise.addPiece(m_operations[i]->extractCurve(dvars));\n }\n\n return piecewise;\n }\n\n\n void resetProblem() {\n m_problem.reset();\n }\n\n void resetGenerator() {\n m_operations.clear();\n m_cumulativeMaxParameters.clear();\n m_cumulativeDecisionVars.clear();\n this->resetProblem();\n }\n\n const QPWrappers::Problem& getProblem() const {\n return this->m_problem;\n }\n\nprivate:\n std::vector> m_operations;\n std::vector m_cumulativeMaxParameters;\n std::vector m_cumulativeDecisionVars;\n QPWrappers::Problem m_problem;\n\n /*\n * fix cumulative structures starting from the given index\n */\n void fixCumulativeStructures(std::size_t idx) {\n for(std::size_t i = idx; i < this->numPieces(); i++) {\n if(i == 0) {\n m_cumulativeMaxParameters[i] = m_operations[i]->maxParameter();\n m_cumulativeDecisionVars[i] =\n m_operations[i]->numDecisionVariables();\n } else {\n m_cumulativeMaxParameters[i] =\n m_operations[i]->maxParameter()\n + m_cumulativeMaxParameters[i-1];\n m_cumulativeDecisionVars[i] =\n m_operations[i]->numDecisionVariables()\n + m_cumulativeDecisionVars[i-1];\n }\n }\n }\n\n /*\n * Given a parameter, return the piece index, piece parameter corresponding\n * to the given parameter, first decision variable index and decision\n * variable count for the piece\n */\n std::tuple pieceInfo(T param) const {\n std::size_t idx = std::lower_bound(\n m_cumulativeMaxParameters.begin(),\n m_cumulativeMaxParameters.end(),\n param) - m_cumulativeMaxParameters.begin();\n\n Index first_dvar_index = 0;\n Index dvar_count = m_operations[idx]->numDecisionVariables();\n if(idx != 0) {\n param -= m_cumulativeMaxParameters[idx-1];\n first_dvar_index = m_cumulativeDecisionVars[idx-1];\n }\n\n return {\n idx,\n std::min(param, m_operations[idx]->maxParameter()),\n first_dvar_index,\n dvar_count\n };\n }\n\n void addConstraints(const std::vector& cons,\n Index first_dvar_index, Index dvar_count) {\n\n Row coeff(this->numDecisionVariables());\n\n for(const auto& constraint: cons) {\n coeff.setZero();\n coeff.block(0, first_dvar_index, 1, dvar_count) \n = constraint.coeff;\n m_problem.add_constraint(coeff, \n constraint.lb,\n constraint.ub,\n constraint.soft_convertible,\n constraint.soft_weight\n ); \n }\n }\n\n\n};\n\n}\n\n#endif", "meta": {"hexsha": "25f06bd4e4f81d3a5f157e55744bb93750727c9d", "size": 15132, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/splx/opt/PiecewiseCurveQPGenerator.hpp", "max_stars_repo_name": "baskinburak/splx", "max_stars_repo_head_hexsha": "0f02bb8c42890e9dde6d8f48f3214e91f88af0fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/splx/opt/PiecewiseCurveQPGenerator.hpp", "max_issues_repo_name": "baskinburak/splx", "max_issues_repo_head_hexsha": "0f02bb8c42890e9dde6d8f48f3214e91f88af0fc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2019-02-21T00:30:06.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-27T01:39:41.000Z", "max_forks_repo_path": "include/splx/opt/PiecewiseCurveQPGenerator.hpp", "max_forks_repo_name": "baskinburak/splx", "max_forks_repo_head_hexsha": "0f02bb8c42890e9dde6d8f48f3214e91f88af0fc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-08-12T08:17:37.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-12T08:17:37.000Z", "avg_line_length": 36.2877697842, "max_line_length": 93, "alphanum_fraction": 0.5724292889, "num_tokens": 3377, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.24275779355327787}} {"text": "// This file is part of OpenMVG, an Open Multiple View Geometry C++ library.\n\n// Copyright (c) 2012, 2013 Pierre MOULON.\n\n// This Source Code Form is subject to the terms of the Mozilla Public\n// License, v. 2.0. If a copy of the MPL was not distributed with this\n// file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n#ifndef OPENMVG_NUMERIC_LM_HPP\n#define OPENMVG_NUMERIC_LM_HPP\n\n// Levenberg Marquardt Non Linear Optimization\n#include \n\nnamespace openMVG\n{\nusing namespace Eigen;\n\n/**\n * @brief Generic functor Levenberg-Marquardt minimization\n * @tparam _Scalar Type of internal computation\n * @tparam NX Number of values per sample at compile time\n * @tparam NY Number of samples at compile time\n */\ntemplate\nstruct Functor\n{\n using Scalar = _Scalar;\n enum\n {\n InputsAtCompileTime = NX,\n ValuesAtCompileTime = NY\n };\n using InputType = Matrix;\n using ValueType = Matrix;\n using JacobianType = Matrix;\n\n\n /// Number of values per sample\n const int m_inputs;\n\n // Number of sample\n const int m_values;\n\n /**\n * @brief Default constructor\n */\n Functor()\n : m_inputs( InputsAtCompileTime ),\n m_values( ValuesAtCompileTime )\n {\n\n }\n\n /**\n * @brief Constructor\n * @param inputs Number of column per sample\n * @param values Number of sample\n */\n Functor( int inputs, int values ) : m_inputs( inputs ), m_values( values ) {}\n\n /**\n * @brief Get number of samples\n * @return Number of samples\n */\n int inputs() const\n {\n return m_inputs;\n }\n\n /**\n * @brief Get number of samples\n * @return Number of samples\n */\n int values() const\n {\n return m_values;\n }\n\n // you should define that in the subclass :\n // void operator() (const InputType& x, ValueType* v, JacobianType* _j=0) const;\n};\n\n}; // namespace openMVG\n\n#endif // OPENMVG_NUMERIC_LM_HPP\n", "meta": {"hexsha": "6a48a30e8725e0159c831bd20c7f8ebb287b75e9", "size": 1988, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "pose_refinement/SA-LMPE/ba/openMVG/numeric/lm.hpp", "max_stars_repo_name": "Aurelio93/satellite-pose-estimation", "max_stars_repo_head_hexsha": "46957a9bc9f204d468f8fe3150593b3db0f0726a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 90.0, "max_stars_repo_stars_event_min_datetime": "2019-05-19T03:48:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-02T15:20:49.000Z", "max_issues_repo_path": "pose_refinement/SA-LMPE/ba/openMVG/numeric/lm.hpp", "max_issues_repo_name": "Aurelio93/satellite-pose-estimation", "max_issues_repo_head_hexsha": "46957a9bc9f204d468f8fe3150593b3db0f0726a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 11.0, "max_issues_repo_issues_event_min_datetime": "2019-05-22T07:45:46.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-20T01:48:26.000Z", "max_forks_repo_path": "pose_refinement/SA-LMPE/ba/openMVG/numeric/lm.hpp", "max_forks_repo_name": "Aurelio93/satellite-pose-estimation", "max_forks_repo_head_hexsha": "46957a9bc9f204d468f8fe3150593b3db0f0726a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 18.0, "max_forks_repo_forks_event_min_datetime": "2019-05-19T03:48:32.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-29T18:19:16.000Z", "avg_line_length": 22.8505747126, "max_line_length": 83, "alphanum_fraction": 0.6966800805, "num_tokens": 529, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266116, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.2425397026294272}} {"text": "\n\n#include \n#include \n\n#include \n\n#if (defined(NTL_WIZARD_HACK) && defined(NTL_GF2X_LIB))\n#undef NTL_GF2X_LIB\n#endif\n\n\n// some crossover points...choice depends\n// if we are using the gf2x lib or not\n\n#ifdef NTL_GF2X_LIB\n\n#define NTL_GF2X_GCD_CROSSOVER (400L*NTL_BITS_PER_LONG) \n#define NTL_GF2X_HalfGCD_CROSSOVER (6L*NTL_BITS_PER_LONG)\n#define NTL_GF2X_BERMASS_CROSSOVER (200L*NTL_BITS_PER_LONG)\n\n#else\n\n#define NTL_GF2X_GCD_CROSSOVER (900L*NTL_BITS_PER_LONG) \n#define NTL_GF2X_HalfGCD_CROSSOVER (6L*NTL_BITS_PER_LONG)\n#define NTL_GF2X_BERMASS_CROSSOVER (450L*NTL_BITS_PER_LONG)\n\n#endif\n\n\nNTL_START_IMPL\n\n/********** data structures for accesss to GF2XRegisters ************/\n\nstatic GF2X GF2XRegisterVec[32];\nstatic long GF2XRegisterTop = 0;\n\n\nclass GF2XRegisterType {\npublic:\n\nGF2X *xrep;\n\nGF2XRegisterType()\n{ xrep = &GF2XRegisterVec[GF2XRegisterTop]; GF2XRegisterTop++; }\n\n~GF2XRegisterType()\n{ xrep->xrep.release(); \n GF2XRegisterTop--; }\n\noperator GF2X& () { return *xrep; }\n\n};\n\n#define GF2XRegister(a) GF2XRegisterType GF2XReg__ ## a ; GF2X& a = GF2XReg__ ## a\n\n\n\n\n\n\n\nstatic vec_GF2X stab; // used by PlainDivRem and PlainRem\n\nstatic WordVector GF2X_rembuf;\n\n\nvoid PlainDivRem(GF2X& q, GF2X& r, const GF2X& a, const GF2X& b)\n{\n long da, sa, posa, db, sb, posb, dq, sq, posq;\n\n da = deg(a);\n db = deg(b);\n\n if (db < 0) Error(\"GF2X: division by zero\");\n\n if (da < db) {\n r = a;\n clear(q);\n return;\n }\n\n sa = a.xrep.length();\n posa = da - NTL_BITS_PER_LONG*(sa-1);\n sb = b.xrep.length();\n posb = db - NTL_BITS_PER_LONG*(sb-1);\n\n dq = da - db;\n sq = dq/NTL_BITS_PER_LONG + 1;\n posq = dq - NTL_BITS_PER_LONG*(sq-1);\n\n _ntl_ulong *ap;\n if (&r == &a)\n ap = r.xrep.elts();\n else {\n GF2X_rembuf = a.xrep;\n ap = GF2X_rembuf.elts();\n }\n\n stab.SetLength(NTL_BITS_PER_LONG);\n long i;\n\n stab[posb] = b;\n for (i = 1; i <= min(dq, NTL_BITS_PER_LONG-1); i++) \n MulByX(stab[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG], \n stab[((_ntl_ulong)(posb+i-1))%NTL_BITS_PER_LONG]);\n\n _ntl_ulong *stab_ptr[NTL_BITS_PER_LONG];\n long stab_cnt[NTL_BITS_PER_LONG];\n\n for (i = 0; i <= min(dq, NTL_BITS_PER_LONG-1); i++) {\n WordVector& st = stab[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG].xrep;\n long k = st.length();\n stab_ptr[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG] = &st[k-1];\n stab_cnt[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG] = -k+1;\n }\n\n q.xrep.SetLength(sq);\n _ntl_ulong *qp = q.xrep.elts();\n for (i = 0; i < sq; i++)\n qp[i] = 0;\n\n _ntl_ulong *atop = &ap[sa-1];\n _ntl_ulong *qtop = &qp[sq-1];\n _ntl_ulong *stab_top;\n\n while (1) {\n if (atop[0] & (1UL << posa)) {\n qtop[0] |= (1UL << posq);\n stab_top = stab_ptr[posa];\n for (i = stab_cnt[posa]; i <= 0; i++)\n atop[i] ^= stab_top[i];\n }\n\n da--;\n if (da < db) break;\n\n posa--;\n if (posa < 0) {\n posa = NTL_BITS_PER_LONG-1;\n atop--;\n }\n\n posq--;\n if (posq < 0) {\n posq = NTL_BITS_PER_LONG-1;\n qtop--;\n }\n }\n\n if (posb == 0) sb--;\n\n r.xrep.SetLength(sb);\n if (&r != &a) {\n _ntl_ulong *rp = r.xrep.elts();\n for (i = 0; i < sb; i++)\n rp[i] = ap[i];\n }\n r.normalize();\n\n GF2X_rembuf.release();\n for (i = 0; i <= min(dq, NTL_BITS_PER_LONG-1); i++) {\n WordVector& st = stab[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG].xrep;\n st.release();\n }\n}\n\n\n\nvoid PlainDiv(GF2X& q, const GF2X& a, const GF2X& b)\n{\n GF2XRegister(r);\n PlainDivRem(q, r, a, b);\n}\n\n\nvoid PlainRem(GF2X& r, const GF2X& a, const GF2X& b)\n{\n long da, sa, posa, db, sb, posb;\n\n da = deg(a);\n db = deg(b);\n\n if (db < 0) Error(\"GF2X: division by zero\");\n\n if (da < db) {\n r = a;\n return;\n }\n\n sa = a.xrep.length();\n posa = da - NTL_BITS_PER_LONG*(sa-1);\n sb = b.xrep.length();\n posb = db - NTL_BITS_PER_LONG*(sb-1);\n\n _ntl_ulong *ap;\n if (&r == &a)\n ap = r.xrep.elts();\n else {\n GF2X_rembuf = a.xrep;\n ap = GF2X_rembuf.elts();\n }\n\n stab.SetLength(NTL_BITS_PER_LONG);\n long i;\n\n stab[posb] = b;\n for (i = 1; i <= min(da-db, NTL_BITS_PER_LONG-1); i++) \n MulByX(stab[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG], \n stab[((_ntl_ulong)(posb+i-1))%NTL_BITS_PER_LONG]);\n\n _ntl_ulong *stab_ptr[NTL_BITS_PER_LONG];\n long stab_cnt[NTL_BITS_PER_LONG];\n\n for (i = 0; i <= min(da-db, NTL_BITS_PER_LONG-1); i++) {\n WordVector& st = stab[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG].xrep;\n long k = st.length();\n stab_ptr[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG] = &st[k-1];\n stab_cnt[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG] = -k+1;\n }\n\n\n _ntl_ulong *atop = &ap[sa-1];\n _ntl_ulong *stab_top;\n\n while (1) {\n if (atop[0] & (1UL << posa)) {\n stab_top = stab_ptr[posa];\n for (i = stab_cnt[posa]; i <= 0; i++)\n atop[i] ^= stab_top[i];\n }\n\n da--;\n if (da < db) break;\n\n posa--;\n if (posa < 0) {\n posa = NTL_BITS_PER_LONG-1;\n atop--;\n }\n }\n\n if (posb == 0) sb--;\n\n r.xrep.SetLength(sb);\n if (&r != &a) {\n _ntl_ulong *rp = r.xrep.elts();\n for (i = 0; i < sb; i++)\n rp[i] = ap[i];\n }\n r.normalize();\n\n GF2X_rembuf.release();\n for (i = 0; i <= min(da-db, NTL_BITS_PER_LONG-1); i++) {\n WordVector& st = stab[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG].xrep;\n st.release();\n }\n}\n\n#define MASK8 ((1UL << 8)-1UL)\n\nstatic _ntl_ulong invtab[128] = {\n1UL, 255UL, 85UL, 219UL, 73UL, 151UL, 157UL, 51UL, 17UL, 175UL,\n69UL, 139UL, 89UL, 199UL, 141UL, 99UL, 33UL, 95UL, 117UL, 123UL,\n105UL, 55UL, 189UL, 147UL, 49UL, 15UL, 101UL, 43UL, 121UL, 103UL,\n173UL, 195UL, 65UL, 191UL, 21UL, 155UL, 9UL, 215UL, 221UL, 115UL,\n81UL, 239UL, 5UL, 203UL, 25UL, 135UL, 205UL, 35UL, 97UL, 31UL,\n53UL, 59UL, 41UL, 119UL, 253UL, 211UL, 113UL, 79UL, 37UL, 107UL,\n57UL, 39UL, 237UL, 131UL, 129UL, 127UL, 213UL, 91UL, 201UL, 23UL,\n29UL, 179UL, 145UL, 47UL, 197UL, 11UL, 217UL, 71UL, 13UL, 227UL,\n161UL, 223UL, 245UL, 251UL, 233UL, 183UL, 61UL, 19UL, 177UL, 143UL,\n229UL, 171UL, 249UL, 231UL, 45UL, 67UL, 193UL, 63UL, 149UL, 27UL,\n137UL, 87UL, 93UL, 243UL, 209UL, 111UL, 133UL, 75UL, 153UL, 7UL,\n77UL, 163UL, 225UL, 159UL, 181UL, 187UL, 169UL, 247UL, 125UL, 83UL,\n241UL, 207UL, 165UL, 235UL, 185UL, 167UL, 109UL, 3UL };\n\n\n\nvoid NewtonInvTrunc(GF2X& c, const GF2X& a, long e)\n{\n if (e == 1) {\n set(c);\n return;\n }\n\n static vec_long E;\n E.SetLength(0);\n append(E, e);\n while (e > 8) {\n e = (e+1)/2;\n append(E, e);\n }\n\n long L = E.length();\n\n GF2XRegister(g);\n GF2XRegister(g0);\n GF2XRegister(g1);\n GF2XRegister(g2);\n\n g.xrep.SetMaxLength((E[0]+NTL_BITS_PER_LONG-1)/NTL_BITS_PER_LONG + 1);\n g0.xrep.SetMaxLength((E[0]+NTL_BITS_PER_LONG-1)/NTL_BITS_PER_LONG + 1);\n g1.xrep.SetMaxLength(((3*E[0]+1)/2+NTL_BITS_PER_LONG-1)/NTL_BITS_PER_LONG+1);\n g2.xrep.SetMaxLength((E[0]+NTL_BITS_PER_LONG-1)/NTL_BITS_PER_LONG + 1);\n\n g.xrep.SetLength(1);\n g.xrep[0] = invtab[(a.xrep[0] & MASK8) >> 1] & ((1UL< 0; i--) {\n // lift from E[i] to E[i-1]\n\n long k = E[i];\n long l = E[i-1]-E[i];\n\n trunc(g0, a, k+l);\n\n mul(g1, g0, g);\n RightShift(g1, g1, k);\n trunc(g1, g1, l);\n\n mul(g2, g1, g);\n trunc(g2, g2, l);\n LeftShift(g2, g2, k);\n\n add(g, g, g2);\n }\n\n c = g;\n}\n\nvoid InvTrunc(GF2X& c, const GF2X& a, long e)\n{\n if (ConstTerm(a) == 0 || e < 0)\n Error(\"inv: bad args\");\n\n if (NTL_OVERFLOW(e, 1, 0))\n Error(\"overflow in InvTrunc\");\n\n if (e == 0) {\n clear(c);\n return;\n }\n\n NewtonInvTrunc(c, a, e);\n}\n\n\n\nstatic \nlong weight1(_ntl_ulong a)\n{\n long res = 0;\n while (a) {\n if (a & 1) res ++;\n a >>= 1;\n }\n return res;\n}\n\nlong weight(const GF2X& a)\n{\n long wlen = a.xrep.length();\n long res = 0;\n long i;\n for (i = 0; i < wlen; i++)\n res += weight1(a.xrep[i]);\n\n return res;\n}\n\n\n\nstatic\nvoid SparsityCheck(const GF2X& f, long& k3, long& k2, long& k1)\n{\n long w = weight(f);\n if (w != 3 && w != 5) {\n k3 = 0;\n return;\n }\n\n if (ConstTerm(f) != 1) {\n k3 = 0;\n return;\n }\n\n GF2X g = f;\n\n long n = deg(f);\n\n trunc(g, g, n);\n \n long t = deg(g);\n\n if (n-t < NTL_BITS_PER_LONG || t > (n+1)/2) {\n k3 = 0;\n return;\n }\n\n if (w == 3) {\n k3 = t;\n k2 = 0;\n return;\n }\n\n k3 = t;\n trunc(g, g, t);\n t = deg(g);\n k2 = t;\n trunc(g, g, t);\n t = deg(g);\n k1 = t;\n}\n\n\n\n\nconst long GF2X_MOD_PLAIN = 0;\nconst long GF2X_MOD_MUL = 1;\nconst long GF2X_MOD_SPECIAL = 2;\nconst long GF2X_MOD_TRI = 3;\nconst long GF2X_MOD_PENT = 4;\n\nvoid build(GF2XModulus& F, const GF2X& f)\n{\n long n = deg(f);\n long i;\n\n if (n <= 0) Error(\"build(GF2XModulus,GF2X): deg(f) <= 0\");\n\n F.tracevec.SetLength(0);\n\n F.f = f;\n F.n = n;\n F.sn = f.xrep.length();\n\n long sb = F.sn;\n long posb = n - NTL_BITS_PER_LONG*(sb-1);\n\n F.posn = posb; \n\n if (F.posn > 0) {\n F.size = F.sn;\n F.msk = (1UL << F.posn) - 1UL;\n }\n else {\n F.size = F.sn-1;\n F.msk = ~0UL;\n }\n\n SparsityCheck(f, F.k3, F.k2, F.k1);\n\n if (F.k3 != 0) {\n if (F.k2 == 0)\n F.method = GF2X_MOD_TRI;\n else\n F.method = GF2X_MOD_PENT;\n\n return;\n }\n\n\n GF2X f0;\n trunc(f0, f, n);\n long deg_f0 = deg(f0);\n\n if (F.sn > 1 && deg_f0 < NTL_BITS_PER_LONG \n && deg_f0 >= NTL_BITS_PER_LONG/2) {\n if (F.size >= 6)\n F.method = GF2X_MOD_MUL;\n else\n F.method = GF2X_MOD_SPECIAL;\n }\n else if (F.sn > 1 && deg_f0 < NTL_BITS_PER_LONG/2) {\n if (F.size >= 4)\n F.method = GF2X_MOD_MUL;\n else\n F.method = GF2X_MOD_SPECIAL;\n }\n else if (F.size >= 8)\n F.method = GF2X_MOD_MUL;\n else \n F.method = GF2X_MOD_PLAIN;\n \n\n if (F.method == GF2X_MOD_SPECIAL) {\n if (!F.stab_cnt) F.stab_cnt = NTL_NEW_OP long[NTL_BITS_PER_LONG];\n long *stab_cnt = F.stab_cnt;\n if (!stab_cnt) Error(\"out of memory\");\n\n if (!F.stab1) F.stab1 = NTL_NEW_OP _ntl_ulong[2*NTL_BITS_PER_LONG];\n _ntl_ulong *stab1 = F.stab1;\n if (!stab1) Error(\"out of memory\");\n\n stab1[posb<<1] = f.xrep[0];\n stab1[(posb<<1)+1] = 0;\n\n stab_cnt[posb] = -sb+1;\n\n for (i = 1; i < NTL_BITS_PER_LONG; i++) {\n long kk0 = ((_ntl_ulong)(posb+i-1))%NTL_BITS_PER_LONG;\n long kk1 = ((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG;\n\n stab1[kk1<<1] = stab1[kk0<<1] << 1;\n stab1[(kk1<<1)+1] = (stab1[(kk0<<1)+1] << 1) \n | (stab1[kk0<<1] >> (NTL_BITS_PER_LONG-1));\n\n if (kk1 < posb) \n stab_cnt[kk1] = -sb;\n else\n stab_cnt[kk1] = -sb+1;\n }\n }\n else if (F.method == GF2X_MOD_PLAIN) {\n vec_GF2X& stab = F.stab;\n stab.SetLength(NTL_BITS_PER_LONG);\n\n\n if (!F.stab_ptr) F.stab_ptr = NTL_NEW_OP _ntl_ulong_ptr[NTL_BITS_PER_LONG];\n _ntl_ulong **stab_ptr = F.stab_ptr;\n if (!stab_ptr) Error(\"out of memory\");\n\n if (!F.stab_cnt) F.stab_cnt = NTL_NEW_OP long[NTL_BITS_PER_LONG];\n long *stab_cnt = F.stab_cnt;\n if (!stab_cnt) Error(\"out of memory\");\n \n \n stab[posb] = f;\n for (i = 1; i < NTL_BITS_PER_LONG; i++) \n MulByX(stab[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG], \n stab[((_ntl_ulong)(posb+i-1))%NTL_BITS_PER_LONG]);\n \n \n for (i = 0; i < NTL_BITS_PER_LONG; i++) {\n WordVector& st = stab[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG].xrep;\n long k = st.length();\n stab_ptr[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG] = &st[k-1];\n stab_cnt[((_ntl_ulong)(posb+i))%NTL_BITS_PER_LONG] = -k+1;\n }\n }\n else if (F.method == GF2X_MOD_MUL) {\n GF2X P1, P2;\n\n CopyReverse(P1, f, n);\n InvTrunc(P2, P1, n-1);\n CopyReverse(P1, P2, n-2);\n trunc(F.h0, P1, n-2);\n F.f0 = f0;\n }\n}\n\nGF2XModulus::GF2XModulus()\n{\n n = -1;\n method = GF2X_MOD_PLAIN;\n stab_ptr = 0;\n stab_cnt = 0;\n stab1 = 0;\n}\n\n\n// The following two routines are total spaghetti...unfortunately,\n// cleaning them up would require too much re-coding in other\n// places.\n\nGF2XModulus::GF2XModulus(const GF2XModulus& F) :\n f(F.f), n(F.n), sn(F.sn), posn(F.posn), k3(F.k3), k2(F.k2), k1(F.k1),\n size(F.size), \n msk(F.msk), method(F.method), stab(F.stab), h0(F.h0), f0(F.f0),\n stab_cnt(0), stab_ptr(0), stab1(0), tracevec(F.tracevec)\n{\n if (method == GF2X_MOD_SPECIAL) {\n long i;\n stab1 = NTL_NEW_OP _ntl_ulong[2*NTL_BITS_PER_LONG];\n if (!stab1) Error(\"GF2XModulus: out of memory\");\n for (i = 0; i < 2*NTL_BITS_PER_LONG; i++)\n stab1[i] = F.stab1[i];\n stab_cnt = NTL_NEW_OP long[NTL_BITS_PER_LONG];\n if (!stab_cnt) Error(\"GF2XModulus: out of memory\");\n for (i = 0; i < NTL_BITS_PER_LONG; i++)\n stab_cnt[i] = F.stab_cnt[i];\n }\n else if (method == GF2X_MOD_PLAIN) {\n long i;\n\n if (F.stab_cnt) {\n stab_cnt = NTL_NEW_OP long[NTL_BITS_PER_LONG];\n if (!stab_cnt) Error(\"GF2XModulus: out of memory\");\n for (i = 0; i < NTL_BITS_PER_LONG; i++)\n stab_cnt[i] = F.stab_cnt[i];\n }\n\n if (F.stab_ptr) {\n stab_ptr = NTL_NEW_OP _ntl_ulong_ptr[NTL_BITS_PER_LONG];\n if (!stab_ptr) Error(\"GF2XModulus: out of memory\");\n \n for (i = 0; i < NTL_BITS_PER_LONG; i++) {\n WordVector& st = stab[((_ntl_ulong)(posn+i))%NTL_BITS_PER_LONG].xrep;\n long k = st.length();\n stab_ptr[((_ntl_ulong)(posn+i))%NTL_BITS_PER_LONG] = &st[k-1];\n stab_cnt[((_ntl_ulong)(posn+i))%NTL_BITS_PER_LONG] = -k+1;\n }\n }\n }\n}\n\nGF2XModulus& GF2XModulus::operator=(const GF2XModulus& F)\n{\n if (this == &F) return *this;\n\n f=F.f; n=F.n; sn=F.sn; posn=F.posn; \n k3=F.k3; k2=F.k2; k1=F.k1;\n size=F.size; \n msk=F.msk; method=F.method; stab=F.stab; h0=F.h0; f0 = F.f0;\n tracevec=F.tracevec;\n\n if (method == GF2X_MOD_SPECIAL) {\n long i;\n if (!stab1) stab1 = NTL_NEW_OP _ntl_ulong[2*NTL_BITS_PER_LONG];\n if (!stab1) Error(\"GF2XModulus: out of memory\");\n for (i = 0; i < 2*NTL_BITS_PER_LONG; i++)\n stab1[i] = F.stab1[i];\n if (!stab_cnt) stab_cnt = NTL_NEW_OP long[NTL_BITS_PER_LONG];\n if (!stab_cnt) Error(\"GF2XModulus: out of memory\");\n for (i = 0; i < NTL_BITS_PER_LONG; i++)\n stab_cnt[i] = F.stab_cnt[i];\n }\n else if (method == GF2X_MOD_PLAIN) {\n long i;\n\n if (F.stab_cnt) {\n if (!stab_cnt) stab_cnt = NTL_NEW_OP long[NTL_BITS_PER_LONG];\n if (!stab_cnt) Error(\"GF2XModulus: out of memory\");\n for (i = 0; i < NTL_BITS_PER_LONG; i++)\n stab_cnt[i] = F.stab_cnt[i];\n }\n\n if (F.stab_ptr) {\n if (!stab_ptr) stab_ptr = NTL_NEW_OP _ntl_ulong_ptr[NTL_BITS_PER_LONG];\n if (!stab_ptr) Error(\"GF2XModulus: out of memory\");\n \n for (i = 0; i < NTL_BITS_PER_LONG; i++) {\n WordVector& st = stab[((_ntl_ulong)(posn+i))%NTL_BITS_PER_LONG].xrep;\n long k = st.length();\n stab_ptr[((_ntl_ulong)(posn+i))%NTL_BITS_PER_LONG] = &st[k-1];\n stab_cnt[((_ntl_ulong)(posn+i))%NTL_BITS_PER_LONG] = -k+1;\n }\n }\n }\n\n return *this;\n}\n \n\n\nGF2XModulus::~GF2XModulus() \n{ \n delete [] stab_ptr; \n delete [] stab_cnt; \n delete [] stab1; \n}\n\n\n\nGF2XModulus::GF2XModulus(const GF2X& ff)\n{\n n = -1;\n method = GF2X_MOD_PLAIN;\n stab_ptr = 0;\n stab_cnt = 0;\n stab1 = 0;\n\n build(*this, ff);\n}\n\n\n\n\n\nvoid UseMulRem21(GF2X& r, const GF2X& a, const GF2XModulus& F)\n{\n GF2XRegister(P1);\n GF2XRegister(P2);\n\n RightShift(P1, a, F.n);\n mul(P2, P1, F.h0);\n RightShift(P2, P2, F.n-2);\n add(P2, P2, P1);\n mul(P1, P2, F.f0);\n trunc(P1, P1, F.n);\n trunc(r, a, F.n);\n add(r, r, P1);\n}\n\nvoid UseMulDivRem21(GF2X& q, GF2X& r, const GF2X& a, const GF2XModulus& F)\n{\n GF2XRegister(P1);\n GF2XRegister(P2);\n\n RightShift(P1, a, F.n);\n mul(P2, P1, F.h0);\n RightShift(P2, P2, F.n-2);\n add(P2, P2, P1);\n mul(P1, P2, F.f0);\n trunc(P1, P1, F.n);\n trunc(r, a, F.n);\n add(r, r, P1);\n q = P2;\n}\n\nvoid UseMulDiv21(GF2X& q, const GF2X& a, const GF2XModulus& F)\n{\n GF2XRegister(P1);\n GF2XRegister(P2);\n\n RightShift(P1, a, F.n);\n mul(P2, P1, F.h0);\n RightShift(P2, P2, F.n-2);\n add(P2, P2, P1);\n q = P2;\n}\n\n\nvoid UseMulRemX1(GF2X& r, const GF2X& aa, const GF2XModulus& F)\n{\n GF2XRegister(buf);\n GF2XRegister(tmp);\n GF2XRegister(a);\n\n clear(buf);\n a = aa;\n\n long n = F.n;\n long a_len = deg(a) + 1;\n\n while (a_len > 0) {\n long old_buf_len = deg(buf) + 1;\n long amt = min(2*n-1-old_buf_len, a_len);\n\n LeftShift(buf, buf, amt);\n RightShift(tmp, a, a_len-amt);\n add(buf, buf, tmp);\n trunc(a, a, a_len-amt);\n\n UseMulRem21(buf, buf, F);\n a_len -= amt;\n }\n\n r = buf;\n}\n \n\nvoid UseMulDivRemX1(GF2X& q, GF2X& r, const GF2X& aa, const GF2XModulus& F)\n{\n GF2XRegister(buf);\n GF2XRegister(tmp);\n GF2XRegister(a);\n GF2XRegister(qq);\n GF2XRegister(qbuf);\n\n clear(buf);\n a = aa;\n clear(qq);\n\n long n = F.n;\n long a_len = deg(a) + 1;\n\n while (a_len > 0) {\n long old_buf_len = deg(buf) + 1;\n long amt = min(2*n-1-old_buf_len, a_len);\n\n LeftShift(buf, buf, amt);\n RightShift(tmp, a, a_len-amt);\n add(buf, buf, tmp);\n trunc(a, a, a_len-amt);\n\n UseMulDivRem21(qbuf, buf, buf, F);\n a_len -= amt;\n\n ShiftAdd(qq, qbuf, a_len);\n }\n\n r = buf;\n q = qq;\n}\n\n\nvoid UseMulDivX1(GF2X& q, const GF2X& aa, const GF2XModulus& F)\n{\n GF2XRegister(buf);\n GF2XRegister(tmp);\n GF2XRegister(a);\n GF2XRegister(qq);\n GF2XRegister(qbuf);\n \n clear(buf);\n a = aa;\n clear(qq);\n\n long n = F.n;\n long a_len = deg(a) + 1;\n\n while (a_len > 0) {\n long old_buf_len = deg(buf) + 1;\n long amt = min(2*n-1-old_buf_len, a_len);\n\n LeftShift(buf, buf, amt);\n RightShift(tmp, a, a_len-amt);\n add(buf, buf, tmp);\n trunc(a, a, a_len-amt);\n\n UseMulDivRem21(qbuf, buf, buf, F);\n a_len -= amt;\n\n ShiftAdd(qq, qbuf, a_len);\n }\n\n q = qq;\n}\n\nstatic\nvoid TrinomReduce(GF2X& x, const GF2X& a, long n, long k)\n{\n long wn = n / NTL_BITS_PER_LONG;\n long bn = n - wn*NTL_BITS_PER_LONG;\n\n long wdiff = (n-k)/NTL_BITS_PER_LONG;\n long bdiff = (n-k) - wdiff*NTL_BITS_PER_LONG;\n\n long m = a.xrep.length()-1;\n\n if (wn > m) {\n x = a;\n return;\n }\n\n GF2XRegister(r);\n\n r = a;\n\n _ntl_ulong *p = r.xrep.elts();\n\n _ntl_ulong *pp;\n\n\n _ntl_ulong w;\n\n if (bn == 0) {\n if (bdiff == 0) {\n // bn == 0 && bdiff == 0\n\n while (m >= wn) {\n w = p[m];\n p[m-wdiff] ^= w;\n p[m-wn] ^= w;\n m--;\n }\n }\n else {\n // bn == 0 && bdiff != 0\n\n while (m >= wn) {\n w = p[m];\n pp = &p[m-wdiff];\n *pp ^= (w >> bdiff);\n *(pp-1) ^= (w << (NTL_BITS_PER_LONG-bdiff));\n p[m-wn] ^= w;\n m--;\n }\n }\n }\n else {\n if (bdiff == 0) {\n // bn != 0 && bdiff == 0\n\n while (m > wn) {\n w = p[m];\n p[m-wdiff] ^= w;\n pp = &p[m-wn];\n *pp ^= (w >> bn);\n *(pp-1) ^= (w << (NTL_BITS_PER_LONG-bn));\n m--;\n }\n\n w = (p[m] >> bn) << bn;;\n\n p[m-wdiff] ^= w;\n p[0] ^= (w >> bn);\n\n p[m] &= ((1UL< wn) {\n w = p[m];\n pp = &p[m-wdiff];\n *pp ^= (w >> bdiff);;\n *(pp-1) ^= (w << (NTL_BITS_PER_LONG-bdiff));\n pp = &p[m-wn];\n *pp ^= (w >> bn);\n *(pp-1) ^= (w << (NTL_BITS_PER_LONG-bn));\n m--;\n }\n\n w = (p[m] >> bn) << bn;;\n\n p[m-wdiff] ^= (w >> bdiff);\n if (m-wdiff-1 >= 0) p[m-wdiff-1] ^= (w << (NTL_BITS_PER_LONG-bdiff));\n p[0] ^= (w >> bn);\n p[m] &= ((1UL<= 0 && p[wn] == 0)\n wn--;\n\n r.xrep.QuickSetLength(wn+1);\n\n x = r;\n}\n\nstatic\nvoid PentReduce(GF2X& x, const GF2X& a, long n, long k3, long k2, long k1)\n{\n long wn = n / NTL_BITS_PER_LONG;\n long bn = n - wn*NTL_BITS_PER_LONG;\n\n long m = a.xrep.length()-1;\n\n if (wn > m) {\n x = a;\n return;\n }\n\n long wdiff1 = (n-k1)/NTL_BITS_PER_LONG;\n long bdiff1 = (n-k1) - wdiff1*NTL_BITS_PER_LONG;\n\n long wdiff2 = (n-k2)/NTL_BITS_PER_LONG;\n long bdiff2 = (n-k2) - wdiff2*NTL_BITS_PER_LONG;\n\n long wdiff3 = (n-k3)/NTL_BITS_PER_LONG;\n long bdiff3 = (n-k3) - wdiff3*NTL_BITS_PER_LONG;\n\n GF2XRegister(r);\n r = a;\n\n _ntl_ulong *p = r.xrep.elts();\n\n _ntl_ulong *pp;\n\n _ntl_ulong w;\n\n while (m > wn) {\n w = p[m];\n\n if (bn == 0) \n p[m-wn] ^= w;\n else {\n pp = &p[m-wn];\n *pp ^= (w >> bn);\n *(pp-1) ^= (w << (NTL_BITS_PER_LONG-bn));\n }\n\n if (bdiff1 == 0) \n p[m-wdiff1] ^= w;\n else {\n pp = &p[m-wdiff1];\n *pp ^= (w >> bdiff1);\n *(pp-1) ^= (w << (NTL_BITS_PER_LONG-bdiff1));\n }\n\n if (bdiff2 == 0) \n p[m-wdiff2] ^= w;\n else {\n pp = &p[m-wdiff2];\n *pp ^= (w >> bdiff2);\n *(pp-1) ^= (w << (NTL_BITS_PER_LONG-bdiff2));\n }\n\n if (bdiff3 == 0) \n p[m-wdiff3] ^= w;\n else {\n pp = &p[m-wdiff3];\n *pp ^= (w >> bdiff3);\n *(pp-1) ^= (w << (NTL_BITS_PER_LONG-bdiff3));\n }\n\n m--;\n }\n\n w = (p[m] >> bn) << bn;\n\n p[0] ^= (w >> bn); \n\n if (bdiff1 == 0)\n p[m-wdiff1] ^= w;\n else {\n p[m-wdiff1] ^= (w >> bdiff1);\n if (m-wdiff1-1 >= 0) p[m-wdiff1-1] ^= (w << (NTL_BITS_PER_LONG-bdiff1));\n }\n\n if (bdiff2 == 0)\n p[m-wdiff2] ^= w;\n else {\n p[m-wdiff2] ^= (w >> bdiff2);\n if (m-wdiff2-1 >= 0) p[m-wdiff2-1] ^= (w << (NTL_BITS_PER_LONG-bdiff2));\n }\n\n if (bdiff3 == 0)\n p[m-wdiff3] ^= w;\n else {\n p[m-wdiff3] ^= (w >> bdiff3);\n if (m-wdiff3-1 >= 0) p[m-wdiff3-1] ^= (w << (NTL_BITS_PER_LONG-bdiff3));\n }\n\n if (bn != 0)\n p[m] &= ((1UL<= 0 && p[wn] == 0)\n wn--;\n\n r.xrep.QuickSetLength(wn+1);\n\n x = r;\n}\n\n\n\n\nstatic\nvoid RightShiftAdd(GF2X& c, const GF2X& a, long n)\n{\n if (n < 0) {\n Error(\"RightShiftAdd: negative shamt\");\n }\n\n if (n == 0) {\n add(c, c, a);\n return;\n }\n\n long sa = a.xrep.length();\n long wn = n/NTL_BITS_PER_LONG;\n long bn = n - wn*NTL_BITS_PER_LONG;\n\n if (wn >= sa) {\n return;\n }\n\n long sc = c.xrep.length();\n long i;\n\n if (sa-wn > sc)\n c.xrep.SetLength(sa-wn);\n\n _ntl_ulong *cp = c.xrep.elts();\n const _ntl_ulong *ap = a.xrep.elts();\n\n for (i = sc; i < sa-wn; i++)\n cp[i] = 0;\n\n\n if (bn == 0) {\n for (i = 0; i < sa-wn; i++)\n cp[i] ^= ap[i+wn];\n }\n else {\n for (i = 0; i < sa-wn-1; i++)\n cp[i] ^= (ap[i+wn] >> bn) | (ap[i+wn+1] << (NTL_BITS_PER_LONG - bn));\n\n cp[sa-wn-1] ^= ap[sa-1] >> bn;\n }\n\n c.normalize();\n}\n\n\nstatic\nvoid TriDiv21(GF2X& q, const GF2X& a, long n, long k)\n{\n GF2XRegister(P1);\n\n RightShift(P1, a, n);\n if (k != 1) \n RightShiftAdd(P1, P1, n-k);\n\n q = P1;\n}\n\nstatic \nvoid TriDivRem21(GF2X& q, GF2X& r, const GF2X& a, long n, long k)\n{\n GF2XRegister(Q);\n TriDiv21(Q, a, n, k);\n TrinomReduce(r, a, n, k);\n q = Q;\n}\n\n\nstatic\nvoid PentDiv21(GF2X& q, const GF2X& a, long n, long k3, long k2, long k1)\n{\n if (deg(a) < n) {\n clear(q);\n return;\n }\n\n GF2XRegister(P1);\n GF2XRegister(P2);\n\n RightShift(P1, a, n);\n \n RightShift(P2, P1, n-k3);\n RightShiftAdd(P2, P1, n-k2);\n if (k1 != 1) {\n RightShiftAdd(P2, P1, n-k1);\n }\n\n add(P2, P2, P1);\n\n q = P2;\n}\n\nstatic \nvoid PentDivRem21(GF2X& q, GF2X& r, const GF2X& a, long n, \n long k3, long k2, long k1)\n{\n GF2XRegister(Q);\n PentDiv21(Q, a, n, k3, k2, k1);\n PentReduce(r, a, n, k3, k2, k1);\n q = Q;\n}\n\nstatic\nvoid TriDivRemX1(GF2X& q, GF2X& r, const GF2X& aa, long n, long k)\n{\n GF2XRegister(buf);\n GF2XRegister(tmp);\n GF2XRegister(a);\n GF2XRegister(qq);\n GF2XRegister(qbuf);\n\n clear(buf);\n a = aa;\n clear(qq);\n\n long a_len = deg(a) + 1;\n\n while (a_len > 0) {\n long old_buf_len = deg(buf) + 1;\n long amt = min(2*n-1-old_buf_len, a_len);\n\n LeftShift(buf, buf, amt);\n RightShift(tmp, a, a_len-amt);\n add(buf, buf, tmp);\n trunc(a, a, a_len-amt);\n\n TriDivRem21(qbuf, buf, buf, n, k);\n a_len -= amt;\n\n ShiftAdd(qq, qbuf, a_len);\n }\n\n r = buf;\n q = qq;\n}\n\n\nstatic\nvoid TriDivX1(GF2X& q, const GF2X& aa, long n, long k)\n{\n GF2XRegister(buf);\n GF2XRegister(tmp);\n GF2XRegister(a);\n GF2XRegister(qq);\n GF2XRegister(qbuf);\n \n clear(buf);\n a = aa;\n clear(qq);\n\n long a_len = deg(a) + 1;\n\n while (a_len > 0) {\n long old_buf_len = deg(buf) + 1;\n long amt = min(2*n-1-old_buf_len, a_len);\n\n LeftShift(buf, buf, amt);\n RightShift(tmp, a, a_len-amt);\n add(buf, buf, tmp);\n trunc(a, a, a_len-amt);\n\n TriDivRem21(qbuf, buf, buf, n, k);\n a_len -= amt;\n\n ShiftAdd(qq, qbuf, a_len);\n }\n\n q = qq;\n}\n\nstatic\nvoid PentDivRemX1(GF2X& q, GF2X& r, const GF2X& aa, long n, \n long k3, long k2, long k1)\n{\n GF2XRegister(buf);\n GF2XRegister(tmp);\n GF2XRegister(a);\n GF2XRegister(qq);\n GF2XRegister(qbuf);\n\n clear(buf);\n a = aa;\n clear(qq);\n\n long a_len = deg(a) + 1;\n\n while (a_len > 0) {\n long old_buf_len = deg(buf) + 1;\n long amt = min(2*n-1-old_buf_len, a_len);\n\n LeftShift(buf, buf, amt);\n RightShift(tmp, a, a_len-amt);\n add(buf, buf, tmp);\n trunc(a, a, a_len-amt);\n\n PentDivRem21(qbuf, buf, buf, n, k3, k2, k1);\n a_len -= amt;\n\n ShiftAdd(qq, qbuf, a_len);\n }\n\n r = buf;\n q = qq;\n}\n\n\nstatic\nvoid PentDivX1(GF2X& q, const GF2X& aa, long n, long k3, long k2, long k1)\n{\n GF2XRegister(buf);\n GF2XRegister(tmp);\n GF2XRegister(a);\n GF2XRegister(qq);\n GF2XRegister(qbuf);\n \n clear(buf);\n a = aa;\n clear(qq);\n\n long a_len = deg(a) + 1;\n\n while (a_len > 0) {\n long old_buf_len = deg(buf) + 1;\n long amt = min(2*n-1-old_buf_len, a_len);\n\n LeftShift(buf, buf, amt);\n RightShift(tmp, a, a_len-amt);\n add(buf, buf, tmp);\n trunc(a, a, a_len-amt);\n\n PentDivRem21(qbuf, buf, buf, n, k3, k2, k1);\n a_len -= amt;\n\n ShiftAdd(qq, qbuf, a_len);\n }\n\n q = qq;\n}\n\n\n\nvoid rem(GF2X& r, const GF2X& a, const GF2XModulus& F)\n{\n long n = F.n;\n\n if (n < 0) Error(\"rem: uninitialized modulus\");\n\n if (F.method == GF2X_MOD_TRI) {\n TrinomReduce(r, a, n, F.k3);\n return;\n }\n\n if (F.method == GF2X_MOD_PENT) {\n PentReduce(r, a, n, F.k3, F.k2, F.k1);\n return;\n }\n\n long da = deg(a);\n\n\n if (da < n) {\n r = a;\n }\n else if (F.method == GF2X_MOD_MUL) {\n if (da <= 2*(n-1)) \n UseMulRem21(r, a, F);\n else\n UseMulRemX1(r, a, F);\n }\n else if (F.method == GF2X_MOD_SPECIAL) {\n long sa = a.xrep.length();\n long posa = da - NTL_BITS_PER_LONG*(sa-1);\n \n _ntl_ulong *ap;\n if (&r == &a)\n ap = r.xrep.elts();\n else {\n GF2X_rembuf = a.xrep;\n ap = GF2X_rembuf.elts();\n }\n \n _ntl_ulong *atop = &ap[sa-1];\n _ntl_ulong *stab_top;\n \n long i;\n \n while (1) {\n if (atop[0] & (1UL << posa)) {\n stab_top = &F.stab1[posa << 1];\n i = F.stab_cnt[posa];\n atop[i] ^= stab_top[0];\n atop[i+1] ^= stab_top[1];\n }\n\n da--;\n if (da < n) break;\n\n posa--;\n if (posa < 0) {\n posa = NTL_BITS_PER_LONG-1;\n atop--;\n }\n }\n \n long sn = F.size;\n r.xrep.SetLength(sn);\n if (&r != &a) {\n _ntl_ulong *rp = r.xrep.elts();\n for (i = 0; i < sn; i++)\n rp[i] = ap[i];\n }\n r.xrep[sn-1] &= F.msk;\n r.normalize();\n }\n else {\n long sa = a.xrep.length();\n long posa = da - NTL_BITS_PER_LONG*(sa-1);\n \n \n _ntl_ulong *ap;\n if (&r == &a)\n ap = r.xrep.elts();\n else {\n GF2X_rembuf = a.xrep;\n ap = GF2X_rembuf.elts();\n }\n \n _ntl_ulong *atop = &ap[sa-1];\n _ntl_ulong *stab_top;\n \n long i;\n \n while (1) {\n if (atop[0] & (1UL << posa)) {\n stab_top = F.stab_ptr[posa];\n for (i = F.stab_cnt[posa]; i <= 0; i++)\n atop[i] ^= stab_top[i];\n }\n \n da--;\n if (da < n) break;\n\n posa--;\n if (posa < 0) {\n posa = NTL_BITS_PER_LONG-1;\n atop--;\n }\n }\n \n long sn = F.size;\n r.xrep.SetLength(sn);\n if (&r != &a) {\n _ntl_ulong *rp = r.xrep.elts();\n for (i = 0; i < sn; i++)\n rp[i] = ap[i];\n }\n r.normalize();\n }\n\n GF2X_rembuf.release();\n}\n\nvoid DivRem(GF2X& q, GF2X& r, const GF2X& a, const GF2XModulus& F)\n{\n long da = deg(a);\n long n = F.n;\n\n if (n < 0) Error(\"DivRem: uninitialized modulus\");\n\n if (da < n) {\n r = a;\n clear(q);\n }\n else if (F.method == GF2X_MOD_TRI) {\n if (da <= 2*(n-1)) \n TriDivRem21(q, r, a, F.n, F.k3);\n else\n TriDivRemX1(q, r, a, F.n, F.k3);\n }\n else if (F.method == GF2X_MOD_PENT) {\n if (da <= 2*(n-1)) \n PentDivRem21(q, r, a, F.n, F.k3, F.k2, F.k1);\n else\n PentDivRemX1(q, r, a, F.n, F.k3, F.k2, F.k1);\n }\n else if (F.method == GF2X_MOD_MUL) {\n if (da <= 2*(n-1)) \n UseMulDivRem21(q, r, a, F);\n else\n UseMulDivRemX1(q, r, a, F);\n }\n else if (F.method == GF2X_MOD_SPECIAL) {\n long sa = a.xrep.length();\n long posa = da - NTL_BITS_PER_LONG*(sa-1);\n \n long dq = da - n;\n long sq = dq/NTL_BITS_PER_LONG + 1;\n long posq = dq - NTL_BITS_PER_LONG*(sq-1);\n \n _ntl_ulong *ap;\n if (&r == &a)\n ap = r.xrep.elts();\n else {\n GF2X_rembuf = a.xrep;\n ap = GF2X_rembuf.elts();\n }\n \n _ntl_ulong *atop = &ap[sa-1];\n _ntl_ulong *stab_top;\n\n long i;\n\n q.xrep.SetLength(sq);\n _ntl_ulong *qp = q.xrep.elts();\n for (i = 0; i < sq; i++)\n qp[i] = 0;\n\n _ntl_ulong *qtop = &qp[sq-1];\n\n \n while (1) {\n if (atop[0] & (1UL << posa)) {\n qtop[0] |= (1UL << posq);\n stab_top = &F.stab1[posa << 1];\n i = F.stab_cnt[posa];\n atop[i] ^= stab_top[0];\n atop[i+1] ^= stab_top[1];\n }\n \n da--;\n if (da < n) break;\n\n posa--;\n if (posa < 0) {\n posa = NTL_BITS_PER_LONG-1;\n atop--;\n }\n\n posq--;\n if (posq < 0) {\n posq = NTL_BITS_PER_LONG-1;\n qtop--;\n }\n }\n \n long sn = F.size;\n r.xrep.SetLength(sn);\n if (&r != &a) {\n _ntl_ulong *rp = r.xrep.elts();\n for (i = 0; i < sn; i++)\n rp[i] = ap[i];\n }\n r.xrep[sn-1] &= F.msk;\n r.normalize();\n }\n else {\n long sa = a.xrep.length();\n long posa = da - NTL_BITS_PER_LONG*(sa-1);\n \n long dq = da - n;\n long sq = dq/NTL_BITS_PER_LONG + 1;\n long posq = dq - NTL_BITS_PER_LONG*(sq-1);\n \n _ntl_ulong *ap;\n if (&r == &a)\n ap = r.xrep.elts();\n else {\n GF2X_rembuf = a.xrep;\n ap = GF2X_rembuf.elts();\n }\n \n _ntl_ulong *atop = &ap[sa-1];\n _ntl_ulong *stab_top;\n \n long i;\n\n q.xrep.SetLength(sq);\n _ntl_ulong *qp = q.xrep.elts();\n for (i = 0; i < sq; i++)\n qp[i] = 0;\n\n _ntl_ulong *qtop = &qp[sq-1];\n \n while (1) {\n if (atop[0] & (1UL << posa)) {\n qtop[0] |= (1UL << posq);\n stab_top = F.stab_ptr[posa];\n for (i = F.stab_cnt[posa]; i <= 0; i++)\n atop[i] ^= stab_top[i];\n }\n \n da--;\n if (da < n) break;\n\n posa--;\n if (posa < 0) {\n posa = NTL_BITS_PER_LONG-1;\n atop--;\n }\n\n posq--;\n if (posq < 0) {\n posq = NTL_BITS_PER_LONG-1;\n qtop--;\n }\n }\n \n long sn = F.size;\n r.xrep.SetLength(sn);\n if (&r != &a) {\n _ntl_ulong *rp = r.xrep.elts();\n for (i = 0; i < sn; i++)\n rp[i] = ap[i];\n }\n r.normalize();\n }\n\n GF2X_rembuf.release();\n}\n\n\n\nvoid div(GF2X& q, const GF2X& a, const GF2XModulus& F)\n{\n long da = deg(a);\n long n = F.n;\n\n if (n < 0) Error(\"div: uninitialized modulus\");\n\n\n if (da < n) {\n clear(q);\n }\n else if (F.method == GF2X_MOD_TRI) {\n if (da <= 2*(n-1)) \n TriDiv21(q, a, F.n, F.k3);\n else\n TriDivX1(q, a, F.n, F.k3);\n }\n else if (F.method == GF2X_MOD_PENT) {\n if (da <= 2*(n-1)) \n PentDiv21(q, a, F.n, F.k3, F.k2, F.k1);\n else\n PentDivX1(q, a, F.n, F.k3, F.k2, F.k1);\n }\n else if (F.method == GF2X_MOD_MUL) {\n if (da <= 2*(n-1)) \n UseMulDiv21(q, a, F);\n else\n UseMulDivX1(q, a, F);\n }\n else if (F.method == GF2X_MOD_SPECIAL) {\n long sa = a.xrep.length();\n long posa = da - NTL_BITS_PER_LONG*(sa-1);\n \n long dq = da - n;\n long sq = dq/NTL_BITS_PER_LONG + 1;\n long posq = dq - NTL_BITS_PER_LONG*(sq-1);\n \n _ntl_ulong *ap;\n GF2X_rembuf = a.xrep;\n ap = GF2X_rembuf.elts();\n\n \n _ntl_ulong *atop = &ap[sa-1];\n _ntl_ulong *stab_top;\n\n long i;\n\n q.xrep.SetLength(sq);\n _ntl_ulong *qp = q.xrep.elts();\n for (i = 0; i < sq; i++)\n qp[i] = 0;\n\n _ntl_ulong *qtop = &qp[sq-1];\n \n while (1) {\n if (atop[0] & (1UL << posa)) {\n qtop[0] |= (1UL << posq);\n stab_top = &F.stab1[posa << 1];\n i = F.stab_cnt[posa];\n atop[i] ^= stab_top[0];\n atop[i+1] ^= stab_top[1];\n }\n \n da--;\n if (da < n) break;\n\n posa--;\n if (posa < 0) {\n posa = NTL_BITS_PER_LONG-1;\n atop--;\n }\n\n posq--;\n if (posq < 0) {\n posq = NTL_BITS_PER_LONG-1;\n qtop--;\n }\n }\n }\n else {\n long sa = a.xrep.length();\n long posa = da - NTL_BITS_PER_LONG*(sa-1);\n \n long dq = da - n;\n long sq = dq/NTL_BITS_PER_LONG + 1;\n long posq = dq - NTL_BITS_PER_LONG*(sq-1);\n \n _ntl_ulong *ap;\n GF2X_rembuf = a.xrep;\n ap = GF2X_rembuf.elts();\n \n _ntl_ulong *atop = &ap[sa-1];\n _ntl_ulong *stab_top;\n \n long i;\n\n q.xrep.SetLength(sq);\n _ntl_ulong *qp = q.xrep.elts();\n for (i = 0; i < sq; i++)\n qp[i] = 0;\n\n _ntl_ulong *qtop = &qp[sq-1];\n \n while (1) {\n if (atop[0] & (1UL << posa)) {\n qtop[0] |= (1UL << posq);\n stab_top = F.stab_ptr[posa];\n for (i = F.stab_cnt[posa]; i <= 0; i++)\n atop[i] ^= stab_top[i];\n }\n \n da--;\n if (da < n) break;\n\n posa--;\n if (posa < 0) {\n posa = NTL_BITS_PER_LONG-1;\n atop--;\n }\n\n posq--;\n if (posq < 0) {\n posq = NTL_BITS_PER_LONG-1;\n qtop--;\n }\n }\n }\n\n GF2X_rembuf.release();\n}\n\n\nvoid MulMod(GF2X& c, const GF2X& a, const GF2X& b, const GF2XModulus& F)\n{\n if (F.n < 0) Error(\"MulMod: uninitialized modulus\");\n\n GF2XRegister(t);\n mul(t, a, b);\n rem(c, t, F);\n}\n\n\nvoid SqrMod(GF2X& c, const GF2X& a, const GF2XModulus& F)\n{\n if (F.n < 0) Error(\"SqrMod: uninitialized modulus\");\n\n GF2XRegister(t);\n sqr(t, a);\n rem(c, t, F);\n}\n\n\n// we need these two versions to prevent a GF2XModulus\n// from being constructed.\n\n\nvoid MulMod(GF2X& c, const GF2X& a, const GF2X& b, const GF2X& f)\n{\n GF2XRegister(t);\n mul(t, a, b);\n rem(c, t, f);\n}\n\nvoid SqrMod(GF2X& c, const GF2X& a, const GF2X& f)\n{\n GF2XRegister(t);\n sqr(t, a);\n rem(c, t, f);\n}\n\n\nstatic\nlong OptWinSize(long n)\n// finds k that minimizes n/(k+1) + 2^{k-1}\n\n{\n long k;\n double v, v_new;\n\n\n v = n/2.0 + 1.0;\n k = 1;\n\n for (;;) {\n v_new = n/(double(k+2)) + double(1L << k);\n if (v_new >= v) break;\n v = v_new;\n k++;\n }\n\n return k;\n}\n \n\n\nvoid PowerMod(GF2X& h, const GF2X& g, const ZZ& e, const GF2XModulus& F)\n// h = g^e mod f using \"sliding window\" algorithm\n{\n if (deg(g) >= F.n) Error(\"PowerMod: bad args\");\n\n if (e == 0) {\n set(h);\n return;\n }\n\n if (e == 1) {\n h = g;\n return;\n }\n\n if (e == -1) {\n InvMod(h, g, F);\n return;\n }\n\n if (e == 2) {\n SqrMod(h, g, F);\n return;\n }\n\n if (e == -2) {\n SqrMod(h, g, F);\n InvMod(h, h, F);\n return;\n }\n\n\n long n = NumBits(e);\n\n GF2X res;\n res.SetMaxLength(F.n);\n set(res);\n\n long i;\n\n if (n < 16) {\n // plain square-and-multiply algorithm\n\n for (i = n - 1; i >= 0; i--) {\n SqrMod(res, res, F);\n if (bit(e, i))\n MulMod(res, res, g, F);\n }\n\n if (e < 0) InvMod(res, res, F);\n\n h = res;\n return;\n }\n\n long k = OptWinSize(n);\n\n k = min(k, 9);\n\n vec_GF2X v;\n\n v.SetLength(1L << (k-1));\n\n v[0] = g;\n \n if (k > 1) {\n GF2X t;\n SqrMod(t, g, F);\n\n for (i = 1; i < (1L << (k-1)); i++)\n MulMod(v[i], v[i-1], t, F);\n }\n\n\n long val;\n long cnt;\n long m;\n\n val = 0;\n for (i = n-1; i >= 0; i--) {\n val = (val << 1) | bit(e, i); \n if (val == 0)\n SqrMod(res, res, F);\n else if (val >= (1L << (k-1)) || i == 0) {\n cnt = 0;\n while ((val & 1) == 0) {\n val = val >> 1;\n cnt++;\n }\n\n m = val;\n while (m > 0) {\n SqrMod(res, res, F);\n m = m >> 1;\n }\n\n MulMod(res, res, v[val >> 1], F);\n\n while (cnt > 0) {\n SqrMod(res, res, F);\n cnt--;\n }\n\n val = 0;\n }\n }\n\n if (e < 0) InvMod(res, res, F);\n\n h = res;\n}\n\n \n\n\nvoid PowerXMod(GF2X& hh, const ZZ& e, const GF2XModulus& F)\n{\n if (F.n < 0) Error(\"PowerXMod: uninitialized modulus\");\n\n if (IsZero(e)) {\n set(hh);\n return;\n }\n\n long n = NumBits(e);\n long i;\n\n GF2X h;\n\n h.SetMaxLength(F.n+1);\n set(h);\n\n for (i = n - 1; i >= 0; i--) {\n SqrMod(h, h, F);\n if (bit(e, i)) {\n MulByX(h, h);\n if (coeff(h, F.n) != 0)\n add(h, h, F.f);\n }\n }\n\n if (e < 0) InvMod(h, h, F);\n\n hh = h;\n}\n\n\n \n\n\nvoid UseMulRem(GF2X& r, const GF2X& a, const GF2X& b)\n{\n GF2XRegister(P1);\n GF2XRegister(P2);\n\n long da = deg(a);\n long db = deg(b);\n\n CopyReverse(P1, b, db);\n InvTrunc(P2, P1, da-db+1);\n CopyReverse(P1, P2, da-db);\n\n RightShift(P2, a, db);\n mul(P2, P1, P2);\n RightShift(P2, P2, da-db);\n mul(P1, P2, b);\n add(P1, P1, a);\n \n r = P1;\n}\n\nvoid UseMulDivRem(GF2X& q, GF2X& r, const GF2X& a, const GF2X& b)\n{\n GF2XRegister(P1);\n GF2XRegister(P2);\n\n long da = deg(a);\n long db = deg(b);\n\n CopyReverse(P1, b, db);\n InvTrunc(P2, P1, da-db+1);\n CopyReverse(P1, P2, da-db);\n\n RightShift(P2, a, db);\n mul(P2, P1, P2);\n RightShift(P2, P2, da-db);\n mul(P1, P2, b);\n add(P1, P1, a);\n \n r = P1;\n q = P2;\n}\n\nvoid UseMulDiv(GF2X& q, const GF2X& a, const GF2X& b)\n{\n GF2XRegister(P1);\n GF2XRegister(P2);\n\n long da = deg(a);\n long db = deg(b);\n\n CopyReverse(P1, b, db);\n InvTrunc(P2, P1, da-db+1);\n CopyReverse(P1, P2, da-db);\n\n RightShift(P2, a, db);\n mul(P2, P1, P2);\n RightShift(P2, P2, da-db);\n \n q = P2;\n}\n\n\nconst long GF2X_DIV_CROSS = 100; \n\nvoid DivRem(GF2X& q, GF2X& r, const GF2X& a, const GF2X& b)\n{\n long sa = a.xrep.length();\n long sb = b.xrep.length();\n\n if (sb < GF2X_DIV_CROSS || sa-sb < GF2X_DIV_CROSS)\n PlainDivRem(q, r, a, b);\n else if (sa < 4*sb)\n UseMulDivRem(q, r, a, b);\n else {\n GF2XModulus B;\n build(B, b);\n DivRem(q, r, a, B);\n }\n}\n\nvoid div(GF2X& q, const GF2X& a, const GF2X& b)\n{\n long sa = a.xrep.length();\n long sb = b.xrep.length();\n\n if (sb < GF2X_DIV_CROSS || sa-sb < GF2X_DIV_CROSS)\n PlainDiv(q, a, b);\n else if (sa < 4*sb)\n UseMulDiv(q, a, b);\n else {\n GF2XModulus B;\n build(B, b);\n div(q, a, B);\n }\n}\n\nvoid rem(GF2X& r, const GF2X& a, const GF2X& b)\n{\n long sa = a.xrep.length();\n long sb = b.xrep.length();\n\n if (sb < GF2X_DIV_CROSS || sa-sb < GF2X_DIV_CROSS)\n PlainRem(r, a, b);\n else if (sa < 4*sb)\n UseMulRem(r, a, b);\n else {\n GF2XModulus B;\n build(B, b);\n rem(r, a, B);\n }\n}\n\n\nstatic inline \nvoid swap(_ntl_ulong_ptr& a, _ntl_ulong_ptr& b) \n{ _ntl_ulong_ptr t; t = a; a = b; b = t; }\n\n\n\n\nstatic\nvoid BaseGCD(GF2X& d, const GF2X& a_in, const GF2X& b_in)\n{\n GF2XRegister(a);\n GF2XRegister(b);\n \n if (IsZero(a_in)) {\n d = b_in;\n return;\n }\n\n if (IsZero(b_in)) {\n d = a_in;\n return;\n }\n \n a.xrep.SetMaxLength(a_in.xrep.length()+1);\n b.xrep.SetMaxLength(b_in.xrep.length()+1);\n\n a = a_in;\n b = b_in;\n\n _ntl_ulong *ap = a.xrep.elts();\n _ntl_ulong *bp = b.xrep.elts();\n\n long da = deg(a);\n long wa = da/NTL_BITS_PER_LONG;\n long ba = da - wa*NTL_BITS_PER_LONG;\n\n long db = deg(b);\n long wb = db/NTL_BITS_PER_LONG;\n long bb = db - wb*NTL_BITS_PER_LONG;\n\n long parity = 0;\n\n for (;;) {\n if (da < db) {\n swap(ap, bp);\n swap(da, db);\n swap(wa, wb);\n swap(ba, bb);\n parity = 1 - parity;\n }\n\n // da >= db\n\n if (db == -1) break;\n\n ShiftAdd(ap, bp, wb+1, da-db);\n\n _ntl_ulong msk = 1UL << ba;\n _ntl_ulong aa = ap[wa];\n\n while ((aa & msk) == 0) {\n da--;\n msk = msk >> 1;\n ba--;\n if (!msk) {\n wa--;\n ba = NTL_BITS_PER_LONG-1;\n msk = 1UL << (NTL_BITS_PER_LONG-1);\n if (wa < 0) break;\n aa = ap[wa];\n }\n }\n }\n\n a.normalize();\n b.normalize();\n\n if (!parity) {\n d = a;\n }\n else {\n d = b;\n }\n}\n\n\nvoid OldGCD(GF2X& d, const GF2X& a, const GF2X& b)\n{\n long sa = a.xrep.length();\n long sb = b.xrep.length();\n\n if (sb >= 10 && 2*sa > 3*sb) {\n GF2XRegister(r);\n\n rem(r, a, b);\n BaseGCD(d, b, r);\n }\n else if (sa >= 10 && 2*sb > 3*sa) {\n GF2XRegister(r);\n\n rem(r, b, a);\n BaseGCD(d, a, r);\n }\n else {\n BaseGCD(d, a, b);\n }\n}\n\n\n\n\n\n#define XX_STEP(ap,da,wa,ba,rp,sr,bp,db,wb,bb,sp,ss) \\\n long delta = da-db; \\\n \\\n if (delta == 0) { \\\n long i; \\\n for (i = wb; i >= 0; i--) ap[i] ^= bp[i]; \\\n for (i = ss-1; i >= 0; i--) rp[i] ^= sp[i]; \\\n if (ss > sr) sr = ss; \\\n } \\\n else if (delta == 1) { \\\n long i; \\\n _ntl_ulong tt, tt1; \\\n \\\n tt = bp[wb] >> (NTL_BITS_PER_LONG-1); \\\n if (tt) ap[wb+1] ^= tt; \\\n tt = bp[wb]; \\\n for (i = wb; i >= 1; i--) \\\n tt1 = bp[i-1], ap[i] ^= (tt << 1) | (tt1 >> (NTL_BITS_PER_LONG-1)), \\\n tt = tt1; \\\n ap[0] ^= tt << 1; \\\n \\\n if (ss > 0) { \\\n long t = ss; \\\n tt = sp[ss-1] >> (NTL_BITS_PER_LONG-1); \\\n if (tt) rp[ss] ^= tt, t++; \\\n tt = sp[ss-1]; \\\n for (i = ss-1; i >= 1; i--) \\\n tt1=sp[i-1], \\\n rp[i] ^= (tt << 1) | (tt1 >> (NTL_BITS_PER_LONG-1)), \\\n tt = tt1; \\\n rp[0] ^= tt << 1; \\\n if (t > sr) sr = t; \\\n } \\\n } \\\n else if (delta < NTL_BITS_PER_LONG) { \\\n long i; \\\n _ntl_ulong tt, tt1; \\\n long rdelta = NTL_BITS_PER_LONG-delta; \\\n \\\n tt = bp[wb] >> rdelta; \\\n if (tt) ap[wb+1] ^= tt; \\\n tt=bp[wb]; \\\n for (i = wb; i >= 1; i--) \\\n tt1=bp[i-1], ap[i] ^= (tt << delta) | (tt1 >> rdelta), \\\n tt=tt1; \\\n ap[0] ^= tt << delta; \\\n \\\n if (ss > 0) { \\\n long t = ss; \\\n tt = sp[ss-1] >> rdelta; \\\n if (tt) rp[ss] ^= tt, t++; \\\n tt=sp[ss-1]; \\\n for (i = ss-1; i >= 1; i--) \\\n tt1=sp[i-1], rp[i] ^= (tt << delta) | (tt1 >> rdelta), \\\n tt=tt1; \\\n rp[0] ^= tt << delta; \\\n if (t > sr) sr = t; \\\n } \\\n } \\\n else { \\\n ShiftAdd(ap, bp, wb+1, da-db); \\\n ShiftAdd(rp, sp, ss, da-db); \\\n long t = ss + (da-db+NTL_BITS_PER_LONG-1)/NTL_BITS_PER_LONG; \\\n if (t > sr) { \\\n while (t > 0 && rp[t-1] == 0) t--; \\\n sr = t; \\\n } \\\n } \\\n \\\n _ntl_ulong msk = 1UL << ba; \\\n _ntl_ulong aa = ap[wa]; \\\n \\\n while ((aa & msk) == 0) { \\\n da--; \\\n msk = msk >> 1; \\\n ba--; \\\n if (!msk) { \\\n wa--; \\\n ba = NTL_BITS_PER_LONG-1; \\\n msk = 1UL << (NTL_BITS_PER_LONG-1); \\\n if (wa < 0) break; \\\n aa = ap[wa]; \\\n } \\\n } \\\n\n\n\n\nstatic\nvoid XXGCD(GF2X& d, GF2X& r_out, const GF2X& a_in, const GF2X& b_in)\n{\n GF2XRegister(a);\n GF2XRegister(b);\n GF2XRegister(r);\n GF2XRegister(s);\n\n if (IsZero(b_in)) {\n d = a_in;\n set(r_out);\n return;\n }\n\n if (IsZero(a_in)) {\n d = b_in;\n clear(r_out);\n return;\n }\n \n a.xrep.SetMaxLength(a_in.xrep.length()+1);\n b.xrep.SetMaxLength(b_in.xrep.length()+1);\n\n long max_sz = max(a_in.xrep.length(), b_in.xrep.length());\n r.xrep.SetLength(max_sz+1);\n s.xrep.SetLength(max_sz+1);\n\n _ntl_ulong *rp = r.xrep.elts();\n _ntl_ulong *sp = s.xrep.elts();\n\n long i;\n for (i = 0; i <= max_sz; i++) {\n rp[i] = sp[i] = 0;\n }\n\n rp[0] = 1;\n\n long sr = 1;\n long ss = 0;\n\n a = a_in;\n b = b_in;\n\n _ntl_ulong *ap = a.xrep.elts();\n _ntl_ulong *bp = b.xrep.elts();\n\n long da = deg(a);\n long wa = da/NTL_BITS_PER_LONG;\n long ba = da - wa*NTL_BITS_PER_LONG;\n\n long db = deg(b);\n long wb = db/NTL_BITS_PER_LONG;\n long bb = db - wb*NTL_BITS_PER_LONG;\n\n long parity = 0;\n\n\n for (;;) {\n if (da == -1 || db == -1) break;\n\n if (da < db || (da == db && parity)) {\n if (da < db && !parity) parity = 1;\n XX_STEP(bp,db,wb,bb,sp,ss,ap,da,wa,ba,rp,sr)\n\n }\n else {\n parity = 0;\n XX_STEP(ap,da,wa,ba,rp,sr,bp,db,wb,bb,sp,ss)\n }\n }\n\n a.normalize();\n b.normalize();\n r.normalize();\n s.normalize();\n\n if (db == -1) {\n d = a;\n r_out = r;\n }\n else {\n d = b;\n r_out = s;\n }\n}\n\n\n\nstatic\nvoid BaseXGCD(GF2X& d, GF2X& s, GF2X& t, const GF2X& a, const GF2X& b)\n{\n if (IsZero(b)) {\n d = a;\n set(s);\n clear(t);\n }\n else {\n GF2XRegister(t1);\n GF2XRegister(b1);\n\n b1 = b;\n XXGCD(d, s, a, b);\n mul(t1, a, s);\n add(t1, t1, d);\n div(t, t1, b1);\n }\n}\n\n\n\n\nvoid OldXGCD(GF2X& d, GF2X& s, GF2X& t, const GF2X& a, const GF2X& b)\n{\n long sa = a.xrep.length();\n long sb = b.xrep.length();\n\n\n if (sb >= 10 && 2*sa > 3*sb) {\n GF2XRegister(r);\n GF2XRegister(q);\n GF2XRegister(s1);\n GF2XRegister(t1);\n\n\n DivRem(q, r, a, b);\n BaseXGCD(d, s1, t1, b, r);\n\n \n mul(r, t1, q);\n add(r, r, s1); // r = s1 - t1*q, but sign doesn't matter\n\n s = t1;\n t = r; \n }\n else if (sa >= 10 && 2*sb > 3*sa) {\n GF2XRegister(r);\n GF2XRegister(q);\n GF2XRegister(s1);\n GF2XRegister(t1);\n\n\n DivRem(q, r, b, a);\n BaseXGCD(d, s1, t1, a, r);\n\n \n mul(r, t1, q);\n add(r, r, s1); // r = s1 - t1*q, but sign doesn't matter\n\n t = t1;\n s = r; \n }\n else {\n BaseXGCD(d, s, t, a, b);\n }\n\n}\n\n\n\n\nstatic\nvoid BaseInvMod(GF2X& d, GF2X& s, const GF2X& a, const GF2X& f)\n{\n if (deg(a) >= deg(f) || deg(f) == 0) Error(\"InvMod: bad args\");\n\n long sa = a.xrep.length();\n long sf = f.xrep.length();\n\n if ((sa >= 10 && 2*sf > 3*sa) || \n sf > NTL_GF2X_GCD_CROSSOVER/NTL_BITS_PER_LONG) {\n GF2XRegister(t);\n\n XGCD(d, s, t, a, f);\n }\n else {\n XXGCD(d, s, a, f);\n }\n\n}\n\n\n\nvoid InvMod(GF2X& c, const GF2X& a, const GF2X& f)\n{ \n GF2XRegister(d);\n GF2XRegister(s);\n BaseInvMod(d, s, a, f);\n\n if (!IsOne(d)) Error(\"InvMod: inverse undefined\");\n\n c = s;\n}\n\n\n\nlong InvModStatus(GF2X& c, const GF2X& a, const GF2X& f)\n{ \n GF2XRegister(d);\n GF2XRegister(s);\n BaseInvMod(d, s, a, f);\n\n if (!IsOne(d)) {\n c = d;\n return 1;\n }\n\n c = s;\n return 0;\n}\n\n\n\n \nvoid diff(GF2X& c, const GF2X& a)\n{\n RightShift(c, a, 1);\n \n // clear odd coeffs\n\n long dc = deg(c);\n long i;\n for (i = 1; i <= dc; i += 2)\n SetCoeff(c, i, 0);\n}\n\nvoid conv(GF2X& c, long a)\n{\n if (a & 1)\n set(c);\n else\n clear(c);\n}\n\nvoid conv(GF2X& c, GF2 a)\n{\n if (a == 1)\n set(c);\n else\n clear(c);\n}\n\nvoid conv(GF2X& x, const vec_GF2& a)\n{\n x.xrep = a.rep;\n x.normalize();\n}\n\nvoid conv(vec_GF2& x, const GF2X& a)\n{\n VectorCopy(x, a, deg(a)+1);\n}\n\nvoid VectorCopy(vec_GF2& x, const GF2X& a, long n)\n{\n if (n < 0) Error(\"VectorCopy: negative length\"); \n\n if (NTL_OVERFLOW(n, 1, 0))\n Error(\"overflow in VectorCopy\");\n\n long wa = a.xrep.length();\n long wx = (n + NTL_BITS_PER_LONG - 1)/NTL_BITS_PER_LONG;\n\n long wmin = min(wa, wx);\n\n x.SetLength(n);\n\n const _ntl_ulong *ap = a.xrep.elts();\n _ntl_ulong *xp = x.rep.elts();\n\n long i;\n for (i = 0; i < wmin; i++)\n xp[i] = ap[i];\n\n if (wa < wx) {\n for (i = wa; i < wx; i++)\n xp[i] = 0;\n }\n else {\n long p = n % NTL_BITS_PER_LONG;\n if (p != 0)\n xp[wx-1] &= (1UL << p) - 1UL;\n }\n}\n\n\nvoid add(GF2X& c, const GF2X& a, long b)\n{\n c = a;\n if (b & 1) {\n long n = c.xrep.length();\n if (n == 0) \n set(c);\n else {\n c.xrep[0] ^= 1;\n if (n == 1 && !c.xrep[0]) c.xrep.SetLength(0);\n }\n }\n}\n\nvoid add(GF2X& c, const GF2X& a, GF2 b)\n{\n add(c, a, rep(b));\n}\n\n\nvoid MulTrunc(GF2X& c, const GF2X& a, const GF2X& b, long n)\n{\n GF2XRegister(t);\n\n mul(t, a, b);\n trunc(c, t, n);\n}\n\nvoid SqrTrunc(GF2X& c, const GF2X& a, long n)\n{\n GF2XRegister(t);\n\n sqr(t, a);\n trunc(c, t, n);\n}\n\n\nlong divide(GF2X& q, const GF2X& a, const GF2X& b)\n{\n if (IsZero(b)) {\n if (IsZero(a)) {\n clear(q);\n return 1;\n }\n else\n return 0;\n }\n\n GF2XRegister(lq);\n GF2XRegister(r);\n\n DivRem(lq, r, a, b);\n if (!IsZero(r)) return 0;\n q = lq;\n return 1;\n}\n\nlong divide(const GF2X& a, const GF2X& b)\n{\n if (IsZero(b)) return IsZero(a);\n GF2XRegister(r);\n rem(r, a, b);\n if (!IsZero(r)) return 0;\n return 1;\n}\n\n\n\n/*** modular composition routines and data structures ***/\n\n\nvoid InnerProduct(GF2X& x, const GF2X& v, long dv, long low, long high, \n const vec_GF2X& H, long n, WordVector& t)\n{\n long i, j;\n\n _ntl_ulong *tp = t.elts();\n\n for (i = 0; i < n; i++)\n tp[i] = 0;\n\n\n long w_low = low/NTL_BITS_PER_LONG;\n long b_low = low - w_low*NTL_BITS_PER_LONG;\n\n \n const _ntl_ulong *vp = &v.xrep[w_low];\n _ntl_ulong msk = 1UL << b_low;\n _ntl_ulong vv = *vp;\n\n high = min(high, dv);\n\n i = low;\n for (;;) {\n if (vv & msk) {\n const WordVector& h = H[i-low].xrep;\n long m = h.length();\n const _ntl_ulong *hp = h.elts();\n for (j = 0; j < m; j++)\n tp[j] ^= hp[j];\n }\n\n i++;\n if (i > high) break;\n\n msk = msk << 1;\n if (!msk) {\n msk = 1UL;\n vp++;\n vv = *vp;\n }\n }\n\n x.xrep = t;\n x.normalize();\n}\n\n\nvoid CompMod(GF2X& x, const GF2X& g, const GF2XArgument& A, const GF2XModulus& F)\n{\n long dg = deg(g);\n if (dg <= 0) {\n x = g;\n return;\n }\n\n GF2X s, t;\n WordVector scratch(INIT_SIZE, F.size);\n\n long m = A.H.length() - 1;\n long l = (((dg+1)+m-1)/m) - 1;\n\n InnerProduct(t, g, dg, l*m, l*m + m - 1, A.H, F.size, scratch);\n for (long i = l-1; i >= 0; i--) {\n InnerProduct(s, g, dg, i*m, i*m + m - 1, A.H, F.size, scratch);\n MulMod(t, t, A.H[m], F);\n add(t, t, s);\n }\n\n x = t;\n}\n\nvoid build(GF2XArgument& A, const GF2X& h, const GF2XModulus& F, long m)\n{\n if (m <= 0 || deg(h) >= F.n) Error(\"build GF2XArgument: bad args\");\n\n if (m > F.n) m = F.n;\n\n long i;\n\n A.H.SetLength(m+1);\n\n set(A.H[0]);\n A.H[1] = h;\n for (i = 2; i <= m; i++) \n MulMod(A.H[i], A.H[i-1], h, F);\n}\n\n\nvoid CompMod(GF2X& x, const GF2X& g, const GF2X& h, const GF2XModulus& F)\n // x = g(h) mod f\n{\n long m = SqrRoot(deg(g)+1);\n\n if (m == 0) {\n clear(x);\n return;\n }\n\n GF2XArgument A;\n\n build(A, h, F, m);\n\n CompMod(x, g, A, F);\n}\n\n\n\n\nvoid Comp2Mod(GF2X& x1, GF2X& x2, const GF2X& g1, const GF2X& g2,\n const GF2X& h, const GF2XModulus& F)\n\n{\n long m = SqrRoot(deg(g1) + deg(g2) + 2);\n\n if (m == 0) {\n clear(x1);\n clear(x2);\n return;\n }\n\n GF2XArgument A;\n\n build(A, h, F, m);\n\n GF2X xx1, xx2;\n\n CompMod(xx1, g1, A, F);\n CompMod(xx2, g2, A, F);\n\n x1 = xx1;\n x2 = xx2;\n}\n\nvoid Comp3Mod(GF2X& x1, GF2X& x2, GF2X& x3, \n const GF2X& g1, const GF2X& g2, const GF2X& g3,\n const GF2X& h, const GF2XModulus& F)\n\n{\n long m = SqrRoot(deg(g1) + deg(g2) + deg(g3) + 3);\n\n if (m == 0) {\n clear(x1);\n clear(x2);\n clear(x3);\n return;\n }\n\n GF2XArgument A;\n\n build(A, h, F, m);\n\n GF2X xx1, xx2, xx3;\n\n CompMod(xx1, g1, A, F);\n CompMod(xx2, g2, A, F);\n CompMod(xx3, g3, A, F);\n\n x1 = xx1;\n x2 = xx2;\n x3 = xx3;\n}\n\n\n\nvoid build(GF2XTransMultiplier& B, const GF2X& b, const GF2XModulus& F)\n{\n long db = deg(b);\n\n if (db >= F.n) Error(\"build TransMultiplier: bad args\");\n\n GF2X t;\n\n LeftShift(t, b, F.n-1);\n div(t, t, F);\n\n // we optimize for low degree b\n\n long d;\n\n d = deg(t);\n if (d < 0)\n B.shamt_fbi = 0;\n else\n B.shamt_fbi = F.n-2 - d; \n\n CopyReverse(B.fbi, t, d);\n\n if (F.method != GF2X_MOD_TRI && F.method != GF2X_MOD_PENT) {\n \n // The following code optimizes the case when \n // f = X^n + low degree poly\n \n trunc(t, F.f, F.n);\n d = deg(t);\n if (d < 0)\n B.shamt = 0;\n else\n B.shamt = d;\n\n CopyReverse(B.f0, t, d);\n }\n\n\n if (db < 0)\n B.shamt_b = 0;\n else\n B.shamt_b = db;\n\n CopyReverse(B.b, b, db);\n}\n\nvoid TransMulMod(GF2X& x, const GF2X& a, const GF2XTransMultiplier& B,\n const GF2XModulus& F)\n{\n if (deg(a) >= F.n) Error(\"TransMulMod: bad args\");\n\n GF2XRegister(t1);\n GF2XRegister(t2);\n GF2XRegister(t3);\n\n mul(t1, a, B.b);\n RightShift(t1, t1, B.shamt_b);\n\n if (F.method == GF2X_MOD_TRI) {\n RightShift(t2, a, F.k3);\n add(t2, t2, a);\n }\n else if (F.method == GF2X_MOD_PENT) {\n RightShift(t2, a, F.k3);\n RightShift(t3, a, F.k2);\n add(t2, t2, t3);\n RightShift(t3, a, F.k1);\n add(t2, t2, t3);\n add(t2, t2, a);\n }\n else {\n mul(t2, a, B.f0);\n RightShift(t2, t2, B.shamt);\n }\n\n trunc(t2, t2, F.n-1);\n\n mul(t2, t2, B.fbi);\n if (B.shamt_fbi > 0) LeftShift(t2, t2, B.shamt_fbi);\n trunc(t2, t2, F.n-1);\n MulByX(t2, t2);\n\n add(x, t1, t2);\n}\n\nvoid UpdateMap(vec_GF2& x, const vec_GF2& a, const GF2XTransMultiplier& B,\n const GF2XModulus& F)\n{\n GF2XRegister(xx);\n GF2XRegister(aa);\n conv(aa, a);\n TransMulMod(xx, aa, B, F);\n conv(x, xx);\n}\n \n\nvoid ProjectPowers(GF2X& x, const GF2X& a, long k, const GF2XArgument& H,\n const GF2XModulus& F)\n{\n long n = F.n;\n\n if (deg(a) >= n || k < 0 || NTL_OVERFLOW(k, 1, 0)) \n Error(\"ProjectPowers: bad args\");\n\n long m = H.H.length()-1;\n long l = (k+m-1)/m - 1;\n\n GF2XTransMultiplier M;\n build(M, H.H[m], F);\n\n GF2X s;\n s = a;\n\n x.SetMaxLength(k);\n clear(x);\n\n long i;\n\n for (i = 0; i <= l; i++) {\n long m1 = min(m, k-i*m);\n for (long j = 0; j < m1; j++)\n SetCoeff(x, i*m+j, InnerProduct(H.H[j].xrep, s.xrep));\n if (i < l)\n TransMulMod(s, s, M, F);\n }\n}\n\n\nvoid ProjectPowers(vec_GF2& x, const vec_GF2& a, long k, \n const GF2XArgument& H, const GF2XModulus& F)\n{\n GF2X xx;\n ProjectPowers(xx, to_GF2X(a), k, H, F);\n VectorCopy(x, xx, k);\n}\n\n\nvoid ProjectPowers(GF2X& x, const GF2X& a, long k, const GF2X& h, \n const GF2XModulus& F)\n{\n if (deg(a) >= F.n || k < 0) Error(\"ProjectPowers: bad args\");\n\n if (k == 0) {\n clear(x);\n return;\n }\n\n long m = SqrRoot(k);\n\n GF2XArgument H;\n build(H, h, F, m);\n\n ProjectPowers(x, a, k, H, F);\n}\n\nvoid ProjectPowers(vec_GF2& x, const vec_GF2& a, long k, const GF2X& H,\n const GF2XModulus& F)\n{\n GF2X xx;\n ProjectPowers(xx, to_GF2X(a), k, H, F);\n VectorCopy(x, xx, k);\n}\n\n\nvoid OldMinPolyInternal(GF2X& h, const GF2X& x, long m)\n{\n GF2X a, b, r, s;\n GF2X a_in, b_in;\n\n if (IsZero(x)) {\n set(h);\n return;\n }\n\n clear(a_in);\n SetCoeff(a_in, 2*m);\n\n CopyReverse(b_in, x, 2*m-1);\n \n a.xrep.SetMaxLength(a_in.xrep.length()+1);\n b.xrep.SetMaxLength(b_in.xrep.length()+1);\n\n long max_sz = max(a_in.xrep.length(), b_in.xrep.length());\n r.xrep.SetLength(max_sz+1);\n s.xrep.SetLength(max_sz+1);\n\n _ntl_ulong *rp = r.xrep.elts();\n _ntl_ulong *sp = s.xrep.elts();\n\n long i;\n for (i = 0; i <= max_sz; i++) {\n rp[i] = sp[i] = 0;\n }\n\n sp[0] = 1;\n\n long sr = 0;\n long ss = 1;\n\n a = a_in;\n b = b_in;\n\n _ntl_ulong *ap = a.xrep.elts();\n _ntl_ulong *bp = b.xrep.elts();\n\n long da = deg(a);\n long wa = da/NTL_BITS_PER_LONG;\n long ba = da - wa*NTL_BITS_PER_LONG;\n\n long db = deg(b);\n long wb = db/NTL_BITS_PER_LONG;\n long bb = db - wb*NTL_BITS_PER_LONG;\n\n long parity = 0;\n\n for (;;) {\n if (da < db) {\n swap(ap, bp);\n swap(da, db);\n swap(wa, wb);\n swap(ba, bb);\n parity = 1 - parity;\n\n swap(rp, sp);\n swap(sr, ss);\n }\n\n // da >= db\n\n if (db < m) break;\n\n ShiftAdd(ap, bp, wb+1, da-db);\n ShiftAdd(rp, sp, ss, da-db);\n long t = ss + (da-db+NTL_BITS_PER_LONG-1)/NTL_BITS_PER_LONG;\n if (t > sr) {\n while (t > 0 && rp[t-1] == 0) t--; \n sr = t;\n }\n\n _ntl_ulong msk = 1UL << ba;\n _ntl_ulong aa = ap[wa];\n\n while ((aa & msk) == 0) {\n da--;\n msk = msk >> 1;\n ba--;\n if (!msk) {\n wa--;\n ba = NTL_BITS_PER_LONG-1;\n msk = 1UL << (NTL_BITS_PER_LONG-1);\n if (wa < 0) break;\n aa = ap[wa];\n }\n }\n }\n\n a.normalize();\n b.normalize();\n r.normalize();\n s.normalize();\n\n if (!parity) {\n h = s;\n }\n else {\n h = r;\n }\n}\n\n\nvoid DoMinPolyMod(GF2X& h, const GF2X& g, const GF2XModulus& F, long m, \n const GF2X& R)\n{\n GF2X x;\n\n ProjectPowers(x, R, 2*m, g, F);\n MinPolyInternal(h, x, m);\n}\n\nvoid MinPolySeq(GF2X& h, const vec_GF2& a, long m)\n{\n if (m < 0 || NTL_OVERFLOW(m, 1, 0)) Error(\"MinPoly: bad args\");\n if (a.length() < 2*m) Error(\"MinPoly: sequence too short\");\n GF2X x;\n x.xrep = a.rep;\n x.normalize();\n MinPolyInternal(h, x, m);\n}\n\nvoid ProbMinPolyMod(GF2X& h, const GF2X& g, const GF2XModulus& F, long m)\n{\n long n = F.n;\n if (m < 1 || m > n) Error(\"ProbMinPoly: bad args\");\n\n GF2X R;\n random(R, n);\n\n DoMinPolyMod(h, g, F, m, R);\n}\n\nvoid ProbMinPolyMod(GF2X& h, const GF2X& g, const GF2XModulus& F)\n{\n ProbMinPolyMod(h, g, F, F.n);\n}\n\nvoid MinPolyMod(GF2X& hh, const GF2X& g, const GF2XModulus& F, long m)\n{\n GF2X h, h1;\n long n = F.n;\n if (m < 1 || m > n) Error(\"MinPoly: bad args\");\n\n /* probabilistically compute min-poly */\n\n ProbMinPolyMod(h, g, F, m);\n if (deg(h) == m) { hh = h; return; }\n CompMod(h1, h, g, F);\n if (IsZero(h1)) { hh = h; return; }\n\n /* not completely successful...must iterate */\n\n\n GF2X h2, h3;\n GF2X R;\n GF2XTransMultiplier H1;\n \n\n for (;;) {\n random(R, n);\n build(H1, h1, F);\n TransMulMod(R, R, H1, F);\n DoMinPolyMod(h2, g, F, m-deg(h), R);\n\n mul(h, h, h2);\n if (deg(h) == m) { hh = h; return; }\n CompMod(h3, h2, g, F);\n MulMod(h1, h3, h1, F);\n if (IsZero(h1)) { hh = h; return; }\n }\n}\n\nvoid IrredPolyMod(GF2X& h, const GF2X& g, const GF2XModulus& F, long m)\n{\n if (m < 1 || m > F.n) Error(\"IrredPoly: bad args\");\n\n GF2X R;\n set(R);\n\n DoMinPolyMod(h, g, F, m, R);\n}\n\n\n\nvoid IrredPolyMod(GF2X& h, const GF2X& g, const GF2XModulus& F)\n{\n IrredPolyMod(h, g, F, F.n);\n}\n\n\n\nvoid MinPolyMod(GF2X& hh, const GF2X& g, const GF2XModulus& F)\n{\n MinPolyMod(hh, g, F, F.n);\n}\n\n\n\nvoid MulByXMod(GF2X& c, const GF2X& a, const GF2XModulus& F)\n{\n long da = deg(a);\n long df = deg(F);\n if (da >= df) Error(\"MulByXMod: bad args\"); \n\n MulByX(c, a);\n\n if (da >= 0 && da == df-1)\n add(c, c, F);\n}\n\nstatic\nvoid MulByXModAux(GF2X& c, const GF2X& a, const GF2X& f)\n{\n long da = deg(a);\n long df = deg(f);\n if (da >= df) Error(\"MulByXMod: bad args\"); \n\n MulByX(c, a);\n\n if (da >= 0 && da == df-1)\n add(c, c, f);\n}\n\nvoid MulByXMod(GF2X& h, const GF2X& a, const GF2X& f)\n{\n if (&h == &f) {\n GF2X hh;\n MulByXModAux(hh, a, f);\n h = hh;\n }\n else\n MulByXModAux(h, a, f);\n}\n\n\n\n\nvoid power(GF2X& x, const GF2X& a, long e)\n{\n if (e < 0) {\n Error(\"power: negative exponent\");\n }\n\n if (e == 0) {\n x = 1;\n return;\n }\n\n if (a == 0 || a == 1) {\n x = a;\n return;\n }\n\n long da = deg(a);\n\n if (da > (NTL_MAX_LONG-1)/e)\n Error(\"overflow in power\");\n\n GF2X res;\n res.SetMaxLength(da*e + 1);\n res = 1;\n \n long k = NumBits(e);\n long i;\n\n for (i = k - 1; i >= 0; i--) {\n sqr(res, res);\n if (bit(e, i))\n mul(res, res, a);\n }\n\n x = res;\n}\n\n\nstatic\nvoid FastTraceVec(vec_GF2& S, const GF2XModulus& f)\n{\n long n = deg(f);\n\n if (n <= 0) Error(\"TraceVec: bad args\");\n\n GF2X x = reverse(-LeftShift(reverse(diff(reverse(f)), n-1), n-1)/f, n-1);\n\n VectorCopy(S, x, n);\n S.put(0, to_GF2(n));\n}\n\nstatic\nvoid PlainTraceVec(vec_GF2& S, const GF2X& f)\n{\n long n = deg(f);\n\n if (n <= 0) \n Error(\"TraceVec: bad args\");\n\n if (n == 0) {\n S.SetLength(0);\n return;\n }\n\n GF2X x = reverse(-LeftShift(reverse(diff(reverse(f)), n-1), n-1)/f, n-1);\n\n VectorCopy(S, x, n); \n S.put(0, to_GF2(n));\n}\n\n\nvoid TraceVec(vec_GF2& S, const GF2X& f)\n{\n PlainTraceVec(S, f);\n}\n\nstatic\nvoid ComputeTraceVec(const GF2XModulus& F)\n{\n vec_GF2& S = *((vec_GF2 *) &F.tracevec);\n\n if (S.length() > 0)\n return;\n\n if (F.method == GF2X_MOD_PLAIN) {\n PlainTraceVec(S, F.f);\n }\n else {\n FastTraceVec(S, F);\n }\n}\n\nvoid TraceMod(GF2& x, const GF2X& a, const GF2XModulus& F)\n{\n long n = F.n;\n\n if (deg(a) >= n)\n Error(\"trace: bad args\");\n\n if (F.tracevec.length() == 0) \n ComputeTraceVec(F);\n\n project(x, F.tracevec, a);\n}\n\nvoid TraceMod(GF2& x, const GF2X& a, const GF2X& f)\n{\n if (deg(a) >= deg(f) || deg(f) <= 0)\n Error(\"trace: bad args\");\n\n project(x, TraceVec(f), a);\n}\n\n\n\n// New versions of GCD, XGCD, and MinPolyInternal\n// and support routines\n\nclass _NTL_GF2XMatrix {\nprivate:\n\n _NTL_GF2XMatrix(const _NTL_GF2XMatrix&); // disable\n GF2X elts[2][2];\n\npublic:\n\n _NTL_GF2XMatrix() { }\n ~_NTL_GF2XMatrix() { }\n\n void operator=(const _NTL_GF2XMatrix&);\n GF2X& operator() (long i, long j) { return elts[i][j]; }\n const GF2X& operator() (long i, long j) const { return elts[i][j]; }\n};\n\n\nvoid _NTL_GF2XMatrix::operator=(const _NTL_GF2XMatrix& M)\n{\n elts[0][0] = M.elts[0][0];\n elts[0][1] = M.elts[0][1];\n elts[1][0] = M.elts[1][0];\n elts[1][1] = M.elts[1][1];\n}\n\n\nstatic\nvoid mul(GF2X& U, GF2X& V, const _NTL_GF2XMatrix& M)\n// (U, V)^T = M*(U, V)^T\n{\n GF2X t1, t2, t3;\n\n mul(t1, M(0,0), U);\n mul(t2, M(0,1), V);\n add(t3, t1, t2);\n mul(t1, M(1,0), U);\n mul(t2, M(1,1), V);\n add(V, t1, t2);\n U = t3;\n}\n\n\nstatic\nvoid mul(_NTL_GF2XMatrix& A, _NTL_GF2XMatrix& B, _NTL_GF2XMatrix& C)\n// A = B*C, B and C are destroyed\n{\n GF2X t1, t2;\n\n mul(t1, B(0,0), C(0,0));\n mul(t2, B(0,1), C(1,0));\n add(A(0,0), t1, t2);\n\n mul(t1, B(1,0), C(0,0));\n mul(t2, B(1,1), C(1,0));\n add(A(1,0), t1, t2);\n\n mul(t1, B(0,0), C(0,1));\n mul(t2, B(0,1), C(1,1));\n add(A(0,1), t1, t2);\n\n mul(t1, B(1,0), C(0,1));\n mul(t2, B(1,1), C(1,1));\n add(A(1,1), t1, t2);\n\n long i, j;\n for (i = 0; i < 2; i++) {\n for (j = 0; j < 2; j++) {\n B(i,j).kill();\n C(i,j).kill();\n }\n }\n}\n\nstatic\nvoid IterHalfGCD(_NTL_GF2XMatrix& M_out, GF2X& U, GF2X& V, long d_red)\n{\n M_out(0,0).SetMaxLength(d_red);\n M_out(0,1).SetMaxLength(d_red);\n M_out(1,0).SetMaxLength(d_red);\n M_out(1,1).SetMaxLength(d_red);\n\n set(M_out(0,0)); clear(M_out(0,1));\n clear(M_out(1,0)); set(M_out(1,1));\n\n long goal = deg(U) - d_red;\n\n if (deg(V) <= goal)\n return;\n\n GF2X Q, t(INIT_SIZE, d_red);\n\n while (deg(V) > goal) {\n DivRem(Q, U, U, V);\n swap(U, V);\n\n mul(t, Q, M_out(1,0));\n sub(t, M_out(0,0), t);\n M_out(0,0) = M_out(1,0);\n M_out(1,0) = t;\n\n mul(t, Q, M_out(1,1));\n sub(t, M_out(0,1), t);\n M_out(0,1) = M_out(1,1);\n M_out(1,1) = t;\n }\n}\n\n\n\nstatic\nvoid HalfGCD(_NTL_GF2XMatrix& M_out, const GF2X& U, const GF2X& V, long d_red)\n{\n if (IsZero(V) || deg(V) <= deg(U) - d_red) {\n set(M_out(0,0)); clear(M_out(0,1));\n clear(M_out(1,0)); set(M_out(1,1));\n\n return;\n }\n\n\n long n = deg(U) - 2*d_red + 2;\n if (n < 0) n = 0;\n\n GF2X U1, V1;\n\n RightShift(U1, U, n);\n RightShift(V1, V, n);\n\n if (d_red <= NTL_GF2X_HalfGCD_CROSSOVER) {\n IterHalfGCD(M_out, U1, V1, d_red);\n return;\n }\n\n long d1 = (d_red + 1)/2;\n if (d1 < 1) d1 = 1;\n if (d1 >= d_red) d1 = d_red - 1;\n\n _NTL_GF2XMatrix M1;\n\n HalfGCD(M1, U1, V1, d1);\n mul(U1, V1, M1);\n\n\n long d2 = deg(V1) - deg(U) + n + d_red;\n\n if (IsZero(V1) || d2 <= 0) {\n M_out = M1;\n return;\n }\n\n\n GF2X Q;\n _NTL_GF2XMatrix M2;\n\n DivRem(Q, U1, U1, V1);\n swap(U1, V1);\n\n HalfGCD(M2, U1, V1, d2);\n\n GF2X t(INIT_SIZE, deg(M1(1,1))+deg(Q)+1);\n\n mul(t, Q, M1(1,0));\n sub(t, M1(0,0), t);\n swap(M1(0,0), M1(1,0));\n swap(M1(1,0), t);\n\n t.kill();\n\n t.SetMaxLength(deg(M1(1,1))+deg(Q)+1);\n\n mul(t, Q, M1(1,1));\n sub(t, M1(0,1), t);\n swap(M1(0,1), M1(1,1));\n swap(M1(1,1), t);\n\n t.kill();\n\n mul(M_out, M2, M1);\n}\n\nstatic\nvoid HalfGCD(GF2X& U, GF2X& V)\n{\n long d_red = (deg(U)+1)/2;\n\n if (IsZero(V) || deg(V) <= deg(U) - d_red) {\n return;\n }\n\n long du = deg(U);\n\n\n long d1 = (d_red + 1)/2;\n if (d1 < 1) d1 = 1;\n if (d1 >= d_red) d1 = d_red - 1;\n\n _NTL_GF2XMatrix M1;\n\n HalfGCD(M1, U, V, d1);\n mul(U, V, M1);\n\n long d2 = deg(V) - du + d_red;\n\n if (IsZero(V) || d2 <= 0) {\n return;\n }\n\n M1(0,0).kill();\n M1(0,1).kill();\n M1(1,0).kill();\n M1(1,1).kill();\n\n\n GF2X Q;\n\n DivRem(Q, U, U, V);\n swap(U, V);\n\n HalfGCD(M1, U, V, d2);\n\n mul(U, V, M1);\n}\n\n\nvoid GCD(GF2X& d, const GF2X& u, const GF2X& v)\n{\n long su = u.xrep.length();\n long sv = v.xrep.length();\n\n if (su <= NTL_GF2X_GCD_CROSSOVER/NTL_BITS_PER_LONG &&\n sv <= NTL_GF2X_GCD_CROSSOVER/NTL_BITS_PER_LONG) {\n OldGCD(d, u, v);\n return;\n }\n \n GF2X u1, v1;\n\n u1 = u;\n v1 = v;\n\n long du1 = deg(u1);\n long dv1 = deg(v1);\n\n if (du1 == dv1) {\n if (IsZero(u1)) {\n clear(d);\n return;\n }\n\n rem(v1, v1, u1);\n }\n else if (du1 < dv1) {\n swap(u1, v1);\n du1 = dv1;\n }\n\n // deg(u1) > deg(v1)\n\n while (du1 >= NTL_GF2X_GCD_CROSSOVER && !IsZero(v1)) {\n HalfGCD(u1, v1);\n\n if (!IsZero(v1)) {\n rem(u1, u1, v1);\n swap(u1, v1);\n }\n\n du1 = deg(u1);\n }\n\n OldGCD(d, u1, v1);\n}\n\nstatic\nvoid XHalfGCD(_NTL_GF2XMatrix& M_out, GF2X& U, GF2X& V, long d_red)\n{\n if (IsZero(V) || deg(V) <= deg(U) - d_red) {\n set(M_out(0,0)); clear(M_out(0,1));\n clear(M_out(1,0)); set(M_out(1,1));\n\n return;\n }\n\n long du = deg(U);\n\n if (d_red <= NTL_GF2X_HalfGCD_CROSSOVER) {\n IterHalfGCD(M_out, U, V, d_red);\n return;\n }\n\n long d1 = (d_red + 1)/2;\n if (d1 < 1) d1 = 1;\n if (d1 >= d_red) d1 = d_red - 1;\n\n _NTL_GF2XMatrix M1;\n\n HalfGCD(M1, U, V, d1);\n mul(U, V, M1);\n\n long d2 = deg(V) - du + d_red;\n\n if (IsZero(V) || d2 <= 0) {\n M_out = M1;\n return;\n }\n\n\n GF2X Q;\n _NTL_GF2XMatrix M2;\n\n DivRem(Q, U, U, V);\n swap(U, V);\n\n XHalfGCD(M2, U, V, d2);\n\n\n GF2X t(INIT_SIZE, deg(M1(1,1))+deg(Q)+1);\n\n mul(t, Q, M1(1,0));\n sub(t, M1(0,0), t);\n swap(M1(0,0), M1(1,0));\n swap(M1(1,0), t);\n\n t.kill();\n\n t.SetMaxLength(deg(M1(1,1))+deg(Q)+1);\n\n mul(t, Q, M1(1,1));\n sub(t, M1(0,1), t);\n swap(M1(0,1), M1(1,1));\n swap(M1(1,1), t);\n\n t.kill();\n\n mul(M_out, M2, M1);\n}\n\n\n\n\nvoid XGCD(GF2X& d, GF2X& s, GF2X& t, const GF2X& a, const GF2X& b)\n{\n // GF2 w;\n\n long sa = a.xrep.length();\n long sb = b.xrep.length();\n\n if (sa <= NTL_GF2X_GCD_CROSSOVER/NTL_BITS_PER_LONG &&\n sb <= NTL_GF2X_GCD_CROSSOVER/NTL_BITS_PER_LONG) {\n OldXGCD(d, s, t, a, b);\n return;\n }\n\n GF2X U, V, Q;\n\n U = a;\n V = b;\n\n long flag = 0;\n\n if (deg(U) == deg(V)) {\n DivRem(Q, U, U, V);\n swap(U, V);\n flag = 1;\n }\n else if (deg(U) < deg(V)) {\n swap(U, V);\n flag = 2;\n }\n\n _NTL_GF2XMatrix M;\n\n XHalfGCD(M, U, V, deg(U)+1);\n\n d = U;\n\n\n if (flag == 0) {\n s = M(0,0);\n t = M(0,1);\n }\n else if (flag == 1) {\n s = M(0,1);\n mul(t, Q, M(0,1));\n sub(t, M(0,0), t);\n }\n else { /* flag == 2 */\n s = M(0,1);\n t = M(0,0);\n }\n\n // normalize\n\n // inv(w, LeadCoeff(d));\n // mul(d, d, w);\n // mul(s, s, w);\n // mul(t, t, 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YES\n2. NO", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.24253969590858088}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n//#include \n\nenum KernelType {CubicSpline = 0, WendlandC2 = 1, WendlandC4 = 2};\n\n#include \"particle_simulator.hpp\"\n#include \"hdr_time.hpp\"\n#include \"hdr_run.hpp\"\n#ifdef USE_INTRINSICS\n#include \"vector_x86.hpp\"\n#endif\n#include \"hdr_dimension.hpp\"\n#include \"hdr_kernel.hpp\"\n#include \"hdr_sph.hpp\"\n#include \"hdr_hgas.hpp\"\n#include \"hdr_bhns.hpp\"\n\nnamespace Alert{\n bool BeyondBoundary = false;\n //PS::F64 MinimumOfDensity = 1.;\n PS::F64 MinimumOfDensity = 1e-6;\n //PS::F64 MaximumOfZCoordinate = 1e8;\n PS::F64 MaximumOfZCoordinate = 5e8;\n}\n\nvoid debugByPrintf(char * str) {\n for(PS::S64 i = 0; i < PS::Comm::getNumberOfProc(); i++) {\n if(i == PS::Comm::getRank()) {\n fprintf(stderr, \"Process %8d %s\\n\", PS::Comm::getRank(), str);\n }\n PS::Comm::barrier();\n }\n}\n\nclass SPHAnalysis : public HelmholtzGas {\npublic:\n SPHAnalysis() {\n this->id = 0;\n this->istar = 0;\n this->mass = 0.;\n this->pos = 0.;\n this->vel = 0.;\n this->uene = 0.;\n this->alph = 0.;\n this->alphu = 0.;\n this->ksr = 0.;\n for(PS::S32 k = 0; k < NR::NumberOfNucleon; k++) {\n this->cmps[k] = 0.;\n } \n }\n\n bool judgeCriterion() {\n /*\n if(this->divv > 0.) {\n return true;\n } else {\n return false;\n }\n */\n PS::F64 vzovercs = ((this->pos[2] > 0.) ? (- this->vel[2] / this->vsnd)\n : (this->vel[2] / this->vsnd));\n if(vzovercs > 4.) {\n //if(vzovercs > 8.) {\n return true;\n } else {\n return false;\n }\n }\n\n void readAscii(FILE * fp) {\n fscanf(fp, \"%lld%lld%lf\", &this->id, &this->istar, &this->mass); // 3\n fscanf(fp, \"%lf%lf%lf\", &this->pos[0], &this->pos[1], &this->pos[2]); // 6\n fscanf(fp, \"%lf%lf%lf\", &this->vel[0], &this->vel[1], &this->vel[2]); // 9\n fscanf(fp, \"%lf%lf%lf\", &this->acc[0], &this->acc[1], &this->acc[2]); // 12\n fscanf(fp, \"%lf%lf%lf\", &this->uene, &this->alph, &this->alphu); // 15\n fscanf(fp, \"%lf%lf%6d\", &this->dens, &this->ksr, &this->np); // 18\n fscanf(fp, \"%lf%lf%lf\", &this->vsnd, &this->pres, &this->temp); // 21\n fscanf(fp, \"%lf%lf%lf\", &this->divv, &this->rotv, &this->bswt); // 24\n fscanf(fp, \"%lf%lf%lf\", &this->pot, &this->abar, &this->zbar); // 27\n fscanf(fp, \"%lf\", &this->enuc); // 28\n fscanf(fp, \"%lf%lf%lf\", &this->vsmx, &this->udot, &this->dnuc); // 31\n for(PS::S32 k = 0; k < NuclearReaction::NumberOfNucleon; k++) { // 32 -- 44\n fscanf(fp, \"%lf\", &this->cmps[k]);\n }\n fscanf(fp, \"%lf\", &this->pot3);\n fscanf(fp, \"%lf%lf%lf\", &this->tempmax[0], &this->tempmax[1], &this->tempmax[2]);\n fscanf(fp, \"%lf\", &this->entr);\n }\n\n void writeAscii(FILE *fp) const {\n fprintf(fp, \"%6d %2d %+e\", this->id, this->istar, this->mass); // 3\n fprintf(fp, \" %+e %+e %+e\", this->pos[0], this->pos[1], this->pos[2]); // 6\n fprintf(fp, \" %+e %+e %+e\", this->vel[0], this->vel[1], this->vel[2]); // 9\n fprintf(fp, \" %+e %+e %+e\", this->acc[0], this->acc[1], this->acc[2]); // 12\n fprintf(fp, \" %+e %+e %+e\", this->uene, this->alph, this->alphu); // 15\n fprintf(fp, \" %+e %+e %6d\", this->dens, this->ksr, this->np); // 18\n fprintf(fp, \" %+e %+e %+e\", this->vsnd, this->pres, this->temp); // 21\n fprintf(fp, \" %+e %+e %+e\", this->divv, this->rotv, this->bswt); // 24\n fprintf(fp, \" %+e %+e %+e\", this->pot, this->abar, this->zbar); // 27\n fprintf(fp, \" %+e\", this->enuc); // 28\n fprintf(fp, \" %+e %+e %+e\", this->vsmx, this->udot, this->dnuc); // 31\n for(PS::S32 k = 0; k < NuclearReaction::NumberOfNucleon; k++) { // 32 -- 44\n fprintf(fp, \" %+.3e\", this->cmps[k]);\n }\n fprintf(fp, \" %+e\", this->pot3); // 45\n fprintf(fp, \" %+e %+e %+e\", this->tempmax[0], this->tempmax[1], this->tempmax[2]);\n // 46 -- 48\n fprintf(fp, \" %+e\", this->entr); // 49\n fprintf(fp, \"\\n\");\n }\n\n void writeAscii1Dimension(FILE *fp) const {\n fprintf(fp, \"%+e %+e %+e\", this->pos[2], this->dens, this->vel[2]);\n fprintf(fp, \"\\n\");\n }\n\n void clear() {\n this->dens = 0.;\n this->vel[2] = 0.;\n }\n\n void copyFromForce(const SPHAnalysis & tmpsph) {\n this->dens = tmpsph.dens;\n this->vel[2] = tmpsph.vel[2];\n }\n\n void copyFromFP(const SPHAnalysis & sph) {\n (*this) = sph;\n }\n\n PS::F64 getRSearch() const {\n return this->ksr;\n }\n\n};\n\nclass SPHFitting {\npublic:\n PS::S64 id;\n PS::F64 mass;\n PS::F64vec pos;\n PS::F64vec vel;\n PS::F64 ksr;\n PS::F64 dens;\n\n SPHFitting() {\n this->id = 0;\n this->mass = 0.;\n this->pos = 0.;\n this->vel = 0.;\n this->ksr = 0.;\n this->dens = 0.;\n }\n\n void copyFromSPHAnalysis(const SPHAnalysis & sph) {\n this->id = sph.id;\n this->mass = sph.mass;\n this->pos = sph.pos;\n this->vel = sph.vel;\n this->ksr = sph.ksr;\n this->dens = sph.dens;\n }\n\n void copyToSPHAnalysis(SPHAnalysis & sph) const {\n sph.id = this->id;\n sph.mass = this->mass;\n sph.pos = this->pos;\n sph.vel = this->vel;\n sph.ksr = this->ksr;\n sph.dens = this->dens;\n }\n\n PS::F64vec getPos() const {\n return this->pos;\n }\n\n void setPos(PS::F64vec pos_new) { \n this->pos = pos_new;\n }\n\n void clear() {\n this->dens = 0.;\n this->vel[2] = 0.;\n }\n\n void copyFromForce(const SPHFitting & tmpsph) {\n this->dens = tmpsph.dens;\n this->vel[2] = tmpsph.vel[2];\n }\n\n void copyFromFP(const SPHFitting & sph) {\n (*this) = sph;\n }\n\n PS::F64 getRSearch() const {\n return this->ksr;\n }\n\n void writeAscii(FILE *fp) const {\n fprintf(fp, \"%6d\", this->id);\n fprintf(fp, \"\\n\");\n }\n};\n\n#if 1\ntemplate \nstruct calcFitting {\n void operator () (const Tsph * epi,\n const PS::S32 nip,\n const Tsph * epj,\n const PS::S32 njp,\n Tsph * back) {\n for(PS::S32 i = 0; i < nip; i++) {\n if(epi[i].mass != 0.) {\n continue;\n }\n PS::F64vec ipos = epi[i].pos;\n PS::F64 dn = 0.;\n PS::F64 vz = 0.;\n for(PS::S32 j = 0; j < njp; j++) {\n PS::F64vec dx = ipos - epj[j].pos;\n PS::F64 r2 = dx * dx;\n PS::F64 r1 = sqrt(r2);\n PS::F64 hi = (epj[j].ksr != 0.) ? 1. / epj[j].ksr : 0.;\n PS::F64 di = (epj[j].dens != 0.) ? 1. / epj[j].dens : 0.;\n PS::F64 qj = r1 * hi;\n PS::F64 dnj = epj[j].mass * ND::calcVolumeInverse(hi) * SK::kernel0th(qj);\n PS::F64 vzj = epj[j].vel[2] * epj[j].mass * di\n * ND::calcVolumeInverse(hi) * SK::kernel0th(qj);\n dn += dnj;\n vz += vzj;\n }\n back[i].dens = (dn > Alert::MinimumOfDensity) ? dn : Alert::MinimumOfDensity;\n back[i].vel[2] = vz;\n }\n }\n};\n#else\ntemplate \nstruct calcFitting {\n void operator () (const Tsph * epi,\n const PS::S32 nip,\n const Tsph * epj,\n const PS::S32 njp,\n Tsph * back) {\n for(PS::S32 i = 0; i < nip; i++) {\n if(epi[i].mass != 0.) {\n continue;\n }\n PS::F64 r2min_m = 1e60;\n PS::F64 r2min_p = 1e60;\n bool prsns_m = false;\n bool prsns_p = false;\n PS::S64 id_m = -100;\n PS::S64 id_p = -100;\n for(PS::S32 j = 0; j < njp; j++) {\n if(epj[j].mass == 0.) {\n continue;\n }\n if(epj[j].pos[2] < 0.) {\n continue;\n }\n PS::F64vec dx = epj[j].pos - epi[i].pos;\n PS::F64 r2 = dx * dx;\n if(dx[2] < 0.) {\n if(r2 < r2min_m) {\n r2min_m = r2;\n id_m = j;\n }\n prsns_m = true;\n } else {\n if(r2 < r2min_p) {\n r2min_p = r2;\n id_p = j;\n }\n prsns_p = true;\n }\n }\n PS::F64 pzm = epj[id_m].pos[2];\n PS::F64 pzp = epj[id_p].pos[2];\n PS::F64 pzi = epi[i].pos[2];\n PS::F64 dnm = epj[id_m].dens;\n PS::F64 dnp = epj[id_p].dens;\n PS::F64 vzm = epj[id_m].vel[2];\n PS::F64 vzp = epj[id_p].vel[2];\n if(prsns_m && prsns_p) {\n back[i].dens = ((pzp - pzi) * dnm + (pzi - pzm) * dnp) / (pzp - pzm);\n back[i].vel[2] = ((pzp - pzi) * vzm + (pzi - pzm) * vzp) / (pzp - pzm);\n } else if(prsns_p) {\n back[i].dens = dnp;\n back[i].vel[2] = 0.;\n } else {\n back[i].dens = Alert::MinimumOfDensity;\n back[i].vel[2] = 0.;\n }\n }\n }\n};\n#endif\n\ntemplate \nvoid insertMassLessParticle(Tsph & sph,\n Tzsph & zsph,\n const PS::S64 nx,\n const PS::S64 ny,\n const PS::F64 bx,\n const PS::F64 by,\n const PS::F64 wx,\n bool maxornot,\n bool rightornot,\n const PS::S32 rkglb,\n const PS::S32 idglb) {\n\n PS::S64 nzsph = zsph.size();\n PS::F64 zpmax = 0.;\n for(PS::S64 i = 0; i < nzsph; i++) {\n if(fabs(zsph[i].pos[2]) > zpmax) {\n zpmax = fabs(zsph[i].pos[2]);\n }\n }\n PS::F64 zmmax = Alert::MaximumOfZCoordinate;\n PS::F64 zmmin = - zmmax;\n assert(zmmax > zpmax);\n PS::S64 nmesh = 800;\n PS::F64 msize = (zmmax - zmmin) / (PS::F64)nmesh;\n \n PS::F64 posx = bx + (PS::F64(idglb % nx) + 0.5) * wx;\n PS::F64 posy = by + (PS::F64(idglb / nx) + 0.5) * wx;\n PS::S64 nloc = sph.getNumberOfParticleLocal();\n sph.setNumberOfParticleLocal(nloc+nmesh);\n for(PS::S64 i = 0; i < nmesh; i++) {\n SPHAnalysis tmpsph;\n if(maxornot) {\n tmpsph.id = -1;\n } else if (rightornot) {\n tmpsph.id = -2;\n } else {\n tmpsph.id = -3;\n }\n tmpsph.mass = 0.;\n tmpsph.pos[0] = posx;\n tmpsph.pos[1] = posy;\n tmpsph.pos[2] = zmmin + (PS::F64)i * msize;\n tmpsph.vel = 0.;\n tmpsph.ksr = 0.;\n sph[nloc+i] = tmpsph;\n }\n\n}\n\ntemplate \nvoid search1Dpoint(Tsph & sph,\n const PS::S64 nx,\n const PS::S64 ny,\n const PS::F64 bx,\n const PS::F64 by,\n const PS::F64 wx,\n PS::S64 * nptcl,\n PS::S64 * pcrit,\n PS::F64 * dnmax,\n bool maxornot,\n bool rightornot,\n PS::F64 dnlimit,\n PS::F64 r2limit,\n char * filename,\n PS::F64 * _dnglb,\n PS::F64 * _r2glb) {\n PS::F64 dnloc = -1.;\n PS::S64 idloc = -1;\n for(PS::S64 i = 0; i < nx * ny; i++) {\n PS::F64 fracpcrit = (PS::F64)pcrit[i] / (PS::F64)nptcl[i];\n if(fracpcrit < 0.01 || 0.02 < fracpcrit) {\n continue;\n }\n if(dnmax[i] > dnlimit && (!maxornot)) {\n continue;\n }\n#if 0\n if(!((bx + (PS::F64)(i % nx) * wx - pxlimit > 0.) ^ (!rightornot)) && (!maxornot)) {\n continue;\n }\n#else\n PS::F64 tmpx = bx + (PS::F64)(i % nx) * wx;\n PS::F64 tmpy = by + (PS::F64)(i / nx) * wx;\n PS::F64 tmpr2 = tmpx * tmpx + tmpy * tmpy;\n if(!((tmpr2 - r2limit > 0.) ^ (!rightornot)) && (!maxornot)) {\n continue;\n }\n#endif\n if(bx + (PS::F64)(i % nx) * wx < 0.) {\n continue;\n }\n if(dnloc < dnmax[i]) {\n dnloc = dnmax[i];\n idloc = i;\n }\n }\n\n PS::F64 dnglb = -1.;\n PS::S32 rkglb = -1;\n PS::Comm::getMaxValue(dnloc, PS::Comm::getRank(), dnglb, rkglb);\n PS::S64 idglb = ((PS::Comm::getRank() == rkglb) ? idloc : -1);\n PS::F64 pxglb = bx + (PS::F64)(idloc % nx) * wx;\n PS::F64 pyglb = by + (PS::F64)(idloc / nx) * wx;\n PS::F64 r2glb = pxglb * pxglb + pyglb * pyglb;\n PS::Comm::broadcast(&r2glb, 1, rkglb);\n\n#if 1\n if(maxornot) {\n // run.hewd0.45_bh3e2_b05.00\n // t0096_0\n //rkglb = 71;\n //idglb = 771;\n // t0097_0\n rkglb = 72;\n idglb = 770;\n // run.hewd0.45_bh3e2_b06.00\n // t0095_0\n //rkglb = 70;\n //idglb = 810;\n // t0096_0\n //rkglb = 71;\n //idglb = 800;\n // t0097_0\n //rkglb = 71;\n //idglb = 796;\n // t0098_0\n //rkglb = 70;\n //idglb = 795;\n }\n#else\n#endif\n\n if(PS::Comm::getRank() == rkglb) {\n\n if(maxornot) {\n printf(\"max: %8d %8d\\n\", rkglb, idglb);\n } else if(rightornot){\n printf(\"right: %8d %8d\\n\", rkglb, idglb);\n } else {\n printf(\"left: %8d %8d\\n\", rkglb, idglb);\n }\n\n PS::ReallocatableArray zsph;\n char ofile[1024];\n sprintf(ofile, filename);\n FILE * fp = fopen(ofile, \"w\");\n assert(fp);\n for(PS::S64 i = 0; i < sph.getNumberOfParticleLocal(); i++) {\n PS::F64 dx = sph[i].pos[0] - bx;\n PS::F64 dy = sph[i].pos[1] - by;\n PS::S64 id = 0;\n id = (PS::S64)(dy / wx) * nx;\n id += (PS::S64)(dx / wx) % nx;\n //assert(id < nx * ny);\n if(id >= nx * ny) {\n continue;\n }\n if(id == idglb) {\n sph[i].writeAscii(fp);\n zsph.push_back(sph[i]);\n }\n }\n fclose(fp);\n insertMassLessParticle(sph, zsph, nx, ny, bx, by, wx, maxornot, rightornot, rkglb, idglb);\n }\n\n *_dnglb = dnglb;\n *_r2glb = r2glb;\n}\n \nint main(int argc, char ** argv) {\n PS::Initialize(argc, argv);\n\n PS::DomainInfo dinfo;\n dinfo.initialize();\n dinfo.setDomain(PS::Comm::getNumberOfProc(), 1);\n PS::ParticleSystem sph;\n sph.initialize();\n sph.createParticle(0);\n sph.setNumberOfParticleLocal(0);\n\n char itype[1024];\n PS::S32 fflag, nfile;\n PS::S64 ibgn, iend;\n PS::F64vec xmin;\n PS::F64 xmax;\n PS::F64 wdth;\n PS::S64 nnxx;\n char otype[1024];\n FILE * fp = fopen(argv[1], \"r\");\n fscanf(fp, \"%s\", itype);\n fscanf(fp, \"%d %d\", &fflag, &nfile);\n fscanf(fp, \"%lf%lf%lf\", &xmin[0], &xmin[1], &xmax);\n fscanf(fp, \"%lf%lld\", &wdth, &nnxx);\n fscanf(fp, \"%s\", otype);\n fclose(fp);\n\n if(PS::Comm::getRank() == 0) {\n fprintf(stderr, \"itype: %s\\n\", itype);\n fprintf(stderr, \"fflag: %d nfile: %d\\n\", fflag, nfile);\n fprintf(stderr, \"xmin[0]: %+e xmin[1]: %+e\\n\", xmin[0], xmin[1]);\n fprintf(stderr, \"xmax: %+e\\n\", xmax);\n fprintf(stderr, \"width: %+e nnxx: %lld\\n\", wdth, nnxx);\n fprintf(stderr, \"otype: %s\\n\", otype);\n fprintf(stderr, \"nproc: %d\\n\", PS::Comm::getNumberOfProc());\n fprintf(stderr, \"nthrd: %d\\n\", PS::Comm::getNumberOfThread());\n }\n\n // input\n if(fflag == 0) {\n char sfile[1024];\n sprintf(sfile, \"%s.dat\", itype);\n fp = fopen(sfile, \"r\");\n assert(fp);\n fprintf(stderr, \"Reading file...\\n\");\n sph.readParticleAscii(sfile);\n fclose(fp);\n } else {\n PS::S64 ntot = 0;\n PS::S64 nrank = PS::Comm::getNumberOfProc();\n PS::S64 irank = PS::Comm::getRank();\n PS::S64 ihead = nfile * irank / nrank;\n PS::S64 itail = nfile * (irank + 1) / nrank;\n for(PS::S64 ifile = ihead; ifile < itail; ifile++) {\n char sfile[1024];\n sprintf(sfile, \"%s_p%06d_i%06d.dat\", itype, nfile, ifile);\n fp = fopen(sfile, \"r\");\n assert(fp);\n PS::S64 ntmp = 0;\n for(PS::S32 c; (c = getc(fp)) != EOF; ntmp += ('\\n' == c ? 1 : 0)) {\n ;\n }\n fclose(fp);\n sph.setNumberOfParticleLocal(ntot+ntmp);\n fp = fopen(sfile, \"r\");\n for(PS::S64 i = 0; i < ntmp; i++) {\n sph[ntot+i].readAscii(fp);\n }\n fclose(fp);\n ntot += ntmp;\n }\n }\n\n // Data distribution\n {\n PS::S64 nrank = PS::Comm::getNumberOfProc();\n PS::F64 dx = wdth / (PS::F64)nrank;\n for(PS::S64 i = 0; i < nrank; i++) {\n PS::F64ort pos;\n pos.low_[0] = xmin[0] + dx * i;\n pos.low_[1] = - xmax;\n pos.low_[2] = - xmax;\n pos.high_[0] = xmin[0] + dx * (i + 1);\n pos.high_[1] = + xmax;\n pos.high_[2] = + xmax;\n dinfo.setPosDomain(i, pos);\n }\n sph.exchangeParticle(dinfo);\n }\n\n // Analyse divergence v\n {\n const PS::F64 wx = wdth / (PS::F64)nnxx;\n const PS::F64 bx = xmin[0] + (wdth / (PS::F64)PS::Comm::getNumberOfProc())\n * PS::Comm::getRank();\n const PS::F64 by = xmin[1];\n const PS::S64 nx = nnxx / PS::Comm::getNumberOfProc();\n const PS::S64 ny = nnxx;\n PS::S64 *nptcl = (PS::S64 *)malloc(sizeof(PS::S64) * nx * ny);\n assert(nptcl);\n PS::S64 *pcrit = (PS::S64 *)malloc(sizeof(PS::S64) * nx * ny);\n assert(pcrit);\n PS::F64 *dnmax = (PS::F64 *)malloc(sizeof(PS::F64) * nx * ny);\n assert(dnmax);\n for(PS::S64 i = 0; i < nx * ny; i++) {\n nptcl[i] = 0;\n pcrit[i] = 0;\n dnmax[i] = 0.;\n } \n for(PS::S64 i = 0; i < sph.getNumberOfParticleLocal(); i++) {\n PS::F64 dx = sph[i].pos[0] - bx;\n PS::F64 dy = sph[i].pos[1] - by;\n PS::S64 id = 0;\n id = (PS::S64)(dy / wx) * nx;\n id += (PS::S64)(dx / wx) % nx;\n //assert(id < nx * ny);\n if(id >= nx * ny) {\n if(Alert::BeyondBoundary == false) {\n fprintf(stderr, \"Caution! A particle is beyond the boundary!\\n\");\n Alert::BeyondBoundary = true;\n }\n continue;\n }\n nptcl[id]++;\n if(sph[i].judgeCriterion()) {\n pcrit[id]++;\n }\n if(dnmax[id] < sph[i].dens) {\n dnmax[id] = sph[i].dens;\n }\n }\n\n PS::F64 dncen, r2cen;\n char ofile[1024];\n sprintf(ofile, \"%s_0.log\", otype);\n search1Dpoint(sph, nx, ny, bx, by, wx, nptcl, pcrit, dnmax,\n true, false, 0., 0., ofile, &dncen, &r2cen);\n PS::F64 dntmp, r2tmp;\n sprintf(ofile, \"%s_1.log\", otype);\n search1Dpoint(sph, nx, ny, bx, by, wx, nptcl, pcrit, dnmax,\n false, true, 0.5*dncen, r2cen, ofile, &dntmp, &r2tmp);\n sprintf(ofile, \"%s_2.log\", otype);\n search1Dpoint(sph, nx, ny, bx, by, wx, nptcl, pcrit, dnmax,\n false, false, 0.5*dncen, r2cen, ofile, &dntmp, &r2tmp);\n\n free(nptcl);\n free(pcrit);\n free(dnmax);\n }\n\n PS::ParticleSystem fsph;\n fsph.initialize();\n fsph.createParticle(0);\n fsph.setNumberOfParticleLocal(sph.getNumberOfParticleLocal());\n for(PS::S64 i = 0; i < sph.getNumberOfParticleLocal(); i++) {\n fsph[i].copyFromSPHAnalysis(sph[i]);\n }\n dinfo.decomposeDomainAll(fsph);\n fsph.exchangeParticle(dinfo);\n PS::TreeForForceShort::Scatter fitting;\n fitting.initialize(0);\n //fsph.writeParticleAscii(\"hoge\", \"%s_p%06d_i%06d.dat\");\n //debugByPrintf(\"hoge a\");\n fitting.calcForceAllAndWriteBack(calcFitting(), fsph, dinfo);\n //debugByPrintf(\"hoge b\");\n sph.setNumberOfParticleLocal(fsph.getNumberOfParticleLocal());\n for(PS::S64 i = 0; i < sph.getNumberOfParticleLocal(); i++) {\n fsph[i].copyToSPHAnalysis(sph[i]);\n }\n\n // output fitting funciton\n {\n for(PS::S64 irank = 0; irank < PS::Comm::getNumberOfProc(); irank++) {\n if(irank == PS::Comm::getRank()) {\n char ofile0[1024];\n char ofile1[1024];\n char ofile2[1024];\n sprintf(ofile0, \"%s_0.dat\", otype);\n sprintf(ofile1, \"%s_1.dat\", otype);\n sprintf(ofile2, \"%s_2.dat\", otype);\n FILE * fp0 = fopen(ofile0, \"a\");\n FILE * fp1 = fopen(ofile1, \"a\");\n FILE * fp2 = fopen(ofile2, \"a\");\n for(PS::S64 i = 0; i < sph.getNumberOfParticleLocal(); i++) {\n if(sph[i].id == -1) {\n sph[i].writeAscii1Dimension(fp0);\n } else if(sph[i].id == -2) {\n sph[i].writeAscii1Dimension(fp1);\n } else if(sph[i].id == -3) {\n sph[i].writeAscii1Dimension(fp2);\n }\n }\n fclose(fp0);\n fclose(fp1);\n fclose(fp2);\n }\n PS::Comm::barrier();\n }\n }\n\n PS::Finalize();\n\n return 0;\n}\n", "meta": {"hexsha": "8e13735524fc2fa04a3a951292f8b9385d3960b0", "size": 22382, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tool.imbh/search/bounce/main.cpp", "max_stars_repo_name": "atrtnkw/sph", "max_stars_repo_head_hexsha": "c6bb3d7fd18f57c51fe60197706741e5a26e6ce4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2018-07-11T00:43:01.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-11T13:41:50.000Z", "max_issues_repo_path": "tool.imbh/search/bounce/main.cpp", "max_issues_repo_name": "atrtnkw/sph", "max_issues_repo_head_hexsha": "c6bb3d7fd18f57c51fe60197706741e5a26e6ce4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tool.imbh/search/bounce/main.cpp", "max_forks_repo_name": "atrtnkw/sph", "max_forks_repo_head_hexsha": "c6bb3d7fd18f57c51fe60197706741e5a26e6ce4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2020-01-06T14:22:25.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-06T14:22:25.000Z", "avg_line_length": 32.8181818182, "max_line_length": 98, "alphanum_fraction": 0.4407112859, "num_tokens": 7149, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.3849121444839335, "lm_q1q2_score": 0.24240789467756008}} {"text": "// Copyright (c) 2015-2021 Daniel Cooke\n// Use of this source code is governed by the MIT license that can be found in the LICENSE file.\n\n#include \"coalescent_model.hpp\"\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \"utils/maths.hpp\"\n#include \"utils/mappable_algorithms.hpp\"\n\nnamespace octopus {\n\nCoalescentModel::CoalescentModel(Haplotype reference, Parameters params,\n std::size_t num_haplotyes_hint, CachingStrategy caching)\n: reference_ {std::move(reference)}\n, reference_repeats_ {}\n, indel_heterozygosity_model_ {make_indel_model(reference_, {params.indel_heterozygosity}, reference_repeats_)}\n, params_ {params}\n, haplotypes_ {}\n, caching_ {caching}\n, index_cache_ {}\n, index_flag_buffer_ {}\n, k_indel_zero_result_cache_ {2 * num_haplotyes_hint, std::vector> {}}\n{\n if (params_.snp_heterozygosity <= 0 || params_.indel_heterozygosity <= 0) {\n throw std::domain_error {\"CoalescentModel: snp and indel heterozygosity must be > 0\"};\n }\n site_buffer1_.reserve(128);\n site_buffer2_.reserve(128);\n if (caching == CachingStrategy::address) {\n difference_address_cache_.reserve(num_haplotyes_hint);\n } else if (caching_ == CachingStrategy::value) {\n difference_value_cache_.reserve(num_haplotyes_hint);\n difference_value_cache_.emplace(std::piecewise_construct,\n std::forward_as_tuple(reference_),\n std::forward_as_tuple());\n }\n k_indel_pos_result_cache_.reserve(2 * num_haplotyes_hint);\n}\n\nvoid CoalescentModel::set_reference(Haplotype reference)\n{\n reference_ = std::move(reference);\n if (caching_ == CachingStrategy::address) {\n difference_address_cache_.clear();\n } else if (caching_ == CachingStrategy::value) {\n difference_value_cache_.clear();\n difference_value_cache_.emplace(std::piecewise_construct,\n std::forward_as_tuple(reference_),\n std::forward_as_tuple());\n }\n}\n\nvoid CoalescentModel::prime(MappableBlock haplotypes)\n{\n haplotypes_ = std::move(haplotypes);\n index_cache_.assign(haplotypes_.size(), boost::none);\n index_flag_buffer_.assign(haplotypes_.size(), false);\n}\n\nvoid CoalescentModel::unprime() noexcept\n{\n haplotypes_.clear();\n haplotypes_.shrink_to_fit();\n index_cache_.clear();\n index_cache_.shrink_to_fit();\n index_flag_buffer_.clear();\n index_flag_buffer_.shrink_to_fit();\n}\n\nbool CoalescentModel::is_primed() const noexcept\n{\n return !index_cache_.empty();\n}\n\nCoalescentModel::LogProbability CoalescentModel::evaluate(const Haplotype& haplotype) const\n{\n return evaluate(count_segregating_sites(haplotype));\n}\n\nCoalescentModel::LogProbability CoalescentModel::evaluate(const std::vector& haplotype_indices) const\n{\n return evaluate(count_segregating_sites(haplotype_indices));\n}\n\nnamespace {\n\nauto powm1(const unsigned i) noexcept // std::pow(-1, i)\n{\n return (i % 2 == 0) ? 1 : -1;\n}\n\ntemplate \nauto coalescent_real_space(const unsigned n, const unsigned k, const RealType theta)\n{\n RealType result {0};\n for (unsigned i {2}; i <= n; ++i) {\n result += powm1(i) * maths::binomial_coefficient(n - 1, i - 1) * ((i - 1) / (theta + i - 1)) * std::pow(theta / (theta + i - 1), k);\n }\n return std::log(result);\n}\n\ntemplate \nauto complex_log_sum_exp(ForwardIt first, ForwardIt last)\n{\n using ComplexType = typename std::iterator_traits::value_type;\n const auto l = [] (const auto& lhs, const auto& rhs) { return lhs.real() < rhs.real(); };\n const auto max = *std::max_element(first, last, l);\n return max + std::log(std::accumulate(first, last, ComplexType {},\n [max] (const auto curr, const auto x) { return curr + std::exp(x - max); }));\n}\n\ntemplate \nauto complex_log_sum_exp(const Container& logs)\n{\n return complex_log_sum_exp(std::cbegin(logs), std::cend(logs));\n}\n\ntemplate \nauto coalescent_log_space(const unsigned n, const unsigned k, const RealType theta)\n{\n std::vector> tmp(n - 1, std::log(std::complex {-1}));\n for (unsigned i {2}; i <= n; ++i) {\n auto& cur = tmp[i - 2];\n cur *= i;\n cur += maths::log_binomial_coefficient(n - 1, i - 1);\n cur += std::log((i - 1) / (theta + i - 1));\n cur += k * std::log(theta / (theta + i - 1));\n }\n return complex_log_sum_exp(tmp).real();\n}\n\ntemplate \nauto coalescent(const unsigned n, const unsigned k, const RealType theta)\n{\n if (n < 30 && k <= 80) {\n auto result = coalescent_real_space(n, k, theta);\n if (std::isnan(result)) {\n result = coalescent_log_space(n, k, theta);\n }\n return result;\n } else {\n return coalescent_log_space(n, k, theta);\n }\n}\n\ntemplate \nauto coalescent(const unsigned n, const unsigned k_snp, const unsigned k_indel,\n const RealType theta_snp, const RealType theta_indel)\n{\n const auto theta = theta_snp + theta_indel;\n const auto k_tot = k_snp + k_indel;\n auto result = coalescent(n, k_tot, theta);\n result += k_snp * std::log(theta_snp / theta);\n result += k_indel * std::log(theta_indel / theta);\n result += maths::log_binomial_coefficient(k_tot, k_snp);\n return result;\n}\n\ntemplate \nauto coalescent(const unsigned n, \n const std::array& k,\n const std::array& theta)\n{\n const auto theta_tot = std::accumulate(std::cbegin(theta), std::cend(theta), RealType {0});\n const auto k_tot = std::accumulate(std::cbegin(k), std::cend(k), 0u);\n auto result = coalescent(n, k_tot, theta_tot);\n for (std::size_t i {0}; i < N; ++i) {\n result += k[i] * std::log(theta[i] / theta_tot);\n }\n result += maths::log_multinomial_coefficient(k);\n return result;\n}\n\n} // namespace\n\nCoalescentModel::LogProbability CoalescentModel::evaluate(const SegregatingSiteCounts& counts) const\n{\n if (counts.repeat_indels + counts.complex_indels == 0) {\n return evaluate_no_indels(counts.snps, counts.haplotypes);\n } else {\n const auto indel_heterozygosities = calculate_buffered_indel_heterozygosities();\n const auto repeat_indel_heterozygosity = maths::round_sf(indel_heterozygosities.second, 6);\n const auto complex_indel_heterozygosity = maths::round_sf(indel_heterozygosities.first, 6);\n SegregatingSiteCountsWithIndelHeterozygosities sites {counts, repeat_indel_heterozygosity, complex_indel_heterozygosity};\n auto itr = k_indel_pos_result_cache_.find(sites);\n if (itr != std::cend(k_indel_pos_result_cache_)) {\n return itr->second;\n }\n const std::array site_counts {counts.snps, counts.repeat_indels, counts.complex_indels};\n const std::array site_heterozygosities {params_.snp_heterozygosity, repeat_indel_heterozygosity, complex_indel_heterozygosity};\n const auto result = coalescent(counts.haplotypes, site_counts, site_heterozygosities);\n k_indel_pos_result_cache_.emplace(sites, result);\n return result;\n }\n}\n\nCoalescentModel::LogProbability CoalescentModel::evaluate_no_indels(const unsigned k_snp, const unsigned n) const\n{\n if (k_indel_zero_result_cache_.size() > n) {\n if (k_indel_zero_result_cache_[n].size() > k_snp) {\n auto& result = k_indel_zero_result_cache_[n][k_snp];\n if (!result) {\n result = coalescent(n, k_snp, 0, params_.snp_heterozygosity, params_.indel_heterozygosity);\n }\n return *result;\n } else {\n k_indel_zero_result_cache_[n].resize(k_snp + 1, boost::none);\n }\n } else {\n k_indel_zero_result_cache_.resize(n + 1);\n k_indel_zero_result_cache_[n].assign(k_snp + 1, boost::none);\n }\n const auto result = coalescent(n, k_snp, 0, params_.snp_heterozygosity, params_.indel_heterozygosity);\n k_indel_zero_result_cache_[n][k_snp] = result;\n return result;\n}\n\nvoid CoalescentModel::fill_site_buffer(const Haplotype& haplotype) const\n{\n assert(site_buffer2_.empty());\n site_buffer1_.clear();\n if (caching_ == CachingStrategy::address) {\n fill_site_buffer_from_address_cache(haplotype);\n } else {\n fill_site_buffer_from_value_cache(haplotype);\n }\n site_buffer1_ = std::move(site_buffer2_);\n site_buffer2_.clear();\n}\n\nvoid CoalescentModel::fill_site_buffer_uncached(const Haplotype& haplotype) const\n{\n // Although we won't retrieve from the cache, we need to make sure all the variants\n // stay in existence as we populate the buffers by reference.\n auto itr = difference_value_cache_.find(reference_);\n if (itr == std::cend(difference_value_cache_)) {\n itr = difference_value_cache_.emplace(reference_, haplotype.difference(reference_)).first;\n } else {\n itr->second = haplotype.difference(reference_);\n }\n std::set_union(std::begin(site_buffer1_), std::end(site_buffer1_),\n std::cbegin(itr->second), std::cend(itr->second),\n std::back_inserter(site_buffer2_));\n}\n\nvoid CoalescentModel::fill_site_buffer_from_value_cache(const Haplotype& haplotype) const\n{\n auto itr = difference_value_cache_.find(haplotype);\n if (itr == std::cend(difference_value_cache_)) {\n itr = difference_value_cache_.emplace(haplotype, haplotype.difference(reference_)).first;\n }\n std::set_union(std::begin(site_buffer1_), std::end(site_buffer1_),\n std::cbegin(itr->second), std::cend(itr->second),\n std::back_inserter(site_buffer2_));\n}\n\nvoid CoalescentModel::fill_site_buffer_from_address_cache(const Haplotype& haplotype) const\n{\n auto itr = difference_address_cache_.find(std::addressof(haplotype));\n if (itr == std::cend(difference_address_cache_)) {\n itr = difference_address_cache_.emplace(std::addressof(haplotype), haplotype.difference(reference_)).first;\n }\n std::set_union(std::begin(site_buffer1_), std::end(site_buffer1_),\n std::cbegin(itr->second), std::cend(itr->second),\n std::back_inserter(site_buffer2_));\n}\n\nCoalescentModel::SegregatingSiteCounts CoalescentModel::count_segregating_sites(const Haplotype& haplotype) const\n{\n fill_site_buffer(haplotype);\n return count_segregating_sites_in_buffer(1);\n}\n\nCoalescentModel::SegregatingSiteCounts CoalescentModel::count_segregating_sites_in_buffer(const unsigned num_haplotypes) const\n{\n unsigned repeat_indels {0}, complex_indels {0};\n for (const auto& site : site_buffer1_) {\n if (is_indel(site)) {\n if (has_overlapped(reference_repeats_, site.get())) {\n ++repeat_indels;\n } else {\n ++complex_indels;\n }\n }\n }\n const auto num_indels = repeat_indels + complex_indels;\n const auto num_snps = static_cast(site_buffer1_.size()) - num_indels;\n return {num_haplotypes + 1, num_snps, repeat_indels, complex_indels};\n}\n\nstd::pair CoalescentModel::calculate_buffered_indel_heterozygosities() const\n{\n boost::optional min_heterozygosity {}, max_heterozygosity {};\n for (const auto& site : site_buffer1_) {\n if (is_indel(site)) {\n auto site_heterozygosity = calculate_heterozygosity(site);\n if (min_heterozygosity) {\n min_heterozygosity = std::min(*min_heterozygosity, site_heterozygosity);\n } else {\n min_heterozygosity = site_heterozygosity;\n }\n if (max_heterozygosity) {\n max_heterozygosity = std::max(*max_heterozygosity, site_heterozygosity);\n } else {\n max_heterozygosity = site_heterozygosity;\n }\n }\n }\n if (!min_heterozygosity) min_heterozygosity = params_.indel_heterozygosity;\n if (!max_heterozygosity) max_heterozygosity = params_.indel_heterozygosity;\n return std::make_pair(*min_heterozygosity, *max_heterozygosity);\n}\n\ndouble CoalescentModel::calculate_heterozygosity(const Variant& indel) const\n{\n assert(is_indel(indel));\n const auto offset = static_cast(begin_distance(reference_, indel));\n return calculate_indel_probability(indel_heterozygosity_model_, offset, indel_size(indel));\n}\n\nCoalescentProbabilityGreater::CoalescentProbabilityGreater(CoalescentModel model)\n: model_ {std::move(model)}\n, buffer_ {}\n, cache_ {}\n{\n buffer_.reserve(1);\n cache_.reserve(100);\n}\n\nbool CoalescentProbabilityGreater::operator()(const Haplotype& lhs, const Haplotype& rhs) const\n{\n if (have_same_alleles(lhs, rhs)) return true;\n auto cache_itr = cache_.find(lhs);\n if (cache_itr == std::cend(cache_)) {\n buffer_.assign({lhs});\n cache_itr = cache_.emplace(lhs, model_.evaluate(buffer_)).first;\n }\n const auto lhs_probability = cache_itr->second;\n cache_itr = cache_.find(rhs);\n if (cache_itr == std::cend(cache_)) {\n buffer_.assign({rhs});\n cache_itr = cache_.emplace(rhs, model_.evaluate(buffer_)).first;\n }\n const auto rhs_probability = cache_itr->second;\n return lhs_probability > rhs_probability;\n}\n\nbool operator==(const CoalescentModel::SegregatingSiteCounts& lhs, const CoalescentModel::SegregatingSiteCounts& rhs) noexcept\n{\n return lhs.haplotypes == rhs.haplotypes && lhs.snps == rhs.snps && lhs.repeat_indels == rhs.repeat_indels && lhs.complex_indels == rhs.complex_indels;\n}\n\nstd::size_t CoalescentModel::SegregatingSiteCountsHash::operator()(const SegregatingSiteCounts& counts) const noexcept\n{\n return boost::hash_value(std::make_tuple(counts.haplotypes, counts.snps, counts.repeat_indels, counts.complex_indels));\n}\n\nstd::size_t CoalescentModel::SegregatingSiteCountsWithIndelHeterozygositiesHash::operator()(const SegregatingSiteCountsWithIndelHeterozygosities& sites) const noexcept\n{\n using boost::hash_combine;\n std::size_t result {};\n hash_combine(result, SegregatingSiteCountsHash{}(sites.counts));\n hash_combine(result, std::hash()(sites.repeat_heterozygosity));\n hash_combine(result, std::hash()(sites.complex_heterozygosity));\n return result;\n}\n\nbool CoalescentModel::SegregatingSiteCountsWithIndelHeterozygositiesEqual::operator()(const SegregatingSiteCountsWithIndelHeterozygosities& lhs, const SegregatingSiteCountsWithIndelHeterozygosities& rhs) const noexcept\n{\n return lhs.counts == rhs.counts && lhs.repeat_heterozygosity == rhs.repeat_heterozygosity && lhs.complex_heterozygosity == rhs.complex_heterozygosity;\n}\n\n} // namespace octopus\n", "meta": {"hexsha": "9b954c05b036b9f70b277c5f8969e16525630a34", "size": 15069, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/core/models/mutation/coalescent_model.cpp", "max_stars_repo_name": "Schaudge/octopus", "max_stars_repo_head_hexsha": "d0cc5d0840aefdfefae5af8595e3330620106054", 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"avg_line_length": 39.5511811024, "max_line_length": 218, "alphanum_fraction": 0.6937421196, "num_tokens": 3959, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.3849121444839335, "lm_q1q2_score": 0.24240788932830792}} {"text": "/*!@file\n * @copyright This code is licensed under the 3-clause BSD license.\n * Copyright ETH Zurich, Laboratory of Physical Chemistry, Reiher Group.\n * See LICENSE.txt for details.\n */\n\n#include \"Molassembler/Interpret.h\"\n\n#include \"Utils/Geometry/AtomCollection.h\"\n#include \"Utils/Bonds/BondOrderCollection.h\"\n\n#include \"Molassembler/BondOrders.h\"\n#include \"Molassembler/Detail/Cartesian.h\"\n#include \"Molassembler/Graph.h\"\n#include \"Molassembler/Graph/GraphAlgorithms.h\"\n#include \"Molassembler/Graph/PrivateGraph.h\"\n#include \"Molassembler/Molecule.h\"\n#include \"Molassembler/Shapes/ContinuousMeasures.h\"\n#include \"Molassembler/Temple/Adaptors/AllPairs.h\"\n#include \"Molassembler/Temple/Functional.h\"\n#include \"Molassembler/Temple/Optionals.h\"\n\n#include \n\nnamespace Scine {\nnamespace Molassembler {\nnamespace Interpret {\nnamespace {\n\nUtils::PositionCollection paste(const std::vector& positions) {\n const unsigned N = positions.size();\n auto matrix = Utils::PositionCollection(N, 3);\n for(unsigned i = 0; i < N; ++i) {\n matrix.row(i) = positions.at(i);\n }\n return matrix;\n}\n\ndouble vectorAngle(const Eigen::Vector3d& a, const Eigen::Vector3d& b) {\n return std::acos(\n a.dot(b) / (\n a.norm() * b.norm()\n )\n );\n}\n\nstruct HapticPlaneGeometry {\n double angle;\n double rmsd;\n};\n\nHapticPlaneGeometry hapticPlaneGeometry(\n const std::vector& positions,\n const AtomIndex v,\n const std::vector& site\n) {\n const unsigned siteSize = site.size();\n\n assert(siteSize >= 1 && \"Passed hapticPlaneGeometry a non-haptic site!\");\n\n Eigen::Vector3d siteCentroid = Eigen::Vector3d::Zero();\n for(AtomIndex siteAtom : site) {\n siteCentroid += positions.at(siteAtom);\n }\n siteCentroid /= siteSize;\n\n if(siteSize == 2) {\n const double frontAngle = Cartesian::angle(\n positions.at(v),\n siteCentroid,\n positions.at(site.front())\n );\n const double backAngle = Cartesian::angle(\n positions.at(v),\n siteCentroid,\n positions.at(site.back())\n );\n\n return HapticPlaneGeometry {\n M_PI / 2.0 - std::min(frontAngle, backAngle),\n 0.0\n };\n }\n\n const Eigen::Vector3d centroidAxis = siteCentroid - positions.at(v).transpose();\n\n Utils::PositionCollection hapticAtoms(siteSize, 3);\n for(unsigned i = 0; i < siteSize; ++i) {\n hapticAtoms.row(i) = positions.at(site.at(i));\n }\n const auto plane = Cartesian::planeOfBestFit(hapticAtoms);\n const double firstAngle = vectorAngle(plane.normal(), centroidAxis);\n const double secondAngle = vectorAngle(plane.normal(), -centroidAxis);\n return HapticPlaneGeometry {\n std::min(firstAngle, secondAngle),\n Cartesian::planeRmsd(plane, hapticAtoms, Temple::iota(siteSize))\n };\n}\n\nboost::optional minimumClassificationProbability(\n const PrivateGraph& graph,\n const std::vector& positions,\n const AtomIndex v\n) {\n auto sites = GraphAlgorithms::sites(graph, v);\n if(sites.size() <= 1) {\n return boost::none;\n }\n\n const unsigned S = sites.size();\n Eigen::Matrix sitePositions(3, S + 1);\n for(unsigned i = 0; i < S; ++i) {\n Eigen::Vector3d averagePosition = Eigen::Vector3d::Zero();\n for(unsigned j : sites[i]) {\n averagePosition += positions.at(j);\n }\n sitePositions.col(i) = averagePosition / sites[i].size();\n }\n sitePositions.col(S) = positions.at(v);\n\n auto normalizedPositions = Shapes::Continuous::normalize(sitePositions);\n\n // Classify all suitable shapes\n std::vector viableShapes;\n for(const Shapes::Shape shape : Shapes::allShapes) {\n if(Shapes::size(shape) == S) {\n viableShapes.push_back(shape);\n }\n }\n\n const auto classifications = Temple::map(viableShapes,\n [&](const Shapes::Shape shape) -> boost::optional {\n const double measure = Shapes::Continuous::shapeCentroidLast(normalizedPositions, shape).measure;\n return Shapes::Continuous::probabilityRandomCloud(measure, shape);\n }\n );\n\n if(Temple::all_of(classifications)) {\n const double minimumProbability = std::min_element(\n std::begin(classifications),\n std::end(classifications),\n [](const auto& a, const auto& b) -> bool {\n return a.value() < b.value();\n }\n )->value();\n\n return minimumProbability;\n }\n\n return boost::none;\n}\n\nstruct BestRemovals {\n std::vector removals;\n double certainty;\n};\n\nboost::optional bestRemovalFromHapticSite(\n const PrivateGraph& graph,\n const std::vector& positions,\n const AtomIndex v,\n const std::vector>& sites,\n const unsigned hapticSiteIndex\n) {\n const unsigned S = sites.size();\n const std::vector& hapticSite = sites.at(hapticSiteIndex);\n const unsigned hapticSiteSize = hapticSite.size();\n return Temple::accumulate(\n hapticSite,\n boost::optional(boost::none),\n [&](const auto& carry, const AtomIndex siteVertexToRemove) -> boost::optional {\n auto graphCopy = graph;\n graphCopy.removeEdge(graphCopy.edge(v, siteVertexToRemove));\n GraphAlgorithms::updateEtaBonds(graphCopy);\n const auto newSites = GraphAlgorithms::sites(graphCopy, v);\n\n /* Possible effects of bond removal\n * - Separating a haptic ligand into two single-atom ligands,\n * changing shapes\n * - recognizable by size change\n * - accept if new certainty is low or significantly\n * lower than old (factor 0.5)\n * - Improves the haptic plane angle by removing a bad bond\n * - recognized by matching sites and comparing angles\n * - accept if angle halves\n *\n * How to compare cases?\n */\n if(newSites.size() > S) {\n const auto priorCertainty = minimumClassificationProbability(graph, positions, v);\n const auto posteriorCertainty = minimumClassificationProbability(graphCopy, positions, v);\n\n if(priorCertainty && posteriorCertainty) {\n if(\n posteriorCertainty.value() <= 0.01\n || posteriorCertainty.value() <= 0.5 * priorCertainty.value()\n ) {\n return BestRemovals {{siteVertexToRemove}, 1 - *posteriorCertainty};\n }\n }\n } else {\n const auto siteFindIter = Temple::find_if(\n newSites,\n [&](const auto& newSite) -> bool {\n if(newSite.size() != hapticSiteSize - 1) {\n return false;\n }\n\n // Match all vertices except the one to remove\n for(const AtomIndex oldSiteAtomIndex : hapticSite) {\n if(oldSiteAtomIndex == siteVertexToRemove) {\n continue;\n }\n\n if(Temple::find(newSite, oldSiteAtomIndex) == std::end(newSite)) {\n return false;\n }\n }\n\n return true;\n }\n );\n\n if(siteFindIter != std::end(newSites)) {\n const double newHapticAngle = hapticPlaneGeometry(positions, v, *siteFindIter).angle;\n const double newHapticAngleCertainty = 1 - newHapticAngle * 2 / M_PI;\n const double carryCertainty = Temple::Optionals::map(carry,\n [](const auto& removal) { return removal.certainty; }\n ).value_or(0.0);\n if(newHapticAngleCertainty > carryCertainty) {\n return BestRemovals {{siteVertexToRemove}, newHapticAngleCertainty};\n }\n }\n }\n\n return carry;\n }\n );\n}\n\n} // namespace\n\nstd::vector> ComponentMap::invert() const {\n const unsigned nComponents = *std::max_element(\n std::begin(map),\n std::end(map)\n ) + 1;\n\n std::vector<\n std::vector\n > inverseMaps (nComponents);\n\n const unsigned N = map.size();\n for(unsigned i = 0; i < N; ++i) {\n inverseMaps.at(map.at(i)).push_back(i);\n }\n\n return inverseMaps;\n}\n\nstd::vector ComponentMap::apply(\n const Utils::AtomCollection& atomCollection\n) const {\n const unsigned nComponents = *std::max_element(\n std::begin(map),\n std::end(map)\n ) + 1;\n std::vector componentSizes(nComponents, 0);\n for(unsigned i : map) {\n componentSizes.at(i) += 1;\n }\n\n /* Allocate the collections */\n std::vector collections = Temple::map(\n componentSizes,\n [](const unsigned size) { return Utils::AtomCollection(size); }\n );\n std::vector collectionSizeCount(nComponents, 0);\n\n for(unsigned i = 0; i < map.size(); ++i) {\n unsigned moleculeIndex = map.at(i);\n Utils::AtomCollection& collection = collections.at(moleculeIndex);\n unsigned& collectionSize = collectionSizeCount.at(moleculeIndex);\n collection.setElement(\n collectionSize,\n atomCollection.getElement(i)\n );\n collection.setPosition(\n collectionSize,\n atomCollection.getPosition(i)\n );\n ++collectionSize;\n }\n\n return collections;\n}\n\nComponentMap::ComponentIndexPair ComponentMap::apply(const unsigned index) const {\n ComponentIndexPair pair;\n pair.component = map.at(index);\n pair.atomIndex = std::count(\n std::begin(map),\n std::begin(map) + index,\n pair.component\n );\n return pair;\n}\n\nunsigned ComponentMap::invert(const ComponentIndexPair& pair) const {\n unsigned count = 0;\n const unsigned N = map.size();\n for(unsigned i = 0; i < N; ++i) {\n if(map[i] == pair.component) {\n ++count;\n }\n\n if(count == pair.atomIndex + 1) {\n return i;\n }\n }\n\n throw std::out_of_range(\"No match found in component map!\");\n}\n\nstruct MoleculeParts {\n PrivateGraph graph;\n std::vector angstromPositions;\n boost::optional<\n std::vector\n > bondStereopermutatorCandidatesOptional;\n};\n\nstruct Parts {\n std::vector precursors;\n ComponentMap componentMap;\n unsigned nZeroLengthPositions = 0;\n};\n\n// Yields a graph structure without element type annotations\nPrivateGraph discretize(\n const Utils::BondOrderCollection& bondOrders,\n const BondDiscretizationOption discretization\n) {\n const PrivateGraph::Vertex N = bondOrders.getSystemSize();\n PrivateGraph graph {N};\n\n if(discretization == BondDiscretizationOption::Binary) {\n for(unsigned i = 0; i < N; ++i) {\n for(unsigned j = i + 1; j < N; ++j) {\n double bondOrder = bondOrders.getOrder(i, j);\n\n if(bondOrder > 0.5) {\n graph.addEdge(i, j, BondType::Single);\n }\n }\n }\n } else if(discretization == BondDiscretizationOption::RoundToNearest) {\n for(unsigned i = 0; i < N; ++i) {\n for(unsigned j = i + 1; j < N; ++j) {\n double bondOrder = bondOrders.getOrder(i, j);\n\n if(bondOrder > 0.5) {\n auto bond = static_cast(\n std::round(bondOrder) - 1\n );\n\n if(bondOrder > 6.5) {\n bond = BondType::Sextuple;\n }\n\n graph.addEdge(i, j, bond);\n }\n }\n }\n }\n\n return graph;\n}\n\nParts construeParts(\n const Utils::ElementTypeCollection& elements,\n const AngstromPositions& angstromWrapper,\n const Utils::BondOrderCollection& bondOrders,\n const BondDiscretizationOption discretization,\n const boost::optional& stereopermutatorThreshold\n) {\n const unsigned N = elements.size();\n\n // Check preconditions\n if(angstromWrapper.positions.rows() != N) {\n throw std::invalid_argument(\n \"Number of positions in angstrom wrapper do not match number of elements\"\n );\n }\n\n if(bondOrders.getSystemSize() != N) {\n throw std::invalid_argument(\n \"Bond order argument system size does not match number of elements\"\n );\n }\n\n PrivateGraph atomCollectionGraph = discretize(bondOrders, discretization);\n\n Parts parts;\n const unsigned numComponents = atomCollectionGraph.connectedComponents(parts.componentMap.map);\n parts.precursors.resize(numComponents);\n\n if(stereopermutatorThreshold) {\n for(auto& precursor : parts.precursors) {\n // Empty-vector-initialize the candidate optionals\n precursor.bondStereopermutatorCandidatesOptional = std::vector {};\n }\n }\n\n // Map from original index to component index\n std::vector indexInComponentMap (N);\n\n /* Maybe\n * - filtered_graph using predicate of componentMap number\n * - copy_graph to new Graph keeping element types and bond orders\n *\n * - alternately, must keep a map of atomcollection index to precursor index\n * and new precursor atom index in order to transfer edges too\n */\n\n for(unsigned i = 0; i < N; ++i) {\n auto& precursor = parts.precursors.at(\n parts.componentMap.apply(i).component\n );\n\n // Add a new vertex with element information\n PrivateGraph::Vertex newIndex = precursor.graph.addVertex(elements.at(i));\n\n // Save new index in precursor graph\n indexInComponentMap.at(i) = newIndex;\n\n if(angstromWrapper.positions.row(i).norm() <= 1e-14) {\n parts.nZeroLengthPositions += 1;\n }\n\n // Copy over position information\n precursor.angstromPositions.emplace_back(\n angstromWrapper.positions.row(i)\n );\n }\n\n // Copy over edges and bond orders\n for(const PrivateGraph::Edge& edge : atomCollectionGraph.edges()) {\n const PrivateGraph::Vertex source = atomCollectionGraph.source(edge);\n const PrivateGraph::Vertex target = atomCollectionGraph.target(edge);\n\n // Both source and target are part of the same component (since they are bonded)\n auto& precursor = parts.precursors.at(\n parts.componentMap.apply(source).component\n );\n\n // Copy over the edge\n precursor.graph.addEdge(\n indexInComponentMap.at(source),\n indexInComponentMap.at(target),\n atomCollectionGraph.bondType(edge)\n );\n\n // If the edge's bond order exceeds the threshold optional\n if(\n stereopermutatorThreshold\n && bondOrders.getOrder(source, target) >= *stereopermutatorThreshold\n ) {\n precursor.bondStereopermutatorCandidatesOptional->emplace_back(\n indexInComponentMap.at(source),\n indexInComponentMap.at(target)\n );\n }\n }\n\n return parts;\n}\n\nMoleculesResult molecules(\n const Utils::ElementTypeCollection& elements,\n const AngstromPositions& angstromWrapper,\n const Utils::BondOrderCollection& bondOrders,\n const BondDiscretizationOption discretization,\n const boost::optional& stereopermutatorThreshold\n) {\n Parts parts = construeParts(\n elements,\n angstromWrapper,\n bondOrders,\n discretization,\n stereopermutatorThreshold\n );\n\n // Collect results\n MoleculesResult result;\n\n /* Transform precursors into Molecules. Positions may only be used if there\n * is at most one position very close to (0, 0, 0). Otherwise, we assume that\n * the given positions are faulty or no positional information is present,\n * and only the graph is used to create the Molecules.\n */\n if(parts.nZeroLengthPositions < 2) {\n result.molecules.reserve(parts.precursors.size());\n for(auto& precursor : parts.precursors) {\n result.molecules.emplace_back(\n Graph {std::move(precursor.graph)},\n AngstromPositions(paste(precursor.angstromPositions), LengthUnit::Angstrom),\n precursor.bondStereopermutatorCandidatesOptional\n );\n }\n } else {\n result.molecules.reserve(parts.precursors.size());\n for(auto& precursor : parts.precursors) {\n result.molecules.emplace_back(\n Graph {std::move(precursor.graph)}\n );\n }\n }\n\n result.componentMap = std::move(parts.componentMap);\n\n return result;\n}\n\nMoleculesResult molecules(\n const Utils::ElementTypeCollection& elements,\n const AngstromPositions& angstromWrapper,\n const BondDiscretizationOption discretization,\n const boost::optional& stereopermutatorThreshold\n) {\n return molecules(\n elements,\n angstromWrapper,\n uffBondOrders(elements, angstromWrapper),\n discretization,\n stereopermutatorThreshold\n );\n}\n\nMoleculesResult molecules(\n const Utils::AtomCollection& atomCollection,\n const Utils::BondOrderCollection& bondOrders,\n const BondDiscretizationOption discretization,\n const boost::optional& stereopermutatorThreshold\n) {\n return molecules(\n atomCollection.getElements(),\n AngstromPositions {atomCollection.getPositions(), LengthUnit::Bohr},\n bondOrders,\n discretization,\n stereopermutatorThreshold\n );\n}\n\nMoleculesResult molecules(\n const Utils::AtomCollection& atomCollection,\n const BondDiscretizationOption discretization,\n const boost::optional& stereopermutatorThreshold\n) {\n AngstromPositions angstromWrapper {atomCollection.getPositions(), LengthUnit::Bohr};\n\n return molecules(\n atomCollection.getElements(),\n angstromWrapper,\n uffBondOrders(atomCollection.getElements(), angstromWrapper),\n discretization,\n stereopermutatorThreshold\n );\n}\n\nGraphsResult graphs(\n const Utils::ElementTypeCollection& elements,\n const AngstromPositions& angstromWrapper,\n const Utils::BondOrderCollection& bondOrders,\n BondDiscretizationOption discretization\n) {\n Parts parts = construeParts(\n elements,\n angstromWrapper,\n bondOrders,\n discretization,\n boost::none\n );\n\n GraphsResult result;\n result.graphs.reserve(parts.precursors.size());\n for(auto& precursor : parts.precursors) {\n result.graphs.emplace_back(std::move(precursor.graph));\n }\n result.componentMap = std::move(parts.componentMap);\n return result;\n}\n\nGraphsResult graphs(\n const Utils::AtomCollection& atomCollection,\n const Utils::BondOrderCollection& bondOrders,\n BondDiscretizationOption discretization\n) {\n return graphs(\n atomCollection.getElements(),\n AngstromPositions {atomCollection.getPositions(), LengthUnit::Bohr},\n bondOrders,\n discretization\n );\n}\n\nstd::vector uncertainBonds(\n const Utils::AtomCollection& atomCollection,\n const Utils::BondOrderCollection& bondOrders\n) {\n std::vector falsePositives;\n\n Parts parts = construeParts(\n atomCollection.getElements(),\n AngstromPositions {atomCollection.getPositions(), LengthUnit::Bohr},\n bondOrders,\n BondDiscretizationOption::Binary,\n boost::none\n );\n\n for(unsigned component = 0; component < parts.precursors.size(); ++component) {\n MoleculeParts& part = parts.precursors[component];\n GraphAlgorithms::updateEtaBonds(part.graph);\n\n std::vector> sketchyClassifications;\n\n for(const PrivateGraph::Vertex v : part.graph.vertices()) {\n /* Detect high uncertainty shape classifications. Adjacent pairs with high\n * uncertainties are candidates for false positives.\n */\n auto classificationUncertainty = minimumClassificationProbability(part.graph, part.angstromPositions, v);\n if(classificationUncertainty && *classificationUncertainty >= 0.5) {\n sketchyClassifications.emplace_back(v, *classificationUncertainty);\n }\n }\n\n // NOTE: Cannot remove bonds with overlapping constituent atoms!\n for(const auto& sketchyPair : Temple::Adaptors::allPairs(sketchyClassifications)) {\n const unsigned i = sketchyPair.first.first;\n const unsigned j = sketchyPair.second.first;\n const double i_random = sketchyPair.first.second;\n const double j_random = sketchyPair.second.second;\n\n if(part.graph.edgeOption(i, j)) {\n falsePositives.push_back(\n FalsePositive {\n parts.componentMap.invert(ComponentMap::ComponentIndexPair {component, i}),\n parts.componentMap.invert(ComponentMap::ComponentIndexPair {component, j}),\n i_random * j_random\n }\n );\n }\n }\n }\n\n return falsePositives;\n}\n\nstd::vector badHapticLigandBonds(\n const Utils::AtomCollection& atomCollection,\n const Utils::BondOrderCollection& bondOrders\n) {\n std::vector falsePositives;\n\n // Avoid duplicate bonds in list with reverse i-j\n auto addFalsePositive = [&](unsigned i, unsigned j, double p) {\n if(i > j) {\n std::swap(i, j);\n }\n auto findIter = std::find_if(\n std::begin(falsePositives),\n std::end(falsePositives),\n [=](const FalsePositive& fp) -> bool {\n return fp.i == i && fp.j == j;\n }\n );\n if(findIter == std::end(falsePositives)) {\n falsePositives.push_back(FalsePositive {i, j, p});\n }\n };\n\n Parts parts = construeParts(\n atomCollection.getElements(),\n AngstromPositions {atomCollection.getPositions(), LengthUnit::Bohr},\n bondOrders,\n BondDiscretizationOption::Binary,\n boost::none\n );\n\n for(unsigned component = 0; component < parts.precursors.size(); ++component) {\n MoleculeParts& part = parts.precursors[component];\n GraphAlgorithms::updateEtaBonds(part.graph);\n\n for(const PrivateGraph::Vertex v : part.graph.vertices()) {\n /* Detect haptic shape planes with large angles to the axis defined by the\n * site position and the central atom or with high rms deviations on\n * their plane fit\n */\n const auto sites = GraphAlgorithms::sites(part.graph, v);\n const unsigned S = sites.size();\n for(unsigned siteIndex = 0; siteIndex < S; ++siteIndex) {\n const auto& site = sites.at(siteIndex);\n const unsigned siteSize = site.size();\n if(siteSize == 1) {\n continue;\n }\n\n const auto geometry = hapticPlaneGeometry(part.angstromPositions, v, site);\n\n // Less than 30° and a good plane fit indicate the haptic site is fine\n if(geometry.angle < M_PI / 6 && geometry.rmsd < 0.2) {\n continue;\n }\n\n if(siteSize == 2) {\n // Suggest the vertex further from the center\n const double frontDistance = (\n part.angstromPositions.at(v)\n - part.angstromPositions.at(site.front())\n ).norm();\n const double backDistance = (\n part.angstromPositions.at(v)\n - part.angstromPositions.at(site.back())\n ).norm();\n const AtomIndex toRemove = frontDistance < backDistance ? site.back() : site.front();\n addFalsePositive(\n parts.componentMap.invert(\n ComponentMap::ComponentIndexPair {component, v}\n ),\n parts.componentMap.invert(\n ComponentMap::ComponentIndexPair {component, toRemove}\n ),\n geometry.angle * 2 / M_PI\n );\n } else {\n const auto suggestedRemovalOption = bestRemovalFromHapticSite(\n part.graph,\n part.angstromPositions,\n v,\n sites,\n siteIndex\n );\n if(suggestedRemovalOption) {\n for(const AtomIndex w : suggestedRemovalOption->removals) {\n addFalsePositive(\n parts.componentMap.invert(\n ComponentMap::ComponentIndexPair {component, v}\n ),\n parts.componentMap.invert(\n ComponentMap::ComponentIndexPair {component, w}\n ),\n suggestedRemovalOption->certainty\n );\n }\n }\n }\n }\n }\n }\n return falsePositives;\n}\n\nUtils::BondOrderCollection removeFalsePositives(\n const Utils::AtomCollection& atoms,\n Utils::BondOrderCollection bonds\n) {\n // First do bad haptic bond orders\n auto haptics = badHapticLigandBonds(atoms, bonds);\n while(!haptics.empty()) {\n Temple::sort(haptics);\n FalsePositive& mostLikely = haptics.back();\n bonds.setOrder(mostLikely.i, mostLikely.j, 0.0);\n haptics = badHapticLigandBonds(atoms, bonds);\n }\n\n // Then do uncertain bonds\n auto uncertains = uncertainBonds(atoms, bonds);\n while(!uncertains.empty()) {\n Temple::sort(uncertains);\n FalsePositive& mostLikely = uncertains.back();\n bonds.setOrder(mostLikely.i, mostLikely.j, 0.0);\n uncertains = uncertainBonds(atoms, bonds);\n }\n\n return bonds;\n}\n\n} // namespace Interpret\n} // namespace Molassembler\n} // namespace Scine\n", "meta": {"hexsha": "036ca3a450456e94927488318cf1eaafaee74a3b", "size": 23939, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Molassembler/Interpret.cpp", "max_stars_repo_name": "Dom1L/molassembler", "max_stars_repo_head_hexsha": "dafc656b1aa846b65b1fd1e06f3740ceedcf22db", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Molassembler/Interpret.cpp", "max_issues_repo_name": "Dom1L/molassembler", "max_issues_repo_head_hexsha": "dafc656b1aa846b65b1fd1e06f3740ceedcf22db", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Molassembler/Interpret.cpp", "max_forks_repo_name": "Dom1L/molassembler", "max_forks_repo_head_hexsha": "dafc656b1aa846b65b1fd1e06f3740ceedcf22db", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.2642225032, "max_line_length": 111, "alphanum_fraction": 0.67220853, "num_tokens": 5711, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.3849121303722487, "lm_q1q2_score": 0.24240788044112735}} {"text": "/*\n Copyright (C) 2020 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n\n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace QuantLib;\n\nusing namespace boost::accumulators;\n\nnamespace ore {\nnamespace analytics {\n\nStaticCreditXvaCalculator::StaticCreditXvaCalculator(\n const boost::shared_ptr portfolio, const boost::shared_ptr market,\n const string& configuration, const string& baseCurrency, const string& dvaName,\n const string& fvaBorrowingCurve, const string& fvaLendingCurve,\n const bool applyDynamicInitialMargin,\n const boost::shared_ptr dimCalculator,\n const boost::shared_ptr tradeExposureCube,\n const boost::shared_ptr nettingSetExposureCube,\n const Size tradeEpeIndex, const Size tradeEneIndex, \n const Size nettingSetEpeIndex, const Size nettingSetEneIndex,\n const bool flipViewXVA, const string& flipViewBorrowingCurvePostfix, const string& flipViewLendingCurvePostfix)\n : ValueAdjustmentCalculator(portfolio, market, configuration, baseCurrency, dvaName,\n fvaBorrowingCurve, fvaLendingCurve, applyDynamicInitialMargin,\n dimCalculator, tradeExposureCube, nettingSetExposureCube, tradeEpeIndex, tradeEneIndex, \n nettingSetEpeIndex, nettingSetEneIndex, \n flipViewXVA, flipViewBorrowingCurvePostfix, flipViewLendingCurvePostfix) {\n for (Size i = 0; i < dates().size(); i++) {\n dateIndexMap_.emplace(dates()[i], i);\n }\n}\n\n\nconst Real StaticCreditXvaCalculator::calculateCvaIncrement(\n const string& tid, const string& cid, const Date& d0, const Date& d1, const Real& rr) {\n Handle dts = market_->defaultCurve(cid, configuration_);\n QL_REQUIRE(!dts.empty(), \"Default curve missing for counterparty \" << cid);\n Real increment = 0.0;\n Real s0 = dts->survivalProbability(d0);\n Real s1 = dts->survivalProbability(d1);\n Real epe = tradeExposureCube_->get(tid, d1, 0, tradeEpeIndex_);\n increment = (1.0 - rr) * (s0 - s1) * epe;\n return increment;\n}\n\nconst Real StaticCreditXvaCalculator::calculateDvaIncrement(\n const string& tid, const Date& d0, const Date& d1, const Real& rr) {\n Handle dts = market_->defaultCurve(dvaName_, configuration_);\n QL_REQUIRE(!dts.empty(), \"Default curve missing for counterparty \" << dvaName_);\n Real increment = 0.0;\n Real s0 = dts->survivalProbability(d0);\n Real s1 = dts->survivalProbability(d1);\n Real ene = tradeExposureCube_->get(tid, d1, 0, tradeEneIndex_);\n increment = (1.0 - rr) * (s0 - s1) * ene;\n return increment;\n}\n\nconst Real StaticCreditXvaCalculator::calculateNettingSetCvaIncrement(\n const string& nid, const string& cid, const Date& d0, const Date& d1, const Real& rr) {\n Handle dts = market_->defaultCurve(cid, configuration_);\n QL_REQUIRE(!dts.empty(), \"Default curve missing for counterparty \" << cid);\n Real increment = 0.0;\n Real s0 = dts->survivalProbability(d0);\n Real s1 = dts->survivalProbability(d1);\n Real epe = nettingSetExposureCube_->get(nid, d1, 0, nettingSetEpeIndex_);\n increment = (1.0 - rr) * (s0 - s1) * epe;\n return increment;\n}\n\nconst Real StaticCreditXvaCalculator::calculateNettingSetDvaIncrement(\n const string& nid, const Date& d0, const Date& d1, const Real& rr) {\n Handle dts = market_->defaultCurve(dvaName_, configuration_);\n QL_REQUIRE(!dts.empty(), \"Default curve missing for counterparty \" << dvaName_);\n Real increment = 0.0;\n Real s0 = dts->survivalProbability(d0);\n Real s1 = dts->survivalProbability(d1);\n Real ene = nettingSetExposureCube_->get(nid, d1, 0, nettingSetEneIndex_);\n increment = (1.0 - rr) * (s0 - s1) * ene;\n return increment;\n}\n\nconst Real StaticCreditXvaCalculator::calculateFbaIncrement(\n const string& tid, const string& cid, const string& dvaName,\n const Date& d0, const Date& d1, const Real& dcf) {\n Handle dts_cid;\n Handle dts_dvaName;\n if (cid != \"\") {\n dts_cid = market_->defaultCurve(cid, configuration_);\n QL_REQUIRE(!dts_cid.empty(), \"Default curve missing for counterparty \" << cid);\n }\n if (dvaName != \"\") {\n dts_dvaName = market_->defaultCurve(dvaName, configuration_);\n QL_REQUIRE(!dts_dvaName.empty(), \"Default curve missing for counterparty \" << cid);\n }\n\n Real increment = 0.0;\n Real s0 = cid == \"\" ? 1.0 : dts_cid->survivalProbability(d0);\n Real s1 = dvaName == \"\" ? 1.0 : dts_dvaName->survivalProbability(d0);\n Real ene = tradeExposureCube_->get(tid, d1, 0, tradeEneIndex_);\n increment = s0 * s1 * ene * dcf;\n return increment;\n}\n\nconst Real StaticCreditXvaCalculator::calculateFcaIncrement(\n const string& tid, const string& cid, const string& dvaName,\n const Date& d0, const Date& d1, const Real& dcf) {\n Handle dts_cid;\n Handle dts_dvaName;\n if (cid != \"\") {\n dts_cid = market_->defaultCurve(cid, configuration_);\n QL_REQUIRE(!dts_cid.empty(), \"Default curve missing for counterparty \" << cid);\n }\n if (dvaName != \"\") {\n dts_dvaName = market_->defaultCurve(dvaName, configuration_);\n QL_REQUIRE(!dts_dvaName.empty(), \"Default curve missing for counterparty \" << cid);\n }\n\n Real increment = 0.0;\n Real s0 = cid == \"\" ? 1.0 : dts_cid->survivalProbability(d0);\n Real s1 = dvaName == \"\" ? 1.0 : dts_dvaName->survivalProbability(d0);\n Real epe = tradeExposureCube_->get(tid, d1, 0, tradeEpeIndex_);\n increment = s0 * s1 * epe * dcf;\n return increment;\n}\n\nconst Real StaticCreditXvaCalculator::calculateNettingSetFbaIncrement(\n const string& nid, const string& cid, const string& dvaName,\n const Date& d0, const Date& d1, const Real& dcf) {\n Handle dts_cid;\n Handle dts_dvaName;\n if (cid != \"\") {\n dts_cid = market_->defaultCurve(cid, configuration_);\n QL_REQUIRE(!dts_cid.empty(), \"Default curve missing for counterparty \" << cid);\n }\n if (dvaName != \"\") {\n dts_dvaName = market_->defaultCurve(dvaName, configuration_);\n QL_REQUIRE(!dts_dvaName.empty(), \"Default curve missing for counterparty \" << cid);\n }\n\n Real increment = 0.0;\n Real s0 = cid == \"\" ? 1.0 : dts_cid->survivalProbability(d0);\n Real s1 = dvaName == \"\" ? 1.0 : dts_dvaName->survivalProbability(d0);\n Real ene = nettingSetExposureCube_->get(nid, d1, 0, nettingSetEneIndex_);\n increment = s0 * s1 * ene * dcf;\n return increment;\n}\n\nconst Real StaticCreditXvaCalculator::calculateNettingSetFcaIncrement(\n const string& nid, const string& cid, const string& dvaName,\n const Date& d0, const Date& d1, const Real& dcf) {\n Handle dts_cid;\n Handle dts_dvaName;\n if (cid != \"\") {\n dts_cid = market_->defaultCurve(cid, configuration_);\n QL_REQUIRE(!dts_cid.empty(), \"Default curve missing for counterparty \" << cid);\n }\n if (dvaName != \"\") {\n dts_dvaName = market_->defaultCurve(dvaName, configuration_);\n QL_REQUIRE(!dts_dvaName.empty(), \"Default curve missing for counterparty \" << cid);\n }\n\n Real increment = 0.0;\n Real s0 = cid == \"\" ? 1.0 : dts_cid->survivalProbability(d0);\n Real s1 = dvaName == \"\" ? 1.0 : dts_dvaName->survivalProbability(d0);\n Real epe = nettingSetExposureCube_->get(nid, d1, 0, nettingSetEpeIndex_);\n increment = s0 * s1 * epe * dcf;\n return increment;\n}\n\nconst Real StaticCreditXvaCalculator::calculateNettingSetMvaIncrement(\n const string& nid, const string& cid, const Date& d0, const Date& d1, const Real& dcf) {\n Handle dts_cid = market_->defaultCurve(cid, configuration_);\n QL_REQUIRE(cid == \"\" || !dts_cid.empty(), \"Default curve missing for counterparty \" << cid);\n Handle dts_dvaName = market_->defaultCurve(dvaName_, configuration_);\n QL_REQUIRE(dvaName_ == \"\" || !dts_dvaName.empty(), \"Default curve missing for counterparty \" << dvaName_);\n\n Real increment = 0.0;\n Real s0 = cid == \"\" ? 1.0 : dts_cid->survivalProbability(d0);\n Real s1 = dvaName_ == \"\" ? 1.0 : dts_dvaName->survivalProbability(d0);\n increment = s0 * s1 * dimCalculator_->expectedIM(nid)[dateIndexMap_[d1]] * dcf;\n return increment;\n}\n\n} // namespace analytics\n} // namespace ore\n", "meta": {"hexsha": "948bb2818df45c5a06d2b025ad6ab33c22644137", "size": 9612, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "OREAnalytics/orea/aggregation/staticcreditxvacalculator.cpp", "max_stars_repo_name": "mrslezak/Engine", "max_stars_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 335.0, "max_stars_repo_stars_event_min_datetime": "2016-10-07T16:31:10.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-02T07:12:03.000Z", "max_issues_repo_path": "OREAnalytics/orea/aggregation/staticcreditxvacalculator.cpp", "max_issues_repo_name": "mrslezak/Engine", "max_issues_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 59.0, "max_issues_repo_issues_event_min_datetime": "2016-10-31T04:20:24.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-03T16:39:57.000Z", "max_forks_repo_path": "OREAnalytics/orea/aggregation/staticcreditxvacalculator.cpp", "max_forks_repo_name": "mrslezak/Engine", "max_forks_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 180.0, "max_forks_repo_forks_event_min_datetime": "2016-10-08T14:23:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T10:43:05.000Z", "avg_line_length": 46.2115384615, "max_line_length": 120, "alphanum_fraction": 0.7059925094, "num_tokens": 2653, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.37387581579519075, "lm_q1q2_score": 0.24219608720751604}} {"text": "/*\n * Copyright (c) 2013, Christian Gehring, Hannes Sommer, Paul Furgale, Remo Diethelm\n * All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions are met:\n * * Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n * * Redistributions in binary form must reproduce the above copyright\n * notice, this list of conditions and the following disclaimer in the\n * documentation and/or other materials provided with the distribution.\n * * Neither the name of the Autonomous Systems Lab, ETH Zurich nor the\n * names of its contributors may be used to endorse or promote products\n * derived from this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\n * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n * DISCLAIMED. IN NO EVENT SHALL Christian Gehring, Hannes Sommer, Paul Furgale,\n * Remo Diethelm BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,\n * OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE\n * GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\n * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n *\n*/\n\n#ifndef KINDR_ROTATIONS_EIGEN_EULERANGLESZYX_HPP_\n#define KINDR_ROTATIONS_EIGEN_EULERANGLESZYX_HPP_\n\n#include \n\n#include \n\n#include \"kindr/common/common.hpp\"\n#include \"kindr/common/assert_macros_eigen.hpp\"\n#include \"kindr/rotations/RotationBase.hpp\"\n#include \"kindr/rotations/eigen/RotationEigenFunctions.hpp\"\n\n\nnamespace kindr {\nnamespace rotations {\nnamespace eigen_impl {\n\n\n/*! \\class EulerAnglesZyx\n * \\brief Implementation of Euler angles (Z-Y'-X'' / yaw-pitch-roll) rotation based on Eigen::Matrix\n *\n * The following typedefs are provided for convenience:\n * - \\ref eigen_impl::EulerAnglesZyxAD \"EulerAnglesZyxAD\" for active rotation and double primitive type\n * - \\ref eigen_impl::EulerAnglesZyxAF \"EulerAnglesZyxAF\" for active rotation and float primitive type\n * - \\ref eigen_impl::EulerAnglesZyxPD \"EulerAnglesZyxPD\" for passive rotation and double primitive type\n * - \\ref eigen_impl::EulerAnglesZyxPF \"EulerAnglesZyxPF\" for passive rotation and float primitive type\n * - EulerAnglesYprAD = EulerAnglesZyxAD\n * - EulerAnglesYprAF = EulerAnglesZyxAF\n * - EulerAnglesYprPD = EulerAnglesZyxPD\n * - EulerAnglesYprPF = EulerAnglesZyxPF\n *\n * \\tparam PrimType_ the primitive type of the data (double or float)\n * \\tparam Usage_ the rotation usage which is either active or passive\n * \\ingroup rotations\n */\ntemplate\nclass EulerAnglesZyx : public EulerAnglesZyxBase, Usage_> {\n private:\n /*! \\brief The base type.\n */\n typedef Eigen::Matrix Base;\n\n /*! \\brief vector of Euler angles [yaw; pitch; roll]\n */\n Base zyx_;\n public:\n /*! \\brief The implementation type.\n * The implementation type is always an Eigen object.\n */\n typedef Base Implementation;\n /*! \\brief The primitive type.\n * Float/Double\n */\n typedef PrimType_ Scalar;\n\n /*! \\brief Euler angles as 3x1-matrix\n */\n typedef Base Vector;\n\n /*! \\brief Default constructor using identity rotation.\n */\n EulerAnglesZyx()\n : zyx_(Base::Zero()) {\n }\n\n /*! \\brief Constructor using three scalars.\n * \\param yaw first rotation angle around Z axis\n * \\param pitch second rotation angle around Y' axis\n * \\param roll third rotation angle around X'' axis\n */\n EulerAnglesZyx(Scalar yaw, Scalar pitch, Scalar roll)\n : zyx_(yaw,pitch,roll) {\n }\n\n /*! \\brief Constructor using Eigen::Matrix.\n * \\param other Eigen::Matrix [roll; pitch; yaw]\n */\n explicit EulerAnglesZyx(const Base& other)\n : zyx_(other) {\n }\n\n /*! \\brief Constructor using another rotation.\n * \\param other other rotation\n */\n template\n inline explicit EulerAnglesZyx(const RotationBase& other)\n : zyx_(internal::ConversionTraits::convert(other.derived()).toImplementation()) {\n }\n\n /*! \\brief Assignment operator using another rotation.\n * \\param other other rotation\n * \\returns referece\n */\n template\n EulerAnglesZyx& operator =(const RotationBase& other) {\n this->toImplementation() = internal::ConversionTraits::convert(other.derived()).toImplementation();\n return *this;\n }\n\n\n /*! \\brief Parenthesis operator to convert from another rotation.\n * \\param other other rotation\n * \\returns reference\n */\n template\n EulerAnglesZyx& operator ()(const RotationBase& other) {\n this->toImplementation() = internal::ConversionTraits::convert(other.derived()).toImplementation();\n return *this;\n }\n\n /*! \\brief Returns the inverse of the rotation.\n * \\returns the inverse of the rotation\n */\n EulerAnglesZyx inverted() const {\n return EulerAnglesZyx(eigen_internal::getInverseYpr(this->toImplementation()));\n }\n\n /*! \\brief Inverts the rotation.\n * \\returns reference\n */\n EulerAnglesZyx& invert() {\n *this = this->inverted();\n return *this;\n }\n\n /*! \\brief Returns the Euler angles in a vector.\n * \\returns vector Eigen::Matrix\n */\n inline const Vector vector() const {\n return this->toImplementation();\n }\n\n /*! \\brief Cast to the implementation type.\n * \\returns the implementation for direct manipulation (recommended only for advanced users)\n */\n inline Base& toImplementation() {\n return static_cast(zyx_);\n }\n\n /*! \\brief Cast to the implementation type.\n * \\returns the implementation for direct manipulation (recommended only for advanced users)\n */\n inline const Base& toImplementation() const {\n return static_cast(zyx_);\n }\n\n /*! \\brief Gets yaw (Z) angle.\n * \\returns yaw angle (scalar)\n */\n inline Scalar yaw() const {\n return zyx_(0);\n }\n\n /*! \\brief Gets pitch (Y') angle.\n * \\returns pitch angle (scalar)\n */\n inline Scalar pitch() const {\n return zyx_(1);\n }\n\n /*! \\brief Gets roll (X'') angle.\n * \\returns roll angle (scalar)\n */\n inline Scalar roll() const {\n return zyx_(2);\n }\n\n /*! \\brief Sets yaw (Z) angle.\n */\n inline void setYaw(Scalar yaw) {\n zyx_(0) = yaw;\n }\n\n /*! \\brief Sets pitch (Y') angle.\n */\n inline void setPitch(Scalar pitch) {\n zyx_(1) = pitch;\n }\n\n /*! \\brief Sets roll (X'') angle.\n */\n inline void setRoll(Scalar roll) {\n zyx_(2) = roll;\n }\n\n /*! \\brief Reading access to yaw (Z) angle.\n * \\returns yaw angle (scalar) with reading access\n */\n inline Scalar z() const {\n return zyx_(0);\n }\n\n /*! \\brief Reading access to pitch (Y') angle.\n * \\returns pitch angle (scalar) with reading access\n */\n inline Scalar y() const {\n return zyx_(1);\n }\n\n /*! \\brief Reading access to roll (X'') angle.\n * \\returns roll angle (scalar) with reading access\n */\n inline Scalar x() const {\n return zyx_(2);\n }\n\n /*! \\brief Writing access to yaw (Z) angle.\n * \\returns yaw angle (scalar) with writing access\n */\n inline void setZ(Scalar z) {\n zyx_(0) = z;\n }\n\n /*! \\brief Writing access to pitch (Y') angle.\n * \\returns pitch angle (scalar) with writing access\n */\n inline void setY(Scalar y) {\n zyx_(1) = y;\n }\n\n /*! \\brief Writing access to roll (X'') angle.\n * \\returns roll angle (scalar) with writing access\n */\n inline void setX(Scalar x) {\n zyx_(2) = x;\n }\n\n /*! \\brief Sets the rotation to identity.\n * \\returns reference\n */\n EulerAnglesZyx& setIdentity() {\n zyx_.setZero();\n return *this;\n }\n\n /*! \\brief Returns a unique Euler angles rotation with angles in [-pi,pi),[-pi/2,pi/2),[-pi,pi).\n * This function is used to compare different rotations.\n * \\returns copy of the Euler angles rotation which is unique\n */\n EulerAnglesZyx getUnique() const {\n Base zyx(kindr::common::floatingPointModulo(z()+M_PI,2*M_PI)-M_PI,\n kindr::common::floatingPointModulo(y()+M_PI,2*M_PI)-M_PI,\n kindr::common::floatingPointModulo(x()+M_PI,2*M_PI)-M_PI); // wrap all angles into [-pi,pi)\n\n const double tol = 1e-3;\n\n // wrap angles into [-pi,pi),[-pi/2,pi/2),[-pi,pi)\n if(zyx.y() < -M_PI/2 - tol)\n {\n if(zyx.z() < 0) {\n zyx.z() = zyx.z() + M_PI;\n } else {\n zyx.z() = zyx.z() - M_PI;\n }\n\n zyx.y() = -(zyx.y() + M_PI);\n\n if(zyx.x() < 0) {\n zyx.x() = zyx.x() + M_PI;\n } else {\n zyx.x() = zyx.x() - M_PI;\n }\n }\n else if(-M_PI/2 - tol <= zyx.y() && zyx.y() <= -M_PI/2 + tol)\n {\n zyx.z() += zyx.x();\n zyx.x() = 0;\n }\n else if(-M_PI/2 + tol < zyx.y() && zyx.y() < M_PI/2 - tol)\n {\n // ok\n }\n else if(M_PI/2 - tol <= zyx.y() && zyx.y() <= M_PI/2 + tol)\n {\n // todo: M_PI/2 should not be in range, other formula?\n zyx.z() -= zyx.x();\n zyx.x() = 0;\n }\n else // M_PI/2 + tol < zyx.y()\n {\n if(zyx.z() < 0) {\n zyx.z() = zyx.z() + M_PI;\n } else {\n zyx.z() = zyx.z() - M_PI;\n }\n\n zyx.y() = -(zyx.y() - M_PI);\n\n if(zyx.x() < 0) {\n zyx.x() = zyx.x() + M_PI;\n } else {\n zyx.x() = zyx.x() - M_PI;\n }\n }\n\n return EulerAnglesZyx(zyx);\n }\n\n /*! \\brief Modifies the Euler angles rotation such that the angles lie in [-pi,pi),[-pi/2,pi/2),[-pi,pi).\n * \\returns reference\n */\n EulerAnglesZyx& setUnique() { // wraps angles into [-pi,pi),[-pi/2,pi/2),[-pi,pi)\n *this = getUnique();\n return *this;\n }\n\n /*! \\brief Concenation operator.\n * This is explicitly specified, because Eigen::Matrix provides also an operator*.\n * \\returns the concenation of two rotations\n */\n using EulerAnglesZyxBase, Usage_>::operator*;\n\n /*! \\brief Equivalence operator.\n * This is explicitly specified, because Eigen::Matrix provides also an operator==.\n * \\returns true if two rotations are similar.\n */\n using EulerAnglesZyxBase, Usage_>::operator==;\n\n /*! \\brief Used for printing the object with std::cout.\n * \\returns std::stream object\n */\n friend std::ostream& operator << (std::ostream& out, const EulerAnglesZyx& zyx) {\n out << zyx.toImplementation().transpose();\n return out;\n }\n};\n\n//! \\brief Active Euler angles rotation (Z,Y',X'' / yaw,pitch,roll) with double primitive type\ntypedef EulerAnglesZyx EulerAnglesZyxAD;\n//! \\brief Active Euler angles rotation (Z,Y',X'' / yaw,pitch,roll) with float primitive type\ntypedef EulerAnglesZyx EulerAnglesZyxAF;\n//! \\brief Passive Euler angles rotation (Z,Y',X'' / yaw,pitch,roll) with double primitive type\ntypedef EulerAnglesZyx EulerAnglesZyxPD;\n//! \\brief Passive Euler angles rotation (Z,Y',X'' / yaw,pitch,roll) with float primitive type\ntypedef EulerAnglesZyx EulerAnglesZyxPF;\n\n//! \\brief Equivalent Euler angles rotation (Z,Y',X'' / yaw,pitch,roll) class\ntemplate \nusing EulerAnglesYpr = EulerAnglesZyx;\n\n//! \\brief Active Euler angles rotation (Z,Y',X'' / yaw,pitch,roll) with double primitive type\ntypedef EulerAnglesYpr EulerAnglesYprAD;\n//! \\brief Active Euler angles rotation (Z,Y',X'' / yaw,pitch,roll) with float primitive type\ntypedef EulerAnglesYpr EulerAnglesYprAF;\n//! \\brief Passive Euler angles rotation (Z,Y',X'' / yaw,pitch,roll) with double primitive type\ntypedef EulerAnglesYpr EulerAnglesYprPD;\n//! \\brief Passive Euler angles rotation (Z,Y',X'' / yaw,pitch,roll) with float primitive type\ntypedef EulerAnglesYpr EulerAnglesYprPF;\n\n} // namespace eigen_impl\n\n\nnamespace internal {\n\ntemplate\nclass get_scalar> {\n public:\n typedef PrimType_ Scalar;\n};\n\ntemplate\nclass get_matrix3X>{\n public:\n typedef int IndexType;\n\n template \n using Matrix3X = Eigen::Matrix;\n};\n\ntemplate\nclass get_other_usage> {\n public:\n typedef eigen_impl::EulerAnglesZyx OtherUsage;\n};\n\ntemplate\nclass get_other_usage> {\n public:\n typedef eigen_impl::EulerAnglesZyx OtherUsage;\n};\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Conversion Traits\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\ntemplate\nclass ConversionTraits, eigen_impl::AngleAxis> {\n public:\n inline static eigen_impl::EulerAnglesZyx convert(const eigen_impl::AngleAxis& aa) {\n// return eigen_impl::EulerAnglesZyx(eigen_impl::getYprFromAngleAxis(aa.toImplementation()));\n return eigen_impl::EulerAnglesZyx(eigen_impl::RotationQuaternion(aa));\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::RotationVector> {\n public:\n inline static eigen_impl::EulerAnglesZyx convert(const eigen_impl::RotationVector& rotationVector) {\n return eigen_impl::EulerAnglesZyx(eigen_impl::AngleAxis(rotationVector));\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::RotationQuaternion> {\n public:\n inline static eigen_impl::EulerAnglesZyx convert(const eigen_impl::RotationQuaternion& q) {\n return eigen_impl::EulerAnglesZyx(eigen_impl::eigen_internal::getYprFromQuaternion(q.toImplementation()));\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::RotationMatrix> {\n public:\n inline static eigen_impl::EulerAnglesZyx convert(const eigen_impl::RotationMatrix& R) {\n return eigen_impl::EulerAnglesZyx(eigen_impl::eigen_internal::getYprFromRotationMatrix(R.toImplementation().transpose()));\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::EulerAnglesXyz> {\n public:\n inline static eigen_impl::EulerAnglesZyx convert(const eigen_impl::EulerAnglesXyz& xyz) {\n return eigen_impl::EulerAnglesZyx(eigen_impl::eigen_internal::getYprFromRpy(xyz.toImplementation()));\n }\n};\n\ntemplate\nclass ConversionTraits, eigen_impl::EulerAnglesZyx> {\n public:\n inline static eigen_impl::EulerAnglesZyx convert(const eigen_impl::EulerAnglesZyx& zyx) {\n return eigen_impl::EulerAnglesZyx(zyx.toImplementation().template cast());\n }\n};\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Multiplication Traits\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\n//template\n//class MultiplicationTraits, RotationUsage::ACTIVE>, RotationBase, RotationUsage::ACTIVE>> {\n// public:\n// inline static eigen_impl::EulerAnglesZyx mult(const eigen_impl::EulerAnglesZyx& a, const eigen_impl::EulerAnglesZyx& b) {\n// return eigen_impl::EulerAnglesZyx(eigen_impl::RotationQuaternion(\n// eigen_impl::RotationQuaternion(a).toImplementation()*\n// eigen_impl::RotationQuaternion(b).toImplementation()));\n// }\n//};\n//\n//template\n//class MultiplicationTraits, RotationUsage::PASSIVE>, RotationBase, RotationUsage::PASSIVE>> {\n// public:\n// inline static eigen_impl::EulerAnglesZyx mult(const eigen_impl::EulerAnglesZyx& a, const eigen_impl::EulerAnglesZyx& b) {\n// return eigen_impl::EulerAnglesZyx(eigen_impl::RotationQuaternion(\n// eigen_impl::RotationQuaternion(a).toImplementation()*\n// eigen_impl::RotationQuaternion(b).toImplementation()));\n// }\n//};\n\n//template\n//class MultiplicationTraits, Usage_>, RotationBase, Usage_>> {\n// public:\n// inline static eigen_impl::EulerAnglesZyx mult(const eigen_impl::EulerAnglesZyx& a, const eigen_impl::EulerAnglesZyx& b) {\n// return eigen_impl::EulerAnglesZyx(eigen_impl::RotationQuaternion(\n// eigen_impl::RotationQuaternion(a).toImplementation()*\n// eigen_impl::RotationQuaternion(b).toImplementation()));\n// }\n//};\n\n\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Rotation Traits\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\n\n\n/* -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n * Comparison Traits\n * ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- */\n\n\n} // namespace internal\n} // namespace rotations\n} // namespace kindr\n\n\n#endif /* KINDR_ROTATIONS_EIGEN_EULERANGLESZYX_HPP_ */\n", "meta": {"hexsha": "f3efa074919d4c15cb0d7f73316025a6cce87a25", "size": 21417, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "IHMCPerception/third-party/kindr/include/kindr/rotations/eigen/EulerAnglesZyx.hpp", "max_stars_repo_name": "wxmerkt/ihmc-open-robotics-software", "max_stars_repo_head_hexsha": "2c47c9a9bd999e7811038e99c3888683f9973a2a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 170.0, "max_stars_repo_stars_event_min_datetime": "2016-02-01T18:58:50.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-17T05:28:01.000Z", "max_issues_repo_path": "IHMCPerception/third-party/kindr/include/kindr/rotations/eigen/EulerAnglesZyx.hpp", "max_issues_repo_name": "wxmerkt/ihmc-open-robotics-software", "max_issues_repo_head_hexsha": "2c47c9a9bd999e7811038e99c3888683f9973a2a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 162.0, "max_issues_repo_issues_event_min_datetime": 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YES\n2. NO", "lm_q1_score": 0.5078118642792043, "lm_q2_score": 0.47657965106367595, "lm_q1q2_score": 0.24201280108417797}} {"text": "/*\n scph.cpp\n\n Copyright (c) 2015 Terumasa Tadano\n\n This file is distributed under the terms of the MIT license.\n Please see the file 'LICENCE.txt' in the root directory \n or http://opensource.org/licenses/mit-license.php for information.\n*/\n\n#include \"mpi_common.h\"\n#include \"scph.h\"\n#include \"dynamical.h\"\n#include \"kpoint.h\"\n#include \"anharmonic_core.h\"\n#include \"dielec.h\"\n#include \"ewald.h\"\n#include \"memory.h\"\n#include \"thermodynamics.h\"\n#include \"write_phonons.h\"\n#include \"constants.h\"\n#include \"system.h\"\n#include \"error.h\"\n#include \"mathfunctions.h\"\n#include \"integration.h\"\n#include \"parsephon.h\"\n#include \"phonon_dos.h\"\n#include \"symmetry_core.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"timer.h\"\n#include \n#include \n#include \n\n#if defined(WIN32) || defined(_WIN32)\n#pragma comment(lib, \"libfftw3-3.lib\")\n#pragma comment(lib, \"libfftw3f-3.lib\")\n#pragma comment(lib, \"libfftw3l-3.lib\")\n#endif\n\nusing namespace PHON_NS;\n\nScph::Scph(PHON *phon) : Pointers(phon)\n{\n set_default_variables();\n}\n\nScph::~Scph()\n{\n deallocate_variables();\n}\n\n\nvoid Scph::set_default_variables()\n{\n im = std::complex(0.0, 1.0);\n restart_scph = false;\n warmstart_scph = false;\n lower_temp = true;\n tolerance_scph = 1.0e-10;\n mixalpha = 0.1;\n maxiter = 100;\n print_self_consistent_fc2 = false;\n selfenergy_offdiagonal = true;\n relax_coordinate = false;\n\n xk_scph = nullptr;\n kvec_na_scph = nullptr;\n xk_interpolate = nullptr;\n relvec_v3 = nullptr;\n relvec_v4 = nullptr;\n invmass_v3 = nullptr;\n invmass_v4 = nullptr;\n evec_index_v3 = nullptr;\n evec_index_v4 = nullptr;\n kmap_interpolate_to_scph = nullptr;\n evec_harmonic = nullptr;\n fcs_group_v3 = nullptr;\n fcs_group_v4 = nullptr;\n omega2_harmonic = nullptr;\n mat_transform_sym = nullptr;\n small_group_at_k = nullptr;\n symop_minus_at_k = nullptr;\n kpoint_map_symmetry = nullptr;\n exp_phase = nullptr;\n exp_phase3 = nullptr;\n mindist_list_scph = nullptr;\n\n bubble = 0;\n phi3_reciprocal = nullptr;\n compute_Cv_anharmonic = 0;\n}\n\n\nvoid Scph::deallocate_variables()\n{\n if (xk_scph) {\n memory->deallocate(xk_scph);\n }\n if (kvec_na_scph) {\n memory->deallocate(kvec_na_scph);\n }\n if (xk_interpolate) {\n memory->deallocate(xk_interpolate);\n }\n if (kmap_interpolate_to_scph) {\n memory->deallocate(kmap_interpolate_to_scph);\n }\n if (mindist_list_scph) {\n memory->deallocate(mindist_list_scph);\n }\n if (evec_harmonic) {\n memory->deallocate(evec_harmonic);\n }\n if (omega2_harmonic) {\n memory->deallocate(omega2_harmonic);\n }\n if (relvec_v3) {\n memory->deallocate(relvec_v3);\n }\n if (relvec_v4) {\n memory->deallocate(relvec_v4);\n }\n if (invmass_v3) {\n memory->deallocate(invmass_v3);\n }\n if (invmass_v4) {\n memory->deallocate(invmass_v4);\n }\n if (evec_index_v3) {\n memory->deallocate(evec_index_v3);\n }\n if (evec_index_v4) {\n memory->deallocate(evec_index_v4);\n }\n if (fcs_group_v3) {\n memory->deallocate(fcs_group_v3);\n }\n if (fcs_group_v4) {\n memory->deallocate(fcs_group_v4);\n }\n if (exp_phase) {\n memory->deallocate(exp_phase);\n }\n if (exp_phase3) {\n memory->deallocate(exp_phase3);\n }\n if (mat_transform_sym) {\n memory->deallocate(mat_transform_sym);\n }\n if (small_group_at_k) {\n memory->deallocate(small_group_at_k);\n }\n if (symop_minus_at_k) {\n memory->deallocate(symop_minus_at_k);\n }\n if (kpoint_map_symmetry) {\n memory->deallocate(kpoint_map_symmetry);\n }\n if (phi3_reciprocal) {\n memory->deallocate(phi3_reciprocal);\n }\n}\n\nvoid Scph::setup_scph()\n{\n relax_coordinate = false;\n MPI_Bcast(&relax_coordinate, 1, MPI_LOGICAL, 0, MPI_COMM_WORLD);\n MPI_Bcast(&bubble, 1, MPI_UNSIGNED, 0, MPI_COMM_WORLD);\n\n setup_kmesh();\n setup_eigvecs();\n setup_transform_ifc();\n setup_pp_interaction();\n setup_transform_symmetry();\n}\n\nvoid Scph::exec_scph()\n{\n const auto nk_ref = kpoint->nk;\n const auto ns = dynamical->neval;\n const auto Tmin = system->Tmin;\n const auto Tmax = system->Tmax;\n const auto dT = system->dT;\n\n std::complex ****delta_dymat_scph = nullptr;\n std::complex ****delta_dymat_scph_plus_bubble = nullptr;\n\n const auto NT = static_cast((Tmax - Tmin) / dT) + 1;\n\n MPI_Bcast(&restart_scph, 1, MPI_LOGICAL, 0, MPI_COMM_WORLD);\n MPI_Bcast(&selfenergy_offdiagonal, 1, MPI_LOGICAL, 0, MPI_COMM_WORLD);\n MPI_Bcast(&ialgo, 1, MPI_UNSIGNED, 0, MPI_COMM_WORLD);\n\n memory->allocate(delta_dymat_scph, NT, ns, ns, nk_interpolate);\n\n if (restart_scph) {\n\n // Read anharmonic correction to the dynamical matrix from the existing file\n load_scph_dymat_from_file(delta_dymat_scph);\n\n } else {\n\n if (dynamical->nonanalytic == 3) {\n error->exit(\"exec_scph\",\n \"Sorry, NONANALYTIC=3 can't be used for the main loop of the SCPH calculation.\");\n }\n // Solve the SCPH equation and obtain the correction to the dynamical matrix\n exec_scph_main(delta_dymat_scph);\n\n if (mympi->my_rank == 0) {\n store_scph_dymat_to_file(delta_dymat_scph);\n write_anharmonic_correction_fc2(delta_dymat_scph, NT);\n }\n }\n\n if (kpoint->kpoint_mode == 2) {\n if (thermodynamics->calc_FE_bubble) {\n compute_free_energy_bubble_SCPH(kmesh_interpolate,\n delta_dymat_scph);\n }\n }\n\n if (bubble) {\n memory->allocate(delta_dymat_scph_plus_bubble, NT, ns, ns, nk_interpolate);\n bubble_correction(delta_dymat_scph,\n delta_dymat_scph_plus_bubble);\n if (mympi->my_rank == 0) {\n write_anharmonic_correction_fc2(delta_dymat_scph_plus_bubble, NT, bubble);\n }\n }\n\n postprocess(delta_dymat_scph,\n delta_dymat_scph_plus_bubble);\n\n memory->deallocate(delta_dymat_scph);\n if (delta_dymat_scph_plus_bubble) memory->deallocate(delta_dymat_scph_plus_bubble);\n\n}\n\nvoid Scph::postprocess(std::complex ****delta_dymat_scph,\n std::complex ****delta_dymat_scph_plus_bubble)\n{\n double ***eval_anharm = nullptr;\n const auto nk_ref = kpoint->nk;\n const auto ns = dynamical->neval;\n const auto Tmin = system->Tmin;\n const auto Tmax = system->Tmax;\n const auto dT = system->dT;\n const auto NT = static_cast((Tmax - Tmin) / dT) + 1;\n\n unsigned int nomega_dielec;\n\n if (mympi->my_rank == 0) {\n\n std::cout << '\\n';\n std::cout << \" Running postprocess of SCPH (calculation of free energy, MSD, DOS)\" << std::endl;\n std::cout << \" The number of temperature points: \" << std::setw(4) << NT << std::endl;\n std::cout << \" \";\n\n std::complex ***evec_tmp = nullptr;\n double **eval_gam = nullptr;\n std::complex ***evec_gam = nullptr;\n double **xk_gam = nullptr;\n memory->allocate(eval_anharm, NT, nk_ref, ns);\n memory->allocate(evec_tmp, nk_ref, ns, ns);\n\n double **dos_scph = nullptr;\n double ***pdos_scph = nullptr;\n double *heat_capacity = nullptr;\n double *heat_capacity_correction = nullptr;\n double *FE_QHA = nullptr;\n double *dFE_scph = nullptr;\n double **msd_scph = nullptr;\n double ***ucorr_scph = nullptr;\n double ****dielec_scph = nullptr;\n double *omega_grid = nullptr;\n\n if (kpoint->kpoint_mode == 2) {\n if (dos->compute_dos) {\n memory->allocate(dos_scph, NT, dos->n_energy);\n\n if (dos->projected_dos) {\n memory->allocate(pdos_scph, NT, ns, dos->n_energy);\n }\n }\n memory->allocate(heat_capacity, NT);\n memory->allocate(FE_QHA, NT);\n memory->allocate(dFE_scph, NT);\n\n if (writes->getPrintMSD()) {\n memory->allocate(msd_scph, NT, ns);\n }\n if (writes->getPrintUcorr()) {\n memory->allocate(ucorr_scph, NT, ns, ns);\n }\n if (compute_Cv_anharmonic) {\n memory->allocate(heat_capacity_correction, NT);\n }\n }\n if (dielec->calc_dielectric_constant) {\n omega_grid = dielec->get_omega_grid(nomega_dielec);\n memory->allocate(dielec_scph, NT, nomega_dielec, 3, 3);\n memory->allocate(eval_gam, 1, ns);\n memory->allocate(evec_gam, 1, ns, ns);\n memory->allocate(xk_gam, 1, 3);\n for (auto i = 0; i < 3; ++i) xk_gam[0][i] = 0.0;\n }\n\n for (auto iT = 0; iT < NT; ++iT) {\n auto T = Tmin + dT * static_cast(iT);\n\n exec_interpolation(kmesh_interpolate,\n delta_dymat_scph[iT],\n nk_ref,\n kpoint->xk,\n kpoint->kvec_na,\n eval_anharm[iT],\n evec_tmp);\n\n if (kpoint->kpoint_mode == 2) {\n\n if (dos->compute_dos) {\n dos->calc_dos_from_given_frequency(eval_anharm[iT],\n dos_scph[iT]);\n }\n\n heat_capacity[iT] = thermodynamics->Cv_tot(T,\n kpoint->nk_irred,\n ns,\n kpoint->kpoint_irred_all,\n &kpoint->weight_k[0],\n eval_anharm[iT]);\n\n FE_QHA[iT] = thermodynamics->free_energy_QHA(T,\n kpoint->nk_irred,\n ns,\n kpoint->kpoint_irred_all,\n &kpoint->weight_k[0],\n eval_anharm[iT]);\n\n dFE_scph[iT] = thermodynamics->FE_scph_correction(iT,\n eval_anharm[iT],\n evec_tmp);\n\n if (writes->getPrintMSD()) {\n double shift[3]{0.0, 0.0, 0.0};\n\n for (auto is = 0; is < ns; ++is) {\n msd_scph[iT][is] = thermodynamics->disp_corrfunc(T, is, is,\n shift, kpoint->nk,\n ns,\n kpoint->xk,\n eval_anharm[iT],\n evec_tmp);\n }\n }\n\n if (writes->getPrintUcorr()) {\n double shift[3];\n for (auto i = 0; i < 3; ++i) shift[i] = static_cast(writes->getShiftUcorr()[i]);\n\n for (auto is = 0; is < ns; ++is) {\n for (auto js = 0; js < ns; ++js) {\n ucorr_scph[iT][is][js] = thermodynamics->disp_corrfunc(T, is, js,\n shift, kpoint->nk,\n ns,\n kpoint->xk,\n eval_anharm[iT],\n evec_tmp);\n }\n }\n }\n }\n\n if (dielec->calc_dielectric_constant) {\n exec_interpolation(kmesh_interpolate,\n delta_dymat_scph[iT],\n 1,\n xk_gam,\n xk_gam,\n eval_gam,\n evec_gam);\n\n for (auto is = 0; is < ns; ++is) {\n if (eval_gam[0][is] < 0.0) {\n eval_gam[0][is] = -std::pow(eval_gam[0][is], 2.0);\n } else {\n eval_gam[0][is] = std::pow(eval_gam[0][is], 2.0);\n }\n }\n\n dielec->compute_dielectric_function(nomega_dielec,\n omega_grid,\n eval_gam[0],\n evec_gam[0],\n dielec_scph[iT]);\n }\n\n std::cout << '.' << std::flush;\n if (iT % 25 == 24) {\n std::cout << std::endl;\n std::cout << std::setw(3);\n }\n }\n std::cout << \"\\n\\n\";\n\n if (kpoint->kpoint_mode == 2 && compute_Cv_anharmonic > 0) {\n double **domega_dt = nullptr;\n memory->allocate(domega_dt, nk_ref, ns);\n if (compute_Cv_anharmonic == 1) {\n // Use central difference to evaluate temperature derivative of\n // anharmonic frequencies\n\n heat_capacity_correction[0] = 0.0;\n heat_capacity_correction[NT - 1] = 0.0;\n\n for (auto iT = 1; iT < NT - 1; ++iT) {\n auto T = Tmin + dT * static_cast(iT);\n\n get_derivative_central_diff(dT, nk_ref,\n eval_anharm[iT - 1],\n eval_anharm[iT + 1],\n domega_dt);\n\n heat_capacity_correction[iT] = thermodynamics->Cv_anharm_correction(T,\n kpoint->nk_irred,\n ns,\n kpoint->kpoint_irred_all,\n &kpoint->weight_k[0],\n eval_anharm[iT],\n domega_dt);\n }\n }\n }\n\n if (kpoint->kpoint_mode == 0) {\n writes->write_scph_energy(eval_anharm);\n } else if (kpoint->kpoint_mode == 1) {\n writes->write_scph_bands(eval_anharm);\n } else if (kpoint->kpoint_mode == 2) {\n if (dos->compute_dos) {\n writes->write_scph_dos(dos_scph);\n }\n writes->write_scph_thermodynamics(heat_capacity,\n heat_capacity_correction,\n FE_QHA,\n dFE_scph);\n if (writes->getPrintMSD()) {\n writes->write_scph_msd(msd_scph);\n }\n if (writes->getPrintUcorr()) {\n writes->write_scph_ucorr(ucorr_scph);\n }\n }\n if (dielec->calc_dielectric_constant) {\n writes->write_scph_dielec(dielec_scph);\n }\n\n // If delta_dymat_scph_plus_bubble != nullptr, run postprocess again with\n // delta_dymat_scph_plus_bubble.\n if (bubble > 0) {\n std::cout << std::endl;\n std::cout << \" \";\n\n for (auto iT = 0; iT < NT; ++iT) {\n auto T = Tmin + dT * static_cast(iT);\n\n exec_interpolation(kmesh_interpolate,\n delta_dymat_scph_plus_bubble[iT],\n nk_ref,\n kpoint->xk,\n kpoint->kvec_na,\n eval_anharm[iT],\n evec_tmp);\n\n if (kpoint->kpoint_mode == 2) {\n\n if (dos->compute_dos) {\n dos->calc_dos_from_given_frequency(eval_anharm[iT],\n dos_scph[iT]);\n }\n\n heat_capacity[iT] = thermodynamics->Cv_tot(T,\n kpoint->nk_irred,\n ns,\n kpoint->kpoint_irred_all,\n &kpoint->weight_k[0],\n eval_anharm[iT]);\n\n if (writes->getPrintMSD()) {\n double shift[3]{0.0, 0.0, 0.0};\n\n for (auto is = 0; is < ns; ++is) {\n msd_scph[iT][is] = thermodynamics->disp_corrfunc(T, is, is,\n shift, kpoint->nk,\n ns,\n kpoint->xk,\n eval_anharm[iT],\n evec_tmp);\n }\n }\n\n if (writes->getPrintUcorr()) {\n double shift[3];\n for (auto i = 0; i < 3; ++i) shift[i] = static_cast(writes->getShiftUcorr()[i]);\n\n for (auto is = 0; is < ns; ++is) {\n for (auto js = 0; js < ns; ++js) {\n ucorr_scph[iT][is][js] = thermodynamics->disp_corrfunc(T, is, js,\n shift, kpoint->nk,\n ns,\n kpoint->xk,\n eval_anharm[iT],\n evec_tmp);\n }\n }\n }\n }\n\n std::cout << '.' << std::flush;\n if (iT % 25 == 24) {\n std::cout << std::endl;\n std::cout << std::setw(3);\n }\n }\n\n std::cout << \"\\n\\n\";\n\n if (kpoint->kpoint_mode == 0) {\n writes->write_scph_energy(eval_anharm, bubble);\n } else if (kpoint->kpoint_mode == 1) {\n writes->write_scph_bands(eval_anharm, bubble);\n } else if (kpoint->kpoint_mode == 2) {\n if (dos->compute_dos) {\n writes->write_scph_dos(dos_scph, bubble);\n }\n if (writes->getPrintMSD()) {\n writes->write_scph_msd(msd_scph, bubble);\n }\n if (writes->getPrintUcorr()) {\n writes->write_scph_ucorr(ucorr_scph, bubble);\n }\n }\n }\n\n memory->deallocate(eval_anharm);\n memory->deallocate(evec_tmp);\n\n if (dos_scph) memory->deallocate(dos_scph);\n if (pdos_scph) memory->deallocate(pdos_scph);\n if (heat_capacity) memory->deallocate(heat_capacity);\n if (heat_capacity_correction) memory->deallocate(heat_capacity_correction);\n if (FE_QHA) memory->deallocate(FE_QHA);\n if (dFE_scph) memory->deallocate(dFE_scph);\n if (dielec_scph) memory->deallocate(dielec_scph);\n\n if (eval_gam) memory->deallocate(eval_gam);\n if (evec_gam) memory->deallocate(evec_gam);\n if (xk_gam) memory->deallocate(xk_gam);\n }\n}\n\n\nvoid Scph::load_scph_dymat_from_file(std::complex ****dymat_out)\n{\n const auto ns = dynamical->neval;\n const auto Tmin = system->Tmin;\n const auto Tmax = system->Tmax;\n const auto dT = system->dT;\n const auto NT = static_cast((Tmax - Tmin) / dT) + 1;\n std::vector Temp_array(NT);\n\n for (int i = 0; i < NT; ++i) {\n Temp_array[i] = Tmin + dT * static_cast(i);\n }\n\n if (mympi->my_rank == 0) {\n\n const auto consider_offdiagonal = selfenergy_offdiagonal;\n double temp;\n std::ifstream ifs_dymat;\n auto file_dymat = input->job_title + \".scph_dymat\";\n bool consider_offdiag_tmp;\n unsigned int nk_interpolate_ref[3];\n unsigned int nk_scph_tmp[3];\n double Tmin_tmp, Tmax_tmp, dT_tmp;\n double dymat_real, dymat_imag;\n std::string str_dummy;\n int nonanalytic_tmp;\n\n std::cout << \" RESTART_SCPH is true.\" << std::endl;\n std::cout << \" Dynamical matrix is read from file ...\";\n\n ifs_dymat.open(file_dymat.c_str(), std::ios::in);\n\n if (!ifs_dymat) {\n error->exit(\"load_scph_dymat_from_file\",\n \"Cannot open scph_dymat file\");\n }\n\n // Read computational settings from file and check the consistency.\n ifs_dymat >> nk_interpolate_ref[0] >> nk_interpolate_ref[1] >> nk_interpolate_ref[2];\n ifs_dymat >> nk_scph_tmp[0] >> nk_scph_tmp[1] >> nk_scph_tmp[2];\n ifs_dymat >> Tmin_tmp >> Tmax_tmp >> dT_tmp;\n ifs_dymat >> nonanalytic_tmp >> consider_offdiag_tmp;\n\n if (nk_interpolate_ref[0] != kmesh_interpolate[0] ||\n nk_interpolate_ref[1] != kmesh_interpolate[1] ||\n nk_interpolate_ref[2] != kmesh_interpolate[2]) {\n error->exit(\"load_scph_dymat_from_file\",\n \"The number of KMESH_INTERPOLATE is not consistent\");\n }\n if (nk_scph_tmp[0] != kmesh_scph[0] ||\n nk_scph_tmp[1] != kmesh_scph[1] ||\n nk_scph_tmp[2] != kmesh_scph[2]) {\n error->exit(\"load_scph_dymat_from_file\",\n \"The number of KMESH_SCPH is not consistent\");\n }\n if (nonanalytic_tmp != dynamical->nonanalytic) {\n error->warn(\"load_scph_dymat_from_file\",\n \"The NONANALYTIC tag is not consistent\");\n }\n if (consider_offdiag_tmp != consider_offdiagonal) {\n error->exit(\"load_scph_dymat_from_file\",\n \"The SELF_OFFDIAG tag is not consistent\");\n }\n\n // Check if the precalculated data for the given temperature range exists\n const auto NT_ref = static_cast((Tmax_tmp - Tmin_tmp) / dT_tmp) + 1;\n std::vector Temp_array_ref(NT_ref);\n for (int i = 0; i < NT_ref; ++i) {\n Temp_array_ref[i] = Tmin_tmp + dT_tmp * static_cast(i);\n }\n std::vector flag_load(NT_ref);\n for (int i = 0; i < NT_ref; ++i) {\n flag_load[i] = 0;\n for (int j = 0; j < NT; ++j) {\n if (std::abs(Temp_array_ref[i] - Temp_array[j]) < eps6) {\n flag_load[i] = 1;\n break;\n }\n }\n }\n int icount = 0;\n for (int iT = 0; iT < NT_ref; ++iT) {\n ifs_dymat >> str_dummy >> temp;\n for (int is = 0; is < ns; ++is) {\n for (int js = 0; js < ns; ++js) {\n for (int ik = 0; ik < nk_interpolate; ++ik) {\n ifs_dymat >> dymat_real >> dymat_imag;\n if (flag_load[iT]) {\n dymat_out[icount][is][js][ik]\n = std::complex(dymat_real, dymat_imag);\n }\n }\n }\n }\n if (flag_load[iT]) icount += 1;\n }\n\n ifs_dymat.close();\n\n if (icount != NT) {\n error->exit(\"load_scph_dymat_from_file\",\n \"The temperature information is not consistent\");\n }\n std::cout << \" done.\" << std::endl;\n }\n // Broadcast to all MPI threads\n mpi_bcast_complex(dymat_out, NT, nk_interpolate, ns);\n}\n\nvoid Scph::store_scph_dymat_to_file(std::complex ****dymat_in)\n{\n int i;\n const auto ns = dynamical->neval;\n const auto Tmin = system->Tmin;\n const auto Tmax = system->Tmax;\n const auto dT = system->dT;\n std::ofstream ofs_dymat;\n auto file_dymat = input->job_title + \".scph_dymat\";\n\n const auto NT = static_cast((Tmax - Tmin) / dT) + 1;\n\n ofs_dymat.open(file_dymat.c_str(), std::ios::out);\n\n if (!ofs_dymat) {\n error->exit(\"store_scph_dymat_to_file\",\n \"Cannot open scph_dymat file\");\n }\n for (i = 0; i < 3; ++i) {\n ofs_dymat << std::setw(5) << kmesh_interpolate[i];\n }\n ofs_dymat << std::endl;\n for (i = 0; i < 3; ++i) {\n ofs_dymat << std::setw(5) << kmesh_scph[i];\n }\n ofs_dymat << std::endl;\n ofs_dymat << std::setw(10) << Tmin;\n ofs_dymat << std::setw(10) << Tmax;\n ofs_dymat << std::setw(10) << dT << std::endl;\n ofs_dymat << std::setw(5) << dynamical->nonanalytic;\n ofs_dymat << std::setw(5) << selfenergy_offdiagonal << std::endl;\n\n for (auto iT = 0; iT < NT; ++iT) {\n const auto temp = Tmin + static_cast(iT) * dT;\n ofs_dymat << \"# \" << temp << std::endl;\n for (auto is = 0; is < ns; ++is) {\n for (auto js = 0; js < ns; ++js) {\n for (auto ik = 0; ik < nk_interpolate; ++ik) {\n ofs_dymat << std::setprecision(15)\n << std::setw(25) << dymat_in[iT][is][js][ik].real();\n ofs_dymat << std::setprecision(15)\n << std::setw(25) << dymat_in[iT][is][js][ik].imag();\n ofs_dymat << std::endl;\n }\n }\n }\n }\n ofs_dymat.close();\n std::cout << \" \" << std::setw(input->job_title.length() + 12) << std::left << file_dymat;\n std::cout << \" : Anharmonic dynamical matrix (restart file)\" << std::endl;\n}\n\nvoid Scph::exec_scph_main(std::complex ****dymat_anharm)\n{\n int ik, is;\n const auto nk = nk_scph;\n const auto ns = dynamical->neval;\n const auto nk_reduced_scph = kp_irred_scph.size();\n const auto nk_irred_interpolate = kp_irred_interpolate.size();\n const auto Tmin = system->Tmin;\n const auto Tmax = system->Tmax;\n const auto dT = system->dT;\n double ***omega2_anharm;\n std::complex ***evec_anharm_tmp;\n std::complex ***v3_array_all;\n std::complex ***v4_array_all;\n\n std::vector vec_temp;\n\n const auto NT = static_cast((Tmax - Tmin) / dT) + 1;\n\n // Compute matrix element of 4-phonon interaction\n\n memory->allocate(omega2_anharm, NT, nk, ns);\n memory->allocate(evec_anharm_tmp, nk, ns, ns);\n memory->allocate(v4_array_all, nk_irred_interpolate * nk_scph,\n ns * ns, ns * ns);\n\n // Calculate v4 array. \n // This operation is the most expensive part of the calculation.\n if (selfenergy_offdiagonal & (ialgo == 1)) {\n compute_V4_elements_mpi_over_band(v4_array_all,\n evec_harmonic,\n selfenergy_offdiagonal);\n } else {\n compute_V4_elements_mpi_over_kpoint(v4_array_all,\n evec_harmonic,\n selfenergy_offdiagonal,\n relax_coordinate);\n }\n\n if (relax_coordinate) {\n memory->allocate(v3_array_all, nk, ns, ns * ns);\n compute_V3_elements_mpi_over_kpoint(v3_array_all,\n evec_harmonic,\n selfenergy_offdiagonal);\n }\n\n if (mympi->my_rank == 0) {\n\n std::complex ***cmat_convert;\n memory->allocate(cmat_convert, nk, ns, ns);\n\n vec_temp.clear();\n\n if (lower_temp) {\n for (int i = NT - 1; i >= 0; --i) {\n vec_temp.push_back(Tmin + static_cast(i) * dT);\n }\n } else {\n for (int i = 0; i < NT; ++i) {\n vec_temp.push_back(Tmin + static_cast(i) * dT);\n }\n }\n\n auto converged_prev = false;\n\n for (double temp : vec_temp) {\n auto iT = static_cast((temp - Tmin) / dT);\n\n // Initialize phonon eigenvectors with harmonic values\n\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n for (int js = 0; js < ns; ++js) {\n evec_anharm_tmp[ik][is][js] = evec_harmonic[ik][is][js];\n }\n }\n }\n if (converged_prev) {\n if (lower_temp) {\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n omega2_anharm[iT][ik][is] = omega2_anharm[iT + 1][ik][is];\n }\n }\n } else {\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n omega2_anharm[iT][ik][is] = omega2_anharm[iT - 1][ik][is];\n }\n }\n }\n }\n\n compute_anharmonic_frequency(v4_array_all,\n omega2_anharm[iT],\n evec_anharm_tmp,\n temp,\n converged_prev,\n cmat_convert,\n selfenergy_offdiagonal,\n writes->getVerbosity());\n\n calc_new_dymat_with_evec(dymat_anharm[iT],\n omega2_anharm[iT],\n evec_anharm_tmp);\n\n if (!warmstart_scph) converged_prev = false;\n }\n\n memory->deallocate(cmat_convert);\n\n }\n\n mpi_bcast_complex(dymat_anharm, NT, nk_interpolate, ns);\n\n memory->deallocate(omega2_anharm);\n memory->deallocate(v4_array_all);\n memory->deallocate(evec_anharm_tmp);\n}\n\n\nvoid Scph::compute_V3_elements_mpi_over_kpoint(std::complex ***v3_out,\n std::complex ***evec_in,\n const bool self_offdiag)\n{\n // Calculate the matrix elements of quartic terms in reciprocal space.\n // This is the most expensive part of the SCPH calculation.\n\n auto ns = dynamical->neval;\n auto ns2 = ns * ns;\n auto ns3 = ns * ns * ns;\n unsigned int is, js, ks;\n double phase3[3];\n int loc3[3];\n unsigned int **ind;\n unsigned int i, j;\n std::complex ret;\n long int ii;\n\n const auto inv2pi = 1.0 / (2.0 * pi);\n const auto dnk_represent = static_cast(nk_represent);\n const auto factor = std::pow(0.5, 2) / static_cast(nk_scph);\n static auto complex_zero = std::complex(0.0, 0.0);\n std::complex *v3_array_at_kpair;\n std::complex ***v3_mpi;\n\n // nk2_prod = nk_reduced_interpolate * nk_scph;\n\n if (mympi->my_rank == 0) {\n if (self_offdiag) {\n std::cout << \" SELF_OFFDIAG = 1: Calculating all components of v3_array ... \";\n } else {\n std::cout << \" SELF_OFFDIAG = 0: Calculating diagonal components of v3_array ... \";\n }\n }\n\n memory->allocate(v3_array_at_kpair, ngroup_v3);\n memory->allocate(ind, ngroup_v3, 3);\n memory->allocate(v3_mpi, nk_scph, ns, ns2);\n\n for (unsigned int ik = mympi->my_rank; ik < nk_scph; ik += mympi->nprocs) {\n\n for (is = 0; is < ngroup_v3; ++is) v3_array_at_kpair[is] = complex_zero;\n\n for (i = 0; i < ngroup_v3; ++i) {\n\n std::complex sum_tmp = std::complex(0.0, 0.0);\n for (j = 0; j < 3; ++j) ind[i][j] = evec_index_v3[i][j];\n\n if (tune_type == 0) {\n for (j = 0; j < fcs_group_v3[i].size(); ++j) {\n const auto phase = xk_scph[ik][0] * (relvec_v3[i][j].vecs[0][0] - relvec_v3[i][j].vecs[1][0])\n + xk_scph[ik][1] * (relvec_v3[i][j].vecs[0][1] - relvec_v3[i][j].vecs[1][1])\n + xk_scph[ik][2] * (relvec_v3[i][j].vecs[0][2] - relvec_v3[i][j].vecs[1][2]);\n\n const int loc = nint(phase * dnk_represent * inv2pi) % nk_represent + nk_represent - 1;\n\n sum_tmp += fcs_group_v3[i][j] * invmass_v3[i] * exp_phase[loc];\n }\n } else if (tune_type == 1) {\n for (j = 0; j < fcs_group_v4[i].size(); ++j) {\n\n for (ii = 0; ii < 3; ++ii) {\n phase3[ii] = xk_scph[ik][ii] * (relvec_v3[i][j].vecs[0][ii] - relvec_v3[i][j].vecs[1][ii]);\n loc3[ii] = nint(phase3[ii] * dnk[ii] * inv2pi) % nk_grid[ii] + nk_grid[ii] - 1;\n }\n\n sum_tmp += fcs_group_v3[i][j] * invmass_v3[i]\n * exp_phase3[loc3[0]][loc3[1]][loc3[2]];\n }\n }\n v3_array_at_kpair[i] = sum_tmp;\n }\n\n#pragma omp parallel for private(is)\n for (ii = 0; ii < ns; ++ii) {\n for (is = 0; is < ns2; ++is) {\n v3_mpi[ik][ii][is] = complex_zero;\n v3_out[ik][ii][is] = complex_zero;\n }\n }\n\n if (self_offdiag) {\n\n // All matrix elements will be calculated when considering the off-diagonal\n // elements of the phonon self-energy (i.e., when considering polarization mixing).\n\n#pragma omp parallel for private(is, js, ks, ret, i)\n for (ii = 0; ii < ns3; ++ii) {\n is = ii / ns2;\n js = (ii - ns2 * is) / ns;\n ks = ii % ns;\n\n ret = std::complex(0.0, 0.0);\n\n for (i = 0; i < ngroup_v3; ++i) {\n\n ret += v3_array_at_kpair[i]\n * evec_in[0][is][ind[i][0]]\n * evec_in[ik][js][ind[i][1]]\n * std::conj(evec_in[ik][ks][ind[i][2]]);\n }\n\n v3_mpi[ik][is][ns * js + ks] = factor * ret;\n }\n\n } else {\n\n // Only diagonal elements will be computed when neglecting the polarization mixing.\n\n if (ik == 0) {\n#pragma omp parallel for private(is, js, ks, ret, i)\n for (ii = 0; ii < ns3; ++ii) {\n is = ii / ns2;\n js = (ii - ns2 * is) / ns;\n ks = ii % ns;\n\n ret = std::complex(0.0, 0.0);\n\n for (i = 0; i < ngroup_v3; ++i) {\n\n ret += v3_array_at_kpair[i]\n * evec_in[0][is][ind[i][0]]\n *evec_in[ik][js][ind[i][1]]\n * std::conj(evec_in[ik][ks][ind[i][2]]);\n }\n\n v3_mpi[ik][is][ns * js + ks] = factor * ret;\n }\n } else {\n\n#pragma omp parallel for private(is, js, ret, i)\n for (ii = 0; ii < ns2; ++ii) {\n is = ii / ns;\n js = ii % ns;\n\n ret = std::complex(0.0, 0.0);\n\n for (i = 0; i < ngroup_v3; ++i) {\n\n ret += v3_array_at_kpair[i]\n * evec_in[0][is][ind[i][0]]\n * evec_in[ik][js][ind[i][1]]\n * std::conj(evec_in[ik][js][ind[i][2]]);\n }\n\n v3_mpi[ik][is][(ns + 1) * js] = factor * ret;\n }\n }\n }\n }\n\n memory->deallocate(v3_array_at_kpair);\n memory->deallocate(ind);\n#ifdef MPI_CXX_DOUBLE_COMPLEX\n MPI_Allreduce(&v3_mpi[0][0][0], &v3_out[0][0][0],\n static_cast(nk_scph) * ns3,\n MPI_CXX_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);\n#else\n MPI_Allreduce(&v3_mpi[0][0][0], &v3_out[0][0][0], static_cast(nk_scph) * ns3,\n MPI_COMPLEX16, MPI_SUM, MPI_COMM_WORLD);\n#endif\n\n memory->deallocate(v3_mpi);\n\n zerofill_elements_acoustic_at_gamma(omega2_harmonic, v3_out, 3);\n\n if (mympi->my_rank == 0) {\n std::cout << \" done !\" << std::endl;\n timer->print_elapsed();\n }\n}\n\nvoid Scph::compute_V4_elements_mpi_over_kpoint(std::complex ***v4_out,\n std::complex ***evec_in,\n const bool self_offdiag,\n const bool relax)\n{\n // Calculate the matrix elements of quartic terms in reciprocal space.\n // This is the most expensive part of the SCPH calculation.\n\n const size_t nk_reduced_interpolate = kp_irred_interpolate.size();\n const size_t ns = dynamical->neval;\n const size_t ns2 = ns * ns;\n const size_t ns3 = ns * ns * ns;\n const size_t ns4 = ns * ns * ns * ns;\n size_t is, js, ks, ls;\n double phase3[3];\n int loc3[3];\n unsigned int **ind;\n unsigned int i, j;\n std::complex ret;\n long int ii;\n\n const auto inv2pi = 1.0 / (2.0 * pi);\n const auto dnk_represent = static_cast(nk_represent);\n const auto factor = std::pow(0.5, 2) / static_cast(nk_scph);\n static auto complex_zero = std::complex(0.0, 0.0);\n std::complex *v4_array_at_kpair;\n std::complex ***v4_mpi;\n\n const size_t nk2_prod = nk_reduced_interpolate * nk_scph;\n\n if (mympi->my_rank == 0) {\n if (self_offdiag) {\n std::cout << \" SELF_OFFDIAG = 1: Calculating all components of v4_array ... \";\n } else {\n std::cout << \" SELF_OFFDIAG = 0: Calculating diagonal components of v4_array ... \";\n }\n }\n\n memory->allocate(v4_array_at_kpair, ngroup_v4);\n memory->allocate(ind, ngroup_v4, 4);\n memory->allocate(v4_mpi, nk2_prod, ns2, ns2);\n\n for (size_t ik_prod = mympi->my_rank; ik_prod < nk2_prod; ik_prod += mympi->nprocs) {\n const auto ik = ik_prod / nk_scph;\n const auto jk = ik_prod % nk_scph;\n\n const unsigned int knum = kmap_interpolate_to_scph[kp_irred_interpolate[ik][0].knum];\n\n for (is = 0; is < ngroup_v4; ++is) v4_array_at_kpair[is] = complex_zero;\n\n for (i = 0; i < ngroup_v4; ++i) {\n\n auto sum_tmp = std::complex(0.0, 0.0);\n for (j = 0; j < 4; ++j) ind[i][j] = evec_index_v4[i][j];\n\n if (tune_type == 0) {\n for (j = 0; j < fcs_group_v4[i].size(); ++j) {\n const auto phase = xk_scph[knum][0] * relvec_v4[i][j].vecs[0][0]\n + xk_scph[knum][1] * relvec_v4[i][j].vecs[0][1]\n + xk_scph[knum][2] * relvec_v4[i][j].vecs[0][2]\n + xk_scph[jk][0] * (relvec_v4[i][j].vecs[1][0] - relvec_v4[i][j].vecs[2][0])\n + xk_scph[jk][1] * (relvec_v4[i][j].vecs[1][1] - relvec_v4[i][j].vecs[2][1])\n + xk_scph[jk][2] * (relvec_v4[i][j].vecs[1][2] - relvec_v4[i][j].vecs[2][2]);\n\n const auto loc = nint(phase * dnk_represent * inv2pi) % nk_represent + nk_represent - 1;\n\n sum_tmp += fcs_group_v4[i][j] * invmass_v4[i] * exp_phase[loc];\n }\n } else if (tune_type == 1) {\n for (j = 0; j < fcs_group_v4[i].size(); ++j) {\n\n for (ii = 0; ii < 3; ++ii) {\n phase3[ii] = xk_scph[knum][ii] * relvec_v4[i][j].vecs[0][ii]\n + xk_scph[jk][ii] * (relvec_v4[i][j].vecs[1][ii] - relvec_v4[i][j].vecs[2][ii]);\n loc3[ii] = nint(phase3[ii] * dnk[ii] * inv2pi) % nk_grid[ii] + nk_grid[ii] - 1;\n }\n\n sum_tmp += fcs_group_v4[i][j] * invmass_v4[i]\n * exp_phase3[loc3[0]][loc3[1]][loc3[2]];\n }\n }\n v4_array_at_kpair[i] = sum_tmp;\n }\n\n#pragma omp parallel for private(is)\n for (ii = 0; ii < ns2; ++ii) {\n for (is = 0; is < ns2; ++is) {\n v4_mpi[ik_prod][ii][is] = complex_zero;\n v4_out[ik_prod][ii][is] = complex_zero;\n }\n }\n\n if (self_offdiag) {\n\n // All matrix elements will be calculated when considering the off-diagonal\n // elements of the phonon self-energy (loop diagram).\n\n#pragma omp parallel for private(is, js, ks, ls, ret, i)\n for (ii = 0; ii < ns4; ++ii) {\n is = ii / ns3;\n js = (ii - ns3 * is) / ns2;\n ks = (ii - ns3 * is - ns2 * js) / ns;\n ls = ii % ns;\n\n if (is < js) continue;\n\n ret = std::complex(0.0, 0.0);\n\n for (i = 0; i < ngroup_v4; ++i) {\n\n ret += v4_array_at_kpair[i]\n * std::conj(evec_in[knum][is][ind[i][0]])\n * evec_in[knum][js][ind[i][1]]\n * evec_in[jk][ks][ind[i][2]]\n * std::conj(evec_in[jk][ls][ind[i][3]]);\n }\n\n v4_mpi[ik_prod][ns * is + js][ns * ks + ls] = factor * ret;\n }\n\n } else {\n\n // Only diagonal elements will be computed when neglecting the polarization mixing.\n\n if (relax && (knum == 0 || jk == 0)) {\n\n#pragma omp parallel for private(is, js, ks, ls, ret, i)\n for (ii = 0; ii < ns4; ++ii) {\n is = ii / ns3;\n js = (ii - ns3 * is) / ns2;\n ks = (ii - ns3 * is - ns2 * js) / ns;\n ls = ii % ns;\n\n if (is < js) continue;\n\n ret = std::complex(0.0, 0.0);\n\n for (i = 0; i < ngroup_v4; ++i) {\n\n ret += v4_array_at_kpair[i]\n * std::conj(evec_in[knum][is][ind[i][0]])\n * evec_in[knum][js][ind[i][1]]\n * evec_in[jk][ks][ind[i][2]]\n * std::conj(evec_in[jk][ls][ind[i][3]]);\n }\n\n v4_mpi[ik_prod][ns * is + js][ns * ks + ls] = factor * ret;\n }\n\n } else {\n\n#pragma omp parallel for private(is, js, ret, i)\n for (ii = 0; ii < ns2; ++ii) {\n is = ii / ns;\n js = ii % ns;\n\n ret = std::complex(0.0, 0.0);\n\n for (i = 0; i < ngroup_v4; ++i) {\n\n ret += v4_array_at_kpair[i]\n * std::conj(evec_in[knum][is][ind[i][0]])\n * evec_in[knum][is][ind[i][1]]\n * evec_in[jk][js][ind[i][2]]\n * std::conj(evec_in[jk][js][ind[i][3]]);\n }\n\n v4_mpi[ik_prod][(ns + 1) * is][(ns + 1) * js] = factor * ret;\n }\n }\n }\n }\n\n memory->deallocate(v4_array_at_kpair);\n memory->deallocate(ind);\n\n// Now, communicate the calculated data.\n// When the data count is larger than 2^31-1, split it.\n\n long maxsize = 1;\n maxsize = (maxsize << 31) - 1;\n\n const size_t count = nk2_prod * ns4;\n const size_t count_sub = ns4;\n\n if (count <= maxsize) {\n#ifdef MPI_CXX_DOUBLE_COMPLEX\n MPI_Allreduce(&v4_mpi[0][0][0], &v4_out[0][0][0], count,\n MPI_CXX_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);\n#else\n MPI_Allreduce(&v4_mpi[0][0][0], &v4_out[0][0][0], count,\n MPI_COMPLEX16, MPI_SUM, MPI_COMM_WORLD);\n#endif\n } else if (count_sub <= maxsize) {\n for (size_t ik_prod = 0; ik_prod < nk2_prod; ++ik_prod) {\n#ifdef MPI_CXX_DOUBLE_COMPLEX\n MPI_Allreduce(&v4_mpi[ik_prod][0][0], &v4_out[ik_prod][0][0],\n count_sub,\n MPI_CXX_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);\n#else\n MPI_Allreduce(&v4_mpi[ik_prod][0][0], &v4_out[ik_prod][0][0],\n count_sub,\n MPI_COMPLEX16, MPI_SUM, MPI_COMM_WORLD);\n#endif\n }\n } else {\n for (size_t ik_prod = 0; ik_prod < nk2_prod; ++ik_prod) {\n for (is = 0; is < ns2; ++is) {\n#ifdef MPI_CXX_DOUBLE_COMPLEX\n MPI_Allreduce(&v4_mpi[ik_prod][is][0], &v4_out[ik_prod][is][0],\n ns2,\n MPI_CXX_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);\n#else\n MPI_Allreduce(&v4_mpi[ik_prod][is][0], &v4_out[ik_prod][is][0],\n ns2,\n MPI_COMPLEX16, MPI_SUM, MPI_COMM_WORLD);\n#endif\n }\n }\n }\n\n memory->deallocate(v4_mpi);\n\n zerofill_elements_acoustic_at_gamma(omega2_harmonic, v4_out, 4);\n\n\n if (mympi->my_rank == 0) {\n std::cout << \" done !\" << std::endl;\n timer->print_elapsed();\n }\n}\n\nvoid Scph::compute_V4_elements_mpi_over_band(std::complex ***v4_out,\n std::complex ***evec_in,\n const bool self_offdiag)\n{\n // Calculate the matrix elements of quartic terms in reciprocal space.\n // This is the most expensive part of the SCPH calculation.\n\n size_t ik_prod;\n const size_t nk_reduced_interpolate = kp_irred_interpolate.size();\n const size_t ns = dynamical->neval;\n const size_t ns2 = ns * ns;\n const size_t ns4 = ns * ns * ns * ns;\n int is, js;\n unsigned int knum;\n double phase3[3];\n int loc3[3];\n unsigned int **ind;\n unsigned int i, j;\n long int *nset_mpi;\n\n auto inv2pi = 1.0 / (2.0 * pi);\n auto dnk_represent = static_cast(nk_represent);\n auto factor = std::pow(0.5, 2) / static_cast(nk_scph);\n static auto complex_zero = std::complex(0.0, 0.0);\n std::complex *v4_array_at_kpair;\n std::complex ***v4_mpi;\n\n std::vector ik_vec, jk_vec, is_vec, js_vec;\n\n auto nk2_prod = nk_reduced_interpolate * nk_scph;\n\n if (mympi->my_rank == 0) {\n if (self_offdiag) {\n std::cout << \" IALGO = 1 : Use different algorithm efficient when nbands >> nk\\n\";\n std::cout << \" SELF_OFFDIAG = 1: Calculating all components of v4_array ... \\n\";\n } else {\n error->exit(\"compute_V4_elements_mpi_over_kpoint\",\n \"This function can be used only when SELF_OFFDIAG = 1\");\n }\n }\n\n memory->allocate(nset_mpi, mympi->nprocs);\n\n long int nset_tot = nk2_prod * ((ns2 - ns) / 2 + ns);\n long int nset_each = nset_tot / mympi->nprocs;\n long int nres = nset_tot - nset_each * mympi->nprocs;\n\n for (i = 0; i < mympi->nprocs; ++i) {\n nset_mpi[i] = nset_each;\n if (nres > i) {\n nset_mpi[i] += 1;\n }\n }\n\n MPI_Bcast(&nset_mpi[0], mympi->nprocs, MPI_LONG, 0, MPI_COMM_WORLD);\n long int nstart = 0;\n for (i = 0; i < mympi->my_rank; ++i) {\n nstart += nset_mpi[i];\n }\n long int nend = nstart + nset_mpi[mympi->my_rank];\n nset_each = nset_mpi[mympi->my_rank];\n memory->deallocate(nset_mpi);\n\n ik_vec.clear();\n jk_vec.clear();\n is_vec.clear();\n js_vec.clear();\n\n long int icount = 0;\n for (ik_prod = 0; ik_prod < nk2_prod; ++ik_prod) {\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n if (is < js) continue;\n\n if (icount >= nstart && icount < nend) {\n ik_vec.push_back(ik_prod / nk_scph);\n jk_vec.push_back(ik_prod % nk_scph);\n is_vec.push_back(is);\n js_vec.push_back(js);\n }\n ++icount;\n }\n }\n }\n\n memory->allocate(v4_array_at_kpair, ngroup_v4);\n memory->allocate(ind, ngroup_v4, 4);\n memory->allocate(v4_mpi, nk2_prod, ns2, ns2);\n\n for (ik_prod = 0; ik_prod < nk2_prod; ++ik_prod) {\n#pragma omp parallel for private (js)\n for (is = 0; is < ns2; ++is) {\n for (js = 0; js < ns2; ++js) {\n v4_mpi[ik_prod][is][js] = complex_zero;\n v4_out[ik_prod][is][js] = complex_zero;\n }\n }\n }\n\n int ik_old = -1;\n int jk_old = -1;\n\n if (mympi->my_rank == 0) {\n std::cout << \" Total number of sets to compute : \" << nset_each << std::endl;\n }\n\n for (long int ii = 0; ii < nset_each; ++ii) {\n\n auto ik_now = ik_vec[ii];\n auto jk_now = jk_vec[ii];\n auto is_now = is_vec[ii];\n auto js_now = js_vec[ii];\n\n if (!(ik_now == ik_old && jk_now == jk_old)) {\n\n // Update v4_array_at_kpair and ind\n\n knum = kmap_interpolate_to_scph[kp_irred_interpolate[ik_now][0].knum];\n\n for (is = 0; is < ngroup_v4; ++is) v4_array_at_kpair[is] = complex_zero;\n\n for (i = 0; i < ngroup_v4; ++i) {\n\n std::complex sum_tmp = std::complex(0.0, 0.0);\n for (j = 0; j < 4; ++j) ind[i][j] = evec_index_v4[i][j];\n\n if (tune_type == 0) {\n for (j = 0; j < fcs_group_v4[i].size(); ++j) {\n auto phase = xk_scph[knum][0] * relvec_v4[i][j].vecs[0][0]\n + xk_scph[knum][1] * relvec_v4[i][j].vecs[0][1]\n + xk_scph[knum][2] * relvec_v4[i][j].vecs[0][2]\n + xk_scph[jk_now][0] * (relvec_v4[i][j].vecs[1][0] - relvec_v4[i][j].vecs[2][0])\n + xk_scph[jk_now][1] * (relvec_v4[i][j].vecs[1][1] - relvec_v4[i][j].vecs[2][1])\n + xk_scph[jk_now][2] * (relvec_v4[i][j].vecs[1][2] - relvec_v4[i][j].vecs[2][2]);\n\n auto loc = nint(phase * dnk_represent * inv2pi) % nk_represent + nk_represent - 1;\n\n sum_tmp += fcs_group_v4[i][j] * invmass_v4[i] * exp_phase[loc];\n }\n } else if (tune_type == 1) {\n for (j = 0; j < fcs_group_v4[i].size(); ++j) {\n\n for (unsigned int k = 0; k < 3; ++k) {\n phase3[k] = xk_scph[knum][k] * relvec_v4[i][j].vecs[0][k]\n + xk_scph[jk_now][k] * (relvec_v4[i][j].vecs[1][k]\n - relvec_v4[i][j].vecs[2][k]);\n loc3[k] = nint(phase3[k] * dnk[k] * inv2pi) % nk_grid[k] + nk_grid[k] - 1;\n }\n\n sum_tmp += fcs_group_v4[i][j] * invmass_v4[i]\n * exp_phase3[loc3[0]][loc3[1]][loc3[2]];\n }\n }\n v4_array_at_kpair[i] = sum_tmp;\n }\n ik_old = ik_now;\n jk_old = jk_now;\n }\n\n ik_prod = ik_now * nk_scph + jk_now;\n int is_prod = ns * is_now + js_now;\n\n#pragma omp parallel for private (i)\n for (js = 0; js < ns2; ++js) {\n\n unsigned int ks = js / ns;\n unsigned int ls = js % ns;\n\n auto ret = std::complex(0.0, 0.0);\n\n for (i = 0; i < ngroup_v4; ++i) {\n\n ret += v4_array_at_kpair[i]\n * std::conj(evec_in[knum][is_now][ind[i][0]])\n * evec_in[knum][js_now][ind[i][1]]\n * evec_in[jk_now][ks][ind[i][2]]\n * std::conj(evec_in[jk_now][ls][ind[i][3]]);\n }\n\n v4_mpi[ik_prod][is_prod][js] = factor * ret;\n }\n\n if (mympi->my_rank == 0) {\n std::cout << \" SET \" << ii + 1 << \" done. \" << std::endl;\n }\n\n } // loop over nk2_prod*ns2\n\n memory->deallocate(v4_array_at_kpair);\n memory->deallocate(ind);\n\n// Now, communicate the calculated data.\n// When the data count is larger than 2^31-1, split it.\n\n long maxsize = 1;\n maxsize = (maxsize << 31) - 1;\n\n const size_t count = nk2_prod * ns4;\n const size_t count_sub = ns4;\n\n if (mympi->my_rank == 0) {\n std::cout << \"Communicating v4_array over MPI ...\";\n }\n if (count <= maxsize) {\n#ifdef MPI_CXX_DOUBLE_COMPLEX\n MPI_Allreduce(&v4_mpi[0][0][0], &v4_out[0][0][0], count,\n MPI_CXX_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);\n#else\n MPI_Allreduce(&v4_mpi[0][0][0], &v4_out[0][0][0], count,\n MPI_COMPLEX16, MPI_SUM, MPI_COMM_WORLD);\n#endif\n } else if (count_sub <= maxsize) {\n for (size_t ik_prod = 0; ik_prod < nk2_prod; ++ik_prod) {\n#ifdef MPI_CXX_DOUBLE_COMPLEX\n MPI_Allreduce(&v4_mpi[ik_prod][0][0], &v4_out[ik_prod][0][0],\n count_sub,\n MPI_CXX_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);\n#else\n MPI_Allreduce(&v4_mpi[ik_prod][0][0], &v4_out[ik_prod][0][0],\n count_sub,\n MPI_COMPLEX16, MPI_SUM, MPI_COMM_WORLD);\n#endif\n }\n } else {\n for (size_t ik_prod = 0; ik_prod < nk2_prod; ++ik_prod) {\n for (is = 0; is < ns2; ++is) {\n#ifdef MPI_CXX_DOUBLE_COMPLEX\n MPI_Allreduce(&v4_mpi[ik_prod][is][0], &v4_out[ik_prod][is][0],\n ns2,\n MPI_CXX_DOUBLE_COMPLEX, MPI_SUM, MPI_COMM_WORLD);\n#else\n MPI_Allreduce(&v4_mpi[ik_prod][is][0], &v4_out[ik_prod][is][0],\n ns2,\n MPI_COMPLEX16, MPI_SUM, MPI_COMM_WORLD);\n#endif\n }\n }\n }\n if (mympi->my_rank == 0) {\n std::cout << \"done.\\n\";\n }\n\n memory->deallocate(v4_mpi);\n\n zerofill_elements_acoustic_at_gamma(omega2_harmonic, v4_out, 4);\n\n if (mympi->my_rank == 0) {\n std::cout << \" done !\" << std::endl;\n timer->print_elapsed();\n }\n}\n\nvoid Scph::zerofill_elements_acoustic_at_gamma(double **omega2,\n std::complex ***v_elems,\n const int fc_order) const\n{\n // Set V3 or V4 elements involving acoustic modes at Gamma point\n // exactly zero.\n\n int jk;\n int is, js, ks, ls;\n const auto ns = dynamical->neval;\n bool *is_acoustic;\n memory->allocate(is_acoustic, ns);\n int nacoustic;\n auto threshould = 1.0e-24;\n const auto nk_reduced_interpolate = kp_irred_interpolate.size();\n static auto complex_zero = std::complex(0.0, 0.0);\n\n\n if (!(fc_order == 3 || fc_order == 4)) {\n error->exit(\"zerofill_elements_acoustic_at_gamma\",\n \"The fc_order must be either 3 or 4.\");\n }\n\n do {\n nacoustic = 0;\n for (is = 0; is < ns; ++is) {\n if (std::abs(omega2[0][is]) < threshould) {\n is_acoustic[is] = true;\n ++nacoustic;\n } else {\n is_acoustic[is] = false;\n }\n }\n if (nacoustic > 3) {\n error->exit(\"zerofill_elements_acoustic_at_gamma\",\n \"Could not assign acoustic modes at Gamma.\");\n }\n threshould *= 2.0;\n } while (nacoustic < 3);\n\n\n if (fc_order == 3) {\n\n // Set V3 to zeros so as to avoid mixing with gamma acoustic modes\n // jk = 0;\n for (is = 0; is < ns; ++is) {\n for (ks = 0; ks < ns; ++ks) {\n for (ls = 0; ls < ns; ++ls) {\n if (is_acoustic[ks] || is_acoustic[ls]) {\n v_elems[0][is][ns * ks + ls] = complex_zero;\n }\n }\n }\n }\n\n // ik = 0;\n for (jk = 0; jk < nk_scph; ++jk) {\n for (is = 0; is < ns; ++is) {\n if (is_acoustic[is]) {\n for (ks = 0; ks < ns; ++ks) {\n for (ls = 0; ls < ns; ++ls) {\n v_elems[jk][is][ns * ks + ls] = complex_zero;\n }\n }\n }\n }\n }\n\n } else if (fc_order == 4) {\n // Set V4 to zeros so as to avoid mixing with gamma acoustic modes\n // jk = 0;\n for (int ik = 0; ik < nk_reduced_interpolate; ++ik) {\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n for (ks = 0; ks < ns; ++ks) {\n for (ls = 0; ls < ns; ++ls) {\n if (is_acoustic[ks] || is_acoustic[ls]) {\n v_elems[nk_scph * ik][ns * is + js][ns * ks + ls] = complex_zero;\n }\n }\n }\n }\n }\n }\n // ik = 0;\n for (jk = 0; jk < nk_scph; ++jk) {\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n if (is_acoustic[is] || is_acoustic[js]) {\n for (ks = 0; ks < ns; ++ks) {\n for (ls = 0; ls < ns; ++ls) {\n v_elems[jk][ns * is + js][ns * ks + ls] = complex_zero;\n }\n }\n }\n }\n }\n }\n\n }\n\n memory->deallocate(is_acoustic);\n}\n\n\nvoid Scph::setup_kmesh()\n{\n unsigned int ik;\n unsigned int i;\n double xtmp[3];\n\n // Setup k points for SCPH equation\n MPI_Bcast(&kmesh_scph[0], 3, MPI_UNSIGNED, 0, MPI_COMM_WORLD);\n MPI_Bcast(&kmesh_interpolate[0], 3, MPI_UNSIGNED, 0, MPI_COMM_WORLD);\n\n // Set up k points for Fourier interpolation\n nk_scph = kmesh_scph[0] * kmesh_scph[1] * kmesh_scph[2];\n nk_interpolate = kmesh_interpolate[0] * kmesh_interpolate[1] * kmesh_interpolate[2];\n\n memory->allocate(xk_scph, nk_scph, 3);\n memory->allocate(xk_interpolate, nk_interpolate, 3);\n memory->allocate(kvec_na_scph, nk_scph, 3);\n\n kpoint->gen_kmesh(true,\n kmesh_scph,\n xk_scph,\n kp_irred_scph);\n\n kpoint->gen_kmesh(true,\n kmesh_interpolate,\n xk_interpolate,\n kp_irred_interpolate);\n\n\n for (ik = 0; ik < nk_scph; ++ik) {\n for (i = 0; i < 3; ++i) {\n kvec_na_scph[ik][i] = 0.0;\n }\n }\n\n for (ik = 0; ik < nk_scph; ++ik) {\n\n for (i = 0; i < 3; ++i) xtmp[i] = xk_scph[ik][i];\n rotvec(xtmp, xtmp, system->rlavec_p, 'T');\n const auto norm = xtmp[0] * xtmp[0] + xtmp[1] * xtmp[1] + xtmp[2] * xtmp[2];\n\n if (norm > eps) {\n for (i = 0; i < 3; ++i) kvec_na_scph[ik][i] = xk_scph[ik][i] / std::sqrt(norm);\n }\n }\n\n if (mympi->my_rank == 0) {\n// if (verbosity > 0) {\n std::cout << \" Setting up the SCPH calculations ...\" << std::endl << std::endl;\n std::cout << \" Gamma-centered uniform grid with the following mesh density:\" << std::endl;\n std::cout << \" nk1:\" << std::setw(5) << kmesh_scph[0] << std::endl;\n std::cout << \" nk2:\" << std::setw(5) << kmesh_scph[1] << std::endl;\n std::cout << \" nk3:\" << std::setw(5) << kmesh_scph[2] << std::endl;\n std::cout << std::endl;\n std::cout << \" Number of k points : \" << nk_scph << std::endl;\n std::cout << \" Number of irreducible k points : \" << kp_irred_scph.size() << std::endl;\n std::cout << std::endl;\n std::cout << \" Fourier interpolation from reciprocal to real space\" << std::endl;\n std::cout << \" will be performed with the following mesh density:\" << std::endl;\n std::cout << \" nk1:\" << std::setw(5) << kmesh_interpolate[0] << std::endl;\n std::cout << \" nk2:\" << std::setw(5) << kmesh_interpolate[1] << std::endl;\n std::cout << \" nk3:\" << std::setw(5) << kmesh_interpolate[2] << std::endl;\n std::cout << std::endl;\n std::cout << \" Number of k points : \" << nk_interpolate << std::endl;\n std::cout << \" Number of irreducible k points : \"\n << kp_irred_interpolate.size() << std::endl;\n// }\n }\n\n memory->allocate(kmap_interpolate_to_scph, nk_interpolate);\n\n for (ik = 0; ik < nk_interpolate; ++ik) {\n for (i = 0; i < 3; ++i) xtmp[i] = xk_interpolate[ik][i];\n\n const auto loc = kpoint->get_knum(xtmp, kmesh_scph);\n\n if (loc == -1)\n error->exit(\"setup_kmesh\",\n \"KMESH_INTERPOLATE should be a integral multiple of KMESH_SCPH\");\n kmap_interpolate_to_scph[ik] = loc;\n }\n}\n\n\nvoid Scph::setup_transform_symmetry()\n{\n // Construct small_group_at_k, symop_minus_at_k, and\n // mat_transport_sym.\n\n unsigned int ik;\n unsigned int is, js;\n unsigned int icrd, jcrd;\n double x1[3], x2[3], k[3], k_minus[3], Sk[3], xtmp[3];\n double S_cart[3][3], S_frac[3][3], S_frac_inv[3][3];\n double S_recip[3][3];\n std::complex im(0.0, 1.0);\n std::complex **gamma_tmp;\n bool *flag;\n\n const auto natmin = system->natmin;\n const auto ns = dynamical->neval;\n const auto nk_irred_interpolate = kp_irred_interpolate.size();\n\n memory->allocate(gamma_tmp, ns, ns);\n memory->allocate(mat_transform_sym, nk_irred_interpolate,\n symmetry->nsym, ns, ns);\n memory->allocate(small_group_at_k, nk_irred_interpolate);\n memory->allocate(symop_minus_at_k, nk_irred_interpolate);\n memory->allocate(kpoint_map_symmetry, nk_interpolate);\n memory->allocate(flag, nk_interpolate);\n\n for (ik = 0; ik < nk_interpolate; ++ik) {\n flag[ik] = false;\n }\n\n for (ik = 0; ik < kp_irred_interpolate.size(); ++ik) {\n\n small_group_at_k[ik].clear();\n symop_minus_at_k[ik].clear();\n\n const auto knum = kp_irred_interpolate[ik][0].knum;\n for (icrd = 0; icrd < 3; ++icrd) {\n k[icrd] = xk_interpolate[knum][icrd];\n k_minus[icrd] = -k[icrd];\n }\n const auto knum_minus = kpoint->get_knum(k_minus, kmesh_interpolate);\n\n unsigned int isym = 0;\n\n for (const auto &it : symmetry->SymmListWithMap) {\n\n for (icrd = 0; icrd < 3; ++icrd) {\n for (jcrd = 0; jcrd < 3; ++jcrd) {\n S_cart[icrd][jcrd] = it.rot[3 * icrd + jcrd];\n S_frac[icrd][jcrd] = it.rot_real[3 * icrd + jcrd];\n S_recip[icrd][jcrd] = it.rot_reciprocal[3 * icrd + jcrd];\n }\n }\n\n invmat3(S_frac_inv, S_frac);\n rotvec(Sk, k, S_recip);\n\n for (auto i = 0; i < 3; ++i) Sk[i] = Sk[i] - nint(Sk[i]);\n\n const auto knum_sym = kpoint->get_knum(Sk, kmesh_interpolate);\n if (knum_sym == -1)\n error->exit(\"setup_transform_symmetry\",\n \"kpoint not found\");\n\n if (knum_sym == knum) small_group_at_k[ik].push_back(isym);\n if (knum_sym == knum_minus) symop_minus_at_k[ik].push_back(isym);\n\n if (!flag[knum_sym]) {\n kpoint_map_symmetry[knum_sym].symmetry_op = isym;\n kpoint_map_symmetry[knum_sym].knum_irred_orig = ik;\n kpoint_map_symmetry[knum_sym].knum_orig = knum;\n flag[knum_sym] = true;\n }\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n gamma_tmp[is][js] = std::complex(0.0, 0.0);\n }\n }\n\n for (unsigned int jat = 0; jat < natmin; ++jat) {\n const auto iat = it.mapping[jat];\n\n // Fractional coordinates of x1 and x2\n for (icrd = 0; icrd < 3; ++icrd) {\n x1[icrd] = system->xr_p[system->map_p2s[iat][0]][icrd];\n x2[icrd] = system->xr_p[system->map_p2s[jat][0]][icrd];\n }\n\n rotvec(xtmp, x1, S_frac_inv);\n for (icrd = 0; icrd < 3; ++icrd) {\n xtmp[icrd] = xtmp[icrd] - x2[icrd];\n }\n\n auto phase = 2.0 * pi * (k[0] * xtmp[0] + k[1] * xtmp[1] + k[2] * xtmp[2]);\n\n for (icrd = 0; icrd < 3; ++icrd) {\n for (jcrd = 0; jcrd < 3; ++jcrd) {\n gamma_tmp[3 * iat + icrd][3 * jat + jcrd]\n = S_cart[icrd][jcrd] * std::exp(im * phase);\n }\n }\n }\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n mat_transform_sym[ik][isym][is][js] = gamma_tmp[is][js];\n }\n }\n\n ++isym;\n }\n }\n\n memory->deallocate(gamma_tmp);\n memory->deallocate(flag);\n}\n\n\nvoid Scph::symmetrize_dynamical_matrix(const unsigned int ik,\n Eigen::MatrixXcd &dymat) const\n{\n // Symmetrize the dynamical matrix of given index ik.\n using namespace Eigen;\n unsigned int i, isym;\n unsigned int is, js;\n const auto ns = dynamical->neval;\n MatrixXcd dymat_sym = MatrixXcd::Zero(ns, ns);\n MatrixXcd dymat_tmp(ns, ns), gamma(ns, ns);\n\n const auto nsym_small = small_group_at_k[ik].size();\n const auto nsym_minus = symop_minus_at_k[ik].size();\n\n for (i = 0; i < nsym_minus; ++i) {\n isym = symop_minus_at_k[ik][i];\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n gamma(is, js) = mat_transform_sym[ik][isym][is][js];\n }\n }\n\n dymat_tmp = gamma * dymat * gamma.transpose().conjugate();\n dymat_sym += dymat_tmp.conjugate();\n }\n\n for (i = 0; i < nsym_small; ++i) {\n isym = small_group_at_k[ik][i];\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n gamma(is, js) = mat_transform_sym[ik][isym][is][js];\n }\n }\n\n dymat_tmp = gamma * dymat * gamma.transpose().conjugate();\n dymat_sym += dymat_tmp;\n }\n\n\n dymat = dymat_sym / static_cast(nsym_small + nsym_minus);\n}\n\nvoid Scph::replicate_dymat_for_all_kpoints(std::complex ***dymat_inout) const\n{\n using namespace Eigen;\n unsigned int i;\n unsigned int is, js;\n const auto ns = dynamical->neval;\n MatrixXcd dymat_tmp(ns, ns), gamma(ns, ns), dymat(ns, ns);\n\n std::complex ***dymat_all;\n\n memory->allocate(dymat_all, ns, ns, nk_interpolate);\n\n for (i = 0; i < nk_interpolate; ++i) {\n\n const auto ik_irred = kpoint_map_symmetry[i].knum_irred_orig;\n const auto ik_orig = kpoint_map_symmetry[i].knum_orig;\n const auto isym = kpoint_map_symmetry[i].symmetry_op;\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n gamma(is, js) = mat_transform_sym[ik_irred][isym][is][js];\n dymat(is, js) = dymat_inout[is][js][ik_orig];\n }\n }\n dymat_tmp = gamma * dymat * gamma.transpose().conjugate();\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dymat_all[is][js][i] = dymat_tmp(is, js);\n }\n }\n }\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n for (i = 0; i < nk_interpolate; ++i) {\n dymat_inout[is][js][i] = dymat_all[is][js][i];\n }\n }\n }\n memory->deallocate(dymat_all);\n}\n\nvoid Scph::setup_eigvecs()\n{\n const auto ns = dynamical->neval;\n\n if (mympi->my_rank == 0) {\n std::cout << std::endl\n << \" Diagonalizing dynamical matrices for all k points ... \";\n }\n\n memory->allocate(evec_harmonic, nk_scph, ns, ns);\n memory->allocate(omega2_harmonic, nk_scph, ns);\n\n // Calculate phonon eigenvalues and eigenvectors for all k-points for scph\n\n#pragma omp parallel for\n for (int ik = 0; ik < nk_scph; ++ik) {\n\n dynamical->eval_k(xk_scph[ik], kvec_na_scph[ik],\n fcs_phonon->fc2_ext, omega2_harmonic[ik],\n evec_harmonic[ik], true);\n }\n\n if (mympi->my_rank == 0) {\n std::cout << \"done !\" << std::endl;\n }\n}\n\nvoid Scph::setup_pp_interaction()\n{\n // Prepare information for calculating ph-ph interaction coefficients.\n\n unsigned int i, j;\n double *invsqrt_mass_p;\n\n if (mympi->my_rank == 0) {\n if (relax_coordinate || bubble > 0) {\n std::cout << \" Preparing for calculating V3 & V4 ...\";\n } else {\n std::cout << \" Preparing for calculating V4 ...\";\n }\n }\n\n if (anharmonic_core->quartic_mode != 1) {\n error->exit(\"setup_pp_interaction\",\n \"quartic_mode should be 1 for SCPH\");\n }\n\n memory->allocate(invsqrt_mass_p, system->natmin);\n\n for (i = 0; i < system->natmin; ++i) {\n invsqrt_mass_p[i] = std::sqrt(1.0 / system->mass[system->map_p2s[i][0]]);\n }\n\n // Setup for V3 if relax_coordinate = True.\n if (relax_coordinate || bubble > 0) {\n\n std::sort(fcs_phonon->force_constant_with_cell[1].begin(),\n fcs_phonon->force_constant_with_cell[1].end());\n\n anharmonic_core->prepare_group_of_force_constants(fcs_phonon->force_constant_with_cell[1], 3,\n ngroup_v3, fcs_group_v3);\n memory->allocate(invmass_v3, ngroup_v3);\n memory->allocate(evec_index_v3, ngroup_v3, 3);\n memory->allocate(relvec_v3, ngroup_v3);\n memory->allocate(phi3_reciprocal, ngroup_v3);\n\n anharmonic_core->prepare_relative_vector(fcs_phonon->force_constant_with_cell[1],\n 3,\n ngroup_v3,\n fcs_group_v3,\n relvec_v3);\n\n int k = 0;\n for (i = 0; i < ngroup_v3; ++i) {\n for (int j = 0; j < 3; ++j) {\n evec_index_v3[i][j] = fcs_phonon->force_constant_with_cell[1][k].pairs[j].index;\n }\n invmass_v3[i]\n = invsqrt_mass_p[evec_index_v3[i][0] / 3]\n * invsqrt_mass_p[evec_index_v3[i][1] / 3]\n * invsqrt_mass_p[evec_index_v3[i][2] / 3];\n k += fcs_group_v3[i].size();\n }\n }\n\n std::sort(fcs_phonon->force_constant_with_cell[2].begin(),\n fcs_phonon->force_constant_with_cell[2].end());\n\n anharmonic_core->prepare_group_of_force_constants(fcs_phonon->force_constant_with_cell[2], 4,\n ngroup_v4, fcs_group_v4);\n\n memory->allocate(invmass_v4, ngroup_v4);\n memory->allocate(evec_index_v4, ngroup_v4, 4);\n memory->allocate(relvec_v4, ngroup_v4);\n // memory->allocate(phi4_reciprocal, ngroup_v4);\n\n anharmonic_core->prepare_relative_vector(fcs_phonon->force_constant_with_cell[2],\n 4,\n ngroup_v4,\n fcs_group_v4,\n relvec_v4);\n\n int k = 0;\n for (i = 0; i < ngroup_v4; ++i) {\n for (int j = 0; j < 4; ++j) {\n evec_index_v4[i][j] = fcs_phonon->force_constant_with_cell[2][k].pairs[j].index;\n }\n invmass_v4[i]\n = invsqrt_mass_p[evec_index_v4[i][0] / 3]\n * invsqrt_mass_p[evec_index_v4[i][1] / 3]\n * invsqrt_mass_p[evec_index_v4[i][2] / 3]\n * invsqrt_mass_p[evec_index_v4[i][3] / 3];\n k += fcs_group_v4[i].size();\n }\n\n memory->deallocate(invsqrt_mass_p);\n\n nk_grid[0] = kmesh_scph[0];\n nk_grid[1] = kmesh_scph[1];\n nk_grid[2] = kmesh_scph[2];\n\n for (i = 0; i < 3; ++i) dnk[i] = static_cast(nk_grid[i]);\n\n if (nk_grid[0] == nk_grid[1] && nk_grid[1] == nk_grid[2]) {\n nk_represent = nk_grid[0];\n tune_type = 0;\n\n } else if (nk_grid[0] == nk_grid[1] && nk_grid[2] == 1) {\n nk_represent = nk_grid[0];\n tune_type = 0;\n\n } else if (nk_grid[1] == nk_grid[2] && nk_grid[0] == 1) {\n nk_represent = nk_grid[1];\n tune_type = 0;\n\n } else if (nk_grid[2] == nk_grid[0] && nk_grid[1] == 1) {\n nk_represent = nk_grid[2];\n tune_type = 0;\n\n } else if (nk_grid[0] == 1 && nk_grid[1] == 1) {\n nk_represent = nk_grid[2];\n tune_type = 0;\n\n } else if (nk_grid[1] == 1 && nk_grid[2] == 1) {\n nk_represent = nk_grid[0];\n tune_type = 0;\n\n } else if (nk_grid[2] == 1 && nk_grid[0] == 1) {\n nk_represent = nk_grid[1];\n tune_type = 0;\n\n } else {\n tune_type = 1;\n }\n\n int ii;\n\n if (tune_type == 0) {\n\n memory->allocate(exp_phase, 2 * nk_represent - 1);\n for (ii = 0; ii < 2 * nk_represent - 1; ++ii) {\n const auto phase = 2.0 * pi * static_cast(ii - nk_represent + 1)\n / static_cast(nk_represent);\n exp_phase[ii] = std::exp(im * phase);\n }\n\n } else if (tune_type == 1) {\n double phase[3];\n\n tune_type = 1;\n memory->allocate(exp_phase3, 2 * nk_grid[0] - 1, 2 * nk_grid[1] - 1, 2 * nk_grid[2] - 1);\n\n for (ii = 0; ii < 2 * nk_grid[0] - 1; ++ii) {\n phase[0] = 2.0 * pi * static_cast(ii - nk_grid[0] + 1) / dnk[0];\n for (int jj = 0; jj < 2 * nk_grid[1] - 1; ++jj) {\n phase[1] = 2.0 * pi * static_cast(jj - nk_grid[1] + 1) / dnk[1];\n for (int kk = 0; kk < 2 * nk_grid[2] - 1; ++kk) {\n phase[2] = 2.0 * pi * static_cast(kk - nk_grid[2] + 1) / dnk[2];\n exp_phase3[ii][jj][kk] = std::exp(im * (phase[0] + phase[1] + phase[2]));\n }\n }\n }\n }\n\n if (mympi->my_rank == 0) {\n std::cout << \" done!\" << std::endl;\n }\n}\n\nvoid Scph::setup_transform_ifc()\n{\n // Compute mindist_list_scph necessary to calculate dynamical matrix\n // from the real-space force constants\n\n int i, j;\n int ix, iy, iz;\n const auto nk = nk_interpolate;\n const auto nat = system->natmin;\n unsigned int iat;\n\n int **shift_cell, **shift_cell_super;\n double **xf_p;\n double ****x_all;\n\n const int nkx = kmesh_interpolate[0];\n const int nky = kmesh_interpolate[1];\n const int nkz = kmesh_interpolate[2];\n\n const auto ncell = nk;\n const auto ncell_s = 27;\n\n memory->allocate(shift_cell, ncell, 3);\n memory->allocate(shift_cell_super, ncell_s, 3);\n memory->allocate(xf_p, nat, 3);\n memory->allocate(x_all, ncell_s, ncell, nat, 3);\n\n unsigned int icell = 0;\n for (ix = 0; ix < nkx; ++ix) {\n for (iy = 0; iy < nky; ++iy) {\n for (iz = 0; iz < nkz; ++iz) {\n\n shift_cell[icell][0] = ix;\n shift_cell[icell][1] = iy;\n shift_cell[icell][2] = iz;\n\n ++icell;\n }\n }\n }\n\n for (i = 0; i < 3; ++i) shift_cell_super[0][i] = 0;\n icell = 1;\n for (ix = -1; ix <= 1; ++ix) {\n for (iy = -1; iy <= 1; ++iy) {\n for (iz = -1; iz <= 1; ++iz) {\n if (ix == 0 && iy == 0 && iz == 0) continue;\n\n shift_cell_super[icell][0] = ix;\n shift_cell_super[icell][1] = iy;\n shift_cell_super[icell][2] = iz;\n\n ++icell;\n }\n }\n }\n\n for (i = 0; i < nat; ++i) {\n rotvec(xf_p[i], system->xr_s[system->map_p2s[i][0]], system->lavec_s);\n rotvec(xf_p[i], xf_p[i], system->rlavec_p);\n for (j = 0; j < 3; ++j) xf_p[i][j] /= 2.0 * pi;\n }\n\n for (i = 0; i < ncell_s; ++i) {\n for (j = 0; j < ncell; ++j) {\n for (iat = 0; iat < nat; ++iat) {\n x_all[i][j][iat][0] = xf_p[iat][0] + static_cast(shift_cell[j][0])\n + static_cast(nkx * shift_cell_super[i][0]);\n x_all[i][j][iat][1] = xf_p[iat][1] + static_cast(shift_cell[j][1])\n + static_cast(nky * shift_cell_super[i][1]);\n x_all[i][j][iat][2] = xf_p[iat][2] + static_cast(shift_cell[j][2])\n + static_cast(nkz * shift_cell_super[i][2]);\n\n rotvec(x_all[i][j][iat], x_all[i][j][iat], system->lavec_p);\n }\n }\n }\n\n double dist;\n std::vector dist_tmp;\n ShiftCell shift_tmp{};\n std::vector vec_tmp;\n\n memory->allocate(mindist_list_scph, nat, nat, ncell);\n\n for (iat = 0; iat < nat; ++iat) {\n for (unsigned int jat = 0; jat < nat; ++jat) {\n for (icell = 0; icell < ncell; ++icell) {\n\n dist_tmp.clear();\n for (i = 0; i < ncell_s; ++i) {\n dist = distance(x_all[0][0][iat], x_all[i][icell][jat]);\n dist_tmp.emplace_back(i, dist);\n }\n std::sort(dist_tmp.begin(), dist_tmp.end());\n\n const auto dist_min = dist_tmp[0].dist;\n mindist_list_scph[iat][jat][icell].dist = dist_min;\n\n for (i = 0; i < ncell_s; ++i) {\n dist = dist_tmp[i].dist;\n\n if (std::abs(dist_min - dist) < eps8) {\n\n shift_tmp.sx = shift_cell[icell][0]\n + nkx * shift_cell_super[dist_tmp[i].cell_s][0];\n shift_tmp.sy = shift_cell[icell][1]\n + nky * shift_cell_super[dist_tmp[i].cell_s][1];\n shift_tmp.sz = shift_cell[icell][2]\n + nkz * shift_cell_super[dist_tmp[i].cell_s][2];\n\n mindist_list_scph[iat][jat][icell].shift.push_back(shift_tmp);\n }\n }\n\n }\n }\n }\n\n memory->deallocate(shift_cell);\n memory->deallocate(shift_cell_super);\n memory->deallocate(xf_p);\n memory->deallocate(x_all);\n}\n\n\nvoid Scph::exec_interpolation(const unsigned int kmesh_orig[3],\n std::complex ***dymat_r,\n const unsigned int nk_dense,\n double **xk_dense,\n double **kvec_dense,\n double **eval_out,\n std::complex ***evec_out)\n{\n unsigned int i, j, is;\n const auto ns = dynamical->neval;\n const auto nk1 = kmesh_orig[0];\n const auto nk2 = kmesh_orig[1];\n const auto nk3 = kmesh_orig[2];\n\n double *eval_real;\n std::complex **mat_tmp;\n std::complex **mat_harmonic, **mat_harmonic_na;\n std::vector eval_vec(ns);\n\n memory->allocate(mat_tmp, ns, ns);\n memory->allocate(eval_real, ns);\n memory->allocate(mat_harmonic, ns, ns);\n\n if (dynamical->nonanalytic) {\n memory->allocate(mat_harmonic_na, ns, ns);\n }\n\n for (int ik = 0; ik < nk_dense; ++ik) {\n\n if (dynamical->nonanalytic == 3) {\n dynamical->calc_analytic_k(xk_dense[ik],\n ewald->fc2_without_dipole,\n mat_harmonic);\n } else {\n dynamical->calc_analytic_k(xk_dense[ik],\n fcs_phonon->fc2_ext,\n mat_harmonic);\n }\n\n r2q(xk_dense[ik], nk1, nk2, nk3, ns, dymat_r, mat_tmp);\n\n for (i = 0; i < ns; ++i) {\n for (j = 0; j < ns; ++j) {\n mat_tmp[i][j] += mat_harmonic[i][j];\n }\n }\n\n if (dynamical->nonanalytic) {\n\n if (dynamical->nonanalytic == 1) {\n dynamical->calc_nonanalytic_k(xk_dense[ik],\n kvec_dense[ik],\n mat_harmonic_na);\n } else if (dynamical->nonanalytic == 2) {\n dynamical->calc_nonanalytic_k2(xk_dense[ik],\n kvec_dense[ik],\n mat_harmonic_na);\n\n } else if (dynamical->nonanalytic == 3) {\n ewald->add_longrange_matrix(xk_dense[ik],\n kvec_dense[ik],\n mat_harmonic_na);\n }\n\n for (i = 0; i < ns; ++i) {\n for (j = 0; j < ns; ++j) {\n mat_tmp[i][j] += mat_harmonic_na[i][j];\n }\n }\n }\n\n diagonalize_interpolated_matrix(mat_tmp, eval_real, evec_out[ik], true);\n\n for (is = 0; is < ns; ++is) {\n const auto eval_tmp = eval_real[is];\n\n if (eval_tmp < 0.0) {\n eval_vec[is] = -std::sqrt(-eval_tmp);\n } else {\n eval_vec[is] = std::sqrt(eval_tmp);\n }\n }\n\n for (is = 0; is < ns; ++is) eval_out[ik][is] = eval_vec[is];\n\n }\n\n memory->deallocate(eval_real);\n memory->deallocate(mat_tmp);\n memory->deallocate(mat_harmonic);\n\n if (dynamical->nonanalytic) {\n memory->deallocate(mat_harmonic_na);\n }\n}\n\n\nvoid Scph::r2q(const double *xk_in,\n const unsigned int nx,\n const unsigned int ny,\n const unsigned int nz,\n const unsigned int ns,\n std::complex ***dymat_r_in,\n std::complex **dymat_k_out) const\n{\n std::complex im(0.0, 1.0);\n\n const auto ncell = nx * ny * nz;\n\n for (unsigned int i = 0; i < ns; ++i) {\n\n const auto iat = i / 3;\n\n for (unsigned int j = 0; j < ns; ++j) {\n\n const auto jat = j / 3;\n\n dymat_k_out[i][j] = std::complex(0.0, 0.0);\n\n for (unsigned int icell = 0; icell < ncell; ++icell) {\n\n auto exp_phase = std::complex(0.0, 0.0);\n\n // This operation is necessary for the Hermiticity of the dynamical matrix.\n for (const auto &it : mindist_list_scph[iat][jat][icell].shift) {\n\n auto phase = 2.0 * pi\n * (static_cast(it.sx) * xk_in[0]\n + static_cast(it.sy) * xk_in[1]\n + static_cast(it.sz) * xk_in[2]);\n\n exp_phase += std::exp(im * phase);\n }\n exp_phase /= static_cast(mindist_list_scph[iat][jat][icell].shift.size());\n\n dymat_k_out[i][j] += dymat_r_in[i][j][icell] * exp_phase;\n }\n\n }\n }\n}\n\n\nvoid Scph::diagonalize_interpolated_matrix(std::complex **mat_in,\n double *eval_out,\n std::complex **evec_out,\n const bool require_evec) const\n{\n unsigned int i, j;\n char JOBZ;\n int INFO;\n double *RWORK;\n std::complex *amat;\n std::complex *WORK;\n\n int ns = dynamical->neval;\n\n int LWORK = (2 * ns - 1) * 10;\n memory->allocate(RWORK, 3 * ns - 2);\n memory->allocate(WORK, LWORK);\n\n\n if (require_evec) {\n JOBZ = 'V';\n } else {\n JOBZ = 'N';\n }\n\n char UPLO = 'U';\n\n memory->allocate(amat, ns * ns);\n\n unsigned int k = 0;\n for (j = 0; j < ns; ++j) {\n for (i = 0; i < ns; ++i) {\n amat[k++] = mat_in[i][j];\n }\n }\n\n zheev_(&JOBZ, &UPLO, &ns, amat, &ns, eval_out, WORK, &LWORK, RWORK, &INFO);\n\n k = 0;\n\n if (require_evec) {\n // Here we transpose the matrix evec_out so that \n // evec_out[i] becomes phonon eigenvector of i-th mode.\n for (j = 0; j < ns; ++j) {\n for (i = 0; i < ns; ++i) {\n evec_out[j][i] = amat[k++];\n }\n }\n }\n\n memory->deallocate(amat);\n memory->deallocate(WORK);\n memory->deallocate(RWORK);\n}\n\nvoid Scph::find_degeneracy(std::vector *degeneracy_out,\n const unsigned int nk_in,\n double **eval_in) const\n{\n // eval is omega^2 in atomic unit\n\n const auto ns = dynamical->neval;\n const auto tol_omega = 1.0e-7;\n\n for (unsigned int ik = 0; ik < nk_in; ++ik) {\n\n degeneracy_out[ik].clear();\n\n auto omega_prev = eval_in[ik][0];\n auto ideg = 1;\n\n for (unsigned int is = 1; is < ns; ++is) {\n const auto omega_now = eval_in[ik][is];\n\n if (std::abs(omega_now - omega_prev) < tol_omega) {\n ++ideg;\n } else {\n degeneracy_out[ik].push_back(ideg);\n ideg = 1;\n omega_prev = omega_now;\n }\n\n }\n degeneracy_out[ik].push_back(ideg);\n }\n}\n\nvoid Scph::calc_new_dymat_with_evec(std::complex ***dymat_out,\n double **omega2_in,\n std::complex ***evec_in)\n{\n std::complex *polarization_matrix, *mat_tmp;\n std::complex *eigval_matrix, *dmat;\n std::complex *beta;\n std::complex ***dymat_q, **dymat_harmonic;\n std::complex im(0.0, 1.0);\n\n unsigned int ik, is, js;\n int ns = dynamical->neval;\n\n const unsigned int ns2 = ns * ns;\n\n auto alpha = std::complex(1.0, 0.0);\n\n char TRANSA[] = \"N\";\n char TRANSB[] = \"C\";\n\n memory->allocate(polarization_matrix, ns2);\n memory->allocate(mat_tmp, ns2);\n memory->allocate(eigval_matrix, ns2);\n memory->allocate(beta, ns);\n memory->allocate(dmat, ns2);\n memory->allocate(dymat_q, ns, ns, nk_interpolate);\n memory->allocate(dymat_harmonic, ns, ns);\n\n for (is = 0; is < ns; ++is) beta[is] = std::complex(0.0, 0.0);\n\n for (ik = 0; ik < nk_interpolate; ++ik) {\n\n const auto knum = kmap_interpolate_to_scph[ik];\n\n // create eigval matrix\n\n for (is = 0; is < ns2; ++is) eigval_matrix[is] = std::complex(0.0, 0.0);\n\n unsigned int m = 0;\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n if (is == js) {\n eigval_matrix[m] = omega2_in[knum][is];\n }\n ++m;\n }\n }\n\n // create polarization matrix\n\n m = 0;\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n polarization_matrix[m++] = evec_in[knum][is][js];\n }\n }\n\n zgemm_(TRANSA, TRANSB, &ns, &ns, &ns, &alpha,\n eigval_matrix, &ns, polarization_matrix, &ns, beta, mat_tmp, &ns);\n zgemm_(TRANSA, TRANSA, &ns, &ns, &ns, &alpha,\n polarization_matrix, &ns, mat_tmp, &ns, beta, dmat, &ns);\n\n m = 0;\n\n for (js = 0; js < ns; ++js) {\n for (is = 0; is < ns; ++is) {\n dymat_q[is][js][ik] = dmat[m];\n ++m;\n }\n }\n\n\n // Subtract harmonic contribution\n dynamical->calc_analytic_k(xk_interpolate[ik],\n fcs_phonon->fc2_ext,\n dymat_harmonic);\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dymat_q[is][js][ik] -= dymat_harmonic[is][js];\n }\n }\n }\n\n memory->deallocate(polarization_matrix);\n memory->deallocate(mat_tmp);\n memory->deallocate(eigval_matrix);\n memory->deallocate(beta);\n memory->deallocate(dmat);\n memory->deallocate(dymat_harmonic);\n\n const auto nk1 = kmesh_interpolate[0];\n const auto nk2 = kmesh_interpolate[1];\n const auto nk3 = kmesh_interpolate[2];\n\n std::vector > xk_dup;\n\n int icell = 0;\n\n for (int ix = 0; ix < nk1; ++ix) {\n for (int iy = 0; iy < nk2; ++iy) {\n for (int iz = 0; iz < nk3; ++iz) {\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dymat_out[is][js][icell] = std::complex(0.0, 0.0);\n }\n }\n\n for (ik = 0; ik < nk_interpolate; ++ik) {\n\n duplicate_xk_boundary(xk_interpolate[ik], xk_dup);\n\n auto cexp_phase = std::complex(0.0, 0.0);\n\n for (const auto &i : xk_dup) {\n\n auto phase = 2.0 * pi * (i[0] * static_cast(ix)\n + i[1] * static_cast(iy)\n + i[2] * static_cast(iz));\n cexp_phase += std::exp(-im * phase);\n\n }\n cexp_phase /= static_cast(xk_dup.size());\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dymat_out[is][js][icell] += dymat_q[is][js][ik] * cexp_phase;\n }\n }\n\n\n }\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dymat_out[is][js][icell] /= static_cast(nk_interpolate);\n }\n }\n\n ++icell;\n }\n }\n }\n\n\n memory->deallocate(dymat_q);\n}\n\n\nvoid Scph::compute_anharmonic_frequency(std::complex ***v4_array_all,\n double **omega2_out,\n std::complex ***evec_anharm_scph,\n const double temp,\n bool &flag_converged,\n std::complex ***cmat_convert,\n const bool offdiag,\n const unsigned int verbosity)\n{\n // This is the main function of the SCPH equation.\n // The detailed algorithm can be found in PRB 92, 054301 (2015).\n // Eigen3 library is used for the compact notation of matrix-matrix products.\n\n using namespace Eigen;\n\n int ik, jk;\n unsigned int i;\n unsigned int is, js, ks;\n unsigned int kk;\n const auto nk = nk_scph;\n const auto ns = dynamical->neval;\n unsigned int knum, knum_interpolate;\n const auto nk_irred_interpolate = kp_irred_interpolate.size();\n const auto nk1 = kmesh_interpolate[0];\n const auto nk2 = kmesh_interpolate[1];\n const auto nk3 = kmesh_interpolate[2];\n int iloop;\n\n MatrixXd omega_now(nk, ns), omega_old(nk, ns);\n MatrixXd omega2_HA(nk, ns);\n MatrixXcd mat_tmp(ns, ns), evec_tmp(ns, ns);\n\n VectorXd eval_tmp(ns);\n MatrixXcd Dymat(ns, ns);\n MatrixXcd Fmat(ns, ns);\n MatrixXcd Qmat = MatrixXcd::Zero(ns, ns);\n MatrixXcd Cmat(ns, ns), Dmat(ns, ns);\n\n double diff;\n double conv_tol = tolerance_scph;\n double alpha = mixalpha;\n\n double **eval_interpolate;\n double re_tmp, im_tmp;\n bool has_negative;\n\n std::complex ctmp;\n std::complex ***mat_omega2_harmonic;\n std::complex ***evec_initial;\n std::complex ***dmat_convert;\n std::complex ***dmat_convert_old;\n std::complex ***evec_new;\n std::complex ***dymat_new, ***dymat_harmonic;\n std::complex ***dymat_q;\n std::complex ***Fmat0;\n\n const auto complex_one = std::complex(1.0, 0.0);\n const auto complex_zero = std::complex(0.0, 0.0);\n\n SelfAdjointEigenSolver saes;\n\n memory->allocate(mat_omega2_harmonic, nk_interpolate, ns, ns);\n memory->allocate(eval_interpolate, nk, ns);\n memory->allocate(evec_initial, nk, ns, ns);\n memory->allocate(evec_new, nk, ns, ns);\n memory->allocate(dmat_convert, nk, ns, ns);\n memory->allocate(dmat_convert_old, nk, ns, ns);\n memory->allocate(dymat_new, ns, ns, nk_interpolate);\n memory->allocate(dymat_q, ns, ns, nk_interpolate);\n memory->allocate(dymat_harmonic, nk_interpolate, ns, ns);\n memory->allocate(Fmat0, nk_irred_interpolate, ns, ns);\n\n const auto T_in = temp;\n\n std::cout << \" Temperature = \" << T_in << \" K\" << std::endl;\n\n // Set initial values\n\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n\n if (flag_converged) {\n if (omega2_out[ik][is] < 0.0 && std::abs(omega2_out[ik][is]) > 1.0e-16 && verbosity > 0) {\n std::cout << \"Warning : Large negative frequency detected\" << std::endl;\n }\n\n if (omega2_out[ik][is] < 0.0) {\n omega_now(ik, is) = std::sqrt(-omega2_out[ik][is]);\n } else {\n omega_now(ik, is) = std::sqrt(omega2_out[ik][is]);\n }\n } else {\n if (omega2_harmonic[ik][is] < 0.0) {\n omega_now(ik, is) = std::sqrt(-omega2_harmonic[ik][is]);\n } else {\n omega_now(ik, is) = std::sqrt(omega2_harmonic[ik][is]);\n }\n }\n\n omega2_HA(ik, is) = omega2_harmonic[ik][is];\n\n for (js = 0; js < ns; ++js) {\n evec_initial[ik][is][js] = evec_harmonic[ik][is][js];\n\n if (!flag_converged) {\n // Initialize Cmat with identity matrix\n if (is == js) {\n cmat_convert[ik][is][js] = complex_one;\n } else {\n cmat_convert[ik][is][js] = complex_zero;\n }\n }\n\n }\n }\n }\n\n // Set initial harmonic dymat and eigenvalues\n\n for (ik = 0; ik < nk_interpolate; ++ik) {\n knum = kmap_interpolate_to_scph[ik];\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n mat_omega2_harmonic[ik][is][js] = complex_zero;\n }\n mat_omega2_harmonic[ik][is][is] = std::complex(omega2_HA(knum, is), 0.0);\n eval_interpolate[knum][is] = omega2_HA(knum, is);\n }\n\n dynamical->calc_analytic_k(xk_interpolate[ik],\n fcs_phonon->fc2_ext,\n dymat_harmonic[ik]);\n }\n\n for (ik = 0; ik < nk_irred_interpolate; ++ik) {\n\n knum_interpolate = kp_irred_interpolate[ik][0].knum;\n\n // Fmat harmonic\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n if (is == js) {\n Fmat0[ik][is][js] = mat_omega2_harmonic[knum_interpolate][is][is];\n } else {\n Fmat0[ik][is][js] = complex_zero;\n }\n }\n }\n }\n\n int icount = 0;\n\n // Main loop\n for (iloop = 0; iloop < maxiter; ++iloop) {\n\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n auto omega1 = omega_now(ik, is);\n if (std::abs(omega1) < eps8) {\n Qmat(is, is) = complex_zero;\n } else {\n // Note that the missing factor 2 in the denominator of Qmat is \n // already considered in the v4_array_all.\n if (thermodynamics->classical) {\n Qmat(is, is) = std::complex(2.0 * T_in * thermodynamics->T_to_Ryd / (omega1 * omega1),\n 0.0);\n } else {\n auto n1 = thermodynamics->fB(omega1, T_in);\n Qmat(is, is) = std::complex((2.0 * n1 + 1.0) / omega1, 0.0);\n }\n }\n }\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n Cmat(is, js) = cmat_convert[ik][is][js];\n }\n }\n\n Dmat = Cmat * Qmat * Cmat.adjoint();\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dmat_convert[ik][is][js] = Dmat(is, js);\n }\n }\n }\n\n // Mixing dmat\n if (iloop > 0) {\n#pragma omp parallel for private(is, js)\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dmat_convert[ik][is][js] = alpha * dmat_convert[ik][is][js]\n + (1.0 - alpha) * dmat_convert_old[ik][is][js];\n }\n }\n }\n }\n\n for (ik = 0; ik < nk_irred_interpolate; ++ik) {\n\n knum_interpolate = kp_irred_interpolate[ik][0].knum;\n knum = kmap_interpolate_to_scph[knum_interpolate];\n\n // Fmat harmonic\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n Fmat(is, js) = Fmat0[ik][is][js];\n }\n }\n\n // Anharmonic correction to Fmat\n\n if (!offdiag) {\n for (is = 0; is < ns; ++is) {\n i = (ns + 1) * is;\n\n re_tmp = 0.0;\n im_tmp = 0.0;\n\n#pragma omp parallel for private(jk, kk, ks), reduction(+:re_tmp, im_tmp)\n for (jk = 0; jk < nk; ++jk) {\n\n kk = nk * ik + jk;\n\n for (ks = 0; ks < ns; ++ks) {\n ctmp = v4_array_all[kk][i][(ns + 1) * ks]\n * dmat_convert[jk][ks][ks];\n re_tmp += ctmp.real();\n im_tmp += ctmp.imag();\n }\n }\n Fmat(is, is) += std::complex(re_tmp, im_tmp);\n }\n } else {\n\n // Anharmonic correction to Fmat\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js <= is; ++js) {\n\n i = ns * is + js;\n\n re_tmp = 0.0;\n im_tmp = 0.0;\n\n#pragma omp parallel for private(jk, kk, ks), reduction(+:re_tmp, im_tmp)\n for (jk = 0; jk < nk; ++jk) {\n\n kk = nk * ik + jk;\n\n for (ks = 0; ks < ns; ++ks) {\n for (unsigned int ls = 0; ls < ns; ++ls) {\n ctmp = v4_array_all[kk][i][ns * ks + ls]\n * dmat_convert[jk][ks][ls];\n re_tmp += ctmp.real();\n im_tmp += ctmp.imag();\n }\n }\n }\n Fmat(is, js) += std::complex(re_tmp, im_tmp);\n }\n }\n\n }\n\n saes.compute(Fmat);\n eval_tmp = saes.eigenvalues();\n\n for (is = 0; is < ns; ++is) {\n\n double omega2_tmp = eval_tmp(is);\n\n if (omega2_tmp < 0.0 && std::abs(omega2_tmp) > 1.0e-16) {\n\n if (verbosity > 1) {\n std::cout << \" Detect imaginary : \";\n std::cout << \" knum = \" << knum + 1 << \" is = \" << is + 1 << '\\n';\n for (int j = 0; j < 3; ++j) {\n std::cout << \" xk = \" << std::setw(15) << xk_scph[knum][j];\n }\n std::cout << '\\n';\n }\n\n if (v4_array_all[nk * ik + knum][(ns + 1) * is][(ns + 1) * is].real() > 0.0) {\n if (verbosity > 1) {\n std::cout << \" onsite V4 is positive\\n\\n\";\n }\n\n if (flag_converged) {\n ++icount;\n eval_tmp(is) = omega2_out[knum][is] * std::pow(0.99, icount);\n } else {\n ++icount;\n eval_tmp(is) = -eval_tmp(is) * std::pow(0.99, icount);\n }\n } else {\n if (verbosity > 1) {\n std::cout << \" onsite V4 is negative\\n\\n\";\n }\n eval_tmp(is) = std::abs(omega2_tmp);\n }\n }\n }\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n evec_tmp(is, js) = evec_initial[knum][is][js];\n\n if (is == js) {\n Dymat(is, js) = std::complex(eval_tmp(is), 0.0);\n } else {\n Dymat(is, js) = complex_zero;\n }\n }\n }\n\n // New eigenvector matrix E_{new}= E_{old} * C\n mat_tmp = evec_tmp.transpose() * saes.eigenvectors();\n Dymat = mat_tmp * Dymat * mat_tmp.adjoint();\n\n#ifdef _DEBUG2\n Dymat_sym = Dymat;\n symmetrize_dynamical_matrix(ik, Dymat_sym);\n std::complex **dymat_exact;\n memory->allocate(dymat_exact, ns, ns);\n std::cout << \"ik = \" << ik + 1 << std::endl;\n std::cout << \"Dymat\" << std::endl;\n std::cout << Dymat << std::endl;\n std::cout << \"Dymat_sym\" << std::endl;\n std::cout << Dymat_sym << std::endl;\n dynamical->calc_analytic_k(xk_interpolate[knum_interpolate], fcs_phonon->fc2_ext, dymat_exact);\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n Dymat_sym(is,js) = dymat_exact[is][js];\n }\n }\n std::cout << \"Dymat_exact\" << std::endl;\n std::cout << Dymat_sym << std::endl;\n memory->deallocate(dymat_exact);\n\n#endif\n symmetrize_dynamical_matrix(ik, Dymat);\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dymat_q[is][js][knum_interpolate] = Dymat(is, js);\n }\n }\n } // close loop ik\n\n replicate_dymat_for_all_kpoints(dymat_q);\n\n#ifdef _DEBUG2\n for (ik = 0; ik < nk_interpolate; ++ik) {\n\n knum = kmap_interpolate_to_scph[ik];\n \n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n Dymat(is,js) = dymat_q[is][js][ik];\n }\n }\n\n saes.compute(Dymat);\n eval_tmp = saes.eigenvalues();\n\n for (is = 0; is < ns; ++is) {\n eval_orig(is) = omega2_harmonic(knum,is);\n }\n\n std::cout << \" ik = \" << std::setw(4) << ik + 1 << \" : \";\n for (i = 0; i < 3; ++i) std::cout << std::setw(15) << xk_scph[knum][i];\n std::cout << std::endl;\n\n for (is = 0; is < ns; ++is) {\n std::cout << std::setw(15) << eval_tmp(is);\n std::cout << std::setw(15) << eval_orig(is);\n std::cout << std::setw(15) << eval_tmp(is) - eval_orig(is) << std::endl;\n }\n \n }\n#endif\n\n // Subtract harmonic contribution to the dynamical matrix\n for (ik = 0; ik < nk_interpolate; ++ik) {\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dymat_q[is][js][ik] -= dymat_harmonic[ik][is][js];\n }\n }\n }\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n fftw_plan plan = fftw_plan_dft_3d(nk1, nk2, nk3,\n reinterpret_cast(dymat_q[is][js]),\n reinterpret_cast(dymat_new[is][js]),\n FFTW_FORWARD, FFTW_ESTIMATE);\n fftw_execute(plan);\n fftw_destroy_plan(plan);\n\n for (ik = 0; ik < nk_interpolate; ++ik)\n dymat_new[is][js][ik] /= static_cast(nk_interpolate);\n }\n }\n\n exec_interpolation(kmesh_interpolate,\n dymat_new,\n nk,\n xk_scph,\n kvec_na_scph,\n eval_interpolate, evec_new);\n\n for (ik = 0; ik < nk; ++ik) {\n\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n mat_tmp(is, js) = evec_initial[ik][js][is];\n evec_tmp(is, js) = evec_new[ik][js][is];\n }\n }\n\n Cmat = mat_tmp.adjoint() * evec_tmp;\n\n for (is = 0; is < ns; ++is) {\n omega_now(ik, is) = eval_interpolate[ik][is];\n for (js = 0; js < ns; ++js) {\n cmat_convert[ik][is][js] = Cmat(is, js);\n }\n }\n }\n\n if (iloop == 0) {\n if (verbosity > 0) {\n std::cout << \" SCPH ITER \" << std::setw(5) << iloop + 1 << \" : DIFF = N/A\" << std::endl;\n }\n\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n omega_old(ik, is) = omega_now(ik, is);\n }\n }\n\n } else {\n\n diff = 0.0;\n\n for (ik = 0; ik < nk_interpolate; ++ik) {\n knum = kmap_interpolate_to_scph[ik];\n for (is = 0; is < ns; ++is) {\n diff += std::pow(omega_now(knum, is) - omega_old(knum, is), 2.0);\n }\n }\n diff /= static_cast(nk_interpolate * ns);\n if (verbosity > 0) {\n std::cout << \" SCPH ITER \" << std::setw(5) << iloop + 1 << \" : \";\n std::cout << \" DIFF = \" << std::setw(15) << std::sqrt(diff) << std::endl;\n }\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n omega_old(ik, is) = omega_now(ik, is);\n }\n }\n if (std::sqrt(diff) < conv_tol) {\n has_negative = false;\n\n for (ik = 0; ik < nk_interpolate; ++ik) {\n knum = kmap_interpolate_to_scph[ik];\n for (is = 0; is < ns; ++is) {\n if (omega_now(knum, is) < 0.0 && std::abs(omega_now(knum, is)) > eps8) {\n has_negative = true;\n break;\n }\n }\n }\n if (!has_negative) {\n if (verbosity > 0) std::cout << \" DIFF < SCPH_TOL : break SCPH loop\\n\";\n break;\n }\n if (verbosity > 0) std::cout << \" DIFF < SCPH_TOL but a negative frequency is detected.\\n\";\n }\n }\n\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n for (js = 0; js < ns; ++js) {\n dmat_convert_old[ik][is][js] = dmat_convert[ik][is][js];\n }\n }\n }\n } // end loop iteration\n\n if (std::sqrt(diff) < conv_tol) {\n if (verbosity > 0) {\n std::cout << \" Temp = \" << T_in;\n std::cout << \" : convergence achieved in \" << std::setw(5)\n << iloop + 1 << \" iterations.\" << std::endl;\n }\n flag_converged = true;\n } else {\n if (verbosity > 0) {\n std::cout << \"Temp = \" << T_in;\n std::cout << \" : not converged.\" << std::endl;\n }\n flag_converged = false;\n }\n\n for (ik = 0; ik < nk; ++ik) {\n for (is = 0; is < ns; ++is) {\n if (eval_interpolate[ik][is] < 0.0) {\n if (std::abs(eval_interpolate[ik][is]) <= eps10) {\n omega2_out[ik][is] = 0.0;\n } else {\n omega2_out[ik][is] = -std::pow(eval_interpolate[ik][is], 2.0);\n }\n } else {\n omega2_out[ik][is] = std::pow(eval_interpolate[ik][is], 2.0);\n }\n for (js = 0; js < ns; ++js) {\n evec_anharm_scph[ik][is][js] = evec_new[ik][is][js];\n }\n }\n }\n\n if (verbosity > 1) {\n std::cout << \"New eigenvalues\" << std::endl;\n for (ik = 0; ik < nk_interpolate; ++ik) {\n knum = kmap_interpolate_to_scph[ik];\n for (is = 0; is < ns; ++is) {\n std::cout << \" ik_interpolate = \" << std::setw(5) << ik + 1;\n std::cout << \" is = \" << std::setw(5) << is + 1;\n std::cout << \" omega2 = \" << std::setw(15) << omega2_out[knum][is] << std::endl;\n }\n std::cout << std::endl;\n }\n }\n\n memory->deallocate(mat_omega2_harmonic);\n memory->deallocate(eval_interpolate);\n memory->deallocate(evec_initial);\n memory->deallocate(dmat_convert);\n memory->deallocate(dmat_convert_old);\n memory->deallocate(evec_new);\n memory->deallocate(dymat_new);\n memory->deallocate(dymat_q);\n memory->deallocate(dymat_harmonic);\n memory->deallocate(Fmat0);\n}\n\n\nvoid Scph::compute_free_energy_bubble_SCPH(const unsigned int kmesh[3],\n std::complex ****delta_dymat_scph)\n{\n const auto NT = static_cast((system->Tmax - system->Tmin) / system->dT) + 1;\n const auto nk_ref = kpoint->nk;\n const auto ns = dynamical->neval;\n double ***eval;\n std::complex ****evec;\n\n if (mympi->my_rank == 0) {\n std::cout << std::endl;\n std::cout << \" -----------------------------------------------------------------\"\n << std::endl;\n std::cout << \" Calculating the vibrational free energy from the Bubble diagram \" << std::endl;\n std::cout << \" on top of the SCPH calculation.\" << std::endl;\n std::cout << '\\n';\n std::cout << \" This calculation requires allocation of additional memory:\" << std::endl;\n\n size_t nsize = nk_ref * ns * ns * NT * sizeof(std::complex < double > )\n + nk_ref * ns * NT * sizeof(double);\n\n const auto nsize_dble = static_cast(nsize) / 100000000.0;\n std::cout << \" Estimated memory usage per MPI process: \" << std::setw(10)\n << std::fixed << std::setprecision(4) << nsize_dble << \" GByte.\" << std::endl;\n\n std::cout << \" To avoid possible faults associated with insufficient memory,\\n\"\n \" please reduce the number of MPI processes per node and/or\\n\"\n \" the number of temperagure grids.\\n\\n\";\n }\n\n memory->allocate(thermodynamics->FE_bubble, NT);\n memory->allocate(eval, NT, nk_ref, ns);\n memory->allocate(evec, NT, nk_ref, ns, ns); // This requires lots of RAM\n\n for (auto iT = 0; iT < NT; ++iT) {\n const auto temp = system->Tmin + system->dT * float(iT);\n\n exec_interpolation(kmesh,\n delta_dymat_scph[iT],\n nk_ref,\n kpoint->xk,\n kpoint->kvec_na,\n eval[iT],\n evec[iT]);\n }\n\n thermodynamics->compute_FE_bubble_SCPH(eval, evec, thermodynamics->FE_bubble);\n\n memory->deallocate(eval);\n memory->deallocate(evec);\n\n if (mympi->my_rank == 0) {\n std::cout << \" done!\" << std::endl << std::endl;\n }\n}\n\nvoid Scph::bubble_correction(std::complex ****delta_dymat_scph,\n std::complex ****delta_dymat_scph_plus_bubble)\n{\n const auto NT = static_cast((system->Tmax - system->Tmin) / system->dT) + 1;\n const auto ns = dynamical->neval;\n unsigned int i;\n double xk_tmp[3];\n\n auto epsilon = integration->epsilon;\n const auto nk_irred_interpolate = kp_irred_interpolate.size();\n\n double **eval = nullptr;\n double ***eval_bubble = nullptr;\n std::complex ***evec;\n double *real_self = nullptr;\n std::vector > omegalist;\n\n if (mympi->my_rank == 0) {\n std::cout << std::endl;\n std::cout << \" -----------------------------------------------------------------\"\n << std::endl;\n std::cout << \" Calculating the bubble self-energy \" << std::endl;\n std::cout << \" on top of the SCPH calculation.\" << std::endl;\n std::cout << '\\n';\n }\n\n epsilon *= time_ry / Hz_to_kayser;\n MPI_Bcast(&epsilon, 1, MPI_DOUBLE, 0, MPI_COMM_WORLD);\n\n memory->allocate(eval, nk_scph, ns);\n memory->allocate(evec, nk_scph, ns, ns);\n\n if (mympi->my_rank == 0) {\n memory->allocate(eval_bubble, NT, nk_scph, ns);\n for (auto iT = 0; iT < NT; ++iT) {\n for (auto ik = 0; ik < nk_scph; ++ik) {\n for (auto is = 0; is < ns; ++is) {\n eval_bubble[iT][ik][is] = 0.0;\n }\n }\n }\n memory->allocate(real_self, ns);\n }\n\n const auto nk_reduced_scph = kp_irred_scph.size();\n\n std::vector *degeneracy_at_k;\n memory->allocate(degeneracy_at_k, nk_scph);\n\n\n for (auto iT = 0; iT < NT; ++iT) {\n const auto temp = system->Tmin + system->dT * float(iT);\n\n exec_interpolation(kmesh_interpolate,\n delta_dymat_scph[iT],\n nk_scph,\n xk_scph,\n kvec_na_scph,\n eval,\n evec);\n\n find_degeneracy(degeneracy_at_k,\n nk_scph,\n eval);\n\n if (mympi->my_rank == 0) std::cout << \" Temperature (K) : \" << std::setw(6) << temp << '\\n';\n\n for (auto ik = 0; ik < nk_irred_interpolate; ++ik) {\n\n auto knum_interpolate = kp_irred_interpolate[ik][0].knum;\n auto knum = kmap_interpolate_to_scph[knum_interpolate];\n\n for (auto m = 0; m < 3; ++m) xk_tmp[m] = -xk_scph[knum][m];\n auto knum_minus = kpoint->get_knum(xk_tmp, kmesh_scph);\n\n if (mympi->my_rank == 0) {\n std::cout << \" Irred. k: \" << std::setw(5) << ik + 1 << \" (\";\n for (auto m = 0; m < 3; ++m) std::cout << std::setw(15) << xk_scph[knum][m];\n std::cout << \")\\n\";\n }\n\n for (unsigned int snum = 0; snum < ns; ++snum) {\n\n\n if (eval[knum][snum] < eps8) {\n if (mympi->my_rank == 0) real_self[snum] = 0.0;\n } else {\n omegalist.clear();\n\n if (bubble == 1) {\n\n omegalist.push_back(im * epsilon);\n\n auto se_bubble = get_bubble_selfenergy(nk_scph,\n ns,\n kmesh_scph,\n xk_scph,\n eval,\n evec,\n knum,\n snum,\n temp,\n omegalist);\n\n if (mympi->my_rank == 0) real_self[snum] = se_bubble[0].real();\n\n } else if (bubble == 2) {\n\n omegalist.push_back(eval[knum][snum] + im * epsilon);\n\n auto se_bubble = get_bubble_selfenergy(nk_scph,\n ns,\n kmesh_scph,\n xk_scph,\n eval,\n evec,\n knum,\n snum,\n temp,\n omegalist);\n\n if (mympi->my_rank == 0) real_self[snum] = se_bubble[0].real();\n\n } else if (bubble == 3) {\n\n auto maxfreq = eval[knum][snum] + 50.0 * time_ry / Hz_to_kayser;\n auto minfreq = eval[knum][snum] - 50.0 * time_ry / Hz_to_kayser;\n\n if (minfreq < 0.0) minfreq = 0.0;\n\n const auto domega = 0.1 * time_ry / Hz_to_kayser;\n auto nomega = static_cast((maxfreq - minfreq) / domega) + 1;\n\n for (auto iomega = 0; iomega < nomega; ++iomega) {\n omegalist.push_back(minfreq + static_cast(iomega) * domega + im * epsilon);\n }\n\n auto se_bubble = get_bubble_selfenergy(nk_scph,\n ns,\n kmesh_scph,\n xk_scph,\n eval,\n evec,\n knum,\n snum,\n temp,\n omegalist);\n\n if (mympi->my_rank == 0) {\n\n std::vector nonlinear_func(nomega);\n for (auto iomega = 0; iomega < nomega; ++iomega) {\n nonlinear_func[iomega] = omegalist[iomega].real() * omegalist[iomega].real()\n - eval[knum][snum] * eval[knum][snum]\n + 2.0 * eval[knum][snum] * se_bubble[iomega].real();\n }\n\n // find a root of nonlinear_func = 0 from the sign change.\n int count_root = 0;\n std::vector root_index;\n\n for (auto iomega = 0; iomega < nomega - 1; ++iomega) {\n if (nonlinear_func[iomega] * nonlinear_func[iomega + 1] < 0.0) {\n ++count_root;\n root_index.push_back(iomega);\n }\n }\n\n if (count_root == 0) {\n error->warn(\"bubble_correction\",\n \"Could not find a root in the nonlinear equation at this temperature. \"\n \"Use the w=0 component.\");\n\n real_self[snum] = se_bubble[0].real();\n\n } else {\n if (count_root > 1) {\n error->warn(\"bubble_correction\",\n \"Multiple roots were found in the nonlinear equation at this temperature. \"\n \"Use the lowest-frequency solution\");\n std::cout << \" solution found at the following frequencies:\\n\";\n for (auto iroot = 0; iroot < count_root; ++iroot) {\n std::cout << std::setw(15)\n << writes->in_kayser(omegalist[root_index[iroot]].real());\n }\n std::cout << '\\n';\n }\n\n // Instead of performing a linear interpolation (secant method) of nonlinear_func,\n // we interpolate the bubble self-energy. Since the frequency grid is dense (0.1 cm^-1 step),\n // this approximation should not make any problems (hopefully).\n\n double omega_solution = omegalist[root_index[0] + 1].real()\n - nonlinear_func[root_index[0] + 1]\n * domega / (nonlinear_func[root_index[0] + 1] -\n nonlinear_func[root_index[0]]);\n\n real_self[snum] = (se_bubble[root_index[0] + 1].real()\n - se_bubble[root_index[0]].real())\n * (omega_solution - omegalist[root_index[0] + 1].real()) / domega\n + se_bubble[root_index[0] + 1].real();\n }\n }\n }\n }\n if (mympi->my_rank == 0) {\n std::cout << \" branch : \" << std::setw(5) << snum + 1;\n std::cout << \" omega = \" << std::setw(15) << writes->in_kayser(eval[knum][snum]) << \" (cm^-1); \";\n std::cout << \" Re[Self] = \" << std::setw(15) << writes->in_kayser(real_self[snum]) << \" (cm^-1)\\n\";\n }\n }\n\n if (mympi->my_rank == 0) {\n // average self energy of degenerate modes\n int ishift = 0;\n double real_self_avg = 0.0;\n\n for (const auto &it : degeneracy_at_k[knum]) {\n for (auto m = 0; m < it; ++m) {\n real_self_avg += real_self[m + ishift];\n }\n real_self_avg /= static_cast(it);\n\n for (auto m = 0; m < it; ++m) {\n real_self[m + ishift] = real_self_avg;\n }\n real_self_avg = 0.0;\n ishift += it;\n }\n\n for (unsigned int snum = 0; snum < ns; ++snum) {\n eval_bubble[iT][knum][snum] = eval[knum][snum] * eval[knum][snum]\n - 2.0 * eval[knum][snum] * real_self[snum];\n for (auto jk = 1; jk < kp_irred_interpolate[ik].size(); ++jk) {\n auto knum2 = kmap_interpolate_to_scph[kp_irred_interpolate[ik][jk].knum];\n eval_bubble[iT][knum2][snum] = eval_bubble[iT][knum][snum];\n }\n }\n\n std::cout << '\\n';\n }\n }\n\n if (mympi->my_rank == 0) {\n calc_new_dymat_with_evec(delta_dymat_scph_plus_bubble[iT],\n eval_bubble[iT],\n evec);\n }\n }\n\n memory->deallocate(eval);\n memory->deallocate(evec);\n memory->deallocate(degeneracy_at_k);\n\n if (eval_bubble) memory->deallocate(eval_bubble);\n\n if (mympi->my_rank == 0) {\n std::cout << \" done!\" << std::endl << std::endl;\n }\n}\n\nstd::vector > Scph::get_bubble_selfenergy(const unsigned int nk_in,\n const unsigned int ns_in,\n const unsigned int kmesh_in[3],\n double **xk_in,\n double **eval_in,\n std::complex ***evec_in,\n const unsigned int knum,\n const unsigned int snum,\n const double temp_in,\n const std::vector > &omegalist)\n{\n unsigned int arr_cubic[3];\n double xk_tmp[3];\n std::complex omega_sum[2], omega_shift;\n\n double factor = 1.0 / (static_cast(nk_in) * std::pow(2.0, 4));\n const auto ns2 = ns_in * ns_in;\n const auto nks = nk_in * ns2;\n\n std::complex im(0.0, 1.0);\n\n double n1, n2;\n double f1, f2;\n for (auto m = 0; m < 3; ++m) xk_tmp[m] = -xk_in[knum][m];\n auto knum_minus = kpoint->get_knum(xk_tmp, kmesh_in);\n\n arr_cubic[0] = ns_in * knum_minus + snum;\n\n std::vector > se_bubble(omegalist.size());\n\n const auto nomega = omegalist.size();\n\n std::complex *ret_sum, *ret_mpi;\n memory->allocate(ret_sum, nomega);\n memory->allocate(ret_mpi, nomega);\n\n for (auto iomega = 0; iomega < nomega; ++iomega) {\n ret_sum[iomega] = std::complex(0.0, 0.0);\n ret_mpi[iomega] = std::complex(0.0, 0.0);\n }\n\n for (auto iks = mympi->my_rank; iks < nks; iks += mympi->nprocs) {\n\n auto ik1 = iks / ns2;\n auto is1 = (iks % ns2) / ns_in;\n auto is2 = iks % ns_in;\n\n for (auto m = 0; m < 3; ++m) xk_tmp[m] = xk_in[knum][m] - xk_in[ik1][m];\n auto ik2 = kpoint->get_knum(xk_tmp, kmesh_in);\n\n double omega1 = eval_in[ik1][is1];\n double omega2 = eval_in[ik2][is2];\n\n arr_cubic[1] = ns_in * ik1 + is1;\n arr_cubic[2] = ns_in * ik2 + is2;\n\n double v3_tmp = std::norm(V3_this(arr_cubic, eval_in, evec_in));\n\n if (thermodynamics->classical) {\n n1 = thermodynamics->fC(omega1, temp_in);\n n2 = thermodynamics->fC(omega2, temp_in);\n f1 = n1 + n2;\n f2 = n2 - n1;\n } else {\n n1 = thermodynamics->fB(omega1, temp_in);\n n2 = thermodynamics->fB(omega2, temp_in);\n f1 = n1 + n2 + 1.0;\n f2 = n2 - n1;\n }\n for (auto iomega = 0; iomega < nomega; ++iomega) {\n omega_sum[0] = 1.0 / (omegalist[iomega] + omega1 + omega2) - 1.0 / (omegalist[iomega] - omega1 - omega2);\n omega_sum[1] = 1.0 / (omegalist[iomega] + omega1 - omega2) - 1.0 / (omegalist[iomega] - omega1 + omega2);\n ret_mpi[iomega] += v3_tmp * (f1 * omega_sum[0] + f2 * omega_sum[1]);\n }\n }\n for (auto iomega = 0; iomega < nomega; ++iomega) {\n ret_mpi[iomega] *= factor;\n }\n MPI_Reduce(&ret_mpi[0], &ret_sum[0], nomega, MPI_COMPLEX16, MPI_SUM, 0, MPI_COMM_WORLD);\n\n for (auto iomega = 0; iomega < nomega; ++iomega) {\n se_bubble[iomega] = ret_sum[iomega];\n }\n\n memory->deallocate(ret_mpi);\n memory->deallocate(ret_sum);\n\n return se_bubble;\n}\n\nstd::complex Scph::V3_this(const unsigned int ks[3],\n double **eval,\n std::complex ***evec)\n{\n int i;\n unsigned int kn[3], sn[3];\n const int ns = dynamical->neval;\n\n double omega[3];\n auto ret = std::complex(0.0, 0.0);\n auto ret_re = 0.0;\n auto ret_im = 0.0;\n\n for (i = 0; i < 3; ++i) {\n kn[i] = ks[i] / ns;\n sn[i] = ks[i] % ns;\n omega[i] = eval[kn[i]][sn[i]];\n }\n\n // Return zero if any of the involving phonon has imaginary frequency\n if (omega[0] < eps8 || omega[1] < eps8 || omega[2] < eps8) return 0.0;\n\n if (kn[1] != kindex_phi3_stored[0] || kn[2] != kindex_phi3_stored[1]) {\n calc_phi3_reciprocal_this(kn[1], kn[2], phi3_reciprocal);\n kindex_phi3_stored[0] = kn[1];\n kindex_phi3_stored[1] = kn[2];\n }\n#ifdef _OPENMP\n#pragma omp parallel for private(ret), reduction(+: ret_re, ret_im)\n#endif\n for (i = 0; i < ngroup_v3; ++i) {\n ret = evec[kn[0]][sn[0]][evec_index_v3[i][0]]\n * evec[kn[1]][sn[1]][evec_index_v3[i][1]]\n * evec[kn[2]][sn[2]][evec_index_v3[i][2]]\n * invmass_v3[i] * phi3_reciprocal[i];\n ret_re += ret.real();\n ret_im += ret.imag();\n }\n\n return std::complex(ret_re, ret_im)\n / std::sqrt(omega[0] * omega[1] * omega[2]);\n}\n\nvoid Scph::calc_phi3_reciprocal_this(const unsigned int ik1,\n const unsigned int ik2,\n std::complex *ret)\n{\n int i, j;\n unsigned int iloc;\n double phase;\n const auto dnk_represent = static_cast(nk_represent) / (2.0 * pi);\n std::complex ret_in;\n unsigned int nsize_group;\n\n\n if (tune_type == 0) {\n\n#pragma omp parallel for private(ret_in, nsize_group, j, phase, iloc)\n for (i = 0; i < ngroup_v3; ++i) {\n\n ret_in = std::complex(0.0, 0.0);\n nsize_group = fcs_group_v3[i].size();\n\n for (j = 0; j < nsize_group; ++j) {\n\n phase = relvec_v3[i][j].vecs[0][0] * xk_scph[ik1][0]\n + relvec_v3[i][j].vecs[0][1] * xk_scph[ik1][1]\n + relvec_v3[i][j].vecs[0][2] * xk_scph[ik1][2]\n + relvec_v3[i][j].vecs[1][0] * xk_scph[ik2][0]\n + relvec_v3[i][j].vecs[1][1] * xk_scph[ik2][1]\n + relvec_v3[i][j].vecs[1][2] * xk_scph[ik2][2];\n\n unsigned int iloc = nint(phase * dnk_represent) % nk_represent + nk_represent - 1;\n ret_in += fcs_group_v3[i][j] * exp_phase[iloc];\n }\n ret[i] = ret_in;\n }\n\n } else if (tune_type == 1) {\n\n // Tuned version is used when nk1=nk2=nk3 doesn't hold.\n\n int loc[3];\n double phase3[3];\n const auto inv2pi = 1.0 / (2.0 * pi);\n\n#pragma omp parallel for private(ret_in, nsize_group, j, phase3, loc)\n for (i = 0; i < ngroup_v3; ++i) {\n\n ret_in = std::complex(0.0, 0.0);\n nsize_group = fcs_group_v3[i].size();\n\n for (j = 0; j < nsize_group; ++j) {\n\n for (auto ii = 0; ii < 3; ++ii) {\n phase3[ii]\n = relvec_v3[i][j].vecs[0][ii] * xk_scph[ik1][ii]\n + relvec_v3[i][j].vecs[1][ii] * xk_scph[ik2][ii];\n\n loc[ii] = nint(phase3[ii] * dnk[ii] * inv2pi) % nk_grid[ii] + nk_grid[ii] - 1;\n }\n\n ret_in += fcs_group_v3[i][j] * exp_phase3[loc[0]][loc[1]][loc[2]];\n }\n ret[i] = ret_in;\n }\n }\n}\n\n\ndouble Scph::distance(double *x1,\n double *x2)\n{\n auto dist = std::pow(x1[0] - x2[0], 2)\n + std::pow(x1[1] - x2[1], 2)\n + std::pow(x1[2] - x2[2], 2);\n dist = std::sqrt(dist);\n\n return dist;\n}\n\nvoid Scph::duplicate_xk_boundary(double *xk_in,\n std::vector > &vec_xk)\n{\n int i;\n int n[3];\n double sign[3];\n std::vector vec_tmp;\n\n vec_xk.clear();\n\n for (i = 0; i < 3; ++i) {\n if (std::abs(std::abs(xk_in[i]) - 0.5) < eps) {\n n[i] = 2;\n } else {\n n[i] = 1;\n }\n }\n\n for (i = 0; i < n[0]; ++i) {\n sign[0] = 1.0 - 2.0 * static_cast(i);\n for (int j = 0; j < n[1]; ++j) {\n sign[1] = 1.0 - 2.0 * static_cast(j);\n for (int k = 0; k < n[2]; ++k) {\n sign[2] = 1.0 - 2.0 * static_cast(k);\n\n vec_tmp.clear();\n for (int l = 0; l < 3; ++l) {\n vec_tmp.push_back(sign[l] * xk_in[l]);\n }\n vec_xk.push_back(vec_tmp);\n\n }\n }\n }\n}\n\nvoid Scph::write_anharmonic_correction_fc2(std::complex ****delta_dymat,\n const unsigned int NT,\n const int type)\n{\n unsigned int i, j;\n const auto Tmin = system->Tmin;\n const auto dT = system->dT;\n double ***delta_fc2;\n double **xtmp;\n const auto ns = dynamical->neval;\n unsigned int is, js, icell;\n unsigned int iat, jat;\n\n std::string file_fc2;\n std::ofstream ofs_fc2;\n\n if (type == 0) {\n file_fc2 = input->job_title + \".scph_dfc2\";\n } else if (type == 1) {\n file_fc2 = input->job_title + \".scph+bubble(0)_dfc2\";\n } else if (type == 2) {\n file_fc2 = input->job_title + \".scph+bubble(w)_dfc2\";\n } else if (type == 3) {\n file_fc2 = input->job_title + \".scph+bubble(wQP)_dfc2\";\n }\n\n ofs_fc2.open(file_fc2.c_str(), std::ios::out);\n if (!ofs_fc2)\n error->exit(\"write_anharmonic_correction_fc2\",\n \"Cannot open file_fc2\");\n\n const auto ncell = kmesh_interpolate[0] * kmesh_interpolate[1] * kmesh_interpolate[2];\n\n memory->allocate(delta_fc2, ns, ns, ncell);\n\n memory->allocate(xtmp, system->natmin, 3);\n\n ofs_fc2.precision(10);\n\n for (i = 0; i < system->natmin; ++i) {\n rotvec(xtmp[i], system->xr_s[system->map_p2s[i][0]], system->lavec_s);\n rotvec(xtmp[i], xtmp[i], system->rlavec_p);\n for (j = 0; j < 3; ++j) xtmp[i][j] /= 2.0 * pi;\n }\n\n for (i = 0; i < 3; ++i) {\n for (j = 0; j < 3; ++j) {\n ofs_fc2 << std::setw(20) << system->lavec_p[j][i];\n }\n ofs_fc2 << std::endl;\n }\n ofs_fc2 << std::setw(5) << system->natmin << std::setw(5) << system->nkd << std::endl;\n for (i = 0; i < system->nkd; ++i) {\n ofs_fc2 << std::setw(5) << system->symbol_kd[i];\n }\n ofs_fc2 << std::endl;\n\n for (i = 0; i < system->natmin; ++i) {\n for (j = 0; j < 3; ++j) {\n ofs_fc2 << std::setw(20) << xtmp[i][j];\n }\n ofs_fc2 << std::setw(5) << system->kd[system->map_p2s[i][0]] + 1 << std::endl;\n }\n\n memory->deallocate(xtmp);\n\n for (unsigned int iT = 0; iT < NT; ++iT) {\n const auto temp = Tmin + dT * static_cast(iT);\n\n ofs_fc2 << \"# Temp = \" << temp << std::endl;\n\n for (is = 0; is < ns; ++is) {\n iat = is / 3;\n\n for (js = 0; js < ns; ++js) {\n jat = js / 3;\n\n for (icell = 0; icell < ncell; ++icell) {\n delta_fc2[is][js][icell]\n = delta_dymat[iT][is][js][icell].real()\n * std::sqrt(system->mass[system->map_p2s[iat][0]]\n * system->mass[system->map_p2s[jat][0]]);\n }\n\n }\n }\n\n\n for (icell = 0; icell < ncell; ++icell) {\n\n for (is = 0; is < ns; ++is) {\n iat = is / 3;\n const auto icrd = is % 3;\n\n for (js = 0; js < ns; ++js) {\n jat = js / 3;\n const auto jcrd = js % 3;\n\n const auto nmulti = mindist_list_scph[iat][jat][icell].shift.size();\n\n for (auto it = mindist_list_scph[iat][jat][icell].shift.cbegin();\n it != mindist_list_scph[iat][jat][icell].shift.cend(); ++it) {\n\n ofs_fc2 << std::setw(4) << (*it).sx;\n ofs_fc2 << std::setw(4) << (*it).sy;\n ofs_fc2 << std::setw(4) << (*it).sz;\n ofs_fc2 << std::setw(5) << iat << std::setw(3) << icrd;\n ofs_fc2 << std::setw(4) << jat << std::setw(3) << jcrd;\n ofs_fc2 << std::setprecision(15) << std::setw(25)\n << delta_fc2[is][js][icell] / static_cast(nmulti) << std::endl;\n\n }\n\n }\n }\n }\n\n ofs_fc2 << std::endl;\n }\n\n memory->deallocate(delta_fc2);\n\n ofs_fc2.close();\n std::cout << \" \" << std::setw(input->job_title.length() + 12) << std::left << file_fc2;\n\n if (type == 0) {\n std::cout << \" : Anharmonic corrections to the second-order IFCs (SCPH)\" << std::endl;\n } else if (type == 1) {\n std::cout << \" : Anharmonic corrections to the second-order IFCs (SCPH+Bubble(0))\" << std::endl;\n } else if (type == 2) {\n std::cout << \" : Anharmonic corrections to the second-order IFCs (SCPH+Bubble(w))\" << std::endl;\n } else if (type == 3) {\n std::cout << \" : Anharmonic corrections to the second-order IFCs (SCPH+Bubble(wQP))\" << std::endl;\n }\n}\n\nvoid Scph::mpi_bcast_complex(std::complex ****data,\n const unsigned int NT,\n const unsigned int nk,\n const unsigned int ns)\n{\n const int _NT = static_cast(NT);\n const int _nk = static_cast(nk);\n const int _ns = static_cast(ns);\n\n#ifdef MPI_CXX_DOUBLE_COMPLEX\n MPI_Bcast(&data[0][0][0][0], _NT * _nk * _ns * _ns,\n MPI_CXX_DOUBLE_COMPLEX, 0, MPI_COMM_WORLD);\n#elif defined MPI_DOUBLE_COMPLEX\n MPI_Bcast(&data[0][0][0][0], _NT * _nk * _ns * _ns, MPI_DOUBLE_COMPLEX, 0, MPI_COMM_WORLD);\n#else\n MPI_Bcast(&data[0][0][0][0], _NT * _nk * _ns * _ns, MPI_COMPLEX16, 0, MPI_COMM_WORLD);\n#endif\n}\n\nvoid Scph::get_derivative_central_diff(const double delta_t,\n const unsigned int nk,\n double **omega0,\n double **omega2,\n double **domega_dt)\n{\n const auto ns = dynamical->neval;\n const auto inv_dt = 1.0 / (2.0 * delta_t);\n for (auto ik = 0; ik < nk; ++ik) {\n for (auto is = 0; is < ns; ++is) {\n domega_dt[ik][is] = (omega2[ik][is] - omega0[ik][is]) * inv_dt;\n // std::cout << \"domega_dt = \" << domega_dt[ik][is] << '\\n';\n }\n }\n}\n", "meta": {"hexsha": "5fc0486b1dc8620df6206d6cc17b5f268f6b1f7f", "size": 139852, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "anphon/scph.cpp", "max_stars_repo_name": "jochym/alamode", "max_stars_repo_head_hexsha": "128cc2315a661f2440a2f264f0b9dd75ed42dd39", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "anphon/scph.cpp", "max_issues_repo_name": "jochym/alamode", "max_issues_repo_head_hexsha": "128cc2315a661f2440a2f264f0b9dd75ed42dd39", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "anphon/scph.cpp", "max_forks_repo_name": "jochym/alamode", "max_forks_repo_head_hexsha": "128cc2315a661f2440a2f264f0b9dd75ed42dd39", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.9516709512, "max_line_length": 125, "alphanum_fraction": 0.4576838372, "num_tokens": 37581, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.24189891699630717}} {"text": "/**\n * \\file\n * \\copyright\n * Copyright (c) 2012-2021, OpenGeoSys Community (http://www.opengeosys.org)\n * Distributed under a Modified BSD License.\n * See accompanying file LICENSE.txt or\n * http://www.opengeosys.org/project/license\n *\n */\n\n#include \"BHE_1P.h\"\n\n#include \n\n#include \"FlowAndTemperatureControl.h\"\n#include \"Physics.h\"\n#include \"ThermoMechanicalFlowProperties.h\"\n\nnamespace ProcessLib\n{\nnamespace HeatTransportBHE\n{\nnamespace BHE\n{\nBHE_1P::BHE_1P(BoreholeGeometry const& borehole,\n RefrigerantProperties const& refrigerant,\n GroutParameters const& grout,\n FlowAndTemperatureControl const& flowAndTemperatureControl,\n PipeConfiguration1PType const& pipes,\n bool const use_python_bcs)\n : BHECommon{borehole, refrigerant, grout, flowAndTemperatureControl,\n use_python_bcs},\n _pipe(pipes)\n{\n _thermal_resistances.fill(std::numeric_limits::quiet_NaN());\n\n // Initialize thermal resistances.\n auto values = visit(\n [&](auto const& control) {\n return control(refrigerant.reference_temperature,\n 0. /* initial time */);\n },\n flowAndTemperatureControl);\n updateHeatTransferCoefficients(values.flow_rate);\n}\n\nstd::array BHE_1P::pipeHeatCapacities()\n const\n{\n double const rho_r = refrigerant.density;\n double const specific_heat_capacity = refrigerant.specific_heat_capacity;\n double const rho_g = grout.rho_g;\n double const porosity_g = grout.porosity_g;\n double const heat_cap_g = grout.heat_cap_g;\n\n return {{\n /*pipe*/ rho_r * specific_heat_capacity,\n /*grout*/ (1.0 - porosity_g) * rho_g * heat_cap_g,\n }};\n}\n\nstd::array BHE_1P::pipeHeatConductions()\n const\n{\n double const lambda_r = refrigerant.thermal_conductivity;\n double const rho_r = refrigerant.density;\n double const Cp_r = refrigerant.specific_heat_capacity;\n double const alpha_L = _pipe.longitudinal_dispersion_length;\n double const porosity_g = grout.porosity_g;\n double const lambda_g = grout.lambda_g;\n\n // Here we calculate the laplace coefficients in the governing\n // equations of the BHE.\n return {{\n // pipe, Eq. 19\n (lambda_r + rho_r * Cp_r * alpha_L * _flow_velocity),\n // grout, Eq. 21\n (1.0 - porosity_g) * lambda_g,\n }};\n}\n\nstd::array\nBHE_1P::pipeAdvectionVectors(Eigen::Vector3d const& elem_direction) const\n{\n double const& rho_r = refrigerant.density;\n double const& Cp_r = refrigerant.specific_heat_capacity;\n Eigen::Vector3d adv_vector = rho_r * Cp_r * _flow_velocity * elem_direction;\n\n return {// pipe, Eq. 19\n adv_vector,\n // grout, Eq. 21\n {0, 0, 0}};\n}\n\ndouble BHE_1P::compute_R_gs(double const chi, double const R_g)\n{\n return (1 - chi) * R_g;\n}\n\nvoid BHE_1P::updateHeatTransferCoefficients(double const flow_rate)\n\n{\n auto const tm_flow_properties = calculateThermoMechanicalFlowPropertiesPipe(\n _pipe.single_pipe, borehole_geometry.length, refrigerant, flow_rate);\n\n _flow_velocity = tm_flow_properties.velocity;\n _thermal_resistances =\n calcThermalResistances(tm_flow_properties.nusselt_number);\n}\n\n// Nu is the Nusselt number.\nstd::array BHE_1P::calcThermalResistances(\n double const Nu)\n{\n constexpr double pi = boost::math::constants::pi();\n\n double const lambda_r = refrigerant.thermal_conductivity;\n double const lambda_g = grout.lambda_g;\n double const lambda_p = _pipe.single_pipe.wall_thermal_conductivity;\n\n // thermal resistances due to advective flow of refrigerant in the pipe\n double const R_adv_i1 = 1.0 / (Nu * lambda_r * pi);\n\n // thermal resistance due to thermal conductivity of the pipe wall material\n double const R_con_a = std::log(_pipe.single_pipe.outsideDiameter() /\n _pipe.single_pipe.diameter) /\n (2.0 * pi * lambda_p);\n\n // thermal resistances of the grout\n double const D = borehole_geometry.diameter;\n double const pipe_outside_diameter = _pipe.single_pipe.outsideDiameter();\n\n double const chi = std::log(std::sqrt(D * D + pipe_outside_diameter *\n pipe_outside_diameter) /\n std::sqrt(2) / pipe_outside_diameter) /\n std::log(D / pipe_outside_diameter);\n double const R_g =\n std::log(D / pipe_outside_diameter) / 2 / (pi * lambda_g);\n\n double const R_con_b = chi * R_g;\n\n // thermal resistances due to grout-soil exchange\n double const R_gs = compute_R_gs(chi, R_g);\n\n // Eq. 29 and 30\n double const R_fg = R_adv_i1 + R_con_a + R_con_b;\n\n return {{R_fg, R_gs}};\n}\n\nstd::array, 2>\nBHE_1P::getBHEInflowDirichletBCNodesAndComponents(\n std::size_t const top_node_id,\n std::size_t const bottom_node_id,\n int const in_component_id)\n{\n return {std::make_pair(top_node_id, in_component_id),\n std::make_pair(bottom_node_id, in_component_id)};\n}\n\nstd::optional<\n std::array, 2>>\nBHE_1P::getBHEBottomDirichletBCNodesAndComponents(\n std::size_t const /*bottom_node_id*/,\n int const /*in_component_id*/,\n int const /*out_component_id*/)\n{\n return {};\n}\n\nstd::array BHE_1P::crossSectionAreas() const\n{\n return {{_pipe.single_pipe.area(),\n borehole_geometry.area() - _pipe.single_pipe.outsideArea()}};\n}\n\ndouble BHE_1P::updateFlowRateAndTemperature(double const T_out,\n double const current_time)\n{\n auto values =\n visit([&](auto const& control) { return control(T_out, current_time); },\n flowAndTemperatureControl);\n updateHeatTransferCoefficients(values.flow_rate);\n return values.temperature;\n}\n} // namespace BHE\n} // namespace HeatTransportBHE\n} // namespace ProcessLib\n", "meta": {"hexsha": "80e41d3b300488f5310d1253d029ee50b05a8d32", "size": 6268, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ProcessLib/HeatTransportBHE/BHE/BHE_1P.cpp", "max_stars_repo_name": "ufz/ogs", "max_stars_repo_head_hexsha": "97d0249e0c578c3055730f4e9d994b9970885098", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 111.0, "max_stars_repo_stars_event_min_datetime": "2015-03-20T22:54:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T04:37:21.000Z", "max_issues_repo_path": "ProcessLib/HeatTransportBHE/BHE/BHE_1P.cpp", "max_issues_repo_name": "ufz/ogs", "max_issues_repo_head_hexsha": "97d0249e0c578c3055730f4e9d994b9970885098", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 3015.0, "max_issues_repo_issues_event_min_datetime": "2015-01-05T21:55:16.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-15T01:09:17.000Z", "max_forks_repo_path": "ProcessLib/HeatTransportBHE/BHE/BHE_1P.cpp", "max_forks_repo_name": "ufz/ogs", "max_forks_repo_head_hexsha": "97d0249e0c578c3055730f4e9d994b9970885098", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 250.0, "max_forks_repo_forks_event_min_datetime": "2015-02-10T15:43:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-30T04:37:20.000Z", "avg_line_length": 33.164021164, "max_line_length": 80, "alphanum_fraction": 0.6727823867, "num_tokens": 1586, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.24189891699630717}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#define REP(i, n) for(int i = 0, i##_MACRO = (n); i < i##_MACRO; i++)\n#define RANGE(i, a, b) for(int i = (a), i##_MACRO = (b); i < i##_MACRO; i++)\n#define EACH(e, a) for(auto&& e : a)\n#define ALL(a) std::begin(a), std::end(a)\n#define RALL(a) std::rbegin(a), std::rend(a)\n#define FILL(a, n) memset((a), n, sizeof(a))\n#define FILLZ(a) FILL(a, 0)\n#define INT(x) (static_cast(x))\n#define PRECISION(x) std::fixed << std::setprecision(x)\n\nusing namespace std;\n\nusing ll = long long;\nusing VI = std::vector;\nusing VI2D = std::vector>;\nusing VLL = std::vector;\nusing VLL2D = std::vector>;\n\nconstexpr int INF = 2e9;\nconstexpr double EPS = 1e-10;\nconstexpr double PI = acos(-1.0);\n\nconstexpr int dx[] = {-1, 0, 1, 0};\nconstexpr int dy[] = {0, -1, 0, 1};\n\ntemplate \nstruct make_vector_type {\n\tusing type =\n\t\ttypename std::vector::type>;\n};\n\ntemplate \nstruct make_vector_type {\n\tusing type = typename std::vector;\n};\n\ntemplate \nauto make_vector_impl(const std::vector& ls, T init_value) {\n\tif constexpr(N == 0) {\n\t\treturn std::vector(ls[N], init_value);\n\t} else {\n\t\treturn typename make_vector_type::type(\n\t\t\tls[N], make_vector_impl(ls, init_value));\n\t}\n}\n\ntemplate \nauto make_vector(const std::size_t (&ls)[N], T init_value) {\n\tstd::vector dimensions(N);\n\tfor(int i = 0; i < N; i++) {\n\t\tdimensions[N - i - 1] = ls[i];\n\t}\n\treturn make_vector_impl(dimensions, init_value);\n}\n\ntemplate \nstd::vector make_vector(std::size_t size, T init_value) {\n\treturn std::vector(size, init_value);\n}\n\ntemplate \nconstexpr int sign(T x) {\n\treturn x < 0 ? -1 : x > 0 ? 1 : 0;\n}\n\ntemplate <>\nconstexpr int sign(double x) {\n\treturn x < -EPS ? -1 : x > EPS ? 1 : 0;\n}\n\ntemplate \nconstexpr void chmax(T& m, U x) {\n\tm = max(m, x);\n}\n\ntemplate \nconstexpr void chmin(T& m, U x) {\n\tm = min(m, x);\n}\n\ntemplate \nconstexpr T square(T x) {\n\treturn x * x;\n}\n\ntemplate \nclass ModInt {\n\tpublic:\n\tlong long n;\n\n\tconstexpr ModInt() : n(0) {}\n\tconstexpr ModInt(long long n) : n(n < 0 ? n + MOD : n % MOD) {}\n\n\tconstexpr long long get() const { return this->n; }\n\tconstexpr long long get_mod() const { return MOD; }\n\n\tconstexpr ModInt inv() const { return pow>(*this, MOD - 2); }\n\n\tconstexpr ModInt& operator=(const long long rhs) {\n\t\treturn *this = ModInt(rhs);\n\t}\n\tconstexpr ModInt& operator+=(const ModInt rhs) {\n\t\treturn *this = ModInt(this->n + rhs.n);\n\t}\n\tconstexpr ModInt& operator-=(const ModInt rhs) {\n\t\treturn *this = ModInt(this->n - rhs.n);\n\t}\n\tconstexpr ModInt& operator*=(const ModInt rhs) {\n\t\treturn *this = ModInt(this->n * rhs.n);\n\t}\n\tconstexpr ModInt& operator/=(const ModInt rhs) {\n\t\treturn *this *= rhs.inv();\n\t}\n\tconstexpr bool operator==(const ModInt rhs) const {\n\t\treturn this->n == rhs.n;\n\t}\n};\n\ntemplate \nconstexpr ModInt operator+(const ModInt& lhs,\n\t\t\t\t\t\t\t\tconst ModInt& rhs) {\n\treturn ModInt(lhs) += rhs;\n}\ntemplate \nconstexpr ModInt operator+(const ModInt& lhs, const long long& rhs) {\n\treturn ModInt(lhs) += rhs;\n}\ntemplate \nconstexpr ModInt operator+(const long long& lhs, const ModInt& rhs) {\n\treturn ModInt(lhs) += rhs;\n}\n\ntemplate \nconstexpr ModInt operator-(const ModInt& lhs,\n\t\t\t\t\t\t\t\tconst ModInt& rhs) {\n\treturn ModInt(lhs) -= rhs;\n}\ntemplate \nconstexpr ModInt operator-(const ModInt& lhs, const long long& rhs) {\n\treturn ModInt(lhs) -= rhs;\n}\ntemplate \nconstexpr ModInt operator-(const long long& lhs, const ModInt& rhs) {\n\treturn ModInt(lhs) -= rhs;\n}\n\ntemplate \nconstexpr ModInt operator*(const ModInt& lhs,\n\t\t\t\t\t\t\t\tconst ModInt& rhs) {\n\treturn ModInt(lhs) *= rhs;\n}\ntemplate \nconstexpr ModInt operator*(const ModInt& lhs, const long long& rhs) {\n\treturn ModInt(lhs) *= rhs;\n}\ntemplate \nconstexpr ModInt operator*(const long long& lhs, const ModInt& rhs) {\n\treturn ModInt(lhs) *= rhs;\n}\n\ntemplate \nconstexpr ModInt operator/(const ModInt& lhs,\n\t\t\t\t\t\t\t\tconst ModInt& rhs) {\n\treturn ModInt(lhs) /= rhs;\n}\ntemplate \nconstexpr ModInt operator/(const ModInt& lhs, const long long& rhs) {\n\treturn ModInt(lhs) /= rhs;\n}\ntemplate \nconstexpr ModInt operator/(const long long& lhs, const ModInt& rhs) {\n\treturn ModInt(lhs) /= rhs;\n}\n\ntemplate \nstd::ostream& operator<<(std::ostream& os, const ModInt& x) {\n\treturn os << x.n;\n}\n\ntemplate \nstd::istream& operator>>(std::istream& is, const ModInt& x) {\n\treturn is >> x.n;\n}\n\nint roundup_pow2(int n) {\n\tif(!(n & (n - 1))) {\n\t\treturn n;\n\t}\n\n\tint ret = 1;\n\twhile(n > ret) {\n\t\tret <<= 1;\n\t}\n\treturn ret;\n}\n\ntemplate \nclass LazySegmentTree {\n\tusing F = std::function;\n\tusing G = std::function;\n\tusing H = std::function;\n\n\tstd::vector tree;\n\tstd::vector lazy;\n\tF merge;\n\tG mapping;\n\tH composition;\n\tT id1;\n\tU id2;\n\tstd::size_t size;\n\n\tpublic:\n\tLazySegmentTree(const vector& a,\n\t\t\t\t\tconst F f,\n\t\t\t\t\tconst T id1,\n\t\t\t\t\tconst G g,\n\t\t\t\t\tconst H h,\n\t\t\t\t\tconst U id2)\n\t\t: tree(roundup_pow2(a.size()) * 2 - 1, id1),\n\t\t lazy(roundup_pow2(a.size()) * 2 - 1, id2), merge(f), id1(id1),\n\t\t mapping(g), composition(h), id2(id2), size(roundup_pow2(a.size())) {\n\t\tint offset = this->size - 1;\n\t\tfor(int i = 0; i < a.size(); i++) {\n\t\t\tthis->tree[i + offset] = a[i];\n\t\t}\n\t\tfor(int i = offset - 1; i >= 0; i--) {\n\t\t\tthis->tree[i] =\n\t\t\t\tthis->merge(this->tree[i * 2 + 1], this->tree[i * 2 + 2]);\n\t\t}\n\t}\n\n\tvoid debug_print() {\n\t\tcout << \"tree: \";\n\t\tREP(i, this->size * 2 - 1) {\n\t\t\tif(i == this->size - 1) {\n\t\t\t\tcout << \"| \";\n\t\t\t}\n\t\t\tcout << \"(\" << this->tree[i].first << \",\" << this->tree[i].second\n\t\t\t\t << \") \";\n\t\t}\n\t\tcout << \"\\nlazy: \";\n\t\tREP(i, this->size * 2 - 1) {\n\t\t\tif(i == this->size - 1) {\n\t\t\t\tcout << \"| \";\n\t\t\t}\n\t\t\tcout << this->lazy[i] << \" \";\n\t\t}\n\t\tcout << endl;\n\t}\n\n\tvoid\n\tupdate(const std::size_t left, const std::size_t right, const U value) {\n\t\tthis->update_impl(left, right, 0, 0, this->size, value);\n\t}\n\n\tT find(const std::size_t left, const std::size_t right) {\n\t\treturn this->find_impl(left, right, 0, 0, this->size);\n\t}\n\n\tprivate:\n\tvoid update_impl(std::size_t query_left,\n\t\t\t\t\t std::size_t query_right,\n\t\t\t\t\t std::size_t node,\n\t\t\t\t\t std::size_t node_left,\n\t\t\t\t\t std::size_t node_right,\n\t\t\t\t\t U value) {\n\t\tthis->force(node);\n\t\tif(node_right <= query_left || query_right <= node_left) {\n\t\t\treturn;\n\t\t}\n\t\tif(query_left <= node_left && node_right <= query_right) {\n\t\t\tthis->lazy[node] = this->composition(this->lazy[node], value);\n\t\t\tthis->force(node);\n\t\t\treturn;\n\t\t}\n\t\tthis->update_impl(query_left,\n\t\t\t\t\t\t query_right,\n\t\t\t\t\t\t node * 2 + 1,\n\t\t\t\t\t\t node_left,\n\t\t\t\t\t\t node_left + (node_right - node_left) / 2,\n\t\t\t\t\t\t value);\n\t\tthis->update_impl(query_left,\n\t\t\t\t\t\t query_right,\n\t\t\t\t\t\t node * 2 + 2,\n\t\t\t\t\t\t node_left + (node_right - node_left) / 2,\n\t\t\t\t\t\t node_right,\n\t\t\t\t\t\t value);\n\t\tthis->tree[node] =\n\t\t\tthis->merge(this->tree[node * 2 + 1], this->tree[node * 2 + 2]);\n\t}\n\n\tT find_impl(size_t query_left,\n\t\t\t\tsize_t query_right,\n\t\t\t\tsize_t node,\n\t\t\t\tsize_t node_left,\n\t\t\t\tsize_t node_right) {\n\t\tthis->force(node);\n\t\tif(node_right <= query_left || query_right <= node_left) {\n\t\t\treturn this->id1;\n\t\t}\n\t\tif(query_left <= node_left && node_right <= query_right) {\n\t\t\treturn this->tree[node];\n\t\t}\n\n\t\treturn this->merge(find_impl(query_left,\n\t\t\t\t\t\t\t\t\t query_right,\n\t\t\t\t\t\t\t\t\t node * 2 + 1,\n\t\t\t\t\t\t\t\t\t node_left,\n\t\t\t\t\t\t\t\t\t node_left + (node_right - node_left) / 2),\n\t\t\t\t\t\t find_impl(query_left,\n\t\t\t\t\t\t\t\t\t query_right,\n\t\t\t\t\t\t\t\t\t node * 2 + 2,\n\t\t\t\t\t\t\t\t\t node_left + (node_right - node_left) / 2,\n\t\t\t\t\t\t\t\t\t node_right));\n\t}\n\n\tvoid force(std::size_t node) {\n\t\tif(this->lazy[node] == this->id2) {\n\t\t\treturn;\n\t\t}\n\t\tif(node * 2 + 1 < this->size * 2 - 1) {\n\t\t\tthis->lazy[node * 2 + 1] =\n\t\t\t\tthis->composition(lazy[node * 2 + 1], lazy[node]);\n\t\t\tthis->lazy[node * 2 + 2] =\n\t\t\t\tthis->composition(lazy[node * 2 + 2], lazy[node]);\n\t\t}\n\t\tthis->tree[node] = this->mapping(this->tree[node], lazy[node]);\n\t\tthis->lazy[node] = this->id2;\n\t}\n};\n\nusing mint = ModInt<998244353>;\n\nint main() {\n\tint n, q;\n\tcin >> n >> q;\n\tVI l(q), r(q);\n\tVI d(q);\n\tREP(i, q) {\n\t\tcin >> l[i] >> r[i] >> d[i];\n\t\tl[i] = n - l[i] + 1;\n\t\tr[i] = n - r[i];\n\t\tswap(l[i], r[i]);\n\t}\n\n\tvector keta(n + 1, 0);\n\tketa[0] = 1;\n\tREP(i, n) { keta[i + 1] = keta[i] * 10; }\n\n\tvector repunit(n + 1, 0);\n\trepunit[0] = 1;\n\tREP(i, n) { repunit[i + 1] = repunit[i] * 10 + 1; }\n\n\tvector> s(n, {1, 1});\n\n\tLazySegmentTree, int> lst(\n\t\ts,\n\t\t[&](pair a, pair b) {\n\t\t\treturn make_pair(a.first + b.first * keta[a.second],\n\t\t\t\t\t\t\t a.second + b.second);\n\t\t},\n\t\t{0, 1},\n\t\t[&](pair a, int b) {\n\t\t\treturn make_pair(b ? b * repunit[a.second - 1] : a.first, a.second);\n\t\t},\n\t\t[](int a, int b) { return b ? b : a; },\n\t\t0);\n\t// lst.debug_print();\n\tREP(i, q) {\n\t\tlst.update(l[i], r[i], d[i]);\n\t\tcout << lst.find(0, n).first << endl;\n\t\t// lst.debug_print();\n\t}\n\treturn 0;\n}\n", "meta": {"hexsha": "8733664c920ea2e7f7be2ca1335c51f73a3a902f", "size": 9904, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "AtCoder/ABL/E.cpp", "max_stars_repo_name": "arlechann/atcoder", "max_stars_repo_head_hexsha": "1af08efa6d3a0e8c75e4eaf13e1eda994820b9e2", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "AtCoder/ABL/E.cpp", "max_issues_repo_name": "arlechann/atcoder", "max_issues_repo_head_hexsha": "1af08efa6d3a0e8c75e4eaf13e1eda994820b9e2", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "AtCoder/ABL/E.cpp", "max_forks_repo_name": "arlechann/atcoder", "max_forks_repo_head_hexsha": "1af08efa6d3a0e8c75e4eaf13e1eda994820b9e2", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 24.76, "max_line_length": 79, "alphanum_fraction": 0.6114701131, "num_tokens": 3145, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.47268347662043286, "lm_q1q2_score": 0.24187998375596864}} {"text": "/*\r\n * Copyright Nick Thompson, 2020\r\n * Use, modification and distribution are subject to the\r\n * Boost Software License, Version 1.0. (See accompanying file\r\n * LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)\r\n */\r\n\r\n#ifndef BOOST_MATH_SPECIAL_DAUBECHIES_WAVELET_HPP\r\n#define BOOST_MATH_SPECIAL_DAUBECHIES_WAVELET_HPP\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\nnamespace boost::math {\r\n\r\n template\r\n std::vector daubechies_wavelet_dyadic_grid(int64_t j_max)\r\n {\r\n if (j_max == 0)\r\n {\r\n throw std::domain_error(\"The wavelet dyadic grid is refined from the scaling integer grid, so its minimum amount of data is half integer widths.\");\r\n }\r\n auto phijk = daubechies_scaling_dyadic_grid(j_max - 1);\r\n //psi_j[l] = psi(-p+1 + l/2^j) = \\sum_{k=0}^{2p-1} (-1)^k c_k \\phi(1-2p+k + l/2^{j-1})\r\n //For derivatives just map c_k -> 2^order c_k.\r\n auto d = boost::math::filters::daubechies_scaling_filter();\r\n Real scale = boost::math::constants::root_two() * (1 << order);\r\n for (size_t i = 0; i < d.size(); ++i)\r\n {\r\n d[i] *= scale;\r\n if (!(i & 1))\r\n {\r\n d[i] = -d[i];\r\n }\r\n }\r\n\r\n std::vector v(2 * p + (2 * p - 1) * ((int64_t(1) << j_max) - 1), std::numeric_limits::quiet_NaN());\r\n v[0] = 0;\r\n v[v.size() - 1] = 0;\r\n\r\n for (int64_t l = 1; l < static_cast(v.size() - 1); ++l)\r\n {\r\n Real term = 0;\r\n for (int64_t k = 0; k < static_cast(d.size()); ++k)\r\n {\r\n int64_t idx = (int64_t(1) << (j_max - 1)) * (1 - 2 * p + k) + l;\r\n if (idx < 0 || idx >= static_cast(phijk.size()))\r\n {\r\n continue;\r\n }\r\n term += d[k] * phijk[idx];\r\n }\r\n v[l] = term;\r\n }\r\n\r\n return v;\r\n }\r\n\r\n\r\n template\r\n class daubechies_wavelet {\r\n //\r\n // Some type manipulation so we know the type of the interpolator, and the vector type it requires:\r\n //\r\n typedef std::vector < std::array < Real, p < 6 ? 2 : p < 10 ? 3 : 4>> vector_type;\r\n //\r\n // List our interpolators:\r\n //\r\n typedef std::tuple<\r\n detail::null_interpolator, detail::matched_holder_aos, detail::linear_interpolation_aos,\r\n interpolators::detail::cardinal_cubic_hermite_detail_aos, interpolators::detail::cardinal_quintic_hermite_detail_aos,\r\n interpolators::detail::cardinal_septic_hermite_detail_aos > interpolator_list;\r\n //\r\n // Select the one we need:\r\n //\r\n typedef std::tuple_element_t<\r\n p == 1 ? 0 :\r\n p == 2 ? 1 :\r\n p == 3 ? 2 :\r\n p <= 5 ? 3 :\r\n p <= 9 ? 4 : 5, interpolator_list> interpolator_type;\r\n public:\r\n daubechies_wavelet(int grid_refinements = -1)\r\n {\r\n static_assert(p < 20, \"Daubechies wavelets are only implemented for p < 20.\");\r\n static_assert(p > 0, \"Daubechies wavelets must have at least 1 vanishing moment.\");\r\n if (grid_refinements == 0)\r\n {\r\n throw std::domain_error(\"The wavelet requires at least 1 grid refinement.\");\r\n }\r\n if constexpr (p == 1)\r\n {\r\n return;\r\n }\r\n else\r\n {\r\n if (grid_refinements < 0)\r\n {\r\n if (std::is_same_v)\r\n {\r\n if (grid_refinements == -2)\r\n {\r\n // Control absolute error:\r\n // p= 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19\r\n std::array r{ -1, -1, 18, 19, 16, 11, 8, 7, 7, 7, 5, 5, 4, 4, 4, 4, 3, 3, 3, 3 };\r\n grid_refinements = r[p];\r\n }\r\n else\r\n {\r\n // Control relative error:\r\n // p= 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19\r\n std::array r{ -1, -1, 21, 21, 21, 17, 16, 15, 14, 13, 12, 11, 11, 11, 11, 11, 11, 11, 11, 11 };\r\n grid_refinements = r[p];\r\n }\r\n }\r\n else if (std::is_same_v)\r\n {\r\n // p= 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19\r\n std::array r{ -1, -1, 21, 21, 21, 21, 21, 21, 21, 21, 20, 20, 19, 18, 18, 18, 18, 18, 18, 18 };\r\n grid_refinements = r[p];\r\n }\r\n else\r\n {\r\n grid_refinements = 21;\r\n }\r\n }\r\n\r\n // Compute the refined grid:\r\n // In fact for float precision I know the grid must be computed in double precision and then cast back down, or else parts of the support are systematically inaccurate.\r\n std::future> t0 = std::async(std::launch::async, [&grid_refinements]() {\r\n // Computing in higher precision and downcasting is essential for 1ULP evaluation in float precision:\r\n auto v = daubechies_wavelet_dyadic_grid::type, p, 0>(grid_refinements);\r\n return detail::daubechies_eval_type::vector_cast(v);\r\n });\r\n // Compute the derivative of the refined grid:\r\n std::future> t1 = std::async(std::launch::async, [&grid_refinements]() {\r\n auto v = daubechies_wavelet_dyadic_grid::type, p, 1>(grid_refinements);\r\n return detail::daubechies_eval_type::vector_cast(v);\r\n });\r\n\r\n // if necessary, compute the second and third derivative:\r\n std::vector d2ydx2;\r\n std::vector d3ydx3;\r\n if constexpr (p >= 6) {\r\n std::future> t3 = std::async(std::launch::async, [&grid_refinements]() {\r\n auto v = daubechies_wavelet_dyadic_grid::type, p, 2>(grid_refinements);\r\n return detail::daubechies_eval_type::vector_cast(v);\r\n });\r\n\r\n if constexpr (p >= 10) {\r\n std::future> t4 = std::async(std::launch::async, [&grid_refinements]() {\r\n auto v = daubechies_wavelet_dyadic_grid::type, p, 3>(grid_refinements);\r\n return detail::daubechies_eval_type::vector_cast(v);\r\n });\r\n d3ydx3 = t4.get();\r\n }\r\n d2ydx2 = t3.get();\r\n }\r\n\r\n\r\n auto y = t0.get();\r\n auto dydx = t1.get();\r\n\r\n if constexpr (p >= 2)\r\n {\r\n vector_type data(y.size());\r\n for (size_t i = 0; i < y.size(); ++i)\r\n {\r\n data[i][0] = y[i];\r\n data[i][1] = dydx[i];\r\n if constexpr (p >= 6)\r\n data[i][2] = d2ydx2[i];\r\n if constexpr (p >= 10)\r\n data[i][3] = d3ydx3[i];\r\n }\r\n if constexpr (p <= 3)\r\n m_interpolator = std::make_shared(std::move(data), grid_refinements, Real(-p + 1));\r\n else\r\n m_interpolator = std::make_shared(std::move(data), Real(-p + 1), Real(1) / (1 << grid_refinements));\r\n }\r\n else\r\n m_interpolator = std::make_shared();\r\n }\r\n }\r\n\r\n\r\n inline Real operator()(Real x) const\r\n {\r\n if (x <= -p + 1 || x >= p)\r\n {\r\n return 0;\r\n }\r\n if constexpr (p == 1)\r\n {\r\n if (x < Real(1) / Real(2))\r\n {\r\n return 1;\r\n }\r\n else if (x == Real(1) / Real(2))\r\n {\r\n return 0;\r\n }\r\n return -1;\r\n }\r\n return (*m_interpolator)(x);\r\n }\r\n\r\n inline Real prime(Real x) const\r\n {\r\n static_assert(p > 2, \"The 3-vanishing moment Daubechies wavelet is the first which is continuously differentiable.\");\r\n if (x <= -p + 1 || x >= p)\r\n {\r\n return 0;\r\n }\r\n return m_interpolator->prime(x);\r\n }\r\n\r\n inline Real double_prime(Real x) const\r\n {\r\n static_assert(p >= 6, \"Second derivatives of Daubechies wavelets require at least 6 vanishing moments.\");\r\n if (x <= -p + 1 || x >= p)\r\n {\r\n return Real(0);\r\n }\r\n return m_interpolator->double_prime(x);\r\n }\r\n\r\n std::pair support() const\r\n {\r\n return { Real(-p + 1), Real(p) };\r\n }\r\n\r\n int64_t bytes() const\r\n {\r\n return m_interpolator->bytes() + sizeof(*this);\r\n }\r\n\r\n private:\r\n std::shared_ptr m_interpolator;\r\n };\r\n\r\n}\r\n#endif\r\n", "meta": {"hexsha": "7eae27daa5a856b88b3d21b323e8d1c9f5011a6f", "size": 9924, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/boost/include/boost/math/special_functions/daubechies_wavelet.hpp", "max_stars_repo_name": "kindlychung/mediasoup-sfu-cpp", "max_stars_repo_head_hexsha": "f69d2f48f7edbf4f0c57244280a47bea985f39cf", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 80.0, "max_stars_repo_stars_event_min_datetime": "2021-09-07T12:44:32.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T01:22:19.000Z", "max_issues_repo_path": "deps/boost/include/boost/math/special_functions/daubechies_wavelet.hpp", "max_issues_repo_name": "kindlychung/mediasoup-sfu-cpp", "max_issues_repo_head_hexsha": "f69d2f48f7edbf4f0c57244280a47bea985f39cf", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2021-12-23T02:49:42.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-15T05:28:24.000Z", "max_forks_repo_path": "deps/boost/include/boost/math/special_functions/daubechies_wavelet.hpp", "max_forks_repo_name": "kindlychung/mediasoup-sfu-cpp", "max_forks_repo_head_hexsha": "f69d2f48f7edbf4f0c57244280a47bea985f39cf", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 25.0, "max_forks_repo_forks_event_min_datetime": "2021-09-14T06:24:25.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T06:55:07.000Z", "avg_line_length": 39.0708661417, "max_line_length": 181, "alphanum_fraction": 0.5020153164, "num_tokens": 2704, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.24187998375596861}} {"text": "// ====================================================================\n// This file is part of FlexibleSUSY.\n//\n// FlexibleSUSY is free software: you can redistribute it and/or modify\n// it under the terms of the GNU General Public License as published\n// by the Free Software Foundation, either version 3 of the License,\n// or (at your option) any later version.\n//\n// FlexibleSUSY is distributed in the hope that it will be useful, but\n// WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n// General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with FlexibleSUSY. If not, see\n// .\n// ====================================================================\n\n// File generated at Sat 26 May 2018 14:35:26\n\n/**\n * @file ScalarSingletDM_Z2_two_scale_ewsb_solver.hpp\n *\n * @brief contains class for solving EWSB when two-scale algorithm is used\n *\n * This file was generated at Sat 26 May 2018 14:35:26 with FlexibleSUSY\n * 2.0.1 (git commit: unknown) and SARAH 4.12.2 .\n */\n\n#ifndef ScalarSingletDM_Z2_TWO_SCALE_EWSB_SOLVER_H\n#define ScalarSingletDM_Z2_TWO_SCALE_EWSB_SOLVER_H\n\n#include \"ScalarSingletDM_Z2_ewsb_solver.hpp\"\n#include \"ScalarSingletDM_Z2_ewsb_solver_interface.hpp\"\n#include \"error.hpp\"\n\n#include \n\nnamespace flexiblesusy {\n\nclass EWSB_solver;\nclass Two_scale;\n\nclass ScalarSingletDM_Z2_mass_eigenstates;\n\ntemplate<>\nclass ScalarSingletDM_Z2_ewsb_solver : public ScalarSingletDM_Z2_ewsb_solver_interface {\npublic:\n ScalarSingletDM_Z2_ewsb_solver() = default;\n ScalarSingletDM_Z2_ewsb_solver(const ScalarSingletDM_Z2_ewsb_solver&) = default;\n ScalarSingletDM_Z2_ewsb_solver(ScalarSingletDM_Z2_ewsb_solver&&) = default;\n virtual ~ScalarSingletDM_Z2_ewsb_solver() {}\n ScalarSingletDM_Z2_ewsb_solver& operator=(const ScalarSingletDM_Z2_ewsb_solver&) = default;\n ScalarSingletDM_Z2_ewsb_solver& operator=(ScalarSingletDM_Z2_ewsb_solver&&) = default;\n\n virtual void set_loop_order(int l) override { loop_order = l; }\n virtual void set_number_of_iterations(int n) override { number_of_iterations = n; }\n virtual void set_precision(double p) override { precision = p; }\n\n virtual int get_loop_order() const override { return loop_order; }\n virtual int get_number_of_iterations() const override { return number_of_iterations; }\n virtual double get_precision() const override { return precision; }\n\n virtual int solve(ScalarSingletDM_Z2_mass_eigenstates&) override;\nprivate:\n static const int number_of_ewsb_equations = 1;\n using EWSB_vector_t = Eigen::Matrix;\n\n class EEWSBStepFailed : public Error {\n public:\n virtual ~EEWSBStepFailed() {}\n virtual std::string what() const { return \"Could not perform EWSB step.\"; }\n };\n\n int number_of_iterations{100}; ///< maximum number of iterations\n int loop_order{2}; ///< loop order to solve EWSB at\n double precision{1.e-5}; ///< precision goal\n\n void set_ewsb_solution(ScalarSingletDM_Z2_mass_eigenstates&, const EWSB_solver*);\n template void set_best_ewsb_solution(ScalarSingletDM_Z2_mass_eigenstates&, It, It);\n\n int solve_tree_level(ScalarSingletDM_Z2_mass_eigenstates&);\n int solve_iteratively(ScalarSingletDM_Z2_mass_eigenstates&);\n int solve_iteratively_at(ScalarSingletDM_Z2_mass_eigenstates&, int);\n int solve_iteratively_with(ScalarSingletDM_Z2_mass_eigenstates&, EWSB_solver*, const EWSB_vector_t&);\n\n EWSB_vector_t initial_guess(const ScalarSingletDM_Z2_mass_eigenstates&) const;\n EWSB_vector_t tadpole_equations(const ScalarSingletDM_Z2_mass_eigenstates&) const;\n EWSB_vector_t ewsb_step(const ScalarSingletDM_Z2_mass_eigenstates&) const;\n};\n\n} // namespace flexiblesusy\n\n#endif\n", "meta": {"hexsha": "757c601df1021d0e98a5cffacaf0614e3e318fca", "size": 3874, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contrib/MassSpectra/flexiblesusy/models/ScalarSingletDM_Z2/ScalarSingletDM_Z2_two_scale_ewsb_solver.hpp", "max_stars_repo_name": "sebhoof/gambit_1.5", "max_stars_repo_head_hexsha": "f9a3f788e3331067c555ae1a030420e903c6fdcd", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-09-08T20:05:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-26T07:57:56.000Z", "max_issues_repo_path": "contrib/MassSpectra/flexiblesusy/models/ScalarSingletDM_Z2/ScalarSingletDM_Z2_two_scale_ewsb_solver.hpp", "max_issues_repo_name": "sebhoof/gambit_1.5", "max_issues_repo_head_hexsha": "f9a3f788e3331067c555ae1a030420e903c6fdcd", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": "2020-10-19T09:56:17.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-28T06:12:03.000Z", "max_forks_repo_path": "contrib/MassSpectra/flexiblesusy/models/ScalarSingletDM_Z2/ScalarSingletDM_Z2_two_scale_ewsb_solver.hpp", "max_forks_repo_name": "patscott/gambit_1.4", "max_forks_repo_head_hexsha": "a50537419918089effc207e8b206489a5cfd2258", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2020-09-08T02:23:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-23T08:48:04.000Z", "avg_line_length": 40.7789473684, "max_line_length": 104, "alphanum_fraction": 0.7527103769, "num_tokens": 1007, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.3775406687981454, "lm_q1q2_score": 0.2418660890923267}} {"text": "// Copyright (c) 2012 Andre Martins\n// All Rights Reserved.\n//\n// This file is part of AD3 2.0.\n//\n// AD3 2.0 is free software: you can redistribute it and/or modify\n// it under the terms of the GNU Lesser General Public License as published by\n// the Free Software Foundation, either version 3 of the License, or\n// (at your option) any later version.\n//\n// AD3 2.0 is distributed in the hope that it will be useful,\n// but WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n// GNU Lesser General Public License for more details.\n//\n// You should have received a copy of the GNU Lesser General Public License\n// along with AD3 2.0. If not, see .\n\n\n\n#ifndef _AD3_QP_HPP_\n#define _AD3_QP_HPP_\n\n\n#include \n#include \n#include \n#include \"dd_grlab.hpp\"\n\n\n#define NEARLY_ZERO_TOL(a,tol) (((a)<=(tol)) && ((a)>=(-(tol))))\n#define NEARLY_EQ_TOL(a,b,tol) (((a)-(b))*((a)-(b))<=(tol))\n#define num_max_iterations_QP_ 10\n\nstruct admm_vertex_program_general:public admm_vertex_program {\n\nvoid Maximize(vertex_type& vertex, vec additional_log_potentials, vec variable_log_potentials,\n Configuration &configuration,\n double *value) {\n \n vector states(vertex.data().nvars,-1);\n int best = -1;\n *value = -1e12;\n for (int index = 0;\n index < additional_log_potentials.size();\n ++index) {\n //cout<<\"enter loop 1 in Maximize ..\"< *value) {\n configuration = index;\n *value = score;\n }\n }\n //cout<<\"exit loop 1 in Maximize ..\"<= 0);\n \n }\n\n\n \n//Configuration CreateConfiguration()\n//{ Configuration *configuration = new Configuration;\n// return *Configuration;\n //}\n \n void DeleteConfiguration(Configuration &configuration) {\n configuration = -1;\n }\n\n\n bool InvertAfterInsertion(vertex_type& vertex, vector & inverse_A_,\n const vector &active_set, const Configuration &inserted_element) {\n\n vector inverse_A = inverse_A_;\n int size_A = active_set.size() + 1;\n vector r(size_A);\n\n r[0] = 1.0;\n for (int i = 0; i < active_set.size(); ++i) {\n // Count how many variable values the new assignment\n // have in common with the i-th assignment.\n int num_common_values = CountCommonValues(vertex, active_set[i], inserted_element);\n r[i+1] = static_cast(num_common_values);\n }\n\n double r0 = static_cast(CountCommonValues(vertex,\n inserted_element, inserted_element));\n double s = r0;\n for (int i = 0; i < size_A; ++i) {\n if (r[i] == 0.0) continue;\n s -= r[i] * r[i] * inverse_A[i * size_A + i];\n for (int j = i+1; j < size_A; ++j) {\n if (r[j] == 0.0) continue;\n s -= 2 * r[i] * r[j] * inverse_A[i * size_A + j];\n }\n }\n\n if (NEARLY_ZERO_TOL(s, 1e-9)) {\n if (opts.verbose> 2) {\n cout << \"Warning: updated matrix will become singular after insertion.\"\n << endl;\n }\n return false;\n }\n\n double invs = 1.0 / s;\n vector d(size_A, 0.0);\n for (int i = 0; i < size_A; ++i) {\n if (r[i] == 0.0) continue;\n for (int j = 0; j < size_A; ++j) {\n d[j] += inverse_A[i * size_A + j] * r[i];\n }\n }\n\n int size_A_after = size_A + 1;\n inverse_A_.resize(size_A_after * size_A_after);\n for (int i = 0; i < size_A; ++i) {\n for (int j = 0; j < size_A; ++j) {\n inverse_A_[i * size_A_after + j] = inverse_A[i * size_A + j] +\n invs * d[i] * d[j];\n }\n inverse_A_[i * size_A_after + size_A] = -invs * d[i];\n inverse_A_[size_A * size_A_after + i] = -invs * d[i];\n }\n inverse_A_[size_A * size_A_after + size_A] = invs;\n\n return true;\n}\n\nvoid InvertAfterRemoval(vector &inverse_A_,const vector &active_set,\n int removed_index) {\n vector inverse_A = inverse_A_;\n int size_A = active_set.size() + 1;\n vector r(size_A);\n\n ++removed_index; // Index in A has an offset of 1.\n double invs = inverse_A[removed_index * size_A + removed_index];\n assert(!NEARLY_ZERO_TOL(invs, 1e-12));\n double s = 1.0 / invs;\n vector d(size_A - 1, 0.0);\n int k = 0;\n for (int i = 0; i < size_A; ++i) {\n if (i == removed_index) continue;\n d[k] = -s * inverse_A[removed_index * size_A + i];\n ++k;\n }\n\n int size_A_after = size_A - 1;\n inverse_A_.resize(size_A_after * size_A_after);\n k = 0;\n for (int i = 0; i < size_A; ++i) {\n if (i == removed_index) continue;\n int l = 0;\n for (int j = 0; j < size_A; ++j) {\n if (j == removed_index) continue;\n inverse_A_[k * size_A_after + l] = inverse_A[i * size_A + j] -\n invs * d[k] * d[l];\n ++l;\n }\n ++k;\n }\n}\n\n// Compute Mnz'*Mnz\nvoid ComputeActiveSetSimilarities(vertex_type& vertex,\n const vector &active_set,\n vector *similarities) {\n int size = active_set.size();\n\n // Compute similarity matrix.\n similarities->resize(size * size);\n (*similarities)[0] = 0.0;\n for (int i = 0; i < active_set.size(); ++i) {\n (*similarities)[i*size + i] = static_cast(\n CountCommonValues(vertex,active_set[i], active_set[i]) );\n for (int j = i+1; j < active_set.size(); ++j) {\n // Count how many variable values the i-th and j-th \n // assignments have in common.\n int num_common_values = CountCommonValues(vertex,active_set[i], active_set[j]);\n (*similarities)[i*size + j] = num_common_values;\n (*similarities)[j*size + i] = num_common_values;\n }\n }\n}\n\nvoid ComputeMarginalsFromSparseDistribution( vertex_type& vertex, \n const vector &active_set,\n const vector &distribution,\n vec &variable_posteriors,\n vec &additional_posteriors) {\n //cout<<\"enter cmfsp ...\"< states(vertex.data().nvars, -1);\n get_configuration_states(vertex, configuration, &states);\n \n int offset = 0;\n \n for (int k = 0; k < vertex.data().nvars; ++k) \n { //cout<<\"loop enter update marginals..\"<size() == states2->size());\n int count = 0;\n vector states1(vertex.data().nvars, -1); \n vector states2(vertex.data().nvars, -1);\n get_configuration_states(vertex, configuration1, &states1);\n get_configuration_states(vertex, configuration2, &states2);\n for(int i = 0; i< vertex.data().nvars; i++)\n { //cout<<\"enter loop in get count common values...\"< states(vertex.data().nvars, -1);\n get_configuration_states(vertex, configuration, &states);\n *value = 0.0;\n int offset = 0;\n for (int i = 0;i *similarities,\n vector *eigenvalues) {\n\n int size = sqrt(similarities->size());\n\n Eigen::SelfAdjointEigenSolver es;\n Eigen::MatrixXd sim(size, size);\n int t = 0;\n for (int i = 0; i < size; ++i) {\n for (int j = 0; j < size; ++j) {\n sim(i, j) = (*similarities)[t];\n ++t;\n }\n }\n es.compute(sim);\n const Eigen::VectorXd &eigvals = es.eigenvalues(); \n eigenvalues->resize(size);\n for (int i = 0; i < size; ++i) {\n (*eigenvalues)[i] = eigvals[i];\n }\n const Eigen::MatrixXd &eigvectors = es.eigenvectors().transpose();\n t = 0;\n for (int i = 0; i < size; ++i) {\n for (int j = 0; j < size; ++j) {\n (*similarities)[t] = eigvectors(i, j);\n ++t;\n }\n }\n\n}\n\n\nvoid SolveQP(vertex_type& vertex,const gather_type& total,\n vec& beliefs, vec& variable_posteriors,\n vec& additional_posteriors) {\n vertex_data& vdata = vertex.data(); \n beliefs = vdata.potentials;\n int num_configurations = vdata.potentials.size();\n for (int index_configuration = 0;\n index_configuration < num_configurations;\n ++index_configuration) {\n vector states(vdata.nvars, -1);\n // This could be made more efficient by defining an iterator over factor\n // configurations.\n get_configuration_states(vertex, index_configuration, &states);\n int offset = 0;\n for (int k = 0; k < vdata.nvars; ++k) {\n //cout< active_set_;\n vector distribution_;\n vector inverse_A_;\n // Initialize the active set.\n \n if (active_set_.size() == 0) {\n variable_posteriors.resize(variable_log_potentials.size()); \n additional_posteriors.resize(additional_log_potentials.size()); \n distribution_.clear();\n // Initialize by solving the LP, discarding the quadratic\n // term.\n Configuration configuration = -1;\n double value;\n //cout<<\"Before Maximize ..\"<(\n -CountCommonValues(vertex,configuration, configuration));\n //cout<<\"count common vals in solve QP ...\"< z;\n int num_max_iterations = num_max_iterations_QP_;\n double tau = 0;\n for (int iter = 0; iter < num_max_iterations; ++iter) {\n bool same_as_before = true;\n bool unbounded = false;\n if (changed_active_set) {\n // Recompute vector b.\n vector b(active_set_.size() + 1, 0.0);\n b[0] = 1.0;\n for (int i = 0; i < active_set_.size(); ++i) {\n const Configuration &configuration = active_set_[i];\n double score;\n // cout<<\"Before eval in solveQP ...\"< 2) {\n cout << \"Warning: value - tau = \"\n << value - tau << \" \" << value << \" \" << tau\n << endl;\n }\n // We have found the solution;\n // the distribution, active set, and inv(A)\n // are cached for the next round.\n DeleteConfiguration(configuration);\n\n // Just in case, clean the cache.\n // This may prevent eventual numerical problems in the future.\n for (int j = 0; j < active_set_.size(); ++j) {\n if (j == k) continue; // This configuration was deleted already.\n DeleteConfiguration(active_set_[j]);\n }\n active_set_.clear();\n inverse_A_.clear();\n distribution_.clear();\n\n // Return.\n return;\n }\n }\n z.push_back(0.0);\n distribution_ = z;\n\n // Update inv(A).\n bool singular = !InvertAfterInsertion(vertex, inverse_A_, active_set_, configuration);\n //cout<<\"Invertafterinsertion in solveQP ..\"< similarities(active_set_.size() * active_set_.size());\n ComputeActiveSetSimilarities(vertex, active_set_, &similarities);\n \n //cout<<\"compute active similarities in solveQP ..\"< padded_similarities((active_set_.size()+2) * \n (active_set_.size()+2), 1.0);\n for (int i = 0; i < active_set_.size(); ++i) {\n for (int j = 0; j < active_set_.size(); ++j) {\n padded_similarities[(i+1)*(active_set_.size()+2) + (j+1)] =\n similarities[i*active_set_.size() + j];\n }\n }\n padded_similarities[0] = 0.0;\n for (int i = 0; i < active_set_.size(); ++i) {\n double value = static_cast(\n CountCommonValues(vertex, configuration, active_set_[i]));\n padded_similarities[(i+1)*(active_set_.size()+2) +\n (active_set_.size()+1)] = value;\n padded_similarities[(active_set_.size()+1)*(active_set_.size()+2) +\n (i+1)] = value;\n }\n double value = static_cast(\n CountCommonValues(vertex, configuration, configuration));\n padded_similarities[(active_set_.size()+1)*(active_set_.size()+2) +\n (active_set_.size()+1)] = value;\n\n vector eigenvalues(active_set_.size()+2);\n EigenDecompose(&padded_similarities, &eigenvalues);\n int zero_eigenvalue = -1;\n for (int i = 0; i < active_set_.size()+2; ++i) {\n if (NEARLY_EQ_TOL(eigenvalues[i], 0.0, 1e-9)) {\n if (zero_eigenvalue >= 0) {\n // If this happens, something failed. Maybe a numerical problem\n // may cause this. In that case, just give up, clean the cache\n // and return. Hopefully the next iteration will fix it.\n cout << \"Multiple zero eigenvalues: \"\n << eigenvalues[zero_eigenvalue] << \" and \"\n << eigenvalues[i] << endl;\n cout << \"Warning: Giving up.\" << endl;\n // Clean the cache.\n for (int j = 0; j < active_set_.size(); ++j) {\n DeleteConfiguration(active_set_[j]);\n }\n active_set_.clear();\n inverse_A_.clear();\n distribution_.clear();\n return;\n }\n zero_eigenvalue = i;\n }\n }\n assert(zero_eigenvalue >= 0);\n vector configurations_to_remove;\n for (int j = 1; j < active_set_.size()+1; ++j) {\n double value = padded_similarities[zero_eigenvalue*(active_set_.size()+2) + j];\n if (!NEARLY_EQ_TOL(value, 0.0, 1e-9)) {\n configurations_to_remove.push_back(j-1);\n }\n }\n if (opts.verbose > 2) {\n cout << \"Pick a configuration to remove (\" << configurations_to_remove.size()\n << \" out of \" << active_set_.size() << \").\" << endl;\n }\n\n assert(configurations_to_remove.size() >= 1);\n int j = configurations_to_remove[0];\n\n // Update inv(A).\n InvertAfterRemoval(inverse_A_, active_set_, j);\n\n // Remove blocking constraint from the active set.\n DeleteConfiguration(active_set_[j]); // Delete configutation.\n active_set_.erase(active_set_.begin() + j);\n\n singular = !InvertAfterInsertion(vertex, inverse_A_, active_set_, configuration);\n assert(!singular);\n }\n\n // Insert configuration to active set.\n if (opts.verbose > 2) {\n cout << \"Inserted one element to the active set (iteration \"\n << iter << \").\" << endl;\n }\n active_set_.push_back(configuration);\n changed_active_set = true;\n } \n } else {\n // Solution has changed from the previous iteration.\n // Look for blocking constraints.\n int blocking = -1;\n bool exist_blocking = false;\n double alpha = 1.0;\n for (int i = 0; i < active_set_.size(); ++i) {\n assert(distribution_[i] >= -1e-12);\n if (z[i] >= distribution_[i]) continue;\n if (z[i] < 0) exist_blocking = true;\n double tmp = distribution_[i] / (distribution_[i] - z[i]);\n if (blocking < 0 || tmp < alpha) {\n alpha = tmp;\n blocking = i;\n }\n }\n\n if (!exist_blocking) {\n // No blocking constraints.\n assert(!unbounded);\n distribution_ = z;\n alpha = 1.0;\n changed_active_set = false;\n } else {\n if (alpha > 1.0 && !unbounded) alpha = 1.0;\n // Interpolate between factor_posteriors_[i] and z.\n if (alpha == 1.0) {\n distribution_ = z;\n } else {\n for (int i = 0; i < active_set_.size(); ++i) {\n z[i] = (1 - alpha) * distribution_[i] + alpha * z[i];\n distribution_[i] = z[i];\n }\n }\n\n // Update inv(A).\n InvertAfterRemoval(inverse_A_, active_set_, blocking);\n\n // Remove blocking constraint from the active set.\n if (opts.verbose > 2) {\n cout << \"Removed one element to the active set (iteration \"\n << iter << \").\" << endl;\n }\n\n DeleteConfiguration(active_set_[blocking]); // Delete configutation.\n active_set_.erase(active_set_.begin() + blocking);\n\n z.erase(z.begin() + blocking);\n distribution_.erase(distribution_.begin() + blocking);\n changed_active_set = true;\n for (int i = 0; i < distribution_.size(); ++i) {\n assert(distribution_[i] > -1e-16);\n }\n }\n }\n }\n\n // Maximum number of iterations reached.\n // Return the best existing solution by computing the variable marginals \n // from the full distribution stored in z.\n //assert(false);\n ComputeMarginalsFromSparseDistribution(vertex, active_set_,\n z,\n variable_posteriors,\n additional_posteriors); \n };\n\n};\n#endif\n", "meta": {"hexsha": "dae0cf069b987f1d3129d6a3e83846f2fb73eea4", "size": 23305, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "toolkits/graphical_models/ad3_qp.hpp", "max_stars_repo_name": "zgdahai/graphlabapi", "max_stars_repo_head_hexsha": "7d66bbda82d4d44cded35f9438e1c9359b0ca64e", "max_stars_repo_licenses": ["ECL-2.0", "Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2016-11-07T05:47:18.000Z", "max_stars_repo_stars_event_max_datetime": "2016-11-07T05:47:18.000Z", "max_issues_repo_path": "toolkits/graphical_models/ad3_qp.hpp", "max_issues_repo_name": "keerthanashanmugam/graphlabapi", "max_issues_repo_head_hexsha": "7d66bbda82d4d44cded35f9438e1c9359b0ca64e", "max_issues_repo_licenses": ["ECL-2.0", "Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "toolkits/graphical_models/ad3_qp.hpp", "max_forks_repo_name": "keerthanashanmugam/graphlabapi", "max_forks_repo_head_hexsha": "7d66bbda82d4d44cded35f9438e1c9359b0ca64e", "max_forks_repo_licenses": ["ECL-2.0", "Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.5855572998, "max_line_length": 127, "alphanum_fraction": 0.5738253594, "num_tokens": 5794, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.24186162614190215}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\n\n// Copyright (c) 2011-2015 Barend Gehrels, Amsterdam, the Netherlands.\n\n// This file was modified by Oracle on 2015.\n// Modifications copyright (c) 2015 Oracle and/or its affiliates.\n\n// Contributed and/or modified by Menelaos Karavelas, on behalf of Oracle\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_GEOMETRY_ALGORITHMS_DETAIL_PARTITION_HPP\n#define BOOST_GEOMETRY_ALGORITHMS_DETAIL_PARTITION_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n\nnamespace boost { namespace geometry\n{\n\nnamespace detail { namespace partition\n{\n\ntemplate \ninline void divide_box(Box const& box, Box& lower_box, Box& upper_box)\n{\n typedef typename coordinate_type::type ctype;\n\n // Divide input box into two parts, e.g. left/right\n ctype two = 2;\n ctype mid = (geometry::get(box)\n + geometry::get(box)) / two;\n\n lower_box = box;\n upper_box = box;\n geometry::set(lower_box, mid);\n geometry::set(upper_box, mid);\n}\n\n// Divide forward_range into three subsets: lower, upper and oversized\n// (not-fitting)\n// (lower == left or bottom, upper == right or top)\ntemplate \ninline void divide_into_subsets(Box const& lower_box,\n Box const& upper_box,\n IteratorVector const& input,\n IteratorVector& lower,\n IteratorVector& upper,\n IteratorVector& exceeding)\n{\n typedef typename boost::range_iterator\n <\n IteratorVector const\n >::type it_type;\n\n for(it_type it = boost::begin(input); it != boost::end(input); ++it)\n {\n bool const lower_overlapping = OverlapsPolicy::apply(lower_box, **it);\n bool const upper_overlapping = OverlapsPolicy::apply(upper_box, **it);\n\n if (lower_overlapping && upper_overlapping)\n {\n exceeding.push_back(*it);\n }\n else if (lower_overlapping)\n {\n lower.push_back(*it);\n }\n else if (upper_overlapping)\n {\n upper.push_back(*it);\n }\n else\n {\n // Is nowhere. That is (since 1.58) possible, it might be\n // skipped by the OverlapsPolicy to enhance performance\n }\n }\n}\n\ntemplate\n<\n typename ExpandPolicy,\n typename Box,\n typename IteratorVector\n>\ninline void expand_with_elements(Box& total, IteratorVector const& input)\n{\n typedef typename boost::range_iterator::type it_type;\n for(it_type it = boost::begin(input); it != boost::end(input); ++it)\n {\n ExpandPolicy::apply(total, **it);\n }\n}\n\n\n// Match forward_range with itself\ntemplate \ninline void handle_one(IteratorVector const& input, Policy& policy)\n{\n if (boost::size(input) == 0)\n {\n return;\n }\n\n typedef typename boost::range_iterator::type it_type;\n\n // Quadratic behaviour at lowest level (lowest quad, or all exceeding)\n for (it_type it1 = boost::begin(input); it1 != boost::end(input); ++it1)\n {\n it_type it2 = it1;\n for (++it2; it2 != boost::end(input); ++it2)\n {\n policy.apply(**it1, **it2);\n }\n }\n}\n\n// Match forward range 1 with forward range 2\ntemplate\n<\n typename Policy,\n typename IteratorVector1,\n typename IteratorVector2\n>\ninline void handle_two(IteratorVector1 const& input1,\n IteratorVector2 const& input2,\n Policy& policy)\n{\n typedef typename boost::range_iterator\n <\n IteratorVector1 const\n >::type iterator_type1;\n\n typedef typename boost::range_iterator\n <\n IteratorVector2 const\n >::type iterator_type2;\n\n if (boost::size(input1) == 0 || boost::size(input2) == 0)\n {\n return;\n }\n\n for(iterator_type1 it1 = boost::begin(input1);\n it1 != boost::end(input1);\n ++it1)\n {\n for(iterator_type2 it2 = boost::begin(input2);\n it2 != boost::end(input2);\n ++it2)\n {\n policy.apply(**it1, **it2);\n }\n }\n}\n\ntemplate \ninline bool recurse_ok(IteratorVector const& input,\n std::size_t min_elements, std::size_t level)\n{\n return boost::size(input) >= min_elements\n && level < 100;\n}\n\ntemplate \ninline bool recurse_ok(IteratorVector1 const& input1,\n IteratorVector2 const& input2,\n std::size_t min_elements, std::size_t level)\n{\n return boost::size(input1) >= min_elements\n && recurse_ok(input2, min_elements, level);\n}\n\ntemplate\n<\n typename IteratorVector1,\n typename IteratorVector2,\n typename IteratorVector3\n>\ninline bool recurse_ok(IteratorVector1 const& input1,\n IteratorVector2 const& input2,\n IteratorVector3 const& input3,\n std::size_t min_elements, std::size_t level)\n{\n return boost::size(input1) >= min_elements\n && recurse_ok(input2, input3, min_elements, level);\n}\n\ntemplate\n<\n int Dimension,\n typename Box,\n typename OverlapsPolicy1,\n typename OverlapsPolicy2,\n typename ExpandPolicy1,\n typename ExpandPolicy2,\n typename VisitBoxPolicy\n>\nclass partition_two_ranges;\n\n\ntemplate\n<\n int Dimension,\n typename Box,\n typename OverlapsPolicy,\n typename ExpandPolicy,\n typename VisitBoxPolicy\n>\nclass partition_one_range\n{\n template \n static inline Box get_new_box(IteratorVector const& input)\n {\n Box box;\n geometry::assign_inverse(box);\n expand_with_elements(box, input);\n return box;\n }\n\n template \n static inline void next_level(Box const& box,\n IteratorVector const& input,\n std::size_t level, std::size_t min_elements,\n Policy& policy, VisitBoxPolicy& box_policy)\n {\n if (recurse_ok(input, min_elements, level))\n {\n partition_one_range\n <\n 1 - Dimension,\n Box,\n OverlapsPolicy,\n ExpandPolicy,\n VisitBoxPolicy\n >::apply(box, input, level + 1, min_elements, policy, box_policy);\n }\n else\n {\n handle_one(input, policy);\n }\n }\n\n // Function to switch to two forward ranges if there are\n // geometries exceeding the separation line\n template \n static inline void next_level2(Box const& box,\n IteratorVector const& input1,\n IteratorVector const& input2,\n std::size_t level, std::size_t min_elements,\n Policy& policy, VisitBoxPolicy& box_policy)\n {\n if (recurse_ok(input1, input2, min_elements, level))\n {\n partition_two_ranges\n <\n 1 - Dimension,\n Box,\n OverlapsPolicy, OverlapsPolicy,\n ExpandPolicy, ExpandPolicy,\n VisitBoxPolicy\n >::apply(box, input1, input2, level + 1, min_elements,\n policy, box_policy);\n }\n else\n {\n handle_two(input1, input2, policy);\n }\n }\n\npublic :\n template \n static inline void apply(Box const& box,\n IteratorVector const& input,\n std::size_t level,\n std::size_t min_elements,\n Policy& policy, VisitBoxPolicy& box_policy)\n {\n box_policy.apply(box, level);\n\n Box lower_box, upper_box;\n divide_box(box, lower_box, upper_box);\n\n IteratorVector lower, upper, exceeding;\n divide_into_subsets(lower_box, upper_box,\n input, lower, upper, exceeding);\n\n if (boost::size(exceeding) > 0)\n {\n // Get the box of exceeding-only\n Box exceeding_box = get_new_box(exceeding);\n\n // Recursively do exceeding elements only, in next dimension they\n // will probably be less exceeding within the new box\n next_level(exceeding_box, exceeding, level, min_elements,\n policy, box_policy);\n\n // Switch to two forward ranges, combine exceeding with\n // lower resp upper, but not lower/lower, upper/upper\n next_level2(exceeding_box, exceeding, lower, level, min_elements,\n policy, box_policy);\n next_level2(exceeding_box, exceeding, upper, level, min_elements,\n policy, box_policy);\n }\n\n // Recursively call operation both parts\n next_level(lower_box, lower, level, min_elements, policy, box_policy);\n next_level(upper_box, upper, level, min_elements, policy, box_policy);\n }\n};\n\ntemplate\n<\n int Dimension,\n typename Box,\n typename OverlapsPolicy1,\n typename OverlapsPolicy2,\n typename ExpandPolicy1,\n typename ExpandPolicy2,\n typename VisitBoxPolicy\n>\nclass partition_two_ranges\n{\n template\n <\n typename Policy,\n typename IteratorVector1,\n typename IteratorVector2\n >\n static inline void next_level(Box const& box,\n IteratorVector1 const& input1,\n IteratorVector2 const& input2,\n std::size_t level, std::size_t min_elements,\n Policy& policy, VisitBoxPolicy& box_policy)\n {\n partition_two_ranges\n <\n 1 - Dimension,\n Box,\n OverlapsPolicy1,\n OverlapsPolicy2,\n ExpandPolicy1,\n ExpandPolicy2,\n VisitBoxPolicy\n >::apply(box, input1, input2, level + 1, min_elements,\n policy, box_policy);\n }\n\n template \n static inline Box get_new_box(IteratorVector const& input)\n {\n Box box;\n geometry::assign_inverse(box);\n expand_with_elements(box, input);\n return box;\n }\n\n template \n static inline Box get_new_box(IteratorVector1 const& input1,\n IteratorVector2 const& input2)\n {\n Box box = get_new_box(input1);\n expand_with_elements(box, input2);\n return box;\n }\n\npublic :\n template\n <\n typename Policy,\n typename IteratorVector1,\n typename IteratorVector2\n >\n static inline void apply(Box const& box,\n IteratorVector1 const& input1,\n IteratorVector2 const& input2,\n std::size_t level,\n std::size_t min_elements,\n Policy& policy, VisitBoxPolicy& box_policy)\n {\n box_policy.apply(box, level);\n\n Box lower_box, upper_box;\n divide_box(box, lower_box, upper_box);\n\n IteratorVector1 lower1, upper1, exceeding1;\n IteratorVector2 lower2, upper2, exceeding2;\n divide_into_subsets(lower_box, upper_box,\n input1, lower1, upper1, exceeding1);\n divide_into_subsets(lower_box, upper_box,\n input2, lower2, upper2, exceeding2);\n\n if (boost::size(exceeding1) > 0)\n {\n // All exceeding from 1 with 2:\n\n if (recurse_ok(exceeding1, exceeding2, min_elements, level))\n {\n Box exceeding_box = get_new_box(exceeding1, exceeding2);\n next_level(exceeding_box, exceeding1, exceeding2, level,\n min_elements, policy, box_policy);\n }\n else\n {\n handle_two(exceeding1, exceeding2, policy);\n }\n\n // All exceeding from 1 with lower and upper of 2:\n\n // (Check sizes of all three forward ranges to avoid recurse into\n // the same combinations again and again)\n if (recurse_ok(lower2, upper2, exceeding1, min_elements, level))\n {\n Box exceeding_box = get_new_box(exceeding1);\n next_level(exceeding_box, exceeding1, lower2, level,\n min_elements, policy, box_policy);\n next_level(exceeding_box, exceeding1, upper2, level,\n min_elements, policy, box_policy);\n }\n else\n {\n handle_two(exceeding1, lower2, policy);\n handle_two(exceeding1, upper2, policy);\n }\n }\n\n if (boost::size(exceeding2) > 0)\n {\n // All exceeding from 2 with lower and upper of 1:\n if (recurse_ok(lower1, upper1, exceeding2, min_elements, level))\n {\n Box exceeding_box = get_new_box(exceeding2);\n next_level(exceeding_box, lower1, exceeding2, level,\n min_elements, policy, box_policy);\n next_level(exceeding_box, upper1, exceeding2, level,\n min_elements, policy, box_policy);\n }\n else\n {\n handle_two(lower1, exceeding2, policy);\n handle_two(upper1, exceeding2, policy);\n }\n }\n\n if (recurse_ok(lower1, lower2, min_elements, level))\n {\n next_level(lower_box, lower1, lower2, level,\n min_elements, policy, box_policy);\n }\n else\n {\n handle_two(lower1, lower2, policy);\n }\n if (recurse_ok(upper1, upper2, min_elements, level))\n {\n next_level(upper_box, upper1, upper2, level,\n min_elements, policy, box_policy);\n }\n else\n {\n handle_two(upper1, upper2, policy);\n }\n }\n};\n\nstruct visit_no_policy\n{\n template \n static inline void apply(Box const&, std::size_t )\n {}\n};\n\nstruct include_all_policy\n{\n template \n static inline bool apply(Item const&)\n {\n return true;\n }\n};\n\n\n}} // namespace detail::partition\n\ntemplate\n<\n typename Box,\n typename ExpandPolicy1,\n typename OverlapsPolicy1,\n typename ExpandPolicy2 = ExpandPolicy1,\n typename OverlapsPolicy2 = OverlapsPolicy1,\n typename IncludePolicy1 = detail::partition::include_all_policy,\n typename IncludePolicy2 = detail::partition::include_all_policy,\n typename VisitBoxPolicy = detail::partition::visit_no_policy\n>\nclass partition\n{\n template\n <\n typename ExpandPolicy,\n typename IncludePolicy,\n typename ForwardRange,\n typename IteratorVector\n >\n static inline void expand_to_range(ForwardRange const& forward_range,\n Box& total, IteratorVector& iterator_vector)\n {\n for(typename boost::range_iterator::type it\n = boost::begin(forward_range);\n it != boost::end(forward_range);\n ++it)\n {\n if (IncludePolicy::apply(*it))\n {\n ExpandPolicy::apply(total, *it);\n iterator_vector.push_back(it);\n }\n }\n }\n\npublic :\n template \n static inline void apply(ForwardRange const& forward_range,\n VisitPolicy& visitor,\n std::size_t min_elements = 16,\n VisitBoxPolicy box_visitor = detail::partition::visit_no_policy()\n )\n {\n typedef typename boost::range_iterator\n <\n ForwardRange const\n >::type iterator_type;\n\n if (std::size_t(boost::size(forward_range)) > min_elements)\n {\n std::vector iterator_vector;\n Box total;\n assign_inverse(total);\n expand_to_range(forward_range,\n total, iterator_vector);\n\n detail::partition::partition_one_range\n <\n 0, Box,\n OverlapsPolicy1,\n ExpandPolicy1,\n VisitBoxPolicy\n >::apply(total, iterator_vector, 0, min_elements,\n visitor, box_visitor);\n }\n else\n {\n for(iterator_type it1 = boost::begin(forward_range);\n it1 != boost::end(forward_range);\n ++it1)\n {\n iterator_type it2 = it1;\n for(++it2; it2 != boost::end(forward_range); ++it2)\n {\n visitor.apply(*it1, *it2);\n }\n }\n }\n }\n\n template\n <\n typename ForwardRange1,\n typename ForwardRange2,\n typename VisitPolicy\n >\n static inline void apply(ForwardRange1 const& forward_range1,\n ForwardRange2 const& forward_range2,\n VisitPolicy& visitor,\n std::size_t min_elements = 16,\n VisitBoxPolicy box_visitor\n = detail::partition::visit_no_policy()\n )\n {\n typedef typename boost::range_iterator\n <\n ForwardRange1 const\n >::type iterator_type1;\n\n typedef typename boost::range_iterator\n <\n ForwardRange2 const\n >::type iterator_type2;\n\n if (std::size_t(boost::size(forward_range1)) > min_elements\n && std::size_t(boost::size(forward_range2)) > min_elements)\n {\n std::vector iterator_vector1;\n std::vector iterator_vector2;\n Box total;\n assign_inverse(total);\n expand_to_range(forward_range1,\n total, iterator_vector1);\n expand_to_range(forward_range2,\n total, iterator_vector2);\n\n detail::partition::partition_two_ranges\n <\n 0, Box, OverlapsPolicy1, OverlapsPolicy2,\n ExpandPolicy1, ExpandPolicy2, VisitBoxPolicy\n >::apply(total, iterator_vector1, iterator_vector2,\n 0, min_elements, visitor, box_visitor);\n }\n else\n {\n for(iterator_type1 it1 = boost::begin(forward_range1);\n it1 != boost::end(forward_range1);\n ++it1)\n {\n for(iterator_type2 it2 = boost::begin(forward_range2);\n it2 != boost::end(forward_range2);\n ++it2)\n {\n visitor.apply(*it1, *it2);\n }\n }\n }\n }\n};\n\n\n}} // namespace boost::geometry\n\n#endif // BOOST_GEOMETRY_ALGORITHMS_DETAIL_PARTITION_HPP\n", "meta": {"hexsha": "8b19add47956fd9181a63fab3c2d37437134ff4a", "size": 19312, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "nheqminer/3rdparty/boost/geometry/algorithms/detail/partition.hpp", "max_stars_repo_name": "EuroLine/nheqminer", "max_stars_repo_head_hexsha": "81c7ef889bb502d16f7d1e7ef020d0592f8af945", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1210.0, "max_stars_repo_stars_event_min_datetime": "2020-08-18T07:57:36.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T15:06:05.000Z", "max_issues_repo_path": "ios/Pods/boost-for-react-native/boost/geometry/algorithms/detail/partition.hpp", "max_issues_repo_name": "c7yrus/alyson-v3", "max_issues_repo_head_hexsha": "5ad95a8f782f5f5d2fd543d44ca6a8b093395965", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 369.0, "max_issues_repo_issues_event_min_datetime": "2016-10-21T07:42:54.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-19T10:49:29.000Z", "max_forks_repo_path": "ios/Pods/boost-for-react-native/boost/geometry/algorithms/detail/partition.hpp", "max_forks_repo_name": "c7yrus/alyson-v3", "max_forks_repo_head_hexsha": "5ad95a8f782f5f5d2fd543d44ca6a8b093395965", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 534.0, "max_forks_repo_forks_event_min_datetime": "2016-10-20T21:00:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T10:02:27.000Z", "avg_line_length": 30.2695924765, "max_line_length": 79, "alphanum_fraction": 0.5945008285, "num_tokens": 4196, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.24186162614190215}} {"text": "/*\n * Copyright (c) 2020, The Regents of the University of California (Regents).\n * All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions are\n * met:\n *\n * 1. Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n *\n * 2. Redistributions in binary form must reproduce the above\n * copyright notice, this list of conditions and the following\n * disclaimer in the documentation and/or other materials provided\n * with the distribution.\n *\n * 3. Neither the name of the copyright holder nor the names of its\n * contributors may be used to endorse or promote products derived\n * from this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS AS IS\n * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\n * LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n * SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n * INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n * CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n * POSSIBILITY OF SUCH DAMAGE.\n *\n * Please contact the author(s) of this library if you have any questions.\n * Authors: David Fridovich-Keil ( dfk@eecs.berkeley.edu )\n */\n\n///////////////////////////////////////////////////////////////////////////////\n//\n// Core open-loop LQ game solver based on Basar and Olsder, Chapter 6. All\n// notation matches the text, though we shall assume that `c` (additive drift in\n// dynamics) is always `0`, which holds because these dynamics are for delta x,\n// delta us. Also, we have modified terms slightly to account for linear terms\n// in the stage cost for control, i.e.\n// control penalty i = 0.5 \\sum_j du_j^T R_ij (du_j + 2 r_ij)\n//\n// Solve a time-varying, finite horizon LQ game (finds open-loop Nash\n// feedback strategies for both players).\n//\n// Assumes that dynamics are given by\n// ``` dx_{k+1} = A_k dx_k + \\sum_i Bs[i]_k du[i]_k ```\n//\n// Returns strategies Ps, alphas. Here, all the Ps are zero (by default), and\n// only the alphas are nonzero.\n//\n// Notation is based on derivation which may be found in the PDF included in\n// this repository named \"open_loop_lq_derivation.pdf\".\n//\n///////////////////////////////////////////////////////////////////////////////\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\nnamespace ilqgames {\n\nstd::vector LQOpenLoopSolver::Solve(\n const std::vector& linearization,\n const std::vector>&\n quadraticization,\n const VectorXf& x0, std::vector* delta_xs,\n std::vector>* costates) {\n CHECK_EQ(linearization.size(), num_time_steps_);\n CHECK_EQ(quadraticization.size(), num_time_steps_);\n\n // Make sure delta_xs and costates are the right size.\n if (delta_xs) CHECK_NOTNULL(costates);\n if (costates) CHECK_NOTNULL(delta_xs);\n if (delta_xs) {\n delta_xs->resize(num_time_steps_);\n costates->resize(num_time_steps_);\n for (size_t kk = 0; kk < num_time_steps_; kk++) {\n (*delta_xs)[kk].resize(dynamics_->XDim());\n (*costates)[kk].resize(dynamics_->NumPlayers());\n for (PlayerIndex ii = 0; ii < dynamics_->NumPlayers(); ii++)\n (*costates)[kk][ii].resize(dynamics_->XDim());\n }\n }\n\n // List of player-indexed strategies (each of which is a time-indexed\n // affine state error-feedback controller). Since this is an open-loop\n // strategy, we will not change the default zero value of the P matrix.\n std::vector strategies;\n for (PlayerIndex ii = 0; ii < dynamics_->NumPlayers(); ii++)\n strategies.emplace_back(num_time_steps_, dynamics_->XDim(),\n dynamics_->UDim(ii));\n\n // Initialize m^i and M^i and index first by time and then by player.\n for (PlayerIndex ii = 0; ii < dynamics_->NumPlayers(); ii++) {\n ms_.back()[ii] = quadraticization.back()[ii].state.grad;\n Ms_.back()[ii] = quadraticization.back()[ii].state.hess;\n }\n\n // (1) Work backward in time and cache \"special\" terms.\n // NOTE: time starts from the second-to-last entry since we'll treat the\n // final entry as a terminal cost as in Basar and Olsder, ch. 6.\n for (int kk = num_time_steps_ - 2; kk >= 0; kk--) {\n // Unpack linearization and quadraticization at this time step.\n const auto& lin = linearization[kk];\n const auto& quad = quadraticization[kk];\n\n // Campute capital lambdas.\n capital_lambdas_[kk].setIdentity();\n for (PlayerIndex ii = 0; ii < dynamics_->NumPlayers(); ii++) {\n const auto control_iter = quad[ii].control.find(ii);\n CHECK(control_iter != quad[ii].control.end());\n\n chol_Rs_[kk][ii].compute(control_iter->second.hess);\n warped_Bs_[kk][ii] = chol_Rs_[kk][ii].solve(lin.Bs[ii].transpose());\n warped_rs_[kk][ii] = chol_Rs_[kk][ii].solve(control_iter->second.grad);\n capital_lambdas_[kk] += lin.Bs[ii] * warped_Bs_[kk][ii] * Ms_[kk + 1][ii];\n }\n\n // Compute inv(capital lambda).\n qr_capital_lambdas_[kk].compute(capital_lambdas_[kk]);\n\n // Compute Ms and ms.\n intermediate_terms_[kk].setZero();\n for (PlayerIndex ii = 0; ii < dynamics_->NumPlayers(); ii++) {\n intermediate_terms_[kk] -=\n lin.Bs[ii] *\n (warped_Bs_[kk][ii] * ms_[kk + 1][ii] + warped_rs_[kk][ii]);\n }\n\n for (PlayerIndex ii = 0; ii < dynamics_->NumPlayers(); ii++) {\n Ms_[kk][ii] =\n quad[ii].state.hess + lin.A.transpose() * Ms_[kk + 1][ii] *\n qr_capital_lambdas_[kk].solve(lin.A);\n ms_[kk][ii] =\n quad[ii].state.grad +\n lin.A.transpose() * (ms_[kk + 1][ii] +\n Ms_[kk + 1][ii] * qr_capital_lambdas_[kk].solve(\n intermediate_terms_[kk]));\n }\n }\n\n // (2) Now compute optimal state and control trajectory forward in time.\n VectorXf x_star = x0;\n VectorXf last_x_star;\n for (size_t kk = 0; kk < num_time_steps_ - 1; kk++) {\n // Maybe set delta_x.\n if (delta_xs) (*delta_xs)[kk] = x_star;\n\n // Unpack linearization at this time step.\n const auto& lin = linearization[kk];\n\n // Compute optimal x.\n last_x_star = x_star;\n x_star = qr_capital_lambdas_[kk].solve(lin.A * last_x_star +\n intermediate_terms_[kk]);\n\n // Compute optimal u and store (sign flipped) in alpha.\n // Also maybe compute costates.\n for (PlayerIndex ii = 0; ii < dynamics_->NumPlayers(); ii++) {\n const VectorXf intermediate_term =\n Ms_[kk + 1][ii] * x_star + ms_[kk + 1][ii];\n strategies[ii].alphas[kk] =\n warped_Bs_[kk][ii] * intermediate_term + warped_rs_[kk][ii];\n\n if (costates) (*costates)[kk][ii] = lin.A.transpose() * intermediate_term;\n }\n\n // Check dynamic feasibility.\n // VectorXf check_x = lin.A * last_x_star;\n // for (PlayerIndex ii = 0; ii < dynamics_->NumPlayers(); ii++)\n // check_x -= lin.Bs[ii] * strategies[ii].alphas[kk];\n\n // CHECK_LE((x_star - check_x).cwiseAbs().maxCoeff(), 1e-1);\n }\n\n // Set delta_x and costate for last time step.\n if (delta_xs) {\n delta_xs->back() = x_star;\n for (PlayerIndex ii = 0; ii < dynamics_->NumPlayers(); ii++)\n costates->back()[ii].setZero();\n }\n\n return strategies;\n}\n\n} // namespace ilqgames\n", "meta": {"hexsha": "03dd91ad6c7e61d443f23047868ab171e736bc8c", "size": 8242, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/lq_open_loop_solver.cpp", "max_stars_repo_name": "anjianli21/ilqgames", "max_stars_repo_head_hexsha": "2be8e2bc6d34a9a6296d341b75d59e37c9057ad5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 53.0, "max_stars_repo_stars_event_min_datetime": "2019-11-25T02:51:12.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-12T18:30:02.000Z", "max_issues_repo_path": "src/lq_open_loop_solver.cpp", "max_issues_repo_name": "anjianli21/ilqgames", "max_issues_repo_head_hexsha": "2be8e2bc6d34a9a6296d341b75d59e37c9057ad5", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 42.0, "max_issues_repo_issues_event_min_datetime": "2019-10-05T20:22:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-09T20:10:35.000Z", "max_forks_repo_path": "src/lq_open_loop_solver.cpp", "max_forks_repo_name": "anjianli21/ilqgames", "max_forks_repo_head_hexsha": "2be8e2bc6d34a9a6296d341b75d59e37c9057ad5", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 23.0, "max_forks_repo_forks_event_min_datetime": "2020-01-02T13:33:12.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-06T01:55:28.000Z", "avg_line_length": 41.6262626263, "max_line_length": 80, "alphanum_fraction": 0.654816792, "num_tokens": 2110, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.24186162614190215}} {"text": "/*\n * Copyright (c) 2021 Abit More, and contributors.\n *\n * The MIT License\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n * THE SOFTWARE.\n */\n#include \n\n#include \n\n#include \n\n#include \n\nusing namespace graphene::chain;\n\n/*\ntarget_CR = max( target_CR, MCR )\n\ntarget_CR = new_collateral / ( new_debt / feed_price )\n = ( collateral - max_amount_to_sell ) * feed_price\n / ( debt - amount_to_get )\n = ( collateral - max_amount_to_sell ) * feed_price\n / ( debt - round_down(max_amount_to_sell * match_price ) )\n = ( collateral - max_amount_to_sell ) * feed_price\n / ( debt - (max_amount_to_sell * match_price - x) )\n\nNote: x is the fraction, 0 <= x < 1\n\n=>\n\nmax_amount_to_sell = ( (debt + x) * target_CR - collateral * feed_price )\n / (target_CR * match_price - feed_price)\n = ( (debt + x) * tCR / DENOM - collateral * fp_debt_amt / fp_coll_amt )\n / ( (tCR / DENOM) * (mp_debt_amt / mp_coll_amt) - fp_debt_amt / fp_coll_amt )\n = ( (debt + x) * tCR * fp_coll_amt * mp_coll_amt - collateral * fp_debt_amt * DENOM * mp_coll_amt)\n / ( tCR * mp_debt_amt * fp_coll_amt - fp_debt_amt * DENOM * mp_coll_amt )\n\nmax_debt_to_cover = max_amount_to_sell * match_price\n = max_amount_to_sell * mp_debt_amt / mp_coll_amt\n = ( (debt + x) * tCR * fp_coll_amt * mp_debt_amt - collateral * fp_debt_amt * DENOM * mp_debt_amt)\n / (tCR * mp_debt_amt * fp_coll_amt - fp_debt_amt * DENOM * mp_coll_amt)\n*/\nshare_type call_order_object::get_max_debt_to_cover( price match_price,\n price feed_price,\n const uint16_t maintenance_collateral_ratio,\n const optional& maintenance_collateralization )const\n{ try {\n // be defensive here, make sure feed_price is in collateral / debt format\n if( feed_price.base.asset_id != call_price.base.asset_id )\n feed_price = ~feed_price;\n\n FC_ASSERT( feed_price.base.asset_id == call_price.base.asset_id\n && feed_price.quote.asset_id == call_price.quote.asset_id );\n\n bool after_core_hardfork_1270 = maintenance_collateralization.valid();\n\n // be defensive here, make sure maintenance_collateralization is in collateral / debt format\n if( after_core_hardfork_1270 )\n {\n FC_ASSERT( maintenance_collateralization->base.asset_id == call_price.base.asset_id\n && maintenance_collateralization->quote.asset_id == call_price.quote.asset_id );\n }\n\n // According to the feed protection rule (https://github.com/cryptonomex/graphene/issues/436),\n // a call order should only be called when its collateral ratio is not higher than required maintenance collateral ratio.\n // Although this should be guaranteed by the caller of this function, we still check here to be defensive.\n // Theoretically this check can be skipped for better performance.\n //\n // Before core-1270 hard fork, we check with call_price; afterwards, we check with collateralization().\n if( ( !after_core_hardfork_1270 && call_price > feed_price )\n || ( after_core_hardfork_1270 && collateralization() > *maintenance_collateralization ) )\n return 0;\n\n if( !target_collateral_ratio.valid() ) // target cr is not set\n return debt;\n\n uint16_t tcr = std::max( *target_collateral_ratio, maintenance_collateral_ratio ); // use mcr if target cr is too small\n\n price target_collateralization = ( after_core_hardfork_1270 ?\n feed_price * ratio_type( tcr, GRAPHENE_COLLATERAL_RATIO_DENOM ) :\n price() );\n\n // be defensive here, make sure match_price is in collateral / debt format\n if( match_price.base.asset_id != call_price.base.asset_id )\n match_price = ~match_price;\n\n FC_ASSERT( match_price.base.asset_id == call_price.base.asset_id\n && match_price.quote.asset_id == call_price.quote.asset_id );\n\n typedef boost::multiprecision::int256_t i256;\n i256 mp_debt_amt = match_price.quote.amount.value;\n i256 mp_coll_amt = match_price.base.amount.value;\n i256 fp_debt_amt = feed_price.quote.amount.value;\n i256 fp_coll_amt = feed_price.base.amount.value;\n\n // firstly we calculate without the fraction (x), the result could be a bit too small\n i256 numerator = fp_coll_amt * mp_debt_amt * debt.value * tcr\n - fp_debt_amt * mp_debt_amt * collateral.value * GRAPHENE_COLLATERAL_RATIO_DENOM;\n if( numerator < 0 ) // feed protected, actually should not be true here, just check to be safe\n return 0;\n\n i256 denominator = fp_coll_amt * mp_debt_amt * tcr - fp_debt_amt * mp_coll_amt * GRAPHENE_COLLATERAL_RATIO_DENOM;\n if( denominator <= 0 ) // black swan\n return debt;\n\n // note: if add 1 here, will result in 1.5x imperfection rate;\n // however, due to rounding, the result could still be a bit too big, thus imperfect.\n i256 to_cover_i256 = ( numerator / denominator );\n if( to_cover_i256 >= debt.value ) // avoid possible overflow\n return debt;\n share_type to_cover_amt = static_cast< int64_t >( to_cover_i256 );\n\n // stabilize\n // note: rounding up-down results in 3x imperfection rate in comparison to down-down-up\n asset to_pay = asset( to_cover_amt, debt_type() ) * match_price;\n asset to_cover = to_pay * match_price;\n to_pay = to_cover.multiply_and_round_up( match_price );\n\n if( to_cover.amount >= debt || to_pay.amount >= collateral ) // to be safe\n return debt;\n FC_ASSERT( to_pay.amount < collateral && to_cover.amount < debt );\n\n // Check whether the collateral ratio after filled is high enough\n // Before core-1270 hard fork, we check with call_price; afterwards, we check with collateralization().\n std::function result_is_good = after_core_hardfork_1270 ?\n std::function( [this,&to_cover,&to_pay,target_collateralization]() -> bool\n {\n price new_collateralization = ( get_collateral() - to_pay ) / ( get_debt() - to_cover );\n return ( new_collateralization > target_collateralization );\n }) :\n std::function( [this,&to_cover,&to_pay,tcr,feed_price]() -> bool\n {\n price new_call_price = price::call_price( get_debt() - to_cover, get_collateral() - to_pay, tcr );\n return ( new_call_price > feed_price );\n });\n\n // if the result is good, we return.\n if( result_is_good() )\n return to_cover.amount;\n\n // be here, to_cover is too small due to rounding. deal with the fraction\n numerator += fp_coll_amt * mp_debt_amt * tcr; // plus the fraction\n to_cover_i256 = ( numerator / denominator ) + 1;\n if( to_cover_i256 >= debt.value ) // avoid possible overflow\n to_cover_i256 = debt.value;\n to_cover_amt = static_cast< int64_t >( to_cover_i256 );\n\n asset max_to_pay = ( ( to_cover_amt == debt.value ) ? get_collateral()\n : asset( to_cover_amt, debt_type() ).multiply_and_round_up( match_price ) );\n if( max_to_pay.amount > collateral )\n max_to_pay.amount = collateral;\n\n asset max_to_cover = ( ( max_to_pay.amount == collateral ) ? get_debt() : ( max_to_pay * match_price ) );\n if( max_to_cover.amount >= debt ) // to be safe\n {\n max_to_pay.amount = collateral;\n max_to_cover.amount = debt;\n }\n\n if( max_to_pay <= to_pay || max_to_cover <= to_cover ) // strange data. should skip binary search and go on, but doesn't help much\n return debt;\n FC_ASSERT( max_to_pay > to_pay && max_to_cover > to_cover );\n\n asset min_to_pay = to_pay;\n asset min_to_cover = to_cover;\n\n // try with binary search to find a good value\n // note: actually binary search can not always provide perfect result here,\n // due to rounding, collateral ratio is not always increasing while to_pay or to_cover is increasing\n bool max_is_ok = false;\n while( true )\n {\n // get the mean\n if( match_price.base.amount < match_price.quote.amount ) // step of collateral is smaller\n {\n to_pay.amount = ( min_to_pay.amount + max_to_pay.amount + 1 ) / 2; // should not overflow. round up here\n if( to_pay.amount == max_to_pay.amount )\n to_cover.amount = max_to_cover.amount;\n else\n {\n to_cover = to_pay * match_price;\n if( to_cover.amount >= max_to_cover.amount ) // can be true when max_is_ok is false\n {\n to_pay.amount = max_to_pay.amount;\n to_cover.amount = max_to_cover.amount;\n }\n else\n {\n to_pay = to_cover.multiply_and_round_up( match_price ); // stabilization, no change or become smaller\n FC_ASSERT( to_pay.amount < max_to_pay.amount );\n }\n }\n }\n else // step of debt is smaller or equal\n {\n to_cover.amount = ( min_to_cover.amount + max_to_cover.amount ) / 2; // should not overflow. round down here\n if( to_cover.amount == max_to_cover.amount )\n to_pay.amount = max_to_pay.amount;\n else\n {\n to_pay = to_cover.multiply_and_round_up( match_price );\n if( to_pay.amount >= max_to_pay.amount ) // can be true when max_is_ok is false\n {\n to_pay.amount = max_to_pay.amount;\n to_cover.amount = max_to_cover.amount;\n }\n else\n {\n to_cover = to_pay * match_price; // stabilization, to_cover should have increased\n if( to_cover.amount >= max_to_cover.amount ) // to be safe\n {\n to_pay.amount = max_to_pay.amount;\n to_cover.amount = max_to_cover.amount;\n }\n }\n }\n }\n\n // check again to see if we've moved away from the minimums, if not, use the maximums directly\n if( to_pay.amount <= min_to_pay.amount || to_cover.amount <= min_to_cover.amount\n || to_pay.amount > max_to_pay.amount || to_cover.amount > max_to_cover.amount )\n {\n to_pay.amount = max_to_pay.amount;\n to_cover.amount = max_to_cover.amount;\n }\n\n // check the mean\n if( to_pay.amount == max_to_pay.amount && ( max_is_ok || to_pay.amount == collateral ) )\n return to_cover.amount;\n FC_ASSERT( to_pay.amount < collateral && to_cover.amount < debt );\n\n // Check whether the result is good\n if( result_is_good() ) // good\n {\n if( to_pay.amount == max_to_pay.amount )\n return to_cover.amount;\n max_to_pay.amount = to_pay.amount;\n max_to_cover.amount = to_cover.amount;\n max_is_ok = true;\n }\n else // not good\n {\n if( to_pay.amount == max_to_pay.amount )\n break;\n min_to_pay.amount = to_pay.amount;\n min_to_cover.amount = to_cover.amount;\n }\n }\n\n // be here, max_to_cover is too small due to rounding. search forward\n for( uint64_t d1 = 0, d2 = 1, d3 = 1; ; d1 = d2, d2 = d3, d3 = d1 + d2 ) // 1,1,2,3,5,8,...\n {\n if( match_price.base.amount > match_price.quote.amount ) // step of debt is smaller\n {\n to_pay.amount += d2;\n if( to_pay.amount >= collateral )\n return debt;\n to_cover = to_pay * match_price;\n if( to_cover.amount >= debt )\n return debt;\n to_pay = to_cover.multiply_and_round_up( match_price ); // stabilization\n if( to_pay.amount >= collateral )\n return debt;\n }\n else // step of collateral is smaller or equal\n {\n to_cover.amount += d2;\n if( to_cover.amount >= debt )\n return debt;\n to_pay = to_cover.multiply_and_round_up( match_price );\n if( to_pay.amount >= collateral )\n return debt;\n to_cover = to_pay * match_price; // stabilization\n if( to_cover.amount >= debt )\n return debt;\n }\n\n // defensive check\n FC_ASSERT( to_pay.amount < collateral && to_cover.amount < debt );\n\n // Check whether the result is good\n if( result_is_good() ) // good\n return to_cover.amount;\n }\n\n} FC_CAPTURE_AND_RETHROW( (*this)(feed_price)(match_price)(maintenance_collateral_ratio) ) }\n\nFC_REFLECT_DERIVED_NO_TYPENAME( graphene::chain::limit_order_object,\n (graphene::db::object),\n (expiration)(seller)(for_sale)(sell_price)(deferred_fee)(deferred_paid_fee)\n )\n\nFC_REFLECT_DERIVED_NO_TYPENAME( graphene::chain::call_order_object, (graphene::db::object),\n (borrower)(collateral)(debt)(call_price)(target_collateral_ratio) )\n\nFC_REFLECT_DERIVED_NO_TYPENAME( graphene::chain::force_settlement_object,\n (graphene::db::object),\n (owner)(balance)(settlement_date)\n )\n\nFC_REFLECT_DERIVED_NO_TYPENAME( graphene::chain::collateral_bid_object, (graphene::db::object),\n (bidder)(inv_swan_price) )\n\nGRAPHENE_IMPLEMENT_EXTERNAL_SERIALIZATION( graphene::chain::limit_order_object )\nGRAPHENE_IMPLEMENT_EXTERNAL_SERIALIZATION( graphene::chain::call_order_object )\nGRAPHENE_IMPLEMENT_EXTERNAL_SERIALIZATION( graphene::chain::force_settlement_object )\nGRAPHENE_IMPLEMENT_EXTERNAL_SERIALIZATION( graphene::chain::collateral_bid_object )\n", "meta": {"hexsha": "b36f43984af5f713ce94ffb3617a7917a28e84a6", "size": 14556, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libraries/chain/market_object.cpp", "max_stars_repo_name": "karthikskkk/core", "max_stars_repo_head_hexsha": "b3e4d09552ca82bb8c4df2a9860cc8e7cc5165fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "libraries/chain/market_object.cpp", "max_issues_repo_name": "karthikskkk/core", "max_issues_repo_head_hexsha": "b3e4d09552ca82bb8c4df2a9860cc8e7cc5165fc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "libraries/chain/market_object.cpp", "max_forks_repo_name": "karthikskkk/core", "max_forks_repo_head_hexsha": "b3e4d09552ca82bb8c4df2a9860cc8e7cc5165fc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.6503067485, "max_line_length": 133, "alphanum_fraction": 0.6482550151, "num_tokens": 3398, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.24181873129329282}} {"text": "#ifndef HW4_RENDER_HPP\n#define HW4_RENDER_HPP\n\n#include \n#include \n#include \n\n#include \n#include \n\n#include \"scene.hpp\"\n\nnamespace hw4 {\n class Image {\n struct pixel {\n unsigned char r;\n unsigned char g;\n unsigned char b;\n };\n\n glm::ivec2 m_size;\n std::unique_ptr m_data;\n public:\n Image() : m_size(glm::ivec2(0)), m_data(nullptr) {}\n Image(glm::ivec2 size)\n : m_size(size), m_data(std::make_unique(size.x * size.y)) {}\n\n glm::ivec2 size() const { return this->m_size; }\n\n Image& fill(glm::vec3 value) {\n std::fill(\n &this->m_data[0],\n &this->m_data[this->m_size.x * this->m_size.y],\n pixel {\n static_cast(std::round(glm::clamp(value.r, 0.0f, 1.0f) * 255.0f)),\n static_cast(std::round(glm::clamp(value.g, 0.0f, 1.0f) * 255.0f)),\n static_cast(std::round(glm::clamp(value.b, 0.0f, 1.0f) * 255.0f))\n }\n );\n\n return *this;\n }\n\n Image& set_pixel(glm::ivec2 pos, glm::vec3 value) {\n assert(pos.x >= 0 && pos.x < this->m_size.x);\n assert(pos.y >= 0 && pos.y < this->m_size.y);\n\n this->m_data[static_cast(pos.x + pos.y * this->m_size.x)] = pixel {\n static_cast(std::round(glm::clamp(value.r, 0.0f, 1.0f) * 255.0f)),\n static_cast(std::round(glm::clamp(value.g, 0.0f, 1.0f) * 255.0f)),\n static_cast(std::round(glm::clamp(value.b, 0.0f, 1.0f) * 255.0f))\n };\n\n return *this;\n }\n\n glm::vec3 get_pixel(glm::ivec2 pos) const {\n assert(pos.x >= 0 && pos.x < this->m_size.x);\n assert(pos.y >= 0 && pos.y < this->m_size.y);\n\n pixel p = this->m_data[static_cast(pos.x + pos.y * this->m_size.x)];\n\n return glm::vec3(p.r / 255.0f, p.g / 255.0f, p.b / 255.0f);\n }\n\n Image& copy_data(const Image& img, glm::ivec2 pos) {\n assert(pos.x >= 0 && pos.x + img.m_size.x <= this->m_size.x);\n assert(pos.y >= 0 && pos.y + img.m_size.y <= this->m_size.y);\n\n // Since image rows are stored separately, each row needs to be copied individually.\n for (int y = 0; y < img.m_size.y; y++) {\n std::copy(\n &img.m_data[y * img.m_size.x],\n &img.m_data[(y + 1) * img.m_size.x],\n &this->m_data[pos.x + (y + pos.y) * this->m_size.x]\n );\n }\n\n return *this;\n }\n\n void save_as_ppm(const boost::filesystem::path& path) const;\n };\n\n class RayTraceRenderer {\n glm::ivec2 m_size;\n int m_max_recursion;\n int m_supersample_level;\n float m_bias;\n Camera m_camera;\n\n float m_img_plane_distance;\n float m_sample_spacing;\n float m_sample_mult;\n\n void update_params();\n public:\n RayTraceRenderer(\n glm::ivec2 size,\n int max_recursion,\n int supersample_level,\n float bias,\n Camera camera\n )\n : m_size(size), m_max_recursion(max_recursion), m_supersample_level(supersample_level),\n m_bias(bias), m_camera(camera) {\n this->update_params();\n }\n\n glm::ivec2 size() const { return this->m_size; }\n int max_recursion() const { return this->m_max_recursion; }\n int supersample_level() const { return this->m_supersample_level; }\n const Camera& camera() const { return this->m_camera; }\n\n Image render(\n const Scene& scene,\n std::function progress_callback\n ) const;\n Image render_patch(\n const Scene& scene,\n const glm::mat4& inv_view_matrix,\n glm::ivec2 start,\n glm::ivec2 size\n ) const;\n glm::vec3 render_pixel(\n const Scene& scene,\n const glm::mat4& inv_view_matrix,\n glm::ivec2 pos\n ) const;\n glm::vec3 render_ray(\n const Scene& scene,\n const Ray& ray\n ) const {\n return this->render_ray(scene, ray, 0);\n }\n private:\n glm::vec3 render_ray(\n const Scene& scene,\n const Ray& ray,\n int recursion\n ) const;\n glm::vec3 render_point_light(\n const Scene& scene,\n const Ray& ray,\n const Intersection& intersection,\n const PointMaterial& material,\n const PointLight& point_light\n ) const;\n float get_visibility(const Scene& scene, glm::vec3 from, glm::vec3 to) const;\n };\n}\n\n#endif\n", "meta": {"hexsha": "103c4bec891ccb57b2fa48bb4ad62d8dba97546f", "size": 4957, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "inc/render.hpp", "max_stars_repo_name": "wasmjit-omr/raytracer", "max_stars_repo_head_hexsha": "f816b3992863b3e204c9e13394d14f292c4bbd98", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "inc/render.hpp", "max_issues_repo_name": "wasmjit-omr/raytracer", "max_issues_repo_head_hexsha": "f816b3992863b3e204c9e13394d14f292c4bbd98", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "inc/render.hpp", "max_forks_repo_name": "wasmjit-omr/raytracer", "max_forks_repo_head_hexsha": "f816b3992863b3e204c9e13394d14f292c4bbd98", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2019-05-03T08:27:48.000Z", "max_forks_repo_forks_event_max_datetime": "2019-05-03T08:27:48.000Z", "avg_line_length": 32.1883116883, "max_line_length": 101, "alphanum_fraction": 0.5245107928, "num_tokens": 1254, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.4687906266262437, "lm_q1q2_score": 0.2417177833927635}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\n\n// Copyright (c) 2007-2012 Barend Gehrels, Amsterdam, the Netherlands.\n// Copyright (c) 2008-2012 Bruno Lalande, Paris, France.\n// Copyright (c) 2009-2012 Mateusz Loskot, London, UK.\n\n// Parts of Boost.Geometry are redesigned from Geodan's Geographic Library\n// (geolib/GGL), copyright (c) 1995-2010 Geodan, Amsterdam, the Netherlands.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_GEOMETRY_STRATEGIES_TRANSFORM_MAP_TRANSFORMER_HPP\n#define BOOST_GEOMETRY_STRATEGIES_TRANSFORM_MAP_TRANSFORMER_HPP\n\n\n#include \n\n#include \n\nnamespace boost { namespace geometry\n{\n\n// Silence warning C4127: conditional expression is constant\n#if defined(_MSC_VER)\n#pragma warning(push) \n#pragma warning(disable : 4127) \n#endif\n\nnamespace strategy { namespace transform\n{\n\n/*!\n\\brief Transformation strategy to map from one to another Cartesian coordinate system\n\\ingroup strategies\n\\tparam Mirror if true map is mirrored upside-down (in most cases pixels\n are from top to bottom, while map is from bottom to top)\n */\ntemplate\n<\n typename CalculationType,\n std::size_t Dimension1,\n std::size_t Dimension2,\n bool Mirror = false,\n bool SameScale = true\n>\nclass map_transformer\n : public ublas_transformer\n{\n typedef boost::numeric::ublas::matrix M;\n\npublic :\n template \n explicit inline map_transformer(B const& box, D const& width, D const& height)\n {\n set_transformation(\n get(box), get(box),\n get(box), get(box),\n width, height);\n }\n\n template \n explicit inline map_transformer(W const& wx1, W const& wy1, W const& wx2, W const& wy2,\n D const& width, D const& height)\n {\n set_transformation(wx1, wy1, wx2, wy2, width, height);\n }\n\n\nprivate :\n template \n inline void set_transformation_point(W const& wx, W const& wy,\n P const& px, P const& py,\n S const& scalex, S const& scaley)\n {\n\n // Translate to a coordinate system centered on world coordinates (-wx, -wy)\n M t1(3,3);\n t1(0,0) = 1; t1(0,1) = 0; t1(0,2) = -wx;\n t1(1,0) = 0; t1(1,1) = 1; t1(1,2) = -wy;\n t1(2,0) = 0; t1(2,1) = 0; t1(2,2) = 1;\n\n // Scale the map\n M s(3,3);\n s(0,0) = scalex; s(0,1) = 0; s(0,2) = 0;\n s(1,0) = 0; s(1,1) = scaley; s(1,2) = 0;\n s(2,0) = 0; s(2,1) = 0; s(2,2) = 1;\n\n // Translate to a coordinate system centered on the specified pixels (+px, +py)\n M t2(3, 3);\n t2(0,0) = 1; t2(0,1) = 0; t2(0,2) = px;\n t2(1,0) = 0; t2(1,1) = 1; t2(1,2) = py;\n t2(2,0) = 0; t2(2,1) = 0; t2(2,2) = 1;\n\n // Calculate combination matrix in two steps\n this->m_matrix = boost::numeric::ublas::prod(s, t1);\n this->m_matrix = boost::numeric::ublas::prod(t2, this->m_matrix);\n }\n\n\n template \n void set_transformation(W const& wx1, W const& wy1, W const& wx2, W const& wy2,\n D const& width, D const& height)\n {\n D px1 = 0;\n D py1 = 0;\n D px2 = width;\n D py2 = height;\n\n // Get the same type, but at least a double\n typedef typename select_most_precise::type type;\n\n\n // Calculate appropriate scale, take min because whole box must fit\n // Scale is in PIXELS/MAPUNITS (meters)\n W wdx = wx2 - wx1;\n W wdy = wy2 - wy1;\n type sx = (px2 - px1) / boost::numeric_cast(wdx);\n type sy = (py2 - py1) / boost::numeric_cast(wdy);\n\n if (SameScale)\n {\n type scale = (std::min)(sx, sy);\n sx = scale;\n sy = scale;\n }\n\n // Calculate centerpoints\n W wtx = wx1 + wx2;\n W wty = wy1 + wy2;\n W two = 2;\n W wmx = wtx / two;\n W wmy = wty / two;\n type pmx = (px1 + px2) / 2.0;\n type pmy = (py1 + py2) / 2.0;\n\n set_transformation_point(wmx, wmy, pmx, pmy, sx, sy);\n\n if (Mirror)\n {\n // Mirror in y-direction\n M m(3,3);\n m(0,0) = 1; m(0,1) = 0; m(0,2) = 0;\n m(1,0) = 0; m(1,1) = -1; m(1,2) = 0;\n m(2,0) = 0; m(2,1) = 0; m(2,2) = 1;\n\n // Translate in y-direction such that it fits again\n M y(3, 3);\n y(0,0) = 1; y(0,1) = 0; y(0,2) = 0;\n y(1,0) = 0; y(1,1) = 1; y(1,2) = height;\n y(2,0) = 0; y(2,1) = 0; y(2,2) = 1;\n\n // Calculate combination matrix in two steps\n this->m_matrix = boost::numeric::ublas::prod(m, this->m_matrix);\n this->m_matrix = boost::numeric::ublas::prod(y, this->m_matrix);\n }\n }\n};\n\n\n}} // namespace strategy::transform\n\n#if defined(_MSC_VER)\n#pragma warning(pop) \n#endif\n\n}} // namespace boost::geometry\n\n\n#endif // BOOST_GEOMETRY_STRATEGIES_TRANSFORM_MAP_TRANSFORMER_HPP\n", "meta": {"hexsha": "baf7216582eda61f38aa21f07ecba5e96129e8d7", "size": 5376, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/geometry/strategies/transform/map_transformer.hpp", "max_stars_repo_name": "ballisticwhisper/boost", "max_stars_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 24.0, "max_stars_repo_stars_event_min_datetime": "2019-02-24T14:48:16.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T21:37:26.000Z", "max_issues_repo_path": "boost/geometry/strategies/transform/map_transformer.hpp", "max_issues_repo_name": "ballisticwhisper/boost", "max_issues_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2019-02-25T20:45:09.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-28T18:05:45.000Z", "max_forks_repo_path": "boost/geometry/strategies/transform/map_transformer.hpp", "max_forks_repo_name": "ballisticwhisper/boost", "max_forks_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 24.0, "max_forks_repo_forks_event_min_datetime": "2017-11-01T03:30:09.000Z", "max_forks_repo_forks_event_max_datetime": "2018-12-28T21:57:33.000Z", "avg_line_length": 31.2558139535, "max_line_length": 91, "alphanum_fraction": 0.5770089286, "num_tokens": 1724, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5195213219520929, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.24152627893750805}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"third_party/eigen3/unsupported/Eigen/CXX11/Tensor\"\n#include \"unsupported/Eigen/CXX11/src/Tensor/TensorDeviceCuda.h\"\n//#include \"unsupported/Eigen/CXX11/Tensor\"\n#include \"tensorflow/core/framework/op.h\"\n#include \"tensorflow/core/framework/op_kernel.h\"\n#include \"tensorflow/core/framework/tensor_shape.h\"\n#include \"tensorflow/core/framework/common_shape_fns.h\"\n#include \"tensorflow/core/util/work_sharder.h\"\n#include \"bboxes.h\"\n#include \"wtoolkit.h\"\n#include \"wtoolkit_cuda.h\"\n#include \n\nusing namespace tensorflow;\nusing namespace std;\n\ntypedef Eigen::ThreadPoolDevice CPUDevice;\ntypedef Eigen::GpuDevice GPUDevice;\n/*\n * num_classes: 类别数，不含背景\n * gaussian_iou: 一般为0.7\n * gbboxes: groundtruth bbox, [B,N,4] 相对坐标\n * glabels: 标签[B,N], 背景为0\n * glength: 有效的groundtruth bbox数量\n * output_size: 输出图的大小[2]=(OH,OW) \n *\n * output:\n * output_heatmaps_c: center heatmaps [B,OH,OW,num_classes]\n * output_hw_offset: [B,OH,OW,4], (h,w,yoffset,xoffset)\n * output_mask: [B,OH,OW,2] (hw_mask,offset_mask)\n */\nREGISTER_OP(\"Center2BoxesEncode\")\n .Attr(\"T: {float,double,int32,int64}\")\n\t.Attr(\"num_classes:int\")\n\t.Attr(\"gaussian_iou:float=0.7\")\n .Input(\"gbboxes: T\")\n .Input(\"glabels: int32\")\n .Input(\"glength: int32\")\n .Input(\"output_size: int32\")\n\t.Output(\"output_heatmaps_c:T\")\n\t.Output(\"output_hw_offset:T\")\n\t.Output(\"output_hw_offset_mask:T\")\n\t.SetShapeFn([](shape_inference::InferenceContext* c) {\n const auto input_shape0 = c->input(0);\n const auto batch_size = c->Dim(input_shape0,0);\n int num_classes;\n c->GetAttr(\"num_classes\",&num_classes);\n auto shape0 = c->MakeShape({batch_size,-1,-1,num_classes});\n auto shape1 = c->MakeShape({batch_size,-1,-1,4});\n auto shape2 = c->MakeShape({batch_size,-1,-1,2});\n\n\t\t\tc->set_output(0, shape0);\n\t\t\tc->set_output(1, shape1);\n\t\t\tc->set_output(2, shape2);\n\n\t\t\treturn Status::OK();\n\t\t\t});\n\ntemplate \nclass Center2BoxesEncodeOp: public OpKernel {\n\tpublic:\n\t\texplicit Center2BoxesEncodeOp(OpKernelConstruction* context) : OpKernel(context) {\n\t\t\tOP_REQUIRES_OK(context, context->GetAttr(\"num_classes\", &num_classes_));\n\t\t\tOP_REQUIRES_OK(context, context->GetAttr(\"gaussian_iou\", &gaussian_iou_));\n\t\t}\n\n\t\tvoid Compute(OpKernelContext* context) override\n {\n TIME_THISV1(\"Center2BoxesEncode\");\n const Tensor &_gbboxes = context->input(0);\n const Tensor &_glabels = context->input(1);\n const Tensor &_gsize = context->input(2);\n auto gbboxes = _gbboxes.template tensor();\n auto glabels = _glabels.template tensor();\n auto gsize = _gsize.template tensor();\n auto output_size = context->input(3).template flat().data();\n const auto batch_size = _gbboxes.dim_size(0);\n\n OP_REQUIRES(context, _gbboxes.dims() == 3, errors::InvalidArgument(\"box data must be 3-dimension\"));\n OP_REQUIRES(context, _glabels.dims() == 2, errors::InvalidArgument(\"labels data must be 2-dimension\"));\n OP_REQUIRES(context, _gsize.dims() == 1, errors::InvalidArgument(\"gsize data must be 1-dimension\"));\n\n int dims_4d0[4] = {int(batch_size),output_size[0],output_size[1],num_classes_};\n int dims_4d1[4] = {int(batch_size),output_size[0],output_size[1],4};\n int dims_4d2[4] = {int(batch_size),output_size[0],output_size[1],2};\n TensorShape outshape0;\n TensorShape outshape1;\n TensorShape outshape2;\n Tensor *output_heatmaps_c = NULL;\n Tensor *output_hw_offset = NULL;\n Tensor *output_mask = NULL;\n\n TensorShapeUtils::MakeShape(dims_4d0, 4, &outshape0);\n TensorShapeUtils::MakeShape(dims_4d1, 4, &outshape1);\n TensorShapeUtils::MakeShape(dims_4d2, 4, &outshape2);\n\n\n OP_REQUIRES_OK(context, context->allocate_output(0, outshape0, &output_heatmaps_c));\n OP_REQUIRES_OK(context, context->allocate_output(1, outshape1, &output_hw_offset));\n OP_REQUIRES_OK(context, context->allocate_output(2, outshape2, &output_mask));\n\n auto heatmaps_c = output_heatmaps_c->template tensor();\n auto hw_offsets = output_hw_offset->template tensor();\n auto o_mask = output_mask->template tensor();\n Eigen::Tensor max_probs(output_size[0],output_size[1]);\n\n heatmaps_c.setZero();\n hw_offsets.setZero();\n o_mask.setZero();\n\n for(auto i=0; i=output_size[0]||xc>=output_size[1]) {\n cout<<\"ERROR bboxes data: \"<\n static void draw_gaussian(DT& data,int cx,int cy,float radius,int batch_index,int class_index,float delta=6,float k=1.0)\n {\n const auto width = data.dimension(2);\n const auto height = data.dimension(1);\n const auto xtl = max(0,int(cx-radius));\n const auto ytl = max(0,int(cy-radius));\n const auto xbr = min(width,int(cx+radius+1));\n const auto ybr = min(height,int(cy+radius+1));\n const auto sigma = (2*radius+1)/delta;\n const auto c_index = class_index-1;\n\n for(auto x=xtl; x\n static void draw_gaussianv2(DT0& data0,DT1& data1,int cx,int cy,float radius,float h,float w,int batch_index,int class_index,float delta=6,float k=1.0)\n {\n const auto width = data1.dimension(2);\n const auto height = data1.dimension(1);\n const auto xtl = max(0,int(cx-radius));\n const auto ytl = max(0,int(cy-radius));\n const auto xbr = min(width,int(cx+radius+1));\n const auto ybr = min(height,int(cy+radius+1));\n const auto sigma = (2 *radius+1)/delta;\n const auto c_index = class_index-1;\n\n for(auto x=xtl; x(\"T\"), Center2BoxesEncodeOp);\n\nREGISTER_OP(\"Center2BoxesDecode\")\n .Attr(\"T: {float,double,int32,int64}\")\n .Attr(\"k:int\")\n .Attr(\"threshold:float\")\n .Input(\"heatmaps: T\")\n .Input(\"offset: T\")\n .Input(\"hw: T\")\n\t.Output(\"output_bboxes:T\")\n\t.Output(\"output_labels:int32\")\n\t.Output(\"output_probs:T\")\n\t.Output(\"output_index:int32\")\n\t.Output(\"output_lens:int32\")\n\t.SetShapeFn([](shape_inference::InferenceContext* c) {\n int k;\n c->GetAttr(\"k\",&k);\n const auto input_shape0 = c->input(0);\n const auto batch_size = c->Dim(input_shape0,0);\n auto shape0 = c->MakeShape({batch_size,k,4});\n auto shape1 = c->MakeShape({batch_size,k});\n auto shape2 = c->MakeShape({batch_size});\n\n\t\t\tc->set_output(0, shape0);\n\t\t\tc->set_output(1, shape1);\n\t\t\tc->set_output(2, shape1);\n\t\t\tc->set_output(3, shape1);\n\t\t\tc->set_output(4, shape2);\n\t\t\treturn Status::OK();\n\t\t\t});\n\ntemplate \nclass Center2BoxesDecodeOp: public OpKernel {\n private:\n struct InterData{\n InterData(int y,int x,int z,float s):y(y),x(x),z(z),score(s){}\n float score;\n int y,x,z;\n bool operator<(const InterData& rhv)const{\n return scoreGetAttr(\"k\", &k_));\n OP_REQUIRES_OK(context, context->GetAttr(\"threshold\", &threshold_));\n }\n void Compute(OpKernelContext* context) override\n {\n TIME_THISV1(\"Center2BoxesDecode\");\n const Tensor &_heatmaps_c = context->input(0);\n const Tensor &_offset_c = context->input(1);\n const Tensor &_hw = context->input(2);\n\n OP_REQUIRES(context, _heatmaps_c.dims() == 4, errors::InvalidArgument(\"heatmap data must be 4-dimension\"));\n OP_REQUIRES(context, _offset_c.dims() == 4, errors::InvalidArgument(\"offset data must be 4-dimension\"));\n OP_REQUIRES(context, _hw.dims() == 4, errors::InvalidArgument(\"hw data must be 4-dimension\"));\n\n auto heatmaps_c_r = _heatmaps_c.template tensor();\n auto offset_c = _offset_c.template tensor();\n auto hw = _hw.template tensor();\n\n const auto batch_size = _heatmaps_c.dim_size(0);\n vector> res_boxes;\n\n auto heatmaps_c = batch_sim_max_pool(heatmaps_c_r);\n\n for(auto i=0; iallocate_output(0, outshape0, &output_boxes));\n OP_REQUIRES_OK(context, context->allocate_output(1, outshape1, &output_labels));\n OP_REQUIRES_OK(context, context->allocate_output(2, outshape1, &output_probs));\n OP_REQUIRES_OK(context, context->allocate_output(3, outshape1, &output_indexs));\n OP_REQUIRES_OK(context, context->allocate_output(4, outshape2, &output_lens));\n\n auto o_boxes = output_boxes->template tensor();\n auto o_labels = output_labels->template tensor();\n auto o_probs = output_probs->template tensor();\n auto o_indexs = output_indexs->template tensor();\n auto o_lens = output_lens->template tensor();\n\n o_boxes.setZero();\n o_labels.setZero();\n o_probs.setZero();\n o_lens.setZero();\n o_indexs.setZero();\n\n for(auto i=0; isize(); ++j) {\n auto& box = (*b_it)[j];\n o_boxes(i,j,0) = box.ymin;\n o_boxes(i,j,1) = box.xmin;\n o_boxes(i,j,2) = box.ymax;\n o_boxes(i,j,3) = box.xmax;\n o_labels(i,j) = box.classes;\n o_probs(i,j) = box.prob;\n o_indexs(i,j) = box.index;\n }\n o_lens(i) = b_it->size();\n }\n }\n\n template\n Eigen::Tensor batch_sim_max_pool(DT& data,int k=3,float neg_value = 0.0f) {\n Eigen::Tensor res_data(Eigen::array(data.dimensions()));\n for(auto i=0; i ldata = data.chip(i,0).chip(j,2);\n sim_max_pool(ldata,k,neg_value);\n res_data.chip(i,0).chip(j,2) = ldata;\n }\n }\n return res_data;\n }\n template\n void sim_max_pool(DT& data,int k=3,float neg_value = 0.0f) {\n\n float buffer[k *k];\n int nr;\n auto H = data.dimension(0);\n auto W = data.dimension(1);\n auto max_i = -1;\n auto max_j = -1;\n auto max_v = neg_value;\n\n for(auto i=0; i(0,i-k);\n auto j_min = max(0,j-k);\n auto i_max = min(H-1,i+k+1);\n auto j_max = min(W-1,j+k+1);\n\n max_i = -1; \n max_v = neg_value;\n for(auto ii=i_min; iimax_v) {\n max_i = ii;\n max_j = jj;\n max_v = data(ii,jj);\n }\n }\n }//end ii\n if((max_i != i) || (max_j != j)) {\n data(i,j) = neg_value;\n }\n }\n }\n }\n\n template\n vector get_top_k(const DT& heatmaps,int batch_index,int k) {\n const auto H = heatmaps.dimension(1);\n const auto W = heatmaps.dimension(2);\n const auto C = heatmaps.dimension(3);\n vector res;\n res.reserve(H*W/4);\n for(auto y=0; ythreshold_)\n res.emplace_back(y,x,z,heatmaps(batch_index,y,x,z));\n }\n }\n }\n auto mid = res.begin()+min(k,res.size());\n partial_sort(res.begin(),mid,res.end(),[this](auto lhv,auto rhv){ return lhv.score>rhv.score;});\n res.erase(mid,res.end());\n return res;\n }\n\n template\n vector get_boxes(const vector& c,int batch_index,const DT0& HW,const DT1& offset) {\n vector boxes;\n const auto H = HW.dimension(1);\n const auto W = HW.dimension(2);\n\n for(auto& id:c) {\n auto cx = id.x+offset(batch_index,id.y,id.x,1);\n auto cy = id.y+offset(batch_index,id.y,id.x,0);\n auto hh = HW(batch_index,id.y,id.x,0)/2;\n auto hw = HW(batch_index,id.y,id.x,1)/2;\n Box box;\n box.xmin = (cx-hw)/(W-1);\n box.ymin = (cy-hh)/(H-1);\n box.xmax = (cx+hw)/(W-1);\n box.ymax = (cy+hh)/(H-1);\n box.prob = id.score;\n box.index = id.x+id.y*W;\n box.classes = id.z+1;\n boxes.push_back(box);\n }\n return boxes;\n }\n static inline pair index_to_yx(int index,int H,int W) {\n return make_pair(index/W,index%W);\n }\n\tprivate:\n int k_ = 0;\n float threshold_ = 1e-3;\n};\nREGISTER_KERNEL_BUILDER(Name(\"Center2BoxesDecode\").Device(DEVICE_CPU).TypeConstraint(\"T\"), Center2BoxesDecodeOp);\n", "meta": {"hexsha": "4a198b56b45678d77c9622a3d6e0bf236ed94a5f", "size": 18385, "ext": "cc", "lang": "C++", "max_stars_repo_path": "tfop/center_net2_encode_decode.cc", "max_stars_repo_name": "vghost2008/wml", "max_stars_repo_head_hexsha": "d0c5a1da6c228e321ae59a563e9ac84aa66266ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6.0, "max_stars_repo_stars_event_min_datetime": "2019-12-10T17:18:56.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-01T01:00:35.000Z", "max_issues_repo_path": "tfop/center_net2_encode_decode.cc", "max_issues_repo_name": "vghost2008/wml", "max_issues_repo_head_hexsha": "d0c5a1da6c228e321ae59a563e9ac84aa66266ff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2021-08-25T16:16:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-10T05:21:19.000Z", "max_forks_repo_path": "tfop/center_net2_encode_decode.cc", "max_forks_repo_name": "vghost2008/wml", "max_forks_repo_head_hexsha": "d0c5a1da6c228e321ae59a563e9ac84aa66266ff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2019-12-07T09:57:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-06T04:58:10.000Z", "avg_line_length": 41.9748858447, "max_line_length": 159, "alphanum_fraction": 0.5296165352, "num_tokens": 4694, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6926419831347361, "lm_q2_score": 0.3486451217982255, "lm_q1q2_score": 0.24148624857257453}} {"text": "#ifndef TVTML_FUNCTIONAL2D_HPP\n#define TVTML_FUNCTIONAL2D_HPP\n\n// Eigen includes\n#include \n#include \n\n// video++ includes\n#include \n\n// system includes\n#include \n#include \n#include \n#include \n\nnamespace tvmtl{\n\n\ntemplate \nclass Functional{\n\n public:\n\t// Manifold typedefs and constants\n\tstatic const MANIFOLD_TYPE mf_type = MANIFOLD::MyType;\n\tstatic const int value_dim = MANIFOLD::value_dim; \n\tstatic const int manifold_dim = MANIFOLD::manifold_dim; \n\ttypedef typename MANIFOLD::scalar_type scalar_type;\n\ttypedef typename MANIFOLD::value_type value_type;\n\ttypedef typename MANIFOLD::ref_type ref_type;\n\ttypedef typename MANIFOLD::cref_type cref_type;\n\ttypedef typename MANIFOLD::deriv1_type deriv1_type;\n\ttypedef typename MANIFOLD::deriv2_type deriv2_type;\n\ttypedef typename MANIFOLD::restricted_deriv2_type restricted_deriv2_type;\n\ttypedef typename MANIFOLD::tm_base_type tm_base_type;\n\n\t// Data typedef and constants\n\tstatic const int img_dim = DATA::img_dim;\n\ttypedef typename DATA::storage_type img_type;\n\ttypedef typename DATA::weights_type weights_type;\n\ttypedef typename DATA::weights_mat weights_mat;\n\ttypedef typename DATA::inp_mat inp_mat;\n\n\ttypedef vpp::box_nbh2d nbh_type;\n\n\t// Functional parameters and return types\n\tstatic const FUNCTIONAL_DISC disc_type;\n\ttypedef double param_type;\n\ttypedef double result_type;\n\t\n\t// Tangent space transformation matrix types\n\ttypedef vpp::imageNd tm_base_mat_type; \n\t\n\t// Gradient and Hessian types\n\ttypedef Eigen::Matrix gradient_type;\n\ttypedef vpp::imageNd hessian_type;\n\ttypedef Eigen::SparseMatrix sparse_hessian_type;\n\n\t//Constructor\n\tFunctional(param_type lambda, DATA& dat):\n\t lambda_(lambda),\n\t data_(dat)\n\t{\n\t eps2_=1e-10;\n\t static_assert(img_dim == 2, \"Dimension of data and functional must match!\");\n\t}\n\t\n\tvoid updateWeights();\n\n\tvoid updateTMBase();\n\t\n\t\n\t// Evaluation functions\n\tresult_type evaluateJ();\n\tvoid evaluateDJ();\n\tvoid evaluateHJ();\n\t\n\ttemplate \n\tvoid output_img(const IMG& img, const char* filename) const;\n\ttemplate \n\tvoid output_matval_img(const IMG& img, const char* filename) const;\n\n\t// Getter and Setter \n\tinline param_type getlambda() const { return lambda_; }\n\tinline void setlambda(param_type lam) { lambda_=lam; }\n\tinline param_type geteps2() const { return eps2_; }\n\tinline void seteps2(param_type eps) { eps2_=eps; }\n\n\tinline const weights_mat& getweightsX() const { return weightsX_; }\n\tinline const weights_mat& getweightsY() const { return weightsY_; }\n\n\tinline const gradient_type& getDJ() const { return DJ_; }\n\tinline const sparse_hessian_type& getHJ() const { return HJ_; }\n\tinline const tm_base_mat_type& getT() const { return T_; }\n\n private:\n\tDATA& data_;\n\n\tparam_type lambda_, eps2_;\n\tweights_mat weightsX_, weightsY_;\n\n\ttm_base_mat_type T_;\n\tgradient_type DJ_;\n\tsparse_hessian_type HJ_;\n};\n\n\n//--------Implementation FIRSTORDER-----/\n\ntemplate \nconst FUNCTIONAL_DISC Functional::disc_type = disc;\n\n// Update the Weights 2D\ntemplate \nvoid Functional::updateWeights(){\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t Update Weights...\" << std::endl;\n #endif\n\n int nr = data_.img_.nrows();\n int nc = data_.img_.ncols();\n \n // Subimage boxes\n vpp::box2d without_last_col(vpp::vint2(0,0), vpp::vint2(nr-1, nc-2)); // subdomain without last column\n vpp::box2d without_first_col(vpp::vint2(0,1), vpp::vint2(nr-1, nc-1)); // subdomain without first column\n vpp::box2d without_last_row(vpp::vint2(0,0), vpp::vint2(nr-2, nc-1)); // subdomain without last row\n vpp::box2d without_first_row(vpp::vint2(1,0), vpp::vint2(nr-1, nc-1)); // subdomain without first row\n\n weightsX_ = weights_mat(data_.img_.domain());\n weightsY_ = weights_mat(data_.img_.domain());\n\n auto calc_dist = [&] (weights_type& w, const value_type i, const value_type n) {\n\tw = MANIFOLD::dist_squared(i, n);\n };\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Horizontal neighbours \" << std::endl;\n #endif\n\n // Horizontal Neighbours\n vpp::fill(weightsX_, 0.0);\n vpp::pixel_wise(weightsX_ | without_last_col, data_.img_ | without_last_col, data_.img_ | without_first_col )(/*vpp::_no_threads*/) | calc_dist;\n\n #ifdef TV_FUNC_DEBUG \n\tdata_.output_weights(weightsX_,\"XWeights.csv\");\n #endif\t\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Vertical neighbours\" << std::endl;\n #endif\n\n // Vertical Neighbours\n vpp::fill(weightsY_, 0.0);\n vpp::pixel_wise(weightsY_ | without_last_row, data_.img_ | without_last_row, data_.img_ | without_first_row )(/*vpp::_no_threads*/) | calc_dist;\n\n #ifdef TV_FUNC_DEBUG \n\tdata_.output_weights(weightsY_,\"YWeights.csv\");\n #endif\t\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Reweighting\" << std::endl;\n #endif\n\n if(disc==ISO){\n\tauto g = [&] (const weights_type& ew, weights_type& x, weights_type& y) { x = ew / std::sqrt(x+y+eps2_); y = x; };\n\tvpp::pixel_wise(data_.edge_weights_, weightsX_, weightsY_) | g ;\n }\n else{\n\tauto g = [&] (const weights_type& ew, weights_type& w) { w = ew / std::sqrt(w+eps2_); };\n\tvpp::pixel_wise(data_.edge_weights_, weightsX_) | g ;\n\tvpp::pixel_wise(data_.edge_weights_, weightsY_) | g ;\n }\n}\n\n// Update the Tangent space ONB\ntemplate \nvoid Functional::updateTMBase(){\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tUpdate tangent space basis...\" << std::endl;\n #endif\n\n\n tm_base_mat_type T(data_.img_.domain());\n vpp::pixel_wise(T, data_.img_)(/*vpp::_no_threads*/) | [&] (tm_base_type& t, const value_type& i) { MANIFOLD::tangent_plane_base(i,t); };\n T_=T;\n \n #ifdef TV_FUNC_DEBUG \n output_img(T_,\"T.csv\");\n #endif\n}\n\n// Evaluation of J\ntemplate \ntypename Functional::result_type Functional::evaluateJ(){\n\n // sum d^2(img, img_noise)\n result_type J1, J2;\n J1 = J2 = 0.0;\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tFunctional evaluation...\" << std::endl;\n\tstd::cout << \"\\t\\t...Fidelity part\" << std::endl;\n #endif\n\n\n if(data_.doInpaint()){\n\tauto f = [] (const value_type& i, const value_type& n, const bool inp ) { return MANIFOLD::dist_squared(i,n)*(1-inp); };\n\tJ1 = vpp::sum(vpp::pixel_wise(data_.img_, data_.noise_img_, data_.inp_) | f);\n }\n else{\n\tauto f = [] (const value_type& i, const value_type& n) { return MANIFOLD::dist_squared(i,n); };\n\tJ1 = vpp::sum(vpp::pixel_wise(data_.img_, data_.noise_img_)(/*vpp::_no_threads*/)| f);\n }\n\n\tupdateWeights();\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...TV part.\" << std::endl;\n #endif\n\n if(disc==ISO)\n\tJ2 = vpp::sum( vpp::pixel_wise(weightsX_) | [&] (const weights_type& w) {return 1.0/w;} );\n else\n\tJ2 = vpp::sum( vpp::pixel_wise(weightsX_, weightsY_) | [&] (const weights_type& wx, const weights_type& wy) {return 1.0/wx +1.0/wy;} );\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"J1: \" << J1 << std::endl;\n\tstd::cout << \"J2: \" << J2 << std::endl;\n #endif\n\n return 0.5 * J1 + lambda_* J2;\n}\n\n// Evaluation of J'\ntemplate \nvoid Functional::evaluateDJ(){\n\n #ifdef TV_FUNC_WONES_DEBUG \n\toutput_img(data_.img_,\"img.csv\");\n\tvpp::fill(weightsX_, 1.0); // Reset for Debugging\n\tvpp::fill(weightsY_, 1.0); // Reset for Debugging\n #endif\n\n img_type grad = img_type(data_.img_.domain());\n int nr = data_.img_.nrows();\n int nc = data_.img_.ncols();\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tGradient evaluation...\" << std::endl;\n\tstd::cout << \"\\t\\t...Fidelity part\" << std::endl;\n #endif\n //GRADIENT OF FIDELITY TERM\n if(data_.doInpaint()){\n\tauto f = [] (value_type& g, const value_type& i, const value_type& n, const bool inp ) { MANIFOLD::deriv1x_dist_squared(i,n,g); g*=(1-inp); };\n\tvpp::pixel_wise(grad, data_.img_, data_.noise_img_, data_.inp_) | f;\n }\n else{\n\tauto f = [] (value_type& g, const value_type& i, const value_type& n) { MANIFOLD::deriv1x_dist_squared(i,n,g); };\n\tvpp::pixel_wise(grad, data_.img_, data_.noise_img_)(/*vpp::_no_threads*/) | f;\n }\n \n //GRADIENT OF TV TERM\n\n // Subimage boxes\n vpp::box2d without_last_col(vpp::vint2(0,0), vpp::vint2(nr-1, nc-2)); // subdomain without last column\n vpp::box2d without_first_col(vpp::vint2(0,1), vpp::vint2(nr-1, nc-1)); // subdomain without first column\n vpp::box2d without_last_row(vpp::vint2(0,0), vpp::vint2(nr-2, nc-1)); // subdomain without last row\n vpp::box2d without_first_row(vpp::vint2(1,0), vpp::vint2(nr-1, nc-1)); // subdomain without first row\n\n auto calc_first_arg_deriv = [&] (value_type& x, const weights_type& w, const value_type& i, const value_type& n) { MANIFOLD::deriv1x_dist_squared(i, n, x); x *= w; };\n auto calc_second_arg_deriv = [&] (value_type& y, const weights_type& w, const value_type& i, const value_type& n) { MANIFOLD::deriv1y_dist_squared(i, n, y); y *= w; };\n auto add_to_gradient = [&] (value_type& g, const value_type& d) { g+=d*lambda_; };\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tGradient evaluation...\" << std::endl;\n\tstd::cout << \"\\t\\t...TV part\" << std::endl;\n\tstd::cout << \"\\t\\t...-> XD1\" << std::endl;\n #endif\n // Horizontal derivatives and weighting\n // ... w.r.t. to first argument\n { // Temporary image XD1 is deallocated after this scope \n\timg_type XD1 = img_type(without_last_col);\n\tvpp::pixel_wise(XD1, weightsX_ | without_last_col, data_.img_ | without_last_col, data_.img_ | without_first_col) | calc_first_arg_deriv;\n\t#ifdef TV_FUNC_DEBUG \n\t output_img(XD1,\"XD1.csv\");\n\t#endif\n\tauto grad_subX1 = grad | without_last_col;\n\tvpp::pixel_wise(grad_subX1, XD1) | add_to_gradient;\n } \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...-> XD2\" << std::endl;\n #endif\n // ... w.r.t. second argument\n {\n\timg_type XD2 = img_type(without_last_col);\n\tvpp::pixel_wise(XD2, weightsX_ | without_last_col, data_.img_ | without_last_col, data_.img_ | without_first_col) | calc_second_arg_deriv;\n\t#ifdef TV_FUNC_DEBUG \n\t output_img(XD2,\"XD2.csv\");\n\t#endif\n\tauto grad_subX2 = grad | without_first_col;\n\tvpp::pixel_wise(grad_subX2, XD2) | add_to_gradient;\n }\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...-> YD1\" << std::endl;\n #endif\n // Vertical derivatives and weighting\n // ... w.r.t. first argument\n {\n\timg_type YD1 = img_type(without_last_row);\n\tvpp::pixel_wise(YD1, weightsY_ | without_last_row, data_.img_ | without_last_row, data_.img_ | without_first_row) | calc_first_arg_deriv;\n\t#ifdef TV_FUNC_DEBUG \n\t output_img(YD1,\"YD1.csv\");\n\t#endif\n\tauto grad_subY1 = grad | without_last_row;\n\tvpp::pixel_wise(grad_subY1, YD1) | add_to_gradient;\n }\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...-> YD2\" << std::endl;\n #endif\n // ... w.r.t second argument\n {\n\timg_type YD2 = img_type(without_last_row);\n\tvpp::pixel_wise(YD2, weightsY_ | without_last_row, data_.img_ | without_last_row, data_.img_ | without_first_row) | calc_second_arg_deriv;\n\t#ifdef TV_FUNC_DEBUG \n\t output_img(YD2,\"YD2.csv\");\n\t#endif\n auto grad_subY2 = grad | without_first_row;\n vpp::pixel_wise(grad_subY2, YD2) | add_to_gradient;\n }\n\n \n #ifdef TV_FUNC_DEBUG \n\toutput_img(grad,\"grad.csv\");\n #endif\n\n DJ_ = gradient_type::Zero(nr*nc*manifold_dim); \n \n // flatten rowwise\n //vpp::pixel_wise(grad, grad.domain()) | [&] (value_type& p, vpp::vint2 coord) { DJ_.segment(3*(nc*coord[0]+coord[1]), value_dim) = p; };\n \n // Apply tangent space restriction and flatten colwise (as in Matlab code)\n updateTMBase();\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Local to global insert\" << std::endl;\n #endif\n auto insert2grad = [&] (const tm_base_type& t, const value_type& p, const vpp::vint2 coord) { \n\tDJ_.segment(manifold_dim * (coord[0] + nr * coord[1]), manifold_dim) = t.transpose() * Eigen::Map(p.data(), p.size()); \n\t//DJ_.segment(manifold_dim * (coord[0] + nr * coord[1]), manifold_dim) = t.transpose()*p; //remove: does not work for matrix valued pixel\n };\n\n vpp::pixel_wise(T_, grad, grad.domain()) | insert2grad; \n\n #ifdef TV_FUNC_DEBUG \n\tstd::fstream f;\n\tf.open(\"gradJ.csv\",std::fstream::out);\n\tf << DJ_;\n\tf.close();\n #endif\n}\n\n// Evaluation of Hessian J\ntemplate \nvoid Functional::evaluateHJ(){\n #ifdef TV_FUNC_WONES_DEBUG \n\tvpp::fill(weightsX_, 1.0); // Reset for Debugging\n\tvpp::fill(weightsY_, 1.0); // Reset for Debugging\n #endif\n\n hessian_type hessian(data_.img_.domain());\n int nr = data_.img_.nrows();\n int nc = data_.img_.ncols();\n int sparsedim = nr*nc*manifold_dim;\n \n \n //HESSIAN OF FIDELITY TERM\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tHessian evaluation...\" << std::endl;\n\tstd::cout << \"\\t\\t...Fidelity part\" << std::endl;\n #endif\n\n sparse_hessian_type HF(sparsedim,sparsedim);\n\n //HF.reserve(Eigen::VectorXi::Constant(nc,manifold_dim));\n typedef Eigen::Triplet Trip;\n std::vector triplist;\n triplist.reserve(sparsedim*manifold_dim);\n\t\n if(data_.doInpaint()){\n\tauto f = [] (deriv2_type& h, const value_type& i, const value_type& n, const bool inp ) { MANIFOLD::deriv2xx_dist_squared(i,n,h); h*=(1-inp); };\n\tvpp::pixel_wise(hessian, data_.img_, data_.noise_img_, data_.inp_) | f;\n }\n else{\n\tauto f = [] (deriv2_type& h, const value_type& i, const value_type& n) { MANIFOLD::deriv2xx_dist_squared(i,n,h); };\n\tvpp::pixel_wise(hessian, data_.img_, data_.noise_img_) | f;\n }\n \n #ifdef TV_FUNC_DEBUG\n\toutput_matval_img(hessian,\"HF0.csv\");\n #endif\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Local to global insert\" << std::endl;\n #endif\n //TODO: Check whether all 2nd-derivative matrices are symmetric s.t. only half the matrix need to be traversed. e.g. local_col=local_row instead of 0\n auto local2globalInsert = [&](const tm_base_type& t, const deriv2_type& h, const vpp::vint2 coord) { \n\tint pos = manifold_dim * (coord[0] + nr * coord[1]); // columnwise flattening\n\trestricted_deriv2_type ht=t.transpose()*h*t;\n\tfor(int local_row = 0; local_rowXD11\" << std::endl;\n #endif\n // Horizontal Second Derivatives and weighting\n // ... w.r.t. first arguments\n { // Temporary image XD11 is deallocated after this scope\n\thessian_type XD11(without_last_col);\n vpp::pixel_wise(XD11, weightsX_ | without_last_col, data_.img_ | without_last_col, data_.img_ | without_first_col) | calc_xx_der;\n #ifdef TV_FUNC_DEBUG \n\t\toutput_matval_img(XD11,\"XD11.csv\");\n #endif\n\tauto hess_subX11 = hessian | without_last_col;\n\tvpp::pixel_wise(hess_subX11, XD11) | [&] (deriv2_type& h, const deriv2_type& d) { h=d; };\n }\n\t#pragma omp parallel for\n\tfor(int r=0; r< nr; r++) \n\t hessian(r,nc-1)=deriv2_type::Zero(); // set last column to zero\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->XD22\" << std::endl;\n #endif\n //... w.r.t. second arguments\n {\n\thessian_type XD22(without_last_col);\n vpp::pixel_wise(XD22, weightsX_ | without_last_col, data_.img_ | without_last_col, data_.img_ | without_first_col) | calc_yy_der;\n\t#ifdef TV_FUNC_DEBUG \n\t output_matval_img(XD22,\"XD22.csv\");\n\t#endif\n\tauto hess_subX22 = hessian | without_first_col;\n\tvpp::pixel_wise(hess_subX22, XD22) | add_to_hessian;\n }\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->YD11\" << std::endl;\n #endif\n // Vertical Second Derivatives weighting\n //... w.r.t. first arguments\n {\n\thessian_type YD11(without_last_row);\n vpp::pixel_wise(YD11, weightsY_ | without_last_row, data_.img_ | without_last_row, data_.img_ | without_first_row) | calc_xx_der;\n\t#ifdef TV_FUNC_DEBUG \n\t output_matval_img(YD11,\"YD11.csv\");\n\t#endif\n\tauto hess_subY11 = hessian | without_last_row;\n\tvpp::pixel_wise(hess_subY11, YD11) | add_to_hessian;\n }\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->YD22\" << std::endl;\n #endif\n //... w.r.t. second arguments\n {\n\thessian_type YD22(without_last_row);\n vpp::pixel_wise(YD22, weightsY_ | without_last_row, data_.img_ | without_last_row, data_.img_ | without_first_row) | calc_yy_der;\n\t#ifdef TV_FUNC_DEBUG \n\t output_matval_img(YD22,\"YD22.csv\");\n\t#endif\n\tauto hess_subY22 = hessian | without_first_row;\n\tvpp::pixel_wise(hess_subY22, YD22) | [&] (deriv2_type& h, const deriv2_type& d) { h+=d; };\n }\n \n #ifdef TV_FUNC_DEBUG\n output_matval_img(T_, \"T.csv\");\n\toutput_matval_img(hessian, \"NonMixedHessian.csv\");\n #endif\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Local to global insert\" << std::endl;\n #endif\n // Insert elementwise into sparse Hessian\n // NOTE: Eventually make single version for both cases, including an offset\n // --> additional parameters sparse_mat, offset\n int row_offset=0;\n int col_offset=0;\n auto local2globalInsertHTV = [&](const tm_base_type& t1, const tm_base_type& t2, const deriv2_type& h, const vpp::vint2 coord) { \n\tint pos = manifold_dim*(coord[0]+nr*coord[1]); // columnwise flattening\n\trestricted_deriv2_type ht = t1.transpose()*h*t2;\n\t #ifdef TV_FUNC_DEBUG_VERBOSE2\n\t\tstd::cout << \"\\n\\nPos: \" << pos << \" coord[0]=\" << coord[0] << \" coord[1]=\" << coord[1] << std::endl;\n\t\tstd::cout << \"\\t\\tT_left:\" << std::endl;\n\t\tstd::cout << t1 << std::endl;\n\t\tstd::cout << \"\\t\\tHbig:\" << std::endl;\n\t\tstd::cout << h << std::endl;\n\t\tstd::cout << \"\\t\\tT_right:\" << std::endl;\n\t\tstd::cout << t2 << std::endl;\n\t\tstd::cout << \"\\t\\tHsmall:\" << std::endl;\n\t\tstd::cout << ht << std::endl;\n\t #endif\n\tfor(int local_row=0; local_row 0 || col_offset > 0)\n\t\t\ttriplist.push_back(Trip(global_col,global_row,e));\n\t\t //HTV.insert(global_row, global_col) = e;\n\t\t //if(global_col != global_row)\n\t\t //\tHTV.insert(global_col, global_row) = e;\n\t\t}\n\t }\n };\n vpp::pixel_wise(T_, T_, hessian, hessian.domain())(vpp::_no_threads) | local2globalInsertHTV;\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->XD12\" << std::endl;\n #endif \n // Horizontal Second Derivatives and weighting\n // ... w.r.t. first and second arguments \n {\n\thessian_type XD12(without_last_col);\n\tvpp::pixel_wise(XD12, weightsX_ | without_last_col, data_.img_ | without_last_col, data_.img_ | without_first_col ) | calc_xy_der;\n\t#ifdef TV_FUNC_DEBUG \n\t output_matval_img(XD12,\"XD12.csv\");\n\t#endif\n\t#ifdef TV_FUNC_DEBUG_VERBOSE\n\t\tstd::cout << \"\\t\\t...Local to global insert:\" << std::endl;\n\t#endif\n\t// Offsets for upper nyth subdiagonal\n\trow_offset=0;\n\tcol_offset=manifold_dim*nr;\n\tvpp::pixel_wise(T_ | without_last_col, T_ | without_first_col, XD12, XD12.domain())(vpp::_no_threads) | local2globalInsertHTV;\n }\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->YD12\" << std::endl;\n #endif\n // Vertical Second Derivatives and weighting\n //... w.r.t. second arguments\n {\n\thessian_type YD12(data_.img_.domain());\n\tvpp::pixel_wise(YD12 | without_last_row, weightsY_ | without_last_row, data_.img_ | without_last_row, data_.img_ | without_first_row) | calc_xy_der;\n\t#ifdef TV_FUNC_DEBUG \n\t output_matval_img(YD12,\"YD12.csv\");\n #endif\n\t\n\t//Set last row to zero\n\tderiv2_type *lastrow = &YD12(nr-1,0);\n\tfor(int c=0; c< nc; c++) \n\t lastrow[c]=deriv2_type::Zero();\n\n\t#ifdef TV_FUNC_DEBUG_VERBOSE\n\t\tstd::cout << \"\\t\\t...Local to global insert:\" << std::endl;\n\t#endif\n\t// Offsets for first upper subdiagonal\n\trow_offset=0;\n\tcol_offset=manifold_dim;\n\tvpp::pixel_wise(T_ | without_last_row, T_ | without_first_row, YD12 | without_last_row, without_last_row)(vpp::_no_threads) | local2globalInsertHTV;\n\t\n\t//Manually insert last row\n\tfor(int c=0; c solver;\n\tsolver.analyzePattern(HJ_);\n\tsolver.factorize(HJ_);\n\tx = solver.solve(DJ_);\n\n\tstd::fstream f;\n\tf.open(\"Sol.csv\",std::fstream::out);\n\tf << x;\n\tf.close();*/\n #endif\n\n}\n\n\ntemplate \ntemplate < class IMG >\nvoid Functional::output_img(const IMG& img, const char* filename) const{\n int nr = img.nrows();\n int nc = img.ncols();\n\n std::fstream f;\n f.open(filename, std::fstream::out);\n Eigen::IOFormat CommaInitFmt(Eigen::StreamPrecision, Eigen::DontAlignCols, \", \", \", \", \"\", \"\", \"\", \"\");\n for (int r=0; r\ntemplate < class IMG >\nvoid Functional::output_matval_img(const IMG& img, const char* filename) const{\n int nr = img.nrows();\n int nc = img.ncols();\n\n std::fstream f;\n f.open(filename, std::fstream::out);\n Eigen::IOFormat CommaInitFmt(Eigen::StreamPrecision, Eigen::DontAlignCols, \", \", \", \", \"\", \"\", \"\", \"\\n\");\n for (int r=0; r\n#include \n#include \n#include \n#include \n#include \"./parser.hpp\"\n#include \"./lattice.hpp\"\n#include \"./site.hpp\"\n#include \"./search_equiv_sites.hpp\"\n#include \"./myhash.hpp\"\n#include \"./myindex.hpp\"\n#include \"./input.hpp\"\n#include \"./metroconf.hpp\"\n\nusing allclusters = std::vector>>>;\n\nvoid dispInput(std::shared_ptr in){\n\t// [INPUT]\n\tstd::cout << \"MAXBODY : \" << in->getDataByString(\"MAXBODY\") << std::endl;\n\tstd::cout << \"TRUNCATION : \" << in->getDataByString(\"TRUNCATION\") << std::endl;\n\tstd::cout << \"SPINCE : \" << in->getDataByString(\"SPINCE\") << std::endl;\n\tstd::cout << \"SPINPOSCAR : \" << in->getDataByString(\"SPINPOSCAR\") << std::endl;\n\t// [OPTION]\n\tstd::cout << \"PAIRTRUNC : \" << in->getDataByString(\"PAIRTRUNC\") << std::endl;\n\tstd::cout << \"TRITRUNC : \" << in->getDataByString(\"TRITRUNC\") << std::endl;\n\tstd::cout << \"QUADTRUNC : \" << in->getDataByString(\"QUADTRUNC\") << std::endl;\n\tstd::cout << \"CLUSTEROUT : \" << in->getDataByString(\"CLUSTEROUT\") << std::endl;\n\tstd::cout << \"CORRDUMP : \" << in->getDataByString(\"CORRDUMP\") << std::endl;\n}\n\nvoid showResult(int is_corrdump, const std::shared_ptr& in){\n\n\tconst ParseClusterOut parse_cluster_out(\"./cluster.out\");\n\n\tif( is_corrdump>0 ){\n\t\tConf2corr PoscarSpin(\"./poscar.spin\", in, parse_cluster_out.getLabel(), parse_cluster_out.getCluster());\n\n\t\tfor(int i=0; i pall_clusters_dummy(new allclusters());\n\t\tfor(int i=0; isize(); ++i){\n\t\t\tfor(const auto eci : ecicar.getEci() ){\n\t\t\t\tif( (i+1) == eci.first ) {\n\t\t\t\t\tpall_clusters_dummy->push_back((*(parse_cluster_out.getCluster()))[i]);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tMetroconf PoscarSpin(\"./poscar.spin\", in, parse_cluster_out.getLabel(), pall_clusters_dummy, ecicar.getEci(), nullptr, nullptr);\n\t\tPoscarSpin.setTotalEnergy();\n\t\tauto compositions = PoscarSpin.getCompositions();\n\n\t\tfor(int i=0; i in(new Input(\"getconf.ini\"));\n\n\tin->setData(\"MAXBODY\", num_max_body, true);\n\tin->setData(\"TRUNCATION\", truncation, true);\n\n\tin->setData(\"PRECISION\", precision);\n\tin->setData(\"PAIRTRUNC\", pair_truncation);\n\tin->setData(\"TRITRUNC\", triplet_truncation);\n\tin->setData(\"QUADTRUNC\", quadruplet_truncation);\n\n\tin->setData(\"CLUSTEROUT\", is_read);\n\tin->setData(\"CORRDUMP\", is_corrdump);\n\n\tif( pair_truncation == 0 ) pair_truncation = truncation;\n\tif( triplet_truncation == 0 ) triplet_truncation = truncation;\n\tif( quadruplet_truncation == 0 ) quadruplet_truncation = truncation;\n\n\tif( pair_truncation < triplet_truncation or triplet_truncation < quadruplet_truncation ) {\n\t\tstd::cerr << \"ERROR : TRITRUNC <= PAIRTRUNC(orTRUNATION) and QUADTRUNC <= TRITRUNC(orTRUNATION)\" << std::endl;\n\t\texit(1);\n\t}\n\n\tif( is_read>0 ){\n\t\tshowResult(is_corrdump, in);\n\t\treturn 1;\n\t}\n\n\tstd::string filename_poscar_in = \"poscar.spin\";\n\n\tParsePoscar poscar(filename_poscar_in.c_str());\n\tconst auto atoms_unit = poscar.getAtoms();\n\n\tEigen::Vector3d unit_x, unit_y, unit_z;\n\tunit_x << 1, 0, 0;\n\tunit_y << 0, 1, 0;\n\tunit_z << 0, 0, 1;\n\n\tdouble unit_length_x = (poscar.getLatticeBasis() * unit_x).norm();\n\tdouble unit_length_y = (poscar.getLatticeBasis() * unit_y).norm();\n\tdouble unit_length_z = (poscar.getLatticeBasis() * unit_z).norm();\n\n\tint N_unit = poscar.getAtoms().size();\n\t/* if use ceil, integer distance cannot be correctly handled. */\n\tint expand_x = std::ceil((pair_truncation*3.)/unit_length_x);\n\tint expand_y = std::ceil((pair_truncation*3.)/unit_length_y);\n\tint expand_z = std::ceil((pair_truncation*3.)/unit_length_z);\n\n\tposcar.expandPoscar(expand_x, expand_y, expand_z);\n\n\tconst auto atoms = poscar.getAtoms();\n\tconst int N = atoms.size();\n\n\t/* setting lattice and site */\n\tstd::vector> vec_lattices;\n\tstd::vector> vec_sites;\n\tsetSiteAndLattice(vec_lattices, vec_sites, poscar);\n\n\tstd::ofstream cluster_info(\"cluster.info\", std::ios::out );\n\tstd::ofstream cluster_out( \"cluster.out\", std::ios::out );\n\n\tfor( int i=0; igetLatticeNum() << \"] : 0 : 1 : 0 0 0 \" << std::endl;\n\t\tcluster_out << num_index << \" \";\n\t\tcluster_out << \"1\" << \" \";\n\t\tcluster_out << N_unit << \" \";\n\t\tcluster_out << N_unit << \" \";\n\t\tfor( int i=0; igetSiteNum() << \" \";\n\t\t}\n\t\tcluster_out << std::endl;\n\t\t++num_index;\n\t}\n\n\t/* set nbody = 2 */\n\tcluster_info << \" -- 2 body cluster\" << std::endl;\n\tstd::unordered_map index_pair_clusters;\n\tif( pair_truncation == 0 ) pair_truncation = truncation;\n\tsetSiteReferencesAndPairIndex(vec_lattices, vec_sites, index_pair_clusters, pair_truncation, precision);\n\n\tstd::vector sorted_vec_pairindex;\n\tfor(const auto& index_pair : index_pair_clusters){\n\t\tsorted_vec_pairindex.push_back(index_pair.first);\n\t}\n\tsort(sorted_vec_pairindex.begin(), sorted_vec_pairindex.end());\n\n\tcluster_info << \" # [index] : distance : multiplicity : coordination \" << std::endl;\n\tfor( const auto& pairindex : sorted_vec_pairindex ){\n\t\tcluster_info << num_index << \" : \";\n\t\tcluster_info << \"[\" << pairindex.lattice_index[0] << \",\" << pairindex.lattice_index[1] << \"] : \";\n\t\tconst auto pair_itr = vec_sites[0]->getLinkedSiteIterator(pairindex.distance);\n\t\tcluster_info << pairindex.distance << \" : \" << std::distance(pair_itr.first, pair_itr.second) << \" : \";\n\t\tcluster_info << index_pair_clusters[pairindex].transpose() << std::endl;\n\n\t\tfor( const auto& site : vec_sites ) {\n\t\t\tconst auto test_pair_itr = site->getLinkedSiteIterator(pairindex.distance);\n\t\t\tassert( std::distance(pair_itr.first, pair_itr.second) == std::distance(test_pair_itr.first, test_pair_itr.second) );\n\t\t}\n\n\t\tcluster_out << num_index << \" \";\n\t\tcluster_out << \"2\" << \" \";\n\t\tcluster_out << N_unit << \" \";\n\t\tcluster_out << N_unit * std::distance(pair_itr.first, pair_itr.second) << \" \";\n\t\tfor(int i=0; igetLinkedSiteIterator(pairindex.distance);\n\t\t\twhile( pair_itr.first != pair_itr.second ){\n\t\t\t\tcluster_out << vec_sites[i]->getSiteNum() << \" \" << (*(pair_itr.first)).site->getSiteNum() << \" \";\n\t\t\t\t++pair_itr.first;\n\t\t\t}\n\t\t}\n\t\tcluster_out << std::endl;\n\t\t++num_index;\n\t}\n\tif( num_max_body == 2 ){\n\t\tcluster_out.close();\n\t\tcluster_info.close();\n\t\tshowResult(is_corrdump, in);\n\t\treturn 1;\n\t}\n\n\t/* set nbody = 3 */\n\tcluster_info << \" -- 3 body cluster\" << std::endl;\n\tstd::vector keys_index_triplet_cluster;\n\tstd::unordered_map>, hash_tripletindex> index_triplet_cluster;\n\tsetTripletIndex(vec_lattices, vec_sites, sorted_vec_pairindex, keys_index_triplet_cluster, index_triplet_cluster, triplet_truncation, precision);\n\n\tfor(int i=2; i>=0; --i){\n\t\tstable_sort(keys_index_triplet_cluster.begin(), keys_index_triplet_cluster.end(), [i](const TripletIndex& lhs, const TripletIndex& rhs){\n\t\t\t\treturn lhs.vec_pairindex[i] < rhs.vec_pairindex[i];\n\t\t});\n\t}\n\n\tfor(const auto& tripletindex : keys_index_triplet_cluster){\n\n\t\tcluster_info << num_index << \" : \";\n\t\tcluster_info << \"[] : \";\n\t\tcluster_info << \" [ \";\n\t\tcluster_info << tripletindex.vec_pairindex[0].distance << \",\";\n\t\tcluster_info << tripletindex.vec_pairindex[1].distance << \",\";\n\t\tcluster_info << tripletindex.vec_pairindex[2].distance;\n\t\tcluster_info << \" ] : \";\n\t\tcluster_info << index_triplet_cluster[tripletindex].size() << \" : \";\n\n\t\tcluster_info << index_triplet_cluster[tripletindex][0][0].transpose() << \",\";\n\t\tcluster_info << index_triplet_cluster[tripletindex][0][1].transpose() << std::endl;\n\n\t\tcluster_out << num_index << \" \";\n\t\tcluster_out << \"3\" << \" \";\n\t\tcluster_out << N_unit << \" \";\n\t\tcluster_out << N_unit*index_triplet_cluster[tripletindex].size() << \" \";\n\t\tfor(int i=0; igetSiteNum() << \" \";\n\t\t\t\tcluster_out << vec_sites[i]->getLinkedSite(index_triplet_cluster[tripletindex][j][0])->getSiteNum() << \" \";\n\t\t\t\tcluster_out << vec_sites[i]->getLinkedSite(index_triplet_cluster[tripletindex][j][1])->getSiteNum() << \" \";\n\t\t\t}\n\t\t}\n\t\tcluster_out << std::endl;\n\t\t++num_index;\n\t}\n\n\tif( num_max_body == 3 ){\n\t\tcluster_out.close();\n\t\tcluster_info.close();\n\t\tshowResult(is_corrdump, in);\n\t\treturn 1;\n\t}\n\n\t// /* set nbody = 4 */\n\tcluster_info << \" -- 4 body cluster\" << std::endl;\n\tstd::vector keys_index_quadruplet_cluster;\n\tstd::unordered_map>, hash_quadrupletindex> index_quadruplet_cluster;\n\tsetQuadrupletIndex(vec_lattices, vec_sites, sorted_vec_pairindex, index_triplet_cluster, keys_index_quadruplet_cluster, index_quadruplet_cluster, quadruplet_truncation, precision);\n\n\tfor(int i=3; i>=0; --i){\n\t\tfor(int j=2; j>=0; --j){\n\t\tstable_sort(keys_index_quadruplet_cluster.begin(), keys_index_quadruplet_cluster.end(),\n\t\t\t[i,j](const QuadrupletIndex& lhs, const QuadrupletIndex& rhs){\n\t\t\t\treturn lhs.vec_tripletindex[i].vec_pairindex[j] < rhs.vec_tripletindex[i].vec_pairindex[j];\n\t\t\t});\n\t\t}\n\t}\n\n\tfor(const auto& key_index_quadruplet_cluster : keys_index_quadruplet_cluster){\n\n\t\tcluster_info << num_index << \" : \";\n\t\tcluster_info << \"[] : \";\n\t\tcluster_info << \" [ \";\n\t\tfor( const auto distance : key_index_quadruplet_cluster.getVecDistance() ){\n\t\t\tcluster_info << distance << \",\";\n\t\t}\n\t\tcluster_info << \" ] : \";\n\t\tcluster_info << index_quadruplet_cluster[key_index_quadruplet_cluster].size() << \" : \";\n\n\t\tcluster_info << index_quadruplet_cluster[key_index_quadruplet_cluster][0][0].transpose() << \",\";\n\t\tcluster_info << index_quadruplet_cluster[key_index_quadruplet_cluster][0][1].transpose() << \",\";\n\t\tcluster_info << index_quadruplet_cluster[key_index_quadruplet_cluster][0][2].transpose() << std::endl;\n\n\t\tcluster_out << num_index << \" \";\n\t\tcluster_out << \"4\" << \" \";\n\t\tcluster_out << N_unit << \" \";\n\t\tcluster_out << N_unit*index_quadruplet_cluster[key_index_quadruplet_cluster].size() << \" \";\n\t\tfor(int i=0; igetSiteNum() << \" \";\n\t\t\t\tcluster_out << vec_sites[i]->getLinkedSite(index_quadruplet_cluster[key_index_quadruplet_cluster][j][0])->getSiteNum() << \" \";\n\t\t\t\tcluster_out << vec_sites[i]->getLinkedSite(index_quadruplet_cluster[key_index_quadruplet_cluster][j][1])->getSiteNum() << \" \";\n\t\t\t\tcluster_out << vec_sites[i]->getLinkedSite(index_quadruplet_cluster[key_index_quadruplet_cluster][j][2])->getSiteNum() << \" \";\n\t\t\t}\n\t\t}\n\t\tcluster_out << std::endl;\n\t\t++num_index;\n\t}\n\tcluster_out.close();\n\tcluster_info.close();\n\n\tshowResult(is_corrdump, in);\n\treturn 1;\n}\n", "meta": {"hexsha": "c022457776593a1a625c2c8802781b612a991bd1", "size": 11965, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/getconf.cpp", "max_stars_repo_name": "KazuhitoT/wlce", "max_stars_repo_head_hexsha": "0ec6551340756f46f7cc634576af63e1cc81bba8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/getconf.cpp", "max_issues_repo_name": "KazuhitoT/wlce", "max_issues_repo_head_hexsha": "0ec6551340756f46f7cc634576af63e1cc81bba8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/getconf.cpp", "max_forks_repo_name": "KazuhitoT/wlce", "max_forks_repo_head_hexsha": "0ec6551340756f46f7cc634576af63e1cc81bba8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.2268370607, "max_line_length": 182, "alphanum_fraction": 0.6830756373, "num_tokens": 3560, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6584175005616829, "lm_q2_score": 0.36658973632215985, "lm_q1q2_score": 0.24136909792080288}} {"text": "///////////////////////////////////////////////////////////////\n// Copyright 2020 Madhur Chauhan. \n// Copyright 2020 John Maddock. Distributed under the Boost\n// Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at https://www.boost.org/LICENSE_1_0.txt\n\n#ifndef BOOST_MP_ADD_UNSIGNED_ADDC_32_HPP\n#define BOOST_MP_ADD_UNSIGNED_ADDC_32_HPP\n\n#include \n#include \n\nnamespace boost { namespace multiprecision { namespace backends {\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void add_unsigned_constexpr(CppInt1& result, const CppInt2& a, const CppInt3& b) noexcept(is_non_throwing_cpp_int::value)\n{\n using ::boost::multiprecision::std_constexpr::swap;\n //\n // This is the generic, C++ only version of addition.\n // It's also used for all constexpr branches, hence the name.\n // Nothing fancy, just let uintmax_t take the strain:\n //\n double_limb_type carry = 0;\n std::size_t m(0), x(0);\n std::size_t as = a.size();\n std::size_t bs = b.size();\n minmax(as, bs, m, x);\n if (x == 1)\n {\n bool s = a.sign();\n result = static_cast(*a.limbs()) + static_cast(*b.limbs());\n result.sign(s);\n return;\n }\n result.resize(x, x);\n typename CppInt2::const_limb_pointer pa = a.limbs();\n typename CppInt3::const_limb_pointer pb = b.limbs();\n typename CppInt1::limb_pointer pr = result.limbs();\n typename CppInt1::limb_pointer pr_end = pr + m;\n\n if (as < bs)\n swap(pa, pb);\n\n // First where a and b overlap:\n while (pr != pr_end)\n {\n carry += static_cast(*pa) + static_cast(*pb);\n#ifdef __MSVC_RUNTIME_CHECKS\n *pr = static_cast(carry & ~static_cast(0));\n#else\n *pr = static_cast(carry);\n#endif\n carry >>= CppInt1::limb_bits;\n ++pr, ++pa, ++pb;\n }\n pr_end += x - m;\n // Now where only a has digits:\n while (pr != pr_end)\n {\n if (!carry)\n {\n if (pa != pr)\n std_constexpr::copy(pa, pa + (pr_end - pr), pr);\n break;\n }\n carry += static_cast(*pa);\n#ifdef __MSVC_RUNTIME_CHECKS\n *pr = static_cast(carry & ~static_cast(0));\n#else\n *pr = static_cast(carry);\n#endif\n carry >>= CppInt1::limb_bits;\n ++pr, ++pa;\n }\n if (carry)\n {\n // We overflowed, need to add one more limb:\n result.resize(x + 1, x + 1);\n if (result.size() > x)\n result.limbs()[x] = static_cast(1u);\n }\n result.normalize();\n result.sign(a.sign());\n}\n//\n// Core subtraction routine for all non-trivial cpp_int's:\n//\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void subtract_unsigned_constexpr(CppInt1& result, const CppInt2& a, const CppInt3& b) noexcept(is_non_throwing_cpp_int::value)\n{\n using ::boost::multiprecision::std_constexpr::swap;\n //\n // This is the generic, C++ only version of subtraction.\n // It's also used for all constexpr branches, hence the name.\n // Nothing fancy, just let uintmax_t take the strain:\n //\n double_limb_type borrow = 0;\n std::size_t m(0), x(0);\n minmax(a.size(), b.size(), m, x);\n //\n // special cases for small limb counts:\n //\n if (x == 1)\n {\n bool s = a.sign();\n limb_type al = *a.limbs();\n limb_type bl = *b.limbs();\n if (bl > al)\n {\n ::boost::multiprecision::std_constexpr::swap(al, bl);\n s = !s;\n }\n result = al - bl;\n result.sign(s);\n return;\n }\n // This isn't used till later, but comparison has to occur before we resize the result,\n // as that may also resize a or b if this is an inplace operation:\n int c = a.compare_unsigned(b);\n // Set up the result vector:\n result.resize(x, x);\n // Now that a, b, and result are stable, get pointers to their limbs:\n typename CppInt2::const_limb_pointer pa = a.limbs();\n typename CppInt3::const_limb_pointer pb = b.limbs();\n typename CppInt1::limb_pointer pr = result.limbs();\n bool swapped = false;\n if (c < 0)\n {\n swap(pa, pb);\n swapped = true;\n }\n else if (c == 0)\n {\n result = static_cast(0);\n return;\n }\n\n std::size_t i = 0;\n // First where a and b overlap:\n while (i < m)\n {\n borrow = static_cast(pa[i]) - static_cast(pb[i]) - borrow;\n pr[i] = static_cast(borrow);\n borrow = (borrow >> CppInt1::limb_bits) & 1u;\n ++i;\n }\n // Now where only a has digits, only as long as we've borrowed:\n while (borrow && (i < x))\n {\n borrow = static_cast(pa[i]) - borrow;\n pr[i] = static_cast(borrow);\n borrow = (borrow >> CppInt1::limb_bits) & 1u;\n ++i;\n }\n // Any remaining digits are the same as those in pa:\n if ((x != i) && (pa != pr))\n std_constexpr::copy(pa + i, pa + x, pr + i);\n BOOST_MP_ASSERT(0 == borrow);\n\n //\n // We may have lost digits, if so update limb usage count:\n //\n result.normalize();\n result.sign(a.sign());\n if (swapped)\n result.negate();\n}\n\n\n#ifdef BOOST_MP_HAS_IMMINTRIN_H\n//\n// This is the key addition routine where all the argument types are non-trivial cpp_int's:\n//\n//\n// This optimization is limited to: GCC, LLVM, ICC (Intel), MSVC for x86_64 and i386.\n// If your architecture and compiler supports ADC intrinsic, please file a bug\n//\n// As of May, 2020 major compilers don't recognize carry chain though adc\n// intrinsics are used to hint compilers to use ADC and still compilers don't\n// unroll the loop efficiently (except LLVM) so manual unrolling is done.\n//\n// Also note that these intrinsics were only introduced by Intel as part of the\n// ADX processor extensions, even though the addc instruction has been available\n// for basically all x86 processors. That means gcc-9, clang-9, msvc-14.2 and up\n// are required to support these intrinsics.\n//\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void add_unsigned(CppInt1& result, const CppInt2& a, const CppInt3& b) noexcept(is_non_throwing_cpp_int::value)\n{\n#ifndef BOOST_MP_NO_CONSTEXPR_DETECTION\n if (BOOST_MP_IS_CONST_EVALUATED(a.size()))\n {\n add_unsigned_constexpr(result, a, b);\n }\n else\n#endif\n {\n using std::swap;\n\n // Nothing fancy, just let uintmax_t take the strain:\n std::size_t m(0), x(0);\n std::size_t as = a.size();\n std::size_t bs = b.size();\n minmax(as, bs, m, x);\n if (x == 1)\n {\n bool s = a.sign();\n result = static_cast(*a.limbs()) + static_cast(*b.limbs());\n result.sign(s);\n return;\n }\n result.resize(x, x);\n typename CppInt2::const_limb_pointer pa = a.limbs();\n typename CppInt3::const_limb_pointer pb = b.limbs();\n typename CppInt1::limb_pointer pr = result.limbs();\n\n if (as < bs)\n swap(pa, pb);\n // First where a and b overlap:\n std::size_t i = 0;\n unsigned char carry = 0;\n#if defined(BOOST_MSVC) && !defined(BOOST_HAS_INT128) && defined(_M_X64)\n //\n // Special case for 32-bit limbs on 64-bit architecture - we can process\n // 2 limbs with each instruction.\n //\n for (; i + 8 <= m; i += 8)\n {\n carry = _addcarry_u64(carry, *(unsigned long long*)(pa + i + 0), *(unsigned long long*)(pb + i + 0), (unsigned long long*)(pr + i));\n carry = _addcarry_u64(carry, *(unsigned long long*)(pa + i + 2), *(unsigned long long*)(pb + i + 2), (unsigned long long*)(pr + i + 2));\n carry = _addcarry_u64(carry, *(unsigned long long*)(pa + i + 4), *(unsigned long long*)(pb + i + 4), (unsigned long long*)(pr + i + 4));\n carry = _addcarry_u64(carry, *(unsigned long long*)(pa + i + 6), *(unsigned long long*)(pb + i + 6), (unsigned long long*)(pr + i + 6));\n }\n#else\n for (; i + 4 <= m; i += 4)\n {\n carry = ::boost::multiprecision::detail::addcarry_limb(carry, pa[i + 0], pb[i + 0], pr + i);\n carry = ::boost::multiprecision::detail::addcarry_limb(carry, pa[i + 1], pb[i + 1], pr + i + 1);\n carry = ::boost::multiprecision::detail::addcarry_limb(carry, pa[i + 2], pb[i + 2], pr + i + 2);\n carry = ::boost::multiprecision::detail::addcarry_limb(carry, pa[i + 3], pb[i + 3], pr + i + 3);\n }\n#endif\n for (; i < m; ++i)\n carry = ::boost::multiprecision::detail::addcarry_limb(carry, pa[i], pb[i], pr + i);\n for (; i < x && carry; ++i)\n // We know carry is 1, so we just need to increment pa[i] (ie add a literal 1) and capture the carry:\n carry = ::boost::multiprecision::detail::addcarry_limb(0, pa[i], 1, pr + i);\n if (i == x && carry)\n {\n // We overflowed, need to add one more limb:\n result.resize(x + 1, x + 1);\n if (result.size() > x)\n result.limbs()[x] = static_cast(1u);\n }\n else if ((x != i) && (pa != pr))\n // Copy remaining digits only if we need to:\n std_constexpr::copy(pa + i, pa + x, pr + i);\n result.normalize();\n result.sign(a.sign());\n }\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void subtract_unsigned(CppInt1& result, const CppInt2& a, const CppInt3& b) noexcept(is_non_throwing_cpp_int::value)\n{\n#ifndef BOOST_MP_NO_CONSTEXPR_DETECTION\n if (BOOST_MP_IS_CONST_EVALUATED(a.size()))\n {\n subtract_unsigned_constexpr(result, a, b);\n }\n else\n#endif\n {\n using std::swap;\n\n // Nothing fancy, just let uintmax_t take the strain:\n std::size_t m(0), x(0);\n minmax(a.size(), b.size(), m, x);\n //\n // special cases for small limb counts:\n //\n if (x == 1)\n {\n bool s = a.sign();\n limb_type al = *a.limbs();\n limb_type bl = *b.limbs();\n if (bl > al)\n {\n ::boost::multiprecision::std_constexpr::swap(al, bl);\n s = !s;\n }\n result = al - bl;\n result.sign(s);\n return;\n }\n // This isn't used till later, but comparison has to occur before we resize the result,\n // as that may also resize a or b if this is an inplace operation:\n int c = a.compare_unsigned(b);\n // Set up the result vector:\n result.resize(x, x);\n // Now that a, b, and result are stable, get pointers to their limbs:\n typename CppInt2::const_limb_pointer pa = a.limbs();\n typename CppInt3::const_limb_pointer pb = b.limbs();\n typename CppInt1::limb_pointer pr = result.limbs();\n bool swapped = false;\n if (c < 0)\n {\n swap(pa, pb);\n swapped = true;\n }\n else if (c == 0)\n {\n result = static_cast(0);\n return;\n }\n\n std::size_t i = 0;\n unsigned char borrow = 0;\n // First where a and b overlap:\n#if defined(BOOST_MSVC) && !defined(BOOST_HAS_INT128) && defined(_M_X64)\n //\n // Special case for 32-bit limbs on 64-bit architecture - we can process\n // 2 limbs with each instruction.\n //\n for (; i + 8 <= m; i += 8)\n {\n borrow = _subborrow_u64(borrow, *reinterpret_cast(pa + i), *reinterpret_cast(pb + i), reinterpret_cast(pr + i));\n borrow = _subborrow_u64(borrow, *reinterpret_cast(pa + i + 2), *reinterpret_cast(pb + i + 2), reinterpret_cast(pr + i + 2));\n borrow = _subborrow_u64(borrow, *reinterpret_cast(pa + i + 4), *reinterpret_cast(pb + i + 4), reinterpret_cast(pr + i + 4));\n borrow = _subborrow_u64(borrow, *reinterpret_cast(pa + i + 6), *reinterpret_cast(pb + i + 6), reinterpret_cast(pr + i + 6));\n }\n#else\n for(; i + 4 <= m; i += 4)\n {\n borrow = boost::multiprecision::detail::subborrow_limb(borrow, pa[i], pb[i], pr + i);\n borrow = boost::multiprecision::detail::subborrow_limb(borrow, pa[i + 1], pb[i + 1], pr + i + 1);\n borrow = boost::multiprecision::detail::subborrow_limb(borrow, pa[i + 2], pb[i + 2], pr + i + 2);\n borrow = boost::multiprecision::detail::subborrow_limb(borrow, pa[i + 3], pb[i + 3], pr + i + 3);\n }\n#endif\n for (; i < m; ++i)\n borrow = boost::multiprecision::detail::subborrow_limb(borrow, pa[i], pb[i], pr + i);\n\n // Now where only a has digits, only as long as we've borrowed:\n while (borrow && (i < x))\n {\n borrow = boost::multiprecision::detail::subborrow_limb(borrow, pa[i], 0, pr + i);\n ++i;\n }\n // Any remaining digits are the same as those in pa:\n if ((x != i) && (pa != pr))\n std_constexpr::copy(pa + i, pa + x, pr + i);\n BOOST_MP_ASSERT(0 == borrow);\n\n //\n // We may have lost digits, if so update limb usage count:\n //\n result.normalize();\n result.sign(a.sign());\n if (swapped)\n result.negate();\n } // constepxr.\n}\n\n#else\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void add_unsigned(CppInt1& result, const CppInt2& a, const CppInt3& b) noexcept(is_non_throwing_cpp_int::value)\n{\n add_unsigned_constexpr(result, a, b);\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void subtract_unsigned(CppInt1& result, const CppInt2& a, const CppInt3& b) noexcept(is_non_throwing_cpp_int::value)\n{\n subtract_unsigned_constexpr(result, a, b);\n}\n\n#endif\n\n} } } // namespaces\n\n\n#endif\n\n\n", "meta": {"hexsha": "1087e1f15e117a3835f1abcc0137949d8952d45f", "size": 14144, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/multiprecision/cpp_int/add_unsigned.hpp", "max_stars_repo_name": "mariospr/multiprecision", "max_stars_repo_head_hexsha": "4720edda9e3058ba68be8ae6c29342536b9ce142", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/boost/multiprecision/cpp_int/add_unsigned.hpp", "max_issues_repo_name": "mariospr/multiprecision", "max_issues_repo_head_hexsha": "4720edda9e3058ba68be8ae6c29342536b9ce142", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/boost/multiprecision/cpp_int/add_unsigned.hpp", "max_forks_repo_name": "mariospr/multiprecision", "max_forks_repo_head_hexsha": "4720edda9e3058ba68be8ae6c29342536b9ce142", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4536082474, "max_line_length": 208, "alphanum_fraction": 0.604638009, "num_tokens": 4046, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.24130556349315269}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\n\n// Copyright (c) 2007-2011 Barend Gehrels, Amsterdam, the Netherlands.\n// Copyright (c) 2008-2011 Bruno Lalande, Paris, France.\n// Copyright (c) 2009-2011 Mateusz Loskot, London, UK.\n\n// Parts of Boost.Geometry are redesigned from Geodan's Geographic Library\n// (geolib/GGL), copyright (c) 1995-2010 Geodan, Amsterdam, the Netherlands.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_GEOMETRY_ALGORITHMS_PERIMETER_HPP\n#define BOOST_GEOMETRY_ALGORITHMS_PERIMETER_HPP\n\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\nnamespace boost { namespace geometry\n{\n\n#ifndef DOXYGEN_NO_DISPATCH\nnamespace dispatch\n{\n\n// Default perimeter is 0.0, specializations implement calculated values\ntemplate \nstruct perimeter : detail::calculate_null\n <\n typename default_length_result::type,\n Geometry,\n Strategy\n >\n{};\n\ntemplate \nstruct perimeter\n : detail::length::range_length\n <\n Geometry,\n Strategy,\n closure::value\n >\n{};\n\ntemplate \nstruct perimeter\n : detail::calculate_polygon_sum\n <\n typename default_length_result::type,\n Polygon,\n Strategy,\n detail::length::range_length\n <\n typename ring_type::type,\n Strategy,\n closure::value\n >\n >\n{};\n\n\n// box,n-sphere: to be implemented\n\n} // namespace dispatch\n#endif // DOXYGEN_NO_DISPATCH\n\n\n/*!\n\\brief \\brief_calc{perimeter}\n\\ingroup perimeter\n\\details The function perimeter returns the perimeter of a geometry,\n using the default distance-calculation-strategy\n\\tparam Geometry \\tparam_geometry\n\\param geometry \\param_geometry\n\\return \\return_calc{perimeter}\n\n\\qbk{[include reference/algorithms/perimeter.qbk]}\n */\ntemplate\ninline typename default_length_result::type perimeter(\n Geometry const& geometry)\n{\n concept::check();\n\n typedef typename point_type::type point_type;\n typedef typename strategy::distance::services::default_strategy\n <\n point_tag, point_type\n >::type strategy_type;\n\n return dispatch::perimeter\n <\n typename tag::type,\n Geometry,\n strategy_type\n >::apply(geometry, strategy_type());\n}\n\n/*!\n\\brief \\brief_calc{perimeter} \\brief_strategy\n\\ingroup perimeter\n\\details The function perimeter returns the perimeter of a geometry,\n using specified strategy\n\\tparam Geometry \\tparam_geometry\n\\tparam Strategy \\tparam_strategy{distance}\n\\param geometry \\param_geometry\n\\param strategy strategy to be used for distance calculations.\n\\return \\return_calc{perimeter}\n\n\\qbk{distinguish,with strategy}\n\\qbk{[include reference/algorithms/perimeter.qbk]}\n */\ntemplate\ninline typename default_length_result::type perimeter(\n Geometry const& geometry, Strategy const& strategy)\n{\n concept::check();\n\n return dispatch::perimeter\n <\n typename tag::type,\n Geometry,\n Strategy\n >::apply(geometry, strategy);\n}\n\n}} // namespace boost::geometry\n\n#endif // BOOST_GEOMETRY_ALGORITHMS_PERIMETER_HPP\n\n", "meta": {"hexsha": "a0536d1da2fcfeec9907b1b5ecde853a6572500b", "size": 4025, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external/boost_1_47_0/boost/geometry/algorithms/perimeter.hpp", "max_stars_repo_name": "zigaosolin/Raytracer", "max_stars_repo_head_hexsha": "df17f77e814b2e4b90c4a194e18cc81fa84dcb27", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 47.0, "max_stars_repo_stars_event_min_datetime": "2015-01-01T14:37:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-25T07:38:07.000Z", "max_issues_repo_path": "external/boost_1_47_0/boost/geometry/algorithms/perimeter.hpp", "max_issues_repo_name": "zigaosolin/Raytracer", "max_issues_repo_head_hexsha": "df17f77e814b2e4b90c4a194e18cc81fa84dcb27", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6.0, "max_issues_repo_issues_event_min_datetime": "2016-01-11T05:20:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-06T11:37:24.000Z", "max_forks_repo_path": "external/boost_1_47_0/boost/geometry/algorithms/perimeter.hpp", "max_forks_repo_name": "zigaosolin/Raytracer", "max_forks_repo_head_hexsha": "df17f77e814b2e4b90c4a194e18cc81fa84dcb27", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 17.0, "max_forks_repo_forks_event_min_datetime": "2015-01-05T15:10:43.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-22T04:59:16.000Z", "avg_line_length": 28.75, "max_line_length": 79, "alphanum_fraction": 0.7095652174, "num_tokens": 855, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.24130556349315269}} {"text": "// This file is distributed under the MIT license.\n// See the LICENSE file for details.\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \n\n#include \n\n#include \n#include \n\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\nusing namespace visionaray;\n\nusing viewer_type = viewer_glut;\n\n\n//-------------------------------------------------------------------------------------------------\n// Phantom Ray-Hair Intersector (Reshetov and Luebke, 2018)\n//\n\n//-------------------------------------------------------------------------------------------------\n// Ray/cone intersection from appendix A\n//\n\nstruct RayConeIntersection\n{\n inline bool intersect(float r, float dr)\n {\n float r2 = r * r;\n float drr = r * dr;\n\n float ddd = cd.x * cd.x + cd.y * cd.y;\n dp = c0.x * c0.x + c0.y * c0.y;\n float cdd = c0.x * cd.x + c0.y * cd.y;\n float cxd = c0.x * cd.y - c0.y * cd.x;\n\n float c = ddd;\n float b = cd.z * (drr - cdd);\n float cdz2 = cd.z * cd.z;\n ddd += cdz2;\n float a = 2.0f * drr * cdd + cxd * cxd - ddd * r2 + dp * cdz2;\n\n float discr = b * b - a * c;\n s = (b - (discr > 0.0f ? sqrtf(discr) : 0.0f)) / c;\n dt = (s * cd.z - cdd) / ddd;\n dc = s * s + dp;\n sp = cdd / cd.z;\n dp += sp * sp;\n\n return discr > 0.0f;\n }\n\n vec3 c0;\n vec3 cd;\n float s;\n float dt;\n float dp;\n float dc;\n float sp;\n};\n\n\ninline bool intersectCylinder(basic_ray const& ray, vec3 p0, vec3 p1, float ra)\n{\n vec3 ba = p1 - p0;\n vec3 oc = ray.ori - p0;\n\n float baba = dot(ba, ba);\n float bard = dot(ba, ray.dir);\n float baoc = dot(ba, oc);\n\n float k2 = baba - bard * bard;\n float k1 = baba * dot(oc, ray.dir) - baoc * bard;\n float k0 = baba * dot(oc, oc) - baoc * baoc - ra * ra * baba;\n\n float h = k1 * k1 - k2 * k0;\n\n if (h < 0.0f)\n {\n return false;\n }\n\n h = sqrtf(h);\n float t = (-k1 - h) / k2;\n\n // body\n float y = baoc + t * bard;\n if (y > 0.0f && y < baba)\n {\n return true;\n }\n\n // caps\n t = ((y < 0.0f ? 0.0f : baba) - baoc) / bard;\n if (fabsf(k1 + k2 * t) < h)\n {\n return true;\n }\n\n return false;\n}\n\n\n//-------------------------------------------------------------------------------------------------\n// Curve class\n//\n\nstruct Curve : primitive\n{\n vec3 w0;\n vec3 w1;\n vec3 w2;\n vec3 w3;\n\n float r;\n\n vec3 f(float t) const\n {\n float tinv = 1.0f - t;\n return tinv * tinv * tinv * w0\n + 3.0f * tinv * tinv * t * w1\n + 3.0f * tinv * t * t * w2\n + t * t * t * w3;\n }\n\n vec3 dfdt(float t) const\n {\n float tinv = 1.0f - t;\n return -3.0f * tinv * tinv * w0\n + 3.0f * (3.0f * t * t - 4.0f * t + 1.0f) * w1\n + 3.0f * (2.0f - 3.0f * t) * t * w2\n + 3.0f * t * t * w3;\n }\n};\n\nCurve make_curve(vec3 const& w0, vec3 const& w1, vec3 const& w2, vec3 const& w3, float r)\n{\n Curve curve;\n curve.w0 = w0;\n curve.w1 = w1;\n curve.w2 = w2;\n curve.w3 = w3;\n curve.r = r;\n return curve;\n}\n\n\n// That's from here: https://www.shadertoy.com/view/MdKBWt\naabb get_bounds(Curve const& curve)\n{\n vec3 p0 = curve.w0;\n vec3 p1 = curve.w1;\n vec3 p2 = curve.w2;\n vec3 p3 = curve.w3;\n\n // extremes\n vec3 mi = min(p0,p3);\n vec3 ma = max(p0,p3);\n\n // note pascal triangle coefficnets\n vec3 c = -1.0f*p0 + 1.0f*p1;\n vec3 b = 1.0f*p0 - 2.0f*p1 + 1.0f*p2;\n vec3 a = -1.0f*p0 + 3.0f*p1 - 3.0f*p2 + 1.0f*p3;\n\n vec3 h = b*b - a*c;\n\n // real solutions\n if (h.x > 0.0f || h.y > 0.0f || h.z > 0.0f)\n {\n vec3 g(\n sqrtf(fabsf(h.x)),\n sqrtf(fabsf(h.y)),\n sqrtf(fabsf(h.z))\n );\n vec3 t1 = clamp((-b - g)/a,vec3(0.0f),vec3(1.0f)); vec3 s1 = 1.0f-t1;\n vec3 t2 = clamp((-b + g)/a,vec3(0.0f),vec3(1.0f)); vec3 s2 = 1.0f-t2;\n vec3 q1 = s1*s1*s1*p0 + 3.0f*s1*s1*t1*p1 + 3.0f*s1*t1*t1*p2 + t1*t1*t1*p3;\n vec3 q2 = s2*s2*s2*p0 + 3.0f*s2*s2*t2*p1 + 3.0f*s2*t2*t2*p2 + t2*t2*t2*p3;\n\n if (h.x > 0.0f)\n {\n mi.x = min(mi.x,min(q1.x,q2.x));\n ma.x = max(ma.x,max(q1.x,q2.x));\n }\n if (h.y > 0.0f)\n {\n mi.y = min(mi.y,min(q1.y,q2.y));\n ma.y = max(ma.y,max(q1.y,q2.y));\n }\n if (h.z > 0.0f)\n {\n mi.z = min(mi.z,min(q1.z,q2.z));\n ma.z = max(ma.z,max(q1.z,q2.z));\n }\n }\n\n return aabb(mi - vec3(curve.r), ma + vec3(curve.r));\n}\n\n\nstd::pair split(Curve const& curve)\n{\n std::pair result;\n\n vec3 p0 = curve.w0;\n vec3 p1 = curve.w1;\n vec3 p2 = curve.w2;\n vec3 p3 = curve.w3;\n\n vec3 q0 = (p0 + p1) / 2.0f;\n vec3 q1 = (p1 + p2) / 2.0f;\n vec3 q2 = (p2 + p3) / 2.0f;\n\n vec3 r0 = (q0 + q1) / 2.0f;\n vec3 r1 = (q1 + q2) / 2.0f;\n\n vec3 s0 = (r0 + r1) / 2.0f;\n\n result.first = make_curve(p0, q0, r0, s0, curve.r);\n result.second = make_curve(s0, r1, q2, p3, curve.r);\n\n return result;\n}\n\n\n//-------------------------------------------------------------------------------------------------\n// Transform entities to ray-centric coordinate system RCC\n//\n\nstruct TransformToRCC\n{\n inline TransformToRCC(basic_ray const& r)\n {\n vec3 e1;\n vec3 e2;\n vec3 e3 = normalize(r.dir);\n make_orthonormal_basis(e1, e2, e3);\n xformInv = mat4(\n vec4(e1, 0.0f),\n vec4(e2, 0.0f),\n vec4(e3, 0.0f),\n vec4(r.ori, 1.0f)\n );\n xform = inverse(xformInv);\n }\n\n inline vec3 xfmPoint(vec3 point)\n {\n return (xform * vec4(point, 1.0f)).xyz();\n }\n\n inline vec3 xfmVector(vec3 vector)\n {\n return (xform * vec4(vector, 0.0f)).xyz();\n }\n\n inline vec3 xfmPointInv(vec3 point)\n {\n return (xformInv * vec4(point, 1.0f)).xyz();\n }\n\n inline vec3 xfmVectorInv(vec3 vector)\n {\n return (xformInv * vec4(vector, 0.0f)).xyz();\n }\n\n mat4 xform;\n mat4 xformInv;\n};\n\ninline hit_record, primitive> intersect(basic_ray const& r, Curve const& curve)\n{\n hit_record, primitive> result;\n result.hit = false;\n\n // Early exit check against enclosing cylinder\n auto distToCylinder = [&curve](vec3 pt) {\n return length(cross(pt - curve.w0, pt - curve.w3)) / length(curve.w3 - curve.w0);\n };\n\n // TODO: could compute tighter bounding cylinder than this one!\n float rmax = distToCylinder(curve.f(0.33333f));\n rmax = fmaxf(rmax, distToCylinder(curve.f(0.66667f)));\n rmax += curve.r;\n\n vec3 axis = normalize(curve.w3 - curve.w0);\n vec3 p0 = curve.w0 - axis * curve.r;\n vec3 p1 = curve.w3 + axis * curve.r;\n\n if (!intersectCylinder(r, p0, p1, rmax))\n {\n return result;\n }\n\n // Transform curve to RCC\n TransformToRCC rcc(r);\n Curve xcurve = make_curve(\n rcc.xfmPoint(curve.w0),\n rcc.xfmPoint(curve.w1),\n rcc.xfmPoint(curve.w2),\n rcc.xfmPoint(curve.w3),\n curve.r\n );\n\n // \"Test for convergence. If the intersection is found,\n // report it, otherwise start at the other endpoint.\"\n\n // Compute curve end to start at\n float tstart = dot(xcurve.w3 - xcurve.w0, r.dir) > 0.0f ? 0.0f : 1.0f;\n\n for (int ep = 0; ep < 2; ++ep)\n {\n float t = tstart;\n\n RayConeIntersection rci;\n\n float told = 0.0f;\n float dt1 = 0.0f;\n float dt2 = 0.0f;\n\n for (int i = 0; i < 40; ++i)\n {\n rci.c0 = xcurve.f(t);\n rci.cd = xcurve.dfdt(t);\n\n bool phantom = !rci.intersect(curve.r, 0.0f/*cylinder*/);\n\n // \"In all examples in this paper we stop iterations when dt < 5x10^−5\"\n if (!phantom && fabsf(rci.dt) < 5e-5f)\n {\n //vec3 n = normalize(curve.dfdt(t));\n rci.s += rci.c0.z;\n result.t = rci.s;\n result.u = t; // abuse param u to store curve's t\n result.hit = true;\n result.isect_pos = r.ori + result.t * r.dir;\n break;\n }\n\n rci.dt = min(rci.dt, 0.5f);\n rci.dt = max(rci.dt, -0.5f);\n\n dt1 = dt2;\n dt2 = rci.dt;\n\n // Regula falsi\n if (dt1 * dt2 < 0.0f)\n {\n float tnext = 0.0f;\n // \"we use the simplest possible approach by switching\n // to the bisection every 4th iteration:\"\n if ((i & 3) == 0)\n {\n tnext = 0.5f * (told + t);\n }\n else\n {\n tnext = (dt2 * told - dt1 * t) / (dt2 - dt1);\n }\n told = t;\n t = tnext;\n }\n else\n {\n told = t;\n t += rci.dt;\n }\n\n if (t < 0.0f || t > 1.0f)\n {\n break;\n }\n }\n\n if (!result.hit)\n {\n tstart = 1.0f - tstart;\n }\n else\n {\n break;\n }\n }\n\n return result;\n}\n\ninline vec3 get_normal(\n hit_record, primitive> const& hr,\n Curve const& curve\n )\n{\n float t = hr.u;\n vec3 curve_pos = curve.f(t);\n return normalize(hr.isect_pos - curve_pos);\n}\n\ninline float area(Curve const& curve)\n{\n VSNRAY_UNUSED(curve);\n\n // TODO: implement this to support curve lights!\n return -1.0f;\n}\n\ninline void split_primitive(aabb& L, aabb& R, float plane, int axis, Curve const& curve)\n{\n VSNRAY_UNUSED(L);\n VSNRAY_UNUSED(R);\n VSNRAY_UNUSED(plane);\n VSNRAY_UNUSED(axis);\n VSNRAY_UNUSED(curve);\n\n // TODO: implement this to support SBVHs\n}\n\n\n//-------------------------------------------------------------------------------------------------\n// I/O utility for camera lookat only - not fit for the general case!\n//\n\nstd::istream& operator>>(std::istream& in, pinhole_camera& cam)\n{\n vec3 eye;\n vec3 center;\n vec3 up;\n\n in >> eye >> std::ws >> center >> std::ws >> up >> std::ws;\n cam.look_at(eye, center, up);\n\n return in;\n}\n\nstd::ostream& operator<<(std::ostream& out, pinhole_camera const& cam)\n{\n out << cam.eye() << '\\n';\n out << cam.center() << '\\n';\n out << cam.up() << '\\n';\n return out;\n}\n\n\n//-------------------------------------------------------------------------------------------------\n// struct with state variables\n//\n\nstruct renderer : viewer_type\n{\n renderer()\n : viewer_type(512, 512, \"Visionaray Phantom Ray-Hair Intersector Example\")\n , host_sched(8)\n {\n using namespace support;\n\n // Add cmdline options\n add_cmdline_option( cl::makeOption(\n cl::Parser<>(),\n \"filename\",\n cl::Desc(\"Input file in wavefront obj format\"),\n cl::Positional,\n cl::Optional,\n cl::init(this->filename)\n ) );\n\n add_cmdline_option( cl::makeOption(\n cl::Parser<>(),\n \"camera\",\n cl::Desc(\"Text file with camera parameters\"),\n cl::ArgRequired,\n cl::init(this->initial_camera)\n ) );\n\n add_cmdline_option( cl::makeOption(\n cl::Parser<>(),\n \"spp\",\n cl::Desc(\"Pixels per sample for path tracing\"),\n cl::ArgRequired,\n cl::init(this->spp)\n ) );\n }\n\n void build_scene()\n {\n std::vector actual_curves;\n\n std::shared_ptr scene;\n\n if (!filename.empty())\n {\n try\n {\n boost::filesystem::path p(filename);\n std::string ext = p.extension().string();\n\n std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);\n\n std::cout << \"Try loading pbrt file...\\n\";\n\n if (ext == \".pbf\")\n {\n scene = pbrt::Scene::loadFrom(filename);\n }\n else if (ext == \".pbrt\")\n {\n scene = pbrt::importPBRT(filename);\n }\n\n for (pbrt::Shape::SP shape : scene->world->shapes)\n {\n if (pbrt::Curve::SP curve = std::dynamic_pointer_cast(shape))\n {\n if (curve->P.size() != 4)\n {\n continue;\n }\n\n vec3 w0(curve->P[0].x, curve->P[0].y, curve->P[0].z);\n vec3 w1(curve->P[1].x, curve->P[1].y, curve->P[1].z);\n vec3 w2(curve->P[2].x, curve->P[2].y, curve->P[2].z);\n vec3 w3(curve->P[3].x, curve->P[3].y, curve->P[3].z);\n // TODO: phantom _should_ also support two radii!\n float r = (curve->width0 + curve->width1) * 0.5f;\n\n actual_curves.push_back(make_curve(w0, w1, w2, w3, r));\n }\n }\n }\n catch (std::runtime_error e)\n {\n std::cout << \"Failed: \" << e.what() << '\\n';\n // ignore\n }\n }\n\n // Add some dummy data when file couldn't be loaded / file name was empty\n if (actual_curves.empty())\n {\n actual_curves.push_back(make_curve({0.0f, 0.0f, 0.0f}, {1.0f, 0.0f, 0.5f}, {2.0f, 0.5f, 0.0f}, {3.0f, 0.0f, 0.0f}, 0.3f));\n actual_curves.push_back(make_curve({0.0f, 0.0f, 0.0f}, {1.0f, 1.0f, 0.0f}, {2.0f, 2.0f, 0.0f}, {3.0f, 3.0f, 0.0f}, 0.08f));\n actual_curves.push_back(make_curve({0.0f, 0.0f, 0.0f}, {0.0f, 20.0f, 1.0f}, {0.0f, 0.0f, 2.0f}, {0.0f, 0.0f, 3.0f}, 0.04f));\n actual_curves.push_back(make_curve({0.0f, 0.0f, 0.0f}, {0.0f, 1.0f, 1.0f}, {0.0f, 1.0f, 2.0f}, {0.0f, 0.0f, 3.0f}, 0.08f));\n actual_curves.push_back(make_curve({-1.0f, 2.0f, 0.0f}, {0.0f, -2.0f, 0.0f}, {1.0f, 4.0f, 0.0f}, {2.0f, -4.0f, 0.0f}, 0.02f));\n actual_curves.push_back(make_curve({-5.0f, 2.0f, 0.0f}, {0.0f, -0.5f, 1.0f}, {1.0f, 4.0f, 0.0f}, {2.0f, -4.0f, 8.0f}, 0.07f));\n actual_curves.push_back(make_curve({0.0f, 10.0f, 0.0f}, {0.0f, 11.0f, 11.0f}, {0.0f, -11.0f, 0.0f}, {0.0f, -10.0f, 0.0f}, 0.3f));\n actual_curves.push_back(make_curve({3.0f, 8.0f, 0.0f}, {0.0f, 15.0f, 11.0f}, {0.0f, -11.0f, 0.0f}, {7.0f, -10.0f, 0.0f}, 0.2f));\n actual_curves.push_back(make_curve({-10.0f, 20.0f, 0.0f}, {0.0f, -20.0f, 0.0f}, {1.0f, 4.0f, 0.0f}, {2.0f, -4.0f, 0.0f}, 0.1f));\n actual_curves.push_back(make_curve({0.0f, 20.0f, 0.0f}, {1.0f, 2.0f, 4.5f}, {2.0f, 3.5f, 0.0f}, {3.0f, 4.0f, 0.0f}, 0.3f));\n }\n\n for (auto const& curve : actual_curves)\n {\n auto p0 = split(curve);\n auto p00 = split(p0.first);\n auto p01 = split(p0.second);\n // auto p000 = split(p00.first);\n // auto p001 = split(p00.second);\n // auto p010 = split(p01.first);\n // auto p011 = split(p01.second);\n\n // 4x\n curves.push_back(p00.first);\n curves.push_back(p00.second);\n curves.push_back(p01.first);\n curves.push_back(p01.second);\n\n // 8x\n // curves.push_back(p000.first);\n // curves.push_back(p000.second);\n // curves.push_back(p001.first);\n // curves.push_back(p001.second);\n // curves.push_back(p010.first);\n // curves.push_back(p010.second);\n // curves.push_back(p011.first);\n // curves.push_back(p011.second);\n }\n\n for (unsigned i = 0; i < curves.size(); ++i)\n {\n curves[i].prim_id = i;\n curves[i].geom_id = 0;\n }\n\n std::cout << \"Curves loaded: \" << actual_curves.size() << '\\n';\n std::cout << \"Curves after splitting: \" << curves.size() << '\\n';\n\n matte m;\n m.cd() = from_rgb(0.78f, 0.70f, 0.55f);\n m.kd() = 1.0f;\n materials.emplace_back(m);\n\n binned_sah_builder builder;\n // TODO: implement get_split() for Curve to support spatial splits / SBVH!\n builder.enable_spatial_splits(false);\n\n std::cout << \"Building BVH...\\n\";\n bvh = builder.build(index_bvh{}, curves.data(), curves.size());\n std::cout << \"Done!\\n\";\n\n bbox.invalidate();\n\n for (auto const& curve : curves)\n {\n bboxes.push_back(get_bounds(curve));\n bbox.insert(get_bounds(curve));\n }\n }\n\n thin_lens_camera cam;\n cpu_buffer_rt host_rt;\n tiled_sched> host_sched;\n\n std::vector curves;\n std::vector bboxes;\n std::vector> materials;\n index_bvh bvh;\n\n aabb bbox;\n\n unsigned frame_num = 0;\n unsigned spp = 1;\n vec3 ambient = vec3(1.0f, 1.0f, 1.0f);\n\n std::string filename;\n std::string initial_camera;\n\nprotected:\n\n void load_camera(std::string filename);\n void screenshot();\n void on_display();\n void on_key_press(visionaray::key_event const& event);\n void on_mouse_move(visionaray::mouse_event const& event);\n void on_resize(int w, int h);\n\n};\n\n\n//-------------------------------------------------------------------------------------------------\n// Load camera from file, reset frame counter and clear frame\n//\n\nvoid renderer::load_camera(std::string filename)\n{\n std::ifstream file(filename);\n if (file.good())\n {\n file >> cam;\n frame_num = 0;\n host_rt.clear_color_buffer();\n std::cout << \"Load camera from file: \" << filename << '\\n';\n }\n}\n\n\n//-------------------------------------------------------------------------------------------------\n// Take a screenshot\n//\n\nvoid renderer::screenshot()\n{\n std::string screenshot_file_base = \"screenshot\";\n#if VSNRAY_COMMON_HAVE_PNG\n static const std::string screenshot_file_suffix = \".png\";\n image::save_option opt1;\n#else\n static const std::string screenshot_file_suffix = \".pnm\";\n image::save_option opt1({\"binary\", true});\n#endif\n\n // Swizzle to RGB8 for compatibility with pnm image\n std::vector>> rgb(host_rt.width() * host_rt.height());\n swizzle(\n rgb.data(),\n PF_RGB8,\n host_rt.color(),\n PF_RGBA32F,\n host_rt.width() * host_rt.height(),\n TruncateAlpha\n );\n\n //if (rt.color_space() == host_device_rt::SRGB)\n {\n for (int y = 0; y < host_rt.height(); ++y)\n {\n for (int x = 0; x < host_rt.width(); ++x)\n {\n auto& color = rgb[y * host_rt.width() + x];\n color.x = powf(color.x, 1 / 2.2f);\n color.y = powf(color.y, 1 / 2.2f);\n color.z = powf(color.z, 1 / 2.2f);\n }\n }\n }\n\n // Flip so that origin is (top|left)\n std::vector>> flipped(host_rt.width() * host_rt.height());\n\n for (int y = 0; y < host_rt.height(); ++y)\n {\n for (int x = 0; x < host_rt.width(); ++x)\n {\n int yy = host_rt.height() - y - 1;\n flipped[yy * host_rt.width() + x] = rgb[y * host_rt.width() + x];\n }\n }\n\n image img(\n host_rt.width(),\n host_rt.height(),\n PF_RGB8,\n reinterpret_cast(flipped.data())\n );\n\n int inc = 0;\n std::string inc_str = \"\";\n\n std::string filename = screenshot_file_base + inc_str + screenshot_file_suffix;\n\n while (boost::filesystem::exists(filename))\n {\n ++inc;\n inc_str = std::to_string(inc);\n\n while (inc_str.length() < 4)\n {\n inc_str = std::string(\"0\") + inc_str;\n }\n\n inc_str = std::string(\"-\") + inc_str;\n\n filename = screenshot_file_base + inc_str + screenshot_file_suffix;\n }\n\n if (img.save(filename, {opt1}))\n {\n std::cout << \"Screenshot saved to file: \" << filename << '\\n';\n }\n else\n {\n std::cerr << \"Error saving screenshot to file: \" << filename << '\\n';\n }\n}\n\n\n//-------------------------------------------------------------------------------------------------\n// Display function\n//\n\nvoid renderer::on_display()\n{\n // some setup\n\n //-----------------------------------------------------\n // Setup scheduler parameters.\n //\n\n // We use path tracing. We therefore use the\n // jittered_blend_type pixel sampler. That way\n // a rand generator is created, and noisy\n // images are blended later on.\n\n // Note how we set sfactor and dfactor based on\n // frame_num so that pixel blending works.\n\n // Note: for the jittered_blend_type pixel sampler to\n // work properly, you have to count frames yourself!\n // (check the occurrences of renderer::frame_num in\n // this file!)\n\n pixel_sampler::jittered_blend_type blend_params;\n blend_params.spp = spp;\n float alpha = 1.0f / ++frame_num;\n blend_params.sfactor = alpha;\n blend_params.dfactor = 1.0f - alpha;\n\n // Note: alternative samplers are:\n // pixel_sampler::uniform_type\n // You can also leave this argument out, then you'll get\n // the default: pixel_sampler::uniform_type\n auto sparams = make_sched_params(\n blend_params,\n cam, // the camera object (note: could also be two matrices!)\n host_rt // render target, that's where we store the pixel result.\n );\n\n // Create bvh \"refs\" that we can pass to the\n // path tracing kernel\n using bvh_ref = index_bvh::bvh_ref;\n aligned_vector primitives;\n primitives.push_back(bvh.ref());\n\n // Construct a parameter object that is\n // compatible with the builtin path tracing kernel.\n auto kparams = make_kernel_params(\n primitives.data(),\n primitives.data() + primitives.size(),\n materials.data(),\n 4, // bounces\n 1e-5f, // scene epsilon\n vec4(background_color(), 1.0f),\n vec4(ambient, 1.0f)\n );\n\n //-----------------------------------------------------\n // Naive path tracing with the builtin kernel.\n //\n\n // Instantiate the path tracing kernel, and\n // call it by executing the scheduler's\n // frame() function.\n pathtracing::kernel kernel;\n kernel.params = kparams;\n host_sched.frame(kernel, sparams);\n\n\n // Display the rendered image with OpenGL.\n\n glEnable(GL_FRAMEBUFFER_SRGB);\n glClearColor(0.0, 0.0, 0.0, 1.0);\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);\n\n // You could also directly access host_rt::color()\n // or host_rt::depth() (this render target however\n // doesn't store a depth buffer).\n host_rt.display_color_buffer();\n}\n\n\nvoid renderer::on_key_press(visionaray::key_event const& event)\n{\n static const std::string camera_file_base = \"visionaray-camera\";\n static const std::string camera_file_suffix = \".txt\";\n\n switch (event.key())\n {\n case 'p':\n screenshot();\n break;\n\n case 'u':\n {\n int inc = 0;\n std::string inc_str = \"\";\n\n std::string filename = camera_file_base + inc_str + camera_file_suffix;\n\n while (boost::filesystem::exists(filename))\n {\n ++inc;\n inc_str = std::to_string(inc);\n\n while (inc_str.length() < 4)\n {\n inc_str = std::string(\"0\") + inc_str;\n }\n\n inc_str = std::string(\"-\") + inc_str;\n\n filename = camera_file_base + inc_str + camera_file_suffix;\n }\n\n std::ofstream file(filename);\n if (file.good())\n {\n std::cout << \"Storing camera to file: \" << filename << '\\n';\n file << cam;\n }\n }\n break;\n\n case 'v':\n {\n std::string filename = camera_file_base + camera_file_suffix;\n\n load_camera(filename);\n }\n break;\n\n default:\n break;\n }\n\n viewer_type::on_key_press(event);\n}\n\n\n//-------------------------------------------------------------------------------------------------\n// mouse handling\n//\n\nvoid renderer::on_mouse_move(visionaray::mouse_event const& event)\n{\n if (event.buttons() != mouse::NoButton)\n {\n frame_num = 0;\n host_rt.clear_color_buffer();\n }\n\n viewer_type::on_mouse_move(event);\n}\n\n\n//-------------------------------------------------------------------------------------------------\n// resize event\n//\n\nvoid renderer::on_resize(int w, int h)\n{\n frame_num = 0;\n host_rt.clear_color_buffer();\n\n cam.set_viewport(0, 0, w, h);\n float aspect = w / static_cast(h);\n cam.perspective(45.0f * constants::degrees_to_radians(), aspect, 0.001f, 1000.0f);\n host_rt.resize(w, h);\n\n viewer_type::on_resize(w, h);\n}\n\n\n//-------------------------------------------------------------------------------------------------\n// Main function, performs initialization\n//\n\nint main(int argc, char** argv)\n{\n renderer rend;\n\n try\n {\n rend.init(argc, argv);\n }\n catch (std::exception const& e)\n {\n std::cerr << e.what() << '\\n';\n return EXIT_FAILURE;\n }\n\n rend.build_scene();\n\n float aspect = rend.width() / static_cast(rend.height());\n\n rend.cam.perspective(45.0f * constants::degrees_to_radians(), aspect, 0.001f, 1000.0f);\n rend.cam.set_lens_radius(0.002f);\n rend.cam.set_focal_distance(2.0f);\n\n // Load camera from file or set view-all\n std::ifstream file(rend.initial_camera);\n if (file.good())\n {\n file >> rend.cam;\n }\n else\n {\n rend.cam.view_all(rend.bbox);\n }\n\n rend.add_manipulator( std::make_shared(rend.cam, mouse::Left) );\n rend.add_manipulator( std::make_shared(rend.cam, mouse::Middle) );\n // Additional \"Alt + LMB\" pan manipulator for setups w/o middle mouse button\n rend.add_manipulator( std::make_shared(rend.cam, mouse::Left, keyboard::Alt) );\n rend.add_manipulator( std::make_shared(rend.cam, mouse::Right) );\n\n rend.event_loop();\n}\n", "meta": {"hexsha": "3a0e909df4b521ac4d3114522f242afed1e7b421", "size": 27801, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/examples/phantom/main.cpp", "max_stars_repo_name": "tjachmann/visionaray", "max_stars_repo_head_hexsha": "5f181268c8da28c7d9b397300cc9759cec2bf7b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/examples/phantom/main.cpp", "max_issues_repo_name": "tjachmann/visionaray", "max_issues_repo_head_hexsha": "5f181268c8da28c7d9b397300cc9759cec2bf7b3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.24118198136642008}} {"text": "/*\n * NormalVectorsFilter.cpp\n *\n * Created on: May 05, 2015\n * Author: Peter Fankhauser, Martin Wermelinger\n * Institute: ETH Zurich, ANYbotics\n */\n\n#include \"grid_map_filters/NormalVectorsFilter.hpp\"\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \n\nnamespace grid_map {\n\nNormalVectorsFilter::NormalVectorsFilter()\n : method_(Method::RasterSerial), estimationRadius_(0.0), parallelizationEnabled_(false), threadCount_(1), gridMapResolution_(0.02) {}\n\nNormalVectorsFilter::~NormalVectorsFilter() = default;\n\nbool NormalVectorsFilter::configure() {\n // Read which algorithm is chosen: area or raster.\n std::string algorithm;\n if (!FilterBase::getParam(std::string(\"algorithm\"), algorithm)) {\n ROS_WARN(\"Could not find the parameter: `algorithm`. Setting to default value: 'area'.\");\n // Default value.\n algorithm = \"area\";\n }\n\n // Read parameters related to area algorithm only if needed, otherwise when using raster method\n // on purpose it throws unwanted errors.\n if (algorithm != \"raster\") {\n // Read radius, if found, its value will be used for area method. If radius parameter is not found, raster method will be used.\n if (!FilterBase::getParam(std::string(\"radius\"), estimationRadius_)) {\n ROS_WARN(\"Could not find the parameter: `radius`. Switching to raster method.\");\n algorithm = \"raster\";\n }\n ROS_DEBUG(\"Normal vectors estimation radius = %f\", estimationRadius_);\n // If radius not positive switch to raster method.\n if (estimationRadius_ <= 0) {\n ROS_WARN(\"Parameter `radius` is not positive. Switching to raster method.\");\n algorithm = \"raster\";\n }\n }\n\n // Read parallelization_enabled to decide whether parallelization has to be used, if parameter is not found an error is thrown and\n // the false default value will be used.\n if (!FilterBase::getParam(std::string(\"parallelization_enabled\"), parallelizationEnabled_)) {\n ROS_WARN(\"Could not find the parameter: `parallelization_enabled`. Setting to default value: 'false'.\");\n parallelizationEnabled_ = false;\n }\n ROS_DEBUG(\"Parallelization_enabled = %d\", parallelizationEnabled_);\n\n // Read thread_number to set the number of threads to be used if parallelization is enebled,\n // if parameter is not found an error is thrown and the default is to set it to automatic.\n if (!FilterBase::getParam(std::string(\"thread_number\"), threadCount_)) {\n ROS_WARN(\"Could not find the parameter: `thread_number`. Setting to default value: 'automatic'.\");\n threadCount_ = tbb::task_scheduler_init::automatic;\n }\n ROS_DEBUG(\"Thread_number = %d\", threadCount_);\n\n // Set wanted method looking at algorithm and parallelization_enabled parameters.\n // parallelization_enabled is used to select whether to use parallelization or not.\n if (algorithm == \"raster\") {\n // If parallelizationEnabled_=true, use the parallel method, otherwise serial.\n if (parallelizationEnabled_) {\n method_ = Method::RasterParallel;\n ROS_DEBUG(\"Method RasterParallel\");\n } else {\n method_ = Method::RasterSerial;\n ROS_DEBUG(\"Method RasterSerial\");\n }\n } else {\n // If parallelizationEnabled_=true, use the parallel method, otherwise serial.\n if (parallelizationEnabled_) {\n method_ = Method::AreaParallel;\n ROS_DEBUG(\"Method AreaParallel\");\n } else {\n method_ = Method::AreaSerial;\n ROS_DEBUG(\"Method AreaSerial\");\n }\n ROS_DEBUG(\"estimationRadius_ = %f\", estimationRadius_);\n }\n\n // Read normal_vector_positive_axis, to define normal vector positive direction.\n std::string normalVectorPositiveAxis;\n if (!FilterBase::getParam(std::string(\"normal_vector_positive_axis\"), normalVectorPositiveAxis)) {\n ROS_ERROR(\"Normal vectors filter did not find parameter `normal_vector_positive_axis`.\");\n return false;\n }\n if (normalVectorPositiveAxis == \"z\") {\n normalVectorPositiveAxis_ = Vector3::UnitZ();\n } else if (normalVectorPositiveAxis == \"y\") {\n normalVectorPositiveAxis_ = Vector3::UnitY();\n } else if (normalVectorPositiveAxis == \"x\") {\n normalVectorPositiveAxis_ = Vector3::UnitX();\n } else {\n ROS_ERROR(\"The normal vector positive axis '%s' is not valid.\", normalVectorPositiveAxis.c_str());\n return false;\n }\n\n // Read input_layer, to define input grid map layer.\n if (!FilterBase::getParam(std::string(\"input_layer\"), inputLayer_)) {\n ROS_ERROR(\"Normal vectors filter did not find parameter `input_layer`.\");\n return false;\n }\n ROS_DEBUG(\"Normal vectors filter input layer is = %s.\", inputLayer_.c_str());\n\n // Read output_layers_prefix, to define output grid map layers prefix.\n if (!FilterBase::getParam(std::string(\"output_layers_prefix\"), outputLayersPrefix_)) {\n ROS_ERROR(\"Normal vectors filter did not find parameter `output_layers_prefix`.\");\n return false;\n }\n ROS_DEBUG(\"Normal vectors filter output_layer = %s.\", outputLayersPrefix_.c_str());\n\n // If everything has been set up correctly\n return true;\n}\n\nbool NormalVectorsFilter::update(const GridMap& mapIn, GridMap& mapOut) {\n std::vector normalVectorsLayers;\n normalVectorsLayers.push_back(outputLayersPrefix_ + \"x\");\n normalVectorsLayers.push_back(outputLayersPrefix_ + \"y\");\n normalVectorsLayers.push_back(outputLayersPrefix_ + \"z\");\n\n mapOut = mapIn;\n for (const auto& layer : normalVectorsLayers) {\n mapOut.add(layer);\n }\n switch (method_) {\n case Method::AreaSerial:\n computeWithAreaSerial(mapOut, inputLayer_, outputLayersPrefix_);\n break;\n case Method::RasterSerial:\n computeWithRasterSerial(mapOut, inputLayer_, outputLayersPrefix_);\n break;\n case Method::AreaParallel:\n computeWithAreaParallel(mapOut, inputLayer_, outputLayersPrefix_);\n break;\n case Method::RasterParallel:\n computeWithRasterParallel(mapOut, inputLayer_, outputLayersPrefix_);\n break;\n }\n\n return true;\n}\n\n// SVD Area based methods.\nvoid NormalVectorsFilter::computeWithAreaSerial(GridMap& map, const std::string& inputLayer, const std::string& outputLayersPrefix) {\n const double start = ros::Time::now().toSec();\n\n // For each cell in submap.\n for (GridMapIterator iterator(map); !iterator.isPastEnd(); ++iterator) {\n // Check if this is an empty cell (hole in the map).\n if (map.isValid(*iterator, inputLayer)) {\n const Index index(*iterator);\n areaSingleNormalComputation(map, inputLayer, outputLayersPrefix, index);\n }\n }\n\n const double end = ros::Time::now().toSec();\n ROS_DEBUG_THROTTLE(2.0, \"NORMAL COMPUTATION TIME = %f\", (end - start));\n}\n\nvoid NormalVectorsFilter::computeWithAreaParallel(GridMap& map, const std::string& inputLayer, const std::string& outputLayersPrefix) {\n const double start = ros::Time::now().toSec();\n grid_map::Size gridMapSize = map.getSize();\n\n // Set number of thread to use for parallel programming.\n std::unique_ptr TBBInitPtr;\n if (threadCount_ != -1) {\n TBBInitPtr.reset(new tbb::task_scheduler_init(threadCount_));\n }\n\n // Parallelized iteration through the map.\n tbb::parallel_for(0, gridMapSize(0) * gridMapSize(1), [&](int range) {\n // Recover Cell index from range iterator.\n const Index index(range / gridMapSize(1), range % gridMapSize(1));\n if (map.isValid(index, inputLayer)) {\n areaSingleNormalComputation(map, inputLayer, outputLayersPrefix, index);\n }\n });\n\n const double end = ros::Time::now().toSec();\n ROS_DEBUG_THROTTLE(2.0, \"NORMAL COMPUTATION TIME = %f\", (end - start));\n}\n\nvoid NormalVectorsFilter::areaSingleNormalComputation(GridMap& map, const std::string& inputLayer, const std::string& outputLayersPrefix,\n const grid_map::Index& index) {\n // Requested position (center) of circle in map.\n Position center;\n map.getPosition(index, center);\n\n // Prepare data computation. Check if area is bigger than cell.\n const double minAllowedEstimationRadius = 0.5 * map.getResolution();\n if (estimationRadius_ <= minAllowedEstimationRadius) {\n ROS_WARN(\"Estimation radius is smaller than allowed by the map resolution (%f < %f)\", estimationRadius_, minAllowedEstimationRadius);\n }\n\n // Gather surrounding data.\n size_t nPoints = 0;\n Position3 sum = Position3::Zero();\n Eigen::Matrix3d sumSquared = Eigen::Matrix3d::Zero();\n for (CircleIterator circleIterator(map, center, estimationRadius_); !circleIterator.isPastEnd(); ++circleIterator) {\n Position3 point;\n if (!map.getPosition3(inputLayer, *circleIterator, point)) {\n continue;\n }\n nPoints++;\n sum += point;\n sumSquared.noalias() += point * point.transpose();\n }\n\n Vector3 unitaryNormalVector = Vector3::Zero();\n if (nPoints < 3) {\n ROS_DEBUG(\"Not enough points to establish normal direction (nPoints = %i)\", static_cast(nPoints));\n unitaryNormalVector = Vector3::UnitZ();\n } else {\n const Position3 mean = sum / nPoints;\n const Eigen::Matrix3d covarianceMatrix = sumSquared / nPoints - mean * mean.transpose();\n\n // Compute Eigenvectors.\n // Eigenvalues are ordered small to large.\n // Worst case bound for zero eigenvalue from : https://eigen.tuxfamily.org/dox/classEigen_1_1SelfAdjointEigenSolver.html\n Eigen::SelfAdjointEigenSolver solver;\n solver.computeDirect(covarianceMatrix, Eigen::DecompositionOptions::ComputeEigenvectors);\n if (solver.eigenvalues()(1) > 1e-8) {\n unitaryNormalVector = solver.eigenvectors().col(0);\n } else { // If second eigenvalue is zero, the normal is not defined.\n ROS_DEBUG(\"Covariance matrix needed for eigen decomposition is degenerated.\");\n ROS_DEBUG(\"Expected cause: data is on a straight line (nPoints = %i)\", static_cast(nPoints));\n unitaryNormalVector = Vector3::UnitZ();\n }\n }\n\n // Check direction of the normal vector and flip the sign towards the user defined direction.\n if (unitaryNormalVector.dot(normalVectorPositiveAxis_) < 0.0) {\n unitaryNormalVector = -unitaryNormalVector;\n }\n\n map.at(outputLayersPrefix + \"x\", index) = unitaryNormalVector.x();\n map.at(outputLayersPrefix + \"y\", index) = unitaryNormalVector.y();\n map.at(outputLayersPrefix + \"z\", index) = unitaryNormalVector.z();\n}\n// Raster based methods.\nvoid NormalVectorsFilter::computeWithRasterSerial(GridMap& map, const std::string& inputLayer, const std::string& outputLayersPrefix) {\n // Inspiration for algorithm: http://www.flipcode.com/archives/Calculating_Vertex_Normals_for_Height_Maps.shtml\n const double start = ros::Time::now().toSec();\n\n const grid_map::Size gridMapSize = map.getSize();\n gridMapResolution_ = map.getResolution();\n // Faster access to grid map values.\n const grid_map::Matrix dataMap = map[inputLayer];\n // Height and width of submap. Submap is Map without the outermost line of cells, no need to check if index is inside.\n const Index submapStartIndex(1, 1);\n const Index submapBufferSize(gridMapSize(0) - 2, gridMapSize(1) - 2);\n\n // For each cell in submap.\n for (SubmapIterator iterator(map, submapStartIndex, submapBufferSize); !iterator.isPastEnd(); ++iterator) {\n const Index index(*iterator);\n rasterSingleNormalComputation(map, outputLayersPrefix, dataMap, index);\n }\n\n const double end = ros::Time::now().toSec();\n ROS_DEBUG_THROTTLE(2.0, \"NORMAL COMPUTATION TIME = %f\", (end - start));\n}\n\nvoid NormalVectorsFilter::computeWithRasterParallel(GridMap& map, const std::string& inputLayer, const std::string& outputLayersPrefix) {\n const double start = ros::Time::now().toSec();\n\n const grid_map::Size gridMapSize = map.getSize();\n gridMapResolution_ = map.getResolution();\n // Faster access to grid map values if copy grid map layer into local matrix.\n const grid_map::Matrix dataMap = map[inputLayer];\n // Height and width of submap. Submap is Map without the outermost line of cells, no need to check if index is inside.\n const Index submapStartIndex(1, 1);\n const Index submapBufferSize(gridMapSize(0) - 2, gridMapSize(1) - 2);\n if (submapBufferSize(1) != 0) {\n // Set number of thread to use for parallel programming\n std::unique_ptr TBBInitPtr;\n if (threadCount_ != -1) {\n TBBInitPtr.reset(new tbb::task_scheduler_init(threadCount_));\n }\n // Parallelized iteration through the map.\n tbb::parallel_for(0, submapBufferSize(0) * submapBufferSize(1), [&](int range) {\n const Index index(range / submapBufferSize(1) + submapStartIndex(0), range % submapBufferSize(1) + submapStartIndex(1));\n rasterSingleNormalComputation(map, outputLayersPrefix, dataMap, index);\n });\n } else {\n ROS_ERROR(\"Grid map size is too small for normal raster computation\");\n }\n\n const double end = ros::Time::now().toSec();\n ROS_DEBUG_THROTTLE(2.0, \"NORMAL COMPUTATION TIME = %f\", (end - start));\n}\n\nvoid NormalVectorsFilter::rasterSingleNormalComputation(GridMap& map, const std::string& outputLayersPrefix,\n const grid_map::Matrix& dataMap, const grid_map::Index& index) {\n // Inspiration for algorithm:\n // http://www.flipcode.com/archives/Calculating_Vertex_Normals_for_Height_Maps.shtml\n const double centralCell = dataMap(index(0), index(1));\n double topCell = dataMap(index(0) - 1, index(1));\n double rightCell = dataMap(index(0), index(1) + 1);\n double bottomCell = dataMap(index(0) + 1, index(1));\n double leftCell = dataMap(index(0), index(1) - 1);\n\n // Neighboring cells configuration checked in X and Y direction independently.\n // Gridmap frame is defined rotated 90 degrees anticlockwise compared to direction of matrix if we take\n // rows as Y coordinate and columns as X coordinate.\n // In Y direction cell numbered as follows: left 0, center 1, right 2.\n // In X direction cell numbered as follows: top 0, center 1, bottom 2.\n // To find configuration we are in, multiply value of map.isValid of cell in question, True or False,\n // by 2^(number of the cell).\n // Each configuration will have a different number associated with it and then use a switch.\n\n const int configurationDirX = 1 * static_cast(std::isfinite(topCell)) + 2 * static_cast(std::isfinite(centralCell)) +\n 4 * static_cast(std::isfinite(bottomCell));\n const int configurationDirY = 1 * static_cast(std::isfinite(leftCell)) + 2 * static_cast(std::isfinite(centralCell)) +\n 4 * static_cast(std::isfinite(rightCell));\n\n // If outer cell height value is missing use the central value, however the formula for the normal calculation\n // has to take into account that the distance of the cells used for normal calculation is different.\n bool validConfiguration = true;\n double distanceX{NAN};\n switch (configurationDirX) {\n case 7: // All 3 cell height values are valid.\n distanceX = 2 * gridMapResolution_; // Top and bottom cell centers are 2 cell resolution distant.\n break;\n case 6: // Top cell height value not valid.\n topCell = centralCell;\n distanceX = gridMapResolution_;\n break;\n case 5: // Central cell height value not valid. Not a problem.\n distanceX = 2 * gridMapResolution_;\n break;\n case 3: // Bottom cell height value not valid.\n bottomCell = centralCell;\n distanceX = gridMapResolution_;\n break;\n\n default: // More than 1 cell height values are not valid, normal vector will not be calculated in this location.\n validConfiguration = false;\n }\n\n double distanceY{NAN};\n switch (configurationDirY) {\n case 7: // All 3 cell height values are valid.\n distanceY = 2 * gridMapResolution_; // Left and right cell centers are 2 call resolution distant.\n break;\n case 6: // Left cell height value not valid.\n leftCell = centralCell;\n distanceY = gridMapResolution_;\n break;\n case 5: // Central cell height value not valid. Not a problem.\n distanceY = 2 * gridMapResolution_;\n break;\n case 3: // Right cell height value not valid.\n rightCell = centralCell;\n distanceY = gridMapResolution_;\n break;\n\n default: // More than 1 cell height values are not valid, normal vector will not be calculated in this location.\n validConfiguration = false;\n }\n\n if (validConfiguration) {\n // Normal vector initialization\n Vector3 normalVector = Vector3::Zero();\n // X DIRECTION\n normalVector(0) = (bottomCell - topCell) / distanceX;\n // Y DIRECTION\n normalVector(1) = (rightCell - leftCell) / distanceY;\n // Z DIRECTION\n normalVector(2) = +1;\n\n normalVector.normalize();\n\n // Check direction of the normal vector and flip the sign towards the user defined direction.\n if (normalVector.dot(normalVectorPositiveAxis_) < 0.0) {\n normalVector = -normalVector;\n }\n\n map.at(outputLayersPrefix + \"x\", index) = normalVector.x();\n map.at(outputLayersPrefix + \"y\", index) = normalVector.y();\n map.at(outputLayersPrefix + \"z\", index) = normalVector.z();\n }\n}\n\n} // namespace grid_map\n", "meta": {"hexsha": "f6cdce240f9ec58c65b81215c12857ce9918a1e2", "size": 17183, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "grid_map_filters/src/NormalVectorsFilter.cpp", "max_stars_repo_name": "martorelltorres/grid_map", "max_stars_repo_head_hexsha": "ccc4ad60f16b2754860ab05355f849a7a756eea3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 358.0, "max_stars_repo_stars_event_min_datetime": "2015-04-24T12:03:50.000Z", "max_stars_repo_stars_event_max_datetime": "2018-08-04T14:04:53.000Z", "max_issues_repo_path": "grid_map_filters/src/NormalVectorsFilter.cpp", "max_issues_repo_name": "martorelltorres/grid_map", "max_issues_repo_head_hexsha": "ccc4ad60f16b2754860ab05355f849a7a756eea3", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 130.0, "max_issues_repo_issues_event_min_datetime": "2015-01-03T11:51:16.000Z", "max_issues_repo_issues_event_max_datetime": "2018-07-30T14:53:48.000Z", "max_forks_repo_path": "grid_map_filters/src/NormalVectorsFilter.cpp", "max_forks_repo_name": "martorelltorres/grid_map", "max_forks_repo_head_hexsha": "ccc4ad60f16b2754860ab05355f849a7a756eea3", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 218.0, "max_forks_repo_forks_event_min_datetime": "2015-03-19T04:41:02.000Z", "max_forks_repo_forks_event_max_datetime": "2018-08-06T02:36:16.000Z", "avg_line_length": 43.282115869, "max_line_length": 137, "alphanum_fraction": 0.7056974917, "num_tokens": 4242, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.43782349911420193, "lm_q1q2_score": 0.24106884341669138}} {"text": "// Software License for MTL\n// \n// Copyright (c) 2007 The Trustees of Indiana University.\n// 2008 Dresden University of Technology and the Trustees of Indiana University.\n// 2010 SimuNova UG (haftungsbeschränkt), www.simunova.com.\n// All rights reserved.\n// Authors: Peter Gottschling and Andrew Lumsdaine\n// \n// This file is part of the Matrix Template Library\n// \n// See also license.mtl.txt in the distribution.\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace mtl;\nusing namespace mtl::recursion; \nusing namespace std; \n\n// Maximum time for a single measurement\n// is 20 min\nconst double max_time= 900;\n\ntypedef assign::plus_sum ama_t;\n\ntypedef recursion::bound_test_static<32> test32_t;\ntypedef recursion::bound_test_static<64> test64_t;\n\ntypedef gen_dense_mat_mat_mult_t base_mult_t;\ntypedef gen_recursive_dense_mat_mat_mult_t rec_mult_t;\n\ntypedef gen_tiling_22_dense_mat_mat_mult_t tiling_22_base_mult_t;\ntypedef gen_tiling_44_dense_mat_mat_mult_t tiling_44_base_mult_t;\n\n// ugly short cuts\ntypedef dense2D dr_t;\ntypedef dense2D > dc_t;\n\nutility::papi_t papi;\nint l1i= papi.add_event(\"PAPI_L1_DCM\");\nint l2i= papi.add_event(\"PAPI_L2_DCM\");\nint tlbi= papi.add_event(\"PAPI_TLB_DM\");\n\n#ifdef MTL_USE_BLAS\nextern \"C\" {\nvoid dgemm_(const char* transa, const char* transb, \n\t const int* m, const int* n, const int* k,\n\t const double* alpha, const double *da, const int* lda,\n\t const double *db, const int* ldb, const double* dbeta,\n\t double *dc, const int* ldc);\n}\n#endif\n\nstruct dgemm_t\n{\n void operator()(const dc_t& a, const dc_t& b, dc_t& c)\n {\n#ifdef MTL_USE_BLAS\n\tint size= a.num_rows();\n\tdouble alpha= 1.0, beta= 0.0;\n\tdgemm_(\"N\", \"N\", &size, &size, &size, &alpha, \n\t const_cast(&a[0][0]), &size, const_cast(&b[0][0]), \n\t &size, &beta, &c[0][0], &size);\n#endif\n }\n};\n\nstruct dgemm_add_t\n{\n void operator()(const dc_t& a, const dc_t& b, dc_t& c)\n {\n\tint size= a.num_rows();\n\tdouble alpha= 1.0, beta= 1.0;\n\tdgemm_(\"N\", \"N\", &size, &size, &size, &alpha, \n\t const_cast(&a[0][0]), &size, const_cast(&b[0][0]), \n\t &size, &beta, &c[0][0], &size);\n\n }\n};\n\n\n\nvoid print_time_and_mflops(double time, double size)\n{\n // time and MFlops of single measure\n std::cout << time << \", \" << 2.0 * size * size * size / time / 1e6f << \", \";\n}\n\n\n// Matrices are only placeholder to provide the type\ntemplate \nvoid single_measure(MatrixA&, MatrixB&, MatrixC&, Mult mult, unsigned size, std::vector& enabled, int i)\n{\n MatrixA a(size, size);\n MatrixB b(size, size);\n MatrixC c(size, size);\n hessian_setup(a, 1.0);\n hessian_setup(b, 1.0);\n\n if (enabled[i]) {\n\tint reps= 0;\n\tboost::timer start;\t\n\tpapi.reset();\n\tfor (; start.elapsed() < 5; reps++)\n\t mult(a, b, c);\n\tpapi.read();\n\tdouble time= start.elapsed() / double(reps);\n\n\tprint_time_and_mflops(time, a.num_rows());\n\tstd::cout << papi[l1i]/reps << \", \" << papi[l2i]/reps << \", \" << papi[tlbi]/reps << \", \";\n\tif (time > max_time)\n\t enabled[i]= 0;\n } else\n\tstd::cout << \", , , , , \";\n}\n\n// The matrices in the following functions are only place holders, the real matrices are used in single_measure\nvoid measure_morton_order(unsigned size, std::vector& enabled)\n{\n morton_dense mda(4, 4), mdb(4, 4), mdc(4, 4);\n morton_dense mzda(4, 4), mzdb(4, 4), mzdc(4, 4);\n dc_t dc(4, 4);\n \n std::cout << size << \", \";\n rec_mult_t mult;\n single_measure(mda, mdb, mdc, mult, size, enabled, 0);\n single_measure(mzda, mzdb, mzdc, mult, size, enabled, 1);\n single_measure(mda, mzdb, mdc, mult, size, enabled, 2);\n single_measure(dc, dc, dc, dgemm_t(), size, enabled, 3);\n \n std::cout << \"0\\n\"; // to not finish with comma\n std::cout.flush();\n}\n\n\nvoid measure_cast(unsigned size, std::vector& enabled)\n{\n morton_dense mda(4, 4), mdb(4, 4), mdc(4, 4);\n morton_dense d32ra(4, 4), d32rb(4, 4), d32rc(4, 4);\n morton_dense d64ra(4, 4), d64rb(4, 4), d64rc(4, 4);\n morton_dense d64ca(4, 4), d64cb(4, 4), d64cc(4, 4); \n \n rec_mult_t mult;\n std::cout << size << \", \";\n single_measure(mda, mdb, mdc, mult, size, enabled, 0);\n single_measure(d32ra, d32rb, d32rc, mult, size, enabled, 1);\n single_measure(d64ra, d64rb, d64rc, mult, size, enabled, 2);\n single_measure(d64ca, d64cb, d64cc, mult, size, enabled, 3);\n\n dc_t dc(4, 4);\n single_measure(dc, dc, dc, dgemm_t(), size, enabled, 4);\n \n std::cout << \"0\\n\"; std::cout.flush();\n}\n\n\nvoid measure_with_unroll(unsigned size, std::vector& enabled)\n{\n morton_dense d32r(4, 4);\n morton_dense d32c(4, 4);\n\n std::cout << size << \", \";\n\n gen_recursive_dense_mat_mat_mult_t mult;\n gen_recursive_dense_mat_mat_mult_t mult_22;\n gen_recursive_dense_mat_mat_mult_t mult_44;\n\n single_measure(d32r, d32r, d32r, mult, size, enabled, 0);\n single_measure(d32r, d32r, d32r, mult_22, size, enabled, 1);\n single_measure(d32r, d32r, d32r, mult_44, size, enabled, 2);\n\n single_measure(d32c, d32c, d32c, mult, size, enabled, 3);\n single_measure(d32c, d32c, d32c, mult_22, size, enabled, 4);\n single_measure(d32c, d32c, d32c, mult_44, size, enabled, 5);\n dc_t dc(4, 4);\n single_measure(dc, dc, dc, dgemm_t(), size, enabled, 6);\n \n std::cout << \"0\\n\"; std::cout.flush();\n}\n\nvoid measure_base_size(unsigned size, std::vector& enabled)\n{\n morton_dense d16r(4, 4);\n morton_dense d32r(4, 4);\n morton_dense d64r(4, 4);\n morton_dense d128r(4, 4);\n \n std::cout << size << \", \";\n\n gen_recursive_dense_mat_mat_mult_t > mult16;\n single_measure(d16r, d16r, d16r, mult16, size, enabled, 0);\n\n gen_recursive_dense_mat_mat_mult_t > mult32;\n single_measure(d32r, d32r, d32r, mult32, size, enabled, 1);\n\n gen_recursive_dense_mat_mat_mult_t > mult64;\n single_measure(d64r, d64r, d64r, mult64, size, enabled, 2);\n\n gen_recursive_dense_mat_mat_mult_t > mult128;\n single_measure(d128r, d128r, d128r, mult128, size, enabled, 3);\n\n dc_t dc(4, 4);\n single_measure(dc, dc, dc, dgemm_t(), size, enabled, 4);\n \n std::cout << \"0\\n\"; std::cout.flush();\n}\n\ntemplate \nvoid measure_unrolling(unsigned size, std::vector& enabled, Matrix& matrix, MatrixB& matrixb)\n{\n std::cout << size << \", \";\n \n gen_recursive_dense_mat_mat_mult_t mult;\n gen_recursive_dense_mat_mat_mult_t mult_22;\n gen_recursive_dense_mat_mat_mult_t mult_44;\n\n typedef gen_tiling_dense_mat_mat_mult_t<2, 2, ama_t> tiling_m22_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m22;\n \n typedef gen_tiling_dense_mat_mat_mult_t<2, 4, ama_t> tiling_m24_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m24;\n\n typedef gen_tiling_dense_mat_mat_mult_t<4, 2, ama_t> tiling_m42_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m42;\n\n typedef gen_tiling_dense_mat_mat_mult_t<3, 5, ama_t> tiling_m35_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m35;\n\n typedef gen_tiling_dense_mat_mat_mult_t<4, 4, ama_t> tiling_m44_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m44;\n\n\n single_measure(matrix, matrixb, matrix, mult, size, enabled, 0);\n single_measure(matrix, matrixb, matrix, mult_22, size, enabled, 1);\n single_measure(matrix, matrixb, matrix, mult_44, size, enabled, 2);\n\n single_measure(matrix, matrixb, matrix, mult_m22, size, enabled, 3);\n single_measure(matrix, matrixb, matrix, mult_m24, size, enabled, 4);\n single_measure(matrix, matrixb, matrix, mult_m42, size, enabled, 5);\n single_measure(matrix, matrixb, matrix, mult_m35, size, enabled, 6);\n single_measure(matrix, matrixb, matrix, mult_m44, size, enabled, 7);\n \n dc_t dc(4, 4);\n single_measure(dc, dc, dc, dgemm_t(), size, enabled, 8);\n\n gen_recursive_dense_mat_mat_mult_t mult_blas;\n single_measure(dc, dc, dc, mult_blas, size, enabled, 9);\n\n\n std::cout << \"0\\n\"; std::cout.flush();\n}\n\nvoid measure_unrolling_hybrid(unsigned size, std::vector& enabled)\n{\n morton_dense d64r(4, 4);\n morton_dense d64c(4, 4);\n measure_unrolling(size, enabled, d64r, d64c);\n}\n\nvoid measure_unrolling_dense(unsigned size, std::vector& enabled)\n{\n dense2D dense(4, 4);\n dense2D > b(4, 4);\n measure_unrolling(size, enabled, dense, b);\n}\n\n\nvoid measure_orientation(unsigned size, std::vector& enabled)\n{\n std::cout << size << \", \";\n \n morton_dense d64r(4, 4);\n morton_dense d64c(4, 4);\n\n typedef gen_tiling_dense_mat_mat_mult_t<4, 2, ama_t> tiling_m42_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m42;\n\n typedef gen_tiling_dense_mat_mat_mult_t<4, 4, ama_t> tiling_m44_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m44;\n \n single_measure(d64r, d64r, d64r, mult_m42, size, enabled, 0);\n single_measure(d64c, d64c, d64c, mult_m42, size, enabled, 1);\n single_measure(d64r, d64c, d64r, mult_m42, size, enabled, 2);\n single_measure(d64c, d64r, d64r, mult_m42, size, enabled, 3);\n \n single_measure(d64r, d64r, d64r, mult_m44, size, enabled, 4);\n single_measure(d64c, d64c, d64c, mult_m44, size, enabled, 5);\n single_measure(d64r, d64c, d64r, mult_m44, size, enabled, 6);\n single_measure(d64c, d64r, d64r, mult_m44, size, enabled, 7);\n \n dc_t dc(4, 4);\n single_measure(dc, dc, dc, dgemm_t(), size, enabled, 8);\n\n std::cout << \"0\\n\"; std::cout.flush();\n}\n\n\nvoid measure_unrolling_32(unsigned size, std::vector& enabled)\n{\n std::cout << size << \", \";\n \n gen_recursive_dense_mat_mat_mult_t mult;\n gen_recursive_dense_mat_mat_mult_t mult_22;\n gen_recursive_dense_mat_mat_mult_t mult_44;\n\n typedef gen_tiling_dense_mat_mat_mult_t<2, 2, ama_t> tiling_m22_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m22;\n \n typedef gen_tiling_dense_mat_mat_mult_t<2, 4, ama_t> tiling_m24_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m24;\n\n typedef gen_tiling_dense_mat_mat_mult_t<4, 2, ama_t> tiling_m42_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m42;\n\n typedef gen_tiling_dense_mat_mat_mult_t<3, 5, ama_t> tiling_m35_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m35;\n\n typedef gen_tiling_dense_mat_mat_mult_t<4, 4, ama_t> tiling_m44_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult_m44;\n\n \n morton_dense d32r(4, 4);\n morton_dense d32c(4, 4);\n\n\n single_measure(d32r, d32c, d32r, mult, size, enabled, 0);\n single_measure(d32r, d32c, d32r, mult_22, size, enabled, 1);\n single_measure(d32r, d32c, d32r, mult_44, size, enabled, 2);\n\n single_measure(d32r, d32c, d32r, mult_m22, size, enabled, 3);\n single_measure(d32r, d32c, d32r, mult_m24, size, enabled, 4);\n single_measure(d32r, d32c, d32r, mult_m42, size, enabled, 5);\n single_measure(d32r, d32c, d32r, mult_m35, size, enabled, 6);\n single_measure(d32r, d32c, d32r, mult_m44, size, enabled, 7);\n \n dc_t dc(4, 4);\n single_measure(dc, dc, dc, dgemm_t(), size, enabled, 8);\n\n std::cout << \"0\\n\"; std::cout.flush();\n}\n\n\n\nvoid measure_hetero_value(unsigned size, std::vector& enabled)\n{\n using std::complex;\n typedef gen_tiling_dense_mat_mat_mult_t<4, 4, ama_t> tiling_m44_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult;\n\n dc_t dc(4, 4);\n dr_t dr(4, 4);\n dense2D > fr(4, 4);\n dense2D > fc(4, 4);\n dense2D, mat::parameters > cc(4, 4);\n dense2D, mat::parameters > zc(4, 4);\n dense2D, mat::parameters > zr(4, 4);\n\n morton_dense d64r(4, 4);\n morton_dense d64c(4, 4);\n \n morton_dense f64r(4, 4);\n morton_dense f64c(4, 4);\n\n morton_dense, doppled_64_row_mask> c64r(4, 4);\n morton_dense, doppled_64_col_mask> c64c(4, 4);\n\n morton_dense, doppled_64_row_mask> z64r(4, 4);\n morton_dense, doppled_64_col_mask> z64c(4, 4);\n\n std::cout << size << \", \";\n\n single_measure(dr, fc, dr, mult, size, enabled, 0);\n single_measure(fr, fc, dr, mult, size, enabled, 1);\n \n //single_measure(fr, zc, zr, mult, size, enabled, 2);\n //single_measure(dr, cc, zr, mult, size, enabled, 3);\n \n single_measure(d64r, f64c, d64r, mult, size, enabled, 4);\n single_measure(d64r, z64c, z64r, mult, size, enabled, 5);\n std::cout << \"0\\n\"; std::cout.flush();\n}\n\n\nvoid measure_hetero_layout(unsigned size, std::vector& enabled)\n{\n using std::complex;\n typedef gen_tiling_dense_mat_mat_mult_t<4, 4, ama_t> tiling_m44_base_mult_t;\n gen_recursive_dense_mat_mat_mult_t mult;\n\n dc_t dc(4, 4);\n dr_t dr(4, 4);\n dense2D > fr(4, 4);\n dense2D > fc(4, 4);\n dense2D, mat::parameters > cc(4, 4);\n dense2D, mat::parameters > zc(4, 4);\n dense2D, mat::parameters > zr(4, 4);\n\n morton_dense d64r(4, 4);\n morton_dense d64c(4, 4);\n \n morton_dense f64r(4, 4);\n morton_dense f64c(4, 4);\n\n morton_dense, doppled_64_row_mask> c64r(4, 4);\n morton_dense, doppled_64_col_mask> c64c(4, 4);\n\n morton_dense, doppled_64_row_mask> z64r(4, 4);\n morton_dense, doppled_64_col_mask> z64c(4, 4);\n\n std::cout << size << \", \";\n\n single_measure(dr, f64c, dr, mult, size, enabled, 0);\n single_measure(fr, f64c, dr, mult, size, enabled, 1);\n \n //single_measure(f64r, zc, zr, mult, size, enabled, 2);\n single_measure(d64r, zc, zr, mult, size, enabled, 3);\n \n single_measure(d64r, f64c, dr, mult, size, enabled, 4);\n //single_measure(f64r, z64c, zr, mult, size, enabled, 5);\n std::cout << \"0\\n\"; std::cout.flush();\n}\n\n\n\n\ntemplate \nvoid series(unsigned steps, unsigned max_size, Measure measure, const string& comment)\n{\n std::cout << \"# \" << comment << '\\n';\n std::cout << \"# Gnu-Format size, (time, MFlops, L1, L2, TLB, time, MFlops, )*\\n\"; std::cout.flush();\n\n std::vector enabled(16, 1);\n for (unsigned i= steps; i <= max_size; i+= steps)\n\tmeasure(i, enabled);\n}\n\n\n\nint main(int argc, char* argv[])\n{\n papi.start();\n\n std::vector scenarii;\n scenarii.push_back(string(\"Comparing Z-, N-order and mixed with recursive multiplication\"));\n scenarii.push_back(string(\"Comparing base case cast (64) for Z-order, hybrid 32, hybrid 64 row and col-major\"));\n scenarii.push_back(string(\"Using unrolled mult on hybrid row- and column-major matrices\"));\n scenarii.push_back(string(\"Comparing base case sizes for corresponding hybrid row-major matrices\"));\n scenarii.push_back(string(\"Comparing different unrolling for hybrid row-major matrices\"));\n scenarii.push_back(string(\"Comparing different unrolling for row-major dense matrices\"));\n scenarii.push_back(string(\"Comparing different orientations for hybrid row-major matrices\"));\n scenarii.push_back(string(\"Comparing different unrolling for hybrid 32 row-major times col-major matrices\"));\n scenarii.push_back(string(\"Multiplying matrices with different value types\"));\n scenarii.push_back(string(\"Multiplying matrices with different matrix layouts\"));\n\n using std::cout;\n if (argc < 4) {\n\tcerr << \"usage: recursive_mult_timing \\nScenarii:\\n\"; \n\tfor (unsigned i= 0; i < scenarii.size(); i++)\n\t cout << i << \": \" << scenarii[i] << \"\\n\";\n\texit(1);\n }\n unsigned int scenario= atoi(argv[1]), steps= atoi(argv[2]), max_size= atoi(argv[3]), size= 32; \n\n switch (scenario) {\n case 0: \tseries(steps, max_size, measure_morton_order, scenarii[0]); break;\n case 1: \tseries(steps, max_size, measure_cast, scenarii[1]); break;\n case 2: \tseries(steps, max_size, measure_with_unroll, scenarii[2]); break;\n case 3: \tseries(steps, max_size, measure_base_size, scenarii[3]); break;\n case 4: \tseries(steps, max_size, measure_unrolling_hybrid, scenarii[4]); break;\n case 5: \tseries(steps, max_size, measure_unrolling_dense, scenarii[5]); break;\n case 6: \tseries(steps, max_size, measure_orientation, scenarii[6]); break;\n case 7: \tseries(steps, max_size, measure_unrolling_32, scenarii[7]); break;\n case 8: \tseries(steps, max_size, measure_hetero_value, scenarii[8]); break;\n case 9: \tseries(steps, max_size, measure_hetero_layout, scenarii[9]); break;\n }\n\n return 0; \n\n}\n \n\n\n\n\n\n#if 0\n\n// scheiss Kommandos\n\n// g++4 matrix_product_timing.cpp -o matrix_product_timing -O3 -DNDEBUG -ffast-math -mcpu=opteron -mtune=opteron -msse2 -mfpmath=sse -I${MTL_BOOST_ROOT} -I${BOOST_ROOT} -I/usr/local/include -L/usr/local/lib -lpapi -DMTL_HAS_PAPI -DMTL_HAS_BLAS -L/u/htor/projekte/mathlibs/goto-blas -lgoto_opteron-64 -lpthread xerbla.o\n\n\n// g++4 matrix_product_timing.cpp -o matrix_product_timing -O3 -DNDEBUG -ffast-math -mcpu=opteron -mtune=opteron -msse2 -mfpmath=sse -I${MTL_BOOST_ROOT} -I${BOOST_ROOT} -I/usr/local/include -L/usr/local/lib -lpapi -DMTL_HAS_PAPI -DMTL_HAS_BLAS -L/u/htor/projekte/mathlibs/acml-2-6-0-gnu-64bit/gnu64/lib -lacml -L/usr/lib/gcc/x86_64-redhat-linux/3.4.3 -lg2c\n\n// g++4 matrix_product_timing.cpp -o matrix_product_timing -O3 -DNDEBUG -ffast-math -mcpu=opteron -mtune=opteron -msse2 -mfpmath=sse -I${MTL_BOOST_ROOT} -I${BOOST_ROOT} -I/usr/local/include -L/usr/local/lib -lpapi -DMTL_HAS_PAPI -DMTL_HAS_BLAS -L/san/atlas/lib -lf77blas -latlas -L/usr/lib/gcc/x86_64-redhat-linux/3.4.3 -lg2c\n\n#endif\n", "meta": {"hexsha": "2842181efc434c2e60537a412bfc23e08799c78c", "size": 20749, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/numeric/mtl/timing/matrix_product_timing.cpp", "max_stars_repo_name": "lit-uriy/mtl4-mirror", "max_stars_repo_head_hexsha": "37cf7c2847165d3537cbc3400cb5fde6f80e3d8b", "max_stars_repo_licenses": ["MTLL"], "max_stars_count": 24.0, "max_stars_repo_stars_event_min_datetime": "2019-03-26T15:25:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T10:00:45.000Z", "max_issues_repo_path": "libs/numeric/mtl/timing/matrix_product_timing.cpp", "max_issues_repo_name": "lit-uriy/mtl4-mirror", "max_issues_repo_head_hexsha": "37cf7c2847165d3537cbc3400cb5fde6f80e3d8b", "max_issues_repo_licenses": ["MTLL"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2020-04-17T12:35:32.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-03T15:46:25.000Z", "max_forks_repo_path": "libs/numeric/mtl/timing/matrix_product_timing.cpp", "max_forks_repo_name": "lit-uriy/mtl4-mirror", "max_forks_repo_head_hexsha": "37cf7c2847165d3537cbc3400cb5fde6f80e3d8b", "max_forks_repo_licenses": ["MTLL"], "max_forks_count": 10.0, "max_forks_repo_forks_event_min_datetime": "2019-12-01T13:40:30.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-14T08:39:54.000Z", "avg_line_length": 40.525390625, "max_line_length": 359, "alphanum_fraction": 0.6795990168, "num_tokens": 6576, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.43782349911420193, "lm_q1q2_score": 0.24106884341669138}} {"text": "///////////////////////////////////////////////////////////////\n// Copyright 2012 John Maddock. Distributed under the Boost\n// Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at https://www.boost.org/LICENSE_1_0.txt\n\n#ifndef BOOST_MATH_FLOAT_BACKEND_HPP\n#define BOOST_MATH_FLOAT_BACKEND_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace boost {\nnamespace multiprecision {\nnamespace backends {\n\n#ifdef BOOST_MSVC\n#pragma warning(push)\n#pragma warning(disable : 4389 4244 4018 4244 4127)\n#endif\n\ntemplate \nstruct arithmetic_backend\n{\n typedef std::tuple signed_types;\n typedef std::tuple unsigned_types;\n typedef std::tuple float_types;\n typedef int exponent_type;\n\n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend() : m_value(0) {}\n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend(const arithmetic_backend& o) : m_value(o.m_value) {}\n template \n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend(const A& o, const typename std::enable_if::value >::type* = 0) : m_value(o) {}\n template \n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend(const arithmetic_backend& o) : m_value(o.data()) {}\n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend& operator=(const arithmetic_backend& o)\n {\n m_value = o.m_value;\n return *this;\n }\n template \n BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value, arithmetic_backend&>::type operator=(A i)\n {\n m_value = static_cast(i);\n return *this;\n }\n template \n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend& operator=(const arithmetic_backend& i)\n {\n m_value = i.data();\n return *this;\n }\n arithmetic_backend& operator=(const char* s)\n {\n#ifndef BOOST_NO_EXCEPTIONS\n try\n {\n#endif\n m_value = boost::lexical_cast(s);\n#ifndef BOOST_NO_EXCEPTIONS\n }\n catch (const bad_lexical_cast&)\n {\n throw std::runtime_error(std::string(\"Unable to interpret the string provided: \\\"\") + s + std::string(\"\\\" as a compatible number type.\"));\n }\n#endif\n return *this;\n }\n BOOST_MP_CXX14_CONSTEXPR void swap(arithmetic_backend& o)\n {\n std::swap(m_value, o.m_value);\n }\n std::string str(std::streamsize digits, std::ios_base::fmtflags f) const\n {\n std::stringstream ss;\n ss.flags(f);\n ss << std::setprecision(digits ? digits : std::numeric_limits::digits10 + 4) << m_value;\n return ss.str();\n }\n BOOST_MP_CXX14_CONSTEXPR void do_negate(const std::integral_constant&)\n {\n m_value = 1 + ~m_value;\n }\n BOOST_MP_CXX14_CONSTEXPR void do_negate(const std::integral_constant&)\n {\n m_value = -m_value;\n }\n BOOST_MP_CXX14_CONSTEXPR void negate()\n {\n do_negate(std::integral_constant::value>());\n }\n BOOST_MP_CXX14_CONSTEXPR int compare(const arithmetic_backend& o) const\n {\n return m_value > o.m_value ? 1 : (m_value < o.m_value ? -1 : 0);\n }\n template \n BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value, int>::type compare(A i) const\n {\n return m_value > static_cast(i) ? 1 : (m_value < static_cast(i) ? -1 : 0);\n }\n BOOST_MP_CXX14_CONSTEXPR Arithmetic& data() { return m_value; }\n BOOST_MP_CXX14_CONSTEXPR const Arithmetic& data() const { return m_value; }\n\n private:\n Arithmetic m_value;\n};\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value>::type eval_convert_to(R* result, const arithmetic_backend& backend)\n{\n typedef typename std::common_type::type c_type;\n constexpr const c_type max = static_cast((std::numeric_limits::max)());\n constexpr const c_type min = static_cast((std::numeric_limits::min)());\n c_type ct = static_cast(backend.data());\n if ((backend.data() < 0) && !std::numeric_limits::is_signed)\n BOOST_THROW_EXCEPTION(std::range_error(\"Attempt to convert negative number to unsigned type.\"));\n if (ct > max)\n *result = boost::multiprecision::detail::is_signed::value ? (std::numeric_limits::max)() : backend.data();\n else if (std::numeric_limits::is_signed && (ct < min))\n *result = (std::numeric_limits::min)();\n else\n *result = backend.data();\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value && !std::is_enum::value>::type eval_convert_to(R* result, const arithmetic_backend& backend)\n{\n *result = backend.data();\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR bool eval_eq(const arithmetic_backend& a, const arithmetic_backend& b)\n{\n return a.data() == b.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value, bool>::type eval_eq(const arithmetic_backend& a, const A2& b)\n{\n return a.data() == static_cast(b);\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR bool eval_lt(const arithmetic_backend& a, const arithmetic_backend& b)\n{\n return a.data() < b.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value, bool>::type eval_lt(const arithmetic_backend& a, const A2& b)\n{\n return a.data() < static_cast(b);\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR bool eval_gt(const arithmetic_backend& a, const arithmetic_backend& b)\n{\n return a.data() > b.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value, bool>::type eval_gt(const arithmetic_backend& a, const A2& b)\n{\n return a.data() > static_cast(b);\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_add(arithmetic_backend& result, const arithmetic_backend& o)\n{\n result.data() += o.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_subtract(arithmetic_backend& result, const arithmetic_backend& o)\n{\n result.data() -= o.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_multiply(arithmetic_backend& result, const arithmetic_backend& o)\n{\n result.data() *= o.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::has_infinity>::type eval_divide(arithmetic_backend& result, const arithmetic_backend& o)\n{\n result.data() /= o.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::has_infinity>::type eval_divide(arithmetic_backend& result, const arithmetic_backend& o)\n{\n if (!o.data())\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Divide by zero\"));\n result.data() /= o.data();\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value>::type eval_add(arithmetic_backend& result, const A2& o)\n{\n result.data() += o;\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value>::type eval_subtract(arithmetic_backend& result, const A2& o)\n{\n result.data() -= o;\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value>::type eval_multiply(arithmetic_backend& result, const A2& o)\n{\n result.data() *= o;\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if<(boost::multiprecision::detail::is_arithmetic::value && !std::numeric_limits::has_infinity)>::type\neval_divide(arithmetic_backend& result, const A2& o)\n{\n if (!o)\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Divide by zero\"));\n result.data() /= o;\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if<(boost::multiprecision::detail::is_arithmetic::value && std::numeric_limits::has_infinity)>::type\neval_divide(arithmetic_backend& result, const A2& o)\n{\n result.data() /= o;\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_add(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n result.data() = a.data() + b.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_subtract(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n result.data() = a.data() - b.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_multiply(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n result.data() = a.data() * b.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::has_infinity>::type eval_divide(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n result.data() = a.data() / b.data();\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::has_infinity>::type eval_divide(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n if (!b.data())\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Divide by zero\"));\n result.data() = a.data() / b.data();\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value>::type eval_add(arithmetic_backend& result, const arithmetic_backend& a, const A2& b)\n{\n result.data() = a.data() + b;\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value>::type eval_subtract(arithmetic_backend& result, const arithmetic_backend& a, const A2& b)\n{\n result.data() = a.data() - b;\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value>::type eval_multiply(arithmetic_backend& result, const arithmetic_backend& a, const A2& b)\n{\n result.data() = a.data() * b;\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if<(boost::multiprecision::detail::is_arithmetic::value && !std::numeric_limits::has_infinity)>::type\neval_divide(arithmetic_backend& result, const arithmetic_backend& a, const A2& b)\n{\n if (!b)\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Divide by zero\"));\n result.data() = a.data() / b;\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if<(boost::multiprecision::detail::is_arithmetic::value && std::numeric_limits::has_infinity)>::type\neval_divide(arithmetic_backend& result, const arithmetic_backend& a, const A2& b)\n{\n result.data() = a.data() / b;\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR bool eval_is_zero(const arithmetic_backend& val)\n{\n return val.data() == 0;\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if<\n (!std::numeric_limits::is_specialized || std::numeric_limits::is_signed), int>::type\neval_get_sign(const arithmetic_backend& val)\n{\n return val.data() == 0 ? 0 : val.data() < 0 ? -1 : 1;\n}\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if<\n !(std::numeric_limits::is_specialized || std::numeric_limits::is_signed), int>::type\neval_get_sign(const arithmetic_backend& val)\n{\n return val.data() == 0 ? 0 : 1;\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR typename std::enable_if::value, T>::type abs(T v) { return v; }\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_abs(arithmetic_backend& result, const arithmetic_backend& o)\n{\n using boost::multiprecision::backends::abs;\n using std::abs;\n result.data() = abs(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_fabs(arithmetic_backend& result, const arithmetic_backend& o)\n{\n result.data() = std::abs(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_floor(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = floor(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_ceil(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = ceil(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_sqrt(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = sqrt(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR int eval_fpclassify(const arithmetic_backend& o)\n{\n return (boost::math::fpclassify)(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_trunc(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = trunc(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_round(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = round(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_frexp(arithmetic_backend& result, const arithmetic_backend& a, int* v)\n{\n BOOST_MATH_STD_USING\n result.data() = frexp(a.data(), v);\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_ldexp(arithmetic_backend& result, const arithmetic_backend& a, int v)\n{\n BOOST_MATH_STD_USING\n result.data() = ldexp(a.data(), v);\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_exp(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = exp(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_log(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = log(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_log10(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = log10(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_sin(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = sin(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_cos(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = cos(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_tan(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = tan(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_acos(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = acos(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_asin(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = asin(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_atan(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = atan(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_sinh(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = sinh(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_cosh(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = cosh(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_tanh(arithmetic_backend& result, const arithmetic_backend& o)\n{\n BOOST_MATH_STD_USING\n result.data() = tanh(o.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_fmod(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n BOOST_MATH_STD_USING\n result.data() = fmod(a.data(), b.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_pow(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n BOOST_MATH_STD_USING\n result.data() = pow(a.data(), b.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_atan2(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n BOOST_MATH_STD_USING\n result.data() = atan2(a.data(), b.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_left_shift(arithmetic_backend& result, I val)\n{\n result.data() <<= val;\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_right_shift(arithmetic_backend& result, I val)\n{\n result.data() >>= val;\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_modulus(arithmetic_backend& result, const arithmetic_backend& a)\n{\n result.data() %= a.data();\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_bitwise_and(arithmetic_backend& result, const arithmetic_backend& a)\n{\n result.data() &= a.data();\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_bitwise_or(arithmetic_backend& result, const arithmetic_backend& a)\n{\n result.data() |= a.data();\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_bitwise_xor(arithmetic_backend& result, const arithmetic_backend& a)\n{\n result.data() ^= a.data();\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_complement(arithmetic_backend& result, const arithmetic_backend& a)\n{\n result.data() = ~a.data();\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_gcd(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n result.data() = boost::integer::gcd(a.data(), b.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR void eval_lcm(arithmetic_backend& result, const arithmetic_backend& a, const arithmetic_backend& b)\n{\n result.data() = boost::integer::lcm(a.data(), b.data());\n}\n\ntemplate \ninline BOOST_MP_CXX14_CONSTEXPR std::size_t hash_value(const arithmetic_backend& a)\n{\n boost::hash hasher;\n return hasher(a.data());\n}\n\n#ifdef BOOST_MSVC\n#pragma warning(pop)\n#endif\n\n} // namespace backends\n\nusing boost::multiprecision::backends::arithmetic_backend;\n\ntemplate \nstruct number_category > : public std::integral_constant::value ? number_kind_integer : number_kind_floating_point>\n{};\n\nnamespace detail {\n\ntemplate \nstruct double_precision_type;\n\ntemplate \nstruct double_precision_type, ET> >\n{\n typedef number::type>, ET> type;\n};\ntemplate <>\nstruct double_precision_type >\n{\n typedef arithmetic_backend type;\n};\n\n} // namespace detail\n\n}} // namespace boost::multiprecision\n#if !(defined(__SGI_STL_PORT) || defined(BOOST_NO_LIMITS_COMPILE_TIME_CONSTANTS))\n//\n// We shouldn't need these to get code to compile, however for the sake of\n// \"level playing field\" performance comparisons they avoid the very slow\n// lexical_cast's that would otherwise take place. Definition has to be guarded\n// by the inverse of pp-logic in real_concept.hpp which defines these as a workaround\n// for STLPort plus some other old/broken standartd libraries.\n//\nnamespace boost { namespace math { namespace tools {\n\ntemplate <>\ninline unsigned int real_cast(concepts::real_concept r)\n{\n return static_cast(r.value());\n}\n\ntemplate <>\ninline int real_cast(concepts::real_concept r)\n{\n return static_cast(r.value());\n}\n\ntemplate <>\ninline long real_cast(concepts::real_concept r)\n{\n return static_cast(r.value());\n}\n\n// Converts from T to narrower floating-point types, float, double & long double.\n\ntemplate <>\ninline float real_cast(concepts::real_concept r)\n{\n return static_cast(r.value());\n}\ntemplate <>\ninline double real_cast(concepts::real_concept r)\n{\n return static_cast(r.value());\n}\ntemplate <>\ninline long double real_cast(concepts::real_concept r)\n{\n return r.value();\n}\n\n}}} // namespace boost::math::tools\n#endif\n\nnamespace std {\n\ntemplate \nclass numeric_limits, ExpressionTemplates> > : public std::numeric_limits\n{\n typedef std::numeric_limits base_type;\n typedef boost::multiprecision::number, ExpressionTemplates> number_type;\n\n public:\n static constexpr number_type(min)() noexcept { return (base_type::min)(); }\n static constexpr number_type(max)() noexcept { return (base_type::max)(); }\n static constexpr number_type lowest() noexcept { return -(max)(); }\n static constexpr number_type epsilon() noexcept { return base_type::epsilon(); }\n static constexpr number_type round_error() noexcept { return epsilon() / 2; }\n static constexpr number_type infinity() noexcept { return base_type::infinity(); }\n static constexpr number_type quiet_NaN() noexcept { return base_type::quiet_NaN(); }\n static constexpr number_type signaling_NaN() noexcept { return base_type::signaling_NaN(); }\n static constexpr number_type denorm_min() noexcept { return base_type::denorm_min(); }\n};\n\ntemplate <>\nclass numeric_limits : public std::numeric_limits\n{\n typedef std::numeric_limits base_type;\n typedef boost::math::concepts::real_concept number_type;\n\n public:\n static const number_type(min)() noexcept { return (base_type::min)(); }\n static const number_type(max)() noexcept { return (base_type::max)(); }\n static const number_type lowest() noexcept { return -(max)(); }\n static const number_type epsilon() noexcept { return base_type::epsilon(); }\n static const number_type round_error() noexcept { return epsilon() / 2; }\n static const number_type infinity() noexcept { return base_type::infinity(); }\n static const number_type quiet_NaN() noexcept { return base_type::quiet_NaN(); }\n static const number_type signaling_NaN() noexcept { return base_type::signaling_NaN(); }\n static const number_type denorm_min() noexcept { return base_type::denorm_min(); }\n};\n\n} // namespace std\n\n#include \n\n#endif\n", "meta": {"hexsha": "12698335dd96d95e3759c36ad5126d960e755a6d", "size": 26000, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lib/boost_1.78.0/libs/multiprecision/performance/arithmetic_backend.hpp", "max_stars_repo_name": "LaudateCorpus1/math", "max_stars_repo_head_hexsha": "990a66b3cccd27a5fd48626360bb91093a48278b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 106.0, "max_stars_repo_stars_event_min_datetime": "2015-08-07T04:23:50.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-27T18:25:15.000Z", "max_issues_repo_path": "lib/boost_1.78.0/libs/multiprecision/performance/arithmetic_backend.hpp", "max_issues_repo_name": "LaudateCorpus1/math", "max_issues_repo_head_hexsha": "990a66b3cccd27a5fd48626360bb91093a48278b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 130.0, "max_issues_repo_issues_event_min_datetime": "2016-06-22T22:11:25.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-29T20:24:09.000Z", "max_forks_repo_path": "lib/boost_1.78.0/libs/multiprecision/performance/arithmetic_backend.hpp", "max_forks_repo_name": "LaudateCorpus1/math", "max_forks_repo_head_hexsha": "990a66b3cccd27a5fd48626360bb91093a48278b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 41.0, "max_forks_repo_forks_event_min_datetime": "2015-07-08T19:18:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-14T16:39:56.000Z", "avg_line_length": 39.039039039, "max_line_length": 243, "alphanum_fraction": 0.7498846154, "num_tokens": 6241, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.2410557442266242}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \"imu/imu.h\"\n\nusing Eigen::Matrix4f;\nusing Eigen::Vector3f;\nusing Eigen::Vector4f;\n\nnamespace {\nconst uint8_t ADDR_ITG3200 = 0x68;\nconst uint8_t ADDR_HMC5883L = 0x1e;\nconst uint8_t ADDR_ADXL345 = 0x53;\n\nconst Vector3f gyrocal_b(-182.11, -179.32, -212.83);\nconst Vector3f gyrocal_m(-0.01169, -0.01123, -0.01164);\nconst float pointcloud_dist = 200.0f;\n\nbool CheckPointCloud(const Vector3f& pt, std::vector *pointcloud) {\n for (int i = 0; i < pointcloud->size(); i++) {\n if (((*pointcloud)[i] - pt).squaredNorm() <\n pointcloud_dist*pointcloud_dist) {\n return false;\n }\n }\n pointcloud->push_back(pt);\n return true;\n}\n\n} // empty namespace\n\n// our motion model should figure out whether the car is on the ground (1g down\n// on the accelerometer) or flying off a ramp (0g); if it's on the ground and\n// the motor is stopped (0 estimated velocity) then we can zero out the gyro,\n// and calibrate the direction of the accelerometer Y direction.\n\nbool IMU::Init() {\n mag_XTX_ = 0.01 * Matrix4f::Identity();\n mag_XTY_ = Vector4f::Zero();\n mag_bias_ = Vector3f::Zero();\n\n // config gyro\n i2c_.Write(ADDR_ITG3200, 0x3E, 0x01); // use X gyro PLL oscillator\n i2c_.Write(ADDR_ITG3200, 0x15, 19); // samplerate 50Hz (1000/(19+1))\n i2c_.Write(ADDR_ITG3200, 0x16, 0x18 + 4); // enable, 20Hz bandwidth\n // config compass\n i2c_.Write(ADDR_HMC5883L, 0x00, 0x38); // CRA: 75Hz rate w/ 2 averages\n i2c_.Write(ADDR_HMC5883L, 0x01, 0x20); // CRB: set gain 1090 LSB/Gauss\n i2c_.Write(ADDR_HMC5883L, 0x02, 0x00); // continuous measurement\n // config accelerometer\n i2c_.Write(ADDR_ADXL345, 0x2c, 0x09); // 25Hz bw, 50Hz samplerate\n i2c_.Write(ADDR_ADXL345, 0x31, 0x08); // FULL_RES\n i2c_.Write(ADDR_ADXL345, 0x38, 0x00); // bypass FIFO, sample @ 50Hz\n i2c_.Write(ADDR_ADXL345, 0x2d, 0x08); // turn on\n\n return true;\n}\n\nbool IMU::ReadRaw(IMURawState *s) {\n uint8_t axis_buf[8];\n if (!i2c_.Read(ADDR_ITG3200, 0x1b, axis_buf, 8))\n return false;\n // temperature is 280 LSB/deg C, -13200 LSB @35 C\n s->gyro_temp = bswap_16(*reinterpret_cast(axis_buf+0));\n s->gyro_x = bswap_16(*reinterpret_cast(axis_buf+2)); // roll\n s->gyro_y = bswap_16(*reinterpret_cast(axis_buf+4)); // pitch\n s->gyro_z = bswap_16(*reinterpret_cast(axis_buf+6)); // yaw\n\n if (!i2c_.Read(ADDR_HMC5883L, 0x03, axis_buf, 6))\n return false;\n s->mag_x = bswap_16(*reinterpret_cast(axis_buf+0)); // front?\n s->mag_z = bswap_16(*reinterpret_cast(axis_buf+2)); // up\n s->mag_y = bswap_16(*reinterpret_cast(axis_buf+4)); // side?\n\n if (!i2c_.Read(ADDR_ADXL345, 0x32, axis_buf, 6))\n return false;\n s->accel_x = (*reinterpret_cast(axis_buf+0)); // toward back\n s->accel_y = (*reinterpret_cast(axis_buf+2)); // toward right\n s->accel_z = (*reinterpret_cast(axis_buf+4)); // toward ground\n\n return true;\n}\n\nbool IMU::ReadCalibrated(IMUState *s) {\n IMURawState rawstate;\n if (!ReadRaw(&rawstate))\n return false;\n\n Calibrate(rawstate, s);\n return true;\n}\n\n// all devices are oriented such that X is to the front of the car, Y is to\n// the left, and Z is up.\nvoid IMU::Calibrate(const IMURawState &s, IMUState *state) {\n // gyro offset calibration (rough -- needs work)\n Vector3f w0(-s.gyro_y, -s.gyro_z, s.gyro_x);\n w0 -= gyrocal_b + s.gyro_temp * gyrocal_m;\n // scale to radians/second\n state->w = w0 * M_PI / (180.0 * 14.375);\n\n // magnetometer calibration via least-squares fit sphere\n // TODO: fix the axes (i think x and z are swapped and one is negated)\n Vector3f m(s.mag_x, s.mag_y, s.mag_z);\n // if there's room in the point cloud for this new point, update calibration\n if (CheckPointCloud(m, &mag_cal_points_)) {\n Vector4f b(s.mag_x, s.mag_y, s.mag_z, 1);\n mag_XTX_ += b * b.transpose();\n float bmag = b.squaredNorm();\n mag_XTY_ += bmag * b;\n Vector4f beta = mag_XTX_.ldlt().solve(mag_XTY_);\n mag_bias_ = beta.block<3, 1>(0, 0) / 2;\n bmag = sqrt(beta[3] + mag_bias_.squaredNorm());\n fprintf(stderr, \"imu: updated mag calibration [%f %f %f] R=%f\\n\",\n beta[0] / 2, beta[1] / 2, beta[2] / 2, bmag);\n }\n state->N = m - mag_bias_;\n\n // TODO: verify axes\n state->g = Vector3f(-s.accel_y, -s.accel_z, -s.accel_x);\n}\n", "meta": {"hexsha": "6411411f06b116157c87c361e39d876d12e1f8b4", "size": 4417, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/hw/imu/generic9dof.cc", "max_stars_repo_name": "ParikhKadam/cycloid", "max_stars_repo_head_hexsha": "c5e64e8379f801417a38755eb6b2fde881dabd8c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 156.0, "max_stars_repo_stars_event_min_datetime": "2017-08-12T03:58:21.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-21T13:38:52.000Z", "max_issues_repo_path": "src/hw/imu/generic9dof.cc", "max_issues_repo_name": "ParikhKadam/cycloid", "max_issues_repo_head_hexsha": "c5e64e8379f801417a38755eb6b2fde881dabd8c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2018-12-11T11:31:06.000Z", "max_issues_repo_issues_event_max_datetime": "2019-12-05T18:43:51.000Z", "max_forks_repo_path": "src/hw/imu/generic9dof.cc", "max_forks_repo_name": "ParikhKadam/cycloid", "max_forks_repo_head_hexsha": "c5e64e8379f801417a38755eb6b2fde881dabd8c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 18.0, "max_forks_repo_forks_event_min_datetime": "2017-08-17T02:05:54.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-24T20:54:27.000Z", "avg_line_length": 35.6209677419, "max_line_length": 79, "alphanum_fraction": 0.676477247, "num_tokens": 1586, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.3849121444839335, "lm_q1q2_score": 0.2410027997701847}} {"text": "/*\n Copyright (c) 2011-2014 University of Zurich\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in\n all copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n THE SOFTWARE.\n*/\n\n#include \n#include \"lll2-internal.hpp\"\n#include \"transform.cpp\"\n\n#include \n#include \n\n#include \n#include \n#if !defined(PLLL_CONFIG_NO_ARITHMETIC_LONGDOUBLE) || !defined(PLLL_CONFIG_NO_ARITHMETIC_DOUBLE)\n #include \"nfp-wrapper.hpp\"\n#endif\n#if !defined(PLLL_CONFIG_NO_ARITHMETIC_QUADDOUBLE) || !defined(PLLL_CONFIG_NO_ARITHMETIC_DOUBLEDOUBLE)\n #include \"ddqd-wrapper.hpp\"\n#endif\n\nnamespace plll\n{\n class LRIISelector : public LRIInterface\n {\n private:\n class Interface\n {\n private:\n long d_max_bits;\n boost::function &)> d_interface_generator;\n mutable const LatticeReduction::GramSchmidtInformer * d_gsi;\n \n public:\n Interface(long max_bits, boost::function &)> interfacegen)\n : d_max_bits(max_bits), d_interface_generator(interfacegen), d_gsi(NULL)\n {\n }\n \n ~Interface()\n {\n }\n \n LRIInterface * generateInterface(linalg::math_matrix & matrix) const\n {\n return d_interface_generator(matrix);\n }\n \n long maxSupportedBits() const\n {\n return d_max_bits;\n }\n \n bool isFine(unsigned bits, unsigned dimension) const\n {\n long bits_needed = 2 * bits + arithmetic::approxLog2(dimension) + 4;\n return bits_needed <= d_max_bits;\n }\n };\n \n std::list d_interfaces;\n mutable std::list::const_iterator d_interface_gen, d_better_interface_gen;\n mutable STD_AUTO_PTR d_interface;\n mutable bool d_continue, d_reselect;\n mutable MaxBitsCallbackFunction d_maxbitscf;\n \n inline bool isBetter(unsigned bits, unsigned dimension) const\n {\n return (d_better_interface_gen == d_interfaces.end()) ? false : d_better_interface_gen->isFine(bits, dimension);\n } \n \n void maxBitsCallbackFunction(unsigned dimension, unsigned maxbits)\n {\n if (!d_maxbitscf.empty())\n d_maxbitscf(dimension, maxbits);\n if (d_verbose_function && Verbose::yieldsOutput(d_verbose_outputlevel, LatticeReduction::VL_Chatter))\n {\n std::ostringstream s;\n s << \"...base has \" << maxbits << \" bits...\";\n d_verbose_function(LatticeReduction::VL_Chatter, s.str());\n }\n if (!d_interface_gen->isFine(maxbits, dimension) || isBetter(maxbits, dimension))\n {\n d_reselect = true;\n throw change_interface_exception();\n }\n }\n \n bool isContinuing() const\n {\n if (d_reselect)\n selectInterface(true);\n bool v = d_continue;\n d_continue = false;\n return v;\n }\n \n unsigned maxBits() const\n {\n unsigned maxbits = 0;\n for (unsigned i = 0; i < d_lattice.rows(); ++i)\n for (unsigned j = 0; j < d_lattice.cols(); ++j)\n {\n long b = arithmetic::approxLog2(d_lattice(i, j));\n if (b > maxbits)\n maxbits = b;\n }\n return maxbits;\n }\n \n void selectInterface(bool withContinue = false) const\n {\n if (d_interface.get())\n {\n if (d_verbose_function && Verbose::yieldsOutput(d_verbose_outputlevel, LatticeReduction::VL_Information))\n d_verbose_function(LatticeReduction::VL_Information, \"Applying interface change\");\n d_interface.reset();\n }\n \n d_continue = withContinue;\n d_reselect = false;\n unsigned bits = maxBits();\n d_interface_gen = d_interfaces.end();\n for (std::list::const_iterator i = d_interfaces.begin(); i != d_interfaces.end(); ++i)\n {\n if (i->isFine(bits, d_lattice.rows()))\n d_interface_gen = i;\n }\n assert(d_interface_gen != d_interfaces.end());\n \n if (d_verbose_function && Verbose::yieldsOutput(d_verbose_outputlevel, LatticeReduction::VL_Information))\n {\n std::ostringstream s;\n s << \"Selecting interface which supports up to \" << d_interface_gen->maxSupportedBits() << \" bits\";\n d_verbose_function(LatticeReduction::VL_Information, s.str());\n }\n \n // Generate interface\n d_interface.reset(d_interface_gen->generateInterface(d_lattice));\n if (d_ensure_min_prec)\n d_interface->ensureMinimumPrecision(d_min_prec);\n if (d_set_verbose)\n d_interface->setupVerbose(d_verbose_outputlevel, d_verbose_function);\n \n // Select next interface\n d_better_interface_gen = d_interface_gen;\n ++d_better_interface_gen;\n }\n \n const LRIInterface & getInterface() const\n {\n return *d_interface;\n }\n \n LRIInterface & getInterface()\n {\n return *d_interface;\n } \n \n void unselectInterface() const\n {\n d_interface.reset();\n }\n \n class GSI : public LatticeReduction::GramSchmidtInformer\n {\n private:\n LRIISelector & d_selector;\n \n public:\n GSI(LRIISelector & selector)\n : d_selector(selector)\n {\n }\n \n virtual ~GSI()\n {\n }\n \n virtual double getGSCoefficientD(unsigned i, unsigned j) const\n {\n d_selector.selectInterface();\n double r = d_selector.getInterface().getInformer()->getGSCoefficientD(i, j);\n d_selector.unselectInterface();\n return r;\n }\n \n virtual long double getGSCoefficientLD(unsigned i, unsigned j) const\n {\n d_selector.selectInterface();\n long double r = d_selector.getInterface().getInformer()->getGSCoefficientLD(i, j);\n d_selector.unselectInterface();\n return r;\n }\n \n virtual arithmetic::Real getGSCoefficientR(unsigned i, unsigned j, const arithmetic::RealContext & rc) const\n {\n d_selector.selectInterface();\n arithmetic::Real r = d_selector.getInterface().getInformer()->getGSCoefficientR(i, j, rc);\n d_selector.unselectInterface();\n return r;\n }\n \n virtual double getGSSqNormD(unsigned i) const\n {\n d_selector.selectInterface();\n double r = d_selector.getInterface().getInformer()->getGSSqNormD(i);\n d_selector.unselectInterface();\n return r;\n }\n \n virtual long double getGSSqNormLD(unsigned i) const\n {\n d_selector.selectInterface();\n long double r = d_selector.getInterface().getInformer()->getGSSqNormLD(i);\n d_selector.unselectInterface();\n return r;\n }\n \n virtual arithmetic::Real getGSSqNormR(unsigned i, const arithmetic::RealContext & rc) const\n {\n d_selector.selectInterface();\n arithmetic::Real r = d_selector.getInterface().getInformer()->getGSSqNormR(i, rc);\n d_selector.unselectInterface();\n return r;\n }\n \n virtual double computeProjectionLengthD(unsigned k, unsigned b, const linalg::math_rowvector & vec) const\n {\n d_selector.selectInterface();\n double r = d_selector.getInterface().getInformer()->computeProjectionLengthD(k, b, vec);\n d_selector.unselectInterface();\n return r;\n }\n \n virtual long double computeProjectionLengthLD(unsigned k, unsigned b, const linalg::math_rowvector & vec) const\n {\n d_selector.selectInterface();\n long double r = d_selector.getInterface().getInformer()->computeProjectionLengthLD(k, b, vec);\n d_selector.unselectInterface();\n return r;\n }\n \n virtual arithmetic::Real computeProjectionLengthR(unsigned k, unsigned b, const linalg::math_rowvector & vec, const arithmetic::RealContext & rc) const\n {\n d_selector.selectInterface();\n arithmetic::Real r = d_selector.getInterface().getInformer()->computeProjectionLengthR(k, b, vec, rc);\n d_selector.unselectInterface();\n return r;\n }\n };\n \n friend class GSI;\n \n GSI d_gsi;\n \n bool d_ensure_min_prec;\n unsigned long d_min_prec;\n \n bool d_set_verbose;\n LatticeReduction::VerboseOutputLevel d_verbose_outputlevel;\n LatticeReduction::VerboseFunction d_verbose_function;\n \n public:\n LRIISelector(linalg::math_matrix & lattice, LatticeReduction::VerboseOutputLevel vol, LatticeReduction::VerboseFunction vf)\n : LRIInterface(lattice), d_interface(), d_continue(false), d_reselect(false), d_gsi(*this),\n d_ensure_min_prec(false), d_min_prec(0), d_set_verbose(false), d_verbose_outputlevel(vol), d_verbose_function(vf)\n {\n }\n \n virtual ~LRIISelector()\n {\n }\n \n void addInterface(long max_bits, boost::function &)> interfacegen)\n {\n d_interfaces.push_back(Interface(max_bits, interfacegen));\n }\n \n virtual void setupVerbose(LatticeReduction::VerboseOutputLevel vol, LatticeReduction::VerboseFunction vf)\n {\n d_set_verbose = true;\n d_verbose_outputlevel = vol;\n d_verbose_function = vf;\n }\n \n virtual const LatticeReduction::GramSchmidtInformer * getInformer() const\n {\n return &d_gsi;\n }\n \n virtual void ensureMinimumPrecision(unsigned long prec)\n {\n d_ensure_min_prec = prec;\n d_min_prec = prec;\n }\n \n virtual void forceGSRebuild(bool b)\n {\n }\n \n virtual double getGSCoefficientD(unsigned i, unsigned j) const\n {\n selectInterface();\n double r = getInterface().getGSCoefficientD(i, j);\n unselectInterface();\n return r;\n }\n \n virtual long double getGSCoefficientLD(unsigned i, unsigned j) const\n {\n selectInterface();\n long double r = getInterface().getGSCoefficientLD(i, j);\n unselectInterface();\n return r;\n }\n \n virtual arithmetic::Real getGSCoefficientR(unsigned i, unsigned j, const arithmetic::RealContext & rc) const\n {\n selectInterface();\n arithmetic::Real r = getInterface().getGSCoefficientR(i, j, rc);\n unselectInterface();\n return r;\n }\n \n virtual double getGSSqNormD(unsigned i) const\n {\n selectInterface();\n double r = getInterface().getGSSqNormD(i);\n unselectInterface();\n return r;\n }\n \n virtual long double getGSSqNormLD(unsigned i) const\n {\n selectInterface();\n long double r = getInterface().getGSSqNormLD(i);\n unselectInterface();\n return r;\n }\n \n virtual arithmetic::Real getGSSqNormR(unsigned i, const arithmetic::RealContext & rc) const\n {\n selectInterface();\n arithmetic::Real r = getInterface().getGSSqNormR(i, rc);\n unselectInterface();\n return r;\n }\n \n virtual void modFlip(Transform & transform, unsigned i)\n {\n selectInterface();\n getInterface().modFlip(transform, i);\n unselectInterface();\n }\n \n virtual void modSwap(Transform & transform, unsigned i, unsigned j)\n {\n selectInterface();\n getInterface().modSwap(transform, i, j);\n unselectInterface();\n }\n \n virtual void modAdd(Transform & transform, unsigned i, unsigned j, const arithmetic::Integer & m)\n {\n selectInterface();\n getInterface().modAdd(transform, i, j, m);\n unselectInterface();\n }\n \n virtual void sortProjected(unsigned & begin, unsigned & end, Transform & transform)\n {\n selectInterface();\n getInterface().sortProjected(begin, end, transform);\n unselectInterface();\n }\n \n virtual void sizereduction(unsigned & begin, unsigned & end, Transform & transform)\n {\n selectInterface();\n getInterface().sizereduction(begin, end, transform);\n unselectInterface();\n }\n \n virtual void lll(unsigned & begin, unsigned & end, Transform & transform, double alpha, LatticeReduction::LLLMode mode,\n LatticeReduction::CallbackFunction cf, LatticeReduction::CallbackFunction_LI cf2, double cf_int,\n LatticeReduction::MinCallbackFunction mcf, LatticeReduction::MinCallbackFunction_LI mcf2,\n MaxBitsCallbackFunction mbcf, bool anneal, LatticeReduction::AnnealCallbackFunction acf,\n LatticeReduction::LLL_AnnealFunction af, LatticeReduction::DIMethod di, LatticeReduction::DIMode di_mode,\n LatticeReduction::DIChoice di_choice, unsigned di_bs)\n {\n selectInterface(true);\n d_maxbitscf = mbcf;\n while (isContinuing())\n getInterface().lll(begin, end, transform, alpha, mode, cf, cf2, cf_int, mcf, mcf2,\n boost::bind(&LRIISelector::maxBitsCallbackFunction, this, _1, _2),\n anneal, acf, af, di, di_mode, di_choice, di_bs);\n unselectInterface();\n }\n \n virtual void bkz(unsigned & begin, unsigned & end, Transform & transform, double alpha, unsigned blocksize, LatticeReduction::BKZMode mode,\n LatticeReduction::CallbackFunction cf, LatticeReduction::CallbackFunction_LI cf2, double cf_int,\n LatticeReduction::MinCallbackFunction mcf, LatticeReduction::MinCallbackFunction_LI mcf2, MaxBitsCallbackFunction mbcf,\n LatticeReduction::EnumCallbackFunction ecf, LatticeReduction::EnumCallbackFunction_LI ecf2, bool anneal,\n LatticeReduction::AnnealCallbackFunction acf, LatticeReduction::BKZ_AnnealFunction af,\n LatticeReduction::DIMethod di, LatticeReduction::DIMode di_mode, LatticeReduction::DIChoice di_choice, unsigned di_bs)\n {\n selectInterface(true);\n d_maxbitscf = mbcf;\n while (isContinuing())\n getInterface().bkz(begin, end, transform, alpha, blocksize, mode, cf, cf2, cf_int, mcf, mcf2,\n boost::bind(&LRIISelector::maxBitsCallbackFunction, this, _1, _2),\n ecf, ecf2, anneal, acf, af, di, di_mode, di_choice, di_bs);\n unselectInterface();\n }\n \n virtual void hkz(unsigned & begin, unsigned & end, Transform & transform, bool dual,\n LatticeReduction::CallbackFunction cf, LatticeReduction::CallbackFunction_LI cf2, double cf_int,\n LatticeReduction::MinCallbackFunction mcf, LatticeReduction::MinCallbackFunction_LI mcf2,\n MaxBitsCallbackFunction mbcf,\n LatticeReduction::EnumCallbackFunction ecf, LatticeReduction::EnumCallbackFunction_LI ecf2)\n {\n selectInterface(true);\n d_maxbitscf = mbcf;\n while (isContinuing())\n getInterface().hkz(begin, end, transform, dual, cf, cf2, cf_int, mcf, mcf2,\n boost::bind(&LRIISelector::maxBitsCallbackFunction, this, _1, _2),\n ecf, ecf2);\n unselectInterface();\n }\n \n virtual void svp(unsigned & begin, unsigned & end, Transform & transform, bool make_basis, bool extreme, bool dual,\n LatticeReduction::CallbackFunction cf, LatticeReduction::CallbackFunction_LI cf2, double cf_int,\n LatticeReduction::MinCallbackFunction mcf, LatticeReduction::MinCallbackFunction_LI mcf2,\n MaxBitsCallbackFunction mbcf,\n LatticeReduction::EnumCallbackFunction ecf, LatticeReduction::EnumCallbackFunction_LI ecf2)\n {\n selectInterface(true);\n d_maxbitscf = mbcf;\n while (isContinuing())\n getInterface().svp(begin, end, transform, make_basis, extreme, dual, cf, cf2, cf_int, mcf, mcf2,\n boost::bind(&LRIISelector::maxBitsCallbackFunction, this, _1, _2),\n ecf, ecf2);\n unselectInterface();\n }\n \n virtual bool isSizeReduced(unsigned begin, unsigned end) const\n {\n selectInterface();\n bool ret = getInterface().isSizeReduced(begin, end);\n unselectInterface();\n return ret;\n }\n \n virtual bool isLLLBasis(unsigned begin, unsigned end, double alpha, LatticeReduction::LLLMode mode,\n LatticeReduction::DIMethod di, LatticeReduction::DIChoice di_choice, unsigned di_bs) const\n {\n selectInterface();\n bool ret = getInterface().isLLLBasis(begin, end, alpha, mode, di, di_choice, di_bs);\n unselectInterface();\n return ret;\n }\n \n virtual bool isBKZBasis(unsigned begin, unsigned end, double alpha, unsigned blocksize, LatticeReduction::BKZMode mode,\n LatticeReduction::DIMethod di, LatticeReduction::DIChoice di_choice, unsigned di_bs) const\n {\n selectInterface();\n bool ret = getInterface().isBKZBasis(begin, end, alpha, blocksize, mode, di, di_choice, di_bs);\n unselectInterface();\n return ret;\n }\n \n virtual bool isHKZBasis(unsigned begin, unsigned end, bool dual) const\n {\n selectInterface();\n bool ret = getInterface().isHKZBasis(begin, end, dual);\n unselectInterface();\n return ret;\n }\n \n virtual bool isSVPBasis(unsigned begin, unsigned end, bool dual) const\n {\n selectInterface();\n bool ret = getInterface().isSVPBasis(begin, end, dual);\n unselectInterface();\n return ret;\n }\n };\n \n template\n LRIInterface * CreateLRIInterfaceWithContexts(LatticeReduction::VerboseOutputLevel vol, LatticeReduction::VerboseFunction vf,\n linalg::math_matrix & lattice, LatticeReduction::GramSchmidt gs, bool gsr,\n LatticeReduction::SVPMode svp, unsigned max_cores,\n const RealTypeContext & rc, const IntTypeContext & ic);\n \n template\n LRIInterface * CreateLRIInterface(LatticeReduction::VerboseOutputLevel vol, LatticeReduction::VerboseFunction vf,\n linalg::math_matrix & lattice, LatticeReduction::GramSchmidt gs, bool gsr,\n LatticeReduction::SVPMode svp, unsigned max_cores,\n LatticeReduction::Integers ints, const RealTypeContext & rc)\n {\n switch (ints)\n {\n#if !defined(PLLL_CONFIG_NO_ARITHMETIC_BIGINT) && !defined(PLLL_CONFIG_NO_ARITHMETIC_LONGINT)\n case LatticeReduction::I_Auto:\n {\n LRIISelector * sel = new LRIISelector(lattice, vol, vf);\n // Add in decreasing order of supported precisions\n sel->addInterface(std::numeric_limits::max(),\n boost::bind(CreateLRIInterfaceWithContexts,\n vol, vf, _1, gs, gsr, svp, max_cores, rc, arithmetic::IntegerContext()));\n sel->addInterface(std::numeric_limits::digits - 1,\n boost::bind(CreateLRIInterfaceWithContexts >,\n vol, vf, _1, gs, gsr, svp, max_cores, rc, arithmetic::NIntContext()));\n return sel;\n }\n#endif\n#ifndef PLLL_CONFIG_NO_ARITHMETIC_BIGINT\n case LatticeReduction::I_ArbitraryPrecision:\n return CreateLRIInterfaceWithContexts(vol, vf, lattice, gs, gsr, svp, max_cores, rc, arithmetic::IntegerContext());\n#endif\n#ifndef PLLL_CONFIG_NO_ARITHMETIC_LONGINT\n case LatticeReduction::I_LongInt:\n return CreateLRIInterfaceWithContexts(vol, vf, lattice, gs, gsr, svp, max_cores, rc, arithmetic::NIntContext());\n#endif\n }\n assert(!\"Integer arithmetic not supported!\");\n return NULL;\n }\n \n LRIInterface * CreateLRIInterface(LatticeReduction::VerboseOutputLevel vol, LatticeReduction::VerboseFunction vf,\n linalg::math_matrix & lattice,\n LatticeReduction::Arithmetic arith, LatticeReduction::Integers ints,\n LatticeReduction::GramSchmidt gs, bool gsr,\n LatticeReduction::SVPMode svp, unsigned max_cores)\n {\n switch(arith)\n {\n default:\n#ifndef PLLL_CONFIG_NO_ARITHMETIC_LONGDOUBLE\n case LatticeReduction::A_LongDouble: return CreateLRIInterface(vol, vf, lattice, gs, gsr, svp, max_cores, ints, arithmetic::NFPContext());\n#endif\n#ifndef PLLL_CONFIG_NO_ARITHMETIC_REAL\n case LatticeReduction::A_Real: return CreateLRIInterface(vol, vf, lattice, gs, gsr, svp, max_cores, ints, arithmetic::RealContext());\n#endif\n#ifndef PLLL_CONFIG_NO_ARITHMETIC_RATIONAL\n case LatticeReduction::A_Rational: return CreateLRIInterface(vol, vf, lattice, gs, gsr, svp, max_cores, ints, arithmetic::RationalContext());\n#endif\n#ifndef PLLL_CONFIG_NO_ARITHMETIC_DOUBLE\n case LatticeReduction::A_Double: return CreateLRIInterface(vol, vf, lattice, gs, gsr, svp, max_cores, ints, arithmetic::NFPContext());\n#endif\n#ifndef PLLL_CONFIG_NO_ARITHMETIC_DOUBLEDOUBLE\n case LatticeReduction::A_DoubleDouble: return CreateLRIInterface(vol, vf, lattice, gs, gsr, svp, max_cores, ints, arithmetic::DDQDContext());\n#endif\n#ifndef PLLL_CONFIG_NO_ARITHMETIC_QUADDOUBLE\n case LatticeReduction::A_QuadDouble: return CreateLRIInterface(vol, vf, lattice, gs, gsr, svp, max_cores, ints, arithmetic::DDQDContext());\n#endif\n }\n assert(!\"Real arithmetic not supported!\");\n return NULL;\n }\n \n // Explicit instantiation of templates\n \n#ifndef PLLL_CONFIG_NO_ARITHMETIC_BIGINT\n template void setUnit(linalg::math_matrix &);\n#endif\n \n#ifndef PLLL_CONFIG_NO_ARITHMETIC_LONGINT\n template void setUnit >(linalg::math_matrix > &);\n#endif\n}\n", "meta": {"hexsha": "36f946f3bd778bffe4b998c60a48cd3b55eec55c", "size": 25692, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "plll/src/lattices/lll2-multiplexer.cpp", 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43.5457627119, "max_line_length": 184, "alphanum_fraction": 0.5875369765, "num_tokens": 5454, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.38491213037224875, "lm_q1q2_score": 0.24100279093451768}} {"text": "// This file is part of the dune-stuff project:\n// https://github.com/wwu-numerik/dune-stuff\n// Copyright holders: Rene Milk, Felix Schindler\n// License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)\n//\n// Contributors: Sven Kaulmann\n\n#ifndef DUNE_STUFF_COMMON_MATRIX_HH\n#define DUNE_STUFF_COMMON_MATRIX_HH\n\n#include \n\n#include \n#include \n\n#include \n#include \n#include \n\nnamespace Dune {\nnamespace Stuff {\nnamespace Common {\n\n/**\n * \\brief Traits to statically extract the information of a (mathematical) matrix.\n *\n * If you want your matrix class to benefit from the operators defined in this header you have to manually\n * specify a specialization of this class in your code with is_matrix defined to true and an appropriate\n * static methods and members (see the specializations below).\n */\ntemplate \nstruct MatrixAbstraction\n{\n typedef MatType MatrixType;\n typedef MatType ScalarType;\n typedef MatType S;\n\n static const bool is_matrix = false;\n\n static const bool has_static_size = false;\n\n static const size_t static_rows = std::numeric_limits::max();\n\n static const size_t static_cols = std::numeric_limits::max();\n\n static inline /*MatrixType*/ void create(const size_t /*rows*/, const size_t /*cols*/)\n {\n static_assert(AlwaysFalse::value, \"Do not call me if is_matrix is false!\");\n }\n\n static inline /*MatrixType*/ void create(const size_t /*rows*/, const size_t /*cols*/, const ScalarType& /*val*/)\n {\n static_assert(AlwaysFalse::value, \"Do not call me if is_matrix is false!\");\n }\n\n static inline /*size_t*/ void rows(const MatrixType& /*mat*/)\n {\n static_assert(AlwaysFalse::value, \"Do not call me if is_matrix is false!\");\n }\n\n static inline /*size_t*/ void cols(const MatrixType& /*mat*/)\n {\n static_assert(AlwaysFalse::value, \"Do not call me if is_matrix is false!\");\n }\n\n static inline void set_entry(MatrixType& /*mat*/, const size_t /*row*/, const size_t /*col*/,\n const ScalarType& /*val*/)\n {\n static_assert(AlwaysFalse::value, \"Do not call me if is_matrix is false!\");\n }\n\n static inline /*ScalarType*/ void get_entry(const MatrixType& /*mat*/, const size_t /*row*/, const size_t /*col*/)\n {\n static_assert(AlwaysFalse::value, \"Do not call me if is_matrix is false!\");\n }\n};\n\ntemplate \nstruct MatrixAbstraction>\n{\n typedef Dune::DynamicMatrix MatrixType;\n typedef K ScalarType;\n typedef ScalarType S;\n\n static const bool is_matrix = true;\n\n static const bool has_static_size = false;\n\n static const size_t static_rows = std::numeric_limits::max();\n\n static const size_t static_cols = std::numeric_limits::max();\n\n static inline MatrixType create(const size_t rows, const size_t cols) { return MatrixType(rows, cols); }\n\n static inline MatrixType create(const size_t rows, const size_t cols, const ScalarType& val)\n {\n return MatrixType(rows, cols, val);\n }\n\n static inline size_t rows(const MatrixType& mat) { return mat.rows(); }\n\n static inline size_t cols(const MatrixType& mat) { return mat.cols(); }\n\n static inline void set_entry(MatrixType& mat, const size_t row, const size_t col, const ScalarType& val)\n {\n mat[row][col] = val;\n }\n\n static inline ScalarType get_entry(const MatrixType& mat, const size_t row, const size_t col)\n {\n return mat[row][col];\n }\n};\n\ntemplate \nstruct MatrixAbstraction>\n{\n typedef Dune::FieldMatrix MatrixType;\n typedef K ScalarType;\n typedef ScalarType S;\n\n static const bool is_matrix = true;\n\n static const bool has_static_size = true;\n\n static const size_t static_rows = N;\n\n static const size_t static_cols = M;\n\n static inline MatrixType create(const size_t rows, const size_t cols)\n {\n if (rows != N)\n DUNE_THROW(Dune::Stuff::Exceptions::shapes_do_not_match, \"rows = \" << rows << \"\\nN = \" << int(N));\n if (cols != M)\n DUNE_THROW(Dune::Stuff::Exceptions::shapes_do_not_match, \"cols = \" << cols << \"\\nM = \" << int(M));\n return MatrixType();\n }\n\n static inline MatrixType create(const size_t rows, const size_t cols, const ScalarType& val)\n {\n if (rows != N)\n DUNE_THROW(Dune::Stuff::Exceptions::shapes_do_not_match, \"rows = \" << rows << \"\\nN = \" << int(N));\n if (cols != M)\n DUNE_THROW(Dune::Stuff::Exceptions::shapes_do_not_match, \"cols = \" << cols << \"\\nM = \" << int(M));\n return MatrixType(val);\n }\n\n static inline size_t rows(const MatrixType& /*mat*/) { return boost::numeric_cast(N); }\n\n static inline size_t cols(const MatrixType& /*mat*/) { return boost::numeric_cast(M); }\n\n static inline void set_entry(MatrixType& mat, const size_t row, const size_t col, const ScalarType& val)\n {\n mat[row][col] = val;\n }\n\n static inline ScalarType get_entry(const MatrixType& mat, const size_t row, const size_t col)\n {\n return mat[row][col];\n }\n};\n\ntemplate \nstruct MatrixAbstraction>\n{\n typedef Dune::Stuff::Common::FieldMatrix MatrixType;\n typedef K ScalarType;\n typedef ScalarType S;\n\n static const bool is_matrix = true;\n\n static const bool has_static_size = true;\n\n static const size_t static_rows = N;\n\n static const size_t static_cols = M;\n\n static inline MatrixType create(const size_t rows, const size_t cols) { return MatrixType(rows, cols); }\n\n static inline MatrixType create(const size_t rows, const size_t cols, const ScalarType& val)\n {\n return MatrixType(rows, cols, val);\n }\n\n static inline size_t rows(const MatrixType& /*mat*/) { return N; }\n\n static inline size_t cols(const MatrixType& /*mat*/) { return M; }\n\n static inline void set_entry(MatrixType& mat, const size_t row, const size_t col, const ScalarType& val)\n {\n mat[row][col] = val;\n }\n\n static inline ScalarType get_entry(const MatrixType& mat, const size_t row, const size_t col)\n {\n return mat[row][col];\n }\n};\n\ntemplate \nstruct is_matrix\n{\n static const bool value = MatrixAbstraction::is_matrix;\n};\n\ntemplate \ntypename std::enable_if::value, MatrixType>::type\n create(const size_t sz, const typename MatrixAbstraction::S& val)\n{\n return MatrixAbstraction::create(sz, val);\n}\n\n} // namespace Common\n} // namespace Stuff\n} // namespace Dune\n\n#endif // DUNE_STUFF_COMMON_MATRIX_HH\n", "meta": {"hexsha": "cb61257e6306393346ec3b8ef202b5af0b0e89c0", "size": 6720, "ext": "hh", "lang": "C++", "max_stars_repo_path": "dune/stuff/common/matrix.hh", "max_stars_repo_name": "ftalbrecht/dune-stuff-simplified", "max_stars_repo_head_hexsha": "fc1f80dedaa78fae6e6d67e8f5424a6b3ec86b5d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dune/stuff/common/matrix.hh", "max_issues_repo_name": "ftalbrecht/dune-stuff-simplified", "max_issues_repo_head_hexsha": "fc1f80dedaa78fae6e6d67e8f5424a6b3ec86b5d", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dune/stuff/common/matrix.hh", "max_forks_repo_name": "ftalbrecht/dune-stuff-simplified", "max_forks_repo_head_hexsha": "fc1f80dedaa78fae6e6d67e8f5424a6b3ec86b5d", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8256880734, "max_line_length": 116, "alphanum_fraction": 0.7040178571, "num_tokens": 1719, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.24077694225848403}} {"text": "/*\nAll modification made by Cambricon Corporation: © 2018-2019 Cambricon Corporation\nAll rights reserved.\nAll other contributions:\nCopyright (c) 2014--2019, the respective contributors\nAll rights reserved.\nFor the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n * Redistributions of source code must retain the above copyright notice,\n this list of conditions and the following disclaimer.\n * Redistributions in binary form must reproduce the above copyright\n notice, this list of conditions and the following disclaimer in the\n documentation and/or other materials provided with the distribution.\n * Neither the name of Intel Corporation nor the names of its contributors\n may be used to endorse or promote products derived from this software\n without specific prior written permission.\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n\n#include \n#include \n\n#include \n\n#include \"caffe/common.hpp\"\n#include \"caffe/util/math_functions.hpp\"\n#include \"caffe/util/rng.hpp\"\n\nnamespace caffe {\n\ntemplate <>\nvoid caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,\n const CBLAS_TRANSPOSE TransB, const int M,\n const int N, const int K, const float alpha,\n const float* A, const float* B, const float beta,\n float* C) {\n int lda = (TransA == CblasNoTrans) ? K : M;\n int ldb = (TransB == CblasNoTrans) ? N : K;\n cblas_sgemm(CblasRowMajor, TransA, TransB, M, N, K, alpha, A, lda, B, ldb,\n beta, C, N);\n}\n\ntemplate <>\nvoid caffe_cpu_gemm(const CBLAS_TRANSPOSE TransA,\n const CBLAS_TRANSPOSE TransB, const int M,\n const int N, const int K, const double alpha,\n const double* A, const double* B, const double beta,\n double* C) {\n int lda = (TransA == CblasNoTrans) ? K : M;\n int ldb = (TransB == CblasNoTrans) ? N : K;\n cblas_dgemm(CblasRowMajor, TransA, TransB, M, N, K, alpha, A, lda, B, ldb,\n beta, C, N);\n}\n\ntemplate <>\nvoid caffe_cpu_gemv(const CBLAS_TRANSPOSE TransA, const int M,\n const int N, const float alpha, const float* A,\n const float* x, const float beta, float* y) {\n cblas_sgemv(CblasRowMajor, TransA, M, N, alpha, A, N, x, 1, beta, y, 1);\n}\n\ntemplate <>\nvoid caffe_cpu_gemv(const CBLAS_TRANSPOSE TransA, const int M,\n const int N, const double alpha, const double* A,\n const double* x, const double beta, double* y) {\n cblas_dgemv(CblasRowMajor, TransA, M, N, alpha, A, N, x, 1, beta, y, 1);\n}\n\ntemplate <>\nvoid caffe_axpy(const int N, const float alpha, const float* X,\n float* Y) {\n cblas_saxpy(N, alpha, X, 1, Y, 1);\n}\n\ntemplate <>\nvoid caffe_axpy(const int N, const double alpha, const double* X,\n double* Y) {\n cblas_daxpy(N, alpha, X, 1, Y, 1);\n}\n\ntemplate \nvoid caffe_set(const int N, const Dtype alpha, Dtype* Y) {\n if (alpha == 0) {\n memset(Y, 0, sizeof(Dtype) * N);\n return;\n }\n for (int i = 0; i < N; ++i) {\n Y[i] = alpha;\n }\n}\n\ntemplate void caffe_set(const int N, const int alpha, int* Y);\ntemplate void caffe_set(const int N, const float alpha, float* Y);\ntemplate void caffe_set(const int N, const double alpha, double* Y);\n\ntemplate <>\nvoid caffe_add_scalar(const int N, const float alpha, float* Y) {\n for (int i = 0; i < N; ++i) {\n Y[i] += alpha;\n }\n}\n\ntemplate <>\nvoid caffe_add_scalar(const int N, const double alpha, double* Y) {\n for (int i = 0; i < N; ++i) {\n Y[i] += alpha;\n }\n}\n\ntemplate \nvoid caffe_copy(const int N, const Dtype* X, Dtype* Y) {\n if (X != Y) {\n if (Caffe::mode() == Caffe::GPU) {\n#ifdef USE_CUDA\n // NOLINT_NEXT_LINE(caffe/alt_fn)\n CUDA_CHECK(cudaMemcpy(Y, X, sizeof(Dtype) * N, cudaMemcpyDefault));\n#else\n NO_GPU;\n#endif\n } else {\n memcpy(Y, X, sizeof(Dtype) * N);\n }\n }\n}\n\ntemplate void caffe_copy(const int N, const int* X, int* Y);\ntemplate void caffe_copy(const int N, const unsigned int* X,\n unsigned int* Y);\ntemplate void caffe_copy(const int N, const float* X, float* Y);\ntemplate void caffe_copy(const int N, const double* X, double* Y);\n\ntemplate <>\nvoid caffe_scal(const int N, const float alpha, float* X) {\n cblas_sscal(N, alpha, X, 1);\n}\n\ntemplate <>\nvoid caffe_scal(const int N, const double alpha, double* X) {\n cblas_dscal(N, alpha, X, 1);\n}\n\ntemplate <>\nvoid caffe_cpu_axpby(const int N, const float alpha, const float* X,\n const float beta, float* Y) {\n cblas_saxpby(N, alpha, X, 1, beta, Y, 1);\n}\n\ntemplate <>\nvoid caffe_cpu_axpby(const int N, const double alpha, const double* X,\n const double beta, double* Y) {\n cblas_daxpby(N, alpha, X, 1, beta, Y, 1);\n}\n\ntemplate <>\nvoid caffe_add(const int n, const float* a, const float* b, float* y) {\n vsAdd(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_add(const int n, const double* a, const double* b,\n double* y) {\n vdAdd(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_sub(const int n, const float* a, const float* b, float* y) {\n vsSub(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_sub(const int n, const double* a, const double* b,\n double* y) {\n vdSub(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_mul(const int n, const float* a, const float* b, float* y) {\n vsMul(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_mul(const int n, const double* a, const double* b,\n double* y) {\n vdMul(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_div(const int n, const float* a, const float* b, float* y) {\n vsDiv(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_div(const int n, const double* a, const double* b,\n double* y) {\n vdDiv(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_powx(const int n, const float* a, const float b, float* y) {\n vsPowx(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_powx(const int n, const double* a, const double b,\n double* y) {\n vdPowx(n, a, b, y);\n}\n\ntemplate <>\nvoid caffe_sqr(const int n, const float* a, float* y) {\n vsSqr(n, a, y);\n}\n\ntemplate <>\nvoid caffe_sqr(const int n, const double* a, double* y) {\n vdSqr(n, a, y);\n}\n\ntemplate <>\nvoid caffe_sqrt(const int n, const float* a, float* y) {\n vsSqrt(n, a, y);\n}\n\ntemplate <>\nvoid caffe_sqrt(const int n, const double* a, double* y) {\n vdSqrt(n, a, y);\n}\n\ntemplate <>\nvoid caffe_exp(const int n, const float* a, float* y) {\n vsExp(n, a, y);\n}\n\ntemplate <>\nvoid caffe_exp(const int n, const double* a, double* y) {\n vdExp(n, a, y);\n}\n\ntemplate <>\nvoid caffe_log(const int n, const float* a, float* y) {\n vsLn(n, a, y);\n}\n\ntemplate <>\nvoid caffe_log(const int n, const double* a, double* y) {\n vdLn(n, a, y);\n}\n\ntemplate <>\nvoid caffe_abs(const int n, const float* a, float* y) {\n vsAbs(n, a, y);\n}\n\ntemplate <>\nvoid caffe_abs(const int n, const double* a, double* y) {\n vdAbs(n, a, y);\n}\n\nunsigned int caffe_rng_rand() { return (*caffe_rng())(); }\n\ntemplate \nDtype caffe_nextafter(const Dtype b) {\n return boost::math::nextafter(b, std::numeric_limits::max());\n}\n\ntemplate float caffe_nextafter(const float b);\n\ntemplate double caffe_nextafter(const double b);\n\ntemplate \nvoid caffe_rng_uniform(const int n, const Dtype a, const Dtype b, Dtype* r) {\n CHECK_GE(n, 0);\n CHECK(r);\n CHECK_LE(a, b);\n boost::uniform_real random_distribution(a, caffe_nextafter(b));\n boost::variate_generator >\n variate_generator(caffe_rng(), random_distribution);\n for (int i = 0; i < n; ++i) {\n r[i] = variate_generator();\n }\n}\n\ntemplate void caffe_rng_uniform(const int n, const float a,\n const float b, float* r);\n\ntemplate void caffe_rng_uniform(const int n, const double a,\n const double b, double* r);\n\ntemplate \nvoid caffe_rng_gaussian(const int n, const Dtype a, const Dtype sigma,\n Dtype* r) {\n CHECK_GE(n, 0);\n CHECK(r);\n CHECK_GT(sigma, 0);\n boost::normal_distribution random_distribution(a, sigma);\n boost::variate_generator >\n variate_generator(caffe_rng(), random_distribution);\n for (int i = 0; i < n; ++i) {\n r[i] = variate_generator();\n }\n}\n\ntemplate void caffe_rng_gaussian(const int n, const float mu,\n const float sigma, float* r);\n\ntemplate void caffe_rng_gaussian(const int n, const double mu,\n const double sigma, double* r);\n\ntemplate \nvoid caffe_rng_bernoulli(const int n, const Dtype p, int* r) {\n CHECK_GE(n, 0);\n CHECK(r);\n CHECK_GE(p, 0);\n CHECK_LE(p, 1);\n boost::bernoulli_distribution random_distribution(p);\n boost::variate_generator >\n variate_generator(caffe_rng(), random_distribution);\n for (int i = 0; i < n; ++i) {\n r[i] = variate_generator();\n }\n}\n\ntemplate void caffe_rng_bernoulli(const int n, const double p, int* r);\n\ntemplate void caffe_rng_bernoulli(const int n, const float p, int* r);\n\ntemplate \nvoid caffe_rng_bernoulli(const int n, const Dtype p, unsigned int* r) {\n CHECK_GE(n, 0);\n CHECK(r);\n CHECK_GE(p, 0);\n CHECK_LE(p, 1);\n boost::bernoulli_distribution random_distribution(p);\n boost::variate_generator >\n variate_generator(caffe_rng(), random_distribution);\n for (int i = 0; i < n; ++i) {\n r[i] = static_cast(variate_generator());\n }\n}\n\ntemplate void caffe_rng_bernoulli(const int n, const double p,\n unsigned int* r);\n\ntemplate void caffe_rng_bernoulli(const int n, const float p,\n unsigned int* r);\n\ntemplate <>\nfloat caffe_cpu_strided_dot(const int n, const float* x, const int incx,\n const float* y, const int incy) {\n return cblas_sdot(n, x, incx, y, incy);\n}\n\ntemplate <>\ndouble caffe_cpu_strided_dot(const int n, const double* x,\n const int incx, const double* y,\n const int incy) {\n return cblas_ddot(n, x, incx, y, incy);\n}\n\ntemplate \nDtype caffe_cpu_dot(const int n, const Dtype* x, const Dtype* y) {\n return caffe_cpu_strided_dot(n, x, 1, y, 1);\n}\n\ntemplate float caffe_cpu_dot(const int n, const float* x,\n const float* y);\n\ntemplate double caffe_cpu_dot(const int n, const double* x,\n const double* y);\n\ntemplate <>\nfloat caffe_cpu_asum(const int n, const float* x) {\n return cblas_sasum(n, x, 1);\n}\n\ntemplate <>\ndouble caffe_cpu_asum(const int n, const double* x) {\n return cblas_dasum(n, x, 1);\n}\n\ntemplate <>\nvoid caffe_cpu_scale(const int n, const float alpha, const float* x,\n float* y) {\n cblas_scopy(n, x, 1, y, 1);\n cblas_sscal(n, alpha, y, 1);\n}\n\ntemplate <>\nvoid caffe_cpu_scale(const int n, const double alpha, const double* x,\n double* y) {\n cblas_dcopy(n, x, 1, y, 1);\n cblas_dscal(n, alpha, y, 1);\n}\n\n} // namespace caffe\n", "meta": {"hexsha": "5cbd3c80d2f009b42e801f43960d530b2494db0e", "size": 12820, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/caffe/util/math_functions.cpp", "max_stars_repo_name": "imoisture/caffe", "max_stars_repo_head_hexsha": "4d6892afeff234c57ce470056939101e0ae71369", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/caffe/util/math_functions.cpp", "max_issues_repo_name": "imoisture/caffe", "max_issues_repo_head_hexsha": "4d6892afeff234c57ce470056939101e0ae71369", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/caffe/util/math_functions.cpp", "max_forks_repo_name": "imoisture/caffe", "max_forks_repo_head_hexsha": "4d6892afeff234c57ce470056939101e0ae71369", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.5763546798, "max_line_length": 92, "alphanum_fraction": 0.6371294852, "num_tokens": 3419, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5964331462646254, "lm_q2_score": 0.4035668537353746, "lm_q1q2_score": 0.24070064830150537}} {"text": "#include \n#include \"vdf.h\"\nusing boost::asio::ip::tcp;\n\nconst int max_length = 2048;\nconst int kMaxProcessesAllowed = 3;\nstd::mutex socket_mutex;\n\nint process_number;\n\nvoid PrintInfo(std::string input) {\n std::cout << \"VDF Client: \" << input << \"\\n\";\n}\n\nvoid CreateAndWriteProof(integer D, form x, int64_t num_iterations, WesolowskiCallback& weso, bool& stop_signal, tcp::socket& sock) {\n Proof result = CreateProofOfTimeNWesolowski(D, x, num_iterations, 0, weso, 2, 0, stop_signal);\n if (stop_signal == true) {\n PrintInfo(\"Got stop signal before completing the proof!\");\n return ;\n }\n std::vector bytes = ConvertIntegerToBytes(integer(num_iterations), 8);\n bytes.insert(bytes.end(), result.y.begin(), result.y.end());\n bytes.insert(bytes.end(), result.proof.begin(), result.proof.end());\n std::string str_result = BytesToStr(bytes);\n std::lock_guard lock(socket_mutex);\n PrintInfo(\"Generated proof = \" + str_result);;\n boost::asio::write(sock, boost::asio::buffer(str_result.c_str(), str_result.size()));\n}\n\nvoid session(tcp::socket& sock) {\n try {\n char disc[350];\n char disc_size[5];\n boost::system::error_code error;\n\n memset(disc,0x00,sizeof(disc)); // For null termination\n memset(disc_size,0x00,sizeof(disc_size)); // For null termination\n\n boost::asio::read(sock, boost::asio::buffer(disc_size, 3), error);\n int disc_int_size = atoi(disc_size);\n\n boost::asio::read(sock, boost::asio::buffer(disc, disc_int_size), error);\n\n integer D(disc);\n PrintInfo(\"Discriminant = \" + to_string(D.impl));\n\n int space_needed = kSwitchIters / 10 + (kMaxItersAllowed - kSwitchIters) / 100;\n forms = (form*) calloc(space_needed, sizeof(form));\n\n PrintInfo(\"Calloc'd \" + to_string(space_needed * sizeof(form)) + \" bytes\");\n\n // Init VDF the discriminant...\n\n if (error == boost::asio::error::eof)\n return ; // Connection closed cleanly by peer.\n else if (error)\n throw boost::system::system_error(error); // Some other error.\n\n if (getenv( \"warn_on_corruption_in_production\" )!=nullptr) {\n warn_on_corruption_in_production=true;\n }\n if (is_vdf_test) {\n PrintInfo( \"=== Test mode ===\" );\n }\n if (warn_on_corruption_in_production) {\n PrintInfo( \"=== Warn on corruption enabled ===\" );\n }\n assert(is_vdf_test); //assertions should be disabled in VDF_MODE==0\n init_gmp();\n allow_integer_constructor=true; //make sure the old gmp allocator isn't used\n set_rounding_mode();\n\n integer L=root(-D, 4);\n form f=form::generator(D);\n\n bool stop_signal = false;\n // (iteration, thread_id)\n std::set > seen_iterations;\n bool stop_vector[100];\n\n std::vector threads;\n WesolowskiCallback weso(1000000);\n\n //mpz_init(weso.forms[0].a.impl);\n //mpz_init(weso.forms[0].b.impl);\n //mpz_init(weso.forms[0].c.impl);\n\n forms[0]=f;\n weso.D = D;\n weso.L = L;\n weso.kl = 10;\n\n bool stopped = false;\n bool got_iters = false;\n std::thread vdf_worker(repeated_square, f, D, L, std::ref(weso), std::ref(stopped));\n\n // Tell client that I'm ready to get the challenges.\n boost::asio::write(sock, boost::asio::buffer(\"OK\", 2));\n char data[20];\n\n while (!stopped) {\n memset(data, 0, sizeof(data));\n boost::asio::read(sock, boost::asio::buffer(data, 2), error);\n int size = (data[0] - '0') * 10 + (data[1] - '0');\n memset(data, 0, sizeof(data));\n boost::asio::read(sock, boost::asio::buffer(data, size), error);\n int iters = atoi(data);\n got_iters = true;\n \n if (iters == 0) {\n PrintInfo(\"Got stop signal!\");\n stopped = true;\n for (int i = 0; i < threads.size(); i++)\n stop_vector[i] = true;\n for (int t = 0; t < threads.size(); t++) {\n threads[t].join();\n }\n vdf_worker.join();\n free(forms);\n } else {\n int max_iter = 0;\n int max_iter_thread_id = -1;\n int min_iter = std::numeric_limits :: max();\n bool unique = true;\n for (auto active_iter: seen_iterations) {\n if (active_iter.first > max_iter) {\n max_iter = active_iter.first;\n max_iter_thread_id = active_iter.second;\n }\n if (active_iter.first < min_iter) {\n min_iter = active_iter.first;\n }\n if (active_iter.first == iters) {\n unique = false;\n break;\n }\n }\n if (!unique) {\n PrintInfo(\"Duplicate iteration \" + to_string(iters) + \"... Ignoring.\");\n continue;\n }\n if (threads.size() < kMaxProcessesAllowed || iters < min_iter) {\n seen_iterations.insert({iters, threads.size()});\n PrintInfo(\"Running proving for iter: \" + to_string(iters));\n stop_vector[threads.size()] = false;\n threads.push_back(std::thread(CreateAndWriteProof, D, f, iters, std::ref(weso), \n std::ref(stop_vector[threads.size()]), std::ref(sock)));\n if (threads.size() > kMaxProcessesAllowed) {\n PrintInfo(\"Stopping proving for iter: \" + to_string(max_iter));\n stop_vector[max_iter_thread_id] = true;\n seen_iterations.erase({max_iter, max_iter_thread_id});\n }\n }\n }\n }\n } catch (std::exception& e) {\n PrintInfo(\"Exception in thread: \" + to_string(e.what()));\n }\n\n try {\n // Tell client I've stopped everything, wait for ACK and close.\n boost::system::error_code error;\n\n PrintInfo(\"Stopped everything! Ready for the next challenge.\");\n\n std::lock_guard lock(socket_mutex);\n boost::asio::write(sock, boost::asio::buffer(\"STOP\", 4));\n\n char ack[5];\n memset(ack,0x00,sizeof(ack));\n boost::asio::read(sock, boost::asio::buffer(ack, 3), error);\n assert (strncmp(ack, \"ACK\", 3) == 0);\n } catch (std::exception& e) {\n PrintInfo(\"Exception in thread: \" + to_string(e.what()));\n }\n}\n\nint main(int argc, char* argv[])\n{\n try {\n if (argc != 4)\n {\n std::cerr << \"Usage: ./vdf_client \\n\";\n return 1;\n }\n\n boost::asio::io_service io_service;\n\n tcp::resolver resolver(io_service);\n tcp::resolver::query query(tcp::v6(), argv[1], argv[2], boost::asio::ip::resolver_query_base::v4_mapped);\n tcp::resolver::iterator iterator = resolver.resolve(query);\n\n tcp::socket s(io_service);\n boost::asio::connect(s, iterator);\n process_number = atoi(argv[3]);\n session(s);\n } catch (std::exception& e) {\n std::cerr << \"Exception: \" << e.what() << \"\\n\";\n }\n return 0;\n}\n", "meta": {"hexsha": "a72aaaa4e1aa08431f3be4f488d18ade85508b05", "size": 7436, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/chiavdf/fast_vdf/vdf_client.cpp", "max_stars_repo_name": "davision/chia-blockchain", "max_stars_repo_head_hexsha": "d5a66579c00cb926e0d266e5b8077ac16b220932", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/chiavdf/fast_vdf/vdf_client.cpp", "max_issues_repo_name": "davision/chia-blockchain", "max_issues_repo_head_hexsha": "d5a66579c00cb926e0d266e5b8077ac16b220932", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/chiavdf/fast_vdf/vdf_client.cpp", "max_forks_repo_name": "davision/chia-blockchain", "max_forks_repo_head_hexsha": "d5a66579c00cb926e0d266e5b8077ac16b220932", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8118811881, "max_line_length": 133, "alphanum_fraction": 0.5477407208, "num_tokens": 1777, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.24067603175975236}} {"text": "#include \"LevelSet.h\"\n\n#include \n\n#include \n#include \n\n#include \"tbb/blocked_range3d.h\"\n#include \"tbb/tbb.h\"\n\nnamespace FluidSim3D\n{\n\nLevelSet::LevelSet()\n : myNarrowBand(0)\n , myPhiGrid(Transform(0, Vec3d::Zero()), Vec3i::Zero())\n , myIsBackgroundNegative(false)\n{\n exactinit();\n}\n\nLevelSet::LevelSet(const Transform& xform, const Vec3i& size)\n : LevelSet(xform, size, size[0] * size[1] * size[2]) \n{}\n\nLevelSet::LevelSet(const Transform& xform, const Vec3i& size, double bandwidth, bool isBoundaryNegative)\n : myNarrowBand(bandwidth * xform.dx())\n , myIsBackgroundNegative(isBoundaryNegative)\n , myPhiGrid(xform, size, myIsBackgroundNegative ? -myNarrowBand : myNarrowBand)\n{\n for (int axis : {0, 1, 2}) assert(size[axis] >= 0);\n\n // In order to deal with triangle meshes, we need to initialize\n // the geometric predicate library.\n exactinit();\n}\n\nvoid LevelSet::initFromMesh(const TriMesh& initialMesh, bool doResizeGrid)\n{\n\t// The internal code can't handle a mesh that falls outside of the bounds.\n\t// If the mesh does, we need to create a new copy and clamp it into the bounds of the grid.\n\n if (!doResizeGrid)\n {\n\t\tbool outOfBounds = false;\n\t\tfor (const Vec3d& vertex : initialMesh.vertices())\n\t\t{\n\t\t\tVec3d indexPoint = worldToIndex(vertex);\n\n for (int axis : {0, 1, 2})\n {\n\t\t\t if (indexPoint[axis] <= 0 || indexPoint[axis] >= size()[axis])\n\t\t\t\t outOfBounds = true;\n }\n\t\t}\n\n\t\tif (outOfBounds)\n\t\t{\n\t\t\tTriMesh clampedMesh = initialMesh;\n\n\t\t\t// Clamp to be inside grid\n\t\t\tfor (int vertIndex = 0; vertIndex < clampedMesh.vertexCount(); ++vertIndex)\n\t\t\t{\n\t\t\t\tconst Vec3d& vertex = clampedMesh.vertex(vertIndex);\n\t\t\t\tVec3d indexPoint = worldToIndex(vertex);\n\n\t\t\t\tdouble offset = 1e-5 * dx();\n\n for (int axis : {0, 1, 2})\n {\n indexPoint[axis] = std::clamp(indexPoint[axis], offset, size()[axis] - 1. - offset);\n }\n\n\t\t\t\tclampedMesh.setVertex(vertIndex, indexToWorld(indexPoint));\n\t\t\t}\n\n\t\t\tinitFromMeshImpl(clampedMesh, doResizeGrid);\n\t\t\treturn;\n\t\t}\n }\n\n initFromMeshImpl(initialMesh, doResizeGrid);\n}\n\nvoid LevelSet::reinit()\n{\n TriMesh tempMesh = buildMesh();\n initFromMeshImpl(tempMesh, false);\n}\n\nbool LevelSet::isGridMatched(const LevelSet& grid) const\n{\n if (size() != grid.size()) return false;\n if (xform() != grid.xform()) return false;\n return true;\n}\n\nbool LevelSet::isGridMatched(const ScalarGrid& grid) const\n{\n if (grid.sampleType() != ScalarGridSettings::SampleType::CENTER) return false;\n if (size() != grid.size()) return false;\n if (xform() != grid.xform()) return false;\n return true;\n}\n\nvoid LevelSet::unionSurface(const LevelSet& unionPhi)\n{\n assert(isGridMatched(unionPhi));\n\n tbb::parallel_for(tbb::blocked_range(0, voxelCount(), tbbLightGrainSize), [&](const tbb::blocked_range& range)\n {\n for (int index = range.begin(); index != range.end(); ++index)\n {\n Vec3i cell = myPhiGrid.unflatten(index);\n myPhiGrid(cell) = std::min(myPhiGrid(cell), unionPhi(cell));\n }\n });\n}\n\nbool LevelSet::isBackgroundNegative() const\n{\n return myIsBackgroundNegative;\n}\n\nvoid LevelSet::setBackgroundNegative()\n{\n myIsBackgroundNegative = true;\n}\n\nTriMesh LevelSet::buildMesh() const\n{\n // Create grid to store index to dual contouring point. Note that phi is\n // center sampled so the DC grid must be node sampled and one cell shorter\n // in each dimension\n UniformGrid dcPointIndices(size() - Vec3i::Ones(), -1);\n std::vector> dcPointPair;\n\n // Build list of dual contouring points\n {\n tbb::enumerable_thread_specific>> parallelDCPoints;\n\n tbb::parallel_for(tbb::blocked_range(0, dcPointIndices.voxelCount(), tbbHeavyGrainSize), [&](const tbb::blocked_range& range)\n {\n auto& localDCPoints = parallelDCPoints.local();\n\n for (int cellIndex = range.begin(); cellIndex != range.end(); ++cellIndex)\n {\n Vec3i cell = dcPointIndices.unflatten(cellIndex);\n\n VecVec3d points;\n VecVec3d normals;\n\n Vec3d averagePoint = Vec3d::Zero();\n\n for (int edgeAxis : {0, 1, 2})\n for (int edgeIndex = 0; edgeIndex < 4; ++edgeIndex)\n {\n Vec3i edge = cellToEdge(cell, edgeAxis, edgeIndex);\n\n Vec3i backwardNode = edgeToNode(edge, edgeAxis, 0);\n Vec3i forwardNode = edgeToNode(edge, edgeAxis, 1);\n\n // Look for zero crossings.\n // Note that nodes for the DC grid fall exactly on the cell centers\n // of the level set grid.\n if ((myPhiGrid(backwardNode) <= 0 && myPhiGrid(forwardNode) > 0) ||\n (myPhiGrid(backwardNode) > 0 && myPhiGrid(forwardNode) <= 0))\n {\n // Find interface point\n Vec3d point = interpolateInterface(backwardNode, forwardNode);\n\n for (int axis : {0, 1, 2})\n assert(point[axis] >= backwardNode[axis] && point[axis] <= forwardNode[axis]);\n\n points.push_back(point);\n\n averagePoint += point;\n\n // Find associated surface normal\n Vec3d localNormal = normal(indexToWorld(point));\n\n normals.push_back(localNormal);\n }\n }\n\n if (points.size() > 0)\n {\n averagePoint.array() /= double(points.size());\n\n Matrix3x3d AtA = Matrix3x3d::Zero();\n\n Vec3d rhs = Vec3d::Zero();\n\n for (int pointIndex = 0; pointIndex < points.size(); ++pointIndex)\n {\n AtA += normals[pointIndex] * normals[pointIndex].transpose();\n rhs += normals[pointIndex] * normals[pointIndex].dot(points[pointIndex] - averagePoint);\n }\n\n Eigen::SelfAdjointEigenSolver eigenSolver(AtA);\n const Vec3d& eigenvalues = eigenSolver.eigenvalues();\n\n // Clamp eigenvalues\n double tolerance = 0.01 * eigenvalues.cwiseAbs().maxCoeff();\n\n Vec3d invEigenvalues;\n int clamped = 0;\n for (int index : {0, 1, 2})\n {\n if (std::fabs(eigenvalues[index] < tolerance))\n {\n invEigenvalues[index] = 0;\n ++clamped;\n }\n else\n {\n invEigenvalues[index] = 1. / eigenvalues[index];\n }\n }\n\n Vec3d qefPoint;\n if (clamped < 3)\n {\n qefPoint = averagePoint + eigenSolver.eigenvectors() * invEigenvalues.asDiagonal() * eigenSolver.eigenvectors().transpose() * rhs;\n }\n else\n {\n qefPoint = averagePoint;\n }\n\n // Clamp to cell\n if (qefPoint[0] < cell[0] || qefPoint[0] > cell[0] + 1 ||\n qefPoint[1] < cell[1] || qefPoint[1] > cell[1] + 1 ||\n qefPoint[2] < cell[2] || qefPoint[2] > cell[2] + 1)\n {\n AlignedBox3d cellBbox(cell.cast());\n cellBbox.extend((cell + Vec3i::Ones()).cast());\n Vec3d rayDirection = averagePoint - qefPoint;\n double alpha = computeRayBBoxIntersection(cellBbox, qefPoint, rayDirection);\n\n if (alpha <= 1)\n {\n qefPoint += alpha * rayDirection;\n }\n else\n {\n qefPoint = averagePoint;\n }\n }\n\n localDCPoints.emplace_back(cell, qefPoint);\n }\n }\n });\n\n mergeLocalThreadVectors(dcPointPair, parallelDCPoints);\n }\n\n VecVec3d vertices(dcPointPair.size());\n\n // Set DC point index for direct look up when building mesh and\n // convert points to world space.\n tbb::parallel_for(tbb::blocked_range(0, dcPointPair.size(), tbbLightGrainSize), [&](const tbb::blocked_range& range)\n {\n for (size_t index = range.begin(); index != range.end(); ++index)\n {\n const Vec3i& cell = dcPointPair[index].first;\n\n assert(dcPointIndices(cell) == -1);\n dcPointIndices(cell) = int(index);\n\n const Vec3d& point = dcPointPair[index].second;\n vertices[index] = indexToWorld(point);\n }\n });\n\n // Build triangle mesh using dual contouring points\n\n VecVec3i triangles;\n\n for (int edgeAxis : {0, 1, 2})\n {\n Vec3i start = Vec3i::Zero();\n ++start[(edgeAxis + 1) % 3];\n ++start[(edgeAxis + 2) % 3];\n\n Vec3i end = dcPointIndices.size();\n\n tbb::enumerable_thread_specific parallelTriangles;\n\n auto loopRange3d = tbb::blocked_range3d(start[0], end[0], int(std::cbrt(tbbLightGrainSize)), start[1], end[1],\n int(std::cbrt(tbbLightGrainSize)), start[2], end[2], int(std::cbrt(tbbLightGrainSize)));\n\n tbb::parallel_for(loopRange3d, [&](const tbb::blocked_range3d& range)\n {\n auto& localTriFaces = parallelTriangles.local();\n\n Vec3i edge;\n for (edge[0] = range.pages().begin(); edge[0] != range.pages().end(); ++edge[0])\n for (edge[1] = range.rows().begin(); edge[1] != range.rows().end(); ++edge[1])\n for (edge[2] = range.cols().begin(); edge[2] != range.cols().end(); ++edge[2])\n {\n Vec3i backwardNode = edgeToNode(edge, edgeAxis, 0);\n Vec3i forwardNode = edgeToNode(edge, edgeAxis, 1);\n\n if ((myPhiGrid(backwardNode) <= 0 && myPhiGrid(forwardNode) > 0) ||\n (myPhiGrid(backwardNode) > 0 && myPhiGrid(forwardNode) <= 0))\n {\n std::array dcCells;\n std::array vertexIndices;\n\n for (int cellIndex = 0; cellIndex < 4; ++cellIndex)\n {\n dcCells[cellIndex] = edgeToCellCCW(edge, edgeAxis, cellIndex);\n vertexIndices[cellIndex] = dcPointIndices(dcCells[cellIndex]);\n assert(vertexIndices[cellIndex] >= 0);\n }\n\n if (myPhiGrid(backwardNode) <= 0.)\n {\n localTriFaces.emplace_back(vertexIndices[0], vertexIndices[1], vertexIndices[2]);\n localTriFaces.emplace_back(vertexIndices[0], vertexIndices[2], vertexIndices[3]);\n }\n else\n {\n localTriFaces.emplace_back(vertexIndices[0], vertexIndices[2], vertexIndices[1]);\n localTriFaces.emplace_back(vertexIndices[0], vertexIndices[3], vertexIndices[2]);\n }\n }\n }\n });\n\n mergeLocalThreadVectors(triangles, parallelTriangles);\n }\n\n return TriMesh(triangles, vertices);\n}\n\nvoid LevelSet::clear()\n{\n myPhiGrid.clear();\n}\n\nvoid LevelSet::resize(const Vec3i& size)\n{\n myPhiGrid.resize(size);\n}\n\n// Find the nearest point on the interface starting from the index position.\n// If the position falls outside of the narrow band, there isn't a defined gradient\n// to use. In this case, the original position will be returned.\nVec3d LevelSet::findSurface(const Vec3d& worldPoint, int iterationLimit, double tolerance) const\n{\n assert(iterationLimit >= 0);\n\n double phi = myPhiGrid.triLerp(worldPoint);\n\n double epsilon = tolerance * dx();\n Vec3d tempPoint = worldPoint;\n\n int iterationCount = 0;\n if (std::fabs(phi) < myNarrowBand)\n {\n while (std::fabs(phi) > epsilon && iterationCount < iterationLimit)\n {\n tempPoint -= phi * .8 * normal(tempPoint);\n phi = myPhiGrid.triLerp(tempPoint);\n ++iterationCount;\n }\n }\n\n return tempPoint;\n}\n\nVec3d LevelSet::interpolateInterface(const Vec3i& startPoint, const Vec3i& endPoint) const\n{\n assert((myPhiGrid(startPoint) <= 0 && myPhiGrid(endPoint) > 0) ||\n (myPhiGrid(startPoint) > 0 && myPhiGrid(endPoint) <= 0));\n\n // Find weight to zero isosurface\n double theta = lengthFraction(myPhiGrid(startPoint), myPhiGrid(endPoint));\n\n assert(theta >= 0 && theta <= 1);\n\n if (myPhiGrid(startPoint) > 0)\n theta = 1. - theta;\n\n return startPoint.cast() + theta * (endPoint - startPoint).cast();\n}\n\nvoid LevelSet::drawGrid(Renderer& renderer, bool doOnlyNarrowBand) const\n{\n if (doOnlyNarrowBand)\n {\n forEachVoxelRange(Vec3i::Zero().eval(), size(), [&](const Vec3i& cell)\n {\n if (std::fabs(myPhiGrid(cell)) < myNarrowBand) myPhiGrid.drawGridCell(renderer, cell);\n });\n }\n else\n myPhiGrid.drawGrid(renderer);\n}\n\nvoid LevelSet::drawGridPlane(Renderer& renderer, Axis planeAxis, double position, bool doOnlyNarrowBand) const\n{\n position = std::clamp(position, double(0), double(1));\n\n Vec3i start = Vec3i::Zero();\n Vec3i end(myPhiGrid.size() - Vec3i::Ones());\n\n if (planeAxis == Axis::XAXIS)\n {\n start[0] = int(std::floor(position * double(myPhiGrid.size()[0] - 1)));\n end[0] = start[0] + 1;\n }\n else if (planeAxis == Axis::YAXIS)\n {\n start[1] = int(std::floor(position * double(myPhiGrid.size()[1] - 1)));\n end[1] = start[1] + 1;\n }\n else if (planeAxis == Axis::ZAXIS)\n {\n start[2] = int(std::floor(position * double(myPhiGrid.size()[2] - 1)));\n end[2] = start[2] + 1;\n }\n\n forEachVoxelRange(start, end, [&](const Vec3i& cell)\n {\n if (doOnlyNarrowBand)\n {\n if (std::fabs(myPhiGrid(cell)) < myNarrowBand) myPhiGrid.drawGridCell(renderer, cell);\n }\n else\n myPhiGrid.drawGridCell(renderer, cell);\n });\n}\n\n// Display a supersampled slice of the grid. The plane will have a normal in the plane_axis direction.\n// The position is from [0,1] where 0 is at the grid origin and 1 is at the origin + size * dx.\nvoid LevelSet::drawSupersampledValuesPlane(Renderer& renderer, Axis planeAxis, double position, double radius,\n int samples, double sampleSize) const\n{\n myPhiGrid.drawSupersampledValuesPlane(renderer, planeAxis, position, radius, samples, sampleSize);\n}\nvoid LevelSet::drawSampleNormalsPlane(Renderer& renderer, Axis planeAxis, double position, const Vec3d& colour,\n double length) const\n{\n myPhiGrid.drawSampleGradientsPlane(renderer, planeAxis, position, colour, length);\n}\n\nvoid LevelSet::drawSurface(Renderer& renderer, const Vec3d& colour, double lineWidth) const\n{\n TriMesh tempMesh = buildMesh();\n tempMesh.drawMesh(renderer, true, colour, lineWidth);\n}\n\n// \n// Private methods\n//\n\nvoid LevelSet::initFromMeshImpl(const TriMesh& initialMesh, bool doResizeGrid)\n{\n if (doResizeGrid)\n {\n // Determine the bounding box of the mesh to build the underlying grids\n\t\tAlignedBox3d bbox = initialMesh.boundingBox();\n\n\t\t// Expand grid beyond the narrow band of the mesh\n\t\tdouble maxPadding = 50. * dx();\n\t\tmaxPadding = std::min(2. * myNarrowBand, maxPadding);\n\n\t\tbbox.extend(bbox.min() - Vec3d::Constant(maxPadding));\n\t\tbbox.extend(bbox.max() + Vec3d::Constant(maxPadding));\n\n\t\tVec3d origin = indexToWorld(floor(worldToIndex(bbox.min())).eval());\n\t\tTransform xform(dx(), origin);\n\t\tVec3d topRight = indexToWorld(ceil(worldToIndex(bbox.max())).eval());\n \n\t\t// TODO: add the ability to reset grid so we don't have ot re-allocate memory\n\t\tmyPhiGrid = ScalarGrid(xform, ((topRight - origin) / dx()).cast(), myIsBackgroundNegative ? -myNarrowBand : myNarrowBand);\n }\n\n // We want to track which cells in the level set contain valid distance information.\n // The first pass will set cells close to the mesh as FINISHED.\n UniformGrid reinitializedCells(size(), VisitedCellLabels::UNVISITED_CELL);\n UniformGrid meshCellParities(size(), 0);\n\n for (const Vec3i& tri : initialMesh.triangles())\n {\n // It's easier to work in our index space and just scale the distance later.\n std::array triVertices;\n for (int localVertexIndex : {0, 1, 2})\n triVertices[localVertexIndex] = worldToIndex(initialMesh.vertex(tri[localVertexIndex]));\n\n\t\tAlignedBox3d triBbox(triVertices[0]);\n\t\ttriBbox.extend(triVertices[1]);\n\t\ttriBbox.extend(triVertices[2]);\n\n Vec3i triCeilMin = ceil(triBbox.min()).cast();\n Vec3i triFloorMin = floor(triBbox.min()).cast() - Vec3i::Ones();\n Vec3i triFloorMax = floor(triBbox.max()).cast();\n\n // Z-axis intersection tests. Iterate along an aligned set of grid edges\n // in decsending order, checking for intersections at each edge.\n // If an intersection is found then we can stop searching along the set.\n for (int i = triCeilMin[0]; i <= triFloorMax[0]; ++i)\n for (int j = triFloorMin[1]; j <= triFloorMax[1]; ++j)\n for (int k = triFloorMax[2]; k >= triFloorMin[2]; --k)\n {\n Vec3d gridPoint(i, j, k);\n IntersectionLabels intersectionResult = exactTriIntersect(gridPoint, triVertices[0], triVertices[1], triVertices[2], Axis::ZAXIS);\n\n for (int axis : {0, 1, 2})\n\t\t\t\t assert(gridPoint[axis] >= 0 && gridPoint[axis] < myPhiGrid.size()[axis] - 1);\n\n if (intersectionResult == IntersectionLabels::NO) continue;\n\n int parityChange = -1;\n double qrs = orient2d(triVertices[0].data(), triVertices[1].data(), triVertices[2].data());\n assert(qrs != 0);\n if (qrs < 0)\n parityChange = 1;\n\n if (intersectionResult == IntersectionLabels::YES)\n meshCellParities(i, j, k + 1) += parityChange;\n else\n {\n assert(intersectionResult == IntersectionLabels::ON);\n\n if (parityChange == 1)\n meshCellParities(i, j, k) += parityChange;\n else\n {\n meshCellParities(i, j, k + 1) += parityChange;\n } \n }\n\n break;\n }\n }\n\n // Now that all the z-axis edge crossings have been found, we can compile the parity changes\n // and label grid nodes that are at the interface\n for (int i = 0; i < size()[0]; ++i)\n for (int j = 0; j < size()[1]; ++j)\n {\n int parity = myIsBackgroundNegative ? 1 : 0;\n\n for (int k = 0; k < size()[2]; ++k)\n {\n Vec3i cell(i, j, k);\n\n parity += meshCellParities(cell);\n meshCellParities(cell) = parity;\n\n if (parity > 0)\n myPhiGrid(cell) = -myNarrowBand;\n else\n myPhiGrid(cell) = myNarrowBand;\n }\n\n assert(myIsBackgroundNegative ? parity == 1 : parity == 0);\n }\n\n // With the parity assigned, loop over the grid once more and label nodes that have a sign change\n // with neighbouring nodes (this means parity goes from -'ve (and zero) to +'ve or vice versa).\n forEachVoxelRange(Vec3i::Ones(), size() - Vec3i::Ones(), [&](const Vec3i& cell)\n {\n bool isCellInside = meshCellParities(cell) > 0;\n\n for (int axis : {0, 1, 2})\n for (int direction : {0, 1})\n {\n Vec3i adjacentCell = cellToCell(cell, axis, direction);\n\n bool isAdjacentCellInside = meshCellParities(adjacentCell) > 0;\n\n if (isCellInside != isAdjacentCellInside)\n reinitializedCells(cell) = VisitedCellLabels::FINISHED_CELL;\n }\n });\n\n // Loop over all the triangles in the mesh. Level set grid cells labelled as FINISHED will be\n // updated with the distance to the surface if it happens to be shorter than the current\n // distance to the surface.\n for (const Vec3i& tri : initialMesh.triangles())\n {\n std::array vertices;\n for (int localVertexIndex : {0, 1, 2})\n vertices[localVertexIndex] = worldToIndex(initialMesh.vertex(tri[localVertexIndex]));\n \n AlignedBox3d triBbox(vertices[0]);\n triBbox.extend(vertices[1]);\n triBbox.extend(vertices[2]);\n\n // Expand outward by 2-voxels in each direction\n triBbox.extend(triBbox.min() - Vec3d::Constant(2));\n triBbox.extend(triBbox.max() + Vec3d::Constant(2));\n\n AlignedBox3d clampBbox;\n clampBbox.extend(Vec3d::Zero());\n clampBbox.extend(size().cast() - Vec3d::Ones());\n\n triBbox.clamp(clampBbox);\n\n for (int axis : {0, 1, 2})\n assert(triBbox.min()[axis] >= 0 && triBbox.max()[axis] < size()[axis]);\n\n forEachVoxelRange(triBbox.min().cast(), triBbox.max().cast() + Vec3i::Ones(), [&](const Vec3i& cell)\n {\n if (reinitializedCells(cell) != VisitedCellLabels::UNVISITED_CELL)\n {\n Vec3d cellPoint = cell.cast();\n\n Vec3d triProjectionPoint = pointToTriangleProjection(cellPoint, vertices[0], vertices[1], vertices[2]);\n\n double surfaceDistance = (cellPoint - triProjectionPoint).norm() * dx();\n\n // If the new distance is closer than existing distance values, update cell\n if (std::fabs(myPhiGrid(cell)) > surfaceDistance)\n {\n // If the parity says the node is inside, set it to be negative\n myPhiGrid(cell) = (meshCellParities(cell) > 0) ? -surfaceDistance : surfaceDistance;\n }\n }\n });\n }\n\n reinitFastMarching(reinitializedCells);\n}\n\nvoid LevelSet::reinitFastMarching(UniformGrid& reinitializedCells)\n{\n assert(reinitializedCells.size() == size());\n\n auto solveEikonal2D = [&](double Ux, double Uy) -> double\n {\n if (std::fabs(Ux - Uy) >= dx())\n return std::min(Ux, Uy) + dx();\n else\n {\n // Quadratic equation from the Eikonal\n double rootEntry = std::pow(Ux + Uy, 2) - 2. * (std::pow(Ux, 2) + std::pow(Uy, 2) - std::pow(dx(), 2));\n assert(rootEntry >= 0);\n return .5 * (Ux + Uy + std::sqrt(rootEntry));\n }\n };\n\n auto solveEikonal = [&](const Vec3i& cell) -> double\n {\n double max = std::numeric_limits::max();\n\n Vec3d Uaxis = Vec3d::Constant(max);\n\t\tfor (int axis : {0, 1, 2})\n\t\t\tfor (int direction : {0, 1})\n\t\t\t{\n\t\t\t\tVec3i adjacentCell = cellToCell(cell, axis, direction);\n\n\t\t\t\tif (adjacentCell[axis] < 0 || adjacentCell[axis] >= size()[axis])\n\t\t\t\t{\n\t\t\t\t\tassert(myPhiGrid(cell) > 0 && !myIsBackgroundNegative || myPhiGrid(cell) < 0 && myIsBackgroundNegative);\n\t\t\t\t\tUaxis[axis] = std::min(max, Uaxis[axis]);\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t{\n\t\t\t\t\tUaxis[axis] = std::min(std::fabs(myPhiGrid(adjacentCell)), Uaxis[axis]);\n\t\t\t\t}\n\t\t\t}\n\n double discrim = std::pow(Uaxis.sum(), 2) - 3. * (Uaxis.squaredNorm() - std::pow(dx(), 2));\n if (discrim < 0.)\n {\n double dist = std::min(std::min(solveEikonal2D(Uaxis[0], Uaxis[1]), solveEikonal2D(Uaxis[1], Uaxis[2])), solveEikonal2D(Uaxis[0], Uaxis[2]));\n\n assert(std::isfinite(dist));\n\n return dist;\n }\n else\n {\n double dist = (Uaxis.sum() + std::sqrt(discrim)) / 3.;\n assert(std::isfinite(dist));\n return dist;\n }\n };\n\n // Load up the BFS queue with the unvisited cells next to the finished ones\n using Node = std::pair;\n auto cmp = [](const Node& a, const Node& b) -> bool { return std::fabs(a.second) > std::fabs(b.second); };\n std::priority_queue, decltype(cmp)> marchingQ(cmp);\n\n forEachVoxelRange(Vec3i::Zero(), reinitializedCells.size(), [&](const Vec3i& cell)\n {\n if (reinitializedCells(cell) == VisitedCellLabels::FINISHED_CELL)\n {\n for (int axis : {0, 1, 2})\n for (int direction : {0, 1})\n {\n Vec3i adjacentCell = cellToCell(cell, axis, direction);\n\n if (adjacentCell[axis] < 0 || adjacentCell[axis] >= reinitializedCells.size()[axis]) continue;\n\n if (reinitializedCells(adjacentCell) == VisitedCellLabels::UNVISITED_CELL)\n {\n double dist = solveEikonal(adjacentCell);\n\n if (!std::isfinite(dist))\n int a = 0;\n assert(dist >= 0);\n\n myPhiGrid(adjacentCell) = myPhiGrid(adjacentCell) < 0 ? -dist : dist;\n\n Node node(adjacentCell, dist);\n\n marchingQ.push(node);\n reinitializedCells(adjacentCell) = VisitedCellLabels::VISITED_CELL;\n }\n }\n }\n });\n\n while (!marchingQ.empty())\n {\n Node localNode = marchingQ.top();\n Vec3i localCell = localNode.first;\n marchingQ.pop();\n\n // Since you can't just update parts of the priority queue,\n // it's possible that a cell has been solidified at a smaller distance\n // and an older insert if doubleing around.\n if (reinitializedCells(localCell) == VisitedCellLabels::FINISHED_CELL)\n {\n // Make sure that the distance assigned to the cell is smaller than\n // what is doubleing around\n assert(std::fabs(myPhiGrid(localCell)) <= std::fabs(localNode.second));\n continue;\n }\n assert(reinitializedCells(localCell) == VisitedCellLabels::VISITED_CELL);\n\n if (std::fabs(myPhiGrid(localCell)) < myNarrowBand)\n {\n // Debug check that there is indeed a FINISHED cell next to it\n bool foundFinishedCell = false;\n\n for (int axis : {0, 1, 2})\n for (int direction : {0, 1})\n {\n Vec3i adjacentCell = cellToCell(localCell, axis, direction);\n\n if (adjacentCell[axis] < 0 || adjacentCell[axis] >= reinitializedCells.size()[axis]) continue;\n\n if (reinitializedCells(adjacentCell) == VisitedCellLabels::FINISHED_CELL)\n foundFinishedCell = true;\n else\n {\n double dist = solveEikonal(adjacentCell);\n assert(dist >= 0);\n\n if (dist > myNarrowBand) dist = myNarrowBand;\n\n if (reinitializedCells(adjacentCell) == VisitedCellLabels::VISITED_CELL &&\n dist > std::fabs(myPhiGrid(adjacentCell)))\n continue;\n\n myPhiGrid(adjacentCell) = myPhiGrid(adjacentCell) < 0 ? -dist : dist;\n\n Node node(adjacentCell, dist);\n\n marchingQ.push(node);\n reinitializedCells(adjacentCell) = VisitedCellLabels::VISITED_CELL;\n }\n }\n assert(foundFinishedCell);\n }\n else\n myPhiGrid(localCell) = myPhiGrid(localCell) < 0 ? -myNarrowBand : myNarrowBand;\n\n reinitializedCells(localCell) = VisitedCellLabels::FINISHED_CELL;\n }\n}\n\nVec3d LevelSet::findSurfaceIndex(const Vec3d& indexPoint, int iterationLimit, double tolerance) const\n{\n Vec3d worldPoint = indexToWorld(indexPoint);\n worldPoint = findSurface(worldPoint, iterationLimit, tolerance);\n return worldToIndex(worldPoint);\n}\n\n}", "meta": {"hexsha": "9a8c13b39a9269c46fa51527201249a006fcf9be", "size": 29023, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Library/SurfaceTrackers/LevelSet.cpp", "max_stars_repo_name": "rgoldade/3dFluidSimulation", "max_stars_repo_head_hexsha": "680d84429e73e26671a52e1a725b1b76ec4ef0db", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 4.0, "max_stars_repo_stars_event_min_datetime": "2020-05-04T16:47:25.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-30T01:31:09.000Z", "max_issues_repo_path": "Library/SurfaceTrackers/LevelSet.cpp", "max_issues_repo_name": "rgoldade/3dFluidSimulation", "max_issues_repo_head_hexsha": "680d84429e73e26671a52e1a725b1b76ec4ef0db", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Library/SurfaceTrackers/LevelSet.cpp", "max_forks_repo_name": "rgoldade/3dFluidSimulation", "max_forks_repo_head_hexsha": "680d84429e73e26671a52e1a725b1b76ec4ef0db", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.7845373891, "max_line_length": 154, "alphanum_fraction": 0.5530785928, "num_tokens": 7031, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011686727232, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.24054145004180058}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace po = boost::program_options;\n\ntemplate< class algorithmVariant, typename GraphType>\nvoid runQueries( GraphType& G, std::vector< std::pair >& queries,\n std::vector& ids, GridChecker& gChecker, const unsigned int showOnScreen)\n{\n typedef typename GraphType::NodeIterator NodeIterator;\n NodeIterator s,t;\n unsigned int sourceId, targetId;\n unsigned int timestamp = 0;\n //create algorithm\n algorithmVariant algorithm( G, NUM_CRITERIA, ×tamp);\n //run queries\n unsigned int query_n = 0;\n double numLabels = 0;\n double totalTime = 0;\n for( std::vector< std::pair >::iterator it = queries.begin();\n it != queries.end(); ++it)\n {\n //clear nodes\n NodeIterator u, lastnode;\n for( u = G.beginNodes(), lastnode = G.endNodes(); u != lastnode; ++u)\n {\n u->g_op.clear();\n u->g_cl.clear();\n }\n sourceId = it->first;\n targetId = it->second;\n s = G.getNodeIterator( ids[sourceId]);\n t = G.getNodeIterator( ids[targetId]);\n Timer timer;\n timer.start();\n algorithm.init( s, t, NUM_CRITERIA);\n auto heuristicTime = 1000 * timer.getElapsedTime();\n timer.start();\n algorithm.runQuery( s, t);\n auto runtime = 1000 * timer.getElapsedTime();\n totalTime += runtime;\n // add Pareto efficient solutions to the results class\n MulticriteriaResults mResults( queries);\n mResults.addResults( t->g_cl);\n numLabels += algorithm.getGeneratedLabels();\n // Two things are checked: First, the set of solutions is the same; second, the solutions have been found in the same order.\n bool order = true;\n bool correctness = mResults.checkParetoCosts( gChecker, query_n, order);\n Color::Modifier def(Color::FG_DEFAULT);\n Color::Modifier red(Color::FG_RED);\n Color::Modifier green(Color::FG_GREEN);\n Color::Modifier yellow(Color::FG_YELLOW);\n Color::Modifier b_blue(Color::BG_BLUE);\n Color::Modifier b_def(Color::BG_DEFAULT);\n\n std::cout << b_blue << \"(\" << sourceId << \"->\" << targetId << \")\" << b_def << \"\\t\\t\";\n\n if ( showOnScreen)\n {\n Color::Modifier blue(Color::FG_BLUE);\n Color::Modifier lblue(Color::FG_LIGHT_BLUE);\n Color::Modifier cyan(Color::FG_CYAN);\n Color::Modifier lcyan(Color::FG_LIGHT_CYAN);\n std::cout << blue << t->g_cl.size() << def << \" efficient paths were found.\\n\";\n std::cout << \"\\t\\t\\t\" << lblue << algorithm.getGeneratedLabels() << def << \" labels were scanned.\\n\";\n std::cout << \"\\t\\t\\t\" << cyan << heuristicTime << def << \" msec. - calculation of heuristic.\\n\";\n std::cout << \"\\t\\t\\t\" << lcyan << runtime << def << \" msec. - algorithm runtime.\\n\";\n }\n std::cout << \"Solutions ... ->\\t\";\n if ( !( correctness))\n {\n std::cout << red << \"Different!!!\\n\" << def;\n t->printLabels( std::cout, G);\n exit (EXIT_FAILURE);\n }\n else\n {\n std::cout << green << \"OK\" << def << \"\\n\";\n if (! (order)) std::cout << yellow << \"Warning!\" << def << \" The same set of solutions was found, but in different order.\\n\";\n }\n std::cout << \"---------------------------------------------------------------------\\n\";\n ++query_n;\n }\n\n if ( showOnScreen)\n {\n std::cout << \"\\tAlgorithm Runtime:\\t\" << totalTime << \" msec. ( \" << (totalTime / queries.size()) << \" msec. per query)\\n\";\n std::cout << \"\\tGenerated labels:\\t \" << numLabels << std::endl;\n }\n}\n\ntemplate< typename GraphType>\nvoid runBenchmarks( GraphType& G, std::vector< std::pair >& queries,\n std::vector& ids, GridChecker& gChecker,\n const std::string& name, const unsigned int& algorithmVariant, const unsigned int showOnScreen)\n{\n switch( algorithmVariant)\n {\n case 1:\n // output message for all queries\n std::cout << \"with NAMOA* (blind) ...\\n\\n\";\n runQueries >( G, queries, ids, gChecker, showOnScreen);\n break;\n case 2:\n std::cout << \"with NAMOA*_tc ...\\n\\n\";\n runQueries >( G, queries, ids, gChecker, showOnScreen);\n break;\n case 3:\n std::cout << \"with NAMOA*_bound_tc ...\\n\\n\";\n runQueries >( G, queries, ids, gChecker, showOnScreen);\n //std::cout << \"with NAMOA* Arc Flags (\" << name << \") ...\\n\\n\";\n //runQueries >( G, queries, ids, gChecker, showOnScreen);\n break;\n case 0: // default\n std::cout << \"with NAMOA* (blind) ...\\n\\n\";\n runQueries >( G, queries, ids, gChecker, showOnScreen);\n std::cout << \"with NAMOA*_tc ...\\n\\n\";\n runQueries >( G, queries, ids, gChecker, showOnScreen);\n std::cout << \"with NAMOA*_bound_tc ...\\n\\n\";\n runQueries >( G, queries, ids, gChecker, showOnScreen);\n break;\n }\n}\n\ntypedef DynamicGraph< AdjacencyListImpl, Node, Edge> Graph;\n//typedef DynamicGraph< AdjacencyListImpl, LWNode, Edge> LWGraph;\ntypedef Graph::NodeIterator NodeIterator;\ntypedef Graph::EdgeIterator EdgeIterator;\ntypedef Graph::NodeDescriptor NodeDescriptor;\n\nint main( int argc, char* argv[])\n{\n std::string gridBenchmarksPath = \"/home/francis/Projects/Benchmarks/\";\n std::string gridsPath = gridBenchmarksPath + \"grids/\";\n std::string gridQueriesPath = gridBenchmarksPath + \"queries/\";\n std::string gridSolutionsPath = gridBenchmarksPath + \"solutions/\";\n\n unsigned int benchmarkVariant = 2;\n unsigned int algorithmVariant = 0;\n unsigned int showOnScreen = 1;\n\n // Declare the supported options.\n po::options_description desc(\"Allowed options\");\n desc.add_options()\n (\"benchmark,b\", po::value< unsigned int>(), \"Benchmark to run. Bicriteria[2], Tri-criterion[3]. Default:2\")\n (\"algorithm,a\", po::value< unsigned int>(), \"Multicriteria Heuristic. All[0], Blind[1], Ideal Point[2], Bounded Ideal point[3]. Default:0\")\n (\"showOnScreen,s\", po::value< unsigned int>(), \"Display stats on screen. Yes[1], No[0]. Default:1\");\n\n po::variables_map vm;\n po::store(po::parse_command_line(argc, argv, desc), vm);\n po::notify(vm); \n\n if (vm.empty()) {\n std::cout << desc << \"\\n\";\n return 0;\n }\n if (vm.count(\"benchmark\"))\n {\n benchmarkVariant = vm[\"benchmark\"].as();\n }\n if (vm.count(\"algorithm\"))\n {\n algorithmVariant = vm[\"algorithm\"].as();\n }\n if (vm.count(\"showOnScreen\"))\n {\n showOnScreen = vm[\"showOnScreen\"].as();\n }\n unsigned int nqueries;\n if ( benchmarkVariant == 2)\n {\n NUM_CRITERIA = 2;\n nqueries = NQUERIES_2;\n }\n else if ( benchmarkVariant == 3)\n {\n NUM_CRITERIA = 3;\n nqueries = NQUERIES_3;\n }\n else\n {\n std::cerr << \"BenchmarkVariant provided not implemented yet\\n\";\n }\n\n for ( unsigned int grid_n = 0; grid_n < NGRIDS; ++grid_n)\n {\n GridChecker gChecker( nqueries, benchmarkVariant,\n gridQueriesPath + \"queries\" + std::to_string( benchmarkVariant) + \".txt\",\n gridSolutionsPath + \"p\" + std::to_string( grid_n) + \"/\" +\n std::to_string( benchmarkVariant) + \".txt\",\n GRIDDIMSIZE);\n std::vector< std::pair< unsigned int ,unsigned int> > queries;\n queries = gChecker.getQueries (benchmarkVariant);\n Graph G;\n std::string gridProblemPath = gridsPath + \"Grid\" + std::to_string( grid_n)+ \".txt\";\n std::cout << \"\\nBenchmark at \" << gridProblemPath;\n GridReader reader( gridProblemPath, benchmarkVariant);\n Timer timer;\n timer.start();\n G.read(&reader);\n std::cout << \"Graph has \" << (double)G.memUsage()/1048576 << \" Mbytes. Time spent to read:\\t\" << timer.getElapsedTime() << \"sec\" << std::endl;\n std::cout << \"Checking correctness of \" << queries.size() << \" queries \\n\";\n timer.start();\n runBenchmarks( G, queries, reader.getIds(), gChecker, \"ADJ\", algorithmVariant, showOnScreen);\n std::cout << \"\\tBenchmark runtime: \\t\" << timer.getElapsedTime() << \" sec\" << std::endl;\n }\n return 0;\n}\n", "meta": {"hexsha": "7a69ba3ee2bbfe048d4e8e3db239bb3f389cdf74", "size": 9567, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "CorrectnessCheckers/grids/benchmark/correctnessChecker.cpp", "max_stars_repo_name": "FrankS101/multicriteriaDIMACS", "max_stars_repo_head_hexsha": "993bd50a2537ad56d33535a546a8426f8120f9fa", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2022-01-15T11:21:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-15T11:21:17.000Z", "max_issues_repo_path": "CorrectnessCheckers/grids/benchmark/correctnessChecker.cpp", "max_issues_repo_name": "FrankS101/multicriteriaDIMACS", "max_issues_repo_head_hexsha": "993bd50a2537ad56d33535a546a8426f8120f9fa", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "CorrectnessCheckers/grids/benchmark/correctnessChecker.cpp", "max_forks_repo_name": "FrankS101/multicriteriaDIMACS", "max_forks_repo_head_hexsha": "993bd50a2537ad56d33535a546a8426f8120f9fa", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.1453744493, "max_line_length": 150, "alphanum_fraction": 0.5997700429, "num_tokens": 2413, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.41111086923216805, "lm_q1q2_score": 0.24054144409323516}} {"text": "// Boost.Geometry - gis-projections (based on PROJ4)\r\n\r\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\r\n\r\n// This file was modified by Oracle on 2017, 2018.\r\n// Modifications copyright (c) 2017-2018, Oracle and/or its affiliates.\r\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle.\r\n\r\n// Use, modification and distribution is subject to the Boost Software License,\r\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\r\n// http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\r\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\r\n// PROJ4 is maintained by Frank Warmerdam\r\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\r\n\r\n// Last updated version of proj: 5.0.0\r\n\r\n// Original copyright notice:\r\n\r\n// Permission is hereby granted, free of charge, to any person obtaining a\r\n// copy of this software and associated documentation files (the \"Software\"),\r\n// to deal in the Software without restriction, including without limitation\r\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\r\n// and/or sell copies of the Software, and to permit persons to whom the\r\n// Software is furnished to do so, subject to the following conditions:\r\n\r\n// The above copyright notice and this permission notice shall be included\r\n// in all copies or substantial portions of the Software.\r\n\r\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\r\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\r\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\r\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\r\n// DEALINGS IN THE SOFTWARE.\r\n\r\n#ifndef BOOST_GEOMETRY_PROJECTIONS_EQDC_HPP\r\n#define BOOST_GEOMETRY_PROJECTIONS_EQDC_HPP\r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n#include \r\n\r\n#include \r\n\r\nnamespace boost { namespace geometry\r\n{\r\n\r\nnamespace projections\r\n{\r\n #ifndef DOXYGEN_NO_DETAIL\r\n namespace detail { namespace eqdc\r\n {\r\n\r\n static const double epsilon10 = 1.e-10;\r\n\r\n template \r\n struct par_eqdc\r\n {\r\n T phi1;\r\n T phi2;\r\n T n;\r\n T rho0;\r\n T c;\r\n detail::en en;\r\n bool ellips;\r\n };\r\n\r\n // template class, using CRTP to implement forward/inverse\r\n template \r\n struct base_eqdc_ellipsoid\r\n : public base_t_fi, T, Parameters>\r\n {\r\n par_eqdc m_proj_parm;\r\n\r\n inline base_eqdc_ellipsoid(const Parameters& par)\r\n : base_t_fi, T, Parameters>(*this, par)\r\n {}\r\n\r\n // FORWARD(e_forward) sphere & ellipsoid\r\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\r\n inline void fwd(T lp_lon, T const& lp_lat, T& xy_x, T& xy_y) const\r\n {\r\n T rho = 0.0;\r\n\r\n rho = this->m_proj_parm.c - (this->m_proj_parm.ellips ? pj_mlfn(lp_lat, sin(lp_lat),\r\n cos(lp_lat), this->m_proj_parm.en) : lp_lat);\r\n xy_x = rho * sin( lp_lon *= this->m_proj_parm.n );\r\n xy_y = this->m_proj_parm.rho0 - rho * cos(lp_lon);\r\n }\r\n\r\n // INVERSE(e_inverse) sphere & ellipsoid\r\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\r\n inline void inv(T xy_x, T xy_y, T& lp_lon, T& lp_lat) const\r\n {\r\n static T const half_pi = detail::half_pi();\r\n\r\n T rho = 0.0;\r\n\r\n if ((rho = boost::math::hypot(xy_x, xy_y = this->m_proj_parm.rho0 - xy_y)) != 0.0 ) {\r\n if (this->m_proj_parm.n < 0.) {\r\n rho = -rho;\r\n xy_x = -xy_x;\r\n xy_y = -xy_y;\r\n }\r\n lp_lat = this->m_proj_parm.c - rho;\r\n if (this->m_proj_parm.ellips)\r\n lp_lat = pj_inv_mlfn(lp_lat, this->m_par.es, this->m_proj_parm.en);\r\n lp_lon = atan2(xy_x, xy_y) / this->m_proj_parm.n;\r\n } else {\r\n lp_lon = 0.;\r\n lp_lat = this->m_proj_parm.n > 0. ? half_pi : -half_pi;\r\n }\r\n }\r\n\r\n static inline std::string get_name()\r\n {\r\n return \"eqdc_ellipsoid\";\r\n }\r\n\r\n };\r\n\r\n // Equidistant Conic\r\n template \r\n inline void setup_eqdc(Params const& params, Parameters& par, par_eqdc& proj_parm)\r\n {\r\n T cosphi, sinphi;\r\n int secant;\r\n\r\n proj_parm.phi1 = pj_get_param_r(params, \"lat_1\", srs::dpar::lat_1);\r\n proj_parm.phi2 = pj_get_param_r(params, \"lat_2\", srs::dpar::lat_2);\r\n\r\n if (fabs(proj_parm.phi1 + proj_parm.phi2) < epsilon10)\r\n BOOST_THROW_EXCEPTION( projection_exception(error_conic_lat_equal) );\r\n\r\n proj_parm.en = pj_enfn(par.es);\r\n\r\n proj_parm.n = sinphi = sin(proj_parm.phi1);\r\n cosphi = cos(proj_parm.phi1);\r\n secant = fabs(proj_parm.phi1 - proj_parm.phi2) >= epsilon10;\r\n if( (proj_parm.ellips = (par.es > 0.)) ) {\r\n double ml1, m1;\r\n\r\n m1 = pj_msfn(sinphi, cosphi, par.es);\r\n ml1 = pj_mlfn(proj_parm.phi1, sinphi, cosphi, proj_parm.en);\r\n if (secant) { /* secant cone */\r\n sinphi = sin(proj_parm.phi2);\r\n cosphi = cos(proj_parm.phi2);\r\n proj_parm.n = (m1 - pj_msfn(sinphi, cosphi, par.es)) /\r\n (pj_mlfn(proj_parm.phi2, sinphi, cosphi, proj_parm.en) - ml1);\r\n }\r\n proj_parm.c = ml1 + m1 / proj_parm.n;\r\n proj_parm.rho0 = proj_parm.c - pj_mlfn(par.phi0, sin(par.phi0),\r\n cos(par.phi0), proj_parm.en);\r\n } else {\r\n if (secant)\r\n proj_parm.n = (cosphi - cos(proj_parm.phi2)) / (proj_parm.phi2 - proj_parm.phi1);\r\n proj_parm.c = proj_parm.phi1 + cos(proj_parm.phi1) / proj_parm.n;\r\n proj_parm.rho0 = proj_parm.c - par.phi0;\r\n }\r\n }\r\n\r\n }} // namespace detail::eqdc\r\n #endif // doxygen\r\n\r\n /*!\r\n \\brief Equidistant Conic projection\r\n \\ingroup projections\r\n \\tparam Geographic latlong point type\r\n \\tparam Cartesian xy point type\r\n \\tparam Parameters parameter type\r\n \\par Projection characteristics\r\n - Conic\r\n - Spheroid\r\n - Ellipsoid\r\n \\par Projection parameters\r\n - lat_1: Latitude of first standard parallel (degrees)\r\n - lat_2: Latitude of second standard parallel (degrees)\r\n \\par Example\r\n \\image html ex_eqdc.gif\r\n */\r\n template \r\n struct eqdc_ellipsoid : public detail::eqdc::base_eqdc_ellipsoid\r\n {\r\n template \r\n inline eqdc_ellipsoid(Params const& params, Parameters const& par)\r\n : detail::eqdc::base_eqdc_ellipsoid(par)\r\n {\r\n detail::eqdc::setup_eqdc(params, this->m_par, this->m_proj_parm);\r\n }\r\n };\r\n\r\n #ifndef DOXYGEN_NO_DETAIL\r\n namespace detail\r\n {\r\n\r\n // Static projection\r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_STATIC_PROJECTION(srs::spar::proj_eqdc, eqdc_ellipsoid, eqdc_ellipsoid)\r\n\r\n // Factory entry(s)\r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_ENTRY_FI(eqdc_entry, eqdc_ellipsoid)\r\n \r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_BEGIN(eqdc_init)\r\n {\r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_ENTRY(eqdc, eqdc_entry);\r\n }\r\n\r\n } // namespace detail\r\n #endif // doxygen\r\n\r\n} // namespace projections\r\n\r\n}} // namespace boost::geometry\r\n\r\n#endif // BOOST_GEOMETRY_PROJECTIONS_EQDC_HPP\r\n\r\n", "meta": {"hexsha": "8b3e562b3d6108a5278f184083515b5ab7d16f79", "size": 9256, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "dep/win/include/boost/geometry/srs/projections/proj/eqdc.hpp", "max_stars_repo_name": "Netis/packet-agent", "max_stars_repo_head_hexsha": "70da3479051a07e3c235abe7516990f9fd21a18a", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 995.0, "max_stars_repo_stars_event_min_datetime": "2018-06-22T10:39:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T01:22:14.000Z", "max_issues_repo_path": "dep/win/include/boost/geometry/srs/projections/proj/eqdc.hpp", "max_issues_repo_name": "Netis/packet-agent", "max_issues_repo_head_hexsha": "70da3479051a07e3c235abe7516990f9fd21a18a", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 32.0, "max_issues_repo_issues_event_min_datetime": "2018-06-23T14:19:37.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-29T10:20:37.000Z", "max_forks_repo_path": "dep/win/include/boost/geometry/srs/projections/proj/eqdc.hpp", "max_forks_repo_name": "Netis/packet-agent", "max_forks_repo_head_hexsha": "70da3479051a07e3c235abe7516990f9fd21a18a", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 172.0, "max_forks_repo_forks_event_min_datetime": "2018-06-22T11:12:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T07:44:33.000Z", "avg_line_length": 40.9557522124, "max_line_length": 114, "alphanum_fraction": 0.5740060501, "num_tokens": 2118, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.37754066879814546, "lm_q1q2_score": 0.2405050321931378}} {"text": "//\n// Copyright (c) 2004, 2005, 2006, 2007 Troy D. Straszheim \n// \n// This file is part of IceTray.\n//\n// IceTray is free software; you can redistribute it and/or modify\n// it under the terms of the GNU General Public License as published by\n// the Free Software Foundation; either version 3 of the License, or\n// (at your option) any later version.\n//\n// IceTray is distributed in the hope that it will be useful,\n// but WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n// GNU General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with this program. If not, see .\n//\n\n#include \n#include \n\n#include \n\nusing namespace boost::python;\n\n#define I3_CONSTANT_NAMES\t\t\t\\\n (c)(n_ice_phase)(n_ice_group)(n_ice)\t\t\\\n (theta_cherenkov)(c_ice)(pi)(e)(NA)\t\t\\\n (SurfaceElev)(OriginElev)(zIceTop)\t\t\\\n (Coordinate_shift_x)(Coordinate_shift_y)\t\\\n (Coordinate_shift_z)(dt_window_l)(dt_window_h)\n\n#define I3CONSTANT_DEF(r,data,t) \\\n .def_readonly(BOOST_PP_STRINGIZE(t), BOOST_PP_CAT(I3Constants::, t))\n\n// dummy class just used as a namespace.\nnamespace {\n struct dummy { };\n}\n\nvoid register_I3Constants()\n{\n class_(\"I3Constants\")\n .def_readonly(\"c\", I3Constants::c, \"Speed of light in vacuum\")\n .def_readonly(\"n_ice_phase\", I3Constants::n_ice_phase, \"the average phase velocity for wavelength of 400nm\") \n .def_readonly(\"n_ice_group\", I3Constants::n_ice_group, \"avg group velocity for 400nm\")\n .def_readonly(\"n_ice\", I3Constants::n_ice, \"index of refraction of ice\")\n .def_readonly(\"theta_cherenkov\", I3Constants::theta_cherenkov, \"cherenkov angle in ice\")\n .def_readonly(\"c_ice\", I3Constants::c_ice, \"c in ice\")\n .def_readonly(\"pi\", I3Constants::pi, \"what is pi doing duplicated here\")\n .def_readonly(\"e\", I3Constants::e, \"OY-ler's number. awww yeah you know how we do.\")\n .def_readonly(\"NA\", I3Constants::NA, \"avogadro's number\")\n .def_readonly(\"SurfaceElev\", I3Constants::SurfaceElev, \"elevation of surface (amanda hole 4)\")\n .def_readonly(\"OriginElev\", I3Constants::OriginElev, \"elev of icecube origin, by definition\")\n .def_readonly(\"zIceTop\", I3Constants::zIceTop, \" Z-coordinate of IceTop (Origin Depth)\"\n\t\t \"Obtained from DEFINED elevation of origin and MEASURED surface elevation\")\n .def_readonly(\"Coordinate_shift_x\", I3Constants::Coordinate_shift_x, \"conversion between icecube and amanda\")\n .def_readonly(\"Coordinate_shift_y\", I3Constants::Coordinate_shift_y, \"conversion between icecube and amanda\")\n .def_readonly(\"Coordinate_shift_z\", I3Constants::Coordinate_shift_z, \"conversion between icecube and amanda\")\n .def_readonly(\"dt_window_l\", I3Constants::dt_window_l, \"default value for time residuals\")\n .def_readonly(\"dt_window_h\", I3Constants::dt_window_h, \"default value for time residuals\")\n .def( freeze() )\n ;\n}\n", "meta": {"hexsha": "2a4adb5aec2af7d97259bec854aae02746797006", "size": 3047, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "dataclasses/private/pybindings/I3Constants.cxx", "max_stars_repo_name": "hschwane/offline_production", "max_stars_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-12-24T22:00:01.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-24T22:00:01.000Z", "max_issues_repo_path": "dataclasses/private/pybindings/I3Constants.cxx", "max_issues_repo_name": "hschwane/offline_production", "max_issues_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dataclasses/private/pybindings/I3Constants.cxx", "max_forks_repo_name": "hschwane/offline_production", "max_forks_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2020-07-17T09:20:29.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-30T16:44:18.000Z", "avg_line_length": 46.1666666667, "max_line_length": 113, "alphanum_fraction": 0.7305546439, "num_tokens": 862, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5736784220301065, "lm_q2_score": 0.4186969093556867, "lm_q1q2_score": 0.2401973822680529}} {"text": "/*\n Copyright (C) 2019 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace QuantExt {\n\nDiscountingForwardBondEngine::DiscountingForwardBondEngine(\n const Handle& discountCurve, const Handle& incomeCurve,\n const Handle& bondReferenceYieldCurve, const Handle& bondSpread,\n const Handle& bondDefaultCurve, const Handle& bondRecoveryRate,\n Period timestepPeriod, boost::optional includeSettlementDateFlows, const Date& settlementDate,\n const Date& npvDate)\n : discountCurve_(discountCurve), incomeCurve_(incomeCurve), bondReferenceYieldCurve_(bondReferenceYieldCurve),\n bondSpread_(bondSpread), bondDefaultCurve_(bondDefaultCurve), bondRecoveryRate_(bondRecoveryRate),\n timestepPeriod_(timestepPeriod), includeSettlementDateFlows_(includeSettlementDateFlows),\n settlementDate_(settlementDate), npvDate_(npvDate) {\n\n bondReferenceYieldCurve_ =\n bondSpread_.empty() ? bondReferenceYieldCurve\n : Handle(\n boost::make_shared(bondReferenceYieldCurve, bondSpread_));\n registerWith(discountCurve_); // curve for discounting of the forward derivative contract. OIS, usually.\n registerWith(incomeCurve_); // this is a curve for compounding of the bond\n registerWith(bondReferenceYieldCurve_); // this is the bond reference curve, for discounting, usually RePo\n registerWith(bondSpread_);\n registerWith(bondDefaultCurve_);\n registerWith(bondRecoveryRate_);\n}\n\nvoid DiscountingForwardBondEngine::calculate() const {\n // Do some checks on data\n QL_REQUIRE(!discountCurve_.empty(), \"discounting term structure handle is empty\");\n QL_REQUIRE(!incomeCurve_.empty(), \"income term structure handle is empty\");\n QL_REQUIRE(!bondReferenceYieldCurve_.empty(), \"bond rerference term structure handle is empty\");\n\n Date npvDate = npvDate_; // this is today when the valuation occurs\n if (npvDate == Null()) {\n npvDate = (*discountCurve_)->referenceDate();\n }\n Date settlementDate = settlementDate_; //\n if (settlementDate == Null()) {\n settlementDate = (*discountCurve_)->referenceDate();\n }\n\n Date maturityDate =\n arguments_.fwdMaturityDate; // this is the date when the forward is executed, i.e. cash and bond change hands\n\n Real cmpPayment = arguments_.compensationPayment;\n if (cmpPayment == Null()) {\n cmpPayment = 0.0;\n }\n Date cmpPaymentDate = arguments_.compensationPaymentDate;\n if (cmpPaymentDate == Null()) {\n cmpPaymentDate = npvDate;\n }\n\n // in case that the premium payment has occured in the past, we set the amount to 0. the date itself is set to the\n // npvDate to have a valid date for \"discounting\"\n Date cmpPaymentDate_use = cmpPaymentDate >= npvDate ? cmpPaymentDate : maturityDate;\n cmpPayment = cmpPaymentDate >= npvDate ? cmpPayment : 0.0; // premium cashflow is not relevant for npv if in the\n // past\n\n // initialize\n results_.value = 0.0; // this is today's npv of the forward contract\n results_.underlyingSpotValue = 0.0; // this is today's value of the \"restricted bond\". Restricted means that only\n // cashflows after maturity are taken into account.\n results_.forwardValue = 0.0; // this the value of the forward contract just before maturity\n\n bool dirty = arguments_.settlementDirty;\n\n results_.underlyingSpotValue = calculateBondNpv(npvDate, maturityDate); // cashflows before maturity will be ignored\n\n boost::tie(results_.forwardValue, results_.value) = calculateForwardContractPresentValue(\n results_.underlyingSpotValue, cmpPayment, npvDate, maturityDate, cmpPaymentDate_use, dirty);\n}\n\nReal DiscountingForwardBondEngine::calculateBondNpv(Date npvDate, Date computeDate) const {\n Real npvValue = 0.0;\n Size numCoupons = 0;\n bool hasLiveCashFlow = false;\n\n // handle case where we wish to price simply with benchmark curve and scalar security spread\n // i.e. credit curve term structure (and recovery) have not been specified\n // we set the default probability and recovery rate to zero in this instance (issuer credit worthiness already\n // captured within security spread)\n boost::shared_ptr creditCurvePtr =\n bondDefaultCurve_.empty()\n ? boost::make_shared(npvDate, 0.0, bondReferenceYieldCurve_->dayCounter())\n : bondDefaultCurve_.currentLink();\n Rate recoveryVal = bondRecoveryRate_.empty() ? 0.0 : bondRecoveryRate_->value(); // setup default bond recovery rate\n\n // load the shared pointer into bd\n boost::shared_ptr bd = arguments_.underlying;\n for (Size i = 0; i < bd->cashflows().size(); i++) {\n // Recovery amount is computed over the whole time interval (npvDate,maturityOfBond)\n\n if (bd->cashflows()[i]->hasOccurred(\n computeDate, includeSettlementDateFlows_)) // Cashflows before computeDate not relevant for npv\n continue;\n\n /* The amount recovered in the case of default is the recoveryrate*Notional*Probability of\n Default; this is added to the NPV value. For coupon bonds the coupon periods are taken\n as the timesteps for integrating over the probability of default.\n */\n boost::shared_ptr coupon = boost::dynamic_pointer_cast(bd->cashflows()[i]);\n\n if (coupon) {\n numCoupons++;\n Date startDate = coupon->accrualStartDate();\n Date endDate = coupon->accrualEndDate();\n Date effectiveStartDate = (startDate <= computeDate && computeDate <= endDate) ? computeDate : startDate;\n Date defaultDate = effectiveStartDate + (endDate - effectiveStartDate) / 2;\n Probability P = creditCurvePtr->defaultProbability(effectiveStartDate, endDate);\n\n npvValue += coupon->nominal() * recoveryVal * P * bondReferenceYieldCurve_->discount(defaultDate);\n }\n\n hasLiveCashFlow = true; // check if a cashflow is available after the date of valuation.\n\n // Coupon value is discounted future payment times the survival probability\n Probability S = creditCurvePtr->survivalProbability(bd->cashflows()[i]->date()) /\n creditCurvePtr->survivalProbability(computeDate);\n npvValue += bd->cashflows()[i]->amount() * S * bondReferenceYieldCurve_->discount(bd->cashflows()[i]->date());\n }\n\n // the ql instrument might not yet be expired and still have not anything to value if\n // the computeDate > evaluation date\n if (!hasLiveCashFlow)\n return 0.0;\n\n if (bd->cashflows().size() > 1 && numCoupons == 0) {\n QL_FAIL(\"DiscountingForwardBondEngine does not support bonds with multiple cashflows but no coupons\");\n }\n\n boost::shared_ptr firstCoupon = boost::dynamic_pointer_cast(bd->cashflows()[0]);\n if (firstCoupon) {\n Date startDate = computeDate; // face value recovery starting with computeDate\n while (startDate < bd->cashflows()[0]->date()) {\n Date stepDate = startDate + timestepPeriod_;\n Date endDate = (stepDate > bd->cashflows()[0]->date()) ? bd->cashflows()[0]->date() : stepDate;\n Date defaultDate = startDate + (endDate - startDate) / 2;\n Probability P = creditCurvePtr->defaultProbability(startDate, endDate);\n\n npvValue += firstCoupon->nominal() * recoveryVal * P * bondReferenceYieldCurve_->discount(defaultDate);\n startDate = stepDate;\n }\n }\n /* If there are no coupon, as in a Zero Bond, we must integrate over the entire period from npv date to\n maturity. The timestepPeriod specified is used as provide the steps for the integration. This only applies\n to bonds with 1 cashflow, identified as a final redemption payment.\n */\n if (bd->cashflows().size() == 1) {\n boost::shared_ptr redemption = boost::dynamic_pointer_cast(bd->cashflows()[0]);\n if (redemption) {\n Date startDate = computeDate;\n while (startDate < redemption->date()) {\n Date stepDate = startDate + timestepPeriod_;\n Date endDate = (stepDate > redemption->date()) ? redemption->date() : stepDate;\n Date defaultDate = startDate + (endDate - startDate) / 2;\n Probability P = creditCurvePtr->defaultProbability(startDate, endDate);\n\n npvValue += redemption->amount() * recoveryVal * P * bondReferenceYieldCurve_->discount(defaultDate);\n startDate = stepDate;\n }\n }\n }\n return npvValue * arguments_.bondNotional;\n}\n\nboost::tuple DiscountingForwardBondEngine::calculateForwardContractPresentValue(\n Real spotValue, Real cmpPayment, Date npvDate, Date computeDate, Date cmpPaymentDate, bool dirty) const {\n // here we go with the true forward computation\n Real forwardBondValue = 0.0;\n Real forwardContractPresentValue = 0.0;\n Real forwardContractForwardValue = 0.0;\n\n // handle case where we wish to price simply with benchmark curve and scalar security spread\n // i.e. credit curve term structure (and recovery) have not been specified\n // we set the default probability and recovery rate to zero in this instance (issuer credit worthiness already\n // captured within security spread)\n boost::shared_ptr creditCurvePtr =\n bondDefaultCurve_.empty()\n ? boost::make_shared(npvDate, 0.0, bondReferenceYieldCurve_->dayCounter())\n : bondDefaultCurve_.currentLink();\n Rate recoveryVal = bondRecoveryRate_.empty() ? 0.0 : bondRecoveryRate_->value(); // setup default bond recovery rate\n\n // load the shared pointer into bd\n boost::shared_ptr bd = arguments_.underlying;\n\n // the case of dirty strike corresponds here to an accrual of 0.0. This will be convenient in the code.\n Real accruedAmount =\n dirty ? 0.0 : bd->accruedAmount(computeDate) * bd->notional(computeDate) / 100 * arguments_.bondNotional;\n\n /* Discounting and compounding, taking account of possible bond default before delivery*/\n\n forwardBondValue =\n spotValue / (incomeCurve_->discount(computeDate)); // compounding to date of maturity of forward contract\n\n // Subtract strike at maturity. Regarding accrual (i.e. strike is given clean vs dirty) there are two\n // cases: long or short.\n\n // Long: forwardBondValue - strike_dirt = (forwardBondValue - accrual) - strike_clean\n // Short: strike_dirt - forwardBondValue = strike_clean - (forwardBondValue - accrual)\n // In total:\n forwardContractForwardValue = (*arguments_.payoff)(forwardBondValue - accruedAmount);\n\n // forwardContractPresentValue adjusted for potential default before computeDate:\n forwardContractPresentValue =\n forwardContractForwardValue * (discountCurve_->discount(computeDate)) *\n creditCurvePtr->survivalProbability(computeDate) -\n cmpPayment *\n (discountCurve_->discount(cmpPaymentDate)); // The forward is a derivative. We use \"OIS curve\" to discount.\n // We subtract the potential payment due to Premium.\n\n // Take account of face value recovery:\n // A) Recovery for time period when coupons are present\n for (Size i = 0; i < bd->cashflows().size(); i++) {\n if (bd->cashflows()[i]->hasOccurred(\n npvDate, includeSettlementDateFlows_)) // Cashflows before npvDate not relevant for npv\n continue;\n if (bd->cashflows()[i]->date() >=\n computeDate) // Cashflows after computeDate do not fall into the forward period\n continue;\n boost::shared_ptr coupon = boost::dynamic_pointer_cast(bd->cashflows()[i]);\n\n if (coupon) {\n Date startDate = coupon->accrualStartDate();\n Date endDate = coupon->accrualEndDate();\n Date effectiveStartDate = (startDate <= npvDate && npvDate <= endDate) ? npvDate : startDate;\n Date effectiveEndDate = (startDate <= computeDate && computeDate <= endDate) ? computeDate : endDate;\n Date defaultDate = effectiveStartDate + (effectiveEndDate - effectiveStartDate) / 2;\n Probability P = creditCurvePtr->defaultProbability(effectiveStartDate, effectiveEndDate);\n\n forwardContractPresentValue +=\n (*arguments_.payoff)(coupon->nominal() * arguments_.bondNotional * recoveryVal - accruedAmount) * P *\n (discountCurve_->discount(defaultDate));\n }\n }\n\n // B) Recovery for time period before coupons are present\n boost::shared_ptr firstCoupon = boost::dynamic_pointer_cast(bd->cashflows()[0]);\n if (firstCoupon) {\n Date startDate = npvDate; // face value recovery starting with npvDate\n Date stopDate = std::min(bd->cashflows()[0]->date(), computeDate);\n while (startDate < stopDate) {\n Date stepDate = startDate + timestepPeriod_;\n Date endDate = (stepDate > stopDate) ? stopDate : stepDate;\n Date defaultDate = startDate + (endDate - startDate) / 2;\n Probability P = creditCurvePtr->defaultProbability(startDate, endDate);\n\n forwardContractPresentValue +=\n (*arguments_.payoff)(firstCoupon->nominal() * arguments_.bondNotional * recoveryVal - accruedAmount) *\n P * (discountCurve_->discount(defaultDate));\n startDate = stepDate;\n }\n }\n // C) ZCB\n /* If there are no coupon, as in a Zero Bond, we must integrate over the entire period from npv date to\n maturity. The timestepPeriod specified is used as provide the steps for the integration. This only applies\n to bonds with 1 cashflow, identified as a final redemption payment.\n */\n if (bd->cashflows().size() == 1) {\n boost::shared_ptr redemption = boost::dynamic_pointer_cast(bd->cashflows()[0]);\n if (redemption) {\n Date startDate = npvDate;\n while (startDate < redemption->date()) {\n Date stepDate = startDate + timestepPeriod_;\n Date endDate = (stepDate > redemption->date()) ? redemption->date() : stepDate;\n Date defaultDate = startDate + (endDate - startDate) / 2;\n Probability P = creditCurvePtr->defaultProbability(startDate, endDate);\n\n forwardContractPresentValue +=\n (*arguments_.payoff)(redemption->amount() * arguments_.bondNotional * recoveryVal - accruedAmount) *\n P * (discountCurve_->discount(defaultDate));\n startDate = stepDate;\n }\n }\n }\n\n return boost::make_tuple(forwardContractForwardValue, forwardContractPresentValue);\n}\n\n} // namespace QuantExt\n", "meta": {"hexsha": "6141a9dbf27daf7c2b6a861ffc984636840b07d5", "size": 16331, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "QuantExt/qle/pricingengines/discountingforwardbondengine.cpp", "max_stars_repo_name": "nvolfango/Engine", "max_stars_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-03-30T17:24:17.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-30T17:24:17.000Z", "max_issues_repo_path": "QuantExt/qle/pricingengines/discountingforwardbondengine.cpp", "max_issues_repo_name": "zhangjiayin/Engine", "max_issues_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "QuantExt/qle/pricingengines/discountingforwardbondengine.cpp", "max_forks_repo_name": "zhangjiayin/Engine", "max_forks_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 53.0227272727, "max_line_length": 120, "alphanum_fraction": 0.685322393, "num_tokens": 3667, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.41869690935568665, "lm_q1q2_score": 0.2401973761644581}} {"text": "// -----------------------------------------------------------------------\n//\n// Copyright (C) 2020 - David Fernández Castellanos\n//\n// This file is part of the MEPLS software. You can use it, redistribute\n// it, and/or modify it under the terms of the Creative Commons Attribution\n// 4.0 International Public License. The full text of the license can be\n// found in the file LICENSE at the top level of the MEPLS distribution.\n//\n// -----------------------------------------------------------------------\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n// MEPLS built-in dynamics\n#include \n\n// to parse command line arguments\n#include \n\n// to parse input parameters files\n#include \n#include \n\n// to save output data in JSON format\n#include \n\n\nstruct Parameters\n{\n\t// these are the parameters and we will use in the simulation, initialized with some default values\n\tunsigned int seed = 1234567;\n\tunsigned int Nx = 32;\n\tunsigned int Ny = 32;\n\tdouble G = 30.;\n\tdouble nu = 0.3;\n\tdouble gamma = 0.05;\n\tdouble k = 6.;\n\tdouble strain_limit = 0.05;\n\tdouble lambda_init = 1.;\n\tdouble lambda_renew = 1.;\n\tstd::string filename = \"out.json\";\n\n\tvoid declare_entries(dealii::ParameterHandler &prm)\n\t{\n\t\t// We declare the entries of the parameters text file. Each entry matches the name of a\n\t\t// simulation parameters (although it doesn't need to) and has a default value. We use as\n\t\t// the default value is the same value of the variables declared above\n\n\t\tprm.enter_subsection(\"Section1\");\n\n\t\tprm.declare_entry(\"seed\", mepls::utils::str::to_string(seed), dealii::Patterns::Integer(0),\n\t\t\t\t\t\t \"\");\n\t\tprm.declare_entry(\"Nx\", mepls::utils::str::to_string(Nx), dealii::Patterns::Integer(0), \"\");\n\t\tprm.declare_entry(\"Ny\", mepls::utils::str::to_string(Ny), dealii::Patterns::Integer(0), \"\");\n\t\tprm.declare_entry(\"G\", mepls::utils::str::to_string(G), dealii::Patterns::Double(0.0), \"\");\n\t\tprm.declare_entry(\"nu\", mepls::utils::str::to_string(nu), dealii::Patterns::Double(0.0),\n\t\t\t\t\t\t \"\");\n\t\tprm.declare_entry(\"gamma\", mepls::utils::str::to_string(gamma),\n\t\t\t\t\t\t dealii::Patterns::Double(0.0), \"\");\n\t\tprm.declare_entry(\"strain_limit\", mepls::utils::str::to_string(strain_limit),\n\t\t\t\t\t\t dealii::Patterns::Double(0.0), \"\");\n\t\tprm.declare_entry(\"lambda_init\", mepls::utils::str::to_string(lambda_init),\n\t\t\t\t\t\t dealii::Patterns::Double(0.0), \"\");\n\t\tprm.declare_entry(\"lambda_renew\", mepls::utils::str::to_string(lambda_renew),\n\t\t\t\t\t\t dealii::Patterns::Double(0.0), \"\");\n\t\tprm.declare_entry(\"k\", mepls::utils::str::to_string(k), dealii::Patterns::Double(0.0), \"\");\n\t\tprm.declare_entry(\"filename\", filename, dealii::Patterns::FileName(), \"\");\n\n\t\tprm.leave_subsection();\n\t}\n\n\tvoid load_entries(dealii::ParameterHandler &prm)\n\t{\n\t\tprm.enter_subsection(\"Section1\");\n\n\t\t// We define how each parameter gets its value from a parameters file entry\n\n\t\tseed = prm.get_integer(\"seed\");\n\t\tNx = prm.get_integer(\"Nx\");\n\t\tNy = prm.get_integer(\"Ny\");\n\t\tG = prm.get_double(\"G\");\n\t\tnu = prm.get_double(\"nu\");\n\t\tgamma = prm.get_double(\"gamma\");\n\t\tk = prm.get_double(\"k\");\n\t\tstrain_limit = prm.get_double(\"strain_limit\");\n\t\tlambda_init = prm.get_double(\"lambda_init\");\n\t\tlambda_renew = prm.get_double(\"lambda_renew\");\n\t\tfilename = prm.get(\"filename\");\n\n\t\tprm.leave_subsection();\n\t}\n\n\tvoid load_file(const std::string &filename)\n\t{\n\t\t// load a parameters file\n\n\t\tdealii::ParameterHandler prm;\n\t\tdeclare_entries(prm);\n\t\tprm.parse_input(filename);\n\t\tload_entries(prm);\n\t}\n\n\tvoid generate_file(const std::string &filename)\n\t{\n\t\t// generate a parameters file template\n\n\t\tstd::ofstream outfile(filename);\n\t\tdealii::ParameterHandler prm;\n\t\tdeclare_entries(prm);\n\t\tprm.print_parameters(outfile, dealii::ParameterHandler::OutputStyle::Text);\n\t}\n\n};\n\n\ntemplate\nvoid write_data(\n\tconst mepls::history::History &sim_history, const Parameters &p)\n{\n\tboost::property_tree::ptree data_tree;\n\n\t// write some metadata, such as a simulation name and description\n\tdata_tree.put(\"Name\", \"Step4\");\n\tdata_tree.put(\"Description\", \"System driven in the athermal quasistatic limit\");\n\n\t// write the simulation parameters\n\tdata_tree.put(\"Parameters.dim\", 2);\n\tdata_tree.put(\"Parameters.seed\", p.seed);\n\tdata_tree.put(\"Parameters.Nx\", p.Nx);\n\tdata_tree.put(\"Parameters.Ny\", p.Ny);\n\tdata_tree.put(\"Parameters.G\", p.G);\n\tdata_tree.put(\"Parameters.nu\", p.nu);\n\tdata_tree.put(\"Parameters.gamma\", p.gamma);\n\tdata_tree.put(\"Parameters.lambda_renew\", p.lambda_renew);\n\tdata_tree.put(\"Parameters.lambda_init\", p.lambda_init);\n\tdata_tree.put(\"Parameters.k\", p.k);\n\tdata_tree.put(\"Parameters.strain_limit\", p.strain_limit);\n\n\n\t// write some metadata, such as a simulation name and description\n\tdata_tree.put(\"Name\", \"Step4\");\n\tdata_tree.put(\"Description\", \"System driven in the athermal quasistatic limit\");\n\n\t// write the simulation parameters\n\tdata_tree.put(\"Parameters.dim\", 2);\n\tdata_tree.put(\"Parameters.seed\", p.seed);\n\tdata_tree.put(\"Parameters.Nx\", p.Nx);\n\tdata_tree.put(\"Parameters.Ny\", p.Ny);\n\tdata_tree.put(\"Parameters.G\", p.G);\n\tdata_tree.put(\"Parameters.nu\", p.nu);\n\tdata_tree.put(\"Parameters.gamma\", p.gamma);\n\tdata_tree.put(\"Parameters.lambda_renew\", p.lambda_renew);\n\tdata_tree.put(\"Parameters.lambda_init\", p.lambda_init);\n\tdata_tree.put(\"Parameters.k\", p.k);\n\tdata_tree.put(\"Parameters.strain_limit\", p.strain_limit);\n\n\t// We write the event histories with CSV format to a string.\n\t// Here, we write only the columns of interest, but there are more available (see the\n\t// documentation).\n\tstd::ostringstream plastic_events_csv;\n\tplastic_events_csv << \"index,element,eigenstrain_00,eigenstrain_11,eigenstrain_01\\n\";\n\tfor(auto &row : sim_history.plastic)\n\t\tplastic_events_csv << row.index << \",\" << row.element << \",\" << row.eigenstrain_00 << \",\"\n\t\t\t\t\t\t << row.eigenstrain_11 << \",\" << row.eigenstrain_01 << \"\\n\";\n\n\tdata_tree.put(\"Data.plastic_events\", plastic_events_csv.str());\n\n\n\tstd::ostringstream driving_events_csv;\n\tdriving_events_csv << \"index,dext_stress,dtotal_strain\\n\";\n\tfor(auto &row : sim_history.driving)\n\t\tdriving_events_csv << row.index << \",\" << row.dext_stress << \",\" << row.dtotal_strain\n\t\t\t\t\t\t << \"\\n\";\n\n\tdata_tree.put(\"Data.driving_events\", driving_events_csv.str());\n\n\n\tstd::ostringstream macro_evolution_csv;\n\tmacro_evolution_csv << \"index,ext_stress,total_strain,time,av_vm_stress,av_vm_plastic_strain\\n\";\n\tfor(auto &row : sim_history.macro_evolution)\n\t\tmacro_evolution_csv << row.index << \",\" << row.ext_stress << \",\" << row.total_strain << \",\"\n\t\t\t\t\t\t\t<< row.time << \",\" << row.av_vm_stress << \",\"\n\t\t\t\t\t\t\t<< row.av_vm_plastic_strain << \"\\n\";\n\n\tdata_tree.put(\"Data.macro_evolution\", macro_evolution_csv.str());\n\n\n\t// write the data tree to a JSON file\n\tstd::ofstream output_file(p.filename);\n\tboost::property_tree::json_parser::write_json(output_file, data_tree);\n\toutput_file.close();\n}\n\n\nvoid run(const Parameters &p)\n{\n\t//----- SETUP ------\n\n\t// we do the same as in the previous tutorial, but this time we use the\n\t// parameters from the input Parameters object\n\n\tconstexpr unsigned dim = 2;\n\tstd::mt19937 generator(p.seed);\n\n\tdealii::SymmetricTensor<4, dim> C = mepls::utils::tensor::make_isotropic_stiffness(p.G,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tp.nu);\n\n\tmepls::element::Vector elements;\n\n\t// we also store the elements into a vector that knows the derived class, so we can access\n\t// the example::element::Scalar::conf struct later (this member cannot be accessed\n\t// through the pointers to the base mepls::element::Element)\n\tstd::vector *> elements_scalar;\n\n\tfor(double n = 0; n < p.Nx * p.Ny; ++n)\n\t{\n\t\texample::element::Scalar::Config conf;\n\t\tconf.number = n;\n\t\tconf.gamma = p.gamma;\n\t\tconf.lambda = p.lambda_init;\n\t\tconf.k = p.k;\n\n\t\tauto element = new example::element::Scalar(conf, generator);\n\t\telement->C(C);\n\n\t\telements_scalar.push_back(element);\n\t\telements.push_back(element);\n\t}\n\n\tmepls::elasticity_solver::LeesEdwards solver(p.Nx, p.Ny);\n\tfor(auto &element : elements)\n\t\tsolver.set_elastic_properties(element->number(), element->C());\n\tsolver.setup_and_assembly();\n\n\tmepls::element::calculate_ext_stress_coefficients(elements, solver);\n\tmepls::element::calculate_local_stress_coefficients_central(elements, solver);\n\n\tmepls::system::Standard system(elements, solver, generator);\n\n\tmepls::history::History sim_history(\"Simulation_history\");\n\n\tsystem.set_history(sim_history);\n\n\t// when the elements were created, their slip systems were initialized with thresholds from a\n\t// Weibull distribution with scale lambda_init. We change it now to lambda_renew. Therefore,\n\t// when plastic deformation occurs, the renew local thresholds will have a different\n\t// average\n\tfor(auto &element : elements_scalar)\n\t\telement->conf.lambda = p.lambda_renew;\n\n\n\t//----- DYNAMICS ------\n\n\t// This object will allow us to check for different conditions by which\n\t// the simulation might stop. Different conditions might be checked, but as long as one of\n\t// them evaluates to false, when continue_simulation() is called it will return false\n\tmepls::utils::ContinueSimulation continue_simulation;\n\n\tsim_history.add_macro(system);\n\n\t// run while the continue_simulation object says so\n\twhile(continue_simulation())\n\t{\n\t\tstd::cout << system.macrostate[\"total_strain\"] << \" \" << system.macrostate[\"ext_stress\"]\n\t\t\t\t << std::endl;\n\n\n\t\t// these are the same dynamics we implemented in the previous tutorial, but using MEPLS built-in\n\n\t\t// apply an external strain increment of 0.01%\n\t\tmepls::dynamics::finite_extremal_dynamics_step(1e-4, system);\n\t\tsim_history.add_macro(system);\n\n\t\t// perform and avalanche of slip events. By passing the continue_simulation object,\n\t\t// the relaxation function can set its own condition for stopping the simulation.\n\t\t// Specifically, it will check if the avalanche size overcomes a certain maximum upper limit\n\t\tmepls::dynamics::relaxation(system, continue_simulation);\n\t\tsim_history.add_macro(system);\n\n\n\t\t// check if the strain has reached the strain limit. If it has, the next time continue_simulation() is called\n\t\t// it will return false, so we will exit the main loop\n\t\tcontinue_simulation(system.macrostate[\"total_strain\"] < p.strain_limit,\n\t\t\t\t\t\t\t\"total strain limit reached\");\n\n\t}\n\n\t// print the message of the stopping condition that was met\n\tstd::cout << continue_simulation << std::endl;\n\n\tfor(auto &element : elements)\n\t\tdelete element;\n\n\n\twrite_data(sim_history, p);\n}\n\n\nint main(int argc, char *argv[])\n{\n\t// Read the command line arguments. We define the -f 'filename' to pass the\n\t// path to the parameters file\n\tcli::Parser parser(argc, argv);\n\tparser.set_optional(\"f\", \"file\", \"./default.prm\",\n\t\t\t\t\t\t\t\t\t \"Name of the input configuration file\");\n\tparser.run_and_exit_if_error();\n\t// you can check https://github.com/FlorianRappl/CmdParser for a further documentation of cli::Parser\n\n\n\n\t// Create the parameters object\n\tParameters p;\n\n\t// We try to load the parameters file, but if it doesn't exist, we generate a new one with the name\n\t// default.prm and default values\n\ttry\n\t{\n\t\tp.load_file(parser.get(\"f\"));\n\t}\n\tcatch(dealii::PathSearch::ExcFileNotFound &)\n\t{\n\t\tp.generate_file(parser.get(\"f\"));\n\t\tstd::cout << \"Configuration file \" << parser.get(\"f\") << \" created\"\n\t\t\t\t << std::endl;\n\t\treturn 1;\n\t}\n\n\n\t// run the simulation\n\trun(p);\n\n\n\treturn 0;\n}\n", "meta": {"hexsha": "393b4fd284fadfc6ff540cd09983956d31c67f37", "size": 11544, "ext": "cc", "lang": "C++", "max_stars_repo_path": "tutorial/step4/step_4.cc", "max_stars_repo_name": "kastellane/MEPLS", "max_stars_repo_head_hexsha": "9d7e4a7b9de73e65e3d4e4aba9b90a6fd563e186", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "tutorial/step4/step_4.cc", "max_issues_repo_name": "kastellane/MEPLS", "max_issues_repo_head_hexsha": "9d7e4a7b9de73e65e3d4e4aba9b90a6fd563e186", "max_issues_repo_licenses": ["CC-BY-4.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tutorial/step4/step_4.cc", "max_forks_repo_name": "kastellane/MEPLS", "max_forks_repo_head_hexsha": "9d7e4a7b9de73e65e3d4e4aba9b90a6fd563e186", "max_forks_repo_licenses": ["CC-BY-4.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.9529411765, "max_line_length": 111, "alphanum_fraction": 0.707033957, "num_tokens": 2922, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.476579651063676, "lm_q1q2_score": 0.24015142691958238}} {"text": "// Copyright Yamaha 2021\n// MIT License\n// https://github.com/yamaha-bps/ros2_pcl_utils/blob/master/LICENSE\n\n#include \"pcl_utils/feature.hpp\"\n\n#include \n\n#include \n#include \n#include \n\n#include \"pcl_utils/pcl_iterator.hpp\"\n\n\nnamespace cbr\n{\n\nstruct ScanInfo\n{\n uint32_t idx = 0; // index in scan\n bool ok = false; // if eligible to be a feature\n float cvalue = 0; // estimate of smoothness\n};\n\n\nstd::pair\npcl_features(const sensor_msgs::msg::PointCloud2 & msg, const PclFeatureParams & prm)\n{\n const uint32_t N = msg.height * msg.width;\n const uint32_t W = prm.window;\n\n if (N < 2 * W + 1) {\n return {nullptr, nullptr}; // too few points to process\n }\n\n\n //////////////////////////\n // STEP 1: VALID POINTS //\n //////////////////////////\n\n std::vector valid(N, false);\n\n std::vector ang_discont(N - 1, false); // angle discontinuity\n std::vector dist_discont(N - 1, 0); // distance discontinuity\n std::vector inc_ang_viol(N - 1, 0); // incidence angle bound violation\n\n const auto cos_ang_disc_thresh = std::cos(prm.ang_disc_thresh);\n const auto sin_ang_inc_thresh = std::sin(prm.ang_inc_thresh);\n\n PclIterator it(msg);\n for (auto i = 0u; i != N - 1; ++i, ++it) {\n const auto p0 = *it;\n const auto p1 = *(it + 1);\n const auto r0 = p0.norm();\n const auto r1 = p1.norm();\n const auto r01 = (p1 - p0).norm();\n\n // incidence angle difference from 90 (perpendicular incidence)\n // | alpha - pi/2 | <= b\n // | cos(alpha) | <= sin(b)\n if (std::abs(p0.dot(p1 - p0)) / (r0 * r01) > sin_ang_inc_thresh) {\n inc_ang_viol[i] = true;\n }\n\n // angular discontinuity\n // cos alpha = dot(p0, p1) / ||p1|| ||p2||\n // |alpha| > b <==> cos(alpha) < cos(b) <==> dot(p0, p1) / (||p1|| ||p2||) < cos(b)\n if (p0.dot(p1) / (r0 * r1) < cos_ang_disc_thresh) {\n ang_discont[i] = true;\n }\n\n // distance discontinuity\n if (2 * r01 > prm.rel_disc_thresh * (r0 + r1)) {\n if (r0 < r1) {\n dist_discont[i] = -1; // p1 further away than p0\n } else {\n dist_discont[i] = 1; // p1 closer than p0\n }\n }\n }\n\n // sliding window count of number of angular discontinuities\n uint32_t num_ang_discont = 0;\n for (auto i = 0u; i < 2u * W; ++i) {\n num_ang_discont += uint32_t(ang_discont[i]);\n }\n\n // sliding window count of distance discontinuities\n uint32_t num_dist_discont_neg_left = 0; // count decreasing on the left\n uint32_t num_dist_discont_pos_rght = 0; // count increasing on the right\n for (auto i = 0u; i < W; ++i) {\n num_dist_discont_neg_left += uint32_t(dist_discont[i] == -1);\n }\n for (auto i = W; i < 2 * W; ++i) {\n num_dist_discont_pos_rght += uint32_t(dist_discont[i] == 1);\n }\n\n it = PclIterator(msg) + W;\n for (auto i = W; i + W < N; ++i, ++it) {\n valid[i] = true;\n\n if (num_dist_discont_neg_left > 0 || num_dist_discont_pos_rght > 0) {\n // distance discontinuity condition (robustly visible)\n valid[i] = false;\n } else if (num_ang_discont > 0) {\n // no angular discontinuities in window\n valid[i] = false;\n } else if (inc_ang_viol[i - 1] && inc_ang_viol[i]) {\n // incidence angle condition\n valid[i] = false;\n } else if (it.intensity() < prm.min_intensity || it.intensity() > prm.max_intensity) {\n // intensity condition\n valid[i] = false;\n } else if (std::atan2((*it).tail<2>().norm(), (*it).x()) > prm.max_angle) {\n // angle condition\n valid[i] = false;\n } else {\n // depth condition\n auto norm = (*it).norm();\n if (norm < prm.min_depth || norm > prm.max_depth) {\n valid[i] = false;\n }\n }\n\n // update sliding window counters\n num_ang_discont -= ang_discont[i - W];\n num_ang_discont += ang_discont[i + W];\n num_dist_discont_neg_left -= uint32_t(dist_discont[i - W] == -1);\n num_dist_discont_neg_left += uint32_t(dist_discont[i] == -1);\n num_dist_discont_pos_rght -= uint32_t(dist_discont[i] == 1);\n num_dist_discont_pos_rght += uint32_t(dist_discont[i + W] == 1);\n }\n\n //////////////////////\n // STEP 2: FEATURES //\n //////////////////////\n\n // cumulative vector sum in window\n Eigen::Vector3f csum(0, 0, 0);\n\n std::vector cands;\n\n // fast iterator: point one past right end of interval\n auto i_f = 0u;\n auto it_f = PclIterator(msg);\n for (; i_f != 2 * W + 1; ++i_f, ++it_f) {\n csum += *it_f;\n }\n\n // mid iterator: points to active point\n auto it_m = PclIterator(msg) + W;\n\n // slow iterator: points to left end of interval\n auto i_s = 0u;\n auto it_s = PclIterator(msg);\n\n // calculate c-values:\n // c = || (2*W + 1) * p - \\sum_i p_i || / ( 2 * W * ||p|| )\n // = || 2 * W * p - \\sum_{i != 0} p_i || / (2 * W * ||p|| )\n // = || p - (1/2W) \\sum_{i != 0} p_i || / || p ||\n\n // if c-value is small it is likely a planar point (p close to average of surrounding pts)\n // if c-value is large it is likely an edge point (p far from average of surrounding pts)\n for (; i_f != N; ++i_s, ++i_f, ++it_s, ++it_m, ++it_f) {\n uint32_t i_mid = i_s + W;\n\n if (valid[i_mid]) {\n cands.push_back(\n {i_mid, true, ((2 * W + 1) * (*it_m) - csum).norm() / (2 * W * (*it_m).norm())}\n );\n }\n\n csum -= *it_s;\n csum += *it_f;\n }\n\n // sort candidates by c-value\n std::sort(\n cands.begin(), cands.end(), [](const auto & item1, const auto & item2) {\n return item1.cvalue < item2.cvalue;\n });\n\n // extract features\n\n // edges from back\n auto edges = std::make_unique();\n edges->fields = msg.fields;\n edges->point_step = msg.point_step;\n edges->is_bigendian = msg.is_bigendian;\n edges->header = msg.header;\n\n for (auto it = cands.crbegin(); it != cands.crend() && it->cvalue > prm.edge_thresh; ++it) {\n auto &[idx, ok, cvalue] = *it;\n if (valid[idx]) {\n std::copy(\n msg.data.begin() + idx * msg.point_step,\n msg.data.begin() + (idx + 1) * msg.point_step,\n std::back_insert_iterator(edges->data)\n );\n for (auto idx_i = idx - W; idx_i != idx + W + 1; ++idx_i) {\n valid[idx_i] = false; // mark surrounding as taken\n }\n }\n }\n\n edges->width = edges->data.size() / edges->point_step;\n edges->row_step = edges->width;\n edges->height = 1;\n edges->is_dense = 1;\n\n // planar from front\n auto planar = std::make_unique();\n planar->fields = msg.fields;\n planar->point_step = msg.point_step;\n planar->is_bigendian = msg.is_bigendian;\n planar->header = msg.header;\n\n for (auto it = cands.cbegin(); it != cands.cend() && it->cvalue < prm.plane_thresh; ++it) {\n auto &[idx, ok, cvalue] = *it;\n if (valid[idx]) {\n std::copy(\n msg.data.begin() + idx * msg.point_step,\n msg.data.begin() + (idx + 1) * msg.point_step,\n std::back_insert_iterator(planar->data)\n );\n for (auto idx_i = idx - W; idx_i != idx + W + 1; ++idx_i) {\n valid[idx_i] = false; // mark surrounding as taken\n }\n }\n }\n\n planar->width = planar->data.size() / planar->point_step;\n planar->row_step = planar->width;\n planar->height = 1;\n planar->is_dense = 1;\n\n return {std::move(edges), std::move(planar)};\n}\n\n} // namespace cbr\n", "meta": {"hexsha": "a08712b5162a60745d02cc4c3d9b9092aedc2dfb", "size": 7348, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/feature.cpp", "max_stars_repo_name": "yamaha-bps/ros2_pcl_utils", "max_stars_repo_head_hexsha": "513615ac1b6b251a39f29eb7ff03f307747b8aa3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-07-05T14:35:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-05T14:35:35.000Z", "max_issues_repo_path": "src/feature.cpp", "max_issues_repo_name": "yamaha-bps/ros2_pcl_utils", "max_issues_repo_head_hexsha": "513615ac1b6b251a39f29eb7ff03f307747b8aa3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/feature.cpp", "max_forks_repo_name": "yamaha-bps/ros2_pcl_utils", "max_forks_repo_head_hexsha": "513615ac1b6b251a39f29eb7ff03f307747b8aa3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2022-03-31T01:35:07.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T01:35:07.000Z", "avg_line_length": 30.3636363636, "max_line_length": 94, "alphanum_fraction": 0.5870985302, "num_tokens": 2343, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.44552953503957277, "lm_q1q2_score": 0.24013294370711535}} {"text": "/*\n ============================================================================\n Name : INSIGHTv3_mpi.cpp\n Author : Jan Mikelson\n Version :\n Copyright : \tThis free software is available under the Creative Commons Attribution Share Alike License.\n You are permitted to use, redistribute and adapt this software as long as appropriate credit\n is given to the original author, and all derivative works are distributed under the same\n license or a compatible one.\n For more information, visit http://creativecommons.org/licenses/by-sa/3.0/ or send a letter to\n Creative Commons, 171 2nd Street, Suite 300, San Francisco, California, 94105, USA.\n Description : \tParallel version of the INSIGHT algorithm. Further details can be found in Lillacci & Khammash 2013\n ============================================================================\n */\n\n#include \n#include \n\n#include /* defines FILENAME_MAX */\n#ifdef WINDOWS\n#include \n#define GetCurrentDir _getcwd\n#else\n#include \n#define GetCurrentDir getcwd\n#endif\n\nchar cCurrentPath[FILENAME_MAX];\n\n#include \n#include \n#include \n#include \n#include \n\n#include \"INSIGHTv3.h\"\n#include \"BirthDeathModel.h\"\n#include \"InsightAlgorithm.h\"\n#include \"InsightParticleSamplerFactory.h\"\n#include \"IllegalArgumentException.h\"\n#include \"ParticleEvaluatorInsight.h\"\n#include \"UniformSamplerFactory.h\"\n#include \"ScaledUniformSamplerFactory.h\"\n#include \"ProblemFileReader.h\"\n#include \"ModelDescription.h\"\n#include \"UniformSampler.h\"\n#include \"InsightIterationMpi.h\"\n#include \"InsightMpiWorker.h\"\n#include \"PrevPopReader.h\"\n\nusing namespace INSIGHTv3;\n\n//Default values\nconst static std::string PROBLEM_FILE = \"\";\nconst static int S = 3;\nconst static double FINAL_TOLERANCE = 0.03;\nconst static int NUM_PARTICLES = 1000;\nconst static std::string OUTPUT_FILE = \"\";\nconst static std::string PREVIOUS_POPULATION_FILE = \"\";\nconst static bool IGNORE_WEIGHTS = false;\nconst static int PRINT = 20;\nconst static double BETA = 0.01;\nconst static double TOL_KOLMOGOROV = 0.0000001;\nconst static double KAPPA_KOLMOGOROV = 0.005224; // kappa value for M = 80000 and beta = 0.01\nconst static double TOL_KAPPA_KOLMOGOROV = 0.01;\n\nstd::string problem_file_name;\nint s;\ndouble final_tolerance;\nint num_particles;\nstd::string output_file_name;\nstd::string prev_pop_file;\nbool ignore_weights;\nint print;\ndouble beta;\ndouble tolerance_kolmogorov;\ndouble kappa_kolmogorov;\ndouble tol_kappa_kolmovorog;\n\nnamespace po = boost::program_options;\n\nvoid handleOptions(int argc, char * argv[]);\nvoid printInfo(ModelDescription& desc);\n\nint main(int argc, char *argv[]) {\n\n\thandleOptions(argc, argv);\n\n\tclock_t tic = clock();\n\tMPI::Init(argc, argv);\n\tint my_rank = MPI::COMM_WORLD.Get_rank();\n\tint num_tasks = MPI::COMM_WORLD.Get_size();\n\n\tif (num_tasks == 1) {\n\t\tstd::cerr\n\t\t\t\t<< \"When using mpi version of INSIGHT at least two processes must be started!\"\n\t\t\t\t<< std::endl;\n\t\tabort();\n\t}\n\n\tint seed = time(NULL) + my_rank;\n#ifdef DEBUG\n\tseed = 2;\n#endif\n\tRngPtr rng = boost::make_shared(seed);\n\n\tProblemFileReader reader;\n\tModelDescription desc = reader.read(rng, problem_file_name);\n\n\tKolmogorovComputerPtr kolmogorov_computer = boost::make_shared<\n\t\t\tKolmogorovComputer>(*desc.original_data, beta, tolerance_kolmogorov,\n\t\t\tkappa_kolmogorov);\n\tevaluator_ptr evaluator = boost::make_shared(\n\t\t\tboost::ref(*desc.model), *desc.times, boost::ref(rng),\n\t\t\tkolmogorov_computer); // need to use boost::ref, see boost\n\tsampler_ptr prior_sampler = boost::make_shared(\n\t\t\tboost::ref(rng), *desc.lower_bounds_parameter,\n\t\t\t*desc.upper_bounds_parameter);\n\n\tif (my_rank == 0) {\n\t\tprintInfo(desc);\n\t\twriter_ptr writer = boost::make_shared(\n\t\t\t\toutput_file_name);\n\n\t\tsampler_factory_ptr factory = boost::make_shared<\n\t\t\t\tScaledUniformSamplerFactory>(boost::ref(rng),\n\t\t\t\tdesc.model->num_params, *desc.lower_bounds_parameter,\n\t\t\t\t*desc.upper_bounds_parameter, ignore_weights);\n\n\t\tInsightIterationMpi iteration(num_tasks, prior_sampler);\n\t\tToleranceProviderPtr tolerance_provider = boost::make_shared<\n\t\t\t\tFixedSequenceToleranceProvider>(s, desc.model->model,\n\t\t\t\tkolmogorov_computer);\n\n\t\tsampler_ptr first_sampler;\n\t\tstd::vector prev_acceptance_rates;\n\t\tstd::vector prev_tolerances;\n\t\tstd::vector prev_num_simulations;\n\t\tif (prev_pop_file.length() > 0) {\n\t\t\tPrevPopReader prev_pop_reader;\n\t\t\tParticleSet particle_set;\n\t\t\tprev_pop_reader.readPreviousPop(prev_pop_file,\n\t\t\t\t\tdesc.model->num_params, &particle_set,\n\t\t\t\t\t&prev_acceptance_rates, &prev_tolerances,\n\t\t\t\t\t&prev_num_simulations);\n\t\t\tfirst_sampler = factory->createSampler(particle_set,\n\t\t\t\t\tkolmogorov_computer->getThresholdForS(s), s);\n\t\t} else {\n\t\t\tfirst_sampler = prior_sampler;\n\t\t}\n\t\tInsightAlgorithm algorithm(iteration, factory, evaluator, writer,\n\t\t\t\ttolerance_provider, final_tolerance, num_particles,\n\t\t\t\tdesc.model->num_params, prev_acceptance_rates, prev_tolerances,\n\t\t\t\tprev_num_simulations);\n\t\tIterationLogger logger(print);\n\n\t\talgorithm.run(first_sampler, logger);\n\n\t\tMPI_INSTRUCTION instruction(DIETAG);\n\t\tfor (int rank = 1; rank < num_tasks; rank++) {\n\t\t\tMPI::COMM_WORLD.Send(&instruction, 1, MPI::INT, rank, INSTRUCTION);\n\t\t}\n\t} else {\n\t\tInsightMpiWorker worker(my_rank, desc.model->num_params, evaluator,\n\t\t\t\tprior_sampler);\n\t\tworker.run();\n\t}\n\n\tclock_t toc = clock();\n\tif (my_rank == 0) {\n\t\tstd::cout << \"\\n\\nSMC inference complete.\" << std::endl;\n\t\tstd::cout << \"\\nElapsed time: \"\n\t\t\t\t<< ((double) (toc - tic)) / CLOCKS_PER_SEC << \"sec.\\n\"\n\t\t\t\t<< std::endl;\n\t}\n\tMPI::Finalize();\n\n\treturn 0;\n}\n\nvoid handleOptions(int argc, char * argv[]) {\n\n\tpo::options_description desc(\"Allowed options\");\n\tdesc.add_options()(\"help\", \"produce help message\")(\"problem_file\",\n\t\t\tpo::value(&problem_file_name)->default_value(\n\t\t\t\t\tPROBLEM_FILE),\n\t\t\t\"problem file. A problem file must always be provided!\")(\n\t\t\t\"number_simulations,s\", po::value(&s)->default_value(S),\n\t\t\t\"starting number of simulations\")(\"final tolerance,e\",\n\t\t\tpo::value(&final_tolerance)->default_value(FINAL_TOLERANCE),\n\t\t\t\"the final tolerance for the algorithm\")(\"pop_size,n\",\n\t\t\tpo::value(&num_particles)->default_value(NUM_PARTICLES),\n\t\t\t\"define the population size\")(\"output_file,O\",\n\t\t\tpo::value(&output_file_name)->default_value(\n\t\t\t\t\tOUTPUT_FILE), \"output file\")(\"previous_pop_file,P\",\n\t\t\tpo::value(&prev_pop_file)->default_value(\n\t\t\t\t\tPREVIOUS_POPULATION_FILE), \"previous population file\")(\n\t\t\t\"ignore_weights,i\",\n\t\t\tpo::value(&ignore_weights)->default_value(IGNORE_WEIGHTS),\n\t\t\t\"ignore weights\")(\"print,p\",\n\t\t\tpo::value(&print)->default_value(PRINT),\n\t\t\t\"define the frequency of print outs\")(\"beta,b\",\n\t\t\tpo::value(&beta)->default_value(BETA),\n\t\t\t\"the confidence of the kolmogorov test for the rejection rule\")(\n\t\t\t\"tol_kol,k\",\n\t\t\tpo::value(&tolerance_kolmogorov)->default_value(\n\t\t\t\t\tTOL_KOLMOGOROV),\n\t\t\t\"the tolerance for the algorithm to compute the inverse of the kolmogorov distribution\")(\n\t\t\t\"kappa,K\",\n\t\t\tpo::value(&kappa_kolmogorov)->default_value(\n\t\t\t\t\tKAPPA_KOLMOGOROV),\n\t\t\t\"Kappa for the computation of the Kolmogorov distance\")(\n\t\t\t\"tol_kol_kap,T\",\n\t\t\tpo::value(&tol_kappa_kolmovorog)->default_value(\n\t\t\t\t\tTOL_KAPPA_KOLMOGOROV),\n\t\t\t\"the tolerance for the algorithm to compute kappa for the computation of the Kolmogorov distance\");\n\n\tpo::positional_options_description p;\n\tp.add(\"problem_file\", -1);\n\n\tpo::variables_map vm;\n\tpo::store(po::parse_command_line(argc, argv, desc), vm);\n\tpo::store(\n\t\t\tpo::command_line_parser(argc, argv).options(desc).positional(p).run(),\n\t\t\tvm);\n\tpo::notify(vm);\n\n\tif (vm.count(\"help\")) {\n\t\tstd::cout << desc << \"\\n\";\n\t\tabort();\n\t}\n\n\tif (problem_file_name.size() == 0) {\n\t\tstd::cerr << \"Problem file is required\" << std::endl;\n\t\tabort();\n\t}\n\n\tif (output_file_name.size() == 0) {\n\t\tGetCurrentDir(cCurrentPath, sizeof(cCurrentPath));\n\t\toutput_file_name = std::string(cCurrentPath) + \"/resutls.txt\";\n\t}\n\n}\n\nvoid printInfo(ModelDescription& desc) {\n\n\tstd::cout << \"\\nThis is INSIGHT v3.0\\n\" << std::endl;\n\tstd::cout << \"Problem file path:\" << std::endl;\n\tstd::cout << problem_file_name << std::endl;\n\tstd::cout << \"\\nThe new particle population will be saved in:\" << std::endl;\n\tstd::cout << output_file_name << std::endl;\n\n\tstd::cout << \"\\nWill estimate \" << desc.model->model_name << \" using \"\n\t\t\t<< num_particles << \" SMC particles.\" << std::endl;\n\tstd::cout << \"\\nThe data is assumed to have been created using \"\n\t\t\t<< desc.model->num_outputs << \" outputs,\" << std::endl;\n\tstd::cout << \"measured at \" << desc.times->size() << \" timepoints.\"\n\t\t\t<< std::endl;\n\n\tif (prev_pop_file.length() > 0) {\n\t\tstd::cout << \"A previous population is loaded from file: \" << std::endl;\n\t\tstd::cout << prev_pop_file << std::endl;\n\t}\n\tstd::cout << \"To estimate the density of each population \"\n\t\t\t<< \"Kernel Density Estimation with uniform Kernel\" << std::endl;\n\tstd::cout << \"is used.\" << std::endl;\n\tif (ignore_weights) {\n\t\tstd::cout << std::endl << \"Particle weights will be ignored by sampler!\"\n\t\t\t\t<< std::endl;\n\t}\n\tstd::cout << \"\\nThe parallel version of the algorithm is used.\"\n\t\t\t<< std::endl;\n}\n", "meta": {"hexsha": "e318488100c57ad2e8626c6f844b1146baafd4de", "size": 9312, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "container/INSIGHT/src/INSIGHTv3_mpi.cpp", "max_stars_repo_name": "mcapuccini/cloud-insight", "max_stars_repo_head_hexsha": "96fa1a12baa7aebd31878a969d2e43e5355714fa", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, 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YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.4726834766204328, "lm_q1q2_score": 0.24003427747659772}} {"text": "#include \"Poisson.h\"\n#include \"config.h\"\n#include \"kernels/poisson/init.h\"\n#include \"kernels/poisson/kernel.h\"\n#include \"kernels/poisson/tensor.h\"\n\n#include \"basis/WarpAndBlend.h\"\n#include \"form/BC.h\"\n#include \"form/DGCurvilinearCommon.h\"\n#include \"form/InverseInequality.h\"\n#include \"form/RefElement.h\"\n#include \"geometry/Curvilinear.h\"\n#include \"quadrules/SimplexQuadratureRule.h\"\n#include \"tensor/EigenMap.h\"\n#include \"util/LinearAllocator.h\"\n\n#include \n#include \n#include \n\nnamespace tensor = tndm::poisson::tensor;\nnamespace init = tndm::poisson::init;\nnamespace kernel = tndm::poisson::kernel;\n\nnamespace tndm {\n\nPoisson::Poisson(std::shared_ptr> cl, functional_t<1> K,\n DGMethod method)\n : DGCurvilinearCommon(std::move(cl), MinQuadOrder()), method_(method),\n space_(PolynomialDegree, ALIGNMENT),\n materialSpace_(PolynomialDegree, WarpAndBlendFactory(), ALIGNMENT),\n fun_K(make_volume_functional(std::move(K))), fun_force(zero_volume_function),\n fun_dirichlet(zero_facet_function), fun_slip(zero_facet_function) {\n\n Minv_ = space_.inverseMassMatrix();\n E_Q = space_.evaluateBasisAt(volRule.points());\n E_Q_T = space_.evaluateBasisAt(volRule.points(), {1, 0});\n Dxi_Q = space_.evaluateGradientAt(volRule.points());\n\n negative_E_Q_T = Managed>(E_Q_T.shape(), std::size_t{ALIGNMENT});\n EigenMap(negative_E_Q_T) = -EigenMap(E_Q_T);\n\n for (std::size_t f = 0; f < DomainDimension + 1u; ++f) {\n auto points = cl_->facetParam(f, fctRule.points());\n E_q.emplace_back(space_.evaluateBasisAt(points));\n E_q_T.emplace_back(space_.evaluateBasisAt(points, {1, 0}));\n Dxi_q.emplace_back(space_.evaluateGradientAt(points));\n Dxi_q_120.emplace_back(space_.evaluateGradientAt(points, {1, 2, 0}));\n matE_q_T.emplace_back(materialSpace_.evaluateBasisAt(points, {1, 0}));\n\n negative_E_q_T.emplace_back(space_.evaluateBasisAt(points, {1, 0}));\n auto E = EigenMap(negative_E_q_T.back());\n E = -E;\n }\n\n matE_Q_T = materialSpace_.evaluateBasisAt(volRule.points(), {1, 0});\n matDxi_Q = materialSpace_.evaluateGradientAt(volRule.points());\n}\n\nvoid Poisson::compute_mass_matrix(std::size_t elNo, double* M) const {\n kernel::massMatrix mm;\n mm.E_Q = E_Q.data();\n mm.J_Q = vol[elNo].get().data();\n mm.M = M;\n mm.W = volRule.weights().data();\n mm.execute();\n}\n\nvoid Poisson::compute_inverse_mass_matrix(std::size_t elNo, double* Minv) const {\n compute_mass_matrix(elNo, Minv);\n\n auto J_Q = vol[elNo].get();\n alignas(ALIGNMENT) double Jinv_Q[tensor::Jinv_Q::size()] = {};\n for (unsigned q = 0; q < tensor::Jinv_Q::Shape[0]; ++q) {\n Jinv_Q[q] = 1.0 / J_Q[q];\n }\n\n kernel::MinvWA wa;\n wa.E_Q = E_Q.data();\n wa.Jinv_Q = Jinv_Q;\n wa.MinvRef = Minv_.data();\n wa.MinvWA = Minv;\n wa.W = volRule.weights().data();\n wa.execute();\n}\n\nvoid Poisson::compute_K_Dx_q(std::size_t fctNo, FacetInfo const& info,\n std::array K_Dx_q) const {\n kernel::K_Dx_q dx;\n for (int i = 0; i < 2; ++i) {\n if (K_Dx_q[i]) {\n auto JInv = (i == 1) ? fct[fctNo].get() : fct[fctNo].get();\n dx.G_q = JInv.data()->data();\n dx.matE_q_T = matE_q_T[info.localNo[i]].data();\n dx.K = material[info.up[i]].get().data();\n dx.K_Dx_q(0) = K_Dx_q[i];\n dx.Dxi_q(0) = Dxi_q[info.localNo[i]].data();\n dx.execute();\n }\n }\n}\n\nvoid Poisson::compute_K_q(std::size_t fctNo, FacetInfo const& info,\n std::array K_q) const {\n kernel::K_q kw;\n for (int i = 0; i < 2; ++i) {\n if (K_q[i]) {\n kw.matE_q_T = matE_q_T[info.localNo[i]].data();\n kw.K = material[info.up[i]].get().data();\n kw.K_q(0) = K_q[i];\n kw.execute();\n }\n }\n}\n\nvoid Poisson::begin_preparation(std::size_t numElements, std::size_t numLocalElements,\n std::size_t numLocalFacets) {\n base::begin_preparation(numElements, numLocalElements, numLocalFacets);\n\n material.setStorage(\n std::make_shared(numElements * materialSpace_.numBasisFunctions()), 0u,\n numElements, materialSpace_.numBasisFunctions());\n\n volPre.setStorage(std::make_shared(numElements * volRule.size()), 0u,\n numLocalElements, volRule.size());\n\n fctPre.setStorage(std::make_shared(numLocalFacets * fctRule.size()), 0u,\n numLocalFacets, fctRule.size());\n\n penalty_.resize(numLocalFacets);\n}\n\nvoid Poisson::prepare_volume(std::size_t elNo, LinearAllocator& scratch) {\n base::prepare_volume(elNo, scratch);\n\n auto Kfield = material[elNo].get().data();\n alignas(ALIGNMENT) double K_Q_raw[tensor::K_Q::size()];\n auto K_Q = Matrix(K_Q_raw, 1, volRule.size());\n fun_K(elNo, K_Q);\n\n alignas(ALIGNMENT) double Mmem[tensor::matM::size()];\n kernel::project_K_lhs krnl_lhs;\n krnl_lhs.matE_Q_T = matE_Q_T.data();\n krnl_lhs.J_Q = vol[elNo].get().data();\n krnl_lhs.matM = Mmem;\n krnl_lhs.W = volRule.weights().data();\n krnl_lhs.execute();\n\n kernel::project_K_rhs krnl_rhs;\n krnl_rhs.matE_Q_T = matE_Q_T.data();\n krnl_rhs.J_Q = vol[elNo].get().data();\n krnl_rhs.K = Kfield;\n krnl_rhs.K_Q = K_Q_raw;\n krnl_rhs.W = volRule.weights().data();\n krnl_rhs.execute();\n\n using MMap = Eigen::Map,\n Eigen::Unaligned,\n Eigen::OuterStride>;\n using KMap = Eigen::Map, Eigen::Unaligned,\n Eigen::InnerStride<1>>;\n\n auto K_eigen = KMap(Kfield);\n K_eigen = MMap(Mmem).fullPivLu().solve(K_eigen);\n}\n\nvoid Poisson::prepare_skeleton(std::size_t fctNo, FacetInfo const& info,\n LinearAllocator& scratch) {\n base::prepare_skeleton(fctNo, info, scratch);\n\n for (int side = 0; side < 2; ++side) {\n kernel::K_G_q k;\n k.G_q = side == 1 ? fct[fctNo].get().data()->data()\n : fct[fctNo].get().data()->data();\n k.K = material[info.up[side]].get().data();\n k.K_G_q(0) = side == 1 ? fctPre[fctNo].get().data()->data()\n : fctPre[fctNo].get().data()->data();\n k.matE_q_T = matE_q_T[info.localNo[side]].data();\n k.execute();\n }\n}\n\nvoid Poisson::prepare_boundary(std::size_t fctNo, FacetInfo const& info,\n LinearAllocator& scratch) {\n base::prepare_boundary(fctNo, info, scratch);\n\n kernel::K_G_q k;\n k.G_q = fct[fctNo].get().data()->data();\n k.K = material[info.up[0]].get().data();\n k.K_G_q(0) = fctPre[fctNo].get().data()->data();\n k.matE_q_T = matE_q_T[info.localNo[0]].data();\n k.execute();\n}\n\nvoid Poisson::prepare_volume_post_skeleton(std::size_t elNo, LinearAllocator& scratch) {\n base::prepare_volume_post_skeleton(elNo, scratch);\n\n auto Kfield = material[elNo].get().data();\n\n kernel::J_W_K_Q krnl;\n krnl.J_W_K_Q = volPre[elNo].get().data()->data();\n krnl.J_Q = vol[elNo].get().data();\n krnl.K = Kfield;\n krnl.matE_Q_T = matE_Q_T.data();\n krnl.W = volRule.weights().data();\n krnl.execute();\n}\n\nvoid Poisson::prepare_penalty(std::size_t fctNo, FacetInfo const& info, LinearAllocator&) {\n auto const p = [&](int side) {\n auto Kfield = material[info.up[side]].get().data();\n auto k0 = *std::min_element(Kfield, Kfield + materialSpace_.numBasisFunctions());\n auto k1 = *std::max_element(Kfield, Kfield + materialSpace_.numBasisFunctions());\n constexpr double c_N_1 = InverseInequality::trace_constant(PolynomialDegree - 1);\n return (Dim + 1) * c_N_1 * (area_[fctNo] / volume_[info.up[side]]) * (k1 * k1 / k0);\n };\n\n if (info.up[0] != info.up[1]) {\n penalty_[fctNo] = (p(0) + p(1)) / 4.0;\n } else {\n penalty_[fctNo] = p(0);\n }\n}\n\nbool Poisson::assemble_volume(std::size_t elNo, Matrix& A00,\n LinearAllocator& scratch) const {\n alignas(ALIGNMENT) double Dx_Q[tensor::Dx_Q::size()];\n\n assert(volRule.size() == tensor::W::Shape[0]);\n assert(Dxi_Q.shape(0) == tensor::Dxi_Q::Shape[0]);\n assert(Dxi_Q.shape(1) == tensor::Dxi_Q::Shape[1]);\n assert(Dxi_Q.shape(2) == tensor::Dxi_Q::Shape[2]);\n\n kernel::Dx_Q dx;\n dx.Dx_Q = Dx_Q;\n dx.Dxi_Q = Dxi_Q.data();\n dx.G_Q = vol[elNo].get().data()->data();\n dx.execute();\n\n kernel::assembleVolume krnl;\n krnl.A = A00.data();\n krnl.Dx_Q = Dx_Q;\n krnl.K = material[elNo].get().data();\n krnl.matE_Q_T = matE_Q_T.data();\n krnl.J_Q = vol[elNo].get().data();\n krnl.W = volRule.weights().data();\n krnl.execute();\n return true;\n}\n\nbool Poisson::assemble_skeleton(std::size_t fctNo, FacetInfo const& info, Matrix& A00,\n Matrix& A01, Matrix& A10, Matrix& A11,\n LinearAllocator& scratch) const {\n assert(fctRule.size() == tensor::w::Shape[0]);\n assert(E_q[0].shape(0) == tensor::E_q::Shape[0][0]);\n assert(E_q[0].shape(1) == tensor::E_q::Shape[0][1]);\n assert(Dxi_q[0].shape(0) == tensor::Dxi_q::Shape[0][0]);\n assert(Dxi_q[0].shape(1) == tensor::Dxi_q::Shape[0][1]);\n assert(Dxi_q[0].shape(2) == tensor::Dxi_q::Shape[0][2]);\n\n alignas(ALIGNMENT) double K_Dx_q0[tensor::K_Dx_q::size(0)];\n alignas(ALIGNMENT) double K_Dx_q1[tensor::K_Dx_q::size(1)];\n auto K_Dx_q = std::array{K_Dx_q0, K_Dx_q1};\n compute_K_Dx_q(fctNo, info, K_Dx_q);\n\n alignas(ALIGNMENT) double L_q[2][std::max(tensor::L_q::size(0), tensor::L_q::size(1))];\n\n if (method_ == DGMethod::BR2) {\n alignas(ALIGNMENT) double Lift0[tensor::Lift::size(0)];\n alignas(ALIGNMENT) double Lift1[tensor::Lift::size(1)];\n alignas(ALIGNMENT) double Minv[2][tensor::M::size()];\n for (int i = 0; i < 2; ++i) {\n compute_inverse_mass_matrix(info.up[i], Minv[i]);\n }\n\n alignas(ALIGNMENT) double K_q0[tensor::K_q::size(0)];\n alignas(ALIGNMENT) double K_q1[tensor::K_q::size(1)];\n auto K_q = std::array{K_q0, K_q1};\n compute_K_q(fctNo, info, K_q);\n\n kernel::lift_skeleton lift;\n lift.Lift(0) = Lift0;\n lift.Lift(1) = Lift1;\n lift.n_q = fct[fctNo].get().data()->data();\n lift.w = fctRule.weights().data();\n for (int i = 0; i < 2; ++i) {\n lift.K_q(i) = K_q[i];\n lift.L_q(i) = L_q[i];\n lift.Minv(i) = Minv[i];\n lift.E_q(i) = E_q[info.localNo[i]].data();\n }\n lift.execute(0);\n lift.execute(1);\n } else { // IP\n kernel::lift_ip lift;\n lift.nl_q = fct[fctNo].get().data();\n for (int i = 0; i < 2; ++i) {\n lift.L_q(i) = L_q[i];\n lift.E_q(i) = E_q[info.localNo[i]].data();\n }\n lift.execute(0);\n lift.execute(1);\n }\n\n kernel::assembleSurface assemble;\n assemble.c00 = -0.5;\n assemble.c01 = -assemble.c00;\n assemble.c10 = epsilon * 0.5;\n assemble.c11 = -assemble.c10;\n assemble.c20 = penalty(fctNo);\n assemble.c21 = -assemble.c20;\n assemble.a(0, 0) = A00.data();\n assemble.a(0, 1) = A01.data();\n assemble.a(1, 0) = A10.data();\n assemble.a(1, 1) = A11.data();\n for (int i = 0; i < 2; ++i) {\n assemble.K_Dx_q(i) = K_Dx_q[i];\n assemble.E_q(i) = E_q[info.localNo[i]].data();\n assemble.L_q(i) = L_q[i];\n }\n assemble.n_q = fct[fctNo].get().data()->data();\n assemble.w = fctRule.weights().data();\n assemble.execute(0, 0);\n assemble.execute(0, 1);\n assemble.execute(1, 0);\n assemble.execute(1, 1);\n\n return true;\n}\n\nbool Poisson::assemble_boundary(std::size_t fctNo, FacetInfo const& info, Matrix& A00,\n LinearAllocator& scratch) const {\n if (info.bc == BC::Natural) {\n return false;\n }\n\n assert(fctRule.size() == tensor::w::Shape[0]);\n assert(E_q[0].shape(0) == tensor::E_q::Shape[0][0]);\n assert(E_q[0].shape(1) == tensor::E_q::Shape[0][1]);\n assert(Dxi_q[0].shape(0) == tensor::Dxi_q::Shape[0][0]);\n assert(Dxi_q[0].shape(1) == tensor::Dxi_q::Shape[0][1]);\n assert(Dxi_q[0].shape(2) == tensor::Dxi_q::Shape[0][2]);\n\n alignas(ALIGNMENT) double L0[tensor::L_q::size(0)];\n if (method_ == DGMethod::BR2) {\n alignas(ALIGNMENT) double Lift0[tensor::Lift::size(0)];\n alignas(ALIGNMENT) double Minv0[tensor::M::size()];\n compute_inverse_mass_matrix(info.up[0], Minv0);\n\n alignas(ALIGNMENT) double K_q[tensor::K_q::size(0)];\n compute_K_q(fctNo, info, {K_q, nullptr});\n\n kernel::lift_boundary lift;\n lift.Lift(0) = Lift0;\n lift.K_q(0) = K_q;\n lift.L_q(0) = L0;\n lift.Minv(0) = Minv0;\n lift.E_q(0) = E_q[info.localNo[0]].data();\n lift.n_q = fct[fctNo].get().data()->data();\n lift.w = fctRule.weights().data();\n lift.execute();\n } else { // IP\n kernel::lift_ip lift;\n lift.nl_q = fct[fctNo].get().data();\n lift.L_q(0) = L0;\n lift.E_q(0) = E_q[info.localNo[0]].data();\n lift.execute(0);\n }\n\n alignas(ALIGNMENT) double K_Dx_q0[tensor::K_Dx_q::size(0)];\n compute_K_Dx_q(fctNo, info, {K_Dx_q0, nullptr});\n\n kernel::assembleSurface assemble;\n assemble.c00 = -1.0;\n assemble.c10 = epsilon;\n assemble.c20 = penalty(fctNo);\n assemble.a(0, 0) = A00.data();\n assemble.K_Dx_q(0) = K_Dx_q0;\n assemble.E_q(0) = E_q[info.localNo[0]].data();\n assemble.L_q(0) = L0;\n assemble.n_q = fct[fctNo].get().data()->data();\n assemble.w = fctRule.weights().data();\n assemble.execute(0, 0);\n return true;\n}\n\nbool Poisson::rhs_volume(std::size_t elNo, Vector& B,\n LinearAllocator& scratch) const {\n assert(tensor::b::Shape[0] == tensor::A::Shape[0]);\n\n alignas(ALIGNMENT) double F_Q_raw[tensor::F_Q::size()];\n assert(tensor::F_Q::size() == volRule.size());\n auto F_Q = Matrix(F_Q_raw, 1, tensor::F_Q::Shape[0]);\n fun_force(elNo, F_Q);\n\n kernel::rhsVolume rhs;\n rhs.E_Q = E_Q.data();\n rhs.F_Q = F_Q_raw;\n rhs.J_Q = vol[elNo].get().data();\n rhs.W = volRule.weights().data();\n rhs.b = B.data();\n rhs.execute();\n return true;\n}\n\nbool Poisson::bc_skeleton(std::size_t fctNo, BC bc, double f_q_raw[]) const {\n assert(tensor::f_q::size() == fctRule.size());\n auto f_q = Matrix(f_q_raw, 1, tensor::f_q::Shape[0]);\n if (bc == BC::Fault) {\n fun_slip(fctNo, f_q, false);\n } else if (bc == BC::Dirichlet) {\n fun_dirichlet(fctNo, f_q, false);\n } else {\n return false;\n }\n return true;\n}\nbool Poisson::bc_boundary(std::size_t fctNo, BC bc, double f_q_raw[]) const {\n assert(tensor::f_q::size() == fctRule.size());\n auto f_q = Matrix(f_q_raw, 1, tensor::f_q::Shape[0]);\n if (bc == BC::Fault) {\n fun_slip(fctNo, f_q, true);\n for (std::size_t q = 0; q < tensor::f_q::Shape[0]; ++q) {\n f_q(0, q) *= 0.5;\n }\n } else if (bc == BC::Dirichlet) {\n fun_dirichlet(fctNo, f_q, true);\n } else {\n return false;\n }\n return true;\n}\n\nbool Poisson::rhs_skeleton(std::size_t fctNo, FacetInfo const& info, Vector& B0,\n Vector& B1, LinearAllocator& scratch) const {\n alignas(ALIGNMENT) double f_q_raw[tensor::f_q::size()];\n if (!bc_skeleton(fctNo, info.bc, f_q_raw)) {\n return false;\n }\n\n alignas(ALIGNMENT) double f_lifted_q[tensor::f_lifted_q::size()];\n if (method_ == DGMethod::BR2) {\n alignas(ALIGNMENT) double f_lifted0[tensor::f_lifted::size(0)];\n alignas(ALIGNMENT) double f_lifted1[tensor::f_lifted::size(1)];\n alignas(ALIGNMENT) double Minv[2][tensor::M::size()];\n compute_inverse_mass_matrix(info.up[0], Minv[0]);\n compute_inverse_mass_matrix(info.up[1], Minv[1]);\n\n alignas(ALIGNMENT) double K_q0[tensor::K_q::size(0)];\n alignas(ALIGNMENT) double K_q1[tensor::K_q::size(1)];\n auto K_q = std::array{K_q0, K_q1};\n compute_K_q(fctNo, info, K_q);\n\n kernel::rhs_lift_skeleton lift;\n for (int i = 0; i < 2; ++i) {\n lift.E_q(i) = E_q[info.localNo[i]].data();\n lift.K_q(i) = K_q[i];\n lift.Minv(i) = Minv[i];\n }\n lift.n_q = fct[fctNo].get().data()->data();\n lift.f_q = f_q_raw;\n lift.f_lifted(0) = f_lifted0;\n lift.f_lifted(1) = f_lifted1;\n lift.f_lifted_q = f_lifted_q;\n lift.w = fctRule.weights().data();\n lift.execute();\n } else { // IP\n kernel::rhs_lift_ip lift;\n lift.nl_q = fct[fctNo].get().data();\n lift.f_q = f_q_raw;\n lift.f_lifted_q = f_lifted_q;\n lift.execute();\n }\n\n alignas(ALIGNMENT) double K_Dx_q0[tensor::K_Dx_q::size(0)];\n alignas(ALIGNMENT) double K_Dx_q1[tensor::K_Dx_q::size(1)];\n compute_K_Dx_q(fctNo, info, {K_Dx_q0, K_Dx_q1});\n\n kernel::rhsFacet rhs;\n rhs.b = B0.data();\n rhs.c10 = 0.5 * epsilon;\n rhs.c20 = penalty(fctNo);\n rhs.f_q = f_q_raw;\n rhs.f_lifted_q = f_lifted_q;\n rhs.n_q = fct[fctNo].get().data()->data();\n rhs.w = fctRule.weights().data();\n rhs.K_Dx_q(0) = K_Dx_q0;\n rhs.E_q(0) = E_q[info.localNo[0]].data();\n rhs.execute();\n\n rhs.b = B1.data();\n rhs.c20 *= -1.0;\n rhs.K_Dx_q(0) = K_Dx_q1;\n rhs.E_q(0) = E_q[info.localNo[1]].data();\n rhs.execute();\n\n return true;\n}\n\nbool Poisson::rhs_boundary(std::size_t fctNo, FacetInfo const& info, Vector& B0,\n LinearAllocator& scratch) const {\n alignas(ALIGNMENT) double f_q_raw[tensor::f_q::size()];\n if (!bc_boundary(fctNo, info.bc, f_q_raw)) {\n return false;\n }\n\n alignas(ALIGNMENT) double f_lifted_q[tensor::f_lifted_q::size()];\n if (method_ == DGMethod::BR2) {\n alignas(ALIGNMENT) double f_lifted0[tensor::f_lifted::size(0)];\n alignas(ALIGNMENT) double M0[tensor::M::size()];\n compute_inverse_mass_matrix(info.up[0], M0);\n\n alignas(ALIGNMENT) double K_q[tensor::K_q::size(0)];\n compute_K_q(fctNo, info, {K_q, nullptr});\n\n kernel::rhs_lift_boundary lift;\n lift.E_q(0) = E_q[info.localNo[0]].data();\n lift.n_q = fct[fctNo].get().data()->data();\n lift.K_q(0) = K_q;\n lift.Minv(0) = M0;\n lift.f_q = f_q_raw;\n lift.f_lifted(0) = f_lifted0;\n lift.f_lifted_q = f_lifted_q;\n lift.w = fctRule.weights().data();\n lift.execute();\n } else { // IP\n kernel::rhs_lift_ip lift;\n lift.nl_q = fct[fctNo].get().data();\n lift.f_q = f_q_raw;\n lift.f_lifted_q = f_lifted_q;\n lift.execute();\n }\n\n alignas(ALIGNMENT) double K_Dx_q0[tensor::K_Dx_q::size(0)];\n compute_K_Dx_q(fctNo, info, {K_Dx_q0, nullptr});\n\n kernel::rhsFacet rhs;\n rhs.b = B0.data();\n rhs.c10 = epsilon;\n rhs.c20 = penalty(fctNo);\n rhs.f_q = f_q_raw;\n rhs.f_lifted_q = f_lifted_q;\n rhs.n_q = fct[fctNo].get().data()->data();\n rhs.w = fctRule.weights().data();\n rhs.K_Dx_q(0) = K_Dx_q0;\n rhs.E_q(0) = E_q[info.localNo[0]].data();\n rhs.execute();\n return true;\n}\n\nvoid Poisson::apply(std::size_t elNo, mneme::span info, Vector const& x_0,\n std::array, NumFacets> const& x_n,\n Vector& y_0) const {\n\n alignas(ALIGNMENT) double Dx_Q[tensor::Dx_Q::size()];\n kernel::apply_volume av;\n av.Dx_Q = Dx_Q;\n av.Dxi_Q = Dxi_Q.data();\n av.G_Q = vol[elNo].get().data()->data();\n av.J_W_K_Q = volPre[elNo].get().data()->data();\n av.U = x_0.data();\n av.U_new = y_0.data();\n av.execute();\n\n alignas(ALIGNMENT) double n_q_flipped[tensor::n_q::size()];\n alignas(ALIGNMENT) double n_unit_q_flipped[tensor::n_unit_q::size()];\n for (std::size_t f = 0; f < NumFacets; ++f) {\n bool is_skeleton_face = elNo != info[f].lid;\n bool is_fault_or_dirichlet = info[f].bc == BC::Fault || info[f].bc == BC::Dirichlet;\n\n auto fctNo = info[f].fctNo;\n double const* n_q = fct[fctNo].get().data()->data();\n double const* n_unit_q = fct[fctNo].get().data()->data();\n double const* K_G_q0 = fctPre[fctNo].get().data()->data();\n double const* K_G_q1 = fctPre[fctNo].get().data()->data();\n if (is_skeleton_face && info[f].side == 1) {\n std::swap(K_G_q0, K_G_q1);\n\n for (int i = 0; i < tensor::n_q::size(); ++i) {\n n_q_flipped[i] = -n_q[i];\n }\n n_q = n_q_flipped;\n for (int i = 0; i < tensor::n_unit_q::size(); ++i) {\n n_unit_q_flipped[i] = -n_unit_q[i];\n }\n n_unit_q = n_unit_q_flipped;\n }\n\n alignas(ALIGNMENT) double u_hat_q[tensor::u_hat_q::size()] = {};\n alignas(ALIGNMENT) double sigma_hat_q[tensor::sigma_hat_q::size()] = {};\n if (info[f].bc == BC::None || (is_skeleton_face && is_fault_or_dirichlet)) {\n kernel::flux_u_skeleton fu;\n fu.negative_E_q_T(0) = negative_E_q_T[f].data();\n fu.E_q_T(1) = E_q_T[info[f].localNo].data();\n fu.U = x_0.data();\n fu.U_ext = x_n[f].data();\n fu.u_hat_q = u_hat_q;\n fu.execute();\n\n kernel::flux_sigma_skeleton fs;\n fs.c00 = -penalty(fctNo);\n fs.Dxi_q_120(0) = Dxi_q_120[f].data();\n fs.Dxi_q_120(1) = Dxi_q_120[info[f].localNo].data();\n fs.E_q_T(0) = E_q_T[f].data();\n fs.negative_E_q_T(1) = negative_E_q_T[info[f].localNo].data();\n fs.K_G_q(0) = K_G_q0;\n fs.K_G_q(1) = K_G_q1;\n fs.U = x_0.data();\n fs.U_ext = x_n[f].data();\n fs.n_unit_q = n_unit_q;\n fs.sigma_hat_q = sigma_hat_q;\n fs.execute();\n } else if (is_fault_or_dirichlet) {\n kernel::flux_u_boundary fu;\n fu.U = x_0.data();\n fu.u_hat_q = u_hat_q;\n fu.negative_E_q_T(0) = negative_E_q_T[f].data();\n fu.execute();\n\n kernel::flux_sigma_boundary fs;\n fs.c00 = -penalty(fctNo);\n fs.Dxi_q_120(0) = Dxi_q_120[f].data();\n fs.E_q_T(0) = E_q_T[f].data();\n fs.K_G_q(0) = K_G_q0;\n fs.U = x_0.data();\n fs.n_unit_q = n_unit_q;\n fs.sigma_hat_q = sigma_hat_q;\n fs.execute();\n } else {\n continue;\n }\n\n kernel::apply_facet af;\n af.Dxi_q(0) = Dxi_q[f].data();\n af.E_q(0) = E_q[f].data();\n af.K_G_q(0) = K_G_q0;\n af.n_q = n_q;\n af.sigma_hat_q = sigma_hat_q;\n af.u_hat_q = u_hat_q;\n af.U_new = y_0.data();\n af.w = fctRule.weights().data();\n af.execute();\n }\n}\n\nstd::size_t Poisson::flops_apply(std::size_t elNo, mneme::span info) const {\n std::size_t flops = kernel::apply_volume::HardwareFlops;\n for (std::size_t f = 0; f < NumFacets; ++f) {\n bool is_skeleton_face = elNo != info[f].lid;\n bool is_fault_or_dirichlet = info[f].bc == BC::Fault || info[f].bc == BC::Dirichlet;\n if (info[f].bc == BC::None || (is_skeleton_face && is_fault_or_dirichlet)) {\n flops += kernel::flux_u_skeleton::HardwareFlops;\n flops += kernel::flux_sigma_skeleton::HardwareFlops;\n } else if (is_fault_or_dirichlet) {\n flops += kernel::flux_u_boundary::HardwareFlops;\n flops += kernel::flux_sigma_boundary::HardwareFlops;\n } else {\n continue;\n }\n flops += kernel::apply_facet::HardwareFlops;\n }\n return flops;\n}\n\nvoid Poisson::coefficients_volume(std::size_t elNo, Matrix& C,\n LinearAllocator&) const {\n auto const coeff_K = material[elNo].get();\n assert(coeff_K.size() == C.shape(0));\n for (std::size_t i = 0; i < coeff_K.size(); ++i) {\n C(i, 0) = coeff_K[i];\n }\n}\n\nTensorBase> Poisson::tractionResultInfo() const {\n return TensorBase>(tensor::grad_u::Shape[0], tensor::grad_u::Shape[1]);\n}\n\nvoid Poisson::traction_skeleton(std::size_t fctNo, FacetInfo const& info, Vector& u0,\n Vector& u1, Matrix& result) const {\n assert(result.size() == tensor::grad_u::size());\n\n alignas(ALIGNMENT) double f_q_raw[tensor::f_q::size()];\n bc_skeleton(fctNo, info.bc, f_q_raw);\n\n alignas(ALIGNMENT) double K_Dx_q0[tensor::K_Dx_q::size(0)];\n alignas(ALIGNMENT) double K_Dx_q1[tensor::K_Dx_q::size(1)];\n compute_K_Dx_q(fctNo, info, {K_Dx_q0, K_Dx_q1});\n\n kernel::grad_u krnl;\n krnl.c00 = -penalty(fctNo);\n krnl.K_Dx_q(0) = K_Dx_q0;\n krnl.K_Dx_q(1) = K_Dx_q1;\n krnl.E_q(0) = E_q[info.localNo[0]].data();\n krnl.E_q(1) = E_q[info.localNo[1]].data();\n krnl.f_q = f_q_raw;\n krnl.grad_u = result.data();\n krnl.n_unit_q = fct[fctNo].get().data()->data();\n krnl.u(0) = u0.data();\n krnl.u(1) = u1.data();\n krnl.execute();\n}\n\nvoid Poisson::traction_boundary(std::size_t fctNo, FacetInfo const& info, Vector& u0,\n Matrix& result) const {\n assert(result.size() == tensor::grad_u::size());\n\n alignas(ALIGNMENT) double f_q_raw[tensor::f_q::size()];\n bc_boundary(fctNo, info.bc, f_q_raw);\n\n alignas(ALIGNMENT) double K_Dx_q0[tensor::K_Dx_q::size(0)];\n compute_K_Dx_q(fctNo, info, {K_Dx_q0, nullptr});\n\n kernel::grad_u_bnd krnl;\n krnl.c00 = -penalty(fctNo);\n krnl.K_Dx_q(0) = K_Dx_q0;\n krnl.E_q(0) = E_q[info.localNo[0]].data();\n krnl.f_q = f_q_raw;\n krnl.grad_u = result.data();\n krnl.n_unit_q = fct[fctNo].get().data()->data();\n krnl.u(0) = u0.data();\n krnl.execute();\n}\n\n} // namespace tndm\n", "meta": {"hexsha": "dd0d69fd44ae3adc393257d24aaa31c1f0b16884", "size": 26667, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "app/localoperator/Poisson.cpp", "max_stars_repo_name": "NicoSchlw/tandem", "max_stars_repo_head_hexsha": "3a08b5a7ae391c1675c5cbfdad77260d4a0115cc", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 4.0, "max_stars_repo_stars_event_min_datetime": "2021-11-03T17:11:00.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-16T07:51:01.000Z", "max_issues_repo_path": "app/localoperator/Poisson.cpp", "max_issues_repo_name": "NicoSchlw/tandem", "max_issues_repo_head_hexsha": "3a08b5a7ae391c1675c5cbfdad77260d4a0115cc", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 12.0, "max_issues_repo_issues_event_min_datetime": "2020-05-18T14:51:13.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-16T12:56:31.000Z", "max_forks_repo_path": "app/localoperator/Poisson.cpp", "max_forks_repo_name": "NicoSchlw/tandem", "max_forks_repo_head_hexsha": "3a08b5a7ae391c1675c5cbfdad77260d4a0115cc", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2020-10-23T08:04:42.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-15T12:23:59.000Z", "avg_line_length": 36.7820689655, "max_line_length": 99, "alphanum_fraction": 0.5899051262, "num_tokens": 8317, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7025300573952054, "lm_q2_score": 0.34158248603300034, "lm_q1q2_score": 0.23997196351796066}} {"text": "/**\n * Copyright (c) 2011, 2012\n * Claudio Kopper \n * and the IceCube Collaboration \n *\n * Permission to use, copy, modify, and/or distribute this software for any\n * purpose with or without fee is hereby granted, provided that the above\n * copyright notice and this permission notice appear in all copies.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES\n * WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF\n * MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY\n * SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES\n * WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION\n * OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN\n * CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.\n *\n *\n * $Id: I3CLSimModuleHelper.cxx 178168 2019-12-19 21:40:33Z jvansanten $\n *\n * @file I3CLSimModuleHelper.cxx\n * @version $Revision: 178168 $\n * @date $Date: 2019-12-19 14:40:33 -0700 (Thu, 19 Dec 2019) $\n * @author Claudio Kopper\n */\n\n#ifndef __STDC_FORMAT_MACROS\n#define __STDC_FORMAT_MACROS\n#endif\n#include \n\n#include \"clsim/I3CLSimModuleHelper.h\"\n\n#include \"clsim/function/I3CLSimFunctionConstant.h\"\n#include \"clsim/function/I3CLSimFunctionFromTable.h\"\n#include \"clsim/function/I3CLSimFunctionDeltaPeak.h\"\n#include \"clsim/random_value/I3CLSimRandomValueInterpolatedDistribution.h\"\n#include \"clsim/random_value/I3CLSimRandomValueWlenCherenkovNoDispersion.h\"\n#include \"clsim/random_value/I3CLSimRandomValueConstant.h\"\n\n#include \n#include \n#include \n\n#include \"dataclasses/physics/I3MCTree.h\"\n#include \"dataclasses/physics/I3MCTreeUtils.h\"\n\n\nnamespace I3CLSimModuleHelper {\n \n namespace {\n double CherenkovYieldDistribution(double wlen, I3CLSimMediumPropertiesConstPtr mediumProperties, double beta=1.)\n {\n I3CLSimFunctionConstPtr nPhaseDist =\n mediumProperties->GetPhaseRefractiveIndex(0); // this assumes the refractive index does not change between layers\n \n if (!nPhaseDist->HasNativeImplementation()) \n log_fatal(\"The refractive index distribution needs a native implementation to be usable!\");\n \n const double nPhase = nPhaseDist->GetValue(wlen);\n \n return (2.*M_PI/(137.*(wlen*wlen)))*(1. - 1./ ( std::pow(beta*nPhase,2.) ) ); // dN/dxdwlen\n }\n\n // the normalization will not be correct here\n double CherenkovYieldDistributionNoDispersion(double wlen)\n {\n return 1./(wlen*wlen); // dN/dxdwlen\n }\n\n };\n \n \n I3CLSimRandomValueConstPtr\n makeWavelengthGenerator(I3CLSimFunctionConstPtr unbiasedSpectrum,\n I3CLSimFunctionConstPtr wavelengthGenerationBias,\n I3CLSimMediumPropertiesConstPtr mediumProperties)\n {\n {\n // special handling for delta peaks\n I3CLSimFunctionDeltaPeakConstPtr deltaPeak =\n boost::dynamic_pointer_cast(unbiasedSpectrum);\n if (deltaPeak) {\n const double peakPosition = deltaPeak->GetPeakPosition();\n \n return I3CLSimRandomValueConstantConstPtr\n (new I3CLSimRandomValueConstant(peakPosition));\n }\n }\n\n // if we get here, it's not a delta peak\n \n double minWlen = unbiasedSpectrum->GetMinWlen();\n double maxWlen = unbiasedSpectrum->GetMaxWlen();\n \n // check if the spectrum is from a tabulated distribution (instead of\n // a parameterized one)\n I3CLSimFunctionFromTableConstPtr unbiasedSpectrumFromTable;\n unbiasedSpectrumFromTable =\n boost::dynamic_pointer_cast(unbiasedSpectrum);\n\n if (!unbiasedSpectrumFromTable) {\n // do not clip wavelengths if they are from a tabulated distribution. In that case,\n // re-use the entire table binning\n if (mediumProperties->GetMinWavelength() > minWlen) minWlen=mediumProperties->GetMinWavelength();\n if (mediumProperties->GetMaxWavelength() < maxWlen) maxWlen=mediumProperties->GetMaxWavelength();\n }\n \n const double wlenRange = maxWlen-minWlen;\n if (wlenRange <= 0.) log_fatal(\"Internal error, wavelength range <= 0!\");\n\n if (wavelengthGenerationBias->GetMinWlen() > minWlen)\n log_fatal(\"wavelength generation bias has to have a wavelength range larger or equal to the spectrum wavelength range!\");\n if (wavelengthGenerationBias->GetMaxWlen() < maxWlen)\n log_fatal(\"wavelength generation bias has to have a wavelength range larger or equal to the spectrum wavelength range!\");\n\n // Check if the spectrum values are from a tabulated distribution.\n // If yes, use the table binning, if not make up a binning.\n if (unbiasedSpectrumFromTable)\n {\n std::size_t wlenPoints = unbiasedSpectrumFromTable->GetNumEntries();\n const double firstWlen = unbiasedSpectrumFromTable->GetFirstWavelength();\n \n std::vector spectrum(wlenPoints, NAN);\n std::vector wavelengths(wlenPoints, NAN);\n for (std::size_t i=0;iGetEntryWavelength(i);\n const double entry = unbiasedSpectrumFromTable->GetEntryValue(i);\n const double bias = wavelengthGenerationBias->GetValue(wavelength);\n \n spectrum[i] = bias * entry;\n wavelengths[i] = wavelength;\n }\n \n if (unbiasedSpectrumFromTable->GetInEqualSpacingMode()) {\n const double wlenStep = unbiasedSpectrumFromTable->GetWavelengthStepping();\n\n return I3CLSimRandomValueInterpolatedDistributionConstPtr\n (new I3CLSimRandomValueInterpolatedDistribution(firstWlen,\n wlenStep,\n spectrum));\n } else {\n // slightly less efficient if non-equally spaced\n return I3CLSimRandomValueInterpolatedDistributionConstPtr\n (new I3CLSimRandomValueInterpolatedDistribution(wavelengths,\n spectrum));\n }\n }\n else\n {\n // use a pre-defined binning of 10ns (an arbitrary value..)\n \n std::size_t wlenPoints = static_cast(wlenRange/(10.*I3Units::nanometer))+2;\n const double firstWlen = minWlen;\n const double wlenStep = wlenRange/static_cast(wlenPoints-1);\n \n std::vector spectrum(wlenPoints, NAN);\n for (std::size_t i=0;i(i)*wlenStep;\n const double entry = unbiasedSpectrum->GetValue(wavelength);\n const double bias = wavelengthGenerationBias->GetValue(wavelength);\n \n spectrum[i] = bias * entry;\n }\n \n return I3CLSimRandomValueInterpolatedDistributionConstPtr\n (new I3CLSimRandomValueInterpolatedDistribution(firstWlen,\n wlenStep,\n spectrum));\n }\n }\n \n I3CLSimRandomValueConstPtr\n makeCherenkovWavelengthGenerator(I3CLSimFunctionConstPtr wavelengthGenerationBias,\n bool generateCherenkovPhotonsWithoutDispersion,\n I3CLSimMediumPropertiesConstPtr mediumProperties)\n {\n const double minWlen = mediumProperties->GetMinWavelength();\n const double maxWlen = mediumProperties->GetMaxWavelength();\n const double wlenRange = maxWlen-minWlen;\n if (wlenRange <= 0.) log_fatal(\"Internal error, wavelength range <= 0!\");\n\n if (wavelengthGenerationBias->GetMinWlen() > minWlen)\n log_fatal(\"wavelength generation bias has to have a wavelength range larger or equal to the medium property range!\");\n if (wavelengthGenerationBias->GetMaxWlen() < maxWlen)\n log_fatal(\"wavelength generation bias has to have a wavelength range larger or equal to the medium property range!\");\n \n bool noBias=false;\n //bool biasIsConstant=false;\n \n {\n I3CLSimFunctionConstantConstPtr wavelengthGenerationBiasConstant =\n boost::dynamic_pointer_cast(wavelengthGenerationBias);\n \n if (wavelengthGenerationBiasConstant)\n {\n //biasIsConstant=true;\n \n if ( std::abs(wavelengthGenerationBiasConstant->GetValue((minWlen+maxWlen)/2.)-1.) < 1e-10 )\n noBias=true;\n }\n }\n \n I3CLSimFunctionFromTableConstPtr wavelengthGenerationBiasFromTable;\n wavelengthGenerationBiasFromTable =\n boost::dynamic_pointer_cast(wavelengthGenerationBias);\n \n \n\n if ((!noBias) && (generateCherenkovPhotonsWithoutDispersion))\n {\n log_warn(\"**********\");\n log_warn(\" Using the \\\"GenerateCherenkovPhotonsWithoutDispersion\\\" option\");\n log_warn(\" with a biased photon spectrum generation does not yield a performance\");\n log_warn(\" increase. You might consider turning this option off to get a better\");\n log_warn(\" approximation of the Cherenkov spectrum.\");\n log_warn(\"**********\");\n }\n \n // Check if the bias values are from a tabulated distribution.\n // If yes, use the table binning, if not make up a binning.\n if (wavelengthGenerationBiasFromTable)\n {\n std::size_t wlenPoints = wavelengthGenerationBiasFromTable->GetNumEntries();\n const double firstWlen = wavelengthGenerationBiasFromTable->GetFirstWavelength();\n \n std::vector spectrum(wlenPoints, NAN);\n std::vector wavelengths(wlenPoints, NAN);\n for (std::size_t i=0;iGetEntryWavelength(i);\n const double bias = wavelengthGenerationBiasFromTable->GetEntryValue(i);\n \n if (generateCherenkovPhotonsWithoutDispersion)\n {\n spectrum[i] =\n bias * CherenkovYieldDistributionNoDispersion(wavelength);\n }\n else\n {\n spectrum[i] =\n bias * CherenkovYieldDistribution(wavelength, mediumProperties);\n }\n \n wavelengths[i] = wavelength;\n }\n \n if (wavelengthGenerationBiasFromTable->GetInEqualSpacingMode()) {\n const double wlenStep = wavelengthGenerationBiasFromTable->GetWavelengthStepping();\n\n return I3CLSimRandomValueInterpolatedDistributionConstPtr\n (new I3CLSimRandomValueInterpolatedDistribution(firstWlen,\n wlenStep,\n spectrum));\n } else {\n // slightly less efficient if non-equally spaced\n return I3CLSimRandomValueInterpolatedDistributionConstPtr\n (new I3CLSimRandomValueInterpolatedDistribution(wavelengths,\n spectrum));\n }\n }\n else if ((noBias) && (generateCherenkovPhotonsWithoutDispersion))\n {\n return I3CLSimRandomValueWlenCherenkovNoDispersionConstPtr\n (new I3CLSimRandomValueWlenCherenkovNoDispersion(minWlen, maxWlen));\n }\n else\n {\n std::size_t wlenPoints = static_cast(wlenRange/(10.*I3Units::nanometer))+2;\n const double firstWlen = minWlen;\n const double wlenStep = wlenRange/static_cast(wlenPoints-1);\n \n std::vector spectrum(wlenPoints, NAN);\n for (std::size_t i=0;i(i)*wlenStep;\n const double bias = wavelengthGenerationBias->GetValue(wavelength);\n \n if (generateCherenkovPhotonsWithoutDispersion)\n {\n spectrum[i] =\n bias * CherenkovYieldDistributionNoDispersion(wavelength);\n }\n else\n {\n spectrum[i] =\n bias * CherenkovYieldDistribution(wavelength, mediumProperties);\n }\n }\n\n return I3CLSimRandomValueInterpolatedDistributionConstPtr\n (new I3CLSimRandomValueInterpolatedDistribution(firstWlen,\n wlenStep,\n spectrum));\n }\n\n \n }\n\n \n I3CLSimStepToPhotonConverterOpenCLPtr initializeOpenCL(const I3CLSimOpenCLDevice &device,\n I3RandomServicePtr rng,\n I3CLSimSimpleGeometryConstPtr geometry,\n I3CLSimMediumPropertiesConstPtr medium,\n I3CLSimFunctionConstPtr wavelengthGenerationBias,\n const std::vector &wavelengthGenerators,\n bool enableDoubleBuffering,\n bool doublePrecision,\n bool stopDetectedPhotons,\n bool saveAllPhotons,\n double saveAllPhotonsPrescale,\n double fixedNumberOfAbsorptionLengths,\n double pancakeFactor,\n uint32_t photonHistoryEntries,\n uint32_t limitWorkgroupSize)\n {\n I3CLSimStepToPhotonConverterOpenCLPtr conv(new I3CLSimStepToPhotonConverterOpenCL(rng, device.GetUseNativeMath()));\n\n conv->SetDevice(device);\n\n conv->SetWlenGenerators(wavelengthGenerators);\n conv->SetWlenBias(wavelengthGenerationBias);\n\n conv->SetMediumProperties(medium);\n conv->SetGeometry(geometry);\n\n conv->SetEnableDoubleBuffering(enableDoubleBuffering);\n conv->SetDoublePrecision(doublePrecision);\n conv->SetStopDetectedPhotons(stopDetectedPhotons);\n conv->SetSaveAllPhotons(saveAllPhotons);\n conv->SetSaveAllPhotonsPrescale(saveAllPhotonsPrescale);\n\n conv->SetFixedNumberOfAbsorptionLengths(fixedNumberOfAbsorptionLengths);\n conv->SetDOMPancakeFactor(pancakeFactor);\n\n conv->SetPhotonHistoryEntries(photonHistoryEntries);\n\n conv->Compile();\n //log_trace(\"%s\", conv.GetFullSource().c_str());\n \n std::size_t maxWorkgroupSize = conv->GetMaxWorkgroupSize();\n if (limitWorkgroupSize!=0) {\n maxWorkgroupSize = std::min(static_cast(limitWorkgroupSize), maxWorkgroupSize);\n }\n \n conv->SetWorkgroupSize(maxWorkgroupSize);\n const std::size_t workgroupSize = conv->GetWorkgroupSize();\n \n // use approximately the given number of work items, convert to a multiple of the workgroup size\n std::size_t maxNumWorkitems = (static_cast(device.GetApproximateNumberOfWorkItems())/workgroupSize)*workgroupSize;\n if (maxNumWorkitems==0) maxNumWorkitems=workgroupSize;\n \n conv->SetMaxNumWorkitems(maxNumWorkitems);\n\n log_info(\"Using OpenCL device: platform=%s device=%s isgpu=%d\", \n device.GetPlatformName().c_str(),\n device.GetDeviceName().c_str(),\n device.IsGPU());\n log_info(\"maximum workgroup size is %zu\", maxWorkgroupSize);\n log_info(\"configured workgroup size is %zu\", workgroupSize);\n if (maxNumWorkitems != device.GetApproximateNumberOfWorkItems()) {\n log_info(\"maximum number of work items is %zu (user configured was %\" PRIu32 \")\", maxNumWorkitems, device.GetApproximateNumberOfWorkItems());\n } else {\n log_debug(\"maximum number of work items is %zu (user configured was %\" PRIu32 \")\", maxNumWorkitems, device.GetApproximateNumberOfWorkItems());\n }\n\n conv->Initialize();\n \n return conv;\n }\n\n}\n", "meta": {"hexsha": "ce5e3825f5f70b096c4d39843a451b45ed6c5690", "size": 17564, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "clsim/private/clsim/I3CLSimModuleHelper.cxx", "max_stars_repo_name": "hschwane/offline_production", "max_stars_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-12-24T22:00:01.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-24T22:00:01.000Z", "max_issues_repo_path": "clsim/private/clsim/I3CLSimModuleHelper.cxx", "max_issues_repo_name": "hschwane/offline_production", "max_issues_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "clsim/private/clsim/I3CLSimModuleHelper.cxx", "max_forks_repo_name": "hschwane/offline_production", "max_forks_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2020-07-17T09:20:29.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-30T16:44:18.000Z", "avg_line_length": 46.8373333333, "max_line_length": 154, "alphanum_fraction": 0.5963903439, "num_tokens": 3593, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6001883592602049, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.23996229266874805}} {"text": "// File: finetune_classifier.cc\n// Author: Karl Moritz Hermann (mail@karlmoritz.com)\n// Created: 29-01-2013\n// Last Update: Thu 03 Oct 2013 11:26:25 AM BST\n\n// STL\n#include \n#include \n#include \n#include \n\n// Boost\n#include \n#include \n\n// L-BFGS\n#include \n\n// Local\n#include \"finetune_classifier.h\"\n#include \"models.h\"\n#include \"fast_math.h\"\n\nusing namespace std;\nnamespace bpo = boost::program_options;\n\nFinetuneClassifier::FinetuneClassifier(RecursiveAutoencoderBase& rae,\n TrainingCorpus& trainC, float lambdaF, float alpha,\n int dynamic_mode, int iterations) : lambda(lambdaF), alpha_rae(alpha),\n mode(dynamic_mode), iterations(iterations), it_count(0), num_batches(100),\n eta(0.1) {\n\n /***************************************************************************\n * Define a couple of frequently needed variables *\n ***************************************************************************/\n\n train_length = min(rae.config.num_sentences,int(trainC.size()));\n bool use_full_corpus = false;\n if (train_length == 0)\n {\n train_length = trainC.size();\n use_full_corpus = true;\n }\n\n label_width = rae.config.label_class_size;\n num_label_types = rae.config.num_label_types; // 1\n\n int multiplier = 1;\n if (mode == 0)\n multiplier = 2;\n if (mode == 3)\n multiplier = 3; // only works for compound test\n if (mode == 4)\n multiplier = 4;\n // Embedding: s1 + s2 + cos_sim(s1,s2) + len(s1) + len(s2) +\n // unigram_overlap(s1,s2) following Blacoe/Lapata 2012\n dynamic_embedding_size = multiplier * rae.config.word_representation_size;\n\n theta_size_ = dynamic_embedding_size * label_width * num_label_types + label_width * num_label_types;\n\n trainI_ = new Real[train_length * dynamic_embedding_size]();\n theta_ = new Real[theta_size_];\n WeightVectorType theta(theta_,theta_size_);\n theta.setZero();\n if (true) {\n std::random_device rd;\n std::mt19937 gen(rd());\n //std::mt19937 gen(0);\n float r = sqrt( 6.0 / dynamic_embedding_size);\n std::uniform_real_distribution<> dis(-r,r);\n for (int i=0; iforwardPropagate(true);\n propagator->setDynamic(trainData[i].vector,mode);\n //cout << \"C: \" << trainData[i].vector[0] << endl;\n\n delete propagator;\n }\n}\n\nvoid FinetuneClassifier::evaluate()\n{\n\n vector& data = trainData;\n int length = train_length;\n\n int right = 0;\n int wrong = 0;\n int tp = 0;\n int fp = 0;\n int tn = 0;\n int fn = 0;\n\n#pragma omp parallel for schedule(dynamic)\n for (auto i = 0; i 0.5)\n {\n wrong += 1;\n if(data[i].label == 0) ++fp;\n else ++fn;\n }\n else\n {\n right += 1;\n if(data[i].label == 0) ++tn;\n else ++tp;\n }\n }\n }\n cout << right << \"/\" << right + wrong << \" \";\n Real precision = 1.0 * tp / (tp + fp);\n Real recall = 1.0 * tp / (tp + fn);\n Real accuracy = 1.0 * (tp + tn) / (tp + tn + fp + fn);\n Real f1score = 2.0 * (precision * recall) / (precision + recall);\n cout << \"Acc/F1: \" << accuracy << \" \" << f1score << endl;\n}\n\nvoid FinetuneClassifier::trainLbfgs(LineSearchType linesearch)\n{\n batch_from = 0;\n batch_to = train_length;\n\n lbfgs_parameter_t param;\n lbfgs_parameter_init(¶m);\n param.linesearch = linesearch;\n param.max_iterations = iterations;\n //param.epsilon = 0.00000001;\n param.m = 25;\n\n const int n = theta_size_;\n auto vars = theta_;\n Real error = 0.0;\n\n int tries = 0;\n\n while (tries < 3 and it_count < 250)\n {\n int ret = lbfgs(n, vars, &error, lbfgs_evaluate_, lbfgs_progress_, this, ¶m);\n cout << \"L-BFGS optimization terminated with status code = \" << ret << endl;\n cout << \"fx=\" << error << endl;\n ++tries;\n }\n}\n\nvoid FinetuneClassifier::trainAdaGrad()\n{\n auto vars = theta_;\n int number_vars = theta_size_;\n\n WeightArrayType theta(vars,number_vars);\n\n Real* Gt_d = new Real[number_vars];\n Real* Ginv_d = new Real[number_vars];\n WeightArrayType Gt(Gt_d,number_vars);\n WeightArrayType Ginv(Ginv_d,number_vars);\n Gt.setZero();\n\n Real* data1 = new Real[number_vars];\n\n int batchsize = (train_length / num_batches) + 1;\n cout << \"Batch size: \" << batchsize << endl;\n\n for (auto iteration = 0; iteration < iterations; ++iteration)\n {\n //unsigned seed = std::chrono::system_clock::now().time_since_epoch().count();\n random_shuffle (mix.begin(), mix.end()); //, std::default_random_engine(seed));\n\n for (auto batch = 0; batch < num_batches; ++batch)\n {\n batch_from = batch*batchsize;\n batch_to = min((batch+1)*batchsize,train_length);\n\n if (batch_to - batch_from > 0)\n {\n WeightArrayType grad(data1,number_vars);\n\n //float err =\n finetuneCostAndGrad_(theta_,data1,number_vars);\n //grad /= (batch_to - batch_from);\n Gt += grad*grad;\n for (int i=0;i(instance)->finetuneCostAndGrad_(x, g, n);\n}\n\nint FinetuneClassifier::lbfgs_progress_(\n void *instance,\n const lbfgsfloatval_t *x,\n const lbfgsfloatval_t *g,\n const lbfgsfloatval_t fx,\n const lbfgsfloatval_t xnorm,\n const lbfgsfloatval_t gnorm,\n const lbfgsfloatval_t step,\n int n,\n int k,\n int ls\n )\n{\n cout << \"N: \" << n << endl;\n printf(\"Iteration %d:\\n\", k);\n printf(\" fx = %f, x[0] = %f, x[1] = %f %f\\n\", fx, x[0], x[1], x[2]);\n printf(\" fx = %f, g[0] = %f, g[1] = %f %f\\n\", fx, g[0], g[1], g[2]);\n printf(\" xnorm = %f, gnorm = %f, step = %f\\n\", xnorm, gnorm, step);\n printf(\"\\n\");\n\n\n reinterpret_cast(instance)->it_count++;\n reinterpret_cast(instance)->evaluate();\n return 0;\n}\n\n// ForwardPropagates and returns error and gradient on self\nlbfgsfloatval_t FinetuneClassifier::finetuneCostAndGrad_(\n const lbfgsfloatval_t *x,\n lbfgsfloatval_t *gradient_location,\n const int n)\n{\n\n WeightVectorType grad(gradient_location,theta_size_);\n grad.setZero();\n\n WeightMatricesType Wcatgrad;\n WeightVectorsType Bcatgrad;\n\n Real* ptr = gradient_location;\n for (auto i=0; i 0.5)\n {\n wrong += 1;\n if(trainData[i].label == 0)\n ++fp;\n else\n ++fn;\n }\n else\n {\n right += 1;\n if(trainData[i].label == 0)\n ++tn;\n else\n ++tp;\n }\n }\n }\n\n /*\n *cout << \"Correct: \" << right << \"/\" << right + wrong << \" \";\n *cout << \"Zero: \" << tp << \"/\" << tp+fn << \" \";\n *cout << \"One: \" << tn << \"/\" << tn+fp << endl;\n */\n\n float lambda_partial = lambda * (batch_to - batch_from) / train_length;\n Wcatgrad[0] += lambda_partial*Wcat[0];\n cost += 0.5*lambda_partial*(Wcat[0].cwiseProduct(Wcat[0])).sum();\n\n Wcatgrad[0] /= (batch_to - batch_from);\n cost /= (batch_to - batch_from);\n\n return cost;\n}\n\n\nFinetuneClassifier::~FinetuneClassifier()\n{\n delete [] trainI_;\n delete [] theta_;\n\n}\n", "meta": {"hexsha": "bb37a94595922002f94e6a9a405fea2ec2e8db25", "size": 10574, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/common/finetune_classifier.cc", "max_stars_repo_name": "karlmoritz/oxcvsm", "max_stars_repo_head_hexsha": "02fd78231a8b3c4d48e9e759a3a075d04bcd0529", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 10.0, "max_stars_repo_stars_event_min_datetime": "2015-02-06T01:41:41.000Z", "max_stars_repo_stars_event_max_datetime": "2017-10-06T15:50:32.000Z", "max_issues_repo_path": "src/common/finetune_classifier.cc", "max_issues_repo_name": "karlmoritz/oxcvsm", "max_issues_repo_head_hexsha": "02fd78231a8b3c4d48e9e759a3a075d04bcd0529", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/common/finetune_classifier.cc", "max_forks_repo_name": "karlmoritz/oxcvsm", "max_forks_repo_head_hexsha": "02fd78231a8b3c4d48e9e759a3a075d04bcd0529", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2015-01-02T10:49:15.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-30T17:59:22.000Z", "avg_line_length": 26.9744897959, "max_line_length": 104, "alphanum_fraction": 0.5860601475, "num_tokens": 3029, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6001883449573377, "lm_q2_score": 0.3998116407397951, "lm_q1q2_score": 0.2399622869502953}} {"text": "#ifndef BOOST_NUMERIC_CHECKED_RESULT_OPERATIONS\n#define BOOST_NUMERIC_CHECKED_RESULT_OPERATIONS\n\n// Copyright (c) 2012 Robert Ramey\n//\n// Distributed under the Boost Software License, Version 1.0. (See\n// accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// Implemenation of arithmetic on \"extended\" integers.\n// Extended integers are defined in terms of C++ primitive integers as\n// a) an interger range\n// b) extra elements +inf, -inf, indeterminate\n//\n// Integer operations are closed on the set of extended integers\n// but operations are not necessarily associative when they result in the\n// extensions +inf, -inf, and indeterminate\n//\n// in this code, the type \"checked_result\" where T is some\n// integer type is an \"extended\" integer.\n\n#include \n\n#include \n\n#include \"checked_result.hpp\"\n#include \"checked_integer.hpp\"\n\n//////////////////////////////////////////////////////////////////////////\n// the following idea of \"value_type\" is used by several of the operations\n// defined by checked_result arithmetic.\n\nnamespace boost {\nnamespace safe_numerics {\n\ntemplate\nconstexpr inline void display(const boost::safe_numerics::checked_result & c){\n switch(c.m_e){\n case safe_numerics_error::success:\n std::terminate();\n case safe_numerics_error::positive_overflow_error: // result is above representational maximum\n std::terminate();\n case safe_numerics_error::negative_overflow_error: // result is below representational minimum\n std::terminate();\n case safe_numerics_error::domain_error: // one operand is out of valid range\n std::terminate();\n case safe_numerics_error::range_error: // result cannot be produced for this operation\n std::terminate();\n case safe_numerics_error::precision_overflow_error: // result lost precision\n std::terminate();\n case safe_numerics_error::underflow_error: // result is too small to be represented\n std::terminate();\n case safe_numerics_error::negative_value_shift: // negative value in shift operator\n std::terminate();\n case safe_numerics_error::negative_shift: // shift a negative value\n std::terminate();\n case safe_numerics_error::shift_too_large: // l/r shift exceeds variable size\n std::terminate();\n case safe_numerics_error::uninitialized_value: // creating of uninitialized value\n std::terminate();\n }\n}\n\n//////////////////////////////////////////////////////////////////////////\n// implement C++ operators for check_result\n\nstruct sum_value_type {\n // characterization of various values\n const enum flag {\n known_value = 0,\n less_than_min,\n greater_than_max,\n indeterminate,\n count\n } m_flag;\n template\n constexpr flag to_flag(const checked_result & t) const {\n switch(static_cast(t)){\n case safe_numerics_error::success:\n return known_value;\n case safe_numerics_error::negative_overflow_error:\n // result is below representational minimum\n return less_than_min;\n case safe_numerics_error::positive_overflow_error:\n // result is above representational maximum\n return greater_than_max;\n default:\n return indeterminate;\n }\n }\n template\n constexpr sum_value_type(const checked_result & t) :\n m_flag(to_flag(t))\n {}\n constexpr operator std::uint8_t () const {\n return static_cast(m_flag);\n }\n};\n\n// integers addition\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator+(\n const checked_result & t,\n const checked_result & u\n){\n using value_type = sum_value_type;\n const std::uint8_t order = static_cast(value_type::count);\n\n // note major pain. Clang constexpr multi-dimensional array is fine.\n // but gcc doesn't permit a multi-dimensional array to be be constexpr.\n // so we need to some ugly gymnastics to make our system work for all\n // all systems.\n const enum safe_numerics_error result[order * order] = {\n // t == known_value\n //{\n // u == ...\n safe_numerics_error::success, // known_value,\n safe_numerics_error::negative_overflow_error, // less_than_min,\n safe_numerics_error::positive_overflow_error, // greater_than_max,\n safe_numerics_error::range_error, // indeterminate,\n //},\n // t == less_than_min,\n //{\n // u == ...\n safe_numerics_error::negative_overflow_error, // known_value,\n safe_numerics_error::negative_overflow_error, // less_than_min,\n safe_numerics_error::range_error, // greater_than_max,\n safe_numerics_error::range_error, // indeterminate,\n //},\n // t == greater_than_max,\n //{\n // u == ...\n safe_numerics_error::positive_overflow_error, // known_value,\n safe_numerics_error::range_error, // less_than_min,\n safe_numerics_error::positive_overflow_error, // greater_than_max,\n safe_numerics_error::range_error, // indeterminate,\n //},\n // t == indeterminate,\n //{\n // u == ...\n safe_numerics_error::range_error, // known_value,\n safe_numerics_error::range_error, // less_than_min,\n safe_numerics_error::range_error, // greater_than_max,\n safe_numerics_error::range_error, // indeterminate,\n //},\n };\n\n const value_type tx(t);\n const value_type ux(u);\n\n const safe_numerics_error e = result[tx * order + ux];\n if(safe_numerics_error::success == e)\n return checked::add(t, u);\n return checked_result(e, \"addition result\");\n}\n\n// unary +\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator+(\n const checked_result & t\n){\n return t;\n}\n\n// integers subtraction\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator-(\n const checked_result & t,\n const checked_result & u\n){\n using value_type = sum_value_type;\n constexpr const std::uint8_t order = static_cast(value_type::count);\n\n constexpr const enum safe_numerics_error result[order * order] = {\n // t == known_value\n //{\n // u == ...\n safe_numerics_error::success, // known_value,\n safe_numerics_error::positive_overflow_error, // less_than_min,\n safe_numerics_error::negative_overflow_error, // greater_than_max,\n safe_numerics_error::range_error, // indeterminate,\n //},\n // t == less_than_min,\n //{\n // u == ...\n safe_numerics_error::negative_overflow_error, // known_value,\n safe_numerics_error::range_error, // less_than_min,\n safe_numerics_error::negative_overflow_error, // greater_than_max,\n safe_numerics_error::range_error, // indeterminate,\n //},\n // t == greater_than_max,\n //{\n // u == ...\n safe_numerics_error::positive_overflow_error, // known_value,\n safe_numerics_error::positive_overflow_error, // less_than_min,\n safe_numerics_error::range_error, // greater_than_max,\n safe_numerics_error::range_error, // indeterminate,\n //},\n // t == indeterminate,\n //{\n // u == ...\n safe_numerics_error::range_error, // known_value,\n safe_numerics_error::range_error, // less_than_min,\n safe_numerics_error::range_error, // greater_than_max,\n safe_numerics_error::range_error, // indeterminate,\n //},\n };\n\n const value_type tx(t);\n const value_type ux(u);\n\n const safe_numerics_error e = result[tx * order + ux];\n if(safe_numerics_error::success == e)\n return checked::subtract(t, u);\n return checked_result(e, \"subtraction result\");\n}\n\n// unary -\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator-(\n const checked_result & t\n){\n// assert(false);\n return checked_result(0) - t;\n}\n\nstruct product_value_type {\n // characterization of various values\n const enum flag {\n less_than_min = 0,\n less_than_zero,\n zero,\n greater_than_zero,\n greater_than_max,\n indeterminate,\n // count of number of cases for values\n count,\n // temporary values for special cases\n t_value,\n u_value,\n z_value\n } m_flag;\n template\n constexpr flag to_flag(const checked_result & t) const {\n switch(static_cast(t)){\n case safe_numerics_error::success:\n return (t < checked_result(0))\n ? less_than_zero\n : (t > checked_result(0))\n ? greater_than_zero\n : zero;\n case safe_numerics_error::negative_overflow_error:\n // result is below representational minimum\n return less_than_min;\n case safe_numerics_error::positive_overflow_error:\n // result is above representational maximum\n return greater_than_max;\n default:\n return indeterminate;\n }\n }\n template\n constexpr product_value_type(const checked_result & t) :\n m_flag(to_flag(t))\n {}\n constexpr operator std::uint8_t () const {\n return static_cast(m_flag);\n }\n};\n\n// integers multiplication\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator*(\n const checked_result & t,\n const checked_result & u\n){\n using value_type = product_value_type;\n const std::uint8_t order = static_cast(value_type::count);\n\n constexpr const enum value_type::flag result[order * order] = {\n // t == less_than_min\n //{\n // u == ...\n value_type::greater_than_max, // less_than_min,\n value_type::greater_than_max, // less_than_zero,\n value_type::zero, // zero,\n value_type::less_than_min, // greater_than_zero,\n value_type::less_than_min, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == less_than_zero,\n //{\n // u == ...\n value_type::greater_than_max, // less_than_min,\n value_type::greater_than_zero, // less_than_zero,\n value_type::zero, // zero,\n value_type::less_than_zero, // greater_than_zero,\n value_type::less_than_min, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == zero,\n //{\n // u == ...\n value_type::zero, // less_than_min,\n value_type::zero, // less_than_zero,\n value_type::zero, // zero,\n value_type::zero, // greater_than_zero,\n value_type::zero, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == greater_than_zero,\n //{\n // u == ...\n value_type::less_than_min, // less_than_min,\n value_type::less_than_zero, // less_than_zero,\n value_type::zero, // zero,\n value_type::greater_than_zero, // greater_than_zero,\n value_type::greater_than_max, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == greater_than_max\n //{\n value_type::less_than_min, // less_than_min,\n value_type::less_than_min, // less_than_zero,\n value_type::zero, // zero,\n value_type::greater_than_max, // greater_than_zero,\n value_type::greater_than_max, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == indeterminate\n //{\n value_type::indeterminate, // less_than_min,\n value_type::indeterminate, // less_than_zero,\n value_type::indeterminate, // zero,\n value_type::indeterminate, // greater_than_zero,\n value_type::indeterminate, // greater than max,\n value_type::indeterminate, // indeterminate,\n //}\n };\n\n const value_type tx(t);\n const value_type ux(u);\n\n switch(result[tx * order + ux]){\n case value_type::less_than_min:\n return safe_numerics_error::negative_overflow_error;\n case value_type::zero:\n return T(0);\n case value_type::greater_than_max:\n return safe_numerics_error::positive_overflow_error;\n case value_type::less_than_zero:\n case value_type::greater_than_zero:\n return checked::multiply(t, u);\n case value_type::indeterminate:\n return safe_numerics_error::range_error;\n default:\n assert(false);\n }\n return checked_result(0); // to suppress msvc warning\n}\n\n// integers division\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator/(\n const checked_result & t,\n const checked_result & u\n){\n using value_type = product_value_type;\n const std::uint8_t order = static_cast(value_type::count);\n\n constexpr const enum value_type::flag result[order * order] = {\n // t == less_than_min\n //{\n // u == ...\n value_type::indeterminate, // less_than_min,\n value_type::greater_than_max, // less_than_zero,\n value_type::less_than_min, // zero,\n value_type::less_than_min, // greater_than_zero,\n value_type::less_than_min, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == less_than_zero,\n //{\n // u == ...\n value_type::zero, // less_than_min,\n value_type::greater_than_zero, // less_than_zero,\n value_type::less_than_min, // zero,\n value_type::less_than_zero, // greater_than_zero,\n value_type::zero, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == zero,\n //{\n // u == ...\n value_type::zero, // less_than_min,\n value_type::zero, // less_than_zero,\n value_type::indeterminate, // zero,\n value_type::zero, // greater_than_zero,\n value_type::zero, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == greater_than_zero,\n //{\n // u == ...\n value_type::zero, // less_than_min,\n value_type::less_than_zero, // less_than_zero,\n value_type::greater_than_max, // zero,\n value_type::greater_than_zero, // greater_than_zero,\n value_type::zero, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == greater_than_max\n //{\n value_type::less_than_min, // less_than_min,\n value_type::less_than_min, // less_than_zero,\n value_type::greater_than_max, // zero,\n value_type::greater_than_max, // greater_than_zero,\n value_type::indeterminate, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == indeterminate\n //{\n value_type::indeterminate, // less_than_min,\n value_type::indeterminate, // less_than_zero,\n value_type::indeterminate, // zero,\n value_type::indeterminate, // greater_than_zero,\n value_type::indeterminate, // greater than max,\n value_type::indeterminate, // indeterminate,\n //}\n };\n\n const value_type tx(t);\n const value_type ux(u);\n\n switch(result[tx * order + ux]){\n case value_type::less_than_min:\n return safe_numerics_error::negative_overflow_error;\n case value_type::zero:\n return 0;\n case value_type::greater_than_max:\n return safe_numerics_error::positive_overflow_error;\n case value_type::less_than_zero:\n case value_type::greater_than_zero:\n return checked::divide(t, u);\n case value_type::indeterminate:\n return safe_numerics_error::range_error;\n default:\n assert(false);\n }\n return checked_result(0); // to suppress msvc warning\n}\n\n// integers modulus\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator%(\n const checked_result & t,\n const checked_result & u\n){\n using value_type = product_value_type;\n const std::uint8_t order = static_cast(value_type::count);\n\n constexpr const enum value_type::flag result[order * order] = {\n // t == less_than_min\n //{\n // u == ...\n value_type::indeterminate, // less_than_min,\n value_type::z_value, // less_than_zero,\n value_type::indeterminate, // zero,\n value_type::z_value, // greater_than_zero,\n value_type::indeterminate, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == less_than_zero,\n //{\n // u == ...\n value_type::t_value, // less_than_min,\n value_type::greater_than_zero, // less_than_zero,\n value_type::indeterminate, // zero,\n value_type::less_than_zero, // greater_than_zero,\n value_type::t_value, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == zero,\n //{\n // u == ...\n value_type::zero, // less_than_min,\n value_type::zero, // less_than_zero,\n value_type::indeterminate, // zero,\n value_type::zero, // greater_than_zero,\n value_type::zero, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == greater_than_zero,\n //{\n // u == ...\n value_type::t_value, // less_than_min,\n value_type::less_than_zero, // less_than_zero,\n value_type::indeterminate, // zero,\n value_type::greater_than_zero, // greater_than_zero,\n value_type::t_value, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == greater_than_max\n //{\n value_type::indeterminate, // less_than_min,\n value_type::u_value, // less_than_zero,\n value_type::indeterminate, // zero,\n value_type::u_value, // greater_than_zero,\n value_type::indeterminate, // greater than max,\n value_type::indeterminate, // indeterminate,\n //},\n // t == indeterminate\n //{\n value_type::indeterminate, // less_than_min,\n value_type::indeterminate, // less_than_zero,\n value_type::indeterminate, // zero,\n value_type::indeterminate, // greater_than_zero,\n value_type::indeterminate, // greater than max,\n value_type::indeterminate, // indeterminate,\n //}\n };\n\n const value_type tx(t);\n const value_type ux(u);\n\n switch(result[tx * order + ux]){\n case value_type::zero:\n return 0;\n case value_type::less_than_zero:\n case value_type::greater_than_zero:\n return checked::modulus(t, u);\n case value_type::indeterminate:\n return safe_numerics_error::range_error;\n case value_type::t_value:\n return t;\n case value_type::u_value:\n return checked::subtract(u, 1);\n case value_type::z_value:\n return checked::subtract(1, u);\n case value_type::greater_than_max:\n case value_type::less_than_min:\n default:\n assert(false);\n }\n // suppress msvc warning\n return checked_result(0);\n}\n\n// comparison operators\n\ntemplate\nconstexpr boost::logic::tribool operator<(\n const checked_result & t,\n const checked_result & u\n){\n using value_type = sum_value_type;\n constexpr const std::uint8_t order = static_cast(value_type::count);\n\n // the question arises about how to order values of type greater_than_min.\n // that is: what should greater_than_min < greater_than_min return.\n //\n // a) return indeterminate because we're talking about the \"true\" values for\n // which greater_than_min is a placholder.\n //\n // b) return false because the two values are \"equal\"\n //\n // for our purposes, a) seems the better interpretation.\n \n enum class result_type : std::uint8_t {\n runtime,\n false_value,\n true_value,\n indeterminate,\n };\n constexpr const result_type resultx[order * order]{\n // t == known_value\n //{\n // u == ...\n result_type::runtime, // known_value,\n result_type::false_value, // less_than_min,\n result_type::true_value, // greater_than_max,\n result_type::indeterminate, // indeterminate,\n //},\n // t == less_than_min\n //{\n // u == ...\n result_type::true_value, // known_value,\n result_type::indeterminate, // less_than_min, see above argument\n result_type::true_value, // greater_than_max,\n result_type::indeterminate, // indeterminate,\n //},\n // t == greater_than_max\n //{\n // u == ...\n result_type::false_value, // known_value,\n result_type::false_value, // less_than_min,\n result_type::indeterminate, // greater_than_max, see above argument\n result_type::indeterminate, // indeterminate,\n //},\n // t == indeterminate\n //{\n // u == ...\n result_type::indeterminate, // known_value,\n result_type::indeterminate, // less_than_min,\n result_type::indeterminate, // greater_than_max,\n result_type::indeterminate, // indeterminate,\n //},\n };\n\n const value_type tx(t);\n const value_type ux(u);\n\n switch(resultx[tx * order + ux]){\n case result_type::runtime:\n return static_cast(t) < static_cast(u);\n case result_type::false_value:\n return false;\n case result_type::true_value:\n return true;\n case result_type::indeterminate:\n return boost::logic::indeterminate;\n default:\n assert(false);\n }\n return true;\n}\n\ntemplate\nconstexpr boost::logic::tribool\noperator>=(\n const checked_result & t,\n const checked_result & u\n){\n return !(t < u);\n}\n\ntemplate\nconstexpr boost::logic::tribool\noperator>(\n const checked_result & t,\n const checked_result & u\n){\n return u < t;\n}\n\ntemplate\nconstexpr boost::logic::tribool\noperator<=(\n const checked_result & t,\n const checked_result & u\n){\n return !(u < t);\n}\n\ntemplate\nconstexpr boost::logic::tribool\noperator==(\n const checked_result & t,\n const checked_result & u\n){\n using value_type = sum_value_type;\n constexpr const std::uint8_t order = static_cast(value_type::count);\n\n enum class result_type : std::uint8_t {\n runtime,\n false_value,\n true_value,\n indeterminate,\n };\n\n constexpr const result_type result[order * order]{\n // t == known_value\n //{\n // u == ...\n result_type::runtime, // known_value,\n result_type::false_value, // less_than_min,\n result_type::false_value, // greater_than_max,\n result_type::indeterminate, // indeterminate,\n //},\n // t == less_than_min\n //{\n // u == ...\n result_type::false_value, // known_value,\n result_type::indeterminate, // less_than_min,\n result_type::false_value, // greater_than_max,\n result_type::indeterminate, // indeterminate,\n //},\n // t == greater_than_max\n //{\n // u == ...\n result_type::false_value, // known_value,\n result_type::false_value, // less_than_min,\n result_type::indeterminate, // greater_than_max,\n result_type::indeterminate, // indeterminate,\n //},\n // t == indeterminate\n //{\n // u == ...\n result_type::indeterminate, // known_value,\n result_type::indeterminate, // less_than_min,\n result_type::indeterminate, // greater_than_max,\n result_type::indeterminate, // indeterminate,\n //},\n };\n\n const value_type tx(t);\n const value_type ux(u);\n\n switch(result[tx * order + ux]){\n case result_type::runtime:\n return static_cast(t) == static_cast(u);\n case result_type::false_value:\n return false;\n case result_type::true_value:\n return true;\n case result_type::indeterminate:\n return boost::logic::indeterminate;\n default:\n assert(false);\n }\n // suppress msvc warning - not all control paths return a value\n return false;\n}\n\ntemplate\nconstexpr boost::logic::tribool\noperator!=(\n const checked_result & t,\n const checked_result & u\n){\n return ! (t == u);\n}\n\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator>>(\n const checked_result & t,\n const checked_result & u\n);\n\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator~(\n const checked_result & t\n){\n// assert(false);\n return ~t.m_r;\n}\n\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator<<(\n const checked_result & t,\n const checked_result & u\n){\n using value_type = product_value_type;\n const std::uint8_t order = static_cast(value_type::count);\n\n constexpr const std::uint8_t result[order * order] = {\n // t == less_than_min\n //{\n // u == ...\n 1, // -1, // less_than_min,\n 2, // safe_numerics_error::negative_overflow_error, // less_than_zero,\n 2, // safe_numerics_error::negative_overflow_error, // zero,\n 2, // safe_numerics_error::negative_overflow_error, // greater_than_zero,\n 2, // safe_numerics_error::negative_overflow_error, // greater than max,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == less_than_zero,\n //{\n // u == ...\n 3, // -1, // less_than_min,\n 4, // - (-t >> -u), // less_than_zero,\n 5, // safe_numerics_error::negative_overflow_error, // zero,\n 6, // - (-t << u), // greater_than_zero,\n 2, // safe_numerics_error::negative_overflow_error, // greater than max,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == zero,\n //{\n // u == ...\n 3, // 0 // less_than_min,\n 3, // 0 // less_than_zero,\n 3, // 0, // zero,\n 3, // 0, // greater_than_zero,\n 3, // 0, // greater than max,\n 3, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == greater_than_zero,\n //{\n // u == ...\n 3, // 0, // less_than_min,\n 7, // t << -u, // less_than_zero,\n 5, // t, // zero,\n 8, // t << u // greater_than_zero,\n 9, // safe_numerics_error::positive_overflow_error, // greater than max,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == greater_than_max\n //{\n // u == ...\n 1, // safe_numerics_error::range_error, // less_than_min,\n 9, // safe_numerics_error::positive_overflow_error), // less_than_zero,\n 9, // safe_numerics_error::positive_overflow_error, // zero,\n 9, // safe_numerics_error::positive_overflow_error), // greater_than_zero,\n 9, // safe_numerics_error::positive_overflow_error, // greater than max,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == indeterminate\n //{\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //}\n };\n\n const value_type tx(t);\n const value_type ux(u);\n assert(tx * order + ux < order * order);\n\n // I had a switch(i) statment here - but it results in an ICE\n // on multiple versions of gcc. So make the equivalent in\n // nested if statments - should be the same (more or less)\n // performancewise.\n const unsigned int i = result[tx * order + ux];\n assert(i <= 9);\n if(1 == i){\n return safe_numerics_error::range_error;\n }\n else\n if(2 == i){\n return safe_numerics_error::negative_overflow_error;\n }\n else\n if(3 == i){\n return checked_result(0);\n // the following gymnastics are to handle the case where \n // a value is changed from a negative to a positive number.\n // For example, and 8 bit number t == -128. Then -t also\n // equals -128 since 128 cannot be held in an 8 bit signed\n // integer.\n }\n else\n if(4 == i){ // - (-t >> -u)\n assert(static_cast(t < checked_result(0)));\n assert(static_cast(u < checked_result(0)));\n return t >> -u;\n }\n else\n if(5 == i){\n return t;\n }\n else\n if(6 == i){ // - (-t << u)\n assert(static_cast(t < checked_result(0)));\n assert(static_cast(u > checked_result(0)));\n const checked_result temp_t = t * checked_result(2);\n const checked_result temp_u = u - checked_result(1);\n return - (-temp_t << temp_u);\n }\n else\n if(7 == i){ // t >> -u\n assert(static_cast(t > checked_result(0)));\n assert(static_cast(u < checked_result(0)));\n return t >> -u;\n }\n else\n if(8 == i){ // t << u\n assert(static_cast(t > checked_result(0)));\n assert(static_cast(u > checked_result(0)));\n checked_result r = checked::left_shift(t, u);\n return (r.m_e == safe_numerics_error::shift_too_large)\n ? checked_result(safe_numerics_error::positive_overflow_error)\n : r;\n }\n else\n if(9 == i){\n return safe_numerics_error::positive_overflow_error;\n }\n else{\n assert(false);\n };\n return checked_result(0); // to suppress msvc warning\n}\n\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator>>(\n const checked_result & t,\n const checked_result & u\n){\n using value_type = product_value_type;\n const std::uint8_t order = static_cast(value_type::count);\n\n const std::uint8_t result[order * order] = {\n // t == less_than_min\n //{\n // u == ...\n 2, // safe_numerics_error::negative_overflow_error, // less_than_min,\n 2, // safe_numerics_error::negative_overflow_error, // less_than_zero,\n 2, // safe_numerics_error::negative_overflow_error, // zero,\n 2, // safe_numerics_error::negative_overflow_error, // greater_than_zero,\n 1, // safe_numerics_error::range_error, // greater than max,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == less_than_zero,\n //{\n // u == ...\n 2, // safe_numerics_error::negative_overflow_error // less_than_min,\n 4, // - (-t << -u), // less_than_zero,\n 5, // safe_numerics_error::negative_overflow_error. // zero,\n 6, // - (-t >> u), // greater_than_zero,\n 3, // 0, ? or -1 // greater than max,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == zero,\n //{\n // u == ...\n 3, // 0 // less_than_min,\n 3, // 0 // less_than_zero,\n 3, // 0, // zero,\n 3, // 0, // greater_than_zero,\n 3, // 0, // greater than max,\n 3, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == greater_than_zero,\n //{\n // u == ...\n 9, // safe_numerics_error::positive_overflow_error // less_than_min,\n 7, // t << -u, // less_than_zero,\n 5, // t, // zero,\n 8, // t >> u // greater_than_zero,\n 3, // 0, // greater than max,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == greater_than_max\n //{\n // u == ...\n 9, // safe_numerics_error::positive_overflow_error, // less_than_min,\n 9, // safe_numerics_error::positive_overflow_error, // less_than_zero,\n 9, // safe_numerics_error::positive_overflow_error, // zero,\n 9, // safe_numerics_error::positive_overflow_error, // greater_than_zero,\n 1, // safe_numerics_error::range_error, // greater than max,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //},\n // t == indeterminate\n //{\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n 1, // safe_numerics_error::range_error, // indeterminate,\n //}\n };\n\n const value_type tx(t);\n const value_type ux(u);\n assert(tx * order + ux < order * order);\n\n // I had a switch(i) statment here - but it results in an ICE\n // on multiple versions of gcc. So make the equivalent in\n // nested if statments - should be the same (more or less)\n // performancewise.\n const unsigned int i = result[tx * order + ux];\n assert(i <= 9);\n if(1 == i){\n return safe_numerics_error::range_error;\n }\n else\n if(2 == i){\n return safe_numerics_error::negative_overflow_error;\n }\n else\n if(3 == i){\n return checked_result(0);\n }\n else\n if(4 == i){ // - (-t << -u)\n assert(static_cast(t < checked_result(0)));\n assert(static_cast(u < checked_result(0)));\n return t << -u;\n }\n else\n if(5 == i){\n return t;\n }\n else\n if(6 == i){ // - (-t >> u)\n assert(static_cast(t < checked_result(0)));\n assert(static_cast(u > checked_result(0)));\n const checked_result temp_t = t / checked_result(2);\n const checked_result temp_u = u - checked_result(1);\n return - (-temp_t >> temp_u);\n }\n else\n if(7 == i){ // t << -u,\n assert(static_cast(t > checked_result(0)));\n assert(static_cast(u < checked_result(0)));\n return t << -u;\n }\n else\n if(8 == i){ // t >> u\n assert(static_cast(t > checked_result(0)));\n assert(static_cast(u > checked_result(0)));\n checked_result r = checked::right_shift(t, u);\n return (r.m_e == safe_numerics_error::shift_too_large)\n ? checked_result(0)\n : r;\n }\n else\n if(9 == i){\n return safe_numerics_error::positive_overflow_error;\n }\n else{\n assert(false);\n };\n return checked_result(0); // to suppress msvc warning\n}\n\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator|(\n const checked_result & t,\n const checked_result & u\n){\n return\n t.exception() || u.exception()\n ? checked_result(safe_numerics_error::range_error)\n : checked::bitwise_or(\n static_cast(t),\n static_cast(u)\n );\n}\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator^(\n const checked_result & t,\n const checked_result & u\n){\n return\n t.exception() || u.exception()\n ? checked_result(safe_numerics_error::range_error)\n : checked::bitwise_xor(\n static_cast(t),\n static_cast(u)\n );\n}\n\ntemplate\ntypename std::enable_if<\n std::is_integral::value,\n checked_result\n>::type\nconstexpr inline operator&(\n const checked_result & t,\n const checked_result & u\n){\n return\n t.exception() || u.exception()\n ? checked_result(safe_numerics_error::range_error)\n : checked::bitwise_and(\n static_cast(t),\n static_cast(u)\n );\n}\n\n} // safe_numerics\n} // boost\n\n#include \n\nnamespace std {\n\ntemplate\ninline std::basic_ostream & operator<<(\n std::basic_ostream & os,\n const boost::safe_numerics::checked_result & r\n){\n bool e = r.exception();\n os << e;\n if(!e)\n os << static_cast(r);\n else\n os << std::error_code(r.m_e).message() << ':' << static_cast(r);\n return os;\n}\n\ntemplate\ninline std::basic_ostream & operator<<(\n std::basic_ostream & os,\n const boost::safe_numerics::checked_result & r\n){\n bool e = r.exception();\n os << e;\n if(! e)\n os << static_cast(r);\n else\n os << std::error_code(r.m_e).message() << ':' << static_cast(r);\n return os;\n}\n\ntemplate\ninline std::basic_istream & operator>>(\n std::basic_istream & is,\n boost::safe_numerics::checked_result & r\n){\n bool e;\n is >> e;\n if(!e)\n is >> static_cast(r);\n else\n is >> std::error_code(r.m_e).message() >> ':' >> static_cast(r);\n return is;\n}\n\ntemplate\ninline std::basic_istream & operator>>(\n std::basic_istream & is, \n boost::safe_numerics::checked_result & r\n){\n bool e;\n is >> e;\n if(!e){\n std::int16_t i;\n is >> i;\n r.m_contents.m_r = static_cast(i);\n }\n else\n is >> std::error_code(r.m_e).message() >> ':' >> static_cast(r);\n return is;\n}\n\n} // std\n\n/////////////////////////////////////////////////////////////////\n// numeric limits for checked\n\n#include \n\nnamespace std {\n\ntemplate\nclass numeric_limits >\n : public std::numeric_limits\n{\n using this_type = boost::safe_numerics::checked_result;\npublic:\n constexpr static this_type min() noexcept {\n return this_type(std::numeric_limits::min());\n }\n constexpr static this_type max() noexcept {\n return this_type(std::numeric_limits::max());\n }\n};\n\n} // std\n\n#endif // BOOST_NUMERIC_CHECKED_RESULT_OPERATIONS\n", "meta": {"hexsha": "f5b8f9af4c8454b36c77707b6e63925d61c8f7e5", "size": 42336, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lib/boost_1.78.0/boost/safe_numerics/checked_result_operations.hpp", "max_stars_repo_name": "LaudateCorpus1/math", "max_stars_repo_head_hexsha": "990a66b3cccd27a5fd48626360bb91093a48278b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 111.0, "max_stars_repo_stars_event_min_datetime": "2018-09-26T00:40:34.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-08T12:02:17.000Z", "max_issues_repo_path": "lib/boost_1.78.0/boost/safe_numerics/checked_result_operations.hpp", "max_issues_repo_name": "LaudateCorpus1/math", "max_issues_repo_head_hexsha": "990a66b3cccd27a5fd48626360bb91093a48278b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 130.0, "max_issues_repo_issues_event_min_datetime": "2016-06-22T22:11:25.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-29T20:24:09.000Z", "max_forks_repo_path": "lib/boost_1.78.0/boost/safe_numerics/checked_result_operations.hpp", "max_forks_repo_name": "LaudateCorpus1/math", "max_forks_repo_head_hexsha": "990a66b3cccd27a5fd48626360bb91093a48278b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 41.0, "max_forks_repo_forks_event_min_datetime": "2015-07-08T19:18:35.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-14T16:39:56.000Z", "avg_line_length": 34.9884297521, "max_line_length": 105, "alphanum_fraction": 0.5624055178, "num_tokens": 9608, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.23986766542353896}} {"text": "#include \n#include \n\n#include \n\n#include \"caffe/common.hpp\"\n#include \"caffe/quantizer.hpp\"\n#include \"caffe/util/math_functions.hpp\"\n#include \"caffe/util/rng.hpp\"\n\nnamespace caffe {\n\n// Integer quantized types\ntemplate\ntypename std::enable_if::value, void>::type\ncaffe_gemv(const CBLAS_TRANSPOSE trans_A,\n const int_tp M, const int_tp N, const Dtype alpha,\n const Dtype* a, const Dtype* x,\n const Dtype beta, Dtype* y,\n const QuantizerValues* const alpha_quant,\n const QuantizerValues* const a_quant,\n const QuantizerValues* const x_quant,\n const QuantizerValues* const beta_quant,\n const QuantizerValues* const y_quant) {\n\n typedef typename std::conditional::type>::type Difftype;\n typedef typename std::conditional::type Acctype;\n\n int8_t shift_bits = (32/sizeof(Dtype)) - 1;\n\n int32_t mult;\n int8_t shift;\n int32_t alpha_mult;\n int8_t alpha_shift;\n int32_t beta_mult;\n int8_t beta_shift;\n Acctype y_max = y_quant->get_max();\n Acctype y_min = y_quant->get_min();\n Dtype lhs_off = a_quant->get_zero();\n Dtype rhs_off = x_quant->get_zero();\n Dtype alpha_off = alpha_quant ? alpha_quant->get_zero() : Dtype(0);\n Dtype beta_off = beta_quant ? beta_quant->get_zero() : Dtype(0);\n const Acctype result_off = y_quant->get_zero();\n\n QuantizerBase::template MultiplicativeQuantVals(\n a_quant, x_quant, y_quant, &mult, &shift, shift_bits);\n QuantizerBase::template MultiplicativeQuantVals(\n y_quant, alpha_quant, y_quant, &alpha_mult, &alpha_shift, shift_bits);\n QuantizerBase::template MultiplicativeQuantVals(\n y_quant, beta_quant, y_quant, &beta_mult, &beta_shift, shift_bits);\n\n int_tp a_inc = (trans_A == CblasNoTrans) ? 1 : N;\n int_tp y_cnt = (trans_A == CblasNoTrans) ? M : N;\n int_tp x_cnt = (trans_A == CblasNoTrans) ? N : M;\n#pragma omp parallel for\n for (int_tp m = 0; m < y_cnt; m++) {\n int_tp a_index = (trans_A == CblasNoTrans) ? m * N : m;\n Acctype acc = 0;\n for (int_tp n = 0; n < x_cnt; n++) {\n Difftype a_diff = a[a_index] - lhs_off;\n Difftype x_diff = x[n] - rhs_off;\n acc += static_cast(a_diff) * static_cast(x_diff);\n a_index += a_inc;\n }\n Acctype reg = acc * (alpha_quant ? Acctype(1) : alpha);\n reg = static_cast((static_cast(reg) *\n static_cast(mult)) / (1ll << shift_bits));\n if (shift >= 0) {\n reg = reg >> shift;\n } else {\n reg = reg << -shift;\n }\n if (alpha_quant) {\n Difftype alpha_diff = alpha - alpha_off;\n reg = static_cast(alpha_diff) * static_cast(reg);\n reg = static_cast((static_cast(reg) *\n static_cast(alpha_mult)) / (1ll << shift_bits));\n if (alpha_shift >= 0) {\n reg = reg >> alpha_shift;\n } else {\n reg = reg << -alpha_shift;\n }\n }\n if (beta_quant) {\n Difftype beta_diff = beta - beta_off;\n Difftype c_diff = y[m] - static_cast(result_off);\n Acctype creg = static_cast(beta_diff)\n * static_cast(c_diff);\n creg = static_cast((static_cast(creg) *\n static_cast(beta_mult)) / (1ll << shift_bits));\n if (beta_shift >= 0) {\n creg = creg >> beta_shift;\n } else {\n creg = creg << -beta_shift;\n }\n reg = reg + creg;\n } else if (beta == Dtype(1)) {\n reg = reg + (y[m] - result_off);\n }\n reg = reg + result_off;\n y[m] = static_cast(std::min(std::max(reg, y_min), y_max));\n }\n}\n\n// Half precision\ntemplate\ntypename std::enable_if::value, void>::type\ncaffe_gemv(const CBLAS_TRANSPOSE trans_A,\n const int_tp M, const int_tp N, const Dtype alpha,\n const Dtype* a, const Dtype* x,\n const Dtype beta, Dtype* y,\n const QuantizerValues* const alpha_quant,\n const QuantizerValues* const a_quant,\n const QuantizerValues* const x_quant,\n const QuantizerValues* const beta_quant,\n const QuantizerValues* const y_quant) {\n int_tp a_inc = (trans_A == CblasNoTrans) ? 1 : N;\n int_tp y_cnt = (trans_A == CblasNoTrans) ? M : N;\n int_tp x_cnt = (trans_A == CblasNoTrans) ? N : M;\n for (int_tp m = 0; m < y_cnt; m++) {\n int_tp a_index = (trans_A == CblasNoTrans) ? m * N : m;\n Dtype acc = 0;\n for (int_tp n = 0; n < x_cnt; n++) {\n acc += a[a_index] * x[n];\n a_index += a_inc;\n }\n if (beta == 0)\n y[m] = acc * alpha;\n else\n y[m] = acc * alpha + beta * y[m];\n }\n}\n\ntemplate\ntypename std::enable_if::value, void>::type\ncaffe_gemv(const CBLAS_TRANSPOSE trans_A,\n const int_tp M, const int_tp N,\n const half_fp alpha,\n const half_fp* a, const half_fp* x,\n const half_fp beta, half_fp* y,\n const QuantizerValues* const alpha_quant,\n const QuantizerValues* const a_quant,\n const QuantizerValues* const x_quant,\n const QuantizerValues* const beta_quant,\n const QuantizerValues* const y_quant);\ntemplate\ntypename std::enable_if::value, void>::type\ncaffe_gemv(const CBLAS_TRANSPOSE trans_A,\n const int_tp M, const int_tp N,\n const uint8_t alpha,\n const uint8_t* a, const uint8_t* x,\n const uint8_t beta, uint8_t* y,\n const QuantizerValues* const alpha_quant,\n const QuantizerValues* const a_quant,\n const QuantizerValues* const x_quant,\n const QuantizerValues* const beta_quant,\n const QuantizerValues* const y_quant);\ntemplate\ntypename std::enable_if::value, void>::type\ncaffe_gemv(const CBLAS_TRANSPOSE trans_A,\n const int_tp M, const int_tp N,\n const uint16_t alpha,\n const uint16_t* a, const uint16_t* x,\n const uint16_t beta, uint16_t* y,\n const QuantizerValues* const alpha_quant,\n const QuantizerValues* const a_quant,\n const QuantizerValues* const x_quant,\n const QuantizerValues* const beta_quant,\n const QuantizerValues* const y_quant);\ntemplate\ntypename std::enable_if::value, void>::type\ncaffe_gemv(const CBLAS_TRANSPOSE trans_A,\n const int_tp M, const int_tp N,\n const uint32_t alpha,\n const uint32_t* a, const uint32_t* x,\n const uint32_t beta, uint32_t* y,\n const QuantizerValues* const alpha_quant,\n const QuantizerValues* const a_quant,\n const QuantizerValues* const x_quant,\n const QuantizerValues* const beta_quant,\n const QuantizerValues* const y_quant);\n\ntemplate\ntypename std::enable_if::value, void>::type\ncaffe_gemv(const CBLAS_TRANSPOSE trans_A,\n const int_tp M, const int_tp N,\n const uint64_t alpha,\n const uint64_t* a, const uint64_t* x,\n const uint64_t beta, uint64_t* y,\n const QuantizerValues* const alpha_quant,\n const QuantizerValues* const a_quant,\n const QuantizerValues* const x_quant,\n const QuantizerValues* const beta_quant,\n const QuantizerValues* const y_quant);\n\ntemplate<>\nvoid caffe_gemv(const CBLAS_TRANSPOSE trans_A, const int_tp M,\n const int_tp N, const float alpha, const float* A,\n const float* x, const float beta, float* y,\n const QuantizerValues* const alpha_quant,\n const QuantizerValues* const a_quant,\n const QuantizerValues* const x_quant,\n const QuantizerValues* const beta_quant,\n const QuantizerValues* const y_quant) {\n cblas_sgemv(CblasRowMajor, trans_A, M, N, alpha, A, N, x, 1, beta, y, 1);\n}\n\ntemplate<>\nvoid caffe_gemv(const CBLAS_TRANSPOSE trans_A, const int_tp M,\n const int_tp N, const double alpha, const double* A,\n const double* x, const double beta, double* y,\n const QuantizerValues* const alpha_quant,\n const QuantizerValues* const a_quant,\n const QuantizerValues* const x_quant,\n const QuantizerValues* const beta_quant,\n const QuantizerValues* const y_quant) {\n cblas_dgemv(CblasRowMajor, trans_A, M, N, alpha, A, N, x, 1, beta, y, 1);\n}\n\n} // namespace caffe\n\n", "meta": {"hexsha": "7e8c579bdffc295698453acda987a19203ed50f0", "size": 9560, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/caffe/util/math_blas_2.cpp", "max_stars_repo_name": "naibaf7/caffe", "max_stars_repo_head_hexsha": "29960153c828820b1abb55a5792283742f57caa2", "max_stars_repo_licenses": ["Intel", "BSD-2-Clause"], "max_stars_count": 89.0, "max_stars_repo_stars_event_min_datetime": "2015-04-20T01:25:01.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-07T17:03:28.000Z", "max_issues_repo_path": "src/caffe/util/math_blas_2.cpp", "max_issues_repo_name": "Miaomz/caffe-opencl", "max_issues_repo_head_hexsha": "505693d54298b89cf83b54778479087cff2f3bd6", "max_issues_repo_licenses": ["Intel", "BSD-2-Clause"], "max_issues_count": 62.0, "max_issues_repo_issues_event_min_datetime": "2015-06-18T13:11:20.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-19T05:00:10.000Z", "max_forks_repo_path": "src/caffe/util/math_blas_2.cpp", "max_forks_repo_name": "Miaomz/caffe-opencl", "max_forks_repo_head_hexsha": "505693d54298b89cf83b54778479087cff2f3bd6", "max_forks_repo_licenses": ["Intel", "BSD-2-Clause"], "max_forks_count": 30.0, "max_forks_repo_forks_event_min_datetime": "2015-07-05T17:08:09.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-10T13:16:02.000Z", "avg_line_length": 42.4888888889, "max_line_length": 78, "alphanum_fraction": 0.6009414226, "num_tokens": 2378, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982315512489, "lm_q2_score": 0.37022539954425293, "lm_q1q2_score": 0.2398313591001216}} {"text": "// Copyright (c) 2015-2021 Daniel Cooke\n// Use of this source code is governed by the MIT license that can be found in the LICENSE file.\n\n#include \"variational_bayes_mixture_mixture_model.hpp\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \"utils/maths.hpp\"\n\nnamespace octopus { namespace model {\n\nVariationalBayesMixtureMixtureModel::VariationalBayesMixtureMixtureModel(Options options)\n: options_ {std::move(options)}\n{}\n\nVariationalBayesMixtureMixtureModel::Inferences\nVariationalBayesMixtureMixtureModel::evaluate(const LogProbabilityVector& genotype_log_priors,\n const HaplotypeLikelihoodMatrix& log_likelihoods,\n const GroupOptionalPriorArray& group_priors,\n const GroupConcentrationVector& group_concentrations,\n const MixtureConcentrationArray& mixture_concentrations,\n std::vector seeds) const\n{\n const auto group_log_priors = to_logs(group_priors);\n const auto expanded_log_likelihoods = expand(log_likelihoods);\n const auto evaluate_seed = [&] (auto&& seed) {\n return this->evaluate(genotype_log_priors, log_likelihoods, expanded_log_likelihoods, group_log_priors, group_concentrations, mixture_concentrations, std::move(seed)); };\n std::vector seed_inferences(seeds.size());\n if (options_.parallel_execution) {\n parallel_transform(std::make_move_iterator(std::begin(seeds)), std::make_move_iterator(std::end(seeds)), std::begin(seed_inferences), evaluate_seed);\n } else {\n std::transform(std::make_move_iterator(std::begin(seeds)), std::make_move_iterator(std::end(seeds)), std::begin(seed_inferences), evaluate_seed);\n }\n Inferences result {};\n compute_evidence_weighted_latents(result.weighted_genotype_posteriors, result.weighted_group_responsibilities, seed_inferences);\n const static auto evidence_less = [] (const auto& lhs, const auto& rhs) { return lhs.approx_log_evidence < rhs.approx_log_evidence; };\n result.map = *std::max_element(std::begin(seed_inferences), std::end(seed_inferences), evidence_less);\n return result;\n}\n\nVariationalBayesMixtureMixtureModel::Inferences\nVariationalBayesMixtureMixtureModel::evaluate(const LogProbabilityVector& genotype_log_priors,\n const HaplotypeLikelihoodMatrix& log_likelihoods,\n const GroupConcentrationVector& group_concentrations,\n const MixtureConcentrationArray& mixture_concentrations,\n std::vector seeds) const\n{\n const auto num_samples = log_likelihoods.size();\n const GroupOptionalPriorArray no_priors(num_samples);\n return evaluate(genotype_log_priors, log_likelihoods, no_priors, group_concentrations, mixture_concentrations, std::move(seeds));\n}\n\nVariationalBayesMixtureMixtureModel::Inferences\nVariationalBayesMixtureMixtureModel::evaluate(const LogProbabilityVector& genotype_log_priors,\n const HaplotypeLikelihoodMatrix& log_likelihoods,\n const GroupOptionalPriorArray& group_priors,\n const double group_concentration,\n const std::vector& mixture_concentrations,\n std::vector seeds) const\n{\n const auto num_samples = log_likelihoods.size();\n assert(mixture_concentrations.size() == num_samples);\n const auto num_groups = log_likelihoods.front().front().size();\n std::vector group_mixture_sizes {};\n group_mixture_sizes.reserve(num_groups);\n for (const auto& genotype : log_likelihoods.front().front()) {\n group_mixture_sizes.push_back(genotype.size());\n }\n const GroupConcentrationVector group_concentrations(num_groups, group_concentration);\n MixtureConcentrationArray group_mixture_concentrations {};\n group_mixture_concentrations.reserve(num_samples);\n for (auto sample_concentration : mixture_concentrations) {\n group_mixture_concentrations.emplace_back(num_groups, ComponentConcentrationVector(sample_concentration));\n unsigned t {0};\n for (auto& sample_group_sample_concentrations : group_mixture_concentrations.back()) {\n sample_group_sample_concentrations.assign(group_mixture_sizes[t++], sample_concentration);\n }\n }\n return evaluate(genotype_log_priors, log_likelihoods, group_priors, group_concentrations, group_mixture_concentrations, std::move(seeds));\n}\n\nVariationalBayesMixtureMixtureModel::Inferences\nVariationalBayesMixtureMixtureModel::evaluate(const LogProbabilityVector& genotype_log_priors,\n const HaplotypeLikelihoodMatrix& log_likelihoods,\n const double group_concentration,\n const std::vector& mixture_concentrations,\n std::vector seeds) const\n{\n const auto num_samples = log_likelihoods.size();\n const GroupOptionalPriorArray no_priors(num_samples);\n return evaluate(genotype_log_priors, log_likelihoods, no_priors, group_concentration, mixture_concentrations, std::move(seeds));\n}\n\nVariationalBayesMixtureMixtureModel::Inferences\nVariationalBayesMixtureMixtureModel::evaluate(const LogProbabilityVector& genotype_log_priors,\n const HaplotypeLikelihoodMatrix& log_likelihoods,\n const GroupOptionalPriorArray& group_priors,\n const double group_concentration,\n const double mixture_concentration,\n std::vector seeds) const\n{\n const auto num_samples = log_likelihoods.size();\n const auto num_groups = log_likelihoods.front().front().size();\n std::vector group_mixture_sizes {};\n group_mixture_sizes.reserve(num_groups);\n for (const auto& genotype : log_likelihoods.front().front()) {\n group_mixture_sizes.push_back(genotype.size());\n }\n const GroupConcentrationVector group_concentrations(num_groups, group_concentration);\n MixtureConcentrationArray mixture_concentrations(num_samples, MixtureConcentrationVector(num_groups));\n for (auto& sample_concentrations : mixture_concentrations) {\n unsigned t {0};\n for (auto& sample_group_sample_concentrations : sample_concentrations) {\n sample_group_sample_concentrations.assign(group_mixture_sizes[t++], mixture_concentration);\n }\n }\n return evaluate(genotype_log_priors, log_likelihoods, group_priors, group_concentrations, mixture_concentrations, std::move(seeds));\n}\n\nVariationalBayesMixtureMixtureModel::Inferences\nVariationalBayesMixtureMixtureModel::evaluate(const LogProbabilityVector& genotype_log_priors,\n const HaplotypeLikelihoodMatrix& log_likelihoods,\n const double group_concentration,\n const double mixture_concentration,\n std::vector seeds) const\n{\n const auto num_samples = log_likelihoods.size();\n const GroupOptionalPriorArray no_priors(num_samples);\n return evaluate(genotype_log_priors, log_likelihoods, no_priors, group_concentration, mixture_concentration, std::move(seeds));\n}\n\n// Private methods\n\nVariationalBayesMixtureMixtureModel::GroupOptionalLogPriorVector\nVariationalBayesMixtureMixtureModel::to_logs(const GroupOptionalPriorVector& prior) const\n{\n GroupOptionalLogPriorVector result {};\n if (prior) {\n result = LogProbabilityVector(prior->size());\n const static auto to_log = [] (auto p) noexcept {\n const static auto min_log_prior = std::log(std::numeric_limits::min());\n return p > 0 ? std::log(p) : min_log_prior;\n };\n std::transform(std::cbegin(*prior), std::cend(*prior), std::begin(*result), to_log);\n }\n return result;\n}\n\nVariationalBayesMixtureMixtureModel::GroupOptionalLogPriorArray\nVariationalBayesMixtureMixtureModel::to_logs(const GroupOptionalPriorArray& priors) const\n{\n GroupOptionalLogPriorArray result {};\n result.reserve(priors.size());\n std::transform(std::cbegin(priors), std::cend(priors), std::back_inserter(result),\n [this] (const auto& sample_priors) { return to_logs(sample_priors); });\n return result;\n}\n\nVariationalBayesMixtureMixtureModel::ExpandedHaplotypeLikelihoodMatrix \nVariationalBayesMixtureMixtureModel::expand(const HaplotypeLikelihoodMatrix& likelihoods) const\n{\n const auto S = likelihoods.size();\n const auto G = likelihoods[0].size();\n const auto T = likelihoods[0][0].size();\n ExpandedHaplotypeLikelihoodMatrix result(S, ExpandedGroupLikelihoodVector(T));\n for (std::size_t s {0}; s < S; ++s) {\n const auto N = likelihoods[s][0][0][0].size();\n for (std::size_t t {0}; t < T; ++t) {\n const auto K = likelihoods[0][0][t].size();\n result[s][t].assign(K, ExpandedGenotype(N, ExpandedLikelihood(G)));\n for (std::size_t k {0}; k < K; ++k) {\n for (std::size_t g {0}; g < G; ++g) {\n for (std::size_t n {0}; n < N; ++n) {\n result[s][t][k][n][g] = likelihoods[s][g][t][k][n];\n }\n }\n }\n }\n }\n return result;\n}\n\nnamespace {\n\nVariationalBayesMixtureMixtureModel::ProbabilityVector&\nexp(const LogProbabilityVector& log_probabilities, ProbabilityVector& result) noexcept\n{\n std::transform(std::cbegin(log_probabilities), std::cend(log_probabilities), std::begin(result),\n [] (const auto lp) noexcept { return std::exp(lp); });\n return result;\n}\n\nauto exp(const VariationalBayesMixtureMixtureModel::LogProbabilityVector& log_probabilities)\n{\n VariationalBayesMixtureMixtureModel::ProbabilityVector result(log_probabilities.size());\n return exp(log_probabilities, result);\n}\n\ntemplate \nauto sum(const std::vector& values) noexcept\n{\n return std::accumulate(std::cbegin(values), std::cend(values), T {0});\n}\n\nauto sum(const VBReadLikelihoodArray& likelihoods) noexcept\n{\n using T = VBReadLikelihoodArray::BaseType::value_type;\n return std::accumulate(std::cbegin(likelihoods), std::cend(likelihoods), T {0});\n}\n\ntemplate \nauto inner_product(const T1& lhs, const T2& rhs) noexcept\n{\n assert(std::distance(std::cbegin(lhs), std::cend(lhs)) == std::distance(std::cbegin(rhs), std::cend(rhs)));\n using T = typename T1::value_type;\n return std::inner_product(std::cbegin(lhs), std::cend(lhs), std::cbegin(rhs), T {0});\n}\n\ntemplate \nauto dirichlet_expectation_log(const std::vector& concentrations)\n{\n std::vector result(concentrations.size());\n if (concentrations.size() > 1) {\n using boost::math::digamma;\n const auto digamma_alpha_0 = digamma(sum(concentrations));\n std::transform(std::cbegin(concentrations), std::cend(concentrations), std::begin(result),\n [=] (auto alpha) { return digamma(alpha) - digamma_alpha_0; });\n }\n return result;\n}\n\ntemplate \nT log_sum_exp(const std::vector& logs)\n{\n return maths::log_sum_exp(logs);\n}\n\nbool all_equal_sizes(const VariationalBayesMixtureMixtureModel::MixtureConcentrationVector& concentrations) noexcept\n{\n const auto size_unequal = [] (const auto& lhs, const auto& rhs) { return lhs.size() != rhs.size(); };\n return std::adjacent_find(std::cbegin(concentrations), std::end(concentrations), size_unequal) == std::cend(concentrations);\n}\n\n} // namespace\n\nVariationalBayesMixtureMixtureModel::PointInferences\nVariationalBayesMixtureMixtureModel::evaluate(const LogProbabilityVector& genotype_log_priors,\n const HaplotypeLikelihoodMatrix& log_likelihoods,\n const ExpandedHaplotypeLikelihoodMatrix& expanded_log_likelihoods,\n const GroupOptionalLogPriorArray& group_log_priors,\n const GroupConcentrationVector& prior_group_concentrations,\n const MixtureConcentrationArray& prior_mixture_concentrations,\n LogProbabilityVector genotype_log_posteriors) const\n{\n Latents latents {};\n latents.genotype_log_posteriors = std::move(genotype_log_posteriors);\n latents.genotype_posteriors = exp(latents.genotype_log_posteriors);\n latents.group_concentrations = prior_group_concentrations;\n latents.mixture_concentrations = prior_mixture_concentrations;\n latents.group_responsibilities = init_responsibilities(group_log_priors, prior_group_concentrations, prior_mixture_concentrations,\n latents.genotype_posteriors, log_likelihoods);\n latents.component_responsibilities = init_responsibilities(prior_group_concentrations, prior_mixture_concentrations,\n latents.genotype_posteriors, latents.group_responsibilities, expanded_log_likelihoods);\n auto prev_evidence = std::numeric_limits::lowest();\n for (unsigned i {0}; i < options_.max_iterations; ++i) {\n update_genotype_log_posteriors(latents.genotype_log_posteriors, genotype_log_priors,\n latents.group_responsibilities, latents.component_responsibilities,\n log_likelihoods);\n exp(latents.genotype_log_posteriors, latents.genotype_posteriors);\n update_group_concentrations(latents.group_concentrations, prior_group_concentrations, latents.group_responsibilities);\n update_mixture_concentrations(latents.mixture_concentrations, prior_mixture_concentrations, latents.component_responsibilities);\n auto curr_evidence = calculate_evidence(prior_group_concentrations, latents.group_concentrations,\n prior_mixture_concentrations, latents.mixture_concentrations,\n genotype_log_priors, latents.genotype_log_posteriors, latents.genotype_posteriors,\n group_log_priors, latents.group_responsibilities, latents.component_responsibilities,\n log_likelihoods);\n// assert(curr_evidence + options_.epsilon >= prev_evidence);\n if (curr_evidence <= prev_evidence || (curr_evidence - prev_evidence) < options_.epsilon) {\n prev_evidence = curr_evidence;\n break;\n }\n prev_evidence = curr_evidence;\n update_responsibilities(latents.group_responsibilities, group_log_priors,latents.group_concentrations,\n latents.mixture_concentrations, latents.genotype_posteriors,\n latents.component_responsibilities, log_likelihoods);\n update_responsibilities(latents.component_responsibilities, latents.group_concentrations, latents.mixture_concentrations,\n latents.genotype_posteriors, latents.group_responsibilities, expanded_log_likelihoods);\n }\n return {std::move(latents), prev_evidence};\n}\n\nnamespace {\n\ntemplate \nstd::size_t max_element_index(ForwardIterator first, ForwardIterator last)\n{\n return std::distance(first, std::max_element(first, last));\n}\n\ntemplate \nstd::size_t max_element_index(const Range& values)\n{\n return max_element_index(std::cbegin(values), std::cend(values));\n}\n\n} // namespace\n\nVariationalBayesMixtureMixtureModel::GroupResponsibilityVector\nVariationalBayesMixtureMixtureModel::init_responsibilities(const GroupOptionalLogPriorArray& group_log_priors,\n const GroupConcentrationVector& group_concentrations,\n const MixtureConcentrationArray& mixture_concentrations,\n const ProbabilityVector& genotype_priors,\n const HaplotypeLikelihoodMatrix& log_likelihoods) const\n{\n const auto S = log_likelihoods.size();\n const auto T = group_concentrations.size();\n GroupResponsibilityVector result(S, Sigma(T));\n const auto G = genotype_priors.size();\n const auto ln_ex_psi = dirichlet_expectation_log(group_concentrations);\n std::size_t max_K {0};\n for (std::size_t s {0}; s < S; ++s) {\n for (std::size_t t {0}; t < T; ++t) {\n max_K = std::max(max_K, mixture_concentrations[s][t].size());\n }\n }\n const auto max_genotype_prior_idx = max_element_index(genotype_priors);\n ComponentResponsibilityVector approx_taus(max_K);\n for (std::size_t s {0}; s < S; ++s) {\n const auto N = log_likelihoods[s][0][0][0].size(); // num reads\n for (auto& tau : approx_taus) tau.resize(N);\n for (std::size_t t {0}; t < T; ++t) {\n result[s][t] = ln_ex_psi[t];\n if (group_log_priors[s]) result[s][t] += (*group_log_priors[s])[t];\n const auto ln_ex_pi = dirichlet_expectation_log(mixture_concentrations[s][t]);\n const auto K = mixture_concentrations[s][t].size();\n // Approximate tau assuming p(t) = 1\n Tau approx_tau(K);\n for (std::size_t n {0}; n < N; ++n) {\n for (std::size_t k {0}; k < K; ++k) {\n approx_tau[k] = ln_ex_pi[k] + log_likelihoods[s][max_genotype_prior_idx][t][k][n];\n }\n maths::normalise_exp(approx_tau);\n for (std::size_t k {0}; k < K; ++k) {\n approx_taus[k][n] = approx_tau[k];\n }\n }\n for (std::size_t k {0}; k < K; ++k) {\n for (std::size_t g {0}; g < G; ++g) {\n result[s][t] += genotype_priors[g] * inner_product(approx_taus[k], log_likelihoods[s][g][t][k]);\n }\n }\n }\n maths::normalise_exp(result[s]);\n }\n return result;\n}\n\nvoid\nVariationalBayesMixtureMixtureModel::update_responsibilities(GroupResponsibilityVector& result,\n const GroupOptionalLogPriorArray& group_log_priors,\n const GroupConcentrationVector& group_concentrations,\n const MixtureConcentrationArray& mixture_concentrations,\n const ProbabilityVector& genotype_posteriors,\n const ComponentResponsibilityMatrix& component_responsibilities,\n const HaplotypeLikelihoodMatrix& log_likelihoods) const\n{\n const auto T = group_concentrations.size();\n const auto S = log_likelihoods.size();\n const auto G = genotype_posteriors.size();\n const auto ln_ex_psi = dirichlet_expectation_log(group_concentrations);\n for (std::size_t s {0}; s < S; ++s) {\n for (std::size_t t {0}; t < T; ++t) {\n const auto max_K = component_responsibilities[s][t].size();\n result[s][t] = ln_ex_psi[t];\n if (group_log_priors[s]) result[s][t] += (*group_log_priors[s])[t];\n const auto ln_ex_pi = dirichlet_expectation_log(mixture_concentrations[s][t]);\n const auto K = ln_ex_pi.size();\n result[s][t] += sum(ln_ex_pi);\n for (std::size_t k {0}; k < max_K; ++k) {\n for (std::size_t g {0}; g < G; ++g) {\n if (k < K) {\n result[s][t] += genotype_posteriors[g] * inner_product(component_responsibilities[s][t][k], log_likelihoods[s][g][t][k]);\n } else {\n result[s][t] += genotype_posteriors[g] * inner_product(component_responsibilities[s][t][k], log_likelihoods[s][g][t][0]);\n }\n }\n }\n }\n maths::normalise_exp(result[s]);\n }\n}\n\nVariationalBayesMixtureMixtureModel::ComponentResponsibilityMatrix\nVariationalBayesMixtureMixtureModel::init_responsibilities(const GroupConcentrationVector& group_concentrations,\n const MixtureConcentrationArray& mixture_concentrations,\n const ProbabilityVector& genotype_priors,\n const GroupResponsibilityVector& group_responsibilities,\n const ExpandedHaplotypeLikelihoodMatrix& log_likelihoods) const\n{\n const auto S = log_likelihoods.size();\n const auto T = group_concentrations.size();\n std::size_t K {0};\n for (std::size_t s {0}; s < S; ++s) {\n for (std::size_t t {0}; t < T; ++t) {\n K = std::max(K, mixture_concentrations[s][t].size());\n }\n }\n ComponentResponsibilityMatrix result(S, ComponentResponsibilityVectorArray(T, ComponentResponsibilityVector(K)));\n for (std::size_t s {0}; s < S; ++s) {\n const auto N = log_likelihoods[s][0][0].size();\n for (std::size_t t {0}; t < T; ++t) {\n for (std::size_t k {0}; k < K; ++k) {\n result[s][t][k].resize(N);\n }\n }\n }\n update_responsibilities(result, group_concentrations, mixture_concentrations,\n genotype_priors, group_responsibilities, log_likelihoods);\n return result;\n}\n\nnamespace {\n\nauto inner_product(const VariationalBayesMixtureMixtureModel::ProbabilityVector& genotype_posteriors,\n const VariationalBayesMixtureMixtureModel::GenotypeCombinationLikelihoodVector& log_likelihoods,\n const std::size_t t, const std::size_t k, const std::size_t n) noexcept\n{\n ProbabilityVector::value_type result {0};\n const auto G = genotype_posteriors.size();\n for (std::size_t g {0}; g < G; ++g) {\n result += genotype_posteriors[g] * log_likelihoods[g][t][k][n];\n }\n return result;\n}\n\n} // namespace\n\nvoid\nVariationalBayesMixtureMixtureModel::update_responsibilities(ComponentResponsibilityMatrix& result,\n const GroupConcentrationVector& group_concentrations,\n const MixtureConcentrationArray& mixture_concentrations,\n const ProbabilityVector& genotype_posteriors,\n const GroupResponsibilityVector& group_responsibilities,\n const ExpandedHaplotypeLikelihoodMatrix& log_likelihoods) const\n{\n const auto T = group_concentrations.size();\n const auto S = log_likelihoods.size();\n const auto max_K = result[0][0].size();\n std::vector float_genotype_posteriors {std::cbegin(genotype_posteriors), std::cend(genotype_posteriors)};\n for (std::size_t s {0}; s < S; ++s) {\n const auto N = log_likelihoods[s][0][0].size();\n for (std::size_t t {0}; t < T; ++t) {\n const auto ln_exp_pi = dirichlet_expectation_log(mixture_concentrations[s][t]);\n const auto K = ln_exp_pi.size();\n for (std::size_t k {0}; k < max_K; ++k) {\n for (std::size_t n {0}; n < N; ++n) {\n if (t == 0) result[s][t][k][n] = 0;\n if (k < K) {\n //result[s][t][k][n] += ln_exp_pi[k] + inner_product(genotype_posteriors, log_likelihoods[s], t, k, n);\n result[s][t][k][n] += ln_exp_pi[k] + inner_product(float_genotype_posteriors, log_likelihoods[s][t][k][n]);\n } else {\n result[s][t][k][n] = options_.null_log_probability;\n }\n }\n }\n std::vector ln_rho(max_K);\n for (std::size_t n {0}; n < N; ++n) {\n for (std::size_t k {0}; k < max_K; ++k) {\n ln_rho[k] = result[s][t][k][n];\n }\n const auto ln_rho_norm = log_sum_exp(ln_rho);\n for (std::size_t k {0}; k < max_K; ++k) {\n result[s][t][k][n] = std::exp(ln_rho[k] - ln_rho_norm);\n }\n }\n }\n }\n}\n\nvoid\nVariationalBayesMixtureMixtureModel::update_genotype_log_posteriors(LogProbabilityVector& result,\n const LogProbabilityVector& genotype_log_priors,\n const GroupResponsibilityVector& group_responsibilities,\n const ComponentResponsibilityMatrix& component_responsibilities,\n const HaplotypeLikelihoodMatrix& log_likelihoods) const\n{\n for (std::size_t g {0}; g < genotype_log_priors.size(); ++g) {\n result[g] = genotype_log_priors[g] + marginalise(group_responsibilities, component_responsibilities, log_likelihoods, g);\n }\n maths::normalise_logs(result);\n}\n\nVariationalBayesMixtureMixtureModel::LogProbability\nVariationalBayesMixtureMixtureModel::marginalise(const GroupResponsibilityVector& group_responsibilities,\n const ComponentResponsibilityMatrix& component_responsibilities,\n const HaplotypeLikelihoodMatrix& log_likelihoods,\n const std::size_t g) const noexcept\n{\n const auto T = group_responsibilities.front().size();\n const auto S = component_responsibilities.size();\n LogProbability result {0};\n for (std::size_t s {0}; s < S; ++s) {\n for (std::size_t t {0}; t < T; ++t) {\n result += group_responsibilities[s][t] * marginalise(component_responsibilities[s][t], log_likelihoods[s][g][t]);\n }\n }\n return result;\n}\n\nVariationalBayesMixtureMixtureModel::LogProbability\nVariationalBayesMixtureMixtureModel::marginalise(const ComponentResponsibilityVector& responsibilities,\n const HaplotypeLikelihoodVector& log_likelihoods) const noexcept\n{\n const auto K = log_likelihoods.size();\n const auto max_K = responsibilities.size();\n assert(K <= max_K);\n LogProbability result {0};\n for (std::size_t k {0}; k < max_K; ++k) {\n if (k < K) {\n result += inner_product(responsibilities[k], log_likelihoods[k]);\n } else {\n result += inner_product(responsibilities[k], log_likelihoods[0]);\n }\n }\n return result;\n}\n\nvoid\nVariationalBayesMixtureMixtureModel::update_group_concentrations(GroupConcentrationVector& result,\n const GroupConcentrationVector& prior_group_concentrations,\n const GroupResponsibilityVector& group_responsibilities) const\n{\n assert(result.size() == prior_group_concentrations.size());\n const auto T = prior_group_concentrations.size();\n const auto S = group_responsibilities.size();\n for (std::size_t t {0}; t < T; ++t) {\n result[t] = prior_group_concentrations[t];\n for (std::size_t s {0}; s < S; ++s) {\n result[t] += group_responsibilities[s][t];\n }\n }\n}\n\nvoid\nVariationalBayesMixtureMixtureModel::update_mixture_concentrations(MixtureConcentrationArray& result,\n const MixtureConcentrationArray& prior_mixture_concentrations,\n const ComponentResponsibilityMatrix& component_responsibilities) const\n{\n const auto S = result.size();\n const auto T = result.front().size();\n for (std::size_t s {0}; s < S; ++s) {\n for (std::size_t t {0}; t < T; ++t) {\n const auto K = prior_mixture_concentrations[s][t].size();\n for (std::size_t k {0}; k < K; ++k) {\n result[s][t][k] = prior_mixture_concentrations[s][t][k] + sum(component_responsibilities[s][t][k]);\n }\n }\n }\n}\n\nnamespace {\n\ntemplate \nauto shannon_entropy(const std::vector& probabilities) noexcept\n{\n return -std::accumulate(std::cbegin(probabilities), std::cend(probabilities), T {0},\n [] (const auto curr, const auto p) noexcept { return curr + (p > 0 ? p * std::log(p) : 0.0); });\n}\n\ntemplate \nauto shannon_entropy(const std::vector>& probabilities) noexcept\n{\n return std::accumulate(std::cbegin(probabilities), std::cend(probabilities), T {0},\n [] (const auto curr, const auto& ps) noexcept { return curr + shannon_entropy(ps); });\n}\n\n} // namespace\n\ndouble\nVariationalBayesMixtureMixtureModel::calculate_evidence(const GroupConcentrationVector& prior_group_concentrations,\n const GroupConcentrationVector& posterior_group_concentrations,\n const MixtureConcentrationArray& prior_mixture_concentrations,\n const MixtureConcentrationArray& posterior_mixture_concentrations,\n const LogProbabilityVector& genotype_log_priors,\n const LogProbabilityVector& genotype_log_posteriors,\n const ProbabilityVector& genotype_posteriors,\n const GroupOptionalPriorArray& group_log_priors,\n const GroupResponsibilityVector& group_responsibilities,\n const ComponentResponsibilityMatrix& component_responsibilities,\n const HaplotypeLikelihoodMatrix& log_likelihoods) const\n{\n const auto G = genotype_log_priors.size();\n const auto S = prior_mixture_concentrations.size();\n const auto T = group_responsibilities.front().size();\n double result {0};\n for (std::size_t g {0}; g < G; ++g) {\n auto w = genotype_log_priors[g] - genotype_log_posteriors[g];\n for (std::size_t s {0}; s < S; ++s) {\n double ss {0};\n for (std::size_t t {0}; t < T; ++t) {\n ss += group_responsibilities[s][t] * marginalise(component_responsibilities[s][t], log_likelihoods[s][g][t]);\n }\n w += ss;\n }\n result += genotype_posteriors[g] * w;\n }\n for (std::size_t s {0}; s < S; ++s) {\n result += shannon_entropy(group_responsibilities[s]);\n for (std::size_t t {0}; t < T; ++t) {\n result += group_responsibilities[s][t] * shannon_entropy(component_responsibilities[s][t]);\n result += group_responsibilities[s][t] * (maths::log_beta(posterior_mixture_concentrations[s][t]) - maths::log_beta(prior_mixture_concentrations[s][t]));\n }\n if (group_log_priors[s]) {\n auto sigma_expected_ln_norm = maths::log_each_copy(group_responsibilities[s]);\n for (std::size_t t {0}; t < T; ++t) {\n sigma_expected_ln_norm[t] += (*group_log_priors[s])[t];\n }\n result += maths::log_sum_exp(sigma_expected_ln_norm);\n }\n }\n result += maths::log_beta(posterior_group_concentrations) - maths::log_beta(prior_group_concentrations); \n return result;\n}\n\nnamespace {\n\ntemplate \nvoid check_normalisation(Range& probabilities) noexcept\n{\n const auto mass = std::accumulate(std::cbegin(probabilities), std::cend(probabilities), 0.0);\n if (mass > 1.0) for (auto& p : probabilities) p /= mass;\n}\n\n} // namespace\n\nvoid\nVariationalBayesMixtureMixtureModel::compute_evidence_weighted_latents(ProbabilityVector& genotype_posteriors,\n GroupResponsibilityVector& group_responsibilities,\n const std::vector& latents) const\n{\n assert(!latents.empty());\n const auto num_genotypes = latents.front().latents.genotype_posteriors.size();\n std::vector map_genotypes(num_genotypes, latents.size());\n for (std::size_t i {0}; i < latents.size(); ++i) {\n const auto map_genotype_idx = max_element_index(latents[i].latents.genotype_posteriors);\n if (map_genotypes[map_genotype_idx] == latents.size()\n || latents[i].approx_log_evidence > latents[map_genotypes[map_genotype_idx]].approx_log_evidence) {\n map_genotypes[map_genotype_idx] = i;\n }\n }\n std::vector modes {};\n modes.reserve(latents.size());\n for (std::size_t g {0}; g < num_genotypes; ++g) {\n if (map_genotypes[g] < latents.size()) {\n modes.push_back(map_genotypes[g]);\n }\n }\n std::vector mode_weights(modes.size());\n std::transform(std::cbegin(modes), std::cend(modes), std::begin(mode_weights),\n [&] (auto mode) { return latents[mode].approx_log_evidence; });\n maths::normalise_exp(mode_weights);\n genotype_posteriors.resize(num_genotypes);\n const auto num_samples = latents.front().latents.group_responsibilities.size();\n const auto num_groups = latents.front().latents.group_responsibilities.front().size();\n group_responsibilities.resize(num_samples, Sigma(num_groups));\n for (std::size_t i {0}; i < modes.size(); ++i) {\n const auto mode_weight = mode_weights[i];\n const auto& mode_genotype_posteriors = latents[modes[i]].latents.genotype_posteriors;\n std::transform(std::cbegin(mode_genotype_posteriors), std::cend(mode_genotype_posteriors),\n std::cbegin(genotype_posteriors), std::begin(genotype_posteriors),\n [mode_weight] (auto seed_posterior, auto curr_posterior) {\n return curr_posterior + mode_weight * seed_posterior;\n });\n const auto& mode_group_responsabilities = latents[modes[i]].latents.group_responsibilities;\n for (std::size_t s {0}; s < num_samples; ++s) {\n for (std::size_t t {0}; t < num_groups; ++t) {\n group_responsibilities[s][t] += mode_weight * mode_group_responsabilities[s][t];\n }\n }\n }\n check_normalisation(genotype_posteriors);\n for (auto& responsibilities : group_responsibilities) check_normalisation(responsibilities);\n}\n\nvoid VariationalBayesMixtureMixtureModel::print_concentrations(const GroupConcentrationVector& concentrations) const\n{\n for (std::size_t t {0}; t < concentrations.size(); ++t) {\n std::cout << \"t: \" << t << \" = \" << concentrations[t] << std::endl;\n }\n}\n\nvoid VariationalBayesMixtureMixtureModel::print_concentrations(const MixtureConcentrationArray& concentrations) const\n{\n for (std::size_t s {0}; s < concentrations.size(); ++s) {\n for (std::size_t t {0}; t < concentrations[s].size(); ++t) {\n for (std::size_t k {0}; k < concentrations[s][t].size(); ++k) {\n std::cout << \"s: \" << s << \" t: \" << t << \" k: \" << k << \" = \" << concentrations[s][t][k] << std::endl;\n }\n }\n }\n}\n\nvoid VariationalBayesMixtureMixtureModel::print(const ProbabilityVector& probabilities) const\n{\n for (std::size_t g {0}; g < probabilities.size(); ++g) {\n std::cout << \"g: \" << g << \" = \" << probabilities[g] << std::endl;\n }\n}\n\nvoid VariationalBayesMixtureMixtureModel::print(const GroupResponsibilityVector& responsibilities) const\n{\n for (std::size_t s {0}; s < responsibilities.size(); ++s) {\n for (std::size_t t {0}; t < responsibilities[s].size(); ++t) {\n std::cout << \"s: \" << s << \" t: \" << t << \" = \" << responsibilities[s][t] << std::endl;\n }\n }\n}\n\nvoid VariationalBayesMixtureMixtureModel::print(const ComponentResponsibilityMatrix& responsibilities) const\n{\n for (std::size_t s {0}; s < responsibilities.size(); ++s) {\n for (std::size_t t {0}; t < responsibilities[s].size(); ++t) {\n for (std::size_t k {0}; k < responsibilities[s].size(); ++k) {\n for (std::size_t n {0}; n < responsibilities[s][k].size(); ++n) {\n std::cout << \"s: \" << s << \" t: \" << t << \" k: \" << k << \" n: \" << n << \" = \" << responsibilities[s][t][k][n] << std::endl;\n }\n }\n }\n }\n}\n\nvoid VariationalBayesMixtureMixtureModel::print(const HaplotypeLikelihoodMatrix& log_likelihoods) const\n{\n for (std::size_t s {0}; s < log_likelihoods.size(); ++s) {\n for (std::size_t g {0}; g < log_likelihoods[s].size(); ++g) {\n for (std::size_t t {0}; t < log_likelihoods[s][g].size(); ++t) {\n for (std::size_t k {0}; k < log_likelihoods[s][g][t].size(); ++k) {\n for (std::size_t n {0}; n < log_likelihoods[s][g][t][k].size(); ++n) {\n std::cout << \"s: \" << s << \" g: \" << g << \" t: \" << t << \" k: \" << k\n << \" n: \" << n << \" = \" << log_likelihoods[s][g][t][k][n] << std::endl;\n }\n }\n }\n }\n }\n}\n\n} // namespace model\n} // namespace octopus\n", "meta": {"hexsha": "73eca45c8959f3bab8687248c0f46c622fb87dff", "size": 38629, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/core/models/genotype/variational_bayes_mixture_mixture_model.cpp", "max_stars_repo_name": "Schaudge/octopus", "max_stars_repo_head_hexsha": "d0cc5d0840aefdfefae5af8595e3330620106054", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 278.0, "max_stars_repo_stars_event_min_datetime": "2016-10-03T16:30:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T05:59:32.000Z", "max_issues_repo_path": "src/core/models/genotype/variational_bayes_mixture_mixture_model.cpp", "max_issues_repo_name": "Schaudge/octopus", "max_issues_repo_head_hexsha": "d0cc5d0840aefdfefae5af8595e3330620106054", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 229.0, "max_issues_repo_issues_event_min_datetime": "2016-10-13T14:07:35.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-19T18:59:58.000Z", "max_forks_repo_path": "src/core/models/genotype/variational_bayes_mixture_mixture_model.cpp", "max_forks_repo_name": "Schaudge/octopus", "max_forks_repo_head_hexsha": "d0cc5d0840aefdfefae5af8595e3330620106054", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 37.0, "max_forks_repo_forks_event_min_datetime": "2016-10-28T22:47:54.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T07:28:43.000Z", "avg_line_length": 50.9617414248, "max_line_length": 178, "alphanum_fraction": 0.6086618861, "num_tokens": 8723, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521105, "lm_q2_score": 0.3702253995442529, "lm_q1q2_score": 0.2398313540653751}} {"text": "// BSD 3-Clause License\n//\n// Copyright (c) 2022, Map IV, Inc.\n// All rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are met:\n//\n// 1. Redistributions of source code must retain the above copyright notice, this\n// list of conditions and the following disclaimer.\n//\n// 2. Redistributions in binary form must reproduce the above copyright notice,\n// this list of conditions and the following disclaimer in the documentation\n// and/or other materials provided with the distribution.\n//\n// 3. Neither the name of the copyright holder nor the names of its\n// contributors may be used to endorse or promote products derived from\n// this software without specific prior written permission.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n// DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n// FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n// DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n// SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n// CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n// OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n#include \n\n#include \n#include \n#include \n#include \n\n#include \n\nnamespace height_converter\n{\n\nstd::vector split(std::string input, char delimiter)\n{\n std::istringstream stream(input);\n std::string field;\n std::vector result;\n\n while (std::getline(stream, field, delimiter))\n {\n if (field != std::string(\" \") && !field.empty() && field != \"\\n\" && field != \"\\r\")\n result.push_back(field);\n }\n return result;\n}\n\nGSIGEO2011::GSIGEO2011()\n{\n}\n\nGSIGEO2011::~GSIGEO2011()\n{\n}\n\nvoid GSIGEO2011::loadGeoidMap(const std::string& geoid_file)\n{\n std::ifstream ifs(geoid_file);\n if (!ifs)\n {\n std::cerr << \"Error: Cannot open Geoid data file: \" << geoid_file << std::endl;\n exit(2);\n }\n\n geoid_map_.reserve(row_size_);\n for (int i = 0; i < row_size_; i++)\n {\n geoid_map_[i].reserve(column_size_);\n }\n\n std::string curr_line;\n // Skip header line\n std::getline(ifs, curr_line);\n\n int i = 0, j = 0;\n\n while (std::getline(ifs, curr_line))\n {\n std::vector str_vec = split(curr_line, ' ');\n\n for (int k = 0; k < str_vec.size(); k++)\n {\n geoid_map_[i][j] = std::stod(str_vec[k]);\n j++;\n \n }\n if (j == column_size_) \n {\n j = 0;\n i++;\n }\n }\n is_geoid_loaded_ = true;\n}\n\ndouble GSIGEO2011::getGeoid(const double& lat, const double& lon)\n{\n if (!is_geoid_loaded_)\n {\n std::cerr << \"Error: Geoid map is not loaded\" << std::endl;\n exit(1);\n }\n const double lat_min = 20;\n const double lon_min = 120;\n const double d_lat = 1.0 / 60.0;\n const double d_lon = 1.5 / 60.0;\n\n const int i_lat = std::floor((lat - lat_min) / d_lat);\n const int i_lon = std::floor((lon - lon_min) / d_lon);\n const int j_lat = i_lat + 1;\n const int j_lon = i_lon + 1;\n\n // const double t = (((lat - lat_min) / d_lat) - i_lat) / d_lat;\n // const double u = (((lon - lon_min) / d_lon) - i_lon) / d_lon;\n const double t = (lat - (lat_min + i_lat * d_lat)) / d_lat;\n const double u = (lon - (lon_min + i_lon * d_lon)) / d_lon;\n\n if (i_lat < 0 || i_lat >= row_size_ - 1 || i_lon < 0 || i_lon >= column_size_ - 1)\n {\n std::cerr << \"Error: latitude/longitude is out of range (20~50, 120~150)\" << std::endl;\n std::cerr << (lat, lon) << std::endl;\n exit(1);\n }\n\n if (geoid_map_[i_lat][i_lon] == 999 || geoid_map_[i_lat][j_lon] == 999 || geoid_map_[j_lat][i_lon] == 999 || geoid_map_[j_lat][j_lon] == 999)\n {\n std::cerr << \"Error: Not supported area\" << std::endl;\n exit(1);\n }\n\n double geoid = (1-t)*(1-u)*geoid_map_[i_lat][i_lon] + (1-t)*u*geoid_map_[i_lat][j_lon] + t*(1-u)*geoid_map_[j_lat][i_lon] + t*u*geoid_map_[j_lat][j_lon];\n\n return geoid;\n}\n} // namespace height_converter\n", "meta": {"hexsha": "16895b1aa03c2042bf01a92b8fd90693989c1a3d", "size": 4386, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/gsigeo.cpp", "max_stars_repo_name": "MapIV/height_converter", "max_stars_repo_head_hexsha": "176cf575a0ef42308a90fa7eaad4fea4ac490c88", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2022-03-24T07:52:59.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-24T11:33:29.000Z", "max_issues_repo_path": "src/gsigeo.cpp", "max_issues_repo_name": "MapIV/height_converter", "max_issues_repo_head_hexsha": "176cf575a0ef42308a90fa7eaad4fea4ac490c88", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/gsigeo.cpp", "max_forks_repo_name": "MapIV/height_converter", "max_forks_repo_head_hexsha": "176cf575a0ef42308a90fa7eaad4fea4ac490c88", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.2482758621, "max_line_length": 155, "alphanum_fraction": 0.6634746922, "num_tokens": 1258, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.23976805794376713}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#define REP(i, n) for(int i = 0, i##_MACRO = (n); i < i##_MACRO; i++)\n#define RANGE(i, a, b) for(int i = (a), i##_MACRO = (b); i < i##_MACRO; i++)\n#define EACH(e, a) for(auto&& e : a)\n#define ALL(a) std::begin(a), std::end(a)\n#define RALL(a) std::rbegin(a), std::rend(a)\n#define FILL(a, n) memset((a), n, sizeof(a))\n#define FILLZ(a) FILL(a, 0)\n#define INT(x) (static_cast(x))\n\nusing namespace std;\n\nusing ll = long long;\nusing VI = vector;\nusing VI2D = vector>;\n\nconstexpr int INF = 2e9;\nconstexpr double EPS = 1e-10;\nconstexpr double PI = acos(-1.0);\n\nconstexpr int dx[] = {-1, 0, 1, 0};\nconstexpr int dy[] = {0, -1, 0, 1};\n\ntemplate \nconstexpr int sign(T x) {\n\treturn x < 0 ? -1 : x > 0 ? 1 : 0;\n}\n\ntemplate <>\nconstexpr int sign(double x) {\n\treturn x < -EPS ? -1 : x > EPS ? 1 : 0;\n}\n\ntemplate \nconstexpr void chmax(T& m, U x) {\n\tm = max(m, x);\n}\n\ntemplate \nconstexpr void chmin(T& m, U x) {\n\tm = min(m, x);\n}\n\ntemplate \nconstexpr T square(T x) {\n\treturn x * x;\n}\n\n// UnionFind(disjoint set)\nclass UnionFind {\n\tstd::vector parents;\n\tstd::vector rank;\n\tstd::vector tree_size;\n\n\tpublic:\n\tUnionFind(size_t size) : parents(size), rank(size, 0), tree_size(size, 1) {\n\t\tstd::iota(this->parents.begin(), this->parents.end(), 0);\n\t}\n\n\t// 併合\n\tbool merge(size_t a, size_t b) {\n\t\tsize_t ar = this->root(a);\n\t\tsize_t br = this->root(b);\n\t\tif(ar == br) {\n\t\t\treturn false;\n\t\t}\n\t\tif(this->rank[ar] < this->rank[br]) {\n\t\t\tstd::swap(ar, br);\n\t\t}\n\t\tif(this->rank[ar] == this->rank[br]) {\n\t\t\tthis->rank[ar]++;\n\t\t}\n\t\tthis->tree_size[ar] = this->tree_size[br] =\n\t\t\tthis->tree_size[ar] + this->tree_size[br];\n\t\treturn this->parents[br] = ar;\n\t}\n\tbool unite(size_t a, size_t b) { return this->merge(a, b); }\n\n\t// 同集合か判定\n\tbool is_same(size_t a, size_t b) { return this->root(a) == this->root(b); }\n\tbool is_union(size_t a, size_t b) { return this->is_same(a, b); }\n\tsize_t size(size_t n) {\n\t\tthis->root(n);\n\t\treturn this->tree_size[n];\n\t}\n\n\tprivate:\n\tsize_t root(int n) {\n\t\tif(this->parents[n] == n) {\n\t\t\treturn n;\n\t\t}\n\n\t\tsize_t r = this->root(this->parents[n]);\n\t\tthis->parents[n] = r;\n\t\tthis->tree_size[n] = this->tree_size[r];\n\t\treturn r;\n\t}\n};\n\nint main() {\n\tint n, m, k;\n\tcin >> n >> m >> k;\n\tVI a(m);\n\tVI b(m);\n\tVI2D friends(n);\n\tREP(i, m) {\n\t\tcin >> a[i] >> b[i];\n\t\tfriends[a[i] - 1].push_back(b[i] - 1);\n\t\tfriends[b[i] - 1].push_back(a[i] - 1);\n\t}\n\tVI c(k);\n\tVI d(k);\n\tVI2D blocks(n);\n\tREP(i, k) {\n\t\tcin >> c[i] >> d[i];\n\t\tblocks[c[i] - 1].push_back(d[i] - 1);\n\t\tblocks[d[i] - 1].push_back(c[i] - 1);\n\t}\n\tUnionFind uf(n);\n\tREP(i, m) { uf.merge(a[i] - 1, b[i] - 1); }\n\tREP(i, n) {\n\t\tint size = uf.size(i) - 1;\n\t\tEACH(e, friends[i]) {\n\t\t\tif(uf.is_same(i, e)) {\n\t\t\t\tsize--;\n\t\t\t}\n\t\t}\n\t\tEACH(e, blocks[i]) {\n\t\t\tif(uf.is_same(i, e)) {\n\t\t\t\tsize--;\n\t\t\t}\n\t\t}\n\t\tcout << size << ' ';\n\t}\n\tcout << endl;\n\treturn 0;\n}\n", "meta": {"hexsha": "8b49ce10c1cb0ca3943717128e39df6f0994c9f9", "size": 3340, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "AtCoder/ABC157/D.cpp", "max_stars_repo_name": "arlechann/atcoder", "max_stars_repo_head_hexsha": "1af08efa6d3a0e8c75e4eaf13e1eda994820b9e2", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "AtCoder/ABC157/D.cpp", "max_issues_repo_name": "arlechann/atcoder", "max_issues_repo_head_hexsha": "1af08efa6d3a0e8c75e4eaf13e1eda994820b9e2", "max_issues_repo_licenses": ["CC0-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "AtCoder/ABC157/D.cpp", "max_forks_repo_name": "arlechann/atcoder", "max_forks_repo_head_hexsha": "1af08efa6d3a0e8c75e4eaf13e1eda994820b9e2", "max_forks_repo_licenses": ["CC0-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.875, "max_line_length": 76, "alphanum_fraction": 0.5925149701, "num_tokens": 1163, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.23976805794376713}} {"text": "#pragma once\n\n#include \n#include \n#include \n#include \"genetic_code.hpp\"\n#include \"xlorad.hpp\"\n\nnamespace lorad {\n\n class QMatrix {\n\n public:\n typedef std::vector freq_xchg_t;\n typedef std::shared_ptr freq_xchg_ptr_t;\n typedef double omega_t;\n typedef std::shared_ptr omega_ptr_t;\n typedef boost::shared_ptr SharedPtr;\n\n QMatrix();\n virtual ~QMatrix();\n \n virtual void clear() = 0;\n\n virtual void setEqualStateFreqs(freq_xchg_ptr_t freq_ptr) = 0;\n virtual void setStateFreqsSharedPtr(freq_xchg_ptr_t freq_ptr) = 0;\n virtual void setStateFreqs(freq_xchg_t & freq) = 0;\n virtual freq_xchg_ptr_t getStateFreqsSharedPtr() = 0;\n virtual const double * getStateFreqs() const = 0;\n void fixStateFreqs(bool is_fixed);\n bool isFixedStateFreqs() const;\n\n virtual void setEqualExchangeabilities(freq_xchg_ptr_t xchg_ptr) = 0;\n virtual void setExchangeabilitiesSharedPtr(freq_xchg_ptr_t xchg) = 0;\n virtual void setExchangeabilities(freq_xchg_t & xchg) = 0;\n virtual freq_xchg_ptr_t getExchangeabilitiesSharedPtr() = 0;\n virtual const double * getExchangeabilities() const = 0;\n void fixExchangeabilities(bool is_fixed);\n bool isFixedExchangeabilities() const;\n\n virtual void setOmegaSharedPtr(omega_ptr_t omega) = 0;\n virtual void setOmega(omega_t omega) = 0;\n virtual omega_ptr_t getOmegaSharedPtr() = 0;\n virtual double getOmega() const = 0;\n void fixOmega(bool is_fixed);\n bool isFixedOmega() const;\n\n#if defined(POLGSS)\n virtual void setStateFreqRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t freq_params_ptr) = 0;\n virtual std::vector getStateFreqRefDistParamsVect() const = 0;\n virtual void setExchangeabilityRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t xchg_params_ptr) = 0;\n virtual std::vector getExchangeabilityRefDistParamsVect() const = 0;\n#endif\n\n virtual const double * getEigenvectors() const = 0;\n virtual const double * getInverseEigenvectors() const = 0;\n virtual const double * getEigenvalues() const = 0;\n\n void setActive(bool activate);\n \n protected:\n \n virtual void recalcRateMatrix() = 0;\n void normalizeFreqsOrExchangeabilities(freq_xchg_ptr_t v);\n\n bool _is_active;\n bool _state_freqs_fixed;\n bool _exchangeabilities_fixed;\n bool _omega_fixed;\n\n#if defined(POLGSS)\n freq_xchg_ptr_t _state_freq_refdist;\n freq_xchg_ptr_t _exchangeability_refdist;\n#endif\n };\n \n inline QMatrix::QMatrix() {\n }\n \n inline QMatrix::~QMatrix() {\n }\n \n inline void QMatrix::setActive(bool activate) {\n _is_active = activate;\n recalcRateMatrix();\n }\n\n inline void QMatrix::clear() {\n _is_active = false;\n _state_freqs_fixed = false;\n _exchangeabilities_fixed = false;\n _omega_fixed = false;\n }\n\n inline void QMatrix::fixStateFreqs(bool is_fixed) {\n _state_freqs_fixed = is_fixed;\n }\n \n inline void QMatrix::fixExchangeabilities(bool is_fixed) {\n _exchangeabilities_fixed = is_fixed;\n }\n \n inline void QMatrix::fixOmega(bool is_fixed) {\n _omega_fixed = is_fixed;\n }\n \n inline bool QMatrix::isFixedStateFreqs() const {\n return _state_freqs_fixed;\n }\n \n inline bool QMatrix::isFixedExchangeabilities() const {\n return _exchangeabilities_fixed;\n }\n \n inline bool QMatrix::isFixedOmega() const {\n return _omega_fixed;\n }\n \n inline void QMatrix::normalizeFreqsOrExchangeabilities(QMatrix::freq_xchg_ptr_t v) {\n // Be sure elements of v sum to 1.0 and assert that they are all positive\n double sum_v = std::accumulate(v->begin(), v->end(), 0.0);\n for (auto & x : *v) {\n assert(x > 0.0);\n x /= sum_v;\n }\n }\n \n class QMatrixNucleotide : public QMatrix {\n\n public:\n typedef Eigen::Matrix eigenMatrix4d_t;\n typedef Eigen::Vector4d eigenVector4d_t;\n \n QMatrixNucleotide();\n ~QMatrixNucleotide();\n \n void clear();\n\n void setEqualStateFreqs(freq_xchg_ptr_t freq_ptr);\n void setStateFreqsSharedPtr(freq_xchg_ptr_t freq_ptr);\n void setStateFreqs(freq_xchg_t & freqs);\n freq_xchg_ptr_t getStateFreqsSharedPtr();\n const double * getStateFreqs() const;\n\n void setEqualExchangeabilities(freq_xchg_ptr_t xchg_ptr);\n void setExchangeabilitiesSharedPtr(freq_xchg_ptr_t xchg_ptr);\n void setExchangeabilities(freq_xchg_t & xchg);\n freq_xchg_ptr_t getExchangeabilitiesSharedPtr();\n const double * getExchangeabilities() const;\n\n void setOmegaSharedPtr(omega_ptr_t omega_ptr);\n void setOmega(omega_t omega);\n omega_ptr_t getOmegaSharedPtr();\n double getOmega() const;\n\n#if defined(POLGSS)\n void setStateFreqRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t freq_params_ptr);\n std::vector getStateFreqRefDistParamsVect() const;\n void setExchangeabilityRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t xchg_params_ptr);\n std::vector getExchangeabilityRefDistParamsVect() const;\n#endif\n\n const double * getEigenvectors() const;\n const double * getInverseEigenvectors() const;\n const double * getEigenvalues() const;\n \n\n EIGEN_MAKE_ALIGNED_OPERATOR_NEW\n \n protected:\n \n virtual void recalcRateMatrix();\n\n private:\n \n // workspaces for computing eigenvectors/eigenvalues\n eigenMatrix4d_t _sqrtPi;\n eigenMatrix4d_t _sqrtPiInv;\n eigenMatrix4d_t _Q;\n eigenMatrix4d_t _eigenvectors;\n eigenMatrix4d_t _inverse_eigenvectors;\n eigenVector4d_t _eigenvalues;\n\n freq_xchg_ptr_t _state_freqs;\n freq_xchg_ptr_t _exchangeabilities;\n };\n \n inline QMatrixNucleotide::QMatrixNucleotide() {\n clear();\n }\n\n inline QMatrixNucleotide::~QMatrixNucleotide() {\n }\n\n inline void QMatrixNucleotide::clear() {\n QMatrix::clear();\n\n QMatrix::freq_xchg_t xchg = {1,1,1,1,1,1};\n _exchangeabilities = std::make_shared(xchg);\n\n QMatrix::freq_xchg_t freq_vect = {0.25, 0.25, 0.25, 0.25};\n _state_freqs = std::make_shared(freq_vect);\n \n#if defined(POLGSS)\n QMatrix::freq_xchg_t freq_param_vect = {1.0, 1.0, 1.0, 1.0};\n _state_freq_refdist = std::make_shared(freq_param_vect);\n \n QMatrix::freq_xchg_t xchg_param_vect = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0};\n _exchangeability_refdist = std::make_shared(xchg_param_vect);\n#endif\n \n recalcRateMatrix();\n }\n\n inline QMatrix::freq_xchg_ptr_t QMatrixNucleotide::getExchangeabilitiesSharedPtr() {\n return _exchangeabilities;\n }\n \n inline QMatrix::freq_xchg_ptr_t QMatrixNucleotide::getStateFreqsSharedPtr() {\n return _state_freqs;\n }\n\n inline QMatrix::omega_ptr_t QMatrixNucleotide::getOmegaSharedPtr() {\n assert(false);\n return nullptr;\n }\n \n inline const double * QMatrixNucleotide::getEigenvectors() const {\n return _eigenvectors.data();\n }\n \n inline const double * QMatrixNucleotide::getInverseEigenvectors() const {\n return _inverse_eigenvectors.data();\n }\n \n inline const double * QMatrixNucleotide::getEigenvalues() const {\n return _eigenvalues.data();\n }\n \n inline const double * QMatrixNucleotide::getExchangeabilities() const {\n return &(*_exchangeabilities)[0];\n }\n\n inline const double * QMatrixNucleotide::getStateFreqs() const {\n return &(*_state_freqs)[0];\n }\n\n inline double QMatrixNucleotide::getOmega() const {\n assert(false);\n return 0.0;\n }\n\n inline void QMatrixNucleotide::setEqualExchangeabilities(QMatrix::freq_xchg_ptr_t xchg_ptr) {\n _exchangeabilities = xchg_ptr;\n _exchangeabilities->assign(6, 1.0/6.0);\n recalcRateMatrix();\n }\n \n inline void QMatrixNucleotide::setExchangeabilitiesSharedPtr(QMatrix::freq_xchg_ptr_t xchg_ptr) {\n if (xchg_ptr->size() != 6)\n throw XLorad(boost::format(\"Expecting 6 exchangeabilities and got %d: perhaps you meant to specify a subset data type other than nucleotide\") % xchg_ptr->size());\n _exchangeabilities = xchg_ptr;\n normalizeFreqsOrExchangeabilities(_exchangeabilities);\n recalcRateMatrix();\n }\n \n inline void QMatrixNucleotide::setExchangeabilities(QMatrix::freq_xchg_t & xchg) {\n if (xchg.size() != 6)\n throw XLorad(boost::format(\"Expecting 6 exchangeabilities and got %d: perhaps you meant to specify a subset data type other than nucleotide\") % xchg.size());\n std::copy(xchg.begin(), xchg.end(), _exchangeabilities->begin());\n recalcRateMatrix();\n }\n \n inline void QMatrixNucleotide::setEqualStateFreqs(QMatrix::freq_xchg_ptr_t freq_ptr) {\n _state_freqs = freq_ptr;\n _state_freqs->assign(4, 0.25);\n recalcRateMatrix();\n }\n \n inline void QMatrixNucleotide::setStateFreqsSharedPtr(QMatrix::freq_xchg_ptr_t freq_ptr) {\n if (freq_ptr->size() != 4)\n throw XLorad(boost::format(\"Expecting 4 state frequencies and got %d: perhaps you meant to specify a subset data type other than nucleotide\") % freq_ptr->size());\n double sum_of_freqs = std::accumulate(freq_ptr->begin(), freq_ptr->end(), 0.0);\n if (std::fabs(sum_of_freqs - 1.0) > 0.001)\n throw XLorad(boost::format(\"Expecting sum of 4 state frequencies to be 1, but instead got %g\") % sum_of_freqs);\n _state_freqs = freq_ptr;\n recalcRateMatrix();\n }\n \n inline void QMatrixNucleotide::setStateFreqs(QMatrix::freq_xchg_t & freqs) {\n if (freqs.size() != 4)\n throw XLorad(boost::format(\"Expecting 4 state frequencies and got %d: perhaps you meant to specify a subset data type other than nucleotide\") % freqs.size());\n std::copy(freqs.begin(), freqs.end(), _state_freqs->begin());\n recalcRateMatrix();\n }\n \n inline void QMatrixNucleotide::setOmegaSharedPtr(QMatrix::omega_ptr_t omega_ptr) {\n assert(false);\n }\n\n inline void QMatrixNucleotide::setOmega(QMatrix::omega_t omega) {\n assert(false);\n }\n\n#if defined(POLGSS)\n inline void QMatrixNucleotide::setStateFreqRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t freq_params_ptr) {\n if (freq_params_ptr->size() != 4)\n throw XLorad(boost::format(\"Expecting 4 state frequency reference distribution parameters and got %d: perhaps you meant to specify a subset data type other than nucleotide\") % freq_params_ptr->size());\n _state_freq_refdist = freq_params_ptr;\n }\n \n inline std::vector QMatrixNucleotide::getStateFreqRefDistParamsVect() const {\n return std::vector(_state_freq_refdist->begin(), _state_freq_refdist->end());\n }\n \n inline void QMatrixNucleotide::setExchangeabilityRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t xchg_params_ptr) {\n if (xchg_params_ptr->size() != 6)\n throw XLorad(boost::format(\"Expecting 6 exchangeability reference distribution parameters and got %d: perhaps you meant to specify a subset data type other than nucleotide\") % xchg_params_ptr->size());\n _exchangeability_refdist = xchg_params_ptr;\n }\n \n inline std::vector QMatrixNucleotide::getExchangeabilityRefDistParamsVect() const {\n return std::vector(_exchangeability_refdist->begin(), _exchangeability_refdist->end());\n }\n#endif\n \n inline void QMatrixNucleotide::recalcRateMatrix() {\n // Must have assigned both _state_freqs and _exchangeabilities to recalculate rate matrix\n if (!_is_active || !(_state_freqs && _exchangeabilities))\n return;\n \n double piA = (*_state_freqs)[0];\n double piC = (*_state_freqs)[1];\n double piG = (*_state_freqs)[2];\n double piT = (*_state_freqs)[3];\n \n Eigen::Map tmp(_state_freqs->data());\n _sqrtPi = tmp.sqrt().matrix().asDiagonal();\n _sqrtPiInv = _sqrtPi.inverse();\n\n assert(_exchangeabilities->size() == 6);\n double rAC = (*_exchangeabilities)[0];\n double rAG = (*_exchangeabilities)[1];\n double rAT = (*_exchangeabilities)[2];\n double rCG = (*_exchangeabilities)[3];\n double rCT = (*_exchangeabilities)[4];\n double rGT = (*_exchangeabilities)[5];\n\n double inverse_scaling_factor = piA*(rAC*piC + rAG*piG + rAT*piT) + piC*(rAC*piA + rCG*piG + rCT*piT) + piG*(rAG*piA + rCG*piC + rGT*piT) + piT*(rAT*piA + rCT*piC + rGT*piG);\n double scaling_factor = 1.0/inverse_scaling_factor;\n\n _Q(0,0) = -scaling_factor*(rAC*piC + rAG*piG + rAT*piT);\n _Q(0,1) = scaling_factor*rAC*piC;\n _Q(0,2) = scaling_factor*rAG*piG;\n _Q(0,3) = scaling_factor*rAT*piT;\n\n _Q(1,0) = scaling_factor*rAC*piA;\n _Q(1,1) = -scaling_factor*(rAC*piA + rCG*piG + rCT*piT);\n _Q(1,2) = scaling_factor*rCG*piG;\n _Q(1,3) = scaling_factor*rCT*piT;\n\n _Q(2,0) = scaling_factor*rAG*piA;\n _Q(2,1) = scaling_factor*rCG*piC;\n _Q(2,2) = -scaling_factor*(rAG*piA + rCG*piC + rGT*piT);\n _Q(2,3) = scaling_factor*rGT*piT;\n\n _Q(3,0) = scaling_factor*rAT*piA;\n _Q(3,1) = scaling_factor*rCT*piC;\n _Q(3,2) = scaling_factor*rGT*piG;\n _Q(3,3) = -scaling_factor*(rAT*piA + rCT*piC + rGT*piG);\n\n // S is a symmetric matrix\n eigenMatrix4d_t S = eigenMatrix4d_t(_sqrtPi*_Q*_sqrtPiInv);\n\n // Can use efficient eigensystem solver because S is symmetric\n Eigen::SelfAdjointEigenSolver solver(S);\n if (solver.info() != Eigen::Success) {\n throw XLorad(\"Error in the calculation of eigenvectors and eigenvalues of the GTR rate matrix\");\n }\n\n _eigenvectors = _sqrtPiInv*solver.eigenvectors();\n _inverse_eigenvectors = solver.eigenvectors().transpose()*_sqrtPi;\n _eigenvalues = solver.eigenvalues();\n }\n \n class QMatrixCodon : public QMatrix {\n\n public:\n typedef Eigen::Matrix eigenMatrixXd_t;\n typedef Eigen::VectorXd eigenVectorXd_t;\n \n QMatrixCodon(GeneticCode::SharedPtr gcode);\n ~QMatrixCodon();\n \n void clear();\n\n void setEqualStateFreqs(freq_xchg_ptr_t freq_ptr);\n void setStateFreqsSharedPtr(freq_xchg_ptr_t freq_ptr);\n void setStateFreqs(freq_xchg_t & freqs);\n freq_xchg_ptr_t getStateFreqsSharedPtr();\n const double * getStateFreqs() const;\n\n void setEqualExchangeabilities(freq_xchg_ptr_t xchg_ptr);\n void setExchangeabilitiesSharedPtr(freq_xchg_ptr_t xchg_ptr);\n void setExchangeabilities(freq_xchg_t & xchg);\n freq_xchg_ptr_t getExchangeabilitiesSharedPtr();\n const double * getExchangeabilities() const;\n\n void setOmegaSharedPtr(omega_ptr_t omega_ptr);\n void setOmega(omega_t omega);\n omega_ptr_t getOmegaSharedPtr();\n double getOmega() const;\n\n#if defined(POLGSS)\n void setStateFreqRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t freq_params_ptr);\n std::vector getStateFreqRefDistParamsVect() const;\n void setExchangeabilityRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t xchg_params_ptr);\n std::vector getExchangeabilityRefDistParamsVect() const;\n#endif\n\n const double * getEigenvectors() const;\n const double * getInverseEigenvectors() const;\n const double * getEigenvalues() const;\n \n protected:\n \n virtual void recalcRateMatrix();\n\n private:\n \n // workspaces for computing eigenvectors/eigenvalues\n eigenMatrixXd_t _sqrtPi;\n eigenMatrixXd_t _sqrtPiInv;\n eigenMatrixXd_t _Q;\n eigenMatrixXd_t _eigenvectors;\n eigenMatrixXd_t _inverse_eigenvectors;\n eigenVectorXd_t _eigenvalues;\n\n freq_xchg_ptr_t _state_freqs;\n omega_ptr_t _omega;\n\n std::vector _codons;\n std::vector _amino_acids;\n \n GeneticCode::SharedPtr _genetic_code;\n };\n\n inline QMatrixCodon::QMatrixCodon(GeneticCode::SharedPtr gcode) {\n assert(gcode);\n _genetic_code = gcode;\n clear();\n }\n\n inline QMatrixCodon::~QMatrixCodon() {\n }\n\n inline void QMatrixCodon::clear() {\n QMatrix::clear();\n\n unsigned nstates = _genetic_code->getNumNonStopCodons();\n _genetic_code->copyCodons(_codons);\n _genetic_code->copyAminoAcids(_amino_acids);\n \n QMatrix::omega_t omega = 0.1;\n _omega = std::make_shared(omega);\n \n QMatrix::freq_xchg_t freq_vect(nstates, 1./nstates);\n _state_freqs = std::make_shared(freq_vect);\n \n _sqrtPi.resize(nstates, nstates);\n _sqrtPiInv.resize(nstates, nstates);\n _Q.resize(nstates, nstates);\n _eigenvectors.resize(nstates, nstates);\n _inverse_eigenvectors.resize(nstates, nstates);\n _eigenvalues.resize(nstates);\n \n recalcRateMatrix();\n }\n\n inline QMatrix::freq_xchg_ptr_t QMatrixCodon::getExchangeabilitiesSharedPtr() {\n assert(false);\n return nullptr;\n }\n \n inline QMatrix::freq_xchg_ptr_t QMatrixCodon::getStateFreqsSharedPtr() {\n return _state_freqs;\n }\n\n inline QMatrix::omega_ptr_t QMatrixCodon::getOmegaSharedPtr() {\n return _omega;\n }\n \n inline const double * QMatrixCodon::getEigenvectors() const {\n return _eigenvectors.data();\n }\n \n inline const double * QMatrixCodon::getInverseEigenvectors() const {\n return _inverse_eigenvectors.data();\n }\n \n inline const double * QMatrixCodon::getEigenvalues() const {\n return _eigenvalues.data();\n }\n \n inline const double * QMatrixCodon::getExchangeabilities() const {\n assert(false);\n return 0;\n }\n\n inline const double * QMatrixCodon::getStateFreqs() const {\n return &(*_state_freqs)[0];\n }\n\n inline double QMatrixCodon::getOmega() const {\n return *_omega;\n }\n\n inline void QMatrixCodon::setEqualExchangeabilities(QMatrix::freq_xchg_ptr_t xchg_ptr) {\n assert(false);\n }\n \n inline void QMatrixCodon::setExchangeabilitiesSharedPtr(QMatrix::freq_xchg_ptr_t xchg_ptr) {\n assert(false);\n }\n \n inline void QMatrixCodon::setExchangeabilities(QMatrix::freq_xchg_t & xchg) {\n assert(false);\n }\n \n inline void QMatrixCodon::setEqualStateFreqs(QMatrix::freq_xchg_ptr_t freq_ptr) {\n _state_freqs = freq_ptr;\n unsigned nstates = _genetic_code->getNumNonStopCodons();\n _state_freqs->assign(nstates, 1./nstates);\n recalcRateMatrix();\n }\n \n inline void QMatrixCodon::setStateFreqsSharedPtr(QMatrix::freq_xchg_ptr_t freq_ptr) {\n unsigned nstates = _genetic_code->getNumNonStopCodons();\n if (freq_ptr->size() != nstates)\n throw XLorad(boost::format(\"Expecting %d state frequencies and got %d: perhaps you meant to specify a subset data type other than codon\") % nstates % freq_ptr->size());\n double sum_of_freqs = std::accumulate(freq_ptr->begin(), freq_ptr->end(), 0.0);\n if (std::fabs(sum_of_freqs - 1.0) > 0.001)\n throw XLorad(boost::format(\"Expecting sum of codon frequencies to be 1, but instead got %g\") % sum_of_freqs);\n _state_freqs = freq_ptr;\n normalizeFreqsOrExchangeabilities(_state_freqs);\n recalcRateMatrix();\n }\n \n inline void QMatrixCodon::setStateFreqs(QMatrix::freq_xchg_t & freqs) {\n unsigned nstates = _genetic_code->getNumNonStopCodons();\n if (freqs.size() != nstates)\n throw XLorad(boost::format(\"Expecting %d state frequencies and got %d: perhaps you meant to specify a subset data type other than codon\") % nstates % freqs.size());\n std::copy(freqs.begin(), freqs.end(), _state_freqs->begin());\n recalcRateMatrix();\n }\n \n inline void QMatrixCodon::setOmegaSharedPtr(QMatrix::omega_ptr_t omega_ptr) {\n _omega = omega_ptr;\n recalcRateMatrix();\n }\n\n inline void QMatrixCodon::setOmega(QMatrix::omega_t omega) {\n *_omega = omega;\n recalcRateMatrix();\n }\n\n inline void QMatrixCodon::recalcRateMatrix() {\n // Must have assigned both _state_freqs and _omega to recalculate rate matrix\n if (!_is_active || !(_state_freqs && _omega))\n return;\n \n unsigned nstates = _genetic_code->getNumNonStopCodons();\n assert(_state_freqs->size() == nstates);\n const double * pi = getStateFreqs();\n double omega = getOmega();\n \n Eigen::Map tmp(_state_freqs->data(), nstates);\n _sqrtPi = tmp.sqrt().matrix().asDiagonal();\n _sqrtPiInv = _sqrtPi.inverse();\n\n // Calculate (unscaled) instantaneous rate matrix\n _Q = Eigen::MatrixXd::Zero(nstates,nstates);\n\n for (unsigned i = 0; i < nstates-1; i++) {\n for (unsigned j = i+1; j < nstates; j++) {\n unsigned diffs = 0;\n if (_codons[i][0] != _codons[j][0])\n diffs++;\n if (_codons[i][1] != _codons[j][1])\n diffs++;\n if (_codons[i][2] != _codons[j][2])\n diffs++;\n if (diffs == 1) {\n bool synonymous = _amino_acids[i] == _amino_acids[j];\n _Q(i,j) = (synonymous ? 1.0 : omega)*pi[j];\n _Q(j,i) = (synonymous ? 1.0 : omega)*pi[i];\n _Q(i,i) -= _Q(i,j);\n _Q(j,j) -= _Q(j,i);\n }\n }\n }\n\n double average_rate = 0.0;\n for (unsigned i = 0; i < nstates; i++)\n average_rate -= pi[i]*_Q(i,i);\n double scaling_factor = 3.0/average_rate;\n\n _Q *= scaling_factor;\n\n // S is a symmetric matrix\n eigenMatrixXd_t S = eigenMatrixXd_t(_sqrtPi*_Q*_sqrtPiInv);\n\n // Can use efficient eigensystem solver because S is symmetric\n Eigen::SelfAdjointEigenSolver solver(S);\n if (solver.info() != Eigen::Success) {\n throw XLorad(\"Error in the calculation of eigenvectors and eigenvalues of the codon model rate matrix\");\n }\n\n _eigenvectors = _sqrtPiInv*solver.eigenvectors();\n _inverse_eigenvectors = solver.eigenvectors().transpose()*_sqrtPi;\n _eigenvalues = solver.eigenvalues();\n }\n \n#if defined(POLGSS)\n inline void QMatrixCodon::setStateFreqRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t freq_params_ptr) {\n if (freq_params_ptr->size() != 61)\n throw XLorad(boost::format(\"Expecting 61 state frequency reference distribution parameters and got %d: perhaps you meant to specify a subset data type other than codon\") % freq_params_ptr->size());\n _state_freq_refdist = freq_params_ptr;\n }\n \n inline std::vector QMatrixCodon::getStateFreqRefDistParamsVect() const {\n return std::vector(_state_freq_refdist->begin(), _state_freq_refdist->end());\n }\n \n inline void QMatrixCodon::setExchangeabilityRefDistParamsSharedPtr(QMatrix::freq_xchg_ptr_t xchg_params_ptr) {\n throw XLorad(\"Not expecting exchangeability reference distribution to be specified for a codon model\");\n }\n \n inline std::vector QMatrixCodon::getExchangeabilityRefDistParamsVect() const {\n throw XLorad(\"Not expecting to copy exchangeability reference distribution parameters for a codon model\");\n return std::vector();\n }\n#endif\n \n} // namespace lorad\n", "meta": {"hexsha": "ce1d8de6bdc3ebf52395ca72611f5aef2227dc37", "size": 27099, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/qmatrix.hpp", "max_stars_repo_name": "plewis/lorad", "max_stars_repo_head_hexsha": "bdc70e966e423e92aef66ef9d52220a5c241e6f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-06-17T17:07:24.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-17T17:07:24.000Z", "max_issues_repo_path": "src/qmatrix.hpp", "max_issues_repo_name": "plewis/hpd-histogram", "max_issues_repo_head_hexsha": "4cc35206e0505127bffe9db6f650852f07bb7f63", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, 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YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.2397680579437671}} {"text": "// External libraries\n#include \n#include \n#include \n\n// Local libraries\n#include \n#include \n#include \n\n// Local headers\n#include \"read.h\"\n#include \"messages.h\"\n#include \"modelParameters.h\"\n#include \"State.h\"\n#include \"write.h\"\n#include \"comptonScattMatrix.h\"\n#include \"thermalProcesses.h\"\n#include \"adafFunctions.h\"\n#include \"globalVariables.h\"\n#include \"flareProcesses.h\"\n#include \"redshiftFunction.h\"\n#include \"thermalDistribution.h\"\n#include \"NTtimescales.h\"\n#include \"NTinjection.h\"\n#include \"injectionNeutrons.h\"\n#include \"distributionNeutrons.h\"\n#include \"NTdistribution.h\"\n#include \"NTradiation.h\"\n#include \"absorption.h\"\n#include \"secondariesProcesses.h\"\n#include \"jetEmission.h\"\n\nusing namespace std;\n\nint main()\n{\n\tstring folder{ prepareOutputfolder() };\n\ttry {\n\t\tGlobalConfig = readConfig();\n\t\tprepareGlobalCfg();\n\t\t\n\t\tState model(GlobalConfig.get_child(\"model\"));\n\t\tredshiftFactor(model);\n\t\tcreateH5file(\"h5prueba.h5\", model);\n\t}\n\tcatch (std::runtime_error& e)\n\t{\n\t\tstd::cout << \"ERROR: \" << e.what() << std::endl;\n\t}\n\treturn 0;\n}\n\n/*\tstring folder{ prepareOutputfolder() };\n\ttry {\n\t\tGlobalConfig = readConfig();\n\t\tprepareGlobalCfg();\n\t\t\n\t\tState model(GlobalConfig.get_child(\"model\"));\n\t\twriteFields(model);\n\t\tredshiftFactor(model);\n\n\t\tif (calculateThermal) {\n\t\t\tif (calculateComptonScatt)\n\t\t\t\tcomptonScattMatrix(model);\n\t\t\telse\n\t\t\t\tcomptonScattMatrixRead(model);\n\t\t\tthermalRadiation(model, \"lumThermal.dat\");\n\t\t}\n\t\telse\n\t\t\treadEandRParamSpace(\"photonDensity\", model.photon.distribution, 0, 0);\n\t\t\n\t\t//writeEandRParamSpace(\"photonDensity_z\",model.photon.injection,0,0);\n\t\t\n\t\tif (calculateJetEmission)\n\t\t\tjetProcesses(model,\"lumJet.txt\");\n\t\t\n\t\tif (calculateFlare)\n\t\t\tflareProcesses(model);\n\t\t\n//***********nonthermal particles**************\t\t\n\t\tif (calculateNonThermal) {\n \n\t\t\tif (calculateLosses) {\n\t\t\t\tif (calculateNTelectrons)\n\t\t\t\t\tnonThermalTimescales(model.ntElectron, model, \"electronCoolingTimes.dat\");\n\t\t\t\tif (calculateNTprotons)\n\t\t\t\t\tnonThermalTimescales(model.ntProton, model, \"protonCoolingTimes.dat\");\n\t\t\t}\n\t\t\n\t\t\tif (calculateNTdistributions) {\n\n\t\t\t\tif (calculateNTelectrons) {\n\t\t\t\t\tinjection(model.ntElectron, model);\n\t\t\t\t\tif (accMethod == 0)\n\t\t\t\t\t\t//distributionSpatialDiffusion(model.ntElectron, model);\n\t\t\t\t\t\tdistributionMultiZone(model.ntElectron, model);\n\t\t\t\t\t\t//distributionSpatialDiffusionSteady(model.ntElectron, model);\n\t\t\t\t\telse\n\t\t\t\t\t\tdistributionSpatialDiffusion(model.ntElectron, model);\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\treadEandRParamSpace(\"electronInjection_vol\", model.ntElectron.injection, 0, 1);\n\t\t\t\t\treadEandRParamSpace(\"electronDistribution_vol\", model.ntElectron.distribution, 0, 1);\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\tif (calculateNTprotons) {\n\t\t\t\t\tif (accMethod == 0)\n\t\t\t\t\t\tinjection(model.ntProton,model);\n\t\t\t\t\telse\n\t\t\t\t\t\tinjection(model.ntProton,model);\n\t\t\t\t\t\t//injectionFokkerPlanckOneZone(model.ntProton,model);\n\t\t\t\t\tif (accMethod == 0)\n\t\t\t\t\t\tdistributionSpatialDiffusionSteady(model.ntProton, model);\n\t\t\t\t\t\t//distributionMultiZoneRadial(model.ntProton, model);\n\t\t\t\t\telse\n\t\t\t\t\t\tdistributionSpatialDiffusionSteady(model.ntProton, model);\n\t\t\t\t\t\t//distributionFokkerPlanckCompleteSteadyState(model.ntProton, model);\n\t\t\t\t\t\t//distributionFokkerPlanckSpatialDiffusionTimeDependent(model.ntProton, model);\n\t\t\t\t\t\t//distributionFokkerPlanckRadial(model.ntProton,model);\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\treadEandRParamSpace(\"protonInjection_vol\", model.ntProton.injection,0,1);\n\t\t\t\t\treadEandRParamSpace(\"protonDistribution_vol\", model.ntProton.distribution,0,1);\n\t\t\t\t}\n\t\t\t}\n\t\t\telse {\n\t\t\t\tif (calculateNTelectrons) {\n\t\t\t\t\treadEandRParamSpace(\"electronInjection_vol\",model.ntElectron.injection,0,1);\n\t\t\t\t\treadEandRParamSpace(\"electronDistribution_vol\",model.ntElectron.distribution,0,1);\n\t\t\t\t}\n\t\t\t\tif (calculateNTprotons) {\n\t\t\t\t\treadEandRParamSpace(\"protonInjection_vol\",model.ntProton.injection,0,1);\n\t\t\t\t\treadEandRParamSpace(\"protonDistribution_vol\",model.ntProton.distribution,0,1);\n\t\t\t\t}\n\t\t\t}\n\t\t\t\n\t\t\tif (calculateNonThermalLum)\n\t\t\t\tnonThermalRadiation(model, \"lumNonThermal.dat\");\n\t\t\telse {\n\t\t\t\treadEandRParamSpace(\"photonDensity\", model.photon.distribution, 0, 0);\n\t\t\t\treadEandRParamSpace(\"NTphotonDensity\", model.ntPhoton.distribution, 0, 0);\n\t\t\t\treadEandRParamSpace(\"NTphotonDensity\", model.ntPhoton.injection, 0, 0);\n\t\t\t\treadEandRParamSpace(\"opticalDepth_gg\", model.tau_gg, 0, 0);\n\t\t\t}\n\t\t\t\n\t\t\tif (calculateSecondaries)\n\t\t\t\tsecondariesProcesses(model);\n\t\t\telse {\n\t\t\t\treadEandRParamSpace(\"muonDistribution\", model.ntMuon.distribution, 0, 0);\n\t\t\t\treadEandRParamSpace(\"pionDistribution\", model.ntChargedPion.distribution, 0, 0);\n\t\t\t}\n\t\t\t\n\t\t\t// NEUTRINO TRANSPORT\n\t\t\tif (calculateNeutrinos)\n\t\t\t\tinjectionNeutrino(model.neutrino, model);\n\t\t\t\t\n\t\t\t// NEUTRON TRANPOSRT\n\t\t\tif (calculateNeutronInj) {\n\t\t\t\tinjectionNeutrons(model);\n\t\t\t\tradiativeLossesNeutron(model.ntNeutron,model,\"neutronTimescales.dat\");\n\t\t\t\tif (calculateNeutronDis)\n\t\t\t\t\tdistributionNeutronsAGN(model);\n\t\t\t\tif (calculateJetDecay)\n\t\t\t\t\tjetNeutronDecay(model);\n\t\t\t}\n\t\t}\n\t}\n\tcatch (std::runtime_error& e)\n\t{\n\t\tstd::cout << \"ERROR: \" << e.what() << std::endl;\n\t}\n\treturn 0;\n}*/", "meta": {"hexsha": "80ca4802768bf2f5cbba9372e55704a4ea801b0c", "size": 5168, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/adaf/main.cpp", "max_stars_repo_name": "eduardomgutierrez/RIAF_radproc", "max_stars_repo_head_hexsha": "0e4166f04cce27fed2cbd2c7078023c10e0e8d12", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-08-30T06:56:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-30T06:56:03.000Z", "max_issues_repo_path": "src/adaf/main.cpp", "max_issues_repo_name": "eduardomgutierrez/RIAF_radproc", "max_issues_repo_head_hexsha": "0e4166f04cce27fed2cbd2c7078023c10e0e8d12", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/adaf/main.cpp", "max_forks_repo_name": "eduardomgutierrez/RIAF_radproc", "max_forks_repo_head_hexsha": "0e4166f04cce27fed2cbd2c7078023c10e0e8d12", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.7011494253, "max_line_length": 90, "alphanum_fraction": 0.7174922601, "num_tokens": 1459, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.2392746598334529}} {"text": "/* _______________________________________________________________________\n\n DAKOTA: Design Analysis Kit for Optimization and Terascale Applications\n Copyright 2014 Sandia Corporation.\n This software is distributed under the GNU Lesser General Public License.\n For more information, see the README file in the top Dakota directory.\n _______________________________________________________________________ */\n\n\n//- Class:\t SensAnalysisGlobal\n//- Description: Utility helper class which has correlations and VBD\n//- Owner: Laura Swiler, Brian Adams, Ahmad Rushdi\n//- Checked by:\n//- Version:\n\n#include \"SensAnalysisGlobal.hpp\"\n#include \"ResultsManager.hpp\"\n#include \"dakota_linear_algebra.hpp\"\n#include \n#include \n\nstatic const char rcsId[]=\"@(#) $Id: SensAnalysisGlobal.cpp 6170 2009-10-06 22:42:15Z lpswile $\";\n\n\nnamespace Dakota {\n\nRealArray SensAnalysisGlobal::rawData = RealArray();\n\n\nbool SensAnalysisGlobal::rank_sort(const int& x, const int& y)\n{ return rawData[x]second.function_value(k))) {\n valid_sample[j] = false; \n break; \n }\n if (valid_sample[j])\n ++num_valid_samples;\n }\n\n return num_valid_samples;\n}\n\n\nvoid SensAnalysisGlobal::\nvalid_sample_matrix(const VariablesArray& vars_samples,\n const IntResponseMap& resp_samples,\n const StringSetArray& dss_vals,\n const BoolDeque is_valid_sample,\n RealMatrix& valid_data) \n{\n int num_obs = vars_samples.size(), num_corr = valid_data.numRows();\n IntRespMCIter it = resp_samples.begin();\n for (size_t j=0, s_cntr=0; jsecond.function_values(), td_col_resp);\n ++s_cntr;\n }\n}\n\nvoid SensAnalysisGlobal::\nvalid_sample_matrix(const RealMatrix& vars_samples,\n const IntResponseMap& resp_samples,\n const BoolDeque is_valid_sample,\n RealMatrix& valid_data)\n{\n int num_obs = vars_samples.numCols(), num_corr = valid_data.numRows();\n IntRespMCIter it = resp_samples.begin();\n for (int j=0, s_cntr=0; jsecond.function_values(), td_col_resp);\n ++s_cntr;\n }\n}\n\n\n/** When converting values to ranks, uses the average ranks of any tied values */\nvoid SensAnalysisGlobal::values_to_ranks(RealMatrix& valid_data)\n{\n int num_corr = valid_data.numRows(), num_valid_samples = valid_data.numCols();\n // for each var/resp\n for (int i=0; ifirst;\n std::pair\n\ttied_range = vals_inds.equal_range(value); \n int num_ties = std::distance(tied_range.first, tied_range.second);\n double avg_rank = (rank + rank+num_ties-1) / 2.0;\n // all tied values get assigned the average rank\n for ( ; tied_range.first != tied_range.second; ++tied_range.first)\n\tvalid_data(i, tied_range.first->second) = avg_rank;\n // increment to the next unequal value\n vi_it = tied_range.second;\n rank += num_ties;\n }\n }\n}\n\n\nvoid SensAnalysisGlobal::center_rows(RealMatrix& data_matrix)\n{\n int num_row = data_matrix.numRows(), num_col = data_matrix.numCols();\n for (int i=0; i 1.0)\n corr_value = corr_value / std::abs(corr_value);\n}\n\n\n/** This version is used when full variables objects are being\n processed. Calculates simple correlation, partial correlation,\n simple rank correlation, and partial rank correlation\n coefficients. */\nvoid SensAnalysisGlobal::\ncompute_correlations(const VariablesArray& vars_samples,\n const IntResponseMap& resp_samples,\n const StringSetArray& dss_vals)\n{\n size_t num_obs = vars_samples.size();\n if (num_obs == 0) {\n Cerr << \"Error: Number of samples must be nonzero in SensAnalysisGlobal::\"\n << \"compute_correlations().\" << std::endl;\n abort_handler(-1);\n }\n if (resp_samples.size() != num_obs) {\n Cerr << \"Error: Mismatch in array lengths in SensAnalysisGlobal::\"\n << \"compute_correlations().\" << std::endl;\n abort_handler(-1);\n }\n\n numVars = vars_samples[0].cv() + vars_samples[0].div() + \n vars_samples[0].dsv() + vars_samples[0].drv();\n numFns = resp_samples.begin()->second.num_functions();\n int num_corr = numVars + numFns;\n\n // determine which samples have valid responses\n BoolDeque is_valid_sample(num_obs);\n int num_valid_samples = find_valid_samples(resp_samples, is_valid_sample);\n \n // The following calls regenerate and destroy the valid_data matrix\n // to save memory\n\n // create a matrix containing only the valid sample data\n RealMatrix valid_data(num_corr, num_valid_samples);\n\n // calculate simple rank correlation coeff\n valid_sample_matrix(vars_samples, resp_samples, dss_vals, is_valid_sample, \n valid_data);\n simple_corr(valid_data, num_corr, simpleCorr);\n\n // calculate partial correlation coeff\n valid_sample_matrix(vars_samples, resp_samples, dss_vals, is_valid_sample, \n valid_data);\n partial_corr(valid_data, numVars, simpleCorr, partialCorr, numericalIssuesRaw);\n\n // calculate simple rank correlation coeff\n valid_sample_matrix(vars_samples, resp_samples, dss_vals, is_valid_sample, \n valid_data);\n values_to_ranks(valid_data);\n simple_corr(valid_data, num_corr, simpleRankCorr);\n\n // calculate partial rank correlation coeff\n valid_sample_matrix(vars_samples, resp_samples, dss_vals, is_valid_sample, \n valid_data);\n values_to_ranks(valid_data);\n partial_corr(valid_data, numVars, simpleRankCorr, partialRankCorr, \n numericalIssuesRank);\n\n corrComputed = true;\n}\n\n/** This version is used when compact samples matrix is being\n processed. Calculates simple correlation, partial correlation,\n simple rank correlation, and partial rank correlation\n coefficients. */\nvoid SensAnalysisGlobal::\ncompute_correlations(const RealMatrix& vars_samples,\n const IntResponseMap& resp_samples)\n{\n int num_obs = vars_samples.numCols();\n if (!num_obs) {\n Cerr << \"Error: Number of samples must be nonzero in SensAnalysisGlobal::\"\n << \"compute_correlations().\" << std::endl;\n abort_handler(-1);\n }\n if (resp_samples.size() != num_obs) {\n Cerr << \"Error: Mismatch in array lengths in SensAnalysisGlobal::\"\n << \"compute_correlations().\" << std::endl;\n abort_handler(-1);\n }\n\n numVars = vars_samples.numRows();\n numFns = resp_samples.begin()->second.num_functions();\n int num_corr = numVars + numFns;\n\n // determine which samples have valid responses\n BoolDeque is_valid_sample(num_obs);\n int num_valid_samples = find_valid_samples(resp_samples, is_valid_sample);\n\n // The following calls regenerate and destroy the valid_data matrix\n // to save memory\n\n // create a matrix containing only the valid sample data\n RealMatrix valid_data(num_corr, num_valid_samples);\n\n // calculate simple rank correlation coeff\n valid_sample_matrix(vars_samples, resp_samples, is_valid_sample, valid_data);\n simple_corr(valid_data, num_corr, simpleCorr);\n\n // calculate partial correlation coeff\n valid_sample_matrix(vars_samples, resp_samples, is_valid_sample, valid_data);\n partial_corr(valid_data, numVars, simpleCorr, partialCorr, numericalIssuesRaw);\n\n // calculate simple rank correlation coeff\n valid_sample_matrix(vars_samples, resp_samples, is_valid_sample, valid_data);\n values_to_ranks(valid_data);\n simple_corr(valid_data, num_corr, simpleRankCorr);\n\n // calculate partial rank correlation coeff\n valid_sample_matrix(vars_samples, resp_samples, is_valid_sample, valid_data);\n values_to_ranks(valid_data);\n partial_corr(valid_data, numVars, simpleRankCorr, partialRankCorr, \n numericalIssuesRank);\n\n corrComputed = true;\n}\n\n\n/** Calculates simple correlation coefficients from a matrix of data\n (oriented factors x observations):\n - num_corr is number of rows of total data \n - num_in indicates whether only pairs of correlations should be\n calculated between pairs of columns (num_in\n vs. num_corr-num_in); if num_in = num_corr, correlations are\n calculated between all columns */\nvoid SensAnalysisGlobal::\nsimple_corr(RealMatrix& total_data, const int& num_in, RealMatrix& corr_matrix)\n{\n int num_corr = total_data.numRows(), num_obs = total_data.numCols();\n\n center_rows(total_data);\n\n for (int i=0; i::quiet_NaN());\n else {\n corr_matrix.multiply(Teuchos::NO_TRANS, Teuchos::TRANS, 1.0, \n\t\t\t total_data, total_data, 0.0);\n for (int i=0; i::quiet_NaN());\n else {\n RealMatrix total_data_in(Teuchos::View, total_data, num_in, num_obs, 0, 0);\n RealMatrix total_data_out(Teuchos::View, total_data, num_out, num_obs, \n\t\t\t\tnum_in, 0);\n corr_matrix.multiply(Teuchos::NO_TRANS, Teuchos::TRANS, 1.0, \n\t\t\t total_data_in, total_data_out, 0.0);\n // snap all finite values to [-1.0, 1.0]\n for (int i=0; i::quiet_NaN());\n numerical_issues = true;\n return;\n }\n \n // For a single input factor, partial = simple (no controlling factors)\n if (num_in == 1) {\n for (int k=0; k::epsilon() * control_factors_Z.normInf();\n int sv_keep = 0;\n for ( ; sv_keep < sing_vals.length(); ++sv_keep)\n if (sing_vals[sv_keep] < tol)\n break;\n // TODO: Could opt for stricter check here:\n // if (sv_keep < std::min(num_obs, num_in-1))\n if (sv_keep == 0)\n numerical_except[i] = true;\n else {\n v_trans.reshape(sv_keep, num_in - 1);\n Zinv_Zt_X.reshape(sv_keep, 1 + num_out);\n Zinv_Zt_X.multiply(Teuchos::NO_TRANS, Teuchos::NO_TRANS, 1.0, \n v_trans, Zt_X, 0.0);\n for (int j=0; j::quiet_NaN();\n else\n corr_matrix(i,k) = partial_cov(0, k+1) / std::sqrt(partial_cov(0,0)) / \n std::sqrt(partial_cov(k+1, k+1));\n }\n\n // snap all finite values to [-1.0, 1.0]\n for (int i=0; i\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"utils/maths.hpp\"\n#include \"core/models/haplotype_likelihood_cache.hpp\"\n\n/**\n *\n * This file contains an implementation of the Variational Bayes mixture model\n * used by some genotype models. The notation follows the documentation.\n *\n */\n\nnamespace octopus { namespace model {\n\n// Types needed for Variational Bayes model\n\nstruct VariationalBayesParameters\n{\n double epsilon;\n unsigned max_iterations;\n};\n\nusing ProbabilityVector = std::vector;\nusing LogProbabilityVector = std::vector;\n\ntemplate \nusing VBAlpha = std::array;\ntemplate \nusing VBAlphaVector = std::vector>;\n\nclass VBReadLikelihoodArray\n{\npublic:\n using BaseType = HaplotypeLikelihoodCache::LikelihoodVector;\n \n VBReadLikelihoodArray() = default;\n \n explicit VBReadLikelihoodArray(const BaseType&);\n \n VBReadLikelihoodArray(const VBReadLikelihoodArray&) = default;\n VBReadLikelihoodArray& operator=(const VBReadLikelihoodArray&) = default;\n VBReadLikelihoodArray(VBReadLikelihoodArray&&) = default;\n VBReadLikelihoodArray& operator=(VBReadLikelihoodArray&&) = default;\n \n ~VBReadLikelihoodArray() = default;\n \n void operator=(const BaseType&);\n void operator=(std::reference_wrapper);\n std::size_t size() const noexcept;\n BaseType::const_iterator begin() const noexcept;\n BaseType::const_iterator end() const noexcept;\n double operator[](const std::size_t n) const noexcept;\n\nprivate:\n const BaseType* likelihoods;\n};\n\ntemplate \nusing VBGenotype = std::array; // One element per haplotype in genotype (i.e. K)\ntemplate \nusing VBGenotypeVector = std::vector>; // Per element per genotype\ntemplate \nusing VBReadLikelihoodMatrix = std::vector>; // One element per sample\n\ntemplate \nusing VBTau = std::array; // One element per haplotype in genotype (i.e. K)\ntemplate \nusing VBResponsabilityVector = std::vector>; // One element per genotype\ntemplate \nusing VBResponsabilityMatrix = std::vector>; // One element per sample\n\ntemplate \nstruct VBLatents\n{\n ProbabilityVector genotype_posteriors;\n LogProbabilityVector genotype_log_posteriors;\n VBAlphaVector alphas;\n VBResponsabilityMatrix responsabilities;\n};\n\n// Main VB method\n\ntemplate \nstd::pair, double>\nrun_variational_bayes(const VBAlphaVector& prior_alphas,\n const LogProbabilityVector& genotype_log_priors,\n const VBReadLikelihoodMatrix& log_likelihoods,\n const VariationalBayesParameters& params,\n std::vector seeds);\n\nnamespace detail {\n\nusing VBInverseLikelihood = std::vector; // One element per genotype\nusing VBInverseGenotype = std::vector; // One element per read\ntemplate \nusing VBInverseGenotypeVector = std::array; // One element per haplotype in genotype\ntemplate \nusing VBInverseReadLikelihoodMatrix = std::vector>; // One element per sample\n\ntemplate \nauto invert(const VBGenotypeVector& likelihoods)\n{\n static_assert(K > 0, \"K == 0\");\n const auto num_genotypes = likelihoods.size();\n assert(num_genotypes > 0);\n const auto num_reads = likelihoods.front().front().size();\n VBInverseGenotypeVector result {};\n for (std::size_t k {0}; k < K; ++k) {\n result[k] = VBInverseGenotype(num_reads, VBInverseLikelihood(num_genotypes));\n for (std::size_t n {0}; n < num_reads; ++n) {\n for (std::size_t g {0}; g < num_genotypes; ++g) {\n result[k][n][g] = likelihoods[g][k][n];\n }\n }\n }\n return result;\n}\n\ntemplate \nauto invert(const VBReadLikelihoodMatrix& matrix)\n{\n VBInverseReadLikelihoodMatrix result {};\n result.reserve(matrix.size());\n std::transform(std::cbegin(matrix), std::cend(matrix), std::back_inserter(result),\n [] (const auto& v) { return invert(v); });\n return result;\n}\n\ninline ProbabilityVector& exp(const LogProbabilityVector& log_probabilities, ProbabilityVector& result) noexcept\n{\n std::transform(std::cbegin(log_probabilities), std::cend(log_probabilities), std::begin(result),\n [] (const auto lp) noexcept { return std::exp(lp); });\n return result;\n}\n\ninline ProbabilityVector exp(const LogProbabilityVector& log_probabilities)\n{\n ProbabilityVector result(log_probabilities.size());\n return exp(log_probabilities, result);\n}\n\ninline auto sum(const VBAlpha<2>& alpha) noexcept\n{\n return alpha[0] + alpha[1];\n}\n\ninline auto sum(const VBAlpha<3>& alpha) noexcept\n{\n return alpha[0] + alpha[1] + alpha[2];\n}\n\ntemplate \nauto sum(const VBAlpha& alpha) noexcept\n{\n return std::accumulate(std::cbegin(alpha), std::cend(alpha), 0.0);\n}\n\ntemplate \ninline auto digamma_diff(const T a, const T b)\n{\n using boost::math::digamma;\n return digamma(a) - digamma(b);\n}\n\ntemplate \ninline T log_sum_exp(const std::array& logs)\n{\n return logs[0];\n}\n\ntemplate \ninline T log_sum_exp(const std::array& logs)\n{\n return maths::log_sum_exp(logs[0], logs[1]);\n}\n\ntemplate \ninline T log_sum_exp(const std::array& logs)\n{\n return maths::log_sum_exp(logs[0], logs[1], logs[2]);\n}\n\ntemplate \nT log_sum_exp(const std::array& logs)\n{\n return maths::log_sum_exp(logs);\n}\n\ntemplate \nauto count_reads(const VBGenotypeVector& likelihoods) noexcept\n{\n return likelihoods[0][0].size();\n}\n\ntemplate \nauto count_reads(const VBInverseGenotypeVector& likelihoods) noexcept\n{\n return likelihoods[0].size();\n}\n\ntemplate \nauto marginalise(const ProbabilityVector& distribution, const VBGenotypeVector& likelihoods,\n const unsigned k, const std::size_t n) noexcept\n{\n return std::inner_product(std::cbegin(distribution), std::cend(distribution),\n std::cbegin(likelihoods), 0.0, std::plus<> {},\n [k, n] (const auto p, const auto& haplotype_likelihoods) noexcept {\n return p * haplotype_likelihoods[k][n];\n });\n}\n\ntemplate \nauto marginalise(const ProbabilityVector& distribution, const VBInverseGenotypeVector& likelihoods,\n const unsigned k, const std::size_t n) noexcept\n{\n return std::inner_product(std::cbegin(distribution), std::cend(distribution), std::cbegin(likelihoods[k][n]), 0.0);\n}\n\ntemplate \nVBResponsabilityVector\ninit_responsabilities(const VBAlpha& prior_alphas,\n const ProbabilityVector& genotype_probabilities,\n const VBLikelihoodVector_& read_likelihoods)\n{\n using T = typename VBAlpha::value_type;\n std::array al; // no need to keep recomputing this\n const auto a0 = sum(prior_alphas);\n for (unsigned k {0}; k < K; ++k) {\n al[k] = digamma_diff(prior_alphas[k], a0);\n }\n const auto N = count_reads(read_likelihoods);\n VBResponsabilityVector result(N);\n std::array ln_rho;\n for (std::size_t n {0}; n < N; ++n) {\n for (unsigned k {0}; k < K; ++k) {\n ln_rho[k] = al[k] + marginalise(genotype_probabilities, read_likelihoods, k, n);\n }\n const auto ln_rho_norm = log_sum_exp(ln_rho);\n for (unsigned k {0}; k < K; ++k) {\n result[n][k] = std::exp(ln_rho[k] - ln_rho_norm);\n }\n }\n return result;\n}\n\ntemplate \nVBResponsabilityMatrix\ninit_responsabilities(const VBAlphaVector& prior_alphas,\n const ProbabilityVector& genotype_probabilities,\n const VBLikelihoodMatrix& read_likelihoods)\n{\n const auto S = read_likelihoods.size(); // num samples\n VBResponsabilityMatrix result{};\n result.reserve(S);\n for (std::size_t s {0}; s < S; ++s) {\n result.push_back(init_responsabilities(prior_alphas[s], genotype_probabilities, read_likelihoods[s]));\n }\n return result;\n}\n\ntemplate \nvoid update_responsabilities(VBResponsabilityVector& result,\n const VBAlpha& posterior_alphas,\n const ProbabilityVector& genotype_probabilities,\n const VBLikelihoodVector_& read_likelihoods)\n{\n using T = typename VBAlpha::value_type;\n std::array al;\n const auto a0 = sum(posterior_alphas);\n for (unsigned k {0}; k < K; ++k) {\n al[k] = digamma_diff(posterior_alphas[k], a0);\n }\n const auto N = count_reads(read_likelihoods);\n std::array ln_rho;\n for (std::size_t n {0}; n < N; ++n) {\n for (unsigned k {0}; k < K; ++k) {\n ln_rho[k] = al[k] + marginalise(genotype_probabilities, read_likelihoods, k, n);\n }\n const auto ln_rho_norm = log_sum_exp(ln_rho);\n for (unsigned k {0}; k < K; ++k) {\n result[n][k] = std::exp(ln_rho[k] - ln_rho_norm);\n }\n }\n}\n\n// same as init_responsabilities but in-place\ntemplate \nvoid update_responsabilities(VBResponsabilityMatrix& result,\n const VBAlphaVector& posterior_alphas,\n const ProbabilityVector& genotype_probabilities,\n const VBLikelihoodMatrix& read_likelihoods)\n{\n const auto S = read_likelihoods.size();\n for (std::size_t s {0}; s < S; ++s) {\n update_responsabilities(result[s], posterior_alphas[s], genotype_probabilities, read_likelihoods[s]);\n }\n}\n\ntemplate \nauto sum(const VBResponsabilityVector& taus, const unsigned k) noexcept\n{\n return std::accumulate(std::cbegin(taus), std::cend(taus), 0.0,\n [k] (const auto curr, const auto& tau) noexcept {\n return curr + tau[k];\n });\n}\n\ntemplate \nvoid update_alpha(VBAlpha& alpha, const VBAlpha& prior_alpha,\n const VBResponsabilityVector& taus) noexcept\n{\n for (unsigned k {0}; k < K; ++k) {\n alpha[k] = prior_alpha[k] + sum(taus, k);\n }\n}\n\ntemplate \nvoid update_alphas(VBAlphaVector& alphas, const VBAlphaVector& prior_alphas,\n const VBResponsabilityMatrix& responsabilities) noexcept\n{\n const auto S = alphas.size();\n assert(S == prior_alphas.size() && S == responsabilities.size());\n for (std::size_t s {0}; s < S; ++s) {\n update_alpha(alphas[s], prior_alphas[s], responsabilities[s]);\n }\n}\n\ntemplate \nauto marginalise(const VBResponsabilityVector& responsabilities,\n const VBGenotypeVector& read_likelihoods,\n const std::size_t g) noexcept\n{\n double result {0};\n const auto N = read_likelihoods[0][0].size(); // num reads in sample s\n assert(responsabilities.size() == N);\n assert(responsabilities[0].size() == K && read_likelihoods[g].size() == K);\n for (unsigned k {0}; k < K; ++k) {\n const auto& k_likelihoods = read_likelihoods[g][k];\n for (std::size_t n {0}; n < N; ++n) {\n result += responsabilities[n][k] * k_likelihoods[n];\n }\n }\n return result;\n}\n\ntemplate \nauto marginalise(const VBResponsabilityMatrix& responsabilities,\n const VBReadLikelihoodMatrix& read_likelihoods,\n const std::size_t g) noexcept\n{\n double result {0};\n const auto S = read_likelihoods.size(); // num samples\n assert(S == responsabilities.size());\n for (std::size_t s {0}; s < S; ++s) {\n result += marginalise(responsabilities[s], read_likelihoods[s], g);\n }\n return result;\n}\n\ntemplate \nvoid update_genotype_log_posteriors(LogProbabilityVector& result,\n const LogProbabilityVector& genotype_log_priors,\n const VBResponsabilityMatrix& responsabilities,\n const VBReadLikelihoodMatrix& read_likelihoods)\n{\n const auto G = result.size();\n for (std::size_t g {0}; g < G; ++g) {\n result[g] = genotype_log_priors[g] + marginalise(responsabilities, read_likelihoods, g);\n }\n maths::normalise_logs(result);\n}\n\ninline auto max_change(const VBAlpha<2>& lhs, const VBAlpha<2>& rhs) noexcept\n{\n return std::max(std::abs(lhs.front() - rhs.front()), std::abs(lhs.back() - rhs.back()));\n}\n\ninline auto max_change(const VBAlpha<3>& lhs, const VBAlpha<3>& rhs) noexcept\n{\n return std::max({std::abs(lhs[0] - rhs[0]), std::abs(lhs[1] - rhs[1]), std::abs(lhs[2] - rhs[2])});\n}\n\ntemplate \nauto max_change(const VBAlpha& lhs, const VBAlpha& rhs) noexcept\n{\n double result {0};\n for (std::size_t k {0}; k < K; ++k) {\n const auto curr = std::abs(lhs[k] - rhs[k]);\n if (curr > result) result = curr;\n }\n return result;\n}\n\ntemplate \nauto max_change(const VBAlphaVector& prior_alphas, const VBAlphaVector& posterior_alphas) noexcept\n{\n const auto S = prior_alphas.size();\n assert(S == posterior_alphas.size());\n double result {0};\n for (std::size_t s {0}; s < S; ++s) {\n const auto curr = max_change(prior_alphas[s], posterior_alphas[s]);\n if (curr > result) result = curr;\n }\n return result;\n}\n\ntemplate \nstd::pair check_convergence(const VBAlphaVector& prior_alphas,\n const VBAlphaVector& posterior_alphas,\n const double prev_max_change,\n const double epsilon) noexcept\n{\n const auto new_max_change = max_change(prior_alphas, posterior_alphas);\n return std::make_pair(std::abs(new_max_change - prev_max_change) < epsilon, new_max_change);\n}\n\ntemplate \nauto entropy(const VBTau& tau) noexcept\n{\n return -std::accumulate(std::cbegin(tau), std::cend(tau), 0.0,\n [] (const auto curr, const auto t) noexcept { return curr + (t * std::log(t)); });\n}\n\n// E [ln q(Z_s)]\ntemplate \nauto sum_entropies(const VBResponsabilityVector& taus) noexcept\n{\n return std::accumulate(std::cbegin(taus), std::cend(taus), 0.0,\n [] (const auto curr, const auto& tau) noexcept { return curr + entropy(tau); });\n}\n\ntemplate \nauto calculate_evidence_lower_bound(const VBAlphaVector& prior_alphas,\n const VBAlphaVector& posterior_alphas,\n const LogProbabilityVector& genotype_log_priors,\n const ProbabilityVector& genotype_posteriors,\n const LogProbabilityVector& genotype_log_posteriors,\n const VBResponsabilityMatrix& taus,\n const VBReadLikelihoodMatrix& log_likelihoods,\n const boost::optional max_posterior_skip = boost::none)\n{\n const auto G = genotype_log_priors.size();\n const auto S = log_likelihoods.size();\n double result {0};\n for (std::size_t g {0}; g < G; ++g) {\n if (!max_posterior_skip || genotype_posteriors[g] >= *max_posterior_skip) {\n auto w = genotype_log_priors[g] - genotype_log_posteriors[g];\n for (std::size_t s {0}; s < S; ++s) {\n const auto N_s = taus[s].size();\n for (std::size_t k {0}; k < K; ++k) {\n for (std::size_t n {0}; n < N_s; ++n) {\n w += taus[s][n][k] * log_likelihoods[s][g][k][n];\n }\n }\n }\n result += genotype_posteriors[g] * w;\n }\n }\n for (std::size_t s {0}; s < S; ++s) {\n result += (maths::log_beta(posterior_alphas[s]) - maths::log_beta(prior_alphas[s]));\n result += sum_entropies(taus[s]);\n }\n return result;\n}\n\n// Main algorithm - single seed\n\n// Starting iteration with given genotype_log_posteriors\ntemplate \nVBLatents\nrun_variational_bayes(const VBAlphaVector& prior_alphas,\n const LogProbabilityVector& genotype_log_priors,\n const VBLikelihoodMatrix1& log_likelihoods1,\n const VBLikelihoodMatrix2& log_likelihoods2,\n LogProbabilityVector genotype_log_posteriors,\n const VariationalBayesParameters& params)\n{\n assert(!prior_alphas.empty());\n assert(!genotype_log_priors.empty());\n assert(!log_likelihoods1.empty());\n assert(log_likelihoods1.size() == log_likelihoods2.size());\n assert(prior_alphas.size() == log_likelihoods1.size()); // num samples\n assert(log_likelihoods1.front().size() == genotype_log_priors.size()); // num genotypes\n assert(params.max_iterations > 0);\n auto genotype_posteriors = exp(genotype_log_posteriors);\n auto posterior_alphas = prior_alphas;\n auto responsabilities = init_responsabilities(posterior_alphas, genotype_posteriors, log_likelihoods2);\n assert(responsabilities.size() == log_likelihoods1.size()); // num samples\n auto prev_evidence = std::numeric_limits::lowest();\n bool is_converged {};\n double max_change {};\n for (unsigned i {0}; i < params.max_iterations; ++i) {\n update_genotype_log_posteriors(genotype_log_posteriors, genotype_log_priors, responsabilities, log_likelihoods1);\n exp(genotype_log_posteriors, genotype_posteriors);\n update_alphas(posterior_alphas, prior_alphas, responsabilities);\n update_responsabilities(responsabilities, posterior_alphas, genotype_posteriors, log_likelihoods2);\n std::tie(is_converged, max_change) = check_convergence(prior_alphas, posterior_alphas, max_change, params.epsilon);\n if (is_converged) break;\n auto curr_evidence = calculate_evidence_lower_bound(prior_alphas, posterior_alphas, genotype_log_priors,\n genotype_posteriors, genotype_log_posteriors, responsabilities,\n log_likelihoods1, 1e-10);\n if (curr_evidence <= prev_evidence || (curr_evidence - prev_evidence) < params.epsilon) break;\n prev_evidence = curr_evidence;\n }\n return VBLatents {\n std::move(genotype_posteriors), std::move(genotype_log_posteriors),\n std::move(posterior_alphas), std::move(responsabilities)\n };\n}\n\n// Not using inverted log likelihoods\ntemplate \nVBLatents\nrun_variational_bayes(const VBAlphaVector& prior_alphas,\n const LogProbabilityVector& genotype_log_priors,\n const VBReadLikelihoodMatrix& log_likelihoods,\n LogProbabilityVector genotype_log_posteriors,\n const VariationalBayesParameters& params)\n{\n return run_variational_bayes(prior_alphas, genotype_log_posteriors, log_likelihoods,\n log_likelihoods, genotype_log_posteriors, params);\n}\n\n// Main algorithm - multiple seed\n\ntemplate \nbool run_vb_with_matrix_inversion(const VBReadLikelihoodMatrix& log_likelihoods,\n const VariationalBayesParameters& params,\n const std::vector& seeds) noexcept\n{\n return true;\n}\n\ntemplate \nstd::vector>\nrun_variational_bayes(const VBAlphaVector& prior_alphas,\n const LogProbabilityVector& genotype_log_priors,\n const VBReadLikelihoodMatrix& log_likelihoods,\n const VariationalBayesParameters& params,\n std::vector&& seeds)\n{\n std::vector> result {};\n result.reserve(seeds.size());\n if (run_vb_with_matrix_inversion(log_likelihoods, params, seeds)) {\n const auto inverted_log_likelihoods = invert(log_likelihoods);\n for (auto& seed : seeds) {\n result.push_back(detail::run_variational_bayes(prior_alphas, genotype_log_priors,\n log_likelihoods, inverted_log_likelihoods,\n std::move(seed), params));\n }\n } else {\n for (auto& seed : seeds) {\n result.push_back(detail::run_variational_bayes(prior_alphas, genotype_log_priors,\n log_likelihoods,\n std::move(seed), params));\n }\n }\n return result;\n}\n\n// lower-bound calculation\n\ntemplate \nauto calculate_evidence_lower_bound(const VBAlphaVector& prior_alphas,\n const LogProbabilityVector& genotype_log_priors,\n const VBReadLikelihoodMatrix& log_likelihoods,\n const VBLatents& latents)\n{\n return calculate_evidence_lower_bound(prior_alphas, latents.alphas, genotype_log_priors,\n latents.genotype_posteriors, latents.genotype_log_posteriors,\n latents.responsabilities, log_likelihoods);\n \n}\n\ntemplate \nstd::pair, double>\nget_max_evidence_latents(const VBAlphaVector& prior_alphas,\n const LogProbabilityVector& genotype_log_priors,\n const VBReadLikelihoodMatrix& log_likelihoods,\n std::vector>&& latents)\n{\n std::vector seed_evidences(latents.size());\n std::transform(std::cbegin(latents), std::cend(latents), std::begin(seed_evidences),\n [&] (const auto& seed_latents) {\n return calculate_evidence_lower_bound(prior_alphas, genotype_log_priors, log_likelihoods, seed_latents);\n });\n const auto max_itr = std::max_element(std::cbegin(seed_evidences), std::cend(seed_evidences));\n const auto max_idx = std::distance(std::cbegin(seed_evidences), max_itr);\n return std::make_pair(std::move(latents[max_idx]), *max_itr);\n}\n\n} // namespace detail\n\ntemplate \nstd::pair, double>\nrun_variational_bayes(const VBAlphaVector& prior_alphas,\n const LogProbabilityVector& genotype_log_priors,\n const VBReadLikelihoodMatrix& log_likelihoods,\n const VariationalBayesParameters& params,\n std::vector seeds)\n{\n assert(!seeds.empty());\n auto latents = detail::run_variational_bayes(prior_alphas, genotype_log_priors, log_likelihoods, params, std::move(seeds));\n return detail::get_max_evidence_latents(prior_alphas, genotype_log_priors, log_likelihoods, std::move(latents));\n}\n\ninline VBReadLikelihoodArray::VBReadLikelihoodArray(const BaseType& underlying_likelihoods)\n: likelihoods{std::addressof(underlying_likelihoods)} {}\n\ninline void VBReadLikelihoodArray::operator=(const BaseType& other)\n{\n likelihoods = std::addressof(other);\n}\n\ninline void VBReadLikelihoodArray::operator=(std::reference_wrapper other)\n{\n likelihoods = std::addressof(other.get());\n}\n\ninline std::size_t VBReadLikelihoodArray::size() const noexcept\n{\n return likelihoods->size();\n}\n\ninline VBReadLikelihoodArray::BaseType::const_iterator VBReadLikelihoodArray::begin() const noexcept\n{\n return likelihoods->begin();\n}\n\ninline VBReadLikelihoodArray::BaseType::const_iterator VBReadLikelihoodArray::end() const noexcept\n{\n return likelihoods->end();\n}\n\ninline double VBReadLikelihoodArray::operator[](const std::size_t n) const noexcept\n{\n return likelihoods->operator[](n);\n}\n\n} // namespace model\n} // namespace octopus\n\n#endif\n", "meta": {"hexsha": "4a80afaa4e8dc011c4e70c2f5a4887e0ba3814af", "size": 25113, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/core/models/genotype/variational_bayes_mixture_model.hpp", "max_stars_repo_name": "alimanfoo/octopus", "max_stars_repo_head_hexsha": "f3cc3f567f02fafe33f5a06e5be693d6ea985ee3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2018-08-21T23:34:28.000Z", "max_stars_repo_stars_event_max_datetime": "2018-08-21T23:34:28.000Z", "max_issues_repo_path": "src/core/models/genotype/variational_bayes_mixture_model.hpp", "max_issues_repo_name": "alimanfoo/octopus", "max_issues_repo_head_hexsha": "f3cc3f567f02fafe33f5a06e5be693d6ea985ee3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, 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YES\n2. NO", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.39606816627404173, "lm_q1q2_score": 0.23919817393835982}} {"text": "\n// Copyright Gavin Band 2008 - 2012.\n// Distributed under the Boost Software License, Version 1.0.\n// (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#define foreach BOOST_FOREACH\n#include \n#include \n#include \"appcontext/CmdLineOptionProcessor.hpp\"\n#include \"appcontext/ApplicationContext.hpp\"\n#include \"appcontext/ProgramFlow.hpp\"\n#include \"appcontext/get_current_time_as_string.hpp\"\n#include \"genfile/FileUtils.hpp\"\n#include \"genfile/utility.hpp\"\n#include \"statfile/BuiltInTypeStatSource.hpp\"\n#include \n\nnamespace globals {\n\tstd::string const program_name = \"overrep\" ;\n\tstd::string const program_version = \"0.1\" ;\n}\n\nstruct OverrepOptions: public appcontext::CmdLineOptionProcessor {\n\tstd::string get_program_name() const { return globals::program_name ; }\n\n\tvoid declare_options( appcontext::OptionProcessor& options ) {\n\t\toptions.set_help_option( \"-help\" ) ;\n\n\t\toptions.declare_group( \"File handling options\" ) ;\n\t\toptions[ \"-p\" ]\n\t\t\t.set_description( \"Specify the path of a file containing pathway definitions to load.\"\n\t\t\t \t\" This file must have at least three columns; the first should be an identifier for the pathway (no whitespace),\"\n\t\t\t\t\" the second should be the pathway name, and the third should contain gene identifiers.\" )\n\t\t\t.set_takes_values_until_next_option()\n\t\t\t.set_minimum_multiplicity( 1 )\n\t\t\t.set_maximum_multiplicity( 100 )\n\t\t;\n\n\t\toptions[ \"-c\" ]\n\t\t\t.set_description( \"Specify the path of a file containing gene clusters to load.\" )\n\t\t\t.set_takes_single_value()\n\t\t;\n\n\t\toptions[ \"-u\" ]\n\t\t\t.set_description( \"Specify the path of a file containing a list of genes to treat as the gene \\\"universe\\\".\" )\n\t\t\t.set_takes_single_value()\n\t\t\t.set_is_required()\n\t\t;\n\n\t\toptions[ \"-g\" ]\n\t\t\t.set_description( \"Specify the path of a file containing a list of genes to test with.\" )\n\t\t\t.set_takes_single_value()\n\t\t;\n\n\t\toptions.declare_group( \"Miscellaneous options\" ) ;\n\t\toptions[ \"-P-value\" ]\n\t\t\t.set_description( \"Output lists of genes for all pathways getting this P-value or better.\" )\n\t\t\t.set_takes_single_value()\n\t\t\t.set_default_value( 0.000001 )\n\t\t;\n\t\toptions[ \"-analysis-name\" ]\n\t\t\t.set_description( \"Specify a name to label results from this analysis with. (This applies to modules which store their results in a qcdb file.)\" )\n\t\t\t.set_takes_single_value()\n\t\t\t.set_is_required() ;\n\n\t\toptions[ \"-intersect\" ]\n\t\t\t.set_description( \"Specify that the universe should only include genes that actuall occur in pathways.\" ) ;\n\n\t\toptions[ \"-log\" ]\n\t\t\t.set_description( \"Specify the path of the log file.\" )\n\t\t\t.set_takes_single_value()\n\t\t\t.set_default_value( \"overrep.log\" ) ;\n\t}\n} ;\n\n// Fisher's exact test between categories\n// red black\n// good | a | b |\n// bad | c | d |\n//\n// Under the null that both rows are distributed the same, a is hypergeometrically\n// distributed. Fishers' p-value is then the mass under the hypergeometric distribution\n// of all values of a greater than or equal to the one given that are consistent with\n// the given margins of the table.\nstruct FishersExactTest {\n\tFishersExactTest( Eigen::Matrix2d const& matrix ):\n\t\tm_matrix( matrix ),\n\t\tm_distribution( m_matrix.col( 0 ).sum(), m_matrix.row( 0 ).sum(), m_matrix.sum() )\n\t{}\n\t\n\tdouble get_OR() const {\n\t\treturn ( m_matrix(0,0) * m_matrix(1,1) ) / ( m_matrix(0,1)*m_matrix(1,0) ) ;\n\t}\n\n\tstd::pair< double, double > get_confidence_interval() const {\n\t\tassert(0) ;\n\t}\n\n\tdouble get_pvalue() const {\n\t\tusing boost::math::cdf ;\n\t\tusing boost::math::complement ;\n\n\t\tif( m_matrix(0,0) == 0 ) {\n\t\t\treturn 1 ;\n\t\t} else {\n\t\t\treturn cdf( complement( m_distribution, m_matrix( 0, 0 ) - 1.0 )) ;\n\t\t}\n\t}\n\n\tprivate:\n\t\tEigen::Matrix2d const m_matrix ;\n\t\tboost::math::hypergeometric m_distribution ;\n} ;\n\nvoid load_list_of_strings( std::string const& filename, boost::function< void ( std::string const& ) > setter ) {\n\tstd::auto_ptr< std::istream > f = genfile::open_text_file_for_input( filename ) ;\n\tstd::string element ;\n\twhile( (*f) >> element ) {\n\t\tsetter( element ) ;\n\t}\n}\n\nvoid load_pathways_list(\n\tstatfile::BuiltInTypeStatSource& source,\n\tboost::function< void ( std::string const&, std::string const& ) > set_pathway_name,\n\tboost::function< void ( std::string const&, std::string const& ) > set_pathway_gene\n) {\n\tstd::string pathway_id ;\n\tstd::string pathway_name ;\n\tstd::string gene_id ;\n\twhile( source >> pathway_id ) {\n\t\tsource >> pathway_name >> gene_id >> statfile::ignore_all() ;\n\t\tset_pathway_name( pathway_id, pathway_name ) ;\n\t\tset_pathway_gene( pathway_id, gene_id ) ;\n\t}\n}\n\nvoid load_clusters(\n\tstatfile::BuiltInTypeStatSource& source,\n\tboost::function< void ( std::string const&, std::string const& ) > set_cluster_member = boost::function< void ( std::string const&, std::string const& ) >(),\n\tboost::function< void ( std::string const&, std::string const& ) > set_cluster_comment = boost::function< void ( std::string const&, std::string const& ) >()\n) {\n\tstd::string cluster_id ;\n\tstd::string cluster_comment ;\n\tstd::string gene_regexp ;\n\twhile( source >> cluster_id ) {\n\t\tsource >> gene_regexp >> cluster_comment >> statfile::ignore_all() ;\n\t\tif( set_cluster_comment ) {\n\t\t\tset_cluster_comment( cluster_id, cluster_comment ) ;\n\t\t}\n\t\tif( set_cluster_member ) {\n\t\t\tset_cluster_member( cluster_id, gene_regexp ) ;\n\t\t}\n\t}\n}\n\nnamespace impl {\n\tstruct MapSetter {\n\t\ttypedef std::string A ;\n\t\ttypedef std::string B ;\n\t\ttypedef std::map< A, B > Map ;\n\t\tMapSetter( Map& map ): m_map( &map ) {} ;\n\t\tMapSetter( MapSetter const& other ): m_map( other.m_map ) {} ;\n\t\tMapSetter& operator=( MapSetter const& other ) {\n\t\t\tm_map = other.m_map ;\n\t\t\treturn *this ;\n\t\t} \n\t\t\n\t\tvoid operator()( std::string const& left, std::string const& right ) const {\n\t\t\t(*m_map)[ left ] = right ;\n\t\t}\n\t\t\n\tprivate:\n\t\tMap* m_map ;\n\t} ;\n\t\n\tstruct MapSetSetter {\n\t\ttypedef std::string A ;\n\t\ttypedef std::set< std::string > B ;\n\t\ttypedef std::map< A, B > Map ;\n\t\tMapSetSetter( Map& map ): m_map( &map ) {} ;\n\t\tMapSetSetter( MapSetSetter const& other ): m_map( other.m_map ) {} ;\n\t\tMapSetSetter& operator=( MapSetSetter const& other ) {\n\t\t\tm_map = other.m_map ;\n\t\t\treturn *this ;\n\t\t} \n\t\t\n\t\tvoid operator()( std::string const& left, std::string const& right ) const {\n\t\t\t(*m_map)[ left ].insert( right ) ;\n\t\t}\n\t\t\n\tprivate:\n\t\tmutable Map* m_map ;\n\t} ;\n\t\n\ttemplate< typename Container >\n\tvoid insert_into( typename Container::value_type const& value, Container* X ) {\n\t\tX->insert( value ) ;\n\t}\n\t\n\ttemplate< typename Container >\n\tbool in( typename Container::value_type const& value, Container const& X ) {\n\t\treturn X.find( value ) != X.end() ;\n\t}\n}\n\nstruct OverrepApplication: public appcontext::ApplicationContext {\npublic:\n\tOverrepApplication( int argc, char **argv ):\n\t\tappcontext::ApplicationContext(\n\t\t\tglobals::program_name,\n\t\t\tglobals::program_version,\n\t\t\tstd::auto_ptr< appcontext::OptionProcessor >( new OverrepOptions ),\n\t\t\targc,\n\t\t\targv,\n\t\t\t\"-log\"\n\t\t)\n\t{}\n\t\n\tvoid process() {\n\t\tunsafe_process() ;\n\t}\n\t\nprivate:\n\ttypedef std::set< std::string > StringSet ;\n\ttypedef std::map< std::string, std::string > StringStringMap ;\n\ttypedef std::map< std::string, std::set< std::string > > StringStringSetMap ;\n\n\tvoid unsafe_process() {\n\t\tload_data() ;\n\t\tm_cluster_mapping = get_cluster_mapping() ;\n\t\tmap_genes_through_clusters( m_cluster_mapping ) ;\n\n\t\tsummarise() ;\n\t\t\n\t\trun_tests() ;\n\t}\n\n\tvoid load_data() {\n\t\tStringStringMap pathway_names ;\n\t\tStringStringSetMap pathway_members ;\n\t\tStringStringSetMap clusters ;\n\t\tStringStringMap cluster_descriptions ;\n\t\tStringSet universe ;\n\t\tStringSet test_genes ;\n\t\t\n\t\n\t\tassert( options().check( \"-u\" ) ) ;\t\n\t\tif( options().check( \"-u\" )) {\n\t\t\tload_list_of_strings(\n\t\t\t\toptions().get< std::string >( \"-u\" ),\n\t\t\t\tboost::bind(\n\t\t\t\t\t&impl::insert_into< StringSet >,\n\t\t\t\t\t_1,\n\t\t\t\t\t&universe\n\t\t\t\t)\n\t\t\t) ;\n\n\t\t\tget_ui_context().logger()\n\t\t\t\t<< \"I loaded \" << universe.size() << \" universe genes. First few are:\\n\" ;\n\t\t\tStringSet::const_iterator\n\t\t\t\ti = universe.begin(),\n\t\t\t\tend_i = universe.end() ;\n\t\t\tfor( std::size_t count = 0; count < 5 && i != end_i; ++count, ++i ) {\n\t\t\t\tget_ui_context().logger() << \" \" << *i << \"\\n\" ;\n\t\t\t}\n\t\t}\n\t\t\n\t\tif( options().check( \"-c\" )) {\n\t\t\tstatfile::BuiltInTypeStatSource::UniquePtr source = statfile::BuiltInTypeStatSource::open(\n\t\t\t\toptions().get< std::string >( \"-c\" )\n\t\t\t) ;\n\t\t\tload_clusters(\n\t\t\t\t*source,\n\t\t\t\timpl::MapSetSetter( clusters ),\n\t\t\t\timpl::MapSetter( cluster_descriptions )\n\t\t\t) ;\n\t\t\tget_ui_context().logger() << \"Loaded \" << clusters.size() << \" clusters:\\n\" ;\n\t\t\tforeach( StringStringSetMap::value_type const& elt, clusters ) {\n\t\t\t\tget_ui_context().logger()\n\t\t\t\t\t<< \" \" << elt.first << \": \" ;\n\t\t\t\tforeach( StringSet::value_type const& value, elt.second ) {\n\t\t\t\t\tget_ui_context().logger() << value << \" \" ;\n\t\t\t\t}\n\t\t\t\tget_ui_context().logger() << \"\\n\" ;\n\t\t\t}\n\t\t}\n\n\t\tif( options().check( \"-p\" )) {\n\t\t\tstd::vector< std::string > elts = options().get_values< std::string >( \"-p\" ) ;\n\t\t\tforeach( std::string const& elt, elts ) {\n\t\t\t\tstatfile::BuiltInTypeStatSource::UniquePtr source = statfile::BuiltInTypeStatSource::open(\n\t\t\t\t\tgenfile::wildcard::find_files_by_chromosome( elt )\n\t\t\t\t) ;\n\t\t\t\tload_pathways_list(\n\t\t\t\t\t*source,\n\t\t\t\t\timpl::MapSetter( pathway_names ),\n\t\t\t\t\timpl::MapSetSetter( pathway_members )\n\t\t\t\t) ;\n\t\t\t}\n\n\t\t\ttypedef std::pair< std::string, std::set< std::string > > StringStringSet ;\n\t\t\tif( options().check( \"-intersect\" )) {\n\t\t\t\tStringSet pathway_universe ;\n\t\t\t\tforeach( StringStringSet const& entry, pathway_members ) {\n\t\t\t\t\tpathway_universe.insert( entry.second.begin(), entry.second.end() ) ;\n\t\t\t\t}\n\t\t\t\tget_ui_context().logger() << \"Created pathway universe of \" << universe.size() << \" genes based on all genes in pathways.\\n\" ;\n\t\t\t\tstd::size_t const N = universe.size() ;\n\t\t\t\tuniverse = genfile::utility::intersect( universe, pathway_universe ) ;\n\t\t\t\tget_ui_context().logger() << \"Formed intersection with universe (\" << universe.size() << \" of \" << N << \" in original universe.\\n\" ;\n\t\t\t\t\n\t\t\t}\n\n\t\t\tstd::map< std::string, StringSet > universe_pathway_members ;\n\t\t\tforeach( StringStringSet const& entry, pathway_members ) {\n\t\t\t\tuniverse_pathway_members[ entry.first ] = genfile::utility::intersect( entry.second, universe ) ;\n\t\t\t}\n\t\t\t\n\t\t\tget_ui_context().logger()\n\t\t\t\t<< \"I loaded \" << pathway_names.size() << \" pathways. First few are:\\n\" ;\n\t\t\tstd::map< std::string, std::string >::const_iterator\n\t\t\t\tpathway_i = pathway_names.begin(),\n\t\t\t\tpathway_end = pathway_names.end() ;\n\t\t\tfor( std::size_t count = 0; count < 5 && pathway_i != pathway_end; ++count, ++pathway_i ) {\n\t\t\t\tget_ui_context().logger() << \" \" << pathway_i->first << \": \" << pathway_i->second\n\t\t\t\t\t<< \" (\" << pathway_members[pathway_i->first].size()\n\t\t\t\t\t<< \" genes of which \"\n\t\t\t\t\t<< universe_pathway_members[pathway_i->first].size()\n\t\t\t\t\t<<\" are in the universe)\\n\" ;\n\t\t\t}\n\t\t\t\n\t\t\tpathway_members = universe_pathway_members ;\n\t\t}\n\t\t\n\t\tif( options().check( \"-g\" )) {\n\t\t\tload_list_of_strings(\n\t\t\t\toptions().get< std::string >( \"-g\" ),\n\t\t\t\tboost::bind(\n\t\t\t\t\timpl::insert_into< StringSet >,\n\t\t\t\t\t_1,\n\t\t\t\t\t&test_genes\n\t\t\t\t)\n\t\t\t) ;\n\t\t\t\n\t\t\tget_ui_context().logger()\n\t\t\t\t<< \"I loaded \" << test_genes.size() << \" test genes \" ;\n\t\t\ttest_genes = genfile::utility::intersect( test_genes, universe ) ;\n\t\t\tget_ui_context().logger()\n\t\t\t\t<< \"of which \" << test_genes.size() << \" are in the universe. First few are:\\n\" ;\n\t\t\tStringSet::const_iterator\n\t\t\t\ti = test_genes.begin(),\n\t\t\t\tend_i = test_genes.end() ;\n\t\t\tfor( std::size_t count = 0; count < 5 && i != end_i; ++count, ++i ) {\n\t\t\t\tget_ui_context().logger() << \" \" << *i << \"\\n\" ;\n\t\t\t}\n\t\t}\n\t\t\n\t\tm_pathway_names = pathway_names ;\n\t\tm_pathway_members = pathway_members ;\n\t\tm_clusters = clusters ;\n\t\tm_cluster_descriptions = cluster_descriptions ;\n\t\tm_universe = universe ;\n\t\tm_test_genes = test_genes ;\n\t}\n\t\n\tStringStringMap get_cluster_mapping() {\n\t\tget_ui_context().logger() << \"Computing gene->cluster mapping...\\n\" ;\n\t\tStringStringMap result ;\n\t\tforeach( StringStringSetMap::value_type const& cluster, m_clusters ) {\n\t\t\tforeach( std::string const& regex_string, cluster.second ) {\n\t\t\t\tboost::regex regex( regex_string ) ;\n\t\t\t\tStringStringMap const& this_map = get_cluster_mapping( regex, cluster.first ) ;\n\t\t\t\tresult.insert( this_map.begin(), this_map.end() ) ;\n\t\t\t}\n\t\t}\n\t\t\n\t\treturn result ;\n\t}\n\t\n\tStringStringMap get_cluster_mapping( boost::regex const& regex, std::string const& replacement ) {\n\t\tStringStringMap result ;\n\t\tget_cluster_mapping( m_universe, regex, replacement, boost::bind( impl::insert_into< StringStringMap >, _1, &result )) ;\n\t\tget_cluster_mapping( m_test_genes, regex, replacement, boost::bind( impl::insert_into< StringStringMap >, _1, &result )) ;\n\t\tforeach( StringStringSetMap::value_type& value, m_pathway_members ) {\n\t\t\tget_cluster_mapping( value.second, regex, replacement, boost::bind( impl::insert_into< StringStringMap >, _1, &result )) ;\n\t\t}\n\t\treturn result ;\n\t}\n\n\t\n\tvoid get_cluster_mapping(\n\t\tStringSet& values,\n\t\tboost::regex const& regex,\n\t\tstd::string const& replacement,\n\t\tboost::function< void ( std::pair< std::string, std::string > const& ) > output\n\t) {\n\t\tStringSet::iterator i = values.begin(), end_i = values.end() ;\n\t\tfor( ; i != end_i; ++i ) {\n\t\t\tstd::string value = *i ;\n\t\t\tvalue = boost::regex_replace( value, regex, replacement ) ;\n\t\t\tif( value != *i ) {\n\t\t\t\toutput( std::make_pair( *i, value ) ) ;\n\t\t\t}\n\t\t}\n\t}\n\n\tvoid map_genes_through_clusters( StringStringMap const& mapping ) {\n\t\tget_ui_context().logger() << \"Mapping genes through clusters...\\n\" ;\n\t\tstd::size_t n = m_universe.size() ;\n\t\tm_universe = map_genes_through_clusters( mapping, m_universe ) ;\n\t\tget_ui_context().logger() << \"Mapped \" << n << \" genes to \" << m_universe.size() << \" in universe.\\n\" ;\n\t\tn = m_test_genes.size() ;\n\t\tm_test_genes = map_genes_through_clusters( mapping, m_test_genes ) ;\n\t\tget_ui_context().logger() << \"Mapped \" << n << \" genes to \" << m_test_genes.size() << \" in test genes.\\n\" ;\n\t\tforeach( StringStringSetMap::value_type& value, m_pathway_members ) {\n\t\t\tvalue.second = map_genes_through_clusters( mapping, value.second ) ;\n\t\t}\n\t}\n\n\tStringSet map_genes_through_clusters( StringStringMap const& mapping, StringSet const& genes ) {\n\t\tStringSet result ;\n\t\tforeach( StringSet::value_type const& value, genes ) {\n\t\t\tStringStringMap::const_iterator where = mapping.find( value ) ;\n\t\t\tif( where != mapping.end() ) {\n\t\t\t\tresult.insert( where->second ) ;\n\t\t\t} else {\n\t\t\t\tresult.insert( value ) ;\n\t\t\t}\n }\n\t\treturn result ;\n\t}\n\n\tvoid summarise() {\n\t\tget_ui_context().logger() << \"\\n-------------------------\\n\\n\" ;\n\t\tif( m_clusters.size() > 0 ) {\n\t\t\tget_ui_context().logger() << std::setw( 12 ) << \"Cluster mapping:\\n\" ;\n StringStringSetMap backMap ;\n\t\t\tforeach( StringStringMap::value_type const& key_value, m_cluster_mapping ) {\n backMap[ key_value.second ].insert( key_value.first ) ;\n\t\t\t}\n foreach( StringStringSetMap::value_type const& k, backMap ) {\n get_ui_context().logger() << std::setw( 12 ) << k.first << \":\" ;\n\t\t\t\tstd::size_t count = 0 ;\n\t\t\t\tfor( StringSet::const_iterator i = k.second.begin(); i != k.second.end(); ++i, ++count ) {\n get_ui_context().logger() << \" \" << *i ;\n\t\t\t\t\tif( count == 10 ) {\n\t\t\t\t\t\tget_ui_context().logger() << \" (+ \" << k.second.size() - 10 << \" others)...\" ;\n\t\t\t\t\t\tbreak ;\n\t\t\t\t\t}\n }\n get_ui_context().logger() << \"\\n\" ;\n }\n\t\t}\n#if 0\n\t\tif( m_clusters.size() > 0 ) {\n\t\t\tget_ui_context().logger() << std::setw( 12 ) << \"Cluster mapping:\\n\" ;\n\t\t\tforeach( StringStringMap::value_type const& key_value, m_cluster_mapping ) {\n\t\t\t\tget_ui_context().logger() << std::setw( 12 ) << key_value.first << \": \" << key_value.second << \"\\n\" ;\n\t\t\t}\n\t\t}\n#endif\n\t\tget_ui_context().logger() << std::setw( 12 ) << \"Test genes:\" << \" \" << m_test_genes.size() << \" genes\\n\" ;\n\t\tget_ui_context().logger() << std::setw( 12 ) << \"Pathways:\" << \" \" << m_pathway_names.size() << \" pathways\\n\" ;\n\t\tget_ui_context().logger() << std::setw( 12 ) << \"Universe:\" << \" \" << m_universe.size() << \" genes\\n\" ;\n\t}\n\n\tvoid run_tests() {\n\t\t// table is\n\t\t// in pathway | not in pathway | \n\t\t// test gene | a | b |\n\t\t// Not test gene | c | d |\n\t\t//\n\t\tEigen::Matrix2d table ;\n\t\t\n\t\tusing std::setw ;\n\t\tstd::string const tab = \"\\t\" ;\n\t\tstd::cout\n\t\t//get_ui_context().logger()\n\t\t\t<< \"analysis\" << tab\n\t\t\t<< \"pathway id\" << tab\n\t\t\t<< \"pathway name\" << tab\n\t\t\t<< \"hits in pathway\" << tab\n\t\t\t<< \"hits not in pathway\" << tab\n\t\t\t<< \"nonhits in pathway\" << tab\n\t\t\t<< \"nonhits not in pathway\" << tab\n\t\t\t<< \"total in pathway\" << tab\n\t\t\t<< \"total genes\" << tab\n\t\t\t<< \"sample odds ratio\" << tab\n\t\t\t<< \"fishers exact test p value\" << tab\n\t\t\t<< \"ids.of.hits.in.pathway\"\n\t\t\t<< \"\\n\" ;\n\n\t\tstd::string const analysis = options().get< std::string >( \"-analysis-name\" ) ;\n\t\tdouble const pvalue_threshhold = options().get< double >( \"-P-value\" ) ;\n\t\tstd::ostringstream hit_genes_in_pathway ;\n\t\tforeach( StringStringSetMap::value_type const& pathway, m_pathway_members ) {\n\t\t\thit_genes_in_pathway.str( \"\" ) ;\n\t\t\ttable = Eigen::Matrix2d::Zero() ;\n\n\t\t\tforeach( std::string const& value, m_universe ) {\n\t\t\t\tint i = 0, j = 0 ;\n\t\t\t\tif( impl::in( value, m_test_genes )) {\n\t\t\t\t\ti = 0 ;\n\t\t\t\t} else {\n\t\t\t\t\ti = 1 ;\n\t\t\t\t}\n\n\t\t\t\tif( impl::in( value, pathway.second ) ) {\n\t\t\t\t\tj = 0 ;\n\t\t\t\t} else {\n\t\t\t\t\tj = 1 ;\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\t++table( i, j ) ;\n\n\t\t\t\tif( i == 0 && j == 0 ) {\n\t\t\t\t\tif( hit_genes_in_pathway.str().size() == 0 ) {\n\t\t\t\t\t\thit_genes_in_pathway << value ;\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\thit_genes_in_pathway << \",\" << value ;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t//get_ui_context().logger()\n\t\t\tstd::cout\n\t\t\t\t<< analysis << tab\t\n\t\t\t\t<< pathway.first << tab\n\t\t\t\t<< \"\\\"\" << m_pathway_names[ pathway.first ] << \"\\\"\" << tab\n\t\t\t\t<< table( 0, 0 ) << tab\n\t\t\t\t<< table( 0, 1 ) << tab\n\t\t\t\t<< table( 1, 0 ) << tab\n\t\t\t\t<< table( 1, 1 ) << tab\n\t\t\t\t<< table.col( 0 ).sum() << tab\n\t\t\t\t<< table.sum() << tab ;\n\t\t\ttry {\n\t\t\t\tFishersExactTest test( table ) ;\n\t\t\t\t//get_ui_context().logger()\n\t\t\t\tstd::cout\n\t\t\t\t\t<< test.get_OR() << tab << test.get_pvalue() ;\n\t\t\t\tif( test.get_pvalue() <= pvalue_threshhold ) {\n\t\t\t\t\tstd::cout << tab << hit_genes_in_pathway.str() ;\n\t\t\t\t} else {\n\t\t\t\t\tstd::cout << tab << \"NA\" ;\n\t\t\t\t}\n\t\t\t}\n\t\t\tcatch( std::exception const& ) {\n\t\t\t\t// get_ui_context().logger()\n\t\t\t\tstd::cout\n\t\t\t\t\t<< \"NA\\tNA\\t\" ;\t\n\t\t\t}\n\t\t\tstd::cout << \"\\n\" ;\n\t\t}\n\t}\n\t\nprivate:\n\tStringStringMap m_pathway_names ;\n\tStringStringSetMap m_pathway_members ;\n\tStringStringSetMap m_clusters ;\n\tStringStringMap m_cluster_descriptions ;\n\tStringStringMap m_cluster_mapping ;\n\tStringSet m_universe ;\n\tStringSet m_test_genes ;\n} ;\n\nint main( int argc, char **argv ) {\n\ttry {\n\t\tOverrepApplication app( argc, argv ) ;\t\n\t\tapp.process() ;\n\t}\n\tcatch( appcontext::HaltProgramWithReturnCode const& e ) {\n\t\treturn e.return_code() ;\n\t}\n\treturn 0 ;\n}\n", "meta": {"hexsha": "edf8e99cfaff18384296b5d16a7ab96c853fbae0", "size": 19123, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "apps/overrep.cpp", "max_stars_repo_name": "CreRecombinase/qctool", "max_stars_repo_head_hexsha": "6dad3a15c461177bf6940ba7b991337402ca5c41", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "apps/overrep.cpp", "max_issues_repo_name": "CreRecombinase/qctool", "max_issues_repo_head_hexsha": "6dad3a15c461177bf6940ba7b991337402ca5c41", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "apps/overrep.cpp", "max_forks_repo_name": "CreRecombinase/qctool", "max_forks_repo_head_hexsha": "6dad3a15c461177bf6940ba7b991337402ca5c41", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.5775127768, "max_line_length": 158, "alphanum_fraction": 0.6366678868, "num_tokens": 5362, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.4035668537353746, "lm_q1q2_score": 0.23918059505750586}} {"text": "// Copyright (c) 2014-2018, The Monero Project\n//\n// All rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without modification, are\n// permitted provided that the following conditions are met:\n//\n// 1. Redistributions of source code must retain the above copyright notice, this list of\n// conditions and the following disclaimer.\n//\n// 2. Redistributions in binary form must reproduce the above copyright notice, this list\n// of conditions and the following disclaimer in the documentation and/or other\n// materials provided with the distribution.\n//\n// 3. Neither the name of the copyright holder nor the names of its contributors may be\n// used to endorse or promote products derived from this software without specific\n// prior written permission.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY\n// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF\n// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL\n// THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,\n// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF\n// THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n//\n// The LWMA-4 Difficulty algorithm as implemented below is under a separate license\n// to the rest of this file.\n//\n// Parts of this file are originally copyright (c) 2012-2013 The Cryptonote developers\n\n#include \n#include \n#include \n#include \n#include \n\n#include \"common/int-util.h\"\n#include \"crypto/hash.h\"\n#include \"cryptonote_config.h\"\n#include \"difficulty.h\"\n\n#undef MONERO_DEFAULT_LOG_CATEGORY\n#define MONERO_DEFAULT_LOG_CATEGORY \"difficulty\"\n\nnamespace cryptonote {\n\n using std::size_t;\n using std::uint64_t;\n using std::vector;\n\n#if defined(__x86_64__)\n static inline void mul(uint64_t a, uint64_t b, uint64_t &low, uint64_t &high) {\n low = mul128(a, b, &high);\n }\n\n#else\n\n static inline void mul(uint64_t a, uint64_t b, uint64_t &low, uint64_t &high) {\n // __int128 isn't part of the standard, so the previous function wasn't portable. mul128() in Windows is fine,\n // but this portable function should be used elsewhere. Credit for this function goes to latexi95.\n\n uint64_t aLow = a & 0xFFFFFFFF;\n uint64_t aHigh = a >> 32;\n uint64_t bLow = b & 0xFFFFFFFF;\n uint64_t bHigh = b >> 32;\n\n uint64_t res = aLow * bLow;\n uint64_t lowRes1 = res & 0xFFFFFFFF;\n uint64_t carry = res >> 32;\n\n res = aHigh * bLow + carry;\n uint64_t highResHigh1 = res >> 32;\n uint64_t highResLow1 = res & 0xFFFFFFFF;\n\n res = aLow * bHigh;\n uint64_t lowRes2 = res & 0xFFFFFFFF;\n carry = res >> 32;\n\n res = aHigh * bHigh + carry;\n uint64_t highResHigh2 = res >> 32;\n uint64_t highResLow2 = res & 0xFFFFFFFF;\n\n //Addition\n\n uint64_t r = highResLow1 + lowRes2;\n carry = r >> 32;\n low = (r << 32) | lowRes1;\n r = highResHigh1 + highResLow2 + carry;\n uint64_t d3 = r & 0xFFFFFFFF;\n carry = r >> 32;\n r = highResHigh2 + carry;\n high = d3 | (r << 32);\n }\n\n#endif\n\n static inline bool cadd(uint64_t a, uint64_t b) {\n return a + b < a;\n }\n\n static inline bool cadc(uint64_t a, uint64_t b, bool c) {\n return a + b < a || (c && a + b == (uint64_t) -1);\n }\n\n bool check_hash(const crypto::hash &hash, difficulty_type difficulty) {\n uint64_t low, high, top, cur;\n // First check the highest word, this will most likely fail for a random hash.\n mul(swap64le(((const uint64_t *) &hash)[3]), difficulty, top, high);\n if (high != 0) {\n return false;\n }\n mul(swap64le(((const uint64_t *) &hash)[0]), difficulty, low, cur);\n mul(swap64le(((const uint64_t *) &hash)[1]), difficulty, low, high);\n bool carry = cadd(cur, low);\n cur = high;\n mul(swap64le(((const uint64_t *) &hash)[2]), difficulty, low, high);\n carry = cadc(cur, low, carry);\n carry = cadc(high, top, carry);\n return !carry;\n }\n\n // LWMA-4 difficulty algorithm\n // Copyright (c) 2017-2018 Zawy\n // MIT license\n //\n // Permission is hereby granted, free of charge, to any person obtaining a copy\n // of this software and associated documentation files (the \"Software\"), to\n // deal in the Software without restriction, including without limitation the\n // rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n // sell copies of the Software, and to permit persons to whom the Software is\n // furnished to do so, subject to the following conditions:\n //\n // The above copyright notice and this permission notice shall be included in\n // all copies or substantial portions of the Software.\n //\n // THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\n // WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF\n // OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n //\n // https://github.com/zawy12/difficulty-algorithms/issues/3\n // See commented version for explanations & required config file changes. Fix FTL and MTP!\n difficulty_type next_difficulty(std::vector timestamps, std::vector cumulative_difficulties, size_t target_seconds) {\n uint64_t T = target_seconds;\n uint64_t N = DIFFICULTY_WINDOW; // N=45, 60, and 90 for T=600, 120, 60.\n uint64_t L(0), ST(0), next_D, prev_D, avg_D, i;\n\n assert(timestamps.size() == cumulative_difficulties.size() && timestamps.size() <= N+1 );\n\n // If it's a new coin, do startup code. Do not remove in case other coins copy your code.\n uint64_t difficulty_guess = 600;\n if (timestamps.size() <= 12 ) { return difficulty_guess; }\n if ( timestamps.size() < N +1 ) { N = timestamps.size()-1; }\n\n // If hashrate/difficulty ratio after a fork is < 1/3 prior ratio, hardcode D for N+1 blocks after fork.\n // This will also cover up a very common type of backwards-incompatible fork.\n // difficulty_guess = 100000; // Dev may change. Guess low than anything expected.\n // if ( height <= UPGRADE_HEIGHT + 1 + N ) { return difficulty_guess; }\n\n // Safely convert out-of-sequence timestamps into > 0 solvetimes.\n std::vectorTS(N+1);\n TS[0] = timestamps[0];\n for ( i = 1; i <= N; i++) {\n if ( timestamps[i] > TS[i-1] ) { TS[i] = timestamps[i]; }\n else { TS[i] = TS[i-1]; }\n }\n\n for ( i = 1; i <= N; i++) {\n // Temper long solvetime drops if they were preceded by 3 or 6 fast solves.\n if ( i > 4 && TS[i]-TS[i-1] > 5*T && TS[i-1] - TS[i-4] < (14*T)/10 ) { ST = 2*T; }\n else if ( i > 7 && TS[i]-TS[i-1] > 5*T && TS[i-1] - TS[i-7] < 4*T ) { ST = 2*T; }\n else { // Assume normal conditions, so get ST.\n // LWMA drops too much from long ST, so limit drops with a 5*T limit\n ST = std::min(5*T ,TS[i] - TS[i-1]);\n }\n L += ST * i ;\n }\n if (L < N*N*T/20 ) { L = N*N*T/20; }\n avg_D = ( cumulative_difficulties[N] - cumulative_difficulties[0] )/ N;\n\n // Prevent round off error for small D and overflow for large D.\n if (avg_D > 2000000*N*N*T) {\n next_D = (avg_D/(200*L))*(N*(N+1)*T*97);\n }\n else { next_D = (avg_D*N*(N+1)*T*97)/(200*L); }\n\n prev_D = cumulative_difficulties[N] - cumulative_difficulties[N-1] ;\n\n // Apply 10% jump rule.\n if (( TS[N] - TS[N-1] < (2*T)/10 ) ||\n ( TS[N] - TS[N-2] < (5*T)/10 ) ||\n ( TS[N] - TS[N-3] < (8*T)/10 ))\n {\n next_D = std::max( next_D, std::min( (prev_D*110)/100, (105*avg_D)/100 ) );\n }\n // Make all insignificant digits zero for easy reading.\n i = 1000000000;\n while (i > 1) {\n if ( next_D > i*100 ) { next_D = ((next_D+i/2)/i)*i; break; }\n else { i /= 10; }\n }\n return next_D;\n }\n}\n", "meta": {"hexsha": "44a19938ab3d1ec51be1e5e6e960dd49c4349dff", "size": 8678, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cryptonote_basic/difficulty.cpp", "max_stars_repo_name": "unprll-project/unprll", "max_stars_repo_head_hexsha": "b72fa7fc3a74964f46956e39c3e1b3a734a4c308", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 8.0, "max_stars_repo_stars_event_min_datetime": "2018-11-28T14:53:04.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-13T15:53:14.000Z", "max_issues_repo_path": "src/cryptonote_basic/difficulty.cpp", "max_issues_repo_name": "unprll-project/unprll", "max_issues_repo_head_hexsha": "b72fa7fc3a74964f46956e39c3e1b3a734a4c308", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 8.0, "max_issues_repo_issues_event_min_datetime": "2018-12-09T03:01:32.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-11T13:56:43.000Z", "max_forks_repo_path": "src/cryptonote_basic/difficulty.cpp", "max_forks_repo_name": "unprll-project/unprll", "max_forks_repo_head_hexsha": "b72fa7fc3a74964f46956e39c3e1b3a734a4c308", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 10.0, "max_forks_repo_forks_event_min_datetime": "2018-11-28T14:47:14.000Z", "max_forks_repo_forks_event_max_datetime": "2019-09-19T19:57:09.000Z", "avg_line_length": 41.3238095238, "max_line_length": 148, "alphanum_fraction": 0.6377045402, "num_tokens": 2381, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.23918059505750583}} {"text": "/*\n Copyright (C) 2021 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nusing namespace QuantLib;\n\nnamespace QuantExt {\n\nCrossCcyFixFloatMtMResetSwap::CrossCcyFixFloatMtMResetSwap(\n Real nominal, const Currency& fixedCurrency, const Schedule& fixedSchedule,\n Rate fixedRate, const DayCounter& fixedDayCount, const BusinessDayConvention& fixedPaymentBdc,\n Natural fixedPaymentLag, const Calendar& fixedPaymentCalendar, const Currency& floatCurrency,\n const Schedule& floatSchedule, const boost::shared_ptr& floatIndex, Spread floatSpread,\n const BusinessDayConvention& floatPaymentBdc, Natural floatPaymentLag, const Calendar& floatPaymentCalendar,\n const boost::shared_ptr& fxIdx, bool resetsOnFloatLeg, bool receiveFixed)\n : CrossCcySwap(3), nominal_(nominal), fixedCurrency_(fixedCurrency),\n fixedSchedule_(fixedSchedule), fixedRate_(fixedRate), fixedDayCount_(fixedDayCount), \n fixedPaymentBdc_(fixedPaymentBdc), fixedPaymentLag_(fixedPaymentLag), fixedPaymentCalendar_(fixedPaymentCalendar),\n floatCurrency_(floatCurrency), floatSchedule_(floatSchedule), floatIndex_(floatIndex),\n floatSpread_(floatSpread), floatPaymentBdc_(floatPaymentBdc),\n floatPaymentLag_(floatPaymentLag), floatPaymentCalendar_(floatPaymentCalendar),\n fxIndex_(fxIdx), resetsOnFloatLeg_(resetsOnFloatLeg), receiveFixed_(receiveFixed) {\n\n registerWith(floatIndex_);\n registerWith(fxIndex_);\n initialize();\n}\n\nvoid CrossCcyFixFloatMtMResetSwap::initialize() {\n\n // if resets on floating leg, set notional to zero, else the fixed\n Real floatNotional, fixedNotional;\n if (resetsOnFloatLeg_) {\n floatNotional = 0.0;\n fixedNotional = nominal_;\n } else {\n floatNotional = nominal_;\n fixedNotional = 0.0;\n }\n\n // Build the float leg\n Leg floatLeg = IborLeg(floatSchedule_, floatIndex_)\n .withNotionals(floatNotional)\n .withSpreads(floatSpread_)\n .withPaymentAdjustment(floatPaymentBdc_)\n .withPaymentLag(floatPaymentLag_)\n .withPaymentCalendar(floatPaymentCalendar_);\n\n // Register with each floating rate coupon\n for (Leg::const_iterator it = floatLeg.begin(); it < floatLeg.end(); ++it)\n registerWith(*it);\n\n // Build the fixed rate leg\n Leg fixedLeg = FixedRateLeg(fixedSchedule_)\n .withNotionals(fixedNotional)\n .withCouponRates(fixedRate_, fixedDayCount_)\n .withPaymentAdjustment(fixedPaymentBdc_)\n .withPaymentLag(fixedPaymentLag_)\n .withPaymentCalendar(fixedPaymentCalendar_);\n\n if (resetsOnFloatLeg_) {\n // Notional exchanges for fixed leg\n // Initial notional exchange\n Date aDate = fixedSchedule_.dates().front();\n aDate = fixedPaymentCalendar_.adjust(aDate, fixedPaymentBdc_);\n boost::shared_ptr aCashflow = boost::make_shared(-fixedNotional, aDate);\n fixedLeg.insert(fixedLeg.begin(), aCashflow);\n\n // Final notional exchange\n aDate = fixedLeg.back()->date();\n aCashflow = boost::make_shared(fixedNotional, aDate);\n fixedLeg.push_back(aCashflow);\n\n // resetting floating leg\n for (Size j = 0; j < floatLeg.size(); ++j) {\n boost::shared_ptr coupon = boost::dynamic_pointer_cast(floatLeg[j]);\n Date fixingDate = fxIndex_->fixingCalendar().advance(coupon->accrualStartDate(),\n -static_cast(fxIndex_->fixingDays()), Days);\n boost::shared_ptr fxLinkedCoupon =\n boost::make_shared(fixingDate, fixedNotional, fxIndex_, coupon);\n floatLeg[j] = fxLinkedCoupon;\n }\n\n // now build a separate leg to store the resetting notionals\n receiveFixed_ ? payer_[2] = -1.0 : payer_[2] = +1.0;\n currencies_[2] = floatCurrency_;\n for (Size j = 0; j < floatLeg.size(); j++) {\n boost::shared_ptr c = boost::dynamic_pointer_cast(floatLeg[j]);\n QL_REQUIRE(c, \"Resetting XCCY - expected Coupon\"); \n // build a pair of notional flows, one at the start and one at the end of\n // the accrual period. Both with the same FX fixing date\n Date fixingDate = fxIndex_->fixingCalendar().advance(c->accrualStartDate(),\n -static_cast(fxIndex_->fixingDays()), Days);\n legs_[2].push_back(boost::shared_ptr(\n new FXLinkedCashFlow(c->accrualStartDate(), fixingDate, -fixedNotional, fxIndex_)));\n legs_[2].push_back(boost::shared_ptr(\n new FXLinkedCashFlow(c->accrualEndDate(), fixingDate, fixedNotional, fxIndex_)));\n }\n } else {\n // Notional exchanges for floating leg\n // Initial notional exchange\n Date aDate = floatSchedule_.dates().front();\n aDate = floatPaymentCalendar_.adjust(aDate, floatPaymentBdc_);\n boost::shared_ptr aCashflow = boost::make_shared(-floatNotional, aDate);\n floatLeg.insert(fixedLeg.begin(), aCashflow);\n\n // Final notional exchange\n aDate = fixedLeg.back()->date();\n aCashflow = boost::make_shared(floatNotional, aDate);\n floatLeg.push_back(aCashflow);\n\n // resetting fixed leg\n for (Size j = 0; j < fixedLeg.size(); ++j) {\n boost::shared_ptr coupon = boost::dynamic_pointer_cast(fixedLeg[j]);\n Date fixingDate = fxIndex_->fixingCalendar().advance(coupon->accrualStartDate(),\n -static_cast(fxIndex_->fixingDays()), Days);\n boost::shared_ptr fxLinkedCoupon = \n boost::make_shared(fixingDate, floatNotional, fxIndex_, coupon);\n floatLeg[j] = fxLinkedCoupon;\n }\n\n // now build a separate leg to store the resetting notionals\n receiveFixed_ ? payer_[2] = -1.0 : payer_[2] = +1.0;\n currencies_[2] = fixedCurrency_;\n for (Size j = 0; j < fixedLeg.size(); j++) {\n boost::shared_ptr c = boost::dynamic_pointer_cast(fixedLeg[j]);\n QL_REQUIRE(c, \"Resetting XCCY - expected Coupon\");\n // build a pair of notional flows, one at the start and one at the end of\n // the accrual period. Both with the same FX fixing date\n Date fixingDate = fxIndex_->fixingCalendar().advance(c->accrualStartDate(),\n -static_cast(fxIndex_->fixingDays()), Days);\n legs_[2].push_back(boost::shared_ptr(\n new FXLinkedCashFlow(c->accrualStartDate(), fixingDate, -floatNotional, fxIndex_)));\n legs_[2].push_back(boost::shared_ptr(\n new FXLinkedCashFlow(c->accrualEndDate(), fixingDate, floatNotional, fxIndex_)));\n }\n }\n\n // Deriving from cross currency swap where:\n // First leg should hold the pay flows\n // Second leg should hold the receive flows\n payer_[0] = -1.0;\n payer_[1] = 1.0;\n if (receiveFixed_) {\n legs_[1] = fixedLeg;\n currencies_[1] = fixedCurrency_;\n legs_[0] = floatLeg;\n currencies_[0] = floatCurrency_;\n } else {\n legs_[0] = fixedLeg;\n currencies_[0] = fixedCurrency_;\n legs_[1] = floatLeg;\n currencies_[1] = floatCurrency_;\n }\n \n // Register the instrument with all cashflows on each leg.\n for (Size legNo = 0; legNo < legs_.size(); legNo++) {\n Leg::iterator it;\n for (it = legs_[legNo].begin(); it != legs_[legNo].end(); ++it) {\n registerWith(*it);\n }\n }\n}\n\nvoid CrossCcyFixFloatMtMResetSwap::setupArguments(PricingEngine::arguments* a) const {\n\n CrossCcySwap::setupArguments(a);\n\n if (CrossCcyFixFloatMtMResetSwap::arguments* args = dynamic_cast(a)) {\n args->fixedRate = fixedRate_;\n args->spread = floatSpread_;\n }\n}\n\nvoid CrossCcyFixFloatMtMResetSwap::fetchResults(const PricingEngine::results* r) const {\n\n CrossCcySwap::fetchResults(r);\n\n // Depending on the pricing engine used, we may have CrossCcyFixFloatSwap::results\n if (const CrossCcyFixFloatMtMResetSwap::results* res = dynamic_cast(r)) {\n // If we have CrossCcyFixFloatSwap::results from the pricing engine\n fairFixedRate_ = res->fairFixedRate;\n fairSpread_ = res->fairSpread;\n } else {\n // If not, set them to Null to indicate a calculation is needed below\n fairFixedRate_ = Null();\n fairSpread_ = Null();\n }\n\n // Calculate fair rate and spread if they are still Null here\n static Spread basisPoint = 1.0e-4;\n\n Size idxFixed = receiveFixed_ ? 1 : 0;\n if (fairFixedRate_ == Null() && legBPS_[idxFixed] != Null())\n fairFixedRate_ = fixedRate_ - NPV_ / (legBPS_[idxFixed] / basisPoint);\n\n Size idxFloat = receiveFixed_ ? 0 : 1;\n if (fairSpread_ == Null() && legBPS_[idxFloat] != Null())\n fairSpread_ = floatSpread_ - NPV_ / (legBPS_[idxFloat] / basisPoint);\n}\n\nvoid CrossCcyFixFloatMtMResetSwap::setupExpired() const {\n CrossCcySwap::setupExpired();\n fairFixedRate_ = Null();\n fairSpread_ = Null();\n}\n\nvoid CrossCcyFixFloatMtMResetSwap::arguments::validate() const {\n CrossCcySwap::arguments::validate();\n QL_REQUIRE(fixedRate != Null(), \"Fixed rate cannot be null\");\n QL_REQUIRE(spread != Null(), \"Spread cannot be null\");\n}\n\nvoid CrossCcyFixFloatMtMResetSwap::results::reset() {\n CrossCcySwap::results::reset();\n fairFixedRate = Null();\n fairSpread = Null();\n}\n} // namespace QuantExt\n", "meta": {"hexsha": "9b8561fcf84b620e696fa7e580c63538f1cd3c8f", "size": 10921, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "QuantExt/qle/instruments/crossccyfixfloatmtmresetswap.cpp", "max_stars_repo_name": "mrslezak/Engine", "max_stars_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 335.0, "max_stars_repo_stars_event_min_datetime": "2016-10-07T16:31:10.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-02T07:12:03.000Z", "max_issues_repo_path": "QuantExt/qle/instruments/crossccyfixfloatmtmresetswap.cpp", "max_issues_repo_name": "mrslezak/Engine", "max_issues_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 59.0, "max_issues_repo_issues_event_min_datetime": "2016-10-31T04:20:24.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-03T16:39:57.000Z", "max_forks_repo_path": "QuantExt/qle/instruments/crossccyfixfloatmtmresetswap.cpp", "max_forks_repo_name": "mrslezak/Engine", "max_forks_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 180.0, "max_forks_repo_forks_event_min_datetime": "2016-10-08T14:23:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T10:43:05.000Z", "avg_line_length": 44.9423868313, "max_line_length": 123, "alphanum_fraction": 0.6877575314, "num_tokens": 2735, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.2391454545110906}} {"text": "// Distributed under the MIT License.\n// See LICENSE.txt for details.\n\n#pragma once\n\n#include \n#include \n#include \n#include \n\n#include \"DataStructures/FixedHashMap.hpp\"\n#include \"DataStructures/Index.hpp\"\n#include \"DataStructures/Variables.hpp\"\n#include \"Domain/Structure/Direction.hpp\"\n#include \"Domain/Structure/DirectionMap.hpp\"\n#include \"Domain/Structure/Element.hpp\"\n#include \"Domain/Structure/ElementId.hpp\"\n#include \"Domain/Structure/MaxNumberOfNeighbors.hpp\"\n#include \"Evolution/DgSubcell/NeighborData.hpp\"\n#include \"Evolution/Systems/Burgers/Tags.hpp\"\n#include \"NumericalAlgorithms/Spectral/Mesh.hpp\"\n#include \"Utilities/ErrorHandling/Assert.hpp\"\n#include \"Utilities/Gsl.hpp\"\n#include \"Utilities/TMPL.hpp\"\n\nnamespace Burgers::fd {\ntemplate \nvoid reconstruct_work(\n const gsl::not_null, 1>*> vars_on_lower_face,\n const gsl::not_null, 1>*> vars_on_upper_face,\n const Reconstructor& reconstruct,\n const Variables> volume_vars, const Element<1>& element,\n const FixedHashMap, ElementId<1>>,\n evolution::dg::subcell::NeighborData,\n boost::hash, ElementId<1>>>>\n neighbor_data,\n const Mesh<1>& subcell_mesh, const size_t ghost_zone_size) {\n const size_t volume_num_pts = subcell_mesh.number_of_grid_points();\n const size_t reconstructed_num_pts = volume_num_pts + 1;\n\n // create views/spans into the face data, which will be filled by the\n // reconstructor\n std::array, 1> upper_face_vars{};\n std::array, 1> lower_face_vars{};\n for (size_t i = 0; i < 1; i++) {\n gsl::at(upper_face_vars, i) =\n gsl::make_span(get(gsl::at(*vars_on_upper_face, i))[0].data(),\n reconstructed_num_pts);\n gsl::at(lower_face_vars, i) =\n gsl::make_span(get(gsl::at(*vars_on_lower_face, i))[0].data(),\n reconstructed_num_pts);\n }\n\n const size_t neighbor_num_ghost_fd_points = ghost_zone_size;\n\n // make span of ghost cell variables for each direction\n DirectionMap<1, gsl::span> ghost_cell_vars{};\n\n // for all Directions, make the pointers in ghost_cell_vars span to point\n // the received neighbor data\n for (const auto& direction : Direction<1>::all_directions()) {\n const auto& neighbors_in_direction = element.neighbors().at(direction);\n\n ASSERT(neighbors_in_direction.size() == 1,\n \"Currently only support one neighbor in each direction, but \"\n \"got \"\n << neighbors_in_direction.size() << \" in direction \"\n << direction);\n\n ghost_cell_vars[direction] = gsl::make_span(\n &neighbor_data.at(std::pair{direction, *neighbors_in_direction.begin()})\n .data_for_reconstruction[0],\n neighbor_num_ghost_fd_points);\n }\n\n // make span of volume variables\n auto& volume_tensor = get(volume_vars);\n const gsl::span volume_vars_span =\n gsl::make_span((volume_tensor)[0].data(), volume_num_pts);\n\n // perform reconstruction\n reconstruct(make_not_null(&upper_face_vars), make_not_null(&lower_face_vars),\n volume_vars_span, ghost_cell_vars, subcell_mesh.extents(), 1);\n}\n\ntemplate \nvoid reconstruct_fd_neighbor_work(\n const gsl::not_null*> vars_on_face,\n const ReconstructLower& reconstruct_lower_neighbor,\n const ReconstructUpper& reconstruct_upper_neighbor,\n const Variables>& subcell_volume_vars,\n const Element<1>& element,\n const FixedHashMap, ElementId<1>>,\n evolution::dg::subcell::NeighborData,\n boost::hash, ElementId<1>>>>\n neighbor_data,\n const Mesh<1>& subcell_mesh, const Direction<1>& direction_to_reconstruct,\n const size_t ghost_zone_size) {\n const std::pair mortar_id{\n direction_to_reconstruct,\n *element.neighbors().at(direction_to_reconstruct).begin()};\n\n Index<1> ghost_data_extents = subcell_mesh.extents();\n ghost_data_extents[direction_to_reconstruct.dimension()] = ghost_zone_size;\n\n // allocate Variable for storing data from neighbor\n Variables> neighbor_vars{ghost_data_extents.product()};\n\n {\n ASSERT(neighbor_data.contains(mortar_id),\n \"The neighbor data does not contain the mortar: (\"\n << mortar_id.first << ',' << mortar_id.second << \")\");\n\n const auto& neighbor_data_in_direction = neighbor_data.at(mortar_id);\n std::copy(\n neighbor_data_in_direction.data_for_reconstruction.begin(),\n std::next(neighbor_data_in_direction.data_for_reconstruction.begin(),\n static_cast(ghost_data_extents.product())),\n neighbor_vars.data());\n }\n\n const auto& tensor_volume = get(subcell_volume_vars);\n const auto& tensor_neighbor = get(neighbor_vars);\n auto& tensor_on_face = get(*vars_on_face);\n\n if (direction_to_reconstruct.side() == Side::Upper) {\n for (size_t tensor_index = 0; tensor_index < tensor_on_face.size();\n ++tensor_index) {\n reconstruct_upper_neighbor(\n make_not_null(&tensor_on_face[tensor_index]),\n tensor_volume[tensor_index], tensor_neighbor[tensor_index],\n subcell_mesh.extents(), ghost_data_extents, direction_to_reconstruct);\n }\n } else {\n for (size_t tensor_index = 0; tensor_index < tensor_on_face.size();\n ++tensor_index) {\n reconstruct_lower_neighbor(\n make_not_null(&tensor_on_face[tensor_index]),\n tensor_volume[tensor_index], tensor_neighbor[tensor_index],\n subcell_mesh.extents(), ghost_data_extents, direction_to_reconstruct);\n }\n }\n}\n} // namespace Burgers::fd\n", "meta": {"hexsha": "3db20e0c49ac6684b183cfa5dc82dabb3ab9176c", "size": 6155, "ext": "tpp", "lang": "C++", "max_stars_repo_path": "src/Evolution/Systems/Burgers/FiniteDifference/ReconstructWork.tpp", "max_stars_repo_name": "kidder/spectre", "max_stars_repo_head_hexsha": "97ae95f72320f9f67895d3303824e64de6fd9077", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Evolution/Systems/Burgers/FiniteDifference/ReconstructWork.tpp", "max_issues_repo_name": "kidder/spectre", "max_issues_repo_head_hexsha": "97ae95f72320f9f67895d3303824e64de6fd9077", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Evolution/Systems/Burgers/FiniteDifference/ReconstructWork.tpp", "max_forks_repo_name": "kidder/spectre", "max_forks_repo_head_hexsha": "97ae95f72320f9f67895d3303824e64de6fd9077", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.5878378378, "max_line_length": 80, "alphanum_fraction": 0.7000812348, "num_tokens": 1442, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.23901246187607117}} {"text": "#ifndef SKYLARK_HASH_TRANSFORM_MIXED_HPP\n#define SKYLARK_HASH_TRANSFORM_MIXED_HPP\n\n#include \n#include \n\n#include \"../base/sparse_vc_star_matrix.hpp\"\n\n#if SKYLARK_HAVE_COMBBLAS\n#include \"../utility/external/combblas_comm_grid.hpp\"\n#endif\n#include \"../utility/external/elemental_comm_grid.hpp\"\n\nnamespace skylark { namespace sketch {\n\n//FIXME:\n// - Benchmark one-sided vs. col/row comm (or midpoint scheme):\n// Most likely the scheme depends on the output Elemental distribution,\n// here we use the same comm-scheme for all output types.\n// - Processing Sparse matrix in blocks?\n// - MPI-3 stuff, see: Enabling highly-scalable remote memory access\n// programming with MPI-3 one sided, R. Gerstenbergerm, M. Besta, and\n// T. Hoefler.\n\n\n/* Specialization: sparse_vc_star for input, distributed Elemental for output */\ntemplate class IdxDistributionType,\n template class ValueDistribution>\nstruct hash_transform_t <\n base::sparse_vc_star_matrix_t,\n El::DistMatrix,\n IdxDistributionType,\n ValueDistribution > :\n public hash_transform_data_t {\n typedef El::Int index_type;\n typedef ValueType value_type;\n typedef base::sparse_vc_star_matrix_t matrix_type;\n typedef El::DistMatrix< value_type, ColDist, RowDist > output_matrix_type;\n typedef hash_transform_data_t data_type;\n\n\n /**\n * Regular constructor\n */\n hash_transform_t (int N, int S, base::context_t& context)\n : data_type(N, S, context)\n {}\n\n /**\n * Copy constructor\n */\n hash_transform_t (\n hash_transform_t<\n matrix_type, output_matrix_type,\n IdxDistributionType, ValueDistribution>& other)\n : data_type(other)\n {}\n\n /**\n * Constructor from data\n */\n hash_transform_t (const data_type& other_data)\n : data_type(other_data)\n {}\n\n template \n void apply (const matrix_type &A, output_matrix_type &sketch_of_A,\n Dimension dimension) const {\n try {\n apply_impl (A, sketch_of_A, dimension);\n } catch(boost::mpi::exception e) {\n SKYLARK_THROW_EXCEPTION (\n base::mpi_exception()\n << base::error_msg(e.what()) );\n } catch (std::bad_alloc e) {\n SKYLARK_THROW_EXCEPTION (\n base::skylark_exception()\n << base::error_msg(\"bad_alloc: out of memory\") );\n }\n }\n\n\nprivate:\n /**\n * Apply the sketching transform that is described in by the sketch_of_A.\n *\n * FIXME: distribution depending schemes would be more efficient\n */\n template \n void apply_impl (const matrix_type &A,\n output_matrix_type &sketch_of_A,\n Dimension dist) const {\n\n typedef size_t offset_idx_t;\n\n boost::mpi::communicator comm = skylark::utility::get_communicator(A);\n\n const size_t rank = comm.rank();\n\n const size_t ncols = sketch_of_A.Width();\n\n size_t comm_size = comm.size();\n\n const int* A_indptr = A.indptr();\n const int* A_indices = A.indices();\n const value_type *A_values = A.locked_values();\n\n std::vector< std::map > array_offsets(comm_size);\n\n // pre-compute processor targets of local sketch application\n for(int i = 0; i < A.width(); i++) {\n for (int j = A_indptr[i]; j < A_indptr[i + 1]; j++) {\n\n // compute global row and column id, and compress in one\n // target position index\n const size_t pos = getPos(\n A.global_row(A_indices[j]), i, ncols, dist);\n\n // compute target processor for this target index\n const size_t target_rank = utility::owner(\n sketch_of_A, pos / ncols, pos % ncols);\n\n assert(target_rank < comm_size);\n\n // map the position to the next empty slot\n if(array_offsets[target_rank].count(pos) == 0) {\n size_t next_pos = array_offsets[target_rank].size();\n array_offsets[target_rank][pos] = next_pos;\n }\n }\n }\n\n\n // constructing array holding start/end indices for one-sided access\n std::vector proc_start_idx(comm_size + 1, 0);\n for(size_t i = 1; i < comm_size + 1; ++i)\n proc_start_idx[i] = proc_start_idx[i-1] + array_offsets[i-1].size();\n\n const size_t my_num_values = proc_start_idx[comm_size];\n // total number of nnz that will result when applying sketch locally\n std::vector indices(my_num_values, 0);\n std::vector values(my_num_values, 0);\n\n // Apply sketch for all local values. Note that some of the resulting\n // values might end up on a different processor. The data structure\n // fills values (sorted by processor id) in one continuous array.\n // Subsequently, one-sided operations can be used to access values for\n // each processor.\n for(int i = 0; i < A.width(); i++) {\n for (int j = A_indptr[i]; j < A_indptr[i + 1]; j++) {\n\n // compute global row and column id, and compress in one\n // target position index\n const size_t pos = getPos(\n A.global_row(A_indices[j]), i, ncols, dist);\n\n // compute target processor for this target index\n const size_t target_rank = utility::owner(\n sketch_of_A, pos / ncols, pos % ncols);\n\n assert(target_rank < comm_size);\n\n // get offset in array for current element\n const size_t ar_idx = proc_start_idx[target_rank] +\n array_offsets[target_rank][pos];\n\n assert(ar_idx < indices.size());\n indices[ar_idx] = pos;\n\n assert(ar_idx < values.size());\n values[ar_idx] += A_values[j] *\n data_type::getValue(A.global_row(A_indices[j]), i, dist);\n }\n }\n\n // tell MPI that we will not use locks\n MPI_Info info;\n MPI_Info_create(&info);\n MPI_Info_set(info, \"no_locks\", \"true\");\n\n MPI_Win start_offset_win, idx_win, val_win;\n\n MPI_Win_create(&proc_start_idx[0], sizeof(size_t) * (comm_size + 1),\n sizeof(size_t), info, comm, &start_offset_win);\n\n MPI_Win_create(&indices[0], sizeof(index_type) * indices.size(),\n sizeof(index_type), info, comm, &idx_win);\n\n MPI_Win_create(&values[0], sizeof(value_type) * values.size(),\n sizeof(value_type), info, comm, &val_win);\n\n MPI_Info_free(&info);\n\n // Synchronize epoch, no subsequent put operations (read only) and no\n // preceding fence calls.\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOPRECEDE, start_offset_win);\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOPRECEDE, idx_win);\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOPRECEDE, val_win);\n\n\n // accumulate values from other procs\n for(size_t p = 0; p < comm_size; ++p) {\n\n // get the start/end offset\n std::vector offset(2);\n MPI_Get(&(offset[0]), 2, boost::mpi::get_mpi_datatype(),\n p, rank, 2, boost::mpi::get_mpi_datatype(),\n start_offset_win);\n\n MPI_Win_fence(MPI_MODE_NOPUT, start_offset_win);\n size_t num_values = offset[1] - offset[0];\n\n // and fill indices/values.\n std::vector add_idx(num_values);\n std::vector add_val(num_values);\n MPI_Get(&(add_idx[0]), num_values,\n boost::mpi::get_mpi_datatype(), p, offset[0],\n num_values, boost::mpi::get_mpi_datatype(),\n idx_win);\n\n MPI_Get(&(add_val[0]), num_values,\n boost::mpi::get_mpi_datatype(), p, offset[0],\n num_values, boost::mpi::get_mpi_datatype(),\n val_win);\n\n MPI_Win_fence(MPI_MODE_NOPUT, idx_win);\n MPI_Win_fence(MPI_MODE_NOPUT, val_win);\n\n // finally, set data in local buffer\n for(size_t i = 0; i < num_values; ++i) {\n index_type lrow = sketch_of_A.LocalRow(add_idx[i] / ncols);\n index_type lcol = sketch_of_A.LocalCol(add_idx[i] % ncols);\n sketch_of_A.UpdateLocal(lrow, lcol, add_val[i]);\n }\n }\n\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOSUCCEED, start_offset_win);\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOSUCCEED, idx_win);\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOSUCCEED, val_win);\n\n MPI_Win_free(&start_offset_win);\n MPI_Win_free(&idx_win);\n MPI_Win_free(&val_win);\n }\n\n inline index_type getPos(index_type rowid, index_type colid, size_t ncols,\n columnwise_tag) const {\n return colid + ncols * data_type::row_idx[rowid];\n }\n\n inline index_type getPos(index_type rowid, index_type colid, size_t ncols,\n rowwise_tag) const {\n return rowid * ncols + data_type::row_idx[colid];\n }\n};\n\n#if SKYLARK_HAVE_COMBBLAS\n/* Specialization: SpParMat for input, distributed Elemental for output */\ntemplate class IdxDistributionType,\n template class ValueDistribution>\nstruct hash_transform_t <\n SpParMat >,\n El::DistMatrix,\n IdxDistributionType,\n ValueDistribution > :\n public hash_transform_data_t {\n typedef IndexType index_type;\n typedef ValueType value_type;\n typedef SpDCCols< index_type, value_type > col_t;\n typedef FullyDistVec< index_type, value_type> mpi_vector_t;\n typedef SpParMat< index_type, value_type, col_t > matrix_type;\n typedef El::DistMatrix< value_type, ColDist, RowDist > output_matrix_type;\n typedef hash_transform_data_t data_type;\n\n\n /**\n * Regular constructor\n */\n hash_transform_t (int N, int S, base::context_t& context) :\n data_type(N, S, context)\n {}\n\n /**\n * Copy constructor\n */\n hash_transform_t (\n hash_transform_t<\n matrix_type, output_matrix_type,\n IdxDistributionType, ValueDistribution>& other)\n : data_type(other)\n {}\n\n /**\n * Constructor from data\n */\n hash_transform_t (const data_type& other_data)\n : data_type(other_data)\n {}\n\n template \n void apply (const matrix_type &A, output_matrix_type &sketch_of_A,\n Dimension dimension) const {\n try {\n apply_impl (A, sketch_of_A, dimension);\n } catch(boost::mpi::exception e) {\n SKYLARK_THROW_EXCEPTION (\n base::mpi_exception()\n << base::error_msg(e.what()) );\n } catch (std::string e) {\n SKYLARK_THROW_EXCEPTION (\n base::combblas_exception()\n << base::error_msg(e) );\n } catch (std::logic_error e) {\n SKYLARK_THROW_EXCEPTION (\n base::combblas_exception()\n << base::error_msg(e.what()) );\n } catch (std::bad_alloc e) {\n SKYLARK_THROW_EXCEPTION (\n base::skylark_exception()\n << base::error_msg(\"bad_alloc: out of memory\") );\n }\n }\n\n\nprivate:\n /**\n * Apply the sketching transform that is described in by the sketch_of_A.\n */\n template \n void apply_impl (const matrix_type &A_,\n output_matrix_type &sketch_of_A,\n Dimension dist) const {\n\n // We are essentially doing a 'const' access to A, but the necessary,\n // 'const' option is missing from the interface\n matrix_type &A = const_cast(A_);\n\n const size_t rank = A.getcommgrid()->GetRank();\n\n // extract columns of matrix\n col_t &data = A.seq();\n\n const size_t ncols = sketch_of_A.Width();\n\n const size_t my_row_offset = utility::cb_my_row_offset(A);\n const size_t my_col_offset = utility::cb_my_col_offset(A);\n\n size_t comm_size = A.getcommgrid()->GetSize();\n std::vector< std::set > proc_set(comm_size);\n\n // pre-compute processor targets of local sketch application\n for(typename col_t::SpColIter col = data.begcol();\n col != data.endcol(); col++) {\n for(typename col_t::SpColIter::NzIter nz = data.begnz(col);\n nz != data.endnz(col); nz++) {\n\n // compute global row and column id, and compress in one\n // target position index\n const index_type rowid = nz.rowid() + my_row_offset;\n const index_type colid = col.colid() + my_col_offset;\n const size_t pos = getPos(rowid, colid, ncols, dist);\n\n // compute target processor for this target index\n const size_t target = utility::owner(\n sketch_of_A, pos / ncols, pos % ncols);\n\n if(proc_set[target].count(pos) == 0) {\n assert(target < comm_size);\n proc_set[target].insert(pos);\n }\n }\n }\n\n // constructing array holding start/end indices for one-sided access\n std::vector proc_start_idx(comm_size + 1, 0);\n for(size_t i = 1; i < comm_size + 1; ++i)\n proc_start_idx[i] = proc_start_idx[i-1] + proc_set[i-1].size();\n\n // total number of nnz that will result when applying sketch locally\n std::vector indicies(proc_start_idx[comm_size], 0);\n std::vector values(proc_start_idx[comm_size], 0);\n\n // Apply sketch for all local values. Note that some of the resulting\n // values might end up on a different processor. The data structure\n // fills values (sorted by processor id) in one continuous array.\n // Subsequently, one-sided operations can be used to access values for\n // each processor.\n for(typename col_t::SpColIter col = data.begcol();\n col != data.endcol(); col++) {\n for(typename col_t::SpColIter::NzIter nz = data.begnz(col);\n nz != data.endnz(col); nz++) {\n\n // compute global row and column id, and compress in one\n // target position index\n const index_type rowid = nz.rowid() + my_row_offset;\n const index_type colid = col.colid() + my_col_offset;\n const size_t pos = getPos(rowid, colid, ncols, dist);\n\n // compute target processor for this target index\n const size_t proc = utility::owner(\n sketch_of_A, pos / ncols, pos % ncols);\n\n // get offset in array for current element\n const size_t ar_idx = proc_start_idx[proc] +\n std::distance(proc_set[proc].begin(), proc_set[proc].find(pos));\n\n indicies[ar_idx] = pos;\n values[ar_idx] += nz.value() *\n data_type::getValue(rowid, colid, dist);\n }\n }\n\n // Creating windows for all relevant arrays\n boost::mpi::communicator comm = utility::get_communicator(A);\n\n // tell MPI that we will not use locks\n MPI_Info info;\n MPI_Info_create(&info);\n MPI_Info_set(info, \"no_locks\", \"true\");\n\n MPI_Win start_offset_win, idx_win, val_win;\n\n MPI_Win_create(&proc_start_idx[0], sizeof(size_t) * (comm_size + 1),\n sizeof(size_t), info, comm, &start_offset_win);\n\n MPI_Win_create(&indicies[0], sizeof(index_type) * indicies.size(),\n sizeof(index_type), info, comm, &idx_win);\n\n MPI_Win_create(&values[0], sizeof(value_type) * values.size(),\n sizeof(value_type), info, comm, &val_win);\n\n MPI_Info_free(&info);\n\n // Synchronize epoch, no subsequent put operations (read only) and no\n // preceding fence calls.\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOPRECEDE, start_offset_win);\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOPRECEDE, idx_win);\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOPRECEDE, val_win);\n\n\n // accumulate values from other procs\n for(size_t p = 0; p < comm_size; ++p) {\n\n // get the start/end offset\n std::vector offset(2);\n MPI_Get(&(offset[0]), 2, boost::mpi::get_mpi_datatype(),\n p, rank, 2, boost::mpi::get_mpi_datatype(),\n start_offset_win);\n\n MPI_Win_fence(MPI_MODE_NOPUT, start_offset_win);\n size_t num_values = offset[1] - offset[0];\n\n // and fill indices/values.\n std::vector add_idx(num_values);\n std::vector add_val(num_values);\n MPI_Get(&(add_idx[0]), num_values,\n boost::mpi::get_mpi_datatype(), p, offset[0],\n num_values, boost::mpi::get_mpi_datatype(),\n idx_win);\n\n MPI_Get(&(add_val[0]), num_values,\n boost::mpi::get_mpi_datatype(), p, offset[0],\n num_values, boost::mpi::get_mpi_datatype(),\n val_win);\n\n MPI_Win_fence(MPI_MODE_NOPUT, idx_win);\n MPI_Win_fence(MPI_MODE_NOPUT, val_win);\n\n // finally, set data in local buffer\n for(size_t i = 0; i < num_values; ++i) {\n index_type lrow = sketch_of_A.LocalRow(add_idx[i] / ncols);\n index_type lcol = sketch_of_A.LocalCol(add_idx[i] % ncols);\n sketch_of_A.UpdateLocal(lrow, lcol, add_val[i]);\n }\n }\n\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOSUCCEED, start_offset_win);\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOSUCCEED, idx_win);\n MPI_Win_fence(MPI_MODE_NOPUT | MPI_MODE_NOSUCCEED, val_win);\n\n MPI_Win_free(&start_offset_win);\n MPI_Win_free(&idx_win);\n MPI_Win_free(&val_win);\n }\n\n inline index_type getPos(index_type rowid, index_type colid, size_t ncols,\n columnwise_tag) const {\n return colid + ncols * data_type::row_idx[rowid];\n }\n\n inline index_type getPos(index_type rowid, index_type colid, size_t ncols,\n rowwise_tag) const {\n return rowid * ncols + data_type::row_idx[colid];\n }\n};\n\n/* Specialization: SpParMat for input, Local Elemental output */\ntemplate class IdxDistributionType,\n template class ValueDistribution>\nstruct hash_transform_t <\n SpParMat >,\n El::Matrix,\n IdxDistributionType,\n ValueDistribution > :\n public hash_transform_data_t {\n typedef IndexType index_type;\n typedef ValueType value_type;\n typedef SpDCCols< index_type, value_type > col_t;\n typedef FullyDistVec< index_type, value_type> mpi_vector_t;\n typedef SpParMat< index_type, value_type, col_t > matrix_type;\n typedef El::Matrix< value_type > output_matrix_type;\n typedef hash_transform_data_t data_type;\n\n\n /**\n * Regular constructor\n */\n hash_transform_t (int N, int S, base::context_t& context) :\n data_type(N, S, context) {\n\n }\n\n /**\n * Copy constructor\n */\n template \n hash_transform_t (hash_transform_t& other) :\n data_type(other) {}\n\n /**\n * Constructor from data\n */\n hash_transform_t (hash_transform_data_t& other_data) :\n data_type(other_data) {}\n\n template \n void apply (const matrix_type &A, output_matrix_type &sketch_of_A,\n Dimension dimension) const {\n try {\n apply_impl (A, sketch_of_A, dimension);\n } catch(boost::mpi::exception e) {\n SKYLARK_THROW_EXCEPTION (\n base::mpi_exception()\n << base::error_msg(e.what()) );\n } catch (std::string e) {\n SKYLARK_THROW_EXCEPTION (\n base::combblas_exception()\n << base::error_msg(e) );\n } catch (std::logic_error e) {\n SKYLARK_THROW_EXCEPTION (\n base::combblas_exception()\n << base::error_msg(e.what()) );\n } catch (std::bad_alloc e) {\n SKYLARK_THROW_EXCEPTION (\n base::skylark_exception()\n << base::error_msg(\"bad_alloc: out of memory\") );\n }\n }\n\n\nprivate:\n /**\n * Apply the sketching transform that is described in by the sketch_of_A.\n */\n template \n void apply_impl (const matrix_type &A_,\n output_matrix_type &sketch_of_A,\n Dimension dist) const {\n\n // We are essentially doing a 'const' access to A, but the necessary,\n // 'const' option is missing from the interface\n matrix_type &A = const_cast(A_);\n\n const size_t rank = A.getcommgrid()->GetRank();\n\n // extract columns of matrix\n col_t &data = A.seq();\n\n const size_t my_row_offset = utility::cb_my_row_offset(A);\n const size_t my_col_offset = utility::cb_my_col_offset(A);\n\n int n_res_cols = A.getncol();\n int n_res_rows = A.getnrow();\n data_type::get_res_size(n_res_rows, n_res_cols, dist);\n\n // Apply sketch for all local values. Subsequently, all values are\n // gathered on processor 0 and the local matrix is populated.\n typedef std::map col_values_t;\n col_values_t col_values;\n for(typename col_t::SpColIter col = data.begcol();\n col != data.endcol(); col++) {\n for(typename col_t::SpColIter::NzIter nz = data.begnz(col);\n nz != data.endnz(col); nz++) {\n\n index_type rowid = nz.rowid() + my_row_offset;\n index_type colid = col.colid() + my_col_offset;\n\n const value_type value =\n nz.value() * data_type::getValue(rowid, colid, dist);\n data_type::finalPos(rowid, colid, dist);\n col_values[colid * n_res_rows + rowid] += value;\n }\n }\n\n std::vector< std::map > result;\n boost::mpi::gather(utility::get_communicator(A), col_values, result, 0);\n\n if(rank == 0) {\n typedef typename std::map::iterator itr_t;\n for(size_t i = 0; i < result.size(); ++i) {\n itr_t proc_itr = result[i].begin();\n for(; proc_itr != result[i].end(); proc_itr++) {\n int row = proc_itr->first % n_res_rows;\n int col = proc_itr->first / n_res_rows;\n sketch_of_A.Update(row, col, proc_itr->second);\n }\n }\n }\n }\n};\n\n\n/* Specialization: SpParMat for input, Elemental[* / *] output */\ntemplate class IdxDistributionType,\n template class ValueDistribution>\nstruct hash_transform_t <\n SpParMat >,\n El::DistMatrix,\n IdxDistributionType,\n ValueDistribution > :\n public hash_transform_data_t {\n typedef IndexType index_type;\n typedef ValueType value_type;\n typedef SpDCCols< index_type, value_type > col_t;\n typedef FullyDistVec< index_type, value_type> mpi_vector_t;\n typedef SpParMat< index_type, value_type, col_t > matrix_type;\n typedef El::DistMatrix< value_type, El::STAR, El::STAR > output_matrix_type;\n typedef hash_transform_data_t data_type;\n\n\n /**\n * Regular constructor\n */\n hash_transform_t (int N, int S, base::context_t& context) :\n data_type(N, S, context) {\n\n }\n\n /**\n * Copy constructor\n */\n template \n hash_transform_t (hash_transform_t& other) :\n data_type(other) {}\n\n /**\n * Constructor from data\n */\n hash_transform_t (hash_transform_data_t& other_data) :\n data_type(other_data) {}\n\n template \n void apply (const matrix_type &A, output_matrix_type &sketch_of_A,\n Dimension dimension) const {\n try {\n apply_impl (A, sketch_of_A, dimension);\n } catch(boost::mpi::exception e) {\n SKYLARK_THROW_EXCEPTION (\n base::mpi_exception()\n << base::error_msg(e.what()) );\n } catch (std::string e) {\n SKYLARK_THROW_EXCEPTION (\n base::combblas_exception()\n << base::error_msg(e) );\n } catch (std::logic_error e) {\n SKYLARK_THROW_EXCEPTION (\n base::combblas_exception()\n << base::error_msg(e.what()) );\n } catch (std::bad_alloc e) {\n SKYLARK_THROW_EXCEPTION (\n base::skylark_exception()\n << base::error_msg(\"bad_alloc: out of memory\") );\n }\n }\n\n\nprivate:\n /**\n * Apply the sketching transform that is described in by the sketch_of_A.\n */\n template \n void apply_impl (const matrix_type &A_,\n output_matrix_type &sketch_of_A,\n Dimension dist) const {\n\n // We are essentially doing a 'const' access to A, but the necessary,\n // 'const' option is missing from the interface\n matrix_type &A = const_cast(A_);\n\n const size_t rank = A.getcommgrid()->GetRank();\n\n // extract columns of matrix\n col_t &data = A.seq();\n\n const size_t my_row_offset = utility::cb_my_row_offset(A);\n const size_t my_col_offset = utility::cb_my_col_offset(A);\n\n int n_res_cols = A.getncol();\n int n_res_rows = A.getnrow();\n data_type::get_res_size(n_res_rows, n_res_cols, dist);\n\n // Apply sketch for all local values. Subsequently, all values are\n // gathered on all processor and the \"local\" matrix is populated.\n typedef std::map col_values_t;\n col_values_t col_values;\n for(typename col_t::SpColIter col = data.begcol();\n col != data.endcol(); col++) {\n for(typename col_t::SpColIter::NzIter nz = data.begnz(col);\n nz != data.endnz(col); nz++) {\n\n index_type rowid = nz.rowid() + my_row_offset;\n index_type colid = col.colid() + my_col_offset;\n\n const value_type value =\n nz.value() * data_type::getValue(rowid, colid, dist);\n data_type::finalPos(rowid, colid, dist);\n col_values[colid * n_res_rows + rowid] += value;\n }\n }\n\n std::vector< std::map > result;\n boost::mpi::all_gather(\n utility::get_communicator(A), col_values, result);\n\n typedef typename std::map::iterator itr_t;\n for(size_t i = 0; i < result.size(); ++i) {\n itr_t proc_itr = result[i].begin();\n for(; proc_itr != result[i].end(); proc_itr++) {\n int row = proc_itr->first % n_res_rows;\n int col = proc_itr->first / n_res_rows;\n sketch_of_A.Update(row, col, proc_itr->second);\n }\n }\n }\n};\n#endif\n\n} } /** namespace skylark::sketch */\n\n#endif // SKYLARK_HASH_TRANSFORM_MIXED_HPP\n", "meta": {"hexsha": "0fa1451fef95e8d0899950f7f59d9ba5f4b617b7", "size": 29534, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "sketch/hash_transform_Mixed.hpp", "max_stars_repo_name": "xdata-skylark/libskylark", "max_stars_repo_head_hexsha": "89c3736136a24d519c14fc0738c21f37f1e10360", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 86.0, "max_stars_repo_stars_event_min_datetime": "2015-01-20T03:12:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-10T04:05:21.000Z", "max_issues_repo_path": "sketch/hash_transform_Mixed.hpp", "max_issues_repo_name": "xdata-skylark/libskylark", "max_issues_repo_head_hexsha": "89c3736136a24d519c14fc0738c21f37f1e10360", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 48.0, "max_issues_repo_issues_event_min_datetime": "2015-05-12T09:31:23.000Z", "max_issues_repo_issues_event_max_datetime": "2018-12-05T14:45:46.000Z", "max_forks_repo_path": "sketch/hash_transform_Mixed.hpp", "max_forks_repo_name": "xdata-skylark/libskylark", "max_forks_repo_head_hexsha": "89c3736136a24d519c14fc0738c21f37f1e10360", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 25.0, "max_forks_repo_forks_event_min_datetime": "2015-01-18T23:02:11.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-12T07:30:35.000Z", "avg_line_length": 37.8641025641, "max_line_length": 84, "alphanum_fraction": 0.5905735762, "num_tokens": 6644, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.23901246187607114}} {"text": "//Copyright (c) 2013 Singapore-MIT Alliance for Research and Technology\n//Licensed under the terms of the MIT License, as described in the file:\n// license.txt (http://opensource.org/licenses/MIT)\n\n#include \n#include \n#include \n#include \n\n#include \"models/IntersectionDrivingModel.hpp\"\n#include \"DriverUpdateParams.hpp\"\n#include \"Driver.hpp\"\n#include \"util/Utils.hpp\"\n\nusing namespace std;\nusing namespace sim_mob;\n\nMITSIM_IntDriving_Model::MITSIM_IntDriving_Model() :\nintersectionAttentivenessFactorMin(0), intersectionAttentivenessFactorMax(0), minimumGap(0), impatienceFactor(0)\n{\n modelType = IntModelType::Int_Model_MITSIM;\n}\n\nMITSIM_IntDriving_Model::MITSIM_IntDriving_Model(DriverUpdateParams& params) :\nintersectionAttentivenessFactorMin(0), intersectionAttentivenessFactorMax(0), minimumGap(0), impatienceFactor(0)\n{\n modelType = IntModelType::Int_Model_MITSIM;\n readDriverParameters(params);\n}\n\nMITSIM_IntDriving_Model::~MITSIM_IntDriving_Model()\n{\n}\n\ndouble MITSIM_IntDriving_Model::getIntersectionAttentivenessFactorMin() const\n{\n return intersectionAttentivenessFactorMin;\n}\n\ndouble MITSIM_IntDriving_Model::getIntersectionAttentivenessFactorMax() const\n{\n return intersectionAttentivenessFactorMax;\n}\n\ndouble MITSIM_IntDriving_Model::getImpatienceFactor() const\n{\n return impatienceFactor;\n}\n\nvoid MITSIM_IntDriving_Model::readDriverParameters(DriverUpdateParams ¶ms)\n{\n string modelName = \"general_driver_model\";\n string critical_gap_addon;\n bool isAMOD = false;\n\n //Check if the vehicle is autonomous\n if (params.driver->getParent()->amodId != \"-1\")\n {\n isAMOD = true;\n }\n\n //Get the parameter manager instance for the respective type of vehicle (normal or AMOD)\n ParameterManager *parameterMgr = ParameterManager::Instance(isAMOD);\n\n //Read the parameter values\n parameterMgr->param(modelName, \"intersection_attentiveness_factor_min\", intersectionAttentivenessFactorMin, 1.0);\n parameterMgr->param(modelName, \"intersection_attentiveness_factor_max\", intersectionAttentivenessFactorMax, 3.0);\n parameterMgr->param(modelName, \"minimum_gap\", minimumGap, 0.0);\n parameterMgr->param(modelName, \"critical_gap_addon\", critical_gap_addon, string(\"0.0 2.5\"));\n parameterMgr->param(modelName, \"impatience_factor\", impatienceFactor, 0.2);\n\n //Vector to store the tokenized parameters\n std::vector gapAddonParams;\n\n //Index\n int index = 0;\n\n //Tokenize the gap add-on parameters\n boost::trim(critical_gap_addon);\n boost::split(gapAddonParams, critical_gap_addon, boost::is_any_of(\" \"), boost::token_compress_on);\n\n //Convert into numeric form\n vector::iterator itStr = gapAddonParams.begin();\n while (itStr != gapAddonParams.end())\n {\n double res = 0;\n\n try\n {\n res = boost::lexical_cast(itStr->c_str());\n }\n catch (boost::bad_lexical_cast&)\n {\n std::stringstream str;\n str << __func__ << \": Could not covert \" << *itStr << \" to type double.\";\n throw std::runtime_error(str.str());\n }\n\n criticalGapAddOn[index] = res;\n\n ++index;\n ++itStr;\n }\n}\n\ndouble MITSIM_IntDriving_Model::calcBrakeToStopAcc(double distance, DriverUpdateParams ¶ms)\n{\n double acc = 0;\n\n if (distance > Math::DOUBLE_EPSILON)\n {\n //v^2 = u^2 + 2as (Equation of motion)\n //So, a = (v^2 - u^2) / 2s\n //v = final velocity, u = initial velocity\n //a = acceleration, s = displacement\n double sqCurrVel = params.perceivedFwdVelocity * params.perceivedFwdVelocity;\n\n acc = -sqCurrVel / distance * 0.5;\n\n if (acc <= params.normalDeceleration)\n {\n acc = params.normalDeceleration;\n }\n else\n {\n double dt = params.nextStepSize;\n double vt = params.perceivedFwdVelocity * dt;\n double a = dt * dt;\n double b = 2.0 * vt - params.normalDeceleration * a;\n double c = sqCurrVel + 2.0 * params.normalDeceleration * (distance - vt);\n double d = b * b - 4.0 * a * c;\n\n if (!(d < 0 || a <= 0.0))\n {\n acc = (sqrt(d) - b) / a * 0.5;\n }\n }\n }\n else\n {\n double dt = params.nextStepSize;\n acc = (dt > 0.0) ? -(params.perceivedFwdVelocity) / dt : params.maxDeceleration;\n }\n\n //Make sure the value is bounded\n if (acc > params.maxAcceleration)\n {\n acc = params.maxAcceleration;\n }\n\n if (acc < params.maxDeceleration)\n {\n acc = params.maxDeceleration;\n }\n\n return acc;\n}\n\ndouble MITSIM_IntDriving_Model::calcCrawlingAcc(double distance, DriverUpdateParams& params)\n{\n //Acceleration to slowly crawl towards the conflict point\n return 2 * ((distance / 2) - params.perceivedFwdVelocity * params.nextStepSize) / (params.nextStepSize * params.nextStepSize);\n}\n\ndouble MITSIM_IntDriving_Model::calcArrivalTime(double distance, DriverUpdateParams& params)\n{\n double arrivalTime = -1, acceleration = 0, finalVel = 0;\n\n //The final velocity is limited by the turning speed, so calculate the acceleration required to\n //achieve the final velocity\n //v^2 = u^2 + 2as\n //So, a = (v^2 - u^2) / (2s)\n\n //Get the speed limit and convert it to m/s\n finalVel = 1; //currTurning->getTurningSpeed() / 3.6;\n\n //Calculate the acceleration\n acceleration = ((finalVel * finalVel) - (params.perceivedFwdVelocity * params.perceivedFwdVelocity)) / (2 * distance);\n\n //We know s = ut + (1/2)at^2\n //To find the time required, we rearrange the equation as follows:\n //(1/2)at^2 + ut - s = 0 This is a quadratic equation AX^2 + BX + C = 0 and we can solve for t\n //A = (1/2)a; B = u; C = -s\n\n if (acceleration == 0 && params.perceivedFwdVelocity != 0)\n {\n //Acceleration is 0, so we have a linear relation : ut - s = 0\n arrivalTime = distance / params.perceivedFwdVelocity;\n }\n else\n {\n //As it is a quadratic equation, we will have two solutions\n double sol1 = 0, sol2 = 0;\n\n //The discriminant (b^2 - 4ac)\n double discriminant = (params.perceivedFwdVelocity * params.perceivedFwdVelocity) - (2 * acceleration * (-distance));\n\n if (discriminant >= 0)\n {\n //Calculate the solutions\n sol1 = (-params.perceivedFwdVelocity - sqrt(discriminant)) / acceleration;\n sol2 = (-params.perceivedFwdVelocity + sqrt(discriminant)) / acceleration;\n\n //As time can be negative, return the solution that is a positive value \n if (sol1 >= 0 && sol2 >= 0)\n {\n arrivalTime = min(sol1, sol2);\n }\n else if (sol1 >= 0)\n {\n arrivalTime = sol1;\n }\n else if (sol2 >= 0)\n {\n arrivalTime = sol2;\n }\n }\n }\n\n return arrivalTime;\n}\n\ndouble MITSIM_IntDriving_Model::makeAcceleratingDecision(DriverUpdateParams ¶ms)\n{\n params.intDebugStr.clear();\n std::stringstream debugStr;\n double acc = params.maxAcceleration;\n const double vehicleLength = params.driver->getVehicleLength();\n const TurningGroup *currTurningGroup = currTurning->getTurningGroup();\n\n //Reduce the reaction time in intersection \n params.reactionTimeCounter = params.reactionTimeCounter * Utils::generateFloat(intersectionAttentivenessFactorMin, intersectionAttentivenessFactorMax);\n\n //Safety margin distance in front of the vehicle (half a vehicle length seems a reasonable margin)\n const double safeDist = 1.5 * vehicleLength;\n double distToStopLine = params.driver->getDistToIntersection() - safeDist;\n\n debugStr << \";t:\" << params.now.ms() << \";dSL:\" << distToStopLine;\n\n //Check if we've stopped close enough to the stop line\n if (distToStopLine <= 1 && params.perceivedFwdVelocity <= 0.1)\n {\n params.hasStoppedForStopSign = true;\n debugStr << \";StpSgn-Dne\";\n }\n\n //We have to stop for the stop sign\n if (currTurningGroup->getRule() == TurningGroupRule::TURNING_GROUP_RULE_STOP_SIGN && !params.hasStoppedForStopSign && distToStopLine <= 5)\n {\n double brakingAcc = calcBrakeToStopAcc(distToStopLine, params);\n\n if (acc > brakingAcc && params.perceivedFwdVelocity != 0)\n {\n acc = brakingAcc;\n params.driver->setYieldingToInIntersection(-1);\n }\n\n debugStr << \";StpSgn\";\n return acc;\n }\n\n //The turning conflicts for the current turning\n const vector &conflicts = currTurning->getConflictsOnPath();\n\n //Select the nearest conflict ahead of us (the vector is sorted according to the distance from the start of the turning,\n //we may have crossed some)\n for (vector::const_iterator itConflicts = conflicts.begin(); itConflicts != conflicts.end(); ++itConflicts)\n {\n const TurningConflict *conflict = *itConflicts;\n bool isGapRejected = false;\n\n debugStr << \";Cfl:\" << conflict->getConflictId();\n\n //The priority of the turnings in the conflict\n //0 - equal priority, 1 - first turning has priority, 2 - second turning has priority\n int priority = conflict->getPriority();\n\n bool isFirstTurning = (conflict->getFirstTurning() == currTurning) ? true : false;\n\n //The distance to conflict point from the start of the turning\n double cfltDistFrmTurning = 0;\n\n //The distance to conflict point from our position\n double distToConflict = 0;\n\n //Check which turning in the conflict has the higher priority. If our turning has higher priority, \n //ignore all vehicles in the conflict as they will slow down for us\n if (isFirstTurning)\n {\n //Our turning has priority, or equal priority but other turning has\n //stop sign, ignore the vehicles in this conflict\n if (priority == 1 ||\n (priority == 0 && currTurningGroup->getRule() == TurningGroupRule::TURNING_GROUP_RULE_NO_STOP_SIGN &&\n conflict->getSecondTurning()->getTurningGroup()->getRule() == TurningGroupRule::TURNING_GROUP_RULE_STOP_SIGN))\n {\n continue;\n }\n\n //Get the distance to conflict point\n cfltDistFrmTurning = conflict->getFirstConflictDistance();\n }\n else\n {\n //Our turning has priority, or equal priority but other turning has\n //stop sign, ignore the vehicles in this conflict\n if (priority == 2 ||\n (priority == 0 && currTurningGroup->getRule() == TurningGroupRule::TURNING_GROUP_RULE_NO_STOP_SIGN &&\n conflict->getFirstTurning()->getTurningGroup()->getRule() == TurningGroupRule::TURNING_GROUP_RULE_STOP_SIGN))\n {\n continue;\n }\n\n //Get the distance to conflict point\n cfltDistFrmTurning = conflict->getSecondConflictDistance();\n }\n\n //Calculate the distance to conflict point from current position\n\n //If the vehicle is approaching the intersection rather than already in it, add the distance to the end\n //of the segment to the distance to conflict\n if (params.isApproachingIntersection)\n {\n distToConflict = cfltDistFrmTurning + params.driver->getDistToIntersection();\n }\n else\n {\n //Vehicle is in the intersection, so subtract the distance covered on the turning\n distToConflict = cfltDistFrmTurning - params.driver->getDistCoveredOnCurrWayPt();\n }\n\n debugStr << \";DstCfl:\" << distToConflict;\n\n //The distance at which the vehicle needs to come to a stand-still when yielding\n double stoppingDist = 0;\n\n //If the turning has a stop sign, we stop just before the intersection\n if (currTurningGroup->getRule() == TurningGroupRule::TURNING_GROUP_RULE_STOP_SIGN)\n {\n stoppingDist = distToStopLine;\n }\n else\n {\n stoppingDist = distToConflict - safeDist;\n }\n\n debugStr << \";StpDis:\" << stoppingDist;\n\n //If we're yet to reach the conflict, calculate the time required for us and the conflict driver\n //else go to the next conflict\n if (distToConflict > vehicleLength)\n {\n //Time taken to reach conflict by current driver\n double timeToConflict = calcArrivalTime(abs(distToConflict), params);\n\n if (timeToConflict == -1)\n {\n if (params.perceivedFwdVelocity > 1.0)\n {\n timeToConflict = abs(distToConflict) / params.perceivedFwdVelocity;\n }\n else\n {\n timeToConflict = 0;\n }\n }\n\n debugStr << \";Time2Cfl:\" << timeToConflict;\n\n //Calculate the critical gap\n //It is the max of: minimumGap and the difference between the \n //criticalGap for the turning conflict and a random value\n double criticalGap = conflict->getCriticalGap();\n\n //Add a random add-on value\n criticalGap += Utils::nRandom(criticalGapAddOn[0], criticalGapAddOn[1]);\n\n //Reduce by impatience on if equal priority\n if (priority == 0)\n {\n criticalGap -= (params.impatienceTimer * impatienceFactor);\n }\n\n criticalGap = max(criticalGap, minimumGap);\n\n debugStr << \";CGap:\" << criticalGap; \n\n //The map entry to the conflict and list of NearestVehicles on the it\n map >::iterator itConflictVehicles = params.conflictVehicles.find(conflict);\n\n //Check if we have found an entry in the map\n if (itConflictVehicles != params.conflictVehicles.end())\n {\n debugStr << \";#veh:\" << itConflictVehicles->second.size();\n\n //Iterator to the list of nearest vehicles on the list\n std::set::iterator itNearestVehicles = itConflictVehicles->second.begin();\n\n //Look for the vehicle reaching the conflict point - the list is sorted according to the distance from the conflict\n //point - distance less than 0 means yet to reach the conflict and greater than 0 means crossed the conflict point\n for (; itNearestVehicles != itConflictVehicles->second.end(); ++itNearestVehicles)\n {\n Driver *drv = const_cast (itNearestVehicles->driver);\n DriverUpdateParams ¶msOtherDriver = drv->getParams();\n\n debugStr << \";Veh:\" << paramsOtherDriver.parentId; \n debugStr << \";DisCfl:\" << itNearestVehicles->distance;\n\n //If a driver is already yielding to us, scan the next conflict\n if (itNearestVehicles->driver->getYieldingToInIntersection() == params.parentId)\n {\n debugStr << \";Yldng:\"; \n break;\n }\n\n if (itNearestVehicles->distance >= -itNearestVehicles->driver->getVehicleLength() / 2 && itNearestVehicles->distance < 0.0)\n {\n //Other vehicle is blocking the conflict\n debugStr << \";StpPtCrsd:\";\n isGapRejected = true;\n }\n else if (itNearestVehicles->distance < -itNearestVehicles->driver->getVehicleLength())\n {\n //The vehicle is yet to arrive at the conflict point\n \n //Time taken to reach conflict point by incoming driver\n double timeToConflictOtherDriver = 0;\n\n //Speed of the incoming vehicle (m/s))\n double speed = itNearestVehicles->driver->getVehicle()->getVelocity();\n\n if (speed > 1.0)\n {\n //Negate the distance while calculating the time\n timeToConflictOtherDriver = abs(itNearestVehicles->distance) / speed;\n } \n\n //The gap between the drivers\n double gap = abs(timeToConflictOtherDriver - timeToConflict);\n\n debugStr << \";T2Cfl:\" << timeToConflictOtherDriver << \";gap:\" << gap;\n\n //The gap was accepted, but we need to check if there's enough space after the conflict\n //point. A vehicle with higher priority might collide with us if we can't go past \n //the conflict point\n if (gap >= criticalGap && timeToConflictOtherDriver != 0 && timeToConflict != 0)\n {\n debugStr << \";gp>=cgap\"; \n\n //Distance of forward vehicle from the conflict point\n double distAheadOfConflict = DBL_MAX;\n \n if (params.nvFwd.driver)\n {\n //The forward driver exists, reject the gap if he's not far enough ahead of the conflict\n distAheadOfConflict = params.nvFwd.distance - distToConflict;\n } \n else if (params.nvFwdNextLink.driver)\n {\n //There is no forward driver, but there is one after the intersection\n distAheadOfConflict = params.nvFwdNextLink.distance - distToConflict;\n }\n\n //Reject the gap if we don't have at least 5 vehicle length of space\n if (distAheadOfConflict < (5 * vehicleLength))\n {\n isGapRejected = true;\n }\n }\n else if (timeToConflict != 0 && timeToConflictOtherDriver == 0)\n {\n //Other driver has stopped\n debugStr << \";Time2Cfl!=0&&T2Cfl!=0\"; \n\n //Assume the vehicle starts moving with the same speed as us \n\n //Assumed time calculated based on our speed\n double assumedTimeToConflict = abs(itNearestVehicles->distance) / params.currSpeed;\n\n debugStr << \";AsmdT2Cfl:\" << assumedTimeToConflict; \n\n //Assumed gap\n double assumedGap = abs(assumedTimeToConflict - timeToConflict);\n\n //Check if the gap is accepted\n if (assumedGap <= criticalGap && itNearestVehicles->driver->getYieldingToInIntersection() == -1)\n {\n debugStr << \";AsmdGap-Rjct\"; \n isGapRejected = true;\n }\n else\n {\n debugStr << \";AsmdGap-Accpt\";\n }\n } \n else if (timeToConflict == 0 && timeToConflictOtherDriver != 0)\n {\n //We have stopped, crawl till we can better judge the gap\n debugStr << \";Time2Cfl==0&&T2Cfl!=0\";\n\n if (distToConflict > abs(itNearestVehicles->distance) && itNearestVehicles->driver->getYieldingToInIntersection() == -1)\n {\n //If we're stationary, crawl to conflict point\n double crawlAcc = calcCrawlingAcc(stoppingDist, params); \n\n if (acc > crawlAcc)\n {\n acc = crawlAcc;\n params.driver->setYieldingToInIntersection(paramsOtherDriver.parentId);\n\n debugStr << \";Crawl:\" << crawlAcc << \";YldVeh:\" << paramsOtherDriver.parentId; \n }\n else\n {\n debugStr << \";XCrawl:\" << crawlAcc;\n isGapRejected = true;\n }\n }\n } \n else if (timeToConflict == 0 && timeToConflictOtherDriver == 0)\n {\n //Both vehicles have stopped, break deadlock\n debugStr << \";Time2Cfl==0&&T2Cfl==0\";\n\n //Compare the distances, if the one nearer to the conflict can go through\n if (distToConflict > abs(itNearestVehicles->distance) && itNearestVehicles->driver->getYieldingToInIntersection() == -1)\n {\n debugStr << \";OthClser\"; \n isGapRejected = true;\n }\n else\n {\n debugStr << \";IClsr\";\n }\n } \n else\n {\n //All other cases, reject gap\n isGapRejected = true;\n }\n }\n\n //If the gap has been rejected (slow down and stop)\n if (isGapRejected)\n {\n //Calculate the deceleration required to stop before conflict\n double brakingAcc = calcBrakeToStopAcc(stoppingDist, params);\n\n debugStr << \";Gap-Rjct\" << \";brake:\" << brakingAcc;\n\n //If this deceleration is smaller than the one we have previously, use this\n if (acc > brakingAcc)\n {\n acc = brakingAcc;\n params.driver->setYieldingToInIntersection(paramsOtherDriver.parentId);\n\n debugStr << \";YldVeh:\" << paramsOtherDriver.parentId << \"Gap-Rjct\";\n }\n }\n }\n }\n }\n else\n {\n debugStr << \";XdCflt\" << conflict->getConflictId() << \";dist:\" << distToConflict;\n }\n }\n\n params.intDebugStr = debugStr.str();\n return acc;\n}\n", "meta": {"hexsha": "3ea3e9ddaa669e1acde14e0af7bf2101b2d5c2cf", "size": 23528, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "dev/Basic/short/entities/roles/driver/MITSIM_IntDriving_Model.cpp", "max_stars_repo_name": "gusugusu1018/simmobility-prod", "max_stars_repo_head_hexsha": "d30a5ba353673f8fd35f4868c26994a0206a40b6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 50.0, "max_stars_repo_stars_event_min_datetime": "2018-12-21T08:21:38.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-24T09:47:59.000Z", "max_issues_repo_path": "dev/Basic/short/entities/roles/driver/MITSIM_IntDriving_Model.cpp", "max_issues_repo_name": "gusugusu1018/simmobility-prod", "max_issues_repo_head_hexsha": "d30a5ba353673f8fd35f4868c26994a0206a40b6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2018-12-19T13:42:47.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-13T04:11:45.000Z", "max_forks_repo_path": "dev/Basic/short/entities/roles/driver/MITSIM_IntDriving_Model.cpp", "max_forks_repo_name": "gusugusu1018/simmobility-prod", "max_forks_repo_head_hexsha": "d30a5ba353673f8fd35f4868c26994a0206a40b6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 27.0, "max_forks_repo_forks_event_min_datetime": "2018-11-28T07:30:34.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-05T02:22:26.000Z", "avg_line_length": 41.2049036778, "max_line_length": 162, "alphanum_fraction": 0.5474753485, "num_tokens": 4962, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.36296921241058616, "lm_q1q2_score": 0.23898528161622076}} {"text": "#ifndef PARMCB_SPTREES_HPP_\n#define PARMCB_SPTREES_HPP_\n\n// Copyright (C) Dimitrios Michail 2019 - 2021.\n// Distributed under the Boost Software License, Version 1.0.\n// (See accompanying file LICENSE_1_0.txt or copy at\n// https://www.boost.org/LICENSE_1_0.txt)\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n\n\nnamespace parmcb {\n\n template class SPNode;\n template class SPTree;\n template struct SPSubtree;\n template class SPTrees;\n template class CandidateCycle;\n template struct SerializableCandidateCycle;\n template struct SerializableMinOddCycle;\n template struct SerializableMinOddCycleMinOp;\n\n template\n class SPNode {\n public:\n typedef typename boost::graph_traits::vertex_descriptor Vertex;\n typedef typename boost::graph_traits::edge_descriptor Edge;\n typedef typename boost::property_traits::value_type WeightType;\n\n SPNode() :\n _vertex(), _parity(false), _weight(WeightType()), _pred(), _has_pred(false) {\n }\n\n SPNode(Vertex vertex, WeightType weight) :\n _vertex(vertex), _parity(false), _weight(WeightType()), _pred(), _has_pred(false) {\n }\n\n SPNode(Vertex vertex, WeightType weight, const Edge &pred) :\n _vertex(vertex), _parity(false), _weight(weight), _pred(pred), _has_pred(true) {\n }\n\n void add_child(std::shared_ptr> c) {\n _children.push_back(c);\n }\n\n std::vector>>& children() {\n return _children;\n }\n\n Vertex& vertex() {\n return _vertex;\n }\n\n bool& parity() {\n return _parity;\n }\n\n WeightType& weight() {\n return _weight;\n }\n\n const Edge& pred() {\n return _pred;\n }\n\n bool has_pred() {\n return _has_pred;\n }\n\n private:\n Vertex _vertex;\n bool _parity;\n WeightType _weight;\n Edge _pred;\n bool _has_pred;\n std::vector>> _children;\n };\n\n template\n struct SPSubtree {\n T info;\n std::shared_ptr> root;\n\n SPSubtree(T info, std::shared_ptr> root) :\n info(info), root(root) {\n }\n };\n\n template\n class SPTree {\n public:\n typedef typename boost::graph_traits::vertex_descriptor Vertex;\n typedef typename boost::graph_traits::vertex_iterator VertexIt;\n typedef typename boost::property_map::type VertexIndexMapType;\n typedef typename boost::graph_traits::edge_descriptor Edge;\n typedef typename boost::property_traits::value_type WeightType;\n\n SPTree(std::size_t id, const Graph &g, const VertexIndexMapType& index_map, const WeightMap &weight_map, const Vertex &source) :\n _id(id), _g(g), _weight_map(weight_map), _index_map(index_map), _source(\n source), _tree_node_map(boost::num_vertices(g)), _first_in_path(boost::num_vertices(g)) {\n initialize();\n }\n\n void update_parities(const std::set &edges) {\n std::stack> stack;\n stack.emplace(false, _root);\n\n while (!stack.empty()) {\n SPSubtree r = stack.top();\n stack.pop();\n\n r.root->parity() = r.info;\n for (auto c : r.root->children()) {\n bool is_signed = edges.find(c->pred()) != edges.end();\n stack.emplace(SPSubtree { static_cast(r.info ^ is_signed), c });\n }\n }\n }\n\n std::shared_ptr> node(const Vertex &v) const {\n return _tree_node_map[_index_map[v]];\n }\n\n const Vertex& source() const {\n return _source;\n }\n\n const Graph& graph() const {\n return _g;\n }\n\n const std::size_t id() const {\n return _id;\n }\n\n const Vertex first(const Vertex &v) {\n auto vindex = _index_map[v];\n return _first_in_path[vindex];\n }\n\n template\n std::vector> create_candidate_cycles(EdgeIterator begin,\n EdgeIterator end) const {\n // collect tree edges\n std::set tree_edges;\n VertexIt vi, viend;\n for (boost::tie(vi, viend) = boost::vertices(_g); vi != viend; ++vi) {\n auto v = *vi;\n auto vindex = _index_map[v];\n std::shared_ptr> n = _tree_node_map[vindex];\n if (n != nullptr && n->has_pred()) {\n tree_edges.insert(n->pred());\n }\n }\n\n // loop over (non-tree) provided edges and create candidate cycles\n std::vector> cycles;\n for (EdgeIterator it = begin; it != end; it++) {\n Edge e = *it;\n if (tree_edges.find(e) != tree_edges.end()) {\n continue;\n }\n\n // non-tree edge\n std::shared_ptr> v = node(boost::source(e, _g));\n if (v == nullptr) {\n continue;\n }\n std::shared_ptr> u = node(boost::target(e, _g));\n if (u == nullptr) {\n continue;\n }\n\n if (_first_in_path[_index_map[v->vertex()]] == _first_in_path[_index_map[u->vertex()]]) {\n // shortest paths start with the same vertex, discard\n continue;\n }\n\n WeightType cycle_weight = boost::get(_weight_map, e) + v->weight() + u->weight();\n cycles.emplace_back(_id, e, cycle_weight);\n }\n return cycles;\n }\n\n std::vector> create_candidate_cycles() const {\n auto itPair = boost::edges(_g);\n return create_candidate_cycles(itPair.first, itPair.second);\n }\n\n std::vector> create_serializable_candidate_cycles(\n const ForestIndex &forest_index) {\n // collect tree edges\n std::set tree_edges;\n VertexIt vi, viend;\n for (boost::tie(vi, viend) = boost::vertices(_g); vi != viend; ++vi) {\n auto v = *vi;\n auto vindex = _index_map[v];\n std::shared_ptr> n = _tree_node_map[vindex];\n if (n != nullptr && n->has_pred()) {\n tree_edges.insert(n->pred());\n }\n }\n\n // loop over all non-tree edges and create candidate cycles\n std::vector> cycles;\n for (const auto &e : boost::make_iterator_range(boost::edges(_g))) {\n if (tree_edges.find(e) != tree_edges.end()) {\n continue;\n }\n\n // non-tree edge\n std::shared_ptr> v = node(boost::source(e, _g));\n if (v == nullptr) {\n continue;\n }\n std::shared_ptr> u = node(boost::target(e, _g));\n if (u == nullptr) {\n continue;\n }\n\n if (_first_in_path[_index_map[v->vertex()]] == _first_in_path[_index_map[u->vertex()]]) {\n // shortest paths start with the same vertex, discard\n continue;\n }\n\n cycles.emplace_back(_source, forest_index(e));\n }\n\n return cycles;\n }\n\n private:\n const std::size_t _id;\n const Graph &_g;\n const WeightMap &_weight_map;\n const VertexIndexMapType &_index_map;\n const Vertex _source;\n\n /*\n * Shortest path tree root\n */\n std::shared_ptr> _root;\n /*\n * Map from vertex to shortest path tree node\n */\n std::vector>> _tree_node_map;\n /*\n * First vertex in shortest path from root to a vertex.\n */\n std::vector _first_in_path;\n\n void initialize() {\n // run shortest path\n std::vector dist(boost::num_vertices(_g), (std::numeric_limits::max)());\n boost::function_property_map, Vertex, WeightType&> dist_map(\n parmcb::detail::VertexIndexFunctor(dist, _index_map));\n std::vector> pred(boost::num_vertices(_g), std::make_tuple(false, Edge()));\n boost::function_property_map>, Vertex,\n std::tuple&> pred_map(\n parmcb::detail::VertexIndexFunctor >(pred, _index_map));\n lex_dijkstra(_g, _weight_map, _source, dist_map, pred_map);\n\n // create tree nodes and mapping\n VertexIt vi, viend;\n for (boost::tie(vi, viend) = boost::vertices(_g); vi != viend; ++vi) {\n auto v = *vi;\n auto vindex = _index_map[v];\n auto p = boost::get(pred_map, v);\n if (v == _source) {\n _tree_node_map[vindex] = std::shared_ptr>(\n new SPNode(v, dist[vindex]));\n _root = _tree_node_map[vindex];\n } else if (std::get<0>(p)) {\n Edge e = std::get<1>(p);\n _tree_node_map[vindex] = std::shared_ptr>(\n new SPNode(v, dist[vindex], e));\n }\n }\n\n // link tree nodes\n for (boost::tie(vi, viend) = boost::vertices(_g); vi != viend; ++vi) {\n auto v = *vi;\n auto p = boost::get(pred_map, v);\n if (std::get<0>(p)) {\n auto e = std::get<1>(p);\n auto u = boost::opposite(e, v, _g);\n auto vindex = _index_map[v];\n auto uindex = _index_map[u];\n _tree_node_map[uindex]->add_child(_tree_node_map[vindex]);\n }\n }\n\n // compute first in path\n compute_first_in_path();\n }\n\n void compute_first_in_path() {\n std::stack> stack;\n stack.emplace(_source, _root);\n\n while (!stack.empty()) {\n SPSubtree r = stack.top();\n stack.pop();\n\n if (r.root == _root) {\n auto v = r.root->vertex();\n auto vindex = _index_map[v];\n _first_in_path[vindex] = v;\n for (auto c : r.root->children()) {\n stack.emplace(SPSubtree { static_cast(c->vertex()), c });\n }\n } else {\n auto v = r.root->vertex();\n auto vindex = _index_map[v];\n _first_in_path[vindex] = r.info;\n for (auto c : r.root->children()) {\n stack.emplace(SPSubtree { static_cast(r.info), c });\n }\n }\n }\n }\n\n };\n\n template\n class CandidateCycle {\n public:\n typedef typename boost::graph_traits::vertex_descriptor Vertex;\n typedef typename boost::graph_traits::edge_descriptor Edge;\n typedef typename boost::property_traits::value_type WeightType;\n\n CandidateCycle(std::size_t tree, const Edge &e, WeightType weight) :\n _tree(tree), _e(e), _weight(weight) {\n }\n\n CandidateCycle(const CandidateCycle &c) :\n _tree(c._tree), _e(c._e), _weight(c._weight) {\n }\n\n CandidateCycle& operator=(const CandidateCycle &other) {\n if (this != &other) {\n _tree = other._tree;\n _e = other._e;\n _weight = other._weight;\n }\n return *this;\n }\n\n std::size_t tree() const {\n return _tree;\n }\n\n const Edge& edge() const {\n return _e;\n }\n\n const WeightType& weight() const {\n return _weight;\n }\n\n private:\n std::size_t _tree;\n Edge _e;\n WeightType _weight;\n };\n\n template\n struct SerializableCandidateCycle {\n typedef typename boost::graph_traits::vertex_descriptor Vertex;\n typedef typename ForestIndex::size_type Edge;\n\n SerializableCandidateCycle() {\n }\n\n SerializableCandidateCycle(Vertex v, Edge e) :\n v(v), e(e) {\n }\n\n template\n void serialize(Archive &ar, const unsigned) {\n ar & v;\n ar & e;\n }\n\n Vertex v;\n Edge e;\n };\n\n template\n struct SerializableMinOddCycle {\n typedef typename ForestIndex::size_type Edge;\n typedef typename boost::property_traits::value_type WeightType;\n\n SerializableMinOddCycle() :\n exists(false) {\n }\n\n SerializableMinOddCycle(std::vector edges, WeightType weight, bool exists) :\n edges(edges), weight(weight), exists(exists) {\n }\n\n SerializableMinOddCycle(const SerializableMinOddCycle &c) :\n edges(c.edges), weight(c.weight), exists(c.exists) {\n }\n\n SerializableMinOddCycle& operator=(const SerializableMinOddCycle &other) {\n if (this != &other) {\n edges = other.edges;\n weight = other.weight;\n exists = other.exists;\n }\n return *this;\n }\n\n template\n void serialize(Archive &ar, const unsigned) {\n ar & edges;\n ar & weight;\n ar & exists;\n }\n\n std::vector edges;\n WeightType weight;\n bool exists;\n };\n\n template\n struct SerializableMinOddCycleMinOp {\n\n const SerializableMinOddCycle& operator()(\n const SerializableMinOddCycle &lhs,\n const SerializableMinOddCycle &rhs) const {\n if (!lhs.exists || !rhs.exists) {\n if (lhs.exists) {\n return lhs;\n } else {\n return rhs;\n }\n }\n // both valid, compare\n if (lhs.weight < rhs.weight) {\n return lhs;\n }\n return rhs;\n }\n\n };\n\n template\n class CandidateCycleToSerializableConverter {\n public:\n CandidateCycleToSerializableConverter(const std::vector> &trees,\n const ForestIndex &forest_index) :\n trees(trees), forest_index(forest_index) {\n }\n\n SerializableCandidateCycle operator()(const CandidateCycle &cycle) const {\n return SerializableCandidateCycle(trees.at(cycle.tree()).source(), forest_index(cycle.edge()));\n }\n\n private:\n const std::vector> &trees;\n const ForestIndex &forest_index;\n };\n\n template\n class CandidateCycleBuilder {\n public:\n typedef typename boost::graph_traits::vertex_descriptor Vertex;\n typedef typename boost::graph_traits::edge_descriptor Edge;\n typedef typename boost::property_traits::value_type WeightType;\n\n CandidateCycleBuilder(const Graph &g, const WeightMap &weight_map) :\n g(g), weight_map(weight_map) {\n }\n\n std::tuple, WeightType, bool> operator()(const std::vector> &trees,\n const CandidateCycle &c, const std::set &signed_edges, bool use_weight_limit,\n WeightType weight_limit) const {\n\n std::shared_ptr> v = trees[c.tree()].node(boost::source(c.edge(), g));\n std::shared_ptr> u = trees[c.tree()].node(boost::target(c.edge(), g));\n\n Edge e = c.edge();\n if (v->parity() ^ u->parity() ^ (signed_edges.find(e) != signed_edges.end())) {\n // odd cycle, validate\n bool valid = true;\n WeightType cycle_weight = boost::get(weight_map, e);\n std::set result;\n result.insert(e);\n\n if (use_weight_limit && cycle_weight > weight_limit) {\n return std::make_tuple(std::set { }, 0.0, false);\n }\n\n // first part\n Vertex w = boost::source(c.edge(), g);\n std::shared_ptr> ws = trees[c.tree()].node(w);\n while (ws->has_pred()) {\n Edge a = ws->pred();\n if (result.insert(a).second == false) {\n valid = false;\n break;\n }\n cycle_weight += boost::get(weight_map, a);\n if (use_weight_limit && cycle_weight > weight_limit) {\n valid = false;\n break;\n }\n w = boost::opposite(a, w, g);\n ws = trees[c.tree()].node(w);\n }\n\n if (!valid) {\n return std::make_tuple(std::set { }, 0.0, false);\n }\n\n // second part\n w = boost::target(c.edge(), g);\n ws = trees[c.tree()].node(w);\n while (ws->has_pred()) {\n Edge a = ws->pred();\n if (result.insert(a).second == false) {\n valid = false;\n break;\n }\n cycle_weight += boost::get(weight_map, a);\n if (use_weight_limit && cycle_weight > weight_limit) {\n valid = false;\n break;\n }\n w = boost::opposite(a, w, g);\n ws = trees[c.tree()].node(w);\n }\n\n if (!valid) {\n return std::make_tuple(std::set { }, 0.0, false);\n }\n\n return std::make_tuple(result, cycle_weight, true);\n }\n return std::make_tuple(std::set { }, 0.0, false);\n }\n\n private:\n const Graph &g;\n const WeightMap &weight_map;\n };\n\n template\n class ShortestOddCycleLookup {\n public:\n typedef typename boost::graph_traits::vertex_descriptor Vertex;\n typedef typename boost::graph_traits::edge_descriptor Edge;\n typedef typename boost::property_traits::value_type WeightType;\n\n ShortestOddCycleLookup(const Graph &g, const WeightMap &weight_map,\n std::vector> &trees,\n std::vector> &cycles, bool sorted_cycles) :\n g(g), weight_map(weight_map), candidate_cycle_builder(g, weight_map), trees(trees), cycles(cycles), sorted_cycles(\n sorted_cycles) {\n }\n\n std::tuple, WeightType, bool> operator()(const std::set &edges) {\n return compute_shortest_odd_cycle(edges);\n }\n\n private:\n\n template\n std::tuple, WeightType, bool> compute_shortest_odd_cycle(const std::set &edges,\n typename std::enable_if::type* = 0) {\n\n for (std::size_t i = 0; i < trees.size(); i++) {\n trees[i].update_parities(edges);\n }\n\n std::tuple, WeightType, bool> min;\n\n for (CandidateCycle c : cycles) {\n std::tuple, WeightType, bool> cc = candidate_cycle_builder(trees, c, edges,\n std::get<2>(min), std::get<1>(min));\n\n if (std::get<2>(cc)) {\n if (sorted_cycles) {\n return cc;\n }\n\n if (!std::get<2>(min)) {\n min = cc;\n } else {\n if (std::get<1>(cc) < std::get<1>(min)) {\n min = cc;\n }\n }\n }\n }\n return min;\n }\n\n template\n std::tuple, WeightType, bool> compute_shortest_odd_cycle(const std::set &edges,\n typename std::enable_if::type* = 0) {\n\n tbb::parallel_for(tbb::blocked_range(0, trees.size()),\n [&](const tbb::blocked_range &r) {\n for (std::size_t i = r.begin(); i != r.end(); ++i) {\n trees[i].update_parities(edges);\n }\n });\n\n std::less compare = std::less();\n typedef std::tuple, WeightType, bool> cycle_t;\n auto cycle_min = [compare](const cycle_t &c1, const cycle_t &c2) {\n if (!std::get<2>(c1) || !std::get<2>(c2)) {\n if (std::get<2>(c1)) {\n return c1;\n } else {\n return c2;\n }\n }\n // both valid, compare\n if (!compare(std::get<1>(c2), std::get<1>(c1))) {\n return c1;\n }\n return c2;\n };\n\n return tbb::parallel_reduce(tbb::blocked_range(0, cycles.size()),\n std::make_tuple(std::set(), (std::numeric_limits::max)(), false),\n [&](tbb::blocked_range r, auto running_min) {\n for (std::size_t i = r.begin(); i < r.end(); i++) {\n auto c = cycles[i];\n auto cc = candidate_cycle_builder(trees, c, edges, std::get<2>(running_min),\n std::get<1>(running_min));\n if (std::get<2>(cc)) {\n if (!std::get<2>(running_min) || compare(std::get<1>(cc), std::get<1>(running_min))) {\n running_min = cc;\n }\n }\n }\n return running_min;\n },\n cycle_min);\n }\n\n const Graph &g;\n const WeightMap &weight_map;\n const CandidateCycleBuilder candidate_cycle_builder;\n std::vector> &trees;\n std::vector> &cycles;\n bool sorted_cycles;\n };\n\n} // parmcb\n\n#endif\n", "meta": {"hexsha": "d5c945684b67acf8facba430fa52107157a3355a", "size": 25325, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/parmcb/sptrees.hpp", "max_stars_repo_name": "d-michail/parmcb", "max_stars_repo_head_hexsha": "19d0b7eb01735600c851b969d9e5a87c78b8c711", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/parmcb/sptrees.hpp", "max_issues_repo_name": "d-michail/parmcb", 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YES\n2. NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.23898001888212939}} {"text": "// this must define the same symbol as the main module file (numpy requirement)\n#define PY_ARRAY_UNIQUE_SYMBOL andres_graph_PyArray_API\n#define NO_IMPORT_ARRAY\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\n#include \"andres/graph/grid-graph.hxx\"\n#include \"andres/functional.hxx\"\n#include \"andres/graph/graph.hxx\"\n#include \"andres/graph/threadpool.hxx\"\n#include \"andres/graph/neighborhood.hxx\"\n#include \"andres/graph/multicut-lifted/lifted_mc_model.hxx\"\n\n#include \"image.hxx\"\n\nnamespace bp = boost::python;\nnamespace agraph = andres::graph;\n\n\n\n\n\ntemplate\nvoid addLongRangeEdges(\n MODEL & model,\n vigra::NumpyArray<2, float> edgePmap, // in 0-1\n const float beta = 0.5f,\n const int minRadius = 2,\n const int maxRadius = 7 \n){ \n GRAPH_CHECK_OP(model.originalGraph().shape(0), == ,edgePmap.shape(0),\"\");\n GRAPH_CHECK_OP(model.originalGraph().shape(1), == ,edgePmap.shape(1),\"\");\n\n typedef vigra::TinyVector Coord;\n const auto shape = edgePmap.shape();\n\n auto clipToImg = [&](const Coord & coord){\n auto c = coord;\n for(auto i=0; i<2; ++i){\n c[i] = std::max(0, c[i]);\n c[i] = std::min(int(shape[i]),c[i]);\n }\n return c;\n };\n\n\n // move a coordinate to local min\n auto moveToMin = [&](const Coord & coord){\n Coord coords[5] = {\n coord,\n clipToImg(coord+Coord(0,1)),\n clipToImg(coord+Coord(1,0)),\n clipToImg(coord+Coord(-1,-1)),\n clipToImg(coord+Coord(-1, 0))\n };\n auto minVal = std::numeric_limits::infinity();\n auto minCoord = Coord();\n for(size_t i=0; i<5; ++i){\n const auto val = edgePmap[coord];\n if(val FCoord;\n auto node = [&](const Coord & coord){\n return size_t(coord[0] + coord[1]*shape[0]);\n };\n\n std::mutex graphMutex;\n std::mutex mapMutex;\n\n std::set processed;\n\n struct ToAdd{\n size_t u,v;\n float w;\n };\n\n size_t bufferSize = 1000000;\n std::vector > buffers(nThreads);\n for(auto & vec : buffers){\n vec.reserve(bufferSize+1);\n }\n\n\n\n auto addToBuffer = [&](std::vector & buffer,const size_t u_, const size_t v_, const float w_){\n\n ToAdd ta;\n ta.u=u_;\n ta.v=v_;\n ta.w=w_;\n buffer.push_back(ta);\n //std::cout<<\"buffer size\"<=bufferSize){\n\n std::unique_lock lock(graphMutex);\n //std::cout<<\"clear buffer\\n\";\n for(const auto & ta : buffer){\n const auto fe = liftedGraph.findEdge(ta.u,ta.v);\n // not yet in lifted graph\n // therefore cannot be processed\n if(!fe.first){\n //std::cout<<\"a\\n\";\n const auto e = model.setCost(ta.u,ta.v,ta.w,false);\n processed.insert(e);\n }\n // edge is in lifted graph\n else{\n //std::cout<<\"b\\n\";\n auto fm = processed.find(fe.second);\n // not yet processed\n if(fm == processed.end()){\n std::cout<<\"b1\\n\";\n const auto e = model.setCost(ta.u,ta.v,ta.w,false);\n processed.insert(e);\n }\n else{\n std::cout<<\"b2\\n\";\n }\n }\n }\n buffer.resize(0);\n }\n };\n\n\n std::cout<<\"lifted graph edge num \"<=,0,\"\");\n GRAPH_CHECK_OP(q[1],>=,0,\"\");\n GRAPH_CHECK_OP(q[0],<,shape[0],\"\");\n GRAPH_CHECK_OP(q[1],<,shape[1],\"\");\n const auto v = node(q);\n\n size_t e;\n if( norm(p-q) < float(minRadius))\n continue;\n if(p==q || v>u){\n continue;\n }\n\n \n GRAPH_CHECK_OP(v,<,originalGraph.numberOfVertices(),\"\");\n GRAPH_CHECK_OP(v,<,liftedGraph.numberOfVertices(),\"\");\n const auto qf = FCoord(q);\n const auto pq = q-p;\n const auto dist = vigra::norm(pq);\n const auto step = pq*(1.0f / (dist * 1.3f + 0.5f));\n auto pOnLine = FCoord(p);\n auto noMax = true;\n auto maxVal = -1.0f*std::numeric_limits::infinity();\n while(Coord(pOnLine)!=q){\n //std::cout<<\"pol \"<\nvoid addLongRangeNH(\n MODEL & model,\n const int radius = 7 \n){ \n for(auto u=0; u\ndouble evalCut(\n LiftedMcModel & liftedMcModel,\n vigra::NumpyArray<1, uint8_t> cut\n){\n auto s = 0.0;\n for(auto e=0; e\nvoid setCosts(\n LiftedMcModel & liftedMcModel,\n vigra::NumpyArray<1, vigra::TinyVector > uv,\n vigra::NumpyArray<1, float> costs,\n const bool overwrite \n){\n GRAPH_CHECK_OP(uv.size(), == , costs.size(), \"shape mismatch: uv and costs have different size\");\n\n const auto u = uv.bindElementChannel(0);\n const auto v = uv.bindElementChannel(1);\n const auto c = vigra::MultiArrayView<1, float>(costs);\n liftedMcModel.setCosts(u.begin(), u.end(), v.begin(), c.begin(), overwrite);\n}\n\n\ntemplate\nvigra::NumpyAnyArray edgeLabelsToNodeLabels(\n const LiftedMcModel & model,\n vigra::NumpyArray<1, uint8_t> edgeLabels,\n vigra::NumpyArray<1, uint64_t> nodeLabels\n){\n vigra::TinyVector shape(model.originalGraph().numberOfVertices());\n nodeLabels.reshapeIfEmpty(shape);\n model.getNodeLabels(edgeLabels, nodeLabels);\n return nodeLabels;\n}\n\n\ntemplate\nvigra::NumpyAnyArray nodeLabelsToEdgeLabels(\n const LiftedMcModel & model,\n vigra::NumpyArray<1, uint64_t> nodeLabels,\n vigra::NumpyArray<1, uint8_t> edgeLabels\n){\n vigra::TinyVector shape(model.liftedGraph().numberOfEdges());\n edgeLabels.reshapeIfEmpty(shape);\n model.getEdgeLabels(nodeLabels, edgeLabels);\n return edgeLabels;\n}\n\n\ntemplate\nvoid fuseGtObjective(\n LiftedMcModel & model,\n vigra::NumpyArray<2, uint64_t> nodeLabels,\n const size_t rr,\n const double beta,\n const bool verbose\n){\n if(verbose)\n std::cout<<\"nodeLabel.shape \"< > extendedNh(nV);\n std::vector > extendedNh2(nV);\n\n\n for(auto n=0; n0.1)\n // std::cout<<\" p1 \"<\nvoid fuseGtObjectiveGrid(\n LiftedMcModel & model,\n vigra::NumpyArray<3, uint64_t> nodeLabels,\n vigra::NumpyArray<1, double> pExpert,\n vigra::NumpyArray<2, float> cutRegularizer,\n const size_t rr,\n const double beta,\n //const double cLocal,\n const bool verbose\n){\n if(verbose)\n std::cout<<\"nodeLabel.shape \"< Coord;\n Coord shape(originalGraph.shape(0), originalGraph.shape(1));\n\n auto nGt = nodeLabels.shape(2);\n\n std::vector > extendedNh(nV);\n std::vector > extendedNh2(nV);\n\n auto clipToImg = [&](const Coord & coord){\n auto c = coord;\n for(auto i=0; i<2; ++i){\n c[i] = std::max(0, c[i]);\n c[i] = std::min(int(shape[i]),c[i]);\n }\n return c;\n };\n auto node = [&](const Coord & coord){\n return size_t(coord[0] + coord[1]*shape[0]);\n };\n\n Coord p;\n\n for(p[1]=0; p[1]0.1)\n // std::cout<<\" p1 \"<\nvoid thinObjectSeededSeg(\n LiftedMcModel & model,\n vigra::NumpyArray<2, uint64_t> linkConstraints,\n vigra::NumpyArray<2, float> cutCosts,\n const double constraintCost,\n const bool verbose\n){\n\n auto shape = linkConstraints.shape();\n auto tshape = cutCosts.shape();\n GRAPH_CHECK_OP(shape[0],==,model.originalGraph().shape(0),\"\");\n GRAPH_CHECK_OP(shape[1],==,model.originalGraph().shape(1),\"\");\n\n GRAPH_CHECK_OP(shape[0]*2-1,==,tshape[0],\"\");\n GRAPH_CHECK_OP(shape[1]*2-1,==,tshape[1],\"\");\n\n typedef vigra::TinyVector Coord;\n /// link constraints\n std::map > constraintSet;\n\n auto node = [&](const Coord & c){\n return c[0] + c[1]*shape[0];\n };\n std::cout<<\"local terms\\n\";\n for(auto y=0; y > cVecVec;\n for(auto iter = constraintSet.begin(); iter!=constraintSet.end(); ++iter){\n cVecVec.push_back(iter->second);\n }\n std::cout<<\"must link terms\\n\";\n // must link constraints\n for(auto & inodes : cVecVec){\n GRAPH_CHECK_OP(inodes.size(),>=,1,\"\");\n std::cout<<\"inodes \"<1){\n for(auto i=0; i\nvoid lmSuperpixel(\n LiftedMcModel & model,\n vigra::NumpyArray<2, float> cutCosts,\n const size_t seedRadius = 10,\n const size_t stepSize = 2,\n const double sigma = 3.0,\n const double seedRepulsion = 1.0\n){\n\n\n const int shape[2] = {\n int(model.originalGraph().shape(0)),\n int(model.originalGraph().shape(1)) \n };\n auto tshape = cutCosts.shape();\n\n\n typedef vigra::TinyVector Coord;\n\n auto node = [&](const Coord & c){\n return c[0] + c[1]*shape[0];\n };\n std::cout<<\"generate seeds\\n\";\n\n\n std::vector seeds;\n\n for(auto y=seedRadius+1; y=,2,\"seedRadius is to large\");\n // seed repulsion\n auto c=0;\n auto r=0;\n for(auto si=0; si\nvoid andresImageSegModel(\n LiftedMcModel & model,\n vigra::NumpyArray<2, float> pmapImage,\n const float bias = 0.5\n){\n const auto & shape = pmapImage.shape();\n // convert to andres image\n Image image(shape.begin(),shape.begin());\n for(auto y=0; y edgeCutProbabilities;\n PixelCutProbabilityFromEdgesWTA pixelCutProbabilityWTA(image);\n constructGraphAndEdgeProbabilities(pixelCutProbabilityWTA, graph, edgeCutProbabilities, bias);\n\n}\n\n\ntemplate\nvoid loadBsdModel(LiftedMcModel & model, const std::string & filename){\n auto fileHandle = agraph::hdf5::openFile(filename);\n agraph::hdf5::load(fileHandle,\"graph\",model._originalGraph());\n agraph::hdf5::load(fileHandle,\"graph-lifted\",model._liftedGraph());\n std::vector shape;\n auto & ec = model._edgeCosts();\n //ec.resize(model.liftedGraph().numberOfEdges());\n std::vector ecd;\n agraph::hdf5::load(fileHandle, \"edge-cut-probabilities\", shape, ecd);\n\n transform(\n ecd.begin(),\n ecd.end(),\n ecd.begin(),\n andres::NegativeLogProbabilityRatio()\n );\n\n ec.resize(ecd.size());\n std::copy(ecd.begin(), ecd.end(),ec.begin());\n agraph::hdf5::closeFile(fileHandle);\n\n\n transform(\n ec.begin(),\n ec.end(),\n ec.begin(),\n andres::NegativeLogProbabilityRatio()\n );\n\n}\n\ntemplate\nvoid exportLiftedMcModelT(const std::string & clsName, F && f){\n typedef OG originalGraph;\n typedef agraph::multicut_lifted::LiftedMcModel LiftedMcModel;\n\n\n auto cls = bp::class_\n (\n clsName.c_str(), \n bp::init<\n const originalGraph&\n >(\n bp::arg(\"originalGraph\")\n )[bp::with_custodian_and_ward<1 /*custodian == self*/, 2 /*ward == const originalGraph & */>()]\n )\n .def(\"originalGraph\",&LiftedMcModel::originalGraph , bp::return_internal_reference<>())\n .def(\"liftedGraph\",&LiftedMcModel::liftedGraph , bp::return_internal_reference<>())\n .def(\"_setCosts\",vigra::registerConverters(&setCosts))\n .def(\"_setCost\",&LiftedMcModel::setCost)\n .def(\"evalCut\",vigra::registerConverters(&evalCut))\n .def(\"edgeLabelsToNodeLabels\",vigra::registerConverters(&edgeLabelsToNodeLabels),\n (\n bp::arg(\"edgeLabels\"),\n bp::arg(\"out\") = bp::object()\n )\n )\n .def(\"loadBsdModel\", &loadBsdModel)\n .def(\"nodeLabelsToEdgeLabels\",vigra::registerConverters(&nodeLabelsToEdgeLabels),\n (\n bp::arg(\"nodeLabels\"),\n bp::arg(\"out\") = bp::object()\n )\n );\n\n f(cls);\n\n\n bp::def(\"fuseGtObjective\",vigra::registerConverters(&fuseGtObjective),\n (\n bp::arg(\"model\"),\n bp::arg(\"nodeLabels\"),\n bp::arg(\"rr\") = 2,\n bp::arg(\"beta\") = 0.5,\n bp::arg(\"verbose\") = true\n )\n );\n}\n\n\ntemplate\nvigra::NumpyAnyArray flattenLabels(\n const MODEL & model,\n vigra::NumpyArray<2, uint64_t> labels2d,\n vigra::NumpyArray<1, uint64_t> out\n){\n vigra::TinyVector shape(labels2d.size());\n out.reshapeIfEmpty(shape);\n auto c=0;\n for(auto y=0; y GridGraph2D;\n typedef agraph::GridGraph<3> GridGraph3D;\n typedef agraph::Graph<> Graph;\n typedef agraph::multicut_lifted::LiftedMcModel LiftedMcModelGridGraph2D;\n typedef agraph::multicut_lifted::LiftedMcModel LiftedMcModelGraph;\n {\n typedef agraph::multicut_lifted::LiftedMcModel LiftedMcModelGridGraph2D; \n exportLiftedMcModelT(\"LiftedMcModelGridGraph2D\",\n [&](\n bp::class_< LiftedMcModelGridGraph2D > & cls\n ){\n cls\n .def(\"flattenLabels\",\n vigra::registerConverters(&flattenLabels< LiftedMcModelGridGraph2D >),\n (\n bp::arg(\"labels\"),\n bp::arg(\"out\") = bp::object()\n )\n )\n ;\n }\n );\n }\n exportLiftedMcModelT(\"LiftedMcModelGridGraph3D\",\n [&](\n bp::class_ > & cls\n ){\n \n }\n );\n exportLiftedMcModelT(\"LiftedMcModelGraph\",\n [&](\n bp::class_ > & cls\n ){\n \n }\n );\n\n\n bp::def(\"addLongRangeEdges\",vigra::registerConverters(&addLongRangeEdges))\n ;\n\n bp::def(\"addLongRangeNH\",vigra::registerConverters(&addLongRangeNH))\n ;\n bp::def(\"addLongRangeNH\",vigra::registerConverters(&addLongRangeNH))\n ;\n\n bp::def(\"fuseGtObjectiveGrid\",vigra::registerConverters(&fuseGtObjectiveGrid),\n (\n bp::arg(\"model\"),\n bp::arg(\"nodeLabels\"),\n bp::arg(\"pExpert\"),\n bp::arg(\"cutRegularizer\"),\n bp::arg(\"rr\") = 2,\n bp::arg(\"beta\") = 0.5,\n //bp::arg(\"cLocal\") = 100,\n bp::arg(\"verbose\") = true\n )\n );\n\n bp::def(\"thinObjectSeededSeg\",vigra::registerConverters(&thinObjectSeededSeg),\n (\n bp::arg(\"model\"),\n bp::arg(\"linkConstraints\"),\n bp::arg(\"cutCosts\"),\n bp::arg(\"c\") = 1000.0,\n bp::arg(\"verbose\") = true\n )\n );\n\n \n\n bp::def(\"lmSuperpixel\",vigra::registerConverters(&lmSuperpixel),\n (\n bp::arg(\"model\"),\n bp::arg(\"cutCosts\"),\n bp::arg(\"seedRadius\") = 10,\n bp::arg(\"stepSize\") = 2,\n bp::arg(\"sigma\") = 3.0,\n bp::arg(\"seedRepulsion\") =1.0\n )\n );\n\n\n bp::def(\"andresImageSegModel\",vigra::registerConverters(&andresImageSegModel),\n (\n bp::arg(\"model\"),\n bp::arg(\"pmap\"),\n bp::arg(\"bias\") = 0.5\n )\n );\n}\n", "meta": {"hexsha": "343654efcab234cbb6e21b80ffc6f2abf7b24d6a", "size": 26507, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "dependencies/graph-1.6/src/andres/graph/python/lifted_mc_model.cxx", "max_stars_repo_name": "ilastik/nature_methods_multicut_pipeline", "max_stars_repo_head_hexsha": "1dc596505ab8c995b50561eeb969c59673b7dcab", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 14.0, "max_stars_repo_stars_event_min_datetime": "2017-02-07T12:41:06.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-19T02:52:32.000Z", "max_issues_repo_path": "dependencies/graph-1.6/src/andres/graph/python/lifted_mc_model.cxx", "max_issues_repo_name": "ilastik/nature_methods_multicut_pipeline", "max_issues_repo_head_hexsha": "1dc596505ab8c995b50561eeb969c59673b7dcab", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 5.0, "max_issues_repo_issues_event_min_datetime": "2017-02-07T01:51:34.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-31T15:00:33.000Z", "max_forks_repo_path": "dependencies/graph-1.6/src/andres/graph/python/lifted_mc_model.cxx", "max_forks_repo_name": "ilastik/nature_methods_multicut_pipeline", "max_forks_repo_head_hexsha": "1dc596505ab8c995b50561eeb969c59673b7dcab", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2017-11-16T04:04:55.000Z", "max_forks_repo_forks_event_max_datetime": "2018-05-11T11:33:51.000Z", "avg_line_length": 31.0023391813, "max_line_length": 124, "alphanum_fraction": 0.5331421889, "num_tokens": 7159, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.23898001888212939}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n// OpenMP\n#include \n\n#include \"box_triangle/aabb_triangle_overlap.h\"\n#include \"box_ray/vector3.h\"\n#include \"box_ray/ray.h\"\n#include \"box_ray/box.h\"\n#include \"triangle_point/vec.h\"\n#include \"triangle_point/makelevelset3.h\"\n#include \"triangle_ray/raytri.h\"\n\n/** \\brief Simple struct representing arbitrary boxes. */\nstruct Box {\n /** \\brief Minimum coordinates. */\n Eigen::Vector3f min;\n /** \\brief Maximum coordinates. */\n Eigen::Vector3f max;\n\n /** \\brief Constructor.\n */\n Box() {\n this->min = Eigen::Vector3f::Zero();\n this->max = Eigen::Vector3f::Zero();\n }\n\n /** \\brief Constructor; construct a box from minimum and maximum coordinates.\n * \\param[in] min minimum coordinates\n * \\param[in] max maximum coordinates\n */\n Box(const Eigen::Vector3f &min, const Eigen::Vector3f &max) {\n this->min = min;\n this->max = max;\n }\n\n /** \\brief Check if a point is inside the box.\n * \\param[in] point point to check\n * \\return contained\n */\n bool contains(const Eigen::Vector3f &point) {\n bool contains = true;\n\n for (int d = 0; d < 3; d++) {\n if (point(d) < this->min(d) || point(d) > this->max(d)) {\n contains = false;\n }\n }\n\n return contains;\n }\n\n /** \\brief Scale the box.\n * \\param[in] scale scale per axis\n */\n void scale(const Eigen::Vector3f &scale) {\n for (int d = 0; d < 3; d++) {\n min(d) *= scale(d);\n max(d) *= scale(d);\n }\n }\n\n /** \\brief Scale the box.\n * \\param[in] scale scale over all axes\n */\n void scale(float scale) {\n this->scale(Eigen::Vector3f(scale, scale, scale));\n }\n\n /** \\brief Scale the box.\n * \\param[in] translation translation per axis\n */\n void translate(const Eigen::Vector3f &translation) {\n for (int d = 0; d < 3; d++) {\n min(d) += translation(d);\n max(d) += translation(d);\n }\n }\n};\n\n/** \\brief Test box ray intersection.\n * \\param[in] box box\n * \\param[in] ray ray to intersect with\n * \\return intersects\n */\nbool box_ray_intersection(const Box &box, const Eigen::Vector3f &ray) {\n for (int d = 0; d < 3; d++) {\n assert(box.min(d) < box.max(d));\n }\n\n // convert to used data structure\n bri::Vector3 _o(0, 0, 0);\n bri::Vector3 _d(ray(0), ray(1), ray(2));\n bri::Ray _ray(_o, _d);\n bri::Vector3 _min(box.min(0), box.min(1), box.min(2));\n bri::Vector3 _max(box.max(0), box.max(1), box.max(2));\n bri::Box _box(_min, _max);\n\n // interval for valid its is [0,1] as the ray will\n // be the full-length vector to an observed point\n return _box.intersect(_ray, 0, 0.99);\n}\n\n/** \\brief Test box ray intersection.\n * \\param[in] box box\n * \\param[in] origin origin point\n * \\param[in] dest destination point\n * \\return intersects\n */\nbool box_ray_intersection(const Box &box, const Eigen::Vector3f &origin, const Eigen::Vector3f &dest) {\n for (int d = 0; d < 3; d++) {\n assert(box.min(d) < box.max(d));\n }\n\n // convert to used data structure\n bri::Vector3 _o(origin(0), origin(1), origin(2));\n bri::Vector3 _d(dest(0), dest(1), dest(2));\n bri::Ray _ray(_o, _d);\n bri::Vector3 _min(box.min(0), box.min(1), box.min(2));\n bri::Vector3 _max(box.max(0), box.max(1), box.max(2));\n bri::Box _box(_min, _max);\n\n // interval for valid its is [0,1] as the ray will\n // be the full-length vector to an observed point\n return _box.intersect(_ray, 0, 0.99);\n}\n\n/** \\brief Compute triangle box intersection.\n * \\param[in] voxel voxel to test intersection for\n * \\param[in] v1 first vertex\n * \\param[in] v2 second vertex\n * \\param[in] v3 third vertex\n * \\return intersects\n */\nbool triangle_box_intersection(const Box &voxel, const Eigen::Vector3f &v1, const Eigen::Vector3f &v2, const Eigen::Vector3f &v3) {\n float half_size[3] = {\n (voxel.max(0) - voxel.min(0))/2.,\n (voxel.max(1) - voxel.min(1))/2.,\n (voxel.max(2) - voxel.min(2))/2.\n };\n\n float center[3] = {\n voxel.max(0) - half_size[0],\n voxel.max(1) - half_size[1],\n voxel.max(2) - half_size[2]\n };\n\n float vertices[3][3] = {{v1(0), v1(1), v1(2)}, {v2(0), v2(1), v2(2)}, {v3(0), v3(1), v3(2)}};\n return triBoxOverlap(center, half_size, vertices);\n}\n\n/** \\brief Compute triangle point distance and corresponding closest point.\n * \\param[in] point point\n * \\param[in] v1 first vertex\n * \\param[in] v2 second vertex\n * \\param[in] v3 third vertex\n * \\param[out] ray corresponding closest point\n * \\return distance\n */\nfloat triangle_point_distance(const Eigen::Vector3f &point, const Eigen::Vector3f &v1, const Eigen::Vector3f &v2, const Eigen::Vector3f &v3,\n Eigen::Vector3f &closest_point) {\n\n Vec3f x0(point.data());\n Vec3f x1(v1.data());\n Vec3f x2(v2.data());\n Vec3f x3(v3.data());\n\n Vec3f r(0);\n float distance = point_triangle_distance_field(x0, x1, x2, x3, r);\n\n for (int d = 0; d < 3; d++) {\n closest_point(d) = r[d];\n }\n\n return distance;\n}\n\n/** \\brief Test triangle ray intersection.\n * \\param[in] origin origin of ray\n * \\param[in] dest destination of ray\n * \\param[in] v1 first vertex\n * \\param[in] v2 second vertex\n * \\param[in] v3 third vertex\n * \\return intersects\n */\nbool triangle_ray_intersection(const Eigen::Vector3f &origin, const Eigen::Vector3f &dest,\n const Eigen::Vector3f &v1, const Eigen::Vector3f &v2, const Eigen::Vector3f &v3, float &t) {\n\n double _origin[3] = {origin(0), origin(1), origin(2)};\n double _dir[3] = {dest(0) - origin(0), dest(1) - origin(1), dest(2) - origin(2)};\n double _v1[3] = {v1(0), v1(1), v1(2)};\n double _v2[3] = {v2(0), v2(1), v2(2)};\n double _v3[3] = {v3(0), v3(1), v3(2)};\n\n // t is the distance, u and v are barycentric coordinates\n // http://fileadmin.cs.lth.se/cs/personal/tomas_akenine-moller/code/raytri_tam.pdf\n double _t, u, v;\n int success = intersect_triangle(_origin, _dir, _v1, _v2, _v3, &_t, &u, &v);\n t = _t;\n\n if (success) {\n return true;\n }\n\n return false;\n}\n\n/** \\brief Simple struct encapsulating a 3D bounding box. */\nstruct BoundingBox {\n /** \\brief Size of the bounding box. */\n Eigen::Vector3f size;\n /** \\brief Translation (i.e. center) of bounding box. */\n Eigen::Vector3f translation;\n /** \\brief Rotation of bounding box (in radians per axis). */\n Eigen::Vector3f rotation;\n /** \\brief Meta information for writing. */\n std::string meta;\n\n /** \\brief Constructor.\n */\n BoundingBox() {\n this->size = Eigen::Vector3f::Zero();\n this->translation = Eigen::Vector3f::Zero();\n this->rotation = Eigen::Vector3f::Zero();\n this->meta = \"\";\n }\n\n /** \\brief Assignment operator.\n * \\param[in] bounding_box bounding box to assign\n * \\return this\n */\n BoundingBox& operator=(const BoundingBox &bounding_box) {\n this->size = bounding_box.size;\n this->translation = bounding_box.translation;\n this->rotation = bounding_box.rotation;\n this->meta = bounding_box.meta;\n return *this;\n }\n\n /** \\brief Convert to regular box.\n * \\param[out] box box to convert to\n */\n void to_box(Box &box) const {\n for (int d = 0 ; d < 3; d++) {\n box.min(d) = this->translation(d) - this->size(d)/2;\n box.max(d) = this->translation(d) + this->size(d)/2;\n }\n }\n\n /** \\brief Given the angle in radians, construct a rotation matrix around the y-axis.\n * \\param[in] radians angle in radians\n * \\param[out] rotation rotation matrix\n */\n static void rotation_matrix_y(const float radians, Eigen::Matrix3f &rotation) {\n rotation = Eigen::Matrix3f::Zero();\n\n rotation(0, 0) = std::cos(radians); rotation(0, 2) = std::sin(radians);\n rotation(1, 1) = 1;\n rotation(2, 0) = -std::sin(radians); rotation(2, 2) = std::cos(radians);\n }\n\n /** \\brief Check if a point lies inside the bounding box.\n * \\param[in] point point to check\n * \\return contained in bounding box\n */\n bool contains(const Eigen::Vector3f &point) const {\n Eigen::Vector3f transformed = point - this->translation;\n Eigen::Matrix3f rotation;\n BoundingBox::rotation_matrix_y(this->rotation(1), rotation);\n transformed = rotation*transformed;\n\n bool contained = true;\n for (int d = 0; d < 3; d++) {\n if (transformed(d) > this->size(d)/2 || transformed(d) < -this->size(d)/2) {\n contained = false;\n }\n }\n\n return contained;\n }\n\n /** \\brief Get largest scale of bounding box.\n * \\return scale\n */\n float scale() const {\n float scale = 0;\n\n for (int d = 0; d < 3; d++) {\n if (this->size(d) > scale) {\n scale = this->size(d);\n }\n }\n\n assert(scale > 0);\n return scale;\n }\n};\n\n/** \\brief Just encapsulating vertices and faces. */\nclass Mesh {\npublic:\n /** \\brief Empty constructor. */\n Mesh() {\n\n }\n\n /** \\brief Add a vertex.\n * \\param[in] vertex vertex to add\n */\n void add_vertex(Eigen::Vector3f& vertex) {\n this->vertices.push_back(vertex);\n }\n\n /** \\brief Get the number of vertices.\n * \\return number of vertices\n */\n int num_vertices() {\n return static_cast(this->vertices.size());\n }\n\n /** \\brief Add a face.\n * \\param[in] face face to add\n */\n void add_face(Eigen::Vector3i& face) {\n this->faces.push_back(face);\n }\n\n /** \\brief Get the number of faces.\n * \\return number of faces\n */\n int num_faces() {\n return static_cast(this->faces.size());\n }\n\n /** \\brief Translate the mesh.\n * \\param[in] translation translation vector\n */\n void translate(const Eigen::Vector3f& translation) {\n for (int v = 0; v < this->num_vertices(); ++v) {\n for (int i = 0; i < 3; ++i) {\n this->vertices[v](i) += translation(i);\n }\n }\n }\n\n /** \\brief Scale the mesh.\n * \\param[in] scale scale vector\n */\n void scale(const Eigen::Vector3f& scale) {\n for (int v = 0; v < this->num_vertices(); ++v) {\n for (int i = 0; i < 3; ++i) {\n this->vertices[v](i) *= scale(i);\n }\n }\n }\n\n /** \\brief Voxelize the given mesh into a dense volume.\n * \\param[in] mesh mesh to voxelize\n * \\param[in] n batch index in dense\n * \\param[in] dense dense pre-initialized volume\n */\n void voxelize_UNSCALED(int n, Eigen::Tensor& sdf) {\n\n int height = sdf.dimension(1);\n int width = sdf.dimension(2);\n int depth = sdf.dimension(3);\n\n for (int h = 0; h < height; ++h) {\n for (int w = 0; w < width; ++w) {\n for (int d = 0; d < depth; ++d) {\n sdf(n, h, w, d) = FLT_MAX;\n\n // [Data] 99% percentile dimensions:\n // [Data] 4.750000 1.870000 1.970000\n // [Data] 2.540102 1 1.053475\n // [Data] 56 22 24\n\n float padding_factor = 1.2;\n float width_factor = (padding_factor*2.545454545)/2.545454545; // = padding_factor\n float height_factor = padding_factor/2.545454545;\n float depth_factor = (padding_factor*1.090909091)/2.545454545;\n\n // the box corresponding to this voxel\n Eigen::Vector3f min(width_factor*static_cast(w)/width, height_factor*static_cast(h)/height,\n depth_factor*static_cast(d)/depth);\n Eigen::Vector3f max(width_factor*static_cast(w + 1)/width, height_factor*static_cast(h + 1)/height,\n depth_factor*static_cast(d + 1)/depth);\n\n Box voxel_box(min, max);\n voxel_box.translate(Eigen::Vector3f(-width_factor/2, -height_factor/2, -depth_factor/2));\n Eigen::Vector3f center((voxel_box.max(0) + voxel_box.min(0))/2., (voxel_box.max(1) + voxel_box.min(1))/2., (voxel_box.max(2) + voxel_box.min(2))/2.);\n\n // count number of intersections.\n int num_intersect = 0;\n for (unsigned int f = 0; f < this->faces.size(); ++f) {\n\n Eigen::Vector3f v1 = this->vertices[this->faces[f](0)];\n Eigen::Vector3f v2 = this->vertices[this->faces[f](1)];\n Eigen::Vector3f v3 = this->vertices[this->faces[f](2)];\n\n Eigen::Vector3f closest_point;\n triangle_point_distance(center, v1, v2, v3, closest_point);\n float distance = (center - closest_point).norm();\n\n if (distance < sdf(n, h, w, d)) {\n sdf(n, h, w, d) = distance;\n }\n\n bool intersect = triangle_ray_intersection(center, Eigen::Vector3f(0, 0, 0), v1, v2, v3, distance);\n\n if (intersect && distance >= 0) {\n num_intersect++;\n }\n }\n\n if (num_intersect%2 == 1) {\n sdf(n, h, w, d) *= -1;\n }\n }\n }\n }\n }\n\n /** \\brief Voxelize the given mesh into a dense volume.\n * \\param[in] mesh mesh to voxelize\n * \\param[in] n batch index in dense\n * \\param[in] dense dense pre-initialized volume\n */\n void voxelize_SCALED(int n, Eigen::Tensor& sdf) {\n\n int height = sdf.dimension(1);\n int width = sdf.dimension(2);\n int depth = sdf.dimension(3);\n\n #pragma omp parallel\n {\n #pragma omp for\n for (int i = 0; i < height*width*depth; i++) {\n int d = i%depth;\n int w = (i/depth)%width;\n int h = (i/depth)/width;\n sdf(n, h, w, d) = FLT_MAX;\n\n // the box corresponding to this voxel\n Eigen::Vector3f min(static_cast(w), static_cast(h), static_cast(d));\n Eigen::Vector3f max(static_cast(w + 1), static_cast(h + 1), static_cast(d + 1));\n\n Box voxel_box(min, max);\n //voxel_box.translate(Eigen::Vector3f(-static_cast(width)/2, -static_cast(height)/2, -static_cast(depth)/2));\n Eigen::Vector3f center((voxel_box.max(0) + voxel_box.min(0))/2., (voxel_box.max(1) + voxel_box.min(1))/2., (voxel_box.max(2) + voxel_box.min(2))/2.);\n\n // count number of intersections.\n int num_intersect = 0;\n for (unsigned int f = 0; f < this->faces.size(); ++f) {\n\n Eigen::Vector3f v1 = this->vertices[this->faces[f](0)];\n Eigen::Vector3f v2 = this->vertices[this->faces[f](1)];\n Eigen::Vector3f v3 = this->vertices[this->faces[f](2)];\n\n Eigen::Vector3f closest_point;\n triangle_point_distance(center, v1, v2, v3, closest_point);\n float distance = (center - closest_point).norm();\n\n if (distance < sdf(n, h, w, d)) {\n sdf(n, h, w, d) = distance;\n }\n\n bool intersect = triangle_ray_intersection(center, Eigen::Vector3f(0, 0, 0), v1, v2, v3, distance);\n\n if (intersect && distance >= 0) {\n num_intersect++;\n }\n }\n\n if (num_intersect%2 == 1) {\n sdf(n, h, w, d) *= -1;\n }\n }\n }\n }\n\n /** \\brief Sample points from the mesh\n * \\param[in] mesh mesh to sample from\n * \\param[in] n batch index in points\n * \\param[in] points pre-initialized tensor holding points\n */\n void sample(int n, Eigen::Tensor& points) {\n\n // The number of points to sample.\n const int N = points.dimension(1);\n\n // Stores the areas of faces.\n std::vector areas(this->faces.size());\n float sum = 0;\n\n // Build a probability distribution over faces.\n for (int f = 0; f < this->faces.size(); f++) {\n Eigen::Vector3f a = this->vertices[this->faces[f][0]];\n Eigen::Vector3f b = this->vertices[this->faces[f][1]];\n Eigen::Vector3f c = this->vertices[this->faces[f][2]];\n\n // Angle between a->b and a->c.\n Eigen::Vector3f ab = b - a;\n Eigen::Vector3f ac = c - a;\n float cos_angle = ab.dot(ac)/(ab.norm()*ac.norm());\n float angle = std::acos(cos_angle);\n\n // Compute triangle area.\n float area = std::max(0., 0.5*ab.norm()*ac.norm()*std::sin(angle));\n //std::cout << area << \" \" << std::pow(area, 1./4.) << \" \" << angle << \" \" << ab.norm() << \" \" << ac.norm() << \" \" << std::sin(angle) << std::endl;\n\n // Accumulate.\n //area = std::sqrt(area);\n areas[f] = area;\n sum += area;\n //areas.push_back(1);\n //sum += 1;\n }\n\n //std::cout << sum << std::endl;\n assert(sum > 1e-8);\n\n for (int f = 0; f < this->faces.size(); f++) {\n //std::cout << areas[f] << \" \";\n areas[f] /= sum;\n //std::cout << areas[f] << std::endl;\n }\n\n std::vector cum_areas(areas.size());\n cum_areas[0] = areas[0];\n\n for (int f = 1; f < this->faces.size(); f++) {\n cum_areas[f] = areas[f] + cum_areas[f - 1];\n }\n\n for (int i = 0; i < N; i++) {\n float r = static_cast(std::rand())/static_cast(RAND_MAX);\n int face = 0;\n\n while (r > cum_areas[face + 1] && face < this->faces.size() - 1) {\n face++;\n }\n\n assert(face >= 0 && face < this->faces.size());\n //int face = std::rand()%this->faces.size();\n\n float r1 = 0;\n float r2 = 0;\n do {\n r1 = static_cast(std::rand())/static_cast(RAND_MAX);\n r2 = static_cast(std::rand())/static_cast(RAND_MAX);\n }\n while (r1 + r2 > 1.f);\n\n int s = std::rand()%3;\n //std::cout << face << \" \" << areas[face] << std::endl;\n\n Eigen::Vector3f a = this->vertices[this->faces[face](s)];\n Eigen::Vector3f b = this->vertices[this->faces[face]((s + 1)%3)];\n Eigen::Vector3f c = this->vertices[this->faces[face]((s + 2)%3)];\n\n Eigen::Vector3f ab = b - a;\n Eigen::Vector3f ac = c - a;\n\n Eigen::Vector3f point = a + r1*ab + r2*ac;\n points(n, i, 0) = point(0);\n points(n, i, 1) = point(1);\n points(n, i, 2) = point(2);\n }\n }\n\n /** \\brief Write mesh to OFF file.\n * \\param[in] filepath path to OFF file to write\n * \\return success\n */\n bool to_off(const std::string filepath) {\n std::ofstream* out = new std::ofstream(filepath, std::ofstream::out);\n if (!static_cast(out)) {\n return false;\n }\n\n (*out) << \"OFF\" << std::endl;\n (*out) << this->vertices.size() << \" \" << this->faces.size() << \" 0\" << std::endl;\n\n for (unsigned int v = 0; v < this->vertices.size(); v++) {\n (*out) << this->vertices[v](0) << \" \" << this->vertices[v](1) << \" \" << this->vertices[v](2) << std::endl;\n }\n\n for (unsigned int f = 0; f < this->faces.size(); f++) {\n (*out) << \"3 \" << this->faces[f](0) << \" \" << this->faces[f](1) << \" \" << this->faces[f](2) << std::endl;\n }\n\n out->close();\n delete out;\n\n return true;\n }\n\nprivate:\n\n /** \\brief Vertices as (x,y,z)-vectors. */\n std::vector vertices;\n\n /** \\brief Faces as list of vertex indices. */\n std::vector faces;\n};\n\n/** \\brief Reading an off file and returning the vertices x, y, z coordinates and the\n * face indices.\n * \\param[in] filepath path to the OFF file\n * \\param[out] mesh read mesh with vertices and faces\n * \\return success\n */\nbool read_off(const std::string filepath, Mesh& mesh) {\n\n std::ifstream* file = new std::ifstream(filepath.c_str());\n std::string line;\n std::stringstream ss;\n int line_nb = 0;\n\n std::getline(*file, line);\n ++line_nb;\n\n if (line != \"off\" && line != \"OFF\") {\n std::cout << \"[Error] Invalid header: \\\"\" << line << \"\\\", \" << filepath << std::endl;\n return false;\n }\n\n size_t n_edges;\n std::getline(*file, line);\n ++line_nb;\n\n int n_vertices;\n int n_faces;\n ss << line;\n ss >> n_vertices;\n ss >> n_faces;\n ss >> n_edges;\n\n for (size_t v = 0; v < n_vertices; ++v) {\n std::getline(*file, line);\n ++line_nb;\n\n ss.clear();\n ss.str(\"\");\n\n Eigen::Vector3f vertex;\n ss << line;\n ss >> vertex(0);\n ss >> vertex(1);\n ss >> vertex(2);\n\n mesh.add_vertex(vertex);\n }\n\n size_t n;\n for (size_t f = 0; f < n_faces; ++f) {\n std::getline(*file, line);\n ++line_nb;\n\n ss.clear();\n ss.str(\"\");\n\n size_t n;\n ss << line;\n ss >> n;\n\n if(n != 3) {\n std::cout << \"[Error] Not a triangle (\" << n << \" points) at \" << (line_nb - 1) << std::endl;\n return false;\n }\n\n Eigen::Vector3i face;\n ss >> face(0);\n ss >> face(1);\n ss >> face(2);\n\n mesh.add_face(face);\n }\n\n if (n_vertices != mesh.num_vertices()) {\n std::cout << \"[Error] Number of vertices in header differs from actual number of vertices.\" << std::endl;\n return false;\n }\n\n if (n_faces != mesh.num_faces()) {\n std::cout << \"[Error] Number of faces in header differs from actual number of faces.\" << std::endl;\n return false;\n }\n\n file->close();\n delete file;\n\n return true;\n}\n\n/** \\brief Write the given set of volumes to h5 file.\n * \\param[in] filepath h5 file to write\n * \\param[in] n number of volumes\n * \\param[in] height height of volumes\n * \\param[in] width width of volumes\n * \\param[in] depth depth of volumes\n * \\param[in] dense volume data\n */\nbool write_hdf5(const std::string filepath, Eigen::Tensor& dense) {\n\n try {\n\n /*\n * Turn off the auto-printing when failure occurs so that we can\n * handle the errors appropriately\n */\n H5::Exception::dontPrint();\n\n /*\n * Create a new file using H5F_ACC_TRUNC access,\n * default file creation properties, and default file\n * access properties.\n */\n H5::H5File file(filepath, H5F_ACC_TRUNC);\n\n /*\n * Define the size of the array and create the data space for fixed\n * size dataset.\n */\n hsize_t rank = 4;\n hsize_t dimsf[rank];\n dimsf[0] = dense.dimension(0);\n dimsf[1] = dense.dimension(1);\n dimsf[2] = dense.dimension(2);\n dimsf[3] = dense.dimension(3);\n H5::DataSpace dataspace(rank, dimsf);\n\n /*\n * Define datatype for the data in the file.\n * We will store little endian INT numbers.\n */\n H5::IntType datatype(H5::PredType::NATIVE_FLOAT);\n datatype.setOrder(H5T_ORDER_LE);\n\n /*\n * Create a new dataset within the file using defined dataspace and\n * datatype and default dataset creation properties.\n */\n H5::DataSet dataset = file.createDataSet(\"tensor\", datatype, dataspace);\n\n /*\n * Write the data to the dataset using default memory space, file\n * space, and transfer properties.\n */\n float* data = static_cast(dense.data());\n dataset.write(data, H5::PredType::NATIVE_FLOAT);\n } // end of try block\n\n // catch failure caused by the H5File operations\n catch(H5::FileIException error) {\n error.printError();\n return false;\n }\n\n // catch failure caused by the DataSet operations\n catch(H5::DataSetIException error) {\n error.printError();\n return false;\n }\n\n // catch failure caused by the DataSpace operations\n catch(H5::DataSpaceIException error) {\n error.printError();\n return false;\n }\n\n // catch failure caused by the DataSpace operations\n catch(H5::DataTypeIException error) {\n error.printError();\n return false;\n }\n\n return true;\n}\n\n/** \\brief Write the given set of volumes to h5 file.\n * \\param[in] filepath h5 file to write\n * \\param[in] n number of volumes\n * \\param[in] height height of volumes\n * \\param[in] width width of volumes\n * \\param[in] depth depth of volumes\n * \\param[in] dense volume data\n */\nbool write_hdf5(const std::string filepath, Eigen::Tensor& dense) {\n\n try {\n\n /*\n * Turn off the auto-printing when failure occurs so that we can\n * handle the errors appropriately\n */\n H5::Exception::dontPrint();\n\n /*\n * Create a new file using H5F_ACC_TRUNC access,\n * default file creation properties, and default file\n * access properties.\n */\n H5::H5File file(filepath, H5F_ACC_TRUNC);\n\n /*\n * Define the size of the array and create the data space for fixed\n * size dataset.\n */\n hsize_t rank = 3;\n hsize_t dimsf[rank];\n dimsf[0] = dense.dimension(0);\n dimsf[1] = dense.dimension(1);\n dimsf[2] = dense.dimension(2);\n H5::DataSpace dataspace(rank, dimsf);\n\n /*\n * Define datatype for the data in the file.\n * We will store little endian INT numbers.\n */\n H5::IntType datatype(H5::PredType::NATIVE_FLOAT);\n datatype.setOrder(H5T_ORDER_LE);\n\n /*\n * Create a new dataset within the file using defined dataspace and\n * datatype and default dataset creation properties.\n */\n H5::DataSet dataset = file.createDataSet(\"tensor\", datatype, dataspace);\n\n /*\n * Write the data to the dataset using default memory space, file\n * space, and transfer properties.\n */\n float* data = static_cast(dense.data());\n dataset.write(data, H5::PredType::NATIVE_FLOAT);\n } // end of try block\n\n // catch failure caused by the H5File operations\n catch(H5::FileIException error) {\n error.printError();\n return false;\n }\n\n // catch failure caused by the DataSet operations\n catch(H5::DataSetIException error) {\n error.printError();\n return false;\n }\n\n // catch failure caused by the DataSpace operations\n catch(H5::DataSpaceIException error) {\n error.printError();\n return false;\n }\n\n // catch failure caused by the DataSpace operations\n catch(H5::DataTypeIException error) {\n error.printError();\n return false;\n }\n\n return true;\n}\n\n/** \\brief Write the given set of volumes to h5 file.\n * \\param[in] filepath h5 file to write\n * \\param[in] n number of volumes\n * \\param[in] height height of volumes\n * \\param[in] width width of volumes\n * \\param[in] depth depth of volumes\n * \\param[in] dense volume data\n */\nbool write_hdf5(const std::string filepath, Eigen::Tensor& dense) {\n\n try {\n\n /*\n * Turn off the auto-printing when failure occurs so that we can\n * handle the errors appropriately\n */\n H5::Exception::dontPrint();\n\n /*\n * Create a new file using H5F_ACC_TRUNC access,\n * default file creation properties, and default file\n * access properties.\n */\n H5::H5File file(filepath, H5F_ACC_TRUNC);\n\n /*\n * Define the size of the array and create the data space for fixed\n * size dataset.\n */\n hsize_t rank = 4;\n hsize_t dimsf[rank];\n dimsf[0] = dense.dimension(0);\n dimsf[1] = dense.dimension(1);\n dimsf[2] = dense.dimension(2);\n dimsf[3] = dense.dimension(3);\n H5::DataSpace dataspace(rank, dimsf);\n\n /*\n * Define datatype for the data in the file.\n * We will store little endian INT numbers.\n */\n H5::IntType datatype(H5::PredType::NATIVE_INT);\n datatype.setOrder(H5T_ORDER_LE);\n\n /*\n * Create a new dataset within the file using defined dataspace and\n * datatype and default dataset creation properties.\n */\n H5::DataSet dataset = file.createDataSet(\"tensor\", datatype, dataspace);\n\n /*\n * Write the data to the dataset using default memory space, file\n * space, and transfer properties.\n */\n int* data = static_cast(dense.data());\n dataset.write(data, H5::PredType::NATIVE_INT);\n } // end of try block\n\n // catch failure caused by the H5File operations\n catch(H5::FileIException error) {\n error.printError();\n return false;\n }\n\n // catch failure caused by the DataSet operations\n catch(H5::DataSetIException error) {\n error.printError();\n return false;\n }\n\n // catch failure caused by the DataSpace operations\n catch(H5::DataSpaceIException error) {\n error.printError();\n return false;\n }\n\n // catch failure caused by the DataSpace operations\n catch(H5::DataTypeIException error) {\n error.printError();\n return false;\n }\n\n return true;\n}\n\n/** \\brief Read all files in a directory matching the given extension.\n * \\param[in] directory path to directory\n * \\param[out] files read file paths\n * \\param[in] extension extension to filter for\n */\nvoid read_directory(const std::string directory, std::map& files, const std::string extension = \".off\") {\n\n boost::filesystem::path dir(directory);\n boost::filesystem::directory_iterator end;\n\n files.clear();\n for (boost::filesystem::directory_iterator it(dir); it != end; ++it) {\n if (it->path().extension().string() == extension) {\n int number = std::stoi(it->path().filename().string());\n files.insert(std::pair(number, it->path().string()));\n }\n }\n}\n\n/** \\brief Small helper for safely retrieving values from Json root element. */\nclass JSON {\npublic:\n\n /** \\brief Constructor.\n * \\param[in] root JSON root element\n */\n JSON(Json::Value root) {\n this->root = root;\n }\n\n /** \\brief Get the value of the given key as int.\n * \\param[in] key key to retrieve\n * \\return value\n */\n int get_int(std::string key) {\n if (!this->root.isMember(key)) {\n std::cout << \"[Error] key \" << key << \" not found\" << std::endl;\n exit(1);\n }\n\n return this->root[key].asInt();\n }\n\n /** \\brief Get the value of the given key as float.\n * \\param[in] key key to retrieve\n * \\return value\n */\n float get_float(std::string key) {\n if (!this->root.isMember(key)) {\n std::cout << \"[Error] key \" << key << \" not found\" << std::endl;\n exit(1);\n }\n\n return this->root[key].asFloat();\n }\n\n /** \\brief Get the value of the given key as string.\n * \\param[in] key key to retrieve\n * \\return value\n */\n std::string get_string(std::string key) {\n if (!this->root.isMember(key)) {\n std::cout << \"[Error] key \" << key << \" not found\" << std::endl;\n exit(1);\n }\n\n return this->root[key].asString();\n }\n\nprivate:\n\n /** \\brief Root element. */\n Json::Value root;\n};\n\nint main(int argc, char** argv) {\n if (argc != 2) {\n std::cout << \"[Error] Usage: voxelize_cuboids config.json\" << std::endl;\n exit(1);\n }\n\n Json::Value root;\n Json::Reader reader;\n\n std::string config_file = argv[1];\n\n if (!boost::filesystem::is_regular_file(boost::filesystem::path(config_file))) {\n std::cout << \"[Error] Config file not found: \" << config_file << std::endl;\n exit(1);\n }\n\n std::ifstream json_file(config_file, std::ifstream::in | std::ifstream::binary);\n reader.parse(json_file, root, false);\n JSON json(root);\n\n // Unsafe!\n int height = json.get_int(\"height\");\n int width = json.get_int(\"width\");\n int depth = json.get_int(\"depth\");\n\n int image_height = json.get_int(\"image_height\");\n int image_width = json.get_int(\"image_width\");\n\n std::string suffix = json.get_string(\"suffix\");\n int multiplier = json.get_int(\"multiplier\");\n\n std::string sdf_file = json.get_string(\"sdf_file\");\n if (sdf_file.empty()) {\n std::cout << \"[Error] Read invalid output file\" << std::endl;\n exit(1);\n }\n\n sdf_file = sdf_file + \"_\" + std::to_string(multiplier) + \"_\"\n + std::to_string(image_height) + \"x\" + std::to_string(image_width) + \"_\"\n + std::to_string(height) + \"x\" + std::to_string(width) + \"x\" + std::to_string(depth)\n + suffix + \".h5\";\n\n std::string output_file = json.get_string(\"output_file\");\n if (output_file.empty()) {\n std::cout << \"[Error] Read invalid output file\" << std::endl;\n exit(1);\n }\n\n output_file = output_file + \"_\" + std::to_string(multiplier) + \"_\"\n + std::to_string(image_height) + \"x\" + std::to_string(image_width) + \"_\"\n + std::to_string(height) + \"x\" + std::to_string(width) + \"x\" + std::to_string(depth)\n + suffix + \".h5\";\n\n std::string off_directory = json.get_string(\"off_dir\");\n if (off_directory.empty()) {\n std::cout << \"[Error] Read invalid off directory\" << std::endl;\n exit(1);\n }\n\n off_directory = off_directory + \"_\" + std::to_string(multiplier) + \"_\"\n + std::to_string(image_height) + \"x\" + std::to_string(image_width) + \"_\"\n + std::to_string(height) + \"x\" + std::to_string(width) + \"x\" + std::to_string(depth) + suffix;\n\n std::string off_gt_directory = json.get_string(\"off_gt_dir\");\n if (off_gt_directory.empty()) {\n std::cout << \"[Error] Read invalid off gt directory\" << std::endl;\n exit(1);\n }\n\n off_gt_directory = off_gt_directory + \"_\" + std::to_string(multiplier) + \"_\"\n + std::to_string(image_height) + \"x\" + std::to_string(image_width) + \"_\"\n + std::to_string(height) + \"x\" + std::to_string(width) + \"x\" + std::to_string(depth) + suffix;\n\n if (!boost::filesystem::is_directory(boost::filesystem::path(off_gt_directory))) {\n boost::filesystem::create_directories(boost::filesystem::path(off_gt_directory));\n }\n\n std::string point_file = json.get_string(\"point_file\");\n if (point_file.empty()) {\n std::cout << \"[Error] Read invalid point file\" << std::endl;\n exit(1);\n }\n\n point_file = point_file + \"_\" + std::to_string(multiplier) + \"_\"\n + std::to_string(image_height) + \"x\" + std::to_string(image_width) + \"_\"\n + std::to_string(height) + \"x\" + std::to_string(width) + \"x\" + std::to_string(depth)\n + suffix + \".h5\";\n\n if (!boost::filesystem::is_directory(boost::filesystem::path(off_directory))) {\n std::cout << \"[Error] OFF directory \" << off_directory << \" not found\" << std::endl;\n exit(1);\n }\n\n std::map files;\n read_directory(off_directory, files);\n\n int N = files.size();\n Eigen::Tensor sdf(N, height, width, depth);\n sdf.setZero();\n\n int N_points = json.get_int(\"n_points\");\n Eigen::Tensor points(N, N_points, 3);\n points.setZero();\n\n std::vector indices;\n for (std::map::iterator it = files.begin(); it != files.end(); it++) {\n indices.push_back(it->first);\n }\n\n int n = 0;\n #pragma omp parallel\n {\n #pragma omp for\n for (unsigned int i = 0; i< indices.size(); i++) {\n int n = indices[i];\n\n Mesh mesh;\n bool success = read_off(files[n], mesh);\n\n if (!success) {\n std::cout << \"[Error] could not read \" << files[n] << std::endl;\n exit(1);\n }\n\n float padding_factor = 1 + json.get_float(\"padding\");\n float scale_factor = static_cast(width)/padding_factor;\n mesh.scale(Eigen::Vector3f(scale_factor, scale_factor, scale_factor));\n mesh.translate(Eigen::Vector3f(static_cast(width)/2, static_cast(height)/2, static_cast(depth)/2));\n\n std::cout << \"[Data] sampling mesh \" << n << \"/\" << files.size() << \" \" << files[n] << std::endl;\n mesh.sample(i, points);\n\n std::string off_file = off_gt_directory + \"/\" + std::to_string(i) + \".off\";\n std::cout << \"[Data] writing \" << off_file << std::endl;\n mesh.to_off(off_file);\n\n if (!success) {\n std::cout << \"[Error] error reading \" << files[n] << std::endl;\n exit(1);\n }\n\n std::cout << \"[Data] voxelizing mesh \" << n << \"/\" << files.size() << \" \" << files[n] << std::endl;\n mesh.voxelize_SCALED(i, sdf);\n\n n++;\n }\n }\n\n bool success = write_hdf5(sdf_file, sdf);\n\n if (success) {\n std::cout <<\"[Data] wrote \" << sdf_file << std::endl;\n }\n else {\n std::cout << \"[Error] error writing \" << sdf_file << std::endl;\n }\n\n success = write_hdf5(point_file, points);\n\n if (success) {\n std::cout <<\"[Data] wrote \" << point_file << std::endl;\n }\n else {\n std::cout << \"[Error] error writing \" << point_file << std::endl;\n }\n\n Eigen::Tensor occ(N, height, width, depth);\n occ.setZero();\n\n for (int n = 0; n < N; n++) {\n for (int h = 0; h < height; h++) {\n for (int w = 0; w < width; w++) {\n for (int d = 0; d < depth; d++) {\n if (sdf(n, h, w, d) <= 0) {\n occ(n, h, w, d) = 1;\n }\n }\n }\n }\n }\n\n success = write_hdf5(output_file, occ);\n\n if (success) {\n std::cout <<\"[Data] wrote \" << output_file << std::endl;\n }\n else {\n std::cout << \"[Error] error writing \" << output_file << std::endl;\n }\n\n exit(0);\n}", "meta": {"hexsha": "0884bc06022a7884f234039a463c9c2bea928f47", "size": 35562, 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"davidstutz/daml-shape-completion", "max_forks_repo_head_hexsha": "d0d1d1c26ba547d02c4102077aeb0a1ea46c4e50", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2020-04-30T01:30:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-05T10:24:46.000Z", "avg_line_length": 28.9592833876, "max_line_length": 159, "alphanum_fraction": 0.6030594455, "num_tokens": 10250, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.4339814648038985, "lm_q1q2_score": 0.2389533910267992}} {"text": "// ====================================================================\n// This file is part of FlexibleSUSY.\n//\n// FlexibleSUSY is free software: you can redistribute it and/or modify\n// it under the terms of the GNU General Public License as published\n// by the Free Software Foundation, either version 3 of the License,\n// or (at your option) any later version.\n//\n// FlexibleSUSY is distributed in the hope that it will be useful, but\n// WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n// General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with FlexibleSUSY. If not, see\n// .\n// ====================================================================\n\n// File generated at Thu 10 May 2018 15:01:38\n\n#ifndef NUHMSSM_susy_parameters_H\n#define NUHMSSM_susy_parameters_H\n\n#include \"betafunction.hpp\"\n#include \"NUHMSSM_input_parameters.hpp\"\n\n#include \n#include \n#include \n#include \n\nnamespace flexiblesusy {\n\n#ifdef TRACE_STRUCT_TYPE\n #undef TRACE_STRUCT_TYPE\n#endif\n#define TRACE_STRUCT_TYPE Susy_traces\n\nclass NUHMSSM_susy_parameters : public Beta_function {\npublic:\n explicit NUHMSSM_susy_parameters(const NUHMSSM_input_parameters& input_ = NUHMSSM_input_parameters());\n NUHMSSM_susy_parameters(double scale_, int loops_, int thresholds_, const NUHMSSM_input_parameters& input_, const Eigen::Matrix& Yd_, const Eigen::Matrix& Ye_\n , const Eigen::Matrix& Yu_, double Mu_, double g1_, double g2_,\n double g3_, double vd_, double vu_\n);\n NUHMSSM_susy_parameters(const NUHMSSM_susy_parameters&) = default;\n NUHMSSM_susy_parameters(NUHMSSM_susy_parameters&&) = default;\n virtual ~NUHMSSM_susy_parameters() = default;\n NUHMSSM_susy_parameters& operator=(const NUHMSSM_susy_parameters&) = default;\n NUHMSSM_susy_parameters& operator=(NUHMSSM_susy_parameters&&) = default;\n\n virtual Eigen::ArrayXd beta() const override;\n virtual Eigen::ArrayXd get() const override;\n virtual void print(std::ostream&) const;\n virtual void set(const Eigen::ArrayXd&) override;\n const NUHMSSM_input_parameters& get_input() const;\n NUHMSSM_input_parameters& get_input();\n void set_input_parameters(const NUHMSSM_input_parameters&);\n\n NUHMSSM_susy_parameters calc_beta() const;\n NUHMSSM_susy_parameters calc_beta(int) const;\n virtual void clear();\n\n void set_Yd(const Eigen::Matrix& Yd_) { Yd = Yd_; }\n void set_Yd(int i, int k, const double& value) { Yd(i,k) = value; }\n void set_Ye(const Eigen::Matrix& Ye_) { Ye = Ye_; }\n void set_Ye(int i, int k, const double& value) { Ye(i,k) = value; }\n void set_Yu(const Eigen::Matrix& Yu_) { Yu = Yu_; }\n void set_Yu(int i, int k, const double& value) { Yu(i,k) = value; }\n void set_Mu(double Mu_) { Mu = Mu_; }\n void set_g1(double g1_) { g1 = g1_; }\n void set_g2(double g2_) { g2 = g2_; }\n void set_g3(double g3_) { g3 = g3_; }\n void set_vd(double vd_) { vd = vd_; }\n void set_vu(double vu_) { vu = vu_; }\n\n const Eigen::Matrix& get_Yd() const { return Yd; }\n double get_Yd(int i, int k) const { return Yd(i,k); }\n const Eigen::Matrix& get_Ye() const { return Ye; }\n double get_Ye(int i, int k) const { return Ye(i,k); }\n const Eigen::Matrix& get_Yu() const { return Yu; }\n double get_Yu(int i, int k) const { return Yu(i,k); }\n double get_Mu() const { return Mu; }\n double get_g1() const { return g1; }\n double get_g2() const { return g2; }\n double get_g3() const { return g3; }\n double get_vd() const { return vd; }\n double get_vu() const { return vu; }\n\n Eigen::Matrix get_SqSq() const;\n Eigen::Matrix get_SlSl() const;\n double get_SHdSHd() const;\n double get_SHuSHu() const;\n Eigen::Matrix get_SdRSdR() const;\n Eigen::Matrix get_SuRSuR() const;\n Eigen::Matrix get_SeRSeR() const;\n\n\nprotected:\n Eigen::Matrix Yd{Eigen::Matrix::Zero()};\n Eigen::Matrix Ye{Eigen::Matrix::Zero()};\n Eigen::Matrix Yu{Eigen::Matrix::Zero()};\n double Mu{};\n double g1{};\n double g2{};\n double g3{};\n double vd{};\n double vu{};\n\n NUHMSSM_input_parameters input{};\n\nprivate:\n static const int numberOfParameters = 33;\n\n struct Susy_traces {\n double traceYdAdjYd{};\n double traceYeAdjYe{};\n double traceYdAdjYdYdAdjYd{};\n double traceYdAdjYuYuAdjYd{};\n double traceYeAdjYeYeAdjYe{};\n double traceYuAdjYu{};\n double traceAdjYdYd{};\n double traceAdjYeYe{};\n double traceAdjYuYu{};\n double traceAdjYdYdAdjYdYd{};\n double traceAdjYeYeAdjYeYe{};\n double traceAdjYuYuAdjYdYd{};\n double traceAdjYuYuAdjYuYu{};\n double traceAdjYdYdAdjYdYdAdjYdYd{};\n double traceAdjYeYeAdjYeYeAdjYeYe{};\n double traceAdjYuYuAdjYuYuAdjYdYd{};\n double traceYuAdjYuYuAdjYu{};\n double traceAdjYdYdAdjYuYuAdjYdYd{};\n double traceAdjYuYuAdjYuYuAdjYuYu{};\n\n };\n Susy_traces calc_susy_traces(int) const;\n\n Eigen::Matrix calc_beta_Yd_1_loop(const TRACE_STRUCT_TYPE&) const;\n Eigen::Matrix calc_beta_Yd_2_loop(const TRACE_STRUCT_TYPE&) const;\n Eigen::Matrix calc_beta_Yd_3_loop(const TRACE_STRUCT_TYPE&) const;\n Eigen::Matrix calc_beta_Ye_1_loop(const TRACE_STRUCT_TYPE&) const;\n Eigen::Matrix calc_beta_Ye_2_loop(const TRACE_STRUCT_TYPE&) const;\n Eigen::Matrix calc_beta_Ye_3_loop(const TRACE_STRUCT_TYPE&) const;\n Eigen::Matrix calc_beta_Yu_1_loop(const TRACE_STRUCT_TYPE&) const;\n Eigen::Matrix calc_beta_Yu_2_loop(const TRACE_STRUCT_TYPE&) const;\n Eigen::Matrix calc_beta_Yu_3_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_Mu_1_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_Mu_2_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_Mu_3_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_g1_1_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_g1_2_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_g1_3_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_g2_1_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_g2_2_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_g2_3_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_g3_1_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_g3_2_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_g3_3_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_vd_1_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_vd_2_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_vd_3_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_vu_1_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_vu_2_loop(const TRACE_STRUCT_TYPE&) const;\n double calc_beta_vu_3_loop(const TRACE_STRUCT_TYPE&) const;\n\n};\n\nstd::ostream& operator<<(std::ostream&, const NUHMSSM_susy_parameters&);\n\n#undef TRACE_STRUCT_TYPE\n\n} // namespace flexiblesusy\n\n#endif\n", "meta": {"hexsha": "f53c140986f85962134552bb8a55339f03358768", "size": 7259, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contrib/MassSpectra/flexiblesusy/models/NUHMSSM/NUHMSSM_susy_parameters.hpp", "max_stars_repo_name": "sebhoof/gambit_1.5", "max_stars_repo_head_hexsha": "f9a3f788e3331067c555ae1a030420e903c6fdcd", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-09-08T20:05:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-26T07:57:56.000Z", "max_issues_repo_path": "contrib/MassSpectra/flexiblesusy/models/NUHMSSM/NUHMSSM_susy_parameters.hpp", "max_issues_repo_name": "sebhoof/gambit_1.5", "max_issues_repo_head_hexsha": "f9a3f788e3331067c555ae1a030420e903c6fdcd", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": "2020-10-19T09:56:17.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-28T06:12:03.000Z", "max_forks_repo_path": "contrib/MassSpectra/flexiblesusy/models/NUHMSSM/NUHMSSM_susy_parameters.hpp", "max_forks_repo_name": "patscott/gambit_1.4", "max_forks_repo_head_hexsha": "a50537419918089effc207e8b206489a5cfd2258", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2020-09-08T02:23:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-23T08:48:04.000Z", "avg_line_length": 41.2443181818, "max_line_length": 185, "alphanum_fraction": 0.7206226753, "num_tokens": 2081, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.23869078194630144}} {"text": "/**\n * @file ffn_impl.hpp\n * @author Marcus Edel\n *\n * Definition of the FFN class, which implements feed forward neural networks.\n *\n * mlpack is free software; you may redistribute it and/or modify it under the\n * terms of the 3-clause BSD license. You should have received a copy of the\n * 3-clause BSD license along with mlpack. If not, see\n * http://www.opensource.org/licenses/BSD-3-Clause for more information.\n */\n#ifndef MLPACK_METHODS_ANN_FFN_IMPL_HPP\n#define MLPACK_METHODS_ANN_FFN_IMPL_HPP\n\n// In case it hasn't been included yet.\n#include \"ffn.hpp\"\n\n#include \"visitor/forward_visitor.hpp\"\n#include \"visitor/backward_visitor.hpp\"\n#include \"visitor/deterministic_set_visitor.hpp\"\n#include \"visitor/gradient_set_visitor.hpp\"\n#include \"visitor/gradient_visitor.hpp\"\n#include \"visitor/set_input_height_visitor.hpp\"\n#include \"visitor/set_input_width_visitor.hpp\"\n\n#include \n\nnamespace mlpack {\nnamespace ann /** Artificial Neural Network. */ {\n\n\ntemplate\nFFN::FFN(\n OutputLayerType outputLayer, InitializationRuleType initializeRule) :\n outputLayer(std::move(outputLayer)),\n initializeRule(std::move(initializeRule)),\n width(0),\n height(0),\n reset(false),\n numFunctions(0),\n deterministic(true)\n{\n /* Nothing to do here */\n}\n\ntemplate\nFFN::~FFN()\n{\n std::for_each(network.begin(), network.end(),\n boost::apply_visitor(deleteVisitor));\n}\n\ntemplate\nvoid FFN::ResetData(\n arma::mat predictors, arma::mat responses)\n{\n numFunctions = responses.n_cols;\n this->predictors = std::move(predictors);\n this->responses = std::move(responses);\n this->deterministic = true;\n ResetDeterministic();\n\n if (!reset)\n ResetParameters();\n}\n\ntemplate\ntemplate\ndouble FFN::Train(\n arma::mat predictors,\n arma::mat responses,\n OptimizerType& optimizer,\n CallbackTypes&&... callbacks)\n{\n ResetData(std::move(predictors), std::move(responses));\n\n // Train the model.\n Timer::Start(\"ffn_optimization\");\n const double out = optimizer.Optimize(*this, parameter, callbacks...);\n Timer::Stop(\"ffn_optimization\");\n\n Log::Info << \"FFN::FFN(): final objective of trained model is \" << out\n << \".\" << std::endl;\n return out;\n}\n\ntemplate\ntemplate\ndouble FFN::Train(\n arma::mat predictors,\n arma::mat responses,\n CallbackTypes&&... callbacks)\n{\n ResetData(std::move(predictors), std::move(responses));\n\n OptimizerType optimizer;\n\n // Train the model.\n Timer::Start(\"ffn_optimization\");\n const double out = optimizer.Optimize(*this, parameter, callbacks...);\n Timer::Stop(\"ffn_optimization\");\n\n Log::Info << \"FFN::FFN(): final objective of trained model is \" << out\n << \".\" << std::endl;\n return out;\n}\n\ntemplate\nvoid FFN::Forward(\n arma::mat inputs, arma::mat& results)\n{\n if (parameter.is_empty())\n ResetParameters();\n\n if (!deterministic)\n {\n deterministic = true;\n ResetDeterministic();\n }\n\n currentInput = std::move(inputs);\n Forward(std::move(currentInput));\n results = boost::apply_visitor(outputParameterVisitor, network.back());\n}\n\ntemplate\nvoid FFN::Forward(\n arma::mat inputs, arma::mat& results, const size_t begin, const size_t end)\n{\n boost::apply_visitor(ForwardVisitor(std::move(inputs), std::move(\n boost::apply_visitor(outputParameterVisitor, network[begin]))),\n network[begin]);\n\n for (size_t i = 1; i < end - begin + 1; ++i)\n {\n boost::apply_visitor(ForwardVisitor(std::move(boost::apply_visitor(\n outputParameterVisitor, network[begin + i - 1])), std::move(\n boost::apply_visitor(outputParameterVisitor, network[begin + i]))),\n network[begin + i]);\n }\n\n results = boost::apply_visitor(outputParameterVisitor, network[end]);\n}\n\ntemplate\ndouble FFN::Backward(\n arma::mat targets, arma::mat& gradients)\n{\n double res = outputLayer.Forward(std::move(boost::apply_visitor(\n outputParameterVisitor, network.back())), std::move(targets));\n\n for (size_t i = 0; i < network.size(); ++i)\n {\n res += boost::apply_visitor(lossVisitor, network[i]);\n }\n\n outputLayer.Backward(std::move(boost::apply_visitor(outputParameterVisitor,\n network.back())), std::move(targets), std::move(error));\n\n gradients = arma::zeros(parameter.n_rows, parameter.n_cols);\n\n Backward();\n ResetGradients(gradients);\n Gradient(std::move(currentInput));\n\n return res;\n}\n\ntemplate\nvoid FFN::Predict(\n arma::mat predictors, arma::mat& results)\n{\n if (parameter.is_empty())\n ResetParameters();\n\n if (!deterministic)\n {\n deterministic = true;\n ResetDeterministic();\n }\n\n arma::mat resultsTemp;\n Forward(std::move(arma::mat(predictors.colptr(0),\n predictors.n_rows, 1, false, true)));\n resultsTemp = boost::apply_visitor(outputParameterVisitor,\n network.back()).col(0);\n\n results = arma::mat(resultsTemp.n_elem, predictors.n_cols);\n results.col(0) = resultsTemp.col(0);\n\n for (size_t i = 1; i < predictors.n_cols; i++)\n {\n Forward(std::move(arma::mat(predictors.colptr(i),\n predictors.n_rows, 1, false, true)));\n\n resultsTemp = boost::apply_visitor(outputParameterVisitor,\n network.back());\n results.col(i) = resultsTemp.col(0);\n }\n}\n\ntemplate\ndouble FFN::Evaluate(\n arma::mat predictors, arma::mat responses)\n{\n if (parameter.is_empty())\n ResetParameters();\n\n if (!deterministic)\n {\n deterministic = true;\n ResetDeterministic();\n }\n\n Forward(std::move(predictors));\n\n double res = outputLayer.Forward(std::move(boost::apply_visitor(\n outputParameterVisitor, network.back())), std::move(responses));\n\n for (size_t i = 0; i < network.size(); ++i)\n {\n res += boost::apply_visitor(lossVisitor, network[i]);\n }\n\n return res;\n}\n\ntemplate\ndouble FFN::Evaluate(\n const arma::mat& parameters)\n{\n double res = 0;\n for (size_t i = 0; i < predictors.n_cols; ++i)\n res += Evaluate(parameters, i, 1, true);\n\n return res;\n}\n\ntemplate\ndouble FFN::Evaluate(\n const arma::mat& /* parameters */,\n const size_t begin,\n const size_t batchSize,\n const bool deterministic)\n{\n if (parameter.is_empty())\n ResetParameters();\n\n if (deterministic != this->deterministic)\n {\n this->deterministic = deterministic;\n ResetDeterministic();\n }\n\n Forward(std::move(predictors.cols(begin, begin + batchSize - 1)));\n double res = outputLayer.Forward(\n std::move(boost::apply_visitor(outputParameterVisitor, network.back())),\n std::move(responses.cols(begin, begin + batchSize - 1)));\n\n for (size_t i = 0; i < network.size(); ++i)\n {\n res += boost::apply_visitor(lossVisitor, network[i]);\n }\n\n return res;\n}\n\ntemplate\ndouble FFN::Evaluate(\n const arma::mat& parameters, const size_t begin, const size_t batchSize)\n{\n return Evaluate(parameters, begin, batchSize, true);\n}\n\ntemplate\ntemplate\ndouble FFN::\nEvaluateWithGradient(const arma::mat& parameters, GradType& gradient)\n{\n double res = 0;\n for (size_t i = 0; i < predictors.n_cols; ++i)\n res += EvaluateWithGradient(parameters, i, gradient, 1);\n\n return res;\n}\n\ntemplate\ntemplate\ndouble FFN::\nEvaluateWithGradient(const arma::mat& /* parameters */,\n const size_t begin,\n GradType& gradient,\n const size_t batchSize)\n{\n if (gradient.is_empty())\n {\n if (parameter.is_empty())\n ResetParameters();\n\n gradient = arma::zeros(parameter.n_rows, parameter.n_cols);\n }\n else\n {\n gradient.zeros();\n }\n\n if (this->deterministic)\n {\n this->deterministic = false;\n ResetDeterministic();\n }\n\n Forward(std::move(predictors.cols(begin, begin + batchSize - 1)));\n double res = outputLayer.Forward(\n std::move(boost::apply_visitor(outputParameterVisitor, network.back())),\n std::move(responses.cols(begin, begin + batchSize - 1)));\n\n for (size_t i = 0; i < network.size(); ++i)\n {\n res += boost::apply_visitor(lossVisitor, network[i]);\n }\n\n outputLayer.Backward(\n std::move(boost::apply_visitor(outputParameterVisitor, network.back())),\n std::move(responses.cols(begin, begin + batchSize - 1)),\n std::move(error));\n\n Backward();\n ResetGradients(gradient);\n Gradient(std::move(predictors.cols(begin, begin + batchSize - 1)));\n\n return res;\n}\n\ntemplate\nvoid FFN::Gradient(\n const arma::mat& parameters,\n const size_t begin,\n arma::mat& gradient,\n const size_t batchSize)\n{\n this->EvaluateWithGradient(parameters, begin, gradient, batchSize);\n}\n\ntemplate\nvoid FFN::Shuffle()\n{\n math::ShuffleData(predictors, responses, predictors, responses);\n}\n\ntemplate\nvoid FFN::ResetParameters()\n{\n ResetDeterministic();\n\n // Reset the network parameter with the given initialization rule.\n NetworkInitialization networkInit(initializeRule);\n networkInit.Initialize(network, parameter);\n}\n\ntemplate\nvoid FFN::ResetDeterministic()\n{\n DeterministicSetVisitor deterministicSetVisitor(deterministic);\n std::for_each(network.begin(), network.end(),\n boost::apply_visitor(deterministicSetVisitor));\n}\n\ntemplate\nvoid FFN::ResetGradients(arma::mat& gradient)\n{\n size_t offset = 0;\n for (size_t i = 0; i < network.size(); ++i)\n {\n offset += boost::apply_visitor(GradientSetVisitor(std::move(gradient),\n offset), network[i]);\n }\n}\n\ntemplate\nvoid FFN::Forward(arma::mat&& input)\n{\n boost::apply_visitor(ForwardVisitor(std::move(input), std::move(\n boost::apply_visitor(outputParameterVisitor, network.front()))),\n network.front());\n\n if (!reset)\n {\n if (boost::apply_visitor(outputWidthVisitor, network.front()) != 0)\n {\n width = boost::apply_visitor(outputWidthVisitor, network.front());\n }\n\n if (boost::apply_visitor(outputHeightVisitor, network.front()) != 0)\n {\n height = boost::apply_visitor(outputHeightVisitor, network.front());\n }\n }\n\n for (size_t i = 1; i < network.size(); ++i)\n {\n if (!reset)\n {\n // Set the input width.\n boost::apply_visitor(SetInputWidthVisitor(width), network[i]);\n\n // Set the input height.\n boost::apply_visitor(SetInputHeightVisitor(height), network[i]);\n }\n\n boost::apply_visitor(ForwardVisitor(std::move(boost::apply_visitor(\n outputParameterVisitor, network[i - 1])), std::move(\n boost::apply_visitor(outputParameterVisitor, network[i]))), network[i]);\n\n if (!reset)\n {\n // Get the output width.\n if (boost::apply_visitor(outputWidthVisitor, network[i]) != 0)\n {\n width = boost::apply_visitor(outputWidthVisitor, network[i]);\n }\n\n // Get the output height.\n if (boost::apply_visitor(outputHeightVisitor, network[i]) != 0)\n {\n height = boost::apply_visitor(outputHeightVisitor, network[i]);\n }\n }\n }\n\n if (!reset)\n reset = true;\n}\n\ntemplate\nvoid FFN::Backward()\n{\n boost::apply_visitor(BackwardVisitor(std::move(boost::apply_visitor(\n outputParameterVisitor, network.back())), std::move(error), std::move(\n boost::apply_visitor(deltaVisitor, network.back()))), network.back());\n\n for (size_t i = 2; i < network.size(); ++i)\n {\n boost::apply_visitor(BackwardVisitor(std::move(boost::apply_visitor(\n outputParameterVisitor, network[network.size() - i])), std::move(\n boost::apply_visitor(deltaVisitor, network[network.size() - i + 1])),\n std::move(boost::apply_visitor(deltaVisitor,\n network[network.size() - i]))), network[network.size() - i]);\n }\n}\n\ntemplate\nvoid FFN::Gradient(arma::mat&& input)\n{\n boost::apply_visitor(GradientVisitor(std::move(input), std::move(\n boost::apply_visitor(deltaVisitor, network[1]))), network.front());\n\n for (size_t i = 1; i < network.size() - 1; ++i)\n {\n boost::apply_visitor(GradientVisitor(std::move(boost::apply_visitor(\n outputParameterVisitor, network[i - 1])), std::move(\n boost::apply_visitor(deltaVisitor, network[i + 1]))), network[i]);\n }\n\n boost::apply_visitor(GradientVisitor(std::move(boost::apply_visitor(\n outputParameterVisitor, network[network.size() - 2])), std::move(error)),\n network[network.size() - 1]);\n}\n\ntemplate\ntemplate\nvoid FFN::serialize(\n Archive& ar, const unsigned int version)\n{\n ar & BOOST_SERIALIZATION_NVP(parameter);\n ar & BOOST_SERIALIZATION_NVP(width);\n ar & BOOST_SERIALIZATION_NVP(height);\n ar & BOOST_SERIALIZATION_NVP(currentInput);\n\n // Earlier versions of the FFN code did not serialize whether or not the model\n // was reset.\n if (version > 0)\n {\n ar & BOOST_SERIALIZATION_NVP(reset);\n }\n\n // Be sure to clear other layers before loading.\n if (Archive::is_loading::value)\n {\n std::for_each(network.begin(), network.end(),\n boost::apply_visitor(deleteVisitor));\n network.clear();\n }\n\n ar & BOOST_SERIALIZATION_NVP(network);\n\n // If we are loading, we need to initialize the weights.\n if (Archive::is_loading::value)\n {\n // The behavior in earlier versions was to always assume the weights needed\n // to be reset.\n if (version == 0)\n reset = false;\n\n size_t offset = 0;\n for (size_t i = 0; i < network.size(); ++i)\n {\n offset += boost::apply_visitor(WeightSetVisitor(std::move(parameter),\n offset), network[i]);\n\n boost::apply_visitor(resetVisitor, network[i]);\n }\n\n deterministic = true;\n ResetDeterministic();\n }\n}\n\ntemplate\nvoid FFN::Swap(FFN& network)\n{\n std::swap(outputLayer, network.outputLayer);\n std::swap(initializeRule, network.initializeRule);\n std::swap(width, network.width);\n std::swap(height, network.height);\n std::swap(reset, network.reset);\n std::swap(this->network, network.network);\n std::swap(predictors, network.predictors);\n std::swap(responses, network.responses);\n std::swap(parameter, network.parameter);\n std::swap(numFunctions, network.numFunctions);\n std::swap(error, network.error);\n std::swap(currentInput, network.currentInput);\n std::swap(deterministic, network.deterministic);\n std::swap(delta, network.delta);\n std::swap(inputParameter, network.inputParameter);\n std::swap(outputParameter, network.outputParameter);\n std::swap(gradient, network.gradient);\n};\n\ntemplate\nFFN::FFN(\n const FFN& network):\n outputLayer(network.outputLayer),\n initializeRule(network.initializeRule),\n width(network.width),\n height(network.height),\n reset(network.reset),\n predictors(network.predictors),\n responses(network.responses),\n parameter(network.parameter),\n numFunctions(network.numFunctions),\n error(network.error),\n currentInput(network.currentInput),\n deterministic(network.deterministic),\n delta(network.delta),\n inputParameter(network.inputParameter),\n outputParameter(network.outputParameter),\n gradient(network.gradient)\n{\n // Build new layers according to source network\n for (size_t i = 0; i < network.network.size(); ++i)\n {\n this->network.push_back(boost::apply_visitor(copyVisitor,\n network.network[i]));\n }\n};\n\ntemplate\nFFN::FFN(\n FFN&& network):\n outputLayer(std::move(network.outputLayer)),\n initializeRule(std::move(network.initializeRule)),\n width(network.width),\n height(network.height),\n reset(network.reset),\n predictors(std::move(network.predictors)),\n responses(std::move(network.responses)),\n parameter(std::move(network.parameter)),\n numFunctions(network.numFunctions),\n error(std::move(network.error)),\n currentInput(std::move(network.currentInput)),\n deterministic(network.deterministic),\n delta(std::move(network.delta)),\n inputParameter(std::move(network.inputParameter)),\n outputParameter(std::move(network.outputParameter)),\n gradient(std::move(network.gradient))\n{\n this->network = std::move(network.network);\n};\n\ntemplate\nFFN&\nFFN::operator = (FFN network)\n{\n Swap(network);\n return *this;\n};\n\n} // namespace ann\n} // namespace mlpack\n\n#endif\n", "meta": {"hexsha": "f39bbfdb130c4b4f6b304cd5b228ae6a47632d50", "size": 20346, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/mlpack/methods/ann/ffn_impl.hpp", "max_stars_repo_name": "tomjpsun/mlpack", "max_stars_repo_head_hexsha": "39b9a852c58b648ddb9b87a3d87aa3db2bacbf0a", "max_stars_repo_licenses": ["BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-01-11T14:14:30.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-11T14:14:30.000Z", "max_issues_repo_path": "src/mlpack/methods/ann/ffn_impl.hpp", "max_issues_repo_name": "tomjpsun/mlpack", "max_issues_repo_head_hexsha": "39b9a852c58b648ddb9b87a3d87aa3db2bacbf0a", "max_issues_repo_licenses": ["BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/mlpack/methods/ann/ffn_impl.hpp", "max_forks_repo_name": "tomjpsun/mlpack", "max_forks_repo_head_hexsha": "39b9a852c58b648ddb9b87a3d87aa3db2bacbf0a", "max_forks_repo_licenses": ["BSD-3-Clause-No-Nuclear-License-2014", "BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.790625, "max_line_length": 80, "alphanum_fraction": 0.7053474884, "num_tokens": 4651, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241632752915, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.23869078194630144}} {"text": "/*\n Copyright (C) 2020 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\nusing namespace std;\n\nnamespace QuantExt {\n\nSpreadedBlackVolatilitySurfaceMoneyness::SpreadedBlackVolatilitySurfaceMoneyness(\n const Handle& referenceVol, const Handle& movingSpot, const std::vector(val.str(0, std::ios_base::fmtflags(0)));\n}\ninline void eval_convert_to(char* result, const tommath_int& val)\n{\n *result = static_cast(boost::lexical_cast(val.str(0,\nstd::ios_base::fmtflags(0))));\n}\ninline void eval_convert_to(unsigned char* result, const tommath_int& val)\n{\n *result = static_cast(boost::lexical_cast(val.str(0,\nstd::ios_base::fmtflags(0))));\n}\ninline void eval_convert_to(signed char* result, const tommath_int& val)\n{\n *result = static_cast(boost::lexical_cast(val.str(0,\nstd::ios_base::fmtflags(0))));\n}\n*/\ninline void eval_abs(tommath_int &result, const tommath_int &val) {\n detail::check_tommath_result(\n mp_abs(const_cast<::mp_int *>(&val.data()), &result.data()));\n}\ninline void eval_gcd(tommath_int &result, const tommath_int &a,\n const tommath_int &b) {\n detail::check_tommath_result(mp_gcd(const_cast<::mp_int *>(&a.data()),\n const_cast<::mp_int *>(&b.data()),\n const_cast<::mp_int *>(&result.data())));\n}\ninline void eval_lcm(tommath_int &result, const tommath_int &a,\n const tommath_int &b) {\n detail::check_tommath_result(mp_lcm(const_cast<::mp_int *>(&a.data()),\n const_cast<::mp_int *>(&b.data()),\n const_cast<::mp_int *>(&result.data())));\n}\ninline void eval_powm(tommath_int &result, const tommath_int &base,\n const tommath_int &p, const tommath_int &m) {\n if (eval_get_sign(p) < 0) {\n BOOST_THROW_EXCEPTION(\n std::runtime_error(\"powm requires a positive exponent.\"));\n }\n detail::check_tommath_result(mp_exptmod(\n const_cast<::mp_int *>(&base.data()), const_cast<::mp_int *>(&p.data()),\n const_cast<::mp_int *>(&m.data()), &result.data()));\n}\n\ninline void eval_qr(const tommath_int &x, const tommath_int &y, tommath_int &q,\n tommath_int &r) {\n detail::check_tommath_result(mp_div(const_cast<::mp_int *>(&x.data()),\n const_cast<::mp_int *>(&y.data()),\n &q.data(), &r.data()));\n}\n\ninline unsigned eval_lsb(const tommath_int &val) {\n int c = eval_get_sign(val);\n if (c == 0) {\n BOOST_THROW_EXCEPTION(std::range_error(\"No bits were set in the operand.\"));\n }\n if (c < 0) {\n BOOST_THROW_EXCEPTION(\n std::range_error(\"Testing individual bits in negative values is not \"\n \"supported - results are undefined.\"));\n }\n return mp_cnt_lsb(const_cast<::mp_int *>(&val.data()));\n}\n\ninline unsigned eval_msb(const tommath_int &val) {\n int c = eval_get_sign(val);\n if (c == 0) {\n BOOST_THROW_EXCEPTION(std::range_error(\"No bits were set in the operand.\"));\n }\n if (c < 0) {\n BOOST_THROW_EXCEPTION(\n std::range_error(\"Testing individual bits in negative values is not \"\n \"supported - results are undefined.\"));\n }\n return mp_count_bits(const_cast<::mp_int *>(&val.data())) - 1;\n}\n\ntemplate \ninline typename enable_if, Integer>::type\neval_integer_modulus(const tommath_int &x, Integer val) {\n static const mp_digit m = (static_cast(1) << DIGIT_BIT) - 1;\n if (val <= m) {\n mp_digit d;\n detail::check_tommath_result(mp_mod_d(const_cast<::mp_int *>(&x.data()),\n static_cast(val), &d));\n return d;\n } else {\n return default_ops::eval_integer_modulus(x, val);\n }\n}\ntemplate \ninline typename enable_if, Integer>::type\neval_integer_modulus(const tommath_int &x, Integer val) {\n return eval_integer_modulus(x,\n boost::multiprecision::detail::unsigned_abs(val));\n}\n\ninline std::size_t hash_value(const tommath_int &val) {\n std::size_t result = 0;\n std::size_t len = val.data().used;\n for (std::size_t i = 0; i < len; ++i)\n boost::hash_combine(result, val.data().dp[i]);\n boost::hash_combine(result, val.data().sign);\n return result;\n}\n\n} // namespace backends\n\nusing boost::multiprecision::backends::tommath_int;\n\ntemplate <>\nstruct number_category : public mpl::int_ {};\n\ntypedef number tom_int;\ntypedef rational_adaptor tommath_rational;\ntypedef number tom_rational;\n\n} // namespace multiprecision\n} // namespace boost\n\nnamespace std {\n\ntemplate \nclass numeric_limits> {\n typedef boost::multiprecision::number\n number_type;\n\npublic:\n BOOST_STATIC_CONSTEXPR bool is_specialized = true;\n //\n // Largest and smallest numbers are bounded only by available memory, set\n // to zero:\n //\n static number_type(min)() { return number_type(); }\n static number_type(max)() { return number_type(); }\n static number_type lowest() { return (min)(); }\n BOOST_STATIC_CONSTEXPR int digits = INT_MAX;\n BOOST_STATIC_CONSTEXPR int digits10 = (INT_MAX / 1000) * 301L;\n BOOST_STATIC_CONSTEXPR int max_digits10 = digits10 + 3;\n BOOST_STATIC_CONSTEXPR bool is_signed = true;\n BOOST_STATIC_CONSTEXPR bool is_integer = true;\n BOOST_STATIC_CONSTEXPR bool is_exact = true;\n BOOST_STATIC_CONSTEXPR int radix = 2;\n static number_type epsilon() { return number_type(); }\n static number_type round_error() { return number_type(); }\n BOOST_STATIC_CONSTEXPR int min_exponent = 0;\n BOOST_STATIC_CONSTEXPR int min_exponent10 = 0;\n BOOST_STATIC_CONSTEXPR int max_exponent = 0;\n BOOST_STATIC_CONSTEXPR int max_exponent10 = 0;\n BOOST_STATIC_CONSTEXPR bool has_infinity = false;\n BOOST_STATIC_CONSTEXPR bool has_quiet_NaN = false;\n BOOST_STATIC_CONSTEXPR bool has_signaling_NaN = false;\n BOOST_STATIC_CONSTEXPR float_denorm_style has_denorm = denorm_absent;\n BOOST_STATIC_CONSTEXPR bool has_denorm_loss = false;\n static number_type infinity() { return number_type(); }\n static number_type quiet_NaN() { return number_type(); }\n static number_type signaling_NaN() { return number_type(); }\n static number_type denorm_min() { return number_type(); }\n BOOST_STATIC_CONSTEXPR bool is_iec559 = false;\n BOOST_STATIC_CONSTEXPR bool is_bounded = false;\n BOOST_STATIC_CONSTEXPR bool is_modulo = false;\n BOOST_STATIC_CONSTEXPR bool traps = false;\n BOOST_STATIC_CONSTEXPR bool tinyness_before = false;\n BOOST_STATIC_CONSTEXPR float_round_style round_style = round_toward_zero;\n};\n\n#ifndef BOOST_NO_INCLASS_MEMBER_INITIALIZATION\n\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits>::digits;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits>::digits10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits>::max_digits10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::is_signed;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::is_integer;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::is_exact;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits>::radix;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits>::min_exponent;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits>::min_exponent10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits>::max_exponent;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits>::max_exponent10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::has_infinity;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::has_quiet_NaN;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits<\n boost::multiprecision::number>::has_signaling_NaN;\ntemplate \nBOOST_CONSTEXPR_OR_CONST float_denorm_style\n numeric_limits>::has_denorm;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::has_denorm_loss;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::is_iec559;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::is_bounded;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::is_modulo;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::traps;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits>::tinyness_before;\ntemplate \nBOOST_CONSTEXPR_OR_CONST float_round_style\n numeric_limits>::round_style;\n\n#endif\n} // namespace std\n\n#endif\n", "meta": {"hexsha": "05df9d65c2c3ac65200b59751601f7f0b67cfe65", "size": 31274, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/boost_1_72_0/boost/multiprecision/tommath.hpp", "max_stars_repo_name": "henrywarhurst/matrix", "max_stars_repo_head_hexsha": "317a2a7c35c1c7e3730986668ad2270dc19809ef", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "libs/boost_1_72_0/boost/multiprecision/tommath.hpp", "max_issues_repo_name": "henrywarhurst/matrix", "max_issues_repo_head_hexsha": "317a2a7c35c1c7e3730986668ad2270dc19809ef", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "libs/boost_1_72_0/boost/multiprecision/tommath.hpp", "max_forks_repo_name": "henrywarhurst/matrix", "max_forks_repo_head_hexsha": "317a2a7c35c1c7e3730986668ad2270dc19809ef", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.1414267835, "max_line_length": 80, "alphanum_fraction": 0.6521071817, "num_tokens": 8001, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.2366694651278724}} {"text": "// Distributed under the MIT License.\n// See LICENSE.txt for details.\n\n#pragma once\n\n#include \n#include // IWYU pragma: keep\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"DataStructures/DataBox/Prefixes.hpp\"\n#include \"DataStructures/Tensor/Tensor.hpp\"\n#include \"DataStructures/Variables.hpp\"\n#include \"Domain/SizeOfElement.hpp\"\n#include \"Domain/Structure/Element.hpp\" // IWYU pragma: keep\n#include \"Domain/Structure/OrientationMapHelpers.hpp\"\n#include \"Domain/Tags.hpp\" // IWYU pragma: keep\n#include \"Evolution/DiscontinuousGalerkin/Limiters/HwenoImpl.hpp\"\n#include \"Evolution/DiscontinuousGalerkin/Limiters/MinmodTci.hpp\"\n#include \"Evolution/DiscontinuousGalerkin/Limiters/MinmodType.hpp\"\n#include \"Evolution/DiscontinuousGalerkin/Limiters/SimpleWenoImpl.hpp\"\n#include \"Evolution/DiscontinuousGalerkin/Limiters/WenoGridHelpers.hpp\"\n#include \"Evolution/DiscontinuousGalerkin/Limiters/WenoHelpers.hpp\"\n#include \"Evolution/DiscontinuousGalerkin/Limiters/WenoType.hpp\"\n#include \"NumericalAlgorithms/Interpolation/RegularGridInterpolant.hpp\"\n#include \"NumericalAlgorithms/LinearOperators/MeanValue.hpp\"\n#include \"NumericalAlgorithms/Spectral/Mesh.hpp\"\n#include \"Options/Options.hpp\"\n#include \"Utilities/Algorithm.hpp\"\n#include \"Utilities/ErrorHandling/Assert.hpp\"\n#include \"Utilities/Gsl.hpp\"\n#include \"Utilities/MakeArray.hpp\"\n#include \"Utilities/TMPL.hpp\"\n#include \"Utilities/TaggedTuple.hpp\"\n\n/// \\cond\ntemplate \nclass Direction;\ntemplate \nclass ElementId;\n\nnamespace PUP {\nclass er;\n} // namespace PUP\n\nnamespace Limiters {\ntemplate \nclass Weno;\n} // namespace Limiters\n/// \\endcond\n\nnamespace Limiters {\n/// \\ingroup LimitersGroup\n/// \\brief A compact-stencil WENO limiter for DG\n///\n/// Implements the simple WENO limiter of \\cite Zhong2013 and the Hermite WENO\n/// (HWENO) limiter of \\cite Zhu2016 for an arbitrary set of tensors. These\n/// limiters require communication only between nearest-neighbor elements, but\n/// preserve the full order of the DG solution when the solution is smooth.\n/// Full volume data is communicated between neighbors.\n///\n/// The limiter uses the minmod-based TVB troubled-cell indicator (TCI) of\n/// \\cite Cockburn1999 to identify elements that need limiting. The simple\n/// WENO implementation follows the paper: it checks the TCI independently\n/// to each tensor component, so that only certain tensor components may be\n/// limited. The HWENO implementation checks the TCI for all tensor components,\n/// and if any single component is troubled, then all components are limited.\n/// Note that the HWENO paper, because it specializes the limiter to the\n/// Newtonian Euler fluid system, uses a more sophisticated TCI that is adapted\n/// to the particulars of the fluid system. We instead use the TVB indicator\n/// because it is easily applied to a general set of tensors.\n///\n/// For each tensor component to limit, the new solution is obtained by WENO\n/// reconstruction --- a linear combination of the local DG solution and a\n/// \"modified\" solution from each neighbor element. For the simple WENO limiter,\n/// the modified solution is obtained by simply extrapolating the neighbor\n/// solution onto the troubled element. For the HWENO limiter, the modified\n/// solution is obtained by a least-squares fit to the solution across multiple\n/// neighboring elements.\n///\n/// To reconstruct the WENO solution from the local solution and the modified\n/// neighbor solutions, the standard WENO procedure is followed. We use the\n/// oscillation indicator of \\cite Dumbser2007, modified for use on the\n/// square/cube grids of SpECTRE. We favor this indicator because portions of\n/// the work can be precomputed, leading to an oscillation measure that is\n/// efficient to evaluate.\n///\n/// \\warning\n/// Limitations:\n/// - Does not support non-Legendre bases; this is ASSERTed.\n/// - Does not support h- or p-refinement; this is ASSERTed.\n/// - Does not support curved elements; this is not enforced.\ntemplate \nclass Weno> {\n public:\n /// \\brief The WenoType\n ///\n /// One of `Limiters::WenoType`. See the `Limiters::Weno`\n /// documentation for details.\n struct Type {\n using type = WenoType;\n static constexpr Options::String help = {\"Type of WENO limiter\"};\n };\n /// \\brief The linear weight given to each neighbor\n ///\n /// This linear weight gets combined with the oscillation indicator to\n /// compute the weight for each WENO estimated solution. The standard value\n /// in the literature is 0.001; larger values may be better suited for\n /// problems with strong shocks, and smaller values may be better suited to\n /// smooth problems.\n struct NeighborWeight {\n using type = double;\n static type lower_bound() noexcept { return 1e-6; }\n static type upper_bound() noexcept { return 0.1; }\n static constexpr Options::String help = {\n \"Linear weight for each neighbor element's solution\"};\n };\n /// \\brief The TVB constant for the minmod TCI\n ///\n /// See `Limiters::Minmod` documentation for details.\n struct TvbConstant {\n using type = double;\n static type lower_bound() noexcept { return 0.0; }\n static constexpr Options::String help = {\"TVB constant 'm'\"};\n };\n /// \\brief Turn the limiter off\n ///\n /// This option exists to temporarily disable the limiter for debugging\n /// purposes. For problems where limiting is not needed, the preferred\n /// approach is to not compile the limiter into the executable.\n struct DisableForDebugging {\n using type = bool;\n static type suggested_value() noexcept { return false; }\n static constexpr Options::String help = {\"Disable the limiter\"};\n };\n using options =\n tmpl::list;\n static constexpr Options::String help = {\"A WENO limiter for DG\"};\n\n Weno(WenoType weno_type, double neighbor_linear_weight, double tvb_constant,\n bool disable_for_debugging = false) noexcept;\n\n Weno() noexcept = default;\n Weno(const Weno& /*rhs*/) = default;\n Weno& operator=(const Weno& /*rhs*/) = default;\n Weno(Weno&& /*rhs*/) noexcept = default;\n Weno& operator=(Weno&& /*rhs*/) noexcept = default;\n ~Weno() = default;\n\n // NOLINTNEXTLINE(google-runtime-references)\n void pup(PUP::er& p) noexcept;\n\n /// \\brief Data to send to neighbor elements\n struct PackagedData {\n Variables> volume_data;\n tuples::TaggedTuple<::Tags::Mean...> means;\n Mesh mesh;\n std::array element_size =\n make_array(std::numeric_limits::signaling_NaN());\n\n // NOLINTNEXTLINE(google-runtime-references)\n void pup(PUP::er& p) noexcept {\n p | volume_data;\n p | means;\n p | mesh;\n p | element_size;\n }\n };\n\n using package_argument_tags =\n tmpl::list,\n domain::Tags::SizeOfElement>;\n\n /// \\brief Package data for sending to neighbor elements\n void package_data(gsl::not_null packaged_data,\n const typename Tags::type&... tensors,\n const Mesh& mesh,\n const std::array& element_size,\n const OrientationMap& orientation_map) const\n noexcept;\n\n using limit_tags = tmpl::list;\n using limit_argument_tags =\n tmpl::list,\n domain::Tags::Element,\n domain::Tags::SizeOfElement>;\n\n /// \\brief Limit the solution on the element\n bool operator()(\n const gsl::not_null>... tensors,\n const Mesh& mesh, const Element& element,\n const std::array& element_size,\n const std::unordered_map<\n std::pair, ElementId>, PackagedData,\n boost::hash, ElementId>>>&\n neighbor_data) const noexcept;\n\n private:\n template \n // NOLINTNEXTLINE(readability-redundant-declaration) false positive\n friend bool operator==(const Weno& lhs,\n const Weno& rhs) noexcept;\n\n WenoType weno_type_;\n double neighbor_linear_weight_;\n double tvb_constant_;\n bool disable_for_debugging_;\n};\n\ntemplate \nWeno>::Weno(\n const WenoType weno_type, const double neighbor_linear_weight,\n const double tvb_constant, const bool disable_for_debugging) noexcept\n : weno_type_(weno_type),\n neighbor_linear_weight_(neighbor_linear_weight),\n tvb_constant_(tvb_constant),\n disable_for_debugging_(disable_for_debugging) {}\n\ntemplate \n// NOLINTNEXTLINE(google-runtime-references)\nvoid Weno>::pup(PUP::er& p) noexcept {\n p | weno_type_;\n p | neighbor_linear_weight_;\n p | tvb_constant_;\n p | disable_for_debugging_;\n}\n\ntemplate \nvoid Weno>::package_data(\n const gsl::not_null packaged_data,\n const typename Tags::type&... tensors, const Mesh& mesh,\n const std::array& element_size,\n const OrientationMap& orientation_map) const noexcept {\n // By always initializing the PackagedData Variables member, we avoid an\n // assertion that arises from having a default-constructed Variables in a\n // disabled limiter. There is a performance cost, because the package_data()\n // function does non-zero work even for a disabled limiter... but since the\n // limiter should never be disabled in a production simulation, this cost\n // should never matter.\n (packaged_data->volume_data).initialize(mesh.number_of_grid_points());\n\n if (UNLIKELY(disable_for_debugging_)) {\n // Do not initialize packaged_data\n // (except for the Variables member \"volume_data\", see above)\n return;\n }\n\n const auto wrap_compute_means = [&mesh, &packaged_data](\n auto tag, const auto tensor) noexcept {\n for (size_t i = 0; i < tensor.size(); ++i) {\n // Compute the mean using the local orientation of the tensor and mesh.\n get<::Tags::Mean>(packaged_data->means)[i] =\n mean_value(tensor[i], mesh);\n }\n return '0';\n };\n expand_pack(wrap_compute_means(Tags{}, tensors)...);\n\n packaged_data->element_size =\n orientation_map.permute_from_neighbor(element_size);\n\n const auto wrap_copy_tensor = [&packaged_data](auto tag,\n const auto tensor) noexcept {\n get(packaged_data->volume_data) = tensor;\n return '0';\n };\n expand_pack(wrap_copy_tensor(Tags{}, tensors)...);\n packaged_data->volume_data = orient_variables(\n packaged_data->volume_data, mesh.extents(), orientation_map);\n\n packaged_data->mesh = orientation_map(mesh);\n}\n\ntemplate \nbool Weno>::operator()(\n const gsl::not_null>... tensors,\n const Mesh& mesh, const Element& element,\n const std::array& element_size,\n const std::unordered_map<\n std::pair, ElementId>, PackagedData,\n boost::hash, ElementId>>>&\n neighbor_data) const noexcept {\n if (UNLIKELY(disable_for_debugging_)) {\n // Do not modify input tensors\n return false;\n }\n\n // Check that basis is LGL or LG\n // A Legendre basis is assumed for the oscillation indicator (used in both\n // SimpleWeno and Hweno) and in the Hweno reconstruction.\n ASSERT(mesh.basis() == make_array(Spectral::Basis::Legendre),\n \"Unsupported basis: \" << mesh);\n ASSERT(mesh.quadrature() ==\n make_array(Spectral::Quadrature::GaussLobatto) or\n mesh.quadrature() ==\n make_array(Spectral::Quadrature::Gauss),\n \"Unsupported quadrature: \" << mesh);\n\n // Enforce restrictions on h-refinement, p-refinement\n if (UNLIKELY(alg::any_of(element.neighbors(),\n [](const auto& direction_neighbors) noexcept {\n return direction_neighbors.second.size() != 1;\n }))) {\n ERROR(\"The Weno limiter does not yet support h-refinement\");\n // Removing this limitation will require:\n // - Generalizing the computation of the modified neighbor solutions.\n // - Generalizing the WENO weighted sum for multiple neighbors in each\n // direction.\n }\n alg::for_each(neighbor_data, [&mesh](const auto& neighbor_and_data) noexcept {\n if (UNLIKELY(neighbor_and_data.second.mesh != mesh)) {\n ERROR(\"The Weno limiter does not yet support p-refinement\");\n // Removing this limitation will require generalizing the\n // computation of the modified neighbor solutions.\n }\n });\n\n if (weno_type_ == WenoType::Hweno) {\n // Troubled-cell detection for HWENO flags the element for limiting if any\n // component of any tensor needs limiting.\n const bool cell_is_troubled =\n Tci::tvb_minmod_indicator(\n tvb_constant_, (*tensors)..., mesh, element, element_size,\n neighbor_data);\n if (not cell_is_troubled) {\n // No limiting is needed\n return false;\n }\n\n std::unordered_map<\n std::pair, ElementId>, DataVector,\n boost::hash, ElementId>>>\n modified_neighbor_solution_buffer{};\n for (const auto& neighbor_and_data : neighbor_data) {\n const auto& neighbor = neighbor_and_data.first;\n modified_neighbor_solution_buffer.insert(\n make_pair(neighbor, DataVector(mesh.number_of_grid_points())));\n }\n\n EXPAND_PACK_LEFT_TO_RIGHT(Weno_detail::hweno_impl(\n make_not_null(&modified_neighbor_solution_buffer), tensors,\n neighbor_linear_weight_, mesh, element, neighbor_data));\n return true; // cell_is_troubled\n\n } else if (weno_type_ == WenoType::SimpleWeno) {\n // Buffers and pre-computations for TCI\n Minmod_detail::BufferWrapper tci_buffer(mesh);\n const auto effective_neighbor_sizes =\n Minmod_detail::compute_effective_neighbor_sizes(element, neighbor_data);\n\n // Buffers for simple WENO implementation\n std::unordered_map<\n std::pair, ElementId>,\n intrp::RegularGrid,\n boost::hash, ElementId>>>\n interpolator_buffer{};\n std::unordered_map<\n std::pair, ElementId>, DataVector,\n boost::hash, ElementId>>>\n modified_neighbor_solution_buffer{};\n\n bool some_component_was_limited = false;\n\n const auto wrap_minmod_tci_and_simple_weno_impl =\n [this, &some_component_was_limited, &tci_buffer, &interpolator_buffer,\n &modified_neighbor_solution_buffer, &mesh, &element, &element_size,\n &neighbor_data,\n &effective_neighbor_sizes](auto tag, const auto tensor) noexcept {\n for (size_t tensor_storage_index = 0;\n tensor_storage_index < tensor->size(); ++tensor_storage_index) {\n // Check TCI\n const auto effective_neighbor_means =\n Minmod_detail::compute_effective_neighbor_means(\n tensor_storage_index, element, neighbor_data);\n const bool component_needs_limiting = Tci::tvb_minmod_indicator(\n make_not_null(&tci_buffer), tvb_constant_,\n (*tensor)[tensor_storage_index], mesh, element, element_size,\n effective_neighbor_means, effective_neighbor_sizes);\n\n if (component_needs_limiting) {\n if (modified_neighbor_solution_buffer.empty()) {\n // Allocate the neighbor solution buffers only if the limiter is\n // triggered. This reduces allocation when no limiting occurs.\n for (const auto& neighbor_and_data : neighbor_data) {\n const auto& neighbor = neighbor_and_data.first;\n modified_neighbor_solution_buffer.insert(make_pair(\n neighbor, DataVector(mesh.number_of_grid_points())));\n }\n }\n Weno_detail::simple_weno_impl(\n make_not_null(&interpolator_buffer),\n make_not_null(&modified_neighbor_solution_buffer), tensor,\n neighbor_linear_weight_, tensor_storage_index, mesh, element,\n neighbor_data);\n some_component_was_limited = true;\n }\n }\n return '0';\n };\n expand_pack(wrap_minmod_tci_and_simple_weno_impl(Tags{}, tensors)...);\n return some_component_was_limited; // cell_is_troubled\n } else {\n ERROR(\"WENO limiter not implemented for WenoType: \" << weno_type_);\n }\n\n return false; // cell_is_troubled\n}\n\ntemplate \nbool operator==(const Weno& lhs,\n const Weno& rhs) noexcept {\n return lhs.weno_type_ == rhs.weno_type_ and\n lhs.neighbor_linear_weight_ == rhs.neighbor_linear_weight_ and\n lhs.tvb_constant_ == rhs.tvb_constant_ and\n lhs.disable_for_debugging_ == rhs.disable_for_debugging_;\n}\n\ntemplate \nbool operator!=(const Weno& lhs,\n const Weno& rhs) noexcept {\n return not(lhs == rhs);\n}\n\n} // namespace Limiters\n", "meta": {"hexsha": "29dda685619d139435d68579e458aa38036f9e48", "size": 18203, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/Evolution/DiscontinuousGalerkin/Limiters/Weno.hpp", "max_stars_repo_name": "trami18/spectre", "max_stars_repo_head_hexsha": "6b1f6497bf2e26d1474bfadf143b3321942c40b4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2021-04-11T04:07:42.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-11T05:07:54.000Z", "max_issues_repo_path": "src/Evolution/DiscontinuousGalerkin/Limiters/Weno.hpp", "max_issues_repo_name": "trami18/spectre", "max_issues_repo_head_hexsha": "6b1f6497bf2e26d1474bfadf143b3321942c40b4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2022-03-25T18:26:16.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-25T19:30:39.000Z", "max_forks_repo_path": "src/Evolution/DiscontinuousGalerkin/Limiters/Weno.hpp", "max_forks_repo_name": "isaaclegred/spectre", "max_forks_repo_head_hexsha": "5765da85dad680cad992daccd479376c67458a8c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.4312354312, "max_line_length": 80, "alphanum_fraction": 0.6995000824, "num_tokens": 4233, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548646660542, "lm_q2_score": 0.3629692055196168, "lm_q1q2_score": 0.23642175773917523}} {"text": "//=============================================================================================================\n/**\n* @file mne_project_to_surface.cpp\n* @author Jana Kiesel ;\n* Matti Hamalainen \n* @version 1.0\n* @date August, 2016\n*\n* @section LICENSE\n*\n* Copyright (C) 2016, Jana Kiesel and Matti Hamalainen. All rights reserved.\n*\n* Redistribution and use in source and binary forms, with or without modification, are permitted provided that\n* the following conditions are met:\n* * Redistributions of source code must retain the above copyright notice, this list of conditions and the\n* following disclaimer.\n* * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and\n* the following disclaimer in the documentation and/or other materials provided with the distribution.\n* * Neither the name of MNE-CPP authors nor the names of its contributors may be used\n* to endorse or promote products derived from this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED\n* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\n* PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,\n* INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\n* HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n* NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n*\n* @brief MNEProjectToSurface class definition.\n*\n*/\n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// INCLUDES\n//=============================================================================================================\n\n#include \"mne_project_to_surface.h\"\n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// INCLUDES\n//=============================================================================================================\n\n#include \n#include \n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// QT INCLUDES\n//=============================================================================================================\n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// Eigen INCLUDES\n//=============================================================================================================\n\n#include \n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// USED NAMESPACES\n//=============================================================================================================\n\nusing namespace MNELIB;\nusing namespace Eigen;\n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// DEFINE GLOBAL METHODS\n//=============================================================================================================\n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// DEFINE MEMBER METHODS\n//=============================================================================================================\n\nMNEProjectToSurface::MNEProjectToSurface()\n: r1(MatrixX3f::Zero(1,3))\n, r12(MatrixX3f::Zero(1,3))\n, r13(MatrixX3f::Zero(1,3))\n, nn(MatrixX3f::Zero(1,3))\n, a(VectorXf::Zero(1))\n, b(VectorXf::Zero(1))\n, c(VectorXf::Zero(1))\n, det(VectorXf::Zero(1))\n{\n\n}\n\n\n//*************************************************************************************************************\n\nMNEProjectToSurface::MNEProjectToSurface(const MNEBemSurface &p_MNEBemSurf)\n: r1(MatrixX3f::Zero(p_MNEBemSurf.ntri,3))\n, r12(MatrixX3f::Zero(p_MNEBemSurf.ntri,3))\n, r13(MatrixX3f::Zero(p_MNEBemSurf.ntri,3))\n, nn(MatrixX3f::Zero(p_MNEBemSurf.ntri,3))\n, a(VectorXf::Zero(p_MNEBemSurf.ntri))\n, b(VectorXf::Zero(p_MNEBemSurf.ntri))\n, c(VectorXf::Zero(p_MNEBemSurf.ntri))\n, det(VectorXf::Zero(p_MNEBemSurf.ntri))\n{\n for (int i = 0; i < p_MNEBemSurf.ntri; ++i)\n {\n r1.row(i) = p_MNEBemSurf.rr.row(p_MNEBemSurf.tris(i,0));\n r12.row(i) = p_MNEBemSurf.rr.row(p_MNEBemSurf.tris(i,1)) - r1.row(i);\n r13.row(i) = p_MNEBemSurf.rr.row(p_MNEBemSurf.tris(i,2)) - r1.row(i);\n a(i) = r12.row(i) * r12.row(i).transpose();\n b(i) = r13.row(i) * r13.row(i).transpose();\n c(i) = r12.row(i) * r13.row(i).transpose();\n }\n\n if (!(p_MNEBemSurf.tri_nn.isZero(0)))\n {\n nn = p_MNEBemSurf.tri_nn.cast();\n }\n else\n {\n for (int i = 0; i < p_MNEBemSurf.ntri; ++i)\n {\n nn.row(i) = r12.row(i).transpose().cross(r13.row(i).transpose()).transpose();\n }\n }\n det = (a.array()*b.array() - c.array()*c.array()).matrix();\n}\n\n\n//*************************************************************************************************************\n\nMNEProjectToSurface::MNEProjectToSurface(const MNESurface &p_MNESurf)\n: r1(MatrixX3f::Zero(p_MNESurf.ntri,3))\n, r12(MatrixX3f::Zero(p_MNESurf.ntri,3))\n, r13(MatrixX3f::Zero(p_MNESurf.ntri,3))\n, nn(MatrixX3f::Zero(p_MNESurf.ntri,3))\n, a(VectorXf::Zero(p_MNESurf.ntri))\n, b(VectorXf::Zero(p_MNESurf.ntri))\n, c(VectorXf::Zero(p_MNESurf.ntri))\n, det(VectorXf::Zero(p_MNESurf.ntri))\n{\n for (int i = 0; i < p_MNESurf.ntri; ++i)\n {\n r1.row(i) = p_MNESurf.rr.row(p_MNESurf.tris(i,0));\n r12.row(i) = p_MNESurf.rr.row(p_MNESurf.tris(i,1)) - r1.row(i);\n r13.row(i) = p_MNESurf.rr.row(p_MNESurf.tris(i,2)) - r1.row(i);\n nn.row(i) = r12.row(i).transpose().cross(r13.row(i).transpose()).transpose();\n a(i) = r12.row(i) * r12.row(i).transpose();\n b(i) = r13.row(i) * r13.row(i).transpose();\n c(i) = r12.row(i) * r13.row(i).transpose();\n }\n\n det = (a.array()*b.array() - c.array()*c.array()).matrix();\n}\n\n\n//*************************************************************************************************************\n\nbool MNEProjectToSurface::mne_find_closest_on_surface(const MatrixXf &r, const int np, MatrixXf &rTri,\n VectorXi &nearest, VectorXf &dist)\n{\n nearest.resize(np);\n dist.resize(np);\n if (this->r1.isZero(0))\n {\n qDebug() << \"No surface loaded to make the projection./n\";\n return false;\n }\n int bestTri = -1;\n float bestDist = -1;\n Vector3f rTriK;\n for (int k = 0; k < np; ++k)\n {\n /*\n * To do: decide_search_restriction for the use in an iterative closest point to plane algorithm\n * For now it's OK to go through all triangles.\n */\n if (!this->mne_project_to_surface(r.row(k).transpose(), rTriK, bestTri, bestDist))\n {\n qDebug() << \"The projection of point number \" << k << \" didn't work./n\";\n return false;\n }\n rTri.row(k) = rTriK.transpose();\n nearest[k] = bestTri;\n dist[k] = bestDist;\n }\n return true;\n}\n\n\n//*************************************************************************************************************\n\nbool MNEProjectToSurface::mne_project_to_surface(const Vector3f &r, Vector3f &rTri, int &bestTri, float &bestDist)\n{\n float p = 0, q = 0, p0 = 0, q0 = 0, dist0 = 0;\n bestDist = 0.0f;\n bestTri = -1;\n for (int tri = 0; tri < a .size(); ++tri)\n {\n if (!this->nearest_triangle_point(r, tri, p0, q0, dist0))\n {\n qDebug() << \"The projection on triangle \" << tri << \" didn't work./n\";\n return false;\n }\n\n if ((bestTri < 0) || (std::fabs(dist0) < std::fabs(bestDist)))\n {\n bestDist = dist0;\n p = p0;\n q = q0;\n bestTri = tri;\n }\n }\n\n if (bestTri >= 0)\n {\n if (!this->project_to_triangle(rTri, p, q, bestTri))\n {\n qDebug() << \"The coordinate transform to cartesian system didn't work./n\";\n return false;\n }\n return true;\n }\n\n qDebug() << \"No best Triangle found./n\";\n return false;\n}\n\n\n//*************************************************************************************************************\n\nbool MNEProjectToSurface::nearest_triangle_point(const Vector3f &r, const int tri, float &p, float &q, float &dist)\n{\n //Calculate some helpers\n Vector3f rr = r - this->r1.row(tri).transpose(); //Vector from triangle corner #1 to r\n float v1 = this->r12.row(tri)*rr;\n float v2 = this->r13.row(tri)*rr;\n\n //Calculate the orthogonal projection of the point r on the plane\n dist = this->nn.row(tri)*rr;\n p = (this->b(tri)*v1 - this->c(tri)*v2)/det(tri);\n q = (this->a(tri)*v2 - this->c(tri)*v1)/det(tri);\n\n //If the point projects into the triangle we are done\n if (p >= 0.0 && p <= 1.0 && q >= 0.0 && q <= 1.0 && (p+q) <= 1.0)\n {\n return true;\n }\n\n /*\n * Tough: must investigate the sides\n * We might do something intelligent here. However, for now it is ok\n * to do it in the hard way\n */\n float p0, q0, t0, dist0, best, bestp, bestq;\n\n /*\n * Side 1 -> 2\n */\n p0 = p + (q * this->c(tri)) / this->a(tri);\n // Place the point in the corner if it is not on the side\n if (p0 < 0.0)\n {\n p0 = 0.0;\n }\n else if (p0 > 1.0)\n {\n p0 = 1.0;\n }\n q0 = 0;\n // Distance\n dist0 = sqrt((p-p0)*(p-p0)*this->a(tri) +\n (q-q0)*(q-q0)*this->b(tri) +\n 2*(p-p0)*(q-q0)*this->c(tri) +\n dist*dist);\n\n best = dist0;\n bestp = p0;\n bestq = q0;\n /*\n * Side 2 -> 3\n */\n t0 = ((a(tri)-c(tri))*(-p) + (b(tri)-c(tri))*q)/(a(tri)+b(tri)-2*c(tri));\n // Place the point in the corner if it is not on the side\n if (t0 < 0.0)\n {\n t0 = 0.0;\n }\n else if (t0 > 1.0)\n {\n t0 = 1.0;\n }\n p0 = 1.0 - t0;\n q0 = t0;\n // Distance\n dist0 = sqrt((p-p0)*(p-p0)*this->a(tri) +\n (q-q0)*(q-q0)*this->b(tri) +\n 2*(p-p0)*(q-q0)*this->c(tri) +\n dist*dist);\n if (dist0 < best)\n {\n best = dist0;\n bestp = p0;\n bestq = q0;\n }\n /*\n * Side 1 -> 3\n */\n p0 = 0.0;\n q0 = q + (p * c(tri))/b(tri);\n // Place the point in the corner if it is not on the side\n if (q0 < 0.0)\n {\n q0 = 0.0;\n\n }\n else if (q0 > 1.0)\n {\n q0 = 1.0;\n }\n // Distance\n dist0 = sqrt((p-p0)*(p-p0)*this->a(tri) +\n (q-q0)*(q-q0)*this->b(tri) +\n 2*(p-p0)*(q-q0)*this->c(tri) +\n dist*dist);\n if (dist0 < best)\n {\n best = dist0;\n bestp = p0;\n bestq = q0;\n }\n dist = best;\n p = bestp;\n q = bestq;\n return true;\n}\n\n\n//*************************************************************************************************************\n\nbool MNEProjectToSurface::project_to_triangle(Vector3f &rTri, const float p, const float q, const int tri)\n{\n rTri = this->r1.row(tri) + p*this->r12.row(tri) + q*this->r13.row(tri);\n return true;\n}\n", "meta": {"hexsha": "84713ba83b656e77f04261c025ef40951c1d3770", "size": 12451, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libraries/mne/mne_project_to_surface.cpp", "max_stars_repo_name": "ChunmingGu/mne-cpp-master", "max_stars_repo_head_hexsha": "36f21b3ab0c65a133027da83fa8e2a652acd1485", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-05-14T07:38:25.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-14T07:38:25.000Z", "max_issues_repo_path": "libraries/mne/mne_project_to_surface.cpp", "max_issues_repo_name": "ChunmingGu/mne-cpp-master", "max_issues_repo_head_hexsha": "36f21b3ab0c65a133027da83fa8e2a652acd1485", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2018-08-23T12:40:56.000Z", "max_issues_repo_issues_event_max_datetime": "2018-08-23T12:40:56.000Z", "max_forks_repo_path": "libraries/mne/mne_project_to_surface.cpp", "max_forks_repo_name": "ChunmingGu/mne-cpp-master", "max_forks_repo_head_hexsha": "36f21b3ab0c65a133027da83fa8e2a652acd1485", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.0732394366, "max_line_length": 116, "alphanum_fraction": 0.4358686049, "num_tokens": 3081, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704796847396, "lm_q2_score": 0.4263215925474903, "lm_q1q2_score": 0.23638273791976905}} {"text": "// Copyright (C) 2011-2012 by the BEM++ Authors\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n#ifndef fiber_numerical_quadrature_hpp\n#define fiber_numerical_quadrature_hpp\n\n#include \"../common/common.hpp\"\n\n/** \\file\n *\n * Low-level functions filling arrays of quadrature points and weights. */\n\n#include \"element_pair_topology.hpp\"\n\n#include \"../common/armadillo_fwd.hpp\"\n#include \n#include \n\nnamespace Fiber\n{\n\nstruct SingleQuadratureDescriptor\n{\n int vertexCount;\n int order;\n\n bool operator<(const SingleQuadratureDescriptor& other) const {\n return std::make_pair(vertexCount, order) <\n std::make_pair(other.vertexCount, other.order);\n }\n\n bool operator==(const SingleQuadratureDescriptor& other) const {\n return vertexCount == other.vertexCount &&\n order == other.order;\n }\n\n bool operator!=(const SingleQuadratureDescriptor& other) const {\n return !operator==(other);\n }\n\n friend std::ostream&\n operator<< (std::ostream& dest, const SingleQuadratureDescriptor& obj)\n {\n dest << obj.vertexCount << \" \" << obj.order;\n return dest;\n }\n};\n\ninline size_t tbb_hasher(const SingleQuadratureDescriptor& d)\n{\n return (d.vertexCount - 3) + 2 * d.order;\n}\n\nstruct DoubleQuadratureDescriptor\n{\n ElementPairTopology topology;\n int testOrder;\n int trialOrder;\n\n bool operator<(const DoubleQuadratureDescriptor& other) const {\n using boost::tuples::make_tuple;\n return make_tuple(topology, testOrder, trialOrder) <\n make_tuple(other.topology, other.testOrder, other.trialOrder);\n }\n\n bool operator==(const DoubleQuadratureDescriptor& other) const {\n return topology == other.topology &&\n testOrder == other.testOrder &&\n trialOrder == other.trialOrder;\n }\n\n bool operator!=(const DoubleQuadratureDescriptor& other) const {\n return !operator==(other);\n }\n\n friend std::ostream&\n operator<< (std::ostream& dest, const DoubleQuadratureDescriptor& obj)\n {\n dest << obj.topology << \" \" << obj.testOrder << \" \" << obj.trialOrder;\n return dest;\n }\n};\n\ninline size_t tbb_hasher(const DoubleQuadratureDescriptor& d)\n{\n const ElementPairTopology& t = d.topology;\n return (t.testVertexCount - 3) + 2 *\n ((t.trialVertexCount - 3) + 2 *\n (t.testSharedVertex0 + 4 *\n (t.trialSharedVertex0 + 4 *\n (t.testSharedVertex1 + 4 *\n (t.trialSharedVertex1 + 4 *\n (d.testOrder + 256 *\n d.trialOrder))))));\n}\n\n/** \\brief Retrieve points and weights for a quadrature over a single element.\n *\n * \\param[in] elementCornerCount\n * Number of corners of the element to be integrated on.\n * \\param[in] accuracyOrder\n * Accuracy order of the quadrature, i.e. its degree of exactness.\n * \\param[out] points\n * Quadrature points.\n * \\param[out] weights\n * Quadrature weights. */\ntemplate \nvoid fillSingleQuadraturePointsAndWeights(int elementCornerCount,\n int accuracyOrder,\n arma::Mat& points,\n std::vector& weights);\n\ntemplate \nvoid fillDoubleSingularQuadraturePointsAndWeights(\n const DoubleQuadratureDescriptor& desc,\n arma::Mat& testPoints,\n arma::Mat& trialPoints,\n std::vector& weights);\n\n} // namespace Fiber\n\n#endif\n", "meta": {"hexsha": "6eee5fa07300d7f30d644088a077f76b4fd985ad", "size": 4650, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lib/fiber/numerical_quadrature.hpp", "max_stars_repo_name": "nicolas-chaulet/bempp", "max_stars_repo_head_hexsha": "0f5cc72e0e542437e787db5704978456b0ad9e35", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2021-07-22T13:34:28.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-22T13:35:20.000Z", "max_issues_repo_path": "lib/fiber/numerical_quadrature.hpp", "max_issues_repo_name": "UCL/bempp", "max_issues_repo_head_hexsha": "f768ec7d319c02d6e0142512fb61db0607cadf10", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/fiber/numerical_quadrature.hpp", "max_forks_repo_name": "UCL/bempp", "max_forks_repo_head_hexsha": "f768ec7d319c02d6e0142512fb61db0607cadf10", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-05-17T09:46:44.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-17T09:46:44.000Z", "avg_line_length": 33.2142857143, "max_line_length": 80, "alphanum_fraction": 0.668172043, "num_tokens": 1038, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.2363827316424768}} {"text": "\n#pragma once\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace verilog\n{\n namespace bdd {\n\n enum class NegP {\n Positive, Negative\n };\n\n enum class Type {\n Zero, One, Input\n };\n\n struct Node {\n std::string input_name;\n Type t;\n };\n\n typedef boost::adjacency_list<\n boost::setS,\n boost::vecS,\n boost::bidirectionalS,\n Node, NegP> GD;\n\n typedef unsigned int hashcode;\n\n struct BDD {\n GD graph;\n\n GD::vertex_descriptor \n /**\n * The nodes representing the sinks of the BDD\n */\n zero,\n one,\n /**\n * The node representing the source of the bdd\n */\n source;\n\n std::map > bdd_heap;\n\n BDD() {\n zero = boost::add_vertex(graph);\n one = boost::add_vertex(graph);\n graph[zero].t = Type::Zero;\n graph[one].t = Type::One;\n }\n\n BDD(const std::string name) {\n zero = boost::add_vertex(graph);\n one = boost::add_vertex(graph);\n source = add_vertex(name);\n\n add_edge(source, one, NegP::Positive);\n add_edge(source, zero, NegP::Negative);\n\n graph[zero].t = Type::Zero;\n graph[one].t = Type::One;\n }\n\n /**\n * Creates a copy of a,\n * merges all nodes of b into the copy.\n * calls the conjunction_internal function for the copy.\n */\n\n void copy_graph(const BDD & o, \n std::map & node_map\n ) {\n\n GD::vertex_iterator v, vend;\n for(boost::tie(v, vend) = boost::vertices(o.graph);\n v != vend; ++v) {\n if (o.graph[*v].t == Type::Input) {\n GD::vertex_descriptor n = add_vertex(o.graph[*v].input_name);\n node_map[*v] = n;\n }\n }\n \n GD::edge_iterator e, eend;\n for(boost::tie(e, eend) = boost::edges(o.graph);\n e != eend; ++e) {\n GD::vertex_descriptor s = boost::source(*e, o.graph);\n\n GD::vertex_descriptor x;\n GD::vertex_descriptor d = boost::target(*e, o.graph);\n switch(o.graph[d].t) {\n case(Type::Zero): \n x = zero;\n break;\n case(Type::One): \n x = one;\n break;\n default: \n x = node_map[d];\n }\n boost::add_edge(node_map[s], x, o.graph[*e], graph);\n }\n }\n\n BDD operator&(const BDD & b) {\n\n std::map node_map_a;\n\n std::map node_map_b;\n\n BDD c;\n c.copy_graph(*this, node_map_a);\n c.copy_graph(b, node_map_b);\n\n c.source = c.conjunction_internal(\n node_map_a[this->source],\n node_map_b[b.source]\n );\n return c;\n }\n\n private:\n /**\n * Internal function that checks for all the cases\n * Accepts the nodes that must be conjoined\n * and returns the new source.\n */\n GD::vertex_descriptor\n conjunction_internal(\n GD::vertex_descriptor & a, \n GD::vertex_descriptor & b) {\n std::cout << \"conjunction_internal \" << a << \" \" << b << \"\\n\";\n add_edge(a, b, NegP::Positive);\n return a;\n }\n\n\n GD::vertex_descriptor add_vertex(const std::string name) {\n GD::vertex_descriptor v = boost::add_vertex(graph);\n graph[v].input_name = name;\n graph[v].t = Type::Input;\n return v;\n }\n\n void add_edge(\n GD::vertex_descriptor & a, \n GD::vertex_descriptor & b, \n NegP p) {\n boost::add_edge(a, b, p, graph);\n }\n\n /**\n * USE WITH CAUTION, as it changes the current BDD\n * - changes zero and one\n */\n public:\n void reverse() {\n GD::vertex_descriptor aux = one;\n one = zero;\n zero = aux;\n\n graph[zero].t = Type::Zero;\n graph[one].t = Type::One;\n }\n\n /**\n * USE WITH CAUTION, as it changes the current BDD\n * merges the input edges of the node b into node a.\n */\n void merge_input_edges(\n const GD::vertex_descriptor a, \n const GD::vertex_descriptor b) {\n GD::in_edge_iterator e, end;\n for (boost::tie(e, end) = in_edges(b, graph); e != end; ++e) {\n GD::vertex_descriptor s = boost::source(*e, graph);\n boost::add_edge(s, a, graph[*e], graph);\n }\n }\n\n std::map > find_layers() {\n // TODO\n }\n\n // Criar um hash o mais unico possivel para um bdd\n hashcode hash(int v) {\n // TODO\n }\n\n // Compares BDDs\n friend bool operator<(const BDD & bdd1, const BDD & bdd2) {\n // TODO\n }\n\n /**\n * USE WITH CAUTION, as it changes the current BDD\n * - minimizes the BDD\n */\n void minimize() {\n // TODO\n }\n\n friend bool operator==(const BDD & bdd1, const BDD & bdd2) {\n return\n (bdd1.source == bdd2.source) &&\n (bdd1.one == bdd2.one) &&\n (bdd1.zero == bdd2.zero);\n // TODO &&\n // (bdd1.graph == bdd2.graph);\n }\n friend bool operator!=(const BDD & bdd1, const BDD & bdd2) {\n return\n (bdd1.source != bdd2.source) &&\n (bdd1.one != bdd2.one) &&\n (bdd1.zero != bdd2.zero);\n // TODO&&\n // (bdd1.graph != bdd2.graph);\n }\n\n public:\n int size() {\n int i = 0;\n GD::vertex_iterator n, e;\n for(boost::tie(n, e) = boost::vertices(graph);\n n != e; ++n) ++i;\n return i;\n }\n\n friend std::ostream& operator<<(std::ostream &o, const BDD & b) {\n\n int i = 0;\n std::map node_map;\n //std::map node_map;\n GD::vertex_iterator v, vend;\n\n for(boost::tie(v, vend) = boost::vertices(b.graph);\n v != vend; ++v) {\n node_map[*v] = ++i;\n //node_map[*v] = *v;\n }\n\n\n o << \"digraph G {\\n\" ;\n for(boost::tie(v, vend) = boost::vertices(b.graph);\n v != vend; ++v) {\n switch(b.graph[*v].t) {\n case(Type::Input) : {\n o << node_map[*v] << \" [label=\\\"\"\n << b.graph[*v].input_name\n << \"\\\", \";\n if (*v == b.source)\n o << \"shape=doublecircle];\\n\";\n else\n o << \"shape=circle];\\n\";\n break;\n }\n case(Type::One) : {\n o << node_map[*v] << \" [label=\\\"1\\\", shape=box]; \\n\";\n break;\n }\n case(Type::Zero) : {\n o << node_map[*v] << \" [label=\\\"0\\\", shape=box]; \\n\";\n break;\n }\n }\n }\n\n GD::edge_iterator e, eend;\n for(boost::tie(e, eend) = boost::edges(b.graph);\n e != eend; ++e) {\n GD::vertex_descriptor s = boost::source(*e, b.graph);\n GD::vertex_descriptor d = boost::target(*e, b.graph);\n\n o << node_map[s] << \"->\" << node_map[d];\n switch(b.graph[*e]) {\n case(NegP::Positive): {\n o << \" [style=solid];\\n\";\n break;\n }\n case(NegP::Negative): {\n o << \" [style=dashed];\\n\";\n break;\n }\n }\n }\n\n\n o << \"}\\n\" ;\n return o;\n }\n };\n }\n}\n", "meta": {"hexsha": "b8a7d50c7faf5670d32f6b9b6cc71643aa3c0f14", "size": 7977, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/bdd_model.hpp", "max_stars_repo_name": "RafaelONascimento/iccad2016uffs", "max_stars_repo_head_hexsha": "ece416e9139cdfa20ccd27536965e4e03ca474b2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2017-05-03T14:11:14.000Z", "max_stars_repo_stars_event_max_datetime": "2017-05-03T14:11:14.000Z", "max_issues_repo_path": "include/bdd_model.hpp", "max_issues_repo_name": "RafaelONascimento/iccad2016uffs", "max_issues_repo_head_hexsha": "ece416e9139cdfa20ccd27536965e4e03ca474b2", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/bdd_model.hpp", "max_forks_repo_name": "RafaelONascimento/iccad2016uffs", "max_forks_repo_head_hexsha": "ece416e9139cdfa20ccd27536965e4e03ca474b2", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.7322580645, "max_line_length": 74, "alphanum_fraction": 0.4825122226, "num_tokens": 2030, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.2363827316424768}} {"text": "/*~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\n| Phycas: Python software for phylogenetic analysis |\n| Copyright (C) 2006 Mark T. Holder, Paul O. Lewis and David L. Swofford |\n| |\n| This program is free software; you can redistribute it and/or modify |\n| it under the terms of the GNU General Public License as published by |\n| the Free Software Foundation; either version 2 of the License, or |\n| (at your option) any later version. |\n| |\n| This program is distributed in the hope that it will be useful, |\n| but WITHOUT ANY WARRANTY; without even the implied warranty of |\n| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |\n| GNU General Public License for more details. |\n| |\n| You should have received a copy of the GNU General Public License along |\n| with this program; if not, write to the Free Software Foundation, Inc., |\n| 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. |\n\\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~*/\n\n#include \n#include \n#include \n#include \"ncl/nxsallocatematrix.h\"\n#include \"q_matrix.hpp\"\n#include \"xlikelihood.hpp\"\nusing namespace phycas;\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\n*/\nQMatrix::QMatrix()\n : dimension(0), flat_length(0)\n , qmat(0), qmat_begin(0), qmat_end(0)\n , w(0), w_begin(0), w_end(0)\n , z(0), z_begin(0), z_end(0)\n , fv(0)\n , q_dirty(true)\n\t{\n\t// Set up Q matrix representing the JC69 model by default\n\trr.assign(6, 1.0);\n\tpi.assign(4, 0.25);\n\tsqrtPi.assign(4, 0.5);\n\trecalcQMatrix();\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\n*/\nQMatrix::~QMatrix()\n\t{\n\tclear();\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReturns object to its just-constructed state, with the exception that the vectors `rr', `pi' and `sqrtPi' are not\n|\tcleared.\n*/\nvoid QMatrix::clearAllExceptFreqsAndRates()\n\t{\n\tdimension\t= 0;\n\tflat_length\t= 0;\n\tqmat_begin\t= 0;\n\tqmat_end\t= 0;\n\tw_begin\t\t= 0;\n\tw_end\t\t= 0;\n\tz_begin\t\t= 0;\n\tz_end\t\t= 0;\n\n\tdim_vect.clear();\n\n\tif (qmat)\n\t\t{\n\t\tDeleteTwoDArray(qmat);\n\t\tqmat = 0;\n\t\t}\n\n\tif (w)\n\t\t{\n\t\tdelete [] w;\n\t\tw = 0;\n\t\t}\n\n\tif (fv)\n\t\t{\n\t\tdelete [] fv;\n\t\tfv = 0;\n\t\t}\n\n\tif (z)\n\t\t{\n\t\tDeleteTwoDArray(z);\n\t\tz = 0;\n\t\t}\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReturns object to its just-constructed state by calling clearAllExceptFreqsAndRates and then also clearing the\n|\t`rr', `pi' and `sqrtPi' vectors.\n*/\nvoid QMatrix::clear()\n\t{\n\tclearAllExceptFreqsAndRates();\n\tpi.clear();\n\tsqrtPi.clear();\n\trr.clear();\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tAccessor function that returns the current value of the data member `dimension', which is the number of rows (and\n|\tcolumns) in the Q matrix.\n*/\nunsigned QMatrix::getDimension()\n\t{\n\treturn dimension;\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tCopies elements from the supplied vector `rates' to the data member `rr' and sets `q_dirty' to true.\n*/\nvoid QMatrix::setRelativeRates(const std::vector & rates)\n\t{\n\trr.resize(rates.size());\n\tstd::copy(rates.begin(), rates.end(), rr.begin());\n\tq_dirty = true;\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tCopies elements from the supplied vector `rates' to the data member `rr' and sets `q_dirty' to true.\n*/\nvoid QMatrix::setStateFreqs(const std::vector & freqs)\n\t{\n\tpi.resize(freqs.size());\n\tstd::copy(freqs.begin(), freqs.end(), pi.begin());\n\n\tsqrtPi.resize(freqs.size());\n\tstd::transform(pi.begin(), pi.end(), sqrtPi.begin(), boost::lambda::bind(static_cast(&std::sqrt), boost::lambda::_1));\n\tq_dirty = true;\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReallocates all arrays and vectors (except `rr', `pi' and `sqrtPi') according to the value of `new_dim' and sets\n|\t`dimension' equal to `new_dim' before returning. Assumes `new_dim' is greater than zero. Note that `qmat', `w', `z'\n|\tand `fv' are allocated but not filled with any values when this function returns.\n*/\nvoid QMatrix::redimension(unsigned new_dim)\n\t{\n\tPHYCAS_ASSERT(new_dim > 0);\n\tclearAllExceptFreqsAndRates();\n\n\tdimension = new_dim;\n\tflat_length = new_dim*new_dim;\n\n\texpwv.resize(new_dim);\n\n\t// Set the shape of a NumArray object that represents Q, P (transition probs), E (eigenvectors) or V (eigenvalues)\n\tdim_vect.push_back((int)dimension);\n\tdim_vect.push_back((int)dimension);\n\n\t// Create and fill qmat with default (Mk model) values\n\tqmat = NewTwoDArray(dimension, dimension);\n\tPHYCAS_ASSERT(qmat);\n\n\tqmat_begin\t\t= &qmat[0][0];\n\tqmat_end\t\t= qmat_begin + flat_length;\n\n\t// Create w (array of eigenvalues)\n\tw = new double[dimension];\n\tw_begin = &w[0];\n\tw_end = w_begin + dimension;\n\n\t// Create z (two-dimensional array of eigenvectors)\n\tz = NewTwoDArray(dimension, dimension);\n\tz_begin\t\t= &z[0][0];\n\tz_end\t\t= z_begin + flat_length;\n\n\t// Create fv (workspace used by EigenRealSymmetric)\n\tfv = new double[dimension];\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tIf `q_dirty' is true, calls recalcQMatrixImpl to recompute the `qmat', `z' and `w' data members. If `q_dirty' is\n|\tfalse, however, this function returns immediately and thus (because it is declared inline) is computationally\n|\tinexpensive.\n*/\nvoid QMatrix::recalcQMatrix()\n\t{\n\tif (!q_dirty)\n\t\treturn;\n\trecalcQMatrixImpl();\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReturns the eigenvalues in the data member `w' as a vector. Calls recalcQMatrix first to ensure that `w' is up to\n|\tdate.\n*/\nstd::vector QMatrix::getEigenValues()\n\t{\n\trecalcQMatrix();\n\tstd::vector v(dimension, 0.0);\n\tstd::copy(w_begin, w_end, v.begin());\n\treturn v;\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\n*/\nstd::string QMatrix::showQMatrix()\n\t{\n\trecalcQMatrix();\n\treturn showMatrixImpl(qmat_begin);\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tStores a flattened version of the supplied 2-dimensional array `twoDarr', storing the result in the supplied std::vector\n|\treference variable `p'. The supplied `twoDarr' should be laid out so that rows occupy contiguous memory.\n*/\nvoid QMatrix::flattenTwoDMatrix(std::vector & p, double * * twoDarr, unsigned dim) const\n\t{\n\tunsigned flat_length = dim*dim;\n\tp.resize(flat_length);\n\tdouble * twoD_begin = &twoDarr[0][0];\n\tdouble * twoD_end = twoD_begin + flat_length;\n\tstd::copy(twoD_begin, twoD_end, p.begin());\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tUses `pi' and `rr' vectors to create the two-dimensional array `qmat'. The data member `qmat' is reallocated if\n|\tchanges in `pi' and `rr' imply a new dimension (or if `qmat' is NULL). Recomputes eigenvalues and eigenvectors\n|\tcorresponding to the new Q matrix. Assumes `q_dirty' is true, and sets `q_dirty' to false when finished. Throws an\n|\texception if lengths of the `pi' and `rr' vectors are incompatible.\n*/\nvoid QMatrix::recalcQMatrixImpl()\n\t{\n\tPHYCAS_ASSERT(q_dirty);\n\n\t// pi, sqrtPi and rr should all be non-empty\n\tPHYCAS_ASSERT(pi.size() > 0);\n\tPHYCAS_ASSERT(sqrtPi.size() == pi.size());\n\tPHYCAS_ASSERT(rr.size() > 0);\n\n\t// First check to make sure lengths of `rr' and `pi' are compatible with each other\n\tunsigned dim_pi = (unsigned)pi.size();\n\tunsigned dim_rr = (unsigned)((1.0 + std::sqrt(1.0 + 8.0*rr.size()))/2.0); // rr.size() = (n^2 - n)/2, so use quadratic formula to get n\n\tif (dim_pi != dim_rr)\n\t\t{\n\t\tthrow XLikelihood(boost::str(boost::format(\"Number of relative rates (%d) and number of state frequencies (%d) specified are incompatible\") % dim_rr % dim_pi));\n\t\t}\n\n\t// If qmat is NULL or dimension differs from dim_pi and dim_rr, reallocate qmat, z, w and dv\n\tif (!qmat || (dimension != dim_pi))\n\t\tredimension(dim_pi);\n\n\t// This vector will hold the row sums\n\tstd::vector row_sum(dimension, 0.0);\n\n\tunsigned i, j, k = 0;\n\tdouble sum_for_scaling = 0.0;\n\tfor (i = 0; i < dimension; ++i)\n\t\t{\n\t\tdouble pi_i = pi[i];\n\t\tdouble sqrtPi_i = sqrtPi[i];\n\t\tfor (j = i + 1; j < dimension; ++j, ++k)\n\t\t\t{\n\t\t\tdouble rr_k = rr[k];\n\t\t\tdouble pi_j = pi[j];\n\t\t\tdouble sqrtPi_j = sqrtPi[j];\n\n\t\t\t// set value in upper triangle\n\t\t\tqmat[i][j] = rr_k*sqrtPi_i*sqrtPi_j;\n\n\t\t\t// set value in lower triangle\n\t\t\tqmat[j][i] = qmat[i][j];\n\n\t\t\t// add to relevant row sums\n\t\t\trow_sum[i] += rr_k*pi_j;\n\t\t\trow_sum[j] += rr_k*pi_i;\n\n\t\t\t// add to total expected number of substitutions\n\t\t\tsum_for_scaling += 2.0*pi_i*rr_k*pi_j;\n\t\t\t}\n\t\tqmat[i][i] = -row_sum[i];\n\t\t}\n\n\tPHYCAS_ASSERT(sum_for_scaling > 0.0);\n\tedgelen_scaler = 1.0/sum_for_scaling;\n\n\t// Calculate eigenvalues (w) and eigenvectors (z)\n\tint err_code = EigenRealSymmetric(dimension, qmat, w, z, fv);\n\n\tif (err_code != 0)\n\t\t{\n\t\tclearAllExceptFreqsAndRates();\n\t\tthrow XLikelihood(\"Error in the calculation of eigenvectors and eigenvalues of the Q matrix\");\n\t\t}\n\n\tq_dirty = false;\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n| Recomputes a transition probability matrix for an edge length `edgelen', storing it in `pmat'. If either state\n| frequencies or relative rates have changed, the Q matrix is reconstructed and eigenvalues and eigenvectors\n| recomputed before the transition matrix is recomputed.\n*/\nvoid QMatrix::recalcPMat(\n double * * pmat,\t\t/**< is the transition matrix to recalculate */\n double edgelen) \t\t/**< is the edge length */\n\t{\n\trecalcQMatrix();\n double t = edgelen;\n\n // The next two lines fix the \"Rota\" bug; see BUGS file for details\n if (t < 1.e-8)\n t = 1.e-8; //TreeNode::edgeLenEpsilon;\n\n\t// Adjust the supplied edgelen to account for the fact that the expected number of substitutions\n\t// implied by the Q matrix is not unity\n\tdouble v = t*edgelen_scaler;\n\n // Precalculate exp to avoid doing the same calculation dimension*dimension times\n\tfor (unsigned k = 0; k < dimension; ++k)\n\t\t{\n\t\texpwv[k] = std::exp(w[k]*v);\n }\n\n\t// Exponentiate eigenvalues and put everything back together again\n\t// Real symmetric matrices can be diagonalized using Z*exp(D)*Z^T, where Z is the\n\t// orthogonal matrix of eigenvectors and D is the diagonal matrix of eigenvalues,\n\t// each multiplied by time (scaled to equal expected number of substitutions)\n\tfor (unsigned i = 0; i < dimension; ++i)\n\t\t{\n\t\tdouble sqrtPi_i = sqrtPi[i];\n\t\tfor (unsigned j = 0; j < dimension; ++j)\n\t\t\t{\n\t\t\tdouble factor = sqrtPi[j]/sqrtPi_i;\n\t\t\tdouble Pij = 0.0;\n\t\t\tfor (unsigned k = 0; k < dimension; ++k)\n\t\t\t\t{\n\t\t\t\tdouble tmp = z[i][k]*z[j][k]*expwv[k];\n\t\t\t\tPij += tmp;\n\t\t\t\t}\n\t\t\tpmat[i][j] = Pij*factor;\n\t\t\t}\n\t\t}\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\n*/\n// void QMatrix::recalcPMatrix(\n// std::vector & P,\t/**< */\n// double edgelen)\t\t\t/**< */\n// \t{\n// \trecalcQMatrix();\n//\n// double t = edgelen;\n//\n// // The next two lines fix the \"Rota\" bug; see BUGS file for details\n// if (t < 1.e-8)\n// t = 1.e-8; //TreeNode::edgeLenEpsilon;\n//\n// // Adjust the supplied edgelen to account for the fact that the expected number of substitutions\n// \t// implied by the Q matrix is not unity\n// \tdouble v = t*edgelen_scaler;\n//\n// \tP.clear();\n// \tP.reserve(flat_length);\n//\n// \t// Exponentiate eigenvalues and put everything back together again\n// \tfor (unsigned i = 0; i < dimension; ++i)\n// \t\t{\n// \t\tdouble sqrtPi_i = sqrtPi[i];\n// \t\tfor (unsigned j = 0; j < dimension; ++j)\n// \t\t\t{\n// \t\t\tdouble factor = sqrtPi[j]/sqrtPi_i;\n// \t\t\tdouble Pij = 0.0;\n// \t\t\tfor (unsigned k = 0; k < dimension; ++k)\n// \t\t\t\t{\n// \t\t\t\tdouble tmp = z[i][k]*z[j][k]*std::exp(w[k]*v);\n// \t\t\t\tPij += tmp;\n// \t\t\t\t}\n// \t\t\tP.push_back(Pij*factor);\n// \t\t\t}\n// \t\t}\n// \t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\n*/\nstd::string QMatrix::showMatrixImpl(const double * q) const\n\t{\n\tunsigned i, j;\n\n\t// Output one column for row labels and a label for every column of qmat\n\tstd::string s = str(boost::format(\"%12s \") % \" \");\n\tfor (i = 0; i < dimension; ++i)\n\t\t{\n\t\ts += str(boost::format(\"%12d \") % i);\n\t\t}\n\ts += \"\\n\";\n\n\t// Output rows of q\n\tdouble * pq = (double *)q;\t//PELIGROSO\n\tfor (i = 0; i < dimension; ++i)\n\t\t{\n\t\ts += str(boost::format(\"%12d \") % i);\n\t\tfor (j = 0; j < dimension; ++j)\n\t\t\t{\n\t\t\ts += str(boost::format(\"%12.5f \") % *pq++);\n\t\t\t}\n\t\ts += \"\\n\";\n\t\t}\n\n\treturn s;\n\t}\n\n#if defined(PYTHON_ONLY)\n#if defined(USING_NUMARRAY)\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReturns the entries in the data member `qmat' as a NumArray. Calls recalcQMatrix first to ensure that `qmat' is up\n|\tto date.\n*/\nboost::python::numeric::array QMatrix::getQMatrix()\n\t{\n\trecalcQMatrix();\n\treturn num_util::makeNum(qmat_begin, dim_vect);\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReturns the eigenvectors in the data member `z' as a NumArray. Calls recalcQMatrix first to ensure that `z' is up\n|\tto date.\n*/\nboost::python::numeric::array QMatrix::getEigenVectors()\n\t{\n\trecalcQMatrix();\n\treturn num_util::makeNum(z_begin, dim_vect);\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReturns the transition probability matrix as a vector of double values (where the 2-dimensional matrix is converted\n| to a 1-dimensional vector by storing rows one after the other. Intended for debugging (not fast).\n*/\nboost::python::numeric::array QMatrix::getPMatrix(double edgelen)\n\t{\n\tdouble * * pMat = NewTwoDArray(dimension, dimension);\n\trecalcPMat(pMat, edgelen);\n\treturn num_util::makeNum(pMat, dim_vect);\n\t}\n#else\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReturns the entries in the data member `qmat' as a NumArray. Calls recalcQMatrix first to ensure that `qmat' is up\n|\tto date.\n*/\nstd::vector QMatrix::getQMatrix()\n\t{\n\trecalcQMatrix();\n\tstd::vector p;\n\tflattenTwoDMatrix(p, qmat, dimension);\n\treturn p;\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReturns the eigenvectors in the data member `z' as a NumArray. Calls recalcQMatrix first to ensure that `z' is up\n|\tto date.\n*/\nstd::vector QMatrix::getEigenVectors()\n\t{\n\trecalcQMatrix();\n\tstd::vector p;\n\tflattenTwoDMatrix(p, z, dimension);\n\treturn p;\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReturns the transition probability matrix as a vector of double values (where the 2-dimensional matrix is converted\n| to a 1-dimensional vector by storing rows one after the other. Intended for debugging (not fast).\n*/\nstd::vector QMatrix::getPMatrix(double edgelen)\n\t{\n\tdouble * * pMat = NewTwoDArray(dimension, dimension);\n\trecalcPMat(pMat, edgelen);\n\tstd::vector p;\n\tflattenTwoDMatrix(p, pMat, dimension);\n\tDeleteTwoDArray(pMat);\n\treturn p;\n\t}\n#endif\n#endif\n\n", "meta": {"hexsha": "2af6b368caa990be4280d81a8e4ccd4b5c81b7c6", "size": 16483, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cpp/q_matrix.cpp", "max_stars_repo_name": "plewis/phycas", "max_stars_repo_head_hexsha": "9f5a4d9b2342dab907d14a46eb91f92ad80a5605", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2015-09-24T23:12:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-12T07:07:01.000Z", "max_issues_repo_path": "src/cpp/q_matrix.cpp", "max_issues_repo_name": "plewis/phycas", "max_issues_repo_head_hexsha": "9f5a4d9b2342dab907d14a46eb91f92ad80a5605", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/cpp/q_matrix.cpp", "max_forks_repo_name": "plewis/phycas", "max_forks_repo_head_hexsha": "9f5a4d9b2342dab907d14a46eb91f92ad80a5605", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2015-11-23T10:35:43.000Z", "max_forks_repo_forks_event_max_datetime": "2015-11-23T10:35:43.000Z", "avg_line_length": 33.7075664622, "max_line_length": 162, "alphanum_fraction": 0.5447430686, "num_tokens": 4092, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.2363385312979336}} {"text": "/*\n * Copyright 2020 IFPEN-CEA\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n * SPDX-License-Identifier: Apache-2.0\n */\n\n/*!\n * \\file NormalizeOpt.cc\n * \\brief NormalizeOpt.cc\n */\n\n#include \"NormalizeOpt.h\"\n\n#include \n#include \n#include \n\n#ifdef ALIEN_USE_EIGEN2\n#include \n#include \n#include \n\n#endif\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\nnamespace Alien\n{\n\nusing namespace Arccore;\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\nNormalizeOpt::NormalizeOpt()\n: m_algo(StdLU)\n, m_sum_first_eq(false)\n{}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\nvoid NormalizeOpt::setAlgo(eAlgoType algo)\n{\n m_algo = algo;\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\nvoid NormalizeOpt::setOpt(eOptType opt, bool flag)\n{\n switch (opt) {\n case SumFirstEq:\n m_sum_first_eq = flag;\n break;\n default:\n FatalErrorException(A_FUNCINFO, String::format(\"Unhandle option type: \", opt));\n break;\n }\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\nNormalizeOpt::eErrorType\nNormalizeOpt::normalize(IMatrix& m, IVector& x) const\n{\n MatrixImpl& A = m.impl()->get(true);\n VectorImpl& b = x.impl()->get(true);\n return _normalize(A, b);\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\nNormalizeOpt::eErrorType\nNormalizeOpt::normalize(\nCompositeMatrix& m, CompositeVector& x, ConstArrayView eq_ids) const\n{\n // need to update timestamp of all submatrices\n MatrixImpl& A00 = m(0, 0).impl()->get(true);\n MatrixImpl& A01 = m(0, 1).impl()->get(true);\n MatrixImpl& A10 = m(1, 0).impl()->get(true);\n MatrixImpl& A11 = m(1, 1).impl()->get(true);\n VectorImpl& b0 = x[0].impl()->get(true);\n VectorImpl& b1 = x[1].impl()->get(true);\n {\n eErrorType error =\n _normalize(A00, A01, m(0, 1).impl()->hasFeature(\"transposed\"), eq_ids, b0);\n if (error != NoError)\n return error;\n }\n {\n eErrorType error =\n _normalize(A11, eq_ids, A10, m(1, 0).impl()->hasFeature(\"transposed\"), b1);\n return error;\n }\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\ntemplate <>\nvoid NormalizeOpt::Op::multInvDiag<0>()\n{}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\ntemplate <>\nvoid NormalizeOpt::Op::multInvDiag<1>()\n{\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow];\n Real diag = m_matrix[off];\n for (Integer col = off; col < m_row_offset[irow + 1]; ++col) {\n m_matrix[col] /= diag;\n }\n m_rhs[irow] /= diag;\n\n if (!m_keep_diag)\n m_matrix[off] = 0.;\n }\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\ntemplate \nvoid NormalizeOpt::Op::multInvDiag()\n{\n#ifdef ALIEN_USE_EIGEN2\n if (m_algo == EigenLU) {\n using namespace Eigen;\n using namespace std;\n typedef Eigen::Matrix MatrixType;\n typedef Eigen::Matrix VectorType;\n const Integer NxN = N * N;\n // TOCHECK : to be removed ?\n // cout<<\"MULT INV DIAG B\"< block(NxN);\n UniqueArray vect(N);\n Map m(block.begin(), N, N);\n Map v(vect.begin(), N);\n if (m_diag_first) {\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow];\n\n Map diag(&m_matrix[off * NxN], N, N);\n if (diag.determinant() == 0) {\n cout << \"Non inversible diagonal : row=\" << irow << endl;\n cout << \" DIAG :\" << endl;\n cout << diag << endl;\n }\n MatrixType inv_diag = diag.inverse();\n // TOCHECK : to be removed ?\n // PartialPivLU< MatrixType > lu(diag);\n // MatrixType inv_diag = lu.inverse() ;\n\n // OFF DIAGONAL treatment\n for (Integer col = off + 1; col < m_row_offset[irow + 1]; ++col) {\n block.copy(ArrayView(NxN, &m_matrix[col * NxN]));\n Map matrix(&m_matrix[col * NxN], N, N);\n matrix = inv_diag * m;\n // TOCHECK : to be removed ?\n // matrix = lu.solve(m) ;\n }\n\n // RHS treatment\n vect.copy(ArrayView(N, &m_rhs[irow * N]));\n Map rhs(&m_rhs[irow * N], N);\n rhs = inv_diag * v;\n // TOCHECK : to be removed\n // rhs = lu.solve(v) ;\n\n if (m_keep_diag)\n diag.setIdentity();\n else\n diag.setZero();\n }\n }\n else {\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow] + m_upper_diag_index[irow];\n\n Map diag(&m_matrix[off * NxN], N, N);\n if (diag.determinant() == 0) {\n cout << \"Non inversible diagonal : row=\" << irow << endl;\n cout << \" DIAG :\" << endl;\n cout << diag << endl;\n }\n MatrixType inv_diag = diag.inverse();\n // TOCHECK : to be removed ?\n // PartialPivLU< MatrixType > lu(diag);\n // MatrixType inv_diag = lu.inverse() ;\n\n // OFF DIAGONAL treatment\n for (Integer col = m_row_offset[irow]; col < off; ++col) {\n block.copy(ArrayView(NxN, &m_matrix[col * NxN]));\n Map matrix(&m_matrix[col * NxN], N, N);\n matrix = inv_diag * m;\n // TOCHECK : to be removed ?\n // matrix = lu.solve(m) ;\n }\n // skip diagonal block\n for (Integer col = off + 1; col < m_row_offset[irow + 1]; ++col) {\n block.copy(ArrayView(NxN, &m_matrix[col * NxN]));\n Map matrix(&m_matrix[col * NxN], N, N);\n matrix = inv_diag * m;\n // TOCHECK : to be removed ?\n // matrix = lu.solve(m) ;\n }\n\n // RHS treatment\n vect.copy(ArrayView(N, &m_rhs[irow * N]));\n Map rhs(&m_rhs[irow * N], N);\n rhs = inv_diag * v;\n // TOCHECK : to be removed ?\n // rhs = lu.solve(v) ;\n\n if (m_keep_diag)\n diag.setIdentity();\n else\n diag.setZero();\n }\n }\n }\n else\n#endif\n {\n if (m_diag_first) {\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow];\n\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag);\n\n // OFF DIAGONAL treatment\n for (Integer col = off + 1; col < m_row_offset[irow + 1]; ++col) {\n lu.template solve(&m_matrix[col * N * N]);\n }\n\n // RHS treatment\n lu.template solve<1, true>(&m_rhs[irow * N]);\n\n if (m_keep_diag)\n lu.setIdentity();\n else\n lu.setZero();\n }\n }\n else {\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow] + m_upper_diag_index[irow];\n\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag);\n\n // OFF DIAGONAL treatment\n for (Integer col = m_row_offset[irow]; col < off; ++col) {\n lu.template solve(&m_matrix[col * N * N]);\n }\n // skip diagonal block\n for (Integer col = off + 1; col < m_row_offset[irow + 1]; ++col) {\n lu.template solve(&m_matrix[col * N * N]);\n }\n // RHS treatment\n lu.template solve<1, true>(&m_rhs[irow * N]);\n\n if (m_keep_diag)\n lu.setIdentity();\n else\n lu.setZero();\n }\n }\n }\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\nNormalizeOpt::eErrorType\nNormalizeOpt::_normalize(MatrixImpl& m, VectorImpl& x) const\n{\n Op op(m, x, m_algo, m_sum_first_eq);\n if (m.block()) {\n switch (m.block()->size()) {\n case 1:\n op.multInvDiag<1>();\n break;\n case 2:\n op.multInvDiag<2>();\n break;\n case 3:\n op.multInvDiag<3>();\n break;\n case 4:\n op.multInvDiag<4>();\n break;\n default:\n op.multInvDiag<0>();\n break;\n }\n }\n return NoError;\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\ntemplate <>\nvoid NormalizeOpt::Op2::multInvDiag<0>()\n{}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\ntemplate <>\nvoid NormalizeOpt::Op2::multInvDiag<1>()\n{\n if (m_submatrix2) {\n if (m_diag_first) {\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix00 and RHS\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow];\n Real diag = m_matrix[off];\n for (Integer col = off + 1; col < m_row_offset[irow + 1]; ++col) {\n m_matrix[col] /= diag;\n }\n m_rhs[irow] /= diag;\n }\n\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix01\n if (m_trans)\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer ieq = m_eq_ids[i];\n for (Integer k = m_extra_eq_row_offset[ieq]; k < m_extra_eq_row_offset[ieq + 1];\n ++k) {\n Integer irow = m_extra_eq_cols[k];\n Integer off = m_row_offset[irow];\n Real diag = m_matrix[off];\n m_extra_eq_matrix[k] /= diag;\n }\n }\n else\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer irow = m_eq_ids[i];\n Integer off = m_row_offset[irow];\n Real diag = m_matrix[off];\n for (Integer k = m_extra_eq_row_offset[irow];\n k < m_extra_eq_row_offset[irow + 1]; ++k) {\n m_extra_eq_matrix[k] /= diag;\n }\n }\n ////////////////////////////////////////////\n //\n // SET DIAG TO Id\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off_diag = m_row_offset[irow];\n m_matrix[off_diag] = m_keep_diag ? 1. : 0.;\n }\n }\n else {\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix00 and RHS\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off_diag = m_row_offset[irow] + m_upper_diag_index[irow];\n Real diag = m_matrix[off_diag];\n for (Integer col = m_row_offset[irow]; col < off_diag; ++col) {\n m_matrix[col] /= diag;\n }\n for (Integer col = off_diag + 1; col < m_row_offset[irow + 1]; ++col) {\n m_matrix[col] /= diag;\n }\n m_rhs[irow] /= diag;\n }\n\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix01\n if (m_trans)\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer ieq = m_eq_ids[i];\n for (Integer k = m_extra_eq_row_offset[ieq]; k < m_extra_eq_row_offset[ieq + 1];\n ++k) {\n Integer irow = m_extra_eq_cols[k];\n Integer off = m_row_offset[irow] + m_upper_diag_index[irow];\n Real diag = m_matrix[off];\n m_extra_eq_matrix[k] /= diag;\n }\n }\n else\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer irow = m_eq_ids[i];\n Integer off = m_row_offset[irow] + m_upper_diag_index[irow];\n Real diag = m_matrix[off];\n for (Integer k = m_extra_eq_row_offset[irow];\n k < m_extra_eq_row_offset[irow + 1]; ++k) {\n m_extra_eq_matrix[k] /= diag;\n }\n }\n ////////////////////////////////////////////\n //\n // SET DIAG TO Id\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off_diag = m_row_offset[irow] + m_upper_diag_index[irow];\n m_matrix[off_diag] = m_keep_diag ? 1. : 0.;\n }\n }\n }\n\n if (m_submatrix1) {\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix10 SubMatrix11 RHS\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer ieq = m_eq_ids[i];\n Real diag = m_matrix[ieq];\n for (Integer j = m_extra_eq_row_offset[ieq]; j < m_extra_eq_row_offset[ieq + 1];\n ++j) {\n for (Integer k = 0; k < m_nuk2; ++k)\n m_extra_eq_matrix[j * m_nuk2 + k] /= diag;\n }\n m_rhs[ieq] /= diag;\n m_matrix[ieq] = 1.;\n }\n }\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\ntemplate \nvoid NormalizeOpt::Op2::multInvDiag()\n{\n if (m_submatrix2) {\n if (m_diag_first) {\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix00\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow];\n\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag);\n\n // OFF DIAGONAL treatment\n for (Integer col = off + 1; col < m_row_offset[irow + 1]; ++col) {\n lu.template solve(&m_matrix[col * N * N]);\n }\n\n // RHS treatment\n lu.template solve<1, true>(&m_rhs[irow * N]);\n }\n\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix01\n if (m_trans)\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer ieq = m_eq_ids[i];\n for (Integer k = m_extra_eq_row_offset[ieq]; k < m_extra_eq_row_offset[ieq + 1];\n ++k) {\n Integer irow = m_extra_eq_cols[k];\n Integer off = m_row_offset[irow];\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag, false);\n\n lu.template solve<1, true>(&m_extra_eq_matrix[k * N]);\n }\n }\n else\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer irow = m_eq_ids[i];\n Integer off = m_row_offset[irow];\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag, false);\n for (Integer k = m_extra_eq_row_offset[irow];\n k < m_extra_eq_row_offset[irow + 1]; ++k) {\n lu.template solve(&m_extra_eq_matrix[k * N]);\n }\n }\n\n ////////////////////////////////////////////\n //\n // SET DIAG TO Id\n if (m_keep_diag)\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow];\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag, false);\n\n lu.setIdentity();\n }\n else\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow];\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag, false);\n lu.setZero();\n }\n }\n else {\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix01\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow] + m_upper_diag_index[irow];\n\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag);\n\n // OFF DIAGONAL treatment\n for (Integer col = m_row_offset[irow]; col < off; ++col) {\n lu.template solve(&m_matrix[col * N * N]);\n }\n // skip diagonal block\n for (Integer col = off + 1; col < m_row_offset[irow + 1]; ++col) {\n lu.template solve(&m_matrix[col * N * N]);\n }\n // RHS treatment\n lu.template solve<1, true>(&m_rhs[irow * N]);\n }\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix01\n if (m_trans)\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer ieq = m_eq_ids[i];\n for (Integer k = m_extra_eq_row_offset[ieq]; k < m_extra_eq_row_offset[ieq + 1];\n ++k) {\n Integer irow = m_extra_eq_cols[k] - m_local_offset;\n\n Integer off = m_row_offset[irow] + m_upper_diag_index[irow];\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag, false);\n\n lu.template solve<1, true>(&m_extra_eq_matrix[k * N]);\n }\n }\n else {\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer irow = m_eq_ids[i];\n Integer off = m_row_offset[irow] + m_upper_diag_index[irow];\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag, false);\n for (Integer k = m_extra_eq_row_offset[irow];\n k < m_extra_eq_row_offset[irow + 1]; ++k) {\n lu.template solve(&m_extra_eq_matrix[k * N]);\n }\n }\n }\n\n ////////////////////////////////////////////\n //\n // SET DIAG TO Id\n if (m_keep_diag)\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow] + m_upper_diag_index[irow];\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag, false);\n\n lu.setIdentity();\n }\n else\n for (Integer irow = 0; irow < m_local_size; ++irow) {\n Integer off = m_row_offset[irow] + m_upper_diag_index[irow];\n Real* diag = &m_matrix[off * N * N];\n LU lu(diag, false);\n lu.setZero();\n }\n }\n }\n\n if (m_submatrix1) {\n ////////////////////////////////////////////\n //\n // NORMALIZE SubMatrix10 SubMatrix11 RHS\n for (Integer i = 0; i < m_eq_ids.size(); ++i) {\n Integer ieq = m_eq_ids[i];\n Real diag = m_matrix[ieq];\n for (Integer j = m_extra_eq_row_offset[ieq]; j < m_extra_eq_row_offset[ieq + 1];\n ++j) {\n for (Integer k = 0; k < N; ++k)\n m_extra_eq_matrix[j * N + k] /= diag;\n }\n m_rhs[ieq] /= diag;\n m_matrix[ieq] = 1.;\n }\n }\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\nNormalizeOpt::eErrorType\nNormalizeOpt::_normalize(MatrixImpl& m, MatrixImpl& m2, bool trans,\n ConstArrayView eq_ids, VectorImpl& x) const\n{\n Op2 op(m, m2, trans, eq_ids, x, m_algo, m_sum_first_eq);\n\n switch (x.block()->size()) {\n case 1:\n op.multInvDiag<1>();\n break;\n case 2:\n op.multInvDiag<2>();\n break;\n case 3:\n op.multInvDiag<3>();\n break;\n case 4:\n op.multInvDiag<4>();\n break;\n default:\n op.multInvDiag<0>();\n break;\n }\n return NoError;\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\nNormalizeOpt::eErrorType\nNormalizeOpt::_normalize(MatrixImpl& m, ConstArrayView eq_ids, MatrixImpl& m2,\n bool trans, VectorImpl& x) const\n{\n Op2 op(m, eq_ids, m2, trans, x, m_algo, m_sum_first_eq);\n\n switch (x.block()->size()) {\n case 1:\n op.multInvDiag<1>();\n break;\n case 2:\n op.multInvDiag<2>();\n break;\n case 3:\n op.multInvDiag<3>();\n break;\n case 4:\n op.multInvDiag<4>();\n break;\n default:\n op.multInvDiag<0>();\n break;\n }\n return NoError;\n}\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n\n} // namespace Alien\n\n/*---------------------------------------------------------------------------*/\n/*---------------------------------------------------------------------------*/\n", "meta": {"hexsha": "78ad816bc45046ec74894373bf1738174090970b", "size": 21178, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/modules/core/src/alien/expression/normalization/NormalizeOpt.cc", "max_stars_repo_name": "sdesrozis/alien", "max_stars_repo_head_hexsha": "af497785c97d00bde17e3c0a08a2f69289931dac", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/modules/core/src/alien/expression/normalization/NormalizeOpt.cc", "max_issues_repo_name": "sdesrozis/alien", "max_issues_repo_head_hexsha": "af497785c97d00bde17e3c0a08a2f69289931dac", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/modules/core/src/alien/expression/normalization/NormalizeOpt.cc", "max_forks_repo_name": "sdesrozis/alien", "max_forks_repo_head_hexsha": "af497785c97d00bde17e3c0a08a2f69289931dac", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.282127031, "max_line_length": 90, "alphanum_fraction": 0.4612333554, "num_tokens": 5277, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.2363385312979336}} {"text": "/*\n * Copyright 2016 The MUSCET Development Team\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\")\n *\n * You may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\n\n#include \n#include \n#include \n#include \n//#include \n#include \n\nnamespace votca { namespace xtp {\n\nvoid Density2Gyration::Initialize(Property* options) {\n string key = Identify();\n\n _state = options->get(key + \".state\").as (); \n _state_no = options->get(key + \".statenumber\").as ();\n _spin = options->get(key + \".spin\").as ();\n if ( options->exists(key+\".ecp\")) {\n _use_ecp=options->get(key + \".ecp\").as ();\n }\n\n _integrationmethod = options->get(key + \".integrationmethod\").as ();\n \n if (!(_integrationmethod==\"numeric\" || _integrationmethod==\"analytic\")){\n std::runtime_error(\"Method not recognized. Only numeric and analytic available\");\n }\n if ( options->exists(key+\".gridsize\")) {\n _gridsize = options->get(key+\".gridsize\").as();\n }\n else _gridsize=\"medium\";\n if ( options->exists(key+\".openmp\")) {\n _openmp_threads = options->get(key+\".openmp\").as();\n }\n else _openmp_threads=0;\n \n // get the path to the shared folders with xml files\n char *votca_share = getenv(\"VOTCASHARE\"); \n if(votca_share == NULL) throw std::runtime_error(\"VOTCASHARE not set, cannot open help files.\");\n\n \n \n}\n\n\n\nvoid Density2Gyration::AnalyzeDensity( Orbitals & _orbitals ){\n int threads=1;\n#ifdef _OPENMP\n if ( _openmp_threads > 0 ) omp_set_num_threads(_openmp_threads); \n threads=omp_get_max_threads();\n#endif\n CTP_LOG(ctp::logDEBUG, *_log) << \"===== Running on \"<< threads << \" threads ===== \" << flush;\n\n vector< ctp::QMAtom* > Atomlist =_orbitals.QMAtoms();\n std::vector< ctp::QMAtom* >::iterator at;\n for (at=Atomlist.begin();at DMAT_tot;\n BasisSet bs;\n bs.LoadBasisSet(_orbitals.getDFTbasis());\n AOBasis basis;\n basis.AOBasisFill(&bs, _Atomlist );\n \n // Analyze geometry\n AnalyzeGeometry( _Atomlist );\n \n \n \n std::vector > DMAT;\n\n //basis.ReorderMOs(_orbitals.MOCoefficients(), _orbitals.getQMpackage(), \"votca\" ); \n \n if(_state==\"transition\"){\n DMAT_tot=_orbitals.TransitionDensityMatrix(_spin, _state_no-1); \n }\n else if (_state==\"ground\" || _state==\"excited\" || _state==\"exciton\" ){\n CTP_LOG(ctp::logDEBUG, *_log) << \"Calculating density matrix: \" << _state << \" No. \" << _state_no << flush;\n \n \n \n ub::matrix DMATGS=_orbitals.DensityMatrixGroundState();\n DMAT_tot=DMATGS;\n if ( _state_no > 0 && ( _state==\"excited\" || _state==\"exciton\" ) ){\n \n DMAT = _orbitals.DensityMatrixExcitedState( _spin, _state_no-1);\n \n if (_state == \"excited\" ){ \n DMAT_tot=DMAT_tot-DMAT[0]+DMAT[1];\n }\n \n } \n\t // Ground state + hole_contribution + electron contribution\n\t}\n else throw std::runtime_error(\"State entry not recognized\");\n\n \n if (_integrationmethod==\"numeric\") {\n\n // setup numerical integration grid\n NumericalIntegration numway;\n numway.GridSetup(_gridsize,&bs,_Atomlist,&basis);\n \n \n if ( _state==\"ground\" || _state==\"excited\") {\n //LOG(logDEBUG, *_log) << TimeStamp() << \" Calculate Densities at Numerical Grid with gridsize \"<< _gridsize << flush; \n ub::vector _analysis=numway.IntegrateGyrationTensor(DMAT_tot);\n\n // convert to eigenframe\n ub::vector _gyration_tensor_diagonal;\n ub::matrix _gyration_tensor_eigenframe;\n CTP_LOG(ctp::logDEBUG, *_log) << ctp::TimeStamp() << \" Converting to Eigenframe \" << flush; \n Convert2Eigenframe( _analysis, _gyration_tensor_diagonal, _gyration_tensor_eigenframe );\n \n // determine quaternion for rotation of xyz to EF\n CTP_LOG(ctp::logDEBUG, *_log) << ctp::TimeStamp() << \" Calculating Quaternion \" << flush; \n ub::vector _quaternion = get_quaternion( _gyration_tensor_eigenframe );\n\n // report results\n CTP_LOG(ctp::logDEBUG, *_log) << ctp::TimeStamp() << \" Reporting \" << flush; \n ReportAnalysis( _state, _analysis, _gyration_tensor_diagonal, _gyration_tensor_eigenframe );\n \n \n } else if ( _state == \"exciton\" ){\n // hole density first\n ub::vector _analysis_hole=numway.IntegrateGyrationTensor(DMAT[0]);\n\n // convert to eigenframe\n ub::vector _gyration_tensor_diagonal_hole;\n ub::matrix _gyration_tensor_eigenframe_hole;\n Convert2Eigenframe( _analysis_hole, _gyration_tensor_diagonal_hole, _gyration_tensor_eigenframe_hole );\n \n // determine quaternion for rotation of xyz to EF\n ub::vector _quaternion_hole = get_quaternion( _gyration_tensor_eigenframe_hole );\n\n // report results\n ReportAnalysis( \"hole\", _analysis_hole, _gyration_tensor_diagonal_hole, _gyration_tensor_eigenframe_hole );\n\n // electron density\n ub::vector _analysis_electron=numway.IntegrateGyrationTensor(DMAT[1]);\n\n // convert to eigenframe\n ub::vector _gyration_tensor_diagonal_electron;\n ub::matrix _gyration_tensor_eigenframe_electron;\n Convert2Eigenframe( _analysis_electron, _gyration_tensor_diagonal_electron, _gyration_tensor_eigenframe_electron );\n \n // determine quaternion for rotation of xyz to EF\n ub::vector _quaternion_electron = get_quaternion( _gyration_tensor_eigenframe_electron );\n \n \n // report results\n ReportAnalysis( \"electron\", _analysis_electron, _gyration_tensor_diagonal_electron, _gyration_tensor_eigenframe_electron );\n \n }\n\n }\n else if (_integrationmethod==\"analytic\") {\n //esp.Fit2Density_analytic(_Atomlist,DMAT_tot,basis);\n }\n }\n\n\n void Density2Gyration::AnalyzeGeometry(vector _atoms){\n \n Elements _elements; \n ub::vector _analysis = ub::zero_vector(10);\n std::vector< ctp::QMAtom* >::iterator at;\n for (at=_atoms.begin();at<_atoms.end();++at){\n \n double m = _elements.getMass( (*at)->type );\n double x = (*at)->x ;\n double y = (*at)->y ;\n double z = (*at)->z ;\n _analysis(0) += m ;\n _analysis(1) += m*x ;\n _analysis(2) += m*y ;\n _analysis(3) += m*z ;\n \n _analysis(4) += m*x*x;\n _analysis(5) += m*x*y;\n _analysis(6) += m*x*z;\n _analysis(7) += m*y*y;\n _analysis(8) += m*y*z;\n _analysis(9) += m*z*z;\n \n }\n \n // normalize\n for ( unsigned i =1 ; i < 4; i++){\n _analysis(i) = _analysis(i)/_analysis(0)/tools::conv::bohr2ang;\n }\n // normalize\n for ( unsigned i =4 ; i < _analysis.size(); i++){\n _analysis(i) = _analysis(i)/_analysis(0)/tools::conv::bohr2ang/tools::conv::bohr2ang;\n }\n \n \n \n // gyration tensor\n _analysis(4) -= _analysis(1)*_analysis(1);\n _analysis(5) -= _analysis(1)*_analysis(2);\n _analysis(6) -= _analysis(1)*_analysis(3);\n _analysis(7) -= _analysis(2)*_analysis(2);\n _analysis(8) -= _analysis(2)*_analysis(3);\n _analysis(9) -= _analysis(3)*_analysis(3);\n\n \n // convert to eigenframe\n ub::vector _gyration_tensor_diagonal;\n ub::matrix _gyration_tensor_eigenframe;\n Convert2Eigenframe( _analysis, _gyration_tensor_diagonal, _gyration_tensor_eigenframe );\n\n cout << \"\\n\";\n \n for ( int i =0 ; i< 3; i++){\n \n cout << _gyration_tensor_eigenframe(0,i) << \" \" << _gyration_tensor_eigenframe(1,i) << \" \" << _gyration_tensor_eigenframe(2,i) << \"\\n\" << endl; \n \n }\n \n /*\n // test for right-handedness\n double check_x = _gyration_tensor_eigenframe(1,0)* _gyration_tensor_eigenframe(2,1) - _gyration_tensor_eigenframe(2,0)* _gyration_tensor_eigenframe(1,1) ;\n double check_y = _gyration_tensor_eigenframe(2,0)* _gyration_tensor_eigenframe(0,1) - _gyration_tensor_eigenframe(0,0)* _gyration_tensor_eigenframe(2,1) ;\n double check_z = _gyration_tensor_eigenframe(0,0)* _gyration_tensor_eigenframe(1,1) - _gyration_tensor_eigenframe(1,0)* _gyration_tensor_eigenframe(0,1) ;\n \n cout << \" check x\" << check_x << \" vs \" << _gyration_tensor_eigenframe(0,2) << \"\\n\" < _quaternion = get_quaternion( _gyration_tensor_eigenframe );\n\n // report results\n ReportAnalysis( \"geometry\", _analysis, _gyration_tensor_diagonal, _gyration_tensor_eigenframe );\n \n \n \n \n \n }\n\n\n\n void Density2Gyration::ReportAnalysis(string label, ub::vector _tensor_elements, ub::vector _tensor_diagonal, ub::matrix _tensor_frame){\n\n \n CTP_LOG(ctp::logINFO, *_log) << \"---------------- \" << label << \" ----------------\" << flush;\n CTP_LOG(ctp::logINFO, *_log) << (boost::format(\" Norm = %1$9.4f \") % (_tensor_elements(0)) ) << flush;\n \n //LOG(logINFO,*_pLog) << (format(\" Level = %1$4d DFT = %2$+1.4f VXC = %3$+1.4f S-X = %4$+1.4f S-C = %5$+1.4f GWA = %6$+1.4f\") % (_i+_qpmin+1) % _dft_energies( _i + _qpmin ) % _vxc(_i,_i) % _sigma_x(_i,_i) % _sigma_c(_i,_i) % _qp_energies(_i + _qpmin ) ).str() << flush;\n \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Centroid x = %1$9.4f Ang\") % (_tensor_elements(1)*tools::conv::bohr2ang) ) << flush;\n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Centroid y = %1$9.4f Ang\") % (_tensor_elements(2)*tools::conv::bohr2ang) ) << flush;\n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Centroid y = %1$9.4f Ang\") % (_tensor_elements(3)*tools::conv::bohr2ang) ) << flush;\n \n double RA2 = tools::conv::bohr2ang *tools::conv::bohr2ang;\n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Gyration Tensor xx = %1$9.4f Ang^2\") % (_tensor_elements(4)*RA2) ) << flush;\n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Gyration Tensor xy = %1$9.4f Ang^2\") % (_tensor_elements(5)*RA2) ) << flush;\n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Gyration Tensor xz = %1$9.4f Ang^2\") % (_tensor_elements(6)*RA2) ) << flush;\n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Gyration Tensor yy = %1$9.4f Ang^2\") % (_tensor_elements(7)*RA2) ) << flush;\n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Gyration Tensor yz = %1$9.4f Ang^2\") % (_tensor_elements(8)*RA2) ) << flush;\n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Gyration Tensor zz = %1$9.4f Ang^2\") % (_tensor_elements(9)*RA2) ) << flush;\n \n \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Gyration Tensor D1 = %1$9.4f Ang^2\") % (_tensor_diagonal(0)*RA2) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Gyration Tensor D2 = %1$9.4f Ang^2\") % (_tensor_diagonal(1)*RA2) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Gyration Tensor D3 = %1$9.4f Ang^2\") % (_tensor_diagonal(2)*RA2) ) << flush; \n\n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Radius of Gyration = %1$9.4f Ang\") % (std::sqrt(_tensor_diagonal(0) + _tensor_diagonal(1) + _tensor_diagonal(2))*tools::conv::bohr2ang )) << flush; \n \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Tensor EF Axis 1 1 = %1$9.4f \") % (_tensor_frame(0,0)) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Tensor EF Axis 1 2 = %1$9.4f \") % (_tensor_frame(1,0)) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Tensor EF Axis 1 3 = %1$9.4f \") % (_tensor_frame(2,0)) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Tensor EF Axis 2 1 = %1$9.4f \") % (_tensor_frame(0,1)) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Tensor EF Axis 2 2 = %1$9.4f \") % (_tensor_frame(1,1)) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Tensor EF Axis 2 3 = %1$9.4f \") % (_tensor_frame(2,1)) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Tensor EF Axis 3 1 = %1$9.4f \") % (_tensor_frame(0,2)) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Tensor EF Axis 3 2 = %1$9.4f \") % (_tensor_frame(1,2)) ) << flush; \n CTP_LOG(ctp::logINFO,*_log) << (boost::format(\" Tensor EF Axis 3 3 = %1$9.4f \") % (_tensor_frame(2,2)) ) << flush; \n\n \n }\n\n ub::vector Density2Gyration::get_quaternion(ub::matrix& eigenframe){\n \n // second coordinate system is assumed to be Cartesian\n \n ub::matrix M=ub::zero_matrix(3,3);\n\n // add all the outer products, if second coordinate system was not Cartesian\n // is anyways, so M=eigenframe\n //M=ub::trans(eigenframe);\n M=eigenframe;\n \n ub::matrix N = ub::zero_matrix(4,4);\n \n N(0,0) = M(0,0) + M(1,1) + M(2,2); // N11=float(M[0][:,0]+M[1][:,1]+M[2][:,2]);\n N(1,1) = M(0,0) - M(1,1) - M(2,2); // N22=float(M[0][:,0]-M[1][:,1]-M[2][:,2])\n N(2,2) = -M(0,0) + M(1,1) - M(2,2); // N33=float(-M[0][:,0]+M[1][:,1]-M[2][:,2])\n N(3,3) = -M(0,0) - M(1,1) + M(2,2); // N44=float(-M[0][:,0]-M[1][:,1]+M[2][:,2])\n N(0,1) = M(1,2) - M(2,1); // N12=float(M[1][:,2]-M[2][:,1])\n N(0,2) = M(2,0) - M(0,2); // N13=float(M[2][:,0]-M[0][:,2])\n N(0,3) = M(0,1) - M(1,0); // N14=float(M[0][:,1]-M[1][:,0])\n N(1,0) = N(0,1); // N21=float(N12)\n N(1,2) = M(0,1) + M(1,0); // N23=float(M[0][:,1]+M[1][:,0])\n N(1,3) = M(2,0) + M(0,2); // N24=float(M[2][:,0]+M[0][:,2])\n N(2,0) = N(0,2); // N31=float(N13)\n N(2,1) = N(1,2); // N32=float(N23)\n N(2,3) = M(1,2) + M(2,1); // N34=float(M[1][:,2]+M[2][:,1])\n N(3,0) = N(0,3); // N41=float(N14)\n N(3,1) = N(1,3); // N42=float(N24)\n N(3,2) = N(2,3); // N43=float(N34)\n\n ub::vector eigenvalues;\n ub::matrix eigenvectors;\n linalg_eigenvalues( N, eigenvalues, eigenvectors );\n \n // find max eigenvalue\n int index=0;\n double maxev = eigenvalues(0);\n\n for (unsigned i = 1; i < eigenvalues.size(); i++ ) {\n if ( eigenvalues(i) > maxev ) {\n maxev = eigenvalues(i);\n index = i;\n }\n }\n \n // return this vector\n ub::vector quaternion = ub::zero_vector(4);\n quaternion(0)= eigenvectors(0,index);\n quaternion(1)= eigenvectors(1,index);\n quaternion(2)= eigenvectors(2,index);\n quaternion(3)= eigenvectors(3,index);\n \n return quaternion;\n \n }\n \n \n \n \n void Density2Gyration::Convert2Eigenframe(ub::vector V, ub::vector &_gyration_tensor_diagonal, ub::matrix &_gyration_tensor_eigenframe ){\n \n ub::matrix _gyration_tensor = ub::zero_matrix(3,3);\n _gyration_tensor(0,0) = V(4);\n _gyration_tensor(1,0) = V(5);\n _gyration_tensor(2,0) = V(6);\n _gyration_tensor(0,1) = V(5);\n _gyration_tensor(1,1) = V(7);\n _gyration_tensor(2,1) = V(8);\n _gyration_tensor(0,2) = V(6);\n _gyration_tensor(1,2) = V(8);\n _gyration_tensor(2,2) = V(9);\n \n linalg_eigenvalues( _gyration_tensor, _gyration_tensor_diagonal, _gyration_tensor_eigenframe );\n \n }\n\n\n\n\n}}\n\n\n", "meta": {"hexsha": "cd9a0696bed0fe68e2926e8f6cc1616d25c6bf71", "size": 17952, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/libxtp/gyration.cc", "max_stars_repo_name": "choudarykvsp/xtp", "max_stars_repo_head_hexsha": "9a249fd34615abcf790d5f0ecd3ddf1ed0ac0e7a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2018-03-05T17:36:53.000Z", "max_stars_repo_stars_event_max_datetime": "2018-03-05T17:36:53.000Z", "max_issues_repo_path": "src/libxtp/gyration.cc", "max_issues_repo_name": "choudarykvsp/xtp", "max_issues_repo_head_hexsha": "9a249fd34615abcf790d5f0ecd3ddf1ed0ac0e7a", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/libxtp/gyration.cc", "max_forks_repo_name": "choudarykvsp/xtp", "max_forks_repo_head_hexsha": "9a249fd34615abcf790d5f0ecd3ddf1ed0ac0e7a", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.6285714286, "max_line_length": 282, "alphanum_fraction": 0.5489081996, "num_tokens": 5257, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.23622818822842856}} {"text": "//=====================================================\n// File : hand_vec_interface.hh\n// Copyright (C) 2008 Gael Guennebaud \n//=====================================================\n//\n// This program is free software; you can redistribute it and/or\n// modify it under the terms of the GNU General Public License\n// as published by the Free Software Foundation; either version 2\n// of the License, or (at your option) any later version.\n//\n// This program is distributed in the hope that it will be useful,\n// but WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n// GNU General Public License for more details.\n// You should have received a copy of the GNU General Public License\n// along with this program; if not, write to the Free Software\n// Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.\n//\n#ifndef HAND_VEC_INTERFACE_HH\n#define HAND_VEC_INTERFACE_HH\n\n#include \n#include \"f77_interface.hh\"\n\nusing namespace Eigen;\n\ntemplate\nclass hand_vec_interface : public f77_interface_base {\n\npublic :\n\n typedef typename internal::packet_traits::type Packet;\n static const int PacketSize = internal::packet_traits::size;\n\n typedef typename f77_interface_base::stl_matrix stl_matrix;\n typedef typename f77_interface_base::stl_vector stl_vector;\n typedef typename f77_interface_base::gene_matrix gene_matrix;\n typedef typename f77_interface_base::gene_vector gene_vector;\n\n static void free_matrix(gene_matrix & A, int N){\n internal::aligned_free(A);\n }\n\n static void free_vector(gene_vector & B){\n internal::aligned_free(B);\n }\n\n static inline void matrix_from_stl(gene_matrix & A, stl_matrix & A_stl){\n int N = A_stl.size();\n A = (real*)internal::aligned_malloc(N*N*sizeof(real));\n for (int j=0;j0)\n {\n// for (size_t j = 0;j0)\n {\n bool aligned0 = (iN0 % PacketSize) == 0;\n if (aligned0)\n for (int j = 0;j0)\n// {\n// // int aligned0 = (iN0 % PacketSize);\n// int aligned1 = (iN1 % PacketSize);\n//\n// if (aligned1==0)\n// {\n// for (int j = 0;j0)\n// {\n// bool aligned0 = (iN0 % PacketSize) == 0;\n// if (aligned0)\n// for (int j = 0;j0)\n// {\n// bool aligned0 = (iN0 % PacketSize) == 0;\n// bool aligned1 = (iN1 % PacketSize) == 0;\n//\n// if (aligned0 && aligned1)\n// {\n// for (int j = 0;j0)\n// {\n// bool aligned0 = (iN0 % PacketSize) == 0;\n// if (aligned0)\n// for (int j = 0;j0)\n// {\n// bool aligned = (iN % PacketSize) == 0;\n// if (aligned)\n// {\n// #ifdef PEELING\n// Packet A0, A1, A2, X0, X1, X2;\n// int ANP = (AN/(8*PacketSize))*8*PacketSize;\n// for (int j = 0;j0)\n {\n int align1 = (iN1 % PacketSize);\n if (align1==0)\n {\n for (int j = 0;j0)\n {\n if (iN0 % PacketSize==0)\n for (int j = 0;j0)\n// {\n// bool aligned = (iN % PacketSize) == 0;\n// if (aligned)\n// {\n// #ifdef PEELING\n// int ANP = (AN/(8*PacketSize))*8*PacketSize;\n// for (int j = 0;j0)\n {\n Packet pcoef = internal::pset1(coef);\n #ifdef PEELING\n const int peelSize = 3;\n int ANP = (AN/(peelSize*PacketSize))*peelSize*PacketSize;\n float* X1 = X + PacketSize;\n float* Y1 = Y + PacketSize;\n float* X2 = X + 2*PacketSize;\n float* Y2 = Y + 2*PacketSize;\n Packet x0,x1,x2,y0,y1,y2;\n for (int j = 0;j\r\n#include \r\n\r\n// mapnik\r\n#include \r\n#include \r\n\r\n// boost\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\nBOOST_GEOMETRY_REGISTER_POINT_2D(mapnik::coord2d, double, cs::cartesian, x, y)\r\n\r\n// register mapnik::box2d\r\nnamespace boost { namespace geometry { namespace traits\r\n{\r\n\r\ntemplate<> struct tag > { typedef box_tag type; };\r\n\r\ntemplate<> struct point_type > { typedef mapnik::coord2d type; };\r\n\r\ntemplate <>\r\nstruct indexed_access, min_corner, 0>\r\n{\r\n typedef coordinate_type::type ct;\r\n static inline ct get(mapnik::box2d const& b) { return b.minx();}\r\n static inline void set(mapnik::box2d &b, ct const& value) { b.set_minx(value); }\r\n};\r\n\r\ntemplate <>\r\nstruct indexed_access, min_corner, 1>\r\n{\r\n typedef coordinate_type::type ct;\r\n static inline ct get(mapnik::box2d const& b) { return b.miny();}\r\n static inline void set(mapnik::box2d &b, ct const& value) { b.set_miny(value); }\r\n};\r\n\r\ntemplate <>\r\nstruct indexed_access, max_corner, 0>\r\n{\r\n typedef coordinate_type::type ct;\r\n static inline ct get(mapnik::box2d const& b) { return b.maxx();}\r\n static inline void set(mapnik::box2d &b, ct const& value) { b.set_maxx(value); }\r\n};\r\n\r\ntemplate <>\r\nstruct indexed_access, max_corner, 1>\r\n{\r\n typedef coordinate_type::type ct;\r\n static inline ct get(mapnik::box2d const& b) { return b.maxy();}\r\n static inline void set(mapnik::box2d &b , ct const& value) { b.set_maxy(value); }\r\n};\r\n\r\n}}}\r\n\r\nnamespace mapnik {\r\n\r\nusing namespace boost::geometry;\r\n\r\ntemplate \r\nstruct polygon_clipper\r\n{\r\n typedef mapnik::coord2d point_2d;\r\n typedef model::polygon polygon_2d;\r\n typedef std::deque polygon_list;\r\n\r\n polygon_clipper(Geometry & geom)\r\n : clip_box_(),\r\n geom_(geom)\r\n {\r\n\r\n }\r\n\r\n polygon_clipper( box2d const& clip_box,Geometry & geom)\r\n : clip_box_(clip_box),\r\n geom_(geom)\r\n {\r\n init();\r\n }\r\n\r\n void set_clip_box(box2d const& clip_box)\r\n {\r\n clip_box_ = clip_box;\r\n init();\r\n }\r\n\r\n unsigned type() const\r\n {\r\n return geom_.type();\r\n }\r\n\r\n void rewind(unsigned path_id)\r\n {\r\n output_.rewind(path_id);\r\n }\r\n\r\n unsigned vertex (double * x, double * y)\r\n {\r\n return output_.vertex(x,y);\r\n }\r\n\r\nprivate:\r\n\r\n void init()\r\n {\r\n polygon_2d subject_poly;\r\n double x,y;\r\n double prev_x, prev_y;\r\n geom_.rewind(0);\r\n unsigned ring_count = 0;\r\n while (true)\r\n {\r\n unsigned cmd = geom_.vertex(&x,&y);\r\n if (cmd == SEG_END) break;\r\n if (cmd == SEG_MOVETO)\r\n {\r\n prev_x = x;\r\n prev_y = y;\r\n if (ring_count == 0)\r\n {\r\n append(subject_poly, make(x,y));\r\n }\r\n else\r\n {\r\n subject_poly.inners().push_back(polygon_2d::inner_container_type::value_type());\r\n append(subject_poly.inners().back(),make(x,y));\r\n }\r\n ++ring_count;\r\n }\r\n else if (cmd == SEG_LINETO)\r\n {\r\n if (std::abs(x - prev_x) < 1e-12 && std::abs(y - prev_y) < 1e-12)\r\n {\r\n std::cerr << std::setprecision(12) << \"coincident vertices:(\" << prev_x << \",\"\r\n << prev_y << \") , (\" << x << \",\" << y << \")\" << std::endl;\r\n continue;\r\n }\r\n prev_x = x;\r\n prev_x = y;\r\n if (ring_count == 1)\r\n {\r\n append(subject_poly, make(x,y));\r\n }\r\n else\r\n {\r\n append(subject_poly.inners().back(),make(x,y));\r\n }\r\n }\r\n }\r\n\r\n polygon_list clipped_polygons;\r\n\r\n try\r\n {\r\n boost::geometry::intersection(clip_box_, subject_poly, clipped_polygons);\r\n }\r\n catch (boost::geometry::exception const& ex)\r\n {\r\n std::cerr << ex.what() << std::endl;\r\n }\r\n\r\n BOOST_FOREACH(polygon_2d const& poly, clipped_polygons)\r\n {\r\n bool move_to = true;\r\n BOOST_FOREACH(point_2d const& c, boost::geometry::exterior_ring(poly))\r\n {\r\n if (move_to)\r\n {\r\n move_to = false;\r\n output_.move_to(c.x,c.y);\r\n }\r\n else\r\n {\r\n output_.line_to(c.x,c.y);\r\n }\r\n }\r\n output_.close_path();\r\n // interior rings\r\n BOOST_FOREACH(polygon_2d::inner_container_type::value_type const& ring, boost::geometry::interior_rings(poly))\r\n {\r\n move_to = true;\r\n BOOST_FOREACH(point_2d const& c, ring)\r\n {\r\n if (move_to)\r\n {\r\n move_to = false;\r\n output_.move_to(c.x,c.y);\r\n }\r\n else\r\n {\r\n output_.line_to(c.x,c.y);\r\n }\r\n }\r\n output_.close_path();\r\n }\r\n }\r\n }\r\n\r\n box2d clip_box_;\r\n Geometry & geom_;\r\n mapnik::geometry_type output_;\r\n\r\n};\r\n\r\n}\r\n\r\n#endif //MAPNIK_POLYGON_CLIPPER_HPP\r\n", "meta": {"hexsha": "629ef8914b3d6a32d1455dc9ed7ed86998ac95bf", "size": 7168, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external/include/mapnik/polygon_clipper.hpp", "max_stars_repo_name": "Wujingli/OpenWebGlobeDataProcessing", "max_stars_repo_head_hexsha": "932eaa00c81fc0571122bc618ade010fa255735e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "external/include/mapnik/polygon_clipper.hpp", "max_issues_repo_name": "Wujingli/OpenWebGlobeDataProcessing", "max_issues_repo_head_hexsha": "932eaa00c81fc0571122bc618ade010fa255735e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "external/include/mapnik/polygon_clipper.hpp", "max_forks_repo_name": "Wujingli/OpenWebGlobeDataProcessing", "max_forks_repo_head_hexsha": "932eaa00c81fc0571122bc618ade010fa255735e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2019-06-08T15:59:26.000Z", "max_forks_repo_forks_event_max_datetime": "2021-02-06T08:13:01.000Z", "avg_line_length": 30.1176470588, "max_line_length": 123, "alphanum_fraction": 0.5358537946, "num_tokens": 1666, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792043, "lm_q2_score": 0.4649015713733885, "lm_q1q2_score": 0.23608253366545198}} {"text": "//=======================================================================\r\n// Copyright (c) 2018 Yi Ji\r\n//\r\n// Distributed under the Boost Software License, Version 1.0.\r\n// (See accompanying file LICENSE_1_0.txt or copy at\r\n// http://www.boost.org/LICENSE_1_0.txt)\r\n//\r\n//=======================================================================\r\n\r\n#ifndef BOOST_GRAPH_MAXIMUM_WEIGHTED_MATCHING_HPP\r\n#define BOOST_GRAPH_MAXIMUM_WEIGHTED_MATCHING_HPP\r\n\r\n#include // for std::iter_swap\r\n#include \r\n#include \r\n#include \r\n\r\nnamespace boost\r\n{\r\ntemplate < typename Graph, typename MateMap, typename VertexIndexMap >\r\ntypename property_traits<\r\n typename property_map< Graph, edge_weight_t >::type >::value_type\r\nmatching_weight_sum(const Graph& g, MateMap mate, VertexIndexMap vm)\r\n{\r\n typedef typename graph_traits< Graph >::vertex_iterator vertex_iterator_t;\r\n typedef\r\n typename graph_traits< Graph >::vertex_descriptor vertex_descriptor_t;\r\n typedef typename property_traits< typename property_map< Graph,\r\n edge_weight_t >::type >::value_type edge_property_t;\r\n\r\n edge_property_t weight_sum = 0;\r\n vertex_iterator_t vi, vi_end;\r\n\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n {\r\n vertex_descriptor_t v = *vi;\r\n if (get(mate, v) != graph_traits< Graph >::null_vertex()\r\n && get(vm, v) < get(vm, get(mate, v)))\r\n weight_sum += get(edge_weight, g, edge(v, mate[v], g).first);\r\n }\r\n return weight_sum;\r\n}\r\n\r\ntemplate < typename Graph, typename MateMap >\r\ninline typename property_traits<\r\n typename property_map< Graph, edge_weight_t >::type >::value_type\r\nmatching_weight_sum(const Graph& g, MateMap mate)\r\n{\r\n return matching_weight_sum(g, mate, get(vertex_index, g));\r\n}\r\n\r\ntemplate < typename Graph, typename MateMap, typename VertexIndexMap >\r\nclass weighted_augmenting_path_finder\r\n{\r\npublic:\r\n template < typename T > struct map_vertex_to_\r\n {\r\n typedef boost::iterator_property_map<\r\n typename std::vector< T >::iterator, VertexIndexMap >\r\n type;\r\n };\r\n typedef typename graph::detail::VERTEX_STATE vertex_state_t;\r\n typedef typename graph_traits< Graph >::vertex_iterator vertex_iterator_t;\r\n typedef\r\n typename graph_traits< Graph >::vertex_descriptor vertex_descriptor_t;\r\n typedef typename std::vector< vertex_descriptor_t >::const_iterator\r\n vertex_vec_iter_t;\r\n typedef\r\n typename graph_traits< Graph >::out_edge_iterator out_edge_iterator_t;\r\n typedef typename graph_traits< Graph >::edge_descriptor edge_descriptor_t;\r\n typedef typename graph_traits< Graph >::edge_iterator edge_iterator_t;\r\n typedef typename property_traits< typename property_map< Graph,\r\n edge_weight_t >::type >::value_type edge_property_t;\r\n typedef std::deque< vertex_descriptor_t > vertex_list_t;\r\n typedef std::vector< edge_descriptor_t > edge_list_t;\r\n typedef typename map_vertex_to_< vertex_descriptor_t >::type\r\n vertex_to_vertex_map_t;\r\n typedef\r\n typename map_vertex_to_< edge_property_t >::type vertex_to_weight_map_t;\r\n typedef typename map_vertex_to_< bool >::type vertex_to_bool_map_t;\r\n typedef typename map_vertex_to_< std::pair< vertex_descriptor_t,\r\n vertex_descriptor_t > >::type vertex_to_pair_map_t;\r\n typedef\r\n typename map_vertex_to_< std::pair< edge_descriptor_t, bool > >::type\r\n vertex_to_edge_map_t;\r\n typedef typename map_vertex_to_< vertex_to_edge_map_t >::type\r\n vertex_pair_to_edge_map_t;\r\n\r\n class blossom\r\n {\r\n public:\r\n typedef boost::shared_ptr< blossom > blossom_ptr_t;\r\n std::vector< blossom_ptr_t > sub_blossoms;\r\n edge_property_t dual_var;\r\n blossom_ptr_t father;\r\n\r\n blossom() : dual_var(0), father(blossom_ptr_t()) {}\r\n\r\n // get the base vertex of a blossom by recursively getting\r\n // its base sub-blossom, which is always the first one in\r\n // sub_blossoms because of how we create and maintain blossoms\r\n virtual vertex_descriptor_t get_base() const\r\n {\r\n const blossom* b = this;\r\n while (!b->sub_blossoms.empty())\r\n b = b->sub_blossoms[0].get();\r\n return b->get_base();\r\n }\r\n\r\n // set a sub-blossom as a blossom's base by exchanging it\r\n // with its first sub-blossom\r\n void set_base(const blossom_ptr_t& sub)\r\n {\r\n for (blossom_iterator_t bi = sub_blossoms.begin();\r\n bi != sub_blossoms.end(); ++bi)\r\n {\r\n if (sub.get() == bi->get())\r\n {\r\n std::iter_swap(sub_blossoms.begin(), bi);\r\n break;\r\n }\r\n }\r\n }\r\n\r\n // get all vertices inside recursively\r\n virtual std::vector< vertex_descriptor_t > vertices() const\r\n {\r\n std::vector< vertex_descriptor_t > all_vertices;\r\n for (typename std::vector< blossom_ptr_t >::const_iterator bi\r\n = sub_blossoms.begin();\r\n bi != sub_blossoms.end(); ++bi)\r\n {\r\n std::vector< vertex_descriptor_t > some_vertices\r\n = (*bi)->vertices();\r\n all_vertices.insert(all_vertices.end(), some_vertices.begin(),\r\n some_vertices.end());\r\n }\r\n return all_vertices;\r\n }\r\n };\r\n\r\n // a trivial_blossom only has one vertex and no sub-blossom;\r\n // for each vertex v, in_blossom[v] is the trivial_blossom that contains it\r\n // directly\r\n class trivial_blossom : public blossom\r\n {\r\n public:\r\n trivial_blossom(vertex_descriptor_t v) : trivial_vertex(v) {}\r\n virtual vertex_descriptor_t get_base() const { return trivial_vertex; }\r\n\r\n virtual std::vector< vertex_descriptor_t > vertices() const\r\n {\r\n std::vector< vertex_descriptor_t > all_vertices;\r\n all_vertices.push_back(trivial_vertex);\r\n return all_vertices;\r\n }\r\n\r\n private:\r\n vertex_descriptor_t trivial_vertex;\r\n };\r\n\r\n typedef boost::shared_ptr< blossom > blossom_ptr_t;\r\n typedef typename std::vector< blossom_ptr_t >::iterator blossom_iterator_t;\r\n typedef\r\n typename map_vertex_to_< blossom_ptr_t >::type vertex_to_blossom_map_t;\r\n\r\n weighted_augmenting_path_finder(\r\n const Graph& arg_g, MateMap arg_mate, VertexIndexMap arg_vm)\r\n : g(arg_g)\r\n , vm(arg_vm)\r\n , null_edge(std::pair< edge_descriptor_t, bool >(\r\n num_edges(g) == 0 ? edge_descriptor_t() : *edges(g).first, false))\r\n , mate_vector(num_vertices(g))\r\n , label_S_vector(num_vertices(g), graph_traits< Graph >::null_vertex())\r\n , label_T_vector(num_vertices(g), graph_traits< Graph >::null_vertex())\r\n , outlet_vector(num_vertices(g), graph_traits< Graph >::null_vertex())\r\n , tau_idx_vector(num_vertices(g), graph_traits< Graph >::null_vertex())\r\n , dual_var_vector(std::vector< edge_property_t >(\r\n num_vertices(g), std::numeric_limits< edge_property_t >::min()))\r\n , pi_vector(std::vector< edge_property_t >(\r\n num_vertices(g), std::numeric_limits< edge_property_t >::max()))\r\n , gamma_vector(std::vector< edge_property_t >(\r\n num_vertices(g), std::numeric_limits< edge_property_t >::max()))\r\n , tau_vector(std::vector< edge_property_t >(\r\n num_vertices(g), std::numeric_limits< edge_property_t >::max()))\r\n , in_blossom_vector(num_vertices(g))\r\n , old_label_vector(num_vertices(g))\r\n , critical_edge_vectors(num_vertices(g),\r\n std::vector< std::pair< edge_descriptor_t, bool > >(\r\n num_vertices(g), null_edge))\r\n ,\r\n\r\n mate(mate_vector.begin(), vm)\r\n , label_S(label_S_vector.begin(), vm)\r\n , label_T(label_T_vector.begin(), vm)\r\n , outlet(outlet_vector.begin(), vm)\r\n , tau_idx(tau_idx_vector.begin(), vm)\r\n , dual_var(dual_var_vector.begin(), vm)\r\n , pi(pi_vector.begin(), vm)\r\n , gamma(gamma_vector.begin(), vm)\r\n , tau(tau_vector.begin(), vm)\r\n , in_blossom(in_blossom_vector.begin(), vm)\r\n , old_label(old_label_vector.begin(), vm)\r\n {\r\n vertex_iterator_t vi, vi_end;\r\n edge_iterator_t ei, ei_end;\r\n\r\n edge_property_t max_weight\r\n = std::numeric_limits< edge_property_t >::min();\r\n for (boost::tie(ei, ei_end) = edges(g); ei != ei_end; ++ei)\r\n max_weight = std::max(max_weight, get(edge_weight, g, *ei));\r\n\r\n typename std::vector<\r\n std::vector< std::pair< edge_descriptor_t, bool > > >::iterator vei;\r\n\r\n for (boost::tie(vi, vi_end) = vertices(g),\r\n vei = critical_edge_vectors.begin();\r\n vi != vi_end; ++vi, ++vei)\r\n {\r\n vertex_descriptor_t u = *vi;\r\n mate[u] = get(arg_mate, u);\r\n dual_var[u] = 2 * max_weight;\r\n in_blossom[u] = boost::make_shared< trivial_blossom >(u);\r\n outlet[u] = u;\r\n critical_edge_vector.push_back(\r\n vertex_to_edge_map_t(vei->begin(), vm));\r\n }\r\n\r\n critical_edge\r\n = vertex_pair_to_edge_map_t(critical_edge_vector.begin(), vm);\r\n\r\n init();\r\n }\r\n\r\n // return the top blossom where v is contained inside\r\n blossom_ptr_t in_top_blossom(vertex_descriptor_t v) const\r\n {\r\n blossom_ptr_t b = in_blossom[v];\r\n while (b->father != blossom_ptr_t())\r\n b = b->father;\r\n return b;\r\n }\r\n\r\n // check if vertex v is in blossom b\r\n bool is_in_blossom(blossom_ptr_t b, vertex_descriptor_t v) const\r\n {\r\n if (v == graph_traits< Graph >::null_vertex())\r\n return false;\r\n blossom_ptr_t vb = in_blossom[v]->father;\r\n while (vb != blossom_ptr_t())\r\n {\r\n if (vb.get() == b.get())\r\n return true;\r\n vb = vb->father;\r\n }\r\n return false;\r\n }\r\n\r\n // return the base vertex of the top blossom that contains v\r\n inline vertex_descriptor_t base_vertex(vertex_descriptor_t v) const\r\n {\r\n return in_top_blossom(v)->get_base();\r\n }\r\n\r\n // add an existed top blossom of base vertex v into new top\r\n // blossom b as its sub-blossom\r\n void add_sub_blossom(blossom_ptr_t b, vertex_descriptor_t v)\r\n {\r\n blossom_ptr_t sub = in_top_blossom(v);\r\n sub->father = b;\r\n b->sub_blossoms.push_back(sub);\r\n if (sub->sub_blossoms.empty())\r\n return;\r\n for (blossom_iterator_t bi = top_blossoms.begin();\r\n bi != top_blossoms.end(); ++bi)\r\n {\r\n if (bi->get() == sub.get())\r\n {\r\n top_blossoms.erase(bi);\r\n break;\r\n }\r\n }\r\n }\r\n\r\n // when a top blossom is created or its base vertex getting an S-label,\r\n // add all edges incident to this blossom into even_edges\r\n void bloom(blossom_ptr_t b)\r\n {\r\n std::vector< vertex_descriptor_t > vertices_of_b = b->vertices();\r\n vertex_vec_iter_t vi;\r\n for (vi = vertices_of_b.begin(); vi != vertices_of_b.end(); ++vi)\r\n {\r\n out_edge_iterator_t oei, oei_end;\r\n for (boost::tie(oei, oei_end) = out_edges(*vi, g); oei != oei_end;\r\n ++oei)\r\n {\r\n if (target(*oei, g) != *vi && mate[*vi] != target(*oei, g))\r\n even_edges.push_back(*oei);\r\n }\r\n }\r\n }\r\n\r\n // assigning a T-label to a non S-vertex, along with outlet and updating pi\r\n // value if updated pi[v] equals zero, augment the matching from its mate\r\n // vertex\r\n void put_T_label(vertex_descriptor_t v, vertex_descriptor_t T_label,\r\n vertex_descriptor_t outlet_v, edge_property_t pi_v)\r\n {\r\n if (label_S[v] != graph_traits< Graph >::null_vertex())\r\n return;\r\n\r\n label_T[v] = T_label;\r\n outlet[v] = outlet_v;\r\n pi[v] = pi_v;\r\n\r\n vertex_descriptor_t v_mate = mate[v];\r\n if (pi[v] == 0)\r\n {\r\n label_T[v_mate] = graph_traits< Graph >::null_vertex();\r\n label_S[v_mate] = v;\r\n bloom(in_top_blossom(v_mate));\r\n }\r\n }\r\n\r\n // get the missing T-label for a to-be-expanded base vertex\r\n // the missing T-label is the last vertex of the path from outlet[v] to v\r\n std::pair< vertex_descriptor_t, vertex_descriptor_t > missing_label(\r\n vertex_descriptor_t b_base)\r\n {\r\n vertex_descriptor_t missing_outlet = outlet[b_base];\r\n\r\n if (outlet[b_base] == b_base)\r\n return std::make_pair(\r\n graph_traits< Graph >::null_vertex(), missing_outlet);\r\n\r\n vertex_iterator_t vi, vi_end;\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n old_label[*vi] = std::make_pair(label_T[*vi], outlet[*vi]);\r\n\r\n std::pair< vertex_descriptor_t, vertex_state_t > child(\r\n outlet[b_base], graph::detail::V_EVEN);\r\n blossom_ptr_t b = in_blossom[child.first];\r\n for (; b->father->father != blossom_ptr_t(); b = b->father)\r\n ;\r\n child.first = b->get_base();\r\n\r\n if (child.first == b_base)\r\n return std::make_pair(\r\n graph_traits< Graph >::null_vertex(), missing_outlet);\r\n\r\n while (true)\r\n {\r\n std::pair< vertex_descriptor_t, vertex_state_t > child_parent\r\n = parent(child, true);\r\n\r\n for (b = in_blossom[child_parent.first];\r\n b->father->father != blossom_ptr_t(); b = b->father)\r\n ;\r\n missing_outlet = child_parent.first;\r\n child_parent.first = b->get_base();\r\n\r\n if (child_parent.first == b_base)\r\n break;\r\n else\r\n child = child_parent;\r\n }\r\n return std::make_pair(child.first, missing_outlet);\r\n }\r\n\r\n // expand a top blossom, put all its non-trivial sub-blossoms into\r\n // top_blossoms\r\n blossom_iterator_t expand_blossom(\r\n blossom_iterator_t bi, std::vector< blossom_ptr_t >& new_ones)\r\n {\r\n blossom_ptr_t b = *bi;\r\n for (blossom_iterator_t i = b->sub_blossoms.begin();\r\n i != b->sub_blossoms.end(); ++i)\r\n {\r\n blossom_ptr_t sub_blossom = *i;\r\n vertex_descriptor_t sub_base = sub_blossom->get_base();\r\n label_S[sub_base] = label_T[sub_base]\r\n = graph_traits< Graph >::null_vertex();\r\n outlet[sub_base] = sub_base;\r\n sub_blossom->father = blossom_ptr_t();\r\n // new top blossoms cannot be pushed back into top_blossoms\r\n // immediately, because push_back() may cause reallocation and then\r\n // invalid iterators\r\n if (!sub_blossom->sub_blossoms.empty())\r\n new_ones.push_back(sub_blossom);\r\n }\r\n return top_blossoms.erase(bi);\r\n }\r\n\r\n // when expanding a T-blossom with base v, it requires more operations:\r\n // supply the missing T-labels for new base vertices by picking the minimum\r\n // tau from vertices of each corresponding new top-blossoms; when label_T[v]\r\n // is null or we have a smaller tau from missing_label(v), replace T-label\r\n // and outlet of v (but don't bloom v)\r\n blossom_iterator_t expand_T_blossom(\r\n blossom_iterator_t bi, std::vector< blossom_ptr_t >& new_ones)\r\n {\r\n blossom_ptr_t b = *bi;\r\n\r\n vertex_descriptor_t b_base = b->get_base();\r\n std::pair< vertex_descriptor_t, vertex_descriptor_t > T_and_outlet\r\n = missing_label(b_base);\r\n\r\n blossom_iterator_t next_bi = expand_blossom(bi, new_ones);\r\n\r\n for (blossom_iterator_t i = b->sub_blossoms.begin();\r\n i != b->sub_blossoms.end(); ++i)\r\n {\r\n blossom_ptr_t sub_blossom = *i;\r\n vertex_descriptor_t sub_base = sub_blossom->get_base();\r\n vertex_descriptor_t min_tau_v\r\n = graph_traits< Graph >::null_vertex();\r\n edge_property_t min_tau\r\n = std::numeric_limits< edge_property_t >::max();\r\n\r\n std::vector< vertex_descriptor_t > sub_vertices\r\n = sub_blossom->vertices();\r\n for (vertex_vec_iter_t v = sub_vertices.begin();\r\n v != sub_vertices.end(); ++v)\r\n {\r\n if (tau[*v] < min_tau)\r\n {\r\n min_tau = tau[*v];\r\n min_tau_v = *v;\r\n }\r\n }\r\n\r\n if (min_tau < std::numeric_limits< edge_property_t >::max())\r\n put_T_label(\r\n sub_base, tau_idx[min_tau_v], min_tau_v, tau[min_tau_v]);\r\n }\r\n\r\n if (label_T[b_base] == graph_traits< Graph >::null_vertex()\r\n || tau[old_label[b_base].second] < pi[b_base])\r\n boost::tie(label_T[b_base], outlet[b_base]) = T_and_outlet;\r\n\r\n return next_bi;\r\n }\r\n\r\n // when vertices v and w are matched to each other by augmenting,\r\n // we must set v/w as base vertex of any blossom who contains v/w and\r\n // is a sub-blossom of their lowest (smallest) common blossom\r\n void adjust_blossom(vertex_descriptor_t v, vertex_descriptor_t w)\r\n {\r\n blossom_ptr_t vb = in_blossom[v], wb = in_blossom[w],\r\n lowest_common_blossom;\r\n std::vector< blossom_ptr_t > v_ancestors, w_ancestors;\r\n\r\n while (vb->father != blossom_ptr_t())\r\n {\r\n v_ancestors.push_back(vb->father);\r\n vb = vb->father;\r\n }\r\n while (wb->father != blossom_ptr_t())\r\n {\r\n w_ancestors.push_back(wb->father);\r\n wb = wb->father;\r\n }\r\n\r\n typename std::vector< blossom_ptr_t >::reverse_iterator i, j;\r\n i = v_ancestors.rbegin();\r\n j = w_ancestors.rbegin();\r\n while (i != v_ancestors.rend() && j != w_ancestors.rend()\r\n && i->get() == j->get())\r\n {\r\n lowest_common_blossom = *i;\r\n ++i;\r\n ++j;\r\n }\r\n\r\n vb = in_blossom[v];\r\n wb = in_blossom[w];\r\n while (vb->father != lowest_common_blossom)\r\n {\r\n vb->father->set_base(vb);\r\n vb = vb->father;\r\n }\r\n while (wb->father != lowest_common_blossom)\r\n {\r\n wb->father->set_base(wb);\r\n wb = wb->father;\r\n }\r\n }\r\n\r\n // every edge weight is multiplied by 4 to ensure integer weights\r\n // throughout the algorithm if all input weights are integers\r\n inline edge_property_t slack(const edge_descriptor_t& e) const\r\n {\r\n vertex_descriptor_t v, w;\r\n v = source(e, g);\r\n w = target(e, g);\r\n return dual_var[v] + dual_var[w] - 4 * get(edge_weight, g, e);\r\n }\r\n\r\n // backtrace one step on vertex v along the augmenting path\r\n // by its labels and its vertex state;\r\n // boolean parameter \"use_old\" means whether we are updating labels,\r\n // if we are, then we use old labels to backtrace and also we\r\n // don't jump to its base vertex when we reach an odd vertex\r\n std::pair< vertex_descriptor_t, vertex_state_t > parent(\r\n std::pair< vertex_descriptor_t, vertex_state_t > v,\r\n bool use_old = false) const\r\n {\r\n if (v.second == graph::detail::V_EVEN)\r\n {\r\n // a paranoid check: label_S shoule be the same as mate in\r\n // backtracing\r\n if (label_S[v.first] == graph_traits< Graph >::null_vertex())\r\n label_S[v.first] = mate[v.first];\r\n return std::make_pair(label_S[v.first], graph::detail::V_ODD);\r\n }\r\n else if (v.second == graph::detail::V_ODD)\r\n {\r\n vertex_descriptor_t w = use_old ? old_label[v.first].first\r\n : base_vertex(label_T[v.first]);\r\n return std::make_pair(w, graph::detail::V_EVEN);\r\n }\r\n return std::make_pair(v.first, graph::detail::V_UNREACHED);\r\n }\r\n\r\n // backtrace from vertices v and w to their free (unmatched) ancesters,\r\n // return the nearest common ancestor (null_vertex if none) of v and w\r\n vertex_descriptor_t nearest_common_ancestor(vertex_descriptor_t v,\r\n vertex_descriptor_t w, vertex_descriptor_t& v_free_ancestor,\r\n vertex_descriptor_t& w_free_ancestor) const\r\n {\r\n std::pair< vertex_descriptor_t, vertex_state_t > v_up(\r\n v, graph::detail::V_EVEN);\r\n std::pair< vertex_descriptor_t, vertex_state_t > w_up(\r\n w, graph::detail::V_EVEN);\r\n vertex_descriptor_t nca;\r\n nca = w_free_ancestor = v_free_ancestor\r\n = graph_traits< Graph >::null_vertex();\r\n\r\n std::vector< bool > ancestor_of_w_vector(num_vertices(g), false);\r\n std::vector< bool > ancestor_of_v_vector(num_vertices(g), false);\r\n vertex_to_bool_map_t ancestor_of_w(ancestor_of_w_vector.begin(), vm);\r\n vertex_to_bool_map_t ancestor_of_v(ancestor_of_v_vector.begin(), vm);\r\n\r\n while (nca == graph_traits< Graph >::null_vertex()\r\n && (v_free_ancestor == graph_traits< Graph >::null_vertex()\r\n || w_free_ancestor == graph_traits< Graph >::null_vertex()))\r\n {\r\n ancestor_of_w[w_up.first] = true;\r\n ancestor_of_v[v_up.first] = true;\r\n\r\n if (w_free_ancestor == graph_traits< Graph >::null_vertex())\r\n w_up = parent(w_up);\r\n if (v_free_ancestor == graph_traits< Graph >::null_vertex())\r\n v_up = parent(v_up);\r\n\r\n if (mate[v_up.first] == graph_traits< Graph >::null_vertex())\r\n v_free_ancestor = v_up.first;\r\n if (mate[w_up.first] == graph_traits< Graph >::null_vertex())\r\n w_free_ancestor = w_up.first;\r\n\r\n if (ancestor_of_w[v_up.first] == true || v_up.first == w_up.first)\r\n nca = v_up.first;\r\n else if (ancestor_of_v[w_up.first] == true)\r\n nca = w_up.first;\r\n else if (v_free_ancestor == w_free_ancestor\r\n && v_free_ancestor != graph_traits< Graph >::null_vertex())\r\n nca = v_up.first;\r\n }\r\n\r\n return nca;\r\n }\r\n\r\n // when a new top blossom b is created by connecting (v, w), we add\r\n // sub-blossoms into b along backtracing from v_prime and w_prime to\r\n // stop_vertex (the base vertex); also, we set labels and outlet for each\r\n // base vertex we pass by\r\n void make_blossom(blossom_ptr_t b, vertex_descriptor_t w_prime,\r\n vertex_descriptor_t v_prime, vertex_descriptor_t stop_vertex)\r\n {\r\n std::pair< vertex_descriptor_t, vertex_state_t > u(\r\n v_prime, graph::detail::V_ODD);\r\n std::pair< vertex_descriptor_t, vertex_state_t > u_up(\r\n w_prime, graph::detail::V_EVEN);\r\n\r\n for (; u_up.first != stop_vertex; u = u_up, u_up = parent(u))\r\n {\r\n if (u_up.second == graph::detail::V_EVEN)\r\n {\r\n if (!in_top_blossom(u_up.first)->sub_blossoms.empty())\r\n outlet[u_up.first] = label_T[u.first];\r\n label_T[u_up.first] = outlet[u.first];\r\n }\r\n else if (u_up.second == graph::detail::V_ODD)\r\n label_S[u_up.first] = u.first;\r\n\r\n add_sub_blossom(b, u_up.first);\r\n }\r\n }\r\n\r\n // the design of recursively expanding augmenting path in\r\n // (reversed_)retrieve_augmenting_path functions is inspired by same\r\n // functions in max_cardinality_matching.hpp; except that in weighted\r\n // matching, we use \"outlet\" vertices instead of \"bridge\" vertex pairs: if\r\n // blossom b is the smallest non-trivial blossom that contains its base\r\n // vertex v, then v and outlet[v] are where augmenting path enters and\r\n // leaves b\r\n void retrieve_augmenting_path(\r\n vertex_descriptor_t v, vertex_descriptor_t w, vertex_state_t v_state)\r\n {\r\n if (v == w)\r\n aug_path.push_back(v);\r\n else if (v_state == graph::detail::V_EVEN)\r\n {\r\n aug_path.push_back(v);\r\n retrieve_augmenting_path(label_S[v], w, graph::detail::V_ODD);\r\n }\r\n else if (v_state == graph::detail::V_ODD)\r\n {\r\n if (outlet[v] == v)\r\n aug_path.push_back(v);\r\n else\r\n reversed_retrieve_augmenting_path(\r\n outlet[v], v, graph::detail::V_EVEN);\r\n retrieve_augmenting_path(label_T[v], w, graph::detail::V_EVEN);\r\n }\r\n }\r\n\r\n void reversed_retrieve_augmenting_path(\r\n vertex_descriptor_t v, vertex_descriptor_t w, vertex_state_t v_state)\r\n {\r\n if (v == w)\r\n aug_path.push_back(v);\r\n else if (v_state == graph::detail::V_EVEN)\r\n {\r\n reversed_retrieve_augmenting_path(\r\n label_S[v], w, graph::detail::V_ODD);\r\n aug_path.push_back(v);\r\n }\r\n else if (v_state == graph::detail::V_ODD)\r\n {\r\n reversed_retrieve_augmenting_path(\r\n label_T[v], w, graph::detail::V_EVEN);\r\n if (outlet[v] != v)\r\n retrieve_augmenting_path(outlet[v], v, graph::detail::V_EVEN);\r\n else\r\n aug_path.push_back(v);\r\n }\r\n }\r\n\r\n // correct labels for vertices in the augmenting path\r\n void relabel(vertex_descriptor_t v)\r\n {\r\n blossom_ptr_t b = in_blossom[v]->father;\r\n\r\n if (!is_in_blossom(b, mate[v]))\r\n { // if v is a new base vertex\r\n std::pair< vertex_descriptor_t, vertex_state_t > u(\r\n v, graph::detail::V_EVEN);\r\n while (label_S[u.first] != u.first\r\n && is_in_blossom(b, label_S[u.first]))\r\n u = parent(u, true);\r\n\r\n vertex_descriptor_t old_base = u.first;\r\n if (label_S[old_base] != old_base)\r\n { // if old base is not exposed\r\n label_T[v] = label_S[old_base];\r\n outlet[v] = old_base;\r\n }\r\n else\r\n { // if old base is exposed then new label_T[v] is not in b,\r\n // we must (i) make b2 the smallest blossom containing v but not\r\n // as base vertex (ii) backtrace from b2's new base vertex to b\r\n label_T[v] = graph_traits< Graph >::null_vertex();\r\n for (b = b->father; b != blossom_ptr_t() && b->get_base() == v;\r\n b = b->father)\r\n ;\r\n if (b != blossom_ptr_t())\r\n {\r\n u = std::make_pair(b->get_base(), graph::detail::V_ODD);\r\n while (!is_in_blossom(\r\n in_blossom[v]->father, old_label[u.first].first))\r\n u = parent(u, true);\r\n label_T[v] = u.first;\r\n outlet[v] = old_label[u.first].first;\r\n }\r\n }\r\n }\r\n else if (label_S[v] == v || !is_in_blossom(b, label_S[v]))\r\n { // if v is an old base vertex\r\n // let u be the new base vertex; backtrace from u's old T-label\r\n std::pair< vertex_descriptor_t, vertex_state_t > u(\r\n b->get_base(), graph::detail::V_ODD);\r\n while (\r\n old_label[u.first].first != graph_traits< Graph >::null_vertex()\r\n && old_label[u.first].first != v)\r\n u = parent(u, true);\r\n label_T[v] = old_label[u.first].second;\r\n outlet[v] = v;\r\n }\r\n else // if v is neither a new nor an old base vertex\r\n label_T[v] = label_S[v];\r\n }\r\n\r\n void augmenting(vertex_descriptor_t v, vertex_descriptor_t v_free_ancestor,\r\n vertex_descriptor_t w, vertex_descriptor_t w_free_ancestor)\r\n {\r\n vertex_iterator_t vi, vi_end;\r\n\r\n // retrieve the augmenting path and put it in aug_path\r\n reversed_retrieve_augmenting_path(\r\n v, v_free_ancestor, graph::detail::V_EVEN);\r\n retrieve_augmenting_path(w, w_free_ancestor, graph::detail::V_EVEN);\r\n\r\n // augment the matching along aug_path\r\n vertex_descriptor_t a, b;\r\n vertex_list_t reversed_aug_path;\r\n while (!aug_path.empty())\r\n {\r\n a = aug_path.front();\r\n aug_path.pop_front();\r\n reversed_aug_path.push_back(a);\r\n b = aug_path.front();\r\n aug_path.pop_front();\r\n reversed_aug_path.push_back(b);\r\n\r\n mate[a] = b;\r\n mate[b] = a;\r\n\r\n // reset base vertex for every blossom in augment path\r\n adjust_blossom(a, b);\r\n }\r\n\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n old_label[*vi] = std::make_pair(label_T[*vi], outlet[*vi]);\r\n\r\n // correct labels for in-blossom vertices along aug_path\r\n while (!reversed_aug_path.empty())\r\n {\r\n a = reversed_aug_path.front();\r\n reversed_aug_path.pop_front();\r\n\r\n if (in_blossom[a]->father != blossom_ptr_t())\r\n relabel(a);\r\n }\r\n\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n {\r\n vertex_descriptor_t u = *vi;\r\n if (mate[u] != graph_traits< Graph >::null_vertex())\r\n label_S[u] = mate[u];\r\n }\r\n\r\n // expand blossoms with zero dual variables\r\n std::vector< blossom_ptr_t > new_top_blossoms;\r\n for (blossom_iterator_t bi = top_blossoms.begin();\r\n bi != top_blossoms.end();)\r\n {\r\n if ((*bi)->dual_var <= 0)\r\n bi = expand_blossom(bi, new_top_blossoms);\r\n else\r\n ++bi;\r\n }\r\n top_blossoms.insert(top_blossoms.end(), new_top_blossoms.begin(),\r\n new_top_blossoms.end());\r\n init();\r\n }\r\n\r\n // create a new blossom and set labels for vertices inside\r\n void blossoming(vertex_descriptor_t v, vertex_descriptor_t v_prime,\r\n vertex_descriptor_t w, vertex_descriptor_t w_prime,\r\n vertex_descriptor_t nca)\r\n {\r\n vertex_iterator_t vi, vi_end;\r\n\r\n std::vector< bool > is_old_base_vector(num_vertices(g));\r\n vertex_to_bool_map_t is_old_base(is_old_base_vector.begin(), vm);\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n {\r\n if (*vi == base_vertex(*vi))\r\n is_old_base[*vi] = true;\r\n }\r\n\r\n blossom_ptr_t b = boost::make_shared< blossom >();\r\n add_sub_blossom(b, nca);\r\n\r\n label_T[w_prime] = v;\r\n label_T[v_prime] = w;\r\n outlet[w_prime] = w;\r\n outlet[v_prime] = v;\r\n\r\n make_blossom(b, w_prime, v_prime, nca);\r\n make_blossom(b, v_prime, w_prime, nca);\r\n\r\n label_T[nca] = graph_traits< Graph >::null_vertex();\r\n outlet[nca] = nca;\r\n\r\n top_blossoms.push_back(b);\r\n bloom(b);\r\n\r\n // set gamma[b_base] = min_slack{critical_edge(b_base, other_base)}\r\n // where each critical edge is updated before, by\r\n // argmin{slack(old_bases_in_b, other_base)};\r\n vertex_vec_iter_t i, j;\r\n std::vector< vertex_descriptor_t > b_vertices = b->vertices(),\r\n old_base_in_b, other_base;\r\n vertex_descriptor_t b_base = b->get_base();\r\n for (i = b_vertices.begin(); i != b_vertices.end(); ++i)\r\n {\r\n if (is_old_base[*i])\r\n old_base_in_b.push_back(*i);\r\n }\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n {\r\n if (*vi != b_base && *vi == base_vertex(*vi))\r\n other_base.push_back(*vi);\r\n }\r\n for (i = other_base.begin(); i != other_base.end(); ++i)\r\n {\r\n edge_property_t min_slack\r\n = std::numeric_limits< edge_property_t >::max();\r\n std::pair< edge_descriptor_t, bool > b_vi = null_edge;\r\n for (j = old_base_in_b.begin(); j != old_base_in_b.end(); ++j)\r\n {\r\n if (critical_edge[*j][*i] != null_edge\r\n && min_slack > slack(critical_edge[*j][*i].first))\r\n {\r\n min_slack = slack(critical_edge[*j][*i].first);\r\n b_vi = critical_edge[*j][*i];\r\n }\r\n }\r\n critical_edge[b_base][*i] = critical_edge[*i][b_base] = b_vi;\r\n }\r\n gamma[b_base] = std::numeric_limits< edge_property_t >::max();\r\n for (i = other_base.begin(); i != other_base.end(); ++i)\r\n {\r\n if (critical_edge[b_base][*i] != null_edge)\r\n gamma[b_base] = std::min(\r\n gamma[b_base], slack(critical_edge[b_base][*i].first));\r\n }\r\n }\r\n\r\n void init()\r\n {\r\n even_edges.clear();\r\n\r\n vertex_iterator_t vi, vi_end;\r\n typename std::vector<\r\n std::vector< std::pair< edge_descriptor_t, bool > > >::iterator vei;\r\n\r\n for (boost::tie(vi, vi_end) = vertices(g),\r\n vei = critical_edge_vectors.begin();\r\n vi != vi_end; ++vi, ++vei)\r\n {\r\n vertex_descriptor_t u = *vi;\r\n out_edge_iterator_t ei, ei_end;\r\n\r\n gamma[u] = tau[u] = pi[u]\r\n = std::numeric_limits< edge_property_t >::max();\r\n std::fill(vei->begin(), vei->end(), null_edge);\r\n\r\n if (base_vertex(u) != u)\r\n continue;\r\n\r\n label_S[u] = label_T[u] = graph_traits< Graph >::null_vertex();\r\n outlet[u] = u;\r\n\r\n if (mate[u] == graph_traits< Graph >::null_vertex())\r\n {\r\n label_S[u] = u;\r\n bloom(in_top_blossom(u));\r\n }\r\n }\r\n }\r\n\r\n bool augment_matching()\r\n {\r\n vertex_descriptor_t v, w, w_free_ancestor, v_free_ancestor;\r\n v = w = w_free_ancestor = v_free_ancestor\r\n = graph_traits< Graph >::null_vertex();\r\n bool found_alternating_path = false;\r\n\r\n // note that we only use edges of zero slack value for augmenting\r\n while (!even_edges.empty() && !found_alternating_path)\r\n {\r\n // search for augmenting paths depth-first\r\n edge_descriptor_t current_edge = even_edges.back();\r\n even_edges.pop_back();\r\n\r\n v = source(current_edge, g);\r\n w = target(current_edge, g);\r\n\r\n vertex_descriptor_t v_prime = base_vertex(v);\r\n vertex_descriptor_t w_prime = base_vertex(w);\r\n\r\n // w_prime == v_prime implies that we get an edge that has been\r\n // shrunk into a blossom\r\n if (v_prime == w_prime)\r\n continue;\r\n\r\n // a paranoid check\r\n if (label_S[v_prime] == graph_traits< Graph >::null_vertex())\r\n {\r\n std::swap(v_prime, w_prime);\r\n std::swap(v, w);\r\n }\r\n\r\n // w_prime may be unlabeled or have a T-label; replace the existed\r\n // T-label if the edge slack is smaller than current pi[w_prime] and\r\n // update it. Note that a T-label is \"deserved\" only when pi equals\r\n // zero. also update tau and tau_idx so that tau_idx becomes T-label\r\n // when a T-blossom is expanded\r\n if (label_S[w_prime] == graph_traits< Graph >::null_vertex())\r\n {\r\n if (slack(current_edge) < pi[w_prime])\r\n put_T_label(w_prime, v, w, slack(current_edge));\r\n if (slack(current_edge) < tau[w])\r\n {\r\n if (in_blossom[w]->father == blossom_ptr_t()\r\n || label_T[w_prime] == v\r\n || label_T[w_prime]\r\n == graph_traits< Graph >::null_vertex()\r\n || nearest_common_ancestor(v_prime, label_T[w_prime],\r\n v_free_ancestor, w_free_ancestor)\r\n == graph_traits< Graph >::null_vertex())\r\n {\r\n tau[w] = slack(current_edge);\r\n tau_idx[w] = v;\r\n }\r\n }\r\n }\r\n\r\n else\r\n {\r\n if (slack(current_edge) > 0)\r\n {\r\n // update gamma and critical_edges when we have a smaller\r\n // edge slack\r\n gamma[v_prime]\r\n = std::min(gamma[v_prime], slack(current_edge));\r\n gamma[w_prime]\r\n = std::min(gamma[w_prime], slack(current_edge));\r\n if (critical_edge[v_prime][w_prime] == null_edge\r\n || slack(critical_edge[v_prime][w_prime].first)\r\n > slack(current_edge))\r\n {\r\n critical_edge[v_prime][w_prime]\r\n = std::pair< edge_descriptor_t, bool >(\r\n current_edge, true);\r\n critical_edge[w_prime][v_prime]\r\n = std::pair< edge_descriptor_t, bool >(\r\n current_edge, true);\r\n }\r\n continue;\r\n }\r\n else if (slack(current_edge) == 0)\r\n {\r\n // if nca is null_vertex then we have an augmenting path;\r\n // otherwise we have a new top blossom with nca as its base\r\n // vertex\r\n vertex_descriptor_t nca = nearest_common_ancestor(\r\n v_prime, w_prime, v_free_ancestor, w_free_ancestor);\r\n\r\n if (nca == graph_traits< Graph >::null_vertex())\r\n found_alternating_path\r\n = true; // to break out of the loop\r\n else\r\n blossoming(v, v_prime, w, w_prime, nca);\r\n }\r\n }\r\n }\r\n\r\n if (!found_alternating_path)\r\n return false;\r\n\r\n augmenting(v, v_free_ancestor, w, w_free_ancestor);\r\n return true;\r\n }\r\n\r\n // slack the vertex and blossom dual variables when there is no augmenting\r\n // path found according to the primal-dual method\r\n bool adjust_dual()\r\n {\r\n edge_property_t delta1, delta2, delta3, delta4, delta;\r\n delta1 = delta2 = delta3 = delta4\r\n = std::numeric_limits< edge_property_t >::max();\r\n\r\n vertex_iterator_t vi, vi_end;\r\n\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n {\r\n delta1 = std::min(delta1, dual_var[*vi]);\r\n delta4 = pi[*vi] > 0 ? std::min(delta4, pi[*vi]) : delta4;\r\n if (*vi == base_vertex(*vi))\r\n delta3 = std::min(delta3, gamma[*vi] / 2);\r\n }\r\n\r\n for (blossom_iterator_t bi = top_blossoms.begin();\r\n bi != top_blossoms.end(); ++bi)\r\n {\r\n vertex_descriptor_t b_base = (*bi)->get_base();\r\n if (label_T[b_base] != graph_traits< Graph >::null_vertex()\r\n && pi[b_base] == 0)\r\n delta2 = std::min(delta2, (*bi)->dual_var / 2);\r\n }\r\n\r\n delta = std::min(std::min(delta1, delta2), std::min(delta3, delta4));\r\n\r\n // start updating dual variables, note that the order is important\r\n\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n {\r\n vertex_descriptor_t v = *vi, v_prime = base_vertex(v);\r\n\r\n if (label_S[v_prime] != graph_traits< Graph >::null_vertex())\r\n dual_var[v] -= delta;\r\n else if (label_T[v_prime] != graph_traits< Graph >::null_vertex()\r\n && pi[v_prime] == 0)\r\n dual_var[v] += delta;\r\n\r\n if (v == v_prime)\r\n gamma[v] -= 2 * delta;\r\n }\r\n\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n {\r\n vertex_descriptor_t v_prime = base_vertex(*vi);\r\n if (pi[v_prime] > 0)\r\n tau[*vi] -= delta;\r\n }\r\n\r\n for (blossom_iterator_t bi = top_blossoms.begin();\r\n bi != top_blossoms.end(); ++bi)\r\n {\r\n vertex_descriptor_t b_base = (*bi)->get_base();\r\n if (label_T[b_base] != graph_traits< Graph >::null_vertex()\r\n && pi[b_base] == 0)\r\n (*bi)->dual_var -= 2 * delta;\r\n if (label_S[b_base] != graph_traits< Graph >::null_vertex())\r\n (*bi)->dual_var += 2 * delta;\r\n }\r\n\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n {\r\n vertex_descriptor_t v = *vi;\r\n if (pi[v] > 0)\r\n pi[v] -= delta;\r\n\r\n // when some T-vertices have zero pi value, bloom their mates so\r\n // that matching can be further augmented\r\n if (label_T[v] != graph_traits< Graph >::null_vertex()\r\n && pi[v] == 0)\r\n put_T_label(v, label_T[v], outlet[v], pi[v]);\r\n }\r\n\r\n // optimal solution reached, halt\r\n if (delta == delta1)\r\n return false;\r\n\r\n // expand odd blossoms with zero dual variables and zero pi value of\r\n // their base vertices\r\n if (delta == delta2 && delta != delta3)\r\n {\r\n std::vector< blossom_ptr_t > new_top_blossoms;\r\n for (blossom_iterator_t bi = top_blossoms.begin();\r\n bi != top_blossoms.end();)\r\n {\r\n const blossom_ptr_t b = *bi;\r\n vertex_descriptor_t b_base = b->get_base();\r\n if (b->dual_var == 0\r\n && label_T[b_base] != graph_traits< Graph >::null_vertex()\r\n && pi[b_base] == 0)\r\n bi = expand_T_blossom(bi, new_top_blossoms);\r\n else\r\n ++bi;\r\n }\r\n top_blossoms.insert(top_blossoms.end(), new_top_blossoms.begin(),\r\n new_top_blossoms.end());\r\n }\r\n\r\n while (true)\r\n {\r\n // find a zero-slack critical edge (v, w) of zero gamma values\r\n std::pair< edge_descriptor_t, bool > best_edge = null_edge;\r\n std::vector< vertex_descriptor_t > base_nodes;\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n {\r\n if (*vi == base_vertex(*vi))\r\n base_nodes.push_back(*vi);\r\n }\r\n for (vertex_vec_iter_t i = base_nodes.begin();\r\n i != base_nodes.end(); ++i)\r\n {\r\n if (gamma[*i] == 0)\r\n {\r\n for (vertex_vec_iter_t j = base_nodes.begin();\r\n j != base_nodes.end(); ++j)\r\n {\r\n if (critical_edge[*i][*j] != null_edge\r\n && slack(critical_edge[*i][*j].first) == 0)\r\n best_edge = critical_edge[*i][*j];\r\n }\r\n }\r\n }\r\n\r\n // if not found, continue finding other augment matching\r\n if (best_edge == null_edge)\r\n {\r\n bool augmented = augment_matching();\r\n return augmented || delta != delta1;\r\n }\r\n // if found, determine either augmenting or blossoming\r\n vertex_descriptor_t v = source(best_edge.first, g),\r\n w = target(best_edge.first, g);\r\n vertex_descriptor_t v_prime = base_vertex(v),\r\n w_prime = base_vertex(w), v_free_ancestor,\r\n w_free_ancestor;\r\n vertex_descriptor_t nca = nearest_common_ancestor(\r\n v_prime, w_prime, v_free_ancestor, w_free_ancestor);\r\n if (nca == graph_traits< Graph >::null_vertex())\r\n {\r\n augmenting(v, v_free_ancestor, w, w_free_ancestor);\r\n return true;\r\n }\r\n else\r\n blossoming(v, v_prime, w, w_prime, nca);\r\n }\r\n\r\n return false;\r\n }\r\n\r\n template < typename PropertyMap > void get_current_matching(PropertyMap pm)\r\n {\r\n vertex_iterator_t vi, vi_end;\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n put(pm, *vi, mate[*vi]);\r\n }\r\n\r\nprivate:\r\n const Graph& g;\r\n VertexIndexMap vm;\r\n const std::pair< edge_descriptor_t, bool > null_edge;\r\n\r\n // storage for the property maps below\r\n std::vector< vertex_descriptor_t > mate_vector;\r\n std::vector< vertex_descriptor_t > label_S_vector, label_T_vector;\r\n std::vector< vertex_descriptor_t > outlet_vector;\r\n std::vector< vertex_descriptor_t > tau_idx_vector;\r\n std::vector< edge_property_t > dual_var_vector;\r\n std::vector< edge_property_t > pi_vector, gamma_vector, tau_vector;\r\n std::vector< blossom_ptr_t > in_blossom_vector;\r\n std::vector< std::pair< vertex_descriptor_t, vertex_descriptor_t > >\r\n old_label_vector;\r\n std::vector< vertex_to_edge_map_t > critical_edge_vector;\r\n std::vector< std::vector< std::pair< edge_descriptor_t, bool > > >\r\n critical_edge_vectors;\r\n\r\n // iterator property maps\r\n vertex_to_vertex_map_t mate;\r\n vertex_to_vertex_map_t label_S; // v has an S-label -> v can be an even\r\n // vertex, label_S[v] is its mate\r\n vertex_to_vertex_map_t\r\n label_T; // v has a T-label -> v can be an odd vertex, label_T[v] is its\r\n // predecessor in aug_path\r\n vertex_to_vertex_map_t outlet;\r\n vertex_to_vertex_map_t tau_idx;\r\n vertex_to_weight_map_t dual_var;\r\n vertex_to_weight_map_t pi, gamma, tau;\r\n vertex_to_blossom_map_t\r\n in_blossom; // map any vertex v to the trivial blossom containing v\r\n vertex_to_pair_map_t old_label; // before\r\n // relabeling or expanding T-blossoms\r\n vertex_pair_to_edge_map_t\r\n critical_edge; // an not matched edge (v, w) is critical if v and w\r\n // belongs to different S-blossoms\r\n\r\n vertex_list_t aug_path;\r\n edge_list_t even_edges;\r\n std::vector< blossom_ptr_t > top_blossoms;\r\n};\r\n\r\ntemplate < typename Graph, typename MateMap, typename VertexIndexMap >\r\nvoid maximum_weighted_matching(const Graph& g, MateMap mate, VertexIndexMap vm)\r\n{\r\n empty_matching< Graph, MateMap >::find_matching(g, mate);\r\n weighted_augmenting_path_finder< Graph, MateMap, VertexIndexMap > augmentor(\r\n g, mate, vm);\r\n\r\n // can have |V| times augmenting at most\r\n for (std::size_t t = 0; t < num_vertices(g); ++t)\r\n {\r\n bool augmented = false;\r\n while (!augmented)\r\n {\r\n augmented = augmentor.augment_matching();\r\n if (!augmented)\r\n {\r\n // halt if adjusting dual variables can't bring potential\r\n // augment\r\n if (!augmentor.adjust_dual())\r\n break;\r\n }\r\n }\r\n if (!augmented)\r\n break;\r\n }\r\n\r\n augmentor.get_current_matching(mate);\r\n}\r\n\r\ntemplate < typename Graph, typename MateMap >\r\ninline void maximum_weighted_matching(const Graph& g, MateMap mate)\r\n{\r\n maximum_weighted_matching(g, mate, get(vertex_index, g));\r\n}\r\n\r\n// brute-force matcher searches all possible combinations of matched edges to\r\n// get the maximum weighted matching which can be used for testing on small\r\n// graphs (within dozens vertices)\r\ntemplate < typename Graph, typename MateMap, typename VertexIndexMap >\r\nclass brute_force_matching\r\n{\r\npublic:\r\n typedef\r\n typename graph_traits< Graph >::vertex_descriptor vertex_descriptor_t;\r\n typedef typename graph_traits< Graph >::vertex_iterator vertex_iterator_t;\r\n typedef\r\n typename std::vector< vertex_descriptor_t >::iterator vertex_vec_iter_t;\r\n typedef typename graph_traits< Graph >::edge_iterator edge_iterator_t;\r\n typedef boost::iterator_property_map< vertex_vec_iter_t, VertexIndexMap >\r\n vertex_to_vertex_map_t;\r\n\r\n brute_force_matching(\r\n const Graph& arg_g, MateMap arg_mate, VertexIndexMap arg_vm)\r\n : g(arg_g)\r\n , vm(arg_vm)\r\n , mate_vector(num_vertices(g))\r\n , best_mate_vector(num_vertices(g))\r\n , mate(mate_vector.begin(), vm)\r\n , best_mate(best_mate_vector.begin(), vm)\r\n {\r\n vertex_iterator_t vi, vi_end;\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n best_mate[*vi] = mate[*vi] = get(arg_mate, *vi);\r\n }\r\n\r\n template < typename PropertyMap > void find_matching(PropertyMap pm)\r\n {\r\n edge_iterator_t ei;\r\n boost::tie(ei, ei_end) = edges(g);\r\n select_edge(ei);\r\n\r\n vertex_iterator_t vi, vi_end;\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n put(pm, *vi, best_mate[*vi]);\r\n }\r\n\r\nprivate:\r\n const Graph& g;\r\n VertexIndexMap vm;\r\n std::vector< vertex_descriptor_t > mate_vector, best_mate_vector;\r\n vertex_to_vertex_map_t mate, best_mate;\r\n edge_iterator_t ei_end;\r\n\r\n void select_edge(edge_iterator_t ei)\r\n {\r\n if (ei == ei_end)\r\n {\r\n if (matching_weight_sum(g, mate)\r\n > matching_weight_sum(g, best_mate))\r\n {\r\n vertex_iterator_t vi, vi_end;\r\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\r\n best_mate[*vi] = mate[*vi];\r\n }\r\n return;\r\n }\r\n\r\n vertex_descriptor_t v, w;\r\n v = source(*ei, g);\r\n w = target(*ei, g);\r\n\r\n select_edge(++ei);\r\n\r\n if (mate[v] == graph_traits< Graph >::null_vertex()\r\n && mate[w] == graph_traits< Graph >::null_vertex())\r\n {\r\n mate[v] = w;\r\n mate[w] = v;\r\n select_edge(ei);\r\n mate[v] = mate[w] = graph_traits< Graph >::null_vertex();\r\n }\r\n }\r\n};\r\n\r\ntemplate < typename Graph, typename MateMap, typename VertexIndexMap >\r\nvoid brute_force_maximum_weighted_matching(\r\n const Graph& g, MateMap mate, VertexIndexMap vm)\r\n{\r\n empty_matching< Graph, MateMap >::find_matching(g, mate);\r\n brute_force_matching< Graph, MateMap, VertexIndexMap > brute_force_matcher(\r\n g, mate, vm);\r\n brute_force_matcher.find_matching(mate);\r\n}\r\n\r\ntemplate < typename Graph, typename MateMap >\r\ninline void brute_force_maximum_weighted_matching(const Graph& g, MateMap mate)\r\n{\r\n brute_force_maximum_weighted_matching(g, mate, get(vertex_index, g));\r\n}\r\n\r\n}\r\n\r\n#endif\r\n", "meta": {"hexsha": "8527664ea4db93693ceb14a33b1148371d4eec67", "size": 51035, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/boost/include/boost/graph/maximum_weighted_matching.hpp", "max_stars_repo_name": "kindlychung/mediasoup-sfu-cpp", "max_stars_repo_head_hexsha": "f69d2f48f7edbf4f0c57244280a47bea985f39cf", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 80.0, "max_stars_repo_stars_event_min_datetime": "2021-09-07T12:44:32.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T01:22:19.000Z", "max_issues_repo_path": "deps/boost/include/boost/graph/maximum_weighted_matching.hpp", "max_issues_repo_name": "kindlychung/mediasoup-sfu-cpp", "max_issues_repo_head_hexsha": "f69d2f48f7edbf4f0c57244280a47bea985f39cf", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2021-12-23T02:49:42.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-15T05:28:24.000Z", "max_forks_repo_path": "deps/boost/include/boost/graph/maximum_weighted_matching.hpp", "max_forks_repo_name": "kindlychung/mediasoup-sfu-cpp", "max_forks_repo_head_hexsha": "f69d2f48f7edbf4f0c57244280a47bea985f39cf", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 25.0, "max_forks_repo_forks_event_min_datetime": "2021-09-14T06:24:25.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T06:55:07.000Z", "avg_line_length": 38.8394216134, "max_line_length": 81, "alphanum_fraction": 0.5525031841, "num_tokens": 11551, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.23597951964532393}} {"text": "/* _______________________________________________________________________\n\n DAKOTA: Design Analysis Kit for Optimization and Terascale Applications\n Copyright 2014 Sandia Corporation.\n This software is distributed under the GNU Lesser General Public License.\n For more information, see the README file in the top Dakota directory.\n _______________________________________________________________________ */\n\n//- Class: NonlinearCGOptimizer\n//- Description: Implementation code for the NonlinearCGOptimizer class\n//- Owner: Brian Adams\n//- Checked by:\n\n#include \"NonlinearCGOptimizer.hpp\"\n#include \"ProblemDescDB.hpp\"\n#include \n\n// uncomment to use the Boost Brent's algorithm\n//#define BOOST_BRENT 1\n\nusing namespace std;\n\nnamespace Dakota {\n\n// wrapper around linesearch evaluator needed for use in Brent's algorithm\nclass boost_ls_eval {\n\npublic:\n\n // constructor\n boost_ls_eval(NonlinearCGOptimizer& pimpl_): pImpl(pimpl_)\n { /* empty constructor */ }\n\n // evaluator\n Real operator()(const Real& trial_step)\n { return ( pImpl.linesearch_eval(trial_step, 1) ); }\n\nprivate:\n\n // pointer to class instance so we have a context when performing evaluations\n NonlinearCGOptimizer& pImpl;\n\n};\n\n\nNonlinearCGOptimizer::\nNonlinearCGOptimizer(ProblemDescDB& problem_db, Model& model): \n Optimizer(problem_db, model, std::shared_ptr(new NonlinearCGTraits())),\n initialStep(0.01), linesearchTolerance(1.0e-2),\n linesearchType(CG_LS_SIMPLE), maxLinesearchIters(10), relFunctionTol(0.0),\n relGradientTol(0.0), resetStep(true), restartIter(1000000),\n updateType(CG_FLETCHER_REEVES)\n{\n if (numFunctions > 1 || numConstraints > 0 || boundConstraintFlag) {\n Cerr << \"ERROR: NonlinearCG only supports unconstrainted single objective \"\n\t << \"problems!\" << endl;\n abort_handler(-1);\n }\n // some of the defaults may be overridden by user-supplied options\n parse_options();\n\n stepLength = initialStep;\n}\n\n\nNonlinearCGOptimizer::~NonlinearCGOptimizer()\n{ }\n\n\nvoid NonlinearCGOptimizer::core_run()\n{\n\n // TODO: Once DAKOTA moves to Teuchos for its numerical data type,\n // remove the various copies below. (Also, could use std::copy for some.)\n copy_data(iteratedModel.continuous_variables(), designVars); // view->copy\n searchDirection.sizeUninitialized(numContinuousVars);\n\n if (linesearchType > CG_FIXED_STEP)\n trialVars.sizeUninitialized(numContinuousVars);\n\n for (iterCurr = 0; iterCurr < maxIterations; iterCurr++) {\n\n // get function and gradient -- should discern whether linesearch\n // is in use and only request gradient if appropriate\n activeSet.request_values(3);\n iteratedModel.evaluate(activeSet);\n const Response& response = iteratedModel.current_response();\n const RealVector& functions = response.function_values();\n\n functionCurr = functions[0];\n gradCurr = response.function_gradient_view(0);\n\n // always store ||g||^2\n gradDotGrad_curr = gradCurr.dot(gradCurr);\n if (iterCurr == 0)\n gradDotGrad_init = gradDotGrad_curr;\n\n // TODO: accumulate stats\n if (outputLevel >= NORMAL_OUTPUT) {\n Cout << \"INFO (NonlinearCG): iteration statistics\\n\"; \n Cout << \"iter J norm(g) steplen\\n\";\n Cout << \"---- ---------------- ---------------- ----------------\\n\";\n Cout << setw(4) << iterCurr << \" \" << functionCurr << \" \" \n\t << sqrt(gradDotGrad_curr) << \" \" << stepLength << \"\\n\" << endl;\n }\n // check convergence: two-norm of gradient\n if (sqrt(gradDotGrad_curr) < convergenceTol) {\n Cout << \"INFO (NonlinearCG): hard convergence reached (gradient norm \"\n\t << \"within tolerance).\" << endl;\n break;\n }\n\n if ( iterCurr > 0)\n // could do as CTK and check this in conjunction with test above...\n if (sqrt(gradDotGrad_curr) < relGradientTol*gradDotGrad_init) {\n\tCout << \"INFO (NonlinearCG): convergence reached (gradient reduction \"\n\t << \"within tolerance).\" << endl;\n\tbreak;\n }\n if ( std::fabs(functionCurr - functionPrev) / \n\t std::max(1.0,std::fabs(functionPrev)) <\n\t relFunctionTol ) {\n\tCout << \"INFO (NonlinearCG): convergence reached (function change \"\n\t << \"within tolerance).\" << endl;\n\tbreak;\n }\n\n // will set searchDirection to steepest descent, or CG direction\n // TODO: recourse to steepest descent\n compute_direction();\n if (sqrt(searchDirection.dot(searchDirection)) < 1e-16) {\n Cout << \"INFO (NonlinearCG): degenerate search direction. Exiting.\"\n\t << endl;\n break;\n }\n\n // will set stepLength if successful, return false if failed\n if (!compute_step()) {\n Cout << \"INFO (NonlinearCG): failure computing step length. Exiting.\"\n\t << endl;\n break;\n }\n\n // update design vars here, gradient evaluated on loop continuation\n // compute designVars <- stepLength * searchDirection + designVars\n designVars.AXPY(designVars.length(), stepLength, searchDirection.values(),\n\t\t 1, designVars.values(), 1);\n\n // TODO: this is a duplicate copy in the linesearch case -- rework to avoid\n iteratedModel.continuous_variables(designVars);\n \n // archive gradient and derived metric for next iteration\n functionPrev = functionCurr;\n gradPrev = response.function_gradient_copy(0);\n gradDotGrad_prev = gradDotGrad_curr;\n\n }\n\n if (iterCurr == maxIterations)\n Cout << \"INFO (NonlinearCG): maxIterations = \" << maxIterations \n\t << \" reached. Exiting.\" << endl; \n\n\n // return the optimal point in best*; should do DB lookup?\n bestVariablesArray.front().continuous_variables(designVars);\n if (!localObjectiveRecast) // else local_objective_recast_retrieve() used\n bestResponseArray.front().function_value(functionCurr, 0);\n}\n\n\nvoid NonlinearCGOptimizer::compute_direction()\n{\n\n if (iterCurr == 0 || (iterCurr % restartIter == 0) || \n updateType == CG_STEEPEST) {\n\n // choose steepest descent on first iteration or on reset\n if (outputLevel >= VERBOSE_OUTPUT && iterCurr > 0)\n Cout << \"INFO (NonlinearCG): Iteration = \" << iterCurr \n\t << \", resetting to steepest descent.\" << endl;\n searchDirection.putScalar(0.0);\n searchDirection -= gradCurr;\n\n }\n else {\n\n double beta = 0.0;\n if (updateType == CG_FLETCHER_REEVES)\n beta = gradDotGrad_curr/gradDotGrad_prev;\t\n else {\n\n // Polak-Ribiere or Hestenes-Stiefel\n gradDiff = iteratedModel.current_response().function_gradient_copy(0);\n gradDiff -= gradPrev;\n Real gradCurr_dot_gradDiff = \n\tgradCurr.dot(gradDiff);\n \n if (updateType == CG_POLAK_RIBIERE)\n\tbeta = gradCurr_dot_gradDiff / gradDotGrad_prev;\n else if (updateType == CG_POLAK_RIBIERE_PLUS)\n\t// use max(B_PR, 0.0))\n\tbeta = std::max(gradCurr_dot_gradDiff / gradDotGrad_prev, 0.0);\n else if (updateType == CG_HESTENES_STIEFEL)\n\t// use (gradCurr - gradPrev)'*searchDirection in denominator\n\tbeta = gradCurr_dot_gradDiff / \n\t gradDiff.dot(searchDirection);\n }\n\n if (outputLevel >= DEBUG_OUTPUT)\n Cout << \"DEBUG (NonlinearCG): beta = \" << beta << endl;\n // searchDirection <-- beta*searchDirection - gradCurr\n searchDirection.scale(beta);\n searchDirection.AXPY(searchDirection.length(), -1.0,\n\t\t\t gradCurr.values(), 1,\n\t\t\t searchDirection.values(), 1);\n }\n if (outputLevel >= DEBUG_OUTPUT)\n Cout << \"DEBUG (NonlinearCG): new search direction is:\\n\"\n\t << searchDirection << endl;\n\n}\n \n\n// returns whether a valid step was found\nbool NonlinearCGOptimizer::compute_step()\n{\n\n // TODO: stepLength should be chosen based on descent direction and \n // previous successful step\n if (resetStep)\n stepLength = initialStep;\n\n switch (linesearchType) {\n\n case CG_FIXED_STEP:\n // do nothing (leave initial stepLength )\n return(true);\n break;\n\n case CG_LS_SIMPLE: {\n\n // value-based line search with simple decrease\n bool decrease = false;\n unsigned bt_iter = 0;\n while (!decrease && bt_iter < maxLinesearchIters) {\n\n if (linesearch_eval(stepLength) < functionCurr) {\n\tif (outputLevel > NORMAL_OUTPUT)\n\t Cout << \"INFO (NonlinearCG_LS): Simple decrease achieved; step = \" \n\t << stepLength << \".\" << endl;\n\tdecrease = true;\n }\n else {\n\tif (outputLevel > NORMAL_OUTPUT) {\n\t if (bt_iter == 0)\n\t Cout << \"INFO (NonlinearCG_LS): Initiating simple linesearch.\\n\";\n\t Cout << \"INFO (NonlinearCG_LS): Backtracking.\" << endl;\n\t}\n\tstepLength /= 2;\n\tbt_iter++;\n }\n\n }\n if (bt_iter == maxLinesearchIters && outputLevel > NORMAL_OUTPUT) {\n Cout << \"INFO (NonlinearCG_LS): Could not find step yielding simple \"\n\t << \"decrease.\" << endl;\n }\n return(decrease);\n\n break;\n }\n\n case CG_LS_BRENT: {\n\n if (outputLevel > NORMAL_OUTPUT)\n Cout << \"INFO (NonlinearCG_LS): Initiating Brent linesearch.\" << endl;\n\n // first bracket the minimum, then use Brent's algorithm from Boost to \n // hone in on the minimum\n Real xa, xb, xc, fa, fb, fc;\n xa = 0.0;\n fa = functionCurr;\n\n if (outputLevel > NORMAL_OUTPUT)\n Cout << \"INFO (NonlinearCG_LS): Evaluating at initial step = \" \n\t << stepLength << \".\" << endl;\n xb = stepLength;\n fb = linesearch_eval(xb);\n\n if (outputLevel > NORMAL_OUTPUT)\n Cout << \"INFO (NonlinearCG_LS): Initiating bracketing procedure.\" << endl;\n bracket_min(xa, xb, xc, fa, fb, fc);\n if (outputLevel > NORMAL_OUTPUT) {\n Cout << \"INFO (NonlinearCG_LS): Bracketing complete:\\n\";\n Cout << \"Bracket: [\" << xa << \", \" << xb << \", \" << xc << \"]\" << endl;\n Cout << \"Functions: [\" << fa << \", \" << fb << \", \" << fc << \"]\" << endl;\n Cout << \"INFO (NonlinearCG_LS): Initiating 1-D minimization.\" << endl;\n }\n\n#ifdef BOOST_BRENT\n if (outputLevel >= DEBUG_OUTPUT)\n Cout << \"INFO (NonlinearCG_LS): Using Boost Brent.\" << endl;\n int exponent;\n frexp(linesearchTolerance, &exponent);\n int bits = 1 - exponent;\n boost::uintmax_t max_it = maxLinesearchIters;\n\n pair opt_len = \n boost::math::tools::brent_find_minima\n (boost_ls_eval(*this), xa, xc, bits, max_it);\n stepLength = opt_len.first;\n#else\n if (outputLevel >= DEBUG_OUTPUT)\n Cout << \"INFO (NonlinearCG_LS): Using native Brent.\" << endl;\n stepLength = brent_minimize(xa, xc, linesearchTolerance);\n#endif\n\n if (outputLevel > NORMAL_OUTPUT)\n Cout << \"INFO (NonlinearCG_LS): Linesearch complete; step = \" \n\t << stepLength << \".\" << endl;\n \n return(true);\n \n break;\n\n }\n\n default:\n Cerr << \"ERROR (NonlinearCG_LS): Requested linesearch type not available.\" \n\t << endl;\n abort_handler(-1);\n break;\n\n }\n\n return(false);\n\n}\n\n\nvoid NonlinearCGOptimizer::bracket_min(Real& xa, Real& xb, Real& xc,\n\t\t\t\t Real& fa, Real& fb, Real& fc) \n{\n // Repeatedly evaluate the function along the search direction until\n // we know we've bracketed a minimum.\n\n \n const Real GOLDEN_RATIO = 1.618033988749895, SMALL_DIV = 1e-16,\n MAX_EXTRAP_FACTOR = 100.0;\n Real tmp, q, r, xm, xlim, fm = 0.0;\n\n // TODO: implement option to do simple backtracking to get a lower\n // function value in the current search direction; for now, just\n // swap the values of a and b if necessary\n // also, need to bound the search >= 0 when changing sign...\n if (fb > fa) {\n Cout << \"swapping points, fa = \" << fa << \"fb = \" << fb << \"diff \" \n\t << fb-fa << endl;\n tmp = xa;\n xa = xb;\n xb = tmp;\n tmp = fa;\n fa = fb;\n fb = tmp;\n }\n\n xc = xb + GOLDEN_RATIO*(xb-xa);\n fc = linesearch_eval(xc);\n\n if (outputLevel >= DEBUG_OUTPUT) {\n Cout << \"Bracket: [\" << xa << \", \" << xb << \", \" << xc << \"]\" << endl;\n Cout << \"Functions: [\" << fa << \", \" << fb << \", \" << fc << \"]\" << endl;\n }\n\n while (fb >= fc) {\n \n if (outputLevel >= DEBUG_OUTPUT) {\n Cout << \"Bracket: [\" << xa << \", \" << xb << \", \" << xc << \"]\" << endl;\n Cout << \"Functions: [\" << fa << \", \" << fb << \", \" << fc << \"]\" << endl;\n }\n\n // find the extremum xm of a quadratic model interpolating a, b, c\n q = (fb-fa)*(xb-xc);\n r = (fb-fc)*(xb-xa);\n // avoid division by small (q-r) by bounding with signed minimum\n tmp = std::fabs(q-r);\n tmp = (tmp < SMALL_DIV) ? SMALL_DIV : tmp;\n tmp = (q-r < 0 ) ? -tmp : tmp;\n xm = xb - (q*(xb-xc) -r*(xb-xa))/2.0/tmp;\n // maximum point for which we trust the interpolation\n xlim = xb + MAX_EXTRAP_FACTOR * (xc-xb);\n\n // now detect which interval xm is in and act accordingly\n // [xb, xc]\n if ( (xb-xm)*(xm-xc) > 0.0 ) {\n \n fm = linesearch_eval(xm);\n if (fm < fc) { \n\t// use points [b, xm, c]\n\txa = xb;\n\tfa = fb;\n xb = xm;\n\tfb = fm;\n\treturn;\n }\n else if (fm > fb) {\n\t// use points [a, b, xm]\n\txc = xm;\n\tfc = fm;\n\treturn;\n }\n xm = xc + GOLDEN_RATIO*(xc-xb);\n fm = linesearch_eval(xm);\n\n }\n // [xc, xlim]\n else if ((xc-xm)*(xm-xlim) > 0.0) {\n if (fm < fc) {\n\txb = xc;\n\tfb = fc;\n\txc = xm;\n\tfc = fm; \n\txm = xc+GOLDEN_RATIO*(xc-xb);\n\tfm = linesearch_eval(xm);\n }\n }\n // [xlim, inf]\n else if ((xm-xlim)*(xlim-xc) >= 0.0 ) {\n xm = xlim;\n fm = linesearch_eval(xm);\n }\n // [0,xb]\n else {\n xm = xc + GOLDEN_RATIO*(xc-xb);\n fm = linesearch_eval(xm);\n }\n\n // shift to newest 3 points before loop\n xa = xb;\n fa = fb;\n xb = xc;\n fb = fc;\n xc = xm;\n fc = fm;\n\n }\n\n}\n\n/** Perform 1-D minimization for the stepLength using Brent's method.\n This is a C translation of fmin.f from Netlib. \n*/\nReal NonlinearCGOptimizer::brent_minimize(Real a, Real b, Real tol)\n{\n\n // c is the squared inverse of the golden ratio\n Real c = 0.5*(3.0-sqrt(5.0));\n\n // eps is approximately the square root of the relative machine\n // precision.\n\n // initialization \n Real eps = std::numeric_limits::epsilon();\n Real tol1 = eps+1.0;\n eps = sqrt(eps);\n\n Real v = a+c*(b-a);\n Real w = v;\n Real x = v;\n Real e = 0.0;\n Real fx = linesearch_eval(x);\n Real fv = fx;\n Real fw = fx;\n Real tol3 = tol/3.0;\n\n Real xm = 0.5*(a+b);\n tol1 = eps*std::fabs(x)+tol3;\n Real t2 = 2.0*tol1;\n\n Real d, p, q, r, u, fu; // temp variables\n\n unsigned iter = 1;\n while ( iter < maxLinesearchIters && std::fabs(x-xm) > (t2-0.5*(b-a)) ) {\n\n d = 0.0;\n p = 0.0;\n q = 0.0;\n r = 0.0;\n if ( std::fabs(e) > tol1 ) { \n\n // fit parabola\n r = (x-w)*(fx-fv);\n q = (x-v)*(fx-fw);\n p = (x-v)*q-(x-w)*r;\n q = 2.0*(q-r);\n \n if (q <= 0.0)\n\tq = -q;\n else\n\tp = -p;\n r = e;\n e = d;\n \n }\n\n if ( (std::fabs(p) < std::fabs(0.5*q*r)) && (p > q*(a-x)) && (p < q*(b-x)) ) {\n\n // a parabolic-interpolation step\n d = p/q;\n u = x+d;\n // f must not be evaluated too close to ax or bx\n if ( ((u-a) < t2) || ((b-u) < t2) ) {\n\td = tol1;\n\tif (x >= xm) \n\t d = -d;\n }\n }\n else {\n\n // a golden-section step\n if (x >= xm)\n\te = a-x;\n else\n\te = b-x;\n d = c*e;\n \n }\n\n // f must not be evaluated too close to x\n if ( std::fabs(d) < tol1 )\n if ( d <= 0.0 )\n\tu = x-tol1;\n else\n\tu = x+tol1;\n else\n u = x+d;\n \n fu = linesearch_eval(u);\n\n // update a, b, v, w, and x\n if (fx <= fu) \n if (u >= x)\n\tb = u;\n else\n\ta = u;\n\n if (fu <= fx) {\n\n if (u >= x)\n\ta = x;\n else\n\tb = x;\n v = w;\n fv = fw;\n w = x;\n fw = fx;\n x = u;\n fx = fu;\n\n }\n else if ( (fu > fw) && (w != x) ) {\n\n if ((fu<=fv) || (v == x) || (v == w)) {\n\tv = u;\n\tfv = fu;\n }\n\n }\n else {\n\n v = w;\n fv = fw;\n w = u;\n fw = fu;\n\n }\n\n xm = 0.5*(a+b);\n tol1 = eps*std::fabs(x)+tol3;\n t2 = 2.0*tol1;\n iter++;\n\n } // end while\n\n if (iter > maxLinesearchIters && outputLevel >= NORMAL_OUTPUT) {\n Cout << \"WARN (NonlinearCG_LS): Step length not found within \"\n\t << \"maxLinesearchIters; using best known.\" << endl;\n }\n\n return(x);\n\n} \n\n\n// Function evaluator to use in linesearches\n// Uses the current designVars and searchDirection \n// TODO: Support gradient-based evals, likely returning a const Response&\nReal NonlinearCGOptimizer::linesearch_eval(const Real& trial_step,\n\t\t\t\t\t short req_val) \n{\n // evaluate function only \n for (size_t i=0; i update_type;\n update_type[\"steepest\"] = 0;\n update_type[\"fletcher_reeves\"] = 1;\n update_type[\"polak_ribiere\"] = 2;\n update_type[\"polak_ribiere_plus\"] = 3;\n update_type[\"hestenes_stiefel\"] = 4;\n\n // Allowed linesearch options\n map search_type;\n search_type[\"fixed_step\"] = 0;\n search_type[\"ls_simple\"] = 1;\n search_type[\"ls_brent\"] = 2;\n search_type[\"ls_wolfe\"] = 3;\n\n map opts;\n const StringArray& db_opts = probDescDB.get_sa(\"method.coliny.misc_options\");\n StringArray::const_iterator db_it = db_opts.begin();\n StringArray::const_iterator db_end = db_opts.end();\n String::const_iterator delim;\n\n for ( ; db_it != db_end; ++db_it)\n if ( (delim = find(db_it->begin(), db_it->end(), '=')) != db_it->end()) {\n\n String opt(*db_it, 0, distance(db_it->begin(), delim));\n String val(*db_it, distance(db_it->begin(), delim+1),\n\t\t distance(delim, db_it->end()));\n \n if (opt == \"initial_step\")\n\tinitialStep = atof(val.c_str());\n else if (opt == \"linesearch_tolerance\")\n\tlinesearchTolerance = atof(val.c_str());\n else if (opt == \"linesearch_type\") {\n\tmap::const_iterator cit;\n\tif ( (cit = search_type.find(val)) != search_type.end())\n\t linesearchType = cit->second;\n\telse {\n\t Cerr << \"ERROR (NonlinearCG): Invalid linesearch_type.\" << endl;\n\t abort_handler(-1);\n\t}\n }\n else if (opt == \"max_linesearch_iters\")\n\tmaxLinesearchIters = atoi(val.c_str());\n else if (opt == \"rel_function_tol\")\n\trelFunctionTol = atof(val.c_str());\n else if (opt == \"rel_gradient_tol\")\n\trelGradientTol = atof(val.c_str());\n else if (opt == \"restart_iter\")\n\trestartIter = atoi(val.c_str());\n else if (opt == \"reset_step\")\n\tresetStep = (val == \"true\") ? true : false;\n else if (opt == \"update_type\") {\n\tmap::const_iterator cit;\n\tif ( (cit = update_type.find(val)) != update_type.end())\n\t updateType = cit->second;\n\telse {\n\t Cerr << \"ERROR (NonlinearCG): Invalid update_type.\" << endl;\n\t abort_handler(-1);\n\t}\n }\n else {\n\tCerr << \"ERROR (NonlinearCG): Unknown misc_option.\" << endl;\n\tabort_handler(-1);\n }\n\n if (outputLevel > NORMAL_OUTPUT)\n\tCout << \"INFO (NonlinearCG): User parameter '\" << opt << \"': \" << val \n\t << endl;\n\n }\n else {\n Cerr << \"ERROR (NonlinearCG): Invalid misc_options format.\" << endl;\n abort_handler(-1);\n }\n\n}\n\n\n} // namespace Dakota\n", "meta": {"hexsha": "bd209bdb92626ccc24fe3392fb8c96856a3f3895", "size": 19105, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/NonlinearCGOptimizer.cpp", "max_stars_repo_name": "jnnccc/Dakota-orb", "max_stars_repo_head_hexsha": "96488e723be9c67f0f85be8162b7af52c312b770", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/NonlinearCGOptimizer.cpp", "max_issues_repo_name": "jnnccc/Dakota-orb", "max_issues_repo_head_hexsha": "96488e723be9c67f0f85be8162b7af52c312b770", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/NonlinearCGOptimizer.cpp", "max_forks_repo_name": "jnnccc/Dakota-orb", "max_forks_repo_head_hexsha": "96488e723be9c67f0f85be8162b7af52c312b770", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.4892086331, "max_line_length": 85, "alphanum_fraction": 0.61444648, "num_tokens": 5415, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.23597951964532393}} {"text": "/*\n * Copyright (C) 2016-2019 Istituto Italiano di Tecnologia (IIT)\n *\n * This software may be modified and distributed under the terms of the\n * BSD 3-Clause license. See the accompanying LICENSE file for details.\n */\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace bfl;\nusing namespace Eigen;\n\n\nclass DecoratedWNA : public StateModelDecorator\n{\npublic:\n DecoratedWNA(std::unique_ptr state_model) noexcept :\n StateModelDecorator(std::move(state_model))\n { };\n\n\n void motion(const Eigen::Ref& cur_states, Eigen::Ref mot_states) override\n {\n std::cout << \"Decorator: DecoratedWNA::motion().\" << std::endl;\n\n StateModelDecorator::motion(cur_states, mot_states);\n }\n};\n\n\nclass DecoratedLinearSensor : public MeasurementModelDecorator\n{\npublic:\n DecoratedLinearSensor(std::unique_ptr observation_model) noexcept :\n MeasurementModelDecorator(std::move(observation_model))\n { }\n\n\n std::pair measure() const override\n {\n std::cout << \"Decorator: DecoratedLinearSensor::measure().\" << std::endl;\n\n return MeasurementModelDecorator::measure();\n }\n};\n\n\nclass DecoratedDrawParticles : public PFPredictionDecorator\n{\npublic:\n DecoratedDrawParticles(std::unique_ptr prediction) noexcept :\n PFPredictionDecorator(std::move(prediction))\n { }\n\nprotected:\n void predictStep(const ParticleSet& prev_particles, ParticleSet& pred_particles) override\n {\n std::cout << \"Decorator: DecoratedDrawParticles::predictStep().\" << std::endl;\n\n PFPredictionDecorator::predictStep(prev_particles, pred_particles);\n }\n};\n\n\nclass DecoratedBootstrapCorrection : public PFCorrectionDecorator\n{\npublic:\n DecoratedBootstrapCorrection(std::unique_ptr correction) noexcept :\n PFCorrectionDecorator(std::move(correction))\n { }\n\nprotected:\n void correctStep(const ParticleSet& pred_particles, ParticleSet& cor_particles) override\n {\n std::cout << \"Decorator: DecoratedBootstrapCorrection::correctStep().\" << std::endl;\n\n PFCorrectionDecorator::correctStep(pred_particles, cor_particles);\n }\n};\n\n\nclass SISSimulation : public SIS\n{\npublic:\n SISSimulation\n (\n unsigned int num_particle,\n std::size_t state_size,\n unsigned int simulation_steps,\n std::unique_ptr initialization,\n std::unique_ptr prediction,\n std::unique_ptr correction,\n std::unique_ptr resampling\n ) noexcept :\n SIS(num_particle, state_size, std::move(initialization), std::move(prediction), std::move(correction), std::move(resampling)),\n simulation_steps_(simulation_steps)\n { }\n\nprotected:\n bool runCondition()\n {\n if (getFilteringStep() < simulation_steps_)\n return true;\n else\n return false;\n }\n\nprivate:\n unsigned int simulation_steps_;\n};\n\n\nint main()\n{\n /* A set of parameters needed to run a SIS particle filter in a simulated environment. */\n double surv_x = 1000.0;\n double surv_y = 1000.0;\n unsigned int num_particle_x = 100;\n unsigned int num_particle_y = 100;\n unsigned int num_particle = num_particle_x * num_particle_y;\n Vector4d initial_state(10.0f, 0.0f, 10.0f, 0.0f);\n unsigned int simulation_time = 10;\n std::size_t state_size = 4;\n\n /* Step 1 - Initialization */\n /* Initialize initialization class. */\n std::unique_ptr grid_initialization = utils::make_unique(surv_x, surv_y, num_particle_x, num_particle_y);\n\n\n /* Step 2 - Prediction */\n /* Step 2.1 - Define the state model */\n /* Initialize a white noise acceleration state model. */\n std::unique_ptr wna = utils::make_unique();\n\n /* Step 2.1.1 - Define a decoration for the state model */\n /* Initialize a white noise acceleration decorator. */\n std::unique_ptr decorated_wna = utils::make_unique(std::move(wna));\n\n /* Step 2.2 - Define the prediction step */\n /* Initialize the particle filter prediction step and pass the ownership of the state model. */\n std::unique_ptr pf_prediction = utils::make_unique();\n pf_prediction->setStateModel(std::move(decorated_wna));\n\n /* Step 2.2.1 - Define a decoration for the prediction step */\n /* Initialize a particle filter prediction decorator. */\n std::unique_ptr decorated_prediction = utils::make_unique(std::move(pf_prediction));\n\n\n /* Step 3 - Correction */\n /* Step 3.1 - Define where the measurement are originated from (either simulated or from a real process) */\n /* Initialize simulaterd target model, a white noise acceleration, and measurements, a MeasurementModel decoration for the linear sensor. */\n std::unique_ptr target_model = utils::make_unique();\n std::unique_ptr simulated_state_model = utils::make_unique(std::move(target_model), initial_state, simulation_time);\n\n /* Initialize a measurement model (a linear sensor reading x and y coordinates). */\n std::unique_ptr simulated_linear_sensor = utils::make_unique(std::move(simulated_state_model));\n\n /* Step 3.1.1 - Define a decoration for the measurement model */\n /* Initialize a white noise acceleration decorator */\n std::unique_ptr decorated_linearsensor = utils::make_unique(std::move(simulated_linear_sensor));\n\n /* Step 3.2 - Define the likelihood model */\n /* Initialize the the exponential likelihood, a PFCorrection decoration of the particle filter correction step. */\n std::unique_ptr exp_likelihood = utils::make_unique();\n\n /* Step 3.3 - Define the correction step */\n /* Initialize the particle filter correction step and pass the ownership of the measurement model. */\n std::unique_ptr pf_correction = utils::make_unique();\n pf_correction->setLikelihoodModel(std::move(exp_likelihood));\n pf_correction->setMeasurementModel(std::move(decorated_linearsensor));\n\n /* Initialize a update particle decorator */\n std::unique_ptr decorated_correction = utils::make_unique(std::move(pf_correction));\n\n\n /* Step 4 - Resampling */\n /* Initialize a resampling algorithm */\n std::unique_ptr resampling = utils::make_unique();\n\n\n /* Step 5 - Assemble the particle filter */\n std::cout << \"Constructing SIS particle filter...\" << std::flush;\n SISSimulation sis_pf(num_particle, state_size, simulation_time, std::move(grid_initialization), std::move(decorated_prediction), std::move(decorated_correction), std::move(resampling));\n std::cout << \"done!\" << std::endl;\n\n\n /* Step 6 - Prepare the filter to be run */\n std::cout << \"Booting SIS particle filter...\" << std::flush;\n sis_pf.boot();\n std::cout << \"completed!\" << std::endl;\n\n\n /* Step 7 - Run the filter and wait until it is closed */\n /* Note that since this is a simulation, the filter will end upon simulation termination */\n std::cout << \"Running SIS particle filter...\" << std::flush;\n sis_pf.run();\n std::cout << \"waiting...\" << std::endl;\n if (!sis_pf.wait())\n return EXIT_FAILURE;\n std::cout << \"completed!\" << std::endl;\n\n\n return EXIT_SUCCESS;\n}\n", "meta": {"hexsha": "1ed9e98c86f115bd46e080472df40811d9c4d1a1", "size": 8462, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/test_SIS_Decorators/main.cpp", "max_stars_repo_name": "vesor/bayes-filters-lib", "max_stars_repo_head_hexsha": "24cfbed786a017f7aebb5bf3ace3694d4f7d5f66", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2021-05-27T02:52:46.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-10T07:06:39.000Z", "max_issues_repo_path": "test/test_SIS_Decorators/main.cpp", "max_issues_repo_name": "vesor/bayes-filters-lib", "max_issues_repo_head_hexsha": "24cfbed786a017f7aebb5bf3ace3694d4f7d5f66", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "test/test_SIS_Decorators/main.cpp", "max_forks_repo_name": "vesor/bayes-filters-lib", "max_forks_repo_head_hexsha": "24cfbed786a017f7aebb5bf3ace3694d4f7d5f66", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-04-14T08:20:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-14T08:20:28.000Z", "avg_line_length": 37.4424778761, "max_line_length": 189, "alphanum_fraction": 0.7240605058, "num_tokens": 1964, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.2359099410213478}} {"text": "#include \"alchemist.h\"\n#include \"data_stream.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"arpackpp/arrssym.h\"\n#include \"spdlog/spdlog.h\"\n#include \n\nusing Eigen::MatrixXd;\nusing Eigen::VectorXd;\n\nnamespace alchemist {\n\nusing namespace El;\n\nstruct Driver {\n mpi::communicator world;\n DataInputStream input;\n DataOutputStream output;\n std::vector workers;\n std::map matrices;\n uint32_t nextMatrixId;\n std::shared_ptr log;\n\n Driver(const mpi::communicator &world, std::istream &is, std::ostream &os, std::shared_ptr log);\n void issue(const Command &cmd);\n MatrixHandle registerMatrix(size_t numRows, size_t numCols);\n void reshapeMatrix(MatrixHandle handle, size_t numRows, size_t numCols);\n int main();\n\n void handle_newMatrix();\n void handle_matrixMul();\n void handle_matrixDims();\n void handle_computeThinSVD();\n void handle_getMatrixRows();\n void handle_getTranspose();\n void handle_kmeansClustering();\n void handle_truncatedSVD();\n void handle_SkylarkKRR();\n void handle_SkylarkLSQR();\n void handle_FactorizedCGSolver();\n void handle_RandomFourierFeatures();\n void handle_ReadHDF5();\n void handle_normalizeMatInPlace();\n};\n\nDriver::Driver(const mpi::communicator &world, std::istream &is, std::ostream &os, std::shared_ptr log) :\n world(world), input(is), output(os), log(log), nextMatrixId(42) {\n}\n\nvoid Driver::issue(const Command &cmd) {\n const Command *cmdptr = &cmd;\n mpi::broadcast(world, cmdptr, 0);\n}\n\nMatrixHandle Driver::registerMatrix(size_t numRows, size_t numCols) {\n MatrixHandle handle{nextMatrixId++};\n MatrixDescriptor info(handle, numRows, numCols);\n matrices.insert(std::make_pair(handle, info));\n return handle;\n}\n\nvoid Driver::reshapeMatrix(MatrixHandle handle, size_t numRows, size_t numCols) {\n matrices.erase(handle);\n MatrixDescriptor info(handle, numRows, numCols);\n matrices.insert(std::make_pair(handle, info));\n}\n\nint Driver::main() {\n\n // get WorkerInfo\n auto numWorkers = world.size() - 1;\n workers.resize(numWorkers);\n for(auto id = 0; id < numWorkers; ++id) {\n world.recv(id + 1, 0, workers[id]);\n }\n log->info(\"{} workers ready, sending hostnames and ports to Spark\", numWorkers);\n\n // handshake\n ENSURE(input.readInt() == 0xABCD);\n ENSURE(input.readInt() == 0x1);\n output.writeInt(0xDCBA);\n output.writeInt(0x1);\n output.writeInt(numWorkers);\n for(auto id = 0; id < numWorkers; ++id) {\n output.writeString(workers[id].hostname);\n output.writeInt(workers[id].port);\n }\n output.flush();\n\n bool shouldExit = false;\n while(!shouldExit) {\n uint32_t typeCode = input.readInt();\n log->info(\"Received code {:#x}\", typeCode);\n\n switch(typeCode) {\n // shutdown\n case 0xFFFFFFFF:\n shouldExit = true;\n issue(HaltCommand());\n output.writeInt(0x1);\n output.flush();\n break;\n\n // new matrix\n case 0x1:\n handle_newMatrix();\n break;\n\n // matrix multiplication\n case 0x2:\n handle_matrixMul();\n break;\n\n // get matrix dimensions\n case 0x3:\n handle_matrixDims();\n break;\n\n // return matrix to Spark\n case 0x4:\n handle_getMatrixRows();\n break;\n\n case 0x5:\n handle_computeThinSVD();\n break;\n\n case 0x6:\n handle_getTranspose();\n break;\n\n case 0x7:\n handle_kmeansClustering();\n break;\n\n case 0x8:\n handle_truncatedSVD();\n break;\n\n case 0x9:\n handle_SkylarkKRR();\n break;\n\n case 0x10:\n handle_SkylarkLSQR();\n break;\n\n case 0x11:\n handle_FactorizedCGSolver();\n break;\n\n case 0x12:\n handle_RandomFourierFeatures();\n break;\n\n case 0x13:\n handle_ReadHDF5();\n break;\n\n case 0x14:\n handle_normalizeMatInPlace();\n break;\n\n default:\n log->error(\"Unknown typeCode {#x}\", typeCode);\n abort();\n }\n log->info(\"Waiting on next command\");\n }\n\n // wait for workers to reach exit\n world.barrier();\n return EXIT_SUCCESS;\n}\n\n// A = USV\n/*\nvoid Driver::handle_computeLowrankSVD() {\n uint32_t inputMat = input.readInt();\n uint32_t kRank = input.readInt();\n uint32_t whichFactors = input.readInt(); // 0 = U, 1 = U and S, 2 = V and S, 3/default = U, S, and V\n\n MatrixHandle U;\n MatrixHandle S;\n MatrixHandle V;\n\n // TODO: register the U, S, V factors with false newmatrixcommands to track them\n switch(whichFactors) {\n case 0: U = MatrixHandle{nextMatrixId++};\n break;\n case 1: U = MatrixHandle{nextMatrixId++};\n S = MatrixHandle{nextMatrixId++};\n break;\n case 2: V = MatrixHandle{nextMatrixId++};\n S = MatrixHandle{nextMatrixId++};\n break;\n default: U = MatrixHandle{nextMatrixId++};\n S = MatrixHandle{nextMatrixId++};\n V = MatrixHandle{nextMatrixId++};\n break;\n }\n\n LowrankSVDCommand cmd(MatrixHandle{inputMat}, whichFactors, krank, U, S, V);\n issue(cmd);\n\n output.writeInt(0x1); // statusCode\n switch(whichFactors) {\n case 0: output.writeInt(U.id);\n break;\n case 1: output.writeInt(U.id);\n output.writeInt(S.id);\n break;\n case 2: output.writeInt(V.id);\n output.writeInt(S.id);\n break;\n default: output.writeInt(U.id);\n output.writeInt(S.id);\n output.writeInt(V.id);\n break;\n }\n output.flush();\n\n // wait for command to finish\n world.barrier();\n output.writeInt(0x1);\n output.flush();\n}\n*/\n\nvoid Driver::handle_RandomFourierFeatures() {\n MatrixHandle A{input.readInt()};\n uint32_t numRandFeatures = input.readInt();\n double sigma = input.readDouble();\n uint32_t seed = input.readInt();\n\n auto numRows = matrices[A].numRows;\n uint32_t d = matrices[A].numCols;\n MatrixHandle X = registerMatrix(numRows, numRandFeatures);\n\n log->info(\"Computing Fourier Random Features on feature matrix {}\");\n log->info(\"Input dim: {}, Output dim: {}, number of points: {}\", d, numRandFeatures, numRows);\n\n RandomFourierFeaturesCommand cmd(A, X, numRandFeatures, sigma, seed);\n issue(cmd);\n\n world.barrier(); // wait for it to finish\n output.writeInt(0x1);\n output.writeInt(X.id);\n output.flush();\n log->info(\"Finished computing Fourier Random Features, stored matrix as {}\", X);\n}\n\nvoid Driver::handle_normalizeMatInPlace() {\n MatrixHandle A{input.readInt()};\n log->info(\"Normalizing matrix {} in place by zero meaning the rows and setting the columns to have stdev 1\", A);\n\n NormalizeMatInPlaceCommand cmd(A);\n issue(cmd);\n\n output.writeInt(0x1);\n log->info(\"Finished normalizing matrix {} in place\", A);\n}\n\nvoid Driver::handle_ReadHDF5() {\n std::string fname = input.readString();\n std::string varname = input.readString();\n int colreplicas = input.readInt();\n MatrixHandle A = registerMatrix(0, 0);\n \n log->info(\"Reading variable {} from file {} and replicating columnwise {} times\", varname, fname, colreplicas);\n ReadHDF5Command cmd(A, fname, varname, colreplicas);\n issue(cmd);\n\n El::Int numRows, numCols;\n world.recv(1, mpi::any_tag, numRows);\n world.recv(1, mpi::any_tag, numCols);\n world.barrier();\n\n reshapeMatrix(A, numRows, numCols);\n log->info(\"Loaded matrix is {}-by-{}\", matrices[A].numRows, matrices[A].numCols);\n output.writeInt(0x1);\n output.writeInt(A.id);\n output.flush();\n log->info(\"Finished loading variable from HDF5 file\");\n}\n\nvoid Driver::handle_FactorizedCGSolver() {\n MatrixHandle featureMat{input.readInt()};\n MatrixHandle targetMat{input.readInt()};\n double lambda = input.readDouble();\n uint32_t maxIters = input.readInt();\n\n auto numfeatures = matrices[featureMat].numCols;\n auto numtargets = matrices[targetMat].numCols;\n MatrixHandle coefs = registerMatrix(numfeatures, numtargets);\n\n log->info(\"Starting factorized CG solver on feature matrix {} and target matrix {}\", featureMat, targetMat);\n log->info(\"lambda={}, maxIters={}\", lambda, maxIters);\n\n FactorizedCGSolverCommand cmd(featureMat, targetMat, coefs, lambda, maxIters);\n issue(cmd);\n\n world.barrier(); // wait for it to finish\n output.writeInt(0x1);\n output.writeInt(coefs.id);\n output.flush();\n log->info(\"Finished CG solve, stored coefficient matrix as {}\", coefs);\n}\n\nvoid Driver::handle_SkylarkKRR() {\n MatrixHandle featureMat{input.readInt()};\n MatrixHandle targetMat{input.readInt()};\n bool regression = input.readInt() > 0? true : false;\n uint32_t lossfunction = input.readInt();\n uint32_t regularizer = input.readInt();\n uint32_t kernel = input.readInt();\n double kernelparam = input.readDouble();\n double kernelparam2 = input.readDouble();\n double kernelparam3 = input.readDouble();\n double lambda = input.readDouble();\n uint32_t maxiter = input.readInt();\n double tolerance = input.readDouble();\n double rho = input.readDouble();\n uint32_t seed = input.readInt();\n uint32_t randomfeatures = input.readInt();\n uint32_t numfeaturepartitions = input.readInt();\n\n log->info(\"Starting Skylark's ADMM KRR solver on feature matrix {} and target matrix {}\", featureMat, targetMat);\n log->info(\"\");\n\n auto numfeatures = matrices[featureMat].numCols;\n auto numtargets = matrices[targetMat].numCols;\n MatrixHandle coefs = registerMatrix(numfeatures, numtargets);\n\n SkylarkKernelSolverCommand cmd(featureMat, targetMat, coefs, regression, \n lossfunction, regularizer, kernel, kernelparam, kernelparam2,\n kernelparam3, lambda, maxiter, tolerance, rho, seed, randomfeatures,\n numfeaturepartitions);\n issue(cmd);\n\n world.barrier(); // wait for it to finish\n log->info(\"Finished calling Skylark's ADMM Kernel solver for this KRR problem\");\n output.writeInt(0x1);\n output.writeInt(coefs.id);\n output.flush();\n log->info(\"Finished KRR solve, stored coeffcient matrix as {}\", coefs);\n}\n\nvoid Driver::handle_SkylarkLSQR() {\n MatrixHandle A{input.readInt()};\n MatrixHandle B{input.readInt()};\n double tolerance = input.readDouble();\n uint32_t iter_lim = input.readInt();\n\n auto p = matrices[A].numCols;\n auto m = matrices[B].numCols;\n MatrixHandle X = registerMatrix(p, m);\n\n log->info(\"Starting Skylark's LSQR solver on feature matrix {} and target matrix {}\", A, B);\n log->info(\"Tolerance: {}\", tolerance);\n log->info(\"Iteration Limit: {}\", iter_lim);\n log->info(\"Result will be a {}-by-{} matrix\", p, m);\n\n SkylarkLSQRSolverCommand cmd(A, B, X, tolerance, iter_lim);\n issue(cmd);\n\n world.barrier(); // wait for it to finish\n log->info(\"Finished call to Skylark's LSQR solver\");\n output.writeInt(0x1);\n output.writeInt(X.id);\n output.flush();\n log->info(\"Finished LSQR computation\");\n}\n\n// TODO: the cluster centers should be stored locally on driver and reduced/broadcasted. the current\n// way of updating kmeans centers is ridiculous\n// TODO: currently only implements kmeans||\nvoid Driver::handle_kmeansClustering() {\n MatrixHandle inputMat{input.readInt()};\n uint32_t numCenters = input.readInt();\n uint32_t maxnumIters = input.readInt(); // how many iteration of Lloyd's algorithm to use\n uint32_t initSteps = input.readInt(); // number of initialization steps to use in kmeans||\n double changeThreshold = input.readDouble(); // if all the centers change by Euclidean distance less than changeThreshold, then we stop the iterations\n uint32_t method = input.readInt(); // which initialization method to use to choose initial cluster center guesses\n uint64_t seed = input.readLong(); // randomness seed used in driver and workers\n\n log->info(\"Starting K-means on matrix {}\", inputMat);\n log->info(\"numCenters = {}, maxnumIters = {}, initSteps = {}, changeThreshold = {}, method = {}, seed = {}\",\n numCenters, maxnumIters, initSteps, changeThreshold, method, seed);\n\n if (changeThreshold < 0.0 || changeThreshold > 1.0) {\n log->error(\"Unreasonable change threshold in k-means: {}\", changeThreshold);\n abort();\n }\n if (method != 1) {\n log->warn(\"Sorry, only k-means|| initialization has been implemented, so ignoring your choice of method {}\", method);\n }\n\n auto n = matrices[inputMat].numRows;\n auto d = matrices[inputMat].numCols;\n MatrixHandle centersHandle = this->registerMatrix(numCenters, d);\n MatrixHandle assignmentsHandle = this->registerMatrix(n, 1);\n KMeansCommand cmd(inputMat, numCenters, method, initSteps, changeThreshold, seed, centersHandle, assignmentsHandle);\n issue(cmd); // initial call initializes stuff and waits for next command\n\n /******** START of kmeans|| initialization ********/\n std::mt19937 gen(seed);\n std::uniform_int_distribution dis(0, n-1);\n uint32_t rowidx = dis(gen);\n std::vector initialCenter(d);\n\n mpi::broadcast(world, rowidx, 0); // tell the workers which row to use as initialization in kmeans||\n world.barrier(); // wait for workers to return oversampled cluster centers and sizes\n\n std::vector clusterSizes;\n std::vector initClusterCenters;\n world.recv(1, mpi::any_tag, clusterSizes);\n world.recv(1, mpi::any_tag, initClusterCenters);\n world.barrier();\n\n log->info(\"Retrieved the k-means|| oversized set of potential cluster centers\");\n log->debug(\"{}\", initClusterCenters);\n\n // use kmeans++ locally to find the initial cluster centers\n std::vector weights;\n weights.reserve(clusterSizes.size());\n std::for_each(clusterSizes.begin(), clusterSizes.end(), [&weights](const uint32_t & cnt){ weights.push_back((double) cnt); });\n MatrixXd clusterCenters(numCenters, d);\n\n kmeansPP(gen(), initClusterCenters, weights, clusterCenters, 30); // same number of maxIters as spark kmeans\n\n log->info(\"Ran local k-means on the driver to determine starting cluster centers\");\n log->debug(\"{}\", clusterCenters);\n\n mpi::broadcast(world, clusterCenters.data(), numCenters*d, 0);\n /******** END of kMeans|| initialization ********/\n\n /******** START of Lloyd's algorithm iterations ********/\n double percentAssignmentsChanged = 1.0;\n bool centersMovedQ = true;\n uint32_t numChanged = 0;\n uint32_t numIters = 0;\n std::vector parClusterSizes(numCenters);\n std::vector zerosVector(numCenters);\n\n for(uint32_t clusterIdx = 0; clusterIdx < numCenters; clusterIdx++)\n zerosVector[clusterIdx] = 0;\n\n uint32_t command = 1; // do another iteration\n while (centersMovedQ && numIters++ < maxnumIters) {\n log->info(\"Starting iteration {} of Lloyd's algorithm, {} percentage changed in last iter\",\n numIters, percentAssignmentsChanged*100);\n numChanged = 0;\n for(uint32_t clusterIdx = 0; clusterIdx < numCenters; clusterIdx++)\n parClusterSizes[clusterIdx] = 0;\n command = 1; // do a basic iteration\n mpi::broadcast(world, command, 0);\n mpi::reduce(world, (uint32_t) 0, numChanged, std::plus(), 0);\n mpi::reduce(world, zerosVector.data(), numCenters, parClusterSizes.data(), std::plus(), 0);\n world.recv(1, mpi::any_tag, centersMovedQ);\n percentAssignmentsChanged = ((double) numChanged)/n;\n\n for(uint32_t clusterIdx = 0; clusterIdx < numCenters; clusterIdx++) {\n if (parClusterSizes[clusterIdx] == 0) {\n // this is an empty cluster, so randomly pick a point in the dataset\n // as that cluster's centroid\n centersMovedQ = true;\n command = 2; // reinitialize this cluster center\n uint32_t rowIdx = dis(gen);\n mpi::broadcast(world, command, 0);\n mpi::broadcast(world, clusterIdx, 0);\n mpi::broadcast(world, rowIdx, 0);\n world.barrier();\n }\n }\n\n }\n command = 0xf; // terminate and finalize the k-means centers and assignments as distributed matrices\n mpi::broadcast(world, command, 0);\n double objVal = 0.0;\n mpi::reduce(world, 0.0, objVal, std::plus(), 0);\n world.barrier();\n\n /******** END of Lloyd's iterations ********/\n\n log->info(\"Finished Lloyd's algorithm: took {} iterations, final objective value {}\", numIters, objVal);\n output.writeInt(0x1);\n output.writeInt(assignmentsHandle.id);\n output.writeInt(centersHandle.id);\n output.writeInt(numIters);\n output.flush();\n}\n\nvoid Driver::handle_getTranspose() {\n MatrixHandle inputMat{input.readInt()};\n log->info(\"Constructing the transpose of matrix {}\", inputMat);\n\n auto numRows = matrices[inputMat].numCols;\n auto numCols = matrices[inputMat].numRows;\n MatrixHandle transposeHandle = registerMatrix(numRows, numCols);\n TransposeCommand cmd(inputMat, transposeHandle);\n issue(cmd);\n\n world.barrier(); // wait for command to finish\n output.writeInt(0x1);\n output.writeInt(transposeHandle.id);\n log->info(\"Wrote handle for transpose\");\n output.writeInt(0x1);\n output.flush();\n}\n\n// CAVEAT: Assumes tall-and-skinny for now, doesn't allow many options for controlling\n// LIMITATIONS: assumes V small enough to fit on one machine (so can use ARPACK instead of PARPACK), but still distributes U,S,V and does distributed computations not needed\nvoid Driver::handle_truncatedSVD() {\n log->info(\"Starting truncated SVD computation\");\n MatrixHandle inputMat{input.readInt()};\n uint32_t k = input.readInt();\n uint32_t method = input.readInt();\n\n int LOCALEIGS = 0; // TODO: make these an enumeration, and global to Alchemist\n int LOCALEIGSPRECOMPUTE = 1;\n int DISTEIGS = 2; \n\n MatrixHandle UHandle{nextMatrixId++};\n MatrixHandle SHandle{nextMatrixId++};\n MatrixHandle VHandle{nextMatrixId++};\n\n auto m = matrices[inputMat].numRows;\n auto n = matrices[inputMat].numCols;\n TruncatedSVDCommand cmd(inputMat, UHandle, SHandle, VHandle, k, method);\n issue(cmd);\n\n if (method == DISTEIGS) {\n log->info(\"using distributed mat-vec prods against A, then A tranpose\");\n }\n if (method == LOCALEIGS) {\n log->info(\"using local mat-vec prods computed on the fly against the local Gramians\");\n }\n if (method == LOCALEIGSPRECOMPUTE) {\n log->info(\"using local mat-vec prods against the precomputed local Gramians\");\n }\n\n ARrcSymStdEig prob(n, k, \"LM\");\n uint32_t command;\n std::vector zerosVector(n);\n for(uint32_t idx = 0; idx < n; idx++)\n zerosVector[idx] = 0;\n\n int iterNum = 0;\n\n while (!prob.ArnoldiBasisFound()) {\n prob.TakeStep();\n ++iterNum;\n if(iterNum % 20 == 0) {\n log->info(\"Computed {} mv products\", iterNum);\n }\n if (prob.GetIdo() == 1 || prob.GetIdo() == -1) {\n command = 1;\n mpi::broadcast(world, command, 0);\n if (method == LOCALEIGS || method == LOCALEIGSPRECOMPUTE) {\n mpi::broadcast(world, prob.GetVector(), n, 0);\n mpi::reduce(world, zerosVector.data(), n, prob.PutVector(), std::plus(), 0);\n }\n if (method == DISTEIGS) {\n world.send(1, 0, prob.GetVector(), n);\n world.recv(1, 0, prob.PutVector(), n);\n }\n }\n }\n\n prob.FindEigenvectors();\n uint32_t nconv = prob.ConvergedEigenvalues();\n uint32_t niters = prob.GetIter();\n log->info(\"Done after {} Arnoldi iterations, converged to {} eigenvectors of size {}\", niters, nconv, n);\n\n //NB: it may be the case that n*nconv > 4 GB, then have to be careful!\n // assuming tall and skinny A for now\n MatrixXd rightVecs(n, nconv);\n log->info(\"Allocated matrix for right eigenvectors of A'*A\");\n // Eigen uses column-major layout by default!\n for(uint32_t idx = 0; idx < nconv; idx++)\n std::memcpy(rightVecs.col(idx).data(), prob.RawEigenvector(idx), n*sizeof(double));\n log->info(\"Copied right eigenvectors into allocated storage\");\n\n // Populate U, V, S\n command = 2;\n mpi::broadcast(world, command, 0);\n mpi::broadcast(world, nconv, 0);\n log->info(\"Broadcasted command and number of converged eigenvectors\");\n mpi::broadcast(world, rightVecs.data(), n*nconv, 0);\n log->info(\"Broadcasted right eigenvectors\"); \n mpi::broadcast(world, prob.RawEigenvalues(), nconv, 0);\n log->info(\"Broadcasted eigenvalues\");\n\n MatrixDescriptor Uinfo(UHandle, m, nconv);\n MatrixDescriptor Sinfo(SHandle, nconv, 1);\n MatrixDescriptor Vinfo(VHandle, n, nconv);\n ENSURE(matrices.insert(std::make_pair(UHandle, Uinfo)).second);\n ENSURE(matrices.insert(std::make_pair(SHandle, Sinfo)).second);\n ENSURE(matrices.insert(std::make_pair(VHandle, Vinfo)).second);\n\n log->info(\"Waiting on workers to store U,S,V\");\n\n world.barrier();\n log->info(\"Writing ok status followed by U,S,V handles\");\n output.writeInt(0x1);\n output.writeInt(UHandle.id);\n output.writeInt(SHandle.id);\n output.writeInt(VHandle.id);\n output.flush();\n}\n\nvoid Driver::handle_computeThinSVD() {\n MatrixHandle inputMat{input.readInt()};\n\n // this needs to be done automatically rather than hand-coded. e.g. what if\n // we switch to determining rank by sing-val thresholding instead of doing thin SVD?\n auto m = matrices[inputMat].numRows;\n auto n = matrices[inputMat].numCols;\n auto k = std::min(m,n);\n MatrixHandle Uhandle = registerMatrix(m, k);\n MatrixHandle Shandle = registerMatrix(k, 1);\n MatrixHandle Vhandle = registerMatrix(n, k);\n ThinSVDCommand cmd(inputMat, Uhandle, Shandle, Vhandle);\n issue(cmd);\n\n output.writeInt(0x1); // statusCode\n output.writeInt(Uhandle.id);\n output.writeInt(Shandle.id);\n output.writeInt(Vhandle.id);\n output.flush();\n\n // wait for command to finish\n world.barrier();\n log->info(\"Done with SVD computation\");\n output.writeInt(0x1);\n output.flush();\n}\n\nvoid Driver::handle_matrixMul() {\n MatrixHandle matA{input.readInt()};\n MatrixHandle matB{input.readInt()};\n log->info(\"Multiplying matrices {} and {}\", matA, matB);\n\n auto numRows = matrices[matA].numRows;\n auto numCols = matrices[matB].numCols;\n MatrixHandle destHandle = registerMatrix(numRows, numCols);\n MatrixMulCommand cmd(destHandle, matA, matB);\n issue(cmd);\n\n // tell spark id of resulting matrix\n output.writeInt(0x1); // statusCode\n output.writeInt(destHandle.id);\n output.flush();\n\n // wait for it to finish\n world.barrier();\n output.writeInt(0x1);\n output.flush();\n}\n\nvoid Driver::handle_matrixDims() {\n MatrixHandle matrixHandle{input.readInt()};\n log->info(\"Looking up dimensions for matrix {}\", matrixHandle.id);\n auto info = matrices[matrixHandle];\n log->info(\"Returning dimensions for matrix {}: {}-by-{}\", matrixHandle.id, info.numRows, info.numCols);\n output.writeInt(0x1);\n output.writeLong(info.numRows);\n output.writeLong(info.numCols);\n output.flush();\n\n}\n\nvoid Driver::handle_getMatrixRows() {\n MatrixHandle handle{input.readInt()};\n uint64_t layoutLen = input.readLong();\n std::vector layout;\n layout.reserve(layoutLen);\n for(uint64_t part = 0; part < layoutLen; ++part) {\n layout.push_back(input.readInt());\n }\n log->info(\"Returning matrix {} to Spark\", handle);\n\n MatrixGetRowsCommand cmd(handle, layout);\n issue(cmd);\n\n // tell Spark to start asking for rows\n output.writeInt(0x1);\n output.flush();\n\n // wait for it to finish\n world.barrier();\n output.writeInt(0x1);\n output.flush();\n}\n\nvoid Driver::handle_newMatrix() {\n // read args\n uint64_t numRows = input.readLong();\n uint64_t numCols = input.readLong();\n\n // assign id and notify workers\n MatrixHandle handle = registerMatrix(numRows, numCols);\n NewMatrixCommand cmd(matrices[handle]);\n log->info(\"Recieving new matrix {}, with dimensions {}x{}\", handle, numRows, numCols);\n issue(cmd);\n\n output.writeInt(0x1);\n output.writeInt(handle.id);\n output.flush();\n\n // tell spark which worker expects each row\n std::vector rowWorkerAssignments(numRows, 0);\n std::vector rowsOnWorker;\n std::vector numRowsOnEachWorker(world.size() -1, 0);\n for(int workerIdx = 1; workerIdx < world.size(); workerIdx++) {\n world.recv(workerIdx, 0, rowsOnWorker);\n numRowsOnEachWorker[workerIdx - 1] = rowsOnWorker.size();\n world.barrier();\n for(auto rowIdx: rowsOnWorker) {\n rowWorkerAssignments[rowIdx] = workerIdx;\n }\n }\n\n log->info(\"Sending list of which worker each row should go to\");\n output.writeInt(0x1); // statusCode\n std::stringstream ss;\n for(auto workerIdx: rowWorkerAssignments) {\n output.writeInt(workerIdx);\n ALCHEMIST_TRACE(ss << workerIdx << ' ');\n }\n ALCHEMIST_TRACE(log->info(ss.str().c_str()));\n \n for(uint32_t workerIdx = 1; workerIdx < world.size(); workerIdx++)\n log->info(\"Worker with rank {} will receive {} rows\", workerIdx, numRowsOnEachWorker[workerIdx-1]);\n\n output.flush();\n\n log->info(\"Waiting for spark to finish sending data to the workers\");\n world.barrier();\n output.writeInt(0x1); // statusCode\n output.flush();\n log->info(\"Entire matrix has been received\");\n}\n\ninline bool exist_test (const std::string& name) {\n struct stat buffer;\n return (stat(name.c_str(), &buffer) == 0); \n}\n\nint driverMain(const mpi::communicator &world, int argc, char *argv[]) {\n //log to console as well as file (single-threaded logging)\n //TODO: allow to specify log directory, log level, etc.\n time_t rawtime = time(0);\n struct tm * timeinfo = localtime(&rawtime);\n char buf[80];\n strftime(buf, 80, \"%F-%T\", timeinfo);\n std::shared_ptr log;\n std::vector sinks;\n sinks.push_back(std::make_shared());\n sinks.push_back(std::make_shared(str(format(\"driver-%s.log\") % buf)));\n log = std::make_shared(\"driver\", std::begin(sinks), std::end(sinks));\n //log->flush_on(spdlog::level::warn); // flush whenever warning or more critical message is logged\n //log->set_level(spdlog::level::info); // only log stuff at or above info level, for production\n log->flush_on(spdlog::level::info); // flush always, for debugging\n log->info(\"Started Driver\");\n log->info(\"Max number of OpenMP threads: {}\", omp_get_max_threads());\n\n char machine[255];\n char port[255];\n\n if (argc == 3) { // we are on a non-NERSC system, so passed in Spark driver machine name and port\n log->info(\"Non-NERSC system assumed\");\n log->info(\"Connecting to Spark executor at {}:{}\", argv[1], argv[2]);\n std::strcpy(machine, argv[1]);\n std::strcpy(port, argv[2]);\n } else { // assume we are on NERSC, so look in a specific location for a file containing the machine name and port\n char const* tmp = std::getenv(\"SPARK_WORKER_DIR\");\n std::string sockPath;\n if (tmp == NULL) {\n log->info(\"Couldn't find the SPARK_WORKER_DIR variable\");\n world.abort(1);\n } else {\n sockPath = std::string(tmp) + \"/connection.info\";\n }\n log->info(\"NERSC system assumed\");\n log->info(\"Searching for connection information in file {}\", sockPath);\n\n while(!exist_test(sockPath)) {\n boost::this_thread::sleep_for(boost::chrono::milliseconds(50));\n }\n // now wait for a while for the connection file to be completely written, hopefully is enough time\n // TODO: need a more robust way of ensuring this is the case\n boost::this_thread::sleep_for(boost::chrono::milliseconds(500));\n\n std::string sockSpec;\n std::ifstream infile(sockPath);\n std::getline(infile, sockSpec);\n infile.close();\n boost::tokenizer<> tok(sockSpec);\n boost::tokenizer<>::iterator iter=tok.begin();\n std::string machineName = *iter;\n std::string portName = *(++iter);\n\n log->info(\"Connecting to Spark executor at {}:{}\", machineName, portName);\n strcpy(machine, machineName.c_str());\n strcpy(port, portName.c_str());\n }\n\n using boost::asio::ip::tcp;\n boost::asio::ip::tcp::iostream stream(machine, port);\n ENSURE(stream);\n stream.rdbuf()->non_blocking(false);\n auto result = Driver(world, stream, stream, log).main();\n return result;\n}\n\n} // namespace alchemist\n", "meta": {"hexsha": "c683d70503e1324ed16d9d51a815fc90734111b0", "size": 28001, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "core/src/main/cpp/driver.cpp", "max_stars_repo_name": "jey/alchemist", "max_stars_repo_head_hexsha": "7b5f0e68a96a3f13f624b18669e1c68fff24d375", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 17.0, "max_stars_repo_stars_event_min_datetime": "2018-02-20T15:18:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-14T07:48:23.000Z", "max_issues_repo_path": "core/src/main/cpp/driver.cpp", "max_issues_repo_name": "jey/Alchemist", "max_issues_repo_head_hexsha": "7b5f0e68a96a3f13f624b18669e1c68fff24d375", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2017-07-17T20:02:25.000Z", "max_issues_repo_issues_event_max_datetime": "2018-10-27T12:17:06.000Z", "max_forks_repo_path": "core/src/main/cpp/driver.cpp", "max_forks_repo_name": "jey/alchemist", "max_forks_repo_head_hexsha": "7b5f0e68a96a3f13f624b18669e1c68fff24d375", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2017-05-27T20:44:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-10-01T15:02:44.000Z", "avg_line_length": 33.7768395657, "max_line_length": 173, "alphanum_fraction": 0.6798685761, "num_tokens": 7267, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.4610167793123159, "lm_q1q2_score": 0.2359099410213478}} {"text": "/*\n * This file is a part of\n *\n * ============================================\n * ### Pteros molecular modeling library ###\n * ============================================\n *\n * (C) 2009-2018, Semen Yesylevskyy\n *\n * All works, which use Pteros, should cite the following papers:\n * \n * 1. Semen O. Yesylevskyy, \"Pteros 2.0: Evolution of the fast parallel\n * molecular analysis library for C++ and python\",\n * Journal of Computational Chemistry, 2015, 36(19), 1480–1488.\n * doi: 10.1002/jcc.23943.\n *\n * 2. Semen O. Yesylevskyy, \"Pteros: Fast and easy to use open-source C++\n * library for molecular analysis\",\n * Journal of Computational Chemistry, 2012, 33(19), 1632–1636.\n * doi: 10.1002/jcc.22989.\n *\n * This is free software distributed under Artistic License:\n * http://www.opensource.org/licenses/artistic-license-2.0.php\n *\n*/\n\n\n#include \"pteros/extras/membrane.h\"\n#include \"pteros/core/pteros_error.h\"\n#include \"pteros/core/distance_search.h\"\n#include \"pteros/core/utilities.h\"\n#include \n#include \n#include \n#include \"voro++.hh\"\n\n#ifndef M_PI\n #define M_PI 3.14159265358979323846\n#endif\n\n#ifndef M_PI_2\n #define M_PI_2 9.86960440108935712052951\n#endif\n\nusing namespace std;\nusing namespace pteros;\nusing namespace Eigen;\n\n\n\nMembrane::Membrane(System *sys, const std::vector &species): system(sys), lipid_species(species)\n{\n log = create_logger(\"membrane\");\n\n Selection all_mid_sel(*system);\n\n // Creating selections\n for(auto& sp: lipid_species){\n vector res;\n system->select(sp.whole_sel_str).split_by_residue(res);\n log->info(\"Lipid {}: {}\", sp.name,res.size());\n for(auto& lip: res){\n auto mol = Lipid(lip,sp);\n mol.set_markers();\n all_mid_sel.append(mol.mid_sel.index(0));\n lipids.push_back(mol);\n index_map[mol.mid_sel.index(0)] = lipids.size()-1;\n }\n }\n\n // Compute connectivity \n std::vector leafs;\n all_mid_sel.split_by_connectivity(2.0,leafs,true);\n leaflets.resize(leafs.size());\n leaflets_sel.resize(leafs.size());\n // Set lipid leaflets\n for(int i=0;iinfo(\"Total number of lipids: {}\",lipids.size());\n log->info(\"Number of leaflets: {}\",leafs.size()); \n}\n\nvoid fit_quad_surface(const MatrixXf& coord,\n Matrix& res,\n Vector3f& smoothed_first_point,\n float& rms){\n int N = coord.cols();\n // We fit with polynomial fit = A*x^2 + B*y^2 + C*xy + D*x + E*y + F\n // Thus we need a linear system of size 6\n Matrix m;\n Matrix rhs; // Right hand side and result\n\n // Functions returning powers after coefficients (x^2, y^2, x*y, x, y, 1)\n vector > coef(6);\n coef[0] = [&coord](int j){return coord.col(j)(0)*coord.col(j)(0);};\n coef[1] = [&coord](int j){return coord.col(j)(1)*coord.col(j)(1);};\n coef[2] = [&coord](int j){return coord.col(j)(0)*coord.col(j)(1);};\n coef[3] = [&coord](int j){return coord.col(j)(0);};\n coef[4] = [&coord](int j){return coord.col(j)(1);};\n coef[5] = [&coord](int j){return 1.0;};\n\n // Now form the matrix\n m.fill(0.0);\n rhs.fill(0.0);\n\n for(int r=0;r<6;++r){ //rows\n for(int c=0;c<6;++c){ //columns\n m(r,c) = 0.0;\n for(int j=0;j& neib){\n // Perform Voronoi computation in the tangent plane\n using namespace voro;\n\n voronoicell_neighbor c;\n\n // Z dimension of cotainer is 1, so volume=area\n container con(-dist,dist,\n -dist,dist,\n -0.5,0.5,\n 10,10,10,false,false,false,8);\n\n\n particle_order po;\n // Cycle over particles, first particle is our target\n for(int i=0;i vert;\n c.vertices(0,0,0,vert);\n for(int i=0; i n;\n c.neighbors(n);\n // Filter out negatives\n neib.clear();\n for(int i: n)\n if(i>0) neib.push_back(i);\n\n/*\n cout << \"Npoints: \" << coord.cols() << endl;\n // Output the particle positions in gnuplot format\n con.draw_particles(\"points.gnu\");\n\n // Output the Voronoi cells in gnuplot format\n con.draw_cells_gnuplot(\"points_v.gnu\");\n*/\n return c.volume();\n}\n\n\nvoid get_curvature(const Matrix& coeffs, float& gaussian_curvature, float& mean_curvature){\n /* Compute the curvatures\n\n First fundamental form: I = E du^2 + 2F du dv + G dv^2\n E= r_u dot r_u, F= r_u dot r_v, G= r_v dot r_v\n\n For us parametric variables (u,v) are just (x,y) in local space.\n Derivatives:\n r_u = {1, 0, 2Ax+Cy+D}\n r_v = {0, 1, 2By+Cx+E}\n\n In central point x=0, y=0 so:\n r_u={1,0,D}\n r_v={0,1,E}\n\n Thus: E_ =1+D^2; F_ = D*E; G_ = 1+E^2;\n\n Second fundamental form: II = L du2 + 2M du dv + N dv2\n L = r_uu dot n, M = r_uv dot n, N = r_vv dot n\n\n Normal is just n = {0, 0, 1}\n Derivatives:\n r_uu = {0, 0, 2A}\n r_uv = {0 ,0, C}\n r_vv = {0, 0, 2B}\n\n Thus: L_ = 2A; M_ = C; N_ = 2B;\n */\n\n float E_ = 1.0+coeffs[3]*coeffs[3];\n float F_ = coeffs[3]*coeffs[4];\n float G_ = 1.0+coeffs[4]*coeffs[4];\n\n float L_ = 2.0*coeffs[0];\n float M_ = coeffs[2];\n float N_ = 2.0*coeffs[1];\n\n //Curvatures:\n gaussian_curvature = (L_*N_-M_*M_)/(E_*G_-F_*F_);\n mean_curvature = 0.5*(E_*N_-2.0*F_*M_+G_*L_)/(E_*G_-F_*F_);\n}\n\nvoid Membrane::compute_properties(float d, bool use_external_normal, Vector3f_const_ref external_pivot, Vector3i_const_ref external_dist_dim)\n{\n // Clear everything\n neighbor_pairs.clear();\n\n Eigen::Matrix3f tr, tr_inv;\n\n // Set markers for all lipids\n // This unwraps each lipid\n for(auto& l: lipids) l.set_markers();\n\n // Compute per leaflet properties\n for(int l=0;l bon;\n search_contacts(d,leaflets_sel[l],bon,false,true); \n\n // Convert the list of bonds to convenient form\n // atom ==> 1 2 3...\n vector > conn(leaflets_sel[l].size());\n for(int i=0;iwarn(\"Empty locality of lipid {} in leaflet {}! Skipped.\",i,l);\n continue;\n }\n\n // Local coordinate axes\n Matrix3f axes;\n\n //-----------------------------------\n // Compute normal\n //-----------------------------------\n\n Vector3f normal;\n\n if(use_external_normal){\n // Compute external normal as a vector from pivot to COM of mid_sel (set as marker currently)\n // over specified dimensions\n axes.col(2) = (lip.mid_marker-external_pivot).array()*external_dist_dim.cast().array();\n\n // Find two vectors perpendicular to normal\n if( axes.col(2).dot(Vector3f(1,0,0)) != 1.0 ){\n axes.col(0) = axes.col(2).cross(Vector3f(1,0,0));\n } else {\n axes.col(0) = axes.col(2).cross(Vector3f(0,1,0));\n }\n axes.col(1) = axes.col(2).cross(axes.col(0));\n } else {\n // Compute normals from inertia axes\n // Create selection for locality of this lipid including itself\n Selection local_self(lip.local_sel);\n\n local_self.append(leaflets_sel[l].index(i)); // Add central atom\n\n // Get inertial axes\n Vector3f moments;\n local_self.inertia(moments,axes,fullPBC); // Have to use periodic variant\n // axes.col(2) will be a normal\n }\n\n normal = axes.col(2);\n\n // transformation matrix to local basis\n for(int j=0;j<3;++j) tr.col(j) = axes.col(j).normalized();\n tr_inv = tr.inverse();\n\n // Need to check direction of the normal\n float ang = angle_between_vectors(normal, lip.head_marker-lip.tail_marker);\n if(ang < M_PI_2){\n lip.normal = normal;\n lip.tilt = ang;\n } else {\n lip.normal = -normal;\n lip.tilt = M_PI-ang;\n }\n lip.normal.normalized();\n\n //-----------------------------------\n // Smooth and find local curvatures\n //-----------------------------------\n\n // Create array of local points in local basis\n MatrixXf coord(3,lip.local_sel.size()+1);\n Vector3f c0 = lip.mid_marker; // Real coord of central point - the marker\n coord.col(0) = Vector3f::Zero(); // Local coord of central point is zero\n for(int j=0; jbox(0).shortest_vector(c0,lip.local_sel.xyz(j));\n\n // Fit a quad surface\n Matrix res;\n Vector3f sm; // smoothed coords of central point\n fit_quad_surface(coord,res,sm,lip.quad_fit_rms);\n // Compute curvatures using quad fit coeefs\n get_curvature(res, lip.gaussian_curvature, lip.mean_curvature);\n\n // Get smoothed surface point in lab coords\n lip.smoothed_mid_xyz = tr*sm + lip.mid_marker;\n\n //-----------------------------------\n // Area and neighbours\n //-----------------------------------\n\n vector neib;\n lip.area = compute_area(coord,10.0,neib);\n // Save coordination number of the lipid\n lip.coord_number = neib.size();\n\n // Add neighbor pairs. Only use nearest neighbor lipids from area computation\n // use only i0){\n int cur_ind = index_map[leaflets_sel[l].index(i)];\n for(int j=0; j 1,2,3...\n splay.resize(neighbor_pairs.size());\n neighbors.resize(lipids.size());\n for(int i=0;ibox(0).shortest_vector(lip1.mid_marker, lip2.mid_marker);\n float d = x.norm();\n x = x-x.dot(n1)*n1;\n x.normalize();\n Vector3f v1 = (lip1.head_marker-lip1.tail_marker).normalized();\n Vector3f v2 = (lip2.head_marker-lip2.tail_marker).normalized();\n\n splay[i] = {\n neighbor_pairs[i](0),\n neighbor_pairs[i](1),\n (v2.dot(x)-v1.dot(x)-n2.dot(x)+n1.dot(x))/d\n };\n }\n\n //-----------------------------------\n // Order parameter\n //-----------------------------------\n\n for(auto& lip: lipids){\n // Compute Sz order parameter if the tails are provided\n for(int t=0; txyz(lip.tail_carbon_indexes[t][at+1]);\n auto coord2 = system->xyz(lip.tail_carbon_indexes[t][at-1]);\n float ang = angle_between_vectors(coord1-coord2,lip.normal);\n lip.order[t][at-1] = 1.5*pow(cos(ang),2)-0.5;\n }\n }\n }\n\n}\n\n\nstring tcl_arrow(Vector3f_const_ref p1, Vector3f_const_ref p2, float r, string color){\n stringstream ss;\n Vector3f p = (p2-p1)*0.8+p1;\n ss << \"draw color \" << color << endl;\n\n ss << \"draw cylinder \\\"\" << p1.transpose()*10.0 << \"\\\" \";\n ss << \"\\\"\" << p.transpose()*10.0 << \"\\\" radius \" << r << endl;\n\n ss << \"draw cone \\\"\" << p.transpose()*10.0 << \"\\\" \";\n ss << \"\\\"\" << p2.transpose()*10.0 << \"\\\" radius \" << r*3.0 << endl;\n\n return ss.str();\n}\n\nvoid Membrane::write_vmd_arrows(const string &fname)\n{\n ofstream f(fname);\n for(auto& lip: lipids){\n f << tcl_arrow(lip.mid_sel.center(true),lip.mid_sel.center(true)+lip.normal,0.2,\"green\");\n f << tcl_arrow(lip.tail_sel.center(true),lip.head_sel.center(true),0.2,\"red\");\n }\n f.close();\n\n}\n\nvoid Membrane::write_smoothed(const string& fname){\n System out;\n for(auto& l: lipids){\n auto s = out.append(l.mid_sel(0,0));\n s.name(0) = \"M\";\n s = out.append(l.head_sel(0,0));\n s.xyz(0) = l.smoothed_mid_xyz;\n s.name(0) = \"S\";\n }\n out().write(fname);\n}\n\n\n//------------------------------------------------------------------------\n\nLipid::Lipid(const Selection &sel, const Lipid_descr &descr){\n name = descr.name;\n whole_sel = sel;\n head_sel = whole_sel(descr.head_sel_str);\n tail_sel = whole_sel(descr.tail_sel_str);\n mid_sel = whole_sel(descr.mid_sel_str);\n // Fill tail indexes if any\n tail_carbon_indexes.resize(descr.tail_carbon_sels.size());\n order.resize(descr.tail_carbon_sels.size());\n for(int t=0; t\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace rwlibs { namespace algorithms {\n\n // We can't define this as a static variable within the class, so we put it\n // here for now.\n const double kdtree_epsilon = 0.000000001;\n\n /** \\addtogroup algorithms */\n /*@{*/\n\n /**\n * @brief a space partitioning structure for organizing points in k-dimensional space.\n * Used for searches involving multi.dimensional search keys, including nearest\n * neighbor and range search.\n *\n * KEY must implement:\n * copyable\n * operator[]\n * operator()\n * TODO: remove one of the operators (change code in KDTREE)\n *\n */\n template< class KEY, size_t DIM > class KDTree\n {\n private:\n struct TreeNode;\n\n size_t _nrOfNodes;\n TreeNode* _root;\n std::vector< TreeNode >* _nodes;\n rw::core::Ptr< rw::math::Metric< KEY > > _metric;\n\n public:\n //! a struct for the node in the tree\n struct KDNode\n {\n KDNode (const KEY& k, boost::any val) : key (k), value (val) {}\n KEY key;\n boost::any value;\n };\n\n struct KDResult\n {\n KDResult (KDNode* node, double d) : n (node), dist (d) {}\n KDNode* n;\n double dist;\n };\n\n /**\n * @brief Constructor\n * @cond\n * @param dim [in] the dimension of the keys in the KDTree\n * @endcond\n * @param metric documentation missing !\n */\n KDTree (rw::core::Ptr< rw::math::Metric< KEY > > metric) :\n _nrOfNodes (0), _root (NULL), _nodes (new std::vector< TreeNode > ())\n {}\n\n /**\n * @brief destructor\n */\n virtual ~KDTree ()\n {\n if (_root != NULL)\n delete _root;\n if (_nodes != NULL)\n delete _nodes;\n };\n\n rw::core::Ptr< rw::math::Metric< KEY > > getMetric () { return _metric; }\n\n /**\n * @brief Builds a KDTree from a list of key values and nodes. This method is more efficient\n * than creating an empty KDTree and then inserting nodes\n * @param nodes [in] a list of KDNode's\n * @param metric documentation missing !\n * @return if build succesfull then a pointer to a KD-tree is returned else NULL\n */\n static KDTree< KEY, DIM >* buildTree (std::vector< KDNode >& nodes,\n rw::core::Ptr< rw::math::Metric< KEY > > metric)\n {\n if (nodes.size () == 0)\n return NULL;\n\n // create all tree nodes in a list\n std::vector< TreeNode >* tNodes = new std::vector< TreeNode > (nodes.size ());\n\n // copy the KDNodes into the tree nodes\n int i = 0;\n for (KDNode& n : nodes) {\n (*tNodes)[i]._kdnode = &n;\n i++;\n }\n\n // create a simple median balanced tree\n TreeNode* root = buildBalancedRec (*tNodes, 0, (int) tNodes->size (), 0, DIM);\n\n return new KDTree< KEY, DIM > (*root, tNodes, metric);\n }\n\n /**\n * @brief Builds a KDTree from a list of key values and nodes. This method is more efficient\n * than creating an empty KDTree and then inserting nodes\n * @param nodes [in] a list of KDNode's\n * @param metric documentation missing !\n * @return if build succesfull then a pointer to a KD-tree is returned else NULL\n */\n static KDTree< KEY, DIM >* buildTree (const std::vector< KDNode* >& nodes,\n rw::core::Ptr< rw::math::Metric< KEY > > metric)\n {\n if (nodes.size () == 0)\n return NULL;\n\n // create all tree nodes in a list\n std::vector< TreeNode >* tNodes = new std::vector< TreeNode > (nodes.size ());\n\n // copy the KDNodes into the tree nodes\n int i = 0;\n for (KDNode* n : nodes) {\n (*tNodes)[i]._kdnode = n;\n i++;\n }\n\n // create a simple median balanced tree\n TreeNode* root = buildBalancedRec (*tNodes, 0, (int) tNodes->size (), 0, DIM);\n\n return new KDTree< KEY, DIM > (*root, tNodes, metric);\n }\n\n /**\n * @brief gets the number of dimensions that this KDTree supports\n * @return the nr of dimensions of this KD-Tree\n */\n size_t getDimensions () const { return DIM; };\n\n /**\n * @brief adds a key value pair to the KDTree.\n * @param key [in] must be the same length as the dimensionality of the KDTree\n * @param val [in] value that is to be stored at the keys position\n */\n void addNode (const KEY& key, boost::any val)\n {\n RW_THROW (\"There is no implementation yet!\");\n }\n\n /**\n * @brief finds the KDNode with key equal to nnkey\n * @param nnkey [in] the key that is to be found\n * @return KDNode with key equal to nnkey if existing, else NULL\n */\n KDNode* search (const KEY& nnkey)\n {\n TreeNode* tmpNode = _root;\n for (size_t lev = 0; tmpNode != NULL; lev = (lev + 1) % DIM) {\n KEY& key = tmpNode->_kdnode->key;\n if (nnkey[lev] == key[lev] && !(tmpNode->_deleted) &&\n (_metric->distance (nnkey, key) < 0.000001)) {\n return tmpNode->_kdnode;\n }\n else if (nnkey (lev) > key (lev)) {\n tmpNode = tmpNode->_right;\n }\n else {\n tmpNode = tmpNode->_left;\n }\n }\n return NULL;\n };\n\n /**\n * @brief finds the KDNode with the key closest too nnkey\n * @param nnkey [in] the key to which the nearest neighbor is found\n * @return the nearest neighbor to nnkey\n */\n KDNode& nnSearch (const KEY& nnkey)\n {\n // std::cout << \"nnSearch \" << DIM << std::endl;\n\n // RW_ASSERT(_metric->size(nnkey)==DIM);\n if (_root == NULL)\n RW_THROW (\"KDTree has no data!\");\n\n KEY min = nnkey, max = nnkey;\n KDResult result (NULL, DBL_MAX);\n for (size_t i = 0; i < DIM; i++) {\n min[i] = -DBL_MAX;\n max[i] = DBL_MAX;\n }\n // std::cout << \"nnSearchRec\" << std::endl;\n nnSearchRec (nnkey, _root, min, max, result);\n if (result.n == NULL)\n RW_THROW (\"KDTree has no data!\");\n return *result.n;\n }\n\n /**\n * @brief finds all neighbors in the hyperelipse with radius radi and center in nnkey.\n * @param nnkey [in] the center of the hyperelipse\n * @param radi [in] the radius of the hyperelipse in euclidean 2-norm\n * @param nodes [out] a container for all nodes that is found within the hyperelipse\n */\n void nnSearchElipse (const KEY& nnkey, const KEY& radi, std::list< const KDNode* >& nodes)\n {\n // typedef std::pair QElem;\n using namespace rw::math;\n std::queue< TreeNode* > unhandled;\n unhandled.push (_root);\n double distRadi = _metric->distance (radi);\n KEY low = nnkey, upp = nnkey;\n for (size_t i = 0; i < DIM; i++) {\n low[i] = nnkey[i] - distRadi;\n upp[i] = nnkey[i] + distRadi;\n }\n\n nnSearchElipseRec (nnkey, _root, low, upp, distRadi, nodes);\n }\n\n /**\n * @brief finds all neighbors in the hyperelipse with radius radi and center in nnkey.\n * @param nnkey [in] the center of the hyperelipse\n * @param radi [in] the radius of the hyperelipse in euclidean 2-norm\n * @param nodes [out] a container for all nodes that is found within the hyperelipse\n */\n void nnSearchElipseRect (const KEY& nnkey, const KEY& radi,\n std::list< const KDNode* >& nodes)\n {\n // typedef std::pair QElem;\n using namespace rw::math;\n std::queue< TreeNode* > unhandled;\n unhandled.push (_root);\n\n double distRadi = _metric->distance (radi);\n KEY low = nnkey, upp = nnkey;\n for (size_t i = 0; i < DIM; i++) {\n low (i) = nnkey (i) - distRadi;\n upp (i) = nnkey (i) + distRadi;\n }\n\n while (!unhandled.empty ()) {\n // std::cout << \"unhandled size: \" << unhandled.size() << std::endl;\n TreeNode* n = unhandled.front ();\n unhandled.pop ();\n\n unsigned char axis = n->_axis;\n KEY& key = n->_kdnode->key;\n\n // std::cout << \"Axis: \" << axis << std::endl;\n\n // if the key is in range then add it to the result\n size_t j;\n for (j = 0; j < DIM && low[j] <= key[j] && upp[j] >= key[j]; j++)\n ;\n // std::cout << j << \"==\" << DIM << \" k:\" << key << std::endl;\n if (j == DIM) { // this is in range if\n double dist = _metric->distance (nnkey, key);\n // std::cout << \"Dist: \" << dist << \" < \" << distSqr << std::endl;\n if (dist < distRadi)\n nodes.push_back (n->_kdnode);\n }\n\n // add the children to the unhandled queue if the current dimension\n if ((low (axis) <= key (axis)) && (n->_left != NULL))\n unhandled.push (n->_left);\n if ((upp (axis) > key (axis)) && (n->_right != NULL))\n unhandled.push (n->_right);\n }\n }\n\n /**\n * @brief finds all neighbors in the hyperrectangle defined by the lower bound and the\n * upper bound\n */\n void nnSearchRect (const KEY& low, const KEY& upp, std::list< const KDNode* >& nodes)\n {\n // typedef std::pair QElem;\n std::queue< TreeNode* > unhandled;\n unhandled.push (_root);\n\n // std::cout << \"nnSearchRect: \"<< std::endl;\n // std::cout << \"- low bound: \"<< low << std::endl;\n // std::cout << \"- upp bound: \"<< upp << std::endl;\n\n while (!unhandled.empty ()) {\n // std::cout << \"unhandled size: \" << unhandled.size() << std::endl;\n TreeNode* n = unhandled.front ();\n unhandled.pop ();\n\n unsigned char axis = n->_axis;\n KEY& key = n->_kdnode->key;\n\n // std::cout << \"Axis: \" << axis << std::endl;\n\n // if the key is in range then add it to the result\n size_t j;\n for (j = 0; j < DIM && low[j] <= key[j] && key[j] <= upp[j]; j++)\n ;\n // std::cout << j << \"==\" << DIM << \" k:\" << key << std::endl;\n if (j == DIM) // this is in range\n nodes.push_back (n->_kdnode);\n\n // add the children to the unhandled queue if the current dimension\n if ((low (axis) <= key (axis)) && (n->_left != NULL))\n unhandled.push (n->_left);\n if ((upp (axis) > key (axis)) && (n->_right != NULL))\n unhandled.push (n->_right);\n }\n };\n\n private:\n KDTree (){};\n\n KDTree (TreeNode& root, std::vector< TreeNode >* nodes,\n rw::core::Ptr< rw::math::Metric< KEY > > metric) :\n _root (&root),\n _nodes (nodes), _metric (metric){};\n\n /**\n * @brief Internal representation of a KD Tree Node. To save processing time when deleting\n * TreeNodes, a boolean is kept that say if the node is deleted or not. If deleted all\n * rutines kan skip the node and forward the call to its children.\n */\n struct TreeNode\n {\n public:\n TreeNode () :\n _left (NULL), _right (NULL), _kdnode (NULL), _deleted (false), _axis (0){};\n\n TreeNode (KDNode* node) :\n _left (NULL), _right (NULL), _kdnode (node), _deleted (false), _axis (0){};\n\n TreeNode (TreeNode* left, TreeNode* right, KDNode* node) :\n _left (left), _right (right), _kdnode (node), _deleted (false), _axis (0){};\n\n static void swap (TreeNode& n1, TreeNode& n2)\n {\n std::swap (n1._left, n2._left);\n std::swap (n1._right, n2._right);\n std::swap (n1._kdnode, n2._kdnode);\n }\n\n TreeNode *_left, *_right;\n KDNode* _kdnode;\n bool _deleted; //\n unsigned char _axis; // the splitting axis\n };\n\n struct SimpleCompare\n {\n private:\n size_t _dim;\n\n public:\n SimpleCompare (size_t dim) : _dim (dim){};\n\n bool operator() (const TreeNode& e1, const TreeNode& e2)\n {\n return e1._kdnode->key[_dim] < e2._kdnode->key[_dim];\n }\n };\n\n static TreeNode* buildBalancedRec (std::vector< TreeNode >& tNodes, int startIdx,\n int endIdx, size_t depth, size_t nrOfDims)\n {\n if (endIdx <= startIdx)\n return NULL;\n\n // std::cout << \"RecBuild(\" << startIdx << \",\" << endIdx << \")\" << std::endl;\n size_t len = endIdx - startIdx;\n\n size_t dim = depth % nrOfDims;\n // the compare func can\n std::sort (&tNodes[startIdx], &tNodes[endIdx - 1], SimpleCompare (dim));\n size_t medianIdx = startIdx + len / 2;\n\n TreeNode& mNode = tNodes[medianIdx];\n mNode._axis = 0xFF & dim;\n mNode._left = buildBalancedRec (tNodes, startIdx, (int) medianIdx, depth + 1, nrOfDims);\n mNode._right =\n buildBalancedRec (tNodes, (int) medianIdx + 1, endIdx, depth + 1, nrOfDims);\n return &mNode;\n }\n\n template< class T > T clamp (const T& val, const T& min, const T& max)\n {\n T result = val;\n for (size_t i = 0; i < DIM; i++) {\n result[i] = rw::math::Math::clamp (val[i], min[i], max[i]);\n }\n return result;\n }\n\n void nnSearchRec (const KEY& nnkey, TreeNode* node, KEY& min, KEY& max, KDResult& out)\n {\n using namespace rw::math;\n if (node == NULL)\n return;\n size_t axis = node->_axis;\n // std::cout << \"nnSearchRec(\"<< axis << \")\" << std::endl;\n\n KEY& key = node->_kdnode->key;\n double distSqr = _metric->distance (nnkey, key);\n // std::cout << \"le\" << std::endl;\n\n // if this node is closer than any other then update out\n if (distSqr < out.dist && !node->_deleted) {\n out.dist = distSqr;\n out.n = node->_kdnode;\n }\n // stop if the distance is very small\n if (distSqr < kdtree_epsilon)\n return;\n // std::cout << \"1\" << std::endl;\n // call nnSearch recursively with closerNode,\n // closestNode and closestDistSqr is updated\n bool isLeftClosest = nnkey (axis) < key (axis);\n if (isLeftClosest) {\n // std::cout << \"left\" << std::endl;\n // left is closest, backup split value and make the recursive call\n double maxTmp = max (axis);\n max (axis) = key (axis);\n nnSearchRec (nnkey, node->_left, min, max, out);\n // undo the change of max\n max (axis) = maxTmp;\n }\n else {\n // std::cout << \"right\" << std::endl;\n // right is closest, backup split value and make the recursive call\n double minTmp = min (axis);\n min (axis) = key (axis);\n nnSearchRec (nnkey, node->_right, min, max, out);\n // undo the change of max\n min (axis) = minTmp;\n }\n // std::cout << \"2\" << std::endl;\n\n // next check if fartherNode split plane lies closer than closestDistSqr\n if (Math::sqr (nnkey (axis) - key (axis)) >= out.dist)\n return;\n // std::cout << \"3\" << std::endl;\n\n bool isLeftFarthest = !isLeftClosest;\n // if closest point in hyperrect of farther node is closer than closest\n // then call nnSearch recursively with farther node\n if (isLeftFarthest) {\n double maxTmp = max (axis);\n max (axis) = key (axis);\n KEY closest = clamp (nnkey, min, max);\n if (_metric->distance (nnkey, closest) < out.dist)\n nnSearchRec (nnkey, node->_left, min, max, out);\n // undo the change of max\n max (axis) = maxTmp;\n }\n else {\n double minTmp = min (axis);\n min (axis) = key (axis);\n KEY closest = clamp (nnkey, min, max);\n if (_metric->distance (nnkey, closest) < out.dist)\n nnSearchRec (nnkey, node->_right, min, max, out);\n // undo the change of max\n min (axis) = minTmp;\n }\n };\n\n void nnSearchElipseRec (const KEY& nnkey, TreeNode* node, KEY& min, KEY& max,\n double maxRadiSqr, std::list< const KDNode* >& nodes)\n {\n using namespace rw::math;\n if (node == NULL)\n return;\n size_t axis = node->_axis;\n // std::cout << \"nnSearchRec(\"<< axis << \")\" << std::endl;\n\n KEY& key = node->_kdnode->key;\n double distSqr = _metric->distance (nnkey, key);\n\n // if this node is closer than any other then update out\n if (distSqr < maxRadiSqr && !node->_deleted) {\n nodes.push_back (node->_kdnode);\n }\n // stop if the distance is very small\n if (distSqr < kdtree_epsilon)\n return;\n\n // call nnSearch recursively with closerNode,\n // closestNode and closestDistSqr is updated\n bool isLeftClosest = nnkey (axis) < key (axis);\n if (isLeftClosest) {\n // left is closest, backup split value and make the recursive call\n double maxTmp = max (axis);\n max (axis) = key (axis);\n nnSearchElipseRec (nnkey, node->_left, min, max, maxRadiSqr, nodes);\n // undo the change of max\n max (axis) = maxTmp;\n }\n else {\n // right is closest, backup split value and make the recursive call\n double minTmp = min (axis);\n min (axis) = key (axis);\n nnSearchElipseRec (nnkey, node->_right, min, max, maxRadiSqr, nodes);\n // undo the change of max\n min (axis) = minTmp;\n }\n\n // next check if fartherNode split plane lies closer than closestDistSqr\n if (Math::sqr (nnkey (axis) - key (axis)) >= maxRadiSqr)\n return;\n\n bool isLeftFarthest = !isLeftClosest;\n // if closest point in hyperrect of farther node is closer than closest\n // then call nnSearch recursively with farther node\n if (isLeftFarthest) {\n double maxTmp = max (axis);\n max (axis) = key (axis);\n KEY closest = clamp (nnkey, min, max);\n if (_metric->distance (nnkey, closest) < maxRadiSqr)\n nnSearchElipseRec (nnkey, node->_left, min, max, maxRadiSqr, nodes);\n // undo the change of max\n max (axis) = maxTmp;\n }\n else {\n double minTmp = min (axis);\n min (axis) = key (axis);\n KEY closest = clamp (nnkey, min, max);\n if (_metric->distance (nnkey, closest) < maxRadiSqr)\n nnSearchElipseRec (nnkey, node->_right, min, max, maxRadiSqr, nodes);\n // undo the change of max\n min (axis) = minTmp;\n }\n };\n };\n\n extern template class KDTree< rw::math::Vector3D<>, 3 >;\n\n /**@}*/\n\n}} // namespace rwlibs::algorithms\n\n#endif /*KDTREE_HPP_*/\n", "meta": {"hexsha": "13d64d8d04cf1a0a63d12b859aee7aa078bed13f", "size": 22017, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "RobWork/src/rwlibs/algorithms/kdtree/KDTree.hpp", "max_stars_repo_name": "ZLW07/RobWork", "max_stars_repo_head_hexsha": "e713881f809d866b9a0749eeb15f6763e64044b3", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-12-29T14:16:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-29T14:16:27.000Z", "max_issues_repo_path": "RobWork/src/rwlibs/algorithms/kdtree/KDTree.hpp", "max_issues_repo_name": "ZLW07/RobWork", "max_issues_repo_head_hexsha": "e713881f809d866b9a0749eeb15f6763e64044b3", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "RobWork/src/rwlibs/algorithms/kdtree/KDTree.hpp", "max_forks_repo_name": "ZLW07/RobWork", "max_forks_repo_head_hexsha": "e713881f809d866b9a0749eeb15f6763e64044b3", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.5586690018, "max_line_length": 100, "alphanum_fraction": 0.4933460508, "num_tokens": 5258, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.4571367168274948, "lm_q1q2_score": 0.2357087954044889}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace System {\nusing namespace std;\nusing namespace BOOST_SPIRIT_CLASSIC_NS;\nnamespace clientp\n{\n\nnamespace {\n\tvoid sad_little_message(int line_no, string input){\n\t\tcerr << \"Input line : \" << line_no << endl; \n\t\tcerr << \"Received : \" << input << endl; \n\t\tcerr << \"Please express equations using arithmetic combinations +/- of one or more terms, \" << endl;\n\t\tcerr << \"terms consisting of products expressed with *, powers with ^ and powers/indices\" << endl;\n\t\tcerr << \"expressed within parenthesis for constants k and variables y (1-based index) eg.,\" << endl;\n\t\tcerr << \" \" << endl;\n\t\tcerr << \"1.0*k(717)*y(516)-1.0*k(416)*y(1)*y(392)-1.0*k(718)*y(1)^(1/2)*y(517)\" << endl;\n\t\tcerr << \" \" << endl;\n\t\tthrow std::invalid_argument( \"Received bad equation format on stdin\" );\n\t}\n}\n\n\ntemplate \nvoid update(std::map &x, const T& key, const T& val){\n\n\tx[key]+=val;\n}\n\n\nnamespace qi = boost::spirit::qi;\nnamespace ascii = boost::spirit::ascii;\nnamespace phoenix = boost::phoenix;\n\nusing qi::float_;\nusing qi::double_;\nusing qi::phrase_parse;\nusing qi::_1;\nusing ascii::space;\nusing phoenix::push_back;\n\n\ntemplate \n\tbool parse_csv(Iterator first, Iterator last, std::vector& k)\n\t{\n\t\tbool r = phrase_parse(first, last,\n\n\t\t\t\t// grammar for csv files\n\t\t\t\t(\n\t\t\t\t //*(float_ >> ',' | float_ [push_back(phoenix::ref(k),_1)])\n\t\t\t\t *(float_ [push_back(phoenix::ref(k),_1)])\n\t\t\t\t)\n\t\t\t\t,\n\t\t\t\tspace);\n\n\t\treturn r;\n\n\t}\n\n\ntemplate \n\tbool parse_k_vals(Iterator first, Iterator last, std::vector& w)\n\t{\t\t\t\n\t\tbool r = phrase_parse(first, last,\n\n\t\t\t\t// grammar for k constants\n\t\t\t\t(\n\t\t\t\t (\"k(\" >> float_[push_back(phoenix::ref(w), _1)] >> ')')\n\t\t\t\t)\n\t\t\t\t,\n\t\t\t\t//skip\n\t\t\t\tspace | '*' | float_ >> '*' | '-'>>float_ >> '*'| '+' >> float_ >> '*'| \"y(\" >> float_ >> ')');\n\n\t\treturn r;\n\t}\n\ntemplate \n\tbool parse_constants(Iterator first, Iterator last, std::vector& v)\n\t{\n\t\tbool r = phrase_parse(first, last,\n\n\t\t\t\t// grammar for numerical constants & sign\n\t\t\t\t(\n\t\t\t\t (float_[push_back(phoenix::ref(v), _1)] >> '*')\n\t\t\t\t)\n\t\t\t\t,\n\t\t\t\t//skip\n\t\t\t\tspace);\n\t\treturn r;\n\t}\ntemplate \n\tbool parse_y_vals(Iterator first, Iterator last, std::map& x)\n\t{\n\t\tfloat base =1.0;\n\t\tfloat exp = 0.0;\n\t\tbool r = phrase_parse(first, last,\n\n\t\t\t\t// grammar for y variables\n\t\t\t\t// this looks like hell\n\t\t\t\t(\n\t\t\t\t *(\"y(\" >> float_[phoenix::ref(base)=_1] >> \")^(\" >> float_[phoenix::ref(exp)=_1] >> '/' >> float_[phoenix::ref(exp)/=_1]\\\n\t\t\t\t\t [boost::bind(&update,boost::ref(x),boost::ref(base),boost::ref(exp))] >> ')' |\n\t\t\t\t\t \"y(\" >> float_[phoenix::ref(base)=_1, boost::bind(&update,boost::ref(x),boost::ref(base),1.0f)] >> ')')\n\t\t\t\t)\n\t\t\t\t,\n\t\t\t\t//skip\n\t\t\t\tspace | '*' | float_ >> '*' | '-'>>float_ >> '*'| '+' >> float_ >> '*'| \"k(\" >> float_ >> ')');\n\n\n\t\treturn r;\n\t}\n\ntemplate \n\tbool parse_csv(Iterator first, Iterator last, std::vector& k)\n\t{\n\t\tbool r = phrase_parse(first, last,\n\n\t\t\t\t// grammar for csv files\n\t\t\t\t(\n\t\t\t\t //*(double_ >> ',' | double_ [push_back(phoenix::ref(k),_1)])\n\t\t\t\t *(double_ [push_back(phoenix::ref(k),_1)])\n\t\t\t\t)\n\t\t\t\t,\n\t\t\t\tspace);\n\n\t\treturn r;\n\n\t}\n\n\ntemplate \n\tbool parse_k_vals(Iterator first, Iterator last, std::vector& w)\n\t{\t\t\t\n\t\tbool r = phrase_parse(first, last,\n\n\t\t\t\t// grammar for k constants\n\t\t\t\t(\n\t\t\t\t (\"k(\" >> double_[push_back(phoenix::ref(w), _1)] >> ')')\n\t\t\t\t)\n\t\t\t\t,\n\t\t\t\t//skip\n\t\t\t\tspace | '*' | double_ >> '*' | '-'>>double_ >> '*'| '+' >> double_ >> '*'| \"y(\" >> double_ >> ')');\n\n\t\treturn r;\n\t}\n\ntemplate \n\tbool parse_constants(Iterator first, Iterator last, std::vector& v)\n\t{\n\t\tbool r = phrase_parse(first, last,\n\n\t\t\t\t// grammar for numerical constants & sign\n\t\t\t\t(\n\t\t\t\t (double_[push_back(phoenix::ref(v), _1)] >> '*')\n\t\t\t\t)\n\t\t\t\t,\n\t\t\t\t//skip\n\t\t\t\tspace);\n\t\treturn r;\n\t}\ntemplate \n\tbool parse_y_vals(Iterator first, Iterator last, std::map& x)\n\t{\n\t\tdouble base =1.0;\n\t\tdouble exp = 0.0;\n\t\tbool r = phrase_parse(first, last,\n\n\t\t\t\t// grammar for y variables\n\t\t\t\t// this looks like hell\n\t\t\t\t(\n\t\t\t\t *(\"y(\" >> double_[phoenix::ref(base)=_1] >> \")^(\" >> double_[phoenix::ref(exp)=_1] >> '/' >> double_[phoenix::ref(exp)/=_1]\\\n\t\t\t\t\t [boost::bind(&update,boost::ref(x),boost::ref(base),boost::ref(exp))] >> ')' |\n\t\t\t\t\t \"y(\" >> double_[phoenix::ref(base)=_1, boost::bind(&update,boost::ref(x),boost::ref(base),1.0f)] >> ')')\n\t\t\t\t)\n\t\t\t\t,\n\t\t\t\t//skip\n\t\t\t\tspace | '*' | double_ >> '*' | '-'>>double_ >> '*'| '+' >> double_ >> '*'| \"k(\" >> double_ >> ')');\n\n\n\t\treturn r;\n\t}\n}\n}\n", "meta": {"hexsha": "59130dec4604997f4bed429c953e9a7933a34bf0", "size": 5182, "ext": "inl", "lang": "C++", "max_stars_repo_path": "inc/equation_system/detail/clientp.inl", "max_stars_repo_name": "pytaunay/CuSolve", "max_stars_repo_head_hexsha": "05f75d288b114de7da22249e54520b8fdb589eba", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-04-24T02:46:41.000Z", "max_stars_repo_stars_event_max_datetime": "2019-04-24T02:46:41.000Z", "max_issues_repo_path": "inc/equation_system/detail/clientp.inl", "max_issues_repo_name": "pytaunay/CuSolve", 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YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.23559260281945738}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\n\n// Copyright (c) 2014, Oracle and/or its affiliates.\n\n// Contributed and/or modified by Menelaos Karavelas, on behalf of Oracle\n\n// Licensed under the Boost Software License version 1.0.\n// http://www.boost.org/users/license.html\n\n#ifndef BOOST_GEOMETRY_ALGORITHMS_DETAIL_CLOSEST_FEATURE_POINT_TO_RANGE_HPP\n#define BOOST_GEOMETRY_ALGORITHMS_DETAIL_CLOSEST_FEATURE_POINT_TO_RANGE_HPP\n\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n\n\nnamespace geofeatures_boost {} namespace boost = geofeatures_boost; namespace geofeatures_boost { namespace geometry\n{\n\n#ifndef DOXYGEN_NO_DETAIL\nnamespace detail { namespace closest_feature\n{\n\n\n// returns the segment (pair of iterators) that realizes the closest\n// distance of the point to the range\ntemplate\n<\n typename Point,\n typename Range,\n closure_selector Closure,\n typename Strategy\n>\nclass point_to_point_range\n{\nprotected:\n typedef typename geofeatures_boost::range_iterator::type iterator_type;\n\n template \n static inline void apply(Point const& point,\n iterator_type first,\n iterator_type last,\n Strategy const& strategy,\n iterator_type& it_min1,\n iterator_type& it_min2,\n Distance& dist_min)\n {\n BOOST_GEOMETRY_ASSERT( first != last );\n\n Distance const zero = Distance(0);\n\n iterator_type it = first;\n iterator_type prev = it++;\n if (it == last)\n {\n it_min1 = it_min2 = first;\n dist_min = strategy.apply(point, *first, *first);\n return;\n }\n\n // start with first segment distance\n dist_min = strategy.apply(point, *prev, *it);\n iterator_type prev_min_dist = prev;\n\n // check if other segments are closer\n for (++prev, ++it; it != last; ++prev, ++it)\n {\n Distance dist = strategy.apply(point, *prev, *it);\n if (geometry::math::equals(dist, zero))\n {\n dist_min = zero;\n it_min1 = prev;\n it_min2 = it;\n return;\n }\n else if (dist < dist_min)\n {\n dist_min = dist;\n prev_min_dist = prev;\n }\n }\n\n it_min1 = it_min2 = prev_min_dist;\n ++it_min2;\n }\n\npublic:\n typedef typename std::pair return_type;\n\n template \n static inline return_type apply(Point const& point,\n iterator_type first,\n iterator_type last,\n Strategy const& strategy,\n Distance& dist_min)\n {\n iterator_type it_min1, it_min2;\n apply(point, first, last, strategy, it_min1, it_min2, dist_min);\n\n return std::make_pair(it_min1, it_min2);\n }\n\n static inline return_type apply(Point const& point,\n iterator_type first,\n iterator_type last,\n Strategy const& strategy)\n {\n typename strategy::distance::services::return_type\n <\n Strategy,\n Point,\n typename geofeatures_boost::range_value::type\n >::type dist_min;\n\n return apply(point, first, last, strategy, dist_min);\n }\n\n template \n static inline return_type apply(Point const& point,\n Range const& range,\n Strategy const& strategy,\n Distance& dist_min)\n {\n return apply(point,\n geofeatures_boost::begin(range),\n geofeatures_boost::end(range),\n strategy,\n dist_min);\n }\n\n static inline return_type apply(Point const& point,\n Range const& range,\n Strategy const& strategy)\n {\n return apply(point, geofeatures_boost::begin(range), geofeatures_boost::end(range), strategy);\n }\n};\n\n\n\n// specialization for open ranges\ntemplate \nclass point_to_point_range\n : point_to_point_range\n{\nprivate:\n typedef point_to_point_range base_type;\n typedef typename base_type::iterator_type iterator_type;\n\n template \n static inline void apply(Point const& point,\n iterator_type first,\n iterator_type last,\n Strategy const& strategy,\n iterator_type& it_min1,\n iterator_type& it_min2,\n Distance& dist_min)\n {\n BOOST_GEOMETRY_ASSERT( first != last );\n\n base_type::apply(point, first, last, strategy,\n it_min1, it_min2, dist_min);\n\n iterator_type it_back = --last;\n Distance const zero = Distance(0);\n Distance dist = strategy.apply(point, *it_back, *first);\n\n if (geometry::math::equals(dist, zero))\n {\n dist_min = zero;\n it_min1 = it_back;\n it_min2 = first;\n }\n else if (dist < dist_min)\n {\n dist_min = dist;\n it_min1 = it_back;\n it_min2 = first;\n }\n } \n\npublic:\n typedef typename std::pair return_type;\n\n template \n static inline return_type apply(Point const& point,\n iterator_type first,\n iterator_type last,\n Strategy const& strategy,\n Distance& dist_min)\n {\n iterator_type it_min1, it_min2;\n\n apply(point, first, last, strategy, it_min1, it_min2, dist_min);\n\n return std::make_pair(it_min1, it_min2);\n }\n\n static inline return_type apply(Point const& point,\n iterator_type first,\n iterator_type last,\n Strategy const& strategy)\n {\n typedef typename strategy::distance::services::return_type\n <\n Strategy,\n Point,\n typename geofeatures_boost::range_value::type\n >::type distance_return_type;\n\n distance_return_type dist_min;\n\n return apply(point, first, last, strategy, dist_min);\n }\n\n template \n static inline return_type apply(Point const& point,\n Range const& range,\n Strategy const& strategy,\n Distance& dist_min)\n {\n return apply(point,\n geofeatures_boost::begin(range),\n geofeatures_boost::end(range),\n strategy,\n dist_min);\n }\n\n static inline return_type apply(Point const& point,\n Range const& range,\n Strategy const& strategy)\n {\n return apply(point, geofeatures_boost::begin(range), geofeatures_boost::end(range), strategy);\n }\n};\n\n\n}} // namespace detail::closest_feature\n#endif // DOXYGEN_NO_DETAIL\n\n}} // namespace geofeatures_boost::geometry\n\n\n#endif // BOOST_GEOMETRY_ALGORITHMS_DETAIL_CLOSEST_FEATURE_POINT_TO_RANGE_HPP\n", "meta": {"hexsha": "70dd7161879e40c875e82b9e7a15334d6856f0aa", "size": 8037, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Pods/GeoFeatures/GeoFeatures/boost/geometry/algorithms/detail/closest_feature/point_to_range.hpp", "max_stars_repo_name": "xarvey/Yuuuuuge", 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"max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 9.0, "max_forks_repo_forks_event_min_datetime": "2015-08-26T03:11:38.000Z", "max_forks_repo_forks_event_max_datetime": "2018-03-21T07:16:29.000Z", "avg_line_length": 32.0199203187, "max_line_length": 116, "alphanum_fraction": 0.5508274232, "num_tokens": 1499, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.4339814648038985, "lm_q1q2_score": 0.23559260281945735}} {"text": "/*\n Author(s): Robert I A Patterson\n Project: sweepc (population balance solver)\n Sourceforge: http://sourceforge.net/projects/mopssuite\n\n Copyright (C) 2011 Robert I A Patterson.\n\n File purpose:\n Implementation of constant additive coagulation kernel\n\tfor the hybrid particle-number and particle model\n\n Licence:\n This file is part of \"sweepc\".\n\n sweepc is free software; you can redistribute it and/or\n modify it under the terms of the GNU General Public License\n as published by the Free Software Foundation; either version 2\n of the License, or (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program; if not, write to the Free Software\n Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.\n\n Contact:\n Dr Markus Kraft\n Dept of Chemical Engineering\n University of Cambridge\n New Museums Site\n Pembroke Street\n Cambridge\n CB2 3RA\n UK\n\n Email: mk306@cam.ac.uk\n Website: http://como.cheng.cam.ac.uk\n*/\n\n#include \"swp_hybrid_constcoag.h\"\n\n#include \"swp_params.h\"\n#include \"swp_cell.h\"\n#include \"swp_mechanism.h\"\n#include \n\nusing namespace Sweep::Processes;\n\nconst double Sweep::Processes::HybridConstantCoagulation::s_MajorantFactor = 1.5;\n\n/**\n * Main way of building a new coagulation object\n * @param[in] mech Mechanism to which coagulation will belong\n *\n */\nSweep::Processes::HybridConstantCoagulation::HybridConstantCoagulation(const Sweep::Mechanism &mech)\n: Coagulation(mech)\n{\n m_name = \"ConstantCoagulation\";\n}\n\n// Stream-reading constructor.\nSweep::Processes::HybridConstantCoagulation::HybridConstantCoagulation(std::istream &in, const Sweep::Mechanism &mech)\n: Coagulation(mech)\n{\n m_name = \"ConstantCoagulation\";\n Deserialize(in, mech);\n}\n\n// Returns the rate of the process for the given system.\ndouble Sweep::Processes::HybridConstantCoagulation::Rate(double t, const Cell &sys,\n const Geometry::LocalGeometry1d &local_geom) const\n{\n // Get the number of particles in the system.\n unsigned int n = sys.ParticleCount() + sys.Particles().GetTotalParticleNumber();\n\n return A() * n * (n - 1) * s_MajorantFactor / sys.SampleVolume() / 2;\n}\n\n/**\n * Number of terms in the expression for the sum of the majorant\n * kernel over all particle pairs.\n */\nunsigned int Sweep::Processes::HybridConstantCoagulation::TermCount() const { return TYPE_COUNT; }\n\n\n/**\n * Calculate the terms in the sum of the majorant kernel over all particle\n * pairs, placing each term in successive positions of the sequence\n * beginning at iterm and return the sum of the terms added to that\n * vector.\n *\n * @param[in] t Time for which rates are requested\n * @param[in] sys Details of the particle population and environment\n * @param[in] local_geom Position information\n * @param[in,out] iterm Pointer to start of sequence to hold the rate terms, returned as one past the end.\n */\ndouble Sweep::Processes::HybridConstantCoagulation::RateTerms(double t, const Cell &sys,\n const Geometry::LocalGeometry1d &local_geom,\n fvector::iterator &iterm) const\n{\n return *(iterm++) = Rate(t, sys, local_geom);\n}\n\n/*!\n * \n *\n * \\param[in] t Time\n * \\param[in,out] sys System to update\n * \\param[in] local_geom Details of local phsyical layout\n * \\param[in] iterm Process term responsible for this event\n * \\param[in,out] rng Random number generator\n *\n * \\return 0 on success, otherwise negative.\n */\nint HybridConstantCoagulation::Perform(double t, Sweep::Cell &sys,\n const Geometry::LocalGeometry1d& local_geom,\n unsigned int iterm,\n Sweep::rng_type &rng) const\n{\n\tPartPtrVector dummy;\n // Select properties by which to choose particles.\n // Note we need to choose 2 particles. One particle must be chosen\n // uniformly and one with probability proportional\n // to particle mass.\n\n int ip1 = -1, ip2 = -1;\n unsigned int index1 = 0, index2 = 0;\n\n // Hybrid particle model flags\n bool hybrid_flag = m_mech->IsHybrid();\n bool ip1_flag = false;\n bool ip2_flag = false;\n bool coag_in_place = false;\n\n double n_incep = sys.Particles().GetTotalParticleNumber();\n double n_other = sys.ParticleCount();\n double n_total = n_incep + n_other;\n\n boost::uniform_01 unifDistrib(rng);\n double alpha1 = unifDistrib() * n_total;\n double alpha2 = unifDistrib() * n_total;\n if (n_total <= 0)\n return -1;\n\n if (n_total < 2) // if there are < 2 SPs but incepting class has weight >= 2, we can still act\n return 1;\n else \n {\n if (hybrid_flag)\n {\n // Particle 1 is picked uniformly. Here, the\n // incepting class has multiple weight 1 particles\n // Account for this by selecting this by default and \n // switching with probability n_other/n_total\n ip1 = -2;\n ip2 = -2;\n if (n_other >= alpha1)\n {\n ip1 = sys.Particles().Select_usingGivenRand(iUniform, alpha1, rng);\n }\n }\n else\n {\n ip1 = sys.Particles().Select(rng);\n ip2 = sys.Particles().Select(rng);\n }\n }\n\n // Choose and get first particle, then update it.\n Particle *sp1 = NULL;\n double dsp1 = 0.0;\n bool must_switch = false;\n\n // Is this an incepting class particle?\n if (hybrid_flag && ip1 == -2)\n {\n // Note don't need to add it to the ensemble unless coagulation is successful\n index1 = m_mech->SetRandomParticle(sys.Particles(), t, alpha1 - n_other, iUniform, rng);\n\t\tif (index1 == 0)\n\t\t\treturn -1;\n sp1 = sys.Particles().GetPNParticleAt(index1)->Clone();\n sp1->SetTime(t);\n ip1_flag = true; // Flag sp1 as an incepting class particle\n }\n else\n {\n if (ip1 >= 0) {\n sp1 = sys.Particles().At(ip1);\n }\n else {\n // Failed to choose a particle.\n return -1;\n }\n }\n\n // Choose and get unique second particle, then update it. Note, we are allowed to do\n // this even if the first particle was invalidated.\n unsigned int guard = 0;\n if (!hybrid_flag)\n {\n while ((ip2 == ip1) && (++guard < 1000))\n {\n ip2 = sys.Particles().Select(rng);\n }\n }\n else\n {\n ip2 = ip1;\n bool unsuitableChoice = true;\n unsigned int n_index1 = sys.Particles().NumberAtIndex(index1);\n while (unsuitableChoice && (++guard < 1000))\n {\n alpha2 = unifDistrib() * n_total;\n if (alpha2 <= n_incep)\n {\n index2 = m_mech->SetRandomParticle(sys.Particles(), t, alpha2, iUniform, rng); \n\t\t\t\tif (index2 == 0)\n\t\t\t\t{\n\t\t\t\t\tif (ip1_flag)\n\t\t\t\t\t{\n\t\t\t\t\t\tdelete sp1;\n\t\t\t\t\t\tsp1 = NULL;\n\t\t\t\t\t}\n\t\t\t\t\treturn -1;\n\t\t\t\t}\n if (!((index2 == index1) && (n_index1 == 1)))\n {\n unsuitableChoice = false;\n ip2 = -2;\n }\n }\n else\n {\n ip2 = sys.Particles().Select_usingGivenRand(iUniform, alpha2 - n_incep, rng);\n if (!(ip2 == ip1))\n unsuitableChoice = false;\n }\n }\n }\n\n // Choose and get second particle, then update it.\n Particle *sp2 = NULL;\n double dsp2 = 0.0;\n\n // Is this an incepting class particle?\n if (hybrid_flag && ip2 == -2)\n {\n // Note don't need to add it to the ensemble unless coagulation is successful\n sp2 = sys.Particles().GetPNParticleAt(index2)->Clone();\n sp2->SetTime(t);\n ip2_flag = true; // Flag sp2 as an incepting class particle\n }\n else\n {\n if ((ip2 >= 0) && (ip2 != ip1)) {\n sp2 = sys.Particles().At(ip2);\n }\n else {\n // Failed to select a unique particle.\n return -1;\n }\n }\n\n //Calculate the majorant rate before updating the particles\n const double majk = MajorantKernel(*sp1, *sp2, sys, Default);\n\n //Update the particles\n if (t > sp1->LastUpdateTime())\n m_mech->UpdateParticle(*sp1, sys, t, ip1, rng, dummy);\n\n // Check that particle is still valid. If not,\n // remove it and cease coagulating.\n if (!sp1->IsValid()) {\n if (!ip1_flag)\n {\n // Must remove first particle now.\n sys.Particles().Remove(ip1);\n }\n else\n {\n // Particle sp1 is not in the ensemble, must manually delete it\n delete sp1;\n sp1 = NULL;\n }\n // Invalidating the index tells this routine not to perform coagulation.\n ip1 = -1;\n return 0;\n }\n\n if (t > sp2->LastUpdateTime())\n m_mech->UpdateParticle(*sp2, sys, t, ip2, rng, dummy);\n\n // Check validity of particles after update.\n if (!sp2->IsValid()) {\n // Tell the ensemble to update particle one before we confuse things\n // by removing particle 2\n if (!ip1_flag)\n sys.Particles().Update(ip1);\n\n if (!ip2_flag)\n {\n // Must remove second particle now.\n sys.Particles().Remove(ip2);\n }\n else\n {\n // Particle sp2 is not in the ensemble, must manually delete it\n delete sp2;\n sp2 = NULL;\n }\n\n // Invalidating the index tells this routine not to perform coagulation.\n ip2 = -1;\n\n return 0;\n }\n\n // Check that both the particles are still valid.\n if ((ip1 != -1) && (ip2 != -1)) {\n // Must check for ficticious event now by comparing the original\n // majorant rate and the current (after updates) true rate.\n\n double truek = CoagKernel(*sp1, *sp2, sys);\n\n if (!Fictitious(majk, truek, rng)) {\n if (ip1_flag)\n {\n sys.Particles().UpdateTotalsWithIndex(index1, -1.0);\n sys.Particles().UpdateNumberAtIndex(index1, -1);\n sys.Particles().UpdateTotalParticleNumber(-1);\n unsigned int index12 = index1 + index2;\n // Allow for coagulation in place if the combined particle is small enough\n\t\t\t\tif ((m_mech->CoagulateInList()) && ip2_flag && (index12 < sys.Particles().GetHybridThreshold()))\n {\n coag_in_place = true;\n sys.Particles().UpdateTotalsWithIndex(index12, 1.0);\n sys.Particles().UpdateNumberAtIndex(index12, 1);\n sys.Particles().UpdateTotalParticleNumber(1);\n if (sp1 != NULL)\n {\n delete sp1;\n sp1 = NULL;\n }\n }\n else\n {\n // otherwise add the particle to the ensemble\n ip1 = sys.Particles().Add(*sp1, rng, ip2, true);\n }\n }\n if (ip2_flag)\n {\n sys.Particles().UpdateTotalsWithIndex(index2, -1.0);\n sys.Particles().UpdateNumberAtIndex(index2, -1);\n sys.Particles().UpdateTotalParticleNumber(-1);\n }\n if (!coag_in_place)\n JoinParticles(t, ip1, sp1, ip2, sp2, sys, rng);\n if (ip2_flag && sp2 != NULL)\n {\n delete sp2;\n sp2 = NULL;\n }\n } else {\n if (!ip1_flag)\n sys.Particles().Update(ip1);\n else if (sp1 != NULL)\n {\n delete sp1;\n sp1 = NULL;\n }\n if (!ip2_flag)\n sys.Particles().Update(ip2);\n else if (sp2 != NULL)\n {\n delete sp2;\n sp2 = NULL;\n }\n return 1; // Ficticious event.\n }\n } else {\n // One or both particles were invalidated on update,\n // but that's not a problem. Information on the update\n // of valid particles must be propagated into the binary\n // tree\n if (ip1 != -1)\n {\n if (!ip1_flag)\n sys.Particles().Update(ip1);\n }\n if (ip2 != -1 && !ip2_flag)\n sys.Particles().Update(ip2);\n\n if (ip1_flag && sp1 != NULL)\n {\n delete sp1;\n sp1 = NULL;\n }\n if (ip2_flag && sp2 != NULL)\n {\n delete sp2;\n sp2 = NULL;\n }\n }\n\n if (ip1_flag && sp1 != NULL && ip1 == -2)\n {\n delete sp1;\n sp1 = NULL;\n }\n\n if (ip2_flag && sp2 != NULL)\n {\n delete sp2;\n sp2 = NULL;\n }\n\n return 0;\n}\n\n/**\n * Calculate the coagulation kernel between two particles in a given environment.\n * Note that the details of the environment are not currently used.\n *\n *@param[in] sp1 First particle\n *@param[in] sp2 Second particle\n *@param[in] sys Details of the environment\n *\n *@return Value of kernel\n */\ndouble Sweep::Processes::HybridConstantCoagulation::CoagKernel(const Particle &sp1,\n const Particle &sp2,\n const Cell& sys) const\n{\n return A();\n}\n\n\n/**\n * Calculate the majorant kernel between two particles in a given environment.\n * Note that the details of the environment are not currently used.\n *\n *@param[in] sp1 First particle\n *@param[in] sp2 Second particle\n *@param[in] sys Details of the environment\n *@param[in] maj Unused flag to indicate which majorant kernel is required\n *\n *@return Value of majorant kernel\n */\ndouble Sweep::Processes::HybridConstantCoagulation::MajorantKernel(const Particle &sp1,\n const Particle &sp2,\n const Cell& sys,\n const MajorantType maj) const\n{\n return CoagKernel(sp1, sp2, sys) * s_MajorantFactor;\n}\n\n", "meta": {"hexsha": "281eeab3bf9099e53e5af6ef23dc052c51f57c97", "size": 14896, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/sweepc/source/swp_hybrid_constcoag.cpp", "max_stars_repo_name": "sm453/MOpS", "max_stars_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2020-09-08T14:06:33.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-04T07:52:19.000Z", "max_issues_repo_path": "src/sweepc/source/swp_hybrid_constcoag.cpp", "max_issues_repo_name": "sm453/MOpS", "max_issues_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/sweepc/source/swp_hybrid_constcoag.cpp", "max_forks_repo_name": "sm453/MOpS", "max_forks_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2021-11-15T05:18:26.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-01T13:51:20.000Z", "avg_line_length": 32.2424242424, "max_line_length": 130, "alphanum_fraction": 0.5491407089, "num_tokens": 3525, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725051, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.23559260281945732}} {"text": "#ifndef VIENNACL_LINALG_AMG_HPP_\n#define VIENNACL_LINALG_AMG_HPP_\n\n/* =========================================================================\n Copyright (c) 2010-2014, Institute for Microelectronics,\n Institute for Analysis and Scientific Computing,\n TU Wien.\n Portions of this software are copyright by UChicago Argonne, LLC.\n\n -----------------\n ViennaCL - The Vienna Computing Library\n -----------------\n\n Project Head: Karl Rupp rupp@iue.tuwien.ac.at\n\n (A list of authors and contributors can be found in the PDF manual)\n\n License: MIT (X11), see file LICENSE in the base directory\n============================================================================= */\n\n/** @file viennacl/linalg/amg.hpp\n @brief Main include file for algebraic multigrid (AMG) preconditioners. Experimental.\n\n Implementation contributed by Markus Wagner\n*/\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"viennacl/forwards.h\"\n#include \"viennacl/tools/tools.hpp\"\n#include \"viennacl/linalg/prod.hpp\"\n#include \"viennacl/linalg/direct_solve.hpp\"\n\n#include \"viennacl/linalg/detail/amg/amg_base.hpp\"\n#include \"viennacl/linalg/detail/amg/amg_coarse.hpp\"\n#include \"viennacl/linalg/detail/amg/amg_interpol.hpp\"\n\n#include \n\n#ifdef VIENNACL_WITH_OPENMP\n #include \n#endif\n\n#include \"viennacl/linalg/detail/amg/amg_debug.hpp\"\n\n#define VIENNACL_AMG_COARSE_LIMIT 50\n#define VIENNACL_AMG_MAX_LEVELS 100\n\nnamespace viennacl\n{\n namespace linalg\n {\n typedef detail::amg::amg_tag amg_tag;\n\n\n\n /** @brief Setup AMG preconditioner\n *\n * @param A Operator matrices on all levels\n * @param P Prolongation/Interpolation operators on all levels\n * @param Pointvector Vector of points on all levels\n * @param tag AMG preconditioner tag\n */\n template\n void amg_setup(InternalType1 & A, InternalType1 & P, InternalType2 & Pointvector, amg_tag & tag)\n {\n typedef typename InternalType2::value_type PointVectorType;\n\n unsigned int i, iterations, c_points, f_points;\n detail::amg::amg_slicing Slicing;\n\n // Set number of iterations. If automatic coarse grid construction is chosen (0), then set a maximum size and stop during the process.\n iterations = tag.get_coarselevels();\n if (iterations == 0)\n iterations = VIENNACL_AMG_MAX_LEVELS;\n\n // For parallel coarsenings build data structures (number of threads set automatically).\n if (tag.get_coarse() == VIENNACL_AMG_COARSE_RS0 || tag.get_coarse() == VIENNACL_AMG_COARSE_RS3)\n Slicing.init(iterations);\n\n for (i=0; i(A[i].size1()));\n Pointvector[i].init_points();\n\n // Construct C and F points on coarse level (i is fine level, i+1 coarse level).\n detail::amg::amg_coarse (i, A, Pointvector, Slicing, tag);\n\n // Calculate number of C and F points on level i.\n c_points = Pointvector[i].get_cpoints();\n f_points = Pointvector[i].get_fpoints();\n\n #if defined (VIENNACL_AMG_DEBUG) //or defined(VIENNACL_AMG_DEBUGBENCH)\n std::cout << \"Level \" << i << \": \";\n std::cout << \"No of C points = \" << c_points << \", \";\n std::cout << \"No of F points = \" << f_points << std::endl;\n #endif\n\n // Stop routine when the maximal coarse level is found (no C or F point). Coarsest level is level i.\n if (c_points == 0 || f_points == 0)\n break;\n\n // Construct interpolation matrix for level i.\n detail::amg::amg_interpol (i, A, P, Pointvector, tag);\n\n // Compute coarse grid operator (A[i+1] = R * A[i] * P) with R = trans(P).\n detail::amg::amg_galerkin_prod(A[i], P[i], A[i+1]);\n\n // Test triple matrix product. Very slow for large matrix sizes (ublas).\n // test_triplematprod(A[i],P[i],A[i+1]);\n\n Pointvector[i].delete_points();\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"Coarse Grid Operator Matrix:\" << std::endl;\n printmatrix (A[i+1]);\n #endif\n\n // If Limit of coarse points is reached then stop. Coarsest level is level i+1.\n if (tag.get_coarselevels() == 0 && c_points <= VIENNACL_AMG_COARSE_LIMIT)\n {\n tag.set_coarselevels(i+1);\n return;\n }\n }\n tag.set_coarselevels(i);\n }\n\n /** @brief Initialize AMG preconditioner\n *\n * @param mat System matrix\n * @param A Operator matrices on all levels\n * @param P Prolongation/Interpolation operators on all levels\n * @param Pointvector Vector of points on all levels\n * @param tag AMG preconditioner tag\n */\n template\n void amg_init(MatrixType const & mat, InternalType1 & A, InternalType1 & P, InternalType2 & Pointvector, amg_tag & tag)\n {\n //typedef typename MatrixType::value_type ScalarType;\n typedef typename InternalType1::value_type SparseMatrixType;\n\n if (tag.get_coarselevels() > 0)\n {\n A.resize(tag.get_coarselevels()+1);\n P.resize(tag.get_coarselevels());\n Pointvector.resize(tag.get_coarselevels());\n }\n else\n {\n A.resize(VIENNACL_AMG_MAX_LEVELS+1);\n P.resize(VIENNACL_AMG_MAX_LEVELS);\n Pointvector.resize(VIENNACL_AMG_MAX_LEVELS);\n }\n\n // Insert operator matrix as operator for finest level.\n SparseMatrixType A0 (mat);\n A.insert_element (0, A0);\n }\n\n /** @brief Save operators after setup phase for CPU computation.\n *\n * @param A Operator matrices on all levels on the CPU\n * @param P Prolongation/Interpolation operators on all levels on the CPU\n * @param R Restriction operators on all levels on the CPU\n * @param A_setup Operators matrices on all levels from setup phase\n * @param P_setup Prolongation/Interpolation operators on all levels from setup phase\n * @param tag AMG preconditioner tag\n */\n template\n void amg_transform_cpu (InternalType1 & A, InternalType1 & P, InternalType1 & R, InternalType2 & A_setup, InternalType2 & P_setup, amg_tag & tag)\n {\n //typedef typename InternalType1::value_type MatrixType;\n\n // Resize internal data structures to actual size.\n A.resize(tag.get_coarselevels()+1);\n P.resize(tag.get_coarselevels());\n R.resize(tag.get_coarselevels());\n\n // Transform into matrix type.\n for (unsigned int i=0; i\n void amg_transform_gpu (InternalType1 & A, InternalType1 & P, InternalType1 & R, InternalType2 & A_setup, InternalType2 & P_setup, amg_tag & tag, viennacl::context ctx)\n {\n // Resize internal data structures to actual size.\n A.resize(tag.get_coarselevels()+1);\n P.resize(tag.get_coarselevels());\n R.resize(tag.get_coarselevels());\n\n // Copy to GPU using the internal sparse matrix structure: std::vector.\n for (unsigned int i=0; i)P_setup[i],P[i]);\n }\n for (unsigned int i=0; i\n void amg_setup_apply (InternalVectorType & result, InternalVectorType & rhs, InternalVectorType & residual, SparseMatrixType const & A, amg_tag const & tag)\n {\n typedef typename InternalVectorType::value_type VectorType;\n\n result.resize(tag.get_coarselevels()+1);\n rhs.resize(tag.get_coarselevels()+1);\n residual.resize(tag.get_coarselevels());\n\n for (unsigned int level=0; level < tag.get_coarselevels()+1; ++level)\n {\n result[level] = VectorType(A[level].size1());\n result[level].clear();\n rhs[level] = VectorType(A[level].size1());\n rhs[level].clear();\n }\n for (unsigned int level=0; level < tag.get_coarselevels(); ++level)\n {\n residual[level] = VectorType(A[level].size1());\n residual[level].clear();\n }\n }\n\n\n /** @brief Setup data structures for precondition phase for later use on the GPU\n *\n * @param result Result vector on all levels\n * @param rhs RHS vector on all levels\n * @param residual Residual vector on all levels\n * @param A Operators matrices on all levels from setup phase\n * @param tag AMG preconditioner tag\n * @param ctx Optional context in which the auxiliary objects are created (one out of multiple OpenCL contexts, CUDA, host)\n */\n template\n void amg_setup_apply (InternalVectorType & result, InternalVectorType & rhs, InternalVectorType & residual, SparseMatrixType const & A, amg_tag const & tag, viennacl::context ctx)\n {\n typedef typename InternalVectorType::value_type VectorType;\n\n result.resize(tag.get_coarselevels()+1);\n rhs.resize(tag.get_coarselevels()+1);\n residual.resize(tag.get_coarselevels());\n\n for (unsigned int level=0; level < tag.get_coarselevels()+1; ++level)\n {\n result[level] = VectorType(A[level].size1(), ctx);\n rhs[level] = VectorType(A[level].size1(), ctx);\n }\n for (unsigned int level=0; level < tag.get_coarselevels(); ++level)\n {\n residual[level] = VectorType(A[level].size1(), ctx);\n }\n }\n\n\n /** @brief Pre-compute LU factorization for direct solve (ublas library).\n * @brief Speeds up precondition phase as this is computed only once overall instead of once per iteration.\n *\n * @param op Operator matrix for direct solve\n * @param Permutation Permutation matrix which saves the factorization result\n * @param A Operator matrix on coarsest level\n */\n template\n void amg_lu(boost::numeric::ublas::compressed_matrix & op, boost::numeric::ublas::permutation_matrix<> & Permutation, SparseMatrixType const & A)\n {\n typedef typename SparseMatrixType::const_iterator1 ConstRowIterator;\n typedef typename SparseMatrixType::const_iterator2 ConstColIterator;\n\n // Copy to operator matrix. Needed\n op.resize(A.size1(),A.size2(),false);\n for (ConstRowIterator row_iter = A.begin1(); row_iter != A.end1(); ++row_iter)\n for (ConstColIterator col_iter = row_iter.begin(); col_iter != row_iter.end(); ++col_iter)\n op (col_iter.index1(), col_iter.index2()) = *col_iter;\n\n // Permutation matrix has to be reinitialized with actual size. Do not clear() or resize()!\n Permutation = boost::numeric::ublas::permutation_matrix<> (op.size1());\n boost::numeric::ublas::lu_factorize(op,Permutation);\n }\n\n /** @brief AMG preconditioner class, can be supplied to solve()-routines\n */\n template\n class amg_precond\n {\n typedef typename MatrixType::value_type ScalarType;\n typedef boost::numeric::ublas::vector VectorType;\n typedef detail::amg::amg_sparsematrix SparseMatrixType;\n typedef detail::amg::amg_pointvector PointVectorType;\n\n typedef typename SparseMatrixType::const_iterator1 InternalConstRowIterator;\n typedef typename SparseMatrixType::const_iterator2 InternalConstColIterator;\n typedef typename SparseMatrixType::iterator1 InternalRowIterator;\n typedef typename SparseMatrixType::iterator2 InternalColIterator;\n\n boost::numeric::ublas::vector A_setup;\n boost::numeric::ublas::vector P_setup;\n boost::numeric::ublas::vector A;\n boost::numeric::ublas::vector P;\n boost::numeric::ublas::vector R;\n boost::numeric::ublas::vector Pointvector;\n\n mutable boost::numeric::ublas::compressed_matrix op;\n mutable boost::numeric::ublas::permutation_matrix<> Permutation;\n\n mutable boost::numeric::ublas::vector result;\n mutable boost::numeric::ublas::vector rhs;\n mutable boost::numeric::ublas::vector residual;\n\n mutable bool done_init_apply;\n\n amg_tag tag_;\n public:\n\n amg_precond(): Permutation(0) {}\n /** @brief The constructor. Saves system matrix, tag and builds data structures for setup.\n *\n * @param mat System matrix\n * @param tag The AMG tag\n */\n amg_precond(MatrixType const & mat, amg_tag const & tag): Permutation(0)\n {\n tag_ = tag;\n // Initialize data structures.\n amg_init (mat,A_setup,P_setup,Pointvector,tag_);\n\n done_init_apply = false;\n }\n\n /** @brief Start setup phase for this class and copy data structures.\n */\n void setup()\n {\n // Start setup phase.\n amg_setup(A_setup,P_setup,Pointvector,tag_);\n // Transform to CPU-Matrixtype for precondition phase.\n amg_transform_cpu(A,P,R,A_setup,P_setup,tag_);\n\n done_init_apply = false;\n }\n\n /** @brief Prepare data structures for preconditioning:\n * Build data structures for precondition phase.\n * Do LU factorization on coarsest level.\n */\n void init_apply() const\n {\n // Setup precondition phase (Data structures).\n amg_setup_apply(result,rhs,residual,A_setup,tag_);\n // Do LU factorization for direct solve.\n amg_lu(op,Permutation,A_setup[tag_.get_coarselevels()]);\n\n done_init_apply = true;\n }\n\n /** @brief Returns complexity measures.\n *\n * @param avgstencil Average stencil sizes on all levels\n * @return Operator complexity of AMG method\n */\n template\n ScalarType calc_complexity(VectorType & avgstencil)\n {\n avgstencil = VectorType (tag_.get_coarselevels()+1);\n unsigned int nonzero=0, systemmat_nonzero=0, level_coefficients=0;\n\n for (unsigned int level=0; level < tag_.get_coarselevels()+1; ++level)\n {\n level_coefficients = 0;\n for (InternalRowIterator row_iter = A_setup[level].begin1(); row_iter != A_setup[level].end1(); ++row_iter)\n {\n for (InternalColIterator col_iter = row_iter.begin(); col_iter != row_iter.end(); ++col_iter)\n {\n if (level == 0)\n systemmat_nonzero++;\n nonzero++;\n level_coefficients++;\n }\n }\n avgstencil[level] = level_coefficients/static_cast(A_setup[level].size1());\n }\n return nonzero/static_cast(systemmat_nonzero);\n }\n\n /** @brief Precondition Operation\n *\n * @param vec The vector to which preconditioning is applied to (ublas version)\n */\n template\n void apply(VectorType & vec) const\n {\n // Build data structures and do lu factorization before first iteration step.\n if (!done_init_apply)\n init_apply();\n\n int level;\n\n // Precondition operation (Yang, p.3)\n rhs[0] = vec;\n for (level=0; level (tag_.get_coarselevels()); level++)\n {\n result[level].clear();\n\n // Apply Smoother presmooth_ times.\n smooth_jacobi (level, tag_.get_presmooth(), result[level], rhs[level]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"After presmooth:\" << std::endl;\n printvector(result[level]);\n #endif\n\n // Compute residual.\n residual[level] = rhs[level] - boost::numeric::ublas::prod (A[level],result[level]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"Residual:\" << std::endl;\n printvector(residual[level]);\n #endif\n\n // Restrict to coarse level. Restricted residual is RHS of coarse level.\n rhs[level+1] = boost::numeric::ublas::prod (R[level],residual[level]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"Restricted Residual: \" << std::endl;\n printvector(rhs[level+1]);\n #endif\n }\n\n // On highest level use direct solve to solve equation.\n result[level] = rhs[level];\n boost::numeric::ublas::lu_substitute(op,Permutation,result[level]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"After direct solve: \" << std::endl;\n printvector (result[level]);\n #endif\n\n for (level=tag_.get_coarselevels()-1; level >= 0; level--)\n {\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"Coarse Error: \" << std::endl;\n printvector(result[level+1]);\n #endif\n\n // Interpolate error to fine level. Correct solution by adding error.\n result[level] += boost::numeric::ublas::prod (P[level], result[level+1]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"Corrected Result: \" << std::endl;\n printvector (result[level]);\n #endif\n\n // Apply Smoother postsmooth_ times.\n smooth_jacobi (level, tag_.get_postsmooth(), result[level], rhs[level]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"After postsmooth: \" << std::endl;\n printvector (result[level]);\n #endif\n }\n vec = result[0];\n }\n\n /** @brief (Weighted) Jacobi Smoother (CPU version)\n * @param level Coarse level to which smoother is applied to\n * @param iterations Number of smoother iterations\n * @param x The vector smoothing is applied to\n * @param rhs The right hand side of the equation for the smoother\n */\n template\n void smooth_jacobi(int level, int const iterations, VectorType & x, VectorType const & rhs) const\n {\n VectorType old_result (x.size());\n long index;\n ScalarType sum = 0, diag = 1;\n\n for (int i=0; i(A_setup[level].size1()); ++index)\n {\n InternalConstRowIterator row_iter = A_setup[level].begin1();\n row_iter += index;\n sum = 0;\n diag = 1;\n for (InternalConstColIterator col_iter = row_iter.begin(); col_iter != row_iter.end(); ++col_iter)\n {\n if (col_iter.index1() == col_iter.index2())\n diag = *col_iter;\n else\n sum += *col_iter * old_result[col_iter.index2()];\n }\n x[index]= static_cast(tag_.get_jacobiweight()) * (rhs[index] - sum) / diag + (1-static_cast(tag_.get_jacobiweight())) * old_result[index];\n }\n }\n }\n\n amg_tag & tag() { return tag_; }\n };\n\n /** @brief AMG preconditioner class, can be supplied to solve()-routines.\n *\n * Specialization for compressed_matrix\n */\n template\n class amg_precond< compressed_matrix >\n {\n typedef viennacl::compressed_matrix MatrixType;\n typedef viennacl::vector VectorType;\n typedef detail::amg::amg_sparsematrix SparseMatrixType;\n typedef detail::amg::amg_pointvector PointVectorType;\n\n typedef typename SparseMatrixType::const_iterator1 InternalConstRowIterator;\n typedef typename SparseMatrixType::const_iterator2 InternalConstColIterator;\n typedef typename SparseMatrixType::iterator1 InternalRowIterator;\n typedef typename SparseMatrixType::iterator2 InternalColIterator;\n\n boost::numeric::ublas::vector A_setup;\n boost::numeric::ublas::vector P_setup;\n boost::numeric::ublas::vector A;\n boost::numeric::ublas::vector P;\n boost::numeric::ublas::vector R;\n boost::numeric::ublas::vector Pointvector;\n\n mutable boost::numeric::ublas::compressed_matrix op;\n mutable boost::numeric::ublas::permutation_matrix<> Permutation;\n\n mutable boost::numeric::ublas::vector result;\n mutable boost::numeric::ublas::vector rhs;\n mutable boost::numeric::ublas::vector residual;\n\n viennacl::context ctx_;\n\n mutable bool done_init_apply;\n\n amg_tag tag_;\n\n public:\n\n amg_precond(): Permutation(0) {}\n\n /** @brief The constructor. Builds data structures.\n *\n * @param mat System matrix\n * @param tag The AMG tag\n */\n amg_precond(compressed_matrix const & mat, amg_tag const & tag): Permutation(0), ctx_(viennacl::traits::context(mat))\n {\n tag_ = tag;\n\n // Copy to CPU. Internal structure of sparse matrix is used for copy operation.\n std::vector > mat2 = std::vector >(mat.size1());\n viennacl::copy(mat, mat2);\n\n // Initialize data structures.\n amg_init (mat2,A_setup,P_setup,Pointvector,tag_);\n\n done_init_apply = false;\n }\n\n /** @brief Start setup phase for this class and copy data structures.\n */\n void setup()\n {\n // Start setup phase.\n amg_setup(A_setup,P_setup,Pointvector, tag_);\n // Transform to GPU-Matrixtype for precondition phase.\n amg_transform_gpu(A,P,R,A_setup,P_setup, tag_, ctx_);\n\n done_init_apply = false;\n }\n\n /** @brief Prepare data structures for preconditioning:\n * Build data structures for precondition phase.\n * Do LU factorization on coarsest level.\n */\n void init_apply() const\n {\n // Setup precondition phase (Data structures).\n amg_setup_apply(result,rhs,residual,A_setup,tag_, ctx_);\n // Do LU factorization for direct solve.\n amg_lu(op,Permutation,A_setup[tag_.get_coarselevels()]);\n\n done_init_apply = true;\n }\n\n /** @brief Returns complexity measures\n *\n * @param avgstencil Average stencil sizes on all levels\n * @return Operator complexity of AMG method\n */\n template\n ScalarType calc_complexity(VectorType & avgstencil)\n {\n avgstencil = VectorType (tag_.get_coarselevels()+1);\n unsigned int nonzero=0, systemmat_nonzero=0, level_coefficients=0;\n\n for (unsigned int level=0; level < tag_.get_coarselevels()+1; ++level)\n {\n level_coefficients = 0;\n for (InternalRowIterator row_iter = A_setup[level].begin1(); row_iter != A_setup[level].end1(); ++row_iter)\n {\n for (InternalColIterator col_iter = row_iter.begin(); col_iter != row_iter.end(); ++col_iter)\n {\n if (level == 0)\n systemmat_nonzero++;\n nonzero++;\n level_coefficients++;\n }\n }\n avgstencil[level] = level_coefficients/(double)A[level].size1();\n }\n return nonzero/static_cast(systemmat_nonzero);\n }\n\n /** @brief Precondition Operation\n *\n * @param vec The vector to which preconditioning is applied to\n */\n template\n void apply(VectorType & vec) const\n {\n if (!done_init_apply)\n init_apply();\n\n int level;\n\n // Precondition operation (Yang, p.3).\n rhs[0] = vec;\n for (level=0; level (tag_.get_coarselevels()); level++)\n {\n result[level].clear();\n\n // Apply Smoother presmooth_ times.\n smooth_jacobi (level, tag_.get_presmooth(), result[level], rhs[level]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"After presmooth: \" << std::endl;\n printvector(result[level]);\n #endif\n\n // Compute residual.\n //residual[level] = rhs[level] - viennacl::linalg::prod (A[level],result[level]);\n residual[level] = viennacl::linalg::prod (A[level],result[level]);\n residual[level] = rhs[level] - residual[level];\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"Residual: \" << std::endl;\n printvector(residual[level]);\n #endif\n\n // Restrict to coarse level. Result is RHS of coarse level equation.\n //residual_coarse[level] = viennacl::linalg::prod(R[level],residual[level]);\n rhs[level+1] = viennacl::linalg::prod(R[level],residual[level]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"Restricted Residual: \" << std::endl;\n printvector(rhs[level+1]);\n #endif\n }\n\n // On highest level use direct solve to solve equation (on the CPU)\n //TODO: Use GPU direct solve!\n result[level] = rhs[level];\n boost::numeric::ublas::vector result_cpu (result[level].size());\n\n copy (result[level],result_cpu);\n boost::numeric::ublas::lu_substitute(op,Permutation,result_cpu);\n copy (result_cpu, result[level]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"After direct solve: \" << std::endl;\n printvector (result[level]);\n #endif\n\n for (level=tag_.get_coarselevels()-1; level >= 0; level--)\n {\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"Coarse Error: \" << std::endl;\n printvector(result[level+1]);\n #endif\n\n // Interpolate error to fine level and correct solution.\n result[level] += viennacl::linalg::prod(P[level],result[level+1]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"Corrected Result: \" << std::endl;\n printvector (result[level]);\n #endif\n\n // Apply Smoother postsmooth_ times.\n smooth_jacobi (level, tag_.get_postsmooth(), result[level], rhs[level]);\n\n #ifdef VIENNACL_AMG_DEBUG\n std::cout << \"After postsmooth: \" << std::endl;\n printvector (result[level]);\n #endif\n }\n vec = result[0];\n }\n\n /** @brief Jacobi Smoother (GPU version)\n * @param level Coarse level to which smoother is applied to\n * @param iterations Number of smoother iterations\n * @param x The vector smoothing is applied to\n * @param rhs The right hand side of the equation for the smoother\n */\n template\n void smooth_jacobi(int level, unsigned int iterations, VectorType & x, VectorType const & rhs) const\n {\n VectorType old_result = x;\n\n viennacl::ocl::context & ctx = const_cast(viennacl::traits::opencl_handle(x).context());\n viennacl::linalg::opencl::kernels::compressed_matrix::init(ctx);\n viennacl::ocl::kernel & k = ctx.get_kernel(viennacl::linalg::opencl::kernels::compressed_matrix::program_name(), \"jacobi\");\n\n for (unsigned int i=0; i 0)\n old_result = x;\n x.clear();\n viennacl::ocl::enqueue(k(A[level].handle1().opencl_handle(), A[level].handle2().opencl_handle(), A[level].handle().opencl_handle(),\n static_cast(tag_.get_jacobiweight()),\n viennacl::traits::opencl_handle(old_result),\n viennacl::traits::opencl_handle(x),\n viennacl::traits::opencl_handle(rhs),\n static_cast(rhs.size())));\n\n }\n }\n\n amg_tag & tag() { return tag_; }\n };\n\n }\n}\n\n\n\n#endif\n\n", "meta": {"hexsha": "3a85ec995ae1283f9c3026bb15949471044c645b", "size": 30737, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "viennacl/linalg/amg.hpp", "max_stars_repo_name": "denis14/ViennaCL-1.5.2", "max_stars_repo_head_hexsha": "fec808905cca30196e10126681611bdf8da5297a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "viennacl/linalg/amg.hpp", "max_issues_repo_name": "denis14/ViennaCL-1.5.2", "max_issues_repo_head_hexsha": "fec808905cca30196e10126681611bdf8da5297a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "viennacl/linalg/amg.hpp", "max_forks_repo_name": "denis14/ViennaCL-1.5.2", "max_forks_repo_head_hexsha": "fec808905cca30196e10126681611bdf8da5297a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.3732833958, "max_line_length": 183, "alphanum_fraction": 0.6277125289, "num_tokens": 7410, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6001883735630721, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.23547600398688803}} {"text": "// Material.cpp\n// created by Kuangdai on 17-May-2016 \n// 3D seismic material properties\n\n#include \"Material.h\"\n#include \"Quad.h\"\n#include \"ExodusModel.h\"\n#include \"SpectralConstants.h\"\n\n#include \"Volumetric3D.h\"\n#include \"XMath.h\"\n#include \"Relabelling.h\"\n\n#include \"Acoustic1D.h\"\n#include \"Acoustic3D.h\"\n\n#include \"AttBuilder.h\"\n#include \"Attenuation1D.h\"\n#include \"Attenuation3D.h\"\n\n#include \"Isotropic1D.h\"\n#include \"Isotropic3D.h\"\n#include \"TransverselyIsotropic1D.h\"\n#include \"TransverselyIsotropic3D.h\"\n\n#include \"Anisotropic1D.h\"\n#include \"Anisotropic3D.h\"\n#include \"Geodesy.h\"\n\n#include \n#include \"SlicePlot.h\"\n\nMaterial::Material(const Quad *myQuad, const ExodusModel &exModel): mMyQuad(myQuad) {\n // read Exodus model\n int quadTag = mMyQuad->getQuadTag();\n if (exModel.isIsotropic()) {\n for (int i = 0; i < 4; i++) {\n mVpv1D(i) = mVph1D(i) = exModel.getElementalVariables(\"VP_\" + std::to_string(i), quadTag);\n mVsv1D(i) = mVsh1D(i) = exModel.getElementalVariables(\"VS_\" + std::to_string(i), quadTag);\n mEta1D(i) = 1.;\n }\n } else {\n for (int i = 0; i < 4; i++) {\n mVpv1D(i) = exModel.getElementalVariables(\"VPV_\" + std::to_string(i), quadTag);\n mVph1D(i) = exModel.getElementalVariables(\"VPH_\" + std::to_string(i), quadTag);\n mVsv1D(i) = exModel.getElementalVariables(\"VSV_\" + std::to_string(i), quadTag);\n mVsh1D(i) = exModel.getElementalVariables(\"VSH_\" + std::to_string(i), quadTag);\n mEta1D(i) = exModel.getElementalVariables(\"ETA_\" + std::to_string(i), quadTag);\n }\n }\n for (int i = 0; i < 4; i++) {\n mRho1D(i) = exModel.getElementalVariables(\"RHO_\" + std::to_string(i), quadTag);\n }\n if (exModel.hasAttenuation()) {\n for (int i = 0; i < 4; i++) {\n mQkp1D(i) = exModel.getElementalVariables(\"QKAPPA_\" + std::to_string(i), quadTag);\n mQmu1D(i) = exModel.getElementalVariables(\"QMU_\" + std::to_string(i), quadTag);\n }\n } else {\n mQkp1D.setZero();\n mQmu1D.setZero();\n }\n \n // initialize 3D properties with 1D reference\n int Nr = mMyQuad->getNr();\n mVpv3D = RDMatXN::Zero(1, nPE);\n mVph3D = RDMatXN::Zero(1, nPE);\n mVsv3D = RDMatXN::Zero(1, nPE);\n mVsh3D = RDMatXN::Zero(1, nPE);\n mRho3D = RDMatXN::Zero(1, nPE);\n mEta3D = RDMatXN::Zero(1, nPE);\n mQkp3D = RDMatXN::Zero(1, nPE);\n mQmu3D = RDMatXN::Zero(1, nPE);\n \n for (int ipol = 0; ipol <= nPol; ipol++) {\n for (int jpol = 0; jpol <= nPol; jpol++) {\n int ipnt = ipol * nPntEdge + jpol;\n const RDCol2 &xieta = SpectralConstants::getXiEta(ipol, jpol, mMyQuad->isAxial());\n // fill with 1D \n mVpv3D.col(ipnt).fill(Mapping::interpolate(mVpv1D, xieta));\n mVph3D.col(ipnt).fill(Mapping::interpolate(mVph1D, xieta));\n mVsv3D.col(ipnt).fill(Mapping::interpolate(mVsv1D, xieta));\n mVsh3D.col(ipnt).fill(Mapping::interpolate(mVsh1D, xieta));\n mRho3D.col(ipnt).fill(Mapping::interpolate(mRho1D, xieta));\n mEta3D.col(ipnt).fill(Mapping::interpolate(mEta1D, xieta));\n mQkp3D.col(ipnt).fill(Mapping::interpolate(mQkp1D, xieta));\n mQmu3D.col(ipnt).fill(Mapping::interpolate(mQmu1D, xieta));\n // rho for mass\n int NrP = mMyQuad->getPointNr(ipol, jpol);\n mRhoMass3D[ipnt] = RDColX::Constant(NrP, mRho3D.col(ipnt)(0));\n mVpFluid3D[ipnt] = RDColX::Constant(NrP, mVpv3D.col(ipnt)(0));\n }\n }\n}\n\nvoid Material::addVolumetric3D(const std::vector &m3D, \n double srcLat, double srcLon, double srcDep, double phi2D) {\n if (m3D.size() == 0) {\n return;\n } \n \n // pointers for fast access to material matrices\n std::vector prop1DPtr = {&mVpv1D, &mVph1D, &mVsv1D, &mVsh1D, &mRho1D, &mEta1D, &mQkp1D, &mQmu1D,\n &mVpv1D, &mVsv1D, // these two take no other effect than occupying the slots \n &mC11_1D, &mC12_1D, &mC13_1D, &mC14_1D, &mC15_1D, &mC16_1D,\n &mC22_1D, &mC23_1D, &mC24_1D, &mC25_1D, &mC26_1D,\n &mC33_1D, &mC34_1D, &mC35_1D, &mC36_1D,\n &mC44_1D, &mC45_1D, &mC46_1D,\n &mC55_1D, &mC56_1D,\n &mC66_1D\n };\n std::vector prop3DPtr = {&mVpv3D, &mVph3D, &mVsv3D, &mVsh3D, &mRho3D, &mEta3D, &mQkp3D, &mQmu3D,\n &mVpv3D, &mVsv3D, // these two take no other effect than occupying the slots \n &mC11_3D, &mC12_3D, &mC13_3D, &mC14_3D, &mC15_3D, &mC16_3D,\n &mC22_3D, &mC23_3D, &mC24_3D, &mC25_3D, &mC26_3D,\n &mC33_3D, &mC34_3D, &mC35_3D, &mC36_3D,\n &mC44_3D, &mC45_3D, &mC46_3D,\n &mC55_3D, &mC56_3D,\n &mC66_3D\n };\n \n // radius at element center \n double rElemCenter = mMyQuad->computeCenterRadius();\n \n // read 3D model\n int Nr = mMyQuad->getNr();\n for (int ipol = 0; ipol <= nPol; ipol++) {\n for (int jpol = 0; jpol <= nPol; jpol++) {\n // geographic oordinates of cardinal points\n const RDCol2 &xieta = SpectralConstants::getXiEta(ipol, jpol, mMyQuad->isAxial());\n const RDMatX3 &rtp = mMyQuad->computeGeocentricGlobal(srcLat, srcLon, srcDep, xieta, Nr, phi2D);\n int ipnt = ipol * nPntEdge + jpol;\n for (int alpha = 0; alpha < Nr; alpha++) {\n double r = rtp(alpha, 0);\n double t = rtp(alpha, 1);\n double p = rtp(alpha, 2);\n for (const auto &model: m3D) {\n if (mMyQuad->isFluid() && !model->makeFluid3D()) {\n continue;\n }\n std::vector properties; \n std::vector refTypes;\n std::vector values;\n if (!model->get3dProperties(r, t, p, rElemCenter, properties, refTypes, values)) {\n // point (r, t, p) not in model range\n continue;\n }\n \n if (!_3Dprepared()) {\n prepare3D();\n }\n \n // deal with VP and VS\n std::vector propertiesTIso; \n std::vector refTypesTIso;\n std::vector valuesTIso;\n for (int iprop = 0; iprop < properties.size(); iprop++) {\n if (properties[iprop] == Volumetric3D::MaterialProperty::VP) {\n propertiesTIso.push_back(Volumetric3D::MaterialProperty::VPV);\n propertiesTIso.push_back(Volumetric3D::MaterialProperty::VPH);\n refTypesTIso.push_back(refTypes[iprop]);\n refTypesTIso.push_back(refTypes[iprop]);\n valuesTIso.push_back(values[iprop]);\n valuesTIso.push_back(values[iprop]);\n } else if (properties[iprop] == Volumetric3D::MaterialProperty::VS) {\n propertiesTIso.push_back(Volumetric3D::MaterialProperty::VSV);\n propertiesTIso.push_back(Volumetric3D::MaterialProperty::VSH);\n refTypesTIso.push_back(refTypes[iprop]);\n refTypesTIso.push_back(refTypes[iprop]);\n valuesTIso.push_back(values[iprop]);\n valuesTIso.push_back(values[iprop]);\n } else {\n propertiesTIso.push_back(properties[iprop]);\n refTypesTIso.push_back(refTypes[iprop]);\n valuesTIso.push_back(values[iprop]);\n }\n }\n // change values\n for (int iprop = 0; iprop < propertiesTIso.size(); iprop++) {\n // initialize anisotropy\n if (!mFullAniso && propertiesTIso[iprop] >= Volumetric3D::MaterialProperty::C11) {\n initAniso();\n }\n \n // // check\n // if (mFullAniso) {\n // if (propertiesTIso[iprop] <= Volumetric3D::MaterialProperty::ANIS_ETA) {\n // throw std::runtime_error(\"Material::addVolumetric3D || \" \n // \"Velocity, density and eta can no longer be changed once \"\n // \"full anisotropy has been activated.\");\n // }\n // } \n \n RDRow4 &row1D = *prop1DPtr[propertiesTIso[iprop]];\n RDMatXN &mat3D = *prop3DPtr[propertiesTIso[iprop]];\n Volumetric3D::MaterialRefType ref_type = refTypesTIso[iprop];\n double value3D = valuesTIso[iprop];\n if (ref_type == Volumetric3D::MaterialRefType::Absolute) {\n mat3D(alpha, ipnt) = value3D;\n } else if (ref_type == Volumetric3D::MaterialRefType::Reference1D) {\n double ref1D = Mapping::interpolate(row1D, xieta);\n mat3D(alpha, ipnt) = ref1D * (1. + value3D);\n } else if (ref_type == Volumetric3D::MaterialRefType::Reference3D) {\n mat3D(alpha, ipnt) *= 1. + value3D;\n } else {\n double ref1D = Mapping::interpolate(row1D, xieta);\n mat3D(alpha, ipnt) = (mat3D(alpha, ipnt) - ref1D) * (1. + value3D) + ref1D;\n }\n }\n }\n }\n }\n }\n \n // form mass point sampling\n if (_3Dprepared()) {\n for (int ipol = 0; ipol <= nPol; ipol++) {\n for (int jpol = 0; jpol <= nPol; jpol++) {\n int ipnt = ipol * nPntEdge + jpol;\n int nr_mass = mMyQuad->getPointNr(ipol, jpol);\n mRhoMass3D[ipnt] = XMath::linearResampling(nr_mass, mRho3D.col(ipnt));\n mVpFluid3D[ipnt] = XMath::linearResampling(nr_mass, mVpv3D.col(ipnt));\n }\n }\n }\n \n // rotate anisotropy from geographic to source-centred\n if (mFullAniso) {\n rotateAniso(srcLat, srcLon, srcDep);\n }\n}\n\narPP_RDColX Material::computeElementalMass() const {\n arPP_RDColX mass, J;\n // Jacobian of topography\n if (mMyQuad->hasRelabelling()) {\n J = mMyQuad->getRelabelling().getMassJacobian();\n } else {\n J = mRhoMass3D; // just to use the size\n for (int ipnt = 0; ipnt < nPntElem; ipnt++) {\n J[ipnt].setOnes();\n }\n }\n // general mass term\n const RDRowN &iFact = mMyQuad->getIntegralFactor();\n for (int ipnt = 0; ipnt < nPntElem; ipnt++) {\n if (mMyQuad->isFluid()) {\n mass[ipnt] = (mRhoMass3D[ipnt].array() * mVpFluid3D[ipnt].array().pow(2.)).pow(-1.).matrix();\n } else {\n mass[ipnt] = mRhoMass3D[ipnt];\n }\n mass[ipnt].array() *= iFact(ipnt) * J[ipnt].array();\n }\n return mass;\n}\n\nAcoustic *Material::createAcoustic(bool elem1D) const {\n const RDRowN &iFact = mMyQuad->getIntegralFactor();\n RDMatXN fluidK;\n if (_3Dprepared()) {\n fluidK = mRho3D.array().pow(-1.); \n } else {\n fluidK = mRho3D.replicate(mMyQuad->getNr(), 1).array().pow(-1.);\n }\n for (int ipnt = 0; ipnt < nPntElem; ipnt++) {\n fluidK.col(ipnt) *= iFact(ipnt);\n }\n if (mMyQuad->hasRelabelling()) {\n fluidK.array() *= mMyQuad->getRelabelling().getStiffJacobian().array();\n }\n if (elem1D) {\n RDMatPP kstruct;\n XMath::structuredUseFirstRow(fluidK, kstruct);\n return new Acoustic1D(kstruct.cast());\n } else {\n return new Acoustic3D(fluidK.cast());\n }\n}\n\nElastic *Material::createElastic(bool elem1D, const AttBuilder *attBuild) const {\n if (mFullAniso) {\n return createElasticAniso(elem1D, attBuild);\n }\n \n // elasticity tensor\n const RDRowN &iFact = mMyQuad->getIntegralFactor(); \n RDMatXN vpv3D, vph3D;\n RDMatXN vsv3D, vsh3D;\n RDMatXN rho3D;\n RDMatXN eta3D;\n RDMatXN qkp3D, qmu3D;\n \n if (_3Dprepared()) {\n vpv3D = mVpv3D;\n vph3D = mVph3D;\n vsv3D = mVsv3D;\n vsh3D = mVsh3D;\n rho3D = mRho3D;\n eta3D = mEta3D;\n qkp3D = mQkp3D;\n qmu3D = mQmu3D;\n } else {\n vpv3D = mVpv3D.replicate(mMyQuad->getNr(), 1);\n vph3D = mVph3D.replicate(mMyQuad->getNr(), 1);\n vsv3D = mVsv3D.replicate(mMyQuad->getNr(), 1);\n vsh3D = mVsh3D.replicate(mMyQuad->getNr(), 1);\n rho3D = mRho3D.replicate(mMyQuad->getNr(), 1);\n eta3D = mEta3D.replicate(mMyQuad->getNr(), 1);\n qkp3D = mQkp3D.replicate(mMyQuad->getNr(), 1);\n qmu3D = mQmu3D.replicate(mMyQuad->getNr(), 1);\n }\n \n RDMatXN A(rho3D), C(rho3D), F(rho3D), L(rho3D), N(rho3D);\n for (int ipnt = 0; ipnt < nPntElem; ipnt++) {\n // A C L N\n A.col(ipnt).array() *= vph3D.col(ipnt).array().pow(2.) * iFact(ipnt);\n C.col(ipnt).array() *= vpv3D.col(ipnt).array().pow(2.) * iFact(ipnt);\n L.col(ipnt).array() *= vsv3D.col(ipnt).array().pow(2.) * iFact(ipnt);\n N.col(ipnt).array() *= vsh3D.col(ipnt).array().pow(2.) * iFact(ipnt);\n }\n // F\n F = eta3D.schur(A - 2. * L);\n // must do relabelling before attenuation\n if (mMyQuad->hasRelabelling()) {\n const RDMatXN &J = mMyQuad->getRelabelling().getStiffJacobian();\n A = A.schur(J);\n C = C.schur(J);\n F = F.schur(J);\n L = L.schur(J);\n N = N.schur(J);\n }\n \n // attenuation\n Attenuation1D *att1D = 0;\n Attenuation3D *att3D = 0;\n if (attBuild) {\n // Voigt average\n RDMatXN kappa = (4. * A + C + 4. * F - 4. * N) / 9.;\n RDMatXN mu = (A + C - 2. * F + 6. * L + 5. * N) / 15.;\n A -= (kappa + 4. / 3. * mu);\n C -= (kappa + 4. / 3. * mu);\n F -= (kappa - 2. / 3. * mu);\n L -= mu;\n N -= mu;\n if (elem1D) {\n att1D = attBuild->createAttenuation1D(qkp3D, qmu3D, kappa, mu, mMyQuad);\n } else {\n att3D = attBuild->createAttenuation3D(qkp3D, qmu3D, kappa, mu, mMyQuad);\n }\n A += (kappa + 4. / 3. * mu);\n C += (kappa + 4. / 3. * mu);\n F += (kappa - 2. / 3. * mu);\n L += mu;\n N += mu;\n }\n \n // Elastic pointers\n if (elem1D) {\n RDMatPP A0, C0, F0, L0, N0;\n XMath::structuredUseFirstRow(A, A0);\n XMath::structuredUseFirstRow(C, C0);\n XMath::structuredUseFirstRow(F, F0);\n XMath::structuredUseFirstRow(L, L0);\n XMath::structuredUseFirstRow(N, N0);\n if (isIsotropic()) {\n return new Isotropic1D(F0.cast(), L0.cast(), att1D);\n } else {\n return new TransverselyIsotropic1D(A0.cast(), C0.cast(), \n F0.cast(), L0.cast(), N0.cast(), att1D);\n } \n } else {\n if (isIsotropic()) {\n return new Isotropic3D(F.cast(), L.cast(), att3D);\n } else {\n return new TransverselyIsotropic3D(A.cast(), C.cast(), \n F.cast(), L.cast(), N.cast(), att3D);\n }\n }\n}\n\nElastic *Material::createElasticAniso(bool elem1D, const AttBuilder *attBuild) const {\n // elasticity tensor\n const RDRowN &iFact = mMyQuad->getIntegralFactor(); \n RDMatXN C11_3D(mC11_3D), C12_3D(mC12_3D), C13_3D(mC13_3D), C14_3D(mC14_3D), C15_3D(mC15_3D), C16_3D(mC16_3D);\n RDMatXN C22_3D(mC22_3D), C23_3D(mC23_3D), C24_3D(mC24_3D), C25_3D(mC25_3D), C26_3D(mC26_3D);\n RDMatXN C33_3D(mC33_3D), C34_3D(mC34_3D), C35_3D(mC35_3D), C36_3D(mC36_3D);\n RDMatXN C44_3D(mC44_3D), C45_3D(mC45_3D), C46_3D(mC46_3D);\n RDMatXN C55_3D(mC55_3D), C56_3D(mC56_3D);\n RDMatXN C66_3D(mC66_3D);\n for (int ipnt = 0; ipnt < nPntElem; ipnt++) {\n C11_3D.col(ipnt) *= iFact(ipnt);\n C12_3D.col(ipnt) *= iFact(ipnt);\n C13_3D.col(ipnt) *= iFact(ipnt);\n C14_3D.col(ipnt) *= iFact(ipnt);\n C15_3D.col(ipnt) *= iFact(ipnt);\n C16_3D.col(ipnt) *= iFact(ipnt);\n C22_3D.col(ipnt) *= iFact(ipnt);\n C23_3D.col(ipnt) *= iFact(ipnt);\n C24_3D.col(ipnt) *= iFact(ipnt);\n C25_3D.col(ipnt) *= iFact(ipnt);\n C26_3D.col(ipnt) *= iFact(ipnt);\n C33_3D.col(ipnt) *= iFact(ipnt);\n C34_3D.col(ipnt) *= iFact(ipnt);\n C35_3D.col(ipnt) *= iFact(ipnt);\n C36_3D.col(ipnt) *= iFact(ipnt);\n C44_3D.col(ipnt) *= iFact(ipnt);\n C45_3D.col(ipnt) *= iFact(ipnt);\n C46_3D.col(ipnt) *= iFact(ipnt);\n C55_3D.col(ipnt) *= iFact(ipnt);\n C56_3D.col(ipnt) *= iFact(ipnt);\n C66_3D.col(ipnt) *= iFact(ipnt);\n }\n // must do relabelling before attenuation\n if (mMyQuad->hasRelabelling()) {\n const RDMatXN &J = mMyQuad->getRelabelling().getStiffJacobian();\n C11_3D = C11_3D.schur(J);\n C12_3D = C12_3D.schur(J);\n C13_3D = C13_3D.schur(J);\n C14_3D = C14_3D.schur(J);\n C15_3D = C15_3D.schur(J);\n C16_3D = C16_3D.schur(J);\n C22_3D = C22_3D.schur(J);\n C23_3D = C23_3D.schur(J);\n C24_3D = C24_3D.schur(J);\n C25_3D = C25_3D.schur(J);\n C26_3D = C26_3D.schur(J);\n C33_3D = C33_3D.schur(J);\n C34_3D = C34_3D.schur(J);\n C35_3D = C35_3D.schur(J);\n C36_3D = C36_3D.schur(J);\n C44_3D = C44_3D.schur(J);\n C45_3D = C45_3D.schur(J);\n C46_3D = C46_3D.schur(J);\n C55_3D = C55_3D.schur(J);\n C56_3D = C56_3D.schur(J);\n C66_3D = C66_3D.schur(J);\n }\n \n // attenuation\n Attenuation1D *att1D = 0;\n Attenuation3D *att3D = 0;\n if (attBuild) {\n // Voigt average\n // https://materialsproject.org/wiki/index.php/Elasticity_calculations\n RDMatXN kappa = (C11_3D + C22_3D + C33_3D + 2. * (C12_3D + C23_3D + C13_3D)) / 9.;\n RDMatXN mu = (C11_3D + C22_3D + C33_3D - (C12_3D + C23_3D + C13_3D) + 3. * (C44_3D + C55_3D + C66_3D)) / 15.;\n C11_3D -= (kappa + 4. / 3. * mu);\n C22_3D -= (kappa + 4. / 3. * mu);\n C33_3D -= (kappa + 4. / 3. * mu);\n C12_3D -= (kappa - 2. / 3. * mu);\n C23_3D -= (kappa - 2. / 3. * mu);\n C13_3D -= (kappa - 2. / 3. * mu);\n C44_3D -= mu;\n C55_3D -= mu;\n C66_3D -= mu;\n if (elem1D) {\n att1D = attBuild->createAttenuation1D(mQkp3D, mQmu3D, kappa, mu, mMyQuad);\n } else {\n att3D = attBuild->createAttenuation3D(mQkp3D, mQmu3D, kappa, mu, mMyQuad);\n }\n C11_3D += (kappa + 4. / 3. * mu);\n C22_3D += (kappa + 4. / 3. * mu);\n C33_3D += (kappa + 4. / 3. * mu);\n C12_3D += (kappa - 2. / 3. * mu);\n C23_3D += (kappa - 2. / 3. * mu);\n C13_3D += (kappa - 2. / 3. * mu);\n C44_3D += mu;\n C55_3D += mu;\n C66_3D += mu;\n }\n \n // Elastic pointers\n if (elem1D) {\n RDMatPP C11_1D, C12_1D, C13_1D, C14_1D, C15_1D, C16_1D;\n RDMatPP C22_1D, C23_1D, C24_1D, C25_1D, C26_1D;\n RDMatPP C33_1D, C34_1D, C35_1D, C36_1D;\n RDMatPP C44_1D, C45_1D, C46_1D;\n RDMatPP C55_1D, C56_1D;\n RDMatPP C66_1D;\n XMath::structuredUseFirstRow(C11_3D, C11_1D);\n XMath::structuredUseFirstRow(C12_3D, C12_1D);\n XMath::structuredUseFirstRow(C13_3D, C13_1D);\n XMath::structuredUseFirstRow(C14_3D, C14_1D);\n XMath::structuredUseFirstRow(C15_3D, C15_1D);\n XMath::structuredUseFirstRow(C16_3D, C16_1D);\n XMath::structuredUseFirstRow(C22_3D, C22_1D);\n XMath::structuredUseFirstRow(C23_3D, C23_1D);\n XMath::structuredUseFirstRow(C24_3D, C24_1D);\n XMath::structuredUseFirstRow(C25_3D, C25_1D);\n XMath::structuredUseFirstRow(C26_3D, C26_1D);\n XMath::structuredUseFirstRow(C33_3D, C33_1D);\n XMath::structuredUseFirstRow(C34_3D, C34_1D);\n XMath::structuredUseFirstRow(C35_3D, C35_1D);\n XMath::structuredUseFirstRow(C36_3D, C36_1D);\n XMath::structuredUseFirstRow(C44_3D, C44_1D);\n XMath::structuredUseFirstRow(C45_3D, C45_1D);\n XMath::structuredUseFirstRow(C46_3D, C46_1D);\n XMath::structuredUseFirstRow(C55_3D, C55_1D);\n XMath::structuredUseFirstRow(C56_3D, C56_1D);\n XMath::structuredUseFirstRow(C66_3D, C66_1D);\n return new Anisotropic1D(\n C11_1D.cast(), C12_1D.cast(), C13_1D.cast(), C14_1D.cast(), C15_1D.cast(), C16_1D.cast(),\n C22_1D.cast(), C23_1D.cast(), C24_1D.cast(), C25_1D.cast(), C26_1D.cast(),\n C33_1D.cast(), C34_1D.cast(), C35_1D.cast(), C36_1D.cast(),\n C44_1D.cast(), C45_1D.cast(), C46_1D.cast(),\n C55_1D.cast(), C56_1D.cast(), \n C66_1D.cast(), \n att1D);\n } else {\n return new Anisotropic3D(\n C11_3D.cast(), C12_3D.cast(), C13_3D.cast(), C14_3D.cast(), C15_3D.cast(), C16_3D.cast(),\n C22_3D.cast(), C23_3D.cast(), C24_3D.cast(), C25_3D.cast(), C26_3D.cast(),\n C33_3D.cast(), C34_3D.cast(), C35_3D.cast(), C36_3D.cast(),\n C44_3D.cast(), C45_3D.cast(), C46_3D.cast(),\n C55_3D.cast(), C56_3D.cast(), \n C66_3D.cast(), \n att3D);\n }\n}\n\ndouble Material::getVMaxRef() const {\n return std::max(mVph1D.maxCoeff(), mVpv1D.maxCoeff());\n}\n\nRDColX Material::getVMax() const {\n RDMatXN vpv3D, vph3D;\n if (_3Dprepared()) {\n vpv3D = mVpv3D;\n vph3D = mVph3D;\n } else {\n vpv3D = mVpv3D.replicate(mMyQuad->getNr(), 1);\n vph3D = mVph3D.replicate(mMyQuad->getNr(), 1);\n }\n const RDColX &vpvMax = vpv3D.rowwise().maxCoeff();\n const RDColX &vphMax = vph3D.rowwise().maxCoeff();\n return (vpvMax.array().max(vphMax.array())).matrix();\n}\n\nbool Material::isFluidPar1D() const {\n return XMath::equalRows(mVpv3D) && XMath::equalRows(mRho3D);\n}\n\nbool Material::isSolidPar1D(bool attenuation) const {\n bool result = false;\n if (mFullAniso) {\n result = XMath::equalRows(mC11_3D, 1e-7) && XMath::equalRows(mC12_3D, 1e-7) && XMath::equalRows(mC13_3D, 1e-7) && XMath::equalRows(mC14_3D, 1e-7) && XMath::equalRows(mC15_3D, 1e-7) && XMath::equalRows(mC16_3D, 1e-7) &&\n XMath::equalRows(mC22_3D, 1e-7) && XMath::equalRows(mC23_3D, 1e-7) && XMath::equalRows(mC24_3D, 1e-7) && XMath::equalRows(mC25_3D, 1e-7) && XMath::equalRows(mC26_3D, 1e-7) &&\n XMath::equalRows(mC33_3D, 1e-7) && XMath::equalRows(mC34_3D, 1e-7) && XMath::equalRows(mC35_3D, 1e-7) && XMath::equalRows(mC36_3D, 1e-7) &&\n XMath::equalRows(mC44_3D, 1e-7) && XMath::equalRows(mC45_3D, 1e-7) && XMath::equalRows(mC46_3D, 1e-7) &&\n XMath::equalRows(mC55_3D, 1e-7) && XMath::equalRows(mC56_3D, 1e-7) &&\n XMath::equalRows(mC66_3D, 1e-7) && \n XMath::equalRows(mRho3D);\n } else {\n result = XMath::equalRows(mVpv3D) && XMath::equalRows(mVph3D) &&\n XMath::equalRows(mVsv3D) && XMath::equalRows(mVsh3D) && \n XMath::equalRows(mRho3D) && XMath::equalRows(mEta3D);\n }\n if (attenuation) {\n result = result && XMath::equalRows(mQkp3D) && XMath::equalRows(mQmu3D);\n } \n return result;\n}\n\nbool Material::isIsotropic() const {\n if (mFullAniso) {\n return false;\n }\n return (mVpv3D - mVph3D).norm() < tinyDouble * mVpv3D.norm() && \n (mVsv3D - mVsh3D).norm() < tinyDouble * mVsv3D.norm() && \n (mEta3D - RDMatXN::Ones(mEta3D.rows(), mEta3D.cols())).norm() < tinyDouble;\n}\n\nRDMatXN Material::getProperty(const std::string &vname, int refType) {\n int Nr = mMyQuad->getNr();\n \n // name\n std::string varname = vname;\n if (boost::iequals(vname, \"vp\")) {\n varname = \"vpv\";\n }\n if (boost::iequals(vname, \"vs\")) {\n varname = \"vsv\";\n }\n RDRow4 data1D;\n RDMatXN data3D;\n if (boost::iequals(varname, \"vpv\")) {\n data1D = mVpv1D;\n data3D = mVpv3D;\n } else if (boost::iequals(varname, \"vsv\")) {\n data1D = mVsv1D;\n data3D = mVsv3D;\n } else if (boost::iequals(varname, \"vph\")) {\n data1D = mVph1D;\n data3D = mVph3D;\n } else if (boost::iequals(varname, \"vsh\")) {\n data1D = mVsh1D;\n data3D = mVsh3D;\n } else if (boost::iequals(varname, \"rho\")) {\n data1D = mRho1D;\n data3D = mRho3D;\n } else if (boost::iequals(varname, \"eta\")) {\n data1D = mEta1D;\n data3D = mEta3D;\n } else if (boost::iequals(varname, \"qkappa\")) {\n data1D = mQkp1D;\n data3D = mQkp3D;\n } else if (boost::iequals(varname, \"qmu\")) {\n data1D = mQmu1D;\n data3D = mQmu3D;\n } else if (mFullAniso) {\n if (boost::iequals(varname, \"c11\")) {\n data1D = mC11_1D;\n data3D = mC11_3D;\n } else if (boost::iequals(varname, \"c12\")) {\n data1D = mC12_1D;\n data3D = mC12_3D;\n } else if (boost::iequals(varname, \"c13\")) {\n data1D = mC13_1D;\n data3D = mC13_3D;\n } else if (boost::iequals(varname, \"c14\")) {\n data1D = mC14_1D;\n data3D = mC14_3D;\n } else if (boost::iequals(varname, \"c15\")) {\n data1D = mC15_1D;\n data3D = mC15_3D;\n } else if (boost::iequals(varname, \"c16\")) {\n data1D = mC16_1D;\n data3D = mC16_3D;\n } else if (boost::iequals(varname, \"c22\")) {\n data1D = mC22_1D;\n data3D = mC22_3D;\n } else if (boost::iequals(varname, \"c23\")) {\n data1D = mC23_1D;\n data3D = mC23_3D;\n } else if (boost::iequals(varname, \"c24\")) {\n data1D = mC24_1D;\n data3D = mC24_3D;\n } else if (boost::iequals(varname, \"c25\")) {\n data1D = mC25_1D;\n data3D = mC25_3D;\n } else if (boost::iequals(varname, \"c26\")) {\n data1D = mC26_1D;\n data3D = mC26_3D;\n } else if (boost::iequals(varname, \"c33\")) {\n data1D = mC33_1D;\n data3D = mC33_3D;\n } else if (boost::iequals(varname, \"c34\")) {\n data1D = mC34_1D;\n data3D = mC34_3D;\n } else if (boost::iequals(varname, \"c35\")) {\n data1D = mC35_1D;\n data3D = mC35_3D;\n } else if (boost::iequals(varname, \"c36\")) {\n data1D = mC36_1D;\n data3D = mC36_3D;\n } else if (boost::iequals(varname, \"c44\")) {\n data1D = mC44_1D;\n data3D = mC44_3D;\n } else if (boost::iequals(varname, \"c45\")) {\n data1D = mC45_1D;\n data3D = mC45_3D;\n } else if (boost::iequals(varname, \"c46\")) {\n data1D = mC46_1D;\n data3D = mC46_3D;\n } else if (boost::iequals(varname, \"c55\")) {\n data1D = mC55_1D;\n data3D = mC55_3D;\n } else if (boost::iequals(varname, \"c56\")) {\n data1D = mC56_1D;\n data3D = mC56_3D;\n } else if (boost::iequals(varname, \"c66\")) {\n data1D = mC66_1D;\n data3D = mC66_3D;\n } \n } else {\n throw std::runtime_error(\"Material::getProperty || Unknown field variable name: \" + vname);\n }\n \n if (data3D.rows() != Nr) {\n data3D = data3D.replicate(Nr, 1);\n }\n \n // 3D\n if (refType == SlicePlot::PropertyRefTypes::Property3D) {\n return data3D;\n }\n \n // fill 1D\n RDMatXN data1DXN(Nr, nPntElem);\n for (int ipol = 0; ipol <= nPol; ipol++) {\n for (int jpol = 0; jpol <= nPol; jpol++) {\n int ipnt = ipol * nPntEdge + jpol;\n const RDCol2 &xieta = SpectralConstants::getXiEta(ipol, jpol, mMyQuad->isAxial());\n data1DXN.col(ipnt).fill(Mapping::interpolate(data1D, xieta));\n }\n }\n \n // 1D\n if (refType == SlicePlot::PropertyRefTypes::Property1D) {\n return data1DXN;\n }\n \n // perturb\n RDMatXN data1DBase = data1DXN.array().max(tinyDouble).matrix(); // in fluid, vs = 0\n return ((data3D - data1DXN).array() / data1DBase.array()).matrix();\n}\n\nvoid Material::initAniso() {\n if (mMyQuad->isFluid()) {\n throw std::runtime_error(\"Material::initAniso || Cannot activate full anisotropy in fluid domain.\");\n }\n \n // 1D elasticity tensor\n RDRow4 A_1D = mRho1D.schur(mVph1D).schur(mVph1D);\n RDRow4 C_1D = mRho1D.schur(mVpv1D).schur(mVpv1D);\n RDRow4 L_1D = mRho1D.schur(mVsv1D).schur(mVsv1D);\n RDRow4 N_1D = mRho1D.schur(mVsh1D).schur(mVsh1D);\n RDRow4 F_1D = mEta1D.schur(A_1D - 2. * L_1D);\n \n mC11_1D = mC22_1D = A_1D;\n mC33_1D = C_1D;\n mC44_1D = mC55_1D = L_1D;\n mC66_1D = N_1D;\n mC12_1D = A_1D - 2. * N_1D;\n mC13_1D = mC23_1D = F_1D;\n mC14_1D = mC15_1D = mC16_1D = RDRow4::Zero();\n mC24_1D = mC25_1D = mC26_1D = RDRow4::Zero();\n mC34_1D = mC35_1D = mC36_1D = RDRow4::Zero();\n mC45_1D = mC46_1D = mC56_1D = RDRow4::Zero();\n \n // 3D elasticity tensor\n RDMatXN A_3D = mRho3D.schur(mVph3D).schur(mVph3D);\n RDMatXN C_3D = mRho3D.schur(mVpv3D).schur(mVpv3D);\n RDMatXN L_3D = mRho3D.schur(mVsv3D).schur(mVsv3D);\n RDMatXN N_3D = mRho3D.schur(mVsh3D).schur(mVsh3D);\n RDMatXN F_3D = mEta3D.schur(A_3D - 2. * L_3D);\n \n mC11_3D = mC22_3D = A_3D;\n mC33_3D = C_3D;\n mC44_3D = mC55_3D = L_3D;\n mC66_3D = N_3D;\n mC12_3D = A_3D - 2. * N_3D;\n mC13_3D = mC23_3D = F_3D;\n mC14_3D = mC15_3D = mC16_3D = RDMatXN::Zero(A_3D.rows(), A_3D.cols());\n mC24_3D = mC25_3D = mC26_3D = RDMatXN::Zero(A_3D.rows(), A_3D.cols());\n mC34_3D = mC35_3D = mC36_3D = RDMatXN::Zero(A_3D.rows(), A_3D.cols());\n mC45_3D = mC46_3D = mC56_3D = RDMatXN::Zero(A_3D.rows(), A_3D.cols());\n\n // initialized\n mFullAniso = true;\n}\n\nvoid Material::rotateAniso(double srcLat, double srcLon, double srcDep) {\n RDMatXX inCijkl(6, 6);\n \n // 3D\n for (int alpha = 0; alpha < mC11_3D.rows(); alpha++) {\n // azimuth of the slice\n double phi = 2. * pi / mC11_3D.rows() * alpha;\n // loop over GLL points\n for (int ipol = 0; ipol <= nPol; ipol++) {\n for (int jpol = 0; jpol <= nPol; jpol++) {\n int ipnt = ipol * nPntEdge + jpol;\n\n inCijkl(1 - 1, 1 - 1) = mC11_3D(alpha, ipnt);\n inCijkl(1 - 1, 2 - 1) = mC12_3D(alpha, ipnt);\n inCijkl(1 - 1, 3 - 1) = mC13_3D(alpha, ipnt);\n inCijkl(1 - 1, 4 - 1) = mC14_3D(alpha, ipnt);\n inCijkl(1 - 1, 5 - 1) = mC15_3D(alpha, ipnt);\n inCijkl(1 - 1, 6 - 1) = mC16_3D(alpha, ipnt);\n \n inCijkl(2 - 1, 1 - 1) = mC12_3D(alpha, ipnt);\n inCijkl(2 - 1, 2 - 1) = mC22_3D(alpha, ipnt);\n inCijkl(2 - 1, 3 - 1) = mC23_3D(alpha, ipnt);\n inCijkl(2 - 1, 4 - 1) = mC24_3D(alpha, ipnt);\n inCijkl(2 - 1, 5 - 1) = mC25_3D(alpha, ipnt);\n inCijkl(2 - 1, 6 - 1) = mC26_3D(alpha, ipnt);\n \n inCijkl(3 - 1, 1 - 1) = mC13_3D(alpha, ipnt);\n inCijkl(3 - 1, 2 - 1) = mC23_3D(alpha, ipnt);\n inCijkl(3 - 1, 3 - 1) = mC33_3D(alpha, ipnt);\n inCijkl(3 - 1, 4 - 1) = mC34_3D(alpha, ipnt);\n inCijkl(3 - 1, 5 - 1) = mC35_3D(alpha, ipnt);\n inCijkl(3 - 1, 6 - 1) = mC36_3D(alpha, ipnt);\n \n inCijkl(4 - 1, 1 - 1) = mC14_3D(alpha, ipnt);\n inCijkl(4 - 1, 2 - 1) = mC24_3D(alpha, ipnt);\n inCijkl(4 - 1, 3 - 1) = mC34_3D(alpha, ipnt);\n inCijkl(4 - 1, 4 - 1) = mC44_3D(alpha, ipnt);\n inCijkl(4 - 1, 5 - 1) = mC45_3D(alpha, ipnt);\n inCijkl(4 - 1, 6 - 1) = mC46_3D(alpha, ipnt);\n \n inCijkl(5 - 1, 1 - 1) = mC15_3D(alpha, ipnt);\n inCijkl(5 - 1, 2 - 1) = mC25_3D(alpha, ipnt);\n inCijkl(5 - 1, 3 - 1) = mC35_3D(alpha, ipnt);\n inCijkl(5 - 1, 4 - 1) = mC45_3D(alpha, ipnt);\n inCijkl(5 - 1, 5 - 1) = mC55_3D(alpha, ipnt);\n inCijkl(5 - 1, 6 - 1) = mC56_3D(alpha, ipnt);\n \n inCijkl(6 - 1, 1 - 1) = mC16_3D(alpha, ipnt);\n inCijkl(6 - 1, 2 - 1) = mC26_3D(alpha, ipnt);\n inCijkl(6 - 1, 3 - 1) = mC36_3D(alpha, ipnt);\n inCijkl(6 - 1, 4 - 1) = mC46_3D(alpha, ipnt);\n inCijkl(6 - 1, 5 - 1) = mC56_3D(alpha, ipnt);\n inCijkl(6 - 1, 6 - 1) = mC66_3D(alpha, ipnt);\n \n // compute backazimuth\n const RDCol2 &xieta = SpectralConstants::getXiEta(ipol, jpol, mMyQuad->isAxial());\n RDCol2 rtheta = Geodesy::rtheta(mMyQuad->mapping(xieta));\n RDCol3 rtpS, rtpG;\n rtpS(0) = rtheta(0);\n rtpS(1) = rtheta(1);\n rtpS(2) = phi;\n rtpG = Geodesy::rotateSrc2Glob(rtpS, srcLat, srcLon, srcDep);\n double recDep = Geodesy::getROuter() - rtheta(0);\n double recLat = Geodesy::theta2Lat_d(rtpG(1), recDep);\n double recLon = Geodesy::phi2Lon(rtpG(2));\n double baz = Geodesy::backAzimuth(srcLat, srcLon, srcDep, recLat, recLon, recDep);\n \n // global => source centred RTZ (theta, phi, r)\n const RDMatXX &outCijkl = bondTransformation(inCijkl, 0., 0., -baz);\n \n // by convention, input is in RTZ \n // // (r, theta, phi) => (R, T, Z)\n // const RDMatXX &RTZ_Cijkl_x = bondTransformation(rtp_Cijkl, 0., 0., pi/2.);\n // const RDMatXX &outCijkl = bondTransformation(RTZ_Cijkl_x, pi/2., 0., 0.);\n \n // copy back\n mC11_3D(alpha, ipnt) = outCijkl(1 - 1, 1 - 1);\n mC12_3D(alpha, ipnt) = outCijkl(1 - 1, 2 - 1);\n mC13_3D(alpha, ipnt) = outCijkl(1 - 1, 3 - 1);\n mC14_3D(alpha, ipnt) = outCijkl(1 - 1, 4 - 1);\n mC15_3D(alpha, ipnt) = outCijkl(1 - 1, 5 - 1);\n mC16_3D(alpha, ipnt) = outCijkl(1 - 1, 6 - 1);\n \n mC22_3D(alpha, ipnt) = outCijkl(2 - 1, 2 - 1);\n mC23_3D(alpha, ipnt) = outCijkl(2 - 1, 3 - 1);\n mC24_3D(alpha, ipnt) = outCijkl(2 - 1, 4 - 1);\n mC25_3D(alpha, ipnt) = outCijkl(2 - 1, 5 - 1);\n mC26_3D(alpha, ipnt) = outCijkl(2 - 1, 6 - 1);\n \n mC33_3D(alpha, ipnt) = outCijkl(3 - 1, 3 - 1);\n mC34_3D(alpha, ipnt) = outCijkl(3 - 1, 4 - 1);\n mC35_3D(alpha, ipnt) = outCijkl(3 - 1, 5 - 1);\n mC36_3D(alpha, ipnt) = outCijkl(3 - 1, 6 - 1);\n \n mC44_3D(alpha, ipnt) = outCijkl(4 - 1, 4 - 1);\n mC45_3D(alpha, ipnt) = outCijkl(4 - 1, 5 - 1);\n mC46_3D(alpha, ipnt) = outCijkl(4 - 1, 6 - 1);\n \n mC55_3D(alpha, ipnt) = outCijkl(5 - 1, 5 - 1);\n mC56_3D(alpha, ipnt) = outCijkl(5 - 1, 6 - 1);\n \n mC66_3D(alpha, ipnt) = outCijkl(6 - 1, 6 - 1);\n }\n }\n }\n}\n\nRDMatXX Material::bondTransformation(RDMatXX inCijkl, double alpha, double beta, double gamma) {\n RDMat33 R1, R2, R3, R;\n R1 << 1., 0., 0.,\n 0., cos(alpha), sin(alpha),\n 0., -sin(alpha), cos(alpha);\n R2 << cos(beta), 0., sin(beta), \n 0., 1., 0.,\n -sin(beta), 0, cos(beta);\n R3 << cos(gamma), sin(gamma), 0., \n -sin(gamma), cos(gamma), 0.,\n 0., 0., 1.;\n \n R = R1 * R2 * R3;\n \n RDMat33 K1, K2, K3, K4;\n K1.array() = R.array().pow(2.);\n K2 << R(0, 1) * R(0, 2), R(0, 2) * R(0, 0), R(0, 0) * R(0, 1),\n R(1, 1) * R(1, 2), R(1, 2) * R(1, 0), R(1, 0) * R(1, 1),\n R(2, 1) * R(2, 2), R(2, 2) * R(2, 0), R(2, 0) * R(2, 1);\n K3 << R(1, 0) * R(2, 0), R(1, 1) * R(2, 1), R(1, 2) * R(2, 2),\n R(2, 0) * R(0, 0), R(2, 1) * R(0, 1), R(2, 2) * R(0, 2),\n R(0, 0) * R(1, 0), R(0, 1) * R(1, 1), R(0, 2) * R(1, 2);\n K4 << R(1, 1) * R(2, 2) + R(1, 2) * R(2, 1), \n R(1, 2) * R(2, 0) + R(1, 0) * R(2, 2), \n R(1, 0) * R(2, 1) + R(1, 1) * R(2, 0),\n R(2, 1) * R(0, 2) + R(2, 2) * R(0, 1), \n R(2, 2) * R(0, 0) + R(2, 0) * R(0, 2), \n R(2, 0) * R(0, 1) + R(2, 1) * R(0, 0),\n R(0, 1) * R(1, 2) + R(0, 2) * R(1, 1), \n R(0, 2) * R(1, 0) + R(0, 0) * R(1, 2), \n R(0, 0) * R(1, 1) + R(0, 1) * R(1, 0);\n \n RDMatXX K(6, 6);\n K.block(0, 0, 3, 3) = K1;\n K.block(0, 3, 3, 3) = 2. * K2;\n K.block(3, 0, 3, 3) = K3;\n K.block(3, 3, 3, 3) = K4;\n \n RDMatXX outCijkl(K * inCijkl * K.transpose());\n return outCijkl;\n}\n\nvoid Material::prepare3D() {\n int Nr = mMyQuad->getNr();\n mVpv3D = RDMatXN::Zero(Nr, nPE);\n mVph3D = RDMatXN::Zero(Nr, nPE);\n mVsv3D = RDMatXN::Zero(Nr, nPE);\n mVsh3D = RDMatXN::Zero(Nr, nPE);\n mRho3D = RDMatXN::Zero(Nr, nPE);\n mEta3D = RDMatXN::Zero(Nr, nPE);\n mQkp3D = RDMatXN::Zero(Nr, nPE);\n mQmu3D = RDMatXN::Zero(Nr, nPE);\n \n for (int ipol = 0; ipol <= nPol; ipol++) {\n for (int jpol = 0; jpol <= nPol; jpol++) {\n int ipnt = ipol * nPntEdge + jpol;\n const RDCol2 &xieta = SpectralConstants::getXiEta(ipol, jpol, mMyQuad->isAxial());\n // fill with 1D \n mVpv3D.col(ipnt).fill(Mapping::interpolate(mVpv1D, xieta));\n mVph3D.col(ipnt).fill(Mapping::interpolate(mVph1D, xieta));\n mVsv3D.col(ipnt).fill(Mapping::interpolate(mVsv1D, xieta));\n mVsh3D.col(ipnt).fill(Mapping::interpolate(mVsh1D, xieta));\n mRho3D.col(ipnt).fill(Mapping::interpolate(mRho1D, xieta));\n mEta3D.col(ipnt).fill(Mapping::interpolate(mEta1D, xieta));\n mQkp3D.col(ipnt).fill(Mapping::interpolate(mQkp1D, xieta));\n mQmu3D.col(ipnt).fill(Mapping::interpolate(mQmu1D, xieta));\n // rho for mass\n int NrP = mMyQuad->getPointNr(ipol, jpol);\n mRhoMass3D[ipnt] = RDColX::Constant(NrP, mRho3D.col(ipnt)(0));\n mVpFluid3D[ipnt] = RDColX::Constant(NrP, mVpv3D.col(ipnt)(0));\n }\n }\n}\n\nbool Material::_3Dprepared() const {\n return mVpv3D.rows() == mMyQuad->getNr();\n}\n\n\n", "meta": {"hexsha": "1ea8c1d5a25a411dbb41f9daa147ff59cefd8613", "size": 39480, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "SOLVER/src/preloop/physics/material/Material.cpp", "max_stars_repo_name": "kuangdai/AxiSEM3D", "max_stars_repo_head_hexsha": "fd9da14e9107783e3b07b936c67af2412146e099", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17.0, "max_stars_repo_stars_event_min_datetime": "2016-12-16T03:13:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-15T01:56:45.000Z", "max_issues_repo_path": "SOLVER/src/preloop/physics/material/Material.cpp", 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YES\n2. NO", "lm_q1_score": 0.6297746074044134, "lm_q2_score": 0.3738758157951908, "lm_q1q2_score": 0.23545749511042105}} {"text": "/* This file is part of the Tomographer project, which is distributed under the\n * terms of the MIT license.\n *\n * The MIT License (MIT)\n *\n * Copyright (c) 2016 ETH Zurich, Institute for Theoretical Physics, Philippe Faist\n * Copyright (c) 2017 Caltech, Institute for Quantum Information and Matter, Philippe Faist\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n * SOFTWARE.\n */\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \n\n\n//\n// Data types for our quantum objects. For the sake of the example, we just\n// leave the size to be dynamic, that is, fixed at run time and not at compile\n// time.\n//\ntypedef Tomographer::DenseDM::DMTypes DMTypes;\n\n\n//\n// The class which will store our tomography data. Just define this as\n// \"DenseLLH\" as a shorthand.\n//\ntypedef Tomographer::DenseDM::IndepMeasLLH DenseLLH;\n\n\n//\n// The type of value calculator we would like to use. Here, we settle for the\n// expectation value of an observable, as we are interested in the square\n// fidelity to the pure Bell Phi+ state (=expectation value of the observable\n// |Phi+>\n ValueCalculator;\n\n//\n// The base CData object we use. See Tomographer:::MHRWTasks::ValueHistogramTools::CDataBase.\n//\ntypedef Tomographer::MHRWTasks::ValueHistogramTools::CDataBase<\n ValueCalculator, // our value calculator\n true, // use binning analysis\n Tomographer::MHWalkerParamsStepSize, // MHWalkerParams\n std::mt19937::result_type, // RngSeedType\n long, // IterCountIntType\n double, // CountRealType\n int // HistCountIntType\n >\n BaseCData;\n\n//\n// We need to define a class which adds the capacity of creating the \"master\"\n// random walk object to the engine in\n// Tomographer::MHRWTasks::ValueHistogramTools, which take care of running the\n// random walks etc. as needed.\n//\nstruct OurCData : public BaseCData\n{\n OurCData(DenseLLH * llh_, // data from the the tomography experiment\n\t ValueCalculator valcalc, // the figure-of-merit calculator\n\t HistogramParams hist_params, // histogram parameters\n\t int binning_num_levels, // number of binning levels in the binning analysis\n\t MHRWParamsType mhrw_params, // parameters of the random walk\n\t RngSeedType base_seed) // a random seed to initialize the random number generator\n : BaseCData(valcalc, hist_params, binning_num_levels,\n mhrw_params, base_seed),\n llh(llh_)\n {\n }\n\n DenseLLH * llh;\n\n // The result of a task run -- just use the default StatsResults type provided\n // by ValueHistogramTools. If we had several stats collectors set, we would\n // need to pick out the result corresponding to the\n // value-histogram-stats-collector (see \"minimal_tomorun_controlled.cxx\" for\n // an example).\n typedef MHRWStatsResultsBaseType MHRWStatsResultsType;\n\n //\n // This function is called automatically by the task manager/dispatcher via\n // MHRWTasks. It should set up the random walk as required (at a minimum,\n // create a MHWalker instance and pass on the default value stats collector\n // from Tomographer::MHRWTasks::ValueHistogramTools), and run it.\n //\n // We should not forget to call run(), to actually run the random walk!\n //\n template\n inline void setupRandomWalkAndRun(Rng & rng, LoggerType & logger, ExecFn run) const\n {\n auto val_stats_collector = createValueStatsCollector(logger);\n\n Tomographer::DenseDM::TSpace::LLHMHWalker mhwalker(\n llh->dmt.initMatrixType(),\n *llh,\n rng,\n logger\n );\n\n run(mhwalker, val_stats_collector);\n };\n\nprivate:\n OurCData() : BaseCData(), llh(NULL) { } // for serialization\n\n friend boost::serialization::access;\n template\n void serialize(Archive & a, unsigned int /* version */)\n {\n a & boost::serialization::base_object(*this);\n a & llh;\n }\n};\n\n\n//\n// The root logger which takes care of handling the log messages. Here, we log to the\n// standard output (recall that stdout/stderr are seen as a \"file\").\n//\n// The level of the logger can be set to one of Tomographer::Logger::LONGDEBUG,\n// Tomographer::Logger::DEBUG, Tomographer::Logger::INFO, Tomographer::Logger::WARNING or\n// Tomographer::Logger::ERROR.\n//\ntypedef Tomographer::Logger::FileLogger BaseLoggerType;\nBaseLoggerType rootlogger(stderr, Tomographer::Logger::DEBUG);\n\n\n// The task type, for the MultiProc interface\ntypedef Tomographer::MHRWTasks::MHRandomWalkTask\n OurMHRandomWalkTask;\n\n\nint main()\n{\n //\n // MPI initializations\n //\n mpi::environment mpi_env;\n mpi::communicator mpi_world;\n\n const bool is_master = (mpi_world.rank() == 0);\n\n\n //\n // Prepare the logger in which we can log debug/info/error messages.\n //\n typedef Tomographer::Logger::OriginPrefixedLogger PrefixLoggerType;\n PrefixLoggerType baselogger(rootlogger, streamstr(mpi_world.rank() << \"/\" << mpi_world.size()<<\"|\"));\n\n auto logger = Tomographer::Logger::makeLocalLogger(\"main()\", baselogger);\n\n\n logger.debug(\"starting up\");\n\n\n DenseLLH * llh = NULL;\n OurCData * taskcdat = NULL;\n\n if (is_master) {\n //\n // Master gets to prepare all the data.\n //\n \n //\n // Specify the dimension of the quantum tomography setting.\n //\n const int dim = 4; // two qubits\n DMTypes dmt(dim);\n\n //\n // Prepare data from the tomography experiment.\n //\n // In this hypothetical experiment, we assumed that the observables\n // \\sigma_x\\otimes\\sigma_x, \\sigma_y\\otimes\\sigma_y and \\sigma_z\\otimes\\sigma_z are each\n // measured 100 times. Each measurement setting has two possible outcomes, +1 or -1, and\n // hence there are in total 6 POVM effects.\n //\n\n llh = new DenseLLH(dmt);\n \n // POVM effects for \\sigma_x \\otimes \\sigma_x\n \n DMTypes::MatrixType Exxplus(dmt.initMatrixType());\n Exxplus <<\n 0.5, 0, 0, 0.5,\n 0, 0.5, 0.5, 0,\n 0, 0.5, 0.5, 0,\n 0.5, 0, 0, 0.5;\n\n llh->addMeasEffect(Exxplus, 95); // 95 counts of \"+1\" out of 100 for \\sigma_x\\otimes\\sigma_x\n\n DMTypes::MatrixType Exxminus(dmt.initMatrixType());\n Exxminus <<\n 0.5, 0, 0, -0.5,\n 0, 0.5, -0.5, 0,\n 0, -0.5, 0.5, 0,\n -0.5, 0, 0, 0.5;\n\n llh->addMeasEffect(Exxminus, 5); // 95 counts of \"-1\" out of 100 for \\sigma_x\\otimes\\sigma_x\n\n // POVM effects for \\sigma_y \\otimes \\sigma_y\n \n DMTypes::MatrixType Eyyplus(dmt.initMatrixType());\n Eyyplus <<\n 0.5, 0, 0, -0.5,\n 0, 0.5, 0.5, 0,\n 0, 0.5, 0.5, 0,\n -0.5, 0, 0, 0.5;\n\n llh->addMeasEffect(Eyyplus, 8); // 8 counts of \"+1\" out of 100 for \\sigma_y\\otimes\\sigma_y\n\n DMTypes::MatrixType Eyyminus(dmt.initMatrixType());\n Eyyminus <<\n 0.5, 0, 0, 0.5,\n 0, 0.5, -0.5, 0,\n 0, -0.5, 0.5, 0,\n 0.5, 0, 0, 0.5;\n\n llh->addMeasEffect(Eyyminus, 92); // 92 counts of \"-1\" out of 100 for \\sigma_y\\otimes\\sigma_y\n\n // POVM effects for \\sigma_z \\otimes \\sigma_z\n \n DMTypes::MatrixType Ezzplus(dmt.initMatrixType());\n Ezzplus <<\n 1, 0, 0, 0,\n 0, 0, 0, 0,\n 0, 0, 0, 0,\n 0, 0, 0, 1;\n\n llh->addMeasEffect(Ezzplus, 98); // 98 counts of \"+1\" out of 100 for \\sigma_z\\otimes\\sigma_z\n\n DMTypes::MatrixType Ezzminus(dmt.initMatrixType());\n Ezzminus <<\n 0, 0, 0, 0,\n 0, 1, 0, 0,\n 0, 0, 1, 0,\n 0, 0, 0, 0;\n\n llh->addMeasEffect(Ezzminus, 2); // 2 counts of \"-1\" out of 100 for \\sigma_z\\otimes\\sigma_z\n\n logger.debug(\"data entered OK\");\n\n //\n // Prepare the figure of merit calculator: Squared fidelity to the pure entangled Bell\n // state |\\Phi^+>\n //\n\n DMTypes::MatrixType phiplus(dmt.initMatrixType());\n phiplus <<\n 0.5, 0, 0, 0.5,\n 0, 0, 0, 0,\n 0, 0, 0, 0,\n 0.5, 0, 0, 0.5;\n\n // our main ValueCalculator instance, which is in fact an alias for the\n // ObservableValueCalculator class. [If we wanted to choose which figure of merit to\n // compute at run-time, then we should use a MultiplexorValueCalculator.]\n ValueCalculator valcalc(dmt, phiplus);\n\n // parameters of the histogram of the figure of merit: cover the range [0.75, 1.0] by\n // dividing it into 50 bins\n const OurCData::HistogramParams hist_params(0.7, 1.0, 50);\n\n // parameters of the random walk\n const OurCData::MHRWParamsType mhrw_params(\n 0.04, // step size (choose such that acceptance ratio ~ 0.25)\n 50, // sweep size (should be chosen such that sweep_size*step_size >~ 1)\n 1024, // # of thermalization sweeps\n 32768 // # of live sweeps in which samples are collected\n );\n\n // seed for random number generator -- just use the current time\n std::mt19937::result_type base_seed =\n (std::mt19937::result_type)std::chrono::system_clock::now().time_since_epoch().count();\n\n // number of levels for the binning analysis\n const int binning_num_levels = 8;\n\n // instantiate the class which stores the shared data.\n taskcdat = new OurCData(llh, valcalc, hist_params, binning_num_levels,\n mhrw_params, base_seed);\n\n logger.debug(\"Master here, data ready\") ;\n } else {\n taskcdat = NULL;\n logger.debug(\"Not master, skipping through all the init process\") ;\n }\n\n //\n // Data is ready, prepare & launch the random walks. Use MPI.\n //\n\n\n // repeat the whole random walk this number of times. These random walks will\n // run in parallel depending on the number of CPUs available.\n const int num_repeats = 20;\n\n // create the task manager/dispatcher, using the MPI implementation !!\n auto tasks =\n Tomographer::MultiProc::MPI::mkTaskDispatcher(\n taskcdat, // constant data\n mpi_world,\n logger.parentLogger(), // the main logger object\n num_repeats // num_runs\n );\n\n if (is_master) {\n // only master gets to do this\n\n // get status reports every X milliseconds printed out on std::cout\n tasks.setStatusReportHandler([&](decltype(tasks)::FullStatusReportType report) {\n std::cout << report.getHumanReport() << \"\\n\" ;\n });\n tasks.requestPeriodicStatusReport(1000) ;\n }\n\n //\n // Finally, run our tomo process\n //\n\n logger.debug(\"all set, ready to go\");\n\n auto time_start = std::chrono::system_clock::now();\n\n tasks.run(); // GO!\n\n auto time_end = std::chrono::system_clock::now();\n\n logger.debug(\"Random walks done.\");\n\n // delta-time, formatted in hours, minutes, seconds and fraction of seconds\n std::string elapsed_s = Tomographer::Tools::fmtDuration(time_end - time_start);\n\n if (!is_master) {\n logger.debug(\"not master, we're done here.\");\n return 0;\n }\n\n // only master beyond this point\n\n const auto & task_results = tasks.collectedTaskResults();\n\n auto aggregated_histogram = taskcdat->aggregateResultHistograms(task_results) ;\n\n const auto & histogram = aggregated_histogram.final_histogram;\n\n // histogram has type Tomographer::AveragedHistogram, you can use it like any other\n // Histogram or HistogramWithErrorBars. You can pretty-print it with:\n logger.info([&](std::ostream & stream) {\n stream << \"Nice little histogram after all that work: \\n\"\n << histogram.prettyPrint()\n << \"\\n\";\n });\n\n logger.info([&](std::ostream & stream) {\n // Tomographer::MHRWTasks::ValueHistogramTools::printFinalReport() will generate a\n // default tomorun-like report with the parameters of the random walk, an overview\n // of each histogram of each task repeat, short info on the convergence of the\n // binning error bars, and the final histogram itself along with error bars\n Tomographer::MHRWTasks::ValueHistogramTools::printFinalReport(\n stream, // where to output\n *taskcdat, // the cdata\n task_results, // the results\n aggregated_histogram // aggregated\n );\n });\n\n\n delete llh;\n delete taskcdat;\n\n logger.debug(\"Finally, all done.\");\n\n // success.\n return 0;\n}\n", "meta": {"hexsha": "1d27d1e5823d30e35b52aa6dfc8fafdcc4b5926d", "size": 13694, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "test/minimal_mpi_tomorun.cxx", "max_stars_repo_name": "Tomographer/tomographer", "max_stars_repo_head_hexsha": "0a64927e639454175803c1746141bd9288af8b29", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12.0, "max_stars_repo_stars_event_min_datetime": "2015-09-24T02:25:11.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-13T02:26:00.000Z", "max_issues_repo_path": "test/minimal_mpi_tomorun.cxx", "max_issues_repo_name": "Tomographer/tomographer", "max_issues_repo_head_hexsha": "0a64927e639454175803c1746141bd9288af8b29", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4.0, "max_issues_repo_issues_event_min_datetime": "2015-10-12T15:48:55.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-21T15:14:59.000Z", "max_forks_repo_path": "test/minimal_mpi_tomorun.cxx", "max_forks_repo_name": "Tomographer/tomographer", "max_forks_repo_head_hexsha": "0a64927e639454175803c1746141bd9288af8b29", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2015-10-12T15:32:29.000Z", "max_forks_repo_forks_event_max_datetime": "2018-05-08T11:39:49.000Z", "avg_line_length": 33.4816625917, "max_line_length": 103, "alphanum_fraction": 0.6736526946, "num_tokens": 3839, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.3998116407397951, "lm_q1q2_score": 0.23544472958611865}} {"text": "//---------------------------------------------------------------------------//\n// MIT License\n//\n// Copyright (c) 2020-2021 Mikhail Komarov \n// Copyright (c) 2020-2021 Nikita Kaskov \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n//---------------------------------------------------------------------------//\n\n#ifndef FILECOIN_STORAGE_PROOFS_CORE_PARAMETER_CACHE_HPP\n#define FILECOIN_STORAGE_PROOFS_CORE_PARAMETER_CACHE_HPP\n\n#define BOOST_FILESYSTEM_NO_DEPRECATED\n\n#include \n\n#include \n#include \n\n#include \n\n#include \n#include \n\n#include \n\n#include \n\n#include \n#include \n\nnamespace nil {\n namespace filecoin {\n constexpr static const std::size_t VERSION = 28;\n constexpr static const std::size_t SRS_MAX_PROOFS_TO_AGGREGATE = 65535;\n constexpr static const char *PARAMETER_CACHE_ENV_VAR = \"FIL_PROOFS_PARAMETER_CACHE\";\n constexpr static const char *PARAMETER_CACHE_DIR = \"/var/tmp/filecoin-proof-parameters/\";\n constexpr static const char *GROTH_PARAMETER_EXT = \"params\";\n constexpr static const char *PARAMETER_METADATA_EXT = \"meta\";\n constexpr static const char *VERIFYING_KEY_EXT = \"vk\";\n constexpr static const char *SRS_SHARED_KEY_NAME = \"fil-inner-product-v1\";\n\n struct parameter_data {\n std::string cid;\n std::string digest;\n std::uint64_t sector_size;\n };\n\n typedef btree::map parameter_map;\n\n static parameter_map PARAMETERS;\n static parameter_map SRS_PARAMETERS;\n\n std::string parameter_cache_dir_name() {\n return std::getenv(PARAMETER_CACHE_ENV_VAR);\n }\n\n boost::filesystem::path parameter_cache_dir() {\n return parameter_cache_dir_name();\n }\n\n boost::filesystem::path parameter_cache_params_path(const std::string ¶meter_set_identifier) {\n return boost::filesystem::path(\n (parameter_cache_dir_name() + \"/v\" + std::to_string(VERSION) + \"-\" + parameter_set_identifier + \".\")\n .append(GROTH_PARAMETER_EXT));\n }\n\n boost::filesystem::path parameter_cache_metadata_path(const std::string ¶meter_set_identifier) {\n return boost::filesystem::path(\n (parameter_cache_dir_name() + \"/v\" + std::to_string(VERSION) + \"-\" + parameter_set_identifier + \".\")\n .append(PARAMETER_METADATA_EXT));\n }\n\n boost::filesystem::path parameter_cache_verifying_key_path(const std::string ¶meter_set_identifier) {\n return boost::filesystem::path(\n (parameter_cache_dir_name() + \"/v\" + std::to_string(VERSION) + \"-\" + parameter_set_identifier + \".\")\n .append(VERIFYING_KEY_EXT));\n }\n\n boost::filesystem::path ensure_ancestor_dirs_exist(const boost::filesystem::path &cache_entry_path) {\n boost::filesystem::path parent_dir = cache_entry_path.parent_path();\n if (boost::filesystem::exists(parent_dir)) {\n return cache_entry_path;\n } else {\n throw std::invalid_argument(cache_entry_path.string() + \" has no parent directory\");\n }\n }\n\n struct parameter_set_metadata {\n virtual std::string identifier() const = 0;\n virtual std::size_t sector_size() const = 0;\n };\n\n struct cache_entry_metadata {\n std::size_t sector_size;\n };\n\n cache_entry_metadata read_cached_metadata(const boost::filesystem::path &cache_entry_path) {\n return serde_json::from_reader(cache_entry_path);\n }\n\n cache_entry_metadata write_cached_metadata(const boost::filesystem::path &cache_entry_path,\n cache_entry_metadata value) {\n serde_json::to_writer(cache_entry_path, value);\n\n return value;\n }\n\n mapped_scheme_params>>\n read_cached_params(const boost::filesystem::path &cache_entry_path) {\n return mapped_scheme_params>>::\n build_mapped_parameters(cache_entry_path, false);\n }\n\n crypto3::zk::snark::r1cs_gg_ppzksnark_verification_key>\n read_cached_verifying_key(const boost::filesystem::path &cache_entry_path) {\n return crypto3::zk::snark::r1cs_gg_ppzksnark_verification_key>::read(\n cache_entry_path);\n }\n\n crypto3::zk::snark::r1cs_gg_ppzksnark_verification_key>\n write_cached_verifying_key(\n const boost::filesystem::path &cache_entry_path,\n const crypto3::zk::snark::r1cs_gg_ppzksnark_verification_key>\n &value) {\n value.write(cache_entry_path);\n\n return value;\n }\n\n scheme_params>> write_cached_params(\n const boost::filesystem::path &cache_entry_path,\n scheme_params>>\n value) {\n value.write(cache_entry_path);\n return value;\n }\n\n template class Circuit, typename ParameterSetMetadata = parameter_set_metadata>\n struct cacheable_parameters {\n typedef Circuit> C;\n typedef ParameterSetMetadata P;\n\n virtual std::string cache_prefix() const = 0;\n\n cache_entry_metadata cache_meta(const P &pub_params) {\n return {pub_params.graph.sector_size()};\n }\n\n virtual std::string cache_identifier(const P &pub_params) {\n using namespace nil::crypto3;\n\n std::string circuit_hash = crypto3::hash>(pub_params.identifier());\n actor::format(\"{}-{:02x}\", cache_prefix(), circuit_hash.iter().format(\"\"));\n }\n\n cache_entry_metadata get_param_metadata(const C &circuit, const P &pub_params) {\n std::string id = cache_identifier(pub_params);\n\n // generate (or load) metadata\n boost::filesystem::path meta_path = ensure_ancestor_dirs_exist(parameter_cache_metadata_path(id));\n try {\n read_cached_metadata(meta_path);\n } catch (...) {\n write_cached_metadata(meta_path, cache_meta(pub_params));\n }\n }\n\n mapped_scheme_params>>\n get_groth_params(const C &circuit, const P &pub_params) {\n std::string id = cache_identifier(pub_params);\n\n const auto generate = [&]() {\n return groth16::generate_random_parameters>(circuit, r);\n };\n\n boost::filesystem::path cache_path = ensure_ancestor_dirs_exist(parameter_cache_params_path(id));\n\n try {\n return read_cached_params(cache_path);\n } catch (...) {\n return write_cached_params(cache_path, generate());\n }\n }\n\n template\n crypto3::zk::snark::r1cs_gg_ppzksnark_verification_key>\n get_verifying_key(UniformRandomGenerator &r, const C &circuit, const P &pub_params) {\n std::string id = cache_identifier(pub_params);\n\n const auto generate = [&]()\n -> crypto3::zk::snark::r1cs_gg_ppzksnark_verification_key> {\n return get_groth_params(r, circuit, pub_params).vk;\n };\n\n boost::filesystem::path cache_path = ensure_ancestor_dirs_exist(parameter_cache_verifying_key_path(id));\n try {\n return read_cached_verifying_key(cache_path);\n } catch (...) {\n return write_cached_verifying_key(cache_path, generate());\n }\n }\n };\n } // namespace filecoin\n} // namespace nil\n\n#endif\n", "meta": {"hexsha": "b4860713fff02f9ddbeaa3d03a7a66e60a6fde9b", "size": 10063, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/storage/include/nil/filecoin/storage/proofs/core/parameter_cache.hpp", "max_stars_repo_name": "NilFoundation/crypto3-fil-proofs", "max_stars_repo_head_hexsha": "1fd78ad608278a1ed62fb29b0a077347b74a55f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, 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YES\n2. NO", "lm_q1_score": 0.6548947155710234, "lm_q2_score": 0.3593641451601019, "lm_q1q2_score": 0.2353456796310489}} {"text": "\n\n#include \"../gain_utils.h\"\n#include \"coela_utility/src/string_utils.h\"\n\n#include \"coela_utility/src/misc_math.h\"\n\n#include \"coela_utility/src/microstats.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nnamespace coela {\nnamespace gain_utils {\n//=====================================================================================================================\n\nvoid append_histogram_data_from_float_bitmap_region(const CcdImage& bmp,\n const PixelRange& rgn, HistogramContainer14bit& hist)\n{\n assert(hist.minimum_has_been_set());\n assert(rgn.is_valid());\n for (int y=rgn.low.y; y<=rgn.high.y; ++y) {\n for (int x=rgn.low.x; x<=rgn.high.x; ++x) {\n hist.count_rounded_value(bmp.pix(x,y));\n }\n }//end pixel scan\n return;\n}\n\nvoid append_histogram_data_from_float_bitmap_region(const CcdImage& bmp,\n const PixelRange& rgn, HistogramContainer10bit& hist)\n{\n assert(hist.minimum_has_been_set());\n assert(rgn.is_valid());\n for (int y=rgn.low.y; y<=rgn.high.y; ++y) {\n for (int x=rgn.low.x; x<=rgn.high.x; ++x) {\n hist.count_rounded_value(bmp.pix(x,y));\n }\n }//end pixel scan\n return;\n}\n\n\n\n\nmap load_histogram_data_from_file(const string& filename)\n{\n ifstream datafile(filename.c_str());\n if (!datafile) { throw runtime_error(\"Could not load histogram data from \\\"\"+filename+\"\\\"\"); }\n int value; long count;\n map histogram_data;\n while ((datafile>>value)) {\n datafile>>count;\n histogram_data[value] = count;\n }\n return histogram_data;\n}\n\n\n\n\ndouble histogram_differences_squared(const map& data,\n const map& model)\n{\n assert(data.size()==model.size());\n double diff_sq_sum=0.0;\n double diff;\n for (map::const_iterator it=data.begin(); it!=data.end(); ++it) {\n map::const_iterator model_it = model.find(it->first);\n if (model_it==model.end()) { throw runtime_error(\"Gain utils: model/data mismatch during fitting\"); }\n diff = it->second - model_it->second;\n diff_sq_sum += diff*diff;\n }\n return diff_sq_sum;\n}\n\ndouble poisson_noise_weighted_histogram_difference(const map& data,\n const map& model)\n{\n assert(data.size()>=model.size());\n double diff_sq_sum=0.0;\n double diff;\n\n// pair data_min_pair = get_min_value_pair(data);\n// pair model_min_pair = get_min_value_pair(model);\n// cerr<<\"Data / model min \"<< data_min_pair.first<<\" / \"<< model_min_pair.first<::const_iterator model_it=model.begin(); model_it!=model.end();\n ++model_it) {\n// map::const_iterator model_it = model.find(it->first);\n// if (model_it==model.end()) throw runtime_error(\"Gain utils: model/data mismatch during fitting\");\n map::const_iterator data_it = data.find(model_it->first);\n if (data_it==data.end()) throw\n runtime_error(\"Gain utils: model/data mismatch during fitting;\"\n \"could not find datapoint for value \" + string_utils::itoa(model_it->first));\n diff = (data_it->second - model_it->second);\n diff_sq_sum += diff*diff / max(data_it->second,\n 1l); //Poisson distribution for each histogram entry, so variance == probability\n// diff_sq_sum += pow(fabs(diff),1.5) / sqrt(model_it->second); //This method less sensitive to noise spikes at the end of the histogram section\n }\n return diff_sq_sum;\n}\n\ndouble log_space_histogram_difference(const map& data,\n const map& model)\n{\n assert(data.size()>=model.size());\n double diff_sq_sum=0.0;\n double diff;\n for (map::const_iterator model_it=model.begin(); model_it!=model.end();\n ++model_it) {\n map::const_iterator data_it = data.find(model_it->first);\n if (data_it==data.end()) throw\n runtime_error(\"Gain utils: model/data mismatch during fitting;\"\n \"could not find datapoint for value \" + string_utils::itoa(model_it->first));\n diff = (log(data_it->second) - log(model_it->second));\n diff_sq_sum += diff*diff ;\n }\n return diff_sq_sum;\n}\n\nmap convolve_histogram_with_gaussian(const map& input_hist,\n const double readout_sigma,\n const int kernel_half_width)\n{\n map readout_kernel;\n\n const double unity_area_gaussian_height = 1.0 / (sqrt(2.0*M_PI)*readout_sigma);\n\n const int kernel_min(-kernel_half_width), kernel_max(kernel_half_width);\n for (int value = kernel_min ; value <= kernel_max; ++value) {\n readout_kernel[value] = misc_math::gaussian_1d_function(value,\n unity_area_gaussian_height,\n readout_sigma);\n }\n\n const int input_min= get_min_value_pair(input_hist).first ;\n const int input_max= get_max_value_pair(input_hist).first ;\n\n// cerr<<\"Kernel min / max\" << kernel_min <<\" / \"<< kernel_max< convolved_hist;\n for (map::const_iterator input_it=input_hist.begin();\n input_it!=input_hist.end(); ++input_it) {\n const int input_value= input_it->first;\n if (input_value + kernel_min >= input_min &&\n input_value + kernel_max <= input_max) {\n if (convolved_hist.find(input_value) == input_hist.end()) {\n convolved_hist[input_value ] = 0.0;\n }\n for (map::const_iterator kernel_it=readout_kernel.begin();\n kernel_it!=readout_kernel.end(); ++kernel_it) {\n\n int kernel_offset_input = kernel_it->first + input_value;\n convolved_hist[ input_value ] +=\n input_hist.find(kernel_offset_input)->second * kernel_it->second;\n\n }\n }\n// else{\n// cerr<<\"rejecting input val \" << input_value<& gauss_pars) const\n{\n assert(gauss_pars.size()==3);\n map model=get_model_histogram(gauss_pars);\n// return histogram_differences_squared(data, model);\n// cerr<<\"Pars: \"<< gauss_pars[0]<<\" \" < GaussianHistogramFitFCN::get_model_histogram(\n const std::vector& gauss_pars) const\n{\n mapmodel;\n for (map::const_iterator it=data.begin(); it!=data.end(); ++it) {\n model[it->first] = misc_math::gaussian_1d_function((double)it->first - gauss_pars[0],\n gauss_pars[1],\n gauss_pars[2]); //FIXME -- should perhaps be measuring distance from centre of bin? (so +0.5 to it->first). but not sure.\n }\n return model;\n}\n\n//==================================================================================\nmap ExponentialHistogramFitFCN::get_model_histogram(\n const std::vector& exp_pars) const\n{\n assert(exp_pars.size()==2);\n mapmodel;\n const double& amplitude = exp_pars[0];\n const double& gain = exp_pars[1];\n for (map::const_iterator it=data.begin(); it!=data.end(); ++it) {\n model[it->first] = amplitude*exp(-1.0*it->first/gain);\n }\n return model;\n}\n\ndouble ExponentialHistogramFitFCN::operator()(const std::vector& exp_pars)\nconst\n{\n assert(exp_pars.size()==2);\n map model=get_model_histogram(exp_pars);\n// return log_space_histogram_difference(data, model);\n return poisson_noise_weighted_histogram_difference(data, model) / (model.size() - 1);\n\n}\n//==================================================================================\n//map full_EMCCD_low_light_level_histogram_model::get_model_histogram(\n// const GainData& inf){\n//\n//}\n//\n\n//==================================================================================\nFullEmccdHistogramFitFCN::FullEmccdHistogramFitFCN(const map& histogram)\n :\n error_def(1.0), data_(histogram)\n{\n fit_rgn_min_ = get_min_value_pair(data_).first;\n fit_rgn_max_ = get_max_value_pair(data_).first;\n}\n\nFullEmccdHistogramFitFCN::FullEmccdHistogramFitFCN(const map& histogram,\n const int fit_region_min, const int fit_region_max):\n error_def(1.0), data_(histogram),\n fit_rgn_min_(fit_region_min), fit_rgn_max_(fit_region_max)\n{}\n\ndouble FullEmccdHistogramFitFCN::operator()(const std::vector& pars) const\n{\n assert(pars.size()==npars_);\n\n// cout<<\"Params: \";\n// for (size_t i=0; i!=pars.size();++i){\n// cout< model = get_model_histogram(pars);\n double val = poisson_noise_weighted_histogram_difference(data_, model);\n//\n// cout<<\" : \"< FullEmccdHistogramFitFCN::generate_pars_vec(const GainData& inf)\n{\n vector pars;\n pars.push_back(inf.bias_pedestal);\n pars.push_back(inf.N_dark_pix);\n pars.push_back(inf.readout_sigma);\n pars.push_back(inf.N_light_pix);\n pars.push_back(inf.gain);\n pars.push_back(inf.N_serial_CIC_pix);\n// pars.push_back(inf.serial_CIC_gain);\n return pars;\n}\n\n\nmap FullEmccdHistogramFitFCN::get_CICIR_model_histogram(\n const double gain,\n const double N_CICIR_pix,\n const int N_EM_stages,\n const int max_val_to_generate)\n{\n\n int min_N_stages=1;\n double prob_per_stage = pow(gain, 1.0/N_EM_stages);\n\n\n map gain_for_N_stages;\n for (int stages_passed = 1; stages_passed<=N_EM_stages; ++stages_passed) {\n gain_for_N_stages[stages_passed] = pow(prob_per_stage, stages_passed);\n }\n\n// cerr<<\"gain at 604:\"<< gain_for_N_stages[604]< CICIR_histogram;\n for (int register_output_val=1; register_output_val<=max_val_to_generate;\n ++register_output_val) {\n double output_frequency=0.0;\n for (int CICIR_stages_passed = min_N_stages; CICIR_stages_passed<=N_EM_stages;\n ++CICIR_stages_passed) {\n const double & g = gain_for_N_stages[CICIR_stages_passed];\n\n output_frequency += exp((0.5-register_output_val)/g) / g;\n }\n CICIR_histogram[register_output_val]= frequency_normalisation * output_frequency;\n }\n return CICIR_histogram;\n}\n\nmap FullEmccdHistogramFitFCN::get_model_histogram(\n const std::vector& pars) const\n{\n assert(pars.size()==npars_);\n\n const double& bias_pedestal = pars[0];\n const double& dark_pix = pars[1];\n const double& RO_sigma = pars[2];\n const double& N_photon_events = pars[3];\n const double& phot_gain = pars[4];\n const double& N_serial_CIC_pix = pars[5];\n// const double& serial_CIC_gain = pars[6];\n// const double serial_CIC_gain = sqrt(phot_gain);\n\n int convolution_half_width=60;\n\n// int data_start = get_min_value_pair(data).first;\n int data_end = get_max_value_pair(data_).first;\n int tail_range = data_end - bias_pedestal;\n\n double total_pix_calc = N_photon_events + N_serial_CIC_pix + dark_pix;\n double photon_freq_est = N_photon_events / total_pix_calc;\n// cerr<<\"Photon freq est: \" << photon_freq_est<<\", calc total: \" << total_pix_calc<0) {\n boost::math::poisson_distribution<> pois_dist(photon_freq_est);\n double p1_prob = boost::math::pdf(pois_dist, 1);\n double p2_prob = boost::math::pdf(pois_dist, 2);\n\n //Note exp( 0.5 / phot_gain) term corrects ~1% normalisation error;\n // since integral from 1 to +inf of the PDF otherwise = exp(-1/g)\n p1_amplitude = (p1_prob / (p1_prob +p2_prob)) *\n N_photon_events * exp(0.5/phot_gain) / phot_gain;\n\n //Note normalisation correction not reqd here, more complex to calc. but also much smaller effect.\n p2_amplitude = (p2_prob / (p1_prob +p2_prob)) *\n N_photon_events / (phot_gain*phot_gain);\n } else {\n p1_amplitude=0.0;\n p2_amplitude=0.0;\n }\n\n map register_output_model;\n int fit_rgn_extension_below_bias_pedestal = bias_pedestal - fit_rgn_min_;\n if (fit_rgn_extension_below_bias_pedestal > 0) {\n for (int i= -(convolution_half_width + fit_rgn_extension_below_bias_pedestal); i<0; ++i) {\n register_output_model[i]=0;\n }\n } else {\n for (int i= -convolution_half_width; i<0; ++i) {\n register_output_model[i]=0;\n }\n }\n\n register_output_model[0] = dark_pix;\n\n map CICIR_register_component =\n FullEmccdHistogramFitFCN::get_CICIR_model_histogram(\n phot_gain, N_serial_CIC_pix,\n FullEmccdHistogramFitFCN::N_EM_serial_register_stages,\n tail_range + convolution_half_width\n );\n\n for (int register_output_val=1; register_output_val<=tail_range + convolution_half_width;\n ++register_output_val) {\n double photon_exponential = exp(-1.0* register_output_val /phot_gain);\n //Contribution from 1 and 2 photo-electron events\n register_output_model[register_output_val] =\n p1_amplitude*photon_exponential;\n register_output_model[register_output_val] +=\n p2_amplitude*(register_output_val - 1)*photon_exponential;\n\n //Contribution from CICIR events\n// register_output_model[register_output_val] +=\n// serial_CIC_amplitude*exp( -1.0* register_output_val /serial_CIC_gain );\n register_output_model[register_output_val] +=\n CICIR_register_component[register_output_val];\n }\n\n map RO_model =\n convolve_histogram_with_gaussian(register_output_model, RO_sigma,\n convolution_half_width);\n\n map biased_RO_model;\n for (map::const_iterator it=RO_model.begin(); it!=RO_model.end(); ++it) {\n int output_value = it->first + bias_pedestal;\n if (output_value>=fit_rgn_min_ && output_value <= fit_rgn_max_) {\n biased_RO_model[output_value] = it->second;\n }\n }\n\n return biased_RO_model;\n}\n\n//==================================================================================\n\n//double calculate_thresholded_SNR(const double readout_noise_in_ADU,\n// const double mean_photon_gain_in_ADU,\n// const double CIC_event_rate_per_pixel_readout,\n// const double light_level,\n// const double threshold_in_photo_electrons,\n// const bool output_to_screen){\n// map histogram_min_max;\n// histogram_min_max[-readout_noise_in_ADU*5]=0;\n// histogram_min_max[10000]=0;\n//\n// FullEmccdHistogramFitFCN full_model(histogram_min_max);\n// GainData full_pars;\n// full_pars.bias_pedestal = 0.0;\n// full_pars.readout_sigma = readout_noise_in_ADU;\n// full_pars.gain = mean_photon_gain_in_ADU;\n// full_pars.N_light_pix = 0.0;\n// full_pars.N_serial_CIC_pix = 0.0;\n// full_pars.N_dark_pix = 0.0;\n//// - mean_photon_gain_in_ADU*CIC_event_rate_per_pixel_readout);\n//\n// GainData photon_hist_pars(full_pars);\n// photon_hist_pars.N_light_pix=1.0;\n// map light_model = full_model.get_model_histogram(\n// full_model.generate_pars_vec(photon_hist_pars));\n//\n// double all_counts = gain_utils::sum_counts_in_histogram(light_model);\n// double thresh_counts = gain_utils::sum_counts_in_histogram_above_threshold(\n// light_model, threshold_in_photo_electrons*mean_photon_gain_in_ADU);\n//\n// double pass_rate_for_photon_events = thresh_counts / all_counts;\n//\n// if (output_to_screen){\n//// gain_utils::write_histogram_data_to_file(light_model, \"photons_only.txt\");\n// cerr<<\"photons hist sum:\" << all_counts << endl;\n// cerr<<\"Filtered photon fraction: \"<< pass_rate_for_photon_events< CICIR_model = full_model.get_model_histogram(\n//// full_model.generate_pars_vec(CICIR_hist_pars));\n////\n//// all_counts = gain_utils::sum_counts_in_histogram(CICIR_model);\n//// thresh_counts = gain_utils::sum_counts_in_histogram_above_threshold(\n//// CICIR_model, threshold_in_photo_electrons*mean_photon_gain_in_ADU);\n////\n//// double CICIR_fraction = thresh_counts / all_counts;\n////\n//// if (output_to_screen){\n//// gain_utils::write_histogram_data_to_file(CICIR_model, \"CICIR_only.txt\");\n//// cerr<<\"CICIR hist sum:\" << all_counts << endl;\n//// cerr<<\"Filtered CICIR fraction: \"<< CICIR_fraction< RO_model = full_model.get_model_histogram(\n//// full_model.generate_pars_vec(RO_hist_pars));\n////\n//// all_counts = gain_utils::sum_counts_in_histogram(RO_model);\n//// thresh_counts = gain_utils::sum_counts_in_histogram_above_threshold(\n//// RO_model, threshold_in_photo_electrons*mean_photon_gain_in_ADU);\n////\n//// double RO_fraction = thresh_counts / all_counts;\n////\n//// if (output_to_screen){\n//// gain_utils::write_histogram_data_to_file(RO_model, \"RO_only.txt\");\n//// cerr<<\"RO hist sum:\" << all_counts << endl;\n//// cerr<<\"Filtered RO fraction: \"<< RO_fraction< full_model_hist = full_model.get_model_histogram(\n// full_model.generate_pars_vec(full_pars));\n//\n// all_counts = gain_utils::sum_counts_in_histogram(full_model_hist);\n// thresh_counts = gain_utils::sum_counts_in_histogram_above_threshold(\n// full_model_hist, threshold_in_photo_electrons*mean_photon_gain_in_ADU);\n//\n// double pass_rate_for_all_events = thresh_counts / all_counts;\n// if (output_to_screen){\n// cerr<<\"Noise hist sum:\" << all_counts << endl;\n// cerr<<\"Filtered noise fraction: \"<< pass_rate_for_all_events < 0.5) {\n throw runtime_error(\"ThresholdedSnrCalculator() ---\"\n \"Light level too high\");\n }\n\n map histogram_min_max;\n histogram_min_max[-RO*5]=0;\n histogram_min_max[10000]=0;\n\n FullEmccdHistogramFitFCN full_model(histogram_min_max);\n GainData full_pars;\n full_pars.bias_pedestal = 0.0;\n full_pars.readout_sigma = RO;\n full_pars.gain = gain;\n full_pars.N_light_pix = 0.0;\n full_pars.N_serial_CIC_pix = 0.0;\n full_pars.N_dark_pix = 0.0;\n// - mean_photon_gain_in_ADU*CIC_event_rate_per_pixel_readout);\n\n GainData photon_hist_pars(full_pars);\n photon_hist_pars.N_light_pix=1.0;\n\n photon_distribution_ = full_model.get_model_histogram(\n full_model.generate_pars_vec(photon_hist_pars));\n\n full_pars.N_light_pix = light_level;\n full_pars.N_serial_CIC_pix = CICIR_rate;\n full_pars.N_dark_pix = 1.0 - (light_level + CICIR_rate);\n\n full_distribution_ = full_model.get_model_histogram(\n full_model.generate_pars_vec(full_pars));\n\n// gain_utils::write_histogram_data_to_file(photon_distribution_, \"photons.txt\");\n// gain_utils::write_histogram_data_to_file(full_distribution_, \"full_dist.txt\");\n\n}\n\ndouble ThresholdedSnrCalculator::calc_SNR_at_threshold(\n const double threshold_in_photo_electrons) const\n{\n\n double all_counts = gain_utils::sum_counts_in_histogram(photon_distribution_);\n double thresh_counts = gain_utils::sum_counts_in_histogram_above_threshold(\n photon_distribution_, threshold_in_photo_electrons*gain_);\n double fraction_photon_events_passed = thresh_counts / all_counts;\n\n all_counts = gain_utils::sum_counts_in_histogram(full_distribution_);\n thresh_counts = gain_utils::sum_counts_in_histogram_above_threshold(\n full_distribution_, threshold_in_photo_electrons*gain_);\n\n double fraction_of_all_events_passed = thresh_counts / all_counts;\n// cerr<<\"Gain\"<< gain_< max_SNR) {\n best_thresh = t;\n max_SNR = s;\n }\n }\n return best_thresh;\n}\n\ndouble calculate_ideal_photon_counter_SNR(const double light_level)\n{\n return light_level /\n sqrt(exp(light_level) - 1.0);\n}\n\ndouble calculate_linear_mode_SNR(const double readout_noise_in_ADU,\n const double mean_photon_gain_in_ADU,\n const double light_level,\n const double CICIR_event_rate_per_pixel_readout\n )\n{\n\n ///Calculate CICIR_signal relative to a photo-electron signal:\n double CICIR_signal = CICIR_event_rate_per_pixel_readout /\n log(mean_photon_gain_in_ADU);\n double normed_readout = readout_noise_in_ADU / mean_photon_gain_in_ADU;\n return light_level /\n sqrt(2.0*(light_level + CICIR_signal) + normed_readout*normed_readout);\n}\n\n//==================================================================================\npair get_mode(const map& histogram_data)\n{\n int peak_value(histogram_data.begin()->first);\n long peak_count(histogram_data.begin()->second);\n for (map::const_iterator it=histogram_data.begin(); it!=histogram_data.end();\n ++it) {\n if (it->second > peak_count) {\n peak_value =it->first; peak_count = it->second;\n }\n }\n pair peak_pair(peak_value, peak_count);\n return peak_pair;\n}\n\n\n\nfloat get_gaussian_HWHM(const map& histogram_data)\n{\n pair peak = get_mode(histogram_data);\n float best_distance_to_HM=peak.second/2.0;\n int best_value = peak.first;\n for (map::const_iterator it=histogram_data.begin(); it!=histogram_data.end();\n ++it) {\n if (it->first < peak.first) {\n float current_dist_to_HM = fabs(it->second - peak.second/2.0);\n if (current_dist_to_HM < best_distance_to_HM) {\n best_distance_to_HM=current_dist_to_HM;\n best_value =it->first;\n }\n }\n }\n// cout <<\"Half peak value: \" < get_gaussian_fitting_section(const map& histogram_data)\n{\n map gaussian_section;\n pair peak = get_mode(histogram_data);\n long count_threshold = max((long)(peak.second*0.005), 300l);\n for (map::const_iterator it=histogram_data.begin(); it!=histogram_data.end();\n ++it) {\n if (it->first < (peak.first+10) && it->second > count_threshold) { gaussian_section[it->first]=it->second; }\n }\n return gaussian_section;\n}\n\n\n\nmap get_tail_fitting_section(const map& histogram_data,\n const double g_sigma_est, const bool output_to_screen)\n{\n pairpeak =get_mode(histogram_data);\n long count_threshold=max((long)25,\n (long)floor(peak.second/6e6)); //FIXME - this may need adjusting\n if (output_to_screen) { cout <<\"\\nTail count cutoff: \" << count_threshold<::const_iterator it=histogram_data.begin(); it!=histogram_data.end();\n ++it) {\n if (it->first > peak.first && it->second >count_threshold) {\n tail_section_end_value= max(tail_section_end_value, it->first);\n }\n }\n tail_section_end_value-=10; //jump back, otherwise may get extended by a noise spike extending the range to... a noise spike!\n if (output_to_screen) { cout <<\"Resulting in cutoff value: \"< tail_section;\n int curve_range = tail_section_end_value - peak.first;\n int peak_offset = max(curve_range*0.25, g_sigma_est*8);\n int tail_start = (peak.first + peak_offset);\n if (output_to_screen) { cout <<\"Tail_start at: \"<= tail_section_end_value -5) { throw runtime_error(\"Not enough data to fit tail of histogram\"); }\n for (map::const_iterator it=histogram_data.begin(); it!=histogram_data.end();\n ++it) {\n if (it->first >= tail_start && it->first <=tail_section_end_value) { tail_section[it->first] = it->second; }\n }\n return tail_section;\n}\n\nmap get_serial_CIC_model_fitting_section(const map& histogram_data,\n const double bias_pedestal,\n const double readout_sigma)\n{\n int low_cutoff = bias_pedestal + 4.0 * readout_sigma;\n int high_cutoff = bias_pedestal + 12*readout_sigma;\n map fitting_region;\n for (map::const_iterator it=histogram_data.begin(); it!=histogram_data.end();\n ++it) {\n if (it->first > low_cutoff && it->first <=high_cutoff) { fitting_region[it->first] = it->second; }\n }\n return fitting_region;\n}\n\nmap get_full_model_fitting_section(const map& histogram_data,\n const double bias_pedestal,\n const double readout_sigma)\n{\n// pairpeak =get_mode(histogram_data);\n// long count_threshold=max((long)50, (long)floor(peak.second/1e6));\n\n map tail_region = get_tail_fitting_section(histogram_data,\n readout_sigma);\n int tail_end = get_max_value_pair(tail_region).first;\n\n int low_cutoff = bias_pedestal - 0.5 * readout_sigma;\n// cerr<<\"low cuttoff, tail end: \" < fitting_region;\n for (map::const_iterator it=histogram_data.begin(); it!=histogram_data.end();\n ++it) {\n if (it->first > low_cutoff && it->first <=tail_end) { fitting_region[it->first] = it->second; }\n }\n return fitting_region;\n}\n\nmap get_points_at_values_greater_than(const map& histogram_data,\n int zero_point)\n{\n map positive_section;\n for (map::const_iterator it=histogram_data.begin(); it!=histogram_data.end();\n ++it) {\n if ((it->first > zero_point)) { positive_section[it->first]=it->second; }\n }\n return positive_section;\n}\n\nmap threshold_above_count(const map& histogram_data,\n int count_threshold)\n{\n map threshed;\n for (map::const_iterator it=histogram_data.begin(); it!=histogram_data.end();\n ++it) {\n if ((it->second > count_threshold)) { threshed[it->first]=it->second; }\n }\n return threshed;\n}\n\n\nfloat estimate_log_slope(const map& tail_section)\n{\n pair tail_section_peak(get_mode(tail_section)),\n tail_end(get_max_value_pair(tail_section));\n return (log(tail_end.second) - log(tail_section_peak.second))/\n (tail_end.first - tail_section_peak.first);\n}\n\n//Returns gauss_par vector: peak value (zero value), peak count, sigma (readout sigma);\nvector fit_readout_gaussian(const map& histogram_data,\n const bool output_to_screen)\n{\n pair peak = get_mode(histogram_data);\n if (output_to_screen) { cout <<\"Data peak count at \" < gaussian_section = get_gaussian_fitting_section(histogram_data);\n gaussian_section.erase(\n 0); //remove weird spike at 0 value. fixme! - track down the origin of this.\n\n GaussianHistogramFitFCN hump_fitter(gaussian_section);\n ROOT::Minuit2::MnUserParameters gauss_pars;\n gauss_pars.Add(\"HistPeakValue\", peak.first, 2);\n gauss_pars.SetLimits(\"HistPeakValue\", -100.0, 20000.0);\n gauss_pars.Add(\"HistPeakCount\", peak.second, peak.second*0.1);\n// gauss_pars.SetLimits(\"HistPeakCount\", peak.first*0.5, peak.first*2.0);\n gauss_pars.SetLowerLimit(\"HistPeakCount\",5);\n float gauss_sigma_est = get_gaussian_HWHM(histogram_data) * 2 /\n misc_math::gaussian_fwhm_to_sigma_ratio;\n if (output_to_screen) { cout <<\"Sigma init est. \" << gauss_sigma_est < end_pars;\n end_pars=min.UserParameters().Params();\n return end_pars;\n}\n\n//Returns amplitude, gain\nvector fit_exponential_tail(const map& histogram_data,\n const double gaussian_sigma_estimate,\n const bool output_to_screen)\n{\n if (output_to_screen) { cout <<\"Fitting tail\"<peak =get_mode(histogram_data);\n map tail_section = get_tail_fitting_section(histogram_data,\n gaussian_sigma_estimate ,output_to_screen);\n\n ExponentialHistogramFitFCN tail_fitter(tail_section);\n double log_slope_estimate = estimate_log_slope(tail_section);\n if (output_to_screen) { cout <<\"Initial slope estimate: \" < fitted_tail_pars = min.UserParameters().Params();\n return fitted_tail_pars;\n}\n\nvector fit_exponential_tail_fixed_gain(const map& histogram_data,\n const double gaussian_sigma_estimate, const double gain_estimate,\n const bool output_to_screen)\n{\n if (output_to_screen) { cout <<\"Fitting tail\"<peak =get_mode(histogram_data);\n map tail_section = get_tail_fitting_section(histogram_data,\n gaussian_sigma_estimate ,output_to_screen);\n\n ExponentialHistogramFitFCN tail_fitter(tail_section);\n\n double gain_init_est=gain_estimate;\n double amplitude_estimate=peak.second*0.1 / exp(\n -1.0*peak.first/gain_init_est); //assumes 1/10th of all pixels contain a photon event\n ROOT::Minuit2::MnUserParameters tail_par;\n if (output_to_screen) { cout <<\"Initial amp estimate: \" < fitted_tail_pars = min.UserParameters().Params();\n return fitted_tail_pars;\n}\n\n\n\nGainData fit_full_CCD_model(const map& histogram_data,\n const GainData& approx_fit_init,\n const bool output_to_screen\n// , const bool sqrt_serial_CIC_gain_mode\n )\n{\n //Hardcoded initial estimates (fixme?)\n GainData approx_fit(approx_fit_init);\n\n approx_fit.N_serial_CIC_pix= approx_fit.actual_number_pix_events_recorded -\n (approx_fit.N_dark_pix + approx_fit.N_light_pix);\n// approx_fit.serial_CIC_gain = sqrt(approx_fit.gain);\n\n\n\n// gain_utils::write_histogram_data_to_file(full_fitting_region, \"full_fitting_region.txt\");\n\n ROOT::Minuit2::MnUserParameters mnpars;\n const string key_BiasPedestal = \"BiasPedestal\";\n const string key_NDarkPix = \"NDarkPix\";\n const string key_ROSigma = \"ROSigma\";\n const string key_NPhotPix = \"NPhotPix\";\n const string key_PhotGain = \"PhotGain\";\n const string key_NSerialCICPix = \"NSerCICPix\";\n// const string key_SerialCICGain = \"SerCICGain\";\n\n //NB! Ensure ordering matches definition for FullEmccdHistogramFitFCN::operator() !!!\n\n mnpars.Add(key_BiasPedestal, approx_fit.bias_pedestal, 2);\n mnpars.SetLimits(key_BiasPedestal, approx_fit.bias_pedestal-2,\n approx_fit.bias_pedestal+2);\n\n mnpars.Add(key_NDarkPix, approx_fit.N_dark_pix, approx_fit.N_dark_pix*0.1);\n mnpars.SetLowerLimit(key_NDarkPix,5);\n// if (output_to_screen) cout <<\"Sigma init est. \" << gauss_sigma_est < serial_CIC_region =\n get_serial_CIC_model_fitting_section(histogram_data,\n approx_fit.bias_pedestal, approx_fit.readout_sigma);\n\n FullEmccdHistogramFitFCN CIC_fit_fcn(serial_CIC_region);\n\n if (output_to_screen) cout<<\"---------------------------------\\n\"\n << \"Init params:\" << mnpars< full_fitting_region =\n get_full_model_fitting_section(histogram_data,\n approx_fit.bias_pedestal, approx_fit.readout_sigma);\n//\n FullEmccdHistogramFitFCN full_fit_fcn(full_fitting_region);\n\n mnpars = fitted_pars;\n mnpars.Release(key_BiasPedestal);\n mnpars.Release(key_NDarkPix);\n mnpars.Release(key_ROSigma);\n mnpars.Release(key_NPhotPix);\n// if (sqrt_serial_CIC_gain_mode == false){\n mnpars.Release(key_PhotGain);\n// }\n// mnpars.Release(key_NSerialCICPix); //Already free\n// mnpars.Release(key_SerialCICGain); //Already free\n mnpars.SetError(key_NDarkPix, mnpars.Value(key_NDarkPix)*0.1);\n mnpars.SetError(key_NPhotPix, mnpars.Value(key_NPhotPix)*0.1);\n mnpars.SetError(key_PhotGain, mnpars.Value(key_PhotGain)*0.05);\n mnpars.SetError(key_NSerialCICPix, mnpars.Value(key_NSerialCICPix)*0.1);\n// mnpars.SetError(key_SerialCICGain, mnpars.Value(key_SerialCICGain)*0.1);\n\n\n if (output_to_screen) cout<<\"---------------------------------\\n\"\n << \"Init params:\" << mnpars<& histogram_bins,\n const bool perform_advanced_fit,\n const bool fixed_gain_mode, const double gain_value,\n const bool output_to_screen\n// , const bool sqrt_serial_CIC_gain_mode\n )\n{\n if (fixed_gain_mode && gain_value==0.0) { throw logic_error(\"Must supply a gain value for fitting in fixed gain mode\"); }\n vector gauss_pars = gain_utils::fit_readout_gaussian(histogram_bins);\n vector exp_pars;\n if (fixed_gain_mode) { exp_pars = gain_utils::fit_exponential_tail_fixed_gain(histogram_bins, gauss_pars.back(), gain_value, true); }\n else { exp_pars= gain_utils::fit_exponential_tail(histogram_bins, gauss_pars.back(), output_to_screen && !perform_advanced_fit); }\n\n GainData approx_results;\n approx_results.bias_pedestal = gauss_pars[0];\n double peak_gaussian_count = gauss_pars[1];\n approx_results.readout_sigma = gauss_pars[2];\n approx_results.gain = exp_pars[1];\n\n approx_results.N_dark_pix= peak_gaussian_count*approx_results.readout_sigma *sqrt(\n 2*M_PI); //area under a gaussian\n\n double area_under_exponential_curve = approx_results.gain *\n exp_pars.front()* exp(\n -1.0*approx_results.bias_pedestal/approx_results.gain); //Integral from bias pedestal to infinity\n approx_results.N_light_pix=area_under_exponential_curve;\n\n approx_results.actual_number_pix_events_recorded = sum_counts_in_histogram(\n histogram_bins);\n\n approx_results.photon_event_freq =\n approx_results.N_light_pix /\n (approx_results.N_light_pix+approx_results.N_dark_pix);\n\n// approx_results.N_serial_CIC_pix =\n// approx_results.actual_number_pix_events_recorded -\n// (approx_results.N_dark_pix+approx_results.N_light_pix);\n\n// approx_results.serial_CIC_rate =\n// approx_results.N_serial_CIC_pix /\n// approx_results.actual_number_pix_events_recorded;\n\n\n// cerr<<\"Approx fit:\" << approx_results< full_pars = gauss_pars; // zero_val, peak_count, sigma,\n//\n// full_pars.push_back(exp_pars[0]); //photon_amplitude\n// full_pars.push_back(exp_pars[1]); //photon_gain,\n// full_pars.push_back(0.00); //serial_CIC_amplitude;\n// approx_results.photon_event_freq = area_under_exponential_curve / (area_under_gaussian_curve + area_under_exponential_curve);\n\n}\n\n//==================================================================================\nGainData::GainData()\n : bias_pedestal(-1),readout_sigma(-1),gain(-1),\n N_dark_pix(-1), N_light_pix(-1), N_serial_CIC_pix(-1),\n photon_event_freq(-1), serial_CIC_rate(-1),\n actual_number_pix_events_recorded(0)\n{}\n\nvoid GainData::write_to_file(const std::string& filename)\n{\n ofstream outputfile(filename.c_str());\n if (outputfile) {\n outputfile<<\"#\"+get_column_headers()< lines;\n string line;\n while (getline(datafile,line)) { lines.push_back(line); }\n// for (size_t i=0; i!=lines.size();++i) cout < line_segments = tokenize_and_strip_spaces(lines[1], \",\");\n if (line_segments.size()!=4) { throw runtime_error(\"\\nIncorrect file format for gain info: \" + filename +\"\\n\"); }\n bias_pedestal=atof(line_segments[0]);\n readout_sigma=atof(line_segments[1]);\n gain=atof(line_segments[2]);\n photon_event_freq=atof(line_segments[3]);\n serial_CIC_rate=atof(line_segments[4]);\n}\n\nstd::ostream& operator<<(std::ostream& os, const GainData& g_inf)\n{\n os<\nCcdImage& normalise_CCD_with_uniform_gain(CcdImage& input,\n const GainData& det_info)\n{\n input.pix-=det_info.bias_pedestal;\n input.pix/=det_info.gain;\n return input;\n}\ntemplate\nCcdImage& normalise_CCD_with_uniform_gain(CcdImage& input,\n const GainData& det_info);\n\ntemplate\nCcdImage& normalise_CCD_with_uniform_gain(CcdImage& input,\n const GainData& det_info);\n\n\n//double_bitmap& normalise_CCD_with_gain_map(double_bitmap& input, const GainData& det_info, const double_bitmap& gain_map){\n//// input.add_normalisation_flag();\n// input-=det_info.bias_pedestal;\n// input/=gain_map;\n// return input;\n//}\n\n\n//==================================================================================\n\nCcdImage threshold_bitmap(const CcdImage& input,\n const double threshold_level)\n{\n CcdImage thresholded(input);\n// assert(input.key_exists(\"CCDNORMD\"));\n for (PixelIterator i(input.pix.range()); i!=i.end; ++i) {\n input.pix(i)>threshold_level ? thresholded.pix(i)=1.0f : thresholded.pix(i)=0.0f;\n }\n// thresholded.add_keyword(\"THRESHED\", string_utils::ftoa(threshold_level),\"Datacount Level at which data was thresholded\");\n return thresholded;\n}\n\n\nCcdImage create_threshold_mask(const CcdImage& input,\n const float threshold_count)\n{\n CcdImage mask(input);\n mask.pix.assign(1.0);\n// if (!input.key_exists(\"CCDNORMD\")) throw logic_error(\"create_threshold_mask takes normalised input\");\n for (PixelIterator pix(input.pix.range()); pix!=pix.end; ++pix) {\n if (input.pix(pix)>threshold_count) {\n //If bright area\n PixelRange mask_region(pix,pix);\n mask_region = PixelRange::pad(mask_region, 4); //FIXME - hardcoded radius\n mask_region = PixelRange::overlap(mask_region, mask.pix.range());\n for (PixelIterator i(mask_region); i!=i.end; ++i) { mask.pix(i) = 0.0; }\n }\n }\n return mask;\n}\n\n\n\nGainData analyse_histogram(const map& full_data_histogram ,\n const string& output_dir)\n{\n gain_utils::write_histogram_data_to_file(full_data_histogram,\n output_dir+\"short_hist.dat\");\n vector hump_pars= gain_utils::fit_readout_gaussian(full_data_histogram, true);\n\n map gauss_data = gain_utils::get_gaussian_fitting_section(full_data_histogram);\n gain_utils::GaussianHistogramFitFCN gauss_fitter(gauss_data);\n map gauss_fitted_bit = gauss_fitter.get_model_histogram(hump_pars);\n gain_utils::write_histogram_data_to_file(gauss_data, output_dir+\"hump_data.dat\");\n gain_utils::write_histogram_data_to_file(gauss_fitted_bit,\n output_dir+\"hump_model_fit.dat\");\n\n map threshed_data = gain_utils::threshold_above_count(full_data_histogram, 100);\n\n gain_utils::GaussianHistogramFitFCN gauss_full(threshed_data);\n map gauss_model = gauss_full.get_model_histogram(hump_pars);\n gain_utils::write_histogram_data_to_file(gauss_model, output_dir+\"hump_model_full.dat\");\n\n vector tail_pars = gain_utils::fit_exponential_tail(full_data_histogram,\n hump_pars.back(), true);\n\n map fitted_tail_data = gain_utils::get_tail_fitting_section(\n full_data_histogram, hump_pars.back());\n gain_utils::ExponentialHistogramFitFCN tail_fitter(fitted_tail_data);\n map fitted_tail_model = tail_fitter.get_model_histogram(tail_pars);\n gain_utils::write_histogram_data_to_file(fitted_tail_data, output_dir+\"tail_data.dat\");\n gain_utils::write_histogram_data_to_file(fitted_tail_model,\n output_dir+\"tail_model_fit.dat\");\n\n map postive_section = gain_utils::get_points_at_values_greater_than(\n full_data_histogram, hump_pars.front()+hump_pars.back()); //\n gain_utils::ExponentialHistogramFitFCN tail_model(postive_section);\n mapfull_tail_model = tail_model.get_model_histogram(tail_pars);\n gain_utils::write_histogram_data_to_file(full_tail_model,\n output_dir+\"tail_model_full.dat\");\n\n gain_utils::write_histogram_data_to_file(gain_utils::sum_histograms(full_tail_model,\n gauss_model), output_dir+\"combined_model.dat\");\n\n map unfitted_data;\n for (map::const_iterator it=full_data_histogram.begin();\n it!=full_data_histogram.end(); ++it) {\n int value = it->first;\n if (gauss_data.count(value)==0 && fitted_tail_data.count(value)==0) {\n unfitted_data[value]=it->second;\n }\n }\n\n gain_utils::write_histogram_data_to_file(unfitted_data, output_dir+\"unfitted_data.dat\");\n\n GainData results;\n results.bias_pedestal = hump_pars.front();\n results.readout_sigma = hump_pars.back();\n results.gain = tail_pars.back();\n\n double peak_gaussian_count = hump_pars[1];\n double deduced_proportion_dark_pix = peak_gaussian_count*results.readout_sigma * sqrt(\n 2*M_PI); //area under a gaussian\n double deduced_proportion_photon_pix = results.gain * tail_pars.front()* exp(\n -1.0*results.bias_pedestal/results.gain); //Amplitude by gain (amplitude corrected for origin shift to gaussian peak)\n results.photon_event_freq = deduced_proportion_photon_pix / (deduced_proportion_dark_pix+\n deduced_proportion_photon_pix);\n results.N_light_pix=deduced_proportion_photon_pix;\n results.N_dark_pix=deduced_proportion_dark_pix;\n return results;\n}\n\n//=====================================================================================================================\n}//end namespace\n}\n\n", "meta": {"hexsha": "4a50a621c510c40859edaf7fb37706c205b96a2a", "size": 53288, "ext": "cc", "lang": "C++", "max_stars_repo_path": "coela_luckypipe/src/implementation/gain_utils.cc", "max_stars_repo_name": "timstaley/coelacanth", "max_stars_repo_head_hexsha": "d8adc49bac5dac54fdce600ea0260c526ce361af", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-10-22T03:08:45.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-22T03:08:45.000Z", "max_issues_repo_path": "coela_luckypipe/src/implementation/gain_utils.cc", "max_issues_repo_name": "timstaley/coelacanth", "max_issues_repo_head_hexsha": "d8adc49bac5dac54fdce600ea0260c526ce361af", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "coela_luckypipe/src/implementation/gain_utils.cc", "max_forks_repo_name": "timstaley/coelacanth", "max_forks_repo_head_hexsha": "d8adc49bac5dac54fdce600ea0260c526ce361af", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.9278033794, "max_line_length": 152, "alphanum_fraction": 0.6537869689, "num_tokens": 12808, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6113819591324418, "lm_q2_score": 0.3849121444839335, "lm_q1q2_score": 0.23532834098845676}} {"text": "// ====================================================================\n// This file is part of FlexibleSUSY.\n//\n// FlexibleSUSY is free software: you can redistribute it and/or modify\n// it under the terms of the GNU General Public License as published\n// by the Free Software Foundation, either version 3 of the License,\n// or (at your option) any later version.\n//\n// FlexibleSUSY is distributed in the hope that it will be useful, but\n// WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n// General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with FlexibleSUSY. If not, see\n// .\n// ====================================================================\n\n// File generated at Thu 10 May 2018 14:43:46\n\n/**\n * @file HSSUSY_mass_eigenstates.hpp\n *\n * @brief contains class for model with routines needed to solve boundary\n * value problem using the two_scale solver by solving EWSB\n * and determine the pole masses and mixings\n *\n * This file was generated at Thu 10 May 2018 14:43:46 with FlexibleSUSY\n * 2.0.1 (git commit: unknown) and SARAH 4.12.2 .\n */\n\n#ifndef HSSUSY_MASS_EIGENSTATES_H\n#define HSSUSY_MASS_EIGENSTATES_H\n\n#include \"HSSUSY_info.hpp\"\n#include \"HSSUSY_physical.hpp\"\n#include \"HSSUSY_soft_parameters.hpp\"\n#include \"loop_corrections.hpp\"\n#include \"threshold_corrections.hpp\"\n#include \"error.hpp\"\n#include \"problems.hpp\"\n#include \"config.h\"\n\n#include \n#include \n#include \n\n#include \n\nnamespace flexiblesusy {\n\nclass HSSUSY_ewsb_solver_interface;\n/**\n * @class HSSUSY_mass_eigenstates\n * @brief model class with routines for determing masses and mixinga and EWSB\n */\nclass HSSUSY_mass_eigenstates : public HSSUSY_soft_parameters {\npublic:\n explicit HSSUSY_mass_eigenstates(const HSSUSY_input_parameters& input_ = HSSUSY_input_parameters());\n HSSUSY_mass_eigenstates(const HSSUSY_mass_eigenstates&) = default;\n HSSUSY_mass_eigenstates(HSSUSY_mass_eigenstates&&) = default;\n virtual ~HSSUSY_mass_eigenstates() = default;\n HSSUSY_mass_eigenstates& operator=(const HSSUSY_mass_eigenstates&) = default;\n HSSUSY_mass_eigenstates& operator=(HSSUSY_mass_eigenstates&&) = default;\n\n /// number of EWSB equations\n static const int number_of_ewsb_equations = 1;\n\n void calculate_DRbar_masses();\n void calculate_pole_masses();\n void check_pole_masses_for_tachyons();\n virtual void clear() override;\n void clear_DRbar_parameters();\n Eigen::ArrayXd get_DRbar_masses() const;\n Eigen::ArrayXd get_DRbar_masses_and_mixings() const;\n Eigen::ArrayXd get_extra_parameters() const;\n void do_calculate_sm_pole_masses(bool);\n bool do_calculate_sm_pole_masses() const;\n void do_calculate_bsm_pole_masses(bool);\n bool do_calculate_bsm_pole_masses() const;\n void do_force_output(bool);\n bool do_force_output() const;\n void reorder_DRbar_masses();\n void reorder_pole_masses();\n void set_ewsb_iteration_precision(double);\n void set_ewsb_loop_order(int);\n void set_loop_corrections(const Loop_corrections&);\n const Loop_corrections& get_loop_corrections() const;\n void set_threshold_corrections(const Threshold_corrections&);\n const Threshold_corrections& get_threshold_corrections() const;\n void set_DRbar_masses(const Eigen::ArrayXd&);\n void set_DRbar_masses_and_mixings(const Eigen::ArrayXd&);\n void set_extra_parameters(const Eigen::ArrayXd&);\n void set_pole_mass_loop_order(int);\n int get_pole_mass_loop_order() const;\n void set_physical(const HSSUSY_physical&);\n double get_ewsb_iteration_precision() const;\n double get_ewsb_loop_order() const;\n const HSSUSY_physical& get_physical() const;\n HSSUSY_physical& get_physical();\n const Problems& get_problems() const;\n Problems& get_problems();\n void set_ewsb_solver(const std::shared_ptr&);\n int solve_ewsb_tree_level();\n int solve_ewsb_one_loop();\n int solve_ewsb(); ///< solve EWSB at ewsb_loop_order level\n\n void calculate_spectrum();\n void clear_problems();\n std::string name() const;\n void run_to(double scale, double eps = -1.0) override;\n void print(std::ostream& out = std::cerr) const override;\n void set_precision(double);\n double get_precision() const;\n\n\n double get_MVG() const { return MVG; }\n double get_MHp() const { return MHp; }\n const Eigen::Array& get_MFv() const { return MFv; }\n double get_MFv(int i) const { return MFv(i); }\n double get_MAh() const { return MAh; }\n double get_Mhh() const { return Mhh; }\n const Eigen::Array& get_MFd() const { return MFd; }\n double get_MFd(int i) const { return MFd(i); }\n const Eigen::Array& get_MFu() const { return MFu; }\n double get_MFu(int i) const { return MFu(i); }\n const Eigen::Array& get_MFe() const { return MFe; }\n double get_MFe(int i) const { return MFe(i); }\n double get_MVWp() const { return MVWp; }\n double get_MVP() const { return MVP; }\n double get_MVZ() const { return MVZ; }\n\n \n\n \n const Eigen::Matrix,3,3>& get_Vd() const { return Vd; }\n std::complex get_Vd(int i, int k) const { return Vd(i,k); }\n const Eigen::Matrix,3,3>& get_Ud() const { return Ud; }\n std::complex get_Ud(int i, int k) const { return Ud(i,k); }\n const Eigen::Matrix,3,3>& get_Vu() const { return Vu; }\n std::complex get_Vu(int i, int k) const { return Vu(i,k); }\n const Eigen::Matrix,3,3>& get_Uu() const { return Uu; }\n std::complex get_Uu(int i, int k) const { return Uu(i,k); }\n const Eigen::Matrix,3,3>& get_Ve() const { return Ve; }\n std::complex get_Ve(int i, int k) const { return Ve(i,k); }\n const Eigen::Matrix,3,3>& get_Ue() const { return Ue; }\n std::complex get_Ue(int i, int k) const { return Ue(i,k); }\n const Eigen::Matrix& get_ZZ() const { return ZZ; }\n double get_ZZ(int i, int k) const { return ZZ(i,k); }\n\n\n\n\n double get_mass_matrix_VG() const;\n void calculate_MVG();\n double get_mass_matrix_Hp() const;\n void calculate_MHp();\n Eigen::Matrix get_mass_matrix_Fv() const;\n void calculate_MFv();\n double get_mass_matrix_Ah() const;\n void calculate_MAh();\n double get_mass_matrix_hh() const;\n void calculate_Mhh();\n Eigen::Matrix get_mass_matrix_Fd() const;\n void calculate_MFd();\n Eigen::Matrix get_mass_matrix_Fu() const;\n void calculate_MFu();\n Eigen::Matrix get_mass_matrix_Fe() const;\n void calculate_MFe();\n double get_mass_matrix_VWp() const;\n void calculate_MVWp();\n Eigen::Matrix get_mass_matrix_VPVZ() const;\n void calculate_MVPVZ();\n\n double get_ewsb_eq_hh_1() const;\n\n double CphhHpconjHp() const;\n double CpbargWpgZHp() const;\n double CpbargZgWpconjHp() const;\n double CpbargWpCgZconjHp() const;\n double CpbargZgWpCHp() const;\n double CpconjHpVPVWp() const;\n double CpconjHpVWpVZ() const;\n double CpAhAhHpconjHp() const;\n double CphhhhHpconjHp() const;\n double CpHpHpconjHpconjHp() const;\n std::complex CpAhconjHpVWp() const;\n double CphhconjHpVWp() const;\n double CpHpconjHpVP() const;\n double CpHpconjHpVZ() const;\n double CpHpconjHpconjVWpVWp() const;\n std::complex CpHpconjHpVZVZ() const;\n std::complex CpbarFdFuconjHpPR(int gI1, int gI2) const;\n std::complex CpbarFdFuconjHpPL(int gI1, int gI2) const;\n double CpbarFeFvconjHpPR(int , int ) const;\n std::complex CpbarFeFvconjHpPL(int gI1, int gI2) const;\n double CpAhAhhh() const;\n std::complex CpbargWpgWpAh() const;\n std::complex CpbargWpCgWpCAh() const;\n double CpAhAhAhAh() const;\n double CpAhAhhhhh() const;\n std::complex CpAhhhVZ() const;\n std::complex CpAhHpconjVWp() const;\n double CpAhAhconjVWpVWp() const;\n std::complex CpAhAhVZVZ() const;\n std::complex CpbarFdFdAhPR(int gI1, int gI2) const;\n std::complex CpbarFdFdAhPL(int gI1, int gI2) const;\n std::complex CpbarFeFeAhPR(int gI1, int gI2) const;\n std::complex CpbarFeFeAhPL(int gI1, int gI2) const;\n std::complex CpbarFuFuAhPR(int gI1, int gI2) const;\n std::complex CpbarFuFuAhPL(int gI1, int gI2) const;\n double Cphhhhhh() const;\n double CphhVZVZ() const;\n double CphhconjVWpVWp() const;\n double CpbargWpgWphh() const;\n double CpbargWpCgWpChh() const;\n double CpbargZgZhh() const;\n double Cphhhhhhhh() const;\n double CphhHpconjVWp() const;\n double CphhhhconjVWpVWp() const;\n std::complex CphhhhVZVZ() const;\n std::complex CpbarFdFdhhPR(int gI1, int gI2) const;\n std::complex CpbarFdFdhhPL(int gI1, int gI2) const;\n std::complex CpbarFeFehhPR(int gI1, int gI2) const;\n std::complex CpbarFeFehhPL(int gI1, int gI2) const;\n std::complex CpbarFuFuhhPR(int gI1, int gI2) const;\n std::complex CpbarFuFuhhPL(int gI1, int gI2) const;\n std::complex CpVGVGVG() const;\n std::complex CpbargGgGVG() const;\n double CpbarFdFdVGPL(int gI1, int gI2) const;\n double CpbarFdFdVGPR(int gI1, int gI2) const;\n double CpbarFuFuVGPL(int gI1, int gI2) const;\n double CpbarFuFuVGPR(int gI1, int gI2) const;\n double CpVGVGVGVG1() const;\n double CpVGVGVGVG2() const;\n double CpVGVGVGVG3() const;\n double CpHpconjVWpVP() const;\n double CpbargWpgWpVP() const;\n double CpbargWpCgWpCVP() const;\n std::complex CpHpconjHpVPVP() const;\n double CpconjVWpVPVWp() const;\n double CpbarFdFdVPPL(int gI1, int gI2) const;\n double CpbarFdFdVPPR(int gI1, int gI2) const;\n double CpbarFeFeVPPL(int gI1, int gI2) const;\n double CpbarFeFeVPPR(int gI1, int gI2) const;\n double CpbarFuFuVPPL(int gI1, int gI2) const;\n double CpbarFuFuVPPR(int gI1, int gI2) const;\n double CpconjVWpVPVPVWp3() const;\n double CpconjVWpVPVPVWp1() const;\n double CpconjVWpVPVPVWp2() const;\n double CpHpconjVWpVZ() const;\n double CpbargWpgWpVZ() const;\n double CpbargWpCgWpCVZ() const;\n double CpconjVWpVWpVZ() const;\n double CpbarFdFdVZPL(int gI1, int gI2) const;\n double CpbarFdFdVZPR(int gI1, int gI2) const;\n double CpbarFeFeVZPL(int gI1, int gI2) const;\n double CpbarFeFeVZPR(int gI1, int gI2) const;\n double CpbarFuFuVZPL(int gI1, int gI2) const;\n double CpbarFuFuVZPR(int gI1, int gI2) const;\n double CpbarFvFvVZPL(int gI1, int gI2) const;\n double CpbarFvFvVZPR(int , int ) const;\n double CpconjVWpVWpVZVZ1() const;\n double CpconjVWpVWpVZVZ2() const;\n double CpconjVWpVWpVZVZ3() const;\n double CpbargPgWpconjVWp() const;\n double CpbargWpCgPconjVWp() const;\n double CpbargWpCgZconjVWp() const;\n double CpbargZgWpconjVWp() const;\n std::complex CpbarFdFuconjVWpPL(int gI1, int gI2) const;\n double CpbarFdFuconjVWpPR(int , int ) const;\n std::complex CpbarFeFvconjVWpPL(int gI1, int gI2) const;\n double CpbarFeFvconjVWpPR(int , int ) const;\n double CpconjVWpconjVWpVWpVWp2() const;\n double CpconjVWpconjVWpVWpVWp1() const;\n double CpconjVWpconjVWpVWpVWp3() const;\n std::complex CpbarUFdFdAhPL(int gO2, int gI1) const;\n std::complex CpbarUFdFdAhPR(int gO1, int gI1) const;\n std::complex CpbarUFdFdhhPL(int gO2, int gI2) const;\n std::complex CpbarUFdFdhhPR(int gO1, int gI2) const;\n std::complex CpbarUFdFdVGPR(int gO2, int gI2) const;\n std::complex CpbarUFdFdVGPL(int gO1, int gI2) const;\n std::complex CpbarUFdFdVPPR(int gO2, int gI2) const;\n std::complex CpbarUFdFdVPPL(int gO1, int gI2) const;\n std::complex CpbarUFdFdVZPR(int gO2, int gI2) const;\n std::complex CpbarUFdFdVZPL(int gO1, int gI2) const;\n std::complex CpbarUFdFuconjHpPL(int gO2, int gI2) const;\n std::complex CpbarUFdFuconjHpPR(int gO1, int gI2) const;\n double CpbarUFdFuconjVWpPR(int , int ) const;\n std::complex CpbarUFdFuconjVWpPL(int gO1, int gI2) const;\n std::complex CpbarUFuFuAhPL(int gO2, int gI1) const;\n std::complex CpbarUFuFuAhPR(int gO1, int gI1) const;\n std::complex CpbarUFuFdHpPL(int gO2, int gI2) const;\n std::complex CpbarUFuFdHpPR(int gO1, int gI2) const;\n double CpbarUFuFdVWpPR(int , int ) const;\n std::complex CpbarUFuFdVWpPL(int gO1, int gI2) const;\n std::complex CpbarUFuFuhhPL(int gO2, int gI2) const;\n std::complex CpbarUFuFuhhPR(int gO1, int gI2) const;\n std::complex CpbarUFuFuVGPR(int gO2, int gI2) const;\n std::complex CpbarUFuFuVGPL(int gO1, int gI2) const;\n std::complex CpbarUFuFuVPPR(int gO2, int gI2) const;\n std::complex CpbarUFuFuVPPL(int gO1, int gI2) const;\n std::complex CpbarUFuFuVZPR(int gO2, int gI2) const;\n std::complex CpbarUFuFuVZPL(int gO1, int gI2) const;\n std::complex CpbarUFeFeAhPL(int gO2, int gI1) const;\n std::complex CpbarUFeFeAhPR(int gO1, int gI1) const;\n std::complex CpbarUFeFehhPL(int gO2, int gI2) const;\n std::complex CpbarUFeFehhPR(int gO1, int gI2) const;\n std::complex CpbarUFeFeVPPR(int gO2, int gI2) const;\n std::complex CpbarUFeFeVPPL(int gO1, int gI2) const;\n std::complex CpbarUFeFeVZPR(int gO2, int gI2) const;\n std::complex CpbarUFeFeVZPL(int gO1, int gI2) const;\n std::complex CpbarUFeFvconjHpPL(int gO2, int gI2) const;\n double CpbarUFeFvconjHpPR(int , int ) const;\n double CpbarUFeFvconjVWpPR(int , int ) const;\n double CpbarUFeFvconjVWpPL(int gO1, int gI2) const;\n double CpbarFvFeHpPL(int , int ) const;\n std::complex CpbarFvFeHpPR(int gO1, int gI2) const;\n double CpbarFvFeVWpPR(int , int ) const;\n std::complex CpbarFvFeVWpPL(int gO1, int gI2) const;\n std::complex CpbarFuFdHpPL(int gO2, int gI2) const;\n std::complex CpbarFuFdHpPR(int gO1, int gI2) const;\n double CpbarFuFdVWpPR(int , int ) const;\n std::complex CpbarFuFdVWpPL(int gO1, int gI2) const;\n std::complex self_energy_Hp_1loop(double p ) const;\n std::complex self_energy_Ah_1loop(double p ) const;\n std::complex self_energy_hh_1loop(double p ) const;\n std::complex self_energy_VG_1loop(double p ) const;\n std::complex self_energy_VP_1loop(double p ) const;\n std::complex self_energy_VZ_1loop(double p ) const;\n std::complex self_energy_VWp_1loop(double p ) const;\n std::complex self_energy_Fd_1loop_1(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fd_1loop_1(double p) const;\n std::complex self_energy_Fd_1loop_PR(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fd_1loop_PR(double p) const;\n std::complex self_energy_Fd_1loop_PL(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fd_1loop_PL(double p) const;\n std::complex self_energy_Fu_1loop_1(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fu_1loop_1(double p) const;\n std::complex self_energy_Fu_1loop_PR(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fu_1loop_PR(double p) const;\n std::complex self_energy_Fu_1loop_PL(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fu_1loop_PL(double p) const;\n std::complex self_energy_Fe_1loop_1(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fe_1loop_1(double p) const;\n std::complex self_energy_Fe_1loop_PR(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fe_1loop_PR(double p) const;\n std::complex self_energy_Fe_1loop_PL(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fe_1loop_PL(double p) const;\n std::complex self_energy_Fv_1loop_1(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fv_1loop_1(double p) const;\n std::complex self_energy_Fv_1loop_PR(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fv_1loop_PR(double p) const;\n std::complex self_energy_Fv_1loop_PL(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fv_1loop_PL(double p) const;\n std::complex self_energy_Fd_1loop_1_heavy_rotated(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fd_1loop_1_heavy_rotated(double p) const;\n std::complex self_energy_Fd_1loop_PR_heavy_rotated(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fd_1loop_PR_heavy_rotated(double p) const;\n std::complex self_energy_Fd_1loop_PL_heavy_rotated(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fd_1loop_PL_heavy_rotated(double p) const;\n std::complex self_energy_Fe_1loop_1_heavy_rotated(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fe_1loop_1_heavy_rotated(double p) const;\n std::complex self_energy_Fe_1loop_PR_heavy_rotated(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fe_1loop_PR_heavy_rotated(double p) const;\n std::complex self_energy_Fe_1loop_PL_heavy_rotated(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fe_1loop_PL_heavy_rotated(double p) const;\n std::complex self_energy_Fu_1loop_1_heavy_rotated(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fu_1loop_1_heavy_rotated(double p) const;\n std::complex self_energy_Fu_1loop_PR_heavy_rotated(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fu_1loop_PR_heavy_rotated(double p) const;\n std::complex self_energy_Fu_1loop_PL_heavy_rotated(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fu_1loop_PL_heavy_rotated(double p) const;\n std::complex self_energy_Fu_1loop_1_heavy(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fu_1loop_1_heavy(double p) const;\n std::complex self_energy_Fu_1loop_PR_heavy(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fu_1loop_PR_heavy(double p) const;\n std::complex self_energy_Fu_1loop_PL_heavy(double p , int gO1, int gO2) const;\n Eigen::Matrix,3,3> self_energy_Fu_1loop_PL_heavy(double p) const;\n std::complex tadpole_hh_1loop() const;\n\n\n /// calculates the tadpoles at current loop order\n void tadpole_equations(double[number_of_ewsb_equations]) const;\n /// calculates the tadpoles at current loop order\n Eigen::Matrix tadpole_equations() const;\n /// calculates the tadpoles divided by VEVs at current loop order\n Eigen::Matrix tadpole_equations_over_vevs() const;\n\n\n\n double self_energy_hh_2loop(double p) const;\n\n\n double self_energy_hh_3loop() const;\n\n void calculate_MVG_pole();\n void calculate_MFv_pole();\n void calculate_Mhh_pole();\n void calculate_MVP_pole();\n void calculate_MVZ_pole();\n void calculate_MFd_pole();\n void calculate_MFu_pole();\n void calculate_MFe_pole();\n void calculate_MVWp_pole();\n double calculate_MVWp_pole(double);\n double calculate_MVZ_pole(double);\n\n double calculate_MFv_DRbar(double, int) const;\n double calculate_MFe_DRbar(double, int) const;\n double calculate_MFu_DRbar(double, int) const;\n double calculate_MFd_DRbar(double, int) const;\n double calculate_MVP_DRbar(double);\n double calculate_MVZ_DRbar(double);\n double calculate_MVWp_DRbar(double);\n\n double ThetaW() const;\n\n\nprivate:\n int ewsb_loop_order{2}; ///< loop order for EWSB\n int pole_mass_loop_order{2}; ///< loop order for pole masses\n bool calculate_sm_pole_masses{false}; ///< switch to calculate the pole masses of the Standard Model particles\n bool calculate_bsm_pole_masses{true}; ///< switch to calculate the pole masses of the BSM particles\n bool force_output{false}; ///< switch to force output of pole masses\n double precision{1.e-3}; ///< RG running precision\n double ewsb_iteration_precision{1.e-5};///< precision goal of EWSB solution\n HSSUSY_physical physical{}; ///< contains the pole masses and mixings\n Problems problems{HSSUSY_info::model_name,\n &HSSUSY_info::particle_names_getter,\n &HSSUSY_info::parameter_names_getter}; ///< problems\n Loop_corrections loop_corrections{}; ///< used pole mass corrections\n std::shared_ptr ewsb_solver{};\n Threshold_corrections threshold_corrections{}; ///< used threshold corrections\n\n int get_number_of_ewsb_iterations() const;\n int get_number_of_mass_iterations() const;\n int solve_ewsb_tree_level_custom();\n void copy_DRbar_masses_to_pole_masses();\n\n // Passarino-Veltman loop functions\n double A0(double) const noexcept;\n double B0(double, double, double) const noexcept;\n double B1(double, double, double) const noexcept;\n double B00(double, double, double) const noexcept;\n double B22(double, double, double) const noexcept;\n double H0(double, double, double) const noexcept;\n double F0(double, double, double) const noexcept;\n double G0(double, double, double) const noexcept;\n\n // DR-bar masses\n double MVG{};\n double MHp{};\n Eigen::Array MFv{Eigen::Array::Zero()};\n double MAh{};\n double Mhh{};\n Eigen::Array MFd{Eigen::Array::Zero()};\n Eigen::Array MFu{Eigen::Array::Zero()};\n Eigen::Array MFe{Eigen::Array::Zero()};\n double MVWp{};\n double MVP{};\n double MVZ{};\n\n // DR-bar mixing matrices\n Eigen::Matrix,3,3> Vd{Eigen::Matrix,3,3>::Zero()};\n Eigen::Matrix,3,3> Ud{Eigen::Matrix,3,3>::Zero()};\n Eigen::Matrix,3,3> Vu{Eigen::Matrix,3,3>::Zero()};\n Eigen::Matrix,3,3> Uu{Eigen::Matrix,3,3>::Zero()};\n Eigen::Matrix,3,3> Ve{Eigen::Matrix,3,3>::Zero()};\n Eigen::Matrix,3,3> Ue{Eigen::Matrix,3,3>::Zero()};\n Eigen::Matrix ZZ{Eigen::Matrix::Zero()};\n\n // phases\n\n // extra parameters\n\n};\n\nstd::ostream& operator<<(std::ostream&, const HSSUSY_mass_eigenstates&);\n\n} // namespace flexiblesusy\n\n#endif\n", "meta": {"hexsha": "788bca5c769ffd1c0770d488213066c0265f9f51", "size": 23198, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contrib/MassSpectra/flexiblesusy/models/HSSUSY/HSSUSY_mass_eigenstates.hpp", "max_stars_repo_name": "sebhoof/gambit_1.5", "max_stars_repo_head_hexsha": "f9a3f788e3331067c555ae1a030420e903c6fdcd", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-09-08T20:05:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-26T07:57:56.000Z", "max_issues_repo_path": "contrib/MassSpectra/flexiblesusy/models/HSSUSY/HSSUSY_mass_eigenstates.hpp", "max_issues_repo_name": "sebhoof/gambit_1.5", "max_issues_repo_head_hexsha": "f9a3f788e3331067c555ae1a030420e903c6fdcd", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": 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YES\n2. NO", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.40733340004593027, "lm_q1q2_score": 0.23523315061190284}} {"text": "/*\n dfc2.cpp\n\nCopyright (c) 2016 Terumasa Tadano\n\nThis file is distributed under the terms of the MIT license.\nPlease see the file 'LICENCE.txt' in the root directory\nor http://opensource.org/licenses/mit-license.php for information.\n*/\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"memory.h\"\n#include \"xml_parser.h\"\n#include \"dfc2.h\"\n#include \"constants.h\"\n#include \"mathfunctions.h\"\n\nusing namespace std;\n\nint main()\n{\n cout << \" DFC2 -- a generator of renormalized harmonic FCs from SCPH outputs.\" << endl;\n cout << \" XML file containing original FC2 : \";\n cin >> original_xml;\n cout << \" Output xml filename with anharmonic correction : \";\n cin >> new_xml;\n cout << \" FC2 correction file from SCPH calculation : \";\n cin >> file_fc2_correction;\n cout << \" Target temperature : \";\n cin >> temp;\n\n // Load original harmonic force constants and structure data of the supercell\n load_fc2_xml(original_xml);\n\n // Load anharmonic correction and structure data of the primitive lattice\n load_delta_fc2(file_fc2_correction, temp);\n\n // Initialize new fc2 by the original values\n fc2_new.clear();\n copy(fc2_orig.begin(), fc2_orig.end(), back_inserter(fc2_new));\n\n // Add delta_fc2 to fc2_new\n calculate_new_fc2(fc2_orig, delta_fc2, fc2_new);\n\n write_new_xml(fc2_new, new_xml);\n\n cout << endl << \" New XML file \" << new_xml << \" was created successfully.\" << endl;\n\n deallocate(xr_s);\n deallocate(kd);\n deallocate(kd_symbol);\n deallocate(map_p2s);\n deallocate(map_s2p);\n deallocate(xr_p);\n deallocate(kd_p);\n}\n\n\nvoid load_fc2_xml(const std::string file_in)\n{\n int i;\n using namespace boost::property_tree;\n\n ptree pt;\n int atm1, atm2, xyz1, xyz2, cell_s;\n stringstream ss1, ss2;\n FcsClassExtent fcext_tmp;\n\n map dict_atomic_kind;\n\n try {\n read_xml(file_in, pt);\n }\n catch (exception &e) {\n cout << \"Cannot open file \" + file_in << endl;\n exit(EXIT_FAILURE);\n }\n\n nat = boost::lexical_cast(\n get_value_from_xml(pt,\n \"Data.Structure.NumberOfAtoms\"));\n nkd = boost::lexical_cast(\n get_value_from_xml(pt,\n \"Data.Structure.NumberOfElements\"));\n\n ntran = boost::lexical_cast(\n get_value_from_xml(pt,\n \"Data.Symmetry.NumberOfTranslations\"));\n\n natmin = nat / ntran;\n\n for (i = 0; i < 3; ++i) {\n ss1.str(\"\");\n ss1.clear();\n ss1 << get_value_from_xml(pt,\n \"Data.Structure.LatticeVector.a\"\n + boost::lexical_cast(i + 1));\n ss1 >> lavec_s[0][i] >> lavec_s[1][i] >> lavec_s[2][i];\n }\n\n // Parse atomic elements and coordinates\n\n allocate(xr_s, nat, 3);\n allocate(kd, nat);\n allocate(kd_symbol, nkd);\n\n i = 0;\n\n BOOST_FOREACH(const ptree::value_type& child_, pt.get_child(\"Data.Structure.AtomicElements\")) {\n const ptree &child = child_.second;\n const unsigned int icount_kd = child.get(\".number\");\n dict_atomic_kind[boost::lexical_cast(child_.second.data())] = icount_kd - 1;\n kd_symbol[i++] = boost::lexical_cast(child_.second.data());\n }\n\n unsigned int index;\n\n BOOST_FOREACH(const ptree::value_type& child_, pt.get_child(\"Data.Structure.Position\")) {\n const ptree &child = child_.second;\n const string str_index = child.get(\".index\");\n const string str_element = child.get(\".element\");\n\n ss1.str(\"\");\n ss1.clear();\n ss1 << child.data();\n\n index = boost::lexical_cast(str_index) - 1;\n\n if (index >= nat) {\n cout << \"index is out of range\" << endl;\n exit(EXIT_FAILURE);\n }\n\n kd[index] = dict_atomic_kind[str_element];\n ss1 >> xr_s[index][0] >> xr_s[index][1] >> xr_s[index][2];\n }\n\n dict_atomic_kind.clear();\n\n // Parse mapping information\n\n allocate(map_p2s, natmin, ntran);\n allocate(map_s2p, nat);\n\n unsigned int tran, atom_p, atom_s;\n\n BOOST_FOREACH(const ptree::value_type& child_, pt.get_child(\"Data.Symmetry.Translations\")) {\n const ptree &child = child_.second;\n const string str_tran = child.get(\".tran\");\n const string str_atom = child.get(\".atom\");\n\n tran = boost::lexical_cast(str_tran) - 1;\n atom_p = boost::lexical_cast(str_atom) - 1;\n atom_s = boost::lexical_cast(child.data()) - 1;\n\n if (tran >= ntran || atom_p >= natmin || atom_s >= nat) {\n cout << \"index is out of range\" << endl;\n exit(EXIT_FAILURE);\n }\n\n map_p2s[atom_p][tran] = atom_s;\n map_s2p[atom_s].atom_num = atom_p;\n map_s2p[atom_s].tran_num = tran;\n }\n\n\n BOOST_FOREACH(const ptree::value_type& child_, pt.get_child(\"Data.ForceConstants.HARMONIC\")) {\n const ptree &child = child_.second;\n const string str_p1 = child.get(\".pair1\");\n const string str_p2 = child.get(\".pair2\");\n\n ss1.str(\"\");\n ss2.str(\"\");\n ss1.clear();\n ss2.clear();\n\n ss1 << str_p1;\n ss2 << str_p2;\n\n ss1 >> atm1 >> xyz1;\n ss2 >> atm2 >> xyz2 >> cell_s;\n\n fcext_tmp.atm1 = atm1 - 1;\n fcext_tmp.xyz1 = xyz1 - 1;\n fcext_tmp.atm2 = atm2 - 1;\n fcext_tmp.xyz2 = xyz2 - 1;\n fcext_tmp.cell_s = cell_s - 1;\n fcext_tmp.fcs_val = boost::lexical_cast(child.data());\n\n fc2_orig.push_back(fcext_tmp);\n }\n}\n\n\nvoid load_delta_fc2(const std::string file_in, const double temp)\n{\n int i;\n ifstream ifs_in;\n stringstream ss;\n\n // Restart\n ifs_in.open(file_in.c_str(), ios::in);\n if (!ifs_in) {\n cout << \"Could not open \" + file_in << endl;\n exit(EXIT_FAILURE);\n }\n\n // Check the consistency\n\n string line_tmp, str_tmp;\n vector str_vec;\n\n int sx, sy, sz;\n int atm1, atm2, xyz1, xyz2;\n double dfc2_tmp;\n bool found_tag = false;\n\n // Get lattice vectors\n for (i = 0; i < 3; ++i) {\n ifs_in >> lavec_p[0][i] >> lavec_p[1][i] >> lavec_p[2][i];\n }\n recips(lavec_p, rlavec_p);\n\n ifs_in >> nat_p >> nkd_p;\n ifs_in.ignore();\n getline(ifs_in, line_tmp);\n\n allocate(xr_p, nat_p, 3);\n allocate(kd_p, nat_p);\n for (i = 0; i < nat_p; ++i) {\n ifs_in >> xr_p[i][0] >> xr_p[i][1] >> xr_p[i][2] >> kd_p[i];\n }\n ifs_in.ignore();\n\n while (getline(ifs_in, line_tmp)) {\n if (line_tmp[0] == '#') {\n boost::split(str_vec, line_tmp, boost::is_space());\n if (abs(boost::lexical_cast(str_vec[3]) - temp) < eps) {\n found_tag = true;\n break;\n }\n }\n }\n if (!found_tag) {\n cout << \"Could not find the # Temp tag for the target temperature\" << endl;\n exit(EXIT_FAILURE);\n }\n\n delta_fc2.clear();\n\n while (getline(ifs_in, line_tmp)) {\n\n if (line_tmp[0] == '#') break;\n\n if (!line_tmp.empty()) {\n stringstream ss1;\n\n ss1 << line_tmp;\n ss1 >> sx >> sy >> sz >> atm1 >> xyz1 >> atm2 >> xyz2 >> dfc2_tmp;\n\n delta_fc2.push_back(DeltaFcs(sx, sy, sz, atm1, xyz1, atm2, xyz2, dfc2_tmp));\n }\n\n }\n\n ifs_in.close();\n}\n\n\nvoid calculate_new_fc2(std::vector fc2_in,\n std::vector delta_fc2,\n std::vector &fc2_out)\n{\n int i, j, k;\n int ix, iy, iz;\n int icell;\n double **xshift_s;\n\n allocate(xshift_s, 27, 3);\n\n for (i = 0; i < 3; ++i) xshift_s[0][i] = 0.0;\n\n icell = 0;\n\n for (ix = -1; ix <= 1; ++ix) {\n for (iy = -1; iy <= 1; ++iy) {\n for (iz = -1; iz <= 1; ++iz) {\n if (ix == 0 && iy == 0 && iz == 0) continue;\n\n ++icell;\n\n xshift_s[icell][0] = static_cast(ix);\n xshift_s[icell][1] = static_cast(iy);\n xshift_s[icell][2] = static_cast(iz);\n }\n }\n }\n\n double mat_convert[3][3];\n\n\n for (i = 0; i < 3; ++i) {\n for (j = 0; j < 3; ++j) {\n mat_convert[i][j] = 0.0;\n for (k = 0; k < 3; ++k) {\n mat_convert[i][j] += rlavec_p[i][k] * lavec_s[k][j] / (2.0 * pi);\n }\n\n mat_convert[i][j] = static_cast(nint(mat_convert[i][j]));\n }\n }\n\n //\n // cout << \"lavec super:\" << endl;\n // for (i = 0; i < 3; ++i) {\n // for (j = 0; j < 3; ++j) {\n // cout << setw(15) << lavec_s[i][j];\n // }\n // cout << endl;\n // }\n // cout << endl;\n //\n // cout << \"lavec primitive:\" << endl;\n // for (i = 0; i < 3; ++i) {\n // for (j = 0; j < 3; ++j) {\n // cout << setw(15) << lavec_p[i][j];\n // }\n // cout << endl;\n // }\n // cout << endl;\n //\n //\n // cout << \"Mat convert:\" << endl;\n // for (i = 0; i < 3; ++i) {\n // for (j = 0; j < 3; ++j) {\n // cout << setw(15) << mat_convert[i][j];\n // }\n // cout << endl;\n // }\n // cout << endl;\n\n double vec[3];\n\n int icount = 0;\n\n vector arr_tmp;\n vector fc2_data;\n\n fc2_data.clear();\n\n for (auto it = fc2_in.cbegin(); it != fc2_in.cend(); ++it) {\n\n arr_tmp.clear();\n\n for (i = 0; i < 3; ++i) {\n vec[i] = xr_s[(*it).atm2][i] + xshift_s[(*it).cell_s][i]\n - xr_s[map_p2s[map_s2p[(*it).atm2].atom_num][0]][i];\n }\n\n rotvec(vec, vec, mat_convert);\n\n arr_tmp.push_back((*it).atm1);\n arr_tmp.push_back((*it).xyz1);\n arr_tmp.push_back(map_s2p[(*it).atm2].atom_num);\n arr_tmp.push_back((*it).xyz2);\n for (i = 0; i < 3; ++i) arr_tmp.push_back(nint(vec[i]));\n\n fc2_data.push_back(FcsTrans(arr_tmp, icount));\n ++icount;\n }\n\n std::sort(fc2_data.begin(), fc2_data.end());\n\n vector::iterator iter_found;\n int index_tmp = 0;\n\n\n for (auto it = delta_fc2.begin(); it != delta_fc2.end(); ++it) {\n\n if (abs((*it).dfc2) > eps10) {\n arr_tmp.clear();\n arr_tmp.push_back((*it).atm1);\n arr_tmp.push_back((*it).xyz1);\n arr_tmp.push_back((*it).atm2);\n arr_tmp.push_back((*it).xyz2);\n arr_tmp.push_back((*it).sx);\n arr_tmp.push_back((*it).sy);\n arr_tmp.push_back((*it).sz);\n\n iter_found = lower_bound(fc2_data.begin(), fc2_data.end(), FcsTrans(arr_tmp, index_tmp));\n\n if (iter_found != fc2_data.end() && arr_tmp == (*iter_found).arr) {\n fc2_new[(*iter_found).fcs_index].fcs_val += (*it).dfc2;\n } else {\n cout << \"Warning: The following force constant doesn't exist in the original file:\" << endl;\n cout << setw(5) << (*it).sx << setw(5) << (*it).sy << setw(5) << (*it).sz;\n cout << setw(5) << (*it).atm1 << setw(5) << (*it).xyz1;\n cout << setw(5) << (*it).atm2 << setw(5) << (*it).xyz2;\n cout << setw(15) << (*it).dfc2 << endl;\n }\n }\n ++index_tmp;\n\n }\n}\n\n\nvoid recips(double vec[3][3], double inverse[3][3])\n{\n double det;\n det = vec[0][0] * vec[1][1] * vec[2][2]\n + vec[1][0] * vec[2][1] * vec[0][2]\n + vec[2][0] * vec[0][1] * vec[1][2]\n - vec[0][0] * vec[2][1] * vec[1][2]\n - vec[2][0] * vec[1][1] * vec[0][2]\n - vec[1][0] * vec[0][1] * vec[2][2];\n\n if (abs(det) < eps12) {\n cout << \"Lattice vector is singular\" << endl;\n exit(EXIT_FAILURE);\n }\n\n double factor = 2.0 * pi / det;\n\n inverse[0][0] = (vec[1][1] * vec[2][2] - vec[1][2] * vec[2][1]) * factor;\n inverse[0][1] = (vec[0][2] * vec[2][1] - vec[0][1] * vec[2][2]) * factor;\n inverse[0][2] = (vec[0][1] * vec[1][2] - vec[0][2] * vec[1][1]) * factor;\n\n inverse[1][0] = (vec[1][2] * vec[2][0] - vec[1][0] * vec[2][2]) * factor;\n inverse[1][1] = (vec[0][0] * vec[2][2] - vec[0][2] * vec[2][0]) * factor;\n inverse[1][2] = (vec[0][2] * vec[1][0] - vec[0][0] * vec[1][2]) * factor;\n\n inverse[2][0] = (vec[1][0] * vec[2][1] - vec[1][1] * vec[2][0]) * factor;\n inverse[2][1] = (vec[0][1] * vec[2][0] - vec[0][0] * vec[2][1]) * factor;\n inverse[2][2] = (vec[0][0] * vec[1][1] - vec[0][1] * vec[1][0]) * factor;\n}\n\n\nvoid write_new_xml(const std::vector fc2_in,\n const std::string xml_out)\n{\n // Write to XML file\n\n int i, j;\n using boost::property_tree::ptree;\n\n ptree pt;\n string str_pos[3], str_tmp;\n\n str_tmp.clear();\n\n pt.put(\"Data.OriginalFC2\", original_xml);\n pt.put(\"Data.SCPH_file\", file_fc2_correction);\n pt.put(\"Data.SCPH_Temperature\", double2string(temp, 5));\n pt.put(\"Data.Structure.NumberOfAtoms\", nat);\n pt.put(\"Data.Structure.NumberOfElements\", nkd);\n\n for (i = 0; i < nkd; ++i) {\n ptree &child = pt.add(\"Data.Structure.AtomicElements.element\", kd_symbol[i]);\n child.put(\".number\", i + 1);\n }\n\n for (i = 0; i < 3; ++i) {\n str_pos[i].clear();\n for (j = 0; j < 3; ++j) {\n str_pos[i] += \" \" + double2string(lavec_s[j][i]);\n }\n }\n pt.put(\"Data.Structure.LatticeVector\", \"\");\n pt.put(\"Data.Structure.LatticeVector.a1\", str_pos[0]);\n pt.put(\"Data.Structure.LatticeVector.a2\", str_pos[1]);\n pt.put(\"Data.Structure.LatticeVector.a3\", str_pos[2]);\n\n pt.put(\"Data.Structure.Position\", \"\");\n\n for (i = 0; i < nat; ++i) {\n str_tmp.clear();\n for (j = 0; j < 3; ++j) str_tmp += \" \" + double2string(xr_s[i][j]);\n ptree &child = pt.add(\"Data.Structure.Position.pos\", str_tmp);\n child.put(\".index\", i + 1);\n child.put(\".element\", kd_symbol[kd[i]]);\n }\n\n pt.put(\"Data.Symmetry.NumberOfTranslations\", ntran);\n for (i = 0; i < ntran; ++i) {\n for (j = 0; j < natmin; ++j) {\n ptree &child = pt.add(\"Data.Symmetry.Translations.map\", map_p2s[j][i] + 1);\n child.put(\".tran\", i + 1);\n child.put(\".atom\", j + 1);\n }\n }\n\n\n pt.put(\"Data.ForceConstants\", \"\");\n str_tmp.clear();\n\n for (auto it = fc2_in.begin(); it != fc2_in.end(); ++it) {\n ptree &child = pt.add(\"Data.ForceConstants.HARMONIC.FC2\", double2string((*it).fcs_val));\n\n child.put(\".pair1\", boost::lexical_cast((*it).atm1 + 1)\n + \" \" + boost::lexical_cast((*it).xyz1 + 1));\n child.put(\".pair2\", boost::lexical_cast((*it).atm2 + 1)\n + \" \" + boost::lexical_cast((*it).xyz2 + 1)\n + \" \" + boost::lexical_cast((*it).cell_s + 1));\n }\n\n using namespace boost::property_tree::xml_parser;\n const int indent = 2;\n\n#if BOOST_VERSION >= 105600\n write_xml(xml_out, pt, std::locale(),\n xml_writer_make_settings(' ', indent, widen(\"utf-8\")));\n#else\n write_xml(xml_out, pt, std::locale(),\n xml_writer_make_settings(' ', indent, widen(\"utf-8\")));\n#endif\n}\n\n\nstring double2string(const double d, const int nprec)\n{\n std::string rt;\n std::stringstream ss;\n\n ss << std::scientific << std::setprecision(nprec) << d;\n ss >> rt;\n return rt;\n}\n", "meta": {"hexsha": "cde44689ef4079e4a6f5a3f187f2a2bd1f6234ac", "size": 15931, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tools/dfc2.cpp", "max_stars_repo_name": "wichoi77/alamode", "max_stars_repo_head_hexsha": "f0b3f4cc9903a807006b8f2d183de77dd461f61c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-01-27T19:05:03.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-27T19:05:03.000Z", "max_issues_repo_path": "tools/dfc2.cpp", "max_issues_repo_name": "wichoi77/alamode", "max_issues_repo_head_hexsha": "f0b3f4cc9903a807006b8f2d183de77dd461f61c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tools/dfc2.cpp", "max_forks_repo_name": "wichoi77/alamode", "max_forks_repo_head_hexsha": "f0b3f4cc9903a807006b8f2d183de77dd461f61c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-04-26T14:01:15.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-26T14:01:15.000Z", "avg_line_length": 29.5018518519, "max_line_length": 108, "alphanum_fraction": 0.538635365, "num_tokens": 4796, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.546738151984614, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.23517796644690528}} {"text": "/**\n * WorldLocation.cpp\n *\n * Copyright 2016-2021 Heartland Software Solutions Inc.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the license at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the LIcense is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n#include \"times_internal.h\"\n#include \"worldlocation.h\"\n#include \"SunriseSunsetCalc.h\"\n#include \"str_printf.h\"\n\n#include \n#include \n#include \n#include \"boost_bimap.h\"\n\nextern bool insideCanadaDetail(class Canada **canada, const double latitude, const double longitude);\nextern void insideCanadaCleanup(class Canada *canada);\n\nusing namespace HSS_Time;\nusing namespace HSS_Time_Private;\n\n\n#define Pi 3.14159265358979323846264\n#define TwoPi 6.28318530717958647692529\n\nstatic __inline double DEGREE_TO_RADIAN(const double X) {\n\treturn (X / 180.0) * 3.14159265358979323846264;\n}\n\n\nstatic __inline double RADIAN_TO_DEGREE(const double X) {\n\treturn (X * 180.0) * 0.318309886183790671537768;\n}\n\n\n// this list is generated from: http://www.timeanddate.com/library/abbreviations/timezones/\n\nconstexpr int STD_TIMEZONE_ID = 0x10000;\nconstexpr int DST_TIMEZONE_ID = 0x20000;\nconstexpr int MIL_TIMEZONE_ID = 0x40000;\nconstexpr std::uint32_t MAKE_ID(int type, int id) { return type | id; }\nconstexpr bool IS_STD(int id) { return (STD_TIMEZONE_ID & id) != 0; }\nconstexpr bool IS_DST(int id) { return (DST_TIMEZONE_ID & id) != 0; }\nconstexpr bool IS_MIL(int id) { return (MIL_TIMEZONE_ID & id) != 0; }\n\n\nconst TimeZoneInfo WorldLocation::m_std_timezones[] = {\n\t{ WTimeSpan(0, 9, 30, 0),\tWTimeSpan(0),\t\t\t\"ACST\",\t\"Australian Central Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 0) },\n\t{ WTimeSpan(0, 10, 0, 0),\tWTimeSpan(0),\t\t\t\"AEST\",\t\"Australian Eastern Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 1) },\n\t{ WTimeSpan(0, -9, 0, 0),\tWTimeSpan(0),\t\t\t\"AKST\",\t\"Alaska Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 2) },\n\t{ WTimeSpan(0, -4, 0, 0),\tWTimeSpan(0),\t\t\t\"AST\",\t\"Atlantic Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 3) },\n\t{ WTimeSpan(0, 8, 0, 0),\tWTimeSpan(0),\t\t\t\"AWST\", \"Australian Western Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 4) },\n\t{ WTimeSpan(0, 1, 0, 0),\tWTimeSpan(0),\t\t\t\"CET\",\t\"Central European Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 5) },\t// 5\n\t{ WTimeSpan(0, -6, 0, 0),\tWTimeSpan(0),\t\t\t\"CST\",\t\"Central Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 6) },\n\t{ WTimeSpan(0, 7, 0, 0),\tWTimeSpan(0),\t\t\t\"CXT\",\t\"Christmas Island Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 7) },\n\t{ WTimeSpan(0, 2, 0, 0),\tWTimeSpan(0),\t\t\t\"EET\",\t\"Eastern European Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 8) },\n\t{ WTimeSpan(0, -5, 0, 0),\tWTimeSpan(0),\t\t\t\"EST\",\t\"Eastern Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 9) },\n\t{ WTimeSpan(0, -10, 0, 0),\tWTimeSpan(0),\t\t\t\"HAST\",\t\"Hawaii-Aleutian Standard Time\",\tMAKE_ID(STD_TIMEZONE_ID, 10) },\t// 10\n\t{ WTimeSpan(0, 3, 0, 0),\tWTimeSpan(0),\t\t\t\"MSK\",\t\"Moscow Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 11) },\n\t{ WTimeSpan(0, -7, 0, 0),\tWTimeSpan(0),\t\t\t\"MST\",\t\"Mountain Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 12) },\n\t{ WTimeSpan(0, 11, 30, 0),\tWTimeSpan(0),\t\t\t\"NFT\",\t\"Norfolk (Island) Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 13) },\n\t{ WTimeSpan(0, -3, -30, 0),\tWTimeSpan(0),\t\t\t\"NST\",\t\"Newfoundland Standard Time\",\t\tMAKE_ID(STD_TIMEZONE_ID, 14) },\n\t{ WTimeSpan(0, 12, 0, 0),\tWTimeSpan(0),\t\t\t\"NZST\",\t\"New Zealand Standard Time\",\t\tMAKE_ID(STD_TIMEZONE_ID, 15) },\t// 15\n\t{ WTimeSpan(0, -8, 0, 0),\tWTimeSpan(0),\t\t\t\"PST\",\t\"Pacific Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 16) },\n\t{ WTimeSpan(0, 0, 0, 0),\tWTimeSpan(0),\t\t\t\"UTC\",\t\"Universal Coordinated Time\",\t\tMAKE_ID(STD_TIMEZONE_ID, 17) },\n\t{ WTimeSpan(0, 2, 0, 0),\tWTimeSpan(0),\t\t\t\"RZ1\",\t\"Russian Zone 1\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 18) },\n\t{ WTimeSpan(0, 3, 0, 0),\tWTimeSpan(0),\t\t\t\"RZ2\",\t\"Russian Zone 2\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 19) },\n\t{ WTimeSpan(0, 4, 0, 0),\tWTimeSpan(0),\t\t\t\"RZ3\",\t\"Russian Zone 3\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 20) },\t// 20\n\t{ WTimeSpan(0, -1, 0, 0),\tWTimeSpan(0),\t\t\t\"WAT\",\t\"West African Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 21) },\n\t{ WTimeSpan(0, -2, 0, 0),\tWTimeSpan(0),\t\t\t\"AT\",\t\"Azores Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 22) },\n\t{ WTimeSpan(0, -11, 0, 0),\tWTimeSpan(0),\t\t\t\"NT\",\t\"Nome Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 23) },\n\t{ WTimeSpan(0, 5, 30, 0),\tWTimeSpan(0),\t\t\t\"IST\",\t\"Indian Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 24) },\n\t{ WTimeSpan(0, 8, 0, 0),\tWTimeSpan(0),\t\t\t\"CCT\",\t\"China Coast Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 25) },\t// 25\n\t{ WTimeSpan(0, 9, 0, 0),\tWTimeSpan(0),\t\t\t\"JST\",\t\"Japan Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 26) },\n\t{ WTimeSpan(0, 10, 0, 0),\tWTimeSpan(0),\t\t\t\"GST\",\t\"Guam Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 27) },\n\t{ WTimeSpan(0, 0, 0, 0),\tWTimeSpan(0),\t\t\tNULL,\tNULL,\t\t\t\t\t\t\t\t0 }\n};\n\n\nconst TimeZoneInfo WorldLocation::m_std_extra_timezones[] = {\n\t{ WTimeSpan(0, -5, 0, 0),\tWTimeSpan(0),\t\t\t\"ACT\",\t\"Acre Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 28) },\n\t{ WTimeSpan(0, +8, 45, 0),\tWTimeSpan(0),\t\t\t\"ACWST\",\"Guam Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 29) },\n\t{ WTimeSpan(0, +4, 30, 0),\tWTimeSpan(0),\t\t\t\"AFT\",\t\"Afghanistan Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 30) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0),\t\t\t\"ALMT\",\t\"Alma-Ata Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 31) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0),\t\t\t\"AMT\",\t\"Amazon Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 32) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0),\t\t\t\"AMT\",\t\"Armenia Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 33) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0),\t\t\t\"ANAT\",\t\"Anadyr Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 34) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"AQTT\",\t\"Aqtobe Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 35) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0),\t\t\t\"ART\",\t\"Argentina Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 36) },\n\t{ WTimeSpan(0, +3, 00, 0),\tWTimeSpan(0),\t\t\t\"AST\",\t\"Arabia Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 37) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0),\t\t\t\"AZT\",\t\"Azerbaijan Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 38) },\n\t{ WTimeSpan(0, -12, 00, 0),\tWTimeSpan(0),\t\t\t\"AoE\",\t\"Anywhere on Earth\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 39) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"BNT\",\t\"Brunei Darussalam Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 40) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0),\t\t\t\"BOT\",\t\"Bolivia Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 41) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0),\t\t\t\"BRT\",\t\"Bras\\u00EDlia Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 42) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0),\t\t\t\"BST\",\t\"Bangladesh Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 43) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0),\t\t\t\"BTT\",\t\"Guam Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 44) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"CAST\",\t\"Casey Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 45) },\n\t{ WTimeSpan(0, +2, 00, 0),\tWTimeSpan(0),\t\t\t\"CAT\",\t\"Central Africa Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 46) },\n\t{ WTimeSpan(0, +6, 30, 0),\tWTimeSpan(0),\t\t\t\"CCT\",\t\"Cocos Islands Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 47) },\n\t{ WTimeSpan(0, +12, 45, 0),\tWTimeSpan(0),\t\t\t\"CHAST\",\"Chatham Island Standard Time\",\t\tMAKE_ID(STD_TIMEZONE_ID, 48) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"CHOT\",\t\"Choibalsan Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 49) },\n\t{ WTimeSpan(0, +10, 00, 0),\tWTimeSpan(0),\t\t\t\"CHUT\",\t\"Chuuk Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 50) },\n\t{ WTimeSpan(0, -10, 00, 0),\tWTimeSpan(0),\t\t\t\"CKT\",\t\"Cook Island Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 51) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0),\t\t\t\"CLT\",\t\"Chile Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 52) },\n\t{ WTimeSpan(0, -5, 00, 0),\tWTimeSpan(0),\t\t\t\"COT\",\t\"Colombia Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 53) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"CST\",\t\"China Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 54) },\n\t{ WTimeSpan(0, -5, 00, 0),\tWTimeSpan(0),\t\t\t\"CST\",\t\"Cuba Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 55) },\n\t{ WTimeSpan(0, -1, 00, 0),\tWTimeSpan(0),\t\t\t\"CVT\",\t\"Cape Verde Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 56) },\n\t{ WTimeSpan(0, +10, 00, 0),\tWTimeSpan(0),\t\t\t\"ChST\",\t\"Chamorro Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 57) },\n\t{ WTimeSpan(0, +7, 00, 0),\tWTimeSpan(0),\t\t\t\"DAVT\",\t\"Davis Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 58) },\n\t{ WTimeSpan(0, -6, 00, 0),\tWTimeSpan(0),\t\t\t\"EAST\",\t\"Easter Island Standard Time\",\t\tMAKE_ID(STD_TIMEZONE_ID, 59) },\n\t{ WTimeSpan(0, +3, 00, 0),\tWTimeSpan(0),\t\t\t\"EAT\",\t\"Eastern Africa Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 60) },\n\t{ WTimeSpan(0, -5, 00, 0),\tWTimeSpan(0),\t\t\t\"ECT\",\t\"Ecuador Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 61) },\n\t{ WTimeSpan(0, -1, 00, 0),\tWTimeSpan(0),\t\t\t\"EGT\",\t\"East Greenland Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 62) },\n\t{ WTimeSpan(0, +3, 00, 0),\tWTimeSpan(0),\t\t\t\"FET\",\t\"Further-Eastern European Time\",\tMAKE_ID(STD_TIMEZONE_ID, 63) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0),\t\t\t\"FJT\",\t\"Fiji Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 64) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0),\t\t\t\"FKT\",\t\"Falkland Island Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 65) },\n\t{ WTimeSpan(0, -2, 00, 0),\tWTimeSpan(0),\t\t\t\"FNT\",\t\"Fernando de Noronha Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 66) },\n\t{ WTimeSpan(0, -6, 00, 0),\tWTimeSpan(0),\t\t\t\"GALT\",\t\"Galapagos Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 67) },\n\t{ WTimeSpan(0, -9, 00, 0),\tWTimeSpan(0),\t\t\t\"GAMT\",\t\"Gambier Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 68) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0),\t\t\t\"GET\",\t\"Georgia Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 69) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0),\t\t\t\"GFT\",\t\"French Guiana Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 70) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0),\t\t\t\"GILT\",\t\"Gilbert Island Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 71) },\n\t{ WTimeSpan(0, +0, 00, 0),\tWTimeSpan(0),\t\t\t\"GMT\",\t\"Greenwich Mean Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 72) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0),\t\t\t\"GST\",\t\"Gulf Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 73) },\n\t{ WTimeSpan(0, -2, 00, 0),\tWTimeSpan(0),\t\t\t\"GST\",\t\"South Georgia Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 74) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0),\t\t\t\"GYT\",\t\"Guyana Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 75) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"HKT\",\t\"Hong Kong Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 76) },\n\t{ WTimeSpan(0, +7, 00, 0),\tWTimeSpan(0),\t\t\t\"HOVT\",\t\"Hovd Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 77) },\n\t{ WTimeSpan(0, +7, 00, 0),\tWTimeSpan(0),\t\t\t\"ICT\",\t\"Indochina Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 78) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0),\t\t\t\"IOT\",\t\"Indian Chagos Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 79) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"IRKT\",\t\"Irkutsk Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 80) },\n\t{ WTimeSpan(0, +3, 30, 0),\tWTimeSpan(0),\t\t\t\"IRST\",\t\"Iran Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 81) },\n\t{ WTimeSpan(0, +1, 00, 0),\tWTimeSpan(0),\t\t\t\"IST\",\t\"Irish Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 82) },\n\t{ WTimeSpan(0, +2, 00, 0),\tWTimeSpan(0),\t\t\t\"IST\",\t\"Israel Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 83) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0),\t\t\t\"KGT\",\t\"Kyrgyzstan Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 84) },\n\t{ WTimeSpan(0, +11, 00, 0),\tWTimeSpan(0),\t\t\t\"KOST\",\t\"Kosrae Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 85) },\n\t{ WTimeSpan(0, +7, 00, 0),\tWTimeSpan(0),\t\t\t\"KRAT\",\t\"Krasnoyarsk Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 86) },\n\t{ WTimeSpan(0, +9, 00, 0),\tWTimeSpan(0),\t\t\t\"KST\",\t\"Korea Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 87) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0),\t\t\t\"KUYT\",\t\"Kuybyshev Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 88) },\n\t{ WTimeSpan(0, +10, 30, 0),\tWTimeSpan(0),\t\t\t\"LHST\",\t\"Lord Howe Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 89) },\n\t{ WTimeSpan(0, +14, 00, 0),\tWTimeSpan(0),\t\t\t\"LINT\",\t\"Line Islands Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 90) },\n\t{ WTimeSpan(0, +10, 00, 0),\tWTimeSpan(0),\t\t\t\"MAGT\",\t\"Magadan Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 91) },\n\t{ WTimeSpan(0, -9, -30, 0),\tWTimeSpan(0),\t\t\t\"MART\",\t\"Marquesas Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 92) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"MAWT\",\t\"Mawson Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 93) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0),\t\t\t\"MHT\",\t\"Marshall Islands Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 94) },\n\t{ WTimeSpan(0, +6, 30, 0),\tWTimeSpan(0),\t\t\t\"MMT\",\t\"Myanmar Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 95) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0),\t\t\t\"MUT\",\t\"Mauritius Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 96) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"MVT\",\t\"Maldives Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 97) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"MYT\",\t\"Malaysia Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 98) },\n\t{ WTimeSpan(0, +11, 00, 0),\tWTimeSpan(0),\t\t\t\"NCT\",\t\"New Caledonia Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 99) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0),\t\t\t\"NOVT\",\t\"Novosibirsk Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 100) },\n\t{ WTimeSpan(0, +5, 45, 0),\tWTimeSpan(0),\t\t\t\"NPT\",\t\"Nepal Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 101) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0),\t\t\t\"NRT\",\t\"Nauru Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 102) },\n\t{ WTimeSpan(0, -11, 00, 0),\tWTimeSpan(0),\t\t\t\"NUT\",\t\"Niue Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 103) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0),\t\t\t\"OMST\",\t\"Omsk Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 104) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"ORAT\",\t\"Oral Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 105) },\n\t{ WTimeSpan(0, -5, 00, 0),\tWTimeSpan(0),\t\t\t\"PET\",\t\"Peru Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 106) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0),\t\t\t\"PETT\",\t\"Kamchatka Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 107) },\n\t{ WTimeSpan(0, +10, 00, 0),\tWTimeSpan(0),\t\t\t\"PGT\",\t\"Papua New Guinea Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 108) },\n\t{ WTimeSpan(0, +13, 00, 0),\tWTimeSpan(0),\t\t\t\"PHOT\",\t\"Phoenix Island Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 109) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"PHT\",\t\"Philippine Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 110) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"PKT\",\t\"Pakistan Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 111) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0),\t\t\t\"PMST\",\t\"Pierre & Miquelon Standard Time\",\tMAKE_ID(STD_TIMEZONE_ID, 112) },\n\t{ WTimeSpan(0, +11, 00, 0),\tWTimeSpan(0),\t\t\t\"PONT\",\t\"Pohnpei Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 113) },\n\t{ WTimeSpan(0, -8, 00, 0),\tWTimeSpan(0),\t\t\t\"PST\",\t\"Pitcairn Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 114) },\n\t{ WTimeSpan(0, +9, 00, 0),\tWTimeSpan(0),\t\t\t\"PWT\",\t\"Palau Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 115) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0),\t\t\t\"PYT\",\t\"Paraguay Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 116) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0),\t\t\t\"QYZT\",\t\"Qyzylorda Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 117) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0),\t\t\t\"RET\",\t\"Reunion Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 118) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0),\t\t\t\"ROTT\",\t\"Rothera Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 119) },\n\t{ WTimeSpan(0, +10, 00, 0),\tWTimeSpan(0),\t\t\t\"SAKT\",\t\"Sakhalin Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 120) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0),\t\t\t\"SAMT\",\t\"Samara Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 121) },\n\t{ WTimeSpan(0, +2, 00, 0),\tWTimeSpan(0),\t\t\t\"SAST\",\t\"South Africa Standard Time\",\t\tMAKE_ID(STD_TIMEZONE_ID, 122) },\n\t{ WTimeSpan(0, +11, 00, 0),\tWTimeSpan(0),\t\t\t\"SBT\",\t\"Solomon Islands Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 123) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0),\t\t\t\"SCT\",\t\"Seychelles Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 124) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"SGT\",\t\"Singapore Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 125) },\n\t{ WTimeSpan(0, +11, 00, 0),\tWTimeSpan(0),\t\t\t\"SRET\",\t\"Srednekolymsk Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 126) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0),\t\t\t\"SRT\",\t\"Suriname Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 127) },\n\t{ WTimeSpan(0, -11, 00, 0),\tWTimeSpan(0),\t\t\t\"SST\",\t\"Samoa Standard Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 128) },\n\t{ WTimeSpan(0, +3, 00, 0),\tWTimeSpan(0),\t\t\t\"SYOT\",\t\"Syowa Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 129) },\n\t{ WTimeSpan(0, -10, 00, 0),\tWTimeSpan(0),\t\t\t\"TAHT\",\t\"Tahiti Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 130) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"TFT\",\t\"French Southern and Antarctic Time\",MAKE_ID(STD_TIMEZONE_ID, 131) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"TJT\",\t\"Tajikistan Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 132) },\n\t{ WTimeSpan(0, +13, 00, 0),\tWTimeSpan(0),\t\t\t\"TKT\",\t\"Tokelau Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 133) },\n\t{ WTimeSpan(0, +9, 00, 0),\tWTimeSpan(0),\t\t\t\"TLT\",\t\"East Timor Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 134) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"TMT\",\t\"Turkmenistan Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 135) },\n\t{ WTimeSpan(0, +13, 00, 0),\tWTimeSpan(0),\t\t\t\"TOT\",\t\"Tonga Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 136) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0),\t\t\t\"TVT\",\t\"Tuvalu Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 137) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"ULAT\",\t\"Ulaanbaatar Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 138) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0),\t\t\t\"UYT\",\t\"Uruguay Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 139) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"UZT\",\t\"Uzbekistan Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 140) },\n\t{ WTimeSpan(0, -4, -30, 0),\tWTimeSpan(0),\t\t\t\"VET\",\t\"Venezuelan Standard Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 141) },\n\t{ WTimeSpan(0, +10, 00, 0),\tWTimeSpan(0),\t\t\t\"VLAT\",\t\"Vladivostok Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 142) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0),\t\t\t\"VOST\",\t\"Vostok Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 143) },\n\t{ WTimeSpan(0, +11, 00, 0),\tWTimeSpan(0),\t\t\t\"VUT\",\t\"Vanuatu Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 144) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0),\t\t\t\"WAKT\",\t\"Wake Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 145) },\n\t{ WTimeSpan(0, +0, 00, 0),\tWTimeSpan(0),\t\t\t\"WET\",\t\"Western European Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 146) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0),\t\t\t\"WFT\",\t\"Wallis and Futuna Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 147) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0),\t\t\t\"WGT\",\t\"West Greenland Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 148) },\n\t{ WTimeSpan(0, +7, 00, 0),\tWTimeSpan(0),\t\t\t\"WIB\",\t\"Western Indonesian Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 149) },\n\t{ WTimeSpan(0, +9, 00, 0),\tWTimeSpan(0),\t\t\t\"WIT\",\t\"Eastern Indonesian Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 150) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0),\t\t\t\"WITA\",\t\"Central Indonesian Time\",\t\t\tMAKE_ID(STD_TIMEZONE_ID, 151) },\n\t{ WTimeSpan(0, +13, 00, 0),\tWTimeSpan(0),\t\t\t\"WST\",\t\"West Samoa Time\",\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 152) },\n\t{ WTimeSpan(0, +0, 00, 0),\tWTimeSpan(0),\t\t\t\"WT\",\t\"Western Sahara Standard Time\",\t\tMAKE_ID(STD_TIMEZONE_ID, 153) },\n\t{ WTimeSpan(0, +9, 00, 0),\tWTimeSpan(0),\t\t\t\"YAKT\",\t\"Yakutsk Time\",\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 154) },\n\t{ WTimeSpan(0, +10, 00, 0),\tWTimeSpan(0),\t\t\t\"YAPT\",\t\"Yap Time\",\t\t\t\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 155) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0),\t\t\t\"YEKT\",\t\"Yekaterinburg Time\",\t\t\t\tMAKE_ID(STD_TIMEZONE_ID, 156) },\n\t{ WTimeSpan(0, 0, 0, 0),\tWTimeSpan(0),\t\t\tNULL,\tNULL,\t\t\t\t\t\t\t\t0 }\n};\n\n\nconst ::TimeZoneInfo WorldLocation::m_dst_timezones[] = {\n\t{ WTimeSpan(0, 9, 30, 0),\tWTimeSpan(0, 1, 0, 0),\t\"ACDT\",\t\"Australian Central Daylight Time\", MAKE_ID(DST_TIMEZONE_ID, 0) },\n\t{ WTimeSpan(0, -4, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"ADT\",\t\"Atlantic Daylight Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 1) },\n\t{ WTimeSpan(0, 10, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"AEDT\",\t\"Australian Eastern Daylight Time\", MAKE_ID(DST_TIMEZONE_ID, 2) },\n\t{ WTimeSpan(0, -9, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"AKDT\",\t\"Alaska Daylight Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 3) },\n\t{ WTimeSpan(0, 8, 0, 0),\tWTimeSpan(0, 1, 0, 0), \t\"AWDT\", \"Australian Western Daylight Time\", MAKE_ID(DST_TIMEZONE_ID, 4) },\n\t{ WTimeSpan(0, 0, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"BST\",\t\"British Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 5) },\t// 5\n\t{ WTimeSpan(0, -6, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"CDT\",\t\"Central Daylight Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 6) },\n\t{ WTimeSpan(0, 1, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"CEDT\",\t\"Central European Daylight Time\",\tMAKE_ID(DST_TIMEZONE_ID, 7) },\n\t{ WTimeSpan(0, -5, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"EDT\",\t\"Eastern Daylight Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 8) },\n\t{ WTimeSpan(0, 2, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"EEDT\",\t\"Eastern European Daylight Time\",\tMAKE_ID(DST_TIMEZONE_ID, 9) },\n\t{ WTimeSpan(0, -10, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"HADT\",\t\"Hawaii-Aleutian Daylight Time\",\tMAKE_ID(DST_TIMEZONE_ID, 10) },\t// 10\n\t{ WTimeSpan(0, 0, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"IST\",\t\"Irish Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 11) },\n\t{ WTimeSpan(0, -7, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"MDT\",\t\"Mountain Daylight Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 12) },\n\t{ WTimeSpan(0, 3, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"MSD\",\t\"Moscow Daylight Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 13) },\n\t{ WTimeSpan(0, -3, -30, 0),\tWTimeSpan(0, 1, 0, 0),\t\"NDT\",\t\"Newfoundland Daylight Time\",\t\tMAKE_ID(DST_TIMEZONE_ID, 14) },\n\t{ WTimeSpan(0, 12, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"NZDT\",\t\"New Zealand Daylight Time\",\t\tMAKE_ID(DST_TIMEZONE_ID, 15) },\t// 15\n\t{ WTimeSpan(0, -8, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"PDT\",\t\"Pacific Daylight Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 16) },\n\t{ WTimeSpan(0, 0, 0, 0),\tWTimeSpan(0, 1, 0, 0),\t\"WEDT\",\t\"Western European Daylight Time\",\tMAKE_ID(DST_TIMEZONE_ID, 17) },\n\t{ WTimeSpan(0, 0, 0, 0),\tWTimeSpan(0),\t\t\tNULL,\tNULL,\t\t\t\t\t\t\t\t0 }\n};\n\n\nconst ::TimeZoneInfo WorldLocation::m_dst_extra_timezones[] = {\n\t{ WTimeSpan(0, +3, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"ADT\",\t\"Arabia Daylight Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 18) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"AMST\",\t\"Amazon Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 19) },\n\t{ WTimeSpan(0, -1, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"AZOST\",\"Azores Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 20) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"AZST\",\t\"Azerbaijan Summer Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 21) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"BRST\",\t\"Bras\\u00EDlia Summer Time \",\t\tMAKE_ID(DST_TIMEZONE_ID, 22) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"CDT\",\t\"Cuba Daylight Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 23) },\n\t{ WTimeSpan(0, +12, 45, 0),\tWTimeSpan(0, 1, 0, 0),\t\"CHADT\",\"Chatham Island Daylight Time\",\t\tMAKE_ID(DST_TIMEZONE_ID, 24) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"CLST\",\t\"Chile Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 25) },\n\t{ WTimeSpan(0, -6, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"EASST\",\"Easter Island Summer Time\",\t\tMAKE_ID(DST_TIMEZONE_ID, 26) },\n\t{ WTimeSpan(0, -1, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"EGST\",\t\"Eastern Greenland Summer Time\",\tMAKE_ID(DST_TIMEZONE_ID, 27) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"FKST\",\t\"Falkland Islands Summer Time\",\t\tMAKE_ID(DST_TIMEZONE_ID, 28) },\n\t{ WTimeSpan(0, +2, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"IDT\",\t\"Israel Daylight Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 29) },\n\t{ WTimeSpan(0, +3, 30, 0),\tWTimeSpan(0, 1, 0, 0),\t\"IRDT\",\t\"Iran Daylight Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 30) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"IRKST\",\"Irkutsk Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 31) },\n\t{ WTimeSpan(0, +7, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"KRAST\",\"Krasnoyarsk Summer Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 32) },\n\t{ WTimeSpan(0, +10, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"LHDT\",\t\"Lord Howe Daylight Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 33) },\n\t{ WTimeSpan(0, +11, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"MAGST\",\"Magadan Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 34) },\n\t{ WTimeSpan(0, +6, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"NOVST\",\"Novosibirsk Summer Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 35) },\n\t{ WTimeSpan(0, +8, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"OMSST\",\"Omsk Summer Time\",\t\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 36) },\n\t{ WTimeSpan(0, +13, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"PETST\",\"Kamchatka Summer Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 37) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"PMDT\",\t\"Pierre & Miquelon Daylight Time\",\tMAKE_ID(DST_TIMEZONE_ID, 38) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"UYST\",\t\"Uruguay Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 39) },\n\t{ WTimeSpan(0, +10, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"VLAST\",\"Vladivostok Summer Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 40) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"WARST\",\"Western Argentine Summer Time\",\tMAKE_ID(DST_TIMEZONE_ID, 41) },\n\t{ WTimeSpan(0, +1, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"WAST\",\t\"West Africa Summer Time\",\t\t\tMAKE_ID(DST_TIMEZONE_ID, 42) },\n\t{ WTimeSpan(0, -3, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"WGST\",\t\"Western Greenland Summer Time\",\tMAKE_ID(DST_TIMEZONE_ID, 43) },\n\t{ WTimeSpan(0, 0, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"WST\",\t\"Western Sahara Summer Time\",\t\tMAKE_ID(DST_TIMEZONE_ID, 44) },\n\t{ WTimeSpan(0, +9, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"YAKST\",\"Yakutsk Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 45) },\n\t{ WTimeSpan(0, +5, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"YEKST\",\"Yekaterinburg Summer Time\",\t\tMAKE_ID(DST_TIMEZONE_ID, 46) },\n\t{ WTimeSpan(0, +12, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"FJST\",\t\"Fiji Summer Time\",\t\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 47) },\n\t{ WTimeSpan(0, -4, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"PYST\",\t\"Paraguay Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 48) },\n\t{ WTimeSpan(0, +4, 00, 0),\tWTimeSpan(0, 1, 0, 0),\t\"AMST\",\t\"Armenia Summer Time\",\t\t\t\tMAKE_ID(DST_TIMEZONE_ID, 49) },\n\t{ WTimeSpan(0, 0, 0, 0),\tWTimeSpan(0),\t\t\tNULL,\tNULL,\t\t\t\t\t\t\t\t0 }\n};\n\n\nconst ::TimeZoneInfo WorldLocation::m_mil_timezones[] = {\n\t{ WTimeSpan(0, 0, 0, 0),\tWTimeSpan(0),\t\t\"Z\",\t\"Zulu Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 0) },\n\t{ WTimeSpan(0, 1, 0, 0),\tWTimeSpan(0),\t\t\"A\",\t\"Alpha Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 1) },\n\t{ WTimeSpan(0, 2, 0, 0),\tWTimeSpan(0),\t\t\"B\",\t\"Bravo Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 2) },\n\t{ WTimeSpan(0, 3, 0, 0),\tWTimeSpan(0),\t\t\"C\",\t\"Charlie Time Zone\",\tMAKE_ID(MIL_TIMEZONE_ID, 3) },\n\t{ WTimeSpan(0, 4, 0, 0),\tWTimeSpan(0),\t\t\"D\",\t\"Delta Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 4) },\n\t{ WTimeSpan(0, 5, 0, 0),\tWTimeSpan(0),\t\t\"E\",\t\"Echo Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 5) },\t// 5\n\t{ WTimeSpan(0, 6, 0, 0),\tWTimeSpan(0),\t\t\"F\",\t\"Foxtrot Time Zone\",\tMAKE_ID(MIL_TIMEZONE_ID, 6) },\n\t{ WTimeSpan(0, 7, 0, 0),\tWTimeSpan(0),\t\t\"G\",\t\"Golf Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 7) },\n\t{ WTimeSpan(0, 8, 0, 0),\tWTimeSpan(0),\t\t\"H\",\t\"Hotel Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 8) },\n\t{ WTimeSpan(0, 9, 0, 0),\tWTimeSpan(0),\t\t\"I\",\t\"India Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 9) },\n\t{ WTimeSpan(0, 10, 0, 0),\tWTimeSpan(0),\t\t\"K\",\t\"Kilo Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 10) },\t// 10\n\t{ WTimeSpan(0, 11, 0, 0),\tWTimeSpan(0),\t\t\"L\",\t\"Lima Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 11) },\n\t{ WTimeSpan(0, 12, 0, 0),\tWTimeSpan(0),\t\t\"M\",\t\"Mike Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 12) },\n\t{ WTimeSpan(0, -1, 0, 0),\tWTimeSpan(0),\t\t\"N\",\t\"November Time Zone\",\tMAKE_ID(MIL_TIMEZONE_ID, 13) },\n\t{ WTimeSpan(0, -2, 0, 0),\tWTimeSpan(0),\t\t\"O\",\t\"Oscar Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 14) },\n\t{ WTimeSpan(0, -3, 0, 0),\tWTimeSpan(0),\t\t\"P\",\t\"Papa Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 15) },\t// 15\n\t{ WTimeSpan(0, -4, 0, 0),\tWTimeSpan(0),\t\t\"Q\",\t\"Quebec Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 16) },\n\t{ WTimeSpan(0, -5, 0, 0),\tWTimeSpan(0),\t\t\"R\",\t\"Romeo Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 17) },\n\t{ WTimeSpan(0, -6, 0, 0),\tWTimeSpan(0),\t\t\"S\",\t\"Sierra Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 18) },\n\t{ WTimeSpan(0, -7, 0, 0),\tWTimeSpan(0),\t\t\"T\",\t\"Tango Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 19) },\n\t{ WTimeSpan(0, -8, 0, 0),\tWTimeSpan(0),\t\t\"U\",\t\"Uniform Time Zone\",\tMAKE_ID(MIL_TIMEZONE_ID, 20) }, // 20\n\t{ WTimeSpan(0, -9, 0, 0),\tWTimeSpan(0),\t\t\"V\",\t\"Vector Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 21) },\n\t{ WTimeSpan(0, -10, 0, 0),\tWTimeSpan(0),\t\t\"W\",\t\"Whiskey Time Zone\",\tMAKE_ID(MIL_TIMEZONE_ID, 22) },\n\t{ WTimeSpan(0, -11, 0, 0),\tWTimeSpan(0),\t\t\"X\",\t\"X-ray Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 23) },\n\t{ WTimeSpan(0, -12, 0, 0),\tWTimeSpan(0),\t\t\"Y\",\t\"Yankee Time Zone\",\t\tMAKE_ID(MIL_TIMEZONE_ID, 24) },\n\t{ WTimeSpan(0),\t\t\t\tWTimeSpan(0),\t\tNULL,\tNULL,\t\t\t\t\t0 }\n};\n\n\nstruct WindowsTimezoneData\n{\n\tconst std::string WindowsName;\n\tconst std::uint32_t HssId;\n};\n\nstruct RegionZone\n{\n\tconst std::string Name;\n\tconst std::uint32_t HssId;\n};\n\n//https://raw.githubusercontent.com/unicode-org/cldr/master/common/supplemental/windowsZones.xml\nconst std::vector WindowsMap =\n{\n\t{ \"Dateline Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 12) },\n\t{ \"UTC-11\", MAKE_ID(MIL_TIMEZONE_ID, 23) },\n\t{ \"Aleutian Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 10) },\n\t{ \"Hawaiian Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 10) },\n\t{ \"Marquesas Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 92) },\n\t{ \"Alaskan Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 2) },\n\t{ \"UTC-09\", MAKE_ID(MIL_TIMEZONE_ID, 21) },\n\t{ \"Pacific Standard Time (Mexico)\", MAKE_ID(STD_TIMEZONE_ID, 92) },\n\t{ \"UTC-08\", MAKE_ID(MIL_TIMEZONE_ID, 20) },\n\t{ \"Pacific Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 16) },\n\t{ \"US Mountain Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 12) },\n\t{ \"Mountain Standard Time (Mexico)\", MAKE_ID(STD_TIMEZONE_ID, 12) },\n\t{ \"Mountain Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 12) },\n\t{ \"Central America Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 6) },\n\t{ \"Central Standard Time (Mexico)\", MAKE_ID(STD_TIMEZONE_ID, 6) },\n\t{ \"Central Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 6) },\n\t{ \"Canada Central Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 6) },\n\t{ \"Easter Island Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 59) },\n\t{ \"SA Pacific Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 53) },\n\t{ \"US Eastern Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 9) },\n\t{ \"Eastern Standard Time (Mexico)\", MAKE_ID(STD_TIMEZONE_ID, 9) },\n\t{ \"Eastern Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 9) },\n\t{ \"Haiti Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 17) },\n\t{ \"Cuba Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 55) },\n\t{ \"Turks And Caicos Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 17) },\n\t{ \"Paraguay Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 116) },\n\t{ \"Atlantic Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 3) },\n\t{ \"Venezuela Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 141) },\n\t{ \"Central Brazilian Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 16) },\n\t{ \"SA Western Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 75) },\n\t{ \"Pacific SA Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 52) },\n\t{ \"Newfoundland Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 14) },\n\t{ \"Tocantins Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 15) },\n\t{ \"E. South America Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 42) },\n\t{ \"SA Eastern Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 119) },\n\t{ \"Argentina Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 36) },\n\t{ \"Greenland Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 148) },\n\t{ \"Montevideo Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 15) },\n\t{ \"Magallanes Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 15) },\n\t{ \"Saint Pierre Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 112) },\n\t{ \"Bahia Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 15) },\n\t{ \"UTC-02\", MAKE_ID(MIL_TIMEZONE_ID, 14) },\n\t{ \"Azores Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 14) },\n\t{ \"Cape Verde Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 56) },\n\t{ \"UTC\", MAKE_ID(STD_TIMEZONE_ID, 17) },\n\t{ \"GMT Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 72) },\n\t{ \"Greenwich Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 72) },\n\t{ \"Sao Tome Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 17) },\n\t{ \"Morocco Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 1) },\n\t{ \"W. Europe Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 5) },\n\t{ \"Central Europe Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 5) },\n\t{ \"Central European Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 5) },\n\t{ \"Romance Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 1) },\n\t{ \"W. Central Africa Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 1) },\n\t{ \"Jordan Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 2) },\n\t{ \"GTB Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 8) },\n\t{ \"E. Europe Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 8) },\n\t{ \"Middle East Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 2) },\n\t{ \"Egypt Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 46) },\n\t{ \"Syria Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 2) },\n\t{ \"West Bank Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 2) },\n\t{ \"South Africa Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 122) },\n\t{ \"FLE Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 8) },\n\t{ \"Israel Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 83) },\n\t{ \"Kaliningrad Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 18) },\n\t{ \"Sudan Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 46) },\n\t{ \"Libya Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 46) },\n\t{ \"Namibia Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 46) },\n\t{ \"Arabic Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 37) },\n\t{ \"Arab Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 37) },\n\t{ \"Turkey Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 63) },\n\t{ \"Belarus Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 63) },\n\t{ \"Russian Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 11) },\n\t{ \"E. Africa Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 60) },\n\t{ \"Iran Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 81) },\n\t{ \"Arabian Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 4) },\n\t{ \"Astrakhan Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 20) },\n\t{ \"Russia Time Zone 3\", MAKE_ID(STD_TIMEZONE_ID, 20) },\n\t{ \"Azerbaijan Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 38) },\n\t{ \"Mauritius Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 96) },\n\t{ \"Saratov Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 20) },\n\t{ \"Georgian Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 69) },\n\t{ \"Volgograd Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 20) },\n\t{ \"Caucasus Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 4) },\n\t{ \"Afghanistan Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 30) },\n\t{ \"West Asia Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 5) },\n\t{ \"Ekaterinburg Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 156) },\n\t{ \"Pakistan Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 111) },\n\t{ \"Qyzylorda Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 5) },\n\t{ \"India Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 24) },\n\t{ \"Sri Lanka Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 24) },\n\t{ \"Nepal Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 101) },\n\t{ \"Central Asia Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 6) },\n\t{ \"Bangladesh Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 43) },\n\t{ \"Omsk Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 104) },\n\t{ \"Myanmar Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 95) },\n\t{ \"SE Asia Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 7) },\n\t{ \"Altai Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 7) },\n\t{ \"W. Mongolia Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 77) },\n\t{ \"North Asia Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 86) },\n\t{ \"N. Central Asia Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 7) },\n\t{ \"Tomsk Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 7) },\n\t{ \"China Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 54) },\n\t{ \"North Asia East Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 80) },\n\t{ \"Singapore Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 125) },\n\t{ \"W. Australia Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 4) },\n\t{ \"Taipei Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 8) },\n\t{ \"Ulaanbaatar Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 138) },\n\t{ \"Aus Central W. Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 29) },\n\t{ \"Transbaikal Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 9) },\n\t{ \"Tokyo Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 26) },\n\t{ \"North Korea Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 87) },\n\t{ \"Korea Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 87) },\n\t{ \"Yakutsk Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 154) },\n\t{ \"Cen. Australia Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 0) },\n\t{ \"AUS Central Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 0) },\n\t{ \"E. Australia Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 1) },\n\t{ \"AUS Eastern Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 1) },\n\t{ \"West Pacific Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 27) },\n\t{ \"Tasmania Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 1) },\n\t{ \"Vladivostok Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 142) },\n\t{ \"Lord Howe Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 89) },\n\t{ \"Bougainville Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 123) },\n\t{ \"Russia Time Zone 10\", MAKE_ID(STD_TIMEZONE_ID, 126) },\n\t{ \"Magadan Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 11) },\n\t{ \"Norfolk Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 11) },\n\t{ \"Sakhalin Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 11) },\n\t{ \"Central Pacific Standard Time\", MAKE_ID(MIL_TIMEZONE_ID, 11) },\n\t{ \"Russia Time Zone 11\", MAKE_ID(MIL_TIMEZONE_ID, 11) },\n\t{ \"New Zealand Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 15) },\n\t{ \"UTC+12\", MAKE_ID(MIL_TIMEZONE_ID, 12) },\n\t{ \"Fiji Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 64) },\n\t{ \"Chatham Islands Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 48) },\n\t{ \"UTC+13\", MAKE_ID(STD_TIMEZONE_ID, 109) },\n\t{ \"Tonga Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 136) },\n\t{ \"Samoa Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 152) },\n\t{ \"Line Islands Standard Time\", MAKE_ID(STD_TIMEZONE_ID, 90) }\n};\n\n\n//https://data.iana.org/time-zones/releases/\nconst std::vector RegionMap =\n{\n#include \"RegionMap.inl\"\n};\n\n\n\nboost::bimap daylightStandardMap =\nmakeBimap({\n\t{ MAKE_ID(STD_TIMEZONE_ID, 0), MAKE_ID(DST_TIMEZONE_ID, 0) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 3), MAKE_ID(DST_TIMEZONE_ID, 1) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 1), MAKE_ID(DST_TIMEZONE_ID, 2) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 2), MAKE_ID(DST_TIMEZONE_ID, 3) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 4), MAKE_ID(DST_TIMEZONE_ID, 4) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 6), MAKE_ID(DST_TIMEZONE_ID, 6) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 5), MAKE_ID(DST_TIMEZONE_ID, 7) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 9), MAKE_ID(DST_TIMEZONE_ID, 8) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 8), MAKE_ID(DST_TIMEZONE_ID, 9) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 10), MAKE_ID(DST_TIMEZONE_ID, 10) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 12), MAKE_ID(DST_TIMEZONE_ID, 12) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 11), MAKE_ID(DST_TIMEZONE_ID, 13) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 14), MAKE_ID(DST_TIMEZONE_ID, 14) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 15), MAKE_ID(DST_TIMEZONE_ID, 15) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 16), MAKE_ID(DST_TIMEZONE_ID, 16) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 146), MAKE_ID(DST_TIMEZONE_ID, 17) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 37), MAKE_ID(DST_TIMEZONE_ID, 18) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 32), MAKE_ID(DST_TIMEZONE_ID, 19) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 22), MAKE_ID(DST_TIMEZONE_ID, 20) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 38), MAKE_ID(DST_TIMEZONE_ID, 21) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 42), MAKE_ID(DST_TIMEZONE_ID, 22) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 48), MAKE_ID(DST_TIMEZONE_ID, 24) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 52), MAKE_ID(DST_TIMEZONE_ID, 25) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 59), MAKE_ID(DST_TIMEZONE_ID, 26) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 62), MAKE_ID(DST_TIMEZONE_ID, 27) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 65), MAKE_ID(DST_TIMEZONE_ID, 28) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 83), MAKE_ID(DST_TIMEZONE_ID, 29) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 81), MAKE_ID(DST_TIMEZONE_ID, 30) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 80), MAKE_ID(DST_TIMEZONE_ID, 31) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 86), MAKE_ID(DST_TIMEZONE_ID, 32) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 89), MAKE_ID(DST_TIMEZONE_ID, 33) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 100), MAKE_ID(DST_TIMEZONE_ID, 35) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 112), MAKE_ID(DST_TIMEZONE_ID, 38) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 139), MAKE_ID(DST_TIMEZONE_ID, 39) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 142), MAKE_ID(DST_TIMEZONE_ID, 40) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 148), MAKE_ID(DST_TIMEZONE_ID, 43) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 153), MAKE_ID(DST_TIMEZONE_ID, 44) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 154), MAKE_ID(DST_TIMEZONE_ID, 45) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 156), MAKE_ID(DST_TIMEZONE_ID, 46) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 64), MAKE_ID(DST_TIMEZONE_ID, 47) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 116), MAKE_ID(DST_TIMEZONE_ID, 48) },\n\t{ MAKE_ID(STD_TIMEZONE_ID, 33), MAKE_ID(DST_TIMEZONE_ID, 49) }\n\t});\n\n\nbool WorldLocation::InsideCanada() const {\n\treturn ((WorldLocation *)this)->InsideCanada(_latitude, _longitude);\n}\n\n\nbool WorldLocation::InsideCanada(const double latitude, const double longitude) {\n\tif (latitude < DEGREE_TO_RADIAN(41.0))\t\treturn false;\n\tif (latitude > DEGREE_TO_RADIAN(83.0))\t\treturn false;\n\tif (longitude < DEGREE_TO_RADIAN(-141.0))\treturn false;\n\tif (longitude > DEGREE_TO_RADIAN(-52.0))\treturn false;\n\n#if defined(_MSC_VER) || defined(_USE_CANADA)\n\treturn insideCanadaDetail(&canada, RADIAN_TO_DEGREE(latitude), RADIAN_TO_DEGREE(longitude));\n#else\n\tif (longitude < DEGREE_TO_RADIAN(-122.8)) {\n\t\tif (latitude < DEGREE_TO_RADIAN(48.3))\treturn false;\n\t}\n\telse if (longitude < DEGREE_TO_RADIAN(-95.153)) {\n\t\tif (latitude < DEGREE_TO_RADIAN(49.0))\treturn false;\n\t}\n\telse if (longitude < DEGREE_TO_RADIAN(-88.0)) {\n\t\tif (latitude < DEGREE_TO_RADIAN(48.0))\treturn false;\n\t}\n\telse if (longitude < DEGREE_TO_RADIAN(-83.5)) {\n\t\tif (latitude < DEGREE_TO_RADIAN(45.5))\treturn false;\n\t}\n\telse if (longitude < DEGREE_TO_RADIAN(-78.7)) {\n\t\tif (latitude < DEGREE_TO_RADIAN(41.66))\treturn false;\n\t}\n\telse if (longitude < DEGREE_TO_RADIAN(-74.75)) {\n\t\tif (latitude < DEGREE_TO_RADIAN(43.65))\treturn false;\n\t}\n\telse if (longitude < DEGREE_TO_RADIAN(-67.31)) {\n\t\tif (latitude < DEGREE_TO_RADIAN(45))\treturn false;\n\t}\n\telse {\n\t\tif (latitude < DEGREE_TO_RADIAN(43.25))\treturn false;\n\t}\n\treturn true;\n#endif\n}\n\n\nbool WorldLocation::InsideNewZealand() const {\n\tif ((_longitude > DEGREE_TO_RADIAN(172.5)) && (_longitude < DEGREE_TO_RADIAN(178.6))) {\n\t\tif ((_latitude > DEGREE_TO_RADIAN(-41.75)) && (_latitude < DEGREE_TO_RADIAN(-34.3))) {\t// general extents of New Zealand's north island\n\t\t\treturn true;\n\t\t}\n\t}\n\n\tif ((_longitude > DEGREE_TO_RADIAN(166.3)) && (_longitude < DEGREE_TO_RADIAN(174.5))) {\n\t\tif ((_latitude > DEGREE_TO_RADIAN(-47.35)) && (_latitude < DEGREE_TO_RADIAN(40.4))) {\t// general extents of New Zealand's south island\n\t\t\treturn true;\n\t\t}\n\t}\n\treturn false;\n}\n\n\nbool WorldLocation::InsideTasmania() const {\n\tif ((_longitude > DEGREE_TO_RADIAN(143.5)) && (_longitude < DEGREE_TO_RADIAN(149.0))) {\n\t\tif ((_latitude > DEGREE_TO_RADIAN(-44.0)) && (_latitude < DEGREE_TO_RADIAN(-39.5))) {\t// general extents of Tasmania\n\t\t\treturn true;\n\t\t}\n\t}\n\n\treturn false;\n}\n\n\nbool WorldLocation::InsideAustraliaMainland() const {\n\tif ((_longitude > DEGREE_TO_RADIAN(113.15)) && (_longitude < DEGREE_TO_RADIAN(153.633333))) {\n\t\tif ((_latitude > DEGREE_TO_RADIAN(-39.133333)) && (_latitude < DEGREE_TO_RADIAN(-10.683333))) {\t// general extents of continental Australia\n\t\t\treturn true;\n\t\t}\n\t}\n\n\treturn false;\n}\n\n\nvoid WorldLocation::SetTimeZoneOffset(const TimeZoneInfo* timezone)\n{\n\t_amtDST = timezone->m_dst;\n\t__timezone = timezone->m_timezone;\n\tif (_startDST.GetTotalMicroSeconds() != 0)\n\t\t_startDST = WTimeSpan(0);\n\tif (_amtDST.GetTotalMicroSeconds() != 0)\n\t\t_endDST = WTimeSpan(366, 0, 0, 0);\n\t_timezoneInfo = timezone;\n}\n\n\nbool WorldLocation::SetTimeZoneOffset(std::uint32_t id)\n{\n\tauto tz = TimeZoneFromId(id);\n\tif (tz)\n\t{\n\t\tSetTimeZoneOffset(tz);\n\t\treturn true;\n\t}\n\treturn false;\n}\n\n\nconst ::TimeZoneInfo *WorldLocation::GuessTimeZone(INTNM::int16_t set) const {\n\tbool valid;\n\tconst TimeZoneInfo *tzi = WorldLocation::TimeZoneFromLatLon(_latitude, _longitude, set, &valid);\n\tif (!valid)\n\t{\n\t\tif (InsideNewZealand()) {\n\t\t\tif (set == 0)\n\t\t\t\treturn &m_std_timezones[15];\n\t\t\telse if (set == 1)\n\t\t\t\treturn &m_dst_timezones[15];\n\t\t} else if (InsideTasmania()) {\n\t\t\tif (set == 0)\n\t\t\t\treturn &m_std_timezones[1];\n\t\t\telse if (set == 1)\n\t\t\t\treturn &m_dst_timezones[2];\n\t\t}\n\n\t\tdouble longitude = _longitude;\n\t\twhile (longitude < -Pi)\n\t\t\tlongitude += TwoPi;\n\t\twhile (longitude > Pi)\n\t\t\tlongitude -= TwoPi;\n\n\t\tconst ::TimeZoneInfo *tz;\n\t\tdouble variation = TwoPi;\n\t\tif (set == 1)\t\ttz = m_dst_timezones;\n\t\telse if (set == -1)\ttz = m_mil_timezones;\n\t\telse if (set == 0)\ttz = m_std_timezones;\n\t\telse\t\t\treturn NULL;\n\n\t\twhile (tz->m_name) {\n\t\t\tdouble ideal_longitude = ((double)tz->m_timezone.GetTotalSeconds()) / (double)(12.0 * 60.0 * 60.0) * Pi;\n\t\t\tdouble offset_longitude = fabs(longitude - ideal_longitude);\n\t\t\tif (variation > offset_longitude) {\n\t\t\t\tvariation = offset_longitude;\n\t\t\t\ttzi = tz;\n\t\t\t}\n\t\t\ttz++;\n\t\t}\n\t}\n\treturn tzi;\n}\n\n\nconst ::TimeZoneInfo *WorldLocation::CurrentTimeZone(INTNM::int16_t set, bool* hidden) const\n{\n\tif (_timezoneInfo != nullptr)\n\t{\n\t\tif (hidden)\n\t\t{\n\t\t\tif (IS_STD(_timezoneInfo->m_id))\n\t\t\t{\n\t\t\t\t*hidden = true;\n\t\t\t\tconst ::TimeZoneInfo* tz = m_std_timezones;\n\t\t\t\twhile (tz->m_code)\n\t\t\t\t{\n\t\t\t\t\tif (tz->m_id == _timezoneInfo->m_id)\n\t\t\t\t\t{\n\t\t\t\t\t\t*hidden = false;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t\ttz++;\n\t\t\t\t}\n\t\t\t}\n\t\t\telse if (IS_DST(_timezoneInfo->m_id))\n\t\t\t{\n\t\t\t\t*hidden = true;\n\t\t\t\tconst ::TimeZoneInfo* tz = m_dst_timezones;\n\t\t\t\twhile (tz->m_code)\n\t\t\t\t{\n\t\t\t\t\tif (tz->m_id == _timezoneInfo->m_id)\n\t\t\t\t\t{\n\t\t\t\t\t\t*hidden = false;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t\ttz++;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn _timezoneInfo;\n\t}\n\telse\n\t{\n\t\tconst ::TimeZoneInfo* tz;\n\t\tconst ::TimeZoneInfo* tzSecondary = nullptr;\n\t\tif (set == -1)\n\t\t\ttz = m_mil_timezones;\n\t\telse if (_startDST == _endDST)\n\t\t{\n\t\t\ttz = m_std_timezones;\n\t\t\ttzSecondary = m_std_extra_timezones;\n\t\t}\n\t\telse\n\t\t{\n\t\t\ttz = m_dst_timezones;\n\t\t\ttzSecondary = m_dst_extra_timezones;\n\t\t}\n\n\t\twhile (tz->m_code)\n\t\t{\n\t\t\tif (tz->m_timezone == __timezone)\n\t\t\t\tbreak;\n\t\t\ttz++;\n\t\t}\n\t\tif (tz->m_code)\n\t\t{\n\t\t\tif (hidden)\n\t\t\t\t*hidden = false;\n\t\t\treturn tz;\n\t\t}\n\t\telse if (tzSecondary)\n\t\t{\n\t\t\twhile (tzSecondary->m_code)\n\t\t\t{\n\t\t\t\tif (tzSecondary->m_timezone == __timezone)\n\t\t\t\t\tbreak;\n\t\t\t\ttzSecondary++;\n\t\t\t}\n\t\t\tif (tzSecondary->m_code)\n\t\t\t{\n\t\t\t\tif (hidden)\n\t\t\t\t\t*hidden = true;\n\t\t\t\treturn tzSecondary;\n\t\t\t}\n\t\t}\n\t\treturn nullptr;\n\t}\n\treturn nullptr;\n}\n\nconst ::TimeZoneInfo* WorldLocation::TimeZoneFromName(const std::string& name, INTNM::int16_t set, bool* hidden)\n{\n\tconst ::TimeZoneInfo *tz;\n\tconst ::TimeZoneInfo* tzSecondary = nullptr;\n\tif (set == -1)\n\t\ttz = m_mil_timezones;\n\telse if (set)\n\t{\n\t\ttz = m_dst_timezones;\n\t\ttzSecondary = m_dst_extra_timezones;\n\t}\n\telse\n\t{\n\t\ttz = m_std_timezones;\n\t\ttzSecondary = m_std_extra_timezones;\n\t}\n\n\twhile (tz->m_code)\n\t{\n\t\tif (boost::iequals(tz->m_code, name) || boost::iequals(tz->m_name, name))\n\t\t\tbreak;\n\t\ttz++;\n\t}\n\tif (tz->m_code)\n\t{\n\t\tif (hidden)\n\t\t\t*hidden = false;\n\t\treturn tz;\n\t}\n\telse if (tzSecondary)\n\t{\n\t\twhile (tzSecondary->m_code)\n\t\t{\n\t\t\tif (boost::iequals(tzSecondary->m_code, name) || boost::iequals(tzSecondary->m_name, name))\n\t\t\t\tbreak;\n\t\t\ttzSecondary++;\n\t\t}\n\t\tif (tzSecondary->m_code)\n\t\t{\n\t\t\tif (hidden)\n\t\t\t\t*hidden = true;\n\t\t\treturn tzSecondary;\n\t\t}\n\t}\n\treturn nullptr;\n}\n\n\nconst ::TimeZoneInfo* WorldLocation::TimeZoneFromId(std::uint32_t id, bool* hidden)\n{\n\tif (IS_STD(id))\n\t{\n\t\tconst TimeZoneInfo* tz = m_std_timezones;\n\t\twhile (tz->m_name)\n\t\t{\n\t\t\tif (tz->m_id == id)\n\t\t\t{\n\t\t\t\tif (hidden)\n\t\t\t\t\t*hidden = false;\n\t\t\t\treturn tz;\n\t\t\t}\n\t\t\ttz++;\n\t\t}\n\t\ttz = m_std_extra_timezones;\n\t\twhile (tz->m_name)\n\t\t{\n\t\t\tif (tz->m_id == id)\n\t\t\t{\n\t\t\t\tif (hidden)\n\t\t\t\t\t*hidden = true;\n\t\t\t\treturn tz;\n\t\t\t}\n\t\t\ttz++;\n\t\t}\n\t}\n\telse if (IS_DST(id))\n\t{\n\t\tconst TimeZoneInfo* tz = m_dst_timezones;\n\t\twhile (tz->m_name)\n\t\t{\n\t\t\tif (tz->m_id == id)\n\t\t\t{\n\t\t\t\tif (hidden)\n\t\t\t\t\t*hidden = false;\n\t\t\t\treturn tz;\n\t\t\t}\n\t\t\ttz++;\n\t\t}\n\t\ttz = m_dst_extra_timezones;\n\t\twhile (tz->m_name)\n\t\t{\n\t\t\tif (tz->m_id == id)\n\t\t\t{\n\t\t\t\tif (hidden)\n\t\t\t\t\t*hidden = true;\n\t\t\t\treturn tz;\n\t\t\t}\n\t\t\ttz++;\n\t\t}\n\t}\n\telse\n\t{\n\t\tconst TimeZoneInfo* tz = m_mil_timezones;\n\t\twhile (tz->m_name)\n\t\t{\n\t\t\tif (tz->m_id == id)\n\t\t\t{\n\t\t\t\tif (hidden)\n\t\t\t\t\t*hidden = false;\n\t\t\t\treturn tz;\n\t\t\t}\n\t\t\ttz++;\n\t\t}\n\t}\n\treturn nullptr;\n}\n\n\nconst ::TimeZoneInfo* WorldLocation::TimeZoneFromWindowsName(const std::string& name)\n{\n\tfor (auto windows : WindowsMap)\n\t{\n\t\tif (boost::equals(windows.WindowsName, name))\n\t\t\treturn TimeZoneFromId(windows.HssId);\n\t}\n\treturn nullptr;\n}\n\n\nconst ::TimeZoneInfo* WorldLocation::TimeZoneFromRegionName(const std::string& regionName)\n{\n\tfor (auto region : RegionMap)\n\t{\n\t\tif (boost::iequals(region.Name, regionName))\n\t\t\treturn TimeZoneFromId(region.HssId);\n\t}\n\treturn nullptr;\n}\n\n\nconst TimeZoneInfo* WorldLocation::GetDaylightSavingsTimeZone(const TimeZoneInfo* info)\n{\n\tif (bimapContainsLeft(daylightStandardMap, info->m_id))\n\t\treturn TimeZoneFromId(daylightStandardMap.left.at(info->m_id));\n\treturn info;\n}\n\n\nconst TimeZoneInfo* WorldLocation::GetStandardTimeZone(const TimeZoneInfo* info)\n{\n\tif (bimapContainsRight(daylightStandardMap, info->m_id))\n\t\treturn TimeZoneFromId(daylightStandardMap.right.at(info->m_id));\n\treturn info;\n}\n\n\nWorldLocation::WorldLocation()\n\t: _timezoneInfo(nullptr)\n#ifdef HSS_USE_CACHING\n\t, m_sunCache(2), m_solarCache(2)\n#endif\n{\n\t_latitude = 1000.0;\n\t_longitude = 1000.0;\n\t__timezone = WTimeSpan(0);\n\t_startDST = WTimeSpan(0);\n\t_endDST = WTimeSpan(0);\n\t_amtDST = WTimeSpan(0, 1, 0, 0);\n\n\tcanada = nullptr;\n}\n\n\nWorldLocation::WorldLocation(const WorldLocation &wl)\n\t: _timezoneInfo(nullptr)\n#ifdef HSS_USE_CACHING\n\t, m_sunCache(2), m_solarCache(2)\n#endif\n{\n\t*this = wl;\n\n\tcanada = nullptr;\n}\n\n\nWorldLocation::WorldLocation(double latitude, double longitude, bool guessTimezone)\n\t: _timezoneInfo(nullptr)\n#ifdef HSS_USE_CACHING\n\t, m_sunCache(2), m_solarCache(2)\n#endif\n{\n\t_latitude = DEGREE_TO_RADIAN(latitude);\n\t_longitude = DEGREE_TO_RADIAN(longitude);\n\tif (guessTimezone)\n\t{\n\t\t_timezoneInfo = GuessTimeZone(0);\n\t\t__timezone = _timezoneInfo->m_timezone;\n\t\tif (_timezoneInfo->m_dst.GetTotalSeconds() > 0)\n\t\t{\n\t\t\t_startDST = WTimeSpan(0);\n\t\t\t_endDST = WTimeSpan(366, 0, 0, 0);\n\t\t\t_amtDST = _timezoneInfo->m_dst;\n\t\t}\n\t}\n\n\tcanada = nullptr;\n}\n\n\nWorldLocation::~WorldLocation() {\n#if defined(_MSC_VER) || defined(_USE_CANADA)\n\tinsideCanadaCleanup(canada);\n#endif\n}\n\n\nWorldLocation &WorldLocation::operator=(const WorldLocation &wl) {\n\tif (&wl != this) {\n\t\t_latitude = wl._latitude;\n\t\t_longitude = wl._longitude;\n\t\t__timezone = wl.__timezone;\n\t\t_startDST = wl._startDST;\n\t\t_endDST = wl._endDST;\n\t\t_amtDST = wl._amtDST;\n\n#ifdef HSS_USE_CACHING\n\t\tm_sunCache.Clear();\n\t\tm_solarCache.Clear();\n#endif\n\n\t}\n\treturn *this;\n}\n\n\nbool WorldLocation::operator==(const WorldLocation &wl) const {\n\tif (&wl == this)\n\t\treturn true;\n\tif ((_latitude == wl._latitude) &&\n\t (_longitude == wl._longitude) &&\n\t (__timezone == wl.__timezone) &&\n\t (_startDST == wl._startDST) &&\n\t (_endDST == wl._endDST) &&\n\t (_amtDST == wl._amtDST))\n\t\treturn true;\n\treturn false;\n}\n\n\nbool WorldLocation::operator!=(const WorldLocation &wl) const {\n\treturn !(*this == wl);\n}\n\n\nWTimeSpan WorldLocation::m_solar_timezone(const WTime &solar_time) const {\n#ifdef HSS_USE_CACHING\n\tstruct sun_key sk;\n\tsk.m_sun_cache_lat = m_latitude;\n\tsk.m_sun_cache_long = m_longitude;\n\tsk.m_sun_cache_tm = solar_time.GetTime(0);\n\tWTimeSpan retval;\n\tif (m_solarCache.Retrieve(&sk, &retval))\n\t\treturn retval;\n#endif\n\n\tINTNM::int32_t\tday = solar_time.GetDay(WTIME_FORMAT_AS_LOCAL),\n\t\tyear = solar_time.GetYear(WTIME_FORMAT_AS_LOCAL),\n\t\tmonth = solar_time.GetMonth(WTIME_FORMAT_AS_LOCAL);\n\n\tCSunriseSunsetCalc calculator;\n\tRISESET_IN_STRUCT sInput;\n\tsInput.Latitude = RADIAN_TO_DEGREE(_latitude);\n\tsInput.Longitude = -RADIAN_TO_DEGREE(_longitude);\n\tsInput.timezone = 0;\n\tsInput.DaytimeSaving = false;\n\tsInput.year = year;\n\tsInput.month = month;\n\tsInput.day = day;\n\tRISESET_OUT_STRUCT sOut;\n\tcalculator.calcSun(sInput,&sOut);\n\n\tWTimeSpan solarTime(0, sOut.SolarNoonHour - 12, sOut.SolarNoonMin, (INTNM::int32_t)sOut.SolarNoonSec);\n\tWTimeSpan result((INTNM::int64_t)0 - solarTime.GetTotalSeconds());\n\t\n#ifdef HSS_USE_CACHING\n\tm_solarCache.Store(&sk, &result);\n#endif\n\treturn result;\n}\n\n\n#if defined(TIMES_WINDOWS) && !defined(_NO_MFC)\nCArchive& HSS_Time::operator>>(CArchive& is, WorldLocation &wl) {\n\tunion {\n\t\tshort svalue[4];\n\t\tdouble dvalue;\n\t} loader;\n\tis >> loader.svalue[0] >> loader.svalue[1];\n\tif (loader.svalue[0] == -1) {\n\t\tif ((loader.svalue[1] < 1) || (loader.svalue[1] > 6))\n\t\t\tAfxThrowArchiveException(CArchiveException::badSchema, _T(\"World Location\"));\n\t\tbool found = false;\n\t\tif (loader.svalue[1] >= 5) {\n\t\t\tuint32_t id;\n\t\t\tis >> id;\n\t\t\tif (id != (uint32_t)-1) {\n\t\t\t\twl.SetTimeZoneOffset(id);\n\t\t\t\tfound = true;\n\t\t\t}\n\t\t}\n\t\tis >> wl._latitude >> wl._longitude;\n\t\tif (!found) {\n\t\t\tif (loader.svalue[1] < 3) {\n\t\t\t\tINTNM::int32_t timezone;\n\t\t\t\tis >> timezone;\n\t\t\t\twl.__timezone = WTimeSpan(timezone);\n\t\t\t} else\tis >> wl.__timezone;\n\t\t\tif (loader.svalue[1] < 4) {\n\t\t\t\tINTNM::int16_t spheroid;\n\t\t\t\tis >> spheroid;\n\t\t\t}\n\t\t\tif (loader.svalue[1] == 2) {\n\t\t\t\tINTNM::int32_t ts;\n\t\t\t\tis >> ts; wl._startDST = WTimeSpan(ts);\n\t\t\t\tis >> ts; wl._endDST = WTimeSpan(ts);\n\t\t\t\tis >> ts; wl._amtDST = WTimeSpan(ts);\n\t\t\t} else if (loader.svalue[1] >= 3)\n\t\t\t\tis >> wl._startDST >> wl._endDST >> wl._amtDST;\n\n\t\t\t// try to guess what the timezone ID is\n\t\t\tconst ::TimeZoneInfo* tz;\n\t\t\tif ((wl._amtDST.GetTotalSeconds() == 0) && (!wl.dstExists()))\ttz = wl.m_std_timezones;\n\t\t\telse\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttz = wl.m_dst_timezones;\n\t\t\twhile (tz->m_name) {\n\t\t\t\tif ((tz->m_timezone == wl.__timezone) && (tz->m_dst == wl._amtDST)) {\n\t\t\t\t\twl._timezoneInfo = tz;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\ttz++;\n\t\t\t}\n\t\t}\n\t} else {\n\t\tis >> loader.svalue[2] >> loader.svalue[3];\n\t\twl._latitude = loader.dvalue;\n\t\tis >> wl._longitude >> wl.__timezone;\n\t}\n\treturn is;\n}\n\n\nCArchive& HSS_Time::operator<<(CArchive& os, const WorldLocation &wl) {\n\tshort hickup = -1;\n\tshort version = 6;\t\t// 4 removed m_spheroid\n\t\t\t\t\t\t\t// 5 added id\n\t\t\t\t\t\t\t// 6 re-added latitude, longitude\n\tos << hickup << version;\n\tuint32_t id;\n\tif (wl.m_timezoneInfo()) {\n\t\tid = wl.m_timezoneInfo()->m_id;\n\t\tos << id;\n\t\tos << wl._latitude << wl._longitude;\n\t} else {\n\t\tid = (uint32_t)-1;\n\t\tos << id << wl._latitude << wl._longitude << wl.__timezone;\n\t\tos << wl._startDST << wl._endDST << wl._amtDST;\n\t}\n\treturn os;\n}\n#endif\n\n\nINTNM::int16_t WorldLocation::m_sun_rise_set(const WTime &daytime, WTime *Rise, WTime *Set, WTime *Noon) const {\n#ifdef HSS_USE_CACHING\n\tstruct sun_key sk;\n\tsk.m_sun_cache_lat = m_latitude;\n\tsk.m_sun_cache_long = m_longitude;\n\tsk.m_sun_cache_tm = daytime.GetTime(0);\n\tstruct sun_val sv;\n\tif (m_sunCache.Retrieve(&sk, &sv)) {\n\t\t*Rise = WTime(sv.m_sun_cache_rise, Rise->GetTimeManager());\n\t\t*Set = WTime(sv.m_sun_cache_set, Set->GetTimeManager());\n\t\t*Noon = WTime(sv.m_sun_cache_noon, Noon->GetTimeManager());\n\t\treturn sv.m_success;\n\t}\n#endif\n\n\tINTNM::int32_t\tday = daytime.GetDay(WTIME_FORMAT_AS_SOLAR),\n\t\tyear = daytime.GetYear(WTIME_FORMAT_AS_SOLAR),\n\t\tmonth = daytime.GetMonth(WTIME_FORMAT_AS_SOLAR);\n\n\tCSunriseSunsetCalc calculator;\n\tRISESET_IN_STRUCT sInput;\n\tsInput.Latitude = RADIAN_TO_DEGREE(_latitude);\n\tsInput.Longitude = -RADIAN_TO_DEGREE(_longitude);\n\tsInput.timezone = 0;\n\tsInput.DaytimeSaving = false;\n\tsInput.year = year;\n\tsInput.month = month;\n\tsInput.day = day;\n\tRISESET_OUT_STRUCT sOut;\n\tINTNM::int16_t success = calculator.calcSun(sInput,&sOut);\n\n\tWTime riseTime(0ULL, Rise->GetTimeManager()),\n\t\tsetTime(0ULL, Set->GetTimeManager()),\n\t\tnoonTime(0ULL, Noon->GetTimeManager());\n\tif (!(success & NO_SUNRISE))\n\t\triseTime = WTime(sOut.YearRise,sOut.MonthRise,sOut.DayRise,sOut.HourRise,sOut.MinRise,(INTNM::int32_t)sOut.SecRise,Rise->GetTimeManager());\n\t*Rise = riseTime;\n\tif (!(success & NO_SUNSET))\n\t\tsetTime = WTime(sOut.YearSet,sOut.MonthSet,sOut.DaySet,sOut.HourSet,sOut.MinSet,(INTNM::int32_t)sOut.SecSet,Set->GetTimeManager());\n\t*Set = setTime;\n\tnoonTime = WTime(sInput.year,sInput.month,sInput.day, sOut.SolarNoonHour, sOut.SolarNoonMin, (INTNM::int32_t)sOut.SolarNoonSec,Noon->GetTimeManager());\n\t*Noon = noonTime;\n\t\t\n#ifdef HSS_USE_CACHING\n\tsk.m_sun_cache_tm = daytime.GetTime(0);\n\tsk.m_sun_cache_lat = m_latitude;\n\tsk.m_sun_cache_long = m_longitude;\n\tsv.m_sun_cache_rise = Rise->GetTotalSeconds();\n\tsv.m_sun_cache_set = Set->GetTotalSeconds();\n\tsv.m_sun_cache_noon = Noon->GetTotalSeconds();\n\tsv.m_success = success;\n\tm_sunCache.Store(&sk, &sv);\n#endif\n\treturn success;\n}\n", "meta": {"hexsha": "f6946dea803b6b2be3883b36740ca9ad8bf03f9f", "size": 55361, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cxx/src/worldlocation.cpp", "max_stars_repo_name": "HeartlandSoftware/WTime", "max_stars_repo_head_hexsha": "9285b780e930a72bb10dd0811ad1177ff6c25aae", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "cxx/src/worldlocation.cpp", "max_issues_repo_name": "HeartlandSoftware/WTime", "max_issues_repo_head_hexsha": "9285b780e930a72bb10dd0811ad1177ff6c25aae", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cxx/src/worldlocation.cpp", "max_forks_repo_name": "HeartlandSoftware/WTime", 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YES\n2. NO", "lm_q1_score": 0.6224593312018545, "lm_q2_score": 0.37754066879814546, "lm_q1q2_score": 0.23500371220159447}} {"text": "// json parser\n#include \n#include \n#include \nnamespace pt = boost::property_tree;\n\n#include \"../ReflectionCoefficients/ReflectionCoefficientsFactory.h\"\n#include \"GreensTensorPlate.h\"\n#include \"GreensTensorVacuum.h\"\n#include \"GreensTensorPlateVacuum.h\"\n\nGreensTensorPlateVacuum::GreensTensorPlateVacuum(\n double v, double beta, double za,\n std::shared_ptr reflection_coefficients,\n double delta_cut, vec::fixed<2> rel_err)\n : GreensTensorPlate(v, beta, za, std::move(reflection_coefficients), delta_cut,\n rel_err) {\n this->vacuum_greens_tensor =\n std::make_shared(v, beta, rel_err(0));\n}\n\nGreensTensorPlateVacuum::GreensTensorPlateVacuum(const std::string &input_file)\n : GreensTensorPlate(input_file) {\n // Create a root\n pt::ptree root;\n\n // Load the json file in this ptree\n pt::read_json(input_file, root);\n\n std::string addvacuum = root.get(\"GreensTensor.addvacuum\");\n assert(addvacuum == \"true\");\n\n this->vacuum_greens_tensor =\n std::make_shared(v, beta, this->rel_err(0));\n}\n\nvoid GreensTensorPlateVacuum::integrate_k(\n double omega, cx_mat::fixed<3, 3> >, \n Tensor_Options fancy_complex, Weight_Options weight_function) const {\n\n //compute the contributions from the planar surface\n GreensTensorPlate::integrate_k(omega, GT, fancy_complex, weight_function);\n\n //compute the contributions from the vacuum\n cx_mat::fixed<3, 3> vac;\n vacuum_greens_tensor->integrate_k(omega, vac, fancy_complex, weight_function);\n\n GT += vac;\n}\n\nvoid GreensTensorPlateVacuum::calculate_tensor(double omega, vec::fixed<2> k,\n cx_mat::fixed<3, 3> >) const {\n\n //compute the contributions from the planar surface\n GreensTensorPlate::calculate_tensor(omega, k, GT);\n\n //compute the contributions from the vacuum\n cx_mat::fixed<3, 3> vac;\n vacuum_greens_tensor->calculate_tensor(omega, k, vac);\n\n GT += vac;\n}\n\nvoid GreensTensorPlateVacuum::print_info(std::ostream &stream) const {\n stream << \"# GreensTensorPlateVacuum\\n#\\n\"\n << \"# v = \" << v << \"\\n\"\n << \"# beta = \" << beta << \"\\n\"\n << \"# za = \" << za << \"\\n\"\n << \"# delta_cut = \" << delta_cut << \"\\n\"\n << \"# rel_err = \" << rel_err(0) << \",\" << rel_err(1) << \"\\n\";\n}\n", "meta": {"hexsha": "9ead8127d91db9b83d7c052be3e804da583c0a7e", "size": 2420, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/GreensTensor/GreensTensorPlateVacuum.cpp", "max_stars_repo_name": "QuaCaTeam/quaca", "max_stars_repo_head_hexsha": "ab2d213f3e0e357bd72930ae1e4e703184130270", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-05-19T09:01:40.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-20T07:57:54.000Z", "max_issues_repo_path": "src/GreensTensor/GreensTensorPlateVacuum.cpp", "max_issues_repo_name": "myoelmy/quaca", "max_issues_repo_head_hexsha": "def47981b710a73f2fb3a7c14c354f8de91cf88f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 31.0, "max_issues_repo_issues_event_min_datetime": "2020-05-19T08:01:46.000Z", "max_issues_repo_issues_event_max_datetime": "2020-05-28T07:33:35.000Z", "max_forks_repo_path": "src/GreensTensor/GreensTensorPlateVacuum.cpp", "max_forks_repo_name": "myoelmy/quaca", "max_forks_repo_head_hexsha": "def47981b710a73f2fb3a7c14c354f8de91cf88f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.6111111111, "max_line_length": 83, "alphanum_fraction": 0.6842975207, "num_tokens": 647, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.23493508547998251}} {"text": "#include \"RouteFinder.h\"\n\n#include \n\nnamespace carto { namespace osrm {\n Result RouteFinder::find(const Query& query) const {\n std::array, 2> nearestNodes;\n std::array, 2> heaps;\n std::unordered_map pathSuffixMap;\n float minWeight = 0.0f;\n for (int i = 0; i < 2; i++) {\n nearestNodes[i] = _graph->findNearestNode(query.getPos(i));\n if (nearestNodes[i].empty()) {\n return Result();\n }\n\n for (const Graph::NearestNode& nearestNode : nearestNodes[i]) {\n Graph::NodePtr node = _graph->getNode(nearestNode.nodeId);\n\n // Calculate end-point weights\n float weight = (i == 0 ? -nearestNode.geometryRelPos : nearestNode.geometryRelPos) * node->nodeData.weight;\n minWeight = std::min(minWeight, weight);\n\n // Special case: we have already added same node but the node is inaccessible along the current direction\n if (i == 1 && nearestNodes[0].size() == 1 && nearestNodes[1].size() == 1) {\n const Graph::NearestNode& otherNearestNode = nearestNodes[1 - i][0];\n if (nearestNode.nodeId == otherNearestNode.nodeId && nearestNode.geometryRelPos < otherNearestNode.geometryRelPos) {\n // Add all backward edges \"leading\" to current node\n for (auto edge = node->firstEdge; edge != node->lastEdge; edge++) {\n if (edge->backward) {\n heaps[i].emplace(edge->targetNodeId, Graph::NodeId(), weight + edge->edgeData.weight);\n pathSuffixMap[edge->targetNodeId] = PathNode(edge->targetNodeId, *edge, nearestNode.nodeId);\n }\n }\n\n // Here comes the tricky part: we must perform another spatial query to find INCOMING edges pointing to current edge\n std::vector geometry = _graph->getNodeGeometry(*node);\n std::vector nearestNodes2 = _graph->findNearestNode(geometry.front());\n for (const Graph::NearestNode& nearestNode2 : nearestNodes2) {\n Graph::NodePtr node2 = _graph->getNode(nearestNode2.nodeId);\n for (auto edge2 = node2->firstEdge; edge2 != node2->lastEdge; edge2++) {\n if (edge2->forward && edge2->targetNodeId == nearestNode.nodeId) {\n heaps[i].emplace(nearestNode2.nodeId, Graph::NodeId(), weight + edge2->edgeData.weight);\n pathSuffixMap[nearestNode2.nodeId] = PathNode(nearestNode2.nodeId, *edge2, nearestNode.nodeId);\n }\n }\n }\n\n continue;\n }\n }\n\n // Add the node to heap, if other nodes were not already added\n heaps[i].emplace(nearestNode.nodeId, Graph::NodeId(), weight);\n }\n }\n\n // Apply bidirectional Dijkstra\n Graph::NodeId bestNodeId;\n float bestWeight = std::numeric_limits::infinity();\n std::array, 2> settledNodes;\n for (int i = 0; !(heaps[0].empty() && heaps[1].empty()); i = 1 - i) {\n if (heaps[i].empty()) {\n continue;\n }\n SearchNode searchNode = heaps[i].top();\n heaps[i].pop();\n \n // Skip all invalid nodes\n if (searchNode.nodeId.blockId.packageId == -1) {\n continue;\n }\n\n // Already shorter path found? In that case we can stop searching in the given direction\n if (searchNode.weight + minWeight > bestWeight) {\n while (!heaps[i].empty()) {\n heaps[i].pop();\n }\n continue;\n }\n \n // Best path to give node?\n auto it0 = settledNodes[i].find(searchNode.nodeId);\n if (it0 != settledNodes[i].end()) {\n if (searchNode.weight >= it0->second.weight) { // not sure if the check is required\n continue;\n }\n }\n\n // Settle the node\n settledNodes[i][searchNode.nodeId] = searchNode;\n \n // Stalling optimization. This implementation is not optimal, we should also look at non-settled heap nodes\n Graph::NodePtr node = _graph->getNode(searchNode.nodeId);\n bool stall = false;\n for (auto edge = node->firstEdge; edge != node->lastEdge; edge++) {\n if ((i == 0 && edge->backward) || (i != 0 && edge->forward)) {\n auto it = settledNodes[i].find(edge->targetNodeId);\n if (it != settledNodes[i].end()) {\n if (it->second.weight + edge->edgeData.weight < searchNode.weight) {\n stall = true;\n break;\n }\n }\n }\n }\n if (stall) {\n continue;\n }\n\n // Recalculate shortest path and middle node\n auto it1 = settledNodes[1 - i].find(searchNode.nodeId);\n if (it1 != settledNodes[1 - i].end()) {\n float totalWeight = searchNode.weight + it1->second.weight;\n if (totalWeight >= 0 && totalWeight < bestWeight) {\n bestWeight = totalWeight;\n bestNodeId = searchNode.nodeId;\n }\n }\n\n // Add target nodes to heap\n for (auto edge = node->firstEdge; edge != node->lastEdge; edge++) {\n if ((i == 0 && edge->forward) || (i != 0 && edge->backward)) {\n heaps[i].emplace(edge->targetNodeId, searchNode.nodeId, searchNode.weight + edge->edgeData.weight);\n }\n }\n }\n\n // Check that path was found\n if (bestNodeId.blockId.packageId == -1) {\n return Result();\n }\n\n // Unpack path\n std::array, 2> paths;\n for (int i = 0; i < 2; i++) {\n std::stack> stack;\n Graph::NodeId nodeId = bestNodeId;\n while (true) {\n auto it = settledNodes[i].find(nodeId);\n assert(it != settledNodes[i].end());\n Graph::NodeId prevNodeId = it->second.prevNodeId;\n if (prevNodeId.blockId.packageId == -1) {\n break;\n }\n stack.emplace(prevNodeId, nodeId);\n nodeId = prevNodeId;\n }\n\n while (!stack.empty()) {\n std::pair nodeIds = stack.top();\n stack.pop();\n\n Graph::NodePtr matchedNode;\n const Graph::Edge* matchedEdge = nullptr;\n\n // Find the edge between prevNodeId and nodeId. Do matching based on node ids.\n Graph::NodeId prevNodeId = nodeIds.first;\n Graph::NodeId nodeId = nodeIds.second;\n for (int j = 0; j < 2 && !matchedEdge; j++) {\n Graph::NodePtr prevNode = _graph->getNode(prevNodeId);\n for (auto edge = prevNode->firstEdge; edge != prevNode->lastEdge; edge++) {\n if (edge->targetNodeId == nodeId && ((j == i && edge->forward) || (j != i && edge->backward))) {\n matchedNode = prevNode;\n matchedEdge = edge;\n break;\n }\n }\n std::swap(nodeId, prevNodeId);\n }\n \n // If the edge was not found, then we have a link between packages with different node encodings. Do slow matching, based on geometry, not node ids\n for (int j = 0; j < 2 && !matchedEdge; j++) {\n Graph::NodePtr prevNode = _graph->getNode(prevNodeId);\n Graph::NodePtr node = _graph->getNode(nodeId);\n std::vector nodeGeometry = _graph->getNodeGeometry(*node);\n for (auto edge = prevNode->firstEdge; edge != prevNode->lastEdge; edge++) {\n if (edge->targetNodeId.blockId.packageId == -1) {\n continue;\n }\n Graph::NodePtr targetNode = _graph->getNode(edge->targetNodeId);\n std::vector targetNodeGeometry = _graph->getNodeGeometry(*targetNode);\n if (nodeGeometry == targetNodeGeometry && ((j == i && edge->forward) || (j != i && edge->backward))) {\n matchedNode = prevNode;\n matchedEdge = edge;\n break;\n }\n }\n std::swap(nodeId, prevNodeId);\n }\n \n // Unpack the matched edge\n if (matchedEdge) {\n if (matchedEdge->contracted) {\n if (matchedEdge->contractedNodeId.blockId.packageId == -1) {\n return Result(); // Contracted node is not available, packing failed\n }\n stack.emplace(matchedEdge->contractedNodeId, nodeIds.second);\n stack.emplace(nodeIds.first, matchedEdge->contractedNodeId);\n }\n else {\n paths[i].emplace_back(nodeIds.first, *matchedEdge, nodeIds.second);\n }\n } else {\n return Result(); // NOTE: this should not happen, unless the graph is broken\n }\n }\n }\n\n // Build joined path. Add pseudo-node at the beginning to simplify processing and add final node, if rerouting in case of one-way street\n std::vector path;\n path = paths[0];\n for (auto it = paths[1].rbegin(); it != paths[1].rend(); it++) {\n path.emplace_back(it->nextNodeId, it->edge, it->prevNodeId);\n }\n if (path.empty()) {\n path.emplace(path.begin(), bestNodeId, Graph::Edge(), bestNodeId);\n }\n else {\n Graph::NodeId firstNodeId = path.front().prevNodeId;\n path.emplace(path.begin(), firstNodeId, Graph::Edge(), firstNodeId);\n }\n\n auto finalNodeIt = pathSuffixMap.find(path.back().nextNodeId);\n if (finalNodeIt != pathSuffixMap.end()) {\n path.push_back(finalNodeIt->second);\n }\n\n // Construct query result\n std::vector instructions;\n std::vector routeVertices;\n for (std::size_t j = 0; j < path.size(); j++) {\n Graph::NodeId nodeId = path[j].nextNodeId;\n Graph::NodePtr node = _graph->getNode(nodeId);\n \n std::vector geometry = _graph->getNodeGeometry(*node);\n std::pair geometryIndex(0, geometry.size());\n std::pair geometryRelPos(0.0f, 1.0f);\n\n std::size_t firstNNIndex = std::numeric_limits::max();\n if (j == 0) {\n for (std::size_t k = 0; k < nearestNodes[0].size(); k++) {\n if (nearestNodes[0][k].nodeId == nodeId) {\n geometryIndex.first = nearestNodes[0][k].geometrySegmentIndex;\n geometryRelPos.first = nearestNodes[0][k].geometryRelPos;\n firstNNIndex = k;\n break;\n }\n }\n }\n\n std::size_t lastNNIndex = std::numeric_limits::max();\n if (j == path.size() - 1) {\n for (std::size_t k = 0; k < nearestNodes[1].size(); k++) {\n if (nearestNodes[1][k].nodeId == nodeId) {\n geometryIndex.second = nearestNodes[1][k].geometrySegmentIndex;\n geometryRelPos.second = nearestNodes[1][k].geometryRelPos;\n lastNNIndex = k;\n break;\n }\n }\n }\n\n double dist = calculateGeometryLength(geometry, geometryRelPos.first, geometryRelPos.second);\n double time = (j > 0 ? path[j].edge.edgeData.weight : node->nodeData.weight) * (geometryRelPos.second - geometryRelPos.first) / 10.0;\n std::string streetName = _graph->getNodeName(*node);\n\n // Initial route instruction/vertex\n if (firstNNIndex != std::numeric_limits::max()) {\n instructions.emplace_back(Instruction::Type::HEAD_ON, Instruction::TravelMode::DEFAULT, streetName, dist, time, routeVertices.size());\n routeVertices.push_back(nearestNodes[0][firstNNIndex].nodePos);\n }\n \n // Middle instructions/vertices\n if (!routeVertices.empty() && geometryIndex.first < geometryIndex.second) {\n if (routeVertices.back() == geometry[geometryIndex.first]) {\n routeVertices.pop_back();\n }\n }\n std::size_t vertexIndex = routeVertices.size();\n routeVertices.insert(routeVertices.end(), geometry.begin() + geometryIndex.first, geometry.begin() + geometryIndex.second);\n if (j > 0) {\n Instruction::Type type = static_cast(path[j].edge.edgeData.turnInstruction);\n Instruction::TravelMode travelMode = static_cast(node->nodeData.travelMode);\n instructions.emplace_back(type, travelMode, streetName, dist, time, vertexIndex);\n }\n\n // Final instruction/vertex\n if (lastNNIndex != std::numeric_limits::max()) {\n instructions.emplace_back(Instruction::Type::REACHED_YOUR_DESTINATION, Instruction::TravelMode::DEFAULT, \"\", 0, 0, routeVertices.size());\n routeVertices.push_back(nearestNodes[1][lastNNIndex].nodePos);\n }\n }\n\n return Result(std::move(instructions), std::move(routeVertices));\n }\n\n double RouteFinder::calculateGeometryLength(const std::vector& geometry, double t0, double t1) {\n double totalLen = 0;\n for (unsigned int j = 1; j < geometry.size(); j++) {\n totalLen += calculateGreatCircleDistance(geometry[j - 1], geometry[j]);\n }\n if (t0 == 0 && t1 == 1) {\n return totalLen;\n }\n\n double pos = 0;\n double len = 0;\n for (unsigned int j = 1; j < geometry.size(); j++) {\n double segmentLen = calculateGreatCircleDistance(geometry[j - 1], geometry[j]);\n double segmentPos0 = std::max(pos, t0 * totalLen);\n double segmentPos1 = std::min(pos + segmentLen, t1 * totalLen);\n len += std::max(0.0, segmentPos1 - segmentPos0);\n pos += segmentLen;\n }\n return len;\n }\n\n double RouteFinder::calculateGreatCircleDistance(const WGSPos& p0, const WGSPos& p1) {\n const double degToRad = boost::math::constants::pi() / 180.0;\n\n double lat1 = p0(0) * degToRad;\n double lng1 = p0(1) * degToRad;\n double lat2 = p1(0) * degToRad;\n double lng2 = p1(1) * degToRad;\n\n double dLng = lng1 - lng2;\n double dLat = lat1 - lat2;\n\n double aHarv = std::pow(std::sin(dLat / 2.0), 2) + std::cos(lat1) * std::cos(lat2) * std::pow(std::sin(dLng / 2.0), 2);\n double cHarv = 2.0 * std::atan2(std::sqrt(aHarv), std::sqrt(1.0 - aHarv));\n return EARTH_RADIUS * cHarv;\n }\n} }\n", "meta": {"hexsha": "787ecb745998cbdce044a61bfe0baa4f7f05bcaa", "size": 16201, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "osrm/src/osrm/RouteFinder.cpp", "max_stars_repo_name": "farfromrefug/mobile-carto-libs", "max_stars_repo_head_hexsha": "c7e81a7c73661aa047de9ba7e8bbdf3a24bbf1df", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 6.0, "max_stars_repo_stars_event_min_datetime": "2018-06-27T17:43:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-29T18:50:49.000Z", "max_issues_repo_path": "osrm/src/osrm/RouteFinder.cpp", "max_issues_repo_name": "farfromrefug/mobile-carto-libs", "max_issues_repo_head_hexsha": "c7e81a7c73661aa047de9ba7e8bbdf3a24bbf1df", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 22.0, "max_issues_repo_issues_event_min_datetime": "2019-04-10T06:38:09.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-20T08:12:02.000Z", "max_forks_repo_path": "osrm/src/osrm/RouteFinder.cpp", "max_forks_repo_name": "farfromrefug/mobile-carto-libs", "max_forks_repo_head_hexsha": "c7e81a7c73661aa047de9ba7e8bbdf3a24bbf1df", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2019-03-12T10:25:20.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-28T10:18:56.000Z", "avg_line_length": 48.3611940299, "max_line_length": 163, "alphanum_fraction": 0.515091661, "num_tokens": 3534, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.640635854839898, "lm_q2_score": 0.36658973632215985, "lm_q1q2_score": 0.2348505291042797}} {"text": "#ifndef LBP_SCSLBP_HPP\n#define LBP_SCSLBP_HPP\n\n#include \n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n\n#include \n\n//\n// @inproceedings{xue2010dynamic,\n// title={Dynamic background subtraction based on spatial extended center-symmetric local binary pattern},\n// author={Xue, Gengjian and Sun, Jun and Song, Li},\n// booktitle={Multimedia and Expo (ICME), 2010 IEEE International Conference on},\n// pages={1050--1054},\n// year={2010},\n// organization={IEEE}\n// }\n//\n\nnamespace lbp {\nnamespace scslbp_detail {\n\nconst auto scslbp = [](auto N, auto S) {\n const auto n = hana::size (N);\n\n return [=](const cv::Mat& src, const cv::Mat& m, const cv::Mat& s,\n size_t i, size_t j) {\n using namespace hana::literals;\n\n const auto f = abs (\n S (src, i, j) - S (m, i, j)) >= 2.5 * S (s, i, j);\n\n return boost::hana::fold_left (\n N, 0, [&, shift = 0](auto accum, auto x) mutable {\n const auto a = S (src, i + x [0_c], j + x [1_c]);\n const auto b = S (src, i - x [0_c], j - x [1_c]);\n return accum + ((a >= b) << shift++);\n }) + (int (f) << n);\n };\n};\n\n} // namespace scslbp_detail\n\ntemplate< typename T, size_t R, size_t P >\nauto scslbp = [](double alpha = 0.05) {\n //\n // Learning rate for both mean and variance:\n //\n LBP_ASSERT (0. <= alpha && alpha <= 1.);\n\n //\n // Mean, variance, standard deviation and previous frame:\n //\n cv::Mat m, v, s, I;\n\n //\n // Elementary operator:\n //\n auto op = scslbp_detail::scslbp (\n detail::semicircular_neighborhood< R, P >,\n detail::nearest_sampler< float >);\n\n return [=, init = 0](const cv::Mat& arg) mutable {\n using value_type = typename boost::uint_t< P/2 + 1 >::least;\n\n //\n // Only works on single-channel, gray images:\n //\n LBP_ASSERT (1 == arg.channels ());\n\n cv::Mat src = lbp::equalize (lbp::convert (arg, CV_32F));\n\n if (0 == init && 1 == ++init) {\n I = src.clone ();\n\n m = I.clone ();\n v = I.clone ();\n s = I.clone ();\n\n return I;\n }\n\n cv::Mat dst (src.size (), opencv_type< value_type >, cv::Scalar (0));\n\n //\n // Exponential moving average for estimating the mean:\n //\n cv::Mat m_ = (1 - alpha) * m + alpha * src;\n\n //\n // Rolling variance with a twist from [2009Gil-Jiménez] (see chapter 2):\n //\n cv::Mat v_ = (1 - alpha) * v + (alpha / 2) * lbp::pow (src - I, 2);\n cv::Mat s_ = lbp::sqrt (v_);\n\n#pragma omp parallel for\n for (size_t i = R; i < src.rows - R - 1; ++i) {\n for (size_t j = R; j < src.cols - R - 1; ++j) {\n dst.at< value_type > (i, j) = op (src, m, s, i, j);\n }\n }\n\n m = m_;\n v = v_;\n s = s_;\n\n I = src.clone ();\n\n return dst;\n };\n};\n\n} // namespace lbp\n\n//\n// References:\n//\n// [2009Gil-Jiménez] Gil-Jiménez, Pedro, et al. \"Continuous variance estimation\n// in video surveillance sequences with high illumination changes.\" Signal\n// Processing 89.7 (2009): 1412-1416.\n//\n//\n\n#endif // LBP_SCSLBP_HPP\n", "meta": {"hexsha": "fe8a0b570ba0d3666e0d5d2f4f2026e807512b12", "size": 3423, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/lbp/scslbp.hpp", "max_stars_repo_name": "thinkoid/lbp", "max_stars_repo_head_hexsha": "9ce3bc41a8961cf0304c5d271cd882b4cae78cb2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2019-11-02T12:45:36.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-25T03:51:09.000Z", "max_issues_repo_path": "include/lbp/scslbp.hpp", "max_issues_repo_name": "thinkoid/lbp", "max_issues_repo_head_hexsha": "9ce3bc41a8961cf0304c5d271cd882b4cae78cb2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/lbp/scslbp.hpp", "max_forks_repo_name": "thinkoid/lbp", "max_forks_repo_head_hexsha": "9ce3bc41a8961cf0304c5d271cd882b4cae78cb2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2018-08-02T09:10:08.000Z", "max_forks_repo_forks_event_max_datetime": "2018-08-02T09:19:34.000Z", "avg_line_length": 25.7368421053, "max_line_length": 108, "alphanum_fraction": 0.5349108969, "num_tokens": 1000, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.23484043695244028}} {"text": "#define BIORBD_API_EXPORTS\n#include \"RigidBody/Integrator.h\"\n\n#include \n#include \n#include \n\n#include \"Utils/Error.h\"\n#include \"Utils/String.h\"\n#include \"RigidBody/GeneralizedCoordinates.h\"\n#include \"RigidBody/GeneralizedVelocity.h\"\n#include \"RigidBody/GeneralizedAcceleration.h\"\n#include \"RigidBody/Joints.h\"\n\nbiorbd::rigidbody::Integrator::Integrator(biorbd::rigidbody::Joints &model) :\n m_steps(std::make_shared()),\n m_model(&model),\n m_x_vec(std::make_shared>()),\n m_times(std::make_shared>()),\n m_u(std::make_shared()) {\n\n}\n\nbiorbd::rigidbody::Integrator::~Integrator()\n{\n\n}\n\nbiorbd::rigidbody::Integrator biorbd::rigidbody::Integrator::DeepCopy() const\n{\n biorbd::rigidbody::Integrator copy(*this->m_model);\n copy.DeepCopy(*this);\n return copy;\n}\n\nvoid biorbd::rigidbody::Integrator::DeepCopy(const biorbd::rigidbody::Integrator &other)\n{\n *m_steps = *other.m_steps;\n m_model = other.m_model;\n m_x_vec->resize(other.m_x_vec->size());\n for (unsigned int i=0; isize(); ++i)\n (*m_x_vec)[i] = (*other.m_x_vec)[i];\n m_times->resize(other.m_times->size());\n for (unsigned int i=0; isize(); ++i)\n (*m_times)[i] = (*other.m_times)[i];\n *m_u = *other.m_u;\n}\n\nvoid biorbd::rigidbody::Integrator::operator() (\n const state_type &x ,\n state_type &dxdt ,\n double ){\n\n // Équation différentielle : x/xdot => xdot/xddot\n biorbd::rigidbody::GeneralizedCoordinates Q(*m_model);\n biorbd::rigidbody::GeneralizedVelocity QDot(*m_model);\n biorbd::rigidbody::GeneralizedAcceleration QDDot(*m_model);\n QDDot.setZero();\n for (unsigned int i=0; i<*m_nQ; i++){\n Q(i) = x[i];\n }\n for (unsigned int i=0; i<*m_nQdot; i++){\n QDot(i) = x[i+*m_nQ];\n }\n\n RigidBodyDynamics::ForwardDynamics (*m_model, Q, QDot, *m_u, QDDot);\n\n // Faire sortir xdot/xddot\n for (unsigned int i=0; i<*m_nQ; i++){\n dxdt[i] = QDot[i];\n }\n for (unsigned int i=0; i<*m_nQdot; i++){\n dxdt[i + *m_nQ] = QDDot[i];\n }\n\n}\n\nvoid biorbd::rigidbody::Integrator::showAll(){\n std::cout << \"Test:\" << std::endl;\n for (unsigned int i=0; i <= *m_steps; i++){\n std::cout << (*m_times)[i];\n for (unsigned int j = 0; j < *m_nQ + *m_nQdot; j++)\n std::cout << \" \" << (*m_x_vec)[i][j];\n std::cout << std::endl;\n }\n}\n\nunsigned int biorbd::rigidbody::Integrator::steps() const\n{\n return *m_steps+1;\n}\n\nbiorbd::utils::Vector biorbd::rigidbody::Integrator::getX(\n unsigned int idx){\n biorbd::utils::Vector out(*m_nQ + *m_nQdot);\n biorbd::utils::Error::check(idx <= *m_steps, \"Trying to get Q outside range\");\n for (unsigned int i=0; i<*m_nQ + *m_nQdot; i++){\n out(i) = (*m_x_vec)[idx][i];\n }\n return out;\n}\n\ndouble biorbd::rigidbody::Integrator::time(unsigned int idx)\n{\n return (*m_times)[idx];\n}\n\nvoid biorbd::rigidbody::Integrator::integrate(\n const biorbd::utils::Vector &Q_Qdot,\n const biorbd::utils::Vector &u,\n double t0,\n double tend,\n double timeStep){\n // These variable can't be computer a construct time because of\n // interaction calls with biorbd::rigidbody::Joints\n m_nQ = std::make_shared(m_model->nbQ());\n m_nQdot = std::make_shared(m_model->nbQdot());\n\n#ifndef SKIP_ASSERT\n biorbd::utils::Error::check(\n Q_Qdot.size() == *m_nQ + *m_nQdot,\n \"Wrong size for Q and Qdot\");\n#endif\n\n // Assume constant torque over the whole integration\n *m_u = u;\n\n // Remplissage de la variable par les positions et vitesse\n state_type x(*m_nQ + *m_nQdot);\n for (unsigned int i=0; i<*m_nQ + *m_nQdot; i++)\n x[i] = Q_Qdot(i);\n\n launchIntegrate(x, t0, tend, timeStep);\n}\n\nvoid biorbd::rigidbody::Integrator::launchIntegrate(\n state_type& x,\n double t0,\n double tend,\n double timeStep)\n{\n // Choix de l'algorithme et intégration\n boost::numeric::odeint::runge_kutta4< state_type > stepper;\n *m_steps = static_cast(\n boost::numeric::odeint::integrate_const(\n stepper, *this, x, t0, tend, timeStep,\n push_back_state_and_time( *m_x_vec , *m_times )));\n}\n", "meta": {"hexsha": "5b86bb26605af61e3e8c30681441a9ee8c429c76", "size": 4410, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/RigidBody/Integrator.cpp", "max_stars_repo_name": "jdowlingmedley/biorbd", "max_stars_repo_head_hexsha": "591a5372d815af626fa500047fe04743c76d7e13", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/RigidBody/Integrator.cpp", "max_issues_repo_name": "jdowlingmedley/biorbd", "max_issues_repo_head_hexsha": "591a5372d815af626fa500047fe04743c76d7e13", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/RigidBody/Integrator.cpp", "max_forks_repo_name": "jdowlingmedley/biorbd", "max_forks_repo_head_hexsha": "591a5372d815af626fa500047fe04743c76d7e13", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.5973154362, "max_line_length": 88, "alphanum_fraction": 0.6253968254, "num_tokens": 1354, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073802837477, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.23473581523096895}} {"text": "/************************************************************************/\n/* */\n/* Copyright 2011 by Ullrich Koethe and Michael Hanselmann */\n/* */\n/* This file is part of the VIGRA computer vision library. */\n/* The VIGRA Website is */\n/* http://hci.iwr.uni-heidelberg.de/vigra/ */\n/* Please direct questions, bug reports, and contributions to */\n/* ullrich.koethe@iwr.uni-heidelberg.de or */\n/* vigra@informatik.uni-hamburg.de */\n/* */\n/* Permission is hereby granted, free of charge, to any person */\n/* obtaining a copy of this software and associated documentation */\n/* files (the \"Software\"), to deal in the Software without */\n/* restriction, including without limitation the rights to use, */\n/* copy, modify, merge, publish, distribute, sublicense, and/or */\n/* sell copies of the Software, and to permit persons to whom the */\n/* Software is furnished to do so, subject to the following */\n/* conditions: */\n/* */\n/* The above copyrigfht notice and this permission notice shall be */\n/* included in all copies or substantial portions of the */\n/* Software. */\n/* */\n/* THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND */\n/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES */\n/* OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND */\n/* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT */\n/* HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, */\n/* WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING */\n/* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR */\n/* OTHER DEALINGS IN THE SOFTWARE. */\n/* */\n/************************************************************************/\n\n#define PY_ARRAY_UNIQUE_SYMBOL vigranumpylearning_PyArray_API\n// #define NO_IMPORT_ARRAY\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace python = boost::python;\nnamespace vigra\n{\n\ntemplate\npython::tuple\npythonPCA(NumpyArray<2,U> features, int nComponents)\n{\n vigra_precondition(!features.axistags(),\n \"principleComponents(): feature matrix must not have axistags\\n\"\n \"(use 'array.view(numpy.ndarray)' to remove them).\");\n \n NumpyArray<2, U> fz(Shape2(features.shape(0), nComponents)); \n NumpyArray<2, U> zv(Shape2(nComponents, features.shape(1))); \n\n {\n PyAllowThreads _pythread;\n principleComponents(features, fz, zv);\n }\n return python::make_tuple(fz, zv);\n}\n\ntemplate\npython::tuple\npythonPLSA(NumpyArray<2,U> features, \n int nComponents,\n int nIterations,\n double minGain,\n bool normalize)\n{\n vigra_precondition(!features.axistags(),\n \"pLSA(): feature matrix must not have axistags\\n\"\n \"(use 'array.view(numpy.ndarray)' to remove them).\");\n \n NumpyArray<2, U> fz(Shape2(features.shape(0), nComponents)); \n NumpyArray<2, U> zv(Shape2(nComponents, features.shape(1))); \n\n {\n PyAllowThreads _pythread;\n pLSA(features, fz, zv,\n RandomNumberGenerator<>(), \n PLSAOptions().maximumNumberOfIterations(nIterations)\n .minimumRelativeGain(minGain)\n .normalizedComponentWeights(normalize));\n }\n return python::make_tuple(fz, zv);\n}\n\n\nvoid defineUnsupervised()\n{\n using namespace python;\n \n docstring_options doc_options(true, true, false);\n\n def(\"principleComponents\", registerConverters(&pythonPCA),\n (arg(\"features\"), arg(\"nComponents\")),\n \"\\nPerform principle component analysis. \\n\\n\"\n \"The imput matrix 'features' must have shape (nFeatures*nSamples). PCA will\\n\"\n \"reduce it to a smaller matrix 'C' with shape (nComponents*nSamples) that \\n\"\n \"preserves as much variance as possible. Specifically, the call::\\n\\n\"\n \" P, C = principleComponents(features, 3)\\n\\n\"\n \"returns a projection matrix 'P' with shape (nComponents*nFeatures)\\n\"\n \"such that ``C = numpy.dot(numpy.transpose(P), features)``. Conversely, the\\n\"\n \"matrix ``f = numpy.dot(P, C)`` is the best possible rank-nComponents\\n\"\n \"approximation to the matrix 'features' under the least-squares criterion.\\n\\n\"\n \"See principleComponents_ in the C++ documentation for more detailed\\ninformation.\\n\\n\");\n\n PLSAOptions options;\n\n def(\"pLSA\", registerConverters(&pythonPLSA),\n (arg(\"features\"), arg(\"nComponents\"), arg(\"nIterations\") = options.max_iterations,\n arg(\"minGain\") = options.min_rel_gain, arg(\"normalize\") = options.normalized_component_weights),\n \"\\nPerform probabilistic latent semantic analysis. \\n\\n\"\n \"The imput matrix 'features' must have shape (nFeatures*nSamples). PCA will\\n\"\n \"reduce it to a smaller matrix 'C' with shape (nComponents*nSamples) that \\n\"\n \"preserves as much information as possible. Specifically, the call::\\n\\n\"\n \" P, C = pLSA(features, 3)\\n\\n\"\n \"returns a projection matrix 'P' with shape (nComponents*nFeatures)\\n\"\n \"such that the matrix ``f = numpy.dot(P, C)`` is a rank-nComponents matrix\\n\"\n \"that approximates the matrix 'features' well under the pLSA criterion.\\n\"\n \"Note that the result of pLSA() is not unique, since the algorithm uses random\\n\"\n \"initialization.\\n\\n\"\n \"See pLSA_ in the C++ documentation for more detailed\\ninformation.\\n\\n\");\n}\n\nvoid defineRandomForest();\nvoid defineRandomForestOld();\n\n} // namespace vigra\n\n\nusing namespace vigra;\nusing namespace boost::python;\n\nBOOST_PYTHON_MODULE_INIT(learning)\n{\n import_vigranumpy();\n defineUnsupervised();\n defineRandomForest();\n defineRandomForestOld();\n}\n\n\n", "meta": {"hexsha": "eb0d6a75e008c3d043b58ed668c9ad7ecb259dd9", "size": 6748, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "vigranumpy/src/core/learning.cxx", "max_stars_repo_name": "burgerdev/vigra", "max_stars_repo_head_hexsha": "f9f8d0b3224f3dc89bc7d0caf1126d49f6d4a3ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vigranumpy/src/core/learning.cxx", "max_issues_repo_name": "burgerdev/vigra", "max_issues_repo_head_hexsha": "f9f8d0b3224f3dc89bc7d0caf1126d49f6d4a3ca", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vigranumpy/src/core/learning.cxx", "max_forks_repo_name": "burgerdev/vigra", "max_forks_repo_head_hexsha": "f9f8d0b3224f3dc89bc7d0caf1126d49f6d4a3ca", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.6887417219, "max_line_length": 105, "alphanum_fraction": 0.5712803794, "num_tokens": 1420, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813030906443134, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.2345946593579748}} {"text": "//\n// cvx_pose_estimation.cpp\n// LoopClosure\n//\n// Created by jimmy on 2016-03-31.\n// Copyright © 2016 jimmy. All rights reserved.\n//\n\n#include \"cvx_pose_estimation.hpp\"\n#include \n#include \n#include \"cvx_calib3d.hpp\"\n\nusing std::cout;\nusing std::endl;\nusing cv::Mat;\n\n\nstruct HypotheseLoss\n{\n double loss_;\n Mat rvec_; // rotation for 3D --> 2D projection\n Mat tvec_; // translation for 3D --> 2D projection\n Mat affine_; // for 3D --> 3D camera to world transformation\n vector inlier_indices_; // camera coordinate index\n vector inlier_candidate_world_pts_indices_; // candidate world point index\n \n // store all inliers from preemptive ransac\n vector camera_pts_;\n vector wld_pts_;\n \n HypotheseLoss()\n {\n loss_ = INT_MAX;\n }\n HypotheseLoss(const double loss)\n {\n loss_ = loss;\n }\n \n HypotheseLoss(const HypotheseLoss & other)\n {\n loss_ = other.loss_;\n rvec_ = other.rvec_;\n tvec_ = other.tvec_;\n affine_ = other.affine_;\n inlier_indices_.clear();\n inlier_indices_.resize(other.inlier_indices_.size());\n inlier_candidate_world_pts_indices_.clear();\n inlier_candidate_world_pts_indices_.resize(other.inlier_candidate_world_pts_indices_.size());\n for(int i = 0; i 0)\n {\n camera_pts_.resize(other.camera_pts_.size());\n wld_pts_.resize(other.wld_pts_.size());\n \n assert(camera_pts_.size() == wld_pts_.size());\n for(int i = 0; i 0)\n {\n camera_pts_.resize(other.camera_pts_.size());\n wld_pts_.resize(other.wld_pts_.size());\n \n assert(camera_pts_.size() == wld_pts_.size());\n for(int i = 0; i & camera_pts,\n const vector> & candidate_wld_pts,\n const PreemptiveRANSAC3DParameter & param,\n cv::Mat & camera_pose)\n{\n assert(camera_pts.size() == candidate_wld_pts.size());\n if (camera_pts.size() < 500) {\n return false;\n }\n \n const int num_iteration = 2048;\n int K = 1024;\n const int N = (int)camera_pts.size();\n const int B = param.sample_number_;\n \n vector affine_candidate;\n for (int i = 0; i sampled_camera_pts;\n vector sampled_wld_pts;\n \n sampled_camera_pts.push_back(camera_pts[k1]);\n sampled_camera_pts.push_back(camera_pts[k2]);\n sampled_camera_pts.push_back(camera_pts[k3]);\n sampled_camera_pts.push_back(camera_pts[k4]);\n \n sampled_wld_pts.push_back(candidate_wld_pts[k1][0]);\n sampled_wld_pts.push_back(candidate_wld_pts[k2][0]);\n sampled_wld_pts.push_back(candidate_wld_pts[k3][0]);\n sampled_wld_pts.push_back(candidate_wld_pts[k4][0]);\n \n Mat affine; \n CvxCalib3D::KabschTransform(sampled_camera_pts, sampled_wld_pts, affine);\n affine_candidate.push_back(affine);\n if (affine_candidate.size() > K) {\n printf(\"initialization repeat %d times\\n\", i);\n break;\n }\n }\n printf(\"init camera parameter number is %lu\\n\", affine_candidate.size());\n \n vector losses;\n for (int i = 0; i 1) {\n // sample random set\n vector sampled_camera_pts;\n vector< vector > sampled_wld_pts; // one camera point may have multiple world points correspondences\n vector sampled_indices;\n for (int i =0; i transformed_pts;\n CvxCalib3D::rigidTransform(sampled_camera_pts, losses[i].affine_, transformed_pts);\n \n // check minimum distance from transformed points to world coordiante\n for (int j = 0; j threshold) {\n losses[i].loss_ += 1.0;\n }\n else {\n losses[i].inlier_indices_.push_back(sampled_indices[j]);\n losses[i].inlier_candidate_world_pts_indices_.push_back(min_index);\n }\n } // end of j\n assert(losses[i].inlier_indices_.size() == losses[i].inlier_candidate_world_pts_indices_.size());\n // printf(\"inlier number is %lu\\n\", losses[i].inlier_indices_.size());\n }\n //getchar();\n \n std::sort(losses.begin(), losses.end());\n losses.resize(losses.size()/2);\n \n for (int i = 0; i 4) {\n vector inlier_camera_pts;\n vector inlier_wld_pts;\n for (int j = 0; j < losses[i].inlier_indices_.size(); j++) {\n int index = losses[i].inlier_indices_[j];\n int wld_index = losses[i].inlier_candidate_world_pts_indices_[j];\n inlier_camera_pts.push_back(camera_pts[index]);\n inlier_wld_pts.push_back(candidate_wld_pts[index][wld_index]);\n }\n Mat affine;\n \n CvxCalib3D::KabschTransform(inlier_camera_pts, inlier_wld_pts, affine);\n losses[i].affine_ = affine;\n losses[i].inlier_indices_.clear();\n losses[i].inlier_candidate_world_pts_indices_.clear(); \n }\n }\n }\n assert(losses.size() == 1);\n \n camera_pose = cv::Mat::eye(4, 4, CV_64F);\n losses[0].affine_.copyTo(camera_pose(cv::Rect(0, 0, 4, 3)));\n //cout<<\"camera pose\\n\"<(0, 0);\n double m01 = rot.at(0, 1);\n double m02 = rot.at(0, 2);\n double m10 = rot.at(1, 0);\n double m11 = rot.at(1, 1);\n double m12 = rot.at(1, 2);\n double m20 = rot.at(2, 0);\n double m21 = rot.at(2, 1);\n double m22 = rot.at(2, 2);\n double theta1 = atan2(m12, m22);\n double c2 = sqrt(m00 * m00 + m01 * m01);\n double theta2 = atan2(-m02, c2);\n double s1 = sin(theta1);\n double c1 = cos(theta1);\n double theta3 = atan2(s1*m20 - c1 * m10, c1*m11 - s1*m21);\n \n double scale = 180.0/3.14159;\n theta1 *= scale;\n theta2 *= scale;\n theta3 *= scale;\n \n Mat eular_angle = cv::Mat::zeros(3, 1, CV_64FC1);\n eular_angle.at(0, 0) = theta1;\n eular_angle.at(1, 0) = theta2;\n eular_angle.at(2, 0) = theta3;\n return eular_angle;\n //printf(\"Eular angle %lf %lf %lf\\n\", theta1, theta2, theta3); \n}\n\nvoid CvxPoseEstimation::poseDistance(const cv::Mat & src_pose,\n const cv::Mat & dst_pose,\n double & angle_distance,\n double & euclidean_disance)\n{\n // http://chrischoy.github.io/research/measuring-rotation/\n assert(src_pose.type() == CV_64F);\n assert(dst_pose.type() == CV_64F);\n \n Mat src_R = src_pose(cv::Rect(0, 0, 3, 3));\n Mat dst_R = dst_pose(cv::Rect(0, 0, 3, 3));\n \n double scale = 180.0/3.14159; \n \n Mat q1 = CvxPoseEstimation::rotationToQuaternion(src_R);\n Mat q2 = CvxPoseEstimation::rotationToQuaternion(dst_R);\n double val_dot = fabs(q1.dot(q2));\n\n //double dot = r1.dot(r2);\n //angle_distance = acos(dot) * scale;\n angle_distance = 2.0 * acos(val_dot) * scale;\n \n euclidean_disance = 0.0;\n double dx = src_pose.at(0, 3) - dst_pose.at(0, 3);\n double dy = src_pose.at(1, 3) - dst_pose.at(1, 3);\n double dz = src_pose.at(2, 3) - dst_pose.at(2, 3);\n euclidean_disance += dx * dx;\n euclidean_disance += dy * dy;\n euclidean_disance += dz * dz;\n euclidean_disance = sqrt(euclidean_disance);\n // printf(\"location distance are %f %f %f\\n\", dx, dy, dz);\n}\n\n\nMat CvxPoseEstimation::rotationToQuaternion(const cv::Mat & rot)\n{\n assert(rot.type() == CV_64FC1);\n assert(rot.rows == 3 && rot.cols == 3);\n \n Mat ret = cv::Mat::zeros(4, 1, CV_64FC1);\n \n float r11 = rot.at(0, 0);\n float r12 = rot.at(0, 1);\n float r13 = rot.at(0, 2);\n float r21 = rot.at(1, 0);\n float r22 = rot.at(1, 1);\n float r23 = rot.at(1, 2);\n float r31 = rot.at(2, 0);\n float r32 = rot.at(2, 1);\n float r33 = rot.at(2, 2);\n \n float q0 = ( r11 + r22 + r33 + 1.0f) / 4.0f;\n float q1 = ( r11 - r22 - r33 + 1.0f) / 4.0f;\n float q2 = (-r11 + r22 - r33 + 1.0f) / 4.0f;\n float q3 = (-r11 - r22 + r33 + 1.0f) / 4.0f;\n if(q0 < 0.0f) q0 = 0.0f;\n if(q1 < 0.0f) q1 = 0.0f;\n if(q2 < 0.0f) q2 = 0.0f;\n if(q3 < 0.0f) q3 = 0.0f;\n q0 = sqrt(q0);\n q1 = sqrt(q1);\n q2 = sqrt(q2);\n q3 = sqrt(q3);\n if(q0 >= q1 && q0 >= q2 && q0 >= q3) {\n q0 *= +1.0f;\n q1 *= CvxPoseEstimation::SIGN(r32 - r23);\n q2 *= CvxPoseEstimation::SIGN(r13 - r31);\n q3 *= CvxPoseEstimation::SIGN(r21 - r12);\n } else if(q1 >= q0 && q1 >= q2 && q1 >= q3) {\n q0 *= CvxPoseEstimation::SIGN(r32 - r23);\n q1 *= +1.0f;\n q2 *= CvxPoseEstimation::SIGN(r21 + r12);\n q3 *= CvxPoseEstimation::SIGN(r13 + r31);\n } else if(q2 >= q0 && q2 >= q1 && q2 >= q3) {\n q0 *= CvxPoseEstimation::SIGN(r13 - r31);\n q1 *= CvxPoseEstimation::SIGN(r21 + r12);\n q2 *= +1.0f;\n q3 *= CvxPoseEstimation::SIGN(r32 + r23);\n } else if(q3 >= q0 && q3 >= q1 && q3 >= q2) {\n q0 *= CvxPoseEstimation::SIGN(r21 - r12);\n q1 *= CvxPoseEstimation::SIGN(r31 + r13);\n q2 *= CvxPoseEstimation::SIGN(r32 + r23);\n q3 *= +1.0f;\n } else {\n printf(\"q0, q1, q2, q3: %f %f %f %f\\n\", q0, q1, q2, q3);\n printf(\"Error: rotation matrix quaternion.\\n\");\n cout<<\"rotation matrix is \\n\"<(0, 0) = q0;\n ret.at(1, 0) = q1;\n ret.at(2, 0) = q2;\n ret.at(3, 0) = q3;\n return ret;\n}\n\nMat CvxPoseEstimation::quaternionToRotation(const cv::Mat & q)\n{\n assert(q.type() == CV_64FC1);\n assert(q.rows == 4);\n assert(q.cols == 1);\n \n double x = q.at(0, 0);\n double y = q.at(1, 0);\n double z = q.at(2, 0);\n double w = q.at(3, 0);\n \n Eigen::Quaterniond quat(w, x, y, z);\n \n Eigen::Matrix eig_mat = quat.matrix();\n cv::Mat rot = cv::Mat::zeros(3, 3, CV_64FC1);\n for (int r = 0; r<3; r++) {\n for (int c = 0; c<3; c++) {\n rot.at(r, c) = eig_mat(r, c);\n }\n } \n return rot;\n}\n\n\n\n\n", "meta": {"hexsha": "cc1fbcf4fe83aba47e9f194a59e73e5c9d83dca0", "size": 15276, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/pose_estimation/cvx_pose_estimation.cpp", "max_stars_repo_name": "LiliMeng/btrf", "max_stars_repo_head_hexsha": "c13da164b11c5ada522fa40deeaffc32192c4bf9", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 9.0, "max_stars_repo_stars_event_min_datetime": "2017-10-28T15:24:04.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-28T13:51:05.000Z", "max_issues_repo_path": "src/pose_estimation/cvx_pose_estimation.cpp", "max_issues_repo_name": "LiliMeng/btrf", "max_issues_repo_head_hexsha": "c13da164b11c5ada522fa40deeaffc32192c4bf9", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/pose_estimation/cvx_pose_estimation.cpp", "max_forks_repo_name": "LiliMeng/btrf", "max_forks_repo_head_hexsha": "c13da164b11c5ada522fa40deeaffc32192c4bf9", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2017-11-08T16:10:39.000Z", "max_forks_repo_forks_event_max_datetime": "2019-05-21T03:40:02.000Z", "avg_line_length": 35.2794457275, "max_line_length": 122, "alphanum_fraction": 0.5520424195, "num_tokens": 4361, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.23459465935797474}} {"text": "//==================================================================================================\n/*!\n @file\n\n @copyright 2016 NumScale SAS\n\n Distributed under the Boost Software License, Version 1.0.\n (See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)\n*/\n//==================================================================================================\n#ifndef BOOST_SIMD_FUNCTION_DEC_HPP_INCLUDED\n#define BOOST_SIMD_FUNCTION_DEC_HPP_INCLUDED\n\n#if defined(DOXYGEN_ONLY)\nnamespace boost { namespace simd\n{\n\n /*!\n @ingroup group-arithmetic\n This function object returns its parameter decremented by 1.\n\n @par Header \n\n @par Decorators\n\n - saturated_ ensures that @c saturated_(dec)(x) will never be\n strictly greater than @c x,\n avoiding the wrap around from @ref Valmin to\n @ref Valmax with integer types\n\n @see inc, minus\n\n @par Example:\n\n @snippet dec.cpp dec\n\n @par Possible output:\n\n @snippet dec.txt dec\n **/\n Value dec(Value const& x);\n} }\n#endif\n\n#include \n#include \n\n#endif\n", "meta": {"hexsha": "4de353e2184737bea0537d4d901cc4af5430fc12", "size": 1174, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "third_party/boost/simd/function/dec.hpp", "max_stars_repo_name": "SylvainCorlay/pythran", "max_stars_repo_head_hexsha": "908ec070d837baf77d828d01c3e35e2f4bfa2bfa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 6.0, "max_stars_repo_stars_event_min_datetime": "2018-02-25T22:23:33.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-15T15:13:12.000Z", "max_issues_repo_path": "third_party/boost/simd/function/dec.hpp", "max_issues_repo_name": "SylvainCorlay/pythran", "max_issues_repo_head_hexsha": "908ec070d837baf77d828d01c3e35e2f4bfa2bfa", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "third_party/boost/simd/function/dec.hpp", "max_forks_repo_name": "SylvainCorlay/pythran", "max_forks_repo_head_hexsha": "908ec070d837baf77d828d01c3e35e2f4bfa2bfa", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 7.0, "max_forks_repo_forks_event_min_datetime": "2017-12-12T12:36:31.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-10T14:27:07.000Z", "avg_line_length": 23.9591836735, "max_line_length": 100, "alphanum_fraction": 0.5843270869, "num_tokens": 251, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.23459465935797474}} {"text": "/* _______________________________________________________________________\n\n DAKOTA: Design Analysis Kit for Optimization and Terascale Applications\n Copyright 2014 Sandia Corporation.\n This software is distributed under the GNU Lesser General Public License.\n For more information, see the README file in the top Dakota directory.\n _______________________________________________________________________ */\n\n//- Class:\t NonDGlobalReliability\n//- Description: Implementation code for NonDGlobalReliability class\n//- Owner: Barron Bichon, Mike Eldred\n//- Checked by:\n//- Version:\n\n#include \"dakota_system_defs.hpp\"\n#include \"dakota_data_io.hpp\"\n#include \"NonDGlobalReliability.hpp\"\n#include \"NonDLHSSampling.hpp\"\n//#include \"DDACEDesignCompExp.hpp\"\n//#include \"FSUDesignCompExp.hpp\"\n#include \"NonDAdaptImpSampling.hpp\"\n//#ifdef HAVE_ACRO\n//#include \"COLINOptimizer.hpp\"\n//#endif\n#include \"RecastModel.hpp\"\n#include \"DataFitSurrModel.hpp\"\n#include \"DakotaApproximation.hpp\"\n#include \"ProblemDescDB.hpp\"\n#ifdef HAVE_NCSU\n#include \"NCSUOptimizer.hpp\"\n#endif\n#include \"pecos_stat_util.hpp\"\n#include \n\n//#define DEBUG\n//#define DEGUG_PLOTS\n\nstatic const char rcsId[] = \"@(#) $Id: NonDGlobalReliability.cpp 4058 2006-10-25 01:39:40Z mseldre $\";\n\nextern \"C\" {\n#ifdef DAKOTA_F90\n #if defined(HAVE_CONFIG_H) && !defined(DISABLE_DAKOTA_CONFIG_H)\n\n // Deprecated; continue to support legacy, clashing macros for ONE RELEASE\n #define BVLS_WRAPPER_FC FC_FUNC_(bvls_wrapper,BVLS_WRAPPER)\n void BVLS_WRAPPER_FC( Dakota::Real* a, int& m, int& n, Dakota::Real* b,\n\t\t Dakota::Real* bnd, Dakota::Real* x, Dakota::Real& rnorm,\n\t\t int& nsetp, Dakota::Real* w, int* index, int& ierr );\n #else\n\n // Use the CMake-generated fortran name mangling macros (eliminate warnings)\n #include \"dak_f90_config.h\"\n #define BVLS_WRAPPER_FC DAK_F90_GLOBAL_(bvls_wrapper,BVLS_WRAPPER)\n void BVLS_WRAPPER_FC( Dakota::Real* a, int& m, int& n, Dakota::Real* b,\n\t\t Dakota::Real* bnd, Dakota::Real* x, Dakota::Real& rnorm,\n\t\t int& nsetp, Dakota::Real* w, int* index, int& ierr );\n #endif // HAVE_CONFIG_H and !DISABLE_DAKOTA_CONFIG_H\n#endif // DAKOTA_F90\n}\n\nnamespace Dakota {\n\n// initialization of statics\nNonDGlobalReliability* NonDGlobalReliability::nondGlobRelInstance(NULL);\n\n\nNonDGlobalReliability::\nNonDGlobalReliability(ProblemDescDB& problem_db, Model& model): \n NonDReliability(problem_db, model),\n meritFunctionType(AUGMENTED_LAGRANGIAN_MERIT), dataOrder(1)\n{\n if (mppSearchType < EGRA_X) {\n Cerr << \"Error: only x-space and u-space EGRA are currently supported in \"\n\t << \"global_reliability.\"<< std::endl;\n abort_handler(-1); \n }\n\n // standard reliability indices are not defined and should be precluded\n // via the input spec. requestedRelLevels is default sized in NonD and\n // cannot be used for the empty() test below.\n if (!probDescDB.get_rva(\"method.nond.reliability_levels\").empty() ||\n respLevelTarget == RELIABILITIES) {\n Cerr << \"Error: reliability indices are not defined for global reliability \"\n\t << \"methods. Use generalized reliability instead.\" << std::endl;\n abort_handler(-1); \n }\n\n // requestedProbLevels & requestedGenRelLevels are not yet supported\n // since PMA EGRA requires additional R&D. Note: requestedProbLevels and\n // requestedGenRelLevels are default sized in NonD and cannot be used for\n // the empty() tests below.\n if (!probDescDB.get_rva(\"method.nond.probability_levels\").empty() ||\n !probDescDB.get_rva(\"method.nond.gen_reliability_levels\").empty()) {\n Cerr << \"Error: Inverse reliability mappings not currently supported in \"\n\t << \"global_reliability.\"<< std::endl;\n abort_handler(-1); \n }\n\n#ifndef DAKOTA_F90\n if (meritFunctionType == LAGRANGIAN_MERIT) {\n Cerr << \"Error: F90 required for standard Lagrangian merit function in \"\n\t << \"global_reliability.\"<< std::endl;\n abort_handler(-1); \n }\n#endif // DAKOTA_F90\n\n // Size the output arrays, augmenting sizing in NonDReliability. Relative to\n // other NonD methods, the output storage for reliability methods is greater\n // since there may be differences between requested and computed levels for\n // the same level type (the request is not always achieved) and since\n // probability and reliability are carried along in parallel (due to their\n // direct correspondence). Relative to NonDLocalReliability, RelLevels are\n // ignored since probabilities are estimated directly from MMAIS.\n size_t i;\n for (i=0; i 0) ? db_samples : \n (numContinuousVars+1)*(numContinuousVars+2)/2;\n\n int lhs_seed = probDescDB.get_int(\"method.random_seed\");\n const String& rng = probDescDB.get_string(\"method.random_number_generator\");\n bool vary_pattern = false; // for consistency across outer loop invocations\n // get point samples file\n const String& import_pts_file\n = probDescDB.get_string(\"method.import_build_points_file\");\n if (!import_pts_file.empty())\n { samples = 0; sample_reuse = \"all\"; }\n\n //int symbols = samples; // symbols needed for DDACE\n Iterator dace_iterator;\n NonDLHSSampling* lhs_sampler_rep;\n // instantiate the Nataf Recast and Gaussian Process DataFit recursions\n if (mppSearchType == EGRA_X) { // Recast( DataFit( iteratedModel ) )\n\n // The following uses on the fly derived ctor:\n lhs_sampler_rep = new NonDLHSSampling(iteratedModel, sample_type, samples,\n lhs_seed, rng, vary_pattern, ACTIVE_UNIFORM);\n //unsigned short dace_method = SUBMETHOD_LHS; // submethod enum\n //lhs_sampler_rep = new DDACEDesignCompExp(iteratedModel, samples, symbols,\n // lhs_seed, dace_method);\n //unsigned short dace_method = FSU_HAMMERSLEY;\n //lhs_sampler_rep = new FSUDesignCompExp(iteratedModel, samples, lhs_seed,\n // dace_method);\n dace_iterator.assign_rep(lhs_sampler_rep, false);\n\n // Construct g-hat(x) using a GP approximation over the active/uncertain\n // vars (same view as iteratedModel: not the typical All view for DACE).\n // For RBDO with GP over {u}+{d}, view set to All from all_variables spec.\n Model g_hat_x_model;\n IntSet surr_fn_indices; // Only want functions with requested levels\n ActiveSet set = iteratedModel.current_response().active_set();// copy\n set.request_values(0);\n for (i=0; iinitialize_random_variables(natafTransform); // shared rep\n\n // Construct G-hat(u) using a GP approximation over the active/uncertain\n // variables (using the same view as iteratedModel/g_u_model: not the\n // typical All view for DACE).\n // For RBDO with GP over {u}+{d}, view set to All from all_variables spec.\n IntSet surr_fn_indices; // Only want functions with requested levels\n ActiveSet set = iteratedModel.current_response().active_set();// copy\n set.request_values(0);\n for (i=0; i max_concurrency within IteratorScheduler::init_iterator().\n // A max of the local derivative concurrency and the DACE concurrency is used\n // for this purpose.\n maxEvalConcurrency = std::max(maxEvalConcurrency,\n\t\t\t\tdace_iterator.maximum_evaluation_concurrency());\n\n // Configure a RecastModel with one objective and no constraints using the\n // alternate minimalist constructor. The RIA/PMA expected improvement/\n // expected feasibility formulations may vary with the level requests, so\n // the recast fn pointers are reset for each level within the run fn.\n SizetArray recast_vars_comps_total; // default: empty; no change in size\n BitArray all_relax_di, all_relax_dr; // default: empty; no discrete relaxation\n short recast_resp_order = 1; // nongradient-based optimizers\n mppModel.assign_rep(\n new RecastModel(uSpaceModel, recast_vars_comps_total, all_relax_di,\n\t\t all_relax_dr, 1, 0, 0, recast_resp_order), false);\n\n // For formulations with one objective and one equality constraint,\n // use the following instead:\n //mppModel.assign_rep(new RecastModel(uSpaceModel, ..., 1, 1, 0, ...), false);\n //RealVector nln_eq_targets(1, 0.);\n //mppModel.nonlinear_eq_constraint_targets(nln_eq_targets);\n\n // must use alternate NoDB ctor chain\n int max_iter = 1000, max_eval = 10000;\n double min_box_size = 1.e-15, vol_box_size = 1.e-15;\n#ifdef HAVE_NCSU \n mppOptimizer.assign_rep(new NCSUOptimizer(mppModel, max_iter, max_eval,\n\t\t\t\t\t min_box_size, vol_box_size), false);\n //#ifdef HAVE_ACRO\n //int coliny_seed = 0; // system-generated, for now\n //mppOptimizer.assign_rep(new\n // COLINOptimizer(mppModel, coliny_seed), false);\n //mppOptimizer.assign_rep(new\n // COLINOptimizer(mppModel, coliny_seed), false);\n //#endif\n#else\n Cerr << \"NCSU DIRECT Optimizer is not available to use in the MPP search \" \n << \"in global reliability optimization: aborting process.\" << std::endl;\n abort_handler(-1);\n#endif //HAVE_NCSU\n\n // The importance sampler uses uSpaceModel (without additional recasting)\n // and may be constructed here. Thus, NonDGlobal applies integration\n // refinement to the G-hat(u) surrogate. Behavior needs to be repeatable\n // and AIS is not part of the EGRA spec: either reuse lhs_seed or hardwire.\n int refine_samples = 1000, refine_seed = 123457;\n // we pass a u-space model and enforce the EGRA GP bounds on the samples;\n // extreme values are needed to define bounds for outer PDF bins.\n bool x_model_flag = false, use_model_bounds = true, track_extreme = pdfOutput;\n integrationRefinement = MMAIS; vary_pattern = true;\n\n NonDAdaptImpSampling* importance_sampler_rep = new\n NonDAdaptImpSampling(uSpaceModel, sample_type, refine_samples, refine_seed,\n\t\t\t rng, vary_pattern, integrationRefinement, cdfFlag,\n\t\t\t x_model_flag, use_model_bounds, track_extreme);\n importanceSampler.assign_rep(importance_sampler_rep, false);\n\n // if approximation is built in x-space, then importanceSampler must perform\n // inverse transformations on gp_inputs; if approximation is built in u-space,\n // only the cdfFlag is needed to define which samples are failures\n if (mppSearchType == EGRA_X) // share the ProbabilityTransformation rep\n importance_sampler_rep->initialize_random_variables(natafTransform);\n}\n\n\nNonDGlobalReliability::~NonDGlobalReliability()\n{ }\n\n\nbool NonDGlobalReliability::resize()\n{\n bool parent_reinit_comms = NonDReliability::resize();\n\n Cerr << \"\\nError: Resizing is not yet supported in method \"\n << method_enum_to_string(methodName) << \".\" << std::endl;\n abort_handler(METHOD_ERROR);\n\n return parent_reinit_comms;\n}\n\n\nvoid NonDGlobalReliability::derived_init_communicators(ParLevLIter pl_iter)\n{\n iteratedModel.init_communicators(pl_iter, maxEvalConcurrency);\n\n // mppModel.init_communicators() recursion is currently sufficient for\n // uSpaceModel. An additional uSpaceModel.init_communicators() call would be\n // motivated by special parallel usage of uSpaceModel below that is not\n // otherwise covered by the recursion.\n //uSpaceMaxConcurrency = maxEvalConcurrency; // local derivative concurrency\n //uSpaceModel.init_communicators(pl_iter, uSpaceMaxConcurrency);\n\n // mppOptimizer and importanceSampler use NoDBBaseConstructor, so no\n // need to manage DB list nodes at this level\n mppOptimizer.init_communicators(pl_iter);\n importanceSampler.init_communicators(pl_iter);\n}\n\n\nvoid NonDGlobalReliability::derived_set_communicators(ParLevLIter pl_iter)\n{\n NonD::derived_set_communicators(pl_iter);\n\n //uSpaceMaxConcurrency = maxEvalConcurrency; // local derivative concurrency\n //uSpaceModel.set_communicators(pl_iter, uSpaceMaxConcurrency);\n\n // mppOptimizer and importanceSampler use NoDBBaseConstructor, so no\n // need to manage DB list nodes at this level\n mppOptimizer.set_communicators(pl_iter);\n importanceSampler.set_communicators(pl_iter);\n}\n\n\nvoid NonDGlobalReliability::derived_free_communicators(ParLevLIter pl_iter)\n{\n // deallocate communicators for MMAIS on uSpaceModel\n importanceSampler.free_communicators(pl_iter);\n\n // deallocate communicators for DIRECT on mppModel\n mppOptimizer.free_communicators(pl_iter);\n\n //uSpaceMaxConcurrency = maxEvalConcurrency; // local derivative concurrency\n //uSpaceModel.free_communicators(pl_iter, uSpaceMaxConcurrency);\n\n iteratedModel.free_communicators(pl_iter, maxEvalConcurrency);\n}\n\n\nvoid NonDGlobalReliability::core_run()\n{\n // initialize the random variable arrays and the correlation Cholesky factor\n initialize_random_variable_parameters();\n transform_correlations();\n\n // set the object instance pointer for use within static member functions\n NonDGlobalReliability* prev_grel_instance = nondGlobRelInstance;\n nondGlobRelInstance = this;\n\n // Optimize the GP for all levels and then use MAIS for _all_ levels\n optimize_gaussian_process();\n importance_sampling();\n numRelAnalyses++;\n\n // restore in case of recursion\n nondGlobRelInstance = prev_grel_instance;\n}\n\n\nvoid NonDGlobalReliability::optimize_gaussian_process()\n{\n // now that variables/labels/bounds/targets have flowed down at run-time from\n // any higher level recursions, propagate them up the instantiate-on-the-fly\n // Model recursion so that they are correct when they propagate back down.\n mppModel.update_from_subordinate_model(); // depth = max\n\n if (mppSearchType == EGRA_X) {\n // assign non-default global variable bounds for use in DACE.\n // This does not affect any uncertain variable distribution bounds.\n // Note 1: the interval defined in x-space will be sampled uniformly by\n // DACE methods, which could result in very irregular coverage in u-space.\n // It would be better to sample uniformly in u-space.\n // Note 2: it would be preferable to set this up in the constructor. The\n // EGRA_U case can do this by setting the bounds in the actualModel of the\n // DataFitSurrModel and then having them copied in the DataFitSurrModel\n // ctor. The EGRA_X case needs trans_U_to_X, which isn't available until\n // after initialize_random_variable_parameters() is executed at run time.\n // Therefore, this case sets the bounds for the DataFitSurrModel, which are\n // then propagated to actualModel in DataFitSurrModel::update_actual_model()\n RealVector u_l_bnds(numContinuousVars, false), x_l_bnds,\n u_u_bnds(numContinuousVars, false), x_u_bnds;\n u_l_bnds = -5.; u_u_bnds = 5.;\n for (size_t i=0; imi_parallel_level_iterator(miPLIndex);\n for (respFnCount=0; respFnCount>>>> Reliability Index Approach (RIA) for response level \"\n\t << levelCount+1 << \" = \" << requestedTargetLevel << '\\n';\n }\n else if (levelCount < rl_len + pl_len) { \n\tsize_t index = levelCount-rl_len;\n\tReal p = requestedProbLevels[respFnCount][index];\n\tCout << \"\\n>>>>> Performance Measure Approach (PMA) for probability \"\n\t << \"level \" << index+1 << \" = \" << p << '\\n';\n\trequestedTargetLevel = -Pecos::NormalRandomVariable::inverse_std_cdf(p);\n\tReal gen_beta_cdf = (cdfFlag) ?\n\t requestedTargetLevel : -requestedTargetLevel;\n\tpmaMaximizeG = (gen_beta_cdf < 0.);\n }\n else {\n\tsize_t index = levelCount-rl_len-pl_len;\n\trequestedTargetLevel = requestedGenRelLevels[respFnCount][index];\n\tCout << \"\\n>>>>> Performance Measure Approach (PMA) for reliability \"\n\t << \"level \" << index+1 << \" = \" << requestedTargetLevel << '\\n';\n\tReal gen_beta_cdf = (cdfFlag) ?\n\t requestedTargetLevel : -requestedTargetLevel;\n\tpmaMaximizeG = (gen_beta_cdf < 0.);\n }\n\n bool pma_aug_lag_flag\n\t= (!ria_flag && meritFunctionType == AUGMENTED_LAGRANGIAN_MERIT);\n if (pma_aug_lag_flag) {\n\taugLagrangeMult = 0.; penaltyParameter = 1.;\n\tlastConstraintViolation = DBL_MAX; lastIterateAccepted = false;\n }\n\n // Iterate until EGRA converges\n approxIters = 0;\n approxConverged = false;\n while (!approxConverged) {\n\n\tapproxIters++;\n\n\t// construct global optimizer and its EI/EF recast model\n\tSizet2DArray vars_map, primary_resp_map, secondary_resp_map;\n\tBoolDequeArray nonlinear_resp_map(1, BoolDeque(1, true));\n\tRecastModel* mpp_model_rep = (RecastModel*)mppModel.model_rep();\n\tif (ria_flag) {\n\t // Standard RIA : min u'u s.t. g = z_bar\n\t // use RIA evaluators to recast g into global opt subproblem\n\t //primary_resp_map.reshape(1); // one objective, no contributors\n\t //secondary_resp_map.reshape(1); // one constraint, one contributor\n\t //secondary_resp_map[0].reshape(1);\n\t //secondary_resp_map[0][0] = respFnCount;\n\t //BoolDequeArray nonlinear_resp_map(2);\n\t //nonlinear_resp_map[1] = BoolDeque(1, false);\n\t //mpp_model_rep->init_maps(vars_map, false, NULL, NULL,\n\t // primary_resp_map, secondary_resp_map, nonlinear_resp_map,\n\t // RIA_objective_eval, RIA_constraint_eval);\n\n\t // EFF formulation : max EFF s.t. bound constraints\n\t // use EFF evaluators to recast g into global opt subproblem\n\t primary_resp_map.resize(1);\n\t primary_resp_map[0].resize(1);\n\t primary_resp_map[0][0] = respFnCount;\n\t mpp_model_rep->init_maps(vars_map, false, NULL, NULL,\n\t primary_resp_map, secondary_resp_map, nonlinear_resp_map,\n\t EFF_objective_eval, NULL);\n\t}\n\telse {\n\t // Standard PMA : min/max g s.t. u'u = beta_bar^2\n\t // use PMA evaluators to recast g into global opt subproblem\n\t //void (*set_map) (const ShortArray& recast_asv,\n\t // ShortArray& sub_model_asv) =\n\t // (integrationOrder == 2) ? PMA2_set_mapping : NULL;\n\t //primary_resp_map.reshape(1); // one objective, one contributor\n\t //primary_resp_map[0].reshape(1);\n\t //primary_resp_map[0][0] = respFnCount;\n\t //secondary_resp_map.reshape(1); // one constraint, no contributors\n\t //BoolDequeArray nonlinear_resp_map(2);\n\t //nonlinear_resp_map[0] = BoolDeque(1, false);\n\t //mpp_model_rep->init_maps(vars_map, false, NULL, set_map,\n\t // primary_resp_map, secondary_resp_map, nonlinear_resp_map,\n\t // PMA_objective_eval, PMA_constraint_eval);\n\n\t // EIF formulation : max Phi(EIF) [merit function on EIF]\n\t // determine fnStar from among sample data\n\t get_best_sample();\n\t // use EIF evaluators to recast g into global opt subproblem\n\t primary_resp_map.resize(1);\n\t primary_resp_map[0].resize(1);\n\t primary_resp_map[0][0] = respFnCount;\n\t mpp_model_rep->init_maps(vars_map, false, NULL, NULL,\n\t primary_resp_map, secondary_resp_map, nonlinear_resp_map,\n\t EIF_objective_eval, NULL);\n\t}\n\n\t// Execute GLOBAL search and retrieve u-space results\n\tCout << \"\\n>>>>> Initiating global reliability optimization\\n\";\n\tmppOptimizer.run(pl_iter);\n\t// Use these two lines for COLINY optimizers\n\t//const VariablesArray& vars_star\n\t// = mppOptimizer.variables_array_results();\n\t//const RealVector& c_vars_u = vars_star[0].continuous_variables();\n\t// Use these two lines for NCSU DIRECT\n\tconst Variables& vars_star = mppOptimizer.variables_results();\n\tconst RealVector& c_vars_u = vars_star.continuous_variables();\n\n\t// Get expected value at u* for output\n\tuSpaceModel.continuous_variables(c_vars_u);\n\tuSpaceModel.evaluate();\n\tconst RealVector& g_hat_fns\n\t = uSpaceModel.current_response().function_values();\n\n\t// Re-evaluate the expected improvement/feasibility at vars_star\n\tReal beta_star = 0.,/* TO DO */ exp_fns_star = (ria_flag) ?\n\t expected_feasibility(g_hat_fns, vars_star) :\n\t expected_improvement(g_hat_fns, vars_star);\n \n\tCout << \"\\nResults of EGRA iteration:\\nFinal point (u-space) =\\n\";\n\twrite_data(Cout, c_vars_u);\n\tsize_t wpp7 = write_precision+7;\n\tif (ria_flag) {\n\t Cout << \"Expected Feasibility =\\n \"\n\t << std::setw(wpp7) << -exp_fns_star << \"\\n \"\n\t << std::setw(wpp7) << g_hat_fns[respFnCount]-requestedTargetLevel\n\t << \" [G_hat(u) - z]\\n\";\n\t// << \" \" << std::setw(wpp7) << beta_star\n\t// << \" [beta*]\\n\";\n\t//Cout << \"RIA optimum =\\n \"\n\t// << std::setw(wpp7) << exp_fns_star << \" [u'u]\\n\"\n\t// << \" \" << std::setw(wpp7) << exp_fns_star[1]\n\t// << \" [G(u) - z]\\n\";\n\t}\n\telse {\n\t // Calculate beta^2 for output (and aug_lag update)\n\t Cout << \"Expected Improvement =\\n \"\n\t << std::setw(wpp7) << -exp_fns_star << \"\\n \"\n\t << std::setw(wpp7) << beta_star - requestedTargetLevel\n\t << \" [beta* - bar-beta*]\\n \"\n\t << std::setw(wpp7) << g_hat_fns[respFnCount] << \" [G_hat(u)]\\n\";\n\t//Cout << \"PMA optimum =\\n \"\n\t// << std::setw(wpp7) << exp_fns_star << \" [\";\n\t//if (pmaMaximizeG) Cout << '-';\n\t//Cout << \"G(u)]\\n \" << std::setw(wpp7)\n\t// << exp_fns_star[1] << \" [u'u - B^2]\\n\";\n\t}\n\n\t// Update parameters for the augmented Lagrangian merit function\n\tif (pma_aug_lag_flag) {\n\t // currently only used for PMA with EIF\n\t Real c_violation = beta_star - requestedTargetLevel;\n\t if (c_violation < lastConstraintViolation)\n\t lastIterateAccepted = true;\n\t lastConstraintViolation = c_violation;\n\t}\n \n\t// Check for convergence based on max EIF/EFF\n\t// BMA: was previously hard-wired: convergenceTol = .001;\n if (maxIterations < 0) \n maxIterations = 25*numContinuousVars;\n\tif (approxIters >= maxIterations || -exp_fns_star < convergenceTol)\n\t approxConverged = true;\n\telse {\n\t // Evaluate response_star_truth\n\t uSpaceModel.component_parallel_mode(TRUTH_MODEL);\n\t RealVector c_vars_x;\n\t natafTransform.trans_U_to_X(c_vars_u, c_vars_x);\n\t iteratedModel.continuous_variables(c_vars_x);\n\t ActiveSet set = iteratedModel.current_response().active_set();\n\t set.request_values(0); set.request_value(dataOrder, respFnCount);\n\t iteratedModel.evaluate(set);\n\t IntResponsePair resp_star_truth(iteratedModel.evaluation_id(),\n\t\t\t\t\t iteratedModel.current_response());\n\n\t // Update the GP approximation\n\t if (mppSearchType == EGRA_X) // update with x-space current vars\n\t uSpaceModel.append_approximation(\n\t iteratedModel.current_variables(), resp_star_truth, true);\n\t else // update with u-space vars_star\n\t uSpaceModel.append_approximation(vars_star, resp_star_truth, true);\n\t}\n } // end approx convergence while loop\n \n if (ria_flag)\n\tCout << \"\\n<<<<< GP model has converged for response level \";\n else\n \tCout << \"\\n<<<<< GP model has converged for response level \";\n Cout << levelCount+1 << \" = \" << requestedTargetLevel << '\\n';\n \n } // end loop over levels\n\n if (num_levels)\n Cout << \"\\n<<<<< GP model has converged for all requested levels of \"\n\t << \"response function \" << respFnCount+1 << '\\n';\n\n#ifdef DEBUG\n // DEBUG - output set of samples used to build the GP\n // If problem is 2d, output a grid of points on the GP, variance, \n // eff, and truth (if requested)\n if (num_levels) { // can only plot if GP was built!\n std::string samsfile(\"egra_sams\");\n std::string tag = \"_\" + boost::lexical_cast(respFnCount+1) +\n \".out\";\n samsfile += tag;\n std::ofstream samsOut(samsfile.c_str(),std::ios::out);\n samsOut << std::scientific;\n const Pecos::SurrogateData& gp_data\n\t= uSpaceModel.approximation_data(respFnCount);\n size_t num_data_pts = gp_data.size(), num_vars = uSpaceModel.cv();\n for (size_t i=0; imi_parallel_level_iterator(miPLIndex);\n // rep needed for access to functions not mapped to Iterator level\n NonDAdaptImpSampling* importance_sampler_rep\n = (NonDAdaptImpSampling*)importanceSampler.iterator_rep();\n\n for (respFnCount=0; respFnCount\n\t initialize(gp_inputs, x_data_flag, respFnCount, 0., z);\n }\n else // not operational (see error traps in ctor)\n\timportance_sampler_rep->initialize(gp_inputs, x_data_flag,\n\t respFnCount, 0., computedRespLevels[respFnCount][levelCount]);\n\n importanceSampler.run(pl_iter);\n\n Real p = importance_sampler_rep->final_probability();\n#ifdef DEBUG\n Cout << \"\\np = \" << p << std::endl;\n#endif // DEBUG\n // RIA z-bar -> p\n computedProbLevels[respFnCount][levelCount] = p;\n // RIA z-bar -> generalized beta\n Real gen_beta = -Pecos::NormalRandomVariable::inverse_std_cdf(p);\n computedGenRelLevels[respFnCount][levelCount] = gen_beta;\n switch (respLevelTarget) {\n case PROBABILITIES:\n\tfinalStatistics.function_value(p, statCount++); break;\n case GEN_RELIABILITIES:\n\tfinalStatistics.function_value(gen_beta, statCount++); break;\n }\n }\n }\n // post-process level mappings to define PDFs (using prob_refined and\n // all_levels_computed modes)\n if (pdfOutput)\n compute_densities(importance_sampler_rep->extreme_values(), true, true);\n}\n\n\nvoid NonDGlobalReliability::\nEIF_objective_eval(const Variables& sub_model_vars,\n\t\t const Variables& recast_vars,\n\t\t const Response& sub_model_response,\n\t\t Response& recast_response)\n{\n const ShortArray& recast_asv = recast_response.active_set_request_vector();\n if (recast_asv[0] & 1) {\n Real ei = nondGlobRelInstance->expected_improvement(\n sub_model_response.function_values(), recast_vars);\n recast_response.function_value(ei, 0);\n }\n}\n\n\nvoid NonDGlobalReliability::\nEFF_objective_eval(const Variables& sub_model_vars,\n\t\t const Variables& recast_vars,\n\t\t const Response& sub_model_response,\n\t\t Response& recast_response)\n{\n const ShortArray& recast_asv = recast_response.active_set_request_vector();\n if (recast_asv[0] & 1) {\n Real ef = nondGlobRelInstance->expected_feasibility(\n sub_model_response.function_values(), recast_vars);\n recast_response.function_value(ef, 0);\n }\n}\n\n\nReal NonDGlobalReliability::\nexpected_improvement(const RealVector& expected_values,\n\t\t const Variables& recast_vars)\n{\n // Get variance from the GP; Expected values are passed in\n // If GP built in x-space, transform input point to x-space to get variance\n RealVector variances;\n if (mppSearchType == EGRA_X) { // uSpaceModel = Recast(DataFit(iteratedModel))\n Model& dfs_model = uSpaceModel.subordinate_model();\n // assume recast_vars have been propagated to GP just prior to call\n variances\n = dfs_model.approximation_variances(dfs_model.current_variables());\n }\n else // EGRA_U: uSpaceModel = DataFit(Recast(iteratedModel))\n variances = uSpaceModel.approximation_variances(recast_vars);\n \n Real mean = expected_values[respFnCount];\n Real stdv = std::sqrt(variances[respFnCount]);\n\n // Calculate and apply penalty to the mean\n Real beta_star = 0.; // TO DO\n // calculate the equality constraint: beta* - bar-beta*\n Real cfn = beta_star - requestedTargetLevel;\n Real penalty = constraint_penalty(cfn, recast_vars.continuous_variables());\n // Calculation of EI will have different form for the CDF/CCDF cases\n Real penalized_mean = (pmaMaximizeG) ? mean - penalty : mean + penalty;\n \n // Calculate the expected improvement\n // Note: This is independent of the CDF/CCDF check\n\n Real ei, cdf, pdf;\n Real snv = (fnStar-penalized_mean); // not normalized yet\n if(std::fabs(snv)>=std::fabs(stdv)*50.0) {\n //this will trap the denominator=0.0 case even if numerator=0.0\n pdf = 0.;\n cdf = (snv > 0.) ? 1. : 0.;\n }\n else{\n snv /= stdv; // now snv is the standard normal variate\n cdf = Pecos::NormalRandomVariable::std_cdf(snv);\n pdf = Pecos::NormalRandomVariable::std_pdf(snv);\n }\n ei = (pmaMaximizeG) ? (penalized_mean - fnStar)*(1.-cdf) + stdv*pdf\n : (fnStar - penalized_mean)*cdf + stdv*pdf;\n return -ei; // return -EI because we are maximizing EI\n}\n\n\nReal NonDGlobalReliability::\nexpected_feasibility(const RealVector& expected_values,\n\t\t const Variables& recast_vars)\n{\n // Get variance from the GP; Expected values are passed in\n // If GP built in x-space, transform input point to x-space to get variance\n RealVector variances;\n if (mppSearchType == EGRA_X) { // uSpaceModel = Recast(DataFit(iteratedModel))\n Model& dfs_model = uSpaceModel.subordinate_model();\n // assume recast_vars have been propagated to GP just prior to call\n variances\n = dfs_model.approximation_variances(dfs_model.current_variables());\n }\n else // EGRA_U: uSpaceModel = DataFit(Recast(iteratedModel))\n variances = uSpaceModel.approximation_variances(recast_vars);\n \n Real mean = expected_values[respFnCount],\n stdv = std::sqrt(variances[respFnCount]),\n zbar = requestedTargetLevel,\n alpha = 2.; // may want to try values other than 2\n\n // calculate standard normal variate +/- alpha\n \n Real cdfz, pdfz, cdfp, pdfp, cdfm, pdfm;\n Real snvz = (zbar - mean);\n if (std::fabs(snvz) >= std::fabs(stdv)*50.) {\n pdfm = pdfp = pdfz = 0.;\n cdfm = cdfp = cdfz = (snvz > 0.) ? 1. : 0.;\n }\n else {\n snvz /= stdv; Real snvp = snvz + alpha, snvm = snvz - alpha;\n pdfz = Pecos::NormalRandomVariable::std_pdf(snvz);\n cdfz = Pecos::NormalRandomVariable::std_cdf(snvz);\n pdfp = Pecos::NormalRandomVariable::std_pdf(snvp);\n cdfp = Pecos::NormalRandomVariable::std_cdf(snvp);\n pdfm = Pecos::NormalRandomVariable::std_pdf(snvm);\n cdfm = Pecos::NormalRandomVariable::std_cdf(snvm);\n }\n // calculate expected feasibility function\n Real ef = (mean - zbar)*(2.*cdfz - cdfm - cdfp) //exploit\n - stdv*(2.*pdfz - pdfm - pdfp //explore\n\t - alpha*cdfp + alpha*cdfm);\n\n return -ef; // return -EF because we are maximizing\n}\n\n\nvoid NonDGlobalReliability::get_best_sample()\n{\n // Pull the samples and responses from data used to build latest GP\n // and apply any penalties to calculate fnStar for use in the \n // expected improvement function\n // This is only done for PMA - there is no \"best solution\" for\n // the expected feasibility function used in RIA\n\n Iterator& dace_iterator = uSpaceModel.subordinate_iterator();\n const RealMatrix& true_vars_x = dace_iterator.all_samples();\n const IntResponseMap& true_responses = dace_iterator.all_responses();\n size_t i, j, num_samples = true_vars_x.numCols(),\n num_vars = true_vars_x.numRows();\n \n // If GP built in x-space, transform true_vars_x to u-space to calculate beta\n RealVectorArray true_c_vars_u(num_samples); RealVector true_vars_x_cv;\n for (i=0; i(true_vars_x), (int)i);\n if (mppSearchType == EGRA_X)\n natafTransform.trans_X_to_U(true_vars_x_cv, true_c_vars_u[i]);\n else\n true_c_vars_u[i] = true_vars_x_cv; // view OK\n }\n\n // Calculation of fnStar will have different form for CDF/CCDF cases\n fnStar = (pmaMaximizeG) ? -DBL_MAX : DBL_MAX; IntRespMCIter it;\n for (i=0, it=true_responses.begin(); isecond.function_value(0) - penalty :\n it->second.function_value(0) + penalty;\n if ( ( pmaMaximizeG && penalized_response > fnStar) ||\n\t (!pmaMaximizeG && penalized_response < fnStar) )\n fnStar = penalized_response;\n }\n}\n\n\nReal NonDGlobalReliability::\nconstraint_penalty(const Real& c_viol, const RealVector& u)\n{\n if (meritFunctionType == PENALTY_MERIT)\n return exp(approxIters/10.)*c_viol*c_viol; // try other schedules\n else if (meritFunctionType == AUGMENTED_LAGRANGIAN_MERIT) {\n if (lastIterateAccepted)\n augLagrangeMult += 2.*penaltyParameter*c_viol;\n else \n penaltyParameter *= 2.;\n return augLagrangeMult*c_viol + penaltyParameter*c_viol*c_viol;\n }\n else if (meritFunctionType == LAGRANGIAN_MERIT) {\n#ifdef DAKOTA_F90\n // form [A] = grad[u'u - beta^2]\n RealVector A(numContAleatUncVars, false);\n for (size_t i=0; i\n#include \n#include \n#include \n#include \n#include \n#include \n\nusing Eigen::Vector3f;\nusing Eigen::Vector2f;\nusing Eigen::AngleAxis;\n\nCamera::Camera() {\n //mtx_ = new std::mutex();\n fov_current_ = 60.0f;\n fov_future_ = 60.0f;\n aspect_ = 1.0f;\n depth_near_ = 1.0f;\n depth_far_ = 1000.f;\n\n roll_correction_ = true;\n always_update_ = false;\n\n rotation_current_ = AngleAxis(0, Vector3f(1.0f, 0.0f, 0.0f));\n rotation_future_ = AngleAxis(0, Vector3f(1.0f, 0.0f, 0.0f));\n\n translation_current_ = Vector3f(0, 0, 0);\n translation_future_ = Vector3f(0, 0, 0);\n\n translation_speed_ = 1;\n\n projection_dirty_ = true;\n\n timer_ = new QTimer();\n timer_->connect(timer_, &QTimer::timeout, [&] () {\n Eigen::Vector3f trans_diff = translation_future_ - translation_current_;\n bool good_enough_rotation = rotation_current_.isApprox(rotation_future_);\n float fov_diff = fov_future_ - fov_current_;\n\n if(trans_diff.norm() > 1e-4 || !good_enough_rotation || fabs(fov_diff) > 1e-10) {\n\n translation_current_ = translation_current_ + trans_diff * 0.5;\n rotation_current_ = rotation_current_.slerp(0.5, rotation_future_);\n fov_current_ = fov_future_ * 0.5 + fov_current_ * 0.5;\n\n if(fabs(fov_diff) > 1e-10)\n projection_dirty_ = true;\n\n emit updated();\n }\n else {\n if(always_update_)\n emit updated();\n translation_speed_ = 1;\n }\n\n\n });\n\n timer_->start(1000/60);\n}\n\nCamera::~Camera() {\n //delete mtx_;\n}\n\ndouble angle (Vector3f a, Vector3f b){\n double dotp = a.dot(b);\n if(dotp >= 1 || dotp <= -1){\n return 0.0;\n }\n return acos(dotp);\n}\n\ndouble normalizeRad(double angle){\n if(angle < -M_PI_2){\n return -M_PI_2 - (angle + M_PI_2);\n } else if(angle > M_PI_2) {\n return M_PI_2 - (angle - M_PI_2);\n }\n return angle;\n}\n\nEigen::Quaternionf rollcorrect(Eigen::Quaternionf rotation){\n Vector3f local_z_axis = rotation.inverse() * -Vector3f::UnitZ();\n Vector3f local_x_axis = rotation.inverse() * Vector3f::UnitX();\n Vector3f local_y_axis = rotation.inverse() * Vector3f::UnitY();\n\n Vector3f z_proj_zx = local_z_axis; z_proj_zx(1) = 0; z_proj_zx.normalize();\n Vector3f x_proj_zx = local_x_axis; x_proj_zx(1) = 0; x_proj_zx.normalize();\n Vector3f y_proj_zx = local_y_axis; y_proj_zx(1) = 0; y_proj_zx.normalize();\n\n double pitch = angle(local_z_axis, z_proj_zx);\n double roll = angle(local_x_axis, x_proj_zx);\n\n int proj_side = local_x_axis.cross(x_proj_zx).dot(local_z_axis) > 0 ? -1 : 1;\n int side_up = local_y_axis.dot(Vector3f::UnitY()) > 0 ? 1 : -1;\n\n if(side_up == -1){\n roll = M_PI - roll;\n }\n\n roll = roll * proj_side * side_up;\n\n double norm_pitch = normalizeRad(pitch);\n double correction_factor = (M_PI_2 - fabs(norm_pitch)) / M_PI_2;\n correction_factor = pow(correction_factor - 0.5, 1);\n\n if(pitch > 0.7){\n correction_factor = 0;\n }\n\n AngleAxis roll_correction(correction_factor*-roll, Vector3f::UnitZ());\n rotation = roll_correction * rotation;\n\n return rotation;\n}\n\nvoid Camera::setFoV(float fov) {\n fov_future_ = fov;\n projection_dirty_ = true;\n}\n\nvoid Camera::setAspect(float aspect) {\n aspect_ = aspect;\n projection_dirty_ = true;\n}\n\nvoid Camera::setDepthRange(float near, float far) {\n depth_near_ = near;\n depth_far_ = far;\n projection_dirty_ = true;\n}\n\nvoid Camera::setPosition(const Eigen::Vector3f& pos) {\n translation_future_ = pos;\n}\n\n\nvoid Camera::recalculateProjectionMatrix() {\n // Code from Mesa project, src/glu/sgi/libutil/project.c\n projection_matrix_.setIdentity();\n float radians = fov_current_ / 2 * M_PI / 180;\n\n float deltaZ = depth_far_ - depth_near_;\n float sine = sin(radians);\n if ((deltaZ == 0) || (sine == 0) || (aspect_ == 0)) {\n return;\n }\n float cotangent = cos(radians) / sine;\n\n projection_matrix_(0, 0) = cotangent / aspect_;\n projection_matrix_(1, 1) = cotangent;\n projection_matrix_(2, 2) = -(depth_far_ + depth_near_) / deltaZ;\n projection_matrix_(3, 2) = -1;\n projection_matrix_(2, 3) = -2 * depth_near_ * depth_far_ / deltaZ;\n projection_matrix_(3, 3) = 0;\n projection_dirty_ = false;\n}\n\nEigen::Affine3f Camera::modelviewMatrix() {\n return rotation_current_ * Eigen::Translation3f(translation_current_) * Eigen::Affine3f::Identity();\n}\n\nEigen::Affine3f Camera::projectionMatrix() const {\n if (projection_dirty_) {\n const_cast(this)->recalculateProjectionMatrix();\n }\n return projection_matrix_;\n}\n\nvoid Camera::translate(const Eigen::Vector3f& pos) {\n translation_future_ = Eigen::Translation3f(rotation_current_.inverse() * (translation_speed_ * pos)) * translation_current_;\n\n if(translation_speed_ < 10){\n translation_speed_ *= 1.1f; // If succesive tranlations are performed, speed things up\n }\n\n emit modified();\n}\n\nvoid Camera::setRotate3D(const Eigen::Vector3f& rot) {\n Eigen::AngleAxis aaZ(rot.z(), Eigen::Vector3f::UnitZ());\n Eigen::AngleAxis aaY(rot.y(), Eigen::Vector3f::UnitY());\n Eigen::AngleAxis aaX(rot.x(), Eigen::Vector3f::UnitX());\n\n rotation_future_ = aaZ * aaY * aaX;\n emit modified();\n}\n\nvoid Camera::rotate2D(float x, float y) {\n Vector2f rot = Vector2f(x, y);\n\n AngleAxis rotX(rot.x(), Vector3f::UnitY()); // look left right\n AngleAxis rotY(rot.y(), Vector3f::UnitX()); // look up down\n\n rotation_future_ = (rotX * rotY) * rotation_current_;\n\n if(roll_correction_){\n rotation_future_ = rollcorrect(rotation_future_);\n }\n\n emit modified();\n}\n\nvoid Camera::rotate3D(float _yaw, float _pitch, float _roll) {\n\n AngleAxis rotX(_yaw, Vector3f::UnitY()); // look left right\n AngleAxis rotY(_pitch, Vector3f::UnitX()); // look up down\n\n rotation_future_ = (rotX * rotY) * rotation_future_;\n\n auto clamp = [] (double num, double low, double high) {\n if (num > high)\n return high;\n if (num < low)\n return low;\n return num;\n };\n\n Eigen::Matrix3f r = rotation_future_.toRotationMatrix();\n double roll = -atan2(r(0,2), r(1, 2));\n double pitch = acos(r(2,2));\n //double yaw = atan2(r(2, 0), r(2, 1));\n\n\n Vector3f dir = rotation_future_ * Vector3f::UnitZ();\n double dotp = dir.dot(Vector3f::UnitZ());\n\n double sign = dir.dot(Vector3f::UnitY()) > 0 ? 1.0 : -1.0;\n double angle = sign * acos(dotp);\n\n/*\n qDebug() << \"Y angle\" << angle;\n qDebug() << \"Y angle (DEG)\" << (angle/M_PI) * 180;\n qDebug() << \"Roll\" << roll;\n qDebug() << \"Pitch\" << pitch;\n qDebug() << \"Yaw\" << yaw;\n*/\n double correction_factor = 1.0 - fabs(pitch-M_PI/2)/(M_PI/2);\n correction_factor = -0.5 + 1.5 * correction_factor;\n correction_factor = clamp(correction_factor, 0, 1);\n\n if(angle < 0)\n correction_factor = 0;\n\n //qDebug() << \"Correction factor:\" << correction_factor;\n\n AngleAxis roll_correction(correction_factor*-roll, Vector3f::UnitZ());\n rotation_future_ = roll_correction * rotation_future_;\n rotation_future_.normalize();\n\n emit modified();\n}\n\nvoid Camera::adjustFov(int val) {\n // Mouse seems to move in increments of 120\n val = -val/60.0f;\n if (fov_future_ + val < 100.0f && fov_future_ + val > 2.0f)\n setFoV(fov_future_ + val);\n\n}\n\nvoid Camera::birds_eye() {\n rotation_future_ = AngleAxis(0, Vector3f(1, 0, 0));\n translation_future_ = 20 * -Vector3f::UnitZ();\n}\n\nvoid Camera::toggleRollCorrection(){\n roll_correction_ = !roll_correction_;\n}\n\nvoid Camera::toggleRollCorrection(bool on){\n roll_correction_ = on;\n}\n", "meta": {"hexsha": "e714f564f507a37368235f198bac3172a4841047", "size": 9541, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/gui/camera.cpp", "max_stars_repo_name": "circlingthesun/cloudclean", "max_stars_repo_head_hexsha": "4b9496bc3b52143c35f0ad83ee68bbc5e8aa32d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2018-10-18T16:10:21.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-28T01:52:24.000Z", "max_issues_repo_path": "src/gui/camera.cpp", "max_issues_repo_name": "circlingthesun/cloudclean", "max_issues_repo_head_hexsha": "4b9496bc3b52143c35f0ad83ee68bbc5e8aa32d5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/gui/camera.cpp", "max_forks_repo_name": "circlingthesun/cloudclean", "max_forks_repo_head_hexsha": "4b9496bc3b52143c35f0ad83ee68bbc5e8aa32d5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2017-12-13T07:39:18.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-29T13:13:48.000Z", "avg_line_length": 30.3853503185, "max_line_length": 128, "alphanum_fraction": 0.6603081438, "num_tokens": 2632, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.42250463481418826, "lm_q1q2_score": 0.2342663413133585}} {"text": "#ifndef INVKIN_H_INCLUDED\n#define INVKIN_H_INCLUDED\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \"pinocchio/math/rpy.hpp\"\n#include \"pinocchio/spatial/explog.hpp\"\n\nclass InvKin {\n /* Planner that outputs current and future locations of footsteps, the\n reference trajectory of the base and the position, velocity, acceleration\n commands for feet in swing phase based on the reference velocity given by\n the user and the current position/velocity of the base */\n\n /*private:\n // Inputs of the constructor\n double dt; // Time step of the contact sequence (time step of the MPC)\n double dt_tsid; // Time step of TSID\n double T_gait; // Gait period\n double T_mpc; // MPC period (prediction horizon)\n double h_ref; // Reference height for the trunk\n int k_mpc; // Number of TSID iterations for one iteration of the MPC\n bool on_solo8; // Whether we are working on solo8 or not\n\n // Predefined quantities\n double k_feedback = 0.03; // Feedback gain for the feedback term of the\n planner double g = 9.81; // Value of the gravity acceleartion\n double L = 0.155; // Value of the maximum allowed deviation due to\n leg length bool is_static = false; // Flag for static gait\n\n // Number of time steps in the prediction horizon\n int n_steps; // T_mpc / time step of the MPC\n\n // Feet index vector\n std::vector feet;\n std::vector t0s;\n double t_remaining = 0.0;\n double t_swing[4] = {0.0, 0.0, 0.0, 0.0};\n\n // Constant sized matrices\n Eigen::MatrixXd fsteps = Eigen::MatrixXd::Zero(N0_gait, 13);\n Eigen::Matrix shoulders = Eigen::Matrix::Zero();\n // Position of shoulders in local frame Eigen::Matrix q_static\n = Eigen::Matrix::Zero(); Eigen::Matrix\n RPY_static = Eigen::Matrix::Zero(); Eigen::Matrix o_feet_contact = Eigen::Matrix::Zero(); // Feet matrix in\n world frame Eigen::Matrix next_footstep = Eigen::Matrix::Zero(); // To store the result of the compute_next_footstep function\n Eigen::Matrix R =\n Eigen::Matrix::Zero(); // Predefined matrices for\n compute_footstep function Eigen::Matrix R_1 =\n Eigen::Matrix::Zero(); // Predefined matrices for\n compute_next_footstep function Eigen::Matrix R_2 =\n Eigen::Matrix::Zero(); Eigen::Matrix dt_cum\n = Eigen::Matrix::Zero(); Eigen::Matrix angle = Eigen::Matrix::Zero(); Eigen::Matrix dx = Eigen::Matrix::Zero();\n Eigen::Matrix dy = Eigen::Matrix::Zero(); Eigen::Matrix q_tmp = Eigen::Matrix::Zero(); Eigen::Matrix q_dxdy = Eigen::Matrix::Zero(); Eigen::Matrix RPY = Eigen::Matrix::Zero(); Eigen::Matrix b_v_cur = Eigen::Matrix::Zero(); Eigen::Matrix b_v_ref = Eigen::Matrix::Zero(); Eigen::Matrix cross = Eigen::Matrix::Zero(); Eigen::Matrix vref_in = Eigen::Matrix::Zero();\n\n Eigen::Matrix gait_p = Eigen::Matrix::Zero(); // Past gait Eigen::MatrixXd gait_f =\n Eigen::MatrixXd::Zero(N0_gait, 5); // Current\n and future gait Eigen::Matrix gait_f_des =\n Eigen::Matrix::Zero(); // Future desired gait\n\n // Time interval vector\n Eigen::Matrix dt_vector;\n\n // Reference trajectory matrix of size 12 by (1 + N) with the current state\n of\n // the robot in column 0 and the N steps of the prediction horizon in the\n others Eigen::MatrixXd xref;\n\n // Foot trajectory generator\n double max_height_feet = 0.05; // * (1000/312.5); // height * correction\n coefficient double t_lock_before_touchdown = 0.07; std::vector\n myTrajGen;\n\n // Variables for foot trajectory generator\n int i_end_gait = 0;\n Eigen::Matrix t_stance =\n Eigen::Matrix::Zero(); // Total duration of current stance\n phase for each foot Eigen::Matrix footsteps_target =\n Eigen::Matrix::Zero(); Eigen::MatrixXd goals =\n Eigen::MatrixXd::Zero(3, 4); // Store 3D target position for feet\n Eigen::MatrixXd vgoals = Eigen::MatrixXd::Zero(3, 4); // Store 3D target\n velocity for feet Eigen::MatrixXd agoals = Eigen::MatrixXd::Zero(3, 4); //\n Store 3D target acceleration for feet Eigen::Matrix mgoals =\n Eigen::Matrix::Zero(); // Storage variable for the\n trajectory generator\n\n Eigen::Matrix res_gen = Eigen::Matrix::Zero();\n // Result of the generator\n */\n\n private:\n // Inputs of the constructor\n double dt; // Time step of the contact sequence (time step of the MPC)\n\n // Matrices initialisation\n Eigen::Matrix feet_position_ref = Eigen::Matrix::Zero();\n Eigen::Matrix feet_velocity_ref = Eigen::Matrix::Zero();\n Eigen::Matrix feet_acceleration_ref = Eigen::Matrix::Zero();\n Eigen::Matrix flag_in_contact = Eigen::Matrix::Zero();\n Eigen::Matrix base_orientation_ref = Eigen::Matrix::Zero();\n Eigen::Matrix base_angularvelocity_ref = Eigen::Matrix::Zero();\n Eigen::Matrix base_angularacceleration_ref = Eigen::Matrix::Zero();\n Eigen::Matrix base_position_ref = Eigen::Matrix::Zero();\n Eigen::Matrix base_linearvelocity_ref = Eigen::Matrix::Zero();\n Eigen::Matrix base_linearacceleration_ref = Eigen::Matrix::Zero();\n Eigen::Matrix x_ref = Eigen::Matrix::Zero();\n Eigen::Matrix x = Eigen::Matrix::Zero();\n Eigen::Matrix dx_ref = Eigen::Matrix::Zero();\n Eigen::Matrix dx = Eigen::Matrix::Zero();\n Eigen::Matrix J = Eigen::Matrix::Zero();\n Eigen::Matrix invJ = Eigen::Matrix::Zero();\n Eigen::Matrix acc = Eigen::Matrix::Zero();\n Eigen::Matrix x_err = Eigen::Matrix::Zero();\n Eigen::Matrix dx_r = Eigen::Matrix::Zero();\n\n Eigen::Matrix pfeet_err = Eigen::Matrix::Zero();\n Eigen::Matrix vfeet_ref = Eigen::Matrix::Zero();\n Eigen::Matrix afeet = Eigen::Matrix::Zero();\n Eigen::Matrix e_basispos = Eigen::Matrix::Zero();\n Eigen::Matrix abasis = Eigen::Matrix::Zero();\n Eigen::Matrix e_basisrot = Eigen::Matrix::Zero();\n Eigen::Matrix awbasis = Eigen::Matrix::Zero();\n\n Eigen::MatrixXd ddq = Eigen::MatrixXd::Zero(18, 1);\n Eigen::MatrixXd q_step = Eigen::MatrixXd::Zero(18, 1);\n Eigen::MatrixXd dq_cmd = Eigen::MatrixXd::Zero(18, 1);\n\n // Gains\n double Kp_base_orientation = 100.0;\n double Kd_base_orientation = 2.0 * std::sqrt(Kp_base_orientation);\n \n double Kp_base_position = 100.0;\n double Kd_base_position = 2.0 * std::sqrt(Kp_base_position);\n\n double Kp_flyingfeet = 1000.0;\n double Kd_flyingfeet = 5.0 * std::sqrt(Kp_flyingfeet);\n\n public:\n InvKin();\n InvKin(double dt_in);\n \n Eigen::Matrix cross3(Eigen::Matrix left, Eigen::Matrix right);\n\n Eigen::MatrixXd refreshAndCompute(const Eigen::MatrixXd &x_cmd, const Eigen::MatrixXd &contacts,\n const Eigen::MatrixXd &goals, const Eigen::MatrixXd &vgoals, const Eigen::MatrixXd &agoals,\n const Eigen::MatrixXd &posf, const Eigen::MatrixXd &vf, const Eigen::MatrixXd &wf, const Eigen::MatrixXd &af,\n const Eigen::MatrixXd &Jf, const Eigen::MatrixXd &posb, const Eigen::MatrixXd &rotb, const Eigen::MatrixXd &vb,\n const Eigen::MatrixXd &ab, const Eigen::MatrixXd &Jb);\n Eigen::MatrixXd computeInvKin(const Eigen::MatrixXd &posf, const Eigen::MatrixXd &vf, const Eigen::MatrixXd &wf, const Eigen::MatrixXd &af,\n const Eigen::MatrixXd &Jf, const Eigen::MatrixXd &posb, const Eigen::MatrixXd &rotb, const Eigen::MatrixXd &vb, const Eigen::MatrixXd &ab,\n const Eigen::MatrixXd &Jb);\n Eigen::MatrixXd get_q_step();\n Eigen::MatrixXd get_dq_cmd();\n /*void Print();\n\n int create_walk();\n int create_trot();\n int create_gait_f();\n int roll(int k);\n int compute_footsteps(Eigen::MatrixXd q_cur, Eigen::MatrixXd v_cur,\n Eigen::MatrixXd v_ref); double get_stance_swing_duration(int i, int j, double\n value); int compute_next_footstep(int i, int j); int\n getRefStates(Eigen::MatrixXd q, Eigen::MatrixXd v, Eigen::MatrixXd vref,\n double z_average); int update_target_footsteps(); int\n update_trajectory_generator(int k, double h_estim); int run_planner(int k,\n const Eigen::MatrixXd &q, const Eigen::MatrixXd &v, const Eigen::MatrixXd\n &b_vref, double h_estim, double z_average);*/\n\n // Accessors (to retrieve C data from Python)\n /*Eigen::MatrixXd get_xref();\n Eigen::MatrixXd get_fsteps();\n Eigen::MatrixXd get_gait();\n Eigen::MatrixXd get_goals();\n Eigen::MatrixXd get_vgoals();\n Eigen::MatrixXd get_agoals();*/\n};\n\ntemplate\n_Matrix_Type_ pseudoInverse(const _Matrix_Type_ &a, double epsilon = std::numeric_limits::epsilon())\n{\n\tEigen::JacobiSVD< _Matrix_Type_ > svd(a ,Eigen::ComputeThinU | Eigen::ComputeThinV);\n\tdouble tolerance = epsilon * static_cast(std::max(a.cols(), a.rows())) *svd.singularValues().array().abs()(0);\n\treturn svd.matrixV() * (svd.singularValues().array().abs() > tolerance).select(svd.singularValues().array().inverse(), 0).matrix().asDiagonal() * svd.matrixU().adjoint();\n}\n\n\n#endif // INVKIN_H_INCLUDED\n", "meta": {"hexsha": "90e694cf7defcf522edfbc189a666af8cf9f4ecb", "size": 10547, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/quadruped-reactive-walking/InvKin.hpp", "max_stars_repo_name": "nim65s/quadruped-reactive-walking", "max_stars_repo_head_hexsha": "1e0f4069fd11af85abf10bfc8f9d66200c672646", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 6.0, "max_stars_repo_stars_event_min_datetime": "2021-03-03T10:59:19.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-13T15:05:25.000Z", "max_issues_repo_path": "include/quadruped-reactive-walking/InvKin.hpp", "max_issues_repo_name": "nim65s/quadruped-reactive-walking", "max_issues_repo_head_hexsha": "1e0f4069fd11af85abf10bfc8f9d66200c672646", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/quadruped-reactive-walking/InvKin.hpp", "max_forks_repo_name": "nim65s/quadruped-reactive-walking", "max_forks_repo_head_hexsha": "1e0f4069fd11af85abf10bfc8f9d66200c672646", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2021-05-17T13:34:29.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-03T10:58:37.000Z", "avg_line_length": 51.4487804878, "max_line_length": 172, "alphanum_fraction": 0.6696691002, "num_tokens": 3173, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.23400269564086654}} {"text": "// this is for emacs file handling -*- mode: c++; indent-tabs-mode: nil -*-\n\n// -- BEGIN LICENSE BLOCK ----------------------------------------------\n// Copyright (c) 2018, FZI Forschungszentrum Informatik\n//\n// Redistribution and use in source and binary forms, with or without modification, are permitted\n// provided that the following conditions are met:\n//\n// 1. Redistributions of source code must retain the above copyright notice, this list of conditions\n// and the following disclaimer.\n//\n// 2. Redistributions in binary form must reproduce the above copyright notice, this list of\n// conditions and the following disclaimer in the documentation and/or other materials provided\n// with the distribution.\n//\n// 3. Neither the name of the copyright holder nor the names of its contributors may be used to\n// endorse or promote products derived from this software without specific prior written\n// permission.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR\n// IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND\n// FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR\n// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n// DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n// DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\n// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY\n// WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n// -- END LICENSE BLOCK ------------------------------------------------\n\n//----------------------------------------------------------------------\n/*!\\file\n *\n * \\author Julius Ziegler \n * \\date 2012-03-06\n *\n */\n//----------------------------------------------------------------------\n\n#if !defined(LOCALGEOGRAPHICCS_HPP)\n#define LOCALGEOGRAPHICCS_HPP\n\n#include \"convert_coordinates.hpp\"\n\n#include \n\n#include \n\nstruct LocalGeographicCS\n{\n LocalGeographicCS();\n LocalGeographicCS( double lat0, double lon0 );\n\n void set_origin( double lat0, double lon0 );\n\n void ll2xy( double lat, double lon, double& x, double& y ) const;\n void xy2ll( double x, double y, double& lat, double& lon ) const;\n\n boost::tuple ll2xy( double lat, double lon ) const;\n boost::tuple xy2ll( double x, double y ) const;\n\n // operate on containers\n template\n void ll2xy( const ItIn& lat_begin, const ItIn& lat_end, const ItIn& lon_begin, const ItOut& x_begin, const ItOut& y_begin ) const;\n\n template\n void xy2ll( const ItIn& x_begin, const ItIn& x_end, const ItIn& y_begin, const ItOut& lat_begin, const ItOut& lon_begin ) const;\n\nprivate:\n double _scale;\n double _x0, _y0;\n};\n\ninline LocalGeographicCS::LocalGeographicCS( double lat0, double lon0 )\n{\n set_origin( lat0, lon0 );\n}\n\ninline LocalGeographicCS::LocalGeographicCS()\n{}\n\ninline void LocalGeographicCS::set_origin( double lat0, double lon0 )\n{\n _scale = convert_coordinates::lat_to_scale( lat0 );\n convert_coordinates::latlon_to_mercator( lat0, lon0, _scale, _x0, _y0 );\n}\n\ninline void LocalGeographicCS::ll2xy( double lat, double lon, double& x, double& y ) const\n{\n convert_coordinates::latlon_to_mercator( lat, lon, _scale, x, y );\n x -= _x0;\n y -= _y0;\n}\n\ninline boost::tuple LocalGeographicCS::ll2xy( double lat, double lon ) const\n{\n double x, y;\n ll2xy( lat, lon, x, y );\n return boost::make_tuple( x, y );\n}\n\ninline void LocalGeographicCS::xy2ll( double x, double y, double& lat, double& lon ) const\n{\n x += _x0;\n y += _y0;\n\n convert_coordinates::mercator_to_latlon( x, y, _scale, lat, lon );\n}\n\ninline boost::tuple LocalGeographicCS::xy2ll( double x, double y ) const\n{\n double lat, lon;\n xy2ll( x, y, lat, lon );\n return boost::make_tuple( lat, lon );\n}\n\n// operate on containers\ntemplate\nvoid LocalGeographicCS::ll2xy( const ItIn& lat_begin, const ItIn& lat_end, const ItIn& lon_begin, const ItOut& x_begin, const ItOut& y_begin ) const\n{\n ItIn lat = lat_begin;\n ItIn lon = lon_begin;\n ItOut x = x_begin;\n ItOut y = y_begin;\n\n for( ; lat != lat_end; lat++, lon++, x++, y++ )\n {\n ll2xy( *lat, *lon, *x, *y );\n }\n}\n\ntemplate\nvoid LocalGeographicCS::xy2ll( const ItIn& x_begin, const ItIn& x_end, const ItIn& y_begin, const ItOut& lat_begin, const ItOut& lon_begin ) const\n{\n ItIn x = x_begin;\n ItIn y = y_begin;\n\n ItOut lat = lat_begin;\n ItOut lon = lon_begin;\n\n for( ; x != x_end; lat++, lon++, x++, y++ )\n xy2ll( *x, *y, *lat, *lon );\n}\n\n#endif\n", "meta": {"hexsha": "fb17aa30fd873ec7c37d441bd507994958553bd8", "size": 4873, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/liblanelet/LocalGeographicCS.hpp", "max_stars_repo_name": "brand666/liblanelet", "max_stars_repo_head_hexsha": "252e436ae9f705f8004d86b504be6a5f0c8bcc19", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 24.0, "max_stars_repo_stars_event_min_datetime": "2017-11-29T12:44:44.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-06T12:45:52.000Z", "max_issues_repo_path": "src/liblanelet/LocalGeographicCS.hpp", "max_issues_repo_name": "brand666/liblanelet", "max_issues_repo_head_hexsha": "252e436ae9f705f8004d86b504be6a5f0c8bcc19", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2018-11-02T09:21:27.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-16T20:03:17.000Z", "max_forks_repo_path": "src/liblanelet/LocalGeographicCS.hpp", "max_forks_repo_name": "brand666/liblanelet", "max_forks_repo_head_hexsha": "252e436ae9f705f8004d86b504be6a5f0c8bcc19", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 12.0, "max_forks_repo_forks_event_min_datetime": "2017-10-26T08:42:06.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-06T12:45:52.000Z", "avg_line_length": 33.6068965517, "max_line_length": 148, "alphanum_fraction": 0.6821260004, "num_tokens": 1293, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832354982645, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.23390873404628146}} {"text": "//=============================================================================================================\n/**\n* @file spectrogram.cpp\n* @author Martin Henfling ;\n* Daniel Knobl ;\n* @version 1.0\n* @date September, 2015\n*\n* @section LICENSE\n*\n* Copyright (C) 2014, Martin Henfling and Daniel Knobl. All rights reserved.\n*\n* Redistribution and use in source and binary forms, with or without modification, are permitted provided that\n* the following conditions are met:\n* * Redistributions of source code must retain the above copyright notice, this list of conditions and the\n* following disclaimer.\n* * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and\n* the following disclaimer in the documentation and/or other materials provided with the distribution.\n* * Neither the name of MNE-CPP authors nor the names of its contributors may be used\n* to endorse or promote products derived from this software without specific prior written permission.\n*\n* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED\n* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\n* PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,\n* INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\n* HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n* NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n* POSSIBILITY OF SUCH DAMAGE.\n*\n*\n* @brief Implementation of spectrogram class.\n*/\n\n//*************************************************************************************************************\n//=============================================================================================================\n// INCLUDES\n//=============================================================================================================\n\n#include \"spectrogram.h\"\n#include \"math.h\"\n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// Eigen INCLUDES\n//=============================================================================================================\n\n#include \n#include \n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// USED NAMESPACES\n//=============================================================================================================\n\nusing namespace UTILSLIB;\n\n\n//*************************************************************************************************************\n//=============================================================================================================\n// DEFINE MEMBER METHODS\n//=============================================================================================================\n\nVectorXd Spectrogram::gauss_window (qint32 sample_count, qreal scale, quint32 translation)\n{\n VectorXd gauss = VectorXd::Zero(sample_count);\n\n for(qint32 n = 0; n < sample_count; n++)\n {\n qreal t = (qreal(n) - translation) / scale;\n gauss[n] = exp(-3.14 * pow(t, 2))*pow(sqrt(scale),(-1))*pow(qreal(2),(0.25));\n }\n\n return gauss;\n}\n\n//-----------------------------------------------------------------------------------------------------------------\n\nMatrixXd Spectrogram::make_spectrogram(VectorXd signal, qint32 window_size = 0)\n{\n if(window_size == 0)\n window_size = signal.rows()/4;\n\n Eigen::FFT fft;\n MatrixXd tf_matrix = MatrixXd::Zero(signal.rows()/2, signal.rows());\n\n for(qint32 translate = 0; translate < signal.rows(); translate++)\n {\n VectorXd envelope = gauss_window(signal.rows(), window_size, translate);\n\n VectorXd windowed_sig = VectorXd::Zero(signal.rows());\n VectorXcd fft_win_sig = VectorXcd::Zero(signal.rows());\n\n VectorXd real_coeffs = VectorXd::Zero(signal.rows()/2);\n\n for(qint32 sample = 0; sample < signal.rows(); sample++)\n windowed_sig[sample] = signal[sample] * envelope[sample];\n\n fft.fwd(fft_win_sig, windowed_sig);\n\n for(qint32 i= 0; i\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"theia/math/util.h\"\n#include \"theia/math/graph/minimum_spanning_tree.h\"\n#include \"theia/util/hash.h\"\n#include \"theia/util/map_util.h\"\n#include \"theia/sfm/twoview_info.h\"\n#include \"theia/sfm/types.h\"\n#include \"theia/sfm/view_graph/view_graph.h\"\n#include \"theia/util/filesystem.h\"\n\n#include \n#include \n\nnamespace theia {\n\nstd::ofstream fileWriter(\"cycle_error_report.txt\", std::ios::out);\n\nbool PrefilterViewPairs(ViewGraph* viewGraph) {\n CHECK_NOTNULL(viewGraph);\n\n// Declare MST extractor and MST and edges\n MinimumSpanningTree mst_extractor;\n std::unordered_set mst;\n std::unordered_map edges;\n\n\n// Create MST\n edges = viewGraph->GetAllEdges();\n\n for (const auto& edge : edges) {\n mst_extractor.AddEdge(edge.first.first, edge.first.second, 1);\n }\n\n// Add values to MST\n mst_extractor.Extract(&mst);\n\n\n\n\n// Preparing variables for creating chains\n std::vector > chainList;\n std::unordered_set visited;\n std::vector oldChain;\n// Create chains\n CreateChains(chainList, mst, visited, oldChain, mst.begin()->first);\n\n\n\n\n// Preparing variables for creating cycles\n// Store cycles as vectors of ViewIdPairs, with a big vector holding all the cycles\n std::vector > cycleList;\n// Create cycles\n CreateCycles(edges, chainList, mst, cycleList);\n\n\n\n\n// Preparing variables for calculating errors\n std::unordered_set allUsedEdges;\n std::vector errors;\n// Calculate and print cycle errors\n CalculateErrors(cycleList, edges, allUsedEdges, errors);\n\n\n// Preparing variables for calculating translation errors\n std::unordered_set allUsedTranslationEdges;\n std::vector > translationCycles;\n std::vector translationErrors;\n// Calculate translation cycle errors\n CalculateTranslationErrors(cycleList, translationCycles, edges, allUsedTranslationEdges, translationErrors);\n\n\n\n// Preparing variables for inferencing\n std::unordered_map number2Edge;\n std::unordered_map edge2Number;\n int it = 0;\n for (auto& edge : allUsedEdges) {\n\t number2Edge.emplace(it, edge);\n\t edge2Number.emplace(edge, it);\n\t ++it;\n }\n\n std::unordered_map number2TranslationEdge;\n std::unordered_map translationEdge2Number;\n int it2 = 0;\n for (auto& edge : allUsedTranslationEdges) {\n\t number2TranslationEdge.emplace(it2, edge);\n\t translationEdge2Number.emplace(edge, it2);\n\t ++it2;\n }\n\n double rotSigma = 2*M_PI/180;\n double transSigma = 0.011;\n\n// Edges that we need to remove\n std::unordered_set viewPairsToRemove;\n// Do inferences to figure out what edges to remove\n DoInference(rotSigma, cycleList, errors, edge2Number, number2Edge, allUsedEdges, viewPairsToRemove);\n// Do inferences based on relative translations\n DoInference(transSigma, translationCycles, translationErrors, translationEdge2Number, number2TranslationEdge, allUsedTranslationEdges, viewPairsToRemove);\n\n\n\n// Remove the edge if it is incorrect\n for (const ViewIdPair viewIdPair : viewPairsToRemove) {\n viewGraph->RemoveEdge(viewIdPair.first, viewIdPair.second);\n }\n VLOG(1) << \"Removed \" << viewPairsToRemove.size()\n << \" view pairs by rotation prefiltering.\";\n return true;\n}\n\n\nvoid CreateChains(std::vector >& chainList,\n\t\t\t std::unordered_set& mst,\n\t\t\t\t std::unordered_set& visited,\n\t\t\t\t std::vector oldChain,\n\t\t\t\t ViewId root) {\n\tfor (ViewIdPair e : mst) {\n\t\tif (visited.find(e) != visited.end()) {\n\t\t\tcontinue;\n\t\t}\n\t\tif (e.first == root) {\n\t\t\toldChain.emplace_back(e);\n\t\t\tstd::vector chain = oldChain;\n\t\t\toldChain.pop_back();\n\t\t\tchainList.emplace_back(chain);\n\t\t\tvisited.insert(e);\n\n\t\t\tCreateChains(chainList, mst, visited, chain, e.second);\n\t\t}\n\t\telse if (e.second == root) {\n\t\t\toldChain.emplace_back(e);\n\t\t\tstd::vector chain = oldChain;\n\t\t\toldChain.pop_back();\n\t\t\tchainList.emplace_back(chain);\n\t\t\tvisited.insert(e);\n\n\t\t\tCreateChains(chainList, mst, visited, chain, e.first);\n\t\t}\n\t}\n}\n\n\nvoid CreateCycles(std::unordered_map& edges,\n\t\t\t\t std::vector >& chainList,\n\t\t\t\t std::unordered_set& mst,\n\t\t\t\t std::vector >& cycleList) {\n\n\tstd::unordered_set otherEdges; // not in MST\n\n\tfor (auto& e : edges) {\n\t\tif (mst.find(e.first) == mst.end()) {\n\t\t\totherEdges.insert(e.first);\n\t\t}\n\t}\n\n\tfor (ViewIdPair e : otherEdges) {\n\t\tif (e.first == mst.begin()->first || e.second == mst.begin()->first) {\n\t\t\tcontinue;\n\t\t}\n\t\tstd::vector cycle;\n\t\tcycle.emplace_back(e);\n\t\tstd::vector chain1;\n\t\tstd::vector chain2;\n\n\t\tfor (std::vector chain : chainList) {\n\t\t\tViewIdPair lastE = chain.end()[-1];\n\t\t\tViewIdPair secondtolastE = chain.end()[-2];\n\t\t\tif (\n\t\t\t\t((e.first == lastE.first || e.first == lastE.second)\n\t\t\t\t&& !(e.first == secondtolastE.first || e.first == secondtolastE.second))\n\t\t\t\t||\n\t\t\t\t((e.first == lastE.first || e.first == lastE.second)\n\t\t\t\t&& (chain.size() == 1))\n\t\t\t\t) {\n\t\t\t\tchain1 = chain;\n\t\t\t\tcontinue;\n\t\t\t}\n\t\t\tif (\n\t\t\t\t((e.second == lastE.first || e.second == lastE.second)\n\t\t\t\t&& !(e.second == secondtolastE.first || e.second == secondtolastE.second))\n\t\t\t\t||\n\t\t\t\t((e.second == lastE.first || e.second == lastE.second)\n\t\t\t\t&& (chain.size() == 1))\n\t\t\t\t) {\n\t\t\t\tchain2 = chain;\n\t\t\t\tcontinue;\n\t\t\t}\n\t\t}\n\n\t\tstd::set_symmetric_difference(chain1.begin(), chain1.end(),\n\t\t\t\t chain2.begin(), chain2.end(),\n\t\t\t\t std::back_inserter(cycle));\n\t\tcycleList.emplace_back(cycle);\n\t}\n}\n\n\nvoid CalculateErrors(std::vector >& cycleList,\n\t\t\t\t\t std::unordered_map& edges,\n\t\t\t\t\t std::unordered_set& allUsedEdges,\n\t\t\t\t\t std::vector& errors) {\n\tfor (std::vector cycle : cycleList) {\n\n\t\tdouble error;\n\t\tEigen::Matrix3d combinedRotation;\n\t\tceres::AngleAxisToRotationMatrix(\n\t\t\tedges[cycle[0]].rotation_2.data(),\n\t\t\tceres::RowMajorAdapter3x3(combinedRotation.data()));\n\n\t\tstd::vector usedEdges;\n\t\tusedEdges.push_back(cycle.front());\n\t\tallUsedEdges.insert(cycle.front());\n\t\tstd::vector nextEdge;\n\t\tnextEdge.push_back(cycle.front().second);\n\t\twhile (usedEdges.size() < cycle.size()) {\n\t\t\tfor (ViewIdPair edge : cycle) {\n\n\t\t\t\tTwoViewInfo info = edges[edge];\n\t\t\t\tEigen::Matrix3d rotation_matrix;\n\n\t\t\t\tif (std::find(usedEdges.begin(), usedEdges.end(), edge) != usedEdges.end()) {\n\t\t\t\t\tcontinue;\n\t\t\t\t} else if (edge.first == nextEdge.back()) {\n\t\t\t\t\tceres::AngleAxisToRotationMatrix(\n\t\t\t\t\t\tinfo.rotation_2.data(),\n\t\t\t\t\t\tceres::RowMajorAdapter3x3(rotation_matrix.data()));\n\t\t\t\t\tcombinedRotation *= rotation_matrix;\n\t\t\t\t\tusedEdges.push_back(edge);\n\t\t\t\t\tif (allUsedEdges.find(edge) == allUsedEdges.end()) {\n\t\t\t\t\t\tallUsedEdges.insert(edge);\n\t\t\t\t\t}\n\t\t\t\t\tnextEdge.push_back(edge.second);\n\t\t\t\t\tfileWriter << rotation_matrix;\n\t\t\t\t\tfileWriter << \"\\n\\n\";\n\t\t\t\t} else if (edge.second == nextEdge.back()) {\n\t\t\t\t\tceres::AngleAxisToRotationMatrix(\n\t\t\t\t\t\tinfo.rotation_2.data(),\n\t\t\t\t\t\tceres::RowMajorAdapter3x3(rotation_matrix.data()));\n\t\t\t\t\tcombinedRotation *= rotation_matrix.transpose();\n\t\t\t\t\tusedEdges.push_back(edge);\n\t\t\t\t\tif (allUsedEdges.find(edge) == allUsedEdges.end()) {\n\t\t\t\t\t\tallUsedEdges.insert(edge);\n\t\t\t\t\t}\n\t\t\t\t\tnextEdge.push_back(edge.first);\n\t\t\t\t\tfileWriter << rotation_matrix.transpose();\n\t\t\t\t\tfileWriter << \"\\n\\n\";\n\t\t\t\t} else {\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tfileWriter << \"Combined Rotation:\\n\";\n\t\tfileWriter << combinedRotation;\n\n\t\tfileWriter << \"\\n\\nError:\\n\";\n\t\terror = acos((combinedRotation.trace()-1)/2);\n\t\tfileWriter << error << \"\\n\\n\\n\\n\";\n\t\terrors.push_back(error);\n\t}\n}\n\n\nvoid CalculateTranslationErrors(std::vector >& cycleList,\n\t\t\t\t\t\t\t\tstd::vector >& translationCycles,\n\t\t \t \t \t \t \t \tstd::unordered_map& edges,\n\t\t\t\t\t\t\t\tstd::unordered_set& allUsedTranslationEdges,\n\t\t\t\t\t\t\t\tstd::vector& translationErrors) {\n\tfor (auto& cycle : cycleList) {\n\t\tif (cycle.size() == 3) {\n\t\t\ttranslationCycles.push_back(cycle);\n\t\t}\n\t}\n\tstd::cout << \"Singular Values: \" << \"\\n\\n\";\n\tfor (auto& cycle : translationCycles) {\n\t\tEigen::Matrix3d translationsMatrix;\n\t\tfor (int i = 0; i < 3; ++i) {\n\t\t\ttranslationsMatrix(i, 0) = edges[cycle.front()].position_2[i];\n\t\t}\n\t\tstd::vector usedEdges;\n\t\tusedEdges.push_back(cycle.front());\n\t\tallUsedTranslationEdges.insert(cycle.front());\n\t\tstd::vector nextEdge;\n\t\tnextEdge.push_back(cycle.front().second);\n\t\twhile (usedEdges.size() < 3) {\n\t\t\tfor (int j = 1; j < 3; ++j) {\n\t\t\t\tif (std::find(usedEdges.begin(), usedEdges.end(), cycle[j]) != usedEdges.end()) {\n\t\t\t\t\tcontinue;\n\t\t\t\t} else if (cycle[j].first == nextEdge.back()) {\n\t\t\t\t\tfor (int i = 0; i < 3; ++i) {\n\t\t\t\t\t\ttranslationsMatrix(i, j) = edges[cycle[j]].position_2[i];\n\t\t\t\t\t}\n\t\t\t\t\tusedEdges.push_back(cycle[j]);\n\t\t\t\t\tif (allUsedTranslationEdges.find(cycle[j]) == allUsedTranslationEdges.end()) {\n\t\t\t\t\t\tallUsedTranslationEdges.insert(cycle[j]);\n\t\t\t\t\t}\n\t\t\t\t\tnextEdge.push_back(cycle[j].second);\n\t\t\t\t} else if (cycle[j].second == nextEdge.back()) {\n\t\t\t\t\tfor (int i = 0; i < 3; ++i) {\n\t\t\t\t\t\ttranslationsMatrix(i, j) = -(edges[cycle[j]].position_2[i]);\n\t\t\t\t\t}\n\t\t\t\t\tusedEdges.push_back(cycle[j]);\n\t\t\t\t\tif (allUsedTranslationEdges.find(cycle[j]) == allUsedTranslationEdges.end()) {\n\t\t\t\t\t\tallUsedTranslationEdges.insert(cycle[j]);\n\t\t\t\t\t}\n\t\t\t\t\tnextEdge.push_back(cycle[j].first);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n//\t\tstd::cout << translationsMatrix << \"\\n\";\n\t\tEigen::JacobiSVD svd(translationsMatrix);\n\t\tdouble error = svd.singularValues()[2];\n\t\ttranslationErrors.push_back(error);\n\t}\n\tfor (int i = 0; i < translationErrors.size(); ++i) {\n\t\tstd::cout << \"Cycle \" << i << \"\\n\" << translationErrors[i] << \"\\n\\n\";\n\t}\n}\n\n\nvoid DoInference(double sigma,\n\t\t\t\t std::vector >& cycleList,\n\t\t\t\t std::vector& errors,\n\t\t\t\t std::unordered_map& edge2Number,\n\t\t\t\t std::unordered_map& number2Edge,\n\t\t\t\t std::unordered_set& allUsedEdges,\n\t\t\t\t std::unordered_set& viewPairsToRemove) {\n\tconst double rhoe = (-log(0.1)) - (-log(0.9));\n\tdouble rhol;\n\n\tconst double sigmaInliers = sigma;\n\tconst double eps = pow(2,-52);\n\n\tusing namespace lemon;\n\n\t// Create an instance of the default LP solver class\n\t// (it will represent an \"empty\" problem at first)\n\tLp lp;\n\n\t// Set up variables\n\tstd::vector xe;\n\tstd::vector xl;\n\tLp::Expr totalEdges;\n\tLp::Expr totalCycles;\n\tstd::map, std::vector > index;\n\tfor (auto& cycle : cycleList) {\n\t\tstd::vector tempVector;\n\t\tfor (auto& edge : cycle) {\n\t\t\ttempVector.push_back(edge2Number[edge]);\n\t\t}\n\t\tindex.emplace(cycle, tempVector);\n\t}\n\n\t// Add columns (variables) to the problem\n\tfor (int i = 0; i < allUsedEdges.size(); ++i) { // For every edge used in any cycle, make a new xe\n\t\txe.push_back(lp.addCol());\n\t}\n\tfor (int i = 0; i < cycleList.size(); ++i) { // For every cycle in the cycleList, make a new xl\n\t\txl.push_back(lp.addCol());\n\t}\n\n\t// Add rows (constraints) to the problem\n\tfor (int cycle = 0; cycle < cycleList.size(); ++cycle) { // For every cycle in the cycleList\n\n\t\tLp::Expr total;\n\t\tfor (int edge = 0; edge < cycleList[cycle].size(); ++edge) {\n\t\t\tlp.addRow(xl[cycle] >= xe[(index[cycleList[cycle]])[edge]]); // xl for this cycle is greater than each xe\n\t\t\ttotal += xe[(index[cycleList[cycle]])[edge]];\n\t\t\ttotalEdges += total;\n\t\t}\n\t\tlp.addRow(xl[cycle] <= total);\n\n\t\t// Calculate rhol for this cycle\n\t\tdouble pErrorInliers = exp(-errors[cycle]/sigmaInliers)/(sigmaInliers*(1-exp(-M_PI/sigmaInliers)));\n\t\tdouble pErrorOutliers = (1-exp(-errors[cycle]/sigmaInliers))/(M_PI-sigmaInliers*(1-exp(-M_PI/sigmaInliers)));\n\t\trhol = (-log(pErrorOutliers + eps))-(-log(pErrorInliers + eps));\n\t\ttotalCycles += rhol * xl[cycle];\n\t}\n\n\t// Set lower and upper bounds for the columns (variables)\n\tfor (int k = 0; k < xe.size(); ++k) {\n\t\tlp.colBounds(xe[k], 0, 1);\n\t}\n\tfor (int k = 0; k < xl.size(); ++k) {\n\t\tlp.colBounds(xl[k], 0, 1);\n\t}\n\n\t// Specify the objective function\n\tlp.min();\n\tlp.obj(rhoe * totalEdges + totalCycles);\n\n\t// Solve the problem using the underlying LP solver\n\tlp.solve();\n\n\t// Print the results\n\tif (lp.primalType() == Lp::OPTIMAL) {\n\t\tstd::cout << \"Objective function value: \" << lp.primal() << \"\\n\\n\";\n\t\tfor (int i = 0; i < xe.size(); ++i) {\n\t\t\tstd::cout << \"Edge \" << i << \": \" << number2Edge[i].first << \" \" << number2Edge[i].second << \":\\t\" << lp.primal(xe[i]) << \"\\n\";\n\t\t\tif (lp.primal(xe[i]) == 1) {\n\t\t\t\tviewPairsToRemove.insert(number2Edge[i]);\n\t\t\t}\n\t\t}\n\t\tfor (int i = 0; i < xl.size(); ++i) {\n\t\t\tstd::cout << \"Cycle \" << i << \": \" << lp.primal(xl[i]) << \"\\n\";\n\t\t}\n\t} else {\n\t\tstd::cout << \"Optimal solution not found.\" << \"\\n\";\n\t}\n}\n\n\n} // namespace theia\n\n", "meta": {"hexsha": "a6f6270cd77313e0c82fc54069095b3c746f1263", "size": 13389, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/theia/sfm/view_graph/prefilter_view_pairs.cc", "max_stars_repo_name": "LEON-MING/TheiaSfM_Leon", "max_stars_repo_head_hexsha": "8ac187b80100ad7f52fe9af49fa4a0db6db226b9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/theia/sfm/view_graph/prefilter_view_pairs.cc", "max_issues_repo_name": "LEON-MING/TheiaSfM_Leon", "max_issues_repo_head_hexsha": "8ac187b80100ad7f52fe9af49fa4a0db6db226b9", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/theia/sfm/view_graph/prefilter_view_pairs.cc", "max_forks_repo_name": "LEON-MING/TheiaSfM_Leon", "max_forks_repo_head_hexsha": "8ac187b80100ad7f52fe9af49fa4a0db6db226b9", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.4988610478, "max_line_length": 156, "alphanum_fraction": 0.6683844947, "num_tokens": 3797, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.4339814648038985, "lm_q1q2_score": 0.23390872761876402}} {"text": "#include \"syanten.hpp\"\r\n\r\n#include \r\n#include \r\n\r\n#include \r\n\r\n#include \"bitutils.hpp\"\r\n\r\nnamespace mahjong\r\n{\r\n\r\nSyantenCalculator::SyantenCalculator() { initialize(); }\r\n\r\n/**\r\n * @brief 向聴数を計算する。\r\n *\r\n * @param[in] hand 手牌\r\n * @param[in] type 計算対象の向聴数の種類\r\n * @return std::tuple (向聴数の種類, 向聴数)\r\n */\r\nstd::tuple SyantenCalculator::calc(const Hand &hand, int type)\r\n{\r\n#ifdef CHECK_ARGUMENT\r\n if (type < 0 || type > 7) {\r\n spdlog::warn(\"Invalid type {} passed.\", type);\r\n return -2;\r\n }\r\n#endif\r\n\r\n std::tuple ret = {SyantenType::Null, std::numeric_limits::max()};\r\n\r\n if (type & SyantenType::Normal) {\r\n int syanten = calc_normal(hand);\r\n if (syanten < std::get<1>(ret))\r\n ret = {SyantenType::Normal, syanten};\r\n }\r\n if (type & SyantenType::Tiitoi) {\r\n int syanten = calc_tiitoi(hand);\r\n if (syanten < std::get<1>(ret))\r\n ret = {SyantenType::Tiitoi, syanten};\r\n }\r\n if (type & SyantenType::Kokusi) {\r\n int syanten = calc_kokusi(hand);\r\n if (syanten < std::get<1>(ret))\r\n ret = {SyantenType::Kokusi, syanten};\r\n }\r\n\r\n return ret;\r\n}\r\n\r\n/**\r\n * @brief 初期化する。\r\n *\r\n * @return 初期化に成功した場合は true、そうでない場合は false を返す。\r\n */\r\nbool SyantenCalculator::initialize()\r\n{\r\n boost::filesystem::path exe_path = boost::dll::this_line_location().parent_path();\r\n\r\n#ifdef USE_UNORDERED_MAP\r\n if (s_tbl_.empty()) {\r\n boost::filesystem::path path = exe_path / \"syupai_table.bin\";\r\n std::ifstream ifs(path.string(), std::ios::binary);\r\n for (size_t i = 0; i < ShuupaiPatternSize; ++i) {\r\n unsigned int key;\r\n Pattern pattern;\r\n ifs.read((char *)&key, sizeof(unsigned int));\r\n ifs.read((char *)&pattern, sizeof(Pattern));\r\n\r\n s_tbl_[key] = pattern;\r\n }\r\n }\r\n\r\n if (z_tbl_.empty()) {\r\n boost::filesystem::path path = exe_path / \"zihai_table.bin\";\r\n std::ifstream ifs(path.string(), std::ios::binary);\r\n\r\n for (size_t i = 0; i < ZihaiPatternSize; ++i) {\r\n unsigned int key;\r\n Pattern pattern;\r\n ifs.read((char *)&key, sizeof(unsigned int));\r\n ifs.read((char *)&pattern, sizeof(Pattern));\r\n\r\n z_tbl_[key] = pattern;\r\n }\r\n }\r\n#else\r\n if (s_tbl_.empty()) {\r\n s_tbl_.resize(ShuupaiTableSize);\r\n\r\n boost::filesystem::path path = exe_path / \"syupai_table.bin\";\r\n std::ifstream ifs(path.string(), std::ios::binary);\r\n for (size_t i = 0; i < ShuupaiPatternSize; ++i) {\r\n unsigned int key;\r\n ifs.read((char *)&key, sizeof(unsigned int));\r\n ifs.read((char *)&s_tbl_[key], sizeof(Pattern));\r\n }\r\n }\r\n\r\n if (z_tbl_.empty()) {\r\n z_tbl_.resize(ZihaiTableSize);\r\n\r\n boost::filesystem::path path = exe_path / \"zihai_table.bin\";\r\n std::ifstream ifs(path.string(), std::ios::binary);\r\n\r\n for (size_t i = 0; i < ZihaiPatternSize; ++i) {\r\n unsigned int key;\r\n ifs.read((char *)&key, sizeof(unsigned int));\r\n ifs.read((char *)&z_tbl_[key], sizeof(Pattern));\r\n }\r\n }\r\n#endif\r\n\r\n return true;\r\n}\r\n\r\n/**\r\n * @brief 通常手の向聴数を計算する。\r\n *\r\n * @param[in] hand 手牌\r\n * @return int 向聴数\r\n */\r\nint SyantenCalculator::calc_normal(const Hand &hand)\r\n{\r\n // 制約条件「面子 + 候補 <= 4」で「面子数 * 2 + 候補」の最大値を計算する。\r\n int n_melds = int(hand.melds.size());\r\n int n_mentu_base = n_melds + s_tbl_[hand.manzu].n_mentu +\r\n s_tbl_[hand.pinzu].n_mentu + s_tbl_[hand.sozu].n_mentu +\r\n z_tbl_[hand.zihai].n_mentu;\r\n int n_kouho_base = s_tbl_[hand.manzu].n_kouho + s_tbl_[hand.pinzu].n_kouho +\r\n s_tbl_[hand.sozu].n_kouho + z_tbl_[hand.zihai].n_kouho;\r\n\r\n // 雀頭なし\r\n int max = n_mentu_base * 2 + std::min(4 - n_mentu_base, n_kouho_base);\r\n\r\n if (s_tbl_[hand.manzu].head) {\r\n // 萬子の雀頭有り\r\n int n_mentu = n_mentu_base + s_tbl_[hand.manzu].n_mentu_diff;\r\n int n_kouho = n_kouho_base + s_tbl_[hand.manzu].n_kouho_diff;\r\n max = std::max(max, n_mentu * 2 + std::min(4 - n_mentu, n_kouho) + 1);\r\n }\r\n\r\n if (s_tbl_[hand.pinzu].head) {\r\n // 筒子の雀頭有り\r\n int n_mentu = n_mentu_base + s_tbl_[hand.pinzu].n_mentu_diff;\r\n int n_kouho = n_kouho_base + s_tbl_[hand.pinzu].n_kouho_diff;\r\n max = std::max(max, n_mentu * 2 + std::min(4 - n_mentu, n_kouho) + 1);\r\n }\r\n\r\n if (s_tbl_[hand.sozu].head) {\r\n // 索子の雀頭有り\r\n int n_mentu = n_mentu_base + s_tbl_[hand.sozu].n_mentu_diff;\r\n int n_kouho = n_kouho_base + s_tbl_[hand.sozu].n_kouho_diff;\r\n max = std::max(max, n_mentu * 2 + std::min(4 - n_mentu, n_kouho) + 1);\r\n }\r\n\r\n if (z_tbl_[hand.zihai].head) {\r\n // 字牌の雀頭有り\r\n int n_mentu = n_mentu_base + z_tbl_[hand.zihai].n_mentu_diff;\r\n int n_kouho = n_kouho_base + z_tbl_[hand.zihai].n_kouho_diff;\r\n max = std::max(max, n_mentu * 2 + std::min(4 - n_mentu, n_kouho) + 1);\r\n }\r\n\r\n return 8 - max;\r\n}\r\n\r\n/**\r\n * @brief 七対子手の向聴数を計算する。\r\n *\r\n * @param[in] hand 手牌\r\n * @return int 向聴数\r\n */\r\nint SyantenCalculator::calc_tiitoi(const Hand &hand)\r\n{\r\n // 牌の種類 (1枚以上の牌) を数える。\r\n int n_types = s_tbl_[hand.manzu].n_ge1 + s_tbl_[hand.pinzu].n_ge1 +\r\n s_tbl_[hand.sozu].n_ge1 + z_tbl_[hand.zihai].n_ge1;\r\n // 対子の数 (2枚以上の牌) を数える。\r\n int n_toitu = s_tbl_[hand.manzu].n_ge2 + s_tbl_[hand.pinzu].n_ge2 +\r\n s_tbl_[hand.sozu].n_ge2 + z_tbl_[hand.zihai].n_ge2;\r\n\r\n int syanten = 6 - n_toitu;\r\n if (n_types < 7)\r\n syanten += 7 - n_types; // 4枚持ちを考慮\r\n\r\n return syanten;\r\n}\r\n\r\n/**\r\n * @brief 国士無双の向聴数を計算する。\r\n *\r\n * @param[in] hand 手牌\r\n * @return int 向聴数\r\n */\r\nint SyantenCalculator::calc_kokusi(const Hand &hand)\r\n{\r\n // 老頭牌を抽出する。\r\n int manzu19 = hand.manzu & Bit::RotohaiMask;\r\n int pinzu19 = hand.pinzu & Bit::RotohaiMask;\r\n int sozu19 = hand.sozu & Bit::RotohaiMask;\r\n\r\n // 幺九牌の種類 (1枚以上の牌) を数える。\r\n int n_yaotyuhai = s_tbl_[manzu19].n_ge1 + s_tbl_[pinzu19].n_ge1 +\r\n s_tbl_[sozu19].n_ge1 + z_tbl_[hand.zihai].n_ge1;\r\n\r\n // 幺九牌の対子があるかどうか\r\n int toitu = ((manzu19 & 0b110'000'000'000'000'000'000'000'110) |\r\n (pinzu19 & 0b110'000'000'000'000'000'000'000'110) |\r\n (sozu19 & 0b110'000'000'000'000'000'000'000'110) |\r\n (hand.zihai & 0b110'110'110'110'110'110'110)) > 0;\r\n\r\n return 13 - toitu - n_yaotyuhai;\r\n}\r\n\r\n#ifdef USE_UNORDERED_MAP\r\nstd::unordered_map SyantenCalculator::s_tbl_;\r\nstd::unordered_map SyantenCalculator::z_tbl_;\r\n#else\r\nstd::vector SyantenCalculator::s_tbl_;\r\nstd::vector SyantenCalculator::z_tbl_;\r\n#endif\r\n\r\nstatic SyantenCalculator inst;\r\n\r\n} // namespace mahjong\r\n", "meta": {"hexsha": "a8131e40d544c541ac5e57bd11dabb7f18c76c18", "size": 6995, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mahjong/syanten.cpp", "max_stars_repo_name": "happapa/mahjong-cpp", "max_stars_repo_head_hexsha": "a392a9a48cd790dbcf4ee31463c3a431c9a614c5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/mahjong/syanten.cpp", "max_issues_repo_name": "happapa/mahjong-cpp", "max_issues_repo_head_hexsha": "a392a9a48cd790dbcf4ee31463c3a431c9a614c5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/mahjong/syanten.cpp", "max_forks_repo_name": "happapa/mahjong-cpp", "max_forks_repo_head_hexsha": "a392a9a48cd790dbcf4ee31463c3a431c9a614c5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8149779736, "max_line_length": 88, "alphanum_fraction": 0.5805575411, "num_tokens": 2368, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631698328917, "lm_q2_score": 0.38491214448393357, "lm_q1q2_score": 0.2338969338242831}} {"text": "#ifndef TVTML_FUNCTIONAL3D_HPP\n#define TVTML_FUNCTIONAL3D_HPP\n\n// Eigen includes\n#include \n#include \n\n// video++ includes\n#include \n\n// system includes\n#include \n#include \n#include \n#include \n\n// own includes \n#include \"enumerators.hpp\"\n#include \"manifold.hpp\"\n#include \"data.hpp\"\n\nnamespace tvmtl{\n\n\ntemplate \nclass Functional{\n\n public:\n\t// Manifold typedefs and constants\n\tstatic const MANIFOLD_TYPE mf_type = MANIFOLD::MyType;\n\tstatic const int value_dim = MANIFOLD::value_dim; \n\tstatic const int manifold_dim = MANIFOLD::manifold_dim; \n\ttypedef typename MANIFOLD::scalar_type scalar_type;\n\ttypedef typename MANIFOLD::value_type value_type;\n\ttypedef typename MANIFOLD::ref_type ref_type;\n\ttypedef typename MANIFOLD::cref_type cref_type;\n\ttypedef typename MANIFOLD::deriv1_type deriv1_type;\n\ttypedef typename MANIFOLD::deriv2_type deriv2_type;\n\ttypedef typename MANIFOLD::restricted_deriv2_type restricted_deriv2_type;\n\ttypedef typename MANIFOLD::tm_base_type tm_base_type;\n\n\t// Data typedef and constants\n\tstatic const int img_dim = DATA::img_dim;\n\ttypedef typename DATA::storage_type img_type;\n\ttypedef typename DATA::weights_type weights_type;\n\ttypedef typename DATA::weights_mat weights_mat;\n\ttypedef typename DATA::inp_mat inp_mat;\n\n\t// Functional parameters and return types\n\tstatic const FUNCTIONAL_DISC disc_type;\n\ttypedef double param_type;\n\ttypedef double result_type;\n\t\n\t// Tangent space transformation matrix types\n\ttypedef vpp::imageNd tm_base_mat_type; \n\t\n\t// Gradient and Hessian types\n\ttypedef Eigen::Matrix gradient_type;\n\ttypedef vpp::imageNd hessian_type;\n\ttypedef Eigen::SparseMatrix sparse_hessian_type;\n\n\t//Constructor\n\tFunctional(param_type lambda, DATA& dat):\n\t lambda_(lambda),\n\t data_(dat)\n\t{\n\t eps2_=1e-10;\n\t static_assert(img_dim == 3, \"Dimension of data and functional must match!\");\n\t}\n\t\n\tvoid updateWeights();\n\n\tvoid updateTMBase();\n\t\n\t\n\t// Evaluation functions\n\tresult_type evaluateJ();\n\tvoid evaluateDJ();\n\tvoid evaluateHJ();\n\t\n\ttemplate \n\tvoid output_img(const IMG& img, const char* filename) const;\n\ttemplate \n\tvoid output_matval_img(const IMG& img, const char* filename) const;\n\n\t// Getter and Setter \n\tinline param_type getlambda() const { return lambda_; }\n\tinline void setlambda(param_type lam) { lambda_=lam; }\n\tinline param_type geteps2() const { return eps2_; }\n\tinline void seteps2(param_type eps) { eps2_=eps; }\n\n\tinline const weights_mat& getweightsX() const { return weightsX_; }\n\tinline const weights_mat& getweightsY() const { return weightsY_; }\n\tinline const weights_mat& getweightsZ() const { return weightsZ_; }\n\n\tinline const gradient_type& getDJ() const { return DJ_; }\n\tinline const sparse_hessian_type& getHJ() const { return HJ_; }\n\tinline const tm_base_mat_type& getT() const { return T_; }\n\n private:\n\tDATA& data_;\n\n\tparam_type lambda_, eps2_;\n\tweights_mat weightsX_, weightsY_, weightsZ_;\n\n\ttm_base_mat_type T_;\n\tgradient_type DJ_;\n\tsparse_hessian_type HJ_;\n};\n\n\n//--------Implementation FIRSTORDER-----/\n\ntemplate \nconst FUNCTIONAL_DISC Functional::disc_type = disc;\n\ntemplate \nvoid Functional::updateWeights(){\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t Update Weights...\" << std::endl;\n #endif\n\n // Neighbourhood box\n int ns = data_.img_.nslices(); // z\n int nr = data_.img_.nrows(); // y\n int nc = data_.img_.ncols(); // x\n \n // Subimage boxes\n vpp::box3d without_last_x(vpp::vint3(0,0,0), vpp::vint3(ns - 1, nr - 1, nc - 2)); // subdomain without last xslice\n vpp::box3d without_last_y(vpp::vint3(0,0,0), vpp::vint3(ns - 1, nr - 2, nc - 1)); // subdomain without last yslice\n vpp::box3d without_last_z(vpp::vint3(0,0,0), vpp::vint3(ns - 2, nr - 1, nc - 1)); // subdomain without last zslice\n vpp::box3d without_first_x(vpp::vint3(0,0,1), vpp::vint3(ns - 1, nr - 1, nc - 1)); // subdomain without first xlice\n vpp::box3d without_first_y(vpp::vint3(0,1,0), vpp::vint3(ns - 1, nr - 1, nc - 1)); // subdomain without first yslice\n vpp::box3d without_first_z(vpp::vint3(1,0,0), vpp::vint3(ns - 1, nr - 1, nc - 1)); // subdomain without first zslice\n\n weightsX_ = weights_mat(data_.img_.domain());\n weightsY_ = weights_mat(data_.img_.domain());\n weightsZ_ = weights_mat(data_.img_.domain());\n\n auto calc_dist = [&] (weights_type& w, const value_type i, const value_type n) {\n\tw = MANIFOLD::dist_squared(i, n);\n };\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...X neighbours \" << std::endl;\n #endif\n \n // X Neighbours\n fill3d(weightsX_, 0.0);\n pixel_wise3d(calc_dist, weightsX_ | without_last_x, data_.img_ | without_last_x, data_.img_ | without_first_x );\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Reweighting\" << std::endl;\n\tstd::cout << \"\\t\\t...Y neighbours\" << std::endl;\n #endif\n\n // Y Neighbours\n fill3d(weightsY_, 0.0);\n pixel_wise3d(calc_dist, weightsY_ | without_last_y, data_.img_ | without_last_y, data_.img_ | without_first_y );\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Reweighting\" << std::endl;\n\tstd::cout << \"\\t\\t...Z neighbours\" << std::endl;\n #endif\n\n // Z Neighbours\n fill3d(weightsZ_, 0.0);\n pixel_wise3d(calc_dist, weightsZ_ | without_last_z, data_.img_ | without_last_z, data_.img_ | without_first_z );\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Reweighting\" << std::endl;\n #endif\n\n if(disc==ISO){\n\tauto g = [&] (const weights_type& ew, weights_type& x, weights_type& y, weights_type& z) { x = ew / std::sqrt(x + y + z + eps2_); y = x; z = x; };\n\tpixel_wise3d(g, data_.edge_weights_, weightsX_, weightsY_, weightsZ_);\n }\n else{\n\tauto g = [&] (const weights_type& ew, weights_type& w) { w = ew / std::sqrt(w+eps2_); };\n\tpixel_wise3d(g, data_.edge_weights_, weightsX_);\n\tpixel_wise3d(g, data_.edge_weights_, weightsY_);\n\tpixel_wise3d(g, data_.edge_weights_, weightsZ_);\n }\n}\n\n// Update the Tangent space ONB\ntemplate \nvoid Functional::updateTMBase(){\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tUpdate tangent space basis...\" << std::endl;\n #endif\n\n\n tm_base_mat_type T(data_.img_.domain());\n pixel_wise3d([&] (tm_base_type& t, const value_type& i) { MANIFOLD::tangent_plane_base(i,t); }, T, data_.img_);\n T_=T;\n \n}\n\n\n// Evaluation of J\ntemplate \ntypename Functional::result_type Functional::evaluateJ(){\n\n // sum d^2(img, img_noise)\n result_type J1, J2;\n J1 = J2 = 0.0;\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tFunctional evaluation...\" << std::endl;\n\tstd::cout << \"\\t\\t...Fidelity part\" << std::endl;\n #endif\n\n\n if(data_.doInpaint()){\n\tauto f = [&] (const value_type& i, const value_type& n, const bool inp ) { J1 += MANIFOLD::dist_squared(i,n)*(1-inp); };\n\tpixel_wise3d_nothreads(f, data_.img_, data_.noise_img_, data_.inp_);\n }\n else{\n\tauto f = [&] (const value_type& i, const value_type& n) { J1 += MANIFOLD::dist_squared(i,n); };\n\tpixel_wise3d_nothreads(f, data_.img_, data_.noise_img_);\n }\n\n\tupdateWeights();\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...TV part.\" << std::endl;\n #endif\n\n if(disc==ISO)\n\tpixel_wise3d_nothreads([&] (const weights_type& w) { J2 += 1.0/w;} ,weightsX_);\n else\n\tpixel_wise3d_nothreads([&] (const weights_type& wx, const weights_type& wy, const weights_type& wz) { J2 += 1.0 / wx + 1.0 / wy+ 1.0 / wz;}, weightsX_, weightsY_, weightsZ_); \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"J1: \" << J1 << std::endl;\n\tstd::cout << \"J2: \" << J2 << std::endl;\n #endif\n\n return 0.5 * J1 + lambda_* J2;\n}\n\n// Evaluation of J'\ntemplate \nvoid Functional::evaluateDJ(){\n \n img_type grad = img_type(data_.img_.domain());\n int ns = data_.img_.nslices();\n int nr = data_.img_.nrows();\n int nc = data_.img_.ncols();\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tGradient evaluation...\" << std::endl;\n\tstd::cout << \"\\t\\t...Fidelity part\" << std::endl;\n #endif\n //GRADIENT OF FIDELITY TERM\n if(data_.doInpaint()){\n\tauto f = [] (value_type& g, const value_type& i, const value_type& n, const bool inp ) { MANIFOLD::deriv1x_dist_squared(i,n,g); g*=(1-inp); };\n\tpixel_wise3d(f, grad, data_.img_, data_.noise_img_, data_.inp_);\n }\n else{\n\tauto f = [] (value_type& g, const value_type& i, const value_type& n) { MANIFOLD::deriv1x_dist_squared(i,n,g); };\n\tpixel_wise3d(f, grad, data_.img_, data_.noise_img_);\n }\n \n //GRADIENT OF TV TERM\n \n // Subimage boxes\n vpp::box3d without_last_x(vpp::vint3(0,0,0), vpp::vint3(ns - 1, nr - 1, nc - 2)); // subdomain without last xslice\n vpp::box3d without_last_y(vpp::vint3(0,0,0), vpp::vint3(ns - 1, nr - 2, nc - 1)); // subdomain without last yslice\n vpp::box3d without_last_z(vpp::vint3(0,0,0), vpp::vint3(ns - 2, nr - 1, nc - 1)); // subdomain without last zslice\n vpp::box3d without_first_x(vpp::vint3(0,0,1), vpp::vint3(ns - 1, nr - 1, nc - 1)); // subdomain without first xlice\n vpp::box3d without_first_y(vpp::vint3(0,1,0), vpp::vint3(ns - 1, nr - 1, nc - 1)); // subdomain without first yslice\n vpp::box3d without_first_z(vpp::vint3(1,0,0), vpp::vint3(ns - 1, nr - 1, nc - 1)); // subdomain without first zslice\n\n auto calc_first_arg_deriv = [&] (value_type& x, const weights_type& w, const value_type& i, const value_type& n) { MANIFOLD::deriv1x_dist_squared(i, n, x); x *= w; };\n auto calc_second_arg_deriv = [&] (value_type& y, const weights_type& w, const value_type& i, const value_type& n) { MANIFOLD::deriv1y_dist_squared(i, n, y); y *= w; };\n auto add_to_gradient = [&] (value_type& g, const value_type& d) { g+=d*lambda_; };\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tGradient evaluation...\" << std::endl;\n\tstd::cout << \"\\t\\t...TV part\" << std::endl;\n\tstd::cout << \"\\t\\t...-> XD1\" << std::endl;\n #endif\n // X neighbors and reweighting\n // ... w.r.t. to first argument\n { // Temporary image XD1 is deallocated after this scope \n\timg_type XD1 = img_type(without_last_x);\n\tpixel_wise3d(calc_first_arg_deriv, XD1, weightsX_ | without_last_x, data_.img_ | without_last_x, data_.img_ | without_first_x);\n\tpixel_wise3d(add_to_gradient, grad | without_last_x, XD1);\n }\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...-> XD2\" << std::endl;\n #endif\n // ... w.r.t. second argument\n {\n\timg_type XD2 = img_type(without_last_x);\n\tpixel_wise3d(calc_second_arg_deriv, XD2, weightsX_ | without_last_x, data_.img_ | without_last_x, data_.img_ | without_first_x);\n\tpixel_wise3d(add_to_gradient, grad | without_first_x, XD2);\n }\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...-> YD1\" << std::endl;\n #endif\n // Vertical derivatives and weighting\n // ... w.r.t. first argument\n {\n\timg_type YD1 = img_type(without_last_y);\n\tpixel_wise3d(calc_first_arg_deriv, YD1, weightsY_ | without_last_y, data_.img_ | without_last_y, data_.img_ | without_first_y);\n\tpixel_wise3d(add_to_gradient, grad | without_first_y, YD1);\n }\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...-> YD2\" << std::endl;\n #endif\n // ... w.r.t second argument\n {\n\timg_type YD2 = img_type(without_last_y);\n\tpixel_wise3d(calc_second_arg_deriv, YD2, weightsY_ | without_last_y, data_.img_ | without_last_y, data_.img_ | without_first_y);\n pixel_wise3d(add_to_gradient, grad | without_first_y, YD2);\n }\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...-> ZD1\" << std::endl;\n #endif\n // Vertical derivatives and weighting\n // ... w.r.t. first argument\n {\n\timg_type ZD1 = img_type(without_last_z);\n\tpixel_wise3d(calc_first_arg_deriv, ZD1, weightsZ_ | without_last_z, data_.img_ | without_last_z, data_.img_ | without_first_z);\n\tpixel_wise3d(add_to_gradient, grad | without_first_z, ZD1);\n }\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...-> ZD2\" << std::endl;\n #endif\n // ... w.r.t second argument\n {\n\timg_type ZD2 = img_type(without_last_z);\n\tpixel_wise3d(calc_second_arg_deriv, ZD2, weightsZ_ | without_last_z, data_.img_ | without_last_z, data_.img_ | without_first_z);\n pixel_wise3d(add_to_gradient, grad | without_first_z, ZD2);\n }\n \n #ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(grad,\"3dgrad.csv\");\n #endif\n \n DJ_ = gradient_type::Zero(ns*nr*nc*manifold_dim); \n \n updateTMBase();\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Local to global insert\" << std::endl;\n #endif\n \n for(int s = 0; s < ns; ++s){\n\t//#pragma omp parallel for\n\tfor(int r = 0; r < nr; ++r){\n\t // Start of row pointers\n\t value_type* p = &grad(s, r, 0);\n\t tm_base_type* t = &T_(s, r, 0);\n\t for(int c = 0; c < nc; ++c)\n\t\tDJ_.segment(manifold_dim * (nr * nc * s + nc * r + c), manifold_dim) = t[c].transpose() * Eigen::Map(p[c].data(), p[c].size()); \n\t//\tDJ_.segment(manifold_dim * (s + ns * r + ns * nr * c), manifold_dim) = t[c].transpose() * Eigen::Map(p[c].data(), p[c].size()); \n\t}\n } \n\n #ifdef TV_FUNC_DEBUG \n\tstd::fstream f;\n\tf.open(\"3dDJ.csv\",std::fstream::out);\n\tf << DJ_;\n\tf.close();\n #endif\n}\n\n// Evaluation of Hessian J\ntemplate \nvoid Functional::evaluateHJ(){\n hessian_type hessian(data_.img_.domain());\n\n int ns = data_.img_.nslices();\n int nr = data_.img_.nrows();\n int nc = data_.img_.ncols();\n int sparsedim = ns*nr*nc*manifold_dim;\n \n \n //HESSIAN OF FIDELITY TERM\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\tHessian evaluation...\" << std::endl;\n\tstd::cout << \"\\t\\t...Fidelity part\" << std::endl;\n #endif\n\n sparse_hessian_type HF(sparsedim,sparsedim);\n\n //HF.reserve(Eigen::VectorXi::Constant(nc,manifold_dim));\n typedef Eigen::Triplet Trip;\n std::vector triplist;\n triplist.reserve(sparsedim*manifold_dim);\n\t\n if(data_.doInpaint()){\n\tauto f = [] (deriv2_type& h, const value_type& i, const value_type& n, const bool inp ) { MANIFOLD::deriv2xx_dist_squared(i,n,h); h*=(1-inp); };\n\tpixel_wise3d(f, hessian, data_.img_, data_.noise_img_, data_.inp_);\n }\n else{\n\tauto f = [] (deriv2_type& h, const value_type& i, const value_type& n) { MANIFOLD::deriv2xx_dist_squared(i,n,h); };\n\tpixel_wise3d(f, hessian, data_.img_, data_.noise_img_);\n }\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Local to global insert\" << std::endl;\n #endif\n for(int s = 0; s < ns; ++s){\n for(int r = 0; r < nr; ++r){\n\t // Start of row pointers\n\t deriv2_type* h = &hessian(s, r, 0);\n\t tm_base_type* t = &T_(s, r, 0);\n\t for(int c = 0; c < nc; ++c){\n\t\tint pos = manifold_dim * (nr * nc * s + nc * r + c); // rowwise flattening\n\t\t//int pos = manifold_dim * (s + ns * r + ns * nr * c); // columnwise flattening\n\t\trestricted_deriv2_type ht=t[c].transpose()*h[c]*t[c];\n\t \n\t\tfor(int local_row = 0; local_rowXD11\" << std::endl;\n #endif\n \n // Horizontal Second Derivatives and weighting\n // ... w.r.t. first arguments\n { // Temporary image XD11 is deallocated after this scope\n\thessian_type XD11(without_last_x);\n pixel_wise3d(calc_xx_der, XD11, weightsX_ | without_last_x, data_.img_ | without_last_x, data_.img_ | without_first_x);\n\tpixel_wise3d([&] (deriv2_type& h, const deriv2_type& d) { h=d; }, hessian | without_last_x, XD11);\n\t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(XD11,\"3dXD11.csv\");\n\t#endif\n }\n \n for(int s=0; s < ns; ++s)\n\tfor(int r=0; rXD22\" << std::endl;\n #endif\n //... w.r.t. second arguments\n {\n\thessian_type XD22(without_last_x);\n pixel_wise3d(calc_yy_der, XD22, weightsX_ | without_last_x, data_.img_ | without_last_x, data_.img_ | without_first_x);\n\tpixel_wise3d(add_to_hessian, hessian | without_first_x, XD22);\n\t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(XD22,\"3dXD22.csv\");\n\t#endif\n }\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->YD11\" << std::endl;\n #endif\n // Vertical Second Derivatives weighting\n //... w.r.t. first arguments\n {\n\thessian_type YD11(without_last_y);\n pixel_wise3d(calc_xx_der, YD11, weightsY_ | without_last_y, data_.img_ | without_last_y, data_.img_ | without_first_y);\n\tpixel_wise3d(add_to_hessian, hessian | without_last_y, YD11);\n\t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(YD11,\"3dYD11.csv\");\n\t#endif\n }\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->YD22\" << std::endl;\n #endif\n //... w.r.t. second arguments\n {\n\thessian_type YD22(without_last_y);\n pixel_wise3d(calc_yy_der, YD22, weightsY_ | without_last_y, data_.img_ | without_last_y, data_.img_ | without_first_y); \n\tpixel_wise3d(add_to_hessian, hessian | without_first_y, YD22);\n\t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(YD22,\"3dYD22.csv\");\n\t#endif\n }\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->ZD11\" << std::endl;\n #endif\n // Vertical Second Derivatives weighting\n //... w.r.t. first arguments\n {\n\thessian_type ZD11(without_last_z);\n pixel_wise3d(calc_xx_der, ZD11, weightsZ_ | without_last_z, data_.img_ | without_last_z, data_.img_ | without_first_z);\n\tpixel_wise3d(add_to_hessian, hessian | without_last_z, ZD11);\n\t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(ZD11,\"3dZD11.csv\");\n\t#endif\n }\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->ZD22\" << std::endl;\n #endif\n //... w.r.t. second arguments\n {\n\thessian_type ZD22(without_last_z);\n pixel_wise3d(calc_yy_der, ZD22, weightsZ_ | without_last_z, data_.img_ | without_last_z, data_.img_ | without_first_z); \n\tpixel_wise3d(add_to_hessian, hessian | without_first_z, ZD22);\n\t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(ZD22,\"3dZD22.csv\");\n\t#endif\n } \n \t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(hessian,\"3dhessian.csv\");\n\t#endif\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...Local to global insert\" << std::endl;\n #endif\n // Insert elementwise into sparse Hessian\n // NOTE: Eventually make single version for both cases, including an offset\n // --> additional parameters sparse_mat, offset\n auto HTV_insert = [&]( hessian_type H, tm_base_mat_type T1, tm_base_mat_type T2, int row_offset, int col_offset){\n\tint Hns = H.nslices();\n\tint Hnr = H.nrows();\n\tint Hnc = H.ncols();\n \n\tfor(int s = 0; s < Hns; ++s){\n\t for(int r = 0; r < Hnr; ++r){\n\t\t// Start of row pointers\n\t\tderiv2_type* h = &H(s, r, 0);\n\t\ttm_base_type* t1 = &T1(s, r, 0);\n\t\ttm_base_type* t2 = &T2(s, r, 0);\n\t\tfor(int c = 0; c < Hnc; ++c){\n\t\t int pos = manifold_dim * (nr * nc * s + nc * r +c); // rowwise flattening\n\t\t // int pos = manifold_dim * (s + ns * r + ns * nr * c); // columnwise flattening\n\t\t restricted_deriv2_type ht = t1[c].transpose()*h[c]*t2[c];\n\t\t\n\t\t for(int local_row = 0; local_row 0 || col_offset > 0)\n\t\t\t\t\ttriplist.push_back(Trip(global_col,global_row,e));\n\t\t\t\t}\n\t\t\t}\n\t\t\n\t\t}\n\t }\n\t}\n };\n\n auto HTV_single_insert = [&] (deriv2_type& h, tm_base_type& t1, tm_base_type& t2, int pos, int row_offset, int col_offset){\n \t\t restricted_deriv2_type ht = t1.transpose()*h*t2;\n\t\t for(int local_row = 0; local_row 0 || col_offset > 0)\n\t\t\t\t\ttriplist.push_back(Trip(global_col,global_row,e));\n\t\t\t\t}\n\t\t\t}\n };\n\n HTV_insert(hessian, T_, T_, 0, 0);\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->ZD12\" << std::endl;\n #endif \n // Z Neighbors Second Derivatives and weighting\n // ... w.r.t. first and second arguments \n {\n\thessian_type ZD12(without_last_z);\n\tpixel_wise3d(calc_xy_der, ZD12, weightsZ_ | without_last_z, data_.img_ | without_last_z, data_.img_ | without_first_z );\n\t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(ZD12,\"3dZD12.csv\");\n\t#endif\n\t#ifdef TV_FUNC_DEBUG_VERBOSE\n\t\tstd::cout << \"\\t\\t...Local to global insert:\" << std::endl;\n\t#endif\n\t// Offsets for upper nx*ny-th subdiagonal\n\tint offset = manifold_dim * nr * nc;\n\tHTV_insert(ZD12, T_ | without_last_z, T_ | without_first_z, 0, offset);\n }\n \n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->YD12\" << std::endl;\n #endif\n // Y Neighbors Second Derivatives and weighting\n //... w.r.t. second arguments\n {\n\thessian_type YD12(data_.img_.domain());\n\tpixel_wise3d(calc_xy_der, YD12 | without_last_y, weightsY_ | without_last_y, data_.img_ | without_last_y, data_.img_ | without_first_y );\n\t\n\t//Set last y-slice to zero\n\t#pragma omp parallel for\n\tfor(int s = 0; s < ns ; ++s){\n\t deriv2_type* row = &YD12(s, nr-1 ,0);\n\t for(int c = 0; c < nc; ++c)\n\t\trow[c] = deriv2_type::Zero();\n\t}\n\t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(YD12,\"3dYD12.csv\");\n\t#endif\n\t#ifdef TV_FUNC_DEBUG_VERBOSE\n\t\tstd::cout << \"\\t\\t...Local to global insert:\" << std::endl;\n\t#endif\n\t// Offsets for first nxth subdiagonal\n\tint offset = manifold_dim * nc;\n\n\t// Insert YD12 except last ns entries\n\tauto t1_it = T_.begin();\n\tauto t2_it = typename tm_base_mat_type::iterator(vpp::vint3(0,1,0), T_); // Iterator at beginning of second row \n\tbool do_break = false;\n\tint k = 0;\n\tint max_entry = ns * nr * nc - nc;\n\n\tfor(int s=0; s < ns; ++s){\n\t for(int r=0; r < nr; ++r){\n\t\t deriv2_type* row = &YD12(s, r, 0);\n\t\t for(int c=0; c < nc; ++c){\n\t\t int pos = manifold_dim * (nr * nc * s + nc * r +c); // rowwise flattening\n\t//\t int pos = manifold_dim * (s + ns * r + ns * nr * c); // columnwise flattening\n\t\t HTV_single_insert(row[c], *t1_it, *t2_it, pos, 0, offset);\n\t\t t1_it.next();\n\t\t t2_it.next();\n\t\t #ifdef TV_FUNC_DEBUG_VERBOSE2\n\t\t\tstd::cout << \"\\n\\t\\t\\tPosition: (\" << s << \",\"<< c << \",\" << r << \")\" << std::endl;\n\t\t\tstd::cout << \"\\t\\t\\tValue(YD12): \" << row[c] << std::endl;\n\t\t\tstd::cout << \"\\t\\t\\tEntry number: \" << k << std::endl;\n\t\t\tstd::cout << \"\\t\\t\\tPos: \" << pos << std::endl;\n\t\t #endif\n\t\t ++k;\n\t\t if(k >= max_entry) {do_break = true; break;}\n\t\t }\n\t\t if(do_break) break;\n\t }\n\t if(do_break) break;\n\t}\n\n\n }\n\n #ifdef TV_FUNC_DEBUG_VERBOSE\n\tstd::cout << \"\\t\\t...->XD12\" << std::endl;\n #endif\n // Z neightbors Second Derivatives and weighting\n //... w.r.t. second arguments\n {\n\thessian_type XD12(data_.img_.domain());\n\tpixel_wise3d(calc_xy_der, XD12 | without_last_x, weightsX_ | without_last_x, data_.img_ | without_last_x, data_.img_ | without_first_x);\n\t\n\t//Set last slice to zero, - actually not necessary but safer than keeping uninitialized data\n\t#pragma omp parallel for\n\tfor(int s = 0; s < ns; ++s){\n\t for(int r = 0; r < nr; ++r)\n\t\tXD12(s, r, nc-1) = deriv2_type::Zero();\n\t}\n\t\n\t#ifdef TV_FUNC_DEBUG\n\t data_.output_matval_img(XD12,\"3dXD12.csv\");\n\t#endif\n\t#ifdef TV_FUNC_DEBUG_VERBOSE\n\t\tstd::cout << \"\\t\\t...Local to global insert:\" << std::endl;\n\t#endif\n\t\n\t// Insert into first upper subdiagonal\n\tauto t1_it = T_.begin();\n\tauto t2_it = T_.begin(); t2_it.next();\n\tfor(int s=0; s < ns; ++s){ \n\t for(int r=0; r < nr; ++r){\n\t\t deriv2_type* row = &XD12(s, r, 0);\n\t\t for(int c=0; c < nc; ++c){\n\t\t int pos = manifold_dim * (nr * nc * s + nc * r +c); // rowwise flattening\n\t\t //int pos = manifold_dim * (s + ns * r + ns * nr * c); // columnwise flattening\n\t\t HTV_single_insert(row[c], *t1_it, *t2_it, pos, 0, manifold_dim);\n\t\t t1_it.next();\n\t\t t2_it.next();\n\t\t if(s==ns-1 && r == nc-1 && c >= nc-2 ) break;\n\t\t }\n\t }\n\t}\n\n\tHTV.setFromTriplets(triplist.begin(),triplist.end()); \n\tHTV.makeCompressed();\n\ttriplist.clear();\n } \t\n\t#ifdef TV_FUNC_DEBUG_VERBOSE\n\t\tstd::cout << \"\\t\\t...Combine Fidelity and TV parts:\" << std::endl;\n\t#endif \n\t\n\tHJ_= HF + lambda_*HTV;\n\t\n\t#ifdef TV_FUNC_DEBUG_VERBOSE\n\t\tstd::cout << \"\\t\\t...Output Hessian (stats):\" << std::endl;\n\t#endif\n #ifdef TV_FUNC_DEBUG\n\tif (sparsedim<200){\n\t if(sparsedim<80){\n\t\tstd::cout << \"\\nFidelity\\n\" << HF << std::endl; \n\t\tstd::cout << \"\\nTV\\n\" << HTV << std::endl; \n\t\tstd::cout << \"\\nHessian\\n\" << HJ_ << std::endl; \n\t }\n\n\t std::fstream f;\n\t f.open(\"H.csv\",std::fstream::out);\n\t Eigen::IOFormat CommaInitFmt(Eigen::StreamPrecision, Eigen::DontAlignCols, \", \", \", \", \"\", \"\", \"\", \"\");\n\t //Eigen::MatrixXd M(HJ_);\n\t //f << M.format(CommaInitFmt);\n\t f << HJ_;\n\t f.close();\n\n\t}\n\telse{\n\t std::cout << \"\\nFidelity Non-Zeros: \" << HJ_.nonZeros() << std::endl; \n\t std::cout << \"\\nTV Non-Zeros: \" << HJ_.nonZeros() << std::endl; \n\t std::cout << \"\\nHessian Non-Zeros: \" << HJ_.nonZeros() << std::endl; \n\t}\n // Test Solver:\n\t#ifdef TV_FUNC_DEBUG_VERBOSE\n\t\tstd::cout << \"\\t\\t...Test Solve\" << std::endl;\n\t#endif\n\tgradient_type x;\n \n\tEigen::SparseLU solver;\n\tsolver.analyzePattern(HJ_);\n\tsolver.factorize(HJ_);\n\tx = solver.solve(DJ_);\n\n\tstd::fstream f;\n\tf.open(\"Sol.csv\",std::fstream::out);\n\tf << x;\n\tf.close();\n #endif\n}\n\n\ntemplate \ntemplate < class IMG >\nvoid Functional::output_img(const IMG& img, const char* filename) const{\n //TODO\n}\n\ntemplate \ntemplate < class IMG >\nvoid Functional::output_matval_img(const IMG& img, const char* filename) const{\n //TODO\n}\n\n}// end namespace tvtml\n\n#endif\n", "meta": {"hexsha": "37405d7e157780059a0ad4ba073840b30fc879ba", "size": 28960, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "mtvmtl/core/functional3d.hpp", "max_stars_repo_name": "pdebus/MTVMTL", "max_stars_repo_head_hexsha": "65a7754b34d1f6a1e86d15e3c2d4346b9418414f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2017-05-08T12:40:46.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-02T05:11:01.000Z", "max_issues_repo_path": "mtvmtl/core/functional3d.hpp", "max_issues_repo_name": "pdebus/MTVMTL", "max_issues_repo_head_hexsha": "65a7754b34d1f6a1e86d15e3c2d4346b9418414f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "mtvmtl/core/functional3d.hpp", "max_forks_repo_name": "pdebus/MTVMTL", "max_forks_repo_head_hexsha": "65a7754b34d1f6a1e86d15e3c2d4346b9418414f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.2453066333, "max_line_length": 176, "alphanum_fraction": 0.6558701657, "num_tokens": 9162, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5736784220301065, "lm_q2_score": 0.4073334000459302, "lm_q1q2_score": 0.23367838217850737}} {"text": "/**********************************************************************************\n * The MIT License (MIT) *\n * *\n * Copyright (c) 2014 Carnegie Mellon University *\n * *\n * Permission is hereby granted, free of charge, to any person obtaining a copy *\n * of this software and associated documentation files (the \"Software\"), to deal *\n * in the Software without restriction, including without limitation the rights *\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell *\n * copies of the Software, and to permit persons to whom the Software is *\n * furnished to do so, subject to the following conditions: *\n * *\n * The above copyright notice and this permission notice shall be included in *\n * all copies or substantial portions of the Software. *\n * *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR *\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, *\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE *\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER *\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, *\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN *\n * THE SOFTWARE. *\n **********************************************************************************/\n/**\n * @file cpdr.cpp\n * @brief A pedestrian dead-reckoning system. Integrates gyros and step counting\n * @author M. George\n */\n\n#include \n#include \n\n#include \n\n#include \"cpdr.h\"\n\nusing namespace BASIL;\n\n/**************************************************************************//**\n* cPDR Constructor. Constant vectors and matrices (gravity,\n* earth rate) are formed here. PDR state is set to ALIGN initially. PDR\n* cannot be used until an alignment method is called.\n* \\arg \\c params. Boost::property_tree object and id of attached IMU.\n******************************************************************************/\ncPDR::cPDR(boost::property_tree::ptree config, unsigned int imuid) : mStepDetector(config)\n{\n mImuId = imuid;\n \n mMessageCounter = 0;\n \n // Clear all the member vectors and matrices.\n mEarthRate = CartesianVector::Zero();\n mGravity = CartesianVector::Zero();\n mAlpha = CartesianVector::Zero();\n mDeltaP = CartesianVector::Zero();\n mDeltaV = CartesianVector::Zero();\n mGravityReaction = CartesianVector::Zero();\n\n mIdentity = CartesianMatrix::Identity();\n mA = CartesianMatrix::Zero();\n \n mAlphaSkew = SkewMatrix::Zero(); \n mAlphaMagnitude = 0.0;\n\n mf1 = mf2 = mf3 = mf4 = 0.0;\n\n mCbn = RotationMatrix::Identity();\n\n mdt = 0;\n\n // INS initially stopped, time = 0, no messages and ready to calibrate.\n mTime = 0.0;\n mPreviousTime = 0.0;\n mPreviousEarthRateTime = 0.0;\n mAlignmentTime = config.get(\"parameters.ins.alignmentperiod\");\n mIMUCount = 0;\n mIMURate = config.get(\"parameters.hardware.imus.xsensmti.rate\");\n mEarthUpdateRate = config.get(\"parameters.ins.earthupdaterate\");\n \n if (mEarthUpdateRate < 0)\n {\n std::cerr << \"Earth update rate must be >= 0\" << std::endl;\n mApplyEarthRateCorrections = false;\n mEarthSubsample = 0;\n }\n else if (mEarthUpdateRate == 0)\n {\n mApplyEarthRateCorrections = false;\n mEarthSubsample = 0;\n }\n else\n {\n mApplyEarthRateCorrections = true;\n mEarthSubsample = mIMURate / mEarthUpdateRate;\n }\n \n // Get initial conditions\n mInitialHeading = config.get(\"parameters.ins.initialheading\")*BASIL::CONSTANTS::DEG_TO_RAD;\n mInitialLatitude = config.get(\"parameters.ins.approximatelatitude\")*BASIL::CONSTANTS::DEG_TO_RAD;\n \n // Step data\n mStepLength = config.get(\"parameters.pdr.steplength\");\n mStepIncrement = CartesianVector::Zero();\n \n // Clear initial INS data structure\n mInsData.Velocity = CartesianVector::Zero();\n mInsData.Position = Array::Zero();\n mInsData.Orientation = AngleArray::Zero();\n mInsData.InsMdl = BASIL::INS::GENERIC_INS;\n mInsData.Id = imuid;\n\n // Clear IMU calibration data structure\n mImuCalibrationData.AccelerationBias = CartesianVector::Zero();\n mImuCalibrationData.AccelerationScaleFactor = CartesianVector::Zero();\n mImuCalibrationData.AngularRateBias << 0.00414044772524217,0.00189662656009109,0.00726575764268722;\n mImuCalibrationData.AngularRateGBias = CartesianVector::Zero();\n mImuCalibrationData.AngularRateScaleFactor = CartesianVector::Zero();\n mImuCalibrationData.ImuMdl = BASIL::IMUS::GENERIC_IMU;\n mImuCalibrationData.Id = imuid;\n \n mCurrentState = ALIGNING;\n}\n\n/**************************************************************************//**\n* cPDR Destructor\n******************************************************************************/\ncPDR::~cPDR()\n{\n}\n\n/**************************************************************************//**\n* Integrates the attitude forward using Savage type exact \n* integration. Ref: Savage, P. Strapdown Analytics, 2nd Ed. Chapters 7, 19 \n* Steps are detected and position is dead-reckoned using heading data and\n* average step length.\n* \\arg \\c omega. Vector of x,y,z gyro rate readings in rad/s\n* \\arg \\c accel. Vector of x,y,z accelerometer readings in m/s/s\n* \\arg \\c time. Time stamp of IMU readings\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::integratePVA(const cImuData& imudata)\n{\n if (mCurrentState < cPDR::RUNNING)\n {\n std::cerr << \"Error (cPDR): INS must be aligned before integrating.\" << std::endl;\n\t return;\n } \n\n // For the first message we receive predict using nominal IMU rate\n if (mMessageCounter == 0)\n {\n mdt = 1.0/double(mIMURate);\n }\n else\n {\n mdt = imudata.Time.getSeconds() - mPreviousTime;\n }\n \n // Catch an out of order packet here\n if (mdt < 0)\n {\n //Some kind of standard error here\n std::cerr << \"Error (cPDR): dt is negative in integratePVA()\" << std::endl;\n return;\n }\n\n if (mdt > 2.0/double(mIMURate))\n {\n // Some kind of standard error here\n std::cerr << \"Warning (cPDR): dt (\" << mdt << \"s) is large in integratePVA()\" << std::endl;\n // Reset the counters so this doesn't persist.\n mInsData.Time = imudata.Time;\n mPreviousTime = imudata.Time.getSeconds();\n \tmMessageCounter++;\n // Returning here is equivalent to assuming no motion during period of missing data.\n return;\n }\n\n // Get rotation vector\n mAlpha = imudata.AngularRate*mdt;\n mAlphaMagnitude = mAlpha.norm();\n \n // Catch zero rotation vector magnitudes which happen with stationary sensors and quantization\n if (mAlphaMagnitude > 0.0)\n { \n // Skew symmetric matrix from rotation vector\n BASIL::MATH::skew(mAlpha, mAlphaSkew);\n \n // Integration constants\n mf1 = std::sin(mAlphaMagnitude)/mAlphaMagnitude;\n mf2 = (1.0 - std::cos(mAlphaMagnitude))/(mAlphaMagnitude*mAlphaMagnitude);\n mf3 = (1.0/(mAlphaMagnitude*mAlphaMagnitude))*(1.0-mf1);\n mf4 = (1.0/(mAlphaMagnitude*mAlphaMagnitude))*(0.5-mf2);\n\t\n\t // Position update\n\t if (mStepDetector.detect(imudata.Acceleration(BASIL::IMUZ)))\n\t {\n\t mStepIncrement(BASIL::NORTH) = mStepLength * std::cos(mInsData.Orientation(BASIL::YAW));\n\t mStepIncrement(BASIL::EAST) = mStepLength * std::sin(mInsData.Orientation(BASIL::YAW));\n\t mStepIncrement(BASIL::DOWN) = 0.0;\n mInsData.Position.noalias() += mStepIncrement;\n //std::cout << mMessageCounter << std::endl;\n }\n \n // Orientation update\n mA.noalias() = mIdentity + mf1*mAlphaSkew + mf2*mAlphaSkew*mAlphaSkew;\n mCbn *= mA;\n cbn2euler(mCbn,mInsData.Orientation);\n }\n else\n {\n // Position update\n\t if (mStepDetector.detect(imudata.Acceleration(BASIL::IMUZ)))\n\t {\n\t mStepIncrement(BASIL::NORTH) = mStepLength * std::cos(mInsData.Orientation(BASIL::YAW));\n\t mStepIncrement(BASIL::EAST) = mStepLength * std::sin(mInsData.Orientation(BASIL::YAW));\n\t mStepIncrement(BASIL::DOWN) = 0.0;\n mInsData.Position.noalias() += mStepIncrement;\n //std::cout << mMessageCounter << std::endl;\n }\n }\n\n // Apply an earth rate correction at a lower frequency\n if ( mApplyEarthRateCorrections && (mMessageCounter % mEarthSubsample == 0) )\n {\n cPDR::earthRateCorrection(imudata.Time.getSeconds());\n }\n\n // Times are set here so that a user can bypass position and velocity integration\n // if desired, effectively using this class as a AHRS system with gyros only.\n mInsData.Time = imudata.Time;\n mPreviousTime = imudata.Time.getSeconds();\n\n mMessageCounter++;\n}\n\n/**************************************************************************//**\n* Called at sub-intervals of the integreatePVA() function. Adds correction \n* to current attitude to account for Earth rotation effects. Position and velocity\n* corrections are taken into account in integratePVA() and as a natural consequence\n* of this update. Not used if no latitude input is used in alignment call.\n* \\arg \\c void\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::earthRateCorrection(const double time)\n{ \n // First time in we use nominal earth correction rate for the dt.\n if (mPreviousEarthRateTime == 0.0)\n {\n mdt = 1.0 / (double)mEarthSubsample;\n }\n else\n {\n mdt = time - mPreviousEarthRateTime;\n }\n\n // Catch negative time increments\n if (mdt < 0)\n {\n //Some kind of standard error here\n std::cerr << \"Error (cPDR): dt is negative in earthRateCorrection()\" << std::endl;\n return;\n }\n\n mAlpha = -1.0*mdt*mEarthRate;\n mAlphaMagnitude = mAlpha.norm();\n mf1 = std::sin(mAlphaMagnitude)/mAlphaMagnitude;\n mf2 = (1.0 - std::cos(mAlphaMagnitude))/(mAlphaMagnitude*mAlphaMagnitude);\n BASIL::MATH::skew(mAlpha, mAlphaSkew);\n \n mA.noalias() = mIdentity + mf1*mAlphaSkew + mf2*mAlphaSkew*mAlphaSkew;\n mCbn = mA*mCbn;\n cbn2euler(mCbn,mInsData.Orientation);\n \n mPreviousEarthRateTime = time;\n}\n\n/**************************************************************************//**\n* Local initial alignment with velocity and position in local navigation coordinates. \n* Roll and pitch angles are coarsely aligned from an input average acceleration vector \n* (2-3s recommended) with gravity. Initial heading (North), velocity and position (Local) \n* are user inputs. User must also input an approximate latitude. The global heading and \n* approximate latitude allow reasonable gravity models and earth rate corrections to be fixed.\n*\\arg \\c Pointer to 3x1 array of time averaged x,y,z accelerometer signals. \n* \t\t Suggested averaging time 2-3s. IMU must be stationary during this time!\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::localAlign(const CartesianVector& gravityReactionForce, const double heading, const CartesianVector& initialUTMPosition, \n const CartesianVector& initialUTMVelocity, const double approximateLatitude)\n{\n gravityAlignRollPitch(gravityReactionForce, mInsData.Orientation);\n mInsData.Orientation(BASIL::YAW) = heading;\n\n cPDR::euler2cbn(mInsData.Orientation,mCbn);\n\n mInsData.Velocity = initialUTMVelocity;\n mInsData.Position = initialUTMPosition;\n\n // Earth rate:\n getEarthRate(approximateLatitude, mEarthRate);\n \n // Gravity\n mGravity(BASIL::DOWN) = getGravityMagnitude(approximateLatitude);\n\n mMode = cPDR::LOCAL;\n // Earth rate corrections will be applied unless already turned off in constructor\n mApplyEarthRateCorrections = mApplyEarthRateCorrections && true;\n \n // Zero counters in case we need to re-align at some point\n mMessageCounter = 0;\n mdt = 0;\n mInsData.Time.setTime(TIME::FREE_RUNNING,0,0.0);\n mPreviousTime = 0.0;\n mPreviousEarthRateTime = 0.0;\n\n // We are now running and ready to call integrate*() functions\n mCurrentState = cPDR::RUNNING;\n}\n\n/**************************************************************************//**\n* Local initial alignment with of roll and pitch angles only. \n* Roll and pitch angles are coarsely aligned from an input average acceleration vector \n* (2-3s recommended) with gravity. Initial heading, velocity and position are all set to 0. \n* User must also input an approximate latitude to allow a coarse estimate of gravity.\n*\\arg \\c Pointer to 3x1 array of time averaged x,y,z accelerometer signals. \n* \t\t Suggested averaging time 2-3s. IMU must be stationary during this time!\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::localAlign(const CartesianVector& gravityReactionForce, const double approximateLatitude)\n{\n gravityAlignRollPitch(gravityReactionForce, mInsData.Orientation);\n \n euler2cbn(mInsData.Orientation,mCbn);\n \n // Earth rate:\n getEarthRate(approximateLatitude, mEarthRate);\n \n // Gravity\n mGravity(BASIL::DOWN) = getGravityMagnitude(approximateLatitude);\n \n mMode = cPDR::LOCAL;\n // Earth rate corrections will be applied unless already turned off in constructor\n mApplyEarthRateCorrections = mApplyEarthRateCorrections && true;\n \n // Zero counters in case we need to re-align at some point\n mMessageCounter = 0;\n mdt = 0;\n mInsData.Time.setTime(TIME::FREE_RUNNING,0,0.0);\n mPreviousTime = 0.0;\n mPreviousEarthRateTime = 0.0;\n \n // We are now running and ready to call integrate*() functions\n mCurrentState = cPDR::RUNNING;\n}\n\n/**************************************************************************//**\n* Get the magnitude of gravity at our current latitude using a Defense Mapping \n* Agency model. Reference: Jay A. Farrell. Aided Inertial Navigation, p.33.\n* \\arg \\c latitude. Reference to latitude data (radians)\n* \\arg \\c gravity. Reference to gravity output.\n* \\return \\c void\n******************************************************************************/\ndouble cPDR::getGravityMagnitude(const double latitude)\n{\n double gravity = BASIL::WGS84::EQUATORIAL_GRAVITY * \n ( ( 1.0 + 0.0019318513530*std::sin(latitude)*std::sin(latitude)) / \n (std::sqrt(1.0 - 0.0066943800229*std::sin(latitude)*std::sin(latitude))) );\n return gravity;\n}\n\n/**************************************************************************//**\n* Correct the current position, velocity and orientation with errors.\n* \\arg \\c position. Position.\n* \\arg \\c velocity. Velocity.\n* \\arg \\c eulers. Euler angles.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::correctPVA(const CartesianVector& positionerror, \n const CartesianVector& velocityerror, \n const AngleArray& skewangles)\n{\n mInsData.Velocity -= velocityerror;\n mInsData.Position -= positionerror;\n \n BASIL::MATH::skew(skewangles, mAlphaSkew); \n mA = mIdentity - mAlphaSkew;\n mCbn = mA.inverse() * mCbn;\n \n cbn2euler(mCbn, mInsData.Orientation);\n}\n\n/**************************************************************************//**\n* Get the components of earth rate at our current latitude.\n* \\arg \\c latitude. Reference to latitude data (radians)\n* \\arg \\c gravity. Pointer to earthRate vector for output.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::getEarthRate(const double latitude, CartesianVector& earthRate)\n{\n earthRate(NORTH) = BASIL::WGS84::EARTH_RATE * std::cos(latitude);\n earthRate(EAST) = 0;\n earthRate(DOWN) = -1.0 * BASIL::WGS84::EARTH_RATE*std::sin(latitude);\n}\n\n/**************************************************************************//**\n* Get roll and pitch angles from a gravity vector. Input vector should be averaged\n* over a period of 2-3 seconds with a stationary IMU. Aligns IMU x,y coordinates\n* to Down gravity vector. 1 mg of accelerometer bias -> 1 mrad of roll, pitch error.\n* Calibrate your accelerometer before averaging if possible.\n* \\arg \\c latitude. Reference to latitude data (radians)\n* \\arg \\c gravity. Pointer to earthRate vector for output.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::gravityAlignRollPitch(const CartesianVector& gravityReactionForce, AngleArray& angles)\n{ \n angles(ROLL) = std::atan2(-gravityReactionForce(BODYY),\n -gravityReactionForce(BODYZ));\n angles(PITCH) = std::atan2(gravityReactionForce(BASIL::BODYX),\n std::sqrt((gravityReactionForce(BODYY) * gravityReactionForce(BODYY))\n +(gravityReactionForce(BODYZ) * gravityReactionForce(BODYZ))));\n}\n\n/**************************************************************************//**\n* Calculate a direction cosine matrix, Cbm, relating body frame to global North,\n* East, Down frame from an Euler angle vector. Cbm is propagated directly by\n* integrate so this function is most useful at alignment time for initial Cbm.\n* \\arg \\c Cbm. Empty Cbm matrix to be filled.\n* \\arg \\c type. Euler angle vector.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::euler2cbn(const AngleArray& eulers, CartesianMatrix& Cbn)\n{\n double roll = eulers(0);\n double pitch = eulers(1);\n double yaw = eulers(2);\n \n Cbn << std::cos(yaw)*std::cos(pitch),\n std::cos(yaw)*std::sin(pitch)*std::sin(roll) - std::sin(yaw)*std::cos(roll),\n std::cos(yaw)*std::sin(pitch)*std::cos(roll) + std::sin(roll)*std::sin(yaw),\n std::cos(pitch)*std::sin(yaw),\n std::cos(roll)*std::cos(yaw) + std::sin(roll)*std::sin(pitch)*std::sin(yaw),\n std::cos(roll)*std::sin(pitch)*std::sin(yaw) - std::sin(roll)*std::cos(yaw),\n -std::sin(pitch),\n std::sin(roll)*std::cos(pitch),\n std::cos(roll)*std::cos(pitch);\n}\n\n/**************************************************************************//**\n* Set the Euler angles from the current Cbm value\n* \\arg \\c Cbm. Current Cbm matrix.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::cbn2euler(const RotationMatrix& Cbn, AngleArray& eulers)\n{\n eulers(0) = std::atan2(Cbn(2,1),Cbn(2,2));\n eulers(1) = std::asin(-Cbn(2,0));\n eulers(2) = std::atan2(Cbn(1,0),Cbn(0,0));\n}\n\n/**************************************************************************//**\n* Copy the current Cbm into the input reference.\n* \\arg \\c out. Empty Cbm matrix to be filled.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::getCbn(RotationMatrix& out)\n{\n out = mCbn;\n}\n\n/**************************************************************************//**\n* Copy the current euler angles into the input reference.\n* \\arg \\c out. Empty position vector to be filled.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::getEulers(AngleArray& out)\n{\n cbn2euler(mCbn,out);\n}\n\n/**************************************************************************//**\n* Copy the current velocity into the input reference.\n* \\arg \\c out. Empty velocity vector to be filled.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::getVelocity(CartesianVector& out)\n{\n out = mInsData.Velocity;\n}\n\n/**************************************************************************//**\n* Copy the current position into the input reference.\n* \\arg \\c out. Empty position vector to be filled.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::getPosition(CartesianVector& out)\n{\n out = mInsData.Position;\n}\n\n/**************************************************************************//**\n* Copy the current gravity vector into the input reference.\n* \\arg \\c out. Empty gravity vector to be filled.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::getGravity(CartesianVector& out)\n{\n out = mGravity;\n}\n\n/**************************************************************************//**\n* Copy the current earth rate vector into the input reference.\n* \\arg \\c out. Empty earth rate vector to be filled.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::getEarthRate(CartesianVector& out)\n{\n out = mEarthRate;\n}\n\n/**************************************************************************//**\n* Copy the current position, velocity and euler angles out.\n* \\arg \\c position. Empty position vector to be filled.\n* \\arg \\c velocity. Empty velocity vector to be filled.\n* \\arg \\c eulers. Empty euler angles vector to be filled.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::getPVA(CartesianVector& position, CartesianVector& velocity, CartesianVector& eulers)\n{\n position = mInsData.Position;\n velocity = mInsData.Velocity;\n cbn2euler(mCbn,eulers);\n}\n\n/**************************************************************************//**\n* Set the current Cbm from the input field.\n* \\arg \\c out. Cbm matrix input.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::setCbn(const RotationMatrix& in)\n{\n mCbn = in;\n cbn2euler(mCbn, mInsData.Orientation);\n}\n\n/**************************************************************************//**\n* Set the current euler angles from the input field.\n* \\arg \\c out. Euler angles (r,p,y) input.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::setEulers(const AngleArray& in)\n{\n mInsData.Orientation = in;\n euler2cbn(mInsData.Orientation, mCbn);\n}\n\n/**************************************************************************//**\n* Set the current velocity from the input field.\n* \\arg \\c out. Velocity vector input.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::setVelocity(const CartesianVector& in)\n{\n mInsData.Velocity = in;\n}\n\n/**************************************************************************//**\n* Set the current position from the input field.\n* \\arg \\c out. Position vector input.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::setPosition(const CartesianVector& in)\n{\n mInsData.Position = in;\n}\n\n/**************************************************************************//**\n* Set the current position, velocity and euler angles.\n* \\arg \\c position. Position.\n* \\arg \\c velocity. Velocity.\n* \\arg \\c eulers. Euler angles.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::setPVA(const CartesianVector& position, \n const CartesianVector& velocity, \n const AngleArray& eulers)\n{\n mInsData.Position = position;\n mInsData.Velocity = velocity;\n mInsData.Orientation = eulers;\n euler2cbn(mInsData.Orientation, mCbn);\n}\n\n/**************************************************************************//**\n* Set the current gravity vector\n* \\arg \\c in. Gravity in NED.\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::setGravity(const CartesianVector& in)\n{\n mGravity = in;\n}\n\n/**************************************************************************//**\n* Reset INS Counters so that a break in data does not cause a spike in the solution\n* \\arg \\c void\n* \\return \\c void\n******************************************************************************/\nvoid cPDR::resetCounters()\n{\n // Zero counters in case we need to re-align at some point\n mMessageCounter = 0;\n mdt = 0;\n mTime = 0.0;\n mPreviousTime = 0.0;\n mPreviousEarthRateTime = 0.0;\n}\n", "meta": {"hexsha": "b245261565d6f31f42865f3b812933afde19a43e", "size": 25154, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ShoppingNavigator/source/inertial/cpdr.cpp", "max_stars_repo_name": "mrmdgeorge/shopping-navigator", "max_stars_repo_head_hexsha": "440bdb3f52f3f72dfcf912971c6d7b543c8de600", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4.0, "max_stars_repo_stars_event_min_datetime": "2016-08-08T03:45:43.000Z", "max_stars_repo_stars_event_max_datetime": "2017-10-20T07:31:45.000Z", "max_issues_repo_path": "ShoppingNavigator/source/inertial/cpdr.cpp", "max_issues_repo_name": "mrmdgeorge/shopping-navigator", "max_issues_repo_head_hexsha": "440bdb3f52f3f72dfcf912971c6d7b543c8de600", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ShoppingNavigator/source/inertial/cpdr.cpp", "max_forks_repo_name": "mrmdgeorge/shopping-navigator", "max_forks_repo_head_hexsha": "440bdb3f52f3f72dfcf912971c6d7b543c8de600", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2015-02-12T14:58:44.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-20T15:10:57.000Z", "avg_line_length": 40.7682333874, "max_line_length": 133, "alphanum_fraction": 0.547268824, "num_tokens": 5700, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.37754066879814546, "lm_q1q2_score": 0.23361477879485967}} {"text": "#ifdef COMPILATION_INSTRUCTIONS\n(echo '#include\"'$0'\"'>$0.cpp)&&$CXX -Wall -Wextra -Wpedantic -Wfatal-errors -D_TEST_MULTI_ADAPTORS_LAPACK_CUDA $0.cpp -o $0x `pkg-config --libs blas` -lcudart -lcublas -lcusolver&&$0x&&rm $0x $0.cpp; exit\n#endif\n// © Alfredo A. Correa 2020\n\n#ifndef MULTI_ADAPTORS_LAPACK_CUDA_HPP\n#define MULTI_ADAPTORS_LAPACK_CUDA_HPP\n\n#include \"../../memory/adaptors/cuda/ptr.hpp\"\n#include \"../../memory/adaptors/cuda/managed/ptr.hpp\"\n#include \"../../memory/adaptors/cuda/managed/allocator.hpp\"\n\n#include \"../../adaptors/cuda.hpp\"\n\n//#include\n#include \n\n//#include // debug\n\n#include \n\n#include\n#include\n\n#include \"../blas/filling.hpp\"\n\nnamespace boost{\nnamespace multi{\n\nnamespace cusolver{\n\nenum class status : typename std::underlying_type::type{\n\tsuccess = CUSOLVER_STATUS_SUCCESS, // \"The operation completed successfully.\"\n\tnot_initialized = CUSOLVER_STATUS_NOT_INITIALIZED, // \"The cuSolver library was not initialized. This is usually caused by the lack of a prior call, an error in the CUDA Runtime API called by the cuSolver routine, or an error in the hardware setup. To correct: call cusolverCreate() prior to the function call; and check that the hardware, an appropriate version of the driver, and the cuSolver library are correctly installed.\"\n\tallocation_failed = CUSOLVER_STATUS_ALLOC_FAILED, // \"Resource allocation failed inside the cuSolver library. This is usually caused by a cudaMalloc() failure. To correct: prior to the function call, deallocate previously allocated memory as much as possible.\"\n\tinvalid_value = CUSOLVER_STATUS_INVALID_VALUE, // \"An unsupported value or parameter was passed to the function (a negative vector size, for example). To correct: ensure that all the parameters being passed have valid values.\"\n\tarchitecture_mismatch = CUSOLVER_STATUS_ARCH_MISMATCH, // \"The function requires a feature absent from the device architecture; usually caused by the lack of support for atomic operations or double precision. To correct: compile and run the application on a device with compute capability 2.0 or above.\"\n\texecution_failed = CUSOLVER_STATUS_EXECUTION_FAILED, // \"The GPU program failed to execute. This is often caused by a launch failure of the kernel on the GPU, which can be caused by multiple reasons. To correct: check that the hardware, an appropriate version of the driver, and the cuSolver library are correctly installed.\"\n\tinternal_error = CUSOLVER_STATUS_INTERNAL_ERROR, // \"An internal cuSolver operation failed. This error is usually caused by a cudaMemcpyAsync() failure. To correct: check that the hardware, an appropriate version of the driver, and the cuSolver library are correctly installed. Also, check that the memory passed as a parameter to the routine is not being deallocated prior to the routine’s completion.\"\n\tmatrix_type_not_supported = CUSOLVER_STATUS_MATRIX_TYPE_NOT_SUPPORTED // \"The matrix type is not supported by this function. This is usually caused by passing an invalid matrix descriptor to the function. To correct: check that the fields in descrA were set correctly.\"\n};\n\nstd::string inline status_string(enum status s){ //https://stackoverflow.com/questions/13041399/equivalent-of-cudageterrorstring-for-cublas\n\tswitch(s){\n\t\tcase status::success : return \"The operation completed successfully.\";\n\t\tcase status::not_initialized : return \"The cuSolver library was not initialized. This is usually caused by the lack of a prior call, an error in the CUDA Runtime API called by the cuSolver routine, or an error in the hardware setup. To correct: call cusolverCreate() prior to the function call; and check that the hardware, an appropriate version of the driver, and the cuSolver library are correctly installed.\";\n\t\tcase status::allocation_failed : return \"Resource allocation failed inside the cuSolver library. This is usually caused by a cudaMalloc() failure. To correct: prior to the function call, deallocate previously allocated memory as much as possible.\";\n\t\tcase status::invalid_value : return \"An unsupported value or parameter was passed to the function (a negative vector size, for example). To correct: ensure that all the parameters being passed have valid values.\";\n\t\tcase status::architecture_mismatch : return \"The function requires a feature absent from the device architecture; usually caused by the lack of support for atomic operations or double precision. To correct: compile and run the application on a device with compute capability 2.0 or above.\";\n\t\tcase status::execution_failed : return \"The GPU program failed to execute. This is often caused by a launch failure of the kernel on the GPU, which can be caused by multiple reasons. To correct: check that the hardware, an appropriate version of the driver, and the cuSolver library are correctly installed.\";\n\t\tcase status::internal_error : return \"An internal cuSolver operation failed. This error is usually caused by a cudaMemcpyAsync() failure. To correct: check that the hardware, an appropriate version of the driver, and the cuSolver library are correctly installed. Also, check that the memory passed as a parameter to the routine is not being deallocated prior to the routine’s completion.\";\n\t\tcase status::matrix_type_not_supported : return \"The matrix type is not supported by this function. This is usually caused by passing an invalid matrix descriptor to the function. To correct: check that the fields in descrA were set correctly.\";\n\t}\n\treturn \"cublas status \";\n}\nstruct error_category : std::error_category{\n\tchar const* name() const noexcept override{return \"cusolver wrapper\";}\n\tstd::string message(int err) const override{return cusolver::status_string(static_cast(err));}\n\tstatic error_category& instance(){static cusolver::error_category instance; return instance;}\n};\ninline std::error_code make_error_code(cusolver::status s) noexcept{\n\treturn std::error_code(int(s), cusolver::error_category::instance());\n}\n\nstruct version_t{\n\tint major = -1, minor =-1, patch=-1;\n\tfriend std::ostream& operator<<(std::ostream& os, version_t const& self){\n\t\treturn os<< self.major <<'.'<< self.minor <<'.'<, decltype(&cusolverDnDestroy)>{\n\tcontext() : std::unique_ptr, decltype(&cusolverDnDestroy)>{\n\t\t[]{\n\t\t\tcusolverDnHandle_t h; \n\t\t\tauto s=cusolverDnCreate(&h); assert(CUSOLVER_STATUS_SUCCESS==s and h);\n\t\t\treturn h;\n\t\t}(), &cusolverDnDestroy\n\t}{}\n\ttemplate int potrf_buffer_size(filling uplo, A const& a);\n};\n\ntemplate\nstruct cusolverDn;//{\n//\ttemplate\n//\tstatic auto potrf_bufferSize(ArgsA3... argsa3, T* ptr, ArgsB2... argsb2);\n//};\n\ntemplate<>\nstruct cusolverDn{\n\ttemplate\n\tstatic auto potrf_bufferSize(A3... a3, double* ptr, B2... b2)\n\t->decltype(cusolverDnSpotrf_bufferSize(a3..., ptr, b2...)){\n\t\treturn cusolverDnSpotrf_bufferSize(a3..., ptr, b2...);}\n\ttemplate static auto syev(A... a)\n\t->decltype(cusolverDnSsyevd_bufferSize(a...)){\n\t\treturn cusolverDnSsyevd_bufferSize(a...);}\n\ttemplate static auto syev(A... a)\n\t->decltype(cusolverDnSsyevd(a...)){\n\t\treturn cusolverDnSsyevd(a...);}\n};\n\ntemplate<>\nstruct cusolverDn{\n\ttemplate\n\tstatic auto potrf_bufferSize(A3... a3, double* ptr, B2... b2)\n\t->decltype(cusolverDnDpotrf_bufferSize(a3..., ptr, b2...)){\n\t\treturn cusolverDnDpotrf_bufferSize(a3..., ptr, b2...);}\n\ttemplate static auto syevd_bufferSize(A... a)\n//\t->decltype(cusolverDnDsyevd_bufferSize(a...)){\n\t{\treturn cusolverDnDsyevd_bufferSize(a...);}\n\ttemplate static auto syevd(A... a)\n\t->decltype(cusolverDnDsyevd(a...))\n\t{\treturn cusolverDnDsyevd(a...);}\n};\n\ntemplate<>\nstruct cusolverDn>{\n\ttemplate\n\tstatic auto potrf_bufferSize(A3... a3, std::complex* ptr, B2... b2)\n\t->decltype(cusolverDnCpotrf_bufferSize(a3..., reinterpret_cast(ptr), b2...)){\n\t\treturn cusolverDnCpotrf_bufferSize(a3..., reinterpret_cast(ptr), b2...);}\n};\n\ntemplate<>\nstruct cusolverDn>{\n\tstatic auto translate(std::complex* p){return reinterpret_cast(p);}\n\ttemplate static auto translate(T t){return t;}\n\ttemplate static auto potrf_bufferSize(A... a)\n\t->decltype(cusolverDnZpotrf_bufferSize(translate(a)...)){\n\t\treturn cusolverDnZpotrf_bufferSize(translate(a)...);}\n\ttemplate static auto potrf(A... a)\n\t->decltype(cusolverDnZpotrf(translate(a)...)){\n\t\treturn cusolverDnZpotrf(translate(a)...);}\n};\n\n}\n}\n\nnamespace memory{\nnamespace cuda{\n\ntemplate::element_type>\nvoid potrf(UL ul, S n, PtrT A, S incx, int& info){\n\tboost::multi::cusolver::dense::context ctx; //BOOST_LOG_TRIVIAL(trace)<<\"cuda::potrf called on size/stride \"<< n <<' '<< incx <<'\\n';\n\tint lwork = -1;\n\t{\n\t\tauto s = cusolver::dense::cusolverDn::potrf_bufferSize(ctx.get(), ul=='U'?CUBLAS_FILL_MODE_UPPER:CUBLAS_FILL_MODE_LOWER, n, raw_pointer_cast(A), incx, &lwork);\n\t\tassert(s == CUSOLVER_STATUS_SUCCESS); assert(lwork >= 0);\n\t}\n\tmulti::cuda::array work(lwork);\n\tmulti::cuda::static_array devInfo;\n\tauto s = cusolver::dense::cusolverDn::potrf(ctx.get(), ul=='U'?CUBLAS_FILL_MODE_UPPER:CUBLAS_FILL_MODE_LOWER, n, raw_pointer_cast(A), incx, raw_pointer_cast(base(work)), lwork, raw_pointer_cast(base(devInfo)) );\n\tassert(s == CUSOLVER_STATUS_SUCCESS);\n\tcudaDeviceSynchronize();\n\tinfo = devInfo();\n}\n\n// https://docs.nvidia.com/cuda/cusolver/index.html#cuds-lt-t-gt-syevd\ntemplate::element_type>\nvoid syev(char jobz, char uplo, S n, PtrT a, S lda, PtrT w, PtrT /*work*/, S /*lwork*/, int& info){\n\tboost::multi::cusolver::dense::context ctx;\n\tint lwork_needed = -1;\n\t{\n\t\tauto s = cusolver::dense::cusolverDn::syevd_bufferSize(\n\t\t\tctx.get(), jobz=='V'?CUSOLVER_EIG_MODE_VECTOR:CUSOLVER_EIG_MODE_NOVECTOR, \n\t\t\tuplo=='U'?CUBLAS_FILL_MODE_UPPER:CUBLAS_FILL_MODE_LOWER, \n\t\t\tn,\n\t\t\traw_pointer_cast(a),\n\t\t\tlda,\n\t\t\traw_pointer_cast(w),\n\t\t\t&lwork_needed\n\t\t);\n\t\tassert(s == CUSOLVER_STATUS_SUCCESS); assert(lwork_needed >= 0);\n\t}\n\tmulti::cuda::array tmp_work(lwork_needed); // buffers needs no-managed memory!\n\tmulti::cuda::static_array devInfo;\n\tauto s = cusolver::dense::cusolverDn::syevd(\n\t\tctx.get(), jobz=='V'?CUSOLVER_EIG_MODE_VECTOR:CUSOLVER_EIG_MODE_NOVECTOR, \n\t\tuplo=='U'?CUBLAS_FILL_MODE_UPPER:CUBLAS_FILL_MODE_LOWER, \n\t\tn,\n\t\traw_pointer_cast(a),\n\t\tlda,\n\t\traw_pointer_cast(w),\n\t\traw_pointer_cast(tmp_work.data_elements()),\n\t\ttmp_work.size(),\n\t\traw_pointer_cast(base(devInfo))\n\t);\n\tif( s != CUSOLVER_STATUS_SUCCESS ) throw std::system_error{cusolver::make_error_code(static_cast(s)), \"cannot call cusolver function \"};\n//\tcudaDeviceSynchronize();\n\tinfo = devInfo();\n}\n\nnamespace managed{\n\ttemplate::element_type>\n\tauto potrf(UL ul, S n, PtrT A, S incx, int& info)\n\t->decltype(cuda::potrf(ul, n, cuda::ptr(A), incx, info)){\n\t\treturn cuda::potrf(ul, n, cuda::ptr(A), incx, info);}\n\n\ttemplate::element_type>\n\tauto syev(char jobz, char uplo, S n, PtrT a, S lda, PtrT w, P2 work, S lwork, int& info)\n\t->decltype(cuda::syev(jobz, uplo, n, cuda::ptr(a), lda, cuda::ptr(w), cuda::ptr(work), lwork, info)){\n\t\treturn cuda::syev(jobz, uplo, n, cuda::ptr(a), lda, cuda::ptr(w), cuda::ptr(work), lwork, info);}\n\n}\n\n}\n}\n\n}}\n\nnamespace std{template<> struct is_error_code_enum<::boost::multi::cusolver::status> : true_type{};}\n\n///////////////////////////////////////////////////////////////////////////////\n\n#if _TEST_MULTI_ADAPTORS_LAPACK_CUDA\n\n#include \"../../array.hpp\"\n#include \"../../utility.hpp\"\n#include\n\nnamespace multi = boost::multi;\n\nint main(){\n\tstd::cout << \"cusolver version \" << multi::cusolver::version() << std::endl;\n\tmulti::cusolver::dense::context c;\n//\tmulti::cublas_context c;\n//\tassert( c.version() >= 10100 );\n}\n\n#endif\n#endif\n\n", "meta": {"hexsha": "f478332ed36d9f6e4e09f325d2d486d2d75cf86a", "size": 12759, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external_codes/boost_multi/multi/adaptors/lapack/cuda.hpp", "max_stars_repo_name": "djstaros/qmcpack", "max_stars_repo_head_hexsha": "280f67e638bae280448b47fa618f05b848c530d2", "max_stars_repo_licenses": ["NCSA"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "external_codes/boost_multi/multi/adaptors/lapack/cuda.hpp", "max_issues_repo_name": "djstaros/qmcpack", "max_issues_repo_head_hexsha": "280f67e638bae280448b47fa618f05b848c530d2", "max_issues_repo_licenses": ["NCSA"], "max_issues_count": 11.0, "max_issues_repo_issues_event_min_datetime": "2020-05-09T20:57:21.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-10T00:00:17.000Z", "max_forks_repo_path": "external_codes/boost_multi/multi/adaptors/lapack/cuda.hpp", "max_forks_repo_name": "djstaros/qmcpack", "max_forks_repo_head_hexsha": "280f67e638bae280448b47fa618f05b848c530d2", "max_forks_repo_licenses": ["NCSA"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 51.8658536585, "max_line_length": 439, "alphanum_fraction": 0.7325025472, "num_tokens": 3319, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.36658973632215985, "lm_q1q2_score": 0.2335289509776597}} {"text": "#include \n\n#include \n\n#include \n\n#include \n\n#include \"global_kernel_functions.hpp\"\n#include \"kernel.hpp\"\n\n#define DO_INTERSECT_2(T) \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect)\n#define DO_INTERSECT_2_SYM \\\n DO_INTERSECT_2(Point_2); \\\n DO_INTERSECT_2(Line_2); \\\n DO_INTERSECT_2(Segment_2); \\\n DO_INTERSECT_2(Ray_2); \\\n DO_INTERSECT_2(Triangle_2); \\\n DO_INTERSECT_2(Iso_rectangle_2)\n\n#define INTERSECTION_2(T) \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection)\n#define INTERSECTION_2_SYM \\\n INTERSECTION_2(Point_2); \\\n INTERSECTION_2(Line_2); \\\n INTERSECTION_2(Segment_2); \\\n INTERSECTION_2(Ray_2); \\\n INTERSECTION_2(Triangle_2); \\\n INTERSECTION_2(Iso_rectangle_2)\n\n#define DO_INTERSECT_3(T) \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect); \\\n cgal.method(\"do_intersect\", &do_intersect)\n#define DO_INTERSECT_3_ALL \\\n DO_INTERSECT_3(Point_3); \\\n DO_INTERSECT_3(Tetrahedron_3); \\\n DO_INTERSECT_3(Segment_3); \\\n DO_INTERSECT_3(Line_3); \\\n DO_INTERSECT_3(Plane_3); \\\n DO_INTERSECT_3(Triangle_3); \\\n DO_INTERSECT_3(Ray_3); \\\n DO_INTERSECT_3(Iso_cuboid_3); \\\n DO_INTERSECT_3(Sphere_3); \\\n DO_INTERSECT_3(Bbox_3)\n\n#define INTERSECTION_3(T) \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection); \\\n cgal.method(\"intersection\", &intersection)\n#define INTERSECTION_3_SYM \\\n INTERSECTION_3(Point_3); \\\n INTERSECTION_3(Segment_3); \\\n INTERSECTION_3(Line_3); \\\n INTERSECTION_3(Plane_3); \\\n INTERSECTION_3(Triangle_3); \\\n INTERSECTION_3(Ray_3)\n\n#define SQUARED_DISTANCE_2(T) \\\n cgal.method(\"squared_distance\", &squared_distance); \\\n cgal.method(\"squared_distance\", &squared_distance); \\\n cgal.method(\"squared_distance\", &squared_distance); \\\n cgal.method(\"squared_distance\", &squared_distance); \\\n cgal.method(\"squared_distance\", &squared_distance)\n#define SQUARED_DISTANCE_2_ALL \\\n SQUARED_DISTANCE_2(Point_2); \\\n SQUARED_DISTANCE_2(Line_2); \\\n SQUARED_DISTANCE_2(Ray_2); \\\n SQUARED_DISTANCE_2(Segment_2); \\\n SQUARED_DISTANCE_2(Triangle_2)\n\n#define SQUARED_DISTANCE_3(T) \\\n cgal.method(\"squared_distance\", &squared_distance); \\\n cgal.method(\"squared_distance\", &squared_distance); \\\n cgal.method(\"squared_distance\", &squared_distance); \\\n cgal.method(\"squared_distance\", &squared_distance); \\\n cgal.method(\"squared_distance\", &squared_distance)\n#define SQUARED_DISTANCE_3_SYM \\\n SQUARED_DISTANCE_3(Point_3); \\\n SQUARED_DISTANCE_3(Segment_3); \\\n SQUARED_DISTANCE_3(Line_3); \\\n SQUARED_DISTANCE_3(Plane_3); \\\n SQUARED_DISTANCE_3(Ray_3)\n\nnamespace jlcgal {\n\ntemplate\ninline\nbool\ndo_intersect(const T1& t1, const T2& t2) {\n return CGAL::do_intersect(t1, t2);\n}\n\ntemplate\ninline\njl_value_t*\nintersection(const T1& t1, const T2& t2) {\n auto result = CGAL::intersection(t1, t2);\n return result ?\n boost::apply_visitor(Intersection_visitor(), *result) :\n jl_nothing;\n}\n\ntemplate\ninline\nFT\nsquared_distance(const T1& t1, const T2& t2) {\n return CGAL::squared_distance(t1, t2);\n}\n\nvoid wrap_global_lk_functions(jlcxx::Module& cgal) {\n cgal.set_override_module(jl_base_module);\n cgal.method(\"angle\", static_cast(&CGAL::angle));\n cgal.method(\"angle\", static_cast(&CGAL::angle));\n cgal.method(\"angle\", static_cast(&CGAL::angle));\n cgal.method(\"angle\", static_cast(&CGAL::angle));\n cgal.method(\"angle\", static_cast(&CGAL::angle));\n cgal.method(\"angle\", static_cast(&CGAL::angle));\n cgal.method(\"angle\", static_cast(&CGAL::angle));\n cgal.unset_override_module();\n\n cgal.method(\"approximate_angle\", static_cast(&CGAL::approximate_angle));\n cgal.method(\"approximate_angle\", static_cast(&CGAL::approximate_angle));\n\n cgal.method(\"approximate_dihedral_angle\", static_cast(&CGAL::approximate_dihedral_angle));\n\n cgal.method(\"area\", static_cast(&CGAL::area));\n\n cgal.method(\"are_ordered_along_line\", static_cast(&CGAL::are_ordered_along_line));\n cgal.method(\"are_ordered_along_line\", static_cast(&CGAL::are_ordered_along_line));\n\n cgal.method(\"are_strictly_ordered_along_line\", static_cast(&CGAL::are_strictly_ordered_along_line));\n cgal.method(\"are_strictly_ordered_along_line\", static_cast(&CGAL::are_strictly_ordered_along_line));\n\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n cgal.method(\"barycenter\", static_cast(&CGAL::barycenter));\n\n cgal.method(\"bisector\", static_cast(&CGAL::bisector));\n cgal.method(\"bisector\", static_cast(&CGAL::bisector));\n cgal.method(\"bisector\", static_cast(&CGAL::bisector));\n cgal.method(\"bisector\", static_cast(&CGAL::bisector));\n\n cgal.method(\"centroid\", static_cast(&CGAL::centroid));\n cgal.method(\"centroid\", static_cast(&CGAL::centroid));\n cgal.method(\"centroid\", static_cast(&CGAL::centroid));\n cgal.method(\"centroid\", static_cast(&CGAL::centroid));\n cgal.method(\"centroid\", static_cast(&CGAL::centroid));\n cgal.method(\"centroid\", static_cast(&CGAL::centroid));\n cgal.method(\"centroid\", static_cast(&CGAL::centroid));\n\n cgal.method(\"circumcenter\", static_cast(&CGAL::circumcenter));\n cgal.method(\"circumcenter\", static_cast(&CGAL::circumcenter));\n cgal.method(\"circumcenter\", static_cast(&CGAL::circumcenter));\n cgal.method(\"circumcenter\", static_cast(&CGAL::circumcenter));\n cgal.method(\"circumcenter\", static_cast(&CGAL::circumcenter));\n cgal.method(\"circumcenter\", static_cast(&CGAL::circumcenter));\n cgal.method(\"circumcenter\", static_cast(&CGAL::circumcenter));\n cgal.method(\"circumcenter\", static_cast(&CGAL::circumcenter));\n\n cgal.method(\"collinear_are_ordered_along_line\", static_cast(&CGAL::collinear_are_ordered_along_line));\n cgal.method(\"collinear_are_ordered_along_line\", static_cast(&CGAL::collinear_are_ordered_along_line));\n\n cgal.method(\"collinear_are_strictly_ordered_along_line\", static_cast(&CGAL::collinear_are_strictly_ordered_along_line));\n cgal.method(\"collinear_are_strictly_ordered_along_line\", static_cast(&CGAL::collinear_are_strictly_ordered_along_line));\n\n cgal.method(\"collinear\", static_cast(&CGAL::collinear));\n cgal.method(\"collinear\", static_cast(&CGAL::collinear));\n\n cgal.method(\"compare_dihedral_angle\", static_cast(&CGAL::compare_dihedral_angle));\n cgal.method(\"compare_dihedral_angle\", static_cast(&CGAL::compare_dihedral_angle));\n cgal.method(\"compare_dihedral_angle\", static_cast(&CGAL::compare_dihedral_angle));\n cgal.method(\"compare_dihedral_angle\", static_cast(&CGAL::compare_dihedral_angle));\n\n cgal.method(\"compare_distance_to_point\", static_cast(&CGAL::compare_distance_to_point));\n cgal.method(\"compare_distance_to_point\", static_cast(&CGAL::compare_distance_to_point));\n\n cgal.method(\"compare_lexicographically\", static_cast(&CGAL::compare_lexicographically));\n cgal.method(\"compare_lexicographically\", static_cast(&CGAL::compare_lexicographically));\n\n cgal.method(\"compare_signed_distance_to_line\", static_cast(&CGAL::compare_signed_distance_to_line));\n cgal.method(\"compare_signed_distance_to_line\", static_cast(&CGAL::compare_signed_distance_to_line));\n\n cgal.method(\"compare_signed_distance_to_plane\", static_cast(&CGAL::compare_signed_distance_to_plane));\n cgal.method(\"compare_signed_distance_to_plane\", static_cast(&CGAL::compare_signed_distance_to_plane));\n\n cgal.method(\"compare_slope\", static_cast(&CGAL::compare_slope));\n cgal.method(\"compare_slope\", static_cast(&CGAL::compare_slope));\n cgal.method(\"compare_slope\", static_cast(&CGAL::compare_slope));\n\n cgal.method(\"compare_squared_distance\", static_cast(&CGAL::compare_squared_distance));\n cgal.method(\"compare_squared_distance\", static_cast(&CGAL::compare_squared_distance));\n\n cgal.method(\"compare_squared_radius\", static_cast(&CGAL::compare_squared_radius));\n cgal.method(\"compare_squared_radius\", static_cast(&CGAL::compare_squared_radius));\n cgal.method(\"compare_squared_radius\", static_cast(&CGAL::compare_squared_radius));\n cgal.method(\"compare_squared_radius\", static_cast(&CGAL::compare_squared_radius));\n\n cgal.method(\"compare_x\", static_cast(&CGAL::compare_x));\n cgal.method(\"compare_x\", static_cast(&CGAL::compare_x));\n cgal.method(\"compare_x\", static_cast(&CGAL::compare_x));\n cgal.method(\"compare_x\", static_cast(&CGAL::compare_x));\n cgal.method(\"compare_x\", static_cast(&CGAL::compare_x));\n\n cgal.method(\"compare_xy\", static_cast(&CGAL::compare_xy));\n\n cgal.method(\"compare_x_at_y\", static_cast(&CGAL::compare_x_at_y));\n cgal.method(\"compare_x_at_y\", static_cast(&CGAL::compare_x_at_y));\n cgal.method(\"compare_x_at_y\", static_cast(&CGAL::compare_x_at_y));\n cgal.method(\"compare_x_at_y\", static_cast(&CGAL::compare_x_at_y));\n\n cgal.method(\"compare_y_at_x\", static_cast(&CGAL::compare_y_at_x));\n cgal.method(\"compare_y_at_x\", static_cast(&CGAL::compare_y_at_x));\n cgal.method(\"compare_y_at_x\", static_cast(&CGAL::compare_y_at_x));\n cgal.method(\"compare_y_at_x\", static_cast(&CGAL::compare_y_at_x));\n cgal.method(\"compare_y_at_x\", static_cast(&CGAL::compare_y_at_x));\n cgal.method(\"compare_y_at_x\", static_cast(&CGAL::compare_y_at_x));\n\n cgal.method(\"compare_y\", static_cast(&CGAL::compare_y));\n cgal.method(\"compare_y\", static_cast(&CGAL::compare_y));\n cgal.method(\"compare_y\", static_cast(&CGAL::compare_y));\n cgal.method(\"compare_y\", static_cast(&CGAL::compare_y));\n cgal.method(\"compare_y\", static_cast(&CGAL::compare_y));\n\n cgal.method(\"compare_xyz\", static_cast(&CGAL::compare_xyz));\n\n cgal.method(\"compare_z\", static_cast(&CGAL::compare_z));\n\n cgal.method(\"compare_yx\", static_cast(&CGAL::compare_yx));\n\n cgal.method(\"coplanar\", static_cast(&CGAL::coplanar));\n\n cgal.method(\"coplanar_orientation\", static_cast(&CGAL::coplanar_orientation));\n cgal.method(\"coplanar_orientation\", static_cast(&CGAL::coplanar_orientation));\n\n cgal.method(\"coplanar_side_of_bounded_circle\", static_cast(&CGAL::coplanar_side_of_bounded_circle));\n\n cgal.method(\"cross_product\", static_cast(&CGAL::cross_product));\n\n cgal.method(\"determinant\", static_cast(&CGAL::determinant));\n cgal.method(\"determinant\", static_cast(&CGAL::determinant));\n\n DO_INTERSECT_2_SYM;\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n cgal.method(\"do_intersect\", &do_intersect);\n\n DO_INTERSECT_3_ALL;\n\n cgal.method(\"has_larger_distance_to_point\", static_cast(&CGAL::has_larger_distance_to_point));\n cgal.method(\"has_larger_distance_to_point\", static_cast(&CGAL::has_larger_distance_to_point));\n\n cgal.method(\"has_larger_signed_distance_to_line\", static_cast(&CGAL::has_larger_signed_distance_to_line));\n cgal.method(\"has_larger_signed_distance_to_line\", static_cast(&CGAL::has_larger_signed_distance_to_line));\n\n cgal.method(\"has_larger_signed_distance_to_plane\", static_cast(&CGAL::has_larger_signed_distance_to_plane));\n cgal.method(\"has_larger_signed_distance_to_plane\", static_cast(&CGAL::has_larger_signed_distance_to_plane));\n\n cgal.method(\"has_smaller_distance_to_point\", static_cast(&CGAL::has_smaller_distance_to_point));\n cgal.method(\"has_smaller_distance_to_point\", static_cast(&CGAL::has_smaller_distance_to_point));\n\n cgal.method(\"has_smaller_signed_distance_to_line\", static_cast(&CGAL::has_smaller_signed_distance_to_line));\n cgal.method(\"has_smaller_signed_distance_to_line\", static_cast(&CGAL::has_smaller_signed_distance_to_line));\n\n cgal.method(\"has_smaller_signed_distance_to_plane\", static_cast(&CGAL::has_smaller_signed_distance_to_plane));\n cgal.method(\"has_smaller_signed_distance_to_plane\", static_cast(&CGAL::has_smaller_signed_distance_to_plane));\n\n INTERSECTION_2_SYM;\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n\n INTERSECTION_3_SYM;\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n cgal.method(\"intersection\", &intersection);\n\n cgal.method(\"l_infinity_distance\", static_cast(&CGAL::l_infinity_distance));\n cgal.method(\"l_infinity_distance\", static_cast(&CGAL::l_infinity_distance));\n\n cgal.method(\"left_turn\", static_cast(&CGAL::left_turn));\n\n cgal.method(\"lexicographically_xy_larger\", static_cast(&CGAL::lexicographically_xy_larger));\n\n cgal.method(\"lexicographically_xy_larger_or_equal\", static_cast(&CGAL::lexicographically_xy_larger_or_equal));\n\n cgal.method(\"lexicographically_xy_smaller\", static_cast(&CGAL::lexicographically_xy_smaller));\n\n cgal.method(\"lexicographically_xy_smaller_or_equal\", static_cast(&CGAL::lexicographically_xy_smaller_or_equal));\n\n cgal.method(\"lexicographically_xyz_smaller\", static_cast(&CGAL::lexicographically_xyz_smaller));\n\n cgal.method(\"lexicographically_xyz_smaller_or_equal\", static_cast(&CGAL::lexicographically_xyz_smaller_or_equal));\n\n cgal.method(\"max_vertex\", static_cast(&CGAL::max_vertex));\n cgal.method(\"max_vertex\", static_cast(&CGAL::max_vertex));\n\n cgal.method(\"midpoint\", static_cast(&CGAL::midpoint));\n cgal.method(\"midpoint\", static_cast(&CGAL::midpoint));\n\n cgal.method(\"min_vertex\", static_cast(&CGAL::min_vertex));\n cgal.method(\"min_vertex\", static_cast(&CGAL::min_vertex));\n\n cgal.method(\"normal\", static_cast(&CGAL::normal));\n\n cgal.method(\"orientation\", static_cast(&CGAL::orientation));\n cgal.method(\"orientation\", static_cast(&CGAL::orientation));\n cgal.method(\"orientation\", static_cast(&CGAL::orientation));\n cgal.method(\"orientation\", static_cast(&CGAL::orientation));\n\n cgal.method(\"orthogonal_vector\", static_cast(&CGAL::orthogonal_vector));\n\n cgal.method(\"parallel\", static_cast(&CGAL::parallel));\n cgal.method(\"parallel\", static_cast(&CGAL::parallel));\n cgal.method(\"parallel\", static_cast(&CGAL::parallel));\n cgal.method(\"parallel\", static_cast(&CGAL::parallel));\n cgal.method(\"parallel\", static_cast(&CGAL::parallel));\n cgal.method(\"parallel\", static_cast(&CGAL::parallel));\n cgal.method(\"parallel\", static_cast(&CGAL::parallel));\n\n cgal.method(\"radical_plane\", static_cast(&CGAL::radical_plane));\n\n cgal.method(\"radical_line\", static_cast(&CGAL::radical_line));\n\n cgal.method(\"rational_rotation_approximation\", static_cast(&CGAL::rational_rotation_approximation));\n\n cgal.method(\"right_turn\", static_cast(&CGAL::right_turn));\n\n cgal.method(\"scalar_product\", static_cast(&CGAL::scalar_product));\n cgal.method(\"scalar_product\", static_cast(&CGAL::scalar_product));\n\n cgal.method(\"side_of_bounded_circle\", static_cast(&CGAL::side_of_bounded_circle));\n cgal.method(\"side_of_bounded_circle\", static_cast(&CGAL::side_of_bounded_circle));\n\n cgal.method(\"side_of_bounded_sphere\", static_cast(&CGAL::side_of_bounded_sphere));\n cgal.method(\"side_of_bounded_sphere\", static_cast(&CGAL::side_of_bounded_sphere));\n cgal.method(\"side_of_bounded_sphere\", static_cast(&CGAL::side_of_bounded_sphere));\n\n cgal.method(\"side_of_oriented_circle\", static_cast(&CGAL::side_of_oriented_circle));\n\n cgal.method(\"side_of_oriented_sphere\", static_cast(&CGAL::side_of_oriented_sphere));\n\n cgal.method(\"squared_area\", static_cast(&CGAL::squared_area));\n\n SQUARED_DISTANCE_2_ALL;\n\n SQUARED_DISTANCE_3_SYM;\n cgal.method(\"squared_distance\", &squared_distance);\n cgal.method(\"squared_distance\", &squared_distance);\n\n cgal.method(\"squared_radius\", static_cast(&CGAL::squared_radius));\n cgal.method(\"squared_radius\", static_cast(&CGAL::squared_radius));\n cgal.method(\"squared_radius\", static_cast(&CGAL::squared_radius));\n cgal.method(\"squared_radius\", static_cast(&CGAL::squared_radius));\n cgal.method(\"squared_radius\", static_cast(&CGAL::squared_radius));\n cgal.method(\"squared_radius\", static_cast(&CGAL::squared_radius));\n cgal.method(\"squared_radius\", static_cast(&CGAL::squared_radius));\n\n cgal.method(\"unit_normal\", static_cast(&CGAL::unit_normal));\n\n cgal.method(\"volume\", static_cast(&CGAL::volume));\n\n cgal.method(\"x_equal\", static_cast(&CGAL::x_equal));\n cgal.method(\"x_equal\", static_cast(&CGAL::x_equal));\n cgal.method(\"y_equal\", static_cast(&CGAL::y_equal));\n cgal.method(\"y_equal\", static_cast(&CGAL::y_equal));\n cgal.method(\"z_equal\", static_cast(&CGAL::z_equal));\n\n cgal.method(\"do_overlap\", static_cast(&CGAL::do_overlap));\n cgal.method(\"do_overlap\", static_cast(&CGAL::do_overlap));\n}\n\n} // jlcgal\n\n#undef DO_INTERSECT_2\n#undef DO_INTERSECT_2_SYM\n\n#undef INTERSECTION_2\n#undef INTERSECTION_2_SYM\n\n#undef DO_INTERSECT_3\n#undef DO_INTERSECT_3_ALL\n\n#undef INTERSECTION_3\n#undef INTERSECTION_3_SYM\n\n#undef SQUARED_DISTANCE_2\n#undef SQUARED_DISTANCE_2_ALL\n\n#undef SQUARED_DISTANCE_3\n#undef SQUARED_DISTANCE_3_SYM\n", "meta": {"hexsha": "0ff805b897bc895dfa5081c03dec7f959e570e4c", "size": 32038, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/global_kernel_functions/global_lk_functions.cpp", "max_stars_repo_name": "rgcv/libcgal-julia", 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"max_forks_repo_forks_event_max_datetime": "2022-03-14T17:17:30.000Z", "avg_line_length": 69.9519650655, "max_line_length": 240, "alphanum_fraction": 0.748829515, "num_tokens": 9557, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.2335112019012587}} {"text": "/*\n * Copyright (c) 2011, Mattia Penati \n * All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without modification,\n * are permitted provided that the following conditions are met:\n *\n * * Redistributions of source code must retain the above copyright notice,\n * this list of conditions and the following disclaimer.\n * * Redistributions in binary form must reproduce the above copyright notice,\n * this list of conditions and the following disclaimer in the documentation\n * and/or other materials provided with the distribution.\n * * Neither the name of the Politecnico di Milano nor the names of its\n * contributors may be used to endorse or promote products derived from\n * this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\n * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR\n * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON\n * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n */\n\n#ifndef AMA_TENSOR_DETAIL_COPY_HPP\n#define AMA_TENSOR_DETAIL_COPY_HPP 1\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace ama\n{\n namespace tensor_\n {\n\n namespace mpl = ::boost::mpl;\n\n\n /* given the order this struct create the first index */\n template \n struct first:\n mpl::fold<\n mpl::range_c\n , mpl::vector0_c\n , mpl::push_back< mpl::_1 , mpl::size_t<0> >\n >::type { };\n\n /* increment the multi-index */\n template <\n typename D /* the dimension */\n , typename I /* the multi-index */\n >\n struct increment:\n mpl::fold<\n I\n , mpl::pair< /* a pair with: */\n mpl::vector0_c /* - the multi-index */\n , mpl::true_ /* - an the carry of sum */\n >\n , mpl::pair<\n mpl::push_back<\n mpl::first\n , mpl::modulus<\n mpl::if_<\n mpl::second\n , mpl::next\n , mpl::_2\n >\n , D\n >\n >\n , mpl::equal_to<\n mpl::if_<\n mpl::second\n , mpl::next\n , mpl::_2\n >\n , D\n >\n >\n > { };\n\n\n /* this struct implement the copy */\n template <\n typename D /* the dimensione of tensor */\n , typename O /* the order of tensor */\n , typename I = first /* the first multi-index */\n , typename R = mpl::false_ /* this type is true for the last multi-index */\n >\n struct copy\n {\n template \n static void apply(SRC const & src, DST & dst)\n {\n dst.template at() = src.template at();\n\n /* increment the multi-index */\n typedef typename increment::type increment_type;\n\n /* the first is the multi-index incremented */\n typedef typename mpl::first::type i;\n /* the second is a boolean flag toidentify the last multi-index */\n typedef typename mpl::second::type r;\n\n /* iterative call */\n copy::apply(src,dst);\n }\n };\n\n\n /* partial specialization, end the iterative call */\n template \n struct copy\n {\n template \n static void apply(SRC const &, DST &) { }\n };\n\n }\n}\n\n#endif /* AMA_TENSOR_DETAIL_COPY_HPP */\n", "meta": {"hexsha": "cde5783932ae54ea0e8789e9ae1a324bea44e16d", "size": 4850, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/ama/tensor/detail/copy.hpp", "max_stars_repo_name": "mattiapenati/amanita", "max_stars_repo_head_hexsha": "c5c16d1f17e71151ce1d8e6972ddff6cec3c7305", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/ama/tensor/detail/copy.hpp", "max_issues_repo_name": "mattiapenati/amanita", "max_issues_repo_head_hexsha": "c5c16d1f17e71151ce1d8e6972ddff6cec3c7305", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/ama/tensor/detail/copy.hpp", "max_forks_repo_name": "mattiapenati/amanita", "max_forks_repo_head_hexsha": "c5c16d1f17e71151ce1d8e6972ddff6cec3c7305", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.9259259259, "max_line_length": 84, "alphanum_fraction": 0.5659793814, "num_tokens": 1010, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5234203638047913, "lm_q2_score": 0.44552953503957266, "lm_q1q2_score": 0.23319923131619263}} {"text": "//Copyright (c) 2008-2016 Emil Dotchevski and Reverge Studios, Inc.\r\n\r\n//Distributed under the Boost Software License, Version 1.0. (See accompanying\r\n//file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n#ifndef UUID_5265FC7CA1C011DE9EBDFFA956D89593\r\n#define UUID_5265FC7CA1C011DE9EBDFFA956D89593\r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\nnamespace\r\nboost\r\n {\r\n namespace\r\n qvm\r\n {\r\n ////////////////////////////////////////////////\r\n\r\n namespace\r\n qvm_detail\r\n {\r\n template \r\n class\r\n col_\r\n {\r\n col_( col_ const & );\r\n col_ & operator=( col_ const & );\r\n ~col_();\r\n\r\n public:\r\n\r\n template \r\n BOOST_QVM_INLINE_TRIVIAL\r\n col_ &\r\n operator=( T const & x )\r\n {\r\n assign(*this,x);\r\n return *this;\r\n }\r\n\r\n template \r\n BOOST_QVM_INLINE_TRIVIAL\r\n operator R() const\r\n {\r\n R r;\r\n assign(r,*this);\r\n return r;\r\n }\r\n };\r\n }\r\n\r\n template \r\n struct\r\n vec_traits< qvm_detail::col_ >\r\n {\r\n typedef qvm_detail::col_ this_vector;\r\n typedef typename mat_traits::scalar_type scalar_type;\r\n static int const dim=mat_traits::rows;\r\n BOOST_QVM_STATIC_ASSERT(Col>=0);\r\n BOOST_QVM_STATIC_ASSERT(Col::cols);\r\n\r\n template \r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type\r\n read_element( this_vector const & x )\r\n {\r\n BOOST_QVM_STATIC_ASSERT(I>=0);\r\n BOOST_QVM_STATIC_ASSERT(I::template read_element(reinterpret_cast(x));\r\n }\r\n\r\n template \r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type &\r\n write_element( this_vector & x )\r\n {\r\n BOOST_QVM_STATIC_ASSERT(I>=0);\r\n BOOST_QVM_STATIC_ASSERT(I::template write_element(reinterpret_cast(x));\r\n }\r\n\r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type\r\n read_element_idx( int i, this_vector const & x )\r\n {\r\n BOOST_QVM_ASSERT(i>=0);\r\n BOOST_QVM_ASSERT(i::read_element_idx(i,Col,reinterpret_cast(x));\r\n }\r\n\r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type &\r\n write_element_idx( int i, this_vector & x )\r\n {\r\n BOOST_QVM_ASSERT(i>=0);\r\n BOOST_QVM_ASSERT(i::write_element_idx(i,Col,reinterpret_cast(x));\r\n }\r\n };\r\n\r\n template \r\n struct\r\n deduce_vec,D>\r\n {\r\n typedef vec::scalar_type,D> type;\r\n };\r\n\r\n template \r\n struct\r\n deduce_vec2,qvm_detail::col_,D>\r\n {\r\n typedef vec::scalar_type,D> type;\r\n };\r\n\r\n template \r\n typename boost::enable_if_c<\r\n is_mat::value,\r\n qvm_detail::col_ const &>::type\r\n BOOST_QVM_INLINE_TRIVIAL\r\n col( A const & a )\r\n {\r\n return reinterpret_cast const &>(a);\r\n }\r\n\r\n template \r\n typename boost::enable_if_c<\r\n is_mat::value,\r\n qvm_detail::col_ &>::type\r\n BOOST_QVM_INLINE_TRIVIAL\r\n col( A & a )\r\n {\r\n return reinterpret_cast &>(a);\r\n }\r\n\r\n ////////////////////////////////////////////////\r\n\r\n namespace\r\n qvm_detail\r\n {\r\n template \r\n class\r\n row_\r\n {\r\n row_( row_ const & );\r\n row_ & operator=( row_ const & );\r\n ~row_();\r\n\r\n public:\r\n\r\n template \r\n BOOST_QVM_INLINE_TRIVIAL\r\n row_ &\r\n operator=( T const & x )\r\n {\r\n assign(*this,x);\r\n return *this;\r\n }\r\n\r\n template \r\n BOOST_QVM_INLINE_TRIVIAL\r\n operator R() const\r\n {\r\n R r;\r\n assign(r,*this);\r\n return r;\r\n }\r\n };\r\n }\r\n\r\n template \r\n struct\r\n vec_traits< qvm_detail::row_ >\r\n {\r\n typedef qvm_detail::row_ this_vector;\r\n typedef typename mat_traits::scalar_type scalar_type;\r\n static int const dim=mat_traits::cols;\r\n BOOST_QVM_STATIC_ASSERT(Row>=0);\r\n BOOST_QVM_STATIC_ASSERT(Row::rows);\r\n\r\n template \r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type\r\n read_element( this_vector const & x )\r\n {\r\n BOOST_QVM_STATIC_ASSERT(I>=0);\r\n BOOST_QVM_STATIC_ASSERT(I::template read_element(reinterpret_cast(x));\r\n }\r\n\r\n template \r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type &\r\n write_element( this_vector & x )\r\n {\r\n BOOST_QVM_STATIC_ASSERT(I>=0);\r\n BOOST_QVM_STATIC_ASSERT(I::template write_element(reinterpret_cast(x));\r\n }\r\n\r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type\r\n read_element_idx( int i, this_vector const & x )\r\n {\r\n BOOST_QVM_ASSERT(i>=0);\r\n BOOST_QVM_ASSERT(i::read_element_idx(Row,i,reinterpret_cast(x));\r\n }\r\n\r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type &\r\n write_element_idx( int i, this_vector & x )\r\n {\r\n BOOST_QVM_ASSERT(i>=0);\r\n BOOST_QVM_ASSERT(i::write_element_idx(Row,i,reinterpret_cast(x));\r\n }\r\n };\r\n\r\n template \r\n struct\r\n deduce_vec,D>\r\n {\r\n typedef vec::scalar_type,D> type;\r\n };\r\n\r\n template \r\n struct\r\n deduce_vec2,qvm_detail::row_,D>\r\n {\r\n typedef vec::scalar_type,D> type;\r\n };\r\n\r\n template \r\n typename boost::enable_if_c<\r\n is_mat::value,\r\n qvm_detail::row_ const &>::type\r\n BOOST_QVM_INLINE_TRIVIAL\r\n row( A const & a )\r\n {\r\n return reinterpret_cast const &>(a);\r\n }\r\n\r\n template \r\n typename boost::enable_if_c<\r\n is_mat::value,\r\n qvm_detail::row_ &>::type\r\n BOOST_QVM_INLINE_TRIVIAL\r\n row( A & a )\r\n {\r\n return reinterpret_cast &>(a);\r\n }\r\n\r\n ////////////////////////////////////////////////\r\n\r\n namespace\r\n qvm_detail\r\n {\r\n template \r\n class\r\n diag_\r\n {\r\n diag_( diag_ const & );\r\n diag_ & operator=( diag_ const & );\r\n ~diag_();\r\n\r\n public:\r\n\r\n template \r\n BOOST_QVM_INLINE_TRIVIAL\r\n diag_ &\r\n operator=( T const & x )\r\n {\r\n assign(*this,x);\r\n return *this;\r\n }\r\n\r\n template \r\n BOOST_QVM_INLINE_TRIVIAL\r\n operator R() const\r\n {\r\n R r;\r\n assign(r,*this);\r\n return r;\r\n }\r\n };\r\n\r\n template \r\n struct diag_bool_dispatch;\r\n\r\n template \r\n struct\r\n diag_bool_dispatch\r\n {\r\n static int const value=X;\r\n };\r\n\r\n template \r\n struct\r\n diag_bool_dispatch\r\n {\r\n static int const value=Y;\r\n };\r\n }\r\n\r\n template \r\n struct\r\n vec_traits< qvm_detail::diag_ >\r\n {\r\n typedef qvm_detail::diag_ this_vector;\r\n typedef typename mat_traits::scalar_type scalar_type;\r\n static int const dim=qvm_detail::diag_bool_dispatch<\r\n mat_traits::rows,\r\n mat_traits::cols,\r\n mat_traits::rows<=mat_traits::cols>::value;\r\n\r\n template \r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type\r\n read_element( this_vector const & x )\r\n {\r\n BOOST_QVM_STATIC_ASSERT(I>=0);\r\n BOOST_QVM_STATIC_ASSERT(I::template read_element(reinterpret_cast(x));\r\n }\r\n\r\n template \r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type &\r\n write_element( this_vector & x )\r\n {\r\n BOOST_QVM_STATIC_ASSERT(I>=0);\r\n BOOST_QVM_STATIC_ASSERT(I::template write_element(reinterpret_cast(x));\r\n }\r\n\r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type\r\n read_element_idx( int i, this_vector const & x )\r\n {\r\n BOOST_QVM_ASSERT(i>=0);\r\n BOOST_QVM_ASSERT(i::read_element_idx(i,i,reinterpret_cast(x));\r\n }\r\n\r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type &\r\n write_element_idx( int i, this_vector & x )\r\n {\r\n BOOST_QVM_ASSERT(i>=0);\r\n BOOST_QVM_ASSERT(i::write_element_idx(i,i,reinterpret_cast(x));\r\n }\r\n };\r\n\r\n template \r\n struct\r\n deduce_vec,D>\r\n {\r\n typedef vec::scalar_type,D> type;\r\n };\r\n\r\n template \r\n struct\r\n deduce_vec2,qvm_detail::diag_,D>\r\n {\r\n typedef vec::scalar_type,D> type;\r\n };\r\n\r\n template \r\n typename boost::enable_if_c<\r\n is_mat::value,\r\n qvm_detail::diag_ const &>::type\r\n BOOST_QVM_INLINE_TRIVIAL\r\n diag( A const & a )\r\n {\r\n return reinterpret_cast const &>(a);\r\n }\r\n\r\n template \r\n typename boost::enable_if_c<\r\n is_mat::value,\r\n qvm_detail::diag_ &>::type\r\n BOOST_QVM_INLINE_TRIVIAL\r\n diag( A & a )\r\n {\r\n return reinterpret_cast &>(a);\r\n }\r\n\r\n ////////////////////////////////////////////////\r\n\r\n namespace\r\n qvm_detail\r\n {\r\n template \r\n class\r\n translation_\r\n {\r\n translation_( translation_ const & );\r\n ~translation_();\r\n\r\n public:\r\n\r\n translation_ &\r\n operator=( translation_ const & x )\r\n {\r\n assign(*this,x);\r\n return *this;\r\n }\r\n\r\n template \r\n BOOST_QVM_INLINE_TRIVIAL\r\n translation_ &\r\n operator=( T const & x )\r\n {\r\n assign(*this,x);\r\n return *this;\r\n }\r\n\r\n template \r\n BOOST_QVM_INLINE_TRIVIAL\r\n operator R() const\r\n {\r\n R r;\r\n assign(r,*this);\r\n return r;\r\n }\r\n };\r\n }\r\n\r\n template \r\n struct\r\n vec_traits< qvm_detail::translation_ >\r\n {\r\n typedef qvm_detail::translation_ this_vector;\r\n typedef typename mat_traits::scalar_type scalar_type;\r\n static int const dim=mat_traits::rows-1;\r\n BOOST_QVM_STATIC_ASSERT(mat_traits::rows==mat_traits::cols);\r\n BOOST_QVM_STATIC_ASSERT(mat_traits::rows>=3);\r\n\r\n template \r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type\r\n read_element( this_vector const & x )\r\n {\r\n BOOST_QVM_STATIC_ASSERT(I>=0);\r\n BOOST_QVM_STATIC_ASSERT(I::template read_element(reinterpret_cast(x));\r\n }\r\n\r\n template \r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type &\r\n write_element( this_vector & x )\r\n {\r\n BOOST_QVM_STATIC_ASSERT(I>=0);\r\n BOOST_QVM_STATIC_ASSERT(I::template write_element(reinterpret_cast(x));\r\n }\r\n\r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type\r\n read_element_idx( int i, this_vector const & x )\r\n {\r\n BOOST_QVM_ASSERT(i>=0);\r\n BOOST_QVM_ASSERT(i::read_element_idx(i,dim,reinterpret_cast(x));\r\n }\r\n\r\n static\r\n BOOST_QVM_INLINE_CRITICAL\r\n scalar_type &\r\n write_element_idx( int i, this_vector & x )\r\n {\r\n BOOST_QVM_ASSERT(i>=0);\r\n BOOST_QVM_ASSERT(i::write_element_idx(i,dim,reinterpret_cast(x));\r\n }\r\n };\r\n\r\n template \r\n struct\r\n deduce_vec,D>\r\n {\r\n typedef vec::scalar_type,D> type;\r\n };\r\n\r\n template \r\n struct\r\n deduce_vec2,qvm_detail::translation_,D>\r\n {\r\n typedef vec::scalar_type,D> type;\r\n };\r\n\r\n template \r\n typename boost::enable_if_c<\r\n is_mat::value && mat_traits::rows==mat_traits::cols && mat_traits::rows>=3,\r\n qvm_detail::translation_ const &>::type\r\n BOOST_QVM_INLINE_TRIVIAL\r\n translation( A const & a )\r\n {\r\n return reinterpret_cast const &>(a);\r\n }\r\n\r\n template \r\n typename boost::enable_if_c<\r\n is_mat::value && mat_traits::rows==mat_traits::cols && mat_traits::rows>=3,\r\n qvm_detail::translation_ &>::type\r\n BOOST_QVM_INLINE_TRIVIAL\r\n translation( A & a )\r\n {\r\n return reinterpret_cast &>(a);\r\n }\r\n\r\n ////////////////////////////////////////////////\r\n }\r\n }\r\n\r\n#endif\r\n", "meta": {"hexsha": "81a2527d218701c5f0f1c7b3486a910d0e791dc3", "size": 18433, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ios/Pods/boost-for-react-native/boost/qvm/map_mat_vec.hpp", "max_stars_repo_name": "rudylee/expo", "max_stars_repo_head_hexsha": "b3e65a7a5b205f14a3eb6cd6fa8d13c8d663b1cc", "max_stars_repo_licenses": ["Apache-2.0", "MIT"], "max_stars_count": 8805.0, "max_stars_repo_stars_event_min_datetime": "2015-11-03T00:52:29.000Z", "max_stars_repo_stars_event_max_datetime": 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"Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.2329014738176742}} {"text": " /*\r\n * Copyright 2007-2015 The OpenMx Project\r\n *\r\n * Licensed under the Apache License, Version 2.0 (the \"License\");\r\n * you may not use this file except in compliance with the License.\r\n * You may obtain a copy of the License at\r\n *\r\n * http://www.apache.org/licenses/LICENSE-2.0\r\n *\r\n * Unless required by applicable law or agreed to in writing, software\r\n * distributed under the License is distributed on an \"AS IS\" BASIS,\r\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n * See the License for the specific language governing permissions and\r\n * limitations under the License.\r\n */\r\n \r\n#include \"omxExpectation.h\"\r\n#include \"omxFitFunction.h\"\r\n#include \"omxDefines.h\"\r\n#include \"omxGREMLExpectation.h\"\r\n#include \r\n#include \r\n#include \r\n \r\nvoid omxInitGREMLExpectation(omxExpectation* ox){\r\n \r\n SEXP rObj = ox->rObj;\r\n SEXP Rmtx, casesToDrop, yXcolnames;\r\n int i=0;\r\n omxState* currentState = ox->currentState;\r\n \r\n if(OMX_DEBUG) { mxLog(\"Initializing GREML expectation.\"); }\r\n \r\n //omxGREMLExpectation *oge = (omxGREMLExpectation*) R_alloc(1, sizeof(omxGREMLExpectation));\r\n omxGREMLExpectation *oge = new omxGREMLExpectation;\r\n \r\n /* Set Expectation Calls and Structures */\r\n ox->computeFun = omxComputeGREMLExpectation;\r\n\tox->destructFun = omxDestroyGREMLExpectation;\r\n\tox->componentFun = omxGetGREMLExpectationComponent;\r\n\tox->populateAttrFun = omxPopulateGREMLAttributes;\r\n\tox->argStruct = (void*) oge;\r\n \r\n \r\n /* Set up expectation structures */\r\n //y:\r\n oge->y = new omxData();\r\n {ScopedProtect p1(Rmtx, R_do_slot(rObj, Rf_install(\"y\")));\r\n\t oge->y->newDataStatic(currentState, Rmtx);\r\n }\r\n //V:\r\n\tif(OMX_DEBUG) { mxLog(\"Processing V.\"); }\r\n\toge->cov = omxNewMatrixFromSlot(rObj, currentState, \"V\");\r\n if( oge->cov->rows != oge->cov->cols ){\r\n Rf_error(\"'V' matrix is not square\");\r\n }\r\n //X:\r\n\tif(OMX_DEBUG) { mxLog(\"Processing X.\"); }\r\n {ScopedProtect p1(Rmtx, R_do_slot(rObj, Rf_install(\"X\")));\r\n\toge->X = omxNewMatrixFromRPrimitive(Rmtx, currentState, 0, 0);\r\n }\r\n //Eigy (local) will have however many rows and 1 column:\r\n Eigen::Map< Eigen::MatrixXd > Eigy(omxMatrixDataColumnMajor(oge->y->dataMat), oge->y->dataMat->cols, 1);\r\n if(oge->X->rows != Eigy.rows()){Rf_error(\"'X' and 'y' matrices have different numbers of rows\");}\r\n //means:\r\n oge->means = omxInitMatrix(Eigy.rows(), 1, 1, currentState);\r\n //logdetV_om:\r\n oge->logdetV_om = omxInitMatrix(1, 1, 1, currentState);\r\n oge->logdetV_om->data[0] = 0;\r\n //cholV_fail_om:\r\n oge->cholV_fail_om = omxInitMatrix(1, 1, 1, currentState);\r\n oge->cholV_fail_om->data[0] = 0;\r\n //quadXinv:\r\n oge->quadXinv.setZero(oge->X->cols, oge->X->cols);\r\n\r\n\r\n //Deal with missing data:\r\n int* casesToDrop_intptr;\r\n oge->numcases2drop = 0;\r\n {\r\n ScopedProtect p1(casesToDrop, R_do_slot(rObj, Rf_install(\"casesToDrop\")));\r\n if(Rf_length(casesToDrop)){\r\n if(OMX_DEBUG) { mxLog(\"Preparing GREML expectation to handle missing data.\"); }\r\n oge->numcases2drop = Rf_length(casesToDrop);\r\n casesToDrop_intptr = INTEGER(casesToDrop);\r\n oge->dropcase.assign(oge->cov->rows,0);\r\n for(i=0; i < Rf_length(casesToDrop); i++){\r\n if(casesToDrop_intptr[i] >= oge->cov->rows){\r\n Rf_warning(\"casesToDrop vector in GREML expectation contains indices greater than the number of datapoints\");\r\n oge->numcases2drop--; \r\n }\r\n //Need to subtract 1 from the index because R begins array indexing with 1, not 0:\r\n else{oge->dropcase[casesToDrop_intptr[i]-1] = 1;}\r\n }}\r\n }\r\n if(Eigy.rows() != oge->cov->rows - oge->numcases2drop){\r\n Rf_error(\"y and V matrices do not have equal numbers of rows\");\r\n }\r\n \r\n \r\n //column names of y and X:\r\n {\r\n ScopedProtect p1(yXcolnames, R_do_slot(rObj, Rf_install(\"yXcolnames\")));\r\n oge->yXcolnames.resize(Rf_length(yXcolnames));\r\n for(i=0; i < Rf_length(yXcolnames); i++){\r\n SEXP elem;\r\n {ScopedProtect p2(elem, STRING_ELT(yXcolnames, i));\r\n \toge->yXcolnames[i] = CHAR(elem);}\r\n }\r\n }\r\n \r\n //Initially compute everything involved in computing means:\r\n oge->alwaysComputeMeans = 1;\r\n oge->cholquadX_fail = 0;\r\n EigenMatrixAdaptor EigX(oge->X);\r\n Eigen::Map< Eigen::MatrixXd > yhat(omxMatrixDataColumnMajor(oge->means), oge->means->rows, oge->means->cols);\r\n Eigen::MatrixXd EigV(Eigy.rows(), Eigy.rows());\r\n Eigen::MatrixXd quadX(oge->X->cols, oge->X->cols);\r\n //Apparently you need to initialize a matrix's elements before you try to write to its lower triangle:\r\n quadX.setZero(oge->X->cols, oge->X->cols);\r\n Eigen::LLT< Eigen::MatrixXd > cholV(Eigy.rows());\r\n Eigen::LLT< Eigen::MatrixXd > cholquadX(oge->X->cols);\r\n if( oge->numcases2drop ){\r\n dropCasesAndEigenize(oge->cov, EigV, oge->numcases2drop, oge->dropcase, 1);\r\n }\r\n else{EigV = Eigen::Map< Eigen::MatrixXd >(omxMatrixDataColumnMajor(oge->cov), oge->cov->rows, oge->cov->cols);}\r\n //invcov:\r\n oge->invcov = omxInitMatrix(EigV.rows(), EigV.cols(), 1, currentState);\r\n Eigen::Map< Eigen::MatrixXd > Vinv(omxMatrixDataColumnMajor(oge->invcov), EigV.rows(), EigV.cols());\r\n cholV.compute(EigV.selfadjointView());\r\n if(cholV.info() != Eigen::Success){\r\n Rf_error(\"Expected covariance matrix is non-positive-definite at initial values\");\r\n }\r\n oge->cholV_vectorD = (( Eigen::MatrixXd )(cholV.matrixL())).diagonal();\r\n for(i=0; i < oge->X->rows; i++){\r\n oge->logdetV_om->data[0] += log(oge->cholV_vectorD[i]);\r\n }\r\n oge->logdetV_om->data[0] *= 2;\r\n Vinv = cholV.solve(Eigen::MatrixXd::Identity( EigV.rows(), EigV.cols() )); //<-- V inverse\r\n oge->XtVinv = EigX.transpose() * Vinv;\r\n quadX.triangularView() = oge->XtVinv * EigX;\r\n cholquadX.compute(quadX.selfadjointView());\r\n if(cholquadX.info() != Eigen::Success){\r\n Rf_error(\"Cholesky factorization failed at initial values; possibly, the matrix of covariates is rank-deficient\");\r\n }\r\n oge->cholquadX_vectorD = (( Eigen::MatrixXd )(cholquadX.matrixL())).diagonal();\r\n oge->quadXinv = ( cholquadX.solve(Eigen::MatrixXd::Identity(oge->X->cols, oge->X->cols)) ).triangularView();\r\n yhat = EigX * oge->quadXinv.selfadjointView() * oge->XtVinv * Eigy;\r\n \r\n /*Prepare y as the data that the FIML fitfunction will use:*/\r\n oge->data2 = ox->data;\r\n ox->data = oge->y;\r\n if (oge->data2->hasDefinitionVariables()) {\r\n\t Rf_error(\"definition variables are incompatible (and unnecessary) with GREML expectation\");\r\n }\r\n}\r\n\r\n\r\nvoid omxComputeGREMLExpectation(omxExpectation* ox, const char *, const char *) {\r\n omxGREMLExpectation* oge = (omxGREMLExpectation*) (ox->argStruct);\r\n\tomxRecompute(oge->cov, NULL);\r\n int i=0;\r\n oge->cholV_fail_om->data[0] = 0;\r\n oge->cholquadX_fail = 0;\r\n oge->logdetV_om->data[0] = 0;\r\n \r\n EigenMatrixAdaptor EigX(oge->X);\r\n Eigen::Map< Eigen::MatrixXd > Eigy(omxMatrixDataColumnMajor(oge->y->dataMat), oge->y->dataMat->cols, 1);\r\n Eigen::Map< Eigen::MatrixXd > yhat(omxMatrixDataColumnMajor(oge->means), oge->means->rows, oge->means->cols);\r\n Eigen::MatrixXd EigV(Eigy.rows(), Eigy.rows());\r\n Eigen::Map< Eigen::MatrixXd > Vinv(omxMatrixDataColumnMajor(oge->invcov), oge->invcov->rows, oge->invcov->cols);\r\n Eigen::MatrixXd quadX(oge->X->cols, oge->X->cols);\r\n quadX.setZero(oge->X->cols, oge->X->cols);\r\n Eigen::LLT< Eigen::MatrixXd > cholV(oge->y->dataMat->rows);\r\n Eigen::LLT< Eigen::MatrixXd > cholquadX(oge->X->cols);\r\n if( oge->numcases2drop ){\r\n dropCasesAndEigenize(oge->cov, EigV, oge->numcases2drop, oge->dropcase, 1);\r\n }\r\n else{EigV = Eigen::Map< Eigen::MatrixXd >(omxMatrixDataColumnMajor(oge->cov), oge->cov->rows, oge->cov->cols);}\r\n cholV.compute(EigV.selfadjointView());\r\n if(cholV.info() != Eigen::Success){\r\n oge->cholV_fail_om->data[0] = 1;\r\n return;\r\n }\r\n oge->cholV_vectorD = (( Eigen::MatrixXd )(cholV.matrixL())).diagonal();\r\n for(i=0; i < oge->X->rows; i++){\r\n oge->logdetV_om->data[0] += log(oge->cholV_vectorD[i]);\r\n }\r\n oge->logdetV_om->data[0] *= 2;\r\n if(oge->alwaysComputeMeans){\r\n \tVinv = cholV.solve(Eigen::MatrixXd::Identity( EigV.rows(), EigV.cols() )); //<-- V inverse\r\n \toge->XtVinv = EigX.transpose() * Vinv;\r\n }\r\n /*alwaysComputeMeans is initialized as true, and the only way it can be set to false is by the GREML \r\n fitfunction. If its false, that means that the GREML fitfunction is being used, and it knows how to handle\r\n a \"half-full\" Vinv.*/\r\n else{\r\n \t//V inverse:\r\n \tVinv.triangularView() = ( cholV.solve(Eigen::MatrixXd::Identity( EigV.rows(), EigV.cols() )) ).triangularView();\r\n \toge->XtVinv = EigX.transpose() * Vinv.selfadjointView();\r\n }\r\n quadX.triangularView() = oge->XtVinv * EigX;\r\n cholquadX.compute(quadX.selfadjointView());\r\n if(cholquadX.info() != Eigen::Success){ \r\n oge->cholquadX_fail = 1;\r\n return;\r\n }\r\n oge->cholquadX_vectorD = (( Eigen::MatrixXd )(cholquadX.matrixL())).diagonal();\r\n oge->quadXinv = ( cholquadX.solve(Eigen::MatrixXd::Identity(oge->X->cols, oge->X->cols)) ).triangularView();\r\n if(oge->alwaysComputeMeans){\r\n yhat = EigX * oge->quadXinv.selfadjointView() * oge->XtVinv * Eigy;\r\n }\r\n}\r\n\r\n\r\nvoid omxDestroyGREMLExpectation(omxExpectation* ox) {\r\n\tif(OMX_DEBUG) { mxLog(\"Destroying GREML Expectation.\"); }\r\n omxGREMLExpectation* argStruct = (omxGREMLExpectation*)(ox->argStruct);\r\n ox->data = argStruct->data2;\r\n omxFreeMatrix(argStruct->means);\r\n omxFreeMatrix(argStruct->invcov);\r\n omxFreeMatrix(argStruct->logdetV_om);\r\n omxFreeMatrix(argStruct->cholV_fail_om);\r\n}\r\n\r\n\r\n/*Possible TODO: it will require some additional computation, but it is probably best to calculate the final\r\nregression coefficients using QR, which is more numerically stable*/\r\nvoid omxPopulateGREMLAttributes(omxExpectation *ox, SEXP algebra) {\r\n if(OMX_DEBUG) { mxLog(\"Populating GREML expectation attributes.\"); }\r\n\r\n omxGREMLExpectation* oge = (omxGREMLExpectation*) (ox->argStruct);\r\n \r\n Rf_setAttrib(algebra, Rf_install(\"numStats\"), Rf_ScalarReal(oge->y->dataMat->cols));\r\n Rf_setAttrib(algebra, Rf_install(\"numFixEff\"), Rf_ScalarInteger(oge->X->cols));\r\n \r\n Eigen::Map< Eigen::MatrixXd > Eigy(omxMatrixDataColumnMajor(oge->y->dataMat), oge->y->dataMat->cols, 1);\r\n SEXP b_ext, bcov_ext, yXcolnames;\r\n oge->quadXinv = oge->quadXinv.selfadjointView();\r\n Eigen::MatrixXd GREML_b = oge->quadXinv * oge->XtVinv * Eigy;\r\n \r\n {\r\n ScopedProtect p1(b_ext, Rf_allocMatrix(REALSXP, GREML_b.rows(), 1));\r\n for(int row = 0; row < GREML_b.rows(); row++){\r\n REAL(b_ext)[0 * GREML_b.rows() + row] = GREML_b(row,0);\r\n }\r\n Rf_setAttrib(algebra, Rf_install(\"b\"), b_ext);\r\n }\r\n \r\n {\r\n ScopedProtect p1(bcov_ext, Rf_allocMatrix(REALSXP, oge->quadXinv.rows(), \r\n \toge->quadXinv.cols()));\r\n for(int row = 0; row < oge->quadXinv.rows(); row++){\r\n for(int col = 0; col < oge->quadXinv.cols(); col++){\r\n REAL(bcov_ext)[col * oge->quadXinv.rows() + row] = oge->quadXinv(row,col);\r\n }} \r\n Rf_setAttrib(algebra, Rf_install(\"bcov\"), bcov_ext);\r\n }\r\n \r\n //yXcolnames:\r\n {\r\n ScopedProtect p1(yXcolnames, Rf_allocVector(STRSXP, oge->yXcolnames.size()));\r\n for(int i=0; i < (int)(oge->yXcolnames.size()); i++){\r\n SET_STRING_ELT(yXcolnames, i, Rf_mkChar(oge->yXcolnames[i]));\r\n }\r\n Rf_setAttrib(algebra, Rf_install(\"yXcolnames\"), yXcolnames);\r\n }\r\n \r\n}\r\n\r\nomxMatrix* omxGetGREMLExpectationComponent(omxExpectation* ox, omxFitFunction* off, const char* component){\r\n/* Return appropriate parts of Expectation to the Fit Function */\r\n if(OMX_DEBUG) { mxLog(\"GREML expectation: %s requested--\", component); }\r\n\r\n\tomxGREMLExpectation* oge = (omxGREMLExpectation*)(ox->argStruct);\r\n\tomxMatrix* retval = NULL;\r\n\r\n\t\r\n if(strEQ(\"y\", component)) {\r\n retval = oge->y->dataMat;\r\n\t}\r\n else if(strEQ(\"invcov\", component)) {\r\n retval = oge->invcov;\r\n }\r\n else if(strEQ(\"means\", component)) {\r\n \tretval = oge->means;\r\n }\r\n else if(strEQ(\"cholV_fail_om\", component)){\r\n retval = oge->cholV_fail_om;\r\n }\r\n else if(strEQ(\"logdetV_om\", component)){\r\n retval = oge->logdetV_om;\r\n }\r\n else if(strEQ(\"cov\", component)) {\r\n\t\tretval = oge->cov;\r\n\t} \r\n else if(strEQ(\"X\", component)) {\r\n\t\tretval = oge->X;\r\n\t} \r\n \r\n\tif (retval) omxRecompute(retval, NULL);\r\n\t\r\n\treturn retval;\r\n}\r\n\r\n\r\n\r\nstatic double omxAliasedMatrixElement(omxMatrix *om, int row, int col)\r\n{\r\n int index = 0;\r\n if(row >= om->originalRows || col >= om->originalCols) {\r\n \tchar *errstr = (char*) calloc(250, sizeof(char));\r\n\t\tsprintf(errstr, \"Requested improper value (%d, %d) from (%d, %d) matrix.\", \r\n\t\t\trow + 1, col + 1, om->originalRows, om->originalCols);\r\n\t\tRf_error(errstr);\r\n\t\tfree(errstr); // TODO not reached\r\n return (NA_REAL);\r\n\t}\r\n\tif(om->colMajor) {\r\n\t\tindex = col * om->originalRows + row;\r\n\t} else {\r\n\t\tindex = row * om->originalCols + col;\r\n\t}\r\n\treturn om->data[index];\r\n}\r\n\r\n\r\n\r\nvoid dropCasesAndEigenize(omxMatrix* om, Eigen::MatrixXd &em, int num2drop, std::vector< int > todrop,\r\n\tint symmetric){\r\n \r\n if(OMX_DEBUG) { mxLog(\"Trimming out cases with missing data...\"); }\r\n \r\n if(num2drop < 1){ return; }\r\n \r\n omxEnsureColumnMajor(om);\r\n \r\n if(om->algebra == NULL){ //i.e., if omxMatrix is from a frontend MxMatrix\r\n \r\n em.setZero(om->rows - num2drop, om->cols - num2drop);\r\n \r\n int nextCol = 0;\r\n int nextRow = 0;\r\n \r\n for(int j = 0; j < om->cols; j++) {\r\n \t if(todrop[j]) continue;\r\n \t\tnextRow = (symmetric ? nextCol : 0);\r\n \t\tfor(int k = (symmetric ? j : 0); k < om->rows; k++) {\r\n \t\t\tif(todrop[k]) continue;\r\n \t\t\tem(nextRow,nextCol) = omxAliasedMatrixElement(om, k, j);\r\n \t\t\tnextRow++;\r\n \t\t}\r\n \t\tnextCol++;\r\n \t}\r\n }\r\n else{ /*If the omxMatrix is from an algebra, then copying is not necessary; it can be resized directly\r\n and Eigen-mapped, since the algebra will be recalculated back to its original dimensions anyhow.*/\r\n if(om->originalRows == 0 || om->originalCols == 0) Rf_error(\"Not allocated\");\r\n if (om->rows != om->originalRows || om->cols != om->originalCols) {\r\n // Feasible, but the code is currently not robust to this case\r\n Rf_error(\"Can only omxRemoveRowsAndColumns once\");\r\n }\r\n \r\n int oldRows = om->originalRows;\r\n int oldCols = om->originalCols;\r\n \r\n int nextCol = 0;\r\n int nextRow = 0;\r\n \r\n om->rows = oldRows - num2drop;\r\n om->cols = oldCols - num2drop;\r\n \r\n for(int j = 0; j < oldCols; j++) {\r\n if(todrop[j]) continue;\r\n nextRow = (symmetric ? nextCol : 0);\r\n for(int k = (symmetric ? j : 0); k < oldRows; k++) {\r\n if(todrop[k]) continue;\r\n omxSetMatrixElement(om, nextRow, nextCol, omxAliasedMatrixElement(om, k, j));\r\n nextRow++;\r\n }\r\n nextCol++;\r\n }\r\n em = Eigen::Map< Eigen::MatrixXd >(om->data, om->rows, om->cols);\r\n omxMarkDirty(om); //<--Need to mark it dirty so that it gets recalculated back to original dimensions.\r\n }\r\n if(OMX_DEBUG) { mxLog(\"Finished trimming out cases with missing data...\"); }\r\n}\r\n", "meta": {"hexsha": "6eef6f4fbb2dec1558f0ef004bce5ecf096425c8", "size": 15080, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/omxGREMLExpectation.cpp", "max_stars_repo_name": "JuKa87/OpenMx", "max_stars_repo_head_hexsha": "f055df183ca433abd194e494a433142825666128", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/omxGREMLExpectation.cpp", "max_issues_repo_name": "JuKa87/OpenMx", "max_issues_repo_head_hexsha": "f055df183ca433abd194e494a433142825666128", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/omxGREMLExpectation.cpp", "max_forks_repo_name": "JuKa87/OpenMx", "max_forks_repo_head_hexsha": "f055df183ca433abd194e494a433142825666128", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.8659793814, "max_line_length": 144, "alphanum_fraction": 0.6561007958, "num_tokens": 4810, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.2329014738176742}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nEigen::MatrixXf V;\nEigen::MatrixXi F;\nEigen::MatrixXf FN;\nEigen::MatrixXf FV;\n\nint* E2E;\nglm::vec3 *pts1, *pts2;\nstd::pair *pframes;\nint *face_indices1, *face_indices2, *group_indices;\nglm::vec2* group_tex;\n\nint* find_queue;\nglm::vec3 *ux_queue, *uy_queue;\nglm::vec2 *coord_queue;\nint* neighbor_hash;\nglm::vec3 *ux_hash, *uy_hash;\nglm::vec2 *coord_hash;\n\nglm::vec3 *cudaV;\nglm::ivec3 *cudaF;\nglm::vec3 *cudaFV;\nglm::vec3 *cudaFN;\nint *cudaE2E;\nint num_v, num_f;\n\nglm::vec3 *p8192, *p1024, *p256, *p64, *p16;\nglm::ivec3 *i1024, *i256, *i64, *i16;\n\nFILE* fps;\n\nextern \"C\" {\n\n\ndouble GetTickCount(void) \n{\n struct timespec now;\n if (clock_gettime(CLOCK_MONOTONIC, &now))\n return 0;\n return now.tv_sec * 1000.0 + now.tv_nsec / 1000000.0;\n}\n\nvoid ReadArray(void* indatav, const char* filename, int size, int stride) {\n\tFILE* fp = fopen(filename, \"rb\");\n\tfread(indatav, stride, size, fp);\n\tfclose(fp);\n}\n\nvoid InitializeE2E(void* vertices, void* faces, int num_v, int num_f, void* pE2E) {\n\tint* iE2E = (int*)pE2E;\n\tV = Eigen::Map((float*)vertices, 3, num_v);\n\tF = Eigen::Map((int*)faces, 3, num_f);\n\t//E2E = Eigen::VectorXi(num_f * 3);\n\tstd::map, int > dedgeid;\n\tfor (int i = 0; i < num_f; ++i) {\n\t\tfor (int j = 0; j < 3; ++j) {\n\t\t\tiE2E[i * 3 + j] = -1;\n\t\t\tint x = F(j, i);\n\t\t\tint y = F((j + 1) % 3, i);\n\t\t\tauto key = std::make_pair(x, y);\n\t\t\tauto reverse_key = std::make_pair(y, x);\n\t\t\tif (dedgeid.count(reverse_key)) {\n\t\t\t\tint deid = dedgeid[reverse_key];\n\t\t\t\tiE2E[i * 3 + j] = deid;\n\t\t\t\tiE2E[deid] = i * 3 + j;\n\t\t\t\tdedgeid.erase(reverse_key);\n\t\t\t} else {\n\t\t\t\tdedgeid[key] = i * 3 + j;\n\t\t\t}\n\t\t}\n\t}\n}\n\nvoid InitializeMesh(void* vertices, void* faces, void* faceV, void* faceN, void* iE2E, int _num_v, int _num_f) {\n\tnum_v = _num_v;\n\tnum_f = _num_f;\n\n\tV = Eigen::Map((float*)vertices, 3, num_v);\n\tF = Eigen::Map((int*)faces, 3, num_f);\n\tFV = Eigen::Map((float*)faceV, 3, num_f);\n\tFN = Eigen::Map((float*)faceN, 3, num_f);\n\tE2E = (int*)iE2E;\n}\nvoid ReadBaryCentry(void* bary_coords, void* bary_indices, const char* filename) {\n\tFILE* fp = fopen(filename, \"rb\");\n\tint num_C, num_I;\n\tfread(&num_C, sizeof(int), 1, fp);\n\tfread(&num_I, sizeof(int), 1, fp);\n\tfread(bary_coords, sizeof(float), 3 * num_C, fp);\n\tfread(bary_indices, sizeof(int), num_I, fp);\n\tfclose(fp);\n}\n\nvoid FurthestSampling(void* pointcloud, void* inds, int num_input, int num_indices) {\n\tint* indices = (int*)inds;\n\tfloat* pts = (float*)pointcloud;\n\tstd::vector distance(num_input, 1e30);\n\tfor (int i = 0; i < num_indices; ++i) {\n\t\tif (i == 0) {\n\t\t\tindices[i] = rand() % num_input;\n\t\t\tcontinue;\n\t\t}\n\t\tint offset_p = indices[i - 1] * 3;\n\t\tfloat px = pts[offset_p];\n\t\tfloat py = pts[offset_p + 1];\n\t\tfloat pz = pts[offset_p + 2];\n\t\tfor (int j = 0; j < num_input; ++j) {\n\t\t\tint offset_j = j * 3;\n\t\t\tfloat x = pts[offset_j] - px;\n\t\t\tfloat y = pts[offset_j + 1] - py;\n\t\t\tfloat z = pts[offset_j + 2] - pz;\n\t\t\tfloat dis = x * x + y * y + z * z;\n\t\t\tif (dis < distance[j]) {\n\t\t\t\tdistance[j] = dis;\n\t\t\t}\n\t\t}\n\n\t\tint max_index = 0;\n\t\tfor(int j = 0; j < num_input; ++j)\n\t\t{\n\t\t\tif(distance[j] > distance[max_index])\n\t\t\t{\n\t\t\t\tmax_index = j;\n\t\t\t}\n\t\t}\n\t\tindices[i] = max_index;\n\t}\n}\n\nvoid ComputeFacesInfo(void* fv, void* fn, void* v, void* f, int num_v, int num_f) {\n\tV = Eigen::Map((float*)v, 3, num_v);\n\tF = Eigen::Map((int*)f, 3, num_f);\n#pragma omp parallel for\n\tfor (int i = 0; i < num_f; ++i) {\n\t\tfloat* fv_f = ((float*)fv) + 3 * i;\n\t\tfloat* fn_f = ((float*)fn) + 3 * i;\n\t\tEigen::Vector3f FV = (V.col(F(0, i)) + V.col(F(1, i)) + V.col(F(2, i))) / 3.0f;\n\t\tEigen::Vector3f FN = (Eigen::Vector3f(V.col(F(1, i)) - V.col(F(0, i))).cross(Eigen::Vector3f(V.col(F(2, i)) - V.col(F(0, i))))).normalized();\n\t\tfv_f[0] = FV[0];\n\t\tfv_f[1] = FV[1];\n\t\tfv_f[2] = FV[2];\n\t\tfn_f[0] = FN[0];\n\t\tfn_f[1] = FN[1];\n\t\tfn_f[2] = FN[2];\n\t}\n}\n\ninline Eigen::Vector3d rotate_vector_into_plane(Eigen::Vector3d q, const Eigen::Vector3d &source_normal,\n const Eigen::Vector3d &target_normal) {\n const double cosTheta = source_normal.dot(target_normal);\n if (cosTheta < 0.9999f) {\n if (cosTheta < -0.9999f) return -q;\n Eigen::Vector3d axis = source_normal.cross(target_normal);\n q = q * cosTheta + axis.cross(q) +\n axis * (axis.dot(q) * (1.0 - cosTheta) / axis.dot(axis));\n }\n return q;\n}\n\n\ninline Eigen::Vector3d Travel(Eigen::Vector3d p, const Eigen::Vector3d& dir, double len, int f, int* E2E, const Eigen::MatrixXf& V, const Eigen::MatrixXi& F, const Eigen::MatrixXf& NF, const double* triangle_space) {\n\tEigen::Vector3f Nf = NF.col(f);\n\tEigen::Vector3d N(Nf[0], Nf[1], Nf[2]);\n\tEigen::Vector3d pt = (dir - dir.dot(N) * N).normalized();\n\n\tint prev_id = -1;\n\tint count = 0;\n\n\twhile (len > 0) {\n\t\tcount += 1;\n\t\t//printf(\"%d %f\\n\", count, len);\n\t\tEigen::Vector3f t0f = V.col(F(0, f));\n\t\tEigen::Vector3f t1f = V.col(F(1, f));\n\t\tEigen::Vector3f t2f = V.col(F(2, f));\n\t\tEigen::Vector3d t0(t0f[0], t0f[1], t0f[2]);\n\t\tEigen::Vector3d t1(t1f[0], t1f[1], t1f[2]);\n\t\tEigen::Vector3d t2(t2f[0], t2f[1], t2f[2]);\n\t\tt1 -= t0;\n\t\tt2 -= t0;\n\n\t\tint edge_id = f * 3;\n\t\tdouble max_len = 1e30;\n\t\tbool found = false;\n\t\tint next_id, next_f;\n\t\tEigen::Vector3d next_q;\n\n\t\tdouble* triangle_space_f = ((double*)triangle_space) + f * 6;\n\t\tEigen::Matrix T = Eigen::Map >(triangle_space_f);\n\n\t\t//const Eigen::MatrixXd& T = triangle_space[f];\n\t\tEigen::VectorXd coord = T * (p - t0);\n\t\tEigen::VectorXd dirs = (T * pt);\n\n\t\tdouble lens[3];\n\t\tlens[0] = -coord.y() / dirs.y();\n\t\tlens[1] = (1 - coord.x() - coord.y()) / (dirs.x() + dirs.y());\n\t\tlens[2] = -coord.x() / dirs.x();\n\n\t\tfor (int fid = 0; fid < 3; ++fid) {\n\t\t\tif (fid + edge_id == prev_id)\n\t\t\t\tcontinue;\n\n\t\t\tif (lens[fid] >= 0 && lens[fid] < max_len) {\n\t\t\t\tmax_len = lens[fid];\n\t\t\t\tnext_id = E2E[edge_id + fid];\n\t\t\t\tnext_f = next_id;\n\t\t\t\tif (next_f != -1)\n\t\t\t\t\tnext_f /= 3;\n\t\t\t\tfound = true;\n\t\t\t}\n\t\t}\n\n\t\tEigen::Vector3f Nf_f = NF.col(f);\n//\t\tprintf(\"status: %f %f %d\\n\", len, max_len, f);\n\t\tif (max_len >= len) {\n\t\t\tp = p + len * pt;\n\t\t\tlen = 0;\n\t\t\tbreak;\n\t\t}\n\t\tp = t0 + t1 * (coord.x() + dirs.x() * max_len) + t2 * (coord.y() + dirs.y() * max_len);\n\t\tif (!found) {\n\t\t\tbreak;\t\t\t\n\t\t}\n\t\tlen -= max_len;\n\t\tif (next_f == -1) {\n\t\t\tbreak;\n\t\t}\n\t\t\n\t\tEigen::Vector3f Nf_nf = NF.col(next_f);\n\t\tpt = rotate_vector_into_plane(pt, Eigen::Vector3d(Nf_f[0], Nf_f[1], Nf_f[2]), Eigen::Vector3d(Nf_nf[0], Nf_nf[1], Nf_nf[2]));\n\t\tf = next_f;\n\t\tprev_id = next_id;\n\t}\n\treturn p;\n}\n\nvoid InitializeTangentSpace(void* vertices, void* faces, void* nf, int num_v, int num_f, void* tangent_space) {\n\tV = Eigen::Map((float*)vertices, 3, num_v);\n\tF = Eigen::Map((int*)faces, 3, num_f);\n\tFN = Eigen::Map((float*)nf, 3, num_f);\n\tfor (int i = 0; i < num_f; ++i) {\n\t\tEigen::Matrix3d p, q;\n\t\tEigen::Vector3f v1 = V.col(F(1, i)) - V.col(F(0, i));\n\t\tEigen::Vector3f v2 = V.col(F(2, i)) - V.col(F(0, i));\n\t\tEigen::Vector3f v3 = FN.col(i);\n\t\tp.col(0) = Eigen::Vector3d(v1[0], v1[1], v1[2]);\n\t\tp.col(1) = Eigen::Vector3d(v2[0], v2[1], v2[2]);\n\t\tp.col(2) = Eigen::Vector3d(v3[0], v3[1], v3[2]);\n\t\tq = p.inverse();\n\t\tdouble* triangle_space_f = ((double*)tangent_space) + 6 * i;\n\t\tfor (int j = 0; j < 2; ++j) {\n\t\t\tfor (int k = 0; k < 3; ++k) {\n\t\t\t\ttriangle_space_f[k * 2 + j] = q(j, k);\n\t\t\t}\n\t\t}\n\t}\n\n}\n\nvoid GetTangentNeighbors(float radius, void* positions, void* frames, void* finds, void* triangle_space, int num_v, void* out_p) {\n#pragma omp parallel for\n\tfor (int v = 0; v < num_v; ++v) {\n\t\tfloat* p_pos = ((float*)positions) + v * 3;\n\t\tfloat* p_frame = ((float*)frames) + v * 6;\n\t\tfloat* p_normal = p_frame + 3;\n\t\tfloat* out = ((float*)out_p) + v * 27;\n\t\tEigen::Vector3d p(p_pos[0], p_pos[1], p_pos[2]);\n\t\tEigen::Vector3d orient(p_frame[0], p_frame[1], p_frame[2]);\n\t\tEigen::Vector3d normal(p_normal[0], p_normal[1], p_normal[2]);\n\t\tEigen::Vector3d orient_y = normal.cross(orient);\n\t\tfor (int i = -1; i <= 1; ++i) {\n\t\t\tfor (int j = -1; j <= 1; ++j) {\n\t\t\t\tEigen::Vector3d dir = orient * i + orient_y * j; \n\t\t\t\tdouble len = dir.norm();\n\t\t\t\tdir = dir / len;\n\t\t\t\tEigen::Vector3d np = Travel(p, dir, len * radius, ((int*)finds)[v], E2E, V, F, FN, (double*)triangle_space);\n\t\t\t\tfloat* outp = out + ((i + 1) * 3 + (j + 1)) * 3;\n\t\t\t\toutp[0] = np[0];\n\t\t\t\toutp[1] = np[1];\n\t\t\t\toutp[2] = np[2];\n\t\t\t}\n\t\t}\n\t}\n}\n\n\nstruct Coordinate {\n\tCoordinate()\n\t{}\n\tCoordinate(int f, const float* end, const float* from, const float* tex, const float* _ux) {\n\t\tfloat* n = ((float*)FN.data()) + f * 3;\n\t\tuy[0] = n[1] * _ux[2] - n[2] * _ux[1];\n\t\tuy[1] = n[2] * _ux[0] - n[0] * _ux[2];\n\t\tuy[2] = n[0] * _ux[1] - n[1] * _ux[0];\n\t\tfloat len = 1.0 / sqrt(uy[0] * uy[0] + uy[1] * uy[1] + uy[2] * uy[2]);\n\t\tuy[0] *= len;\n\t\tuy[1] *= len;\n\t\tuy[2] *= len;\n\n\t\tux[0] = uy[1] * n[2] - uy[2] * n[1];\n\t\tux[1] = uy[2] * n[0] - uy[0] * n[2];\n\t\tux[2] = uy[0] * n[1] - uy[1] * n[0];\n\n\t\tfloat dx = ux[0] * (end[0] - from[0]) + ux[1] * (end[1] - from[1]) + ux[2] * (end[2] - from[2]);\n\t\tfloat dy = uy[0] * (end[0] - from[0]) + uy[1] * (end[1] - from[1]) + uy[2] * (end[2] - from[2]);\n\t\tcoord[0] = tex[0] + dx;\n\t\tcoord[1] = tex[1] + dy;\n\t\tfind = f;\n\t}\n\tint find;\n\tfloat ux[3];\n\tfloat uy[3];\n\tfloat coord[2];\n};\nvoid GetNeighborhood(float radius,\n\tvoid* positions, void* frames, void* finds, int num_group_pts,\n\tvoid* pointcloud, void* pointcloud_finds, int num_pts,\n\tvoid* neighbor_inds, void* neighbor_tex, int num_indices)\n{\n\tfloat radius2 = radius * radius;\n\tstd::vector indices(num_pts);\n\tstd::iota(indices.begin(), indices.end(), 0);\n\tstd::random_shuffle(indices.begin(), indices.end());\n#pragma omp parallel for\n\tfor (int pt_ind = 0; pt_ind < num_group_pts; ++pt_ind) {\n\t\tEigen::MatrixXf pts = Eigen::Map((float*)pointcloud, 3, num_pts);\n\t\tEigen::Vector3f p = Eigen::Map(((float*)positions) + 3 * pt_ind);\n\t\tint find = ((int*)finds)[pt_ind];\n\n\t\tstd::unordered_map neighbor_faces;\n\t\tstd::vector q;\n\t\tq.reserve(1024);\n\t\tint front = 0;\n\t\tfloat* fv = (float*)FV.data();\n\t\t//q.push(std::make_pair(find, c));\n\t\tfloat tex[] = {0, 0};\n\t\tq.emplace_back(Coordinate(find, fv + find * 3, (float*)&p, tex, (float*)frames + 6 * pt_ind));\n\t\tneighbor_faces[find] = q.size() - 1;\n\t\twhile (front < q.size()) {\n\t\t\tauto& info = q[front];\n\t\t\tint f = info.find;\n\t\t\tint* nf = E2E + f * 3;\n\t\t\tfor (int i = 0; i < 3; ++i) {\n\t\t\t\tint next_f = *nf++;\n\t\t\t\tif (next_f == -1)\n\t\t\t\t\tcontinue;\n\t\t\t\tnext_f /= 3;\n\t\t\t\tbool flag = false;\n\t\t\t\tfor (int j = 0; j < 3; ++j) {\n\t\t\t\t\tauto diff = V.col(F(j, next_f)) - p;\n\t\t\t\t\tif (diff.squaredNorm() <= radius2 * 2) {\n\t\t\t\t\t\tflag = true;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif (flag && !neighbor_faces.count(next_f)) {\n\t\t\t\t\tq.emplace_back(Coordinate(next_f, fv + next_f * 3, fv + f * 3, info.coord, info.ux));\n\t\t\t\t\tneighbor_faces[next_f] = q.size() - 1;\n\t\t\t\t}\n\t\t\t}\n\t\t\tfront += 1;\n\t\t}\n\t\tint offset_i = rand() % indices.size();\n\t\tint offset = 0;\n\t\tint* neighbor_ind = ((int*)neighbor_inds) + num_indices * pt_ind;\n\t\tfloat* neighbor_texs = ((float*)neighbor_tex) + num_indices * pt_ind * 2;\n\t\tauto it = indices.begin() + offset_i;\n\t\tbool found = false;\n\t\tfor (int i = 0; i < num_pts; ++i) {\n\t\t\tint seed = *it++;\n\t\t\tif (it == indices.end())\n\t\t\t\tit = indices.begin();\n\t\t\tEigen::Vector3f qs = pts.col(seed) - p;\n\t\t\tif (qs.squaredNorm() <= radius2 * 2) {\n\t\t\t\tint find = ((int*)pointcloud_finds)[seed];\n\t\t\t\tauto it = neighbor_faces.find(find);\n\t\t\t\tif (it != neighbor_faces.end()) {\n\t\t\t\t\tauto diff = pts.col(seed) - FV.col(find);\n\t\t\t\t\tauto& coord = q[it->second];\n\t\t\t\t\tEigen::Vector3f ux(coord.ux[0],coord.ux[1],coord.ux[2]);\n\t\t\t\t\tEigen::Vector3f uy(coord.uy[0],coord.uy[1],coord.uy[2]);\n\t\t\t\t\tfloat x = coord.coord[0] + diff.dot(ux);\n\t\t\t\t\tfloat y = coord.coord[1] + diff.dot(uy);\n\t\t\t\t\tif (std::abs(x) <= radius && std::abs(y) <= radius) {\n\t\t\t\t\t\tif (offset == num_indices) {\n\t\t\t\t\t\t\tif (x < 1e-5 && y < 1e-5) {\n\t\t\t\t\t\t\t\tneighbor_texs[0] = x;\n\t\t\t\t\t\t\t\tneighbor_texs[1] = y;\n\t\t\t\t\t\t\t\tneighbor_ind[0] = seed;\n\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tif (x < 1e-5 && y < 1e-5) {\n\t\t\t\t\t\t\t\tfound = true;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tneighbor_texs[offset * 2] = x;\n\t\t\t\t\t\t\tneighbor_texs[offset * 2 + 1] = y;\n\t\t\t\t\t\t\tneighbor_ind[offset++] = seed;\n\t\t\t\t\t\t\tif (offset == num_indices && found)\n\t\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tfor (; offset < num_indices; ++offset) {\n\t\t\tneighbor_ind[offset] = neighbor_ind[0];\n\t\t\tneighbor_texs[offset * 2] = 1e30;\n\t\t\tneighbor_texs[offset * 2 + 1] = 1e30;\n\t\t}\n\t}\n}\n\n\n\nvoid ComputeMask(void* maskp, void* indicesp, int num) {\n\tstd::unordered_set uset;\n\tint* mask = (int*)maskp;\n\tint* indices = (int*)indicesp;\n\tfor (int i = 0; i < num; ++i) {\n\t\tint d = indices[i];\n\t\tif (uset.count(d)) {\n\t\t\tmask[i] = 0;\n\t\t} else {\n\t\t\tmask[i] = 1;\n\t\t\tuset.insert(d);\n\t\t}\n\t}\n}\n}", "meta": {"hexsha": "47080d02ada199eeddfebf45e3b3eb25be749623", "size": 13118, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "data/Neighbors/neighbor.cpp", "max_stars_repo_name": "hjwdzh/TextureNet", "max_stars_repo_head_hexsha": "f3515537909ffb4ab04694b91109b535bb5c85d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 89.0, "max_stars_repo_stars_event_min_datetime": "2019-03-30T03:59:25.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-25T05:16:51.000Z", "max_issues_repo_path": "data/Neighbors/neighbor.cpp", "max_issues_repo_name": "jtpils/TextureNet", "max_issues_repo_head_hexsha": "f3515537909ffb4ab04694b91109b535bb5c85d5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2019-06-29T11:21:11.000Z", "max_issues_repo_issues_event_max_datetime": "2019-07-12T04:09:41.000Z", "max_forks_repo_path": "data/Neighbors/neighbor.cpp", "max_forks_repo_name": "jtpils/TextureNet", "max_forks_repo_head_hexsha": "f3515537909ffb4ab04694b91109b535bb5c85d5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 12.0, "max_forks_repo_forks_event_min_datetime": "2019-04-12T01:20:44.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-12T16:10:33.000Z", "avg_line_length": 29.6787330317, "max_line_length": 216, "alphanum_fraction": 0.5801189206, "num_tokens": 4968, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.42250463481418826, "lm_q1q2_score": 0.232634163901469}} {"text": "#pragma once\n#include \n#include \"xtensor/xtensor.hpp\"\n\n\nnamespace mutex_watershed {\n\n // the datastructure to hold the mutex edges for a single cluster and all clusters\n typedef std::unordered_set MutexSet;\n typedef std::vector MutexStorage;\n\n\n template\n inline bool check_mutex(const uint32_t u, const uint32_t rv,\n UFD & ufd, const MutexStorage & mutexes) {\n // the mutex storages are symmetric, so we only need to check one of them\n const auto & mutex_u = mutexes[u];\n bool have_mutex = false;\n // we check for all representatives of mutex edges if\n // they are the same as the reperesentative of v\n for(const auto mu : mutex_u) {\n if(ufd.find_set(mu) == rv) {\n have_mutex = true;\n break;\n }\n }\n return have_mutex;\n }\n\n\n template\n inline void insert_mutex(const uint32_t u, const uint32_t v, const uint32_t rv,\n UFD & ufd, MutexStorage & mutexes) {\n\n auto & mutex_u = mutexes[u];\n // if we don't have a mutex yet, insert it\n if(mutex_u.size() == 0) {\n mutex_u.insert(v);\n }\n\n // otherwise check if v is already in the mutexes\n // and filter the mutexes in the process\n else {\n\n bool have_mutex = false;\n std::unordered_set mutex_representatives;\n\n // iterate over all current mutexes\n auto mutex_it = mutex_u.begin();\n while(mutex_it != mutex_u.end()) {\n const uint32_t rm = ufd.find_set(*mutex_it);\n\n // check if this mutex is already present in the list\n // if it is not, insert it, otherwise delete this mutex\n if(mutex_representatives.find(rm) == mutex_representatives.end()) {\n mutex_representatives.insert(rm);\n ++mutex_it; // we don't erase, so we need to increase by hand\n } else {\n mutex_it = mutex_u.erase(mutex_it);\n }\n\n // if we have not already found v as mutex, check for it\n if(!have_mutex) {\n have_mutex = rv == rm;\n }\n }\n\n // insert the v mutex if it is not present\n if(!have_mutex) {\n // std::cout << \"Inserting mutex \" << u << \" \" << v << std::endl;\n mutex_u.insert(v);\n }\n }\n }\n\n\n template\n inline void merge_mutexes(const uint32_t u, const uint32_t v,\n UFD & ufd, MutexStorage & mutexes) {\n auto & mutex_u = mutexes[u];\n auto & mutex_v = mutexes[v];\n\n // extract all representatives (which should be unique here)\n std::unordered_map mutex_reps_u;\n for(const auto mu : mutex_u) {\n mutex_reps_u[ufd.find_set(mu)] = mu;\n }\n\n std::unordered_map mutex_reps_v;\n for(const auto mv : mutex_v) {\n mutex_reps_v[ufd.find_set(mv)] = mv;\n }\n\n // merge u into v\n for(const auto mu : mutex_reps_u) {\n if(mutex_reps_v.find(mu.first) == mutex_reps_v.end()) {\n mutex_v.insert(mu.second);\n }\n }\n\n // merge v into u\n for(const auto mv : mutex_reps_v) {\n if(mutex_reps_u.find(mv.first) == mutex_reps_u.end()) {\n mutex_u.insert(mv.second);\n }\n }\n\n }\n\n\n template\n void compute_mws_clustering(const size_t number_of_labels,\n const xt::xexpression & uvs_exp,\n const xt::xexpression & mutex_uvs_exp,\n const xt::xexpression & weights_exp,\n const xt::xexpression & mutex_weights_exp,\n xt::xexpression & node_labeling_exp) {\n\n // casts\n const auto & uvs = uvs_exp.derived_cast();\n const auto & mutex_uvs = mutex_uvs_exp.derived_cast();\n const auto & weights = weights_exp.derived_cast();\n const auto & mutex_weights = mutex_weights_exp.derived_cast();\n auto & node_labeling = node_labeling_exp.derived_cast();\n\n // make ufd\n std::vector ranks(number_of_labels);\n std::vector parents(number_of_labels);\n boost::disjoint_sets ufd(&ranks[0], &parents[0]);\n for(uint64_t label = 0; label < number_of_labels; ++label) {\n ufd.make_set(label);\n }\n\n // determine number of edge types\n const size_t num_edges = uvs.shape()[0];\n const size_t num_mutex = mutex_uvs.shape()[0];\n\n // argsort ALL edges\n // we sort in ascending order\n std::vector indices(num_edges + num_mutex);\n std::iota(indices.begin(), indices.end(), 0);\n std::sort(indices.begin(), indices.end(), [&](const size_t a, const size_t b){\n const double val_a = (a < num_edges) ? weights(a) : mutex_weights(a - num_edges);\n const double val_b = (b < num_edges) ? weights(b) : mutex_weights(b - num_edges);\n return val_a < val_b;\n });\n\n MutexStorage mutexes(number_of_labels);\n\n // iterate over all edges\n for(const size_t edge_id : indices) {\n\n // check whether this edge is mutex via the edge offset\n const bool is_mutex = edge_id >= num_edges;\n\n if(is_mutex) {\n // find the mutex id and the connected nodes\n const size_t mutex_id = edge_id - num_edges;\n const uint32_t u = mutex_uvs(mutex_id, 0);\n const uint32_t v = mutex_uvs(mutex_id, 1);\n\n // find the current representatives\n const uint32_t ru = ufd.find_set(u);\n const uint32_t rv = ufd.find_set(v);\n\n // if the nodes are already connected, do nothing\n if(ru == rv) {\n continue;\n }\n\n // otherwise, insert the mutex\n insert_mutex(u, v, rv, ufd, mutexes);\n insert_mutex(v, u, ru, ufd, mutexes);\n\n } else {\n\n // find the connected nodes\n const uint32_t u = uvs(edge_id, 0);\n const uint32_t v = uvs(edge_id, 1);\n\n // find the current representatives\n const uint32_t ru = ufd.find_set(u);\n const uint32_t rv = ufd.find_set(v);\n\n // if the nodes are already connected, do nothing\n if(ru == rv) {\n continue;\n }\n\n // otherwise, check if we have an active constraint / mutex edge\n const bool have_mutex = check_mutex(u, rv, ufd, mutexes) || check_mutex(v, ru, ufd, mutexes);\n //const bool have_mutex = check_mutex_edge(u, v, mutexes);\n\n // only merge if we don't have a mutex\n if(!have_mutex) {\n ufd.link(u, v);\n merge_mutexes(u, v, ufd, mutexes);\n }\n\n }\n }\n\n // get node labeling into output\n for(size_t label = 0; label < number_of_labels; ++label) {\n node_labeling[label] = ufd.find_set(label);\n }\n }\n}\n", "meta": {"hexsha": "4d3164a21f5697b3aae55df8760a8507882c624b", "size": 7610, "ext": "hxx", "lang": "C++", "max_stars_repo_path": "include/mutex_watershed/mutex_watershed.hxx", "max_stars_repo_name": "constantinpape/mutex-watershed", "max_stars_repo_head_hexsha": "1316e98c43511eeded30491f83235bd78b32ce4c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-01-19T03:05:43.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-19T03:05:43.000Z", "max_issues_repo_path": "include/mutex_watershed/mutex_watershed.hxx", "max_issues_repo_name": "constantinpape/mutex-watershed", "max_issues_repo_head_hexsha": "1316e98c43511eeded30491f83235bd78b32ce4c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2018-09-19T05:15:15.000Z", "max_issues_repo_issues_event_max_datetime": "2018-09-19T07:45:01.000Z", "max_forks_repo_path": "include/mutex_watershed/mutex_watershed.hxx", "max_forks_repo_name": "constantinpape/mutex-watershed", "max_forks_repo_head_hexsha": "1316e98c43511eeded30491f83235bd78b32ce4c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2018-09-21T21:01:25.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-21T21:01:25.000Z", "avg_line_length": 36.4114832536, "max_line_length": 109, "alphanum_fraction": 0.5408672799, "num_tokens": 1725, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.550607350786733, "lm_q2_score": 0.42250463481418826, "lm_q1q2_score": 0.23263415767015627}} {"text": "/* Author: Mincheul Kang */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#define foreach BOOST_FOREACH\n\nnamespace ompl {\n namespace magic {\n/** \\brief The number of nearest neighbors to consider by\n default in the construction of the PRM roadmap */\n static const unsigned int DEFAULT_NEAREST_NEIGHBORS_LAZY = 5;\n\n/** \\brief When optimizing solutions with lazy planners, this is the minimum\n number of path segments to add before attempting a new optimized solution\n extraction */\n static const unsigned int MIN_ADDED_SEGMENTS_FOR_LAZY_OPTIMIZATION = 5;\n }\n}\n\nompl::geometric::HarmoniousLazyPRMstarMulti::HarmoniousLazyPRMstarMulti(const base::SpaceInformationPtr &si,\n base::HarmoniousSampler &hs,\n const std::vector &isContinuous,\n bool starStrategy) :\n base::Planner(si, \"HarmoniousLazyPRMstarMulti\"),\n starStrategy_(starStrategy),\n userSetConnectionStrategy_(false),\n maxDistance_(0.0),\n indexProperty_(boost::get(boost::vertex_index_t(), g_)),\n stateProperty_(boost::get(vertex_state_t(), g_)),\n radiusProperty_(boost::get(vertex_radius_t(), g_)),\n witnessProperty_(boost::get(vertex_witness_t(), g_)),\n costProperty_(boost::get(vertex_cost_t(), g_)),\n childrenProperty_(boost::get(vertex_children_t(), g_)),\n predecessorProperty_(boost::get(boost::vertex_predecessor_t(), g_)),\n colorProperty_(boost::get(boost::vertex_color_t(), g_)),\n weightProperty_(boost::get(boost::edge_weight_t(), g_)),\n vertexValidityProperty_(boost::get(vertex_flags_t(), g_)),\n edgeValidityProperty_(boost::get(edge_flags_t(), g_)),\n bestCost_(std::numeric_limits::quiet_NaN()),\n iterations_(0),\n increaseIterations_(0),\n BisectionCC_(true),\n rewireFactor_(1.5),\n hs_(hs),\n isContinuous_(isContinuous)\n{\n specs_.recognizedGoal = base::GOAL_SAMPLEABLE_REGION;\n specs_.approximateSolutions = false;\n specs_.optimizingPaths = true;\n\n Planner::declareParam(\"BisectionCC\", this,\n &HarmoniousLazyPRMstarMulti::setBisectionCC, std::string(\".\"));\n Planner::declareParam(\"RewireFactor\", this,\n &HarmoniousLazyPRMstarMulti::setRewireFactor, std::string(\".\"));\n\n addPlannerProgressProperty(\"iterations INTEGER\",\n std::bind(&HarmoniousLazyPRMstarMulti::getIterationCount, this));\n addPlannerProgressProperty(\"best cost REAL\",\n std::bind(&HarmoniousLazyPRMstarMulti::getBestCost, this));\n addPlannerProgressProperty(\"milestone count INTEGER\",\n std::bind(&HarmoniousLazyPRMstarMulti::getMilestoneCountString, this));\n addPlannerProgressProperty(\"edge count INTEGER\",\n std::bind(&HarmoniousLazyPRMstarMulti::getEdgeCountString, this));\n}\n\nompl::geometric::HarmoniousLazyPRMstarMulti::~HarmoniousLazyPRMstarMulti() {\n}\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::setup() {\n Planner::setup();\n tools::SelfConfig sc(si_, getName());\n sc.configurePlannerRange(maxDistance_);\n\n if (!nn_) {\n nn_.reset(tools::SelfConfig::getDefaultNearestNeighbors(this));\n nn_->setDistanceFunction(std::bind(&HarmoniousLazyPRMstarMulti::distanceFunctionHarmonious, this, std::placeholders::_1, std::placeholders::_2));\n }\n\n if (!nnB_) {\n nnB_.reset(tools::SelfConfig::getDefaultNearestNeighbors(this));\n nnB_->setDistanceFunction(std::bind(&HarmoniousLazyPRMstarMulti::distanceFunctionHarmonious, this, std::placeholders::_1, std::placeholders::_2));\n }\n\n if (!nnM_) {\n nnM_.reset(tools::SelfConfig::getDefaultNearestNeighbors(this));\n nnM_->setDistanceFunction(std::bind(&HarmoniousLazyPRMstarMulti::distanceFunctionHarmonious, this, std::placeholders::_1, std::placeholders::_2));\n }\n\n if (!connectionStrategy_) {\n// if (starStrategy_)\n// connectionStrategy_ = KStarStrategy(std::bind(&HarmoniousLazyPRMstarMulti::milestoneCount, this), nn_, si_->getStateDimension());\n// else\n// connectionStrategy_ = KBoundedStrategy(magic::DEFAULT_NEAREST_NEIGHBORS_LAZY, maxDistance_, nn_);\n }\n\n double dimDbl = static_cast(si_->getStateDimension());\n // double prunedMeasure_ = si_->getSpaceMeasure();\n\n k_rrgConstant_ = rewireFactor_ * boost::math::constants::e() + (boost::math::constants::e() / dimDbl);\n\n // Setup optimization objective\n //\n // If no optimization objective was specified, then default to\n // optimizing path length as computed by the distance() function\n // in the state space.\n if (pdef_) {\n if (pdef_->hasOptimizationObjective()) {\n opt_ = pdef_->getOptimizationObjective();\n }\n else {\n opt_.reset(new base::PathLengthOptimizationObjective(si_));\n\n if (!starStrategy_) {\n opt_->setCostThreshold(opt_->infiniteCost());\n }\n }\n }\n else {\n OMPL_ERROR(\"%s: problem definition is not set, deferring setup completion...\\n\", getName().c_str());\n setup_ = false;\n }\n sampler_ = si_->allocStateSampler();\n}\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::setRange(double distance) {\n maxDistance_ = distance;\n\n if (!userSetConnectionStrategy_) {\n connectionStrategy_.clear();\n }\n\n if (isSetup()) {\n setup();\n }\n}\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::setMaxNearestNeighbors(unsigned int k) {\n if (starStrategy_) {\n throw Exception(\"Cannot set the maximum nearest neighbors for \" + getName());\n }\n\n if (!nn_) {\n nn_.reset(tools::SelfConfig::getDefaultNearestNeighbors(this));\n nn_->setDistanceFunction(std::bind(&HarmoniousLazyPRMstarMulti::distanceFunctionHarmonious, this,std::placeholders::_1, std::placeholders::_2));\n }\n\n if (!nnB_) {\n nnB_.reset(tools::SelfConfig::getDefaultNearestNeighbors(this));\n nnB_->setDistanceFunction(std::bind(&HarmoniousLazyPRMstarMulti::distanceFunctionHarmonious, this, std::placeholders::_1, std::placeholders::_2));\n }\n\n if (!nnM_) {\n nnM_.reset(tools::SelfConfig::getDefaultNearestNeighbors(this));\n nnM_->setDistanceFunction(std::bind(&HarmoniousLazyPRMstarMulti::distanceFunctionHarmonious, this, std::placeholders::_1, std::placeholders::_2));\n }\n\n if (!userSetConnectionStrategy_) {\n connectionStrategy_.clear();\n }\n\n if (isSetup()) {\n setup();\n }\n}\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::setProblemDefinition(const base::ProblemDefinitionPtr &pdef) {\n Planner::setProblemDefinition(pdef);\n clearQuery();\n}\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::clearQuery() {\n startM_.clear();\n goalM_.clear();\n pis_.restart();\n}\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::clear() {\n Planner::clear();\n freeMemory();\n\n if (nn_) {\n nn_->clear();\n }\n\n if (nnB_) {\n nnB_->clear();\n }\n\n if (nnM_) {\n nnM_->clear();\n }\n\n clearQuery();\n\n iterations_ = 0;\n bestCost_ = base::Cost(std::numeric_limits::quiet_NaN());\n}\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::freeMemory() {\n foreach (Vertex v, boost::vertices(g_)) {\n si_->freeState(stateProperty_[v]);\n }\n\n g_.clear();\n}\n\n// Add newly sampled vertex and its adjancency edges connected to neigh neighbors.\nompl::geometric::HarmoniousLazyPRMstarMulti::Vertex ompl::geometric::HarmoniousLazyPRMstarMulti::addMilestone(base::State *state, bool isM, bool isChecked) {\n Vertex m = boost::add_vertex(g_);\n stateProperty_[m] = state;\n radiusProperty_[m] = std::numeric_limits::infinity();\n costProperty_[m] = std::numeric_limits::infinity();\n childrenProperty_[m] = new std::vector();\n witnessProperty_[m] = NULL;\n predecessorProperty_[m] = NULL;\n colorProperty_[m] = 0;\n vertexValidityProperty_[m] = (isChecked) ? VALIDITY_TRUE : VALIDITY_UNKNOWN;\n\n std::vector neighbors;\n std::vector neighbors_costs;\n\n if(isM){\n // manipulation regions\n unsigned long int bSize = nnB_->size();\n unsigned int max_number_of_neighbors = std::ceil(k_rrgConstant_ * log(static_cast(milestoneCount()-bSize) + 1u));\n neighbors.reserve(max_number_of_neighbors);\n neighbors_costs.reserve(max_number_of_neighbors);\n\n nnM_->nearestK(m, max_number_of_neighbors, neighbors);\n\n foreach (Vertex n, neighbors) {\n const double weight = distanceFunctionHarmonious(n, m);\n\n ompl::base::Cost cost_weight(weight);\n const Graph::edge_property_type properties(cost_weight);\n\n neighborProperty_[m].push_back(type_neighbor(n, weight));\n neighborProperty_[n].push_back(type_neighbor(m, weight));\n\n // If collision-free or optimized well,\n const Edge &e = boost::add_edge(n, m, properties, g_).first;\n edgeValidityProperty_[e] = VALIDITY_UNKNOWN;\n }\n\n // base regions\n max_number_of_neighbors = std::ceil(k_rrgConstant_ * log(static_cast(bSize) + 1u));\n\n neighbors.reserve(max_number_of_neighbors);\n neighbors_costs.reserve(max_number_of_neighbors);\n\n nnB_->nearestK(m, max_number_of_neighbors, neighbors);\n\n foreach (Vertex n, neighbors) {\n const double weight = distanceFunctionHarmonious(n, m);\n\n ompl::base::Cost cost_weight(weight);\n const Graph::edge_property_type properties(cost_weight);\n\n neighborProperty_[m].push_back(type_neighbor(n, weight));\n neighborProperty_[n].push_back(type_neighbor(m, weight));\n\n // If collision-free or optimized well,\n const Edge &e = boost::add_edge(n, m, properties, g_).first;\n edgeValidityProperty_[e] = VALIDITY_UNKNOWN;\n }\n\n nnM_->add(m);\n }\n else{\n unsigned int max_number_of_neighbors = std::ceil(k_rrgConstant_ * log(static_cast(milestoneCount()) + 1u));\n\n neighbors.reserve(max_number_of_neighbors);\n neighbors_costs.reserve(max_number_of_neighbors);\n\n nn_->nearestK(m, max_number_of_neighbors, neighbors);\n\n foreach (Vertex n, neighbors) {\n const double weight = distanceFunctionHarmonious(n, m);\n\n ompl::base::Cost cost_weight(weight);\n const Graph::edge_property_type properties(cost_weight);\n\n neighborProperty_[m].push_back(type_neighbor(n, weight));\n neighborProperty_[n].push_back(type_neighbor(m, weight));\n\n // If collision-free or optimized well,\n const Edge &e = boost::add_edge(n, m, properties, g_).first;\n edgeValidityProperty_[e] = VALIDITY_UNKNOWN;\n }\n\n nnB_->add(m);\n }\n\n nn_->add(m);\n\n return m;\n}\n\nompl::base::PlannerStatus ompl::geometric::HarmoniousLazyPRMstarMulti::solve(const base::PlannerTerminationCondition &ptc) {\n // Initial checkup for start/goal configurations.\n checkValidity();\n\n // Add the valid start states as milestones\n while (const base::State *st = pis_.nextStart()) {\n Vertex st_vert = addMilestone(si_->cloneState(st), false);\n costProperty_[st_vert] = 0.0; // Initialize with 0 cost.\n startM_.push_back(st_vert);\n }\n\n if (startM_.size() == 0) {\n OMPL_ERROR(\"error-%s: There are no valid initial states!\", getName().c_str());\n return base::PlannerStatus::INVALID_START;\n }\n\n unsigned long int nrStartStates = boost::num_vertices(g_);\n OMPL_INFORM(\"%s: Starting planning with %lu states already in datastructure\", getName().c_str(), nrStartStates);\n\n bestCost_ = opt_->infiniteCost();\n base::State *workState = si_->allocState();\n bool fullyOptimized = false;\n base::PathPtr bestSolution;\n\n base::RealVectorStateSpace::StateType *rstate = static_cast(workState);\n\n Vertex startV = startM_[0];\n\n do{\n if(hs_.makeGoal(si_, workState)){\n goalM_.push_back(addMilestone(si_->cloneState(workState), true));\n }\n } while(goalM_.size() == 0 && ptc == false);\n\n bool isM = false;\n while (ptc == false) {\n ++iterations_;\n\n int r_val = std::rand() % 100;\n if(r_val < hs_.params_.prob_goal_ && goalM_.size() < hs_.params_.max_num_goal_){\n if(hs_.makeGoal(si_, workState)){\n goalM_.push_back(addMilestone(si_->cloneState(workState), true));\n }\n else{\n continue;\n }\n }\n else if(r_val < hs_.params_.prob_uniform_){\n hs_.uniformSampling(workState);\n if(hs_.getReachable(workState)){\n isM = true;\n }\n else{\n isM = false;\n }\n }\n else{\n hs_.biasedSampling(workState);\n\n if(hs_.getReachable(workState)){\n hs_.jointSampling(workState);\n isM = true;\n }\n else{\n hs_.setBasicPose(workState);\n isM = false;\n }\n }\n\n if (!si_->isValid(workState)) {\n continue;\n }\n\n // Add collision-free vertices.\n Vertex addedVertex = addMilestone(si_->cloneState(workState), isM);\n\n // DSPT update.\n Decrease(addedVertex);\n\n // Only support a single pair of start and goal node.\n base::PathPtr solution;\n\n for (int i = 0; i < (int)goalM_.size(); i++) {\n Vertex goalV = goalM_[i];\n do {\n if (predecessorProperty_[goalV] == NULL || bestCost_.value() <= costProperty_[goalV])\n break;\n solution = constructSolution(startV, goalV);\n } while (!solution);\n\n if (solution) {\n base::Cost c(costProperty_[goalV]);\n\n if (opt_->isCostBetterThan(c, bestCost_)) {\n bestSolution = solution;\n\n bestCost_ = c;\n }\n }\n }\n }\n if (goalM_.empty()) {\n OMPL_ERROR(\"%s: Unable to find any valid goal states\", getName().c_str());\n return base::PlannerStatus::INVALID_GOAL;\n }\n\n si_->freeState(workState);\n\n if (bestSolution) {\n base::PlannerSolution psol(bestSolution);\n psol.setPlannerName(getName());\n // If the solution was optimized, we mark it as such\n psol.setOptimized(opt_, bestCost_, fullyOptimized);\n pdef_->addSolutionPath(psol);\n }\n\n OMPL_INFORM(\"%s: Created %lu vertices and %lu edges.\", getName().c_str(), boost::num_vertices(g_) - 1, boost::num_edges(g_));\n OMPL_INFORM(\"%s: Goalstates %lu \", getName().c_str(), goalM_.size());\n OMPL_INFORM(\"Cost: %.6f\", bestCost_.value());\n\n return bestSolution ? base::PlannerStatus::EXACT_SOLUTION :\n base::PlannerStatus::TIMEOUT;\n}\n\n// outedge, inedge? - doesn't matter, need to scan all the neighbors.\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::Decrease(const Vertex &v) {\n typedef std::pair weight_vertex;\n std::priority_queue pq;\n\n // Initialize cost of v, i.e., finding best parent vertex in G(g_).\n BGL_FORALL_OUTEDGES(v, e, g_, Graph) {\n Vertex w = target(e, g_);\n double weight = weightProperty_[e].value();\n\n if (costProperty_[v] > costProperty_[w] + weight) {\n predecessorProperty_[v] = w;\n costProperty_[v] = costProperty_[w] + weight;\n }\n }\n\n // No need to invoke cancelAdoption since v is newly sampled.\n if (predecessorProperty_[v] != NULL) {\n childrenProperty_[predecessorProperty_[v]]->push_back(v);\n }\n\n // At this point, v has a best parent. From now on construct its subtree of descendants.\n\n pq.push(weight_vertex(-costProperty_[v], v)); // Invert the cost value for mimicking min-heap.\n\n while (!pq.empty()) {\n weight_vertex top = pq.top();\n pq.pop();\n\n double cost = -top.first; // Invert the cost value to be like min-heap.\n Vertex vert = top.second;\n\n if (cost > costProperty_[vert]) {\n continue;\n }\n\n BGL_FORALL_OUTEDGES(vert, e, g_, Graph) {\n Vertex w = target(e, g_);\n double weight = weightProperty_[e].value();\n double cost_w = costProperty_[w];\n\n if (cost_w > cost + weight) {\n costProperty_[w] = cost + weight;\n cancelAdoption(w);\n\n predecessorProperty_[w] = vert;\n childrenProperty_[vert]->push_back(w);\n pq.push(weight_vertex(-costProperty_[w], w));\n }\n }\n }\n\n // Now, DSPT is stable.\n}\n\n#define RED (increaseIterations_ + 1) // I know, it's bad #define.\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::Increase(const Vertex vs) {\n // reds;\n typedef std::pair weight_vertex;\n std::priority_queue pq; // Max-heap by default.\n\n pq.push(weight_vertex(-costProperty_[vs], vs)); // It works as if it is min-heap.\n\n // costProperty_[vert]) {\n continue; // Instead of heap-improve\n }\n\n // Probability to get a pink node? almost impossible!\n /*\n // If there exist a non-red neighbor q of z such that Dist(q) + w_(q, z) = D(z)\n // set pink, that means it can keep the current cost, thus it is not necessary to\n // iterate its children.\n // Otherwise, set red and enqueue all the children of z.\n bool pink_flag = false;\n BGL_FORALL_OUTEDGES(vert, e, g_, Graph) {\n Vertex w = target(e, g_);\n double weight = weightProperty_[e].value();\n\n if (colorProperty_[w] != RED && costProperty_[w] + weight == cost) {\n // Actually, '<' should not be happened all the time.\n // And even '==' would very rarely occur, but possible.\n pink_flag = true;\n\n cancelAdoption(vert);\n predecessorProperty_[vert] = w;\n childrenProperty_[w]->push_back(vert);\n break; // If there exsits, take anyone among them.\n }\n }\n\n if (pink_flag) {\n continue;\n }*/\n\n colorProperty_[vert] = RED; // Set to 'red'\n reds.push_back(vert);\n // Even with multiple starting red nodes, there will be no re-visit since each starting node is\n // a root node of sub'tree' in DSPT. That is, if statement within for loop might be useless.\n // Someone would be curious, e.g., then why do we have to use priority queue in step2 ?\n // Just for 'pink' case. Yeap. We need to identify all the other parent candidates are red or not\n // prior to checking current node.\n std::vector *children = childrenProperty_[vert];\n\n for (unsigned int i = 0; i < children->size(); i++) {\n pq.push(weight_vertex(-costProperty_[(*children)[i]], (*children)[i]));\n }\n }\n\n // 'pq' is empty at here.\n\n // ::infinity();\n cancelAdoption(reds[i]);\n\n BGL_FORALL_OUTEDGES(reds[i], e, g_, Graph) {\n Vertex w = target(e, g_);\n double weight = weightProperty_[e].value();\n\n if (colorProperty_[w] == RED) {\n continue; // If red, put aside for a while.\n }\n\n if (costProperty_[reds[i]] > costProperty_[w] + weight) {\n costProperty_[reds[i]] = costProperty_[w] + weight;\n predecessorProperty_[reds[i]] = w;\n }\n }\n\n if (predecessorProperty_[reds[i]] != NULL) {\n childrenProperty_[predecessorProperty_[reds[i]]]->push_back(reds[i]);\n }\n\n pq.push(weight_vertex(-costProperty_[reds[i]], reds[i]));\n }\n\n // push_back(w);\n pq.push(weight_vertex(-costProperty_[w], w));\n }\n }\n }\n\n // The end!\n // colorProperty_ is not necessary to be cleansed out, just increase variable, 'RED'.\n}\n\n// TODO : sync between children & edges.\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::cancelAdoption(const Vertex &child) {\n if (predecessorProperty_[child] == NULL)\n return;\n\n std::vector *children = childrenProperty_[predecessorProperty_[child]];\n\n for (unsigned int i = 0; i < children->size(); i++) if ((*children)[i] == child) {\n std::swap((*children)[i], children->back());\n children->pop_back();\n break;\n }\n\n predecessorProperty_[child] = NULL;\n}\n\n// Vertex first.\nompl::base::PathPtr ompl::geometric::HarmoniousLazyPRMstarMulti::constructSolution(const Vertex &start, const Vertex &goal) {\n std::vector solution_path;\n\n // Construct a solution from DSPT.\n for (Vertex vert = goal; vert != NULL; vert = predecessorProperty_[vert])\n solution_path.push_back(vert);\n\n if (solution_path.empty() || solution_path.size() == 1)\n return base::PathPtr();\n\n // Goal = 0, Start = n - 1.\n // TODO : From goal or start ? which one is better?\n\n // auto from = solution_path.rbegin();\n std::vector::reverse_iterator from = solution_path.rbegin();\n for (std::vector::reverse_iterator to = from + 1; to != solution_path.rend(); ++to) {\n Edge e = boost::lookup_edge(*from, *to, g_).first; // Exhaustive search O(E) at worst case.\n unsigned int &evd = edgeValidityProperty_[e];\n\n if ((evd & VALIDITY_TRUE) == 0) { // Unknown\n bool result = true;\n // double weight = weightProperty_[e].value();\n\n result &= checkMotion(stateProperty_[*from], stateProperty_[*to]);\n\n if (result) {\n evd |= VALIDITY_TRUE;\n }\n else {\n boost::remove_edge(e, g_); // O(log(E/V)) time...\n\n cancelAdoption(*to);\n Increase(*to);\n return base::PathPtr();\n }\n }\n\n from = to;\n }\n\n PathGeometric *p = new PathGeometric(si_);\n\n // Feasible path is found, fetch optimized edges if possible.\n\n for (std::vector::const_reverse_iterator sol = solution_path.rbegin(); sol != solution_path.rend(); ++sol) {\n p->append(stateProperty_[*sol]);\n }\n\n return base::PathPtr(p);\n}\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::getPlannerData(base::PlannerData &data) const {\n Planner::getPlannerData(data);\n // Caution : it handles directional information regardless of the search graph setting\n // which is undirectional graph.\n}\n\ndouble ompl::geometric::HarmoniousLazyPRMstarMulti::distanceFunction(const base::State *a, const base::State *b) const {\n return si_->distance(a, b);\n}\n\ndouble ompl::geometric::HarmoniousLazyPRMstarMulti::distanceFunctionHarmonious(const Vertex a, const Vertex b) const {\n double base = distanceFunctionBase(stateProperty_[a], stateProperty_[b]);\n double arm = distanceFunctionJoints(stateProperty_[a], stateProperty_[b]);\n\n return base + arm;\n}\n\n\ndouble ompl::geometric::HarmoniousLazyPRMstarMulti::distanceFunctionBase(const base::State *a, const base::State *b) const {\n double dist = 0.0;\n int dim = si_->getStateDimension();\n\n std::vector ca, cb;\n si_->getStateSpace()->copyToReals(ca, a);\n si_->getStateSpace()->copyToReals(cb, b);\n\n for (int i = 0; i < NUM_BASE_DOF; i++) {\n double dd = fabs(ca[i]-cb[i]);\n\n if(isContinuous_[i]) {\n if (dd > boost::math::constants::pi()) {\n dd = 2.0 * boost::math::constants::pi() - dd;\n }\n }\n\n dist += hs_.params_.weights_[i] * dd * hs_.params_.weights_[i] * dd;\n }\n\n return std::sqrt(dist);\n}\n\ndouble ompl::geometric::HarmoniousLazyPRMstarMulti::distanceFunctionJoints(const base::State *a, const base::State *b) const {\n double dist = 0.0;\n\n std::vector ca, cb;\n si_->getStateSpace()->copyToReals(ca, a);\n si_->getStateSpace()->copyToReals(cb, b);\n\n for (int i = NUM_BASE_DOF; i < ca.size(); i++) {\n double dd = fabs(ca[i]-cb[i]);\n\n if(isContinuous_[i+3]){\n if (dd > boost::math::constants::pi()) {\n dd = 2.0 * boost::math::constants::pi() - dd;\n }\n }\n\n dist += hs_.params_.weights_[i] * dd * hs_.params_.weights_[i] * dd;\n }\n\n return std::sqrt(dist);\n}\n\n\nbool ompl::geometric::HarmoniousLazyPRMstarMulti::checkMotion(base::State *s1, base::State *s2) const {\n// /* Assume motion starts/ends in a valid configuration so v1/v2 are valid */\n bool result = true;\n int dim = si_->getStateDimension();\n int nd;\n\n int nd_j = validSegmentCount_compare(distanceFunctionJoints(s1, s2), hs_.params_.factor_joint_);\n int nd_b = validSegmentCount_compare(distanceFunctionBase(s1, s2), hs_.params_.factor_base_);\n if(nd_j > nd_b){\n nd = nd_j;\n }\n else{\n nd = nd_b;\n }\n\n if (nd > 1) {\n /* Temporary storage for the checked state */\n base::State *test = si_->allocState();\n std::queue > q;\n q.push(std::make_pair(1, nd - 1));\n\n while (!q.empty()) {\n std::pair range = q.front();\n unsigned int mid;\n\n mid = (range.first + range.second) / 2;\n interpolate(s1, s2, (double)mid / (double)nd, test);\n// si_->getStateSpace()->interpolate(s1, s2, (double)mid / (double)nd, test);\n\n if (!si_->isValid(test)) {\n result = false;\n break;\n }\n\n q.pop();\n if (range.first < mid)\n q.push(std::make_pair(range.first, mid - 1));\n if (mid < range.second) {\n q.push(std::make_pair(mid + 1, range.second));\n } // if mid == first, no more recursion.\n }\n }\n\n return result;\n}\n\nunsigned int ompl::geometric::HarmoniousLazyPRMstarMulti::validSegmentCount_compare(const double dist,\n const double longestValidSegment) const {\n return si_->getStateSpace()->getValidSegmentCountFactor() * (unsigned int)ceil(dist / longestValidSegment);\n}\n\nvoid ompl::geometric::HarmoniousLazyPRMstarMulti::interpolate(const base::State *from, const base::State *to, const double t, base::State *state) const\n{\n const base::RealVectorStateSpace::StateType *rfrom = static_cast(from);\n const base::RealVectorStateSpace::StateType *rto = static_cast(to);\n const base::RealVectorStateSpace::StateType *rstate = static_cast(state);\n for (unsigned int i = 0 ; i < isContinuous_.size() ; ++i){\n if(isContinuous_[i]){\n double diff = rto->values[i] - rfrom->values[i];\n if (fabs(diff) <= boost::math::constants::pi())\n rstate->values[i] = rfrom->values[i] + diff * t;\n else\n {\n double &v = rstate->values[i];\n if (diff > 0.0)\n diff = 2.0 * boost::math::constants::pi() - diff;\n else\n diff = -2.0 * boost::math::constants::pi() - diff;\n v = rfrom->values[i] - diff * t;\n // input states are within bounds, so the following check is sufficient\n if (v > boost::math::constants::pi())\n v -= 2.0 * boost::math::constants::pi();\n else\n if (v < -boost::math::constants::pi())\n v += 2.0 * boost::math::constants::pi();\n }\n }\n else{\n rstate->values[i] = rfrom->values[i] + (rto->values[i] - rfrom->values[i]) * t;\n }\n }\n}\n", "meta": {"hexsha": "18eaf99a121145b31560a9a4a7862fc5457e880f", "size": 30511, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "harmonious_sampling/src/HarmoniousLazyPRMstarMulti.cpp", "max_stars_repo_name": "cheulkang/HarmoniousSampling", "max_stars_repo_head_hexsha": "40a5f89ab1f4a680160a61b13daba09cdf661b8b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 7.0, "max_stars_repo_stars_event_min_datetime": "2019-11-06T06:01:14.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-22T04:46:19.000Z", "max_issues_repo_path": "harmonious_sampling/src/HarmoniousLazyPRMstarMulti.cpp", "max_issues_repo_name": "cheulkang/HarmoniousSampling", "max_issues_repo_head_hexsha": "40a5f89ab1f4a680160a61b13daba09cdf661b8b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "harmonious_sampling/src/HarmoniousLazyPRMstarMulti.cpp", "max_forks_repo_name": "cheulkang/HarmoniousSampling", "max_forks_repo_head_hexsha": "40a5f89ab1f4a680160a61b13daba09cdf661b8b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2020-01-01T12:50:13.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-27T07:33:42.000Z", "avg_line_length": 36.1076923077, "max_line_length": 157, "alphanum_fraction": 0.6104028055, "num_tokens": 7408, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.531209388216861, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.23257595311142057}} {"text": "/* Copyright (c) 2010-2019, Delft University of Technology\n * All rigths reserved\n *\n * This file is part of the Tudat. Redistribution and use in source and\n * binary forms, with or without modification, are permitted exclusively\n * under the terms of the Modified BSD license. You should have received\n * a copy of the license with this file. If not, please or visit:\n * http://tudat.tudelft.nl/LICENSE.\n */\n\n#include \n\n#include \"tudat/interface/spice/spiceEphemeris.h\"\n#include \"tudat/astro/ephemerides/customEphemeris.h\"\n#include \"tudat/astro/ephemerides/keplerEphemeris.h\"\n#include \"tudat/astro/ephemerides/multiArcEphemeris.h\"\n#include \"tudat/astro/ephemerides/tabulatedEphemeris.h\"\n#include \"tudat/astro/ephemerides/approximatePlanetPositions.h\"\n#include \"tudat/astro/ephemerides/approximatePlanetPositionsCircularCoplanar.h\"\n#include \"tudat/astro/ephemerides/constantEphemeris.h\"\n#include \"tudat/math/interpolators/lagrangeInterpolator.h\"\n#include \"tudat/simulation/environment_setup/createEphemeris.h\"\n\nnamespace tudat\n{\n\nnamespace simulation_setup\n{\n\nusing namespace ephemerides;\n\n//! Function to create an ephemeris model.\nstd::shared_ptr< ephemerides::Ephemeris > createBodyEphemeris(\n const std::shared_ptr< EphemerisSettings > ephemerisSettings,\n const std::string& bodyName )\n{\n // Declare return object.\n std::shared_ptr< ephemerides::Ephemeris > ephemeris;\n\n if( ephemerisSettings->getMakeMultiArcEphemeris( ) )\n {\n std::map< double, std::shared_ptr< Ephemeris > > singleArcEphemerides;\n ephemerisSettings->resetMakeMultiArcEphemeris( false );\n\n singleArcEphemerides[ -std::numeric_limits< double >::lowest( ) ] = createBodyEphemeris(\n ephemerisSettings, bodyName );\n\n ephemeris = std::make_shared< MultiArcEphemeris >(\n singleArcEphemerides, ephemerisSettings->getFrameOrigin( ), ephemerisSettings->getFrameOrientation( ) );\n }\n else\n {\n\n // Check which type of ephemeris model is to be created.\n switch( ephemerisSettings->getEphemerisType( ) )\n {\n case direct_spice_ephemeris:\n {\n // Check consistency of type and class.\n std::shared_ptr< DirectSpiceEphemerisSettings > directEphemerisSettings =\n std::dynamic_pointer_cast< DirectSpiceEphemerisSettings >( ephemerisSettings );\n if( directEphemerisSettings == nullptr )\n {\n throw std::runtime_error(\n \"Error, expected direct spice ephemeris settings for body \" + bodyName );\n }\n else\n {\n std::string inputName = ( directEphemerisSettings->getBodyNameOverride( ) == \"\" ) ?\n bodyName : directEphemerisSettings->getBodyNameOverride( );\n\n // Create corresponding ephemeris object.\n ephemeris = std::make_shared< SpiceEphemeris >(\n inputName,\n directEphemerisSettings->getFrameOrigin( ),\n directEphemerisSettings->getCorrectForStellarAberration( ),\n directEphemerisSettings->getCorrectForLightTimeAberration( ),\n directEphemerisSettings->getConvergeLighTimeAberration( ),\n directEphemerisSettings->getFrameOrientation( ) );\n }\n break;\n }\n case interpolated_spice:\n {\n // Check consistency of type and class.\n std::shared_ptr< InterpolatedSpiceEphemerisSettings > interpolatedEphemerisSettings =\n std::dynamic_pointer_cast< InterpolatedSpiceEphemerisSettings >(\n ephemerisSettings );\n if( interpolatedEphemerisSettings == nullptr )\n {\n throw std::runtime_error(\n \"Error, expected tabulated spice ephemeris settings for body \" + bodyName );\n }\n else\n {\n // Since only the barycenters of planetary systems are included in the standard DE\n // ephemerides, append 'Barycenter' to body name.\n std::string inputName = ( interpolatedEphemerisSettings->getBodyNameOverride( ) == \"\" ) ?\n bodyName : interpolatedEphemerisSettings->getBodyNameOverride( );\n // Create corresponding ephemeris object.\n if( !interpolatedEphemerisSettings->getUseLongDoubleStates( ) )\n {\n ephemeris = createTabulatedEphemerisFromSpice< double, double >(\n inputName,\n interpolatedEphemerisSettings->getInitialTime( ),\n interpolatedEphemerisSettings->getFinalTime( ),\n interpolatedEphemerisSettings->getTimeStep( ),\n interpolatedEphemerisSettings->getFrameOrigin( ),\n interpolatedEphemerisSettings->getFrameOrientation( ),\n interpolatedEphemerisSettings->getInterpolatorSettings( ) );\n }\n else\n {\n#if( TUDAT_BUILD_WITH_EXTENDED_PRECISION_PROPAGATION_TOOLS )\n\n ephemeris = createTabulatedEphemerisFromSpice< long double, double >(\n inputName,\n static_cast< long double >( interpolatedEphemerisSettings->getInitialTime( ) ),\n static_cast< long double >( interpolatedEphemerisSettings->getFinalTime( ) ),\n static_cast< long double >( interpolatedEphemerisSettings->getTimeStep( ) ),\n interpolatedEphemerisSettings->getFrameOrigin( ),\n interpolatedEphemerisSettings->getFrameOrientation( ),\n interpolatedEphemerisSettings->getInterpolatorSettings( ) );\n#else\n throw std::runtime_error( \"Error, long double compilation is turned off; requested long double tabulated ephemeris\" );\n#endif\n }\n }\n break;\n }\n case tabulated_ephemeris:\n {\n // Check consistency of type and class.\n std::shared_ptr< TabulatedEphemerisSettings > tabulatedEphemerisSettings =\n std::dynamic_pointer_cast< TabulatedEphemerisSettings >( ephemerisSettings );\n if( tabulatedEphemerisSettings == nullptr )\n {\n throw std::runtime_error(\n \"Error, expected tabulated ephemeris settings for body \" + bodyName );\n }\n else\n {\n // Create corresponding ephemeris object.\n if( !tabulatedEphemerisSettings->getUseLongDoubleStates( ) )\n {\n if( tabulatedEphemerisSettings->getBodyStateHistory( ).size( ) != 0 )\n {\n ephemeris = std::make_shared< TabulatedCartesianEphemeris< > >(\n std::make_shared<\n interpolators::LagrangeInterpolator< double, Eigen::Vector6d > >\n ( tabulatedEphemerisSettings->getBodyStateHistory( ), 6,\n interpolators::huntingAlgorithm,\n interpolators::lagrange_cubic_spline_boundary_interpolation ),\n tabulatedEphemerisSettings->getFrameOrigin( ),\n tabulatedEphemerisSettings->getFrameOrientation( ) );\n }\n else\n {\n ephemeris = std::make_shared< TabulatedCartesianEphemeris< > >(\n std::shared_ptr< interpolators::OneDimensionalInterpolator< double, Eigen::Vector6d > >( ),\n tabulatedEphemerisSettings->getFrameOrigin( ),\n tabulatedEphemerisSettings->getFrameOrientation( ) );\n }\n }\n else\n {\n#if( TUDAT_BUILD_WITH_EXTENDED_PRECISION_PROPAGATION_TOOLS )\n\n // Cast input history to required type.\n if( tabulatedEphemerisSettings->getBodyStateHistory( ).size( ) != 0 )\n {\n std::map< double, Eigen::Vector6d > originalStateHistory =\n tabulatedEphemerisSettings->getBodyStateHistory( );\n std::map< double, Eigen::Matrix< long double, 6, 1 > > longStateHistory;\n\n for( std::map< double, Eigen::Vector6d >::const_iterator stateIterator =\n originalStateHistory.begin( ); stateIterator != originalStateHistory.end( ); stateIterator++ )\n {\n longStateHistory[ stateIterator->first ] = stateIterator->second.cast< long double >( );\n ephemeris =\n std::make_shared< TabulatedCartesianEphemeris< long double, double > >(\n std::make_shared< interpolators::LagrangeInterpolator<\n double, Eigen::Matrix< long double, 6, 1 > > >\n ( longStateHistory, 6,\n interpolators::huntingAlgorithm,\n interpolators::lagrange_cubic_spline_boundary_interpolation ),\n tabulatedEphemerisSettings->getFrameOrigin( ),\n tabulatedEphemerisSettings->getFrameOrientation( ) );\n }\n }\n else\n {\n ephemeris = std::make_shared< TabulatedCartesianEphemeris< long double, double > >(\n std::shared_ptr< interpolators::OneDimensionalInterpolator<\n double, Eigen::Matrix< long double, 6, 1 > > >( ),\n tabulatedEphemerisSettings->getFrameOrigin( ),\n tabulatedEphemerisSettings->getFrameOrientation( ) );\n }\n#else\n throw std::runtime_error( \"Error, long double compilation is turned off; requested long double tabulated ephemeris\" );\n#endif\n }\n }\n break;\n }\n case auto_generated_tabulated_ephemeris:\n {\n // Check consistency of type and class.\n std::shared_ptr< AutoGeneratedTabulatedEphemerisSettings > tabulatedEphemerisSettings =\n std::dynamic_pointer_cast< AutoGeneratedTabulatedEphemerisSettings >( ephemerisSettings );\n if( tabulatedEphemerisSettings == nullptr )\n {\n throw std::runtime_error( \"Error, expected auto-generate tabulated ephemeris settings for \" + bodyName );\n }\n else\n {\n // Create ephemeris\n ephemeris = getTabulatedEphemeris(\n createBodyEphemeris( tabulatedEphemerisSettings->getEphemerisSettings( ), bodyName ),\n tabulatedEphemerisSettings->getStartTime( ), tabulatedEphemerisSettings->getEndTime( ),\n tabulatedEphemerisSettings->getTimeStep( ), tabulatedEphemerisSettings->getInterpolatorSettings( ) );\n }\n break;\n }\n case constant_ephemeris:\n {\n // Check consistency of type and class.\n std::shared_ptr< ConstantEphemerisSettings > constantEphemerisSettings =\n std::dynamic_pointer_cast< ConstantEphemerisSettings >( ephemerisSettings );\n if( constantEphemerisSettings == nullptr )\n {\n throw std::runtime_error( \"Error, expected constant ephemeris settings for \" + bodyName );\n }\n else\n {\n // Create ephemeris\n ephemeris = std::make_shared< ConstantEphemeris >(\n [ = ]( ){ return constantEphemerisSettings->getConstantState( ); },\n constantEphemerisSettings->getFrameOrigin( ),\n constantEphemerisSettings->getFrameOrientation( ) );\n }\n break;\n }\n case custom_ephemeris:\n {\n // Check consistency of type and class.\n std::shared_ptr< CustomEphemerisSettings > customEphemerisSettings =\n std::dynamic_pointer_cast< CustomEphemerisSettings >( ephemerisSettings );\n if( customEphemerisSettings == nullptr )\n {\n throw std::runtime_error( \"Error, expected custom ephemeris settings for \" + bodyName );\n }\n else\n {\n // Create ephemeris\n ephemeris = std::make_shared< CustomEphemeris >(\n customEphemerisSettings->getCustomStateFunction( ),\n customEphemerisSettings->getFrameOrigin( ),\n customEphemerisSettings->getFrameOrientation( ) );\n }\n break;\n }\n\n case kepler_ephemeris:\n {\n // Check consistency of type and class.\n std::shared_ptr< KeplerEphemerisSettings > keplerEphemerisSettings =\n std::dynamic_pointer_cast< KeplerEphemerisSettings >( ephemerisSettings );\n if( keplerEphemerisSettings == nullptr )\n {\n throw std::runtime_error( \"Error, expected Kepler ephemeris settings for \" + bodyName );\n }\n else\n {\n // Create ephemeris\n ephemeris = std::make_shared< KeplerEphemeris >(\n keplerEphemerisSettings->getInitialStateInKeplerianElements( ),\n keplerEphemerisSettings->getEpochOfInitialState( ),\n keplerEphemerisSettings->getCentralBodyGravitationalParameter( ),\n keplerEphemerisSettings->getFrameOrigin( ),\n keplerEphemerisSettings->getFrameOrientation( ),\n keplerEphemerisSettings->getRootFinderAbsoluteTolerance( ),\n keplerEphemerisSettings->getRootFinderMaximumNumberOfIterations( ) );\n }\n break;\n }\n case approximate_planet_positions:\n {\n // Check consistency of type and class.\n std::shared_ptr< ApproximateJplEphemerisSettings > approximateEphemerisSettings =\n std::dynamic_pointer_cast< ApproximateJplEphemerisSettings >(\n ephemerisSettings );\n if( approximateEphemerisSettings == nullptr )\n {\n throw std::runtime_error(\n \"Error, expected approximate ephemeris settings for body \" + bodyName );\n }\n else\n {\n\n// ephemerides::ApproximatePlanetPositionsBase::BodiesWithEphemerisData bodyToUse;\n// if( approximateEphemerisSettings->getBodyIdentifier( ) ==\n// ephemerides::ApproximatePlanetPositionsBase::BodiesWithEphemerisData::undefined )\n// {\n// try\n// {\n// bodyToUse = ephemerides::ApproximatePlanetPositionsBase::getBodiesWithEphemerisDataId( bodyName );\n// }\n// catch( std::runtime_error const& )\n\n// {\n// throw std::runtime_error( \"Error, approximate ephemeris not available for body: \" + bodyName + \" when creating ephemeris.\" );\n// }\n// }\n// else\n// {\n// bodyToUse = approximateEphemerisSettings->getBodyIdentifier( );\n// }\n//=======\n//>>>>>>> origin/feature/mga_estimation_refactor_merge\n\n // Create corresponding ephemeris object.\n if( approximateEphemerisSettings->getUseCircularCoplanarApproximation( ) )\n {\n ephemeris = std::make_shared< ApproximateJplCircularCoplanarEphemeris >(\n approximateEphemerisSettings->getBodyName( ) );\n }\n else\n {\n ephemeris = std::make_shared< ApproximateJplEphemeris >(\n approximateEphemerisSettings->getBodyName( ) );\n }\n }\n break;\n }\n\t\tcase direct_tle_ephemeris:\n\t\t{\n\t\t\t// Check consistency of type and class.\n\t\t\tstd::shared_ptr< DirectTleEphemerisSettings > directTleEphemerisSettings =\n\t\t\t\t\tstd::dynamic_pointer_cast< DirectTleEphemerisSettings >( ephemerisSettings );\n\t\t\tif( directTleEphemerisSettings == nullptr )\n\t\t\t{\n\t\t\t\tthrow std::runtime_error(\n\t\t\t\t\t\t\"Error, expected direct TLE ephemeris settings for body \" + bodyName );\n\t\t\t}\n\t\t\t// Check if the Earth is present in the simulation\n\t\t\telse\n\t\t\t{\n\t\t\t\t//std::string inputName = bodyName;\n\n\t\t\t\t// Check period of the satellite for correct SDP setting\n\n\n\t\t\t\t// Create corresponding ephemeris object.\n\t\t\t\tephemeris = std::make_shared< TleEphemeris >(\n\t\t\t\t\t\tdirectTleEphemerisSettings->getFrameOrigin(),\n\t\t\t\t\t\tdirectTleEphemerisSettings->getFrameOrientation(),\n\t\t\t\t\t\tdirectTleEphemerisSettings->getTle()\n\t\t\t\t\t\t);\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n\t\tcase interpolated_tle_ephemeris:\n\t\t{\n\t\t\t// Check consistency of type and class.\n\t\t\tstd::shared_ptr< InterpolatedTleEphemerisSettings > interpolatedTleEphemerisSettings =\n\t\t\t\t\tstd::dynamic_pointer_cast< InterpolatedTleEphemerisSettings >(\n\t\t\t\t\t\t\tephemerisSettings );\n\t\t\tif( interpolatedTleEphemerisSettings == nullptr )\n\t\t\t{\n\t\t\t\tthrow std::runtime_error(\n\t\t\t\t\t\t\"Error, expected interpolated TLE ephemeris settings for body \" + bodyName );\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\n\t\t\t\tephemeris = createTabulatedEphemerisFromTLE< double, double >(\n\t\t\t\t\t\tbodyName,\n\t\t\t\t\t\tinterpolatedTleEphemerisSettings->getInitialTime( ),\n\t\t\t\t\t\tinterpolatedTleEphemerisSettings->getFinalTime( ),\n\t\t\t\t\t\tinterpolatedTleEphemerisSettings->getTimeStep( ),\n\t\t\t\t\t\tinterpolatedTleEphemerisSettings->getFrameOrigin( ),\n\t\t\t\t\t\tinterpolatedTleEphemerisSettings->getFrameOrientation( ),\n\t\t\t\t\t\tinterpolatedTleEphemerisSettings->getTle( ),\n\t\t\t\t\t\tinterpolatedTleEphemerisSettings->getInterpolatorSettings( )\n\t\t\t\t\t\t);\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n case scaled_ephemeris:\n {\n // Check consistency of type and class.\n std::shared_ptr< ScaledEphemerisSettings > scaledEphemeriSettings =\n std::dynamic_pointer_cast< ScaledEphemerisSettings >(\n ephemerisSettings );\n if( scaledEphemeriSettings == nullptr )\n {\n throw std::runtime_error(\n \"Error, expected scaled ephemeris settings for body \" + bodyName );\n }\n else\n {\n std::shared_ptr< Ephemeris > baseEphemeris = createBodyEphemeris(\n scaledEphemeriSettings->getBaseSettings( ), bodyName );\n ephemeris = std::make_shared< ScaledEphemeris >(\n baseEphemeris, scaledEphemeriSettings->getScaling( ), scaledEphemeriSettings->getIsScalingAbsolute( ) );\n }\n break;\n }\n default:\n {\n throw std::runtime_error(\n \"Error, did not recognize ephemeris model settings type \" +\n std::to_string( ephemerisSettings->getEphemerisType( ) ) );\n }\n }\n }\n return ephemeris;\n\n}\n\n//! Function that retrieves the time interval at which an ephemeris can be safely interrogated\nstd::pair< double, double > getSafeInterpolationInterval( const std::shared_ptr< ephemerides::Ephemeris > ephemerisModel )\n{\n // Make default output pair\n std::pair< double, double > safeInterval = std::make_pair(\n std::numeric_limits< double >::lowest( ), std::numeric_limits< double >::max( ) );\n\n // Check if model is tabulated, and retrieve safe interval from model\n if( isTabulatedEphemeris( ephemerisModel ) )\n {\n safeInterval = getTabulatedEphemerisSafeInterval( ephemerisModel );\n }\n // Check if model is multi-arc, and retrieve safe intervals from first and last arc.\n else if( std::dynamic_pointer_cast< ephemerides::MultiArcEphemeris >( ephemerisModel ) != nullptr )\n {\n std::shared_ptr< ephemerides::MultiArcEphemeris > multiArcEphemerisModel =\n std::dynamic_pointer_cast< ephemerides::MultiArcEphemeris >( ephemerisModel );\n safeInterval.first = getSafeInterpolationInterval( multiArcEphemerisModel->getSingleArcEphemerides( ).at( 0 ) ).first;\n safeInterval.second = getSafeInterpolationInterval(\n multiArcEphemerisModel->getSingleArcEphemerides( ).at(\n multiArcEphemerisModel->getSingleArcEphemerides( ).size( ) - 1 ) ).second;\n }\n return safeInterval;\n}\n\n\n} // namespace simulation_setup\n\n} // namespace tudat\n", "meta": {"hexsha": "51e834759eda97b40f7a08b525c5fa2fa18f971e", "size": 21474, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/simulation/environment_setup/createEphemeris.cpp", "max_stars_repo_name": "kimonito98/tudat", "max_stars_repo_head_hexsha": "c28f2a3e78b8492e2e054ad5e0d1f9ad785cd092", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/simulation/environment_setup/createEphemeris.cpp", "max_issues_repo_name": "kimonito98/tudat", "max_issues_repo_head_hexsha": "c28f2a3e78b8492e2e054ad5e0d1f9ad785cd092", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/simulation/environment_setup/createEphemeris.cpp", "max_forks_repo_name": "kimonito98/tudat", "max_forks_repo_head_hexsha": "c28f2a3e78b8492e2e054ad5e0d1f9ad785cd092", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.6141906874, "max_line_length": 151, "alphanum_fraction": 0.5736704852, "num_tokens": 4656, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.23252493517189746}} {"text": "#include \"diffuclass.h\"\n#include \n#include \n#include \n\n//------------------------------------------------------------------------------\n// Declare functions\n//------------------------------------------------------------------------------\n\nextern PetscErrorCode ComputeMatrix(KSP, Mat, Mat, void*);\n\nPetscErrorCode MyMatMult(Mat,Vec,Vec);\n\n\n//------------------------------------------------------------------------------\n//\t\t\t\t\t\t\t Public Methods\n//------------------------------------------------------------------------------\n//TODO: include all boundary condition elements and individually parallelized grids\nvoid diffusionPETSc::initDiffusion(eQ::diffusionSolver::params &initParams)\n//void diffusionPETSc::initDiffusion(MPI_Comm comm, std::vector filePaths, int argc, char* argv[])\n{\n auto argc = initParams.argc;\n auto argv = initParams.argv;\n int \t i, j;\n\n /*\n //Split input communicator into subcommunicators for each desired diffusion grid\n MPI_Comm subComm;\n int \t myRankMPI, commSize, i, j;\n\n MPI_Comm_size(comm, &commSize);\n MPI_Comm_rank(comm, &myRankMPI);\n\n MPI_Comm_split(comm, myRankMPI, 0, &subComm);\n\n initData.subCommunicator = subComm;\n //Record vector of diffusion constants and choose the appropriate constant for each processor\n std::vector\t\td_vector;\n d_vector = std::vector(eQ::data::parameters[\"D_HSL\"].get>());\n initData.diffusionConstant = d_vector.at(myRankMPI % d_vector.size());\n\n //Set domain size based on parameters\n initData.xLengthMicrons = int(eQ::data::parameters[\"simulationTrapWidthMicrons\"]);\n initData.yLengthMicrons = int(eQ::data::parameters[\"simulationTrapHeightMicrons\"]);\n\n //Set time step\n initData.dt = eQ::data::parameters[\"dt\"];\n\n //Set grid spacing in domain\n initData.h = 1 / double(eQ::data::parameters[\"nodesPerMicronSignaling\"]);\n\n //Set the appropriate filepath to each grid for data recording\n initData.directoryName = filePaths.at(myRankMPI % d_vector.size());\n*/\n initData.subCommunicator = initParams.comm;\n initData.diffusionConstant = initParams.D_HSL;//direct read from init params\n initData.xLengthMicrons = initParams.trapWidthMicrons;\n initData.yLengthMicrons = initParams.trapHeightMicrons;\n initData.dt = initParams.dt;\n initData.h = 1.0 / initParams.nodesPerMicron;\n\n initData.directoryName = initParams.filePath + \"petsc\";\n boost::filesystem::path dstFolder = initData.directoryName;\n boost::filesystem::create_directory(dstFolder);\n\n initData.objectName = \"grid\";\n\n //Set initial boundary conditions\n if(\"DIRICHLET_0\" == eQ::data::parameters[\"boundaryType\"]){\n initData.homogeneousDirichlet = PETSC_TRUE;\n\n initData.topDirichletCoefficient = 1;\n initData.bottomDirichletCoefficient = 1;\n initData.leftDirichletCoefficient = 1;\n initData.rightDirichletCoefficient = 1;\n\n initData.topNeumannCoefficient = 0;\n initData.bottomNeumannCoefficient = 0;\n initData.leftNeumannCoefficient = 0;\n initData.rightNeumannCoefficient = 0;\n\n initData.topBoundaryValue = 0;\n initData.bottomBoundaryValue = 0;\n initData.leftBoundaryValue = 0;\n initData.rightBoundaryValue = 0;\n }\n\n //Initialize PETSc element of diffusion\n InitializeDiffusion(&initData, argc, argv);\n\n //Resize and populate vectors for data transfer\n solution_vector.resize(gridNodesX * gridNodesY);\n allXCoordinates.resize(gridNodesX * gridNodesY);\n allYCoordinates.resize(gridNodesX * gridNodesY);\n\n for (j=0; j < gridNodesY; j++){\n for (i=0; i < gridNodesX; i++){\n allXCoordinates.at(i + j * gridNodesX) = i*initData.h;\n allYCoordinates.at(i + j * gridNodesX) = j*initData.h;\n solution_vector.at(i + j * gridNodesX) = 0;\n }\n }\n\n //Copy initial conditions to solution vector\n ReadGridValues(allXCoordinates, allYCoordinates, &solution_vector);\n}\n\nvoid diffusionPETSc::stepDiffusion(void)\n{\n //Copy modified solution vector to u0\n //solution_vector is our public vector of all solution data - copy that over.\n\n WriteGridValues(allXCoordinates, allYCoordinates, solution_vector);\n\n TimeStep();\n\n ReadGridValues(allXCoordinates, allYCoordinates, &solution_vector);\n}\n\n//TODO: implement each wall separately\nvoid diffusionPETSc::setBoundaryValues(const eQ::data::parametersType &bvals)\n{\n //JW: over-ride for now: set boundaries explicitly by writing the data structures\n return;\n\n if(bool(bvals[\"allBoundaries\"]) == true){\n gridData->topBoundaryValue = double(bvals[\"boundaryValue\"]);\n gridData->bottomBoundaryValue = double(bvals[\"boundaryValue\"]);\n gridData->leftBoundaryValue = double(bvals[\"boundaryValue\"]);\n gridData->rightBoundaryValue = double(bvals[\"boundaryValue\"]);\n }\n}\n\n//TODO: verify accuracy, check about choosing walls\neQ::data::parametersType diffusionPETSc::getBoundaryFlux(void)\n{\n //Compute and return boundary flux across all walls\n eQ::data::parametersType \tfluxData;\n double \t\t\t \ttotalBoundaryFlux, boundarySlope;\n int \t\t\t\ti, j;\n\n for (i = 0; i < gridNodesX; i++){\n for (j = 0; j < gridNodesY; j++){\n if (i == 0){\n boundarySlope = boundarySlope + (solution_vector.at(2 + (gridNodesX) * j) - \\\n solution_vector.at((gridNodesX)*j))/(2*gridData->h);\n }\n else if (i == gridNodesX - 1){\n boundarySlope = boundarySlope + (solution_vector.at(i - 2 + (gridNodesX-1)*j) - \\\n solution_vector.at(i + (gridNodesX-1)*j))/(2*gridData->h);\n }\n else if (j == 0){\n boundarySlope = boundarySlope + (solution_vector.at(i + (gridNodesX-1) * 2) - \\\n solution_vector.at(i))/(2*gridData->h);\n }\n else if (j == gridNodesY - 1){\n boundarySlope = boundarySlope + (solution_vector.at(i + (gridNodesX-1) * (j - 2)) - \\\n solution_vector.at(i + (gridNodesX-1)*j))/(2*gridData->h);\n }\n }\n }\n\n totalBoundaryFlux = gridData->diffusionConstant * gridData->dt * boundarySlope;\n fluxData[\"totalFlux\"] = totalBoundaryFlux;\n return fluxData;\n}\n\nvoid diffusionPETSc::writeDiffusionFiles(double timestamp)\n{\n //Work in timestamp\n RecordData();\n}\n\n/*\nvoid diffusionPETSc::writeDataFiles(double dt)\n{\n //Need to write full function\n}\n*/\n\nvoid diffusionPETSc::finalize(void)\n{\n DiffusionFinalize(PETSC_TRUE);\n}\n\n\n//------------------------------------------------------------------------------\n// Private Methods\n//------------------------------------------------------------------------------\n\nPetscErrorCode diffusionPETSc::ApplyBoundaryConditions()\n{\n //Applys Neumann/Robin Boundary Conditions to RHS vector\n PetscInt i,j,xm,ym,xs,ys;\n PetscScalar **RHSarray;\n\n //Get subgrid information for each processor and get arrays from vector\n ierr = DMDAGetCorners(distributedArray,&xs,&ys,0,&xm,&ym,0);CHKERRQ(ierr);\n ierr = DMDAVecGetArray(distributedArray, globalVector, &RHSarray);CHKERRQ(ierr);\n\n //JW NOTE: each node can check its range whether it has boundary points\n // then, traverse the boundary explicitly, rather than traversing the entire i,j space\n const double twoFh = 2 * gridData->fourierNumber * gridData->h;//\n// double topD = gridData->topDirichletCoefficient;\n// double bottomD = gridData->bottomDirichletCoefficient;\n// double leftD = gridData->leftDirichletCoefficient;\n// double rightD = gridData->rightDirichletCoefficient;\n const double topN = gridData->topNeumannCoefficient;\n const double bottomN = gridData->bottomNeumannCoefficient;\n const double leftN = gridData->leftNeumannCoefficient;\n const double rightN = gridData->rightNeumannCoefficient;\n\n //Alter values in arrays to match Boundary Conditions\n for (j = ys; j < ys+ym; j++){\n for (i = xs; i < xs+xm; i++){\n// if (i == 0 || i == gridNodesX-1 || j == 0 || j == gridNodesY-1){\n\n //TOP WALL\n if(j == gridNodesY-1)\n {\n const double topBV = topBoundaryValue[i];\n //Set non-Dirichlet for top wall\n if (topN != 0)\n {//if TL (TR) corner, only set to N if left (right) wall is N\n if ((i != 0 || leftN != 0) && (i != gridNodesX-1 || rightN != 0))\n RHSarray[j][i] += (twoFh * topBV) / topN;\n }\n else\n// RHSarray[j][i] = topBV / topD;\n RHSarray[j][i] = topBV;//if N==0, then D must be 1\n }\n //BOTTOM WALL\n else if(j == 0)\n {\n const double bottomBV = bottomBoundaryValue[i];\n //Set non-Dirichlet for bottom wall\n if (bottomN != 0)\n {\n if ((i != 0 || leftN != 0) && (i != gridNodesX-1 || rightN != 0))\n RHSarray[j][i] += (twoFh * bottomBV) / bottomN;\n }\n else\n// RHSarray[j][i] = bottomBV / bottomD;\n RHSarray[j][i] = bottomBV;//if N==0, then D must be 1\n }\n //RIGHT WALL\n if(i == gridNodesX-1)\n {\n const double rightBV = gridData->rightBoundaryValue;\n //Set non-Dirichlet for right wall\n if (rightN != 0)\n {\n if ((j != 0 || bottomN != 0) && (j != gridNodesY-1 || topN != 0))\n RHSarray[j][i] += (twoFh * rightBV) / rightN;\n }\n else\n// RHSarray[j][i] = rightBV / rightD;\n RHSarray[j][i] = rightBV;//if N==0, then D must be 1\n }\n //LEFT WALL\n else if(i == 0)\n {\n const double leftBV = gridData->leftBoundaryValue;\n //Set non-Dirichlet for left wall\n if (leftN != 0)\n {\n if ((j != 0 || bottomN != 0) && (j != gridNodesY-1 || topN != 0))\n RHSarray[j][i] += (twoFh * leftBV) / leftN;\n }\n else\n// RHSarray[j][i] = leftBV / leftD;\n RHSarray[j][i] = leftBV;//if N==0, then D must be 1\n }\n// }\n }\n }\n/*\n //Alter values in arrays to match Boundary Conditions\n for (j = ys; j < ys+ym; j++){\n for (i = xs; i < xs+xm; i++){\n if (i == 0 || i == gridNodesX-1 || j == 0 || j == gridNodesY-1){\n //Set non-Dirichlet for top wall\n if (gridData->topNeumannCoefficient != 0 && j == gridNodesY-1){\n //Only change corners if they are also Neumann Boundary Conditions\n if ((i != 0 || gridData->leftNeumannCoefficient != 0) &&\n (i != gridNodesX-1 || gridData->rightNeumannCoefficient != 0)){\n RHSarray[j][i] = RHSarray[j][i] + (2 * gridData->fourierNumber * gridData->h * \\\n gridData->topBoundaryValue) / gridData->topNeumannCoefficient;\n }\n }\n //Set non-Dirichlet for bottom wall\n if (gridData->bottomNeumannCoefficient != 0 && j == 0){\n //Only change corners if they are also Neumann Boundary Conditions\n if ((i != 0 || gridData->leftNeumannCoefficient != 0) &&\n (i != gridNodesX-1 || gridData->rightNeumannCoefficient != 0)){\n RHSarray[j][i] = RHSarray[j][i] + (2 * gridData->fourierNumber * gridData->h * \\\n gridData->bottomBoundaryValue) / gridData->bottomNeumannCoefficient;\n }\n }\n //Set non-Dirichlet for left wall\n if (gridData->leftNeumannCoefficient != 0 && i == 0){\n //Only change corners if they are also Neumann Boundary Conditions\n if ((j != 0 || gridData->bottomNeumannCoefficient != 0) &&\n (j != gridNodesY-1 || gridData->topNeumannCoefficient != 0)){\n RHSarray[j][i] = RHSarray[j][i] + (2 * gridData->fourierNumber * gridData->h * \\\n gridData->leftBoundaryValue) / gridData->leftNeumannCoefficient;\n }\n }\n //Set non-Dirichlet for right wall\n if (gridData->rightNeumannCoefficient != 0 && i == gridNodesX-1){\n //Only change corners if they are also Neumann Boundary Conditions\n if ((j != 0 || gridData->bottomNeumannCoefficient != 0) &&\n (j != gridNodesY-1 || gridData->topNeumannCoefficient != 0)){\n RHSarray[j][i] = RHSarray[j][i] + (2 * gridData->fourierNumber * gridData->h * \\\n gridData->rightBoundaryValue) / gridData->rightNeumannCoefficient;\n }\n }\n\n //Otherwise, set Dirichlet conditions\n if (gridData->topNeumannCoefficient == 0 && j == gridNodesY-1){\n RHSarray[j][i] = gridData->topBoundaryValue / gridData->topDirichletCoefficient;\n }\n else if (gridData->bottomNeumannCoefficient == 0 && j == 0){\n RHSarray[j][i] = gridData->bottomBoundaryValue / gridData->bottomDirichletCoefficient;\n }\n else if (gridData->leftNeumannCoefficient == 0 && i == 0){\n RHSarray[j][i] = gridData->leftBoundaryValue / gridData->leftDirichletCoefficient;\n }\n else if (gridData->rightNeumannCoefficient == 0 && i == gridNodesX-1){\n RHSarray[j][i] = gridData->rightBoundaryValue / gridData->rightDirichletCoefficient;\n }\n }\n }\n }\n*/\n ierr = DMDAVecRestoreArray(distributedArray, globalVector, &RHSarray);CHKERRQ(ierr);\n\n return ierr;\n}\n\n//Figure out argc and argv\nPetscErrorCode diffusionPETSc::InitializeDiffusion (DiffusionData *dataStruct, int argc, char **argv)\n{\n //Initializes sets data values, inializes PETSc, and otherwise takes care of\n //all preparation needed in order to iterate. It should be noted that this\n //function does NOT set any initial conditions.\n\n gridData = dataStruct;\n //Initialize PETSc and assign passed communicator to DIFFU_COMM\n DIFFU_COMM = gridData->subCommunicator;\n ierr = PetscInitialize(&argc,&argv,(char*)0,NULL);\n MPI_Comm_size(DIFFU_COMM, &commSize);\n\n //Set up viewer for print statements\n printViewer = PETSC_VIEWER_STDOUT_(DIFFU_COMM);\n\n //Calculate crucial values for grid\n gridNodesX = (gridData->xLengthMicrons/gridData->h) + 1;\n gridNodesY = (gridData->yLengthMicrons/gridData->h) + 1;\n gridData->totalNodes = gridNodesX * gridNodesY;\n gridData->fourierNumber = (gridData->diffusionConstant*gridData->dt) / (gridData->h*gridData->h);\n\n //Set boundary and stencil types for distributed array\n boundary = DM_BOUNDARY_NONE;\n stencilType = DMDA_STENCIL_STAR;\n\n //Create Krylov Subspace method(KSP) context and Distributed Array(DMDA)\n ierr = KSPCreate(DIFFU_COMM, &krylovSolver);CHKERRQ(ierr);\n ierr = DMDACreate2d(DIFFU_COMM, boundary, boundary, stencilType, gridNodesX, gridNodesY, PETSC_DECIDE, \\\n PETSC_DECIDE, 1, 1, NULL, NULL, &distributedArray);CHKERRQ(ierr);\n\n //Configure DMDA\n ierr = DMSetFromOptions(distributedArray);CHKERRQ(ierr);\n ierr = DMSetUp(distributedArray);CHKERRQ(ierr);\n ierr = DMDASetUniformCoordinates(distributedArray, 0, gridData->xLengthMicrons, 0, \\\n gridData->yLengthMicrons, 0, 0);CHKERRQ(ierr);\n ierr = DMView(distributedArray, printViewer);CHKERRQ(ierr);\n\n //Create solution vector and local helper vector for scattering\n ierr = DMCreateGlobalVector(distributedArray, &globalVector);CHKERRQ(ierr);\n ierr = DMCreateLocalVector(distributedArray, &gridData->localVector);CHKERRQ(ierr);\n\n //Obtain additional needed values for data structure\n ierr = VecGetOwnershipRange(globalVector, &gridData->localMinimum, &gridData->localMaximum);CHKERRQ(ierr);\n\n //Configure KSP\n ierr = KSPSetDM(krylovSolver, distributedArray);CHKERRQ(ierr);\n ierr = KSPSetComputeOperators(krylovSolver, ComputeMatrix, gridData);CHKERRQ(ierr);\n ierr = KSPGetPC(krylovSolver, &preconditioner);CHKERRQ(ierr);\n ierr = PCSetType(preconditioner, PCNONE);CHKERRQ(ierr);\n ierr = KSPSetType(krylovSolver, KSPFBCGSR);\n ierr = KSPSetFromOptions(krylovSolver);CHKERRQ(ierr);\n ierr = KSPSetUp(krylovSolver);CHKERRQ(ierr);\n\n //Prepare Application Ordering (AO)\n ierr = DMDAGetAO(distributedArray, &appOrder);CHKERRQ(ierr);\n\n ierr = PetscPrintf(DIFFU_COMM, \"\\nPETSC CLASS InitializeDiffusion(): %s \\n\", gridData->directoryName.c_str());CHKERRQ(ierr);\n\n //Set initial timestep number\n step = 0;\n return ierr;\n}\n\nPetscErrorCode diffusionPETSc::TimeStep()\n{\n //Takes a step forward in time in the simulation\n step++;\n ierr = ApplyBoundaryConditions();CHKERRQ(ierr);\n ierr = KSPSolve(krylovSolver, globalVector, globalVector);CHKERRQ(ierr);\n ierr = PetscPrintf(DIFFU_COMM,\"\\r%d steps complete\", step);CHKERRQ(ierr);\n return ierr;\n}\n\n//Ensure vector references work as desired\nPetscErrorCode diffusionPETSc::WriteGridValues(vector xCoordinates, vector \\\n yCoordinates, vector values)\n{\n //Takes values and their coordinates in vector form, converts coordinates to\n //a discretized form, and then inserts the values into the grid based on these\n //coordinates.\n\n //Declare temporary variables for use in method\n PetscInt xm, ym, xs, ys, xindex, yindex;\n unsigned int size, i;\n PetscScalar **globalarray;\n\n //Record size variable and check for size consistency across vectors\n size = xCoordinates.size();\n if (size != yCoordinates.size() || size != values.size()){\n ierr = PetscPrintf(PETSC_COMM_SELF, \\\n \"Error: vector sizes for WriteGridValues must match\\n\");CHKERRQ(ierr);\n }\n\n //Get coordinate range and array for all data on processor\n ierr = DMDAGetCorners(distributedArray,&xs,&ys,0,&xm,&ym,0);CHKERRQ(ierr);\n ierr = DMDAVecGetArray(distributedArray, globalVector, &globalarray);CHKERRQ(ierr);\n\n //Iterate through all coordinates, filter out those not on processor, and\n //insert values into grid\n for (i=0; ih);\n yindex = round(yCoordinates.at(i)/gridData->h);\n if (xindex >= xs && xindex < xs+xm && yindex >= ys && yindex < ys+ym){\n globalarray[yindex][xindex] = values.at(i);\n }\n }\n\n //Restore array to vector, finalizing the insert of these new values.\n ierr = DMDAVecRestoreArray(distributedArray, globalVector, &globalarray);CHKERRQ(ierr);\n\n return ierr;\n}\n\n//TODO: Ensure vector references work as desired\n//Extensively test commSize > 1 case - need MPI to properly share data?\nPetscErrorCode diffusionPETSc::ReadGridValues(vector xCoordinates, vector \\\n yCoordinates, vector *values)\n{\n //Given some coordinates and a vector for values, discretizes the coordinates\n //and finds the values associated with them in the grid. Puts these values\n //into the given vector. Note that this only does so for on-process values.\n\n //Record size variable and check for size consistency across vectors\n unsigned int readSize;\n readSize = xCoordinates.size();\n if (readSize != yCoordinates.size() || readSize != values->size()){\n ierr = PetscPrintf(PETSC_COMM_SELF, \\\n \"Error: vector sizes for ReadGridValues must match\\n\");CHKERRQ(ierr);\n return ierr;\n }\n\n //Read values from grid - use alternative algorithm if grid is parallelized\n if (commSize == 1){\n //Declare temporary variables for use in method\n PetscInt xm, ym, xs, ys, xindex, yindex;\n unsigned int i;\n PetscScalar **readarray;\n\n\n //Get coordinate range and array for all data on processor\n ierr = DMDAGetCorners(distributedArray,&xs,&ys,0,&xm,&ym,0);CHKERRQ(ierr);\n ierr = DMDAVecGetArrayRead(distributedArray, globalVector, &readarray);CHKERRQ(ierr);\n\n //Get list of natural grid coordinates\n for (i=0; i < readSize; i++){\n xindex = round(xCoordinates.at(i)/gridData->h);\n yindex = round(yCoordinates.at(i)/gridData->h);\n if (xindex >= xs && xindex < xs+xm && yindex >= ys && yindex < ys+ym){\n values->at(i) = readarray[yindex][xindex];\n }\n }\n\n //Restore read array to vector\n ierr = DMDAVecRestoreArrayRead(distributedArray, globalVector, &readarray);CHKERRQ(ierr);\n }\n\n else{\n //Declare temporary variables for use in method\n PetscInt \t\t \txindex, yindex, globalIndex[readSize],\n localIndex[readSize];\n unsigned int\t\ti;\n const PetscScalar\t*readarray;\n IS\t\t\t\t\tfrom, to;\n Vec\t\t\t\t\tsequentialVector;\n VecScatter\t\t\tscatter;\n\n //Create index vector for later PETSc vector scattering\n for (i=0; i < readSize; i++){\n localIndex[i] = i;\n }\n\n //Get list of natural grid coordinates\n for (i=0; i < readSize; i++){\n xindex = round(xCoordinates.at(i)/gridData->h);\n yindex = round(yCoordinates.at(i)/gridData->h);\n globalIndex[i] = yindex*gridNodesX + xindex;\n }\n\n //Replace natural grid coordinates with PETSc grid coordinates\n ierr = AOApplicationToPetsc(appOrder, readSize, globalIndex);CHKERRQ(ierr);\n\n //Prepares index sets for PETSc vector scattering\n ierr = ISCreateGeneral(DIFFU_COMM, readSize, globalIndex, PETSC_COPY_VALUES, &from);\n CHKERRQ(ierr);\n ierr = ISCreateGeneral(PETSC_COMM_SELF, readSize, localIndex, PETSC_COPY_VALUES, &to);\n CHKERRQ(ierr);\n\n //Create PETSc vector to be scattered to\n ierr = VecCreateSeq(PETSC_COMM_SELF, readSize, &sequentialVector);CHKERRQ(ierr);\n\n //Create PETSc vector scatter and execute it\n ierr = VecScatterCreate(globalVector, from, sequentialVector, to, &scatter);CHKERRQ(ierr);\n ierr = VecScatterBegin(scatter, globalVector, sequentialVector, INSERT_VALUES, SCATTER_FORWARD);\n CHKERRQ(ierr);\n ierr = VecScatterEnd(scatter, globalVector, sequentialVector, INSERT_VALUES, SCATTER_FORWARD);\n CHKERRQ(ierr);\n\n //Obtain data array from scattered PETSc vector\n ierr = VecGetArrayRead(sequentialVector, &readarray);CHKERRQ(ierr);\n\n //Copy data array into vector (non-PETSc vector)\n for (i=0; i < readSize; i++){\n values->at(i) = readarray[i];\n }\n\n //Restore array to vector\n ierr = VecRestoreArrayRead(sequentialVector, &readarray);CHKERRQ(ierr);\n\n //Destroy unnecessary objects\n ierr = ISDestroy(&from);CHKERRQ(ierr);\n ierr = ISDestroy(&to);CHKERRQ(ierr);\n ierr = VecScatterDestroy(&scatter);CHKERRQ(ierr);\n ierr = VecDestroy(&sequentialVector);CHKERRQ(ierr);\n }\n\n return ierr;\n}\n\nPetscErrorCode diffusionPETSc::RecordData()\n{\n //Writes grid data into a .vtr file to be read by ParaView\n\n //Declare temporary variables for use in method\n char filename[100];\n PetscViewer vtrviewer;\n\n //Get name of file to be written\n sprintf(filename, \"%s/%s%04d.vtr\", gridData->directoryName.c_str(), \\\n gridData->objectName.c_str(), step);\n\n // sprintf(filename, \"%s/%s%d.vtr\", gridData->directoryName.c_str(),\n\n //Open viewer and write data to file\n ierr = PetscViewerVTKOpen(DIFFU_COMM, filename, FILE_MODE_WRITE, \\\n &vtrviewer);CHKERRQ(ierr);\n ierr = PetscViewerPushFormat(vtrviewer, PETSC_VIEWER_VTK_VTR);CHKERRQ(ierr);\n ierr = VecView(globalVector, vtrviewer);CHKERRQ(ierr);\n\n //Destroy viewer and return ierr\n ierr = PetscViewerDestroy(&vtrviewer);CHKERRQ(ierr);\n return ierr;\n}\n\nPetscErrorCode diffusionPETSc::DiffusionFinalize(PetscBool last)\n{\n //Destroys all remaining objects in class and finalizes PETSc if \"last\" is set\n //to true.\n\n //Destroy objects created for an instance of this class\n ierr = DMDestroy(&distributedArray);CHKERRQ(ierr);\n ierr = KSPDestroy(&krylovSolver);CHKERRQ(ierr);\n ierr = VecDestroy(&globalVector);CHKERRQ(ierr);\n ierr = VecDestroy(&gridData->localVector);CHKERRQ(ierr);\n\n ierr = PetscPrintf(DIFFU_COMM, \"\\nDone\\n\");CHKERRQ(ierr);\n\n //If this is the last instance of this class to be finished, finalize PETSc.\n if (last){\n ierr = PetscFinalize();\n }\n\n return ierr;\n}\n\n//------------------------------------------------------------------------------\n// Helper functions (external to class)\n//------------------------------------------------------------------------------\n\nPetscErrorCode ComputeMatrix(KSP krylovSolver, Mat A, Mat jac, void *user){\n //Establishes shell matrix for KSP operation\n\n //Initialize function---------------------------------------------------------\n //Declare variables\n PetscErrorCode ierr;\n DiffusionData *gridData = (DiffusionData*)user;\n\n //Begin function\n PetscFunctionBegin;\n\n //Create matrices-------------------------------------------------------------\n //Form matrix for KSP process\n ierr = MatSetSizes(A, gridData->localMaximum-gridData->localMinimum, gridData->localMaximum-\\\n gridData->localMinimum, gridData->totalNodes, gridData->totalNodes);CHKERRQ(ierr);\n ierr = MatSetType(A,MATSHELL);CHKERRQ(ierr);\n ierr = MatShellSetContext(A,gridData);CHKERRQ(ierr);\n ierr = MatSetUp(A);CHKERRQ(ierr);\n\n //Set multiplication function for KSP process matrix\n ierr = MatShellSetOperation(A,MATOP_MULT,(void(*)(void))MyMatMult);\n CHKERRQ(ierr);\n\n //Return error code-----------------------------------------------------------\n PetscFunctionReturn(ierr);\n}\n\nPetscErrorCode MyMatMult(Mat A, Vec X, Vec Y){\n //Defines the multiplication function for shell matrix\n\n //Initialize function---------------------------------------------------------\n //Declare variables\n PetscErrorCode ierr;\n void *ptr;\n DiffusionData *user;\n PetscScalar **yarray, **xarray;\n DM distributedArray;\n PetscInt i,j,xm,ym,xs,ys,gxm,gym,gxs,gys;\n\n //Begin function\n PetscFunctionBegin;\n\n //Retrieve context and DM\n ierr = MatShellGetContext(A,&ptr);CHKERRQ(ierr);\n ierr = VecGetDM(X,&distributedArray);CHKERRQ(ierr);\n user = (DiffusionData*)ptr;\n\n //Prepare for computation-----------------------------------------------------\n //Begin scattering\n ierr = DMGlobalToLocalBegin(distributedArray, X, INSERT_VALUES, user->localVector);CHKERRQ(ierr);\n\n //Get local domain in grid\n ierr = DMDAGetCorners(distributedArray,&xs,&ys,0,&xm,&ym,0);CHKERRQ(ierr);\n ierr = DMDAGetGhostCorners(distributedArray,&gxs,&gys,0,&gxm,&gym,0);CHKERRQ(ierr);\n\n //Get local array for product vector\n ierr = DMDAVecGetArray(distributedArray, Y, &yarray);CHKERRQ(ierr);\n\n //Finish scattering and get local array from scattered vector\n ierr = DMGlobalToLocalEnd(distributedArray, X, INSERT_VALUES, user->localVector);CHKERRQ(ierr);\n ierr = DMDAVecGetArrayRead(distributedArray, user->localVector, &xarray);CHKERRQ(ierr);\n\n //Compute product-------------------------------------------------------------\n/*\n const double h = user->h;\n const double F = user->fourierNumber;\n const double twoF = 2*user->fourierNumber;\n const double topD = user->topDirichletCoefficient;\n const double bottomD = user->bottomDirichletCoefficient;\n const double leftD = user->leftDirichletCoefficient;\n const double rightD = user->rightDirichletCoefficient;\n const double topN = user->topNeumannCoefficient;\n const double bottomN = user->bottomNeumannCoefficient;\n const double leftN = user->leftNeumannCoefficient;\n const double rightN = user->rightNeumannCoefficient;\n\n for (j=ys; jleftNeumannCoefficient != 0 && i == gxs) && (j != gys && j != gys+gym-1)){\n if (leftN != 0)\n yarray[j][i] =\n -F * (xarray[j-1][i] + xarray[j+1][i] + 2*xarray[j][i+1])\n + (1 + (2 + (user->h*leftD/leftN)) * twoF) * xarray[j][i];\n //Evaluate wall in case of Dirichlet condition\n else\n yarray[j][i] = xarray[j][i];\n }\n //RIGHT WALL\n else if(i == gxs+gxm-1)\n {\n// else if ((user->rightNeumannCoefficient != 0 && i == gxs+gxm-1) && (j != gys && j != gys+gym-1)){\n if (rightN != 0)\n yarray[j][i] =\n -F * (xarray[j-1][i] + xarray[j+1][i] - 2*xarray[j][i-1])\n + (1 + (2 + (h*rightD/rightN)) * twoF) * xarray[j][i];\n //Evaluate wall in case of Dirichlet condition\n else\n yarray[j][i] = xarray[j][i];\n }\n\n //Sets value for non-edge points\n else\n yarray[j][i] = 1.0 - F*(\n xarray[j-1][i] + xarray[j+1][i] + xarray[j][i-1] + xarray[j][i+1] - 4*xarray[j][i]);\n // yarray[j][i] = -user->fourierNumber*xarray[j-1][i]-user->fourierNumber*xarray[j+1][i]-\\\n // user->fourierNumber*xarray[j][i-1] -user->fourierNumber*xarray[j][i+1]+\\\n // (1+4*user->fourierNumber)*xarray[j][i];\n }\n }\n*/\n\n///*\n for (j=ys; jtopNeumannCoefficient != 0 && j == gys+gym-1){\n //Evaluate left corner when left wall is Neumann/Robin\n if (i == gxs && user->leftNeumannCoefficient != 0){\n yarray[j][i] = -2*user->fourierNumber*xarray[j][i+1]-2*user->fourierNumber*xarray[j-1][i]\\\n +(1+(4 + (2*user->h*user->topDirichletCoefficient/user->topNeumannCoefficient)\\\n +(2*user->h*user->leftDirichletCoefficient/user->leftNeumannCoefficient))*\\\n user->fourierNumber)*xarray[j][i];\n }\n //Evaluate right corner when right wall is Neumann/Robin\n else if (i == gxs+gxm-1 && user->rightNeumannCoefficient != 0){\n yarray[j][i] = -2*user->fourierNumber*xarray[j][i-1]-2*user->fourierNumber*xarray[j-1][i]\\\n +(1+(4 + (2*user->h*user->topDirichletCoefficient/user->topNeumannCoefficient)\\\n +(2*user->h*user->rightDirichletCoefficient/user->rightNeumannCoefficient))*\\\n user->fourierNumber)*xarray[j][i];\n }\n //Evaluate all other edge points on top wall\n else{\n yarray[j][i] = -2*user->fourierNumber*xarray[j-1][i]-user->fourierNumber*xarray[j][i-1]\\\n -user->fourierNumber*xarray[j][i+1]+(1 + (4 + 2*user->h*user->topDirichletCoefficient\\\n /user->topNeumannCoefficient)*user->fourierNumber)*xarray[j][i];\n }\n }\n //Evaluate in case where bottom wall is Neumann/Robin\n else if (user->bottomNeumannCoefficient != 0 && j == gys){\n //Evaluate left corner when left wall is Neumann/Robin\n if (i == gxs && user->leftNeumannCoefficient != 0){\n yarray[j][i] = -2*user->fourierNumber*xarray[j][i+1]-2*user->fourierNumber*xarray[j+1][i]\\\n +(1+(4 + (2*user->h*user->leftDirichletCoefficient/user->leftNeumannCoefficient) + \\\n (2*user->h*user->bottomDirichletCoefficient/user->bottomNeumannCoefficient))*\\\n user->fourierNumber)*xarray[j][i];\n }\n //Evaluate right corner when right wall is Neumann/Robin\n else if (i == gxs+gxm-1 && user->rightNeumannCoefficient != 0){\n yarray[j][i] = -2*user->fourierNumber*xarray[j][i-1]-2*user->fourierNumber*xarray[j+1][i]\\\n +(1+(4 + (2*user->h*user->rightDirichletCoefficient/user->rightNeumannCoefficient) + \\\n (2*user->h*user->bottomDirichletCoefficient/user->bottomNeumannCoefficient))*\\\n user->fourierNumber)*xarray[j][i];\n }\n //Evaluate all other edge points on bottom wall\n else{\n yarray[j][i] =\n -2*user->fourierNumber*xarray[j+1][i]\n -user->fourierNumber*xarray[j][i-1]\n -user->fourierNumber*xarray[j][i+1]\n +(1+(4 + (2*user->h*user->bottomDirichletCoefficient\\\n /user->bottomNeumannCoefficient))*user->fourierNumber)*xarray[j][i];\n }\n }\n //Evaluate in case where left wall is Neumann/Robin\n else if ((user->leftNeumannCoefficient != 0 && i == gxs) && (j != gys && j != gys+gym-1)){\n yarray[j][i] = -user->fourierNumber*xarray[j-1][i]-user->fourierNumber*xarray[j+1][i]-\\\n 2*user->fourierNumber*xarray[j][i+1]+(1+(4 + (2*user->h*user->leftDirichletCoefficient\\\n /user->leftNeumannCoefficient))*user->fourierNumber)*xarray[j][i];\n }\n //Evaluate in case where right wall is Neumann/Robin\n else if ((user->rightNeumannCoefficient != 0 && i == gxs+gxm-1) && (j != gys && j != gys+gym-1)){\n yarray[j][i] = -user->fourierNumber*xarray[j-1][i]-user->fourierNumber*xarray[j+1][i]-\\\n 2*user->fourierNumber*xarray[j][i-1]+(1+(4 + (2*user->h*user->rightDirichletCoefficient\\\n /user->rightNeumannCoefficient))*user->fourierNumber)*xarray[j][i];\n }\n //Evaluate wall in case of Dirichlet condition\n else{\n yarray[j][i] = xarray[j][i];\n }\n }\n //Sets value for non-edge points\n else\n yarray[j][i] = -user->fourierNumber*xarray[j-1][i]-user->fourierNumber*xarray[j+1][i]-\\\n user->fourierNumber*xarray[j][i-1] -user->fourierNumber*xarray[j][i+1]+\\\n (1+4*user->fourierNumber)*xarray[j][i];\n }\n }\n//*/\n\n //Finish function-------------------------------------------------------------\n //Restore arrays\n ierr = DMDAVecRestoreArray(distributedArray, Y, &yarray);CHKERRQ(ierr);\n ierr = DMDAVecRestoreArrayRead(distributedArray, user->localVector, &xarray);CHKERRQ(ierr);\n\n //Return error code\n PetscFunctionReturn(ierr);\n}\n", "meta": {"hexsha": "c7808f4e399ead4e8a845839d42b7e4ad66e30ea", "size": 37564, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "diffuclass.cpp", "max_stars_repo_name": "jwinkle/eQ", "max_stars_repo_head_hexsha": "dbc94575fad9c8e4f1feaddc6a1c1c9067967ed2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "diffuclass.cpp", 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NO", "lm_q1_score": 0.5926665855647395, "lm_q2_score": 0.39233683016710835, "lm_q1q2_score": 0.2325249295264332}} {"text": "#include \"midgard/util.h\"\n#include \"midgard/constants.h\"\n#include \"midgard/distanceapproximator.h\"\n#include \"midgard/logging.h\"\n#include \"midgard/point2.h\"\n#include \"midgard/polyline2.h\"\n#include \"midgard/vector2.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace {\n\nconstexpr double RAD_PER_METER = 1.0 / 6378160.187;\nconstexpr double RAD_PER_DEG = valhalla::midgard::kPiDouble / 180.0;\nconstexpr double DEG_PER_RAD = 180.0 / valhalla::midgard::kPiDouble;\n\nstd::vector\nresample_at_1hz(const std::vector& segments) {\n std::vector resampled;\n float time_remainder = 0.0;\n for (const auto& segment : segments) {\n // get the speed of this edge\n auto meters = valhalla::midgard::Polyline2::Length(segment.shape);\n // trim the shape to account of the portion of the previous second that bled onto this edge\n auto to_trim = segment.speed * time_remainder;\n auto trimmed = valhalla::midgard::trim_polyline(segment.shape.cbegin(), segment.shape.cend(),\n to_trim / meters, 1.f);\n // resample it at 1 second intervals\n auto second_interval =\n valhalla::midgard::resample_spherical_polyline(trimmed, segment.speed, false);\n resampled.insert(resampled.end(), second_interval.begin(), second_interval.end());\n // figure out how much of the last second will bleed into the next edge\n double intpart;\n time_remainder = std::modf((meters - to_trim) / segment.speed, &intpart);\n }\n return resampled;\n}\n\n} // namespace\n\nnamespace valhalla {\nnamespace midgard {\n\n// scalar * vector operator.\nVector2 operator*(float s, const Vector2& v) {\n return Vector2(v.x() * s, v.y() * s);\n}\n\nVector2d operator*(double s, const Vector2d& v) {\n return Vector2d(v.x() * s, v.y() * s);\n}\n\n// Trim the front of a polyline (represented as a list or vector of Point2).\n// Returns the trimmed portion of the polyline. The supplied polyline is\n// altered (the trimmed part is removed).\ntemplate container_t trim_front(container_t& pts, const float dist) {\n // Return if less than 2 points\n if (pts.size() < 2) {\n return {};\n }\n\n // Walk the polyline and accumulate length until it exceeds dist\n container_t result;\n result.push_back(pts.front());\n double d = 0.0f;\n for (auto p1 = pts.begin(), p2 = std::next(pts.begin()); p2 != pts.end(); ++p1, ++p2) {\n double segdist = p1->Distance(*p2);\n if ((d + segdist) > dist) {\n double frac = (dist - d) / segdist;\n auto midpoint = p1->PointAlongSegment(*p2, frac);\n result.push_back(midpoint);\n\n // Remove used part of polyline\n pts.erase(pts.begin(), p1);\n pts.front() = midpoint;\n return result;\n } else {\n d += segdist;\n result.push_back(*p2);\n }\n }\n\n // Used all of the polyline without exceeding dist\n pts.clear();\n return result;\n}\n\nvoid trim_shape(float start,\n PointLL start_vertex, // NOLINT\n float end,\n PointLL end_vertex, // NOLINT\n std::vector& shape) {\n // clip up to the start point if the start_vertex is valid\n float along = 0.f;\n if (start_vertex.IsValid()) {\n // find the spot at which we cross the distance threshold and stop\n auto current = shape.begin();\n for (; !shape.empty() && (current != shape.end() - 1) && along <= start; ++current) {\n along += (current + 1)->Distance(*current);\n }\n // we found the spot to stop for the beginning of the shape so set it to the new beginning\n *(--current) = start_vertex;\n shape.erase(shape.begin(), current);\n along = start;\n }\n\n // clip after the end point if the end vertex is valid\n if (end_vertex.IsValid()) {\n // find the point at which we cross the distance threshold and stop\n auto current = shape.begin();\n for (; !shape.empty() && (current != shape.end() - 1) && along <= end; ++current) {\n along += (current + 1)->Distance(*current);\n }\n // found the spot to stop for the end of the shape so set it to the new end\n *(current) = end_vertex;\n shape.erase(++current, shape.end());\n }\n}\n\nfloat tangent_angle(size_t index,\n const PointLL& point,\n const std::vector& shape,\n const float sample_distance,\n bool forward) {\n // depending on if we are going forward or backward we choose a different increment\n auto increment = forward ? -1 : 1;\n auto first_end = forward ? shape.cbegin() : shape.cend() - 1;\n auto second_end = forward ? shape.cend() - 1 : shape.cbegin();\n\n // u and v will be points we move along the shape until we have enough distance between them or\n // run out of points\n\n // move backwards until we have enough or run out\n float remaining = sample_distance;\n auto u = point;\n auto i = shape.cbegin() + index + forward;\n while (remaining > 0 && i != first_end) {\n // move along and see how much distance that added\n i += increment;\n auto d = u.Distance(*i);\n // are we done yet?\n if (remaining <= d) {\n auto coef = remaining / d;\n u = u.PointAlongSegment(*i, coef);\n return u.Heading(point);\n }\n // next one\n u = *i;\n remaining -= d;\n }\n\n // move forwards until we have enough or run out\n auto v = point;\n i = shape.cbegin() + index + !forward;\n while (remaining > 0 && i != second_end) {\n // move along and see how much distance that added\n i -= increment;\n auto d = v.Distance(*i);\n // are we done yet?\n if (remaining <= d) {\n auto coef = remaining / d;\n v = v.PointAlongSegment(*i, coef);\n return u.Heading(v);\n }\n // next one\n v = *i;\n remaining -= d;\n }\n return u.Heading(v);\n}\n\n// Explicit instantiations\ntemplate std::vector trim_front>(std::vector&, const float);\ntemplate std::vector trim_front>(std::vector&, const float);\ntemplate std::list trim_front>(std::list&, const float);\ntemplate std::list trim_front>(std::list&, const float);\n\nmemory_status::memory_status(const std::unordered_set& interest) {\n // grab the vm stats from the file\n std::ifstream file(\"/proc/self/status\");\n std::string line;\n while (std::getline(file, line)) {\n // did we find a memory metric\n if (line.find_first_of(\"Vm\") == 0) {\n // grab the name of it and see if we care about it\n std::string name = line.substr(0, line.find_first_of(':'));\n if (interest.size() > 0 && interest.find(name) == interest.end()) {\n continue;\n }\n // try to get the number of bytes\n line.erase(std::remove_if(line.begin(), line.end(),\n [](const char c) { return !std::isdigit(c); }),\n line.end());\n if (line.size() == 0) {\n continue;\n }\n auto bytes = std::stod(line) * 1024.0;\n // get the units and scale\n std::pair metric = std::make_pair(bytes, \"b\");\n for (auto unit : {\"B\", \"KB\", \"MB\", \"GB\"}) {\n metric.second = unit;\n if (metric.first > 1024.0) {\n metric.first /= 1024.0;\n } else {\n break;\n }\n }\n metrics.emplace(std::piecewise_construct, std::forward_as_tuple(name),\n std::forward_as_tuple(metric));\n }\n line.clear();\n }\n}\n\nbool memory_status::supported() {\n struct stat s;\n return stat(\"/proc/self/status\", &s) == 0;\n}\n\nstd::ostream& operator<<(std::ostream& stream, const memory_status& s) {\n for (const auto& metric : s.metrics) {\n stream << metric.first << \": \" << metric.second.first << metric.second.second << std::endl;\n }\n return stream;\n}\n\n/* This method makes use of several computations explained and demonstrated at:\n * http://williams.best.vwh.net/avform.htm (reference no longer active)\n * New Reference:\n * http://www.movable-type.co.uk/scripts/latlong.html\n */\ntemplate \ncontainer_t\nresample_spherical_polyline(const container_t& polyline, double resolution, bool preserve) {\n if (polyline.size() == 0) {\n return {};\n };\n\n // for each point\n container_t resampled = {polyline.front()};\n resolution *= RAD_PER_METER;\n double remaining = resolution;\n auto last = resampled.back();\n for (auto p = std::next(polyline.cbegin()); p != polyline.cend(); ++p) {\n // radians\n auto lon2 = p->first * -RAD_PER_DEG;\n auto lat2 = p->second * RAD_PER_DEG;\n // how much do we have left on this segment from where we are (in great arc radians)\n // double d = 2.0 * asin(sqrt(pow(sin((resampled.back().second * RAD_PER_DEG - lat2) /\n // 2.0), 2.0) + cos(resampled.back().second * RAD_PER_DEG) * cos(lat2)\n // *pow(sin((resampled.back().first * -RAD_PER_DEG - lon2) / 2.0), 2.0)));\n auto d = (last == *p) ? 0.0\n : acos(sin(last.second * RAD_PER_DEG) * sin(lat2) +\n cos(last.second * RAD_PER_DEG) * cos(lat2) *\n cos(last.first * -RAD_PER_DEG - lon2));\n if (std::isnan(d)) {\n // set d to 0, do not skip in case we are preserving coordinates\n d = 0.0;\n }\n\n // keep placing points while we can fit them\n while (d > remaining) {\n // some precomputed stuff\n auto lon1 = last.first * -RAD_PER_DEG;\n auto lat1 = last.second * RAD_PER_DEG;\n auto sd = sin(d);\n auto a = sin(d - remaining) / sd;\n auto acs1 = a * cos(lat1);\n auto b = sin(remaining) / sd;\n auto bcs2 = b * cos(lat2);\n // find the interpolated point along the arc\n auto x = acs1 * cos(lon1) + bcs2 * cos(lon2);\n auto y = acs1 * sin(lon1) + bcs2 * sin(lon2);\n auto z = a * sin(lat1) + b * sin(lat2);\n last.first = atan2(y, x) * -DEG_PER_RAD;\n last.second = atan2(z, sqrt(x * x + y * y)) * DEG_PER_RAD;\n resampled.push_back(last);\n // we just consumed a bit\n d -= remaining;\n // we need another bit\n remaining = resolution;\n }\n // we're going to the next point so consume whatever's left\n remaining -= d;\n last = *p;\n if (preserve) {\n resampled.push_back(last);\n }\n }\n\n // TODO: do we want to let them know remaining?\n\n // hand it back\n return resampled;\n}\n\n// explicit instantiations\ntemplate std::vector\nresample_spherical_polyline>(const std::vector&, double, bool);\ntemplate std::vector\nresample_spherical_polyline>(const std::vector&, double, bool);\ntemplate std::list\nresample_spherical_polyline>(const std::list&, double, bool);\ntemplate std::list\nresample_spherical_polyline>(const std::list&, double, bool);\n\n/* Resample a polyline at uniform intervals using more accurate spherical interpolation between\n * points. The length and number of samples is specified. The interval is computed based on\n * the number of samples and the algorithm guarantees that the secified number of samples\n * is exactly produced.\n * This method makes use of several computations explained and demonstrated at:\n * http://williams.best.vwh.net/avform.htm (reference no longer active)\n * New Reference:\n * http://www.movable-type.co.uk/scripts/latlong.html\n */\nstd::vector uniform_resample_spherical_polyline(const std::vector& polyline,\n const double length,\n const uint32_t n) {\n if (polyline.size() == 0) {\n return {};\n }\n\n // Compute sample distance that splits the polyline equally to create n vertices.\n // Divisor is n-1 since there is 1 more vertex than edge on the subdivided polyline.\n double sample_distance = length / (n - 1);\n double d0 = sample_distance;\n\n // for each point\n std::vector resampled = {polyline.front()};\n sample_distance *= RAD_PER_METER;\n double remaining = sample_distance;\n PointLL last = resampled.back();\n for (auto p = std::next(polyline.cbegin()); p != polyline.cend(); ++p) {\n // Distance between this vertex and last (in great arc radians)\n auto lon2 = p->first * -RAD_PER_DEG;\n auto lat2 = p->second * RAD_PER_DEG;\n auto d = (last == *p) ? 0.0\n : acos(sin(last.second * RAD_PER_DEG) * sin(lat2) +\n cos(last.second * RAD_PER_DEG) * cos(lat2) *\n cos(last.first * -RAD_PER_DEG - lon2));\n if (std::isnan(d)) {\n continue;\n }\n\n // Place resampled points on this segment as long as remaining distance is < d\n while (remaining < d) {\n // some precomputed stuff\n auto lon1 = last.first * -RAD_PER_DEG;\n auto lat1 = last.second * RAD_PER_DEG;\n auto sd = sin(d);\n auto a = sin(d - remaining) / sd;\n auto acs1 = a * cos(lat1);\n auto b = sin(sample_distance) / sd;\n auto bcs2 = b * cos(lat2);\n\n // find the interpolated point along the arc\n auto x = acs1 * cos(lon1) + bcs2 * cos(lon2);\n auto y = acs1 * sin(lon1) + bcs2 * sin(lon2);\n auto z = a * sin(lat1) + b * sin(lat2);\n last.first = atan2(y, x) * -DEG_PER_RAD;\n last.second = atan2(z, sqrt(x * x + y * y)) * DEG_PER_RAD;\n resampled.push_back(last);\n\n // Update to reduce d and update...\n d -= remaining;\n remaining = sample_distance;\n }\n // we're going to the next point so consume whatever's left\n remaining -= d;\n last = *p;\n }\n\n if (resampled.size() < n) {\n // Append the last polyline point\n resampled.push_back(std::move(polyline.back()));\n } else if (resampled.size() == n) {\n resampled.back() = polyline.back();\n }\n\n if (resampled.size() != n) {\n LOG_ERROR(\"resampled polyline not expected size! n: \" + std::to_string(n) +\n \" actual: \" + std::to_string(resampled.size()) + \" length: \" + std::to_string(length) +\n \" d: \" + std::to_string(d0));\n }\n return resampled;\n}\n\n// Resample the polyline to the specified resolution. This is a faster and less precise\n// method than resample_spherical_polyline.\nstd::vector\nresample_polyline(const std::vector& polyline, const float length, const float resolution) {\n if (polyline.size() == 0) {\n return {};\n }\n\n // Add the first point\n std::vector resampled = {polyline.front()};\n\n // Compute sample distance that is near the resolution but splits the polyline equally\n size_t n = std::round(length / resolution);\n float sample_distance = length / n;\n\n // Iterate through line segments of the polyline\n float accumulated_d = 0.0f;\n auto p0 = polyline.cbegin();\n for (auto p1 = std::next(polyline.cbegin()); p1 != polyline.cend(); ++p0, ++p1) {\n // break if we have sampled enough\n if (resampled.size() == n) {\n break;\n }\n\n // Find distance (meters) between the 2 points of the input polyline.\n float d = p0->Distance(*p1);\n\n // Interpolate between the prior polyline point if we exceed the resolution\n // (including distance accumulated so far)\n if (d + accumulated_d > sample_distance) {\n float dlon = p1->first - p0->first;\n float dlat = p1->second - p0->second;\n\n // Form the first interpolated point\n float p = (sample_distance - accumulated_d) / d;\n resampled.emplace_back(p0->first + p * dlon, p0->second + p * dlat);\n\n // Continue to interpolate along the segment while accumulated distance is less than resolution\n float dp = sample_distance / d;\n while (p + dp < 1.0f && resampled.size() < n) {\n p += dp;\n resampled.emplace_back(p0->first + p * dlon, p0->second + p * dlat);\n }\n\n // Set the accumulated distance to the distance remaining on this segment\n accumulated_d = d * (1.0f - p);\n\n } else {\n // Have not accumulated enough distance. Add d to the accumulated distance\n accumulated_d += d;\n }\n }\n\n // Append the last polyline point\n resampled.push_back(std::move(polyline.back()));\n\n return resampled;\n}\n\n// Return the intersection of two infinite lines if any\ntemplate \nbool intersect(const coord_t& u, const coord_t& v, const coord_t& a, const coord_t& b, coord_t& i) {\n auto uv_xd = u.first - v.first;\n auto uv_yd = u.second - v.second;\n auto ab_xd = a.first - b.first;\n auto ab_yd = a.second - b.second;\n auto d_cross = uv_xd * ab_yd - ab_xd * uv_yd;\n // parallel or very close to it\n if (std::abs(d_cross) < 1e-5) {\n return false;\n }\n auto uv_cross = u.first * v.second - u.second * v.first;\n auto ab_cross = a.first * b.second - a.second * b.first;\n i.first = (uv_cross * ab_xd - uv_xd * ab_cross) / d_cross;\n i.second = (uv_cross * ab_yd - uv_yd * ab_cross) / d_cross;\n return true;\n}\ntemplate bool intersect(const PointLL& u,\n const PointLL& v,\n const PointLL& a,\n const PointLL& b,\n PointLL& i);\ntemplate bool\nintersect(const Point2& u, const Point2& v, const Point2& a, const Point2& b, Point2& i);\n\n// Return the intercept of the line passing through uv with the horizontal line defined by y\ntemplate \ntypename coord_t::first_type\ny_intercept(const coord_t& u, const coord_t& v, const typename coord_t::second_type y) {\n if (std::abs(u.first - v.first) < 1e-5) {\n return u.first;\n }\n if (std::abs(u.second - u.second) < 1e-5) {\n return NAN;\n }\n auto m = (v.second - u.second) / (v.first - u.first);\n auto b = u.second - (u.first * m);\n return (y - b) / m;\n}\ntemplate PointXY::first_type y_intercept>(const PointXY&,\n const PointXY&,\n const PointXY::first_type);\ntemplate GeoPoint::first_type y_intercept>(const GeoPoint&,\n const GeoPoint&,\n const GeoPoint::first_type);\ntemplate PointXY::first_type y_intercept>(const PointXY&,\n const PointXY&,\n const PointXY::first_type);\ntemplate GeoPoint::first_type\ny_intercept>(const GeoPoint&,\n const GeoPoint&,\n const GeoPoint::first_type);\n\n// Return the intercept of the line passing through uv with the vertical line defined by x\ntemplate \ntypename coord_t::first_type\nx_intercept(const coord_t& u, const coord_t& v, const typename coord_t::second_type x) {\n if (std::abs(u.second - v.second) < 1e-5) {\n return u.second;\n }\n if (std::abs(u.first - v.first) < 1e-5) {\n return NAN;\n }\n auto m = (v.second - u.second) / (v.first - u.first);\n auto b = u.second - (u.first * m);\n return x * m + b;\n}\ntemplate PointXY::first_type x_intercept>(const PointXY&,\n const PointXY&,\n const PointXY::first_type);\ntemplate GeoPoint::first_type x_intercept>(const GeoPoint&,\n const GeoPoint&,\n const GeoPoint::first_type);\ntemplate PointXY::first_type x_intercept>(const PointXY&,\n const PointXY&,\n const PointXY::first_type);\ntemplate GeoPoint::first_type\nx_intercept>(const GeoPoint&,\n const GeoPoint&,\n const GeoPoint::first_type);\n\ntemplate \ntypename container_t::value_type::first_type polygon_area(const container_t& polygon) {\n typename container_t::value_type::first_type area =\n polygon.back() == polygon.front() ? 0.\n : (polygon.back().first + polygon.front().first) *\n (polygon.back().second + polygon.front().second);\n for (auto p1 = polygon.cbegin(), p2 = std::next(polygon.cbegin()); p2 != polygon.cend();\n ++p1, ++p2) {\n area += (p1->first + p2->first) * (p1->second + p2->second);\n }\n return area * .5;\n}\n\ntemplate PointLL::first_type polygon_area(const std::list&);\ntemplate PointLL::first_type polygon_area(const std::vector&);\ntemplate Point2::first_type polygon_area(const std::list&);\ntemplate Point2::first_type polygon_area(const std::vector&);\n\nstd::vector simulate_gps(const std::vector& segments,\n std::vector& accuracies,\n float smoothing,\n float accuracy,\n size_t sample_rate,\n unsigned seed) {\n // resample the coords along a given edge at one second intervals\n auto resampled = resample_at_1hz(segments);\n\n // a way to get noise but only allow for slow change\n std::mt19937 generator(seed);\n std::uniform_real_distribution distribution(-1, 1);\n ring_queue_t> noises(smoothing);\n auto get_noise = [&]() {\n // we generate a vector whose magnitude is no more than accuracy\n auto lon_adj = distribution(generator);\n auto lat_adj = distribution(generator);\n auto len = std::sqrt((lon_adj * lon_adj) + (lat_adj * lat_adj));\n lon_adj /= len;\n lat_adj /= len; // norm\n auto scale = (distribution(generator) + 1.f) / 2.f;\n lon_adj *= scale * accuracy;\n lat_adj *= scale * accuracy; // random scale <= accuracy\n noises.emplace_back(std::make_pair(lon_adj, lat_adj));\n // average over last n to smooth\n std::pair noise{0, 0};\n std::for_each(noises.begin(), noises.end(), [&noise](const std::pair& n) {\n noise.first += n.first;\n noise.second += n.second;\n });\n noise.first /= noises.size();\n noise.second /= noises.size();\n return noise;\n };\n // fill up the noise queue so the first points arent unsmoothed\n while (!noises.full()) {\n get_noise();\n }\n\n // for each point of the 1hz shape\n std::vector simulated;\n for (size_t i = 0; i < resampled.size(); ++i) {\n const auto& p = resampled[i];\n // is this a harmonic of the desired sampling rate\n if (i % sample_rate == 0) {\n // meters of noise with extremely low likelihood its larger than accuracy\n auto noise = get_noise();\n // use the number of meters per degree in both axis to offset the point by the noise\n auto metersPerDegreeLon = DistanceApproximator::MetersPerLngDegree(p.second);\n simulated.emplace_back(midgard::PointLL(p.first + noise.first / metersPerDegreeLon,\n p.second + noise.second / kMetersPerDegreeLat));\n // keep the distance to use for accuracy\n accuracies.emplace_back(simulated.back().Distance(p));\n }\n }\n return simulated;\n}\n\npolygon_t to_boundary(const std::unordered_set& region, const Tiles& tiles) {\n // do we have this tile in this region\n auto member = [®ion](int32_t tile) { return region.find(tile) != region.cend(); };\n // get the neighbor tile giving -1 if no neighbor\n auto neighbor = [&tiles](int32_t tile, int side) -> int32_t {\n if (tile == -1) {\n return -1;\n }\n auto rc = tiles.GetRowColumn(tile);\n switch (side) {\n default:\n case 0:\n return rc.second == 0 ? -1 : tile - 1;\n case 1:\n return rc.first == 0 ? -1 : tile - tiles.ncolumns();\n case 2:\n return rc.second == tiles.ncolumns() - 1 ? -1 : tile + 1;\n case 3:\n return rc.first == tiles.nrows() - 1 ? -1 : tile + tiles.ncolumns();\n }\n };\n // get the beginning coord of the counter clockwise winding given edge of the given tile\n auto coord = [&tiles](uint32_t tile, int side) -> PointLL {\n auto box = tiles.TileBounds(tile);\n switch (side) {\n default:\n case 0:\n return PointLL(box.minx(), box.maxy());\n case 1:\n return box.minpt();\n case 2:\n return PointLL(box.maxx(), box.miny());\n case 3:\n return box.maxpt();\n }\n };\n // trace a ring of the polygon\n polygon_t polygon;\n std::array, 4> used;\n auto trace = [&member, &neighbor, &coord, &polygon, &used](uint32_t start_tile, int start_side,\n bool ccw) {\n auto tile = start_tile;\n auto side = start_side;\n polygon.emplace_back();\n auto& ring = polygon.back();\n // walk until you see the starting edge again\n do {\n // add this edges geometry\n if (ccw) {\n ring.push_back(coord(tile, side));\n } else {\n ring.push_front(coord(tile, side));\n }\n auto inserted = used[side].insert(tile);\n if (!inserted.second) {\n throw std::logic_error(\"Any tile edge can only be used once as part of the geometry\");\n }\n // we need to go to the first existing neighbor tile following our winding\n // starting with the one on the other side of the current side\n auto adjc = neighbor(tile, (side + 1) % 4);\n auto diag = neighbor(adjc, side);\n if (member(diag)) {\n tile = diag;\n side = (side + 3) % 4;\n } // next one keep following winding\n else if (member(adjc)) {\n tile = adjc;\n } // if neither of those were there we stay on this tile and go to the next side\n else {\n side = (side + 1) % 4;\n }\n } while (tile != start_tile || side != start_side);\n };\n\n // the smallest numbered tile has a left edge on the outer ring of the polygon\n auto start_tile = *region.cbegin();\n int start_side = 0;\n for (auto tile : region) {\n if (tile < start_tile) {\n start_tile = tile;\n }\n }\n\n // trace the outer\n trace(start_tile, start_side, true);\n\n // trace the inners\n for (auto start_tile : region) {\n // if the neighbor isnt a member and we didnt already use the side between them\n for (start_side = 0; start_side < 4; ++start_side) {\n if (!member(neighbor(start_tile, start_side)) &&\n used[start_side].find(start_tile) == used[start_side].cend()) {\n // build the inner ring\n if (start_side != -1) {\n trace(start_tile, start_side, false);\n }\n }\n }\n }\n\n // close all the rings\n for (auto& ring : polygon) {\n ring.push_back(ring.front());\n }\n\n // give it back\n return polygon;\n}\n\nconstexpr char PADDING_ENCODED = '=';\nconstexpr char ZERO_ENCODED = 'A';\n\nstd::string encode64(const std::string& text) {\n using namespace boost::archive::iterators;\n using Base64Encode = base64_from_binary>;\n // Encode and add padding to string per octet encoding described here:\n // https://tools.ietf.org/html/rfc4648#section-4\n std::string encoded(Base64Encode(text.begin()), Base64Encode(text.end()));\n size_t num_pad_chars = (3 - text.size() % 3) % 3;\n encoded.append(num_pad_chars, PADDING_ENCODED);\n return encoded;\n}\n\nstd::string decode64(const std::string& encoded) {\n using namespace boost::archive::iterators;\n using Base64Decode =\n transform_width>, 8, 6>;\n // NOTE(mookerji): Ugh, for more details, see:\n // https://stackoverflow.com/questions/10521581/base64-encode-using-boost-throw-exception\n size_t num_pad_chars = (4 - encoded.size() % 4) % 4;\n std::string padded = encoded;\n padded.append(num_pad_chars, PADDING_ENCODED);\n size_t pad_chars = std::count(padded.begin(), padded.end(), PADDING_ENCODED);\n std::replace(padded.begin(), padded.end(), PADDING_ENCODED, ZERO_ENCODED);\n std::string decoded(Base64Decode(padded.begin()), Base64Decode(padded.end()));\n decoded.erase(decoded.end() - pad_chars, decoded.end());\n return decoded;\n}\n\n} // namespace midgard\n} // namespace valhalla\n", "meta": {"hexsha": "c8b54dff14e46c2b97bb53240c73fcbad3683f4b", "size": 29178, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/midgard/util.cc", "max_stars_repo_name": "mesozoic-drones/valhalla", "max_stars_repo_head_hexsha": "cafc34e63f3189d017348391a18847e8250d2b30", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/midgard/util.cc", "max_issues_repo_name": "mesozoic-drones/valhalla", "max_issues_repo_head_hexsha": "cafc34e63f3189d017348391a18847e8250d2b30", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2020-08-24T18:48:12.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-05T21:18:44.000Z", "max_forks_repo_path": "src/midgard/util.cc", "max_forks_repo_name": "mesozoic-drones/valhalla", "max_forks_repo_head_hexsha": "cafc34e63f3189d017348391a18847e8250d2b30", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-01-24T16:46:01.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-24T16:46:01.000Z", "avg_line_length": 38.3416557162, "max_line_length": 101, "alphanum_fraction": 0.6134759065, "num_tokens": 7354, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.38491214448393357, "lm_q1q2_score": 0.23246069175375653}} {"text": "/**\n * Copyright (C) 2019 Sergey Morozov \n *\n * Permission is hereby granted, free of charge, to any person\n * obtaining a copy of this software and associated documentation\n * files (the \"Software\"), to deal in the Software without restriction,\n * including without limitation the rights to use, copy, modify, merge,\n * publish, distribute, sublicense, and/or sell copies of the Software,\n * and to permit persons to whom the Software is furnished to do so,\n * subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be\n * included in all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\n * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\n * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\n * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH\n * THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n */\n\n#ifndef LIDAR_OBSTACLE_DETECTION_KDTREE_HPP\n#define LIDAR_OBSTACLE_DETECTION_KDTREE_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\nnamespace ser94mor::lidar_obstacle_detection\n{\n\n /**\n * A data structure representing the KD-Tree. Implements \"Insert\" and \"Search\" operations.\n * @tparam dims a number of dimentions, that is, \"K\" from the KD-Tree\n * @tparam id_type a type of the point identifier\n */\n template\n class KDTree\n {\n public:\n\n enum ConstructionWay\n {\n AS_IS, SHUFFLE, SORT,\n };\n\n /**\n * A data structure representing the k-dimensional point.\n */\n struct KDPoint\n {\n using vector_type = Eigen::Matrix;\n\n id_type id;\n vector_type data;\n\n bool IsInHypersphere(const vector_type& center, const double_t radius) const\n {\n auto diff = data - center;\n double_t dist = std::sqrt(diff.transpose() * diff);\n\n return dist <= radius;\n }\n\n bool IsGreaterThanOrEqualTo(const vector_type& point, size_t depth, const double_t distance_tolerance = 0.0) const\n {\n auto ind = depth % dims;\n return data(ind) >= point(ind)-distance_tolerance;\n }\n\n bool IsLessThanOrEqualTo(const vector_type& point, size_t depth, const double_t distance_tolerance = 0.0) const\n {\n auto ind = depth % dims;\n return data(ind) <= point(ind)+distance_tolerance;\n }\n\n bool operator==(const KDPoint& rhs) const\n {\n return id == rhs.id && data.isApprox(rhs.data);\n }\n\n bool operator!=(const KDPoint& rhs) const\n {\n return !(rhs == *this);\n }\n\n };\n\n using point_type = KDPoint;\n using vector_type = typename point_type::vector_type;\n\n private:\n /**\n * A data structure representing the KD-Tree's node.\n */\n class KDNode\n {\n private:\n KDPoint kd_point_;\n std::unique_ptr children_[2];\n\n public:\n explicit KDNode(const KDPoint& kd_point)\n : kd_point_{kd_point}, children_{nullptr, nullptr}\n {\n\n }\n\n std::unique_ptr* SubtreeFor(const KDPoint& point, const size_t depth)\n {\n size_t dim_index = depth % dims;\n size_t child_index = not (point.data(dim_index) < kd_point_.data(dim_index));\n return &(children_[child_index]);\n }\n\n const std::unique_ptr& Left() const\n {\n return children_[0];\n }\n\n const std::unique_ptr& Right() const\n {\n return children_[1];\n }\n\n const KDPoint& Point() const\n {\n return kd_point_;\n }\n };\n\n\n using node_type = KDNode;\n\n public:\n\n /**\n * Constructor.\n */\n KDTree() : root_{nullptr}\n {\n\n }\n\n /**\n * Constructor.\n */\n KDTree(std::vector& points, ConstructionWay construction_way) : root_{nullptr}\n {\n switch (construction_way)\n {\n case AS_IS:\n {\n break;\n }\n case SHUFFLE:\n {\n std::random_device rd;\n std::mt19937 g(rd());\n std::shuffle(points.begin(), points.end(), g);\n break;\n }\n case SORT:\n {\n throw std::logic_error(\n \"a balanced k-d tree construction (when points are sorted by each dimension) is not implemented yet\");\n }\n default:\n {\n throw std::logic_error(\"construction for the given ConstructionWay is not implemented\");\n }\n }\n\n for (auto& point : points)\n {\n this->Insert(point);\n }\n }\n\n /**\n * Insert the K-dimensional point into the KDTree.\n * @param kd_point K-dimensional point\n */\n void Insert(const point_type& kd_point)\n {\n\n size_t cur_depth = 0;\n\n for (auto* cur_node = &root_; ; ++cur_depth)\n {\n if (*cur_node == nullptr)\n {\n *cur_node = std::make_unique(kd_point);\n break;\n }\n\n cur_node = (*cur_node)->SubtreeFor(kd_point, cur_depth);\n }\n }\n\n /**\n * Search the KD-Tree for the points lying in the k-dimensional sphere with the center {@param target_point}\n * and radius {@param distance_tolerance}.\n * @param target_point a K-dimensional point, the center of the sphere\n * @param distance_tolerance the K-dimensional sphere radius\n * @return a vector of point identifiers that lie inside the K-dimensional sphere\n */\n std::vector Search(const vector_type& target_point, const double_t distance_tolerance) const\n {\n std::vector ids;\n\n SearchInternal(ids, root_, 0, target_point, distance_tolerance);\n\n return std::move(ids);\n }\n\n /**\n * @return Pointer to the KD-Tree's root node.\n */\n const std::unique_ptr& Root() const\n {\n return root_;\n }\n\n private:\n std::unique_ptr root_; // the root node\n\n /**\n * The main logic of the \"Search\" operation of the KD-Tree.\n *\n * @param ids a vector where to store point indices\n * @param node the node to consider during the given \"SearchInternal\" invocation\n * @param depth the depth of the {@param node}\n * @param target_point the K-dimensional point, i.e., the center of the search sphere\n * @param distance_tolerance the radius of the K-dimensional search sphere\n */\n void SearchInternal(std::vector& ids,\n const std::unique_ptr& node,\n const size_t depth,\n const vector_type& target_point,\n const double_t distance_tolerance) const\n {\n if (node == nullptr)\n return;\n\n if (node->Point().IsInHypersphere(target_point, distance_tolerance))\n ids.push_back(node->Point().id);\n\n if (node->Point().IsGreaterThanOrEqualTo(target_point, depth, distance_tolerance))\n SearchInternal(ids, node->Left(), depth+1, target_point, distance_tolerance);\n\n if (node->Point().IsLessThanOrEqualTo(target_point, depth, distance_tolerance))\n SearchInternal(ids, node->Right(), depth+1, target_point, distance_tolerance);\n }\n };\n}\n\n#endif //LIDAR_OBSTACLE_DETECTION_KDTREE_HPP\n", "meta": {"hexsha": "2982afb360afefa8631f11466b6969e78a3c3d01", "size": 7481, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/KDTree.hpp", "max_stars_repo_name": "ser94mor/lidar-obstacle-detection", "max_stars_repo_head_hexsha": "e0271e6cd7c925fbd1804ecbe24eb0b7706c1f64", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 23.0, "max_stars_repo_stars_event_min_datetime": "2019-08-05T08:53:42.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T07:14:29.000Z", "max_issues_repo_path": "lidar-pcl/src/KDTree.hpp", "max_issues_repo_name": "YinRui1991/pointpillars-on-openvino", "max_issues_repo_head_hexsha": "125a51e3817d1597c4a9804a42de60e1e10816a9", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 8.0, "max_issues_repo_issues_event_min_datetime": "2019-06-20T09:23:31.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-25T09:14:06.000Z", "max_forks_repo_path": "lidar-pcl/src/KDTree.hpp", "max_forks_repo_name": "YinRui1991/pointpillars-on-openvino", "max_forks_repo_head_hexsha": "125a51e3817d1597c4a9804a42de60e1e10816a9", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 7.0, "max_forks_repo_forks_event_min_datetime": "2019-08-07T18:38:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-23T21:43:04.000Z", "avg_line_length": 28.1240601504, "max_line_length": 120, "alphanum_fraction": 0.6317337254, "num_tokens": 1728, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290806, "lm_q2_score": 0.46101677931231594, "lm_q1q2_score": 0.2323091998129194}} {"text": "// Copyright (c) 2022 CNES\n//\n// All rights reserved. Use of this source code is governed by a\n// BSD-style license that can be found in the LICENSE file.\n#pragma once\n#include \n#include \n\n#include \n#include \n\n#include \"pyinterp/geodetic/algorithm.hpp\"\n#include \"pyinterp/geodetic/point.hpp\"\n\nnamespace pyinterp::geodetic {\n\n/// Forward declaration\nclass Box;\n\nclass Polygon : public boost::geometry::model::polygon {\n public:\n using Base = boost::geometry::model::polygon;\n using Base::polygon;\n\n /// Create a new instance from Python\n Polygon(const pybind11::list& outer, const pybind11::list& inners);\n\n /// Returns the outer ring\n [[nodiscard]] auto outer() const -> pybind11::list {\n auto outer = pybind11::list();\n\n for (const auto& item : Base::outer()) {\n outer.append(item);\n }\n return outer;\n }\n\n /// Returns the inner rings\n [[nodiscard]] auto inners() const -> pybind11::list {\n auto inners = pybind11::list();\n\n for (const auto& inner : Base::inners()) {\n auto buffer = pybind11::list();\n for (const auto& item : inner) {\n buffer.append(item);\n }\n inners.append(buffer);\n }\n return inners;\n }\n\n /// Calculates the envelope of this polygon.\n [[nodiscard]] auto envelope() const -> Box;\n\n /// Get a tuple that fully encodes the state of this instance\n [[nodiscard]] auto getstate() const -> pybind11::tuple {\n return pybind11::make_tuple(this->outer(), this->inners());\n }\n\n /// Calculate the area\n [[nodiscard]] auto area(const std::optional& wgs) const -> double {\n return geodetic::area(*this, wgs);\n }\n\n /// Calculate the distance between two polygons\n [[nodiscard]] auto distance(const Polygon& other) const -> double {\n return geodetic::distance(*this, other);\n }\n\n /// Calculate the distance between this instance and a point\n [[nodiscard]] auto distance(const Point& other) const -> double {\n return geodetic::distance(*this, other);\n }\n\n /// Create a new instance from a registered state of an instance of this\n /// object.\n static auto setstate(const pybind11::tuple& state) -> Polygon {\n if (state.size() != 2) {\n throw std::runtime_error(\"invalid state\");\n }\n return {state[0].cast(), state[1].cast()};\n }\n\n /// @brief Test if the given point is inside or on border of this instance\n ///\n /// @param pt Point to test\n // @return True if the given point is inside or on border of this Polygon\n [[nodiscard]] auto covered_by(const Point& point) const -> bool {\n return boost::geometry::covered_by(point, *this);\n }\n\n /// @brief Test if the coordinates of the points provided are located inside\n /// or at the edge of this Polygon.\n ///\n /// @param lon Longitudes coordinates in degrees to check\n /// @param lat Latitude coordinates in degrees to check\n /// @return Returns a vector containing a flag equal to 1 if the coordinate is\n /// located in the Polygon or at the edge otherwise 0.\n [[nodiscard]] auto covered_by(const Eigen::Ref& lon,\n const Eigen::Ref& lat,\n const size_t num_threads) const\n -> pybind11::array_t {\n return geodetic::covered_by(*this, lon, lat, num_threads);\n }\n\n /// Converts a Polygon into a string with the same meaning as that of this\n /// instance.\n [[nodiscard]] auto to_string() const -> std::string {\n std::stringstream ss;\n ss << boost::geometry::dsv(*this);\n return ss.str();\n }\n};\n\n} // namespace pyinterp::geodetic\n\nnamespace boost::geometry::traits {\nnamespace pg = pyinterp::geodetic;\n\ntemplate <>\nstruct tag {\n using type = polygon_tag;\n};\ntemplate <>\nstruct ring_const_type {\n using type = const model::polygon::ring_type&;\n};\ntemplate <>\nstruct ring_mutable_type {\n using type = model::polygon::ring_type&;\n};\ntemplate <>\nstruct interior_const_type {\n using type = const model::polygon::inner_container_type&;\n};\ntemplate <>\nstruct interior_mutable_type {\n using type = model::polygon::inner_container_type&;\n};\n\ntemplate <>\nstruct exterior_ring {\n static auto get(model::polygon& p)\n -> model::polygon::ring_type& {\n return p.outer();\n }\n static auto get(model::polygon const& p)\n -> model::polygon::ring_type const& {\n return p.outer();\n }\n};\n\ntemplate <>\nstruct interior_rings {\n static auto get(model::polygon& p)\n -> model::polygon::inner_container_type& {\n return p.inners();\n }\n static auto get(model::polygon const& p)\n -> model::polygon::inner_container_type const& {\n return p.inners();\n }\n};\n\n} // namespace boost::geometry::traits", "meta": {"hexsha": "24f67bb75ef9647b92c7dc2c5476772fe9703be3", "size": 4978, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/pyinterp/core/include/pyinterp/geodetic/polygon.hpp", "max_stars_repo_name": "readthedocs-assistant/pangeo-pyinterp", "max_stars_repo_head_hexsha": "e9dc18445dce36638d5a90f64c8e2f1b53164f90", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/pyinterp/core/include/pyinterp/geodetic/polygon.hpp", "max_issues_repo_name": "readthedocs-assistant/pangeo-pyinterp", "max_issues_repo_head_hexsha": "e9dc18445dce36638d5a90f64c8e2f1b53164f90", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/pyinterp/core/include/pyinterp/geodetic/polygon.hpp", "max_forks_repo_name": "readthedocs-assistant/pangeo-pyinterp", "max_forks_repo_head_hexsha": "e9dc18445dce36638d5a90f64c8e2f1b53164f90", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.1696969697, "max_line_length": 80, "alphanum_fraction": 0.6639212535, "num_tokens": 1256, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.46101677931231594, "lm_q1q2_score": 0.23230919981291934}} {"text": "#ifndef NEIGHBOURFINDER_HPP\n#define NEIGHBOURFINDER_HPP\n\n#include \n#include \n#include \"../global.hpp\"\n\ntypedef Eigen::Matrix< int, Eigen::Dynamic, Eigen::Dynamic> MatDynInt; //matrix MxN of type unsigned int\ntypedef Eigen::Matrix< float, Eigen::Dynamic, Eigen::Dynamic> MatDynFloat;\ntypedef Eigen::Matrix< float, Eigen::Dynamic, registration::NUM_FEATURES> FeatureMat;\ntypedef Eigen::Matrix< float, Eigen::Dynamic, 3> Vec3Mat;\n\nnamespace registration {\n\ntemplate \nclass NeighbourFinder\n{\n /*\n GOAL\n This class searches for the k nearest neighbours in 'inSourcePoints' for\n each element in the 'inQueriedPoints' set. It outputs the indices of each\n neighbour and the squared (!) distances between each element of\n 'inQueriedPoints' and its neighbours.\n\n INPUT\n -inQueriedPoints:\n -inSourcePoints:\n\n PARAMETERS\n -numNeighbours(= 3): number of nearest neighbours\n -leafSize(= 15): should be between 5 and 50 or so\n\n OUTPUT\n -outNeighbourIndices\n -outNeighbourSquaredDistances.\n */\n\n public:\n //NeighbourFinder();\n ~NeighbourFinder(); //destructor\n\n void set_source_points(const VecMatType * const inQueriedPoints);\n void set_queried_points(const VecMatType * const _inSourcePoints);\n MatDynInt get_indices() const { return _outNeighbourIndices;}\n MatDynFloat get_distances() const { return _outNeighbourSquaredDistances;}\n void set_parameters(const size_t numNeighbours);\n void update();\n\n protected:\n\n private:\n //# Inputs\n const VecMatType * _inQueriedPoints = NULL;\n const VecMatType * _inSourcePoints = NULL;\n\n //# Outputs\n MatDynInt _outNeighbourIndices;\n MatDynFloat _outNeighbourSquaredDistances;\n //# User parameters\n\n //# Internal Data structures\n nanoflann::KDTreeEigenMatrixAdaptor * _kdTree = NULL;\n\n //# Interal parameters\n size_t _numDimensions = 0;\n size_t _numSourceElements = 0;\n size_t _numQueriedElements = 0;\n size_t _numNeighbours = 3;\n size_t _leafSize = 15;\n\n};\n\n/*\nSee http://stackoverflow.com/questions/495021/why-can-templates-only-be-implemented-in-the-header-file\nfor an explanation why we instantiate our templated class with every matrix type we will use here.\n*/\n//\ntemplate class NeighbourFinder;\ntemplate class NeighbourFinder;\n//\n\n\ntemplate \nNeighbourFinder::~NeighbourFinder()\n{\n //destructor\n if (_kdTree != NULL) { delete _kdTree; _kdTree = NULL;}\n}\n\ntemplate \nvoid NeighbourFinder::set_source_points(const VecMatType * const inSourcePoints){\n //# Set input\n _inSourcePoints = inSourcePoints;\n\n //# Update internal parameters\n _numDimensions = _inSourcePoints->cols();\n _numSourceElements = _inSourcePoints->rows();\n\n //# Update internal data structures\n //## The kd-tree has to be rebuilt.\n if (_kdTree != NULL) { delete _kdTree; _kdTree = NULL;}\n _kdTree = new nanoflann::KDTreeEigenMatrixAdaptor(*_inSourcePoints,\n _leafSize);\n _kdTree->index->buildIndex();\n}\n\n\ntemplate \nvoid NeighbourFinder::set_queried_points(const VecMatType * const inQueriedPoints){\n //# Set input\n _inQueriedPoints = inQueriedPoints;\n\n //# Update internal parameters\n _numQueriedElements = _inQueriedPoints->rows();\n\n //# Adjust internal data structures\n //## The indices and distance matrices have to be resized.\n _outNeighbourIndices.setZero(_numQueriedElements,_numNeighbours);\n _outNeighbourSquaredDistances.setZero(_numQueriedElements,_numNeighbours);\n\n}\n\n\ntemplate \nvoid NeighbourFinder::set_parameters(const size_t numNeighbours){\n //# Check if what user requests, changes the parameter value\n bool parameterChanged = false;\n if (_numNeighbours != numNeighbours){ parameterChanged = true;}\n\n //# Set parameter\n _numNeighbours = numNeighbours;\n\n //# Resize the output matrices if the parameter is changed\n if (parameterChanged == true) {\n _outNeighbourIndices.setZero(_numQueriedElements,_numNeighbours);\n _outNeighbourSquaredDistances.setZero(_numQueriedElements,_numNeighbours);\n }\n}\n\ntemplate \nvoid NeighbourFinder::update(){\n\n //# Query the kd-tree\n //## Loop over the queried features\n //### Initialize variables we'll need during the loop\n unsigned int i = 0;\n unsigned int j = 0;\n std::vector queriedFeature(_numDimensions);\n std::vector neighbourIndices(_numNeighbours);\n std::vector neighbourSquaredDistances(_numNeighbours);\n nanoflann::KNNResultSet knnResultSet(_numNeighbours);\n\n //### Execute loop\n for ( ; i < _numQueriedElements ; ++i ) {\n //### Initiliaze the knnResultSet\n knnResultSet.init(&neighbourIndices[0], &neighbourSquaredDistances[0]);\n\n //### convert input features to 'queriedFeature' std::vector structure\n //### (required by nanoflann's kd-tree).\n for (j = 0 ; j < _numDimensions ; ++j) {\n queriedFeature[j] = (*_inQueriedPoints)(i,j);\n }\n\n //### Query the kd-tree\n// size_t numNeighboursFound = kdTree.knnSearch(&queriedFeature[0], _numNeighbours, &neighbourIndices[0], &neighbourSquaredDistances[0]);\n _kdTree->index->findNeighbors(knnResultSet, &queriedFeature[0],\n nanoflann::SearchParams(32, 0.0001 /*eps*/, true));\n\n //### Copy the result into the outputs by looping over the k nearest\n //### neighbours\n for (j = 0 ; j < _numNeighbours ; ++j) {\n _outNeighbourIndices(i,j) = neighbourIndices[j];\n _outNeighbourSquaredDistances(i,j) = neighbourSquaredDistances[j];\n }\n }\n}//end k_nearest_neighbours()\n\n} //namespace registration\n\n#endif // NEIGHBOURFINDER_HPP\n", "meta": {"hexsha": "f397c8665ad0070d21f28d0c637fa21c5e7318fa", "size": 6074, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/NeighbourFinder.hpp", "max_stars_repo_name": "brisyramshere/meshmonk", "max_stars_repo_head_hexsha": "a0a7cf79902541cf9c800d83a4d4f14fcd756f6f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 53.0, "max_stars_repo_stars_event_min_datetime": "2017-02-03T14:59:54.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-14T05:40:58.000Z", "max_issues_repo_path": "src/NeighbourFinder.hpp", "max_issues_repo_name": "brisyramshere/meshmonk", "max_issues_repo_head_hexsha": "a0a7cf79902541cf9c800d83a4d4f14fcd756f6f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 16.0, "max_issues_repo_issues_event_min_datetime": "2018-07-16T10:34:15.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-18T04:37:12.000Z", "max_forks_repo_path": "src/NeighbourFinder.hpp", "max_forks_repo_name": "brisyramshere/meshmonk", "max_forks_repo_head_hexsha": "a0a7cf79902541cf9c800d83a4d4f14fcd756f6f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 23.0, "max_forks_repo_forks_event_min_datetime": "2018-07-05T14:59:52.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-14T07:01:47.000Z", "avg_line_length": 33.5580110497, "max_line_length": 144, "alphanum_fraction": 0.6934474811, "num_tokens": 1518, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266116, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.23222279986964126}} {"text": "//-*****************************************************************************\n// Copyright 2015 Christopher Jon Horvath\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n// http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n//-*****************************************************************************\n\n//-*****************************************************************************\n// The basic architecture of these Waves is based on the TweakWaves application\n// written by Chris Horvath for Tweak Films in 2001. This, in turn, was based\n// on the SIGGRAPH papers and courses by Jerry Tessendorf, and by the paper\n// \"A Simple Fluid Solver based on the FTT\" by Jos Stam.\n//\n// The TMA, JONSWAP, and Pierson Moskowitz Wave Spectra, as well as the\n// directional spreading functions are formulated based on the descriptions\n// given in \"Ocean Waves: The Stochastic Approach\",\n// by Michel K. Ochi, published by Cambridge Ocean Technology Series, 1998,2005.\n//\n// This library is written as a working implementation of the paper:\n// Christopher J. Horvath. 2015.\n// Empirical directional wave spectra for computer graphics.\n// In Proceedings of the 2015 Symposium on Digital Production (DigiPro '15),\n// Los Angeles, Aug. 8, 2015, pp. 29-39.\n//-*****************************************************************************\n\n#include \"OceanTestShaders.h\"\n#include \"OceanTestFoundation.h\"\n#include \"OceanTestViewScene.h\"\n#include \n#include \n\nnamespace po = boost::program_options;\nusing namespace OceanTest;\n\nint main(int argc, char *argv[]) {\n OceanTest::ewav::Parametersf params;\n OceanTest::Sky::Parameters sparams;\n OceanTest::Mesh::DrawParameters dparams;\n OceanTest::ViewScene::Parameters vparams;\n\n int threads = -1;\n int dispersion = 2;\n int spectrum = 2;\n int directionalSpreading = 3;\n int filter = 0;\n int random = 0;\n\n po::options_description desc(\"Encino Waves 2015\");\n desc.add_options()\n\n // clang-format off\n\n ( \"help,h\", \"prints this help message\" )\n\n\n ( \"threads\",\n po::value( &threads )\n ->default_value( threads ),\n \"Threads to use. Default of -1 means use all.\" )\n\n ( \"resolution\",\n po::value( ¶ms.resolutionPowerOfTwo )\n ->default_value( params.resolutionPowerOfTwo ),\n \"Power of Two of the sim resolution\" )\n\n ( \"domain\",\n po::value( ¶ms.domain )\n ->default_value( params.domain ),\n \"Size, in meters, of largest wave\" )\n\n ( \"gravity\",\n po::value( ¶ms.gravity )\n ->default_value( params.gravity ),\n \"Gravitational constant, in meters per second squared\" )\n\n ( \"surfaceTension\",\n po::value( ¶ms.surfaceTension )\n ->default_value( params.surfaceTension ),\n \"Surface tension constant, in Newtons per meter\" )\n\n ( \"density\",\n po::value( ¶ms.density )\n ->default_value( params.density ),\n \"Water density, in kilograms per meter cubed\" )\n\n ( \"depth\",\n po::value( ¶ms.depth )\n ->default_value( params.depth ),\n \"Average depth of the ocean, in meters\" )\n\n ( \"windSpeed\",\n po::value( ¶ms.windSpeed )\n ->default_value( params.windSpeed ),\n \"Average wind speed, in meters per second\" )\n\n ( \"fetch\",\n po::value( ¶ms.fetch )\n ->default_value( params.fetch ),\n \"Wind fetch, in KILOMETERS\" )\n\n ( \"pinch\",\n po::value( ¶ms.pinch )\n ->default_value( params.pinch ),\n \"Lateral displacement, normalized\" )\n\n ( \"amplitudeGain\",\n po::value( ¶ms.amplitudeGain )\n ->default_value( params.amplitudeGain ),\n \"Gain on the wave height\" )\n\n ( \"dispersion\",\n po::value( &dispersion )\n ->default_value( dispersion ),\n \"Dispersion: 0 for Deep, 1 for Finite Depth, 2 for Capillary\" )\n\n ( \"spectrum\",\n po::value( &spectrum )\n ->default_value( spectrum ),\n \"Spectrum: 0 for Pierson-Moskowitz, 1 for JONSWAP, 2 for TMA\" )\n\n ( \"directionalSpreading\",\n po::value( &directionalSpreading )\n ->default_value( directionalSpreading ),\n \"Directional Spreading: 0 for Balanced Cos2 Theta, \"\n \"1 for Mitsuyasu, 2 for Hasselmann, 3 for Donelan-Banner\" )\n\n ( \"swell\",\n po::value( ¶ms.directionalSpreading.swell )\n ->default_value( params.directionalSpreading.swell ),\n \"The mix between a wind-driven local sea and a swell caused by a \"\n \"distant storm.\" )\n\n ( \"filter\",\n po::value( &filter )\n ->default_value( filter ),\n \"Filter: 0 for nullptr, 1 for Smoothed Invertible Band-Pass\" )\n\n ( \"filterSoftWidth\",\n po::value( ¶ms.filter.softWidth )\n ->default_value( params.filter.softWidth ),\n \"Size in meters of the softness of wavelength filter falloff.\" )\n\n ( \"filterSmall\",\n po::value( ¶ms.filter.smallWavelength )\n ->default_value( params.filter.smallWavelength ),\n \"Size in meters of the smallest kept wavelengths.\" )\n\n ( \"filterBig\",\n po::value( ¶ms.filter.bigWavelength )\n ->default_value( params.filter.bigWavelength ),\n \"Size in meters of the biggest kept wavelengths.\" )\n\n ( \"filterMin\",\n po::value( ¶ms.filter.min )\n ->default_value( params.filter.min ),\n \"Minimum value of filter.\" )\n\n ( \"filterInvert\", \"Invert the filter\" )\n\n ( \"troughDamping\",\n po::value( ¶ms.troughDamping )\n ->default_value( params.troughDamping ),\n \"Trough damping.\" )\n\n ( \"troughDampingSmallWavelength\",\n po::value( ¶ms.troughDampingSmallWavelength )\n ->default_value( params.troughDampingSmallWavelength ),\n \"Trough damping small wavelength.\" )\n\n ( \"troughDampingBigWavelength\",\n po::value( ¶ms.troughDampingBigWavelength )\n ->default_value( params.troughDampingBigWavelength ),\n \"Trough damping big wavelength.\" )\n\n ( \"troughDampingSoftWidth\",\n po::value( ¶ms.troughDampingSoftWidth )\n ->default_value( params.troughDampingSoftWidth ),\n \"Trough damping soft width.\" )\n\n ( \"random\",\n po::value( &random )\n ->default_value( random ),\n \"Random Distribution: 0 for Normal, 1 for Log-Normal\" )\n\n ( \"seed\",\n po::value( ¶ms.random.seed )\n ->default_value( params.random.seed ),\n \"Random Seed\" )\n\n ( \"repeat\",\n po::value( &dparams.repeat )\n ->default_value( dparams.repeat ),\n \"Number of times, between 1 & 3, to repeat the drawing\" )\n\n ( \"time\",\n po::value( &sparams.time )\n ->default_value( sparams.time ),\n \"Time of day, 0 - 24\" )\n\n ( \"turbidity\",\n po::value( &sparams.turbidity )\n ->default_value( sparams.turbidity ),\n \"Turbidity\" )\n\n ( \"skyTexture\",\n po::value( &sparams.filename ),\n \"Sky Texture\" )\n\n ( \"outFileBase\",\n po::value()\n ->default_value( \"EncinoWaves\" ),\n \"The output file name base\" )\n\n ;\n\n // clang-format on\n\n po::variables_map vm;\n po::store(po::command_line_parser(argc, argv).options(desc).run(), vm);\n po::notify(vm);\n\n //-*************************************************************************\n if (vm.count(\"help\")) {\n std::cout << desc << std::endl;\n return 0;\n }\n\n if (vm.count(\"filterInvert\")) {\n params.filter.invert = true;\n }\n\n if (vm.count(\"outFileBase\")) {\n vparams.outputFileBase = vm[\"outFileBase\"].as();\n }\n\n params.dispersion.type = (ewav::DispersionType)dispersion;\n params.spectrum.type = (ewav::SpectrumType)spectrum;\n params.directionalSpreading.type =\n (ewav::DirectionalSpreadingType)directionalSpreading;\n params.filter.type = (ewav::FilterType)filter;\n params.random.type = (ewav::RandomType)random;\n\n std::shared_ptr simPtr(\n new OceanTest::ViewScene(params, sparams, dparams, vparams));\n\n EncinoWaves::SimpleSimViewer::SimpleViewSim(simPtr, true);\n\n return 0;\n}\n", "meta": {"hexsha": "3b76c43afd0a0bf74ae489aef4783d8cacb53153", "size": 8587, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/EncinoWaves/Tests/OceanTestMain.cpp", "max_stars_repo_name": "NTForked/EncinoWaves", "max_stars_repo_head_hexsha": "b7db46962e8405e2c7fb91b3eb7fda03d78489d4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 64.0, "max_stars_repo_stars_event_min_datetime": "2015-08-08T08:53:03.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T15:45:21.000Z", "max_issues_repo_path": "src/EncinoWaves/Tests/OceanTestMain.cpp", "max_issues_repo_name": "NTForked/EncinoWaves", "max_issues_repo_head_hexsha": "b7db46962e8405e2c7fb91b3eb7fda03d78489d4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2018-10-10T18:50:26.000Z", "max_issues_repo_issues_event_max_datetime": "2018-10-10T18:50:26.000Z", "max_forks_repo_path": "src/EncinoWaves/Tests/OceanTestMain.cpp", "max_forks_repo_name": "NTForked/EncinoWaves", "max_forks_repo_head_hexsha": "b7db46962e8405e2c7fb91b3eb7fda03d78489d4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 15.0, "max_forks_repo_forks_event_min_datetime": "2015-08-09T02:40:15.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-11T19:39:29.000Z", "avg_line_length": 33.1544401544, "max_line_length": 80, "alphanum_fraction": 0.624082916, "num_tokens": 2120, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.4339814648038986, "lm_q1q2_score": 0.23222279343467941}} {"text": "#include \"PurificationDecorator.hpp\"\n#include \"IRProvider.hpp\"\n#include \"InstructionIterator.hpp\"\n#include \"PauliOperator.hpp\"\n#include \"XACC.hpp\"\n#include \n#include \"xacc_service.hpp\"\n\nusing namespace xacc::quantum;\n\nnamespace xacc {\nnamespace vqe {\nvoid PurificationDecorator::execute(std::shared_ptr buffer,\n const std::shared_ptr function) {\n\n if (!decoratedAccelerator) {\n xacc::error(\"Null Decorated Accelerator Error\");\n }\n\n return;\n}\n\nstd::vector> PurificationDecorator::execute(\n std::shared_ptr buffer,\n const std::vector> functions) {\n\n std::vector> buffers;\n\n if (!decoratedAccelerator) {\n xacc::error(\"PurificationDecorator - Null Decorated Accelerator Error\");\n }\n\n // Here I expect the ansatz to be functions[0]->getInstruction(0);\n auto ansatz =\n std::dynamic_pointer_cast(functions[0]->getInstruction(0));\n\n if (!ansatz)\n xacc::error(\"ANSATZ IS NULL\");\n\n // Generate all nQubit Pauli Strings\n std::vector XYZ;\n std::set temp, PauliStrings;\n std::vector Paulis;\n\n for (int i = 0; i < buffer->size(); i++)\n XYZ.push_back(\"X\" + std::to_string(i));\n for (int i = 0; i < buffer->size(); i++)\n XYZ.push_back(\"Y\" + std::to_string(i));\n for (int i = 0; i < buffer->size(); i++)\n XYZ.push_back(\"Z\" + std::to_string(i));\n\n std::function &, std::string, const int,\n const int)>\n y;\n y = [&](std::vector &set, std::string prefix, const int n,\n const int k) {\n if (k == 0) {\n PauliOperator op;\n op.fromString(prefix);\n PauliStrings.insert(op.toString());\n return;\n }\n\n for (int i = 0; i < n; i++) {\n auto newPrefix = prefix + \" \" + set[i];\n y(set, newPrefix, n, k - 1);\n }\n };\n\n auto x = [&](std::vector &set, const int k) {\n auto n = set.size();\n y(set, std::string(\"\"), n, k);\n };\n\n // Get all Permutations of XYZ\n x(XYZ, buffer->size());\n\n PauliOperator all;\n std::map opMap;\n for (auto &s : PauliStrings) {\n PauliOperator op;\n op.fromString(s);\n Paulis.push_back(op);\n all += op;\n opMap.insert({op.getTerms().begin()->second.id(), op});\n }\n\n for (auto &kv : opMap)\n std::cout << kv.first << \", \" << kv.second.toString() << \"\\n\";\n\n std::cout << all.toString() << \"\\n\";\n std::cout << Paulis.size() << \" operators to measure\\n\";\n\n auto kernels = all.toXACCIR()->getKernels();\n for (auto &k : kernels) {\n k->insertInstruction(0, ansatz);\n }\n\n// std::cout << \"EXECUTING\\n\";\n buffers = decoratedAccelerator->execute(buffer, kernels);\n\n std::size_t dim = 1;\n std::size_t two = 2;\n for (int i = 0; i < buffer->size(); i++)\n dim *= two;\n\n Eigen::MatrixXcd rho(dim, dim);\n rho.setZero();\n\n std::map> bufMap;\n for (auto &b : buffers) {\n auto id = b->name();\n bufMap.insert({id,b});\n auto op = opMap[id];\n\n auto data = op.toDenseMatrix(buffer->size()).data();\n Eigen::MatrixXcd p = Eigen::Map(data, dim,dim);\n rho += b->getExpectationValueZ() * p;\n }\n\n // Add the Identity term Tr(I rho) * I = * I\n rho += Eigen::MatrixXcd::Identity(dim, dim);\n\n// std::cout << \"RHO:\\n\" << rho << \"\\n\";\n// std::cout << rho.trace() << \"\\n\";\n\n Eigen::MatrixXcd rhosq = rho * rho;\n Eigen::MatrixXcd diff = rhosq - rho;\n Eigen::MatrixXcd diffsq = diff * diff;\n\n int counter = 0;\n auto tr = std::real(diffsq.trace());\n while (tr > 1e-4) {\n rho = 3. * rhosq - 2. * rhosq * rho;\n rho /= rho.trace();\n\n rhosq = rho * rho;\n diff = rhosq - rho;\n diffsq = diff * diff;\n tr = std::real(diffsq.trace());\n // std::cout << counter << \", TRACE: \" << tr << \"\\n\";\n counter++;\n if (counter > 100)\n break;\n }\n\n// std::cout << rho.trace() << \"\\n\" << rho << \"\\n\";\n\n // new E = Tr(H*rho)\n // so get H\n PauliOperator HOp;\n auto ir = xacc::getService(\"gate\")->createIR();\n for (auto &f : functions) {\n ir->addKernel(f);\n }\n HOp.fromXACCIR(ir);\n\n// std::cout << \"MADE IT HERE\\n\";\n// std::cout << HOp.toString() << \"\\n\";\n\n auto identityCoeff = mpark::get(buffer->getInformation(\"identity-coeff\"));\n xacc::info(std::to_string(identityCoeff));\n auto ID = -1 * identityCoeff * Eigen::MatrixXcd::Identity(dim,dim);\n auto data = HOp.toDenseMatrix(buffer->size()).data();\n Eigen::MatrixXcd H = Eigen::Map(data, dim,dim) - ID;\n\n// std::cout << \"CONJ:\\n\" << (rho - rho.conjugate()) << \"\\n\";\n// std::cout << \"IDEMP:\\n\" << (rho*rho - rho) << \"\\n\";\n// std::cout << \"H:\\n\" << H << \"\\n\";\n// std::cout << \"HI\\n\";\n std::cout << \"Energy: \" << std::real((rho * H).trace()) << \"\\n\";\n\n // Need to take new rho and compute

= Tr(P rho) for each of our\n // input functions\n\n std::vector> retBuffers;\n for (auto& f : functions) {\n// if (f->name() == \"I\") {\n// continue;\n// }\n// auto tmp = xacc::getService(\"gate\")->createIR();\n// tmp->addKernel(f);\n\n// PauliOperator o;\n// o.fromXACCIR(tmp);\n\n// auto mat = o.toDenseMatrix(buffer->size());\n// auto expVal = std::real((rho * mat).trace());\n\n if (bufMap.count(f->name())) {\n auto b = bufMap[f->name()];\n b->addExtraInfo(\"purified-energy\",ExtraInfo(std::real((rho*H).trace())));\n// b->addExtraInfo(\"exp-val-z\", ExtraInfo(expVal));\n retBuffers.push_back(b);\n }\n }\n\n return retBuffers;\n}\n\n} // namespace vqe\n} // namespace xacc\n", "meta": {"hexsha": "5972a526df9103acc7ff7542ef5a63650b4604af", "size": 5743, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "decorators/PurificationDecorator.cpp", "max_stars_repo_name": "zpparks314/xacc-vqe", "max_stars_repo_head_hexsha": "37aaadb12d856324532c42ca9f7e56147edbd2e7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 14.0, "max_stars_repo_stars_event_min_datetime": "2017-09-15T19:05:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-04T05:24:51.000Z", "max_issues_repo_path": "decorators/PurificationDecorator.cpp", "max_issues_repo_name": "zpparks314/xacc-vqe", "max_issues_repo_head_hexsha": "37aaadb12d856324532c42ca9f7e56147edbd2e7", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 25.0, "max_issues_repo_issues_event_min_datetime": "2017-08-08T16:03:55.000Z", "max_issues_repo_issues_event_max_datetime": "2019-12-22T12:18:27.000Z", "max_forks_repo_path": "decorators/PurificationDecorator.cpp", "max_forks_repo_name": "zpparks314/xacc-vqe", "max_forks_repo_head_hexsha": "37aaadb12d856324532c42ca9f7e56147edbd2e7", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 17.0, "max_forks_repo_forks_event_min_datetime": "2018-06-25T20:20:16.000Z", "max_forks_repo_forks_event_max_datetime": "2021-04-03T18:31:44.000Z", "avg_line_length": 28.2906403941, "max_line_length": 84, "alphanum_fraction": 0.5904579488, "num_tokens": 1742, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.4186969093556867, "lm_q1q2_score": 0.2321550700079415}} {"text": "/* Copyright (C) 2013-2016, The Regents of The University of Michigan.\nAll rights reserved.\nThis software was developed in the APRIL Robotics Lab under the\ndirection of Edwin Olson, ebolson@umich.edu. This software may be\navailable under alternative licensing terms; contact the address above.\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n1. Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n2. Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND\nANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR\nANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\nON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\nThe views and conclusions contained in the software and documentation are those\nof the authors and should not be interpreted as representing official policies,\neither expressed or implied, of the Regents of The University of Michigan.\n*/\n\n#include \n#include \n\n#include \"opencv2/opencv.hpp\"\n#include \"opencv2/core/eigen.hpp\"\n#include \"apriltag_pose.h\"\n#include \n#include \n#include \n\nextern \"C\" {\n#include \"apriltag.h\"\n#include \"tag36h11.h\"\n#include \"common/getopt.h\"\n}\n#include \n\n// #include \n// #include \n// #include \n// #include \n// #include \n// #include \n// #include \n// #include \n\n\nEigen::Matrix3d cam_R;\nEigen::Vector3d cam_t;\nEigen::Vector3d t_last, t_curr;\ndouble update_t = 0;\n\ntypedef std::vector > VecSE3;\ntypedef std::vector > VecVec3d;\ntypedef std::vector > VecVec2d;\n// apriltag 4-corner 3d points and 2d pixel\nVecVec3d tagpoints;\nVecVec2d tagpixels;\n\ndouble fx = 9.7185090185201193e+02;\ndouble fy = 9.3550247129566696e+02;\ndouble cx = 3.2389824020348124e+02;\ndouble cy = 2.1394131657430057e+02;\n\n#define RATIO 0.4 // orb \n\n// // g2o vertex that use sophus::SE3 as pose\n// class VertexSophus : public g2o::BaseVertex<6, Sophus::SE3> {\n// public:\n// EIGEN_MAKE_ALIGNED_OPERATOR_NEW\n\n// VertexSophus() {}\n// ~VertexSophus() {}\n\n// bool read(std::istream &is) {}\n// bool write(std::ostream &os) const {}\n\n// virtual void setToOriginImpl() {\n// _estimate = Sophus::SE3();\n// }\n\n// virtual void oplusImpl(const double *update_) {\n// Eigen::Map> update(update_);\n// setEstimate(Sophus::SE3::exp(update) * estimate());\n// }\n// };\n\n// class EdgeProjectXYZ2UVPoseOnly: public g2o::BaseUnaryEdge<2, Eigen::Vector2d, VertexSophus >\n// {\n// public:\n// EIGEN_MAKE_ALIGNED_OPERATOR_NEW\n \n// virtual void computeError();\n// virtual void linearizeOplus();\n \n// virtual bool read( std::istream& in ){}\n// virtual bool write(std::ostream& os) const {};\n \n// Eigen::Vector3d point_;\n// };\n\n// void EdgeProjectXYZ2UVPoseOnly::computeError()\n// {\n// // compute projection error ...\n// const VertexSophus *vertexTcw = static_cast( vertex(0) );\n// Eigen::Vector3d p_c = vertexTcw->estimate()*point_;\n// Eigen::Vector2d p_uv = Eigen::Vector2d (\n// fx * p_c ( 0,0 ) / p_c ( 2,0 ) + cx,\n// fy * p_c ( 1,0 ) / p_c ( 2,0 ) + cy\n// );\n\n// _error = _measurement - p_uv;\n// }\n\n// void EdgeProjectXYZ2UVPoseOnly::linearizeOplus()\n// {\n// const VertexSophus* vertexTcw = static_cast( vertex(0) );\n// Eigen::Vector3d xyz_trans = vertexTcw->estimate()*point_;\n// double x = xyz_trans[0];\n// double y = xyz_trans[1];\n// double z = xyz_trans[2];\n// double z_2 = z*z;\n\n// _jacobianOplusXi ( 0,0 ) = x*y/z_2 *fx;\n// _jacobianOplusXi ( 0,1 ) = - ( 1+ ( x*x/z_2 ) ) *fx;\n// _jacobianOplusXi ( 0,2 ) = y/z * fx;\n// _jacobianOplusXi ( 0,3 ) = -1./z *fx;\n// _jacobianOplusXi ( 0,4 ) = 0;\n// _jacobianOplusXi ( 0,5 ) = x/z_2 *fx;\n\n// _jacobianOplusXi ( 1,0 ) = ( 1+y*y/z_2 ) *fy;\n// _jacobianOplusXi ( 1,1 ) = -x*y/z_2 *fy;\n// _jacobianOplusXi ( 1,2 ) = -x/z *fy;\n// _jacobianOplusXi ( 1,3 ) = 0;\n// _jacobianOplusXi ( 1,4 ) = -1./z *fy;\n// _jacobianOplusXi ( 1,5 ) = y/z_2 *fy;\n// }\n\n// plot the poses and points for you, need pangolin\nvoid Draw(const VecSE3 &poses, const VecVec3d &points, const apriltag_detection_info_t &info);\n\nvoid triangulation (\n const std::vector& keypoint_1,\n const std::vector& keypoint_2,\n const std::vector< cv::DMatch >& matches,\n cv::Mat T1, cv::Mat T2,\n std::vector& points);\n\nint main(int argc, char *argv[])\n{\n getopt_t *getopt = getopt_create();\n\n getopt_add_bool(getopt, 'h', \"help\", 0, \"Show this help\");\n getopt_add_bool(getopt, 'd', \"debug\", 1, \"Enable debugging output (slow)\");\n getopt_add_bool(getopt, 'q', \"quiet\", 0, \"Reduce output\");\n getopt_add_string(getopt, 'f', \"family\", \"tag36h11\", \"Tag family to use\");\n getopt_add_int(getopt, 't', \"threads\", \"1\", \"Use this many CPU threads\");\n getopt_add_double(getopt, 'x', \"decimate\", \"2.0\", \"Decimate input image by this factor\");\n getopt_add_double(getopt, 'b', \"blur\", \"0.0\", \"Apply low-pass blur to input\");\n getopt_add_bool(getopt, '0', \"refine-edges\", 1, \"Spend more time trying to align edges of tags\");\n\n if (!getopt_parse(getopt, argc, argv, 1) ||\n getopt_get_bool(getopt, \"help\")) {\n printf(\"Usage: %s [options]\\n\", argv[0]);\n getopt_do_usage(getopt);\n exit(0);\n }\n\n // Initialize camera\n cv::VideoCapture cap(0);\n if (!cap.isOpened()) {\n std::cerr << \"Couldn't open video capture device\" << std::endl;\n return -1;\n }\n\n // Initialize tag detector with options\n apriltag_family_t *tf = NULL;\n const char *famname = getopt_get_string(getopt, \"family\");\n if (!strcmp(famname, \"tag36h11\")) {\n tf = tag36h11_create();\n } else {\n printf(\"Unrecognized tag family name. Use e.g. \\\"tag36h11\\\".\\n\");\n exit(-1);\n }\n\n apriltag_detection_info_t info;\n info.tagsize = 13.8; //cm\n info.fx = fx;\n info.fy = fy;\n info.cx = cx;\n info.cy = cy;\n\n double scale = info.tagsize/2.0; // apriltag 4-corner 3d points \n\n tagpoints.push_back( Eigen::Vector3d ( -scale, scale, 0 ) );\n tagpoints.push_back( Eigen::Vector3d ( scale, scale, 0 ) );\n tagpoints.push_back( Eigen::Vector3d ( scale, -scale, 0 ) );\n tagpoints.push_back( Eigen::Vector3d ( -scale, -scale, 0 ) );\n\n\n double camera_matrix[] =\n {\n info.fx, 0.0, info.cx,\n 0.0, info.fy, info.cy,\n 0.0, 0.0, 1.0\n };\n double dist_coeff[] = {-1.0351230255308908e+00, -1.9662350850900434e+00, 0.0, 0.0};\n\n cv::Mat m_camera_matrix = cv::Mat(3, 3, CV_64FC1, camera_matrix).clone();\n cv::Mat m_dist_coeff = cv::Mat(1, 4, CV_64FC1, dist_coeff).clone();\n\n\n apriltag_detector_t *td = apriltag_detector_create();\n apriltag_detector_add_family(td, tf);\n td->quad_decimate = getopt_get_double(getopt, \"decimate\");\n td->quad_sigma = getopt_get_double(getopt, \"blur\");\n td->nthreads = getopt_get_int(getopt, \"threads\");\n td->debug = getopt_get_bool(getopt, \"debug\");\n td->refine_edges = getopt_get_bool(getopt, \"refine-edges\");\n\n cv::Mat frame, gray;\n VecSE3 poses;\n VecVec3d points;\n std::vector points_cv;\n\n \n cv::Mat last_img, cur_img;\n cv::Mat T1, T2;\n // cv::Mat T1 = (cv::Mat_ (3,4) <<\n // 1,0,0,0,\n // 0,1,0,0,\n // 0,0,1,0);\n // cv::Mat T2 = (cv::Mat_ (3,4) <<\n // R.at(0,0), R.at(0,1), R.at(0,2), t.at(0,0),\n // R.at(1,0), R.at(1,1), R.at(1,2), t.at(1,0),\n // R.at(2,0), R.at(2,1), R.at(2,2), t.at(2,0)\n // );\n bool initial = false;\n int limit_min_times = 0;\n while (true) {\n cap >> frame;\n cv::cvtColor(frame, gray, cv::COLOR_BGR2GRAY);\n \n // Make an image_u8_t header for the Mat data\n image_u8_t im = { .width = gray.cols,\n .height = gray.rows,\n .stride = gray.cols,\n .buf = gray.data\n };\n\n zarray_t *detections = apriltag_detector_detect(td, &im);\n // cout << zarray_size(detections) << \" tags detected\" << endl;\n\n // Draw detection outlines\n for (int i = 0; i < zarray_size(detections); i++) {\n limit_min_times++;\n\n apriltag_detection_t *det;\n zarray_get(detections, i, &det);\n info.det = det; \n apriltag_pose_t pose;\n\t\t\tdouble err = estimate_tag_pose(&info, &pose);\n double wx, wy, wz;\n\n double scale = 107.0/220.0; //depth scale = realvalue / measurement = 107cm/220\n\n wx = pose.t->data[0]*scale;\n wy = pose.t->data[1]*scale;\n wz = pose.t->data[2]*scale;\n\t\t\t// cout << wx <<\" \"<< wy << \" \" << wz << endl;\n\n\n if( !initial ){\n last_img = gray.clone();\n cur_img = gray.clone();\n T1 = (cv::Mat_ (3,4) <<\n pose.R->data[0], pose.R->data[1], pose.R->data[2], pose.t->data[0],\n pose.R->data[3], pose.R->data[4], pose.R->data[5], pose.t->data[1],\n pose.R->data[6], pose.R->data[7], pose.R->data[8], pose.t->data[2]\n );\n T2 = (cv::Mat_ (3,4) <<\n pose.R->data[0], pose.R->data[1], pose.R->data[2], pose.t->data[0],\n pose.R->data[3], pose.R->data[4], pose.R->data[5], pose.t->data[1],\n pose.R->data[6], pose.R->data[7], pose.R->data[8], pose.t->data[2]\n );\n // initial = true;\n if( limit_min_times > 10){\n initial = true;\n }\n }else{\n // std::cout << \"initial is ok!!!!\" << \"\\n\";\n cur_img = gray.clone();\n T2 = (cv::Mat_ (3,4) <<\n pose.R->data[0], pose.R->data[1], pose.R->data[2], pose.t->data[0],\n pose.R->data[3], pose.R->data[4], pose.R->data[5], pose.t->data[1],\n pose.R->data[6], pose.R->data[7], pose.R->data[8], pose.t->data[2]\n );\n }\n // std::cout << \"initial:\" << initial << \"||\" << T1.size() << \"||\" << T2.size() << \"\\n\";\n // std::cout << \"last_img\" << last_img.size() << \" cur_img\" << cur_img.size() << \"\\n\";\n\n \n double depth = sqrt( wx*wx + wy*wy + wz*wz );\n \n // cout << \"depth = \" << depth << endl;\n\t\t\t// cout << \"R size:\" << pose.R->nrows <<\" \"<< pose.R->ncols << endl; //3*3\n // cout << \"R:\" << endl;\n // cout << pose.R->data[0] <<\" \"<< pose.R->data[1] << \" \" << pose.R->data[2] << endl; //3*3\n // cout << pose.R->data[3] <<\" \"<< pose.R->data[4] << \" \" << pose.R->data[5] << endl; //3*3\n // cout << pose.R->data[6] <<\" \"<< pose.R->data[7] << \" \" << pose.R->data[8] << endl; //3*3\n\n\n cv::line(frame, cv::Point(det->p[0][0], det->p[0][1]),\n cv::Point(det->p[1][0], det->p[1][1]),\n cv::Scalar(0xff, 0xff, 0xff), 2);\n cv::line(frame, cv::Point(det->p[0][0], det->p[0][1]),\n cv::Point(det->p[3][0], det->p[3][1]),\n cv::Scalar(0xff, 0xff, 0xff), 2);\n cv::line(frame, cv::Point(det->p[1][0], det->p[1][1]),\n cv::Point(det->p[2][0], det->p[2][1]),\n cv::Scalar(0xff, 0xff, 0xff), 2);\n cv::line(frame, cv::Point(det->p[2][0], det->p[2][1]),\n cv::Point(det->p[3][0], det->p[3][1]),\n cv::Scalar(0xff, 0xff, 0xff), 2);\n\n cv::circle(frame, cv::Point(det->p[0][0], det->p[0][1]),\n 2,\n cv::Scalar(0xff, 0xff, 0), 2); \n\n std::stringstream ss;\n ss << det->id;\n cv::String text = ss.str();\n int fontface = cv::FONT_HERSHEY_SCRIPT_SIMPLEX;\n double fontscale = 1.0;\n int baseline;\n cv::Size textsize = getTextSize(text, fontface, fontscale, 2,\n &baseline);\n putText(frame, text, cv::Point(det->c[0]-textsize.width/2,\n det->c[1]+textsize.height/2),\n fontface, fontscale, cv::Scalar(0xff, 0x99, 0), 2);\n\n\n cam_t << wx, wy, wz;\n cam_R << pose.R->data[0], pose.R->data[1], pose.R->data[2],\n pose.R->data[3], pose.R->data[4], pose.R->data[5],\n pose.R->data[6], pose.R->data[7], pose.R->data[8];\n\n\n // three-dimensional cube test (cudePoints)\n std::vector< cv::Point3f > cubePoints;\n cubePoints.push_back(cv::Point3f(-0.5, -0.5, 0.0));\n cubePoints.push_back(cv::Point3f( 0.5, -0.5, 0.0));\n cubePoints.push_back(cv::Point3f( 0.5, 0.5, 0.0));\n cubePoints.push_back(cv::Point3f(-0.5, 0.5, 0.0));\n cubePoints.push_back(cv::Point3f(-0.5, -0.5, 1.0));\n cubePoints.push_back(cv::Point3f( 0.5, -0.5, 1.0));\n cubePoints.push_back(cv::Point3f( 0.5, 0.5, 1.0));\n cubePoints.push_back(cv::Point3f(-0.5, 0.5, 1.0));\n\n\n std::vector< cv::Point2f > imagePoints;\n bool solvePnP_isok = false;\n cv::Mat cv_cam_R, cv_cam_t;\n if ( solvePnP_isok ){\n cv::Point3f corners_3d[] =\n {\n cv::Point3f(-0.5f, -0.5f, 0),\n cv::Point3f(-0.5f, 0.5f, 0),\n cv::Point3f( 0.5f, 0.5f, 0),\n cv::Point3f( 0.5f, -0.5f, 0)\n };\n std::vector m_corners_3d = std::vector(corners_3d, corners_3d + 4);\n std::vector m_corners;\n m_corners.push_back(cv::Point2f(det->p[0][0], det->p[0][1]));\n m_corners.push_back(cv::Point2f(det->p[1][0], det->p[1][1]));\n m_corners.push_back(cv::Point2f(det->p[2][0], det->p[2][1]));\n m_corners.push_back(cv::Point2f(det->p[3][0], det->p[3][1]));\n\n cv::Mat rot_vec;\n cv::solvePnP(m_corners_3d, m_corners, m_camera_matrix, m_dist_coeff, rot_vec, cv_cam_t);\n cv::Rodrigues(rot_vec, cv_cam_R);\n cv::projectPoints(cubePoints, cv_cam_R, cv_cam_t, m_camera_matrix, m_dist_coeff, imagePoints);\n }else{\n for ( auto &p:cubePoints ){\n p = p*5;\n }\n cv::eigen2cv(cam_R, cv_cam_R);\n cv::eigen2cv(cam_t, cv_cam_t);\n cv::projectPoints(cubePoints, cv_cam_R, cv_cam_t, m_camera_matrix, m_dist_coeff, imagePoints);\n }\n \n\n\n // draw cube lines\n cv::line(frame, imagePoints[0], imagePoints[1], cv::Scalar(0, 0, 0xff), 2);\n cv::line(frame, imagePoints[1], imagePoints[2], cv::Scalar(0, 0, 0xff), 2);\n cv::line(frame, imagePoints[2], imagePoints[3], cv::Scalar(0, 0, 0xff), 2);\n cv::line(frame, imagePoints[3], imagePoints[0], cv::Scalar(0, 0, 0xff), 2);\n\n cv::line(frame, imagePoints[4], imagePoints[5], cv::Scalar(0xff, 0, 0), 2);\n cv::line(frame, imagePoints[5], imagePoints[6], cv::Scalar(0xff, 0, 0), 2);\n cv::line(frame, imagePoints[6], imagePoints[7], cv::Scalar(0xff, 0, 0), 2);\n cv::line(frame, imagePoints[7], imagePoints[4], cv::Scalar(0xff, 0, 0), 2);\n\n cv::line(frame, imagePoints[0], imagePoints[4], cv::Scalar(0, 0xff, 0), 2);\n cv::line(frame, imagePoints[1], imagePoints[5], cv::Scalar(0, 0xff, 0), 2);\n cv::line(frame, imagePoints[2], imagePoints[6], cv::Scalar(0, 0xff, 0), 2);\n cv::line(frame, imagePoints[3], imagePoints[7], cv::Scalar(0, 0xff, 0), 2);\n\n\n // Note that every variable that we compute is proportional to the scale factor of H.\n // or pnp, cv::solvePnP cv::Rodrigues\n\n double H00 = MATD_EL(det->H, 0, 0);\n double H01 = MATD_EL(det->H, 0, 1);\n double H02 = MATD_EL(det->H, 0, 2);\n double H10 = MATD_EL(det->H, 1, 0);\n double H11 = MATD_EL(det->H, 1, 1);\n double H12 = MATD_EL(det->H, 1, 2);\n double H20 = MATD_EL(det->H, 2, 0);\n double H21 = MATD_EL(det->H, 2, 1);\n double H22 = MATD_EL(det->H, 2, 2);\n // cout << \"H = \" << H00 << \" \" << H01 << endl;\n \n for( int i = 0; i < 4; i++ ){\n tagpixels.push_back( Eigen::Vector2d(det->p[i][0], det->p[i][1]) );\n }\n Sophus::SE3 SE3_Rt( cam_R, cam_t );\n poses.push_back( SE3_Rt );\n\n // last_img = gray.clone();\n // cur_img = gray.clone();\n\n if( initial ){\n // ORB Features\n std::vector keypoints_sence, keypoints_obj;\n cv::Mat descriptors_box, descriptors_sence;\n cv::Ptr detector = cv::ORB::create();\n\n detector->detectAndCompute(last_img, cv::Mat(), keypoints_sence, descriptors_sence);\n detector->detectAndCompute(cur_img, cv::Mat(), keypoints_obj, descriptors_box);\n std::vector matches;\n // 初始化flann匹配\n // cv::Ptr matcher = cv::FlannBasedMatcher::create(); // default is bad, using local sensitive hash(LSH)\n cv::Ptr matcher = cv::makePtr(cv::makePtr(12, 20, 2));\n matcher->match(descriptors_box, descriptors_sence, matches);\n // 发现匹配\n std::vector goodMatches;\n \n float maxdist = 0;\n for (unsigned int i = 0; i < matches.size(); ++i) {\n // printf(\"dist : %.2f \\n\", matches[i].distance);\n maxdist = cv::max(maxdist, matches[i].distance);\n }\n for (unsigned int i = 0; i < matches.size(); ++i) {\n if (matches[i].distance < maxdist*RATIO)\n goodMatches.push_back(matches[i]);\n }\n\n // cv::Mat dst;\n // cv::drawMatches(cur_img, keypoints_obj, last_img, keypoints_sence, goodMatches, dst);\n // cv::imshow(\"output\", dst);\n // cv::waitKey(0);\n // cv::Mat R_cv = (cv::Mat_ (3,3) <<\n // pose.R->data[0], pose.R->data[1], pose.R->data[2],\n // pose.R->data[3], pose.R->data[4], pose.R->data[5],\n // pose.R->data[6], pose.R->data[7], pose.R->data[8]);\n \n // cv::Mat t_cv = (cv::Mat_ (3,1) <<\n // wx, wy, wz);\n \n // std::cout << R_cv << \"\\n\";\n // std::cout << t_cv << \"\\n\";\n // std::cout << \"keypoints_sence:\" << keypoints_sence.size() << \"\\n\";\n // std::cout << \"keypoints_obj:\" << keypoints_obj.size() << \"\\n\";\n // std::cout << \"T1:\" << T1 << \"\\n\\n\";\n // std::cout << \"T2:\" << T2 << \"\\n\";\n // printf(\"total match points : %d || goodMatches: %d\\n\", matches.size(), goodMatches.size());\n \n // 绘制关键点\n // cv::Mat keypoint_img;\n cv::drawKeypoints(frame, keypoints_obj, frame, cv::Scalar::all(-1), cv::DrawMatchesFlags::DEFAULT);\n // cv::imshow(\"KeyPoints Image\", keypoint_img);\n\n\n \n t_last << T1.at(0,3), T1.at(1,3), T1.at(2,3);\n t_curr << T2.at(0,3), T2.at(1,3), T2.at(2,3);\n Eigen::Vector3d t_tmp = t_last - t_curr;\n update_t = fabsf(t_tmp[0]) + fabsf(t_tmp[1]) + fabsf(t_tmp[2]);\n std::cout << \"update_t:\"<< update_t << \"\\n\";\n // std::cout << \"t_last:\" << t_last << \"\\n\";\n // std::cout << \"t_curr:\" << t_curr << \"\\n\";\n if(goodMatches.size()>0 && update_t > 10){\n // std::cout << \"keypoints_sence:\" << keypoints_sence.size() << \"\\n\";\n // std::cout << \"keypoints_obj:\" << keypoints_obj.size() << \"\\n\";\n // std::cout << \"T1:\" << T1 << \"\\n\";\n // std::cout << \"T2:\" << T2 << \"\\n\";\n triangulation ( keypoints_sence, keypoints_obj, goodMatches, T1, T2, points_cv );\n }\n }\n }\n if(update_t > 10){\n last_img = cur_img.clone();\n T1 = T2.clone();\n }\n\n \n\n \n \n apriltag_detections_destroy(detections);\n\n imshow(\"Tag Detections\", frame);\n\n if (cv::waitKey(30) == 'q')\n break;\n }\n // // using bundle adjustment to optimize the pose\n // typedef g2o::BlockSolver> Block;\n // Block::LinearSolverType* linearSolver = new g2o::LinearSolverDense();\n // Block* solver_ptr = new Block( linearSolver );\n // g2o::OptimizationAlgorithmLevenberg* solver = new g2o::OptimizationAlgorithmLevenberg ( solver_ptr );\n // g2o::SparseOptimizer optimizer;\n // optimizer.setAlgorithm ( solver );\n\n\n // // add pose vertices\n // for( int j = 0; j < poses.size(); j++ ){\n // VertexSophus* vertexTcw = new VertexSophus();\n // vertexTcw->setEstimate( poses[j] );\n // vertexTcw->setId( j );\n // optimizer.addVertex( vertexTcw );\n // }\n\n // // edges\n // for( int c = 0; c < poses.size(); c++ )\n // for ( int i=0; i<4; i++ ){\n // // 3D -> 2D projection\n // EdgeProjectXYZ2UVPoseOnly* edge = new EdgeProjectXYZ2UVPoseOnly();\n // // edge->setId ( i );\n // edge->setVertex ( 0, dynamic_cast(optimizer.vertex(c)) );\n // edge->point_ = tagpoints[i];\n // edge->setMeasurement ( tagpixels[c*4 + i] );\n // edge->setInformation ( Eigen::Matrix2d::Identity() );\n // optimizer.addEdge ( edge );\n // }\n \n // optimizer.initializeOptimization();\n // optimizer.optimize ( 10 );\n\n // // fetch data from the optimizer\n // for(int c = 0; c < poses.size(); c++){\n // // Eigen::Vector3d Pw = dynamic_cast(optimizer.vertex(p))->estimate();\n // // points[p] = Pw;\n // Sophus::SE3 Tcw = dynamic_cast( optimizer.vertex(c) )->estimate();\n // poses[c] = Tcw;\n // }\n\n for(int pt_indx = 0; pt_indx < points_cv.size(); pt_indx++){\n points.push_back( Eigen::Vector3d ( points_cv[pt_indx].x, points_cv[pt_indx].y, points_cv[pt_indx].z ) );\n }\n // points.push_back( Eigen::Vector3d ( -scale, scale, 0 ) );\n // points.push_back( Eigen::Vector3d ( scale, scale, 0 ) );\n // points.push_back( Eigen::Vector3d ( scale, -scale, 0 ) );\n // points.push_back( Eigen::Vector3d ( -scale, -scale, 0 ) );\n\n Draw(poses, points, info);\n apriltag_detector_destroy(td);\n\n if (!strcmp(famname, \"tag36h11\")) {\n tag36h11_destroy(tf);\n } \n\n getopt_destroy(getopt);\n\n return 0;\n}\n\n\ncv::Point2d pixel2cam ( const cv::Point2d& p, const cv::Mat& K )\n{\n return cv::Point2d\n (\n ( p.x - K.at ( 0,2 ) ) / K.at ( 0,0 ),\n ( p.y - K.at ( 1,2 ) ) / K.at ( 1,1 )\n );\n}\n\n\n\nvoid triangulation (\n const std::vector& keypoint_1,\n const std::vector& keypoint_2,\n const std::vector< cv::DMatch >& matches,\n cv::Mat T1, cv::Mat T2,\n std::vector& points)\n{\n // cv::Mat T1 = (cv::Mat_ (3,4) <<\n // 1,0,0,0,\n // 0,1,0,0,\n // 0,0,1,0);\n // cv::Mat T2 = (cv::Mat_ (3,4) <<\n // R.at(0,0), R.at(0,1), R.at(0,2), t.at(0,0),\n // R.at(1,0), R.at(1,1), R.at(1,2), t.at(1,0),\n // R.at(2,0), R.at(2,1), R.at(2,2), t.at(2,0)\n // );\n\n // info.fx, 0.0, info.cx,\n // 0.0, info.fy, info.cy,\n // 0.0, 0.0, 1.0\n cv::Mat K = ( cv::Mat_ ( 3,3 ) << fx, 0, cx, 0, fy, cy, 0, 0, 1 );\n\n std::vector pts_1, pts_2;\n for ( cv::DMatch m:matches )\n {\n // 将像素坐标转换至相机坐标\n pts_1.push_back ( pixel2cam( keypoint_1[m.queryIdx].pt, K) );\n pts_2.push_back ( pixel2cam( keypoint_2[m.trainIdx].pt, K) );\n }\n\n cv::Mat pts_4d;\n cv::triangulatePoints( T1, T2, pts_1, pts_2, pts_4d );\n\n // 转换成非齐次坐标\n for ( int i=0; i(3,0) < 1.0) continue;\n \n x /= x.at(3,0); // 归一化\n cv::Point3d p (\n x.at(0,0),\n x.at(1,0),\n x.at(2,0)\n );\n points.push_back( p );\n }\n}\n\nvoid Draw(const VecSE3 &poses, const VecVec3d &points, const apriltag_detection_info_t &info) {\n if (poses.empty() || points.empty()) {\n std::cerr << \"parameter is empty!\" << std::endl;\n return;\n }\n\n // create pangolin window and plot the trajectory\n pangolin::CreateWindowAndBind(\"Trajectory Viewer\", 1024, 768);\n glEnable(GL_DEPTH_TEST);\n glEnable(GL_BLEND);\n glBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA);\n\n pangolin::OpenGlRenderState s_cam(\n pangolin::ProjectionMatrix(1024, 768, 500, 500, 512, 389, 0.1, 1000),\n pangolin::ModelViewLookAt(0, -0.1, -1.8, 0, 0, 0, 0.0, -1.0, 0.0)\n );\n\n pangolin::View &d_cam = pangolin::CreateDisplay()\n .SetBounds(0.0, 1.0, pangolin::Attach::Pix(175), 1.0, -1024.0f / 768.0f)\n .SetHandler(new pangolin::Handler3D(s_cam));\n\n\n while (pangolin::ShouldQuit() == false) {\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT);\n\n d_cam.Activate(s_cam);\n glClearColor(0.0f, 0.0f, 0.0f, 0.0f);\n\n // intrinsics\n float fx = info.fx;\n float fy = info.fy;\n float cx = info.cx;\n float cy = info.cy;\n\n // draw poses\n float sz = 0.1;\n int width = 640, height = 480;\n for (auto &Tcw: poses) {\n glPushMatrix();\n Sophus::Matrix4f m = Tcw.inverse().matrix().cast();\n glMultMatrixf((GLfloat *) m.data());\n glColor3f(1, 0, 0);\n glLineWidth(2);\n glBegin(GL_LINES);\n glVertex3f(0, 0, 0);\n glVertex3f(sz * (0 - cx) / fx, sz * (0 - cy) / fy, sz);\n glVertex3f(0, 0, 0);\n glVertex3f(sz * (0 - cx) / fx, sz * (height - 1 - cy) / fy, sz);\n glVertex3f(0, 0, 0);\n glVertex3f(sz * (width - 1 - cx) / fx, sz * (height - 1 - cy) / fy, sz);\n glVertex3f(0, 0, 0);\n glVertex3f(sz * (width - 1 - cx) / fx, sz * (0 - cy) / fy, sz);\n glVertex3f(sz * (width - 1 - cx) / fx, sz * (0 - cy) / fy, sz);\n glVertex3f(sz * (width - 1 - cx) / fx, sz * (height - 1 - cy) / fy, sz);\n glVertex3f(sz * (width - 1 - cx) / fx, sz * (height - 1 - cy) / fy, sz);\n glVertex3f(sz * (0 - cx) / fx, sz * (height - 1 - cy) / fy, sz);\n glVertex3f(sz * (0 - cx) / fx, sz * (height - 1 - cy) / fy, sz);\n glVertex3f(sz * (0 - cx) / fx, sz * (0 - cy) / fy, sz);\n glVertex3f(sz * (0 - cx) / fx, sz * (0 - cy) / fy, sz);\n glVertex3f(sz * (width - 1 - cx) / fx, sz * (0 - cy) / fy, sz);\n glEnd();\n glPopMatrix();\n }\n\n // points\n glPointSize(2);\n glBegin(GL_POINTS);\n for (size_t i = 0; 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YES\n2. NO", "lm_q1_score": 0.5698526368038304, "lm_q2_score": 0.4073334000459302, "lm_q1q2_score": 0.2321200120744428}} {"text": "// Copyright (c) 2012-2017, The CryptoNote developers, The Bytecoin developers\n// Copyright (c) 2017-2019, The Iridium developers\n// Copyright (c) 2018-2019, The MonetaVerde developers\n//\n// This file is part of Bytecoin.\n//\n// Bytecoin is free software: you can redistribute it and/or modify\n// it under the terms of the GNU Lesser General Public License as published by\n// the Free Software Foundation, either version 3 of the License, or\n// (at your option) any later version.\n//\n// Bytecoin is distributed in the hope that it will be useful,\n// but WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n// GNU Lesser General Public License for more details.\n//\n// You should have received a copy of the GNU Lesser General Public License\n// along with Bytecoin. If not, see .\n\n#include \"Currency.h\"\n#include \n#include \n#include \n#include \"../Common/Math.h\"\n#include \"../Common/Base58.h\"\n#include \"../Common/int-util.h\"\n#include \"../Common/StringTools.h\"\n\n#include \"Account.h\"\n#include \"CryptoNoteBasicImpl.h\"\n#include \"CryptoNoteFormatUtils.h\"\n#include \"CryptoNoteTools.h\"\n#include \"TransactionExtra.h\"\n#include \"UpgradeDetector.h\"\n\n#undef ERROR\n\nusing namespace Logging;\nusing namespace Common;\n\nnamespace CryptoNote {\n\n/* BLACKROSEDIAMONDV2 COMPATIBILITY */\n#if defined(_MSC_VER)\n /* #define NOMINMAX 1\n #include \n #include \n\n static inline void mul(uint64_t a, uint64_t b, uint64_t &low, uint64_t &high) {\n low = mul128(a, b, &high);\n }\n */\n #include \n\n #pragma intrinsic(_umul128)\n\n static inline void mul(uint64_t a, uint64_t b, uint64_t &low, uint64_t &high) {\n low = _umul128(a, b, &high);\n }\n\n#else\n\n static inline void mul(uint64_t a, uint64_t b, uint64_t &low, uint64_t &high) {\n typedef unsigned __int128 uint128_t;\n uint128_t res = (uint128_t) a * (uint128_t) b;\n low = (uint64_t) res;\n high = (uint64_t) (res >> 64);\n }\n\n#endif\n\n\t\n\tuint64_t log2_fix(uint64_t x, size_t log_fix_precision)\n {\n assert(x != 0);\n assert(1 <= log_fix_precision && log_fix_precision < sizeof(uint64_t) * 8 / 2 - 1); // \"Invalid log precision\"\n\n uint64_t b = UINT64_C(1) << (log_fix_precision - 1);\n uint64_t y = 0;\n\n while (x >= (UINT64_C(2) << log_fix_precision))\n {\n x >>= 1;\n y += UINT64_C(1) << log_fix_precision;\n }\n\n // 64 bits are enough, because of x < 2 * (1 << log_fix_precision) <= 2^32\n uint64_t z = x;\n for (size_t i = 0; i < log_fix_precision; i++)\n {\n z = (z * z) >> log_fix_precision;\n if (z >= (UINT64_C(2) << log_fix_precision))\n {\n z >>= 1;\n y += b;\n }\n b >>= 1;\n }\n\n return y;\n }\n/* /END BLACKROSEDIAMONDV2 */\n\nconst std::vector Currency::PRETTY_AMOUNTS = {\n 1, 2, 3, 4, 5, 6, 7, 8, 9,\n 10, 20, 30, 40, 50, 60, 70, 80, 90,\n 100, 200, 300, 400, 500, 600, 700, 800, 900,\n 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000,\n 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000,\n 100000, 200000, 300000, 400000, 500000, 600000, 700000, 800000, 900000,\n 1000000, 2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000,\n 10000000, 20000000, 30000000, 40000000, 50000000, 60000000, 70000000, 80000000, 90000000,\n 100000000, 200000000, 300000000, 400000000, 500000000, 600000000, 700000000, 800000000, 900000000,\n 1000000000, 2000000000, 3000000000, 4000000000, 5000000000, 6000000000, 7000000000, 8000000000, 9000000000,\n 10000000000, 20000000000, 30000000000, 40000000000, 50000000000, 60000000000, 70000000000, 80000000000, 90000000000,\n 100000000000, 200000000000, 300000000000, 400000000000, 500000000000, 600000000000, 700000000000, 800000000000, 900000000000,\n 1000000000000, 2000000000000, 3000000000000, 4000000000000, 5000000000000, 6000000000000, 7000000000000, 8000000000000, 9000000000000,\n 10000000000000, 20000000000000, 30000000000000, 40000000000000, 50000000000000, 60000000000000, 70000000000000, 80000000000000, 90000000000000,\n 100000000000000, 200000000000000, 300000000000000, 400000000000000, 500000000000000, 600000000000000, 700000000000000, 800000000000000, 900000000000000,\n 1000000000000000, 2000000000000000, 3000000000000000, 4000000000000000, 5000000000000000, 6000000000000000, 7000000000000000, 8000000000000000, 9000000000000000,\n 10000000000000000, 20000000000000000, 30000000000000000, 40000000000000000, 50000000000000000, 60000000000000000, 70000000000000000, 80000000000000000, 90000000000000000,\n 100000000000000000, 200000000000000000, 300000000000000000, 400000000000000000, 500000000000000000, 600000000000000000, 700000000000000000, 800000000000000000, 900000000000000000,\n 1000000000000000000, 2000000000000000000, 3000000000000000000, 4000000000000000000, 5000000000000000000, 6000000000000000000, 7000000000000000000, 8000000000000000000, 9000000000000000000,\n 10000000000000000000ull\n};\n\nbool Currency::init() {\n if (!generateGenesisBlock()) {\n logger(ERROR, BRIGHT_RED) << \"Failed to generate genesis block\";\n return false;\n }\n\n try {\n cachedGenesisBlock->getBlockHash();\n } catch (std::exception& e) {\n logger(ERROR, BRIGHT_RED) << \"Failed to get genesis block hash: \" << e.what();\n return false;\n }\n\n if (isTestnet()) {\n m_upgradeHeightV2 = m_testnetUpgradeHeightV2;\n m_upgradeHeightV3 = m_testnetUpgradeHeightV3;\n m_upgradeHeightV4 = m_testnetUpgradeHeightV4;\n m_difficultyTarget = m_testnet_DifficultyTarget;\n m_blocksFileName = \"testnet_\" + m_blocksFileName;\n m_blockIndexesFileName = \"testnet_\" + m_blockIndexesFileName;\n m_txPoolFileName = \"testnet_\" + m_txPoolFileName;\n logger(INFO, RED) << \"V2 Height : \" << m_upgradeHeightV2;\n logger(INFO, RED) << \"V3 Height : \" << m_upgradeHeightV3;\n logger(INFO, RED) << \"V4 Height : \" << m_upgradeHeightV4;\n logger(INFO, RED) << \"Target : \" << m_difficultyTarget << \"s\";\n }\n\n return true;\n}\n\nbool Currency::generateGenesisBlock() {\n genesisBlockTemplate = boost::value_initialized();\n\n //account_public_address ac = boost::value_initialized();\n //std::vector sz;\n //constructMinerTx(0, 0, 0, 0, 0, ac, m_genesisBlock.baseTransaction); // zero fee in genesis\n //BinaryArray txb = toBinaryArray(m_genesisBlock.baseTransaction);\n //std::string hex_tx_represent = Common::toHex(txb);\n\n // Hard code coinbase tx in genesis block, because through generating tx use random, but genesis should be always the same\n // This is the BlackRoseDiamondV2 genesis transaction\n std::string genesisCoinbaseTxHex = GENESIS_COINBASE_TX_HEX;\n BinaryArray minerTxBlob;\n\n bool r =\n fromHex(genesisCoinbaseTxHex, minerTxBlob) &&\n fromBinaryArray(genesisBlockTemplate.baseTransaction, minerTxBlob);\n\n if (!r) {\n logger(ERROR, BRIGHT_RED) << \"failed to parse coinbase tx from hard coded blob\";\n return false;\n }\n\n genesisBlockTemplate.majorVersion = BLOCK_MAJOR_VERSION_1;\n genesisBlockTemplate.minorVersion = BLOCK_MINOR_VERSION_0;\n genesisBlockTemplate.timestamp = 0;\n genesisBlockTemplate.nonce = 10000;\n if (m_testnet) {\n ++genesisBlockTemplate.nonce;\n }\n //miner::find_nonce_for_given_block(bl, 1, 0);\n cachedGenesisBlock.reset(new CachedBlock(genesisBlockTemplate));\n return true;\n}\n\nsize_t Currency::difficultyWindowByBlockVersion(uint8_t blockMajorVersion) const {\n if (blockMajorVersion >= BLOCK_MAJOR_VERSION_3) {\n return CryptoNote::parameters::DIFFICULTY_WINDOW_V3;\n } else {\n return CryptoNote::parameters::DIFFICULTY_WINDOW;\n }\n}\n\nsize_t Currency::difficultyLagByBlockVersion(uint8_t blockMajorVersion) const {\n if (blockMajorVersion >= BLOCK_MAJOR_VERSION_2) {\n return CryptoNote::parameters::DIFFICULTY_LAG_V2; // lag = 0 since V2\n } else {\n return CryptoNote::parameters::DIFFICULTY_LAG;\n }\n}\n\nsize_t Currency::difficultyCutByBlockVersion(uint8_t blockMajorVersion) const {\n return CryptoNote::parameters::DIFFICULTY_CUT;\n}\n\nsize_t Currency::difficultyBlocksCountByBlockVersion(uint8_t blockMajorVersion) const {\n if (blockMajorVersion >= BLOCK_MAJOR_VERSION_2) {\n return DIFFICULTY_BLOCKS_COUNT;\n } else {\n return difficultyWindowByBlockVersion(blockMajorVersion) + difficultyLagByBlockVersion(blockMajorVersion);\n }\n}\n\nsize_t Currency::blockGrantedFullRewardZoneByBlockVersion(uint8_t blockMajorVersion) const {\n if (blockMajorVersion >= BLOCK_MAJOR_VERSION_2) {\n return CryptoNote::parameters::CRYPTONOTE_BLOCK_GRANTED_FULL_REWARD_ZONE_CURRENT; // does not change since V2\n } else {\n return CryptoNote::parameters::CRYPTONOTE_BLOCK_GRANTED_FULL_REWARD_ZONE_V1;\n }\n}\n\nuint32_t Currency::upgradeHeight(uint8_t majorVersion) const {\n if (majorVersion == BLOCK_MAJOR_VERSION_4) {\n return m_upgradeHeightV4;\n } else if (majorVersion == BLOCK_MAJOR_VERSION_3) {\n return m_upgradeHeightV3;\n } else if (majorVersion == BLOCK_MAJOR_VERSION_2) {\n return m_upgradeHeightV2;\n } else {\n return static_cast(-1);\n }\n}\n\nbool Currency::getBlockReward(uint8_t blockMajorVersion, size_t medianSize, size_t currentBlockSize, uint64_t alreadyGeneratedCoins,\n\tuint64_t fee, uint64_t& reward, int64_t& emissionChange, const Difficulty diff) const {\n\n size_t log_fix_precision = blockMajorVersion > BLOCK_MAJOR_VERSION_2? (parameters::BLOCK_REWARD_LOG_FIX_PRECISION_V3):(parameters::BLOCK_REWARD_LOG_FIX_PRECISION);\n\n assert(diff != 0);\n assert(static_cast(diff) < (UINT64_C(1) << (sizeof(uint64_t) * 8 - log_fix_precision)));\n uint64_t baseReward = log2_fix(diff << log_fix_precision, log_fix_precision) << 20;\n // logger(Logging::INFO, Logging::BRIGHT_GREEN) << \"baseReward: \" << baseReward << \", con diff \" << (int)diff << \", log fix: \" << log_fix_precision << \", b.majorv: \" << (int)blockMajorVersion;\n\t// assert(alreadyGeneratedCoins <= m_moneySupply);\n\tassert(m_emissionSpeedFactor > 0 && m_emissionSpeedFactor <= 8 * sizeof(uint64_t));\n\n\t// Tail emission\n/*\n\tuint64_t baseReward = (m_moneySupply - alreadyGeneratedCoins) >> m_emissionSpeedFactor;\n\tif (alreadyGeneratedCoins + CryptoNote::parameters::TAIL_EMISSION_REWARD >= m_moneySupply || baseReward < CryptoNote::parameters::TAIL_EMISSION_REWARD)\n\t{\n\t\tbaseReward = CryptoNote::parameters::TAIL_EMISSION_REWARD;\n\t}\n*/\n\tsize_t blockGrantedFullRewardZone = blockGrantedFullRewardZoneByBlockVersion(blockMajorVersion);\n\tmedianSize = std::max(medianSize, blockGrantedFullRewardZone);\n\tif (currentBlockSize > UINT64_C(2) * medianSize) {\n\t\tlogger(TRACE) << \"Block cumulative size is too big: \" << currentBlockSize << \", expected less than \" << 2 * medianSize;\n\t\treturn false;\n\t}\n\n\tuint64_t penalizedBaseReward = getPenalizedAmount(baseReward, medianSize, currentBlockSize);\n\tuint64_t penalizedFee = blockMajorVersion >= BLOCK_MAJOR_VERSION_4 ? getPenalizedAmount(fee, medianSize, currentBlockSize) : fee;\n\tif (parameters::CRYPTONOTE_COIN_VERSION == 1) {\n\t\tpenalizedFee = getPenalizedAmount(fee, medianSize, currentBlockSize);\n\t}\n // logger(Logging::INFO, Logging::BRIGHT_GREEN) << \"[getBlockReward] baseReward: \" << baseReward << \", con diff \" << (int)diff << \", log fix: \" << log_fix_precision << \", b.majorv: \" << (int)blockMajorVersion << \", penalizedBaseReward: \" << penalizedBaseReward << \", penalizedFee: \" << penalizedFee << \", fee:\" << fee;\n\t//std::cout << \"BlockSize: \" << currentBlockSize << \", medianSize:\" << medianSize << \", baseReward: \" << formatAmount(baseReward) << \", penalizedBaseReward: \" << formatAmount(penalizedBaseReward) << \", fee: \" << formatAmount(fee)\n\t//\t\t\t<< \", penalizedFee: \" << formatAmount(penalizedFee) << std::endl;\n\temissionChange = penalizedBaseReward - (fee - penalizedFee);\n\treward = penalizedBaseReward + penalizedFee;\n\n\treturn true;\n}\n/*\nbool Currency::getBlockReward(uint8_t blockMajorVersion, size_t medianSize, size_t currentBlockSize, uint64_t alreadyGeneratedCoins,\n uint64_t fee, uint64_t& reward, int64_t& emissionChange) const {\n assert(alreadyGeneratedCoins <= m_moneySupply);\n assert(m_emissionSpeedFactor > 0 && m_emissionSpeedFactor <= 8 * sizeof(uint64_t));\n\n uint64_t baseReward = (m_moneySupply - alreadyGeneratedCoins) >> m_emissionSpeedFactor;\n\n size_t blockGrantedFullRewardZone = blockGrantedFullRewardZoneByBlockVersion(blockMajorVersion);\n medianSize = std::max(medianSize, blockGrantedFullRewardZone);\n if (currentBlockSize > UINT64_C(2) * medianSize) {\n logger(TRACE) << \"Block cumulative size is too big: \" << currentBlockSize << \", expected less than \" << 2 * medianSize;\n return false;\n }\n\n uint64_t penalizedBaseReward = getPenalizedAmount(baseReward, medianSize, currentBlockSize);\n uint64_t penalizedFee = blockMajorVersion >= BLOCK_MAJOR_VERSION_2 ? getPenalizedAmount(fee, medianSize, currentBlockSize) : fee;\n emissionChange = penalizedBaseReward - (fee - penalizedFee);\n reward = penalizedBaseReward + penalizedFee;\n\n return true;\n}\n*/\nsize_t Currency::maxBlockCumulativeSize(uint64_t height) const {\n assert(height <= std::numeric_limits::max() / m_maxBlockSizeGrowthSpeedNumerator);\n size_t maxSize = static_cast(m_maxBlockSizeInitial +\n (height * m_maxBlockSizeGrowthSpeedNumerator) / m_maxBlockSizeGrowthSpeedDenominator);\n assert(maxSize >= m_maxBlockSizeInitial);\n return maxSize;\n}\n\nbool Currency::constructMinerTx(uint8_t blockMajorVersion, uint32_t height, size_t medianSize, uint64_t alreadyGeneratedCoins, size_t currentBlockSize,\n uint64_t fee, const AccountPublicAddress& minerAddress, Transaction& tx, const BinaryArray& extraNonce/* = BinaryArray()*/, size_t maxOuts/* = 1*/,\n const Difficulty diff/* = 0*/) const {\n\n tx.inputs.clear();\n tx.outputs.clear();\n tx.extra.clear();\n\n KeyPair txkey = generateKeyPair();\n addTransactionPublicKeyToExtra(tx.extra, txkey.publicKey);\n if (!extraNonce.empty()) {\n if (!addExtraNonceToTransactionExtra(tx.extra, extraNonce)) {\n return false;\n }\n }\n\n BaseInput in;\n in.blockIndex = height;\n\n uint64_t blockReward;\n int64_t emissionChange;\n if (!getBlockReward(blockMajorVersion, medianSize, currentBlockSize, alreadyGeneratedCoins, fee, blockReward, emissionChange, diff)) {\n logger(INFO) << \"Block is too big\";\n return false;\n }\n\n std::vector outAmounts;\n decompose_amount_into_digits(blockReward, m_defaultDustThreshold,\n [&outAmounts](uint64_t a_chunk) { outAmounts.push_back(a_chunk); },\n [&outAmounts](uint64_t a_dust) { outAmounts.push_back(a_dust); });\n\n if (!(1 <= maxOuts)) { logger(ERROR, BRIGHT_RED) << \"max_out must be non-zero\"; return false; }\n while (maxOuts < outAmounts.size()) {\n outAmounts[outAmounts.size() - 2] += outAmounts.back();\n outAmounts.resize(outAmounts.size() - 1);\n }\n\n uint64_t summaryAmounts = 0;\n for (size_t no = 0; no < outAmounts.size(); no++) {\n Crypto::KeyDerivation derivation = boost::value_initialized();\n Crypto::PublicKey outEphemeralPubKey = boost::value_initialized();\n\n bool r = Crypto::generate_key_derivation(minerAddress.viewPublicKey, txkey.secretKey, derivation);\n\n if (!(r)) {\n logger(ERROR, BRIGHT_RED)\n << \"while creating outs: failed to generate_key_derivation(\"\n << minerAddress.viewPublicKey << \", \" << txkey.secretKey << \")\";\n return false;\n }\n\n r = Crypto::derive_public_key(derivation, no, minerAddress.spendPublicKey, outEphemeralPubKey);\n\n if (!(r)) {\n logger(ERROR, BRIGHT_RED)\n << \"while creating outs: failed to derive_public_key(\"\n << derivation << \", \" << no << \", \"\n << minerAddress.spendPublicKey << \")\";\n return false;\n }\n\n KeyOutput tk;\n tk.key = outEphemeralPubKey;\n\n TransactionOutput out;\n summaryAmounts += out.amount = outAmounts[no];\n out.target = tk;\n tx.outputs.push_back(out);\n }\n\n if (!(summaryAmounts == blockReward)) {\n logger(ERROR, BRIGHT_RED) << \"Failed to construct miner tx, summaryAmounts = \" << summaryAmounts << \" not equal blockReward = \" << blockReward;\n return false;\n }\n\n tx.version = CURRENT_TRANSACTION_VERSION;\n //lock\n tx.unlockTime = height + m_minedMoneyUnlockWindow;\n tx.inputs.push_back(in);\n return true;\n}\n\nbool Currency::isFusionTransaction(const std::vector& inputsAmounts, const std::vector& outputsAmounts, size_t size) const {\n if (size > fusionTxMaxSize()) {\n return false;\n }\n\n if (inputsAmounts.size() < fusionTxMinInputCount()) {\n return false;\n }\n\n if (inputsAmounts.size() < outputsAmounts.size() * fusionTxMinInOutCountRatio()) {\n return false;\n }\n\n uint64_t inputAmount = 0;\n for (auto amount: inputsAmounts) {\n if (amount < defaultDustThreshold()) {\n return false;\n }\n\n inputAmount += amount;\n }\n\n std::vector expectedOutputsAmounts;\n expectedOutputsAmounts.reserve(outputsAmounts.size());\n decomposeAmount(inputAmount, defaultDustThreshold(), expectedOutputsAmounts);\n std::sort(expectedOutputsAmounts.begin(), expectedOutputsAmounts.end());\n\n return expectedOutputsAmounts == outputsAmounts;\n}\n\nbool Currency::isFusionTransaction(const Transaction& transaction, size_t size) const {\n assert(getObjectBinarySize(transaction) == size);\n\n std::vector outputsAmounts;\n outputsAmounts.reserve(transaction.outputs.size());\n for (const TransactionOutput& output : transaction.outputs) {\n outputsAmounts.push_back(output.amount);\n }\n\n return isFusionTransaction(getInputsAmounts(transaction), outputsAmounts, size);\n}\n\nbool Currency::isFusionTransaction(const Transaction& transaction) const {\n return isFusionTransaction(transaction, getObjectBinarySize(transaction));\n}\n\nbool Currency::isAmountApplicableInFusionTransactionInput(uint64_t amount, uint64_t threshold) const {\n uint8_t ignore;\n return isAmountApplicableInFusionTransactionInput(amount, threshold, ignore);\n}\n\nbool Currency::isAmountApplicableInFusionTransactionInput(uint64_t amount, uint64_t threshold, uint8_t& amountPowerOfTen) const {\n if (amount >= threshold) {\n return false;\n }\n\n if (amount < defaultDustThreshold()) {\n return false;\n }\n\n auto it = std::lower_bound(PRETTY_AMOUNTS.begin(), PRETTY_AMOUNTS.end(), amount);\n if (it == PRETTY_AMOUNTS.end() || amount != *it) {\n return false;\n }\n\n amountPowerOfTen = static_cast(std::distance(PRETTY_AMOUNTS.begin(), it) / 9);\n return true;\n}\n\nstd::string Currency::accountAddressAsString(const AccountBase& account) const {\n return getAccountAddressAsStr(m_publicAddressBase58Prefix, account.getAccountKeys().address);\n}\n\nstd::string Currency::accountAddressAsString(const AccountPublicAddress& accountPublicAddress) const {\n return getAccountAddressAsStr(m_publicAddressBase58Prefix, accountPublicAddress);\n}\n\nbool Currency::parseAccountAddressString(const std::string& str, AccountPublicAddress& addr) const {\n uint64_t prefix;\n if (!CryptoNote::parseAccountAddressString(prefix, addr, str)) {\n return false;\n }\n\n if (prefix != m_publicAddressBase58Prefix) {\n logger(DEBUGGING) << \"Wrong address prefix: \" << prefix << \", expected \" << m_publicAddressBase58Prefix;\n return false;\n }\n\n return true;\n}\n\nstd::string Currency::formatAmount(uint64_t amount) const {\n std::string s = std::to_string(amount);\n if (s.size() < m_numberOfDecimalPlaces + 1) {\n s.insert(0, m_numberOfDecimalPlaces + 1 - s.size(), '0');\n }\n s.insert(s.size() - m_numberOfDecimalPlaces, \".\");\n return s;\n}\n\nstd::string Currency::formatAmount(int64_t amount) const {\n std::string s = formatAmount(static_cast(std::abs(amount)));\n\n if (amount < 0) {\n s.insert(0, \"-\");\n }\n\n return s;\n}\n\nbool Currency::parseAmount(const std::string& str, uint64_t& amount) const {\n std::string strAmount = str;\n boost::algorithm::trim(strAmount);\n\n size_t pointIndex = strAmount.find_first_of('.');\n size_t fractionSize;\n if (std::string::npos != pointIndex) {\n fractionSize = strAmount.size() - pointIndex - 1;\n while (m_numberOfDecimalPlaces < fractionSize && '0' == strAmount.back()) {\n strAmount.erase(strAmount.size() - 1, 1);\n --fractionSize;\n }\n if (m_numberOfDecimalPlaces < fractionSize) {\n return false;\n }\n strAmount.erase(pointIndex, 1);\n } else {\n fractionSize = 0;\n }\n\n if (strAmount.empty()) {\n return false;\n }\n\n if (!std::all_of(strAmount.begin(), strAmount.end(), ::isdigit)) {\n return false;\n }\n\n if (fractionSize < m_numberOfDecimalPlaces) {\n strAmount.append(m_numberOfDecimalPlaces - fractionSize, '0');\n }\n\n return Common::fromString(strAmount, amount);\n}\n\nDifficulty Currency::nextDifficulty(\n uint8_t version,\n uint32_t blockIndex,\n std::vector timestamps,\n std::vector cumulativeDifficulties\n ) const {\n Difficulty nextDiff;\n if (version >= BLOCK_MAJOR_VERSION_3) {\n nextDiff = nextDifficultyLWMA4(version, timestamps, cumulativeDifficulties);\n } else {\n nextDiff = nextDifficultyOriginal(timestamps,cumulativeDifficulties);\n }\n if(nextDiff < 1) {\n nextDiff = 1;\n }\n return nextDiff;\n}\n\n\n\n/**\n * Original cryptonote difficulty algo\n */\nDifficulty Currency::nextDifficultyOriginal(\n std::vector timestamps,\n std::vector cumulativeDifficulties\n ) const {\n\n uint64_t target_seconds = static_cast(m_difficultyTarget); //CryptoNote::parameters::DIFFICULTY_TARGET;\n\tsize_t m_difficultyWindow_2 = CryptoNote::parameters::DIFFICULTY_WINDOW;\n\tassert(m_difficultyWindow_2 >= 2);\n\n\tif (timestamps.size() > m_difficultyWindow_2) {\n\t\ttimestamps.resize(m_difficultyWindow_2);\n\t\tcumulativeDifficulties.resize(m_difficultyWindow_2);\n\t}\n\n\tsize_t length = timestamps.size();\n\tassert(length == cumulativeDifficulties.size());\n\tassert(length <= m_difficultyWindow_2);\n\tif (length <= 1) {\n\t\treturn 1;\n\t}\n\n\tstatic_assert(CryptoNote::parameters::DIFFICULTY_WINDOW >= 2, \"Window is too small\");\n assert(length <= CryptoNote::parameters::DIFFICULTY_WINDOW);\n sort(timestamps.begin(), timestamps.end());\n size_t cut_begin, cut_end;\n static_assert(2 * CryptoNote::parameters::DIFFICULTY_CUT <= CryptoNote::parameters::DIFFICULTY_WINDOW - 2, \"Cut length is too large\");\n if (length <= CryptoNote::parameters::DIFFICULTY_WINDOW - 2 * CryptoNote::parameters::DIFFICULTY_CUT) {\n cut_begin = 0;\n cut_end = length;\n } else {\n cut_begin = (length - (CryptoNote::parameters::DIFFICULTY_WINDOW - 2 * CryptoNote::parameters::DIFFICULTY_CUT) + 1) / 2;\n cut_end = cut_begin + (CryptoNote::parameters::DIFFICULTY_WINDOW - 2 * CryptoNote::parameters::DIFFICULTY_CUT);\n }\n assert(/*cut_begin >= 0 &&*/ cut_begin + 2 <= cut_end && cut_end <= length);\n uint64_t time_span = timestamps[cut_end - 1] - timestamps[cut_begin];\n if (time_span == 0) {\n time_span = 1;\n }\n Difficulty total_work = cumulativeDifficulties[cut_end - 1] - cumulativeDifficulties[cut_begin];\n assert(total_work > 0);\n uint64_t low, high;\n mul(total_work, target_seconds, low, high);\n if (high != 0 || low + time_span - 1 < low) {\n return 0;\n }\n return (low + time_span - 1) / time_span;\n}\n\n// LWMA difficulty algorithm Hard fork v3\n// Copyright (c) 2017-2018 Zawy\n// MIT license http://www.opensource.org/licenses/mit-license.php.\n// Tom Harding, Karbowanec, Masari, Bitcoin Gold, and Bitcoin Candy have contributed.\n// https://github.com/zawy12/difficulty-algorithms/issues/3\n// Zawy's LWMA difficulty algorithm implementation V4 (60 solvetimes limits -7T/7T)\n// (60 solvetimes - limits -7T/7T - adjust = 0.9909)\nDifficulty Currency::nextDifficultyLWMA4(uint8_t &version,\n std::vector ×tamps,\n std::vector &cumulativeDifficulties\n ) const {\n const size_t c_difficultyWindow = difficultyWindowByBlockVersion(version); // 61\n const int64_t c_difficultyTarget = static_cast(m_difficultyTarget);\n if (timestamps.size() > c_difficultyWindow) {\n timestamps.resize(c_difficultyWindow);\n cumulativeDifficulties.resize(c_difficultyWindow);\n }\n size_t length = timestamps.size();\n assert(length == cumulativeDifficulties.size());\n assert(length <= c_difficultyWindow);\n if (length <= 1) {\n return 1;\n }\n int64_t solveTime(0),LWMA(0),minWST(0);\n uint64_t aimedTarget(0),low,high;\n Difficulty totalWork(0),nextDiff(0);\n const double_t adjust = 0.9909;\n for (int64_t i = 1; i < length; i++) { // lenght = 61\n solveTime = static_cast(timestamps[i]) - static_cast(timestamps[i-1]);\n solveTime = std::max(- blockFutureTimeLimit(), solveTime);\n LWMA += solveTime * i;\n }\n // Keep LWMA sane in case something unforeseen occurs.if ( LWMA < T*N*(N+1)/8 ) { LWMA = T*N*(N+1)/8; N=lenght-1}\n minWST = c_difficultyTarget * length*(length-1)/8;\n if(LWMA < minWST){\n LWMA = minWST;\n }\n totalWork = cumulativeDifficulties.back() - cumulativeDifficulties.front();\n aimedTarget = adjust * (length / 2.0) * c_difficultyTarget ;\n assert(totalWork > 0);\n low = mul128(totalWork, aimedTarget, &high);\n if (high != 0) {\n return 0;\n }\n nextDiff = low/LWMA;\n return nextDiff;\n}\n\n\nbool Currency::checkProofOfWorkV1(Crypto::cn_context& context, const CachedBlock& block, Difficulty currentDifficulty) const {\n if (BLOCK_MAJOR_VERSION_1 != block.getBlock().majorVersion) {\n return false;\n }\n\n return check_hash(block.getBlockLongHash(context), currentDifficulty);\n}\n\nbool Currency::checkProofOfWorkV2(Crypto::cn_context& context, const CachedBlock& cachedBlock, Difficulty currentDifficulty) const {\n const auto& block = cachedBlock.getBlock();\n if (block.majorVersion < BLOCK_MAJOR_VERSION_2) {\n return false;\n }\n\n if (!check_hash(cachedBlock.getBlockLongHash(context), currentDifficulty)) {\n return false;\n }\n\n TransactionExtraMergeMiningTag mmTag;\n if (!getMergeMiningTagFromExtra(block.parentBlock.baseTransaction.extra, mmTag)) {\n logger(ERROR) << \"merge mining tag wasn't found in extra of the parent block miner transaction\";\n return false;\n }\n\n if (8 * sizeof(cachedGenesisBlock->getBlockHash()) < block.parentBlock.blockchainBranch.size()) {\n return false;\n }\n\n Crypto::Hash auxBlocksMerkleRoot;\n Crypto::tree_hash_from_branch(block.parentBlock.blockchainBranch.data(), block.parentBlock.blockchainBranch.size(),\n cachedBlock.getAuxiliaryBlockHeaderHash(), &cachedGenesisBlock->getBlockHash(), auxBlocksMerkleRoot);\n\n if (auxBlocksMerkleRoot != mmTag.merkleRoot) {\n logger(ERROR, BRIGHT_YELLOW) << \"Aux block hash wasn't found in merkle tree\";\n return false;\n }\n\n return true;\n}\n\nbool Currency::checkProofOfWork(Crypto::cn_context& context, const CachedBlock& block, Difficulty currentDiffic) const {\n switch (block.getBlock().majorVersion) {\n case BLOCK_MAJOR_VERSION_1:\n return checkProofOfWorkV1(context, block, currentDiffic);\n\n case BLOCK_MAJOR_VERSION_2:\n case BLOCK_MAJOR_VERSION_3:\n case BLOCK_MAJOR_VERSION_4:\n return checkProofOfWorkV2(context, block, currentDiffic);\n }\n\n logger(ERROR, BRIGHT_RED) << \"Unknown block major version: \" << block.getBlock().majorVersion << \".\" << block.getBlock().minorVersion;\n return false;\n}\n\nsize_t Currency::getApproximateMaximumInputCount(size_t transactionSize, size_t outputCount, size_t mixinCount) const {\n const size_t KEY_IMAGE_SIZE = sizeof(Crypto::KeyImage);\n const size_t OUTPUT_KEY_SIZE = sizeof(decltype(KeyOutput::key));\n const size_t AMOUNT_SIZE = sizeof(uint64_t) + 2; //varint\n const size_t GLOBAL_INDEXES_VECTOR_SIZE_SIZE = sizeof(uint8_t);//varint\n const size_t GLOBAL_INDEXES_INITIAL_VALUE_SIZE = sizeof(uint32_t);//varint\n const size_t GLOBAL_INDEXES_DIFFERENCE_SIZE = sizeof(uint32_t);//varint\n const size_t SIGNATURE_SIZE = sizeof(Crypto::Signature);\n const size_t EXTRA_TAG_SIZE = sizeof(uint8_t);\n const size_t INPUT_TAG_SIZE = sizeof(uint8_t);\n const size_t OUTPUT_TAG_SIZE = sizeof(uint8_t);\n const size_t PUBLIC_KEY_SIZE = sizeof(Crypto::PublicKey);\n const size_t TRANSACTION_VERSION_SIZE = sizeof(uint8_t);\n const size_t TRANSACTION_UNLOCK_TIME_SIZE = sizeof(uint64_t);\n\n const size_t outputsSize = outputCount * (OUTPUT_TAG_SIZE + OUTPUT_KEY_SIZE + AMOUNT_SIZE);\n const size_t headerSize = TRANSACTION_VERSION_SIZE + TRANSACTION_UNLOCK_TIME_SIZE + EXTRA_TAG_SIZE + PUBLIC_KEY_SIZE;\n const size_t inputSize = INPUT_TAG_SIZE + AMOUNT_SIZE + KEY_IMAGE_SIZE + SIGNATURE_SIZE + GLOBAL_INDEXES_VECTOR_SIZE_SIZE + GLOBAL_INDEXES_INITIAL_VALUE_SIZE +\n mixinCount * (GLOBAL_INDEXES_DIFFERENCE_SIZE + SIGNATURE_SIZE);\n\n return (transactionSize - headerSize - outputsSize) / inputSize;\n}\n\nCurrency::Currency(Currency&& currency) :\n m_maxBlockHeight(currency.m_maxBlockHeight),\n m_maxBlockBlobSize(currency.m_maxBlockBlobSize),\n m_maxTxSize(currency.m_maxTxSize),\n m_publicAddressBase58Prefix(currency.m_publicAddressBase58Prefix),\n m_minedMoneyUnlockWindow(currency.m_minedMoneyUnlockWindow),\n m_timestampCheckWindow(currency.m_timestampCheckWindow),\n m_timestampCheckWindowV3(currency.m_timestampCheckWindowV3),\n m_blockFutureTimeLimit(currency.m_blockFutureTimeLimit),\n m_moneySupply(currency.m_moneySupply),\n m_emissionSpeedFactor(currency.m_emissionSpeedFactor),\n m_rewardBlocksWindow(currency.m_rewardBlocksWindow),\n m_blockGrantedFullRewardZone(currency.m_blockGrantedFullRewardZone),\n m_minerTxBlobReservedSize(currency.m_minerTxBlobReservedSize),\n m_minMixin(currency.m_minMixin),\n m_maxMixin(currency.m_maxMixin),\n m_mandatoryMixinBlockVersion(currency.m_mandatoryMixinBlockVersion),\n m_numberOfDecimalPlaces(currency.m_numberOfDecimalPlaces),\n m_coin(currency.m_coin),\n m_mininumFee(currency.m_mininumFee),\n m_defaultDustThreshold(currency.m_defaultDustThreshold),\n m_difficultyTarget(currency.m_difficultyTarget),\n m_testnet_DifficultyTarget(currency.m_testnet_DifficultyTarget),\n m_difficultyWindow(currency.m_difficultyWindow),\n m_difficultyLag(currency.m_difficultyLag),\n m_difficultyCut(currency.m_difficultyCut),\n m_maxBlockSizeInitial(currency.m_maxBlockSizeInitial),\n m_maxBlockSizeGrowthSpeedNumerator(currency.m_maxBlockSizeGrowthSpeedNumerator),\n m_maxBlockSizeGrowthSpeedDenominator(currency.m_maxBlockSizeGrowthSpeedDenominator),\n m_lockedTxAllowedDeltaSeconds(currency.m_lockedTxAllowedDeltaSeconds),\n m_lockedTxAllowedDeltaBlocks(currency.m_lockedTxAllowedDeltaBlocks),\n m_mempoolTxLiveTime(currency.m_mempoolTxLiveTime),\n m_numberOfPeriodsToForgetTxDeletedFromPool(currency.m_numberOfPeriodsToForgetTxDeletedFromPool),\n m_fusionTxMaxSize(currency.m_fusionTxMaxSize),\n m_fusionTxMinInputCount(currency.m_fusionTxMinInputCount),\n m_fusionTxMinInOutCountRatio(currency.m_fusionTxMinInOutCountRatio),\n m_upgradeHeightV2(currency.m_upgradeHeightV2),\n m_upgradeHeightV3(currency.m_upgradeHeightV3),\n m_upgradeHeightV4(currency.m_upgradeHeightV4),\n m_testnetUpgradeHeightV2(currency.m_testnetUpgradeHeightV2),\n m_testnetUpgradeHeightV3(currency.m_testnetUpgradeHeightV3),\n m_testnetUpgradeHeightV4(currency.m_testnetUpgradeHeightV4),\n m_upgradeVotingThreshold(currency.m_upgradeVotingThreshold),\n m_upgradeVotingWindow(currency.m_upgradeVotingWindow),\n m_upgradeWindow(currency.m_upgradeWindow),\n m_blocksFileName(currency.m_blocksFileName),\n m_blockIndexesFileName(currency.m_blockIndexesFileName),\n m_txPoolFileName(currency.m_txPoolFileName),\n m_testnet(currency.m_testnet),\n genesisBlockTemplate(std::move(currency.genesisBlockTemplate)),\n cachedGenesisBlock(new CachedBlock(genesisBlockTemplate)),\n logger(currency.logger) {\n}\n\nCurrencyBuilder::CurrencyBuilder(Logging::ILogger& log) : m_currency(log) {\n maxBlockNumber(parameters::CRYPTONOTE_MAX_BLOCK_NUMBER);\n maxBlockBlobSize(parameters::CRYPTONOTE_MAX_BLOCK_BLOB_SIZE);\n maxTxSize(parameters::CRYPTONOTE_MAX_TX_SIZE);\n publicAddressBase58Prefix(parameters::CRYPTONOTE_PUBLIC_ADDRESS_BASE58_PREFIX);\n minedMoneyUnlockWindow(parameters::CRYPTONOTE_MINED_MONEY_UNLOCK_WINDOW);\n\n timestampCheckWindow(parameters::BLOCKCHAIN_TIMESTAMP_CHECK_WINDOW);\n timestampCheckWindowV3(parameters::BLOCKCHAIN_TIMESTAMP_CHECK_WINDOW_V3);\n blockFutureTimeLimit(parameters::CRYPTONOTE_BLOCK_FUTURE_TIME_LIMIT);\n\n moneySupply(parameters::MONEY_SUPPLY);\n emissionSpeedFactor(parameters::EMISSION_SPEED_FACTOR);\n\n rewardBlocksWindow(parameters::CRYPTONOTE_REWARD_BLOCKS_WINDOW);\n\n minMixin(parameters::MIN_MIXIN);\n maxMixin(parameters::MAX_MIXIN);\n mandatoryMixinBlockVersion(parameters::MANDATORY_MIXIN_BLOCK_VERSION);\n\n blockGrantedFullRewardZone(parameters::CRYPTONOTE_BLOCK_GRANTED_FULL_REWARD_ZONE);\n minerTxBlobReservedSize(parameters::CRYPTONOTE_COINBASE_BLOB_RESERVED_SIZE);\n\n numberOfDecimalPlaces(parameters::CRYPTONOTE_DISPLAY_DECIMAL_POINT);\n\n mininumFee(parameters::MINIMUM_FEE);\n defaultDustThreshold(parameters::DEFAULT_DUST_THRESHOLD);\n\n difficultyTarget(parameters::DIFFICULTY_TARGET);\n testnetDifficultyTarget(parameters::TESTNET_DIFFICULTY_TARGET);\n\n difficultyWindow(parameters::DIFFICULTY_WINDOW);\n difficultyLag(parameters::DIFFICULTY_LAG);\n difficultyCut(parameters::DIFFICULTY_CUT);\n\n maxBlockSizeInitial(parameters::MAX_BLOCK_SIZE_INITIAL);\n maxBlockSizeGrowthSpeedNumerator(parameters::MAX_BLOCK_SIZE_GROWTH_SPEED_NUMERATOR);\n maxBlockSizeGrowthSpeedDenominator(parameters::MAX_BLOCK_SIZE_GROWTH_SPEED_DENOMINATOR);\n\n lockedTxAllowedDeltaSeconds(parameters::CRYPTONOTE_LOCKED_TX_ALLOWED_DELTA_SECONDS);\n lockedTxAllowedDeltaBlocks(parameters::CRYPTONOTE_LOCKED_TX_ALLOWED_DELTA_BLOCKS);\n\n mempoolTxLiveTime(parameters::CRYPTONOTE_MEMPOOL_TX_LIVETIME);\n mempoolTxFromAltBlockLiveTime(parameters::CRYPTONOTE_MEMPOOL_TX_FROM_ALT_BLOCK_LIVETIME);\n numberOfPeriodsToForgetTxDeletedFromPool(parameters::CRYPTONOTE_NUMBER_OF_PERIODS_TO_FORGET_TX_DELETED_FROM_POOL);\n\n fusionTxMaxSize(parameters::FUSION_TX_MAX_SIZE);\n fusionTxMinInputCount(parameters::FUSION_TX_MIN_INPUT_COUNT);\n fusionTxMinInOutCountRatio(parameters::FUSION_TX_MIN_IN_OUT_COUNT_RATIO);\n\n upgradeHeightV2(parameters::UPGRADE_HEIGHT_V2);\n upgradeHeightV3(parameters::UPGRADE_HEIGHT_V3);\n upgradeHeightV4(parameters::UPGRADE_HEIGHT_V4);\n\n testnetUpgradeHeightV2(parameters::TESTNET_UPGRADE_HEIGHT_V2);\n testnetUpgradeHeightV3(parameters::TESTNET_UPGRADE_HEIGHT_V3);\n testnetUpgradeHeightV4(parameters::TESTNET_UPGRADE_HEIGHT_V4);\n\n upgradeVotingThreshold(parameters::UPGRADE_VOTING_THRESHOLD);\n upgradeVotingWindow(parameters::UPGRADE_VOTING_WINDOW);\n upgradeWindow(parameters::UPGRADE_WINDOW);\n\n blocksFileName(parameters::CRYPTONOTE_BLOCKS_FILENAME);\n blockIndexesFileName(parameters::CRYPTONOTE_BLOCKINDEXES_FILENAME);\n txPoolFileName(parameters::CRYPTONOTE_POOLDATA_FILENAME);\n\n testnet(false);\n}\n\nCurrencyBuilder& CurrencyBuilder::emissionSpeedFactor(unsigned int val) {\n if (val <= 0 || val > 8 * sizeof(uint64_t)) {\n throw std::invalid_argument(\"val at emissionSpeedFactor()\");\n }\n\n m_currency.m_emissionSpeedFactor = val;\n return *this;\n}\n\nCurrencyBuilder& CurrencyBuilder::numberOfDecimalPlaces(size_t val) {\n m_currency.m_numberOfDecimalPlaces = val;\n m_currency.m_coin = 1;\n for (size_t i = 0; i < m_currency.m_numberOfDecimalPlaces; ++i) {\n m_currency.m_coin *= 10;\n }\n\n return *this;\n}\n\nCurrencyBuilder& CurrencyBuilder::difficultyWindow(size_t val) {\n if (val < 2) {\n throw std::invalid_argument(\"val at difficultyWindow()\");\n }\n m_currency.m_difficultyWindow = val;\n return *this;\n}\n\nCurrencyBuilder& CurrencyBuilder::upgradeVotingThreshold(unsigned int val) {\n if (val <= 0 || val > 100) {\n throw std::invalid_argument(\"val at upgradeVotingThreshold()\");\n }\n\n m_currency.m_upgradeVotingThreshold = val;\n return *this;\n}\n\nCurrencyBuilder& CurrencyBuilder::upgradeWindow(uint32_t val) {\n if (val <= 0) {\n throw std::invalid_argument(\"val at upgradeWindow()\");\n }\n\n m_currency.m_upgradeWindow = val;\n return *this;\n}\n\n}\n", "meta": {"hexsha": "87e4329f2b324b2edb11955e4e97887c0e7d6b48", "size": 37428, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/CryptoNoteCore/Currency.cpp", "max_stars_repo_name": "Camellia73/BlackRoseCoin_Diamond_V2", "max_stars_repo_head_hexsha": "f155f7f9eb7f8db711b9e3a0d417038321a92abe", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/CryptoNoteCore/Currency.cpp", "max_issues_repo_name": "Camellia73/BlackRoseCoin_Diamond_V2", "max_issues_repo_head_hexsha": "f155f7f9eb7f8db711b9e3a0d417038321a92abe", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/CryptoNoteCore/Currency.cpp", "max_forks_repo_name": "Camellia73/BlackRoseCoin_Diamond_V2", "max_forks_repo_head_hexsha": "f155f7f9eb7f8db711b9e3a0d417038321a92abe", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.3569060773, "max_line_length": 322, "alphanum_fraction": 0.723121727, "num_tokens": 9712, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6791786991753929, "lm_q2_score": 0.341582499438317, "lm_q1q2_score": 0.23199555762959548}} {"text": "// Copyright (c) 2019 Tim Perkins\n\n// Permission is hereby granted, free of charge, to any person obtaining\n// a copy of this software and associated documentation files (the\n// “Software”), to deal in the Software without restriction, including\n// without limitation the rights to use, copy, modify, merge, publish,\n// distribute, sublicense, and/or sell copies of the Software, and to\n// permit persons to whom the Software is furnished to do so, subject to\n// the following conditions:\n\n// The above copyright notice and this permission notice shall be\n// included in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,\n// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS\n// BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN\n// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\n// CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n\n#ifndef TTFRM_TFRM_HPP\n#define TTFRM_TFRM_HPP\n\n#include \n#include \n\n#include \n#include \n\nnamespace ttfrm {\n\nusing Quat = Eigen::Quaterniond;\nusing Vec3 = Eigen::Vector3d;\n\ntemplate \nclass Tfrm;\n\ntemplate \nstruct FramePair {\n using FrameId = FrameId_;\n\n FrameId to_frame;\n FrameId from_frame;\n};\n\ntemplate \nstd::string Stringify(const Tfrm& tfrm);\n\ntemplate \nstd::string Stringify(const FramePair& frame_pair);\n\ntemplate \nclass Tfrm {\n public:\n using FrameId = FrameId_;\n\n Tfrm(const FrameId& to_frame, const FrameId& from_frame, const Quat& rot, const Vec3& trans);\n Tfrm(const Tfrm& other_tfrm);\n Tfrm(Tfrm&& other_tfrm);\n\n static Tfrm Identity(const FrameId& to_frame, const FrameId& from_frame);\n static Tfrm FromRotation(const FrameId& to_frame, const FrameId& from_frame,\n const Quat& rot);\n static Tfrm FromTranslation(const FrameId& to_frame, const FrameId& from_frame,\n const Vec3& trans);\n static Tfrm FromIsometry(const FrameId& to_frame, const FrameId& from_frame,\n const Eigen::Isometry3d& isometry);\n\n Tfrm& operator=(const Tfrm& other_tfrm);\n Tfrm& operator=(Tfrm&& other_tfrm);\n\n bool operator==(const Tfrm& other_tfrm) const;\n bool operator!=(const Tfrm& other_tfrm) const;\n bool IsApprox(const Tfrm& other_tfrm, double precision = 1.0e-6) const;\n\n FramePair Frames() const;\n FrameId ToFrame() const;\n FrameId FromFrame() const;\n\n Quat Rotation() const;\n Vec3 Translation() const;\n\n Tfrm Inverse() const;\n\n Tfrm Compose(const Tfrm& other_tfrm) const;\n Tfrm operator*(const Tfrm& other_tfrm) const;\n Tfrm operator()(const Tfrm& other_tfrm) const;\n\n Vec3 Apply(const Vec3& trans) const;\n Vec3 operator*(const Vec3& trans) const;\n Vec3 operator()(const Vec3& trans) const;\n\n Tfrm Interpolate(const FrameId& interp_to_frame, const Tfrm& other_tfrm,\n double t) const;\n\n Eigen::Isometry3d AsIsometry() const;\n\n private:\n FrameId to_frame_;\n FrameId from_frame_;\n Quat rot_;\n Vec3 trans_;\n\n friend std::string Stringify(const Tfrm& tfrm);\n friend std::string Stringify(const FramePair& frame_pair);\n};\n\nclass TfrmComposeException : public std::runtime_error {\n public:\n template \n TfrmComposeException(const Tfrm& tfrm, const Tfrm& other_tfrm);\n TfrmComposeException(const TfrmComposeException& other_comp_excp) = default;\n TfrmComposeException(TfrmComposeException&& other_comp_excp) = default;\n};\n\nclass TfrmInterpolateException : public std::runtime_error {\n public:\n template \n TfrmInterpolateException(const Tfrm& tfrm, const Tfrm& other_tfrm);\n TfrmInterpolateException(const TfrmInterpolateException& other_comp_excp) = default;\n TfrmInterpolateException(TfrmInterpolateException&& other_comp_excp) = default;\n};\n\ntemplate \nTfrm::Tfrm(const FrameId& to_frame, const FrameId& from_frame, const Quat& rot,\n const Vec3& trans)\n : to_frame_(to_frame), from_frame_(from_frame), rot_(rot.normalized()), trans_(trans)\n{\n // Do nothing\n}\n\ntemplate \nTfrm::Tfrm(const Tfrm& other_tfrm)\n : to_frame_(other_tfrm.to_frame_),\n from_frame_(other_tfrm.from_frame_),\n rot_(other_tfrm.rot_),\n trans_(other_tfrm.trans_)\n{\n // Do nothing\n}\n\ntemplate \nTfrm::Tfrm(Tfrm&& other_tfrm)\n : to_frame_(std::move(other_tfrm.to_frame_)),\n from_frame_(std::move(other_tfrm.from_frame_)),\n rot_(std::move(other_tfrm.rot_)),\n trans_(std::move(other_tfrm.trans_))\n{\n // Do nothing\n}\n\ntemplate \nTfrm Tfrm::Identity(const FrameId& to_frame, const FrameId& from_frame)\n{\n return Tfrm(to_frame, from_frame, Quat::Identity(), Vec3::Zero());\n}\n\ntemplate \nTfrm Tfrm::FromRotation(const FrameId& to_frame, const FrameId& from_frame,\n const Quat& rot)\n{\n return Tfrm(to_frame, from_frame, rot, Vec3::Zero());\n}\n\ntemplate \nTfrm Tfrm::FromTranslation(const FrameId& to_frame, const FrameId& from_frame,\n const Vec3& trans)\n{\n return Tfrm(to_frame, from_frame, Quat::Identity(), trans);\n}\n\ntemplate \nTfrm Tfrm::FromIsometry(const FrameId& to_frame, const FrameId& from_frame,\n const Eigen::Isometry3d& isometry)\n{\n return Tfrm(to_frame, from_frame, Quat(isometry.rotation()), isometry.translation());\n}\n\ntemplate \nTfrm& Tfrm::operator=(const Tfrm& other_tfrm)\n{\n to_frame_ = other_tfrm.to_frame_;\n from_frame_ = other_tfrm.from_frame_;\n rot_ = other_tfrm.rot_;\n trans_ = other_tfrm.trans_;\n return *this;\n}\n\ntemplate \nTfrm& Tfrm::operator=(Tfrm&& other_tfrm)\n{\n to_frame_ = std::move(other_tfrm.to_frame_);\n from_frame_ = std::move(other_tfrm.from_frame_);\n rot_ = std::move(other_tfrm.rot_);\n trans_ = std::move(other_tfrm.trans_);\n return *this;\n}\n\ntemplate \nbool Tfrm::operator==(const Tfrm& other_tfrm) const\n{\n static auto QuatEquality = [](const Quat& q1, const Quat& q2) {\n return (q1.w() == q2.w() && q1.x() == q2.x() && q1.y() == q2.y() && q1.z() == q2.z());\n };\n return (to_frame_ == other_tfrm.to_frame_ && from_frame_ == other_tfrm.from_frame_\n && QuatEquality(rot_, other_tfrm.rot_) && trans_ == other_tfrm.trans_);\n}\n\ntemplate \nbool Tfrm::operator!=(const Tfrm& other_tfrm) const\n{\n return !(*this == other_tfrm);\n}\n\ntemplate \nbool Tfrm::IsApprox(const Tfrm& other_tfrm, double precision) const\n{\n return (to_frame_ == other_tfrm.to_frame_ && from_frame_ == other_tfrm.from_frame_\n && rot_.isApprox(other_tfrm.rot_, precision)\n && trans_.isApprox(other_tfrm.trans_, precision));\n}\n\ntemplate \nFramePair Tfrm::Frames() const\n{\n return {to_frame_, from_frame_};\n}\n\ntemplate \nFrameId Tfrm::ToFrame() const\n{\n return to_frame_;\n}\n\ntemplate \nFrameId Tfrm::FromFrame() const\n{\n return from_frame_;\n}\n\ntemplate \nQuat Tfrm::Rotation() const\n{\n return rot_;\n}\n\ntemplate \nVec3 Tfrm::Translation() const\n{\n return trans_;\n}\n\ntemplate \nTfrm Tfrm::Inverse() const\n{\n const Quat inv_rot = rot_.inverse();\n return Tfrm(from_frame_, to_frame_, inv_rot, inv_rot * -trans_);\n}\n\ntemplate \nTfrm Tfrm::Compose(const Tfrm& other_tfrm) const\n{\n if (from_frame_ != other_tfrm.to_frame_) {\n throw TfrmComposeException(*this, other_tfrm);\n }\n // T2(T1(v)) = R2 * (R1 * v + t1) + t2 = (R2 * R1) * v + (t2 + R2 * t1)\n return Tfrm(to_frame_, other_tfrm.from_frame_, rot_ * other_tfrm.rot_,\n trans_ + rot_ * other_tfrm.trans_);\n}\n\ntemplate \nTfrm Tfrm::operator*(const Tfrm& other_tfrm) const\n{\n return Compose(other_tfrm);\n}\n\ntemplate \nTfrm Tfrm::operator()(const Tfrm& other_tfrm) const\n{\n return Compose(other_tfrm);\n}\n\ntemplate \nVec3 Tfrm::Apply(const Vec3& trans) const\n{\n // T(v) = R * v + t\n return Vec3(rot_ * trans + trans_);\n}\n\ntemplate \nVec3 Tfrm::operator*(const Vec3& trans) const\n{\n return Apply(trans);\n}\n\ntemplate \nVec3 Tfrm::operator()(const Vec3& trans) const\n{\n return Apply(trans);\n}\n\ntemplate \nTfrm Tfrm::Interpolate(const FrameId& interp_to_frame,\n const Tfrm& other_tfrm, double ratio) const\n{\n if (from_frame_ != other_tfrm.from_frame_) {\n throw TfrmInterpolateException(*this, other_tfrm);\n }\n return Tfrm(interp_to_frame, from_frame_, rot_.slerp(ratio, other_tfrm.rot_),\n trans_ + ratio * (other_tfrm.trans_ - trans_));\n}\n\ntemplate \nEigen::Isometry3d Tfrm::AsIsometry() const\n{\n // The concatenation of the translation and the rotation\n return Eigen::Translation3d(trans_) * rot_;\n}\n\ntemplate \nTfrmComposeException::TfrmComposeException(const Tfrm& tfrm,\n const Tfrm& other_tfrm)\n : std::runtime_error(fmt::format(\"Cannot compose transforms {} and {}\",\n Stringify(tfrm.Frames()), Stringify(other_tfrm.Frames())))\n{\n // Do nothing\n}\n\ntemplate \nTfrmInterpolateException::TfrmInterpolateException(const Tfrm& tfrm,\n const Tfrm& other_tfrm)\n : std::runtime_error(fmt::format(\"Cannot interpolate transforms {} and {}\",\n Stringify(tfrm.Frames()), Stringify(other_tfrm.Frames())))\n{\n // Do nothing\n}\n\ntemplate \nstd::string Stringify(const Tfrm& tfrm)\n{\n return fmt::format(\n \"([{}] <- [{}], ROT: (W: {}, X: {}, Y: {}, Z: {}), TRANS: (X: {}, Y: {}, Z: {}))\",\n tfrm.to_frame_, tfrm.from_frame_, tfrm.rot_.w(), tfrm.rot_.x(), tfrm.rot_.y(), tfrm.rot_.z(),\n tfrm.trans_.x(), tfrm.trans_.y(), tfrm.trans_.z());\n}\n\ntemplate \nstd::string Stringify(const FramePair& frame_pair)\n{\n return fmt::format(\"([{}] <- [{}])\", frame_pair.to_frame, frame_pair.from_frame);\n}\n\n} // namespace ttfrm\n\n#endif // TTFRM_TFRM_HPP\n", "meta": {"hexsha": "c0ae959454d8d09d9fe21d6bf96697cfc2a5ae24", "size": 11267, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ttfrm/tfrm.hpp", "max_stars_repo_name": "tprk77/ttfrm", "max_stars_repo_head_hexsha": "a228043e0b4a937188c586280964c8a9798e9dba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-01-05T03:09:50.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-05T03:09:50.000Z", "max_issues_repo_path": "ttfrm/tfrm.hpp", "max_issues_repo_name": "tprk77/ttfrm", "max_issues_repo_head_hexsha": "a228043e0b4a937188c586280964c8a9798e9dba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2019-10-07T13:45:13.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-24T02:11:37.000Z", "max_forks_repo_path": "ttfrm/tfrm.hpp", "max_forks_repo_name": "tprk77/ttfrm", "max_forks_repo_head_hexsha": "a228043e0b4a937188c586280964c8a9798e9dba", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.4720670391, "max_line_length": 99, "alphanum_fraction": 0.6973462324, "num_tokens": 3053, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.45326184801538616, "lm_q1q2_score": 0.23194161390836884}} {"text": "///////////////////////////////////////////////////////////////\n// Copyright 2013 John Maddock. Distributed under the Boost\n// Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_\n\n#ifndef BOOST_MP_FLOAT128_HPP\n#define BOOST_MP_FLOAT128_HPP\n\n#include \n#include \n#include \n#include \n\n#if defined(BOOST_INTEL) && !defined(BOOST_MP_USE_FLOAT128) && !defined(BOOST_MP_USE_QUAD)\n# if defined(BOOST_INTEL_CXX_VERSION) && (BOOST_INTEL_CXX_VERSION >= 1310) && defined(__GNUC__)\n# if (__GNUC__ > 4) || ((__GNUC__ == 4) && (__GNUC_MINOR__ >= 6))\n# define BOOST_MP_USE_FLOAT128\n# endif\n# endif\n\n# ifndef BOOST_MP_USE_FLOAT128\n# define BOOST_MP_USE_QUAD\n# endif\n#endif\n\n#if defined(__GNUC__) && !defined(BOOST_MP_USE_FLOAT128) && !defined(BOOST_MP_USE_QUAD)\n# define BOOST_MP_USE_FLOAT128\n#endif\n\n#if !defined(BOOST_MP_USE_FLOAT128) && !defined(BOOST_MP_USE_QUAD)\n# error \"Sorry compiler is neither GCC, not Intel, don't know how to configure this header.\"\n#endif\n#if defined(BOOST_MP_USE_FLOAT128) && defined(BOOST_MP_USE_QUAD)\n# error \"Oh dear, both BOOST_MP_USE_FLOAT128 and BOOST_MP_USE_QUAD are defined, which one should I be using?\"\n#endif\n\n#if defined(BOOST_MP_USE_FLOAT128)\n\nextern \"C\" {\n#include \n}\n\ntypedef __float128 float128_type;\n\n#elif defined(BOOST_MP_USE_QUAD)\n\n#include \n\ntypedef _Quad float128_type;\n\nextern \"C\" {\n_Quad __ldexpq(_Quad, int);\n_Quad __frexpq(_Quad, int*);\n_Quad __fabsq(_Quad);\n_Quad __floorq(_Quad);\n_Quad __ceilq(_Quad);\n_Quad __sqrtq(_Quad);\n_Quad __truncq(_Quad);\n_Quad __expq(_Quad);\n_Quad __powq(_Quad, _Quad);\n_Quad __logq(_Quad);\n_Quad __log10q(_Quad);\n_Quad __sinq(_Quad);\n_Quad __cosq(_Quad);\n_Quad __tanq(_Quad);\n_Quad __asinq(_Quad);\n_Quad __acosq(_Quad);\n_Quad __atanq(_Quad);\n_Quad __sinhq(_Quad);\n_Quad __coshq(_Quad);\n_Quad __tanhq(_Quad);\n_Quad __fmodq(_Quad, _Quad);\n_Quad __atan2q(_Quad, _Quad);\n\n#define ldexpq __ldexpq\n#define frexpq __frexpq\n#define fabsq __fabsq\n#define floorq __floorq\n#define ceilq __ceilq\n#define sqrtq __sqrtq\n#define truncq __truncq\n#define expq __expq\n#define powq __powq\n#define logq __logq\n#define log10q __log10q\n#define sinq __sinq\n#define cosq __cosq\n#define tanq __tanq\n#define asinq __asinq\n#define acosq __acosq\n#define atanq __atanq\n#define sinhq __sinhq\n#define coshq __coshq\n#define tanhq __tanhq\n#define fmodq __fmodq\n#define atan2q __atan2q\n}\n\ninline _Quad isnanq(_Quad v)\n{\n return v != v;\n}\ninline _Quad isinfq(_Quad v)\n{\n return __fabsq(v) > 1.18973149535723176508575932662800702e4932Q;\n}\n\n#endif\n\nnamespace boost{\nnamespace multiprecision{\nnamespace backends{\n\nstruct float128_backend;\n\n}\n\nusing backends::float128_backend;\n\ntemplate<>\nstruct number_category : public mpl::int_ {};\n#if defined(BOOST_MP_USE_QUAD)\ntemplate<>\nstruct number_category : public mpl::int_ {};\n#endif\n\ntypedef number float128;\n\n#ifndef BOOST_NO_CXX11_CONSTEXPR\n\nnamespace quad_constants {\n constexpr __float128 quad_min = static_cast<__float128>(1)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN) / 1073741824;\n\n constexpr __float128 quad_denorm_min = static_cast<__float128>(1)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN)\n * static_cast<__float128>(DBL_MIN) / 5.5751862996326557854e+42;\n\n constexpr double dbl_mult = 8.9884656743115795386e+307; // This has one bit set only.\n constexpr __float128 quad_max = (static_cast<__float128>(1) - 9.62964972193617926527988971292463659e-35) // This now has all bits sets to 1\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult)\n * static_cast<__float128>(dbl_mult) * 65536;\n}\n\n#define BOOST_MP_QUAD_MIN boost::multiprecision::quad_constants::quad_min\n#define BOOST_MP_QUAD_DENORM_MIN boost::multiprecision::quad_constants::quad_denorm_min\n#define BOOST_MP_QUAD_MAX boost::multiprecision::quad_constants::quad_max\n\n#else\n\n#define BOOST_MP_QUAD_MIN 3.36210314311209350626267781732175260e-4932Q\n#define BOOST_MP_QUAD_DENORM_MIN 6.475175119438025110924438958227646552e-4966Q\n#define BOOST_MP_QUAD_MAX 1.18973149535723176508575932662800702e4932Q\n\n#endif\n\nnamespace backends{\n\nstruct float128_backend\n{\n typedef mpl::list signed_types;\n typedef mpl::list unsigned_types;\n typedef mpl::list float_types;\n typedef int exponent_type;\n\nprivate:\n float128_type m_value;\npublic:\n BOOST_CONSTEXPR float128_backend() BOOST_NOEXCEPT : m_value(0) {}\n BOOST_CONSTEXPR float128_backend(const float128_backend& o) BOOST_NOEXCEPT : m_value(o.m_value) {}\n float128_backend& operator = (const float128_backend& o) BOOST_NOEXCEPT\n {\n m_value = o.m_value;\n return *this;\n }\n template \n BOOST_CONSTEXPR float128_backend(const T& i, const typename enable_if_c::value>::type* = 0) BOOST_NOEXCEPT_IF(noexcept(std::declval() = std::declval()))\n : m_value(i) {}\n template \n typename enable_if_c::value || is_convertible::value, float128_backend&>::type operator = (const T& i) BOOST_NOEXCEPT_IF(noexcept(std::declval() = std::declval()))\n {\n m_value = i;\n return *this;\n }\n float128_backend(long double const& f)\n {\n BOOST_STATIC_CONSTEXPR __float128 inf_val = static_cast<__float128>(HUGE_VAL);\n if(boost::math::isinf(f))\n m_value = (f < 0) ? -inf_val : inf_val;\n else\n m_value = f;\n }\n float128_backend& operator=(long double const& f)\n {\n BOOST_STATIC_CONSTEXPR __float128 inf_val = static_cast<__float128>(HUGE_VAL);\n if(boost::math::isinf(f))\n m_value = (f < 0) ? -inf_val : inf_val;\n else\n m_value = f;\n return *this;\n }\n float128_backend& operator = (const char* s)\n {\n#ifndef BOOST_MP_USE_QUAD\n char* p_end;\n m_value = strtoflt128(s, &p_end);\n if(p_end - s != (std::ptrdiff_t)std::strlen(s))\n {\n BOOST_THROW_EXCEPTION(std::runtime_error(\"Unable to interpret input string as a floating point value\"));\n }\n#else\n boost::multiprecision::detail::convert_from_string(*this, s);\n#endif\n return *this;\n }\n void swap(float128_backend& o) BOOST_NOEXCEPT\n {\n std::swap(m_value, o.value());\n }\n std::string str(std::streamsize digits, std::ios_base::fmtflags f)const\n {\n#ifndef BOOST_MP_USE_QUAD\n char buf[100];\n boost::scoped_array buf2;\n std::string format = \"%\";\n if(f & std::ios_base::showpos)\n format += \"+\";\n if(f & std::ios_base::showpoint)\n format += \"#\";\n format += \".*\";\n if(digits == 0)\n digits = 36;\n format += \"Q\";\n if(f & std::ios_base::scientific)\n format += \"e\";\n else if(f & std::ios_base::fixed)\n format += \"f\";\n else\n format += \"g\";\n\n int v = quadmath_snprintf (buf, 100, format.c_str(), digits, m_value);\n\n if((v < 0) || (v >= 99))\n {\n int v_max = v;\n buf2.reset(new char[v+3]);\n v = quadmath_snprintf (&buf2[0], v_max + 3, format.c_str(), digits, m_value);\n if(v >= v_max + 3)\n {\n BOOST_THROW_EXCEPTION(std::runtime_error(\"Formatting of float128_type failed.\"));\n }\n return &buf2[0];\n }\n return buf;\n#else\n return boost::multiprecision::detail::convert_to_string(*this, digits ? digits : 37, f);\n#endif\n }\n void negate() BOOST_NOEXCEPT\n {\n m_value = -m_value;\n }\n int compare(const float128_backend& o)const\n {\n return m_value == o.m_value ? 0 : m_value < o.m_value ? -1 : 1;\n }\n template \n int compare(const T& i)const\n {\n return m_value == i ? 0 : m_value < i ? -1 : 1;\n }\n float128_type& value()\n {\n return m_value;\n }\n const float128_type& value()const\n {\n return m_value;\n }\n};\n\ninline void eval_add(float128_backend& result, const float128_backend& a)\n{\n result.value() += a.value();\n}\ntemplate \ninline void eval_add(float128_backend& result, const A& a)\n{\n result.value() += a;\n}\ninline void eval_subtract(float128_backend& result, const float128_backend& a)\n{\n result.value() -= a.value();\n}\ntemplate \ninline void eval_subtract(float128_backend& result, const A& a)\n{\n result.value() -= a;\n}\ninline void eval_multiply(float128_backend& result, const float128_backend& a)\n{\n result.value() *= a.value();\n}\ntemplate \ninline void eval_multiply(float128_backend& result, const A& a)\n{\n result.value() *= a;\n}\ninline void eval_divide(float128_backend& result, const float128_backend& a)\n{\n result.value() /= a.value();\n}\ntemplate \ninline void eval_divide(float128_backend& result, const A& a)\n{\n result.value() /= a;\n}\n\ninline void eval_add(float128_backend& result, const float128_backend& a, const float128_backend& b)\n{\n result.value() = a.value() + b.value();\n}\ntemplate \ninline void eval_add(float128_backend& result, const float128_backend& a, const A& b)\n{\n result.value() = a.value() + b;\n}\ninline void eval_subtract(float128_backend& result, const float128_backend& a, const float128_backend& b)\n{\n result.value() = a.value() - b.value();\n}\ntemplate \ninline void eval_subtract(float128_backend& result, const float128_backend& a, const A& b)\n{\n result.value() = a.value() - b;\n}\ntemplate \ninline void eval_subtract(float128_backend& result, const A& a, const float128_backend& b)\n{\n result.value() = a - b.value();\n}\ninline void eval_multiply(float128_backend& result, const float128_backend& a, const float128_backend& b)\n{\n result.value() = a.value() * b.value();\n}\ntemplate \ninline void eval_multiply(float128_backend& result, const float128_backend& a, const A& b)\n{\n result.value() = a.value() * b;\n}\ninline void eval_divide(float128_backend& result, const float128_backend& a, const float128_backend& b)\n{\n result.value() = a.value() / b.value();\n}\n\ntemplate \ninline void eval_convert_to(R* result, const float128_backend& val)\n{\n *result = static_cast(val.value());\n}\n\ninline void eval_frexp(float128_backend& result, const float128_backend& arg, int* exp)\n{\n result.value() = frexpq(arg.value(), exp);\n}\n\ninline void eval_ldexp(float128_backend& result, const float128_backend& arg, int exp)\n{\n result.value() = ldexpq(arg.value(), exp);\n}\n\ninline void eval_floor(float128_backend& result, const float128_backend& arg)\n{\n result.value() = floorq(arg.value());\n}\ninline void eval_ceil(float128_backend& result, const float128_backend& arg)\n{\n result.value() = ceilq(arg.value());\n}\ninline void eval_sqrt(float128_backend& result, const float128_backend& arg)\n{\n result.value() = sqrtq(arg.value());\n}\ninline int eval_fpclassify(const float128_backend& arg)\n{\n if(isnanq(arg.value()))\n return FP_NAN;\n else if(isinfq(arg.value()))\n return FP_INFINITE;\n else if(arg.value() == 0)\n return FP_ZERO;\n\n float128_backend t(arg);\n if(t.value() < 0)\n t.negate();\n if(t.value() < BOOST_MP_QUAD_MIN)\n return FP_SUBNORMAL;\n return FP_NORMAL;\n}\n\ninline void eval_increment(float128_backend& arg)\n{\n ++arg.value();\n}\ninline void eval_decrement(float128_backend& arg)\n{\n --arg.value();\n}\n\n/*********************************************************************\n*\n* abs/fabs:\n*\n*********************************************************************/\n\ninline void eval_abs(float128_backend& result, const float128_backend& arg)\n{\n result.value() = fabsq(arg.value());\n}\ninline void eval_fabs(float128_backend& result, const float128_backend& arg)\n{\n result.value() = fabsq(arg.value());\n}\n\n/*********************************************************************\n*\n* Floating point functions:\n*\n*********************************************************************/\n\ninline void eval_trunc(float128_backend& result, const float128_backend& arg)\n{\n result.value() = truncq(arg.value());\n}\n/*\n// \n// This doesn't actually work... rely on our own default version instead.\n//\ninline void eval_round(float128_backend& result, const float128_backend& arg)\n{\n if(isnanq(arg.value()) || isinf(arg.value()))\n {\n result = boost::math::policies::raise_rounding_error(\n \"boost::multiprecision::trunc<%1%>(%1%)\", 0, \n number(arg), \n number(arg), \n boost::math::policies::policy<>()).backend();\n return;\n }\n result.value() = roundq(arg.value());\n}\n*/\n\ninline void eval_exp(float128_backend& result, const float128_backend& arg)\n{\n result.value() = expq(arg.value());\n}\ninline void eval_log(float128_backend& result, const float128_backend& arg)\n{\n result.value() = logq(arg.value());\n}\ninline void eval_log10(float128_backend& result, const float128_backend& arg)\n{\n result.value() = log10q(arg.value());\n}\ninline void eval_sin(float128_backend& result, const float128_backend& arg)\n{\n result.value() = sinq(arg.value());\n}\ninline void eval_cos(float128_backend& result, const float128_backend& arg)\n{\n result.value() = cosq(arg.value());\n}\ninline void eval_tan(float128_backend& result, const float128_backend& arg)\n{\n result.value() = tanq(arg.value());\n}\ninline void eval_asin(float128_backend& result, const float128_backend& arg)\n{\n result.value() = asinq(arg.value());\n}\ninline void eval_acos(float128_backend& result, const float128_backend& arg)\n{\n result.value() = acosq(arg.value());\n}\ninline void eval_atan(float128_backend& result, const float128_backend& arg)\n{\n result.value() = atanq(arg.value());\n}\ninline void eval_sinh(float128_backend& result, const float128_backend& arg)\n{\n result.value() = sinhq(arg.value());\n}\ninline void eval_cosh(float128_backend& result, const float128_backend& arg)\n{\n result.value() = coshq(arg.value());\n}\ninline void eval_tanh(float128_backend& result, const float128_backend& arg)\n{\n result.value() = tanhq(arg.value());\n}\ninline void eval_fmod(float128_backend& result, const float128_backend& a, const float128_backend& b)\n{\n result.value() = fmodq(a.value(), b.value());\n}\ninline void eval_pow(float128_backend& result, const float128_backend& a, const float128_backend& b)\n{\n result.value() = powq(a.value(), b.value());\n}\ninline void eval_atan2(float128_backend& result, const float128_backend& a, const float128_backend& b)\n{\n result.value() = atan2q(a.value(), b.value());\n}\n#ifndef BOOST_MP_USE_QUAD\ninline void eval_multiply_add(float128_backend& result, const float128_backend& a, const float128_backend& b, const float128_backend& c)\n{\n result.value() = fmaq(a.value(), b.value(), c.value());\n}\ninline int eval_signbit BOOST_PREVENT_MACRO_SUBSTITUTION(const float128_backend& arg)\n{\n return ::signbitq(arg.value());\n}\n#endif\n\ninline std::size_t hash_value(const float128_backend& val)\n{\n return boost::hash_value(static_cast(val.value()));\n}\n\n} // namespace backends\n\n template\n inline boost::multiprecision::number asinh BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return asinhq(arg.backend().value());\n }\n template\n inline boost::multiprecision::number acosh BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return acoshq(arg.backend().value());\n }\n template\n inline boost::multiprecision::number atanh BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return atanhq(arg.backend().value());\n }\n template\n inline boost::multiprecision::number cbrt BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return cbrtq(arg.backend().value());\n }\n template\n inline boost::multiprecision::number erf BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return erfq(arg.backend().value());\n }\n template\n inline boost::multiprecision::number erfc BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return erfcq(arg.backend().value());\n }\n template\n inline boost::multiprecision::number expm1 BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return expm1q(arg.backend().value());\n }\n template\n inline boost::multiprecision::number lgamma BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return lgammaq(arg.backend().value());\n }\n template\n inline boost::multiprecision::number tgamma BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return tgammaq(arg.backend().value());\n }\n template\n inline boost::multiprecision::number log1p BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& arg)\n {\n return log1pq(arg.backend().value());\n }\n\n#ifndef BOOST_MP_USE_QUAD\n template \n inline boost::multiprecision::number copysign BOOST_PREVENT_MACRO_SUBSTITUTION(const boost::multiprecision::number& a, const boost::multiprecision::number& b)\n {\n return ::copysignq(a.backend().value(), b.backend().value());\n }\n\n inline void eval_remainder(float128_backend& result, const float128_backend& a, const float128_backend& b)\n {\n result.value() = remainderq(a.value(), b.value());\n }\n inline void eval_remainder(float128_backend& result, const float128_backend& a, const float128_backend& b, int* pi)\n {\n result.value() = remquoq(a.value(), b.value(), pi);\n }\n#endif\n\n} // namespace multiprecision\n\nnamespace math {\n\n using boost::multiprecision::signbit;\n using boost::multiprecision::copysign;\n\n} // namespace math\n\n} // namespace boost\n\nnamespace boost{ \nnamespace archive{\n\nclass binary_oarchive;\nclass binary_iarchive;\n\n}\n \nnamespace serialization{ namespace float128_detail{\n\ntemplate \nvoid do_serialize(Archive& ar, boost::multiprecision::backends::float128_backend& val, const mpl::false_&, const mpl::false_&)\n{\n // saving\n // non-binary\n std::string s(val.str(0, std::ios_base::scientific));\n ar & s;\n}\ntemplate \nvoid do_serialize(Archive& ar, boost::multiprecision::backends::float128_backend& val, const mpl::true_&, const mpl::false_&)\n{\n // loading\n // non-binary\n std::string s;\n ar & s;\n val = s.c_str();\n}\n\ntemplate \nvoid do_serialize(Archive& ar, boost::multiprecision::backends::float128_backend& val, const mpl::false_&, const mpl::true_&)\n{\n // saving\n // binary\n ar.save_binary(&val, sizeof(val));\n}\ntemplate \nvoid do_serialize(Archive& ar, boost::multiprecision::backends::float128_backend& val, const mpl::true_&, const mpl::true_&)\n{\n // loading\n // binary\n ar.load_binary(&val, sizeof(val));\n}\n\n} // detail\n\ntemplate \nvoid serialize(Archive& ar, boost::multiprecision::backends::float128_backend& val, unsigned int /*version*/)\n{\n typedef typename Archive::is_loading load_tag;\n typedef typename mpl::bool_::value || boost::is_same::value> binary_tag;\n\n float128_detail::do_serialize(ar, val, load_tag(), binary_tag());\n}\n\n} // namepsace archive\n\n} // namespace boost\n\nnamespace std{\n\ntemplate \nclass numeric_limits >\n{\n typedef boost::multiprecision::number number_type;\npublic:\n BOOST_STATIC_CONSTEXPR bool is_specialized = true;\n static number_type (min)() BOOST_NOEXCEPT { return BOOST_MP_QUAD_MIN; }\n static number_type (max)() BOOST_NOEXCEPT { return BOOST_MP_QUAD_MAX; }\n static number_type lowest() BOOST_NOEXCEPT { return -(max)(); }\n BOOST_STATIC_CONSTEXPR int digits = 113;\n BOOST_STATIC_CONSTEXPR int digits10 = 33;\n BOOST_STATIC_CONSTEXPR int max_digits10 = 36;\n BOOST_STATIC_CONSTEXPR bool is_signed = true;\n BOOST_STATIC_CONSTEXPR bool is_integer = false;\n BOOST_STATIC_CONSTEXPR bool is_exact = false;\n BOOST_STATIC_CONSTEXPR int radix = 2;\n static number_type epsilon() { return 1.92592994438723585305597794258492732e-34; /* this double value has only one bit set and so is exact */ }\n static number_type round_error() { return 0.5; }\n BOOST_STATIC_CONSTEXPR int min_exponent = -16381;\n BOOST_STATIC_CONSTEXPR int min_exponent10 = min_exponent * 301L / 1000L;\n BOOST_STATIC_CONSTEXPR int max_exponent = 16384;\n BOOST_STATIC_CONSTEXPR int max_exponent10 = max_exponent * 301L / 1000L;\n BOOST_STATIC_CONSTEXPR bool has_infinity = true;\n BOOST_STATIC_CONSTEXPR bool has_quiet_NaN = true;\n BOOST_STATIC_CONSTEXPR bool has_signaling_NaN = false;\n BOOST_STATIC_CONSTEXPR float_denorm_style has_denorm = denorm_present;\n BOOST_STATIC_CONSTEXPR bool has_denorm_loss = true;\n static number_type infinity() { return HUGE_VAL; /* conversion from double infinity OK */ }\n static number_type quiet_NaN() { return number_type(\"nan\"); }\n static number_type signaling_NaN() { return 0; }\n static number_type denorm_min() { return BOOST_MP_QUAD_DENORM_MIN; }\n BOOST_STATIC_CONSTEXPR bool is_iec559 = true;\n BOOST_STATIC_CONSTEXPR bool is_bounded = false;\n BOOST_STATIC_CONSTEXPR bool is_modulo = false;\n BOOST_STATIC_CONSTEXPR bool traps = false;\n BOOST_STATIC_CONSTEXPR bool tinyness_before = false;\n BOOST_STATIC_CONSTEXPR float_round_style round_style = round_to_nearest;\n};\n\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_specialized;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::digits;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::digits10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::max_digits10;\n\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_signed;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_integer;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_exact;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::radix;\n\n\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::min_exponent;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::max_exponent;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::min_exponent10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::max_exponent10;\n\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::has_infinity;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::has_quiet_NaN;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::has_signaling_NaN;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::has_denorm_loss;\n\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_iec559;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_bounded;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_modulo;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::traps;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::tinyness_before;\n\ntemplate \nBOOST_CONSTEXPR_OR_CONST float_round_style numeric_limits >::round_style;\ntemplate \nBOOST_CONSTEXPR_OR_CONST float_denorm_style numeric_limits >::has_denorm;\n\n} // namespace std\n\n\n#endif\n", "meta": {"hexsha": "b4b16c59745808d5163749fb91af619fa0319bdd", "size": 31173, "ext": "hpp", "lang": "C++", "max_stars_repo_path": 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NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.23194161390836882}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"snark.hpp\"\n#include \"utils.cpp\"\n\nusing namespace libsnark;\nusing namespace std;\n\nint verifyProof(r1cs_ppzksnark_verification_key verificationKey_in, string proofFileName, string publicInputs)\n{\n // Read proof in from file\n //libsnark::r1cs_ppzksnark_proof proof_in;\n boost::optional> proof_in;\n //r1cs_ppzksnark_proof proof_in;\n \n cout << proofFileName << endl;\n ifstream proofFileIn(proofFileName);\n stringstream proofFromFile;\n if (proofFileIn) {\n proofFromFile << proofFileIn.rdbuf();\n proofFileIn.close();\n } else {\n cout << \"Failed to read from proof file\" << endl;\n return 1;\n }\n\n proofFromFile >> proof_in;\n \n // Hashes to validate against\n std::vector h_startBalance_bv(256);\n std::vector h_endBalance_bv(256);\n std::vector h_incoming_bv(256);\n std::vector h_outgoing_bv(256);\n vector> values = fillValuesFromfile(publicInputs);\n h_startBalance_bv = int_list_to_bits_local(values[0], 8);\n h_endBalance_bv = int_list_to_bits_local(values[1], 8);\n h_incoming_bv = int_list_to_bits_local(values[2], 8);\n h_outgoing_bv = int_list_to_bits_local(values[3], 8);\n\n cout << \"proof read ... starting verification\" << endl;\n // Verify the proof\n bool isVerified = verify_payment_in_out_proof(verificationKey_in, *proof_in, h_startBalance_bv, h_endBalance_bv, h_incoming_bv, h_outgoing_bv);\n\n if(isVerified){\n cout << \"Proof was verified!!\" << proofFileName << endl;\n return 0;\n } else {\n cout << \"Proof was not verified!!\" << proofFileName << endl;\n return 1;\n }\n}\n\nint main(int argc, char *argv[])\n{\n // Initialize the curve parameters.\n default_r1cs_ppzksnark_pp::init_public_params();\n\n // Read verification key in from file\n r1cs_ppzksnark_verification_key verificationKey_in;\n ifstream fileIn(\"verificationKey_single\");\n stringstream verificationKeyFromFile;\n if (fileIn) {\n verificationKeyFromFile << fileIn.rdbuf();\n fileIn.close();\n }\n verificationKeyFromFile >> verificationKey_in;\n\n string proofName = \"proof_single_\";\n string proofNameWithId = proofName + argv[1];\n string publicInputs = \"publicInputParameters_single_\";\n string publicInputsWithId = publicInputs + argv[1];\n return verifyProof(verificationKey_in, proofNameWithId, publicInputsWithId);\n}\n\n\n", "meta": {"hexsha": "2d25dc7f62dbc9b16e47d0e4cf179050b08aa8cb", "size": 2612, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/payment_in_out_verify_proof.cpp", "max_stars_repo_name": "agiletechvn/ZKP", "max_stars_repo_head_hexsha": "d1294da076585e2a906aa64560a71fc68114c75c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 118.0, "max_stars_repo_stars_event_min_datetime": "2017-08-29T05:25:07.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-28T13:59:39.000Z", "max_issues_repo_path": "src/payment_in_out_verify_proof.cpp", "max_issues_repo_name": "technologiespro/zero-knowledge-proofs", "max_issues_repo_head_hexsha": "6fdfb0c25ae24e33b6325baa2b261860e89ae087", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 10.0, "max_issues_repo_issues_event_min_datetime": "2017-08-29T03:44:57.000Z", "max_issues_repo_issues_event_max_datetime": "2019-01-10T23:39:26.000Z", "max_forks_repo_path": "src/payment_in_out_verify_proof.cpp", "max_forks_repo_name": "technologiespro/zero-knowledge-proofs", "max_forks_repo_head_hexsha": "6fdfb0c25ae24e33b6325baa2b261860e89ae087", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 59.0, "max_forks_repo_forks_event_min_datetime": "2017-08-29T01:50:49.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-25T13:38:35.000Z", "avg_line_length": 31.8536585366, "max_line_length": 145, "alphanum_fraction": 0.7473200613, "num_tokens": 723, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.23184220331106553}} {"text": "#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \"texturing.h\"\n#include \"seam_leveling.h\"\n#include \"progress_counter.h\"\n\nTEX_NAMESPACE_BEGIN\n\n/*\n* 寻找以vertex为边的所有边界(一般有两条), 边界的左右两边的图片分别是label1, label2\n* */\nvoid find_seam_edges_for_vertex_label_combination(UniGraph const & graph, mve::TriangleMesh::ConstPtr & mesh,\n mve::MeshInfo const & mesh_info, std::size_t vertex, std::size_t label1, std::size_t label2,\n std::vector * seam_edges){\n \n assert(label1 != 0 && label2 != 0 && label1 < label2);\n\n mve::TriangleMesh::VertexList const & vertices = mesh->get_vertices();\n\n std::vector const & adj_verts = mesh_info[vertex].verts; // 顶点相邻的顶点\n for (std::size_t i = 0; i < adj_verts.size(); ++i){\n std::size_t adj_vertex = adj_verts[i];\n if (vertex == adj_vertex) continue;\n \n std::vector edge_faces;\n mesh_info.get_faces_for_edge(vertex, adj_vertex, &edge_faces); // 返回两个顶点相邻的面片\n\n for (std::size_t j = 0; j < edge_faces.size(); ++j) {\n for(std::size_t k = j + 1; k < edge_faces.size(); ++k) {\n std::size_t face_label1 = graph.get_label(edge_faces[j]);\n std::size_t face_label2 = graph.get_label(edge_faces[k]);\n if (!(face_label1 < face_label2)) std::swap(face_label1, face_label2);\n \n if (face_label1 != label1 || face_label2 != label2) continue;\n\n math::Vec3f v1 = vertices[vertex];\n math::Vec3f v2 = vertices[adj_vertex];\n\n if ((v2 - v1).norm() == 0.0f) continue; // 边长为0, 退出\n\n MeshEdge seam_edge = {vertex, adj_vertex};\n seam_edges->push_back(seam_edge);\n }\n }\n }\n}\n\n\n\nmath::Vec3f calculate_difference(VertexProjectionInfos const & vertex_projection_infos,\n mve::TriangleMesh::ConstPtr & mesh, std::vector const & texture_patches,\n std::vector const & seam_edges, int label1, int label2) {\n\n assert(label1 != 0 && label2 != 0 && label1 < label2);\n assert(!seam_edges.empty());\n \n mve::TriangleMesh::VertexList const & vertices = mesh->get_vertices();\n\n math::Accum color1_accum(math::Vec3f(0.0f)); // 一个累加器(支持基本整型的数据相加)\n math::Accum color2_accum(math::Vec3f(0.0f));\n\n for (MeshEdge const & seam_edge : seam_edges) {\n math::Vec3f v1 = vertices[seam_edge.v1];\n math::Vec3f v2 = vertices[seam_edge.v2];\n \n if ((v2 - v1).norm() == 0.0f) continue; // 边长为0, 退出\n\n std::vector edge_projection_infos;\n // find_mesh_edge_projections(vertex_projection_infos, seam_edge, &edge_projection_infos);\n\n \n }\n}\n\nvoid global_seam_leveling(UniGraph const & graph, mve::TriangleMesh::ConstPtr mesh,\n mve::MeshInfo const & mesh_info,\n std::vector > const & vertex_projection_infos,\n std::vector * texture_patches) {\n \n mve::TriangleMesh::VertexList const & vertices = mesh->get_vertices();\n std::size_t const num_vertices = vertices.size();\n\n std::cout << \"\\t 创建用于优化的矩阵... \" << std::flush;\n\n std::vector > vertlabel2row; // 把顶点和view对应起来, 一个顶点可能被多个面共用, 所以一个顶点可能对多个view, 相同点不同的view,点的序号也不同 vertlabel2row[顶点_id(即在mesh中所有顶点的索引)][view_id] = 点的新序号(即x_row)\n vertlabel2row.resize(num_vertices);\n\n std::vector > labels; // 索引和顶点在mesh中的索引一样 {{view的id}}\n labels.resize(num_vertices);\n\n // 为每个标签的每个顶点 指定一个新索引（row）\n std::size_t x_row = 0; // 顶点的序号\n for (std::size_t i = 0; i < num_vertices; ++i) { // 遍历每个顶点\n std::set label_set; // 集合 如果使用当前顶点的多个面来自同一个view, 只保留一个\n \n std::vector faces = mesh_info[i].faces; // 相邻面\n std::set::iterator it = label_set.begin();\n for (std::size_t j = 0; j < faces.size(); ++j) { // 遍历每个相邻面, 找出这个顶点相邻的面片用到的view有哪些\n std::size_t label = graph.get_label(faces[j]); // 面用的是哪个view\n label_set.insert(it, label);\n }\n\n for (it = label_set.begin(); it != label_set.end(); ++it) { // 遍历顶点相邻的面用到的view\n std::size_t label = *it;\n if (label == 0) continue;\n\n vertlabel2row[i][label] = x_row; // \n labels[i].push_back(label);\n ++x_row;\n }\n }\n\n std::size_t x_rows = x_row;\n assert(x_rows < static_cast(std::numeric_limits::max()));\n\n float const lambda = 0.1f;\n\n /* Fill the Tikhonov matrix Gamma(regularization constraints). */\n std::size_t Gamma_row = 0; // 能表示有多少对在同一个patch又相邻的顶点\n std::vector > coefficients_Gamma; // A small structure to hold a non zero as a triplet (i,j,value). 用来选中在同一个纹理块的顶点, 给他们的g加个正则项防止过大 [从0开始的索引][顶点的序号]:+/-λ\n coefficients_Gamma.reserve(2 * num_vertices);\n for (std::size_t i = 0; i < num_vertices; ++i) { // 遍历每个顶点\n for (std::size_t j = 0; j < labels[i].size(); ++j) { // 遍历每个顶点连接的view\n std::vector const & adj_verts = mesh_info[i].verts; // 相邻顶点\n for (std::size_t k = 0; k < adj_verts.size(); ++k) { // 遍历相邻顶点\n std::size_t adj_vertex = adj_verts[k];\n for (std::size_t l = 0; l < labels[adj_vertex].size(); ++l) { // 遍历相邻顶点连接的view\n std::size_t label = labels[i][j]; // 当前顶点的一个view\n std::size_t adj_vertex_label = labels[adj_vertex][l]; // 相邻顶点的一个view\n if (i < adj_vertex && label == adj_vertex_label) { // 两个顶点用的是同一个view\n Eigen::Triplet t1(Gamma_row, vertlabel2row[i][label], lambda);\n Eigen::Triplet t2(Gamma_row, vertlabel2row[adj_vertex][adj_vertex_label], -lambda);\n coefficients_Gamma.push_back(t1);\n coefficients_Gamma.push_back(t2);\n Gamma_row++;\n }\n }\n }\n }\n }\n\n std::size_t Gamma_rows = Gamma_row;\n assert(Gamma_rows < static_cast(std::numeric_limits::max()));\n\n Eigen::SparseMatrix Gamma(Gamma_rows, x_rows); // (相邻顶点且相同view的个数, 顶点的view的组合个数) 行为同一个patch又相邻的顶点对的对数, 列为顶点(如果是不同view的顶点, 要区别对待)\n Gamma.setFromTriplets(coefficients_Gamma.begin(), coefficients_Gamma.end());\n\n /* Fill the matrix A and the coefficients for the Vector b of the linear equation system. */\n std::vector > coefficients_A; // [从0开始的索引][view的序号]:+/-1\n std::vector coefficients_b;\n std::size_t A_row = 0;\n for (std::size_t i = 0; i < num_vertices; ++i) {\n for (std::size_t j = 0; j < labels[i].size(); ++j) { // 遍历顶点连接的view\n for (std::size_t k = 0; k < labels[i].size(); ++k) {\n std::size_t label1 = labels[i][j];\n std::size_t label2 = labels[i][k];\n if (label1 < label2) { // 不同的patch才有seam\n\n std::vector seam_edges; // 纹理的边缘\n find_seam_edges_for_vertex_label_combination(graph, mesh, mesh_info, i, label1, label2, &seam_edges);\n\n if (seam_edges.empty()) continue;\n\n Eigen::Triplet t1(A_row, vertlabel2row[i][label1], 1.0f);\n Eigen::Triplet t2(A_row, vertlabel2row[i][label2], -1.0f);\n coefficients_A.push_back(t1);\n coefficients_A.push_back(t2);\n\n coefficients_b.push_back(calculate_difference(vertex_projection_infos, mesh, *texture_patches, seam_edges, label1, label2));\n\n ++A_row;\n }\n }\n }\n }\n\n std::size_t A_rows = A_row;\n assert(A_rows < static_cast(std::numeric_limits::max()));\n\n Eigen::SparseMatrix A(A_rows, x_rows); // (接缝的数量, 顶点的view的组合个数), 每一行中有一个-1, 1, 且这两个元素位于接缝处\n A.setFromTriplets(coefficients_A.begin(), coefficients_A.end());\n\n Eigen::SparseMatrix Lhs = A.transpose() * A + Gamma.transpose() * Gamma;\n /* Only keep lower triangle (CG only uses the lower), prune the rest and compress matrix. */\n Lhs.prune([](const int& row, const int& col, const float& value) -> bool { // left hand side\n return col <= row && value != 0.0f; // 下三角\n }); // value != 0.0f is only to suppress a compiler warning\n\n std::vector > adjust_values(num_vertices); // 每个顶点的调整值1\n std::cout << \"完成.\" << std::endl;\n std::cout << \"\\tLhs的维度: \" << Lhs.rows() << \" x \" << Lhs.cols() << std::endl;\n\n util::WallTimer timer;\n std::cout << \"\\t 计算颜色调整:\"<< std::endl;\n #pragma omp parallel for\n for (std::size_t channel = 0; channel < 3; ++channel) {\n /* Prepare solver. */\n Eigen::ConjugateGradient, Eigen::Lower> cg;\n cg.setMaxIterations(1000);\n cg.setTolerance(0.0001); // The tolerance corresponds to the relative residual error: |Ax-b|/|b|\n cg.compute(Lhs);\n\n /* Prepare right hand side. */\n Eigen::VectorXf b(A_rows);\n for (std::size_t i = 0; i < coefficients_b.size(); ++i) {\n b[i] = coefficients_b[i][channel];\n }\n Eigen::VectorXf Rhs = Eigen::SparseMatrix(A.transpose()) * b;\n\n /* Solve for x. */\n Eigen::VectorXf x(x_rows);\n x = cg.solve(Rhs);\n\n /* 减去平均值，因为系统是欠约束的，我们寻求最小调整的解。 */\n x = x.array() - x.mean();\n\n #pragma omp critical\n std::cout << \"\\t\\t 颜色通道\" << channel << \": CG 花了 \"\n << cg.iterations() << \"次迭代. 残差是\" << cg.error() << std::endl;\n\n #pragma omp critical\n for (std::size_t i = 0; i < num_vertices; ++i) {\n for (std::size_t j = 0; j < labels[i].size(); ++j) {\n std::size_t label = labels[i][j];\n adjust_values[i][label][channel] = x[vertlabel2row[i][label]];\n }\n }\n }\n // std::cout << \"\\t\\t 花费了\" << timer.get_elapsed_sec() << \"秒\" << std::endl;\n\n // mve::TriangleMesh::FaceList const & mesh_faces = mesh->get_faces();\n\n // ProgressCounter texture_patch_counter(\"\\t 调整纹理块\", texture_patches->size());\n // #pragma omp parallel for schedule(dynamic)\n // for (std::size_t i = 0; i < texture_patches->size(); ++i) {\n // texture_patch_counter.progress();\n\n // TexturePatch::Ptr texture_patch = texture_patches->at(i);\n\n // int label = texture_patch->get_label();\n // std::vector const & faces = texture_patch->get_faces();\n // std::vector patch_adjust_values(faces.size() * 3, math::Vec3f(0.0f)); // 三通道\n\n // /* Only adjust texture_patches originating form input images. */\n // if (label == 0) {\n // texture_patch->adjust_colors(patch_adjust_values);\n // texture_patch_counter.inc();\n // continue;\n // };\n\n // for (std::size_t j = 0; j < faces.size(); ++j) {\n // for (std::size_t k = 0; k < 3; ++k) {\n // std::size_t face_pos = faces[j] * 3 + k;\n // std::size_t vertex = mesh_faces[face_pos];\n // patch_adjust_values[j * 3 + k] = adjust_values[vertex].find(label)->second;\n // }\n // }\n\n // texture_patch->adjust_colors(patch_adjust_values);\n // texture_patch_counter.inc();\n // }\n}\n\nTEX_NAMESPACE_END\n", "meta": {"hexsha": "a62551958a7f8f9b67229fd0c5e2708be719d4f1", "size": 11590, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/tex/global_seam_leveling.cpp", "max_stars_repo_name": "sucongCJS/mvs-texturing", "max_stars_repo_head_hexsha": "413ad0619eaadb1a9416ae347ff1f9989b29ca59", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "libs/tex/global_seam_leveling.cpp", "max_issues_repo_name": "sucongCJS/mvs-texturing", "max_issues_repo_head_hexsha": "413ad0619eaadb1a9416ae347ff1f9989b29ca59", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "libs/tex/global_seam_leveling.cpp", "max_forks_repo_name": "sucongCJS/mvs-texturing", "max_forks_repo_head_hexsha": "413ad0619eaadb1a9416ae347ff1f9989b29ca59", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.7675276753, "max_line_length": 198, "alphanum_fraction": 0.5917169974, "num_tokens": 3680, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.23178467793877455}} {"text": "/*\n * LikelihoodBasedTopPairReconstruction.cpp\n *\n * Created on: 25 Aug 2011\n * Author: kreczko\n */\n\n#include \"../../interface/ReconstructionModules/LikelihoodBasedTopPairReconstruction.h\"\n#include \n\nusing namespace std;\n\nnamespace BAT {\n\nLikelihoodBasedTopPairReconstruction::LikelihoodBasedTopPairReconstruction(const LeptonPointer lepton, const METPointer met, const JetCollection jets, const JetCollection bjets):\n\t\tsolutions(),\n\t\talreadyReconstructed(false),\n\t\tmet(met),\n\t\tjets(jets),\n\t\tbjets(bjets),\n\t\tleptonFromW(lepton) {\n}\n\nbool LikelihoodBasedTopPairReconstruction::meetsInitialCriteria() const {\n\treturn met != 0 && leptonFromW != 0 && jets.size() >= 4;\n}\n\nstd::string LikelihoodBasedTopPairReconstruction::getDetailsOnFailure() const {\n\tstd::string msg = \"Initial Criteria not met: \\n\";\n\tif (leptonFromW == 0)\n\t\tmsg += \"Electron from W: not filled \\n\";\n\telse\n\t\tmsg += \"Electron from W: filled \\n\";\n\n\tif (met == 0)\n\t\tmsg += \"Missing transverse energy: not filled \\n\";\n\telse\n\t\tmsg += \"Missing transverse energy: filled \\n\";\n\tstd::string nJets(boost::lexical_cast(jets.size()));\n\tif (jets.size() < 4)\n\t\tmsg += \"Number of jets is too small:\" + nJets + \", should be >= 4 \\n\";\n\telse\n\t\tmsg += \"Number of jets is OK:\" + nJets + \"\\n\";\n\n\treturn msg;\n}\n\n\n\nLikelihoodBasedTopPairReconstruction::~LikelihoodBasedTopPairReconstruction() {\n}\n\nTtbarHypothesisCollection LikelihoodBasedTopPairReconstruction::getAllSolutions(){\n\tif(!alreadyReconstructed)\n\t\treconstruct();\n\n\tSmallerDiscriminatorIsBetter solutionComparator = SmallerDiscriminatorIsBetter();\n\tstd::sort(solutions.begin(), solutions.end(), solutionComparator);\n\treturn solutions;\n}\n\nconst TtbarHypothesisPointer LikelihoodBasedTopPairReconstruction::getBestSolution() {\n\tconst TtbarHypothesisPointer bestSolution = getAllSolutions().front();//sorted by quality, front == best\n\treturn bestSolution;\n}\n\nvoid LikelihoodBasedTopPairReconstruction::reconstruct() {\n\ttypedef unsigned short ushort;\n\n\t// Loop b jets to get hadronic b candidate\n\tfor (ushort hadBindex = 0; hadBindex < bjets.size(); ++hadBindex) {\n\t\tJetPointer hadBJet = bjets[hadBindex];\n\t\tLeptonPointer signallepton = leptonFromW;\n\t\tMETPointer MET = met;\n\n\t\tif (!meetsHadronicBJetRequirement(hadBJet))\n\t\t\tcontinue;\n\t\t// Loop b jets to get leptonic b candidate\n\t\tfor (ushort lepBindex = 0; lepBindex < bjets.size(); ++lepBindex) {\n\t\t\tJetPointer lepBJet = bjets[lepBindex];\n\t\t\tif (lepBindex == hadBindex || !meetsLeptonicBJetRequirement(lepBJet))\n\t\t\t\tcontinue;\n\n\t\t\t// Loop light jets to get jets from W\n\n\n\t\t\tfor ( ushort jet1Index=0; jet1Index < (jets.size()-1); ++jet1Index ) {\n\t\t\t\tfor ( ushort jet2Index=jet1Index+1; jet2Index < jets.size(); ++jet2Index ) {\n\n\t\t\t// for (ushort jet1Index = 0; jet1Index < jets.size(); ++jet1Index) {\n\t\t\t// \tfor (ushort jet2Index = 0; jet2Index < jets.size(); ++jet2Index) {\n\t\t\t\t\tJetPointer jet1FromW = jets[jet1Index];\n\t\t\t\t\tJetPointer jet2FromW = jets[jet2Index];\n\n\t\t\t\t\tif (jet2Index == jet1Index || !meetsJetFromWRequirement(jet1FromW, jet2FromW))\n\t\t\t\t\t\tcontinue;\n\n\t\t\t\t\t// Put all particles together\n\t\t\t\t\tTtbarHypothesisPointer solution(new TtbarHypothesis());\n\t\t\t\t\t//leptons\n\t\t\t\t\tsolution->leptonFromW = leptonFromW;\n\t\t\t\t\tdouble nuChi2 = -1;\n\t\t\t\t\tsolution->neutrinoFromW = getNeutrinoSolution( lepBJet, signallepton, MET, nuChi2 );\n\t\t\t\t\tsolution->neutrinoChi2 = nuChi2;\n\n\t\t\t\t\t//jets\n\t\t\t\t\tsolution->hadronicBJet = hadBJet;\n\t\t\t\t\tsolution->leptonicBjet = lepBJet;\n\t\t\t\t\tsolution->jet1FromW = jet1FromW;\n\t\t\t\t\tsolution->jet2FromW = jet2FromW;\n\n\t\t\t\t\t//combine reconstructed objects\n\t\t\t\t\tsolution->combineReconstructedObjects();\n\n\t\t\t\t\t// Get discrimnant for this solution\n\t\t\t\t\tsolution->discriminator = getDiscriminator(solution);\n\n\n\t\t\t\t\t// Store if event is physical (checks masses of tops and Ws > 0 )\n\t\t\t\t\tif (meetsGlobalRequirement(solution)){\n\t\t\t\t\t\tsolutions.push_back(solution);\n\t\t\t\t\t}\n\n\t\t\t\t\telse\n\t\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\talreadyReconstructed = true;\n}\n\n\nParticlePointer LikelihoodBasedTopPairReconstruction::getNeutrinoSolution(JetPointer BJet, LeptonPointer Lepton, METPointer met, double& neutrinoChi2) {\n\t// Do the neutrino reconstruction\n\tNeutrinoSolver neutrinoSolver( &(Lepton->getFourVector()), &(BJet->getFourVector()), 80, 173 );\n\tdouble test = -1;\n\tFourVector neutrino = neutrinoSolver.GetBest(met->getFourVector().Px(), met->getFourVector().Py(), 25., 25., 0., test );\n\tneutrinoChi2 = test;\n\n\treturn ParticlePointer (new Particle(neutrino.Energy(), neutrino.Px(), neutrino.Py(), neutrino.Pz()));\n}\n\nbool LikelihoodBasedTopPairReconstruction::meetsHadronicBJetRequirement(JetPointer hadBJet){\n\treturn true;\n}\n\nbool LikelihoodBasedTopPairReconstruction::meetsLeptonicBJetRequirement(JetPointer lepBJet){\n\treturn true;\n}\n\nbool LikelihoodBasedTopPairReconstruction::meetsJetFromWRequirement(JetPointer jet1, JetPointer jet2){\n\treturn true;\n}\n\nbool LikelihoodBasedTopPairReconstruction::meetsGlobalRequirement(const TtbarHypothesisPointer solution){\n\treturn solution->isPhysical();\n}\n\ndouble LikelihoodBasedTopPairReconstruction::getDiscriminator(const TtbarHypothesisPointer solution) const{\n\t// Given all the info for this solution/permutation, calculate the discriminant\n\t// cout << \"------------------------------------------------------\" << endl;\n\n\t// Print csv of hadronic b jet and probablitity from correct b histogram\n\tJetPointer hadBJet = solution->hadronicBJet;\n\tJetPointer lepBJet = solution->leptonicBjet;\n\tParticlePointer hadTop = solution->hadronicTop;\n\tParticlePointer lepTop = solution->leptonicTop;\n\tParticlePointer hadW = solution->hadronicW;\n\tParticlePointer lepW = solution->leptonicW;\n\n\tdouble NeutrinoChi2 = solution->neutrinoChi2;\n\n\tdouble hadBJetCSV = hadBJet->getBTagDiscriminator(BAT::BtagAlgorithm::value::CombinedSecondaryVertexV2);\n\tdouble lepBJetCSV = lepBJet->getBTagDiscriminator(BAT::BtagAlgorithm::value::CombinedSecondaryVertexV2);\n\n\tdouble Wmass = hadW->mass();\n\tdouble Topmass = hadTop->mass();\n\n\n\tdouble probCorrectHadB = Globals::csvCorrectPermHistogram->Interpolate( hadBJetCSV );\n\tdouble probIncorrectHadB = Globals::csvIncorrectPermHistogram->Interpolate( hadBJetCSV );\n\n\tdouble probCorrectLepB = Globals::csvCorrectPermHistogram->Interpolate( lepBJetCSV );\n\tdouble probIncorrectLepB = Globals::csvIncorrectPermHistogram->Interpolate( lepBJetCSV );\n\n\tdouble probCorrectNuChi2 = Globals::NuChiCorrectPermHistogram->Interpolate( NeutrinoChi2 );\n\tdouble probIncorrectNuChi2 = Globals::NuChiIncorrectPermHistogram->Interpolate( NeutrinoChi2 );\n\n\tdouble probCorrectWBMass = Globals::HadronicRecoCorrectPermHistogram->Interpolate( Wmass , Topmass );\n\tdouble probIncorrectWBMass = Globals::HadronicRecoIncorrectPermHistogram->Interpolate( Wmass , Topmass );\n\n\tif (probCorrectWBMass == 0){\n\t\tprobCorrectWBMass = 0.000000001;\n\t}\n\tif (probIncorrectWBMass == 0){\n\t\tprobIncorrectWBMass = 0.000000001;\n\t}\n\n\tdouble NuChi2Disc = - log(probCorrectNuChi2/probIncorrectNuChi2);\n\tdouble CSVDisc = - log((probCorrectHadB/probIncorrectHadB)*(probCorrectLepB/probIncorrectLepB));\n\tdouble MassDisc = - log(probCorrectWBMass/probIncorrectWBMass);\n\tdouble likelihoodratio = 20;\n\t// cout << \"Mass Hadronic T : \" << hadTop->mass() << \", Mass Hadronic W : \" << hadW->getFourVector().M() << endl;\n\t// cout << \"Mass Leptonic T : \" << lepTop->getFourVector().M() << \", Mass Leptonic W : \" << lepW->getFourVector().M() << endl;\n\t// cout << \"Neutrino Chi Sq : \" << NeutrinoChi2 << endl;\n\n\tif (NeutrinoChi2 >= 0.0 && Wmass <= 490 && Topmass <= 490){\n\n\n\t\tsolution->CSVDiscriminator = CSVDisc;\n\t\tsolution->MassDiscriminator = MassDisc;\n\t\tsolution->NuChi2Discriminator = NuChi2Disc;\n\n\t\tlikelihoodratio = - log(probCorrectWBMass/probIncorrectWBMass) - log(probCorrectNuChi2/probIncorrectNuChi2) - log((probCorrectHadB/probIncorrectHadB)*(probCorrectLepB/probIncorrectLepB));\n\n\t\t// if (solution->isCorrect()){\n\n\t\t// \tcout << \"Mass Hadronic T : \" << hadTop->mass() << \", Mass Hadronic W : \" << hadW->mass() << endl;\n\t\t// \tcout << \"Mass Leptonic T : \" << lepTop->mass() << \", Mass Leptonic W : \" << lepW->mass() << endl;\n\n\t\t// \tcout << \"probCorrectHadB : \" << hadBJetCSV << \" prob : \" << probCorrectHadB << endl;\n\t\t// \tcout << \"probIncorrectHadB : \" << hadBJetCSV << \" prob : \" << probIncorrectHadB << endl;\t\n\t\t// \t// cout << \"probCorrectLepB : \" << lepBJetCSV << \" prob : \" << probCorrectLepB << endl;\n\t\t// \t// cout << \"probIncorrectLepB : \" << lepBJetCSV << \" prob : \" << probIncorrectLepB << endl;\n\n\t\t// \t// cout << \"CorrectNu : \" << NeutrinoChi2 << \" prob : \" << probCorrectNuChi2 << endl;\n\t\t// \t// cout << \"IncorrectNu : \" << NeutrinoChi2 << \" prob : \" << probIncorrectNuChi2 << endl;\n\n\t\t// \t// cout << \"CorrectMassReco : \" << Wmass << \" \" << Topmass << \" prob : \" << probCorrectWBMass << endl;\n\t\t// \t// cout << \"IncorrectMassReco : \" << Wmass << \" \" << Topmass << \" prob : \" << probIncorrectWBMass << endl;\n\n\t\t// \tcout << \"MassDisc : \" << MassDisc << endl;\n\t\t// \tcout << \"CSVDisc : \" << CSVDisc << endl;\n\t\t// \tcout << \"NuChi2Disc : \" << NuChi2Disc << endl;\n\n\t\t// \tcout << \"Likelihood Test : \" << likelihoodratio << endl;\n\t\t// \t}\n\t}\n\n\telse{\n\t\tsolution->CSVDiscriminator = 10;\n\t\tsolution->MassDiscriminator = 10;\n\t\tsolution->NuChi2Discriminator = 10;\n\t}\nreturn likelihoodratio; \n}\n\n} /* namespace BAT */\n", "meta": {"hexsha": "bd7066d7764f6ed126bb7eecfebfbda736f92678", "size": 9222, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/ReconstructionModules/LikelihoodBasedTopPairReconstruction.cpp", "max_stars_repo_name": "kreczko/AnalysisSoftware", "max_stars_repo_head_hexsha": "fa83a3775a8d644e6098d28dbc6f3d7f1a11b400", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/ReconstructionModules/LikelihoodBasedTopPairReconstruction.cpp", "max_issues_repo_name": "kreczko/AnalysisSoftware", "max_issues_repo_head_hexsha": "fa83a3775a8d644e6098d28dbc6f3d7f1a11b400", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/ReconstructionModules/LikelihoodBasedTopPairReconstruction.cpp", "max_forks_repo_name": "kreczko/AnalysisSoftware", "max_forks_repo_head_hexsha": "fa83a3775a8d644e6098d28dbc6f3d7f1a11b400", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.888, "max_line_length": 189, "alphanum_fraction": 0.7109086966, "num_tokens": 2688, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.23173994123009906}} {"text": "#include \n#include \n\nusing Eigen::MatrixXd;\nusing namespace std;\n\nint main()\n{\n\tMatrixXd R(3,3);\n\tR << 1, 0, 0,\n\t\t 0, 0, -1,\n\t\t 0, 1, 0;\n\tcout << R;\t\n}\n", "meta": {"hexsha": "f1d4d611ca0ec3a809b54f00a2f54b318762139b", "size": 175, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Cpp/rotation/rotation.cpp", "max_stars_repo_name": "silvajhonatan/robotics", "max_stars_repo_head_hexsha": "d1097809e88c744658dab6d661092b6ea8f0e13a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2017-11-16T18:34:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-28T15:33:46.000Z", "max_issues_repo_path": "Cpp/rotation/rotation.cpp", "max_issues_repo_name": "sjhonatan/robotics", "max_issues_repo_head_hexsha": "d1097809e88c744658dab6d661092b6ea8f0e13a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Cpp/rotation/rotation.cpp", "max_forks_repo_name": "sjhonatan/robotics", "max_forks_repo_head_hexsha": "d1097809e88c744658dab6d661092b6ea8f0e13a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 11.6666666667, "max_line_length": 22, "alphanum_fraction": 0.5714285714, "num_tokens": 68, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.3557749071749625, "lm_q1q2_score": 0.23173571651452574}} {"text": "#pragma once\n\n#include \n#include \n#include \n\n\nnamespace boltzmann {\n// homogeneous\nnamespace hg {\ntemplate \nclass RK4\n{\n public:\n typedef NUMERIC_T numeric_t;\n\n private:\n typedef Eigen::VectorXd vec_t;\n\n public:\n RK4(int N_)\n : N(N_)\n , v_k1(N_)\n , v_k2(N_)\n , v_k3(N_)\n , v_k4(N_)\n { /* empty */\n }\n\n void apply(NUMERIC_T* dst,\n const NUMERIC_T* src,\n const std::function& f,\n double dt);\n\n private:\n int N;\n vec_t v_k1;\n vec_t v_k2;\n vec_t v_k3;\n vec_t v_k4;\n};\n\n// ----------------------------------------------------------------------\ntemplate \nvoid\nRK4::apply(NUMERIC_T* dst,\n const NUMERIC_T* src,\n const std::function& f,\n double dt)\n{\n assert(src != dst);\n\n typedef Eigen::Map map_vec_t;\n typedef Eigen::Map map_const_vec_t;\n\n map_const_vec_t v_in(src, N);\n map_vec_t v_dst(dst, N);\n\n f(v_k1.data(), v_in.data());\n\n v_dst = v_in + 0.5 * dt * v_k1;\n f(v_k2.data(), v_dst.data());\n\n v_dst = v_in + 0.5 * dt * v_k2;\n f(v_k3.data(), v_dst.data());\n\n v_dst = v_in + dt * v_k3;\n f(v_k4.data(), v_dst.data());\n\n v_dst = v_in + dt / 6 * (v_k1 + 2 * v_k2 + 2 * v_k3 + v_k4);\n}\n\n} // end hg\n} // end boltzmann\n", "meta": {"hexsha": "ca76aebf513396fdb1ec4b06b81e38ee511e36b4", "size": 1455, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/collision_tensor/time_stepping/rk4.hpp", "max_stars_repo_name": "simonpp/2dBoltzmann", "max_stars_repo_head_hexsha": "bc6b7bbeffa242ce80937947444383b416ba3fc9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/collision_tensor/time_stepping/rk4.hpp", "max_issues_repo_name": "simonpp/2dBoltzmann", "max_issues_repo_head_hexsha": "bc6b7bbeffa242ce80937947444383b416ba3fc9", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/collision_tensor/time_stepping/rk4.hpp", "max_forks_repo_name": "simonpp/2dBoltzmann", "max_forks_repo_head_hexsha": "bc6b7bbeffa242ce80937947444383b416ba3fc9", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 19.4, "max_line_length": 89, "alphanum_fraction": 0.55395189, "num_tokens": 444, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.2317157923092633}} {"text": "/*\n * VSCSoundParameters.cpp\n * SynthStation\n *\n * Created by Jonathan Thorpe on 12/11/2011.\n * Copyright 2011 NXP. All rights reserved.\n *\n */\n\n#include \"VSCSoundParameters.h\"\n#include \"VSCException.h\"\n#include \"VSCMath.h\"\n\n#include \n#include \n#include \n\nconst unsigned int VSCSParameter::kChannelNotFound = kVSCSVoidChannel;\nconst unsigned int VSCSParameter::kIndexAll = UINT_MAX;\n\n\nbool VSCSParameter::Key::operator<(const Key& otherKey) const {\n\tif (domain < otherKey.domain) return true;\n\telse return false;\n\tif (code < otherKey.code) return true;\n\telse return false;\n\tif (index < otherKey.index) return true;\n\treturn false;\n}\n\nbool VSCSParameter::Key::operator==(const Key& otherKey) const {\n\tif (domain != otherKey.domain) return false;\n\tif (code != otherKey.code) return false;\n\tif (index != otherKey.index) return false;\n\treturn true;\n}\n\nVSCSParameter::VSCSParameter() {\n\t\n}\n\nVSCSParameter::~VSCSParameter() {\n\t\n}\n\nVSCSParameter& VSCSParameter::sharedInstance(void) {\n\tstatic VSCSParameter singletonInstance;\n\treturn singletonInstance;\n}\n\n/*\n *\tThis should convert so that linear volume gain [0 : 1] becomes [-infinity : 0]\n */\nVSCSFloat VSCSParameter::linearToDB(VSCSFloat linear) {\n\tVSCSFloat dB = 10.0*std::log10(linear);\n return dB;\n}\n\nVSCSFloat VSCSParameter::dBToLinear(VSCSFloat dB) {\n return std::pow(10.0, 0.1*dB);\n}\n\n\n/*\n *\tParameter labels\n */\nstd::string VSCSParameter::getLabelForParameterWithKey(VSCSParameter::Key k) {\n\t\n\t/*\n\t *\tCheck if there are exact matches\n\t */\n\tKeyLabelMap::iterator labelIterator = customizedKeyLabels.find(k);\n\tif (labelIterator != customizedKeyLabels.end()) \n\t\treturn labelIterator->second;\n\t\n\tswitch (k.code) {\n\t\tcase CodeGain:\n\t\t\treturn \"Gain (Lin)\";\n\t\tcase CodeDBGain:\n\t\t\treturn \"Gain (dB)\";\n\t\tcase CodeFrequency:\n\t\t\treturn \"Freq (Hz)\";\n\t\tcase CodeLogFrequency:\n\t\t\treturn \"Freq (Log Hz)\";\n//\t\tcase CodeDegPhase:\n//\t\t\treturn \"Phase (deg)\";\n\t\tcase CodeRadPhase:\n\t\t\treturn \"Phase (rad)\";\n\t\tcase CodeHarmonics:\n\t\t\treturn \"Harmonics\";\n\t\tdefault:\n\t\t\treturn \"\";\n\t\t\tbreak;\n\t}\n\t\n\tthrow VSCSBadParameterException();\n\t\n}\n\nvoid VSCSParameter::setLabelForParameterWithKey(std::string label, Key k) {\n\t\n\tKeyLabelMap::iterator labelIterator = customizedKeyLabels.find(k);\n\tif (labelIterator != customizedKeyLabels.end()) \n\t\tcustomizedKeyLabels.erase(k);\n\t\n\tcustomizedKeyLabels.insert(KeyLabelPair (k, label));\n\t\n}\n\nvoid VSCSParameter::revertLabelForParameterWithKeyToDefault(Key k) {\n\t\n\tKeyLabelMap::iterator labelIterator = customizedKeyLabels.find(k);\n\tif (labelIterator != customizedKeyLabels.end()) \n\t\tcustomizedKeyLabels.erase(k);\n\t\n}\n\n\nVSCSParameter::ValueRange VSCSParameter::getRangeForParameterWithKey(Key k) {\n\t\n\tKeyRangeMap::iterator rangeIterator = customizedKeyRanges.find(k);\n\tif (rangeIterator != customizedKeyRanges.end()) \n\t\treturn rangeIterator->second;\n\t\n\tswitch (k.code) {\n\t\tcase CodeGain:\n\t\t\treturn ValueRange (0.0, 1.0);\n\t\tcase CodeDBGain:\n\t\t\treturn ValueRange (-30.0, 0.0);\n\t\tcase CodeFrequency:\n\t\t\treturn ValueRange (20.0, 20000.0);\n\t\tcase CodeLogFrequency:\n\t\t\treturn ValueRange (std::log10(20.0), std::log10(20000.0));\n//\t\tcase CodeDegPhase:\n//\t\t\treturn ValueRange (-180.0, 180.0);\n\t\tcase CodeRadPhase:\n\t\t\treturn ValueRange (-vsc::kPI, vsc::kPI);\n\t\tcase CodeHarmonics:\n\t\t\treturn ValueRange (0.0, 10.0);\n\t\tdefault:\n\t\t\treturn ValueRange (0.0, 0.0);\n\t\t\tbreak;\n\t}\n\t\n\tthrow VSCSBadParameterException();\n\t\n}\n\nvoid VSCSParameter::setRangeForParameterWithKey(ValueRange valRange, Key k) {\n\t\n\tKeyRangeMap::iterator rangeIterator = customizedKeyRanges.find(k);\n\tif (rangeIterator != customizedKeyRanges.end()) \n\t\tcustomizedKeyRanges.erase(k);\n\t\n\tcustomizedKeyRanges.insert(KeyRangePair (k, valRange));\n\t\n}\n\nvoid VSCSParameter::revertRangeForParameterWithKeyToDefault(Key k) {\n\t\n\tKeyRangeMap::iterator rangeIterator = customizedKeyRanges.find(k);\n\tif (rangeIterator != customizedKeyRanges.end()) \n\t\tcustomizedKeyRanges.erase(k);\n\t\n}\n\n", "meta": {"hexsha": "09ecf608510fe5d6286fe7fa0110a9e54eea5300", "size": 3934, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Sound/Params/VSCSoundParameters.cpp", "max_stars_repo_name": "jbat100/VirtualSoundControl", "max_stars_repo_head_hexsha": "f84ba15bba4bfce579c185e04df0e1be4f419cd7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Sound/Params/VSCSoundParameters.cpp", "max_issues_repo_name": "jbat100/VirtualSoundControl", "max_issues_repo_head_hexsha": "f84ba15bba4bfce579c185e04df0e1be4f419cd7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Sound/Params/VSCSoundParameters.cpp", "max_forks_repo_name": "jbat100/VirtualSoundControl", "max_forks_repo_head_hexsha": "f84ba15bba4bfce579c185e04df0e1be4f419cd7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.5568862275, "max_line_length": 81, "alphanum_fraction": 0.7262328419, "num_tokens": 1101, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.44939263446475963, "lm_q1q2_score": 0.2317157923092633}} {"text": "#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\n\nnamespace po = boost::program_options;\nnamespace mpi = boost::mpi;\n\n\nusing R = double;\n\n#ifdef ASSUME_REAL\nusing C = R;\n#else\nusing C = std::complex;\n#endif\n\n///////////////////////////////////////////////////////////////////////////////\n/// \\brief Returns the rank of the admin process.\n///////////////////////////////////////////////////////////////////////////////\nconstexpr auto admin_rank() -> int { return 0; }\n\n\n\n// ============================================================================\n// SETTINGS \n// ============================================================================\n\n///////////////////////////////////////////////////////////////////////////////\n/// \\brief Class representing the configurations options.\n\n/// It is a small wrapper around boost::program_options, which read the\n/// options from command line during construction.\n///////////////////////////////////////////////////////////////////////////////\n\n\nauto init_options() -> po::options_description\n{\n\tpo::options_description description;\n\tdescription.add_options()\n\t\t( \"help\", \"Produce the help message.\" )\n\t\t( \"out.file.log\" \n\t\t, po::value()->default_value(\"sample\")\n\t\t, \"Name of the file that will be used for logging. This is \"\n\t\t \"not the full name, but rather a 'base'. The actual file name \"\n\t\t \"will be \\\"[log-file].[PROCESS_RANK].log\\\".\" )\n\t\t( \"out.file.eps\" \n\t\t, po::value()->default_value(\"Epsilon\")\n\t\t, \"Name of the file that will be used for saving the computed \"\n\t\t \"dielectric functions. This is, again, a base rather than the \"\n\t\t \"actual name. The actual file name will be \"\n\t\t \"\\\"[eps-file].[PROCESS_RANK].bin\\\", which indicates that \"\n\t\t \"dielectric functions will be stored in binary format of the \"\n\t\t \"boost::serialization library.\" )\n\t\t( \"in.file.energies\"\n\t\t, po::value()->required()\n\t\t, \"Name of the BIN file where the eigenenergies of the hamiltonian\"\n\t\t \" are read from. This file must be in the format of the \"\n\t\t \"boost::serialization library. This option is REQUIRED.\" )\n\t\t( \"in.file.states\"\n\t\t, po::value()->required()\n\t\t, \"Name of the BIN file where the eigenstates of the hamiltonian \"\n\t\t \"are read from. This file must be in the format of the \"\n\t\t \"boost::serialization library. This option is REQUIRED.\" )\n\t\t( \"in.file.potential\"\n\t\t, po::value()->required()\n\t\t, \"Name of the BIN file where the interaction potential \"\n\t\t \"is read from. This file must be in the format of the \"\n\t\t \"boost::serialization library. This option is REQUIRED.\" )\n\t\t( \"in.frequency.start\"\n\t\t, po::value()->required()\n\t\t, \"Starting frequency in eV. Must be a real value. This option \"\n\t\t \"is REQUIRED.\" )\n\t\t( \"in.frequency.stop\"\n\t\t, po::value()->required()\n\t\t, \"Stopping frequency in eV. Must be a real value. This option \"\n\t\t \"is REQUIRED.\" )\n\t\t( \"in.frequency.step\"\n\t\t, po::value()->required()\n\t\t, \"Step in frequency in eV. Must be a real value.\" );\n\tdescription.add(tcm::init_constants_options());\n\treturn description;\n}\n\n\n\n\ntemplate \nauto parse_command_line( int argc, char** argv\n , _Help&& help\n , _Proceed&& proceed ) -> bool\n{\n\tauto const desc = init_options();\n\tpo::variables_map vm;\n\n\tpo::store(po::command_line_parser(argc, argv).options(desc).run(), vm);\n\n\tif (vm.count(\"help\")) {\n\t\thelp(desc);\n\t\treturn false;\n\t}\n\n\tpo::notify(vm);\n\tproceed(vm);\n\treturn true;\n}\n\n\nauto log_file_name(int const rank, std::string file_name_base)\n{\n\treturn file_name_base + \".\" + std::to_string(rank) + \".log\";\t\n}\n\n\nauto initialize_logging( int const rank\n , std::string const& file_name_base) -> void\n{\n\tusing namespace boost::log;\n\tregister_simple_formatter_factory(\"Severity\");\n\tadd_file_log\n\t( \n\t\tkeywords::file_name = log_file_name(rank, file_name_base),\n\t\tkeywords::format = \"%LineID%: [%TimeStamp%] [%Severity%] %Message%\",\n\t\tkeywords::auto_flush = true\n\t);\n\n\tadd_common_attributes();\n}\n\n\ntemplate \nstruct IPackage {\n\tstd::tuple<_R, _R, _R> frequency_range;\n\tstd::string log_file_name_base;\n\tstd::string eps_file_name_base;\n\ttcm::Matrix<_R> E;\n\ttcm::Matrix<_C> Psi;\n\ttcm::Matrix> V;\n\tstd::map constants;\n\nprivate:\n\tfriend boost::serialization::access;\n\n\ttemplate\n\tauto save(_Archive & ar, unsigned int const version) const -> void\n\t{\n\t\tar << std::get<0>(frequency_range)\n\t\t << std::get<1>(frequency_range)\n\t\t << std::get<2>(frequency_range)\n\t\t << log_file_name_base\n\t\t << eps_file_name_base\n\t\t << E \n\t\t << Psi\n\t\t << V\n\t\t << constants;\n\t}\n\n\ttemplate\n\tauto load(_Archive & ar, unsigned int const version) -> void\n\t{\n\t\tar >> std::get<0>(frequency_range)\n\t\t >> std::get<1>(frequency_range)\n\t\t >> std::get<2>(frequency_range)\n\t\t >> log_file_name_base\n\t\t >> eps_file_name_base\n\t\t >> E \n\t\t >> Psi\n\t\t >> V\n\t\t >> constants;\n\t}\n\n\tBOOST_SERIALIZATION_SPLIT_MEMBER()\n};\n\n\ntemplate\nauto load_matrix(std::string const& file_name) -> tcm::Matrix<_T>\n{\n\tstd::ifstream in_stream{file_name};\n\tif(not in_stream)\n\t\tthrow std::runtime_error{\"Failed to open `\" + file_name + \"`.\"};\n\tboost::archive::binary_iarchive in_archive{in_stream};\n\n\ttcm::Matrix<_T> A;\n\tin_archive >> A;\n\treturn A;\n}\n\n\n\n\nauto load_ipackage(po::variables_map const& vm) -> IPackage\n{\n\treturn { std::make_tuple( vm[\"in.frequency.start\"].as()\n\t , vm[\"in.frequency.stop\"].as()\n\t , vm[\"in.frequency.step\"].as() )\n\t , vm[\"out.file.log\"].as()\n\t , vm[\"out.file.eps\"].as()\n\t , load_matrix(vm[\"in.file.energies\"].as())\n\t , load_matrix(vm[\"in.file.states\"].as())\n\t , load_matrix>(vm[\"in.file.potential\"].as())\n\t\t , tcm::load_constants>(vm)\n\t\t };\n}\n\n\ntemplate \nauto cache( std::string const& message\n , tcm::Matrix<_T> const& X\n\t\t , std::string const& file_name\n , _Logger & lg ) -> void\n{\n\tLOG(lg, info) << \"Caching: \" << message << \"...\";\n\n\tstd::ofstream out_stream{file_name};\n\tif(not out_stream)\n\t\tthrow std::runtime_error{\"Could not open `\" + file_name + \"`.\"};\n\tboost::archive::binary_oarchive out_archive{out_stream};\n\tout_archive << X;\n\n\tLOG(lg, info) << \"Caching successfully finished.\";\n}\n\n\ntemplate\nauto get_job( mpi::communicator const& world\n , std::tuple<_Real, _Real, _Real> const& range\n\t\t\t, _Logger & lg ) -> std::vector<_Real>\n{\t\n\tLOG(lg, info) << \"Calculating homework...\";\n\n\tauto const rank = world.rank();\n\tauto const size = world.size();\n\tauto const& begin = std::get<0>(range);\n\tauto const& end = std::get<1>(range);\n\tauto const& step = std::get<2>(range);\n\n\tstd::vector<_Real> homework;\n\tfor(auto i = 0; begin + i * step <= end; ++i) {\n\t\tif(i % size == rank) \n\t\t\thomework.push_back(begin + i * step);\n\t}\n\t\n\tauto record = lg.open_record(boost::log::keywords::severity = \n\t tcm::severity_level::info);\n\tif (record) {\n\t\tboost::log::record_ostream stream{record};\n\t\tstream << \"Need to perform calculations for the following \"\n\t\t\t << \"frequencies: {\";\n\t\tif(not homework.empty()) {\n\t\t\tfor(std::size_t i = 0; i < homework.size() - 1; ++i)\n\t\t\t\tstream << homework[i] << \", \";\n\t\t\tstream << homework.back();\n\t\t}\n\t\tstream << \"}\";\n\t\tstream.flush();\n\t\tlg.push_record(std::move(record));\n\t}\n\t\n\treturn homework;\n}\n\n\ntemplate\nauto calculate_single( _Number const omega\n , tcm::Matrix<_R> const& E\n\t\t\t\t\t , tcm::Matrix<_C> const& Psi\n\t\t\t\t\t , tcm::Matrix> const& V\n\t\t\t\t\t , std::map const& cs\n , _Logger & lg \n\t\t\t\t\t , std::string const& file_name_base ) -> void\n{\n\tusing namespace std::complex_literals;\n\tLOG(lg, info) << \"Calculating dielectric function for omega = \"\n\t << omega << \"...\";\n\n\tauto const file_name_matrix = \n\t\tfile_name_base + \".\" + std::to_string(std::real(omega)) \n\t\t+ \".matrix.bin\";\n\tauto const file_name_eigenvalues = \n\t\tfile_name_base + \".\" + std::to_string(std::real(omega)) \n\t\t+ \".eigenvalues.bin\";\n\tauto const file_name_eigenstates = \n\t\tfile_name_base + \".\" + std::to_string(std::real(omega)) \n\t\t+ \".eigenstates.bin\";\n\n\tauto epsilon = tcm::dielectric_function::make(omega, E, Psi, V, cs, lg);\n\tcache(\"Dielectric function matrix\", epsilon, file_name_matrix, lg);\n\n\tLOG(lg, info) << \"Diagonalizing dielectric function for omega = \"\n\t << omega << \"...\";\n\n\tusing epsilon_type = typename decltype(epsilon)::value_type;\n\ttcm::Matrix W{epsilon.height(), 1};\n\ttcm::Matrix Z{epsilon.height(), epsilon.height()};\n\ttcm::lapack::geev(epsilon, W, Z);\n\n\tcache(\"Dielectric function eigenvalues\", W, file_name_eigenvalues, lg);\n\tcache(\"Dielectric function eigenstates\", Z, file_name_eigenstates, lg);\n\n\tLOG(lg, info) << \"Done for omega = \" << omega << \"!\";\n}\n\n\n\nauto run( mpi::communicator & world\n , IPackage & input ) -> void\n{\n\tmpi::broadcast(world, input, admin_rank());\n\n\tinitialize_logging(world.rank(), input.log_file_name_base);\n\tboost::log::sources::severity_logger lg;\n\n\tauto const homework = get_job(world, input.frequency_range, lg);\n\tif (homework.empty()) \n\t\treturn;\n\tfor(auto const& w : homework) {\n\t\tcalculate_single( std::complex{w, input.constants.at(\"tau\")}\n\t\t , input.E\n\t\t\t\t\t\t, input.Psi\n\t\t\t\t\t\t, input.V\n\t\t\t\t\t\t, input.constants\n\t\t\t\t\t\t, lg\n\t\t\t\t\t\t, input.eps_file_name_base );\n\t}\n\n\tauto record = lg.open_record(boost::log::keywords::severity = \n\t tcm::severity_level::info);\n\tif (record) {\n\t\tboost::log::record_ostream stream{record};\n\t\tstream << \"Timings:\\n\";\n\t\ttcm::timing::report(stream);\n\t\tstream.flush();\n\t\tlg.push_record(std::move(record));\n\t}\n}\n\n\n\n\n\n\nint main(int argc, char** argv)\n{\n\tmpi::environment env;\n\tmpi::communicator world;\n\n\tIPackage input;\n\tbool proceed_with_calculation;\n\tif (world.rank() == admin_rank()) {\n\t\tproceed_with_calculation = \n\t\t\tparse_command_line( argc, argv\n\t\t\t , [&env] (auto _desc) { std::cout << _desc << '\\n'; }\n\t\t\t , [&input] (auto _vm) { input = load_ipackage(_vm); } );\n\t}\n\t\n\tmpi::broadcast(world, proceed_with_calculation, admin_rank());\n\tif(not proceed_with_calculation)\n\t\treturn EXIT_SUCCESS;\n\t\n\trun(world, input);\n\n\treturn EXIT_SUCCESS;\n}\n", "meta": {"hexsha": "e764482d24bbfcbcb7729593fa48114a09bb3fb1", "size": 11238, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/hello.cpp", "max_stars_repo_name": "twesterhout/plasmon-cpp", "max_stars_repo_head_hexsha": "a0e343ee718d9b30602b322ade6077e42e08d8e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-01-05T11:12:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-05T11:12:07.000Z", "max_issues_repo_path": "src/hello.cpp", "max_issues_repo_name": "twesterhout/plasmon-cpp", "max_issues_repo_head_hexsha": "a0e343ee718d9b30602b322ade6077e42e08d8e5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/hello.cpp", "max_forks_repo_name": "twesterhout/plasmon-cpp", "max_forks_repo_head_hexsha": "a0e343ee718d9b30602b322ade6077e42e08d8e5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.4188481675, "max_line_length": 81, "alphanum_fraction": 0.607225485, "num_tokens": 2826, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.23165309545076215}} {"text": "/*\n Copyright (C) 2016 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\nusing QuantExt::AnalyticJyCpiCapFloorEngine;\nusing QuantExt::AnalyticJyYoYCapFloorEngine;\nusing QuantExt::CpiCapFloorHelper;\nusing QuantExt::InfDkParametrization;\nusing QuantExt::IrLgm1fParametrization;\nusing QuantExt::JyYoYInflationCouponPricer;\nusing QuantExt::YoYCapFloorHelper;\nusing QuantExt::YoYSwapHelper;\nusing QuantLib::DiscountingSwapEngine;\nusing std::vector;\n\nnamespace ore {\nnamespace data {\n\nCrossAssetModelBuilder::CrossAssetModelBuilder(\n const boost::shared_ptr& market, const boost::shared_ptr& config,\n const std::string& configurationLgmCalibration, const std::string& configurationFxCalibration,\n const std::string& configurationEqCalibration, const std::string& configurationInfCalibration,\n const std::string& configurationCrCalibration, const std::string& configurationFinalModel,\n const DayCounter& dayCounter, const bool dontCalibrate, const bool continueOnError,\n const std::string& referenceCalibrationGrid)\n : market_(market), config_(config), configurationLgmCalibration_(configurationLgmCalibration),\n configurationFxCalibration_(configurationFxCalibration), configurationEqCalibration_(configurationEqCalibration),\n configurationInfCalibration_(configurationInfCalibration),\n configurationCrCalibration_(configurationCrCalibration), configurationFinalModel_(configurationFinalModel),\n dayCounter_(dayCounter), dontCalibrate_(dontCalibrate), continueOnError_(continueOnError),\n referenceCalibrationGrid_(referenceCalibrationGrid),\n optimizationMethod_(boost::shared_ptr(new LevenbergMarquardt(1E-8, 1E-8, 1E-8))),\n endCriteria_(EndCriteria(1000, 500, 1E-8, 1E-8, 1E-8)) {\n buildModel();\n registerWithSubBuilders();\n // register market observer with correlations\n marketObserver_ = boost::make_shared();\n for (auto const& c : config->correlations())\n marketObserver_->addObservable(c.second);\n // reset market observer's updated flag\n marketObserver_->hasUpdated(true);\n}\n\nHandle CrossAssetModelBuilder::model() const {\n calculate();\n return model_;\n}\n\nconst std::vector& CrossAssetModelBuilder::swaptionCalibrationErrors() {\n calculate();\n return swaptionCalibrationErrors_;\n}\nconst std::vector& CrossAssetModelBuilder::fxOptionCalibrationErrors() {\n calculate();\n return fxOptionCalibrationErrors_;\n}\nconst std::vector& CrossAssetModelBuilder::eqOptionCalibrationErrors() {\n calculate();\n return eqOptionCalibrationErrors_;\n}\nconst std::vector& CrossAssetModelBuilder::inflationCalibrationErrors() {\n calculate();\n return inflationCalibrationErrors_;\n}\n\nvoid CrossAssetModelBuilder::unregisterWithSubBuilders() {\n for (auto okv : subBuilders_)\n for (auto ikv : okv.second)\n unregisterWith(ikv.second);\n}\n\nvoid CrossAssetModelBuilder::registerWithSubBuilders() {\n for (auto okv : subBuilders_)\n for (auto ikv : okv.second)\n registerWith(ikv.second);\n}\n\nbool CrossAssetModelBuilder::requiresRecalibration() const {\n for (auto okv : subBuilders_)\n for (auto ikv : okv.second)\n if (ikv.second->requiresRecalibration())\n return true;\n\n return marketObserver_->hasUpdated(false);\n}\n\nvoid CrossAssetModelBuilder::performCalculations() const {\n // if any of the sub models requires a recalibration, we rebuilt the model\n // TODO we could do this more selectively\n if (!dontCalibrate_ && requiresRecalibration()) {\n // reset market observer update flag\n marketObserver_->hasUpdated(true);\n // the cast is a bit ugly, but we pretty much know what we are doing here\n const_cast(this)->unregisterWithSubBuilders();\n buildModel();\n const_cast(this)->registerWithSubBuilders();\n }\n}\n\nvoid CrossAssetModelBuilder::buildModel() const {\n\n QL_REQUIRE(market_ != NULL, \"CrossAssetModelBuilder: no market given\");\n LOG(\"Start building CrossAssetModel\");\n DLOG(\"configurations: LgmCalibration \"\n << configurationLgmCalibration_ << \", FxCalibration \" << configurationFxCalibration_ << \", EqCalibration \"\n << configurationEqCalibration_ << \", InfCalibration \" << configurationInfCalibration_ << \", CrCalibration\"\n << configurationCrCalibration_ << \", FinalModel \" << configurationFinalModel_);\n if (dontCalibrate_) {\n DLOG(\"Calibration of the model is disabled.\");\n }\n\n QL_REQUIRE(config_->irConfigs().size() > 0, \"missing IR configurations\");\n QL_REQUIRE(config_->irConfigs().size() == config_->fxConfigs().size() + 1,\n \"FX configuration size \" << config_->fxConfigs().size() << \" inconsisitent with IR configuration size \"\n << config_->irConfigs().size());\n\n swaptionBaskets_.resize(config_->irConfigs().size());\n optionExpiries_.resize(config_->irConfigs().size());\n swaptionMaturities_.resize(config_->irConfigs().size());\n swaptionCalibrationErrors_.resize(config_->irConfigs().size());\n fxOptionBaskets_.resize(config_->fxConfigs().size());\n fxOptionExpiries_.resize(config_->fxConfigs().size());\n fxOptionCalibrationErrors_.resize(config_->fxConfigs().size());\n eqOptionBaskets_.resize(config_->eqConfigs().size());\n eqOptionExpiries_.resize(config_->eqConfigs().size());\n eqOptionCalibrationErrors_.resize(config_->eqConfigs().size());\n inflationCalibrationErrors_.resize(config_->infConfigs().size());\n\n subBuilders_.clear();\n\n // Store information on the number of factors for each process. This is used when requesting a correlation matrix\n // from the CorrelationMatrixBuilder below.\n using ProcessInfo = CorrelationMatrixBuilder::ProcessInfo;\n namespace CT = QuantExt::CrossAssetModelTypes;\n ProcessInfo processInfo;\n\n /*******************************************************\n * Build the IR parametrizations and calibration baskets\n */\n std::vector> irParametrizations;\n std::vector> irDiscountCurves;\n std::vector currencies, regions, crNames, eqNames, infIndices;\n std::vector> irBuilder;\n\n for (Size i = 0; i < config_->irConfigs().size(); i++) {\n boost::shared_ptr ir = config_->irConfigs()[i];\n DLOG(\"IR Parametrization \" << i << \" ccy \" << ir->ccy());\n boost::shared_ptr builder =\n boost::make_shared(market_, ir, configurationLgmCalibration_, config_->bootstrapTolerance(),\n continueOnError_, referenceCalibrationGrid_);\n if (dontCalibrate_)\n builder->freeze();\n irBuilder.push_back(builder);\n boost::shared_ptr parametrization = builder->parametrization();\n swaptionBaskets_[i] = builder->swaptionBasket();\n currencies.push_back(ir->ccy());\n irParametrizations.push_back(parametrization);\n irDiscountCurves.push_back(builder->discountCurve());\n subBuilders_[CT::IR][i] = builder;\n processInfo[CT::IR].emplace_back(ir->ccy(), 1);\n }\n\n QL_REQUIRE(irParametrizations.size() > 0, \"missing IR parametrizations\");\n\n QuantLib::Currency domesticCcy = irParametrizations[0]->currency();\n\n /*******************************************************\n * Build the FX parametrizations and calibration baskets\n */\n std::vector> fxParametrizations;\n for (Size i = 0; i < config_->fxConfigs().size(); i++) {\n DLOG(\"FX Parametrization \" << i);\n boost::shared_ptr fx = config_->fxConfigs()[i];\n QuantLib::Currency ccy = ore::data::parseCurrency(fx->foreignCcy());\n QuantLib::Currency domCcy = ore::data::parseCurrency(fx->domesticCcy());\n\n QL_REQUIRE(ccy.code() == irParametrizations[i + 1]->currency().code(),\n \"FX parametrization currency[\" << i << \"]=\" << ccy << \" does not match IR currrency[\" << i + 1\n << \"]=\" << irParametrizations[i + 1]->currency().code());\n\n QL_REQUIRE(domCcy == domesticCcy, \"FX parametrization [\" << i << \"]=\" << ccy << \"/\" << domCcy\n << \" does not match domestic ccy \" << domesticCcy);\n\n boost::shared_ptr builder =\n boost::make_shared(market_, fx, configurationFxCalibration_, referenceCalibrationGrid_);\n boost::shared_ptr parametrization = builder->parametrization();\n\n fxOptionBaskets_[i] = builder->optionBasket();\n fxParametrizations.push_back(parametrization);\n subBuilders_[CT::FX][i] = builder;\n processInfo[CT::FX].emplace_back(ccy.code() + domCcy.code(), 1);\n }\n\n /*******************************************************\n * Build the EQ parametrizations and calibration baskets\n */\n std::vector> eqParametrizations;\n for (Size i = 0; i < config_->eqConfigs().size(); i++) {\n DLOG(\"EQ Parametrization \" << i);\n boost::shared_ptr eq = config_->eqConfigs()[i];\n string eqName = eq->eqName();\n QuantLib::Currency eqCcy = ore::data::parseCurrency(eq->currency());\n QL_REQUIRE(std::find(currencies.begin(), currencies.end(), eqCcy.code()) != currencies.end(),\n \"Currency (\" << eqCcy << \") for equity \" << eqName << \" not covered by CrossAssetModelData\");\n boost::shared_ptr builder = boost::make_shared(\n market_, eq, domesticCcy, configurationEqCalibration_, referenceCalibrationGrid_);\n boost::shared_ptr parametrization = builder->parametrization();\n eqOptionBaskets_[i] = builder->optionBasket();\n eqParametrizations.push_back(parametrization);\n eqNames.push_back(eqName);\n subBuilders_[CT::EQ][i] = builder;\n processInfo[CT::EQ].emplace_back(eqName, 1);\n }\n\n // Build the INF parametrizations and calibration baskets\n vector> infParameterizations;\n for (Size i = 0; i < config_->infConfigs().size(); i++) {\n boost::shared_ptr imData = config_->infConfigs()[i];\n DLOG(\"Inflation parameterisation (\" << i << \") for index \" << imData->index());\n if (auto dkData = boost::dynamic_pointer_cast(imData)) {\n boost::shared_ptr builder = boost::make_shared(\n market_, dkData, configurationInfCalibration_, referenceCalibrationGrid_, dontCalibrate_);\n if (dontCalibrate_)\n builder->freeze();\n infParameterizations.push_back(builder->parametrization());\n subBuilders_[CT::INF][i] = builder;\n processInfo[CT::INF].emplace_back(dkData->index(), 1);\n } else if (auto jyData = boost::dynamic_pointer_cast(imData)) {\n boost::shared_ptr builder = boost::make_shared(\n market_, jyData, configurationInfCalibration_, referenceCalibrationGrid_);\n infParameterizations.push_back(builder->parameterization());\n subBuilders_[CT::INF][i] = builder;\n processInfo[CT::INF].emplace_back(jyData->index(), 2);\n } else {\n QL_FAIL(\"CrossAssetModelBuilder expects either DK or JY inflation model data.\");\n }\n infIndices.push_back(imData->index());\n }\n\n /*******************************************************\n * Build the CR parametrizations and calibration baskets\n */\n // LGM (if any)\n std::vector> crLgmParametrizations;\n for (Size i = 0; i < config_->crLgmConfigs().size(); ++i) {\n LOG(\"CR LGM Parametrization \" << i);\n boost::shared_ptr cr = config_->crLgmConfigs()[i];\n string crName = cr->name();\n boost::shared_ptr builder =\n boost::make_shared(market_, cr, configurationCrCalibration_);\n boost::shared_ptr parametrization = builder->parametrization();\n crLgmParametrizations.push_back(parametrization);\n crNames.push_back(crName);\n subBuilders_[CT::CR][i] = builder;\n processInfo[CT::CR].emplace_back(crName, 1);\n }\n\n // CIR (if any)\n std::vector> crCirParametrizations;\n for (Size i = 0; i < config_->crCirConfigs().size(); ++i) {\n LOG(\"CR CIR Parametrization \" << i);\n boost::shared_ptr cr = config_->crCirConfigs()[i];\n string crName = cr->name();\n boost::shared_ptr builder =\n boost::make_shared(market_, cr, configurationCrCalibration_);\n boost::shared_ptr parametrization = builder->parametrization();\n crCirParametrizations.push_back(parametrization);\n crNames.push_back(crName);\n subBuilders_[CT::CR][i] = builder;\n processInfo[CT::CR].emplace_back(crName, 1);\n }\n\n std::vector> parametrizations;\n for (Size i = 0; i < irParametrizations.size(); i++)\n parametrizations.push_back(irParametrizations[i]);\n for (Size i = 0; i < fxParametrizations.size(); i++)\n parametrizations.push_back(fxParametrizations[i]);\n for (Size i = 0; i < eqParametrizations.size(); i++)\n parametrizations.push_back(eqParametrizations[i]);\n parametrizations.insert(parametrizations.end(), infParameterizations.begin(), infParameterizations.end());\n for (Size i = 0; i < crLgmParametrizations.size(); i++)\n parametrizations.push_back(crLgmParametrizations[i]);\n for (Size i = 0; i < crCirParametrizations.size(); i++)\n parametrizations.push_back(crCirParametrizations[i]);\n\n QL_REQUIRE(fxParametrizations.size() == irParametrizations.size() - 1, \"mismatch in IR/FX parametrization sizes\");\n\n Measure::Type measure = Measure::LGM;\n if (config_->measure() == \"BA\") {\n measure = Measure::BA;\n DLOG(\"Setting measure to BA\");\n } else if (config_->measure() == \"LGM\") {\n measure = Measure::LGM;\n DLOG(\"Setting measure to BA\");\n } else if (config_->measure() == \"\") {\n DLOG(\"Defaulting to LGM measure\");\n } else {\n QL_FAIL(\"Measure \" << config_->measure() << \" not recognized\");\n }\n\n // Tag on the parametrization and process info for the auxiliary state variable in the bank account measure\n if (measure == Measure::BA) {\n parametrizations.push_back(\n irParametrizations[0]); // FIXME: Is index 0 safe to reference the domestic IR parameters?\n processInfo[CT::AUX].emplace_back(config_->domesticCurrency(), 1);\n }\n\n /******************************\n * Build the correlation matrix\n */\n DLOG(\"CrossAssetModelBuilder: adding correlations.\");\n CorrelationMatrixBuilder cmb;\n\n // Perfect instantaneous correlation of auxiliary variable and domestic LGM state variable in the bank account\n // measure\n CorrelationFactor domesticFactorIR = {CrossAssetModelTypes::IR, config_->domesticCurrency(), 0};\n CorrelationFactor domesticFactorAUX = {CrossAssetModelTypes::AUX, config_->domesticCurrency(), 0};\n if (measure == Measure::BA)\n cmb.addCorrelation(domesticFactorAUX, domesticFactorIR, 1.0);\n\n for (auto it = config_->correlations().begin(); it != config_->correlations().end(); it++) {\n cmb.addCorrelation(it->first.first, it->first.second, it->second);\n if (measure == Measure::BA) {\n // Copy correlation(domesticIR, other) to correlation(domesticAUX, other)\n if (it->first.first == domesticFactorIR)\n cmb.addCorrelation(domesticFactorAUX, it->first.second, it->second);\n if (it->first.second == domesticFactorIR)\n cmb.addCorrelation(it->first.first, domesticFactorAUX, it->second);\n }\n }\n\n Matrix corrMatrix = cmb.correlationMatrix(processInfo);\n\n TLOG(\"CAM correlation matrix:\");\n TLOGGERSTREAM << corrMatrix;\n\n /*****************************\n * Build the cross asset model\n */\n\n SalvagingAlgorithm::Type salvaging = SalvagingAlgorithm::None;\n\n model_.linkTo(boost::make_shared(parametrizations, corrMatrix, salvaging, measure));\n\n /*************************\n * Calibrate IR components\n */\n\n for (Size i = 0; i < irBuilder.size(); i++) {\n DLOG(\"IR Calibration \" << i);\n swaptionCalibrationErrors_[i] = irBuilder[i]->error();\n }\n\n /*************************\n * Relink LGM discount curves to curves used for FX calibration\n */\n\n for (Size i = 0; i < irParametrizations.size(); i++) {\n auto p = irParametrizations[i];\n irDiscountCurves[i].linkTo(*market_->discountCurve(p->currency().code(), configurationFxCalibration_));\n DLOG(\"Relinked discounting curve for \" << p->currency().code() << \" for FX calibration\");\n }\n\n /*************************\n * Calibrate FX components\n */\n\n for (Size i = 0; i < fxParametrizations.size(); i++) {\n boost::shared_ptr fx = config_->fxConfigs()[i];\n\n if (fx->calibrationType() == CalibrationType::None || !fx->calibrateSigma()) {\n DLOG(\"FX Calibration \" << i << \" skipped\");\n continue;\n }\n\n DLOG(\"FX Calibration \" << i);\n\n // attach pricing engines to helpers\n boost::shared_ptr engine =\n boost::make_shared(*model_, i);\n // enable caching for calibration\n // TODO: review this\n engine->cache(true);\n for (Size j = 0; j < fxOptionBaskets_[i].size(); j++)\n fxOptionBaskets_[i][j]->setPricingEngine(engine);\n\n if (!dontCalibrate_) {\n\n if (fx->calibrationType() == CalibrationType::Bootstrap && fx->sigmaParamType() == ParamType::Piecewise)\n model_->calibrateBsVolatilitiesIterative(CrossAssetModelTypes::FX, i, fxOptionBaskets_[i],\n *optimizationMethod_, endCriteria_);\n else\n model_->calibrateBsVolatilitiesGlobal(CrossAssetModelTypes::FX, i, fxOptionBaskets_[i],\n *optimizationMethod_, endCriteria_);\n\n DLOG(\"FX \" << fx->foreignCcy() << \" calibration errors:\");\n fxOptionCalibrationErrors_[i] = getCalibrationError(fxOptionBaskets_[i]);\n if (fx->calibrationType() == CalibrationType::Bootstrap) {\n if (fabs(fxOptionCalibrationErrors_[i]) < config_->bootstrapTolerance()) {\n // we check the log level here to avoid unncessary computations\n if (Log::instance().filter(ORE_DATA)) {\n TLOGGERSTREAM << \"Calibration details:\";\n TLOGGERSTREAM << getCalibrationDetails(fxOptionBaskets_[i], fxParametrizations[i],\n irParametrizations[0]);\n TLOGGERSTREAM << \"rmse = \" << fxOptionCalibrationErrors_[i];\n }\n } else {\n std::string exceptionMessage = \"FX BS \" + std::to_string(i) + \" calibration error \" +\n std::to_string(fxOptionCalibrationErrors_[i]) +\n \" exceeds tolerance \" +\n std::to_string(config_->bootstrapTolerance());\n WLOG(StructuredModelErrorMessage(\"Failed to calibrate FX BS Model\", exceptionMessage));\n WLOGGERSTREAM << \"Calibration details:\";\n WLOGGERSTREAM << getCalibrationDetails(fxOptionBaskets_[i], fxParametrizations[i],\n irParametrizations[0]);\n WLOGGERSTREAM << \"rmse = \" << fxOptionCalibrationErrors_[i];\n if (!continueOnError_)\n QL_FAIL(exceptionMessage);\n }\n }\n }\n }\n\n /*************************\n * Relink LGM discount curves to curves used for EQ calibration\n */\n\n for (Size i = 0; i < irParametrizations.size(); i++) {\n auto p = irParametrizations[i];\n irDiscountCurves[i].linkTo(*market_->discountCurve(p->currency().code(), configurationEqCalibration_));\n DLOG(\"Relinked discounting curve for \" << p->currency().code() << \" for EQ calibration\");\n }\n\n /*************************\n * Calibrate EQ components\n */\n\n for (Size i = 0; i < eqParametrizations.size(); i++) {\n boost::shared_ptr eq = config_->eqConfigs()[i];\n if (!eq->calibrateSigma()) {\n DLOG(\"EQ Calibration \" << i << \" skipped\");\n continue;\n }\n DLOG(\"EQ Calibration \" << i);\n // attach pricing engines to helpers\n Currency eqCcy = eqParametrizations[i]->currency();\n Size eqCcyIdx = model_->ccyIndex(eqCcy);\n boost::shared_ptr engine =\n boost::make_shared(*model_, i, eqCcyIdx);\n for (Size j = 0; j < eqOptionBaskets_[i].size(); j++)\n eqOptionBaskets_[i][j]->setPricingEngine(engine);\n\n if (!dontCalibrate_) {\n\n if (eq->calibrationType() == CalibrationType::Bootstrap && eq->sigmaParamType() == ParamType::Piecewise)\n model_->calibrateBsVolatilitiesIterative(CrossAssetModelTypes::EQ, i, eqOptionBaskets_[i],\n *optimizationMethod_, endCriteria_);\n else\n model_->calibrateBsVolatilitiesGlobal(CrossAssetModelTypes::EQ, i, eqOptionBaskets_[i],\n *optimizationMethod_, endCriteria_);\n DLOG(\"EQ \" << eq->eqName() << \" calibration errors:\");\n eqOptionCalibrationErrors_[i] = getCalibrationError(eqOptionBaskets_[i]);\n if (eq->calibrationType() == CalibrationType::Bootstrap) {\n if (fabs(eqOptionCalibrationErrors_[i]) < config_->bootstrapTolerance()) {\n // we check the log level here to avoid unncessary computations\n if (Log::instance().filter(ORE_DATA)) {\n TLOGGERSTREAM << \"Calibration details:\";\n TLOGGERSTREAM << getCalibrationDetails(eqOptionBaskets_[i], eqParametrizations[i],\n irParametrizations[0]);\n TLOGGERSTREAM << \"rmse = \" << eqOptionCalibrationErrors_[i];\n }\n } else {\n std::string exceptionMessage = \"EQ BS \" + std::to_string(i) + \" calibration error \" +\n std::to_string(eqOptionCalibrationErrors_[i]) +\n \" exceeds tolerance \" +\n std::to_string(config_->bootstrapTolerance());\n WLOG(StructuredModelErrorMessage(\"Failed to calibrate EQ BS Model\", exceptionMessage));\n WLOGGERSTREAM << \"Calibration details:\";\n WLOGGERSTREAM << getCalibrationDetails(eqOptionBaskets_[i], eqParametrizations[i],\n irParametrizations[0]);\n WLOGGERSTREAM << \"rmse = \" << eqOptionCalibrationErrors_[i];\n if (!continueOnError_)\n QL_FAIL(exceptionMessage);\n }\n }\n }\n }\n\n /*************************\n * Relink LGM discount curves to curves used for INF calibration\n */\n\n for (Size i = 0; i < irParametrizations.size(); i++) {\n auto p = irParametrizations[i];\n irDiscountCurves[i].linkTo(*market_->discountCurve(p->currency().code(), configurationInfCalibration_));\n DLOG(\"Relinked discounting curve for \" << p->currency().code() << \" for INF calibration\");\n }\n\n // Calibrate INF components\n for (Size i = 0; i < infParameterizations.size(); i++) {\n boost::shared_ptr imData = config_->infConfigs()[i];\n if (auto dkData = boost::dynamic_pointer_cast(imData)) {\n auto dkParam = boost::dynamic_pointer_cast(infParameterizations[i]);\n QL_REQUIRE(dkParam, \"Expected DK model data to have given a DK parameterisation.\");\n const auto& builder = subBuilders_.at(CT::INF).at(i);\n const auto& dkBuilder = boost::dynamic_pointer_cast(builder);\n calibrateInflation(*dkData, i, dkBuilder->optionBasket(), dkParam);\n } else if (auto jyData = boost::dynamic_pointer_cast(imData)) {\n auto jyParam = boost::dynamic_pointer_cast(infParameterizations[i]);\n QL_REQUIRE(jyParam, \"Expected JY model data to have given a JY parameterisation.\");\n const auto& builder = subBuilders_.at(CT::INF).at(i);\n const auto& jyBuilder = boost::dynamic_pointer_cast(builder);\n calibrateInflation(*jyData, i, jyBuilder, jyParam);\n } else {\n QL_FAIL(\"CrossAssetModelBuilder expects either DK or JY inflation model data.\");\n }\n }\n\n /*************************\n * Relink LGM discount curves to final model curves\n */\n\n for (Size i = 0; i < irParametrizations.size(); i++) {\n auto p = irParametrizations[i];\n irDiscountCurves[i].linkTo(*market_->discountCurve(p->currency().code(), configurationFinalModel_));\n DLOG(\"Relinked discounting curve for \" << p->currency().code() << \" as final model curves\");\n }\n\n // play safe (although the cache of the model should be empty at\n // this point from all what we know...)\n model_->update();\n\n DLOG(\"Building CrossAssetModel done\");\n}\n\nvoid CrossAssetModelBuilder::forceRecalculate() {\n forceCalibration_ = true;\n ModelBuilder::forceRecalculate();\n forceCalibration_ = false;\n}\n\nvoid CrossAssetModelBuilder::calibrateInflation(const InfDkData& data, Size modelIdx,\n const vector>& cb,\n const boost::shared_ptr& inflationParam) const {\n\n LOG(\"Calibrate DK inflation model for inflation index \" << data.index());\n\n if ((!data.volatility().calibrate() && !data.reversion().calibrate()) ||\n (data.calibrationType() == CalibrationType::None)) {\n LOG(\"Calibration of DK inflation model for inflation index \" << data.index() << \" not requested.\");\n return;\n }\n\n Handle zInfIndex =\n market_->zeroInflationIndex(model_->infdk(modelIdx)->name(), configurationInfCalibration_);\n Real baseCPI = dontCalibrate_ ? 100. : zInfIndex->fixing(zInfIndex->zeroInflationTermStructure()->baseDate());\n auto engine = boost::make_shared(*model_, modelIdx, baseCPI);\n for (Size j = 0; j < cb.size(); j++)\n cb[j]->setPricingEngine(engine);\n\n if (dontCalibrate_)\n return;\n\n if (data.volatility().calibrate() && !data.reversion().calibrate()) {\n if (data.calibrationType() == CalibrationType::Bootstrap && data.volatility().type() == ParamType::Piecewise) {\n model_->calibrateInfDkVolatilitiesIterative(modelIdx, cb, *optimizationMethod_, endCriteria_);\n } else {\n model_->calibrateInfDkVolatilitiesGlobal(modelIdx, cb, *optimizationMethod_, endCriteria_);\n }\n } else if (!data.volatility().calibrate() && data.reversion().calibrate()) {\n if (data.calibrationType() == CalibrationType::Bootstrap && data.reversion().type() == ParamType::Piecewise) {\n model_->calibrateInfDkReversionsIterative(modelIdx, cb, *optimizationMethod_, endCriteria_);\n } else {\n model_->calibrateInfDkReversionsGlobal(modelIdx, cb, *optimizationMethod_, endCriteria_);\n }\n } else {\n model_->calibrate(cb, *optimizationMethod_, endCriteria_);\n }\n\n DLOG(\"INF (DK) \" << data.index() << \" calibration errors:\");\n inflationCalibrationErrors_[modelIdx] = getCalibrationError(cb);\n if (data.calibrationType() == CalibrationType::Bootstrap) {\n if (fabs(inflationCalibrationErrors_[modelIdx]) < config_->bootstrapTolerance()) {\n // we check the log level here to avoid unncessary computations\n if (Log::instance().filter(ORE_DATA)) {\n TLOGGERSTREAM << \"Calibration details:\";\n TLOGGERSTREAM << getCalibrationDetails(cb, inflationParam);\n TLOGGERSTREAM << \"rmse = \" << inflationCalibrationErrors_[modelIdx];\n }\n } else {\n string exceptionMessage = \"INF (DK) \" + std::to_string(modelIdx) + \" calibration error \" +\n std::to_string(inflationCalibrationErrors_[modelIdx]) + \" exceeds tolerance \" +\n std::to_string(config_->bootstrapTolerance());\n WLOG(StructuredModelErrorMessage(\"Failed to calibrate INF DK Model\", exceptionMessage));\n WLOGGERSTREAM << \"Calibration details:\";\n WLOGGERSTREAM << getCalibrationDetails(cb, inflationParam);\n WLOGGERSTREAM << \"rmse = \" << inflationCalibrationErrors_[modelIdx];\n if (!continueOnError_)\n QL_FAIL(exceptionMessage);\n }\n }\n}\n\nvoid CrossAssetModelBuilder::calibrateInflation(const InfJyData& data, Size modelIdx,\n const boost::shared_ptr& jyBuilder,\n const boost::shared_ptr& inflationParam) const {\n\n LOG(\"Calibrate JY inflation model for inflation index \" << data.index());\n\n const auto& rrVol = data.realRateVolatility();\n const auto& rrRev = data.realRateReversion();\n const auto& idxVol = data.indexVolatility();\n\n // Check if calibration is needed at all.\n if ((!rrVol.calibrate() && !rrRev.calibrate() && !idxVol.calibrate()) ||\n (data.calibrationType() == CalibrationType::None)) {\n LOG(\"Calibration of JY inflation model for inflation index \" << data.index() << \" not requested.\");\n return;\n }\n\n // We will need the 2 baskets of helpers\n auto rrBasket = jyBuilder->realRateBasket();\n auto idxBasket = jyBuilder->indexBasket();\n\n // Attach engines to the helpers.\n setJyPricingEngine(modelIdx, rrBasket);\n setJyPricingEngine(modelIdx, idxBasket);\n\n if (dontCalibrate_)\n return;\n\n // Single basket of helpers is useful in various places below.\n vector> allHelpers = rrBasket;\n allHelpers.insert(allHelpers.end(), idxBasket.begin(), idxBasket.end());\n\n // Calibration configuration.\n const auto& cc = data.calibrationConfiguration();\n\n if (data.calibrationType() == CalibrationType::BestFit) {\n\n // If calibration type is BestFit, do a global optimisation on the parameters that need to be calibrated.\n DLOG(\"Calibration BestFit of JY inflation model for inflation index \" << data.index() << \" requested.\");\n\n // Indicate the parameters to calibrate\n map toCalibrate;\n toCalibrate[0] = rrVol.calibrate();\n toCalibrate[1] = rrRev.calibrate();\n toCalibrate[2] = idxVol.calibrate();\n\n // Calibrate the model.\n model_->calibrateInfJyGlobal(modelIdx, allHelpers, *optimizationMethod_, endCriteria_, toCalibrate);\n\n } else {\n\n // Calibration type is now Bootstrap, there are multiple options.\n QL_REQUIRE(data.calibrationType() == CalibrationType::Bootstrap,\n \"JY inflation calibration expected a \"\n << \"calibration type of None, BestFit or Bootstrap.\");\n QL_REQUIRE(!(rrRev.calibrate() && rrVol.calibrate()),\n \"Calibrating both the \"\n << \"real rate reversion and real rate volatility using Bootstrap is not supported.\");\n\n if ((!rrVol.calibrate() && !rrRev.calibrate()) && idxVol.calibrate()) {\n\n // Bootstrap the inflation index volatility only.\n DLOG(\"Bootstrap calibration of JY index volatility for index \" << data.index() << \".\");\n QL_REQUIRE(idxVol.type() == ParamType::Piecewise, \"Index volatility parameter should be Piecewise for \"\n << \"a Bootstrap calibration.\");\n model_->calibrateInfJyIterative(modelIdx, 2, idxBasket, *optimizationMethod_, endCriteria_);\n\n } else if (rrVol.calibrate() && !idxVol.calibrate()) {\n\n // Bootstrap the real rate volatility only\n DLOG(\"Bootstrap calibration of JY real rate volatility for index \" << data.index() << \".\");\n QL_REQUIRE(rrVol.type() == ParamType::Piecewise, \"Real rate volatility parameter should be \"\n << \"Piecewise for a Bootstrap calibration.\");\n model_->calibrateInfJyIterative(modelIdx, 0, rrBasket, *optimizationMethod_, endCriteria_);\n\n } else if (rrRev.calibrate() && !idxVol.calibrate()) {\n\n // Bootstrap the real rate reversion only\n DLOG(\"Bootstrap calibration of JY real rate reversion for index \" << data.index() << \".\");\n QL_REQUIRE(rrRev.type() == ParamType::Piecewise, \"Real rate reversion parameter should be \"\n << \"Piecewise for a Bootstrap calibration.\");\n model_->calibrateInfJyIterative(modelIdx, 1, rrBasket, *optimizationMethod_, endCriteria_);\n\n } else if ((rrVol.calibrate() && idxVol.calibrate()) || (rrRev.calibrate() && idxVol.calibrate())) {\n\n if (rrVol.calibrate()) {\n DLOG(\"Bootstrap calibration of JY real rate volatility and index volatility for index \" << data.index()\n << \".\");\n } else {\n DLOG(\"Bootstrap calibration of JY real rate reversion and index volatility for index \" << data.index()\n << \".\");\n }\n\n // Bootstrap the real rate volatility and the index volatility\n Size rrIdx = rrVol.calibrate() ? 0 : 1;\n Size numIts = 0;\n inflationCalibrationErrors_[modelIdx] = getCalibrationError(allHelpers);\n\n while (inflationCalibrationErrors_[modelIdx] > cc.rmseTolerance() && numIts < cc.maxIterations()) {\n model_->calibrateInfJyIterative(modelIdx, 2, idxBasket, *optimizationMethod_, endCriteria_);\n model_->calibrateInfJyIterative(modelIdx, rrIdx, rrBasket, *optimizationMethod_, endCriteria_);\n numIts++;\n inflationCalibrationErrors_[modelIdx] = getCalibrationError(allHelpers);\n }\n\n DLOG(\"Bootstrap calibration of JY model stopped with number of iterations \"\n << numIts << \" and rmse equal to \" << std::scientific << std::setprecision(6)\n << inflationCalibrationErrors_[modelIdx] << \".\");\n\n } else {\n QL_FAIL(\"JY inflation bootstrap calibration does not support the combination of real rate volatility = \"\n << std::boolalpha << rrVol.calibrate() << \", real rate reversion = \" << rrRev.calibrate() << \" and \"\n << \"index volatility = \" << idxVol.calibrate() << \".\");\n }\n }\n\n // Log the calibration details.\n DLOG(\"INF (JY) \" << data.index() << \" calibration errors:\");\n inflationCalibrationErrors_[modelIdx] = getCalibrationError(allHelpers);\n if (data.calibrationType() == CalibrationType::Bootstrap) {\n if (fabs(inflationCalibrationErrors_[modelIdx]) < cc.rmseTolerance()) {\n // we check the log level here to avoid unncessary computations\n if (Log::instance().filter(ORE_DATA)) {\n TLOGGERSTREAM << \"Calibration details:\";\n TLOGGERSTREAM << getCalibrationDetails(rrBasket, idxBasket, inflationParam, rrVol.calibrate());\n TLOGGERSTREAM << \"rmse = \" << inflationCalibrationErrors_[modelIdx];\n }\n } else {\n std::stringstream ss;\n ss << \"INF (JY) \" << modelIdx << \" calibration error \" << std::scientific\n << inflationCalibrationErrors_[modelIdx] << \" exceeds tolerance \" << cc.rmseTolerance();\n string exceptionMessage = ss.str();\n WLOG(StructuredModelErrorMessage(\"Failed to calibrate INF JY Model\", exceptionMessage));\n WLOGGERSTREAM << \"Calibration details:\";\n WLOGGERSTREAM << getCalibrationDetails(rrBasket, idxBasket, inflationParam, rrVol.calibrate());\n WLOGGERSTREAM << \"rmse = \" << inflationCalibrationErrors_[modelIdx];\n if (!continueOnError_)\n QL_FAIL(exceptionMessage);\n }\n }\n\n LOG(\"Finished calibrating JY inflation model for inflation index \" << data.index());\n}\n\nvoid CrossAssetModelBuilder::setJyPricingEngine(\n Size modelIdx, const vector>& calibrationBasket) const {\n\n DLOG(\"Start setting pricing engines on JY calibration instruments.\");\n\n // JY supports three types of calibration helpers. Generally, all of the calibration instruments in a basket will\n // be of the same type but we support all three here.\n boost::shared_ptr cpiCapFloorEngine;\n boost::shared_ptr yoyCapFloorEngine;\n boost::shared_ptr yoySwapEngine;\n boost::shared_ptr yoyCouponPricer;\n\n for (auto& ci : calibrationBasket) {\n\n if (boost::shared_ptr h = boost::dynamic_pointer_cast(ci)) {\n if (!cpiCapFloorEngine) {\n cpiCapFloorEngine = boost::make_shared(*model_, modelIdx);\n }\n h->setPricingEngine(cpiCapFloorEngine);\n continue;\n }\n\n if (boost::shared_ptr h = boost::dynamic_pointer_cast(ci)) {\n if (!yoyCapFloorEngine) {\n yoyCapFloorEngine = boost::make_shared(*model_, modelIdx);\n }\n h->setPricingEngine(yoyCapFloorEngine);\n continue;\n }\n\n if (boost::shared_ptr h = boost::dynamic_pointer_cast(ci)) {\n // Here we need to attach the coupon pricer to all the YoY coupons and then the generic discounting swap\n // engine to the helper.\n if (!yoyCouponPricer) {\n yoyCouponPricer = boost::make_shared(*model_, modelIdx);\n\n Size irIdx = model_->ccyIndex(model_->infjy(modelIdx)->currency());\n auto yts = model_->irlgm1f(irIdx)->termStructure();\n yoySwapEngine = boost::make_shared(yts);\n }\n\n const auto& yoyLeg = h->yoySwap()->yoyLeg();\n for (const auto& cf : yoyLeg) {\n if (auto yoyCoupon = boost::dynamic_pointer_cast(cf))\n yoyCoupon->setPricer(yoyCouponPricer);\n }\n\n h->setPricingEngine(yoySwapEngine);\n continue;\n }\n\n QL_FAIL(\"Only CPI cap floors, YoY cap floors and YoY swaps are supported for JY calibration.\");\n }\n\n DLOG(\"Finished setting pricing engines on JY calibration instruments.\");\n}\n\n} // namespace data\n} // namespace ore\n", "meta": {"hexsha": "a19f886097a4ebe7e1b746350b1d3359b5f00fde", "size": 43218, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "OREData/ored/model/crossassetmodelbuilder.cpp", "max_stars_repo_name": "mrslezak/Engine", "max_stars_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 335.0, "max_stars_repo_stars_event_min_datetime": "2016-10-07T16:31:10.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-02T07:12:03.000Z", "max_issues_repo_path": "OREData/ored/model/crossassetmodelbuilder.cpp", "max_issues_repo_name": "mrslezak/Engine", "max_issues_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 59.0, "max_issues_repo_issues_event_min_datetime": "2016-10-31T04:20:24.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-03T16:39:57.000Z", "max_forks_repo_path": "OREData/ored/model/crossassetmodelbuilder.cpp", "max_forks_repo_name": "mrslezak/Engine", "max_forks_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 180.0, "max_forks_repo_forks_event_min_datetime": "2016-10-08T14:23:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T10:43:05.000Z", "avg_line_length": 50.0787949015, "max_line_length": 120, "alphanum_fraction": 0.6361701143, "num_tokens": 9921, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.3812195662561499, "lm_q1q2_score": 0.23165309003353207}} {"text": "//---------------------------------------------------------------------------//\n// Copyright (c) 2020 Mikhail Komarov \n// Copyright (c) 2019 Alexey Moskvin\n// Copyright (c) 2020 Ilias Khairullin \n//\n// Distributed under the Boost Software License, Version 1.0\n// See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt\n//---------------------------------------------------------------------------//\n\n#ifndef BOOST_MULTIPRECISION_MODULAR_ADAPTOR_HPP\n#define BOOST_MULTIPRECISION_MODULAR_ADAPTOR_HPP\n\n#include \n#include \n\n#include \n#include \n\n#include \n#include \n\n#include \n\n#include \n#include \n#include \n\nnamespace nil {\n namespace crypto3 {\n namespace multiprecision {\n namespace backends {\n\n template\n struct modular_adaptor {\n typedef modular_params modulus_type;\n typedef Backend backend_type;\n\n protected:\n Backend m_base;\n modulus_type m_mod;\n\n public:\n inline Backend& base_data() {\n return m_base;\n }\n inline const Backend& base_data() const {\n return m_base;\n }\n inline modulus_type& mod_data() {\n return m_mod;\n }\n inline const modulus_type& mod_data() const {\n return m_mod;\n }\n\n typedef typename Backend::signed_types signed_types;\n typedef typename Backend::unsigned_types unsigned_types;\n\n modular_adaptor() {\n }\n\n modular_adaptor(const modular_adaptor& o) : m_base(o.base_data()), m_mod(o.mod_data()) {\n }\n\n#ifndef BOOST_NO_CXX11_RVALUE_REFERENCES\n\n modular_adaptor(modular_adaptor&& o) :\n m_base(std::move(o.base_data())), m_mod(std::move(o.mod_data())) {\n }\n\n#endif\n // TODO: implement create_internal_representation\n // modular_adaptor(const Backend& val, const modular_params& mod) : m_mod(mod),\n // m_base(mod.create_internal_representation(val))\n // {\n //\n // }\n\n modular_adaptor(const Backend& val, const Backend& mod) : m_base(val), m_mod(mod) {\n }\n\n modular_adaptor(Backend& val, Backend& mod) : m_base(val), m_mod(mod) {\n }\n\n // TODO: maybe initialize modulus rather than base\n modular_adaptor(const Backend& val) :\n m_base(val), m_mod(typename boost::mpl::front::type(0u)) {\n }\n\n modular_adaptor(const modular_params& mod) :\n m_base(typename boost::mpl::front::type(0u)), m_mod(mod) {\n }\n\n modular_adaptor& operator=(const modular_adaptor& o) {\n m_base = o.base_data();\n m_mod = o.mod_data();\n return *this;\n }\n\n#ifndef BOOST_NO_CXX11_RVALUE_REFERENCES\n\n modular_adaptor& operator=(modular_adaptor&& o)\n\n BOOST_NOEXCEPT {\n m_base = std::move(o.base_data());\n m_mod = std::move(o.mod_data());\n return *this;\n }\n#endif\n\n modular_adaptor& operator=(const char* s) {\n typedef typename boost::mpl::front::type ui_type;\n ui_type zero = 0u;\n\n using default_ops::eval_fpclassify;\n\n if (s && (*s == '(')) {\n std::string part;\n const char* p = ++s;\n while (*p && (*p != ',') && (*p != ')'))\n ++p;\n part.assign(s, p);\n if (!part.empty())\n m_base() = part.c_str();\n else\n m_base() = zero;\n s = p;\n if (*p && (*p != ')')) {\n ++p;\n while (*p && (*p != ')'))\n ++p;\n part.assign(s + 1, p);\n } else\n part.erase();\n if (!part.empty())\n m_mod() = part.c_str();\n else\n m_mod() = zero;\n } else {\n base_data() = s;\n m_mod() = zero;\n }\n return *this;\n }\n\n // TODO: maybe change behaviour to check a congruence relation\n int compare(const modular_adaptor& o) const {\n // They are either equal or not:<\n if (m_mod.compare(o.mod_data()) != 0) {\n BOOST_THROW_EXCEPTION(\n std::runtime_error(\"Could not compare modular number with different mod.\"));\n }\n Backend tmp1, tmp2;\n mod_data().adjust_regular(tmp1, base_data());\n mod_data().adjust_regular(tmp2, o.base_data());\n return tmp1.compare(tmp2);\n }\n\n // TODO: maybe change behaviour to check a congruence relation\n template\n int compare(const T& val) const {\n using default_ops::eval_lt;\n if (!eval_lt(m_mod, val)) {\n BOOST_THROW_EXCEPTION(std::runtime_error(\n \"Could not compare modular number with mod bigger than compared number.\"));\n }\n Backend tmp;\n mod_data().adjust_regular(tmp, base_data());\n return tmp.compare(val);\n }\n\n inline void swap(modular_adaptor& o) {\n base_data().swap(o.base_data());\n std::swap(mod_data(), o.mod_data());\n }\n\n inline std::string str(std::streamsize dig, std::ios_base::fmtflags f) const {\n Backend tmp;\n mod_data().adjust_regular(tmp, base_data());\n return tmp.str(dig, f);\n }\n\n inline void negate() {\n base_data().negate();\n eval_add(base_data(), mod_data().get_mod().backend());\n }\n\n template\n operator number() {\n return base_data();\n };\n };\n\n template\n constexpr void eval_convert_to(Result* result, const modular_adaptor& val) {\n using default_ops::eval_convert_to;\n eval_convert_to(result, val.base_data());\n }\n\n template\n constexpr typename boost::enable_if, bool>::type eval_eq(const modular_adaptor& a,\n const T& b) {\n return a.compare(b) == 0;\n }\n\n template\n constexpr void eval_redc(Backend1& result, const modular_params& mod) {\n mod.reduce(result);\n eval_modulus(result, mod.get_mod().backend());\n }\n\n template\n constexpr void eval_add(modular_adaptor& result, const modular_adaptor& o) {\n BOOST_ASSERT(result.mod_data().get_mod() == o.mod_data().get_mod());\n using default_ops::eval_lt;\n\n eval_add(result.base_data(), o.base_data());\n if (!eval_lt(result.base_data(), result.mod_data().get_mod())) {\n eval_subtract(result.base_data(), result.mod_data().get_mod().backend());\n }\n }\n\n template\n constexpr void eval_subtract(modular_adaptor& result, const modular_adaptor& o) {\n BOOST_ASSERT(result.mod_data().get_mod() == o.mod_data().get_mod());\n typedef typename boost::mpl::front::type ui_type;\n using default_ops::eval_lt;\n eval_subtract(result.base_data(), o.base_data());\n if (eval_lt(result.base_data(), ui_type(0u))) {\n eval_add(result.base_data(), result.mod_data().get_mod().backend());\n }\n }\n\n template\n constexpr void eval_multiply(modular_adaptor& result, const modular_adaptor& o) {\n BOOST_ASSERT(result.mod_data().get_mod() == o.mod_data().get_mod());\n eval_multiply(result.base_data(), o.base_data());\n eval_redc(result.base_data(), result.mod_data());\n }\n\n template\n constexpr void eval_divide(modular_adaptor& result, const modular_adaptor& o) {\n BOOST_ASSERT(result.mod_data().get_mod() == o.mod_data().get_mod());\n Backend tmp1, tmp2;\n result.mod_data().adjust_regular(tmp1, result.base_data());\n result.mod_data().adjust_regular(tmp2, o.base_data());\n eval_divide(tmp1, tmp2);\n result.base_data() = tmp1;\n result.mod_data().adjust_modular(result.base_data());\n result.mod_data().adjust_regular(tmp2, result.base_data());\n }\n\n template\n constexpr void eval_modulus(modular_adaptor& result, const modular_adaptor& o) {\n BOOST_ASSERT(result.mod_data().get_mod() == o.mod_data().get_mod());\n Backend tmp1, tmp2;\n result.mod_data().adjust_regular(tmp1, result.base_data());\n result.mod_data().adjust_regular(tmp2, o.base_data());\n eval_modulus(tmp1, tmp2);\n result.base_data() = tmp1;\n result.mod_data().adjust_modular(result.base_data());\n result.mod_data().adjust_regular(tmp2, result.base_data());\n }\n\n template\n constexpr bool eval_is_zero(const modular_adaptor& val)\n\n BOOST_NOEXCEPT {\n using default_ops::eval_is_zero;\n return eval_is_zero(val.base_data());\n }\n\n // TODO: check returned value\n template\n constexpr int eval_get_sign(const modular_adaptor&) {\n return 1;\n }\n\n // TODO: is the function required\n // template \n // constexpr typename boost::disable_if_c::value>::type\n // eval_convert_to(Result* result, const modular_adaptor& val)\n // {\n // using default_ops::eval_convert_to;\n // eval_convert_to(result, val.base_data());\n // }\n\n template\n constexpr void assign_components(modular_adaptor& result, const T& a, const V& b) {\n result.base_data() = a;\n result.mod_data() = b;\n result.mod_data().adjust_modular(result.base_data());\n }\n\n template\n constexpr void eval_sqrt(modular_adaptor& result, const modular_adaptor& val) {\n eval_sqrt(result.base_data(), val.base_data());\n }\n\n template\n constexpr void eval_abs(modular_adaptor& result, const modular_adaptor& val) {\n result = val;\n }\n\n size_t window_bits(size_t exp_bits) {\n BOOST_STATIC_CONSTEXPR size_t wsize_count = 6;\n BOOST_STATIC_CONSTEXPR size_t wsize[wsize_count][2] = {{1434, 7}, {539, 6}, {197, 4},\n {70, 3}, {17, 2}, {0, 0}};\n\n size_t window_bits = 1;\n\n size_t j = wsize_count - 1;\n while (wsize[j][0] > exp_bits) {\n --j;\n }\n window_bits += wsize[j][1];\n\n return window_bits;\n };\n\n template\n inline void find_modular_pow(modular_adaptor& result,\n const modular_adaptor& b,\n const Backend& exp) {\n using default_ops::eval_bit_set;\n using default_ops::eval_convert_to;\n using default_ops::eval_decrement;\n using default_ops::eval_multiply;\n\n typedef number> modular_type;\n modular_params mod = b.mod_data();\n size_t m_window_bits;\n unsigned long cur_exp_index;\n size_t exp_bits = eval_msb(exp);\n m_window_bits = window_bits(exp_bits + 1);\n\n std::vector m_g(1U << m_window_bits);\n modular_type* p_g = m_g.data();\n modular_type x(1, mod);\n Backend nibble = exp;\n Backend mask;\n eval_bit_set(mask, m_window_bits);\n eval_decrement(mask);\n *p_g = x;\n ++p_g;\n *p_g = b;\n ++p_g;\n for (size_t i = 2; i < (1U << m_window_bits); i++) {\n eval_multiply((*p_g).backend(), m_g[i - 1].backend(), b);\n ++p_g;\n }\n size_t exp_nibbles = (exp_bits + 1 + m_window_bits - 1) / m_window_bits;\n std::vector exp_index;\n\n for (size_t i = 0; i < exp_nibbles; ++i) {\n Backend tmp = nibble;\n eval_bitwise_and(tmp, mask);\n eval_convert_to(&cur_exp_index, tmp);\n eval_right_shift(nibble, m_window_bits);\n exp_index.push_back(cur_exp_index);\n }\n\n x = x * m_g[exp_index[exp_nibbles - 1]];\n for (size_t i = exp_nibbles - 1; i > 0; --i) {\n\n for (size_t j = 0; j != m_window_bits; ++j) {\n x = x * x;\n }\n\n x = x * m_g[exp_index[i - 1]];\n }\n result = x.backend();\n }\n\n template\n constexpr void eval_pow(modular_adaptor& result, const modular_adaptor& b,\n const T& e) {\n find_modular_pow(result, b, e);\n }\n\n template\n constexpr void eval_pow(modular_adaptor& result,\n const modular_adaptor& b,\n const modular_adaptor& e) {\n Backend exp;\n e.mod_data().adjust_regular(exp, e.base_data());\n find_modular_pow(result, b, exp);\n }\n\n template\n constexpr void eval_powm(modular_adaptor& result, const modular_adaptor& b,\n const T& e) {\n eval_pow(result, b, e);\n }\n\n template\n constexpr void eval_powm(modular_adaptor& result,\n const modular_adaptor& b,\n const modular_adaptor& e) {\n eval_pow(result, b, e);\n }\n\n template\n constexpr void eval_left_shift(modular_adaptor& t, UI i) {\n using default_ops::eval_left_shift;\n Backend tmp;\n t.mod_data().adjust_regular(tmp, t.base_data());\n eval_left_shift(tmp, i);\n t.base_data() = tmp;\n t.mod_data().adjust_modular(t.base_data());\n }\n\n template\n constexpr void eval_right_shift(modular_adaptor& t, UI i) {\n using default_ops::eval_right_shift;\n Backend tmp;\n t.mod_data().adjust_regular(tmp, t.base_data());\n eval_right_shift(tmp, i);\n t.base_data() = tmp;\n t.mod_data().adjust_modular(t.base_data());\n }\n\n template\n constexpr void eval_left_shift(modular_adaptor& t, const modular_adaptor& v, UI i) {\n using default_ops::eval_left_shift;\n Backend tmp1, tmp2;\n t.mod_data().adjust_regular(tmp1, t.base_data());\n t.mod_data().adjust_regular(tmp2, v.base_data());\n eval_left_shift(tmp1, tmp2, static_cast(i));\n t.base_data() = tmp1;\n t.mod_data().adjust_modular(t.base_data());\n }\n\n template\n constexpr void eval_right_shift(modular_adaptor& t, const modular_adaptor& v, UI i) {\n using default_ops::eval_right_shift;\n Backend tmp1, tmp2;\n t.mod_data().adjust_regular(tmp1, t.base_data());\n t.mod_data().adjust_regular(tmp2, v.base_data());\n eval_right_shift(tmp1, tmp2, static_cast(i));\n t.base_data() = tmp1;\n t.mod_data().adjust_modular(t.base_data());\n }\n\n template\n constexpr void eval_bitwise_and(modular_adaptor& result, const modular_adaptor& v) {\n using default_ops::eval_bitwise_and;\n BOOST_ASSERT(result.mod_data().get_mod() == v.mod_data().get_mod());\n\n Backend tmp1, tmp2;\n result.mod_data().adjust_regular(tmp1, result.base_data());\n v.mod_data().adjust_regular(tmp2, v.base_data());\n eval_bitwise_and(tmp1, tmp1, tmp2);\n result.base_data() = tmp1;\n result.mod_data().adjust_modular(result.base_data());\n }\n\n template\n constexpr void eval_bitwise_or(modular_adaptor& result, const modular_adaptor& v) {\n using default_ops::eval_bitwise_or;\n BOOST_ASSERT(result.mod_data().get_mod() == v.mod_data().get_mod());\n\n Backend tmp1, tmp2;\n result.mod_data().adjust_regular(tmp1, result.base_data());\n v.mod_data().adjust_regular(tmp2, v.base_data());\n eval_bitwise_or(tmp1, tmp1, tmp2);\n result.base_data() = tmp1;\n result.mod_data().adjust_modular(result.base_data());\n }\n\n template\n constexpr void eval_bitwise_xor(modular_adaptor& result, const modular_adaptor& v) {\n using default_ops::eval_bitwise_xor;\n BOOST_ASSERT(result.mod_data().get_mod() == v.mod_data().get_mod());\n\n Backend tmp1, tmp2;\n result.mod_data().adjust_regular(tmp1, result.base_data());\n v.mod_data().adjust_regular(tmp2, v.base_data());\n eval_bitwise_xor(tmp1, tmp1, tmp2);\n result.base_data() = tmp1;\n result.mod_data().adjust_modular(result.base_data());\n }\n\n template\n constexpr int eval_msb(const modular_adaptor& m) {\n using default_ops::eval_msb;\n Backend tmp;\n m.mod_data().adjust_regular(tmp, m.base_data());\n return eval_msb(tmp);\n }\n\n template\n constexpr unsigned eval_lsb(const modular_adaptor& m) {\n using default_ops::eval_lsb;\n Backend tmp;\n m.mod_data().adjust_regular(tmp, m.base_data());\n return eval_lsb(tmp);\n }\n\n template\n constexpr bool eval_bit_test(const modular_adaptor& m, unsigned index) {\n using default_ops::eval_bit_test;\n Backend tmp;\n m.mod_data().adjust_regular(tmp, m.base_data());\n return eval_bit_test(tmp, index);\n }\n\n template\n constexpr void eval_bit_set(modular_adaptor& result, unsigned index) {\n using default_ops::eval_bit_set;\n Backend tmp;\n result.mod_data().adjust_regular(tmp, result.base_data());\n eval_bit_set(tmp, index);\n result.mod_data().adjust_modular(result.base_data(), tmp);\n }\n\n template\n constexpr void eval_bit_unset(modular_adaptor& result, unsigned index) {\n using default_ops::eval_bit_unset;\n Backend tmp;\n result.mod_data().adjust_regular(tmp, result.base_data());\n eval_bit_unset(tmp, index);\n result.mod_data().adjust_modular(result.base_data(), tmp);\n }\n\n template\n constexpr void eval_bit_flip(modular_adaptor& result, unsigned index) {\n using default_ops::eval_bit_flip;\n Backend tmp;\n result.mod_data().adjust_regular(tmp, result.base_data());\n eval_bit_flip(tmp, index);\n result.mod_data().adjust_modular(result.base_data(), tmp);\n }\n\n } // namespace backends\n\n using nil::crypto3::multiprecision::backends::modular_adaptor;\n\n template\n struct number_category>\n : public boost::mpl::int_ { };\n\n template\n struct component_type, ExpressionTemplates>> {\n typedef number type;\n };\n\n } // namespace multiprecision\n } // namespace crypto3\n} // namespace nil\n\n#endif\n", "meta": {"hexsha": "e0e07a4cd8140122f63a1f134f19977c0bb55d9e", "size": 25326, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "snark-logic/libs-source/multiprecision/include/nil/crypto3/multiprecision/modular/modular_adaptor.hpp", "max_stars_repo_name": "podlodkin/podlodkin-freeton-year-control", "max_stars_repo_head_hexsha": "e394c11f2414804d2fbde93a092ae589d4359739", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": 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"lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.23146209301246015}} {"text": "#include \"itkTractsToVectorImageFilter.h\"\n\n// VTK\n#include \n#include \n#include \n\n// ITK\n#include \n#include \n\n// misc\n#define _USE_MATH_DEFINES\n#include \n#include \n\n\nnamespace itk{\n\nstatic bool CompareVectorLengths(const vnl_vector_fixed< double, 3 >& v1, const vnl_vector_fixed< double, 3 >& v2)\n{\n return (v1.magnitude()>v2.magnitude());\n}\n\ntemplate< class PixelType >\nTractsToVectorImageFilter< PixelType >::TractsToVectorImageFilter():\n m_AngularThreshold(0.7),\n m_Epsilon(0.999),\n m_MaskImage(NULL),\n m_NormalizeVectors(false),\n m_UseWorkingCopy(true),\n m_UseTrilinearInterpolation(false),\n m_MaxNumDirections(3),\n m_Thres(0.5),\n m_NumDirectionsImage(NULL)\n{\n this->SetNumberOfRequiredOutputs(1);\n}\n\n\ntemplate< class PixelType >\nTractsToVectorImageFilter< PixelType >::~TractsToVectorImageFilter()\n{\n}\n\n\ntemplate< class PixelType >\nvnl_vector_fixed TractsToVectorImageFilter< PixelType >::GetVnlVector(double point[])\n{\n vnl_vector_fixed vnlVector;\n vnlVector[0] = point[0];\n vnlVector[1] = point[1];\n vnlVector[2] = point[2];\n return vnlVector;\n}\n\n\ntemplate< class PixelType >\nitk::Point TractsToVectorImageFilter< PixelType >::GetItkPoint(double point[])\n{\n itk::Point itkPoint;\n itkPoint[0] = point[0];\n itkPoint[1] = point[1];\n itkPoint[2] = point[2];\n return itkPoint;\n}\n\ntemplate< class PixelType >\nvoid TractsToVectorImageFilter< PixelType >::GenerateData()\n{\n mitk::BaseGeometry::Pointer geometry = m_FiberBundle->GetGeometry();\n\n // calculate new image parameters\n itk::Vector spacing;\n itk::Point origin;\n itk::Matrix direction;\n ImageRegion<3> imageRegion;\n if (!m_MaskImage.IsNull())\n {\n spacing = m_MaskImage->GetSpacing();\n imageRegion = m_MaskImage->GetLargestPossibleRegion();\n origin = m_MaskImage->GetOrigin();\n direction = m_MaskImage->GetDirection();\n }\n else\n {\n spacing = geometry->GetSpacing();\n origin = geometry->GetOrigin();\n mitk::BaseGeometry::BoundsArrayType bounds = geometry->GetBounds();\n origin[0] += bounds.GetElement(0);\n origin[1] += bounds.GetElement(2);\n origin[2] += bounds.GetElement(4);\n\n for (int i=0; i<3; i++)\n for (int j=0; j<3; j++)\n direction[j][i] = geometry->GetMatrixColumn(i)[j];\n imageRegion.SetSize(0, geometry->GetExtent(0));\n imageRegion.SetSize(1, geometry->GetExtent(1));\n imageRegion.SetSize(2, geometry->GetExtent(2));\n\n\n m_MaskImage = ItkUcharImgType::New();\n m_MaskImage->SetSpacing( spacing );\n m_MaskImage->SetOrigin( origin );\n m_MaskImage->SetDirection( direction );\n m_MaskImage->SetRegions( imageRegion );\n m_MaskImage->Allocate();\n m_MaskImage->FillBuffer(1);\n }\n OutputImageType::RegionType::SizeType outImageSize = imageRegion.GetSize();\n m_OutImageSpacing = m_MaskImage->GetSpacing();\n m_ClusteredDirectionsContainer = ContainerType::New();\n\n // initialize crossings image\n m_CrossingsImage = ItkUcharImgType::New();\n m_CrossingsImage->SetSpacing( spacing );\n m_CrossingsImage->SetOrigin( origin );\n m_CrossingsImage->SetDirection( direction );\n m_CrossingsImage->SetRegions( imageRegion );\n m_CrossingsImage->Allocate();\n m_CrossingsImage->FillBuffer(0);\n\n // initialize num directions image\n m_NumDirectionsImage = ItkUcharImgType::New();\n m_NumDirectionsImage->SetSpacing( spacing );\n m_NumDirectionsImage->SetOrigin( origin );\n m_NumDirectionsImage->SetDirection( direction );\n m_NumDirectionsImage->SetRegions( imageRegion );\n m_NumDirectionsImage->Allocate();\n m_NumDirectionsImage->FillBuffer(0);\n\n // resample fiber bundle\n double minSpacing = 1;\n if(m_OutImageSpacing[0]GetDeepCopy();\n\n // resample fiber bundle for sufficient voxel coverage\n m_FiberBundle->ResampleFibers(minSpacing/3);\n\n // iterate over all fibers\n vtkSmartPointer fiberPolyData = m_FiberBundle->GetFiberPolyData();\n vtkSmartPointer vLines = fiberPolyData->GetLines();\n vLines->InitTraversal();\n int numFibers = m_FiberBundle->GetNumFibers();\n itk::TimeProbe clock;\n m_DirectionsContainer = ContainerType::New();\n\n if (m_UseTrilinearInterpolation)\n MITK_INFO << \"Generating directions from tractogram (trilinear interpolation)\";\n else\n MITK_INFO << \"Generating directions from tractogram\";\n\n boost::progress_display disp(numFibers);\n for( int i=0; iGetNextCell ( numPoints, points );\n if (numPoints<2)\n continue;\n\n itk::Index<3> index; index.Fill(0);\n itk::ContinuousIndex contIndex;\n vnl_vector_fixed dir, wDir;\n itk::Point worldPos;\n vnl_vector v;\n for( int j=0; jGetPoint(points[j]);\n worldPos = GetItkPoint(temp);\n v = GetVnlVector(temp);\n\n dir = GetVnlVector(fiberPolyData->GetPoint(points[j+1]))-v;\n dir.normalize();\n\n m_MaskImage->TransformPhysicalPointToIndex(worldPos, index);\n m_MaskImage->TransformPhysicalPointToContinuousIndex(worldPos, contIndex);\n\n if (m_MaskImage->GetPixel(index)==0)\n continue;\n\n if (!m_UseTrilinearInterpolation)\n {\n if (index[0] < 0 || (unsigned long)index[0] >= outImageSize[0])\n continue;\n if (index[1] < 0 || (unsigned long)index[1] >= outImageSize[1])\n continue;\n if (index[2] < 0 || (unsigned long)index[2] >= outImageSize[2])\n continue;\n\n unsigned int idx = index[0] + outImageSize[0]*(index[1] + outImageSize[1]*index[2]);\n DirectionContainerType::Pointer dirCont = DirectionContainerType::New();\n if (m_DirectionsContainer->IndexExists(idx))\n {\n dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull())\n {\n dirCont = DirectionContainerType::New();\n dirCont->InsertElement(0, dir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n else\n dirCont->InsertElement(dirCont->Size(), dir);\n }\n else\n {\n dirCont->InsertElement(0, dir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n\n continue;\n }\n\n double frac_x = contIndex[0] - index[0];\n double frac_y = contIndex[1] - index[1];\n double frac_z = contIndex[2] - index[2];\n\n if (frac_x<0)\n {\n index[0] -= 1;\n frac_x += 1;\n }\n if (frac_y<0)\n {\n index[1] -= 1;\n frac_y += 1;\n }\n if (frac_z<0)\n {\n index[2] -= 1;\n frac_z += 1;\n }\n\n frac_x = 1-frac_x;\n frac_y = 1-frac_y;\n frac_z = 1-frac_z;\n\n // int coordinates inside image?\n if (index[0] < 0 || (unsigned long)index[0] >= outImageSize[0]-1)\n continue;\n if (index[1] < 0 || (unsigned long)index[1] >= outImageSize[1]-1)\n continue;\n if (index[2] < 0 || (unsigned long)index[2] >= outImageSize[2]-1)\n continue;\n\n DirectionContainerType::Pointer dirCont;\n int idx;\n wDir = dir;\n double weight = ( frac_x)*( frac_y)*( frac_z);\n if (weight>m_Thres)\n {\n wDir *= weight;\n idx = index[0] + outImageSize[0]*(index[1] + outImageSize[1]*index[2] );\n dirCont = DirectionContainerType::New();\n if (m_DirectionsContainer->IndexExists(idx))\n {\n dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull())\n {\n dirCont = DirectionContainerType::New();\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n else\n dirCont->InsertElement(dirCont->Size(), wDir);\n }\n else\n {\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n }\n\n wDir = dir;\n weight = ( frac_x)*(1-frac_y)*( frac_z);\n if (weight>m_Thres)\n {\n wDir *= weight;\n idx = index[0] + outImageSize[0]*(index[1]+1+ outImageSize[1]*index[2] );\n dirCont = DirectionContainerType::New();\n if (m_DirectionsContainer->IndexExists(idx))\n {\n dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull())\n {\n dirCont = DirectionContainerType::New();\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n else\n dirCont->InsertElement(dirCont->Size(), wDir);\n }\n else\n {\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n }\n\n wDir = dir;\n weight = ( frac_x)*( frac_y)*(1-frac_z);\n if (weight>m_Thres)\n {\n wDir *= weight;\n idx = index[0] + outImageSize[0]*(index[1] + outImageSize[1]*index[2]+outImageSize[1]);\n dirCont = DirectionContainerType::New();\n if (m_DirectionsContainer->IndexExists(idx))\n {\n dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull())\n {\n dirCont = DirectionContainerType::New();\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n else\n dirCont->InsertElement(dirCont->Size(), wDir);\n }\n else\n {\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n }\n\n wDir = dir;\n weight = ( frac_x)*(1-frac_y)*(1-frac_z);\n if (weight>m_Thres)\n {\n wDir *= weight;\n idx = index[0] + outImageSize[0]*(index[1]+1+ outImageSize[1]*index[2]+outImageSize[1]);\n dirCont = DirectionContainerType::New();\n if (m_DirectionsContainer->IndexExists(idx))\n {\n dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull())\n {\n dirCont = DirectionContainerType::New();\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n else\n dirCont->InsertElement(dirCont->Size(), wDir);\n }\n else\n {\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n }\n\n wDir = dir;\n weight = (1-frac_x)*( frac_y)*( frac_z);\n if (weight>m_Thres)\n {\n wDir *= weight;\n idx = index[0]+1 + outImageSize[0]*(index[1] + outImageSize[1]*index[2] );\n dirCont = DirectionContainerType::New();\n if (m_DirectionsContainer->IndexExists(idx))\n {\n dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull())\n {\n dirCont = DirectionContainerType::New();\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n else\n dirCont->InsertElement(dirCont->Size(), wDir);\n }\n else\n {\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n }\n\n wDir = dir;\n weight = (1-frac_x)*( frac_y)*(1-frac_z);\n if (weight>m_Thres)\n {\n wDir *= weight;\n idx = index[0]+1 + outImageSize[0]*(index[1] + outImageSize[1]*index[2]+outImageSize[1]);\n dirCont = DirectionContainerType::New();\n if (m_DirectionsContainer->IndexExists(idx))\n {\n dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull())\n {\n dirCont = DirectionContainerType::New();\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n else\n dirCont->InsertElement(dirCont->Size(), wDir);\n }\n else\n {\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n }\n\n wDir = dir;\n weight = (1-frac_x)*(1-frac_y)*( frac_z);\n if (weight>m_Thres)\n {\n wDir *= weight;\n idx = index[0]+1 + outImageSize[0]*(index[1]+1+ outImageSize[1]*index[2] );\n dirCont = DirectionContainerType::New();\n if (m_DirectionsContainer->IndexExists(idx))\n {\n dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull())\n {\n dirCont = DirectionContainerType::New();\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n else\n dirCont->InsertElement(dirCont->Size(), wDir);\n }\n else\n {\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n }\n\n wDir = dir;\n weight = (1-frac_x)*(1-frac_y)*(1-frac_z);\n if (weight>m_Thres)\n {\n wDir *= weight;\n idx = index[0]+1 + outImageSize[0]*(index[1]+1+ outImageSize[1]*index[2]+outImageSize[1]);\n dirCont = DirectionContainerType::New();\n if (m_DirectionsContainer->IndexExists(idx))\n {\n dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull())\n {\n dirCont = DirectionContainerType::New();\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n else\n dirCont->InsertElement(dirCont->Size(), wDir);\n }\n else\n {\n dirCont->InsertElement(0, wDir);\n m_DirectionsContainer->InsertElement(idx, dirCont);\n }\n }\n }\n clock.Stop();\n }\n\n vtkSmartPointer m_VtkCellArray = vtkSmartPointer::New();\n vtkSmartPointer m_VtkPoints = vtkSmartPointer::New();\n\n itk::ImageRegionIterator dirIt(m_NumDirectionsImage, m_NumDirectionsImage->GetLargestPossibleRegion());\n itk::ImageRegionIterator crossIt(m_CrossingsImage, m_CrossingsImage->GetLargestPossibleRegion());\n\n m_DirectionImageContainer = DirectionImageContainerType::New();\n unsigned int maxNumDirections = 0;\n\n MITK_INFO << \"Clustering directions\";\n boost::progress_display disp2(outImageSize[0]*outImageSize[1]*outImageSize[2]);\n for(crossIt.GoToBegin(); !crossIt.IsAtEnd(); ++crossIt)\n {\n ++disp2;\n OutputImageType::IndexType index = crossIt.GetIndex();\n int idx = index[0]+(index[1]+index[2]*outImageSize[1])*outImageSize[0];\n\n if (!m_DirectionsContainer->IndexExists(idx))\n {\n ++dirIt;\n continue;\n }\n DirectionContainerType::Pointer dirCont = m_DirectionsContainer->GetElement(idx);\n if (dirCont.IsNull() || index[0] < 0 || (unsigned long)index[0] >= outImageSize[0] || index[1] < 0 || (unsigned long)index[1] >= outImageSize[1] || index[2] < 0 || (unsigned long)index[2] >= outImageSize[2])\n {\n ++dirIt;\n continue;\n }\n\n std::vector< DirectionType > directions;\n\n for (unsigned int i=0; iSize(); i++)\n if (dirCont->ElementAt(i).magnitude()>0.0001)\n directions.push_back(dirCont->ElementAt(i));\n\n if (!directions.empty())\n directions = FastClustering(directions);\n\n std::sort( directions.begin(), directions.end(), CompareVectorLengths );\n\n if ( directions.size() > maxNumDirections )\n {\n for (unsigned int i=maxNumDirections; i(directions.size(), m_MaxNumDirections); i++)\n {\n ItkDirectionImageType::Pointer directionImage = ItkDirectionImageType::New();\n directionImage->SetSpacing( spacing );\n directionImage->SetOrigin( origin );\n directionImage->SetDirection( direction );\n directionImage->SetRegions( imageRegion );\n directionImage->Allocate();\n Vector< float, 3 > nullVec; nullVec.Fill(0.0);\n directionImage->FillBuffer(nullVec);\n m_DirectionImageContainer->InsertElement(i, directionImage);\n }\n maxNumDirections = std::min(directions.size(), m_MaxNumDirections);\n }\n\n unsigned int numDir = directions.size();\n if (numDir>m_MaxNumDirections)\n numDir = m_MaxNumDirections;\n\n for (unsigned int i=0; i container = vtkSmartPointer::New();\n itk::ContinuousIndex center;\n center[0] = index[0];\n center[1] = index[1];\n center[2] = index[2];\n itk::Point worldCenter;\n m_MaskImage->TransformContinuousIndexToPhysicalPoint( center, worldCenter );\n DirectionType dir = directions.at(i);\n\n // set direction image pixel\n ItkDirectionImageType::Pointer directionImage = m_DirectionImageContainer->GetElement(i);\n Vector< float, 3 > pixel;\n pixel.SetElement(0, dir[0]);\n pixel.SetElement(1, dir[1]);\n pixel.SetElement(2, dir[2]);\n directionImage->SetPixel(index, pixel);\n\n // add direction to vector field (with spacing compensation)\n itk::Point worldStart;\n worldStart[0] = worldCenter[0]-dir[0]/2*minSpacing;\n worldStart[1] = worldCenter[1]-dir[1]/2*minSpacing;\n worldStart[2] = worldCenter[2]-dir[2]/2*minSpacing;\n vtkIdType id = m_VtkPoints->InsertNextPoint(worldStart.GetDataPointer());\n container->GetPointIds()->InsertNextId(id);\n itk::Point worldEnd;\n worldEnd[0] = worldCenter[0]+dir[0]/2*minSpacing;\n worldEnd[1] = worldCenter[1]+dir[1]/2*minSpacing;\n worldEnd[2] = worldCenter[2]+dir[2]/2*minSpacing;\n id = m_VtkPoints->InsertNextPoint(worldEnd.GetDataPointer());\n container->GetPointIds()->InsertNextId(id);\n m_VtkCellArray->InsertNextCell(container);\n }\n dirIt.Set(numDir);\n ++dirIt;\n }\n\n vtkSmartPointer directionsPolyData = vtkSmartPointer::New();\n directionsPolyData->SetPoints(m_VtkPoints);\n directionsPolyData->SetLines(m_VtkCellArray);\n m_OutputFiberBundle = mitk::FiberBundleX::New(directionsPolyData);\n}\n\n\ntemplate< class PixelType >\nstd::vector< vnl_vector_fixed< double, 3 > > TractsToVectorImageFilter< PixelType >::FastClustering(std::vector< vnl_vector_fixed< double, 3 > >& inDirs)\n{\n std::vector< vnl_vector_fixed< double, 3 > > outDirs;\n if (inDirs.empty())\n return outDirs;\n vnl_vector_fixed< double, 3 > oldMean, currentMean, workingMean;\n\n std::vector< vnl_vector_fixed< double, 3 > > normalizedDirs;\n std::vector< int > touched;\n for (unsigned int i=0; i0.0001)\n {\n counter = 0;\n oldMean = currentMean;\n workingMean = oldMean;\n workingMean.normalize();\n currentMean.fill(0.0);\n for (unsigned int i=0; i=m_AngularThreshold)\n {\n currentMean += inDirs[i];\n touched[i] = 1;\n counter++;\n }\n else if (-angle>=m_AngularThreshold)\n {\n currentMean -= inDirs[i];\n touched[i] = 1;\n counter++;\n }\n }\n }\n\n // found stable mean\n if (counter>0)\n {\n currentMean /= counter;\n double mag = currentMean.magnitude();\n\n if (mag>0)\n {\n if (mag>max)\n max = mag;\n\n outDirs.push_back(currentMean);\n }\n }\n\n // find next unused seed\n free = false;\n for (unsigned int i=0; i0)\n for (unsigned int i=0; i\nstd::vector< vnl_vector_fixed< double, 3 > > TractsToVectorImageFilter< PixelType >::Clustering(std::vector< vnl_vector_fixed< double, 3 > >& inDirs)\n{\n std::vector< vnl_vector_fixed< double, 3 > > outDirs;\n if (inDirs.empty())\n return outDirs;\n vnl_vector_fixed< double, 3 > oldMean, currentMean, workingMean;\n\n std::vector< vnl_vector_fixed< double, 3 > > normalizedDirs;\n std::vector< int > touched;\n for (std::size_t i=0; i0.0001)\n {\n counter = 0;\n oldMean = currentMean;\n workingMean = oldMean;\n workingMean.normalize();\n currentMean.fill(0.0);\n for (std::size_t i=0; i=m_AngularThreshold)\n {\n currentMean += inDirs[i];\n counter++;\n }\n else if (-angle>=m_AngularThreshold)\n {\n currentMean -= inDirs[i];\n counter++;\n }\n }\n }\n\n // found stable mean\n if (counter>0)\n {\n bool add = true;\n vnl_vector_fixed< double, 3 > normMean = currentMean;\n normMean.normalize();\n for (std::size_t i=0; i dir = outDirs[i];\n dir.normalize();\n if ((normMean-dir).magnitude()<=0.0001)\n {\n add = false;\n break;\n }\n }\n\n currentMean /= counter;\n if (add)\n {\n double mag = currentMean.magnitude();\n if (mag>0)\n {\n if (mag>max)\n max = mag;\n\n outDirs.push_back(currentMean);\n }\n }\n }\n }\n\n if (m_NormalizeVectors)\n for (std::size_t i=0; i0)\n for (std::size_t i=0; i\nTractsToVectorImageFilter< PixelType >::DirectionContainerType::Pointer TractsToVectorImageFilter< PixelType >::MeanShiftClustering(DirectionContainerType::Pointer dirCont)\n{\n DirectionContainerType::Pointer container = DirectionContainerType::New();\n\n double max = 0;\n for (DirectionContainerType::ConstIterator it = dirCont->Begin(); it!=dirCont->End(); ++it)\n {\n vnl_vector_fixed mean = ClusterStep(dirCont, it.Value());\n\n if (mean.is_zero())\n continue;\n bool addMean = true;\n\n for (DirectionContainerType::ConstIterator it2 = container->Begin(); it2!=container->End(); ++it2)\n {\n vnl_vector_fixed dir = it2.Value();\n double angle = fabs(dot_product(mean, dir)/(mean.magnitude()*dir.magnitude()));\n if (angle>=m_Epsilon)\n {\n addMean = false;\n break;\n }\n }\n\n if (addMean)\n {\n if (m_NormalizeVectors)\n mean.normalize();\n else if (mean.magnitude()>max)\n max = mean.magnitude();\n container->InsertElement(container->Size(), mean);\n }\n }\n\n // max normalize voxel directions\n if (max>0 && !m_NormalizeVectors)\n for (std::size_t i=0; iSize(); i++)\n container->ElementAt(i) /= max;\n\n if (container->Size()Size())\n return MeanShiftClustering(container);\n else\n return container;\n}\n\n\ntemplate< class PixelType >\nvnl_vector_fixed TractsToVectorImageFilter< PixelType >::ClusterStep(DirectionContainerType::Pointer dirCont, vnl_vector_fixed currentMean)\n{\n vnl_vector_fixed newMean; newMean.fill(0);\n\n for (DirectionContainerType::ConstIterator it = dirCont->Begin(); it!=dirCont->End(); ++it)\n {\n vnl_vector_fixed dir = it.Value();\n double angle = dot_product(currentMean, 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YES\n2. NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.23146209301246015}} {"text": "#include \"precompiled.h\"\n#include \"finite_difference_weights.h\"\n\n#ifndef AUTOMATIC_PRECOMPILATION\n#include \n#include \n#include \n#include \n#endif\n\n#include \"Eigen.h\"\n#include \"utility.h\"\n#include \"Math.h\"\n\n#ifndef AUTOMATIC_PRECOMPILATION\n#include \n#endif\n\n#include \"finite_difference_stencils.h\"\n#include \"logging.h\"\n#include \"finite_difference_weights_algo.h\"\n#include \"grid_striding_info_inline.h\"\n#include \"global_config.h\"\n#include \"exceptions.h\"\n#include \"PDE.h\"\n\nnamespace fipster { namespace finite_difference_weights {\n\n\t// #################################################\n\t// ############## PHASE 2:\t\t\t############\n\t// ############## Node Factories ############\n\t// #################################################\n\n\tfdweights::sender_ptr fdweights::setup_node( const arg_t& arg )\n\t{\n\t\tauto PDE=arg.first.PDE;\n\n\t\tauto pde = dynamic_pointer_cast(PDE);\n\t\tif(pde) return create_node(body_t(arg,pde));\n\n\t\t// check for different PDEs\n\t\t// auto pde = dynamic_cast(PDE);\n\t\t// if(pde) return create_node(body_t(arg,pde));\n\n\t\tFIPSTER_THROW_EXCEPTION(runtime_error(\"PDE type \"+PDE->type+\" not implemented!\"));\n\t\treturn 0;\n\t}\n\n\n\t// #################################################\n\t// ############## PHASE 3:\t\t\t############\n\t// ############## Node Bodies ############\n\t// #################################################\n\n\n\tusing namespace Eigen;\n\n\ttemplate\n\tbody_t::body_t( const fd_weights_arg_t& args\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t, shared_ptr pde_ptr ) \n\t\t:\tpde(pde_ptr),args(args),D(args.second->D)\n\t{}\n\n\n\t//############################################################################\n\t//Constructs the multi_indices corresponding to partial derivatives \n\tint references::generatePDindices(uint order)\n\t{\n\t\tuint i__,i__1,i__2,i__3,k,j;\n\t\tbool more;\n\t\t/* Calculate number of partial derivatives to include */\n\t\tint numpds = 0;\n\t\ti__1 = order;\n\t\tfor (i__ = 1; i__ <= i__1; ++i__) {\n\t\t\ti__2 = i__ + D - 1;\n\t\t\ti__3 = D - 1;\n\t\t\tnumpds += (int) binom(i__2, i__3);\n\t\t}\n\n\t\tmulti_indices.resize(numpds,D);\n\t\tposition_t ind(D);\n\t\tmore=0;\n\t\tj = 0;\n\t\t/* Calculate multi indices for partial derivatives */\n\t\ti__1 = numpds;\n\t\tint h,t;\n\t\tfor (i__ = 0; i__ < i__1; ++i__) {\n\t\t\tif (! more) {\n\t\t\t\t++j;\n\t\t\t\tif (j > order)\n\t\t\t\t\tBOOST_THROW_EXCEPTION(invalid_argument(\n\t\t\t\t\t\"Number of Partial derivatives is to high. Specified order exceeded\"));\n\t\t\t}\n\t\t\tcomp_next(j, D, &ind[0], &more, &h, &t);\n\t\t\tfor (k = 0; k < D; ++k) {\n\t\t\t\tmulti_indices(i__,k) = ind[k];\n\t\t\t}\n\t\t}\n\t\t\n\t\treturn numpds;\n\t}\n\n\t//############################################################################\n\t// Compute line-wise constant factorials in denominator for weight optimization systems\n\tvoid references::generateFactorialDenoms(double HigherOrderWeight){\n\n\t\tfactorialdenoms.resize(multi_indices.rows());\n\n\t\tuint j;\t\n\n\t\tfor (int i = 0; i < factorialdenoms.size(); ++i) {\n\t\t\tuint sum=0;\n\t\t\tfactorialdenoms[i]=1.0;\n\t\t\tfor (j=0; j < D; ++j) {\n\t\t\t\tfactorialdenoms[i] /= factorial(multi_indices(i,j));\n\t\t\t\tif(multi_indices(i,j)!=0) sum++;\n\t\t\t}\n\t\t\tif (sum>2)\n\t\t\t\tfactorialdenoms[i] *= HigherOrderWeight;\n\t\t}\n\t}\n\n\ttemplate\n\tvoid test_A_o(const F& A_o, const G& A_io, const H& gsi, const I& stencil_points){\n\t\tauto& grid=*gsi.grid;\n\t\tauto end_b = grid.template b_end();\t\t\t\n\t\tset> obinds;\n\t\tfor(auto bit = boundary_iterator(grid); bit != end_b; ++bit)\n\t\t{\n\t\t\tobinds.clear();\n\n\t\t\t//loop over neighbors\n\t\t\tconst auto s_end=end(gsi.sorted_neighs);\n\t\t\tfor(auto s=begin(gsi.sorted_neighs);s!=s_end;++s){\n\t\t\t\t//get neighbor position\n\t\t\t\tauto pos=bit.pos()+stencil_points.at(s->second);\n\t\t\t\t//check, if points lies on outer boundary:\n\t\t\t\tboundary_index obind;\n\t\t\t\tif(obind.make_ind_from_pos(pos,grid)){\n\t\t\t\t\tFIPSTER_ASSERT(A_o.coeff(bit.b_ind,obind)\n\t\t\t\t\t\t\t\t\t\t==A_io.at(s->second).at(bit.g_ind));\n\t\t\t\t\tif(A_io.at(s->second).at(bit.g_ind)!=0)\n\t\t\t\t\t\tobinds.insert(obind);\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t//compare number of nonzeros in this A_o row and the number of unique accesses\n\t\t\tFIPSTER_ASSERT(obinds.size()==\n\t\t\t\t\t\t\t\t\t\t\t(uint)A_o->innerVector(bit.b_ind.ind).nonZeros());\n\t\t}\n\t}\n\n\t//############################################################################\n\t/** Generate the finite difference weights for the selected stencil,\n\t\tto get a finite difference approximation of the differential operator.\n\t\t\n\t\tIt's exactly done as described in the paper \n\t\t\"Ito, K., & Toivanen, J. (2009). Lagrange Multiplier Approach with \n\t\tOptimized Finite Difference Stencils for Pricing American Options \n\t\tunder Stochastic Volatility. SIAM Journal on Scientific Computing, \n\t\t31(4), 2646. doi:10.1137/07070574X\"\n\t\n\t\tWith the following additions:\n\t\t1. The sign choices specified in PDE.h dicate that the weights \n\t\t\tconstitute a NEGATIVE M-MATRIX. \n\t\t2. This is achieved by using non-negative least squares for the off-diagonal weights,\n\t\t\tas opposed to the proposed solution of a quadratic programming problem.\n\t\t3. Extension to higher dimensional PDEs\n\t\t4. Eq. 40 contains (dx_i)^delta_i. I use 0^0 = 1 (Reason: If there is no \n\t\t\tderivative present in one direction (delta_i=0) then it is \n\t\t\tignored (i.e. factor 1). In all other cases a stepsize of \n\t\t\tzero in one direction (dx_i=0) will yield a 0 in the matrix d.)\n\t\n\t\t \\todo verbesserung: wie vorgeschlagen, die partiellen Ableitungen\n\t\t die man mit einem gröberen Gitter erhällt für die optimierung\n\t\t benutzen...\n\t\n\t\\return 1. A 2D array, where each row corresponds to one point in the multi-dim \n\t\t\tgrid and the i-th column represent the weights of the i-th stencil point,\n\t\t\tif sorted lexicically by its direction vector. The last weight \n\t\t\tcorresponds to the unfinished (see note) center weight.\n\t\t\t2. The so called \"grid_striding_info\"\n\n\t\\note To get the final center weight all non-center weights have\n\t\t\tto be subtracted.\n\n\t*/\n\ttemplate\n\ttypename body_t::result_sptr_t body_t::operator()( const input_t& )\n\t{\n\t\tconst grid_ptr& grid = args.second;\n\t\t\n\t\t//gcc: was editable_result(new result_t) (i.e. possibly without initizalitaion?)\n\t\tfd_weights_result_ptr_t result = make_shared(); \n\n\t\t//create object to hold stuff\n\t\treferences r(D);\n\n\t\t//#### Create Stencil Points (excl. center point), sorted, such that:\n\t\t// [ points(i) , points(points.size()-i-1) ] is\n\t\t// the i-th direction\n\t\t\n\t\ttry{\n\n\t\t\tr.stencil_points.construct(D,args.first.stencil_type);\n\n\t\t}catch(boost::exception& e){\n\t\t\te<A_io.resize(*grid,r.stencil_points.nDirs());\n\n\t\t//#### Construct the multi_indices corresponding to partial derivatives\n\t\tuint numpds = r.generatePDindices(args.first.partial_derivative_order);\n\t\t\n\t\tthread_logger()<<\"WEIGHTS| for \"+toS(proper_neighs)+\" neighbors and \"+toS(numpds)+\" of PDE terms\"<> body\n\t\t\t\t(r,result->A_io,pde,*grid);\n\t\t\tthread_logger()<<\"WEIGHTS| nSites: \"<>(grid_index(),end_ind,100),\n\t\t\t\tbody/*,simple_partitioner()*/);\n\t\t\ttick_count t1 = tick_count::now();\n\t\t\tthread_logger()<<\"WEIGHTS| DONE, maxNorm:\"<b_end();\n\n\t\tauto& gsi = *new grid_striding_info_collection(r.stencil_points.nDirs(),r.stencil_points,grid);\n\t\tthread_logger()<< \"&gsi: \" << &gsi << endl;\n\t\tresult->grid_striding_infos.reset(&gsi);\t\t\n\n\t\tauto& A_io=result->A_io;\n\t\tauto& A_o=*result->A_o;\n\t\tA_o.resize(*grid);\n\n\t\t//test for uniqueness of the systems:\n\t\t//map,int> m;\n\n\t\tA_o->reserve(VectorXi::Constant(grid->Nbound[J_SGS],proper_neighs));\n\n\t\t//iterate over boundary and check for system beginnings\n\t\tfor(connected_set_setup_iterator it(*grid);it!=b_end;++it){\n\n\t\t\t//cout<<\"at \"<2, as a corner (of the inner boundary) can \n\t\t\t\t\t//belong to more than one system (in D>2)!\n\t\t\t\t\t//int a=it.inner_start_b_ind().ind;int b=it.inner_end_b_ind.ind;\n\t\t\t\t\t//use this instead:\n\t\t\t\t\tint a=it.outer_start_b_ind().ind;int b=it.outer_end_b_ind().ind;\n\t\t\t\t\tif(a==2 && b==2){\n\t\t\t\t\t\tcout<<\"pos: \"<1){\n\t\t\t\t\t\tcout<makeCompressed();\n\n\t\tif(global_config::get().test_A_o){\n\t\t\ttest_A_o(A_o,A_io,gsi,r.stencil_points);\n\t\t}\n\n\t\t//A_o.print();\n\n\t\ttick_count t1 = tick_count::now();\n\t\tthread_logger()<<\"WEIGHTS| DONE systems information in \"<\n#include \n#include \n#include \n#include \n#include \n\n#include \n\nnamespace GIMLI{\n\n#if USE_BOOST_THREAD\n #include \n boost::mutex eraseMutex__;\n#else\n #include \n #include \n std::mutex eraseMutex__;\n#endif\n\ntemplate < class ValueType > class CreateSensitivityColMT : public GIMLI::BaseCalcMT{\npublic:\n CreateSensitivityColMT(Matrix < ValueType > & S,\n const std::vector < Cell * > & para,\n const DataContainerERT & data,\n const Matrix < ValueType > & pots,\n const std::map< long, uint > & currPatternIdx,\n const RVector & weights,\n const RVector & k,\n bool calc1,\n bool verbose)\n : BaseCalcMT(verbose), S_(&S), para_(¶), //cellMapIndex_ (&cellMapIndex),\n data_(&data), pots_(&pots), currPatternIdx_(&currPatternIdx),\n weights_(&weights), k_(&k), calc1_(calc1){\n nData_ = data.size();\n nElecs_ = data.sensorCount();\n }\n\n virtual ~CreateSensitivityColMT(){}\n\n virtual void calc(){\n\n if (calc1_){\n calc1();\n }else {\n calc2();\n }\n }\n\n virtual void calc2(){\n ElementMatrix < double > S_i;\n ElementMatrix < double > S1_i;\n\n Cell * cell = NULL;\n int modelIdx = 0;\n\n const Vector < ValueType > *va;\n const Vector < ValueType > *vb;\n const Vector < ValueType > *vm;\n const Vector < ValueType > *vn;\n Vector < ValueType > dummy((*pots_)[0].size(), ValueType(0));\n\n const RVector *da = &(*data_)(\"a\");\n const RVector *db = &(*data_)(\"b\");\n const RVector *dm = &(*data_)(\"m\");\n const RVector *dn = &(*data_)(\"n\");\n\n for (Index cellID = start_; cellID < end_; cellID ++) {\n\n cell = (*para_)[cellID];\n modelIdx = cell->marker();\n\n if (modelIdx < 0) continue;\n\n S1_i.ux2uy2uz2(*cell);\n\n for (Index kIdx = 0; kIdx < weights_->size(); kIdx ++){\n S_i.u2(*cell);\n S_i *= (*k_)[kIdx] * (*k_)[kIdx];\n S_i += S1_i;\n int a = 0, b = 0, m = 0, n = 0;\n for (Index dataIdx = 0; dataIdx < nData_; dataIdx ++ ){\n\n a = (int)(*da)[dataIdx];\n b = (int)(*db)[dataIdx];\n m = (int)(*dm)[dataIdx];\n n = (int)(*dn)[dataIdx];\n\n if (a > -1) va = &(*pots_)[a + nElecs_ * kIdx]; else va = &dummy;\n if (b > -1) vb = &(*pots_)[b + nElecs_ * kIdx]; else vb = &dummy;\n if (m > -1) vm = &(*pots_)[m + nElecs_ * kIdx]; else vm = &dummy;\n if (n > -1) vn = &(*pots_)[n + nElecs_ * kIdx]; else vn = &dummy;\n\n (*S_)[dataIdx][modelIdx] +=\n S_i.mult((*va), (*vb), (*vm), (*vn)) * (*weights_)[kIdx];\n }\n }\n }\n }\n\n virtual void calc1(){\n // log(Debug, \"Thread #\" + str(_threadNumber) + \": on CPU \" + str(schedGetCPU()) +\n // \" slice \" + str(start_) + \":\" + str(end_));\n bool haveCurrentPatterns = false;\n\n if (currPatternIdx_->size() * weights_->size() == pots_->rows()) {\n //** we have current and measurements pattern instead of pol pol potentials;\n haveCurrentPatterns = true;\n }\n\n ElementMatrix < double > S_i;\n Cell * cell = NULL;\n int modelIdx = 0;\n // Index si, sj;\n\n const Vector < ValueType > *va;\n const Vector < ValueType > *vb;\n const Vector < ValueType > *vm;\n const Vector < ValueType > *vn;\n\n const RVector *da = &(*data_)(\"a\");\n const RVector *db = &(*data_)(\"b\");\n const RVector *dm = &(*data_)(\"m\");\n const RVector *dn = &(*data_)(\"n\");\n\n Vector < ValueType > dummy((*pots_)[0].size(), ValueType(0));\n\n for (Index cellID = start_; cellID < end_; cellID ++) {\n\n cell = (*para_)[cellID];\n modelIdx = cell->marker();\n\n if (modelIdx < 0) continue;\n\n if (verbose_) {\n // \tcout << \"\\r\";\n // \tfor (int i = 0; i < tNr; i ++) cout << \"\\t\\t\\t\";\n // \tcout <<\tcellID << \"/\" << para_->size() - 1;\n }\n\n S_i.ux2uy2uz2(*cell);\n Index cellNodeCount = cell->nodeCount();\n\n// ValueType tmpPotA = ValueType(0);\n// ValueType tmpPotM = ValueType(0);\n ValueType sum = ValueType(0);\n\n int a = 0, b = 0, m = 0, n = 0;\n\n double weightsFactor = 1.0;\n //** if weights_->size() > 1, assuming 2.5D so we need to double the weights\n //** we integrate from 0 to \\infty but need we need from -\\infty to \\infty\n if (weights_->size() > 1) weightsFactor = 2.0;\n\n for (Index dataIdx = 0; dataIdx < nData_; dataIdx ++ ){\n\n if (haveCurrentPatterns){\n a = currPatternIdx_->find(data_->electrodeToCurrentPattern(a, b))->second;\n m = currPatternIdx_->find(data_->electrodeToCurrentPattern(m, n))->second;\n b = -1;\n n = -1;\n } else {\n a = (int)(*da)[dataIdx];\n b = (int)(*db)[dataIdx];\n m = (int)(*dm)[dataIdx];\n n = (int)(*dn)[dataIdx];\n }\n\n for (Index kIdx = 0; kIdx < weights_->size(); kIdx ++){\n sum = ValueType(0);\n\n if (a > -1) va = &(*pots_)[a + nElecs_ * kIdx]; else va = &dummy;\n if (b > -1) vb = &(*pots_)[b + nElecs_ * kIdx]; else vb = &dummy;\n if (m > -1) vm = &(*pots_)[m + nElecs_ * kIdx]; else vm = &dummy;\n if (n > -1) vn = &(*pots_)[n + nElecs_ * kIdx]; else vn = &dummy;\n\n (*S_)[dataIdx][modelIdx] += S_i.mult((*va), (*vb), (*vm), (*vn)) * (weightsFactor * (*weights_)[kIdx]);\n continue;\n// std::cout << cell->id() << std::endl;\n for (Index i = 0; i < cellNodeCount; i ++){\n// si = S_i.idx(i);\n// std::cout << cell->node(i).id() << std::endl;\n for (Index j = 0; j < cellNodeCount; j ++){\n// sj = S_i.idx(j);\n // very most time criticle section here\n// tmpPotA = (*va)[si] - (*vb)[si];\n// tmpPotM = (*vm)[sj] - (*vn)[sj];\n//\n// sum += S_i.getVal(i, j) * tmpPotA * tmpPotM;\n\n// sum += S_i.getVal(i, j) *\n// ((*va)[si] - (*vb)[si]) *\n// ((*vm)[sj] - (*vn)[sj]);\n sum += S_i.getVal(i, j) *\n ((*va)[S_i.idx(i)] - (*vb)[S_i.idx(i)]) *\n ((*vm)[S_i.idx(j)] - (*vn)[S_i.idx(j)]);\n\n /* std::cout << i<<\" \"< nModel-1){\n// \t std::cerr << WHERE_AM_I << \" index out of bounds: [0 -- \" << nModel-1 << \"]\" << cellMapIndex[cellID] - 2 << std::endl;\n// \t }\n\n//\t (*S_)[dataIdx][(*cellMapIndex_)[cellID] - 2] += sum * (data_->k(dataIdx) * 2.0 * (*weights_)[kIdx]);\n\n /*! Dangerous!!! mt writing into matrix*/\n\t //(*S_)[dataIdx][(*cellMapIndex_)[cellID]] += sum * (2.0 * (*weights_)[kIdx]);\n {\n// #ifdef HAVE_LIBBOOST_THREAD\n// boost::mutex::scoped_lock lock(eraseMutex__); // slows down alot\n// #endif\n\n (*S_)[dataIdx][modelIdx] += sum * (weightsFactor * (*weights_)[kIdx]);\n// std::cout << \"b: \" << dataIdx<<\" \"< * S_;\n const std::vector < Cell * > * para_;\n const DataContainerERT * data_;\n const Matrix < ValueType > * pots_;\n const std::map< long, uint > * currPatternIdx_;\n const RVector * weights_;\n const RVector * k_;\n uint nData_;\n uint nElecs_;\n bool calc1_;\n\n};\n\nbool lessCellMarker(const Cell * c1, const Cell * c2) { return c1->marker() < c2->marker(); }\n\ntemplate < class ValueType >\nvoid createSensitivityCol_(Matrix < ValueType > & S,\n const Mesh & mesh,\n const DataContainerERT & data,\n const Matrix < ValueType > & pots,\n const RVector & weights,\n const RVector & k,\n std::vector < std::pair < Index, Index > > & matrixClusterIds,\n uint nThreads, bool verbose){\n\nMEMINFO\n\n Index nData = data.size();\n Index nModel = max(mesh.cellMarkers()) + 1;\n Index maxRows = weights.size() * data.sensorCount();\n\n if (pots.rows() >= maxRows){\n// if (pots[0].size() < mesh.nodeCount()){\n// std::stringstream str; str << WHERE_AM_I << \" potential matrix colsize to small. \"\n// << pots[0].size() << \"< \" << mesh.nodeCount() << std::endl;\n// }\n } else {\n std::stringstream str1; str1 << WHERE_AM_I << \" potential matrix rowsize to small.\"\n << pots.rows() << \" < \" << maxRows << std::endl;\n throwLengthError(str1.str());\n }\n\n Stopwatch swatch(true);\n std::map< long, uint > currPatternIdx;\n //std::cout << \"CreateSensitivityColMT \" << nThreads << std::endl;\n\n std::vector< Cell * > cells(mesh.findCellByMarker(0, -1));\n std::sort(cells.begin(), cells.end(), lessCellMarker);\n\n double maxMemSize = max(0.0, getEnvironment(\"SENSMATMAXMEM\", 0.0, verbose));\n double maxSizeNeeded = mByte((double)nData * nModel * sizeof(double));\n\n if (maxMemSize > 0 && verbose){\n std::cout << \"Size of S: \" << maxSizeNeeded << \" MB\" << std::endl;\n }\n\n //** avoid MT problems\n for (std::vector< Cell * >::iterator it = cells.begin();\n it != cells.end(); it ++){\n (*it)->pShape()->invJacobian();\n }\n\n// ShapeFunctionCache::instance().shapeFunctions(cells[0]->shape());\n// ShapeFunctionCache::instance().deriveShapeFunctions(cells[0]->shape(), 0);\n// ShapeFunctionCache::instance().deriveShapeFunctions(cells[0]->shape(), 1);\n// ShapeFunctionCache::instance().deriveShapeFunctions(cells[0]->shape(), 2);\n// ShapeFunctionCache::instance().shapeFunctions(*cells[0]);\n// ShapeFunctionCache::instance().deriveShapeFunctions(*cells[0], 0);\n// ShapeFunctionCache::instance().deriveShapeFunctions(*cells[0], 1);\n// ShapeFunctionCache::instance().deriveShapeFunctions(*cells[0], 2);\n// //cells[0]->createShapefunctionts();\n\n if (maxMemSize > 0 && maxMemSize < maxSizeNeeded){\n\n uint modelCluster = std::floor((double)nModel / (maxSizeNeeded / maxMemSize));\n\n if (modelCluster < 1) {\n throwError(WHERE_AM_I + \" sorry, size of single sensitivity-row exceeds memory limitations.\");\n }\n\n std::cout << \"Size of S cluster: \" << mByte((double)nData * modelCluster * sizeof(ValueType)) << \" MB\" << std::endl;\n std::cout << \"Using model cluster \" << nModel << \" x \" << modelCluster << std::endl;\n\n S.resize(nData, modelCluster);\n\n matrixClusterIds.clear();\n matrixClusterIds.push_back(std::pair < Index, Index >(nData, nModel));\n\n bool calc1 = getEnvironment(\"SENSMAT1\", false, true);\n for (uint i = 0; i < nModel; i += modelCluster ){\nMEMINFO\n Index start = i;\n Index end = min(start + modelCluster, nModel);\n std::cout << \" \" << start << \" \" << end<< std::endl;\n\n S.resize(nData, end - start);\n\n std::vector< Cell * > cellsCluster(mesh.findCellByMarker(start, end));\n std::sort(cellsCluster.begin(), cellsCluster.end(), lessCellMarker);\n\nMEMINFO\n // subtract marker start index\n for (std::vector< Cell * >::iterator it = cellsCluster.begin(); it != cellsCluster.end(); it ++){\n (*it)->setMarker((*it)->marker() - start);\n }\n\n S *= ValueType(0);\nMEMINFO\n\n distributeCalc(CreateSensitivityColMT< ValueType >(S, cellsCluster,\n data, pots,\n currPatternIdx,\n weights, k,\n calc1,\n verbose),\n cellsCluster.size(), nThreads, verbose);\n\nMEMINFO\n\n //** fight against the Lorenz butterfly\n //** 1e-8 is to coarse, need adaptive tolerance\n //S.round(1e-8);\nMEMINFO\n\n S.save(\"sensPart_\" + str(start) + \"-\" + str(end));\n\n matrixClusterIds.push_back(std::pair < Index, Index >(start, end));\n\n // add marker start index\n for (std::vector< Cell * >::iterator it = cellsCluster.begin(); it != cellsCluster.end(); it ++){\n (*it)->setMarker((*it)->marker() + start);\n }\nMEMINFO\n }\n\n S.clear();\n } else {\n\n // __MS(nData << \" \"<< S.rows() << \" \"<< nModel << \" \"<< S.cols())\n if (S.rows() != nData || S.cols() != nModel) S.resize(nData, nModel);\n S *= ValueType(0);\nMEMINFO\n\n if (verbose){\n std::cout << \"S(\" << numberOfCPU() << \"/\" << nThreads; //**check!!!\n #if USE_BOOST_THREAD\n std::cout << \"-boost::mt\";\n #else\n std::cout << \"-std::mt\";\n #endif\n std::cout << \"): \" << swatch.duration() << \":\";\n//swatch.stop(verbose);\n }\n bool calc1 = getEnvironment(\"SENSMAT1\", false, true);\n distributeCalc(CreateSensitivityColMT< ValueType >(S, cells, data,\n pots, currPatternIdx,\n weights, k, calc1, verbose),\n cells.size(), nThreads, verbose);\n if (verbose){\n swatch.stop(verbose);\n }\nMEMINFO\n //** fight against the Lorenz butterfly\n //** 1e-8 is to coarse, need adaptive tolerance\n //S.round(1e-8);\n }\n}\n\nvoid createSensitivityCol(RMatrix & S,\n const Mesh & mesh,\n const DataContainerERT & data,\n const RMatrix & pots,\n const RVector & weights,\n const RVector & k,\n std::vector < std::pair < Index, Index > > & matrixClusterIds,\n uint nThreads, bool verbose){\n createSensitivityCol_(S, mesh, data, pots, weights, k, matrixClusterIds, nThreads, verbose);\n}\n\nvoid createSensitivityCol(CMatrix & S,\n const Mesh & mesh,\n const DataContainerERT & data,\n const CMatrix & pots,\n const RVector & weights,\n const RVector & k,\n std::vector < std::pair < Index, Index > > & matrixClusterIds,\n uint nThreads, bool verbose){\n createSensitivityCol_(S, mesh, data, pots, weights, k, matrixClusterIds, nThreads, verbose);\n}\n\n\nvoid sensitivityDCFEMSingle(const std::vector < Cell * > & para, const RVector & p1, const RVector & p2,\n\t\t RVector & sens, bool verbose){\n uint nCells = para.size();\n if (sens.size() != nCells) sens.resize(nCells);\n\n ElementMatrix < double > S_i;\n double sum = 0.0, a_jk = 0.0, tmppot = 0.0;\n // cout << nCells << std::endl;\n\n for (uint i = 0; i < nCells; i ++){\n //cout << \"Nr. \" << i << std::endl;\n S_i.ux2uy2uz2(*para[i]);\n\n sum = 0.0;\n for (int j = 0, jmax = para[i]->nodeCount(); j < jmax; j ++){\n for (int k = 0, kmax = para[i]->nodeCount(); k < kmax; k ++){\n\t a_jk = S_i.getVal(j, k);\n\t tmppot = p1[S_i.idx(j)] * p2[S_i.idx(k)];\n\t sum += a_jk * tmppot;\n\t//\tcout << \"\\tS_mn: \" << a_jk << \"\\tp1*p2: \" << tmppot << \"\\tp*S_mn: \" << a_jk * tmppot << \"\\tsum: \" << sum << std::endl;\n }\n }\n sens[i] = sum;\n }\n}\n\nRVector prepExportSensitivityData(const Mesh & mesh, const RVector & data, double logdrop){\n Index nModel = unique(sort(mesh.cellMarkers())).size();\n\n ASSERT_EQUAL(nModel, data.size())\n\n //data have always the right length since it comes from S directly\n RVector modelSizes(nModel, 0.0);\n for (Index i = 0; i < mesh.cellCount(); i ++ ){\n modelSizes[mesh.cell(i).marker()] += mesh.cell(i).size();\n }\n\n return logTransDropTol(data/modelSizes, logdrop, true)(mesh.cellMarkers());\n\n // //RVector tmp(data/mesh.cellSizes());\n // if ((uint)data.size() != (uint)mesh.cellCount()){\n\n // throwLengthError(WHERE_AM_I + \" Datasize missmatch: \" + str(mesh.cellCount())+\n // \" \" + str(data.size()));\n // } else {\n // //for (uint i = 0; i < tmp.size(); i ++) tmp[i] = tmp[i] / mesh.cell(i).shape().domainSize();\n // }\n // RVector tmp(data/mesh.cellSizes());\n\n // RVector s(sign(tmp));\n\n // double tmpMax = max(abs(tmp));\n // tmp /= tmpMax;\n\n // for (uint i = 0; i < tmp.size(); i ++) {\n // tmp[i] = std::fabs(tmp[i] / logdrop);\n // if (tmp[i] < 1.0) tmp[i] = 1.0;\n // }\n\n // tmp = log10(tmp);\n // tmp /= max(tmp) * s;\n // return tmp;\n}\n\nvoid exportSensitivityVTK(const std::string & fileName,\n const Mesh & mesh, const RVector & data,\n double logdrop){\n std::map< std::string, RVector > res;\n res.insert(std::make_pair(\"Sensitivity\" ,\n prepExportSensitivityData(mesh, data, logdrop)));\n mesh.exportVTK(fileName, res);\n}\n\n// void exportSensMatrixDC(const std::string & filename, const Mesh & mesh, const RMatrix & S) {\n// exportSensMatrixDC(filename, mesh, S\n// }\n\nvoid exportSensMatrixDC(const std::string & filename, const Mesh & mesh,\n const RMatrix & S, const IVector & idx,\n double logdrop) {\n std::map< std::string, RVector > res;\n\n for (std::map < std::string, RVector >::const_iterator\n it = mesh.dataMap().begin();\n it != mesh.dataMap().end(); it ++){\n res.insert(std::make_pair(it->first, it->second));\n }\n\n std::string add;\n// RVector tmp(mesh.cellCount());\n\n for (size_t i = 0; i < S.rows(); i ++) {\n if (i < 100000) add = \"0\";\n if (i < 10000) add = \"00\";\n if (i < 1000) add = \"000\";\n if (i < 100) add = \"0000\";\n if (i < 10) add = \"00000\";\n\n// for (uint j = 0; j < tmp.size(); j ++) tmp[j] = S[i][mesh.cell(j).marker()];\n\n//#res.insert(std::make_pair(\"sens-\" + add + str(i), log10(RVector(abs(tmp)))));\n\n res.insert(std::make_pair(\"sens-\" + add + str(i),\n prepExportSensitivityData(mesh, S[i], logdrop)));\n\n }\n mesh.exportVTK(filename, res);\n}\n\nRVector coverageDC(const RMatrix & sensMatrix) {\n RVector cov;\n if (sensMatrix.rows() > 0) {\n cov.resize(sensMatrix.cols(), 0.0);\n\n for (size_t i = 0; i < sensMatrix.rows(); i ++) {\n cov += abs(sensMatrix[i]);\n }\n } else {\n std::cout << \"Sensmatrix invalid\" << std::endl;\n }\n return cov;\n}\n\nRVector coverageDCtrans(const MatrixBase & S, const RVector & dd, const RVector & mm) {\n RVector cov;\n\n if (S.rows() > 0) {\n cov.resize(S.cols(), 0.0);\n } else {\n std::cout << \"Sensmatrix invalid\" << std::endl;\n }\n\n if (S.rtti() == GIMLI_MATRIX_RTTI){\n const RMatrix *Sl = dynamic_cast < const RMatrix * >(&S);\n\n for (size_t i = 0; i < S.rows(); i ++) {\n cov += abs((*Sl)[i] * dd[i]);\n }\n } else if (S.rtti() == GIMLI_SPARSE_MAP_MATRIX_RTTI){\n\n const RSparseMapMatrix * Sl = dynamic_cast< const RSparseMapMatrix * >(&S);\n\n for (RSparseMapMatrix::const_iterator it = Sl->begin(); it != Sl->end(); it ++){\n Index row = (*it).first.first;\n Index col = (*it).first.second;\n cov[col] += (*it).second * dd[row];\n }\n } else {\n CERR_TO_IMPL\n }\n\n return cov / abs(mm);\n}\n\nRVector createCoverage(const MatrixBase & S, const Mesh & mesh){\n return createCoverage(S, mesh, RVector(S.rows(), 1.0), RVector(S.cols(), 1.0));\n}\n\nRVector createCoverage(const MatrixBase & S, const Mesh & mesh,\n const RVector & response, const RVector & model){\n\n RVector covModel(coverageDCtrans(S, 1.0 / response, 1.0 / model));\n\tRVector covMesh(covModel(mesh.cellMarkers()));\n if (mesh.cellCount() == model.size()) {\n covMesh /= mesh.cellSizes();\n } else {\n RVector modelCellSizes(covMesh.size(), 0.0);\n for (Index i = 0; i < mesh.cellCount(); i ++){\n Cell *c = &mesh.cell(i);\n modelCellSizes[c->marker()] += c->shape().domainSize();\n }\n if (min(modelCellSizes) > TOLERANCE){\n covMesh /= modelCellSizes;\n } else {\n log(Error, \"Coverage fails:\" + str(mesh.cellCount()) + \" \" + str(model.size()));\n }\n }\n\n return covMesh;\n}\n} // namespace GIMLI\n", "meta": {"hexsha": "a9348dfe437a4bbc20cf1d8256fe9296c158d423", "size": 24076, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "core/src/bert/bertJacobian.cpp", "max_stars_repo_name": "JuliusHen/gimli", "max_stars_repo_head_hexsha": "a5c5779261acfe5a53015c9ee6f7c9ed2dd6c57f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 224.0, "max_stars_repo_stars_event_min_datetime": "2015-02-20T21:36:24.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T07:27:43.000Z", "max_issues_repo_path": "core/src/bert/bertJacobian.cpp", "max_issues_repo_name": "JuliusHen/gimli", "max_issues_repo_head_hexsha": "a5c5779261acfe5a53015c9ee6f7c9ed2dd6c57f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 341.0, "max_issues_repo_issues_event_min_datetime": "2015-05-21T14:39:51.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T01:54:07.000Z", "max_forks_repo_path": "core/src/bert/bertJacobian.cpp", "max_forks_repo_name": "JuliusHen/gimli", "max_forks_repo_head_hexsha": "a5c5779261acfe5a53015c9ee6f7c9ed2dd6c57f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 107.0, "max_forks_repo_forks_event_min_datetime": "2015-01-24T14:40:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-25T12:12:13.000Z", "avg_line_length": 38.5833333333, "max_line_length": 126, "alphanum_fraction": 0.4731683004, "num_tokens": 6019, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.7057850154599563, "lm_q2_score": 0.3276683139517237, "lm_q1q2_score": 0.23126338602815513}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"drake/solvers/snopt_solver.h\"\n#include \"drake/systems/analysis/simulator.h\"\n#include \"drake/systems/framework/diagram.h\"\n#include \"drake/systems/framework/diagram_builder.h\"\n#include \"drake/multibody/parsing/parser.h\"\n#include \n#include \"drake/geometry/geometry_visualization.h\"\n#include \"drake/solvers/solve.h\"\n\n#include \"common/find_resource.h\"\n#include \"systems/trajectory_optimization/dircon/dircon.h\"\n#include \"multibody/kinematic/world_point_evaluator.h\"\n#include \"solvers/nonlinear_constraint.h\"\n#include \"multibody/kinematic/distance_evaluator.h\"\n#include \"multibody/multibody_utils.h\"\n#include \"multibody/visualization_utils.h\"\n#include \"multibody/kinematic/kinematic_constraints.h\"\n\n#include \"examples/Spirit/animate_spirit.h\"\n#include \"examples/Spirit/spirit_utils.h\"\n\nDEFINE_double(duration, 1, \"The stand duration\");\nDEFINE_double(front2BackToeDistance, 0.35, \"Nominal distance between the back and front toes.\");\nDEFINE_double(side2SideToeDistance, 0.2, \"Nominal distance between the back and front toes.\");\nDEFINE_double(bodyHeight, 0.104, \"The spirit body start height (defined in URDF)\");\nDEFINE_double(lowerHeight,0.15, \"The sitting height of the bottom of the robot\");\nDEFINE_double(upperHeight, 0.35, \"The standing height.\");\nDEFINE_double(inputCost, 3, \"The standing height.\");\nDEFINE_double(velocityCost, 10, \"The standing height.\");\nDEFINE_double(eps, 1e-2, \"The wiggle room.\");\nDEFINE_double(optTol, 1e-4,\"Optimization Tolerance\");\nDEFINE_double(feasTol, 1e-4,\"Feasibility Tolerance\");\nDEFINE_bool(autodiff, false, \"Double or autodiff version\");\nDEFINE_bool(runInitTraj, false, \"Animate initial conditions?\");\n// Parameters which enable dircon-improving features\nDEFINE_bool(scale_constraint, true, \"Scale the nonlinear constraint values\");\nDEFINE_bool(scale_variable, false, \"Scale the decision variable\");\n\nusing drake::AutoDiffXd;\nusing drake::multibody::MultibodyPlant;\nusing drake::geometry::SceneGraph;\nusing drake::multibody::Parser;\nusing drake::trajectories::PiecewisePolynomial;\n\nusing Eigen::Vector3d;\nusing Eigen::VectorXd;\nusing Eigen::Matrix3d;\nusing Eigen::MatrixXd;\n\nnamespace dairlib {\nnamespace {\n\nusing systems::trajectory_optimization::DirconModeSequence;\nusing systems::trajectory_optimization::DirconMode;\nusing systems::trajectory_optimization::Dircon;\nusing systems::trajectory_optimization::KinematicConstraintType;\n\nusing std::vector;\nusing std::cout;\nusing std::endl;\n\n\ntemplate \nvoid addConstraints(const MultibodyPlant& plant, Dircon& trajopt){\n // Get position and velocity dictionaries \n int num_knotpoints =10; ///DEBUG\n auto positions_map = multibody::makeNameToPositionsMap(plant);\n auto velocities_map = multibody::makeNameToVelocitiesMap(plant);\n auto x0 = trajopt.initial_state();\n auto xmid = trajopt.state_vars(0, (num_knotpoints - 1) / 2);\n // auto xf = trajopt.final_state();\n\n\n // Initial body positions\n trajopt.AddBoundingBoxConstraint(-FLAGS_eps, FLAGS_eps, x0(positions_map.at(\"base_x\"))); // Give the initial condition room to choose the x_init position (helps with positive knee constraint)\n trajopt.AddBoundingBoxConstraint(-FLAGS_eps, FLAGS_eps, x0(positions_map.at(\"base_y\")));\n trajopt.AddBoundingBoxConstraint(FLAGS_lowerHeight-FLAGS_eps, FLAGS_lowerHeight+FLAGS_eps, x0(positions_map.at(\"base_z\")));\n \n // Mid body positions\n trajopt.AddBoundingBoxConstraint(-0, 0, xmid(positions_map.at(\"base_x\"))); // Give the initial condition room to choose the x_init position (helps with positive knee constraint)\n trajopt.AddBoundingBoxConstraint(-0, 0, xmid(positions_map.at(\"base_y\")));\n trajopt.AddBoundingBoxConstraint(FLAGS_upperHeight-FLAGS_eps, FLAGS_upperHeight+FLAGS_eps, xmid(positions_map.at(\"base_z\")));\n\n return;\n}\n\n /// See runSpiritSquat()\n // std::unique_ptr> plant_ptr,\n // MultibodyPlant* plant_double_ptr,\n // std::unique_ptr> scene_graph_ptr,\n // double duration,\n // PiecewisePolynomial init_x_traj,\n // PiecewisePolynomial init_u_traj,\n // vector> init_l_traj,\n // vector> init_lc_traj,\n // vector> init_vc_traj\n ///\n /// Given an initial guess for state, control, and forces optimizes a standing behaviour for the spirit robot\ntemplate \nvoid runSpiritSquat(\n std::unique_ptr> plant_ptr,\n MultibodyPlant* plant_double_ptr,\n std::unique_ptr> scene_graph_ptr,\n double duration,\n PiecewisePolynomial init_x_traj,\n PiecewisePolynomial init_u_traj,\n vector> init_l_traj,\n vector> init_lc_traj,\n vector> init_vc_traj\n ) {\n\n drake::systems::DiagramBuilder builder;\n MultibodyPlant& plant = *plant_ptr;\n SceneGraph& scene_graph =\n *builder.AddSystem(std::move(scene_graph_ptr));\n \n // Get position and velocity dictionaries \n auto positions_map = multibody::makeNameToPositionsMap(plant);\n auto velocities_map = multibody::makeNameToVelocitiesMap(plant);\n /// For Spirit front left leg->0, back left leg->1, front right leg->2, back right leg->3\n /// Get the frame of each toe and attach a world point to the toe tip (frame is at toe ball center).\n\n int num_legs = 4;\n double toeRadius = 0.02; // Radius of toe ball\n Vector3d toeOffset(toeRadius,0,0); // vector to \"contact point\"\n double mu = 1; // Coeff of friction\n \n\n int num_knotpoints_per_mode = 10; \n \n\n auto sequence = DirconModeSequence(plant);\n\n dairlib::ModeSequenceHelper msh;\n \n msh.addMode( // FIRST MODE 1\n (Eigen::Matrix() << 1,1,1,1 ).finished(), // contact bools \n num_knotpoints_per_mode, // number of knot points in the collocation\n Eigen::Vector3d::UnitZ(), // normal\n Eigen::Vector3d::Zero(), // world offset\n mu //friction\n );\n\n // auto [modeVector, toeEvals, toeEvalSets] = createSpiritModeSequence(plant, msh.modes , msh.knots , msh.normals , msh.offsets, msh.mus, msh.minTs, msh.maxTs);\n auto [modeVector, toeEvals, toeEvalSets] = createSpiritModeSequence(plant, msh);\n \n for (auto& mode : modeVector){\n for (int i = 0; i < num_legs; i++ ){\n mode->MakeConstraintRelative(i,0);\n mode->MakeConstraintRelative(i,1);\n }\n mode->SetDynamicsScale(\n {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17}, 1.0 / 150.0);\n mode->SetKinVelocityScale(\n {0, 1, 2, 3}, {0, 1, 2}, 1.0 / 500.0 * 500 * 1 / 1);\n sequence.AddMode(mode.get());\n } \n\n\n \n \n\n ///Setup trajectory optimization\n auto trajopt = Dircon(sequence);\n\n // Set up Trajectory Optimization options\n trajopt.SetSolverOption(drake::solvers::SnoptSolver::id(),\n \"Print file\", \"../snopt.out\");\n trajopt.SetSolverOption(drake::solvers::SnoptSolver::id(),\n \"Major iterations limit\", 20000);\n trajopt.SetSolverOption(drake::solvers::SnoptSolver::id(), \"Iterations limit\", 100000);\n trajopt.SetSolverOption(drake::solvers::SnoptSolver::id(),\n \"Major optimality tolerance\",\n FLAGS_optTol); // target optimality\n trajopt.SetSolverOption(drake::solvers::SnoptSolver::id(), \"Major feasibility tolerance\", FLAGS_feasTol);\n trajopt.SetSolverOption(drake::solvers::SnoptSolver::id(), \"Verify level\",\n 0); // 0\n\n // Add duration constraint, currently constrained not bounded\n trajopt.AddDurationBounds(0, duration*2);\n // Initialize the trajectory control state and forces\n for (int j = 0; j < sequence.num_modes(); j++) {\n trajopt.drake::systems::trajectory_optimization::MultipleShooting::\n SetInitialTrajectory(init_u_traj, init_x_traj);\n trajopt.SetInitialForceTrajectory(j, init_l_traj[j], init_lc_traj[j],\n init_vc_traj[j]);\n }\n\n /// Setup all the optimization constraints \n int num_knotpoints = trajopt.N(); // number of knot points total in the collocation\n int n_v = plant.num_velocities();\n // int n_q = plant.num_positions();\n auto u = trajopt.input();\n auto x = trajopt.state();\n auto x0 = trajopt.initial_state();\n auto xmid = trajopt.state_vars(0, (num_knotpoints - 1) / 2);\n auto xf = trajopt.final_state();\n addConstraints(plant,trajopt);\n\n // // Initial body positions\n // trajopt.AddBoundingBoxConstraint(-FLAGS_eps, FLAGS_eps, x0(positions_map.at(\"base_x\"))); // Give the initial condition room to choose the x_init position (helps with positive knee constraint)\n // trajopt.AddBoundingBoxConstraint(-FLAGS_eps, FLAGS_eps, x0(positions_map.at(\"base_y\")));\n // trajopt.AddBoundingBoxConstraint(FLAGS_lowerHeight-FLAGS_eps, FLAGS_lowerHeight+FLAGS_eps, x0(positions_map.at(\"base_z\")));\n \n // // Mid body positions\n // trajopt.AddBoundingBoxConstraint(-0, 0, xmid(positions_map.at(\"base_x\"))); // Give the initial condition room to choose the x_init position (helps with positive knee constraint)\n // trajopt.AddBoundingBoxConstraint(-0, 0, xmid(positions_map.at(\"base_y\")));\n // trajopt.AddBoundingBoxConstraint(FLAGS_upperHeight-FLAGS_eps, FLAGS_upperHeight+FLAGS_eps, xmid(positions_map.at(\"base_z\")));\n\n // Final Position Constraints\n bool isPeriodic = 1;\n if (isPeriodic){\n trajopt.AddLinearConstraint( x0(positions_map.at(\"base_x\"))==xf(positions_map.at(\"base_x\")) );\n trajopt.AddLinearConstraint( x0(positions_map.at(\"base_y\"))==xf(positions_map.at(\"base_y\")) );\n trajopt.AddLinearConstraint( x0(positions_map.at(\"base_z\"))==xf(positions_map.at(\"base_z\")) );\n }else{\n trajopt.AddBoundingBoxConstraint(-FLAGS_eps, FLAGS_eps, xf(positions_map.at(\"base_x\"))); // Give the initial condition room to choose the x_init position (helps with positive knee constraint)\n trajopt.AddBoundingBoxConstraint(-FLAGS_eps, FLAGS_eps, xf(positions_map.at(\"base_y\")));\n trajopt.AddBoundingBoxConstraint(FLAGS_lowerHeight-FLAGS_eps, FLAGS_lowerHeight+FLAGS_eps, xf(positions_map.at(\"base_z\")));\n }\n\n \n \n /// Decide whether or not to constrain the body orientation at all knotpoints or only at the beginning and end\n bool isConstrainOrientation = false;\n if (isConstrainOrientation){\n // Body pose constraints (keep the body flat) at all the knotpoints including the ends\n for (int i = 0; i < num_knotpoints; i++) {\n auto xi = trajopt.state(i);\n trajopt.AddBoundingBoxConstraint(1, 1, xi(positions_map.at(\"base_qw\")));\n trajopt.AddBoundingBoxConstraint(0, 0, xi(positions_map.at(\"base_qx\")));\n trajopt.AddBoundingBoxConstraint(0, 0, xi(positions_map.at(\"base_qy\")));\n trajopt.AddBoundingBoxConstraint(0, 0, xi(positions_map.at(\"base_qz\")));\n }\n }else{\n // Body pose constraints (keep the body flat) at initial state\n trajopt.AddBoundingBoxConstraint(1, 1, x0(positions_map.at(\"base_qw\")));\n trajopt.AddBoundingBoxConstraint(0, 0, x0(positions_map.at(\"base_qx\")));\n trajopt.AddBoundingBoxConstraint(0, 0, x0(positions_map.at(\"base_qy\")));\n trajopt.AddBoundingBoxConstraint(0, 0, x0(positions_map.at(\"base_qz\")));\n\n // Body pose constraints (keep the body flat) at mid state\n trajopt.AddBoundingBoxConstraint(1, 1, xmid(positions_map.at(\"base_qw\")));\n trajopt.AddBoundingBoxConstraint(0, 0, xmid(positions_map.at(\"base_qx\")));\n trajopt.AddBoundingBoxConstraint(0, 0, xmid(positions_map.at(\"base_qy\")));\n trajopt.AddBoundingBoxConstraint(0, 0, xmid(positions_map.at(\"base_qz\")));\n\n // Body pose constraints (keep the body flat) at final state\n trajopt.AddBoundingBoxConstraint(1, 1, xf(positions_map.at(\"base_qw\")));\n trajopt.AddBoundingBoxConstraint(0, 0, xf(positions_map.at(\"base_qx\")));\n trajopt.AddBoundingBoxConstraint(0, 0, xf(positions_map.at(\"base_qy\")));\n trajopt.AddBoundingBoxConstraint(0, 0, xf(positions_map.at(\"base_qz\")));\n }\n\n\n /// Start/End velocity constraints of the behavior\n trajopt.AddBoundingBoxConstraint(VectorXd::Zero(n_v), VectorXd::Zero(n_v),\n x0.tail(n_v));\n trajopt.AddBoundingBoxConstraint(VectorXd::Zero(n_v), VectorXd::Zero(n_v),\n xmid.tail(n_v));\n trajopt.AddBoundingBoxConstraint(VectorXd::Zero(n_v), VectorXd::Zero(n_v),\n xf.tail(n_v));\n\n \n\n for (int i = 0; i < num_knotpoints; i++){\n auto xi = trajopt.state(i); \n // legs lined up (front and back hips equal)\n trajopt.AddLinearConstraint( xi( positions_map.at(\"joint_8\") ) == xi( positions_map.at(\"joint_9\") ) );\n trajopt.AddLinearConstraint( xi( positions_map.at(\"joint_10\") ) == xi( positions_map.at(\"joint_11\") ) );\n }\n setSpiritSymmetry(plant, trajopt, \"sagittal\");\n setSpiritJointLimits(plant, trajopt);\n setSpiritActuationLimits(plant,trajopt);\n\n double upperLegLength = 0.206; // length of the upper leg link\n\n \n// Quick check for allowed heights for Spirit\n try{\n if (FLAGS_lowerHeight=upperLegLength*2){\n throw \"Body can't stand to that height without leaving the ground\" ;\n }\n }catch(char const* exc){\n std::cerr<< exc < visualizer_poses; // Ghosts for visualizing during optimization\n visualizer_poses.push_back(num_ghosts); \n\n trajopt.CreateVisualizationCallback(\n dairlib::FindResourceOrThrow(\"examples/Spirit/spirit_drake.urdf\"),\n visualizer_poses, 0.2); // setup which URDF, how many poses, and alpha transparency \n\n /// Run the optimization using your initial guess\n auto start = std::chrono::high_resolution_clock::now();\n const auto result = Solve(trajopt, trajopt.initial_guess());\n auto finish = std::chrono::high_resolution_clock::now();\n std::chrono::duration elapsed = finish - start;\n std::cout << \"Solve time:\" << elapsed.count() < pp_xtraj =\n trajopt.ReconstructStateTrajectory(result);\n multibody::connectTrajectoryVisualizer(plant_double_ptr,\n &builder, &scene_graph, pp_xtraj);\n auto diagram = builder.Build();\n while (true) {\n \n drake::systems::Simulator simulator(*diagram);\n simulator.set_target_realtime_rate(0.25);\n simulator.Initialize();\n simulator.AdvanceTo(pp_xtraj.end_time());\n sleep(2);\n }\n}\n} // namespace\n} // namespace dairlib\n\n\nint main(int argc, char* argv[]) {\n gflags::ParseCommandLineFlags(&argc, &argv, true);\n std::srand(time(0)); // Initialize random number generator.\n \n auto plant = std::make_unique>(0.0);\n auto plant_vis = std::make_unique>(0.0);\n auto scene_graph = std::make_unique>();\n Parser parser(plant.get());\n Parser parser_vis(plant_vis.get(), scene_graph.get());\n std::string full_name =\n dairlib::FindResourceOrThrow(\"examples/Spirit/spirit_drake.urdf\");\n\n parser.AddModelFromFile(full_name);\n parser_vis.AddModelFromFile(full_name);\n \n plant->mutable_gravity_field().set_gravity_vector(-9.81 *\n Eigen::Vector3d::UnitZ());\n\n plant->Finalize();\n plant_vis->Finalize();\n Eigen::VectorXd x0 = Eigen::VectorXd::Zero(plant->num_positions() +\n plant->num_velocities());\n\n int nu = plant->num_actuators();\n int nx = plant->num_positions() + plant->num_velocities();\n int nq = plant->num_positions();\n int nv = plant->num_velocities();\n int N = 20; // number of timesteps\n\n std::vector init_x;\n std::vector init_u;\n std::vector> init_l_traj;\n std::vector> init_lc_traj;\n std::vector> init_vc_traj;\n\n // Initialize state trajectory\n std::vector init_time;\n\n VectorXd xInit(nx);\n VectorXd xMid(nx);\n VectorXd xState(nx);\n xInit = Eigen::VectorXd::Zero(plant->num_positions() + plant->num_velocities());\n xMid = Eigen::VectorXd::Zero(plant->num_positions() + plant->num_velocities());\n xState = Eigen::VectorXd::Zero(plant->num_positions() + plant->num_velocities());\n\n auto positions_map = dairlib::multibody::makeNameToPositionsMap(*plant);\n auto velocities_map = dairlib::multibody::makeNameToVelocitiesMap(*plant);\n auto actuators_map = dairlib::multibody::makeNameToActuatorsMap(*plant);\n int num_joints = 12;\n\n // Print joint dictionary\n std::cout<<\"**********************Joints***********************\"< (N-1)/2 ){\n xState.tail(nv) = -xInit.tail(nv);\n }\n // Integrate the positions based on constant velocity for joints and xyz\n for (int j = 0; j < num_joints; j++){\n xState(positions_map.at(\"joint_\" + std::to_string(j))) = \n xState(positions_map.at(\"joint_\" + std::to_string(j))) + xState(nq + velocities_map.at(\"joint_\" + std::to_string(j)+\"dot\" )) * dt;\n }\n xState(positions_map.at(\"base_x\")) = \n xState(positions_map.at(\"base_x\")) + xState(nq + velocities_map.at(\"base_vx\")) * dt;\n \n xState(positions_map.at(\"base_y\")) = \n xState(positions_map.at(\"base_y\")) + xState(nq + velocities_map.at(\"base_vy\")) * dt;\n \n xState(positions_map.at(\"base_z\")) = \n xState(positions_map.at(\"base_z\")) + xState(nq + velocities_map.at(\"base_vz\")) * dt;\n // Save timestep state into matrix\n init_x.push_back(xState);\n init_u.push_back(Eigen::VectorXd::Zero(nu));\n }\n // Make matrix into trajectory\n auto init_x_traj = PiecewisePolynomial::ZeroOrderHold(init_time, init_x);\n auto init_u_traj = PiecewisePolynomial::ZeroOrderHold(init_time, init_u);\n\n \n // Four contacts so forces are 12 dimensional\n Eigen::VectorXd init_l_vec(12);\n // Initial guess\n init_l_vec << 0, 0, 3*9.81, 0, 0, 3*9.81, 0, 0, 3*9.81, 0, 0, 3*9.81; //gravity and mass distributed\n \n //Initialize force trajectories\n int num_modes = 1; // MAKE DYNAMIC DEBUG\n for (int j = 0; j < num_modes; j++) { \n std::vector init_l_j;\n std::vector init_lc_j;\n std::vector init_vc_j;\n std::vector init_time_j;\n for (int i = 0; i < N; i++) {\n init_time_j.push_back(i*FLAGS_duration/(N-1));\n init_l_j.push_back(init_l_vec);\n init_lc_j.push_back(init_l_vec);\n init_vc_j.push_back(VectorXd::Zero(12));\n }\n\n auto init_l_traj_j = PiecewisePolynomial::ZeroOrderHold(init_time_j,init_l_j);\n auto init_lc_traj_j = PiecewisePolynomial::ZeroOrderHold(init_time_j,init_lc_j);\n auto init_vc_traj_j = PiecewisePolynomial::ZeroOrderHold(init_time_j,init_vc_j);\n\n init_l_traj.push_back(init_l_traj_j);\n init_lc_traj.push_back(init_lc_traj_j);\n init_vc_traj.push_back(init_vc_traj_j);\n }\n\n // if (FLAGS_autodiff) {\n // std::unique_ptr> plant_autodiff =\n // drake::systems::System::ToAutoDiffXd(*plant);\n // dairlib::runSpiritSquat(\n // std::move(plant_autodiff), plant_vis.get(), std::move(scene_graph),\n // FLAGS_duration, init_x_traj, init_u_traj, init_l_traj,\n // init_lc_traj, init_vc_traj);\n // } else \n if (FLAGS_runInitTraj){\n dairlib::runAnimate(\n std::move(plant), plant_vis.get(), std::move(scene_graph), init_x_traj);\n }else {\n dairlib::runSpiritSquat(\n std::move(plant), plant_vis.get(), std::move(scene_graph),\n FLAGS_duration, init_x_traj, init_u_traj, init_l_traj,\n init_lc_traj, init_vc_traj);\n }\n\n}\n\n", "meta": {"hexsha": "8be1efd879d3b5df5ef42053020b1c4a72762d69", "size": 22039, "ext": "cc", "lang": "C++", "max_stars_repo_path": "examples/Spirit/run_spirit_squat.cc", "max_stars_repo_name": "KodlabPenn/dairlib", "max_stars_repo_head_hexsha": "e544e16d2a97c3834e1eb5d534e436a45b8e4f4b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "examples/Spirit/run_spirit_squat.cc", "max_issues_repo_name": "KodlabPenn/dairlib", "max_issues_repo_head_hexsha": "e544e16d2a97c3834e1eb5d534e436a45b8e4f4b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2021-01-25T20:54:53.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-02T04:00:54.000Z", "max_forks_repo_path": "examples/Spirit/run_spirit_squat.cc", "max_forks_repo_name": "KodlabPenn/dairlib", "max_forks_repo_head_hexsha": "e544e16d2a97c3834e1eb5d534e436a45b8e4f4b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.8151093439, "max_line_length": 196, "alphanum_fraction": 0.7056127773, "num_tokens": 5955, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.23118063874073336}} {"text": "#include \"AST.h\"\n#include \"InterpreterContext.h\"\n#include \"StringPool.h\"\n#include \"VariablesScope.h\"\n#include \n#include \n#include \n#include \n\nnamespace\n{\n\ntemplate \nCValue ExecuteSafely(TFunction && fn)\n{\n try\n {\n return fn();\n }\n catch (std::exception const&)\n {\n return CValue::FromError(std::current_exception());\n }\n}\n\nclass CSinFunction : public IFunctionAst\n{\npublic:\n CValue Call(CInterpreterContext &context, const std::vector &arguments) const override\n {\n (void)context;\n return ExecuteSafely([&] {\n double radians = arguments.at(0).AsDouble();\n return CValue::FromDouble(std::sin(radians));\n });\n }\n\n unsigned GetNameId() const override\n {\n return 0;\n }\n};\n\nclass CRandFunction : public IFunctionAst\n{\npublic:\n CValue Call(CInterpreterContext &context, const std::vector &arguments) const override\n {\n (void)context;\n return ExecuteSafely([&] {\n double minimum = arguments.at(0).AsDouble();\n double maximum = arguments.at(1).AsDouble();\n if (minimum > maximum)\n {\n return CValue::FromErrorMessage(\"invalid arguments for rand - range maximum is lesser than minimum.\");\n }\n double rand0to1 = double(std::rand()) / std::numeric_limits::max();\n\n return CValue::FromDouble((maximum - minimum) * rand0to1 + minimum);\n });\n }\n\n unsigned GetNameId() const override\n {\n return 0;\n }\n};\n\n}\n\nCInterpreterContext::CInterpreterContext(std::ostream &output, std::ostream &errors, CStringPool &pool)\n : m_pool(pool)\n , m_output(output)\n , m_errors(errors)\n{\n AddBuiltin(\"sin\", std::unique_ptr(new CSinFunction));\n AddBuiltin(\"rand\", std::unique_ptr(new CRandFunction));\n}\n\nCInterpreterContext::~CInterpreterContext()\n{\n}\n\nvoid CInterpreterContext::DefineVariable(unsigned nameId, const CValue &value)\n{\n if (ValidateValue(value))\n {\n m_scopes.back()->AssignVariable(nameId, value);\n }\n}\n\nvoid CInterpreterContext::AssignVariable(unsigned nameId, const CValue &value)\n{\n if (ValidateValue(value))\n {\n if (CVariablesScope *pScope = FindScopeWithVariable(nameId))\n {\n pScope->AssignVariable(nameId, value);\n }\n else\n {\n DefineVariable(nameId, value);\n }\n }\n}\n\nCValue CInterpreterContext::GetVariableValue(unsigned nameId) const\n{\n if (CVariablesScope *pScope = FindScopeWithVariable(nameId))\n {\n return *pScope->GetVariableValue(nameId);\n }\n return CValue::FromErrorMessage(\"unknown variable \" + m_pool.GetString(nameId));\n}\n\nvoid CInterpreterContext::PushScope(std::unique_ptr &&scope)\n{\n m_scopes.emplace_back(std::move(scope));\n}\n\nstd::unique_ptr CInterpreterContext::PopScope()\n{\n std::unique_ptr ret(m_scopes.back().release());\n m_scopes.pop_back();\n return ret;\n}\n\nsize_t CInterpreterContext::GetScopesCount() const\n{\n return m_scopes.size();\n}\n\nIFunctionAst *CInterpreterContext::GetFunction(unsigned nameId) const\n{\n try\n {\n return m_functions.at(nameId);\n }\n catch (std::exception const&)\n {\n return nullptr;\n }\n}\n\nvoid CInterpreterContext::AddFunction(unsigned nameId, IFunctionAst *function)\n{\n if (function)\n {\n m_functions[nameId] = function;\n }\n}\n\nstd::string CInterpreterContext::GetStringLiteral(unsigned stringId) const\n{\n return m_pool.GetString(stringId);\n}\n\nvoid CInterpreterContext::PrintResults(const std::vector &values)\n{\n m_output << \" \";\n for (const auto &value : values)\n {\n if (ValidateValue(value))\n {\n m_output << value.ToString();\n }\n }\n m_output << std::endl;\n}\n\nvoid CInterpreterContext::PrintError(const std::string &message)\n{\n m_errors << \" Error: \" << message << std::endl;\n}\n\nbool CInterpreterContext::ValidateValue(const CValue &value)\n{\n try\n {\n value.RethrowIfException();\n return true;\n }\n catch (std::exception const& ex)\n {\n PrintError(ex.what());\n return false;\n }\n}\n\nvoid CInterpreterContext::SetReturnValue(boost::optional const& valueOpt)\n{\n m_returnValueOpt = valueOpt;\n}\n\nboost::optional CInterpreterContext::GetReturnValue() const\n{\n return m_returnValueOpt;\n}\n\nCVariablesScope *CInterpreterContext::FindScopeWithVariable(unsigned nameId) const\n{\n auto range = boost::adaptors::reverse(m_scopes);\n auto it = boost::find_if(range, [=](const auto &pScope) {\n return pScope->HasVariable(nameId);\n });\n if (it != range.end())\n {\n return it->get();\n }\n return nullptr;\n}\n\nvoid CInterpreterContext::AddBuiltin(const std::string &name, std::unique_ptr &&function)\n{\n m_builtins.emplace_back(std::move(function));\n unsigned nameRand = m_pool.Insert(name);\n m_functions[nameRand] = m_builtins.back().get();\n}\n", "meta": {"hexsha": "a33ee9a4b2b0f93f87cd426e4bddbfc66fca254e", "size": 5157, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lemon-based-parser/lemon-9-final/InterpreterContext.cpp", "max_stars_repo_name": "PS-Group/compiler-theory-samples", "max_stars_repo_head_hexsha": "c916af50eb42020024257ecd17f9be1580db7bf0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lemon-based-parser/lemon-9-final/InterpreterContext.cpp", "max_issues_repo_name": "PS-Group/compiler-theory-samples", "max_issues_repo_head_hexsha": "c916af50eb42020024257ecd17f9be1580db7bf0", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lemon-based-parser/lemon-9-final/InterpreterContext.cpp", "max_forks_repo_name": "PS-Group/compiler-theory-samples", "max_forks_repo_head_hexsha": "c916af50eb42020024257ecd17f9be1580db7bf0", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.5479452055, "max_line_length": 118, "alphanum_fraction": 0.6525111499, "num_tokens": 1235, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5234203489363239, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.2311806387407333}} {"text": "/*\n * Implementation of EDM methods, including S-map and cross-mapping\n *\n * - Patrick Laub, Department of Management and Marketing,\n * The University of Melbourne, patrick.laub@unimelb.edu.au\n * - Edoardo Tescari, Melbourne Data Analytics Platform,\n * The University of Melbourne, e.tescari@unimelb.edu.au\n *\n */\n\n#pragma warning(disable : 4018)\n\n#include \"edm.h\"\n#include \"cpu.h\"\n#include \"distances.h\"\n#include \"stats.h\" // for correlation and mean_absolute_error\n#include \"thread_pool.h\"\n#include \"train_predict_split.h\"\n\n#ifndef FMT_HEADER_ONLY\n#define FMT_HEADER_ONLY\n#endif\n\n#define EIGEN_NO_DEBUG\n#define EIGEN_DONT_PARALLELIZE\n#include \n#include // std::partial_sort\n#include \n#include // just to create low-level input dumps\n\nstd::atomic numTasksStarted = 0;\nstd::atomic numTasksFinished = 0;\nThreadPool workerPool(0), taskRunnerPool(0);\n\nstd::vector> launch_task_group(const ManifoldGenerator& generator, Options opts,\n const std::vector& Es, const std::vector& libraries,\n int k, int numReps, int crossfold, bool explore, bool full,\n bool saveFinalPredictions, bool saveFinalCoPredictions,\n bool saveSMAPCoeffs, bool copredictMode,\n const std::vector& usable, const std::string& rngState,\n IO* io, bool keep_going(), void all_tasks_finished())\n{\n\n workerPool.set_num_workers(opts.nthreads);\n taskRunnerPool.set_num_workers(num_physical_cores());\n\n // Construct the instance which will (repeatedly) split the data\n // into either the training manifold or the prediction manifold.\n TrainPredictSplitter splitter = TrainPredictSplitter(explore, full, crossfold, usable, rngState);\n\n int numLibraries = (explore ? 1 : libraries.size());\n\n opts.numTasks = numReps * Es.size() * numLibraries;\n opts.configNum = 0;\n opts.taskNum = 0;\n\n int maxE = Es[Es.size() - 1];\n\n std::vector cousable;\n\n if (copredictMode) {\n opts.numTasks *= 2;\n cousable = generator.generate_usable(maxE, true);\n }\n\n int E, kAdj, library, trainSize;\n\n std::vector> futures;\n\n bool newTrainPredictSplit = true;\n\n // Note: the 'numReps' either refers to the 'replicate' option\n // used for bootstrap resampling, or the 'crossfold' number of\n // cross-validation folds. Both options can't be used together,\n // so numReps = max(replicate, crossfold).\n for (int iter = 1; iter <= numReps; iter++) {\n if (explore) {\n newTrainPredictSplit = true;\n trainSize = splitter.next_training_size(iter);\n }\n\n for (int i = 0; i < Es.size(); i++) {\n E = Es[i];\n\n // 'libraries' is implicitly set to one value in explore mode\n // though in xmap mode it is a user-supplied list which we loop over.\n for (int l = 0; l == 0 || l < libraries.size(); l++) {\n if (!explore) {\n newTrainPredictSplit = true;\n }\n\n if (explore) {\n library = trainSize;\n } else {\n library = libraries[l];\n }\n\n // Set the number of neighbours to use\n if (k > 0) {\n kAdj = k;\n } else if (k < 0) {\n kAdj = -1; // Leave a sentinel value so we know to skip the nearest neighbours calculation\n } else if (k == 0) {\n bool isSMap = opts.algorithm == Algorithm::SMap;\n int defaultK = generator.E_actual(E) + 1 + isSMap;\n kAdj = defaultK < library ? defaultK : library;\n }\n\n bool lastConfig = (E == maxE) && (l + 1 == numLibraries);\n\n if (explore) {\n opts.savePrediction = saveFinalPredictions && ((iter == numReps) || (crossfold > 0)) && lastConfig;\n } else {\n opts.savePrediction = saveFinalPredictions && (iter == numReps) && lastConfig;\n }\n opts.saveSMAPCoeffs = saveSMAPCoeffs;\n\n if (newTrainPredictSplit) {\n splitter.update_train_predict_split(library, iter);\n newTrainPredictSplit = false;\n }\n\n opts.copredict = false;\n opts.k = kAdj;\n\n futures.emplace_back(launch_edm_task(generator, opts, E, splitter.trainingRows(), splitter.predictionRows(), io,\n keep_going, all_tasks_finished));\n\n opts.taskNum += 1;\n\n if (copredictMode) {\n opts.copredict = true;\n if (explore) {\n opts.savePrediction = saveFinalCoPredictions && ((iter == numReps) || (crossfold > 0)) && lastConfig;\n } else {\n opts.savePrediction = saveFinalCoPredictions && ((iter == numReps)) && lastConfig;\n }\n opts.saveSMAPCoeffs = false;\n futures.emplace_back(\n launch_edm_task(generator, opts, E, splitter.trainingRows(), cousable, io, keep_going, all_tasks_finished));\n\n opts.taskNum += 1;\n }\n\n opts.configNum += opts.thetas.size();\n }\n }\n }\n\n return futures;\n}\n\nstd::future launch_edm_task(const ManifoldGenerator& generator, Options opts, int E,\n const std::vector& trainingRows, const std::vector& predictionRows,\n IO* io, bool keep_going(), void all_tasks_finished())\n{\n // Expand the 'metrics' vector now that we know the value of E.\n std::vector metrics;\n\n // For the Wasserstein distance, it's more convenient to have one 'metric' for each variable (before taking lags).\n // However, for the L^1 / L^2 distances, it's more convenient to have one 'metric' for each individual\n // point of each observations, so metrics.size() == M.E_actual().\n if (opts.distance == Distance::Wasserstein) {\n // Add a metric for the main variable and for the dt variable.\n // These are always treated as a continuous values (though perhaps in the future this will change).\n metrics.push_back(Metric::Diff);\n if (generator.E_dt(E) > 0) {\n metrics.push_back(Metric::Diff);\n }\n\n // Add in the metrics for the 'extra' variables as they were supplied to us.\n for (int k = 0; k < generator.numExtras(); k++) {\n metrics.push_back(opts.metrics[k]);\n }\n } else {\n // Add metrics for the main variable and the dt variable and their lags.\n // These are always treated as a continuous values (though perhaps in the future this will change).\n for (int lagNum = 0; lagNum < E + generator.E_dt(E); lagNum++) {\n metrics.push_back(Metric::Diff);\n }\n\n // The user specified how to treat the extra variables.\n for (int k = 0; k < generator.numExtras(); k++) {\n int numLags = (k < generator.numExtrasLagged()) ? E : 1;\n for (int lagNum = 0; lagNum < numLags; lagNum++) {\n metrics.push_back(opts.metrics[k]);\n }\n }\n }\n\n opts.metrics = metrics;\n\n if (opts.taskNum == 0) {\n numTasksStarted = 0;\n numTasksFinished = 0;\n }\n\n numTasksStarted += 1;\n\n // This hack is simply to dump some really low level data structures\n // purely for the purpose of generating microbenchmarks.\n if (io != nullptr && io->verbosity > 4) {\n json lowLevelInputDump;\n lowLevelInputDump[\"generator\"] = generator;\n lowLevelInputDump[\"opts\"] = opts;\n lowLevelInputDump[\"E\"] = E;\n lowLevelInputDump[\"trainingRows\"] = trainingRows;\n lowLevelInputDump[\"predictionRows\"] = predictionRows;\n\n std::ofstream o(\"lowLevelInputDump.json\");\n o << lowLevelInputDump << std::endl;\n }\n\n // Note, we can't have missing data inside the training manifold when using the S-Map algorithm\n bool skipMissing = (opts.algorithm == Algorithm::SMap);\n\n Manifold M = generator.create_manifold(E, trainingRows, opts.copredict, false, opts.dtWeight, skipMissing);\n Manifold Mp = generator.create_manifold(E, predictionRows, opts.copredict, true, opts.dtWeight);\n\n return taskRunnerPool.enqueue([opts, M, Mp, predictionRows, io, keep_going, all_tasks_finished] {\n return edm_task(opts, M, Mp, predictionRows, io, keep_going, all_tasks_finished);\n });\n}\n\nPrediction edm_task(const Options opts, const Manifold M, const Manifold Mp, const std::vector predictionRows,\n IO* io, bool keep_going(), void all_tasks_finished())\n{\n bool multiThreaded = opts.nthreads > 1;\n int numThetas = (int)opts.thetas.size();\n int numPredictions = Mp.nobs();\n int numCoeffCols = M.E_actual() + 1;\n\n auto ystar = std::make_unique(numThetas * numPredictions);\n std::fill_n(ystar.get(), numThetas * numPredictions, MISSING_SENTINEL);\n Eigen::Map ystarView(ystar.get(), numThetas, numPredictions);\n\n // If we're saving the coefficients (i.e. in xmap mode), then we're not running with multiple 'theta' values.\n auto coeffs = std::make_unique(numPredictions * numCoeffCols);\n std::fill_n(coeffs.get(), numPredictions * numCoeffCols, MISSING_SENTINEL);\n Eigen::Map coeffsView(coeffs.get(), numPredictions, numCoeffCols);\n\n auto rc = std::make_unique(numThetas * numPredictions);\n std::fill_n(rc.get(), numThetas * numPredictions, UNKNOWN_ERROR);\n Eigen::Map rcView(rc.get(), numThetas, numPredictions);\n\n std::vector kUsed;\n for (int i = 0; i < numPredictions; i++) {\n kUsed.push_back(-1);\n }\n\n if (opts.numTasks > 1 && opts.taskNum == 0) {\n io->progress_bar(0.0);\n }\n\n if (multiThreaded) {\n std::vector> results(numPredictions);\n for (int i = 0; i < numPredictions; i++) {\n results[i] = workerPool.enqueue(\n [&, i] { make_prediction(i, opts, M, Mp, ystarView, rcView, coeffsView, &(kUsed[i]), keep_going); });\n }\n\n if (opts.numTasks == 1) {\n io->progress_bar(0.0);\n }\n for (int i = 0; i < numPredictions; i++) {\n results[i].get();\n if (opts.numTasks == 1) {\n io->progress_bar((i + 1) / ((double)numPredictions));\n }\n }\n } else {\n if (opts.numTasks == 1) {\n io->progress_bar(0.0);\n }\n for (int i = 0; i < numPredictions; i++) {\n if (keep_going != nullptr && keep_going() == false) {\n break;\n }\n make_prediction(i, opts, M, Mp, ystarView, rcView, coeffsView, &(kUsed[i]), keep_going);\n if (opts.numTasks == 1) {\n io->progress_bar((i + 1) / ((double)numPredictions));\n }\n }\n }\n\n Prediction pred;\n\n // Store the results, so long as we weren't interrupted by a 'break'.\n if (keep_going == nullptr || keep_going() == true) {\n // Start by calculating the MAE & rho of prediction, if requested\n for (int t = 0; t < numThetas * opts.calcRhoMAE; t++) {\n PredictionStats stats;\n\n std::vector y1, y2;\n\n for (int i = 0; i < Mp.ySize(); i++) {\n if (Mp.y(i) != MISSING_SENTINEL && ystarView(t, i) != MISSING_SENTINEL) {\n y1.push_back(Mp.y(i));\n y2.push_back(ystarView(t, i));\n }\n }\n\n if (!(y1.empty() || y2.empty())) {\n stats.mae = mean_absolute_error(y1, y2);\n stats.rho = correlation(y1, y2);\n } else {\n stats.mae = MISSING_SENTINEL;\n stats.rho = MISSING_SENTINEL;\n }\n\n pred.stats.push_back(stats);\n }\n\n pred.configNum = opts.configNum;\n\n // Check if any make_prediction call failed, and if so find the most serious error\n pred.rc = *std::max_element(rc.get(), rc.get() + numThetas * numPredictions);\n\n // If we're storing the prediction and/or the SMAP coefficients, put them\n // into the resulting Prediction struct. Otherwise, let them be deleted.\n if (opts.savePrediction) {\n // Take only the predictions for the largest theta value.\n if (numThetas == 1) {\n pred.ystar = std::move(ystar);\n } else {\n pred.ystar = std::make_unique(numPredictions);\n for (int i = 0; i < numPredictions; i++) {\n pred.ystar[i] = ystarView(numThetas - 1, i);\n }\n }\n } else {\n pred.ystar = nullptr;\n }\n\n if (opts.saveSMAPCoeffs) {\n pred.coeffs = std::move(coeffs);\n } else {\n pred.coeffs = nullptr;\n }\n\n if (opts.savePrediction || opts.saveSMAPCoeffs) {\n pred.predictionRows = std::move(predictionRows);\n }\n\n if (opts.saveKUsed) {\n pred.kUsed = kUsed;\n }\n\n pred.cmdLine = opts.cmdLine;\n pred.copredict = opts.copredict;\n\n pred.numThetas = numThetas;\n pred.numPredictions = numPredictions;\n pred.numCoeffCols = numCoeffCols;\n\n if (opts.numTasks > 1) {\n io->progress_bar((numTasksFinished + 1) / ((double)opts.numTasks));\n }\n }\n\n numTasksFinished += 1;\n\n if (numTasksFinished == opts.numTasks) {\n if (all_tasks_finished != nullptr) {\n all_tasks_finished();\n }\n }\n\n return pred;\n}\n\n// Use a training manifold 'M' to make a prediction about the prediction manifold 'Mp'.\n// Specifically, predict the 'Mp_i'-th value of the prediction manifold 'Mp'.\n//\n// The predicted value is stored in 'ystar', along with any return codes in 'rc'.\n// Optionally, the user may ask to store some S-map intermediate values in 'coeffs'.\n//\n// The 'opts' value specifies the kind of prediction to make (e.g. S-map, or simplex method).\n// This function is usually run in a worker thread, and the 'keep_going' callback is frequently called to\n// see whether the user still wants this result, or if they have given up & simply want the execution\n// to terminate.\n//\n// We sometimes let 'M' and 'Mp' be the same manifold, so we train and predict using the same values.\n// In this case, the algorithm may cheat by pulling out the identical trajectory from the training manifold\n// and using this as the prediction. As such, we throw away any neighbours which have a distance of 0 from\n// the target point.\nvoid make_prediction(int Mp_i, const Options& opts, const Manifold& M, const Manifold& Mp, Eigen::Map ystar,\n Eigen::Map rc, Eigen::Map coeffs, int* kUsed, bool keep_going())\n{\n // An impatient user may want to cancel a long-running EDM command, so we occasionally check using this\n // callback to see whether we ought to keep going with this EDM command. Of course, this adds a tiny inefficiency,\n // but there doesn't seem to be a simple way to easily kill running worker threads across all OSs.\n if (keep_going != nullptr && keep_going() == false) {\n rc(0, Mp_i) = BREAK_HIT;\n return;\n }\n\n // Create a list of indices which may potentially be the neighbours of Mp(Mp_i,.)\n std::vector tryInds = potential_neighbour_indices(Mp_i, opts, M, Mp);\n\n DistanceIndexPairs potentialNN;\n if (opts.distance == Distance::Wasserstein) {\n potentialNN = wasserstein_distances(Mp_i, opts, M, Mp, tryInds);\n } else {\n potentialNN = lp_distances(Mp_i, opts, M, Mp, tryInds);\n }\n\n if (keep_going != nullptr && keep_going() == false) {\n rc(0, Mp_i) = BREAK_HIT;\n return;\n }\n\n // Do we have enough distances to find k neighbours?\n int numValidDistances = potentialNN.inds.size();\n int k = opts.k;\n *kUsed = numValidDistances;\n if (k > numValidDistances) {\n if (opts.forceCompute) {\n k = numValidDistances;\n } else {\n rc(0, Mp_i) = INSUFFICIENT_UNIQUE;\n return;\n }\n }\n\n if (k == 0) {\n // Whether we throw an error or just silently ignore this prediction\n // depends on whether we are in 'strict' mode or not.\n rc(0, Mp_i) = opts.forceCompute ? SUCCESS : INSUFFICIENT_UNIQUE;\n return;\n }\n\n // If we asked for all of the neighbours to be considered (e.g. with k = -1), return this index vector directly.\n DistanceIndexPairs kNNs;\n if (k < 0 || k == potentialNN.inds.size()) {\n kNNs = potentialNN;\n } else {\n kNNs = kNearestNeighbours(potentialNN, k);\n }\n\n if (keep_going != nullptr && keep_going() == false) {\n rc(0, Mp_i) = BREAK_HIT;\n return;\n }\n\n if (opts.algorithm == Algorithm::Simplex) {\n for (int t = 0; t < opts.thetas.size(); t++) {\n simplex_prediction(Mp_i, t, opts, M, kNNs.dists, kNNs.inds, ystar, rc, kUsed);\n }\n } else if (opts.algorithm == Algorithm::SMap) {\n for (int t = 0; t < opts.thetas.size(); t++) {\n smap_prediction(Mp_i, t, opts, M, Mp, kNNs.dists, kNNs.inds, ystar, coeffs, rc, kUsed);\n }\n } else {\n rc(0, Mp_i) = INVALID_ALGORITHM;\n }\n}\n\nstd::vector potential_neighbour_indices(int Mp_i, const Options& opts, const Manifold& M, const Manifold& Mp)\n{\n bool skipOtherPanels = opts.panelMode && (opts.idw < 0);\n\n std::vector inds;\n\n for (int i = 0; i < M.nobs(); i++) {\n if (skipOtherPanels && (M.panel(i) != Mp.panel(Mp_i))) {\n continue;\n }\n\n inds.push_back(i);\n }\n\n return inds;\n}\n\n// For a given point, find the k nearest neighbours of this point.\n//\n// If there are many potential neighbours with the exact same distances, we\n// prefer the neighbours with the smallest index value. This corresponds\n// to a stable sort in C++ STL terminology.\n//\n// In typical use-cases of 'edm explore' the value of 'k' is small, like 5-20.\n// However for a typical 'edm xmap' the value of 'k' is set as large as possible.\n// If 'k' is small, the partial_sort is efficient as it only finds the 'k' smallest\n// distances. If 'k' is larger, then it is faster to simply sort the entire distance\n// vector.\nDistanceIndexPairs kNearestNeighbours(const DistanceIndexPairs& potentialNeighbours, int k)\n{\n std::vector idx(potentialNeighbours.inds.size());\n std::iota(idx.begin(), idx.end(), 0);\n\n if (k >= (int)(idx.size() / 2)) {\n auto comparator = [&potentialNeighbours](int i1, int i2) {\n return potentialNeighbours.dists[i1] < potentialNeighbours.dists[i2];\n };\n std::stable_sort(idx.begin(), idx.end(), comparator);\n } else {\n auto stableComparator = [&potentialNeighbours](int i1, int i2) {\n if (potentialNeighbours.dists[i1] != potentialNeighbours.dists[i2])\n return potentialNeighbours.dists[i1] < potentialNeighbours.dists[i2];\n else\n return i1 < i2;\n };\n std::partial_sort(idx.begin(), idx.begin() + k, idx.end(), stableComparator);\n }\n\n std::vector kNNInds(k);\n std::vector kNNDists(k);\n\n for (int i = 0; i < k; i++) {\n kNNInds[i] = potentialNeighbours.inds[idx[i]];\n kNNDists[i] = potentialNeighbours.dists[idx[i]];\n }\n\n return { kNNInds, kNNDists };\n}\n\n// An alternative version of 'kNearestNeighbours' which doesn't sort the neighbours.\n// This version splits ties differently on different OS's, so it can't be used directly,\n// though perhaps a platform-independent implementation of std::nth_element would solve this problem.\nDistanceIndexPairs kNearestNeighboursUnstable(const DistanceIndexPairs& potentialNeighbours, int k)\n{\n std::vector indsToPartition(potentialNeighbours.inds.size());\n std::iota(indsToPartition.begin(), indsToPartition.end(), 0);\n\n auto comparator = [&potentialNeighbours](int i1, int i2) {\n return potentialNeighbours.dists[i1] < potentialNeighbours.dists[i2];\n };\n std::nth_element(indsToPartition.begin(), indsToPartition.begin() + k, indsToPartition.end(), comparator);\n\n std::vector kNNInds(k);\n std::vector kNNDists(k);\n\n for (int i = 0; i < k; i++) {\n kNNInds[i] = potentialNeighbours.inds[indsToPartition[i]];\n kNNDists[i] = potentialNeighbours.dists[indsToPartition[i]];\n }\n\n return { kNNInds, kNNDists };\n}\n\nvoid simplex_prediction(int Mp_i, int t, const Options& opts, const Manifold& M, const std::vector& dists,\n const std::vector& kNNInds, Eigen::Map ystar, Eigen::Map rc,\n int* kUsed)\n{\n int k = kNNInds.size();\n\n // Find the smallest distance (closest neighbour) among the supplied neighbours.\n double minDist = *std::min_element(dists.begin(), dists.end());\n\n // Calculate our weighting of each neighbour, and the total sum of these weights.\n std::vector w(k);\n double sumw = 0.0;\n const double theta = opts.thetas[t];\n\n int numNonZeroWeights = 0;\n for (int j = 0; j < k; j++) {\n w[j] = exp(-theta * (dists[j] / minDist));\n sumw = sumw + w[j];\n numNonZeroWeights += (w[j] > 0);\n }\n\n // For the sake of debugging, count how many neighbours we end up with.\n if (opts.saveKUsed) {\n *kUsed = numNonZeroWeights;\n }\n\n // Make the simplex projection/prediction.\n double r = 0.0;\n for (int j = 0; j < k; j++) {\n r = r + M.y(kNNInds[j]) * (w[j] / sumw);\n }\n\n // Store the results & return value.\n ystar(t, Mp_i) = r;\n rc(t, Mp_i) = SUCCESS;\n}\n\nvoid smap_prediction(int Mp_i, int t, const Options& opts, const Manifold& M, const Manifold& Mp,\n const std::vector& dists, const std::vector& kNNInds, Eigen::Map ystar,\n Eigen::Map coeffs, Eigen::Map rc, int* kUsed)\n{\n int k = kNNInds.size();\n\n // Pull out the nearest neighbours from the manifold, and\n // simultaneously prepend a column of ones in front of the manifold data.\n MatrixXd X_ls_cj(k, M.E_actual() + 1);\n X_ls_cj << Eigen::VectorXd::Ones(k), M.map()(kNNInds, Eigen::all);\n\n // Calculate the weight for each neighbour\n Eigen::Map distsMap(&(dists[0]), dists.size());\n Eigen::VectorXd w = Eigen::exp(-opts.thetas[t] * (distsMap.array() / distsMap.mean()));\n\n // For the sake of debugging, count how many neighbours we end up with.\n if (opts.saveKUsed) {\n int numNonZeroWeights = 0;\n for (double& w_i : w) {\n if (w_i > 0) {\n numNonZeroWeights += 1;\n }\n }\n *kUsed = numNonZeroWeights;\n }\n\n // Scale everything by our weights vector\n X_ls_cj.array().colwise() *= w.array();\n Eigen::VectorXd y_ls = M.yMap()(kNNInds).array() * w.array();\n\n // The old way to solve this system:\n // Eigen::BDCSVD svd(X_ls_cj, Eigen::ComputeThinU | Eigen::ComputeThinV);\n // Eigen::VectorXd ics = svd.solve(y_ls);\n\n // The pseudo-inverse of X can be calculated as (X^T * X)^(-1) * X^T\n // see https://scicomp.stackexchange.com/a/33375\n const int svdOpts = Eigen::ComputeThinU | Eigen::ComputeThinV; // 'ComputeFull*' would probably work identically here.\n Eigen::BDCSVD svd(X_ls_cj.transpose() * X_ls_cj, svdOpts);\n Eigen::VectorXd ics = svd.solve(X_ls_cj.transpose() * y_ls);\n\n double r = ics(0);\n for (int j = 0; j < M.E_actual(); j++) {\n if (Mp(Mp_i, j) != MISSING_SENTINEL) {\n r += Mp(Mp_i, j) * ics(j + 1);\n }\n }\n\n // If the 'savesmap' option is given, save the 'ics' coefficients\n // for the largest value of theta.\n if (opts.saveSMAPCoeffs && t == opts.thetas.size() - 1) {\n for (int j = 0; j < M.E_actual() + 1; j++) {\n if (ics(j) == 0.) {\n coeffs(Mp_i, j) = MISSING_SENTINEL;\n } else {\n coeffs(Mp_i, j) = ics(j);\n }\n }\n }\n\n ystar(t, Mp_i) = r;\n rc(t, Mp_i) = SUCCESS;\n}\n", "meta": {"hexsha": "a1e6e9c16c4ec66f94af02a467de2da5c65a3b9b", "size": 22882, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/edm.cpp", "max_stars_repo_name": "9prady9/EDM", "max_stars_repo_head_hexsha": "db89a7fecf5a0f30b9db2b913a4d82f3c4ced33c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/edm.cpp", "max_issues_repo_name": "9prady9/EDM", "max_issues_repo_head_hexsha": "db89a7fecf5a0f30b9db2b913a4d82f3c4ced33c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/edm.cpp", "max_forks_repo_name": "9prady9/EDM", "max_forks_repo_head_hexsha": "db89a7fecf5a0f30b9db2b913a4d82f3c4ced33c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.6973478939, "max_line_length": 120, "alphanum_fraction": 0.6389738659, "num_tokens": 6261, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5234203489363239, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.23118063874073327}} {"text": "//\r\n// Copyright (c) 2016 - 2017 Mesh Consultants Inc.\r\n// Permission is hereby granted, free of charge, to any person obtaining a copy\r\n// of this software and associated documentation files (the \"Software\"), to deal\r\n// in the Software without restriction, including without limitation the rights\r\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\r\n// copies of the Software, and to permit persons to whom the Software is\r\n// furnished to do so, subject to the following conditions:\r\n//\r\n// The above copyright notice and this permission notice shall be included in\r\n// all copies or substantial portions of the Software.\r\n//\r\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\r\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\r\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\r\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\r\n// THE SOFTWARE.\r\n//\r\n\r\n\r\n#include \"Geomlib_TriMeshThicken.h\"\n\n#include \n\n#include \n\n#pragma warning(push, 0)\n#include \n#include \n#include \n#pragma warning(pop)\n\n#include \n\n#include \"ConversionUtilities.h\"\n#include \"TriMesh.h\"\n\n#pragma warning(disable : 4244)\n\nusing Urho3D::Variant;\nusing Urho3D::VariantMap;\nusing Urho3D::VariantVector;\nusing Urho3D::Vector3;\n\nbool Geomlib::TriMeshThicken(\n\tconst Urho3D::Variant& meshIn,\n\tfloat d,\n\tUrho3D::Variant& meshOut\n)\n{\n\t// meshIn: verify and parse\n\tif (!TriMesh_Verify(meshIn)) {\n\t\tstd::cerr << \"ERROR: Geomlib::Thicken --- meshIn unverified\\n\";\n\t\tmeshOut = Variant();\n\t\treturn false;\n\t}\n\tVariantMap meshMap = meshIn.GetVariantMap();\n\tconst VariantVector vertexList = meshMap[\"vertices\"].GetVariantVector();\n\tconst VariantVector faceList = meshMap[\"faces\"].GetVariantVector();\n\n\t// Compute vertices for outer part of solid, using unit normals\n\tVariantVector vertNormals = TriMesh_ComputeVertexNormals(meshIn, true);\n\tassert(vertNormals.Size() == vertexList.Size());\n\tVariantVector outerVertexList;\n\tfor (unsigned i = 0; i < vertexList.Size(); ++i) {\n\t\tVector3 vert = vertexList[i].GetVector3();\n\t\tVector3 unorm = vertNormals[i].GetVector3();\n\n\t\tVector3 newVert = vert + d * unorm;\n\t\touterVertexList.Push(Variant(newVert));\n\t}\n\t// Compute faces for outer part of solid\n\t// (using convention that outer vertices are listed in the 2nd half of final vertex list\n\tVariantVector outerFaceList;\n\tunsigned numVertices = vertexList.Size();\n\tfor (unsigned i = 0; i < faceList.Size(); ++i) {\n\t\tunsigned v = faceList[i].GetUInt();\n\t\touterFaceList.Push(numVertices + faceList[i].GetUInt());\n\t}\n\n\t// Compute faces to form panels connecting exposed edges\n\t// on inner and outer surfaces.\n\tVariantVector sideFaceList;\n\n\t// Determine which edges are boundary edges, using libigl\n\tEigen::MatrixXf V;\n\tEigen::MatrixXi F;\n\tbool matrixSuccess = IglMeshToMatrices(meshIn, V, F);\n\tassert(matrixSuccess);\n\tEigen::MatrixXi EE; // EE is used to precompute # of edges, then is discarded\n\tigl::edges(F, EE);\n\tEigen::MatrixXi E, EI;\n\tEigen::MatrixXi EF(EE.rows(), 2);\n\tEF = Eigen::MatrixXi::Constant(EE.rows(), 2, -1); // we use the -1 default values to help identify boundary edges\n\tEigen::VectorXi EMAP;\n\tigl::edge_flaps(F, E, EMAP, EF, EI);\n\tEigen::VectorXi B;\n\tigl::is_boundary_edge(E, F, B);\n\tassert(E.rows() == B.rows());\n\n\t// Loop over boundary edges, constructing \"thickened\" outer edges\n\tunsigned numFaces = F.rows();\n\tfor (unsigned e = 0; e < B.rows(); ++e) {\n\t\tif (B(e) == 1) {\n\t\t\t// Boundary found! Edge e is a boundary edge.\n\t\t\t// E\n\t\t\tunsigned f0 = EF(e, 0);\n\t\t\tunsigned f1 = EF(e, 1);\n\n\t\t\tassert(\n\t\t\t\t(f0 == -1 && (f1 >= 0 && f1 < numFaces)) ||\n\t\t\t\t((f0 >= 0 && f0 < numFaces) && f1 == -1)\n\t\t\t);\n\n\t\t\tunsigned v0 = -1;\n\t\t\tunsigned v1 = -1;\n\n\t\t\tif (f0 == -1) {\n\t\t\t\t// f1 is the face with boundary edge e opposite vertex EI(e, 1)\n\t\t\t\tunsigned v = EI(e, 1);\n\n\t\t\t\tv0 = F(f1, (v + 1) % 3);\n\t\t\t\tv1 = F(f1, (v + 2) % 3);\n\t\t\t}\n\t\t\telse if (f1 == -1) {\n\t\t\t\t// f0 is the face with boundary edge e opposite vertex EI(e, 0)\n\t\t\t\tunsigned v = EI(e, 0);\n\n\t\t\t\tv0 = F(f0, (v + 1) % 3);\n\t\t\t\tv1 = F(f0, (v + 2) % 3);\n\t\t\t}\n\t\t\telse {\n\t\t\t\tmeshOut = Variant();\n\t\t\t\treturn false;\n\t\t\t}\n\n\t\t\t// v0, v1: indices into vertexList of vertices (in order) of edge on the boundary\n\t\t\tunsigned v2 = v0 + vertexList.Size();\n\t\t\tunsigned v3 = v1 + vertexList.Size();\n\n\t\t\tsideFaceList.Push(Variant(v0));\n\t\t\tsideFaceList.Push(Variant(v1));\n\t\t\tsideFaceList.Push(Variant(v2));\n\t\t\t//\n\t\t\tsideFaceList.Push(Variant(v2));\n\t\t\tsideFaceList.Push(Variant(v1));\n\t\t\tsideFaceList.Push(Variant(v3));\n\t\t}\n\t}\n\n\tVariantVector innerFaceList;\n\tfor (unsigned i = 0; i < faceList.Size(); i += 3) {\n\t\tinnerFaceList.Push(faceList[i]);\n\t\tinnerFaceList.Push(faceList[i + 2]);\n\t\tinnerFaceList.Push(faceList[i + 1]);\n\t}\n\n\t// Collect all the inner, outer, and side panel data\n\t/*\n\tSpeculative: if d < 0, reverse all orientations....\n\t*/\n\n\tVariantVector newVertexList = vertexList;\n\tnewVertexList.Push(outerVertexList);\n\tVariant newVertices(newVertexList);\n\n\tVariantVector newFaceList = innerFaceList;\n\tnewFaceList.Push(outerFaceList);\n\tnewFaceList.Push(sideFaceList);\n\tif (d < 0.0f) {\n\t\tfor (unsigned i = 0; i < newFaceList.Size(); i += 3) {\n\t\t\tint i1 = newFaceList[i + 1].GetInt();\n\t\t\tint i2 = newFaceList[i + 2].GetInt();\n\n\t\t\tnewFaceList[i + 1] = Variant(i2);\n\t\t\tnewFaceList[i + 2] = Variant(i1);\n\t\t}\n\t}\n\tVariant newFaces(newFaceList);\n\n\tmeshOut = TriMesh_Make(newVertices, newFaces);\n\treturn true;\n}", "meta": {"hexsha": "19874d7770b53755f50ffea0740e46138a1c2d9d", "size": 5691, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Geometry/Geomlib_TriMeshThicken.cpp", "max_stars_repo_name": "elix22/IogramSource", "max_stars_repo_head_hexsha": "3a4ce55d94920e060776b4aa4db710f57a4280bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 28.0, "max_stars_repo_stars_event_min_datetime": "2017-03-01T04:09:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-01T13:33:50.000Z", "max_issues_repo_path": "Geometry/Geomlib_TriMeshThicken.cpp", "max_issues_repo_name": "elix22/IogramSource", "max_issues_repo_head_hexsha": "3a4ce55d94920e060776b4aa4db710f57a4280bc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2017-03-09T05:22:49.000Z", "max_issues_repo_issues_event_max_datetime": "2017-08-02T18:38:05.000Z", "max_forks_repo_path": "Geometry/Geomlib_TriMeshThicken.cpp", "max_forks_repo_name": "elix22/IogramSource", "max_forks_repo_head_hexsha": "3a4ce55d94920e060776b4aa4db710f57a4280bc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 17.0, "max_forks_repo_forks_event_min_datetime": "2017-03-01T14:00:01.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-08T06:36:54.000Z", "avg_line_length": 30.7621621622, "max_line_length": 114, "alphanum_fraction": 0.6847654191, "num_tokens": 1645, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5234203340678567, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.2311806321737327}} {"text": "/* ----------------------------------------------------------------------------\n\n * GTSAM Copyright 2010, Georgia Tech Research Corporation,\n * Atlanta, Georgia 30332-0415\n * All Rights Reserved\n * Authors: Frank Dellaert, et al. (see THANKS for the full author list)\n\n * See LICENSE for the license information\n\n * -------------------------------------------------------------------------- */\n\n/**\n * @file JacobianFactor.cpp\n * @author Richard Roberts\n * @date Dec 8, 2010\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n//#include \n//#include \n\n#include \n#include \n#include \n\nusing namespace std;\n//using namespace boost::lambda;\n\nnamespace gtsam {\n\n /* ************************************************************************* */\n void JacobianFactor::assertInvariants() const {\n#ifndef NDEBUG\n GaussianFactor::assertInvariants(); // The base class checks for unique keys\n assert((size() == 0 && Ab_.rows() == 0 && Ab_.nBlocks() == 0) || size()+1 == Ab_.nBlocks());\n#endif\n }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor(const JacobianFactor& gf) :\n GaussianFactor(gf), model_(gf.model_), Ab_(matrix_) {\n Ab_.assignNoalias(gf.Ab_);\n assertInvariants();\n }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor(const GaussianFactor& gf) : Ab_(matrix_) {\n // Copy the matrix data depending on what type of factor we're copying from\n if(const JacobianFactor* rhs = dynamic_cast(&gf))\n *this = JacobianFactor(*rhs);\n else if(const HessianFactor* rhs = dynamic_cast(&gf))\n *this = JacobianFactor(*rhs);\n else\n throw std::invalid_argument(\"In JacobianFactor(const GaussianFactor& rhs), rhs is neither a JacobianFactor nor a HessianFactor\");\n assertInvariants();\n }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor() : Ab_(matrix_) { assertInvariants(); }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor(const Vector& b_in) : Ab_(matrix_) {\n size_t dims[] = { 1 };\n Ab_.copyStructureFrom(BlockAb(matrix_, dims, dims+1, b_in.size()));\n getb() = b_in;\n model_ = noiseModel::Unit::Create(this->rows());\n assertInvariants();\n }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor(Index i1, const Matrix& A1,\n const Vector& b, const SharedDiagonal& model) :\n GaussianFactor(i1), model_(model), Ab_(matrix_) {\n\n if(model->dim() != (size_t) b.size())\n throw InvalidNoiseModel(b.size(), model->dim());\n\n size_t dims[] = { A1.cols(), 1};\n Ab_.copyStructureFrom(BlockAb(matrix_, dims, dims+2, b.size()));\n Ab_(0) = A1;\n getb() = b;\n assertInvariants();\n }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor(Index i1, const Matrix& A1, Index i2, const Matrix& A2,\n const Vector& b, const SharedDiagonal& model) :\n GaussianFactor(i1,i2), model_(model), Ab_(matrix_) {\n\n if(model->dim() != (size_t) b.size())\n throw InvalidNoiseModel(b.size(), model->dim());\n\n size_t dims[] = { A1.cols(), A2.cols(), 1};\n Ab_.copyStructureFrom(BlockAb(matrix_, dims, dims+3, b.size()));\n Ab_(0) = A1;\n Ab_(1) = A2;\n getb() = b;\n assertInvariants();\n }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor(Index i1, const Matrix& A1, Index i2, const Matrix& A2,\n Index i3, const Matrix& A3, const Vector& b, const SharedDiagonal& model) :\n GaussianFactor(i1,i2,i3), model_(model), Ab_(matrix_) {\n\n if(model->dim() != (size_t) b.size())\n throw InvalidNoiseModel(b.size(), model->dim());\n\n size_t dims[] = { A1.cols(), A2.cols(), A3.cols(), 1};\n Ab_.copyStructureFrom(BlockAb(matrix_, dims, dims+4, b.size()));\n Ab_(0) = A1;\n Ab_(1) = A2;\n Ab_(2) = A3;\n getb() = b;\n assertInvariants();\n }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor(const std::vector > &terms,\n \t\tconst Vector &b, const SharedDiagonal& model) :\n \tGaussianFactor(GetKeys(terms.size(), terms.begin(), terms.end())),\n\t\tmodel_(model), Ab_(matrix_)\n {\n\n if(model->dim() != (size_t) b.size())\n throw InvalidNoiseModel(b.size(), model->dim());\n\n size_t* dims = (size_t*)alloca(sizeof(size_t)*(terms.size()+1)); // FIXME: alloca is bad, just ask Google.\n for(size_t j=0; j > &terms,\n const Vector &b, const SharedDiagonal& model) :\n GaussianFactor(GetKeys(terms.size(), terms.begin(), terms.end())),\n model_(model), Ab_(matrix_)\n {\n\n if(model->dim() != (size_t) b.size())\n throw InvalidNoiseModel(b.size(), model->dim());\n\n size_t* dims=(size_t*)alloca(sizeof(size_t)*(terms.size()+1)); // FIXME: alloca is bad, just ask Google.\n size_t j=0;\n std::list >::const_iterator term=terms.begin();\n for(; term!=terms.end(); ++term,++j)\n dims[j] = term->second.cols();\n dims[j] = 1;\n Ab_.copyStructureFrom(BlockAb(matrix_, dims, dims+terms.size()+1, b.size()));\n j = 0;\n for(term=terms.begin(); term!=terms.end(); ++term,++j)\n Ab_(j) = term->second;\n getb() = b;\n assertInvariants();\n }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor(const GaussianConditional& cg) :\n\t\tGaussianFactor(cg),\n\t\tmodel_(noiseModel::Diagonal::Sigmas(cg.get_sigmas(), true)),\n\t\tAb_(matrix_) {\n Ab_.assignNoalias(cg.rsd_);\n assertInvariants();\n }\n\n /* ************************************************************************* */\n JacobianFactor::JacobianFactor(const HessianFactor& factor) : Ab_(matrix_) {\n keys_ = factor.keys_;\n Ab_.assignNoalias(factor.info_);\n\n\t\t// Do Cholesky to get a Jacobian\n size_t maxrank;\n\t\tbool success;\n\t\tboost::tie(maxrank, success) = choleskyCareful(matrix_);\n\n\t\t// Check for indefinite system\n\t\tif(!success)\n\t\t\tthrow IndeterminantLinearSystemException(factor.keys().front());\n\n // Zero out lower triangle\n matrix_.topRows(maxrank).triangularView() =\n Matrix::Zero(maxrank, matrix_.cols());\n // FIXME: replace with triangular system\n Ab_.rowEnd() = maxrank;\n model_ = noiseModel::Unit::Create(maxrank);\n\n assertInvariants();\n }\n\n /* ************************************************************************* */\n JacobianFactor& JacobianFactor::operator=(const JacobianFactor& rhs) {\n this->Base::operator=(rhs); // Copy keys\n model_ = rhs.model_; // Copy noise model\n Ab_.assignNoalias(rhs.Ab_); // Copy matrix and block structure\n assertInvariants();\n return *this;\n }\n\n /* ************************************************************************* */\n void JacobianFactor::print(const string& s, const IndexFormatter& formatter) const {\n cout << s << \"\\n\";\n if (empty()) {\n cout << \" empty, keys: \";\n BOOST_FOREACH(const Index& key, keys()) { cout << formatter(key) << \" \"; }\n cout << endl;\n } else {\n for(const_iterator key=begin(); key!=end(); ++key)\n \tcout << boost::format(\"A[%1%]=\\n\")%formatter(*key) << getA(key) << endl;\n cout << \"b=\" << getb() << endl;\n model_->print(\"model\");\n }\n }\n\n /* ************************************************************************* */\n // Check if two linear factors are equal\n bool JacobianFactor::equals(const GaussianFactor& f_, double tol) const {\n if(!dynamic_cast(&f_))\n return false;\n else {\n const JacobianFactor& f(static_cast(f_));\n if (empty()) return (f.empty());\n if(keys()!=f.keys() /*|| !model_->equals(lf->model_, tol)*/)\n return false;\n\n if (!(Ab_.rows() == f.Ab_.rows() && Ab_.cols() == f.Ab_.cols()))\n \treturn false;\n\n constABlock Ab1(Ab_.range(0, Ab_.nBlocks()));\n constABlock Ab2(f.Ab_.range(0, f.Ab_.nBlocks()));\n for(size_t row=0; row< (size_t) Ab1.rows(); ++row)\n if(!equal_with_abs_tol(Ab1.row(row), Ab2.row(row), tol) &&\n !equal_with_abs_tol(-Ab1.row(row), Ab2.row(row), tol))\n return false;\n\n return true;\n }\n }\n\n /* ************************************************************************* */\n Vector JacobianFactor::unweighted_error(const VectorValues& c) const {\n Vector e = -getb();\n if (empty()) return e;\n for(size_t pos=0; poswhiten(-getb());\n return model_->whiten(unweighted_error(c));\n }\n\n /* ************************************************************************* */\n double JacobianFactor::error(const VectorValues& c) const {\n if (empty()) return 0;\n Vector weighted = error_vector(c);\n return 0.5 * weighted.dot(weighted);\n }\n\n /* ************************************************************************* */\n Matrix JacobianFactor::computeInformation() const {\n Matrix AbWhitened = Ab_.full();\n model_->WhitenInPlace(AbWhitened);\n return AbWhitened.transpose() * AbWhitened;\n }\n\n /* ************************************************************************* */\n Vector JacobianFactor::operator*(const VectorValues& x) const {\n Vector Ax = zero(Ab_.rows());\n if (empty()) return Ax;\n\n // Just iterate over all A matrices and multiply in correct config part\n for(size_t pos=0; poswhiten(Ax);\n }\n\n /* ************************************************************************* */\n void JacobianFactor::transposeMultiplyAdd(double alpha, const Vector& e,\n VectorValues& x) const {\n Vector E = alpha * model_->whiten(e);\n // Just iterate over all A matrices and insert Ai^e into VectorValues\n for(size_t pos=0; pos JacobianFactor::matrix(bool weight) const {\n Matrix A(Ab_.range(0, size()));\n Vector b(getb());\n // divide in sigma so error is indeed 0.5*|Ax-b|\n if (weight) model_->WhitenSystem(A,b);\n return make_pair(A, b);\n }\n\n /* ************************************************************************* */\n Matrix JacobianFactor::matrix_augmented(bool weight) const {\n if (weight) { Matrix Ab(Ab_.range(0,Ab_.nBlocks())); model_->WhitenInPlace(Ab); return Ab; }\n else return Ab_.range(0, Ab_.nBlocks());\n }\n\n /* ************************************************************************* */\n std::vector >\n JacobianFactor::sparse(const std::vector& columnIndices) const {\n\n std::vector > entries;\n\n // iterate over all variables in the factor\n for(const_iterator var=begin(); varWhiten(getA(var)));\n // find first column index for this key\n size_t column_start = columnIndices[*var];\n for (size_t i = 0; i < (size_t) whitenedA.rows(); i++)\n for (size_t j = 0; j < (size_t) whitenedA.cols(); j++) {\n \tdouble s = whitenedA(i,j);\n if (std::abs(s) > 1e-12) entries.push_back(\n\t\t\t\t\t\t\tboost::make_tuple(i, column_start + j, s));\n }\n }\n\n Vector whitenedb(model_->whiten(getb()));\n size_t bcolumn = columnIndices.back();\n for (size_t i = 0; i < (size_t) whitenedb.size(); i++)\n entries.push_back(boost::make_tuple(i, bcolumn, whitenedb(i)));\n\n // return the result\n return entries;\n }\n\n /* ************************************************************************* */\n JacobianFactor JacobianFactor::whiten() const {\n JacobianFactor result(*this);\n result.model_->WhitenInPlace(result.matrix_);\n result.model_ = noiseModel::Unit::Create(result.model_->dim());\n return result;\n }\n\n /* ************************************************************************* */\n GaussianFactor::shared_ptr JacobianFactor::negate() const {\n \tHessianFactor hessian(*this);\n \treturn hessian.negate();\n }\n\n /* ************************************************************************* */\n GaussianConditional::shared_ptr JacobianFactor::eliminateFirst() {\n return this->eliminate(1);\n }\n\n /* ************************************************************************* */\n GaussianConditional::shared_ptr JacobianFactor::splitConditional(size_t nrFrontals) {\n \tassert(Ab_.rowStart() == 0 && Ab_.rowEnd() == (size_t) matrix_.rows() && Ab_.firstBlock() == 0);\n \tassert(size() >= nrFrontals);\n \tassertInvariants();\n\n \tconst bool debug = ISDEBUG(\"JacobianFactor::splitConditional\");\n\n \tif(debug) cout << \"Eliminating \" << nrFrontals << \" frontal variables\" << endl;\n \tif(debug) this->print(\"Splitting JacobianFactor: \");\n\n \tsize_t frontalDim = Ab_.range(0,nrFrontals).cols();\n\n \t// Check for singular factor\n \tif(model_->dim() < frontalDim)\n\t\t\tthrow IndeterminantLinearSystemException(this->keys().front());\n\n \t// Extract conditional\n \ttic(3, \"cond Rd\");\n\n \t// Restrict the matrix to be in the first nrFrontals variables\n \tAb_.rowEnd() = Ab_.rowStart() + frontalDim;\n \tconst Eigen::VectorBlock sigmas = model_->sigmas().segment(Ab_.rowStart(), Ab_.rowEnd()-Ab_.rowStart());\n \tGaussianConditional::shared_ptr conditional(new GaussianConditional(begin(), end(), nrFrontals, Ab_, sigmas));\n \tif(debug) conditional->print(\"Extracted conditional: \");\n \tAb_.rowStart() += frontalDim;\n \tAb_.firstBlock() += nrFrontals;\n \ttoc(3, \"cond Rd\");\n\n \tif(debug) conditional->print(\"Extracted conditional: \");\n\n \ttic(4, \"remaining factor\");\n \t// Take lower-right block of Ab to get the new factor\n \tAb_.rowEnd() = model_->dim();\n \tkeys_.erase(begin(), begin() + nrFrontals);\n \t// Set sigmas with the right model\n \tif (model_->isConstrained())\n \t\tmodel_ = noiseModel::Constrained::MixedSigmas(sub(model_->sigmas(), frontalDim, model_->dim()));\n \telse\n \t\tmodel_ = noiseModel::Diagonal::Sigmas(sub(model_->sigmas(), frontalDim, model_->dim()));\n \tif(debug) this->print(\"Eliminated factor: \");\n \tassert(Ab_.rows() <= Ab_.cols()-1);\n \ttoc(4, \"remaining factor\");\n\n \tif(debug) print(\"Eliminated factor: \");\n\n \tassertInvariants();\n\n \treturn conditional;\n }\n\n /* ************************************************************************* */\n GaussianConditional::shared_ptr JacobianFactor::eliminate(size_t nrFrontals) {\n\n assert(Ab_.rowStart() == 0 && Ab_.rowEnd() == (size_t) matrix_.rows() && Ab_.firstBlock() == 0);\n assert(size() >= nrFrontals);\n assertInvariants();\n\n const bool debug = ISDEBUG(\"JacobianFactor::eliminate\");\n\n if(debug) cout << \"Eliminating \" << nrFrontals << \" frontal variables\" << endl;\n if(debug) this->print(\"Eliminating JacobianFactor: \");\n if(debug) gtsam::print(matrix_, \"Augmented Ab: \");\n\n size_t frontalDim = Ab_.range(0,nrFrontals).cols();\n\n if(debug) cout << \"frontalDim = \" << frontalDim << endl;\n\n // Use in-place QR dense Ab appropriate to NoiseModel\n tic(2, \"QR\");\n SharedDiagonal noiseModel = model_->QR(matrix_);\n toc(2, \"QR\");\n\n // Zero the lower-left triangle. todo: not all of these entries actually\n // need to be zeroed if we are careful to start copying rows after the last\n // structural zero.\n if(matrix_.rows() > 0)\n for(size_t j=0; j<(size_t) matrix_.cols(); ++j)\n for(size_t i=j+1; idim(); ++i)\n matrix_(i,j) = 0.0;\n\n if(debug) gtsam::print(matrix_, \"QR result: \");\n if(debug) noiseModel->print(\"QR result noise model: \");\n\n // Start of next part\n model_ = noiseModel;\n return splitConditional(nrFrontals);\n }\n\n /* ************************************************************************* */\n void JacobianFactor::allocate(const VariableSlots& variableSlots, vector<\n\t\t\tsize_t>& varDims, size_t m) {\n\t\tkeys_.resize(variableSlots.size());\n\t\tstd::transform(variableSlots.begin(), variableSlots.end(), begin(),\n\t\t\t\tboost::bind(&VariableSlots::const_iterator::value_type::first, _1));\n\t\tvarDims.push_back(1);\n\t\tAb_.copyStructureFrom(BlockAb(matrix_, varDims.begin(), varDims.end(), m));\n\t}\n\n /* ************************************************************************* */\n\tvoid JacobianFactor::setModel(bool anyConstrained, const Vector& sigmas) {\n if((size_t) sigmas.size() != this->rows())\n throw InvalidNoiseModel(this->rows(), sigmas.size());\n\t\tif (anyConstrained)\n\t\t\tmodel_ = noiseModel::Constrained::MixedSigmas(sigmas);\n\t\telse\n\t\t\tmodel_ = noiseModel::Diagonal::Sigmas(sigmas);\n\t}\n\n /* ************************************************************************* */\n const char* JacobianFactor::InvalidNoiseModel::what() const throw() {\n if(description_.empty())\n description_ = (boost::format(\n \"A JacobianFactor was attempted to be constructed or modified to use a\\n\"\n \"noise model of incompatible dimension. The JacobianFactor has\\n\"\n \"dimensionality (i.e. length of error vector) %d but the provided noise\\n\"\n \"model has dimensionality %d.\") % factorDims % noiseModelDims).str();\n return description_.c_str();\n }\n\n}\n", "meta": {"hexsha": "759be49487088004d76357f69bb35ad579128bc4", "size": 18626, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "gtsam/linear/JacobianFactor.cpp", "max_stars_repo_name": "sdmiller/gtsam_pcl", "max_stars_repo_head_hexsha": "1e607bd75090d35e325a8fb37a6c5afe630f1207", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 11.0, "max_stars_repo_stars_event_min_datetime": "2015-02-04T16:41:47.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-10T07:02:44.000Z", "max_issues_repo_path": "gtsam/linear/JacobianFactor.cpp", "max_issues_repo_name": "sdmiller/gtsam_pcl", "max_issues_repo_head_hexsha": "1e607bd75090d35e325a8fb37a6c5afe630f1207", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "gtsam/linear/JacobianFactor.cpp", "max_forks_repo_name": "sdmiller/gtsam_pcl", "max_forks_repo_head_hexsha": "1e607bd75090d35e325a8fb37a6c5afe630f1207", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 6.0, "max_forks_repo_forks_event_min_datetime": "2015-09-10T12:06:08.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-10T07:02:48.000Z", "avg_line_length": 37.7809330629, "max_line_length": 135, "alphanum_fraction": 0.5573928917, "num_tokens": 4561, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.23111686994105915}} {"text": "/**\n * Copyright (c) 2012, Akamai Technologies\n * All rights reserved.\n * \n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions\n * are met:\n * \n * Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n * \n * Redistributions in binary form must reproduce the above\n * copyright notice, this list of conditions and the following\n * disclaimer in the documentation and/or other materials provided\n * with the distribution.\n * \n * Neither the name of the Akamai Technologies nor the names of its\n * contributors may be used to endorse or promote products derived\n * from this software without specific prior written permission.\n * \n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n * \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n * COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,\n * INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\n * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,\n * STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n * ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED\n * OF THE POSSIBILITY OF SUCH DAMAGE.\n */\n\n#ifndef __LOSERTREE_HH__\n#define __LOSERTREE_HH__\n\n#include \n#include \n#include \n#include \n#include \n#include \n\nclass Bithack\n{\npublic:\n static uint32_t roundUpPow2(uint32_t v) {\n v--;\n v |= v >> 1;\n v |= v >> 2;\n v |= v >> 4;\n v |= v >> 8;\n v |= v >> 16;\n return ++v;\n }\n static uint32_t logBase2(uint32_t v) {\n static const int MultiplyDeBruijnBitPosition[32] = \n {\n\t0, 9, 1, 10, 13, 21, 2, 29, 11, 14, 16, 18, 22, 25, 3, 30,\n\t8, 12, 20, 28, 15, 17, 24, 7, 19, 27, 23, 6, 26, 5, 4, 31\n };\n \n v |= v >> 1; // first round down to one less than a power of 2 \n v |= v >> 2;\n v |= v >> 4;\n v |= v >> 8;\n v |= v >> 16;\n \n return MultiplyDeBruijnBitPosition[(uint32_t)(v * 0x07C4ACDDU) >> 27]; \n }\n};\n\n/**\n * A Tournament tree of losers.\n * Many of the implementation details here are taken\n * from Graefe \"Implementing Sorting in Database Systems\".\n */\ntemplate >\nclass LoserTree\n{\npublic:\n class Node \n {\n public:\n /**\n * Player in the tournament that lost here.\n */\n uint32_t InputNumber;\n /**\n * Prefix of normalized key.\n */\n uint32_t KeyPrefix;\n /**\n * The full record in the tree. Required \n * to resolve equality when KeyPrefixes are equal.\n */\n _Data Value;\n };\n\nprivate:\n class SentinelPolicy\n {\n public:\n static const uint32_t MaxPlayers = 8192;\n static const uint32_t MaxPlayersLog2 = 13;\n // We've reserved 2*8192 values for low/high sentinels,\n // this leaves us with 2^32 - 2*8192 for real key\n // values. For simplicity we take only 2^31 values.\n static const uint32_t KeyPrefixLog2 = 31;\n static uint32_t getMaxPlayers() {\n return MaxPlayers;\n }\n /**\n * Get high sorting sentinel value for player i.\n * Guarantees that sentinels are in increasing order with\n * player index.\n */\n static uint32_t getHighSentinel(uint32_t i) {\n return std::numeric_limits::max() - MaxPlayers + i;\n }\n static bool isHighSentinel(uint32_t i) {\n return i >=std::numeric_limits::max() - MaxPlayers;\n }\n /**\n * Get low sorting sentinel value for player i.\n * Guarantees that sentinels are in increasing order with\n * player index.\n */\n static uint32_t getLowSentinel(uint32_t i) {\n return i;\n }\n static bool isLowSentinel(uint32_t i) {\n return i < MaxPlayers;\n }\n /**\n * Apply correction of a user key value so that it may\n * be inserted into a node.\n */\n static uint32_t insertKey(uint32_t i) {\n\treturn i + MaxPlayers;\n }\n /** \n * Apply correction to a node key so that the user\n * key is recreated.\n */\n static uint32_t removeKey(uint32_t i) {\n return i - MaxPlayers;\n }\n };\n\n uint32_t mNumPlayers;\n Node * mNodes;\n _Compare mCompare;\n\n void internalUpdate(uint32_t player, uint32_t keyPrefix, _Data val);\n\npublic:\n LoserTree();\n LoserTree(uint32_t numberOfPlayers, const _Compare& eq = _Compare());\n ~LoserTree();\n\n /**\n * Initialize a loser tournament tree for\n * numberOfPlayers inputs.\n */\n void init(uint32_t numberOfPlayers, const _Compare& eq = _Compare());\n /**\n * Replace the entry at position player with the value val whose\n * key prefix is keyPrefix.\n */\n void update(uint32_t player, uint32_t keyPrefix, _Data val)\n {\n // Apply sentinel correction to prefix\n internalUpdate(player, SentinelPolicy::insertKey(keyPrefix), val);\n }\n /**\n * Return the root node of the tree.\n */\n Node& getRoot()\n {\n return mNodes[0];\n }\n /**\n * Check if high sentinel.\n */\n bool isHighSentinel() const;\n /**\n * Check if tree is empty.\n */\n bool empty() const\n {\n return SentinelPolicy::isLowSentinel(mNodes[0].KeyPrefix);\n }\n /**\n * Close the input to the tree.\n */\n void close(uint32_t input)\n {\n internalUpdate(input, SentinelPolicy::getLowSentinel(input), NULL);\n }\n /**\n * Get the key of the top.\n */\n uint32_t getKeyPrefix() const \n {\n return SentinelPolicy::removeKey(mNodes[0].KeyPrefix);\n }\n /**\n * The input/player corresponding to the top.\n */\n uint32_t getInput() const\n {\n return mNodes[0].InputNumber;\n }\n /**\n * The value corresponding to the top.\n */\n _Data getValue() const\n {\n return mNodes[0].Value;\n }\n};\n\ntemplate \nLoserTree<_Data,_Compare>::LoserTree()\n :\n mNumPlayers(0),\n mNodes(NULL),\n mCompare(_Compare())\n{\n}\n\ntemplate \nLoserTree<_Data,_Compare>::LoserTree(uint32_t numberOfPlayers, const _Compare& eq)\n :\n mNumPlayers(0),\n mNodes(NULL),\n mCompare(eq)\n{\n init(numberOfPlayers,eq);\n}\n\ntemplate \nLoserTree<_Data,_Compare>::~LoserTree()\n{\n delete [] mNodes;\n}\n\ntemplate \nvoid LoserTree<_Data,_Compare>::init(uint32_t numberOfPlayers, \n\t\t\t\t const _Compare& eq)\n{\n // Reset state.\n delete [] mNodes;\n mNumPlayers = numberOfPlayers;\n mCompare = eq;\n\n // There is an easy to compute closed from for initializing\n // a tree that is a power of 2. I don't know of a closed form\n // for initializing arbitrary tree (though I suspect it isn't\n // too hard to calculate). For the moment we round up to \n // nearest power of 2, build tree and then close inputs that\n // we aren't using. There is a little memory wastage but it\n // shouldn't be macroscopic (nor are there cache implications\n // because we'll never touch the tail of the array after we\n // init).\n numberOfPlayers = Bithack::roundUpPow2(mNumPlayers);\n uint32_t numberOfPlayersLog2 = Bithack::logBase2(numberOfPlayers);\n // Our use of sentinels limits the number of inputs to the tree.\n if (numberOfPlayers > SentinelPolicy::getMaxPlayers()) {\n throw std::runtime_error((boost::format(\"Cannot merge more than %1% streams\") %\n\t\t\t SentinelPolicy::getMaxPlayers()).str());\n }\n // TODO: Assume number of players is a power of 2 for now.\n // Gotta figure out clean logic for initializing an arbitrary\n // tree.\n // Initialize tree.\n mNodes = new Node [numberOfPlayers];\n\n for(uint32_t lev=1; lev<=numberOfPlayersLog2; lev++) {\n uint32_t levelBegin = numberOfPlayers >> lev;\n uint32_t numInLevel = levelBegin;\n uint32_t levelInc = (1<\nvoid LoserTree<_Data,_Compare>::internalUpdate(uint32_t player, uint32_t keyPrefix, _Data val)\n{\n // Bottom up tournament. Start playing in bracket player/2.\n for(std::size_t idx = (Bithack::roundUpPow2(mNumPlayers)>>1) + (player >> 1);\n idx>=1;\n idx >>= 1) {\n Node & n (mNodes[idx]);\n bool cmp = keyPrefix < n.KeyPrefix ||\n (keyPrefix==n.KeyPrefix && mCompare(val, n.Value));\n\n if(cmp) {\n std::swap(player, n.InputNumber);\n std::swap(keyPrefix, n.KeyPrefix);\n std::swap(val, n.Value);\n } \n }\n\n // Update the top of the tree\n mNodes[0].InputNumber = player;\n mNodes[0].KeyPrefix = keyPrefix;\n mNodes[0].Value = val;\n}\n\ntemplate \nbool LoserTree<_Data,_Compare>::isHighSentinel() const {\n return SentinelPolicy::isHighSentinel(mNodes[0].KeyPrefix);\n}\n\n\n// Extract configured number of bits from\n// normalized keys. Make them into a N-bit\n// integer that can be compared using integer\n// comparison instructions.\n// TODO: Do we assume that we have to build\n// the entire normalized key and then extract\n// the prefix?\nclass KeyPrefixBuilder {\nprivate:\n uint32_t mPrefix;\n uint32_t mBitsPos;\n const uint32_t mBitsEnd;\n\n // Copy the bits at the position of the current bit iterator.\n typedef uint8_t _IntTy;\n bool copyTo(const _IntTy * begin, uint32_t beginBitPos, uint32_t endBitPos) {\n while(mBitsPos>= beginOffsetWithinByte;\n // Extract correct bits.\n uint32_t newBits = ((uint32_t) (begin[beginByte] & sourceMask)) << mBitsPos;\n mPrefix |= newBits;\n mBitsPos += bitsToCopy;\n beginBitPos += bitsToCopy;\n }\n return mBitsPos == mBitsEnd;\n }\n\n static uint32_t byteSwap(uint32_t val)\n {\n return ((((val) & 0xff000000) >> 24) |\n\t (((val) & 0x00ff0000) >> 8) |\n\t (((val) & 0x0000ff00) << 8) |\n\t (((val) & 0x000000ff) << 24));\n }\n\npublic:\n KeyPrefixBuilder(uint32_t bits)\n :\n mPrefix(0),\n mBitsPos(0),\n mBitsEnd(bits)\n {\n }\n\n bool add(uint32_t v)\n {\n // Byte swap\n uint32_t ret = byteSwap(v);\n // Consume up to the available 32-bits\n return copyTo((const uint8_t *) &ret, 0, 32); \n }\n\n bool add(const uint8_t * begin, const uint8_t * end)\n {\n return copyTo(begin, 0, 8*(end-begin)); \n }\n\n bool add(const char * begin, const char * end)\n {\n return copyTo((const uint8_t *) begin, 0, 8*(end-begin)); \n }\n\n uint32_t getPrefix() const\n {\n return byteSwap(mPrefix) >> (32-mBitsEnd);\n }\n\n void clear() \n {\n mPrefix = 0;\n mBitsPos = 0;\n }\n};\n\n#endif\n", "meta": {"hexsha": "6b53925395eba614d9043eb6f091524399e93e3f", "size": 11789, "ext": "hh", "lang": "C++", "max_stars_repo_path": "ads-df/LoserTree.hh", "max_stars_repo_name": "lairofthegoldinblair/trecul", "max_stars_repo_head_hexsha": "41953c22f18f76e5add7a35a13775f70459fcd96", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 7.0, "max_stars_repo_stars_event_min_datetime": "2015-04-24T13:24:22.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-17T05:47:43.000Z", "max_issues_repo_path": "ads-df/LoserTree.hh", "max_issues_repo_name": "lairofthegoldinblair/trecul", "max_issues_repo_head_hexsha": "41953c22f18f76e5add7a35a13775f70459fcd96", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ads-df/LoserTree.hh", "max_forks_repo_name": "lairofthegoldinblair/trecul", "max_forks_repo_head_hexsha": "41953c22f18f76e5add7a35a13775f70459fcd96", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2015-12-24T16:29:58.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-04T19:30:18.000Z", "avg_line_length": 28.3389423077, "max_line_length": 99, "alphanum_fraction": 0.6646874205, "num_tokens": 3311, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.23087156540677792}} {"text": "#ifndef GUNEROTRANSACTIONSEND_GADGET_H_\n#define GUNEROTRANSACTIONSEND_GADGET_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"uint252.h\"\n\nusing namespace libsnark;\n\nnamespace gunero {\n\n///// TRANSACTION SEND PROOF /////\n// With this proof, we are validating that the sender of the token is accepting that this token's ownership should\n// be transferred to the new transaction hash given. The \"account view hash\" validates that this proof is consistent\n// with the others generated, and also serves as an additional precaution for others using this proof to validate an\n// unauthorized release of the token to a party not covered in the transaction.\n// Public Parameters:\n// Current Authorization Root Hash (W)\n// Token UID (T)\n// Sender Account View Hash (V_S)\n// Receiver Account View Hash (V_R)\n// Previous Transaction Hash (L_P)\n\n// Private Parameters:\n// Sender Private Key (s_S)\n// Sender Account View Randomizer (r_S)\n// Receiver Account View Randomizer (r_R)\n// Previous Sender Account Address (A_PS)\n// Previous Authorization Root Hash (W_P)\n// alt: Receiver Proof Public Key (P_proof_R)\n\n//1) Obtain A_S from s_S through EDCSA operations\n//1 alt) Obtain P_proof_S from s_S through PRF operations\n//2) Validate V_S == hash(A_S, hash(W, r_S)) (View Hash is consistent for Sender)\n//2 alt) Validate V_S == hash(P_proof_S, hash(W, r_S)) (View Hash is consistent for Sender)\n//3) Validate V_R == hash(A_R, hash(W, r_R)) (View Hash is consistent for Receiver)\n//3 alt) Validate V_R == hash(P_proof_R, hash(W, r_R)) (View Hash is consistent for Receiver)\n//4) Validate L_P == hash(A_PS, hash(s_S, hash(T, W_P))) (The send proof is valid, sender owns token)\ntemplate\nclass gunerotransactionsend_gadget : public gadget {\npublic:\n // Verifier inputs\n pb_variable_array zk_packed_inputs;\n pb_variable_array zk_unpacked_inputs;\n std::shared_ptr> unpacker;\n std::shared_ptr> W;\n std::shared_ptr> T;\n std::shared_ptr> V_S;\n std::shared_ptr> V_R;\n std::shared_ptr> L_P;\n\n // Aux inputs\n // pb_variable ZERO;\n std::shared_ptr> ZERO;\n std::shared_ptr> s_S;\n std::shared_ptr> r_S;\n std::shared_ptr> r_R;\n std::shared_ptr> A_PS;\n std::shared_ptr> W_P;\n std::shared_ptr> P_proof_R;//alt\n\n // Computed variables\n std::shared_ptr> P_proof_S;\n // std::shared_ptr> spend_authority;\n std::shared_ptr spend_authority;\n std::shared_ptr> view_hash_1_digest;\n std::shared_ptr view_hash_1_hasher;\n std::shared_ptr view_hash_2_hasher;\n std::shared_ptr> view_hash_2_digest;\n std::shared_ptr view_hash_3_hasher;\n std::shared_ptr view_hash_4_hasher;\n std::shared_ptr> transaction_hash_1_digest;\n std::shared_ptr transaction_hash_1_hasher;\n std::shared_ptr> transaction_hash_2_digest;\n std::shared_ptr transaction_hash_2_hasher;\n std::shared_ptr transaction_hash_3_hasher;\n\n gunerotransactionsend_gadget(protoboard& pb)\n : gadget(pb, \"guneromembership_gadget\")\n {\n // Verifier inputs\n {\n // The verification inputs are all bit-strings of various\n // lengths (256-bit digests and 64-bit integers) and so we\n // pack them into as few field elements as possible. (The\n // more verification inputs you have, the more expensive\n // verification is.)\n zk_packed_inputs.allocate(pb, verifying_field_element_size());\n pb.set_input_sizes(verifying_field_element_size());\n\n alloc_uint256(zk_unpacked_inputs, W);\n alloc_uint256(zk_unpacked_inputs, T);\n alloc_uint256(zk_unpacked_inputs, V_S);\n alloc_uint256(zk_unpacked_inputs, V_R);\n alloc_uint256(zk_unpacked_inputs, L_P);\n\n assert(zk_unpacked_inputs.size() == verifying_input_bit_size());\n\n // This gadget will ensure that all of the inputs we provide are\n // boolean constrained.\n unpacker.reset(new multipacking_gadget(\n pb,\n zk_unpacked_inputs,\n zk_packed_inputs,\n FieldT::capacity(),\n \"unpacker\"\n ));\n }\n\n // We need a constant \"zero\" variable in some contexts. In theory\n // it should never be necessary, but libsnark does not synthesize\n // optimal circuits.\n //\n // The first variable of our constraint system is constrained\n // to be one automatically for us, and is known as `ONE`.\n // ZERO.allocate(pb);\n ZERO.reset(new digest_variable(pb, 256, \"\"));\n\n //We enforce 256 bits instead of 252 because of hash size compliance\n s_S.reset(new digest_variable(pb, 256, \"\"));//252\n\n r_S.reset(new digest_variable(pb, 256, \"\"));\n\n r_R.reset(new digest_variable(pb, 256, \"\"));\n\n A_PS.reset(new digest_variable(pb, 256, \"\"));\n\n W_P.reset(new digest_variable(pb, 256, \"\"));\n\n P_proof_R.reset(new digest_variable(pb, 256, \"\"));\n\n P_proof_S.reset(new digest_variable(pb, 256, \"\"));\n\n // spend_authority.reset(new PRF_addr_a_pk_simple_gadget(\n // pb,\n // ZERO,\n // s_S->bits,\n // P_proof_S\n // ));\n spend_authority.reset(new HashT(\n pb,\n *s_S,\n *ZERO,\n *P_proof_S,\n \"spend_authority\"));\n\n //hash(P_proof_S, hash(W, r_S)) == V_S\n view_hash_1_digest.reset(new digest_variable(pb, 256, \"\"));\n\n view_hash_1_hasher.reset(new HashT(\n pb,\n *W,\n *r_S,\n *view_hash_1_digest,\n \"view_hash_1_hasher\"));\n\n view_hash_2_hasher.reset(new HashT(\n pb,\n *P_proof_S,\n *view_hash_1_digest,\n *V_S,\n \"view_hash_2_hasher\"));\n\n //hash(P_proof_R, hash(W, r_R)) == V_R\n view_hash_2_digest.reset(new digest_variable(pb, 256, \"\"));\n\n view_hash_3_hasher.reset(new HashT(\n pb,\n *W,\n *r_R,\n *view_hash_2_digest,\n \"view_hash_3_hasher\"));\n\n view_hash_4_hasher.reset(new HashT(\n pb,\n *P_proof_R,\n *view_hash_2_digest,\n *V_R,\n \"view_hash_4_hasher\"));\n\n //hash(A_PS, hash(s_S, hash(T, W_P))) == L_P\n transaction_hash_1_digest.reset(new digest_variable(pb, 256, \"\"));\n\n transaction_hash_1_hasher.reset(new HashT(\n pb,\n *T,\n *W_P,\n *transaction_hash_1_digest,\n \"transaction_hash_1_hasher\"));\n\n transaction_hash_2_digest.reset(new digest_variable(pb, 256, \"\"));\n\n transaction_hash_2_hasher.reset(new HashT(\n pb,\n *s_S,\n *transaction_hash_1_digest,\n *transaction_hash_2_digest,\n \"transaction_hash_2_hasher\"));\n\n transaction_hash_3_hasher.reset(new HashT(\n pb,\n *A_PS,\n *transaction_hash_2_digest,\n *L_P,\n \"transaction_hash_3_hasher\"));\n }\n\n ~gunerotransactionsend_gadget()\n {\n\n }\n\n static size_t verifying_input_bit_size() {\n size_t acc = 0;\n\n //W\n acc += HashT::get_digest_len(); // the merkle root (anchor) => libff::bit_vector root(digest_len); \n\n //T\n acc += HashT::get_digest_len();\n\n //V_S\n acc += HashT::get_digest_len();\n\n //V_R\n acc += HashT::get_digest_len();\n\n //L_P\n acc += HashT::get_digest_len();\n\n return acc;\n }\n\n static size_t verifying_field_element_size() {\n return div_ceil(verifying_input_bit_size(), FieldT::capacity());\n }\n\n void generate_r1cs_constraints(\n const std::string& r1csPath,\n const std::string& pkPath,\n const std::string& vkPath\n )\n {\n#ifdef DEBUG\n libff::print_header(\"Gunero constraints\");\n#endif\n\n // The true passed here ensures all the inputs\n // are boolean constrained.\n unpacker->generate_r1cs_constraints(true);\n\n // Constrain `ZERO`\n // generate_r1cs_equals_const_constraint(this->pb, ZERO, FieldT::zero(), \"ZERO\");\n ZERO->generate_r1cs_constraints();\n\n s_S->generate_r1cs_constraints();\n\n r_S->generate_r1cs_constraints();\n\n r_R->generate_r1cs_constraints();\n\n A_PS->generate_r1cs_constraints();\n\n W_P->generate_r1cs_constraints();\n\n P_proof_R->generate_r1cs_constraints();\n\n P_proof_S->generate_r1cs_constraints();\n\n spend_authority->generate_r1cs_constraints();\n\n view_hash_1_digest->generate_r1cs_constraints();\n\n view_hash_1_hasher->generate_r1cs_constraints();\n\n view_hash_2_hasher->generate_r1cs_constraints();\n\n view_hash_2_digest->generate_r1cs_constraints();\n\n view_hash_3_hasher->generate_r1cs_constraints();\n\n view_hash_4_hasher->generate_r1cs_constraints();\n\n transaction_hash_1_digest->generate_r1cs_constraints();\n\n transaction_hash_1_hasher->generate_r1cs_constraints();\n\n transaction_hash_2_digest->generate_r1cs_constraints();\n\n transaction_hash_2_hasher->generate_r1cs_constraints();\n\n transaction_hash_3_hasher->generate_r1cs_constraints();\n\n //Calculate constraints\n r1cs_constraint_system constraint_system = this->pb.get_constraint_system();\n\n if (r1csPath.length() > 0)\n {\n saveToFile(r1csPath, constraint_system);\n }\n\n#ifdef DEBUG\n printf(\"\\n\"); libff::print_indent(); libff::print_mem(\"after generator\"); libff::print_time(\"after generator\");\n#endif\n\n r1cs_ppzksnark_keypair keypair = r1cs_ppzksnark_generator(constraint_system);\n\n //Verify\n r1cs_ppzksnark_processed_verification_key vk_precomp = r1cs_ppzksnark_verifier_process_vk(keypair.vk);\n\n if (pkPath.length() > 0)\n {\n saveToFile(pkPath, keypair.pk);\n }\n\n if (vkPath.length() > 0)\n {\n saveToFile(vkPath, keypair.vk);\n }\n\n#ifdef DEBUG\n printf(\"\\n\"); libff::print_indent(); libff::print_mem(\"after constraints\"); libff::print_time(\"after constraints\");\n#endif\n }\n\n // Public Parameters:\n // Current Authorization Root Hash (W)\n // Token UID (T)\n // Sender Account View Hash (V_S)\n // Receiver Account View Hash (V_R)\n // Previous Transaction Hash (L_P)\n\n // Private Parameters:\n // Sender Private Key (s_S)\n // Sender Account View Randomizer (r_S)\n // Receiver Account View Randomizer (r_R)\n // Previous Sender Account Address (A_PS)\n // Previous Authorization Root Hash (W_P)\n // alt: Receiver Proof Public Key (P_proof_R)\n void generate_r1cs_witness(\n const uint256& pW,\n const uint256& pT,\n const uint256& pV_S,\n const uint256& pV_R,\n const uint256& pL_P,\n const uint252& ps_S,\n const uint256& pr_S,\n const uint256& pr_R,\n const uint160& pA_PS,\n const uint256& pW_P,\n const uint256& pP_proof_R\n )\n {\n // Witness W\n W->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pW)\n );\n\n // Witness T\n T->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pT)\n );\n\n // Witness view hash. This is not a sanity check.\n V_S->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pV_S)\n );\n\n // Witness view hash. This is not a sanity check.\n V_R->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pV_R)\n );\n\n // Witness view hash. This is not a sanity check.\n L_P->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pL_P)\n );\n\n // Witness `zero`\n // this->pb.val(ZERO) = FieldT::zero();\n ZERO->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(uint256())\n );\n\n // Witness s_S for the input\n s_S->bits.fill_with_bits(\n this->pb,\n uint252_to_bool_vector_256(ps_S)\n );\n\n // Witness P_proof_S for s_S with PRF_addr\n spend_authority->generate_r1cs_witness();\n\n // Witness r_S for the input\n r_S->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pr_S)\n );\n\n // Witness r_R for the input\n r_R->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pr_R)\n );\n\n // Witness A_PS for the input\n A_PS->bits.fill_with_bits(\n this->pb,\n uint160_to_bool_vector_256_rpad(pA_PS)\n );\n\n // Witness W_P for the input\n W_P->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pW_P)\n );\n\n // Witness P_proof_R for the input\n P_proof_R->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pP_proof_R)\n );\n\n // Witness hash(W, r_S) = view_hash_1_digest\n view_hash_1_hasher->generate_r1cs_witness();\n\n // Witness hash(P_proof_S, view_hash_1_digest) = V_S\n view_hash_2_hasher->generate_r1cs_witness();\n\n // Witness hash(W, r_R) = view_hash_2_digest\n view_hash_3_hasher->generate_r1cs_witness();\n\n // Witness hash(P_proof_R, view_hash_2_digest) = V_R\n view_hash_4_hasher->generate_r1cs_witness();\n\n // Witness //transaction_hash_1_digest = hash(T, W_P)\n transaction_hash_1_hasher->generate_r1cs_witness();\n\n // Witness //transaction_hash_2_digest = hash(s_S, transaction_hash_1_digest)\n transaction_hash_2_hasher->generate_r1cs_witness();\n\n // Witness //L_P == hash(A_PS, transaction_hash_2_digest)\n transaction_hash_3_hasher->generate_r1cs_witness();\n\n // [SANITY CHECK] Ensure that the intended root\n // was witnessed by the inputs, even if the read\n // gadget overwrote it. This allows the prover to\n // fail instead of the verifier, in the event that\n // the roots of the inputs do not match the\n // hash provided to the proving hashers.\n V_S->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pV_S)\n );\n\n // [SANITY CHECK] Ensure that the intended root\n // was witnessed by the inputs, even if the read\n // gadget overwrote it. This allows the prover to\n // fail instead of the verifier, in the event that\n // the roots of the inputs do not match the\n // hash provided to the proving hashers.\n V_R->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pV_R)\n );\n\n // [SANITY CHECK] Ensure that the intended root\n // was witnessed by the inputs, even if the read\n // gadget overwrote it. This allows the prover to\n // fail instead of the verifier, in the event that\n // the roots of the inputs do not match the\n // hash provided to the proving hashers.\n L_P->bits.fill_with_bits(\n this->pb,\n uint256_to_bool_vector(pL_P)\n );\n\n // This happens last, because only by now are all the\n // verifier inputs resolved.\n unpacker->generate_r1cs_witness_from_bits();\n }\n\n static r1cs_primary_input witness_map(\n const uint256& pW,\n const uint256& pT,\n const uint256& pV_S,\n const uint256& pV_R,\n const uint256& pL_P\n ) {\n std::vector verify_inputs;\n\n insert_uint256(verify_inputs, pW);\n\n insert_uint256(verify_inputs, pT);\n\n insert_uint256(verify_inputs, pV_S);\n\n insert_uint256(verify_inputs, pV_R);\n\n insert_uint256(verify_inputs, pL_P);\n\n assert(verify_inputs.size() == verifying_input_bit_size());\n auto verify_field_elements = libff::pack_bit_vector_into_field_element_vector(verify_inputs);\n assert(verify_field_elements.size() == verifying_field_element_size());\n return verify_field_elements;\n }\n\n void alloc_uint256(\n pb_variable_array& packed_into,\n std::shared_ptr>& var\n ) {\n var.reset(new digest_variable(this->pb, 256, \"\"));\n packed_into.insert(packed_into.end(), var->bits.begin(), var->bits.end());\n }\n};\n\n} // end namespace `gunero`\n\n#endif /* GUNEROTRANSACTIONSEND_GADGET_H_ */", "meta": {"hexsha": "4740b270bece85c7168670ad80589c9408e3f811", "size": 18384, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/gunerotransactionsend_gadget.hpp", "max_stars_repo_name": "GunClear/Silencer", "max_stars_repo_head_hexsha": "625b5ce0860af98763aa2ce14d6b406d5ef368e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2018-10-17T22:27:54.000Z", "max_stars_repo_stars_event_max_datetime": "2018-10-17T22:27:54.000Z", "max_issues_repo_path": "src/gunerotransactionsend_gadget.hpp", "max_issues_repo_name": "GunClear/Silencer", "max_issues_repo_head_hexsha": "625b5ce0860af98763aa2ce14d6b406d5ef368e4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": "2018-11-09T19:57:52.000Z", "max_issues_repo_issues_event_max_datetime": "2019-01-04T15:49:02.000Z", "max_forks_repo_path": "src/gunerotransactionsend_gadget.hpp", "max_forks_repo_name": "GunClear/Silencer", "max_forks_repo_head_hexsha": "625b5ce0860af98763aa2ce14d6b406d5ef368e4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.7321100917, "max_line_length": 124, "alphanum_fraction": 0.6455069626, "num_tokens": 4607, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.36658973632215985, "lm_q1q2_score": 0.2308689123653951}} {"text": "/* Copyright (c) 2010-2016, Delft University of Technology\n * All rigths reserved\n *\n * This file is part of the Tudat. Redistribution and use in source and\n * binary forms, with or without modification, are permitted exclusively\n * under the terms of the Modified BSD license. You should have received\n * a copy of the license with this file. If not, please or visit:\n * http://tudat.tudelft.nl/LICENSE.\n */\n\n#include \n#include \n\n#include \"Tudat/Astrodynamics/Aerodynamics/exponentialAtmosphere.h\"\n#include \"Tudat/Astrodynamics/Aerodynamics/tabulatedAtmosphere.h\"\n#include \"Tudat/SimulationSetup/createAtmosphereModel.h\"\n\nnamespace tudat\n{\n\nnamespace simulation_setup\n{\n\n//! Function to create an atmosphere model.\nboost::shared_ptr< aerodynamics::AtmosphereModel > createAtmosphereModel(\n const boost::shared_ptr< AtmosphereSettings > atmosphereSettings,\n const std::string& body )\n{\n using namespace tudat::aerodynamics;\n\n // Declare return object.\n boost::shared_ptr< AtmosphereModel > atmosphereModel;\n\n // Check which type of atmosphere model is to be created.\n switch( atmosphereSettings->getAtmosphereType( ) )\n {\n case exponential_atmosphere:\n {\n // Check whether settings for atmosphere are consistent with its type.\n boost::shared_ptr< ExponentialAtmosphereSettings > exponentialAtmosphereSettings =\n boost::dynamic_pointer_cast< ExponentialAtmosphereSettings >( atmosphereSettings );\n if( exponentialAtmosphereSettings == NULL )\n {\n throw std::runtime_error(\n \"Error, expected exponential atmosphere settings for body \" + body );\n }\n else\n {\n // Create and initialize exponential atmosphere model.\n boost::shared_ptr< ExponentialAtmosphere > exponentialAtmosphereModel =\n boost::make_shared< ExponentialAtmosphere >(\n exponentialAtmosphereSettings->getDensityScaleHeight( ) ,\n exponentialAtmosphereSettings->getConstantTemperature( ),\n exponentialAtmosphereSettings->getDensityAtZeroAltitude( ),\n exponentialAtmosphereSettings->getSpecificGasConstant( ) );\n atmosphereModel = exponentialAtmosphereModel;\n }\n break;\n }\n case tabulated_atmosphere:\n {\n // Check whether settings for atmosphere are consistent with its type\n boost::shared_ptr< TabulatedAtmosphereSettings > tabulatedAtmosphereSettings =\n boost::dynamic_pointer_cast< TabulatedAtmosphereSettings >( atmosphereSettings );\n if( tabulatedAtmosphereSettings == NULL )\n {\n throw std::runtime_error(\n \"Error, expected tabulated atmosphere settings for body \" + body );\n }\n else\n {\n // Create and initialize tabulatedl atmosphere model.\n atmosphereModel = boost::make_shared< TabulatedAtmosphere >(\n tabulatedAtmosphereSettings->getAtmosphereFile( ) );\n }\n break;\n }\n default:\n throw std::runtime_error(\n \"Error, did not recognize atmosphere model settings type \" +\n boost::lexical_cast< std::string >( atmosphereSettings->getAtmosphereType( ) ) );\n }\n return atmosphereModel;\n}\n\n\n} // namespace simulation_setup\n\n} // namespace tudat\n", "meta": {"hexsha": "254c20846307a77de0d0a9918715f93774ce1428", "size": 3482, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Tudat/SimulationSetup/createAtmosphereModel.cpp", "max_stars_repo_name": "JPelamatti/ThesisTUDAT", "max_stars_repo_head_hexsha": "b94ce35fb7c8fa44ae83238e296a979dfa3adfe8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Tudat/SimulationSetup/createAtmosphereModel.cpp", "max_issues_repo_name": "JPelamatti/ThesisTUDAT", "max_issues_repo_head_hexsha": "b94ce35fb7c8fa44ae83238e296a979dfa3adfe8", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Tudat/SimulationSetup/createAtmosphereModel.cpp", "max_forks_repo_name": "JPelamatti/ThesisTUDAT", "max_forks_repo_head_hexsha": "b94ce35fb7c8fa44ae83238e296a979dfa3adfe8", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2019-05-30T03:42:22.000Z", "max_forks_repo_forks_event_max_datetime": "2019-05-30T03:42:22.000Z", "avg_line_length": 38.6888888889, "max_line_length": 99, "alphanum_fraction": 0.6651349799, "num_tokens": 725, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093733737562, "lm_q2_score": 0.4339814648038986, "lm_q1q2_score": 0.23053502197430378}} {"text": "#include \"focal_grid.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\n\n//-------------------------------------------------\n\ninline bool sortByPix0(const voxel_prop &lhs, const voxel_prop &rhs) {\n\treturn lhs.pix_0 < rhs.pix_0;\n}\n\nFocalGrid::FocalGrid() {\n\t// empty\n}\n\nbool FocalGrid::LoadCalibration(session_param &ses_par, vox_grid &vox, frames &frame) {\n\n\tbool grid_loaded = true;\n\n\ttry {\n\n\t\tvox.image_size.clear();\n\t\tvox.calib_mat.clear();\n\t\tvox.X_xyz.clear();\n\t\tvox.X_uv.clear();\n\t\tvox.uv_offset.clear();\n\n\t\timage_size.clear();\n\t\tcalib_mat.clear();\n\t\tX_xyz.clear();\n\t\tX_uv.clear();\n\t\tuv_offset.clear();\n\n\t\tstring calib_file = ses_par.session_loc + \"/\" + ses_par.cal_loc + \"/\" + ses_par.cal_name;\n\n\t\tarma::Mat CalibMatrix;\n\n\t\tCalibMatrix.load(calib_file);\n\n\t\tfor (int i=0; i uv_off_i = {CalibMatrix(10,i)/2.0-CalibMatrix(12,i)/2.0, CalibMatrix(9,i)/2.0-CalibMatrix(11,i)/2.0, 0.0};\n\t\t\tvox.uv_offset.push_back(uv_off_i);\n\n\t\t\timage_size.push_back(frame.image_size[i]);\n\t\t\tcalib_mat.push_back(CalibMatrix.col(i));\n\t\t\tX_xyz.push_back(FocalGrid::Camera2WorldMatrix(CalibMatrix.col(i)));\n\t\t\tX_uv.push_back(FocalGrid::World2CameraMatrix(CalibMatrix.col(i)));\n\t\t\tuv_offset.push_back(uv_off_i);\n\t\t}\n\n\t\t// Populate internal parameters\n\n\t\tN_cam = vox.N_cam;\n\t\tN_threads = vox.N_threads;\n\t\tnx = vox.nx;\n\t\tny = vox.ny;\n\t\tnz = vox.nz;\n\t\tds = vox.ds;\n\t\tx0 = vox.x0;\n\t\ty0 = vox.y0;\n\t\tz0 = vox.z0;\n\n\t}\n\tcatch (...) {\n\t\tgrid_loaded = false;\n\t}\n\n\treturn grid_loaded;\n}\n\nbool FocalGrid::ConstructFocalGrid(vox_grid &vox) {\n\n\tbool grid_build = true;\n\n\ttry {\n\n\t\tvox.voxel_list.clear();\n\n\t\tint vec_size = 0;\n\t\tint i = 0;\n\t\tvector>> vox_results;\n\t\t\n\t\twhile (i vox_results_thread=vox_results.at(j).get();\n\t\t\t\tvec_size = vox_results_thread.size();\n\t\t\t\tif (vec_size>1) {\n\t\t\t\t\tcopy(vox_results_thread.begin(),vox_results_thread.end(),back_inserter(vox.voxel_list));\n\t\t\t\t}\n\t\t\t}\n\t\t\ti+=N_threads;\n\t\t}\n\n\t\tcout << \"Sorting\" << endl;\n\t\tsort(vox.voxel_list.begin(),vox.voxel_list.end(),sortByPix0);\n\n\t\tcout << \"Voxel list size:\" << endl;\n\t\tcout << vox.voxel_list.size() << endl;\n\n\t}\n\tcatch (...) {\n\t\tgrid_build = false;\n\t}\n\n\treturn grid_build;\n}\n\nvector> FocalGrid::ProjectImage2Cloud(vector> &frame_in, vox_grid &vox) {\n\n\tvector> pcl_now;\n\n\ttuple> view_check;\n\ttuple> code_check;\n\tvector> code_list;\n\n\tint N_row = get<0>(vox.image_size[0]);\n\tint N_col = get<1>(vox.image_size[0]);\n\n\tint vox_ind = 0;\n\tint max_ind = vox.voxel_list.size();\n\n\tfor (int i=0; i<(N_row*N_col); i++) {\n\n\t\tif (frame_in[0](i)>0) {\n\t\t\twhile (vox.voxel_list[vox_ind].pix_0<=i) {\n\t\t\t\tif (vox.voxel_list[vox_ind].pix_0==i) {\n\t\t\t\t\tview_check = FocalGrid::CheckInView(frame_in, vox, vox_ind);\n\t\t\t\t\tif (get<0>(view_check)==true) {\n\t\t\t\t\t\tif (FocalGrid::CheckNeighbors(frame_in, vox, vox_ind)==true) {\n\t\t\t\t\t\t\tcode_check = FocalGrid::CheckCode(code_list, get<1>(view_check));\n\t\t\t\t\t\t\tif (get<1>(code_check)==true) {\n\t\t\t\t\t\t\t\tcode_list.push_back(get<2>(code_check));\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\tpcl_now.push_back(make_tuple(\n\t\t\t\t\t\t\t\tvox.voxel_list[vox_ind].x,\n\t\t\t\t\t\t\t\tvox.voxel_list[vox_ind].y,\n\t\t\t\t\t\t\t\tvox.voxel_list[vox_ind].z,\n\t\t\t\t\t\t\t\tget<0>(code_check)));\n\t\t\t\t\t\t\t\t//get<1>(view_check)));\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tvox_ind++;\n\t\t\t\tif (vox_ind >= max_ind) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (vox_ind >= max_ind) {\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\n\tif (pcl_now.size() <= 0) {\n\t\tpcl_now.push_back(make_tuple(0.0,0.0,0.0,0));\n\t}\n\n\treturn pcl_now;\n}\n\ntuple> FocalGrid::CheckCode(vector> code_list, vector code_in) {\n\n\tint code_out = 0;\n\tvector new_code;\n\n\tint i=0;\n\tbool code_found = false;\n\tbool add_code = false;\n\t\n\tint N_codes = code_list.size();\n\n\tif (N_codes > 0) {\n\t\twhile ((i> FocalGrid::ProjectImage2Cloud(vector> &frame_in, vox_grid &vox) {\n\n\tvector> pcl_now;\n\n\tvector>>> future_pcl;\n\n\tint N_row = get<0>(vox.image_size[0]);\n\tint N_col = get<1>(vox.image_size[0]);\n\n\tint vec_size = 0;\n\n\t// Launch threads:\n\tfor (int j=0; j> pcl_results_thread=future_pcl.at(j).get();\n\t\tvec_size = pcl_results_thread.size();\n\t\tif (pcl_results_thread.size()>1) {\n\t\t\tcopy(pcl_results_thread.begin(),pcl_results_thread.end(),back_inserter(pcl_now));\n\t\t}\n\t}\n\tcout << \"pcl now size\" << endl;\n\tcout << pcl_now.size() << endl;\n\tif (pcl_now.size() <= 0) {\n\t\tpcl_now.push_back(make_tuple(0.0,0.0,0.0,0));\n\t}\n\n\treturn pcl_now;\n}\n\n\nvector> FocalGrid::ProjectImage2CloudThread(int i_start, int i_end, vox_grid &vox, vector> &frame_in) {\n\n\tvector> pcl_i;\n\n\tfor (int i = i_start; i view_check;\n\n\t\tif (frame_in[0](i) > 0) {\n\t\t\twhile (vox.voxel_list[vox_ind].pix_0<=i) {\n\t\t\t\tif (vox.voxel_list[vox_ind].pix_0==i) {\n\t\t\t\t\tview_check = FocalGrid::CheckInView(frame_in, vox, vox_ind);\n\t\t\t\t\tif (get<0>(view_check)==true) {\n\t\t\t\t\t\tif (FocalGrid::CheckNeighbors(frame_in, vox, vox_ind)==true) {\n\t\t\t\t\t\t\tpcl_i.push_back(make_tuple(\n\t\t\t\t\t\t\t\tvox.voxel_list[vox_ind].x,\n\t\t\t\t\t\t\t\tvox.voxel_list[vox_ind].y,\n\t\t\t\t\t\t\t\tvox.voxel_list[vox_ind].z,\n\t\t\t\t\t\t\t\tget<1>(view_check)));\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tvox_ind++;\n\t\t\t\tif (vox_ind >= max_ind) {\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn pcl_i;\n}\n*/\n\nvector> FocalGrid::ProjectCloud2Image(vector> &cloud_in, vox_grid &vox) {\n\n\tvector> frame_now;\n\n\tint N_vox = cloud_in.size();\n\n\tfor (int n=0; n frame_n;\n\t\tframe_n.zeros(get<0>(image_size[n])*get<1>(image_size[n]));\n\t\tframe_now.push_back(frame_n);\n\t}\n\n\tarma::Col uv;\n\tarma::Col xyz;\n\tint u = 0;\n\tint v = 0;\n\n\tfor (int i=0; i(cloud_in[i]),get<1>(cloud_in[i]),get<2>(cloud_in[i]),1.0};\n\t\t\tuv = X_uv[j]*xyz-uv_offset[j];\n\t\t\tif (uv(0)>=0 && uv(0)<(get<1>(image_size[j]))) {\n\t\t\t\tif (uv(1)>=0 && uv(1)<(get<0>(image_size[j]))) {\n\t\t\t\t\tu = (int) uv(0);\n\t\t\t\t\tv = (int) uv(1);\n\t\t\t\t\tif (frame_now[j](get<0>(image_size[j])*u+v)==0) {\n\t\t\t\t\t\tframe_now[j](get<0>(image_size[j])*u+v) = get<3>(cloud_in[i]);\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tif (frame_now[j](get<0>(image_size[j])*u+v)>get<3>(cloud_in[i])) {\n\t\t\t\t\t\t\tframe_now[j](get<0>(image_size[j])*u+v) = get<3>(cloud_in[i]);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\treturn frame_now;\n}\n\ntuple> FocalGrid::CheckInView(vector> &frame_in, vox_grid &vox, int vox_ind) {\n\n\tbool in_view = true;\n\tvector seg_codes;\n\n\tint pix_ind = 0;\n\tint intensity_now = 0;\n\n\tfor (int i=0; i FocalGrid::CheckInView(vector> &frame_in, vox_grid &vox, int vox_ind) {\n\n\tint intensity_sum = 0;\n\tint intensity_now = 0;\n\tbool in_view = true;\n\tint n = 0;\n\tint pix_ind = 0;\n\n\twhile (in_view==true && n0) {\n\t\t\t\tif (intensity_now0) {\n\t\t\t\tif (intensity_now> &frame_in, vox_grid &vox, int vox_ind) {\n\n\tbool pixel_on = true;\n\n\tint uv;\n\tint on_count = 0;\n\tint in_view = 0;\n\n\tfor (int m=0; m<6; m++) {\n\t\tin_view =0;\n\t\tfor (int n=0; n0) {\n\t\t\t\tin_view++;\n\t\t\t}\n\t\t}\n\t\tif (in_view == N_cam) {\n\t\t\ton_count++;\n\t\t}\n\t}\n\n\tif (on_count==6) {\n\t\tpixel_on = false;\n\t}\n\n\tif (on_count<2) {\n\t\tpixel_on = false;\n\t}\n\n\treturn pixel_on;\n}\n\nvector FocalGrid::CheckVoxel(int i) {\n\n\tvector voxel_array;\n\n\tvoxel_array.resize(1);\n\n\tint vox_index = 0;\n\n\tint is_neighbor = 0;\n\n\tarma::Col xyz(4);\n\tarma::Col xyz_0(4);\n\tarma::Col xyz_1(4);\n\tarma::Col xyz_2(4);\n\tarma::Col xyz_3(4);\n\tarma::Col xyz_4(4);\n\tarma::Col xyz_5(4);\n\n\tarma::Mat uv(3,N_cam);\n\tarma::Mat uv_0(3,N_cam);\n\tarma::Mat uv_1(3,N_cam);\n\tarma::Mat uv_2(3,N_cam);\n\tarma::Mat uv_3(3,N_cam);\n\tarma::Mat uv_4(3,N_cam);\n\tarma::Mat uv_5(3,N_cam);\n\n\tfor (int j=0; j(image_size[m])+((int) uv(0,m));\n\t\t\t\t\t\t\tvox_now.vox_ind = vox_index;\n\t\t\t\t\t\t\tvox_now.x = xyz(0);\n\t\t\t\t\t\t\tvox_now.y = xyz(1);\n\t\t\t\t\t\t\tvox_now.z = xyz(2);\n\t\t\t\t\t\t\tvox_now.neighbors.resize(6);\n\t\t\t\t\t\t\tvox_now.neighbors[0].push_back(((int) uv_0(1,m))*get<1>(image_size[m])+((int) uv_0(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[1].push_back(((int) uv_1(1,m))*get<1>(image_size[m])+((int) uv_1(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[2].push_back(((int) uv_2(1,m))*get<1>(image_size[m])+((int) uv_2(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[3].push_back(((int) uv_3(1,m))*get<1>(image_size[m])+((int) uv_3(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[4].push_back(((int) uv_4(1,m))*get<1>(image_size[m])+((int) uv_4(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[5].push_back(((int) uv_5(1,m))*get<1>(image_size[m])+((int) uv_5(0,m)));\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tvox_now.pix_n.push_back(((int) uv(1,m))*get<1>(image_size[m])+((int) uv(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[0].push_back(((int) uv_0(1,m))*get<1>(image_size[m])+((int) uv_0(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[1].push_back(((int) uv_1(1,m))*get<1>(image_size[m])+((int) uv_1(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[2].push_back(((int) uv_2(1,m))*get<1>(image_size[m])+((int) uv_2(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[3].push_back(((int) uv_3(1,m))*get<1>(image_size[m])+((int) uv_3(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[4].push_back(((int) uv_4(1,m))*get<1>(image_size[m])+((int) uv_4(0,m)));\n\t\t\t\t\t\t\tvox_now.neighbors[5].push_back(((int) uv_5(1,m))*get<1>(image_size[m])+((int) uv_5(0,m)));\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tvoxel_array.push_back(vox_now);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\t\n\treturn voxel_array;\n}\n\ninline int FocalGrid::IsVoxel(arma::Mat uv) {\n\n\tint is_voxel = 0;\n\n\tint count = 0;\n\n\tfor (int n=0; n=0 && uv(0,n)<(get<1>(image_size[n]))) {\n\t\t\tif (uv(1,n)>=0 && uv(1,n)<(get<0>(image_size[n]))) {\n\t\t\t\tcount++;\n\t\t\t}\n\t\t}\n\t}\n\n\tif (count == N_cam) {\n\t\tis_voxel = 1;\n\t}\n\n\treturn is_voxel;\n\n}\n\narma::Mat FocalGrid::TransformXYZ2UV(arma::Col xyz_pos) {\n\n\tarma::Mat uv_mat(2,N_cam);\n\n\tfor (int n=0; n uv = X_uv[n]*xyz_pos-uv_offset[n];\n\t\tuv_mat(0,n) = int(uv(0));\n\t\tuv_mat(1,n) = int(uv(1));\n\t}\n\n\treturn uv_mat;\n}\n\ntuple, arma::Col> FocalGrid::RayCasting(int cam_nr, arma::Col xyz_pos_prev, arma::Col uv_pos_prev, arma::Col uv_pos_now) {\n\n\t// Calculate the 3D translation vector:\n\tarma::Col xyz_uv_prev = X_xyz[cam_nr]*(uv_pos_prev+uv_offset[cam_nr]);\n\tarma::Col xyz_uv_now = X_xyz[cam_nr]*(uv_pos_now+uv_offset[cam_nr]);\n\tarma::Col trans_vec = xyz_uv_now-xyz_uv_prev;\n\ttrans_vec(3) = 0.0;\n\n\t// Add the translation to the xyz position:\n\tarma::Col xyz_pos_now = xyz_pos_prev + trans_vec;\n\n\t// Project the new position back to the camera views:\n\n\tarma::Mat uv_mat(3,N_cam);\n\n\tfor (int n=0; n uv = X_uv[n]*xyz_pos_now-uv_offset[n];\n\t\tif (uv(0)>=0 && uv(0)<(get<1>(image_size[n])) && uv(1)>=0 && uv(1)<(get<0>(image_size[n]))) {\n\t\t\tuv_mat(0,n) = int(uv(0));\n\t\t\tuv_mat(1,n) = int(uv(1));\n\t\t\tuv_mat(2,n) = int(uv(2));\n\t\t}\n\t\telse {\n\t\t\tarma::Col uv_old = X_uv[n]*xyz_pos_prev-uv_offset[n];\n\t\t\tuv_mat(0,n) = int(uv_old(0));\n\t\t\tuv_mat(1,n) = int(uv_old(1));\n\t\t\tuv_mat(2,n) = int(uv_old(2));\n\t\t}\n\t}\n\n\treturn make_tuple(uv_mat,xyz_pos_now);\n}\n\narma::Mat FocalGrid::Camera2WorldMatrix(arma::Col calib_param) {\n\n\t// return the world to camera projection matrix\n\n\tarma::Mat C = {{calib_param(0), calib_param(2), 0, 0},\n\t\t \t\t\t{0, calib_param(1), 0, 0},\n\t\t \t\t\t{0, 0, 0, 1}};\n\n\tdouble theta = sqrt(pow(calib_param(3),2)+pow(calib_param(4),2)+pow(calib_param(5),2));\n\n\tarma::Mat omega = {{0, -calib_param(5), calib_param(4)},\n\t\t\t \t\t\t{calib_param(5), 0, -calib_param(3)},\n\t\t\t \t\t\t{-calib_param(4), calib_param(3), 0}};\n\n\tarma::Mat R(3,3); R.eye();\n\n\tR = R+(sin(theta)/theta)*omega+((1-cos(theta))/pow(theta,2))*(omega*omega);\n\n\tarma::Col T = {calib_param(6), calib_param(7), calib_param(8)};\n\n\tarma::Mat K = {{R(0,0), R(0,1), R(0,2), T(0)},\n\t\t \t\t\t\t{R(1,0), R(1,1), R(1,2), T(1)},\n\t\t \t\t\t\t{R(2,0), R(2,1), R(2,2), T(2)},\n\t\t \t\t\t\t{0, 0, 0, 1}};\n\n\treturn arma::inv(K)*arma::pinv(C);\n\n}\n\narma::Mat FocalGrid::World2CameraMatrix(arma::Col calib_param) {\n\n\t// return the world to camera projection matrix\n\n\tarma::Mat C = {{calib_param(0), calib_param(2), 0, 0},\n\t\t \t\t\t{0, calib_param(1), 0, 0},\n\t\t \t\t\t{0, 0, 0, 1}};\n\n\tdouble theta = sqrt(pow(calib_param(3),2)+pow(calib_param(4),2)+pow(calib_param(5),2));\n\n\tarma::Mat omega = {{0, -calib_param(5), calib_param(4)},\n\t\t\t \t\t\t{calib_param(5), 0, -calib_param(3)},\n\t\t\t \t\t\t{-calib_param(4), calib_param(3), 0}};\n\n\tarma::Mat R(3,3); R.eye();\n\n\tR = R+(sin(theta)/theta)*omega+((1-cos(theta))/pow(theta,2))*(omega*omega);\n\n\tarma::Col T = {calib_param(6), calib_param(7), calib_param(8)};\n\n\tarma::Mat K = {{R(0,0), R(0,1), R(0,2), T(0)},\n\t\t \t\t\t\t{R(1,0), R(1,1), R(1,2), T(1)},\n\t\t \t\t\t\t{R(2,0), R(2,1), R(2,2), T(2)},\n\t\t \t\t\t\t{0, 0, 0, 1}};\n\n\treturn C*K;\n\n}", "meta": {"hexsha": "2406730fc8236c82ed97a4ae66a822a14a37bc41", "size": 19116, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "FlyTrackApp/focal_grid_old.cpp", "max_stars_repo_name": "jmmelis/FlyTrackApp", "max_stars_repo_head_hexsha": "7c03eb0aeda7b0bd4e0c6181bc776c92e4fbc582", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FlyTrackApp/focal_grid_old.cpp", "max_issues_repo_name": "jmmelis/FlyTrackApp", "max_issues_repo_head_hexsha": "7c03eb0aeda7b0bd4e0c6181bc776c92e4fbc582", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FlyTrackApp/focal_grid_old.cpp", "max_forks_repo_name": "jmmelis/FlyTrackApp", "max_forks_repo_head_hexsha": "7c03eb0aeda7b0bd4e0c6181bc776c92e4fbc582", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.6246648794, "max_line_length": 169, "alphanum_fraction": 0.6080246914, "num_tokens": 6833, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.35577489351363034, "lm_q1q2_score": 0.23047034201023156}} {"text": "/* Siconos is a program dedicated to modeling, simulation and control\n * of non smooth dynamical systems.\n *\n * Copyright 2016 INRIA.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n*/\n#include \"OSNSMultipleImpact.hpp\"\n#include \"LagrangianDS.hpp\"\n#include \"MultipleImpactNSL.hpp\"\n#include \"Simulation.hpp\"\n#include \"ioMatrix.hpp\"\n#include \n#include \n#include \"OSNSMatrix.hpp\"\n#include \"Model.hpp\"\n#include \"NonSmoothDynamicalSystem.hpp\"\n\n//Default constructor\nOSNSMultipleImpact::OSNSMultipleImpact(): LinearOSNS()\n{\n _typeCompLaw = \"BiStiffness\";\n _nStepSave = 100;\n _tolImpact = DEFAULT__tolImpact;\n _Tol_Vel = DEFAULT_TOL_VEL;\n _Tol_Ener = DEFAULT_TOL_ENER;\n _ZeroVel_EndIm = DEFAULT_TOL_VEL;\n _ZeroEner_EndIm = DEFAULT_TOL_ENER;\n _saveData = false;\n _sizeDataSave = 1000;\n _nStepMax = 100000;\n _stepMinSave = 1;\n _stepMaxSave = _nStepMax;\n _namefile = \"DataMultipleImpact.dat\";\n}\n//------------------------------ -------------------------------------------------------------\nOSNSMultipleImpact::OSNSMultipleImpact(std::string newTypeLaw, double newDelP = 1.0e-5): LinearOSNS()\n{\n _typeCompLaw = newTypeLaw;\n _deltaP = newDelP;\n _nStepSave = 100;\n _tolImpact = DEFAULT__tolImpact;\n _Tol_Vel = DEFAULT_TOL_VEL;\n _Tol_Ener = DEFAULT_TOL_ENER;\n _ZeroVel_EndIm = DEFAULT_TOL_VEL;\n _ZeroEner_EndIm = DEFAULT_TOL_ENER;\n _saveData = false;\n _namefile = \"DataMultipleImpact.dat\";\n _sizeDataSave = 1000;\n _nStepMax = 100000;\n _stepMinSave = 1;\n _stepMaxSave = _nStepMax;\n if ((_typeCompLaw != \"MonoStiffness\") && (_typeCompLaw != \"BiStiffness\"))\n RuntimeException::selfThrow(\"OSNSMultipleImpact::_typeCompLaw type of the compliance model must be either MonoStiffness or BiStiffness!\");\n}\n//-------------------------------------------------------------------------------------------------\nOSNSMultipleImpact::~OSNSMultipleImpact() {}\n//------------------------------------------------------------------------------------------------\n\nvoid OSNSMultipleImpact::setTolImpact(double newTolZero)\n{\n _tolImpact = newTolZero;\n};\n\nvoid OSNSMultipleImpact::SetSaveData(bool var)\n{\n _saveData = var;\n};\n\nvoid OSNSMultipleImpact::SetNameOutput(std::string file_name)\n{\n _namefile = file_name;\n};\n\nvoid OSNSMultipleImpact::SetTolVel(double _var)\n{\n _Tol_Vel = _var;\n};\n\nvoid OSNSMultipleImpact::SetTolEner(double _var)\n{\n _Tol_Ener = _var;\n};\n\nvoid OSNSMultipleImpact::SetZeroVelEndImp(double _var)\n{\n _ZeroVel_EndIm = _var;\n};\n\nvoid OSNSMultipleImpact::SetZeroEnerEndImp(double _var)\n{\n _ZeroEner_EndIm = _var;\n};\n\nvoid OSNSMultipleImpact::SetNstepSave(unsigned int var)\n{\n _nStepSave = var;\n};\n\nvoid OSNSMultipleImpact::SetNstepMax(unsigned int var)\n{\n _nStepMax = var;\n};\n\nvoid OSNSMultipleImpact::SetStepMinMaxSave(unsigned int var1, unsigned int var2)\n{\n _stepMinSave = var1;\n _stepMaxSave = var2;\n}\n\nvoid OSNSMultipleImpact::set_typeCompLaw(std::string newTypeLaw)\n{\n _typeCompLaw = newTypeLaw;\n if ((_typeCompLaw != \"MonoStiffness\") && (_typeCompLaw != \"BiStiffness\"))\n RuntimeException::selfThrow(\"OSNSMultipleImpact::_typeCompLaw type of the compliance model must be either MonoStiffness or BiStiffness!\");\n};\n\nvoid OSNSMultipleImpact::SetSizeDataSave(unsigned int var)\n{\n _sizeDataSave = var;\n}\n//---------------------------------------------------------------------------------------------------\nvoid OSNSMultipleImpact::WriteVectorIntoMatrix(const SiconosVector m, const unsigned int pos_row, const unsigned int pos_col)\n{\n for (unsigned int i = 0; i < m.size(); ++i)\n {\n (*_DataMatrix)(pos_row, pos_col + i) = m(i);\n }\n}\n//----------------------------------------------------------------------------------------------------\nbool OSNSMultipleImpact::isZero(double Var)\n{\n if (std::abs(Var) <= _tolImpact)\n return true;\n else\n return false;\n}\n//------------------------------------------------------------------------------------------------\nbool OSNSMultipleImpact::isVelNegative(double Var)\n{\n if (Var < - _Tol_Vel)\n return true;\n else\n return false;\n}\n//-------------------------------------------------------------------------------------------------\n\nbool OSNSMultipleImpact::isEnerZero(double Var)\n{\n if (std::abs(Var) <= _Tol_Ener)\n return true;\n else\n return false;\n}\n//--------------------------------------------------------------------------------------------------\nunsigned int OSNSMultipleImpact::EstimateNdataCols()\n{\n unsigned int _numberCols = 1;\n // Number of columns for data at contacts\n SP::InteractionsGraph indexSet = simulation()->indexSet(0); // get indexSet[0]\n InteractionsGraph::VIterator ui, uiend;\n for (std11::tie(ui, uiend) = indexSet->vertices(); ui != uiend; ++ui)\n {\n //_numberCols = _numberCols + 3*(indexSet->bundle(*ui)->interaction()->nonSmoothLaw()->size()) + 1;\n _numberCols = _numberCols + (indexSet->bundle(*ui)->getSizeOfY());\n }\n // Number of columns for data at particles\n SP::DynamicalSystemsGraph DSG = simulation()->nonSmoothDynamicalSystem()->dynamicalSystems();\n DynamicalSystemsGraph::VIterator dsi, dsiend;\n for (std11::tie(dsi, dsiend) = DSG->vertices(); dsi != dsiend; ++dsi)\n {\n _numberCols = _numberCols + (DSG->bundle(*dsi)->dimension());\n }\n return(_numberCols);\n}\n//-----------------------------------------------------------------------------------------------\nvoid OSNSMultipleImpact::AllocateMemory()\n{\n if (!_velocityContact)\n _velocityContact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_velocityContact->size() != maxSize())\n _velocityContact->resize(maxSize());\n };\n //\n if (!_oldVelocityContact)\n _oldVelocityContact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_oldVelocityContact->size() != maxSize())\n _oldVelocityContact->resize(maxSize());\n };\n //\n if (! _energyContact)\n _energyContact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_energyContact->size() != maxSize())\n _energyContact->resize(maxSize());\n };\n //\n if (!_WorkcContact)\n _WorkcContact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_WorkcContact->size() != maxSize())\n _WorkcContact->resize(maxSize());\n };\n //\n if (!_distributionVector)\n _distributionVector.reset(new SiconosVector(maxSize()));\n else\n {\n if (_distributionVector->size() != maxSize())\n _distributionVector->resize(maxSize());\n };\n //\n if (!_stateContact)\n _stateContact.reset(new IndexInt(maxSize()));\n else\n {\n if (_stateContact->size() != maxSize())\n _stateContact->resize(maxSize());\n };\n //\n if (!_Kcontact)\n _Kcontact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_Kcontact->size() != maxSize())\n _Kcontact->resize(maxSize());\n };\n //\n if (!_restitutionContact)\n _restitutionContact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_restitutionContact->size() != maxSize())\n _restitutionContact->resize(maxSize());\n };\n //\n if (!_elasticyCoefficientcontact)\n _elasticyCoefficientcontact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_elasticyCoefficientcontact->size() != maxSize())\n _elasticyCoefficientcontact->resize(maxSize());\n };\n if (!_tolImpulseContact)\n _tolImpulseContact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_tolImpulseContact->size() != maxSize())\n _tolImpulseContact->resize(maxSize());\n };\n //\n if (!_deltaImpulseContact)\n _deltaImpulseContact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_deltaImpulseContact->size() != maxSize())\n _deltaImpulseContact->resize(maxSize());\n };\n //\n if (!_impulseContactUpdate)\n _impulseContactUpdate.reset(new SiconosVector(maxSize()));\n else\n {\n if (_impulseContactUpdate->size() != maxSize())\n _impulseContactUpdate->resize(maxSize());\n }\n //\n if (!_forceContact)\n _forceContact.reset(new SiconosVector(maxSize()));\n else\n {\n if (_forceContact->size() != maxSize())\n _forceContact->resize(maxSize());\n };\n // for the data matrix\n unsigned int _numberCols = EstimateNdataCols();\n if (!_DataMatrix)\n _DataMatrix.reset(new SimpleMatrix(_sizeDataSave, _numberCols));\n else\n {\n if ((_DataMatrix->size(0) != _sizeDataSave) || (_DataMatrix->size(1) != _numberCols))\n _DataMatrix->resize(_sizeDataSave, _numberCols);\n }\n}\n//=====================================================================================\nvoid OSNSMultipleImpact::BuildParaContact()\n{\n SP::InteractionsGraph indexSet = simulation()->indexSet(1); // get indexSet[1]\n //Loop over the Interactionof the indexSet(1)\n InteractionsGraph::VIterator ui, uiend;\n for (std11::tie(ui, uiend) = indexSet->vertices(); ui != uiend; ++ui)\n {\n SP::Interaction inter = indexSet->bundle(*ui);\n SP::NonSmoothLaw nslaw = inter->nslaw();\n SP::MultipleImpactNSL Mulnslaw = std11::dynamic_pointer_cast(nslaw);\n assert(Mulnslaw && \"In OSNSMultipleImpact::BuildStiffResCofVec, non-smooth law used must be MultipleImpactNSL!!!\");\n // Get the relative position of inter-interactionBlock in the vector _velocityContact\n unsigned int pos = _M->getPositionOfInteractionBlock(*inter);\n (*_restitutionContact)(pos) = Mulnslaw->ResCof();\n (*_Kcontact)(pos) = Mulnslaw->Stiff();\n (*_elasticyCoefficientcontact)(pos) = Mulnslaw->ElasCof();\n }\n /*\n std::cout << \" Restitution coefficients: \" <display();\n std::cout << \"Stiffnesses: \" <display();\n std::cout << \"Elasticity coeffients at contacts: \" <display();\n */\n\n}\n//========================================================================================\nvoid OSNSMultipleImpact::PreComputeImpact()\n{\n //1. Get the number of contacts and bodies involved in the impact\n if (indexSetLevel() != 1)\n RuntimeException::selfThrow(\"OSNSMultipleImpact::PreComputeImpact==> the levelMin must be equal to 1 in the multiple impact model !!\");\n SP::InteractionsGraph indexSet = simulation()->indexSet(indexSetLevel()); // get indexSet[1]\n _nContact = indexSet->size();\n //2. Compute matrix _M\n SP::Topology topology = simulation()->nonSmoothDynamicalSystem()->topology();\n bool isLinear = simulation()->nonSmoothDynamicalSystem()->isLinear();\n if (!_hasBeenUpdated || !isLinear)\n {\n // Computes new _unitaryBlocks if required\n updateInteractionBlocks();\n // Updates matrix M\n _M->fill(indexSet, !_hasBeenUpdated);\n _sizeOutput = _M->size();\n }\n if (_nContact != _sizeOutput)\n RuntimeException::selfThrow(\"OSNSMultipleImpact::ComputeWMinvWtrans: number of contacts different from the size of output--> this case is not yet implemented!\");\n //3. Checks size of vectors\n if (_velocityContact->size() != _sizeOutput)\n {\n _velocityContact->resize(_sizeOutput);\n }\n _velocityContact->zero();\n //\n if (_oldVelocityContact->size() != _sizeOutput)\n {\n _oldVelocityContact->resize(_sizeOutput);\n }\n _oldVelocityContact->zero();\n //\n if (_energyContact->size() != _sizeOutput)\n {\n _energyContact->resize(_sizeOutput);\n }\n _energyContact->zero();\n //\n if (_WorkcContact->size() != _sizeOutput)\n {\n _WorkcContact->resize(_sizeOutput);\n }\n _WorkcContact->zero();\n //\n if (_distributionVector->size() != _sizeOutput)\n {\n _distributionVector->resize(_sizeOutput);\n }\n _distributionVector->zero();\n //\n if (_stateContact->size() != _sizeOutput)\n {\n _stateContact->resize(_sizeOutput);\n }\n //\n if (_Kcontact->size() != _sizeOutput)\n {\n _Kcontact->resize(_sizeOutput);\n }\n _Kcontact->zero();\n //\n if (_restitutionContact->size() != _sizeOutput)\n {\n _restitutionContact->resize(_sizeOutput);\n }\n _restitutionContact->zero();\n //\n if (_elasticyCoefficientcontact->size() != _sizeOutput)\n {\n _elasticyCoefficientcontact->resize(_sizeOutput);\n }\n _elasticyCoefficientcontact->zero();\n //\n if (_tolImpulseContact->size() != _sizeOutput)\n {\n _tolImpulseContact->resize(_sizeOutput);\n }\n _tolImpulseContact->zero();\n //\n if (_deltaImpulseContact->size() != _sizeOutput)\n {\n _deltaImpulseContact->resize(_sizeOutput);\n }\n _deltaImpulseContact->zero();\n //\n if (_impulseContactUpdate->size() != _sizeOutput)\n {\n _impulseContactUpdate->resize(_sizeOutput);\n }\n _impulseContactUpdate->zero();\n //\n if (_forceContact->size() != _sizeOutput)\n {\n _forceContact->resize(_sizeOutput);\n }\n _forceContact->zero();\n //4. Initialize the relative velocity, potential energy, impulse at contacts\n InitializeInput();\n //5. Build the vectors of stifnesseses, of restitution coefficients, and of elaticity coefficients\n BuildParaContact();\n}\n//=======================================================================================\nvoid OSNSMultipleImpact::InitializeInput()\n{\n //Loop over alls Interactioninvolved in the indexSet[1]\n SP::InteractionsGraph indexSet = simulation()->indexSet(indexSetLevel()); // get indexSet[1]\n InteractionsGraph::VIterator ui, uiend;\n for (std11::tie(ui, uiend) = indexSet->vertices(); ui != uiend; ++ui)\n {\n SP::Interaction inter = indexSet->bundle(*ui);\n //SP::SiconosVector Vc0 = inter->y(1); // Relative velocity at beginning of impact\n SP::SiconosVector Vc0 = inter->yOld(1); // Relative velocity at beginning of impact\n unsigned int pos_inter = _M->getPositionOfInteractionBlock(*inter);\n setBlock(*Vc0, _velocityContact, Vc0->size(), 0, pos_inter);\n SP::SiconosVector ener0(new SiconosVector(Vc0->size()));\n ener0->zero(); // We suppose that the initial potential energy before impact is equal to zero at any contact\n // at the beginning of impact\n setBlock(*ener0, _energyContact, ener0->size(), 0, pos_inter);\n //SP::SiconosVector impulse0= (inter)->lambda(1))->vector(inter->number());\n SP::SiconosVector impulse0(new SiconosVector(Vc0->size()));\n impulse0->zero(); // We suppose that the impulse before impact is equal to zero at any contact\n // at the beginning of impact\n setBlock(*impulse0, _tolImpulseContact, impulse0->size(), 0, pos_inter);\n };\n /*\n std::cout << \"Initial relative velocity at contacts\" <display();\n std::cout<< \"Initial energy at contacts\" <display();\n std::cout << \"Impulse at contact\" <display();\n */\n\n}\n//=========================================================================================\nvoid OSNSMultipleImpact::initialize(SP::Simulation sim)\n{\n\n // General initialize for OneStepNSProblem\n OneStepNSProblem::initialize(sim);\n // Allocate the memory\n AllocateMemory();\n // get topology\n SP::Topology topology = simulation()->nonSmoothDynamicalSystem()->topology();\n // Note that _interactionBlocks is up to date since updateInteractionBlocks\n // has been called during OneStepNSProblem::initialize()\n\n if (! _M)\n {\n if (_MStorageType == 0)\n _M.reset(new OSNSMatrix(maxSize(), 0));\n\n else // if(_MStorageType == 1) size = number of _interactionBlocks\n // = number of Interactionin the largest considered indexSet\n _M.reset(new OSNSMatrix(simulation()->indexSet(indexSetLevel())->size(), 1));\n }\n\n};\n//========================================================================================\nvoid OSNSMultipleImpact::PrimConVelocity()\n{\n getMin(*_velocityContact, _relativeVelocityPrimaryContact, _primaryContactId);\n _energyPrimaryContact = (*_energyContact)(_primaryContactId);\n if (!isVelNegative(_relativeVelocityPrimaryContact))\n {\n RuntimeException::selfThrow(\"OSNSMultipleImpact::PrimConVelocity, the velocity at the primary contact must be negative !!\");\n }\n /*\n std::cout << \"Primary contact according to relative velocity: \" << _primaryContactId < _ZeroEner_EndIm) || ((*_velocityContact)(i) < -_ZeroVel_EndIm)) // if potential energy is not equal to zero or the relative velocity is negative\n {\n _IsImpactEnd = false;\n }\n }\n return _IsImpactEnd;\n // bool var = true;\n // for(unsigned int i = 0; i < _nContact;++i)\n // {\n // if ((*_stateContact)[i] != 0)\n // {\n // var = false;\n // break;\n // };\n // };\n // return var;\n //\n //cout << \"Is the multiple impacts is terminated: \" << _IsImpactEnd <defaultMatrix()), *_deltaImpulseContact); // compute the relative velocity at the end of the step\n //\n /*\n std::cout << \"Relative velocity at contacts at the beginning of step:\" <display();\n std::cout << \"Relative velocity at contacts at the end of step:\" <display();\n */\n //\n}\n//=======================================================================================\nvoid OSNSMultipleImpact::Compute_energyContact()\n{\n if (_typeCompLaw == \"BiStiffness\")\n // For Bistiffness model\n {\n for (unsigned int i = 0; i < _nContact; ++i)\n {\n if ((0.5 * ((*_oldVelocityContact)(i) + (*_velocityContact)(i))) <= 0.0) // Contact located in the compression phase\n {\n (*_energyContact)(i) = (*_energyContact)(i) - 0.5 * ((*_oldVelocityContact)(i) + (*_velocityContact)(i)) * ((*_deltaImpulseContact)(i));\n }\n else // Contact located in the expansion phase\n {\n if (!isZero((*_restitutionContact)(i)))\n {\n (*_energyContact)(i) = (*_energyContact)(i) - (1.0 / std::pow((*_restitutionContact)(i), 2)) * 0.5 * ((*_oldVelocityContact)(i) + (*_velocityContact)(i)) * ((*_deltaImpulseContact)(i));\n //\n if ((*_energyContact)(i) < 0.0)\n {\n (*_energyContact)(i) = 0.0;\n };\n }\n else // restitution coefficient equal to zero\n {\n (*_energyContact)(i) = 0.0; // In this case, no potential energy at contacts when the contact is located in the compression phase\n }\n };\n //\n if ((*_energyContact)(i) < 0.0)\n {\n RuntimeException::selfThrow(\"OSNSMultipleImpact::Compute_energyContact, the potential energy during compression phase must be positive!!!\");\n };\n };\n }\n else\n // For the mono-stiffness model\n {\n for (unsigned int i = 0; i < _nContact; ++i)\n {\n //1: dertermine the work done by the last compression phase at contacts (nessessary for the mono-stiffness compliance model)\n // if Vc(k) < 0 and Vc(k +1) >= 0 ==> transition from the compression phase to the expansion phase, Wc = E(k)\n if (((*_oldVelocityContact)(i) < 0.0) && ((*_velocityContact)(i) >= 0.0))\n {\n (*_WorkcContact)(i) = (*_energyContact)(i);\n };\n //2: Calculate the potential energy at the end of stap\n (*_energyContact)(i) = (*_energyContact)(i) - 0.5 * ((*_oldVelocityContact)(i) + (*_velocityContact)(i)) * ((*_deltaImpulseContact)(i));\n //3: Check if the termination condition is verified or not (if Vc(k+1) > 0.0 and E(k+1) <= (1-e^2)*Wc). If yes, discard the potential energy\n // in order to respect the energetic constraint\n if (((*_stateContact)[i] == 2) && (((*_velocityContact)(i) > 0.0) && ((*_energyContact)(i) <= ((1.0 - std::pow((*_restitutionContact)(i), 2)) * (*_WorkcContact)(i)))))\n {\n (*_energyContact)(i) = 0.0; // potential energy at this contact is completely dissipated before the compression phase finishes\n };\n };\n }\n /*\n\n std::cout << \"Potential energy at contacts at the end of step:\" <display();\n std::cout << \"Work done during the compression phase at contacts\" <display();\n\n */\n}\n//======================================================================================\nvoid OSNSMultipleImpact::UpdateDuringImpact()\n{\n //1. Copy _velocityContact/_deltaImpulseContact into the vector y/lambda for Interactions\n SP::InteractionsGraph indexSet = simulation()->indexSet(indexSetLevel());\n // y and lambda vectors\n SP::SiconosVector lambda;\n SP::SiconosVector y;\n // === Loop through \"active\" Interactions (ie present in indexSets[1]) ===\n unsigned int pos;\n InteractionsGraph::VIterator ui, uiend;\n for (std11::tie(ui, uiend) = indexSet->vertices(); ui != uiend; ++ui)\n {\n Interaction& inter = *indexSet->bundle(*ui);\n // Get the relative position of inter-interactionBlock in the vector _velocityContact/_tolImpulseContact\n pos = _M->getPositionOfInteractionBlock(inter);\n // Get Y and Lambda for the current Interaction\n y = inter.y(inputOutputLevel());\n lambda = inter.lambda(inputOutputLevel());\n // Copy _velocityContact/_tolImpulseContact, starting from index pos into y/lambda\n // save into y !!\n setBlock(*_velocityContact, y, y->size(), pos, 0);\n // saved into lambda[1] !!\n setBlock(*_impulseContactUpdate, lambda, lambda->size(), pos, 0);\n //setBlock(*_deltaImpulseContact, lambda, lambda->size(), pos, 0);\n };\n //2. Update the Input[1], state of DS systems, Output[1]\n simulation()->update(inputOutputLevel());\n _impulseContactUpdate->zero(); // reset input[1] to zero after each update\n}\n//--------------------------------------------------------------------------------------\nvoid OSNSMultipleImpact::SaveDataOneStep(unsigned int _ithPoint)\n{\n // Save the total impulse at the primary contacts (time-like independent variable) and the time evolution during impact\n if (_ithPoint >= _DataMatrix->size(0))\n RuntimeException::selfThrow(\"In OSNSMultipleImpact::ComputeImpact, number of points saved exceeds the size of matrix allocated!!!\");\n //(*_DataMatrix)(_ithPoint,0) = _timeVariable;\n (*_DataMatrix)(_ithPoint, 0) = _impulseVariable;\n // Save the data related to UnitaryRelations\n SP::InteractionsGraph indexSet0 = simulation()->indexSet(0);\n SP::InteractionsGraph indexSet1 = simulation()->indexSet(indexSetLevel());\n unsigned int pos;\n InteractionsGraph::VIterator ui, uiend;\n unsigned int col_pos = 1;\n for (std11::tie(ui, uiend) = indexSet0->vertices(); ui != uiend; ++ui)\n {\n SP::Interaction inter = indexSet0->bundle(*ui);\n SP::SiconosVector ydot = inter->y(1);\n SP::SiconosVector P_inter(new SiconosVector(inter->getSizeOfY()));\n SP::SiconosVector F_inter(new SiconosVector(inter->getSizeOfY()));\n SP::SiconosVector E_inter(new SiconosVector(1));\n if (indexSet1->is_vertex(inter)) // if Interaction belongs to the IndexSet[1]\n {\n pos = _M->getPositionOfInteractionBlock(*inter);\n setBlock(*_tolImpulseContact, P_inter, P_inter->size(), pos, 0);\n setBlock(*_forceContact, F_inter, F_inter->size(), pos, 0);\n setBlock(*_energyContact, E_inter, E_inter->size(), pos, 0);\n }\n else\n {\n P_inter->zero(); // no impulse at this Interaction\n F_inter->zero(); // no force at this Interaction\n E_inter->zero(); // no potential at this Interaction\n };\n // Write the force at the Interaction\n WriteVectorIntoMatrix(*F_inter, _ithPoint, col_pos);\n //WriteVectorIntoMatrix(*P_inter, _ithPoint, col_pos);\n //WriteVectorIntoMatrix(*E_inter, _ithPoint, col_pos);\n col_pos = col_pos + F_inter->size();\n } // Save the data related to DS\n SP::DynamicalSystemsGraph DSG = simulation()->nonSmoothDynamicalSystem()->dynamicalSystems();\n DynamicalSystemsGraph::VIterator dsi, dsiend;\n for (std11::tie(dsi, dsiend) = DSG->vertices(); dsi != dsiend; ++dsi)\n {\n SP::DynamicalSystem ds = DSG->bundle(*dsi); // DS\n SP::LagrangianDS Lagds = std11::dynamic_pointer_cast(ds);\n SP::SiconosVector qdot = Lagds->velocity();\n // Write\n\n WriteVectorIntoMatrix(*qdot, _ithPoint, col_pos);\n col_pos = col_pos + qdot->size();\n }\n}\n//=======================================================================================\nvoid OSNSMultipleImpact::ComputeImpact()\n{\n _impulseVariable = 0.0;\n _timeVariable = 0.0;\n unsigned int number_step = 1;\n unsigned int point_save = 0;\n unsigned int _counterstepsave = 0;\n // Show computation progress\n //cout << \"*********** Impact computation progress *************\" <display();\n std::cout << \"Impulse at contact: \";\n _tolImpulseContact->display();\n */\n //cout << \"-------------------Multiple impacts computation starts:-----------------------\" <= _nStepSave))\n {\n if ((number_step >= _stepMinSave) && (number_step <= _stepMaxSave))\n {\n UpdateDuringImpact(); // Update state of dynamical system\n SaveDataOneStep(point_save); // Save the data\n point_save++;\n _counterstepsave = 0; // reset the counter to 0\n }\n }\n //\n if (number_step > _nStepMax)\n {\n RuntimeException::selfThrow(\"In OSNSMultipleImpact::ComputeImpact, number of integration steps perfomed exceeds the maximal number of steps allowed!!!\");\n //cout << \"Causion: so long computation, the computation is stopped even when the impact is not yet terminated!!! \" <display();\n // std::cout << \"Incremental Impulse: \";\n // _deltaImpulseContact->display();\n // std::cout << \"Impulse at contact: \";\n // _tolImpulseContact->display();\n // std::cout << \"Velocity at contacts: \";\n // _velocityContact->display();\n // std::cout << \"Potential energy at contacts: \";\n // _energyContact->display();\n\n }\n\n //\n // std::cout << \"*****************Impact computation is terminated******************\" <display();\n // std::cout << \"Impulse at contact: \";\n // _tolImpulseContact->display();\n // std::cout << \"Duration of the multiple impacts process: \" << _timeVariable << \" s\" <indexSet(indexSetLevel());\n // y and lambda vectors\n SP::SiconosVector lambda;\n SP::SiconosVector y;\n // === Loop through \"active\" Interactions (ie present in indexSets[1]) ===\n unsigned int pos;\n InteractionsGraph::VIterator ui, uiend;\n for (std11::tie(ui, uiend) = indexSet->vertices(); ui != uiend; ++ui)\n {\n Interaction& inter = *indexSet->bundle(*ui);\n // Get the relative position of inter-interactionBlock in the vector _velocityContact/_tolImpulseContact\n pos = _M->getPositionOfInteractionBlock(inter);\n // Get Y and Lambda for the current Interaction\n y = inter.y(inputOutputLevel());\n lambda = inter.lambda(inputOutputLevel());\n // Copy _velocityContact/_tolImpulseContact, starting from index pos into y/lambda\n // save into y !!\n setBlock(*_velocityContact, y, y->size(), pos, 0);// Warning: yEquivalent is\n // saved into lambda[1] !!\n setBlock(*_impulseContactUpdate, lambda, lambda->size(), pos, 0);\n // If the update is performed at the end of the impact process, we update the total normal impulse at contacts\n // from the beginning to the end of impact (vector _tolImpulseContact). Otherwise, we must reset the lambda[1] to zero because\n // the post-impact velocity has been calculated during impact\n // if (!_saveData) // we update the impact state at the end of impact\n // {\n // // Copy _velocityContact/_tolImpulseContact, starting from index pos into y/lambda\n // // save into y !!\n // setBlock(*_velocityContact, y, y->size(), pos, 0);// Warning: yEquivalent is\n // // saved into lambda[1] !!\n // setBlock(*_tolImpulseContact, lambda, lambda->size(), pos, 0);\n // }\n // else //\n // lambda->zero();\n }\n}\n//========================================================================================\nint OSNSMultipleImpact::compute(double time)\n{\n // Pre-compute for impact\n PreComputeImpact();\n // solve the multiple impacts\n if ((_nContact != 0) && IsVcminNegative()) // if there is at least one contact and the vilocity before impact is negative\n {\n ComputeImpact();\n };\n // Post-compute for multiple impacts\n PostComputeImpact();\n return 0;\n}\n\n//========================================================================================\nvoid OSNSMultipleImpact::display() const\n{\n std::cout << \"<<<<<<<<<<<<<<<<< Information about the multiple impact >>>>>>>>>>>>>>>>>>>>>\" <nonSmoothDynamicalSystem()->topology()->dSG(0);\n DynamicalSystemsGraph::VIterator ui, uiend;\n for (std11::tie(ui, uiend) = DSG0->vertices(); ui != uiend; ++ui)\n {\n SP::DynamicalSystem ds = DSG0->bundle(*ui);\n SP::LagrangianDS lag_ds = std11::dynamic_pointer_cast(ds);\n std::cout << \"DS number: \" << ds->number() <velocityMemory()->getSiconosVector(1))->display();\n std::cout << \"Post-impact velocity: \";\n (lag_ds->velocity())->display();\n }\n // Display impulses at contact points\n SP::InteractionsGraph IndexSet0 = simulation()->nonSmoothDynamicalSystem()->topology()->indexSet(0);\n InteractionsGraph::VIterator vi, viend;\n for (std11::tie(vi, viend) = IndexSet0->vertices(); vi != viend; ++vi)\n {\n SP::Interaction inter = IndexSet0->bundle(*vi);\n std::cout << \"Impulse at contact point \" << inter->number() << \":\";\n (inter->lambda(1))->display();\n }\n};\n", "meta": {"hexsha": "590dce2b12da788c6c7bbbf8cbd69dd091517360", "size": 41641, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "kernel/src/simulationTools/OSNSMultipleImpact.cpp", "max_stars_repo_name": "siconos/siconos-deb", "max_stars_repo_head_hexsha": "2739a23f23d797dbfecec79d409e914e13c45c67", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "kernel/src/simulationTools/OSNSMultipleImpact.cpp", "max_issues_repo_name": "siconos/siconos-deb", "max_issues_repo_head_hexsha": "2739a23f23d797dbfecec79d409e914e13c45c67", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "kernel/src/simulationTools/OSNSMultipleImpact.cpp", "max_forks_repo_name": "siconos/siconos-deb", "max_forks_repo_head_hexsha": "2739a23f23d797dbfecec79d409e914e13c45c67", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.7358139535, "max_line_length": 256, "alphanum_fraction": 0.6216229197, "num_tokens": 10852, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.3960681662740416, "lm_q1q2_score": 0.23023564916092618}} {"text": "\n//此源码被清华学神尹成大魔王专业翻译分析并修改\n//尹成QQ77025077\n//尹成微信18510341407\n//尹成所在QQ群721929980\n//尹成邮箱 yinc13@mails.tsinghua.edu.cn\n//尹成毕业于清华大学,微软区块链领域全球最有价值专家\n//https://mvp.microsoft.com/zh-cn/PublicProfile/4033620\n//版权所有（c）2017-2018比特币核心开发商\n//根据MIT软件许可证分发，请参见随附的\n//文件复制或http://www.opensource.org/licenses/mit-license.php。\n\n#include \n\n#include \n#include \n\n#include \n\n//降阶比较器\nstruct {\n bool operator()(const OutputGroup& a, const OutputGroup& b) const\n {\n return a.effective_value > b.effective_value;\n }\n} descending;\n\n/*\n *这是Murch设计的分支绑定硬币选择算法。它搜索输入\n *设置可支付支出目标，且不超过支出目标\n *创建和支出变更输出的成本。该算法对二进制文件使用深度优先搜索。\n *树。在二叉树中，每个节点对应于utxo的包含或省略。UTXOS\n *按其有效值排序，并根据包含内容对树进行确定性探索。\n *先分支。在每个节点上，算法检查选择是否在目标范围内。\n *虽然选择未达到目标范围，但包含更多的utxo。当选定内容\n *值超出目标范围，可省略从此选择派生的完整子树。\n *此时，取消选择最后一个包含的utxo，并探索相应的省略分支。\n *相反。搜索完成树后或经过有限的尝试后，搜索结束。\n *\n *找到一个解决方案后，搜索会继续搜索更好的解决方案。最好的\n *通过最小化浪费指标来选择解决方案。废物指标定义为\n *以给定的费率减去长期预期成本来花费当前投入\n *输入，加上所选金额超出支出目标：\n *\n *浪费=选择总计-目标+输入×（当前频率-长期频率）\n *\n *该算法使用了两个额外的优化。展望跟踪\n *未经探索的utxos。如果lookahead指示目标范围，则不探索子树。\n *无法联系。此外，无需测试等效组合。这让我们\n *跳过测试是否包含与省略项的有效值和浪费相匹配的utxo。\n *前身。\n *\n *分支定界算法在Murch的硕士论文中有详细描述：\n *https://murch.one/wp-content/uploads/2016/11/erhardt2016coinselection.pdf\n *\n *@param const std:：vector&utxo_pool我们正在选择的utxos集。\n *这些utxo将按有效值和cinputcoins的降序排序。\n *值是它们的有效值。\n *@param const camount&target_value这是我们要选择的值。它是较低的\n *范围的界限。\n *@param const camount&cost_change这是创建和花费更改输出的成本。\n *这个加上目标值是范围的上限。\n *@param std:：set&out\\set->这是一组cinputcoins的输出参数\n *已选定。\n *@param camount&value_ret->这是Cinputcoins总值的输出参数\n *已选定。\n *@param camount not_input_fees->输出和固定大小需要支付的费用\n *开销（版本、锁定时间、标记和标志）\n **/\n\n\nstatic const size_t TOTAL_TRIES = 100000;\n\nbool SelectCoinsBnB(std::vector& utxo_pool, const CAmount& target_value, const CAmount& cost_of_change, std::set& out_set, CAmount& value_ret, CAmount not_input_fees)\n{\n out_set.clear();\n CAmount curr_value = 0;\n\nstd::vector curr_selection; //在此索引中选择utxo\n curr_selection.reserve(utxo_pool.size());\n CAmount actual_target = not_input_fees + target_value;\n\n//计算当前可用值\n CAmount curr_available_value = 0;\n for (const OutputGroup& utxo : utxo_pool) {\n//断言这个utxo不是负数。它不应该是负数，有效值计算应该删除它。\n assert(utxo.effective_value > 0);\n curr_available_value += utxo.effective_value;\n }\n if (curr_available_value < actual_target) {\n return false;\n }\n\n//分类utxo_池\n std::sort(utxo_pool.begin(), utxo_pool.end(), descending);\n\n CAmount curr_waste = 0;\n std::vector best_selection;\n CAmount best_waste = MAX_MONEY;\n\n//选择utxos的深度优先搜索循环\n for (size_t i = 0; i < TOTAL_TRIES; ++i) {\n//开始回溯的条件\n bool backtrack = false;\nif (curr_value + curr_available_value < actual_target || //无法达到当前可用价值中剩余金额的目标。\ncurr_value > actual_target + cost_of_change || //所选值超出范围，请返回并尝试其他分支\n(curr_waste > best_waste && (utxo_pool.at(0).fee - utxo_pool.at(0).long_term_fee) > 0)) { //不要选择那些我们知道如果浪费增加会更浪费的东西。\n backtrack = true;\n} else if (curr_value >= actual_target) { //所选值在范围内\ncurr_waste += (curr_value - actual_target); //这是为进行以下比较而添加到废物中的超额价值。\n//如果长期费用高于当前费用，在检查后再添加一个utxo可以减少浪费。\n//但是，我们不会去探索这一点，因为只有当我们达到目标时，才会对废物进行优化。\n//价值。再加上任何一个utxo都只会烧掉utxo，而这完全是要收费的。所以我们不会\n//探索更多的UTXO，以避免像那样烧钱。\n if (curr_waste <= best_waste) {\n best_selection = curr_selection;\n best_selection.resize(utxo_pool.size());\n best_waste = curr_waste;\n }\ncurr_waste -= (curr_value - actual_target); //去掉多余的价值，因为我们现在要选择不同的硬币\n backtrack = true;\n }\n\n//回溯，向后移动\n if (backtrack) {\n//向后走，找到最后一个包含的utxo，它仍然需要遍历其省略分支。\n while (!curr_selection.empty() && !curr_selection.back()) {\n curr_selection.pop_back();\n curr_available_value += utxo_pool.at(curr_selection.size()).effective_value;\n }\n\nif (curr_selection.empty()) { //我们已经走回了第一个utxo，没有一个分支是不受欢迎的。已搜索所有解决方案\n break;\n }\n\n//输出包含在以前的迭代中，请尝试立即排除。\n curr_selection.back() = false;\n OutputGroup& utxo = utxo_pool.at(curr_selection.size() - 1);\n curr_value -= utxo.effective_value;\n curr_waste -= utxo.fee - utxo.long_term_fee;\n} else { //向前走，继续沿着这条路走\n OutputGroup& utxo = utxo_pool.at(curr_selection.size());\n\n//将此utxo从当前可用的utxo值中删除\n curr_available_value -= utxo.effective_value;\n\n//如果先前的utxo具有相同的值和相同的浪费并且被排除在外，则避免搜索分支。因为费用与\n//长期费用是一样的，我们只需要检查其中一个值是否匹配就可以知道浪费是一样的。\n if (!curr_selection.empty() && !curr_selection.back() &&\n utxo.effective_value == utxo_pool.at(curr_selection.size() - 1).effective_value &&\n utxo.fee == utxo_pool.at(curr_selection.size() - 1).fee) {\n curr_selection.push_back(false);\n } else {\n//包容性分支第一（最大的第一次勘探）\n curr_selection.push_back(true);\n curr_value += utxo.effective_value;\n curr_waste += utxo.fee - utxo.long_term_fee;\n }\n }\n }\n\n//检查解决方案\n if (best_selection.empty()) {\n return false;\n }\n\n//集合输出集合\n value_ret = 0;\n for (size_t i = 0; i < best_selection.size(); ++i) {\n if (best_selection.at(i)) {\n util::insert(out_set, utxo_pool.at(i).m_outputs);\n value_ret += utxo_pool.at(i).m_value;\n }\n }\n\n return true;\n}\n\nstatic void ApproximateBestSubset(const std::vector& groups, const CAmount& nTotalLower, const CAmount& nTargetValue,\n std::vector& vfBest, CAmount& nBest, int iterations = 1000)\n{\n std::vector vfIncluded;\n\n vfBest.assign(groups.size(), true);\n nBest = nTotalLower;\n\n FastRandomContext insecure_rand;\n\n for (int nRep = 0; nRep < iterations && nBest != nTargetValue; nRep++)\n {\n vfIncluded.assign(groups.size(), false);\n CAmount nTotal = 0;\n bool fReachedTarget = false;\n for (int nPass = 0; nPass < 2 && !fReachedTarget; nPass++)\n {\n for (unsigned int i = 0; i < groups.size(); i++)\n {\n//这里的解算器使用随机算法，\n//随机性不是真正的安全目的，只是\n//需要防止退化行为，这很重要\n//RNG很快。我们不使用常数随机序列，\n//因为通过\n//随机选择。\n if (nPass == 0 ? insecure_rand.randbool() : !vfIncluded[i])\n {\n nTotal += groups[i].m_value;\n vfIncluded[i] = true;\n if (nTotal >= nTargetValue)\n {\n fReachedTarget = true;\n if (nTotal < nBest)\n {\n nBest = nTotal;\n vfBest = vfIncluded;\n }\n nTotal -= groups[i].m_value;\n vfIncluded[i] = false;\n }\n }\n }\n }\n }\n}\n\nbool KnapsackSolver(const CAmount& nTargetValue, std::vector& groups, std::set& setCoinsRet, CAmount& nValueRet)\n{\n setCoinsRet.clear();\n nValueRet = 0;\n\n//小于目标值的列表\n boost::optional lowest_larger;\n std::vector applicable_groups;\n CAmount nTotalLower = 0;\n\n Shuffle(groups.begin(), groups.end(), FastRandomContext());\n\n for (const OutputGroup& group : groups) {\n if (group.m_value == nTargetValue) {\n util::insert(setCoinsRet, group.m_outputs);\n nValueRet += group.m_value;\n return true;\n } else if (group.m_value < nTargetValue + MIN_CHANGE) {\n applicable_groups.push_back(group);\n nTotalLower += group.m_value;\n } else if (!lowest_larger || group.m_value < lowest_larger->m_value) {\n lowest_larger = group;\n }\n }\n\n if (nTotalLower == nTargetValue) {\n for (const auto& group : applicable_groups) {\n util::insert(setCoinsRet, group.m_outputs);\n nValueRet += group.m_value;\n }\n return true;\n }\n\n if (nTotalLower < nTargetValue) {\n if (!lowest_larger) return false;\n util::insert(setCoinsRet, lowest_larger->m_outputs);\n nValueRet += lowest_larger->m_value;\n return true;\n }\n\n//用随机逼近法求解子集和\n std::sort(applicable_groups.begin(), applicable_groups.end(), descending);\n std::vector vfBest;\n CAmount nBest;\n\n ApproximateBestSubset(applicable_groups, nTotalLower, nTargetValue, vfBest, nBest);\n if (nBest != nTargetValue && nTotalLower >= nTargetValue + MIN_CHANGE) {\n ApproximateBestSubset(applicable_groups, nTotalLower, nTargetValue + MIN_CHANGE, vfBest, nBest);\n }\n\n//如果我们有一个更大的硬币（或者随机近似没有找到一个好的解决方案，\n//或者下一个更大的硬币更近），把更大的硬币还给我\n if (lowest_larger &&\n ((nBest != nTargetValue && nBest < nTargetValue + MIN_CHANGE) || lowest_larger->m_value <= nBest)) {\n util::insert(setCoinsRet, lowest_larger->m_outputs);\n nValueRet += lowest_larger->m_value;\n } else {\n for (unsigned int i = 0; i < applicable_groups.size(); i++) {\n if (vfBest[i]) {\n util::insert(setCoinsRet, applicable_groups[i].m_outputs);\n nValueRet += applicable_groups[i].m_value;\n }\n }\n\n if (LogAcceptCategory(BCLog::SELECTCOINS)) {\n /*打印（bclog:：selectcoins，“selectcoins（）最佳子集：”）；/*续*/\n for（unsigned int i=0；i::iterator OutputGroup::Discard(const CInputCoin& output) {\n auto it = m_outputs.begin();\n while (it != m_outputs.end() && it->outpoint != output.outpoint) ++it;\n if (it == m_outputs.end()) return it;\n m_value -= output.effective_value;\n effective_value -= output.effective_value;\n return m_outputs.erase(it);\n}\n\nbool OutputGroup::EligibleForSpending(const CoinEligibilityFilter& eligibility_filter) const\n{\n return m_depth >= (m_from_me ? eligibility_filter.conf_mine : eligibility_filter.conf_theirs)\n && m_ancestors <= eligibility_filter.max_ancestors\n && m_descendants <= eligibility_filter.max_descendants;\n}\n", "meta": {"hexsha": "3507a5dccb79d0c898925168fcd5285d7ddda69d", "size": 10966, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/wallet/coinselection.cpp", "max_stars_repo_name": "yinchengtsinghua/BitCoinCppChinese", "max_stars_repo_head_hexsha": "76f64ad8cee5b6c5671b3629f39e7ae4ef84be0a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 13.0, "max_stars_repo_stars_event_min_datetime": "2019-01-23T04:36:05.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-21T11:20:25.000Z", "max_issues_repo_path": "src/wallet/coinselection.cpp", "max_issues_repo_name": "yinchengtsinghua/BitCoinCppChinese", "max_issues_repo_head_hexsha": "76f64ad8cee5b6c5671b3629f39e7ae4ef84be0a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/wallet/coinselection.cpp", "max_forks_repo_name": "yinchengtsinghua/BitCoinCppChinese", "max_forks_repo_head_hexsha": "76f64ad8cee5b6c5671b3629f39e7ae4ef84be0a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2019-01-24T07:48:15.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-11T13:34:44.000Z", "avg_line_length": 32.1583577713, "max_line_length": 191, "alphanum_fraction": 0.630494255, "num_tokens": 3898, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813030761371503, "lm_q2_score": 0.39606816627404173, "lm_q1q2_score": 0.23023564341510078}} {"text": "/************************************************************************\n * *\n * Copyright (C) 2012 OVSM/IPGP *\n * *\n * This program is free software: you can redistribute it and/or modify *\n * it under the terms of the GNU General Public License as published by *\n * the Free Software Foundation, either version 3 of the License, or *\n * (at your option) any later version. *\n * *\n * This program is distributed in the hope that it will be useful, *\n * but WITHOUT ANY WARRANTY; without even the implied warranty of *\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the *\n * GNU General Public License for more details. *\n * *\n * This program is part of 'Projet TSUAREG - INTERREG IV Caraïbes'. *\n * It has been co-financed by the European Union and le Ministère de *\n * l'Ecologie, du Développement Durable, des Transports et du Logement. *\n * *\n ************************************************************************/\n\n\n#define SEISCOMP_COMPONENT Md\n\n#include \"md.h\"\n#include \"l4c1hz.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\n#define _DEPTH_MAX 200.0\n#define _SIGNAL_WINDOW_END 30.0\n#define _LINEAR_CORRECTION 1.0\n#define _OFFSET 0.0\n#define _SNR_MIN 1.2\n#define _DELTA_MAX 400.0\n#define _MD_MAX 5.0\n#define _FMA -0.87\n#define _FMB 2.0\n#define _FMD 0.0035\n#define _FMF 0.0\n#define _FMZ 0.0\n#define _STACOR 0.0 //! not fully implemented !\n/**\n * Seismometer selection\n * 1 for Wood-Anderson\n * 2 for Seismometer 5 sec\n * 3 for WWSSN LP ? filter\n * 4 for WWSSN SP? filter\n * 5 for Generic Seismometer ? filter\n * 6 for Butterworth Low Pass ? filter\n * 7 for Butterwoth High Pass ? filter\n * 8 for Butterworth Band Pass ? filter\n * 9 for L4C 1Hz seismometer\n **/\n#define _SEISMO 9\n#define _BUTTERWORTH \"\"\n\nADD_SC_PLUGIN(\"Md duration magnitude plugin\", \"IPGP \", 0, 1, 1)\n\n#define AMPTAG \"[Amp] [Md]\"\n#define MAGTAG \"[Mag] [Md]\"\n\n\nusing namespace Seiscomp;\nusing namespace Seiscomp::Math;\nusing namespace Seiscomp::Processing;\n\n\n/*----[ AMPLITUDE PROCESSOR CLASS ]----*/\n\nIMPLEMENT_SC_CLASS_DERIVED(AmplitudeProcessor_Md, AmplitudeProcessor, \"AmplitudeProcessor_Md\");\nREGISTER_AMPLITUDEPROCESSOR(AmplitudeProcessor_Md, \"Md\");\n\nstruct ampConfig {\n\n\t\tdouble DEPTH_MAX;\n\t\tdouble SIGNAL_WINDOW_END;\n\t\tdouble SNR_MIN;\n\t\tdouble DELTA_MAX;\n\t\tdouble MD_MAX;\n\t\tdouble FMA;\n\t\tdouble FMB;\n\t\tdouble FMD;\n\t\tdouble FMF;\n\t\tdouble FMZ;\n\t\tdouble STACOR;\n\t\tint SEISMO;\n\t\tstd::string BUTTERWORTH;\n};\nampConfig aFile;\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nAmplitudeProcessor_Md::AmplitudeProcessor_Md() :\n\t\tAmplitudeProcessor(\"Md\") {\n\n\tsetSignalStart(0.);\n\tsetSignalEnd(150.);\n\tsetMinSNR(aFile.SNR_MIN);\n\tsetMaxDist(8);\n\t_computeAbsMax = true;\n\t_isInitialized = false;\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nAmplitudeProcessor_Md::AmplitudeProcessor_Md(const Core::Time& trigger) :\n\t\tAmplitudeProcessor(trigger, \"Md\") {\n\n\tsetSignalStart(0.);\n\tsetSignalEnd(150.);\n\tsetMinSNR(aFile.SNR_MIN);\n\tsetMaxDist(8);\n\t_computeAbsMax = true;\n\t_isInitialized = false;\n\n\tcomputeTimeWindow();\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nbool AmplitudeProcessor_Md::setup(const Settings& settings) {\n\n\tif ( !AmplitudeProcessor::setup(settings) )\n\t\treturn false;\n\n\tbool isButterworth = false;\n\ttry {\n\t\taFile.SEISMO = settings.getInt(\"md.seismo\");\n\t\tstd::string type;\n\t\tswitch ( aFile.SEISMO ) {\n\t\t\tcase 1:\n\t\t\t\ttype = \"WoodAnderson\";\n\t\t\tbreak;\n\t\t\tcase 2:\n\t\t\t\ttype = \"Seismo5sec\";\n\t\t\tbreak;\n\t\t\tcase 3:\n\t\t\t\ttype = \"WWSSN LP\";\n\t\t\tbreak;\n\t\t\tcase 4:\n\t\t\t\ttype = \"WWSSN SP\";\n\t\t\tbreak;\n\t\t\tcase 5:\n\t\t\t\ttype = \"Generic Seismometer\";\n\t\t\tbreak;\n\t\t\tcase 6:\n\t\t\t\ttype = \"Butterworth Low Pass\";\n\t\t\t\tisButterworth = true;\n\t\t\tbreak;\n\t\t\tcase 7:\n\t\t\t\ttype = \"Butterworth High Pass\";\n\t\t\t\tisButterworth = true;\n\t\t\tbreak;\n\t\t\tcase 8:\n\t\t\t\ttype = \"Butterworth Band Pass\";\n\t\t\t\tisButterworth = true;\n\t\t\tbreak;\n\t\t\tcase 9:\n\t\t\t\ttype = \"L4C 1Hz Seismometer\";\n\t\t\tbreak;\n\t\t\tdefault:\n\t\t\t\tbreak;\n\t\t}\n\t\tSEISCOMP_DEBUG(\"%s sets SEISMO to %s [%s.%s]\", AMPTAG, type.c_str(),\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.SEISMO = _SEISMO;\n\t\tSEISCOMP_ERROR(\"%s can not read SEISMO value from configuration file [%s.%s]\", AMPTAG,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\tif ( isButterworth == true ) {\n\t\ttry {\n\t\t\taFile.BUTTERWORTH = settings.getString(\"md.butterworth\");\n\t\t\tSEISCOMP_DEBUG(\"%s sets Butterworth filter to %s [%s.%s]\", AMPTAG,\n\t\t\t aFile.BUTTERWORTH.c_str(), settings.networkCode.c_str(),\n\t\t\t settings.stationCode.c_str());\n\t\t}\n\t\tcatch ( ... ) {\n\t\t\taFile.BUTTERWORTH = _BUTTERWORTH;\n\t\t\tSEISCOMP_ERROR(\"%s can not read Butterworth filter value from configuration file [%s.%s]\", AMPTAG,\n\t\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t\t}\n\t}\n\n\ttry {\n\t\taFile.DEPTH_MAX = settings.getDouble(\"md.depthmax\");\n\t\tSEISCOMP_DEBUG(\"%s sets DEPTH MAX to %.2f [%s.%s]\", AMPTAG, aFile.DEPTH_MAX,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.DEPTH_MAX = _DEPTH_MAX;\n\t\tSEISCOMP_ERROR(\"%s can not read DEPTH MAX value from configuration file [%s.%s]\", AMPTAG,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\taFile.DELTA_MAX = settings.getDouble(\"md.deltamax\");\n\t\tSEISCOMP_DEBUG(\"%s sets DELTA MAX to %.2f [%s.%s]\", AMPTAG, aFile.DELTA_MAX,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.DELTA_MAX = _DELTA_MAX;\n\t\tSEISCOMP_ERROR(\"%s can not read DELTA MAX value from configuration file [%s.%s]\", AMPTAG,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\taFile.SNR_MIN = settings.getDouble(\"md.snrmin\");\n\t\tSEISCOMP_DEBUG(\"%s sets SNR MIN to %.2f [%s.%s]\", AMPTAG, aFile.SNR_MIN,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.SNR_MIN = _SNR_MIN;\n\t\tSEISCOMP_ERROR(\"%s can not read SNR MIN value from configuration file [%s.%s]\", AMPTAG,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\taFile.MD_MAX = settings.getDouble(\"md.mdmax\");\n\t\tSEISCOMP_DEBUG(\"%s sets MD MAX to %.2f [%s.%s]\", AMPTAG, aFile.MD_MAX,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.MD_MAX = _MD_MAX;\n\t\tSEISCOMP_ERROR(\"%s can not read MD MAX value from configuration file [%s.%s]\", AMPTAG,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\taFile.FMA = settings.getDouble(\"md.fma\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMA to %.4f [%s.%s]\", AMPTAG, aFile.FMA,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.FMA = _FMA;\n\t\tSEISCOMP_ERROR(\"%s can not read FMA value from configuration file [%s.%s]\", AMPTAG,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\taFile.FMB = settings.getDouble(\"md.fmb\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMB to %.4f [%s.%s]\", AMPTAG, aFile.FMB,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.FMB = _FMB;\n\t\tSEISCOMP_ERROR(\"%s can not read FMB value from configuration file [%s.%s]\", AMPTAG,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\taFile.FMD = settings.getDouble(\"md.fmd\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMD to %.4f [%s.%s]\", AMPTAG, aFile.FMD,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.FMD = _FMD;\n\t\tSEISCOMP_ERROR(\"%s can not read FMD value from configuration file [%s.%s]\", AMPTAG,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\taFile.FMF = settings.getDouble(\"md.fmf\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMF to %.4f [%s.%s]\", AMPTAG, aFile.FMF,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.FMF = _FMF;\n\t\tSEISCOMP_ERROR(\"%s can not read FMF value from configuration file [%s.%s]\", AMPTAG,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\taFile.FMZ = settings.getDouble(\"md.fmz\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMZ to %.4f [%s.%s]\", AMPTAG, aFile.FMZ,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\taFile.FMZ = _FMZ;\n\t\tSEISCOMP_ERROR(\"%s can not read FMZ value from configuration file [%s.%s]\",\n\t\t AMPTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\t_isInitialized = true;\n\n\treturn true;\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nvoid AmplitudeProcessor_Md::initFilter(double fsamp) {\n\n\tif ( !_enableResponses ) {\n\n\t\tSEISCOMP_DEBUG(\"Using custom responses\");\n\n\t\tMath::Filtering::InPlaceFilter* f;\n\t\tswitch ( aFile.SEISMO ) {\n\t\t\tcase 1:\n\t\t\t\tAmplitudeProcessor::setFilter(new Filtering::IIR::WoodAndersonFilter<\n\t\t\t\t double>(Velocity));\n\t\t\tbreak;\n\t\t\tcase 2:\n\t\t\t\tAmplitudeProcessor::setFilter(new Filtering::IIR::Seismometer5secFilter<\n\t\t\t\t double>(Velocity));\n\t\t\tbreak;\n\t\t\tcase 3:\n\t\t\t\tAmplitudeProcessor::setFilter(new Filtering::IIR::WWSSN_LP_Filter<\n\t\t\t\t double>(Velocity));\n\t\t\tbreak;\n\t\t\tcase 4:\n\t\t\t\tAmplitudeProcessor::setFilter(new Filtering::IIR::WWSSN_SP_Filter<\n\t\t\t\t double>(Velocity));\n\t\t\tbreak;\n\t\t\tcase 5:\n\t\t\t\tAmplitudeProcessor::setFilter(new Filtering::IIR::GenericSeismometer<\n\t\t\t\t double>(Velocity));\n\t\t\tbreak;\n\t\t\tcase 6:\n\t\t\t\tf = new Math::Filtering::IIR::ButterworthLowpass(3, 1, 15);\n\t\t\t\tAmplitudeProcessor::setFilter(f);\n\t\t\tbreak;\n\t\t\tcase 7:\n\t\t\t\tf = new Math::Filtering::IIR::ButterworthHighpass(3, 1, 15);\n\t\t\t\tAmplitudeProcessor::setFilter(f);\n\t\t\tbreak;\n\t\t\tcase 8:\n\t\t\t\tf = new Math::Filtering::IIR::ButterworthBandpass(3, 1, 15, 1, true);\n\t\t\t\tAmplitudeProcessor::setFilter(f);\n\t\t\tbreak;\n\t\t\tcase 9:\n\t\t\t\tAmplitudeProcessor::setFilter(new Filtering::IIR::L4C_1Hz_Filter<\n\t\t\t\t double>(Velocity));\n\t\t\tbreak;\n\t\t\tdefault:\n\t\t\t\tSEISCOMP_ERROR(\"%s can not initialize the chosen filter, \"\n\t\t\t\t\t\"please review your configuration file\", AMPTAG);\n\t\t\tbreak;\n\t\t}\n\t}\n\telse\n\t\tAmplitudeProcessor::setFilter(NULL);\n\n\tAmplitudeProcessor::initFilter(fsamp);\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nint AmplitudeProcessor_Md::capabilities() const {\n\treturn MeasureType;\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nAmplitudeProcessor::IDList\nAmplitudeProcessor_Md::capabilityParameters(Capability cap) const {\n\n\tif ( cap == MeasureType ) {\n\t\tIDList params;\n\t\tparams.push_back(\"AbsMax\");\n\t\tparams.push_back(\"MinMax\");\n\t\treturn params;\n\t}\n\n\treturn AmplitudeProcessor::capabilityParameters(cap);\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nbool AmplitudeProcessor_Md::setParameter(Capability cap,\n const std::string& value) {\n\n\tif ( cap == MeasureType ) {\n\n\t\tif ( value == \"AbsMax\" ) {\n\t\t\t_computeAbsMax = true;\n\t\t\treturn true;\n\t\t}\n\t\telse if ( value == \"MinMax\" ) {\n\t\t\t_computeAbsMax = false;\n\t\t\treturn true;\n\t\t}\n\t\treturn false;\n\t}\n\n\treturn AmplitudeProcessor::setParameter(cap, value);\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nbool AmplitudeProcessor_Md::deconvolveData(Response* resp,\n DoubleArray& data,\n int numberOfIntegrations) {\n\tif ( numberOfIntegrations < -1 )\n\t\treturn false;\n\n\tSEISCOMP_DEBUG(\"Inside deconvolve function\");\n\n\tdouble m, n;\n\tMath::Restitution::FFT::TransferFunctionPtr tf =\n\t\tresp->getTransferFunction(numberOfIntegrations < 0 ? 0 : numberOfIntegrations);\n\n\tif ( !tf )\n\t\treturn false;\n\n\tMath::GroundMotion gm;\n\n\tif ( numberOfIntegrations < 0 )\n\t\tgm = Math::Displacement;\n\telse\n\t\tgm = Math::Velocity;\n\n\tMath::Restitution::FFT::TransferFunctionPtr cascade;\n\tMath::SeismometerResponse::WoodAnderson woodAndersonResp(gm);\n\tMath::SeismometerResponse::Seismometer5sec seis5sResp(gm);\n\tMath::SeismometerResponse::L4C_1Hz l4c1hzResp(gm);\n\n\tMath::Restitution::FFT::PolesAndZeros woodAnderson(woodAndersonResp);\n\tMath::Restitution::FFT::PolesAndZeros seis5sec(seis5sResp);\n\tMath::Restitution::FFT::PolesAndZeros l4c1hz(l4c1hzResp);\n\n\tSEISCOMP_DEBUG(\"SEISMO = %d\", aFile.SEISMO);\n\n\tswitch ( aFile.SEISMO ) {\n\t\tcase 1:\n\t\t\tcascade = *tf / woodAnderson;\n\t\tbreak;\n\t\tcase 2:\n\t\t\tcascade = *tf / seis5sec;\n\t\tbreak;\n\t\tcase 9:\n\t\t\tSEISCOMP_INFO(\"%s Applying filter L4C 1Hz to data\", AMPTAG);\n\t\t\tcascade = *tf / l4c1hz;\n\t\tbreak;\n\t\tdefault:\n\t\t\tcascade = tf;\n\t\t\tSEISCOMP_INFO(\"%s No seismometer specified, no signal reconvolution performed\", AMPTAG);\n\t\t\treturn false;\n\t\tbreak;\n\t}\n\n\t// Remove linear trend\n\tMath::Statistics::computeLinearTrend(data.size(), data.typedData(), m, n);\n\tMath::Statistics::detrend(data.size(), data.typedData(), m, n);\n\n\treturn Math::Restitution::transformFFT(data.size(), data.typedData(),\n\t _stream.fsamp, cascade.get(), _config.respTaper, _config.respMinFreq,\n\t _config.respMaxFreq);\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nbool AmplitudeProcessor_Md::computeAmplitude(const DoubleArray& data, size_t i1,\n size_t i2, size_t si1, size_t si2,\n double offset, AmplitudeIndex* dt,\n AmplitudeValue* amplitude,\n double* period, double* snr) {\n\n\tdouble amax, Imax, ofs_sig, amp_sig;\n\tDoubleArrayPtr d;\n\n\tif ( *snr < aFile.SNR_MIN )\n\t\tSEISCOMP_DEBUG(\"%s computed SNR is under configured SNR MIN\", AMPTAG);\n\n\tif ( _computeAbsMax ) {\n\t\tsize_t imax = find_absmax(data.size(), data.typedData(), si1, si2, offset);\n\t\tamax = fabs(data[imax] - offset);\n\t\tdt->index = imax;\n\t}\n\telse {\n\t\tint lmin, lmax;\n\t\tfind_minmax(lmin, lmax, data.size(), data.typedData(), si1, si2, offset);\n\t\tamax = data[lmax] - data[lmin];\n\t\tdt->index = (lmin + lmax) * 0.5;\n\t\tdt->begin = lmin - dt->index;\n\t\tdt->end = lmax - dt->index;\n\t}\n\n\tImax = dt->index;\n\n\tSEISCOMP_DEBUG(\"%s Amplitude max: %.2f\", AMPTAG, amax);\n\n\t//! searching for Coda second by second through the end of the window\n\t//! TODO: elevate accuracy by using a nanometers scale (maybe)\n\tunsigned int i = si1;\n\tbool hasEndSignal = false;\n\tdouble calculatedSnr = -1;\n\n\tfor (i = (int) Imax; i < i2; i = i + 1 * (int) _stream.fsamp) {\n\n\t\tint window_end = i + 1 * (int) _stream.fsamp;\n\t\td = static_cast(data.slice(i, window_end));\n\n\t\t//! computes pre-arrival offset\n\t\tofs_sig = d->median();\n\n\t\t//! computes rms after removing offset\n\t\tamp_sig = 2 * d->rms(ofs_sig);\n\n\t\tif ( amp_sig / *_noiseAmplitude <= aFile.SNR_MIN ) {\n\t\t\tSEISCOMP_DEBUG(\"%s End of signal found! (%.2f <= %.2f)\", AMPTAG,\n\t\t\t (amp_sig / *_noiseAmplitude), aFile.SNR_MIN);\n\t\t\thasEndSignal = true;\n\t\t\tcalculatedSnr = amp_sig / *_noiseAmplitude;\n\t\t\tbreak;\n\t\t}\n\t}\n\n\tif ( !hasEndSignal ) {\n\t\tSEISCOMP_ERROR(\"%s SNR stayed over configured SNR_MIN! (%.2f > %.2f), \"\n\t\t\t\"skipping magnitude calculation for this station\", AMPTAG,\n\t\t calculatedSnr, aFile.SNR_MIN);\n\t\treturn false;\n\t}\n\n\tdt->index = i;\n\n\t//amplitude->value = 2 * amp_sig; //! actually it would have to be max. peak-to-peak\n\tamplitude->value = amp_sig;\n\n\tif ( _streamConfig[_usedComponent].gain != 0.0 )\n\t\tamplitude->value /= _streamConfig[_usedComponent].gain;\n\telse {\n\t\tsetStatus(MissingGain, 0.0);\n\t\treturn false;\n\t}\n\n\t// Convert m/s to nm/s\n\tamplitude->value *= 1.E09;\n\n\t*period = i - i1 + (_config.signalBegin * _stream.fsamp);\n\n\tSEISCOMP_DEBUG(\"%s calculated event amplitude = %.2f\", AMPTAG, amplitude->value);\n\tSEISCOMP_DEBUG(\"%s calculated signal end at %.2f ms from P phase\", AMPTAG, *period);\n\n\treturn true;\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\ndouble AmplitudeProcessor_Md::timeWindowLength(double distance_deg) const {\n\n\tif ( !_isInitialized ) {\n\n\t\taFile.MD_MAX = _MD_MAX;\n\t\taFile.FMA = _FMA;\n\t\taFile.FMZ = _FMZ;\n\t\taFile.DEPTH_MAX = _DEPTH_MAX;\n\t\taFile.STACOR = _STACOR;\n\t\taFile.FMD = _FMD;\n\t\taFile.FMB = _FMB;\n\t\taFile.FMF = _FMF;\n\t\taFile.SNR_MIN = _SNR_MIN;\n\t\taFile.DELTA_MAX = _DELTA_MAX;\n\t\taFile.SIGNAL_WINDOW_END = _SIGNAL_WINDOW_END;\n\t\taFile.SEISMO = _SEISMO;\n\t\taFile.BUTTERWORTH = _BUTTERWORTH;\n\t}\n\n\tdouble distance_km = Math::Geo::deg2km(distance_deg);\n\tdouble windowLength = (aFile.MD_MAX - aFile.FMA - (aFile.FMZ * aFile.DEPTH_MAX)\n\t - aFile.STACOR - (aFile.FMD * distance_km)) / (aFile.FMB + aFile.FMF);\n\n\twindowLength = pow(10, windowLength) + aFile.SIGNAL_WINDOW_END;\n\tSEISCOMP_DEBUG(\"%s Requesting stream of %.2fsec for current station\", AMPTAG, windowLength);\n\n\treturn windowLength;\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n/*----[ END OF AMPLITUDE PROCESSOR CLASS ]----*/\n\n\n\n\n/*----[ MAGNITUDE PROCESSOR CLASS ]----*/\n\nIMPLEMENT_SC_CLASS_DERIVED(MagnitudeProcessor_Md, MagnitudeProcessor, \"MagnitudeProcessor_Md\");\nREGISTER_MAGNITUDEPROCESSOR(MagnitudeProcessor_Md, \"Md\");\n\nstruct magConfig {\n\n\t\tdouble DEPTH_MAX;\n\t\tdouble LINEAR_CORRECTION;\n\t\tdouble OFFSET;\n\t\tdouble DELTA_MAX;\n\t\tdouble MD_MAX;\n\t\tdouble FMA;\n\t\tdouble FMB;\n\t\tdouble FMD;\n\t\tdouble FMF;\n\t\tdouble FMZ;\n\t\tdouble STACOR;\n};\nmagConfig mFile;\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nMagnitudeProcessor_Md::MagnitudeProcessor_Md() :\n\t\tMagnitudeProcessor(\"Md\") {\n\n\t_linearCorrection = mFile.LINEAR_CORRECTION;\n\t_constantCorrection = mFile.OFFSET;\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nbool MagnitudeProcessor_Md::setup(const Settings& settings) {\n\n\ttry {\n\t\tmFile.DELTA_MAX = settings.getDouble(\"md.deltamax\");\n\t\tSEISCOMP_DEBUG(\"%s sets DELTA MAX to %.2f [%s.%s]\", MAGTAG, mFile.DELTA_MAX,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.DELTA_MAX = _DELTA_MAX;\n\t\tSEISCOMP_ERROR(\"%s can not read DELTA MAX value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.DEPTH_MAX = settings.getDouble(\"md.depthmax\");\n\t\tSEISCOMP_DEBUG(\"%s sets DEPTH MAX to %.2f [%s.%s]\", MAGTAG, mFile.DEPTH_MAX,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.DEPTH_MAX = _DEPTH_MAX;\n\t\tSEISCOMP_ERROR(\"%s can not read DEPTH MAX value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.MD_MAX = settings.getDouble(\"md.mdmax\");\n\t\tSEISCOMP_DEBUG(\"%s sets MD MAX to %.2f [%s.%s]\", MAGTAG, mFile.MD_MAX,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.MD_MAX = _MD_MAX;\n\t\tSEISCOMP_ERROR(\"%s can not read MD MAX value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.LINEAR_CORRECTION = settings.getDouble(\"md.linearcorrection\");\n\t\tSEISCOMP_DEBUG(\"%s sets LINEAR CORRECTION to %.2f [%s.%s]\", MAGTAG,\n\t\t mFile.LINEAR_CORRECTION, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.LINEAR_CORRECTION = _LINEAR_CORRECTION;\n\t\tSEISCOMP_ERROR(\"%s can not read LINEAR CORRECTION value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.OFFSET = settings.getDouble(\"md.offset\");\n\t\tSEISCOMP_DEBUG(\"%s sets OFFSET to %.2f [%s.%s]\", MAGTAG, mFile.OFFSET,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.OFFSET = _OFFSET;\n\t\tSEISCOMP_ERROR(\"%s can not read OFFSET value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.FMA = settings.getDouble(\"md.fma\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMA to %.4f [%s.%s]\", MAGTAG, mFile.FMA,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.FMA = _FMA;\n\t\tSEISCOMP_ERROR(\"%s can not read FMA value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.FMB = settings.getDouble(\"md.fmb\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMB to %.4f [%s.%s]\", MAGTAG, mFile.FMB,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.FMB = _FMB;\n\t\tSEISCOMP_ERROR(\"%s can not read FMB value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.FMD = settings.getDouble(\"md.fmd\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMD to %.4f [%s.%s]\", MAGTAG, mFile.FMD,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.FMD = _FMD;\n\t\tSEISCOMP_ERROR(\"%s can not read FMD value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.FMF = settings.getDouble(\"md.fmf\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMF to %.4f [%s.%s]\", MAGTAG, mFile.FMF,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.FMF = _FMF;\n\t\tSEISCOMP_ERROR(\"%s can not read FMF value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.FMZ = settings.getDouble(\"md.fmz\");\n\t\tSEISCOMP_DEBUG(\"%s sets FMZ to %.4f [%s.%s]\", MAGTAG, mFile.FMZ,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.FMZ = _FMZ;\n\t\tSEISCOMP_ERROR(\"%s can not read FMZ value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\ttry {\n\t\tmFile.STACOR = settings.getDouble(\"md.stacor\");\n\t\tSEISCOMP_DEBUG(\"%s sets STACOR to %.4f [%s.%s]\", MAGTAG, mFile.STACOR,\n\t\t settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\tcatch ( ... ) {\n\t\tmFile.STACOR = _STACOR;\n\t\tSEISCOMP_ERROR(\"%s can not read STACOR value from configuration file [%s.%s]\",\n\t\t MAGTAG, settings.networkCode.c_str(), settings.stationCode.c_str());\n\t}\n\n\treturn true;\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n\n\n\n// >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>\nMagnitudeProcessor::Status\nMagnitudeProcessor_Md::computeMagnitude(double amplitude, double period,\n double delta, double depth,\n double& value) {\n\n\tdouble epdistkm;\n\tepdistkm = Math::Geo::deg2km(delta);\n\n\tSEISCOMP_DEBUG(\"%s --------------------------------\", MAGTAG);\n\tSEISCOMP_DEBUG(\"%s | PARAMETERS | VALUE |\", MAGTAG);\n\tSEISCOMP_DEBUG(\"%s --------------------------------\", MAGTAG);\n\tSEISCOMP_DEBUG(\"%s | delta max | %.2f \", MAGTAG, mFile.DELTA_MAX);\n\tSEISCOMP_DEBUG(\"%s | depth max | %.2f \", MAGTAG, mFile.DEPTH_MAX);\n\tSEISCOMP_DEBUG(\"%s | md max | %.2f \", MAGTAG, mFile.MD_MAX);\n\tSEISCOMP_DEBUG(\"%s | fma | %.4f \", MAGTAG, mFile.FMA);\n\tSEISCOMP_DEBUG(\"%s | fmb | %.4f \", MAGTAG, mFile.FMB);\n\tSEISCOMP_DEBUG(\"%s | fmd | %.4f \", MAGTAG, mFile.FMD);\n\tSEISCOMP_DEBUG(\"%s | fmf | %.4f \", MAGTAG, mFile.FMF);\n\tSEISCOMP_DEBUG(\"%s | fmz | %.4f \", MAGTAG, mFile.FMZ);\n\tSEISCOMP_DEBUG(\"%s | stacor | %.4f \", MAGTAG, mFile.STACOR);\n\tSEISCOMP_DEBUG(\"%s --------------------------------\", MAGTAG);\n\tSEISCOMP_DEBUG(\"%s | (f-p) | %.2f sec \", MAGTAG, period);\n\tSEISCOMP_DEBUG(\"%s | seismic depth | %.2f km \", MAGTAG, depth);\n\tSEISCOMP_DEBUG(\"%s | epicenter dist | %.2f km \", MAGTAG, epdistkm);\n\tSEISCOMP_DEBUG(\"%s --------------------------------\", MAGTAG);\n\n\tif ( amplitude <= 0. ) {\n\t\tvalue = 0;\n\t\tSEISCOMP_ERROR(\"%s calculated amplitude is wrong, \"\n\t\t\t\"no magnitude will be calculated\", MAGTAG);\n\t\treturn Error;\n\t}\n\n\tif ( (mFile.DELTA_MAX) < epdistkm ) {\n\t\tSEISCOMP_ERROR(\"%s epicenter distance is out of configured range, \"\n\t\t\t\"no magnitude will be calculated\", MAGTAG);\n\t\treturn DistanceOutOfRange;\n\t}\n\n\tvalue = mFile.FMA + mFile.FMB * log10(period) + (mFile.FMF * period)\n\t + (mFile.FMD * epdistkm) + (mFile.FMZ * depth) + mFile.STACOR;\n\n\tif ( value > mFile.MD_MAX )\n\t\tSEISCOMP_WARNING(\"%s Calculated magnitude is beyond max Md value [value= %.2f]\",\n\t\t MAGTAG, value);\n\n\treturn OK;\n}\n// <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n\n/*----[ END OF MAGNITUDE PROCESSOR CLASS ]----*/\n\n\n", "meta": {"hexsha": "10225fd673e96ccf460606a66a940dadc96ca136", "size": 26170, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/ipgp/plugins/magnitudes/md/md.cpp", "max_stars_repo_name": "CelsoReyes/seiscomp3", "max_stars_repo_head_hexsha": "ed9b984651c3b43132f12853bf392ae0dcde87b0", "max_stars_repo_licenses": ["Naumen", "Condor-1.1", "MS-PL"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/ipgp/plugins/magnitudes/md/md.cpp", "max_issues_repo_name": "CelsoReyes/seiscomp3", "max_issues_repo_head_hexsha": "ed9b984651c3b43132f12853bf392ae0dcde87b0", 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YES\n2. NO", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.2302306262661209}} {"text": "#include \n#include \n#include \n#include \"../include/grasp_sampler/RRTFunction.h\"\n#include \n#include \n\nusing namespace std;\nusing namespace Eigen;\n\n#define REACHED 0\n#define ADVANCED 1\n#define TRAPPED -1\n\nbool RRT::solveRRT(Robotmodel& model, std::ostream& sout) {\n\n\t_model = model;\n\n\tnt_start._nodes.clear(); // NodeTree\n\tnt_goal._nodes.clear(); // NodeTree\n\n\tinitConfig.clear();// std::vector\n\tDOF_weights.clear();// std::vector\n\tint ActiveDoFs = dofSize;\n\tstep_size = 0.3 / M_PI *180.0;\n\tstd::string start_string[dofSize];\n\n\n\tfor (int i = 0; i < ActiveDoFs; i++) {\n\t\tinitConfig.push_back(qinit(i) * 180.0 / M_PI);\n\t\tDOF_weights.push_back(1.0);\n\t}\n\n\n\tauto start_node = std::make_shared(initConfig);\n\n\tnt_start.addNode(start_node);\n\tnt_pointer = &nt_start;\n\n\t// mulitple seed\n\tfor (int i = 0; i < qgoals.size(); i++)\n\t{\n\t\tstd::vector goalConfig; \n\t\tfor (int j = 0; j < ActiveDoFs; j++)\n\t\t{\n\t\t\tgoalConfig.push_back(qgoals[i][j] * 180.0 / M_PI);\n\t\t}\n\n\t\tauto goal_node = std::make_shared(goalConfig);\n\n\t\tnt_goal.addNode(goal_node);\n\t}\n\n\tbool finished = false;\n\tint iter = 1;\n\n std::vector q_rand;\n RRTNodePtr q_a_near;\n std::vector q_a_reached ;\n RRTNodePtr q_b_near;\n std::vector q_b_reached ;\n\tint root_tree_index, goal_tree_index;\n\n\twhile (iter < 50) {\n\n\t\tnt_pointer = &nt_start;\t\t\t\t\t\t\t\t //\t\t// start tree\n\t\tq_rand = generateRandomConfig();\n\t\tq_a_near = nt_pointer->getNearest(q_rand, DOF_weights); \n\n\t\tq_a_reached = this->extendNode(q_rand, q_a_near);\n\n\t\tnt_pointer = &nt_goal;\n\t\tq_b_near = nt_pointer->getNearest(q_a_reached, DOF_weights); // random config�� ���� ����� c_tree�� ��� ã��\n\t\tq_b_reached = this->extendNode(q_a_reached, q_b_near); // near��� ���� random config�� Extend\n\n\t\tif (getEuclideanNorm(q_a_reached, q_b_reached, dofSize) < step_size)\n\t\t{\n\t\t\tnt_pointer = &nt_start; //\t\t// start tree\n\t\t\troot_tree_index = nt_pointer->getNearestIndex(q_a_reached, DOF_weights);\n\t\t\tnt_pointer = &nt_goal;\n\t\t\tgoal_tree_index = nt_pointer->getNearestIndex(q_b_reached, DOF_weights);\n\n\t\t\t// q_a_reached가 tree내에 있는 노드가 아니여서 불연속이 생김..\n\t\t\t//cout << \"t_turn \\t\" << t_turn << endl;\n\t\t\t//cout << q_a_reached[0] << \"\\t\" << q_a_reached[1] << \"\\t\" << q_a_reached[2] << \"\\t\" << q_a_reached[3] << \"\\t\" << q_a_reached[4] << \"\\t\" << q_a_reached[5] << \"\\t\" << q_a_reached[6] << endl;\n\t\t\t//cout << q_b_reached[0] << \"\\t\" << q_b_reached[1] << \"\\t\" << q_b_reached[2] << \"\\t\" << q_b_reached[3] << \"\\t\" << q_b_reached[4] << \"\\t\" << q_b_reached[5] << \"\\t\" << q_b_reached[6] << endl;\n\n\t\t\t//cout << getEuclideanNorm(q_a_reached, q_b_reached, dofSize) << endl;\n\t\t\t//cout << \"reached\" << endl;\n\t\t\tfinished = true;\n\n\t\t\tbreak;\n\t\t}\n\t\titer++;\n\t}\n\tif (finished) {\n\n\t\tpath = nt_start.getPathWithIndex(root_tree_index);\n\t\tvector> p2;\n\t\tp2 = nt_goal.getPathWithIndex(goal_tree_index);\n\n\n\t\treverse(p2.begin(), p2.end());\n\t\tpath.insert(path.end(), p2.begin(), p2.end());\n\n\t\tfor (int i = 0; i < path.size(); i++) {\n\t\t\tstd::vector node = path[i];\n\t\t\tfor (int j = 0; j(initConfig);\n\n\tnt_start.addNode(start_node);\n\tnt_pointer = &nt_start;\n\tt_turn = 1;\n\n\t// mulitple seed\n\tfor (int i = 0; i < qgoals.size(); i++)\n\t{\n\t\tstd::vector goalConfig; \n\t\tfor (int j = 0; j < ActiveDoFs; j++)\n\t\t{\n\t\t\tgoalConfig.push_back(qgoals[i][j] * 180.0 / M_PI);\n\t\t}\n\t\tauto goal_node = std::make_shared(goalConfig);\n\n\t\tnt_goal.addNode(goal_node);\n\t}\n\t/// single seed\n\t// RRTNode g(goal, 0); // ���� RRT\n\t// gTree.addNode(g);\n\n\n\tdx_from_init_to_goal.resize(6);\n\tVector3d pos_init = CalcBodyToBaseCoordinates(_model.model_, qinit, _model.body_id_vec.back(), _model.body_com_pos.back());\n\tMatrix3d Rot_init = CalcBodyWorldOrientation(_model.model_, qinit, _model.body_id_vec.back(), true).transpose();\n\tdx_from_init_to_goal.head(3) = refer_pos - pos_init;\n\tdx_from_init_to_goal.tail(3) = getPhi(Rot_init, refer_rot);\n\n\tdx_from_extend_to_goal.resize(6);\n\n\n\tbool finished = false;\n\tint iter = 1;\n int a = 1;\n std::vector q_rand;\n RRTNodePtr q_a_near;\n std::vector q_a_reached ;\n RRTNodePtr q_b_near;\n std::vector q_b_reached ;\n\tint root_tree_index, goal_tree_index;\n\twhile (iter < 50000)\n\t{\n\t\tif (t_turn == 1)\n\t\t{\n\t\t\tnt_pointer = &nt_start;\t\t\t\t\t\t\t\t //\t\t// start tree\n\t\t\tq_rand = generateRandomConfig(); //ConstrainedRandomConfig(_model);\t\t\t\t\t // last node of goal tree(g2) or random config\n\n\t\t\t//cout << \"start tree -> goal tree\" << \"\\t\" <getNearest(q_rand, DOF_weights); // random config�� ���� ����� c_tree�� ��� ã��\n\t\t\tq_a_reached = this->extendNodeWithConstraints(q_rand, q_a_near); // near��� ���� random config�� Extend\n\n\t\t\tnt_pointer = &nt_goal;\n\t\t\tq_b_near = nt_pointer->getNearest(q_a_reached, DOF_weights); // random config�� ���� ����� c_tree�� ��� ã��\n\t\t\t\n\t\t\t//cout << q_b_near->getConfiguration()[0] << \"\\t\" << q_b_near->getConfiguration()[1] << endl;\n\t\t\t//cout << \"goal tree -> start tree\" << endl;\n\t\t\tq_b_reached = this->extendNodeWithConstraints(q_a_reached, q_b_near); // near��� ���� random config�� Extend\n\n\t\t\tif (getEuclideanNorm(q_a_reached, q_b_reached, dofSize) < step_size)\n\t\t\t{\n\t\t\t\tnt_pointer = &nt_start;\t\t\t\t\t\t\t\t //\t\t// start tree\n\t\t\t\troot_tree_index = nt_pointer->getNearestIndex(q_a_reached, DOF_weights);\n\t\t\t\tnt_pointer = &nt_goal;\n\t\t\t\tgoal_tree_index = nt_pointer->getNearestIndex(q_b_reached, DOF_weights);\n\n\t\t\t\t// q_a_reached가 tree내에 있는 노드가 아니여서 불연속이 생김..\n\t\t\t\tcout << \"t_turn \\t\" << t_turn << endl;\n\t\t\t\tcout << q_a_reached[0] << \"\\t\" << q_a_reached[1] << \"\\t\" << q_a_reached[2] << \"\\t\" << q_a_reached[3] << \"\\t\" << q_a_reached[4] << \"\\t\" << q_a_reached[5] << \"\\t\" << q_a_reached[6] << endl;\n\t\t\t\tcout << q_b_reached[0] << \"\\t\" << q_b_reached[1] << \"\\t\" << q_b_reached[2] << \"\\t\" << q_b_reached[3] << \"\\t\" << q_b_reached[4] << \"\\t\" << q_b_reached[5] << \"\\t\" << q_b_reached[6] << endl;\n\n\t\t\t\tcout << getEuclideanNorm(q_a_reached, q_b_reached, dofSize) << endl;\n\t\t\t\tcout << \"reached\" << endl;\n\t\t\t\ta = REACHED;\n\t\t\t\tfinished = true;\n\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\t//t_turn = 2;\n\t\t}\n\t\t// if (t_turn == 2)\n\t\t// {\n\t\t// \tc_tree = &gTree;\t// goal tree\n\t\t// \tq_rand = RandomConfig();// ConstrainedRandomConfig(_model);\t\t\t\t\t // last node of goal tree(g2) or random config\n\n\t\t// \t//cout << \"goal tree -> start _tree \" << \"\\t\" <getNearest(q_rand, DOF_weights); // random config�� ���� ����� c_tree�� ��� ã��\n\n\t\t// \tq_a_reached = this->ConstrainedExtend(q_rand, q_a_near); // near��� ���� random config�� Extend\n\n\t\t// \tc_tree = &tree;\n\t\t// \tq_b_near = c_tree->getNearest(q_a_reached, DOF_weights); // random config�� ���� ����� c_tree�� ��� ã��\n\t\t// \t//cout << \"start tree -> goal tree\" << endl;\n\n\t\t// \tq_b_reached = this->ConstrainedExtend(q_a_reached, q_b_near); // near��� ���� random config�� Extend\n\t\t// \tif (getEuclideanNorm(q_a_reached, q_b_reached, dofSize) < step_size)\n\t\t// \t{\n\t\t// \t\tc_tree = &gTree;\t\t\t\t\t\t\t\t //\t\t// start tree\n\t\t// \t\troot_tree_index = c_tree->getNearestIndex(q_a_reached, DOF_weights);\n\t\t// \t\tc_tree = &tree;\n\t\t// \t\tgoal_tree_index = c_tree->getNearestIndex(q_b_reached, DOF_weights);\n\n\t\t// \t\t// q_a_reached가 tree내에 있는 노드가 아니여서 불연속이 생김..\n\t\t// \t\tcout << \"t_turn \\t\" << t_turn << endl;\n\t\t// \t\tcout << q_a_reached[0] << \"\\t\" << q_a_reached[1] << \"\\t\" << q_a_reached[2] << \"\\t\" << q_a_reached[3] << \"\\t\" << q_a_reached[4] << \"\\t\" << q_a_reached[5] << \"\\t\" << q_a_reached[6] << endl;\n\t\t// \t\tcout << q_b_reached[0] << \"\\t\" << q_b_reached[1] << \"\\t\" << q_b_reached[2] << \"\\t\" << q_b_reached[3] << \"\\t\" << q_b_reached[4] << \"\\t\" << q_b_reached[5] << \"\\t\" << q_b_reached[6] << endl;\n\n\t\t// \t\tcout << getEuclideanNorm(q_a_reached, q_b_reached, dofSize) << endl;\n\t\t// \t\tcout << \"reached\" << endl;\n\t\t// \t\ta = REACHED;\n\t\t// \t\tfinished = true;\n\t\t// \t}\n\t\t// \tt_turn = 1;\n\n\t\t// }\n\t\titer++;\n\t}\n\n\tif (finished) {\n\n\t\tpath = nt_start.getPathWithIndex(root_tree_index);\n\t\tvector> p2;\n\t\tp2 = nt_goal.getPathWithIndex(goal_tree_index);\n\n\t\treverse(p2.begin(), p2.end());\n\t\tpath.insert(path.end(), p2.begin(), p2.end());\n\t\t\n\t\tstd::vector end;\n\t\tend = path[path.size()-1];\n\n\t\tfor (int i = 0; i < path.size(); i++) {\n\t\t\tstd::vector node = path[i];\n\t\t\tfor (int j = 0; j < node.size() - 1; j++){\n\t\t\t\tsout << node[j] << \",\";\n\t\t\t}\n\t\t\tsout << node[node.size() - 1] << \"\\n\";\n\n\t\t}//error\n\n\t}\n\telse {\n\n\t\treturn false;\n\t}\n\n\treturn true;\n}\nstd::vector RRT::generateRandomConfig() {\n\n \tstd::vector R;\n\tdo {\n\t\tfor (int i = 0; i < initConfig.size(); i++) {\n\t\t\tdouble jointrange = upper_limit(i) - lower_limit(i); // angle\n\t\t\tdouble r = ((double)rand() / (double)RAND_MAX)*jointrange;\n\t\t\tR.push_back(lower_limit(i) + r);\n\t\t}\n\t} while (R.size() != initConfig.size());\n\n\n\treturn R;\n}\n// std::vector RRT::ConstrainedRandomConfig(Robotmodel model){\n// \tstd::vector R;\n// \tstd::vector R_project;\n\n// \twhile (R_project.empty())\n// \t{\n// \t\tdo\n// \t\t{\n// \t\t\tfor (int i = 0; i < start.size(); i++)\n// \t\t\t{\n// \t\t\t\tdouble jointrange = upper_limit(i) - lower_limit(i); // angle\n// \t\t\t\tdouble r = ((double)rand() / (double)RAND_MAX) * jointrange;\n// \t\t\t\tR.push_back(lower_limit(i) + r);\n// \t\t\t}\n// \t\t} while (R.size() != start.size());\n\n// \t\tR_project = ProjectConfigForRandomConfig(model, R);\n// \t}\n\n\n// \treturn R_project;\n//}\n\nstd::vector RRT::extendNode(std::vector &node, RRTNodePtr &near){\n\tstd::vector qs, qs_old;\n\tqs.clear();\n\tqs_old.clear();\n\tfor (int i = 0; i < dofSize; i++)\n\t{\n\t\tqs.push_back(near->getConfiguration()[i]);\n\t\tqs_old.push_back(near->getConfiguration()[i]);\n\t}\n\n\twhile(true){\n\t\tif (getEuclideanNorm(node, qs, dofSize) < step_size)\n\t\t{\n\t\t\treturn qs;\n\t\t}\n\t\telse if (getEuclideanNorm(node, qs, dofSize) - getEuclideanNorm(qs_old, node, dofSize) > 0.0)\n\t\t{\n\t\t\treturn qs_old; // qs_old is close to node\n\t\t}\n\n\t\tfor (int i = 0; i < initConfig.size(); i++)\n\t\t{\n\t\t\tqs[i] = (qs[i] + min(step_size, getEuclideanNorm(node, qs, dofSize)) * (node[i] - qs[i]) / getEuclideanNorm(node, qs, dofSize));\n\t\t}\n\n\t\tif (!checkSelfCollision(_model, qs))\n\t\t{\n\t\t\tRRTNodePtr old = near;\n\t\t\tnear = std::make_shared(qs, old);\n\t\t\tnt_pointer->addNode(near);\n\t\t\tqs_old = qs;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tcout << \"collision config \\t\" << qs[0] << \"\\t\" < RRT::extendNodeWithConstraints(std::vector &node, RRTNodePtr &near)\n{\n\t// node = q_rand, near : q_near\n\tstd::vector qs, qs_old;\n\tqs.clear();\n\tqs_old.clear();\n\tbool project_flag = false;\n\tint iteration_ = 0;\n\n\tfor (int i = 0; i < dofSize; i++)\n\t{\n\t\tqs.push_back(near->getConfiguration()[i]);\n\t\tqs_old.push_back(near->getConfiguration()[i]);\n\t}\n\t//cout << \"input\" << node[0] << \"\\t\" << node[1] << \"\\t\" << node[2] << \"\\t\" << node[3] << \"\\t\" << node[4] << \"\\t\" << node[5] << \"\\t\" << node[6] << endl;\n\t//cout << \"q_near\" << qs[0] << \"\\t\"<< qs[1] << \"\\t\"<< qs[2] << \"\\t\"<< qs[3] << \"\\t\"<< qs[4] << \"\\t\"<< qs[5] << \"\\t\"<< qs[6] < 0.0)\n\t\t{\n\t\t\treturn qs_old; // qs_old is close to node\n\t\t}\n\n\t\tfor (int i = 0; i < initConfig.size(); i++) // Move from 'qs' to 'node'\n\t\t\tqs[i] = (qs[i] + min(step_size, getEuclideanNorm(node, qs, dofSize)) * (node[i] - qs[i]) / getEuclideanNorm(node, qs, dofSize));\n\n\t\t//cout << \"desired_dir\" < 3.0*step_size)){\n\t\t// \tcout << \"output1\" <(qs, old) ; //(new RRTNode(qs, old)); // qs : configuration, old : parent node\n\t\t\tnt_pointer->addNode(near);\n\n\t\t\tif((getEuclideanNorm(qs, qs_old, dofSize) < step_size / 2.0)){\n\t\t\t\treturn qs;\n\t\t\t}\n\t\t\tqs_old = qs;\n\n\t\t}\n\t\telse\n\t\t{\t\n\t\t\t//cout << \"qs_old\" << qs_old[0] << \"\\t\"<< qs_old[1] << \"\\t\"<< qs_old[2] << \"\\t\"<< qs_old[3] << \"\\t\"<< qs_old[4] << \"\\t\"<< qs_old[5] << \"\\t\"<< qs_old[6] < RRT::projectConfig(Robotmodel model, std::vector qold, std::vector &qs)\n{\n\tmodel.q.resize(dofSize);\n\n\t// Tc\n\tMatrix4d T0_c, T0_obj, Tc_obj;\n\tT0_c.setIdentity();\n\t//T0_c.topLeftCorner(3,3) = refer_rot;\n\tT0_c.topRightCorner(3, 1) = local_rot.transpose()*(refer_pos - local_pos); // global to local pos\n\n\tMatrixXd J_temp(6, dofSize), J(6, dofSize);\n\tVectorXd d_c(6), dx(6), q_error(dofSize), q_old(dofSize);\n\tdx.setZero();\n\tVector3d phi;\n\tVector3d s[3], v[3], w[3];\n\n\tfor (int i = 0; i < dofSize; i++)\n\t{ // deg -> rad\n\t\tqs[i] = qs[i] * M_PI / 180.0;\n\t\tqold[i] = qold[i] * M_PI / 180.0;\n\n\t\tq_old(i) = qold[i] * M_PI / 180.0;\n\t}\n\n\twhile (true)\n\t{\n\t\tfor (int i = 0; i < dofSize; i++)\n\t\t\tmodel.q(i) = qs[i];\n\n\t\tMatrix3d Rot_temp = CalcBodyWorldOrientation(model.model_, model.q, model.body_id_vec.back(), true).transpose();\n\t\tVector3d pos_temp = CalcBodyToBaseCoordinates(model.model_, model.q, model.body_id_vec.back(), model.body_com_pos.back()); // get position and rotation in EE frame;\n\n\t\tT0_obj.setIdentity();\n\t\tT0_obj.topLeftCorner(3, 3) = Rot_temp;\n\t\tT0_obj.topRightCorner(3, 1) = pos_temp;\n\t\tTc_obj = T0_c.inverse() * T0_obj;\n\n\t\td_c.head(3) = Tc_obj.topRightCorner(3, 1);\n\n\t\tMatrix3d Tc_obj_rot = refer_rot.transpose()*Rot_temp;\n\t\td_c(3) = atan2(Tc_obj_rot(2, 1), Tc_obj_rot(2, 2));\n\t\td_c(4) = -asin(Tc_obj_rot(2, 0));\n\t\td_c(5) = atan2(Tc_obj_rot(1, 0), Tc_obj_rot(0, 0));\n\n\t\t// 위치가 베이스 프레임 기준이 아니게 됨.\n\t\tfor (int i = 0; i < 6; i++)\n\t\t{\n\t\t\tif (d_c(i) > C(i, 0)) // max\n\t\t\t\tdx(i) = d_c(i) - C(i, 0);\n\t\t\telse if (d_c(i) < C(i, 1)) // min\n\t\t\t\tdx(i) = d_c(i) - C(i, 1) ;\n\t\t\telse\n\t\t\t\tdx(i) = 0.0;\n\t\t}\n\n\t\t// Algorithm 4 - line 3\n\t\tif (dx.norm() < 0.01)\n\t\t{\n\t\t\tVector3d q_old_pos = CalcBodyToBaseCoordinates(model.model_, q_old, model.body_id_vec.back(), model.body_com_pos.back());\n\t\t\tdouble dot_product;\n\n\t\t\tdx_from_extend_to_goal.head(3) = refer_pos - pos_temp;\n\t\t\tdx_from_extend_to_goal.tail(3) = getPhi(Rot_temp, refer_rot);\n\n\t\t\tdot_product = (dx_from_init_to_goal.head(3)/dx_from_init_to_goal.head(3).norm()).dot((pos_temp - q_old_pos) / (pos_temp - q_old_pos) .norm());\n\n\t\t\t// TODO : focal point - init position, final positon. projected point is inside the ellipsoid\n\t\t\tif ((dx_from_init_to_goal.head(3).norm() < dx_from_extend_to_goal.head(3).norm()) || (dot_product < 0.0))\n\t\t\t{\n\t\t\t\tqs.clear();\n\t\t\t\treturn qs;\n\t\t\t}\n\n\t\t\tfor (int i = 0; i < dofSize; i++)\n\t\t\t{\n\t\t\t\tqs[i] = qs[i] / M_PI * 180.0;\n\t\t\t}\n\t\t\t\treturn qs; // convert rad to angle\n\t\t}\n\n\t\t// Algorithm 4 - line 4\n\t\tCalcPointJacobian6D(model.model_, model.q, model.body_id_vec.back(), model.body_com_pos.back(), J_temp, true);\n\t\tJ.topLeftCorner(3, dofSize) = J_temp.bottomLeftCorner(3, dofSize);\n\n\t\trpy(0) = atan2(Rot_temp(2, 1), Rot_temp(2, 2));\n\t\trpy(1) = -asin(Rot_temp(2, 0));\n\t\trpy(2) = atan2(Rot_temp(1, 0), Rot_temp(0, 0));\n\t\t\n\t\tE_rpy(0,0) = cos(rpy(2))/cos(rpy(1));\n\t\tE_rpy(0,1) = sin(rpy(2))/cos(rpy(1));\n\t\tE_rpy(0,2) = 0.0;\n\n\t\tE_rpy(1,0) = sin(rpy(2));\n\t\tE_rpy(1,1) = cos(rpy(2));\n\t\tE_rpy(1,2) = 0.0;\n\n\t\tE_rpy(2,0) = -cos(rpy(2))*sin(rpy(1))/cos(rpy(1));\n\t\tE_rpy(2,1) = sin(rpy(2))*sin(rpy(1))/cos(rpy(1));\n\t\tE_rpy(2,2) = 1.0;\n\t\tJ.bottomLeftCorner(3, dofSize) = E_rpy*J_temp.topLeftCorner(3, dofSize);\n\t\t\n\n\t\t// Algorithm 4 - line 5\n\t\tq_error = J.transpose() * (J * J.transpose()).inverse() * dx * 0.3;\n\n\t\t// Algorithm 4 - line 6\n\t\tfor (int i = 0; i < dofSize; i++)\n\t\t\tqs[i] -= q_error(i);\n\n\t\t// Algorithm 4 - line 7 : stuck here\n\t\tif (!checkJointLimit(qs) || (getEuclideanNorm(qs, qold, dofSize) > 3.0*step_size/180.0*M_PI))\n\t\t{\n\t\t\t//cout << \"joint limit\" << \"\\t\" << qs[0]/M_PI*180.0<< \"\\t\" << qs[1]/M_PI*180.0<< \"\\t\" << qs[2]/M_PI*180.0<< \"\\t\" << qs[3]/M_PI*180.0<< \"\\t\" << qs[4]/M_PI*180.0<< \"\\t\" << qs[5]/M_PI*180.0<< \"\\t\" << qs[6]/M_PI*180.0 << endl;\n\t\t\tqs.clear();\n\t\t\treturn qs;\n\t\t\t// flag = false;\n\t\t\t// break;\n\t\t}\n\t}\n}\n\n// std::vector RRT::ProjectConfigForRandomConfig(Robotmodel model, std::vector &qs)\n// {\n// \tmodel.q.resize(dofSize);\n\n// \t// Tc\n// \tMatrix4d T0_c, T0_obj, Tc_obj;\n// \tT0_c.setIdentity();\n// \t//T0_c.topLeftCorner(3,3) = refer_rot;\n// \tT0_c.topRightCorner(3, 1) = local_rot*(refer_pos - local_pos); // global to local pos\n\n// \tMatrixXd J_temp(6, dofSize), J(6, dofSize);\n// \tVectorXd d_c(6), dx(6), q_error(dofSize);\n// \tdx.setZero();\n// \tVector3d phi;\n// \tVector3d s[3], v[3], w[3];\n\n// \tfor (int i = 0; i < dofSize; i++)\n// \t{ // deg -> rad\n// \t\tqs[i] = qs[i] * M_PI / 180.0;\n// \t}\n\n// \twhile (true)\n// \t{\n// \t\tfor (int i = 0; i < dofSize; i++)\n// \t\t\tmodel.q(i) = qs[i];\n\n// \t\tMatrix3d Rot_temp = CalcBodyWorldOrientation(model.model_, model.q, model.body_id_vec.back(), true).transpose();\n// \t\tVector3d pos_temp = CalcBodyToBaseCoordinates(model.model_, model.q, model.body_id_vec.back(), model.body_com_pos.back()); // get position and rotation in EE frame;\n\n// \t\tT0_obj.setIdentity();\n// \t\tT0_obj.topLeftCorner(3, 3) = Rot_temp;\n// \t\tT0_obj.topRightCorner(3, 1) = pos_temp;\n// \t\tTc_obj = T0_c.inverse() * T0_obj;\n\n// \t\td_c.head(3) = Tc_obj.topRightCorner(3, 1);\n\n// \t\tMatrix3d Tc_obj_rot = refer_rot.transpose()*Rot_temp;\n// \t\td_c(3) = atan2(Tc_obj_rot(2, 1), Tc_obj_rot(2, 2));\n// \t\td_c(4) = -asin(Tc_obj_rot(2, 0));\n// \t\td_c(5) = atan2(Tc_obj_rot(1, 0), Tc_obj_rot(0, 0));\n\n// \t\tfor (int i = 0; i < 6; i++)\n// \t\t{\n// \t\t\tif (d_c(i) > C(i, 0)) // max\n// \t\t\t\tdx(i) = d_c(i) - C(i, 0);\n// \t\t\telse if (d_c(i) < C(i, 1)) // min\n// \t\t\t\tdx(i) = d_c(i) - C(i, 1) ;\n// \t\t\telse\n// \t\t\t\tdx(i) = 0.0;\n// \t\t}\n\n// \t\t// Algorithm 4 - line 3\n// \t\tif (dx.norm() < 0.01)\n// \t\t{\n// \t\t\tdx_from_extend_to_goal.head(3) = refer_pos - pos_temp;\n// \t\t\tdx_from_extend_to_goal.tail(3) = getPhi(Rot_temp, refer_rot);\n\n// \t\t\tif (dx_from_init_to_goal.norm() < dx_from_extend_to_goal.norm()){\n// \t\t\t\tqs.clear();\n// \t\t\t\treturn qs;\n// \t\t\t}\n\n// \t\t\tfor (int i = 0; i < dofSize; i++)\n// \t\t\t{\n// \t\t\t\tqs[i] = qs[i] / M_PI * 180.0;\n// \t\t\t}\n// \t\t\t\treturn qs; // convert rad to angle\n// \t\t}\n\n// \t\t// Algorithm 4 - line 4\n// \t\tCalcPointJacobian6D(model.model_, model.q, model.body_id_vec.back(), model.body_com_pos.back(), J_temp, true);\n// \t\tJ.topLeftCorner(3, dofSize) = J_temp.bottomLeftCorner(3, dofSize);\n\n// \t\trpy(0) = atan2(Rot_temp(2, 1), Rot_temp(2, 2));\n// \t\trpy(1) = -asin(Rot_temp(2, 0));\n// \t\trpy(2) = atan2(Rot_temp(1, 0), Rot_temp(0, 0));\n\t\t\n// \t\tE_rpy(0,0) = cos(rpy(2))/cos(rpy(1));\n// \t\tE_rpy(0,1) = sin(rpy(2))/cos(rpy(1));\n// \t\tE_rpy(0,2) = 0.0;\n\n// \t\tE_rpy(1,0) = sin(rpy(2));\n// \t\tE_rpy(1,1) = cos(rpy(2));\n// \t\tE_rpy(1,2) = 0.0;\n\n// \t\tE_rpy(2,0) = -cos(rpy(2))*sin(rpy(1))/cos(rpy(1));\n// \t\tE_rpy(2,1) = sin(rpy(2))*sin(rpy(1))/cos(rpy(1));\n// \t\tE_rpy(2,2) = 1.0;\n// \t\tJ.bottomLeftCorner(3, dofSize) = E_rpy*J_temp.topLeftCorner(3, dofSize);\n\t\t\n\n// \t\t// Algorithm 4 - line 5\n// \t\tq_error = J.transpose() * (J * J.transpose()).inverse() * dx * 0.3;\n\n// \t\t// Algorithm 4 - line 6\n// \t\tfor (int i = 0; i < dofSize; i++)\n// \t\t\tqs[i] -= q_error(i);\n\n// \t\t// Algorithm 4 - line 7 : stuck here\n// \t\tif (!OutsideJointLimit(qs))\n// \t\t{\n// \t\t\t//cout << \"joint limit\" << \"\\t\" << qs[0]/M_PI*180.0<< \"\\t\" << qs[1]/M_PI*180.0<< \"\\t\" << qs[2]/M_PI*180.0<< \"\\t\" << qs[3]/M_PI*180.0<< \"\\t\" << qs[4]/M_PI*180.0<< \"\\t\" << qs[5]/M_PI*180.0<< \"\\t\" << qs[6]/M_PI*180.0 << endl;\n// \t\t\tqs.clear();\n// \t\t\treturn qs;\n// \t\t\t// flag = false;\n// \t\t\t// break;\n// \t\t}\n// \t}\n// }\n\n\ndouble RRT::getDistance(std::vector &a, std::vector &b) {\n\n\t//Gets squared Euclidean Distance on C-Space between node and a given\n\tdouble accumulated = 0.0f;\n\tstd::vector diff = b;\n\n\tfor (int i = 0; i< diff.size(); i++) {\n\t\tdiff[i] -= a[i];\n\t\t//double dif = _configuration[i] - (config)[i];\n\t\taccumulated += (diff[i] * diff[i] * DOF_weights[i] * DOF_weights[i]);\n\t}\n\treturn sqrt(accumulated);\n}\nbool RRT::smoothPath(std::ostream& sout, std::istream& sinput)\n{\n\tstd::string fullinput;\n\t//Parse input\n\twhile (sinput) {\n\t\tstd::string input;\n\t\tsinput >> input;\n\t\tfullinput += input;\n\t}\n\tint nSmooth = 5; // atoi(fullinput.c_str());\n\n\twhile (nSmooth) { // while(x) -> while( x != 0 )\n\t\tshortcutSmoothing();\n\t\tnSmooth--;\n\t}\n\t\n\tfor (int i = 0; i< path.size(); i++) {\n\t\tstd::vector node = path[i];\n\t\tfor (int j = 0; j &a, std::vector &b) {\n\tstd::vector u = getUnitVector(a, b); // a->b\n\tstd::vector p = a;\n\twhile (p != b) {\n\t\tif (checkExternalCollision(_model, p)) { return false;\n\t\tbreak;\n\t\t}\n\t\tif (getDistance(p, b) < step_size / 2) {\n\t\t\tp = b;\n\t\t}\n\t\telse {\n\t\t\tfor (int i = 0; i < u.size(); i++) {\n\t\t\t\tp[i] += u[i] * step_size / 2;\n\t\t\t}\n\t\t}\n\t}\n\treturn true;\n}\nstd::vector RRT::getUnitVector(std::vector &a, std::vector &b) {\n\n\t//Gets squared Euclidean Distance on C-Space between node and a given\n\tdouble accumulated = 0.0f;\n\tstd::vector diff = b;\n\n\tfor (int i = 0; i< diff.size(); i++) {\n\t\tdiff[i] -= a[i];//double dif = _configuration[i] - (config)[i];\n\t\taccumulated += (diff[i] * diff[i] * DOF_weights[i] * DOF_weights[i]);\n\t}\n\taccumulated = sqrt(accumulated);\n\tfor (int i = 0; i < diff.size(); i++) {\n\t\tdiff[i] /= accumulated;\n\t}\n\treturn diff;\n}\nbool RRT::checkExternalCollision(Robotmodel model, std::vector &config) {\n\tmodel.q.resize(dofSize);\n\tfor (int i = 0; i < dofSize; i++)\n\t\tmodel.q(i) = config[i] * M_PI / 180.0;\n\n\t// Update Box modeling for link\n\tfor (int i = 0; i < box_num_link; i++)\n\t{\n\t\tMatrix3d Rot_temp3 = Box_link[i].vRot * CalcBodyWorldOrientation(model.model_, model.q, model.body_id_vec[i], true).transpose();\n\t\tBox_link[i].vAxis[0] = Rot_temp3.col(0);\n\t\tBox_link[i].vAxis[1] = Rot_temp3.col(1);\n\t\tBox_link[i].vAxis[2] = Rot_temp3.col(2);\n\t\tBox_link[i].vPos = CalcBodyToBaseCoordinates(model.model_, model.q, model.body_id_vec[i], (Box_link[i].vCenter + model.body_com_pos[i]), true);\n\t}\n\n\tbool chk;\n\tif (box_num_obs == 0)\n\t{\n\t\tchk = false;\n\t\treturn chk;\n\t}\n\tfor (int i = 0; i < box_num_obs; i++)\n\t{\n\t\tfor (int j = 0; j < box_num_link; j++)\n\t\t{\n\t\t\tchk = checkOBBCollision(&Box_obs[i], &Box_link[j]);\n\t\t\tif (chk)\n\t\t\t{\n\t\t\t\t// cout << \"----------------Collision \\n\"\n\t\t\t\t// \t << i << \"\\t\" << j << \"\\t\" << chk << endl;\n\t\t\t\t// cout << Box_obs[i].vPos.transpose() << endl;\n\t\t\t\t// cout << Box_link[j].vPos.transpose() << endl;\n\t\t\t\t// //cout << CalcBodyToBaseCoordinates(model.model_, model.q, model.body_id_vec[j], Vector3d::Zero(), true).transpose() << endl;\n\t\t\t\t// cout << model.q(0) * 180.0 / M_PI << \"\\t\" << model.q(1) * 180.0 / M_PI << \"\\t\" << model.q(2) * 180.0 / M_PI << \"\\t\" << model.q(3) * 180.0 / M_PI << \"\\t\" << model.q(4) * 180.0 / M_PI << \"\\t\" << model.q(5) * 180.0 / M_PI << \"\\t\" << model.q(6) * 180.0 / M_PI << \"\\t\" << endl;\n\n\t\t\t\t//cout << i << \"\\t\" << j << \"\\t\" << chk << endl;\n\n\t\t\t\treturn chk;\n\t\t\t}\n\t\t\t//cout << \"collision\" << i << \"\\t\" << j << endl;\n\t\t}\n\t}\n\treturn chk;\n}\n\n\nbool RRT::checkSelfCollision(Robotmodel model, std::vector &config) {\n\tmodel.q.resize(dofSize);\n\tfor (int i = 0; i < dofSize; i++)\n\t\tmodel.q(i) = config[i] * M_PI / 180.0;\n\n\n\t// Update Box modeling for link\n\tfor (int i = 0; i < box_num_link; i++)\n\t{\n\t\tMatrix3d Rot_temp3 = Box_link[i].vRot * CalcBodyWorldOrientation(model.model_, model.q, model.body_id_vec[i], true).transpose();\n\t\tBox_link[i].vAxis[0] = Rot_temp3.col(0);\n\t\tBox_link[i].vAxis[1] = Rot_temp3.col(1);\n\t\tBox_link[i].vAxis[2] = Rot_temp3.col(2);\n\t\tBox_link[i].vPos = CalcBodyToBaseCoordinates(model.model_, model.q, model.body_id_vec[i], (Box_link[i].vCenter + model.body_com_pos[i]), true);\n\t}\n\n\tbool chk;\n\tif (box_num_link == 0)\n\t{\n\t\tchk = false;\n\t\treturn chk;\n\t}\n\tfor (int i = 0; i < box_num_link; i++)\n\t{\n\t\tfor (int j = i + 2; j < box_num_link; j++)\n\t\t{\n\t\t\tchk = checkOBBCollision(&Box_link[i], &Box_link[j]);\n\t\t\tif (chk)\n\t\t\t{\n\t\t\t\t// cout << \"----------------Collision \\n\" << i << \"\\t\" << j << \"\\t\" << chk << endl;\n\t\t\t\t// cout << Box_link[i].vPos.transpose() << endl;\n\t\t\t\t// cout << Box_link[j].vPos.transpose() << endl;\n\t\t\t\t// //cout << CalcBodyToBaseCoordinates(model.model_, model.q, model.body_id_vec[j], Vector3d::Zero(), true).transpose() << endl;\n\t\t\t\t// cout << model.q(0) * 180.0 / M_PI << \"\\t\" << model.q(1) * 180.0 / M_PI << \"\\t\" << model.q(2) * 180.0 / M_PI << \"\\t\" << model.q(3) * 180.0 / M_PI << \"\\t\" << model.q(4) * 180.0 / M_PI << \"\\t\" << model.q(5) * 180.0 / M_PI << \"\\t\" << model.q(6) * 180.0 / M_PI << \"\\t\" << endl;\n\t\t\t\treturn chk;\n\t\t\t}\n\t\t\t// cout << \"No collision \\n\" << i << \"\\t\" << j << \"\\t\" << chk << endl;\n\t\t\t// cout << Box_link[i].vPos.transpose() << endl;\n\t\t\t// cout << Box_link[j].vPos.transpose() << endl;\n\t\t\t// \tcout << CalcBodyToBaseCoordinates(model.model_, model.q, model.body_id_vec[j], Vector3d::Zero(), true).transpose() << endl;\n\t\t\t//cout << model.q(0) * 180.0 / M_PI << \"\\t\" << model.q(1) * 180.0 / M_PI << \"\\t\" << model.q(2) * 180.0 / M_PI << \"\\t\" << model.q(3) * 180.0 / M_PI << \"\\t\" << model.q(4) * 180.0 / M_PI << \"\\t\" << model.q(5) * 180.0 / M_PI << \"\\t\" << model.q(6) * 180.0 / M_PI << \"\\t\" << endl;\n\t\t}\n\t}\n\treturn chk;\n}\n\n\nbool RRT::checkJointLimit(std::vector q) { // radian\n\tfor (int i = 0;i < q.size();i++) {\n\t\tif (q[i] > upper_limit(i)/180.0*M_PI) {\n\t\t\t//cout << q[i] << \"\\t\" << upper_limit(i)/180.0*M_PI << \"\\t\" << i << endl;\n\t\t\treturn false;\n\t\t}\n\t\telse if (q[i] < lower_limit(i)/180.0*M_PI) {\n\t\t\t//cout << q[i] << \"\\t\" << lower_limit(i)/180.0*M_PI << \"\\t\" << i << endl;\n\n\t\t\treturn false;\n\t\t}\n\t}\n\treturn true;\n\n}\n\nbool RRT::checkOBBCollision(ST_OBB* box0, ST_OBB* box1) // Collision-free : False\n{\n\t// compute difference of box centers,D=C1-C0\n\tVector3d D = Vector3d(box1->vPos(0) - box0->vPos(0), box1->vPos(1) - box0->vPos(1), box1->vPos(2) - box0->vPos(2));\n\n\tfloat C[3][3]; //matrix C=A^T B,c_{ij}=Dot(A_i,B_j)\n\tfloat absC[3][3]; //|c_{ij}|\n\tfloat AD[3]; //Dot(A_i,D)\n\tfloat R0, R1, R; //interval radii and distance between centers\n\tfloat R01; //=R0+R1\n\n\t\t\t\t\t //A0\n\tC[0][0] = FDotProduct(box0->vAxis[0], box1->vAxis[0]);// vAxis : direction // 3D Dot product\n\tC[0][1] = FDotProduct(box0->vAxis[0], box1->vAxis[1]);\n\tC[0][2] = FDotProduct(box0->vAxis[0], box1->vAxis[2]);\n\tAD[0] = FDotProduct(box0->vAxis[0], D);\n\tabsC[0][0] = (float)fabsf(C[0][0]);\n\tabsC[0][1] = (float)fabsf(C[0][1]);\n\tabsC[0][2] = (float)fabsf(C[0][2]);\n\tR = (float)fabsf(AD[0]);\n\tR1 = box1->fAxis(0) * absC[0][0] + box1->fAxis(1) * absC[0][1] + box1->fAxis(2) * absC[0][2];\n\tR01 = box0->fAxis(0) + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A1\n\tC[1][0] = FDotProduct(box0->vAxis[1], box1->vAxis[0]);\n\tC[1][1] = FDotProduct(box0->vAxis[1], box1->vAxis[1]);\n\tC[1][2] = FDotProduct(box0->vAxis[1], box1->vAxis[2]);\n\tAD[1] = FDotProduct(box0->vAxis[1], D);\n\tabsC[1][0] = (float)fabsf(C[1][0]);\n\tabsC[1][1] = (float)fabsf(C[1][1]);\n\tabsC[1][2] = (float)fabsf(C[1][2]);\n\tR = (float)fabsf(AD[1]);\n\tR1 = box1->fAxis(0) * absC[1][0] + box1->fAxis(1) * absC[1][1] + box1->fAxis(2) * absC[1][2];\n\tR01 = box0->fAxis(1) + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A2\n\tC[2][0] = FDotProduct(box0->vAxis[2], box1->vAxis[0]);\n\tC[2][1] = FDotProduct(box0->vAxis[2], box1->vAxis[1]);\n\tC[2][2] = FDotProduct(box0->vAxis[2], box1->vAxis[2]);\n\tAD[2] = FDotProduct(box0->vAxis[2], D);\n\tabsC[2][0] = (float)fabsf(C[2][0]);\n\tabsC[2][1] = (float)fabsf(C[2][1]);\n\tabsC[2][2] = (float)fabsf(C[2][2]);\n\tR = (float)fabsf(AD[2]);\n\tR1 = box1->fAxis(0) * absC[2][0] + box1->fAxis(1) * absC[2][1] + box1->fAxis(2) * absC[2][2];\n\tR01 = box0->fAxis(2) + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//B0\n\tR = (float)fabsf(FDotProduct(box1->vAxis[0], D));\n\tR0 = box0->fAxis(0) * absC[0][0] + box0->fAxis(1) * absC[1][0] + box0->fAxis(2) * absC[2][0];\n\tR01 = R0 + box1->fAxis(0);\n\tif (R > R01)\n\t\treturn 0;\n\n\t//B1\n\tR = (float)fabsf(FDotProduct(box1->vAxis[1], D));\n\tR0 = box0->fAxis(0) * absC[0][1] + box0->fAxis(1) * absC[1][1] + box0->fAxis(2) * absC[2][1];\n\tR01 = R0 + box1->fAxis(1);\n\tif (R > R01)\n\t\treturn 0;\n\n\t//B2\n\tR = (float)fabsf(FDotProduct(box1->vAxis[2], D));\n\tR0 = box0->fAxis(0) * absC[0][2] + box0->fAxis(1) * absC[1][2] + box0->fAxis(2) * absC[2][2];\n\tR01 = R0 + box1->fAxis(2);\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A0xB0\n\tR = (float)fabsf(AD[2] * C[1][0] - AD[1] * C[2][0]);\n\tR0 = box0->fAxis(1) * absC[2][0] + box0->fAxis(2) * absC[1][0];\n\tR1 = box1->fAxis(1) * absC[0][2] + box1->fAxis(2) * absC[0][1];\n\tR01 = R0 + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A0xB1\n\tR = (float)fabsf(AD[2] * C[1][1] - AD[1] * C[2][1]);\n\tR0 = box0->fAxis(1) * absC[2][1] + box0->fAxis(2) * absC[1][1];\n\tR1 = box1->fAxis(0) * absC[0][2] + box1->fAxis(2) * absC[0][0];\n\tR01 = R0 + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A0xB2\n\tR = (float)fabsf(AD[2] * C[1][2] - AD[1] * C[2][2]);\n\tR0 = box0->fAxis(1) * absC[2][2] + box0->fAxis(2) * absC[1][2];\n\tR1 = box1->fAxis(0) * absC[0][1] + box1->fAxis(1) * absC[0][0];\n\tR01 = R0 + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A1xB0\n\tR = (float)fabsf(AD[0] * C[2][0] - AD[2] * C[0][0]);\n\tR0 = box0->fAxis(0) * absC[2][0] + box0->fAxis(2) * absC[0][0];\n\tR1 = box1->fAxis(1) * absC[1][2] + box1->fAxis(2) * absC[1][1];\n\tR01 = R0 + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A1xB1\n\tR = (float)fabsf(AD[0] * C[2][1] - AD[2] * C[0][1]);\n\tR0 = box0->fAxis(0) * absC[2][1] + box0->fAxis(2) * absC[0][1];\n\tR1 = box1->fAxis(0) * absC[1][2] + box1->fAxis(2) * absC[1][0];\n\tR01 = R0 + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A1xB2\n\tR = (float)fabsf(AD[0] * C[2][2] - AD[2] * C[0][2]);\n\tR0 = box0->fAxis(0) * absC[2][2] + box0->fAxis(2) * absC[0][2];\n\tR1 = box1->fAxis(0) * absC[1][1] + box1->fAxis(1) * absC[1][0];\n\tR01 = R0 + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A2xB0\n\tR = (float)fabsf(AD[1] * C[0][0] - AD[0] * C[1][0]);\n\tR0 = box0->fAxis(0) * absC[1][0] + box0->fAxis(1) * absC[0][0];\n\tR1 = box1->fAxis(1) * absC[2][2] + box1->fAxis(2) * absC[2][1];\n\tR01 = R0 + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A2xB1\n\tR = (float)fabsf(AD[1] * C[0][1] - AD[0] * C[1][1]);\n\tR0 = box0->fAxis(0) * absC[1][1] + box0->fAxis(1) * absC[0][1];\n\tR1 = box1->fAxis(0) * absC[2][2] + box1->fAxis(2) * absC[2][0];\n\tR01 = R0 + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\t//A2xB2\n\tR = (float)fabsf(AD[1] * C[0][2] - AD[0] * C[1][2]);\n\tR0 = box0->fAxis(0) * absC[1][2] + box0->fAxis(1) * absC[0][2];\n\tR1 = box1->fAxis(0) * absC[2][1] + box1->fAxis(1) * absC[2][0];\n\tR01 = R0 + R1;\n\tif (R > R01)\n\t\treturn 0;\n\n\treturn 1;\n}\nMatrixXd RRT::MergeRRTResults(MatrixXd joint_target1, MatrixXd joint_target2, int case_){\n\t// case 0 : RRT / RRT\n\t// case 1 : RRT / CBiRRT\n\t// case 2 : CBiRRT / RRT\n\t// case 3 : CBiRRT / CBiRRT \n\tMatrixXd merged_joint_target;\n\tint row[2];\n\tint reminder[2];\n\tswitch(case_ )\n\t{\n\t\tcase 0 :\n\t\t\trow[0] = joint_target1.rows();\n\t\t\trow[1] = joint_target2.rows();\n\t\t\tmerged_joint_target.resize(row[0] + row[1] -1, dofSize);\n\t\t\tmerged_joint_target.topRows(row[0]) = joint_target1.topRows(row[0]);\n\t\t\tmerged_joint_target.bottomRows(row[1]-1) = joint_target2.bottomRows(row[1]-1);\n\t\tbreak;\n\t\tcase 1 :\n\t\t\trow[0] = joint_target1.rows();\n\t\t\trow[1] = joint_target2.rows() / 20;\n\t\t\treminder[1] = joint_target2.rows() % 20;\n\t\t\tif (reminder[1] == 0)\n\t\t\t{\n\t\t\t\tmerged_joint_target.resize(row[0] + row[1], dofSize);\n\t\t\t\tmerged_joint_target.topRows(row[0]) = joint_target1.topRows(row[0]);\n\t\t\t\tfor (int i = 0; i < row[1]; i++)\n\t\t\t\t\tmerged_joint_target.row(row[0] + i) = joint_target2.row(20 * (i + 1) - 1);\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tmerged_joint_target.resize(row[0] + row[1] + 1,dofSize);\n\t\t\t\tmerged_joint_target.topRows(row[0]) = joint_target1.topRows(row[0]);\n\t\t\t\tfor (int i = 0; i < row[1]; i++)\n\t\t\t\t\tmerged_joint_target.row(row[0] + i) = joint_target2.row(20 * (i + 1)- 1);\n\n\t\t\t\tmerged_joint_target.bottomRows(1) = joint_target2.bottomRows(1);\n\t\t\t}\n\t\t\tbreak; \n\t\tcase 2 :\n\t\t\trow[0] = joint_target1.rows() /20;\n\t\t\trow[1] = joint_target2.rows() ;\n\t\t\treminder[0] = joint_target1.rows() % 20;\n\t\t\tif (reminder[0] == 0)\n\t\t\t{\n\t\t\t\tmerged_joint_target.resize(row[0] + row[1],dofSize);\n\t\t\t\tfor (int i = 0; i < row[0]; i++)\n\t\t\t\t\tmerged_joint_target.row(i) = joint_target1.row(20 * (i));\n\n\t\t\t\tmerged_joint_target.bottomRows(row[1]) = joint_target2.bottomRows(row[1]);\n\t\t\t}\n\t\t\telse{\n\t\t\t\tmerged_joint_target.resize(row[0] + row[1] + 1,dofSize);\n\t\t\t\tfor (int i = 0; i < row[0] + 1; i++)\n\t\t\t\t\tmerged_joint_target.row(i) = joint_target1.row(20 * (i));\n\n\t\t\t\tmerged_joint_target.bottomRows(row[1]) = joint_target2.bottomRows(row[1]);\n\t\t\t}\n\t\tbreak;\n\t\tcase 3 :\n\t\t\trow[0] = joint_target1.rows() /20;\n\t\t\trow[1] = joint_target2.rows() /20;\n\t\t\treminder[0] = joint_target1.rows() % 20;\n\t\t\treminder[1] = joint_target2.rows() % 20;\n\t\t\tif (reminder[0] == 0){\n\t\t\t\tif (reminder[1] == 0){\n\t\t\t\t\tmerged_joint_target.resize(row[0] + row[1] + 1,dofSize);\n\t\t\t\t\tfor (int i = 0; i < row[0] + 1; i++)\n\t\t\t\t\t\tmerged_joint_target.row(i) = joint_target1.row(20 * (i));\n\n\t\t\t\t\tfor (int i = 0; i < row[1]; i++)\n\t\t\t\t\t\tmerged_joint_target.row(row[0] + 1 + i) = joint_target2.row(20 * (i+1));\n\t\t\t\t}\n\t\t\t\telse{\n\t\t\t\t\tmerged_joint_target.resize(row[0] + row[1] + 2,dofSize);\n\t\t\t\t\tfor (int i = 0; i < row[0] + 1; i++)\n\t\t\t\t\t\tmerged_joint_target.row(i) = joint_target1.row(20 * (i));\n\n\t\t\t\t\tfor (int i = 0; i < row[1]; i++)\n\t\t\t\t\t\tmerged_joint_target.row(row[0] + 1 + i) = joint_target2.row(20 * (i+1));\n\n\t\t\t\t\tmerged_joint_target.bottomRows(1) = joint_target2.bottomRows(1);\n\t\t\t\t}\n\t\t\t}\n\t\t\telse{\n\t\t\t\tif (reminder[1] == 0){\n\t\t\t\t\tmerged_joint_target.resize(row[0] + row[1] + 2,dofSize);\n\t\t\t\t\tfor (int i = 0; i < row[0] + 1; i++)\n\t\t\t\t\t\tmerged_joint_target.row(i) = joint_target1.row(20 * (i));\n\n\t\t\t\t\tmerged_joint_target.row(row[0]+1) = joint_target1.bottomRows(1);\n\n\t\t\t\t\tfor (int i = 0; i < row[1]; i++)\n\t\t\t\t\t\tmerged_joint_target.row(row[0] + 2 + i) = joint_target2.row(20 * (i+1));\n\t\t\t\t}\n\t\t\t\telse{\n\t\t\t\t\tmerged_joint_target.resize(row[0] + row[1] + 3,dofSize);\n\t\t\t\t\tfor (int i = 0; i < row[0] + 1; i++)\n\t\t\t\t\t\tmerged_joint_target.row(i) = joint_target1.row(20 * (i));\n\n\t\t\t\t\tmerged_joint_target.row(row[0]+1) = joint_target1.bottomRows(1);\n\n\t\t\t\t\tfor (int i = 0; i < row[1]; i++)\n\t\t\t\t\t\tmerged_joint_target.row(row[0] + 2 + i) = joint_target2.row(20 * (i+1));\n\n\t\t\t\t\tmerged_joint_target.bottomRows(1) = joint_target2.bottomRows(1);\n\t\t\t\t}\n\t\t\t}\n\t\tbreak;\n\t}\n\n\n\treturn merged_joint_target;\n}", "meta": {"hexsha": "baf9006e37c3e21d0bdba411c8c25acd7a6c10bb", "size": 36054, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "updated/catkin_ws/src/grasp_sampler/src/RRTFunction.cpp", "max_stars_repo_name": "JisuHann/Point-Cloud--Grasp", "max_stars_repo_head_hexsha": "083244632412709dbc29ac7841b6a837e4ed3cb6", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, 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YES\n2. NO", "lm_q1_score": 0.5078118642792043, "lm_q2_score": 0.4532618480153861, "lm_q1q2_score": 0.2301717440473306}} {"text": "/*****************************************************************************/\n/* Copyright (c) 2015, Karl Pauwels */\n/* All rights reserved. */\n/* */\n/* Redistribution and use in source and binary forms, with or without */\n/* modification, are permitted provided that the following conditions */\n/* are met: */\n/* */\n/* 1. Redistributions of source code must retain the above copyright */\n/* notice, this list of conditions and the following disclaimer. */\n/* */\n/* 2. Redistributions in binary form must reproduce the above copyright */\n/* notice, this list of conditions and the following disclaimer in the */\n/* documentation and/or other materials provided with the distribution. */\n/* */\n/* 3. Neither the name of the copyright holder nor the names of its */\n/* contributors may be used to endorse or promote products derived from */\n/* this software without specific prior written permission. */\n/* */\n/* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS */\n/* \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT */\n/* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR */\n/* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT */\n/* HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, */\n/* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT */\n/* LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, */\n/* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY */\n/* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT */\n/* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE */\n/* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. */\n/*****************************************************************************/\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace pose {\n\nD_MultipleRigidPoses::D_MultipleRigidPoses(int n_cols, int n_rows,\n float nodal_point_x,\n float nodal_point_y,\n float focal_length_x,\n float focal_length_y, float baseline,\n Parameters parameters)\n : parameters_{ parameters }, _n_cols{ n_cols }, _n_rows{ n_rows },\n _nodal_point_x{ nodal_point_x }, _nodal_point_y{ nodal_point_y },\n _focal_length_x{ focal_length_x }, _focal_length_y{ focal_length_y },\n _baseline{ baseline }, _currentSparseObject{ 0 },\n _multipleRigidModelsOgre{\n std::unique_ptr{ new MultipleRigidModelsOgre(\n _n_cols, _n_rows, _focal_length_x, _focal_length_y, _nodal_point_x,\n _nodal_point_y, parameters_.near_plane_, parameters_.far_plane_) }\n },\n cub_radix_sorter_{\n std::unique_ptr >{\n new util::CubRadixSorter(\n _n_rows * _n_cols * _N_FLOWS, 0, parameters_.getKeyBits())\n }\n },\n _n_objects{ 0 }, _running{ true }, render_state_changed_{ true } {\n\n camera_pose_.setValid(true);\n previous_camera_pose_.setValid(false);\n\n /*******************************************************/\n /* Pre-allocate (and initiate )all device space needed */\n /* with maximal possible size */\n /* to avoid re-allocation and re-computation */\n /*******************************************************/\n\n // linear index to store pixel locations\n // goes from 0 to n_rows*n_cols*n_flows\n // used to identify flow source\n std::vector h_linear_ind(_n_rows * _n_cols * _N_FLOWS);\n std::iota(h_linear_ind.begin(), h_linear_ind.end(), 0);\n d_linear_ind_ =\n util::Device1D::make_unique(_n_rows * _n_cols * _N_FLOWS);\n d_linear_ind_->copyFrom(h_linear_ind);\n\n // 0,1,2,...,n_objects valid locations\n d_valid_flow_Zbuffer_ =\n util::Device1D::make_unique(_n_rows * _n_cols * _N_FLOWS);\n d_valid_disparity_Zbuffer_ =\n util::Device1D::make_unique(_n_rows * _n_cols);\n d_valid_flow_Zbuffer_sub_ =\n util::Device1D::make_unique(_n_rows * _n_cols * _N_FLOWS);\n d_valid_disparity_Zbuffer_sub_ =\n util::Device1D::make_unique(_n_rows * _n_cols);\n d_extra_disparity_buffer_ =\n util::Device1D::make_unique(_n_rows * _n_cols);\n\n // index of valid locations\n d_ind_flow_Zbuffer_ =\n util::Device1D::make_unique(_n_rows * _n_cols * _N_FLOWS);\n d_ind_disparity_Zbuffer_ =\n util::Device1D::make_unique(_n_rows * _n_cols);\n d_ind_flow_Zbuffer_sub_ =\n util::Device1D::make_unique(_n_rows * _n_cols * _N_FLOWS);\n d_ind_disparity_Zbuffer_sub_ =\n util::Device1D::make_unique(_n_rows * _n_cols);\n\n // starting index of sorted segments\n d_seg_start_inds_ =\n util::Device1D::make_unique(parameters_.max_objects_);\n\n // Gathering\n d_flow_compact_ =\n util::Device1D::make_unique(_n_cols * _n_rows * _N_FLOWS);\n d_Zbuffer_flow_compact_ =\n util::Device1D::make_unique(_n_cols * _n_rows * _N_FLOWS);\n d_disparity_compact_ = util::Device1D::make_unique(_n_cols * _n_rows);\n d_Zbuffer_normals_compact_ =\n util::Device1D::make_unique(_n_cols * _n_rows);\n d_n_values_flow_ = util::Device1D::make_unique(parameters_.max_objects_);\n d_start_ind_flow_ =\n util::Device1D::make_unique(parameters_.max_objects_);\n d_n_values_disparity_ =\n util::Device1D::make_unique(parameters_.max_objects_);\n d_start_ind_disparity_ =\n util::Device1D::make_unique(parameters_.max_objects_);\n\n // Normal equations\n d_CO_ = util::Device1D::make_unique(\n 4 * _MAX_N_VAL_ACCUM * _N_CON_FLOW * parameters_.max_objects_);\n d_CO_reduced_ = util::Device1D::make_unique(_N_CON_FLOW *\n parameters_.max_objects_);\n d_CD_ = util::Device1D::make_unique(\n 4 * _MAX_N_VAL_ACCUM * _N_CON_DISP * parameters_.max_objects_);\n d_CD_reduced_ = util::Device1D::make_unique(_N_CON_DISP *\n parameters_.max_objects_);\n d_abs_res_ =\n util::Device1D::make_unique((_N_FLOWS + 1) * _n_rows * _n_cols);\n\n h_CO_reduced_.resize(_N_CON_FLOW * parameters_.max_objects_);\n h_CD_reduced_.resize(_N_CON_DISP * parameters_.max_objects_);\n segment_normal_eqs_.resize(parameters_.max_objects_);\n\n // Segment starting indices in single residual structure\n d_offset_ind_res_flow_ =\n util::Device1D::make_unique(parameters_.max_objects_);\n d_offset_ind_res_disparity_ =\n util::Device1D::make_unique(parameters_.max_objects_);\n d_dTR_ = util::Device1D::make_unique(6 * parameters_.max_objects_);\n d_delta_T_accum_ =\n util::Device1D::make_unique(3 * parameters_.max_objects_);\n d_delta_Rmat_accum_ =\n util::Device1D::make_unique(9 * parameters_.max_objects_);\n d_segment_translation_table_ =\n util::Device1D::make_unique(parameters_.max_objects_);\n\n // Approximate median\n // pre-generate maximum number of random numbers required (uniform between\n // [0,1[ , used for sampling (with replacement) of absolute residuals\n std::vector h_random_numbers(_n_rows * _n_cols * (_N_FLOWS + 1));\n srand(0);\n for (auto &it : h_random_numbers)\n it = (float)((double)rand() / double(RAND_MAX));\n d_random_numbers_ =\n util::Device1D::make_unique(_n_rows * _n_cols * (_N_FLOWS + 1));\n d_random_numbers_->copyFrom(h_random_numbers);\n int pp = int(ceil(double(_n_rows * _n_cols * (_N_FLOWS + 1)) /\n 243.0)); // second stage reduction\n d_median_tmp_ = util::Device1D::make_unique(pp);\n d_abs_res_scales_ =\n util::Device1D::make_unique(parameters_.max_objects_);\n\n // temps used inside median calculation\n d_median_n_in_ = util::Device1D::make_unique(parameters_.max_objects_);\n d_median_start_inds_ =\n util::Device1D::make_unique(parameters_.max_objects_);\n\n // Initial Zbuffer\n d_init_Z_ = util::Device1D::make_unique(_n_rows * _n_cols);\n\n // Residual flow\n d_res_flowx_ = util::Device1D::make_unique(_n_rows * _n_cols);\n d_res_flowy_ = util::Device1D::make_unique(_n_rows * _n_cols);\n\n // Residual ar flow\n d_res_ar_flowx_ = util::Device1D::make_unique(_n_rows * _n_cols);\n d_res_ar_flowy_ = util::Device1D::make_unique(_n_rows * _n_cols);\n}\n\nvoid D_MultipleRigidPoses::addModel(const char *obj_filename, float obj_scale,\n TranslationRotation3D initial_pose) {\n if (_n_objects > (parameters_.max_objects_ - 2))\n throw std::runtime_error(\"D_MultipleRigidPoses::addModel: Max objects \"\n \"exceeded, increase key_bits\\n\");\n\n _n_objects++;\n _currentPoses.push_back(initial_pose);\n setSparsePose(initial_pose, _n_objects - 1);\n\n _multipleRigidModelsOgre->addModel(obj_filename);\n}\n\nvoid D_MultipleRigidPoses::removeAllModels() {\n _multipleRigidModelsOgre->removeAllModels();\n _currentPoses.clear();\n _currentSparseObject = 0;\n _n_objects = 0;\n}\n\nvoid D_MultipleRigidPoses::update(const util::Device1D &d_flowx,\n const util::Device1D &d_flowy,\n const util::Device1D &d_ar_flowx,\n const util::Device1D &d_ar_flowy,\n const util::Device2D &d_disparity,\n std::bitset<32> segments_to_update) {\n// small inaccuracy here is that the optical flow (frame 1 -> frame 2) is\n// segmented according to the region estimated at frame 2\n// this is necessary in order to update the residual flow according to the\n// updated model depth\n// have shown (in simulation) that the resulting pose estimates are a lot more\n// accurate than those based on initial depth\n\n#ifdef TIME_STEPS\n // Setup timers\n cudaEvent_t start, end;\n float elapsed_time;\n cudaEventCreate(&start);\n cudaEventCreate(&end);\n#endif\n\n if (_running) {\n\n // reset timers\n int n_timers = 14;\n _compTimes.clear();\n _compTimes.assign(n_timers, 0.0);\n\n /* 0 preprocess-total\n * 1 ols-total\n * 2 robust-total\n * 3 mark valids\n * 4 radix sort\n * 5 gather\n * 6 compose normal equations\n * 7 reduce normal equations\n * 8 solve normal equations (cpu)\n * 9 compute absolute residuals\n * 10 median absolute residuals\n * 11 render\n * 12 pose accumulation (cpu)\n * 13 residual flow\n */\n\n std::vector delta_poses_accum(\n _n_objects, TranslationRotation3D());\n\n std::vector explained_delta_poses(\n _n_objects, TranslationRotation3D());\n\n#ifdef TIME_STEPS\n cudaEventRecord(start, 0);\n#endif\n\n // first rendering\n render(_currentPoses);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end, 0);\n cudaEventSynchronize(end);\n cudaEventElapsedTime(&elapsed_time, start, end);\n _compTimes.at(11) += (double)elapsed_time;\n#endif\n\n#ifdef TIME_STEPS\n cudaEventRecord(start, 0);\n#endif\n\n // store initial Zbuffer as initial model for residual flow\n convertZbufferToZ(d_init_Z_->data(), _multipleRigidModelsOgre->getZBuffer(),\n _n_cols, _n_rows, _nodal_point_x, _nodal_point_y,\n parameters_.near_plane_, parameters_.far_plane_);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end, 0);\n cudaEventSynchronize(end);\n cudaEventElapsedTime(&elapsed_time, start, end);\n _compTimes.at(13) += (double)elapsed_time;\n#endif\n\n // start iterations\n std::vector dT_accum(3 * _n_objects);\n std::vector dR_accum(9 * _n_objects);\n std::vector poses_accum = _currentPoses;\n\n for (int it = 1; it <= parameters_.n_icp_outer_it_; it++) {\n\n// update residual flow\n#ifdef TIME_STEPS\n cudaEventRecord(start, 0);\n#endif\n\n for (int o = 0; o < _n_objects; o++) {\n auto delta_pose =\n poses_accum.at(o) * _currentPoses.at(o).inverseTransform();\n if (previous_camera_pose_.isValid()) {\n explained_delta_poses.at(o) = camera_pose_.inverseTransform() *\n delta_pose * previous_camera_pose_;\n } else {\n explained_delta_poses.at(o) = delta_pose;\n }\n }\n\n for (int o = 0; o < _n_objects; o++) {\n explained_delta_poses.at(o).getT(&dT_accum[3 * o]);\n explained_delta_poses.at(o).getR_mat(&dR_accum[9 * o]);\n }\n d_delta_T_accum_->copyFrom(dT_accum, 3 * _n_objects);\n d_delta_Rmat_accum_->copyFrom(dR_accum, 9 * _n_objects);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end, 0);\n cudaEventSynchronize(end);\n cudaEventElapsedTime(&elapsed_time, start, end);\n _compTimes.at(12) += (double)elapsed_time;\n#endif\n\n#ifdef TIME_STEPS\n cudaEventRecord(start, 0);\n#endif\n computeResidualFlow(\n d_res_flowx_->data(), d_res_flowy_->data(), d_res_ar_flowx_->data(),\n d_res_ar_flowy_->data(), d_flowx.data(), d_flowy.data(),\n d_ar_flowx.data(), d_ar_flowy.data(), d_delta_T_accum_->data(),\n d_delta_Rmat_accum_->data(), d_init_Z_->data(),\n _multipleRigidModelsOgre->getSegmentIND(), _n_cols, _n_rows,\n _nodal_point_x, _nodal_point_y, _focal_length_x, _focal_length_y);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end, 0);\n cudaEventSynchronize(end);\n cudaEventElapsedTime(&elapsed_time, start, end);\n _compTimes.at(13) += (double)elapsed_time;\n#endif\n\n robustPoseUpdates(d_res_flowx_->data(), d_res_flowy_->data(),\n d_res_ar_flowx_->data(), d_res_ar_flowy_->data(),\n d_disparity.data(), d_disparity.pitch(),\n (int)segments_to_update.to_ulong());\n\n // accumulate delta poses\n for (int s = 0; s < _segment_info.size(); s++) {\n SegmentINFO &tmp = _segment_info.at(s);\n delta_poses_accum.at(tmp.segment_ind) =\n _robustDeltaPoses.at(s) * delta_poses_accum.at(tmp.segment_ind);\n }\n\n#ifdef TIME_STEPS\n cudaEventRecord(start, 0);\n#endif\n\n // update poses, invalidating if magnitude T too large\n poses_accum = _currentPoses;\n\n // transform to camera frame\n for (auto &it : poses_accum)\n it = camera_pose_.inverseTransform() * it;\n\n for (int o = 0; o < _n_objects; o++)\n if (delta_poses_accum.at(o).normT2() <\n parameters_.max_t_update_norm_squared_)\n poses_accum.at(o) = delta_poses_accum.at(o) * poses_accum.at(o);\n else // invalidate\n poses_accum.at(o).setValid(false);\n\n // transform back to world frame\n for (auto &it : poses_accum)\n it = camera_pose_ * it;\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end, 0);\n cudaEventSynchronize(end);\n cudaEventElapsedTime(&elapsed_time, start, end);\n _compTimes.at(12) += (double)elapsed_time;\n#endif\n\n#ifdef TIME_STEPS\n cudaEventRecord(start, 0);\n#endif\n\n // render at updated pose (unless last iteration)\n if (it < parameters_.n_icp_outer_it_)\n render(poses_accum);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end, 0);\n cudaEventSynchronize(end);\n cudaEventElapsedTime(&elapsed_time, start, end);\n _compTimes.at(11) += (double)elapsed_time;\n#endif\n }\n\n // update current pose\n _currentPoses = poses_accum;\n }\n}\n\nvoid D_MultipleRigidPoses::updateNormalEquations(\n const util::Device1D &d_flowx, const util::Device1D &d_flowy,\n const util::Device1D &d_ar_flowx,\n const util::Device1D &d_ar_flowy,\n const util::Device2D &d_disparity,\n const std::vector &explained_delta_poses,\n std::bitset<32> segments_to_update) {\n // ensure rendering is up-to-date\n render(_currentPoses);\n\n // store initial Zbuffer as initial model for residual flow\n convertZbufferToZ(d_init_Z_->data(), _multipleRigidModelsOgre->getZBuffer(),\n _n_cols, _n_rows, _nodal_point_x, _nodal_point_y,\n parameters_.near_plane_, parameters_.far_plane_);\n\n // initialize residual flow (will improve later)\n d_res_flowx_->copyFrom(d_flowx);\n d_res_flowy_->copyFrom(d_flowy);\n d_res_ar_flowx_->copyFrom(d_ar_flowx);\n d_res_ar_flowy_->copyFrom(d_ar_flowy);\n\n // subtract camera-motion-induced flow\n std::vector dT_accum(3 * _n_objects);\n std::vector dR_accum(9 * _n_objects);\n\n for (int o = 0; o < _n_objects; o++) {\n explained_delta_poses.at(o).getT(&dT_accum[3 * o]);\n explained_delta_poses.at(o).getR_mat(&dR_accum[9 * o]);\n }\n d_delta_T_accum_->copyFrom(dT_accum, 3 * _n_objects);\n d_delta_Rmat_accum_->copyFrom(dR_accum, 9 * _n_objects);\n\n computeResidualFlow(\n d_res_flowx_->data(), d_res_flowy_->data(), d_res_ar_flowx_->data(),\n d_res_ar_flowy_->data(), d_flowx.data(), d_flowy.data(),\n d_ar_flowx.data(), d_ar_flowy.data(), d_delta_T_accum_->data(),\n d_delta_Rmat_accum_->data(), d_init_Z_->data(),\n _multipleRigidModelsOgre->getSegmentIND(), _n_cols, _n_rows,\n _nodal_point_x, _nodal_point_y, _focal_length_x, _focal_length_y);\n\n // reset normal equations\n for (auto &it : segment_normal_eqs_)\n it.reset();\n\n // perform robust iteration\n robustPoseUpdates(d_res_flowx_->data(), d_res_flowy_->data(),\n d_res_ar_flowx_->data(), d_res_ar_flowy_->data(),\n d_disparity.data(), d_disparity.pitch(),\n (int)segments_to_update.to_ulong());\n\n // prepare normal equations for (external) constrained update\n int min_samples = 1000;\n for (auto &it : _segment_info) {\n // reset if not enough samples or update norm too large\n if (((it.n_values_flow + it.n_values_disparity) < min_samples) &&\n (segment_normal_eqs_.at(it.segment_ind).squaredNormDeltaT() >=\n parameters_.max_t_update_norm_squared_))\n segment_normal_eqs_.at(it.segment_ind).reset();\n }\n}\n\nvoid D_MultipleRigidPoses::setWeights(float w_flow, float w_ar_flow,\n float w_disp) {\n parameters_.w_flow_ = w_flow;\n parameters_.w_ar_flow_ = w_ar_flow;\n parameters_.w_disp_ = w_disp;\n}\n\nvoid D_MultipleRigidPoses::getWeights(float &w_flow, float &w_ar_flow,\n float &w_disp) const {\n w_flow = parameters_.w_flow_;\n w_ar_flow = parameters_.w_ar_flow_;\n w_disp = parameters_.w_disp_;\n}\n\ncudaArray *D_MultipleRigidPoses::getTexture() {\n render(_currentPoses);\n return (_multipleRigidModelsOgre->getTexture());\n}\n\ncudaArray *D_MultipleRigidPoses::getSegmentIND() {\n render(_currentPoses);\n return (_multipleRigidModelsOgre->getSegmentIND());\n}\n\ncudaArray *D_MultipleRigidPoses::getZbuffer() {\n render(_currentPoses);\n return _multipleRigidModelsOgre->getZBuffer();\n}\n\ncudaArray *D_MultipleRigidPoses::getNormalX() {\n render(_currentPoses);\n return _multipleRigidModelsOgre->getNormalX();\n}\n\ncudaArray *D_MultipleRigidPoses::getNormalY() {\n render(_currentPoses);\n return _multipleRigidModelsOgre->getNormalY();\n}\n\ncudaArray *D_MultipleRigidPoses::getNormalZ() {\n render(_currentPoses);\n return _multipleRigidModelsOgre->getNormalZ();\n}\n\nvoid D_MultipleRigidPoses::setCameraParameters(float focal_length_x,\n float focal_length_y,\n float nodal_point_x,\n float nodal_point_y) {\n render_state_changed_ = true;\n _nodal_point_x = nodal_point_x;\n _nodal_point_y = nodal_point_y;\n _focal_length_x = focal_length_x;\n _focal_length_y = focal_length_y;\n _multipleRigidModelsOgre->updateProjectionMatrix(\n _focal_length_x, _focal_length_y, _nodal_point_x, _nodal_point_y,\n parameters_.near_plane_, parameters_.far_plane_);\n}\n\nvoid\nD_MultipleRigidPoses::setCameraPose(const TranslationRotation3D &camera_pose) {\n render_state_changed_ = true;\n\n // if this is the first camera pose received, make previous and current equal\n previous_camera_pose_ =\n previous_camera_pose_.isValid() ? camera_pose_ : camera_pose;\n\n camera_pose_ = camera_pose;\n _multipleRigidModelsOgre->updateCameraPose(camera_pose_.rotateX180());\n}\n\nbool D_MultipleRigidPoses::isDenseWinner() {\n\n bool denseWinner = true;\n double thres = parameters_.reliability_threshold_;\n\n // make sure that sparse does not negatively affect any object's proportion ar\n // valid\n // add some margin to allow minor decrease in reliability (due to noise)\n double margin = parameters_.sparse_intro_allowed_reliability_decrease_;\n bool sparseRelDecrease = false;\n for (int o = 0; o < _n_objects; o++)\n if (_S_ar_flow_prop_valid[o] < (_D_ar_flow_prop_valid[o] - margin))\n sparseRelDecrease = true;\n\n if (!sparseRelDecrease) {\n\n // compare reliability at the current sparse object\n int o = _currentSparseObject;\n\n // we require a pretty high reliability for sparse introduction\n double intro_thres = parameters_.sparse_intro_reliability_threshold_;\n if ((_S_ar_flow_prop_valid[o] > (_D_ar_flow_prop_valid[o] + intro_thres)) ||\n ((_S_ar_flow_prop_valid[o] > intro_thres) &&\n (_D_ar_flow_prop_valid[o] < thres)))\n denseWinner = false;\n }\n\n return (denseWinner);\n}\n\nvoid D_MultipleRigidPoses::render(\n const std::vector &renderPoses) {\n if ((renderPoses != _lastPosesRendered) || (render_state_changed_)) {\n\n // util::TimerGPU render_timer;\n _multipleRigidModelsOgre->render(renderPoses);\n // std::cout << \"render time: \" << render_timer.read() << \" ms\\n\";\n _lastPosesRendered = renderPoses;\n render_state_changed_ = false;\n }\n}\n\nvoid\nD_MultipleRigidPoses::getSegmentLengths(std::vector &lengths,\n const std::vector &starting_inds,\n int n_segments, int total_length) {\n\n for (int i = 0; i < (n_segments - 1); i++) {\n int s = starting_inds.at(i);\n if (s >= 0) {\n\n // find end-point\n int e = starting_inds.at(i + 1);\n for (int j = (i + 2); ((e < 0) && (j < n_segments)); j++)\n e = starting_inds.at(j);\n\n if (e < 0)\n e = total_length;\n\n lengths.at(i) = e - s;\n\n } else\n\n lengths.at(i) = 0;\n }\n\n // final segment\n int s = starting_inds.at(n_segments - 1);\n if (s >= 0)\n lengths.at(n_segments - 1) = total_length - s;\n else\n lengths.at(n_segments - 1) = 0;\n}\n\nint D_MultipleRigidPoses::getNextSelectedSparseObject(bool dense) {\n\n std::vector probabilities =\n dense ? _D_ar_flow_prop_valid : _S_ar_flow_prop_valid;\n\n double sum = 0.0;\n for (int o = 0; o < _n_objects; o++) {\n probabilities.at(o) = 1.0 - probabilities.at(o);\n sum += probabilities.at(o);\n }\n\n for (int o = 0; o < _n_objects; o++)\n probabilities.at(o) /= sum;\n\n double r = ((double)rand() / double(RAND_MAX));\n\n int object = 0;\n double prob_ulim = probabilities.at(object);\n\n while (r > prob_ulim) {\n object++;\n prob_ulim += probabilities.at(object);\n }\n\n return (object);\n}\n\nvoid D_MultipleRigidPoses::evaluateARFlowPoseError(\n bool dense, const util::Device1D &d_ar_flowx,\n std::vector &poses) {\n if (dense)\n computeARFlowPoseError(d_ar_flowx, poses, _D_ar_flow_prop_valid);\n else\n computeARFlowPoseError(d_ar_flowx, poses, _S_ar_flow_prop_valid);\n}\n\nvoid D_MultipleRigidPoses::computeARFlowPoseError(\n const util::Device1D &d_ar_flowx,\n std::vector &poses,\n std::vector &ar_flow_prop_valid) {\n std::vector ar_flow_abs_valid; // discarded\n computeARFlowPoseError(d_ar_flowx, poses, ar_flow_prop_valid,\n ar_flow_abs_valid);\n}\n\nvoid D_MultipleRigidPoses::computeARFlowPoseError(\n const util::Device1D &d_ar_flowx,\n std::vector &poses,\n std::vector &ar_flow_prop_valid,\n std::vector &ar_flow_abs_valid) {\n ar_flow_prop_valid.clear();\n ar_flow_abs_valid.clear();\n\n render(poses);\n\n // re-purposing some already allocated data\n auto d_valid_ar_flow_Zbuffer = d_valid_disparity_Zbuffer_.get();\n auto d_valid_Zbuffer = d_valid_disparity_Zbuffer_sub_.get();\n markValidFlowZbufferAndZbufferZeroBased(\n d_valid_ar_flow_Zbuffer->data(), d_valid_Zbuffer->data(),\n d_ar_flowx.data(), _multipleRigidModelsOgre->getSegmentIND(), _n_cols,\n _n_rows, _n_objects);\n\n // additional buffers required for sorting\n auto d_value = d_ind_disparity_Zbuffer_.get();\n auto d_value_buf = d_ind_disparity_Zbuffer_sub_.get();\n auto d_key_buf = d_extra_disparity_buffer_.get();\n\n // Radix sort all the indices using the valid marks\n // indices could be ignored here to speed up sorting (but diff is minimal)\n cub_radix_sorter_->sort(*d_valid_ar_flow_Zbuffer, *d_value, *d_key_buf,\n *d_value_buf);\n pose::extractLabelStartingIndices(d_seg_start_inds_->data(),\n d_valid_ar_flow_Zbuffer->data(),\n _n_cols * _n_rows, _n_objects);\n std::vector h_seg_start_inds_flow_Zbuffer(_n_objects + 1);\n d_seg_start_inds_->copyTo(h_seg_start_inds_flow_Zbuffer, _n_objects + 1);\n\n cub_radix_sorter_->sort(*d_valid_Zbuffer, *d_value, *d_key_buf, *d_value_buf);\n pose::extractLabelStartingIndices(d_seg_start_inds_->data(),\n d_valid_Zbuffer->data(), _n_cols * _n_rows,\n _n_objects);\n std::vector h_seg_start_inds_Zbuffer(_n_objects + 1);\n d_seg_start_inds_->copyTo(h_seg_start_inds_Zbuffer, _n_objects + 1);\n\n // for(int o=0;o<(_n_objects+1);o++)\n // printf(\"obj %d - flow+z %09d - z\n // %09d\\n\",o,h_seg_start_inds_flow_Zbuffer[o],h_seg_start_inds_Zbuffer[o]);\n\n int n_valid_flow_Zbuffer = (h_seg_start_inds_flow_Zbuffer.at(_n_objects) >= 0)\n ? h_seg_start_inds_flow_Zbuffer.at(_n_objects)\n : (_n_cols * _n_rows);\n\n _seg_lengths_flow_Zbuffer.assign(_n_objects, 0);\n\n getSegmentLengths(_seg_lengths_flow_Zbuffer, h_seg_start_inds_flow_Zbuffer,\n _n_objects, n_valid_flow_Zbuffer);\n\n int n_valid_Zbuffer = (h_seg_start_inds_Zbuffer.at(_n_objects) >= 0)\n ? h_seg_start_inds_Zbuffer.at(_n_objects)\n : (_n_cols * _n_rows);\n\n _seg_lengths_Zbuffer.assign(_n_objects, 0);\n getSegmentLengths(_seg_lengths_Zbuffer, h_seg_start_inds_Zbuffer, _n_objects,\n n_valid_Zbuffer);\n\n // for(int o=0;o<(_n_objects+1);o++)\n // printf(\"obj %d - start+z %09d - length+z %09d - start %09d - length\n // %09d\\n\",o,h_seg_start_inds_flow_Zbuffer[o],seg_lengths_flow_Zbuffer[o],h_seg_start_inds_Zbuffer[o],seg_lengths_Zbuffer[o]);\n\n // save proportion valid for each object\n auto bounding_boxes =\n _multipleRigidModelsOgre->getBoundingBoxesInCameraImage(poses);\n\n for (int o = 0; o < _n_objects; o++) {\n\n double absolute_count = (double)_seg_lengths_flow_Zbuffer[o];\n double prop = (_seg_lengths_Zbuffer[o] > 0)\n ? (double)_seg_lengths_flow_Zbuffer[o] /\n (double)_seg_lengths_Zbuffer[o]\n : 0.0;\n\n if (parameters_.check_reliability_) {\n // does the proportion valid AR flow exceed the threshold?\n bool valid = prop > parameters_.reliability_threshold_;\n\n // is the projected shape 'sufficiently two-dimensional'?\n // the AR flow-based reliability measure fails on near-edge-like object\n // projections\n Eigen::Map > bb_pixel(\n bounding_boxes.at(o).data());\n\n // subtract mean\n Eigen::Vector2d mn = bb_pixel.rowwise().mean();\n bb_pixel.colwise() -= mn;\n\n // covariance\n double ratio = parameters_.max_proportion_projected_bounding_box_;\n Eigen::Matrix2d cov = bb_pixel * bb_pixel.transpose();\n // check validity\n if (std::isfinite(cov(0, 0)) && std::isfinite(cov(0, 1)) &&\n std::isfinite(cov(1, 0)) && std::isfinite(cov(1, 1))) {\n Eigen::EigenSolver eigen_solver(cov);\n // rotate bounding box\n Eigen::Matrix2d real_eigenvectors = eigen_solver.eigenvectors().real();\n // Eigen::VectorXd w = eigen_solver.eigenvalues().real();\n bb_pixel = real_eigenvectors.transpose() * bb_pixel;\n // find extent\n auto extent =\n bb_pixel.rowwise().maxCoeff() - bb_pixel.rowwise().minCoeff();\n ratio = extent.maxCoeff() / extent.minCoeff();\n }\n valid =\n valid && (ratio < parameters_.max_proportion_projected_bounding_box_);\n\n poses.at(o).setValid(valid);\n }\n\n ar_flow_prop_valid.push_back(prop);\n ar_flow_abs_valid.push_back(absolute_count);\n }\n}\n\nvoid D_MultipleRigidPoses::robustPoseUpdates(\n const float *d_flowx, const float *d_flowy, const float *d_ar_flowx,\n const float *d_ar_flowy, const float *d_disparity, size_t d_disparity_pitch,\n int segments_to_update) {\n\n cudaArray *d_ZbufferArray = _multipleRigidModelsOgre->getZBuffer();\n cudaArray *d_normalXArray = _multipleRigidModelsOgre->getNormalX();\n cudaArray *d_normalYArray = _multipleRigidModelsOgre->getNormalY();\n cudaArray *d_normalZArray = _multipleRigidModelsOgre->getNormalZ();\n cudaArray *d_segmentINDArray = _multipleRigidModelsOgre->getSegmentIND();\n\n#ifdef TIME_STEPS\n // Setup timers\n cudaEvent_t start, end, start_sub, end_sub;\n float elapsed_time;\n cudaEventCreate(&start);\n cudaEventCreate(&end);\n cudaEventCreate(&start_sub);\n cudaEventCreate(&end_sub);\n#endif\n#ifdef TIME_STEPS\n cudaEventRecord(start, 0); // preprocessing\n#endif\n\n // initialize normal equations to zero\n std::fill(h_CO_reduced_.begin(), h_CO_reduced_.end(), 0);\n std::fill(h_CD_reduced_.begin(), h_CD_reduced_.end(), 0);\n cudaMemset(d_CO_reduced_->data(), 0,\n _N_CON_FLOW * parameters_.max_objects_ * sizeof(float));\n cudaMemset(d_CD_reduced_->data(), 0,\n _N_CON_DISP * parameters_.max_objects_ * sizeof(float));\n\n // Determine Zbuffer conversion constants\n // depth = Z_conv1/(Zbuffer+Z_conv2)\n float Z_conv1, Z_conv2;\n get_GL_conv_constants(Z_conv1, Z_conv2, parameters_.far_plane_,\n parameters_.near_plane_);\n\n// Mark valid locations with segment index\n// If a cue's weight equals 0 mark everything invalid\n// No initialization required\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n mark_with_zero_based_segmentIND(\n d_valid_flow_Zbuffer_->data(), d_valid_disparity_Zbuffer_->data(),\n d_flowx, d_ar_flowx, (const char *)d_disparity, d_segmentINDArray,\n _n_cols, _n_rows, _n_objects, d_disparity_pitch, parameters_.w_flow_ > 0,\n parameters_.w_ar_flow_ > 0, parameters_.w_disp_ > 0, segments_to_update);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(3) += (double)elapsed_time;\n#endif\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n // Radix sort all the indices using the valid marks\n // the _sub_ buffers are only used for double-buffering (ping-pong)\n d_ind_flow_Zbuffer_->copyFrom(*d_linear_ind_);\n d_ind_disparity_Zbuffer_->copyFrom(*d_linear_ind_, _n_cols * _n_rows);\n\n // util::TimerGPU sort_timer;\n cub_radix_sorter_->sort(*d_valid_flow_Zbuffer_, *d_ind_flow_Zbuffer_,\n *d_valid_flow_Zbuffer_sub_, *d_ind_flow_Zbuffer_sub_);\n // std::cout << \"flow sort time: \" << sort_timer.read();\n // sort_timer.reset();\n cub_radix_sorter_->sort(\n *d_valid_disparity_Zbuffer_, *d_ind_disparity_Zbuffer_,\n *d_valid_disparity_Zbuffer_sub_, *d_ind_disparity_Zbuffer_sub_);\n// std::cout << \" disp sort time: \" << sort_timer.read() << std::endl;\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(4) += (double)elapsed_time;\n#endif\n\n // Get starting indices\n pose::extractLabelStartingIndices(d_seg_start_inds_->data(),\n d_valid_flow_Zbuffer_->data(),\n _n_cols * _n_rows * _N_FLOWS, _n_objects);\n std::vector h_seg_start_inds_flow(_n_objects + 1);\n d_seg_start_inds_->copyTo(h_seg_start_inds_flow, _n_objects + 1);\n pose::extractLabelStartingIndices(d_seg_start_inds_->data(),\n d_valid_disparity_Zbuffer_->data(),\n _n_cols * _n_rows, _n_objects);\n std::vector h_seg_start_inds_disparity(_n_objects + 1);\n d_seg_start_inds_->copyTo(h_seg_start_inds_disparity, _n_objects + 1);\n\n // printf(\"before sub\\n\");\n // for(int i=0;i<(_n_objects+1);i++)\n // printf(\"se %d f %+07d d\n // %+07d\\n\",i,h_seg_start_inds_flow[i],h_seg_start_inds_disparity[i]);\n\n // Regularly subsample flow and disparity indices if required when a maximum\n // number of samples has been enforced\n // Relative proportion flow/disp will be maintained approximately\n\n int n_valid_flow_Zbuffer = (h_seg_start_inds_flow.at(_n_objects) >= 0)\n ? h_seg_start_inds_flow.at(_n_objects)\n : (_n_cols * _n_rows * _N_FLOWS);\n int n_valid_disparity_Zbuffer =\n (h_seg_start_inds_disparity.at(_n_objects) >= 0)\n ? h_seg_start_inds_disparity.at(_n_objects)\n : (_n_cols * _n_rows);\n\n // printf(\"before sub %s\\n\",cudaGetErrorString(cudaGetLastError()));\n // printf(\"val flow %d val disp\n // %d\\n\",n_valid_flow_Zbuffer,n_valid_disparity_Zbuffer);\n\n int n_valid_total = n_valid_flow_Zbuffer + n_valid_disparity_Zbuffer;\n\n if (n_valid_total > parameters_.max_samples_) {\n\n // proportionally subsample flow and disparity\n double sub_factor =\n (double)parameters_.max_samples_ / (double)n_valid_total;\n double inv_sub_factor = 1.0 / sub_factor;\n\n // flow\n int n_valid_flow_Zbuffer_sub =\n (int)floor((double)n_valid_flow_Zbuffer * sub_factor);\n\n if (n_valid_flow_Zbuffer_sub > 0) {\n subsample_ind_and_labels(\n d_ind_flow_Zbuffer_sub_->data(), d_ind_flow_Zbuffer_->data(),\n d_valid_flow_Zbuffer_sub_->data(), d_valid_flow_Zbuffer_->data(),\n n_valid_flow_Zbuffer_sub, (float)inv_sub_factor);\n\n // update all regular variables to account for subsampling\n // swap full and subsampled storage!\n n_valid_flow_Zbuffer = n_valid_flow_Zbuffer_sub;\n d_ind_flow_Zbuffer_->swap(*d_ind_flow_Zbuffer_sub_);\n d_valid_flow_Zbuffer_->swap(*d_valid_flow_Zbuffer_sub_);\n\n // update segment starting indices\n pose::extractLabelStartingIndices(d_seg_start_inds_->data(),\n d_valid_flow_Zbuffer_->data(),\n n_valid_flow_Zbuffer, _n_objects);\n d_seg_start_inds_->copyTo(h_seg_start_inds_flow, _n_objects + 1);\n // printf(\"valsub flow %d\\n\",n_valid_flow_Zbuffer);\n }\n\n // disparity\n int n_valid_disparity_Zbuffer_sub =\n (int)floor((double)n_valid_disparity_Zbuffer * sub_factor);\n\n if (n_valid_disparity_Zbuffer_sub > 0) {\n subsample_ind_and_labels(d_ind_disparity_Zbuffer_sub_->data(),\n d_ind_disparity_Zbuffer_->data(),\n d_valid_disparity_Zbuffer_sub_->data(),\n d_valid_disparity_Zbuffer_->data(),\n n_valid_disparity_Zbuffer_sub,\n (float)inv_sub_factor);\n\n // update all regular variables to account for subsampling\n // swap full and subsampled storage!\n n_valid_disparity_Zbuffer = n_valid_disparity_Zbuffer_sub;\n d_ind_disparity_Zbuffer_->swap(*d_ind_disparity_Zbuffer_sub_);\n d_valid_disparity_Zbuffer_->swap(*d_valid_disparity_Zbuffer_sub_);\n\n // update segment starting indices\n pose::extractLabelStartingIndices(d_seg_start_inds_->data(),\n d_valid_disparity_Zbuffer_->data(),\n n_valid_disparity_Zbuffer, _n_objects);\n d_seg_start_inds_->copyTo(h_seg_start_inds_disparity, _n_objects + 1);\n // printf(\"valsub disp %d\\n\",n_valid_disparity_Zbuffer);\n }\n\n // printf(\"after sub\\n\");\n // for(int i=0;i<(_n_objects+1);i++)\n // printf(\"se %d f %+07d d\n // %+07d\\n\",i,h_seg_start_inds_flow[i],h_seg_start_inds_disparity[i]);\n\n } // subsample\n\n // compute segment lengths\n std::vector seg_lengths_flow(_n_objects);\n getSegmentLengths(seg_lengths_flow, h_seg_start_inds_flow, _n_objects,\n n_valid_flow_Zbuffer);\n std::vector seg_lengths_disparity(_n_objects);\n getSegmentLengths(seg_lengths_disparity, h_seg_start_inds_disparity,\n _n_objects, n_valid_disparity_Zbuffer);\n\n// printf(\"after subsampling\\n\");\n// printf(\"n_f %07d n_d %07d\\n\", n_valid_flow_Zbuffer,\n// n_valid_disparity_Zbuffer);\n// for (int i = 0; i < _n_objects; i++)\n// printf(\"se %d f %+07d (%07d) d %+07d (%07d)\\n\", i,\n// h_seg_start_inds_flow[i],\n// seg_lengths_flow[i], h_seg_start_inds_disparity[i],\n// seg_lengths_disparity[i]);\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n // Gather flow and disparity\n if (n_valid_flow_Zbuffer > 0)\n gather_valid_flow_Zbuffer(\n d_flow_compact_->data(), d_Zbuffer_flow_compact_->data(), d_flowx,\n d_flowy, d_ar_flowx, d_ar_flowy, d_ind_flow_Zbuffer_->data(),\n d_ZbufferArray, n_valid_flow_Zbuffer, _n_cols, _n_rows, Z_conv1,\n Z_conv2);\n\n if (n_valid_disparity_Zbuffer > 0)\n gather_valid_disparity_Zbuffer(\n d_disparity_compact_->data(), d_Zbuffer_normals_compact_->data(),\n (const char *)d_disparity, d_ind_disparity_Zbuffer_->data(),\n d_ZbufferArray, d_normalXArray, d_normalYArray, d_normalZArray,\n n_valid_disparity_Zbuffer, _n_cols, _n_rows, Z_conv1, Z_conv2,\n d_disparity_pitch);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(5) += (double)elapsed_time;\n#endif\n\n // Gather index information\n _segment_info.clear();\n\n for (int o = 0; o < _n_objects; o++) {\n int nf = seg_lengths_flow[o];\n int nd = seg_lengths_disparity[o];\n if ((nf > 0) || (nd > 0)) {\n SegmentINFO tmp;\n tmp.segment_ind = o;\n tmp.n_values_flow = nf;\n tmp.n_values_disparity = nd;\n tmp.start_ind_flow = h_seg_start_inds_flow[o];\n tmp.start_ind_disparity = h_seg_start_inds_disparity[o];\n _segment_info.push_back(tmp);\n }\n }\n\n// printf(\"------------------------------------\\n\");\n// for(int o=0;o<_segment_info.size();o++) {\n// SegmentINFO &tmp = _segment_info.at(o);\n// printf(\"seg %d f_start %06d f_size %06d d_start %06d d_size\n// %06d\\n\",tmp.segment_ind,tmp.start_ind_flow,tmp.n_values_flow,tmp.start_ind_disparity,tmp.n_values_disparity);\n// }\n// printf(\"------------------------------------\\n\");\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end, 0);\n cudaEventSynchronize(end);\n cudaEventElapsedTime(&elapsed_time, start, end);\n // printf(\"preproc time : %2.4f ms\\n\",elapsed_time);\n _compTimes.at(0) += (double)elapsed_time;\n#endif\n\n // preparatory book-keeping\n\n int n_segments = _segment_info.size();\n\n if (n_segments > 0) {\n\n std::vector n_values_flow(n_segments);\n std::vector start_ind_flow(n_segments);\n std::vector n_values_disparity(n_segments);\n std::vector start_ind_disparity(n_segments);\n std::vector start_ind_res_flow(n_segments);\n std::vector start_ind_res_disparity(n_segments);\n\n for (int s = 0; s < n_segments; s++) {\n SegmentINFO &tmp = _segment_info.at(s);\n n_values_flow.at(s) = tmp.n_values_flow;\n start_ind_flow.at(s) = tmp.start_ind_flow;\n n_values_disparity.at(s) = tmp.n_values_disparity;\n start_ind_disparity.at(s) = tmp.start_ind_disparity;\n\n start_ind_res_flow.at(s) = (s > 0) ? (start_ind_res_disparity.at(s - 1) +\n n_values_disparity.at(s - 1))\n : 0;\n start_ind_res_disparity.at(s) =\n start_ind_res_flow.at(s) + n_values_flow.at(s);\n }\n\n // Compute offset between residual input and output indices. Residuals are\n // stored as [seg1_flow seg1_disp seg2_flow seg2_disp ... ] to facilitate\n // median extraction\n\n std::vector offset_ind_res_flow(n_segments);\n std::vector offset_ind_res_disparity(n_segments);\n\n for (int s = 0; s < n_segments; s++) {\n offset_ind_res_flow.at(s) =\n start_ind_res_flow.at(s) - start_ind_flow.at(s);\n offset_ind_res_disparity.at(s) =\n start_ind_res_disparity.at(s) - start_ind_disparity.at(s);\n }\n\n // Create segment translation table (map zero-based original segment indices\n // to compressed segment indices)\n\n std::vector segment_translation_table(_n_objects);\n for (int s = 0; s < n_segments; s++)\n segment_translation_table.at(_segment_info.at(s).segment_ind) = s;\n\n d_segment_translation_table_->copyFrom(segment_translation_table,\n _n_objects);\n d_n_values_flow_->copyFrom(n_values_flow, n_segments);\n d_start_ind_flow_->copyFrom(start_ind_flow, n_segments);\n d_n_values_disparity_->copyFrom(n_values_disparity, n_segments);\n d_start_ind_disparity_->copyFrom(start_ind_disparity, n_segments);\n d_offset_ind_res_flow_->copyFrom(offset_ind_res_flow, n_segments);\n d_offset_ind_res_disparity_->copyFrom(offset_ind_res_disparity, n_segments);\n }\n\n // take care here since _d_CO and _d_CD's maximum sizes are not exactly\n // determined in this way (especially the times 4)\n\n dim3 threadBlock_normal(64, 1, 1);\n int gridDim_x_normal_equations =\n (n_segments > 0)\n ? divUp(_MAX_N_VAL_ACCUM, n_segments * threadBlock_normal.x) * 4\n : 0;\n dim3 blockGrid_normal(gridDim_x_normal_equations, n_segments);\n // dim3 threadBlock_reduce_64(64,1);\n // dim3 blockGrid_reduce_flow(_N_CON_FLOW, n_segments);\n // dim3 blockGrid_reduce_disparity(_N_CON_DISP, n_segments);\n\n dim3 threadBlock_reduce_64_mult(64, 4);\n dim3 blockGrid_reduce_flow_mult(divUp(_N_CON_FLOW, 4), n_segments);\n dim3 blockGrid_reduce_disparity_mult(divUp(_N_CON_DISP, 4), n_segments);\n\n/****************/\n/* OLS Estimate */\n/****************/\n\n#ifdef TIME_STEPS\n cudaEventRecord(start, 0); // ols-time\n#endif\n\n if (n_segments > 0) {\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n normal_eqs_flow(blockGrid_normal, threadBlock_normal, d_CO_->data(),\n d_flow_compact_->data(), d_Zbuffer_flow_compact_->data(),\n d_ind_flow_Zbuffer_->data(), _focal_length_x,\n _focal_length_y, _nodal_point_x, _nodal_point_y, _n_rows,\n _n_cols, d_n_values_flow_->data(),\n d_start_ind_flow_->data());\n normal_eqs_disparity(blockGrid_normal, threadBlock_normal, d_CD_->data(),\n d_disparity_compact_->data(),\n d_Zbuffer_normals_compact_->data(),\n d_ind_disparity_Zbuffer_->data(), _focal_length_x,\n _focal_length_y, _nodal_point_x, _nodal_point_y,\n _baseline, _n_cols, d_n_values_disparity_->data(),\n d_start_ind_disparity_->data(), parameters_.w_disp_);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(6) += (double)elapsed_time;\n#endif\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n reduce_normal_eqs_64_mult_constr(blockGrid_reduce_flow_mult,\n threadBlock_reduce_64_mult,\n d_CO_reduced_->data(), d_CO_->data(),\n gridDim_x_normal_equations, _N_CON_FLOW);\n reduce_normal_eqs_64_mult_constr(blockGrid_reduce_disparity_mult,\n threadBlock_reduce_64_mult,\n d_CD_reduced_->data(), d_CD_->data(),\n gridDim_x_normal_equations, _N_CON_DISP);\n\n// reduce_normal_eqs_64_GPU<<>>(_d_CO_reduced,\n// _d_CO, gridDim_x_normal_equations);\n// reduce_normal_eqs_64_GPU<<>>(_d_CD_reduced,\n// _d_CD, gridDim_x_normal_equations);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(7) += (double)elapsed_time;\n#endif\n }\n\n d_CO_reduced_->copyTo(h_CO_reduced_);\n d_CD_reduced_->copyTo(h_CD_reduced_);\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n // Solve systems\n std::vector dTdR(6 * n_segments);\n for (int s = 0; s < n_segments; s++) {\n int curr_part = _segment_info.at(s).segment_ind;\n segment_normal_eqs_.at(curr_part).compose(\n &h_CO_reduced_.at(_N_CON_FLOW * s), &h_CD_reduced_.at(_N_CON_DISP * s));\n segment_normal_eqs_.at(curr_part).solve(&dTdR.at(6 * s));\n }\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(8) += (double)elapsed_time;\n#endif\n\n // Copy to device memory\n d_dTR_->copyFrom(dTdR, 6 * n_segments);\n\n // for(int s=0;s 0) {\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n dim3 threadBlock_res(256, 1);\n\n if (n_valid_flow_Zbuffer > 0) {\n dim3 blockGrid_res(divUp(n_valid_flow_Zbuffer, threadBlock_res.x), 1);\n flow_absolute_residual_scalable(\n blockGrid_res, threadBlock_res, d_abs_res_->data(),\n d_flow_compact_->data(), d_Zbuffer_flow_compact_->data(),\n d_ind_flow_Zbuffer_->data(), d_valid_flow_Zbuffer_->data(),\n _focal_length_x, _focal_length_y, _nodal_point_x, _nodal_point_y,\n _n_rows, _n_cols, n_valid_flow_Zbuffer,\n d_offset_ind_res_flow_->data(),\n d_segment_translation_table_->data(), parameters_.w_flow_,\n parameters_.w_ar_flow_, d_dTR_->data());\n }\n\n if (n_valid_disparity_Zbuffer > 0) {\n dim3 blockGrid_res(divUp(n_valid_disparity_Zbuffer, threadBlock_res.x),\n 1);\n disp_absolute_residual_scalable(\n blockGrid_res, threadBlock_res, d_abs_res_->data(),\n d_disparity_compact_->data(), d_Zbuffer_normals_compact_->data(),\n d_ind_disparity_Zbuffer_->data(),\n d_valid_disparity_Zbuffer_->data(), _focal_length_x,\n _focal_length_y, _nodal_point_x, _nodal_point_y, _baseline, _n_cols,\n n_valid_disparity_Zbuffer, d_offset_ind_res_disparity_->data(),\n d_segment_translation_table_->data(), parameters_.w_disp_,\n d_dTR_->data());\n }\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(9) += (double)elapsed_time;\n#endif\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n // Median absolute residuals\n std::vector abs_res_scales(n_segments);\n int pp[n_segments];\n for (int s = 0; s < n_segments; s++) {\n SegmentINFO &tmp = _segment_info.at(s);\n pp[s] = tmp.n_values_flow + tmp.n_values_disparity;\n }\n approx_multiple_medians_shuffle_cuda(\n abs_res_scales.data(), d_abs_res_->data(), d_median_tmp_->data(),\n d_random_numbers_->data(), pp, n_segments, d_median_n_in_->data(),\n d_median_start_inds_->data());\n\n for (int s = 0; s < n_segments; s++)\n abs_res_scales.at(s) *= 6.9460f;\n\n d_abs_res_scales_->copyFrom(abs_res_scales, n_segments);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(10) += (double)elapsed_time;\n#endif\n\n// for(int s=0;sdata(),\n d_flow_compact_->data(), d_Zbuffer_flow_compact_->data(),\n d_ind_flow_Zbuffer_->data(), _focal_length_x, _focal_length_y,\n _nodal_point_x, _nodal_point_y, _n_rows, _n_cols,\n d_n_values_flow_->data(), d_start_ind_flow_->data(),\n d_abs_res_scales_->data(), parameters_.w_flow_,\n parameters_.w_ar_flow_, d_dTR_->data());\n\n normal_eqs_disparity_weighted(\n blockGrid_normal, threadBlock_normal, d_CD_->data(),\n d_disparity_compact_->data(), d_Zbuffer_normals_compact_->data(),\n d_ind_disparity_Zbuffer_->data(), _focal_length_x, _focal_length_y,\n _nodal_point_x, _nodal_point_y, _baseline, _n_cols,\n d_n_values_disparity_->data(), d_start_ind_disparity_->data(),\n d_abs_res_scales_->data(), parameters_.w_disp_, d_dTR_->data());\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(6) += (double)elapsed_time;\n#endif\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n reduce_normal_eqs_64_mult_constr(blockGrid_reduce_flow_mult,\n threadBlock_reduce_64_mult,\n d_CO_reduced_->data(), d_CO_->data(),\n gridDim_x_normal_equations, _N_CON_FLOW);\n reduce_normal_eqs_64_mult_constr(blockGrid_reduce_disparity_mult,\n threadBlock_reduce_64_mult,\n d_CD_reduced_->data(), d_CD_->data(),\n gridDim_x_normal_equations, _N_CON_DISP);\n\n// reduce_normal_eqs_64_GPU<<>>(_d_CO_reduced,\n// _d_CO, gridDim_x_normal_equations);\n// reduce_normal_eqs_64_GPU<<>>(_d_CD_reduced,\n// _d_CD, gridDim_x_normal_equations);\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(7) += (double)elapsed_time;\n#endif\n }\n\n d_CO_reduced_->copyTo(h_CO_reduced_);\n d_CD_reduced_->copyTo(h_CD_reduced_);\n\n#ifdef TIME_STEPS\n cudaEventRecord(start_sub, 0);\n#endif\n\n // Solve systems\n for (int s = 0; s < n_segments; s++) {\n int curr_part = _segment_info.at(s).segment_ind;\n segment_normal_eqs_.at(curr_part)\n .compose(&h_CO_reduced_.at(_N_CON_FLOW * s),\n &h_CD_reduced_.at(_N_CON_DISP * s));\n segment_normal_eqs_.at(curr_part).solve(&dTdR.at(6 * s));\n }\n\n#ifdef TIME_STEPS\n cudaThreadSynchronize();\n cudaEventRecord(end_sub, 0);\n cudaEventSynchronize(end_sub);\n cudaEventElapsedTime(&elapsed_time, start_sub, end_sub);\n _compTimes.at(8) += (double)elapsed_time;\n#endif\n\n // Copy to device memory\n d_dTR_->copyFrom(dTdR, 6 * n_segments);\n\n // printf(\"ROBUST ITERATION %d\\n\",it);\n // printf(\"-------------------\\n\");\n // for(int s=0;s\n#include \n\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n\n\nnamespace Dune {\nnamespace GDT {\n\nnamespace Spaces {\nnamespace DG {\ntemplate\nclass FemBased;\n}\ntemplate\nclass Block;\n}\n\nnamespace Operators {\n\n\n/**\n * \\note The automatic detection of the right integration order might fail, so you might want to specify\n * over_integrate. The reason is that in order to locally evaluate the source we first have to create a\n * quadrature, the correct order of wich we guess by taking the sources order on the first entity.\n * \\note We would have liked to do something like this and match on implementations of SpaceInterface:\\code\ntemplate< class T, class VS, class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\nvoid apply(const ConstDiscreteFunction< SpaceInterface< T >, VS >& source,\n DiscreteFunction< Spaces::DG::FemBased< GPR, pR, RR, rR, rCR >, VR >& range) const\n{\n static_assert(Dune::AlwaysFalse< T >::value, \"Not implemented for this combination of source and range!\");\n}\\endcode\n * but that gave compile errors (the compiler just could not match the first argument for whatever reason). This\n * is why we need all combinations of spaces below which are just compile time checks and forwards.\n *\n * \\todo Create a Redirect class templatized with space/vector to check that its a const/discrete function with a\n * method which extracts the correct space if needed. This should give better compile errors.\n */\ntemplate< class GridViewType >\nclass L2Prolongation\n{\n typedef typename GridViewType::template Codim< 0 >::Entity EntityType;\n typedef typename GridViewType::ctype DomainFieldType;\n static const size_t dimDomain = GridViewType::dimension;\n\npublic:\n L2Prolongation(const GridViewType& grid_view)\n : grid_view_(grid_view)\n {}\n\n // Source: Spaces::CG::FemBased\n // Range: Spaces::DG::FemBased\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n void apply(const ConstDiscreteFunction< Spaces::CG::FemBased< GPS, pS, RS, rS, rCS >, VS >& /*source*/,\n DiscreteFunction< Spaces::DG::FemBased< GPR, pR, RR, rR, rCR >, VR >& /*range*/) const\n {\n static_assert(Dune::AlwaysFalse< GPS >::value, \"Not implemented for this combination of source and range!\");\n }\n\n template< class GPS, int pS, class R, size_t r, size_t rC, class VS, class GPR, int pR, class VR >\n inline void apply(const ConstDiscreteFunction< Spaces::CG::FemBased< GPS, pS, R, r, rC >, VS >& source,\n DiscreteFunction< Spaces::DG::FemBased< GPR, pR, R, r, rC >, VR >&\n range) const\n {\n prolong_onto_dg_fem_localfunctions_wrapper(source, range);\n }\n\n // Source: Spaces::DG::FemBased\n // Range: Spaces::DG::FemBased\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n void apply(const ConstDiscreteFunction< Spaces::DG::FemBased< GPS, pS, RS, rS, rCS >, VS >& /*source*/,\n DiscreteFunction< Spaces::DG::FemBased< GPR, pR, RR, rR, rCR >, VR >& /*range*/) const\n {\n static_assert(Dune::AlwaysFalse< GPS >::value, \"Not implemented for this combination of source and range!\");\n }\n\n template< class GPS, int pS, class R, size_t r, size_t rC, class VS, class GPR, int pR, class VR >\n inline void apply(const ConstDiscreteFunction\n < Spaces::DG::FemBased< GPS, pS, R, r, rC >, VS >& source,\n DiscreteFunction< Spaces::DG::FemBased< GPR, pR, R, r, rC >, VR >&\n range) const\n {\n prolong_onto_dg_fem_localfunctions_wrapper(source, range);\n }\n\n template< class GPS, int pS, class R, size_t r, size_t rC, class VS, class GPR, int pR, class VR >\n inline void apply(const ConstDiscreteFunction\n < Spaces::Block< Spaces::DG::FemBased< GPS, pS, R, r, rC > >, VS >& source,\n DiscreteFunction< Spaces::Block< Spaces::DG::FemBased< GPR, pR, R, r, rC > >, VR >&\n range) const\n {\n prolong_onto_dg_fem_localfunctions_wrapper(source, range);\n }\n\n template< class GPS, int pS, class R, size_t r, size_t rC, class VS, class GPR, int pR, class VR >\n inline void apply(const ConstDiscreteFunction\n < Spaces::Block< Spaces::DG::FemBased< GPS, pS, R, r, rC > >, VS >& source,\n DiscreteFunction< Spaces::DG::FemBased< GPR, pR, R, r, rC >, VR >& range) const\n {\n prolong_onto_dg_fem_localfunctions_wrapper(source, range);\n }\n\nprivate:\n template< class SourceFunctionType, class RangeFunctionType >\n void prolong_onto_dg_fem_localfunctions_wrapper(const SourceFunctionType& source, RangeFunctionType& range) const\n {\n typedef typename RangeFunctionType::DomainType DomainType;\n typedef typename RangeFunctionType::RangeType RangeType;\n typedef typename RangeFunctionType::RangeFieldType RangeFieldType;\n typedef typename Stuff::LA::Container< RangeFieldType, Stuff::LA::default_dense_backend >::MatrixType\n LocalMatrixType;\n typedef typename Stuff::LA::Container< RangeFieldType, Stuff::LA::default_dense_backend >::VectorType\n LocalVectorType;\n // clear\n range.vector() *= 0.0;\n // create search in the source grid part\n typedef typename SourceFunctionType::SpaceType::GridViewType SourceGridViewType;\n typedef Stuff::Grid::EntityInlevelSearch< SourceGridViewType > EntitySearch;\n EntitySearch entity_search(source.space().grid_view());\n // guess the polynomial order of the source by hoping that they are the same for all entities\n const size_t source_order = source.local_function(*source.space().grid_view().template begin< 0 >())->order();\n // walk the grid\n RangeType source_value(0);\n std::vector< RangeType > basis_values(range.space().mapper().maxNumDofs());\n const auto entity_it_end = grid_view_.template end< 0 >();\n for (auto entity_it = grid_view_.template begin< 0 >(); entity_it != entity_it_end; ++entity_it) {\n // prepare\n const auto& entity = *entity_it;\n const auto local_basis = range.space().base_function_set(entity);\n auto local_range = range.local_discrete_function(entity);\n LocalMatrixType local_matrix(local_basis.size(), local_basis.size(), RangeFieldType(0));\n LocalVectorType local_vector(local_basis.size(), RangeFieldType(0));\n LocalVectorType local_DoFs(local_basis.size(), RangeFieldType(0));\n // create quadrature\n const auto integrand_order = std::max(source_order, local_basis.order()) + local_basis.order();\n const auto& quadrature = QuadratureRules< DomainFieldType, dimDomain >::rule(entity.type(),\n boost::numeric_cast< int >(integrand_order));\n // get global quadrature points\n std::vector< DomainType > quadrature_points;\n for (const auto& quadrature_point : quadrature)\n quadrature_points.emplace_back(entity.geometry().global(quadrature_point.position()));\n // get source entities\n const auto source_entity_ptr_unique_ptrs = entity_search(quadrature_points);\n assert(source_entity_ptr_unique_ptrs.size() >= quadrature_points.size());\n // loop over all quadrature points\n size_t pp = 0;\n for (const auto& quadrature_point : quadrature) {\n const auto local_point = quadrature_point.position();\n const auto quadrature_weight = quadrature_point.weight();\n const auto integration_element = entity.geometry().integrationElement(local_point);\n // evaluate source\n const auto& source_entity_ptr_unique_ptr = source_entity_ptr_unique_ptrs[pp];\n if (source_entity_ptr_unique_ptr) {\n const auto source_entity_ptr = *source_entity_ptr_unique_ptr;\n const auto& source_entity = *source_entity_ptr;\n const auto local_source = source.local_function(source_entity);\n local_source->evaluate(source_entity.geometry().local(entity.geometry().global(local_point)), source_value);\n } else\n source_value *= 0.0;\n // evaluate\n local_basis.evaluate(local_point, basis_values);\n // compute integrals\n for (size_t ii = 0; ii < local_basis.size(); ++ii) {\n local_vector[ii] += integration_element * quadrature_weight * (source_value * basis_values[ii]);\n for (size_t jj = 0; jj < local_basis.size(); ++jj) {\n local_matrix.add_to_entry(ii,\n jj,\n integration_element * quadrature_weight * (basis_values[ii] * basis_values[jj]));\n }\n }\n ++pp;\n } // loop over all quadrature points\n // compute local DoFs\n try {\n Stuff::LA::Solver< LocalMatrixType >(local_matrix).apply(local_vector, local_DoFs);\n } catch (Stuff::Exceptions::linear_solver_failed& ee) {\n DUNE_THROW(Exceptions::prolongation_error,\n \"L2 prolongation failed because a local matrix could not be inverted!\\n\\n\"\n << \"This was the original error: \" << ee.what());\n }\n // set local DoFs\n auto local_range_vector = local_range->vector();\n assert(local_range_vector.size() == local_DoFs.size());\n for (size_t ii = 0; ii < local_range_vector.size(); ++ii)\n local_range_vector.set(ii, local_DoFs.get_entry(ii));\n } // walk the grid\n } // ... prolong_onto_dg_fem_localfunctions_wrapper(...)\n\n const GridViewType& grid_view_;\n}; // class L2Prolongation\n\n\n/**\n * \\note We would have liked to do something like this and match on implementations of SpaceInterface:\\code\ntemplate< class T, class VS, class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\nvoid apply(const ConstDiscreteFunction< SpaceInterface< T >, VS >& source,\n DiscreteFunction< Spaces::CG::FemBased< GPR, pR, RR, rR, rCR >, VR >& range) const\n{\n static_assert(Dune::AlwaysFalse< T >::value, \"Not implemented for this combination of source and range!\");\n}\\endcode\n * but that gave compile errors (the compiler just could not match the first argument for whatever reason). This\n * is why we need all combinations of spaces below which are just compile time checks and forwards.\n */\ntemplate< class GridViewType >\nclass LagrangeProlongation\n{\npublic:\n typedef typename GridViewType::ctype DomainFieldType;\n static const size_t dimDomain = GridViewType::dimension;\n\n LagrangeProlongation(const GridViewType& grid_view)\n : grid_view_(grid_view)\n {}\n\n // Source: Spaces::CG::FemBased\n // Range: Spaces::CG::FemBased\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n void apply(const ConstDiscreteFunction< Spaces::CG::FemBased< GPS, pS, RS, rS, rCS >, VS >& /*source*/,\n DiscreteFunction< Spaces::CG::FemBased< GPR, pR, RR, rR, rCR >, VR >& /*range*/) const\n {\n static_assert(Dune::AlwaysFalse< GPS >::value, \"Not implemented for this combination of source and range!\");\n }\n\n template< class GPS, int pS, class R, size_t r, class VS, class GPR, int pR, class VR >\n inline void apply(const ConstDiscreteFunction< Spaces::CG::FemBased< GPS, pS, R, r, 1 >, VS >& source,\n DiscreteFunction< Spaces::CG::FemBased< GPR, pR, R, r, 1 >, VR >& range) const\n {\n redirect_to_appropriate_apply(source, range);\n }\n\n // Source: Spaces::DG::FemBased\n // Range: Spaces::CG::FemBased\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n void apply(const ConstDiscreteFunction< Spaces::DG::FemBased< GPS, pS, RS, rS, rCS >, VS >& /*source*/,\n DiscreteFunction< Spaces::CG::FemBased< GPR, pR, RR, rR, rCR >, VR >& /*range*/) const\n {\n static_assert(Dune::AlwaysFalse< GPS >::value, \"Not implemented for this combination of source and range!\");\n }\n\n template< class GPS, int pS, class R, size_t r, class VS, class GPR, int pR, class VR >\n inline void apply(const ConstDiscreteFunction\n < Spaces::DG::FemBased< GPS, pS, R, r, 1 >, VS >& source,\n DiscreteFunction< Spaces::CG::FemBased< GPR, pR, R, r, 1 >, VR >& range) const\n {\n redirect_to_appropriate_apply(source, range);\n }\n\n // Source: Spaces::CG::PdelabBased\n // Range: Spaces::CG::PdelabBased\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n void apply(const ConstDiscreteFunction< Spaces::CG::PdelabBased< GPS, pS, RS, rS, rCS >, VS >& /*source*/,\n DiscreteFunction< Spaces::CG::PdelabBased< GPR, pR, RR, rR, rCR >, VR >& /*range*/) const\n {\n static_assert(Dune::AlwaysFalse< GPS >::value, \"Not implemented for this combination of source and range!\");\n }\n\n template< class GPS, int pS, class R, size_t r, size_t rC, class VS, class GPR, class VR >\n inline void apply(const ConstDiscreteFunction < Spaces::CG::PdelabBased< GPS, pS, R, r, rC >, VS >&\n source,\n DiscreteFunction< Spaces::CG::PdelabBased< GPR, 1, R, r, rC >, VR >& range) const\n {\n redirect_to_appropriate_apply(source, range);\n }\n\nprivate:\n template< class SourceType, class RangeType >\n void redirect_to_appropriate_apply(const SourceType& source, RangeType& range) const\n {\n // create search in the source grid part\n typedef typename SourceType::SpaceType::GridViewType SourceGridViewType;\n typedef Stuff::Grid::EntityInlevelSearch< SourceGridViewType > EntitySearch;\n EntitySearch entity_search(source.space().grid_view());\n // set all range dofs to infinity\n const auto infinity = std::numeric_limits< typename RangeType::RangeFieldType >::infinity();\n for (size_t ii = 0; ii < range.vector().size(); ++ii)\n range.vector().set_entry(ii, infinity);\n // walk the grid\n const auto entity_it_end = grid_view_.template end< 0 >();\n for (auto entity_it = grid_view_.template begin< 0 >();\n entity_it != entity_it_end;\n ++entity_it) {\n const auto& entity = *entity_it;\n // get global lagrange point coordinates\n const auto lagrange_point_set = range.space().lagrange_points(entity);\n typedef FieldVector< typename SourceGridViewType::ctype, SourceGridViewType::dimension > DomainType;\n std::vector< DomainType > lagrange_points(lagrange_point_set.size());\n for (size_t ii = 0; ii < lagrange_point_set.size(); ++ii)\n lagrange_points[ii] = entity.geometry().global(lagrange_point_set[ii]);\n // get source entities\n const auto source_entity_ptrs = entity_search(lagrange_points);\n assert(source_entity_ptrs.size() == lagrange_points.size());\n // get range\n auto local_range = range.local_discrete_function(entity);\n auto local_range_DoF_vector = local_range->vector();\n // do the actual work (see below)\n apply_local(source, lagrange_points, source_entity_ptrs, local_range_DoF_vector);\n } // walk the grid\n } // ... redirect_to_appropriate_apply(...)\n\n template< class SourceType, class LagrangePointsType, class EntityPointers, class LocalDoFVectorType >\n void apply_local(const SourceType& source,\n const LagrangePointsType& lagrange_points,\n const EntityPointers& source_entity_ptr_unique_ptrs,\n LocalDoFVectorType& range_DoF_vector) const\n {\n static const size_t dimRange = SourceType::dimRange;\n size_t kk = 0;\n assert(source_entity_ptr_unique_ptrs.size() >= lagrange_points.size());\n for (size_t ii = 0; ii < lagrange_points.size(); ++ii) {\n if (std::isinf(range_DoF_vector.get(kk))) {\n const auto& global_point = lagrange_points[ii];\n // evaluate source function\n const auto& source_entity_ptr_unique_ptr = source_entity_ptr_unique_ptrs[ii];\n if (source_entity_ptr_unique_ptr) {\n const auto source_entity_ptr = *source_entity_ptr_unique_ptr;\n const auto& source_entity = *source_entity_ptr;\n const auto local_source_point = source_entity.geometry().local(global_point);\n const auto local_source = source.local_function(source_entity);\n const auto source_value = local_source->evaluate(local_source_point);\n for (size_t jj = 0; jj < dimRange; ++jj, ++kk)\n range_DoF_vector.set(kk, source_value[jj]);\n } else\n for (size_t jj = 0; jj < dimRange; ++jj, ++kk)\n range_DoF_vector.set(kk, 0.0);\n }\n else\n kk += dimRange;\n }\n } // ... apply_local(...)\n\n const GridViewType& grid_view_;\n}; // class LagrangeProlongation\n\n\ntemplate< class GridViewType >\nclass Prolongation\n{\npublic:\n typedef typename GridViewType::ctype DomainFieldType;\n static const size_t dimDomain = GridViewType::dimension;\n\n Prolongation(const GridViewType& grid_view)\n : l2_prolongation_operator_(grid_view)\n , lagrange_prolongation_operator_(grid_view)\n {}\n\n template< class SourceType, class RangeType >\n void apply(const SourceType& source, RangeType& range) const\n {\n redirect_to_appropriate_operator(source, range);\n }\n\nprivate:\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n inline void redirect_to_appropriate_operator(const ConstDiscreteFunction< Spaces::CG::FemBased\n < GPS, pS, RS, rS, rCS >, VS >& source,\n DiscreteFunction< Spaces::DG::FemBased\n < GPR, pR, RR, rR, rCR >, VR >& range) const\n {\n l2_prolongation_operator_.apply(source, range);\n }\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n inline void redirect_to_appropriate_operator(const ConstDiscreteFunction\n < Spaces::DG::FemBased\n < GPS, pS, RS, rS, rCS >, VS >& source,\n DiscreteFunction\n < Spaces::DG::FemBased\n < GPR, pR, RR, rR, rCR >, VR >& range) const\n {\n l2_prolongation_operator_.apply(source, range);\n }\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n inline void redirect_to_appropriate_operator(const ConstDiscreteFunction\n < Spaces::Block< Spaces::DG::FemBased\n < GPS, pS, RS, rS, rCS > >, VS >& source,\n DiscreteFunction\n < Spaces::Block< Spaces::DG::FemBased\n < GPR, pR, RR, rR, rCR > >, VR >& range) const\n {\n l2_prolongation_operator_.apply(source, range);\n }\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n inline void redirect_to_appropriate_operator(const ConstDiscreteFunction\n < Spaces::Block< Spaces::DG::FemBased\n < GPS, pS, RS, rS, rCS > >, VS >& source,\n DiscreteFunction\n < Spaces::DG::FemBased\n < GPR, pR, RR, rR, rCR >, VR >& range) const\n {\n l2_prolongation_operator_.apply(source, range);\n }\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n inline void redirect_to_appropriate_operator(const ConstDiscreteFunction\n < Spaces::CG::FemBased\n < GPS, pS, RS, rS, rCS >, VS >& source,\n DiscreteFunction\n < Spaces::CG::FemBased\n < GPR, pR, RR, rR, rCR >, VR >& range) const\n {\n lagrange_prolongation_operator_.apply(source, range);\n }\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n inline void redirect_to_appropriate_operator(const ConstDiscreteFunction\n < Spaces::DG::FemBased\n < GPS, pS, RS, rS, rCS >, VS >& source,\n DiscreteFunction\n < Spaces::CG::FemBased\n < GPR, pR, RR, rR, rCR >, VR >& range) const\n {\n lagrange_prolongation_operator_.apply(source, range);\n }\n\n template< class GPS, int pS, class RS, size_t rS, size_t rCS, class VS,\n class GPR, int pR, class RR, size_t rR, size_t rCR, class VR >\n inline void redirect_to_appropriate_operator(const ConstDiscreteFunction\n < Spaces::CG::PdelabBased\n < GPS, pS, RS, rS, rCS >, VS >& source,\n DiscreteFunction\n < Spaces::CG::PdelabBased\n < GPR, pR, RR, rR, rCR >, VR >& range) const\n {\n lagrange_prolongation_operator_.apply(source, range);\n }\n\n const L2Prolongation< GridViewType > l2_prolongation_operator_;\n const LagrangeProlongation< GridViewType > lagrange_prolongation_operator_;\n}; // class Prolongation\n\n\ntemplate< class GridViewType, class SourceType, class RangeType >\nvoid prolong(const GridViewType& grid_view, const SourceType& source, RangeType& range)\n{\n const Prolongation< GridViewType > prolongation_operator(grid_view);\n prolongation_operator.apply(source, range);\n}\n\n\ntemplate< class SourceType, class RangeType >\nvoid prolong(const SourceType& source, RangeType& range)\n{\n const Prolongation< typename RangeType::SpaceType::GridViewType > prolongation_operator(range.space().grid_view());\n prolongation_operator.apply(source, range);\n}\n\n\n} // namespace Operators\n} // namespace GDT\n} // namespace Dune\n\n#endif // DUNE_GDT_OPERATORS_PROLONGATIONS_HH\n", "meta": {"hexsha": "6c570e05b2f07d9c935216eb53cfa4f7ab24c402", "size": 23907, "ext": "hh", "lang": "C++", "max_stars_repo_path": "dune/gdt/operators/prolongations.hh", "max_stars_repo_name": "ftalbrecht/dune-gdt", "max_stars_repo_head_hexsha": "574bc4a3b28d2a6a6195a6b4df6727c61f0d73c9", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "dune/gdt/operators/prolongations.hh", "max_issues_repo_name": "ftalbrecht/dune-gdt", "max_issues_repo_head_hexsha": "574bc4a3b28d2a6a6195a6b4df6727c61f0d73c9", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dune/gdt/operators/prolongations.hh", "max_forks_repo_name": "ftalbrecht/dune-gdt", "max_forks_repo_head_hexsha": "574bc4a3b28d2a6a6195a6b4df6727c61f0d73c9", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2018-08-13T11:51:27.000Z", "max_forks_repo_forks_event_max_datetime": "2018-08-13T11:51:27.000Z", "avg_line_length": 47.9098196393, "max_line_length": 128, "alphanum_fraction": 0.6398126072, "num_tokens": 5939, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.23017117030618783}} {"text": "//Tencent is pleased to support the open source community by making FeatherCNN available.\n\n//Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved.\n\n//Licensed under the BSD 3-Clause License (the \"License\"); you may not use this file except\n//in compliance with the License. You may obtain a copy of the License at\n//\n//https://opensource.org/licenses/BSD-3-Clause\n//\n//Unless required by applicable law or agreed to in writing, software distributed\n//under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n//CONDITIONS OF ANY KIND, either express or implied. See the License for the\n//specific language governing permissions and limitations under the License.\n\n#include \n#include \n\n#include \n#include \n#include \n#if 0\nvoid fully_connected_inference_direct(const int input_size, const int output_size, const float *x, const float *y, float *z, const int num_threads)\n{\n #pragma omp parallel for schedule(static) num_threads(num_threads)\n for (int i = 0; i < output_size; i++)\n {\n float sum = 0.f;\n for (int j = 0; j < input_size; j++)\n sum += x[j] * y[i * input_size + j];\n z[i] = sum;\n }\n}\n\nvoid fully_connected_transpose_inference_sse8(const int input_size, const int output_size, const float *x, const float *y, float *z, const int num_threads)\n{\n assert(input_size % 8 == 0);\n assert(output_size % 8 == 0);\n #pragma omp parallel for schedule(static) num_threads(num_threads)\n for (int k = 0; k < output_size / 8; k++)\n {\n const float *yPtr = y + k * 8 * input_size;\n __m128 res = {0.0, 0.0, 0.0, 0.0};\n __m128 res1 = {0.0, 0.0, 0.0, 0.0};\n __m128 va0, va1, va2, va3, vb0, vb1, vb2, vb3, vb4, vb5, vb6, vb7;\n for (int i = 0; i < input_size; i += 4)\n {\n // __m128 v1, v2;\n //va = _mm_load_ps(x + i);\n\n vb0 = _mm_load_ps(yPtr);\n vb1 = _mm_load_ps(yPtr + 4);\n vb2 = _mm_load_ps(yPtr + 8);\n vb3 = _mm_load_ps(yPtr + 12);\n vb4 = _mm_load_ps(yPtr + 16);\n vb5 = _mm_load_ps(yPtr + 20);\n vb6 = _mm_load_ps(yPtr + 24);\n vb7 = _mm_load_ps(yPtr + 28);\n\n va0 = _mm_broadcast_ss(x + i);\n va1 = _mm_broadcast_ss(x + i + 1);\n va2 = _mm_broadcast_ss(x + i + 2);\n va3 = _mm_broadcast_ss(x + i + 3);\n\n res = _mm_fmadd_ps(vb0, va0, res);\n res1 = _mm_fmadd_ps(vb1, va0, res1);\n res = _mm_fmadd_ps(vb2, va1, res);\n res1 = _mm_fmadd_ps(vb3, va1, res1);\n res = _mm_fmadd_ps(vb4, va2, res);\n res1 = _mm_fmadd_ps(vb5, va2, res1);\n res = _mm_fmadd_ps(vb6, va3, res);\n res1 = _mm_fmadd_ps(vb7, va3, res1);\n\n yPtr += 32;\n }\n _mm_store_ps((z + 8 * k), res);\n _mm_store_ps((z + 8 * k + 4), res1);\n }\n}\n\n#include \n#include \n\n//For fully connected layers, the weights matrix is transposed.\n//To reduce memory allocation, a KxSTRIDE packed matrix is sufficient.\ntemplate\nvoid packed_sgemv_transposed_init(const int N, const int K, float* matrix, float* packed_buffer)\n{\n size_t N_aligned = N - N % STRIDE;\n for (int j = 0; j < N_aligned; j += STRIDE)\n {\n float* pMatrix = matrix + j * K;\n float* pPacked = packed_buffer;\n for (int k = 0; k < K; ++k)\n {\n for (int i = 0; i < STRIDE; ++i)\n pPacked[i] = pMatrix[i * K + k];\n pPacked += STRIDE;\n }\n memcpy(pMatrix, packed_buffer, STRIDE * K * sizeof(float));\n }\n int rem = N % STRIDE;\n if (rem > 0)\n {\n float* pMatrix = matrix + N_aligned * K;\n float* pPacked = packed_buffer;\n for (int k = 0; k < K; ++k)\n {\n for (int i = 0; i < rem; ++i)\n pPacked[i] = pMatrix[i * K + k];\n pPacked += rem;\n }\n memcpy(pMatrix, packed_buffer, rem * K * sizeof(float));\n }\n}\ntemplate void packed_sgemv_transposed_init<8>(const int N, const int K, float* matrix, float* packed_matrix);\ntemplate void packed_sgemv_transposed_init<16>(const int N, const int K, float* matrix, float* packed_matrix);\n\n//The packed matrix will only be scanned once. Cache miss would only occur with very large K, which is not a very common case.\n\ntemplate\nvoid packed_sgemv(const int N, const int K, const float* A, const float* B, float* C, const float* bias_data, const int num_threads)\n{\n assert(K % 4 == 0);\n int N_aligned = N - N % 16;\n __m256 vZero = _mm256_set1_ps(0.f);\n #pragma omp parallel for\n for (int j = 0; j < N_aligned; j += 16)\n {\n const float *pB = B + j * K;\n float *pC = C + j;\n __m256 acc0 = _mm256_set1_ps(0.f);\n __m256 acc1 = _mm256_set1_ps(0.f);\n if (fuseBias)\n {\n acc0 = _mm256_load_ps(bias_data + j);\n acc1 = _mm256_load_ps(bias_data + j + 8);\n }\n __m256 va0, va1, va2, va3;\n __m256 vb0, vb1, vb2, vb3, vb4, vb5, vb6, vb7;\n for (int k = 0; k < K; k += 4)\n {\n vb0 = _mm256_load_ps(pB);\n vb1 = _mm256_load_ps(pB + 8);\n vb2 = _mm256_load_ps(pB + 16);\n vb3 = _mm256_load_ps(pB + 24);\n vb4 = _mm256_load_ps(pB + 32);\n vb5 = _mm256_load_ps(pB + 40);\n vb6 = _mm256_load_ps(pB + 48);\n vb7 = _mm256_load_ps(pB + 56);\n\n //print_floats(pB, 8);\n va0 = _mm256_broadcast_ss(A + k);\n va1 = _mm256_broadcast_ss(A + k + 1);\n va2 = _mm256_broadcast_ss(A + k + 2);\n va3 = _mm256_broadcast_ss(A + k + 3);\n\n acc0 = _mm256_fmadd_ps(vb0, va0, acc0);\n acc1 = _mm256_fmadd_ps(vb1, va0, acc1);\n acc0 = _mm256_fmadd_ps(vb2, va1, acc0);\n acc1 = _mm256_fmadd_ps(vb3, va1, acc1);\n acc0 = _mm256_fmadd_ps(vb4, va2, acc0);\n acc1 = _mm256_fmadd_ps(vb5, va2, acc1);\n acc0 = _mm256_fmadd_ps(vb6, va3, acc0);\n acc1 = _mm256_fmadd_ps(vb7, va3, acc1);\n pB += 64;\n }\n if (fuseRelu)\n {\n acc0 = _mm256_max_ps(vZero, acc0);\n acc1 = _mm256_max_ps(vZero, acc1);\n }\n _mm256_store_ps(pC, acc0);\n _mm256_store_ps(pC + 8, acc1);\n }\n\n int rem = N % 16;\n if (rem == 8)\n {\n const float *pB = B + N_aligned * K;\n __m256 vacc = _mm256_set1_ps(0.f);\n if (fuseBias)\n vacc = _mm256_load_ps(bias_data + N_aligned);\n __m256 va, vb;\n float acc[4];\n for (int k = 0; k < K; ++k)\n {\n vb = _mm256_loadu_ps(pB);\n va = _mm256_broadcast_ss(A + k);\n vacc = _mm256_fmadd_ps(vb, va, vacc);\n pB += rem;\n }\n if (fuseRelu)\n vacc = _mm256_max_ps(vZero, vacc);\n _mm256_store_ps(C + N_aligned, vacc);\n }\n else if (rem > 0)\n {\n const float *pB = B + N_aligned * K;\n float *pC = C + N_aligned;\n __m256 vacc = _mm256_set1_ps(0.f);\n if (fuseBias && rem >= 8)\n vacc = _mm256_load_ps(bias_data + N_aligned);\n\n __m256 va, vb;\n float acc[4];\n for (int i = 0; i < rem - 8; ++i)\n {\n if (fuseBias)\n acc[i] = bias_data[N_aligned + i];\n else\n acc[i] = 0;\n }\n for (int k = 0; k < K; ++k)\n {\n if (rem >= 8)\n {\n vb = _mm256_loadu_ps(pB);\n va = _mm256_broadcast_ss(A + k);\n vacc = _mm256_fmadd_ps(vb, va, vacc);\n }\n if (rem - 8 > 0)\n {\n for (int i = 0; i < rem - 8; ++i)\n acc[i] = A[k] * pB[i + 8];\n }\n pB += rem;\n }\n if (fuseRelu)\n {\n vacc = _mm256_max_ps(vZero, vacc);\n }\n _mm256_store_ps(pC, vacc);\n for (int i = 0; i < rem - 8; ++i)\n {\n if (fuseRelu)\n pC[i + 8] = ((acc[i] > 0.f) ? acc[i] : 0.f);\n else\n pC[i + 8] = acc[i];\n }\n }\n}\n\ntemplate void packed_sgemv(const int N, const int K, const float* A, const float* B, float* C, const float* bias_data, const int num_threads);\ntemplate void packed_sgemv(const int N, const int K, const float* A, const float* B, float* C, const float* bias_data, const int num_threads);\ntemplate void packed_sgemv(const int N, const int K, const float* A, const float* B, float* C, const float* bias_data, const int num_threads);\ntemplate void packed_sgemv(const int N, const int K, const float* A, const float* B, float* C, const float* bias_data, const int num_threads);\n\n\n\nvoid fully_connected_inference_direct_BiasReLU(int input_size, int output_size, float *x, float *y, float *z, float* biasArr, int num_threads)\n{\n #pragma omp parallel for schedule(static) num_threads(num_threads)\n for (int i = 0; i < output_size; i++)\n {\n float sum = 0.f;\n for (int j = 0; j < input_size; j++)\n sum += x[j] * y[i * input_size + j];\n\n sum += biasArr[i];\n if (sum < 0.f) sum = 0.f;\n z[i] = sum;\n }\n}\n\nvoid fully_connected_transpose_inference_sse8_BiasReLU(int input_size, int output_size, float *x, float *y, float *z, float* biasArr, int num_threads)\n{\n assert(input_size % 8 == 0);\n assert(output_size % 8 == 0);\n #pragma omp parallel for schedule(static) num_threads(num_threads)\n for (int k = 0; k < output_size / 8; k++)\n {\n float *yPtr = y + k * 8 * input_size;\n const __m128 vzero = _mm_set1_ps(0.f);\n\n __m128 res = _mm_load_ps(biasArr + k * 8);\n __m128 res1 = _mm_load_ps(biasArr + k * 8 + 4);\n\n __m128 va0, va1, va2, va3, vb0, vb1, vb2, vb3, vb4, vb5, vb6, vb7;\n for (int i = 0; i < input_size; i += 4)\n {\n vb0 = _mm_load_ps(yPtr);\n vb1 = _mm_load_ps(yPtr + 4);\n vb2 = _mm_load_ps(yPtr + 8);\n vb3 = _mm_load_ps(yPtr + 12);\n vb4 = _mm_load_ps(yPtr + 16);\n vb5 = _mm_load_ps(yPtr + 20);\n vb6 = _mm_load_ps(yPtr + 24);\n vb7 = _mm_load_ps(yPtr + 28);\n\n va0 = _mm_broadcast_ss(x + i);\n va1 = _mm_broadcast_ss(x + i + 1);\n va2 = _mm_broadcast_ss(x + i + 2);\n va3 = _mm_broadcast_ss(x + i + 3);\n\n res = _mm_fmadd_ps(vb0, va0, res);\n res1 = _mm_fmadd_ps(vb1, va0, res1);\n res = _mm_fmadd_ps(vb2, va1, res);\n res1 = _mm_fmadd_ps(vb3, va1, res1);\n res = _mm_fmadd_ps(vb4, va2, res);\n res1 = _mm_fmadd_ps(vb5, va2, res1);\n res = _mm_fmadd_ps(vb6, va3, res);\n res1 = _mm_fmadd_ps(vb7, va3, res1);\n\n yPtr += 32;\n }\n\n res = _mm_max_ps(res, vzero);\n res1 = _mm_max_ps(res1, vzero);\n\n _mm_store_ps((z + 8 * k), res);\n _mm_store_ps((z + 8 * k + 4), res1);\n }\n}\n\n#else\n\ntemplate \nvoid fully_connected_inference_direct(const int input_size, const int output_size, const float *x, const float *y, float *z, const int num_threads, float *bias_arr)\n{\n #pragma omp parallel for schedule(static) num_threads(num_threads)\n for (int i = 0; i < output_size; i++)\n {\n float sum = 0;\n for (int j = 0; j < input_size; j++)\n sum += x[j] * y[i * input_size + j];\n if (fuseBias)\n sum += bias_arr[i];\n if (fuseRelu)\n sum = (sum > 0.f) ? sum : 0.f;\n z[i] = sum;\n }\n}\n\ntemplate \nvoid fully_connected_transpose_inference(const int input_size, const int output_size, const float *x, const float *y, float *z, const int num_threads, float *bias_arr)\n{\n assert(input_size % 8 == 0);\n assert(output_size % 8 == 0);\n #pragma omp parallel for schedule(static) num_threads(num_threads)\n for (int k = 0; k < output_size / 8; k++)\n {\n __m128 vBias, vBias1;\n const __m128 vZero = _mm_set1_ps(0.f);\n __m128 res = _mm_set1_ps(0.f);\n __m128 res1 = _mm_set1_ps(0.f);\n\n if (fuseBias)\n {\n vBias = _mm_load_ps(bias_arr + k * 8);\n vBias1 = _mm_load_ps(bias_arr + k * 8 + 4);\n }\n const float *yPtr = y + k * 8 * input_size;\n __m128 va0, va1, va2, va3, vb0, vb1, vb2, vb3, vb4, vb5, vb6, vb7;\n for (int i = 0; i < input_size; i += 4)\n {\n vb0 = _mm_load_ps(yPtr);\n vb1 = _mm_load_ps(yPtr + 4);\n vb2 = _mm_load_ps(yPtr + 8);\n vb3 = _mm_load_ps(yPtr + 12);\n vb4 = _mm_load_ps(yPtr + 16);\n vb5 = _mm_load_ps(yPtr + 20);\n vb6 = _mm_load_ps(yPtr + 24);\n vb7 = _mm_load_ps(yPtr + 28);\n\n va0 = _mm_broadcast_ss(x + i);\n va1 = _mm_broadcast_ss(x + i + 1);\n va2 = _mm_broadcast_ss(x + i + 2);\n va3 = _mm_broadcast_ss(x + i + 3);\n\n res = _mm_fmadd_ps(vb0, va0, res);\n res1 = _mm_fmadd_ps(vb1, va0, res1);\n res = _mm_fmadd_ps(vb2, va1, res);\n res1 = _mm_fmadd_ps(vb3, va1, res1);\n res = _mm_fmadd_ps(vb4, va2, res);\n res1 = _mm_fmadd_ps(vb5, va2, res1);\n res = _mm_fmadd_ps(vb6, va3, res);\n res1 = _mm_fmadd_ps(vb7, va3, res1);\n\n yPtr += 32;\n }\n\n if (fuseBias)\n {\n res = _mm_add_ps(res, vBias);\n res1 = _mm_add_ps(res1, vBias1);\n }\n if (fuseRelu)\n {\n res = _mm_max_ps(res, vZero);\n res1 = _mm_max_ps(res1, vZero);\n }\n _mm_store_ps((z + 8 * k), res);\n _mm_store_ps((z + 8 * k + 4), res1);\n }\n}\n\ntemplate void fully_connected_inference_direct(const int, const int, const float *, const float *, float *, const int, float *);\ntemplate void fully_connected_inference_direct(const int, const int, const float *, const float *, float *, const int, float *);\ntemplate void fully_connected_inference_direct(const int, const int, const float *, const float *, float *, const int, float *);\ntemplate void fully_connected_inference_direct(const int, const int, const float *, const float *, float *, const int, float *);\n\ntemplate void fully_connected_transpose_inference(const int, const int, const float *, const float *, float *, const int, float *);\ntemplate void fully_connected_transpose_inference(const int, const int, const float *, const float *, float *, const int, float *);\ntemplate void fully_connected_transpose_inference(const int, const int, const float *, const float *, float *, const int, float *);\ntemplate void fully_connected_transpose_inference(const int, const int, const float *, const float *, float *, const int, float *);\n\n#endif\n\nvoid matrixTranspose(float *array, size_t m, size_t n, float *buffer) // A[m][n] -> A[n][m]\n{\n for (int i = 0; i < m; i++)\n for (int j = 0; j < n; j++)\n buffer[j * m + i] = array[i * n + j];\n memcpy(array, buffer, m * n * sizeof(float));\n}\n", "meta": {"hexsha": "c97a1e1302d909c93139ba4783cd9a312bdac145", "size": 15377, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/booster/avx/sgemv.cpp", "max_stars_repo_name": "chenaili6/FeatherCNN", "max_stars_repo_head_hexsha": "52cd8c8749ed584461a88b1f04749bb35a48f9a6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 4.0, "max_stars_repo_stars_event_min_datetime": "2018-05-14T09:00:33.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-14T08:11:54.000Z", "max_issues_repo_path": "src/booster/avx/sgemv.cpp", "max_issues_repo_name": "nihui/FeatherCNN", "max_issues_repo_head_hexsha": "2805f371bd8f33ef742cc9523979f29295d926fb", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/booster/avx/sgemv.cpp", "max_forks_repo_name": "nihui/FeatherCNN", "max_forks_repo_head_hexsha": "2805f371bd8f33ef742cc9523979f29295d926fb", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.9639423077, "max_line_length": 167, "alphanum_fraction": 0.5554399428, "num_tokens": 4787, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.22997364162397252}} {"text": "//\n// main.cpp\n// PhyloAcc\n//\n// Created by hzr on 3/8/16.\n// Copyright © 2016 hzr. All rights reserved.\n//\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"profile.h\"\n#include \"newick.h\"\n#include \"bpp.hpp\"\n#include \"bpp_c.hpp\"\n#include \"utils.h\"\n#include \n#include \n\nusing namespace std;\nusing namespace arma;\n\n// parameters, input and output files paths\nstring params_path;\nstring phytree_path;\nstring align_path;\nstring output_path=\"\";\nstring output_path2=\"\";\nstring segment_path;\nstring id_path=\"\";\nstring result_prefix=\"test\";\n\n//string refspecies;\nstring outgroup;\nstring targetspecies;\nstring conservegroup; // can't be missing in more than 50%\ndouble conserve_prop = 0.8;\n\nint num_thread = 1;\n\n// running parameters\nint num_burn = 200; // num of burn-in updates\nint num_mcmc = 800; // num of MCMC updates,10\nint num_thin = 500; // num of updates between two samples, adaptive frequency\nint num_chain; // outer loop of updates Q matrix and hyperparameter of substitution rates\n\n\ndouble prep_lrate = 0.3;\ndouble prep_lrate2 = 0.0;//0.1\ndouble prep_grate = 0.5; // initalization\n\ndouble prior_lrate2_a = 0,prior_lrate2_b = 1 ; // beta prior for lrate2, 0.5\ndouble prior_lrate_a = 1 ,prior_lrate_b = 9 ; // beta prior for lrate, 2\ndouble prior_grate_a = 3,prior_grate_b = 1 ; // beta prior for grate\n\n\ndouble ratio0 = 0.5; //initial conserved rate\ndouble ratio1 = 1; // initial accelerated rate\ndouble missing_thres = 0.8;\n\ndouble nprior_a = 10, nprior_b = 0.2; //around 1\ndouble cprior_a = 5, cprior_b = 0.04; //around ratio\nint ropt = 1;\ndouble cub = 1;\ndouble nlb = 0.6;\n\nint batch = -1 ;\nint seed = 5;\ndouble indel = 0;\ndouble indel2;\nbool sample_indel = 0;\nbool sample_hyper;\nchar gapchar = '-';\nbool verbose = 0;\ndouble consToMis = 0.01;\nbool prune=0;\ndouble revgap=1;\nint min_length = 50;\n\n\n// load the program parameters\nvoid LoadParams(int argc, char* argv[])\n{\n cout << \"Loading input data and running parameters......\" << endl;\n \n if (argc > 1)\n params_path = string(argv[1]);\n else\n params_path = \"/Users/hzr/Phd_2/Bird/PhyloAcc_init3-3/params2s.txt\"; //params2\n \n cout << \"Loading program configurations from \" << params_path << \"......\" <> tmp;\n \n // input and output file paths\n if (tmp==\"PHYTREE_FILE\")\n line_stream >> phytree_path;\n else if (tmp==\"ALIGN_FILE\")\n line_stream >> align_path;\n else if (tmp==\"SEG_FILE\")\n line_stream >> segment_path;\n else if (tmp==\"ID_FILE\")\n line_stream >> id_path;\n else if (tmp==\"BATCH\")\n line_stream >> batch;\n else if (tmp==\"RESULT_FOLDER\")\n line_stream >> output_path;\n else if (tmp==\"PREFIX\")\n line_stream >> result_prefix;\n \n //else if (tmp==\"RESULT_INDIV\")\n // line_stream >> output_path2;\n \n else if (tmp==\"SEED\")\n line_stream >> seed;\n else if (tmp==\"INIT_CONSERVE_RATE\")\n line_stream >> ratio0;\n else if (tmp==\"INIT_ACCE_RATE\")\n line_stream >> ratio1;\n else if (tmp==\"CONSERVE_PRIOR_A\")\n line_stream >> cprior_a;\n else if (tmp==\"CONSERVE_PRIOR_B\")\n line_stream >> cprior_b;\n else if (tmp==\"ACCE_PRIOR_A\")\n line_stream >> nprior_a;\n else if (tmp==\"ACCE_PRIOR_B\")\n line_stream >> nprior_b;\n else if (tmp==\"ROPT\")\n line_stream >> ropt;\n else if (tmp==\"CUB\")\n line_stream >> cub;\n else if (tmp==\"NLB\")\n line_stream >> nlb;\n\n\n\n \n // running parameters\n else if (tmp==\"BURNIN\")\n line_stream >> num_burn;\n else if (tmp==\"MCMC\")\n line_stream >> num_mcmc;\n else if (tmp==\"ADAPT_FREQ\")\n line_stream >> num_thin;\n else if (tmp==\"INIT_LRATE\")\n line_stream >> prep_lrate;\n else if (tmp==\"INIT_GRATE\")\n line_stream >> prep_grate;\n else if (tmp==\"HYPER_LRATE_A\")\n line_stream >> prior_lrate_a;\n else if (tmp==\"HYPER_LRATE_B\")\n line_stream >> prior_lrate_b;\n else if (tmp==\"HYPER_GRATE_A\")\n line_stream >> prior_grate_a;\n else if (tmp==\"HYPER_GRATE_B\")\n line_stream >> prior_grate_b;\n else if (tmp==\"CHAIN\")\n line_stream >> num_chain;\n \n // constraint\n else if (tmp == \"OUTGROUP\")\n line_stream >> outgroup;\n else if (tmp == \"TARGETSPECIES\")\n line_stream >> targetspecies;\n else if (tmp == \"CONSERVE\")\n line_stream >> conservegroup;\n else if (tmp == \"CONSERVE_PROP\")\n line_stream >> conserve_prop;\n else if (tmp == \"GAP_PROP\")\n line_stream >> missing_thres;\n //else if (tmp == \"REF\")\n // line_stream >> refspecies;\n else if (tmp == \"CONSTOMIS\")\n line_stream >> consToMis;\n \n \n // treat indel as additional character\n else if (tmp == \"GAPCHAR\")\n line_stream >> gapchar;\n else if (tmp == \"PRUNE_TREE\")\n line_stream >> prune;\n else if (tmp == \"TRIM_GAP_PERCENT\")\n line_stream >> revgap;\n else if (tmp == \"MIN_LEN\")\n line_stream >> min_length;\n else if (tmp == \"INDEL\") // not used\n line_stream >> indel;\n else if (tmp == \"INDEL2\") // not used\n line_stream >> indel2;\n else if(tmp == \"SAMPLE_INDEL\") // not used\n line_stream >> sample_indel;\n else if(tmp == \"SAMPLE_HYPER\")\n line_stream >> sample_hyper;\n else if(tmp == \"VERBOSE\")\n line_stream >> verbose;\n else if(tmp == \"NUM_THREAD\")\n line_stream >> num_thread;\n else if(tmp != \"\")\n cout << \"Unknown parameter: \" << tmp < ids;\n if(id_path==\"\")\n {\n if(batch==-1)\n {\n for(int c =0;c= bpp.C) break;\n ids.push_back(c);\n }\n \n }\n \n }else{\n ifstream in_params(id_path.c_str());\n if (!in_params)\n {\n cerr << \"Cannot open the id file: \" << id_path.c_str() << endl;\n exit(1);\n }\n while(!in_params.eof())\n {\n const int BUFF_SIZE = 1024;\n char line_buff[BUFF_SIZE];\n in_params.getline(line_buff, BUFF_SIZE);\n istringstream line_stream(line_buff);\n string tmp; line_stream >> tmp;\n tmp = strutils::trim(tmp);\n if(tmp==\"\") continue;\n ids.push_back(atoi(tmp.c_str()));\n \n }\n\n }\n\n cout << ids.size() << \" of elements to be computed\" << endl; \n //gsl_set_error_handler_off();\n for(int iter =0; iter\n#define __STDC_FORMAT_MACROS\n#include \n//#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \"rnnlmlib_dynamic.h\"\n\n#ifndef exp10\n #define exp10(x) pow((double)10, (x))\n#endif\n\nnamespace RNNLM{\n\n inline void FreadAllOrDie_dyn(void* ptr, size_t size, size_t count, FILE* fo, const char* message) {/*{{{*/\n size_t read = fread(ptr, size, count, fo);\n if (read != count) {\n fprintf(\n stderr, \"ERROR: expected to read %zu elements, but read %zu elements (%s)\\n\",\n count, read, message);\n exit(1);\n }\n }/*}}}*/\n\n void CRnnLM_dyn::ReadFRHeader(FILE* file) {/*{{{*/\n const uint64_t kVersionStepSize = 10000;\n const int kCurrentVersion = 6;\n const unsigned kMaxLayerTypeName = 64; // maximum size of layer name type in bytes (including \\0)\n const std::string kDefaultLayerType = \"sigmoid\";\n const char* error_message = \"error@reading config\";\n\n // READ ... \n \n // leyer1 のサイズ\n uint64_t quazi_layer_size; //10000 *version + layer size が入っている\n FreadAllOrDie_dyn(&quazi_layer_size, sizeof(int64_t), 1, file, \"failed to read layer size.\");\n unsigned int layer_size = quazi_layer_size % kVersionStepSize; //\n int version = quazi_layer_size / kVersionStepSize;\n if(version > kCurrentVersion || version < kCurrentVersion){\n fprintf(stderr, \"unknown version: %\" PRIu64, quazi_layer_size / kVersionStepSize );\n exit(1);\n }\n\n // direct\n unsigned long int maxent_hash_size;\n FreadAllOrDie_dyn(&maxent_hash_size, sizeof(int64_t), 1, file, error_message);\n unsigned int maxent_order;\n FreadAllOrDie_dyn(&maxent_order, sizeof(int), 1, file, error_message);\n\n // nce\n real nce_lnz_ = 9; // default value\n bool use_nce_;\n FreadAllOrDie_dyn(&use_nce_, sizeof(bool), 1, file, error_message);\n use_nce = use_nce_;\n FreadAllOrDie_dyn(&nce_lnz_, sizeof(real), 1, file, error_message);\n nce_lnz = nce_lnz_;\n\n // 読み込むだけのパラメタ (RNNLM toolkit に関係の無いパラメタ)\n bool reverse_sentence;\n char buffer[kMaxLayerTypeName];\n int layer_count;\n int hs_arity;\n FreadAllOrDie_dyn(&reverse_sentence, sizeof(bool), 1, file, error_message);\n FreadAllOrDie_dyn(&buffer, sizeof(char), kMaxLayerTypeName, file, error_message); // 固定長\n FreadAllOrDie_dyn(&layer_count, sizeof(int), 1, file, error_message);\n FreadAllOrDie_dyn(&hs_arity, sizeof(int), 1, file, error_message);\n \n // 決め打ちでセットするパラメタ (faster-rnnlm には無いパラメタ)\n independent = true; // 文は独立\n\n // RNNLM 用のパラメタを設定\n layer0_size = vocab_size + layer_size;\n layer1_size = layer_size;\n layerc_size = 0;\n layer2_size = vocab_size;\n direct_size = maxent_hash_size;\n direct_order = maxent_order;\n\n }/*}}}*/\n\n real CRnnLM_dyn::calc_direct_score(int word, context* c){/*{{{*/\n unsigned long long hash[MAX_NGRAM_ORDER] = {};\n unsigned long long hash_max = direct_size - vocab_size;\n unsigned int a,b;\n unsigned int d_o = direct_order;\n double direct_score = 0;\n\n for (a=0; ahistory[b-1]+1);\n }\n hash[a]=(hash[a]%(hash_max));\n }\n\n for (a=0; a0 && c->history[a-1] == 0) break;\n }\n return direct_score;\n }/*}}}*/\n\n real CRnnLM_dyn::random(real min, real max)/*{{{*/\n {\n return rand()/(real)RAND_MAX*(max-min)+min;\n }/*}}}*/\n\n void CRnnLM_dyn::setRnnLMFile(const char *str)/*{{{*/\n {\n strcpy(rnnlm_file, str);\n }/*}}}*/\n\n int CRnnLM_dyn::searchVocab(char *word)\n {//{{{\n //*mmfstr = word;\n //std::string stdkey = word;\n //auto vitr = vocab_map->find(*mmfstr);\n auto vitr = vocab_map->find(shash(std::string(word)));\n if( vitr == vocab_map->end()){\n return -1;\n }else{\n return vitr->second;\n }\n\n return -1; //return OOV if not found\n }//}}}\n\n void CRnnLM_dyn::saveFullContext(context *dest)\t\t\n {//{{{\n int a;\n \n dest->l1_neuron.resize(layer1_size);\n dest->history.resize(MAX_NGRAM_ORDER);\n dest->last_word = history[0];\n dest->have_recurrent = false;\n \n //vector l1_neuron = layer1_size;\n //vector history = layer1_size;\n for (a=0; al1_neuron[a] = neu1[a].ac;\n for (a=0; ahistory[a] = history[a];\n }//}}}\n\n void CRnnLM_dyn::CacheRecurrent(context *dest)\t\t\n {//{{{\n dest->have_recurrent = true;\n for (int a=0; al1_neuron[a] = neu1[a].ac;\n }//}}}\n\n void CRnnLM_dyn::restoreFullContext(const context *dest) \n {//{{{\n //std::cerr << \"restoreFullContext history_size:\" << dest->history.size() << \" \" << (int)dest->last_word << \" ,\" << (int)dest->history[0] << \",\" << (int)dest->history[1] << std::endl; //H?\n int a;\n for (a=0; al1_neuron[a];\n for (a=0; ahistory[a];\n if(dest->have_recurrent){\n for (a=0; al1_neuron[a];\n }else{\n // TODO: 直接 neu0に 移したほうが速い... 下を後で試す\n for (a=0; al1_neuron[a];\n\n //for (a=0; al1_neuron[a];\n //copyHiddenLayerToInput(); // neu0 に移す\n }\n }//}}}\n\n void CRnnLM_dyn::initNetFR()\n {//{{{\n\n neu0=(struct neuron *)calloc(layer0_size, sizeof(struct neuron));\n neu1=(struct neuron *)calloc(layer1_size, sizeof(struct neuron));\n neu2=(struct neuron *)calloc(layer2_size, sizeof(struct neuron));\n\n }//}}}\n\n // RNNLM モデル，語彙の読み込みを行う(faster-rnnlm 対応版)\n void CRnnLM_dyn::restoreNet_FR() //will read whole network structure\n {//{{{\n int a, b;\n float fl;\n\n // Filename\n const std::string model_vocab_file = rnnlm_file; \n const std::string model_weight_file = model_vocab_file + \".nnet\"; // ネットワークの重み\n\n // 語彙 (vocab, vocab_map)\n FILE *vocab_file = fopen(model_vocab_file.c_str(), \"rb\");\n if (vocab_file == NULL){\n fprintf(stderr, \"Error: vocaburary file %s not found. \\n\", model_vocab_file.c_str());\n exit(1);\n }\n \n std::string MapVocabFilePath= model_vocab_file+\".map\";\n if( access( MapVocabFilePath.c_str(), F_OK ) != -1 ){ //メモリマップからの読み込み\n if (debug_mode>0) \n std::cerr << \"read mapped file\" << std::endl;\n p_file_vocab = new bip::managed_mapped_file(bip::open_read_only, MapVocabFilePath.c_str());\n vocab_map = p_file_vocab->find(\"map_vocab\").first;\n }else{ //モデルファイルの読み込み\n if (debug_mode>0) \n std::cerr << \"read original file\" << std::endl;\n \n // 大きめに1GB (10M文で85MB程度)とる(あとでshrink)\n unsigned long long map_vocab_size = 1024 * 1024 * 1024; \n // メモリマップファイルを作成\n p_file_vocab = new bip::managed_mapped_file(bip::create_only, MapVocabFilePath.c_str(), map_vocab_size ); \n // メモリマップ内にvocablary 用の領域を確保\n vocab_map = p_file_vocab->construct(\"map_vocab\")( 0, boost::hash(), std::equal_to(), p_file_vocab->get_allocator());\n \n // 語彙の vocab, と vocab_map への登録\n for (int line_number = 0; !feof(vocab_file); ++line_number) {\n char buffer[MAX_STRING];\n uint64_t count; \n if (fscanf(vocab_file, \"%s %\" PRIu64 \" \", buffer, &count) != 2) {\n fprintf(stderr, \"WARNING: Skipping ill-formed line #%d in the vocabulary\\n\", line_number);\n continue;\n }\n \n int wid = line_number;\n auto hash_key = shash(std::string(buffer));\n if(vocab_map->find(hash_key) == vocab_map->end())\n (*vocab_map)[shash(std::string(buffer))] = wid; \n else\n std::cerr << \"Collision:\" << buffer << \", \" << wid << std::endl; \n\n if(debug_mode>1)\n std::cerr << buffer << \", \" << wid << std::endl; \n }\n // メモリマップへの書き込みを flush\n p_file_vocab->flush();\n // メモリマップのサイズ shrink\n bip::managed_mapped_file::shrink_to_fit(MapVocabFilePath.c_str());\n }\n fclose(vocab_file);\n \n // vocab_map を全て読み込んでから，vocab に逆向きのmap を作成(デバッグ用)\n vocab_size = vocab_map->size();\n if (debug_mode>0) {\n vocab_file = fopen(model_vocab_file.c_str(), \"rb\");\n\n if (vocab!=NULL) free(vocab);\n vocab_max_size=vocab_size+1000;\n vocab=(struct vocab_word *)calloc(vocab_max_size, sizeof(struct vocab_word)); //initialize memory for vocabulary\n\n for (int line_number = 0; !feof(vocab_file); ++line_number) {\n char buffer[MAX_STRING];\n uint64_t count; \n if (fscanf(vocab_file, \"%s %\" PRIu64 \" \", buffer, &count) != 2) {\n fprintf(stderr, \"WARNING: Skipping ill-formed line #%d in the vocabulary\\n\", line_number);\n continue;\n }\n \n auto wid = searchVocab(buffer);\n // Debug に必要であれば vocab ファイルに登録\n if(wid != -1)\n strncpy(vocab[wid].word, buffer, strlen(buffer));\n }\n\n fclose(vocab_file);\n }\n \n // ニューラルネットワークの重み 読み出し\n FILE *model_file = fopen(model_weight_file.c_str(), \"rb\");\n if (model_file == NULL){\n fprintf(stderr, \"Error: model file %s not found. \\n\", model_weight_file.c_str());\n exit(1);\n }\n \n ReadFRHeader(model_file);\n \n if (debug_mode>0) \n std::cerr << \"reading model file\" << std::endl;\n \n if (neu0==NULL) initNetFR(); //memory allocation (neu0, neu1, neu2)\n \n if (use_nce != true){\n std::cerr << \"err:use nce\" << std::endl;\n exit(1); //DEB\n }\n\n const std::string MapFilePath( model_vocab_file + \".nnet.map\" );\n if( access( MapFilePath.c_str(), F_OK ) != -1 ){ //読み込みのみ\n p_file_syn = new bip::managed_mapped_file(bip::open_read_only, MapFilePath.c_str());\n if(debug_mode>0)\n std::cerr << \"reading RNNLM model\" << std::endl;\n syn_vocab_l1 = p_file_syn->find(\"syn_vocab_l1\").first;\n syn_rec = p_file_syn->find(\"syn_rec\").first;\n syn_l1_l2 = p_file_syn->find(\"syn_l1_l2\").first;\n }else{ //通常読み込み\n unsigned long long syn_size = sizeof(real)*(vocab_size*layer1_size + layer1_size*layer1_size + layer2_size*layer1_size) + 4096;\n p_file_syn = new bip::managed_mapped_file(bip::create_only, MapFilePath.c_str(), syn_size );\n syn_vocab_l1 = p_file_syn->construct(\"syn_vocab_l1\")(p_file_syn->get_segment_manager());\n syn_rec = p_file_syn->construct(\"syn_rec\")(p_file_syn->get_segment_manager());\n syn_l1_l2 = p_file_syn->construct(\"syn_l1_l2\")(p_file_syn->get_segment_manager());\n\n if(debug_mode>0)\n std::cerr << \"reading embedding\" << std::endl;\n syn_vocab_l1->resize(vocab_size*layer1_size);\n for (a=0; a0)\n std::cerr << \"reading NCE layer \" << std::endl; \n // W:layer1 -> output (word embedding => layer1\n syn_l1_l2->resize(layer1_size*layer2_size);\n for (b=0; b0)\n std::cerr << \"reading reccurent weight\" << std::endl;\n // Recurrent weight\n syn_rec->resize(layer1_size*layer1_size);\n for (b=0; bflush();\n }\n \n if(debug_mode>0)\n std::cerr << \"reading direct weight\" << std::endl;\n\n const std::string FilePath( model_vocab_file + \".direct\" );\n if( access( FilePath.c_str(), F_OK ) != -1 ){ //読み込みのみ\n p_file_direct = new bip::managed_mapped_file( bip::open_read_only, FilePath.c_str() ); \n syn_d = p_file_direct->find(\"MyVector\").first;\n if(debug_mode>0)\n std::cerr << \"read finished\"<< std::endl;\n }else{\n // 初回起動時は Memory mapped file に読み込み\n p_file_direct = new bip::managed_mapped_file( bip::create_only, FilePath.c_str(), direct_size*sizeof(real)+4096 );\n syn_d = p_file_direct->construct(\"MyVector\")(p_file_direct->get_segment_manager());\n if(debug_mode>0)\n std::cerr << \"Creating memory mapped file:\" << std::endl;\n\n real fl;\n (*syn_d).resize(direct_size);\n for (unsigned long long b=0; bflush(); \n if(debug_mode>0)\n std::cerr << \"Creating memory mapped file: finished.\"<< std::endl;\n }\n \n fclose(model_file);\n return;\n }//}}}\n\n void CRnnLM_dyn::matrixXvector(struct neuron *dest, struct neuron *srcvec, vector_syn *srcmatrix, int matrix_width, int from, int to, int from2, int to2, int type)\n {//{{{\n int a, b;\n real val1, val2, val3, val4;\n real val5, val6, val7, val8;\n\n if (type==0) {\t\t//ac mod\n for (b=0; b<(to-from)/8; b++) {\n val1=0;\n val2=0;\n val3=0;\n val4=0;\n\n val5=0;\n val6=0;\n val7=0;\n val8=0;\n\n for (a=from2; a0)\n for (a=from2; agradient_cutoff) dest[a].er=gradient_cutoff;\n if (dest[a].er<-gradient_cutoff) dest[a].er=-gradient_cutoff;\n }\n }\n\n //this is normal implementation (about 3x slower):\n\n /*if (type==0) {\t\t//ac mod\n for (b=from; bhistory[0];\n\n \n // 語彙数次元のベクトル\n if (last_word_local!=-1) neu0[last_word_local].ac=1;\n \n // 0-(vocab)-(layer1)-layer0_size\n // 0...vocab(layer0_size-layer1_size -1) が語彙の次元\n // layer0_size-layer1_size > layer0_size がコンテクストの次元\n restoreFullContext(context); // コンテクストをneu0 のコンテクストへコピー，history をセット\n \n // 未知語に対しては計算しない\n if(word == -1) return; \n\n if(context->have_recurrent){\n // context が recurrent に含まれるなら，それを読み込むだけに留める.\n \n }else{\n //propagate 0->1\n for (a=0; a2 class\n double direct_score = 0.0;\n if (direct_size>0) { //RNNME を使う場合\n direct_score = calc_direct_score(word, context);\n }\n \n double rnn_score = 0.0;\n for (a=0; a 0)\n std::cerr << \"p(\" << vocab[word].word << \"|\" << vocab[context->history[0]].word << \", \" << vocab[context->history[1]].word << \", ...) = \" << neu2[word].ac << \" (nn_score:\" << rnn_score << \" direct_score:\" << direct_score << \")\"<< std::endl;\n\n }//}}}\n \n void CRnnLM_dyn::copyHiddenLayerToInput()\n {//{{{\n //std::cout << \"copyHiddenLayerToInput\"<< std::endl;\n int a;\n\n for (a=0; a0) std::cerr << \"initializing RNNLM FR\" << std::endl;\n restoreNet_FR(); // initialize モデル読込 重い\n //computeNet(0, 0); // initialize \n copyHiddenLayerToInput();\n //saveContext(); \n //saveContext2(); //必要？　\n for (int a=0; alast_word;\n real senp;\n\n real lambda=1;\n real logp=0;\n senp=0; //\n\n int word = searchVocab((char*)next_word.c_str());\n\n // 文区切りを0に対応させる アドホックな対処\n if(next_word == \"\" || next_word == \"\")\n word = 0;\n \n // RNN の実行 (結果はneu2[word].ac に書き込まれる\n computeNet_selfnm(word,c);\n\n double ln_score = neu2[word].ac;\n \n if (word!=-1) { //OOVでない\n logp+=log10(ln_score);\n senp+=log10(ln_score*lambda); \n } else {\n //assign to OOVs some score to correctly rescore nbest lists, reasonable value can be less than 1/|V| or backoff LM score (in case it is trained on more data)\n //this means that PPL results from nbest list rescoring are not true probabilities anymore (as in open vocabulary LMs)\n \n // 文字の長さに対してlinear に設定する\n real oov_penalty=-5; //log penalty\n if(lpenalty){ //penalty を文字の長さに対して線形に与える.\n oov_penalty -= lweight * word_length;\n }\n \n logp+=oov_penalty;\n senp+=oov_penalty;\n }\n \n copyHiddenLayerToInput(); //必要？\n if (last_word!=-1) neu0[last_word].ac=0; //delete previous activation\n \n // history の更新\n for (int a=MAX_NGRAM_ORDER-1; a>0; a--)\n history[a] = c->history[a-1];\n history[0] = word;\n \n // context の保存 // history の渡し方が暗黙的\n saveFullContext(new_c);\n\n return senp;\n }//}}}\n\n}\n", "meta": {"hexsha": "83a5e447ad5315f7142bdd7c848e9d7d2ddd0cac", "size": 24281, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/rnnlm/rnnlmlib_dynamic.cpp", "max_stars_repo_name": "yingfeng/jumanpp", "max_stars_repo_head_hexsha": "314e0a7672b89e5a973b99906e9d7b2c6fdd3d99", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/rnnlm/rnnlmlib_dynamic.cpp", "max_issues_repo_name": "yingfeng/jumanpp", "max_issues_repo_head_hexsha": "314e0a7672b89e5a973b99906e9d7b2c6fdd3d99", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/rnnlm/rnnlmlib_dynamic.cpp", "max_forks_repo_name": "yingfeng/jumanpp", "max_forks_repo_head_hexsha": "314e0a7672b89e5a973b99906e9d7b2c6fdd3d99", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.0579937304, "max_line_length": 252, "alphanum_fraction": 0.5170709608, "num_tokens": 7048, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.22997364162397252}} {"text": "/*\n * (C) Copyright 2021 Met Office UK\n *\n * This software is licensed under the terms of the Apache Licence Version 2.0\n * which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.\n */\n\n#include \n#include \n#include \n\n#include \"ioda/ObsDataVector.h\"\n#include \"oops/util/IntSetParser.h\"\n#include \"oops/util/missingValues.h\"\n#include \"ufo/filters/obsfunctions/CloudCostFunction.h\"\n#include \"ufo/filters/Variable.h\"\n#include \"ufo/utils/ufo_utils.interface.h\"\n\nnamespace ufo {\n\nstatic ObsFunctionMaker makerCloudCostFunction_(\"CloudCostFunction\");\n\nCloudCostFunction::CloudCostFunction(const eckit::LocalConfiguration & conf)\n : invars_() {\n // Initialize options\n options_.deserialize(conf);\n\n // List of field names for B matrix\n fields_ = options_.field_names.value();\n\n // Get channels for computing scattering index from options\n std::set chanset = oops::parseIntSet(options_.chanlist.value());\n channels_.assign(chanset.begin(), chanset.end());\n\n // List of required data\n for (size_t i = 0; i < fields_.size(); ++i) {\n invars_ += Variable(\"brightness_temperature_jacobian_\"+fields_[i]+\"@ObsDiag\", channels_);\n }\n invars_ += Variable(\"brightness_temperature@ObsValue\", channels_);\n invars_ += Variable(\"brightness_temperature@\"+options_.HofXGroup.value(), channels_);\n invars_ += Variable(\"latitude@MetaData\");\n if (options_.qtotal_lnq_gkg.value()) {\n invars_ += Variable(\"specific_humidity@GeoVaLs\");\n invars_ += Variable(\"mass_content_of_cloud_liquid_water_in_atmosphere_layer@GeoVaLs\");\n invars_ += Variable(\"mass_content_of_cloud_ice_in_atmosphere_layer@GeoVaLs\");\n invars_ += Variable(\"air_pressure@GeoVaLs\");\n invars_ += Variable(\"air_temperature@GeoVaLs\");\n invars_ += Variable(\"surface_pressure@GeoVaLs\");\n invars_ += Variable(\"surface_temperature@GeoVaLs\");\n invars_ += Variable(\"specific_humidity_at_two_meters_above_surface@GeoVaLs\");\n }\n}\n\n// -----------------------------------------------------------------------------\n\nvoid CloudCostFunction::compute(const ObsFilterData & in,\n ioda::ObsDataVector & out) const {\n // Get dimensions\n const size_t nlocs = in.nlocs();\n const size_t nchans = channels_.size();\n ASSERT(nchans > 0);\n ASSERT(out.nvars() == 1);\n\n // B, R error covariance objects\n eckit::LocalConfiguration bMatrixConf;\n bMatrixConf.set(\"BMatrix\", options_.bmatrix_filepath.value());\n bMatrixConf.set(\"background fields\", options_.field_names.value());\n bMatrixConf.set(\"qtotal\", options_.qtotal_lnq_gkg.value());\n MetOfficeBMatrixStatic staticB(bMatrixConf);\n\n eckit::LocalConfiguration rMatrixConf;\n rMatrixConf.set(\"RMatrix\", options_.rmatrix_filepath.value());\n MetOfficeRMatrixRadiance staticR(rMatrixConf);\n\n bool split_rain = options_.qtotal_split_rain.value();\n\n const std::string clw_name = \"mass_content_of_cloud_liquid_water_in_atmosphere_layer\";\n const std::string ciw_name = \"mass_content_of_cloud_ice_in_atmosphere_layer\";\n std::vector gv_pres(nlocs), gv_temp(nlocs), gv_qgas(nlocs), gv_clw(nlocs), gv_ciw(nlocs),\n humidity_total(nlocs);\n\n // Determine if pressure is ascending or descending (B-matrix assumption)\n size_t np = in.nlevs(Variable(\"air_pressure@GeoVaLs\"));\n std::vector gv_pres_1(nlocs), gv_pres_N(nlocs);\n in.get(Variable(\"air_pressure@GeoVaLs\"), 1, gv_pres_1);\n in.get(Variable(\"air_pressure@GeoVaLs\"), np, gv_pres_N);\n const float missing = util::missingValue(missing);\n ASSERT(gv_pres_1[0] != missing);\n ASSERT(gv_pres_N[0] != missing);\n bool p_ascending = (gv_pres_N[0] > gv_pres_1[0]);\n\n // Assemble combined Jacobian from component fields\n std::vector>>\n jac_vec(nlocs, std::vector>(nchans, std::vector()));\n for (size_t ifield = 0; ifield < fields_.size(); ++ifield) {\n if (options_.qtotal_lnq_gkg.value() &&\n (fields_[ifield] == clw_name || fields_[ifield] == ciw_name)) {\n // qtotal ln(g/kg) Jacobian calculated when \"specific_humidity\" is reached in field list\n continue;\n }\n std::string jac_name = \"brightness_temperature_jacobian_\"+fields_[ifield]+\"@ObsDiag\";\n size_t nlevs = in.nlevs(Variable(jac_name, channels_)[0]);\n std::vector jac_store(nlocs);\n for (size_t ilev = 0; ilev < nlevs; ++ilev) {\n int level_gv = (p_ascending ? ilev+1 : nlevs-ilev);\n int level_jac = (options_.reverse_Jacobian.value() ? nlevs-level_gv+1 : level_gv);\n if (fields_[ifield] == \"specific_humidity\" && options_.qtotal_lnq_gkg.value()) {\n in.get(Variable(\"air_pressure@GeoVaLs\"), level_gv, gv_pres);\n in.get(Variable(\"air_temperature@GeoVaLs\"), level_gv, gv_temp);\n in.get(Variable(\"specific_humidity@GeoVaLs\"), level_gv, gv_qgas);\n in.get(Variable(clw_name+\"@GeoVaLs\"), level_gv, gv_clw);\n in.get(Variable(ciw_name+\"@GeoVaLs\"), level_gv, gv_ciw);\n std::vector qsaturated(nlocs);\n ufo_ops_satrad_qsatwat_f90(qsaturated.data(), gv_temp.data(), gv_pres.data(),\n static_cast(nlocs));\n for (size_t iloc = 0; iloc < nlocs; ++iloc) {\n // Ensure specific humidity is within limits\n gv_qgas[iloc] = std::max(gv_qgas[iloc], options_.min_q.value());\n gv_qgas[iloc] = std::min(gv_qgas[iloc], qsaturated[iloc]);\n humidity_total[iloc] = gv_qgas[iloc] + gv_clw[iloc] + gv_ciw[iloc];\n }\n }\n\n for (size_t ichan = 0; ichan < nchans; ++ichan) {\n in.get(Variable(jac_name, channels_)[ichan], level_jac, jac_store);\n\n if (fields_[ifield] == \"specific_humidity\" && options_.qtotal_lnq_gkg.value()) {\n std::vector jac_clw(nlocs), jac_ciw(nlocs);\n in.get(Variable(\"brightness_temperature_jacobian_\"+clw_name+\"@ObsDiag\", channels_)[ichan],\n level_jac, jac_clw);\n in.get(Variable(\"brightness_temperature_jacobian_\"+ciw_name+\"@ObsDiag\", channels_)[ichan],\n level_jac, jac_ciw);\n std::vector dq_dqtotal(nlocs), dql_dqtotal(nlocs), dqi_dqtotal(nlocs);\n int qsplit_mode = 2; // compute derivatives\n ufo_ops_satrad_qsplit_f90(qsplit_mode, static_cast(nlocs), gv_pres.data(),\n gv_temp.data(), humidity_total.data(), dq_dqtotal.data(),\n dql_dqtotal.data(), dqi_dqtotal.data(), split_rain);\n // Jacobian dy/dx for observation y, humdity x in units kg/kg\n // For alternative units of B-matrix humidity z in ln(g/kg)\n // chain rule gives dy/dz = x.(dy/dx)\n // Gradient due to ice is ignored unless we are using scattering radiative transfer\n // dTb/dln(qt) = qt*(dTb/dq*dq/dqt + dTb/dql*dql/dqt [+ dTb/dqi*dqi/dqt])\n for (size_t iloc = 0; iloc < nlocs; ++iloc) {\n jac_store[iloc] *= dq_dqtotal[iloc];\n jac_store[iloc] += jac_clw[iloc]*dql_dqtotal[iloc];\n if (options_.scattering_switch.value()) {\n jac_store[iloc] += jac_ciw[iloc]*dqi_dqtotal[iloc];\n }\n jac_store[iloc] *= humidity_total[iloc];\n }\n }\n\n if (fields_[ifield] == \"specific_humidity_at_two_meters_above_surface\"\n && options_.qtotal_lnq_gkg.value()) {\n in.get(Variable(\"surface_pressure@GeoVaLs\"), level_gv, gv_pres);\n in.get(Variable(\"surface_temperature@GeoVaLs\"), level_gv, gv_temp);\n in.get(Variable(\"specific_humidity_at_two_meters_above_surface@GeoVaLs\"),\n level_gv, gv_qgas);\n std::vector qsaturated(nlocs);\n ufo_ops_satrad_qsatwat_f90(qsaturated.data(), gv_temp.data(), gv_pres.data(),\n static_cast(nlocs));\n for (size_t iloc = 0; iloc < nlocs; ++iloc) {\n gv_qgas[iloc] = std::max(gv_qgas[iloc], options_.min_q.value());\n gv_qgas[iloc] = std::min(gv_qgas[iloc], qsaturated[iloc]);\n // dTb/dln(q2m) = q2m*(dTb/dq2m)\n jac_store[iloc] *= gv_qgas[iloc];\n }\n }\n\n for (size_t iloc = 0; iloc < nlocs; ++iloc) {\n jac_vec[iloc][ichan].push_back(jac_store[iloc]);\n }\n }\n }\n }\n\n const size_t sizeB = staticB.getsize();\n for (size_t iloc = 0; iloc < nlocs; ++iloc) {\n for (size_t ichan = 0; ichan < nchans; ++ichan) {\n ASSERT(jac_vec[iloc][ichan].size() == sizeB);\n }\n }\n\n // Get departures = ObsValue - HofX (where HofX is bias corrected)\n std::vector> departures(nlocs, std::vector(nchans));\n std::vector obsvalues(nlocs);\n std::vector bgvalues(nlocs);\n std::vector is_out_of_bounds(nlocs, false);\n for (size_t ichan = 0; ichan < nchans; ++ichan) {\n in.get(Variable(\"brightness_temperature@ObsValue\", channels_)[ichan], obsvalues);\n in.get(Variable(\"brightness_temperature@\"+options_.HofXGroup.value(), channels_)[ichan],\n bgvalues);\n for (size_t iloc = 0; iloc < nlocs; ++iloc) {\n departures[iloc][ichan] = obsvalues[iloc] - bgvalues[iloc];\n // Flag observations outside expected bounds\n if (obsvalues[iloc] < options_.minTb.value() || obsvalues[iloc] > options_.maxTb.value()) {\n is_out_of_bounds[iloc] = true;\n }\n }\n }\n\n std::vector latitude(nlocs);\n in.get(Variable(\"latitude@MetaData\"), latitude);\n Eigen::MatrixXf Hmatrix(nchans, sizeB);\n\n for (size_t iloc = 0; iloc < nlocs; ++iloc) {\n if (is_out_of_bounds[iloc]) {\n out[0][iloc] = options_.maxCost.value();\n continue;\n }\n\n for (size_t ichan = 0; ichan < nchans; ++ichan) {\n Hmatrix.row(ichan) = Eigen::Map(jac_vec[iloc][ichan].data(), sizeB);\n }\n\n // Matrix of departures dy\n Eigen::Map dy(departures[iloc].data(), nchans);\n\n // Calculate Scratch_matrix = H.B.H^T + R\n Eigen::MatrixXf BHT;\n staticB.multiply(latitude[iloc], Hmatrix.transpose(), BHT);\n Eigen::MatrixXf HBHT = Hmatrix*BHT;\n Eigen::MatrixXf Scratch_matrix;\n staticR.add(channels_, HBHT, Scratch_matrix);\n\n // Calculate Scratch_matrix2 = Scratch_matrix^-1.dy using Cholesky decomposition\n Eigen::LLT decomposition(Scratch_matrix);\n if (decomposition.info() == Eigen::NumericalIssue) {\n oops::Log::warning() <<\n \"CloudCostFunction Scratch_matrix appears not to be positive definite\" << std::endl;\n out[0][iloc] = options_.maxCost.value();\n continue;\n }\n Eigen::VectorXf Scratch_matrix2 = decomposition.solve(dy);\n\n // Final cost\n float Cost_final = 0.5*dy.transpose()*Scratch_matrix2;\n Cost_final /= static_cast(nchans); // normalise by number of channels\n out[0][iloc] = std::min(Cost_final, options_.maxCost.value());\n }\n}\n\n// -----------------------------------------------------------------------------\n\nconst ufo::Variables & CloudCostFunction::requiredVariables() const {\n return invars_;\n}\n\n// -----------------------------------------------------------------------------\n\n} // namespace ufo\n", "meta": {"hexsha": "ab11430aa361bbed3dbcd4e2cf3d9cd2b0da74dd", "size": 11127, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/ufo/filters/obsfunctions/CloudCostFunction.cc", "max_stars_repo_name": "fmahebert/ufo", "max_stars_repo_head_hexsha": "2af9b91433553ca473c72fcd131400a01c3aabdb", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-10-08T16:37:25.000Z", "max_stars_repo_stars_event_max_datetime": "2021-10-08T16:37:25.000Z", "max_issues_repo_path": "src/ufo/filters/obsfunctions/CloudCostFunction.cc", "max_issues_repo_name": "fmahebert/ufo", "max_issues_repo_head_hexsha": "2af9b91433553ca473c72fcd131400a01c3aabdb", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": "2021-06-25T17:18:06.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-08T17:40:31.000Z", "max_forks_repo_path": "src/ufo/filters/obsfunctions/CloudCostFunction.cc", "max_forks_repo_name": "fmahebert/ufo", "max_forks_repo_head_hexsha": "2af9b91433553ca473c72fcd131400a01c3aabdb", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 31.0, "max_forks_repo_forks_event_min_datetime": "2021-06-24T18:07:53.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-08T15:40:39.000Z", "avg_line_length": 44.1547619048, "max_line_length": 100, "alphanum_fraction": 0.6490518558, "num_tokens": 3093, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.3738758227716966, "lm_q1q2_score": 0.22996647805794782}} {"text": "#ifndef RLSS_INTERNAL_UTIL_HPP\n#define RLSS_INTERNAL_UTIL_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#define RLSS_HARD_QP_SOLVER CPLEX\n#define RLSS_SOFT_QP_SOLVER CPLEX\n#define RLSS_SVM_QP_SOLVER qpOASES\n#define RLSS_HP_PRUNING_LP_SOLVER GUROBI\n\nnamespace rlss {\n\n#ifdef ENABLE_RLSS_DEBUG_MESSAGES\nnamespace internal {\n // methods for debugging\n template\n void debug_message_internal(bool first, const T& message) {\n if(first) {\n std::cout << \"[DEBUG] \";\n }\n std::cout << message << std::endl;\n }\n\n template\n void debug_message_internal(bool first, const T& message, const Args&... args) {\n if(first) {\n std::cout << \"[DEBUG] \";\n }\n std::cout << message;\n debug_message_internal(false, args...);\n }\n} // namespace internal\n\n template\n void debug_message(const Args&... args) {\n internal::debug_message_internal(true, args...);\n }\n#else\n template\n void debug_message(const Args&... args) {\n\n }\n#endif\n\nnamespace internal {\n namespace debug {\n namespace colors {\n constexpr char RESET[] = \"\\033[0m\";\n constexpr char RED[] = \"\\033[31m\";\n constexpr char GREEN[] = \"\\033[32m\";\n }\n }\n}\n\nnamespace internal {\n\ntemplate\nusing AlignedBox = Eigen::AlignedBox;\n\ntemplate\nusing Hyperplane = Eigen::Hyperplane;\n\ntemplate\nusing VectorDIM = Eigen::Matrix;\n\ntemplate\nusing StdVectorVectorDIM = std::vector,\n Eigen::aligned_allocator>>;\n\ntemplate\nusing Vector = Eigen::Matrix;\n\ntemplate\nusing Row = Eigen::Matrix;\n\ntemplate\nusing Matrix = Eigen::Matrix;\n\ntemplate\nusing MatrixRC = Eigen::Matrix; // row column\n\n// method to get corner points of the Eigen AlignedBox passed as argument\ntemplate\nStdVectorVectorDIM cornerPoints(const AlignedBox& box) {\n StdVectorVectorDIM pts(1<\nclass VectorDIMHasher {\npublic:\n std::size_t operator()(const VectorDIM& vec) const {\n std::size_t seed = 0;\n for(unsigned int d = 0; d < DIM; d++) {\n boost::hash_combine(seed, vec(d));\n }\n return seed;\n }\n};\n\n// linear interpolation of two vector points. Returns a vector container of\n// points based on number of interpolated points passed\ntemplate \nStdVectorVectorDIM linearInterpolate(\n const VectorDIM& start,\n const VectorDIM& end,\n std::size_t num_points\n) {\n\n using StdVectorVectorDIM = StdVectorVectorDIM;\n using VectorDIM = VectorDIM;\n\n if(num_points < 2) {\n throw std::domain_error(\n absl::StrCat\n (\n \"linear interpolate can't have less than 2 number of points\",\n \" given: \",\n num_points\n )\n );\n }\n\n StdVectorVectorDIM result(num_points);\n\n VectorDIM step_vec = (end - start) / (num_points - 1);\n\n result[0] = start;\n for(std::size_t step = 1; step < num_points - 1; step++) {\n result[step] = start + step * step_vec;\n }\n result.back() = end;\n\n return result;\n\n\n}\n\n// method to check if the discrete path search algorithm splits the path segments\n// to the minimum required number of segments\ntemplate \nStdVectorVectorDIM bestSplitSegments(\n const StdVectorVectorDIM& segments,\n std::size_t num_pieces\n) {\n // if current number of segments is larger than the number of pieces, throws error\n if(num_pieces + 1 < segments.size()) {\n throw std::domain_error(\n absl::StrCat\n (\n \"nothing to split since num_pieces=\",\n num_pieces,\n \"is less than current number of segments \",\n segments.size() - 1\n )\n );\n }\n\n\n using VectorDIM = VectorDIM;\n using StdVectorVectorDIM = StdVectorVectorDIM;\n\n // if segements is currently of size 1, return a vector of size num_pieces + 1 with\n // all of values of that segment\n if(segments.size() == 1) {\n return StdVectorVectorDIM(num_pieces + 1, segments[0]);\n }\n\n // define segment priority queue element\n struct SegmentPQElem {\n VectorDIM start;\n VectorDIM end;\n T length;\n unsigned int num_pieces;\n std::size_t segment_idx;\n\n SegmentPQElem(const VectorDIM& s, const VectorDIM& e, std::size_t i)\n : start(s),\n end(e),\n length((s-e).norm()),\n num_pieces(1),\n segment_idx(i)\n {\n\n }\n\n // operator to compare the average length of the segment\n bool operator<(const SegmentPQElem& rhs) const {\n return this->length / this->num_pieces\n < rhs.length / rhs.num_pieces;\n }\n };\n\n std::priority_queue\n <\n SegmentPQElem,\n std::vector\n > pq;\n\n // initialize and store segmentPQelements in PQ\n // store current segment point and next segment point and the index of that segment\n for(std::size_t i = 0; i < segments.size()-1; i++) {\n pq.push({segments[i], segments[i+1], i});\n }\n\n // enters iteration only if there are excess segments\n // remove excess segments, such that the segments vector container size equals to the\n // number of segments to be generated\n for(std::size_t p_count = segments.size() - 1;\n p_count < num_pieces; p_count++) {\n SegmentPQElem t = pq.top();\n pq.pop();\n t.num_pieces++;\n pq.push(t);\n }\n\n // iteratively process the priority queue to store into the segment pq elements vector\n std::vector segment_pqelems;\n while(!pq.empty()) {\n segment_pqelems.push_back(pq.top());\n pq.pop();\n }\n\n // sort the segments based on their segment length from shortest to longest\n std::sort(\n segment_pqelems.begin(),\n segment_pqelems.end(),\n [] (const SegmentPQElem& lhs, const SegmentPQElem& rhs) -> bool {\n return lhs.segment_idx < rhs.segment_idx;\n }\n );\n\n StdVectorVectorDIM result;\n\n // interpolate every segment and split them into the number of pieces that segment\n // is to be interpolated, and store the segment points into the vector\n for(const SegmentPQElem& pqelem: segment_pqelems) {\n StdVectorVectorDIM interpolate\n = linearInterpolate(\n pqelem.start,\n pqelem.end,\n pqelem.num_pieces + 1\n );\n\n // append to the back of results vector\n result.insert(result.end(), interpolate.begin(), interpolate.end() - 1);\n }\n\n result.push_back(segments.back());\n return result;\n}\n\ntemplate\nStdVectorVectorDIM firstSegmentFix(\n const StdVectorVectorDIM& segments\n) {\n assert(segments.size() > 1);\n\n StdVectorVectorDIM result;\n result.push_back(segments[0]);\n result.push_back((segments[0] + segments[1]) / 2);\n for(std::size_t i = 1; i < segments.size(); i++) {\n result.push_back(segments[i]);\n }\n return result;\n}\n// shift hyperplane hp creating hyperplane shp\n// such that whenever the center_of_mass of the robot\n// is to the negative side of the shp,\n// the collision shape box of the\n// robot is to the negative side of the hyperplane hp.\n// box should be the bounding box of the collision shape at given\n// center_of_mass\ntemplate\nHyperplane shiftHyperplane(\n const VectorDIM& center_of_mass,\n const AlignedBox& box,\n const Hyperplane& hp\n) {\n using Hyperplane = Hyperplane;\n using StdVectorVectorDIM = StdVectorVectorDIM;\n Hyperplane shp {hp.normal(), std::numeric_limits::lowest()};\n\n\n StdVectorVectorDIM corner_points = cornerPoints(box);\n\n for(const auto& pt : corner_points) {\n shp.offset()\n = std::max(shp.offset(),\n hp.normal().dot(pt - center_of_mass) + hp.offset()\n );\n }\n\n return shp;\n}\n\n/*\n* Buffer 'box' so that when center_of_mass is inside the buffered box\n* com_box corresponding to robot with center of mass 'center_of_mass'\n* is inside the 'box'\n*/\ntemplate\nAlignedBox bufferAlignedBox(\n const VectorDIM& center_of_mass,\n const AlignedBox& com_box,\n const AlignedBox& box\n) {\n return AlignedBox(box.min() + (center_of_mass - com_box.min()),\n box.max() + (center_of_mass - com_box.max()));\n}\n\n/*\n * Prunes hyperplanes h in hps such that if the space S bounded by the bounding\n * box bbox and the hyperplane h contains the space S' bounded by the bounding\n * box bbox and an hyperplane h' in hps (that is S' is a subset of S).\n */\ntemplate\nstd::vector> pruneHyperplanes(\n const std::vector>& hps,\n const AlignedBox& bbox) {\n\n using Hyperplane = Hyperplane;\n using AlignedBox = AlignedBox;\n using LP = LPWrappers::Problem;\n using LPEngine = LPWrappers::RLSS_HP_PRUNING_LP_SOLVER::Engine;\n using Vector = typename LPEngine::Vector;\n\n std::vector result = hps;\n LP lp(DIM, 2);\n LPEngine solver;\n\n for(unsigned int d = 0; d < DIM; d++) {\n lp.set_var_limits(d, bbox.min()(d), bbox.max()(d));\n }\n\n Vector c(DIM);\n for(unsigned int d = 0; d < DIM; d++) c(d) = 1;\n lp.add_c(c);\n\n for(std::size_t i = 0; i < result.size(); i++) {\n bool should_remove_i = false;\n\n lp.set_constraint(1, result[i].normal().transpose(),\n result[i].offset(), LP::infinity);\n\n for(std::size_t j = 0; j < result.size(); j++) {\n if(i == j) continue;\n\n lp.set_constraint(0, result[j].normal().transpose(),\n LP::minus_infinity, result[j].offset());\n\n Vector soln;\n auto ret = solver.init(lp, soln);\n if(ret != LPWrappers::OptReturnType::Optimal) {\n should_remove_i = true;\n break;\n }\n }\n\n if(should_remove_i) {\n std::swap(result[i], result.back());\n result.pop_back();\n i--;\n }\n }\n\n\n return result;\n}\n\n\n} // namespace internal\n\n// ellipsoid class\ntemplate\nclass Ellipsoid {\npublic:\n using VectorDIM = rlss::internal::VectorDIM;\n using MatrixDIMDIM = rlss::internal::MatrixRC;\n using AlignedBox = rlss::internal::AlignedBox;\n using Index = Eigen::Index;\n\n Ellipsoid(const VectorDIM& cnt, const MatrixDIMDIM& m)\n : center(cnt), mtr(m) {\n mtrInvSq = mtr.inverse();\n mtrInvSq = mtrInvSq * mtrInvSq;\n\n }\n\n AlignedBox boundingBox() const {\n // https://members.loria.fr/samuel.hornus/ellipsoid-bbox.html\n VectorDIM delta;\n for(Index r = 0; r < DIM; r++) {\n delta(r) = mtr.row(r).norm();\n }\n\n debug_message(center - delta, center + delta);\n\n return AlignedBox(center - delta, center + delta);\n }\n\n bool intersects(const AlignedBox& box) const {\n if(box.contains(this->center)) {\n return true;\n }\n\n auto crnr = internal::cornerPoints(box);\n for(const auto& pt: crnr) {\n VectorDIM d = pt - center;\n if(d.transpose() * mtrInvSq * d <= 1) {\n return true;\n }\n }\n return false;\n }\n\nprivate:\n VectorDIM center;\n // affine transformation matrix that transforms unit sphere\n // at origin to the ellipsoid at origin\n MatrixDIMDIM mtr;\n MatrixDIMDIM mtrInvSq;\n};\n\n} // namespace rlss\n\n#endif // RLSS_INTERNAL_UTIL_HPP", "meta": {"hexsha": "4acae79fca63d612c9496b1bf987da8872d808a2", "size": 13184, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/rlss/internal/Util.hpp", "max_stars_repo_name": "sieniven/rlss", "max_stars_repo_head_hexsha": "b1f7ff1abf316242a0644b76559ad921fbca3099", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/rlss/internal/Util.hpp", "max_issues_repo_name": "sieniven/rlss", "max_issues_repo_head_hexsha": "b1f7ff1abf316242a0644b76559ad921fbca3099", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/rlss/internal/Util.hpp", "max_forks_repo_name": "sieniven/rlss", "max_forks_repo_head_hexsha": "b1f7ff1abf316242a0644b76559ad921fbca3099", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.3630289532, "max_line_length": 90, "alphanum_fraction": 0.6163531553, "num_tokens": 3241, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.538983220687684, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.2297801849999489}} {"text": "//---------------------------------------------------------------------------//\n// Copyright (c) 2015 Muhammad Junaid Muzammil \n//\n// Distributed under the Boost Software License, Version 1.0\n// See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt\n//\n// See http://boostorg.github.com/compute for more information.\n//---------------------------------------------------------------------------//\n\n#ifndef BOOST_COMPUTE_RANDOM_THREEFRY_HPP\n#define BOOST_COMPUTE_RANDOM_THREEFRY_HPP\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace boost {\nnamespace compute {\n\n/// \\class threefry_engine\n/// \\brief Threefry pseudorandom number generator.\ntemplate\nclass threefry_engine\n{\npublic:\n static const size_t threads = 1024;\n typedef T result_type;\n\n /// Creates a new threefry_engine and seeds it with \\p value.\n explicit threefry_engine(command_queue &queue)\n : m_context(queue.get_context())\n {\n // setup program\n load_program();\n }\n\n /// Creates a new threefry_engine object as a copy of \\p other.\n threefry_engine(const threefry_engine &other)\n : m_context(other.m_context),\n m_program(other.m_program)\n {\n }\n\n /// Copies \\p other to \\c *this.\n threefry_engine& operator=(const threefry_engine &other)\n {\n if(this != &other){\n m_context = other.m_context;\n m_program = other.m_program;\n }\n\n return *this;\n }\n\n /// Destroys the threefry_engine object.\n ~threefry_engine()\n {\n }\n\nprivate:\n /// \\internal_\n void load_program()\n {\n boost::shared_ptr cache =\n program_cache::get_global_cache(m_context);\n std::string cache_key =\n std::string(\"threefry_engine_32x2\");\n\n // Copyright 2010-2012, D. E. Shaw Research.\n // All rights reserved.\n\n // Redistribution and use in source and binary forms, with or without\n // modification, are permitted provided that the following conditions are\n // met:\n\n // * Redistributions of source code must retain the above copyright\n // notice, this list of conditions, and the following disclaimer.\n\n // * Redistributions in binary form must reproduce the above copyright\n // notice, this list of conditions, and the following disclaimer in the\n // documentation and/or other materials provided with the distribution.\n\n // * Neither the name of D. E. Shaw Research nor the names of its\n // contributors may be used to endorse or promote products derived from\n // this software without specific prior written permission.\n\n // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n // \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n // A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n // OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n // LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n // DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n // THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n // OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n const char source[] =\n \"#define THREEFRY2x32_DEFAULT_ROUNDS 20\\n\"\n \"#define SKEIN_KS_PARITY_32 0x1BD11BDA\\n\"\n\n \"enum r123_enum_threefry32x2 {\\n\"\n \" R_32x2_0_0=13,\\n\"\n \" R_32x2_1_0=15,\\n\"\n \" R_32x2_2_0=26,\\n\"\n \" R_32x2_3_0= 6,\\n\"\n \" R_32x2_4_0=17,\\n\"\n \" R_32x2_5_0=29,\\n\"\n \" R_32x2_6_0=16,\\n\"\n \" R_32x2_7_0=24\\n\"\n \"};\\n\"\n\n \"static uint RotL_32(uint x, uint N)\\n\"\n \"{\\n\"\n \" return (x << (N & 31)) | (x >> ((32-N) & 31));\\n\"\n \"}\\n\"\n\n \"struct r123array2x32 {\\n\"\n \" uint v[2];\\n\"\n \"};\\n\"\n \"typedef struct r123array2x32 threefry2x32_ctr_t;\\n\"\n \"typedef struct r123array2x32 threefry2x32_key_t;\\n\"\n\n \"threefry2x32_ctr_t threefry2x32_R(unsigned int Nrounds, threefry2x32_ctr_t in, threefry2x32_key_t k)\\n\"\n \"{\\n\"\n \" threefry2x32_ctr_t X;\\n\"\n \" uint ks[3];\\n\"\n \" uint i; \\n\"\n \" ks[2] = SKEIN_KS_PARITY_32;\\n\"\n \" for (i=0;i < 2; i++) {\\n\"\n \" ks[i] = k.v[i];\\n\"\n \" X.v[i] = in.v[i];\\n\"\n \" ks[2] ^= k.v[i];\\n\"\n \" }\\n\"\n \" X.v[0] += ks[0]; X.v[1] += ks[1];\\n\"\n \" if(Nrounds>0){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_0_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>1){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_1_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>2){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_2_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>3){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_3_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>3){\\n\"\n \" X.v[0] += ks[1]; X.v[1] += ks[2];\\n\"\n \" X.v[1] += 1;\\n\"\n \" }\\n\"\n \" if(Nrounds>4){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_4_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>5){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_5_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>6){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_6_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>7){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_7_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>7){\\n\"\n \" X.v[0] += ks[2]; X.v[1] += ks[0];\\n\"\n \" X.v[1] += 2;\\n\"\n \" }\\n\"\n \" if(Nrounds>8){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_0_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>9){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_1_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>10){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_2_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>11){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_3_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>11){\\n\"\n \" X.v[0] += ks[0]; X.v[1] += ks[1];\\n\"\n \" X.v[1] += 3;\\n\"\n \" }\\n\"\n \" if(Nrounds>12){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_4_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>13){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_5_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>14){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_6_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>15){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_7_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>15){\\n\"\n \" X.v[0] += ks[1]; X.v[1] += ks[2];\\n\"\n \" X.v[1] += 4;\\n\"\n \" }\\n\"\n \" if(Nrounds>16){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_0_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>17){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_1_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>18){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_2_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>19){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_3_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>19){\\n\"\n \" X.v[0] += ks[2]; X.v[1] += ks[0];\\n\"\n \" X.v[1] += 5;\\n\"\n \" }\\n\"\n \" if(Nrounds>20){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_4_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>21){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_5_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>22){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_6_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>23){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_7_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>23){\\n\"\n \" X.v[0] += ks[0]; X.v[1] += ks[1];\\n\"\n \" X.v[1] += 6;\\n\"\n \" }\\n\"\n \" if(Nrounds>24){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_0_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>25){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_1_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>26){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_2_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>27){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_3_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>27){\\n\"\n \" X.v[0] += ks[1]; X.v[1] += ks[2];\\n\"\n \" X.v[1] += 7;\\n\"\n \" }\\n\"\n \" if(Nrounds>28){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_4_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>29){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_5_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>30){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_6_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>31){ X.v[0] += X.v[1]; X.v[1] = RotL_32(X.v[1],R_32x2_7_0); X.v[1] ^= X.v[0]; }\\n\"\n \" if(Nrounds>31){\\n\"\n \" X.v[0] += ks[2]; X.v[1] += ks[0];\\n\"\n \" X.v[1] += 8;\\n\"\n \" }\\n\"\n \" return X;\\n\"\n \"}\\n\"\n\n \"__kernel void generate_rng(__global uint *ctr, __global uint *key, const uint offset) {\\n\"\n \" threefry2x32_ctr_t in;\\n\"\n \" threefry2x32_key_t k;\\n\"\n \" const uint i = get_global_id(0);\\n\"\n \" in.v[0] = ctr[2 * (offset + i)];\\n\"\n \" in.v[1] = ctr[2 * (offset + i) + 1];\\n\"\n \" k.v[0] = key[2 * (offset + i)];\\n\"\n \" k.v[1] = key[2 * (offset + i) + 1];\\n\"\n \" in = threefry2x32_R(20, in, k);\\n\"\n \" ctr[2 * (offset + i)] = in.v[0];\\n\"\n \" ctr[2 * (offset + i) + 1] = in.v[1];\\n\"\n \"}\\n\";\n\n m_program = cache->get_or_build(cache_key, std::string(), source, m_context);\n }\n\npublic:\n\n\n /// Generates Threefry random numbers using both the counter and key values, and then stores\n /// them to the range [\\p first_ctr, \\p last_ctr).\n template\n void generate(OutputIterator first_ctr, OutputIterator last_ctr, OutputIterator first_key, OutputIterator last_key, command_queue &queue) {\n const size_t size_ctr = detail::iterator_range_size(first_ctr, last_ctr);\n const size_t size_key = detail::iterator_range_size(first_key, last_key);\n if(!size_ctr || !size_key || (size_ctr != size_key)) {\n return;\n }\n kernel rng_kernel = m_program.create_kernel(\"generate_rng\");\n\n rng_kernel.set_arg(0, first_ctr.get_buffer());\n rng_kernel.set_arg(1, first_key.get_buffer());\n size_t offset = 0;\n\n for(;;){\n size_t count = 0;\n size_t size = size_ctr/2;\n if(size > threads){\n count = (std::min)(static_cast(threads), size - offset);\n }\n else {\n count = size;\n }\n rng_kernel.set_arg(2, static_cast(offset));\n queue.enqueue_1d_range_kernel(rng_kernel, 0, count, 0);\n\n offset += count;\n\n if(offset >= size){\n break;\n }\n\n }\n }\n\n template\n void generate(OutputIterator first_ctr, OutputIterator last_ctr, command_queue &queue) {\n const size_t size_ctr = detail::iterator_range_size(first_ctr, last_ctr);\n if(!size_ctr) {\n return;\n }\n boost::compute::vector vector_key(size_ctr, m_context);\n vector_key.assign(size_ctr, 0, queue);\n kernel rng_kernel = m_program.create_kernel(\"generate_rng\");\n\n rng_kernel.set_arg(0, first_ctr.get_buffer());\n rng_kernel.set_arg(1, vector_key);\n size_t offset = 0;\n\n for(;;){\n size_t count = 0;\n size_t size = size_ctr/2;\n if(size > threads){\n count = (std::min)(static_cast(threads), size - offset);\n }\n else {\n count = size;\n }\n rng_kernel.set_arg(2, static_cast(offset));\n queue.enqueue_1d_range_kernel(rng_kernel, 0, count, 0);\n\n offset += count;\n\n if(offset >= size){\n break;\n }\n\n }\n }\nprivate:\n context m_context;\n program m_program;\n};\n\n} // end compute namespace\n} // end boost namespace\n\n#endif // BOOST_COMPUTE_RANDOM_THREEFRY_HPP\n", "meta": {"hexsha": "d521ca14c7da9890d25b217faa7064ea2ef4846f", "size": 13640, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/src/boost_1_65_1/boost/compute/random/threefry_engine.hpp", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2018-12-15T20:03:51.000Z", "max_stars_repo_stars_event_max_datetime": "2018-12-15T20:03:51.000Z", "max_issues_repo_path": "deps/src/boost_1_65_1/boost/compute/random/threefry_engine.hpp", "max_issues_repo_name": "shreyasvj25/turicreate", "max_issues_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2021-09-08T02:18:00.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-12T00:39:44.000Z", "max_forks_repo_path": "deps/src/boost_1_65_1/boost/compute/random/threefry_engine.hpp", "max_forks_repo_name": "shreyasvj25/turicreate", "max_forks_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2019-01-12T01:07:34.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-12T01:07:34.000Z", "avg_line_length": 43.7179487179, "max_line_length": 143, "alphanum_fraction": 0.5019061584, "num_tokens": 4661, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.2297239013092188}} {"text": "/*!\n * @file swp_surfvolhydrogen_primary.cpp\n * @author Robert I A Patterson Robert.Patterson@wias-berlin.de\n *\n \n Copyright (C) 2012 Robert I A Patterson\n\n File purpose:\n Implementation of the SurfVolHydrogenPrimary class declared in the\n swp_surfvol_primary.h header file.\n @brief Implementation of particle described by its volume and surface area.\n\n Licence:\n This file is part of \"sweepc\".\n\n sweepc is free software; you can redistribute it and/or\n modify it under the terms of the GNU General Public License\n as published by the Free Software Foundation; either version 2\n of the License, or (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program; if not, write to the Free Software\n Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.\n\n Contact:\n Dr Markus Kraft\n Dept of Chemical Engineering\n University of Cambridge\n New Museums Site\n Pembroke Street\n Cambridge\n CB2 3RA\n UK\n\n Email: mk306@cam.ac.uk\n Website: http://como.cheng.cam.ac.uk\n*/\n\n#include \"swp_primary.h\"\n#include \"swp_surfvolhydrogen_primary.h\"\n#include \"swp_aggmodel_type.h\"\n#include \"swp_model_factory.h\"\n\n#include \n#include \n\nusing namespace Sweep;\nusing namespace Sweep::AggModels;\n\n\n// CONSTRUCTORS AND DESTRUCTORS.\n\n// Default constructor (protected).\nSurfVolHydrogenPrimary::SurfVolHydrogenPrimary(void)\n: iH(1u)\n{\n}\n\n// Initialising constructor.\nSurfVolHydrogenPrimary::SurfVolHydrogenPrimary(double time, const Sweep::ParticleModel &model)\n: Primary(time, model)\n, iH(1u)\n{\n}\n\n// Copy constructor.\nSurfVolHydrogenPrimary::SurfVolHydrogenPrimary(const SurfVolHydrogenPrimary ©)\n: iH(1u)\n{\n *this = copy;\n}\n\n// Stream-reading constructor.\nSurfVolHydrogenPrimary::SurfVolHydrogenPrimary(std::istream &in, const Sweep::ParticleModel &model)\n: iH(1u)\n{\n Deserialize(in, model);\n}\n\n// Default destructor.\nSurfVolHydrogenPrimary::~SurfVolHydrogenPrimary()\n{\n releaseMem();\n}\n\n\n// OPERATOR OVERLOADS.\n\n// Assignment operator (Primary RHS).\nSurfVolHydrogenPrimary &SurfVolHydrogenPrimary::operator=(const Primary &rhs)\n{\n // Attempt to cast the primary to a SurfVolHydrogenPrimary. This will\n // throw an exception if the cast fails.\n operator=(dynamic_cast(rhs));\n\n return *this;\n}\n\n// Assignment operator (SurfVolHydrogenPrimary RHS).\nSurfVolHydrogenPrimary &SurfVolHydrogenPrimary::operator=(const SurfVolHydrogenPrimary &rhs)\n{\n // First copy everything for spherical primaries.\n Primary::operator=(rhs);\n\n return *this;\n}\n\n\n// AGGREGATION MODEL.\n\n// Returns the aggregation model which this primary describes.\nAggModels::AggModelType SurfVolHydrogenPrimary::AggID(void) const {return AggModels::SurfVolHydrogen_ID;}\n\n\n// BASIC DERIVED PARTICLE PROPERTIES.\n\n// Calculates the derived properties from the unique properties. This\n// function is broadly similar to the version in the spherical Primary\n// class except that it uses the surface-volume model. Therefore the\n// surface area is not altered and the collision diameter is calculated\n// using the arithmetic mean function.\nvoid SurfVolHydrogenPrimary::UpdateCache(void)\n{\n // Store the correct surface area.\n const double s = m_surf;\n\n // Pretend that the primary is spherical and set the cache\n // accordingly. This will set m_surf the the surface are\n // of an equivalent volume sphere.\n Primary::UpdateCache();\n\n // The surface area is now set to that of a sphere. Set \n // correct surface area (minimum is spherical surface area).\n m_surf = std::max(s, m_surf);\n\n \n // Calculate diameters.\n m_dcol = (m_diam + sqrt(m_surf / PI)) * 0.5;\n m_dmob = m_dcol; // TODO: Correct expression for Dmob.\n}\n\n// Returns the equivalent spherical particle surface area.\ndouble SurfVolHydrogenPrimary::SphSurfaceArea(void) const {return PI * std::pow(6.0 * m_vol / PI, TWO_THIRDS);}\n\n// Returns the number of primary particles if the aggregate is assumed\n// to consist of mono-sized primaries.\nunsigned int SurfVolHydrogenPrimary::PP_Count(void) const\n{\n // Note the minimum number of primary particles must be 1.\n return std::max(1u, (unsigned int)((m_surf * m_surf * m_surf) /\n (36.0 * PI * m_vol * m_vol)));\n}\n\n// Returns the primary particle diameter if the aggregate is assumed\n// to consist of mono-sized primaries.\ndouble SurfVolHydrogenPrimary::PP_Diameter(void) const\n{\n // This should always be <= equiv. sphere diameter.\n return 6.0 * m_vol / m_surf;\n}\n\n\n// OPERATIONS.\n\n// Adjusts the primary with the given composition and \n// tracker values changes n times. If the particle cannot be adjust\n// n times, then this function returns the number of times\n// it was adjusted.\nunsigned int SurfVolHydrogenPrimary::Adjust(const fvector &dcomp, const fvector &dvalues, rng_type &rng,\n unsigned int n)\n{\n // Calculate change in volume.\n double dvol = 0.0;\n for (unsigned int i=0; i!=dcomp.size(); ++i) {\n dvol += dcomp[i] * m_pmodel->Components(i)->MolWt() / \n m_pmodel->Components(i)->Density();\n }\n dvol *= (double)n / NA;\n\n // Calculate change in surface area.\n double invRadius = 0.0;\n if (dvol > 0.0) {\n // Inverse growth radius.\n invRadius = sqrt(4.0 * PI / m_surf);\n } else {\n // Inverse oxidation radius. \n invRadius = m_surf / (3.0 * m_vol);\n }\n\n // Save new and old surface areas\n const double oldS = m_surf;\n const double s = m_surf + (2.0 * dvol * invRadius);\n\n // Adjust the particle assuming that it is spherical.\n Primary::Adjust(dcomp, dvalues, rng, n);\n\n // Maintain the concentration of H per unit surface area during oxidation\n if(dvol < 0.0) {\n fvector comp = Composition();\n //comp[iH] = static_cast(s * comp[iH] / oldS + 0.5);\n comp[iH] = s * comp[iH] / oldS;\n SetComposition(comp);\n }\n\n\n // Set correct surface area, which was incorrectly set by\n // Primary::Adjust.\n m_surf = std::max(s, m_surf);\n\n // This has a knock-on affect of changing the collision diameter.\n // Note, we can avoid recalling UpdateCache() here, because only\n // a couple of cached values will have changed.\n m_dcol = (m_diam + sqrt(m_surf / PI)) * 0.5;\n m_dmob = m_dcol;\n\n return n;\n}\n\n/*!\n * Combines this primary with another.\n *\n * \\param[in] rhs Particle to add to current instance\n * \\param[in,out] rng Random number generator\n *\n * \\return Reference to the current instance after rhs has been added\n */\nSurfVolHydrogenPrimary &SurfVolHydrogenPrimary::Coagulate(const Primary &rhs, rng_type &rng)\n\n{\n // Store the resultant surface area.\n const double s = m_surf + rhs.SurfaceArea();\n\n // Perform the coagulation.\n Primary::Coagulate(rhs, rng);\n\n // The spherical particle Coagulate() routine has set the\n // surface area incorrectly. We now replace the surface area\n // to the correct point-contact value.\n m_surf = std::max(m_surf, s);\n\n // This has a knock-on affect of changing the collision diameter.\n // Note, we can avoid recalling UpdateCache() here, because only\n // a couple of cached values will have changed.\n m_dcol = (m_diam + sqrt(m_surf / PI)) * 0.5;\n m_dmob = m_dcol;\n\n return *this;\n}\n\n/*!\n * Combines this primary with another.\n *\n * \\param[in] rhs Particle to add to current instance\n * \\param[in,out] rng Random number generator\n *\n * \\return Reference to the current instance after rhs has been added\n */\nSurfVolHydrogenPrimary &SurfVolHydrogenPrimary::Fragment(const Primary &rhs, rng_type &rng)\n\n{\n // Store the resultant surface area.\n const double s = m_surf + rhs.SurfaceArea();\n\n // Perform the coagulation.\n Primary::Fragment(rhs, rng);\n\n // The spherical particle Coagulate() routine has set the\n // surface area incorrectly. We now replace the surface area\n // to the correct point-contact value.\n m_surf = std::max(m_surf, s);\n\n // This has a knock-on affect of changing the collision diameter.\n // Note, we can avoid recalling UpdateCache() here, because only\n // a couple of cached values will have changed.\n m_dcol = (m_diam + sqrt(m_surf / PI)) * 0.5;\n m_dmob = m_dcol;\n\n return *this;\n}\n\n// This routine sinters the Primary for the given length of\n// time using the provided sintering model.\nvoid SurfVolHydrogenPrimary::Sinter(double dt, Cell &sys,\n const Processes::SinteringModel &model,\n rng_type &rng,\n double wt)\n{\n throw std::runtime_error(\"sintering not implemented at present for SurfVolHydrogenPrimary\");\n}\n\n\n// READ/WRITE/COPY.\n\n// Returns a copy of the model data.\nSurfVolHydrogenPrimary *const SurfVolHydrogenPrimary::Clone(void) const\n{\n return new SurfVolHydrogenPrimary(*this);\n}\n\n/*!\n * \\return Number of surface Hydrogens (active sites)\n */\ndouble SurfVolHydrogenPrimary::GetSites() const\n{\n return Composition()[iH];\n}\n\n\n// Writes the object to a binary stream.\nvoid SurfVolHydrogenPrimary::Serialize(std::ostream &out) const\n{\n if (out.good()) {\n // Output the version ID (=0 at the moment).\n const unsigned int version = 0;\n out.write((char*)&version, sizeof(version));\n\n // Output base class.\n Primary::Serialize(out);\n } else {\n throw std::invalid_argument(\"Output stream not ready (Sweep, SurfVolHydrogenPrimary::Serialize).\");\n }\n}\n\n// Reads the object from a binary stream.\nvoid SurfVolHydrogenPrimary::Deserialize(std::istream &in, const Sweep::ParticleModel &model)\n{\n if (in.good()) {\n // Read the output version. Currently there is only one\n // output version, so we don't do anything with this variable.\n // Still needs to be read though.\n unsigned int version = 0;\n in.read(reinterpret_cast(&version), sizeof(version));\n\n switch (version) {\n case 0:\n // Read base class.\n Primary::Deserialize(in, model);\n break;\n default:\n throw std::runtime_error(\"Serialized version number is invalid (Sweep, SurfVolHydrogenPrimary::Deserialize).\");\n }\n } else {\n throw std::invalid_argument(\"Input stream not ready (Sweep, SurfVolHydrogenPrimary::Deserialize).\");\n }\n}\n", "meta": {"hexsha": "7fadf646eef7aa36054acfee5bbdb8564d3ca0b8", "size": 10840, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/sweepc/source/swp_surfvolhydrogen_primary.cpp", "max_stars_repo_name": "sm453/MOpS", "max_stars_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2020-09-08T14:06:33.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-04T07:52:19.000Z", "max_issues_repo_path": "src/sweepc/source/swp_surfvolhydrogen_primary.cpp", "max_issues_repo_name": "sm453/MOpS", "max_issues_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/sweepc/source/swp_surfvolhydrogen_primary.cpp", "max_forks_repo_name": "sm453/MOpS", "max_forks_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2021-11-15T05:18:26.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-01T13:51:20.000Z", "avg_line_length": 30.8831908832, "max_line_length": 127, "alphanum_fraction": 0.6795202952, "num_tokens": 2703, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.44552953503957266, "lm_q1q2_score": 0.22972390130921877}} {"text": "#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \"analysis.h\"\n#include \"filter.h\"\n\n#define N_BINS 4\n\nnamespace fs = std::filesystem;\n\nstd::pair calculate_rotation_and_translation_errors(const Eigen::Matrix4f &transformation,\n const Eigen::Matrix4f &transformation_gt) {\n Eigen::Matrix3f rotation_diff = transformation.block<3, 3>(0, 0).inverse() * transformation_gt.block<3, 3>(0, 0);\n Eigen::Vector3f translation_diff = transformation.block<3, 1>(0, 3) - transformation_gt.block<3, 1>(0, 3);\n float rotation_error = Eigen::AngleAxisf(rotation_diff).angle();\n float translation_error = translation_diff.norm();\n return {rotation_error, translation_error};\n}\n\ninline float dist2(const PointN &p1, const PointN &p2) {\n return (p1.x - p2.x) * (p1.x - p2.x) + (p1.y - p2.y) * (p1.y - p2.y) + (p1.z - p2.z) * (p1.z - p2.z);\n};\n\nfloat calculate_point_cloud_rmse(const PointNCloud::ConstPtr &pcd,\n const Eigen::Matrix4f &transformation,\n const Eigen::Matrix4f &transformation_gt) {\n PointNCloud pcd_transformed;\n Eigen::Matrix4f transformation_diff = transformation.inverse() * transformation_gt;\n pcl::transformPointCloud(*pcd, pcd_transformed, transformation_diff);\n\n float rmse = 0.f;\n for (int i = 0; i < pcd->size(); ++i) {\n rmse += dist2(pcd->points[i], pcd_transformed.points[i]);\n }\n rmse = std::sqrt(rmse / (float) pcd->size());\n return rmse;\n}\n\nfloat calculate_overlap_rmse(const PointNCloud::ConstPtr &src, const PointNCloud::ConstPtr &tgt,\n const Eigen::Matrix4f &transformation,\n const Eigen::Matrix4f &transformation_gt,\n float inlier_threshold) {\n PointNCloud src_aligned, src_aligned_gt;\n pcl::transformPointCloud(*src, src_aligned, transformation);\n pcl::transformPointCloud(*src, src_aligned_gt, transformation_gt);\n\n pcl::KdTreeFLANN tree_tgt;\n tree_tgt.setInputCloud(tgt);\n\n int overlap_size = 0;\n float rmse = 0.f;\n pcl::Indices nn_indices;\n std::vector nn_dists;\n for (int i = 0; i < src->size(); ++i) {\n tree_tgt.nearestKSearch(src_aligned_gt[i], 1, nn_indices, nn_dists);\n if (nn_dists[0] < inlier_threshold * inlier_threshold) { // ith point in overlap\n rmse += dist2(src_aligned.points[i], src_aligned_gt.points[i]);\n overlap_size++;\n }\n }\n if (overlap_size != 0) {\n rmse = std::sqrt(rmse / (float)overlap_size);\n } else {\n rmse = std::numeric_limits::quiet_NaN();\n }\n return rmse;\n\n}\n\nfloat calculate_correspondence_uniformity(const PointNCloud::ConstPtr &src, const PointNCloud::ConstPtr &tgt,\n const pcl::Correspondences &correct_correspondences,\n const AlignmentParameters ¶meters,\n const Eigen::Matrix4f &transformation_gt) {\n PointNCloud::Ptr src_aligned(new PointNCloud);\n pcl::transformPointCloud(*src, *src_aligned, transformation_gt);\n pcl::KdTreeFLANN::Ptr tree(new pcl::KdTreeFLANN());\n tree->setInputCloud(tgt);\n\n pcl::Indices indices;\n std::vector distances;\n // calculate bbox of overlapping area\n PointN min_point, max_point;\n float error_thr = parameters.distance_thr_coef * parameters.voxel_size;\n for (int i = 0; i < src_aligned->size(); ++i) {\n (float) tree->nearestKSearch(*src_aligned, i, 1, indices, distances);\n const PointN &p_src(src_aligned->points[i]), p_tgt(tgt->points[indices[0]]);\n if (std::sqrt(distances[0]) < error_thr && std::isfinite(p_src.normal_x) && std::isfinite(p_tgt.normal_x)) {\n min_point.x = std::min(min_point.x, p_src.x);\n min_point.y = std::min(min_point.y, p_src.y);\n min_point.z = std::min(min_point.z, p_src.z);\n max_point.x = std::max(max_point.x, p_src.x);\n max_point.y = std::max(max_point.y, p_src.y);\n max_point.z = std::max(max_point.z, p_src.z);\n }\n }\n int count[3][N_BINS][N_BINS]{0};\n\n for (auto const &corr: correct_correspondences) {\n const auto &point = src_aligned->points[corr.index_query];\n if (pointInBoundingBox(point, min_point, max_point)) {\n int bin[3];\n bin[0] = std::floor((point.x - min_point.x) / (max_point.x - min_point.x) * N_BINS);\n bin[1] = std::floor((point.y - min_point.y) / (max_point.y - min_point.y) * N_BINS);\n bin[2] = std::floor((point.z - min_point.z) / (max_point.z - min_point.z) * N_BINS);\n // count 3D points projected to YZ, ZX, XY and fallen in 2D bin\n for (int k = 0; k < 3; ++k) {\n count[k][bin[(k + 1) % 3]][bin[(k + 2) % 3]]++;\n }\n }\n }\n float entropy[3]{0.f};\n float n = correct_correspondences.size();\n for (int k = 0; k < 3; ++k) {\n for (int i = 0; i < N_BINS; ++i) {\n for (int j = 0; j < N_BINS; ++j) {\n float p = (float) count[k][i][j] / n;\n if (p == 0.f) continue;\n entropy[k] -= p * std::log(p);\n }\n }\n entropy[k] /= std::log((float) (N_BINS * N_BINS));\n }\n return std::cbrt(entropy[0] * entropy[1] * entropy[2]);\n}\n\nfloat calculate_normal_difference(const PointNCloud::ConstPtr &src, const PointNCloud::ConstPtr &tgt,\n const AlignmentParameters ¶meters, const Eigen::Matrix4f &transformation_gt) {\n PointNCloud::Ptr src_aligned(new PointNCloud);\n pcl::transformPointCloud(*src, *src_aligned, transformation_gt);\n pcl::KdTreeFLANN::Ptr tree(new pcl::KdTreeFLANN());\n tree->setInputCloud(tgt);\n\n pcl::Indices indices;\n std::vector distances;\n float error_thr = parameters.distance_thr_coef * parameters.voxel_size;\n float difference = 0.f;\n int n_points_overlap = 0;\n for (int i = 0; i < src_aligned->size(); ++i) {\n (float) tree->nearestKSearch(*src_aligned, i, 1, indices, distances);\n const PointN &p_src(src_aligned->points[i]), p_tgt(tgt->points[indices[0]]);\n if (std::sqrt(distances[0]) < error_thr && std::isfinite(p_src.normal_x) && std::isfinite(p_tgt.normal_x)) {\n float cos = std::clamp(p_src.normal_x * p_tgt.normal_x + p_src.normal_y * p_tgt.normal_y +\n p_src.normal_z * p_tgt.normal_z, -1.f, 1.f);\n difference += std::abs(std::acos(cos));\n n_points_overlap++;\n }\n }\n return difference / (float) n_points_overlap;\n}\n\nvoid buildCorrectCorrespondences(const PointNCloud::ConstPtr &src, const PointNCloud::ConstPtr &tgt,\n const pcl::Correspondences &correspondences,\n pcl::Correspondences &correct_correspondences,\n const Eigen::Matrix4f &transformation_gt, float error_threshold) {\n correct_correspondences.clear();\n correct_correspondences.reserve(correspondences.size());\n\n PointNCloud input_transformed;\n input_transformed.resize(src->size());\n pcl::transformPointCloud(*src, input_transformed, transformation_gt);\n\n for (const auto &correspondence: correspondences) {\n PointN source_point(input_transformed.points[correspondence.index_query]);\n PointN target_point(tgt->points[correspondence.index_match]);\n float e = pcl::L2_Norm(source_point.data, target_point.data, 3);\n if (e < error_threshold) {\n correct_correspondences.push_back(correspondence);\n }\n }\n}\n\nAlignmentAnalysis::AlignmentAnalysis(const AlignmentParameters ¶meters,\n const PointNCloud::ConstPtr &src, const PointNCloud::ConstPtr &tgt,\n const pcl::Correspondences &correspondences,\n int iterations, const Eigen::Matrix4f &transformation, double time) {\n parameters_ = parameters;\n src_ = src;\n tgt_ = tgt;\n correspondences_ = correspondences;\n iterations_ = iterations;\n transformation_ = transformation;\n time_ = time;\n has_converged_ = true;\n metric_estimator_ = getMetricEstimator(parameters);\n metric_estimator_->setSourceCloud(src);\n metric_estimator_->setTargetCloud(tgt);\n metric_estimator_->setCorrespondences(correspondences);\n metric_estimator_->setInlierThreshold(parameters.distance_thr_coef * parameters.voxel_size);\n}\n\nvoid AlignmentAnalysis::start(const Eigen::Matrix4f &transformation_gt, const std::string &testname) {\n testname_ = testname;\n float error_thr = parameters_.distance_thr_coef * parameters_.voxel_size;\n transformation_gt_ = transformation_gt;\n\n buildCorrectCorrespondences(src_, tgt_, correspondences_, correct_correspondences_, transformation_gt_, error_thr);\n metric_estimator_->buildInlierPairsAndEstimateMetric(transformation_, inlier_pairs_, rmse_, fitness_);\n metric_estimator_->buildCorrectInlierPairs(inlier_pairs_, correct_inlier_pairs_, transformation_gt_);\n pcd_error_ = calculate_point_cloud_rmse(src_, transformation_, transformation_gt_);\n overlap_error_ = calculate_overlap_rmse(src_, tgt_, transformation_, transformation_gt_, error_thr);\n normal_diff_ = calculate_normal_difference(src_, tgt_, parameters_, transformation_gt_);\n corr_uniformity_ = calculate_correspondence_uniformity(src_, tgt_, correct_correspondences_,\n parameters_, transformation_gt_);\n std::tie(r_error_, t_error_) = calculate_rotation_and_translation_errors(transformation_, transformation_gt_);\n\n print();\n save(testname);\n}\n\nvoid AlignmentAnalysis::print() {\n // Print results\n printf(\"\\n\");\n printTransformation(transformation_);\n printTransformation(transformation_gt_);\n pcl::console::print_info(\"fitness: %0.7f\\n\", fitness_);\n pcl::console::print_info(\"inliers_rmse: %0.7f\\n\", rmse_);\n pcl::console::print_info(\"correct inliers: %i/%i\\n\", correct_inlier_pairs_.size(), inlier_pairs_.size());\n pcl::console::print_info(\"correct correspondences: %i/%i\\n\",\n correct_correspondences_.size(), correspondences_.size());\n pcl::console::print_info(\"rotation error: %0.7f\\n\", r_error_);\n pcl::console::print_info(\"translation error: %0.7f\\n\", t_error_);\n pcl::console::print_info(\"point cloud mean error: %0.7f\\n\", pcd_error_);\n pcl::console::print_info(\"normal mean difference: %0.7f\\n\", normal_diff_);\n pcl::console::print_info(\"uniformity of correct correspondences' distribution: %0.7f\\n\", corr_uniformity_);\n}\n\nvoid AlignmentAnalysis::save(const std::string &testname) {\n // Save test parameters and results\n std::string filepath = constructPath(\"test\", \"results\", \"csv\", false);\n bool file_exists = std::filesystem::exists(filepath);\n std::fstream fout;\n if (!file_exists) {\n fout.open(filepath, std::ios_base::out);\n } else {\n fout.open(filepath, std::ios_base::app);\n }\n if (fout.is_open()) {\n if (!file_exists) {\n printAnalysisHeader(fout);\n }\n fout << *this;\n fout.close();\n } else {\n perror((\"error while opening file \" + filepath).c_str());\n }\n}\n\nvoid printAnalysisHeader(std::ostream &out) {\n out << \"version,descriptor,testname,fitness,rmse,correspondences,correct_correspondences,inliers,correct_inliers,\";\n out << \"voxel_size,normal_radius_coef,feature_radius_coef,distance_thr_coef,edge_thr,\";\n out << \"iteration,matching,randomness,filter,threshold,n_random,r_err,t_err,pcd_err,use_normals,\";\n out << \"normal_diff,corr_uniformity,lrf,metric,time,overlap_rmse\\n\";\n}\n\nstd::ostream &operator<<(std::ostream &stream, const AlignmentAnalysis &analysis) {\n stream << VERSION << \",\" << analysis.parameters_.descriptor_id << \",\" << analysis.testname_ << \",\"\n << analysis.fitness_ << \",\" << analysis.rmse_ << \",\";\n stream << analysis.correspondences_.size() << \",\" << analysis.correct_correspondences_.size() << \",\";\n stream << analysis.inlier_pairs_.size() << \",\" << analysis.correct_inlier_pairs_.size() << \",\";\n stream << analysis.parameters_.voxel_size << \",\";\n stream << analysis.parameters_.normal_radius_coef << \",\";\n stream << analysis.parameters_.feature_radius_coef << \",\";\n stream << analysis.parameters_.distance_thr_coef << \",\";\n stream << analysis.parameters_.edge_thr_coef << \",\";\n stream << analysis.iterations_ << \",\";\n stream << analysis.parameters_.matching_id << \",\";\n stream << analysis.parameters_.randomness << \",\";\n auto func = getUniquenessFunction(analysis.parameters_.func_id);\n if (func != nullptr) {\n stream << analysis.parameters_.func_id << \",\" << UNIQUENESS_THRESHOLD << \",\" << N_RANDOM_FEATURES << \",\";\n } else {\n stream << \",,,\";\n }\n stream << analysis.r_error_ << \",\" << analysis.t_error_ << \",\" << analysis.pcd_error_ << \",\";\n stream << analysis.parameters_.use_normals << \",\" << analysis.normal_diff_ << \",\";\n stream << analysis.corr_uniformity_ << \",\" << analysis.parameters_.lrf_id << \",\";\n stream << analysis.parameters_.metric_id << \",\" << analysis.time_ << \",\" << analysis.overlap_error_ << \"\\n\";\n return stream;\n}\n", "meta": {"hexsha": "ca04f6ce49c75e507f1a262f8609e6758ce842f5", "size": 13639, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/analysis.cpp", "max_stars_repo_name": "aleksandrina-streltsova/lidar-global-registration", "max_stars_repo_head_hexsha": "00cc919f17fe5b6854b575ca0aea3712ce034df6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/analysis.cpp", "max_issues_repo_name": "aleksandrina-streltsova/lidar-global-registration", "max_issues_repo_head_hexsha": "00cc919f17fe5b6854b575ca0aea3712ce034df6", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/analysis.cpp", "max_forks_repo_name": "aleksandrina-streltsova/lidar-global-registration", "max_forks_repo_head_hexsha": "00cc919f17fe5b6854b575ca0aea3712ce034df6", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 47.3576388889, "max_line_length": 119, "alphanum_fraction": 0.6448419972, "num_tokens": 3340, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032313, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.22955673216721226}} {"text": "// __BEGIN_LICENSE__\n// Copyright (c) 2009-2013, United States Government as represented by the\n// Administrator of the National Aeronautics and Space Administration. All\n// rights reserved.\n//\n// The NGT platform is licensed under the Apache License, Version 2.0 (the\n// \"License\"); you may not use this file except in compliance with the\n// License. You may obtain a copy of the License at\n// http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n// __END_LICENSE__\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n\nusing namespace vw;\n\nnamespace asp {\n\n void RPCModel::initialize(DiskImageResourceGDAL* resource) {\n // Extract the datum (by means of georeference)\n cartography::GeoReference georef;\n cartography::read_georeference(georef, *resource);\n m_datum = georef.datum();\n\n // Extract RPC Info\n boost::shared_ptr dataset = resource->get_dataset_ptr();\n if (!dataset)\n vw_throw(NotFoundErr() << \"RPCModel: Could not read data. No file has been opened.\");\n\n GDALRPCInfo gdal_rpc;\n if (!GDALExtractRPCInfo(dataset->GetMetadata(\"RPC\"),\n &gdal_rpc))\n vw_throw(NotFoundErr() << \"RPCModel: GDAL resource appears not to have RPC metadata.\");\n\n // Copy information over to our data structures.\n m_lonlatheight_offset = Vector3(gdal_rpc.dfLONG_OFF,\n gdal_rpc.dfLAT_OFF,\n gdal_rpc.dfHEIGHT_OFF);\n m_lonlatheight_scale = Vector3(gdal_rpc.dfLONG_SCALE,\n gdal_rpc.dfLAT_SCALE,\n gdal_rpc.dfHEIGHT_SCALE);\n m_xy_offset = Vector2(gdal_rpc.dfSAMP_OFF, gdal_rpc.dfLINE_OFF);\n m_xy_scale = Vector2(gdal_rpc.dfSAMP_SCALE, gdal_rpc.dfLINE_SCALE);\n\n m_line_num_coeff = CoeffVec(gdal_rpc.adfLINE_NUM_COEFF);\n m_line_den_coeff = CoeffVec(gdal_rpc.adfLINE_DEN_COEFF);\n m_sample_num_coeff = CoeffVec(gdal_rpc.adfSAMP_NUM_COEFF);\n m_sample_den_coeff = CoeffVec(gdal_rpc.adfSAMP_DEN_COEFF);\n }\n\n RPCModel::RPCModel(std::string const& filename) {\n std::string ext = get_extension(filename);\n if (ext == \".rpb\") {\n load_rpb_file(filename);\n return;\n }\n\n // Must have this check, otherwise GDAL prints an error.\n if (has_image_extension(filename)) {\n boost::scoped_ptr s_ptr(new DiskImageResourceGDAL(filename));\n initialize(s_ptr.get());\n }else{\n // Throw an error. It will be caught, but it will get printed\n // only if no other approaches turn out to work later on.\n vw_throw(ArgumentErr() << \"Not an image \" << filename);\n }\n \n }\n\n RPCModel::RPCModel(DiskImageResourceGDAL* resource ) {\n initialize(resource);\n }\n\n // The constructor just copies all of the input data\n RPCModel::RPCModel(cartography::Datum const& datum,\n Vector const& line_num_coeff,\n Vector const& line_den_coeff,\n Vector const& samp_num_coeff,\n Vector const& samp_den_coeff,\n Vector2 const& xy_offset,\n Vector2 const& xy_scale,\n Vector3 const& lonlatheight_offset,\n Vector3 const& lonlatheight_scale) :\n m_datum(datum), \n m_line_num_coeff(line_num_coeff),\n m_line_den_coeff(line_den_coeff), \n m_sample_num_coeff(samp_num_coeff),\n m_sample_den_coeff(samp_den_coeff), \n m_xy_offset(xy_offset),\n m_xy_scale(xy_scale), \n m_lonlatheight_offset(lonlatheight_offset),\n m_lonlatheight_scale(lonlatheight_scale) {}\n \n\n void RPCModel::load_rpb_file(std::string const& filename) {\n //vw_out() << \"Reading RPC model from RPB file, defaulting to WGS84 datum.\\n\";\n m_datum.set_well_known_datum(\"WGS84\");\n std::ifstream f(filename.c_str());\n \n std::string line;\n std::vector tokens;\n bool lineNumCoeffs = false,\n lineDenCoeffs = false,\n sampNumCoeffs = false,\n sampDenCoeffs = false;\n int coeff_index = 0, max_coeff_index = 0;\n\n // Read through each line in the file \n while (std::getline(f, line)) {\n \n try {\n // Break up the line\n boost::split(tokens, line, boost::is_any_of(\"=,;\"));\n \n // Parse keywords\n if (line.find(\"lineOffset\") != std::string::npos)\n m_xy_offset[1] = atof(tokens[1].c_str());\n if (line.find(\"sampOffset\") != std::string::npos)\n m_xy_offset[0] = atof(tokens[1].c_str());\n if (line.find(\"latOffset\") != std::string::npos)\n m_lonlatheight_offset[1] = atof(tokens[1].c_str());\n if (line.find(\"longOffset\") != std::string::npos)\n m_lonlatheight_offset[0] = atof(tokens[1].c_str());\n if (line.find(\"heightOffset\") != std::string::npos)\n m_lonlatheight_offset[2] = atof(tokens[1].c_str());\n if (line.find(\"lineScale\") != std::string::npos)\n m_xy_scale[1] = atof(tokens[1].c_str());\n if (line.find(\"sampScale\") != std::string::npos)\n m_xy_scale[0] = atof(tokens[1].c_str()); \n if (line.find(\"latScale\") != std::string::npos)\n m_lonlatheight_scale[1] = atof(tokens[1].c_str());\n if (line.find(\"longScale\") != std::string::npos)\n m_lonlatheight_scale[0] = atof(tokens[1].c_str());\n if (line.find(\"heightScale\") != std::string::npos)\n m_lonlatheight_scale[2] = atof(tokens[1].c_str());\n\n // Handle the RPC coefficients.\n if (lineNumCoeffs) {\n m_line_num_coeff[coeff_index] = atof(tokens[0].c_str());\n ++coeff_index;\n }\n if (lineDenCoeffs) {\n m_line_den_coeff[coeff_index] = atof(tokens[0].c_str());\n ++coeff_index;\n }\n if (sampNumCoeffs) {\n m_sample_num_coeff[coeff_index] = atof(tokens[0].c_str());\n ++coeff_index;\n }\n if (sampDenCoeffs) {\n m_sample_den_coeff[coeff_index] = atof(tokens[0].c_str());\n ++coeff_index;\n }\n\n // Start of a coefficient sequence\n if (line.find(\"lineNumCoef\") != std::string::npos)\n lineNumCoeffs = true;\n if (line.find(\"lineDenCoef\") != std::string::npos)\n lineDenCoeffs = true;\n if (line.find(\"sampNumCoef\") != std::string::npos)\n sampNumCoeffs = true;\n if (line.find(\"sampDenCoef\") != std::string::npos)\n sampDenCoeffs = true; \n \n // Done reading a coefficient sequence.\n if (line.find(\")\") != std::string::npos) {\n lineNumCoeffs = false;\n lineDenCoeffs = false;\n sampNumCoeffs = false;\n sampDenCoeffs = false;\n if (coeff_index > max_coeff_index)\n max_coeff_index = coeff_index;\n coeff_index = 0;\n }\n } catch(...) {\n vw_throw(ArgumentErr() << \"Error reading file \" << filename\n << \", line = \" << line);\n }\n } // End loop through lines.\n f.close();\n // Basic error check\n if (max_coeff_index != 20)\n vw_throw(ArgumentErr() << \"Error reading file \" << filename\n << \", loaded wrong number of coefficients!\");\n }\n\n // All of these implementations are largely inspired by the GDAL\n // code. We don't use the GDAL code unfortunately because they don't\n // make that part of the API available. However I believe this is a\n // safe reinterpretation that is safe to distribute.\n Vector2 RPCModel::point_to_pixel(Vector3 const& point) const {\n return geodetic_to_pixel(m_datum.cartesian_to_geodetic(point));\n }\n\n Vector2 RPCModel::geodetic_to_pixel(Vector3 const& geodetic) const {\n\n // Should we verify that the input geodetic is in the box?\n\n Vector3 normalized_geodetic =\n elem_quot(geodetic - m_lonlatheight_offset, m_lonlatheight_scale);\n\n Vector2 normalized_pixel = normalized_geodetic_to_normalized_pixel(normalized_geodetic);\n\n return elem_prod(normalized_pixel, m_xy_scale) + m_xy_offset;\n }\n\n Vector2 RPCModel::normalized_geodetic_to_normalized_pixel\n (Vector3 const& normalized_geodetic,\n RPCModel::CoeffVec const& line_num_coeff,\n RPCModel::CoeffVec const& line_den_coeff,\n RPCModel::CoeffVec const& sample_num_coeff,\n RPCModel::CoeffVec const& sample_den_coeff){\n\n CoeffVec term = calculate_terms(normalized_geodetic);\n Vector2 normalized_pixel(dot_prod(term,sample_num_coeff) /\n dot_prod(term,sample_den_coeff),\n dot_prod(term,line_num_coeff) /\n dot_prod(term,line_den_coeff));\n\n return normalized_pixel;\n }\n\n Vector2 RPCModel::normalized_geodetic_to_normalized_pixel\n (Vector3 const& normalized_geodetic) const {\n\n return normalized_geodetic_to_normalized_pixel(normalized_geodetic,\n m_line_num_coeff,\n m_line_den_coeff,\n m_sample_num_coeff,\n m_sample_den_coeff\n );\n }\n\n RPCModel::CoeffVec RPCModel::calculate_terms(vw::Vector3 const& normalized_geodetic) {\n\n double x = normalized_geodetic.x(); // normalized lon\n double y = normalized_geodetic.y(); // normalized lat\n double z = normalized_geodetic.z(); // normalized height\n CoeffVec result;\n result[ 0] = 1.0;\n result[ 1] = x;\n result[ 2] = y;\n result[ 3] = z;\n result[ 4] = x*y;\n result[ 5] = x*z;\n result[ 6] = y*z;\n result[ 7] = x*x;\n result[ 8] = y*y;\n result[ 9] = z*z;\n result[10] = x*y*z;\n result[11] = x*x*x;\n result[12] = x*y*y;\n result[13] = x*z*z;\n result[14] = x*x*y;\n result[15] = y*y*y;\n result[16] = y*z*z;\n result[17] = x*x*z;\n result[18] = y*y*z;\n result[19] = z*z*z;\n return result;\n }\n\n vw::Vector RPCModel::get_coeff_order() {\n vw::Vector result;\n for (int i= 0; i< 3; ++i) result[i] = 1;\n for (int i= 3; i<10; ++i) result[i] = 2;\n for (int i=10; i<20; ++i) result[i] = 3;\n return result;\n }\n\n vw::Matrix RPCModel::terms_Jacobian2(vw::Vector3 const& normalized_geodetic) {\n // Partial derivatives of the terms returned by the\n // calculate_terms() function in respect to the first two\n // variables only (unlike the terms_Jacobian3() function).\n\n vw::Matrix M;\n double x = normalized_geodetic.x(); // normalized lon\n double y = normalized_geodetic.y(); // normalized lat\n double z = normalized_geodetic.z(); // normalized height\n\n // df/dx df/dy // f\n M[ 0][0] = 0.0; M[ 0][1] = 0.0; // 1\n M[ 1][0] = 1.0; M[ 1][1] = 0.0; // x\n M[ 2][0] = 0.0; M[ 2][1] = 1.0; // y\n M[ 3][0] = 0.0; M[ 3][1] = 0.0; // z\n M[ 4][0] = y; M[ 4][1] = x; // xy\n M[ 5][0] = z; M[ 5][1] = 0.0; // xz\n M[ 6][0] = 0.0; M[ 6][1] = z; // yz\n M[ 7][0] = 2.0*x; M[ 7][1] = 0.0; // xx\n M[ 8][0] = 0.0; M[ 8][1] = 2.0*y; // yy\n M[ 9][0] = 0.0; M[ 9][1] = 0.0; // zz\n M[10][0] = y*z; M[10][1] = x*z; // xyz\n M[11][0] = 3.0*x*x; M[11][1] = 0.0; // xxx\n M[12][0] = y*y; M[12][1] = 2.0*x*y; // xyy\n M[13][0] = z*z; M[13][1] = 0.0; // xzz\n M[14][0] = 2.0*x*y; M[14][1] = x*x; // xxy\n M[15][0] = 0.0; M[15][1] = 3.0*y*y; // yyy\n M[16][0] = 0.0; M[16][1] = z*z; // yzz\n M[17][0] = 2.0*x*z; M[17][1] = 0.0; // xxz\n M[18][0] = 0.0; M[18][1] = 2.0*y*z; // yyz\n M[19][0] = 0.0; M[19][1] = 0.0; // zzz\n\n return M;\n }\n\n vw::Matrix RPCModel::terms_Jacobian3(vw::Vector3 const& normalized_geodetic) {\n // Partial derivatives of the terms returned by the\n // calculate_terms() function in respect to all three\n // variables only (unlike the terms_Jacobian2() function).\n\n vw::Matrix M;\n double x = normalized_geodetic.x(); // normalized lon\n double y = normalized_geodetic.y(); // normalized lat\n double z = normalized_geodetic.z(); // normalized height\n\n // df/dx df/dy df/dz // f\n M[ 0][0] = 0.0; M[ 0][1] = 0.0; M[ 0][2] = 0.0; // 1\n M[ 1][0] = 1.0; M[ 1][1] = 0.0; M[ 1][2] = 0.0; // x\n M[ 2][0] = 0.0; M[ 2][1] = 1.0; M[ 2][2] = 0.0; // y\n M[ 3][0] = 0.0; M[ 3][1] = 0.0; M[ 3][2] = 1.0; // z\n M[ 4][0] = y; M[ 4][1] = x; M[ 4][2] = 0.0; // xy\n M[ 5][0] = z; M[ 5][1] = 0.0; M[ 5][2] = x; // xz\n M[ 6][0] = 0.0; M[ 6][1] = z; M[ 6][2] = y; // yz\n M[ 7][0] = 2.0*x; M[ 7][1] = 0.0; M[ 7][2] = 0.0; // xx\n M[ 8][0] = 0.0; M[ 8][1] = 2.0*y; M[ 8][2] = 0.0; // yy\n M[ 9][0] = 0.0; M[ 9][1] = 0.0; M[ 9][2] = 2.0*z; // zz\n M[10][0] = y*z; M[10][1] = x*z; M[10][2] = x*y; // xyz\n M[11][0] = 3.0*x*x; M[11][1] = 0.0; M[11][2] = 0.0; // xxx\n M[12][0] = y*y; M[12][1] = 2.0*x*y; M[12][2] = 0.0; // xyy\n M[13][0] = z*z; M[13][1] = 0.0; M[13][2] = 2.0*x*z; // xzz\n M[14][0] = 2.0*x*y; M[14][1] = x*x; M[14][2] = 0.0; // xxy\n M[15][0] = 0.0; M[15][1] = 3.0*y*y; M[15][2] = 0.0; // yyy\n M[16][0] = 0.0; M[16][1] = z*z; M[16][2] = 2.0*y*z; // yzz\n M[17][0] = 2.0*x*z; M[17][1] = 0.0; M[17][2] = x*x; // xxz\n M[18][0] = 0.0; M[18][1] = 2.0*y*z; M[18][2] = y*y; // yyz\n M[19][0] = 0.0; M[19][1] = 0.0; M[19][2] = 3.0*z*z; // zzz\n\n return M;\n }\n\n RPCModel::CoeffVec\n RPCModel::quotient_Jacobian(RPCModel::CoeffVec const& c,\n RPCModel::CoeffVec const& d,\n RPCModel::CoeffVec const& u) {\n\n // Return the Jacobian of dot_prod(c, u) / dot_prod(d, u)\n // as a vector with 20 elements.\n\n double cu = dot_prod(c, u);\n double du = dot_prod(d, u);\n double den = du*du;\n\n return elem_quot(du * c - cu * d, den);\n }\n\n vw::Matrix3x3 RPCModel::normalization_Jacobian(Vector3 const& q) {\n\n // Return the Jacobian of the function\n // f(x1, x2, x3) = ((x1 - c1)/q1, (x2 - c2)/q2, (x3 - c3)/q3)\n\n vw::Matrix3x3 M;\n M[0][0] = 1.0/q[0]; M[0][1] = 0.0; M[0][2] = 0.0;\n M[1][0] = 0.0; M[1][1] = 1.0/q[1]; M[1][2] = 0.0;\n M[2][0] = 0.0; M[2][1] = 0.0; M[2][2] = 1.0/q[2];\n return M;\n }\n\n Matrix RPCModel::geodetic_to_pixel_Jacobian(Vector3 const& geodetic) const {\n\n Vector3 normalized_geodetic = elem_quot(geodetic - m_lonlatheight_offset, m_lonlatheight_scale);\n\n CoeffVec term = calculate_terms(normalized_geodetic);\n\n CoeffVec Qs = quotient_Jacobian(sample_num_coeff(), sample_den_coeff(), term);\n CoeffVec Ql = quotient_Jacobian(line_num_coeff(), line_den_coeff(), term);\n Matrix MN = terms_Jacobian3(normalized_geodetic) *\n normalization_Jacobian(m_lonlatheight_scale);\n\n Matrix J;\n select_row(J, 0) = m_xy_scale[0] * transpose(Qs) * MN;\n select_row(J, 1) = m_xy_scale[1] * transpose(Ql) * MN;\n\n return J;\n }\n\n Matrix RPCModel::normalized_geodetic_to_pixel_Jacobian(Vector3 const& normalized_geodetic) const {\n\n // This function is different from geodetic_to_pixel_Jacobian() in several respects:\n\n // 1. The input is the normalized geodetic, and the derivatives\n // are in respect to the normalized geodetic as well.\n\n // 2. The derivatives are taken only in respect to the first two\n // variables (normalized lon and lat, no height).\n\n // 3. The output is in normalized pixels (see m_xy_scale and m_xy_offset).\n\n CoeffVec term = calculate_terms(normalized_geodetic);\n\n CoeffVec Qs = quotient_Jacobian(sample_num_coeff(), sample_den_coeff(), term);\n CoeffVec Ql = quotient_Jacobian(line_num_coeff(), line_den_coeff(), term);\n\n Matrix Jt = terms_Jacobian2(normalized_geodetic);\n\n Matrix J;\n select_row(J, 0) = transpose(Qs) * Jt;\n select_row(J, 1) = transpose(Ql) * Jt;\n\n return J;\n }\n\n Matrix RPCModel::geodetic_to_pixel_numerical_Jacobian(Vector3 const& geodetic, double tol) const {\n\n // Find the Jacobian of geodetic_to_pixel using numerical\n // differentiation. This is used for testing purposes.\n\n Matrix J;\n\n Vector2 B = geodetic_to_pixel(geodetic);\n\n Vector2 B0 = (geodetic_to_pixel(geodetic + Vector3(tol, 0, 0 )) - B)/tol;\n Vector2 B1 = (geodetic_to_pixel(geodetic + Vector3(0, tol, 0 )) - B)/tol;\n Vector2 B2 = (geodetic_to_pixel(geodetic + Vector3(0, 0, tol)) - B)/tol;\n\n select_col(J, 0) = B0;\n select_col(J, 1) = B1;\n select_col(J, 2) = B2;\n\n return J;\n }\n\n Vector2 RPCModel::image_to_ground(Vector2 const& pixel, double height, Vector2 lonlat_guess) const {\n\n // The absolute tolerance is experimental, needs more investigation\n double abs_tolerance = 1e-6;\n\n Vector2 normalized_pixel = elem_quot(pixel - m_xy_offset, m_xy_scale);\n\n // Initial guess for the normalized lon and lat\n if (lonlat_guess == Vector2(0.0, 0.0)){\n lonlat_guess = subvector(m_lonlatheight_offset, 0, 2);\n }\n Vector2 normalized_lonlat = elem_quot(lonlat_guess - subvector(m_lonlatheight_offset, 0, 2),\n subvector(m_lonlatheight_scale, 0, 2)\n );\n double len = norm_2(normalized_lonlat);\n if (len != len || len > 1.5){\n // If the input guess is NaN or unreasonable, use 0 as initial guess\n normalized_lonlat = Vector2(0.0, 0.0);\n }\n\n // 10 iterations should be enough for Newton's method to converge\n for (int iter = 0; iter < 10; iter++){\n\n Vector3 normalized_geodetic;\n normalized_geodetic[0] = normalized_lonlat[0];\n normalized_geodetic[1] = normalized_lonlat[1];\n normalized_geodetic[2] = (height - m_lonlatheight_offset[2])/m_lonlatheight_scale[2];\n\n Vector2 p = normalized_geodetic_to_normalized_pixel(normalized_geodetic);\n Matrix J = normalized_geodetic_to_pixel_Jacobian(normalized_geodetic);\n\n // The inverse matrix computed analytically\n double det = J[0][0]*J[1][1] - J[0][1]*J[1][0];\n Matrix invJ;\n invJ[0][0] = J[1][1];\n invJ[0][1] = -J[0][1];\n invJ[1][0] = -J[1][0];\n invJ[1][1] = J[0][0];\n invJ /= det;\n\n // Newton's method for F(x) = y is\n // x = x - J^{-1}(F(x) - y)\n Vector2 error_try = p - normalized_pixel;\n normalized_lonlat -= invJ*error_try;\n\n // Absolute error convergence criterion\n double norm_try = norm_2(error_try);\n if (norm_try < abs_tolerance) {\n break;\n }\n\n }\n\n Vector2 lonlat = elem_prod(normalized_lonlat, subvector(m_lonlatheight_scale, 0, 2))\n + subvector(m_lonlatheight_offset, 0, 2);\n\n return lonlat;\n\n }\n\n void RPCModel::point_and_dir(Vector2 const& pix, Vector3 & P, Vector3 & dir) const {\n\n // For an RPC model there is no defined origin so it and the ray need to be computed.\n\n // Center of valid region to bottom of valid region (normalized)\n const double VERT_SCALE_FACTOR = 0.9; // - The virtual center should be above the terrain\n double height_up = m_lonlatheight_offset[2] + m_lonlatheight_scale[2]*VERT_SCALE_FACTOR;\n double height_dn = m_lonlatheight_offset[2] - m_lonlatheight_scale[2]*VERT_SCALE_FACTOR;\n\n //vw_out() << \"m_lonlatheight_offset = \" << m_lonlatheight_offset << std::endl;\n //vw_out() << \"m_lonlatheight_scale = \" << m_lonlatheight_scale << std::endl;\n\n //vw_out() << \"Height up = \" << height_up << std::endl;\n //vw_out() << \"Height dn = \" << height_dn << std::endl;\n\n // Given the pixel and elevation, estimate lon-lat.\n // Use m_lonlatheight_offset as initial guess for lonlat_up,\n // and then use lonlat_up as initial guess for lonlat_dn.\n Vector2 lonlat_up = image_to_ground(pix, height_up, subvector(m_lonlatheight_offset, 0, 2));\n Vector2 lonlat_dn = image_to_ground(pix, height_dn, lonlat_up);\n\n //vw_out() << \"lonlat_up = \" << lonlat_up << std::endl;\n //vw_out() << \"lonlat_dn = \" << lonlat_dn << std::endl;\n\n Vector3 geo_up = Vector3(lonlat_up[0], lonlat_up[1], height_up);\n Vector3 geo_dn = Vector3(lonlat_dn[0], lonlat_dn[1], height_dn);\n\n //vw_out() << \"geo_up = \" << geo_up << std::endl;\n //vw_out() << \"geo_dn = \" << geo_dn << std::endl;\n\n Vector3 P_up = m_datum.geodetic_to_cartesian(geo_up);\n Vector3 P_dn = m_datum.geodetic_to_cartesian(geo_dn);\n\n dir = normalize(P_dn - P_up);\n \n // Set the origin location very far in the opposite direction of the pointing vector,\n // to put it high above the terrain.\n const double LONG_SCALE_UP = 10000; // This is a distance in meters approx from the top of the llh valid cube\n P = P_up - dir*LONG_SCALE_UP;\n }\n\n Vector3 RPCModel::camera_center(Vector2 const& pix) const{\n // Return an arbitrarily chosen point on the ray back-projected\n // through the camera from the current pixel.\n Vector3 P;\n Vector3 dir;\n point_and_dir(pix, P, dir);\n return P;\n }\n\n Vector3 RPCModel::pixel_to_vector(Vector2 const& pix) const {\n // Find the normalized direction of the ray back-projected through\n // the camera from the current pixel.\n Vector3 P;\n Vector3 dir;\n point_and_dir(pix, P, dir);\n return dir;\n }\n\n std::ostream& operator<<(std::ostream& os, const RPCModel& rpc) {\n os << \"RPC Model:\" << std::endl\n << \"Line Numerator: \" << rpc.line_num_coeff() << std::endl\n << \"Line Denominator: \" << rpc.line_den_coeff() << std::endl\n << \"Samp Numerator: \" << rpc.sample_num_coeff() << std::endl\n << \"Samp Denominator: \" << rpc.sample_den_coeff() << std::endl\n << \"XY Offset: \" << rpc.xy_offset() << std::endl\n << \"XY Scale: \" << rpc.xy_scale() << std::endl\n << \"Geodetic Offset: \" << rpc.lonlatheight_offset() << std::endl\n << \"Geodetic Scale: \" << rpc.lonlatheight_scale();\n return os;\n }\n}\n", "meta": {"hexsha": "974e186244c6cd5187839ebf06a1e144c00267de", "size": 22953, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/asp/Camera/RPCModel.cc", "max_stars_repo_name": "PicoJr/StereoPipeline", "max_stars_repo_head_hexsha": "146110a4d43ce6cb5e950297b8dca3f3b5e3f3b4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 323.0, "max_stars_repo_stars_event_min_datetime": "2015-01-10T12:34:24.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-24T03:52:22.000Z", "max_issues_repo_path": "src/asp/Camera/RPCModel.cc", "max_issues_repo_name": "PicoJr/StereoPipeline", "max_issues_repo_head_hexsha": "146110a4d43ce6cb5e950297b8dca3f3b5e3f3b4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 252.0, "max_issues_repo_issues_event_min_datetime": "2015-07-27T16:36:31.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T02:34:28.000Z", "max_forks_repo_path": "src/asp/Camera/RPCModel.cc", "max_forks_repo_name": "PicoJr/StereoPipeline", "max_forks_repo_head_hexsha": "146110a4d43ce6cb5e950297b8dca3f3b5e3f3b4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 105.0, "max_forks_repo_forks_event_min_datetime": "2015-02-28T02:37:27.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-14T09:17:30.000Z", "avg_line_length": 39.5060240964, "max_line_length": 114, "alphanum_fraction": 0.5913388228, "num_tokens": 7135, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.2295567321672122}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\n\n// Copyright (c) 2007-2014 Barend Gehrels, Amsterdam, the Netherlands.\n\n// This file was modified by Oracle on 2014-2021.\n// Modifications copyright (c) 2014-2021, Oracle and/or its affiliates.\n\n// Contributed and/or modified by Vissarion Fysikopoulos, on behalf of Oracle\n// Contributed and/or modified by Menelaos Karavelas, on behalf of Oracle\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_GEOMETRY_STRATEGIES_SPHERICAL_DISTANCE_CROSS_TRACK_HPP\n#define BOOST_GEOMETRY_STRATEGIES_SPHERICAL_DISTANCE_CROSS_TRACK_HPP\n\n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#ifdef BOOST_GEOMETRY_DEBUG_CROSS_TRACK\n# include \n#endif\n\n\nnamespace boost { namespace geometry\n{\n\nnamespace strategy { namespace distance\n{\n\n\nnamespace comparable\n{\n\n/*\n Given a spherical segment AB and a point D, we are interested in\n computing the distance of D from AB. This is usually known as the\n cross track distance.\n\n If the projection (along great circles) of the point D lies inside\n the segment AB, then the distance (cross track error) XTD is given\n by the formula (see http://williams.best.vwh.net/avform.htm#XTE):\n\n XTD = asin( sin(dist_AD) * sin(crs_AD-crs_AB) )\n\n where dist_AD is the great circle distance between the points A and\n B, and crs_AD, crs_AB is the course (bearing) between the points A,\n D and A, B, respectively.\n\n If the point D does not project inside the arc AB, then the distance\n of D from AB is the minimum of the two distances dist_AD and dist_BD.\n\n Our reference implementation for this procedure is listed below\n (this was the old Boost.Geometry implementation of the cross track distance),\n where:\n * The member variable m_strategy is the underlying haversine strategy.\n * p stands for the point D.\n * sp1 stands for the segment endpoint A.\n * sp2 stands for the segment endpoint B.\n\n ================= reference implementation -- start =================\n\n return_type d1 = m_strategy.apply(sp1, p);\n return_type d3 = m_strategy.apply(sp1, sp2);\n\n if (geometry::math::equals(d3, 0.0))\n {\n // \"Degenerate\" segment, return either d1 or d2\n return d1;\n }\n\n return_type d2 = m_strategy.apply(sp2, p);\n\n return_type crs_AD = geometry::detail::course(sp1, p);\n return_type crs_AB = geometry::detail::course(sp1, sp2);\n return_type crs_BA = crs_AB - geometry::math::pi();\n return_type crs_BD = geometry::detail::course(sp2, p);\n return_type d_crs1 = crs_AD - crs_AB;\n return_type d_crs2 = crs_BD - crs_BA;\n\n // d1, d2, d3 are in principle not needed, only the sign matters\n return_type projection1 = cos( d_crs1 ) * d1 / d3;\n return_type projection2 = cos( d_crs2 ) * d2 / d3;\n\n if (projection1 > 0.0 && projection2 > 0.0)\n {\n return_type XTD\n = radius() * math::abs( asin( sin( d1 / radius() ) * sin( d_crs1 ) ));\n\n // Return shortest distance, projected point on segment sp1-sp2\n return return_type(XTD);\n }\n else\n {\n // Return shortest distance, project either on point sp1 or sp2\n return return_type( (std::min)( d1 , d2 ) );\n }\n\n ================= reference implementation -- end =================\n\n\n Motivation\n ----------\n In what follows we develop a comparable version of the cross track\n distance strategy, that meets the following goals:\n * It is more efficient than the original cross track strategy (less\n operations and less calls to mathematical functions).\n * Distances using the comparable cross track strategy can not only\n be compared with other distances using the same strategy, but also with\n distances computed with the comparable version of the haversine strategy.\n * It can serve as the basis for the computation of the cross track distance,\n as it is more efficient to compute its comparable version and\n transform that to the actual cross track distance, rather than\n follow/use the reference implementation listed above.\n\n Major idea\n ----------\n The idea here is to use the comparable haversine strategy to compute\n the distances d1, d2 and d3 in the above listing. Once we have done\n that we need also to make sure that instead of returning XTD (as\n computed above) that we return a distance CXTD that is compatible\n with the comparable haversine distance. To achieve this CXTD must satisfy\n the relation:\n XTD = 2 * R * asin( sqrt(XTD) )\n where R is the sphere's radius.\n\n Below we perform the mathematical analysis that show how to compute CXTD.\n\n\n Mathematical analysis\n ---------------------\n Below we use the following trigonometric identities:\n sin(2 * x) = 2 * sin(x) * cos(x)\n cos(asin(x)) = sqrt(1 - x^2)\n\n Observation:\n The distance d1 needed when the projection of the point D is within the\n segment must be the true distance. However, comparable::haversine<>\n returns a comparable distance instead of the one needed.\n To remedy this, we implicitly compute what is needed. \n More precisely, we need to compute sin(true_d1):\n\n sin(true_d1) = sin(2 * asin(sqrt(d1)))\n = 2 * sin(asin(sqrt(d1)) * cos(asin(sqrt(d1)))\n = 2 * sqrt(d1) * sqrt(1-(sqrt(d1))^2)\n = 2 * sqrt(d1 - d1 * d1)\n This relation is used below.\n\n As we mentioned above the goal is to find CXTD (named \"a\" below for\n brevity) such that (\"b\" below stands for \"d1\", and \"c\" for \"d_crs1\"):\n\n 2 * R * asin(sqrt(a)) == R * asin(2 * sqrt(b-b^2) * sin(c))\n\n Analysis:\n 2 * R * asin(sqrt(a)) == R * asin(2 * sqrt(b-b^2) * sin(c))\n <=> 2 * asin(sqrt(a)) == asin(sqrt(b-b^2) * sin(c))\n <=> sin(2 * asin(sqrt(a))) == 2 * sqrt(b-b^2) * sin(c)\n <=> 2 * sin(asin(sqrt(a))) * cos(asin(sqrt(a))) == 2 * sqrt(b-b^2) * sin(c)\n <=> 2 * sqrt(a) * sqrt(1-a) == 2 * sqrt(b-b^2) * sin(c)\n <=> sqrt(a) * sqrt(1-a) == sqrt(b-b^2) * sin(c)\n <=> sqrt(a-a^2) == sqrt(b-b^2) * sin(c)\n <=> a-a^2 == (b-b^2) * (sin(c))^2\n\n Consider the quadratic equation: x^2-x+p^2 == 0,\n where p = sqrt(b-b^2) * sin(c); its discriminant is:\n d = 1 - 4 * p^2 = 1 - 4 * (b-b^2) * (sin(c))^2\n\n The two solutions are:\n a_1 = (1 - sqrt(d)) / 2\n a_2 = (1 + sqrt(d)) / 2\n\n Which one to choose?\n \"a\" refers to the distance (on the unit sphere) of D from the\n supporting great circle Circ(A,B) of the segment AB.\n The two different values for \"a\" correspond to the lengths of the two\n arcs delimited D and the points of intersection of Circ(A,B) and the\n great circle perperdicular to Circ(A,B) passing through D.\n Clearly, the value we want is the smallest among these two distances,\n hence the root we must choose is the smallest root among the two.\n\n So the answer is:\n CXTD = ( 1 - sqrt(1 - 4 * (b-b^2) * (sin(c))^2) ) / 2\n\n Therefore, in order to implement the comparable version of the cross\n track strategy we need to:\n (1) Use the comparable version of the haversine strategy instead of\n the non-comparable one.\n (2) Instead of return XTD when D projects inside the segment AB, we\n need to return CXTD, given by the following formula:\n CXTD = ( 1 - sqrt(1 - 4 * (d1-d1^2) * (sin(d_crs1))^2) ) / 2;\n\n\n Complexity Analysis\n -------------------\n In the analysis that follows we refer to the actual implementation below.\n In particular, instead of computing CXTD as above, we use the more\n efficient (operation-wise) computation of CXTD shown here:\n\n return_type sin_d_crs1 = sin(d_crs1);\n return_type d1_x_sin = d1 * sin_d_crs1;\n return_type d = d1_x_sin * (sin_d_crs1 - d1_x_sin);\n return d / (0.5 + math::sqrt(0.25 - d));\n\n Notice that instead of computing:\n 0.5 - 0.5 * sqrt(1 - 4 * d) = 0.5 - sqrt(0.25 - d)\n we use the following formula instead:\n d / (0.5 + sqrt(0.25 - d)).\n This is done for numerical robustness. The expression 0.5 - sqrt(0.25 - x)\n has large numerical errors for values of x close to 0 (if using doubles\n the error start to become large even when d is as large as 0.001).\n To remedy that, we re-write 0.5 - sqrt(0.25 - x) as:\n 0.5 - sqrt(0.25 - d)\n = (0.5 - sqrt(0.25 - d) * (0.5 - sqrt(0.25 - d)) / (0.5 + sqrt(0.25 - d)).\n The numerator is the difference of two squares:\n (0.5 - sqrt(0.25 - d) * (0.5 - sqrt(0.25 - d))\n = 0.5^2 - (sqrt(0.25 - d))^ = 0.25 - (0.25 - d) = d,\n which gives the expression we use.\n\n For the complexity analysis, we distinguish between two cases:\n (A) The distance is realized between the point D and an\n endpoint of the segment AB\n\n Gains:\n Since we are using comparable::haversine<> which is called\n 3 times, we gain:\n -> 3 calls to sqrt\n -> 3 calls to asin\n -> 6 multiplications\n\n Loses: None\n\n So the net gain is:\n -> 6 function calls (sqrt/asin)\n -> 6 arithmetic operations\n\n If we use comparable::cross_track<> to compute\n cross_track<> we need to account for a call to sqrt, a call\n to asin and 2 multiplications. In this case the net gain is:\n -> 4 function calls (sqrt/asin)\n -> 4 arithmetic operations\n\n\n (B) The distance is realized between the point D and an\n interior point of the segment AB\n\n Gains:\n Since we are using comparable::haversine<> which is called\n 3 times, we gain:\n -> 3 calls to sqrt\n -> 3 calls to asin\n -> 6 multiplications\n Also we gain the operations used to compute XTD:\n -> 2 calls to sin\n -> 1 call to asin\n -> 1 call to abs\n -> 2 multiplications\n -> 1 division\n So the total gains are:\n -> 9 calls to sqrt/sin/asin\n -> 1 call to abs\n -> 8 multiplications\n -> 1 division\n\n Loses:\n To compute a distance compatible with comparable::haversine<>\n we need to perform a few more operations, namely:\n -> 1 call to sin\n -> 1 call to sqrt\n -> 2 multiplications\n -> 1 division\n -> 1 addition\n -> 2 subtractions\n\n So roughly speaking the net gain is:\n -> 8 fewer function calls and 3 fewer arithmetic operations\n\n If we were to implement cross_track directly from the\n comparable version (much like what haversine<> does using\n comparable::haversine<>) we need additionally\n -> 2 function calls (asin/sqrt)\n -> 2 multiplications\n\n So it pays off to re-implement cross_track<> to use\n comparable::cross_track<>; in this case the net gain would be:\n -> 6 function calls\n -> 1 arithmetic operation\n\n Summary/Conclusion\n ------------------\n Following the mathematical and complexity analysis above, the\n comparable cross track strategy (as implemented below) satisfies\n all the goal mentioned in the beginning:\n * It is more efficient than its non-comparable counter-part.\n * Comparable distances using this new strategy can also be compared\n with comparable distances computed with the comparable haversine\n strategy.\n * It turns out to be more efficient to compute the actual cross\n track distance XTD by first computing CXTD, and then computing\n XTD by means of the formula:\n XTD = 2 * R * asin( sqrt(CXTD) )\n*/\n\ntemplate\n<\n typename CalculationType = void,\n typename Strategy = comparable::haversine\n>\nclass cross_track\n{\npublic:\n template \n struct return_type\n : promote_floating_point\n <\n typename select_calculation_type\n <\n Point,\n PointOfSegment,\n CalculationType\n >::type\n >\n {};\n\n typedef typename Strategy::radius_type radius_type;\n\n cross_track() = default;\n\n explicit inline cross_track(typename Strategy::radius_type const& r)\n : m_strategy(r)\n {}\n\n inline cross_track(Strategy const& s)\n : m_strategy(s)\n {}\n\n // It might be useful in the future\n // to overload constructor with strategy info.\n // crosstrack(...) {}\n\n\n template \n inline typename return_type::type\n apply(Point const& p, PointOfSegment const& sp1, PointOfSegment const& sp2) const\n {\n\n#if !defined(BOOST_MSVC)\n BOOST_CONCEPT_ASSERT\n (\n (concepts::PointDistanceStrategy)\n );\n#endif\n\n typedef typename return_type::type return_type;\n\n // http://williams.best.vwh.net/avform.htm#XTE\n return_type d1 = m_strategy.apply(sp1, p);\n return_type d3 = m_strategy.apply(sp1, sp2);\n\n if (geometry::math::equals(d3, 0.0))\n {\n // \"Degenerate\" segment, return either d1 or d2\n return d1;\n }\n\n return_type d2 = m_strategy.apply(sp2, p);\n\n return_type lon1 = geometry::get_as_radian<0>(sp1);\n return_type lat1 = geometry::get_as_radian<1>(sp1);\n return_type lon2 = geometry::get_as_radian<0>(sp2);\n return_type lat2 = geometry::get_as_radian<1>(sp2);\n return_type lon = geometry::get_as_radian<0>(p);\n return_type lat = geometry::get_as_radian<1>(p);\n\n return_type crs_AD = geometry::formula::spherical_azimuth\n (lon1, lat1, lon, lat).azimuth;\n\n geometry::formula::result_spherical result =\n geometry::formula::spherical_azimuth\n (lon1, lat1, lon2, lat2);\n return_type crs_AB = result.azimuth;\n return_type crs_BA = result.reverse_azimuth - geometry::math::pi();\n\n return_type crs_BD = geometry::formula::spherical_azimuth\n (lon2, lat2, lon, lat).azimuth;\n\n return_type d_crs1 = crs_AD - crs_AB;\n return_type d_crs2 = crs_BD - crs_BA;\n\n // d1, d2, d3 are in principle not needed, only the sign matters\n return_type projection1 = cos( d_crs1 ) * d1 / d3;\n return_type projection2 = cos( d_crs2 ) * d2 / d3;\n\n#ifdef BOOST_GEOMETRY_DEBUG_CROSS_TRACK\n std::cout << \"Course \" << dsv(sp1) << \" to \" << dsv(p) << \" \"\n << crs_AD * geometry::math::r2d() << std::endl;\n std::cout << \"Course \" << dsv(sp1) << \" to \" << dsv(sp2) << \" \"\n << crs_AB * geometry::math::r2d() << std::endl;\n std::cout << \"Course \" << dsv(sp2) << \" to \" << dsv(sp1) << \" \"\n << crs_BA * geometry::math::r2d() << std::endl;\n std::cout << \"Course \" << dsv(sp2) << \" to \" << dsv(p) << \" \"\n << crs_BD * geometry::math::r2d() << std::endl;\n std::cout << \"Projection AD-AB \" << projection1 << \" : \"\n << d_crs1 * geometry::math::r2d() << std::endl;\n std::cout << \"Projection BD-BA \" << projection2 << \" : \"\n << d_crs2 * geometry::math::r2d() << std::endl;\n std::cout << \" d1: \" << (d1 )\n << \" d2: \" << (d2 )\n << std::endl;\n#endif\n\n if (projection1 > 0.0 && projection2 > 0.0)\n {\n#ifdef BOOST_GEOMETRY_DEBUG_CROSS_TRACK\n return_type XTD = radius() * geometry::math::abs( asin( sin( d1 ) * sin( d_crs1 ) ));\n\n std::cout << \"Projection ON the segment\" << std::endl;\n std::cout << \"XTD: \" << XTD\n << \" d1: \" << (d1 * radius())\n << \" d2: \" << (d2 * radius())\n << std::endl;\n#endif\n return_type const half(0.5);\n return_type const quarter(0.25);\n\n return_type sin_d_crs1 = sin(d_crs1);\n /*\n This is the straightforward obvious way to continue:\n \n return_type discriminant\n = 1.0 - 4.0 * (d1 - d1 * d1) * sin_d_crs1 * sin_d_crs1;\n return 0.5 - 0.5 * math::sqrt(discriminant);\n \n Below we optimize the number of arithmetic operations\n and account for numerical robustness:\n */\n return_type d1_x_sin = d1 * sin_d_crs1;\n return_type d = d1_x_sin * (sin_d_crs1 - d1_x_sin);\n return d / (half + math::sqrt(quarter - d));\n }\n else\n {\n#ifdef BOOST_GEOMETRY_DEBUG_CROSS_TRACK\n std::cout << \"Projection OUTSIDE the segment\" << std::endl;\n#endif\n\n // Return shortest distance, project either on point sp1 or sp2\n return return_type( (std::min)( d1 , d2 ) );\n }\n }\n\n template \n inline radius_type vertical_or_meridian(T1 lat1, T2 lat2) const\n {\n return m_strategy.radius() * (lat1 - lat2);\n }\n\n inline typename Strategy::radius_type radius() const\n { return m_strategy.radius(); }\n\nprivate :\n Strategy m_strategy;\n};\n\n} // namespace comparable\n\n\n/*!\n\\brief Strategy functor for distance point to segment calculation\n\\ingroup strategies\n\\details Class which calculates the distance of a point to a segment, for points on a sphere or globe\n\\see http://williams.best.vwh.net/avform.htm\n\\tparam CalculationType \\tparam_calculation\n\\tparam Strategy underlying point-point distance strategy, defaults to haversine\n\n\\qbk{\n[heading See also]\n[link geometry.reference.algorithms.distance.distance_3_with_strategy distance (with strategy)]\n}\n\n*/\ntemplate\n<\n typename CalculationType = void,\n typename Strategy = haversine\n>\nclass cross_track\n{\npublic :\n typedef within::spherical_point_point equals_point_point_strategy_type;\n\n typedef intersection::spherical_segments\n <\n CalculationType\n > relate_segment_segment_strategy_type;\n\n static inline relate_segment_segment_strategy_type get_relate_segment_segment_strategy()\n {\n return relate_segment_segment_strategy_type();\n }\n\n typedef within::spherical_winding\n <\n void, void, CalculationType\n > point_in_geometry_strategy_type;\n\n static inline point_in_geometry_strategy_type get_point_in_geometry_strategy()\n {\n return point_in_geometry_strategy_type();\n }\n\n template \n struct return_type\n : promote_floating_point\n <\n typename select_calculation_type\n <\n Point,\n PointOfSegment,\n CalculationType\n >::type\n >\n {};\n\n typedef typename Strategy::radius_type radius_type;\n\n inline cross_track()\n {}\n\n explicit inline cross_track(typename Strategy::radius_type const& r)\n : m_strategy(r)\n {}\n\n inline cross_track(Strategy const& s)\n : m_strategy(s)\n {}\n\n // It might be useful in the future\n // to overload constructor with strategy info.\n // crosstrack(...) {}\n\n\n template \n inline typename return_type::type\n apply(Point const& p, PointOfSegment const& sp1, PointOfSegment const& sp2) const\n {\n\n#if !defined(BOOST_MSVC)\n BOOST_CONCEPT_ASSERT\n (\n (concepts::PointDistanceStrategy)\n );\n#endif\n typedef typename return_type::type return_type;\n typedef cross_track this_type;\n\n typedef typename services::comparable_type\n <\n this_type\n >::type comparable_type;\n\n comparable_type cstrategy\n = services::get_comparable::apply(m_strategy);\n\n return_type const a = cstrategy.apply(p, sp1, sp2);\n return_type const c = return_type(2.0) * asin(math::sqrt(a));\n return c * radius();\n }\n\n template \n inline radius_type vertical_or_meridian(T1 lat1, T2 lat2) const\n {\n return m_strategy.radius() * (lat1 - lat2);\n }\n\n inline typename Strategy::radius_type radius() const\n { return m_strategy.radius(); }\n\nprivate :\n\n Strategy m_strategy;\n};\n\n\n\n#ifndef DOXYGEN_NO_STRATEGY_SPECIALIZATIONS\nnamespace services\n{\n\ntemplate \nstruct tag >\n{\n typedef strategy_tag_distance_point_segment type;\n};\n\n\ntemplate \nstruct return_type, P, PS>\n : cross_track::template return_type\n{};\n\n\ntemplate \nstruct comparable_type >\n{\n typedef comparable::cross_track\n <\n CalculationType, typename comparable_type::type\n > type;\n};\n\n\ntemplate\n<\n typename CalculationType,\n typename Strategy\n>\nstruct get_comparable >\n{\n typedef typename comparable_type\n <\n cross_track\n >::type comparable_type;\npublic :\n static inline comparable_type\n apply(cross_track const& strategy)\n {\n return comparable_type(strategy.radius());\n }\n};\n\n\ntemplate\n<\n typename CalculationType,\n typename Strategy,\n typename P,\n typename PS\n>\nstruct result_from_distance, P, PS>\n{\nprivate :\n typedef typename cross_track\n <\n CalculationType, Strategy\n >::template return_type::type return_type;\npublic :\n template \n static inline return_type\n apply(cross_track const& , T const& distance)\n {\n return distance;\n }\n};\n\n\n// Specializations for comparable::cross_track\ntemplate \nstruct tag >\n{\n typedef strategy_tag_distance_point_segment type;\n};\n\n\ntemplate\n<\n typename RadiusType,\n typename CalculationType,\n typename P,\n typename PS\n>\nstruct return_type, P, PS>\n : comparable::cross_track\n <\n RadiusType, CalculationType\n >::template return_type\n{};\n\n\ntemplate \nstruct comparable_type >\n{\n typedef comparable::cross_track type;\n};\n\n\ntemplate \nstruct get_comparable >\n{\nprivate :\n typedef comparable::cross_track this_type;\npublic :\n static inline this_type apply(this_type const& input)\n {\n return input;\n }\n};\n\n\ntemplate\n<\n typename RadiusType,\n typename CalculationType,\n typename P,\n typename PS\n>\nstruct result_from_distance\n <\n comparable::cross_track, P, PS\n >\n{\nprivate :\n typedef comparable::cross_track strategy_type;\n typedef typename return_type::type return_type;\npublic :\n template \n static inline return_type apply(strategy_type const& strategy,\n T const& distance)\n {\n return_type const s\n = sin( (distance / strategy.radius()) / return_type(2.0) );\n return s * s;\n }\n};\n\n\n\n/*\n\nTODO: spherical polar coordinate system requires \"get_as_radian_equatorial<>\"\n\ntemplate \nstruct default_strategy\n <\n segment_tag, Point, PointOfSegment,\n spherical_polar_tag, spherical_polar_tag,\n Strategy\n >\n{\n typedef cross_track\n <\n void,\n std::conditional_t\n <\n std::is_void::value,\n typename default_strategy\n <\n point_tag, Point, PointOfSegment,\n spherical_polar_tag, spherical_polar_tag\n >::type,\n Strategy\n >\n > type;\n};\n*/\n\ntemplate \nstruct default_strategy\n <\n point_tag, segment_tag, Point, PointOfSegment,\n spherical_equatorial_tag, spherical_equatorial_tag,\n Strategy\n >\n{\n typedef cross_track\n <\n void,\n std::conditional_t\n <\n std::is_void::value,\n typename default_strategy\n <\n point_tag, point_tag, Point, PointOfSegment,\n spherical_equatorial_tag, spherical_equatorial_tag\n >::type,\n Strategy\n >\n > type;\n};\n\n\ntemplate \nstruct default_strategy\n <\n segment_tag, point_tag, PointOfSegment, Point,\n spherical_equatorial_tag, spherical_equatorial_tag,\n Strategy\n >\n{\n typedef typename default_strategy\n <\n point_tag, segment_tag, Point, PointOfSegment,\n spherical_equatorial_tag, spherical_equatorial_tag,\n Strategy\n >::type type;\n};\n\n\n} // namespace services\n#endif // DOXYGEN_NO_STRATEGY_SPECIALIZATIONS\n\n}} // namespace strategy::distance\n\n}} // namespace boost::geometry\n\n#endif // BOOST_GEOMETRY_STRATEGIES_SPHERICAL_DISTANCE_CROSS_TRACK_HPP\n", 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YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.39233683016710835, "lm_q1q2_score": 0.22955673216721217}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifndef IIWA_14_FK_FAST_HPP\n#define IIWA_14_FK_FAST_HPP\n\nnamespace IIWA_14_FK_FAST\n{\n const size_t IIWA_14_NUM_ACTIVE_JOINTS = 7;\n const size_t IIWA_14_NUM_LINKS = 8;\n\n const std::string IIWA_14_ACTIVE_JOINT_1_NAME = \"iiwa_joint_1\";\n const std::string IIWA_14_ACTIVE_JOINT_2_NAME = \"iiwa_joint_2\";\n const std::string IIWA_14_ACTIVE_JOINT_3_NAME = \"iiwa_joint_3\";\n const std::string IIWA_14_ACTIVE_JOINT_4_NAME = \"iiwa_joint_4\";\n const std::string IIWA_14_ACTIVE_JOINT_5_NAME = \"iiwa_joint_5\";\n const std::string IIWA_14_ACTIVE_JOINT_6_NAME = \"iiwa_joint_6\";\n const std::string IIWA_14_ACTIVE_JOINT_7_NAME = \"iiwa_joint_7\";\n\n const std::string IIWA_14_LINK_1_NAME = \"iiwa_link_0\";\n const std::string IIWA_14_LINK_2_NAME = \"iiwa_link_1\";\n const std::string IIWA_14_LINK_3_NAME = \"iiwa_link_2\";\n const std::string IIWA_14_LINK_4_NAME = \"iiwa_link_3\";\n const std::string IIWA_14_LINK_5_NAME = \"iiwa_link_4\";\n const std::string IIWA_14_LINK_6_NAME = \"iiwa_link_5\";\n const std::string IIWA_14_LINK_7_NAME = \"iiwa_link_6\";\n const std::string IIWA_14_LINK_8_NAME = \"iiwa_link_7\";\n\n inline Eigen::Isometry3d Get_link_0_joint_1_LinkJointTransform(const double joint_val)\n {\n const Eigen::Translation3d pre_joint_translation(0.0, 0.0, 0.1575);\n const Eigen::Quaterniond pre_joint_rotation = EigenHelpers::QuaternionFromUrdfRPY(0.0, 0.0, 0.0);\n const Eigen::Isometry3d pre_joint_transform = pre_joint_translation * pre_joint_rotation;\n const Eigen::Translation3d joint_translation(0.0, 0.0, 0.0);\n const Eigen::Quaterniond joint_rotation(Eigen::AngleAxisd(joint_val, Eigen::Vector3d::UnitZ()));\n const Eigen::Isometry3d joint_transform = joint_translation * joint_rotation;\n return (pre_joint_transform * joint_transform);\n }\n\n inline Eigen::Isometry3d Get_link_1_joint_2_LinkJointTransform(const double joint_val)\n {\n const Eigen::Translation3d pre_joint_translation(0.0, 0.0, 0.2025);\n const Eigen::Quaterniond pre_joint_rotation = EigenHelpers::QuaternionFromUrdfRPY(M_PI_2, 0.0, M_PI);\n const Eigen::Isometry3d pre_joint_transform = pre_joint_translation * pre_joint_rotation;\n const Eigen::Translation3d joint_translation(0.0, 0.0, 0.0);\n const Eigen::Quaterniond joint_rotation(Eigen::AngleAxisd(joint_val, Eigen::Vector3d::UnitZ()));\n const Eigen::Isometry3d joint_transform = joint_translation * joint_rotation;\n return (pre_joint_transform * joint_transform);\n }\n\n inline Eigen::Isometry3d Get_link_2_joint_3_LinkJointTransform(const double joint_val)\n {\n const Eigen::Translation3d pre_joint_translation(0.0, 0.2045, 0.0);\n const Eigen::Quaterniond pre_joint_rotation = EigenHelpers::QuaternionFromUrdfRPY(M_PI_2, 0.0, M_PI);\n const Eigen::Isometry3d pre_joint_transform = pre_joint_translation * pre_joint_rotation;\n const Eigen::Translation3d joint_translation(0.0, 0.0, 0.0);\n const Eigen::Quaterniond joint_rotation(Eigen::AngleAxisd(joint_val, Eigen::Vector3d::UnitZ()));\n const Eigen::Isometry3d joint_transform = joint_translation * joint_rotation;\n return (pre_joint_transform * joint_transform);\n }\n\n inline Eigen::Isometry3d Get_link_3_joint_4_LinkJointTransform(const double joint_val)\n {\n const Eigen::Translation3d pre_joint_translation(0.0, 0.0, 0.2155);\n const Eigen::Quaterniond pre_joint_rotation = EigenHelpers::QuaternionFromUrdfRPY(M_PI_2, 0.0, 0.0);\n const Eigen::Isometry3d pre_joint_transform = pre_joint_translation * pre_joint_rotation;\n const Eigen::Translation3d joint_translation(0.0, 0.0, 0.0);\n const Eigen::Quaterniond joint_rotation(Eigen::AngleAxisd(joint_val, Eigen::Vector3d::UnitZ()));\n const Eigen::Isometry3d joint_transform = joint_translation * joint_rotation;\n return (pre_joint_transform * joint_transform);\n }\n\n inline Eigen::Isometry3d Get_link_4_joint_5_LinkJointTransform(const double joint_val)\n {\n const Eigen::Translation3d pre_joint_translation(0.0, 0.1845, 0.0);\n const Eigen::Quaterniond pre_joint_rotation = EigenHelpers::QuaternionFromUrdfRPY(-M_PI_2, M_PI, 0.0);\n const Eigen::Isometry3d pre_joint_transform = pre_joint_translation * pre_joint_rotation;\n const Eigen::Translation3d joint_translation(0.0, 0.0, 0.0);\n const Eigen::Quaterniond joint_rotation(Eigen::AngleAxisd(joint_val, Eigen::Vector3d::UnitZ()));\n const Eigen::Isometry3d joint_transform = joint_translation * joint_rotation;\n return (pre_joint_transform * joint_transform);\n }\n\n inline Eigen::Isometry3d Get_link_5_joint_6_LinkJointTransform(const double joint_val)\n {\n const Eigen::Translation3d pre_joint_translation(0.0, 0.0, 0.2155);\n const Eigen::Quaterniond pre_joint_rotation = EigenHelpers::QuaternionFromUrdfRPY(M_PI_2, 0.0, 0.0);\n const Eigen::Isometry3d pre_joint_transform = pre_joint_translation * pre_joint_rotation;\n const Eigen::Translation3d joint_translation(0.0, 0.0, 0.0);\n const Eigen::Quaterniond joint_rotation(Eigen::AngleAxisd(joint_val, Eigen::Vector3d::UnitZ()));\n const Eigen::Isometry3d joint_transform = joint_translation * joint_rotation;\n return (pre_joint_transform * joint_transform);\n }\n\n inline Eigen::Isometry3d Get_link_6_joint_7_LinkJointTransform(const double joint_val)\n {\n const Eigen::Translation3d pre_joint_translation(0.0, 0.081, 0.0);\n const Eigen::Quaterniond pre_joint_rotation = EigenHelpers::QuaternionFromUrdfRPY(-M_PI_2, M_PI, 0.0);\n const Eigen::Isometry3d pre_joint_transform = pre_joint_translation * pre_joint_rotation;\n const Eigen::Translation3d joint_translation(0.0, 0.0, 0.0);\n const Eigen::Quaterniond joint_rotation(Eigen::AngleAxisd(joint_val, Eigen::Vector3d::UnitZ()));\n const Eigen::Isometry3d joint_transform = joint_translation * joint_rotation;\n return (pre_joint_transform * joint_transform);\n }\n\n inline EigenHelpers::VectorIsometry3d GetLinkTransforms(const std::vector& configuration, const Eigen::Isometry3d& base_transform=Eigen::Isometry3d::Identity())\n {\n assert(configuration.size() == IIWA_14_NUM_ACTIVE_JOINTS);\n EigenHelpers::VectorIsometry3d link_transforms(IIWA_14_NUM_LINKS);\n link_transforms[0] = base_transform;\n link_transforms[1] = link_transforms[0] * Get_link_0_joint_1_LinkJointTransform(configuration[0]);\n link_transforms[2] = link_transforms[1] * Get_link_1_joint_2_LinkJointTransform(configuration[1]);\n link_transforms[3] = link_transforms[2] * Get_link_2_joint_3_LinkJointTransform(configuration[2]);\n link_transforms[4] = link_transforms[3] * Get_link_3_joint_4_LinkJointTransform(configuration[3]);\n link_transforms[5] = link_transforms[4] * Get_link_4_joint_5_LinkJointTransform(configuration[4]);\n link_transforms[6] = link_transforms[5] * Get_link_5_joint_6_LinkJointTransform(configuration[5]);\n link_transforms[7] = link_transforms[6] * Get_link_6_joint_7_LinkJointTransform(configuration[6]);\n return link_transforms;\n }\n\n inline EigenHelpers::VectorIsometry3d GetLinkTransforms(const std::map& configuration, const Eigen::Isometry3d& base_transform=Eigen::Isometry3d::Identity())\n {\n std::vector configuration_vector(IIWA_14_NUM_ACTIVE_JOINTS);\n configuration_vector[0] = arc_helpers::RetrieveOrDefault(configuration, IIWA_14_ACTIVE_JOINT_1_NAME, 0.0);\n configuration_vector[1] = arc_helpers::RetrieveOrDefault(configuration, IIWA_14_ACTIVE_JOINT_2_NAME, 0.0);\n configuration_vector[2] = arc_helpers::RetrieveOrDefault(configuration, IIWA_14_ACTIVE_JOINT_3_NAME, 0.0);\n configuration_vector[3] = arc_helpers::RetrieveOrDefault(configuration, IIWA_14_ACTIVE_JOINT_4_NAME, 0.0);\n configuration_vector[4] = arc_helpers::RetrieveOrDefault(configuration, IIWA_14_ACTIVE_JOINT_5_NAME, 0.0);\n configuration_vector[5] = arc_helpers::RetrieveOrDefault(configuration, IIWA_14_ACTIVE_JOINT_6_NAME, 0.0);\n configuration_vector[6] = arc_helpers::RetrieveOrDefault(configuration, IIWA_14_ACTIVE_JOINT_7_NAME, 0.0);\n return GetLinkTransforms(configuration_vector, base_transform);\n }\n\n inline EigenHelpers::MapStringIsometry3d GetLinkTransformsMap(const std::vector& configuration, const Eigen::Isometry3d& base_transform=Eigen::Isometry3d::Identity())\n {\n const EigenHelpers::VectorIsometry3d link_transforms = GetLinkTransforms(configuration, base_transform);\n EigenHelpers::MapStringIsometry3d link_transforms_map;\n link_transforms_map[IIWA_14_LINK_1_NAME] = link_transforms[0];\n link_transforms_map[IIWA_14_LINK_2_NAME] = link_transforms[1];\n link_transforms_map[IIWA_14_LINK_3_NAME] = link_transforms[2];\n link_transforms_map[IIWA_14_LINK_4_NAME] = link_transforms[3];\n link_transforms_map[IIWA_14_LINK_5_NAME] = link_transforms[4];\n link_transforms_map[IIWA_14_LINK_6_NAME] = link_transforms[5];\n link_transforms_map[IIWA_14_LINK_7_NAME] = link_transforms[6];\n link_transforms_map[IIWA_14_LINK_8_NAME] = link_transforms[7];\n return link_transforms_map;\n }\n\n inline EigenHelpers::MapStringIsometry3d GetLinkTransformsMap(const std::map& configuration, const Eigen::Isometry3d& base_transform=Eigen::Isometry3d::Identity())\n {\n const EigenHelpers::VectorIsometry3d link_transforms = GetLinkTransforms(configuration, base_transform);\n EigenHelpers::MapStringIsometry3d link_transforms_map;\n link_transforms_map[IIWA_14_LINK_1_NAME] = link_transforms[0];\n link_transforms_map[IIWA_14_LINK_2_NAME] = link_transforms[1];\n link_transforms_map[IIWA_14_LINK_3_NAME] = link_transforms[2];\n link_transforms_map[IIWA_14_LINK_4_NAME] = link_transforms[3];\n link_transforms_map[IIWA_14_LINK_5_NAME] = link_transforms[4];\n link_transforms_map[IIWA_14_LINK_6_NAME] = link_transforms[5];\n link_transforms_map[IIWA_14_LINK_7_NAME] = link_transforms[6];\n link_transforms_map[IIWA_14_LINK_8_NAME] = link_transforms[7];\n return link_transforms_map;\n }\n}\n\n#endif // IIWA_14_FK_FAST_HPP\n", "meta": 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"max_issues_repo_issues_event_max_datetime": "2022-03-31T11:57:40.000Z", "max_forks_repo_path": "Modules/planning/FastPlanner/plan_env/ThirdParty/arc_utilities/include/arc_utilities/iiwa_14_fk_fast.hpp", "max_forks_repo_name": "473867143/Prometheus", "max_forks_repo_head_hexsha": "df1e1b0d861490223ac8b94d8cc4796537172292", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 270.0, "max_forks_repo_forks_event_min_datetime": "2020-07-02T13:28:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T05:43:08.000Z", "avg_line_length": 61.2674418605, "max_line_length": 188, "alphanum_fraction": 0.755551338, "num_tokens": 2874, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6859494678483918, "lm_q2_score": 0.33458944788835565, "lm_q1q2_score": 0.22951145372670478}} {"text": "#include \"Common/Common.h\"\n#include \"GL/glew.h\"\n#include \"Demos/Visualization/MiniGL.h\"\n#include \"Demos/Visualization/Selection.h\"\n#include \"GL/glut.h\"\n#include \"Demos/Simulation/TimeManager.h\"\n#include \n#include \"FluidModel.h\"\n#include \"TimeStepFluidModel.h\"\n#include \n#include \"Demos/Utils/Logger.h\"\n#include \"Demos/Utils/Timing.h\"\n#include \"Demos/Utils/FileSystem.h\"\n\n#define _USE_MATH_DEFINES\n#include \"math.h\"\n#include \"Demos/Simulation/Constraints.h\"\n#include \"Demos/Visualization/Visualization.h\"\n\n\n// Enable memory leak detection\n#if defined(_DEBUG) && !defined(EIGEN_ALIGN)\n\t#define new DEBUG_NEW \n#endif\n\nINIT_TIMING\nINIT_LOGGING\n\nusing namespace PBD;\nusing namespace Eigen;\nusing namespace std;\n\nvoid timeStep ();\nvoid buildModel ();\nvoid renderWorld();\nvoid createBreakingDam();\nvoid addWall(const Vector3r &minX, const Vector3r &maxX, std::vector &boundaryParticles, std::vector &boundaryActives,bool active);\nvoid initBoundaryData(std::vector &boundaryParticles, std::vector &boundaryActives);\nvoid render ();\nvoid cleanup();\nvoid reset();\nvoid selection(const Eigen::Vector2i &start, const Eigen::Vector2i &end);\nvoid createSphereBuffers(Real radius, int resolution);\nvoid renderSphere(const Vector3r &x, const float color[]);\nvoid releaseSphereBuffers();\nvoid TW_CALL setTimeStep(const void *value, void *clientData);\nvoid TW_CALL getTimeStep(void *value, void *clientData);\nvoid TW_CALL setVelocityUpdateMethod(const void *value, void *clientData);\nvoid TW_CALL getVelocityUpdateMethod(void *value, void *clientData);\nvoid TW_CALL setTempUpdateMethod(const void *value, void *clientData);\nvoid TW_CALL getTempUpdateMethod(void *value, void *clientData);\nvoid TW_CALL setViscosity(const void *value, void *clientData);\nvoid TW_CALL getViscosity(void *value, void *clientData);\nvoid TW_CALL setStiffness(const void *value, void *clientData);\nvoid TW_CALL getStiffness(void *value, void *clientData);\nvoid TW_CALL setDilation(const void *value, void *clientData);\nvoid TW_CALL getDilation(void *value, void *clientData);\nvoid TW_CALL setMelting(const void *value, void *clientData);\nvoid TW_CALL getMelting(void *value, void *clientData);\nvoid TW_CALL setEvaporation(const void *value, void *clientData);\nvoid TW_CALL getEvaporation(void *value, void *clientData);\nvoid TW_CALL setDifusion(const void *value, void *clientData);\nvoid TW_CALL getDifusion(void *value, void *clientData);\nvoid TW_CALL getTempContact(void *value, void *clientData);\nvoid TW_CALL setTempContact(const void *value, void *clientData);\nvoid TW_CALL getContact(void *value, void *clientData);\nvoid TW_CALL setContact(const void *value, void *clientData);\nvoid TW_CALL getXPBD(void *value, void *clientData);\nvoid TW_CALL setXPBD(const void *value, void *clientData);\n\n\nvoid TW_CALL getRadioSol(void *value, void *clientData);\nvoid TW_CALL setRadioSol(const void *value, void *clientData);\nvoid TW_CALL getNumConstraint(void *value, void *clientData);\nvoid TW_CALL setNumConstraint(const void *value, void *clientData);\n\nvoid TW_CALL getDesWidth(void *value, void *clientData);\nvoid TW_CALL setDesWidth(const void *value, void *clientData);\n\nvoid TW_CALL getDesHeight(void *value, void *clientData);\nvoid TW_CALL setDesHeight(const void *value, void *clientData);\n//Core\nFluidModel model;\nTimeStepFluidModel simulation;\n\nReal compact = 0.98;// 0.928;\n\n//const Real particleRadius = 0.025;\nconst Real particleRadius = 0.025;//0.015625//;\nint width = 30;\nint depth = 30;\nint height = 80;\nbool doPause = true;\nbool thermal = true;\nbool record = false;\nbool capture = false;\nbool rendering = true;\nbool renderingWall = false;\nbool renderingAir = false;\n\n\nstd::vector selectedParticles;\nVector3r oldMousePos;\n// initiate buffers\nGLuint elementbuffer;\nGLuint normalbuffer;\nGLuint vertexbuffer;\nint vertexBufferSize = 0;\nGLint context_major_version, context_minor_version;\nstring exePath;\nstring dataPath;\n\nGLuint textureFloor;\nofstream file;\n\n\n// main \nint main( int argc, char **argv )\n{\n\tREPORT_MEMORY_LEAKS\n\n\tstd::string logPath = FileSystem::normalizePath(FileSystem::getProgramPath() + \"/log\");\n\tFileSystem::makeDirs(logPath);\n\tlogger.addSink(unique_ptr(new ConsoleSink(LogLevel::INFO)));\n\tlogger.addSink(unique_ptr(new FileSink(LogLevel::DEBUG, logPath + \"/PBD.log\")));\n\n\texePath = FileSystem::getProgramPath();\n\tdataPath = exePath + \"/\" + std::string(PBD_DATA_PATH);\n\n\t// OpenGL\n\tMiniGL::init (argc, argv, 1280+620, 780, 0, 0, \"Fluid demo\");\n\n\tMiniGL::setClientIdleFunc (60, timeStep);\n\t\n\tMiniGL::initLights ();\n\tMiniGL::loadTexture(textureFloor);\n\n\tMiniGL::setKeyFunc(0, 'r', reset);\n\t//MiniGL::setKeyFunc(1, '1', demo1);\n\t//MiniGL::setKeyFunc(2, '2', demo2);\n\tMiniGL::setSelectionFunc(selection);\n\n\t//MiniGL::desWidth = 350;\n\t//MiniGL::desHeight = 100;\n\t\n\tMiniGL::getOpenGLVersion(context_major_version, context_minor_version);\n\tMiniGL::setClientSceneFunc(render);\t\t\t\n\t\n\t//MiniGL::setViewport (40.0, 0.1f, 500.0, Vector3r (0.0, 3.0, 8.0), Vector3r (0.0, 0.0, 0.0));\n\tMiniGL::setViewport(40.0, 0.1f, 800.0, Vector3r(0.0, 2.1, 7.0), Vector3r(0.0, 0.0, 0.0));\n\n\tTwAddVarRW(MiniGL::getTweakBar(), \"Pause\", TW_TYPE_BOOLCPP, &doPause, \" label='Pause' group=Simulation key=SPACE \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"TimeStepSize\", TW_TYPE_REAL, setTimeStep, getTimeStep, &model, \" label='Time step size' min=0.0 max = 0.1 step=0.0001 precision=4 group=Simulation \");\n\tTwType enumType = TwDefineEnum(\"VelocityUpdateMethodType\", NULL, 0);\n\tTwAddVarCB(MiniGL::getTweakBar(), \"VelocityUpdateMethod\", enumType, setVelocityUpdateMethod, getVelocityUpdateMethod, &simulation, \" label='Velocity update method' enum='0 {First Order Update}, 1 {Second Order Update}' group=Simulation\");\n\n\tTwAddVarCB(MiniGL::getTweakBar(), \"XPBD\", TW_TYPE_BOOL32, setXPBD, getXPBD, &model, \" label='XPBD' group=Simulation \");\n\t//TwAddVarRW(MiniGL::getTweakBar(), \"XPDB\", TW_TYPE_BOOLCPP, &XPBD, \" label='XPBD' group=Simulation key=x \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Viscosity\", TW_TYPE_REAL, setViscosity, getViscosity, &model, \" label='Viscosity' min=0.0 max = 5 step=0.001 precision=4 group=Simulation \");\n\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Radio Solidification\", TW_TYPE_REAL, setRadioSol, getRadioSol, &model, \" label='Radio Solidification' min=0.045 max = 0.1 step=0.01 precision=4 group=Simulation \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Num Constraints\", TW_TYPE_REAL, setNumConstraint, getNumConstraint, &model, \" label='Num Constraints' min=1 max = 10 step=1 precision=4 group=Simulation \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Stiffness\", TW_TYPE_REAL, setStiffness, getStiffness, &model, \" label='Stiffness' min=0.01 max = 1 step=0.01 precision=4 group=Simulation \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Coef Dilation\", TW_TYPE_REAL, setDilation, getDilation, &model, \" label='Coef Dilation' min=0 max = 0.01 step=0.001 precision=4 group=Simulation \");\n\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Temp Melting\", TW_TYPE_REAL, setMelting, getMelting, &model, \" label='Temp Melting' min=0.0 max = 300 step=1.0 precision=4 group=Simulation \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Temp Evaporation\", TW_TYPE_REAL, setEvaporation, getEvaporation, &model, \" label='Temp Evaporation' min=1 max = 500.0 step=1.0 precision=4 group=Simulation \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Coef Difusion\", TW_TYPE_REAL, setDifusion, getDifusion, &model, \" label='Coef Difusion' min=1 max = 1000 step=5 precision=4 group=Simulation \");\n\n\tTwType enumType2 = TwDefineEnum(\"TemperatureUpdateMethodType\", NULL, 0);\n\tTwAddVarCB(MiniGL::getTweakBar(), \"TemperatureUpdateMethod\", enumType2, setTempUpdateMethod, getTempUpdateMethod, &model, \" label='Heat Transfer method' enum='0 {Our Method}, 1 {Cleary Method}' group=Simulation\");\n\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Contact\", TW_TYPE_BOOL32, setContact, getContact, &model, \" label='Contact' group=Simulation \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Temp Contact\", TW_TYPE_REAL, setTempContact, getTempContact, &model, \" label='Temp Contact' min=-50 max = 500 step=10 precision=4 group=Simulation \");\n\n\tTwAddVarRW(MiniGL::getTweakBar(), \"Thermal\", TW_TYPE_BOOLCPP, &thermal, \" label='Thermal' group=Simulation key=t \");\n\n\tTwAddVarRW(MiniGL::getTweakBar(), \"start\", TW_TYPE_BOOLCPP, &record, \" label='start' group=Record key=g \");\n\tTwAddVarRW(MiniGL::getTweakBar(), \"capture\", TW_TYPE_BOOLCPP, &capture, \" label='capture' group=Record key=c \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Des Width\", TW_TYPE_INT32, setDesWidth, getDesWidth, &model, \" label='Des Width' min=0.0 max = 900 step=1.0 group=Record \");\n\tTwAddVarCB(MiniGL::getTweakBar(), \"Des Height\", TW_TYPE_INT32, setDesHeight, getDesHeight, &model, \" label='Des Height' min=0.0 max = 900 step=1.0 group=Record \");\n\tTwAddVarRW(MiniGL::getTweakBar(), \"rendering\", TW_TYPE_BOOLCPP, &rendering, \" label='rendering' group=Record key=n \");\n\tTwAddVarRW(MiniGL::getTweakBar(), \"renderingWall\", TW_TYPE_BOOLCPP, &renderingWall, \" label='renderingWall' group=Record key=m \");\n\tTwAddVarRW(MiniGL::getTweakBar(), \"renderingAir\", TW_TYPE_BOOLCPP, &renderingAir, \" label='renderingAir' group=Record key=i \");\n\n\t\n\tbuildModel();\n\n\tif (context_major_version >= 3)\n\t\tcreateSphereBuffers((Real)particleRadius, 4);\n\t\t//createSphereBuffers((Real)particleRadius, 25);\n\n\tglutMainLoop ();\t\n\n\tcleanup ();\n\tfile.close();\n\n\t//Timing::printAverageTimes();\n\t\n\treturn 0;\n}\n\nvoid cleanup()\n{\n\tdelete TimeManager::getCurrent();\n\tif (context_major_version >= 3)\n\t\treleaseSphereBuffers();\n}\n\nint test = 0;\nint index = 0;\nint frameIndex = 0;\n\nint numFrames = 0;\nint totalFrames = 0;\nint numFile = 0;\nint seconds = 10;\nint totalFrame = 64 * seconds;\n\nint timeEmision = 0;\nint numberFiles = 10;\nint firtTimeAugmented = 0;\nint tempAugmented = 0;\nbool addWallTop = true;\nbool temperatureFloor = true;\n\nint numParticlesAir = 0;\nvoid reset()\n{\n\tTiming::printAverageTimes();\n\tTiming::reset();\n\n\tmodel.reset();\n\tsimulation.reset();\n\tTimeManager::getCurrent()->setTime(0.0);\n\n\ttest++;\n\tindex = 0;\n\tnumFile = 0;\n\tnumFrames = 0;\n\tframeIndex=0;\n\tcapture = false;\n\trecord = false;\n\tdoPause = true;\n}\n\nvoid mouseMove(int x, int y)\n{\n\tVector3r mousePos;\n\tMiniGL::unproject(x, y, mousePos);\n\tconst Vector3r diff = mousePos - oldMousePos;\n\n\tTimeManager *tm = TimeManager::getCurrent();\n\tconst Real h = tm->getTimeStepSize();\n\n\tParticleData &pd = model.getParticles();\n\tfor (unsigned int j = 0; j < selectedParticles.size(); j++)\n\t{\n\t\tpd.getVelocity(selectedParticles[j]) += 5.0*diff/h;\n\t}\n\toldMousePos = mousePos;\n}\n\nvoid selection(const Eigen::Vector2i &start, const Eigen::Vector2i &end)\n{\n\tstd::vector hits;\n\tselectedParticles.clear();\n\tParticleData &pd = model.getParticles();\n\tSelection::selectRect(start, end, &pd.getPosition(0), &pd.getPosition(pd.size() - 1), selectedParticles);\n\tif (selectedParticles.size() > 0)\n\t\tMiniGL::setMouseMoveFunc(GLUT_MIDDLE_BUTTON, mouseMove);\n\telse\n\t\tMiniGL::setMouseMoveFunc(-1, NULL);\n\n\tMiniGL::unproject(end[0], end[1], oldMousePos);\n}\n\nstring nameFile = \"condensation\";\n\n//*\nvoid initData(int numParticles) {\n\n\tchar bufPath[255];\n\tsprintf(bufPath, \"/%s%d.txt\",nameFile ,numFile++);\n\tfile = ofstream(dataPath + bufPath, ios::out | ios::trunc | ios::binary);\n\tfile.seekp(0, ios::beg);\n\tfile.write(reinterpret_cast(&numParticles), sizeof(int));\n}\nvoid saveData(ParticleData* p) {\n\n\tif (numFrames == totalFrame) {\n\t\tfile.close();\n\n\t\tif (numFile >= numberFiles) {\n\t\t\treset();\n\t\t\treturn;\n\t\t\t//exit(0);\n\t\t}\n\t\tchar bufPath[255];\n\t\tsprintf(bufPath, \"/%s%d.txt\", nameFile, numFile++);\n\t\tfile = ofstream(dataPath + bufPath, ios::out | ios::trunc | ios::binary);\n\t\tfile.seekp(0, ios::beg);\n\t\tnumFrames = 0;\n\t}\n\n\tint numParticles = p->getNumberOfParticles();\n\n\tfile.seekp(0, ios::end);\n\tfloat x, y, z, t;\n\tfor (int i = numParticlesAir; i < numParticles; i++) {\n\t\tVector3r position = p->getPosition(i);\n\n\t\tt = p->getTemp(i);\n\t\tx = position[0];\n\t\ty = position[1];\n\t\tz = position[2];\n\n\t\tfile.write(reinterpret_cast(&t), sizeof(float));\n\t\tfile.write(reinterpret_cast(&x), sizeof(float));\n\t\tfile.write(reinterpret_cast(&y), sizeof(float));\n\t\tfile.write(reinterpret_cast(&z), sizeof(float));\n\t}\n\tnumFrames++;\n\ttotalFrames++;\n}\n//*/\nvoid timeStep()\n{\n\n\tif (capture) {\n\t\tMiniGL::saveFrame(\"D:\\\\heat-video\\\\frame\", test, TimeManager::getCurrent()->getTime(), 255, 4);\n\t\tcapture = false;\n\t}\n\tif (doPause) {\n\t\treturn;\n\t}\n\n\tif (timeEmision > 0 && totalFrames >= 64 * timeEmision)\n\t\tmodel.setContact(true);\n\tif (model.getContact() && firtTimeAugmented > 0 && totalFrames >= 64 * firtTimeAugmented) {\n\t\tmodel.setTempContact(model.getTempContact() + tempAugmented);\n\t\tfirtTimeAugmented = 0;\n\t}\n\n\t// Simulation code\n\tfor (unsigned int i = 0; i < 4; i++) //Iterations Per Frame\n\t{\n\t\tsimulation.step(model);\n\t\t// Draw simulation model\n\t\t/*ParticleData &pd = model.getParticles();\n\t\tconst unsigned int nParticles = pd.size();\n\t\tfor (unsigned int i = 0; i < nParticles; i++)\n\t\t{\n\t\t\tconst Real t = 6.0f;\n\t\t\tif (pd.getPosition(i)[1] > 3) {\n\t\t\t\tpd.setTemp(i,t);\n\t\t\t}\n\t\t}*/\n\n\t}\n\t/* Measure Temp\n\tParticleData &pd = model.getParticles();\n\tLOG_INFO << TimeManager::getCurrent()->getTime() << \"\\t\" << pd.getTemp(0) << \"\\t\" << pd.getTemp(pd.getNumberOfParticles() - 1) << \"\\t\" << pd.getTemp(0) + pd.getTemp(pd.getNumberOfParticles() - 1);\n\t//*/\n\tsaveData(&model.getParticles());\n\n\t\n\t\n\n\t/*\n\tif (TimeManager::getCurrent()->getTime()>5.0f) {\n\t\tTiming::printAverageTimes();\n\t\texit(0);\n\t}*/\n\t\n\tif ( record) {\n\t\tMiniGL::saveFrame(\"D:\\\\heat-video\\\\frame\", test, TimeManager::getCurrent()->getTime(), 255, 4);\n\t}\n\t\n}\n\nvoid buildModel ()\n{\n\t//TimeManager::getCurrent ()->setTimeStepSize (0.05025);\n\tTimeManager::getCurrent()->setTimeStepSize(0.00390625);\n\t//TimeManager::getCurrent()->setTimeStepSize(0.001953125);\n\n\tcreateBreakingDam();\n\t\t\n}\n\n\nvoid renderWorld()\n{\n\n\t/*glDepthMask(GL_TRUE);\n\tglEnable(GL_DEPTH_TEST);*/\n\n\tglEnable(GL_TEXTURE_2D);\n\tglBindTexture(GL_TEXTURE_2D, textureFloor);\n\n\t//float color[4] = { 1.0f, 1.0f, 1.0f, 0.9f };\n\tfloat color[4] = {0.9f, 0.9f, 0.9f, 1.0f};\n\t//MiniGL::hsvToRgb(1.0f, 1.0f, 1.0f, surfaceColor);\n\t//*\n\t\n\n\n\tfloat c = 40.0f;\n\n\t\n\tglBegin(GL_QUADS);\n\tfloat speccolor[4] = { 1.0, 1.0, 1.0, 1.0 };\n\tglMaterialfv(GL_FRONT_AND_BACK, GL_AMBIENT, color);\n\tglMaterialfv(GL_FRONT_AND_BACK, GL_DIFFUSE, color);\n\tglMaterialfv(GL_FRONT_AND_BACK, GL_SPECULAR, speccolor);\n\tglMaterialf(GL_FRONT_AND_BACK, GL_SHININESS, 100.0);\n\n\tglTexCoord2d(-2.5, -2.5); glVertex3d(-c, 0.0, -c);\n\tglTexCoord2d(2.5, -2.5); glVertex3d(c, 0.0, -c);\n\tglTexCoord2d(2.5, 2.5); glVertex3d(c, 0.0, c);\n\tglTexCoord2d(-2.5, 2.5); glVertex3d(-c, 0.0, c);\n\tglEnd();\n\tglBindTexture(GL_TEXTURE_2D, 0);\n}\n\n\nvoid render ()\n{\n\tif (!rendering) {\n\t\tMiniGL::drawTime(TimeManager::getCurrent()->getTime());\n\t\treturn;\n\t}\n\t//renderWorld();\n\t\n\t// Draw simulation model\n\tconst ParticleData &pd = model.getParticles();\n\tconst unsigned int nParticles = pd.size();\n\n\tint particlesIni = 0;\n\n\tif (renderingAir) {\n\t\tparticlesIni = numParticlesAir;\n\t}\n\t\n\t//glPointSize(4.0);\n\t\n\tconst Real supportRadius = model.getSupportRadius();\n\tReal vmax = 0.4*2.0*supportRadius / TimeManager::getCurrent()->getTimeStepSize();\n\tReal vmin = 0.0;\n\n\tif (context_major_version > 3)\n\t{\n\t\tif (thermal) {\n\n\t\t\tfor (unsigned int i = particlesIni; i < nParticles; i++)\n\t\t\t{\n\t\t\t\tReal v = pd.getVelocity(i).norm();\n\t\t\t\tv = 0.5*((v - vmin) / (vmax - vmin));\n\t\t\t\tv = min(128.0*v*v, 0.2);\n\t\t\t\tfloat fluidColor[4] = { 1.0f, 1.0f, 1.0f, 1.0f };\n\t\t\t\tReal t = min((pd.getTemp(i)) *0.0065, 0.651);\n\n\t\t\t\tt = max(t, -0.05);\n\t\t\t\t//Real t = min((pd.getTemp(i)) *0.0075, 0.61);\n\n\t\t\t\t/*Real t = min((pd.getTemp(i)) *0.0081, 0.648);\n\t\t\t\tMiniGL::hsvToRgb(0.648 - t, 1.0f, 0.8f + (float)v, fluidColor);*/\n\n\t\t\t\t//if (t < 0)\n\t\t\t\t\t//t = 0;\n\t\t\t\tMiniGL::hsvToRgb(0.65 - t, 1.0f, 0.8f + (float)v, fluidColor);\n\t\t\t\t\n\t\t\t\trenderSphere(pd.getPosition(i), fluidColor);\n\t\t\t}\n\n\t\t}\n\t\telse {\n\t\t\tconst vector c = model.getConstraints();\n\t\t\tconst unsigned int nc = c.size();\n\n\t\t\tfor (unsigned int i = particlesIni; i < nParticles; i++)\n\t\t\t{\n\t\t\t\tReal v = pd.getVelocity(i).norm();\n\t\t\t\tv = 0.5*((v - vmin) / (vmax - vmin));\n\t\t\t\tv = min(128.0*v*v, 0.2);\n\t\t\t\tfloat fluidColor[4] = { 0.2f, 0.2f, 0.2f, 1.0 };\n\t\t\t\tMiniGL::hsvToRgb((pd.getState(i) == 0 ? 1 : 0.55), 1.0f, 0.8f + (float)v, fluidColor);\n\t\t\t\trenderSphere(pd.getPosition(i), fluidColor);\n\t\t\t}\n\n\t\t\tfloat color[4] = { 0.2f, 0.2f, 0.2f, 1.0 };\n\t\t\tMiniGL::hsvToRgb(0.25f, 1.0f, 1.0f, color);\n\n\t\t\tglLineWidth(3);\n\t\t\tglMaterialfv(GL_FRONT_AND_BACK, GL_AMBIENT, color);\n\t\t\tglMaterialfv(GL_FRONT_AND_BACK, GL_DIFFUSE, color);\n\t\t\tglBegin(GL_LINES);\n\n\t\t\tfor (unsigned int i = 0; i < nc; i++)\n\t\t\t{\n\t\t\t\tVector3r x1 = pd.getPosition(c[i].m_bodies[0]);\n\t\t\t\tVector3r x2 = pd.getPosition(c[i].m_bodies[1]);\n\n\t\t\t\tglVertex3f(x1[0], x1[1], x1[2]);\n\t\t\t\tglVertex3f(x2[0], x2[1], x2[2]);\n\n\t\t\t}\n\t\t\tglEnd();\n\t\t}\n\t\tif (renderingWall) {\n\t\t\tfloat surfaceColor[4] = { 0.2f, 0.2f, 0.2f, 1.0 };\n\t\t\tfor (unsigned int i = 0; i < model.numBoundaryParticles(); i++)\n\t\t\t\trenderSphere(model.getBoundaryX(i), surfaceColor);\n\t\t}\n\t}\n\telse\n\t{\n\t\tglDisable(GL_LIGHTING);\n\t\tglBegin(GL_POINTS);\n\t\tfor (unsigned int i = particlesIni; i < nParticles; i++)\n\t\t{\n\t\t\tReal v = pd.getVelocity(i).norm();\n\t\t\tv = 0.5*((v - vmin) / (vmax - vmin));\n\t\t\tv = min(128.0*v*v, 0.5);\n\t\t\tfloat fluidColor[4] = { 0.2f, 0.2f, 0.2f, 1.0 };\n\t\t\tMiniGL::hsvToRgb(0.55f, 1.0f, 0.5f + (float)v, fluidColor);\n\n\t\t\tglColor3fv(fluidColor);\n\t\t\tglVertex3v(&pd.getPosition(i)[0]);\n\t\t}\n\t\tglEnd();\n\n\t\t// \tglBegin(GL_POINTS);\n\t\t// \tfor (unsigned int i = 0; i < model.numBoundaryParticles(); i++)\n\t\t// \t{\n\t\t// \t\tglColor3fv(surfaceColor);\n\t\t// \t\tglVertex3fv(&model.getBoundaryX(i)[0]);\n\t\t// \t}\n\t\t// \tglEnd();*/\n\n\t\t//glEnable(GL_LIGHTING);\n\t}\n\n\n\n\tfloat red[4] = { 1.0f, 1.0f, 1.0f, 1 };\n\tfor (unsigned int j = 0; j < selectedParticles.size(); j++)\n\t{\n\t\tMiniGL::drawSphere(pd.getPosition(selectedParticles[j]), 0.05f, red);\n\t}\t\n\n\t/*\n\tTetModel *tetModel = model.getTetModel();\n\tconst IndexedFaceMesh &surfaceMesh = tetModel->getSurfaceMesh();\n\tVisualization::drawMesh(pd, surfaceMesh, tetModel->getIndexOffset(), surfaceColor);\n\t//*/\n\tMiniGL::drawTime(TimeManager::getCurrent()->getTime());\n\n\tauto str = \"s = \"+ std::to_string(TimeManager::getCurrent()->getTime());\n\t\n\t//MiniGL::drawBitmapText(-0.33,0.25, str.c_str(), str.size()-4, red);\n\t//frameIndex++;\n\n\trenderWorld();\n}\n\n/** Create a breaking dam scenario\n*/\nvoid createBreakingDam()\n{\n\tconst Real diam = 2.0*particleRadius*compact;\n\tconst Real startX = particleRadius;\n\tconst Real startY = particleRadius;\n\tconst Real startZ = particleRadius;\n\n\tmodel.setParticleRadius(particleRadius);\n\n\t/* Test for simulation complete (check in)\n\twidth = 80;\n\theight = 70;\n\tdepth = 60;\n\tfloat temperatureContact = 50;\n\tfloat temperatureSolid = -5;\n\n\ttimeEmision = 2;//2s\n\tnumberFiles = 7;\n\n\tfirtTimeAugmented = 25;//20s\n\ttempAugmented = 80;\n\n\tmodel.addModel(dataPath + \"/bunny4k.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.5, startY, -1.2), false,0.09);\n\t//model.addModel(dataPath + \"/bunny-6001.txt\", diam, temperatureSolid, 0.55f, Vector3r(-0.6, startY, -0.5), false, 0.07);\n\tmodel.setDilation(0.0001);\n\tmodel.setDifusion(25);\n\tmodel.setTempContact(temperatureContact);\n\tnameFile = \"simulation\";\n\t//*/\n\n\n\t/* Test for simulation complete - inverse (check in)\n\twidth = 40;\n\theight = 100;\n\tdepth = 40;\n\tfloat temperatureGas = 105.0;\n\tfloat temperatureLiquid = 5.1;\n\tfloat temperatureContact = 0;\n\tfloat temperatureSolid = -50;\n\n\tnumberFiles = 3;\n\n\tmodel.addGas(diam, temperatureGas, Vector3r(width, 4, depth), Vector3r(0, 45*diam, 0));\t\n\n\t//model.addModel(dataPath + \"/bunny4k.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.5, 2*startY, -1.2), false);\n\t//model.addModel(dataPath + \"/bunny-14032.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.2, startY, -0.9), false, 0.09);\n\tmodel.addModel(dataPath + \"/bunny-6001.txt\", diam, temperatureSolid, 0.55f, Vector3r(-0.75, startY, -0.6), false, 0.07);\n\n\tmodel.setContact(true);\n\tmodel.setTempContact(temperatureContact);\n\tmodel.setDifusion(50);\n\tmodel.setDilation(0.0001);\n\tmodel.setNumMaxConstraints(4);\n\tmodel.setRadioSolidification(0.055);\n\n\ttemperatureFloor = false;\n\n\tnameFile = \"simulationI\";\n\n\t//*/\n\n\t/* Test for heat and cool wather -> compare Transfer Heat (check in)\n\twidth = 60;\n\theight = 20;//80;\n\tdepth = 30;\n\n\tnumberFiles = 4;\n\n\tfloat temperatureLiquid = 1, temperatureLiquid2 = 99;\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(width*0.5, 10, depth), Vector3r(-width*diam*0.25, 0, 0));\n\tmodel.addFluid(diam, temperatureLiquid2, Vector3r(width*0.5, 10, depth), Vector3r(width*diam*0.25, 0, 0));\n\n\tnameFile = \"heat\";\n\t//*/\n\n\n\n\t/* Test for liquid evaporation - convection (check in)\n\theight = 70;\n\tdepth = 30;//30;\n\twidth = 30;\n\tfloat temperatureLiquid = 20;//80;\n\tfloat temperatureContact = 140;\n\n\ttimeEmision = 2;//2s\n\tnumberFiles = 8;\n\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(width, 20, depth), Vector3r(0,0.0045,0));\n\t//model.setContact(true);\n\tmodel.setTempContact(temperatureContact);\n\tmodel.setDifusion(40);\n\n\tnameFile = \"evaporation\";\n\t//*/\n\n\t/* Test for condensation (check in)\n\twidth = 120;\n\theight = 175;\n\tdepth = 40;\n\tfloat temperatureGas = 120.0;\n\t//float temperatureLiquid = 15.1;\n\tfloat temperatureContact = 20;\n\tfloat temperatureSolid = 0.0;\n\n\tnumberFiles = 2;\n\n\t/*float temperatureAir = 10;\n\t//float temperatureAir1 = 10;\n\tfloat temperatureAir2 = 7;\n\tfloat temperatureAir3 = 3;\n\tfloat temperatureAir4 = 0;\n\t//model.addAir(diam, temperatureAir, Vector3r(width, 3, depth), Vector3r(0, height*diam - 155 * 2 * startY, 0),14);\n\t//model.addAir(diam, temperatureAir2, Vector3r(width, 3, depth), Vector3r(0, height*diam - 115 * 2 * startY, 0),13);\n\t//model.addAir(diam, temperatureAir3, Vector3r(width, 4, depth), Vector3r(0, height*diam - 77 * 2 * startY, 0),11);\n\t//model.addAir(diam, temperatureAir4, Vector3r(width, 4, depth), Vector3r(0, height*diam - 35 * 2 * startY, 0),10);\n\n\tnumParticlesAir = model.getParticles().size();* /\n\n\tmodel.addGas(diam, temperatureGas, Vector3r(width, 17, depth), Vector3r(0, diam , 0));\n\n\tmodel.setContact(true);\n\tmodel.setTempContact(temperatureContact);\n\tmodel.setDifusion(20);\n\tmodel.setViscosity(0.001);\n\n\tnameFile = \"condensation\";\n\t//*/\n\n\t/* Test for melting bunny with heat liquid (check in)\n\n\t// change time step = 0.001953125, comment code add border top\n\twidth = 80;\n\theight = 100;\n\tdepth = 60;\n\tfloat temperatureLiquid = 99.0f;\n\tfloat temperatureSolid = -5;\n\n\tnumberFiles = 2;\n\n\tmodel.addModel(dataPath + \"/bunny4k.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.5, startY, -1.2), false, 0.09);\t\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(5, 300, 5), Vector3r(0.35, 3.0, 0.4));\n\t\n\tmodel.setDilation(0.0001);\n\tmodel.setDifusion(30);\n\n\tnameFile = \"melting\";\n\taddWallTop = false;\n\t//*/\n\n\n\t/* Test for solidify bunny (check) -\n\twidth = 80;\n\theight = 100;\n\tdepth = 60;\n\tfloat temperatureLiquid = 10.0f;\n\tfloat temperatureSolid = -40;\n\n\tnumberFiles = 1;\n\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(8, 200,4), Vector3r(-0.25, 3.2, 0.1));\n\t//model.addFluid(diam, temperatureLiquid, Vector3r(5, 130, 2), Vector3r(-0.0, 3.0, 0.4));\n\t//model.addModel(dataPath + \"/bunny-6001.txt\", diam, temperatureSolid,0.55f, Vector3r(-0.75, startY, -0.6),false,0.07);\n\tmodel.addModel(dataPath + \"/bunny4k.txt\", diam, temperatureSolid,0.55f, Vector3r(-1.5, startY, -1.2),false,0.09);\n\tmodel.setDilation(0.0001);\n\tmodel.setNumMaxConstraints(6);\n\tmodel.setRadioSolidification(0.1);\n\tmodel.setDifusion(20);\n\n\n\tnameFile = \"solidify\";\n\taddWallTop = false;\n\t//*/\n\n\n\t/* Test for ( dilatation) --\n\twidth = 60;\n\theight = 60;\n\tdepth = 50;\n\tfloat temperatureSolid = -50;\n\tfloat temperarureContact = -1;\n\t//model.addSolid(diam, temperatureSolid,Vector3r(20,20,20),Vector3r(0, 0,0));\n\tmodel.addModel(dataPath + \"/bunny4k.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.5, startY, -1.2), false, 0.09);\n\t//model.setContact(true);\n\tmodel.setDilation(0.01);\n\tmodel.setTempContact(temperarureContact);\n\tmodel.setDifusion(50);\n\t//*/\n\n\t/* Test for armadillo on liquid - caendo -- (check in)\n\twidth = 60;\n\theight = 80;\n\tdepth = 60;\n\tfloat temperatureLiquid = 50;\n\tfloat temperatureSolid = -40;\n\n\tnumberFiles = 15;\n\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(width, 40, depth), Vector3r(0, 0, 0));\n\tmodel.addModel(dataPath + \"/armadillo4k.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.25, 4.0, -depth*diam * 0.25), false,0.07);\n\tmodel.setContact(false);\n\tmodel.setDilation(0.0001);\n\tmodel.setTempContact(temperatureLiquid);\n\tmodel.setDifusion(20);\n\n\tnameFile = \"armadillo\";\n\taddWallTop = false;\n\t//*/\n\n\n\n\t/////////////////////////////////////////////////\n\n\n\t/* Test for solidification del liquido\n\twidth = 100;\n\theight = 50;\n\tdepth = 30;\n\tfloat temperatureLiquid = 10;\n\tfloat temperatureSolid = 0;\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(width*0.25, 20, depth), Vector3r(-width*diam*3/8 + particleRadius, startY, 0));\n\n\tmodel.setContact(true);\n\tmodel.setTempContact(-5);\n\t//model.addSolid(diam, 0, Vector3r(20, 20, 20), Vector3r(0.24, 2 * startY, 0));\n\n\tnameFile = \"simulationI\";\n\n\t//*/\n\t\n\n\t/* Test for multiples ice on liquid - caendo --\n\twidth = 80;\n\theight = 120;\n\tdepth = 50;\n\tfloat temperatureLiquid = 10;\n\tfloat temperatureSolid = 0;\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(width, 20, depth), Vector3r(startX, 2*startY, startZ));\n\tmodel.addSolid(diam, temperatureSolid, Vector3r(14, 14, 14), Vector3r(-1.5, 1.7, -0.5));\n\tmodel.addSolid(diam, temperatureSolid, Vector3r(17, 17, 17), Vector3r(1.3, 1.5, 0.5));\n\tmodel.addSolid(diam, temperatureSolid, Vector3r(10, 10, 10), Vector3r(-0.5, 2.7, -0.5));\n\tmodel.addSolid(diam, temperatureSolid, Vector3r(5, 5, 5), Vector3r(0.0, 3, -0.0));\n\tmodel.addSolid(diam, temperatureSolid, Vector3r(5, 5, 5), Vector3r(1.0, 3.2, -0.2));\n\tmodel.addSolid(diam, temperatureSolid, Vector3r(10, 10, 10), Vector3r(-1.2, 2.5, 0.5));\n\t//model.addModel(dataPath + \"/armadillo4k.txt\", diam, temperatureSolid, 0.45f, Vector3r(-width*diam * 0.25, 2.0, -depth*diam * 0.25),false);\n\tmodel.setContact(true);\n\tmodel.setDilation(0.0001);\n\tmodel.setTempContact(temperatureLiquid);\n\tmodel.setDifusion(25);\n\t//*/\n\n\t/* Test for armadillo on liquid - caendo -- (check in)\n\twidth = 100;\n\theight = 250;\n\tdepth = 70;\n\tfloat temperatureLiquid = 50;\n\tfloat temperatureSolid = 0;\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(width, 15, depth), Vector3r(startX, 2 * startY, startZ));\n\tmodel.addModel(dataPath + \"/armadillo4k.txt\", diam, temperatureSolid, 0.55f, Vector3r(-width*diam * 0.25, 2.0, -depth*diam * 0.25), false);\n\tmodel.setContact(false);\n\tmodel.setDilation(0.0001);\n\tmodel.setTempContact(temperatureLiquid);\n\tmodel.setDifusion(20);\n\t//*/\n\n\t\n\n\n\t/* Test for meeltin ( derretimiento) --\n\twidth = 50;\n\theight = 50;\n\tdepth = 50;\n\tfloat temperatureSolid = 0;\n\tfloat temperarureContact = 10;\n\tmodel.addSolid(diam, temperatureSolid,Vector3r(30,40,20),Vector3r(particleRadius, 2 * startY,0));\n\tmodel.setContact(true);\n\tmodel.setDilation(0.0001);\n\tmodel.setTempContact(temperarureContact);\n\tmodel.setDifusion(10);\n\t//*/\n\n\t\n\n\t\n\n\t/* Test for heat and cool wather -> compare Transfer Heat cleary\n\twidth = 80;\n\theight = 80;\n\tdepth = 40;\n\tfloat temperatureLiquid = 6, temperatureLiquid2 = 80;\n\tmodel.heatTransferModel = 1;//model cleary\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(width*0.5, 15, depth), Vector3r(-width*diam*0.25 , 0, 0));\n\tmodel.addFluid(diam, temperatureLiquid2, Vector3r(width*0.5, 15, depth), Vector3r(width*diam*0.25 , 0, 0));\n\n\tmodel.setContact(false);\n\tmodel.setTempContact(temperatureLiquid2);\n\t//*/\n\n\t/* Test for liquidos caendo\n\tmodel.addFluid(diam, 21, Vector3r(3, 20, 3), Vector3r(-0.36, 2.0, 0.0));\n\tmodel.addFluid(diam, 55, Vector3r(3, 20, 3), Vector3r(0.16, 0.8, 0.18));\t\n\tmodel.addFluid(diam, 99, Vector3r(3, 20, 3), Vector3r(0.22, 2.8, -0.2));\n\tmodel.addSolid(diam, 0, Vector3r(25, 10, 15), Vector3r(0.025, 0, 0));\n\t//*/\n\n\t\n\t\n\n\t\n\n\t\n\n\t/* Test for simulation complete - inverse (check in)\n\twidth = 100;\n\theight = 290;\n\tdepth = 60;\n\tfloat temperatureGas = 109.0;\n\t//float temperatureLiquid = 15.1;\n\tfloat temperatureContact = 0;\n\tfloat temperatureSolid = 0.0;\n\n\tfloat temperatureAir = 10;\n\t//float temperatureAir1 = 10;\n\tfloat temperatureAir2 = 7;\n\tfloat temperatureAir3 = 3;\n\tfloat temperatureAir4 = 0;\n\tmodel.addAir(diam, temperatureAir, Vector3r(width, 3, depth), Vector3r(particleRadius, height*diam - 155 * 2 * startY, particleRadius), 14);\n\tmodel.addAir(diam, temperatureAir2, Vector3r(width, 3, depth), Vector3r(particleRadius, height*diam - 115 * 2 * startY, particleRadius), 13);\n\tmodel.addAir(diam, temperatureAir3, Vector3r(width, 4, depth), Vector3r(particleRadius, height*diam - 77 * 2 * startY, particleRadius), 11);\n\tmodel.addAir(diam, temperatureAir4, Vector3r(width, 4, depth), Vector3r(particleRadius, height*diam - 35 * 2 * startY, particleRadius), 10);\n\n\tnumParticlesAir = model.getParticles().size();\n\n\tmodel.addGas(diam, temperatureGas, Vector3r(width, 20, depth), Vector3r(particleRadius, height*diam - 265 * 2 * startY, particleRadius));\n\t//model.addModel(dataPath + \"/bunny.txt\", diam, temperatureSolid,0.55f, Vector3r(-0.5, 2*startY, -0.3),false);\n\t//model.addModel(dataPath + \"/bunny4k.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.5, 2 * startY, -1.2), false);\n\n\t//printf(\"%f\",);\n\t//model.addFluid(diam, temperatureLiquid, Vector3r(width, 10, depth), Vector3r(particleRadius, 2 * startY, particleRadius));\n\n\tmodel.setContact(true);\n\tmodel.setTempContact(temperatureContact);\n\tmodel.setDifusion(30);\n\t//*/\n\n\t/* Test for heat and cool wather -> measure temp (check in)\n\twidth = 50;\n\theight = 20;\n\tdepth = 1;\n\tfloat temperatureLiquid = 1, temperatureLiquid2 = 99;\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(width*0.5, 10, depth), Vector3r(-width*diam*0.25, 0, 0));\n\tmodel.addFluid(diam, temperatureLiquid2, Vector3r(width*0.5, 10, depth), Vector3r(width*diam*0.25, 0, 0));\n\tnameFile = \"measure temp\";\n\t//*/\n\n\t/* Test for melting bunny - number particles (check in)\n\twidth = 80;\n\theight = 250;\n\tdepth = 60;\n\tfloat temperatureSolid = -5;\n\t\n\ttimeEmision = 2;\n\tnumberFiles = 1;\n\t\n\n\t//model.addModel(dataPath + \"/bunny-46607.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.5, startY, -1.2), false,0.09);nameFile = \"46607Particles\";//0.09\n\t//model.addModel(dataPath + \"/bunny-27152.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.5, startY, -1.2), false, 0.09); nameFile = \"27152Particles\";//0.09\n\t//model.addModel(dataPath + \"/bunny-14032.txt\", diam, temperatureSolid, 0.55f, Vector3r(-1.2, startY, -0.9), false, 0.09);nameFile = \"14032Particles\";//0.09\n\tmodel.addModel(dataPath + \"/bunny-6001.txt\", diam, temperatureSolid, 0.55f, Vector3r(-0.8, startY, -0.7), false,0.08);nameFile = \"6001Particles\";//0.08\n\t//model.addModel(dataPath + \"/bunny-1849.txt\", diam, temperatureSolid, 0.55f, Vector3r(-0.6, startY, -0.5), false,0.07);nameFile = \"1849Particles\";//0.07\n\t\n\n\tmodel.setDilation(0.001);\n\tmodel.setDifusion(50);\n\tmodel.setTempContact(50);\t\n\t\n\t//*/\n\t/* Test for solidify bunny (check)\n\twidth = 80;\n\theight = 300;\n\tdepth = 60;\n\tfloat temperatureLiquid = 5.6f;\n\tfloat temperatureSolid = 0;\n\n\tmodel.addFluid(diam, temperatureLiquid, Vector3r(8, 200,4), Vector3r(-0.25, 3.2, 0.1));\n\t//model.addFluid(diam, temperatureLiquid, Vector3r(5, 130, 2), Vector3r(-0.0, 3.0, 0.4));\n\t//model.addModel(dataPath + \"/bunny.txt\", diam, temperatureSolid,0.55f, Vector3r(-0.3, 2*startY, 0.0),false);\n\tmodel.addModel(dataPath + \"/bunny4k.txt\", diam, temperatureSolid,0.55f, Vector3r(-1.5, 2*startY, -1.2),false);\n\tmodel.setDilation(0.0001);\n\tmodel.setNumMaxConstraints(6);\n\tmodel.setRadioSolidification(0.05);\n\tmodel.setDifusion(50);\n\t//*/\n\n\t//* Test for multiples demos - number particles (check in)\n\twidth = 80;\n\theight = 90;\n\tdepth = 60;\n\tfloat temperatureSolid = 50;\n\n\t//timeEmision = 1;\n\tnumberFiles = 1;\n\n\tfloat scale = 0.6f;\n\n\t//model.addModel(dataPath + \"/bunny-46607.txt\", diam, temperatureSolid, scale, Vector3r(-1.65, startY, -1.3), false,0.09);//0.09\n\t//model.addModel(dataPath + \"/bunny-27152.txt\", diam, temperatureSolid, scale, Vector3r(-1.5, startY, -1.2), false, 0.09);//0.09\n\t//model.addModel(dataPath + \"/bunny-14032.txt\", diam, temperatureSolid, scale, Vector3r(-1.2, startY, -0.9), false, 0.09);//0.09\n\t//model.addModel(dataPath + \"/bunny-6001.txt\", diam, temperatureSolid, scale, Vector3r(-0.8, startY, -0.7), false,0.08);//0.08\n\tmodel.addModel(dataPath + \"/bunny-1849.txt\", diam, temperatureSolid, scale, Vector3r(-0.6, startY, -0.5), false,0.07);//0.07\n\n\n\tmodel.setDilation(0.001);\n\tmodel.setDifusion(50);\n\tmodel.setTempContact(-10);\n\tmodel.setContact(true);\n\tmodel.setRadioSolidification(0.05);\n\tmodel.setNumMaxConstraints(3);\n\tnameFile = \"demos\";\n\n\t//*/\n\n\n\n\tstd::vector boundaryParticles;\n\tstd::vector boundaryActives;\n\tinitBoundaryData(boundaryParticles, boundaryActives);\n\n\tmodel.initModel( (unsigned int)boundaryParticles.size(), boundaryParticles.data(), &boundaryActives);\n\t\n\t\n\tLOG_INFO << \"Number particles:\" << (model.getParticles().size() - numParticlesAir);\n\t//LOG_INFO << \"Number of particles Total: \" << model.getParticles().size();\n\tinitData(model.getParticles().size() - numParticlesAir);\n}\n\n\nvoid addWall(const Vector3r &minX, const Vector3r &maxX, std::vector &boundaryParticles, std::vector &boundaryActives,bool active)\n{\n\tconst Real particleDistance = 2*model.getParticleRadius()*compact;\n\n\tconst Vector3r diff = maxX - minX;\n\tconst unsigned int stepsX = (unsigned int)(round(diff[0] / particleDistance)) + 1u;\n\tconst unsigned int stepsY = (unsigned int)(round(diff[1] / particleDistance)) + 1u;\n\tconst unsigned int stepsZ = (unsigned int)(round(diff[2] / particleDistance)) + 1u;\n\n\t//printf(\"%d %d %d %f\\n\",stepsX, (unsigned int)(41.00),(unsigned int)(diff[0] / particleDistance), diff[0] / particleDistance);\n\n\tconst unsigned int startIndex = (unsigned int) boundaryParticles.size();\n\tboundaryParticles.resize(startIndex + stepsX*stepsY*stepsZ);\n\tboundaryActives.resize(startIndex + stepsX*stepsY*stepsZ);\n\n\t#pragma omp parallel default(shared)\n\t{\n\t\t#pragma omp for schedule(static) \n\t\tfor (int j = 0; j < (int)stepsX; j++)\n\t\t{\n\t\t\tfor (unsigned int k = 0; k < stepsY; k++)\n\t\t\t{\n\t\t\t\tfor (unsigned int l = 0; l < stepsZ; l++)\n\t\t\t\t{\n\t\t\t\t\tconst Vector3r currPos = minX + Vector3r(j*particleDistance, k*particleDistance, l*particleDistance);\n\t\t\t\t\tboundaryParticles[startIndex + j*stepsY*stepsZ + k*stepsZ + l] = currPos;\n\t\t\t\t\tboundaryActives[startIndex + j*stepsY*stepsZ + k*stepsZ + l] = active;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n}\nvoid initBoundaryData(std::vector &boundaryParticles, std::vector &boundaryActives)\n{\n\n\tconst Real containerWidth = (width)*particleRadius*compact;\n\tconst Real containerDepth = (depth)*particleRadius*compact;\n\tconst Real containerHeight = (height)*particleRadius*compact;\n\n\n\tconst Real diameter = 2*particleRadius*compact;// compact;// *0.8;\n\n\tconst Real x1 = -containerWidth - particleRadius * compact;\n\tconst Real x2 = containerWidth + particleRadius * compact;\n\tconst Real y1 = -(sqrt(3) - 0.80)*particleRadius;\n\tconst Real y2 = 2*containerHeight+ diameter - (sqrt(3) - 0.80)*particleRadius;\n\tconst Real z1 = -containerDepth - particleRadius * compact;\n\tconst Real z2 = containerDepth + particleRadius * compact;\n\n\tfloat rest = 0.001f;\n\n\t// Floor\n\taddWall(Vector3r(x1, y1, z1), Vector3r(x2, y1, z2), boundaryParticles, boundaryActives,temperatureFloor);\n\t// Top\n\tif(addWallTop)\n\t\taddWall(Vector3r(x1, y2, z1), Vector3r(x2, y2, z2), boundaryParticles, boundaryActives, true);\n\t// Left\n\taddWall(Vector3r(x1 - rest, y1+ diameter, z1+ diameter), Vector3r(x1 - rest, y2 - diameter, z2- diameter), boundaryParticles, boundaryActives, false);\n\t// Right\n\taddWall(Vector3r(x2 + rest, y1 + diameter, z1+ diameter), Vector3r(x2 + rest, y2 - diameter, z2- diameter), boundaryParticles, boundaryActives, false);\n\t// Back\n\taddWall(Vector3r(x1, y1 + diameter, z1), Vector3r(x2, y2 - diameter, z1), boundaryParticles, boundaryActives, false);\n\t// Front\n\taddWall(Vector3r(x1, y1 + diameter, z2), Vector3r(x2, y2- diameter, z2), boundaryParticles, boundaryActives, false);\n}\n\n\nvoid createSphereBuffers(Real radius, int resolution)\n{\n\tReal PI = static_cast(M_PI);\n\t// vectors to hold our data\n\t// vertice positions\n\tstd::vector v;\n\t// normals\n\tstd::vector n;\n\tstd::vector indices;\n\n\t// initiate the variable we are going to use\n\tReal X1, Y1, X2, Y2, Z1, Z2;\n\tReal inc1, inc2, inc3, inc4, radius1, radius2;\n\n\tfor (int w = 0; w < resolution; w++)\n\t{\n\t\tfor (int h = (-resolution / 2); h < (resolution / 2); h++)\n\t\t{\n\t\t\tinc1 = (w / (Real)resolution) * 2 * PI;\n\t\t\tinc2 = ((w + 1) / (Real)resolution) * 2 * PI;\n\t\t\tinc3 = (h / (Real)resolution)*PI;\n\t\t\tinc4 = ((h + 1) / (Real)resolution)*PI;\n\n\t\t\tX1 = sin(inc1);\n\t\t\tY1 = cos(inc1);\n\t\t\tX2 = sin(inc2);\n\t\t\tY2 = cos(inc2);\n\n\t\t\t// store the upper and lower radius, remember everything is going to be drawn as triangles\n\t\t\tradius1 = radius*cos(inc3);\n\t\t\tradius2 = radius*cos(inc4);\n\n\t\t\tZ1 = radius*sin(inc3);\n\t\t\tZ2 = radius*sin(inc4);\n\n\t\t\t// insert the triangle coordinates\n\t\t\tv.push_back(Vector3r(radius1*X1, Z1, radius1*Y1));\n\t\t\tv.push_back(Vector3r(radius1*X2, Z1, radius1*Y2));\n\t\t\tv.push_back(Vector3r(radius2*X2, Z2, radius2*Y2));\n\n\t\t\tindices.push_back((unsigned short)v.size() - 3);\n\t\t\tindices.push_back((unsigned short)v.size() - 2);\n\t\t\tindices.push_back((unsigned short)v.size() - 1);\n\n\t\t\tv.push_back(Vector3r(radius1*X1, Z1, radius1*Y1));\n\t\t\tv.push_back(Vector3r(radius2*X2, Z2, radius2*Y2));\n\t\t\tv.push_back(Vector3r(radius2*X1, Z2, radius2*Y1));\n\n\t\t\tindices.push_back((unsigned short)v.size() - 3);\n\t\t\tindices.push_back((unsigned short)v.size() - 2);\n\t\t\tindices.push_back((unsigned short)v.size() - 1);\n\n\t\t\t// insert the normal data\n\t\t\tn.push_back(Vector3r(X1, Z1, Y1));\n\t\t\tn.push_back(Vector3r(X2, Z1, Y2));\n\t\t\tn.push_back(Vector3r(X2, Z2, Y2));\n\t\t\tn.push_back(Vector3r(X1, Z1, Y1));\n\t\t\tn.push_back(Vector3r(X2, Z2, Y2));\n\t\t\tn.push_back(Vector3r(X1, Z2, Y1));\n\t\t}\n\t}\n\n\tfor (unsigned int i = 0; i < n.size(); i++)\n\t\tn[i].normalize();\n\n\n\tglGenBuffersARB(1, &vertexbuffer);\n\tglBindBufferARB(GL_ARRAY_BUFFER_ARB, vertexbuffer);\n\tglBufferDataARB(GL_ARRAY_BUFFER_ARB, v.size() * sizeof(Vector3r), &v[0], GL_STATIC_DRAW);\n\n\tglGenBuffersARB(1, &normalbuffer);\n\tglBindBufferARB(GL_ARRAY_BUFFER_ARB, normalbuffer);\n\tglBufferDataARB(GL_ARRAY_BUFFER_ARB, n.size() * sizeof(Vector3r), &n[0], GL_STATIC_DRAW);\n\tglBindBufferARB(GL_ARRAY_BUFFER_ARB, 0);\n\n\t// Generate a buffer for the indices as well\n\tglGenBuffersARB(1, &elementbuffer);\n\tglBindBufferARB(GL_ELEMENT_ARRAY_BUFFER_ARB, elementbuffer);\n\tglBufferDataARB(GL_ELEMENT_ARRAY_BUFFER_ARB, indices.size() * sizeof(unsigned short), &indices[0], GL_STATIC_DRAW);\n\tglBindBufferARB(GL_ELEMENT_ARRAY_BUFFER_ARB, 0);\n\n\t// store the number of indices for later use\n\tvertexBufferSize = (unsigned int)indices.size();\n\n\t// clean up after us\n\tindices.clear();\n\tn.clear();\n\tv.clear();\n}\n\nvoid renderSphere(const Vector3r &x, const float color[])\n{\n\tglEnableClientState(GL_VERTEX_ARRAY);\n\tglEnableClientState(GL_NORMAL_ARRAY);\n\n\tglMaterialfv(GL_FRONT_AND_BACK, GL_AMBIENT, color);\n\tglMaterialfv(GL_FRONT_AND_BACK, GL_DIFFUSE, color);\n\n\n\tglBindBufferARB(GL_ARRAY_BUFFER_ARB, vertexbuffer);\n\tglVertexPointer(3, GL_REAL, 0, 0);\n\n\tglBindBufferARB(GL_ARRAY_BUFFER_ARB, normalbuffer);\n\tglNormalPointer(GL_REAL, 0, 0);\n\n\tglBindBufferARB(GL_ELEMENT_ARRAY_BUFFER_ARB, elementbuffer);\n\n\tglPushMatrix();\n\tglTranslated(x[0], x[1], x[2]);\n\tglDrawElements(GL_TRIANGLES, (GLsizei)vertexBufferSize, GL_UNSIGNED_SHORT, 0);\n\tglPopMatrix();\n\tglBindBufferARB(GL_ELEMENT_ARRAY_BUFFER_ARB, 0);\n\tglBindBufferARB(GL_ARRAY_BUFFER_ARB, 0);\n\n\tglDisableClientState(GL_NORMAL_ARRAY);\n\tglDisableClientState(GL_VERTEX_ARRAY);\n}\n\nvoid releaseSphereBuffers()\n{\n\tif (elementbuffer != 0)\n\t{\n\t\tglDeleteBuffersARB(1, &elementbuffer);\n\t\telementbuffer = 0;\n\t}\n\tif (normalbuffer != 0)\n\t{\n\t\tglDeleteBuffersARB(1, &normalbuffer);\n\t\tnormalbuffer = 0;\n\t}\n\tif (vertexbuffer != 0)\n\t{\n\t\tglDeleteBuffersARB(1, &vertexbuffer);\n\t\tvertexbuffer = 0;\n\t}\n}\n\n\n\n\nvoid TW_CALL setTimeStep(const void *value, void *clientData)\n{\n\tconst Real val = *(const Real *)(value);\n\tTimeManager::getCurrent()->setTimeStepSize(val);\n}\n\nvoid TW_CALL getTimeStep(void *value, void *clientData)\n{\n\t*(Real *)(value) = TimeManager::getCurrent()->getTimeStepSize();\n}\n\nvoid TW_CALL setVelocityUpdateMethod(const void *value, void *clientData)\n{\n\tconst short val = *(const short *)(value);\n\t((TimeStepFluidModel*)clientData)->setVelocityUpdateMethod((unsigned int)val);\n}\n\nvoid TW_CALL getVelocityUpdateMethod(void *value, void *clientData)\n{\n\t*(short *)(value) = (short)((TimeStepFluidModel*)clientData)->getVelocityUpdateMethod();\n}\n//////\n\nvoid TW_CALL setTempUpdateMethod(const void *value, void *clientData)\n{\n\tconst short val = *(const short *)(value);\n\t((FluidModel*)clientData)->heatTransferModel = (unsigned int)val;\n}\n\nvoid TW_CALL getTempUpdateMethod(void *value, void *clientData)\n{\n\t*(short *)(value) = (short)((FluidModel*)clientData)->heatTransferModel ;\n}\n\n//////\nvoid TW_CALL setViscosity(const void *value, void *clientData)\n{\n\tconst Real val = *(const Real *)(value);\n\t((FluidModel*)clientData)->setViscosity(val);\n}\n\nvoid TW_CALL getViscosity(void *value, void *clientData)\n{\n\t*(Real *)(value) = ((FluidModel*)clientData)->getViscosity();\n}\n\nvoid TW_CALL getRadioSol(void *value, void *clientData)\n{\n\t*(Real *)(value) = ((FluidModel*)clientData)->getRadioSolidification();\n}\nvoid TW_CALL setRadioSol(const void *value, void *clientData) {\n\tconst Real val = *(const Real *)(value);\n\t((FluidModel*)clientData)->setRadioSolidification(val);\n}\nvoid TW_CALL getNumConstraint(void *value, void *clientData)\n{\n\t*(Real *)(value) = ((FluidModel*)clientData)->getNumMaxConstraints();\n}\nvoid TW_CALL setNumConstraint(const void *value, void *clientData)\n{\n\tconst Real val = *(const Real *)(value);\n\t((FluidModel*)clientData)->setNumMaxConstraints(val);\n}\nvoid TW_CALL setStiffness(const void *value, void *clientData)\n{\n\tconst Real val = *(const Real *)(value);\n\t((FluidModel*)clientData)->setClothStiffness(val);\n}\n\nvoid TW_CALL getStiffness(void *value, void *clientData)\n{\n\t*(Real *)(value) = ((FluidModel*)clientData)->getClothStiffness();\n}\n\nvoid TW_CALL setDilation(const void *value, void *clientData)\n{\n\tconst Real val = *(const Real *)(value);\n\t((FluidModel*)clientData)->setDilation(val);\n}\n\nvoid TW_CALL getDilation(void *value, void *clientData)\n{\n\t*(Real *)(value) = ((FluidModel*)clientData)->getDilation();\n}\n\nvoid TW_CALL setMelting(const void *value, void *clientData)\n{\n\tconst Real val = *(const Real *)(value);\n\t((FluidModel*)clientData)->setMelting(val);\n}\n\nvoid TW_CALL getMelting(void *value, void *clientData)\n{\n\t*(Real *)(value) = ((FluidModel*)clientData)->getMelting();\n}\n\nvoid TW_CALL setEvaporation(const void *value, void *clientData)\n{\n\tconst Real val = *(const Real *)(value);\n\t((FluidModel*)clientData)->setEvaporation(val);\n}\n\nvoid TW_CALL getEvaporation(void *value, void *clientData)\n{\n\t*(Real *)(value) = ((FluidModel*)clientData)->getEvaporation();\n}\n\nvoid TW_CALL setDifusion(const void *value, void *clientData)\n{\n\tconst Real val = *(const Real *)(value);\n\t((FluidModel*)clientData)->setDifusion(val);\n}\n\nvoid TW_CALL getDifusion(void *value, void *clientData)\n{\n\t*(Real *)(value) = ((FluidModel*)clientData)->getDifusion();\n}\n\n\nvoid TW_CALL setTempContact(const void *value, void *clientData)\n{\n\tconst Real val = *(const Real *)(value);\n\t((FluidModel*)clientData)->setTempContact(val);\n}\n\nvoid TW_CALL getTempContact(void *value, void *clientData)\n{\n\t*(Real *)(value) = ((FluidModel*)clientData)->getTempContact();\n}\n\nvoid TW_CALL setContact(const void *value, void *clientData)\n{\n\tconst bool val = *(const bool *)(value);\n\t((FluidModel*)clientData)->setContact(val);\n}\n\nvoid TW_CALL getContact(void *value, void *clientData)\n{\n\t*(bool *)(value) = ((FluidModel*)clientData)->getContact();\n}\n\nvoid TW_CALL setXPBD(const void *value, void *clientData)\n{\n\tconst bool val = *(const bool *)(value);\n\t((FluidModel*)clientData)->setXPBD(val);\n}\n\nvoid TW_CALL getXPBD(void *value, void *clientData)\n{\n\t*(bool *)(value) = ((FluidModel*)clientData)->getXPBD();\n}\n\nvoid TW_CALL setDesWidth(const void *value, void *clientData)\n{\n\tint val = *( int *)(value);\n\tMiniGL::desWidth = val;\n\tMiniGL::reziseDesWidth();\n}\n\nvoid TW_CALL getDesWidth(void *value, void *clientData)\n{\n\t*(int*)(value) = MiniGL::desWidth;\n}\n\nvoid TW_CALL setDesHeight(const void *value, void *clientData)\n{\n\tint val = *(int *)(value);\n\tMiniGL::desHeight = val;\n\tMiniGL::reziseDesHeight();\n}\n\nvoid TW_CALL getDesHeight(void *value, void 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NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.22945854377344055}} {"text": "/*\n * odeint_rk4_array\n *\n * Copyright 2011 Mario Mulansky\n * Copyright 2012 Karsten Ahnert\n *\n * Distributed under the Boost Software License, Version 1.0.\n * (See accompanying file LICENSE_1_0.txt or\n * copy at http://www.boost.org/LICENSE_1_0.txt)\n */\n\n#include \n\n#include \n#include \n\n#include \n#include \n#include \n\n#include \"lorenz.hpp\"\n\ntypedef boost::timer timer_type;\n\ntypedef boost::array< double , 3 > state_type;\n\nusing namespace boost::numeric::odeint;\n\n//typedef boost::numeric::odeint::runge_kutta4_classic< state_type > rk4_odeint_type;\n\n// use the never resizer explicitely for optimal performance with gcc, \n// for the intel compiler this doesnt matter and the above definition\n// gives the same performance\ntypedef runge_kutta4_classic< state_type , double , state_type , double ,\n array_algebra, default_operations, never_resizer > rk4_odeint_type;\n\n\nconst int loops = 21;\nconst int num_of_steps = 20000000;\nconst double dt = 1E-10;\n\n\nint main()\n{\n double min_time = 1E6; // something big\n rk4_odeint_type stepper;\n std::clog.precision(16);\n std::cout.precision(16);\n for( int n=0; n\n#include \n \n#ifdef _DEBUG\n#define new DEBUG_NEW\n#endif\nconst int LINE_BUFF_SIZE = 4096;\nconst float g_fFOV = 12.5936f;// used to be 45.5f;\nconst float g_fAspect = 1.0f;\n// CAboutDlg dialog used for App About\n\nclass CAboutDlg : public CDialog\n{\npublic:\n\tCAboutDlg();\n\n// Dialog Data\n#ifdef AFX_DESIGN_TIME\n\tenum { IDD = IDD_ABOUTBOX };\n#endif\n\n\tprotected:\n\tvirtual void DoDataExchange(CDataExchange* pDX); // DDX/DDV support\n\n// Implementation\nprotected:\n\tDECLARE_MESSAGE_MAP()\n};\n//---------------------------------------------------------------//\nCAboutDlg::CAboutDlg() : CDialog(IDD_ABOUTBOX)\n{\n}\n//---------------------------------------------------------------//\nvoid CAboutDlg::DoDataExchange(CDataExchange* pDX)\n{\n\tCDialog::DoDataExchange(pDX);\n}\n//---------------------------------------------------------------//\nBEGIN_MESSAGE_MAP(CAboutDlg, CDialog)\nEND_MESSAGE_MAP()\n\n\n// CFaceMorphDlg dialog\n\n\n//---------------------------------------------------------------//\nCFaceMorphDlg::CFaceMorphDlg(CWnd* pParent /*=nullptr*/)\n\t: CDialog(IDD_FACEMORPH_DIALOG, pParent)\n{\n\tm_hIcon = AfxGetApp()->LoadIcon(IDR_MAINFRAME);\n\t//cblas_dgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, m, n, k, alpha, A, k, B, n, beta, C, n);\n}\n//---------------------------------------------------------------//\nCFaceMorphDlg::~CFaceMorphDlg()\n{\n \n}\n//---------------------------------------------------------------//\nvoid CFaceMorphDlg::DoDataExchange(CDataExchange* pDX)\n{\n\tfor (int h = 0; h < NUM_SLIDERS; h++)\n\t{\n\t\tDDX_Control(pDX, IDC_SLIDER1 + h, m_Slider[h]);\n\t}\n\tCDialog::DoDataExchange(pDX);\n}\n//---------------------------------------------------------------//\nBEGIN_MESSAGE_MAP(CFaceMorphDlg, CDialog)\n\tON_WM_TIMER()\n\tON_WM_SYSCOMMAND()\n\tON_WM_PAINT()\n\tON_WM_HSCROLL() \n\tON_WM_QUERYDRAGICON() \n\tON_WM_CLOSE()\n\tON_BN_CLICKED(IDC_RESET, &CFaceMorphDlg::OnBnClickedReset)\n\tON_BN_CLICKED(IDC_CHECK1, &CFaceMorphDlg::OnBnClickedCheck1)\n\tON_BN_CLICKED(IDC_EXPORT, &CFaceMorphDlg::OnBnClickedExport)\nEND_MESSAGE_MAP()\n//---------------------------------------------------------------//\n// CFaceMorphDlg message handlers\nBOOL CFaceMorphDlg::OnInitDialog()\n{\n\tCDialog::OnInitDialog();\n\n\t// Add \"About...\" menu item to system menu.\n\n\t// IDM_ABOUTBOX must be in the system command range.\n\tASSERT((IDM_ABOUTBOX & 0xFFF0) == IDM_ABOUTBOX);\n\tASSERT(IDM_ABOUTBOX < 0xF000);\n\n\tCMenu* pSysMenu = GetSystemMenu(FALSE);\n\tif (pSysMenu != nullptr)\n\t{\n\t\tBOOL bNameValid;\n\t\tCString strAboutMenu;\n\t\tbNameValid = strAboutMenu.LoadString(IDS_ABOUTBOX);\n\t\tASSERT(bNameValid);\n\t\tif (!strAboutMenu.IsEmpty())\n\t\t{\n\t\t\tpSysMenu->AppendMenu(MF_SEPARATOR);\n\t\t\tpSysMenu->AppendMenu(MF_STRING, IDM_ABOUTBOX, strAboutMenu);\n\t\t}\n\t}\n\n\t// Set the icon for this dialog. The framework does this automatically\n\t// when the application's main window is not a dialog\n\tSetIcon(m_hIcon, TRUE);\t\t\t// Set big icon\n\tSetIcon(m_hIcon, FALSE);\t\t// Set small icon\n \n\tSetDlgItemText(IDC_EXPORT_PATH, L\"C:\\\\_3DMMExport\");\n\n\tCRect rtFrame;\n\tGetDlgItem(IDC_FRAME)->GetWindowRect(rtFrame);\n\tScreenToClient(rtFrame);\n\n\tfor (int h = 0; h < NUM_SLIDERS; h++)\n\t{\n\t\tm_Slider[h].SetRange(0, 2 * SLIDER_CENTER);\n\t\tm_Slider[h].SetPos(SLIDER_CENTER);\n\t}\n\tm_pRenderWnd = new CDxWnd();\n \n\tBOOL b2 = m_pRenderWnd->Create(NULL, _T(\"DxWnd\"), WS_VISIBLE, rtFrame, this, 1);\n\t \n\t//calculate the eigenvalues here and pass them to CDxDlg\n\t//first we need to find the Meshess folder//\n\t//This will be at a higher level. Recursively search until we find it\n\tCString strMeshPath;\n\tTCHAR szFilePath[MAX_PATH + _ATL_QUOTES_SPACE];\n\tDWORD dwFLen = ::GetModuleFileName(NULL, szFilePath + 0, MAX_PATH);\n\tstrMeshPath = CString(szFilePath);\n\n\tlong nRight = strMeshPath.ReverseFind(_T('\\\\'));//remove exe name\n\tstrMeshPath = strMeshPath.Left(nRight);//this will ne the same folder as the EXe\n\tbool bFoundMeshFolder = false;\n\tint nNumAttempts = 0;\n\twhile (!bFoundMeshFolder && nNumAttempts < 3)\n\t{\n\t\tnRight = strMeshPath.ReverseFind(_T('\\\\'));//move one folder up\n\t\tstrMeshPath = strMeshPath.Left(nRight);//this will ne the same folder as \n\t\tnNumAttempts++;\n\t\tif (DoesDirExist(strMeshPath + L\"\\\\Meshes\")) {\n\t\t\tbFoundMeshFolder = true;\n\t\t\tstrMeshPath.Append(L\"\\\\Meshes\\\\\");\n\t\t}\t\t \n\t\t\n\t}\n\tif (bFoundMeshFolder)\n\t{\n\t\tCalcEigenValues(strMeshPath);\n\t}\n\telse\n\t{\n\t\tAfxMessageBox(L\"Failed to find Mesh Folder\");\n\t}\n\n\t// Draw the background gradient.\n\tm_pRenderWnd->Initialize(this);\n\tm_pRenderWnd->MoveWindow(rtFrame);\n\tSetTimer(1, 50, NULL);\n\t \n \n\n\t/*dgesvd(\"All\", \"All\", &m, &n, a, &lda, s, u, &ldu, vt, &ldvt, work, &lwork,\n\t\t&info);*/\n\n\treturn TRUE; // return TRUE unless you set the focus to a control\n} \n//---------------------------------------------------------------//\nbool CFaceMorphDlg::CalcEigenValues(CString strMeshPath)\n{\n\tif (!m_pRenderWnd)\n\t{\n\t\tAfxMessageBox(L\"m_pRenderWnd not created\");\n\t\treturn false;\n\t}\n\tstd::vector > vMeshes;\n\n\t \n\tint nNumVertices = -1;\n\tint nI1 = 0;\n\tint nI2 = 0;\n\tint nI3 = 0;\n\n\t////////////////////////////////////////////////////////////////////////////////////////\n\t//STEP ONE: load the files\n\t////////////////////////////////////////////////////////////////////////////////////////\n\tCFileFind finder;\n\t//count the number of files we will have to load\n\tint nNumFiles = 0;\n\tBOOL bWorking = finder.FindFile(strMeshPath + _T(\"*.mesh\"));\n\twhile (bWorking)\n\t{\n\t\tbWorking = finder.FindNextFile();\n\t\tnNumFiles++;\n\t}\n\tfinder.Close(); \n\n\tint nFileCounter = 0;\n\tbWorking = finder.FindFile(strMeshPath + _T(\"*.mesh\"));\n\twhile (bWorking)\n\t{\n\t\tbWorking = finder.FindNextFile();\n\t\tif (!bWorking) {\n\t\t\tlong ert = 0;\n\t\t}\n\t\tCString strPath = finder.GetFilePath(); \n\t\tint nCounter = 0; \n\t\tCFileHandle file(strPath, TEXT(\"r\"));\n\t\tif (file.GetFile() != 0)\n\t\t{\n\t\t\t//cound the vertices in advance and make sure they fit the vector\n\t\t\twhile (!feof(file.GetFile()))\n\t\t\t{\n\t\t\t\tCHAR buffer[LINE_BUFF_SIZE];\n\t\t\t\tfgets(buffer, LINE_BUFF_SIZE, file.GetFile());\n\t\t\t\tif (buffer[0] == 'v')\n\t\t\t\t{\n\t\t\t\t\tnCounter++;\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (nNumVertices == -1)\n\t\t\t{\n\t\t\t\tnNumVertices = nCounter;\n\t\t\t}\n\t\t\telse if (nNumVertices != nCounter)\n\t\t\t{\n\t\t\t\tfinder.Close();\n\t\t\t\tAfxMessageBox(L\"wrong size\");\n\t\t\t\treturn false;\n\t\t\t}\n\t\t\tif (nFileCounter == 0)\n\t\t\t{\n\t\t\t\tm_pRenderWnd->m_nNumVertices = nNumVertices;\n\t\t\t\t//we have enough info now to preallocate memory to avoid performance penalties caused by dynamic memory allocations...\n\t\t\t\tvMeshes.reserve(nNumFiles);\n\t\t\t\tvMeshes.resize(nNumFiles);\n\t\t\t\tint nNumVerticesXYZ = nNumVertices * 3;\n\t\t\t\tfor (int h = 0; h < nNumFiles; h++)\n\t\t\t\t{\n\t\t\t\t\tvMeshes[h].reserve(nNumVerticesXYZ);\n\t\t\t\t\tvMeshes[h].resize(nNumVerticesXYZ);\n\t\t\t\t}\n\n\t\t\t\tm_pRenderWnd->m_AveFace.reserve(nNumVerticesXYZ);\n\t\t\t\tm_pRenderWnd->m_AveFace.resize(nNumVerticesXYZ);\n\t\t\t\tm_pRenderWnd->m_Mesh.reserve(nNumVerticesXYZ);\n\t\t\t\tm_pRenderWnd->m_Mesh.resize(nNumVerticesXYZ);\n\t\t\t}\n\t\t\trewind(file.GetFile());\n\t\t\tint nIndex = 0;\n\t\t\tfloat x = 0;\n\t\t\tfloat y = 0;\n\t\t\tfloat z = 0;\n\t\t\twhile (!feof(file.GetFile()))\n\t\t\t{\n\t\t\t\tCHAR buffer[LINE_BUFF_SIZE];\n\t\t\t\tfgets(buffer, LINE_BUFF_SIZE, file.GetFile());\n\t\t\t\tif (0 == strncmp(\"v \", buffer, 2))\n\t\t\t\t{\n\t\t\t\t\t//load vertices into the vectors\n\t\t\t\t\tsscanf_s(buffer + 1, \"%f %f %f\", &x, &y, &z);\n\t\t\t\t\tm_pRenderWnd->m_AveFace[nIndex] += x;\n\t\t\t\t\tm_pRenderWnd->m_AveFace[nIndex + nNumVertices] += y;\n\t\t\t\t\tm_pRenderWnd->m_AveFace[nIndex + 2 * nNumVertices] += z;\n\t\t\t\t\tvMeshes[nFileCounter][nIndex] = x;\n\t\t\t\t\tvMeshes[nFileCounter][nIndex + nNumVertices] = y;\n\t\t\t\t\tvMeshes[nFileCounter][nIndex + 2 * nNumVertices] = z;\n\t\t\t\t\tnIndex++;\n\t\t\t\t}\n\t\t\t\telse if (nFileCounter == 0 && 0 == strncmp(\"f \", buffer, 2))\n\t\t\t\t{\n\t\t\t\t\t//load face information (list of three points forming a triangle). Do it for the first mesh only as the rest should be the same\n\t\t\t\t\tsscanf_s(buffer + 1, \"%d %d %d\", &nI1, &nI2, &nI3);\n\t\t\t\t\tm_pRenderWnd->m_vIndices.push_back(nI1 - 1);\n\t\t\t\t\tm_pRenderWnd->m_vIndices.push_back(nI2 - 1);\n\t\t\t\t\tm_pRenderWnd->m_vIndices.push_back(nI3 - 1);\n\t\t\t\t}\n\t\t\t}\n\t\t\tnFileCounter++; \n\t\t}\n\t}\n\t//finder.Close(); //note, CFileFinder calls Close on destructor, so we don't need to call this here\n\n\t////////////////////////////////////////////////////////////////////////////////////////\n\t//STEP TWO: calculate the mean/average face mesh\n\t////////////////////////////////////////////////////////////////////////////////////////\n\tfor_each(m_pRenderWnd->m_AveFace.begin(), m_pRenderWnd->m_AveFace.end(), [nNumFiles](float& x) {x = x / nNumFiles; });\n\n\t////////////////////////////////////////////////////////////////////////////////////////\n\t//STEP THREE: subtract the mean from each face mesh\n\t////////////////////////////////////////////////////////////////////////////////////////\n\tauto t = m_pRenderWnd->m_AveFace;\n\tfor (auto& m : vMeshes)\n\t{\n\t\tint j = 0;\n\t\tfor_each(m.begin(), m.end(), [&j, &t](float& x) {x = x - t[j]; j++; });\n\t}\n\t/*for (int h = 0; h < nNumFiles; h++)//older style for loop - but is it easier to read??\n\t{\n\t\tfor (int j = 0; j < nNumVertices * 3; j++){\n\t\t\tvMeshes[h][j] = vMeshes[h][j] - m_pRenderWnd->m_AveFace[j];\n\t\t}\n\t}*/\n\t////////////////////////////////////////////////////////////////////////////////////////\n\t//STEP FOUR: calculate the eigenvectors\n\t////////////////////////////////////////////////////////////////////////////////////////\n\tint nNumVerticesXYZ = nNumVertices * 3;\n\tEigen::MatrixXf X(nNumVerticesXYZ, nNumFiles);\n\tint h = 0;\n\tfor (auto& m : vMeshes)\n\t{\n\t\tint j = 0;\n\t\tfor_each(m.begin(), m.end(), [&j, h, &X](float& x) {X(j, h) = x; j++; });\n\t\th++;\n\t}\n\t/*for (int h = 0; h < nNumFiles; h++)//older style for loop - is this easier to read??\n\t{\n\t\tfor (int j = 0; j < nNumVerticesXYZ; j++) {\n\t\t\tX(j, h) = vMeshes[h][j];\n\t\t}\n\t}*/\n\n\t//Use Principle Component Analysis. SVD routine in the Eigen library to do this\n\tEigen::JacobiSVD svd(X, Eigen::ComputeThinU | Eigen::ComputeThinV);\n\tEigen::MatrixXf U = svd.matrixU();\n\tEigen::MatrixXf V = svd.matrixV();\n\tEigen::VectorXf s = svd.singularValues();\n \n\tif (s.rows() < NUM_EIGENS)\n\t{\n\t\tCString strMsg;\n\t\tstrMsg.Format(L\"Not enough eigenvectors found. We need at least %d. Please increase the number of meshes and restart the application.\", NUM_EIGENS);\n\t\tAfxMessageBox(strMsg);\n\t\treturn false;\n\t}\n\n\t//There will be nNumFiles eigenvectors, but only take the first 20 (NUM_EIGENS)\n\tm_pRenderWnd->m_Eigen.reserve(NUM_EIGENS);\n\tm_pRenderWnd->m_Eigen.resize(NUM_EIGENS);\n\tfor (int h = 0; h < NUM_EIGENS; h++)\n\t{\n\t\tm_pRenderWnd->m_Eigen[h].reserve(nNumVerticesXYZ);\n\t\tm_pRenderWnd->m_Eigen[h].resize(nNumVerticesXYZ);\n\t\tint j = 0;\n\t\tfor_each(m_pRenderWnd->m_Eigen[h].begin(), m_pRenderWnd->m_Eigen[h].end(), [&j, h, &U](float& x) {x = U(j, h); j++; });\n\t\t/*for (int j = 0; j < nNumVerticesXYZ; j++) \t{//older style, again, isn't it easier to read?\n\t\t\tm_pRenderWnd->m_Eigen[h][j] = U(j, h);\n\t\t}*/\n\t}\n\t//We now have the eigenvectors. The mesh that is rendered will be the average/mean mesh plus a linear combination of the \n\t//the eignevector meshes (m_Eigen). That is each eigenvector is multipled by the scalar corresponding to each slider in the UI.\n\treturn true;\n}\n//---------------------------------------------------------------//\nvoid CFaceMorphDlg::OnTimer(UINT_PTR nIdEvent)\n{\n\tif (nIdEvent == 1)\n\t{\n\t\tif(m_pRenderWnd) m_pRenderWnd->Render();\n\t}\n} \n//---------------------------------------------------------------//\nvoid CFaceMorphDlg::OnSysCommand(UINT nID, LPARAM lParam)\n{\n\tif ((nID & 0xFFF0) == IDM_ABOUTBOX)\n\t{\n\t\tCAboutDlg dlgAbout;\n\t\tdlgAbout.DoModal();\n\t}\n\telse\n\t{\n\t\tCDialog::OnSysCommand(nID, lParam);\n\t}\n}\n//---------------------------------------------------------------//\n// If you add a minimize button to your dialog, you will need the code below\n// to draw the icon. For MFC applications using the document/view model,\n// this is automatically done for you by the framework.\nvoid CFaceMorphDlg::OnPaint()\n{\n\tif (IsIconic())\n\t{\n\t\tCPaintDC dc(this); // device context for painting\n\n\t\tSendMessage(WM_ICONERASEBKGND, reinterpret_cast(dc.GetSafeHdc()), 0);\n\n\t\t// Center icon in client rectangle\n\t\tint cxIcon = GetSystemMetrics(SM_CXICON);\n\t\tint cyIcon = GetSystemMetrics(SM_CYICON);\n\t\tCRect rect;\n\t\tGetClientRect(&rect);\n\t\tint x = (rect.Width() - cxIcon + 1) / 2;\n\t\tint y = (rect.Height() - cyIcon + 1) / 2;\n\n\t\t// Draw the icon\n\t\tdc.DrawIcon(x, y, m_hIcon);\n\t}\n\telse\n\t{\n\t\tCDialog::OnPaint();\n\t}\n\n\tfor (int h = 0; h < 20; h++)\n\t{\n\t\tm_Slider[h].SetRange(0, 2 * SLIDER_CENTER);\n\t\tm_Slider[h].SetPos(SLIDER_CENTER);\n\t}\t\n}\n//---------------------------------------------------------------//\n// The system calls this function to obtain the cursor to display while the user drags\n// the minimized window.\nHCURSOR CFaceMorphDlg::OnQueryDragIcon()\n{\n\treturn static_cast(m_hIcon);\n} \n//---------------------------------------------------------------//\nvoid CFaceMorphDlg::OnSlider()\n{\n\tif (!m_pRenderWnd) return;\n\tfor (int h = 0; h < NUM_SLIDERS; h++)\n\t{\n\t\tm_Scalars.m_Next[h] = (float)m_Slider[h].GetPos() - SLIDER_CENTER;\n\t}\n\tm_pRenderWnd->RecalcMesh(m_Scalars);\n}\n//---------------------------------------------------------------//\nvoid CFaceMorphDlg::OnHScroll(UINT nSBCode, UINT nPos, CScrollBar* pScrollBar)\n{\n\tCSliderCtrl* pSlider = reinterpret_cast(pScrollBar);\n\tfor (int h = 0; h < NUM_SLIDERS; h++)\n\t{\n\t\tif (pSlider == &m_Slider[h])\n\t\t{\n\t\t\tOnSlider();\n\t\t}\n\t}\n\tCDialog::OnHScroll(nSBCode, nPos, pScrollBar);\n}\n//---------------------------------------------------------------//\nvoid CFaceMorphDlg::OnClose()\n{\n\tif (m_pRenderWnd)\n\t{\n\t\tm_pRenderWnd->ShutDown();\n\t\tm_pRenderWnd->DestroyWindow();\n\t\tdelete m_pRenderWnd;\n\t\tm_pRenderWnd = 0;\n\t}\n\n\tCDialog::OnClose();\n}\n//---------------------------------------------------------------//\nvoid CFaceMorphDlg::OnBnClickedReset()\n{\n\tfor (int h = 0; h < NUM_SLIDERS; h++)\n\t{\n\t\tm_Slider[h].SetPos(SLIDER_CENTER);\n\t};\n\tOnSlider();\n}\n//---------------------------------------------------------------//\nvoid CFaceMorphDlg::OnBnClickedCheck1()\n{\n\tif (!m_pRenderWnd) return;\n\tm_pRenderWnd->m_bWireframe = !m_pRenderWnd->m_bWireframe;\n}\n//-------------------------------------------------------------------------------------//\nbool CFaceMorphDlg::DoesDirExist(CString str)\n{\n\tDWORD dwRetVal = GetFileAttributes(str);\n\tif (dwRetVal == 0xFFFFFFFF)\n\t{\n\t\treturn false;\n\t}\n\telse if (dwRetVal & FILE_ATTRIBUTE_DIRECTORY)\n\t{\n\t\treturn true;\n\t}\n\treturn false;\n}\n//---------------------------------------------------------------//\nvoid CFaceMorphDlg::OnBnClickedExport()\n{ \n\tCString strLine;\n\tCString strExportPath;\n\tCWaitCursor wait;\n\n\tGetDlgItemText(IDC_EXPORT_PATH, strExportPath);\n\tif (!DoesDirExist(strExportPath))\n\t{\n\t\tAfxMessageBox(L\"Folder doesn't exist!\");\n\t\treturn;\n\t}\n\t//export eigenvalues \n\tCStdioFile f;\n\tint nNumVerticesXYZ = m_pRenderWnd->m_nNumVertices * 3;\n\tif (f.Open(strExportPath + L\"\\\\Eigen.dat\", CFile::modeCreate | CFile::modeWrite))\n\t{\t\t\n\t\tfor (int j = 0; j < nNumVerticesXYZ; j++)\n\t\t{\n\t\t\tfor (int h = 0; h < NUM_EIGENS; h++)\n\t\t\t{\n\t\t\t\tstrLine.Format(L\"%f\", m_pRenderWnd->m_Eigen[h][j]);\n\t\t\t\tf.WriteString(strLine);\n\t\t\t}\n\t\t\tf.WriteString(L\"\\n\");\n\t\t}\n\t\tf.Close();\n\t}\n\telse\n\t{\n\t\tAfxMessageBox(L\"Could not save EigenVector file\");\n\t\treturn;\n\t}\n\n\tif (f.Open(strExportPath + L\"\\\\MeanFace.dat\", CFile::modeCreate | CFile::modeWrite))\n\t{\n\t\tfor (int j = 0; j < nNumVerticesXYZ; j++)\n\t\t{\n\t\t\tstrLine.Format(L\"%f\\n\", m_pRenderWnd->m_AveFace[j]);\n\t\t\tf.WriteString(strLine);\n\t\t}\n\t\tf.Close();\n\t}\n\telse\n\t{\n\t\tAfxMessageBox(L\"Could not save file\");\n\t\treturn;\n\t}\n\n\tif (f.Open(strExportPath + L\"\\\\MeanFace.obj\", CFile::modeCreate | CFile::modeWrite))\n\t{\n\t\tfor (int j = 0; j < m_pRenderWnd->m_nNumVertices; j++)\n\t\t{\n\t\t\tstrLine.Format(L\"v %f %f %f\\n\", \n\t\t\t\tm_pRenderWnd->m_AveFace[j], \n\t\t\t\tm_pRenderWnd->m_AveFace[m_pRenderWnd->m_nNumVertices + j], \n\t\t\t\tm_pRenderWnd->m_AveFace[2 * m_pRenderWnd->m_nNumVertices + j]);\n\t\t\tf.WriteString(strLine);\n\t\t}\n\t\tsize_t nNumTriangles = m_pRenderWnd->m_vIndices.size() / 3;\n\t\tfor (int j = 0; j < nNumTriangles; j++)\n\t\t{\n\t\t\tstrLine.Format(L\"f %d %d %d\\n\",\n\t\t\t\tm_pRenderWnd->m_vIndices[3 * j + 0] + 1,\n\t\t\t\tm_pRenderWnd->m_vIndices[3 * j + 1] + 1,\n\t\t\t\tm_pRenderWnd->m_vIndices[3 * j + 2] + 1);\n\t\t\tf.WriteString(strLine);\n\t\t}\n\t\tf.Close();\n\t}\n\telse\n\t{\n\t\tAfxMessageBox(L\"Could not save file\");\n\t\treturn;\n\t}\n\t//To load this into matlab using a .,m file, something along the following should work\n\t//fid = fopen('C:\\_3DMMExport\\MeanFace.dat');\n\t//avgFace = [];\n\t//while ~feof(fid)\n\t//\ttline = fgetl(fid);\n\t//avgFace = [avgFace; sscanf(tline(1:end), '%f')']; \n\t//\tend\n\t//\tfclose(fid);\n\t//fid = fopen('C:\\_3DMMExport\\Eigen.dat');\n\t//egn = [length(avgFace), 20];\n\t//k = 1;\n\t//while ~feof(fid)\n\t//\ttline = fgetl(fid);\n\t//egn(k, 1:20) = sscanf(tline(1:end), '%f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f %f');\n\t//k = k + 1;\n\t//end\n\t//\tfclose(fid);\n}\n", "meta": {"hexsha": "e3475692459e149621a913962994be60b2264532", "size": 16937, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "FaceMorph/FaceMorphDlg.cpp", "max_stars_repo_name": "nodecomplete/3DMM-Face-Sample", "max_stars_repo_head_hexsha": "fc9584e32674477097eae1520207c0166d797ce3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "FaceMorph/FaceMorphDlg.cpp", "max_issues_repo_name": "nodecomplete/3DMM-Face-Sample", "max_issues_repo_head_hexsha": "fc9584e32674477097eae1520207c0166d797ce3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "FaceMorph/FaceMorphDlg.cpp", "max_forks_repo_name": "nodecomplete/3DMM-Face-Sample", "max_forks_repo_head_hexsha": "fc9584e32674477097eae1520207c0166d797ce3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8534923339, "max_line_length": 150, "alphanum_fraction": 0.5906004605, "num_tokens": 4995, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632683808533, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.22936224696128907}} {"text": "// Copyright 2005 Mitsubishi Electric Research Laboratories All Rights\n// Reserved.\n\n// Permission to use, copy and modify this software and its documentation\n// without fee for educational, research and non-profit purposes, is hereby\n// granted, provided that the above copyright notice and the following three\n// paragraphs appear in all copies.\n\n// To request permission to incorporate this software into commercial products\n// contact: Vice President of Marketing and Business Development; Mitsubishi\n// Electric Research Laboratories (MERL), 201 Broadway, Cambridge, MA 02139 or\n// .\n\n// IN NO EVENT SHALL MERL BE LIABLE TO ANY PARTY FOR DIRECT, INDIRECT, SPECIAL,\n// INCIDENTAL, OR CONSEQUENTIAL DAMAGES, INCLUDING LOST PROFITS, ARISING OUT OF\n// THE USE OF THIS SOFTWARE AND ITS DOCUMENTATION, EVEN IF MERL HAS BEEN\n// ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.\n\n// MERL SPECIFICALLY DISCLAIMS ANY WARRANTIES, INCLUDING, BUT NOT LIMITED TO,\n// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n// PURPOSE. THE SOFTWARE PROVIDED HEREUNDER IS ON AN \"AS IS\" BASIS, AND MERL\n// HAS NO OBLIGATIONS TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS OR\n// MODIFICATIONS.\n\n\n#include \n#include \n#include \n\n// Include Eigen\n#include \n\n#define BRDF_SAMPLING_RES_THETA_H 90\n#define BRDF_SAMPLING_RES_THETA_D 90\n#define BRDF_SAMPLING_RES_PHI_D 360\n\n#define RED_SCALE (1.0/1500.0)\n#define GREEN_SCALE (1.15/1500.0)\n#define BLUE_SCALE (1.66/1500.0)\n\nstruct MerlBRDF {\n\n /* Storage of the BRDF data */\n double* brdf;\n\n /* Constructor and destructor */\n MerlBRDF() : brdf(nullptr) {\n }\n\n ~MerlBRDF() {\n if(brdf != nullptr) {\n delete[] brdf;\n }\n }\n\n /* Evaluate the BRDF for a given couple of vectors */\n template\n RGB const value(const Vector& wi, const Vector& wo) const {\n double ti = acos(wi.z);\n double pi = atan2(wi.y, wi.x);\n double to = acos(wo.z);\n double po = atan2(wo.y, wo.x);\n\n double r, g, b;\n lookup_brdf_val(ti, pi, to, po, r, g, b);\n return RGB(r, g, b);\n }\n\n /* Read BRDF data */\n bool read_brdf(const std::string& filename)\n {\n FILE *f = fopen(filename.c_str(), \"rb\");\n if (!f) {\n return false;\n }\n\n int dims[3];\n fread(dims, sizeof(int), 3, f);\n int n = dims[0] * dims[1] * dims[2];\n if (n != BRDF_SAMPLING_RES_THETA_H *\n BRDF_SAMPLING_RES_THETA_D *\n BRDF_SAMPLING_RES_PHI_D / 2)\n {\n fprintf(stderr, \"Dimensions don't match\\n\");\n fclose(f);\n return false;\n }\n\n brdf = (double*) malloc (sizeof(double)*3*n);\n fread(brdf, sizeof(double), 3*n, f);\n\n fclose(f);\n return true;\n }\n\n /* Project this BRDF to SH\n *\n * Return a std::vector containing an Eigen::MatrixXf for each input\n * elevation. This matrix size is SH::Terms(order) x RGB.\n */\n template\n std::vector projectToSH(int nbElev, int order, int M=10000) const {\n\n // Random sampler\n std::mt19937 gen(0);\n std::uniform_real_distribution dist(0.0,1.0);\n\n // Return vector\n std::vector res;\n res.reserve(nbElev);\n\n // Sample each incident direction\n for(int e=0; e(wi, wo);\n const auto ylm = SH::FastBasis(wo, order);\n shCoeffs.col(0) += rgb[0]*ylm;\n shCoeffs.col(1) += rgb[1]*ylm;\n shCoeffs.col(2) += rgb[2]*ylm;\n }\n shCoeffs *= 4.0*M_PI / float(M);\n res.push_back(shCoeffs);\n }\n\n return res;\n }\n\n // cross product of two vectors\n void cross_product (double* v1, double* v2, double* out) const\n {\n out[0] = v1[1]*v2[2] - v1[2]*v2[1];\n out[1] = v1[2]*v2[0] - v1[0]*v2[2];\n out[2] = v1[0]*v2[1] - v1[1]*v2[0];\n }\n\n // normalize vector\n void normalize(double* v) const\n {\n // normalize\n double len = sqrt(v[0]*v[0]+v[1]*v[1]+v[2]*v[2]);\n v[0] = v[0] / len;\n v[1] = v[1] / len;\n v[2] = v[2] / len;\n }\n\n // rotate vector along one axis\n void rotate_vector(double* vector, double* axis, double angle, double* out) const\n {\n double temp;\n double cross[3];\n double cos_ang = cos(angle);\n double sin_ang = sin(angle);\n\n out[0] = vector[0] * cos_ang;\n out[1] = vector[1] * cos_ang;\n out[2] = vector[2] * cos_ang;\n\n temp = axis[0]*vector[0]+axis[1]*vector[1]+axis[2]*vector[2];\n temp = temp*(1.0-cos_ang);\n\n out[0] += axis[0] * temp;\n out[1] += axis[1] * temp;\n out[2] += axis[2] * temp;\n\n cross_product (axis,vector,cross);\n\n out[0] += cross[0] * sin_ang;\n out[1] += cross[1] * sin_ang;\n out[2] += cross[2] * sin_ang;\n }\n\n\n // convert standard coordinates to half vector/difference vector coordinates\n void std_coords_to_half_diff_coords(double theta_in, double fi_in,\n double theta_out, double fi_out,\n double& theta_half,double& fi_half,\n double& theta_diff,double& fi_diff ) const\n {\n\n // compute in vector\n double in_vec_z = cos(theta_in);\n double proj_in_vec = sin(theta_in);\n double in_vec_x = proj_in_vec*cos(fi_in);\n double in_vec_y = proj_in_vec*sin(fi_in);\n double in[3]= {in_vec_x,in_vec_y,in_vec_z};\n normalize(in);\n\n\n // compute out vector\n double out_vec_z = cos(theta_out);\n double proj_out_vec = sin(theta_out);\n double out_vec_x = proj_out_vec*cos(fi_out);\n double out_vec_y = proj_out_vec*sin(fi_out);\n double out[3]= {out_vec_x,out_vec_y,out_vec_z};\n normalize(out);\n\n\n // compute halfway vector\n double half_x = (in_vec_x + out_vec_x)/2.0f;\n double half_y = (in_vec_y + out_vec_y)/2.0f;\n double half_z = (in_vec_z + out_vec_z)/2.0f;\n double half[3] = {half_x,half_y,half_z};\n normalize(half);\n\n // compute theta_half, fi_half\n theta_half = acos(half[2]);\n fi_half = atan2(half[1], half[0]);\n\n\n double bi_normal[3] = {0.0, 1.0, 0.0};\n double normal[3] = { 0.0, 0.0, 1.0 };\n double temp[3];\n double diff[3];\n\n // compute diff vector\n rotate_vector(in, normal , -fi_half, temp);\n rotate_vector(temp, bi_normal, -theta_half, diff);\n\n // compute theta_diff, fi_diff\n theta_diff = acos(diff[2]);\n fi_diff = atan2(diff[1], diff[0]);\n\n }\n\n\n // Lookup theta_half index\n // This is a non-linear mapping!\n // In: [0 .. pi/2]\n // Out: [0 .. 89]\n inline int theta_half_index(double theta_half) const\n {\n if (theta_half <= 0.0)\n return 0;\n double theta_half_deg = ((theta_half / (M_PI/2.0))*BRDF_SAMPLING_RES_THETA_H);\n double temp = theta_half_deg*BRDF_SAMPLING_RES_THETA_H;\n temp = sqrt(temp);\n int ret_val = (int)temp;\n if (ret_val < 0) ret_val = 0;\n if (ret_val >= BRDF_SAMPLING_RES_THETA_H)\n ret_val = BRDF_SAMPLING_RES_THETA_H-1;\n return ret_val;\n }\n\n\n // Lookup theta_diff index\n // In: [0 .. pi/2]\n // Out: [0 .. 89]\n inline int theta_diff_index(double theta_diff) const\n {\n int tmp = int(theta_diff / (M_PI * 0.5) * BRDF_SAMPLING_RES_THETA_D);\n if (tmp < 0)\n return 0;\n else if (tmp < BRDF_SAMPLING_RES_THETA_D - 1)\n return tmp;\n else\n return BRDF_SAMPLING_RES_THETA_D - 1;\n }\n\n\n // Lookup phi_diff index\n inline int phi_diff_index(double phi_diff) const\n {\n // Because of reciprocity, the BRDF is unchanged under\n // phi_diff -> phi_diff + M_PI\n if (phi_diff < 0.0)\n phi_diff += M_PI;\n\n // In: phi_diff in [0 .. pi]\n // Out: tmp in [0 .. 179]\n int tmp = int(phi_diff / M_PI * BRDF_SAMPLING_RES_PHI_D / 2);\n if (tmp < 0)\n return 0;\n else if (tmp < BRDF_SAMPLING_RES_PHI_D / 2 - 1)\n return tmp;\n else\n return BRDF_SAMPLING_RES_PHI_D / 2 - 1;\n }\n\n // Given a pair of incoming/outgoing angles, look up the BRDF.\n void lookup_brdf_val(double theta_in, double fi_in,\n double theta_out, double fi_out,\n double& red_val, double& green_val,\n double& blue_val) const\n {\n // Convert to halfangle / difference angle coordinates\n double theta_half, fi_half, theta_diff, fi_diff;\n\n std_coords_to_half_diff_coords(theta_in, fi_in, theta_out, fi_out,\n theta_half, fi_half, theta_diff, fi_diff);\n\n\n // Find index.\n // Note that phi_half is ignored, since isotropic BRDFs are assumed\n int ind = phi_diff_index(fi_diff) +\n theta_diff_index(theta_diff) * BRDF_SAMPLING_RES_PHI_D / 2 +\n theta_half_index(theta_half) * BRDF_SAMPLING_RES_PHI_D / 2 *\n BRDF_SAMPLING_RES_THETA_D;\n\n red_val = brdf[ind] * RED_SCALE;\n green_val = brdf[ind + BRDF_SAMPLING_RES_THETA_H*BRDF_SAMPLING_RES_THETA_D*BRDF_SAMPLING_RES_PHI_D/2] * GREEN_SCALE;\n blue_val = brdf[ind + BRDF_SAMPLING_RES_THETA_H*BRDF_SAMPLING_RES_THETA_D*BRDF_SAMPLING_RES_PHI_D] * BLUE_SCALE;\n\n\n if (red_val < 0.0 || green_val < 0.0 || blue_val < 0.0)\n fprintf(stderr, \"Below horizon.\\n\");\n\n }\n\n\n\n//#include \"EXR_IO.h\"\n//\n//int main(int argc, char *argv[])\n//{\n// if(argc != 3) {\n// fprintf(stderr, \"Uncorrect number of arguments to the command line\");\n// fprintf(stderr, \"Usage: brdf_read [input].binary [output].exr\");\n// return 1;\n// }\n//\n//\n//\tconst char *filename = argv[1];\n//\tdouble* brdf;\n//\n//\t// read brdf\n//\tif (!read_brdf(filename, brdf))\n//\t{\n//\t\tfprintf(stderr, \"Error reading %s\\n\", filename);\n//\t\texit(1);\n//\t}\n//\n// const auto phi = 90;\n//\n// const auto SKIP_PHI = BRDF_SAMPLING_RES_PHI_D / 2;\n//\n// const auto SKIP = SKIP_PHI\n// * BRDF_SAMPLING_RES_THETA_D\n// * BRDF_SAMPLING_RES_THETA_H;\n//\n// const auto RES = BRDF_SAMPLING_RES_THETA_H\n// * BRDF_SAMPLING_RES_THETA_D;\n//\n//\n// double* img = new double[RES*3];\n//\n// for(auto i=0; i::SaveEXR(argv[2], BRDF_SAMPLING_RES_THETA_H, BRDF_SAMPLING_RES_THETA_D, img);\n//\n//\treturn 0;\n//}\n\n};\n", "meta": {"hexsha": "3aba9a278f442d0cd5192cdff71eb0cdb5252e6c", "size": 11693, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "utils/Merl.hpp", "max_stars_repo_name": "belcour/IntegralSH", "max_stars_repo_head_hexsha": "092bed8dc974f0f4c467f54a33d3e3e8968264ae", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 48.0, "max_stars_repo_stars_event_min_datetime": "2018-02-27T07:07:01.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-08T04:40:06.000Z", "max_issues_repo_path": "utils/Merl.hpp", "max_issues_repo_name": "belcour/IntegralSH", "max_issues_repo_head_hexsha": "092bed8dc974f0f4c467f54a33d3e3e8968264ae", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "utils/Merl.hpp", "max_forks_repo_name": "belcour/IntegralSH", "max_forks_repo_head_hexsha": "092bed8dc974f0f4c467f54a33d3e3e8968264ae", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2018-05-08T09:28:28.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-01T03:40:39.000Z", "avg_line_length": 30.1365979381, "max_line_length": 122, "alphanum_fraction": 0.5946292654, "num_tokens": 3419, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.2291657286788779}} {"text": "/*\nanharmonic_core.cpp\n\nCopyright (c) 2014 Terumasa Tadano\n\nThis file is distributed under the terms of the MIT license.\nPlease see the file 'LICENCE.txt' in the root directory\nor http://opensource.org/licenses/mit-license.php for information.\n*/\n\n#include \"mpi_common.h\"\n#include \"anharmonic_core.h\"\n#include \"constants.h\"\n#include \"dynamical.h\"\n#include \"error.h\"\n#include \"fcs_phonon.h\"\n#include \"integration.h\"\n#include \"kpoint.h\"\n#include \"mathfunctions.h\"\n#include \"memory.h\"\n#include \"mode_analysis.h\"\n#include \"phonon_dos.h\"\n#include \"system.h\"\n#include \"thermodynamics.h\"\n#include \n#include \n#include \n\n#ifdef _OPENMP\n\n#include \n\n#endif\n\nusing namespace PHON_NS;\n\nAnharmonicCore::AnharmonicCore(PHON *phon) : Pointers(phon)\n{\n set_default_variables();\n}\n\nAnharmonicCore::~AnharmonicCore()\n{\n deallocate_variables();\n};\n\nvoid AnharmonicCore::set_default_variables()\n{\n quartic_mode = 0;\n use_tuned_ver = true;\n use_triplet_symmetry = true;\n use_quartet_symmetry = true;\n relvec_v3 = nullptr;\n relvec_v4 = nullptr;\n invmass_v3 = nullptr;\n invmass_v4 = nullptr;\n evec_index_v3 = nullptr;\n evec_index_v4 = nullptr;\n fcs_group_v3 = nullptr;\n fcs_group_v4 = nullptr;\n phi3_reciprocal = nullptr;\n phi4_reciprocal = nullptr;\n phase_storage_dos = nullptr;\n}\n\nvoid AnharmonicCore::deallocate_variables()\n{\n if (relvec_v3) {\n deallocate(relvec_v3);\n }\n if (relvec_v4) {\n deallocate(relvec_v4);\n }\n if (invmass_v3) {\n deallocate(invmass_v3);\n }\n if (invmass_v4) {\n deallocate(invmass_v4);\n }\n if (evec_index_v3) {\n deallocate(evec_index_v3);\n }\n if (evec_index_v4) {\n deallocate(evec_index_v4);\n }\n if (fcs_group_v3) {\n deallocate(fcs_group_v3);\n }\n if (fcs_group_v4) {\n deallocate(fcs_group_v4);\n }\n if (phi3_reciprocal) {\n deallocate(phi3_reciprocal);\n }\n if (phi4_reciprocal) {\n deallocate(phi4_reciprocal);\n }\n if (phase_storage_dos) delete phase_storage_dos;\n}\n\nvoid AnharmonicCore::setup()\n{\n sym_permutation = true;\n use_tuned_ver = true;\n MPI_Bcast(&use_tuned_ver, 1, MPI_CXX_BOOL, 0, MPI_COMM_WORLD);\n\n if (fcs_phonon->maxorder >= 2) setup_cubic();\n if (fcs_phonon->maxorder >= 3) setup_quartic();\n\n if (!mode_analysis->calc_fstate_k && dos->kmesh_dos) {\n phase_storage_dos = new PhaseFactorStorage(dos->kmesh_dos->nk_i);\n phase_storage_dos->create(use_tuned_ver);\n }\n}\n\nvoid AnharmonicCore::prepare_relative_vector(const std::vector &fcs_in,\n const unsigned int N,\n const int number_of_groups,\n std::vector *fcs_group,\n std::vector *&vec_out) const\n{\n int i, j, k;\n\n double vecs[3][3];\n double **xshift_s;\n\n std::vector atm_super, atm_prim;\n std::vector cells;\n\n double mat_convert[3][3];\n\n for (i = 0; i < 3; ++i) {\n for (j = 0; j < 3; ++j) {\n mat_convert[i][j] = 0.0;\n for (k = 0; k < 3; ++k) {\n mat_convert[i][j] += system->rlavec_p[i][k] * system->lavec_s_anharm[k][j];\n }\n }\n }\n\n allocate(xshift_s, 27, 3);\n\n for (i = 0; i < 3; ++i) xshift_s[0][i] = 0.0;\n\n unsigned int icell = 0;\n\n for (int ix = -1; ix <= 1; ++ix) {\n for (int iy = -1; iy <= 1; ++iy) {\n for (int iz = -1; iz <= 1; ++iz) {\n if (ix == 0 && iy == 0 && iz == 0) continue;\n\n ++icell;\n\n xshift_s[icell][0] = static_cast(ix);\n xshift_s[icell][1] = static_cast(iy);\n xshift_s[icell][2] = static_cast(iz);\n }\n }\n }\n\n atm_prim.resize(N);\n cells.resize(N);\n atm_super.resize(N);\n\n unsigned int icount = 0;\n\n for (auto igroup = 0; igroup < number_of_groups; ++igroup) {\n\n unsigned int nsize_group = fcs_group[igroup].size();\n\n for (j = 0; j < nsize_group; ++j) {\n\n for (i = 0; i < N; ++i) {\n atm_prim[i] = fcs_in[icount].pairs[i].index / 3;\n const auto tran_tmp = fcs_in[icount].pairs[i].tran;\n cells[i] = fcs_in[icount].pairs[i].cell_s;\n atm_super[i] = system->map_p2s_anharm[atm_prim[i]][tran_tmp];\n }\n\n for (i = 0; i < N - 1; ++i) {\n for (k = 0; k < 3; ++k) {\n vecs[i][k] = system->xr_s_anharm[atm_super[i + 1]][k] + xshift_s[cells[i + 1]][k]\n - system->xr_s_anharm[system->map_p2s_anharm[atm_prim[i + 1]][0]][k];\n }\n rotvec(vecs[i], vecs[i], mat_convert);\n }\n\n if (N == 3) {\n vec_out[igroup].emplace_back(vecs[0], vecs[1]);\n } else if (N == 4) {\n vec_out[igroup].emplace_back(vecs[0], vecs[1], vecs[2]);\n }\n ++icount;\n }\n }\n\n deallocate(xshift_s);\n}\n\nvoid AnharmonicCore::prepare_group_of_force_constants(const std::vector &fcs_in,\n const unsigned int N,\n int &number_of_groups,\n std::vector *&fcs_group_out) const\n{\n // Find the number of groups which has different evecs.\n\n unsigned int i;\n std::vector arr_old, arr_tmp;\n\n number_of_groups = 0;\n\n arr_old.clear();\n for (i = 0; i < N; ++i) {\n arr_old.push_back(-1);\n }\n\n for (const auto &it: fcs_in) {\n\n arr_tmp.clear();\n\n for (i = 0; i < it.pairs.size(); ++i) {\n arr_tmp.push_back(it.pairs[i].index);\n }\n\n if (arr_tmp != arr_old) {\n ++number_of_groups;\n arr_old.clear();\n arr_old.reserve(arr_tmp.size());\n std::copy(arr_tmp.begin(), arr_tmp.end(), std::back_inserter(arr_old));\n }\n }\n\n allocate(fcs_group_out, number_of_groups);\n\n int igroup = -1;\n\n arr_old.clear();\n for (i = 0; i < N; ++i) {\n arr_old.push_back(-1);\n }\n\n for (const auto &it: fcs_in) {\n\n arr_tmp.clear();\n\n for (i = 0; i < it.pairs.size(); ++i) {\n arr_tmp.push_back(it.pairs[i].index);\n }\n\n if (arr_tmp != arr_old) {\n ++igroup;\n arr_old.clear();\n arr_old.reserve(arr_tmp.size());\n std::copy(arr_tmp.begin(), arr_tmp.end(), std::back_inserter(arr_old));\n }\n\n fcs_group_out[igroup].push_back(it.fcs_val);\n }\n}\n\nstd::complex AnharmonicCore::V3(const unsigned int ks[3])\n{\n return V3(ks,\n dos->kmesh_dos->xk,\n dos->dymat_dos->get_eigenvalues(),\n dos->dymat_dos->get_eigenvectors(),\n this->phase_storage_dos);\n}\n\nstd::complex AnharmonicCore::V3(const unsigned int ks[3],\n const double *const *xk_in,\n const double *const *eval_in,\n const std::complex *const *const *evec_in)\n{\n return V3(ks,\n xk_in,\n eval_in,\n evec_in,\n this->phase_storage_dos);\n}\n\nstd::complex AnharmonicCore::V4(const unsigned int ks[4])\n{\n return V4(ks,\n dos->kmesh_dos->xk,\n dos->dymat_dos->get_eigenvalues(),\n dos->dymat_dos->get_eigenvectors(),\n this->phase_storage_dos);\n}\n\nstd::complex AnharmonicCore::Phi3(const unsigned int ks[3])\n{\n return Phi3(ks,\n dos->kmesh_dos->xk,\n dos->dymat_dos->get_eigenvalues(),\n dos->dymat_dos->get_eigenvectors(),\n this->phase_storage_dos);\n}\n\nstd::complex AnharmonicCore::Phi4(const unsigned int ks[4])\n{\n return Phi4(ks,\n dos->kmesh_dos->xk,\n dos->dymat_dos->get_eigenvalues(),\n dos->dymat_dos->get_eigenvectors(),\n this->phase_storage_dos);\n}\n\nstd::complex AnharmonicCore::V3(const unsigned int ks[3],\n const double *const *xk_in,\n const double *const *eval_in,\n const std::complex *const *const *evec_in,\n const PhaseFactorStorage *phase_storage_in)\n{\n int i;\n unsigned int kn[3], sn[3];\n const int ns = dynamical->neval;\n\n double omega[3];\n auto ret = std::complex(0.0, 0.0);\n auto ret_re = 0.0;\n auto ret_im = 0.0;\n\n for (i = 0; i < 3; ++i) {\n kn[i] = ks[i] / ns;\n sn[i] = ks[i] % ns;\n omega[i] = eval_in[kn[i]][sn[i]];\n }\n\n // Return zero if any of the involving phonon has imaginary frequency\n if (omega[0] < eps8 || omega[1] < eps8 || omega[2] < eps8) return 0.0;\n\n// for (i = 0; i < ngroup_v3; ++i) {\n// std::cout << \"invmass_v3[i] = \" << invmass_v3[i] << std::endl;\n// }\n//\n// for (i = 0; i < ngroup_v3; ++i) {\n// std::cout << \"evec_index_v3 = \" << evec_index_v3[i][0]\n// << evec_index_v3[i][1] << evec_index_v3[i][2] << std::endl;\n// }\n\n if (kn[1] != kindex_phi3_stored[0] || kn[2] != kindex_phi3_stored[1]) {\n calc_phi3_reciprocal(xk_in[kn[1]],\n xk_in[kn[2]],\n phase_storage_in,\n phi3_reciprocal);\n\n// for (i = 0; i < ngroup_v3; ++i) {\n// std::cout << phi3_reciprocal[i] << std::endl;\n// }\n kindex_phi3_stored[0] = kn[1];\n kindex_phi3_stored[1] = kn[2];\n }\n#ifdef _OPENMP\n#pragma omp parallel for private(ret), reduction(+: ret_re, ret_im)\n#endif\n for (i = 0; i < ngroup_v3; ++i) {\n ret = evec_in[kn[0]][sn[0]][evec_index_v3[i][0]]\n * evec_in[kn[1]][sn[1]][evec_index_v3[i][1]]\n * evec_in[kn[2]][sn[2]][evec_index_v3[i][2]]\n * invmass_v3[i] * phi3_reciprocal[i];\n ret_re += ret.real();\n ret_im += ret.imag();\n }\n\n return std::complex(ret_re, ret_im)\n / std::sqrt(omega[0] * omega[1] * omega[2]);\n}\n\nstd::complex AnharmonicCore::Phi3(const unsigned int ks[3],\n const double *const *xk_in,\n const double *const *eval_in,\n const std::complex *const *const *evec_in,\n const PhaseFactorStorage *phase_storage_in)\n{\n int i;\n unsigned int kn[3], sn[3];\n const auto ns = dynamical->neval;\n\n double omega[3];\n std::complex ret = std::complex(0.0, 0.0);\n double ret_re = 0.0;\n double ret_im = 0.0;\n\n for (i = 0; i < 3; ++i) {\n kn[i] = ks[i] / ns;\n sn[i] = ks[i] % ns;\n omega[i] = eval_in[kn[i]][sn[i]];\n }\n\n if (kn[1] != kindex_phi3_stored[0] || kn[2] != kindex_phi3_stored[1]) {\n calc_phi3_reciprocal(xk_in[kn[1]],\n xk_in[kn[2]],\n phase_storage_in,\n phi3_reciprocal);\n kindex_phi3_stored[0] = kn[1];\n kindex_phi3_stored[1] = kn[2];\n }\n#ifdef _OPENMP\n#pragma omp parallel for private(ret), reduction(+: ret_re, ret_im)\n#endif\n for (i = 0; i < ngroup_v3; ++i) {\n ret = evec_in[kn[0]][sn[0]][evec_index_v3[i][0]]\n * evec_in[kn[1]][sn[1]][evec_index_v3[i][1]]\n * evec_in[kn[2]][sn[2]][evec_index_v3[i][2]]\n * invmass_v3[i] * phi3_reciprocal[i];\n ret_re += ret.real();\n ret_im += ret.imag();\n }\n\n return std::complex(ret_re, ret_im);\n}\n\nvoid AnharmonicCore::calc_phi3_reciprocal(const double *xk1,\n const double *xk2,\n const PhaseFactorStorage *phase_storage_in,\n std::complex *ret)\n{\n int i, j;\n double phase;\n std::complex ret_in;\n unsigned int nsize_group;\n\n const auto tune_type_now = phase_storage_in->get_tune_type();\n\n if (tune_type_now == 1) {\n\n#pragma omp parallel for private(ret_in, nsize_group, j, phase)\n for (i = 0; i < ngroup_v3; ++i) {\n\n ret_in = std::complex(0.0, 0.0);\n nsize_group = fcs_group_v3[i].size();\n\n for (j = 0; j < nsize_group; ++j) {\n phase = relvec_v3[i][j].vecs[0][0] * xk1[0]\n + relvec_v3[i][j].vecs[0][1] * xk1[1]\n + relvec_v3[i][j].vecs[0][2] * xk1[2]\n + relvec_v3[i][j].vecs[1][0] * xk2[0]\n + relvec_v3[i][j].vecs[1][1] * xk2[1]\n + relvec_v3[i][j].vecs[1][2] * xk2[2];\n\n ret_in += fcs_group_v3[i][j] * phase_storage_in->get_exp_type1(phase);\n }\n ret[i] = ret_in;\n }\n\n } else if (tune_type_now == 2) {\n\n // Tuned version is used when nk1=nk2=nk3 doesn't hold.\n\n double phase3[3];\n\n#pragma omp parallel for private(ret_in, nsize_group, j, phase3)\n for (i = 0; i < ngroup_v3; ++i) {\n\n ret_in = std::complex(0.0, 0.0);\n nsize_group = fcs_group_v3[i].size();\n\n for (j = 0; j < nsize_group; ++j) {\n for (auto ii = 0; ii < 3; ++ii) {\n phase3[ii]\n = relvec_v3[i][j].vecs[0][ii] * xk1[ii]\n + relvec_v3[i][j].vecs[1][ii] * xk2[ii];\n }\n ret_in += fcs_group_v3[i][j] * phase_storage_in->get_exp_type2(phase3);\n }\n ret[i] = ret_in;\n }\n } else {\n // Original version\n#pragma omp parallel for private(ret_in, nsize_group, phase, j)\n for (i = 0; i < ngroup_v3; ++i) {\n\n ret_in = std::complex(0.0, 0.0);\n nsize_group = fcs_group_v3[i].size();\n\n for (j = 0; j < nsize_group; ++j) {\n phase\n = relvec_v3[i][j].vecs[0][0] * xk1[0]\n + relvec_v3[i][j].vecs[0][1] * xk1[1]\n + relvec_v3[i][j].vecs[0][2] * xk1[2]\n + relvec_v3[i][j].vecs[1][0] * xk2[0]\n + relvec_v3[i][j].vecs[1][1] * xk2[1]\n + relvec_v3[i][j].vecs[1][2] * xk2[2];\n ret_in += fcs_group_v3[i][j] * std::exp(im * phase);\n }\n ret[i] = ret_in;\n }\n }\n}\n\nstd::complex AnharmonicCore::V4(const unsigned int ks[4],\n const double *const *xk_in,\n const double *const *eval_in,\n const std::complex *const *const *evec_in,\n const PhaseFactorStorage *phase_storage_in)\n{\n int i;\n const int ns = dynamical->neval;\n unsigned int kn[4], sn[4];\n double omega[4];\n auto ret_re = 0.0;\n auto ret_im = 0.0;\n auto ret = std::complex(0.0, 0.0);\n\n for (i = 0; i < 4; ++i) {\n kn[i] = ks[i] / ns;\n sn[i] = ks[i] % ns;\n omega[i] = eval_in[kn[i]][sn[i]];\n }\n // Return zero if any of the involving phonon has imaginary frequency\n if (omega[0] < eps8 || omega[1] < eps8 || omega[2] < eps8 || omega[3] < eps8) return 0.0;\n\n if (kn[1] != kindex_phi4_stored[0]\n || kn[2] != kindex_phi4_stored[1]\n || kn[3] != kindex_phi4_stored[2]) {\n\n calc_phi4_reciprocal(xk_in[kn[1]],\n xk_in[kn[2]],\n xk_in[kn[3]],\n phase_storage_in,\n phi4_reciprocal);\n\n kindex_phi4_stored[0] = kn[1];\n kindex_phi4_stored[1] = kn[2];\n kindex_phi4_stored[2] = kn[3];\n }\n\n#ifdef _OPENMP\n#pragma omp parallel for private(ret), reduction(+: ret_re, ret_im)\n#endif\n for (i = 0; i < ngroup_v4; ++i) {\n ret = evec_in[kn[0]][sn[0]][evec_index_v4[i][0]]\n * evec_in[kn[1]][sn[1]][evec_index_v4[i][1]]\n * evec_in[kn[2]][sn[2]][evec_index_v4[i][2]]\n * evec_in[kn[3]][sn[3]][evec_index_v4[i][3]]\n * invmass_v4[i] * phi4_reciprocal[i];\n ret_re += ret.real();\n ret_im += ret.imag();\n }\n\n return std::complex(ret_re, ret_im)\n / std::sqrt(omega[0] * omega[1] * omega[2] * omega[3]);\n}\n\nstd::complex AnharmonicCore::Phi4(const unsigned int ks[4],\n const double *const *xk_in,\n const double *const *eval_in,\n const std::complex *const *const *evec_in,\n const PhaseFactorStorage *phase_storage_in)\n{\n int i;\n int ns = dynamical->neval;\n unsigned int kn[4], sn[4];\n double omega[4];\n double ret_re = 0.0;\n double ret_im = 0.0;\n std::complex ret = std::complex(0.0, 0.0);\n\n for (i = 0; i < 4; ++i) {\n kn[i] = ks[i] / ns;\n sn[i] = ks[i] % ns;\n omega[i] = eval_in[kn[i]][sn[i]];\n }\n\n if (kn[1] != kindex_phi4_stored[0]\n || kn[2] != kindex_phi4_stored[1]\n || kn[3] != kindex_phi4_stored[2]) {\n\n calc_phi4_reciprocal(xk_in[kn[1]],\n xk_in[kn[2]],\n xk_in[kn[3]],\n phase_storage_in,\n phi4_reciprocal);\n\n kindex_phi4_stored[0] = kn[1];\n kindex_phi4_stored[1] = kn[2];\n kindex_phi4_stored[2] = kn[3];\n }\n\n#ifdef _OPENMP\n#pragma omp parallel for private(ret), reduction(+: ret_re, ret_im)\n#endif\n for (i = 0; i < ngroup_v4; ++i) {\n ret = evec_in[kn[0]][sn[0]][evec_index_v4[i][0]]\n * evec_in[kn[1]][sn[1]][evec_index_v4[i][1]]\n * evec_in[kn[2]][sn[2]][evec_index_v4[i][2]]\n * evec_in[kn[3]][sn[3]][evec_index_v4[i][3]]\n * invmass_v4[i] * phi4_reciprocal[i];\n ret_re += ret.real();\n ret_im += ret.imag();\n }\n\n return std::complex(ret_re, ret_im);\n}\n\nvoid AnharmonicCore::calc_phi4_reciprocal(const double *xk1,\n const double *xk2,\n const double *xk3,\n const PhaseFactorStorage *phase_storage_in,\n std::complex *ret)\n{\n int i, j;\n double phase;\n std::complex ret_in;\n unsigned int nsize_group;\n\n const auto tune_type_now = phase_storage_in->get_tune_type();\n\n if (tune_type_now == 1) {\n\n#pragma omp parallel for private(ret_in, nsize_group, j, phase)\n for (i = 0; i < ngroup_v4; ++i) {\n\n ret_in = std::complex(0.0, 0.0);\n nsize_group = fcs_group_v4[i].size();\n\n for (j = 0; j < nsize_group; ++j) {\n phase = relvec_v4[i][j].vecs[0][0] * xk1[0]\n + relvec_v4[i][j].vecs[0][1] * xk1[1]\n + relvec_v4[i][j].vecs[0][2] * xk1[2]\n + relvec_v4[i][j].vecs[1][0] * xk2[0]\n + relvec_v4[i][j].vecs[1][1] * xk2[1]\n + relvec_v4[i][j].vecs[1][2] * xk2[2]\n + relvec_v4[i][j].vecs[2][0] * xk3[0]\n + relvec_v4[i][j].vecs[2][1] * xk3[1]\n + relvec_v4[i][j].vecs[2][2] * xk3[2];\n\n ret_in += fcs_group_v4[i][j] * phase_storage_in->get_exp_type1(phase);\n }\n ret[i] = ret_in;\n }\n\n } else if (tune_type_now == 2) {\n\n // Tuned version is used when nk1=nk2=nk3 doesn't hold.\n\n double phase3[3];\n\n#pragma omp parallel for private(ret_in, nsize_group, j, phase3)\n for (i = 0; i < ngroup_v4; ++i) {\n\n ret_in = std::complex(0.0, 0.0);\n nsize_group = fcs_group_v4[i].size();\n\n for (j = 0; j < nsize_group; ++j) {\n for (auto ii = 0; ii < 3; ++ii) {\n phase3[ii]\n = relvec_v4[i][j].vecs[0][ii] * xk1[ii]\n + relvec_v4[i][j].vecs[1][ii] * xk2[ii]\n + relvec_v4[i][j].vecs[2][ii] * xk3[ii];\n }\n ret_in += fcs_group_v4[i][j] * phase_storage_in->get_exp_type2(phase3);\n }\n ret[i] = ret_in;\n }\n } else {\n // Original version\n#pragma omp parallel for private(ret_in, nsize_group, phase)\n for (i = 0; i < ngroup_v4; ++i) {\n\n ret_in = std::complex(0.0, 0.0);\n nsize_group = fcs_group_v4[i].size();\n\n for (j = 0; j < nsize_group; ++j) {\n phase = relvec_v4[i][j].vecs[0][0] * xk1[0]\n + relvec_v4[i][j].vecs[0][1] * xk1[1]\n + relvec_v4[i][j].vecs[0][2] * xk1[2]\n + relvec_v4[i][j].vecs[1][0] * xk2[0]\n + relvec_v4[i][j].vecs[1][1] * xk2[1]\n + relvec_v4[i][j].vecs[1][2] * xk2[2]\n + relvec_v4[i][j].vecs[2][0] * xk3[0]\n + relvec_v4[i][j].vecs[2][1] * xk3[1]\n + relvec_v4[i][j].vecs[2][2] * xk3[2];\n\n ret_in += fcs_group_v4[i][j] * std::exp(im * phase);\n }\n ret[i] = ret_in;\n }\n }\n}\n\nstd::complex AnharmonicCore::V3_mode(int mode,\n const double *xk2,\n const double *xk3,\n int is,\n int js,\n double **eval,\n std::complex ***evec) const\n{\n std::complex ctmp = std::complex(0.0, 0.0);\n\n // Return zero if any of the involving phonon has imaginary frequency\n if (eval[0][mode] < eps8 || eval[1][is] < eps8 || eval[2][js] < eps8) return 0.0;\n\n unsigned int ielem = 0;\n\n for (int i = 0; i < ngroup_v3; ++i) {\n\n auto vec_tmp = evec[0][mode][evec_index_v3[i][0]]\n * evec[1][is][evec_index_v3[i][1]]\n * evec[2][js][evec_index_v3[i][2]]\n * invmass_v3[i];\n\n auto ret_in = std::complex(0.0, 0.0);\n\n const int nsize_group = fcs_group_v3[i].size();\n\n for (auto j = 0; j < nsize_group; ++j) {\n\n auto phase = relvec_v3[i][j].vecs[0][0] * xk2[0]\n + relvec_v3[i][j].vecs[0][1] * xk2[1]\n + relvec_v3[i][j].vecs[0][2] * xk2[2]\n + relvec_v3[i][j].vecs[1][0] * xk3[0]\n + relvec_v3[i][j].vecs[1][1] * xk3[1]\n + relvec_v3[i][j].vecs[1][2] * xk3[2];\n\n ret_in += fcs_group_v3[i][j] * std::exp(im * phase);\n\n ++ielem;\n }\n ctmp += ret_in * vec_tmp;\n }\n\n return ctmp / std::sqrt(eval[0][mode] * eval[1][is] * eval[2][js]);\n}\n\nvoid AnharmonicCore::calc_damping_smearing(const unsigned int ntemp,\n const double *temp_in,\n const double omega_in,\n const unsigned int ik_in,\n const unsigned int is_in,\n const KpointMeshUniform *kmesh_in,\n const double *const *eval_in,\n const std::complex *const *const *evec_in,\n double *ret)\n{\n // This function returns the imaginary part of phonon self-energy \n // for the given frequency omega_in.\n // Lorentzian or Gaussian smearing will be used.\n // This version employs the crystal symmetry to reduce the computational cost\n\n const auto nk = kmesh_in->nk;\n const auto ns = dynamical->neval;\n const auto ns2 = ns * ns;\n unsigned int i;\n int ik;\n unsigned int is, js;\n unsigned int arr[3];\n\n int k1, k2;\n\n double T_tmp;\n double n1, n2;\n double omega_inner[2];\n\n double multi;\n\n for (i = 0; i < ntemp; ++i) ret[i] = 0.0;\n\n double **v3_arr;\n double ***delta_arr;\n double ret_tmp;\n\n double f1, f2;\n\n const auto epsilon = integration->epsilon;\n\n std::vector triplet;\n\n kmesh_in->get_unique_triplet_k(ik_in,\n symmetry->SymmList,\n false,\n false,\n triplet);\n\n const auto npair_uniq = triplet.size();\n\n allocate(v3_arr, npair_uniq, ns * ns);\n allocate(delta_arr, npair_uniq, ns * ns, 2);\n\n const auto knum = kmesh_in->kpoint_irred_all[ik_in][0].knum;\n const auto knum_minus = kmesh_in->kindex_minus_xk[knum];\n#ifdef _OPENMP\n#pragma omp parallel for private(multi, arr, k1, k2, is, js, omega_inner)\n#endif\n for (ik = 0; ik < npair_uniq; ++ik) {\n multi = static_cast(triplet[ik].group.size());\n\n arr[0] = ns * knum_minus + is_in;\n\n k1 = triplet[ik].group[0].ks[0];\n k2 = triplet[ik].group[0].ks[1];\n\n for (is = 0; is < ns; ++is) {\n arr[1] = ns * k1 + is;\n omega_inner[0] = eval_in[k1][is];\n\n for (js = 0; js < ns; ++js) {\n arr[2] = ns * k2 + js;\n omega_inner[1] = eval_in[k2][js];\n\n if (integration->ismear == 0) {\n delta_arr[ik][ns * is + js][0]\n = delta_lorentz(omega_in - omega_inner[0] - omega_inner[1], epsilon)\n - delta_lorentz(omega_in + omega_inner[0] + omega_inner[1], epsilon);\n delta_arr[ik][ns * is + js][1]\n = delta_lorentz(omega_in - omega_inner[0] + omega_inner[1], epsilon)\n - delta_lorentz(omega_in + omega_inner[0] - omega_inner[1], epsilon);\n } else if (integration->ismear == 1) {\n delta_arr[ik][ns * is + js][0]\n = delta_gauss(omega_in - omega_inner[0] - omega_inner[1], epsilon)\n - delta_gauss(omega_in + omega_inner[0] + omega_inner[1], epsilon);\n delta_arr[ik][ns * is + js][1]\n = delta_gauss(omega_in - omega_inner[0] + omega_inner[1], epsilon)\n - delta_gauss(omega_in + omega_inner[0] - omega_inner[1], epsilon);\n }\n }\n }\n }\n\n for (ik = 0; ik < npair_uniq; ++ik) {\n\n k1 = triplet[ik].group[0].ks[0];\n k2 = triplet[ik].group[0].ks[1];\n\n multi = static_cast(triplet[ik].group.size());\n\n for (int ib = 0; ib < ns2; ++ib) {\n is = ib / ns;\n js = ib % ns;\n\n arr[0] = ns * knum_minus + is_in;\n arr[1] = ns * k1 + is;\n arr[2] = ns * k2 + js;\n\n v3_arr[ik][ib] = std::norm(V3(arr,\n kmesh_in->xk,\n eval_in,\n evec_in,\n phase_storage_dos)) * multi;\n }\n }\n\n for (i = 0; i < ntemp; ++i) {\n T_tmp = temp_in[i];\n ret_tmp = 0.0;\n#ifdef _OPENMP\n#pragma omp parallel for private(k1, k2, is, js, omega_inner, n1, n2, f1, f2), reduction(+:ret_tmp)\n#endif\n for (ik = 0; ik < npair_uniq; ++ik) {\n\n k1 = triplet[ik].group[0].ks[0];\n k2 = triplet[ik].group[0].ks[1];\n\n for (is = 0; is < ns; ++is) {\n\n omega_inner[0] = eval_in[k1][is];\n\n for (js = 0; js < ns; ++js) {\n\n omega_inner[1] = eval_in[k2][js];\n\n if (thermodynamics->classical) {\n f1 = thermodynamics->fC(omega_inner[0], T_tmp);\n f2 = thermodynamics->fC(omega_inner[1], T_tmp);\n\n n1 = f1 + f2;\n n2 = f1 - f2;\n } else {\n f1 = thermodynamics->fB(omega_inner[0], T_tmp);\n f2 = thermodynamics->fB(omega_inner[1], T_tmp);\n\n n1 = f1 + f2 + 1.0;\n n2 = f1 - f2;\n }\n\n ret_tmp += v3_arr[ik][ns * is + js]\n * (n1 * delta_arr[ik][ns * is + js][0]\n - n2 * delta_arr[ik][ns * is + js][1]);\n }\n }\n }\n ret[i] = ret_tmp;\n }\n\n deallocate(v3_arr);\n deallocate(delta_arr);\n triplet.clear();\n\n for (i = 0; i < ntemp; ++i) ret[i] *= pi * std::pow(0.5, 4) / static_cast(nk);\n}\n\nvoid AnharmonicCore::calc_damping_tetrahedron(const unsigned int ntemp,\n const double *temp_in,\n const double omega_in,\n const unsigned int ik_in,\n const unsigned int is_in,\n const KpointMeshUniform *kmesh_in,\n const double *const *eval_in,\n const std::complex *const *const *evec_in,\n double *ret)\n{\n // This function returns the imaginary part of phonon self-energy \n // for the given frequency omega_in.\n // Tetrahedron method will be used.\n // This version employs the crystal symmetry to reduce the computational cost\n\n const int nk = kmesh_in->nk;\n const int ns = dynamical->neval;\n\n int ik, ib;\n const auto ns2 = ns * ns;\n\n unsigned int i;\n unsigned int jk;\n unsigned int is, js;\n unsigned int k1, k2;\n unsigned int arr[3];\n\n double T_tmp;\n double n1, n2;\n double f1, f2;\n double multi;\n\n double xk_tmp[3];\n double omega_inner[2];\n\n double ret_tmp;\n\n unsigned int *kmap_identity;\n double **energy_tmp;\n double **weight_tetra;\n double **v3_arr;\n double ***delta_arr;\n\n std::vector triplet;\n\n for (i = 0; i < ntemp; ++i) ret[i] = 0.0;\n\n kmesh_in->get_unique_triplet_k(ik_in,\n symmetry->SymmList,\n use_triplet_symmetry,\n sym_permutation,\n triplet);\n\n const auto npair_uniq = triplet.size();\n\n allocate(v3_arr, npair_uniq, ns2);\n allocate(delta_arr, npair_uniq, ns2, 2);\n\n const auto knum = kmesh_in->kpoint_irred_all[ik_in][0].knum;\n const auto knum_minus = kmesh_in->kindex_minus_xk[knum];\n const auto xk = kmesh_in->xk;\n\n allocate(kmap_identity, nk);\n\n for (i = 0; i < nk; ++i) kmap_identity[i] = i;\n\n#ifdef _OPENMP\n#pragma omp parallel private(is, js, k1, k2, xk_tmp, energy_tmp, i, weight_tetra, ik, jk, arr)\n#endif\n {\n allocate(energy_tmp, 3, nk);\n allocate(weight_tetra, 3, nk);\n\n#ifdef _OPENMP\n#pragma omp for\n#endif\n for (ib = 0; ib < ns2; ++ib) {\n is = ib / ns;\n js = ib % ns;\n\n for (k1 = 0; k1 < nk; ++k1) {\n\n // Prepare two-phonon frequency for the tetrahedron method\n\n for (i = 0; i < 3; ++i) xk_tmp[i] = xk[knum][i] - xk[k1][i];\n\n k2 = kmesh_in->get_knum(xk_tmp);\n\n energy_tmp[0][k1] = eval_in[k1][is] + eval_in[k2][js];\n energy_tmp[1][k1] = eval_in[k1][is] - eval_in[k2][js];\n energy_tmp[2][k1] = -energy_tmp[1][k1];\n }\n\n for (i = 0; i < 3; ++i) {\n integration->calc_weight_tetrahedron(nk, kmap_identity,\n energy_tmp[i], omega_in,\n dos->tetra_nodes_dos->get_ntetra(),\n dos->tetra_nodes_dos->get_tetras(),\n weight_tetra[i]);\n }\n\n for (ik = 0; ik < npair_uniq; ++ik) {\n jk = triplet[ik].group[0].ks[0];\n delta_arr[ik][ib][0] = weight_tetra[0][jk];\n delta_arr[ik][ib][1] = weight_tetra[1][jk] - weight_tetra[2][jk];\n }\n }\n\n deallocate(energy_tmp);\n deallocate(weight_tetra);\n }\n\n for (ik = 0; ik < npair_uniq; ++ik) {\n\n k1 = triplet[ik].group[0].ks[0];\n k2 = triplet[ik].group[0].ks[1];\n\n multi = static_cast(triplet[ik].group.size());\n\n for (ib = 0; ib < ns2; ++ib) {\n is = ib / ns;\n js = ib % ns;\n\n if (delta_arr[ik][ib][0] > 0.0 || std::abs(delta_arr[ik][ib][1]) > 0.0) {\n\n arr[0] = ns * knum_minus + is_in;\n arr[1] = ns * k1 + is;\n arr[2] = ns * k2 + js;\n\n v3_arr[ik][ib] = std::norm(V3(arr,\n kmesh_in->xk,\n eval_in,\n evec_in,\n phase_storage_dos)) * multi;\n\n } else {\n v3_arr[ik][ib] = 0.0;\n }\n }\n }\n\n for (i = 0; i < ntemp; ++i) {\n T_tmp = temp_in[i];\n ret_tmp = 0.0;\n#ifdef _OPENMP\n#pragma omp parallel for private(k1, k2, is, js, omega_inner, n1, n2, f1, f2), reduction(+:ret_tmp)\n#endif\n for (ik = 0; ik < npair_uniq; ++ik) {\n\n k1 = triplet[ik].group[0].ks[0];\n k2 = triplet[ik].group[0].ks[1];\n\n for (is = 0; is < ns; ++is) {\n\n omega_inner[0] = eval_in[k1][is];\n\n for (js = 0; js < ns; ++js) {\n\n omega_inner[1] = eval_in[k2][js];\n\n if (thermodynamics->classical) {\n f1 = thermodynamics->fC(omega_inner[0], T_tmp);\n f2 = thermodynamics->fC(omega_inner[1], T_tmp);\n\n n1 = f1 + f2;\n n2 = f1 - f2;\n } else {\n f1 = thermodynamics->fB(omega_inner[0], T_tmp);\n f2 = thermodynamics->fB(omega_inner[1], T_tmp);\n\n n1 = f1 + f2 + 1.0;\n n2 = f1 - f2;\n }\n\n ret_tmp += v3_arr[ik][ns * is + js]\n * (n1 * delta_arr[ik][ns * is + js][0]\n - n2 * delta_arr[ik][ns * is + js][1]);\n }\n }\n }\n ret[i] = ret_tmp;\n }\n\n deallocate(v3_arr);\n deallocate(delta_arr);\n deallocate(kmap_identity);\n\n for (i = 0; i < ntemp; ++i) ret[i] *= pi * std::pow(0.5, 4);\n}\n\nvoid AnharmonicCore::setup_cubic()\n{\n int i;\n double *invsqrt_mass_p;\n\n // Sort force_constant[1] using the operator defined in fcs_phonons.h\n // This sorting is necessary.\n std::sort(fcs_phonon->force_constant_with_cell[1].begin(),\n fcs_phonon->force_constant_with_cell[1].end());\n prepare_group_of_force_constants(fcs_phonon->force_constant_with_cell[1],\n 3, ngroup_v3, fcs_group_v3);\n\n allocate(invmass_v3, ngroup_v3);\n allocate(evec_index_v3, ngroup_v3, 3);\n allocate(relvec_v3, ngroup_v3);\n allocate(phi3_reciprocal, ngroup_v3);\n\n prepare_relative_vector(fcs_phonon->force_constant_with_cell[1],\n 3,\n ngroup_v3,\n fcs_group_v3,\n relvec_v3);\n\n allocate(invsqrt_mass_p, system->natmin);\n\n for (i = 0; i < system->natmin; ++i) {\n invsqrt_mass_p[i] = std::sqrt(1.0 / system->mass[system->map_p2s[i][0]]);\n }\n\n int k = 0;\n for (i = 0; i < ngroup_v3; ++i) {\n for (int j = 0; j < 3; ++j) {\n evec_index_v3[i][j] = fcs_phonon->force_constant_with_cell[1][k].pairs[j].index;\n }\n invmass_v3[i]\n = invsqrt_mass_p[evec_index_v3[i][0] / 3]\n * invsqrt_mass_p[evec_index_v3[i][1] / 3]\n * invsqrt_mass_p[evec_index_v3[i][2] / 3];\n k += fcs_group_v3[i].size();\n }\n\n deallocate(invsqrt_mass_p);\n}\n\nvoid AnharmonicCore::setup_quartic()\n{\n int i;\n double *invsqrt_mass_p;\n std::sort(fcs_phonon->force_constant_with_cell[2].begin(),\n fcs_phonon->force_constant_with_cell[2].end());\n prepare_group_of_force_constants(fcs_phonon->force_constant_with_cell[2],\n 4, ngroup_v4, fcs_group_v4);\n\n allocate(invmass_v4, ngroup_v4);\n allocate(evec_index_v4, ngroup_v4, 4);\n allocate(relvec_v4, ngroup_v4);\n allocate(phi4_reciprocal, ngroup_v4);\n\n prepare_relative_vector(fcs_phonon->force_constant_with_cell[2],\n 4,\n ngroup_v4,\n fcs_group_v4,\n relvec_v4);\n\n allocate(invsqrt_mass_p, system->natmin);\n\n for (i = 0; i < system->natmin; ++i) {\n invsqrt_mass_p[i] = std::sqrt(1.0 / system->mass[system->map_p2s[i][0]]);\n }\n\n int k = 0;\n for (i = 0; i < ngroup_v4; ++i) {\n for (int j = 0; j < 4; ++j) {\n evec_index_v4[i][j] = fcs_phonon->force_constant_with_cell[2][k].pairs[j].index;\n }\n invmass_v4[i]\n = invsqrt_mass_p[evec_index_v4[i][0] / 3]\n * invsqrt_mass_p[evec_index_v4[i][1] / 3]\n * invsqrt_mass_p[evec_index_v4[i][2] / 3]\n * invsqrt_mass_p[evec_index_v4[i][3] / 3];\n k += fcs_group_v4[i].size();\n }\n\n deallocate(invsqrt_mass_p);\n}\n\nvoid PhaseFactorStorage::create(const bool use_tuned_ver,\n const bool switch_to_type2)\n{\n // For accelerating function V3 and V4 by avoiding continual call of std::exp.\n\n if (use_tuned_ver) {\n\n const auto inv2pi = 1.0 / (2.0 * pi);\n\n for (auto i = 0; i < 3; ++i) dnk[i] = static_cast(nk_grid[i]) * inv2pi;\n\n tune_type = 1;\n\n if (nk_grid[0] == nk_grid[1] && nk_grid[1] == nk_grid[2]) {\n nk_represent = nk_grid[0];\n } else if (nk_grid[0] == nk_grid[1] && nk_grid[2] == 1) {\n nk_represent = nk_grid[0];\n } else if (nk_grid[1] == nk_grid[2] && nk_grid[0] == 1) {\n nk_represent = nk_grid[1];\n } else if (nk_grid[2] == nk_grid[0] && nk_grid[1] == 1) {\n nk_represent = nk_grid[2];\n } else if (nk_grid[0] == 1 && nk_grid[1] == 1) {\n nk_represent = nk_grid[2];\n } else if (nk_grid[1] == 1 && nk_grid[2] == 1) {\n nk_represent = nk_grid[0];\n } else if (nk_grid[2] == 1 && nk_grid[0] == 1) {\n nk_represent = nk_grid[1];\n } else {\n tune_type = 2;\n }\n\n // Force using tune_type == 2 version\n if (switch_to_type2) tune_type = 2;\n\n int ii, jj, kk;\n\n if (tune_type == 1) {\n\n double phase;\n dnk_represent = static_cast(nk_represent) * inv2pi;\n const auto inv_dnk_represent = 1.0 / dnk_represent;\n\n // Pre-calculate the phase factor exp[i 2pi * phase]\n // for different phase angles ranging from [-2pi + 2pi/nk_represent: 2pi*(nk_represent-1)/nk_represent].\n // The redundancy of the data here is intentional and helpful for accepting\n // both positive and negative modulo.\n allocate(exp_phase, 2 * nk_represent - 1);\n#ifdef _OPENMP\n#pragma omp parallel for private(phase)\n#endif\n for (ii = 0; ii < 2 * nk_represent - 1; ++ii) {\n phase = static_cast(ii - nk_represent + 1) * inv_dnk_represent;\n exp_phase[ii] = std::exp(im * phase);\n }\n\n } else if (tune_type == 2) {\n\n double phase[3];\n double inv_dnk[3];\n\n for (auto i = 0; i < 3; ++i) inv_dnk[i] = 1.0 / dnk[i];\n\n allocate(exp_phase3,\n 2 * nk_grid[0] - 1,\n 2 * nk_grid[1] - 1,\n 2 * nk_grid[2] - 1);\n#ifdef _OPENMP\n#pragma omp parallel for private(phase, jj, kk)\n#endif\n for (ii = 0; ii < 2 * nk_grid[0] - 1; ++ii) {\n phase[0] = static_cast(ii - nk_grid[0] + 1) * inv_dnk[0];\n for (jj = 0; jj < 2 * nk_grid[1] - 1; ++jj) {\n phase[1] = static_cast(jj - nk_grid[1] + 1) * inv_dnk[1];\n for (kk = 0; kk < 2 * nk_grid[2] - 1; ++kk) {\n phase[2] = static_cast(kk - nk_grid[2] + 1) * inv_dnk[2];\n exp_phase3[ii][jj][kk] = std::exp(im * (phase[0] + phase[1] + phase[2]));\n }\n }\n }\n }\n } else {\n tune_type = 0;\n }\n}\n\nunsigned int PhaseFactorStorage::get_tune_type() const\n{\n return tune_type;\n}\n\nstd::complex PhaseFactorStorage::get_exp_type1(const double phase_in) const\n{\n int iloc = nint(phase_in * dnk_represent) % nk_represent + nk_represent - 1;\n return exp_phase[iloc];\n}\n\nstd::complex PhaseFactorStorage::get_exp_type2(const double *phase3_in) const\n{\n int loc[3];\n for (auto i = 0; i < 3; ++i) {\n loc[i] = nint(phase3_in[i] * dnk[i]) % nk_grid[i] + nk_grid[i] - 1;\n }\n return exp_phase3[loc[0]][loc[1]][loc[2]];\n}\n\nvoid AnharmonicCore::calc_self3omega_tetrahedron(const double Temp,\n const KpointMeshUniform *kmesh_in,\n const double *const *eval_in,\n const std::complex *const *const *evec_in,\n const unsigned int ik_in,\n const unsigned int snum,\n const unsigned int nomega,\n const double *omega,\n double *ret)\n{\n // This function returns the imaginary part of phonon self-energy \n // for the given frequency range of omega, phonon frequency (eval) and phonon eigenvectors (evec).\n // The tetrahedron method will be used.\n // This version employs the crystal symmetry to reduce the computational cost\n // In addition, both MPI and OpenMP parallelization are used in a hybrid way inside this function.\n\n const auto nk = kmesh_in->nk;\n const int ns = dynamical->neval;\n\n int ik, ib, iomega;\n const auto ns2 = ns * ns;\n\n unsigned int i;\n unsigned int is, js;\n unsigned int k1, k2;\n unsigned int arr[3];\n unsigned int nk_tmp;\n\n double n1, n2;\n double f1, f2;\n double omega_inner[2];\n\n unsigned int *kmap_identity;\n int **kpairs;\n double **energy_tmp;\n double **weight_tetra;\n double **v3_arr, *v3_arr_loc;\n double *ret_private;\n\n std::vector triplet;\n std::vector vk_l;\n\n const int knum = kmesh_in->kpoint_irred_all[ik_in][0].knum;\n const int knum_minus = kmesh_in->kindex_minus_xk[knum];\n\n kmesh_in->get_unique_triplet_k(ik_in,\n symmetry->SymmList,\n false,\n false,\n triplet);\n\n const auto npair_uniq = triplet.size();\n\n if (npair_uniq != nk) {\n exit(\"calc_self3omega_tetrahedron\", \"Something is wrong.\");\n }\n\n allocate(kpairs, nk, 2);\n allocate(kmap_identity, nk);\n\n for (i = 0; i < nk; ++i) kmap_identity[i] = i;\n\n for (iomega = 0; iomega < nomega; ++iomega) ret[iomega] = 0.0;\n\n for (ik = 0; ik < npair_uniq; ++ik) {\n kpairs[ik][0] = triplet[ik].group[0].ks[0];\n kpairs[ik][1] = triplet[ik].group[0].ks[1];\n }\n\n if (nk % mympi->nprocs != 0) {\n nk_tmp = nk / mympi->nprocs + 1;\n } else {\n nk_tmp = nk / mympi->nprocs;\n }\n\n vk_l.clear();\n\n for (ik = 0; ik < nk; ++ik) {\n if (ik % mympi->nprocs == mympi->my_rank) {\n vk_l.push_back(ik);\n }\n }\n\n if (vk_l.size() < nk_tmp) {\n vk_l.push_back(-1);\n }\n\n allocate(v3_arr_loc, ns2);\n allocate(v3_arr, nk_tmp * mympi->nprocs, ns2);\n\n for (ik = 0; ik < nk_tmp; ++ik) {\n\n int ik_now = vk_l[ik];\n\n if (ik_now == -1) {\n\n for (ib = 0; ib < ns2; ++ib) v3_arr_loc[ib] = 0.0; // do nothing\n\n } else {\n#ifdef _OPENMP\n#pragma omp parallel for private(is, js, arr)\n#endif\n for (ib = 0; ib < ns2; ++ib) {\n\n is = ib / ns;\n js = ib % ns;\n\n arr[0] = ns * knum_minus + snum;\n arr[1] = ns * kpairs[ik_now][0] + is;\n arr[2] = ns * kpairs[ik_now][1] + js;\n\n v3_arr_loc[ib] = std::norm(V3(arr,\n kmesh_in->xk,\n eval_in,\n evec_in,\n phase_storage_dos));\n }\n }\n MPI_Gather(&v3_arr_loc[0], ns2, MPI_DOUBLE,\n v3_arr[ik * mympi->nprocs], ns2,\n MPI_DOUBLE, 0, MPI_COMM_WORLD);\n }\n deallocate(v3_arr_loc);\n\n if (mympi->my_rank == 0) {\n\n#ifdef _OPENMP\n#pragma omp parallel private(is, js, k1, k2, energy_tmp, i, \\\n iomega, weight_tetra, ik, \\\n omega_inner, f1, f2, n1, n2)\n#endif\n {\n allocate(energy_tmp, 2, nk);\n allocate(weight_tetra, 2, nk);\n#ifdef _OPENMP\n const int nthreads = omp_get_num_threads();\n const int ithread = omp_get_thread_num();\n#else\n const int nthreads = 1;\n const int ithread = 0;\n#endif\n\n#ifdef _OPENMP\n#pragma omp single\n#endif\n {\n allocate(ret_private, nthreads * nomega);\n for (i = 0; i < nthreads * nomega; ++i) ret_private[i] = 0.0;\n }\n#ifdef _OPENMP\n#pragma omp for\n#endif\n for (ib = 0; ib < ns2; ++ib) {\n\n is = ib / ns;\n js = ib % ns;\n\n for (ik = 0; ik < nk; ++ik) {\n k1 = kpairs[ik][0];\n k2 = kpairs[ik][1];\n\n energy_tmp[0][ik] = eval_in[k1][is] + eval_in[k2][js];\n energy_tmp[1][ik] = eval_in[k1][is] - eval_in[k2][js];\n }\n for (iomega = 0; iomega < nomega; ++iomega) {\n for (i = 0; i < 2; ++i) {\n integration->calc_weight_tetrahedron(nk, kmap_identity,\n energy_tmp[i], omega[iomega],\n dos->tetra_nodes_dos->get_ntetra(),\n dos->tetra_nodes_dos->get_tetras(),\n weight_tetra[i]);\n }\n\n for (ik = 0; ik < nk; ++ik) {\n k1 = kpairs[ik][0];\n k2 = kpairs[ik][1];\n\n omega_inner[0] = eval_in[k1][is];\n omega_inner[1] = eval_in[k2][js];\n if (thermodynamics->classical) {\n f1 = thermodynamics->fC(omega_inner[0], Temp);\n f2 = thermodynamics->fC(omega_inner[1], Temp);\n n1 = f1 + f2;\n n2 = f1 - f2;\n } else {\n f1 = thermodynamics->fB(omega_inner[0], Temp);\n f2 = thermodynamics->fB(omega_inner[1], Temp);\n n1 = f1 + f2 + 1.0;\n n2 = f1 - f2;\n }\n\n //#pragma omp critical\n ret_private[nomega * ithread + iomega]\n += v3_arr[ik][ib] * (n1 * weight_tetra[0][ik] - 2.0 * n2 * weight_tetra[1][ik]);\n }\n }\n }\n#ifdef _OPENMP\n#pragma omp for\n#endif\n for (iomega = 0; iomega < nomega; ++iomega) {\n for (int t = 0; t < nthreads; t++) {\n ret[iomega] += ret_private[nomega * t + iomega];\n }\n }\n deallocate(energy_tmp);\n deallocate(weight_tetra);\n }\n#ifdef _OPENMP\n#pragma omp parallel for\n#endif\n for (iomega = 0; iomega < nomega; ++iomega) {\n ret[iomega] *= pi * std::pow(0.5, 4);\n }\n deallocate(ret_private);\n }\n\n deallocate(v3_arr);\n deallocate(kmap_identity);\n deallocate(kpairs);\n}\n\nint AnharmonicCore::get_ngroup_fcs(const unsigned int order) const\n{\n if (order == 3) return ngroup_v3;\n if (order == 4) return ngroup_v4;\n return 0;\n}\n\nstd::vector *AnharmonicCore::get_fcs_group(const unsigned int order) const\n{\n if (order == 3) return fcs_group_v3;\n if (order == 4) return fcs_group_v4;\n return nullptr;\n}\n\ndouble *AnharmonicCore::get_invmass_factor(const unsigned int order) const\n{\n if (order == 3) return invmass_v3;\n if (order == 4) return invmass_v4;\n return nullptr;\n}\n\nint **AnharmonicCore::get_evec_index(const unsigned int order) const\n{\n if (order == 3) return evec_index_v3;\n if (order == 4) return evec_index_v4;\n return nullptr;\n}\n", "meta": {"hexsha": "9bdc547ee274aa82ecf7347f60de543685d03b3a", "size": 50314, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "anphon/anharmonic_core.cpp", "max_stars_repo_name": "r-masuki/alamode", "max_stars_repo_head_hexsha": "6a75ac277e1e4bdaff4218b28a8d9ab7e5130b3c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 93.0, "max_stars_repo_stars_event_min_datetime": "2015-01-06T17:19:59.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-24T14:26:02.000Z", "max_issues_repo_path": "anphon/anharmonic_core.cpp", "max_issues_repo_name": "r-masuki/alamode", "max_issues_repo_head_hexsha": "6a75ac277e1e4bdaff4218b28a8d9ab7e5130b3c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 32.0, "max_issues_repo_issues_event_min_datetime": "2016-05-28T12:31:55.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-24T05:46:08.000Z", "max_forks_repo_path": "anphon/anharmonic_core.cpp", "max_forks_repo_name": "r-masuki/alamode", "max_forks_repo_head_hexsha": "6a75ac277e1e4bdaff4218b28a8d9ab7e5130b3c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 49.0, "max_forks_repo_forks_event_min_datetime": "2015-07-02T02:17:57.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-04T09:13:40.000Z", "avg_line_length": 33.0144356955, "max_line_length": 116, "alphanum_fraction": 0.4797670628, "num_tokens": 14185, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.2291657286788779}} {"text": "\n/*\n * This program is free software; you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation; either version 3 of the License, or\n * (at your option) any later version.\n *\n * Written (W) 2013 Nicolo' Navarin\n */\n\n#include \"DDK_FeatureGenerator.h\"\n //#include \n#include \n#include \n//#include \n//#include \n//#include \n//#include \n//#include \n\n//#include \n//#include \n\n// #include \n\n //TODO eliminare\n double lossy_e=0.001;\n\n DDkernel_FeatureGenerator::DDkernel_FeatureGenerator()\n : learn_rate(0.1), max_iter(1000), NSPDK_FeatureGenerator(\"DDK\")\n {\n }\n/*\n DDkernel_FeatureGenerator::DDkernel_FeatureGenerator(CGraphFeatures* traindat, CLabels* trainlab)\n : learn_rate(.1), max_iter(1000), NSPDK_FeatureGenerator(\"DDK\")\n {\n //TODO inizializzazione\n // set_features(traindat);\n // set_labels(trainlab);\n }*/\n\n DDkernel_FeatureGenerator::~DDkernel_FeatureGenerator()\n {\n\t NSPDK_FeatureGenerator(\"ddk\");\n }\n DDkernel_FeatureGenerator::DDkernel_FeatureGenerator(const std::string& id) :\n \t\tNSPDK_FeatureGenerator(id) {\n\t\tnew_flag(&mTreeLambda, \"mTreeLambda\", \"(double)\\n tree_lambda parameter for DDK kernel\");\n }\n void DDkernel_FeatureGenerator::OutputParameters(ostream& out) const {\n \tout << \"Radius: \" << mRadius << endl;\n \tout << \"Radius2: \" << mRadiusTwo << endl;\n\n \tout << \"Distance: \" << mDistance << endl;\n \tout << \"Match_Type: \" << mMatchType << endl;\n \tout << \"Hash_Bit_Size: \" << mHashBitSize << endl;\n \tout << \"Hash_Bit_mask: \" << mHashBitMask << endl;\n \tout << \"Min_Kernel: \" << mMinKernel << endl;\n \tout << \"Normalization: \" << mNormalization << endl;\n \tout << \"Vertex Degree Threshold: \" << mVertexDegreeThreshold << endl;\n \tout << \"Debug_Verbosity: \" << mDebugVerbosity << endl;\n \tout << \"Tree_lambda: \" << mTreeLambda << endl;\n\n }\n\n void DDkernel_FeatureGenerator::generate_feature_vector(const GraphClass& aG, SVector& x, const vector& aFirstEndpointList) {\n }\n\t /*\n\t // OutputParameters(cout);\n\t vector graphs;\n\t graphs.push_back(aG);\n\t CGraphFeatures* data = new CGraphFeatures(graphs);\n\t// set_lambda(mTreeLambda);\n\t //TODO dag_h is now set as the radius parameter r\n\t// cout<<\"dag_h=\"<;\n\t graphs.clear();\n\t delete data;\n\n }*/\n\n\n\n void DDkernel_FeatureGenerator::generate_vertex_feature_vector(const GraphClass& aG, vector& x_list, const vector& aFirstEndpointList) {\n\n }\n\n void DDkernel_FeatureGenerator::GenerateAbstractVertexFeatures(unsigned aSrcID, const GraphClass& aG, unsigned aRadius, unsigned aDistance, SVector& x) {\n\n }\n\n //---------------------------------------------------------------------------------\n\n\n//TODO output on svector\n /*\n void DDkernel_FeatureGenerator::svectorFeatures(CGraphFeatures* data,SVector& out_vector, int dag_h, int max_features = 10000000){\n // vector HashMaps = vector(lhs->get_num_vectors());\n // for di tutti i grafi cosi creo i bigDAG.\n\n long time = 1.0 / lossy_e;\n\n vector BER_total;\n\n OnlineFeatures onlinefeatures(max_features);\n // OnlineFeatures onlinefeatures_old;\n\n int64_t numero = 0;\n\n\n\n int training = data->get_num_vectors();\n string a;\n ofstream output(a.c_str());\n\n for (int idx_a=0;idx_aget_feature_vector(a,idx_a);\n\n // \t try{\n OnlineHashBig2DAGLossy* test = onlinefeatures.generateFeatures(a, dag_h,idx_a,data->get_num_vectors());\n\n OnlineHashBig2DAGOrdered ordered_map;\n //TODO add features to a OnlineHashBig2DAGOrdered map\n // cout<<\"test: \"<size();\n BOOST_FOREACH(OnlineHashBig2DAGLossy::value_type local_row, *test) {\n ordered_map[local_row.second.get<2>()]=local_row.second;\n }\n\n // cout<<\"ordered_map: \"<() % (unsigned int)pow(2,mHashBitSize), pow((double)mTreeLambda, (double)i_row.second.get<0>() / 2.0)* (double)i_row.second.get<3>());\n\n ASSERT((unsigned int)i_row.second.get<2>() >= last);\n last = (unsigned int)i_row.second.get<2>();\n }\n // if (mNormalization){\n // \t\t\t\tz.normalize();}\n\n\t\tout_vector.add(z);\n\t\t//if (mNormalization){\n\t\t//\t\tout_vector.normalize();}\n\n delete a;\n test->clear();\n ordered_map.clear();\n delete test;\n\n // \t }catch(shogun::ShogunException e){\n // \t\t cout< p1, const tuple p2)\n{\n /// a graph is less than another graph\n\n\n return p1.get<0>()();\n}\n\n\n\n\n\n\n\nbool FeaturesSortCriterion (const tuple p1, const tuple p2)\n{\n // a graph is less than another graph\n\n\n return p1.get<0>()();\n}\n\n*/\n\nvoid DDkernel_FeatureGeneratorNew::generate_feature_vector(const GraphClass& aG, SVector& x, const vector& aFirstEndpointList) {\n//\t OnlineFeaturesEDeN of;\n//\tcout<<\"Lambda=\"<, int> oSrcDestMaptoDistance;\n\t\t \tmap > MapNodetoParents;\n\t\t \tmap > MapNodetoChildren;\n\n\t\t \tvector TopologicalSort;\n\t\t \tint maxLevel = 0;\n\t\t \t//aG. // codice calcolo features here\n\t\t \taG.SingleVertexBoundedBreadthFirstVisitTree(i,dag_h, oSrcDestMaptoDistance, MapNodetoParents,MapNodetoChildren, TopologicalSort, maxLevel);\n\t\t// cout<<\"MaxLevel: \"< > MapNodeToProductionsID;\n\t\t \tmap > MapNodetoFrequencies;\n\n\t\t \tmap MapProductionIDtoSize;\n\n\t\t // \tcout<<\"topological sort size \"<=0; ii--){\n\t\t \t\t// cout< vertex_adjacency_list = MapNodetoChildren[TopologicalSort[ii]];\n\t\t \t\tint max_child_heigth=0;\n\t\t \tfor (unsigned j = 0; j < vertex_adjacency_list.size(); ++j) {\n\t\t \t\t\tint child_vertex_id= vertex_adjacency_list[j];\n\t\t \t\t\tint child_heigth=MapNodeToProductionsID[child_vertex_id].size();\n\t\t \t\t\tif(child_heigth > max_child_heigth){\n\t\t \t\t\t\tmax_child_heigth = child_heigth;\n\t\t \t\t\t}\n\t\t \t}\n\n\t\t \t\tfor (int depth=0; depth<= max_child_heigth; ++depth){ //era maxLevel\n\t\t \t\t\tunsigned hash_subgraph_code = 1;\n\t\t \t\t//depth 0; only label\n\t\t \t\t\tif(depth==0){\n\t\t \t\t//\t\tcout<<\"depth=0\"< 0; the length of the children ID list\n\t\t \t\t//imposta valore feature a 1 (TODO lambda)\n\t\t \t\t//\t\t\t\t\t\t\tlevel(o-mRadius)\n\t\t\t \tint frequency = 0;\n\t\t\t \tif(max_child_heigth==0){\n\t\t \t\t//leaf node\n\t\t\t \tfrequency= maxLevel - oSrcDestMaptoDistance[make_pair(i,TopologicalSort[ii])];\n\t\t\t \t}\n\t\t\t \tSVector z;\n\t\t \t\tz.set(enc, (double)(frequency+1.0) * sqrt(mTreeLambda));\n\t\t\t //\tcout<<\"Feature: \"<add(z);\n\t\t\t \tMapNodetoFrequencies[TopologicalSort[ii]].push_back(frequency);\n\t\t \t\tMapProductionIDtoSize[enc]= 1; //sqrt(mTreeLambda);\n\t\t \t\t\t}\n\t\t \t\t\telse{\n\t\t \t//\t\t\tcout<<\"depth=\"< vertex_adjacency_list = MapNodetoChildren[TopologicalSort[ii]];\n\t\t \t\tvector vertex_label_id_list;\n\t\t \t\tint min_freq_children =INT_MAX;\n\t\t\t \tfor (unsigned j = 0; j < vertex_adjacency_list.size(); ++j) {\n\n\t\t\t \t\tunsigned child_vertex_id = vertex_adjacency_list[j];\n\t\t\t \t\tint size_map = MapNodeToProductionsID[child_vertex_id].size();//min (depth-1) e dim\n\t\t\t \t\tunsigned child_hash = MapNodeToProductionsID[child_vertex_id][min(size_map,depth)-1];// id hash del figlio;\n\t\t\t \t\tint freq_child = MapNodetoFrequencies[child_vertex_id][min(size_map,depth)-1];\n\t\t\t \t\tif (freq_child < min_freq_children){\n\t\t\t \t\t\tmin_freq_children = freq_child;\n\t\t\t \t\t}\n\t\t\t \t\tvertex_label_id_list.push_back(child_hash);\n\t\t\t \t\tsize += MapProductionIDtoSize[child_hash];\n\t\t\t \t}\n\n\t\t\t \t// vertex_label_id_list contiene gli hash delle prod dei figli. Va ordinato e usato x generare l hash attuale\n\t\t\t \tsort(vertex_label_id_list.begin(), vertex_label_id_list.end());\n\n\t\t\t \tif (vertex_label_id_list.size() > 0){\n\t\t\t \t\tstd::stringstream ss;\n\t\t\t \t\tss <<\":\"<add(z);\n\n\t\t\t //\tcout<<\"weight \"<& aFirstEndpointList){\n//cout< features; //(dag_h+1);\n\t\tSVector DD= generate_vertex_feature_vector_DIVIDED(i,ag2 ,mRadius,features);\n\t//\tcout<<\"got DD vector\"<add(DD);\n\t\t//NSPDK\n\t//\tcout<<\"start generation of NSPKD feature vector.. mDistance\"< first_endpoint_list= vector();\n\t\tfirst_endpoint_list.push_back(i);\n\t\t\t//GetFirstEndpoints(aG, first_endpoint_list);\n\n\t\tint horizon=max(mDistance, mRadiusTwo); // max(mDistance, mRadius);\n\t\t\taG.ComputePairwiseDistanceInformation(horizon, first_endpoint_list);\n\t\t\tif (aG.Check() == false)\n\t\t\t\tthrow logic_error(\"ERROR10: the graph data structure is not sound and it has not passed the checkup procedure\"); //check graph data structure soundness\n\n\t\t\tInitFeatureCache(aG, mRadiusTwo ); //mRadius\n\t\t\tfor (unsigned r = 0; r <= mRadius ; r++) { //max(1.0,mRadius / 2.0)\n\t\t\t\tfor (unsigned r2 = 0; r2 <= mRadiusTwo ; r2++) {\n\t\t\t\tfor (unsigned d = 0; d <= mDistance; d++) { // era 0\n\t\t\t\t\tSVector z;\n\t\t\t\t\tfor (unsigned i = 0; i < first_endpoint_list.size(); i++) {\n\t\t\t\t\t\tunsigned src_id = first_endpoint_list[i];\n\t\t\t\t\t\tif (aG.GetVertexViewPoint(src_id) && aG.GetVertexKernelPoint(src_id) && aG.GetVertexAlive(src_id)) { //proceed to extract features only if the *src* vertex is a kernel point and is alive\n\t\t\t\t\t\t\tSVector zv;\n\n\t\t\t\t\t\t\tvector endpoint_list(4);\n\t\t\t\t\t\t\tendpoint_list[0] = r2;\n\t\t\t\t\t\t\tendpoint_list[1] = d;\n\n\t\t\t\t\t\t//\tunsigned src_code = GenerateVertexNeighbourhoodHashCode(aSrcID, aG, aRadius);\n\t\t\t\t//\tTODO\tfor(unsigned src_code in DD)\n\n\t\t\t\t\t\t\tvector dest_id_list = aG.GetFixedDistanceVertexIDList(i, d);\n\t\t\t\t\t\t\tfor (unsigned dest_j = 0; dest_j < dest_id_list.size(); dest_j++) {\n\t\t\t\t\t\t\t\tunsigned dest_id = dest_id_list[dest_j];\n\t\t\t\t\t\t\t\tunsigned dest_code = 0;\n\t\t\t\t\t\t\t\tif (aG.GetVertexKernelPoint(dest_id) && aG.GetVertexAlive(dest_id)) { //proceed to extract features only if the *dest* vertex is a kernel point and is alive\n\t\t\t\t\t\t\t\t\tdest_code = GenerateVertexNeighbourhoodHashCode(dest_id, aG, r2); //\n\t\t\t\t\t\t\t\t\t// first is the DDK feature, second NSPDK feature\n\t\t\t\t\t\t// for(const SVector::Pair *p = DD; p->i>=0; p++) {\n\t\t\t\t\t\t for(const SVector::Pair *p = features[r]; p->i>=0; p++) {\n\n\t\t\t\t\t\t\t\t\tendpoint_list[2] = p->i;\n\t\t\t\t\t\t\t\t\t\t//TODO scorrere le feature di DD\n\t\t\t\t\t\t\t\t\t\tendpoint_list[3] = dest_code;\n\t\t\t\t\t\t\t\t\tunsigned code = HashFunc(endpoint_list, mHashBitMask);\n\n\t\t\t\t\t\t\t\t\tSVector z;\n\t\t\t\t\t\t\t\t\tz.set(code, p->v);\n\t\t\t\t\t\t\t\t\tzv.add(z);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\n\n\t\t\t\t\t\t\tz.add(zv);\n\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\tif (mNormalization)\n\t\t\t\t\t\tz.normalize();\n\t\t\t\tout->add(z);\n\t\t\t\t} //d\n\t\t\t} //r\n\t\t\t} //r2\n\n\n\n\n\n\n\n\t//\tcout<<\"features size DD:\"<sparse_size()< > features; //(dag_h+1);\n\t\tSVector DD= generate_vertex_feature_vector_DIVIDED_DISTANCE(i,ag2 ,dag_h,features);\n\t\t//cout<add(DD);\n\t\t//NSPDK\n\t//\tcout<<\"start generation of NSPKD feature vector.. mDistance\"< first_endpoint_list= vector();\n\t\tfirst_endpoint_list.push_back(i);\n\t\t\t//GetFirstEndpoints(aG, first_endpoint_list);\n\n\t\tint horizon=max(mDistance, mRadiusTwo); // max(mDistance, mRadius);\n\t\t\taG.ComputePairwiseDistanceInformation(horizon, first_endpoint_list);\n\t\t\tif (aG.Check() == false)\n\t\t\t\tthrow logic_error(\"ERROR10: the graph data structure is not sound and it has not passed the checkup procedure\"); //check graph data structure soundness\n\n\t\t\tInitFeatureCache(aG, mRadiusTwo ); //mRadius\n\t\t\tfor (unsigned r = 0; r <= mRadius ; r++) { //max(1.0,mRadius / 2.0)\n\t\t\t\tfor (unsigned r2 = 0; r2 <= mRadiusTwo ; r2++) {\n\t\t\t\tfor (unsigned d = 0; d <= mDistance; d++) { // era 0\n\t\t\t\t\tSVector z;\n\t\t\t\t\tfor (unsigned i = 0; i < first_endpoint_list.size(); i++) {\n\t\t\t\t\t\tunsigned src_id = first_endpoint_list[i];\n\t\t\t\t\t\tif (aG.GetVertexViewPoint(src_id) && aG.GetVertexKernelPoint(src_id) && aG.GetVertexAlive(src_id)) { //proceed to extract features only if the *src* vertex is a kernel point and is alive\n\t\t\t\t\t\t\tSVector zv;\n\n\t\t\t\t\t\t\tvector endpoint_list(5);\n\t\t\t\t\t\t\tendpoint_list[0] = r2;\n\t\t\t\t\t\t\tendpoint_list[1] = d;\n\n\t\t\t\t\t\t//\tunsigned src_code = GenerateVertexNeighbourhoodHashCode(aSrcID, aG, aRadius);\n\t\t\t\t//\tTODO\tfor(unsigned src_code in DD)\n\n\t\t\t\t\t\t\tvector dest_id_list = aG.GetFixedDistanceVertexIDList(i, d);\n\t\t\t\t\t\t\tfor (unsigned dest_j = 0; dest_j < dest_id_list.size(); dest_j++) {\n\t\t\t\t\t\t\t\tunsigned dest_id = dest_id_list[dest_j];\n\t\t\t\t\t\t\t\tunsigned dest_code = 0;\n\t\t\t\t\t\t\t\tif (aG.GetVertexKernelPoint(dest_id) && aG.GetVertexAlive(dest_id)) { //proceed to extract features only if the *dest* vertex is a kernel point and is alive\n\t\t\t\t\t\t\t\t\tdest_code = GenerateVertexNeighbourhoodHashCode(dest_id, aG, r2); //\n\t\t\t\t\t\t\t\t\t// first is the DDK feature, second NSPDK feature\n\t\t\t\t\t\t// for(const SVector::Pair *p = DD; p->i>=0; p++) {\n\t\t\t\t\t\t\t\t\tfor(int distance=0; distancei>=0; p++) {\n\n\t\t\t\t\t\t\t\t\tendpoint_list[2] = p->i;\n\t\t\t\t\t\t\t\t\t\t//TODO scorrere le feature di DD\n\t\t\t\t\t\t\t\t\t\tendpoint_list[3] = dest_code;\n\t\t\t\t\t\t\t\t\t\t//TEST output\n\t\t\t\t\t\t\t\t//\t\tcout<<\"R \"<v);\n\t\t\t\t\t\t\t\t\tzv.add(z);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\n\n\t\t\t\t\t\t\tz.add(zv);\n\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\tif (mNormalization)\n\t\t\t\t\t\tz.normalize();\n\t\t\t\tout->add(z);\n\t\t\t\t} //d\n\t\t\t} //r\n\t\t\t}//r2\n\n\n\n\n\n\n\n\t//\tcout<<\"features size DD:\"<sparse_size()<, int> oSrcDestMaptoDistance;\n\t\t \tmap > MapNodetoParents;\n\t\t \tmap > MapNodetoChildren;\n\n\t\t \tvector TopologicalSort;\n\t\t \tint maxLevel = 0;\n\t\t \t//aG. // codice calcolo features here\n\t\t \taG.SingleVertexBoundedBreadthFirstVisitTree(i,dag_h, oSrcDestMaptoDistance, MapNodetoParents,MapNodetoChildren, TopologicalSort, maxLevel);\n\t\t// cout<<\"MaxLevel: \"< > MapNodeToProductionsID;\n\t\t \tmap > MapNodetoFrequencies;\n\n\t\t \tmap MapProductionIDtoSize;\n\n\t\t // \tcout<<\"topological sort size \"<=0; ii--){\n\t\t \t\t// cout< vertex_adjacency_list = MapNodetoChildren[TopologicalSort[ii]];\n\t\t \t\tint max_child_heigth=0;\n\t\t \tfor (unsigned j = 0; j < vertex_adjacency_list.size(); ++j) {\n\t\t \t\t\tint child_vertex_id= vertex_adjacency_list[j];\n\t\t \t\t\tint child_heigth=MapNodeToProductionsID[child_vertex_id].size();\n\t\t \t\t\tif(child_heigth > max_child_heigth){\n\t\t \t\t\t\tmax_child_heigth = child_heigth;\n\t\t \t\t\t}\n\t\t \t}\n\n\t\t \t\tfor (int depth=0; depth<= max_child_heigth; ++depth){ //era maxLevel\n\t\t \t\t\tunsigned hash_subgraph_code = 1;\n\t\t \t\t//depth 0; only label\n\t\t \t\t\tif(depth==0){\n\t\t \t\t//\t\tcout<<\"depth=0\"< 0; the length of the children ID list\n\t\t \t\t//imposta valore feature a 1 (TODO lambda)\n\t\t \t\t//\t\t\t\t\t\t\tlevel(o-mRadius)\n\t\t\t \tint frequency = 0;\n\t\t\t \tif(max_child_heigth==0){\n\t\t \t\t//leaf node\n\t\t\t \tfrequency= maxLevel - oSrcDestMaptoDistance[make_pair(i,TopologicalSort[ii])];\n\t\t\t \t}\n\t\t\t \tSVector z;\n\t\t \t\tz.set(enc, (double)(frequency+1.0) * sqrt(mTreeLambda));\n\t\t\t //\tcout<<\"Feature: \"<add(z);\n\t\t\t \tMapNodetoFrequencies[TopologicalSort[ii]].push_back(frequency);\n\t\t \t\tMapProductionIDtoSize[enc]= 1; //sqrt(mTreeLambda);\n\t\t \t\t\t}\n\t\t \t\t\telse{\n\t\t \t//\t\t\tcout<<\"depth=\"< vertex_adjacency_list = MapNodetoChildren[TopologicalSort[ii]];\n\t\t \t\tvector vertex_label_id_list;\n\t\t \t\tint min_freq_children =INT_MAX;\n\t\t\t \tfor (unsigned j = 0; j < vertex_adjacency_list.size(); ++j) {\n\n\t\t\t \t\tunsigned child_vertex_id = vertex_adjacency_list[j];\n\t\t\t \t\tint size_map = MapNodeToProductionsID[child_vertex_id].size();//min (depth-1) e dim\n\t\t\t \t\tunsigned child_hash = MapNodeToProductionsID[child_vertex_id][min(size_map,depth)-1];// id hash del figlio;\n\t\t\t \t\tint freq_child = MapNodetoFrequencies[child_vertex_id][min(size_map,depth)-1];\n\t\t\t \t\tif (freq_child < min_freq_children){\n\t\t\t \t\t\tmin_freq_children = freq_child;\n\t\t\t \t\t}\n\t\t\t \t\tvertex_label_id_list.push_back(child_hash);\n\t\t\t \t\tsize += MapProductionIDtoSize[child_hash];\n\t\t\t \t}\n\n\t\t\t \t// vertex_label_id_list contiene gli hash delle prod dei figli. Va ordinato e usato x generare l hash attuale\n\t\t\t \tsort(vertex_label_id_list.begin(), vertex_label_id_list.end());\n\n\t\t\t \tif (vertex_label_id_list.size() > 0){\n\t\t\t \t\tstd::stringstream ss;\n\t\t\t \t\tss <<\":\"<add(z);\n\n\t\t\t //\tcout<<\"weight \"< & features_grouped_by_depth){\n\t//cout<<\"start generation of feature vector..\"<(dag_h+1);\n\t\t/*\tfor(int i=0; i, int> oSrcDestMaptoDistance;\n\t\t \tmap > MapNodetoParents;\n\t\t \tmap > MapNodetoChildren;\n\n\t\t \tvector TopologicalSort;\n\t\t \tint maxLevel = 0;\n\t\t \t//aG. // codice calcolo features here\n\t\t \taG.SingleVertexBoundedBreadthFirstVisitTree(i,dag_h, oSrcDestMaptoDistance, MapNodetoParents,MapNodetoChildren, TopologicalSort, maxLevel);\n\t\t// cout<<\"MaxLevel: \"< > MapNodeToProductionsID;\n\t\t \tmap > MapNodetoFrequencies;\n\n\t\t \tmap MapProductionIDtoSize;\n\n\t\t // \tcout<<\"topological sort size \"<=0; ii--){\n\t\t \t\t// cout< vertex_adjacency_list = MapNodetoChildren[TopologicalSort[ii]];\n\t\t \t\tint max_child_heigth=0;\n\t\t \tfor (unsigned j = 0; j < vertex_adjacency_list.size(); ++j) {\n\t\t \t\t\tint child_vertex_id= vertex_adjacency_list[j];\n\t\t \t\t\tint child_heigth=MapNodeToProductionsID[child_vertex_id].size();\n\t\t \t\t\tif(child_heigth > max_child_heigth){\n\t\t \t\t\t\tmax_child_heigth = child_heigth;\n\t\t \t\t\t}\n\t\t \t}\n\n\t\t \t\tfor (int depth=0; depth<= max_child_heigth; ++depth){ //era maxLevel\n\t\t \t\t\tunsigned hash_subgraph_code = 1;\n\t\t \t\t//depth 0; only label\n\t\t \t\t\tif(depth==0){\n\t\t \t\t//\t\tcout<<\"depth=0\"< 0; the length of the children ID list\n\t\t \t\t//imposta valore feature a 1 (TODO lambda)\n\t\t \t\t//\t\t\t\t\t\t\tlevel(o-mRadius)\n\t\t\t \tint frequency = 0;\n\t\t\t \tif(max_child_heigth==0){\n\t\t \t\t//leaf node\n\t\t\t \tfrequency= maxLevel - oSrcDestMaptoDistance[make_pair(i,TopologicalSort[ii])];\n\t\t\t \t}\n\t\t\t \tSVector z;\n\t\t \t\tz.set(enc, (double)(frequency+1.0) * sqrt(mTreeLambda));\n\t\t\t //\tcout<<\"Feature: \"<add(z);\n\t\t\t \tfeatures_grouped_by_depth[depth].add(z);\n\t\t\t \tMapNodetoFrequencies[TopologicalSort[ii]].push_back(frequency);\n\t\t \t\tMapProductionIDtoSize[enc]= 1; //sqrt(mTreeLambda);\n\t\t \t\t\t}\n\t\t \t\t\telse{\n\t\t \t//\t\t\tcout<<\"depth=\"< vertex_adjacency_list = MapNodetoChildren[TopologicalSort[ii]];\n\t\t \t\tvector vertex_label_id_list;\n\t\t \t\tint min_freq_children =INT_MAX;\n\t\t\t \tfor (unsigned j = 0; j < vertex_adjacency_list.size(); ++j) {\n\n\t\t\t \t\tunsigned child_vertex_id = vertex_adjacency_list[j];\n\t\t\t \t\tint size_map = MapNodeToProductionsID[child_vertex_id].size();//min (depth-1) e dim\n\t\t\t \t\tunsigned child_hash = MapNodeToProductionsID[child_vertex_id][min(size_map,depth)-1];// id hash del figlio;\n\t\t\t \t\tint freq_child = MapNodetoFrequencies[child_vertex_id][min(size_map,depth)-1];\n\t\t\t \t\tif (freq_child < min_freq_children){\n\t\t\t \t\t\tmin_freq_children = freq_child;\n\t\t\t \t\t}\n\t\t\t \t\tvertex_label_id_list.push_back(child_hash);\n\t\t\t \t\tsize += MapProductionIDtoSize[child_hash];\n\t\t\t \t}\n\n\t\t\t \t// vertex_label_id_list contiene gli hash delle prod dei figli. Va ordinato e usato x generare l hash attuale\n\t\t\t \tsort(vertex_label_id_list.begin(), vertex_label_id_list.end());\n\n\t\t\t \tif (vertex_label_id_list.size() > 0){\n\t\t\t \t\tstd::stringstream ss;\n\t\t\t \t\tss <<\":\"<add(z);\n\t\t\t \tfeatures_grouped_by_depth[depth].add(z);\n\t\t\t //\tcout<<\"weight \"< > & features_grouped_by_depth){\n\t//cout<<\"start generation of feature vector..\"< >(dag_h+1);\n\n\n\t\t//\tcout<<\"n of nodes \"<, int> oSrcDestMaptoDistance;\n\t\t \tmap > MapNodetoParents;\n\t\t \tmap > MapNodetoChildren;\n\n\t\t \tvector TopologicalSort;\n\t\t \tint maxLevel = 0;\n\t\t \t//aG. // codice calcolo features here\n\t\t \taG.SingleVertexBoundedBreadthFirstVisitTree(i,dag_h, oSrcDestMaptoDistance, MapNodetoParents,MapNodetoChildren, TopologicalSort, maxLevel);\n\t\t// cout<<\"MaxLevel: \"<(dag_h+1));\n\n\t\t\t\t}\n\n\t\t \t// \tcout<<\"Breadth first visit ended..\"< > MapNodeToProductionsID;\n\t\t \tmap > MapNodetoFrequencies;\n\n\t\t \tmap MapProductionIDtoSize;\n\n\t\t // \tcout<<\"topological sort size \"<=0; ii--){\n\t\t \t\t// cout< vertex_adjacency_list = MapNodetoChildren[TopologicalSort[ii]];\n\t\t \t\tint max_child_heigth=0;\n\t\t \tfor (unsigned j = 0; j < vertex_adjacency_list.size(); ++j) {\n\t\t \t\t\tint child_vertex_id= vertex_adjacency_list[j];\n\t\t \t\t\tint child_heigth=MapNodeToProductionsID[child_vertex_id].size();\n\t\t \t\t\tif(child_heigth > max_child_heigth){\n\t\t \t\t\t\tmax_child_heigth = child_heigth;\n\t\t \t\t\t}\n\t\t \t}\n\n\t\t \t\tfor (int depth=0; depth<= max_child_heigth; ++depth){ //era maxLevel\n\t\t \t\t\tunsigned hash_subgraph_code = 1;\n\t\t \t\t//depth 0; only label\n\t\t \t\t\tif(depth==0){\n\t\t \t\t//\t\tcout<<\"depth=0\"< 0; the length of the children ID list\n\t\t \t\t//imposta valore feature a 1 (TODO lambda)\n\t\t \t\t//\t\t\t\t\t\t\tlevel(o-mRadius)\n\t\t\t \tint frequency = 0;\n\t\t\t \tif(max_child_heigth==0){\n\t\t \t\t//leaf node\n\t\t\t \tfrequency= maxLevel - oSrcDestMaptoDistance[make_pair(i,TopologicalSort[ii])];\n\t\t\t \t}\n\t\t\t \tSVector z;\n\t\t \t\tz.set(enc, (double)(frequency+1.0) * sqrt(mTreeLambda));\n\t\t\t //\tcout<<\"Feature: \"<add(z);\n\t\t\t \tfeatures_grouped_by_depth[depth][oSrcDestMaptoDistance[make_pair(i,TopologicalSort[ii])]].add(z);\n\t\t\t \tMapNodetoFrequencies[TopologicalSort[ii]].push_back(frequency);\n\t\t \t\tMapProductionIDtoSize[enc]= 1; //sqrt(mTreeLambda);\n\t\t \t\t\t}\n\t\t \t\t\telse{\n\t\t \t//\t\t\tcout<<\"depth=\"< vertex_adjacency_list = MapNodetoChildren[TopologicalSort[ii]];\n\t\t \t\tvector vertex_label_id_list;\n\t\t \t\tint min_freq_children =INT_MAX;\n\t\t\t \tfor (unsigned j = 0; j < vertex_adjacency_list.size(); ++j) {\n\n\t\t\t \t\tunsigned child_vertex_id = vertex_adjacency_list[j];\n\t\t\t \t\tint size_map = MapNodeToProductionsID[child_vertex_id].size();//min (depth-1) e dim\n\t\t\t \t\tunsigned child_hash = MapNodeToProductionsID[child_vertex_id][min(size_map,depth)-1];// id hash del figlio;\n\t\t\t \t\tint freq_child = MapNodetoFrequencies[child_vertex_id][min(size_map,depth)-1];\n\t\t\t \t\tif (freq_child < min_freq_children){\n\t\t\t \t\t\tmin_freq_children = freq_child;\n\t\t\t \t\t}\n\t\t\t \t\tvertex_label_id_list.push_back(child_hash);\n\t\t\t \t\tsize += MapProductionIDtoSize[child_hash];\n\t\t\t \t}\n\n\t\t\t \t// vertex_label_id_list contiene gli hash delle prod dei figli. Va ordinato e usato x generare l hash attuale\n\t\t\t \tsort(vertex_label_id_list.begin(), vertex_label_id_list.end());\n\n\t\t\t \tif (vertex_label_id_list.size() > 0){\n\t\t\t \t\tstd::stringstream ss;\n\t\t\t \t\tss <<\":\"<add(z);\n\t\t\t \tfeatures_grouped_by_depth[depth][oSrcDestMaptoDistance[make_pair(i,TopologicalSort[ii])]].add(z); //TODO distance from root\n\t\t\t //\tcout<<\"weight \"<& aFirstEndpointList) {\n\t//cout<<\"generate_vertex_feature_vector.... OK for abstract\"< first_endpoint_list = aFirstEndpointList;\n\tGetFirstEndpoints(aG, first_endpoint_list);\n\tif (first_endpoint_list.size() == 0)\n\t\tthrow std::logic_error(\"ERROR6: Something went wrong: cannot generate features over an empty set of first endpoints!\");\n\tint horizon = max(mDistance, mRadius);\n\taG.ComputePairwiseDistanceInformation(horizon, first_endpoint_list);\n\tif (aG.Check() == false)\n\t\tthrow logic_error(\"ERROR11: the graph data structure is not sound and it has not passed the checkup procedure\"); //check graph data structure soundness\n\n\tInitFeatureCache(aG, mRadiusTwo);\n\tfor (unsigned i = 0; i < first_endpoint_list.size(); i++) {\n\t\tSVector z;\n\t\tunsigned src_id = first_endpoint_list[i];\n\t\t//InitFeatureCache(aG, mRadiusTwo);\n\n\t\tif (aG.GetVertexViewPoint(src_id) && aG.GetVertexKernelPoint(src_id) && aG.GetVertexAlive(src_id)) { //proceed to extract features only if the *src* vertex is a kernel point and is alive\n\t\t\t//for (unsigned r = 0; r <= mRadius; r++) {\n\t\t\t//\tfor (unsigned d = 0; d <= mDistance; d++) {\n\t\t\t\t//\tGenerateVertexFeatures(src_id, aG, r, d, z);\n\t\t\tGenerateVertexFeatures(src_id, aG, mRadius, mDistance, z);\n\t\t\t\t//}\n\t\t\t//}\n\t\t} else if (aG.GetVertexAbstraction(src_id) && aG.GetVertexAlive(src_id)) {\n\t\t\t//for (unsigned r = 0; r <= mRadius; r++) {\n\t\t\t//\tfor (unsigned d = 0; d <= mDistance; d++) {\n\t\t\t\t//\tGenerateAbstractVertexFeatures(src_id, aG, r, d, z);\n\t\t\tGenerateAbstractVertexFeatures(src_id, aG, mRadius, mDistance, z);\n\n\t\t\t//\t}\n\t\t\t//}\n\t\t}\n\t\tif (mMinKernel)\n\t\t\tConvertSparseVectorToMinFeatureVector(z);\n\t\t//x_list.push_back(z);\n\t\tx.add(z);\n\t}\n\tif (mDebugVerbosity > 0) {\n\n\t\tOutputFeatureMap(cout);\n\t\taG.Output(cout);\n\t}\n}\nvoid ANSDDkernel_FeatureGenerator::GenerateVertexFeatures(unsigned aRootVertexIndex, const GraphClass& aG, unsigned aRadius, unsigned aDistance, SVector& x) {\n\tx= generateVertexFeatures_DISTANCE(aRootVertexIndex, aG ,aRadius);\n}\n\nSVector& ANSDDkernel_FeatureGenerator::generateVertexFeatures_DISTANCE(unsigned i, const GraphClass& aG , int dag_h){\n // test rNSPDK 1/2 d=1/2\n\tGraphClass ag2 = aG;\n\tSVector* out = new SVector();\n//\tcout<<\"Generate feature vector core\"< > features; //(dag_h+1);\n\t\tSVector DD= generate_vertex_feature_vector_DIVIDED_DISTANCE(i,ag2 ,dag_h,features);\n\t\t//cout<add(DD);\n\t\t//NSPDK\n\t//\tcout<<\"start generation of NSPKD feature vector.. mDistance\"< first_endpoint_list= vector();\n\t\tfirst_endpoint_list.push_back(i);\n\t\t\t//GetFirstEndpoints(aG, first_endpoint_list);\n\n\t\tint horizon=max(mDistance, mRadiusTwo); // max(mDistance, mRadius);\n\t\t\taG.ComputePairwiseDistanceInformation(horizon, first_endpoint_list);\n\t\t\tif (aG.Check() == false)\n\t\t\t\tthrow logic_error(\"ERROR10: the graph data structure is not sound and it has not passed the checkup procedure\"); //check graph data structure soundness\n\n\t\t\tInitFeatureCache(aG, mRadiusTwo );\n\t\t\tfor (unsigned r = 0; r <= mRadius ; r++) { //max(1.0,mRadius / 2.0)\n\t\t\t\tfor (unsigned d = 0; d <= mDistance; d++) { // era 0\n\t\t\t\t\tfor (unsigned r2 = 0; r2 <= mRadiusTwo ; r2++) {\n\t\t\t\t\tSVector z;\n\t\t\t\t\tfor (unsigned i = 0; i < first_endpoint_list.size(); i++) {\n\t\t\t\t\t\tunsigned src_id = first_endpoint_list[i];\n\t\t\t\t\t\tif (aG.GetVertexViewPoint(src_id) && aG.GetVertexKernelPoint(src_id) && aG.GetVertexAlive(src_id)) { //proceed to extract features only if the *src* vertex is a kernel point and is alive\n\t\t\t\t\t\t\tSVector zv;\n\n\t\t\t\t\t\t\tvector endpoint_list(5);\n\t\t\t\t\t\t\tendpoint_list[0] = r2;\n\t\t\t\t\t\t\tendpoint_list[1] = d;\n\n\t\t\t\t\t\t//\tunsigned src_code = GenerateVertexNeighbourhoodHashCode(aSrcID, aG, aRadius);\n\t\t\t\t//\tTODO\tfor(unsigned src_code in DD)\n\n\t\t\t\t\t\t\tvector dest_id_list = aG.GetFixedDistanceVertexIDList(i, d);\n\t\t\t\t\t\t\tfor (unsigned dest_j = 0; dest_j < dest_id_list.size(); dest_j++) {\n\t\t\t\t\t\t\t\tunsigned dest_id = dest_id_list[dest_j];\n\t\t\t\t\t\t\t\tunsigned dest_code = 0;\n\t\t\t\t\t\t\t\tif (aG.GetVertexKernelPoint(dest_id) && aG.GetVertexAlive(dest_id)) { //proceed to extract features only if the *dest* vertex is a kernel point and is alive\n\t\t\t\t\t\t\t\t\tdest_code = GenerateVertexNeighbourhoodHashCode(dest_id, aG, r2); //\n\t\t\t\t\t\t\t\t\t// first is the DDK feature, second NSPDK feature\n\t\t\t\t\t\t// for(const SVector::Pair *p = DD; p->i>=0; p++) {\n\t\t\t\t\t\t\t\t\tfor(int distance=0; distancei>=0; p++) {\n\n\t\t\t\t\t\t\t\t\tendpoint_list[2] = p->i;\n\t\t\t\t\t\t\t\t\t\t//TODO scorrere le feature di DD\n\t\t\t\t\t\t\t\t\t\tendpoint_list[3] = dest_code;\n\t\t\t\t\t\t\t\t\t\t//TEST output\n\t\t\t\t\t\t\t\t//\t\tcout<<\"R \"<v);\n\t\t\t\t\t\t\t\t\tzv.add(z);\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\n\n\t\t\t\t\t\t\tz.add(zv);\n\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\n\t\t\t\t\tif (mNormalization)\n\t\t\t\t\t\tz.normalize();\n\t\t\t\tout->add(z);\n\t\t\t\t} //d\n\t\t\t} //r\n\t\t\t} //r2\n\n\n\n\n\n\n\n\t//\tcout<<\"features size DD:\"<sparse_size()< endpoint_list(5);\n\tfor (unsigned r = 0; r <= mRadius ; r++) { //max(1.0,mRadius / 2.0)\n\t\t\t\t\tfor (unsigned d = 0; d <= mDistance; d++) { // era 0\n\t\t\t\t\t\tfor (unsigned r2 = 0; r2 <= mRadiusTwo ; r2++) { //max(1.0,mRadius / 2.0)\n\n\n\tendpoint_list[0] = r;\n\tendpoint_list[1] = d;\n\n\t//ensure that the src vertex is of the abstraction type\n\tif (aG.GetVertexAbstraction(aSrcID) == false)\n\t\tthrow std::logic_error(\"ERROR2: Something went wrong: expecting an abstraction vertex, but abstraction test fails.\");\n\t//extract all adjacent vertices and partition them into part_of (lower level) and abstraction_of (upper level)\n\tvector part_of_list;\n\tvector abstraction_of_list;\n\n\tvector vertex_adjacency_list = aG.GetVertexAdjacentList(aSrcID);\n\tvector edge_adjacency_list = aG.GetEdgeAdjacentList(aSrcID);\n\tif (vertex_adjacency_list.size() != edge_adjacency_list.size())\n\t\tthrow std::logic_error(\"ERROR3: Something went wrong: expecting vertex adjacency list to be the same size as the edge adjacency list.\");\n\tif (vertex_adjacency_list.size() == 0)\n\t\tthrow std::logic_error(\"ERROR3b: Something went wrong: expecting a non empty vertex adjacency list for each abstract vertex.\");\n\tfor (unsigned i = 0; i < vertex_adjacency_list.size(); ++i) {\n\t\tunsigned child_vertex_id = vertex_adjacency_list[i];\n\t\tunsigned child_edge_id = edge_adjacency_list[i];\n\t\tif (aG.GetEdgePartOf(child_edge_id))\n\t\t\tpart_of_list.push_back(child_vertex_id);\n\t\tif (aG.GetEdgeAbstractionOf(child_edge_id))\n\t\t\tabstraction_of_list.push_back(child_vertex_id);\n\t}\n\t//----------------------------\n\n\t//----------------------------------------\n\t\t\t\t\t\t//starting from each upper level vertex find all vertices at distance aDistance\n\tif (abstraction_of_list.size() == 0)\n\t\tthrow std::logic_error(\"ERROR5: Something went wrong: expecting a non empty abstraction_of list.\");\n\tset abstraction_of_set;\n\tfor (unsigned i = 0; i < abstraction_of_list.size(); ++i) {\n\t\tunsigned v = abstraction_of_list[i];\n\t\tvector dest_id_list = aG.GetFixedDistanceVertexIDList(v, d);\n\t\t//-----------?????????????????????????????\n\t//\tabstraction_of_set.insert(abstraction_of_list.begin(), abstraction_of_list.end());\n\t\tabstraction_of_set.insert(dest_id_list.begin(), dest_id_list.end());\n\n\t}\n\t//starting from each lower level vertex find all vertices at distance aDistance\n\tif (part_of_list.size() == 0)\n\t\tthrow std::logic_error(\"ERROR4: Something went wrong: expecting a non empty part_of list.\");\n\tset part_of_set;\n\tfor (unsigned i = 0; i < part_of_list.size(); ++i) {\n\t\tunsigned v = part_of_list[i];\n\t\tvector dest_id_list = aG.GetFixedDistanceVertexIDList(v, d);\n\t\t//-----------?????????????????????????????\n\t\tpart_of_set.insert(dest_id_list.begin(), dest_id_list.end());\n\t}\n\t//make all possible pairs of distant lower level vertices with distant upper level vertices\n\tfor (set::iterator it = part_of_set.begin(); it != part_of_set.end(); ++it) {\n\t\tunsigned part_of_id = *it;\n\n\t\t//TODO modifica con mio kernel\n\t\tvector< vector< SVector> > features; //(dag_h+1);\n\t\tSVector DD= generate_vertex_feature_vector_DIVIDED_DISTANCE(part_of_id, aG ,aRadius,features);\n\t\t//SVector DD= generate_vertex_feature_vector_DIVIDED(part_of_id, aG ,aRadius,features);\n\n\t\t//For every feature in DD, by height and distance from the root\n\t\tfor(int distance=0; distancei>=0; p++) {\n\n\t\t\tendpoint_list[2] = p->i;\n\t\t\t//TODO scorrere le feature di DD\n\t\t\t\t//TEST output\n\t\t\t//\t\tcout<<\"R \"<::iterator jt = abstraction_of_set.begin(); jt != abstraction_of_set.end(); ++jt) {\n\t\t\tunsigned abstraction_of_id = *jt;\n\t\t\t//build features with one neighborhood graph signature from the lower level and one from the upper\n\t\t\tunsigned abstraction_of_code = GenerateVertexNeighbourhoodHashCode(abstraction_of_id, aG, r2);\n\t\t\tendpoint_list[3] = abstraction_of_code;\n\t\t\tunsigned code = HashFunc(endpoint_list, mHashBitMask);\n\t\t\tif (mDebugVerbosity > 0)\n\t\t\t\tmDebugInfo.StoreFeatureCodeToFeatureInfo(code, endpoint_list);\n\t\t\tSVector z;\n\t\t\tz.set(code, 1);\n\t\t\tx.add(z);\n\t\t}\n\t}\n\t\t}\n\t}\n\n\n\t//i have to repeat for both sides for ddk\n\t//make all possible pairs of distant lower level vertices with distant upper level vertices\n\tfor (set::iterator it = part_of_set.begin(); it != part_of_set.end(); ++it) {\n\t\tunsigned part_of_id = *it;\n\n\t\t//TODO modifica con mio kernel\n\t\tvector< vector< SVector> > features; //(dag_h+1);\n\t\tSVector DD= generate_vertex_feature_vector_DIVIDED_DISTANCE(part_of_id, aG ,aRadius,features);\n\n\t\t\t//For every feature in DD, by height and distance from the root\n\t\t\tfor(int distance=0; distancei>=0; p++) {\n\n\t\t\t\tendpoint_list[2] = p->i;\n\n\t\tunsigned part_of_code = GenerateVertexNeighbourhoodHashCode(part_of_id, aG, aRadius);\n\t\tendpoint_list[2] = part_of_code;\n\t\tfor (set::iterator jt = abstraction_of_set.begin(); jt != abstraction_of_set.end(); ++jt) {\n\t\t\tunsigned abstraction_of_id = *jt;\n\t\t\t//build features with one neighborhood graph signature from the lower level and one from the upper\n\t\t\tunsigned abstraction_of_code = GenerateVertexNeighbourhoodHashCode(abstraction_of_id, aG, aRadius);\n\t\t\tendpoint_list[3] = abstraction_of_code;\n\t\t\tunsigned code = HashFunc(endpoint_list, mHashBitMask);\n\t\t\tif (mDebugVerbosity > 0)\n\t\t\t\tmDebugInfo.StoreFeatureCodeToFeatureInfo(code, endpoint_list);\n\t\t\tSVector z;\n\t\t\tz.set(code, 1);\n\t\t\tx.add(z);\n\t\t}\n\t}\n\t}\n\n}\n\t\t\t\t\t}\n\t}\n}\n}\n\n\n\n", "meta": {"hexsha": "ea9e17a5eef4372693e35ab59995555f40702884", "size": 47847, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "EDeN/DDK_FeatureGenerator.cpp", "max_stars_repo_name": "asangphukieo/GraphProt", "max_stars_repo_head_hexsha": "1ce3db40e30ebd9442203dbf8c05aa0e72ffe170", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11.0, "max_stars_repo_stars_event_min_datetime": "2017-09-27T16:05:06.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-18T09:07:16.000Z", "max_issues_repo_path": "EDeN/DDK_FeatureGenerator.cpp", "max_issues_repo_name": "asangphukieo/GraphProt", "max_issues_repo_head_hexsha": "1ce3db40e30ebd9442203dbf8c05aa0e72ffe170", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6.0, "max_issues_repo_issues_event_min_datetime": "2018-11-22T15:52:50.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-15T07:50:58.000Z", "max_forks_repo_path": "EDeN/DDK_FeatureGenerator.cpp", "max_forks_repo_name": "asangphukieo/GraphProt", "max_forks_repo_head_hexsha": "1ce3db40e30ebd9442203dbf8c05aa0e72ffe170", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2019-09-04T10:09:58.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-15T20:52:43.000Z", "avg_line_length": 38.8053527981, "max_line_length": 192, "alphanum_fraction": 0.6560076912, "num_tokens": 12859, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.2291657286788779}} {"text": "// Copyright (c) 2018 yshurik\n//\n// Distributed under the MIT/X11 software license, see the accompanying\n// file COPYING or http://www.opensource.org/licenses/mit-license.php.\n//\n// The use in another cyptocurrency project the code is licensed under\n// Jelurida Public License (JPL). See https://www.jelurida.com/resources/jpl\n\n#include \"pegdata.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \"util.h\"\n\n#include \n#include \n\nusing namespace std;\nusing namespace boost;\n\nCFractions::CFractions()\n :nFlags(VALUE)\n ,f(new int64_t[PEG_SIZE])\n{\n f[0] = 0; // fast init first item\n}\nCFractions::CFractions(int64_t value, uint32_t flags)\n :nFlags(flags)\n ,f(new int64_t[PEG_SIZE])\n{\n if (flags & VALUE)\n f[0] = value; // fast init first item\n else if (flags & STD) {\n f[0] = value;\n nFlags = VALUE;\n ToStd();\n nFlags = flags;\n }\n else {\n assert(0);\n }\n}\nCFractions::CFractions(const CFractions & o)\n :nFlags(o.nFlags)\n ,nLockTime(o.nLockTime)\n ,sReturnAddr(o.sReturnAddr)\n ,f(new int64_t[PEG_SIZE])\n{\n for(int i=0; i< PEG_SIZE; i++) {\n f[i] = o.f[i];\n }\n}\n\nCFractions& CFractions::operator=(const CFractions& o)\n{\n nFlags = o.nFlags;\n nLockTime = o.nLockTime;\n sReturnAddr = o.sReturnAddr;\n for(int i=0; i< PEG_SIZE; i++) {\n f[i] = o.f[i];\n }\n return *this;\n}\n\nvoid CFractions::ToDeltas(int64_t* deltas) const\n{\n int64_t fp = 0;\n for(int i=0; i(deltas);\n int res = ::compress2(zout, &zlen, src, n, zlevel);\n if (res == Z_OK) {\n if (report_len) *report_len = zlen;\n out << nVersion;\n out << uint32_t(nFlags | SER_ZDELTA);\n out << nLockTime;\n out << sReturnAddr;\n auto ser = reinterpret_cast(zout);\n out << zlen;\n out.write(ser, zlen);\n }\n else {\n if (report_len) *report_len = PEG_SIZE*sizeof(int64_t);\n out << nVersion;\n out << uint32_t(nFlags | SER_RAW);\n out << nLockTime;\n out << sReturnAddr;\n auto ser = reinterpret_cast(f.get());\n out.write(ser, PEG_SIZE*sizeof(int64_t));\n }\n } else {\n if (report_len) *report_len = PEG_SIZE*sizeof(int64_t);\n out << nVersion;\n out << uint32_t(nFlags | SER_RAW);\n out << nLockTime;\n out << sReturnAddr;\n auto ser = reinterpret_cast(f.get());\n out.write(ser, PEG_SIZE*sizeof(int64_t));\n }\n return true;\n}\n\nbool CFractions::Unpack(CDataStream& inp)\n{\n uint32_t nSerFlags = 0;\n inp >> nVersion;\n inp >> nSerFlags;\n inp >> nLockTime;\n inp >> sReturnAddr;\n\n if (nSerFlags & SER_VALUE) {\n nFlags = nSerFlags | VALUE;\n inp >> f[0];\n }\n else if (nSerFlags & SER_ZDELTA) {\n unsigned long zlen = 0;\n inp >> zlen;\n\n if (zlen>(2*PEG_SIZE*sizeof(int64_t))) {\n // data are broken, no read\n return false;\n }\n\n unsigned char zinp[2*PEG_SIZE*sizeof(int64_t)];\n unsigned long n = PEG_SIZE*sizeof(int64_t);\n auto ser = reinterpret_cast(zinp);\n inp.read(ser, zlen);\n\n int64_t deltas[PEG_SIZE];\n auto src = reinterpret_cast(ser);\n auto dst = reinterpret_cast(deltas);\n int res = ::uncompress(dst, &n, src, zlen);\n if (res != Z_OK) {\n // data are broken, can not uncompress\n return false;\n }\n FromDeltas(deltas);\n nFlags = nSerFlags | STD;\n }\n else if (nSerFlags & SER_RAW) {\n auto ser = reinterpret_cast(f.get());\n inp.read(ser, PEG_SIZE*sizeof(int64_t));\n nFlags = nSerFlags | STD;\n }\n nFlags &= SER_MASK;\n\n return true;\n}\n\nCFractions CFractions::Std() const\n{\n if ((nFlags & VALUE) ==0)\n return *this;\n\n CFractions fstd;\n fstd.sReturnAddr = sReturnAddr;\n fstd.nLockTime = nLockTime;\n fstd.nFlags = nFlags;\n fstd.nFlags &= ~uint32_t(VALUE);\n fstd.nFlags |= STD;\n\n int64_t v = f[0];\n for(int i=0;i0)\n return false;\n }\n return true;\n}\n\nint64_t CFractions::Total() const\n{\n int64_t nValue =0;\n if (nFlags & VALUE)\n return f[0];\n\n for(int i=0;i= PEG_SIZE) return 0;\n\n if (peglevel.nShiftLastPart >0 &&\n peglevel.nShiftLastTotal >0) {\n // partial value to use\n int64_t v = f[to];\n int64_t vpart = ::RatioPart(v,\n peglevel.nShiftLastPart,\n peglevel.nShiftLastTotal);\n if (vpart < v) vpart++; // better rounding\n nValue += vpart;\n }\n\n for(int i=0;i= PEG_SIZE) return 0;\n\n if (peglevel.nShiftLastPart >0 &&\n peglevel.nShiftLastTotal >0) {\n // partial value to use\n int64_t v = f[from];\n int64_t vpart = ::RatioPart(v,\n peglevel.nShiftLastPart,\n peglevel.nShiftLastTotal);\n if (vpart < v) vpart++; // better rounding\n nValue += (v - vpart);\n from++;\n }\n\n for(int i=from;i total/2) {\n return i;\n }\n }\n return 0;\n}\n\nvoid CFractions::ToStd()\n{\n if ((nFlags & VALUE) == 0)\n return;\n\n nFlags &= ~uint32_t(VALUE);\n nFlags |= STD;\n\n int64_t v = f[0];\n for(int i=0;i=0) continue;\n frNegative.f[i] = f[i];\n if (total) *total += f[i];\n }\n return frNegative;\n}\n\n\nCFractions CFractions::LowPart(int supply, int64_t* total) const\n{\n if ((nFlags & STD) == 0) {\n return Std().LowPart(supply, total);\n }\n CFractions frLowPart(0, CFractions::STD);\n for(int i=0; i=0 &&\n to 0 &&\n peglevel.nShiftLastTotal >0) {\n // partial value to use\n int64_t v = f[to];\n int64_t vpart = ::RatioPart(v,\n peglevel.nShiftLastPart,\n peglevel.nShiftLastTotal);\n if (vpart < v) vpart++;\n frLowPart.f[to] = vpart;\n if (total) *total += vpart;\n }\n\n for(int i=0; i=0 &&\n from 0 &&\n peglevel.nShiftLastTotal >0) {\n // partial value to use\n int64_t v = f[from];\n int64_t vpart = ::RatioPart(v,\n peglevel.nShiftLastPart,\n peglevel.nShiftLastTotal);\n if (vpart < v) vpart++;\n frHighPart.f[from] = (v - vpart);\n if (total) *total += (v - vpart);\n from++;\n }\n\n for(int i=from; i0 &&\n peglevel_low.nShiftLastTotal >0) {\n int64_t vpart = ::RatioPart(vone,\n peglevel_low.nShiftLastPart,\n peglevel_low.nShiftLastTotal);\n v -= vpart;\n }\n\n if (peglevel_high.nShiftLastPart >0 &&\n peglevel_high.nShiftLastTotal >0) {\n int64_t vpart = ::RatioPart(vone,\n peglevel_high.nShiftLastPart,\n peglevel_high.nShiftLastTotal);\n if (vpart < vone) vpart++;\n v -= (vone - vpart);\n }\n\n mid.f[from] = v;\n return mid;\n}\n\n/** Take a part as ration part/total where part is also value (sum fraction)\n * Returned fractions are also adjusted for part for rounding differences.\n * #NOTE8\n */\nCFractions CFractions::RatioPart(int64_t nPartValue) const {\n if ((nFlags & STD) == 0) {\n return Std().RatioPart(nPartValue);\n }\n int64_t nTotalValue = Total();\n int64_t nPartValueSum = 0;\n CFractions fPart(0, CFractions::STD);\n\n if (nPartValue == 0 && nTotalValue == 0)\n return fPart;\n if (nPartValue == 0)\n return fPart;\n if (nPartValue > nTotalValue)\n return Std();\n\n int adjust_from = PEG_SIZE;\n for(int i=0; i()) {\n long m_test;\n has_overflow = __builtin_smull_overflow(v, nPartValue, &m_test);\n } else if (std::is_same()) {\n long long m_test;\n has_overflow = __builtin_smulll_overflow(v, nPartValue, &m_test);\n } else {\n assert(0); // todo: compile error\n }\n\n if (has_overflow) {\n multiprecision::uint128_t v128(v);\n multiprecision::uint128_t part128(nPartValue);\n multiprecision::uint128_t f128 = (v128*part128)/nTotalValue;\n fPart.f[i] = f128.convert_to();\n }\n else {\n fPart.f[i] = (v*nPartValue)/nTotalValue;\n }\n\n nPartValueSum += fPart.f[i];\n }\n\n if (nPartValueSum == nPartValue)\n return fPart;\n if (nPartValueSum > nPartValue)\n return fPart; // todo:peg: validate if possible\n\n int idx = adjust_from;\n int64_t nAdjustValue = nPartValue - nPartValueSum;\n while(nAdjustValue >0) {\n // todo:peg: review all possible cases if rounding mismatch with adjust_from\n if (fPart.f[idx] < f[idx]) {\n nAdjustValue--;\n fPart.f[idx]++;\n }\n idx++;\n if (idx >= PEG_SIZE) {\n idx = adjust_from;\n }\n }\n\n return fPart;\n}\n\n/** Take a part as ration part/total where part is also value (sum fraction)\n * Add taken part into destination fractions. Adjusted from adjust_from.\n * Returns not completed amount (if source(\"this\") has no enough)\n * #NOTE8\n */\nint64_t CFractions::MoveRatioPartTo(int64_t nValueToMove,\n CFractions& b)\n{\n int64_t nTotalValue = Total();\n int64_t nPartValue = nValueToMove;\n\n if (nTotalValue == 0)\n return nValueToMove;\n if (nValueToMove == 0)\n return 0;\n\n if ((nFlags & STD) == 0)\n ToStd();\n if ((b.nFlags & STD) == 0)\n b.ToStd();\n\n if (nPartValue >= nTotalValue) {\n nPartValue = nTotalValue;\n b += *this; // move all\n for(int i=0; i()) {\n long m_test;\n has_overflow = __builtin_smull_overflow(v, nPartValue, &m_test);\n } else if (std::is_same()) {\n long long m_test;\n has_overflow = __builtin_smulll_overflow(v, nPartValue, &m_test);\n } else {\n assert(0); // todo: compile error\n }\n\n int64_t vp = 0;\n\n if (has_overflow) {\n multiprecision::uint128_t v128(v);\n multiprecision::uint128_t part128(nPartValue);\n multiprecision::uint128_t f128 = (v128*part128)/nTotalValue;\n vp = f128.convert_to();\n }\n else {\n vp = (v*nPartValue)/nTotalValue;\n }\n\n nPartValueSum += vp;\n b.f[i] += vp;\n f[i] -= vp;\n }\n\n if (nPartValueSum == nPartValue)\n return 0;\n if (nPartValueSum > nPartValue)\n return 0; // todo:peg: validate if possible\n\n int idx = adjust_from;\n int64_t nAdjustValue = nPartValue - nPartValueSum;\n while(nAdjustValue >0) {\n if (f[idx] >0) {\n nAdjustValue--;\n b.f[idx]++;\n f[idx]--;\n }\n idx++;\n if (idx >= PEG_SIZE) {\n idx = adjust_from;\n }\n }\n\n return 0;\n}\n\nCFractions& CFractions::operator+=(const CFractions& b)\n{\n if ((b.nFlags & STD) == 0) {\n return operator+=(b.Std());\n }\n if ((nFlags & STD) == 0) {\n ToStd();\n }\n for(int i=0; i=0 && vb >=0) a.f[i] = std::min(va, vb);\n else if (va >=0 && vb < 0) a.f[i] = 0;\n else if (va < 0 && vb >=0) a.f[i] = 0;\n else if (va < 0 && vb < 0) a.f[i] = std::max(va, vb);\n }\n return a;\n}\n\nCFractions CFractions::operator-() const\n{\n CFractions a = *this;\n for(int i=0; i vb) {\n nDiff += (va - vb);\n }\n }\n\n return double(nDiff) / double(nTotalA);\n }\n\n else if (nTotalA < nTotalB) {\n if (nTotalA == 0) {\n return nTotalB; // does not make sense to scale\n }\n\n CFractions b1 = b.RatioPart(nTotalA);\n return Distortion(b1);\n }\n\n else if (nTotalA > nTotalB) {\n if (nTotalB == 0) {\n return nTotalA; // does not make sense to scale\n }\n\n CFractions a1 = RatioPart(nTotalB);\n return a1.Distortion(b);\n }\n\n return 0;\n}\n\n", "meta": {"hexsha": "db53783d4f1225c20c96f81be5ad7fd1407098b4", "size": 20184, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/peg/pegfractions.cpp", "max_stars_repo_name": "bitbaymarket/BitBay", "max_stars_repo_head_hexsha": "99498d8509b439bee6d18be21c78577f1c654c01", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29.0, "max_stars_repo_stars_event_min_datetime": "2016-07-12T01:05:24.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-30T03:11:36.000Z", "max_issues_repo_path": "src/peg/pegfractions.cpp", "max_issues_repo_name": "bitbaymarket/BitBay", "max_issues_repo_head_hexsha": "99498d8509b439bee6d18be21c78577f1c654c01", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14.0, "max_issues_repo_issues_event_min_datetime": "2016-05-06T11:02:05.000Z", "max_issues_repo_issues_event_max_datetime": "2018-12-26T12:43:20.000Z", "max_forks_repo_path": "src/peg/pegfractions.cpp", "max_forks_repo_name": "bitbaymarket/BitBay", "max_forks_repo_head_hexsha": "99498d8509b439bee6d18be21c78577f1c654c01", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 23.0, "max_forks_repo_forks_event_min_datetime": "2017-01-04T11:55:46.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-10T19:20:16.000Z", "avg_line_length": 25.1985018727, "max_line_length": 87, "alphanum_fraction": 0.5396848989, "num_tokens": 5844, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5888891307678321, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.22885293194751755}} {"text": "/*~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\n| Phycas: Python software for phylogenetic analysis |\n| Copyright (C) 2006 Mark T. Holder, Paul O. Lewis and David L. Swofford |\n| |\n| This program is free software; you can redistribute it and/or modify |\n| it under the terms of the GNU General Public License as published by |\n| the Free Software Foundation; either version 2 of the License, or |\n| (at your option) any later version. |\n| |\n| This program is distributed in the hope that it will be useful, |\n| but WITHOUT ANY WARRANTY; without even the implied warranty of |\n| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |\n| GNU General Public License for more details. |\n| |\n| You should have received a copy of the GNU General Public License along |\n| with this program; if not, write to the Free Software Foundation, Inc., |\n| 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. |\n\\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~*/\n\n#if ! defined(GTR_MODEL_HPP)\n#define GTR_MODEL_HPP\n\n#include \"states_patterns.hpp\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \"xlikelihood.hpp\"\n#include \"mcmc_param.hpp\"\n#include \"basic_cdf.hpp\"\n#include \"q_matrix.hpp\"\n#include \"model.hpp\"\n#include \"multivariate_probability_distribution.hpp\"\n\nnamespace phycas{\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tSpecialization of the base class Model that represents the General Time Reversible (GTR) model.\n*/\nclass GTR: public Model\n\t{\n\tpublic:\n GTR();\n ~GTR()\n {\n //std::cerr << \"GTR dying...\" << std::endl;\n }\n\n\t\tvirtual std::string getModelName() const;\n\t\tvirtual void createParameters(JointPriorManagerShPtr jpm, TreeShPtr t, MCMCUpdaterVect & edgelens, MCMCUpdaterVect & edgelen_hyperparams, MCMCUpdaterVect & parameters, int subset_pos);\n\t\tdouble calcUniformizationLambda() const;\n double calcLMat(double * * lMat) const;\n double calcUMat(double * * uMat) const;\n\t\tvoid calcPMat(double * * pMat, double edgeLength) const;\n\n void fixRelRates();\n\t\tvoid freeRelRates();\n\n std::vector getRelRates();\n\t\tvoid setRelRates(const std::vector & rates);\n\n void calcRelRatesFromRatios();\n\t\tvoid setRelRateRatio(unsigned which, double value);\n\n void setStateFreqRatio(unsigned ratio_index, double value);\n\n\t\tvoid setRelRateUnnorm(unsigned param_index, double value);\n\t\tdouble getRelRateUnnorm(unsigned param_index);\n\n\t\tvoid setRelRateParamPrior(ProbDistShPtr d);\n\t\tProbDistShPtr getRelRateParamPrior();\n\n\t\tvoid setRelRatePrior(MultivarProbDistShPtr d);\n\t\tMultivarProbDistShPtr getRelRatePrior();\n\n void setNucleotideFreqs(double freqA, double freqC, double freqG, double freqT);\n void setAllFreqsEqual();\n\t\tvirtual void setStateFreqUnnorm(unsigned param_index, double value);\n virtual void setStateFreqsUnnorm(const std::vector & values);\n\n void setStateFreqPrior(MultivarProbDistShPtr d);\n\t\tMultivarProbDistShPtr getStateFreqPrior();\n\n void setStateFreqParamPrior(ProbDistShPtr d);\n\t\tProbDistShPtr getStateFreqParamPrior();\n\n virtual std::string paramHeader() const;\n\t\tvirtual std::string paramReport(unsigned ndecimals, bool include_edgelen_hyperparams) const;\n\t\tdouble calcTRatio();\n\n virtual unsigned getNumFreeParameters() const;\n virtual void appendPWKParamNames(std::vector & names, std::string prefix = \"\") const;\n virtual void appendFreeParamNames(std::vector & names, std::string prefix = \"\") const;\n virtual void appendParamNames(std::vector & names, std::string prefix = \"\") const;\n virtual void appendUntransformedParamValues(std::vector & values) const;\n virtual void appendTransformedParamValues(std::vector & values) const;\n virtual bool setParamValueFromTransformed(std::string parameter_name, double transformed_value, TreeShPtr tree);\n virtual double calcLogDetJacobian() const;\n\n\tprotected:\n\n\t\tMultivarProbDistShPtr rel_rate_prior;\t\t /**< The prior distribution governing each relative rate (usually a gamma distribution with scale 1 and shape equal to the desired Dirichlet parameter) */\n\t\tProbDistShPtr rel_rate_param_prior;\t/**< The joint prior distribution governing all six relative rates */\n\t\tProbDistShPtr freq_param_prior;\t /**< The prior distribution governing each frequency parameter (usually a gamma distribution with scale 1 and shape equal to the desired Dirichlet parameter; used if frequencies are updated separately by slice sampling) */\n \tMultivarProbDistShPtr freq_prior;\t /**< The prior distribution governing the vector of frequencies (used if frequencies are updated jointly by StateFreqMove) */\n\t\tbool rel_rates_fixed;\t /**< If true, the relative rate values will not change during MCMC updates */\n\t\tmutable MCMCUpdaterVect rel_rate_params;\t /**< A vector containing copies of all six relative rate parameters (saved so that fixed/free status can be changed) */\n\t\tmutable QMatrix q_matrix;\t\t\t /**< A QMatrix object used to compute transition probabilities */\n\n\t\tstring_vect_t relrate_name;\t\t\t/**< Holds names of the 6 relative rates (e.g. rAC, rAG, rAT, rCG, rCT, rGT) used as headers in the param file */\n\t\tstring_vect_t freq_name;\t\t\t\t/**< Holds names of the 4 state frequencies (e.g. freqA, freqC, freqG, freqT) used as headers in the param file */\n\n\t\t// Below here are quantities that directly affect likelihood calculations and which should increment time_stamp when modified\n\t\tstd::vector rel_rates;\t\t\t /**< A vector containing the six relative rates */\n\t\tstd::vector rel_rate_ratios;\t /**< A vector for temporary storage of the five relative rate ratios: rAG/rAC, rAT/rAC, rCG/rAC, rCT/rAC, and rGT/rAC */\n\t};\n\ntypedef boost::shared_ptr GTRShPtr;\n\n} // namespace phycas\n\n//#include \"phycas/src/gtr_model.inl\"\n\n#endif\n\n", "meta": {"hexsha": "8e458e1da40ca45470c9d6b8dafc90dfd6120e6e", "size": 7710, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/cpp/gtr.hpp", "max_stars_repo_name": "plewis/phycas", "max_stars_repo_head_hexsha": "9f5a4d9b2342dab907d14a46eb91f92ad80a5605", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2015-09-24T23:12:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-12T07:07:01.000Z", "max_issues_repo_path": "src/cpp/gtr.hpp", "max_issues_repo_name": "plewis/phycas", "max_issues_repo_head_hexsha": "9f5a4d9b2342dab907d14a46eb91f92ad80a5605", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/cpp/gtr.hpp", "max_forks_repo_name": "plewis/phycas", "max_forks_repo_head_hexsha": "9f5a4d9b2342dab907d14a46eb91f92ad80a5605", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2015-11-23T10:35:43.000Z", "max_forks_repo_forks_event_max_datetime": "2015-11-23T10:35:43.000Z", "avg_line_length": 60.234375, "max_line_length": 278, "alphanum_fraction": 0.5629053178, "num_tokens": 1484, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.22885293194751752}} {"text": "#include \n#include \n#include \n#include \"cnpy.h\"\n#include \"float_conversion.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace Eigen;\nusing namespace std::string_literals;\n\nconstexpr size_t BLUE = 0;\nconstexpr size_t GREEN = 1;\nconstexpr size_t RED = 2;\nconstexpr size_t RE1 = 3;\nconstexpr size_t RE2 = 4;\nconstexpr size_t RE3 = 5;\nconstexpr size_t NIR = 6;\nconstexpr size_t SWIR1 = 7;\nconstexpr size_t SWIR2 = 8;\nconstexpr size_t CLOUDS = 9;\nconstexpr size_t SHAPE = 10;\n\nconstexpr size_t NUM_THREADS = 8;\nconstexpr size_t BANDS = 6;\n\nconst array QUANTILES {0.2f, 0.5f, 0.8f};\nconst size_t QS = QUANTILES.size();\n\ntypedef array Point;\ntypedef vector PointCloud;\ntypedef pair, vector> timeseries;\n\nconstexpr float START_DAY = 31 + 28; // Only use images starting in March\nconstexpr float MAX_DAY = 365 - START_DAY - 31; // End with November\n\ntime_t basetime;\n\nstruct result\n{\n string number;\n char type;\n vector features;\n};\n\narray threads;\narray current;\narray, NUM_THREADS> results;\nmutex queue_lock;\nqueue todo;\n\n#define DATA(b) half_to_float(raw.data()[ b * W * H * T + x * H * T + y * T + t ])\n\nfloat clamp(float x, float min=-100, float max=100)\n{\n if (x > max)\n return max;\n else if (x < min)\n return min;\n else if (isnormal(x))\n return x;\n else\n return 0;\n}\n\ntimeseries parse(string filename)\n{\n vector timestamps;\n vector all_points;\n\n cnpy::NpyArray raw = cnpy::npy_load(filename + \".npy\"s);\n ifstream indexfile(filename + \".idx\"s);\n if(indexfile.is_open())\n {\n string line;\n // Skip two lines\n getline(indexfile, line);\n getline(indexfile, line);\n // Skip the label\n getline(indexfile, line, '~');\n // Get entries\n while(getline(indexfile, line, ','))\n {\n struct tm tm;\n strptime(line.c_str(), \"%Y-%m-%dT%H:%M:%S\", &tm);\n float diff = difftime(mktime(&tm), basetime) / (60 * 60 * 24) - START_DAY;\n timestamps.push_back(diff);\n }\n indexfile.close();\n }\n\n size_t W = raw.shape[1];\n size_t H = raw.shape[2];\n size_t T = raw.shape[3];\n vector used_timestamps;\n\n assert(timestamps.size() == T);\n\n for(size_t t = 0; t < T; ++t)\n {\n if(timestamps[t] <= 0)\n continue;\n PointCloud points;\n size_t glitchy_points = 0,\n cloudy_points = 0,\n total_points = 0;\n for(size_t x = 0; x < W; ++x) for(size_t y = 0; y < H; ++y)\n {\n if(DATA(SHAPE) > 0)\n {\n ++total_points;\n if(DATA(CLOUDS) > 0)\n ++cloudy_points;\n float red = DATA(RED),\n grn = DATA(GREEN),\n blu = DATA(BLUE),\n nir = DATA(NIR),\n swir1= DATA(SWIR1),\n swir2= DATA(SWIR2),\n re1 = DATA(RE1),\n re2 = DATA(RE2),\n re3 = DATA(RE3);\n if(red == 0 && grn == 0 && blu == 0)\n ++glitchy_points;\n float ndvi = (nir - red) / (nir + red); // Normalized Difference Vegetation Index\n float cai = (swir1 - swir2) / (swir1 + swir2); // Cellulose Absorption index\n float n = (2 * (nir * nir - red * red) + 1.5f * nir + 0.5 * red ) / (nir + red + 0.5f);\n float gemi = n * (1.0f - 0.25f * n) - (red - 0.125f) / (1 - red); // Global Environment Monitoring Index\n float gli = (2 * grn - red - blu) / (2 * grn + red + blu); // Green Leaf Index\n float cvi = (nir * red) / (grn * grn); // Chlorophyll vegetation index\n float ccci = ((nir - re1) / (nir + re1)) / ndvi; // Canopy Chlorophyll Content Index\n float dswi = (re3 - grn) / (swir1 + red); // Disease-Water Stress Index 5\n float nd790_670 = (re3 - red) / (re3 + red); // Normalized Difference 790 / 670\n float ndwi = (nir - swir2) / (nir + swir2); // Normalized Difference Water Index\n points.push_back({\n clamp(ndvi),\n // clamp(cai),\n clamp(gemi),\n clamp(gli),\n clamp(cvi),\n // clamp(ccci),\n clamp(dswi),\n // clamp(nd790_670),\n clamp(ndwi),\n });\n }\n }\n if(cloudy_points > 0.2 * total_points)\n continue;\n if(glitchy_points > 0.2 * total_points)\n continue;\n all_points.push_back(points);\n used_timestamps.push_back(timestamps[t] / MAX_DAY);\n }\n\n return make_pair(used_timestamps, all_points);\n}\n\ninline float hat(float t, float center, float width)\n{\n return max(1 - abs(t-center) / width, 0.0f) / width;\n}\n\nconst vector get_features(timeseries data, float subsample=1.0f)\n{\n vector time = data.first;\n vector point_series = data.second;\n int count = point_series[0].size();\n\n vector used(count, true);\n if(subsample < 1.0f)\n {\n count = 0;\n random_device rd;\n mt19937 random(rd());\n uniform_real_distribution<> real(0.0, 1.0);\n for(auto &&b : used)\n {\n b = real(random) < subsample;\n count += b;\n }\n }\n\n vector features;\n array, BANDS * QS> Q;\n\n for(PointCloud point_cloud : point_series)\n {\n for(size_t band = 0; band < BANDS; ++band)\n {\n vector points;\n for(size_t i = 0; i < point_cloud.size(); ++i)\n {\n if(used[i] && !isnan(point_cloud[i][band]))\n {\n points.push_back(point_cloud[i][band]);\n }\n }\n if(points.size() > 0)\n {\n sort(points.begin(), points.end());\n for(size_t q = 0; q < QS; ++q)\n Q[band * QS + q].push_back(points[size_t(points.size() * QUANTILES[q])]);\n }\n else\n {\n for(float q : QUANTILES)\n Q[band * QS + q].push_back(0);\n }\n }\n }\n\n for(auto q : Q)\n {\n constexpr size_t DIM = 8;\n // vector> coefficients(NUM_FOURIER, 0);\n Eigen::Matrix model(q.size(), DIM);\n Eigen::Matrix target(q.size(), 1);\n for(size_t k = 0; k < q.size(); ++k)\n {\n target(k, 0) = q[k];\n model(k, 0) = hat(time[k], 0.0f , 1.0f ); // Left Edge\n model(k, 1) = hat(time[k], 1.0f , 1.0f ); // Right Edge\n model(k, 2) = hat(time[k], 0.5f , 0.5f ); // 1/2\n model(k, 3) = hat(time[k], 0.25f , 0.25f ); // 1/4\n model(k, 4) = hat(time[k], 0.75f , 0.25f ); // 3/4\n model(k, 5) = hat(time[k], 0.125f, 0.125f); // 1/8\n model(k, 6) = hat(time[k], 0.375f, 0.125f); // 3/8\n model(k, 7) = hat(time[k], 0.625f, 0.125f); // 5/8\n // model(k, 8) = hat(time[k], 0.875f, 0.125f); // 7/8\n }\n Eigen::Matrix H = model.transpose() * model;\n H = H.completeOrthogonalDecomposition().pseudoInverse();\n Eigen::Matrix weight = H * model.transpose() * target;\n for(size_t k = 0; k < DIM; ++k)\n {\n features.push_back(weight(k, 0));\n }\n }\n return features;\n}\n\ninline bool file_exists (const std::string& name)\n{\n struct stat buffer;\n return (stat (name.c_str(), &buffer) == 0); \n}\n\nvoid work(size_t thread_id)\n{\n current[thread_id] = \"Initializing\";\n bool running = true;\n string number = \"\";\n while(running)\n {\n {\n lock_guard lock(queue_lock);\n if(todo.empty())\n {\n break;\n } else {\n number = todo.front();\n current[thread_id] = number;\n todo.pop();\n }\n }\n \n string filename = \"/home/konrad/dev/remote_sensing/ibiss_processed/cubes/\" + number;\n if(!file_exists(filename + \".npy\"s))\n continue;\n timeseries data = parse(filename);\n\n results[thread_id].push_back({ number, 'F', get_features(data, 0.5) });\n }\n current[thread_id] = \"Done\";\n}\n\nint main(int argc, char **argv)\n{\n struct tm tm;\n strptime(\"2017-01-01T00:00:00\", \"%Y-%m-%dT%H:%M:%S\", &tm);\n basetime = mktime(&tm);\n\n size_t count = 0;\n if(argc == 1)\n {\n string number = \"\";\n while(getline(cin, number))\n {\n todo.push(number);\n ++count;\n }\n }\n else\n {\n string number = \"\";\n ifstream input(argv[1]);\n if(!input.is_open())\n return 1;\n while(getline(input, number))\n {\n todo.push(number);\n ++count;\n }\n input.close();\n }\n\n cout << \"Beginning feature extraction\" << endl;\n auto features = vector>>(count);\n size_t num = features.size();\n\n for(size_t i = 0; i < 100; ++i)\n cout << '_';\n cout << endl;\n\n for(size_t i = 0; i < NUM_THREADS; ++i)\n {\n threads[i] = thread(work, i);\n }\n\n size_t progress = 100;\n while(progress > 0)\n {\n if(progress * num > todo.size() * 100)\n {\n cout << '#' << flush;\n --progress;\n }\n else\n {\n this_thread::sleep_for(100ms);\n }\n }\n\n cout << endl;\n for(size_t i = 0; i < NUM_THREADS; ++i)\n {\n cout << \"Thread (\" << i << \") processed \" << results[i].size() << \" elements\" << endl;\n }\n\n for(size_t i = 0; i < NUM_THREADS; ++i)\n {\n threads[i].join();\n }\n cout << \"\\nCollected all threads\" << endl;\n\n cout << \"\\nWriting CSV...\" << endl;\n\n ofstream out(\"/home/konrad/dev/remote_sensing/data/cfeatures.csv\");\n\n for(size_t i = 0; i < 8*NUM_THREADS; ++i)\n cout << '_';\n cout << endl;\n\n for(size_t i = 0; i < NUM_THREADS; ++i)\n {\n for(auto p : results[i])\n {\n out << p.number << ',' << p.type;\n for(auto val : p.features)\n out << \",\" << val;\n out << '\\n';\n }\n cout << \"########\" << flush;\n results[i].clear();\n }\n cout << endl;\n out.close();\n\n return 0;\n}\n", "meta": {"hexsha": "0904d8dccd6f616aa006d99f310a05b878d1376d", "size": 9710, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "feature_extractor.cpp", "max_stars_repo_name": "cloud-oak/feature_extractor", "max_stars_repo_head_hexsha": "fd9734981e459ed5a288d919d02589628062c811", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "feature_extractor.cpp", "max_issues_repo_name": "cloud-oak/feature_extractor", "max_issues_repo_head_hexsha": "fd9734981e459ed5a288d919d02589628062c811", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "feature_extractor.cpp", "max_forks_repo_name": "cloud-oak/feature_extractor", "max_forks_repo_head_hexsha": "fd9734981e459ed5a288d919d02589628062c811", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.5526315789, "max_line_length": 112, "alphanum_fraction": 0.5615859938, "num_tokens": 3022, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.22884559723156375}} {"text": "#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\nusing namespace std;\r\nusing namespace boost::filesystem;\r\nusing namespace cv;\r\n\r\ntypedef struct IrisCode\r\n{\r\n string name;\r\n unsigned char **data;\r\n int stepsize;\r\n} IrisCode;\r\n\r\nstatic const int CODE_WIDTH = 512;\r\nstatic const int CODE_HEIGHT = 20;\r\n\r\nstatic const int MIN_SHIFT = -16;\r\nstatic const int MAX_SHIFT = 16;\r\n\r\nstatic const int htlut[256] = {0,1,1,2,1,2,2,3,1,2,2,3,2,3,3,4,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,1,2,2,3,2,3,3,4,2,3,3,4,3,4,4,5,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,2,3,3,4,3,4,4,5,3,4,4,5,4,5,5,6,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,3,4,4,5,4,5,5,6,4,5,5,6,5,6,6,7,4,5,5,6,5,6,6,7,5,6,6,7,6,7,7,8};\r\n\r\n\r\nstatic const int ALG_MINHD = 0, ALG_3A = 1, ALG_3AL = 2, ALG_3AS = 3;\r\nstatic const int MODE_MAIN = 1, MODE_HELP = 2;\r\nenum MAIN_MODE { STATIC, DYNAMIC};\r\n\r\nvoid printUsage() {\r\n\tprintf(\"+-----------------------------------------------------------------------------+\\n\");\r\n\tprintf(\"| TripleA - calculates Hamming distance of iris-codes |\\n\");\r\n\tprintf(\"| |\\n\");\r\n\tprintf(\"| MODES |\\n\");\r\n\tprintf(\"| |\\n\");\r\n\tprintf(\"| (# 1) HD calculation of the input images (cross comparison) |\\n\");\r\n\tprintf(\"| (# 2) usage |\\n\");\r\n\tprintf(\"| |\\n\");\r\n\tprintf(\"| ARGUMENTS |\\n\");\r\n\tprintf(\"| |\\n\");\r\n\tprintf(\"+------+------------+---+---+-------------------------------------------------+\\n\");\r\n\tprintf(\"| Name | Parameters | # | ? | Description |\\n\");\r\n\tprintf(\"+------+------------+---+---+-------------------------------------------------+\\n\");\r\n\tprintf(\"| -f | folder | 1 | N | folder of iris-codes generated using LG or QSW |\\n\");\r\n\tprintf(\"| -o | file | 1 | N | output file for the results |\\n\");\r\n\tprintf(\"| -s | step-size | 1 | Y | step-size used in static mode (5) |\\n\");\r\n\tprintf(\"| -c | constant | 1 | Y | constant used in dynamic mode (1) |\\n\");\r\n\tprintf(\"| -a | algorithm | 1 | Y | TripleA algorithm (3a) (default) |\\n\"); \r\n\tprintf(\"| | | | | TripleA-limited algorithm (3al) |\\n\");\r\n\tprintf(\"| | | | | TripleA-single-sided algorithm (3as) |\\n\");\r\n\tprintf(\"| -h | | 2 | N | prints usage |\\n\");\r\n\tprintf(\"+------+------------+---+---+-------------------------------------------------+\\n\");\r\n\tprintf(\"| |\\n\");\r\n\tprintf(\"| The default is to run the program in dynamic mode. When a step size (-s) is |\\n\");\r\n\tprintf(\"| set the program automatically switches to static-mode. |\\n\");\r\n\tprintf(\"| |\\n\");\r\n\tprintf(\"| EXAMPLE USAGE |\\n\");\r\n printf(\"| |\\n\");\r\n printf(\"| -f codes -s 4 -c 0.33 -a 3a -o compare.txt |\\n\");\r\n printf(\"| |\\n\");\r\n\tprintf(\"| AUTHOR |\\n\");\r\n\tprintf(\"| |\\n\");\r\n\tprintf(\"| Christian Rathgeb |\\n\");\r\n\tprintf(\"| |\\n\");\r\n\tprintf(\"| COPYRIGHT |\\n\");\r\n\tprintf(\"| |\\n\");\r\n\tprintf(\"| (C) 2016 All rights reserved. Do not distribute without written permission. |\\n\");\r\n\tprintf(\"+-----------------------------------------------------------------------------+\\n\");\r\n}\r\n\r\n// estimate the Hamming distance between two given codes using the TripleA algorithm\r\nvoid TripleA(IrisCode code1, IrisCode code2, int start, int stop, ofstream& log, int stepsize){\r\n\t\r\n\tfloat mindist = CODE_HEIGHT*CODE_WIDTH;\r\n\tunsigned int dist;\r\n\tint pos_min = 0;\r\n\t\r\n\tint w = CODE_WIDTH;\r\n\tint h;\r\n\tint border;\r\n\t\r\n\tif (CODE_HEIGHT%8 == 0) h = CODE_HEIGHT/8;\r\n\telse h = CODE_HEIGHT/8 + 1;\r\n\r\n\tauto start_time = chrono::high_resolution_clock::now();\r\n\t\r\n\t// alignmnet with step-size\r\n\tborder = stop%stepsize;\r\n\tif (border != 0){\r\n\t\tint k=start;\r\n\t\tdist = 0;\r\n\t\tfor (int i=0; i < h; i++){\r\n\t\t\tfor (int j=0; j < w; j++){\r\n\t\t\t\tdist+= htlut[code1.data[j][i] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][i]];\r\n\t\t\t}\r\n\t\t}\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t}\r\n\t\tstart+=border;\r\n\t}\r\n\t\r\n\tfor (int k = start; k <= stop; k+=stepsize){\r\n\t\tdist = 0;\r\n\t\tfor (int i=0; i < h; i++){\r\n\t\t\tfor (int j=0; j < w; j++){\r\n\t\t\t\tdist+= htlut[code1.data[j][i] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][i]];\r\n\t\t\t}\r\n\t\t}\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t}\r\n\t}\r\n\r\n\tif (border != 0){\r\n\t\tint k=stop;\r\n\t\tdist = 0;\r\n\t\tfor (int i=0; i < h; i++){\r\n\t\t\tfor (int j=0; j < w; j++){\r\n\t\t\t\tdist+= htlut[code1.data[j][i] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][i]];\r\n\t\t\t}\r\n\t\t}\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t}\r\n\t}\r\n\t\r\n\tstart = max(pos_min - stepsize + 1, start); \r\n\tstop = min(pos_min + stepsize - 1, stop);\r\n\t\r\n\tfor (int k = start; k <= stop; k++){\r\n\t\tdist = 0;\r\n\t\tfor (int i=0; i < h; i++){\r\n\t\t\tfor (int j=0; j < w; j++){\r\n\t\t\t\tdist+= htlut[code1.data[j][i] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][i]];\r\n\t\t\t}\r\n\t\t}\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t}\r\n\t}\r\n\r\n\tauto end_time = chrono::high_resolution_clock::now();\r\n\t\r\n\tlog << \"hd(\" << code1.name << \",\" << code2.name << \"):\" << mindist/(CODE_HEIGHT*CODE_WIDTH) << \"\\t time: \" << chrono::duration_cast(end_time - start_time).count() << \" micro seconds \\t step-size: \" << stepsize << endl; \r\n\tlog.flush();\r\n}\r\n\r\n// estimate the Hamming distance between two given codes using the TripleA-limited algorithm\r\nvoid TripleA_limited(IrisCode code1, IrisCode code2, int start, int stop, ofstream& log, int stepsize){\r\n\t\r\n\tfloat mindist = CODE_HEIGHT*CODE_WIDTH;\r\n\tunsigned int dist;\r\n\tint pos_min = 0;\r\n\t\r\n\tint w = CODE_WIDTH;\r\n\tint h;\r\n\tint border;\r\n\t\r\n\tif (CODE_HEIGHT%8 == 0) h = CODE_HEIGHT/8;\r\n\telse h = CODE_HEIGHT/8 + 1;\r\n\r\n\tauto start_time = chrono::high_resolution_clock::now();\r\n\t\r\n\t// limited alignment with step-size\r\n\tborder = stop%stepsize;\r\n\tif (border != 0){\r\n\t\tint k=start;\r\n\t\tdist = 0;\r\n\t\tfor (int j=0; j < w; j++){\r\n\t\t\tdist+= htlut[code1.data[j][0] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][0]];\r\n\t\t}\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t}\r\n\t\tstart+=border;\r\n\t}\r\n\t\r\n\tfor (int k = start; k <= stop; k+=stepsize){\r\n\t\tdist = 0;\r\n\t\tfor (int j=0; j < w; j++){\r\n\t\t\tdist+= htlut[code1.data[j][0] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][0]];\r\n\t\t}\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t}\r\n\t}\r\n\r\n\tif (border != 0){\r\n\t\tint k=stop;\r\n\t\tdist = 0;\r\n\t\tfor (int j=0; j < w; j++){\r\n\t\t\tdist+= htlut[code1.data[j][0] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][0]];\r\n\t\t}\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t}\r\n\t}\r\n\tmindist = CODE_HEIGHT*CODE_WIDTH;\r\n\t\r\n\tstart = max(pos_min - stepsize + 1, start); // note that we already tested the boundaries\r\n\tstop = min(pos_min + stepsize - 1, stop);\r\n\t\r\n\tfor (int k = start; k <= stop; k++){\r\n\t\tdist = 0;\r\n\t\tfor (int i=0; i < h; i++){\r\n\t\t\tfor (int j=0; j < w; j++){\r\n\t\t\t\tdist+= htlut[code1.data[j][i] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][i]];\r\n\t\t\t}\r\n\t\t}\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t}\r\n\t}\r\n\r\n\tauto end_time = chrono::high_resolution_clock::now();\r\n\t\r\n\tlog << \"hd(\" << code1.name << \",\" << code2.name << \"):\" << mindist/(CODE_HEIGHT*CODE_WIDTH) << \"\\t time: \" << chrono::duration_cast(end_time - start_time).count() << \" micro seconds \\t step-size: \" << stepsize << endl;\r\n\tlog.flush();\r\n}\r\n\r\n// estimate the Hamming distance between two given codes using the TripleA-single-sided algorithm\r\nvoid TripleA_singlesided(IrisCode code1, IrisCode code2, int start, int stop, ofstream& log, int stepsize){\r\n\t\r\n\tfloat mindist = CODE_HEIGHT*CODE_WIDTH;\r\n\tunsigned int dist;\r\n\tint pos_min = 0;\r\n\tint idx_min = 0;\r\n\t\r\n\tint w = CODE_WIDTH;\r\n\tint h;\r\n\tint border;\r\n\tint off = 0;\r\n\t\r\n\tif (CODE_HEIGHT%8 == 0) h = CODE_HEIGHT/8;\r\n\telse h = CODE_HEIGHT/8 + 1;\r\n\t\r\n\tborder = stop%stepsize;\r\n\tif (border > 0) off=2;\r\n\t\r\n\tint cnt=0;\r\n\tint scores[(abs(start)+stop)/stepsize + 1 + off];\r\n\t\r\n\tauto start_time = chrono::high_resolution_clock::now();\r\n\t\r\n\tif (border != 0){\r\n\t\tint k=start;\r\n\t\tdist = 0;\r\n\t\tfor (int j=0; j < w; j++){\r\n\t\t\tdist+= htlut[code1.data[j][0] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][0]];\r\n\t\t}\r\n\t\tscores[cnt] = dist;\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t\tidx_min = cnt;\r\n\t\t}\r\n\t\tcnt++;\r\n\t\tstart+=border;\r\n\t}\r\n\t\r\n\tfor (int k = start; k <= stop; k+=stepsize){\r\n\t\tdist = 0;\r\n\r\n\t\tfor (int j=0; j < w; j++){\r\n\t\t\tdist+= htlut[code1.data[j][0] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][0]];\r\n\t\t}\r\n\t\tscores[cnt] = dist;\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t\tidx_min = cnt;\r\n\t\t}\r\n\t\tcnt++;\r\n\t}\r\n\r\n\tif (border != 0){\r\n\t\tint k=stop;\r\n\t\tdist = 0;\r\n\t\tfor (int j=0; j < w; j++){\r\n\t\t\tdist+= htlut[code1.data[j][0] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][0]];\r\n\t\t}\r\n\t\tscores[cnt] = dist;\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t\tidx_min = cnt;\r\n\t\t}\r\n\t}\r\n\t\r\n\tmindist = CODE_HEIGHT*CODE_WIDTH;\r\n\tif (idx_min == 0){\r\n\t\tstart = pos_min;\r\n\t\tstop = pos_min + stepsize - 1;\r\n\t}\r\n\telse if (idx_min == (abs(start)+stop)/stepsize + off){\r\n\t\tstart = pos_min - stepsize + 1;\r\n\t\tstop = pos_min;\r\n\t}\r\n\telse if (scores[idx_min +1] > scores[idx_min -1]){\r\n\t\tstart = pos_min - stepsize + 1;\r\n\t\tstop = pos_min;\r\n\t}\r\n\telse{\r\n\t\tstart = pos_min;\r\n\t\tstop = pos_min + stepsize - 1;\r\n\t}\r\n\t\r\n\tfor (int k = start; k <= stop; k++){\r\n\t\tdist = 0;\r\n\t\tfor (int i=0; i < h; i++){\r\n\t\t\tfor (int j=0; j < w; j++){\r\n\t\t\t\tdist+= htlut[code1.data[j][i] ^ code2.data[(j+k+CODE_WIDTH)%CODE_WIDTH][i]];\r\n\t\t\t}\r\n\t\t}\r\n\t\tif (dist < mindist){\r\n\t\t\tmindist = dist;\r\n\t\t\tpos_min = k;\r\n\t\t}\r\n\t}\r\n\tauto end_time = chrono::high_resolution_clock::now();\r\n\tlog << \"hd(\" << code1.name << \",\" << code2.name << \"):\" << mindist/(CODE_HEIGHT*CODE_WIDTH) << \"\\t time: \" << chrono::duration_cast(end_time - start_time).count() << \" micro seconds \\t step-size: \" << stepsize << endl;\r\n\tlog.flush();\r\n}\r\n\r\n// read an iris-code form a given file name, extract the (dynamic) step size\r\n// and map it to a more efficient representation\r\nIrisCode getCode(string filename, float k)\r\n{\r\n\tIrisCode iriscode;\r\n\tMat img = imread(filename, CV_LOAD_IMAGE_UNCHANGED);\r\n\tCV_Assert(img.data != 0);\r\n\tCV_Assert(img.type() == CV_8UC1);\r\n\r\n\tint w = CODE_WIDTH;\r\n\tint h;\r\n\r\n\tiriscode.name=filename;\r\n\t\r\n\tif (CODE_HEIGHT%8 == 0) h = CODE_HEIGHT/8;\r\n\telse h = CODE_HEIGHT/8 + 1;\r\n\r\n\tunsigned char c;\r\n\tunsigned char bincode[CODE_WIDTH][CODE_HEIGHT];\r\n\t\r\n\t// initialize with 0s\r\n\tfor (int i=0; i < CODE_HEIGHT; i++){\r\n\t\tfor (int j=0; j < CODE_WIDTH; j++){\r\n\t\t\tbincode[j][i] = 0;\r\n\t\t}\r\n\t}\r\n\t\r\n\t// now map the given codes to 0/1 representation\r\n\tfor (int i=0; i < CODE_HEIGHT; i++){\r\n\t\tfor (int j=0; j < CODE_WIDTH; j++){\r\n\t\t\tc=pow(2,7-(j%8));\r\n\t\t\tbincode[j][i]=htlut[img.data[j/8+i*CODE_WIDTH/8]&c];\r\n\t\t}\r\n\t}\r\n\t\r\n\tfloat mu = 0;\r\n\tint length;\r\n\tint count=0;\r\n\tfor (int i=0; i < CODE_HEIGHT; i++){\r\n\t\tlength = 1;\r\n\t\tfor (int j=1; j < CODE_WIDTH; j++){\r\n\t\t\tif (bincode[j][i] == bincode[j-1][i]) length++;\r\n\t\t\telse {\r\n\t\t\t\tmu+=length;\r\n\t\t\t\tlength=1;\r\n\t\t\t\tcount++;\r\n\t\t\t}\r\n\t\t}\r\n\t\tmu+=length;\r\n\t\tlength=1;\r\n\t\tcount++;\r\n\t}\r\n\tmu/=count;\r\n\t\r\n\t// assign the average dynamic step-size according to the given constant k\r\n\tiriscode.stepsize = max((int)(mu*k),1);\r\n\t\r\n\tunsigned char **code;\r\n\tcode = (unsigned char **)malloc(w * sizeof(unsigned char *)); \r\n\tfor (int i = 0; i < w; i++) code[i] = (unsigned char *)malloc(h * sizeof(unsigned char));\r\n\t\r\n\t// initialize with 0s\r\n\tfor (int i=0; i < h; i++){\r\n\t\tfor (int j=0; j < w; j++){\r\n\t\t\tcode[j][i] = 0;\r\n\t\t}\r\n\t}\r\n\t\r\n\t// now map the given codes to a more efficient representation\r\n\t// create bytes out of 8 adjacent row-bits\r\n\tfor (int i=0; i < h; i++){\r\n\t\tfor (int j=0; j < w; j++){\r\n\t\t\tc=pow(2,7-(j%8));\r\n\t\t\tfor (int k = 0; k < 8; k++){\r\n\t\t\t\tif (i*8+k >=CODE_HEIGHT) break;\r\n\t\t\t\tcode[j][i]+=htlut[img.data[j/8+i*w+k*w/8]&c] * pow(2,7-k);\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\r\n\tiriscode.data=code;\r\n\treturn iriscode;\r\n}\r\n\r\n/** ------------------------------- commandline functions ------------------------------- **/\r\n\r\n/**\r\n * Parses a command line\r\n * This routine should be called for parsing command lines for executables.\r\n * Note, that all options require '-' as prefix and may contain an arbitrary\r\n * number of optional arguments.\r\n *\r\n * cmd: commandline representation\r\n * argc: number of parameters\r\n * argv: string array of argument values\r\n */\r\nvoid cmdRead(map >& cmd, int argc, char *argv[]){\r\n\tfor (int i=1; i< argc; i++){\r\n\t\tchar * argument = argv[i];\r\n\t\tif (strlen(argument) > 1 && argument[0] == '-' && (argument[1] < '0' || argument[1] > '9')){\r\n\t\t\tcmd[argument]; // insert\r\n\t\t\tchar * argument2;\r\n\t\t\twhile (i + 1 < argc && (strlen(argument2 = argv[i+1]) <= 1 || argument2[0] != '-' || (argument2[1] >= '0' && argument2[1] <= '9'))){\r\n\t\t\t\tcmd[argument].push_back(argument2);\r\n\t\t\t\ti++;\r\n\t\t\t}\r\n\t\t}\r\n\t\telse {\r\n\t\t\tCV_Error(CV_StsBadArg,\"Invalid command line format\");\r\n\t\t}\r\n\t}\r\n}\r\n\r\n/**\r\n * Checks, if each command line option is valid, i.e. exists in the options array\r\n *\r\n * cmd: commandline representation\r\n * validOptions: list of valid options separated by pipe (i.e. |) character\r\n */\r\nvoid cmdCheckOpts(map >& cmd, const string validOptions){\r\n\tvector tokens;\r\n\tconst string delimiters = \"|\";\r\n\tstring::size_type lastPos = validOptions.find_first_not_of(delimiters,0); // skip delimiters at beginning\r\n\tstring::size_type pos = validOptions.find_first_of(delimiters, lastPos); // find first non-delimiter\r\n\twhile (string::npos != pos || string::npos != lastPos){\r\n\t\ttokens.push_back(validOptions.substr(lastPos,pos - lastPos)); // add found token to vector\r\n\t\tlastPos = validOptions.find_first_not_of(delimiters,pos); // skip delimiters\r\n\t\tpos = validOptions.find_first_of(delimiters,lastPos); // find next non-delimiter\r\n\t}\r\n\tsort(tokens.begin(), tokens.end());\r\n\tfor (map >::iterator it = cmd.begin(); it != cmd.end(); it++){\r\n\t\tif (!binary_search(tokens.begin(),tokens.end(),it->first)){\r\n\t\t\tCV_Error(CV_StsBadArg,\"Command line parameter '\" + it->first + \"' not allowed.\");\r\n\t\t\ttokens.clear();\r\n\t\t\treturn;\r\n\t\t}\r\n\t}\r\n\ttokens.clear();\r\n}\r\n\r\n/*\r\n * Checks, if a specific required option exists in the command line\r\n *\r\n * cmd: commandline representation\r\n * option: option name\r\n */\r\nvoid cmdCheckOptExists(map >& cmd, const string option){\r\n\tmap >::iterator it = cmd.find(option);\r\n if (it == cmd.end()) CV_Error(CV_StsBadArg,\"Command line parameter '\" + option + \"' is required, but does not exist.\");\r\n}\r\n\r\n/*\r\n * Checks, if a specific option has the appropriate number of parameters\r\n *\r\n * cmd: commandline representation\r\n * option: option name\r\n * size: appropriate number of parameters for the option\r\n */\r\nvoid cmdCheckOptSize(map >& cmd, const string option, const unsigned int size = 1){\r\n\tmap >::iterator it = cmd.find(option);\r\n\tif (it->second.size() != size) CV_Error(CV_StsBadArg,\"Command line parameter '\" + option + \"' has unexpected size.\");\r\n}\r\n\r\n/*\r\n * Checks, if a specific option has the appropriate number of parameters\r\n *\r\n * cmd: commandline representation\r\n * option: option name\r\n * min: minimum appropriate number of parameters for the option\r\n * max: maximum appropriate number of parameters for the option\r\n */\r\nvoid cmdCheckOptRange(map >& cmd, string option, unsigned int min = 0, unsigned int max = 1){\r\n\tmap >::iterator it = cmd.find(option);\r\n\tunsigned int size = it->second.size();\r\n\tif (size < min || size > max) CV_Error(CV_StsBadArg,\"Command line parameter '\" + option + \"' is out of range.\");\r\n}\r\n\r\n/*\r\n * Returns the list of parameters for a given option\r\n *\r\n * cmd: commandline representation\r\n * option: name of the option\r\n */\r\nvector * cmdGetOpt(map >& cmd, const string option){\r\n\tmap >::iterator it = cmd.find(option);\r\n\treturn (it != cmd.end()) ? &(it->second) : 0;\r\n}\r\n\r\n/*\r\n * Returns number of parameters in an option\r\n *\r\n * cmd: commandline representation\r\n * option: name of the option\r\n */\r\nunsigned int cmdSizePars(map >& cmd, const string option){\r\n\tmap >::iterator it = cmd.find(option);\r\n\treturn (it != cmd.end()) ? it->second.size() : 0;\r\n}\r\n\r\n/*\r\n * Returns a specific parameter type (int) given an option and parameter index\r\n *\r\n * cmd: commandline representation\r\n * option: name of option\r\n * param: name of parameter\r\n */\r\nint cmdGetParInt(map >& cmd, string option, unsigned int param = 0){\r\n\tmap >::iterator it = cmd.find(option);\r\n\tif (it != cmd.end()) {\r\n\t\tif (param < it->second.size()) {\r\n\t\t\treturn atoi(it->second[param].c_str());\r\n\t\t}\r\n\t}\r\n\treturn 0;\r\n}\r\n\r\n/*\r\n * Returns a specific parameter type (float) given an option and parameter index\r\n *\r\n * cmd: commandline representation\r\n * option: name of option\r\n * param: name of parameter\r\n */\r\nfloat cmdGetParFloat(map >& cmd, const string option, const unsigned int param = 0){\r\n\tmap >::iterator it = cmd.find(option);\r\n\tif (it != cmd.end()) {\r\n\t\tif (param < it->second.size()) {\r\n\t\t\treturn atof(it->second[param].c_str());\r\n\t\t}\r\n\t}\r\n\treturn 0;\r\n}\r\n\r\n/*\r\n * Returns a specific parameter type (string) given an option and parameter index\r\n *\r\n * cmd: commandline representation\r\n * option: name of option\r\n * param: name of parameter\r\n */\r\nstring cmdGetPar(map >& cmd, const string option, const unsigned int param = 0){\r\n\tmap >::iterator it = cmd.find(option);\r\n\tif (it != cmd.end()) {\r\n\t\tif (param < it->second.size()) {\r\n\t\t\treturn it->second[param];\r\n\t\t}\r\n\t}\r\n\treturn 0;\r\n}\r\n\r\n/** ------------------------------- timing functions ------------------------------- **/\r\n\r\n/**\r\n * Class for handling timing progress information\r\n */\r\nclass Timing{\r\npublic:\r\n\t/** integer indicating progress with respect tot total **/\r\n\tint progress;\r\n\t/** total count for progress **/\r\n\tint total;\r\n\r\n\t/*\r\n\t * Default constructor for timing initializing time.\r\n\t * Automatically calls init()\r\n\t *\r\n\t * seconds: update interval in seconds\r\n\t * eraseMode: if true, outputs sends erase characters at each print command\r\n\t */\r\n\tTiming(long seconds, bool eraseMode){\r\n\t\tupdateInterval = seconds;\r\n\t\tprogress = 1;\r\n\t\ttotal = 100;\r\n\t\teraseCount=0;\r\n\t\terase = eraseMode;\r\n\t\tinit();\r\n\t}\r\n\r\n\t/*\r\n\t * Destructor\r\n\t */\r\n\t~Timing(){}\r\n\r\n\t/*\r\n\t * Initializes timing variables\r\n\t */\r\n\tvoid init(void){\r\n\t\tstart = boost::posix_time::microsec_clock::universal_time();\r\n\t\tlastPrint = start - boost::posix_time::seconds(updateInterval);\r\n\t}\r\n\r\n\t/*\r\n\t * Clears printing (for erase option only)\r\n\t */\r\n\tvoid clear(void){\r\n\t\tstring erase(eraseCount,'\\r');\r\n\t\terase.append(eraseCount,' ');\r\n\t\terase.append(eraseCount,'\\r');\r\n\t\tprintf(\"%s\",erase.c_str());\r\n\t\teraseCount = 0;\r\n\t}\r\n\r\n\t/*\r\n\t * Updates current time and returns true, if output should be printed\r\n\t */\r\n\tbool update(void){\r\n\t\tcurrent = boost::posix_time::microsec_clock::universal_time();\r\n\t\treturn ((current - lastPrint > boost::posix_time::seconds(updateInterval)) || (progress == total));\r\n\t}\r\n\r\n\t/*\r\n\t * Prints timing object to STDOUT\r\n\t */\r\n\tvoid print(void){\r\n\t\tlastPrint = current;\r\n\t\tfloat percent = 100.f * progress / total;\r\n\t\tboost::posix_time::time_duration passed = (current - start);\r\n\t\tboost::posix_time::time_duration togo = passed * (total - progress) / max(1,progress);\r\n\t\tif (erase) {\r\n\t\t\tstring erase(eraseCount,'\\r');\r\n\t\t\tprintf(\"%s\",erase.c_str());\r\n\t\t\tint newEraseCount = (progress != total) ? printf(\"Progress ... %3.2f%% (%i/%i Total %i:%02i:%02i.%03i Remaining ca. %i:%02i:%02i.%03i)\",percent,progress,total,passed.hours(),passed.minutes(),passed.seconds(),(int)(passed.total_milliseconds()%1000),togo.hours(),togo.minutes(),togo.seconds(),(int)(togo.total_milliseconds() % 1000)) : printf(\"Progress ... %3.2f%% (%i/%i Total %i:%02i:%02i.%03d)\",percent,progress,total,passed.hours(),passed.minutes(),passed.seconds(),(int)(passed.total_milliseconds()%1000));\r\n\t\t\tif (newEraseCount < eraseCount) {\r\n\t\t\t\tstring erase(newEraseCount-eraseCount,' ');\r\n\t\t\t\terase.append(newEraseCount-eraseCount,'\\r');\r\n\t\t\t\tprintf(\"%s\",erase.c_str());\r\n\t\t\t}\r\n\t\t\teraseCount = newEraseCount;\r\n\t\t}\r\n\t\telse {\r\n\t\t\teraseCount = (progress != total) ? printf(\"Progress ... %3.2f%% (%i/%i Total %i:%02i:%02i.%03i Remaining ca. %i:%02i:%02i.%03i)\\n\",percent,progress,total,passed.hours(),passed.minutes(),passed.seconds(),(int)(passed.total_milliseconds()%1000),togo.hours(),togo.minutes(),togo.seconds(),(int)(togo.total_milliseconds() % 1000)) : printf(\"Progress ... %3.2f%% (%i/%i Total %i:%02i:%02i.%03d)\\n\",percent,progress,total,passed.hours(),passed.minutes(),passed.seconds(),(int)(passed.total_milliseconds()%1000));\r\n\t\t}\r\n\t}\r\nprivate:\r\n\tlong updateInterval;\r\n\tboost::posix_time::ptime start;\r\n\tboost::posix_time::ptime current;\r\n\tboost::posix_time::ptime lastPrint;\r\n\tint eraseCount;\r\n\tbool erase;\r\n};\r\n\r\n/** ------------------------------- file pattern matching functions ------------------------------- **/\r\n\r\n\r\n/*\r\n * Formats a given string, such that it can be used as a regular expression\r\n * I.e. escapes special characters and uses * and ? as wildcards\r\n *\r\n * pattern: regular expression path pattern\r\n * pos: substring starting index\r\n * n: substring size\r\n *\r\n * returning: escaped substring\r\n */\r\nstring patternSubstrRegex(string& pattern, size_t pos, size_t n){\r\n\tstring result;\r\n\tfor (size_t i=pos, e=pos+n; i < e; i++ ) {\r\n\t\tchar c = pattern[i];\r\n\t\tif ( c == '\\\\' || c == '.' || c == '+' || c == '[' || c == '{' || c == '|' || c == '(' || c == ')' || c == '^' || c == '$' || c == '}' || c == ']') {\r\n\t\t\tresult.append(1,'\\\\');\r\n\t\t\tresult.append(1,c);\r\n\t\t}\r\n\t\telse if (c == '*'){\r\n\t\t\tresult.append(\"([^/\\\\\\\\]*)\");\r\n\t\t}\r\n\t\telse if (c == '?'){\r\n\t\t\tresult.append(\"([^/\\\\\\\\])\");\r\n\t\t}\r\n\t\telse {\r\n\t\t\tresult.append(1,c);\r\n\t\t}\r\n\t}\r\n\treturn result;\r\n}\r\n\r\n/*\r\n * Converts a regular expression path pattern into a list of files matching with this pattern by replacing wildcards\r\n * starting in position pos assuming that all prior wildcards have been resolved yielding intermediate directory path.\r\n * I.e. this function appends the files in the specified path according to yet unresolved pattern by recursive calling.\r\n *\r\n * pattern: regular expression path pattern\r\n * files: the list to which new files can be applied\r\n * pos: an index such that positions 0...pos-1 of pattern are already considered/matched yielding path\r\n * path: the current directory (or empty)\r\n */\r\nvoid patternToFiles(string& pattern, vector& files, const size_t& pos, const string& path){\r\n\tsize_t first_unknown = pattern.find_first_of(\"*?\",pos); // find unknown * in pattern\r\n\tif (first_unknown != string::npos){\r\n\t\tsize_t last_dirpath = pattern.find_last_of(\"/\\\\\",first_unknown);\r\n\t\tsize_t next_dirpath = pattern.find_first_of(\"/\\\\\",first_unknown);\r\n\t\tif (next_dirpath != string::npos){\r\n\t\t\tboost::regex expr((last_dirpath != string::npos && last_dirpath > pos) ? patternSubstrRegex(pattern,last_dirpath+1,next_dirpath-last_dirpath-1) : patternSubstrRegex(pattern,pos,next_dirpath-pos));\r\n\t\t\tboost::filesystem::directory_iterator end_itr; // default construction yields past-the-end\r\n\t\t\ttry {\r\n\t\t\t\tfor ( boost::filesystem::directory_iterator itr( ((path.length() > 0) ? path + pattern[pos-1] : (last_dirpath != string::npos && last_dirpath > pos) ? \"\" : \"./\") + ((last_dirpath != string::npos && last_dirpath > pos) ? pattern.substr(pos,last_dirpath-pos) : \"\")); itr != end_itr; ++itr )\r\n\t\t\t\t{\r\n\t\t\t\t\tif (boost::filesystem::is_directory(itr->path())){\r\n\t\t\t\t\t\tboost::filesystem::path p = itr->path().filename();\r\n\t\t\t\t\t\tstring s = p.string();\r\n\t\t\t\t\t\tif (boost::regex_match(s.c_str(), expr)){\r\n\t\t\t\t\t\t\tpatternToFiles(pattern,files,(int)(next_dirpath+1),((path.length() > 0) ? path + pattern[pos-1] : \"\") + ((last_dirpath != string::npos && last_dirpath > pos) ? pattern.substr(pos,last_dirpath-pos) + pattern[last_dirpath] : \"\") + s);\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\tcatch (boost::filesystem::filesystem_error &e){}\r\n\t\t}\r\n\t\telse {\r\n\t\t\tboost::regex expr((last_dirpath != string::npos && last_dirpath > pos) ? patternSubstrRegex(pattern,last_dirpath+1,pattern.length()-last_dirpath-1) : patternSubstrRegex(pattern,pos,pattern.length()-pos));\r\n\t\t\tboost::filesystem::directory_iterator end_itr; // default construction yields past-the-end\r\n\t\t\ttry {\r\n\t\t\t\tfor ( boost::filesystem::directory_iterator itr(((path.length() > 0) ? path + pattern[pos-1] : (last_dirpath != string::npos && last_dirpath > pos) ? \"\" : \"./\") + ((last_dirpath != string::npos && last_dirpath > pos) ? pattern.substr(pos,last_dirpath-pos) : \"\")); itr != end_itr; ++itr )\r\n\t\t\t\t{\r\n\t\t\t\t\tboost::filesystem::path p = itr->path().filename();\r\n\t\t\t\t\tstring s = p.string();\r\n\t\t\t\t\tif (boost::regex_match(s.c_str(), expr)){\r\n\t\t\t\t\t\tfiles.push_back(((path.length() > 0) ? path + pattern[pos-1] : \"\") + ((last_dirpath != string::npos && last_dirpath > pos) ? pattern.substr(pos,last_dirpath-pos) + pattern[last_dirpath] : \"\") + s);\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\tcatch (boost::filesystem::filesystem_error &e){}\r\n\t\t}\r\n\t}\r\n\telse { // no unknown symbols\r\n\t\tboost::filesystem::path file(((path.length() > 0) ? path + \"/\" : \"\") + pattern.substr(pos,pattern.length()-pos));\r\n\t\tif (boost::filesystem::exists(file)){\r\n\t\t\tfiles.push_back(file.string());\r\n\t\t}\r\n\t}\r\n}\r\n\r\n/**\r\n * Converts a regular expression path pattern into a list of files matching with this pattern\r\n *\r\n * pattern: regular expression path pattern\r\n * files: the list to which new files can be applied\r\n */\r\nvoid patternToFiles(string& pattern, vector& files){\r\n\tpatternToFiles(pattern,files,0,\"\");\r\n}\r\n\r\n/*\r\n * Renames a given filename corresponding to the actual file pattern using a renaming pattern.\r\n * Wildcards can be referred to as ?1, ?2, ... in the order they appeared in the file pattern.\r\n *\r\n * pattern: regular expression path pattern\r\n * renamePattern: renaming pattern using ?1, ?2, ... as placeholders for wildcards\r\n * infile: path of the file (matching with pattern) to be renamed\r\n * outfile: path of the renamed file\r\n * par: used parameter (default: '?')\r\n */\r\nvoid patternFileRename(string& pattern, const string& renamePattern, const string& infile, string& outfile, const char par = '?'){\r\n\tsize_t first_unknown = renamePattern.find_first_of(par,0); // find unknown ? in renamePattern\r\n\tif (first_unknown != string::npos){\r\n\t\tstring formatOut = \"\";\r\n\t\tfor (size_t i=0, e=renamePattern.length(); i < e; i++ ) {\r\n\t\t\tchar c = renamePattern[i];\r\n\t\t\tif ( c == par && i+1 < e) {\r\n\t\t\t\tc = renamePattern[i+1];\r\n\t\t\t\tif (c > '0' && c <= '9'){\r\n\t\t\t\t\tformatOut.append(1,'$');\r\n\t\t\t\t\tformatOut.append(1,c);\r\n\t\t\t\t}\r\n\t\t\t\telse {\r\n\t\t\t\t\tformatOut.append(1,par);\r\n\t\t\t\t\tformatOut.append(1,c);\r\n\t\t\t\t}\r\n\t\t\t\ti++;\r\n\t\t\t}\r\n\t\t\telse {\r\n\t\t\t\tformatOut.append(1,c);\r\n\t\t\t}\r\n\t\t}\r\n\t\tboost::regex patternOut(patternSubstrRegex(pattern,0,pattern.length()));\r\n\t\toutfile = boost::regex_replace(infile,patternOut,formatOut,boost::match_default | boost::format_perl);\r\n\t} else {\r\n\t\toutfile = renamePattern;\r\n\t}\r\n}\r\n\r\n/** ------------------------------- Program ------------------------------- **/\r\n\r\n/*\r\n * Main program\r\n */\r\nint main(int argc, char *argv[])\r\n{\t\r\n\tstd::vector iriscodes;\r\n\t\r\n\tint mode = MODE_HELP;\r\n\tmap > cmd;\r\n\ttry {\r\n\t\tcmdRead(cmd,argc,argv);\r\n\t\tif (cmd.size() == 0 || cmdGetOpt(cmd,\"-h\") != 0) mode = MODE_HELP;\r\n\t\telse mode = MODE_MAIN;\r\n\t\tif (mode == MODE_MAIN){\r\n\t\t\t// validate command line\r\n\t\t\tcmdCheckOpts(cmd,\"-f|-s|-a|-c|-o|-h\");\r\n\t\t\tstring folder;\r\n\t\t\tif (cmdGetOpt(cmd,\"-f\") != 0){\r\n\t\t\t\tcmdCheckOptSize(cmd,\"-f\",1);\r\n\t\t\t\tfolder = cmdGetPar(cmd,\"-f\");\r\n\t\t\t}\r\n\t\t\tint alg = ALG_3A;\r\n\t\t\tif (cmdGetOpt(cmd,\"-a\") != 0){\r\n\t\t\t\tcmdCheckOptSize(cmd,\"-a\",1);\r\n\t\t\t\tstring algo = cmdGetPar(cmd,\"-a\");\r\n\t\t\t\tif (algo == \"3al\"){\r\n\t\t\t\t\talg = ALG_3AL;\r\n\t\t\t\t}\r\n\t\t\t\tif (algo == \"3as\"){\r\n\t\t\t\t\talg = ALG_3AS;\r\n\t\t\t\t}\r\n\t\t\t}\r\n MAIN_MODE main_mode = DYNAMIC;\r\n string outfilename = \"dynamic.txt\";\t\t\t\r\n\t\t\t// parameter for static mode\r\n\t\t\tint s = 5;\r\n\t\t\tif (cmdGetOpt(cmd,\"-s\") != 0){\r\n\t\t\t\tcmdCheckOptSize(cmd,\"-s\",1);\r\n\t\t\t\ts = cmdGetParInt(cmd,\"-s\"); \r\n main_mode = STATIC;\r\n outfilename=\"static.txt\";\r\n }\r\n\t\t\tfloat c = 0.33;\r\n\t\t\tif (cmdGetOpt(cmd,\"-c\") != 0){\r\n cmdCheckOptSize(cmd,\"-c\",1);\r\n c = cmdGetParFloat(cmd,\"-c\");\r\n main_mode = DYNAMIC;\r\n outfilename=\"dynamic.txt\";\r\n }\r\n\r\n if (cmdGetOpt(cmd,\"-o\") != 0){\r\n \r\n cmdCheckOptSize(cmd,\"-o\",1);\r\n outfilename = cmdGetPar(cmd,\"-o\");\r\n }\r\n\r\n\t\t\t// starting routine\r\n\t\t\tpath p(folder);\r\n\t\t\tcout << \"loading iriscodes...\" << endl;\r\n\t\t\tfor (auto i = directory_iterator(p); i != directory_iterator(); i++){\r\n\t\t\t\tif (!is_directory(i->path())){\r\n\t\t\t\t\tiriscodes.push_back(getCode(i->path().string(), c));\r\n\t\t\t\t}\r\n\t\t\t\telse continue;\r\n\t\t\t}\r\n\t\t\tcout << \"loaded \" << (int)iriscodes.size() << \" iriscodes\" << endl;\r\n\t\t\t\t\r\n\t\t\tofstream log;\r\n log.open(outfilename, std::ios_base::app); \r\n cout << \"output written to \" << outfilename << endl;\r\n \r\n if ( main_mode == STATIC){\r\n // evaluation with static step size\r\n cout << \"evaluation with static step size of \" << s << \" bit\" << endl;\r\n for (int i=0; i<(int)iriscodes.size()-1; i++){\r\n for (int j=i+1; j<(int)iriscodes.size(); j++){\r\n if (i!=j) {\r\n if (alg == ALG_3A){\r\n TripleA(iriscodes.at(i), iriscodes.at(j), MIN_SHIFT, MAX_SHIFT, log, s);\r\n }\r\n else if (alg == ALG_3AL){\r\n TripleA_limited(iriscodes.at(i), iriscodes.at(j), MIN_SHIFT, MAX_SHIFT, log, s);\r\n }\r\n else {\r\n TripleA_singlesided(iriscodes.at(i), iriscodes.at(j), MIN_SHIFT, MAX_SHIFT, log, s);\r\n }\r\n }\r\n }\r\n }\r\n } else if ( main_mode == DYNAMIC){\r\n // evaluation with dynamic step size\r\n cout << \"evaluation with dynamic step size and constant of \" << c << \" bit\" << endl;\r\n for (int i=0; i<(int)iriscodes.size()-1; i++){\r\n for (int j=i+1; j<(int)iriscodes.size(); j++){\r\n if (i!=j) {\r\n if (alg == ALG_3A){\r\n TripleA(iriscodes.at(i), iriscodes.at(j), MIN_SHIFT, MAX_SHIFT, log, iriscodes.at(i).stepsize);\r\n }\r\n else if (alg == ALG_3AL){\r\n TripleA_limited(iriscodes.at(i), iriscodes.at(j), MIN_SHIFT, MAX_SHIFT, log, iriscodes.at(i).stepsize);\r\n }\r\n else {\r\n TripleA_singlesided(iriscodes.at(i), iriscodes.at(j), MIN_SHIFT, MAX_SHIFT, log, iriscodes.at(i).stepsize);\r\n }\r\n }\r\n }\r\n }\r\n }\r\n\t\t\tlog.close();\r\n \t}\r\n \telse if (mode == MODE_HELP){\r\n\t\t\t// validate command line\r\n\t\t\tcmdCheckOpts(cmd,\"-h\");\r\n\t\t\tif (cmdGetOpt(cmd,\"-h\") != 0) cmdCheckOptSize(cmd,\"-h\",0);\r\n\t\t\t// starting routine\r\n\t\t\tprintUsage();\r\n \t}\r\n }\r\n\tcatch (...){\r\n\t \tprintf(\"Exit with errors.\\n\");\r\n\t \texit(EXIT_FAILURE);\r\n\t}\r\n return EXIT_SUCCESS;\r\n}\r\n", "meta": {"hexsha": "474e9c9a056423d661823236ee3b4cf70c194dfb", "size": 33544, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "packages/TripleA/TripleA.cpp", "max_stars_repo_name": "ngoclamvt123/usit-v2.2.0", "max_stars_repo_head_hexsha": "3b2d27b7096e44eb41c786b4497b296ffd5a1519", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2020-12-20T12:40:14.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-17T20:04:22.000Z", "max_issues_repo_path": "packages/TripleA/TripleA.cpp", "max_issues_repo_name": 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YES\n2. NO", "lm_q1_score": 0.5312093733737562, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.22849830350066827}} {"text": "#include \n#include \n#include \n#include \n\n#include \"hh_post_det_model.hpp\"\n//#include \"vgcc_det_model.hpp\"\n//#include \"pmca_det_model.hpp\"\n#include \"ca_sensor_gillespie_class.hpp\"\n#include \"vgcc_gillespie_class.hpp\"\n#include \"pmca_gillespie_class.hpp\"\n\n#include \"gillespie_class.hpp\"\n#include \"physical_constants.hpp\"\n#include \"stl_vector_operation_functions.hpp\"\n\n\n#include /* Specifying the file within < > lets c++ search for it at -I path */\n#include \n#include \n\nnamespace bno=boost::numeric::odeint;\nnamespace gill=astron::gillespie;\nnamespace g_vgcc=gill::vgcc;\nnamespace g_pmca=gill::pmca;\nnamespace consts=astron::phy_const;\n\ntypedef std::vector< double > state_type;\ntypedef state_type::iterator state_type_it;\ntypedef bno::runge_kutta4 < state_type > rk4_stepper_type;\n\n//-----------------\nint main(int argc, char *argv[])\n{\n //-------------------------------------\n std::string filename; // Output filename variable\n // decode arguments passed\n if (argc < 2){\n filename = argv[0];\n std::cout << \"No filename prefix provided\" << std::endl;\n filename = filename.substr(2,filename.length()-2) + \"_run\";\n }\n else{\n filename = std::string(argv[1]);\n }\n std::ofstream fout(filename);\n if (!fout){\n std::cout << \"Error opening file \" << filename << \"\\n\";\n return (0);\n }\n std::cout << \"File = \" << filename << std::endl;\n // ----------------------------------\n const double vol_pre = 1E-18;\t// Volume of whole presynaptic terminal\n const double vol_pmca = 1E-18;\n const double vol_vgcc = 1E-18;\n\n double conCaIn = 100E-09;\t// Baseline Calcium concentration\n int numCaIn = conCaIn * consts::Av * vol_pre * 1000; // Molarity in Liters 1L = 1000 * 10^-6 meter^3\n \n \n // Initialize deterministic objects\n rk4_stepper_type stepper;\n \n HH_POST hh_post(500E-12); // constructs an hh_pre object with 25 micron radius\n state_type X_hh_post = {0,0,0,-80E-03}; //{m,h,n,V}\n\n //PMCA pmca(100E-09); // constructs an PMCA object with 100 nM calcium concentration (intracellular)\n //state_type X_pmca = {100E-09, 17E-03, 0, 0}; //{conCaIn, PMCA0, PMCA1, PMCA2}\n\n //VGCC vgcc; // constructs VGCCs\n //state_type X_vgcc = {50, 0, 0, 0, 0}; //{C0, C1, C2, C3, O}\n //-------------------------------------\n\n // Initialize MCMC: PMCA\n const int n_pmca = 1;\n const int n_states_pmca = 5; \t// [NCa, Leak, PMCA0, PMCA1, PMCA2]\n std::vector X_pmca = {62,1530,1530,0,0}; // [NCa, NLeak, NPMCA0, PMCA1, PMCA2]\n std::vector gpmca;\n for(int i=0; i X_vgcc = {50,0,0,0,0};\n std::vector gvgcc;\n for(int i=0; i X_cas(n_states_ca_sensor); // State vector for ca_sensor initialization in state X0Y0;\n //for(int i=0;i ca_sensors; // ca_sensor object construction\n //for(int i=0; i Y(11); // Ouput vector\n //--------------------------------------\n double t = 0.0;\n const double dt = 10E-06;\n const double tmax = 100E-03;\n // -------------------------------------\n \n while (t <= tmax){\n //std::cout << \"conCaIn = \" << conCaIn << \" numCaIn = \" << numCaIn << std::endl;\n\n if( (t > 22E-03) && ( t < (24E-03 )) ){\n hh_post.IExt = 20E-02;\t// External current to trigger presynaptic spike \n }\n if( (t >= 24E-03) && ( t <= (tmax )) ){\n hh_post.IExt = 0;\n }\n stepper.do_step( hh_post, X_hh_post, t, dt); // update voltage\n\n //gpmca[0].X[0] = numCaIn; \n for(int i=0; i // NOLINT\n#include \n#include \n\n#include \n#include \"cpp/sensor_fusion/geometry_toolbox_mahony.h\"\n#include \"cpp/sensor_fusion/low_pass_filter.h\"\n#include \"cpp/sensor_fusion/orientation_filter.h\"\n#include \"cpp/sensor_fusion/stationary_detector.h\"\n\nnamespace {\n\n// Minimum time step between sensor updates. This corresponds to 1000 Hz.\nstatic const double kMinTimestepS = 0.001f;\n\n// Maximum time step between sensor updates. This corresponds to 1 Hz.\nstatic const double kMaxTimestepS = 1.0f;\n\n// Gravity constant in m.s^(-2).\nstatic const double kMagnitudeOfGravity = 9.81;\n\n// Small threshold to check if close to zero.\nstatic const double kEpsilon = 1e-9;\n\n// Number of runs for bias correction to remain static.\n// This is less than 1 sec.\nstatic const int kMagBiasCorrectionStaticCount = 40;\n\n// Small threshold used for mag initialization.\nstatic const double kMagInitTolerance = 1e-6;\n\n// Number of mag samples used for mag measurement initialization. 25 samples\n// correspond to ~.4 seconds for mag at 60 Hz.\nstatic const int kNumMagForInitialization = 25;\n\n// Number of continuous invalid mag measurement that results in temporarily\n// disabling mag fusion, unless new mag calibration is performed.\nstatic const int kNumMagForFilterOutlierRejection = 10;\n\n// Threshold of rejecting using one mag measurement. The value for this\n// threshold is in rad, which corresponds to 5 degree.\nstatic const double kMaxAllowedMagDeviationRadians = 0.0872665;\n\n// Threshold of using mag measurements on its timestamp. If the timestamp\n// between mag and gyro is larger than the given value, we simply do not use\n// the current mag measurement. This behavior happens in this multi-threaded\n// sensor fusion system, but not often.\nstatic const double kMaxTimeDifferentInMagAndGyroInSeconds = 0.003f;\n\n// Helper method to validate the time steps of sensor timestamps.\nbool IsTimestampDeltaValid(double timestamp_delta_s) {\n if (timestamp_delta_s <= kMinTimestepS) {\n return false;\n }\n if (timestamp_delta_s > kMaxTimestepS) {\n return false;\n }\n return true;\n}\n\nmahony_filter::StationaryDetector::StationaryDetectorConfiguration\nGetStationaryDetectorConfig(double stationary_bias_correction_gain) {\n mahony_filter::StationaryDetector::StationaryDetectorConfiguration\n stationary_config;\n stationary_config.stationary_bias_correction_gain =\n stationary_bias_correction_gain;\n return stationary_config;\n}\n\n} // namespace\n\nOrientationFilter::OrientationFilter(\n const OrientationFilterConfiguration& config)\n : config_(config),\n state_(Eigen::Matrix::Zero()),\n next_state_(Eigen::Matrix::Zero()),\n is_orientation_initialized_(false),\n first_accel_timestamp_s_(0.0),\n has_received_gyro_sample_(false),\n mag_is_available_(false),\n mag_low_pass_filter_(config.magnetometer_low_pass_cutoff_frequency),\n accel_aligned_R_yaw_mag_aligned_(Eigen::Matrix3d::Identity()),\n num_mag_measurements_for_yaw_initialization_(kNumMagForInitialization),\n mag_meas_for_init_index_(0),\n mag_bias_(Eigen::Vector3d::Zero()),\n new_mag_calibration_available_(false),\n mag_status_(MagStatus::kInitial),\n accumulated_num_of_outlier_mag_measurement_(0),\n current_mag_sample_fits_calibration_(false),\n stationary_detector_(\n GetStationaryDetectorConfig(config.stationary_bias_correction_gain)) {\n // Initialize quaternions to start with the identity rotation.\n state_(3) = 1.0;\n next_state_(3) = 1.0;\n\n // Initialize the data buffer for mag initialization.\n projected_mag_measurement_vector_.resize(\n Eigen::NoChange, num_mag_measurements_for_yaw_initialization_);\n\n // Set bias of magnetometer calibrator when it's provided in configuration.\n if (config_.init_mag_bias != Eigen::Vector3d::Zero()) {\n SetMagBias(config_.init_mag_bias);\n }\n}\n\nvoid OrientationFilter::Run() {\n // Initialization for the filter orientation.\n if (!is_orientation_initialized_ &&\n current_accel_measurement_.timestamp_s > 0.0 &&\n (config_.init_config ==\n OrientationFilterConfiguration::InitialOrientationConfiguration::\n kDontUseMagToInitOrientation ||\n current_mag_measurement_.timestamp_s > 0.0)) {\n is_orientation_initialized_ = OrientationFromAccelAndMag();\n }\n\n // Only start to propagate once the orientation has already been initialized\n // by the accel / mag.\n if (is_orientation_initialized_) {\n FilterPropagate();\n }\n}\n\nvoid OrientationFilter::RegisterOnBadMagnetometerCalibrationDetectedCallback(\n std::function* on_bad_mag_calibration_detected_callback) {\n on_bad_mag_calibration_detected_callbacks_.insert(\n on_bad_mag_calibration_detected_callback);\n}\n\nvoid OrientationFilter::UnRegisterOnBadMagnetometerCalibrationDetectedCallback(\n std::function* on_bad_mag_calibration_detected_callback) {\n on_bad_mag_calibration_detected_callbacks_.erase(\n on_bad_mag_calibration_detected_callback);\n}\n\nvoid OrientationFilter::BadMagnetometerCalibrationDetectedBroadcast() const {\n for (auto callback : on_bad_mag_calibration_detected_callbacks_) {\n if (callback) {\n (*callback)();\n }\n }\n}\n\nvoid OrientationFilter::AddAccelMeasurement(const Eigen::Vector3d& sample,\n double timestamp_s) {\n if (first_accel_timestamp_s_ == 0.0) {\n first_accel_timestamp_s_ = timestamp_s;\n if (is_orientation_initialized_) {\n // Check if the state is aligned with gravity and fix it if it is not.\n const Eigen::Vector3d g_from_acc = sample.normalized();\n const Eigen::Vector3d g_est = ComputeGravityEstimate();\n\n double dot_product = g_from_acc.dot(g_est);\n // Clamp dot_prodcut.\n if (std::abs(dot_product) > 1.0) {\n dot_product = (dot_product > 0.0 ? 1.0 : -1.0);\n }\n const double angular_error_deg = 180. / M_PI * acos(dot_product);\n // TODO: Add a better gravity / acceleration check here.\n if (std::abs(angular_error_deg) > 44) {\n // The error after SetPose is too large. Correct directly the aligment\n // with gravity.\n const Eigen::Vector4d correction_quat =\n geometry_toolbox::RotateInto(g_est, g_from_acc);\n\n state_.head<4>() = geometry_toolbox::QuaternionMultiplication(\n correction_quat, state_.head<4>());\n }\n }\n }\n\n current_accel_measurement_ = SensorSample(sample, timestamp_s);\n\n if (IsStationaryBiasCorrectionEnabled()) {\n stationary_detector_.AddAccelMeasurement(current_accel_measurement_.sample,\n timestamp_s);\n }\n\n if (!has_received_gyro_sample_) {\n // If we have not received a gyro sample supply fake gyro data to make use\n // of every accel sample.\n current_gyro_measurement_ =\n SensorSample(Eigen::Vector3d::Zero(), timestamp_s);\n Run();\n }\n}\n\nvoid OrientationFilter::AddGyroMeasurement(const Eigen::Vector3d& sample,\n double timestamp_s) {\n current_gyro_measurement_ = SensorSample(sample, timestamp_s);\n\n if (IsStationaryBiasCorrectionEnabled()) {\n stationary_detector_.AddGyroMeasurement(current_gyro_measurement_.sample,\n timestamp_s);\n }\n\n const double delta_t_s =\n (timestamp_s - previous_gyro_measurement_.timestamp_s);\n if (IsTimestampDeltaValid(delta_t_s)) {\n if (!has_received_gyro_sample_) {\n has_received_gyro_sample_ = true;\n }\n Run();\n }\n\n previous_gyro_measurement_ = current_gyro_measurement_;\n}\n\nvoid OrientationFilter::AddMagMeasurement(const Eigen::Vector3d& sample,\n double timestamp_s,\n bool fits_calibration) {\n // First sample is only used for setting up the timeline.\n if (current_mag_measurement_.timestamp_s == 0.0) {\n current_mag_measurement_ = SensorSample(sample, timestamp_s);\n current_mag_sample_fits_calibration_ = fits_calibration;\n return;\n }\n\n const double mag_delta_time_s =\n (timestamp_s - previous_mag_measurement_.timestamp_s);\n\n // Use uncalibrated mag correction only if stationary correction is not\n // enabled.\n if (!IsStationaryBiasCorrectionEnabled()) {\n // Now that the timestep is valid we can initialize the low_pass filter.\n mag_low_pass_filter_.AddSampleData(sample, mag_delta_time_s);\n if (mag_low_pass_filter_.IsInitialized()) {\n previous_mag_measurement_ =\n SensorSample(mag_low_pass_filter_.GetFilteredData(),\n current_mag_measurement_.timestamp_s);\n }\n } else {\n previous_mag_measurement_ = current_mag_measurement_;\n }\n\n current_mag_measurement_ = SensorSample(sample, timestamp_s);\n current_mag_sample_fits_calibration_ = fits_calibration;\n\n // Only process valid mag sample.\n mag_is_available_ = IsTimestampDeltaValid(mag_delta_time_s);\n}\n\nbool OrientationFilter::ComputeIterativeSolution(\n const Eigen::VectorXd& mag_vector, const double initial_solution,\n Eigen::Matrix3d* accel_aligned_R_yaw_mag_aligned) {\n CHECK(accel_aligned_R_yaw_mag_aligned != nullptr);\n double current_solution = initial_solution;\n\n // Parameters for Gauss-Newton algorithm.\n constexpr int kMaxIteration = 25;\n constexpr double kNormCorrectionForConvergence = 1e-5;\n\n int current_iteration = 0;\n for (; current_iteration < kMaxIteration; ++current_iteration) {\n // Estimated measurement and Jacobian for Gauss-Newton.\n Eigen::Vector2d z_est(-std::sin(current_solution),\n std::cos(current_solution));\n Eigen::Vector2d Jacobian(-std::cos(current_solution),\n -std::sin(current_solution));\n\n double residual = 0;\n double hessian = 0;\n\n for (int i = 0; i < mag_vector.rows() / 2; ++i) {\n const Eigen::Vector2d residual_i = z_est - mag_vector.segment<2>(i * 2);\n residual += Jacobian.transpose() * residual_i;\n hessian += Jacobian.transpose() * Jacobian;\n }\n\n double correction = -residual / hessian;\n if (hessian < kMagInitTolerance) {\n // Invalid Hessian, mag initialization fails.\n return false;\n }\n\n current_solution += correction;\n\n if (std::abs(correction) < kNormCorrectionForConvergence) {\n break;\n }\n }\n\n if (current_iteration >= kMaxIteration) {\n // Maximum iteration reached, mag initialization fails.\n return false;\n }\n\n *accel_aligned_R_yaw_mag_aligned << std::cos(current_solution),\n -std::sin(current_solution), 0, std::sin(current_solution),\n std::cos(current_solution), 0, 0, 0, 1;\n\n return true;\n}\n\nbool OrientationFilter::ComputeYawAlignmentMatrix(\n const Eigen::Matrix3Xd& mag_proj,\n Eigen::Matrix3d* accel_aligned_R_yaw_mag_aligned) {\n CHECK(accel_aligned_R_yaw_mag_aligned != nullptr);\n const int num_rows = 2 * mag_proj.cols();\n Eigen::VectorXd mag_vector(num_rows, 1);\n for (int i = 0; i < mag_proj.cols(); ++i) {\n if (mag_proj.col(i).head<2>().squaredNorm() < kMagInitTolerance) {\n return false;\n }\n\n mag_vector.segment<2>(i * 2) = mag_proj.col(i).head<2>().normalized();\n }\n\n const double init_solution = -std::atan2(mag_vector(0), mag_vector(1));\n\n return ComputeIterativeSolution(mag_vector, init_solution,\n accel_aligned_R_yaw_mag_aligned);\n}\n\nbool OrientationFilter::OrientationFromAccelAndMag() {\n Eigen::Vector3d L_x;\n Eigen::Vector3d L_y;\n Eigen::Vector3d L_z;\n Eigen::Matrix L_R_G;\n\n // East-North-Up frame of reference:\n // - Gravity vector lies along +z axis\n // - Horizontal component of Mag vector lies along +y axis\n // - x-axis points east\n L_z = current_accel_measurement_.sample.normalized();\n\n switch (config_.init_config) {\n case OrientationFilterConfiguration::InitialOrientationConfiguration::\n kDontUseMagToInitOrientation: {\n // Depending on whether we are portrait or landscape, this will\n // be the direction we expect gravity to be pointing if the device\n // is right-side-up.\n Eigen::Vector3d canonical_down;\n // Is it more in landscape or in portrait mode?\n if (std::abs(L_z.dot(Eigen::Vector3d::UnitY())) <\n std::abs(L_z.dot(Eigen::Vector3d::UnitX()))) {\n // Landscape\n L_y = Eigen::Vector3d::UnitY();\n canonical_down = Eigen::Vector3d::UnitX();\n } else {\n // Portrait\n L_y = -Eigen::Vector3d::UnitX();\n canonical_down = Eigen::Vector3d::UnitY();\n }\n // Is it right-side-up or upside-down?\n if (L_z.dot(canonical_down) < 0) {\n L_y = -L_y;\n }\n break;\n }\n case OrientationFilterConfiguration::InitialOrientationConfiguration::\n kUseMagToInitOrientation:\n L_y = current_mag_measurement_.sample.normalized();\n L_y -= L_z * L_z.transpose() * L_y;\n }\n\n L_x = -L_z.cross(L_y);\n if (L_x.norm() == 0.0) {\n return false;\n }\n\n L_x = L_x / L_x.norm();\n L_y = L_z.cross(L_x);\n if (L_y.norm() == 0.0) {\n return false;\n }\n\n L_R_G.block<3, 1>(0, 0) = L_x;\n L_R_G.block<3, 1>(0, 1) = L_y;\n L_R_G.block<3, 1>(0, 2) = L_z;\n\n // Compute and assign the quaternion of orientation from the resulting\n // rotation matrix.\n state_.block<4, 1>(0, 0) =\n geometry_toolbox::RotationMatrixToQuaternion(L_R_G);\n\n state_from_previous_mag_ = state_;\n mag_is_available_ = false;\n return true;\n}\n\nvoid OrientationFilter::FilterPropagate() {\n // TODO: Orientation filter should be robust to different update\n // frequency.\n const double delta_t = current_gyro_measurement_.timestamp_s -\n previous_gyro_measurement_.timestamp_s;\n if (!IsTimestampDeltaValid(delta_t)) {\n return;\n }\n\n // Ignore the current mag measurement if it is too old.\n if (mag_is_available_) {\n const double gyro_time_ahead = current_gyro_measurement_.timestamp_s -\n current_mag_measurement_.timestamp_s;\n\n if (gyro_time_ahead > kMaxTimeDifferentInMagAndGyroInSeconds) {\n // If this behavior happens very often, we should project previous mag\n // measurement onto current timestamp, to do not waste too much data.\n // In practice, in current experiments, this happens at a very low\n // frequency.\n mag_is_available_ = false;\n }\n }\n\n Eigen::Matrix gyro_measurements;\n gyro_measurements.head<3>() = previous_gyro_measurement_.sample;\n gyro_measurements.tail<3>() = current_gyro_measurement_.sample;\n // Bias compensate the gyro data. Since imu_measurement format is\n // [prev_x, prev_y, prev_z, curr_x, curr_y, curr_z] this can be accomplished\n // by subtracting the bias_x, bias_y, bias_z from the appropriate elements.\n const Eigen::Vector3d rate_correction = ComputeAccelAndMagRateCorrection();\n\n gyro_measurements.head<3>() +=\n (-state_.tail<3>() +\n (IsInitializing()\n ? config_.attitude_correction_gain_during_initialization\n : config_.attitude_correction_gain) *\n rate_correction);\n gyro_measurements.tail<3>() +=\n (-state_.tail<3>() +\n (IsInitializing()\n ? config_.attitude_correction_gain_during_initialization\n : config_.attitude_correction_gain) *\n rate_correction);\n\n Eigen::Matrix current_q, next_q;\n current_q = state_.head<4>();\n next_q = state_.head<4>();\n quaternion_integrator_.Integrate(current_q, gyro_measurements, delta_t,\n &next_q);\n next_state_.head<4>() = next_q;\n next_state_.tail<3>() =\n state_.tail<3>() -\n ((IsInitializing() ? 0.0 : config_.gyroscope_bias_correction_gain) *\n delta_t * rate_correction);\n\n // Bias correction if stationary.\n if (IsStationaryBiasCorrectionEnabled()) {\n next_state_.tail<3>() -=\n delta_t *\n stationary_detector_.GetGyroBiasCorrection(\n next_state_.tail<3>(), current_gyro_measurement_.timestamp_s);\n }\n state_ = next_state_;\n\n if (mag_is_available_) {\n const double mag_delta_t = (current_mag_measurement_.timestamp_s -\n previous_mag_measurement_.timestamp_s);\n if (!IsStationaryBiasCorrectionEnabled()) {\n // Only apply this correction if the stationary bias correction is not\n // enabled.\n const Eigen::Vector3d mag_correction = EstimateBiasUpdateUsingMag();\n state_.tail<3>() -=\n mag_delta_t *\n config_.magnetometer_gain_for_gyroscope_bias_estimation *\n mag_correction;\n }\n\n state_from_previous_mag_ = state_;\n mag_is_available_ = false;\n }\n}\n\nEigen::Vector3d OrientationFilter::EstimateBiasUpdateUsingMag() {\n if (!mag_low_pass_filter_.IsInitialized()) {\n return Eigen::Vector3d::Zero();\n }\n // Check that gyro norm is below threshold. This is to ensure we only estimate\n // drift when device is still and gyro signal is most likely due to drift.\n const bool is_gyro_static =\n current_gyro_measurement_.sample.norm() <\n config_.maximum_allowed_gyro_norm_changed_for_mag_bias_correction;\n\n // Check that mag norm is below threshold. This is to ensure we only estimate\n // drift when device is still and mag signal is similar to noise.\n const bool is_mag_static =\n (previous_mag_measurement_.sample -\n mag_low_pass_filter_.GetFilteredData())\n .norm() < config_.maximum_allowed_magnitude_magnetometer_change_mt;\n mag_low_pass_filter_.SetIsStatic(is_gyro_static && is_mag_static);\n\n if (!mag_low_pass_filter_.GetIsStaticForN(kMagBiasCorrectionStaticCount)) {\n return Eigen::Vector3d::Zero();\n }\n\n // Get down direction from both previous mag and current state.\n const Eigen::Vector3d previous_accel_est =\n geometry_toolbox::QuaternionToRotationMatrix(\n state_from_previous_mag_.head<4>())\n .col(2);\n const Eigen::Vector3d previous_mag_est =\n geometry_toolbox::QuaternionToRotationMatrix(\n state_from_previous_mag_.head<4>())\n .col(1);\n const Eigen::Vector3d current_accel_est =\n geometry_toolbox::QuaternionToRotationMatrix(state_.head<4>()).col(2);\n const Eigen::Vector3d current_mag_est =\n geometry_toolbox::QuaternionToRotationMatrix(state_.head<4>()).col(1);\n\n // Grab the previous mag, remove gravity and normalize.\n Eigen::Vector3d previous_mag_meas = previous_mag_measurement_.sample;\n previous_mag_meas -=\n previous_accel_est * previous_accel_est.transpose() * previous_mag_meas;\n previous_mag_meas.normalize();\n\n // Grab the current mag, remove gravity and normalize.\n Eigen::Vector3d mag_meas = mag_low_pass_filter_.GetFilteredData();\n mag_meas -= current_accel_est * current_accel_est.transpose() * mag_meas;\n mag_meas.normalize();\n\n return mag_meas.cross(previous_mag_meas) -\n current_mag_est.cross(previous_mag_est);\n}\n\nEigen::Vector3d OrientationFilter::ComputeAccelAndMagRateCorrection() {\n Eigen::Vector3d accel_meas = current_accel_measurement_.sample;\n const double accel_magnitude = accel_meas.norm();\n if (accel_magnitude < 1e-6) {\n return Eigen::Vector3d::Zero();\n }\n\n accel_meas.normalize();\n Eigen::Matrix L_R_G_accel_aligned =\n geometry_toolbox::QuaternionToRotationMatrix(state_.head<4>());\n Eigen::Vector3d accel_est = L_R_G_accel_aligned.col(2);\n\n // Dampen the effect of body acceleration. This is only applied when not\n // initializing because it would otherwise slow down convergence.\n double gain = 1.0;\n const double gyro_norm = current_gyro_measurement_.sample.norm();\n if (!IsInitializing()) {\n // Don't update filter while moving too fast.\n // TODO: Consider using something different than this, since most\n // accelerometer sensors don't report 1G at rest.\n gain /= (1.0 + std::abs(accel_magnitude - kMagnitudeOfGravity));\n\n // Don't update filter while moving to fast.\n // Use full gain between 0 and 0.1, dampen toward 0 between 0.1 to\n // 0.3 rad.s^(-1).\n // 1 __ //\n // \\ //\n // \\ //\n // \\______ 0.0 //\n // ^ ^ ^ //\n // 0 .1 .3 //\n gain *= std::max(0.0, std::min(1.0, 1.5 - 5.0 * gyro_norm));\n } else {\n // Use full gain between 0 and 0.04, dampen toward 0 between 0.04 to\n // 0.1 rad.s^(-1).\n // 1 __ //\n // \\ //\n // \\ //\n // \\______ 0.0 //\n // ^ ^ ^ //\n // 0 .04 .1 //\n gain *= std::max(0.0, std::min(1.0, 1.5 - 15.0 * gyro_norm));\n }\n\n Eigen::Vector3d rate_correction_vector = gain / 2 *\n config_.accel_yaw_correction_gain *\n accel_meas.cross(accel_est);\n\n // Check if there is a valid mag sample ready to be consumed.\n if (mag_is_available_ && current_mag_sample_fits_calibration_ &&\n config_.mag_yaw_correction_gain > 0 &&\n current_mag_measurement_.timestamp_s > 0) {\n // First check if received a new mag calibration.\n if (new_mag_calibration_available_) {\n // Invalidate the existing mag alignement and force recompute.\n mag_status_ = MagStatus::kAligning;\n mag_meas_for_init_index_ = 0;\n LOG(INFO) << \"SensorFusion: Received new bias, estimating alignment.\";\n new_mag_calibration_available_ = false;\n }\n\n const Eigen::Vector3d current_calibrated_mag_measurement =\n current_mag_measurement_.sample - mag_bias_;\n\n // Compute estimated mag value, projected on yaw and represented locally.\n Eigen::Vector3d mag_est_projection = current_calibrated_mag_measurement;\n mag_est_projection -=\n accel_est * accel_est.transpose() * mag_est_projection;\n mag_est_projection.normalize();\n\n // Estimate alignment between the filter orientation and magnetic north.\n if (mag_status_ == MagStatus::kAligning) {\n // Store the projected mag in a buffer that will be used by the iterative\n // solver.\n projected_mag_measurement_vector_.col(mag_meas_for_init_index_) =\n L_R_G_accel_aligned.transpose() * mag_est_projection;\n\n ++mag_meas_for_init_index_;\n\n // Check if we have enough sample to run the iterative solver.\n if (mag_meas_for_init_index_ ==\n num_mag_measurements_for_yaw_initialization_) {\n if (ComputeYawAlignmentMatrix(projected_mag_measurement_vector_,\n &accel_aligned_R_yaw_mag_aligned_)) {\n // In this case, mag is successfully initialized.\n mag_status_ = MagStatus::kAligned;\n } else {\n // Mag initialization fails. Clear mag measurements, and re-start data\n // collection.\n mag_meas_for_init_index_ = 0;\n LOG(INFO) << \"SensorFusion: Mag alignment failed in orientation \"\n \"tracker. Will retry.\";\n }\n }\n }\n\n Eigen::Vector3d mag_meas = current_calibrated_mag_measurement;\n mag_meas -= accel_meas * accel_meas.transpose() * mag_meas;\n if (mag_meas.norm() < 1e-6) {\n return Eigen::Vector3d();\n }\n mag_meas.normalize();\n\n if (mag_status_ == MagStatus::kAligned) {\n // Magnetometer is calibrated and aligned with the filter now so we can\n // use it for yaw correction.\n mag_is_available_ = false;\n\n const Eigen::Vector3d mag_est =\n (L_R_G_accel_aligned * accel_aligned_R_yaw_mag_aligned_).col(1);\n\n // Test whether the current mag estimate and mag measurement diverge too\n // much.\n const double angle_between_est_and_meas =\n std::acos(mag_est_projection.dot(mag_est));\n\n if (angle_between_est_and_meas > kMaxAllowedMagDeviationRadians) {\n ++accumulated_num_of_outlier_mag_measurement_;\n if (accumulated_num_of_outlier_mag_measurement_ >\n kNumMagForFilterOutlierRejection) {\n // Invalidate the existing mag alignement and force recompute.\n mag_status_ = MagStatus::kAligning;\n state_from_previous_mag_ = state_;\n mag_meas_for_init_index_ = 0;\n LOG(INFO) << \"Consistently received outlier measurements, resetting.\";\n BadMagnetometerCalibrationDetectedBroadcast();\n accumulated_num_of_outlier_mag_measurement_ = 0;\n }\n } else {\n rate_correction_vector +=\n config_.mag_yaw_correction_gain * mag_meas.cross(mag_est);\n\n accumulated_num_of_outlier_mag_measurement_ = 0;\n }\n }\n }\n\n return rate_correction_vector;\n}\n\nvoid OrientationFilter::Recenter() {\n // This function enforces two constraints on the updated L_R_G_recenter\n // - The gravity direction is kept\n // L_R_G_recenter * z = L_R_G * z\n // - yaw is equal to zero\n // L_R_G_recenter^{-1} * -z =[sin(pitch) 0 cos(pitch)]\n // Notation:\n // L_R_G^{-1} * -z = [cos(yaw) * sin(pitch) sin(yaw) cos(yaw) * cos(pitch)]\n\n //\n // We can deduce that\n // L_R_G_recenter(:,3) == L_R_G(:,3) (1)\n // L_R_G_recenter(3,2) = 0.0 (2)\n //\n // As L_R_G is an orthonormal matrix\n // |L_R_G(:,2)| = 1 (3)\n //\n // Combining (2) and (3)\n // |L_R_G(1:2,2)| = 1 (4)\n // L_R_G(1:2,2) . L_R_G(1:2,3) = 0 (5) Orthogonality\n //\n // if L_R_G(1,3) > Eps\n // L_R_G(2,2) = +/- sqrt( 1 / ( 1 + (L_R_G(2,3) / L_R_G(1,3))^2)) (6)\n // L_R_G(1,2) = - L_R_G(2,2) * L_R_G(2,3) / L_R_G(1,3)\n //\n // Finally we use the orthogonality constraint for the first column\n // L_R_G(:,1) = L_R_G(:,2) x L_R_G(:,3)\n\n const Eigen::Matrix3d L_R_G =\n geometry_toolbox::QuaternionToRotationMatrix(state_.head<4>());\n\n Eigen::Matrix3d L_R_G_recentered;\n // Keep down direction the same (1).\n L_R_G_recentered.block<3, 1>(0, 2) = L_R_G.block<3, 1>(0, 2);\n\n if (std::abs(L_R_G(0, 2)) < kEpsilon) {\n // Arbitrary deciding to use X axis.\n L_R_G_recentered(0, 1) = 1.0;\n L_R_G_recentered(1, 1) = 0.0;\n } else {\n const double x_y_ratio = L_R_G(1, 2) / L_R_G(0, 2); // Eq. (6)\n // The sign is kept positive to enforce \"forwardness\".\n L_R_G_recentered(1, 1) = sqrt(1.0 / (1 + x_y_ratio * x_y_ratio));\n L_R_G_recentered(0, 1) = -L_R_G_recentered(1, 1) * x_y_ratio;\n }\n L_R_G_recentered(2, 1) = 0.0; // Eq. (2)\n\n // Enforce orthogonality.\n L_R_G_recentered.block<3, 1>(0, 0) = L_R_G_recentered.block<3, 1>(0, 1).cross(\n L_R_G_recentered.block<3, 1>(0, 2));\n\n state_.head<4>() =\n geometry_toolbox::RotationMatrixToQuaternion(L_R_G_recentered);\n\n // Reset the mag based bias update by setting the previous state to current\n // state. This avoids using the change in pose due to recentering for bias\n // update.\n state_from_previous_mag_ = state_;\n}\n", "meta": {"hexsha": "4ac4f81524b778fa7d2aeba607f48ff1b4e5b472", "size": 27226, "ext": "cc", "lang": "C++", "max_stars_repo_path": "cpp/sensor_fusion/orientation_filter.cc", "max_stars_repo_name": "google/vr180", "max_stars_repo_head_hexsha": "f55eaa1c6835b911b7a11830ec546636a16d49da", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 37.0, "max_stars_repo_stars_event_min_datetime": "2018-12-09T17:58:51.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-06T11:43:24.000Z", "max_issues_repo_path": "cpp/sensor_fusion/orientation_filter.cc", "max_issues_repo_name": "google/vr180", "max_issues_repo_head_hexsha": "f55eaa1c6835b911b7a11830ec546636a16d49da", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2019-05-21T06:01:29.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-11T09:49:29.000Z", "max_forks_repo_path": "cpp/sensor_fusion/orientation_filter.cc", "max_forks_repo_name": "google/vr180", "max_forks_repo_head_hexsha": "f55eaa1c6835b911b7a11830ec546636a16d49da", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 12.0, "max_forks_repo_forks_event_min_datetime": "2019-05-25T02:18:14.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-11T09:35:03.000Z", "avg_line_length": 37.3983516484, "max_line_length": 80, "alphanum_fraction": 0.6868067289, "num_tokens": 6766, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6619228758499942, "lm_q2_score": 0.34510527095787247, "lm_q1q2_score": 0.22843307342342642}} {"text": "#ifndef BOOST_GEOMETRY_PROJECTIONS_GSTMERC_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_GSTMERC_HPP\n\n// Boost.Geometry - extensions-gis-projections (based on PROJ4)\n// This file is automatically generated. DO NOT EDIT.\n\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\n\n// Last updated version of proj: 4.9.1\n\n// Original copyright notice:\n\n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace boost { namespace geometry { namespace projections\n{\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail { namespace gstmerc\n {\n\n struct par_gstmerc\n {\n double lamc;\n double phic;\n double c;\n double n1;\n double n2;\n double XS;\n double YS;\n };\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_gstmerc_spheroid : public base_t_fi,\n Geographic, Cartesian, Parameters>\n {\n\n typedef double geographic_type;\n typedef double cartesian_type;\n\n par_gstmerc m_proj_parm;\n\n inline base_gstmerc_spheroid(const Parameters& par)\n : base_t_fi,\n Geographic, Cartesian, Parameters>(*this, par) {}\n\n // FORWARD(s_forward) spheroid\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\n inline void fwd(geographic_type& lp_lon, geographic_type& lp_lat, cartesian_type& xy_x, cartesian_type& xy_y) const\n {\n double L, Ls, sinLs1, Ls1;\n L= this->m_proj_parm.n1*lp_lon;\n Ls= this->m_proj_parm.c+this->m_proj_parm.n1*log(pj_tsfn(-1.0*lp_lat,-1.0*sin(lp_lat),this->m_par.e));\n sinLs1= sin(L)/cosh(Ls);\n Ls1= log(pj_tsfn(-1.0*asin(sinLs1),0.0,0.0));\n xy_x= (this->m_proj_parm.XS + this->m_proj_parm.n2*Ls1)*this->m_par.ra;\n xy_y= (this->m_proj_parm.YS + this->m_proj_parm.n2*atan(sinh(Ls)/cos(L)))*this->m_par.ra;\n /*fprintf(stderr,\"fwd:\\nL =%16.13f\\nLs =%16.13f\\nLs1 =%16.13f\\nLP(%16.13f,%16.13f)=XY(%16.4f,%16.4f)\\n\",L,Ls,Ls1,lp_lon+this->m_par.lam0,lp_lat,(xy_x*this->m_par.a + this->m_par.x0)*this->m_par.to_meter,(xy_y*this->m_par.a + this->m_par.y0)*this->m_par.to_meter);*/\n }\n\n // INVERSE(s_inverse) spheroid\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\n inline void inv(cartesian_type& xy_x, cartesian_type& xy_y, geographic_type& lp_lon, geographic_type& lp_lat) const\n {\n double L, LC, sinC;\n L= atan(sinh((xy_x*this->m_par.a - this->m_proj_parm.XS)/this->m_proj_parm.n2)/cos((xy_y*this->m_par.a - this->m_proj_parm.YS)/this->m_proj_parm.n2));\n sinC= sin((xy_y*this->m_par.a - this->m_proj_parm.YS)/this->m_proj_parm.n2)/cosh((xy_x*this->m_par.a - this->m_proj_parm.XS)/this->m_proj_parm.n2);\n LC= log(pj_tsfn(-1.0*asin(sinC),0.0,0.0));\n lp_lon= L/this->m_proj_parm.n1;\n lp_lat= -1.0*pj_phi2(exp((LC-this->m_proj_parm.c)/this->m_proj_parm.n1),this->m_par.e);\n /*fprintf(stderr,\"inv:\\nL =%16.13f\\nsinC =%16.13f\\nLC =%16.13f\\nXY(%16.4f,%16.4f)=LP(%16.13f,%16.13f)\\n\",L,sinC,LC,((xy_x/this->m_par.ra)+this->m_par.x0)/this->m_par.to_meter,((xy_y/this->m_par.ra)+this->m_par.y0)/this->m_par.to_meter,lp_lon+this->m_par.lam0,lp_lat);*/\n }\n\n static inline std::string get_name()\n {\n return \"gstmerc_spheroid\";\n }\n\n };\n\n // Gauss-Schreiber Transverse Mercator (aka Gauss-Laborde Reunion)\n template \n void setup_gstmerc(Parameters& par, par_gstmerc& proj_parm)\n {\n proj_parm.lamc= par.lam0;\n proj_parm.n1= sqrt(1.0+par.es*pow(cos(par.phi0),4.0)/(1.0-par.es));\n proj_parm.phic= asin(sin(par.phi0)/proj_parm.n1);\n proj_parm.c= log(pj_tsfn(-1.0*proj_parm.phic,0.0,0.0))\n -proj_parm.n1*log(pj_tsfn(-1.0*par.phi0,-1.0*sin(par.phi0),par.e));\n proj_parm.n2= par.k0*par.a*sqrt(1.0-par.es)/(1.0-par.es*sin(par.phi0)*sin(par.phi0));\n proj_parm.XS= 0;/* -par.x0 */\n proj_parm.YS= -1.0*proj_parm.n2*proj_parm.phic;/* -par.y0 */\n /*fprintf(stderr,\"a (m) =%16.4f\\ne =%16.13f\\nl0(rad)=%16.13f\\np0(rad)=%16.13f\\nk0 =%16.4f\\nX0 (m)=%16.4f\\nY0 (m)=%16.4f\\n\\nlC(rad)=%16.13f\\npC(rad)=%16.13f\\nc =%16.13f\\nn1 =%16.13f\\nn2 (m) =%16.4f\\nXS (m) =%16.4f\\nYS (m) =%16.4f\\n\", par.a, par.e, par.lam0, par.phi0, par.k0, par.x0, par.y0, proj_parm.lamc, proj_parm.phic, proj_parm.c, proj_parm.n1, proj_parm.n2, proj_parm.XS +par.x0, proj_parm.YS + par.y0);*/\n }\n\n }} // namespace detail::gstmerc\n #endif // doxygen\n\n /*!\n \\brief Gauss-Schreiber Transverse Mercator (aka Gauss-Laborde Reunion) projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Cylindrical\n - Spheroid\n - Ellipsoid\n \\par Projection parameters\n - lat_0: Latitude of origin\n - lon_0: Central meridian\n - k_0: Scale factor\n \\par Example\n \\image html ex_gstmerc.gif\n */\n template \n struct gstmerc_spheroid : public detail::gstmerc::base_gstmerc_spheroid\n {\n inline gstmerc_spheroid(const Parameters& par) : detail::gstmerc::base_gstmerc_spheroid(par)\n {\n detail::gstmerc::setup_gstmerc(this->m_par, this->m_proj_parm);\n }\n };\n\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail\n {\n\n // Factory entry(s)\n template \n class gstmerc_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n inline void gstmerc_init(detail::base_factory& factory)\n {\n factory.add_to_factory(\"gstmerc\", new gstmerc_entry);\n }\n\n } // namespace detail\n #endif // doxygen\n\n}}} // namespace boost::geometry::projections\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_GSTMERC_HPP\n\n", "meta": {"hexsha": "b73ef80a226e862cdc32ba4d3c58746ad08e1ebd", "size": 9230, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/gstmerc.hpp", "max_stars_repo_name": "bowlofstew/omim", "max_stars_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-01-11T05:02:05.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-11T05:02:05.000Z", "max_issues_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/gstmerc.hpp", "max_issues_repo_name": "bowlofstew/omim", "max_issues_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/gstmerc.hpp", "max_forks_repo_name": "bowlofstew/omim", "max_forks_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2018-04-04T10:55:01.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-23T18:52:06.000Z", "avg_line_length": 49.8918918919, "max_line_length": 448, "alphanum_fraction": 0.6334777898, "num_tokens": 2450, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926666143433998, "lm_q2_score": 0.38491214448393357, "lm_q1q2_score": 0.22812457749095047}} {"text": "//---------------------------------------------------------------------------//\n// MIT License\n//\n// Copyright (c) 2020-2021 Mikhail Komarov \n// Copyright (c) 2020-2021 Nikita Kaskov \n\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n//---------------------------------------------------------------------------//\n\n#ifndef FILECOIN_STORAGE_PROOFS_CORE_MERKLE_PROOF_HPP\n#define FILECOIN_STORAGE_PROOFS_CORE_MERKLE_PROOF_HPP\n\n#include \n#include \n\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n\nnamespace nil {\n namespace filecoin {\n namespace merkletree {\n template\n std::size_t base_path_length(std::size_t leaves) {\n std::size_t l;\n if (C > 0) {\n l = leaves / C / B;\n } else if (B > 0) {\n l = leaves / B;\n } else {\n l = leaves;\n }\n\n return graph_height(l) - 1;\n }\n\n template\n std::size_t compound_path_length(std::size_t leaves) {\n std::size_t len = base_path_length(leaves);\n if (B > 0) {\n len += 1;\n }\n\n if (C > 0) {\n len += 1;\n }\n\n return len;\n }\n\n template\n std::size_t compound_tree_height(std::size_t leaves) {\n // base layer\n std::size_t a = graph_height(leaves) - 1;\n\n // sub tree layer\n std::size_t b;\n if (B > 0) {\n b = B - 1;\n } else {\n b = 0;\n }\n\n // top tree layer\n std::size_t c;\n if (C > 0) {\n c = C - 1;\n } else {\n c = 0;\n }\n\n return a + b + c;\n }\n\n template\n struct Proof_basic_policy {\n typedef std::array hash_result_type;\n constexpr static const std::size_t hash_digest_size = Hash::digest_size;\n };\n\n template\n struct Proof {\n\n typedef typename MerkleTree_basic_policy::hash_result_type element;\n constexpr static const std::size_t element_size = MerkleTree_basic_policy::hash_digest_size;\n\n // Optional proofs at immediate lower level from current. Should\n // be None at the base layer.\n std::shared_ptr> sub_tree_proof;\n std::size_t top_layer_nodes; // arity of top layer\n std::size_t sub_tree_layer_nodes; // arity of sub-tree\n std::vector lemma; // layer\n std::vector path; // branch index\n\n /// Creates new MT inclusion proof\n template\n Proof(std::shared_ptr> sub_tree_proof, const std::vector &lemma, const\n std::vector &path) : sub_tree_proof(sub_tree_proof), top_layer_nodes(TopLayerArity),\n sub_tree_layer_nodes(SubTreeArity), lemma(lemma), path(path){\n if (TopLayerArity == 0 && SubTreeArity == 0) {\n BOOST_ASSERT_MSG(lemma.size() > 2, \"Invalid lemma length (short)\");\n BOOST_ASSERT_MSG(lemma.size() == utilities::get_merkle_proof_lemma_len(path.size() + 1, BaseTreeArity),\n \"Invalid lemma length\");\n }\n }\n\n /// Return proof target leaf\n element item() {\n return *lemma.begin();\n }\n\n /// Return sub tree root\n element sub_tree_root() {\n assert(sub_tree_layer_nodes > 0 && sub_tree_proof.is_some());\n return sub_tree_proof.root();\n }\n\n /// Return tree root\n element root() {\n return *(lemma.end() - 1);\n }\n\n /// Validates sub-tree proofs with the specified arity.\n bool validate_sub_tree_proof(std::size_t arity) {\n // Ensure that the sub_tree validates to the root of that\n // sub_tree.\n bool valid = sub_tree_proof.unwrap().validate::>();\n if (!valid) {\n return valid;\n }\n\n // Validate top-most/current layer\n //\n // Check that the remaining proof matches the tree root (note\n // that Proof::validate at the base layer cannot handle a\n // proof this small, so this is a version specific for what we\n // know we have in this case).\n auto a = Algorithm::default();\n a.reset();\n const auto node_count = arity;\n const auto h = {\n std::vector nodes;\n nodes.reserve(node_count);\n\n auto cur_index = 0;\n for (const auto j = 0; j < node_count; ++j) {\n if j == self.path()[0] {\n nodes.push(self.sub_tree_root().clone());\n } else {\n nodes.push(self.lemma()[cur_index].clone());\n cur_index += 1;\n }\n }\n\n if cur_index != node_count - 1 {\n return false;\n }\n\n a.multi_node(&nodes, 0)\n };\n\n return h == root();\n }\n\n /// Verifies MT inclusion proof\n bool validate() {\n if (top_layer_nodes > 0) {\n // Special Top layer handling here.\n BOOST_ASSERT_MSG(sub_tree_proof,\n \"Sub tree proof must be present for validation\");\n\n return validate_sub_tree_proof(top_layer_nodes);\n }\n\n if (sub_tree_layer_nodes > 0) {\n // Sub-tree layer handling here.\n BOOST_ASSERT_MSG(sub_tree_proof,\n \"Sub tree proof must be present for validation\");\n\n return validate_sub_tree_proof(sub_tree_layer_nodes);\n }\n\n // Base layer handling here.\n BOOST_ASSERT_MSG(sub_tree_layer_nodes == 0, \"Base layer proof must have 0 as sub-tree layer node count\");\n BOOST_ASSERT_MSG(top_layer_nodes == 0, \"Base layer proof must have 0 as top layer node count\");\n BOOST_ASSERT_MSG(!sub_tree_proof, \"Sub tree proof must be None\");\n\n std::size_t size = lemma.size();\n if (size < 2) {\n return false;\n }\n\n std::size_t branches = BaseTreeArity;\n auto a = Algorithm::default();\n auto h = this->item();\n auto path_index = 1;\n\n for (size_t i = 1; i < size - 1; i += branches - 1) {\n a.reset();\n h = {\n std::vector nodes;\n nodes.reserve(branches);\n auto cur_index = 0;\n for (j = 0; j < branches; ++j) {\n if j == self.path[path_index - 1] {\n nodes.push(h.clone());\n } else {\n nodes.push(self.lemma[i + cur_index].clone());\n cur_index += 1;\n }\n }\n\n if cur_index != branches - 1 {\n return false;\n }\n\n path_index += 1;\n a.multi_node(&nodes, i - 1)\n };\n }\n\n return h == root();\n }\n\n /// Verifies MT inclusion proof and that leaf_data is the original leaf data for which proof was generated.\n template class Algorithm>\n bool validate_with_data(leaf_data: &dyn Hashable) {\n auto a = Algorithm::default();\n leaf_data.hash(&a);\n const auto item = a.hash();\n a.reset();\n const auto leaf_hash = a.leaf(item);\n\n if (leaf_hash == item()) {\n return validate();\n } else {\n return false;\n }\n }\n };\n\n// /// Interface to abstract over the concept of Merkle Proof.\n// template::scalar_field_type>\n// struct BasicMerkleProof {\n// typedef Hash hash_type;\n// typedef FieldType field_type;\n// typedef typename field_type::value_type fr_value_type;\n//\n// constexpr static const std::size_t base_arity = BaseArity;\n// constexpr static const std::size_t sub_tree_arity = SubTreeArity;\n// constexpr static const std::size_t top_tree_arity = TopTreeArity;\n//\n// /// Try to convert a merkletree proof into this structure.\n// static BasicMerkleProof\n// try_from_proof(const Proof &p) {\n// }\n//\n// std::vector, std::size_t>> as_options() {\n// return path()\n// .iter()\n// .map(| v | {(v .0.iter().copied().map(Into::into).map(Some).collect(), Some(v .1), )})\n// .collect::>();\n// }\n//\n// std::pair, std::size_t>>>\n// into_options_with_leaf() {\n// const auto leaf = leaf();\n// const auto path = path();\n// (Some(leaf.into()),\n// path.into_iter()\n// .map(| (a, b) | {(a.iter().copied().map(Into::into).map(Some).collect(), Some(b), )})\n// .collect::>(), )\n// }\n//\n// std::vector, std::size_t>> as_pairs() {\n// for (int i = 0; i < path().size(); i++) {\n//\n// }\n// .iter()\n// .map(| v | (v .0.iter().copied().map(Into::into).collect(), v .1))\n// .collect::>();\n// }\n//\n// virtual bool verify() const = 0;\n//\n// /// Validates the MerkleProof and that it corresponds to the supplied node.\n// ///\n// /// TODO: audit performance and usage in case verification is\n// /// unnecessary based on how it's used.\n// virtual bool validate(std::size_t node) {\n// if (!verify()) {\n// return false;\n// }\n//\n// return node == path_index();\n// }\n//\n// virtual bool validate_data(const typename Hash::digest_type &data) {\n// if (!verify()) {\n// return false;\n// }\n//\n// return leaf() == data;\n// }\n//\n// virtual typename Hash::digest_type leaf() = 0;\n// virtual typename Hash::digest_type root() = 0;\n// virtual std::size_t size() = 0;\n// virtual std::vector, std::size_t>> path() = 0;\n//\n// std::size_t path_index() {\n// return std::accumulate(\n// path().begin(), path().end(), 0,\n// [&](std::size_t acc, typename std::vector,\n// std::size_t>>::value_type &val) -> std::size_t {\n// return (acc + BaseArity) + val.second;\n// });\n// }\n//\n// bool proves_challenge(std::size_t challenge) {\n// path_index() == challenge;\n// }\n//\n// /// Calcluates the exected length of the full path, given the number of leaves in the base layer.\n// std::size_t expected_len(std::size_t leaves) {\n// return compound_path_length(leaves);\n// }\n// };\n//\n// template\n// struct InclusionPath {\n// /// Calculate the root of this path, given the leaf as input.\n// typename Hash::digest_type root(const typename Hash::digest_type &leaf) {\n// using namespace nil::crypto3;\n// accumulator_set acc;\n// std::accumulate(path.begin(), path.end(), leaf,\n// [&](typename Hash::digest_type acc,\n// typename std::vector>::value_type &v) {\n//\n// });\n// auto a = H::Function::default();\n// (0..self.path.len())\n// .fold(\n// leaf, | h, height | {\n// a.reset();\n//\n// const auto index = self.path[height].index;\n// auto nodes = self.path[height].hashes.clone();\n// nodes.insert(index, h);\n//\n// a.multi_node(&nodes, height)\n// })\n// }\n//\n// std::size_t size() {\n// return path.size();\n// }\n//\n// bool empty() {\n// return path.empty();\n// }\n//\n// std::size_t path_index() {\n// return std::accumulate(\n// path.begin(), path.end(), 0,\n// [&](std::size_t acc, typename std::vector>::value_type &v) {\n// return (acc * BaseArity) + v.index;\n// });\n// }\n//\n// std::vector> path;\n// };\n//\n// template\n// struct SingleProof {\n// template class Proof>\n// static SingleProof try_from_proof(const Proof &p) {\n// return proof_to_single(p, 1);\n// }\n//\n// bool verify() {\n// return root == path.root(leaf);\n// }\n//\n// std::size_t size() {\n// return path.size() * (BaseArity - 1) + 2;\n// }\n//\n// std::vector, std::size_t>> path() {\n// return path.iter().map(| x | (x.hashes.clone(), x.index)).collect::>();\n// }\n//\n// std::size_t path_index() {\n// return path.path_index();\n// }\n//\n// /// Root of the merkle tree.\n// typename Hash::digest_type root;\n// /// The original leaf data for this prof.\n// typename Hash::digest_type leaf;\n// /// The path from leaf to root.\n// InclusionPath path;\n// };\n//\n// template\n// struct SubProof {\n// static SubProof\n// try_from_proof(const Proof &p) {\n// BOOST_ASSERT_MSG(p.sub_layer_nodes() == SubTreeArity, \"sub arity mismatch\");\n// BOOST_ASSERT_MSG(p.sub_tree_proof, \"Cannot generate sub proof without a base-proof\");\n// std::shared_ptr> base_p = p.sub_tree_proof;\n//\n// // Generate SubProof\n// typename Hash::digest_type root = p.root();\n// typename Hash::digest_type leaf = base_p.item();\n// InclusionPath base_proof =\n// extract_path(base_p.lemma(), base_p.path(), 1);\n// InclusionPath sub_proof =\n// extract_path(p.lemma(), p.path(), 0);\n//\n// return {base_proof, sub_proof, root, leaf};\n// }\n//\n// bool verify() {\n// root == sub_proof.root(base_proof.root(leaf));\n// }\n//\n// std::size_t size() {\n// return SubTreeArity;\n// }\n//\n// std::vector, std::size_t>> path() {\n// return base_proof.iter()\n// .map(| x | (x.hashes.clone(), x.index))\n// .chain(self.sub_proof.iter().map(| x | (x.hashes.clone(), x.index)))\n// .collect();\n// }\n//\n// std::size_t path_index() {\n// std::size_t base_proof_leaves = 1;\n// for (int i = 0; i < base_proof.size(); i++) {\n// base_proof_leaves *= BaseArity;\n// }\n//\n// std::size_t sub_proof_index = sub_proof.path_index();\n//\n// return (sub_proof_index * base_proof_leaves) + base_proof.path_index();\n// }\n//\n// InclusionPath base_proof;\n//\n// InclusionPath sub_proof;\n//\n// typename Hash::digest_type root;\n// /// The original leaf data for this prof.\n//\n// typename Hash::digest_type leaf;\n// };\n//\n// template\n// struct TopProof {\n// TopProof\n// try_from_proof(const Proof &p) {\n// BOOST_ASSERT_MSG(p.top_layer_nodes() == TopTreeArity, \"top arity mismatch\");\n// BOOST_ASSERT_MSG(p.sub_layer_nodes() == SubTreeArity, \"sub arity mismatch\");\n//\n// BOOST_ASSERT_MSG(p.sub_tree_proof, \"Cannot generate top proof without a sub-proof\");\n// const auto sub_p = p.sub_tree_proof;\n//\n// BOOST_ASSERT_MSG(sub_p.sub_tree_proof, \"Cannot generate top proof without a base-proof\");\n// const auto base_p = sub_p.sub_tree_proof;\n//\n// const auto root = p.root();\n// const auto leaf = base_p.item();\n//\n// return {extract_path(base_p.lemma(), base_p.path(), 1), extract_path(sub_p.lemma(), sub_p.path(), 0), extract_path(p.lemma(), p.path(), 0), root, leaf};\n// }\n//\n// bool verify() {\n// root == top_proof.root(sub_proof.root(base_proof.root(leaf)));\n// }\n//\n// std::size_t size() {\n// return TopTreeArity;\n// }\n//\n// std::vector, std::size_t>> path() {\n// return base_proof.iter()\n// .map(| x | (x.hashes.clone(), x.index))\n// .chain(self.sub_proof.iter().map(| x | (x.hashes.clone(), x.index)))\n// .chain(self.top_proof.iter().map(| x | (x.hashes.clone(), x.index)))\n// .collect();\n// }\n//\n// std::size_t path_index() {\n// std::size_t base_proof_leaves = 1;\n// for (int i = 0; i < base_proof.size(); i++) {\n// base_proof_leaves *= BaseArity;\n// }\n//\n// return (sub_proof.path_index() * base_proof_leaves) +\n// (top_proof.path_index() * base_proof_leaves * SubTreeArity) + base_proof.path_index();\n// }\n//\n// InclusionPath base_proof;\n//\n// InclusionPath sub_proof;\n//\n// InclusionPath top_proof;\n// /// Root of the merkle tree.\n//\n// typename Hash::digest_type root;\n// /// The original leaf data for this prof.\n// typename Hash::digest_type leaf;\n// };\n//\n// template\n// using ProofData = boost::variant, SubProof,\n// TopProof>;\n//\n// template\n// struct MerkleProof : public BasicMerkleProof {\n// typedef typename Hash::digest_type digest_type;\n// MerkleProof(std::size_t n) :\n// data(SingleProof(std::vector>(n), root, leaf)) {\n// }\n//\n// virtual bool verify() const override {\n// return false;\n// }\n// virtual bool validate(std::size_t node) override {\n// }\n// virtual bool validate_data(const digest_type &data) override {\n// }\n// virtual digest_type leaf() override {\n// return nullptr;\n// }\n// virtual digest_type root() override {\n// return nullptr;\n// }\n// virtual std::vector, std::size_t>> path() override {\n// return std::vector, std::size_t>>();\n// }\n//\n// ProofData data;\n// };\n//\n// /// 'lemma_start_index' is required because sub/top proofs start at\n// /// index 0 and base proofs start at index 1 (skipping the leaf at the\n// /// front)\n// template\n// InclusionPath extract_path(const std::vector &lemma,\n// const std::vector &path,\n// std::size_t lemma_start_index) {\n// std::vector> res;\n//\n// for (int i = 0; i < path.size(); i++) {\n// res.emplace_back(\n// std::vector(lemma.begin() + lemma_start_index + BaseArity * i,\n// lemma.begin() + lemma_start_index + BaseArity * (i + 1)),\n// index);\n// }\n//\n// return InclusionPath(res);\n// }\n//\n// /// Converts a merkle_light proof to a SingleProof\n// template class Proof>\n// SingleProof\n// proof_to_single(const Proof &proof, std::size_t lemma_start_index,\n// typename Hash::digest_type &sub_root = typename Hash::digest_type()) {\n// typename Hash::digest_type root = proof.root();\n// typename Hash::digest_type leaf = sub_root.emplty() ? sub_root : proof.item();\n//\n// InclusionPath path =\n// extract_path(proof.lemma(), proof.path(), lemma_start_index);\n//\n// return {path, root, leaf};\n// }\n } // namespace merkletree \n } // namespace filecoin\n} // namespace nil\n\n#endif\n", "meta": {"hexsha": "4c4305560fe534f1b45dea9afd9740f21208f3d3", "size": 27699, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/storage/include/nil/filecoin/storage/proofs/core/merkle/old_proof.hpp", "max_stars_repo_name": "NilFoundation/crypto3-fil-proofs", "max_stars_repo_head_hexsha": "1fd78ad608278a1ed62fb29b0a077347b74a55f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "libs/storage/include/nil/filecoin/storage/proofs/core/merkle/old_proof.hpp", "max_issues_repo_name": "NilFoundation/crypto3-fil-proofs", "max_issues_repo_head_hexsha": "1fd78ad608278a1ed62fb29b0a077347b74a55f1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4.0, "max_issues_repo_issues_event_min_datetime": "2021-09-06T13:07:46.000Z", "max_issues_repo_issues_event_max_datetime": "2021-11-16T12:38:09.000Z", "max_forks_repo_path": "libs/storage/include/nil/filecoin/storage/proofs/core/merkle/old_proof.hpp", "max_forks_repo_name": "NilFoundation/crypto3-fil-proofs", "max_forks_repo_head_hexsha": "1fd78ad608278a1ed62fb29b0a077347b74a55f1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 45.4827586207, "max_line_length": 231, "alphanum_fraction": 0.4707751182, "num_tokens": 5623, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266116, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.22812401426175666}} {"text": "// Boost.Geometry - gis-projections (based on PROJ4)\n\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\n\n// This file was modified by Oracle on 2017, 2018.\n// Modifications copyright (c) 2017-2018, Oracle and/or its affiliates.\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\n\n// Last updated version of proj: 5.0.0\n\n// Original copyright notice:\n\n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#ifndef BOOST_GEOMETRY_PROJECTIONS_BONNE_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_BONNE_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \n\nnamespace boost { namespace geometry\n{\n\nnamespace projections\n{\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail { namespace bonne\n {\n\n static const double epsilon10 = 1e-10;\n\n template \n struct par_bonne\n {\n T phi1;\n T cphi1;\n T am1;\n T m1;\n detail::en en;\n };\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_bonne_ellipsoid\n : public base_t_fi, T, Parameters>\n {\n par_bonne m_proj_parm;\n\n inline base_bonne_ellipsoid(const Parameters& par)\n : base_t_fi, T, Parameters>(*this, par)\n {}\n\n // FORWARD(e_forward) ellipsoid\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\n inline void fwd(T const& lp_lon, T const& lp_lat, T& xy_x, T& xy_y) const\n {\n T rh, E, c;\n\n rh = this->m_proj_parm.am1 + this->m_proj_parm.m1 - pj_mlfn(lp_lat, E = sin(lp_lat), c = cos(lp_lat), this->m_proj_parm.en);\n E = c * lp_lon / (rh * sqrt(1. - this->m_par.es * E * E));\n xy_x = rh * sin(E);\n xy_y = this->m_proj_parm.am1 - rh * cos(E);\n }\n\n // INVERSE(e_inverse) ellipsoid\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\n inline void inv(T const& xy_x, T xy_y, T& lp_lon, T& lp_lat) const\n {\n static const T half_pi = detail::half_pi();\n\n T s, rh;\n\n rh = boost::math::hypot(xy_x, xy_y = this->m_proj_parm.am1 - xy_y);\n lp_lat = pj_inv_mlfn(this->m_proj_parm.am1 + this->m_proj_parm.m1 - rh, this->m_par.es, this->m_proj_parm.en);\n if ((s = fabs(lp_lat)) < half_pi) {\n s = sin(lp_lat);\n lp_lon = rh * atan2(xy_x, xy_y) *\n sqrt(1. - this->m_par.es * s * s) / cos(lp_lat);\n } else if (fabs(s - half_pi) <= epsilon10)\n lp_lon = 0.;\n else\n BOOST_THROW_EXCEPTION( projection_exception(error_tolerance_condition) );\n }\n\n static inline std::string get_name()\n {\n return \"bonne_ellipsoid\";\n }\n\n };\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_bonne_spheroid\n : public base_t_fi, T, Parameters>\n {\n par_bonne m_proj_parm;\n\n inline base_bonne_spheroid(const Parameters& par)\n : base_t_fi, T, Parameters>(*this, par)\n {}\n\n // FORWARD(s_forward) spheroid\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\n inline void fwd(T const& lp_lon, T const& lp_lat, T& xy_x, T& xy_y) const\n {\n T E, rh;\n\n rh = this->m_proj_parm.cphi1 + this->m_proj_parm.phi1 - lp_lat;\n if (fabs(rh) > epsilon10) {\n xy_x = rh * sin(E = lp_lon * cos(lp_lat) / rh);\n xy_y = this->m_proj_parm.cphi1 - rh * cos(E);\n } else\n xy_x = xy_y = 0.;\n }\n\n // INVERSE(s_inverse) spheroid\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\n inline void inv(T const& xy_x, T xy_y, T& lp_lon, T& lp_lat) const\n {\n static const T half_pi = detail::half_pi();\n\n T rh;\n\n rh = boost::math::hypot(xy_x, xy_y = this->m_proj_parm.cphi1 - xy_y);\n lp_lat = this->m_proj_parm.cphi1 + this->m_proj_parm.phi1 - rh;\n if (fabs(lp_lat) > half_pi) {\n BOOST_THROW_EXCEPTION( projection_exception(error_tolerance_condition) );\n }\n if (fabs(fabs(lp_lat) - half_pi) <= epsilon10)\n lp_lon = 0.;\n else\n lp_lon = rh * atan2(xy_x, xy_y) / cos(lp_lat);\n }\n\n static inline std::string get_name()\n {\n return \"bonne_spheroid\";\n }\n\n };\n\n // Bonne (Werner lat_1=90)\n template \n inline void setup_bonne(Params const& params, Parameters& par, par_bonne& proj_parm)\n {\n static const T half_pi = detail::half_pi();\n\n T c;\n\n proj_parm.phi1 = pj_get_param_r(params, \"lat_1\", srs::dpar::lat_1);\n if (fabs(proj_parm.phi1) < epsilon10)\n BOOST_THROW_EXCEPTION( projection_exception(error_lat1_is_zero) );\n\n if (par.es != 0.0) {\n proj_parm.en = pj_enfn(par.es);\n proj_parm.m1 = pj_mlfn(proj_parm.phi1, proj_parm.am1 = sin(proj_parm.phi1),\n c = cos(proj_parm.phi1), proj_parm.en);\n proj_parm.am1 = c / (sqrt(1. - par.es * proj_parm.am1 * proj_parm.am1) * proj_parm.am1);\n } else {\n if (fabs(proj_parm.phi1) + epsilon10 >= half_pi)\n proj_parm.cphi1 = 0.;\n else\n proj_parm.cphi1 = 1. / tan(proj_parm.phi1);\n }\n }\n\n }} // namespace detail::bonne\n #endif // doxygen\n\n /*!\n \\brief Bonne (Werner lat_1=90) projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Conic\n - Spheroid\n - Ellipsoid\n \\par Projection parameters\n - lat_1: Latitude of first standard parallel (degrees)\n \\par Example\n \\image html ex_bonne.gif\n */\n template \n struct bonne_ellipsoid : public detail::bonne::base_bonne_ellipsoid\n {\n template \n inline bonne_ellipsoid(Params const& params, Parameters const& par)\n : detail::bonne::base_bonne_ellipsoid(par)\n {\n detail::bonne::setup_bonne(params, this->m_par, this->m_proj_parm);\n }\n };\n\n /*!\n \\brief Bonne (Werner lat_1=90) projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Conic\n - Spheroid\n - Ellipsoid\n \\par Projection parameters\n - lat_1: Latitude of first standard parallel (degrees)\n \\par Example\n \\image html ex_bonne.gif\n */\n template \n struct bonne_spheroid : public detail::bonne::base_bonne_spheroid\n {\n template \n inline bonne_spheroid(Params const& params, Parameters const& par)\n : detail::bonne::base_bonne_spheroid(par)\n {\n detail::bonne::setup_bonne(params, this->m_par, this->m_proj_parm);\n }\n };\n\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail\n {\n\n // Static projection\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_STATIC_PROJECTION(srs::spar::proj_bonne, bonne_spheroid, bonne_ellipsoid)\n\n // Factory entry(s)\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_ENTRY_FI2(bonne_entry, bonne_spheroid, bonne_ellipsoid)\n\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_BEGIN(bonne_init)\n {\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_ENTRY(bonne, bonne_entry);\n }\n\n } // namespace detail\n #endif // doxygen\n\n} // namespace projections\n\n}} // namespace boost::geometry\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_BONNE_HPP\n\n", "meta": {"hexsha": "70bdc87fd23511ac525367ac8bfab19b77081c98", "size": 11032, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/boost/geometry/srs/projections/proj/bonne.hpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 12278.0, "max_stars_repo_stars_event_min_datetime": "2015-01-29T17:11:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T21:12:00.000Z", "max_issues_repo_path": "3rdParty/boost/1.71.0/boost/geometry/srs/projections/proj/bonne.hpp", "max_issues_repo_name": "rajeev02101987/arangodb", "max_issues_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 9469.0, "max_issues_repo_issues_event_min_datetime": "2015-01-30T05:33:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T16:17:21.000Z", "max_forks_repo_path": "3rdParty/boost/1.71.0/boost/geometry/srs/projections/proj/bonne.hpp", "max_forks_repo_name": "rajeev02101987/arangodb", "max_forks_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 892.0, "max_forks_repo_forks_event_min_datetime": "2015-01-29T16:26:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T07:44:30.000Z", "avg_line_length": 39.4, "max_line_length": 144, "alphanum_fraction": 0.5833937636, "num_tokens": 2548, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.37754065479083276, "lm_q1q2_score": 0.22800881995392666}} {"text": "/****************************************************************************\n * Copyright (c) 2021 by the Picasso authors *\n * All rights reserved. *\n * *\n * This file is part of the Picasso library. Picasso is distributed under a *\n * BSD 3-clause license. For the licensing terms see the LICENSE file in *\n * the top-level directory. *\n * *\n * SPDX-License-Identifier: BSD-3-Clause *\n ****************************************************************************/\n\n#ifndef PICASSO_FACETGEOMETRY_HPP\n#define PICASSO_FACETGEOMETRY_HPP\n\n#include \n\n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\nnamespace Picasso\n{\n//---------------------------------------------------------------------------//\ntemplate \nstruct FacetGeometryData\n{\n // Get the number of volumes.\n KOKKOS_FUNCTION\n int numVolume() const { return volume_offsets.extent( 0 ); }\n\n // Given a local volume id get the facets associated with the volume.\n KOKKOS_FUNCTION\n Kokkos::View\n volumeFacets( const int volume_id ) const\n {\n Kokkos::pair facet_bounds(\n ( 0 == volume_id ) ? 0 : volume_offsets( volume_id - 1 ),\n volume_offsets( volume_id ) );\n return Kokkos::subview( volume_facets, facet_bounds, Kokkos::ALL(),\n Kokkos::ALL() );\n }\n\n // Get the number of surfaces.\n KOKKOS_FUNCTION\n int numSurface() const { return surface_offsets.extent( 0 ); }\n\n // Given a local surface id get the facets associated with the surface.\n KOKKOS_FUNCTION\n Kokkos::View\n surfaceFacets( const int surface_id ) const\n {\n Kokkos::pair facet_bounds(\n ( 0 == surface_id ) ? 0 : surface_offsets( surface_id - 1 ),\n surface_offsets( surface_id ) );\n return Kokkos::subview( surface_facets, facet_bounds, Kokkos::ALL(),\n Kokkos::ALL() );\n }\n\n // Volume facets. Ordered as (facet,vector,dim) where vector=0,1,2 are the\n // vertices and vector=3 is the unit normal facing outward from the\n // volume.\n Kokkos::View volume_facets;\n\n // Volume face offsets. Inclusive scan of volume counts giving the offset\n // into the facet array for each volume.\n Kokkos::View volume_offsets;\n\n // Surface facets. Ordered as (facet,vector,dim) where vector=0,1,2 are the\n // vertices and vector=3 is the unit normal facing outward from the\n // surface.\n Kokkos::View surface_facets;\n\n // Surface face offsets. Inclusive scan of surface counts giving the offset\n // into the facet array for each surface.\n Kokkos::View surface_offsets;\n\n // Local id of the volume the representes the global axis-aligned bounding\n // box.\n int global_bounding_volume_id;\n\n // Axis aligned bounding box for all volumes.\n Kokkos::View volume_bounding_boxes;\n};\n\n//---------------------------------------------------------------------------//\ntemplate \nclass FacetGeometry\n{\n public:\n // Compute the axis-aligned bounding box of a volume.\n struct BoundingBoxReduce\n {\n typedef float value_type[];\n typedef Kokkos::View facet_view_type;\n typedef Kokkos::View offset_view_type;\n typedef typename facet_view_type::size_type size_type;\n size_type value_count;\n\n facet_view_type facets;\n offset_view_type offsets;\n int volume_id;\n\n BoundingBoxReduce( const facet_view_type& f, const offset_view_type& o,\n int v )\n : value_count( 6 )\n , facets( f )\n , offsets( o )\n , volume_id( v )\n {\n }\n\n KOKKOS_FUNCTION\n void operator()( const size_type facet_id, value_type result ) const\n {\n auto f_start = ( volume_id == 0 ) ? 0 : offsets( volume_id - 1 );\n auto f = f_start + facet_id;\n result[0] =\n fmin( result[0],\n fmin( facets( f, 0, 0 ),\n fmin( facets( f, 1, 0 ), facets( f, 2, 0 ) ) ) );\n result[1] =\n fmin( result[1],\n fmin( facets( f, 0, 1 ),\n fmin( facets( f, 1, 1 ), facets( f, 2, 1 ) ) ) );\n result[2] =\n fmin( result[2],\n fmin( facets( f, 0, 2 ),\n fmin( facets( f, 1, 2 ), facets( f, 2, 2 ) ) ) );\n result[3] =\n fmax( result[3],\n fmax( facets( f, 0, 0 ),\n fmax( facets( f, 1, 0 ), facets( f, 2, 0 ) ) ) );\n result[4] =\n fmax( result[4],\n fmax( facets( f, 0, 1 ),\n fmax( facets( f, 1, 1 ), facets( f, 2, 1 ) ) ) );\n result[5] =\n fmax( result[5],\n fmax( facets( f, 0, 2 ),\n fmax( facets( f, 1, 2 ), facets( f, 2, 2 ) ) ) );\n }\n\n KOKKOS_FUNCTION\n void join( volatile value_type dst,\n const volatile value_type src ) const\n {\n dst[0] = fmin( dst[0], src[0] );\n dst[1] = fmin( dst[1], src[1] );\n dst[2] = fmin( dst[2], src[2] );\n dst[3] = fmax( dst[3], src[3] );\n dst[4] = fmax( dst[4], src[4] );\n dst[5] = fmax( dst[5], src[5] );\n }\n\n KOKKOS_FUNCTION\n void init( value_type v ) const\n {\n v[0] = FLT_MAX;\n v[1] = FLT_MAX;\n v[2] = FLT_MAX;\n v[3] = -FLT_MAX;\n v[4] = -FLT_MAX;\n v[5] = -FLT_MAX;\n }\n };\n\n public:\n using memory_space = MemorySpace;\n\n // Default constructor.\n FacetGeometry() = default;\n\n // Create the geometry from an ASCII STL file.\n template \n FacetGeometry( const boost::property_tree::ptree& ptree,\n const ExecutionSpace& exec_space )\n {\n // Get the geometry parameters.\n const auto& params = ptree.get_child( \"geometry\" );\n\n // Read the stl file and create the facet geometry.\n auto stl_ascii_filename = params.get( \"stl_file\" );\n\n // Containers.\n std::vector volume_ids;\n std::vector surface_ids;\n std::vector volume_facets;\n std::vector surface_facets;\n\n // Load the file.\n std::ifstream file( stl_ascii_filename );\n if ( !file.is_open() )\n throw std::runtime_error( \"Unable to open STL ASCII file\" );\n\n // Read the file.\n std::string buffer;\n std::vector tokens;\n tokens.reserve( 10 );\n bool read_volume = false;\n bool read_surface = false;\n while ( !file.eof() )\n {\n // Get the current line.\n std::getline( file, buffer );\n\n // Break the line up into tokens.\n std::istringstream line( buffer );\n tokens.resize( 0 );\n while ( !line.eof() )\n {\n tokens.push_back( std::string() );\n line >> tokens.back();\n }\n\n // Parse the line.\n if ( tokens.size() > 0 )\n {\n // New solid.\n if ( tokens[0].compare( \"solid\" ) == 0 )\n {\n if ( tokens.size() != 3 )\n throw std::runtime_error(\n \"STL READER: Expected 3 solid line entries\" );\n\n // New volume.\n if ( tokens[1].compare( \"Volume\" ) == 0 ||\n tokens[1].compare( \"Body\" ) == 0 )\n {\n volume_ids.push_back( std::atoi( tokens[2].c_str() ) );\n _volume_facet_count.push_back( 0 );\n read_volume = true;\n }\n\n // New surface.\n else if ( tokens[1].compare( \"Surface\" ) == 0 )\n {\n surface_ids.push_back( std::atoi( tokens[2].c_str() ) );\n _surface_facet_count.push_back( 0 );\n read_surface = true;\n }\n\n else\n {\n throw std::runtime_error(\n \"STL READER: Solids execpted to be Volume/Body or \"\n \"Surface\" );\n }\n }\n\n // Read facet.\n else if ( tokens[0].compare( \"facet\" ) == 0 )\n {\n if ( tokens.size() != 5 )\n throw std::runtime_error(\n \"STL READER: Expected 5 facet line entries\" );\n\n // Volume facet.\n if ( read_volume )\n {\n ++_volume_facet_count.back();\n }\n\n // Surface facet.\n else if ( read_surface )\n {\n ++_surface_facet_count.back();\n }\n }\n\n // Read vertex.\n else if ( tokens[0].compare( \"vertex\" ) == 0 )\n {\n if ( tokens.size() != 4 )\n throw std::runtime_error(\n \"STL READER: Expected 4 vertex line entries\" );\n\n // Volume vertex coordinates.\n if ( read_volume )\n {\n volume_facets.push_back(\n std::atof( tokens[1].c_str() ) );\n volume_facets.push_back(\n std::atof( tokens[2].c_str() ) );\n volume_facets.push_back(\n std::atof( tokens[3].c_str() ) );\n }\n\n // Surface vertex coordinates.\n if ( read_surface )\n {\n surface_facets.push_back(\n std::atof( tokens[1].c_str() ) );\n surface_facets.push_back(\n std::atof( tokens[2].c_str() ) );\n surface_facets.push_back(\n std::atof( tokens[3].c_str() ) );\n }\n }\n\n // Finish reading a solid.\n else if ( tokens[0].compare( \"endsolid\" ) == 0 )\n {\n read_volume = false;\n read_surface = false;\n }\n }\n }\n\n // Close the file.\n file.close();\n\n // Put volume data on device.\n putFileDataOnDevice( volume_ids, _volume_facet_count, volume_facets,\n _volume_ids, _data.volume_facets,\n _data.volume_offsets );\n\n // Put surface data on device.\n putFileDataOnDevice( surface_ids, _surface_facet_count, surface_facets,\n _surface_ids, _data.surface_facets,\n _data.surface_offsets );\n\n // Get the volume id of the global bounding box. The user is required\n // to make an axis-aligned bounding box of their geometry that defines\n // the global bounds of the problem. The user input is the global id\n // of this volume.\n _data.global_bounding_volume_id =\n localVolumeId( params.get( \"global_bounding_volume_id\" ) );\n\n // Compute the bounding boxes of all the volumes.\n _data.volume_bounding_boxes = Kokkos::View(\n Kokkos::ViewAllocateWithoutInitializing( \"volume_bounding_boxes\" ),\n volume_ids.size() );\n auto host_boxes = Kokkos::create_mirror_view(\n Kokkos::HostSpace(), _data.volume_bounding_boxes );\n for ( int v = 0; v < _data.numVolume(); ++v )\n {\n BoundingBoxReduce reducer( _data.volume_facets,\n _data.volume_offsets, v );\n float box[6];\n Kokkos::parallel_reduce(\n \"Picasso::FacetGeometry::VolumeBoundingBox\",\n Kokkos::RangePolicy( exec_space, 0,\n _volume_facet_count[v] ),\n reducer, box );\n for ( int i = 0; i < 6; ++i )\n host_boxes( v, i ) = box[i];\n }\n Kokkos::deep_copy( _data.volume_bounding_boxes, host_boxes );\n\n // Extract the global bounding box to the host.\n auto global_box = Kokkos::subview(\n host_boxes, _data.global_bounding_volume_id, Kokkos::ALL() );\n for ( int i = 0; i < 6; ++i )\n _global_bounding_box[i] = global_box( i );\n }\n\n // Given a global volume id get the local volume id.\n int localVolumeId( const int global_id ) const\n {\n return _volume_ids.find( global_id )->second;\n }\n\n // Given a global surface id get the local surface id.\n int localSurfaceId( const int global_id ) const\n {\n return _surface_ids.find( global_id )->second;\n }\n\n // Given a local volume id get the number of facets that compose the\n // volume.\n int numVolumeFacet( const int local_id ) const\n {\n return _volume_facet_count[local_id];\n }\n\n // Given a local surface id get the number of facets that compose the\n // surface.\n int numSurfaceFacet( const int local_id ) const\n {\n return _surface_facet_count[local_id];\n }\n\n // Get the global bounding box.\n const Kokkos::Array& globalBoundingBox() const\n {\n return _global_bounding_box;\n }\n\n // Get the geometry data.\n const FacetGeometryData& data() const { return _data; }\n\n private:\n // Put file data on device.\n void putFileDataOnDevice(\n const std::vector& solid_ids,\n const std::vector& solid_facet_counts,\n const std::vector& solid_facets,\n std::unordered_map& id_map,\n Kokkos::View& device_facets,\n Kokkos::View& device_offsets )\n {\n // Allocate solid data.\n int num_solid = solid_ids.size();\n device_offsets = Kokkos::View(\n Kokkos::ViewAllocateWithoutInitializing( \"offsets\" ), num_solid );\n\n int num_solid_facet = solid_facets.size() / 9;\n device_facets = Kokkos::View(\n Kokkos::ViewAllocateWithoutInitializing( \"facets\" ),\n num_solid_facet );\n\n // Compute the offsets for the facet-to-solid mapping.\n auto host_offsets =\n Kokkos::create_mirror_view( Kokkos::HostSpace(), device_offsets );\n for ( int i = 0; i < num_solid; ++i )\n {\n // Map global solid ids to local ids.\n id_map.emplace( solid_ids[i], i );\n\n // Compute the offset via inclusive scan.\n host_offsets( i ) =\n ( 0 == i ) ? solid_facet_counts[i]\n : solid_facet_counts[i] + host_offsets( i - 1 );\n }\n\n // Build the facets.\n auto host_facets =\n Kokkos::create_mirror_view( Kokkos::HostSpace(), device_facets );\n for ( int f = 0; f < num_solid_facet; ++f )\n {\n // Extract the vertices.\n for ( int v = 0; v < 3; ++v )\n for ( int d = 0; d < 3; ++d )\n host_facets( f, v, d ) = solid_facets[9 * f + 3 * v + d];\n\n // Compute the normals.\n float v10[3];\n float v20[3];\n for ( int d = 0; d < 3; ++d )\n {\n v10[d] = host_facets( f, 1, d ) - host_facets( f, 0, d );\n v20[d] = host_facets( f, 2, d ) - host_facets( f, 0, d );\n }\n host_facets( f, 3, 0 ) = v10[1] * v20[2] - v10[2] * v20[1];\n host_facets( f, 3, 1 ) = v10[2] * v20[0] - v10[0] * v20[2];\n host_facets( f, 3, 2 ) = v10[0] * v20[1] - v10[1] * v20[0];\n\n // Scale them to make it a unit normal.\n float nmag =\n std::sqrt( host_facets( f, 3, 0 ) * host_facets( f, 3, 0 ) +\n host_facets( f, 3, 1 ) * host_facets( f, 3, 1 ) +\n host_facets( f, 3, 2 ) * host_facets( f, 3, 2 ) );\n for ( int d = 0; d < 3; ++d )\n host_facets( f, 3, d ) /= nmag;\n }\n\n // Copy to device.\n Kokkos::deep_copy( device_facets, host_facets );\n Kokkos::deep_copy( device_offsets, host_offsets );\n }\n\n public:\n // Volume ids - global-to-local mapping.\n std::unordered_map _volume_ids;\n\n // Surface ids - global-to-local mapping.\n std::unordered_map _surface_ids;\n\n // Volume facet counts.\n std::vector _volume_facet_count;\n\n // Surface facet counts.\n std::vector _surface_facet_count;\n\n // Global bounding box.\n Kokkos::Array _global_bounding_box;\n\n // Data.\n FacetGeometryData _data;\n};\n\n//---------------------------------------------------------------------------//\n// Facet Geometry operations.\n//---------------------------------------------------------------------------//\nnamespace FacetGeometryOps\n{\n//---------------------------------------------------------------------------//\n// Project a point x along the given direction, r, and determine if it\n// projects to the facet. If returns true, the projection solution is\n// valid.\n//\n// y[0] = projection barycentric coordinate 1\n// y[1] = projection barycentric coordinate 2\n// y[2] = distance to triangle\ntemplate \nKOKKOS_FUNCTION bool pointFacetProjection( const float x[3], const float r[3],\n const FacetView& facets, const int f,\n float y[3] )\n{\n // Build the system of equations to solve for intersection. Fire the ray\n // in the Y direction - this choice is arbitary.\n Mat3 A;\n Vec3 b;\n for ( int i = 0; i < 3; ++i )\n {\n A( i, 0 ) = facets( f, 1, i ) - facets( f, 0, i );\n A( i, 1 ) = facets( f, 2, i ) - facets( f, 0, i );\n A( i, 2 ) = -r[i];\n b( i ) = x[i] - facets( f, 0, i );\n }\n\n // Check the determinant of the matrix. If zero then the ray is parallel\n // so no intersection.\n auto det_A = !A;\n if ( 0.0 == det_A )\n return false;\n\n // Solve the system.\n VecView3 y_view( y, 1 );\n y_view = A ^ b;\n\n // Check the solution for inclusion in the triangle.\n return ( y[0] >= 0.0 && y[1] >= 0.0 && y[0] + y[1] <= 1.0 );\n}\n\n//---------------------------------------------------------------------------//\n// Fire a ray from point x along the given direction, r, and determine if it\n// intersects the facet.\ntemplate \nKOKKOS_FUNCTION bool rayFacetIntersect( const float x[3], const float r[3],\n const FacetView& facets, const int f )\n{\n // Project the point and check the distance.\n float y[3];\n auto projects = pointFacetProjection( x, r, facets, f, y );\n return projects && ( y[2] > 0.0 );\n}\n\n//---------------------------------------------------------------------------//\n// Compute the signed distance from a point to the plane defined by a facet.\ntemplate \nKOKKOS_FUNCTION float\ndistanceToFacetPlane( const float x[3], const FacetView& facets, const int f )\n{\n return ( x[0] - facets( f, 0, 0 ) ) * facets( f, 3, 0 ) +\n ( x[1] - facets( f, 0, 1 ) ) * facets( f, 3, 1 ) +\n ( x[2] - facets( f, 0, 2 ) ) * facets( f, 3, 2 );\n}\n\n//---------------------------------------------------------------------------//\n// Determine if a point is in a volume represented by a view of facets.\ntemplate \nKOKKOS_FUNCTION bool pointInVolume( const float x[3],\n const FacetView& volume_facets )\n{\n // The choice of ray direction is arbitrary so generate a random one. This\n // could potentially help with robustness as floating point noise may\n // avoid edge and vertex intersections which could lead to multiple\n // positive intersections and therefore an incorrect point-in-volume\n // determination. The facet geometry has no connectivity so it is\n // difficult to resolve multiple intersections without accumulating the\n // intersection points and checking for duplicates within some tolerance.\n //\n // Note: Duan has indicated that doing tests with 3 different random rays\n // has been enough to be robust as one is likely to pass out of the 3 if\n // is a true intersection.\n using rand_type =\n Kokkos::Random_XorShift64;\n rand_type rng( 0 );\n float r[3] = { Kokkos::rand::draw( rng ),\n Kokkos::rand::draw( rng ),\n Kokkos::rand::draw( rng ) };\n float r_mag_inv = 1.0 / sqrt( r[0] * r[0] + r[1] * r[1] + r[2] * r[2] );\n for ( int d = 0; d < 3; ++d )\n r[d] *= r_mag_inv;\n\n // Fire rays through each facet and count intersections. If an\n // odd number of intersections, the point is in the volume. This works for\n // convex and non-convex volumes.\n int count = 0;\n for ( std::size_t f = 0; f < volume_facets.extent( 0 ); ++f )\n if ( rayFacetIntersect( x, r, volume_facets, f ) )\n ++count;\n return ( 1 == count % 2 );\n}\n\n//---------------------------------------------------------------------------//\n// Given a point determine the volume in the given facet geometry in which it\n// is located.If it is in the implicit complement, return -1. If it is outside\n// of the entire domain, return -2;\ntemplate \nKOKKOS_FUNCTION int locatePoint( const float x[3],\n const FacetGeometryData& geom )\n{\n // Get the global bounding volume id.\n int gbv = geom.global_bounding_volume_id;\n\n // Start by checking that the point is in the global bounding volume.\n if ( geom.volume_bounding_boxes( gbv, 0 ) <= x[0] &&\n geom.volume_bounding_boxes( gbv, 1 ) <= x[1] &&\n geom.volume_bounding_boxes( gbv, 2 ) <= x[2] &&\n geom.volume_bounding_boxes( gbv, 3 ) >= x[0] &&\n geom.volume_bounding_boxes( gbv, 4 ) >= x[1] &&\n geom.volume_bounding_boxes( gbv, 5 ) >= x[2] )\n {\n // Check each volume except the global bounding volume for point\n // inclusion.\n for ( int v = 0; v < geom.numVolume(); ++v )\n {\n if ( v != gbv )\n {\n // First check if the point is in the axis-aligned\n // bounding box of the volume.\n if ( geom.volume_bounding_boxes( v, 0 ) <= x[0] &&\n geom.volume_bounding_boxes( v, 1 ) <= x[1] &&\n geom.volume_bounding_boxes( v, 2 ) <= x[2] &&\n geom.volume_bounding_boxes( v, 3 ) >= x[0] &&\n geom.volume_bounding_boxes( v, 4 ) >= x[1] &&\n geom.volume_bounding_boxes( v, 5 ) >= x[2] )\n {\n // If in the bounding box, check against each volume\n // facet for point inclusion.\n if ( pointInVolume( x, geom.volumeFacets( v ) ) )\n {\n return v;\n }\n }\n }\n }\n\n // If the point was not in any volume it is in the implicit\n // complement so return -1.\n return -1;\n }\n\n // Otherwise point is outside of the global domain including the\n // implicit complement so return -2.\n return -2;\n}\n\n//---------------------------------------------------------------------------//\n\n} // end namespace FacetGeometryOps\n\n//---------------------------------------------------------------------------//\n\n} // end namespace Picasso\n\n#endif // end PICASSO_FACETGEOMETRY_HPP\n", "meta": {"hexsha": "0ebc777779a762953b9c3219c70dbb18d9709798", "size": 24901, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/Picasso_FacetGeometry.hpp", "max_stars_repo_name": "picassodev/picasso", "max_stars_repo_head_hexsha": "540771ac54454c6abcd29e3f0a24328e4ad89775", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2021-02-17T14:59:09.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-29T23:29:52.000Z", "max_issues_repo_path": "src/Picasso_FacetGeometry.hpp", "max_issues_repo_name": "picassodev/picasso", "max_issues_repo_head_hexsha": "540771ac54454c6abcd29e3f0a24328e4ad89775", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 48.0, "max_issues_repo_issues_event_min_datetime": "2021-02-17T17:24:12.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-18T14:12:02.000Z", "max_forks_repo_path": "src/Picasso_FacetGeometry.hpp", "max_forks_repo_name": "picassodev/picasso", "max_forks_repo_head_hexsha": "540771ac54454c6abcd29e3f0a24328e4ad89775", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2021-02-17T14:39:58.000Z", "max_forks_repo_forks_event_max_datetime": "2021-10-05T18:42:21.000Z", "avg_line_length": 37.901065449, "max_line_length": 80, "alphanum_fraction": 0.4996184892, "num_tokens": 5872, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704796847395, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.2279488408667704}} {"text": "/*!\n \\file gpp_python_expected_improvement.cpp\n \\rst\n This file has the logic to invoke C++ functions pertaining to expected improvement from Python.\n The data flow follows the basic 4 step from gpp_python_common.hpp.\n\n .. NoteL: several internal functions of this source file are only called from ``Export*()`` functions,\n so their description, inputs, outputs, etc. comments have been moved. These comments exist in\n ``Export*()`` as Python docstrings, so we saw no need to repeat ourselves.\n\\endrst*/\n// This include violates the Google Style Guide by placing an \"other\" system header ahead of C and C++ system headers. However,\n// it needs to be at the top, otherwise compilation fails on some systems with some versions of python: OS X, python 2.7.3.\n// Putting this include first prevents pyport from doing something illegal in C++; reference: http://bugs.python.org/issue10910\n#include \"Python.h\" // NOLINT(build/include)\n\n#include \"gpp_python_expected_improvement.hpp\"\n\n// NOLINT-ing the C, C++ header includes as well; otherwise cpplint gets confused\n#include // NOLINT(build/include_order)\n#include // NOLINT(build/include_order)\n\n#include // NOLINT(build/include_order)\n#include // NOLINT(build/include_order)\n#include // NOLINT(build/include_order)\n#include // NOLINT(build/include_order)\n#include // NOLINT(build/include_order)\n#include // NOLINT(build/include_order)\n#include // NOLINT(build/include_order)\n\n#include \"gpp_common.hpp\"\n#include \"gpp_domain.hpp\"\n#include \"gpp_exception.hpp\"\n#include \"gpp_expected_improvement_gpu.hpp\"\n#include \"gpp_geometry.hpp\"\n#include \"gpp_heuristic_expected_improvement_optimization.hpp\"\n#include \"gpp_math.hpp\"\n#include \"gpp_optimization.hpp\"\n#include \"gpp_optimizer_parameters.hpp\"\n#include \"gpp_python_common.hpp\"\n\nnamespace optimal_learning {\n\nnamespace {\n\ndouble ComputeExpectedImprovementWrapper(const GaussianProcess& gaussian_process,\n const boost::python::list& points_to_sample,\n const boost::python::list& points_being_sampled,\n int num_to_sample, int num_being_sampled,\n int max_int_steps, double best_so_far,\n bool force_monte_carlo,\n RandomnessSourceContainer& randomness_source) {\n PythonInterfaceInputContainer input_container(points_to_sample, points_being_sampled,\n gaussian_process.dim(), num_to_sample, num_being_sampled);\n\n bool configure_for_gradients = false;\n if ((num_to_sample == 1) && (num_being_sampled == 0) && (force_monte_carlo == false)) {\n OnePotentialSampleExpectedImprovementEvaluator ei_evaluator(gaussian_process, best_so_far);\n OnePotentialSampleExpectedImprovementEvaluator::StateType ei_state(ei_evaluator,\n input_container.points_to_sample.data(),\n configure_for_gradients);\n return ei_evaluator.ComputeExpectedImprovement(&ei_state);\n } else {\n ExpectedImprovementEvaluator ei_evaluator(gaussian_process, max_int_steps, best_so_far);\n ExpectedImprovementEvaluator::StateType ei_state(ei_evaluator, input_container.points_to_sample.data(),\n input_container.points_being_sampled.data(),\n input_container.num_to_sample,\n input_container.num_being_sampled,\n configure_for_gradients,\n randomness_source.normal_rng_vec.data());\n return ei_evaluator.ComputeExpectedImprovement(&ei_state);\n }\n}\n\nboost::python::list ComputeGradExpectedImprovementWrapper(const GaussianProcess& gaussian_process,\n const boost::python::list& points_to_sample,\n const boost::python::list& points_being_sampled,\n int num_to_sample, int num_being_sampled,\n int max_int_steps, double best_so_far,\n bool force_monte_carlo,\n RandomnessSourceContainer& randomness_source) {\n PythonInterfaceInputContainer input_container(points_to_sample, points_being_sampled, gaussian_process.dim(),\n num_to_sample, num_being_sampled);\n\n std::vector grad_EI(num_to_sample*input_container.dim);\n bool configure_for_gradients = true;\n if ((num_to_sample == 1) && (num_being_sampled == 0) && (force_monte_carlo == false)) {\n OnePotentialSampleExpectedImprovementEvaluator ei_evaluator(gaussian_process, best_so_far);\n OnePotentialSampleExpectedImprovementEvaluator::StateType ei_state(ei_evaluator,\n input_container.points_to_sample.data(),\n configure_for_gradients);\n ei_evaluator.ComputeGradExpectedImprovement(&ei_state, grad_EI.data());\n } else {\n ExpectedImprovementEvaluator ei_evaluator(gaussian_process, max_int_steps, best_so_far);\n ExpectedImprovementEvaluator::StateType ei_state(ei_evaluator, input_container.points_to_sample.data(),\n input_container.points_being_sampled.data(),\n input_container.num_to_sample,\n input_container.num_being_sampled,\n configure_for_gradients,\n randomness_source.normal_rng_vec.data());\n ei_evaluator.ComputeGradExpectedImprovement(&ei_state, grad_EI.data());\n }\n\n return VectorToPylist(grad_EI);\n}\n\n/*!\\rst\n Utility that dispatches EI optimization based on optimizer type and num_to_sample.\n This is just used to reduce copy-pasted code.\n\n \\param\n :optimizer_parameters: python/cpp_wrappers/optimization._CppOptimizerParameters\n Python object containing the DomainTypes domain_type and OptimizerTypes optimzer_type to use as well as\n appropriate parameter structs e.g., NewtonParameters for type kNewton).\n See comments on the python interface for multistart_expected_improvement_optimization_wrapper\n :gaussian_process: GaussianProcess object (holds points_sampled, values, noise_variance, derived quantities) that describes the\n underlying GP\n :input_container: PythonInterfaceInputContainer object containing data about points_being_sampled\n :domain: object specifying the domain to optimize over (see gpp_domain.hpp)\n :optimizer_type: type of optimization to use (e.g., null, gradient descent)\n :num_to_sample: how many simultaneous experiments you would like to run (i.e., the q in q,p-EI)\n :best_so_far: value of the best sample so far (must be min(points_sampled_value))\n :max_int_steps: maximum number of MC iterations\n :max_num_threads: maximum number of threads for use by OpenMP (generally should be <= # cores)\n :randomness_source: object containing randomness sources (sufficient for multithreading) used in EI computation\n :status: pydict object; cannot be None\n \\output\n :randomness_source: PRNG internal states modified\n :status: modified on exit to describe whether convergence occurred\n :best_points_to_sample[num_to_sample][dim]: next set of points to evaluate\n\\endrst*/\ntemplate \nvoid DispatchExpectedImprovementOptimization(const boost::python::object& optimizer_parameters,\n const GaussianProcess& gaussian_process,\n const PythonInterfaceInputContainer& input_container,\n const DomainType& domain,\n OptimizerTypes optimizer_type,\n int num_to_sample, double best_so_far,\n int max_int_steps, int max_num_threads,\n bool use_gpu, int which_gpu,\n RandomnessSourceContainer& randomness_source,\n boost::python::dict& status,\n double * restrict best_points_to_sample) {\n#ifndef OL_GPU_ENABLED\n (void) which_gpu; // quiet the compiler warning (unused variable)\n#endif\n\n bool found_flag = false;\n switch (optimizer_type) {\n case OptimizerTypes::kNull: {\n ThreadSchedule thread_schedule(max_num_threads, omp_sched_static);\n // optimizer_parameters must contain an int num_random_samples field, extract it\n int num_random_samples = boost::python::extract(optimizer_parameters.attr(\"num_random_samples\"));\n\n if (use_gpu == true) {\n#ifdef OL_GPU_ENABLED\n CudaComputeOptimalPointsToSampleViaLatinHypercubeSearch(gaussian_process, domain, thread_schedule,\n input_container.points_being_sampled.data(),\n num_random_samples, num_to_sample,\n input_container.num_being_sampled,\n best_so_far, max_int_steps, which_gpu, &found_flag,\n &randomness_source.uniform_generator,\n best_points_to_sample);\n#else\n OL_THROW_EXCEPTION(OptimalLearningException, \"GPU is not installed or enabled!\");\n#endif\n } else {\n ComputeOptimalPointsToSampleViaLatinHypercubeSearch(gaussian_process, domain, thread_schedule,\n input_container.points_being_sampled.data(),\n num_random_samples, num_to_sample,\n input_container.num_being_sampled,\n best_so_far, max_int_steps,\n &found_flag, &randomness_source.uniform_generator,\n randomness_source.normal_rng_vec.data(),\n best_points_to_sample);\n }\n status[std::string(\"lhc_\") + domain.kName + \"_domain_found_update\"] = found_flag;\n break;\n } // end case kNull optimizer_type\n case OptimizerTypes::kGradientDescent: {\n // optimizer_parameters must contain a optimizer_parameters field\n // of type GradientDescentParameters. extract it\n const GradientDescentParameters& gradient_descent_parameters = boost::python::extract(optimizer_parameters.attr(\"optimizer_parameters\"));\n ThreadSchedule thread_schedule(max_num_threads, omp_sched_dynamic);\n int num_random_samples = boost::python::extract(optimizer_parameters.attr(\"num_random_samples\"));\n\n bool random_search_only = false;\n if (use_gpu == true) {\n#ifdef OL_GPU_ENABLED\n CudaComputeOptimalPointsToSample(gaussian_process, gradient_descent_parameters, domain, thread_schedule,\n input_container.points_being_sampled.data(), num_to_sample,\n input_container.num_being_sampled, best_so_far, max_int_steps,\n random_search_only, num_random_samples, which_gpu, &found_flag,\n &randomness_source.uniform_generator, best_points_to_sample);\n#else\n OL_THROW_EXCEPTION(OptimalLearningException, \"GPU is not installed or enabled!\");\n#endif\n } else {\n ComputeOptimalPointsToSample(gaussian_process, gradient_descent_parameters, domain, thread_schedule,\n input_container.points_being_sampled.data(), num_to_sample,\n input_container.num_being_sampled, best_so_far, max_int_steps,\n random_search_only, num_random_samples, &found_flag,\n &randomness_source.uniform_generator,\n randomness_source.normal_rng_vec.data(), best_points_to_sample);\n }\n status[std::string(\"gradient_descent_\") + domain.kName + \"_domain_found_update\"] = found_flag;\n break;\n } // end case kGradientDescent optimizer_type\n default: {\n std::fill(best_points_to_sample, best_points_to_sample + input_container.dim*num_to_sample, 0.0);\n OL_THROW_EXCEPTION(OptimalLearningException, \"ERROR: invalid optimizer choice. Setting all coordinates to 0.0.\");\n break;\n }\n } // end switch over optimizer_type\n}\n\nboost::python::list MultistartExpectedImprovementOptimizationWrapper(const boost::python::object& optimizer_parameters,\n const GaussianProcess& gaussian_process,\n const boost::python::list& domain_bounds,\n const boost::python::list& points_being_sampled,\n int num_to_sample, int num_being_sampled,\n double best_so_far, int max_int_steps,\n int max_num_threads, bool use_gpu, int which_gpu,\n RandomnessSourceContainer& randomness_source,\n boost::python::dict& status) {\n // TODO(GH-131): make domain objects constructible from python; and pass them in through\n // the optimizer_parameters python object\n\n // abort if we do not have enough sources of randomness to run with max_num_threads\n if (unlikely(max_num_threads > static_cast(randomness_source.normal_rng_vec.size()))) {\n OL_THROW_EXCEPTION(LowerBoundException, \"Fewer randomness_sources than max_num_threads.\", randomness_source.normal_rng_vec.size(), max_num_threads);\n }\n\n int num_to_sample_input = 0; // No points to sample; we are generating these via EI optimization\n const boost::python::list points_to_sample_dummy;\n PythonInterfaceInputContainer input_container(points_to_sample_dummy, points_being_sampled, gaussian_process.dim(), num_to_sample_input, num_being_sampled);\n std::vector domain_bounds_C(input_container.dim);\n CopyPylistToClosedIntervalVector(domain_bounds, input_container.dim, domain_bounds_C);\n\n std::vector best_points_to_sample_C(input_container.dim*num_to_sample);\n\n DomainTypes domain_type = boost::python::extract(optimizer_parameters.attr(\"domain_type\"));\n OptimizerTypes optimizer_type = boost::python::extract(optimizer_parameters.attr(\"optimizer_type\"));\n switch (domain_type) {\n case DomainTypes::kTensorProduct: {\n TensorProductDomain domain(domain_bounds_C.data(), input_container.dim);\n\n DispatchExpectedImprovementOptimization(optimizer_parameters, gaussian_process, input_container,\n domain, optimizer_type, num_to_sample, best_so_far,\n max_int_steps, max_num_threads, use_gpu, which_gpu,\n randomness_source,\n status, best_points_to_sample_C.data());\n break;\n } // end case OptimizerTypes::kTensorProduct\n case DomainTypes::kSimplex: {\n SimplexIntersectTensorProductDomain domain(domain_bounds_C.data(), input_container.dim);\n\n DispatchExpectedImprovementOptimization(optimizer_parameters, gaussian_process, input_container,\n domain, optimizer_type, num_to_sample, best_so_far,\n max_int_steps, max_num_threads, use_gpu, which_gpu,\n randomness_source,\n status, best_points_to_sample_C.data());\n break;\n } // end case OptimizerTypes::kSimplex\n default: {\n std::fill(best_points_to_sample_C.begin(), best_points_to_sample_C.end(), 0.0);\n OL_THROW_EXCEPTION(OptimalLearningException, \"ERROR: invalid domain choice. Setting all coordinates to 0.0.\");\n break;\n }\n } // end switch over domain_type\n\n return VectorToPylist(best_points_to_sample_C);\n}\n\n/*!\\rst\n Utility that dispatches heuristic EI optimization (solving q,0-EI) based on optimizer type and num_to_sample.\n This is just used to reduce copy-pasted code.\n\n \\param\n :optimizer_parameters: python/cpp_wrappers/optimization._CppOptimizerParameters\n Python object containing the DomainTypes domain_type and OptimizerTypes optimzer_type to use as well as\n appropriate parameter structs e.g., NewtonParameters for type kNewton).\n See comments on the python interface for multistart_expected_improvement_optimization_wrapper\n :gaussian_process: GaussianProcess object (holds points_sampled, values, noise_variance, derived quantities) that describes the\n underlying GP\n :domain: object specifying the domain to optimize over (see gpp_domain.hpp)\n :optimizer_type: type of optimization to use (e.g., null, gradient descent)\n :estimation_policy: the policy to use to produce (heuristic) objective function estimates during multi-points EI optimization\n :num_to_sample: how many simultaneous experiments you would like to run (i.e., the q in q,0-EI)\n :best_so_far: value of the best sample so far (must be min(points_sampled_value))\n :max_num_threads: maximum number of threads for use by OpenMP (generally should be <= # cores)\n :randomness_source: object containing randomness sources (sufficient for multithreading) used in EI computation\n :status: pydict object; cannot be None\n \\output\n :randomness_source: PRNG internal states modified\n :status: modified on exit to describe whether convergence occurred\n :best_points_to_sample[num_to_sample][dim]: next set of points to evaluate\n\\endrst*/\ntemplate \nvoid DispatchHeuristicExpectedImprovementOptimization(const boost::python::object& optimizer_parameters,\n const GaussianProcess& gaussian_process,\n const DomainType& domain,\n OptimizerTypes optimizer_type,\n const ObjectiveEstimationPolicyInterface& estimation_policy,\n int num_to_sample, double best_so_far, int max_num_threads,\n RandomnessSourceContainer& randomness_source,\n boost::python::dict& status,\n double * restrict best_points_to_sample) {\n ThreadSchedule thread_schedule(max_num_threads, omp_sched_dynamic);\n bool found_flag = false;\n switch (optimizer_type) {\n case OptimizerTypes::kNull: {\n // optimizer_parameters must contain an int num_multistarts field, extract it\n int num_random_samples = boost::python::extract(optimizer_parameters.attr(\"num_random_samples\"));\n\n bool random_search_only = true;\n GradientDescentParameters gradient_descent_parameters(0, 0, 0, 0, 1.0, 1.0, 1.0, 0.0); // dummy struct; we aren't using gradient descent\n ComputeHeuristicPointsToSample(gaussian_process, gradient_descent_parameters, domain,\n estimation_policy, thread_schedule, best_so_far,\n random_search_only, num_random_samples, num_to_sample,\n &found_flag, &randomness_source.uniform_generator,\n best_points_to_sample);\n\n status[std::string(\"lhc_\") + domain.kName + \"_domain_found_update\"] = found_flag;\n break;\n } // end case kNull optimizer_type\n case OptimizerTypes::kGradientDescent: {\n // optimizer_parameters must contain a optimizer_parameters field\n // of type GradientDescentParameters. extract it\n const GradientDescentParameters& gradient_descent_parameters = boost::python::extract(optimizer_parameters.attr(\"optimizer_parameters\"));\n int num_random_samples = boost::python::extract(optimizer_parameters.attr(\"num_random_samples\"));\n\n bool random_search_only = false;\n ComputeHeuristicPointsToSample(gaussian_process, gradient_descent_parameters, domain,\n estimation_policy, thread_schedule, best_so_far,\n random_search_only, num_random_samples, num_to_sample,\n &found_flag, &randomness_source.uniform_generator,\n best_points_to_sample);\n\n status[std::string(\"gradient_descent_\") + domain.kName + \"_domain_found_update\"] = found_flag;\n break;\n } // end case kGradientDescent optimizer_type\n default: {\n std::fill(best_points_to_sample, best_points_to_sample + gaussian_process.dim()*num_to_sample, 0.0);\n OL_THROW_EXCEPTION(OptimalLearningException, \"ERROR: invalid optimizer choice. Setting all coordinates to 0.0.\");\n break;\n }\n } // end switch over optimizer_type\n}\n\nboost::python::list HeuristicExpectedImprovementOptimizationWrapper(const boost::python::object& optimizer_parameters,\n const GaussianProcess& gaussian_process,\n const boost::python::list& domain_bounds,\n const ObjectiveEstimationPolicyInterface& estimation_policy,\n int num_to_sample, double best_so_far, int max_num_threads,\n RandomnessSourceContainer& randomness_source,\n boost::python::dict& status) {\n // TODO(GH-131): make domain objects constructible from python; and pass them in through\n // the optimizer_parameters python object\n int dim = gaussian_process.dim();\n std::vector domain_bounds_C(dim);\n CopyPylistToClosedIntervalVector(domain_bounds, dim, domain_bounds_C);\n\n std::vector best_points_to_sample_C(dim*num_to_sample);\n\n DomainTypes domain_type = boost::python::extract(optimizer_parameters.attr(\"domain_type\"));\n OptimizerTypes optimizer_type = boost::python::extract(optimizer_parameters.attr(\"optimizer_type\"));\n switch (domain_type) {\n case DomainTypes::kTensorProduct: {\n TensorProductDomain domain(domain_bounds_C.data(), dim);\n\n DispatchHeuristicExpectedImprovementOptimization(optimizer_parameters, gaussian_process, domain,\n optimizer_type, estimation_policy, num_to_sample,\n best_so_far, max_num_threads, randomness_source,\n status, best_points_to_sample_C.data());\n break;\n } // end case OptimizerTypes::kTensorProduct\n case DomainTypes::kSimplex: {\n SimplexIntersectTensorProductDomain domain(domain_bounds_C.data(), dim);\n\n DispatchHeuristicExpectedImprovementOptimization(optimizer_parameters, gaussian_process, domain,\n optimizer_type, estimation_policy, num_to_sample,\n best_so_far, max_num_threads, randomness_source,\n status, best_points_to_sample_C.data());\n break;\n } // end case OptimizerTypes::kSimplex\n default: {\n std::fill(best_points_to_sample_C.begin(), best_points_to_sample_C.end(), 0.0);\n OL_THROW_EXCEPTION(OptimalLearningException, \"ERROR: invalid domain choice. Setting all coordinates to 0.0.\");\n break;\n }\n } // end switch over domain_type\n\n return VectorToPylist(best_points_to_sample_C);\n}\n\nboost::python::list EvaluateEIAtPointListWrapper(const GaussianProcess& gaussian_process,\n const boost::python::list& initial_guesses,\n const boost::python::list& points_being_sampled,\n int num_multistarts, int num_to_sample,\n int num_being_sampled, double best_so_far,\n int max_int_steps, int max_num_threads,\n RandomnessSourceContainer& randomness_source,\n boost::python::dict& status) {\n // abort if we do not have enough sources of randomness to run with max_num_threads\n if (unlikely(max_num_threads > static_cast(randomness_source.normal_rng_vec.size()))) {\n OL_THROW_EXCEPTION(LowerBoundException, \"Fewer randomness_sources than max_num_threads.\", randomness_source.normal_rng_vec.size(), max_num_threads);\n }\n\n int num_to_sample_input = 0; // No points to sample; we are generating these via EI optimization\n const boost::python::list points_to_sample_dummy;\n PythonInterfaceInputContainer input_container(points_to_sample_dummy, points_being_sampled, gaussian_process.dim(),\n num_to_sample_input, num_being_sampled);\n std::vector result_point_C(input_container.dim); // not used\n std::vector result_function_values_C(num_multistarts);\n std::vector initial_guesses_C(input_container.dim * num_multistarts);\n\n CopyPylistToVector(initial_guesses, input_container.dim * num_multistarts, initial_guesses_C);\n\n ThreadSchedule thread_schedule(max_num_threads, omp_sched_static);\n bool found_flag = false;\n EvaluateEIAtPointList(gaussian_process, thread_schedule, initial_guesses_C.data(),\n input_container.points_being_sampled.data(), num_multistarts,\n num_to_sample, input_container.num_being_sampled, best_so_far,\n max_int_steps, &found_flag, randomness_source.normal_rng_vec.data(),\n result_function_values_C.data(), result_point_C.data());\n\n status[\"evaluate_EI_at_point_list\"] = found_flag;\n\n return VectorToPylist(result_function_values_C);\n}\n\n} // end unnamed namespace\n\nvoid ExportEstimationPolicies() {\n boost::python::class_(\"ObjectiveEstimationPolicyInterface\", R\"%%(\n Pure abstract (in the C++ sense) base class for objective function estimation (e.g., Constant Liar, Kriging Believer).\n Serves no purpose in Python but is needed by boost to allow pointer casts of derived types to this type.\n )%%\", boost::python::no_init);\n\n boost::python::class_ >(\"ConstantLiarEstimationPolicy\", R\"%%(\n Produces objective function estimates at a \"point\" using the \"Constant Liar\" heuristic.\n\n Always outputs function_value = lie_value (the \"constant lie\") and noise_variance = lie_noise_variance, regardless\n of the value of \"point\".\n\n :ivar lie_value: (*float64*) the \"constant lie\" that this estimator should return\n :ivar lie_noise_variance: (*float64*) the noise_variance to associate to the lie_value (MUST be >= 0.0)\n )%%\", boost::python::init(R\"%%(\n Constructs a ConstantLiarEstimationPolicy object.\n\n :param lie_value: the \"constant lie\" that this estimator should return\n :type lie_value: float64 (finite)\n :param lie_noise_variance: the noise_variance to associate to the lie_value (MUST be >= 0.0)\n :type lie_noise_variance: float64 >= 0.0\n )%%\"));\n\n boost::python::class_ >(\"KrigingBelieverEstimationPolicy\", R\"%%(\n Produces objective function estimates at a \"point\" using the \"Kriging Believer\" heuristic.\n\n Requires a valid GaussianProcess (GP) to produce estimates. Computes estimates as:\n\n * function_value = GP.Mean(point) + std_deviation_coef * sqrt(GP.Variance(point))\n * noise_variance = kriging_noise_variance\n\n :ivar std_deviation_coef: (*float64*) the relative amount of bias (in units of GP std deviation) to introduce into the GP mean\n :ivar kriging_noise_variance: (*float64*) the noise_variance to associate to each function value estimate (MUST be >= 0.0)\n )%%\", boost::python::init(R\"%%(\n Constructs for KrigingBelieverEstimationPolicy object.\n\n :param std_deviation_coef: the relative amount of bias (in units of GP std deviation) to introduce into the GP mean\n :type std_deviation_coef: float64 (finite)\n :param kriging_noise_variance: the noise_variance to associate to each function value estimate (MUST be >= 0.0)\n :type kriging_noise_variance: float64 >= 0.0\n )%%\"));\n}\n\nvoid ExportExpectedImprovementFunctions() {\n boost::python::def(\"compute_expected_improvement\", ComputeExpectedImprovementWrapper, R\"%%(\n Compute expected improvement.\n If ``num_to_sample == 1`` and ``num_being_sampled == 0`` AND ``force_monte_carlo is false``, this will\n use (fast/accurate) analytic evaluation.\n Otherwise monte carlo-based EI computation is used.\n\n :param gaussian_process: GaussianProcess object (holds points_sampled, values, noise_variance, derived quantities)\n :type gaussian_process: GPP.GaussianProcess (boost::python ctor wrapper around optimal_learning::GaussianProcess)\n :param points_to_sample: initial points to load into state (must be a valid point for the problem);\n i.e., points at which to evaluate EI and/or its gradient\n :type points_to_sample: list of float64 with shape (num_to_sample, dim)\n :param points_being_sampled: points that are being sampled in concurrently experiments\n :type points_being_sampled: list of float64 with shape (num_being_sampled, dim)\n :param num_to_sample: number of potential future samples; gradients are evaluated wrt these points (i.e., the \"q\" in q,p-EI)\n :type num_to_sample: int > 0\n :param num_being_sampled: number of points being sampled concurrently (i.e., the p in q,p-EI)\n :type num_being_sampled: int >= 0\n :param max_int_steps: number of MC integration points in EI\n :type max_int_steps: int >= 0\n :param best_so_far: best known value of objective so far\n :type best_so_far: float64\n :param force_monte_carlo: true to force monte carlo evaluation of EI\n :type force_monte_carlo: bool\n :param randomness_source: object containing randomness sources; only thread 0's source is used\n :type randomness_source: GPP.RandomnessSourceContainer\n :return: computed EI\n :rtype: float64 >= 0.0\n )%%\");\n\n boost::python::def(\"compute_grad_expected_improvement\", ComputeGradExpectedImprovementWrapper, R\"%%(\n Compute the gradient of expected improvement evaluated at points_to_sample.\n If num_to_sample = 1 and num_being_sampled = 0 AND force_monte_carlo is false, this will\n use (fast/accurate) analytic evaluation.\n Otherwise monte carlo-based EI computation is used.\n\n :param gaussian_process: GaussianProcess object (holds points_sampled, values, noise_variance, derived quantities)\n :type gaussian_process: GPP.GaussianProcess (boost::python ctor wrapper around optimal_learning::GaussianProcess)\n :param points_to_sample: initial points to load into state (must be a valid point for the problem);\n i.e., points at which to evaluate EI and/or its gradient\n :type points_to_sample: list of float64 with shape (num_to_sample, dim)\n :param points_being_sampled: points that are being sampled in concurrently experiments\n :type points_being_sampled: list of float64 with shape (num_being_sampled, dim)\n :param num_to_sample: number of potential future samples; gradients are evaluated wrt these points (i.e., the \"q\" in q,p-EI)\n :type num_to_sample: int > 0\n :param num_being_sampled: number of points being sampled concurrently (i.e., the p in q,p-EI)\n :type num_being_sampled: int >= 0\n :param max_int_steps: number of MC integration points in EI\n :type max_int_steps: int >= 0\n :param best_so_far: best known value of objective so far\n :type best_so_far: float64\n :param force_monte_carlo: true to force monte carlo evaluation of EI\n :type force_monte_carlo: bool\n :param randomness_source: object containing randomness sources; only thread 0's source is used\n :type randomness_source: GPP.RandomnessSourceContainer\n :return: gradient of EI (computed at points_to_sample + points_being_sampled, wrt points_to_sample)\n :rtype: list of float64 with shape (num_to_sample, dim)\n )%%\");\n\n boost::python::def(\"multistart_expected_improvement_optimization\", MultistartExpectedImprovementOptimizationWrapper, R\"%%(\n Optimize expected improvement (i.e., solve q,p-EI) over the specified domain using the specified optimization method.\n Can optimize for num_to_sample new points to sample (i.e., aka \"q\", experiments to run) simultaneously.\n Allows the user to specify num_being_sampled (aka \"p\") ongoing/concurrent experiments.\n\n The _CppOptimizerParameters object is a python class defined in:\n python/cpp_wrappers/optimization._CppOptimizerParameters\n See that class definition for more details.\n\n This function expects it to have the fields:\n\n * domain_type (DomainTypes enum from this file)\n * optimizer_type (OptimizerTypes enum from this file)\n * num_random_samples (int, number of samples to 'dumb' search over, if 'dumb' search is being used.\n e.g., if optimizer = kNull or if to_sample > 1)\n * optimizer_parameters (*Parameters struct (gpp_optimizer_parameters.hpp) where * matches optimizer_type\n unused if optimizer_type == kNull)\n\n This function also has the option of using GPU to compute general q,p-EI via MC simulation. To enable it,\n make sure you have installed GPU components of MOE, otherwise, it will throw Runtime excpetion.\n\n .. WARNING:: this function FAILS and returns an EMPTY LIST if the number of random sources < max_num_threads\n\n :param optimizer_parameters: python object containing the DomainTypes domain_type and\n OptimizerTypes optimzer_type to use as well as\n appropriate parameter structs e.g., NewtonParameters for type kNewton)\n :type optimizer_parameters: _CppOptimizerParameters\n :param gaussian_process: GaussianProcess object (holds points_sampled, values, noise_variance, derived quantities)\n :type gaussian_process: GPP.GaussianProcess (boost::python ctor wrapper around optimal_learning::GaussianProcess)\n :param domain: [lower, upper] bound pairs for each dimension\n :type domain: list of float64 with shape (dim, 2)\n :param points_being_sampled: points that are being sampled in concurrently experiments\n :type points_being_sampled: list of float64 with shape (num_being_sampled, dim)\n :param num_to_sample: number of potential future samples; gradients are evaluated wrt these points (i.e., the \"q\" in q,p-EI)\n :type num_to_sample: int > 0\n :param num_being_sampled: number of points being sampled concurrently (i.e., the p in q,p-EI)\n :type num_being_sampled: int >= 0\n :param best_so_far: best known value of objective so far\n :type best_so_far: float64\n :param max_int_steps: number of MC integration points in EI\n :type max_int_steps: int >= 0\n :param max_num_threads: max number of threads to use during EI optimization\n :type max_num_threads: int >= 1\n :param use_gpu: set to 1 if user wants to use GPU for MC computation\n :type use_gpu: bool\n :param which_gpu: GPU device ID\n :type which_gpu: int >= 0\n :param randomness_source: object containing randomness sources; only thread 0's source is used\n :type randomness_source: GPP.RandomnessSourceContainer\n :param status: pydict object (cannot be None!); modified on exit to describe whether convergence occurred\n :type status: dict\n :return: next set of points to eval\n :rtype: list of float64 with shape (num_to_sample, dim)\n )%%\");\n\n boost::python::def(\"heuristic_expected_improvement_optimization\", HeuristicExpectedImprovementOptimizationWrapper, R\"%%(\n Compute a heuristic approximation to the result of multistart_expected_improvement_optimization(). That is, it\n optimizes an approximation to q,0-EI over the specified domain using the specified optimization method.\n Can optimize for num_to_sample (aka \"q\") new points to sample (i.e., experiments to run) simultaneously.\n\n Computing q,p-EI for q > 1 or p > 1 is expensive. To avoid that cost, this method \"solves\" q,0-EI by repeatedly\n optimizing 1,0-EI. We do the following (in C++)::\n\n for i in xrange(num_to_sample):\n new_point = optimize_1_EI(gaussian_process, ...)\n new_function_value, new_noise_variance = estimation_policy.compute_estimate(new_point, gaussian_process, i)\n gaussian_process.add_point(new_point, new_function_value, new_noise_variance)\n\n So using estimation_policy, we guess what the real-world objective function value would be, evaluated at the result\n of each 1-EI optimization. Then we treat this estimate as *truth* and feed it back to the gaussian process. The\n ConstantLiar and KrigingBelieverEstimationPolicy objects reproduce the heuristics described in Ginsbourger 2008.\n\n See gpp_heuristic_expected_improvement_optimization.hpp for further details on the algorithm.\n\n The _CppOptimizerParameters object is a python class defined in:\n ``python/cpp_wrappers/optimization._CppOptimizerParameters``\n See that class definition for more details.\n\n This function expects it to have the fields:\n\n * domain_type (DomainTypes enum from this file)\n * optimizer_type (OptimizerTypes enum from this file)\n * num_random_samples (int, number of samples to 'dumb' search over, if 'dumb' search is being used.\n e.g., if optimizer = kNull or if to_sample > 1)\n * optimizer_parameters (*Parameters struct (gpp_optimizer_parameters.hpp) where * matches optimizer_type\n unused if optimizer_type == kNull)\n\n .. WARNING:: this function FAILS and returns an EMPTY LIST if the number of random sources < max_num_threads\n\n :param optimizer_parameters: python object containing the DomainTypes domain_type and\n OptimizerTypes optimzer_type to use as well as\n appropriate parameter structs e.g., NewtonParameters for type kNewton)\n :type optimizer_parameters: _CppOptimizerParameters\n :param gaussian_process: GaussianProcess object (holds points_sampled, values, noise_variance, derived quantities)\n :type gaussian_process: GPP.GaussianProcess (boost::python ctor wrapper around optimal_learning::GaussianProcess)\n :param domain: [lower, upper] bound pairs for each dimension\n :type domain: list of float64 with shape (dim, 2)\n :param estimation_policy: the policy to use to produce (heuristic) objective function estimates\n during q,0-EI optimization (e.g., ConstantLiar, KrigingBeliever)\n :type estimation_policy: ObjectiveEstimationPolicyInterface\n :param num_to_sample: number of potential future samples; gradients are evaluated wrt these points (i.e., the \"q\" in q,p-EI)\n :type num_to_sample: int > 0\n :param best_so_far: best known value of objective so far\n :type best_so_far: float64\n :param max_num_threads: max number of threads to use during EI optimization\n :type max_num_threads: int >= 1\n :param randomness_source: object containing randomness sources; only thread 0's source is used\n :type randomness_source: GPP.RandomnessSourceContainer\n :param status: pydict object (cannot be None!); modified on exit to describe whether convergence occurred\n :type status: dict\n :return: next set of points to eval\n :rtype: list of float64 with shape (num_to_sample, dim)\n )%%\");\n\n boost::python::def(\"evaluate_EI_at_point_list\", EvaluateEIAtPointListWrapper, R\"%%(\n Evaluates the expected improvement at each point in initial_guesses; can handle q,p-EI.\n Useful for plotting.\n\n Equivalent to::\n\n result = []\n for point in initial_guesses:\n result.append(compute_expected_improvement(point, ...))\n\n But this method is substantially faster (loop in C++ and multithreaded).\n\n .. WARNING:: this function FAILS and returns an EMPTY LIST if the number of random sources < max_num_threads\n\n\n :param num_to_sample: number of potential future samples; gradients are evaluated wrt these points (i.e., the \"q\" in q,p-EI)\n :type num_to_sample: int > 0\n\n\n :param gaussian_process: GaussianProcess object (holds points_sampled, values, noise_variance, derived quantities)\n :type gaussian_process: GPP.GaussianProcess (boost::python ctor wrapper around optimal_learning::GaussianProcess)\n :param initial_guesses: points at which to evaluate EI\n :type initial_guesses: list of flaot64 with shape (num_multistarts, num_to_sample, dim)\n :param points_being_sampled: points that are being sampled in concurrently experiments\n :type points_being_sampled: list of float64 with shape (num_being_sampled, dim)\n :param num_multistarts: number of points at which to evaluate EI\n :type num_multistarts: int > 0\n :param num_to_sample: number of potential future samples; gradients are evaluated wrt these points (i.e., the \"q\" in q,p-EI)\n :type num_to_sample: int > 0\n :param num_being_sampled: number of points being sampled concurrently (i.e., the p in q,p-EI)\n :type num_being_sampled: int >= 0\n :param best_so_far: best known value of objective so far\n :type best_so_far: float64\n :param max_int_steps: number of MC integration points in EI\n :type max_int_steps: int >= 0\n :param max_num_threads: max number of threads to use during EI optimization\n :type max_num_threads: int >= 1\n :param randomness_source: object containing randomness sources; only thread 0's source is used\n :type randomness_source: GPP.RandomnessSourceContainer\n :param status: pydict object (cannot be None!); modified on exit to describe whether convergence occurred\n :type status: dict\n :return: EI values at each point of the initial_guesses list, in the same order\n :rtype: list of float64 with shape (num_multistarts, )\n )%%\");\n}\n\n} // end namespace optimal_learning\n", "meta": {"hexsha": "f6a85f2486eb069e497afeda664ca74810b10d1b", "size": 43907, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "moe/optimal_learning/cpp/gpp_python_expected_improvement.cpp", "max_stars_repo_name": "dstoeckel/MOE", "max_stars_repo_head_hexsha": "5b5a6a2c6c3cf47320126f7f5894e2a83e347f5c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 966.0, "max_stars_repo_stars_event_min_datetime": "2015-01-10T05:27:30.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T21:04:36.000Z", "max_issues_repo_path": "moe/optimal_learning/cpp/gpp_python_expected_improvement.cpp", "max_issues_repo_name": "dstoeckel/MOE", "max_issues_repo_head_hexsha": "5b5a6a2c6c3cf47320126f7f5894e2a83e347f5c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 46.0, "max_issues_repo_issues_event_min_datetime": "2015-01-16T22:33:08.000Z", "max_issues_repo_issues_event_max_datetime": "2019-09-04T16:33:27.000Z", "max_forks_repo_path": "moe/optimal_learning/cpp/gpp_python_expected_improvement.cpp", "max_forks_repo_name": "dstoeckel/MOE", "max_forks_repo_head_hexsha": "5b5a6a2c6c3cf47320126f7f5894e2a83e347f5c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 143.0, "max_forks_repo_forks_event_min_datetime": "2015-01-07T03:57:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-28T01:10:45.000Z", "avg_line_length": 62.9942611191, "max_line_length": 171, "alphanum_fraction": 0.6741749607, "num_tokens": 8901, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.41111086923216805, "lm_q1q2_score": 0.22794883481344563}} {"text": "// This file is part of LatNet Builder.\n//\n// Copyright (C) 2012-2021 The LatNet Builder author's, supervised by Pierre L'Ecuyer, Universite de Montreal.\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n// http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\n#include \"latbuilder/Parser/MeritFilter.h\"\n#include \"latbuilder/Parser/LevelWeights.h\"\n#include \"latbuilder/MeritFilter.h\"\n#include \"latbuilder/Norm/Normalizer.h\"\n#include \"latbuilder/Norm/PAlphaSL10.h\"\n#include \"latbuilder/Norm/PAlphaDPW08.h\"\n#include \"latbuilder/Norm/PAlphaTilde.h\"\n#include \"latbuilder/Norm/IAAlpha.h\"\n#include \"latbuilder/Norm/IB.h\"\n#include \"latbuilder/Functor/LowPass.h\"\n\n#include \n\nnamespace LatBuilder { namespace Parser {\n\nnamespace {\n\n template \n void setLevelWeights(\n Norm::Normalizer&,\n const std::string&,\n const LatBuilder::SizeParam&\n )\n {}\n\n template \n void setLevelWeights(\n Norm::Normalizer& normalizer,\n const std::string& levelWeights,\n const LatBuilder::SizeParam& sizeParam\n )\n { normalizer.setWeights(LevelWeights::parse(levelWeights, sizeParam)); }\n\n template \n std::unique_ptr> createNormalizer(\n unsigned int alpha,\n const LatBuilder::SizeParam& sizeParam,\n const LatticeTester::Weights& weights,\n Real normType,\n const std::string& levelWeights\n )\n {\n auto normalizer = new LatBuilder::Norm::Normalizer(NORM(alpha, weights, normType));\n setLevelWeights(*normalizer, levelWeights, sizeParam);\n return std::unique_ptr>(normalizer);\n }\n\n template void correctLevelWeights(\n const LatBuilder::SizeParam& sizeParam, \n const std::string& combiner,\n std::string& levelWeights);\n\n template \n void correctLevelWeights(\n const LatBuilder::SizeParam& sizeParam, \n const std::string& combiner,\n std::string& levelWeights)\n {}\n\n template \n void correctLevelWeights(\n const LatBuilder::SizeParam& sizeParam, \n const std::string& combiner,\n std::string& levelWeights)\n {\n const auto strCombinerSplit = splitPair<>(combiner, ':');\n if (strCombinerSplit.first == \"level\") {\n if (strCombinerSplit.second == \"max\"){\n levelWeights = \"select:\" + std::to_string(sizeParam.maxLevel());\n }\n else {\n try {\n levelWeights = \"select:\" + std::to_string(boost::lexical_cast(strCombinerSplit.second)) + \",\" + std::to_string(boost::lexical_cast(strCombinerSplit.second));\n }\n catch (boost::bad_lexical_cast&) {}\n }\n }\n }\n\n /**\n * Parses the bound PAlphaSL10.\n *\n * Example strings: \\c P2-SL10, \\c P4-SL10, \\c P2-DPW08, \\c P4-DPW08\n */\n template \n std::unique_ptr> parseNormalizer(\n const std::string& str,\n const std::string& figure,\n const LatBuilder::SizeParam& sizeParam,\n const LatticeTester::Weights& weights,\n Real normType,\n std::string combiner\n )\n {\n auto figureSplit = splitPair(figure, ':');\n std::string figureString = (figureSplit.second == \"\") ? figure : figureSplit.second;\n std::set admissibleNormalizations;\n if (figureString[0] == 'P') {\n admissibleNormalizations.insert(\"P\" + figureString.substr(1));\n if (LR == LatticeType::ORDINARY)\n {\n admissibleNormalizations.insert(\"P\" + figureString.substr(1) + \"-SL10\");\n admissibleNormalizations.insert(\"P\" + figureString.substr(1) + \"-DPW08\");\n }\n }\n else if (figureString[0] == 'I')\n {\n if (figureString[1] == 'A')\n {\n admissibleNormalizations.insert(\"IA\" + figureString.substr(2));\n }\n else if (figureString[1] == 'B') {\n admissibleNormalizations.insert(\"IB\");\n }\n }\n\n std::string newNormString = str;\n if (figure != \"\" && admissibleNormalizations.size() == 0)\n throw BadFilter(\"No normalizations are available with figure \" + figure + \".\");\n\n if ( (str.size() >= 6 && str.substr(0,6)==\"select\") || str.size() == 0)\n {\n newNormString = *admissibleNormalizations.begin() + \":\" + newNormString;\n }\n\n auto args = splitPair(newNormString, ':');\n if (figure != \"\" && admissibleNormalizations.find(args.first) == admissibleNormalizations.end())\n {\n throw BadFilter(\"Normalization \" + str + \" is incompatible with figure\" + figure + ( (LR == LatticeType::POLYNOMIAL) ? \" with lattice type polynomial.\" : \".\"));\n } \n const auto strSplit = splitPair<>(args.first, '-');\n\n correctLevelWeights(sizeParam, combiner, args.second);\n\n \n try {\n if (strSplit.first[0] == 'P') {\n const auto alpha = boost::lexical_cast(strSplit.first.substr(1));\n if (strSplit.second == \"\")\n {\n if (LR == LatticeType::ORDINARY)\n {\n return createNormalizer(alpha, sizeParam, weights, normType, args.second);\n }\n else if (LR == LatticeType::POLYNOMIAL)\n {\n return createNormalizer(alpha, sizeParam, weights, normType, args.second);\n }\n }\n if (strSplit.second == \"SL10\")\n return createNormalizer(alpha, sizeParam, weights, normType, args.second);\n else if (strSplit.second == \"DPW08\")\n return createNormalizer(alpha, sizeParam, weights, normType, args.second); \n }\n if (strSplit.first[0] == 'I')\n {\n if (strSplit.first[1] == 'A')\n {\n const auto alpha = boost::lexical_cast(strSplit.first.substr(2));\n return createNormalizer(alpha, sizeParam, weights, normType, args.second);\n }\n if (strSplit.first[1] == 'B')\n {\n return createNormalizer(weights.interlacingFactor(), sizeParam, weights, normType, args.second);\n }\n }\n }\n catch (boost::bad_lexical_cast&) {}\n throw BadFilter(\"cannot parse norm: \" + str);\n \n }\n}\n\ntemplate \nstd::unique_ptr>\nMeritFilter::parse(\n const std::string& str,\n const std::string& figure,\n const LatBuilder::SizeParam& sizeParam,\n const LatticeTester::Weights& weights,\n Real normType,\n std::string combiner\n )\n{\n const auto x = splitPair(str, ':');\n if (x.first == \"norm\")\n return parseNormalizer(x.second, figure, sizeParam, weights, normType, combiner);\n else if (x.first == \"low-pass\") {\n auto threshold = boost::lexical_cast(x.second);\n return std::unique_ptr>(new LatBuilder::MeritFilter(Functor::LowPass(threshold), str));\n }\n throw BadFilter(x.first);\n}\n\ntemplate struct LatBuilder::Parser::MeritFilter ;\ntemplate struct LatBuilder::Parser::MeritFilter ;\ntemplate struct LatBuilder::Parser::MeritFilter ;\ntemplate struct LatBuilder::Parser::MeritFilter ;\n\n}}\n", "meta": {"hexsha": "8a838219ed333055ceccc1ee26b52a572f10df48", "size": 8786, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/LatBuilder/Parser/MeritFilter.cc", "max_stars_repo_name": "YochevedDarmon/latnetbuilder", "max_stars_repo_head_hexsha": "7df3a7ba89e10ca51d4af347b75cdb0987e5151c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 10.0, "max_stars_repo_stars_event_min_datetime": "2020-01-21T06:08:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-24T16:11:31.000Z", "max_issues_repo_path": "src/LatBuilder/Parser/MeritFilter.cc", "max_issues_repo_name": "YochevedDarmon/latnetbuilder", "max_issues_repo_head_hexsha": "7df3a7ba89e10ca51d4af347b75cdb0987e5151c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2019-10-31T07:49:28.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-03T12:07:49.000Z", "max_forks_repo_path": "src/LatBuilder/Parser/MeritFilter.cc", "max_forks_repo_name": "YochevedDarmon/latnetbuilder", "max_forks_repo_head_hexsha": "7df3a7ba89e10ca51d4af347b75cdb0987e5151c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2016-07-27T19:25:26.000Z", "max_forks_repo_forks_event_max_datetime": "2018-05-22T20:00:47.000Z", "avg_line_length": 40.4884792627, "max_line_length": 201, "alphanum_fraction": 0.618028682, "num_tokens": 2175, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.44167300566462564, "lm_q1q2_score": 0.22773539795792525}} {"text": "///////////////////////////////////////////////////////////////\n// Copyright 2012 John Maddock. Distributed under the Boost\n// Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at https://www.boost.org/LICENSE_1_0.txt\n\n#ifndef BOOST_MATH_FLOAT_BACKEND_HPP\n#define BOOST_MATH_FLOAT_BACKEND_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace nil {\n namespace crypto3 {\n namespace multiprecision {\n namespace backends {\n\n#ifdef BOOST_MSVC\n#pragma warning(push)\n#pragma warning(disable : 4389 4244 4018 4244 4127)\n#endif\n\n template\n struct arithmetic_backend {\n typedef boost::mpl::list signed_types;\n typedef boost::mpl::list\n unsigned_types;\n typedef boost::mpl::list float_types;\n typedef int exponent_type;\n\n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend() : m_value(0) {\n }\n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend(const arithmetic_backend& o) : m_value(o.m_value) {\n }\n template\n BOOST_MP_CXX14_CONSTEXPR\n arithmetic_backend(const A& o,\n const typename boost::enable_if>::type* = 0) :\n m_value(o) {\n }\n template\n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend(const arithmetic_backend& o) : m_value(o.data()) {\n }\n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend& operator=(const arithmetic_backend& o) {\n m_value = o.m_value;\n return *this;\n }\n template\n BOOST_MP_CXX14_CONSTEXPR\n typename boost::enable_if, arithmetic_backend&>::type\n operator=(A i) {\n m_value = i;\n return *this;\n }\n template\n BOOST_MP_CXX14_CONSTEXPR arithmetic_backend& operator=(const arithmetic_backend& i) {\n m_value = i.data();\n return *this;\n }\n arithmetic_backend& operator=(const char* s) {\n#ifndef BOOST_NO_EXCEPTIONS\n try {\n#endif\n m_value = boost::lexical_cast(s);\n#ifndef BOOST_NO_EXCEPTIONS\n } catch (const boost::bad_lexical_cast&) {\n throw std::runtime_error(std::string(\"Unable to interpret the string provided: \\\"\") + s +\n std::string(\"\\\" as a compatible number type.\"));\n }\n#endif\n return *this;\n }\n BOOST_MP_CXX14_CONSTEXPR void swap(arithmetic_backend& o) {\n std::swap(m_value, o.m_value);\n }\n std::string str(std::streamsize digits, std::ios_base::fmtflags f) const {\n std::stringstream ss;\n ss.flags(f);\n ss << std::setprecision(digits ? digits : std::numeric_limits::digits10 + 4)\n << m_value;\n return ss.str();\n }\n BOOST_MP_CXX14_CONSTEXPR void do_negate(const boost::mpl::true_&) {\n m_value = 1 + ~m_value;\n }\n BOOST_MP_CXX14_CONSTEXPR void do_negate(const boost::mpl::false_&) {\n m_value = -m_value;\n }\n BOOST_MP_CXX14_CONSTEXPR void negate() {\n do_negate(boost::mpl::bool_::value>());\n }\n BOOST_MP_CXX14_CONSTEXPR int compare(const arithmetic_backend& o) const {\n return m_value > o.m_value ? 1 : (m_value < o.m_value ? -1 : 0);\n }\n template\n BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if, int>::type\n compare(A i) const {\n return m_value > static_cast(i) ? 1 :\n (m_value < static_cast(i) ? -1 : 0);\n }\n BOOST_MP_CXX14_CONSTEXPR Arithmetic& data() {\n return m_value;\n }\n BOOST_MP_CXX14_CONSTEXPR const Arithmetic& data() const {\n return m_value;\n }\n\n private:\n Arithmetic m_value;\n };\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if_c::value>::type\n eval_convert_to(R* result, const arithmetic_backend& backend) {\n typedef typename boost::common_type::type c_type;\n BOOST_CONSTEXPR const c_type max = static_cast((std::numeric_limits::max)());\n BOOST_CONSTEXPR const c_type min = static_cast((std::numeric_limits::min)());\n c_type ct = static_cast(backend.data());\n if ((backend.data() < 0) && !std::numeric_limits::is_signed)\n BOOST_THROW_EXCEPTION(std::range_error(\"Attempt to convert negative number to unsigned type.\"));\n if (ct > max)\n *result = boost::is_signed::value ? (std::numeric_limits::max)() : backend.data();\n else if (std::numeric_limits::is_signed && (ct < min))\n *result = (std::numeric_limits::min)();\n else\n *result = backend.data();\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::disable_if_c::value>::type\n eval_convert_to(R* result, const arithmetic_backend& backend) {\n *result = backend.data();\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR bool eval_eq(const arithmetic_backend& a,\n const arithmetic_backend& b) {\n return a.data() == b.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if, bool>::type\n eval_eq(const arithmetic_backend& a, const A2& b) {\n return a.data() == static_cast(b);\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR bool eval_lt(const arithmetic_backend& a,\n const arithmetic_backend& b) {\n return a.data() < b.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if, bool>::type\n eval_lt(const arithmetic_backend& a, const A2& b) {\n return a.data() < static_cast(b);\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR bool eval_gt(const arithmetic_backend& a,\n const arithmetic_backend& b) {\n return a.data() > b.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if, bool>::type\n eval_gt(const arithmetic_backend& a, const A2& b) {\n return a.data() > static_cast(b);\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_add(arithmetic_backend& result,\n const arithmetic_backend& o) {\n result.data() += o.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_subtract(arithmetic_backend& result,\n const arithmetic_backend& o) {\n result.data() -= o.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_multiply(arithmetic_backend& result,\n const arithmetic_backend& o) {\n result.data() *= o.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::enable_if_c::has_infinity>::type\n eval_divide(arithmetic_backend& result, const arithmetic_backend& o) {\n result.data() /= o.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::disable_if_c::has_infinity>::type\n eval_divide(arithmetic_backend& result, const arithmetic_backend& o) {\n if (!o.data())\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Divide by zero\"));\n result.data() /= o.data();\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if>::type\n eval_add(arithmetic_backend& result, const A2& o) {\n result.data() += o;\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if>::type\n eval_subtract(arithmetic_backend& result, const A2& o) {\n result.data() -= o;\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if>::type\n eval_multiply(arithmetic_backend& result, const A2& o) {\n result.data() *= o;\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::enable_if_c<(boost::is_arithmetic::value &&\n !std::numeric_limits::has_infinity)>::type\n eval_divide(arithmetic_backend& result, const A2& o) {\n if (!o)\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Divide by zero\"));\n result.data() /= o;\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::enable_if_c<(boost::is_arithmetic::value &&\n std::numeric_limits::has_infinity)>::type\n eval_divide(arithmetic_backend& result, const A2& o) {\n result.data() /= o;\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_add(arithmetic_backend& result,\n const arithmetic_backend& a,\n const arithmetic_backend& b) {\n result.data() = a.data() + b.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_subtract(arithmetic_backend& result,\n const arithmetic_backend& a,\n const arithmetic_backend& b) {\n result.data() = a.data() - b.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_multiply(arithmetic_backend& result,\n const arithmetic_backend& a,\n const arithmetic_backend& b) {\n result.data() = a.data() * b.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::enable_if_c::has_infinity>::type\n eval_divide(arithmetic_backend& result, const arithmetic_backend& a,\n const arithmetic_backend& b) {\n result.data() = a.data() / b.data();\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::disable_if_c::has_infinity>::type\n eval_divide(arithmetic_backend& result, const arithmetic_backend& a,\n const arithmetic_backend& b) {\n if (!b.data())\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Divide by zero\"));\n result.data() = a.data() / b.data();\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if>::type\n eval_add(arithmetic_backend& result, const arithmetic_backend& a,\n const A2& b) {\n result.data() = a.data() + b;\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if>::type\n eval_subtract(arithmetic_backend& result, const arithmetic_backend& a,\n const A2& b) {\n result.data() = a.data() - b;\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if>::type\n eval_multiply(arithmetic_backend& result, const arithmetic_backend& a,\n const A2& b) {\n result.data() = a.data() * b;\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::enable_if_c<(boost::is_arithmetic::value &&\n !std::numeric_limits::has_infinity)>::type\n eval_divide(arithmetic_backend& result, const arithmetic_backend& a,\n const A2& b) {\n if (!b)\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Divide by zero\"));\n result.data() = a.data() / b;\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::enable_if_c<(boost::is_arithmetic::value &&\n std::numeric_limits::has_infinity)>::type\n eval_divide(arithmetic_backend& result, const arithmetic_backend& a,\n const A2& b) {\n result.data() = a.data() / b;\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR bool eval_is_zero(const arithmetic_backend& val) {\n return val.data() == 0;\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::enable_if_c<(!std::numeric_limits::is_specialized ||\n std::numeric_limits::is_signed),\n int>::type\n eval_get_sign(const arithmetic_backend& val) {\n return val.data() == 0 ? 0 : val.data() < 0 ? -1 : 1;\n }\n template\n inline BOOST_MP_CXX14_CONSTEXPR\n typename boost::disable_if_c<(std::numeric_limits::is_specialized ||\n std::numeric_limits::is_signed),\n int>::type\n eval_get_sign(const arithmetic_backend& val) {\n return val.data() == 0 ? 0 : 1;\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR typename boost::enable_if, T>::type abs(T v) {\n return v;\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_abs(arithmetic_backend& result,\n const arithmetic_backend& o) {\n using nil::crypto3::multiprecision::backends::abs;\n using std::abs;\n result.data() = abs(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_fabs(arithmetic_backend& result,\n const arithmetic_backend& o) {\n result.data() = std::abs(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_floor(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = floor(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_ceil(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = ceil(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_sqrt(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = sqrt(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR int eval_fpclassify(const arithmetic_backend& o) {\n return (boost::math::fpclassify)(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_trunc(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = trunc(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_round(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = round(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_frexp(arithmetic_backend& result,\n const arithmetic_backend& a, int* v) {\n BOOST_MATH_STD_USING\n result.data() = frexp(a.data(), v);\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_ldexp(arithmetic_backend& result,\n const arithmetic_backend& a, int v) {\n BOOST_MATH_STD_USING\n result.data() = ldexp(a.data(), v);\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_exp(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = exp(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_log(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = log(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_log10(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = log10(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_sin(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = sin(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_cos(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = cos(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_tan(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = tan(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_acos(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = acos(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_asin(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = asin(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_atan(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = atan(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_sinh(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = sinh(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_cosh(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = cosh(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_tanh(arithmetic_backend& result,\n const arithmetic_backend& o) {\n BOOST_MATH_STD_USING\n result.data() = tanh(o.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_fmod(arithmetic_backend& result,\n const arithmetic_backend& a,\n const arithmetic_backend& b) {\n BOOST_MATH_STD_USING\n result.data() = fmod(a.data(), b.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_pow(arithmetic_backend& result,\n const arithmetic_backend& a,\n const arithmetic_backend& b) {\n BOOST_MATH_STD_USING\n result.data() = pow(a.data(), b.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_atan2(arithmetic_backend& result,\n const arithmetic_backend& a,\n const arithmetic_backend& b) {\n BOOST_MATH_STD_USING\n result.data() = atan2(a.data(), b.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_left_shift(arithmetic_backend& result, I val) {\n result.data() <<= val;\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_right_shift(arithmetic_backend& result, I val) {\n result.data() >>= val;\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_modulus(arithmetic_backend& result,\n const arithmetic_backend& a) {\n result.data() %= a.data();\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_bitwise_and(arithmetic_backend& result,\n const arithmetic_backend& a) {\n result.data() &= a.data();\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_bitwise_or(arithmetic_backend& result,\n const arithmetic_backend& a) {\n result.data() |= a.data();\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_bitwise_xor(arithmetic_backend& result,\n const arithmetic_backend& a) {\n result.data() ^= a.data();\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_complement(arithmetic_backend& result,\n const arithmetic_backend& a) {\n result.data() = ~a.data();\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_gcd(arithmetic_backend& result,\n const arithmetic_backend& a,\n const arithmetic_backend& b) {\n result.data() = boost::integer::gcd(a.data(), b.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR void eval_lcm(arithmetic_backend& result,\n const arithmetic_backend& a,\n const arithmetic_backend& b) {\n result.data() = boost::integer::lcm(a.data(), b.data());\n }\n\n template\n inline BOOST_MP_CXX14_CONSTEXPR std::size_t hash_value(const arithmetic_backend& a) {\n boost::hash hasher;\n return hasher(a.data());\n }\n\n#ifdef BOOST_MSVC\n#pragma warning(pop)\n#endif\n\n } // namespace backends\n\n using nil::crypto3::multiprecision::backends::arithmetic_backend;\n\n template\n struct number_category>\n : public boost::mpl::int_::value ? number_kind_integer :\n number_kind_floating_point> { };\n\n namespace detail {\n\n template\n struct double_precision_type;\n\n template\n struct double_precision_type, ET>> {\n typedef number::type>, ET> type;\n };\n template<>\n struct double_precision_type> {\n typedef arithmetic_backend type;\n };\n\n } // namespace detail\n\n } // namespace multiprecision\n } // namespace crypto3\n} // namespace nil\n#if !(defined(__SGI_STL_PORT) || defined(BOOST_NO_LIMITS_COMPILE_TIME_CONSTANTS))\n //\n// We shouldn't need these to get code to compile, however for the sake of\n// \"level playing field\" performance comparisons they avoid the very slow\n// lexical_cast's that would otherwise take place. Definition has to be guarded\n// by the inverse of pp-logic in real_concept.hpp which defines these as a workaround\n// for STLPort plus some other old/broken standartd libraries.\n//\nnamespace boost {\n namespace math {\n namespace tools {\n\n template<>\n inline unsigned int real_cast(concepts::real_concept r) {\n return static_cast(r.value());\n }\n\n template<>\n inline int real_cast(concepts::real_concept r) {\n return static_cast(r.value());\n }\n\n template<>\n inline long real_cast(concepts::real_concept r) {\n return static_cast(r.value());\n }\n\n // Converts from T to narrower floating-point types, float, double & long double.\n\n template<>\n inline float real_cast(concepts::real_concept r) {\n return static_cast(r.value());\n }\n template<>\n inline double real_cast(concepts::real_concept r) {\n return static_cast(r.value());\n }\n template<>\n inline long double real_cast(concepts::real_concept r) {\n return r.value();\n }\n\n } // namespace tools\n } // namespace math\n} // namespace boost\n#endif\n\nnamespace std {\n\n template\n class numeric_limits, ExpressionTemplates>>\n : public std::numeric_limits {\n typedef std::numeric_limits base_type;\n typedef nil::crypto3::multiprecision::number,\n ExpressionTemplates>\n number_type;\n\n public:\n BOOST_STATIC_CONSTEXPR number_type(min)() BOOST_NOEXCEPT {\n return (base_type::min)();\n }\n BOOST_STATIC_CONSTEXPR number_type(max)() BOOST_NOEXCEPT {\n return (base_type::max)();\n }\n BOOST_STATIC_CONSTEXPR number_type lowest() BOOST_NOEXCEPT {\n return -(max)();\n }\n BOOST_STATIC_CONSTEXPR number_type epsilon() BOOST_NOEXCEPT {\n return base_type::epsilon();\n }\n BOOST_STATIC_CONSTEXPR number_type round_error() BOOST_NOEXCEPT {\n return epsilon() / 2;\n }\n BOOST_STATIC_CONSTEXPR number_type infinity() BOOST_NOEXCEPT {\n return base_type::infinity();\n }\n BOOST_STATIC_CONSTEXPR number_type quiet_NaN() BOOST_NOEXCEPT {\n return base_type::quiet_NaN();\n }\n BOOST_STATIC_CONSTEXPR number_type signaling_NaN() BOOST_NOEXCEPT {\n return base_type::signaling_NaN();\n }\n BOOST_STATIC_CONSTEXPR number_type denorm_min() BOOST_NOEXCEPT {\n return base_type::denorm_min();\n }\n };\n\n template<>\n class numeric_limits : public std::numeric_limits {\n typedef std::numeric_limits base_type;\n typedef boost::math::concepts::real_concept number_type;\n\n public:\n static const number_type(min)() BOOST_NOEXCEPT {\n return (base_type::min)();\n }\n static const number_type(max)() BOOST_NOEXCEPT {\n return (base_type::max)();\n }\n static const number_type lowest() BOOST_NOEXCEPT {\n return -(max)();\n }\n static const number_type epsilon() BOOST_NOEXCEPT {\n return base_type::epsilon();\n }\n static const number_type round_error() BOOST_NOEXCEPT {\n return epsilon() / 2;\n }\n static const number_type infinity() BOOST_NOEXCEPT {\n return base_type::infinity();\n }\n static const number_type quiet_NaN() BOOST_NOEXCEPT {\n return base_type::quiet_NaN();\n }\n static const number_type signaling_NaN() BOOST_NOEXCEPT {\n return base_type::signaling_NaN();\n }\n static const number_type denorm_min() BOOST_NOEXCEPT {\n return base_type::denorm_min();\n }\n };\n\n} // namespace std\n\n#include \n\n#endif\n", "meta": {"hexsha": "8d2966f5cc77d663814766b4297ff5109042b2f9", "size": 37638, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "snark-logic/libs-source/multiprecision/performance/arithmetic_backend.hpp", "max_stars_repo_name": "idealatom/podlodkin-freeton-year-control", "max_stars_repo_head_hexsha": "6aa96e855fe065c9a75c76da976a87fe2d1668e6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "snark-logic/libs-source/multiprecision/performance/arithmetic_backend.hpp", "max_issues_repo_name": "idealatom/podlodkin-freeton-year-control", "max_issues_repo_head_hexsha": "6aa96e855fe065c9a75c76da976a87fe2d1668e6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "snark-logic/libs-source/multiprecision/performance/arithmetic_backend.hpp", "max_forks_repo_name": "idealatom/podlodkin-freeton-year-control", "max_forks_repo_head_hexsha": "6aa96e855fe065c9a75c76da976a87fe2d1668e6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-09-15T20:27:27.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-15T20:27:27.000Z", "avg_line_length": 51.5589041096, "max_line_length": 120, "alphanum_fraction": 0.5137626866, "num_tokens": 6576, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.22773539795792522}} {"text": "/*\n Tencent is pleased to support the open source community by making\n Plato available.\n Copyright (C) 2019 THL A29 Limited, a Tencent company.\n All rights reserved.\n\n Licensed under the BSD 3-Clause License (the \"License\"); you may\n not use this file except in compliance with the License. You may\n obtain a copy of the License at\n\n https://opensource.org/licenses/BSD-3-Clause\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" basis,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n implied. See the License for the specific language governing\n permissions and limitations under the License.\n\n See the AUTHORS file for names of contributors.\n*/\n\n#ifndef __PLATO_ALGO_FAST_UNFOLDING_LOUVAIN_HPP__\n#define __PLATO_ALGO_FAST_UNFOLDING_LOUVAIN_HPP__\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"glog/logging.h\"\n\n#include \"plato/util/perf.hpp\"\n#include \"plato/util/atomic.hpp\"\n#include \"plato/graph/graph.hpp\"\n#include \"plato/engine/dualmode.hpp\"\n\nnamespace plato { namespace algo {\nstruct louvain_opts_t {\n int alpha_ = -1;\n bool part_by_in_ = false;\n int outer_iteration_ = 3;\n int inner_iteration_ = 2;\n};\n\ntemplate\nclass louvain_epoch_t {\npublic:\n using partition_t = typename GRAPH::partition_t;\n using edge_value_t = typename GRAPH::edata_t;\n using louvain_value_t = plato::dense_state_t;\n using adj_unit_list_spec_t = typename GRAPH::adj_unit_list_spec_t;\n struct epoch_msg_type_t {\n vid_t v_i;\n vid_t from;\n vid_t to;\n edge_value_t ki;\n };\n\n struct sync_val_msg_type_t{\n vid_t v_i;\n edge_value_t val;\n };\n\n\npublic:\n /**\n * @brief\n * @param graph\n * @param graph_info\n * @param m\n * @param opts\n */\n explicit louvain_epoch_t(\n std::shared_ptr graph, const graph_info_t& graph_info, double m,\n const louvain_opts_t& opts = louvain_opts_t());\n /**\n * @brief\n */\n ~louvain_epoch_t();\n\n /**\n * @brief\n */\n void compute();\n\n /**\n * @brief getter\n * @return\n */\n std::shared_ptr graph() { return graph_; };\n /**\n * @brief getter\n * @return\n */\n std::vector& labels() { return labels_; };\n\nprivate:\n std::shared_ptr graph_;\n graph_info_t graph_info_;\n louvain_value_t ki_;\n std::vector labels_;\n std::vector sigma_tot_;\n bitmap_t<> local_bit_;\n double m_;\n louvain_opts_t opts_;\n};\n\ntemplate \nlouvain_epoch_t::louvain_epoch_t(\n std::shared_ptr graph,\n const graph_info_t& graph_info, double m, const louvain_opts_t& opts) :\n graph_(graph), graph_info_(graph_info), ki_(graph_info.max_v_i_, graph->partitioner()),\n labels_(graph_info.max_v_i_ + 1), sigma_tot_(graph_info.max_v_i_ + 1, 0),\n local_bit_(graph_info.max_v_i_ + 1), m_(2 * m), opts_(opts) {\n\n auto& cluster_info = plato::cluster_info_t::get_instance();\n plato::stop_watch_t watch;\n\n {// init labels\n watch.mark(\"t0\");\n#pragma omp parallel for num_threads(cluster_info.threads_)\n for (vid_t v_i = 0; v_i <= graph_info.max_v_i_; ++v_i) {\n labels_[v_i] = v_i;\n }\n LOG(INFO) << \"epoch init labels: \" << watch.show(\"t0\") / 1000.0;\n }\n\n {//calc ki_ and sync sigma_tot_\n bitmap_t<> active_all(graph_info.max_v_i_ + 1);\n active_all.fill();\n auto active_view_all = plato::create_active_v_view(graph->partitioner()->self_v_view(), active_all);\n watch.mark(\"t0\");\n edge_value_t max_ki = 0;\n using push_context_t = plato::template mepa_bc_context_t;\n plato::broadcast_message(\n active_view_all,\n /**\n * @brief\n * @param context\n * @param v_i\n */\n [&](const push_context_t& context, vid_t v_i) {\n edge_value_t local_sum = 0;\n auto neighbours = graph->neighbours(v_i);\n for (auto it = neighbours.begin_; neighbours.end_ != it; ++it) {\n local_sum += it->edata_;\n }\n if (neighbours.begin_ != neighbours.end_) {\n ki_[v_i] = local_sum;\n local_bit_.set_bit(v_i);\n plato::write_max(&max_ki, local_sum);\n context.send(sync_val_msg_type_t{ v_i, local_sum});\n }\n },\n /**\n * @brief\n * @param p_i\n * @param msg\n * @return\n */\n [&](int p_i, sync_val_msg_type_t& msg) {\n sigma_tot_[msg.v_i] = msg.val;\n return 0;\n }\n );\n LOG(INFO) << \"epoch sync sigma_tot: \" << watch.show(\"t0\") / 1000.0;\n MPI_Allreduce(MPI_IN_PLACE, &max_ki, 1, get_mpi_data_type(), MPI_MAX, MPI_COMM_WORLD);\n LOG(INFO) << \"max ki: \" << max_ki;\n }\n}\n\ntemplate \nlouvain_epoch_t::~louvain_epoch_t() {\n}\n\ntemplate \nvoid louvain_epoch_t::compute() {\n auto& cluster_info = plato::cluster_info_t::get_instance();\n auto try_change = [&](vid_t v_i, vid_t from, vid_t to, edge_value_t ki_in_from, edge_value_t ki_in_to) {\n double x = (double)ki_[v_i] + (double)sigma_tot_[to] - (double)sigma_tot_[from];\n return (double)ki_in_to - (double)ki_in_from - 2.0 * (double)ki_[v_i] * x / m_;\n };\n auto do_change = [&](epoch_msg_type_t& msg) {\n labels_[msg.v_i] = msg.to;\n plato::write_add(&sigma_tot_[msg.from], -msg.ki);\n plato::write_add(&sigma_tot_[msg.to], msg.ki);\n };\n\n auto active_view = plato::create_active_v_view(graph_->partitioner()->self_v_view(), local_bit_);\n plato::stop_watch_t watch;\n using push_context_t = plato::template mepa_bc_context_t;\n for (int try_time = 0; try_time < opts_.inner_iteration_; ++try_time){\n watch.mark(\"t0\");\n auto exec_once = [&](std::function condition) {\n plato::broadcast_message(\n active_view,\n /**\n * @brief\n * @param context\n * @param v_i\n */\n [&](const push_context_t& context, plato::vid_t v_i) {\n vid_t target = (vid_t)-1;\n vid_t from = labels_[v_i];\n edge_value_t ki_in_from = 0;\n std::unordered_map ki_in_map;\n auto neighbours = graph_->neighbours(v_i);\n edge_value_t self_cycle = 0;\n for (auto it = neighbours.begin_; neighbours.end_ != it; ++it) {\n vid_t to = labels_[it->neighbour_];\n if (condition(from, to)) {\n ki_in_map[to] += it->edata_;\n } else if (to == from) {\n ki_in_from += it->edata_;\n }\n\n if (it->neighbour_ == v_i) {\n self_cycle = it->edata_;\n }\n }\n\n double best_delta = 0;\n for (auto& it: ki_in_map) {\n double delta = try_change(v_i, from, it.first, ki_in_from, it.second + self_cycle);\n if (delta > best_delta) {\n target = it.first;\n best_delta = delta;\n }\n }\n\n if (target != (vid_t)-1) {\n epoch_msg_type_t msg = epoch_msg_type_t{ v_i, from, target, ki_[v_i] };\n context.send(msg);\n do_change(msg);\n }\n },\n /**\n * @brief\n * @param p_i\n * @param msg\n * @return\n */\n [&](int p_i, epoch_msg_type_t& msg) {\n if (p_i != cluster_info.partition_id_) {\n do_change(msg);\n }\n return 0;\n }\n );\n };\n\n exec_once([&](vid_t from, vid_t to){\n if (from < to) return true;\n return false;\n });\n exec_once([&](vid_t from, vid_t to){\n if (from > to) return true;\n return false;\n });\n LOG(INFO) << \"try_time: \" << try_time << \" cost: \" << watch.show(\"t0\") / 1000.0;\n }\n}\n\ntemplate\nclass louvain_fast_unfolding_t {\npublic:\n using partition_t = typename GRAPH::partition_t;\n using edge_value_t = typename GRAPH::edata_t;\n using adj_unit_list_spec_t = plato::adj_unit_list_t;\n using louvain_value_t = plato::dense_state_t;\n\n struct edge_sync_msg_type_t {\n vid_t src;\n vid_t dst;\n edge_value_t data;\n };\n\n struct degree_sync_msg_type_t {\n vid_t src;\n vid_t degree;\n };\n\npublic:\n /**\n * @brief\n * @param graph\n * @param graph_info\n * @param opts\n */\n explicit louvain_fast_unfolding_t(\n std::shared_ptr graph, const graph_info_t& graph_info,\n const louvain_opts_t& opts = louvain_opts_t());\n\n /**\n * @brief destructor\n */\n ~louvain_fast_unfolding_t();\n\n /**\n * @brief compute\n */\n void compute();\n\n /**\n * @brief save to storage.\n * @tparam STREAM\n * @param streams\n */\n template \n void save(std::vector& streams);\n\nprivate:\n /**\n * @brief\n * @param graph\n * @param labels\n * @return\n */\n std::shared_ptr rebuild(std::shared_ptr graph, std::vector& labels);\n /**\n * @brief\n * @param labels\n */\n void update_local_label(std::vector& labels);\nprivate:\n std::shared_ptr graph_;\n graph_info_t graph_info_;\n louvain_value_t local_label_;\n louvain_value_t local_comm_size_;\n louvain_opts_t opts_;\n};\n\ntemplate\nlouvain_fast_unfolding_t::louvain_fast_unfolding_t(\n std::shared_ptr graph, const graph_info_t& graph_info,\n const louvain_opts_t& opts)\n : graph_(graph), graph_info_(graph_info),\n local_label_(graph_info.max_v_i_, graph->partitioner()),\n local_comm_size_(graph_info.max_v_i_, graph->partitioner()), opts_(opts) {\n}\n\ntemplate\nlouvain_fast_unfolding_t::~louvain_fast_unfolding_t() {\n}\n\ntemplate\nvoid louvain_fast_unfolding_t::compute() {\n //first calc m and init label\n plato::stop_watch_t watch;\n watch.mark(\"t0\");\n double m = local_label_.template foreach (\n /**\n * @brief\n * @param v_i\n * @param pval\n * @return\n */\n [&](vid_t v_i, vid_t* pval) {\n *pval = v_i;\n double local_sum = 0;\n auto neighbours = graph_->neighbours(v_i);\n for (auto it = neighbours.begin_; neighbours.end_ != it; ++it) {\n local_sum += it->edata_;\n }\n return local_sum;\n }\n );\n m /= 2; //undirected\n LOG(INFO) << \"calc m: \" << m << \" cost: \" << watch.show(\"t0\") / 1000.0;\n\n //epoch\n louvain_epoch_t* cur = new louvain_epoch_t(graph_, graph_info_, m, opts_);\n for (int epoch = 0; epoch < opts_.outer_iteration_; epoch++) {\n LOG(INFO) << \"epoch \" << epoch << \" begin!\";\n watch.mark(\"compute\");\n cur->compute();\n LOG(INFO) << \"compute cost: \" << watch.show(\"compute\") / 1000.0;\n update_local_label(cur->labels());\n if (epoch == opts_.outer_iteration_ - 1) {\n delete cur;\n break;\n }\n //rebuild from current graph\n watch.mark(\"rebuild\");\n graph_info_t graph_info_next(graph_info_);\n graph_info_next.is_directed_ = true;\n auto graph_next = rebuild(cur->graph(), cur->labels());\n louvain_epoch_t* nxt = new louvain_epoch_t(graph_next, graph_info_next, m, opts_);\n LOG(INFO) << \"rebuild cost: \" << watch.show(\"rebuild\") / 1000.0;\n delete cur;\n cur = nxt;\n }\n}\n\ntemplate\nstd::shared_ptr louvain_fast_unfolding_t::rebuild(std::shared_ptr graph, std::vector& labels) {\n plato::stop_watch_t watch;\n watch.mark(\"t0\");\n eid_t total_edge = local_label_.template foreach (\n [&](vid_t v_i, vid_t* pval) {\n auto neighbours = graph->neighbours(v_i);\n return (eid_t)(neighbours.end_ - neighbours.begin_);\n }\n );\n LOG(INFO) << \"rebuild before total edge: \" << total_edge << \" cost: \" << watch.show(\"t0\") / 1000.0;\n\n watch.mark(\"t0\");\n auto& cluster_info = plato::cluster_info_t::get_instance();\n vid_t v_begin = graph->partitioner()->offset_[cluster_info.partition_id_];\n vid_t v_end = graph->partitioner()->offset_[cluster_info.partition_id_ + 1];\n std::vector edge_idx(v_end - v_begin + 1, 0);\n std::vector tmp_idx(v_end - v_begin + 1, 0);\n\n bitmap_t<> active_all(graph_info_.max_v_i_ + 1);\n active_all.fill();\n auto active_view_all = plato::create_active_v_view(graph->partitioner()->self_v_view(), active_all);\n\n {\n //first, calc degree\n using push_context_t = plato::template mepa_sd_context_t;\n plato::spread_message(\n active_view_all,\n /**\n * @brief\n * @param context\n * @param v_i\n */\n [&](const push_context_t& context, vid_t v_i) {\n auto neighbours = graph->neighbours(v_i);\n if (neighbours.begin_ == neighbours.end_) return;\n vid_t src = labels[v_i];\n auto send_to = graph->partitioner()->get_partition_id(src);\n context.send(send_to, degree_sync_msg_type_t { src, (vid_t)(neighbours.end_ - neighbours.begin_) } );\n },\n /**\n * @brief\n * @param msg\n * @return\n */\n [&](degree_sync_msg_type_t& msg) {\n vid_t pos = msg.src - v_begin + 1;\n plato::write_add(&edge_idx[pos], (eid_t)msg.degree);\n return 0;\n }\n );\n\n for (int i = 1; i < (int)edge_idx.size(); ++i) {\n edge_idx[i] = edge_idx[i - 1] + edge_idx[i];\n tmp_idx[i] = edge_idx[i];\n }\n\n LOG(INFO) << \"rebuild calc degree cost: \" << watch.show(\"t0\") / 1000.0;\n }\n eid_t total_local_edge = edge_idx[edge_idx.size() - 1];\n std::vector > edges(total_local_edge);\n LOG(INFO) << \"rebuild local edge: \" << edges.size();\n watch.mark(\"t0\");\n {\n //second, transfer edge\n using push_context_t = plato::template mepa_sd_context_t;\n plato::spread_message(\n active_view_all,\n /**\n * @brief\n * @param context\n * @param v_i\n */\n [&](const push_context_t& context, vid_t v_i) {\n auto neighbours = graph->neighbours(v_i);\n if (neighbours.begin_ == neighbours.end_) return;\n vid_t src = labels[v_i];\n auto send_to = graph->partitioner()->get_partition_id(src);\n for (auto it = neighbours.begin_; neighbours.end_ != it; ++it) {\n vid_t dst = labels[it->neighbour_];\n context.send(send_to, edge_sync_msg_type_t{ src, dst, it->edata_ });\n }\n },\n /**\n * @brief\n * @param msg\n * @return\n */\n [&](edge_sync_msg_type_t& msg) {\n vid_t pos = msg.src - v_begin;\n vid_t idx = __sync_fetch_and_add(&tmp_idx[pos], (eid_t)1);\n edges[idx].first = msg.dst;\n edges[idx].second = msg.data;\n return 0;\n }\n );\n LOG(INFO) << \"rebuild transfer edge cost: \" << watch.show(\"t0\") / 1000.0;\n }\n\n watch.mark(\"t0\");\n edge_cache_t edge_cache;\n {\n //third, sort and aggregate\n#pragma omp parallel for num_threads(cluster_info.threads_)\n for (vid_t v_i = v_begin; v_i < v_end; ++v_i) {\n vid_t pos = v_i - v_begin;\n eid_t e_start = edge_idx[pos];\n eid_t e_end = edge_idx[pos + 1];\n if (e_start == e_end) continue;\n std::sort(edges.begin() + e_start, edges.begin() + e_end);\n vid_t pre = (vid_t)-1;\n edge_value_t local_sum = 0;\n for (eid_t e = e_start; e < e_end; ++e) {\n if (pre != edges[e].first) {\n if (pre != (vid_t)-1) {\n edge_cache.push_back(edge_unit_t { v_i, pre, local_sum });\n }\n pre = edges[e].first;\n local_sum = 0;\n }\n local_sum += edges[e].second;\n }\n if (pre != (vid_t)-1) {\n edge_cache.push_back(edge_unit_t { v_i, pre, local_sum });\n }\n }\n\n eid_t edge_num_new = edge_cache.size();\n MPI_Allreduce(MPI_IN_PLACE, &edge_num_new, 1, get_mpi_data_type(), MPI_SUM, MPI_COMM_WORLD);\n LOG(INFO) << \"rebuild new edge num: \" << edge_num_new;\n LOG(INFO) << \"rebuild aggregate edge cost: \" << watch.show(\"t0\") / 1000.0;\n }\n\n watch.mark(\"t0\");\n graph_info_t graph_info_next(graph_info_);\n graph_info_next.is_directed_ = true;\n std::shared_ptr pgraph(new GRAPH(graph->partitioner()));\n pgraph->load_from_cache(graph_info_next, edge_cache);\n LOG(INFO) << \"rebuild load cache cost: \" << watch.show(\"t0\") / 1000.0;\n\n return pgraph;\n}\n\ntemplate\nvoid louvain_fast_unfolding_t::update_local_label(std::vector& labels) {\n local_label_.template foreach (\n /**\n * @brief\n * @param v_i\n * @param pval\n * @return\n */\n [&] (vid_t v_i, vid_t* pval) {\n if (labels[*pval] != *pval) {\n *pval = labels[*pval];\n }\n return 0;\n }\n );\n}\n\ntemplate\ntemplate\nvoid louvain_fast_unfolding_t::save(std::vector& ss) {\n struct louvain_msg_type_t {\n vid_t src;\n vid_t label;\n };\n boost::lockfree::queue que(1024);\n LOG_IF(FATAL, !que.is_lock_free())\n << \"boost::lockfree::queue is not lock free\\n\";\n\n // start a thread to pop and edge and write to output\n std::atomic done(false);\n std::thread pop_write([&done, &ss, &que](void) {\n#pragma omp parallel num_threads(ss.size())\n {\n int tid = omp_get_thread_num();\n louvain_msg_type_t vb;\n while (!done) {\n if (que.pop(vb)) {\n *ss[tid] << vb.src << \",\" << vb.label << \"\\n\";\n }\n }\n\n while (que.pop(vb)) {\n *ss[tid] << vb.src << \",\" << vb.label << \"\\n\";\n }\n }\n });\n\n // traverse\n local_label_.template foreach (\n /**\n * @brief\n * @param v_i\n * @param pval\n * @return\n */\n [&] (vid_t v_i, vid_t* pval) {\n while (!que.push(louvain_msg_type_t {v_i, *pval} )) {\n }\n return 0;\n }\n );\n\n done = true;\n pop_write.join();\n}\n\n} // namespace plato\n} // namespace algo\n#endif\n", "meta": {"hexsha": "2d6c2a439b1cf8ac5896adfd1748c8b2cef2e9c3", "size": 17965, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "plato/algo/fast_unfolding/louvain.hpp", "max_stars_repo_name": "zhaofeng-shu33/plato", "max_stars_repo_head_hexsha": "36012f8951221550576fd270a0e355c7e3b44b72", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-11-15T05:30:03.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-15T05:30:03.000Z", "max_issues_repo_path": "plato/algo/fast_unfolding/louvain.hpp", "max_issues_repo_name": "zhaofeng-shu33/plato", "max_issues_repo_head_hexsha": "36012f8951221550576fd270a0e355c7e3b44b72", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "plato/algo/fast_unfolding/louvain.hpp", "max_forks_repo_name": "zhaofeng-shu33/plato", "max_forks_repo_head_hexsha": "36012f8951221550576fd270a0e355c7e3b44b72", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2022-02-11T02:15:42.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-11T02:15:42.000Z", "avg_line_length": 29.2589576547, "max_line_length": 123, "alphanum_fraction": 0.6234344559, "num_tokens": 5111, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5195213219520929, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.22745864304150112}} {"text": "// Boost.Geometry - gis-projections (based on PROJ4)\r\n\r\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\r\n\r\n// This file was modified by Oracle on 2017, 2018.\r\n// Modifications copyright (c) 2017-2018, Oracle and/or its affiliates.\r\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle.\r\n\r\n// Use, modification and distribution is subject to the Boost Software License,\r\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\r\n// http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\r\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\r\n// PROJ4 is maintained by Frank Warmerdam\r\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\r\n\r\n// Last updated version of proj: 5.0.0\r\n\r\n// Original copyright notice:\r\n\r\n// Permission is hereby granted, free of charge, to any person obtaining a\r\n// copy of this software and associated documentation files (the \"Software\"),\r\n// to deal in the Software without restriction, including without limitation\r\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\r\n// and/or sell copies of the Software, and to permit persons to whom the\r\n// Software is furnished to do so, subject to the following conditions:\r\n\r\n// The above copyright notice and this permission notice shall be included\r\n// in all copies or substantial portions of the Software.\r\n\r\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\r\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\r\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\r\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\r\n// DEALINGS IN THE SOFTWARE.\r\n\r\n#ifndef BOOST_GEOMETRY_PROJECTIONS_POLY_HPP\r\n#define BOOST_GEOMETRY_PROJECTIONS_POLY_HPP\r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\nnamespace boost { namespace geometry\r\n{\r\n\r\nnamespace srs { namespace par4\r\n{\r\n struct poly {}; // Polyconic (American)\r\n\r\n}} //namespace srs::par4\r\n\r\nnamespace projections\r\n{\r\n #ifndef DOXYGEN_NO_DETAIL\r\n namespace detail { namespace poly\r\n {\r\n\r\n static const double tolerance = 1e-10;\r\n static const double conv_tolerance = 1e-10;\r\n static const int n_iter = 10;\r\n static const int i_iter = 20;\r\n static const double i_tolerance = 1.e-12;\r\n\r\n template \r\n struct par_poly\r\n {\r\n T ml0;\r\n detail::en en;\r\n };\r\n\r\n // template class, using CRTP to implement forward/inverse\r\n template \r\n struct base_poly_ellipsoid\r\n : public base_t_fi, T, Parameters>\r\n {\r\n par_poly m_proj_parm;\r\n\r\n inline base_poly_ellipsoid(const Parameters& par)\r\n : base_t_fi, T, Parameters>(*this, par)\r\n {}\r\n\r\n // FORWARD(e_forward) ellipsoid\r\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\r\n inline void fwd(T& lp_lon, T& lp_lat, T& xy_x, T& xy_y) const\r\n {\r\n T ms, sp, cp;\r\n\r\n if (fabs(lp_lat) <= tolerance) {\r\n xy_x = lp_lon;\r\n xy_y = -this->m_proj_parm.ml0;\r\n } else {\r\n sp = sin(lp_lat);\r\n ms = fabs(cp = cos(lp_lat)) > tolerance ? pj_msfn(sp, cp, this->m_par.es) / sp : 0.;\r\n xy_x = ms * sin(lp_lon *= sp);\r\n xy_y = (pj_mlfn(lp_lat, sp, cp, this->m_proj_parm.en) - this->m_proj_parm.ml0) + ms * (1. - cos(lp_lon));\r\n }\r\n }\r\n\r\n // INVERSE(e_inverse) ellipsoid\r\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\r\n inline void inv(T& xy_x, T& xy_y, T& lp_lon, T& lp_lat) const\r\n {\r\n xy_y += this->m_proj_parm.ml0;\r\n if (fabs(xy_y) <= tolerance) {\r\n lp_lon = xy_x;\r\n lp_lat = 0.;\r\n } else {\r\n T r, c, sp, cp, s2ph, ml, mlb, mlp, dPhi;\r\n int i;\r\n\r\n r = xy_y * xy_y + xy_x * xy_x;\r\n for (lp_lat = xy_y, i = i_iter; i ; --i) {\r\n sp = sin(lp_lat);\r\n s2ph = sp * ( cp = cos(lp_lat));\r\n if (fabs(cp) < i_tolerance) {\r\n BOOST_THROW_EXCEPTION( projection_exception(error_tolerance_condition) );\r\n }\r\n c = sp * (mlp = sqrt(1. - this->m_par.es * sp * sp)) / cp;\r\n ml = pj_mlfn(lp_lat, sp, cp, this->m_proj_parm.en);\r\n mlb = ml * ml + r;\r\n mlp = this->m_par.one_es / (mlp * mlp * mlp);\r\n lp_lat += ( dPhi =\r\n ( ml + ml + c * mlb - 2. * xy_y * (c * ml + 1.) ) / (\r\n this->m_par.es * s2ph * (mlb - 2. * xy_y * ml) / c +\r\n 2.* (xy_y - ml) * (c * mlp - 1. / s2ph) - mlp - mlp ));\r\n if (fabs(dPhi) <= i_tolerance)\r\n break;\r\n }\r\n if (!i) {\r\n BOOST_THROW_EXCEPTION( projection_exception(error_tolerance_condition) );\r\n }\r\n c = sin(lp_lat);\r\n lp_lon = asin(xy_x * tan(lp_lat) * sqrt(1. - this->m_par.es * c * c)) / sin(lp_lat);\r\n }\r\n }\r\n\r\n static inline std::string get_name()\r\n {\r\n return \"poly_ellipsoid\";\r\n }\r\n\r\n };\r\n\r\n // template class, using CRTP to implement forward/inverse\r\n template \r\n struct base_poly_spheroid\r\n : public base_t_fi, T, Parameters>\r\n {\r\n par_poly m_proj_parm;\r\n\r\n inline base_poly_spheroid(const Parameters& par)\r\n : base_t_fi, T, Parameters>(*this, par)\r\n {}\r\n\r\n // FORWARD(s_forward) spheroid\r\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\r\n inline void fwd(T& lp_lon, T& lp_lat, T& xy_x, T& xy_y) const\r\n {\r\n T cot, E;\r\n\r\n if (fabs(lp_lat) <= tolerance) {\r\n xy_x = lp_lon;\r\n xy_y = this->m_proj_parm.ml0;\r\n } else {\r\n cot = 1. / tan(lp_lat);\r\n xy_x = sin(E = lp_lon * sin(lp_lat)) * cot;\r\n xy_y = lp_lat - this->m_par.phi0 + cot * (1. - cos(E));\r\n }\r\n }\r\n\r\n // INVERSE(s_inverse) spheroid\r\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\r\n inline void inv(T& xy_x, T& xy_y, T& lp_lon, T& lp_lat) const\r\n {\r\n T B, dphi, tp;\r\n int i;\r\n\r\n if (fabs(xy_y = this->m_par.phi0 + xy_y) <= tolerance) {\r\n lp_lon = xy_x;\r\n lp_lat = 0.;\r\n } else {\r\n lp_lat = xy_y;\r\n B = xy_x * xy_x + xy_y * xy_y;\r\n i = n_iter;\r\n do {\r\n tp = tan(lp_lat);\r\n lp_lat -= (dphi = (xy_y * (lp_lat * tp + 1.) - lp_lat -\r\n .5 * ( lp_lat * lp_lat + B) * tp) /\r\n ((lp_lat - xy_y) / tp - 1.));\r\n } while (fabs(dphi) > conv_tolerance && --i);\r\n if (! i) {\r\n BOOST_THROW_EXCEPTION( projection_exception(error_tolerance_condition) );\r\n }\r\n lp_lon = asin(xy_x * tan(lp_lat)) / sin(lp_lat);\r\n }\r\n }\r\n\r\n static inline std::string get_name()\r\n {\r\n return \"poly_spheroid\";\r\n }\r\n\r\n };\r\n\r\n // Polyconic (American)\r\n template \r\n inline void setup_poly(Parameters& par, par_poly& proj_parm)\r\n {\r\n if (par.es != 0.0) {\r\n proj_parm.en = pj_enfn(par.es);\r\n proj_parm.ml0 = pj_mlfn(par.phi0, sin(par.phi0), cos(par.phi0), proj_parm.en);\r\n } else {\r\n proj_parm.ml0 = -par.phi0;\r\n }\r\n }\r\n\r\n }} // namespace detail::poly\r\n #endif // doxygen\r\n\r\n /*!\r\n \\brief Polyconic (American) projection\r\n \\ingroup projections\r\n \\tparam Geographic latlong point type\r\n \\tparam Cartesian xy point type\r\n \\tparam Parameters parameter type\r\n \\par Projection characteristics\r\n - Conic\r\n - Spheroid\r\n - Ellipsoid\r\n \\par Example\r\n \\image html ex_poly.gif\r\n */\r\n template \r\n struct poly_ellipsoid : public detail::poly::base_poly_ellipsoid\r\n {\r\n inline poly_ellipsoid(const Parameters& par) : detail::poly::base_poly_ellipsoid(par)\r\n {\r\n detail::poly::setup_poly(this->m_par, this->m_proj_parm);\r\n }\r\n };\r\n\r\n /*!\r\n \\brief Polyconic (American) projection\r\n \\ingroup projections\r\n \\tparam Geographic latlong point type\r\n \\tparam Cartesian xy point type\r\n \\tparam Parameters parameter type\r\n \\par Projection characteristics\r\n - Conic\r\n - Spheroid\r\n - Ellipsoid\r\n \\par Example\r\n \\image html ex_poly.gif\r\n */\r\n template \r\n struct poly_spheroid : public detail::poly::base_poly_spheroid\r\n {\r\n inline poly_spheroid(const Parameters& par) : detail::poly::base_poly_spheroid(par)\r\n {\r\n detail::poly::setup_poly(this->m_par, this->m_proj_parm);\r\n }\r\n };\r\n\r\n #ifndef DOXYGEN_NO_DETAIL\r\n namespace detail\r\n {\r\n\r\n // Static projection\r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_STATIC_PROJECTION(srs::par4::poly, poly_spheroid, poly_ellipsoid)\r\n\r\n // Factory entry(s)\r\n template \r\n class poly_entry : public detail::factory_entry\r\n {\r\n public :\r\n virtual base_v* create_new(const Parameters& par) const\r\n {\r\n if (par.es)\r\n return new base_v_fi, T, Parameters>(par);\r\n else\r\n return new base_v_fi, T, Parameters>(par);\r\n }\r\n };\r\n\r\n template \r\n inline void poly_init(detail::base_factory& factory)\r\n {\r\n factory.add_to_factory(\"poly\", new poly_entry);\r\n }\r\n\r\n } // namespace detail\r\n #endif // doxygen\r\n\r\n} // namespace projections\r\n\r\n}} // namespace boost::geometry\r\n\r\n#endif // BOOST_GEOMETRY_PROJECTIONS_POLY_HPP\r\n\r\n", "meta": {"hexsha": "2346413dacb451e966e8ee04fb6c06b2ec462a19", "size": 12425, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/geometry/srs/projections/proj/poly.hpp", "max_stars_repo_name": "Talustus/boost_src", "max_stars_repo_head_hexsha": "ffe074de008f6e8c46ae1f431399cf932164287f", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2018-12-23T01:40:07.000Z", "max_stars_repo_stars_event_max_datetime": "2018-12-23T01:40:07.000Z", "max_issues_repo_path": "jeff/common/include/boost/geometry/srs/projections/proj/poly.hpp", "max_issues_repo_name": "jeffphi/advent-of-code-2018", "max_issues_repo_head_hexsha": "8e54bd23ebfe42fcbede315f0ab85db903551532", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "jeff/common/include/boost/geometry/srs/projections/proj/poly.hpp", "max_forks_repo_name": "jeffphi/advent-of-code-2018", "max_forks_repo_head_hexsha": "8e54bd23ebfe42fcbede315f0ab85db903551532", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.4723127036, "max_line_length": 130, "alphanum_fraction": 0.5095372233, "num_tokens": 2713, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5195213219520929, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.22745864304150112}} {"text": "/*\n * Copyright (c) 2009, 2010, Universitaet Bremen\n * All rights reserved.\n *\n * Author: Christoph Hertzberg \n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions\n * are met:\n *\n * * Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n * * Redistributions in binary form must reproduce the above\n * copyright notice, this list of conditions and the following\n * disclaimer in the documentation and/or other materials provided\n * with the distribution.\n * * Neither the name of the Universitaet Bremen nor the names of its\n * contributors may be used to endorse or promote products derived\n * from this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n * \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\n * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n * POSSIBILITY OF SUCH DAMAGE.\n */\n\n#ifndef __UKF_MTKWRAP_HPP__\n#define __UKF_MTKWRAP_HPP__\n\n#include \n\n#include \n//#include \n\n//#include \n\n\nnamespace ukfom {\n\n\n\n// import most common Eigen types \nUSING_PART_OF_NAMESPACE_EIGEN\n\n\n/**\n * mtkwrap wraps an MTK-Manifold M to a ukf-compatible manifold.\n * M has to have an enum DOF and implement the methods boxplus and boxminus.\n */\ntemplate\nstruct mtkwrap : public M{\n\ttypedef mtkwrap self;\npublic:\n\ttypedef double scalar; // MTK only works with double\n\ttypedef scalar scalar_type;\n\n\tenum {\n\t\tDOF = M::DOF\n\t};\n\t\n\ttypedef Matrix vectorized_type;\n\n\tmtkwrap(const M &m=M()) : M(m) {}\n\t\n\n\t/*\n\t * manifold operator (+)\n\t *\n\t */\n\tself& operator+=(const vectorized_type &delta_state)\n\t{\n\t\tassert(delta_state.stride() == DOF);\n\t\tM::boxplus(delta_state.data());\n\t\treturn *this;\n\t}\n\n\tconst self operator+(const vectorized_type &delta_state) const\n\t{\n\t\tself result = *this;\n\t\tresult += delta_state;\n\t\t\n\t\treturn result;\n\t}\n\n\t/*\n\t * manifold operator (-)\n\t */\n\tconst vectorized_type operator-(const self &other) const\n\t{\n\t\tvectorized_type result;\n\t\tassert(result.stride()==DOF);\n\t\tM::boxminus(result.data(), other);\n\n\t\treturn result;\n\t}\n\n\tbool operator==(const self &other) const\n\t{\n\t\tvectorized_type diff = (*this) - other;\n\t\treturn diff.isZero(1e-12);\n\t}\n\n\tbool operator!=(const self &other) const\n\t{\n\t\treturn !(*this == other);\n\t}\n\npublic:\n\tEIGEN_MAKE_ALIGNED_OPERATOR_NEW\n};\n\n} // namespace ukfom\n#endif /* __UKF_MTKWRAP_HPP__ */\n", "meta": {"hexsha": "5b8b1ed96808d69582645aaaa446524e77922308", "size": 3209, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "examples/slam_and_orientation/ukfom/mtkwrap.hpp", "max_stars_repo_name": "mfkiwl/ADEKF", "max_stars_repo_head_hexsha": "178092b7585b0f311ed7889820704e74dab12bd1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 90.0, "max_stars_repo_stars_event_min_datetime": "2021-01-04T11:04:59.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-23T01:43:07.000Z", "max_issues_repo_path": "examples/slam_and_orientation/ukfom/mtkwrap.hpp", "max_issues_repo_name": "mfkiwl/ADEKF", "max_issues_repo_head_hexsha": "178092b7585b0f311ed7889820704e74dab12bd1", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/slam_and_orientation/ukfom/mtkwrap.hpp", "max_forks_repo_name": "mfkiwl/ADEKF", "max_forks_repo_head_hexsha": "178092b7585b0f311ed7889820704e74dab12bd1", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 11.0, "max_forks_repo_forks_event_min_datetime": "2021-04-26T09:18:07.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-23T01:43:10.000Z", "avg_line_length": 26.3032786885, "max_line_length": 76, "alphanum_fraction": 0.7192271736, "num_tokens": 794, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5583270090337583, "lm_q2_score": 0.40733340004593027, "lm_q1q2_score": 0.2274252389271956}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef ENABLE_PARALLEL\n #include \n#endif\n\n#ifndef SIMPLE_HAUSDORFF_DISTANCE_HPP\n#define SIMPLE_HAUSDORFF_DISTANCE_HPP\n\nnamespace simple_hausdorff_distance\n{\n class SimpleHausdorffDistance\n {\n private:\n\n SimpleHausdorffDistance() {}\n\n static inline size_t GetNumOMPThreads()\n {\n#ifdef ENABLE_PARALLEL\n size_t num_threads = 0;\n #pragma omp parallel\n {\n num_threads = (size_t)omp_get_num_threads();\n }\n return num_threads;\n#else\n return 1;\n#endif\n }\n\n public:\n\n template>\n static double ComputeDistance(const std::vector& first_distribution, const std::vector& second_distribution, const std::function& distance_fn)\n {\n // Compute the Hausdorff distance - the \"maximum minimum\" distance\n double maximum_minimum_distance = 0.0;\n#ifdef ENABLE_PARALLEL\n std::vector thread_temp_storage(GetNumOMPThreads(), 0.0);\n #pragma omp parallel for schedule(guided)\n#endif\n for (size_t idx = 0; idx < first_distribution.size(); idx++)\n {\n const Datatype& first = first_distribution[idx];\n double minimum_distance = INFINITY;\n for (size_t jdx = 0; jdx < second_distribution.size(); jdx++)\n {\n const Datatype& second = second_distribution[jdx];\n const double& current_distance = distance_fn(first, second);\n if (current_distance < minimum_distance)\n {\n minimum_distance = current_distance;\n }\n }\n#ifdef ENABLE_PARALLEL\n const size_t current_thread_id = (size_t)omp_get_thread_num();\n if (minimum_distance > thread_temp_storage[current_thread_id])\n {\n thread_temp_storage[current_thread_id] = minimum_distance;\n }\n#else\n if (minimum_distance > maximum_minimum_distance)\n {\n maximum_minimum_distance = minimum_distance;\n }\n#endif\n }\n#ifdef ENABLE_PARALLEL\n for (size_t idx = 0; idx < thread_temp_storage.size(); idx++)\n {\n const double& temp_minimum_distance = thread_temp_storage[idx];\n if (temp_minimum_distance > maximum_minimum_distance)\n {\n maximum_minimum_distance = temp_minimum_distance;\n }\n }\n#endif\n return maximum_minimum_distance;\n }\n };\n}\n#endif // SIMPLE_HAUSDORFF_DISTANCE_HPP\n", "meta": {"hexsha": "f1e05c3fc97f8f1cada848981ec0b9a0afaebbe6", "size": 3025, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "arc_utilities/include/arc_utilities/simple_hausdorff_distance.hpp", "max_stars_repo_name": "UM-ARM-Lab/mab_ms", "max_stars_repo_head_hexsha": "f199f05b88060182cfbb47706bd1ff3479032c43", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2018-08-20T12:12:43.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-06T09:43:27.000Z", "max_issues_repo_path": "arc_utilities/include/arc_utilities/simple_hausdorff_distance.hpp", "max_issues_repo_name": "UM-ARM-Lab/mab_ms", "max_issues_repo_head_hexsha": "f199f05b88060182cfbb47706bd1ff3479032c43", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "arc_utilities/include/arc_utilities/simple_hausdorff_distance.hpp", "max_forks_repo_name": "UM-ARM-Lab/mab_ms", "max_forks_repo_head_hexsha": "f199f05b88060182cfbb47706bd1ff3479032c43", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2022-03-31T03:12:23.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T03:12:23.000Z", "avg_line_length": 32.5268817204, "max_line_length": 234, "alphanum_fraction": 0.5963636364, "num_tokens": 585, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.3886180267058489, "lm_q1q2_score": 0.2273808559697743}} {"text": "//============================================================================\n// Name : TDEModel.cpp\n// Author : Jordan Frank (jordan.frank@cs.mcgill.ca)\n// Copyright : MIT\n// Description : Implementation for the time-delay embedding model.\n//============================================================================\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n//#include \n\n#include \"Utils.h\"\n#include \"BuildTree.h\"\n#include \"TDEModel.h\"\n\nusing namespace std;\nusing namespace boost;\n\nTDEModel::TDEModel(Settings* settings) {\n ANNcoord *data, *projecteddata;\n\n length = settings->length;\n embdim = settings->embdim;\n delay = settings->delay;\n use_pca = settings->pcaembset;\n\n get_embedding(settings, data, length);\n\n if (use_pca) {\n \tcerr << \"Computing PCA bases.\\n\";\n \tcomputePCABases(data, length, embdim, settings->pcaembdim);\n \tprojecteddata = projectData(data, length, embdim);\n \tdelete [] data;\n \tdata = projecteddata;\n }\n else {\n \tavg = NULL;\n \tbases = NULL;\n }\n /*\n\tfor (uint i = 0; i < (unsigned)bases->cols; i++) {\n\t\tcout << data[i] << \" \" ;\n\t}\n\tcout << endl;\n\t*/\n get_ann_points(dataPts, data, length, settings->pcaembdim);\n kdTree = new ANNkd_tree(dataPts,length,settings->pcaembdim);\n settings->length = length;\n delete [] data;\n}\n\nTDEModel::TDEModel(ifstream* model_file, uint verbosity) {\n\tint avgsize, basesrows, basescols, i, j;\n\t*model_file >> delay;\n\t*model_file >> embdim;\n\t*model_file >> avgsize;\n\n\tif (avgsize > 0) {\n\t\tuse_pca = 1;\n\t\tavg = cvCreateMat(1,avgsize,MAT_TYPE);\n\t\tANNcoord* ptr = (ANNcoord*)avg->data.ptr;\n\t\tfor (i = 0; i < avgsize; i++) {\n\t\t\t*model_file >> *ptr++;\n\t\t}\n\t}\n\telse {\n\t\tuse_pca = 0;\n\t\tavg = NULL;\n\t\tbases = NULL;\n\t}\n\t*model_file >> basesrows >> basescols;\n\tif (verbosity > 0) {\n\t\tif (basescols > 0)\n\t\t\tcerr << \"Model params: d=\" << delay << \" m=\" << embdim << \" p=\" << basescols << endl;\n\t\telse\n\t\t\tcerr << \"Model params: d=\" << delay << \" m=\" << embdim << endl;\n\t}\n\tif (use_pca) {\n\t\tbases = cvCreateMat(basesrows,basescols,MAT_TYPE);\n\t\tANNcoord* ptr = (ANNcoord*)bases->data.ptr;\n\t\tfor (i = 0; i < basesrows; i++) {\n\t\t\tfor (j = 0; j < basescols; j++) {\n\t\t\t\t*model_file >> *ptr++;\n\t\t\t}\n\t\t}\n\t}\n\n\tkdTree = new ANNkd_tree(*model_file);\n\tdataPts = kdTree->thePoints();\n\tlength = kdTree->nPoints();\n\n\tif (verbosity > 0)\n\t\tcerr << \"Loaded \" << length << \" points.\" << endl;\n\tmodel_file->close();\n\tdelete model_file;\n}\n\nTDEModel::~TDEModel() {\n\tif (avg != NULL) cvReleaseMat(&avg);\n\tif (bases != NULL) cvReleaseMat(&bases);\n\tannDeallocPts(dataPts);\n\tdelete kdTree;\n}\n\nvoid TDEModel::DumpTree(char* outfile) {\n ofstream fout(outfile);\n fout << delay << endl;\n fout << embdim << endl;\n if (avg == NULL) {\n \tfout << 0 << endl;\n }\n else {\n \tfout << avg->cols << endl;\n \tfor (int i = 0; i < avg->cols; i++) {\n \t\tfout << \" \" << CV_MAT_ELEM(*avg, ANNcoord, 0, i);\n \t}\n }\n\tfout << endl;\n if (bases == NULL) {\n \tfout << \"0 0\" << endl;\n }\n else {\n \tfout << bases->rows << \" \" << bases->cols << endl;\n \tfor (int i = 0; i < bases->rows; i++) {\n \tfout << CV_MAT_ELEM(*bases, ANNcoord, i, 0);\n \t\tfor (int j = 1; j < bases->cols; j++) {\n \t\t\tfout << \" \" << CV_MAT_ELEM(*bases, ANNcoord, i, j);\n \t\t}\n \t\tfout << endl;\n \t}\n }\n kdTree->Dump(ANNtrue, fout);\n fout.close();\n}\n\nvoid TDEModel::getKNN(ANNpoint ap, uint k, ANNidxArray nn_idx, ANNdistArray dists) {\n\tkdTree->annkSearch(ap, k, nn_idx, dists);\n// for (uint i = 0; i < k; i++) {\n// cout << \"Point \" << i+1 << \": [\" << dataPts[nn_idx[i]][0] << \",\" << dataPts[nn_idx[i]][1] << \",\" << dataPts[nn_idx[i]][2] << \"], Dist: \" << sqrt(dists[i]) << endl;\n// }\n}\n\nvoid TDEModel::simulateTrajectory(ANNpoint s0, ANNpointArray trajectory, uint dim, ulong N) {\n ANNidxArray nn_idx;\n ANNdistArray dists;\n uint i,j,k;\n variate_generator > generator(mt19937(time(0)), normal_distribution<>(0.0,0.1));\n uint neighbours = 4;\n\t// +1 in case one of the neighbours is the last point in the model.\n nn_idx = new ANNidx[neighbours+1];\n dists = new ANNdist[neighbours+1];\n\n for (i = 0; i < dim; i++) {\n \ttrajectory[0][i] = s0[i];\n }\n\n for (i = 1; i < N; i++) {\n \tgetKNN(trajectory[i-1], neighbours+1, nn_idx, dists);\n \tfor (j = 0; j < dim; j++) {\n \t\ttrajectory[i][j] = 0.0;\n \t\tfor (k = 0; k < neighbours; k++) {\n \t\t\tif (nn_idx[k] == ANN_NULL_IDX) break;\n \t\t\telse if (nn_idx[k] == (int)length-1) nn_idx[k] = nn_idx[neighbours];\n \t\t\ttrajectory[i][j] += dataPts[nn_idx[k]+1][j];\n \t\t}\n \t\ttrajectory[i][j] = trajectory[i][j] / (ANNcoord)k + generator();\n \t}\n }\n}\n\nANNpoint TDEModel::getDataPoint(uint idx) {\n\treturn dataPts[idx];\n}\n\nvoid TDEModel::computePCABases(ANNcoord *data, uint rows, uint cols, uint numbases) {\n\tCvMat **embedding, *cov, *eigenvectors, *eigenvalues, *vector;\n\tANNcoord *basesdata;\n\tuint i, j, offset;\n\n\tcov = cvCreateMat(cols, cols, MAT_TYPE);\n\teigenvectors = cvCreateMat(cols,cols,MAT_TYPE);\n\teigenvalues = cvCreateMat(cols,cols,MAT_TYPE);\n\tembedding = new CvMat*[rows];\n\tfor (i = 0; i < rows; i++) {\n\t\tvector = cvCreateMatHeader(1, cols, MAT_TYPE);\n\t\tcvInitMatHeader(vector, 1, cols, MAT_TYPE, data + i * cols);\n\t\tembedding[i] = vector;\n\t}\n\tavg = cvCreateMat(1,cols,MAT_TYPE);\n\tcvCalcCovarMatrix((const CvArr **)embedding, rows, cov, avg, CV_COVAR_NORMAL);\n\tcvSVD(cov, eigenvalues, eigenvectors, 0, CV_SVD_MODIFY_A);\n\n\tbasesdata = new ANNcoord[cols*numbases];\n\tfor (i = 0, offset = 0; i < cols; i++) {\n\t\tfor (j = 0; j < numbases; j++) {\n\t\t\tbasesdata[offset++] = ((ANNcoord*)eigenvectors->data.ptr)[i*cols+j];\n\t\t}\n\t}\n\tbases = cvCreateMatHeader(cols, numbases, MAT_TYPE);\n\tcvInitMatHeader(bases, cols, numbases, MAT_TYPE, basesdata);\n\n\tfor (i = 0; i < rows; i++) {\n \tcvReleaseMat(&embedding[i]);\n }\n delete [] embedding;\n cvReleaseMat(&cov);\n cvReleaseMat(&eigenvectors);\n cvReleaseMat(&eigenvalues);\n}\n\nANNcoord* TDEModel::projectData(ANNcoord* data, uint rows, uint cols) {\n\tif (!use_pca) return data;\n\tANNcoord* shifteddata = new ANNcoord[rows*cols];\n\tANNcoord* projecteddata = new ANNcoord[rows*bases->cols];\n\tuint i, j, offset;\n\n\tfor (i = 0, offset = 0; i < rows; i++) {\n\t\tfor (j = 0; j < cols; j++) {\n\t\t\tshifteddata[offset] = data[offset] - ((ANNcoord*)avg->data.ptr)[j];\n\t\t\toffset++;\n\t\t}\n\t}\n\tCvMat projected = cvMat(rows, bases->cols, MAT_TYPE, projecteddata);\n\tCvMat dataMat = cvMat(rows, cols, MAT_TYPE, shifteddata);\n\tcvGEMM(&dataMat, bases, 1.0, NULL, 0.0, &projected, 0);\n\t/*\n\tfor (i = 0; i < (unsigned)bases->cols; i++) {\n\t\tcout << projecteddata[i] << \" \" ;\n\t}\n\tcout << endl;\n\t*/\n\tdelete [] shifteddata;\n\treturn projecteddata;\n}\n", "meta": {"hexsha": "4d2798a3beb7ac8c8c6fac6c765aaf545aaba4ba", "size": 6864, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "TDEModel.cpp", "max_stars_repo_name": "jwf-zz/tdetools", "max_stars_repo_head_hexsha": "7beb6e4f5dec719a3a59fefd8e92e90475392d48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-05-16T00:47:38.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-23T11:52:37.000Z", "max_issues_repo_path": "TDEModel.cpp", "max_issues_repo_name": "jwf-zz/tdetools", "max_issues_repo_head_hexsha": "7beb6e4f5dec719a3a59fefd8e92e90475392d48", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "TDEModel.cpp", "max_forks_repo_name": "jwf-zz/tdetools", "max_forks_repo_head_hexsha": "7beb6e4f5dec719a3a59fefd8e92e90475392d48", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2019-02-18T06:34:54.000Z", "max_forks_repo_forks_event_max_datetime": "2019-02-18T06:34:54.000Z", "avg_line_length": 28.4813278008, "max_line_length": 177, "alphanum_fraction": 0.592511655, "num_tokens": 2193, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011397337391, "lm_q2_score": 0.38861801254413975, "lm_q1q2_score": 0.2273808420606367}} {"text": "/*\n * Copyright (c) 2018 Eliane Briand\n *\n * This file is part of SmolDock.\n *\n * SmolDock is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 3 of the License, or\n * (at your option) any later version.\n *\n * SmolDock is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with SmolDock. If not, see .\n *\n */\n\n#include \"VinaLikeRigid.h\"\n\n#undef BOOST_LOG\n\n#include \n\n#include \n#include \n#include \n#include \n\n#include \n\n#include \"VinaLikeCommon.h\"\n\nnamespace SmolDock {\n namespace Score {\n\n const std::array\n VinaLikeRigid::coefficientsNames = {\"Gauss1\", \"Gauss2\", \"RepulsionExceptCovalent\", \"Hydrophobic\",\"Hydrogen\"};\n\n\n double vina_like_rigid_inter_scoring_func(const iConformer &ligand, iTransform &transform,\n const iProtein &protein) {\n\n BOOST_ASSERT(!ligand.x.empty());\n BOOST_ASSERT(!protein.x.empty());\n\n if(std::abs(transform.rota.norm() - 1) > 0.1) {\n transform.rota.normalize();\n }\n\n double score_raw = 0;\n\n\n Eigen::Vector3d ProtCenterPosition = {protein.center_x, protein.center_y, protein.center_z};\n\n for (unsigned int idxLig = 0; idxLig < ligand.x.size(); idxLig++) {\n for (unsigned int idxProt = 0; idxProt < protein.x.size(); idxProt++) {\n\n\n Eigen::Vector3d LigPosition = {ligand.x[idxLig], ligand.y[idxLig], ligand.z[idxLig]};\n applyRigidTransformInPlace(LigPosition, transform);\n\n Eigen::Vector3d ProtPosition = {protein.x[idxProt], protein.y[idxProt], protein.z[idxProt]};\n\n Eigen::Vector3d distToCenterVector = LigPosition - ProtCenterPosition;\n\n double distanceToProteinCenter = distToCenterVector.norm();\n\n if (distanceToProteinCenter > (protein.radius - 1)) {\n score_raw += std::pow((distanceToProteinCenter - protein.radius), 4) + 10;\n continue;\n }\n\n Eigen::Vector3d distVect = ProtPosition - LigPosition;\n\n double atomicRadiusLig = ligand.atomicRadius[idxLig];\n double atomicRadiusProt = protein.atomicRadius[idxProt];\n double rawDist = distVect.norm();\n\n\n const double cutoff = 8.0;\n if (rawDist >= cutoff)\n continue;\n\n double radToRemove = (atomicRadiusLig + atomicRadiusProt);\n double distance = rawDist - radToRemove;\n\n score_raw += scoreForAtomCouple(distance, ligand.type[idxLig], ligand.variant[idxLig],\n protein.type[idxProt], protein.variant[idxProt]);\n/*\n // Special rubber band term not in Vina scoring func\n double x_diff_center = std::pow(LigPosition.x - protein.center_x, 2);\n double y_diff_center = std::pow(LigPosition.y - protein.center_y, 2);\n double z_diff_center = std::pow(LigPosition.z - protein.center_z, 2);\n double rawDistCenter = std::sqrt(x_diff_center + y_diff_center + z_diff_center);\n\n score += 0.00001 * rawDistCenter;\n*/\n\n } // for\n } // for\n /*\n BOOST_LOG_TRIVIAL(debug) << \"Intermolecular scoring contribution without weighting :\";\n BOOST_LOG_TRIVIAL(debug) << \"Gauss 1 : \" << std::fixed<< std::setprecision(5) << score_gauss1;\n BOOST_LOG_TRIVIAL(debug) << \"Gauss 2 : \" << std::fixed<< std::setprecision(5) << score_gauss2;\n BOOST_LOG_TRIVIAL(debug) << \"Repulsion : \" << std::fixed<< std::setprecision(5) << score_repulsion;\n BOOST_LOG_TRIVIAL(debug) << \"Hydrophobic : \" << std::fixed<< std::setprecision(5) << score_hydrophobic;\n BOOST_LOG_TRIVIAL(debug) << \"Hydrogen : \" << std::fixed<< std::setprecision(5) << score_hydrogen;\n BOOST_LOG_TRIVIAL(debug) << \"------------------------------------------\";\n BOOST_LOG_TRIVIAL(debug) << \"Raw Score : \" << score_raw;\n BOOST_LOG_TRIVIAL(debug) << \"Nrotatable : \" << ligand.num_rotatable_bond;\n //*/\n double final_score = score_raw / (1 + (0.058459999999999998 * ligand.num_rotatable_bond));\n /*\n BOOST_LOG_TRIVIAL(debug) << \"------------------------------------------\";\n BOOST_LOG_TRIVIAL(debug) << \"Final Score : \" << final_score;\n //*/\n\n return final_score;\n }\n\n double VinaLikeRigid::Evaluate(const arma::mat &x) {\n BOOST_ASSERT(x.n_rows == 7);\n\n iTransform tr = this->internalToExternalRepr(x);\n tr.doHousekeeping();\n\n double score_ = vina_like_rigid_inter_scoring_func(this->startingConformation, tr, this->prot);\n\n return score_;\n }\n\n double VinaLikeRigid::EvaluateWithGradient(const arma::mat &x, arma::mat &grad) {\n\n BOOST_ASSERT(!x.has_nan());\n BOOST_ASSERT(!grad.has_nan());\n BOOST_ASSERT(x.n_rows == 7);\n BOOST_ASSERT(grad.n_rows == 7);\n\n iTransform tr = this->internalToExternalRepr(x);\n tr.doHousekeeping();\n\n double score_ = vina_like_rigid_inter_scoring_func(this->startingConformation, tr, this->prot);\n\n // Translation\n {\n iTransform transform_dx = tr;\n transform_dx.transl.x() += this->differential_epsilon;\n grad[0] = vina_like_rigid_inter_scoring_func(this->startingConformation, transform_dx, this->prot) -\n score_;\n }\n\n {\n iTransform transform_dy = tr;\n transform_dy.transl.y() += this->differential_epsilon;\n grad[1] = vina_like_rigid_inter_scoring_func(this->startingConformation, transform_dy, this->prot) -\n score_;\n }\n\n {\n iTransform transform_dz = tr;\n transform_dz.transl.z() += this->differential_epsilon;\n grad[2] = vina_like_rigid_inter_scoring_func(this->startingConformation, transform_dz, this->prot) -\n score_;\n }\n\n // Rotation\n\n {\n iTransform transform_ds = tr;\n transform_ds.rota.w() += this->differential_epsilon;\n transform_ds.doHousekeeping();\n grad[3] = vina_like_rigid_inter_scoring_func(this->startingConformation, transform_ds, this->prot) -\n score_;\n }\n\n {\n iTransform transform_du = tr;\n transform_du.rota.x() += this->differential_epsilon;\n transform_du.doHousekeeping();\n grad[4] = vina_like_rigid_inter_scoring_func(this->startingConformation, transform_du, this->prot) -\n score_;\n }\n\n {\n iTransform transform_dv = tr;\n transform_dv.rota.y() += this->differential_epsilon;\n transform_dv.doHousekeeping();\n grad[5] = vina_like_rigid_inter_scoring_func(this->startingConformation, transform_dv, this->prot) -\n score_;\n }\n\n {\n iTransform transform_dt = tr;\n transform_dt.rota.z() += this->differential_epsilon;\n transform_dt.doHousekeeping();\n grad[6] = vina_like_rigid_inter_scoring_func(this->startingConformation, transform_dt, this->prot) -\n score_;\n }\n /*\n BOOST_LOG_TRIVIAL(debug) << \"Transform: \" << x.t();\n BOOST_LOG_TRIVIAL(debug) << \"Score: \" << score_;\n BOOST_LOG_TRIVIAL(debug) << \"Gradient\";\n BOOST_LOG_TRIVIAL(debug) << \" ds: \" << grad[3];\n BOOST_LOG_TRIVIAL(debug) << \" du: \" << grad[4] << \" dx: \" << grad[0];\n BOOST_LOG_TRIVIAL(debug) << \" dv: \" << grad[5] << \" dy: \" << grad[1];\n BOOST_LOG_TRIVIAL(debug) << \" dt: \" << grad[6] << \" dx: \" << grad[2];\n //*/\n\n BOOST_ASSERT(score_ == score_); // catches NaN\n return score_;\n }\n\n\n VinaLikeRigid::VinaLikeRigid(const iConformer &startingConformation_,\n const iProtein &p,\n const iTransform &initialTransform_,\n double differential_epsilon_) :\n\n startingConformation(startingConformation_),\n prot(p),\n initialTransform(initialTransform_),\n differential_epsilon(differential_epsilon_) {}\n\n\n double VinaLikeRigid::getDifferentialEpsilon() const {\n return this->differential_epsilon;\n }\n\n arma::mat VinaLikeRigid::getStartingConditions() const {\n return this->externalToInternalRepr(this->initialTransform);\n }\n\n iConformer VinaLikeRigid::getConformerForParamMatrix(const arma::mat &x) {\n BOOST_ASSERT(x.n_rows == 7);\n\n iTransform tr = this->internalToExternalRepr(x);\n normalizeQuaternionInPlace(\n tr.rota); //!< Note that the internal representation arma::mat is not by itself normalized\n //! (because we always normalize it in the scoring function, so no constraint)\n\n iConformer ret = this->startingConformation;\n applyRigidTransformInPlace(ret, tr);\n\n return ret;\n }\n\n unsigned int VinaLikeRigid::getParamVectorDimension() const {\n return 7;\n }\n\n\n std::vector> VinaLikeRigid::EvaluateSubcomponents(const arma::mat &x) {\n std::vector> ret;\n\n BOOST_ASSERT(x.n_rows == 7);\n\n iTransform tr = this->internalToExternalRepr(x);\n normalizeQuaternionInPlace(tr.rota);\n\n BOOST_ASSERT(!this->startingConformation.x.empty());\n BOOST_ASSERT(!this->prot.x.empty());\n\n if(std::abs(tr.rota.norm() - 1) > 0.1) {\n tr.rota.normalize();\n }\n\n BOOST_ASSERT(tr.bondRotationsAngles.size() == this->startingConformation.num_rotatable_bond);\n\n\n double gauss1_total = 0.0;\n double gauss2_total = 0.0;\n double repulsion_total = 0.0;\n double hydrogen_total = 0.0;\n double hydrophobic_total = 0.0;\n double score_raw = 0.0;\n\n iConformer ligand = this->startingConformation;\n applyBondRotationInPlace(ligand, tr);\n\n Eigen::Vector3d ProtCenterPosition = {this->prot.center_x, this->prot.center_y, this->prot.center_z};\n\n for (unsigned int idxLig = 0; idxLig < ligand.x.size(); idxLig++) {\n for (unsigned int idxProt = 0; idxProt < this->prot.x.size(); idxProt++) {\n\n\n Eigen::Vector3d LigPosition = {ligand.x[idxLig], ligand.y[idxLig], ligand.z[idxLig]};\n applyRigidTransformInPlace(LigPosition, tr);\n\n Eigen::Vector3d ProtPosition = {this->prot.x[idxProt], this->prot.y[idxProt], this->prot.z[idxProt]};\n Eigen::Vector3d distToCenterVector = LigPosition - ProtCenterPosition;\n\n double distanceToProteinCenter = distToCenterVector.norm();\n\n if (distanceToProteinCenter > (this->prot.radius - 1)) {\n //FIXME : temporary disable\n //score_raw += std::pow((distanceToProteinCenter - this->prot.radius), 4) + 10;\n continue;\n }\n\n Eigen::Vector3d distVect = ProtPosition - LigPosition;\n\n double rawDist = distVect.norm();\n\n const double cutoff = 8.0;\n if (rawDist >= cutoff)\n continue;\n\n double distance = distanceFromRawDistance(rawDist, ligand.atomicRadius[idxLig], this->prot.atomicRadius[idxProt]);\n\n unsigned char atom1AtomicNumber = ligand.type[idxLig];\n unsigned int atom1AtomVariant = ligand.variant[idxLig];\n unsigned char atom2AtomicNumber = this->prot.type[idxProt];\n unsigned int atom2AtomVariant = this->prot.variant[idxProt];\n\n gauss1_total += vinaGaussComponent(distance, 0.0, 0.5);\n gauss2_total += vinaGaussComponent(distance, 3.0, 2.0);\n repulsion_total += vinaRepulsionComponent(distance, 0.0);\n hydrogen_total += vinaHydrophobicComponent(distance,\n atom1AtomicNumber, atom1AtomVariant,\n atom2AtomicNumber, atom2AtomVariant);\n hydrophobic_total += vinaHydrogenComponent(distance,\n atom1AtomicNumber, atom1AtomVariant,\n atom2AtomicNumber, atom2AtomVariant);\n score_raw += scoreForAtomCouple(distance,\n atom1AtomicNumber, atom1AtomVariant,\n atom2AtomicNumber, atom2AtomVariant);\n\n } // for\n } // for\n\n double final_score = score_raw / (1 + (0.058459999999999998 * ligand.num_rotatable_bond));\n\n\n ret.emplace_back(std::make_tuple(\"Gauss1\",gauss1_total));\n ret.emplace_back(std::make_tuple(\"Gauss2\",gauss2_total));\n ret.emplace_back(std::make_tuple(\"Repulsion\",repulsion_total));\n ret.emplace_back(std::make_tuple(\"Hydrophobic\",hydrogen_total));\n ret.emplace_back(std::make_tuple(\"Hydrogen\",hydrophobic_total));\n ret.emplace_back(std::make_tuple(\"ScoreRaw\",score_raw));\n ret.emplace_back(std::make_tuple(\"Score\",final_score));\n return ret;\n }\n\n double VinaLikeRigid::EvaluateOnlyIntermolecular(const arma::mat &x) {\n return this->Evaluate(x);\n }\n\n unsigned int VinaLikeRigid::getCoefficientsVectorWidth() {\n return this->numCoefficients;\n }\n\n std::vector VinaLikeRigid::getCoefficientsNames() {\n return std::vector(this->coefficientsNames.begin(),this->coefficientsNames.end());\n }\n\n std::vector VinaLikeRigid::getCurrentCoefficients() {\n return {VinaClassic::coeff_gauss1, VinaClassic::coeff_gauss2,\n VinaClassic::coeff_repulsion, VinaClassic::coeff_hydrophobic,\n VinaClassic::coeff_hydrogen };\n }\n\n bool VinaLikeRigid::setNonDefaultCoefficients(std::vector coeffs) {\n BOOST_LOG_TRIVIAL(debug) << \"Non default coefficients not supported yet on Vina rigid scoring function.\";\n return false;\n }\n\n\n }\n\n\n\n\n}\n", "meta": {"hexsha": "02839d51a6bd1fd1b0f30dd7393b93740c0e6fbf", "size": 15944, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Engines/ScoringFunctions/VinaLikeRigid.cpp", "max_stars_repo_name": "ElianeBriand/SMolDock", "max_stars_repo_head_hexsha": "de0cb746ef995ae0eef0f812ebd61727311c332f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-05-30T02:33:13.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-30T02:33:13.000Z", "max_issues_repo_path": "Engines/ScoringFunctions/VinaLikeRigid.cpp", "max_issues_repo_name": "ElianeBriand/SMolDock", "max_issues_repo_head_hexsha": "de0cb746ef995ae0eef0f812ebd61727311c332f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Engines/ScoringFunctions/VinaLikeRigid.cpp", "max_forks_repo_name": "ElianeBriand/SMolDock", "max_forks_repo_head_hexsha": "de0cb746ef995ae0eef0f812ebd61727311c332f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2020-12-30T19:11:51.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-25T02:45:49.000Z", "avg_line_length": 42.2917771883, "max_line_length": 135, "alphanum_fraction": 0.5590190667, "num_tokens": 3449, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5964331462646255, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.2273719853197914}} {"text": "//\n// main.cpp\n// PhyloAcc\n//\n// Created by hzr on 3/8/16.\n// Copyright © 2016 hzr. All rights reserved.\n//\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n//#include //libiomp/\n#include \n#include \n#include \"profile.h\"\n#include \"newick.h\"\n#include \"newick2.h\"\n#include \"bpp.hpp\"\n#include \"bpp_c.hpp\"\n#include \"utils.h\"\n#include \n#include \n\nusing namespace std;\nusing namespace arma;\n\n// parameters, input and output files paths\nstring params_path;\nstring phytree_path;\nstring align_path;\nstring output_path=\"\";\nstring output_path2=\"\";\nstring segment_path;\nstring id_path=\"\";\nstring result_prefix=\"test\";\nstring tree_coal_unit=\"\";\n\n//string refspecies;\nstring outgroup;\nstring targetspecies;\nstring conservegroup; // can't be missing in more than 50%\nstring deepcoal_species = \"\"; //species in which deep coal can happen\ndouble conserve_prop = 0.8;\n\nint num_thread = 1;\n\n// running parameters\nint num_burn = 200; // num of burn-in updates is num_burn * num_thin\nint num_mcmc = 800; // num of MCMC updates, num_burn * num_mcmc\nint num_thin = 1; // num of updates between two samples, adaptive frequency = 50 * num_thin\nint num_chain; // outer loop of updates Q matrix and hyperparameter of substitution rates\n\n\ndouble prep_lrate = 0.5;\ndouble prep_lrate2 = 0.0; //0.1\ndouble prep_grate = 0.8; // initalization\n\ndouble prior_lrate2_a = 0.0,prior_lrate2_b = 1 ; // beta prior for lrate2, 0.5\ndouble prior_lrate_a = 1 ,prior_lrate_b = 1 ; // beta prior for lrate, 1,9\ndouble prior_grate_a = 1,prior_grate_b = 1; // beta prior for grate, 3,1\n\n\ndouble ratio0 = 0.5; //initial conserved rate,0.5\ndouble ratio1 = 1; // initial accelerated rate\ndouble missing_thres = 0.8;\n\ndouble nprior_a = 10, nprior_b = 0.2; //around 1\ndouble cprior_a = 5, cprior_b = 0.04; //around ratio\nint ropt = 1;\ndouble cub = 1;\ndouble nlb = 0.6;\n\nint batch = -1 ;\nint seed = 1;\nint seed2 = 1;\ndouble indel = 0;\ndouble indel2;\nbool sample_indel = 0;\nbool sample_hyper = false;\nchar gapchar = '-';\nbool verbose = 0;\ndouble consToMis = 0.5; //for simulation, 0.01;\nint block = 15; // 25; //90;\nbool prune=0;\ndouble revgap=0.9; // 1\nint min_length = 50;\nbool WL = true;\nbool simulate=false;\nbool verboseGT = 1; //Han*: output trace_genetrees\ndouble br_sample_cutoff = 10.0;\ndouble theta_cutoff = 1.0;\n\n//Han*: add Dirichlet prior for stationary distribution pi:\n//vector prior_dir_par(4,10);\nvector prior_dir_par(2,10); //Han: it's beta prior for pi. Keep the old name.\n\n// load the program parameters\nvoid LoadParams(int argc, char* argv[])\n{\n cout << \"Loading input data and running parameters......\" << endl;\n\n if (argc > 1)\n params_path = string(argv[1]);\n else\n params_path = \"/Users/zhiruihu/Phylogenetics/test_mammalData/param_chr2_Long2.txt\"; //params2\n\n cout << \"Loading program configurations from \" << params_path << \"......\" <> tmp;\n \n // simulate or inference\n if (tmp==\"SIMULATE\")\n line_stream >> simulate;\n // input and output file paths\n else if (tmp==\"PHYTREE_FILE\")\n line_stream >> phytree_path;\n else if (tmp==\"TREE_IN_COALESCENT_UNIT\")\n line_stream >> tree_coal_unit;\n else if (tmp==\"ALIGN_FILE\")\n line_stream >> align_path;\n else if (tmp==\"SEG_FILE\")\n line_stream >> segment_path;\n else if (tmp==\"ID_FILE\")\n line_stream >> id_path;\n else if (tmp==\"BATCH\")\n line_stream >> batch;\n else if (tmp==\"RESULT_FOLDER\")\n line_stream >> output_path;\n else if (tmp==\"PREFIX\")\n line_stream >> result_prefix;\n else if (tmp==\"SEED\")\n line_stream >> seed;\n else if (tmp==\"SEEDS\")\n line_stream >> seed2;\n else if (tmp==\"INIT_CONSERVE_RATE\")\n line_stream >> ratio0;\n else if (tmp==\"INIT_ACCE_RATE\")\n line_stream >> ratio1;\n else if (tmp==\"CONSERVE_PRIOR_A\")\n line_stream >> cprior_a;\n else if (tmp==\"CONSERVE_PRIOR_B\")\n line_stream >> cprior_b;\n else if (tmp==\"ACCE_PRIOR_A\")\n line_stream >> nprior_a;\n else if (tmp==\"ACCE_PRIOR_B\")\n line_stream >> nprior_b;\n else if (tmp==\"ROPT\")\n line_stream >> ropt;\n else if (tmp==\"CUB\")\n line_stream >> cub;\n else if (tmp==\"NLB\")\n line_stream >> nlb;\n\n // running parameters\n else if (tmp==\"BURNIN\")\n line_stream >> num_burn;\n else if (tmp==\"MCMC\")\n line_stream >> num_mcmc;\n else if (tmp==\"THIN\") //ADAPT_FREQ\n line_stream >> num_thin;\n else if (tmp==\"INIT_LRATE\")\n line_stream >> prep_lrate;\n else if (tmp==\"INIT_GRATE\")\n line_stream >> prep_grate;\n else if (tmp==\"HYPER_LRATE_A\")\n line_stream >> prior_lrate_a;\n else if (tmp==\"HYPER_LRATE_B\")\n line_stream >> prior_lrate_b;\n else if (tmp==\"HYPER_GRATE_A\")\n line_stream >> prior_grate_a;\n else if (tmp==\"HYPER_GRATE_B\")\n line_stream >> prior_grate_b;\n else if (tmp==\"CHAIN\")\n line_stream >> num_chain;\n else if (tmp==\"WL\")\n line_stream >> WL;\n else if (tmp==\"BLK_WL\")\n line_stream >> block; \n\n // constraint\n else if (tmp == \"OUTGROUP\")\n line_stream >> outgroup;\n else if (tmp == \"TARGETSPECIES\")\n line_stream >> targetspecies;\n else if (tmp == \"CONSERVE\")\n line_stream >> conservegroup;\n else if (tmp == \"CONSERVE_PROP\")\n line_stream >> conserve_prop;\n else if (tmp == \"GAP_PROP\")\n line_stream >> missing_thres;\n //else if (tmp == \"REF\")\n // line_stream >> refspecies;\n else if (tmp == \"CONSTOMIS\")\n line_stream >> consToMis;\n else if (tmp==\"BR_SAMPLE_THRESHOLD\")\n line_stream >> br_sample_cutoff;\n else if (tmp==\"THETA_CUTOFF\")\n line_stream >> theta_cutoff;\n else if( tmp==\"DEEP_COAL_BRANCH\")\n line_stream >> deepcoal_species;\n\n // treat indel as additional character\n else if (tmp == \"GAPCHAR\")\n line_stream >> gapchar;\n else if (tmp == \"PRUNE_TREE\")\n line_stream >> prune;\n else if (tmp == \"TRIM_GAP_PERCENT\")\n line_stream >> revgap;\n else if (tmp == \"MIN_LEN\")\n line_stream >> min_length;\n else if (tmp == \"INDEL\") // not used\n line_stream >> indel;\n else if (tmp == \"INDEL2\") // not used\n line_stream >> indel2;\n else if(tmp == \"SAMPLE_INDEL\") // not used\n line_stream >> sample_indel;\n else if(tmp == \"SAMPLE_HYPER\")\n line_stream >> sample_hyper;\n else if(tmp == \"VERBOSE\")\n line_stream >> verbose;\n else if(tmp == \"VERBOSE_GENETREE\") //Han*: output sampled gene trees\n line_stream>>verboseGT;\n else if(tmp == \"NUM_THREAD\")\n line_stream >> num_thread;\n else if(tmp != \"\")\n cout << \"Unknown parameter: \" << tmp < pos_cum=vector (N,0);\n for(int i=0; i<(N-1); i++){\n if(phytree.nodes_names[i]!=tree2.nodes_names[i]){\n cout<<\"i=\"<= theta_cutoff){\n pos_cum[i]=1;\n }else{\n theta_cum+=phytree.thetas[i];\n count_cum+=1;\n }\n }else{\n pos_cum[i]=1;\n }\n }\n }\n }\n double theta_aver=theta_cum/count_cum;\n pos_cum[N-1]=1;\n for(int i=phytree.S; i ids;\n if(id_path==\"\")\n {\n if(batch==-1)\n {\n for(int c =0;c<500;c++) //bpp.C,\n {\n ids.push_back(c);\n }\n }else{\n int temp = ceil(bpp.C/3);\n for(int c =batch*temp ;c< (batch+1)*temp;c++)\n {\n if(c >= bpp.C) break;\n ids.push_back(c);\n }\n\n }\n\n }else{\n ifstream in_params(id_path.c_str());\n if (!in_params)\n {\n cerr << \"Cannot open the id file: \" << id_path.c_str() << endl;\n exit(1);\n }\n while(!in_params.eof())\n {\n const int BUFF_SIZE = 1024;\n char line_buff[BUFF_SIZE];\n in_params.getline(line_buff, BUFF_SIZE);\n istringstream line_stream(line_buff);\n string tmp; line_stream >> tmp;\n tmp = strutils::trim(tmp);\n if(tmp==\"\") continue;\n ids.push_back(atoi(tmp.c_str()));\n\n }\n\n }\n\n cout << ids.size() << \" elements to be computed\" << endl;\n\n if(sample_hyper)\n {\n for(int iter =0; iter\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace fl\n{\n// Forward declarations\ntemplate \nclass MultiSensorSigmaPointUpdatePolizzle;\n\n/**\n * \\internal\n */\ntemplate \nclass MultiSensorSigmaPointUpdatePolizzle\n{\n static_assert(\n std::is_base_of::value,\n \"\\n\\n\\n\"\n \"====================================================================\\n\"\n \"= Static Assert: You are using the wrong observation model type =\\n\"\n \"====================================================================\\n\"\n \" Observation model type must be a JointSensor<...>. \\n\"\n \" For single observation model, use the regular Gaussian filter \\n\"\n \" or the regular SigmaPointUpdatePolicy if you are specifying \\n\"\n \" the update policy explicitly fo the GaussianFilter. \\n\"\n \"====================================================================\"\n \"\\n\");\n};\n\ntemplate \nclass MultiSensorSigmaPointUpdatePolizzle<\n SigmaPointQuadrature,\n JointSensor>\n : public MultiSensorSigmaPointUpdatePolizzle<\n SigmaPointQuadrature,\n NonAdditive>>\n{\n};\n\ntemplate \nclass MultiSensorSigmaPointUpdatePolizzle<\n SigmaPointQuadrature,\n NonAdditive>>\n : public Descriptor\n{\npublic:\n typedef JointSensor JointModel;\n typedef typename MultipleOfLocalSensor::Type FeatureSensor;\n typedef typename FeatureSensor::EmbeddedSensor BodyTailModel;\n typedef typename BodyTailModel::BodySensor BodyModel;\n typedef typename BodyTailModel::TailSensor TailModel;\n\n typedef typename JointModel::State State;\n typedef typename JointModel::Obsrv Obsrv;\n typedef typename JointModel::Noise Noise;\n\n typedef typename Traits::LocalObsrv LocalFeature;\n typedef Vector1d LocalObsrvNoise;\n\n template \n void operator()(JointModel& obsrv_function,\n const SigmaPointQuadrature& quadrature,\n const Belief& prior_belief,\n const Obsrv& y,\n Belief& posterior_belief)\n {\n auto& feature_model = obsrv_function.local_sensor();\n auto& body_tail_model = feature_model.embedded_sensor();\n\n /* ------------------------------------------ */\n /* - Determine the number of quadrature - */\n /* - points needed for the given quadrature - */\n /* - in conjunction with the joint Gaussian - */\n /* - p(State, LocalObsrvNoise) - */\n /* ------------------------------------------ */\n enum : signed int\n {\n NumberOfPoints = SigmaPointQuadrature::number_of_points(\n JoinSizes::Value,\n SizeOf::Value>::Size)\n };\n\n /* ------------------------------------------ */\n /* - PointSets - */\n /* - [p_X, p_Q] ~ p(State, LocalObsrvNoise) - */\n /* ------------------------------------------ */\n PointSet p_X;\n PointSet p_R;\n\n /* ------------------------------------------ */\n /* - Transform p(State, LocalObsrvNoise) to - */\n /* - point sets [p_X, p_Q] - */\n /* ------------------------------------------ */\n quadrature.transform_to_points(\n prior_belief,\n Gaussian(feature_model.noise_dimension()),\n p_X,\n p_R);\n\n auto mu_x = p_X.mean();\n auto X = p_X.centered_points();\n\n auto W = p_X.covariance_weights_vector().asDiagonal();\n auto c_xx = (X * W * X.transpose()).eval();\n auto c_xx_inv = c_xx.inverse().eval();\n\n auto C = c_xx_inv;\n auto D = State();\n D.setZero(mu_x.size());\n\n const int sensor_count = obsrv_function.count_local_models();\n const int dim_y = y.size() / sensor_count;\n\n auto h_body = [&](const State& x, const typename BodyModel::Noise& w)\n {\n return feature_model.feature_obsrv(\n body_tail_model.body_model().observation(x, w));\n };\n auto h_tail = [&](const State& x, const typename TailModel::Noise& w)\n {\n return feature_model.feature_obsrv(\n body_tail_model.tail_model().observation(x, w));\n };\n PointSet p_Y_body;\n PointSet p_Y_tail;\n\n\n for (int i = 0; i < sensor_count; ++i)\n {\n // validate sensor value, i.e. make sure it is finite\n if (!is_valid(y, i * dim_y, i * dim_y + dim_y)) continue;\n\n feature_model.id(i);\n\n /* ------------------------------------------ */\n /* - Integrate body - */\n /* ------------------------------------------ */\n\n quadrature.propagate_points(h_body, p_X, p_R, p_Y_body);\n auto mu_y_body = p_Y_body.mean();\n\n // validate sensor value, i.e. make sure it is finite\n if (!is_valid(mu_y_body, 0, dim_y)) continue;\n\n auto Y_body = p_Y_body.centered_points();\n auto c_yy_body = (Y_body * W * Y_body.transpose()).eval();\n auto c_xy_body = (X * W * Y_body.transpose()).eval();\n\n /* ------------------------------------------ */\n /* - Integrate tail - */\n /* ------------------------------------------ */\n quadrature.propagate_points(h_tail, p_X, p_R, p_Y_tail);\n auto mu_y_tail = p_Y_tail.mean();\n auto Y_tail = p_Y_tail.centered_points();\n auto c_yy_tail = (Y_tail * W * Y_tail.transpose()).eval();\n auto c_xy_tail = (X * W * Y_tail.transpose()).eval();\n\n /* ------------------------------------------ */\n /* - Fuse and center - */\n /* ------------------------------------------ */\n auto w = body_tail_model.tail_weight();\n auto mu_y = ((1.0 - w) * mu_y_body + w * mu_y_tail).eval();\n\n // non centered moments\n auto m_yy_body =\n (c_yy_body + mu_y_body * mu_y_body.transpose()).eval();\n auto m_yy_tail =\n (c_yy_tail + mu_y_tail * mu_y_tail.transpose()).eval();\n auto m_yy = ((1.0 - w) * m_yy_body + w * m_yy_tail).eval();\n\n // center\n auto c_yy = (m_yy - mu_y * mu_y.transpose()).eval();\n auto c_xy = ((1.0 - w) * c_xy_body + w * c_xy_tail).eval();\n\n auto c_yx = c_xy.transpose().eval();\n auto A_i = (c_yx * c_xx_inv).eval();\n auto c_yy_given_x = (c_yy - c_yx * c_xx_inv * c_xy).eval();\n auto innovation = (y.middleRows(i * dim_y, dim_y) - mu_y).eval();\n\n C += A_i.transpose() * solve(c_yy_given_x, A_i);\n D += A_i.transpose() * solve(c_yy_given_x, innovation);\n }\n\n /* ------------------------------------------ */\n /* - Update belief according to PAPER REF - */\n /* ------------------------------------------ */\n posterior_belief.dimension(prior_belief.dimension());\n posterior_belief.covariance(C.inverse());\n posterior_belief.mean(mu_x + posterior_belief.covariance() * D);\n }\n\n virtual std::string name() const\n {\n return \"MultiSensorSigmaPointUpdatePolizzle<\" +\n this->list_arguments(\"SigmaPointQuadrature\",\n \"NonAdditive\") +\n \">\";\n }\n\n virtual std::string description() const\n {\n return \"Multi-Sensor Sigma Point based filter update policy \"\n \"for joint observation model of multiple local observation \"\n \"models with non-additive noise.\";\n }\n\nprivate:\n /**\n * \\brief Checks whether all vector components within the range (start, end)\n * are finiate, i.e. not NAN nor Inf.\n */\n template \n bool is_valid(Vector&& vector, int start, int end) const\n {\n for (int k = start; 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YES\n2. NO", "lm_q1_score": 0.6076631840431539, "lm_q2_score": 0.3738758157951908, "lm_q1q2_score": 0.22719056866283732}} {"text": "// This file is part of DM-HEOM (https://github.com/noma/dm-heom)\n//\n// Copyright (c) 2015-2019 Matthias Noack, Zuse Institute Berlin\n//\n// Licensed under the 3-clause BSD License, see accompanying LICENSE,\n// CONTRIBUTORS.md, and README.md for further information.\n\n#include \n#include \n\n#include \n#include \n#include \n\n#include \"heom/command_line.hpp\"\n#include \"heom/common.hpp\"\n#include \"heom/constants.hpp\"\n#include \"heom/dipole_matrix.hpp\"\n#include \"heom/dipole_pseudo_pathway.hpp\"\n#include \"heom/e_field.hpp\"\n#include \"heom/handle_main_exception.hpp\"\n#include \"heom/hierarchy_graph.hpp\"\n#include \"heom/hierarchy_mask.hpp\"\n#include \"heom/hierarchy_norm.hpp\"\n#include \"heom/instance.hpp\"\n#include \"heom/make_file_observer_list.hpp\"\n#include \"heom/population_dynamics_solver.hpp\"\n#include \"heom/ocl_config.hpp\"\n#include \"heom/ode.hpp\"\n#include \"heom/population_dynamics_solver.hpp\"\n#include \"heom/sites_to_states.hpp\"\n#include \"heom/transient_absorption_config.hpp\"\n\nnamespace bmt = ::heom::bmt;\nnamespace num = ::heom::num;\nnamespace ocl = ::heom::ocl;\nusing heom::int_t;\nusing heom::real_t;\nusing heom::real_format;\nusing heom::complex_t;\nusing heom::default_delimiter;\n\n\nint main(int argc, char* argv[])\n{\n\t// start runtime measurement\n\tbmt::timer app_timer;\n\n\t// output compile time configuration\n\tstd::cout << \"-------------------- Compile-Time Configuration ------------\" << std::endl;\n\theom::write_compile_config(std::cout);\n\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\t// command line parsing\n\t// usage: app_* [partition_file]\n\tstd::string ocl_config_file = \"ocl_config.cfg\";\n\tstd::string heom_config_file = \"transient_absorption.cfg\";\n\n\tif (argc >= 2)\n\t\tocl_config_file = argv[1];\n\tif (argc >= 3)\n\t\theom_config_file = argv[2];\n\n\tstd::exception_ptr eptr;\n\ttry {\n\t\t// command line parsing\n\t\theom::command_line command_line { argc, argv };\n\n\t\tstd::cout << \"Parsing OpenCL configuration from file: \" << command_line.ocl_config_filename() << std::endl;\n\t\theom::ocl_config ocl_config(command_line.ocl_config_filename());\n\t\tstd::cout << \"Parsing HEOM configuration from file: \" << command_line.heom_config_filename() << std::endl;\n\t\theom::transient_absorption_config heom_config(command_line.heom_config_filename());\n\n\t\theom::hierarchy_graph complete_graph(heom_config.baths_number(), heom_config.baths_matsubaras(), heom_config.system_ado_depth());\n\n\t\tusing stepper_t = num::meta_stepper; // uses solver_stepper_type from config\n\t\tusing solver_t = heom::population_dynamics_solver;\n\n\t\t// prepare output file (so we run into possible file errors before starting the computation)\n\t\tauto& observation_filename = heom_config.observations().get().front().get().second; // filename of first specified observation, guaranteed to exist by config checks\n\t\tstd::ofstream observation_file(observation_filename);\n\t\tif (observation_file.fail())\n\t\t\tthrow std::runtime_error(\"Error: could not open output file: \" + observation_filename);\n\n\t\t// transient absorption computation over all combinations of dipole matrices and phases\n\t\tconst size_t num_dipole_matrices = heom_config.dipole_tensor_prefactors().size();\n\t\tconst size_t num_pulse_phases = heom_config.probe_pulse_phases().size();\n\t\tconst size_t num_experiments = num_dipole_matrices * num_pulse_phases;\n\n\t\t// solver iterations and steps\n\t\tconst int_t iterations = heom_config.solver_steps() / heom_config.program_observe_steps();\n\t\tconst int_t steps_per_iteration = heom_config.program_observe_steps();\n\t\tconst int_t total_steps = iterations * heom_config.program_observe_steps();\n\n\t\t// output data structure for averaged traces\n\t\tconst int_t num_observations = iterations + 1;\n\t\tstd::vector observations(num_observations);\n\t\t// e_field is needed once, and is the same across runs\n\t\tstd::vector e_field_observations(num_observations);\n\n\t\t// transient absorption pseudo pathway\n\t\tconst auto& pathway_spec = heom_config.spectra_include_esa() ? heom::dipole_pseudo_pathway_esa_spec : heom::dipole_pseudo_pathway_wgs_spec;\n\t\tconst auto sts_mode = pathway_spec.sites_to_states_mode();\n\n\t\t// for progress estimate\n\t\tconst size_t solver_runs = num_dipole_matrices * num_pulse_phases;\n\t\tsize_t solver_run = 0;\n\n\t\t// iterate over all dipoles and compute population dynamics\n\t\tfor (size_t tensor_index = 0; tensor_index < num_dipole_matrices; ++tensor_index) {\n\t\t\t// loop over all pulse phases\n\t\t\tfor (size_t phase_index = 0; phase_index < num_pulse_phases; ++phase_index)\n\t\t\t{\n\t\t\t\tconst bool first_solver_run = tensor_index == 0 && phase_index == 0; // only observe first solver run\n\n\t\t\t\theom::instance heom_instance(heom_config, sts_mode, complete_graph);\n\t\t\t\theom_instance.set_hierarchy_top(); // NOTE: hierarchy top is initialised to zero with first element being complex_t(1.0,0.0) here\n\n\t\t\t\t// OpenCL range\n\t\t\t\tocl::nd_range range {\n\t\t\t\t\t{}, // offset\n\t\t\t\t\t{1, static_cast(heom_instance.matrices())}, // global size\n\t\t\t\t\t{1, 1} // local size\n\t\t\t\t};\n\n\t\t\t\t// create a solver from configuration and instance\n\t\t\t\tsolver_t solver(ocl_config, range, heom_config, heom_instance);\n\t\t\t\tstd::cout << \"-------------------- OpenCL Runtime Configuration ----------\" << std::endl;\n\t\t\t\tsolver.write_ocl_runtime_config(std::cout);\n\t\t\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\t\t\t\t++solver_run; // for progress estimate\n\n\t\t\t\t// compute dipole matrices\n\t\t\t\tauto get_dipole_matrix = [&](size_t spec_index) {\n\t\t\t\t\treturn heom::get_dipole_matrix_for_pathway(pathway_spec, spec_index, heom_config.system_sites(), heom_config, tensor_index);\n\t\t\t\t};\n\t\t\t\tauto dipole_matrix_minus = get_dipole_matrix(0);\n\t\t\t\tauto dipole_matrix_plus = get_dipole_matrix(1);\n\t\t\t\t// TODO: check scaling\n\t\t\t\tdipole_matrix_minus.scale(heom::get_dipole_tensor_prefactor(pathway_spec, heom_config, tensor_index));\n\n\t\t\t\t// TODO: remove this scaling, i.e. fix unit of dipole matrices across all applications, maybe use factor during initialisation of config object or change input file format\n\t\t\t\t// scaling dipole strengths in dipole_config_entries does work for this app, but messes with the result of others (tested with linear_absorption)\n\t\t\t\tdipole_matrix_minus.scale(heom::constants::debye_to_coulomb_meter);\n\t\t\t\tdipole_matrix_plus.scale(heom::constants::debye_to_coulomb_meter);\n//std::cout << dipole_matrix_minus << std::endl << std::endl;\n//std::cout << dipole_matrix_plus << std::endl << std::endl;\n\n\t\t\t\t// TODO: check if this is really plus and not prescaled... prefactor might be one in config (?) vs. other apps\n\t\t\t\t// setup observer with pre-scaled dipole matrix\n\t\t\t\theom::matrix_trace_observer trace_observer(complete_graph, solver, dipole_matrix_plus.data()); // D+ from left via observer\n\n\t\t\t\t// extract hamiltonian as complex_matrix_t for the e_field\n\t\t\t\theom::complex_matrix_t hamiltonian(dipole_matrix_plus.rows(), dipole_matrix_plus.cols());\n\t\t\t\tfor(size_t i = 0; i < hamiltonian.rows(); ++i) {\n\t\t\t\t\tfor(size_t j = 0; j < hamiltonian.cols(); ++j) {\n\t\t\t\t\t\tconst size_t real_index = 2 * (i * hamiltonian.rows() + j);\n\t\t\t\t\t\tconst size_t imag_index = real_index + 1;\n\t\t\t\t\t\thamiltonian.at(i, j) = heom::complex_t(heom_instance.hamiltonian()[real_index], heom_instance.hamiltonian()[imag_index]);\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// add e-field pre-evaluation action to the ODE for transient spectra computation\n\t\t\t\tauto e_field = heom::e_field(heom_config, phase_index, dipole_matrix_plus, dipole_matrix_minus, hamiltonian);\n\t\t\t\tsolver.ode().clear_pre_evaluate_actions();\n\t\t\t\tsolver.ode().add_pre_evaluate_action(e_field);\n\n\t\t\t\t// with create_hierarchy_mask we create mask corresponding to configured filtering strategy, then update solver\n\t\t\t\t// NOTE: .data() returns pointer to mask_t array\n\t\t\t\theom::hierarchy_mask hierarchy_mask = heom::create_hierarchy_mask(heom_config.filtering_strategy(), complete_graph, heom_config.baths_matsubaras(), heom_config.filtering_first_layer());\n\t\t\t\tsolver.update_hierarchy_mask(hierarchy_mask.data());\n\n\t\t\t\tstd::cout << \"-------------------- Hierarchy Mask Counter ----------------\" << std::endl;\n\t\t\t\theom::write_hierarchy_mask_stats(hierarchy_mask, std::cout);\n\t\t\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\t\t\t\t// generate first observation with initial value\n\t\t\t\tobservations[0] += trace_observer.observe_trace(0.0);\n\t\t\t\tif (first_solver_run)\n\t\t\t\t\te_field_observations[0] = e_field.compute_field(0.0);\n\n\t\t\t\t// setup output per propagation output\n\t\t\t\theom::config::obervation_type_list per_propagation_observations;\n\n\t\t\t\t// build single-propagation observation list from config list\n\t\t\t\tfor (auto& pair : heom_config.observations().get()) {\n\t\t\t\t\tauto& obs_type = pair.get().first;\n\t\t\t\t\tauto& filename = pair.get().second;\n\n\t\t\t\t\tif (heom::is_single_propagation_observation(obs_type)) {\n\t\t\t\t\t\tstd::remove_const::type>::type new_pair;\n\t\t\t\t\t\tnew_pair.get().first = pair.get().first; // copy observation type\n\n\t\t\t\t\t\tstd::stringstream new_filename;\n\t\t\t\t\t\tsize_t dot_pos = filename.find_first_of('.');\n\t\t\t\t\t\tif (dot_pos != std::string::npos) {\n\t\t\t\t\t\t\tnew_filename << filename.substr(0, dot_pos); // copy everything prior to first dot\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tnew_filename << filename; // copy whole filename, in case not dot was found\n\t\t\t\t\t\t}\n\t\t\t\t\t\tnew_filename << \"_d\" << tensor_index << \"_p\" << phase_index; // add create suffix\n\t\t\t\t\t\tif (dot_pos != std::string::npos) {\n\t\t\t\t\t\t\tnew_filename << filename.substr(dot_pos); // copy file ending\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tnew_pair.get().second = new_filename.str(); // set new filename\n\t\t\t\t\t\tper_propagation_observations.get().push_back(new_pair);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\theom::observer_list observer_list = heom::make_file_observer_list(per_propagation_observations, complete_graph, solver);\n\t\t\t\tobserver_list.observe(0.0);\n\n\t\t\t\t// write file header once\n\t\t\t\tif (first_solver_run) {\n\t\t\t\t\ttrace_observer.write_header(observation_file, false);\n\t\t\t\t\tobservation_file << default_delimiter;\n\t\t\t\t\tobservation_file << \"e-field_real\";\n\t\t\t\t\tobservation_file << default_delimiter;\n\t\t\t\t\tobservation_file << \"e-field_imag\";\n\t\t\t\t\tobservation_file << '\\n';\n\t\t\t\t}\n\n\t\t\t\t// propagate system\n\t\t\t\tfor (int_t k = 0; k < iterations; ++k)\n\t\t\t\t{\n\t\t\t\t\t// propagate\n\t\t\t\t\tsolver.step_forward(steps_per_iteration);\n\n\t\t\t\t\t// after propagation:\n\t\t\t\t\tconst auto current_step = (k + 1) * steps_per_iteration;\n\t\t\t\t\tconst real_t current_time = current_step * heom_config.solver_step_size();\n\n\t\t\t\t\t// observe trace of dipole_matrix_minus - rho\n\t\t\t\t\tobservations[k + 1] += trace_observer.observe_trace(current_time);\n\t\t\t\t\tif (first_solver_run)\n\t\t\t\t\t\te_field_observations[k + 1] = e_field.compute_field(current_time);\n\n\t\t\t\t\t// observe single-propagation observation types\n\t\t\t\t\tobserver_list.observe(current_time);\n\n\t\t\t\t\t// update status\n\t\t\t\t\tstd::stringstream status_prefix;\n\t\t\t\t\tstatus_prefix << \"Calculation transient absorption (\"\n\t\t\t\t\t << \"dipole: \" << tensor_index + 1 << '/' << num_dipole_matrices\n\t\t\t\t\t << \", \"\n\t\t\t\t\t << \"phase: \" << phase_index + 1 << '/' << num_pulse_phases\n\t\t\t\t\t << \"): \";\n\t\t\t\t\theom::write_progress(current_step - 1, total_steps, solver_run, solver_runs, status_prefix.str(), std::cout);\n\t\t\t\t}\n\n\t\t\t\t// write_complex_matrix(result_buffer_top, heom_instance.states(), std::cout);\n\t\t\t\tstd::cout << \"-------------------- Solver Runtime Summary ----------------\" << std::endl;\n\t\t\t\tsolver.write_runtime_summary(std::cout);\n\t\t\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\t\t\t}\n\t\t}\n\n\t\t// write averaged traces, with added columns for e-field\n\t\tauto format = real_format;\n\t\t//for (auto& obs : observations) {\n\t\tfor (int_t i = 0; i < num_observations; ++i) {\n\t\t\tobservations[i].avg(num_experiments);\n\t\t\tobservation_file << observations[i];\n\t\t\tobservation_file << default_delimiter;\n\t\t\tobservation_file << format % e_field_observations[i].real();\n\t\t\tobservation_file << default_delimiter;\n\t\t\tobservation_file << format % e_field_observations[i].imag();\n\t\t\tobservation_file << '\\n';\n\t\t}\n\t\tobservation_file << std::flush; // tell the OS to write to disk\n\n\t} catch (...) {\n\t\teptr = std::current_exception();\n\t}\n\tint ret = heom::handle_main_exception(eptr);\n\n\t// print application runtime\n\tstd::cout << \"main(): application runtime: \" << boost::format(\"%11.2f\") % std::chrono::duration_cast(bmt::duration(app_timer.elapsed())).count() << \" s\" << std::endl;\n\n\treturn ret;\n}\n", "meta": {"hexsha": "4a5b50451d481078022823249445536c659cf25a", "size": 12675, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "dm-heom/src/app_transient_absorption.cpp", "max_stars_repo_name": "noma/dm-heom", "max_stars_repo_head_hexsha": "85c94f947190064d21bd38544094731113c15412", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 7.0, "max_stars_repo_stars_event_min_datetime": "2019-07-28T01:02:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-09T04:01:36.000Z", "max_issues_repo_path": "dm-heom/src/app_transient_absorption.cpp", "max_issues_repo_name": "noma/dm-heom", "max_issues_repo_head_hexsha": "85c94f947190064d21bd38544094731113c15412", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dm-heom/src/app_transient_absorption.cpp", "max_forks_repo_name": "noma/dm-heom", "max_forks_repo_head_hexsha": "85c94f947190064d21bd38544094731113c15412", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2020-04-04T15:20:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-24T15:16:40.000Z", "avg_line_length": 44.9468085106, "max_line_length": 189, "alphanum_fraction": 0.6998816568, "num_tokens": 3175, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.22684102821147467}} {"text": "// Copyright 2016. All rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are met:\n//\n// * Redistributions of source code must retain the above copyright notice,\n// this list of conditions and the following disclaimer.\n// * Redistributions in binary form must reproduce the above copyright notice,\n// this list of conditions and the following disclaimer in the documentation\n// and/or other materials provided with the distribution.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\n// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\n// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\n// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\n// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\n// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n// POSSIBILITY OF SUCH DAMAGE.\n//\n// Author: ryan.latture@gmail.com (Ryan Latture)\n\n#include \"explicit_system.h\"\n#include \n#include \n\nnamespace explicit_fea {\n\n ExplicitSystem::ExplicitSystem(Mesh _mesh,\n ForceList _external_forces,\n const ColumnVector& _initial_displacements,\n const ColumnVector& _initial_velocities,\n double _t_0,\n const Options& _options) :\n mesh(std::move(_mesh)),\n external_forces(std::move(_external_forces)),\n displacements_0(_initial_displacements),\n velocities_0(_initial_velocities),\n velocities_1(ColumnVector::Zero(_initial_displacements.size())),\n accelerations_0(ColumnVector::Zero(_initial_displacements.size())),\n accelerations_1(ColumnVector::Zero(_initial_displacements.size())),\n forces(ColumnVector::Zero(_initial_displacements.size())),\n RHS(ColumnVector::Zero(_initial_displacements.size())),\n t_0(_t_0),\n dt_0(0.0),\n options(_options) {\n if (displacements_0.size() != velocities_0.size())\n throw std::runtime_error(\"Size of initial velocities and initial displacements are not equal.\");\n if (displacements_0.size() != mesh.get_global_stiffness_matrix().cols())\n throw std::runtime_error(\"Size of displacements and velocities does not match the number of columns in the global matrices.\");\n\n LHS.resize(mesh.get_global_stiffness_matrix().rows(), mesh.get_global_stiffness_matrix().cols());\n assemble_damping_matrix();\n apply_bcs(t_0);\n }\n\n void ExplicitSystem::Options::load(const rapidjson::Document &config_doc) {\n if (config_doc.HasMember(\"options\")) {\n if (config_doc[\"options\"].HasMember(\"beta\")) {\n if (!config_doc[\"options\"][\"beta\"].IsNumber()) {\n throw std::runtime_error(\"beta provided in options configuration is not a number.\");\n }\n beta = config_doc[\"options\"][\"beta\"].GetDouble();\n }\n if (config_doc[\"options\"].HasMember(\"gamma\")) {\n if (!config_doc[\"options\"][\"gamma\"].IsNumber()) {\n throw std::runtime_error(\"gamma provided in options configuration is not a number.\");\n }\n gamma = config_doc[\"options\"][\"gamma\"].GetDouble();\n }\n if (config_doc[\"options\"].HasMember(\"damping_beta\")) {\n if (!config_doc[\"options\"][\"damping_beta\"].IsNumber()) {\n throw std::runtime_error(\"damping_beta provided in options configuration is not a number.\");\n }\n damping_beta = config_doc[\"options\"][\"damping_beta\"].GetDouble();\n }\n if (config_doc[\"options\"].HasMember(\"damping_alpha\")) {\n if (!config_doc[\"options\"][\"damping_alpha\"].IsNumber()) {\n throw std::runtime_error(\"damping_alpha provided in options configuration is not a number.\");\n }\n damping_alpha = config_doc[\"options\"][\"damping_alpha\"].GetDouble();\n }\n }\n }\n\n void ExplicitSystem::update(double dt_1) {\n double t_1 = t_0 + dt_1;\n if (!compare.equal(dt_1, dt_0)) {\n assemble_LHS(dt_1);\n }\n\n apply_external_forces(t_1);\n RHS = forces;\n RHS.noalias() -= damping_matrix * (velocities_0 + (1.0 - options.gamma) * dt_1 * accelerations_0);\n RHS.noalias() -= mesh.get_global_stiffness_matrix() * (displacements_0 + dt_1 * velocities_0 + (0.5 - options.beta) * dt_1 * dt_1 * accelerations_0);\n apply_bcs(t_1);\n\n accelerations_1 = solver.solve(RHS);\n velocities_1.noalias() += (1.0 - options.gamma) * dt_1 * accelerations_0 + options.gamma * dt_1 * accelerations_1;\n\n // advance all current time values to next iteration\n displacements_0.noalias() += dt_1 * velocities_0 + dt_1 * dt_1 * (0.5 - options.beta) * accelerations_0 + dt_1 * dt_1 * options.beta * accelerations_1;\n velocities_0 = velocities_1;\n accelerations_0 = accelerations_1;\n t_0 = t_1;\n dt_0 = dt_1;\n }\n\n\tMesh const& ExplicitSystem::getMesh() const {\n\t\treturn mesh;\n\t}\n\n ColumnVector const& ExplicitSystem::getDisplacements() const {\n return displacements_0;\n }\n\n ColumnVector ExplicitSystem::getForces() const {\n ColumnVector out(displacements_0.size());\n out = mesh.get_global_stiffness_matrix() * displacements_0;\n out += mesh.get_mass_matrix() * accelerations_0;\n return out;\n }\n\n ColumnVector const& ExplicitSystem::getVelocities() const {\n return velocities_0;\n }\n\n double ExplicitSystem::getTime() const {\n return t_0;\n }\n\n void ExplicitSystem::assemble_damping_matrix() {\n damping_matrix.resize(mesh.get_global_stiffness_matrix().rows(), mesh.get_global_stiffness_matrix().cols());\n damping_matrix = options.damping_alpha * mesh.get_mass_matrix() + options.damping_beta * mesh.get_global_stiffness_matrix();\n damping_matrix.prune(1.e-14);\n damping_matrix.makeCompressed();\n }\n\n void ExplicitSystem::assemble_LHS(double dt){\n LHS = mesh.get_mass_matrix() + options.gamma * dt * damping_matrix + options.beta * dt * dt * mesh.get_global_stiffness_matrix();\n LHS.prune(1.e-14);\n LHS.makeCompressed();\n solver.compute(LHS);\n }\n\n void ExplicitSystem::apply_bcs(double time) {\n for (BCList::const_iterator it = mesh.get_bcs().begin(); it != mesh.get_bcs().end(); ++it) {\n RHS[(*it)->global_index] = 0.0;\n if ((*it)->type == BC::Type::DISPLACEMENT) {\n displacements_0[(*it)->global_index] = (*it)->get_value(time);\n velocities_0[(*it)->global_index] = 0.0;\n } else if ((*it)->type == BC::Type::VELOCITY) {\n velocities_0[(*it)->global_index] = (*it)->get_value(time);\n }\n }\n }\n\n void ExplicitSystem::apply_external_forces(double time) {\n for (ForceList::const_iterator it = external_forces.begin(); it != external_forces.end(); ++it) {\n forces[(*it)->global_index] = (*it)->get_value(time);\n }\n }\n\n} // namespace explicit_fea", "meta": {"hexsha": "c14caebddd45ed1a8ee82fd1c7456f862b055ea0", "size": 7705, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/explicit_system.cpp", "max_stars_repo_name": "latture/explicit-beam-fea", "max_stars_repo_head_hexsha": "003e940bda203e1d867494c891c9cee3477cd682", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2018-07-07T07:42:45.000Z", "max_stars_repo_stars_event_max_datetime": "2018-07-07T07:42:45.000Z", "max_issues_repo_path": "src/explicit_system.cpp", "max_issues_repo_name": "latture/explicit-beam-fea", "max_issues_repo_head_hexsha": "003e940bda203e1d867494c891c9cee3477cd682", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/explicit_system.cpp", "max_forks_repo_name": "latture/explicit-beam-fea", "max_forks_repo_head_hexsha": "003e940bda203e1d867494c891c9cee3477cd682", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.1377245509, "max_line_length": 159, "alphanum_fraction": 0.6369889682, "num_tokens": 1725, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.22684102821147467}} {"text": "#ifndef STAN_MCMC_HMC_INTEGRATORS_IMPL_LEAPFROG_HPP\n#define STAN_MCMC_HMC_INTEGRATORS_IMPL_LEAPFROG_HPP\n\n#include \n#include \n\nnamespace stan {\n namespace mcmc {\n\n template \n class impl_leapfrog: public base_leapfrog {\n public:\n impl_leapfrog(): base_leapfrog(),\n max_num_fixed_point_(10),\n fixed_point_threshold_(1e-8) {}\n\n void begin_update_p(typename Hamiltonian::PointType& z,\n Hamiltonian& hamiltonian,\n double epsilon,\n callbacks::logger& logger) {\n hat_phi(z, hamiltonian, epsilon, logger);\n hat_tau(z, hamiltonian, epsilon, this->max_num_fixed_point_,\n logger);\n }\n\n void update_q(typename Hamiltonian::PointType& z,\n Hamiltonian& hamiltonian,\n double epsilon,\n callbacks::logger& logger) {\n // hat{T} = dT/dp * d/dq\n Eigen::VectorXd q_init = z.q + 0.5 * epsilon * hamiltonian.dtau_dp(z);\n Eigen::VectorXd delta_q(z.q.size());\n\n for (int n = 0; n < this->max_num_fixed_point_; ++n) {\n delta_q = z.q;\n z.q.noalias() = q_init + 0.5 * epsilon * hamiltonian.dtau_dp(z);\n hamiltonian.update_metric(z, logger);\n\n delta_q -= z.q;\n if (delta_q.cwiseAbs().maxCoeff() < this->fixed_point_threshold_)\n break;\n }\n hamiltonian.update_gradients(z, logger);\n }\n\n void end_update_p(typename Hamiltonian::PointType& z,\n Hamiltonian& hamiltonian,\n double epsilon,\n callbacks::logger& logger) {\n hat_tau(z, hamiltonian, epsilon, 1, logger);\n hat_phi(z, hamiltonian, epsilon, logger);\n }\n\n // hat{phi} = dphi/dq * d/dp\n void hat_phi(typename Hamiltonian::PointType& z,\n Hamiltonian& hamiltonian,\n double epsilon,\n callbacks::logger& logger) {\n z.p -= epsilon * hamiltonian.dphi_dq(z, logger);\n }\n\n // hat{tau} = dtau/dq * d/dp\n void hat_tau(typename Hamiltonian::PointType& z,\n Hamiltonian& hamiltonian,\n double epsilon,\n int num_fixed_point,\n callbacks::logger& logger) {\n Eigen::VectorXd p_init = z.p;\n Eigen::VectorXd delta_p(z.p.size());\n\n for (int n = 0; n < num_fixed_point; ++n) {\n delta_p = z.p;\n z.p.noalias() = p_init - epsilon * hamiltonian.dtau_dq(z, logger);\n delta_p -= z.p;\n if (delta_p.cwiseAbs().maxCoeff() < this->fixed_point_threshold_)\n break;\n }\n }\n\n int max_num_fixed_point() {\n return this->max_num_fixed_point_;\n }\n\n void set_max_num_fixed_point(int n) {\n if (n > 0) this->max_num_fixed_point_ = n;\n }\n\n double fixed_point_threshold() {\n return this->fixed_point_threshold_;\n }\n\n void set_fixed_point_threshold(double t) {\n if (t > 0) this->fixed_point_threshold_ = t;\n }\n\n private:\n int max_num_fixed_point_;\n double fixed_point_threshold_;\n };\n\n } // mcmc\n} // stan\n\n#endif\n", "meta": {"hexsha": "604181ccf14fc108776ea65404b7fca0b6ea76e5", "size": 3349, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/stan/mcmc/hmc/integrators/impl_leapfrog.hpp", "max_stars_repo_name": "drezap/stan", "max_stars_repo_head_hexsha": "9b319ed125e2a7d14d0c9c246d2f462dad668537", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-07-05T01:40:40.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-05T01:40:40.000Z", "max_issues_repo_path": "src/stan/mcmc/hmc/integrators/impl_leapfrog.hpp", "max_issues_repo_name": "drezap/stan", "max_issues_repo_head_hexsha": "9b319ed125e2a7d14d0c9c246d2f462dad668537", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/stan/mcmc/hmc/integrators/impl_leapfrog.hpp", "max_forks_repo_name": "drezap/stan", "max_forks_repo_head_hexsha": "9b319ed125e2a7d14d0c9c246d2f462dad668537", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2020-07-14T11:36:09.000Z", "max_forks_repo_forks_event_max_datetime": "2020-07-14T11:36:09.000Z", "avg_line_length": 31.8952380952, "max_line_length": 78, "alphanum_fraction": 0.5622573903, "num_tokens": 811, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.22684102821147467}} {"text": "/*=========================================================================\n *\n * Copyright Universitat Pompeu Fabra, Department of Information and\n * Comunication Technologies.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0.txt\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n *=========================================================================*/\n\n#ifndef itkCudaRWSegmentationFilter_hxx\n#define itkCudaRWSegmentationFilter_hxx\n\n#include \n#include \n\n#include \n#include \n#include \n\n#define CLEANUP(s) \\\n do \\\n { \\\n printf(\"%s\\n\", s); \\\n fflush(stdout); \\\n } while (0)\n\nnamespace itk\n{\ntemplate \nvoid CudaRWSegmentationFilter::GenerateData()\n{\n if (m_LabelImage == nullptr) // Exit if SetLabelImage has not been called\n {\n std::cout << \"Label image has not been set\" << std::endl;\n return;\n }\n if (OutputImageType::ImageDimension != 2 && OutputImageType::ImageDimension != 3)\n {\n std::cout << \"Exit segmentation. Image dimension must be 2 or 3 but is \" << OutputImageType::ImageDimension << std::endl;\n return;\n }\n\n // Get regions to iterate for original and label images\n typename OutputImageType::RegionType regionLabel = m_LabelImage->GetLargestPossibleRegion();\n\n // Set label image iterator to get the bounding box size and the quantity of marked nodes\n typedef itk::ImageRegionIterator IteratorLabelType;\n IteratorLabelType itLabel(m_LabelImage, regionLabel);\n\n // Crop image to bounds containin labels\n typename OutputImageType::RegionType regionLabelCrop;\n typename InputImageType::RegionType regionCrop;\n\n for (int i = 0; i != OutputImageType::ImageDimension; ++i)\n {\n regionLabelCrop.SetSize(i, regionLabel.GetIndex()[i]);\n regionLabelCrop.SetIndex(i, regionLabel.GetSize()[i]);\n }\n\n int markedLength = 0;\n\n itLabel.GoToBegin();\n while (!itLabel.IsAtEnd())\n {\n // Get image boundaries to crop image\n if (itLabel.Get() != 0)\n {\n ++markedLength;\n // Get bounding region\n for (int i = 0; i != OutputImageType::ImageDimension; ++i)\n {\n if (itLabel.GetIndex()[i] < regionLabelCrop.GetIndex()[i])\n {\n regionCrop.SetIndex(i, itLabel.GetIndex()[i]);\n regionLabelCrop.SetIndex(i, itLabel.GetIndex()[i]);\n }\n else if (itLabel.GetIndex()[i] > regionLabelCrop.GetSize()[i])\n {\n regionCrop.SetSize(i, itLabel.GetIndex()[i]);\n regionLabelCrop.SetSize(i, itLabel.GetIndex()[i]);\n }\n }\n }\n ++itLabel;\n }\n\n int totalNodes = 1;\n for (int i = 0; i != OutputImageType::ImageDimension; ++i)\n {\n regionCrop.SetIndex(i, regionCrop.GetIndex()[i]);\n regionLabelCrop.SetIndex(i, regionLabelCrop.GetIndex()[i]);\n regionCrop.SetSize(i, regionCrop.GetSize()[i] - regionCrop.GetIndex()[i] + 1);\n regionLabelCrop.SetSize(i, regionLabelCrop.GetSize()[i] - regionLabelCrop.GetIndex()[i] + 1);\n\n totalNodes *= regionCrop.GetSize()[i];\n }\n\n /////////////////////// Build Graph /////////////////////////////\n /*\n // Create graph. Each node will correspond to a pixel connected to its neighbors by an edge.\n // Neighbors are those nodes which distance is 1 ( d = sqrt((x-xi)² + (y-yi)² + (z-zi)²) = 1 ), \n // where x, y, and z are the indices of a pixel and xi, yi, and zi are the indeces of a neighboring pixel\n // Iterate through all pixels of input and label images\n */\n\n int unmarkedLength = totalNodes - markedLength; /* Quantity of unmarked nodes */\n\n // Define vectors to store graph data\n std::vector *nodes = new std::vector(totalNodes); /* Pixel intensity for all nodes/pixels */\n std::vector *labels = new std::vector(totalNodes); /* Label of each node/pixel */\n /* For node 'i'. If 'i' is a marked node, previousFound->at(i) is how many unmarked nodes there are before node 'i'.\n If 'i' is an unmarked node, previousFound->at(i) is how many marked nodes there are before node 'i'.\n This values are needed to build ordered Lu and BT matrices.\n */\n std::vector *previousFound = new std::vector(totalNodes);\n std::vector *unmarked = new std::vector(unmarkedLength); /* Indices of unmarked nodes ordered */\n std::vector *marked = new std::vector(markedLength); /* Store labels of marked nodes */\n std::vector *markedIdx = new std::vector(markedLength); /* Indices of marked nodes ordered */\n std::vector *nameLabels = new std::vector(); /* Values of the different labels of the prior */\n\n // Set bounding box image iterators\n typedef itk::ImageRegionConstIterator ConstIteratorImageType;\n ConstIteratorImageType itImageCrop(this->GetInput(), regionCrop);\n IteratorLabelType itLabelCrop(m_LabelImage, regionLabelCrop);\n\n int foundMarked = 0;\n int foundUnmarked = 0;\n int NodeIdx = 0;\n\n itImageCrop.GoToBegin();\n itLabelCrop.GoToBegin();\n while (!itImageCrop.IsAtEnd())\n {\n // Store intensity in a std::vector\n nodes->at(NodeIdx) = itImageCrop.Get();\n // Store labels in a std::vector. Each index of 'nodes' and 'labels' correspond to the same pixel\n labels->at(NodeIdx) = itLabelCrop.Get();\n\n if (itLabelCrop.Get() == 0)\n {\n // Get the index of each node that is unmarked\n unmarked->at(foundUnmarked) = NodeIdx;\n // Store how many marked points have been found before this unmarked node\n previousFound->at(NodeIdx) = foundMarked;\n ++foundUnmarked;\n }\n else\n {\n // Set to label value if label != 0\n marked->at(foundMarked) = itLabelCrop.Get();\n // Get the index of each node that is marked\n markedIdx->at(foundMarked) = NodeIdx;\n // Store how many unmarked points have been found before this marked node\n previousFound->at(NodeIdx) = foundUnmarked;\n ++foundMarked;\n bool found;\n\n // Store the different labels in a std::vector\n found = std::find(nameLabels->begin(), nameLabels->end(), itLabelCrop.Get()) != nameLabels->end();\n if (!found)\n {\n nameLabels->push_back(itLabelCrop.Get());\n }\n }\n ++itImageCrop;\n ++itLabelCrop;\n ++NodeIdx;\n }\n\n // Sort labels\n std::sort(nameLabels->begin(), nameLabels->end());\n\n int totalLabels = nameLabels->size();\n if (m_SolveForAllLabels)\n {\n totalLabels += 1;\n }\n // Linear system: Lu * X = -BT * M\n // Convert marked (M) into a Eigen::Sparse matrix markedRHS. Needed for the computation of -BT * M (Eigen::SparseMatrix * Eigen::SparseMatrix)\n Eigen::SparseMatrix *markedRHS = new Eigen::SparseMatrix(markedLength, totalLabels - 1);\n for (int i = 0; i != totalLabels - 1; ++i)\n {\n int pos = 0;\n for (auto itMarked = marked->begin(); itMarked != marked->end(); ++itMarked)\n {\n if (*itMarked == nameLabels->at(i))\n markedRHS->insert(pos, i) = 1;\n ++pos;\n }\n }\n\n marked->clear();\n delete marked;\n\n /////////////////////// Build Laplacian matrix /////////////////////////////\n // Normalize intensity gradient over image spacing\n std::vector spacing;\n typename InputImageType::SpacingType space = this->GetInput()->GetSpacing();\n if (OutputImageType::ImageDimension == 2)\n spacing = {space[1], space[0], space[0], space[1]};\n else if (OutputImageType::ImageDimension == 3)\n spacing = {space[2], space[1], space[0], space[0], space[1], space[2]};\n\n std::vector neighbors;\n int x = regionCrop.GetSize()[0];\n int y = regionCrop.GetSize()[1];\n // Right hand of the equation: -BT * M\n Eigen::SparseMatrix *BTxM = new Eigen::SparseMatrix(markedLength, totalLabels - 1);\n\n // Build BT\n // Compare whether NumCols < NumRows for less iterations during BT building\n if (markedIdx->size() < unmarked->size())\n {\n Eigen::SparseMatrix *BT = new Eigen::SparseMatrix(unmarkedLength, markedLength);\n BT->reserve(Eigen::VectorXi::Constant(markedLength, 6));\n\n int node;\n float valNode, valNeighbor, w;\n\n // Iterate through marked nodes to build BT. Rows correspond to unmarked nodes, columns to marked nodes\n for (auto itMarked = markedIdx->begin(); itMarked != markedIdx->end(); ++itMarked)\n {\n valNode = nodes->at(*itMarked); // Intensity of node\n // Obtain neighbors indexes. right and left, top and bottom, front and back.\n node = *itMarked;\n if (OutputImageType::ImageDimension == 2)\n neighbors = {node - x, node - 1, node + 1, node + x};\n else if (OutputImageType::ImageDimension == 3)\n neighbors = {node - x * y, node - x, node - 1, node + 1, node + x, node + x * y};\n for (int i = 0; i != neighbors.size(); ++i)\n {\n // Make sure all the neighbors computed fall within the bounding box dimension\n if (neighbors.at(i) >= 0 && neighbors.at(i) < totalNodes && labels->at(neighbors.at(i)) == 0)\n {\n valNeighbor = nodes->at(neighbors.at(i)); // Intensity of neighbor pixel\n w = (exp(-m_Beta * pow((valNode - valNeighbor) / spacing.at(i), 2)) + 1e-6); // Intensity gradient following a Gaussian function\n // Columns of BT correspond to marked nodes, rows to unmarked\n BT->insert(neighbors.at(i) - previousFound->at(neighbors.at(i)), node - previousFound->at(node)) = -w;\n }\n }\n }\n markedIdx->clear();\n // Right hand of the equation: -BT * M\n *BTxM = -*BT * *markedRHS;\n BT->resize(0, 0);\n BT->data().squeeze();\n markedRHS->resize(0, 0);\n markedRHS->data().squeeze();\n delete markedIdx, markedRHS, BT;\n }\n else\n {\n markedIdx->clear();\n delete markedIdx;\n\n Eigen::SparseMatrix *BT = new Eigen::SparseMatrix(unmarkedLength, markedLength);\n BT->reserve(Eigen::VectorXi::Constant(markedLength, 6));\n\n int node;\n float valNode, valNeighbor, w;\n\n // Iterate through unmarked nodes to build BT. Rows correspond to unmarked nodes, columns to marked nodes\n for (auto itUnmarked = unmarked->begin(); itUnmarked != unmarked->end(); ++itUnmarked)\n {\n valNode = nodes->at(*itUnmarked); // Intensity of node\n // Obtain neighbors indexes. right and left, top and bottom, front and back.\n node = *itUnmarked;\n if (OutputImageType::ImageDimension == 2)\n neighbors = {node - x, node - 1, node + 1, node + x};\n else if (OutputImageType::ImageDimension == 3)\n neighbors = {node - x * y, node - x, node - 1, node + 1, node + x, node + x * y};\n for (int i = 0; i != neighbors.size(); ++i)\n {\n // Make sure all the neighbors computed fall within the bounding box dimension\n if (neighbors.at(i) >= 0 && neighbors.at(i) < totalNodes && labels->at(neighbors.at(i)) != 0)\n {\n valNeighbor = nodes->at(neighbors.at(i)); // Intensity of neighbor pixel\n w = (exp(-m_Beta * pow((valNode - valNeighbor) / spacing.at(i), 2)) + 1e-6); // Intensity gradient following a Gaussian function\n // Columns of BT correspond to marked nodes, rows to unmarked\n BT->insert(node - previousFound->at(node), neighbors.at(i) - previousFound->at(neighbors.at(i))) = -w;\n }\n }\n }\n // Right hand of the equation: -BT * M\n *BTxM = -*BT * *markedRHS;\n BT->resize(0, 0);\n BT->data().squeeze();\n markedRHS->resize(0, 0);\n markedRHS->data().squeeze();\n delete markedRHS, BT;\n }\n\n int nnz = 0;\n\n // Build Lu. LHS of the equation\n Eigen::SparseMatrix *Lu = new Eigen::SparseMatrix(unmarkedLength, unmarkedLength);\n Lu->reserve(Eigen::VectorXi::Constant(unmarkedLength, 7));\n\n // Iterate through unmarked nodes to build Lu. Rows correspond to unmarked nodes, columns to unmarked nodes\n for (auto itUnmarked = unmarked->begin(); itUnmarked != unmarked->end(); ++itUnmarked)\n {\n int node;\n float valNode, valNeighbor, w, degree;\n\n valNode = nodes->at(*itUnmarked); // Intensity of node\n // Obtain neighbors indexes. right and left, top and bottom, front and back.\n node = *itUnmarked;\n if (OutputImageType::ImageDimension == 2)\n neighbors = {node - x, node - 1, node + 1, node + x};\n else if (OutputImageType::ImageDimension == 3)\n neighbors = {node - x * y, node - x, node - 1, node + 1, node + x, node + x * y};\n degree = 0.0;\n for (int i = 0; i != neighbors.size(); ++i)\n {\n // Make sure all the neighbors computed fall within the bounding box dimension\n if (neighbors.at(i) >= 0 && neighbors.at(i) < totalNodes)\n {\n valNeighbor = nodes->at(neighbors.at(i)); // Intensity of neighbor pixel\n w = (exp(-m_Beta * pow((valNode - valNeighbor) / spacing.at(i), 2)) + 1e-6); // Intensity gradient following a Gaussian function\n degree += w; // Sum of the weights\n // Columns of Lu correspond to unmarked nodes\n if (labels->at(neighbors.at(i)) == 0) // If neighbor is an unmarked node, build Lu\n {\n Lu->insert(node - previousFound->at(node), neighbors.at(i) - previousFound->at(neighbors.at(i))) = -w;\n ++nnz;\n }\n }\n }\n // Add node degree to diagonal of Lu\n Lu->insert(node - previousFound->at(node), node - previousFound->at(node)) = degree;\n ++nnz;\n }\n Lu->makeCompressed();\n\n Eigen::SparseMatrix *Lu_PreconditionerGpu = new Eigen::SparseMatrix(unmarkedLength, unmarkedLength);\n Lu_PreconditionerGpu->reserve(Eigen::VectorXi::Constant(unmarkedLength, 1));\n for (int i = 0; i != unmarkedLength; ++i)\n Lu_PreconditionerGpu->insert(i, i) = 1 / Lu->coeffRef(i, i);\n Lu_PreconditionerGpu->makeCompressed();\n\n nodes->clear();\n unmarked->clear();\n previousFound->clear();\n neighbors.clear();\n delete nodes, unmarked, previousFound;\n\n // /////////////////////// Solve linear system /////////////////////////////\n // Convert BTxM into a float array for bicgstab cuda solver\n float *bhost_all_labels = new float[unmarkedLength * (totalLabels - 1)];\n\n for (int i = 0; i != unmarkedLength * (totalLabels - 1); ++i)\n bhost_all_labels[i] = 0;\n\n for (int k = 0; k < BTxM->outerSize(); ++k)\n {\n for (Eigen::SparseMatrix::InnerIterator itMat(*BTxM, k); itMat; ++itMat)\n {\n bhost_all_labels[itMat.row() + k * unmarkedLength] = itMat.value();\n }\n }\n\n // Get pointers for CSR matrices Lu and Lu_PreconditionerGpu\n cooValAhost = Lu->valuePtr();\n csrColPtrAhost = Lu->outerIndexPtr();\n cooRowPtrAhost = Lu->innerIndexPtr();\n\n cooValAhostM = Lu_PreconditionerGpu->valuePtr();\n csrColPtrAhostM = Lu_PreconditionerGpu->outerIndexPtr();\n cooRowPtrAhostM = Lu_PreconditionerGpu->innerIndexPtr();\n\n int bicgstab_exit_status, cuda_mem_cpy_exit_status, cuda_mem_free_exit_status;\n\n // Result vector\n probabilities = new float[unmarkedLength * (totalLabels - 1)];\n probabilitiesInner = new float[unmarkedLength];\n\n // Allocate GPU memory\n cuda_mem_cpy_exit_status = this->AllocGPUMemory(unmarkedLength, nnz);\n if (cuda_mem_cpy_exit_status != 0)\n {\n std::cout << \"GPU memory allocation failed with error \" << cuda_mem_cpy_exit_status << std::endl;\n return;\n }\n\n // Solve S-1 linear systems\n for (int linearSystem = 0; linearSystem != totalLabels - 1; ++linearSystem)\n {\n // Get pinter for each linear system RHS\n bhost = &bhost_all_labels[linearSystem * unmarkedLength];\n // Call CUDA BiCGStab solver\n bicgstab_exit_status = this->BiCGStab(unmarkedLength, nnz);\n if (bicgstab_exit_status != 0)\n {\n std::cout << \"BiCStab failed with error\" << bicgstab_exit_status << std::endl;\n return;\n }\n\n for (int i = 0; i != unmarkedLength; ++i)\n probabilities[i + linearSystem * unmarkedLength] = probabilitiesInner[i];\n }\n\n // Free GPU memeory when all linear systems have been solved\n cuda_mem_free_exit_status = this->FreeGPUMemory();\n if (cuda_mem_free_exit_status != 0)\n {\n std::cout << \"GPU memory free failed with error\" << cuda_mem_free_exit_status << std::endl;\n }\n Lu->resize(0, 0);\n Lu->data().squeeze();\n Lu_PreconditionerGpu->resize(0, 0);\n Lu_PreconditionerGpu->data().squeeze();\n BTxM->resize(0, 0);\n BTxM->data().squeeze();\n delete Lu, Lu_PreconditionerGpu, BTxM;\n delete[] probabilitiesInner, bhost, bhost_all_labels, cooRowPtrAhost, csrColPtrAhost, cooValAhost, cooRowPtrAhostM, csrColPtrAhostM, cooValAhostM;\n\n std::vector *RWLabels = new std::vector(unmarkedLength);\n\n /* Assign a label to each unmarked node according to the result of the solver. \n The label that is assigned is that one corresponding to the highest probability.\n Since we solver for S-1 systems, last label probability is computed by subtraction */\n for (int i = 0; i != unmarkedLength; ++i)\n {\n float maxProbability = probabilities[i];\n int maxLabelPos = 0;\n float accumulatedProbability = maxProbability;\n for (int j = 1; j != totalLabels - 1; ++j)\n {\n accumulatedProbability += probabilities[i + j * unmarkedLength];\n if (probabilities[i + j * unmarkedLength] > maxProbability)\n {\n maxProbability = probabilities[i + j * unmarkedLength];\n maxLabelPos = j;\n }\n }\n RWLabels->at(i) = 0.95 - accumulatedProbability > maxProbability ? nameLabels->back() : nameLabels->at(maxLabelPos);\n }\n\n delete[] probabilities;\n\n int valBackground;\n if (!m_WriteBackground)\n valBackground = 0;\n else\n valBackground = nameLabels->back();\n\n typename OutputImageType::Pointer outputLabels = this->GetOutput();\n\n outputLabels->Graft(m_LabelImage);\n outputLabels->FillBuffer(valBackground);\n\n // Iterate through original label image to create segmentation image\n // Assign labels known from label image to their original value. Assign unmarked labels\n // according to the result from the solver\n\n IteratorLabelType itOut1(outputLabels, regionLabelCrop);\n\n // Set computed labels to segmentation image\n int unmarkedIdx = 0;\n int idxOutput = 0;\n\n itOut1.GoToBegin();\n while (!itOut1.IsAtEnd())\n {\n if (labels->at(idxOutput) == 0)\n {\n if (RWLabels->at(unmarkedIdx) != nameLabels->back())\n itOut1.Set(RWLabels->at(unmarkedIdx));\n else\n itOut1.Set(valBackground);\n ++unmarkedIdx;\n }\n else if (labels->at(idxOutput) != nameLabels->back())\n itOut1.Set(labels->at(idxOutput));\n\n ++idxOutput;\n ++itOut1;\n }\n\n labels->clear();\n RWLabels->clear();\n nameLabels->clear();\n delete labels, RWLabels, nameLabels;\n\n return;\n}\n\ntemplate \nint CudaRWSegmentationFilter::BiCGStab(int M, int nnz)\n{\n\n int iter, flag;\n\n cusparseStatus_t status;\n cusparseHandle_t handle = 0;\n cusparseMatDescr_t descra = 0;\n\n int nnzM = M;\n int N = M;\n int N2 = N;\n\n float timesOne[1] = {1.0};\n float timesZero[1] = {0.0};\n float timesMinusOne[1] = {-1.0};\n\n cudaStat4 = cudaMemcpy(r, bhost,\n (size_t)(N * sizeof(r[0])),\n cudaMemcpyHostToDevice);\n\n cudaStat5 = cudaMemcpy(xdev, xhost,\n (size_t)(N * sizeof(xdev[0])),\n cudaMemcpyHostToDevice);\n\n // Copy right hand side to GPU memory\n if ((cudaStat4 != cudaSuccess) ||\n (cudaStat5 != cudaSuccess))\n {\n CLEANUP(\"Memcpy from Host to Device failed\");\n return EXIT_FAILURE;\n }\n\n /* initialize cusparse library */\n status = cusparseCreate(&handle);\n if (status != CUSPARSE_STATUS_SUCCESS)\n {\n CLEANUP(\"CUSPARSE Library initialization failed\");\n return EXIT_FAILURE;\n }\n /* create and setup matrix descriptor */\n status = cusparseCreateMatDescr(&descra);\n if (status != CUSPARSE_STATUS_SUCCESS)\n {\n CLEANUP(\"Matrix descriptor initialization failed\");\n return EXIT_FAILURE;\n }\n cusparseSetMatType(descra, CUSPARSE_MATRIX_TYPE_GENERAL);\n cusparseSetMatIndexBase(descra, CUSPARSE_INDEX_BASE_ZERO);\n\n bnrm2 = cublasSnrm2(N, r, 1);\n\n float tol = m_Tolerance * bnrm2;\n\n status = cusparseScsrmv(handle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nnz, timesMinusOne, descra, cooValAdev, csrColPtrAdev, cooRowPtrAdev, xdev, timesOne, r); /* r = r - A*x; r is now residual */\n\n if (status != CUSPARSE_STATUS_SUCCESS)\n {\n CLEANUP(\"Matrix‐vector multiplication failed\");\n return EXIT_FAILURE;\n }\n\n error = cublasSnrm2(N, r, 1) / bnrm2; /* norm_r = norm(b) */\n if (error < tol)\n {\n CLEANUP(\"Error smaller than tolerance failed\");\n return EXIT_FAILURE; /* x is close enough already */\n }\n\n omega = 1.0;\n\n cudaStat1 = cudaMemcpy(r_tld, r, (size_t)(N * sizeof(r[0])),\n cudaMemcpyDeviceToDevice); /* r_tld = r */\n\n if ((cudaStat1 != cudaSuccess))\n {\n CLEANUP(\"Memcpy from r to r_tld failed\");\n return EXIT_FAILURE;\n }\n\n // Loop of the BiCGStab solver\n for (iter = 0; iter < m_MaximumNumberOfIterations; ++iter)\n {\n rho = cublasSdot(N, r_tld, 1, r, 1); /* rho = r_tld'*r */\n\n if (rho == 0.0)\n break;\n\n if (iter > 0)\n {\n beta = (rho / rho_1) * (alpha / omega);\n cublasSaxpy(N, -omega, v, 1, p, 1);\n cublasSaxpy(N, 1.0 / beta, r, 1, p, 1);\n cublasSscal(N, beta, p, 1); /* p = r + beta*( p - omega*v ) */\n }\n else\n {\n cudaStat1 = cudaMemcpy(p, r,\n (size_t)(N * sizeof(r[0])),\n cudaMemcpyDeviceToDevice); /* p = r */\n if ((cudaStat1 != cudaSuccess))\n {\n CLEANUP(\"Memcpy from r to p failed\");\n return EXIT_FAILURE;\n }\n }\n\n status = cusparseScsrmv(handle, CUSPARSE_OPERATION_NON_TRANSPOSE, N2, N2, nnzM, timesOne, descra, cooValAdevM, csrColPtrAdevM, cooRowPtrAdevM, p, timesZero, p_hat);\n\n status = cusparseScsrmv(handle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nnz, timesOne, descra, cooValAdev, csrColPtrAdev, cooRowPtrAdev, p_hat, timesZero, v); /* v = A*p_hat */\n\n alpha = rho / cublasSdot(N, r_tld, 1, v, 1); /* alph = rho / ( r_tld'*v ) */\n\n cudaStat1 = cudaMemcpy(s, r, (size_t)(N * sizeof(r[0])),\n cudaMemcpyDeviceToDevice); /* s = r */\n if ((cudaStat1 != cudaSuccess))\n {\n CLEANUP(\"Memcpy from r to s failed\");\n return EXIT_FAILURE;\n }\n cublasSaxpy(N, -alpha, v, 1, s, 1);\n snrm2 = cublasSnrm2(N, s, 1);\n\n cublasSaxpy(N, alpha, p_hat, 1, xdev, 1); /* h = x + alph*p_hat */\n\n if (snrm2 < tol)\n {\n // cublasSaxpy(N, alpha, p_hat, 1, s, 1);\n resid = snrm2 / bnrm2;\n\n cudaStat5 = cudaMemcpy(probabilitiesInner, xdev,\n (size_t)(N * sizeof(probabilitiesInner[0])),\n cudaMemcpyDeviceToHost); /* x = h */\n if ((cudaStat5 != cudaSuccess))\n {\n CLEANUP(\"Memcpy from x to xhost failed\");\n return EXIT_FAILURE;\n }\n\n break;\n }\n\n status = cusparseScsrmv(handle, CUSPARSE_OPERATION_NON_TRANSPOSE, N2, N2, nnzM, timesOne, descra, cooValAdevM, csrColPtrAdevM, cooRowPtrAdevM, s, timesZero, s_hat);\n\n status = cusparseScsrmv(handle, CUSPARSE_OPERATION_NON_TRANSPOSE, N, N, nnz, timesOne, descra, cooValAdev, csrColPtrAdev, cooRowPtrAdev, s_hat, timesZero, t); /* t = A*s_hat */\n\n omega = cublasSdot(N, t, 1, s, 1) / cublasSdot(N, t, 1, t, 1); /* omega = ( t'*s) / ( t'*t ) */\n\n cublasSaxpy(N, omega, s_hat, 1, xdev, 1); /* x = h + omega*s_hat */\n\n cudaStat1 = cudaMemcpy(r, s, (size_t)(N * sizeof(r[0])),\n cudaMemcpyDeviceToDevice); /* r = s */\n if ((cudaStat1 != cudaSuccess))\n {\n CLEANUP(\"Memcpy from s to r failed\");\n return EXIT_FAILURE;\n }\n\n cublasSaxpy(N, -omega, t, 1, r, 1); /* r = s - omega*t */\n snrm2 = cublasSnrm2(N, r, 1);\n\n if (snrm2 <= tol)\n {\n resid = snrm2 / bnrm2;\n\n cudaStat5 = cudaMemcpy(probabilitiesInner, xdev,\n (size_t)(N * sizeof(probabilitiesInner[0])),\n cudaMemcpyDeviceToHost); /* x = x */\n if ((cudaStat5 != cudaSuccess))\n {\n CLEANUP(\"Memcpy from x to xhost failed\");\n return EXIT_FAILURE;\n }\n break;\n }\n if (omega == 0.0)\n break;\n rho_1 = rho;\n }\n ++iter;\n\n error = snrm2 / bnrm2;\n\n if (error <= tol)\n {\n flag = 0;\n }\n else if (omega == 0.0)\n {\n flag = -2;\n }\n else if (rho == 0.0)\n {\n flag = -1;\n }\n else\n flag = 1;\n\n m_SolverIterations += iter;\n m_SolverError = std::min(m_SolverError, error);\n\n return flag;\n}\n\n// Allocate GPU memory and copy data to device\ntemplate \nint CudaRWSegmentationFilter::AllocGPUMemory(int M, int nnz)\n{\n\n int nnzM = M; // Lu has size MxM, Lu_PreconditionerGpu has size MxM\n int N = M;\n\n // Define initial guess\n xhost = new float[N];\n for (int i = 0; i < N; i++)\n {\n xhost[i] = 0;\n }\n\n cudaStat1 = cudaMalloc((void **)&cooRowPtrAdev, nnz * sizeof(cooRowPtrAdev[0]));\n cudaStat3 = cudaMalloc((void **)&cooValAdev, nnz * sizeof(cooValAdev[0]));\n cudaStat4 = cudaMalloc((void **)&r, N * sizeof(r[0]));\n cudaStat5 = cudaMalloc((void **)&s, N * sizeof(s[0]));\n cudaStat6 = cudaMalloc((void **)&t, N * sizeof(t[0]));\n cudaStat7 = cudaMalloc((void **)&r_tld, N * sizeof(r_tld[0]));\n cudaStat8 = cudaMalloc((void **)&p, N * sizeof(p[0]));\n cudaStat9 = cudaMalloc((void **)&p_hat, N * sizeof(p_hat[0]));\n cudaStat10 = cudaMalloc((void **)&s_hat, N * sizeof(s_hat[0]));\n cudaStat11 = cudaMalloc((void **)&v, N * sizeof(v[0]));\n cudaStat12 = cudaMalloc((void **)&xdev, N * sizeof(xdev[0]));\n\n cudaStat13 = cudaMalloc((void **)&csrColPtrAdev, (N + 1) * sizeof(csrColPtrAdev[0]));\n\n cudaStat14 = cudaMalloc((void **)&cooRowPtrAdevM, nnzM * sizeof(cooRowPtrAdevM[0]));\n cudaStat16 = cudaMalloc((void **)&cooValAdevM, nnzM * sizeof(cooValAdevM[0]));\n cudaStat17 = cudaMalloc((void **)&csrColPtrAdevM, (N + 1) * sizeof(csrColPtrAdevM[0]));\n\n if ((cudaStat1 != cudaSuccess) ||\n (cudaStat3 != cudaSuccess) ||\n (cudaStat4 != cudaSuccess) ||\n (cudaStat5 != cudaSuccess) ||\n (cudaStat6 != cudaSuccess) ||\n (cudaStat7 != cudaSuccess) ||\n (cudaStat8 != cudaSuccess) ||\n (cudaStat9 != cudaSuccess) ||\n (cudaStat10 != cudaSuccess) ||\n (cudaStat11 != cudaSuccess) ||\n (cudaStat12 != cudaSuccess) ||\n\n (cudaStat14 != cudaSuccess) ||\n (cudaStat16 != cudaSuccess) ||\n (cudaStat17 != cudaSuccess))\n {\n CLEANUP(\"Device malloc failed\");\n return EXIT_FAILURE;\n }\n\n cudaStat1 = cudaMemcpy(cooRowPtrAdev, cooRowPtrAhost,\n (size_t)(nnz * sizeof(cooRowPtrAdev[0])),\n cudaMemcpyHostToDevice);\n cudaStat13 = cudaMemcpy(csrColPtrAdev, csrColPtrAhost,\n (size_t)((N + 1) * sizeof(csrColPtrAdev[0])),\n cudaMemcpyHostToDevice);\n cudaStat3 = cudaMemcpy(cooValAdev, cooValAhost,\n (size_t)(nnz * sizeof(cooValAdev[0])),\n cudaMemcpyHostToDevice);\n\n cudaStat14 = cudaMemcpy(cooRowPtrAdevM, cooRowPtrAhostM,\n (size_t)(nnzM * sizeof(cooRowPtrAdevM[0])),\n cudaMemcpyHostToDevice);\n cudaStat17 = cudaMemcpy(csrColPtrAdevM, csrColPtrAhostM,\n (size_t)((N + 1) * sizeof(cooColPtrAdevM[0])),\n cudaMemcpyHostToDevice);\n cudaStat16 = cudaMemcpy(cooValAdevM, cooValAhostM,\n (size_t)(nnzM * sizeof(cooValAdevM[0])),\n cudaMemcpyHostToDevice);\n\n if ((cudaStat1 != cudaSuccess) ||\n (cudaStat13 != cudaSuccess) ||\n (cudaStat3 != cudaSuccess) ||\n (cudaStat14 != cudaSuccess) ||\n (cudaStat17 != cudaSuccess) ||\n (cudaStat16 != cudaSuccess))\n {\n CLEANUP(\"Memcpy from Host to Device failed\");\n return EXIT_FAILURE;\n }\n\n return 0;\n}\n\n// Free GPU memeory when all linear systems have been solved\ntemplate \nint CudaRWSegmentationFilter::FreeGPUMemory()\n{\n /* shutdown CUBLAS */\n cublas_status = cublasShutdown();\n if (cublas_status != CUBLAS_STATUS_SUCCESS)\n {\n fprintf(stderr, \"!!!! shutdown error (A)\\n\");\n return EXIT_FAILURE;\n }\n\n cudaStat1 = cudaFree(cooRowPtrAdev);\n cudaStat3 = cudaFree(cooValAdev);\n cudaStat4 = cudaFree(r);\n cudaStat5 = cudaFree(s);\n cudaStat6 = cudaFree(t);\n cudaStat7 = cudaFree(r_tld);\n cudaStat8 = cudaFree(p);\n cudaStat9 = cudaFree(p_hat);\n cudaStat10 = cudaFree(s_hat);\n cudaStat11 = cudaFree(v);\n cudaStat12 = cudaFree(xdev);\n\n cudaStat13 = cudaFree(csrColPtrAdev);\n\n cudaStat14 = cudaFree(cooRowPtrAdevM);\n cudaStat16 = cudaFree(cooValAdevM);\n cudaStat17 = cudaFree(csrColPtrAdevM);\n\n if ((cudaStat1 != cudaSuccess) ||\n (cudaStat3 != cudaSuccess) ||\n (cudaStat4 != cudaSuccess) ||\n (cudaStat5 != cudaSuccess) ||\n (cudaStat6 != cudaSuccess) ||\n (cudaStat7 != cudaSuccess) ||\n (cudaStat8 != cudaSuccess) ||\n (cudaStat9 != cudaSuccess) ||\n (cudaStat10 != cudaSuccess) ||\n (cudaStat11 != cudaSuccess) ||\n (cudaStat12 != cudaSuccess) ||\n\n (cudaStat14 != cudaSuccess) ||\n (cudaStat16 != cudaSuccess) ||\n (cudaStat17 != cudaSuccess))\n {\n CLEANUP(\"Device memory free failed\");\n return EXIT_FAILURE;\n }\n\n return 0;\n}\n\ntemplate \nvoid CudaRWSegmentationFilter::PrintSelf(std::ostream &os, Indent indent) const\n{\n Superclass::PrintSelf(os, indent);\n os << indent << \"Beta: \" << m_Beta << std::endl;\n os << indent << \"Tolerance: \" << m_Tolerance << std::endl;\n os << indent << \"MaximumNumberOfIterations: \" << m_MaximumNumberOfIterations << std::endl;\n os << indent << \"WriteBackground: \" << m_WriteBackground << std::endl;\n}\n\n} // namespace itk\n#endif", "meta": {"hexsha": "772d914c177d0a5f56b92d307506729d1c6f4a9f", "size": 30686, "ext": "hxx", "lang": "C++", "max_stars_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5467381372136563, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.22684102208302143}} {"text": "// Prepare for G2O-------------------------------------------------\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"targetTypes6D.hpp\"\n// #include \"continuous_to_discrete.h\"\n// #include \"targetTypes3D.hpp\"\nusing namespace Eigen;\nusing namespace std;\nusing namespace g2o;\n\n\n\n// Prepare for ROS-------------------------------------------------\n#include \n#include \n#include \"sensor_msgs/Imu.h\"\n// #include \"sensor_msgs/MagneticField.h\"\n// #include \"sensor_msgs/Temperature.h\"\n// #include \"sensor_msgs/FluidPressure.h\"\n// #include \"sensor_msgs/Joy.h\"\n// #include \"sensor_msgs/NavSatFix.h\"\n// #include \"sensor_msgs/Range.h\"\n\n// #include \"geometry_msgs/PointStamped.h\"\n#include \"geometry_msgs/PoseWithCovarianceStamped.h\"\n#include \"geometry_msgs/PoseArray.h\"\n// #include \"geometry_msgs/PoseStamped.h\"\n// #include \"geometry_msgs/TwistWithCovarianceStamped.h\"\n// #include \n// #include \n#include \n\n// #include \"nav_msgs/Odometry.h\"\n// #include \"crossover_nav/Ack.h\"\n// #include \"crossover_nav/Navdata.h\"\n// #include \"crossover_nav/odom_data.h\"\n// #include \"crossover_nav/Status.h\"\n\n#include \n#include \n\n\n\n\n// #include \n// #include \n// #include \n\n// #include \n\n//function convert arduino to ros\n//------------ROS version-------\n// #include \ninline long millis() {\n return (1e3 * ros::Time::now().sec + 1e-6 * ros::Time::now().nsec);\n}\ninline long micros() {\n return (1e6 * ros::Time::now().sec + 1e-3 * ros::Time::now().nsec);\n}\n//not use millis() it give me online floating point behind sec\n//go use ros::Time::now();\n#define constrain(amt,low,high) ((amt)<(low)?(low):((amt)>(high)?(high):(amt)))\n\n#define delay(x) ros::Duration(x/1000.0).sleep()\n#define say(x) (std::cout << x)\n#define sayend(x) (std::cout << x << std::endl)\n#define saytab(x) (std::cout << x << \"\\t\")\n#define saycomma(x) (std::cout << x << \" ,\")\n#define Min(a,b) ((a)<(b)?(a):(b))\n#define Max(a,b) ((a)>(b)?(a):(b))\n#define isfinite(X) std::isfinite(X)\n#define wrap_180(x) (x < -180 ? x+360 : (x > 180 ? x - 360: x))\n#define wrap_pi(x) (x < -3.14 ? x+6.28 : (x > 3.14 ? x - 6.28: x))\n#define HALF_M_PI 1.570796\n\nsensor_msgs::Imu imu;\ngeometry_msgs::PoseWithCovarianceStamped gps_pose , poseop_msgs;\nsensor_fusion_comm::DoubleArrayStamped state_out_msf;\n#define NUM_ITERATE 1\n\nVector3d gps[5];\nVector6d msf_state[17];\nVector3d acc[17];\ndouble dtime[200];\nint Iterate = 0;\nVector6d state;\nint Numimu = 0;\nint Numstate = 0;\n\nint num_state = 0; //round max = 17\nfloat GPS_lOOCKUP[5][2] = {{0,0},\n {3,0},\n {3,-3},\n {0,-3},\n {0,0}\n };\n\nfloat STATE_LOOCKUP[17][2];\n\n\n\n\n\n\n\n\n// void imucallback(const sensor_msgs::Imu::ConstPtr &data)\n// {\n// // imu = *data;\n// sensor_msgs::Imu imu_bf = *data;\n\n// static ros::Time st = ros::Time::now();\n// static ros::Duration ct = ros::Time::now()-st;\n// static ros::Duration pt= ct;\n// ct = ros::Time::now()-st;\n// dtime[Numimu] = (ct - pt).toSec();\n\n// //transform bf to ef\n// tf::Quaternion qimu(imu_bf.linear_acceleration.x,imu_bf.linear_acceleration.y,imu_bf.linear_acceleration.z,0);\n// tf::Quaternion q(imu_bf.orientation.x,imu_bf.orientation.y,imu_bf.orientation.z,imu_bf.orientation.w);\n\n// qimu = q*qimu*q.inverse() - tf::Quaternion(0,0,9.81,0);\n\n// imu.linear_acceleration.x = qimu.x();\n// imu.linear_acceleration.y = qimu.y();\n// imu.linear_acceleration.z = qimu.z();\n// imu.orientation = imu_bf.orientation;\n\n// acc[Numimu][0] = imu.linear_acceleration.x;\n// acc[Numimu][1] = imu.linear_acceleration.y;\n// acc[Numimu][2] = imu.linear_acceleration.z;\n\n// Numimu++;\n// // tf::Quaternion q(imu.orientation.x, imu.orientation.y, imu.orientation.z, imu.orientation.w);\n// // tf::Matrix3x3 m(q);\n// // m.getRPY(roll, pitch, yaw);\n// //ROS_INFO(\"ax:[%f]\", yaw);\n// }\n// void gpscallback(/*const geometry_msgs::PoseWithCovarianceStamped::ConstPtr& data*/) {\n// // gps_pose = *data;\n\n// if(Iterate>=NUM_ITERATE) return;\n\n// gps[num_state][0] = GPS_lOOCKUP[0][0];\n// gps[num_state][1] = gps_pose.pose.pose.position.y;\n// gps[num_state][2] = 0;\n\n// Iterate++;\n\n// }\n// void state_out_callback(/*const sensor_fusion_comm::DoubleArrayStamped::ConstPtr& data*/) {\n\n// state_out_msf = *data;\n\n// for(int i =0;i<6;i++)\n// msf_state[Numstate][i] = state_out_msf.data[i];\n// Numstate++;\n// }\n\nvoid Optimize_test(double dt);\n\n\nint main( int argc, char** argv )\n{\n ros::init(argc, argv, \"gps_tester\");\n ros::NodeHandle n;\n ros::Rate r(200);\n\n // ros::Subscriber imu_sub = n.subscribe(\"/imu_max\", 2, imucallback);\n // ros::Subscriber gps_sub = n.subscribe(\"/imu_max/pose\", 2, gpscallback);\n // ros::Subscriber state_out_msf_sub = n.subscribe(\"/msf_core/state_out\", 2, state_out_callback);\n ros::Publisher poseop_pub = n.advertise(\"/optimized\", 10);\n\n static ros::Time start_time = ros::Time::now();\n static ros::Duration cur_time = ros::Time::now()-start_time;\n static ros::Duration prev_time= cur_time;\n static double dt = 0.25; \n\n poseop_msgs.header.frame_id = \"odom\";\n\n state_out_msf.data.resize(36);\n // state.setZero();\n // Iterate over the simulation steps\n\n\n state.setZero();\n for (int k = 0; k < 3; k++)\n {\n state[k] = 0;\n }\n\n\n\n STATE_LOOCKUP[0][0] = 0;\n STATE_LOOCKUP[0][1] = 0;\n\n for(int i=1;i<=4;i++) {\n /*x*/STATE_LOOCKUP[i][0] = (3.0/4.0)*i;\n /*y*/STATE_LOOCKUP[i][1] = 0;\n if(i==1 || i==4) {\n acc[i] = {16*3/3,0,0};\n if(i==4) acc[i]*=-1;\n }else\n acc[i] = {0,0,0};\n }\n for(int i=1;i<=4;i++) {\n /*x*/STATE_LOOCKUP[i+4][0] = 3.0+(1.0/4.0)*i;\n /*y*/STATE_LOOCKUP[i+4][1] = -i;\n if(i==1 || i==4) {\n acc[i+4] = {16*1/3,-16*4/3,0};\n if(i==4) acc[i+4]*=-1;\n }else\n acc[i+4] = {0,0,0};\n }\n for(int i=1;i<=4;i++) {\n /*x*/STATE_LOOCKUP[i+8][0] = 4.0+ (-3.0/4.0)*i;\n /*y*/STATE_LOOCKUP[i+8][1] = -4.0 + (1.0/4.0)*i;\n if(i==1 || i==4) {\n acc[i+8] = {-16*3/3,16*1/3,0};\n if(i==4) acc[i+8]*=-1;\n }else\n acc[i+8] = {0,0,0};\n }\n for(int i=1;i<=4;i++) {\n /*x*/STATE_LOOCKUP[i+12][0] = 1.0;\n /*y*/STATE_LOOCKUP[i+12][1] = -3.0+i;\n if(i==1 || i==4) {\n acc[i+12] = {16*0/3,16*4/3,0};\n if(i==4) acc[i+12]*=-1;\n }else\n acc[i+12] = {0,0,0};\n }\n\n for(int i=0;i<17;i++) {\n saytab(acc[i][0]); sayend(acc[i][1]);\n }\n\n\n while(ros::ok() && Iterate <= 3)\n {\n\n gps[Iterate+1][0] = GPS_lOOCKUP[Iterate+1][0];\n gps[Iterate+1][1] = GPS_lOOCKUP[Iterate+1][1];\n gps[Iterate+1][2] = 0;\n\n for(int i = 1;i<=4;i++) {\n msf_state[Iterate*4+i][0] = STATE_LOOCKUP[Iterate*4+i][0];\n msf_state[Iterate*4+i][1] = STATE_LOOCKUP[Iterate*4+i][1];\n msf_state[Iterate*4+i][2] = 0;\n msf_state[Iterate*4+i][3] = 0;\n msf_state[Iterate*4+i][4] = 0;\n msf_state[Iterate*4+i][5] = 0;\n // Numstate++;\n }\n \n Iterate ++;\n }\n saytab(\"START Iterate = \");sayend(Iterate);\n Optimize_test(dt);\n sayend(\"END\");\n \n \n}\n\n\n\nvoid Optimize_test(double dt) {\n // Set up the parameters of the simulation\n int numberOfTimeSteps = NUM_ITERATE;\n const double processNoiseSigma = 0.05;\n const double accelerometerNoiseSigma = 0.01;\n const double gpsNoiseSigma = 0.001;\n // const double dt = 1; \n\n // Set up the optimiser and block solver\n SparseOptimizer optimizer;\n optimizer.setVerbose(false);\n\n typedef BlockSolver< BlockSolverTraits<6, 6> > BlockSolver;\n BlockSolver::LinearSolverType * linearSolver\n = new LinearSolverCholmod();\n BlockSolver* blockSolver = new BlockSolver(linearSolver);\n OptimizationAlgorithm* optimizationAlgorithm = new OptimizationAlgorithmGaussNewton(blockSolver);\n optimizer.setAlgorithm(optimizationAlgorithm);\n\n // Sample the start location of the target\n \n\n state = msf_state[0];\n\n // Construct the first vertex; this corresponds to the initial\n // condition and register it with the optimiser\n saytab(\"add vertex state \");sayend(0);\n VertexPositionVelocity3D* stateNode = new VertexPositionVelocity3D();\n stateNode->setEstimate(state);\n stateNode->setId(0);\n optimizer.addVertex(stateNode);\n\n // Construct the GPS observation\n Vector3d gpsMeasurement = gps[0];\n saytab(\"add edge gps \");sayend(0);\n // Add the GPS observation\n GPSObservationEdgePositionVelocity3D* goe = new GPSObservationEdgePositionVelocity3D(gpsMeasurement, gpsNoiseSigma);\n goe->setMeasurement(gpsMeasurement);\n goe->setVertex(0, stateNode);\n optimizer.addEdge(goe);\n\n // Construct the msf observation\n Vector3d msfMeasurement(0,0,0);\n\n saytab(\"add edge statemsf \");sayend(0);\n // Add the msf observation\n GPSObservationEdgePositionVelocity3D* goe2 = new GPSObservationEdgePositionVelocity3D(msfMeasurement, 0.1);\n goe2->setMeasurement(msfMeasurement);\n goe2->setVertex(0, stateNode);\n optimizer.addEdge(goe2);\n\n\n // Set up last estimate\n VertexPositionVelocity3D* lastStateNode = stateNode;\n\n // Iterate over the simulation steps\n for (int k = 0; k < Iterate; ++k)\n { \n for(int j = 1; j <= 4/*Numstate per round*/; j++) \n {\n // saytab(j);saytab(Numstate);sayend(Iterate);\n\n // state = msf_state[k*4+j];\n \n\n // Construct vertex which corresponds to the current state of the target\n VertexPositionVelocity3D* stateNode = new VertexPositionVelocity3D();\n \n stateNode->setId(k*4+j);\n stateNode->setEstimate(state);\n stateNode->setMarginalized(false);\n optimizer.addVertex(stateNode);\n\n saytab(\"add vertex state \");sayend(k*4+j);\n\n\n\n\n\n\n\n\n\n\n\n\n // // Construct the accelerometer measurement\n Vector3d accelerometerMeasurement/*(msf_state[k*4+j][0],msf_state[k*4+j][1],0);//*/ = acc[k*4+j];\n\n TargetOdometry3DEdge* toe = new TargetOdometry3DEdge(dt, accelerometerNoiseSigma);\n toe->setVertex(0, lastStateNode);\n toe->setVertex(1, stateNode);\n VertexPositionVelocity3D* vPrev= dynamic_cast(lastStateNode);\n VertexPositionVelocity3D* vCurr= dynamic_cast(stateNode);\n toe->setMeasurement(accelerometerMeasurement);\n optimizer.addEdge(toe);\n // compute the initial guess via the odometry\n g2o::OptimizableGraph::VertexSet vPrevSet;\n vPrevSet.insert(vPrev);\n toe->initialEstimate(vPrevSet,vCurr);\n\n lastStateNode = stateNode;\n }\n // Construct the msf observation\n Vector3d gpsMeasurement2 = gps[k+1];\n\n // Add the msf observation\n GPSObservationEdgePositionVelocity3D* goe2 = new GPSObservationEdgePositionVelocity3D(gpsMeasurement2, 0.1);\n goe2->setMeasurement(gpsMeasurement2);\n goe2->setVertex(0, stateNode);\n optimizer.addEdge(goe2);\n\n\n // Construct the GPS observation\n Vector3d gpsMeasurement = gps[k+1];\n\n saytab(\"add edge gps \");sayend(k+1);\n // Add the GPS observation\n GPSObservationEdgePositionVelocity3D* goe = new GPSObservationEdgePositionVelocity3D(gpsMeasurement, gpsNoiseSigma);\n goe->setMeasurement(gpsMeasurement);\n goe->setVertex(0, stateNode);\n optimizer.addEdge(goe);\n }\n\n // Configure and set things going\n optimizer.initializeOptimization();\n optimizer.setVerbose(true);\n optimizer.optimize(5);\n cerr << \"number of vertices:\" << optimizer.vertices().size() << endl;\n cerr << \"number of edges:\" << optimizer.edges().size() << endl;\n\n // Print the results\n\n // cout << \"state=\\n\" << state << endl;\n\n#if 0\n for (int k = 0; k < numberOfTimeSteps; k++)\n {\n cout << \"computed estimate \" << k << \"\\n\"\n << dynamic_cast(optimizer.vertices().find(k)->second)->estimate() << endl;\n }\n#endif\n\n // Vector6d v1 = dynamic_cast(optimizer.vertices().find((std::max)(numberOfTimeSteps-2,0))->second)->estimate();\n // Vector6d v2 = dynamic_cast(optimizer.vertices().find((std::max)(numberOfTimeSteps-1,0))->second)->estimate();\n // cout << \"v1=\\n\" << v1 << endl;\n // cout << \"v2=\\n\" << v2 << endl;\n // cout << \"delta state=\\n\" << v2-v1 << endl;\n // Vector6d v[4];\n Vector6d v[17];\n for(int i=0;i<17;i++)\n v[i] = dynamic_cast(optimizer.vertices().find(i)->second)->estimate();\n for(int i=0;i<17;i++)\n cout << v[i][0] << \"\\t\" << v[i][1] < spinv;\n\n optimizer.computeMarginals(spinv, stateNode);\n\n\n\n //optimizer.solver()->computeMarginals();\n\n // covariance\n //\n cout << \"covariance\\n\" << spinv << endl;\n\n // for(int u =0;u<6;u++)\n // for(int v=0;v<6;v++)\n // poseop_msgs.pose.covariance[6*u+v] = spinv(u, v);\n poseop_msgs.pose.covariance[0]=0.1;\n poseop_msgs.pose.covariance[7]=0.1;\n poseop_msgs.pose.covariance[14]=0.1;\n poseop_msgs.pose.covariance[21]=0.1;\n poseop_msgs.pose.covariance[28]=0.1;\n poseop_msgs.pose.covariance[35]=0.1;\n \n}\n\n\n\n\n\n\n\n\n\n// #ifndef G2O_TARGET_TYPES_6D_HPP_\n// #define G2O_TARGET_TYPES_6D_HPP_\n\n// #include \n// #include \n// #include \n// #include \n\n// using namespace g2o;\n\n// typedef Eigen::Matrix Vector6d;\n// typedef Eigen::Matrix Matrix6d;\n\n// // This header file specifies a set of types for the different\n// // tracking examples; note that \n\n// class VertexPosition3D : public g2o::BaseVertex<3, Eigen::Vector3d>\n// {\n// public:\n// EIGEN_MAKE_ALIGNED_OPERATOR_NEW\n// VertexPosition3D()\n// {\n// }\n \n// virtual void setToOriginImpl() {\n// _estimate.setZero();\n// }\n \n// virtual void oplusImpl(const double* update)\n// {\n// _estimate[0] += update[0];\n// _estimate[1] += update[1];\n// _estimate[2] += update[2];\n// }\n \n// virtual bool read(std::istream& /*is*/)\n// {\n// return false;\n// }\n \n// virtual bool write(std::ostream& /*os*/) const\n// {\n// return false;\n// }\n \n// };\n\n// class PositionVelocity3DEdge\n// {\n// };\n \n// class VertexPositionVelocity3D : public g2o::BaseVertex<6, Vector6d>\n// {\n// public:\n// EIGEN_MAKE_ALIGNED_OPERATOR_NEW\n// VertexPositionVelocity3D()\n// {\n// }\n \n// virtual void setToOriginImpl() {\n// _estimate.setZero();\n// }\n \n// virtual void oplusImpl(const double* update)\n// {\n// for (int k = 0; k < 6; k++)\n// _estimate[k] += update[k];\n// }\n \n\n// virtual bool read(std::istream& /*is*/)\n// {\n// return false;\n// }\n \n// virtual bool write(std::ostream& /*os*/) const\n// {\n// return false;\n// }\n \n// };\n\n// // The odometry which links pairs of nodes together\n// class TargetOdometry3DEdge : public g2o::BaseBinaryEdge<6, Eigen::Vector3d, VertexPositionVelocity3D, VertexPositionVelocity3D>\n// {\n// public:\n// TargetOdometry3DEdge(double dt, double noiseSigma)\n// {\n// _dt = dt;\n\n// double q = noiseSigma * noiseSigma;\n// double dt2 = dt * dt;\n\n// // Process noise covariance matrix; this assumes an \"impulse\"\n// // noise model; we add a small stabilising term on the diagonal to make it invertible\n// Matrix6d Q=Matrix6d::Zero();\n// Q(0, 0) = Q(1,1) = Q(2,2) = dt2*dt2*q/4 + 1e-4;\n// Q(0, 3) = Q(1, 4) = Q(2, 5) = dt*dt2*q/2;\n// Q(3, 3) = Q(4,4) = Q(5,5) = dt2 * q + 1e-4;\n// Q(3, 0) = Q(4, 1) = Q(5, 2) = dt*dt2*q/2;\n\n// setInformation(Q.inverse());\n// }\n\n// /** set the estimate of the to vertex, based on the estimate of the from vertex in the edge. */\n// virtual void initialEstimate(const g2o::OptimizableGraph::VertexSet& from, g2o::OptimizableGraph::Vertex* to){\n// assert(from.size() == 1);\n// const VertexPositionVelocity3D* vi = static_cast(*from.begin());\n// VertexPositionVelocity3D* vj = static_cast(to);\n// Vector6d viEst=vi->estimate();\n// Vector6d vjEst=viEst;\n\n// for (int m = 0; m < 3; m++)\n// {\n// vjEst[m] += _dt * (vjEst[m+3] + 0.5 * _dt * _dt* _measurement[m]);\n// }\n\n// for (int m = 0; m < 3; m++)\n// {\n// vjEst[m+3] =0;//+= _dt * _measurement[m];\n// }\n\n// vjEst[0] = _measurement[0];\n// vjEst[1] = _measurement[1];\n// vjEst[2] = 0;\n// vj->setEstimate(vjEst);\n// }\n\n// /** override in your class if it's not possible to initialize the vertices in certain combinations */\n// virtual double initialEstimatePossible(const g2o::OptimizableGraph::VertexSet& from, g2o::OptimizableGraph::Vertex* to) {\n// //only works on sequential vertices\n// const VertexPositionVelocity3D* vi = static_cast(*from.begin());\n// return (to->id() - vi->id() == 1) ? 1.0 : -1.0;\n// }\n\n\n// void computeError()\n// {\n// const VertexPositionVelocity3D* vi = static_cast(_vertices[0]);\n// const VertexPositionVelocity3D* vj = static_cast(_vertices[1]);\n \n// for (int k = 0; k < 3; k++)\n// {\n// _error[k] = vi->estimate()[k] /*+ _dt * (vi->estimate()[k+3] + 0.5 * _dt * _measurement[k]) - vj->estimate()[k]*/-_measurement[k];\n// }\n// for (int k = 3; k < 6; k++)\n// {\n// _error[k] = 0;//vi->estimate()[k] + _dt * _measurement[k-3]- vj->estimate()[k];\n// }\n// }\n \n// virtual bool read(std::istream& /*is*/)\n// {\n// return false;\n// }\n \n// virtual bool write(std::ostream& /*os*/) const\n// {\n// return false;\n// }\n\n// private:\n// double _dt;\n// };\n\n// // The GPS \n// class GPSObservationEdgePositionVelocity3D : public g2o::BaseUnaryEdge<3, Eigen::Vector3d, VertexPositionVelocity3D>\n// {\n// public:\n// GPSObservationEdgePositionVelocity3D(const Eigen::Vector3d& measurement, double noiseSigma)\n// {\n// setMeasurement(measurement);\n// setInformation(Eigen::Matrix3d::Identity() / (noiseSigma*noiseSigma));\n// }\n \n// void computeError()\n// {\n// const VertexPositionVelocity3D* v = static_cast(_vertices[0]);\n// for (int k = 0; k < 3; k++)\n// {\n// _error[k] = v->estimate()[k] - _measurement[k];\n// } \n// }\n \n// virtual bool read(std::istream& /*is*/)\n// {\n// return false;\n// }\n \n// virtual bool write(std::ostream& /*os*/) const\n// {\n// return false;\n// }\n// };\n\n\n// #endif // __TARGET_TYPES_6D_HPP__\n", "meta": {"hexsha": "c19dc2f6108bf18122471a72092f68390a3e5934", "size": 19956, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "data/crossover_nav/src/simulate_optimize_gps.cpp", "max_stars_repo_name": "khairulislam/phys", "max_stars_repo_head_hexsha": "fc702520fcd3b23022b9253e7d94f878978b4500", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "data/crossover_nav/src/simulate_optimize_gps.cpp", "max_issues_repo_name": "khairulislam/phys", "max_issues_repo_head_hexsha": "fc702520fcd3b23022b9253e7d94f878978b4500", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "data/crossover_nav/src/simulate_optimize_gps.cpp", "max_forks_repo_name": "khairulislam/phys", "max_forks_repo_head_hexsha": "fc702520fcd3b23022b9253e7d94f878978b4500", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.3039647577, "max_line_length": 141, "alphanum_fraction": 0.614251353, "num_tokens": 6106, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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NO", "lm_q1_score": 0.6001883449573376, "lm_q2_score": 0.37754065479083276, "lm_q1q2_score": 0.22659550075301946}} {"text": "// Volumetric3D_EMC.cpp\n// created by Kuangdai on 16-May-2017 \n// genetral Volumetric3D model with IRIS-EMC format\n\n#include \"Volumetric3D_EMC.h\"\n#include \n#include \"XMPI.h\"\n#include \"XMath.h\"\n#include \"Geodesy.h\"\n#include \"Parameters.h\"\n#include \"NetCDF_Reader.h\"\n#include \"NetCDF_ReaderAscii.h\"\n\n#include \n#include \n#include \n#include \n#include \n\nbool compareFunc(const std::pair &a, \n const std::pair &b) {\n return a.second < b.second;\n}\n\nvoid Volumetric3D_EMC::initialize() {\n // meta data\n Eigen::Matrix fdata, fdep, flat, flon;\n if (XMPI::root()) {\n std::vector dims;\n std::string fname = Parameters::sInputDirectory + \"/\" + mFileName;\n if (mOneFilePerDepth) {\n // get all files\n DIR *dir = opendir(fname.c_str());\n if (!dir) {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || \" \n \"Error opening directory of data files || Directory = \" + fname); \n }\n struct dirent *entry;\n std::vector> fileDepth;\n while ((entry = readdir(dir)) != NULL) {\n // file name\n std::string fn(entry->d_name);\n if (fn.find(\".grd\") == std::string::npos) {\n continue;\n }\n // depth\n std::string depthStr(fn);\n boost::replace_first(depthStr, \".grd\", \"\");\n std::size_t found = depthStr.find_last_of(\"_\");\n if (found == std::string::npos) {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || \" \n \"Error processing file name || File = \" + fn); \n }\n depthStr = depthStr.substr(found + 1);\n float depth = 0.;\n try {\n depth = boost::lexical_cast(depthStr);\n } catch (std::exception) {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || \" \n \"Error processing file name || File = \" + fn);\n }\n // add pair\n fileDepth.push_back(std::make_pair(fname + \"/\" + fn, depth));\n }\n closedir(dir);\n \n // sort depth\n std::sort(fileDepth.begin(), fileDepth.end(), compareFunc);\n \n // read lat and lon\n Eigen::Matrix dlat, dlon;\n if (NetCDF_Reader::checkNetCDF_isAscii(fileDepth[0].first)) {\n NetCDF_ReaderAscii reader;\n reader.open(fileDepth[0].first);\n reader.read1D(\"lat\", dlat);\n reader.read1D(\"lon\", dlon);\n reader.close();\n } else {\n NetCDF_Reader reader;\n reader.open(fileDepth[0].first);\n reader.read1D(\"lat\", dlat);\n reader.read1D(\"lon\", dlon);\n reader.close();\n }\n flat = dlat.cast();\n flon = dlon.cast();\n \n // depths and data\n size_t depthLen = flat.size() * flon.size(); \n size_t totalLen = depthLen * fileDepth.size();\n fdep.resize(fileDepth.size());\n fdata.resize(totalLen);\n for (int i = 0; i < fileDepth.size(); i++) {\n fdep(i) = fileDepth[i].second;\n Eigen::Matrix temp;\n if (NetCDF_Reader::checkNetCDF_isAscii(fileDepth[i].first)) {\n NetCDF_ReaderAscii reader;\n reader.open(fileDepth[i].first);\n reader.readMetaData(mVarName, temp, dims);\n reader.close();\n } else {\n NetCDF_Reader reader;\n reader.open(fileDepth[i].first);\n reader.readMetaData(mVarName, temp, dims);\n reader.close();\n }\n fdata.block(i * depthLen, 0, depthLen, 1) = temp;\n }\n dims.insert(dims.begin(), fdep.size());\n \n // perturbations are given in percentage\n fdata *= .01f;\n } else {\n if (NetCDF_Reader::checkNetCDF_isAscii(fname)) {\n NetCDF_ReaderAscii reader;\n reader.open(fname);\n reader.read1D(\"depth\", fdep);\n reader.read1D(\"latitude\", flat);\n reader.read1D(\"longitude\", flon);\n reader.readMetaData(mVarName, fdata, dims);\n reader.close();\n } else {\n NetCDF_Reader reader;\n reader.open(fname);\n reader.read1D(\"depth\", fdep);\n reader.read1D(\"latitude\", flat);\n reader.read1D(\"longitude\", flon);\n reader.readMetaData(mVarName, fdata, dims);\n reader.close();\n }\n }\n \n // check dimensions\n if (dims.size() != 3) {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || Inconsistent data dimensions || \"\n \"File/Directory = \" + fname);\n }\n if (dims[0] != fdep.size() || dims[1] != flat.size() || dims[2] != flon.size()) {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || Inconsistent data dimensions || \"\n \"File/Directory = \" + fname);\n }\n if (!XMath::sortedAscending(fdep) || !XMath::sortedAscending(flat) || !XMath::sortedAscending(flon)) {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || Grid coordinates are not sorted ascendingly || \"\n \"File/Directory = \" + fname);\n }\n }\n XMPI::bcastEigen(fdep);\n XMPI::bcastEigen(flat);\n XMPI::bcastEigen(flon);\n XMPI::bcastEigen(fdata);\n \n mGridDep = fdep.cast();\n mGridLat = flat.cast();\n mGridLon = flon.cast();\n RDColX data = fdata.cast();\n \n // SI\n mGridDep *= 1e3;\n if (mReferenceType == Volumetric3D::MaterialRefType::Absolute) {\n // convert to SI\n data *= MaterialPropertyAbsSI[mMaterialProp];\n }\n \n // apply factor\n data *= mFactor;\n \n // special flag\n if (!boost::iequals(mModelFlag, \"none\")) {\n if (boost::iequals(mModelFlag, \"abs\")) {\n data.array() = data.array().abs();\n data *= mModelFlagFactor;\n } else if (boost::iequals(mModelFlag, \"pow\")) {\n double absmax = data.array().abs().maxCoeff();\n double scalefact = std::abs(absmax / std::pow(absmax, mModelFlagFactor));\n data.array() = data.array().sign() * (data.array().abs().pow(mModelFlagFactor) * scalefact);\n } else {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || \"\n \"Unknown special model flag, flag = \" + mModelFlag);\n }\n }\n \n // reshape data\n int pos = 0;\n for (int i = 0; i < mGridDep.size(); i++) {\n RDMatXX mat(mGridLat.size(), mGridLon.size());\n for (int j = 0; j < mGridLat.size(); j++) {\n for (int k = 0; k < mGridLon.size(); k++) {\n mat(j, k) = data(pos++);\n }\n }\n mGridData.push_back(mat);\n }\n}\n\nvoid Volumetric3D_EMC::initialize(const std::vector ¶ms) {\n if (params.size() < 4) throw std::runtime_error(\"Volumetric3D_EMC::initialize || \"\n \"Not enough parameters to initialize a Volumetric3D_EMC object, at least 4 needed.\");\n \n const std::string source = \"Volumetric3D_EMC::initialize\";\n \n // initialize data\n Parameters::castValue(mFileName, params[0], source);\n Parameters::castValue(mVarName, params[1], source);\n \n // property name\n bool found = false;\n for (int i = 0; i < Volumetric3D::MaterialPropertyString.size(); i++) {\n if (boost::iequals(params[2], Volumetric3D::MaterialPropertyString[i])) {\n mMaterialProp = Volumetric3D::MaterialProperty(i);\n found = true;\n break;\n }\n }\n if (!found) {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || \"\n \"Unknown material property, name = \" + params[2]);\n }\n \n // reference type\n found = false;\n for (int i = 0; i < Volumetric3D::MaterialRefTypeString.size(); i++) {\n if (boost::iequals(params[3], Volumetric3D::MaterialRefTypeString[i]) ||\n boost::iequals(params[3], Volumetric3D::MaterialRefTypeStringShort[i])) {\n mReferenceType = Volumetric3D::MaterialRefType(i);\n found = true;\n break;\n }\n }\n if (!found) {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || \"\n \"Unknown material reference type, type = \" + params[3]);\n }\n \n try {\n int ipar = 4;\n Parameters::castValue(mFactor, params.at(ipar++), source);\n Parameters::castValue(mGeographic, params.at(ipar++), source);\n Parameters::castValue(mOneFilePerDepth, params.at(ipar++), source);\n Parameters::castValue(mVerticalDiscontinuities, params.at(ipar++), source);\n Parameters::castValue(mModelFlag, params.at(ipar++), source);\n Parameters::castValue(mModelFlagFactor, params.at(ipar++), source);\n } catch (std::out_of_range) {\n // nothing\n }\n \n if (!boost::iequals(mModelFlag, \"none\")) {\n if (mReferenceType == MaterialRefType::Absolute) {\n throw std::runtime_error(\"Volumetric3D_EMC::initialize || \"\n \"Imposing special model flag on an absolute model.\");\n }\n }\n initialize();\n}\n\nbool Volumetric3D_EMC::get3dProperties(double r, double theta, double phi, double rElemCenter,\n std::vector &properties, \n std::vector &refTypes,\n std::vector &values) const {\n \n // header\n properties = std::vector(1, mMaterialProp);\n refTypes = std::vector(1, mReferenceType);\n values = std::vector(1, 0.);\n \n // to geocentric\n if (mGeographic) {\n // which radius to use?\n theta = pi / 2. - Geodesy::theta2Lat_d(theta, 0.) * degree;\n }\n \n // regularise\n double dep = Geodesy::getROuter() - r;\n double lat = 90. - theta / degree;\n double lon = phi / degree;\n XMath::checkLimits(dep, 0., Geodesy::getROuter());\n XMath::checkLimits(lat, -90., 90.);\n if (mGridLon[0] < 0.) {\n // lon starts from -180.\n if (lon > 180.) {\n lon -= 360.;\n }\n XMath::checkLimits(lon, -180., 180.);\n } else {\n // lon starts from 0.\n XMath::checkLimits(lon, 0., 360.);\n }\n \n // check center\n double dmin = mGridDep[0];\n double dmax = mGridDep[mGridDep.size() - 1];\n double dcenter = Geodesy::getROuter() - rElemCenter;\n if (dcenter < dmin || dcenter > dmax) {\n return false;\n }\n if (dep < dmin && dep > dmin * 0.999999) {\n dep = dmin;\n }\n if (dep > dmax && dep < dmax * 1.000001) {\n dep = dmax;\n }\n \n // interpolation\n int ldep0, llat0, llon0, ldep1, llat1, llon1;\n double wdep0, wlat0, wlon0, wdep1, wlat1, wlon1;\n if (mVerticalDiscontinuities) {\n // use element center depth to locate layer\n XMath::interpLinear(dcenter, mGridDep, ldep0, wdep0);\n if (ldep0 < 0) {\n return false;\n }\n // use point depth to determine value\n wdep0 = 1. - 1. / (mGridDep(ldep0 + 1) - mGridDep(ldep0)) * (dep - mGridDep(ldep0));\n } else {\n XMath::interpLinear(dep, mGridDep, ldep0, wdep0);\n }\n XMath::interpLinear(lat, mGridLat, llat0, wlat0);\n XMath::interpLinear(lon, mGridLon, llon0, wlon0); \n if (ldep0 < 0 || llat0 < 0 || llon0 < 0) {\n return false;\n }\n \n ldep1 = ldep0 + 1;\n llat1 = llat0 + 1;\n llon1 = llon0 + 1;\n wdep1 = 1. - wdep0;\n wlat1 = 1. - wlat0;\n wlon1 = 1. - wlon0;\n \n values[0] += mGridData[ldep0](llat0, llon0) * wdep0 * wlat0 * wlon0;\n values[0] += mGridData[ldep0](llat1, llon0) * wdep0 * wlat1 * wlon0;\n values[0] += mGridData[ldep0](llat0, llon1) * wdep0 * wlat0 * wlon1;\n values[0] += mGridData[ldep0](llat1, llon1) * wdep0 * wlat1 * wlon1;\n values[0] += mGridData[ldep1](llat0, llon0) * wdep1 * wlat0 * wlon0;\n values[0] += mGridData[ldep1](llat1, llon0) * wdep1 * wlat1 * wlon0;\n values[0] += mGridData[ldep1](llat0, llon1) * wdep1 * wlat0 * wlon1;\n values[0] += mGridData[ldep1](llat1, llon1) * wdep1 * wlat1 * wlon1;\n return true;\n}\n\nstd::string Volumetric3D_EMC::verbose() const {\n std::stringstream ss;\n ss << \"\\n======================= 3D Volumetric =======================\" << std::endl;\n ss << \" Model Name = EMC\" << std::endl;\n ss << \" Data File = \" << mFileName << std::endl;\n ss << \" Variable Name = \" << mVarName << std::endl;\n ss << \" Material Property = \" << MaterialPropertyString[mMaterialProp] << std::endl;\n ss << \" Reference Type = \" << MaterialRefTypeString[mReferenceType] << std::endl;\n ss << \" Num. Depths = \" << mGridDep.size() << std::endl;\n ss << \" Num. Latitudes = \" << mGridLat.size() << std::endl;\n ss << \" Num. Longitudes = \" << mGridLon.size() << std::endl;\n ss << \" Depth Range = [\" << mGridDep.minCoeff() << \", \" << mGridDep.maxCoeff() << \"]\" << std::endl;\n ss << \" Latitude Range = [\" << mGridLat.minCoeff() << \", \" << mGridLat.maxCoeff() << \"]\" << std::endl;\n ss << \" Longitude Range = [\" << mGridLon.minCoeff() << \", \" << mGridLon.maxCoeff() << \"]\" << std::endl;\n ss << \" Factor = \" << mFactor << std::endl;\n ss << \" Use Geographic = \" << (mGeographic ? \"YES\" : \"NO\") << std::endl;\n ss << \" One File per Depth = \" << (mOneFilePerDepth ? \"YES\" : \"NO\") << std::endl;\n if (!boost::iequals(mModelFlag, \"none\")) {\n ss << \" Special Model Flag = \" << mModelFlag << std::endl;\n ss << \" Model Flag Factor = \" << mModelFlagFactor << std::endl;\n }\n ss << \"======================= 3D Volumetric =======================\\n\" << std::endl;\n return ss.str();\n}\n\n\n\n", "meta": {"hexsha": "8306f5861b5ff3e99fc54651890de3a158555d11", "size": 14464, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "SOLVER/src/3d_model/3d_volumetric/EMC/Volumetric3D_EMC.cpp", "max_stars_repo_name": "kuangdai/AxiSEM3D", "max_stars_repo_head_hexsha": "fd9da14e9107783e3b07b936c67af2412146e099", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17.0, "max_stars_repo_stars_event_min_datetime": "2016-12-16T03:13:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-15T01:56:45.000Z", "max_issues_repo_path": "SOLVER/src/3d_model/3d_volumetric/EMC/Volumetric3D_EMC.cpp", "max_issues_repo_name": "syzeng-duduxi/AxiSEM3D", "max_issues_repo_head_hexsha": "fd9da14e9107783e3b07b936c67af2412146e099", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6.0, "max_issues_repo_issues_event_min_datetime": "2018-01-15T17:17:20.000Z", "max_issues_repo_issues_event_max_datetime": "2020-03-18T09:53:58.000Z", "max_forks_repo_path": "SOLVER/src/3d_model/3d_volumetric/EMC/Volumetric3D_EMC.cpp", "max_forks_repo_name": "syzeng-duduxi/AxiSEM3D", "max_forks_repo_head_hexsha": "fd9da14e9107783e3b07b936c67af2412146e099", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 19.0, "max_forks_repo_forks_event_min_datetime": "2016-12-28T16:55:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-23T01:02:16.000Z", "avg_line_length": 39.4114441417, "max_line_length": 118, "alphanum_fraction": 0.5330475664, "num_tokens": 3944, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.39233683016710835, "lm_q1q2_score": 0.2265726953073636}} {"text": "// #ifdef HAVE_CONFIG_H\n// #include \"config.h\"\n// #endif\n\n\n// #include \n// #include \n// #include \n// #include \n// #include \n// #include \n\n// #ifdef _OPENMP\n// #include \n// #endif\n\n// #include \n// #include \"cxx/sugar.hpp\"\n// #include \"quadrature.h\"\n// #include \"memory.h\"\n\n// using namespace rysq;\n\n\n// parallel::Fock::Fock(const Quartet &shells) : shells(shells) {} \n\n// parallel::Fock::~Fock() {}\n\n// void parallel::Fock::operator()(const std::vector

¢ers,\n// \t\t\t\tconst std::vector &quartets,\n// \t\t\t\tconst matrix::Adapter &D,\n// \t\t\t\tmatrix::Adapter &F,\n// \t\t\t\tconst Parameters ¶meters) {\n\n// if (quartets.empty()) return;\n\n// #ifdef _OPENMP\n// #pragma omp parallel \n// #endif\n// {\n\n\n// #ifdef _OPENMP\n// \tint num_threads = omp_get_num_threads();\n// \tint thread_num = omp_get_thread_num();\n// #else\n// \tint num_threads = 1;\n// \tint thread_num = 0;\n// #endif\n\n// \trysq::Fock fock(this->shells);\n\n// \tint nij = shells.size(0,1);\n// \tint nkl = shells.size(2,3);\n// \tint nik = shells.size(0,2);\n// \tint nil = shells.size(0,3);\n// \tint njk = shells.size(1,2);\n// \tint njl = shells.size(1,3);\n\n// \tmemory::Map myMemory(32*1024);\n\n// #ifdef _OPENMP\n// #pragma omp for schedule(dynamic,1) nowait\n// #endif\n// \tfor (uint q = 0; q < quartets.size(); ++q) {\n\n// \t int i, j, k, l;\n// \t util::unpack(quartets[q], i, j, k, l);\n\n// \t double *Fij = myMemory.get(i, j, nij);\n// \t double *Fkl = myMemory.get(k, l, nkl);\n// \t double *Fik = myMemory.get(i, k, nik);\n// \t double *Fil = myMemory.get(i, l, nil);\n// \t double *Fjk = myMemory.get(j, k, njk);\n// \t double *Fjl = myMemory.get(j, l, njl);\n\n// \t Quartet
C(centers[i], centers[j], centers[k], centers[l]);\n\n// \t matrix::Density<6> D6 = matrix::index(D, i, j, k, l);\n// \t double *f[] = { Fij, Fkl, Fik, Fil, Fjk, Fjl };\n// \t matrix::Fock<6> F6(f);\n\t\t\n// \t Parameters p = parameters;\n// \t p.scale /= Quartet::symmetry(i, j, k, l);\n\n// \t // fock(C, D6, F6, p);\n \n// \t}\n\n// \ttypedef boost::tuple Block;\n// \tstd::vector blocks;\n// \ttypedef std::vector::iterator block_it;\n\n// \twhile (!myMemory.empty()) {\n// \t memory::Map::Entry kv = myMemory.pop();\n// \t blocks.push_back(Block(kv.j, kv.i, kv.ptr, kv.n));\n// \t}\n// \tstd::sort(blocks.begin(), blocks.end());\n\n// \tstd::vector columns;\n// \ttypedef std::vector::iterator column_it;\n// \tblock_it it = blocks.begin();\n\n// \twhile (it < blocks.end()) {\n// \t int j = it->get<0>();\n// \t columns.push_back(it);\n// \t while (it < blocks.end() && it->get<0>() == j) ++it;\n// \t}\n// \tcolumns.push_back(it);\t \n\n// \tfor (int t = 0; t < num_threads; ++t) {\n// \t int k = (thread_num + t)%num_threads;\n\n// \t for (column_it col = columns.begin(); col < columns.end() - 1; ++col) {\n// \t\tblock_it block = *col;\n// \t\tint j = block->get<0>(); \n// \t\tif (j%num_threads != k) continue;\n\n// \t\twhile (block < *(col+1)) {\n// \t\t double *dest = F(block->get<1>(), block->get<0>());\n// \t\t double *source = block->get<2>();\n// \t\t size_t size = block->get<3>();\n// \t\t util::add(size, dest, source);\n// \t\t ++block;\n// \t\t}\n// \t }\n// #ifdef _OPENMP\n// #pragma omp barrier\n// #endif\n// \t}\n\n// }\n\n// }\n", "meta": {"hexsha": "147ce338f7ff40cf063e8f1e942ef0b00232d012", "size": 3375, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "gamess/libqc/rysq/src/parallel.cpp", "max_stars_repo_name": "andremirt/v_cond", "max_stars_repo_head_hexsha": "6b5c364d7cd4243686488b2bd4318be3927e07ea", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "gamess/libqc/rysq/src/parallel.cpp", "max_issues_repo_name": "andremirt/v_cond", "max_issues_repo_head_hexsha": "6b5c364d7cd4243686488b2bd4318be3927e07ea", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "gamess/libqc/rysq/src/parallel.cpp", "max_forks_repo_name": "andremirt/v_cond", "max_forks_repo_head_hexsha": "6b5c364d7cd4243686488b2bd4318be3927e07ea", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0, "max_line_length": 79, "alphanum_fraction": 0.5611851852, "num_tokens": 1104, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093585306514, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.22646601970491811}} {"text": "///////////////////////////////////////////////////////////////////////////////\n// Copyright 2011 John Maddock. Distributed under the Boost\n// Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_MATH_MP_TOMMATH_BACKEND_HPP\n#define BOOST_MATH_MP_TOMMATH_BACKEND_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace boost{ namespace multiprecision{ namespace backends{\n\nnamespace detail{\n\ninline void check_tommath_result(unsigned v)\n{\n if(v != MP_OKAY)\n {\n BOOST_THROW_EXCEPTION(std::runtime_error(mp_error_to_string(v)));\n }\n}\n\n}\n\nstruct tommath_int;\n\nvoid eval_multiply(tommath_int& t, const tommath_int& o);\nvoid eval_add(tommath_int& t, const tommath_int& o);\n\nstruct tommath_int\n{\n typedef mpl::list signed_types;\n typedef mpl::list unsigned_types;\n typedef mpl::list float_types;\n\n tommath_int()\n {\n detail::check_tommath_result(mp_init(&m_data));\n }\n tommath_int(const tommath_int& o)\n {\n detail::check_tommath_result(mp_init_copy(&m_data, const_cast< ::mp_int*>(&o.m_data)));\n }\n#ifndef BOOST_NO_CXX11_RVALUE_REFERENCES\n tommath_int(tommath_int&& o) BOOST_NOEXCEPT\n {\n m_data = o.m_data;\n o.m_data.dp = 0;\n }\n tommath_int& operator = (tommath_int&& o)\n {\n mp_exch(&m_data, &o.m_data);\n return *this;\n }\n#endif\n tommath_int& operator = (const tommath_int& o)\n {\n if(m_data.dp == 0)\n detail::check_tommath_result(mp_init(&m_data));\n if(o.m_data.dp)\n detail::check_tommath_result(mp_copy(const_cast< ::mp_int*>(&o.m_data), &m_data));\n return *this;\n }\n tommath_int& operator = (boost::ulong_long_type i)\n {\n if(m_data.dp == 0)\n detail::check_tommath_result(mp_init(&m_data));\n boost::ulong_long_type mask = ((1uLL << std::numeric_limits::digits) - 1);\n unsigned shift = 0;\n ::mp_int t;\n detail::check_tommath_result(mp_init(&t));\n mp_zero(&m_data);\n while(i)\n {\n detail::check_tommath_result(mp_set_int(&t, static_cast(i & mask)));\n if(shift)\n detail::check_tommath_result(mp_mul_2d(&t, shift, &t));\n detail::check_tommath_result((mp_add(&m_data, &t, &m_data)));\n shift += std::numeric_limits::digits;\n i >>= std::numeric_limits::digits;\n }\n mp_clear(&t);\n return *this;\n }\n tommath_int& operator = (boost::long_long_type i)\n {\n if(m_data.dp == 0)\n detail::check_tommath_result(mp_init(&m_data));\n bool neg = i < 0;\n *this = boost::multiprecision::detail::unsigned_abs(i);\n if(neg)\n detail::check_tommath_result(mp_neg(&m_data, &m_data));\n return *this;\n }\n //\n // Note that although mp_set_int takes an unsigned long as an argument\n // it only sets the first 32-bits to the result, and ignores the rest.\n // So use uint32_t as the largest type to pass to this function.\n //\n tommath_int& operator = (boost::uint32_t i)\n {\n if(m_data.dp == 0)\n detail::check_tommath_result(mp_init(&m_data));\n detail::check_tommath_result((mp_set_int(&m_data, i)));\n return *this;\n }\n tommath_int& operator = (boost::int32_t i)\n {\n if(m_data.dp == 0)\n detail::check_tommath_result(mp_init(&m_data));\n bool neg = i < 0;\n *this = boost::multiprecision::detail::unsigned_abs(i);\n if(neg)\n detail::check_tommath_result(mp_neg(&m_data, &m_data));\n return *this;\n }\n tommath_int& operator = (long double a)\n {\n using std::frexp;\n using std::ldexp;\n using std::floor;\n\n if(m_data.dp == 0)\n detail::check_tommath_result(mp_init(&m_data));\n\n if (a == 0) {\n detail::check_tommath_result(mp_set_int(&m_data, 0));\n return *this;\n }\n\n if (a == 1) {\n detail::check_tommath_result(mp_set_int(&m_data, 1));\n return *this;\n }\n\n BOOST_ASSERT(!(boost::math::isinf)(a));\n BOOST_ASSERT(!(boost::math::isnan)(a));\n\n int e;\n long double f, term;\n detail::check_tommath_result(mp_set_int(&m_data, 0u));\n ::mp_int t;\n detail::check_tommath_result(mp_init(&t));\n\n f = frexp(a, &e);\n\n static const int shift = std::numeric_limits::digits - 1;\n\n while(f)\n {\n // extract int sized bits from f:\n f = ldexp(f, shift);\n term = floor(f);\n e -= shift;\n detail::check_tommath_result(mp_mul_2d(&m_data, shift, &m_data));\n if(term > 0)\n {\n detail::check_tommath_result(mp_set_int(&t, static_cast(term)));\n detail::check_tommath_result(mp_add(&m_data, &t, &m_data));\n }\n else\n {\n detail::check_tommath_result(mp_set_int(&t, static_cast(-term)));\n detail::check_tommath_result(mp_sub(&m_data, &t, &m_data));\n }\n f -= term;\n }\n if(e > 0)\n detail::check_tommath_result(mp_mul_2d(&m_data, e, &m_data));\n else if(e < 0)\n {\n tommath_int t2;\n detail::check_tommath_result(mp_div_2d(&m_data, -e, &m_data, &t2.data()));\n }\n mp_clear(&t);\n return *this;\n }\n tommath_int& operator = (const char* s)\n {\n //\n // We don't use libtommath's own routine because it doesn't error check the input :-(\n //\n if(m_data.dp == 0)\n detail::check_tommath_result(mp_init(&m_data));\n std::size_t n = s ? std::strlen(s) : 0;\n *this = static_cast(0u);\n unsigned radix = 10;\n bool isneg = false;\n if(n && (*s == '-'))\n {\n --n;\n ++s;\n isneg = true;\n }\n if(n && (*s == '0'))\n {\n if((n > 1) && ((s[1] == 'x') || (s[1] == 'X')))\n {\n radix = 16;\n s +=2;\n n -= 2;\n }\n else\n {\n radix = 8;\n n -= 1;\n }\n }\n if(n)\n {\n if(radix == 8 || radix == 16)\n {\n unsigned shift = radix == 8 ? 3 : 4;\n unsigned block_count = DIGIT_BIT / shift;\n unsigned block_shift = shift * block_count;\n boost::ulong_long_type val, block;\n while(*s)\n {\n block = 0;\n for(unsigned i = 0; (i < block_count); ++i)\n {\n if(*s >= '0' && *s <= '9')\n val = *s - '0';\n else if(*s >= 'a' && *s <= 'f')\n val = 10 + *s - 'a';\n else if(*s >= 'A' && *s <= 'F')\n val = 10 + *s - 'A';\n else\n val = 400;\n if(val > radix)\n {\n BOOST_THROW_EXCEPTION(std::runtime_error(\"Unexpected content found while parsing character string.\"));\n }\n block <<= shift;\n block |= val;\n if(!*++s)\n {\n // final shift is different:\n block_shift = (i + 1) * shift;\n break;\n }\n }\n detail::check_tommath_result(mp_mul_2d(&data(), block_shift, &data()));\n if(data().used)\n data().dp[0] |= block;\n else\n *this = block;\n }\n }\n else\n {\n // Base 10, we extract blocks of size 10^9 at a time, that way\n // the number of multiplications is kept to a minimum:\n boost::uint32_t block_mult = 1000000000;\n while(*s)\n {\n boost::uint32_t block = 0;\n for(unsigned i = 0; i < 9; ++i)\n {\n boost::uint32_t val;\n if(*s >= '0' && *s <= '9')\n val = *s - '0';\n else\n BOOST_THROW_EXCEPTION(std::runtime_error(\"Unexpected character encountered in input.\"));\n block *= 10;\n block += val;\n if(!*++s)\n {\n static const boost::uint32_t block_multiplier[9] = { 10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 1000000000 };\n block_mult = block_multiplier[i];\n break;\n }\n }\n tommath_int t;\n t = block_mult;\n eval_multiply(*this, t);\n t = block;\n eval_add(*this, t);\n }\n }\n }\n if(isneg)\n this->negate();\n return *this;\n }\n std::string str(std::streamsize /*digits*/, std::ios_base::fmtflags f)const\n {\n BOOST_ASSERT(m_data.dp);\n int base = 10;\n if((f & std::ios_base::oct) == std::ios_base::oct)\n base = 8;\n else if((f & std::ios_base::hex) == std::ios_base::hex)\n base = 16;\n //\n // sanity check, bases 8 and 16 are only available for positive numbers:\n //\n if((base != 10) && m_data.sign)\n BOOST_THROW_EXCEPTION(std::runtime_error(\"Formatted output in bases 8 or 16 is only available for positive numbers\"));\n int s;\n detail::check_tommath_result(mp_radix_size(const_cast< ::mp_int*>(&m_data), base, &s));\n boost::scoped_array a(new char[s+1]);\n detail::check_tommath_result(mp_toradix_n(const_cast< ::mp_int*>(&m_data), a.get(), base, s+1));\n std::string result = a.get();\n if((base != 10) && (f & std::ios_base::showbase))\n {\n int pos = result[0] == '-' ? 1 : 0;\n const char* pp = base == 8 ? \"0\" : \"0x\";\n result.insert(static_cast(pos), pp);\n }\n if((f & std::ios_base::showpos) && (result[0] != '-'))\n result.insert(static_cast(0), 1, '+');\n return result;\n }\n ~tommath_int()\n {\n if(m_data.dp)\n mp_clear(&m_data);\n }\n void negate()\n {\n BOOST_ASSERT(m_data.dp);\n mp_neg(&m_data, &m_data);\n }\n int compare(const tommath_int& o)const\n {\n BOOST_ASSERT(m_data.dp && o.m_data.dp);\n return mp_cmp(const_cast< ::mp_int*>(&m_data), const_cast< ::mp_int*>(&o.m_data));\n }\n template \n int compare(V v)const\n {\n tommath_int d;\n tommath_int t(*this);\n detail::check_tommath_result(mp_shrink(&t.data()));\n d = v;\n return t.compare(d);\n }\n ::mp_int& data() \n { \n BOOST_ASSERT(m_data.dp);\n return m_data; \n }\n const ::mp_int& data()const \n { \n BOOST_ASSERT(m_data.dp);\n return m_data; \n }\n void swap(tommath_int& o)BOOST_NOEXCEPT\n {\n mp_exch(&m_data, &o.data());\n }\nprotected:\n ::mp_int m_data;\n};\n\n#define BOOST_MP_TOMMATH_BIT_OP_CHECK(x)\\\n if(SIGN(&x.data()))\\\n BOOST_THROW_EXCEPTION(std::runtime_error(\"Bitwise operations on libtommath negative valued integers are disabled as they produce unpredictable results\"))\n\nint eval_get_sign(const tommath_int& val);\n\ninline void eval_add(tommath_int& t, const tommath_int& o)\n{\n detail::check_tommath_result(mp_add(&t.data(), const_cast< ::mp_int*>(&o.data()), &t.data()));\n}\ninline void eval_subtract(tommath_int& t, const tommath_int& o)\n{\n detail::check_tommath_result(mp_sub(&t.data(), const_cast< ::mp_int*>(&o.data()), &t.data()));\n}\ninline void eval_multiply(tommath_int& t, const tommath_int& o)\n{\n detail::check_tommath_result(mp_mul(&t.data(), const_cast< ::mp_int*>(&o.data()), &t.data()));\n}\ninline void eval_divide(tommath_int& t, const tommath_int& o)\n{\n using default_ops::eval_is_zero;\n tommath_int temp;\n if(eval_is_zero(o))\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Integer division by zero\"));\n detail::check_tommath_result(mp_div(&t.data(), const_cast< ::mp_int*>(&o.data()), &t.data(), &temp.data()));\n}\ninline void eval_modulus(tommath_int& t, const tommath_int& o)\n{\n using default_ops::eval_is_zero;\n if(eval_is_zero(o))\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Integer division by zero\"));\n bool neg = eval_get_sign(t) < 0;\n bool neg2 = eval_get_sign(o) < 0;\n detail::check_tommath_result(mp_mod(&t.data(), const_cast< ::mp_int*>(&o.data()), &t.data()));\n if((neg != neg2) && (eval_get_sign(t) != 0))\n {\n t.negate();\n detail::check_tommath_result(mp_add(&t.data(), const_cast< ::mp_int*>(&o.data()), &t.data()));\n t.negate();\n }\n else if(neg && (t.compare(o) == 0))\n {\n mp_zero(&t.data());\n }\n}\ntemplate \ninline void eval_left_shift(tommath_int& t, UI i)\n{\n detail::check_tommath_result(mp_mul_2d(&t.data(), static_cast(i), &t.data()));\n}\ntemplate \ninline void eval_right_shift(tommath_int& t, UI i)\n{\n tommath_int d;\n detail::check_tommath_result(mp_div_2d(&t.data(), static_cast(i), &t.data(), &d.data()));\n}\ntemplate \ninline void eval_left_shift(tommath_int& t, const tommath_int& v, UI i)\n{\n detail::check_tommath_result(mp_mul_2d(const_cast< ::mp_int*>(&v.data()), static_cast(i), &t.data()));\n}\ntemplate \ninline void eval_right_shift(tommath_int& t, const tommath_int& v, UI i)\n{\n tommath_int d;\n detail::check_tommath_result(mp_div_2d(const_cast< ::mp_int*>(&v.data()), static_cast(i), &t.data(), &d.data()));\n}\n\ninline void eval_bitwise_and(tommath_int& result, const tommath_int& v)\n{\n BOOST_MP_TOMMATH_BIT_OP_CHECK(result);\n BOOST_MP_TOMMATH_BIT_OP_CHECK(v);\n detail::check_tommath_result(mp_and(&result.data(), const_cast< ::mp_int*>(&v.data()), &result.data()));\n}\n\ninline void eval_bitwise_or(tommath_int& result, const tommath_int& v)\n{\n BOOST_MP_TOMMATH_BIT_OP_CHECK(result);\n BOOST_MP_TOMMATH_BIT_OP_CHECK(v);\n detail::check_tommath_result(mp_or(&result.data(), const_cast< ::mp_int*>(&v.data()), &result.data()));\n}\n\ninline void eval_bitwise_xor(tommath_int& result, const tommath_int& v)\n{\n BOOST_MP_TOMMATH_BIT_OP_CHECK(result);\n BOOST_MP_TOMMATH_BIT_OP_CHECK(v);\n detail::check_tommath_result(mp_xor(&result.data(), const_cast< ::mp_int*>(&v.data()), &result.data()));\n}\n\ninline void eval_add(tommath_int& t, const tommath_int& p, const tommath_int& o)\n{\n detail::check_tommath_result(mp_add(const_cast< ::mp_int*>(&p.data()), const_cast< ::mp_int*>(&o.data()), &t.data()));\n}\ninline void eval_subtract(tommath_int& t, const tommath_int& p, const tommath_int& o)\n{\n detail::check_tommath_result(mp_sub(const_cast< ::mp_int*>(&p.data()), const_cast< ::mp_int*>(&o.data()), &t.data()));\n}\ninline void eval_multiply(tommath_int& t, const tommath_int& p, const tommath_int& o)\n{\n detail::check_tommath_result(mp_mul(const_cast< ::mp_int*>(&p.data()), const_cast< ::mp_int*>(&o.data()), &t.data()));\n}\ninline void eval_divide(tommath_int& t, const tommath_int& p, const tommath_int& o)\n{\n using default_ops::eval_is_zero;\n tommath_int d;\n if(eval_is_zero(o))\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Integer division by zero\"));\n detail::check_tommath_result(mp_div(const_cast< ::mp_int*>(&p.data()), const_cast< ::mp_int*>(&o.data()), &t.data(), &d.data()));\n}\ninline void eval_modulus(tommath_int& t, const tommath_int& p, const tommath_int& o)\n{\n using default_ops::eval_is_zero;\n if(eval_is_zero(o))\n BOOST_THROW_EXCEPTION(std::overflow_error(\"Integer division by zero\"));\n bool neg = eval_get_sign(p) < 0;\n bool neg2 = eval_get_sign(o) < 0;\n detail::check_tommath_result(mp_mod(const_cast< ::mp_int*>(&p.data()), const_cast< ::mp_int*>(&o.data()), &t.data()));\n if((neg != neg2) && (eval_get_sign(t) != 0))\n {\n t.negate();\n detail::check_tommath_result(mp_add(&t.data(), const_cast< ::mp_int*>(&o.data()), &t.data()));\n t.negate();\n }\n else if(neg && (t.compare(o) == 0))\n {\n mp_zero(&t.data());\n }\n}\n\ninline void eval_bitwise_and(tommath_int& result, const tommath_int& u, const tommath_int& v)\n{\n BOOST_MP_TOMMATH_BIT_OP_CHECK(u);\n BOOST_MP_TOMMATH_BIT_OP_CHECK(v);\n detail::check_tommath_result(mp_and(const_cast< ::mp_int*>(&u.data()), const_cast< ::mp_int*>(&v.data()), &result.data()));\n}\n\ninline void eval_bitwise_or(tommath_int& result, const tommath_int& u, const tommath_int& v)\n{\n BOOST_MP_TOMMATH_BIT_OP_CHECK(u);\n BOOST_MP_TOMMATH_BIT_OP_CHECK(v);\n detail::check_tommath_result(mp_or(const_cast< ::mp_int*>(&u.data()), const_cast< ::mp_int*>(&v.data()), &result.data()));\n}\n\ninline void eval_bitwise_xor(tommath_int& result, const tommath_int& u, const tommath_int& v)\n{\n BOOST_MP_TOMMATH_BIT_OP_CHECK(u);\n BOOST_MP_TOMMATH_BIT_OP_CHECK(v);\n detail::check_tommath_result(mp_xor(const_cast< ::mp_int*>(&u.data()), const_cast< ::mp_int*>(&v.data()), &result.data()));\n}\n/*\ninline void eval_complement(tommath_int& result, const tommath_int& u)\n{\n //\n // Although this code works, it doesn't really do what the user might expect....\n // and it's hard to see how it ever could. Disabled for now:\n //\n result = u;\n for(int i = 0; i < result.data().used; ++i)\n {\n result.data().dp[i] = MP_MASK & ~(result.data().dp[i]);\n }\n //\n // We now need to pad out the left of the value with 1's to round up to a whole number of\n // CHAR_BIT * sizeof(mp_digit) units. Otherwise we'll end up with a very strange number of\n // bits set!\n //\n unsigned shift = result.data().used * DIGIT_BIT; // How many bits we're actually using\n // How many bits we actually need, reduced by one to account for a mythical sign bit:\n int padding = result.data().used * std::numeric_limits::digits - shift - 1; \n while(padding >= std::numeric_limits::digits) \n padding -= std::numeric_limits::digits;\n\n // Create a mask providing the extra bits we need and add to result:\n tommath_int mask;\n mask = static_cast((1u << padding) - 1);\n eval_left_shift(mask, shift);\n add(result, mask);\n}\n*/\ninline bool eval_is_zero(const tommath_int& val)\n{\n return mp_iszero(&val.data());\n}\ninline int eval_get_sign(const tommath_int& val)\n{\n return mp_iszero(&val.data()) ? 0 : SIGN(&val.data()) ? -1 : 1;\n}\ntemplate \ninline void eval_convert_to(A* result, const tommath_int& val)\n{\n *result = boost::lexical_cast(val.str(0, std::ios_base::fmtflags(0)));\n}\ninline void eval_convert_to(char* result, const tommath_int& val)\n{\n *result = static_cast(boost::lexical_cast(val.str(0, std::ios_base::fmtflags(0))));\n}\ninline void eval_convert_to(unsigned char* result, const tommath_int& val)\n{\n *result = static_cast(boost::lexical_cast(val.str(0, std::ios_base::fmtflags(0))));\n}\ninline void eval_convert_to(signed char* result, const tommath_int& val)\n{\n *result = static_cast(boost::lexical_cast(val.str(0, std::ios_base::fmtflags(0))));\n}\ninline void eval_abs(tommath_int& result, const tommath_int& val)\n{\n detail::check_tommath_result(mp_abs(const_cast< ::mp_int*>(&val.data()), &result.data()));\n}\ninline void eval_gcd(tommath_int& result, const tommath_int& a, const tommath_int& b)\n{\n detail::check_tommath_result(mp_gcd(const_cast< ::mp_int*>(&a.data()), const_cast< ::mp_int*>(&b.data()), const_cast< ::mp_int*>(&result.data())));\n}\ninline void eval_lcm(tommath_int& result, const tommath_int& a, const tommath_int& b)\n{\n detail::check_tommath_result(mp_lcm(const_cast< ::mp_int*>(&a.data()), const_cast< ::mp_int*>(&b.data()), const_cast< ::mp_int*>(&result.data())));\n}\ninline void eval_powm(tommath_int& result, const tommath_int& base, const tommath_int& p, const tommath_int& m)\n{\n if(eval_get_sign(p) < 0)\n {\n BOOST_THROW_EXCEPTION(std::runtime_error(\"powm requires a positive exponent.\"));\n }\n detail::check_tommath_result(mp_exptmod(const_cast< ::mp_int*>(&base.data()), const_cast< ::mp_int*>(&p.data()), const_cast< ::mp_int*>(&m.data()), &result.data()));\n}\n\n\ninline void eval_qr(const tommath_int& x, const tommath_int& y, \n tommath_int& q, tommath_int& r)\n{\n detail::check_tommath_result(mp_div(const_cast< ::mp_int*>(&x.data()), const_cast< ::mp_int*>(&y.data()), &q.data(), &r.data()));\n}\n\ninline unsigned eval_lsb(const tommath_int& val)\n{\n int c = eval_get_sign(val);\n if(c == 0)\n {\n BOOST_THROW_EXCEPTION(std::range_error(\"No bits were set in the operand.\"));\n }\n if(c < 0)\n {\n BOOST_THROW_EXCEPTION(std::range_error(\"Testing individual bits in negative values is not supported - results are undefined.\"));\n }\n return mp_cnt_lsb(const_cast< ::mp_int*>(&val.data()));\n}\n\ninline unsigned eval_msb(const tommath_int& val)\n{\n int c = eval_get_sign(val);\n if(c == 0)\n {\n BOOST_THROW_EXCEPTION(std::range_error(\"No bits were set in the operand.\"));\n }\n if(c < 0)\n {\n BOOST_THROW_EXCEPTION(std::range_error(\"Testing individual bits in negative values is not supported - results are undefined.\"));\n }\n return mp_count_bits(const_cast< ::mp_int*>(&val.data())) - 1;\n}\n\ntemplate \ninline typename enable_if, Integer>::type eval_integer_modulus(const tommath_int& x, Integer val)\n{\n static const mp_digit m = (static_cast(1) << DIGIT_BIT) - 1;\n if(val <= m)\n {\n mp_digit d;\n detail::check_tommath_result(mp_mod_d(const_cast< ::mp_int*>(&x.data()), static_cast(val), &d));\n return d;\n }\n else\n {\n return default_ops::eval_integer_modulus(x, val);\n }\n}\ntemplate \ninline typename enable_if, Integer>::type eval_integer_modulus(const tommath_int& x, Integer val)\n{\n return eval_integer_modulus(x, boost::multiprecision::detail::unsigned_abs(val));\n}\n\n} // namespace backends\n\nusing boost::multiprecision::backends::tommath_int;\n\ntemplate<>\nstruct number_category : public mpl::int_{};\n\ntypedef number tom_int;\ntypedef rational_adaptor tommath_rational;\ntypedef number tom_rational;\n\n}} // namespaces\n\nnamespace std{\n\ntemplate \nclass numeric_limits >\n{\n typedef boost::multiprecision::number number_type;\npublic:\n BOOST_STATIC_CONSTEXPR bool is_specialized = true;\n //\n // Largest and smallest numbers are bounded only by available memory, set\n // to zero:\n //\n static number_type (min)()\n { \n return number_type();\n }\n static number_type (max)() \n { \n return number_type();\n }\n static number_type lowest() { return (min)(); }\n BOOST_STATIC_CONSTEXPR int digits = INT_MAX;\n BOOST_STATIC_CONSTEXPR int digits10 = (INT_MAX / 1000) * 301L;\n BOOST_STATIC_CONSTEXPR int max_digits10 = digits10 + 2;\n BOOST_STATIC_CONSTEXPR bool is_signed = true;\n BOOST_STATIC_CONSTEXPR bool is_integer = true;\n BOOST_STATIC_CONSTEXPR bool is_exact = true;\n BOOST_STATIC_CONSTEXPR int radix = 2;\n static number_type epsilon() { return number_type(); }\n static number_type round_error() { return number_type(); }\n BOOST_STATIC_CONSTEXPR int min_exponent = 0;\n BOOST_STATIC_CONSTEXPR int min_exponent10 = 0;\n BOOST_STATIC_CONSTEXPR int max_exponent = 0;\n BOOST_STATIC_CONSTEXPR int max_exponent10 = 0;\n BOOST_STATIC_CONSTEXPR bool has_infinity = false;\n BOOST_STATIC_CONSTEXPR bool has_quiet_NaN = false;\n BOOST_STATIC_CONSTEXPR bool has_signaling_NaN = false;\n BOOST_STATIC_CONSTEXPR float_denorm_style has_denorm = denorm_absent;\n BOOST_STATIC_CONSTEXPR bool has_denorm_loss = false;\n static number_type infinity() { return number_type(); }\n static number_type quiet_NaN() { return number_type(); }\n static number_type signaling_NaN() { return number_type(); }\n static number_type denorm_min() { return number_type(); }\n BOOST_STATIC_CONSTEXPR bool is_iec559 = false;\n BOOST_STATIC_CONSTEXPR bool is_bounded = false;\n BOOST_STATIC_CONSTEXPR bool is_modulo = false;\n BOOST_STATIC_CONSTEXPR bool traps = false;\n BOOST_STATIC_CONSTEXPR bool tinyness_before = false;\n BOOST_STATIC_CONSTEXPR float_round_style round_style = round_toward_zero;\n};\n\n#ifndef BOOST_NO_INCLASS_MEMBER_INITIALIZATION\n\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::digits;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::digits10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::max_digits10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_signed;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_integer;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_exact;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::radix;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::min_exponent;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::min_exponent10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::max_exponent;\ntemplate \nBOOST_CONSTEXPR_OR_CONST int numeric_limits >::max_exponent10;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::has_infinity;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::has_quiet_NaN;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::has_signaling_NaN;\ntemplate \nBOOST_CONSTEXPR_OR_CONST float_denorm_style numeric_limits >::has_denorm;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::has_denorm_loss;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_iec559;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_bounded;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::is_modulo;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::traps;\ntemplate \nBOOST_CONSTEXPR_OR_CONST bool numeric_limits >::tinyness_before;\ntemplate \nBOOST_CONSTEXPR_OR_CONST float_round_style numeric_limits >::round_style;\n\n#endif\n}\n\n#endif\n", "meta": {"hexsha": "9c373443c6659a1187b74b1f616b899eb77cff9a", "size": 29849, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "clm/src/main/clm/jni/boost/armv7a/include/boost/multiprecision/tommath.hpp", "max_stars_repo_name": "BruceNUAA/gaze-detection-android-app", "max_stars_repo_head_hexsha": "5daa2c8a0e51eb506fe435a6f8d03758162d0579", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1210.0, "max_stars_repo_stars_event_min_datetime": "2020-08-18T07:57:36.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T15:06:05.000Z", "max_issues_repo_path": "clm/src/main/clm/jni/boost/armv7a/include/boost/multiprecision/tommath.hpp", "max_issues_repo_name": "BruceNUAA/gaze-detection-android-app", "max_issues_repo_head_hexsha": "5daa2c8a0e51eb506fe435a6f8d03758162d0579", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 37.0, "max_issues_repo_issues_event_min_datetime": "2020-08-24T02:48:38.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-30T06:41:52.000Z", "max_forks_repo_path": "clm/src/main/clm/jni/boost/armv7a/include/boost/multiprecision/tommath.hpp", "max_forks_repo_name": "BruceNUAA/gaze-detection-android-app", "max_forks_repo_head_hexsha": "5daa2c8a0e51eb506fe435a6f8d03758162d0579", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 275.0, "max_forks_repo_forks_event_min_datetime": "2020-08-18T08:35:16.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T15:06:07.000Z", "avg_line_length": 39.223390276, "max_line_length": 168, "alphanum_fraction": 0.6665549935, "num_tokens": 7682, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.22636067265082674}} {"text": "// Author: Francesco Regazzoni - MOX, Politecnico di Milano\n// Email: francesco.regazzoni@polimi.it\n// Date: 2020\n\n#include \n\n#include \n#include \n\n#include \"model_RDQ20_SE.hpp\"\n\nmodel_RDQ20_SE::model_RDQ20_SE(std::string parameters_file)\n : sarcomere(\"RDQ20-SE\") {\n // Read JSON options file\n boost::property_tree::ptree root;\n boost::property_tree::read_json(parameters_file, root);\n\n prm_n_RU = root.get_child(\"geometry\")\n .get_child(\"n_RU\")\n .get_value(); // [-]\n prm_LA = root.get_child(\"geometry\")\n .get_child(\"LA\")\n .get_value(); // [micro m]\n prm_LM = root.get_child(\"geometry\")\n .get_child(\"LM\")\n .get_value(); // [micro m]\n prm_LB = root.get_child(\"geometry\")\n .get_child(\"LB\")\n .get_value(); // [micro m]\n prm_SL0 = root.get_child(\"geometry\")\n .get_child(\"SL0\")\n .get_value(); // [micro m]\n prm_Lsmooth = root.get_child(\"geometry\")\n .get_child(\"Lsmooth\")\n .get_value(); // [micro m]\n prm_Q = root.get_child(\"RU_steady_state\")\n .get_child(\"Q\")\n .get_value(); // [-]\n prm_Kd0 = root.get_child(\"RU_steady_state\")\n .get_child(\"Kd0\")\n .get_value(); // [micro M]\n prm_alphaKd = root.get_child(\"RU_steady_state\")\n .get_child(\"alphaKd\")\n .get_value(); // [micro M / micro m]\n prm_mu = root.get_child(\"RU_steady_state\")\n .get_child(\"mu\")\n .get_value(); // [-]\n prm_gamma = root.get_child(\"RU_steady_state\")\n .get_child(\"gamma\")\n .get_value(); // [-]\n prm_Koff = root.get_child(\"RU_kinetics\")\n .get_child(\"Koff\")\n .get_value(); // [s^-1]\n prm_Kbasic = root.get_child(\"RU_kinetics\")\n .get_child(\"Kbasic\")\n .get_value(); // [s^-1]\n prm_r0 = root.get_child(\"XB_cycling\")\n .get_child(\"r0\")\n .get_value(); // [s^-1]\n prm_alpha = root.get_child(\"XB_cycling\")\n .get_child(\"alpha\")\n .get_value(); // [-]\n prm_mu0_fP = root.get_child(\"XB_cycling\")\n .get_child(\"mu0_fP\")\n .get_value(); // [s^-1]\n prm_mu1_fP = root.get_child(\"XB_cycling\")\n .get_child(\"mu1_fP\")\n .get_value(); // [s^-1]\n prm_a_XB = root.get_child(\"upscaling\")\n .get_child(\"a_XB\")\n .get_value(); // [kPa]\n\n allocate_variables();\n\n initialize_rates();\n}\n\nvoid model_RDQ20_SE::allocate_variables() {\n // Variable numbers\n n_states_RU = (prm_n_RU - 2) * 4 * 4 * 4;\n n_states_XB = prm_n_RU * 4;\n n_variables = n_states_RU + n_states_XB;\n\n // Allocation of state_RU\n std::array, 4>, 4> base_RU_state;\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n base_RU_state[RU_L][RU_C][RU_R] = 0.0;\n base_RU_state[0][0][0] = 1.0;\n\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n state_RU.push_back(base_RU_state);\n\n // Allocation of state_XB\n std::array base_XB_state;\n for (i_XB = 0; i_XB < 4; ++i_XB)\n base_XB_state[i_XB] = 0.0;\n\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU)\n state_XB.push_back(base_XB_state);\n\n // Allocation of initial_state\n for (unsigned int i = 0; i < n_variables; ++i)\n initial_state.push_back(0.0);\n serialize_state(initial_state);\n\n // Allocation of rates_RU\n std::array, 4>, 4>, 4>\n base_RU_rates_or_flux;\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new)\n base_RU_rates_or_flux[RU_L][RU_C][RU_R][RU_new] = 0.0;\n\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU) {\n rates_RU.push_back(base_RU_rates_or_flux);\n }\n\n // Allocation of flux_RU_*\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU) {\n flux_RU_L.push_back(base_RU_rates_or_flux);\n flux_RU_C.push_back(base_RU_rates_or_flux);\n flux_RU_R.push_back(base_RU_rates_or_flux);\n }\n}\n\nvoid model_RDQ20_SE::solve_time_step(std::vector &state,\n const double &calcium,\n const double &sarcomere_length,\n const double &dSL_dt, const double &dt) {\n // Deserialize state\n deserialize_state(state);\n\n // Update RU transition rates\n RU_update_rates(calcium, sarcomere_length);\n\n // Advance RU state\n double RU_dt = 0.0;\n double time_advanced = 0.0;\n while (time_advanced <=\n dt - 1e-10) // Cover the time-step up to a given tolerance\n {\n RU_dt = std::min(prm_time_step_update_RU_state, dt - time_advanced);\n RU_update_state(RU_dt);\n time_advanced += RU_dt;\n }\n\n // Advance XB state\n XB_update_state(sarcomere_length, dSL_dt, dt);\n\n // Re-serialize state\n serialize_state(state);\n}\n\nvoid model_RDQ20_SE::deserialize_state(const std::vector &state) {\n unsigned int i_current = 0;\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n state_RU[i_RU][RU_L][RU_C][RU_R] = state[i_current];\n i_current++;\n }\n\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU)\n for (i_XB = 0; i_XB < 4; ++i_XB) {\n state_XB[i_RU][i_XB] = state[i_current];\n i_current++;\n }\n}\n\nvoid model_RDQ20_SE::serialize_state(std::vector &state) {\n unsigned int i_current = 0;\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n state[i_current] = state_RU[i_RU][RU_L][RU_C][RU_R];\n i_current++;\n }\n\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU)\n for (i_XB = 0; i_XB < 4; ++i_XB) {\n state[i_current] = state_XB[i_RU][i_XB];\n i_current++;\n }\n}\n\ndouble model_RDQ20_SE::get_active_tension(const std::vector &state,\n const double & /*sarcomere_length*/) {\n deserialize_state(state);\n\n double active_tension = 0.0;\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU)\n active_tension += state_XB[i_RU][1] + state_XB[i_RU][3];\n\n return prm_a_XB * active_tension / prm_n_RU;\n}\n\ndouble\nmodel_RDQ20_SE::get_active_stiffness(const std::vector &state,\n const double & /*sarcomere_length*/) {\n deserialize_state(state);\n\n double active_stiffness = 0.0;\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU)\n active_stiffness += state_XB[i_RU][0] + state_XB[i_RU][2];\n\n return prm_a_XB * active_stiffness / prm_n_RU;\n}\n\ndouble model_RDQ20_SE::get_permissivity(const std::vector &state) {\n deserialize_state(state);\n\n double permissivity = 0;\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU) {\n if (i_RU == 0) // First RU\n {\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n permissivity +=\n state_RU[0][2][RU_C][RU_R] + state_RU[0][3][RU_C][RU_R];\n } else if (i_RU == prm_n_RU - 1) // Last RU\n {\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n permissivity += state_RU[prm_n_RU - 3][RU_L][RU_C][2] +\n state_RU[prm_n_RU - 3][RU_L][RU_C][3];\n } else // Intermediate RUs\n {\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n permissivity += state_RU[i_RU - 1][RU_L][2][RU_R] +\n state_RU[i_RU - 1][RU_L][3][RU_R];\n }\n }\n return permissivity / prm_n_RU;\n}\n\nvoid model_RDQ20_SE::initialize_rates() {\n int permissive_neighbors;\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n permissive_neighbors =\n permissivity_of_state[RU_L] + permissivity_of_state[RU_R];\n rates_RU[i_RU][RU_L][1][RU_R][0] = prm_Koff;\n rates_RU[i_RU][RU_L][2][RU_R][3] = prm_Koff / prm_mu;\n rates_RU[i_RU][RU_L][3][RU_R][0] =\n prm_Kbasic * std::pow(prm_gamma, 2 - permissive_neighbors);\n rates_RU[i_RU][RU_L][2][RU_R][1] =\n prm_Kbasic * std::pow(prm_gamma, 2 - permissive_neighbors);\n }\n}\n\nvoid model_RDQ20_SE::RU_update_rates(const double &calcium,\n const double &sarcomere_length) {\n double Kon = prm_Koff / (prm_Kd0 - prm_alphaKd * (2.15 - sarcomere_length));\n int permissive_neighbors;\n double ChiSF_i_RU;\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU) {\n ChiSF_i_RU = ChiSF(sarcomere_length, i_RU);\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n permissive_neighbors =\n permissivity_of_state[RU_L] + permissivity_of_state[RU_R];\n rates_RU[i_RU][RU_L][0][RU_R][1] = Kon * calcium;\n rates_RU[i_RU][RU_L][3][RU_R][2] = Kon * calcium;\n rates_RU[i_RU][RU_L][1][RU_R][2] =\n ChiSF_i_RU * std::pow(prm_gamma, permissive_neighbors) * prm_Q *\n prm_Kbasic;\n rates_RU[i_RU][RU_L][0][RU_R][3] =\n ChiSF_i_RU * std::pow(prm_gamma, permissive_neighbors) * prm_Q *\n prm_Kbasic / prm_mu;\n }\n }\n}\n\nvoid model_RDQ20_SE::RU_update_state(const double &dt) {\n // Compute fluxes associated with center units\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n flux_RU_C[i_RU][RU_L][RU_C][RU_R][RU_new] =\n rates_RU[i_RU + 1][RU_L][RU_C][RU_R][RU_new] *\n state_RU[i_RU][RU_L][RU_C][RU_R];\n }\n\n double prob_tot;\n double rate_tot;\n\n // Compute fluxes associated with left units\n // --- Most left-ward triplet\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n flux_RU_L[0][RU_L][RU_C][RU_R][RU_new] =\n rates_RU[0][0][RU_L][RU_C][RU_new] *\n state_RU[0][RU_L][RU_C][RU_R];\n }\n\n // --- Other triplets\n for (i_RU = 1; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C) {\n prob_tot = 0.0;\n for (RU_dummy = 0; RU_dummy < 4; ++RU_dummy)\n prob_tot += state_RU[i_RU - 1][RU_dummy][RU_L][RU_C];\n\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n rate_tot = 0.0;\n if (prob_tot > 1e-12) {\n for (RU_dummy = 0; RU_dummy < 4; ++RU_dummy)\n rate_tot += flux_RU_C[i_RU - 1][RU_dummy][RU_L][RU_C][RU_new];\n\n rate_tot /= prob_tot;\n }\n\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n flux_RU_L[i_RU][RU_L][RU_C][RU_R][RU_new] =\n rate_tot * state_RU[i_RU][RU_L][RU_C][RU_R];\n }\n }\n }\n\n // Compute fluxes associated with right units\n // --- Most right-ward triplet\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n flux_RU_R[prm_n_RU - 3][RU_L][RU_C][RU_R][RU_new] =\n rates_RU[prm_n_RU - 1][RU_C][RU_R][0][RU_new] *\n state_RU[prm_n_RU - 3][RU_L][RU_C][RU_R];\n }\n\n // --- Other triplets\n for (i_RU = 0; i_RU < prm_n_RU - 3; ++i_RU)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_C = 0; RU_C < 4; ++RU_C) {\n prob_tot = 0.0;\n for (RU_dummy = 0; RU_dummy < 4; ++RU_dummy)\n prob_tot += state_RU[i_RU + 1][RU_C][RU_R][RU_dummy];\n\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n rate_tot = 0.0;\n if (prob_tot > 1e-12) {\n for (RU_dummy = 0; RU_dummy < 4; ++RU_dummy)\n rate_tot += flux_RU_C[i_RU + 1][RU_C][RU_R][RU_dummy][RU_new];\n\n rate_tot /= prob_tot;\n }\n\n for (RU_L = 0; RU_L < 4; ++RU_L) {\n flux_RU_R[i_RU][RU_L][RU_C][RU_R][RU_new] =\n rate_tot * state_RU[i_RU][RU_L][RU_C][RU_R];\n }\n }\n }\n\n // Forward Euler advance\n for (i_RU = 0; i_RU < prm_n_RU - 2; ++i_RU)\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R)\n for (RU_new = 0; RU_new < 4; ++RU_new) {\n state_RU[i_RU][RU_L][RU_C][RU_R] +=\n dt * (flux_RU_L[i_RU][RU_new][RU_C][RU_R][RU_L] +\n flux_RU_C[i_RU][RU_L][RU_new][RU_R][RU_C] +\n flux_RU_R[i_RU][RU_L][RU_C][RU_new][RU_R] -\n flux_RU_L[i_RU][RU_L][RU_C][RU_R][RU_new] -\n flux_RU_C[i_RU][RU_L][RU_C][RU_R][RU_new] -\n flux_RU_R[i_RU][RU_L][RU_C][RU_R][RU_new]);\n }\n}\n\nvoid model_RDQ20_SE::XB_update_state(const double &sarcomere_length,\n const double &dSL_dt, const double &dt) {\n double v = -dSL_dt / prm_SL0;\n\n double r = prm_r0 + prm_alpha * std::abs(v);\n double k_PN;\n double k_NP;\n double permissivity;\n double flux_PN;\n double flux_NP;\n double ChiSFChiMF_i;\n double diag_P;\n double diag_N;\n\n for (i_RU = 0; i_RU < prm_n_RU; ++i_RU) {\n permissivity = 0;\n flux_PN = 0;\n flux_NP = 0;\n\n if (i_RU == 0) // First RU\n {\n for (RU_C = 0; RU_C < 4; ++RU_C)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n permissivity +=\n state_RU[0][2][RU_C][RU_R] + state_RU[0][3][RU_C][RU_R];\n flux_NP += state_RU[0][0][RU_C][RU_R] * rates_RU[0][0][0][RU_C][3] +\n state_RU[0][1][RU_C][RU_R] * rates_RU[0][0][1][RU_C][2];\n flux_PN += state_RU[0][3][RU_C][RU_R] * rates_RU[0][0][3][RU_C][0] +\n state_RU[0][2][RU_C][RU_R] * rates_RU[0][0][2][RU_C][1];\n }\n } else if (i_RU == prm_n_RU - 1) // Last RU\n {\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_C = 0; RU_C < 4; ++RU_C) {\n permissivity += state_RU[prm_n_RU - 3][RU_L][RU_C][2] +\n state_RU[prm_n_RU - 3][RU_L][RU_C][3];\n flux_NP += state_RU[prm_n_RU - 3][RU_L][RU_C][0] *\n rates_RU[prm_n_RU - 1][RU_C][0][0][3] +\n state_RU[prm_n_RU - 3][RU_L][RU_C][1] *\n rates_RU[prm_n_RU - 1][RU_C][1][0][2];\n flux_PN += state_RU[prm_n_RU - 3][RU_L][RU_C][3] *\n rates_RU[prm_n_RU - 1][RU_C][3][0][0] +\n state_RU[prm_n_RU - 3][RU_L][RU_C][2] *\n rates_RU[prm_n_RU - 1][RU_C][2][0][1];\n }\n } else // Intermediate RUs\n {\n for (RU_L = 0; RU_L < 4; ++RU_L)\n for (RU_R = 0; RU_R < 4; ++RU_R) {\n permissivity += state_RU[i_RU - 1][RU_L][2][RU_R] +\n state_RU[i_RU - 1][RU_L][3][RU_R];\n flux_NP += state_RU[i_RU - 1][RU_L][0][RU_R] *\n rates_RU[i_RU][RU_L][0][RU_R][3] +\n state_RU[i_RU - 1][RU_L][1][RU_R] *\n rates_RU[i_RU][RU_L][1][RU_R][2];\n flux_PN += state_RU[i_RU - 1][RU_L][3][RU_R] *\n rates_RU[i_RU][RU_L][3][RU_R][0] +\n state_RU[i_RU - 1][RU_L][2][RU_R] *\n rates_RU[i_RU][RU_L][2][RU_R][1];\n }\n }\n\n if (permissivity >= 1e-12)\n k_PN = 0;\n else\n k_PN = flux_PN / permissivity;\n\n if (1 - permissivity >= 1e-12)\n k_NP = 0;\n else\n k_NP = flux_NP / (1 - permissivity);\n\n diag_P = r + k_PN;\n diag_N = r + k_NP;\n\n // Fill matrix\n XB_A(0, 0) = -diag_P;\n XB_A(1, 1) = -diag_P;\n XB_A(2, 2) = -diag_N;\n XB_A(3, 3) = -diag_N;\n XB_A(0, 2) = k_NP;\n XB_A(1, 3) = k_NP;\n XB_A(2, 0) = k_PN;\n XB_A(3, 1) = k_PN;\n XB_A(1, 0) = -v;\n XB_A(3, 2) = -v;\n\n XB_A *= -dt;\n for (i_XB = 0; i_XB < 4; ++i_XB)\n XB_A(i_XB, i_XB) += 1.0;\n\n ChiSFChiMF_i =\n ChiSF(sarcomere_length, i_RU) * ChiMF(sarcomere_length, i_RU);\n\n // Fill rhs\n XB_rhs(0) =\n state_XB[i_RU][0] + dt * permissivity * prm_mu0_fP * ChiSFChiMF_i;\n XB_rhs(1) =\n state_XB[i_RU][1] + dt * permissivity * prm_mu1_fP * ChiSFChiMF_i;\n XB_rhs(2) = state_XB[i_RU][2];\n XB_rhs(3) = state_XB[i_RU][3];\n\n // Implicit Euler advance\n XB_sol = XB_A.colPivHouseholderQr().solve(XB_rhs);\n for (i_XB = 0; i_XB < 4; ++i_XB)\n state_XB[i_RU][i_XB] = XB_sol(i_XB);\n }\n}", "meta": {"hexsha": "943261aa0aee8cc5313481d6c4a959bc562626cb", "size": 16860, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "models_cpp/model_RDQ20_SE.cpp", "max_stars_repo_name": "FrancescoRegazzoni/cardiac-activation", "max_stars_repo_head_hexsha": "26f05df28891df7b3c69f16bb136cdced6b63c4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-12-17T00:26:28.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-17T00:26:28.000Z", "max_issues_repo_path": "models_cpp/model_RDQ20_SE.cpp", "max_issues_repo_name": "FrancescoRegazzoni/cardiac-activation", "max_issues_repo_head_hexsha": "26f05df28891df7b3c69f16bb136cdced6b63c4d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "models_cpp/model_RDQ20_SE.cpp", "max_forks_repo_name": "FrancescoRegazzoni/cardiac-activation", "max_forks_repo_head_hexsha": "26f05df28891df7b3c69f16bb136cdced6b63c4d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.4785276074, "max_line_length": 80, "alphanum_fraction": 0.5389679715, "num_tokens": 5950, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.41111086923216794, "lm_q1q2_score": 0.22636067265082668}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n// to the time conversions (do not forget the files!!):\n#include \"time_conversion.c\"\n#include \"time_conversion.h\"\n\n/***\n\n THIS IS THE DEPRECATED VERSION, PLEASE, USE THE FUNCTIONS AND STRUCTS OF THE FILE\n \"logreader2.hpp\"\n\n***/\n\n//#include \n\n\n///\nconst int toUTC = 3;\n///\n\nusing namespace std;\n\n//const long double pi = 4*atan(1);\n//const int gpsLibLeap = 14;\n\n\nstruct gpsTime\n{\n\tunsigned short week;\n\tdouble seconds;\n};\n\n//alternative for the standart definition\nstruct gpsTime2\n{\n\tint week;\n\tdouble seconds;\n\n};\n\n//struct that contains the INS/GNSS observations\nstruct obs\n{\n\n\tgpsTime obsTime;\n\n\t//position\n\tdouble lat;\n\tdouble lgt;\n\tdouble h;\n\n\t//velocity\n\tdouble velX;\n\tdouble velY;\n\tdouble velZ;\n\n\t//attitude\n\tdouble pitch; //along Y axis (\"phi\")\n\tdouble roll; //along X axis (\"omega\")\n\tdouble azimuth; //along Z axis (\"kappa\")\n\n\t//status\n\tstring status;\n};\n\n//struct that contains the covariance matrices of the INS/GNSS observations\nstruct obsCov\n{\n\tgpsTime covTime;\n\n\tdouble pos_mvc[3][3];\n\tdouble vel_mvc[3][3];\n\tdouble att_mvc[3][3];\n\n};\n\n//struct that contains all the others of the INS nested\nstruct obsWcov\n{\n\tobs observation;\n\n\tobsCov obsCovariances;\n};\n\n//struct for the phototimestamp (actually of the stereopair)\n\nstruct moment\n{\ndouble hour;\ndouble minute;\ndouble second;\n};\n\nstruct stereopairTimestamp\n{\ndouble tsyear;\ndouble tsmonth;\ndouble tsday;\n\ndouble leapSeconds;\n\nmoment leftStart;\nmoment leftEnd;\nmoment RightStart;\nmoment RightEnd;\n\ngpsTime _leftStart;\ngpsTime _leftEnd;\ngpsTime _RightStart;\ngpsTime _RightEnd;\n\ngpsTime finalTime;\n};\n\n//struct for the closest observations to the timestamps of the photos\nstruct intervals\n{\ndouble shortestNeg; int shortestNegPos;\ndouble shortestPos; int shortestPosPos;\ndouble theTime;\n};\n\nstruct survData\n{ //store the INS/GNSS data in:\n\tstd::vector pvaWcov;\n\t//the iterator:\n\t//std::vector::iterator pvaWcovIt;\n\n\n\t//store the stereopair timestamps\n\tstd::vector timestamps;\n\t//std::vector::iterator timestampsIt;\n\n\t//store the shortests intervals of time\n\tstd::vector finalIntervals;\n\n\t //store the final ones:\n\tstd::vector finalObsWcovs;\n\n\tstring log;\n\n\tstring log2_;\n\tdouble year;\n\tdouble month;\n\tdouble day;\n\tdouble LeapSeconds;\n\n\tsurvData(string NavLog,string PhotoTimesLog,double Year,double Month,double Day,double LeapSeconds_);\n\n\tvoid logReader();\n\n\tvoid photoTimestampReader();\n\n\tvoid dataInterpolation();\n\n\t};\n\n\tsurvData::survData(string NavLog,string PhotoTimesLog,double Year,double Month,double Day,double LeapSeconds_)\n\t{\n log = NavLog;\n log2_ = PhotoTimesLog;\n year = Year;\n month = Month;\n day = Day;\n LeapSeconds = LeapSeconds_;\n\n logReader();\n photoTimestampReader();\n dataInterpolation();\n\n\t}\n\n//\n// //store the INS/GNSS data in:\n//\tstd::vector pvaWcov;\n//\t//the iterator:\n//\t//std::vector::iterator pvaWcovIt;\n//\n//\n//\t//store the stereopair timestamps\n//\tstd::vector timestamps;\n//\t//std::vector::iterator timestampsIt;\n//\n//\t//store the shortests intervals of time\n//\tstd::vector finalIntervals;\n//\n//\t //store the final ones:\n//\tstd::vector finalObsWcovs;\n\ndouble linTerp(double time,double timeBefore,double timeAfter,double valBefore,double valAfter)\n{\n//the classic linear interpolation\ndouble value = valBefore + ( (valAfter-valBefore)* ((time-timeBefore)/(timeAfter-timeBefore)) );\n\nreturn value;\n}\n\nvoid survData::logReader()\n{\n //3 steps, to pull of the separators\n\tstd::ifstream infile2(log);\n\tstring line2;\n ofstream arquivo;\n\tarquivo.open(\"parte1.txt\");\n\twhile (std::getline(infile2, line2,','))\n\t{\n arquivo< ( value ) < minNeg)\n {\n minNeg = delta;posMinNeg=j;\n }\n\n if (delta >= 0 && delta < minPos)\n {\n minPos = delta;posMinPos=j;\n }\n\n\n deltas<\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \"cnn/training.h\"\n#include \"cnn/cnn.h\"\n#include \"cnn/expr.h\"\n#include \"cnn/nodes.h\"\n#include \"cnn/lstm.h\"\n#include \"cnn/rnn.h\"\n#include \"c2.h\"\n\ncpyp::Corpus corpus;\nvolatile bool requested_stop = false;\nunsigned LAYERS = 2;\nunsigned INPUT_DIM = 40;\nunsigned HIDDEN_DIM = 60;\nunsigned ACTION_DIM = 36;\nunsigned PRETRAINED_DIM = 50;\nunsigned LSTM_INPUT_DIM = 60;\nunsigned POS_DIM = 10;\nunsigned REL_DIM = 8;\n\n\nbool USE_POS = false;\n\nconstexpr const char* ROOT_SYMBOL = \"ROOT\";\nunsigned kROOT_SYMBOL = 0;\nunsigned ACTION_SIZE = 0;\nunsigned VOCAB_SIZE = 0;\nunsigned POS_SIZE = 0;\n\nusing namespace cnn::expr;\nusing namespace cnn;\nusing namespace std;\nnamespace po = boost::program_options;\n\nvector possible_actions;\nunordered_map> pretrained;\n\nvoid InitCommandLine(int argc, char** argv, po::variables_map* conf) {\n po::options_description opts(\"Configuration options\");\n opts.add_options()\n (\"training_data,T\", po::value(), \"List of Transitions - Training corpus\")\n (\"dev_data,d\", po::value(), \"Development corpus\")\n (\"test_data,p\", po::value(), \"Test corpus\")\n (\"unk_strategy,o\", po::value()->default_value(1), \"Unknown word strategy: 1 = singletons become UNK with probability unk_prob\")\n (\"unk_prob,u\", po::value()->default_value(0.2), \"Probably with which to replace singletons with UNK in training data\")\n (\"model,m\", po::value(), \"Load saved model from this file\")\n (\"use_pos_tags,P\", \"make POS tags visible to parser\")\n (\"layers\", po::value()->default_value(2), \"number of LSTM layers\")\n (\"action_dim\", po::value()->default_value(16), \"action embedding size\")\n (\"input_dim\", po::value()->default_value(32), \"input embedding size\")\n (\"hidden_dim\", po::value()->default_value(64), \"hidden dimension\")\n (\"pretrained_dim\", po::value()->default_value(50), \"pretrained input dimension\")\n (\"pos_dim\", po::value()->default_value(12), \"POS dimension\")\n (\"rel_dim\", po::value()->default_value(10), \"relation dimension\")\n (\"lstm_input_dim\", po::value()->default_value(60), \"LSTM input dimension\")\n (\"train,t\", \"Should training be run?\")\n (\"words,w\", po::value(), \"Pretrained word embeddings\")\n (\"help,h\", \"Help\");\n po::options_description dcmdline_options;\n dcmdline_options.add(opts);\n po::store(parse_command_line(argc, argv, dcmdline_options), *conf);\n if (conf->count(\"help\")) {\n cerr << dcmdline_options << endl;\n exit(1);\n }\n if (conf->count(\"training_data\") == 0) {\n cerr << \"Please specify --traing_data (-T): this is required to determine the vocabulary mapping, even if the parser is used in prediction mode.\\n\";\n exit(1);\n }\n}\n\nstruct ParserBuilder {\n\n LSTMBuilder stack_lstm; // (layers, input, hidden, trainer)\n LSTMBuilder buffer_lstm;\n LSTMBuilder action_lstm;\n LookupParameters* p_w; // word embeddings\n LookupParameters* p_t; // pretrained word embeddings (not updated)\n LookupParameters* p_a; // input action embeddings\n LookupParameters* p_r; // relation embeddings\n LookupParameters* p_p; // pos tag embeddings\n Parameters* p_pbias; // parser state bias\n Parameters* p_A; // action lstm to parser state\n Parameters* p_B; // buffer lstm to parser state\n Parameters* p_S; // stack lstm to parser state\n Parameters* p_H; // head matrix for composition function\n Parameters* p_D; // dependency matrix for composition function\n Parameters* p_R; // relation matrix for composition function\n Parameters* p_w2l; // word to LSTM input\n Parameters* p_p2l; // POS to LSTM input\n Parameters* p_t2l; // pretrained word embeddings to LSTM input\n Parameters* p_ib; // LSTM input bias\n Parameters* p_cbias; // composition function bias\n Parameters* p_p2a; // parser state to action\n Parameters* p_action_start; // action bias\n Parameters* p_abias; // action bias\n Parameters* p_buffer_guard; // end of buffer\n Parameters* p_stack_guard; // end of stack\n\n explicit ParserBuilder(Model* model, const unordered_map>& pretrained) :\n stack_lstm(LAYERS, LSTM_INPUT_DIM, HIDDEN_DIM, model),\n buffer_lstm(LAYERS, LSTM_INPUT_DIM, HIDDEN_DIM, model),\n action_lstm(LAYERS, ACTION_DIM, HIDDEN_DIM, model),\n p_w(model->add_lookup_parameters(VOCAB_SIZE, {INPUT_DIM})),\n p_a(model->add_lookup_parameters(ACTION_SIZE, {ACTION_DIM})),\n p_r(model->add_lookup_parameters(ACTION_SIZE, {REL_DIM})),\n p_pbias(model->add_parameters({HIDDEN_DIM})),\n p_A(model->add_parameters({HIDDEN_DIM, HIDDEN_DIM})),\n p_B(model->add_parameters({HIDDEN_DIM, HIDDEN_DIM})),\n p_S(model->add_parameters({HIDDEN_DIM, HIDDEN_DIM})),\n p_H(model->add_parameters({LSTM_INPUT_DIM, LSTM_INPUT_DIM})),\n p_D(model->add_parameters({LSTM_INPUT_DIM, LSTM_INPUT_DIM})),\n p_R(model->add_parameters({LSTM_INPUT_DIM, REL_DIM})),\n p_w2l(model->add_parameters({LSTM_INPUT_DIM, INPUT_DIM})),\n p_ib(model->add_parameters({LSTM_INPUT_DIM})),\n p_cbias(model->add_parameters({LSTM_INPUT_DIM})),\n p_p2a(model->add_parameters({ACTION_SIZE, HIDDEN_DIM})),\n p_action_start(model->add_parameters({ACTION_DIM})),\n p_abias(model->add_parameters({ACTION_SIZE})),\n p_buffer_guard(model->add_parameters({LSTM_INPUT_DIM})),\n p_stack_guard(model->add_parameters({LSTM_INPUT_DIM})) {\n if (USE_POS) {\n p_p = model->add_lookup_parameters(POS_SIZE, {POS_DIM});\n p_p2l = model->add_parameters({LSTM_INPUT_DIM, POS_DIM});\n }\n if (pretrained.size() > 0) {\n p_t = model->add_lookup_parameters(VOCAB_SIZE, {PRETRAINED_DIM});\n for (auto it : pretrained)\n p_t->Initialize(it.first, it.second);\n p_t2l = model->add_parameters({LSTM_INPUT_DIM, PRETRAINED_DIM});\n } else {\n p_t = nullptr;\n p_t2l = nullptr;\n }\n }\n\nstatic bool IsActionForbidden(const string& a, unsigned bsize, unsigned ssize, const vector& stacki) {\n if (a[1]=='W' && ssize<3) return true;\n if (a[1]=='W') {\n int top=stacki[stacki.size()-1];\n int sec=stacki[stacki.size()-2];\n if (sec>top) return true;\n }\n\n bool is_shift = (a[0] == 'S' && a[1]=='H');\n bool is_reduce = !is_shift;\n if (is_shift && bsize == 1) return true;\n if (is_reduce && ssize < 3) return true;\n if (bsize == 2 && // ROOT is the only thing remaining on buffer\n ssize > 2 && // there is more than a single element on the stack\n is_shift) return true;\n // only attach left to ROOT\n if (bsize == 1 && ssize == 3 && a[0] == 'R') return true;\n return false;\n}\n\n// take a vector of actions and return a parse tree (labeling of every\n// word position with its head's position)\nstatic map compute_heads(unsigned sent_len, const vector& actions, const vector& setOfActions, map* pr = nullptr) {\n map heads;\n map r;\n map& rels = (pr ? *pr : r);\n for(unsigned i=0;i bufferi(sent_len + 1, 0), stacki(1, -999);\n for (unsigned i = 0; i < sent_len; ++i)\n bufferi[sent_len - i] = i;\n bufferi[0] = -999;\n for (auto action: actions) { // loop over transitions for sentence\n const string& actionString=setOfActions[action];\n const char ac = actionString[0];\n const char ac2 = actionString[1];\n if (ac =='S' && ac2=='H') { // SHIFT\n assert(bufferi.size() > 1); // dummy symbol means > 1 (not >= 1)\n stacki.push_back(bufferi.back());\n bufferi.pop_back();\n } else if (ac=='S' && ac2=='W') { // SWAP\n assert(stacki.size() > 2);\n unsigned ii = 0, jj = 0;\n jj = stacki.back();\n stacki.pop_back();\n ii = stacki.back();\n stacki.pop_back();\n bufferi.push_back(ii);\n stacki.push_back(jj);\n } else { // LEFT or RIGHT\n assert(stacki.size() > 2); // dummy symbol means > 2 (not >= 2)\n assert(ac == 'L' || ac == 'R');\n unsigned depi = 0, headi = 0;\n (ac == 'R' ? depi : headi) = stacki.back();\n stacki.pop_back();\n (ac == 'R' ? headi : depi) = stacki.back();\n stacki.pop_back();\n stacki.push_back(headi);\n heads[depi] = headi;\n rels[depi] = actionString;\n }\n }\n assert(bufferi.size() == 1);\n //assert(stacki.size() == 2);\n return heads;\n}\n\n// *** if correct_actions is empty, this runs greedy decoding ***\n// returns parse actions for input sentence (in training just returns the reference)\n// OOV handling: raw_sent will have the actual words\n// sent will have words replaced by appropriate UNK tokens\n// this lets us use pretrained embeddings, when available, for words that were OOV in the\n// parser training data\nvector log_prob_parser(ComputationGraph* hg,\n const vector& raw_sent, // raw sentence\n const vector& sent, // sent with oovs replaced\n const vector& sentPos,\n const vector& correct_actions,\n const vector& setOfActions,\n const map& intToWords,\n double *right) {\n vector results;\n const bool build_training_graph = correct_actions.size() > 0;\n\n stack_lstm.new_graph(*hg);\n buffer_lstm.new_graph(*hg);\n action_lstm.new_graph(*hg);\n stack_lstm.start_new_sequence();\n buffer_lstm.start_new_sequence();\n action_lstm.start_new_sequence();\n // variables in the computation graph representing the parameters\n Expression pbias = parameter(*hg, p_pbias);\n Expression H = parameter(*hg, p_H);\n Expression D = parameter(*hg, p_D);\n Expression R = parameter(*hg, p_R);\n Expression cbias = parameter(*hg, p_cbias);\n Expression S = parameter(*hg, p_S);\n Expression B = parameter(*hg, p_B);\n Expression A = parameter(*hg, p_A);\n Expression ib = parameter(*hg, p_ib);\n Expression w2l = parameter(*hg, p_w2l);\n Expression p2l;\n if (USE_POS)\n p2l = parameter(*hg, p_p2l);\n Expression t2l;\n if (p_t2l)\n t2l = parameter(*hg, p_t2l);\n Expression p2a = parameter(*hg, p_p2a);\n Expression abias = parameter(*hg, p_abias);\n Expression action_start = parameter(*hg, p_action_start);\n\n action_lstm.add_input(action_start);\n\n vector buffer(sent.size() + 1); // variables representing word embeddings (possibly including POS info)\n vector bufferi(sent.size() + 1); // position of the words in the sentence\n // precompute buffer representation from left to right\n\n for (unsigned i = 0; i < sent.size(); ++i) {\n assert(sent[i] < VOCAB_SIZE);\n Expression w =lookup(*hg, p_w, sent[i]);\n\n vector args = {ib, w2l, w}; // learn embeddings\n if (USE_POS) { // learn POS tag?\n Expression p = lookup(*hg, p_p, sentPos[i]);\n args.push_back(p2l);\n args.push_back(p);\n }\n if (p_t && pretrained.count(raw_sent[i])) { // include fixed pretrained vectors?\n Expression t = const_lookup(*hg, p_t, raw_sent[i]);\n args.push_back(t2l);\n args.push_back(t);\n }\n buffer[sent.size() - i] = rectify(affine_transform(args));\n bufferi[sent.size() - i] = i;\n }\n // dummy symbol to represent the empty buffer\n buffer[0] = parameter(*hg, p_buffer_guard);\n bufferi[0] = -999;\n for (auto& b : buffer)\n buffer_lstm.add_input(b);\n\n vector stack; // variables representing subtree embeddings\n vector stacki; // position of words in the sentence of head of subtree\n stack.push_back(parameter(*hg, p_stack_guard));\n stacki.push_back(-999); // not used for anything\n // drive dummy symbol on stack through LSTM\n stack_lstm.add_input(stack.back());\n vector log_probs;\n string rootword;\n unsigned action_count = 0; // incremented at each prediction\n while(stack.size() > 2 || buffer.size() > 1) {\n // get list of possible actions for the current parser state\n vector current_valid_actions;\n for (auto a: possible_actions) {\n if (IsActionForbidden(setOfActions[a], buffer.size(), stack.size(), stacki))\n continue;\n current_valid_actions.push_back(a);\n }\n\n // p_t = pbias + S * slstm + B * blstm + A * almst\n Expression p_t = affine_transform({pbias, S, stack_lstm.back(), B, buffer_lstm.back(), A, action_lstm.back()});\n Expression nlp_t = rectify(p_t);\n // r_t = abias + p2a * nlp\n Expression r_t = affine_transform({abias, p2a, nlp_t});\n\n // adist = log_softmax(r_t, current_valid_actions)\n Expression adiste = log_softmax(r_t, current_valid_actions);\n vector adist = as_vector(hg->incremental_forward());\n double best_score = adist[current_valid_actions[0]];\n unsigned best_a = current_valid_actions[0];\n for (unsigned i = 1; i < current_valid_actions.size(); ++i) {\n if (adist[current_valid_actions[i]] > best_score) {\n best_score = adist[current_valid_actions[i]];\n best_a = current_valid_actions[i];\n }\n }\n unsigned action = best_a;\n if (build_training_graph) { // if we have reference actions (for training) use the reference action\n action = correct_actions[action_count];\n if (best_a == action) { (*right)++; }\n }\n ++action_count;\n log_probs.push_back(pick(adiste, action));\n results.push_back(action);\n\n // add current action to action LSTM\n Expression actione = lookup(*hg, p_a, action);\n action_lstm.add_input(actione);\n\n // get relation embedding from action (TODO: convert to relation from action?)\n Expression relation = lookup(*hg, p_r, action);\n\n // do action\n const string& actionString=setOfActions[action];\n const char ac = actionString[0];\n const char ac2 = actionString[1];\n\n\n if (ac =='S' && ac2=='H') { // SHIFT\n assert(buffer.size() > 1); // dummy symbol means > 1 (not >= 1)\n stack.push_back(buffer.back());\n stack_lstm.add_input(buffer.back());\n buffer.pop_back();\n buffer_lstm.rewind_one_step();\n stacki.push_back(bufferi.back());\n bufferi.pop_back();\n } else if (ac=='S' && ac2=='W'){ //SWAP --- Miguel\n assert(stack.size() > 2); // dummy symbol means > 2 (not >= 2)\n\n Expression toki, tokj;\n unsigned ii = 0, jj = 0;\n tokj=stack.back();\n jj=stacki.back();\n stack.pop_back();\n stacki.pop_back();\n\n toki=stack.back();\n ii=stacki.back();\n stack.pop_back();\n stacki.pop_back();\n\n buffer.push_back(toki);\n bufferi.push_back(ii);\n\n stack_lstm.rewind_one_step();\n stack_lstm.rewind_one_step();\n\n buffer_lstm.add_input(buffer.back());\n\n stack.push_back(tokj);\n stacki.push_back(jj);\n\n stack_lstm.add_input(stack.back());\n } else { // LEFT or RIGHT\n assert(stack.size() > 2); // dummy symbol means > 2 (not >= 2)\n assert(ac == 'L' || ac == 'R');\n Expression dep, head;\n unsigned depi = 0, headi = 0;\n (ac == 'R' ? dep : head) = stack.back();\n (ac == 'R' ? depi : headi) = stacki.back();\n stack.pop_back();\n stacki.pop_back();\n (ac == 'R' ? head : dep) = stack.back();\n (ac == 'R' ? headi : depi) = stacki.back();\n stack.pop_back();\n stacki.pop_back();\n if (headi == sent.size() - 1) rootword = intToWords.find(sent[depi])->second;\n // composed = cbias + H * head + D * dep + R * relation\n Expression composed = affine_transform({cbias, H, head, D, dep, R, relation});\n Expression nlcomposed = tanh(composed);\n stack_lstm.rewind_one_step();\n stack_lstm.rewind_one_step();\n stack_lstm.add_input(nlcomposed);\n stack.push_back(nlcomposed);\n stacki.push_back(headi);\n }\n }\n assert(stack.size() == 2); // guard symbol, root\n assert(stacki.size() == 2);\n assert(buffer.size() == 1); // guard symbol\n assert(bufferi.size() == 1);\n Expression tot_neglogprob = -sum(log_probs);\n assert(tot_neglogprob.pg != nullptr);\n return results;\n }\n};\n\nvoid signal_callback_handler(int /* signum */) {\n if (requested_stop) {\n cerr << \"\\nReceived SIGINT again, quitting.\\n\";\n _exit(1);\n }\n cerr << \"\\nReceived SIGINT terminating optimization early...\\n\";\n requested_stop = true;\n}\n\nunsigned compute_correct(const map& ref, const map& hyp, unsigned len) {\n unsigned res = 0;\n for (unsigned i = 0; i < len; ++i) {\n auto ri = ref.find(i);\n auto hi = hyp.find(i);\n assert(ri != ref.end());\n assert(hi != hyp.end());\n if (ri->second == hi->second) ++res;\n }\n return res;\n}\n\nvoid output_conll(const vector& sentence, const vector& pos,\n const vector& sentenceUnkStrings, \n const map& intToWords, \n const map& intToPos, \n const map& hyp, const map& rel_hyp) {\n for (unsigned i = 0; i < (sentence.size()-1); ++i) {\n auto index = i + 1;\n assert(i < sentenceUnkStrings.size() && \n ((sentence[i] == corpus.get_or_add_word(cpyp::Corpus::UNK) &&\n sentenceUnkStrings[i].size() > 0) ||\n (sentence[i] != corpus.get_or_add_word(cpyp::Corpus::UNK) &&\n sentenceUnkStrings[i].size() == 0 &&\n intToWords.find(sentence[i]) != intToWords.end())));\n string wit = (sentenceUnkStrings[i].size() > 0)? \n sentenceUnkStrings[i] : intToWords.find(sentence[i])->second;\n auto pit = intToPos.find(pos[i]);\n assert(hyp.find(i) != hyp.end());\n auto hyp_head = hyp.find(i)->second + 1;\n if (hyp_head == (int)sentence.size()) hyp_head = 0;\n auto hyp_rel_it = rel_hyp.find(i);\n assert(hyp_rel_it != rel_hyp.end());\n auto hyp_rel = hyp_rel_it->second;\n size_t first_char_in_rel = hyp_rel.find('(') + 1;\n size_t last_char_in_rel = hyp_rel.rfind(')') - 1;\n hyp_rel = hyp_rel.substr(first_char_in_rel, last_char_in_rel - first_char_in_rel + 1);\n cout << index << '\\t' // 1. ID \n << wit << '\\t' // 2. FORM\n << \"_\" << '\\t' // 3. LEMMA \n << \"_\" << '\\t' // 4. CPOSTAG \n << pit->second << '\\t' // 5. POSTAG\n << \"_\" << '\\t' // 6. FEATS\n << hyp_head << '\\t' // 7. HEAD\n << hyp_rel << '\\t' // 8. DEPREL\n << \"_\" << '\\t' // 9. PHEAD\n << \"_\" << endl; // 10. PDEPREL\n }\n cout << endl;\n}\n\n\nint main(int argc, char** argv) {\n cnn::Initialize(argc, argv);\n\n cerr << \"COMMAND:\"; \n for (unsigned i = 0; i < static_cast(argc); ++i) cerr << ' ' << argv[i];\n cerr << endl;\n unsigned status_every_i_iterations = 100;\n\n po::variables_map conf;\n InitCommandLine(argc, argv, &conf);\n USE_POS = conf.count(\"use_pos_tags\");\n\n LAYERS = conf[\"layers\"].as();\n INPUT_DIM = conf[\"input_dim\"].as();\n PRETRAINED_DIM = conf[\"pretrained_dim\"].as();\n HIDDEN_DIM = conf[\"hidden_dim\"].as();\n ACTION_DIM = conf[\"action_dim\"].as();\n LSTM_INPUT_DIM = conf[\"lstm_input_dim\"].as();\n POS_DIM = conf[\"pos_dim\"].as();\n REL_DIM = conf[\"rel_dim\"].as();\n const unsigned unk_strategy = conf[\"unk_strategy\"].as();\n cerr << \"Unknown word strategy: \";\n if (unk_strategy == 1) {\n cerr << \"STOCHASTIC REPLACEMENT\\n\";\n } else {\n abort();\n }\n const double unk_prob = conf[\"unk_prob\"].as();\n assert(unk_prob >= 0.); assert(unk_prob <= 1.);\n ostringstream os;\n os << \"parser_\" << (USE_POS ? \"pos\" : \"nopos\")\n << '_' << LAYERS\n << '_' << INPUT_DIM\n << '_' << HIDDEN_DIM\n << '_' << ACTION_DIM\n << '_' << LSTM_INPUT_DIM\n << '_' << POS_DIM\n << '_' << REL_DIM\n << \"-pid\" << getpid() << \".params\";\n int best_correct_heads = 0;\n const string fname = os.str();\n cerr << \"Writing parameters to file: \" << fname << endl;\n bool softlinkCreated = false;\n corpus.load_correct_actions(conf[\"training_data\"].as());\t\n const unsigned kUNK = corpus.get_or_add_word(cpyp::Corpus::UNK);\n kROOT_SYMBOL = corpus.get_or_add_word(ROOT_SYMBOL);\n\n if (conf.count(\"words\")) {\n pretrained[kUNK] = vector(PRETRAINED_DIM, 0);\n cerr << \"Loading from \" << conf[\"words\"].as() << \" with\" << PRETRAINED_DIM << \" dimensions\\n\";\n ifstream in(conf[\"words\"].as().c_str());\n string line;\n getline(in, line);\n vector v(PRETRAINED_DIM, 0);\n string word;\n while (getline(in, line)) {\n istringstream lin(line);\n lin >> word;\n for (unsigned i = 0; i < PRETRAINED_DIM; ++i) lin >> v[i];\n unsigned id = corpus.get_or_add_word(word);\n pretrained[id] = v;\n }\n }\n\n set training_vocab; // words available in the training corpus\n set singletons;\n { // compute the singletons in the parser's training data\n map counts;\n for (auto sent : corpus.sentences)\n for (auto word : sent.second) { training_vocab.insert(word); counts[word]++; }\n for (auto wc : counts)\n if (wc.second == 1) singletons.insert(wc.first);\n }\n\n cerr << \"Number of words: \" << corpus.nwords << endl;\n VOCAB_SIZE = corpus.nwords + 1;\n ACTION_SIZE = corpus.nactions + 1;\n POS_SIZE = corpus.npos + 10; // bad way of dealing with the fact that we may see new POS tags in the test set\n possible_actions.resize(corpus.nactions);\n for (unsigned i = 0; i < corpus.nactions; ++i)\n possible_actions[i] = i;\n\n Model model;\n ParserBuilder parser(&model, pretrained);\n if (conf.count(\"model\")) {\n ifstream in(conf[\"model\"].as().c_str());\n boost::archive::text_iarchive ia(in);\n ia >> model;\n }\n\n // OOV words will be replaced by UNK tokens\n corpus.load_correct_actionsDev(conf[\"dev_data\"].as());\n //TRAINING\n if (conf.count(\"train\")) {\n signal(SIGINT, signal_callback_handler);\n SimpleSGDTrainer sgd(&model);\n //MomentumSGDTrainer sgd(&model);\n sgd.eta_decay = 0.08;\n //sgd.eta_decay = 0.05;\n vector order(corpus.nsentences);\n for (unsigned i = 0; i < corpus.nsentences; ++i)\n order[i] = i;\n double tot_seen = 0;\n status_every_i_iterations = min(status_every_i_iterations, corpus.nsentences);\n unsigned si = corpus.nsentences;\n cerr << \"NUMBER OF TRAINING SENTENCES: \" << corpus.nsentences << endl;\n unsigned trs = 0;\n double right = 0;\n double llh = 0;\n bool first = true;\n int iter = -1;\n time_t time_start = std::chrono::system_clock::to_time_t(std::chrono::system_clock::now());\n //cerr << \"TRAINING STARTED AT: \" << put_time(localtime(&time_start), \"%c %Z\") << endl;\n while(!requested_stop) {\n ++iter;\n for (unsigned sii = 0; sii < status_every_i_iterations; ++sii) {\n if (si == corpus.nsentences) {\n si = 0;\n if (first) { first = false; } else { sgd.update_epoch(); }\n cerr << \"**SHUFFLE\\n\";\n random_shuffle(order.begin(), order.end());\n }\n tot_seen += 1;\n const vector& sentence=corpus.sentences[order[si]];\n vector tsentence=sentence;\n if (unk_strategy == 1) {\n for (auto& w : tsentence)\n if (singletons.count(w) && cnn::rand01() < unk_prob) w = kUNK;\n }\n\t const vector& sentencePos=corpus.sentencesPos[order[si]]; \n\t const vector& actions=corpus.correct_act_sent[order[si]];\n ComputationGraph hg;\n parser.log_prob_parser(&hg,sentence,tsentence,sentencePos,actions,corpus.actions,corpus.intToWords,&right);\n double lp = as_scalar(hg.incremental_forward());\n if (lp < 0) {\n cerr << \"Log prob < 0 on sentence \" << order[si] << \": lp=\" << lp << endl;\n assert(lp >= 0.0);\n }\n hg.backward();\n sgd.update(1.0);\n llh += lp;\n ++si;\n trs += actions.size();\n }\n sgd.status();\n //time_t time_now = std::chrono::system_clock::to_time_t(std::chrono::system_clock::now());\n time_t time_now = time(NULL);\n std::string t_n(asctime(localtime(&time_now)));\n cerr << \"update #\" << iter << \" (epoch \" << (tot_seen / corpus.nsentences) << \" |time=\" << t_n.substr(0, t_n.size() - 1) << \")\\tllh: \"<< llh<<\" ppl: \" << exp(llh / trs) << \" err: \" << (trs - right) / trs << endl;\n llh = trs = right = 0;\n\n static int logc = 0;\n ++logc;\n if (logc % 25 == 1) { // report on dev set\n unsigned dev_size = corpus.nsentencesDev;\n // dev_size = 100;\n double llh = 0;\n double trs = 0;\n double right = 0;\n double correct_heads = 0;\n double total_heads = 0;\n auto t_start = std::chrono::high_resolution_clock::now();\n for (unsigned sii = 0; sii < dev_size; ++sii) {\n const vector& sentence=corpus.sentencesDev[sii];\n\t const vector& sentencePos=corpus.sentencesPosDev[sii]; \n\t const vector& actions=corpus.correct_act_sentDev[sii];\n vector tsentence=sentence;\n for (auto& w : tsentence)\n if (training_vocab.count(w) == 0) w = kUNK;\n\n ComputationGraph hg;\n\t vector pred = parser.log_prob_parser(&hg,sentence,tsentence,sentencePos,vector(),corpus.actions,corpus.intToWords,&right);\n\t double lp = 0;\n llh -= lp;\n trs += actions.size();\n map ref = parser.compute_heads(sentence.size(), actions, corpus.actions);\n map hyp = parser.compute_heads(sentence.size(), pred, corpus.actions);\n //output_conll(sentence, corpus.intToWords, ref, hyp);\n correct_heads += compute_correct(ref, hyp, sentence.size() - 1);\n total_heads += sentence.size() - 1;\n }\n auto t_end = std::chrono::high_resolution_clock::now();\n cerr << \" **dev (iter=\" << iter << \" epoch=\" << (tot_seen / corpus.nsentences) << \")\\tllh=\" << llh << \" ppl: \" << exp(llh / trs) << \" err: \" << (trs - right) / trs << \" uas: \" << (correct_heads / total_heads) << \"\\t[\" << dev_size << \" sents in \" << std::chrono::duration(t_end-t_start).count() << \" ms]\" << endl;\n if (correct_heads > best_correct_heads) {\n\t cerr << \"saving current best model\" << endl;\n best_correct_heads = correct_heads;\n ofstream out(fname);\n boost::archive::text_oarchive oa(out);\n oa << model;\n // Create a soft link to the most recent model in order to make it\n // easier to refer to it in a shell script.\n if (!softlinkCreated) {\n string softlink = \" latest_model\";\n if (system((string(\"rm -f \") + softlink).c_str()) == 0 && \n system((string(\"ln -s \") + fname + softlink).c_str()) == 0) {\n cerr << \"Created \" << softlink << \" as a soft link to \" << fname \n << \" for convenience.\" << endl;\n }\n softlinkCreated = true;\n }\n }\n }\n }\n } // should do training?\n if (true) { // do test evaluation\n double llh = 0;\n double trs = 0;\n double right = 0;\n double correct_heads = 0;\n double total_heads = 0;\n auto t_start = std::chrono::high_resolution_clock::now();\n unsigned corpus_size = corpus.nsentencesDev;\n for (unsigned sii = 0; sii < corpus_size; ++sii) {\n const vector& sentence=corpus.sentencesDev[sii];\n const vector& sentencePos=corpus.sentencesPosDev[sii]; \n const vector& sentenceUnkStr=corpus.sentencesStrDev[sii]; \n const vector& actions=corpus.correct_act_sentDev[sii];\n vector tsentence=sentence;\n for (auto& w : tsentence)\n if (training_vocab.count(w) == 0) w = kUNK;\n ComputationGraph cg;\n double lp = 0;\n vector pred;\n pred = parser.log_prob_parser(&cg,sentence,tsentence,sentencePos,vector(),corpus.actions,corpus.intToWords,&right);\n llh -= lp;\n trs += actions.size();\n map rel_ref, rel_hyp;\n map ref = parser.compute_heads(sentence.size(), actions, corpus.actions, &rel_ref);\n map hyp = parser.compute_heads(sentence.size(), pred, corpus.actions, &rel_hyp);\n output_conll(sentence, sentencePos, sentenceUnkStr, corpus.intToWords, corpus.intToPos, hyp, rel_hyp);\n correct_heads += compute_correct(ref, hyp, sentence.size() - 1);\n total_heads += sentence.size() - 1;\n }\n auto t_end = std::chrono::high_resolution_clock::now();\n cerr << \"TEST llh=\" << llh << \" ppl: \" << exp(llh / trs) << \" err: \" << (trs - right) / trs << \" uas: \" << (correct_heads / total_heads) << \"\\t[\" << corpus_size << \" sents in \" << std::chrono::duration(t_end-t_start).count() << \" ms]\" << endl;\n }\n for (unsigned i = 0; i < corpus.actions.size(); ++i) {\n //cerr << corpus.actions[i] << '\\t' << parser.p_r->values[i].transpose() << endl;\n //cerr << corpus.actions[i] << '\\t' << parser.p_p2a->values.col(i).transpose() << endl;\n }\n}\n", "meta": {"hexsha": "0f774b63c5dc2d829bea2a1ff8ddb4cf8ea3df06", "size": 29843, "ext": "cc", "lang": "C++", "max_stars_repo_path": "parser/lstm-parse.cc", "max_stars_repo_name": "lstmparser/lstm-parser", "max_stars_repo_head_hexsha": "e95b6d314341a4e54dbf8f02315124ecfd28348f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "parser/lstm-parse.cc", "max_issues_repo_name": "lstmparser/lstm-parser", "max_issues_repo_head_hexsha": "e95b6d314341a4e54dbf8f02315124ecfd28348f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "parser/lstm-parse.cc", "max_forks_repo_name": "lstmparser/lstm-parser", "max_forks_repo_head_hexsha": "e95b6d314341a4e54dbf8f02315124ecfd28348f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.1060606061, "max_line_length": 341, "alphanum_fraction": 0.6207485843, "num_tokens": 7930, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.22630079924588875}} {"text": "\n// #pragma GCC optimize (\"O0\")\n\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"navigation.hpp\"\n#include \"constants.hpp\"\n#include \"antenna.hpp\"\n#include \"common.hpp\"\n#include \"enums.h\"\n\n#include \"eigenIncluder.hpp\"\n\n\n/* decode antenna field */\nint decodef(char *p, int n, double *v)\n{\n\tint i;\n\tfor (i = 0; i < n; i++)\n\t\tv[i] = 0;\n\n\tfor (i = 0, p = strtok(p,\" \"); p && i < n; p = strtok(nullptr, \" \"))\n\t{\n\t\tv[i] = atof(p) * 1E-3;\n\t\ti++;\n\t}\n\treturn i;\n}\n\nbool findAntenna(\n\tstring\t\t\t\tcode,\n\tGTime\t\t\t\ttime,\n\tnav_t&\t\t\t\tnav,\n\tE_FType\t\t\t\tft,\n\tPhaseCenterData**\tpcd_ptr_ptr)\n{\n// \tBOOST_LOG_TRIVIAL(debug)\n// \t<< \"Searching for \" << type << \", \" << code;\n\n\tauto it1 = nav.pcvMap.find(code);\n\tif (it1 == nav.pcvMap.end())\n\t{\n\t\treturn false;\n\t}\n\t\n\tauto& [dummyCode, pcvFreqMap] = *it1;\n\t\n\tauto it2 = pcvFreqMap.find(ft);\n\tif (it2 == pcvFreqMap.end())\n\t{\n\t\treturn false;\n\t}\n\t\n\tauto& [dummy2, pcvTimeMap] = *it2;\n\t\n\tauto it3 = pcvTimeMap.lower_bound(time);\n\tif (it3 == pcvTimeMap.end())\n\t{\n\t\t//just use the first chronologically, (last when sorted as they are) instead\n\t\tauto it4 = pcvTimeMap.rbegin();\n\t\t\n\t\tauto& [dummyTime, pcd] = *it4;\n\t\t\n\t\tif (pcd_ptr_ptr)\n\t\t\t*pcd_ptr_ptr = &pcd;\n\t\t\n\t\treturn true;\n\t}\n\t\n\tauto& [dummyTime, pcd] = *it3;\n\t\t\n\tif (pcd_ptr_ptr)\n\t\t*pcd_ptr_ptr = &pcd;\n\t\n\treturn true;\n}\n\t\n/* linear interpolate pcv ------------------------------------------------------\n*\n* args : double x1 I x1 lower bound (degree)\n* double x2 I x2 upper bound (degree)\n* double y1 I y1 lower bound (m)\n* double y2 I y2 upper bound (m)\n* double x O x current point (degree)\n*\n* return : interpolated pcv (m)\n*----------------------------------------------------------------------------*/\ndouble interp(double x1, double x2, double y1, double y2, double x)\n{\n#if (0)\n\treturn (y2-y1)*(x-x1)/(x2-x1)+y1;\n#endif\n\treturn y2-(y2-y1)*(x2-x)/(x2-x1);\n}\n\nVector3d makeAntPco(\n\tstring\t\tid,\n\tE_FType\t\tft,\n\tGTime\t\ttime)\n{\n\tif (ft == F1)\t\treturn Vector3d::Zero();\n\tif (ft == F2)\t\treturn Vector3d::Zero();\n\t\n\tVector3d pco1 = antPco(id, F1, time);\n\tVector3d pco2 = antPco(id, F2, time);\n\t\n\tif (pco1.isZero())\treturn Vector3d::Zero();\n\tif (pco2.isZero())\treturn Vector3d::Zero();\n\t\n\tdouble lam1 = lam_carr[F1];\n\tdouble lam2 = lam_carr[F2];\n\tdouble lamX = lam_carr[ft];\n\t\n\tif (lamX == 0)\t\treturn Vector3d::Zero();\n\t\n\tdouble k32 = (lamX-lam2)/(lam1-lam2);\n\tdouble k31 = (lamX-lam1)/(lam1-lam2);\n\t\n\tVector3d pco\t= k32 * pco1\n\t\t\t\t\t- k31 * pco2;\n\t\t\t\t\t\n\treturn pco;\n}\n\t\n/** fetch pco\n */\nVector3d antPco(\n\tstring\t\tid,\n\tE_FType\t\tft,\n\tGTime\t\ttime,\n\tbool\t\tinterp)\n{\n\tauto it1 = nav.pcoMap.find(id);\n\tif (it1 == nav.pcoMap.end())\n\t{\n\t\treturn Vector3d::Zero();\n\t}\n\t\n\tauto& [dummy1, pcoFreqMap] = *it1;\n\t\n\tauto it2 = pcoFreqMap.find(ft);\n\tif (it2 == pcoFreqMap.end())\n\t{\n\t\tif (interp)\t\treturn makeAntPco(id, ft, time);\n\t\telse\t\t\treturn Vector3d::Zero();\n\t}\n\t\n\tauto& [dummy2, pcoTimeMap] = *it2;\n\t\n\tauto it3 = pcoTimeMap.lower_bound(time);\n\tif (it3 == pcoTimeMap.end())\n\t{\n\t\treturn Vector3d::Zero();\n\t}\n\t\n\tauto& [dummy3, pco] = *it3;\n\t\n\treturn pco;\n}\n\n/** find and interpolate antenna pcv\n*/\ndouble antPcv(\n\tstring\t\tid,\t\t///< antenna id\n\tE_FType\t\tft,\t\t///< frequency\n\tGTime\t\ttime,\t///< time\n\tdouble\t\taCos,\t///< angle between target and antenna axis (radians)\n\tdouble\t\tazi)\t///< azimuth angle (radians)\n{\n\tauto it1 = nav.pcvMap.find(id);\n\tif (it1 == nav.pcvMap.end())\n\t{\n\t\treturn 0;\n\t}\n\t\n\tauto& [dummy1, pcvFreqMap] = *it1;\n\n\tauto it2 = pcvFreqMap.find(ft);\n\tif (it2 == pcvFreqMap.end())\n\t{\n\t\treturn 0;\n\t}\n\t\n\tauto& [dummy2, pcvTimeMap] = *it2;\n\t\n\tauto it3 = pcvTimeMap.lower_bound(time);\n\tif (it3 == pcvTimeMap.end())\n\t{\n\t\treturn 0;\n\t}\n\t\n\tauto& [dummy3, pcd] = *it3;\n\t\n\tauto& pcvMap1D = pcd.PCVMap1D;\n\tauto& pcvMap2D = pcd.PCVMap2D;\n\n\tint\t\tnz\t\t= pcd.nz;\n\tint\t\tnaz\t\t= pcd.naz;\n\tdouble\tzen1\t= pcd.zenStart;\n\tdouble\tdzen\t= pcd.zenDelta;\n\tdouble\tdazi\t= pcd.aziDelta;\n\tdouble\tzen\t\t= aCos * R2D;\n\tazi *= R2D;\n\t\n\tdouble\tpcv;\n\t\n\t/* select zenith angle range */\n\tint zen_n;\n\tfor (zen_n = 1; zen_n < nz; zen_n++)\n\t{\n\t\tif ((zen1 + dzen * zen_n) >= zen)\n\t\t{\n\t\t\tbreak;\n\t\t}\n\t}\n\n\tdouble xz1 = zen1 + dzen * (zen_n - 1);\n\tdouble xz2 = zen1 + dzen * (zen_n);\n\n\tif\t( naz == 0\n\t\t||azi == 0)\n\t{\n\t\t/* linear interpolate receiver pcv - non azimuth-dependent */\n\t\t/* interpolate */\n\n\t\tdouble\tyz1 = pcvMap1D[zen_n - 1];\t\t// lower bound\n\t\tdouble\tyz2 = pcvMap1D[zen_n];\t\t\t// upper bound\n\t\tpcv = interp(xz1, xz2, yz1, yz2, zen);\n\t}\n\telse\n\t{\n\t\t/* bilinear interpolate receiver pcv - azimuth-dependent */\n\t\t/* select azimuth angle range */\n\t\tint az_n;\n\t\tfor (az_n = 1; az_n < naz; az_n++)\n\t\t{\n\t\t\tif ((dazi * az_n) >= azi)\n\t\t\t{\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\n\t\tdouble xa1 = dazi * (az_n -1);\n\t\tdouble xa2 = dazi * (az_n);\n\n\t\tdouble yz3 = pcvMap2D[az_n-1]\t[zen_n-1];\t\tdouble yz1 = pcvMap2D[az_n-1]\t[zen_n];\n\t\tdouble yz4 = pcvMap2D[az_n]\t\t[zen_n-1];\t\tdouble yz2 = pcvMap2D[az_n]\t\t[zen_n];\n\n\t\t/* linear interpolation along zenith angle */\n\t\tdouble ya1\t= interp(xz1, xz2, yz3, yz1, zen);\n\t\tdouble ya2 \t= interp(xz1, xz2, yz4, yz2, zen);\n\n\t\t/* linear interpolation along azimuth angle */\n\t\tpcv\t= interp(xa1, xa2, ya1, ya2, azi);\n\t}\n\n\treturn pcv;\n}\n\n//=============================================================================\n// radomeNoneAntennaType = radome2none(antennaType)\n//\n// e,g, \"AOAD/M_T JPLA\" => \"AOAD/M_T NONE\"\n//\n// Change the last four characters of antenna type to NONE\n// This function is useful for when searching for an antenna model in ANTEX\n//\n// The IGS convention is to default to NONE for the radome if the calibration\n// value is not available\n//=============================================================================\n//void radome2none(char *restrict antenna_type)\nvoid radome2none(string& antenna_type)\n{\n\tsize_t length = antenna_type.size();\n\tif (length != 20)\n\t{\n\t\tprintf(\"\\n*** ERROR radome2none(): string length is less then 20 characters received %ld characters\\n\",length);\n\t\treturn;\n\t}\n\tantenna_type.replace(length - 4, 4, \"NONE\");\n}\n\nmap antexCodes =\n{\n\t{\"G01\", L1 },\n\t{\"G02\", L2 },\n\t{\"G05\", L5 },\n\t{\"R01\", G1 },\n\t{\"R02\", G2 },\n\t{\"E01\", E1 },\n\t{\"E05\", E5A },\n\t{\"E07\", E5B },\n\t{\"E08\", E5AB },\n\t{\"E06\", E6 },\n\t{\"C01\", E1 },\n\t{\"C02\", E2 },\n\t{\"C07\", E5B },\n\t{\"C06\", E6 },\n\t{\"J01\", L1 },\n\t{\"J02\", L2 },\n\t{\"J05\", L5 },\n\t{\"J06\", LEX },\n\t{\"S01\", L1 },\n\t{\"S05\", L5 }\n};\n\n/** Read antex file */\nint readantexf(\n\tstring\tfile,\n\tnav_t&\tnav)\n{\n\tint offset;\n\tint noazi_flag\t\t= 0;\n\tint num_azi_rd\t\t= 0;\n\tint new_antenna\t\t= 0;\n\tint irms\t\t\t= 0;\n\n\tchar tmp[10];\n\tchar *p;\n\n\tFILE* fp = fopen(file.c_str(),\"r\");\n\tif (fp == nullptr)\n\t{\n\t\tBOOST_LOG_TRIVIAL(warning)\n\t\t<< \"Warning: ANTEX file opening error\";\n\n\t\treturn 0;\n\t}\n\n\tconst PhaseCenterData pcv0 = {};\n\tPhaseCenterData recPcv;\n\tPhaseCenterData freqPcv;\n\tVector3d\tpco = Vector3d::Zero();\n\tstring\t\tid;\n\tGTime\t\ttime;\n\t\n\tE_FType\tft = FTYPE_NONE;\n\n\tchar buff[512];\n\twhile (fgets(buff, sizeof(buff), fp))\n\t{\n\t\tchar* comment = buff + 60;\n\n\t\tif (irms) \n\t\t\tcontinue;\n\t\t\n\t\t/* Read in the ANTEX header information */\n\t\t\n\t\tif (strlen(buff) < 60 )\t\t\t\t\t\t\t\t{\tcontinue;\t}\n\t\tif (strstr(comment, \"ANTEX VERSION / SYST\"))\t\t{\tcontinue;\t}\n\t\tif (strstr(comment, \"PCV TYPE / REFANT\")) \t\t\t{\tcontinue;\t}\n\t\tif (strstr(comment, \"COMMENT\")) \t\t\t\t\t{\tcontinue;\t}\n\t\tif (strstr(comment, \"END OF HEADER\"))\t\t\t\t{\tcontinue;\t}\n\t\t\n\t\t/* Read in specific Antenna information now */\n\t\t\n\t\tif (strstr(comment, \"START OF ANTENNA\"))\n\t\t{\n\t\t\trecPcv\t= pcv0;\n\t\t\tfreqPcv\t= pcv0;\n\t\t\tpco\t\t= Vector3d::Zero();\n\t\t\tid\t\t= \"\";\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n// \t\tif (strstr(comment, \"END OF ANTENNA\"))\n// \t\t{\n// \t\t\tGTime time = epoch2time(recPcv.tf);\n// \t\t\t\n// \t\t\tcontinue;\n// \t\t}\n\t\t\n\n\t\tif (strstr(comment, \"METH / BY / # / DATE\"))\n\t\t{\n// \t\t\tint num_calibrated;\n// \t\t\tchar cal_method[20];\n// \t\t\tchar cal_agency[20];\n// \t\t\tchar cal_date[10];\n// \t\t\tstrncpy(cal_method,\tbuff,\t\t20);/* Should be CHAMBER or FIELD or ROBOT or COPIED ot CONVERTED */\n// \t\t\tcal_method[19] = '\\0';\n// \t\t\tstrncpy(cal_agency, buff + 20,\t20);\n// \t\t\tcal_agency[19] = '\\0';\n// \t\t\tstrncpy(tmp,\t\tbuff + 40,\t10);\n// \t\t\tnum_calibrated = atoi(tmp);\n// \t\t\tstrncpy(cal_date,\tbuff + 50,\t10);\n// \t\t\tcal_date[9] = '\\0';\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"DAZI\"))\n\t\t{\n\t\t\tstrncpy(tmp,buff ,8);\t\ttmp[8] = '\\0';\n\t\t\trecPcv.aziDelta = atof(tmp);\n\n\t\t\tif (recPcv.aziDelta < 0.0001)\trecPcv.naz = 0;\n\t\t\telse\t\t\t\t\t\t\trecPcv.naz = (360 / recPcv.aziDelta) + 1;\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"SINEX CODE\"))\n\t\t{\n\t\t\trecPcv.calibModel\t.assign(buff,\t\t10);\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"TYPE / SERIAL NO\"))\n\t\t{\n\t\t\trecPcv.type\t\t\t.assign(buff,\t\t20);\n\t\t\trecPcv.code\t\t\t.assign(buff+20,\t20);\n\t\t\trecPcv.svn\t\t\t.assign(buff+40,\t4);\n\t\t\trecPcv.cospar\t\t.assign(buff+50,\t10);\n\t\t\t\n\t\t\t/* stack antenna pco and pcv */\n\t\t\tstring satId = recPcv.code;\n\t\t\tif (satId.find_first_not_of(' ') == satId.npos)\t\t{ id = recPcv.type;\t}\n\t\t\telse\t\t\t\t\t\t\t\t\t\t\t\t{ id = recPcv.code;\t}\n\t\t\n\t\t\tboost::trim_right(id);\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"ZEN1 / ZEN2 / DZEN\"))\n\t\t{\n\t\t\tstrncpy(tmp, buff,\t\t8);\ttmp[8] = '\\0'; \trecPcv.zenStart\t= atof(tmp);\n\t\t\tstrncpy(tmp, buff+8,\t7);\ttmp[8] = '\\0'; \trecPcv.zenStop\t= atof(tmp);\n\t\t\tstrncpy(tmp, buff+16,\t7);\ttmp[8] = '\\0'; \trecPcv.zenDelta\t= atof(tmp);\n\n\t\t\trecPcv.nz = (recPcv.zenStop - recPcv.zenStart) / recPcv.zenDelta + 1 ;\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"# OF FREQUENCIES\"))\n\t\t{\n//\t\t\tstrncpy(tmp, buff,\t\t8);\n// \t\t\ttmp[8] = '\\0'; \t\n// \t\t\tpcv.nf\t\t= atoi(tmp);\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"VALID FROM\"))\n\t\t{\n\t\t\tchar valid_from[44];\n\t\t\t/* if (!str2time(buff,0,43,pcv.ts)) continue;*/\n\t\t\tstrncpy(valid_from, buff, 43);\n\t\t\tvalid_from[43] = '\\0';\n\t\t\tp = strtok(valid_from, \" \");\n\t\t\tint j = 0;\n\t\t\twhile (p != nullptr)\n\t\t\t{\n\t\t\t\trecPcv.tf[j] = (double) atoi(p);\n\t\t\t\tp = strtok(nullptr, \" \");\n\t\t\t\tj++;\n\t\t\t}\n\t\t\t\n\t\t\ttime = epoch2time(recPcv.tf);\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"VALID UNTIL\"))\n\t\t{\n\t\t\tchar valid_until[44];\n\t\t\t/* if (!str2time(buff,0,43,pcv.te)) continue;*/\n\t\t\tstrncpy(valid_until, buff ,43);\n\t\t\tvalid_until[43] = '\\0';\n\t\t\tp = strtok(valid_until, \" \");\n\t\t\tint j = 0;\n\t\t\twhile (p != nullptr)\n\t\t\t{\n\t\t\t\trecPcv.tu[j] = (double) atoi(p);\n\t\t\t\tp = strtok(nullptr, \" \");\n\t\t\t\tj++;\n\t\t\t}\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"NORTH / EAST / UP\"))\n\t\t{\n\t\t\tdouble neu[3];\n\t\t\tif (decodef(buff, 3, neu) < 3)\n\t\t\t{\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\t/* assign pco value in ENU */\n\t\t\tVector3d enu;\n\t\t\tenu[0] = neu[1];\n\t\t\tenu[1] = neu[0];\n\t\t\tenu[2] = neu[2];\n\t\t\t\n\t\t\tpco = enu;\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"START OF FREQUENCY\"))\n\t\t{\n\t\t\tnum_azi_rd = 0;\n\t\t\tnoazi_flag = 0;\n\n\t\t\tstring antexFCode;\n\t\t\tantexFCode.assign(&buff[3], 3);\n\n\t\t\tft = antexCodes[antexFCode];\n\t\t\t\n\t\t\tfreqPcv = recPcv;\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"END OF FREQUENCY\"))\n\t\t{\t\n\t\t\tnoazi_flag\t= 0;\t\n\t\t\t\n\t\t\tnav.pcvMap[id][ft][time] = freqPcv;\n\t\t\tnav.pcoMap[id][ft][time] = pco;\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif (strstr(comment, \"START OF FREQ RMS\"))\t{\tirms\t\t= 1;\tcontinue;\t}\n\t\tif (strstr(comment, \"END OF FREQ RMS\"))\t\t{\tirms\t\t= 0;\tcontinue;\t}\n\t\t\n\t\tif\t( irms == 0 \n\t\t\t&& strstr(buff, \"NOAZI\"))\n\t\t{\n\t\t\tfor (int i = 0; i < recPcv.nz; i++)\n\t\t\t{\n\t\t\t\toffset = i * 8 + 8;\n\t\t\t\tstrncpy(tmp, buff + offset, 8);\n\t\t\t\ttmp[8]='\\0';\n\t\t\t\tdouble pcv_val = atof(tmp);\n\t\t\t\tfreqPcv.PCVMap1D\t\t\t.push_back(pcv_val * 1e-3);\n\t\t\t}\n\t\t\tnoazi_flag = 1;\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t\t\n\t\tif\t( irms == 0\n\t\t\t&& noazi_flag == 1)\n\t\t{\n\t\t\tstrncpy(tmp, buff, 8);\n\t\t\ttmp[8]='\\0';\n\n\t\t\tfor (int i = 0; i < recPcv.nz; i++)\n\t\t\t{\n\t\t\t\toffset = i * 8 + 8;\n\t\t\t\tstrncpy(tmp, buff + offset, 8);\n\t\t\t\ttmp[8]='\\0';\n\t\t\t\tdouble pcv_val = atof(tmp);\n\t\t\t\tfreqPcv.PCVMap2D[num_azi_rd].push_back(pcv_val * 1e-3);\n\t\t\t}\n\t\t\tnum_azi_rd++;\n\t\t\t\n\t\t\tcontinue;\n\t\t}\n\t}\n\n\tfclose(fp);\n\n\treturn 1;\n}\n", "meta": {"hexsha": "ad91394ce2591b1a768ec77eca6a0d7636a5804e", "size": 11977, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cpp/common/antenna.cpp", "max_stars_repo_name": "HiTMonitor/ginan", "max_stars_repo_head_hexsha": "f348e2683507cfeca65bb58880b3abc2f9c36bcf", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/cpp/common/antenna.cpp", "max_issues_repo_name": "HiTMonitor/ginan", "max_issues_repo_head_hexsha": "f348e2683507cfeca65bb58880b3abc2f9c36bcf", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/cpp/common/antenna.cpp", "max_forks_repo_name": "HiTMonitor/ginan", "max_forks_repo_head_hexsha": "f348e2683507cfeca65bb58880b3abc2f9c36bcf", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 20.6856649396, "max_line_length": 113, "alphanum_fraction": 0.5563162729, "num_tokens": 4279, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.22630079924588875}} {"text": "/*===========================================================================\n NetEvo Library\n Copyright (C) 2011 Thomas E. Gorochowski \n Bristol Centre for Complexity Sciences, University of Bristol, Bristol, UK\n ---------------------------------------------------------------------------- \n NetEvo is a computing framework designed to allow researchers to investigate \n evolutionary aspects of dynamical complex networks. By providing tools to \n easily integrate each of these factors in a coherent way, it is hoped a \n greater understanding can be gained of key attributes and features displayed \n by complex systems.\n \n NetEvo is open-source software released under the Open Source Initiative \n (OSI) approved Non-Profit Open Software License (\"Non-Profit OSL\") 3.0. \n Detailed information about this licence can be found in the COPYING file \n included as part of the source distribution.\n \n This library is distributed in the hope that it will be useful, but\n WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n ============================================================================*/\n\n#include \"simulate.h\"\n#include \n#include \n\nusing namespace boost::numeric::odeint;\n\nnamespace netevo {\n\n void SimulateMap::simulate (System &sys, double tMax, State &initial, SimObserver &obs, ChangeLog &logger) {\n \n // We are in discrete time so use integers for time\n int t = 0, tEnd = (int)tMax;\n int states = (countNodes(sys)*sys.nodeStates()) + (countArcs(sys)*sys.arcStates());\n\n // Check to ensure that initial conditions are correct size\n if (initial.size() < states || initial.size() > states) {\n cerr << \"Incorrect number of states for initial conditions (SimulateMap::simulate)\" << endl;\n return;\n }\n\n // Vector to hold the output. Copy the initial conditions and add to the output\n State y1 = State(initial);\n State y2 = State(initial);\n\n // Check that the state IDs are correct, if not refresh\n if (!sys.validStateIDs()) { sys.refreshStateIDs(); }\n\n // Send the observer the initial conditions\n logger.newState (sys, y1);\n logger.endStep(SIM_STEP);\n logger.commit();\n obs(y1, 0.0);\n \n // Loop through all time steps and calculate new states\n for (t = 1; t <= tEnd; t++) {\n if (t%2 == 0) {\n // Use y1 as old and y2 as new\n // Simulate the dynamics\n sys(y1, y2, (double)t);\n // Log the state change\n logger.newState (sys, y2);\n logger.endStep(SIM_STEP);\n logger.commit();\n // Send result to the observer\n obs(y2, (double)t);\n }\n else {\n // Use y2 as old and y1 as new\n // Simulate the dynamics\n sys(y2, y1, (double)t);\n // Log the state change\n logger.newState (sys, y1);\n logger.endStep(SIM_STEP);\n logger.commit();\n // Send result to the observer\n obs(y1, (double)t);\n }\n }\n \n // Ensure the initial vector is updated to the final result\n initial = ( t%2 == 0 ) ? y1 : y2;\n }\n\n void SimulateOdeFixed::simulate (System &sys, double tMax, State &initial, SimObserver &obs, ChangeLog &logger) {\n \n // Check to ensure that initial conditions are correct size\n int states = (countNodes(sys)*sys.nodeStates()) + (countArcs(sys)*sys.arcStates());\n if (initial.size() < states || initial.size() > states) {\n cerr << \"Incorrect number of states for initial conditions (SimulateOdeFixed::simulate)\" << endl;\n return;\n }\t \n\n // Check that the state IDs are correct, if not refresh\n if (!sys.validStateIDs()) { sys.refreshStateIDs(); }\n\n // Create the required steppers\n typedef runge_kutta4 rk4_stepper_type;\n typedef adams_bashforth_moulton<5,State> adams_bash_moul_stepper_type;\n \n // Solve the system\n switch (mStepper) {\n case RK_4:\n integrate_const(rk4_stepper_type(),\n Simulator(&sys), initial, 0.0, tMax, mStepSize, ObserverPassThrough(sys, obs, logger));\n break;\n case ADAM_BASH_MOUL:\n integrate_const(adams_bash_moul_stepper_type(), \n Simulator(&sys), initial, 0.0, tMax, mStepSize, ObserverPassThrough(sys, obs, logger));\n break;\n default:\n // Do nothing\n break;\n }\n }\n\n\n void SimulateOdeConst::simulate (System &sys, double tMax, State &initial, SimObserver &obs, ChangeLog &logger) {\n \n // Check to ensure that initial conditions are correct size\n int states = (countNodes(sys)*sys.nodeStates()) + (countArcs(sys)*sys.arcStates());\n if (initial.size() < states || initial.size() > states) {\n cerr << \"Incorrect number of states for initial conditions (SimulateOdeConst::simulate)\" << endl;\n return;\n }\t\n\n // Check that the state IDs are correct, if not refresh\n if (!sys.validStateIDs()) { sys.refreshStateIDs(); }\n \n // Create the required steppers\n typedef runge_kutta_cash_karp54 rkck54_error_stepper_type;\n typedef runge_kutta_dopri5 dopri5_error_stepper_type; \n \n // Solve the system\n switch (mStepper) {\n case RK_CASH_KARP_54:\n integrate_const(make_controlled( mEpsAbs , mEpsRel , rkck54_error_stepper_type() ), \n Simulator(&sys), initial, 0.0, tMax, mOutputStep, ObserverPassThrough(sys, obs, logger));\n break;\n case RK_DOPRI_5:\n integrate_const(make_controlled( mEpsAbs , mEpsRel , dopri5_error_stepper_type() ), \n Simulator(&sys), initial, 0.0, tMax, mOutputStep, ObserverPassThrough(sys, obs, logger));\n break;\n case RK_DOPRI_5_DENSE:\n integrate_const(make_dense_output( mEpsAbs , mEpsRel , dopri5_error_stepper_type() ),\n Simulator(&sys), initial, 0.0, tMax, mOutputStep, ObserverPassThrough(sys, obs, logger));\n break;\n default:\n // Do nothing\n break;\n }\n }\n\n void SimulateOdeAdaptive::simulate (System &sys, double tMax, State &initial, SimObserver &obs, ChangeLog &logger) {\n \n // Check to ensure that initial conditions are correct size\n int states = (countNodes(sys)*sys.nodeStates()) + (countArcs(sys)*sys.arcStates());\n if (initial.size() < states || initial.size() > states) {\n cerr << \"Incorrect number of states for initial conditions (SimulateOdeAdaptive::simulate)\" << endl;\n return;\n }\t\n\n // Check that the state IDs are correct, if not refresh\n if (!sys.validStateIDs()) { sys.refreshStateIDs(); }\n \n // Create the required steppers\n typedef runge_kutta_cash_karp54 rkck54_error_stepper_type;\n typedef runge_kutta_dopri5 dopri5_error_stepper_type; \n \n // Solve the system\n switch (mStepper) {\n case RK_CASH_KARP_54:\n integrate_adaptive(make_controlled( mEpsAbs , mEpsRel , rkck54_error_stepper_type() ), \n Simulator(&sys), initial, 0.0, tMax, mInitialStep, ObserverPassThrough(sys, obs, logger));\n break;\n case RK_DOPRI_5:\n integrate_adaptive(make_controlled( mEpsAbs , mEpsRel , dopri5_error_stepper_type() ),\n Simulator(&sys), initial, 0.0, tMax, mInitialStep, ObserverPassThrough(sys, obs, logger));\n break;\n case RK_DOPRI_5_DENSE:\n integrate_adaptive(make_dense_output( mEpsAbs , mEpsRel , dopri5_error_stepper_type() ),\n Simulator(&sys), initial, 0.0, tMax, mInitialStep, ObserverPassThrough(sys, obs, logger));\n break;\n default:\n // Do nothing\n break;\n }\n }\n\n} // netevo namespace\n", "meta": {"hexsha": "9d5e3694e11556fc97c3f77b79f14380b93ae78d", "size": 8203, "ext": "cc", "lang": "C++", "max_stars_repo_path": "lib/netevo/simulate.cc", "max_stars_repo_name": "chofski/dynamic_nets", "max_stars_repo_head_hexsha": "5de19e200cffbb432fa4a1f81af15cc614961bde", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-01-06T08:06:16.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-06T08:06:16.000Z", "max_issues_repo_path": "lib/netevo/simulate.cc", "max_issues_repo_name": "chofski/dynamic_nets", "max_issues_repo_head_hexsha": "5de19e200cffbb432fa4a1f81af15cc614961bde", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/netevo/simulate.cc", "max_forks_repo_name": "chofski/dynamic_nets", "max_forks_repo_head_hexsha": "5de19e200cffbb432fa4a1f81af15cc614961bde", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.9476439791, "max_line_length": 129, "alphanum_fraction": 0.6003901012, "num_tokens": 1922, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.22624518377989256}} {"text": "/* Copyright (c) 2021, United States Government, as represented by the\n * Administrator of the National Aeronautics and Space Administration.\n *\n * All rights reserved.\n *\n * The \"ISAAC - Integrated System for Autonomous and Adaptive Caretaking\n * platform\" software is licensed under the Apache License, Version 2.0\n * (the \"License\"); you may not use this file except in compliance with the\n * License. You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n * License for the specific language governing permissions and limitations\n * under the License.\n */\n\n#include \n#include \n#include \n\n#include // from the isaac repo\n#include // from the isaac repo\n#include // from the astrobee repo\n\n// Get rid of warnings beyond our control\n#pragma GCC diagnostic ignored \"-Wdeprecated-declarations\"\n#pragma GCC diagnostic ignored \"-Wunused-function\"\n#pragma GCC diagnostic push\n#include \n#include \n#include \n#include \n#include \n#pragma GCC diagnostic pop\n\n#include \n\n#include \n#include \n\n// SIFT is doing so much better than SURF for haz cam images.\nDEFINE_string(refiner_feature_detector, \"SIFT\", \"The feature detector to use. SIFT or SURF.\");\nDEFINE_int32(sift_nFeatures, 10000, \"Number of SIFT features\");\nDEFINE_int32(sift_nOctaveLayers, 3, \"Number of SIFT octave layers\");\nDEFINE_double(sift_contrastThreshold, 0.02,\n \"SIFT contrast threshold\"); // decrease for more ip\nDEFINE_double(sift_edgeThreshold, 10, \"SIFT edge threshold\");\nDEFINE_double(sift_sigma, 1.6, \"SIFT sigma\");\n\nnamespace dense_map {\n\nvoid detectFeatures(const cv::Mat& image, bool verbose,\n // Outputs\n cv::Mat* descriptors, Eigen::Matrix2Xd* keypoints) {\n bool histogram_equalization = false;\n\n // If using histogram equalization, need an extra image to store it\n cv::Mat* image_ptr = const_cast(&image);\n cv::Mat hist_image;\n if (histogram_equalization) {\n cv::equalizeHist(image, hist_image);\n image_ptr = &hist_image;\n }\n\n std::vector storage;\n\n if (FLAGS_refiner_feature_detector == \"SIFT\") {\n cv::Ptr sift =\n cv::xfeatures2d::SIFT::create(FLAGS_sift_nFeatures, FLAGS_sift_nOctaveLayers, FLAGS_sift_contrastThreshold,\n FLAGS_sift_edgeThreshold, FLAGS_sift_sigma);\n sift->detect(image, storage);\n sift->compute(image, storage, *descriptors);\n\n } else if (FLAGS_refiner_feature_detector == \"SURF\") {\n interest_point::FeatureDetector detector(\"SURF\");\n detector.Detect(*image_ptr, &storage, descriptors);\n\n // Undo the shift in the detector\n for (cv::KeyPoint& key : storage) {\n key.pt.x += image.cols / 2.0;\n key.pt.y += image.rows / 2.0;\n }\n\n } else {\n LOG(FATAL) << \"Unknown feature detector: \" << FLAGS_refiner_feature_detector;\n }\n\n if (verbose) std::cout << \"Features detected \" << storage.size() << std::endl;\n\n // Copy to data structures expected by subsequent code\n keypoints->resize(2, storage.size());\n Eigen::Vector2d output;\n for (size_t j = 0; j < storage.size(); j++) {\n keypoints->col(j) = Eigen::Vector2d(storage[j].pt.x, storage[j].pt.y);\n }\n}\n\n// This really likes haz cam first and nav cam second\nvoid matchFeatures(std::mutex* match_mutex, int left_image_index, int right_image_index,\n cv::Mat const& left_descriptors, cv::Mat const& right_descriptors,\n Eigen::Matrix2Xd const& left_keypoints,\n Eigen::Matrix2Xd const& right_keypoints, bool verbose,\n // output\n MATCH_PAIR* matches) {\n std::vector cv_matches;\n interest_point::FindMatches(left_descriptors, right_descriptors, &cv_matches);\n\n std::vector left_vec;\n std::vector right_vec;\n for (size_t j = 0; j < cv_matches.size(); j++) {\n int left_ip_index = cv_matches.at(j).queryIdx;\n int right_ip_index = cv_matches.at(j).trainIdx;\n\n // Get the keypoints from the good matches\n left_vec.push_back(cv::Point2f(left_keypoints.col(left_ip_index)[0], left_keypoints.col(left_ip_index)[1]));\n right_vec.push_back(cv::Point2f(right_keypoints.col(right_ip_index)[0], right_keypoints.col(right_ip_index)[1]));\n }\n\n if (left_vec.empty()) return;\n\n // These may need some tweaking but works reasonably well.\n double ransacReprojThreshold = 20.0;\n cv::Mat inlier_mask;\n int maxIters = 10000;\n double confidence = 0.8;\n\n // affine2D works better than homography\n // cv::Mat H = cv::findHomography(left_vec, right_vec, cv::RANSAC,\n // ransacReprojThreshold, inlier_mask, maxIters, confidence);\n cv::Mat H = cv::estimateAffine2D(left_vec, right_vec, inlier_mask, cv::RANSAC,\n ransacReprojThreshold, maxIters, confidence);\n\n std::vector left_ip, right_ip;\n for (size_t j = 0; j < cv_matches.size(); j++) {\n int left_ip_index = cv_matches.at(j).queryIdx;\n int right_ip_index = cv_matches.at(j).trainIdx;\n\n if (inlier_mask.at(j, 0) == 0) continue;\n\n cv::Mat left_desc = left_descriptors.row(left_ip_index);\n cv::Mat right_desc = right_descriptors.row(right_ip_index);\n\n InterestPoint left;\n left.setFromCvKeypoint(left_keypoints.col(left_ip_index), left_desc);\n\n InterestPoint right;\n right.setFromCvKeypoint(right_keypoints.col(right_ip_index), right_desc);\n\n left_ip.push_back(left);\n right_ip.push_back(right);\n }\n\n // Update the shared variable using a lock\n match_mutex->lock();\n\n // Print the verbose message inside the lock, otherwise the text\n // may get messed up.\n if (verbose)\n std::cout << \"Number of matches for pair \"\n << left_image_index << ' ' << right_image_index << \": \"\n << left_ip.size() << std::endl;\n\n *matches = std::make_pair(left_ip, right_ip);\n match_mutex->unlock();\n}\n\n\n// Match features while assuming that the input cameras can be used to filter out\n// outliers by reprojection error.\nvoid matchFeaturesWithCams(std::mutex* match_mutex, int left_image_index, int right_image_index,\n camera::CameraParameters const& left_params,\n camera::CameraParameters const& right_params,\n Eigen::Affine3d const& left_world_to_cam,\n Eigen::Affine3d const& right_world_to_cam,\n double reprojection_error,\n cv::Mat const& left_descriptors, cv::Mat const& right_descriptors,\n Eigen::Matrix2Xd const& left_keypoints,\n Eigen::Matrix2Xd const& right_keypoints,\n bool verbose,\n // output\n MATCH_PAIR* matches) {\n // Match by using descriptors first\n std::vector cv_matches;\n interest_point::FindMatches(left_descriptors, right_descriptors, &cv_matches);\n\n // Do filtering\n std::vector left_vec;\n std::vector right_vec;\n std::vector filtered_cv_matches;\n for (size_t j = 0; j < cv_matches.size(); j++) {\n int left_ip_index = cv_matches.at(j).queryIdx;\n int right_ip_index = cv_matches.at(j).trainIdx;\n\n Eigen::Vector2d dist_left_ip(left_keypoints.col(left_ip_index)[0],\n left_keypoints.col(left_ip_index)[1]);\n\n Eigen::Vector2d dist_right_ip(right_keypoints.col(right_ip_index)[0],\n right_keypoints.col(right_ip_index)[1]);\n\n Eigen::Vector2d undist_left_ip;\n Eigen::Vector2d undist_right_ip;\n left_params.Convert\n (dist_left_ip, &undist_left_ip);\n right_params.Convert\n (dist_right_ip, &undist_right_ip);\n\n Eigen::Vector3d X =\n dense_map::TriangulatePair(left_params.GetFocalLength(), right_params.GetFocalLength(),\n left_world_to_cam, right_world_to_cam,\n undist_left_ip, undist_right_ip);\n\n // Project back into the cameras\n Eigen::Vector3d left_cam_X = left_world_to_cam * X;\n Eigen::Vector2d undist_left_pix\n = left_params.GetFocalVector().cwiseProduct(left_cam_X.hnormalized());\n Eigen::Vector2d dist_left_pix;\n left_params.Convert(undist_left_pix,\n &dist_left_pix);\n\n Eigen::Vector3d right_cam_X = right_world_to_cam * X;\n Eigen::Vector2d undist_right_pix\n = right_params.GetFocalVector().cwiseProduct(right_cam_X.hnormalized());\n Eigen::Vector2d dist_right_pix;\n right_params.Convert(undist_right_pix,\n &dist_right_pix);\n\n // Filter out points whose reprojection error is too big\n bool is_good = ((dist_left_ip - dist_left_pix).norm() <= reprojection_error &&\n (dist_right_ip - dist_right_pix).norm() <= reprojection_error);\n\n // If any values above are Inf or NaN, is_good will be false as well\n if (!is_good) continue;\n\n // Get the keypoints from the good matches\n left_vec.push_back(cv::Point2f(left_keypoints.col(left_ip_index)[0],\n left_keypoints.col(left_ip_index)[1]));\n right_vec.push_back(cv::Point2f(right_keypoints.col(right_ip_index)[0],\n right_keypoints.col(right_ip_index)[1]));\n\n filtered_cv_matches.push_back(cv_matches[j]);\n }\n\n if (left_vec.empty()) return;\n\n // Filter using geometry constraints\n // These may need some tweaking but works reasonably well.\n double ransacReprojThreshold = 20.0;\n cv::Mat inlier_mask;\n int maxIters = 10000;\n double confidence = 0.8;\n\n // affine2D works better than homography\n // cv::Mat H = cv::findHomography(left_vec, right_vec, cv::RANSAC,\n // ransacReprojThreshold, inlier_mask, maxIters, confidence);\n cv::Mat H = cv::estimateAffine2D(left_vec, right_vec, inlier_mask, cv::RANSAC,\n ransacReprojThreshold, maxIters, confidence);\n\n std::vector left_ip, right_ip;\n for (size_t j = 0; j < filtered_cv_matches.size(); j++) {\n int left_ip_index = filtered_cv_matches.at(j).queryIdx;\n int right_ip_index = filtered_cv_matches.at(j).trainIdx;\n\n if (inlier_mask.at(j, 0) == 0) continue;\n\n cv::Mat left_desc = left_descriptors.row(left_ip_index);\n cv::Mat right_desc = right_descriptors.row(right_ip_index);\n\n InterestPoint left;\n left.setFromCvKeypoint(left_keypoints.col(left_ip_index), left_desc);\n\n InterestPoint right;\n right.setFromCvKeypoint(right_keypoints.col(right_ip_index), right_desc);\n\n left_ip.push_back(left);\n right_ip.push_back(right);\n }\n\n // Update the shared variable using a lock\n match_mutex->lock();\n\n // Print the verbose message inside the lock, otherwise the text\n // may get messed up.\n if (verbose)\n std::cout << \"Number of matches for pair \"\n << left_image_index << ' ' << right_image_index << \": \"\n << left_ip.size() << std::endl;\n\n *matches = std::make_pair(left_ip, right_ip);\n match_mutex->unlock();\n}\n\nvoid writeIpRecord(std::ofstream& f, InterestPoint const& p) {\n f.write(reinterpret_cast(&(p.x)), sizeof(p.x));\n f.write(reinterpret_cast(&(p.y)), sizeof(p.y));\n f.write(reinterpret_cast(&(p.ix)), sizeof(p.ix));\n f.write(reinterpret_cast(&(p.iy)), sizeof(p.iy));\n f.write(reinterpret_cast(&(p.orientation)), sizeof(p.orientation));\n f.write(reinterpret_cast(&(p.scale)), sizeof(p.scale));\n f.write(reinterpret_cast(&(p.interest)), sizeof(p.interest));\n f.write(reinterpret_cast(&(p.polarity)), sizeof(p.polarity));\n f.write(reinterpret_cast(&(p.octave)), sizeof(p.octave));\n f.write(reinterpret_cast(&(p.scale_lvl)), sizeof(p.scale_lvl));\n uint64_t size = p.size();\n f.write(reinterpret_cast((&size)), sizeof(uint64));\n for (size_t i = 0; i < p.descriptor.size(); ++i)\n f.write(reinterpret_cast(&(p.descriptor[i])), sizeof(p.descriptor[i]));\n}\n\n// Write matches to disk\nvoid writeMatchFile(std::string match_file, std::vector const& ip1,\n std::vector const& ip2) {\n std::ofstream f;\n f.open(match_file.c_str(), std::ios::binary | std::ios::out);\n std::vector::const_iterator iter1 = ip1.begin();\n std::vector::const_iterator iter2 = ip2.begin();\n uint64 size1 = ip1.size();\n uint64 size2 = ip2.size();\n f.write(reinterpret_cast(&size1), sizeof(uint64));\n f.write(reinterpret_cast(&size2), sizeof(uint64));\n for (; iter1 != ip1.end(); ++iter1) writeIpRecord(f, *iter1);\n for (; iter2 != ip2.end(); ++iter2) writeIpRecord(f, *iter2);\n f.close();\n}\n\nvoid saveImagesAndMatches(std::string const& left_prefix, std::string const& right_prefix,\n std::pair const& index_pair, MATCH_PAIR const& match_pair,\n std::vector const& images) {\n // Add 10000 to have them be listed nicely\n std::ostringstream oss_left;\n oss_left << left_prefix << \"_image\" << index_pair.first + 10000 << \".jpg\";\n std::string left_image_file = oss_left.str();\n std::cout << \"Writing: \" << left_image_file << std::endl;\n cv::imwrite(left_image_file, images[index_pair.first]);\n\n std::ostringstream oss_right;\n oss_right << right_prefix << \"_image\" << index_pair.second + 10000 << \".jpg\";\n std::string right_image_file = oss_right.str();\n std::cout << \"Writing: \" << right_image_file << std::endl;\n cv::imwrite(right_image_file, images[index_pair.second]);\n\n std::string left_stem = boost::filesystem::path(left_image_file).stem().string();\n std::string right_stem = boost::filesystem::path(right_image_file).stem().string();\n\n std::string match_file = left_stem + \"__\" + right_stem + \".match\";\n std::cout << \"Writing: \" << left_image_file << ' ' << right_image_file << ' ' << match_file << std::endl;\n writeMatchFile(match_file, match_pair.first, match_pair.second);\n}\n\n// Triangulate rays emanating from given undistorted and centered pixels\nEigen::Vector3d TriangulatePair(double focal_length1, double focal_length2,\n Eigen::Affine3d const& world_to_cam1,\n Eigen::Affine3d const& world_to_cam2,\n Eigen::Vector2d const& pix1,\n Eigen::Vector2d const& pix2) {\n Eigen::Matrix3d k1;\n k1 << focal_length1, 0, 0, 0, focal_length1, 0, 0, 0, 1;\n\n Eigen::Matrix3d k2;\n k2 << focal_length2, 0, 0, 0, focal_length2, 0, 0, 0, 1;\n\n openMVG::Mat34 cid_to_p1, cid_to_p2;\n openMVG::P_From_KRt(k1, world_to_cam1.linear(), world_to_cam1.translation(), &cid_to_p1);\n openMVG::P_From_KRt(k2, world_to_cam2.linear(), world_to_cam2.translation(), &cid_to_p2);\n\n openMVG::Triangulation tri;\n tri.add(cid_to_p1, pix1);\n tri.add(cid_to_p2, pix2);\n\n Eigen::Vector3d solution = tri.compute();\n return solution;\n}\n\n// Triangulate n rays emanating from given undistorted and centered pixels\nEigen::Vector3d Triangulate(std::vector const& focal_length_vec,\n std::vector const& world_to_cam_vec,\n std::vector const& pix_vec) {\n if (focal_length_vec.size() != world_to_cam_vec.size() ||\n focal_length_vec.size() != pix_vec.size())\n LOG(FATAL) << \"All inputs to Triangulate() must have the same size.\";\n\n if (focal_length_vec.size() <= 1)\n LOG(FATAL) << \"At least two rays must be passed to Triangulate().\";\n\n openMVG::Triangulation tri;\n\n for (size_t it = 0; it < focal_length_vec.size(); it++) {\n Eigen::Matrix3d k;\n k << focal_length_vec[it], 0, 0, 0, focal_length_vec[it], 0, 0, 0, 1;\n\n openMVG::Mat34 cid_to_p;\n openMVG::P_From_KRt(k, world_to_cam_vec[it].linear(), world_to_cam_vec[it].translation(),\n &cid_to_p);\n\n tri.add(cid_to_p, pix_vec[it]);\n }\n\n Eigen::Vector3d solution = tri.compute();\n return solution;\n}\n\nvoid detectMatchFeatures( // Inputs\n std::vector const& cams,\n std::vector const& cam_names,\n std::vector const& cam_params,\n std::vector const& world_to_cam, int num_overlaps,\n int initial_max_reprojection_error, int num_match_threads,\n bool verbose,\n // Outputs\n std::vector>>& keypoint_vec,\n std::vector>& pid_to_cid_fid,\n std::vector & image_files) {\n // Wipe the outputs\n keypoint_vec.clear();\n pid_to_cid_fid.clear();\n image_files.clear();\n\n if (verbose) {\n int count = 10000;\n for (size_t it = 0; it < cams.size(); it++) {\n std::ostringstream oss;\n oss << count << \"_\" << cam_names[cams[it].camera_type] << \".jpg\";\n std::string name = oss.str();\n std::cout << \"Writing: \" << name << std::endl;\n cv::imwrite(name, cams[it].image);\n count++;\n image_files.push_back(name);\n }\n }\n\n // Detect features using multiple threads. Too many threads may result\n // in high memory usage.\n std::ostringstream oss;\n oss << num_match_threads;\n std::string num_threads = oss.str();\n google::SetCommandLineOption(\"num_threads\", num_threads.c_str());\n if (!gflags::GetCommandLineOption(\"num_threads\", &num_threads))\n LOG(FATAL) << \"Failed to get the value of --num_threads in Astrobee software.\\n\";\n std::cout << \"Using \" << num_threads << \" threads for feature detection/matching.\" << std::endl;\n\n std::cout << \"Detecting features.\" << std::endl;\n\n std::vector cid_to_descriptor_map;\n std::vector cid_to_keypoint_map;\n cid_to_descriptor_map.resize(cams.size());\n cid_to_keypoint_map.resize(cams.size());\n {\n // Make the thread pool go out of scope when not needed to not use up memory\n ff_common::ThreadPool thread_pool;\n for (size_t it = 0; it < cams.size(); it++) {\n thread_pool.AddTask\n (&dense_map::detectFeatures, // multi-thread // NOLINT\n // dense_map::detectFeatures( // single-thread // NOLINT\n cams[it].image, verbose, &cid_to_descriptor_map[it], &cid_to_keypoint_map[it]);\n }\n thread_pool.Join();\n }\n\n MATCH_MAP matches;\n\n std::vector > image_pairs;\n for (size_t it1 = 0; it1 < cams.size(); it1++) {\n for (size_t it2 = it1 + 1; it2 < std::min(cams.size(), it1 + num_overlaps + 1); it2++) {\n image_pairs.push_back(std::make_pair(it1, it2));\n }\n }\n\n {\n std::cout << \"Matching features.\" << std::endl;\n ff_common::ThreadPool thread_pool;\n std::mutex match_mutex;\n for (size_t pair_it = 0; pair_it < image_pairs.size(); pair_it++) {\n auto pair = image_pairs[pair_it];\n int left_image_it = pair.first, right_image_it = pair.second;\n thread_pool.AddTask\n (&dense_map::matchFeaturesWithCams, // multi-threaded // NOLINT\n // dense_map::matchFeaturesWithCams( // single-threaded // NOLINT\n &match_mutex, left_image_it, right_image_it, cam_params[cams[left_image_it].camera_type],\n cam_params[cams[right_image_it].camera_type], world_to_cam[left_image_it],\n world_to_cam[right_image_it], initial_max_reprojection_error,\n cid_to_descriptor_map[left_image_it], cid_to_descriptor_map[right_image_it],\n cid_to_keypoint_map[left_image_it], cid_to_keypoint_map[right_image_it], verbose,\n &matches[pair]);\n }\n thread_pool.Join();\n }\n cid_to_descriptor_map = std::vector(); // Wipe, takes memory\n\n // Give all interest points in a given image a unique id, and put\n // them in a vector with the id corresponding to the interest point\n std::vector, int>> keypoint_map(cams.size());\n for (auto it = matches.begin(); it != matches.end(); it++) {\n std::pair const& index_pair = it->first; // alias\n\n int left_index = index_pair.first;\n int right_index = index_pair.second;\n\n dense_map::MATCH_PAIR const& match_pair = it->second; // alias\n std::vector const& left_ip_vec = match_pair.first;\n std::vector const& right_ip_vec = match_pair.second;\n for (size_t ip_it = 0; ip_it < left_ip_vec.size(); ip_it++) {\n auto dist_left_ip = std::make_pair(left_ip_vec[ip_it].x, left_ip_vec[ip_it].y);\n auto dist_right_ip = std::make_pair(right_ip_vec[ip_it].x, right_ip_vec[ip_it].y);\n // Initialize to zero for the moment\n keypoint_map[left_index][dist_left_ip] = 0;\n keypoint_map[right_index][dist_right_ip] = 0;\n }\n }\n keypoint_vec.resize(cams.size());\n for (size_t cid = 0; cid < cams.size(); cid++) {\n keypoint_vec[cid].resize(keypoint_map[cid].size());\n int fid = 0;\n for (auto ip_it = keypoint_map[cid].begin(); ip_it != keypoint_map[cid].end();\n ip_it++) {\n auto& dist_ip = ip_it->first; // alias\n keypoint_map[cid][dist_ip] = fid;\n keypoint_vec[cid][fid] = dist_ip;\n fid++;\n }\n }\n\n // If feature A in image I matches feather B in image J, which\n // matches feature C in image K, then (A, B, C) belong together in\n // a track, and will have a single triangulated xyz. Build such a track.\n\n openMVG::matching::PairWiseMatches match_map;\n for (auto it = matches.begin(); it != matches.end(); it++) {\n std::pair const& index_pair = it->first; // alias\n\n int left_index = index_pair.first;\n int right_index = index_pair.second;\n\n dense_map::MATCH_PAIR const& match_pair = it->second; // alias\n std::vector const& left_ip_vec = match_pair.first;\n std::vector const& right_ip_vec = match_pair.second;\n\n std::vector mvg_matches;\n\n for (size_t ip_it = 0; ip_it < left_ip_vec.size(); ip_it++) {\n auto dist_left_ip = std::make_pair(left_ip_vec[ip_it].x, left_ip_vec[ip_it].y);\n auto dist_right_ip = std::make_pair(right_ip_vec[ip_it].x, right_ip_vec[ip_it].y);\n\n int left_id = keypoint_map[left_index][dist_left_ip];\n int right_id = keypoint_map[right_index][dist_right_ip];\n mvg_matches.push_back(openMVG::matching::IndMatch(left_id, right_id));\n }\n match_map[index_pair] = mvg_matches;\n }\n\n if (verbose) {\n for (auto it = matches.begin(); it != matches.end(); it++) {\n std::pair index_pair = it->first;\n dense_map::MATCH_PAIR const& match_pair = it->second;\n\n int left_index = index_pair.first;\n int right_index = index_pair.second;\n\n std::string left_image = image_files[left_index];\n std::string right_image = image_files[right_index];\n\n std::string left_stem = boost::filesystem::path(left_image).stem().string();\n std::string right_stem = boost::filesystem::path(right_image).stem().string();\n\n std::string match_file = left_stem + \"__\" + right_stem + \".match\";\n\n std::cout << \"Writing: \" << left_image << ' ' << right_image << ' '\n << match_file << std::endl;\n dense_map::writeMatchFile(match_file, match_pair.first, match_pair.second);\n }\n }\n\n // De-allocate data not needed anymore and take up a lot of RAM\n matches.clear(); matches = MATCH_MAP();\n keypoint_map.clear(); keypoint_map.shrink_to_fit();\n cid_to_keypoint_map.clear(); cid_to_keypoint_map.shrink_to_fit();\n\n {\n // Build tracks\n // De-allocate these as soon as not needed to save memory\n openMVG::tracks::TracksBuilder trackBuilder;\n trackBuilder.Build(match_map); // Build: Efficient fusion of correspondences\n trackBuilder.Filter(); // Filter: Remove tracks that have conflict\n // trackBuilder.ExportToStream(std::cout);\n // Export tracks as a map (each entry is a sequence of imageId and featureIndex):\n // {TrackIndex => {(imageIndex, featureIndex), ... ,(imageIndex, featureIndex)}\n openMVG::tracks::STLMAPTracks map_tracks;\n trackBuilder.ExportToSTL(map_tracks);\n match_map = openMVG::matching::PairWiseMatches(); // wipe this, no longer needed\n trackBuilder = openMVG::tracks::TracksBuilder(); // wipe it\n\n if (map_tracks.empty())\n LOG(FATAL) << \"No tracks left after filtering. Perhaps images are too dis-similar?\\n\";\n\n // Populate the filtered tracks\n size_t num_elems = map_tracks.size();\n pid_to_cid_fid.resize(num_elems);\n size_t curr_id = 0;\n for (auto itr = map_tracks.begin(); itr != map_tracks.end(); itr++) {\n for (auto itr2 = (itr->second).begin(); itr2 != (itr->second).end(); itr2++) {\n pid_to_cid_fid[curr_id][itr2->first] = itr2->second;\n }\n curr_id++;\n }\n }\n\n return;\n}\n\n} // end namespace dense_map\n", "meta": {"hexsha": "17ed579e458708042766db86d4f2491ddb770bc2", "size": 25775, "ext": "cc", "lang": "C++", "max_stars_repo_path": "dense_map/geometry_mapper/src/interest_point.cc", "max_stars_repo_name": "CodeMasterBond/isaac", "max_stars_repo_head_hexsha": "b21a533cf30eed012fe12ece047b6d87418d7c6f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 19.0, "max_stars_repo_stars_event_min_datetime": "2021-11-18T19:29:16.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-23T01:55:51.000Z", "max_issues_repo_path": "dense_map/geometry_mapper/src/interest_point.cc", "max_issues_repo_name": "CodeMasterBond/isaac", "max_issues_repo_head_hexsha": "b21a533cf30eed012fe12ece047b6d87418d7c6f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 13.0, "max_issues_repo_issues_event_min_datetime": "2021-11-30T17:14:46.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-22T21:38:33.000Z", "max_forks_repo_path": "dense_map/geometry_mapper/src/interest_point.cc", "max_forks_repo_name": "CodeMasterBond/isaac", "max_forks_repo_head_hexsha": "b21a533cf30eed012fe12ece047b6d87418d7c6f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 6.0, "max_forks_repo_forks_event_min_datetime": "2021-12-03T02:38:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-23T01:52:03.000Z", "avg_line_length": 42.1160130719, "max_line_length": 117, "alphanum_fraction": 0.6649466537, "num_tokens": 6616, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.22608156617653244}} {"text": "#include \"basic.h\"\n\n#if defined(__GNUC__)\n#pragma GCC diagnostic push\n#if __GNUC_PREREQ(9, 0)\n#pragma GCC diagnostic ignored \"-Wdeprecated-copy\"\n#endif\n#pragma GCC diagnostic ignored \"-Wdeprecated-declarations\"\n#pragma GCC diagnostic ignored \"-Wunused-parameter\"\n#endif\n\n#include \n#include \n#include \n\n#if defined(__GNUC__)\n#pragma GCC diagnostic pop\n#endif\n\n#if defined(_MSC_VER)\n#pragma warning(push)\n#pragma warning(disable : 4554)\n#pragma warning(disable : 26450)\n#pragma warning(disable : 26451)\n#pragma warning(disable : 26454)\n#pragma warning(disable : 26495)\n#pragma warning(disable : 26812)\n#elif defined(__GNUC__)\n#pragma GCC diagnostic push\n#pragma GCC diagnostic ignored \"-Wunused-parameter\"\n#endif\n\n#include \n\n#if defined(_MSC_VER)\n#pragma warning(pop)\n#elif defined(__GNUC__)\n#pragma GCC diagnostic pop\n#endif\n\n#include \n\nnamespace ba = boost::accumulators;\n\nnamespace WorldEngine\n{\n\nvoid AntiAlias(boost::multi_array& mapData, size_t steps)\n{\n typedef Eigen::Tensor Tensor2D;\n typedef Eigen::TensorMap Tensor2DMap;\n\n static const float w = 1.0f / 11.0f;\n\n Tensor2D kernel(3, 3);\n kernel.setValues({{w, w, w}, {w, w, w}, {w, w, w}});\n\n const uint32_t width = static_cast(mapData.shape()[1]);\n const uint32_t height = static_cast(mapData.shape()[0]);\n\n Tensor2DMap map(mapData.data(), width, height);\n\n Tensor2D mapPart = map * (2.0f / 11.0f);\n\n auto AntiAliasStep = [&width,\n &height,\n &mapPart = std::as_const(mapPart),\n &kernel = std::as_const(kernel)](Tensor2DMap& map) {\n // Specify first and second dimension for convolution\n static const Eigen::array dimensions({0, 1});\n\n // Create a circular boundary\n const Eigen::array, 2> paddings(\n {std::make_pair(1, 1), std::make_pair(1, 1)});\n Tensor2D paddedMap = map.pad(paddings);\n\n for (uint32_t x = 0; x <= width + 1; x++)\n {\n paddedMap(x, 0) = paddedMap(x, height);\n paddedMap(x, height + 1) = paddedMap(x, 1);\n }\n for (uint32_t y = 0; y <= height + 1; y++)\n {\n paddedMap(0, y) = paddedMap(width, y);\n paddedMap(width + 1, y) = paddedMap(1, y);\n }\n\n map = paddedMap.convolve(kernel, dimensions);\n\n map += mapPart;\n };\n\n for (size_t i = 0; i < steps; i++)\n {\n AntiAliasStep(map);\n }\n}\n\ntemplate\nboost::multi_array\nCountNeighbors(const boost::multi_array& mask, int32_t radius)\n{\n const int32_t width = static_cast(mask.shape()[1]);\n const int32_t height = static_cast(mask.shape()[0]);\n\n boost::multi_array neighbors(boost::extents[height][width]);\n\n std::fill(neighbors.data(), neighbors.data() + neighbors.num_elements(), 0u);\n\n for (int32_t y = 0; y < height; y++)\n {\n for (int32_t x = 0; x < width; x++)\n {\n for (int32_t ny = y - radius; ny <= y + radius; ny++)\n {\n if (0 <= ny && ny < height)\n {\n for (int32_t nx = x - radius; nx <= x + radius; nx++)\n {\n if ((nx != x || ny != y) && 0 <= nx && nx < width)\n {\n if (mask[ny][nx])\n {\n neighbors[y][x]++;\n }\n }\n }\n }\n }\n }\n }\n\n return neighbors;\n}\ntemplate boost::multi_array\nCountNeighbors(const boost::multi_array& mask, int32_t radius);\ntemplate boost::multi_array\nCountNeighbors(const boost::multi_array& mask, int32_t radius);\n\nfloat FindThresholdF(const boost::multi_array& mapData,\n float landPercentage,\n const OceanArrayType* ocean)\n{\n typedef ba::accumulator_set>\n accumulator_t;\n\n const uint32_t width = static_cast(mapData.shape()[1]);\n const uint32_t height = static_cast(mapData.shape()[0]);\n float quantile = 1.0f - landPercentage;\n\n accumulator_t accumulator(ba::quantile_probability = quantile);\n\n if (ocean == nullptr || ocean->size() != mapData.size())\n {\n BOOST_LOG_TRIVIAL(trace) << \"Calculating threshold (\" << landPercentage\n << \") without ocean data...\";\n\n for (uint32_t y = 0; y < height; y++)\n {\n for (uint32_t x = 0; x < width; x++)\n {\n accumulator(mapData[y][x]);\n }\n }\n }\n else\n {\n BOOST_LOG_TRIVIAL(trace) << \"Calculating threshold (\" << landPercentage\n << \") with ocean data...\";\n\n for (uint32_t y = 0; y < height; y++)\n {\n for (uint32_t x = 0; x < width; x++)\n {\n if (!(*ocean)[y][x])\n {\n accumulator(mapData[y][x]);\n }\n }\n }\n }\n\n float threshold = ba::p_square_quantile(accumulator);\n\n BOOST_LOG_TRIVIAL(trace) << \"Threshold: \" << threshold;\n\n return threshold;\n}\n\ndouble Noise(const OpenSimplexNoise::Noise& noise,\n double x,\n double y,\n uint32_t octaves)\n{\n static const double persistence = 0.5;\n static const double lacunarity = 2.0;\n\n double freq = 1.0;\n double amp = 1.0;\n double max = 1.0;\n double total = noise.eval(x, y);\n\n for (uint32_t i = 1; i < octaves; i++)\n {\n freq *= lacunarity;\n amp *= persistence;\n max += amp;\n total += noise.eval(x * freq, y * freq) * amp;\n }\n\n return total / max;\n}\n\n} // namespace WorldEngine\n", "meta": {"hexsha": "945bda1e151f6148f42644482981555c4c890b24", "size": 5990, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "worldengine/source/basic.cpp", "max_stars_repo_name": "dpaulat/worldengine-cpp", "max_stars_repo_head_hexsha": "9f7961aaf62db2633fc9d44b6018384812a6704e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2021-06-09T12:44:37.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-10T21:52:10.000Z", "max_issues_repo_path": "worldengine/source/basic.cpp", "max_issues_repo_name": "dpaulat/worldengine-cpp", "max_issues_repo_head_hexsha": "9f7961aaf62db2633fc9d44b6018384812a6704e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2021-06-09T12:49:33.000Z", "max_issues_repo_issues_event_max_datetime": "2021-06-17T15:28:37.000Z", "max_forks_repo_path": "worldengine/source/basic.cpp", "max_forks_repo_name": "dpaulat/worldengine-cpp", "max_forks_repo_head_hexsha": "9f7961aaf62db2633fc9d44b6018384812a6704e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.8604651163, "max_line_length": 80, "alphanum_fraction": 0.5686143573, "num_tokens": 1566, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.22608156617653244}} {"text": "// Copyright Daniel Trebbien 2010.\n// Distributed under the Boost Software License, Version 1.0.\n// (See accompanying file LICENSE_1_0.txt or the copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_GRAPH_STOER_WAGNER_MIN_CUT_HPP\n#define BOOST_GRAPH_STOER_WAGNER_MIN_CUT_HPP 1\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace boost {\n \n namespace detail {\n \n /**\n * \\brief Performs a phase of the Stoer-Wagner min-cut algorithm\n *\n * Performs a phase of the Stoer-Wagner min-cut algorithm.\n *\n * As described by Stoer & Wagner (1997), a phase is simply a maximum adjacency search\n * (also called a maximum cardinality search), which results in the selection of two vertices\n * \\em s and \\em t, and, as a side product, a minimum s-t cut of\n * the input graph. Here, the input graph is basically \\p g, but some vertices are virtually\n * assigned to others as a way of viewing \\p g as a graph with some sets of\n * vertices merged together.\n *\n * This implementation is a translation of pseudocode by Professor Uri Zwick,\n * School of Computer Science, Tel Aviv University.\n *\n * \\pre \\p g is a connected, undirected graph\n * \\param[in] g the input graph\n * \\param[in] assignments a read/write property map from each vertex to the vertex that it is assigned to\n * \\param[in] assignedVertices a list of vertices that are assigned to others\n * \\param[in] weights a readable property map from each edge to its weight (a non-negative value)\n * \\param[out] pq a keyed, updatable max-priority queue\n * \\returns a tuple (\\em s, \\em t, \\em w) of the \"s\" and \"t\"\n * of the minimum s-t cut and the cut weight \\em w\n * of the minimum s-t cut.\n * \\see http://www.cs.tau.ac.il/~zwick/grad-algo-08/gmc.pdf\n *\n * \\author Daniel Trebbien\n * \\date 2010-09-11\n */\n template \n boost::tuple::vertex_descriptor, typename boost::graph_traits::vertex_descriptor, typename boost::property_traits::value_type>\n stoer_wagner_phase(const UndirectedGraph& g, VertexAssignmentMap assignments, const std::set::vertex_descriptor>& assignedVertices, WeightMap weights, KeyedUpdatablePriorityQueue& pq) {\n typedef typename boost::graph_traits::vertex_descriptor vertex_descriptor;\n typedef typename boost::property_traits::value_type weight_type;\n \n assert(pq.empty());\n typename KeyedUpdatablePriorityQueue::key_map keys = pq.keys();\n \n BGL_FORALL_VERTICES_T(v, g, UndirectedGraph) {\n if (v == get(assignments, v)) { // foreach u \\in V do\n put(keys, v, weight_type(0));\n \n pq.push(v);\n }\n }\n \n assert(pq.size() >= 2);\n \n vertex_descriptor s, t;\n weight_type w;\n while (!pq.empty()) { // while PQ \\neq {} do\n const vertex_descriptor u = pq.top(); // u = extractmax(PQ)\n w = get(keys, u);\n pq.pop();\n \n s = t; t = u;\n \n BGL_FORALL_OUTEDGES_T(u, e, g, UndirectedGraph) { // foreach (u, v) \\in E do\n const vertex_descriptor v = get(assignments, target(e, g));\n \n if (pq.contains(v)) { // if v \\in PQ then\n put(keys, v, get(keys, v) + get(weights, e)); // increasekey(PQ, v, wA(v) + w(u, v))\n pq.update(v);\n }\n }\n \n typename std::set::const_iterator assignedVertexIt, assignedVertexEnd = assignedVertices.end();\n for (assignedVertexIt = assignedVertices.begin(); assignedVertexIt != assignedVertexEnd; ++assignedVertexIt) {\n const vertex_descriptor uPrime = *assignedVertexIt;\n \n if (get(assignments, uPrime) == u) {\n BGL_FORALL_OUTEDGES_T(uPrime, e, g, UndirectedGraph) { // foreach (u, v) \\in E do\n const vertex_descriptor v = get(assignments, target(e, g));\n \n if (pq.contains(v)) { // if v \\in PQ then\n put(keys, v, get(keys, v) + get(weights, e)); // increasekey(PQ, v, wA(v) + w(u, v))\n pq.update(v);\n }\n }\n }\n }\n }\n \n return boost::make_tuple(s, t, w);\n }\n \n /**\n * \\brief Computes a min-cut of the input graph\n *\n * Computes a min-cut of the input graph using the Stoer-Wagner algorithm.\n *\n * \\pre \\p g is a connected, undirected graph\n * \\pre pq.empty()\n * \\param[in] g the input graph\n * \\param[in] weights a readable property map from each edge to its weight (a non-negative value)\n * \\param[out] parities a writable property map from each vertex to a bool type object for\n * distinguishing the two vertex sets of the min-cut\n * \\param[out] assignments a read/write property map from each vertex to a \\c vertex_descriptor object. This\n * map serves as work space, and no particular meaning should be derived from property values\n * after completion of the algorithm.\n * \\param[out] pq a keyed, updatable max-priority queue\n * \\returns the cut weight of the min-cut\n * \\see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.6687&rep=rep1&type=pdf\n * \\see http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.614&rep=rep1&type=pdf\n *\n * \\author Daniel Trebbien\n * \\date 2010-09-11\n */\n template \n typename boost::property_traits::value_type\n stoer_wagner_min_cut(const UndirectedGraph& g, WeightMap weights, ParityMap parities, VertexAssignmentMap assignments, KeyedUpdatablePriorityQueue& pq) {\n BOOST_CONCEPT_ASSERT((boost::IncidenceGraphConcept));\n BOOST_CONCEPT_ASSERT((boost::VertexListGraphConcept));\n typedef typename boost::graph_traits::vertex_descriptor vertex_descriptor;\n typedef typename boost::graph_traits::vertices_size_type vertices_size_type;\n typedef typename boost::graph_traits::edge_descriptor edge_descriptor;\n BOOST_CONCEPT_ASSERT((boost::Convertible::directed_category, boost::undirected_tag>));\n BOOST_CONCEPT_ASSERT((boost::ReadablePropertyMapConcept));\n typedef typename boost::property_traits::value_type weight_type;\n BOOST_CONCEPT_ASSERT((boost::WritablePropertyMapConcept));\n typedef typename boost::property_traits::value_type parity_type;\n BOOST_CONCEPT_ASSERT((boost::ReadWritePropertyMapConcept));\n BOOST_CONCEPT_ASSERT((boost::Convertible::value_type>));\n BOOST_CONCEPT_ASSERT((boost::KeyedUpdatableQueueConcept));\n \n vertices_size_type n = num_vertices(g);\n if (n < 2)\n throw boost::bad_graph(\"the input graph must have at least two vertices.\");\n else if (!pq.empty())\n throw std::invalid_argument(\"the max-priority queue must be empty initially.\");\n \n std::set assignedVertices;\n \n // initialize `assignments` (all vertices are initially assigned to themselves)\n BGL_FORALL_VERTICES_T(v, g, UndirectedGraph) {\n put(assignments, v, v);\n }\n \n vertex_descriptor s, t;\n weight_type bestW;\n \n boost::tie(s, t, bestW) = boost::detail::stoer_wagner_phase(g, assignments, assignedVertices, weights, pq);\n assert(s != t);\n BGL_FORALL_VERTICES_T(v, g, UndirectedGraph) {\n put(parities, v, parity_type(v == t ? 1 : 0));\n }\n put(assignments, t, s);\n assignedVertices.insert(t);\n --n;\n \n for (; n >= 2; --n) {\n weight_type w;\n boost::tie(s, t, w) = boost::detail::stoer_wagner_phase(g, assignments, assignedVertices, weights, pq);\n assert(s != t);\n \n if (w < bestW) {\n BGL_FORALL_VERTICES_T(v, g, UndirectedGraph) {\n put(parities, v, parity_type(get(assignments, v) == t ? 1 : 0));\n \n if (get(assignments, v) == t) // all vertices that were assigned to t are now assigned to s\n put(assignments, v, s);\n }\n \n bestW = w;\n } else {\n BGL_FORALL_VERTICES_T(v, g, UndirectedGraph) {\n if (get(assignments, v) == t) // all vertices that were assigned to t are now assigned to s\n put(assignments, v, s);\n }\n }\n put(assignments, t, s);\n assignedVertices.insert(t);\n }\n \n assert(pq.empty());\n \n return bestW;\n }\n \n } // end `namespace detail` within `namespace boost`\n \n template \n inline typename boost::property_traits::value_type\n stoer_wagner_min_cut(const UndirectedGraph& g, WeightMap weights, const boost::bgl_named_params& params) {\n typedef typename boost::graph_traits::vertex_descriptor vertex_descriptor;\n typedef typename std::vector::size_type heap_container_size_type;\n typedef typename boost::property_traits::value_type weight_type;\n \n typedef boost::bgl_named_params params_type;\n BOOST_GRAPH_DECLARE_CONVERTED_PARAMETERS(params_type, params)\n \n BOOST_AUTO(pq, (boost::detail::make_priority_queue_from_arg_pack_gen >(choose_param(get_param(params, boost::distance_zero_t()), weight_type(0)))(g, arg_pack)));\n \n return boost::detail::stoer_wagner_min_cut(g,\n weights,\n choose_param(get_param(params, boost::parity_map_t()), boost::dummy_property_map()),\n boost::detail::make_property_map_from_arg_pack_gen(vertex_descriptor())(g, arg_pack),\n pq\n );\n }\n \n template \n inline typename boost::property_traits::value_type\n stoer_wagner_min_cut(const UndirectedGraph& g, WeightMap weights) {\n return boost::stoer_wagner_min_cut(g, weights, boost::vertex_index_map(get(boost::vertex_index, g)));\n }\n \n} // end `namespace boost`\n\n#include \n\n#endif // !BOOST_GRAPH_STOER_WAGNER_MIN_CUT_HPP\n", "meta": {"hexsha": "9cd2d0da06c7c500e75ccde2c6fdfadcd4861af2", "size": 11463, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "projects/MonkVG-Android/jni/boost/include/boost/graph/stoer_wagner_min_cut.hpp", "max_stars_repo_name": "smartmobili/MonkVG", "max_stars_repo_head_hexsha": "3fad9b73d30c13411e0ca47bd3e2cc803ac89a9d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 27.0, "max_stars_repo_stars_event_min_datetime": "2020-11-12T19:24:54.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-27T23:10:45.000Z", "max_issues_repo_path": "projects/MonkVG-Android/jni/boost/include/boost/graph/stoer_wagner_min_cut.hpp", "max_issues_repo_name": "smartmobili/MonkVG", "max_issues_repo_head_hexsha": "3fad9b73d30c13411e0ca47bd3e2cc803ac89a9d", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2020-11-02T06:30:39.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-23T18:39:55.000Z", "max_forks_repo_path": "projects/MonkVG-Android/jni/boost/include/boost/graph/stoer_wagner_min_cut.hpp", "max_forks_repo_name": "smartmobili/MonkVG", "max_forks_repo_head_hexsha": "3fad9b73d30c13411e0ca47bd3e2cc803ac89a9d", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2017-03-04T08:50:46.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-23T14:27:04.000Z", "avg_line_length": 47.5643153527, "max_line_length": 272, "alphanum_fraction": 0.6741690657, "num_tokens": 2838, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.4416730056646256, "lm_q1q2_score": 0.2260114108481858}} {"text": "//\n// Copyright (c) 2020 Kenshi Abe\n//\n\n#include \"Trainer.hpp\"\n\n#include \n#include \n#include \n#include \n#include \n#include \"Node.hpp\"\n\nnamespace Trainer {\n\n/// @param mode variant of CFR algorithm\n/// @param seed random seed\n/// @param strategyPaths paths to the binary files that represent fixed strategies\ntemplate \nTrainer::Trainer(const std::string &mode, const uint32_t seed, const std::vector &strategyPaths) : mEngine(seed), mNodeTouchedCnt(0), mModeStr(mode) {\n mGame = new T(mEngine);\n mFolderPath = \"../strategies/\" + mGame->name();\n boost::filesystem::create_directories(mFolderPath);\n mFixedStrategies = new std::unordered_map[mGame->playerNum()];\n mUpdate = new bool[mGame->playerNum()];\n for (int i = 0; i < mGame->playerNum(); ++i) {\n if (strategyPaths.size() >= i + 1 && !strategyPaths[i].empty()) {\n std::cout << \"load strategy \\\"\" << strategyPaths[i] << \"\\\" as static player \" << i << std::endl;\n std::ifstream ifs(strategyPaths[i]);\n boost::archive::binary_iarchive ia(ifs);\n ia >> mFixedStrategies[i];\n ifs.close();\n mUpdate[i] = false;\n } else {\n mUpdate[i] = true;\n }\n }\n}\n\ntemplate \nTrainer::~Trainer() {\n for (auto &itr : mNodeMap) {\n delete itr.second;\n }\n for (int i = 0; i < mGame->playerNum(); ++i) {\n if (mUpdate[i]) {\n continue;\n }\n for (auto &itr : mFixedStrategies[i]) {\n delete itr.second;\n }\n }\n delete[] mFixedStrategies;\n delete[] mUpdate;\n delete mGame;\n}\n\n/// @brief Calculate the expected payoff of each player\n/// @param game game\n/// @param strategies list of strategies for each player\n/// @return list of expected payoffs\ntemplate \nstd::vector Trainer::CalculatePayoff(const T &game, const std::vector> &strategies) {\n // return payoff for terminal states\n if (game.done()) {\n std::vector payoffs(game.playerNum());\n for (int i = 0; i < game.playerNum(); ++i) {\n payoffs[i] = game.payoff(i);\n }\n return payoffs;\n }\n\n // chance node turn\n const int actionNum = game.actionNum();\n if (game.isChanceNode()) {\n std::vector nodeUtils(game.playerNum());\n for (int i = 0; i < game.playerNum(); ++i) {\n nodeUtils[i] = 0.0;\n }\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n const double chanceProbability = game_cp.chanceProbability();\n std::vector utils = CalculatePayoff(game_cp, strategies);\n for (int i = 0; i < game.playerNum(); ++i) {\n nodeUtils[i] += chanceProbability * utils[i];\n }\n }\n return nodeUtils;\n }\n\n // for each action, recursively calculate payoff with additional history and probability\n const int player = game.currentPlayer();\n std::vector nodeUtils(game.playerNum());\n for (int i = 0; i < game.playerNum(); ++i) {\n nodeUtils[i] = 0.0;\n }\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n std::vector utils = CalculatePayoff(game_cp, strategies);\n for (int i = 0; i < game.playerNum(); ++i) {\n nodeUtils[i] += strategies[player](game)[a] * utils[i];\n }\n }\n return nodeUtils;\n}\n\n/// @brief Calculate exploitability of a given strategy profile\n/// @param game game\n/// @param strategies list of strategies for each player\n/// @return exploitability of a given strategy profile\ntemplate \ndouble Trainer::CalculateExploitability(const T &game, const std::vector> &strategies) {\n InfoSets infoSets;\n for (int p = 0; p < game.playerNum(); ++p) {\n auto game_cp(game);\n game_cp.reset(false);\n CreateInfoSets(game_cp, p, strategies, 1.0, infoSets);\n }\n\n double exploitability = 0.0;\n for (int p = 0; p < game.playerNum(); ++p) {\n auto game_cp(game);\n game_cp.reset(false);\n std::unordered_map> bestResponseStrategies;\n exploitability += CalculateBestResponseValue(game_cp, p, strategies, bestResponseStrategies, 1.0, infoSets);\n }\n return exploitability;\n}\n\n/// @brief Fill the ordered map that maps information sets to game nodes and reach probabilities\n/// @param game game\n/// @param playerIndex player whose strategy is updated in the current iteration\n/// @param strategies list of strategies for each player\n/// @param po the probability of reaching the current game node if the acting player always chooses actions leading to the current game node\n/// @return exploitability of a given strategy profile\ntemplate \nvoid Trainer::CreateInfoSets(const T &game, const int playerIndex, const std::vector> &strategies, const double po, InfoSets &infoSets) {\n // return at terminal states\n if (game.done()) {\n return;\n }\n\n // chance node turn\n const int actionNum = game.actionNum();\n if (game.isChanceNode()) {\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n const double chanceProbability = game_cp.chanceProbability();\n CreateInfoSets(game_cp, playerIndex, strategies, po * chanceProbability, infoSets);\n }\n return;\n }\n\n const int player = game.currentPlayer();\n if (player == playerIndex) {\n std::string infoSet = game.infoSetStr();\n if (infoSets.count(infoSet) == 0) {\n infoSets[infoSet] = std::vector>();\n }\n infoSets[infoSet].push_back(std::make_tuple(game, po));\n }\n\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n if (player == playerIndex) {\n CreateInfoSets(game_cp, playerIndex, strategies, po, infoSets);\n } else {\n const double actionProb = strategies[player](game)[a];\n CreateInfoSets(game_cp, playerIndex, strategies, po * actionProb, infoSets);\n }\n }\n}\n\n/// @brief Calculate best response value for a given player\n/// @param game game\n/// @param playerIndex player whose strategy is updated in the current iteration\n/// @param strategies list of strategies for each player\n/// @param bestResponseStrategies best response strategy for a given player\n/// @param po the probability of reaching the current game node if the acting player always chooses actions leading to the current game node\n/// @param infoSets ordered map that maps information sets to game nodes and reach probabilities\n/// @return best response value for a given player\ntemplate \ndouble Trainer::CalculateBestResponseValue(const T &game, const int playerIndex,\n const std::vector> &strategies,\n std::unordered_map> &bestResponseStrategies,\n const double po,\n const InfoSets &infoSets) {\n // return payoff for terminal states\n if (game.done()) {\n return game.payoff(playerIndex);\n }\n\n // chance node turn\n const int actionNum = game.actionNum();\n if (game.isChanceNode()) {\n double nodeUtil = 0.0;\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n const double chanceProbability = game_cp.chanceProbability();\n nodeUtil += chanceProbability * CalculateBestResponseValue(game_cp, playerIndex, strategies, bestResponseStrategies, po * chanceProbability, infoSets);\n }\n return nodeUtil;\n }\n\n const int player = game.currentPlayer();\n if (player == playerIndex) {\n // get information set string representation\n std::string infoSet = game.infoSetStr();\n if (bestResponseStrategies.count(infoSet) == 0) {\n // calculate action values\n double actionValues[actionNum];\n for (int a = 0; a < actionNum; ++a) {\n actionValues[a] = 0.0;\n }\n for (int i = 0; i < infoSets.at(infoSet).size(); ++i) {\n auto game_ = std::get<0>(infoSets.at(infoSet)[i]);\n auto po_ = std::get<1>(infoSets.at(infoSet)[i]);\n double brValues[actionNum];\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game_);\n game_cp.step(a);\n brValues[a] = CalculateBestResponseValue(game_cp, playerIndex, strategies, bestResponseStrategies, po_, infoSets);\n actionValues[a] += po_ * brValues[a];\n }\n }\n // calculate best response strategy\n int brAction = 0;\n for (int a = 0; a < actionNum; ++a) {\n if (actionValues[a] > actionValues[brAction]) {\n brAction = a;\n }\n }\n bestResponseStrategies[infoSet] = std::vector(actionNum, 0.0);\n bestResponseStrategies[infoSet][brAction] = 1.0;\n }\n // calculate best response value\n double utils[actionNum];\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n utils[a] = CalculateBestResponseValue(game_cp, playerIndex, strategies, bestResponseStrategies, po, infoSets);\n }\n double bestResponseValue = 0.0;\n for (int a = 0; a < actionNum; ++a) {\n bestResponseValue += utils[a] * bestResponseStrategies.at(infoSet)[a];\n }\n return bestResponseValue;\n } else {\n // for each action, recursively calculate payoff with additional history and probability\n double nodeUtil = 0.0;\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n const double actionProb = strategies[player](game)[a];\n nodeUtil += actionProb * CalculateBestResponseValue(game_cp, playerIndex, strategies, bestResponseStrategies, po * actionProb, infoSets);\n }\n return nodeUtil;\n }\n}\n\n/// @brief Execute the CFR algorithm to compute an approximate Nash equilibrium\n/// @param iterations number of iterations of CFR\ntemplate \nvoid Trainer::train(const int iterations) {\n double utils[mGame->playerNum()];\n\n for (int i = 0; i < iterations; ++i) {\n for (int p = 0; p < mGame->playerNum(); ++p) {\n if (!mUpdate[p]) {\n continue;\n }\n if (mModeStr == \"vanilla\") {\n mGame->reset(false);\n utils[p] = CFR(*mGame, p, 1.0, 1.0);\n for (auto & itr : mNodeMap) {\n itr.second->updateStrategy();\n }\n } else {\n mGame->reset();\n if (mModeStr == \"chance\") {\n utils[p] = chanceSamplingCFR(*mGame, p, 1.0, 1.0);\n for (auto & itr : mNodeMap) {\n itr.second->updateStrategy();\n }\n } else if (mModeStr == \"external\") {\n utils[p] = externalSamplingCFR(*mGame, p);\n } else if (mModeStr == \"outcome\") {\n utils[p] = std::get<0>(outcomeSamplingCFR(*mGame, p, i, 1.0, 1.0, 1.0));\n } else {\n assert(false);\n }\n }\n }\n if (i % 1000 == 0) {\n std::cout << \"iteration:\" << i << \", cumulative nodes touched: \" << mNodeTouchedCnt << \", infosets num: \" << mNodeMap.size() << \", expected payoffs: (\";\n for (int p = 0; p < mGame->playerNum(); ++p) {\n std::cout << utils[p] << \",\";\n }\n std::cout << \")\" << std::endl;\n }\n if (i != 0 && i % 10000000 == 0) {\n writeStrategyToBin(i);\n }\n }\n\n writeStrategyToBin();\n}\n\n/// @brief Main procedure of vanilla CFR\n/// @param game game\n/// @param playerIndex player whose strategy is updated in the current iteration\n/// @param pi the probability of reaching the current game node if all players other than the acting player always choose actions leading to the current game node\n/// @param po the probability of reaching the current game node if the acting player always chooses actions leading to the current game node\n/// @return expected payoff of the specified player at the current game node\ntemplate \ndouble Trainer::CFR(const T &game, const int playerIndex, const double pi, const double po) {\n ++mNodeTouchedCnt;\n\n // return payoff for terminal states\n if (game.done()) {\n return game.payoff(playerIndex);\n }\n\n // chance node turn\n const int actionNum = game.actionNum();\n if (game.isChanceNode()) {\n double nodeUtil = 0.0;\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n const double chanceProbability = game_cp.chanceProbability();\n nodeUtil += chanceProbability * CFR(game_cp, playerIndex, pi, po * chanceProbability);\n }\n return nodeUtil;\n }\n\n // get information set string representation\n std::string infoSet = game.infoSetStr();\n\n // treat static player as chance node\n const int player = game.currentPlayer();\n if (!mUpdate[player]) {\n double nodeUtil = 0.0;\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n const auto chanceProbability = double(mFixedStrategies[player].at(infoSet)->averageStrategy()[a]);\n nodeUtil += chanceProbability * CFR(game_cp, playerIndex, pi, po * chanceProbability);\n }\n return nodeUtil;\n }\n\n // get information set node or create it if nonexistant\n Node *node = mNodeMap[infoSet];\n if (node == nullptr) {\n node = new Node(actionNum);\n mNodeMap[infoSet] = node;\n }\n\n // get current strategy through regret-matching\n const double *strategy = node->strategy();\n\n // for each action, recursively call CFR with additional history and probability\n double utils[actionNum];\n double nodeUtil = 0;\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n if (player == playerIndex) {\n utils[a] = CFR(game_cp, playerIndex, pi * strategy[a], po);\n } else {\n utils[a] = CFR(game_cp, playerIndex, pi, po * strategy[a]);\n }\n nodeUtil += strategy[a] * utils[a];\n }\n\n if (player == playerIndex) {\n // for each action, compute and accumulate counterfactual regret\n for (int a = 0; a < actionNum; ++a) {\n const double regret = utils[a] - nodeUtil;\n const double regretSum = node->regretSum(a) + po * regret;\n node->regretSum(a, regretSum);\n }\n // update average strategy across all training iterations\n node->strategySum(strategy, pi);\n }\n\n return nodeUtil;\n}\n\n/// @brief Main procedure of chance-sampling MCCFR\n/// @param game game\n/// @param playerIndex player whose strategy is updated in the current iteration\n/// @param pi the probability of reaching the current game node if all players other than the acting player always choose actions leading to the current game node\n/// @param po the probability of reaching the current game node if the acting player and the chance player always choose actions leading to the current game node\n/// @return estimated expected payoff of the specified player at the current game node\ntemplate \ndouble Trainer::chanceSamplingCFR(const T &game, const int playerIndex, const double pi, const double po) {\n ++mNodeTouchedCnt;\n\n // return payoff for terminal states\n if (game.done()) {\n return game.payoff(playerIndex);\n }\n\n // get information set string representation\n std::string infoSet = game.infoSetStr();\n\n // treat static player as chance node\n const int actionNum = game.actionNum();\n const int player = game.currentPlayer();\n if (!mUpdate[player]) {\n auto game_cp(game);\n auto strategy = mFixedStrategies[player].at(infoSet)->averageStrategy();\n std::discrete_distribution dist(strategy, strategy + actionNum);\n game_cp.step(dist(mEngine));\n return chanceSamplingCFR(game_cp, playerIndex, pi, po);\n }\n\n // get information set node or create it if nonexistant\n Node *node = mNodeMap[infoSet];\n if (node == nullptr) {\n node = new Node(actionNum);\n mNodeMap[infoSet] = node;\n }\n\n // get current strategy through regret-matching\n const double *strategy = node->strategy();\n\n // for each action, recursively call cfr with additional history and probability\n double utils[actionNum];\n double nodeUtil = 0;\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n if (player == playerIndex) {\n utils[a] = chanceSamplingCFR(game_cp, playerIndex, pi * strategy[a], po);\n } else {\n utils[a] = chanceSamplingCFR(game_cp, playerIndex, pi, po * strategy[a]);\n }\n nodeUtil += strategy[a] * utils[a];\n }\n\n if (player == playerIndex) {\n // for each action, compute and accumulate counterfactual regret\n for (int a = 0; a < actionNum; ++a) {\n const double regret = utils[a] - nodeUtil;\n const double regretSum = node->regretSum(a) + po * regret;\n node->regretSum(a, regretSum);\n }\n // update average strategy across all training iterations\n node->strategySum(strategy, pi);\n }\n\n return nodeUtil;\n}\n\n/// @brief Main procedure of external-sampling MCCFR\n/// @param game game\n/// @param playerIndex player whose strategy is updated in the current iteration\n/// @return estimated expected payoff of the specified player at the current game node\ntemplate \ndouble Trainer::externalSamplingCFR(const T &game, const int playerIndex) {\n ++mNodeTouchedCnt;\n\n // return payoff for terminal states\n if (game.done()) {\n return game.payoff(playerIndex);\n }\n\n // get information set string representation\n std::string infoSet = game.infoSetStr();\n\n // external sampling with stochastically-weighted averaging cannot treat static player\n const int actionNum = game.actionNum();\n const int player = game.currentPlayer();\n assert(mUpdate[player] && \"External sampling with stochastically-weighted averaging cannot treat static player.\");\n\n // get information set node or create it if nonexistant\n Node *node = mNodeMap[infoSet];\n if (node == nullptr) {\n node = new Node(actionNum);\n mNodeMap[infoSet] = node;\n }\n\n // get current strategy through regret-matching\n node->updateStrategy();\n const double *strategy = node->strategy();\n\n // if current player is not the target player, sample a single action and recursively call cfr\n if (player != playerIndex) {\n auto game_cp(game);\n std::discrete_distribution dist(strategy, strategy + actionNum);\n game_cp.step(dist(mEngine));\n const double util = externalSamplingCFR(game_cp, playerIndex);\n // update average strategy\n node->strategySum(strategy, 1.0);\n return util;\n }\n\n // for each action, recursively call cfr with additional history and probability\n double utils[actionNum];\n double nodeUtil = 0;\n for (int a = 0; a < actionNum; ++a) {\n auto game_cp(game);\n game_cp.step(a);\n utils[a] = externalSamplingCFR(game_cp, playerIndex);\n nodeUtil += strategy[a] * utils[a];\n }\n\n // for each action, compute and accumulate counterfactual regret\n for (int a = 0; a < actionNum; ++a) {\n const double regret = utils[a] - nodeUtil;\n const double regretSum = node->regretSum(a) + regret;\n node->regretSum(a, regretSum);\n }\n\n return nodeUtil;\n}\n\n/// @brief Main procedure of outcome-sampling MCCFR\n/// @param game game\n/// @param playerIndex player whose strategy is updated in the current iteration\n/// @param pi the probability of reaching the current game node if all players other than the acting player always choose actions leading to the current game node\n/// @param po the probability of reaching the current game node if the acting player and the chance player always choose actions leading to the current game node\n/// @param s the probability of reaching the current game node if the chance player always chooses actions leading to the current game node and the other players act according to the sample profile\n/// @return estimated expected payoff of the specified player at the current game node, and the probability of reaching the terminal game node if the chance player always chooses actions leading to the terminal game node\ntemplate \nstd::tuple Trainer::outcomeSamplingCFR(const T &game, const int playerIndex, const int iteration , const double pi, const double po, const double s) {\n ++mNodeTouchedCnt;\n\n // return payoff for terminal states\n if (game.done()) {\n return std::make_tuple(game.payoff(playerIndex) / s, 1.0);\n }\n\n // get information set string representation\n std::string infoSet = game.infoSetStr();\n\n // outcome sampling with stochastically-weighted averaging cannot treat static player\n const int actionNum = game.actionNum();\n const int player = game.currentPlayer();\n assert(mUpdate[player] && \"Outcome sampling with stochastically-weighted averaging cannot treat static player.\");\n\n // get information set node or create it if nonexistant\n Node *node = mNodeMap[infoSet];\n if (node == nullptr) {\n node = new Node(actionNum);\n mNodeMap[infoSet] = node;\n }\n\n // get current strategy through regret-matching\n node->updateStrategy();\n const double *strategy = node->strategy();\n\n // if current player is the target player, sample a single action according to epsilon-on-policy\n // otherwise, sample a single action according to the player's strategy\n const double epsilon = 0.6;\n double probability[actionNum];\n if (player == playerIndex) {\n for (int a = 0; a < actionNum; ++a) {\n probability[a] = (epsilon / (double) actionNum) + (1.0 - epsilon) * strategy[a];\n }\n } else {\n for (int a = 0; a < actionNum; ++a) {\n probability[a] = strategy[a];\n }\n }\n std::discrete_distribution dist(probability, probability + actionNum);\n const int action = dist(mEngine);\n\n // for sampled action, recursively call cfr with additional history and probability\n double util, pTail;\n auto game_cp(game);\n game_cp.step(action);\n const double newPi = pi * (player == playerIndex ? strategy[action] : 1.0);\n const double newPo = po * (player == playerIndex ? 1.0 : strategy[action]);\n std::tuple ret = outcomeSamplingCFR(game_cp, playerIndex, iteration, newPi, newPo, s * probability[action]);\n util = std::get<0>(ret);\n pTail = std::get<1>(ret);\n if (player == playerIndex) {\n // for each action, compute and accumulate counterfactual regret\n const double W = util * po;\n for (int a = 0; a < actionNum; ++a) {\n const double regret = a == action ? W * (1.0 - strategy[action]) * pTail : -W * pTail * strategy[action];\n const double regretSum = node->regretSum(a) + regret;\n node->regretSum(a, regretSum);\n }\n } else {\n // update average strategy\n node->strategySum(strategy, po / s);\n }\n return std::make_tuple(util, pTail * strategy[action]);\n}\n\n/// @brief Save the current average strategy as a binary file\n/// @param iteration current iteration\ntemplate \nvoid Trainer::writeStrategyToBin(const int iteration) const {\n for (auto &itr : mNodeMap) {\n for (char c : itr.first) {\n std::cout << int(c);\n }\n std::cout << \":\";\n for (int i = 0; i < itr.second->actionNum(); ++i) {\n std::cout << itr.second->averageStrategy()[i] << \",\";\n }\n std::cout << std::endl;\n }\n std::string path = iteration > 0 ? \"strategy_\" + std::to_string(iteration)\n : \"strategy\";\n path += \"_\" + mModeStr + \".bin\";\n std::ofstream ofs(mFolderPath + \"/\" + path);\n boost::archive::binary_oarchive oa(ofs);\n oa << mNodeMap;\n ofs.close();\n}\n\n} // namespace\n\n", "meta": {"hexsha": "70e8588cb0d3b80303a51683165c8cddfff1a16e", "size": 25043, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "RegretMinimization/Trainer/Trainer.cpp", "max_stars_repo_name": "bakanaouji/open-cfr", "max_stars_repo_head_hexsha": "36f9aebed39e39e33394a5e994c1366948074913", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-01-06T03:27:09.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-06T03:27:09.000Z", "max_issues_repo_path": "RegretMinimization/Trainer/Trainer.cpp", "max_issues_repo_name": "bakanaouji/open-cfr", "max_issues_repo_head_hexsha": "36f9aebed39e39e33394a5e994c1366948074913", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2020-01-06T02:52:03.000Z", "max_issues_repo_issues_event_max_datetime": "2020-01-06T23:09:43.000Z", "max_forks_repo_path": "RegretMinimization/Trainer/Trainer.cpp", "max_forks_repo_name": "bakanaouji/open-cfr", "max_forks_repo_head_hexsha": "36f9aebed39e39e33394a5e994c1366948074913", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.0047923323, "max_line_length": 220, "alphanum_fraction": 0.61466278, "num_tokens": 5915, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.22601141084818577}} {"text": "#ifndef JHMI_VOLUME_IMAGE_HPP_NRC20150429\n#define JHMI_VOLUME_IMAGE_HPP_NRC20150429\n\n#include \"cube.hpp\"\n#include \"range/v3/view.hpp\"\n#include \"range/v3/core.hpp\"\n#include \"range/v3/view_facade.hpp\"\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace jhmi {\n namespace jhmi_detail {\n template class by_location_view;\n inline std::string to_dtype(float) { return \"float32\"; }\n inline std::string to_dtype(double) { return \"float64\"; }\n inline std::string to_dtype(std::uint8_t) { return \"uint8\"; }\n inline std::string to_dtype(std::uint32_t) { return \"uint32\"; }\n inline std::string to_dtype(std::int32_t) { return \"int32\"; }\n template \n inline std::string to_dtype(boost::units::quantity) { return to_dtype(T{}); }\n }\n\n template \n class volume_image {\n using reference = T&;\n using const_reference = T const&;\n\n public:\n struct physical_pixel_ref {\n physical_pixel_ref(m3 const& pp, int3 const&, reference v) : loc{pp}, value{v} {}\n m3 const loc;\n reference value;\n };\n struct image_pixel_ref {\n image_pixel_ref(m3 const&, int3 const& ip, reference v) : loc{ip}, value{v} {}\n int3 const loc;\n reference value;\n };\n struct pixel_ref {\n pixel_ref(m3 const& pp, int3 const& ip, reference v) : physical_loc{pp}, image_loc{ip}, value{v} {}\n m3 const physical_loc;\n int3 const image_loc;\n reference value;\n };\n struct physical_pixel {\n physical_pixel(m3 const& pp, int3 const&, const_reference v) : loc{pp}, value{v} {}\n m3 const loc;\n const_reference value;\n };\n struct image_pixel {\n image_pixel(m3 const&, int3 const& ip, const_reference v) : loc{ip}, value{v} {}\n int3 const loc;\n const_reference value;\n };\n struct pixel {\n pixel(m3 const& pp, int3 const& ip, const_reference v) : physical_loc{pp}, image_loc{ip}, value{v} {}\n m3 const physical_loc;\n int3 const image_loc;\n const_reference value;\n };\n\n volume_image() = default;\n volume_image(int3 const& size, cube const& physical_extents)\n : physical_extents_(physical_extents),\n w_(size.x), h_(size.y), d_(size.z),\n volume_(new T[w_*h_*d_]()),//Note, we default initialize each element of volume_\n half_pixel_sizes_{element_divide(jhmi::dimensions(physical_extents), 2. * size)}\n {}\n\n explicit volume_image(boost::filesystem::path const& filename) {\n std::ifstream file(filename.string(), std::ios::binary);\n if (!file.is_open()) {\n throw std::runtime_error(fmt::format(\n \"Invalid file \\\"{}\\\" provided to volume_image\", filename.string()));\n }\n boost::iostreams::filtering_istream in;\n in.push(boost::iostreams::zlib_decompressor{});\n in.push(file);\n std::string line;\n getline(in, line);\n std::vector e;\n boost::split(e, line, boost::is_any_of(\" \"), boost::token_compress_on);\n physical_extents_ = cube{dbl3{stod(e[0]), stod(e[1]), stod(e[2])}*mm,\n dbl3{stod(e[3]), stod(e[4]), stod(e[5])}*mm};\n getline(in, line);\n w_ = stoi(line);\n getline(in, line);\n h_ = stoi(line);\n getline(in, line);\n d_ = stoi(line);\n volume_.reset(new T[w_ * h_ * d_]);\n getline(in, line);\n if (line != jhmi_detail::to_dtype(T{})) {\n throw std::invalid_argument(fmt::format(\n \"Invalid attempt to construct {} from {}\", jhmi_detail::to_dtype(T()), line));\n }\n in.read(reinterpret_cast(volume_.get()), w_ * h_ * d_ * sizeof(T));\n half_pixel_sizes_ = element_divide(\n jhmi::dimensions(physical_extents_), 2. * int3{w_,h_,d_});\n }\n\n dbl3 to_index(m3 const& pt) const {\n return dbl3(element_divide(\n element_multiply(pt - physical_extents_.ul() - half_pixel_sizes_, dbl3(w_,h_,d_)),\n jhmi::dimensions(physical_extents_)));\n }\n\n reference operator()(m3 const& pt) {\n return (*this)(int3(to_index(pt)));\n }\n const_reference operator()(m3 const& pt) const {\n return (*this)(int3(to_index(pt)));\n }\n\n reference operator()(int x, int y, int z) {\n assert(x + y * w_ + z * w_ * h_ < w_ * h_ * d_);\n return volume_[x + y * w_ + z * w_ * h_];\n }\n reference operator()(int3 const& pt) { return (*this)(pt.x, pt.y, pt.z); }\n const_reference operator()(int x, int y, int z) const {\n assert(x + y * w_ + z * w_ * h_ < w_ * h_ * d_);\n return volume_[x + y * w_ + z * w_ * h_];\n }\n const_reference operator()(int3 const& pt) const { return (*this)(pt.x, pt.y, pt.z); }\n\n //TODO: Is this returning a pointer to const? I don't think so...\n auto begin() const { return volume_.get(); }\n auto begin() { return volume_.get(); }\n\n auto end() const { return volume_.get() + w_ * h_ * d_; }\n auto end() { return volume_.get() + w_ * h_ * d_; }\n\n int width() const { return w_; }\n int height() const { return h_; }\n int depth() const { return d_; }\n\n int3 dimensions() const { return {w_, h_, d_}; }\n\n void write(boost::filesystem::path const& filename) const {\n std::ofstream file{filename.string(), std::ios::binary};\n boost::iostreams::filtering_ostream out;\n out.push(boost::iostreams::zlib_compressor{});\n out.push(file);\n out << fmt::format(\"{} {} {} {} {} {}\\n{}\\n{}\\n{}\\n{}\\n\",\n physical_extents_.ul().x.value()*1000,\n physical_extents_.ul().y.value()*1000,\n physical_extents_.ul().z.value()*1000,\n physical_extents_.lr().x.value()*1000,\n physical_extents_.lr().y.value()*1000,\n physical_extents_.lr().z.value()*1000,\n width(), height(), depth(), jhmi_detail::to_dtype(T()));\n write_binary(out);\n }\n\n private:\n template \n void write_binary(Stream& out) const {\n out.write(reinterpret_cast(volume_.get()),\n w_ * h_ * d_ * sizeof(T));\n }\n\n template friend class jhmi_detail::by_location_view;\n friend cube extents(volume_image const& v) {\n return v.physical_extents_;\n }\n cube physical_extents_;\n int w_, h_, d_;\n std::unique_ptr volume_;\n m3 half_pixel_sizes_;\n };\n\n namespace jhmi_detail {\n template \n class by_location_view : public ranges::view_facade> {\n public:\n friend struct ranges::range_access;\n V* img_;\n m3 step_;\n m3 center_;\n struct cursor {\n V* img_;\n m3 mul_;\n m3 step_;\n int3 iul_;\n int3 ilr_;\n int3 ipt_;\n\n cursor() = default;\n\n bool equal(cursor const& other) const { return ipt_ == other.ipt_; }\n auto read() const {\n return P{mul_ + element_multiply(dbl3{ipt_}, step_), ipt_, (*img_)(ipt_)};\n }\n void next() {\n if (++ipt_.x >= ilr_.x) {\n ipt_.x = iul_.x;\n if (++ipt_.y >= ilr_.y) {\n ipt_.y = iul_.y;\n ++ipt_.z;\n }\n }\n }\n };\n\n cursor make_cursor(bool do_ul) const {\n auto pe = img_->physical_extents_;\n auto d = img_->dimensions();\n int3 iul{0,0,0};\n int3 iend{0,0,d.z};\n auto ipt = do_ul ? iul : iend;\n\n auto hstep = step_ / 2.;\n auto mul = element_multiply(ceil(element_divide(pe.ul() - center_, hstep)), hstep) + center_;\n return {img_, mul + hstep, step_, iul, d, ipt};\n }\n\n public:\n cursor begin_cursor() const {\n return make_cursor(true);\n }\n cursor end_cursor() const {\n return make_cursor(false);\n }\n\n explicit by_location_view(V& img) : img_(&img),\n step_(element_divide(dimensions(extents(img)), dbl3{img.dimensions()})),\n center_{extents(img).ul() + step_ / 2.} {}\n by_location_view(V& img, m3 const& step, m3 const& center)\n : img_(&img), step_(step), center_{center} {}\n };\n }//jhmi_detail\n\n template \n struct by_const_physical_location_view : jhmi_detail::by_location_view::physical_pixel, volume_image const> {\n using jhmi_detail::by_location_view::physical_pixel, volume_image const>::by_location_view;\n };\n template \n struct by_physical_location_view : jhmi_detail::by_location_view::physical_pixel_ref, volume_image> {\n using jhmi_detail::by_location_view::physical_pixel_ref, volume_image>::by_location_view;\n };\n template \n struct by_const_image_location_view : jhmi_detail::by_location_view::image_pixel, volume_image const> {\n using jhmi_detail::by_location_view::image_pixel, volume_image const>::by_location_view;\n };\n template \n struct by_image_location_view : jhmi_detail::by_location_view::image_pixel_ref, volume_image> {\n using jhmi_detail::by_location_view::image_pixel_ref, volume_image>::by_location_view;\n };\n template \n struct by_const_location_view : jhmi_detail::by_location_view::pixel, volume_image const> {\n using jhmi_detail::by_location_view::pixel, volume_image const>::by_location_view;\n };\n template \n struct by_location_view : jhmi_detail::by_location_view::pixel_ref, volume_image> {\n using jhmi_detail::by_location_view::pixel_ref, volume_image>::by_location_view;\n };\n\n namespace view {\n struct by_physical_location_fn {\n template auto operator()(volume_image const& img) const {\n return by_const_physical_location_view{img};\n }\n template auto operator()(volume_image& img) const {\n return by_physical_location_view{img};\n }\n };\n template auto operator|(T& img, by_physical_location_fn fn) {\n return fn(img);\n }\n struct by_image_location_fn {\n template auto operator()(volume_image const& img) const {\n return by_const_image_location_view{img};\n }\n template auto operator()(volume_image& img) const {\n return by_image_location_view{img};\n }\n };\n template auto operator|(T& img, by_image_location_fn fn) {\n return fn(img);\n }\n struct by_location_fn {\n template auto operator()(volume_image const& img) const {\n return by_const_location_view{img};\n }\n template auto operator()(volume_image& img) const {\n return by_location_view{img};\n }\n };\n template auto operator|(T& img, by_location_fn fn) {\n return fn(img);\n }\n constexpr by_image_location_fn by_image_location{};\n constexpr by_physical_location_fn by_physical_location{};\n constexpr by_location_fn by_location{};\n\n struct equal_step {\n m step_;\n m3 center_;\n explicit equal_step(m step, m3 const& center = m3{}) : step_{step}, center_{center} {}\n template auto operator()(volume_image const& img) const {\n return by_const_location_view(img, dbl3{1,1,1}*step_, center_);\n }\n template auto operator()(volume_image& img) const {\n return by_location_view(img, dbl3{1,1,1}*step_, center_);\n }\n };\n template auto operator|(T& img, equal_step fn) {\n return fn(img);\n }\n\n struct x_slice {\n int x1, x2, y, z;\n x_slice(int x1, int x2, int y, int z) : x1(x1), x2(x2), y(y), z(z) {}\n\n template auto operator()(volume_image const& img) const {\n return ranges::make_iterator_range(&img(x1,y,z), &img(x2,y,z));\n }\n template auto operator()(volume_image& img) const {\n return ranges::make_iterator_range(&img(x1,y,z), &img(x2,y,z));\n }\n };\n template auto operator|(T& img, x_slice fn) {\n return fn(img);\n }\n }\n\n template \n auto gaussian_filter(volume_image& in, double sigma, int num_dev = 3) {\n volume_image out{in.dimensions(), extents(in)};\n volume_image tmp{in.dimensions(), extents(in)};\n volume_image tmp1{in.dimensions(), extents(in)};\n int range = static_cast(std::ceil(num_dev * sigma));\n auto filter = ranges::view::closed_indices(-range, range)\n | ranges::view::transform([&](int x) {\n return /*1. / (sigma * 2.506628274) */ std::exp(-x*x/(2*sigma*sigma)); })\n | ranges::to_vector;\n\n auto ds = inflate(cube{int3{}, in.dimensions()}, -range*int3{1,1,1});\n traverse(ds, 1, [&](int3 const& pt) {\n for (int i = 0; i < filter.size(); ++i) {\n auto new_pt = pt + int3{i-range,0,0};\n tmp1(pt) += in(new_pt) * filter[i];\n }\n });\n traverse(ds, 1, [&](int3 const& pt) {\n for (int i = 0; i < filter.size(); ++i) {\n auto new_pt = pt + int3{0,i-range,0};\n tmp(pt) += tmp1(new_pt) * filter[i];\n }\n });\n traverse(ds, 1, [&](int3 const& pt) {\n for (int i = 0; i < filter.size(); ++i) {\n auto new_pt = pt + int3{0,0,i-range};\n out(pt) += tmp(new_pt) * filter[i];\n }\n });\n return out;\n }\n template auto convolve(\n volume_image const& lhs, volume_image const& rhs) {\n auto half_size = int3(ceil(rhs.dimensions() / 2.));\n cube ds{int3{}, lhs.dimensions() - rhs.dimensions()};\n using Out = decltype(T{} * U{});\n volume_image out_img{lhs.dimensions(), extents(lhs)};\n //Reverse rhs once, simplify calculations later.\n auto kernel = rhs;\n traverse(cube{int3{}, rhs.dimensions()}, 1, [&](int3 const& pt) {\n kernel(rhs.dimensions() - int3{1,1,1} - pt) = rhs(pt);\n });\n traverse(ds, 1, [&](int3 const& ul) {\n Out out;\n traverse(cube{int3{}, kernel.dimensions()}, 1, [&](int3 const& pt) {\n out += lhs(ul + pt) * kernel(pt);\n });\n out_img(ul+half_size) = out;\n });\n return out_img;\n }\n namespace jhmi_detail {\n struct prev_type{}; static const constexpr prev_type prev{};\n struct next_type{}; static const constexpr next_type next{};\n struct Top {\n int operator()() const { return 0; }\n int operator()(prev_type) const { return 0; }\n int operator()(next_type) const { return 1; }\n };\n struct Mid {\n int v;\n explicit Mid(int v) : v(v) {}\n int operator()() const { return v; }\n int operator()(prev_type) const { return v-1; }\n int operator()(next_type) const { return v+1; }\n };\n struct Bottom {\n int v;\n explicit Bottom(int v) : v(v) {}\n int operator()() const { return v; }\n int operator()(prev_type) const { return v-1; }\n int operator()(next_type) const { return v; }\n };\n template \n void traverse_x(F f, U1 y, U2 z, volume_image const& in, volume_image& out) {\n out(0, y(), z()) = f(in(0, y(), z()), in(1, y(), z()),\n in(0, y(prev), z()), in(0, y(next), z()),\n in(0, y(), z(prev)), in(0, y(), z(next)));\n auto l = in.width()-1;\n for (int x = 1; x < l; ++x)\n out(x, y(), z()) = f(in(x, y(), z()), in(x-1, y(), z()), in(x+1, y(), z()),\n in(x, y(prev), z()), in(x, y(next), z()),\n in(x, y(), z(prev)), in(x, y(), z(next)));\n out(l, y(), z()) = f(in(l, y(), z()), in(l-1, y(), z()),\n in(l, y(prev), z()), in(l, y(next), z()),\n in(l, y(), z(prev)), in(l, y(), z(next)));\n }\n template \n void traverse_y(F f, U1 z, volume_image const& in, volume_image& out) {\n traverse_x(f, Top(), z, in, out);\n for (int y = 1; y < in.height()-1; ++y)\n traverse_x(f, Mid(y), z, in, out);\n traverse_x(f, Bottom(in.height()-1), z, in, out);\n }\n template \n void traverse_z(F f, volume_image const& in, volume_image& out) {\n traverse_y(f, Top(), in, out);\n for (int z = 1; z < in.depth()-1; ++z)\n traverse_y(f, Mid(z), in, out);\n traverse_y(f, Bottom(in.depth()-1), in, out);\n }\n }//jhmi_detail\n template auto erode(volume_image const& img) {\n auto out = volume_image{img.dimensions(), extents(img)};\n jhmi_detail::traverse_z([](auto ...vals) { return std::min({vals...}); }, img, out);\n return out;\n }\n template auto dilate(volume_image const& img) {\n auto out = volume_image{img.dimensions(), extents(img)};\n jhmi_detail::traverse_z([](auto ...vals) { return std::max({vals...}); }, img, out);\n return out;\n }\n}\n\n#endif\n", "meta": {"hexsha": "5b2437f25fc9dd8feb4fe35815ece6fa3ea566d1", "size": 17172, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "utility/volume_image.hpp", "max_stars_repo_name": "ncrookston/liver_source", "max_stars_repo_head_hexsha": "9876ac4e9ea57d8e23767af9be061a9b10c6f1e5", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "utility/volume_image.hpp", "max_issues_repo_name": "ncrookston/liver_source", "max_issues_repo_head_hexsha": "9876ac4e9ea57d8e23767af9be061a9b10c6f1e5", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "utility/volume_image.hpp", "max_forks_repo_name": "ncrookston/liver_source", "max_forks_repo_head_hexsha": "9876ac4e9ea57d8e23767af9be061a9b10c6f1e5", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.4161073826, "max_line_length": 142, "alphanum_fraction": 0.6081993944, "num_tokens": 4726, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.3812195803163617, "lm_q1q2_score": 0.22593611250201553}} {"text": "#include \"MBTB_JointR.hpp\"\n#include \"NewtonEulerJointR.hpp\"\n#include \n#include \"op3x3.h\"\n#include \"RotationQuaternion.hpp\"\n//#define MBTB_JOINTR_DEBUG\n\nMBTB_JointR::MBTB_JointR()\n{\n _M.reset(new SimpleMatrix(6,6));\n _F.reset(new SiconosVector(6));\n}\n\n/*\n *\n *\n * ML_A_abs = M_FA_A + M_FB_A = F2/\\BA\n * the system is :\n * FL=F1+F2\n * F1.e0 = F2.e0\n * ML_A_abs . ei = F2 /\\ BA .ei for i=1,2\n *\n */\n\nvoid MBTB_JointR::computeEquivalentForces()\n{\n if(!_G0C1)\n return;\n double q1=_ds1->q()->getValue(3);\n double q2=_ds1->q()->getValue(4);\n double q3=_ds1->q()->getValue(5);\n double q4=_ds1->q()->getValue(6);\n ::boost::math::quaternion quattrf(q1,q2,q3,q4);\n ::boost::math::quaternion cquattrf(q1,-q2,-q3,-q4);\n ::boost::math::quaternion quatbuff;\n SP::SiconosVector Blambda=_jointR->contactForce();\n SiconosVector FL(3);\n FL.setValue(0,Blambda->getValue(0));\n FL.setValue(1,Blambda->getValue(1));\n FL.setValue(2,Blambda->getValue(2));\n\n SiconosVector ML_G(3);\n SiconosVector ML_G_abs(3);\n ML_G.setValue(0,Blambda->getValue(3));\n ML_G.setValue(1,Blambda->getValue(4));\n ML_G.setValue(2,Blambda->getValue(5));\n SP::SiconosVector spML_G_abs(new SiconosVector(3));\n *spML_G_abs = ML_G;\n changeFrameBodyToAbs(_ds1->q(),spML_G_abs);\n ML_G_abs = * spML_G_abs;\n#ifdef MBTB_JOINTR_DEBUG\n printf(\"MBTB_JointR::computeEquivalentForces Blambda\\n\");\n Blambda->display();\n printf(\"MBTB_JointR::computeEquivalentForces ML_G_abs (in abs frame):\\n\");\n ML_G_abs.display();\n#endif\n\n SiconosVector AB0(3);\n AB0=(*_G0C2)-(*_G0C1);\n ::boost::math::quaternion quatAB0(0,AB0.getValue(0),AB0.getValue(1),AB0.getValue(2));\n quatbuff = quattrf*quatAB0*cquattrf;\n SiconosVector AB(3);\n AB.setValue(0,quatbuff.R_component_2());\n AB.setValue(1,quatbuff.R_component_3());\n AB.setValue(2,quatbuff.R_component_4());\n#ifdef MBTB_JOINTR_DEBUG\n printf(\"MBTB_JointR::computeEquivalentForces AB0:\\n\");\n AB0.display();\n printf(\"MBTB_JointR::computeEquivalentForces AB:\\n\");\n AB.display();\n#endif\n\n ::boost::math::quaternion quatG0C1(0,_G0C1->getValue(0),_G0C1->getValue(1),_G0C1->getValue(2));\n quatbuff = quattrf*quatG0C1*cquattrf;\n SiconosVector GA(3);\n GA.setValue(0,quatbuff.R_component_2());\n GA.setValue(1,quatbuff.R_component_3());\n GA.setValue(2,quatbuff.R_component_4());\n#ifdef MBTB_JOINTR_DEBUG\n printf(\"MBTB_JointR::computeEquivalentForces GA:\\n\");\n GA.display();\n#endif\n SiconosVector ML_A_abs(3);\n SiconosVector GA_FL(3);\n cross_product(GA,FL,GA_FL);\n ML_A_abs=ML_G_abs - GA_FL;\n#ifdef MBTB_JOINTR_DEBUG\n printf(\"MBTB_JointR::computeEquivalentForces GA_FL:\\n\");\n GA_FL.display();\n printf(\"MBTB_JointR::computeEquivalentForces ML_A_abs :\\n\");\n ML_A_abs.display();\n#endif\n double normAB=1.0/AB.norm2();\n double e0_x =normAB*AB.getValue(0);\n double e0_y =normAB*AB.getValue(1);\n double e0_z =normAB*AB.getValue(2);\n#ifdef MBTB_JOINTR_DEBUG\n printf(\"MBTB_JointR::computeEquivalentForces e0_x . ML_A_abs (must be zero):%e\\n\",\n e0_x*ML_A_abs.getValue(0)+e0_y*ML_A_abs.getValue(1)+e0_z*ML_A_abs.getValue(2));\n#endif\n double e1_x,e1_y,e1_z,e2_x,e2_y,e2_z;\n int info = orthoBaseFromVector(&e0_x,&e0_y,&e0_z,&e1_x,&e1_y,&e1_z,&e2_x,&e2_y,&e2_z);\n if(info)\n {\n std::cout << \"something wrong happened in orthoBaseFromVector\"<zero();\n //FL= F1+F2.\n _M->setValue(0,0,1);\n _M->setValue(0,3,1);\n _M->setValue(1,1,1);\n _M->setValue(1,4,1);\n _M->setValue(2,2,1);\n _M->setValue(2,5,1);\n /*F1.e0=F2.e0*/\n _M->setValue(3,0,e0_x);\n _M->setValue(3,1,e0_y);\n _M->setValue(3,2,e0_z);\n _M->setValue(3,3,-e0_x);\n _M->setValue(3,4,-e0_y);\n _M->setValue(3,5,-e0_z);\n\n /*\n * F2x BAx F2y*BAz-F2z*BAy\n * FB/\\BA . ei=F2y/\\BAy.ei=F2z*BAx-F2x*BAz .ei = F2x*(BAy *ei_z-BAz*ei_y)+F2y*(BAz*ei_x-BAx*ei_z)+F2z*(BAx*ei_y-BAy*ei_x)\n * F2z BAz F2x*BAy-F2y*BAx\n *\n */\n _M->setValue(4,3,-AB.getValue(1) *e1_z+AB.getValue(2)*e1_y);\n _M->setValue(4,4,-AB.getValue(2) *e1_x+AB.getValue(0)*e1_z);\n _M->setValue(4,5,-AB.getValue(0) *e1_y+AB.getValue(1)*e1_x);\n\n _M->setValue(5,3,-AB.getValue(1) *e2_z+AB.getValue(2)*e2_y);\n _M->setValue(5,4,-AB.getValue(2) *e2_x+AB.getValue(0)*e2_z);\n _M->setValue(5,5,-AB.getValue(0) *e2_y+AB.getValue(1)*e2_x);\n#ifdef MBTB_JOINTR_DEBUG\n printf(\"MBTB_JointR::computeEquivalentForces the sytem M is:\\n\");\n _M->display();\n#endif\n _F->setValue(0,FL.getValue(0));\n _F->setValue(1,FL.getValue(1));\n _F->setValue(2,FL.getValue(2));\n _F->setValue(3,0);\n _F->setValue(4,e1_x * ML_A_abs.getValue(0) + e1_y * ML_A_abs.getValue(1) +e1_z * ML_A_abs.getValue(2));\n _F->setValue(5,e2_x * ML_A_abs.getValue(0) + e2_y * ML_A_abs.getValue(1) +e2_z * ML_A_abs.getValue(2));\n#ifdef MBTB_JOINTR_DEBUG\n printf(\"MBTB_JointR::computeEquivalentForces Mx=b, b:\");\n _F->display();\n#endif\n\n /*Solve the system.*/\n try\n {\n _M->PLUFactorizationInPlace();\n _M->PLUSolve(*_F);\n#ifdef MBTB_JOINTR_DEBUG\n printf(\"MBTB_JointR::computeEquivalentForces Forces equivalent:\");\n _F->display();\n printf(\"MBTB_JointR::computeEquivalentForces checking ML_G_abs = MF1_G+MF2_G:\");\n SiconosVector Maux1(3),Maux2(3),F1(3),F2(3),GC1(3),GC2(3),dif(3);\n F1.setValue(0,_F->getValue(0));\n F1.setValue(1,_F->getValue(1));\n F1.setValue(2,_F->getValue(2));\n F2.setValue(0,_F->getValue(3));\n F2.setValue(1,_F->getValue(4));\n F2.setValue(2,_F->getValue(5));\n //FL=F1+F2\n dif = FL-F1-F2;\n printf(\"MBTB_JointR::computeEquivalentForces FL-F1-F2(must be zero):\\n\");\n dif.display();\n //MF1_G=F1/\\C1G\n cross_product(GA,F1,Maux1);\n ::boost::math::quaternion quatG0C2(0,_G0C2->getValue(0),_G0C2->getValue(1),_G0C2->getValue(2));\n quatbuff = quattrf*quatG0C2*cquattrf;\n SiconosVector GB(3);\n GB.setValue(0,quatbuff.R_component_2());\n GB.setValue(1,quatbuff.R_component_3());\n GB.setValue(2,quatbuff.R_component_4());\n cross_product(GB,F2,Maux2);\n dif=Maux1+Maux2;\n printf(\"MBTB_JointR::computeEquivalentForces momentum (must be ML_G_abs):\\n\");\n dif.display();\n dif=Maux1+Maux2 - ML_G_abs;\n printf(\"MBTB_JointR::computeEquivalentForces dif momentum(must be zero):\\n\");\n dif.display();\n#endif\n }\n catch(const std::exception& e)\n {\n printf(\"MBTB_JointR: exception caught.\\n\");\n }\n\n\n}\n", "meta": {"hexsha": "28f69b2d03bc9ca2cc843c173e243d687296a1ea", "size": 6517, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "mechanisms/src/MBTB/MBTB_JointR.cpp", "max_stars_repo_name": "fperignon/sandbox", "max_stars_repo_head_hexsha": "649f09d6db7bbd84c2418de74eb9453c0131f070", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "mechanisms/src/MBTB/MBTB_JointR.cpp", "max_issues_repo_name": "fperignon/sandbox", "max_issues_repo_head_hexsha": "649f09d6db7bbd84c2418de74eb9453c0131f070", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "mechanisms/src/MBTB/MBTB_JointR.cpp", "max_forks_repo_name": "fperignon/sandbox", "max_forks_repo_head_hexsha": "649f09d6db7bbd84c2418de74eb9453c0131f070", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.9460784314, "max_line_length": 123, "alphanum_fraction": 0.6746969464, "num_tokens": 2467, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926665855647395, "lm_q2_score": 0.3812195803163617, "lm_q1q2_score": 0.2259361070165211}} {"text": "#ifndef STAN_MATH_PRIM_SCAL_PROB_FRECHET_LPDF_HPP\n#define STAN_MATH_PRIM_SCAL_PROB_FRECHET_LPDF_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace stan {\nnamespace math {\n\n// Frechet(y|alpha, sigma) [y > 0; alpha > 0; sigma > 0]\n// FIXME: document\ntemplate \ntypename return_type::type frechet_lpdf(\n const T_y& y, const T_shape& alpha, const T_scale& sigma) {\n static const char* function = \"frechet_lpdf\";\n typedef typename stan::partials_return_type::type\n T_partials_return;\n\n using std::log;\n\n if (size_zero(y, alpha, sigma))\n return 0.0;\n\n T_partials_return logp(0.0);\n check_positive(function, \"Random variable\", y);\n check_positive_finite(function, \"Shape parameter\", alpha);\n check_positive_finite(function, \"Scale parameter\", sigma);\n check_consistent_sizes(function, \"Random variable\", y, \"Shape parameter\",\n alpha, \"Scale parameter\", sigma);\n\n if (!include_summand::value)\n return 0.0;\n\n scalar_seq_view y_vec(y);\n scalar_seq_view alpha_vec(alpha);\n scalar_seq_view sigma_vec(sigma);\n size_t N = max_size(y, alpha, sigma);\n\n VectorBuilder::value, T_partials_return,\n T_shape>\n log_alpha(length(alpha));\n for (size_t i = 0; i < length(alpha); i++)\n if (include_summand::value)\n log_alpha[i] = log(value_of(alpha_vec[i]));\n\n VectorBuilder::value, T_partials_return,\n T_y>\n log_y(length(y));\n for (size_t i = 0; i < length(y); i++)\n if (include_summand::value)\n log_y[i] = log(value_of(y_vec[i]));\n\n VectorBuilder::value,\n T_partials_return, T_scale>\n log_sigma(length(sigma));\n for (size_t i = 0; i < length(sigma); i++)\n if (include_summand::value)\n log_sigma[i] = log(value_of(sigma_vec[i]));\n\n VectorBuilder::value,\n T_partials_return, T_y>\n inv_y(length(y));\n for (size_t i = 0; i < length(y); i++)\n if (include_summand::value)\n inv_y[i] = 1.0 / value_of(y_vec[i]);\n\n VectorBuilder::value,\n T_partials_return, T_y, T_shape, T_scale>\n sigma_div_y_pow_alpha(N);\n for (size_t i = 0; i < N; i++)\n if (include_summand::value) {\n const T_partials_return alpha_dbl = value_of(alpha_vec[i]);\n sigma_div_y_pow_alpha[i]\n = pow(inv_y[i] * value_of(sigma_vec[i]), alpha_dbl);\n }\n\n operands_and_partials ops_partials(y, alpha, sigma);\n for (size_t n = 0; n < N; n++) {\n const T_partials_return alpha_dbl = value_of(alpha_vec[n]);\n if (include_summand::value)\n logp += log_alpha[n];\n if (include_summand::value)\n logp -= (alpha_dbl + 1.0) * log_y[n];\n if (include_summand::value)\n logp += alpha_dbl * log_sigma[n];\n if (include_summand::value)\n logp -= sigma_div_y_pow_alpha[n];\n\n if (!is_constant_struct::value) {\n const T_partials_return inv_y_dbl = value_of(inv_y[n]);\n ops_partials.edge1_.partials_[n]\n += -(alpha_dbl + 1.0) * inv_y_dbl\n + alpha_dbl * sigma_div_y_pow_alpha[n] * inv_y_dbl;\n }\n if (!is_constant_struct::value)\n ops_partials.edge2_.partials_[n]\n += 1.0 / alpha_dbl\n + (1.0 - sigma_div_y_pow_alpha[n]) * (log_sigma[n] - log_y[n]);\n if (!is_constant_struct::value)\n ops_partials.edge3_.partials_[n] += alpha_dbl / value_of(sigma_vec[n])\n * (1 - sigma_div_y_pow_alpha[n]);\n }\n return ops_partials.build(logp);\n}\n\ntemplate \ninline typename return_type::type frechet_lpdf(\n const T_y& y, const T_shape& alpha, const T_scale& sigma) {\n return frechet_lpdf(y, alpha, sigma);\n}\n\n} // namespace math\n} // namespace stan\n#endif\n", "meta": {"hexsha": "0b1ab5e96ab9a081c739ac81de3319efc077d05e", "size": 5394, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "stan/math/prim/scal/prob/frechet_lpdf.hpp", "max_stars_repo_name": "jrmie/math", "max_stars_repo_head_hexsha": "2850ec262181075a5843968e805dc9ad1654e069", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "stan/math/prim/scal/prob/frechet_lpdf.hpp", "max_issues_repo_name": "jrmie/math", "max_issues_repo_head_hexsha": "2850ec262181075a5843968e805dc9ad1654e069", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "stan/math/prim/scal/prob/frechet_lpdf.hpp", "max_forks_repo_name": "jrmie/math", "max_forks_repo_head_hexsha": "2850ec262181075a5843968e805dc9ad1654e069", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.2537313433, "max_line_length": 80, "alphanum_fraction": 0.6928068224, "num_tokens": 1540, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.22593610416899762}} {"text": "/**\n * Parrot Drones Awesome Video Viewer\n * Desktop application\n *\n * Copyright (c) 2018 Parrot Drones SAS\n * Copyright (c) 2016 Aurelien Barre\n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions are met:\n * * Redistributions of source code must retain the above copyright\n * notice, this list of conditions and the following disclaimer.\n * * Redistributions in binary form must reproduce the above copyright\n * notice, this list of conditions and the following disclaimer in the\n * documentation and/or other materials provided with the distribution.\n * * Neither the name of the copyright holders nor the names of its\n * contributors may be used to endorse or promote products derived from\n * this software without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n * ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS BE LIABLE FOR ANY\n * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n */\n\n#include \n\n#include \"pdraw_desktop.h\"\n\n\nvoid pdraw_desktop_view_create_matrices(struct pdraw_desktop *self,\n\t\t\t\t\tunsigned int width,\n\t\t\t\t\tunsigned int height,\n\t\t\t\t\tfloat *view_mat,\n\t\t\t\t\tfloat *proj_mat,\n\t\t\t\t\tfloat near,\n\t\t\t\t\tfloat far)\n{\n\tfloat w = 1.f;\n\tfloat h = (float)width / (float)height;\n\tfloat a = (far + near) / (far - near);\n\tfloat b = -((2 * far * near) / (far - near));\n\n\tEigen::Matrix4f p_mat;\n\tp_mat << w, 0, 0, 0, 0, h, 0, 0, 0, 0, a, b, 0, 0, 1, 0;\n\n\tEigen::Matrix4f v_mat = Eigen::Matrix4f::Identity();\n\n\tunsigned int i, j;\n\tfor (i = 0; i < 4; i++) {\n\t\tfor (j = 0; j < 4; j++)\n\t\t\tview_mat[j * 4 + i] = v_mat(i, j);\n\t}\n\tfor (i = 0; i < 4; i++) {\n\t\tfor (j = 0; j < 4; j++)\n\t\t\tproj_mat[j * 4 + i] = p_mat(i, j);\n\t}\n}\n", "meta": {"hexsha": "3f01b0885726ffb25fe7b7ed9b6ebb9bb88258c0", "size": 2393, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "apps/pdraw_desktop/pdraw_desktop_view.cpp", "max_stars_repo_name": "Parrot-Developers/pdraw", "max_stars_repo_head_hexsha": "7d0983e88291d9224ec79be2bcf3ad045bb09044", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 15.0, "max_stars_repo_stars_event_min_datetime": "2019-05-06T17:01:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-18T08:59:42.000Z", "max_issues_repo_path": "apps/pdraw_desktop/pdraw_desktop_view.cpp", "max_issues_repo_name": "Parrot-Developers/pdraw", "max_issues_repo_head_hexsha": "7d0983e88291d9224ec79be2bcf3ad045bb09044", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2021-03-18T14:32:48.000Z", "max_issues_repo_issues_event_max_datetime": "2021-03-18T14:32:48.000Z", "max_forks_repo_path": "apps/pdraw_desktop/pdraw_desktop_view.cpp", "max_forks_repo_name": "Parrot-Developers/pdraw", "max_forks_repo_head_hexsha": "7d0983e88291d9224ec79be2bcf3ad045bb09044", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 10.0, "max_forks_repo_forks_event_min_datetime": "2019-07-18T19:35:59.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-14T13:41:33.000Z", "avg_line_length": 37.390625, "max_line_length": 80, "alphanum_fraction": 0.6974508985, "num_tokens": 626, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813031051514763, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.22590486564194934}} {"text": "#include \"QAlg/QITE/QITE.h\"\n#include \"Core/QuantumMachine/QuantumMachineFactory.h\"\n#include \n\nQPANDA_BEGIN\n\nQITE::QITE()\n{\n}\n\nint QITE::exec()\n{\n initEnvironment();\n srand((int)time(0));\n\n auto tmp_ansatz = m_ansatz;\n for (int t = 0; t < m_upthrow_num; t++)\n {\n for (int i = 0; i < m_iter_num; i++)\n {\n calcParaA();\n calcParaC();\n\n Eigen::MatrixXd A_inverse = pseudoinverse(m_A);\n Eigen::VectorXd theta_dot = -A_inverse * m_C;\n double delta_k = m_delta_tau * pow(m_Q, i);\n \n for (int j = 0; j < m_theta_index_vec.size(); j++)\n {\n auto t_j = m_theta_index_vec[j];\n double r = (rand() / double(RAND_MAX));\n if (m_update_mode == UpdateMode::GD_VALUE)\n {\n m_ansatz[t_j].theta += theta_dot[j];\n }\n else if(m_update_mode == UpdateMode::GD_DIRECTION)\n {\n if (theta_dot[j] > 0)\n {\n m_ansatz[t_j].theta -= delta_k * r;\n }\n else\n {\n m_ansatz[t_j].theta += delta_k * r;\n }\n }\n else\n {\n return -1;\n }\n\n int n_period = int(m_ansatz[t_j].theta / (2 * PI));\n if (m_ansatz[t_j].theta < 0)\n {\n m_ansatz[t_j].theta += (n_period + 1) * 2 * PI;\n }\n else\n {\n m_ansatz[t_j].theta -= n_period * 2 * PI;\n }\n }\n\n double tmp_exp = getExpectation(m_ansatz);\n m_log_writer << tmp_exp << std::endl;\n\n if (tmp_exp < m_expectation)\n {\n m_expectation = tmp_exp;\n m_best_ansatz = m_ansatz;\n }\n }\n }\n\n std::fstream para_out;\n para_out.open(m_log_file + \"_para.txt\", std::ios::out);\n para_out << \"Last_para:\" << std::endl;\n for (int i = 0; i < m_theta_index_vec.size(); i++)\n {\n para_out << m_ansatz[m_theta_index_vec[i]].theta << \", \";\n }\n\n para_out << std::endl << \"Best_para:\" << std::endl;\n for (int i = 0; i < m_theta_index_vec.size(); i++)\n {\n para_out << m_best_ansatz[m_theta_index_vec[i]].theta << \", \";\n }\n para_out.close();\n m_log_writer.close();\n\n m_ansatz = tmp_ansatz;\n //m_machine->Free_Qubits(m_qlist);\n\n return 0;\n}\n\nprob_tuple QITE::getResult()\n{\n QProg prog;\n prog << constructCircuit(m_best_ansatz);\n m_machine->directlyRun(prog);\n\n auto temp = dynamic_cast(m_machine.get());\n if (nullptr == temp)\n {\n QCERR(\"m_machine is not ideal machine\");\n throw std::runtime_error(\"m_machine is not ideal machine\");\n }\n auto measure_qubits = m_qlist;\n measure_qubits.pop_back();\n auto result = temp->PMeasure(measure_qubits, -1);\n\n std::fstream fout;\n fout.open(m_log_file + \"_measure.txt\", std::ios::out);\n\n std::cout << \"Measure result: \" << std::endl;\n for (auto& i : result)\n {\n if (fabs(i.second) < 1e-4)\n {\n break;\n }\n std::cout << i.first << \" \" << i.second << std::endl;\n fout << i.first << \" \" << i.second << std::endl;\n }\n\n fout.close();\n\n return result;\n}\n\nvoid QITE::initEnvironment()\n{\n if (!m_log_file.empty())\n {\n m_log_writer.open(m_log_file, std::ios::out);\n }\n else\n {\n m_log_writer.open(\"test.log\", std::ios::out);\n }\n\n m_machine.reset(QuantumMachineFactory::GetFactoryInstance()\n .CreateByType(m_quantum_machine_type));\n m_machine->init();\n\n m_theta_index_vec.clear();\n m_hamiltonian = m_pauli.toHamiltonian();\n auto qubits_num = 0;\n for (int i = 0; i < m_ansatz.size(); i++)\n {\n if (m_ansatz[i].target > qubits_num)\n {\n qubits_num = m_ansatz[i].target;\n }\n\n if (m_ansatz[i].control > qubits_num)\n {\n qubits_num = m_ansatz[i].control;\n }\n\n if (m_ansatz[i].type == AnsatzGateType::AGT_RX ||\n m_ansatz[i].type == AnsatzGateType::AGT_RY ||\n m_ansatz[i].type == AnsatzGateType::AGT_RZ)\n {\n m_theta_index_vec.push_back(i);\n }\n }\n qubits_num++;\n\n m_best_ansatz = m_ansatz;\n m_qlist = m_machine->allocateQubits(qubits_num+1);\n m_expectation = getExpectation(m_ansatz);\n}\n\ndouble QITE::getExpectation(const std::vector& ansatz)\n{\n QCircuit circuit = constructCircuit(ansatz);\n double expectation = 0.0;\n for (size_t i = 0; i < m_hamiltonian.size(); i++)\n {\n expectation += getExpectationOneTerm(circuit, m_hamiltonian[i]);\n }\n\n return expectation;\n}\n\ndouble QITE::getExpectationOneTerm( QCircuit c, const QHamiltonianItem& component)\n{\n if (component.first.empty())\n {\n return component.second;\n }\n\n QProg prog;\n prog << c;\n \n for (auto iter : component.first)\n {\n if (iter.second == 'X')\n {\n prog << H(m_qlist[iter.first]);\n }\n else if (iter.second == 'Y')\n {\n prog << RX(m_qlist[iter.first], PI / 2);\n }\n }\n\n m_machine->directlyRun(prog);\n double expectation = 0;\n\n auto temp = dynamic_cast(m_machine.get());\n if (nullptr == temp)\n {\n QCERR(\"m_machine is not ideal machine\");\n throw std::runtime_error(\"m_machine is not ideal machine\");\n }\n auto measure_qubit = m_qlist;\n measure_qubit.pop_back();\n auto result = temp->PMeasure(measure_qubit, -1);\n\n for (auto i = 0u; i < result.size(); i++)\n {\n if (ParityCheck(result[i].first, component.first))\n {\n expectation -= result[i].second;\n }\n else\n {\n expectation += result[i].second;\n }\n }\n\n return expectation * component.second;\n}\n\nbool QITE::ParityCheck(size_t state, const QTerm& paulis) const\n{\n size_t check = 0;\n for (auto iter = paulis.begin(); iter != paulis.end(); iter++)\n {\n auto value = state >> iter->first;\n if ((value % 2) == 1)\n {\n check++;\n }\n }\n\n return 1 == check % 2;\n}\n\nvoid QITE::calcParaA()\n{\n int theta_num = m_theta_index_vec.size();\n m_A = Eigen::MatrixXd(theta_num, theta_num);\n for (int i = 0; i < theta_num; i++)\n {\n for (int j = 0; j < theta_num; j++)\n {\n auto t_i = m_theta_index_vec[i];\n auto t_j = m_theta_index_vec[j];\n if (i > j)\n {\n m_A.row(i)[j] = m_A.row(j)[i];\n continue;\n }\n\n int k = getAnsatzDerivativeParaNum(t_i);\n int l = getAnsatzDerivativeParaNum(t_j);\n\n double sum = 0;\n for (int p = 0; p < k; p++)\n {\n for (int q = 0; q < l; q++)\n {\n auto f_ik = getAnsatzDerivativePara(t_i, p);\n auto f_jl = getAnsatzDerivativePara(t_j, q);\n auto ff = complexDagger(f_ik) * f_jl;\n auto die_len = std::abs(ff);\n\n if ((i ==j) &&(p == q))\n {\n // Identity\n sum += die_len;\n }\n else\n {\n auto phase = std::arg(ff);\n auto value = calcSubCircuit(\n t_i,\n t_j,\n phase,\n getAnsatzDerivativeCircuit(t_i, p),\n getAnsatzDerivativeCircuit(t_j, q));\n\n sum += die_len * value;\n }\n }\n }\n\n m_A.row(i)[j] = sum;\n }\n }\n}\n\nvoid QITE::calcParaC()\n{\n int theta_num = m_theta_index_vec.size();\n m_C = Eigen::VectorXd(theta_num);\n for (int i = 0; i < theta_num; i++)\n {\n auto t_i = m_theta_index_vec[i];\n double sum = 0;\n int k = getAnsatzDerivativeParaNum(t_i);\n int l = getHamiltonianItemNum();\n for (int p = 0; p < k; p++)\n {\n for (int q = 0; q < l; q++)\n {\n auto f_ik = getAnsatzDerivativePara(t_i, p);\n auto f_l = getHamiltonianItemPara(q);\n auto ff = complexDagger(f_ik) * f_l;\n auto die_len = std::abs(ff);\n auto phase = std::arg(ff);\n\n auto value = calcSubCircuit(\n t_i,\n m_ansatz.size(),\n phase,\n getAnsatzDerivativeCircuit(t_i, p),\n getHamiltonianItemCircuit(q));\n\n sum += die_len * value;\n }\n }\n m_C[i] = sum;\n }\n m_C *= -1;\n}\n\nQCircuit QITE::constructCircuit(const std::vector& ansatz)\n{\n QCircuit circuit;\n for (int i = 0; i < ansatz.size(); i++)\n {\n circuit << convertAnsatzToCircuit(ansatz[i]);\n }\n\n return circuit;\n}\n\nstd::complex QITE::complexDagger(std::complex& value)\n{\n return std::complex(value.real(), -value.imag());\n}\n\nint QITE::getAnsatzDerivativeParaNum(int i)\n{\n if (i < 0 ||\n i >= m_ansatz.size())\n {\n QCERR_AND_THROW_ERRSTR(std::runtime_error, \n \"bad para of i in getAnsatzDerivativeParaNum\");\n }\n\n auto& u = m_ansatz[i];\n\n return u.control == -1 ? 1 : 2;\n}\n\nint QITE::getHamiltonianItemNum()\n{\n return m_hamiltonian.size();\n}\n\nstd::complex QITE::getAnsatzDerivativePara(int i, int cnt)\n{\n if (i < 0 ||\n i >= m_ansatz.size())\n {\n QCERR_AND_THROW_ERRSTR(std::runtime_error,\n \"bad para of i in getAnsatzDerivativePara\");\n }\n\n auto& u = m_ansatz[i];\n\n if (u.control != -1)\n {\n if (cnt < 0 ||\n cnt > 1)\n {\n QCERR_AND_THROW_ERRSTR(std::runtime_error,\n \"bad para of cnt in getAnsatzDerivativePara\");\n }\n\n return cnt == 0 ? std::complex(0, -0.25) :\n std::complex(0, 0.25);\n }\n else\n {\n if (cnt != 0)\n {\n QCERR_AND_THROW_ERRSTR(std::runtime_error,\n \"bad para of cnt in getAnsatzDerivativePara\");\n }\n\n return std::complex(0, -0.5);\n }\n}\n\ndouble QITE::getHamiltonianItemPara(int i)\n{\n if (i < 0 ||\n i >= m_hamiltonian.size())\n {\n QCERR_AND_THROW_ERRSTR(std::runtime_error,\n \"bad para of i in getHamiltonianItemPara\");\n }\n\n return m_hamiltonian[i].second;\n}\n\nQCircuit QITE::getAnsatzDerivativeCircuit(int i, int cnt)\n{\n if (i < 0 ||\n i >= m_ansatz.size())\n {\n QCERR_AND_THROW_ERRSTR(std::runtime_error,\n \"bad para of i in getAnsatzDerivativePara\");\n }\n\n QCircuit sub_cir;\n int anc = m_qlist.size() - 1;\n\n auto& u = m_ansatz[i];\n\n if (u.control != -1)\n {\n if (cnt < 0 ||\n cnt > 1)\n {\n QCERR_AND_THROW_ERRSTR(std::runtime_error,\n \"bad para of cnt in getAnsatzDerivativePara\");\n }\n\n sub_cir << (cnt == 0 ? I(m_qlist[u.control]) : Z(m_qlist[u.control]));\n }\n\n switch (u.type)\n {\n case AnsatzGateType::AGT_RX:\n sub_cir << X(m_qlist[u.target]);\n break;\n case AnsatzGateType::AGT_RY:\n sub_cir << Y(m_qlist[u.target]);\n break;\n case AnsatzGateType::AGT_RZ:\n sub_cir << Z(m_qlist[u.target]);\n break;\n }\n\n return sub_cir.control({ m_qlist[anc] });\n}\n\nQCircuit QITE::getHamiltonianItemCircuit(int cnt)\n{\n if (cnt < 0 ||\n cnt >= m_hamiltonian.size())\n {\n QCERR_AND_THROW_ERRSTR(std::runtime_error,\n \"bad para of cnt in getHamiltonianItemPara\");\n }\n\n QCircuit cir;\n auto term = m_hamiltonian[cnt].first;\n\n for (auto& iter : term)\n {\n switch (iter.second)\n {\n case 'X':\n cir << X(m_qlist[iter.first]);\n break;\n case 'Y':\n cir << Y(m_qlist[iter.first]);\n break;\n case 'Z':\n cir << Z(m_qlist[iter.first]);\n break;\n default:\n cir << I(m_qlist[iter.first]);\n break;\n }\n }\n\n return cir.control({ m_qlist[m_qlist.size() - 1] });\n}\n\ndouble QITE::calcSubCircuit(\n int index1, \n int index2, \n double theta, \n QCircuit cir1, \n QCircuit cir2)\n{\n auto anc_quibit = m_qlist[m_qlist.size() - 1];\n\n QProg prog;\n for (int i = 0; i < index1; i++)\n {\n prog << convertAnsatzToCircuit(m_ansatz[i]);\n }\n\n prog << H(anc_quibit)\n << U1(anc_quibit, theta)\n << X(anc_quibit)\n << cir1\n << X(anc_quibit);\n\n for (int i = index1; i < index2; i++)\n {\n prog << convertAnsatzToCircuit(m_ansatz[i]);\n }\n\n prog << cir2\n << H(anc_quibit);\n\n auto temp = dynamic_cast(m_machine.get());\n auto result = temp->probRunDict(prog, { anc_quibit }, -1);\n\n return 2 * result[\"0\"] - 1;\n}\n\nQCircuit QITE::convertAnsatzToCircuit(const AnsatzGate& u)\n{\n if (u.target < 0 || u.target >= m_qlist.size())\n {\n QCERR_AND_THROW_ERRSTR(std::runtime_error,\n \"bad para of target in convertAnsatzToCircuit\");\n }\n\n QCircuit sub_cir;\n switch (u.type)\n {\n case AnsatzGateType::AGT_NOT:\n sub_cir << X(m_qlist[u.target]);\n break;\n case AnsatzGateType::AGT_H:\n sub_cir << H(m_qlist[u.target]);\n break;\n case AnsatzGateType::AGT_RX:\n sub_cir << RX(m_qlist[u.target], u.theta);\n break;\n case AnsatzGateType::AGT_RY:\n sub_cir << RY(m_qlist[u.target], u.theta);\n break;\n case AnsatzGateType::AGT_RZ:\n sub_cir << RZ(m_qlist[u.target], u.theta);\n break;\n default:\n break;\n }\n\n if (u.control != -1)\n {\n sub_cir.setControl({ m_qlist[u.control] });\n }\n\n return sub_cir;\n}\n\nEigen::MatrixXd QITE::pseudoinverse(Eigen::MatrixXd matrix)\n{\n auto svd = matrix.jacobiSvd(Eigen::ComputeFullU | Eigen::ComputeFullV);\n const auto& singularValues = svd.singularValues();\n Eigen::Matrix \n singularValuesInv(matrix.cols(), matrix.rows());\n singularValuesInv.setZero();\n double pinvtoler = m_arbitary_cofficient;\n for (unsigned int i = 0; i < singularValues.size(); ++i) {\n if (singularValues(i) > pinvtoler)\n singularValuesInv(i, i) = 1.0 / singularValues(i);\n else\n singularValuesInv(i, i) = 0.0;\n }\n Eigen::MatrixXd pinvmat = \n svd.matrixV() * singularValuesInv * svd.matrixU().transpose();\n\n return pinvmat;\n}\n\nprob_tuple qite(\n const PauliOperator& h, \n const std::vector& ansatz_gate, \n size_t iter_num, \n std::string log_file,\n QITE::UpdateMode mode, \n size_t up_throw_num, \n double delta_tau, \n double convergence_factor_Q, \n double arbitary_cofficient, \n QMachineType type)\n{\n QITE alg;\n alg.setHamiltonian(h);\n alg.setAnsatzGate(ansatz_gate);\n alg.setIterNum(iter_num);\n alg.setLogFile(log_file);\n alg.setParaUpdateMode(mode);\n alg.setUpthrowNum(up_throw_num);\n alg.setDeltaTau(delta_tau);\n alg.setConvergenceFactorQ(convergence_factor_Q);\n alg.setArbitaryCofficient(arbitary_cofficient);\n alg.setQuantumMachineType(type);\n\n if (alg.exec() != 0)\n {\n return prob_tuple();\n }\n\n return alg.getResult();\n}\n\nQPANDA_END\n", "meta": {"hexsha": "c784593ca393b2b12263cc0625f83a35d3343913", "size": 15757, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "QAlg/QITE/QITE.cpp", "max_stars_repo_name": "QianJianhua1/QPanda-2", "max_stars_repo_head_hexsha": "a13c7b733031b1d0007dceaf1dae6ad447bb969c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 631.0, "max_stars_repo_stars_event_min_datetime": "2019-01-21T01:33:38.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T07:33:04.000Z", "max_issues_repo_path": "QAlg/QITE/QITE.cpp", "max_issues_repo_name": "yinxx/QPanda-2", "max_issues_repo_head_hexsha": "c70c4117a90978916b871424e204c5159f645642", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 24.0, "max_issues_repo_issues_event_min_datetime": "2019-02-01T10:12:45.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-02T01:49:57.000Z", "max_forks_repo_path": "QAlg/QITE/QITE.cpp", "max_forks_repo_name": "yinxx/QPanda-2", "max_forks_repo_head_hexsha": "c70c4117a90978916b871424e204c5159f645642", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 80.0, "max_forks_repo_forks_event_min_datetime": "2019-01-21T03:04:20.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-29T15:38:45.000Z", "avg_line_length": 25.2112, "max_line_length": 82, "alphanum_fraction": 0.522688329, "num_tokens": 4222, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.3886180267058489, "lm_q1q2_score": 0.22590486000420423}} {"text": "// --------------------------------------------------------------------------\n// OpenMS -- Open-Source Mass Spectrometry\n// --------------------------------------------------------------------------\n// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,\n// ETH Zurich, and Freie Universitaet Berlin 2002-2013.\n//\n// This software is released under a three-clause BSD license:\n// * Redistributions of source code must retain the above copyright\n// notice, this list of conditions and the following disclaimer.\n// * Redistributions in binary form must reproduce the above copyright\n// notice, this list of conditions and the following disclaimer in the\n// documentation and/or other materials provided with the distribution.\n// * Neither the name of any author or any participating institution\n// may be used to endorse or promote products derived from this software\n// without specific prior written permission.\n// For a full list of authors, refer to the file AUTHORS.\n// --------------------------------------------------------------------------\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n// ARE DISCLAIMED. IN NO EVENT SHALL ANY OF THE AUTHORS OR THE CONTRIBUTING\n// INSTITUTIONS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\n// OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\n// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\n// OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF\n// ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n//\n// --------------------------------------------------------------------------\n// $Maintainer: Alexandra Zerck $\n// $Authors: $\n// --------------------------------------------------------------------------\n//\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#ifdef DEBUG_DECONV\n#include \n#include \n#endif\n\nnamespace OpenMS\n{\n\n\n const DoubleReal OptimizePeakDeconvolution::dist_ = 1.003;\n namespace OptimizationFunctions\n {\n // Evaluation of the target function for nonlinear optimization.\n Int residualDC(const gsl_vector * x, void * params, gsl_vector * f)\n {\n // According to the gsl conventions, x contains the parameters to be optimized.\n // The first two entries are the left and right width, respectively.They are equal\n // for all peaks. Then the height and position of all peaks are stored.\n //\n // Params might contain any additional parameters. We handle these using class members\n // instead.\n // The vector f is supposed to contain the result when we return from this function.\n // Note: GSL wants the values for each data point i as one component of the results vector\n std::vector & signal = static_cast(params)->signal;\n std::vector & positions = static_cast(params)->positions;\n std::vector & peaks = static_cast(params)->peaks;\n OptimizationFunctions::PenaltyFactorsIntensity & penalties = static_cast(params)->penalties;\n Int charge = static_cast(params)->charge;\n DoubleReal leftwidth = gsl_vector_get(x, 0);\n DoubleReal rightwidth = gsl_vector_get(x, 1);\n //DoubleReal posP1 = gsl_vector_get(x,2);\n\n // iterate over all points of the signal\n for (Size current_point = 0; current_point < positions.size(); current_point++)\n {\n DoubleReal computed_signal = 0.;\n DoubleReal current_position = positions[current_point];\n DoubleReal experimental_signal = signal[current_point];\n\n //iterate over all peaks\n for (Size current_peak = 0; current_peak < peaks.size(); current_peak++)\n {\n //Store the current parameters for this peak\n DoubleReal p_height = gsl_vector_get(x, 2 + 2 * current_peak);\n DoubleReal p_position = gsl_vector_get(x, 2 + 2 * current_peak + 1);\n DoubleReal p_width = (current_position <= p_position) ? leftwidth : rightwidth;\n\n //is it a Lorentz or a Sech - Peak?\n if (peaks[current_peak].type == PeakShape::LORENTZ_PEAK)\n {\n computed_signal += p_height / (1. + pow(p_width * (current_position - p_position), 2));\n }\n else // It's a Sech - Peak\n {\n computed_signal += p_height / pow(cosh(p_width * (current_position - p_position)), 2);\n }\n }\n gsl_vector_set(f, current_point, computed_signal - experimental_signal);\n }\n\n // penalties : especially negative heights have to be penalised\n DoubleReal penalty = 0.;\n\n DoubleReal penalty_pos = penalties.pos;\n DoubleReal penalty_lwidth = penalties.lWidth;\n DoubleReal penalty_rwidth = penalties.rWidth;\n DoubleReal penalty_intensity = penalties.height;\n\n\n //iterate over all peaks again to compute the penalties\n for (Size current_peak = 0; current_peak < peaks.size(); current_peak++)\n {\n DoubleReal p_position = gsl_vector_get(x, 2 + 2 * current_peak + 1);\n if (current_peak < peaks.size() - 1)\n {\n\n DoubleReal next_p_position = gsl_vector_get(x, 2 + 2 * current_peak + 3);\n // if distance between peaks does not match the peptide mass rule\n if (fabs(fabs(p_position - next_p_position) - 1.003 / charge) > 0.05)\n {\n // penalize it\n penalty += penalty_pos * 10000\n * pow(fabs(fabs(p_position - next_p_position) - 1.003 / charge), 2);\n }\n }\n DoubleReal old_position = peaks[current_peak].mz_position;\n DoubleReal old_width_l = peaks[current_peak].left_width;\n DoubleReal old_width_r = peaks[current_peak].right_width;\n DoubleReal old_height = peaks[current_peak].height;\n\n DoubleReal p_width_l = gsl_vector_get(x, 0);\n DoubleReal p_width_r = gsl_vector_get(x, 1);\n DoubleReal p_height = gsl_vector_get(x, 2 + 2 * current_peak);\n\n if (p_height < 1)\n {\n penalty += 100000 * penalty_intensity * pow(fabs(p_height - old_height), 2);\n\n }\n if (p_width_l < 0)\n {\n penalty += penalty_lwidth * peaks.size() * 10000 * pow(fabs(p_width_l - old_width_l), 2);\n }\n else if (p_width_l < 1.5)\n penalty += 10000 * pow(fabs(p_width_l - old_width_l), 2);\n if (p_width_r < 0)\n {\n penalty += penalty_rwidth * peaks.size() * 10000 * pow(fabs(p_width_r - old_width_r), 2);\n }\n else if (p_width_r < 1.5)\n penalty += 10000 * pow(fabs(p_width_r - old_width_r), 2);\n if (fabs(old_position - p_position) > 0.1)\n {\n penalty += 10000 * penalty_pos * pow(fabs(old_position - p_position), 2);\n }\n\n\n\n }\n gsl_vector_set(f, f->size - 1, penalty);\n return GSL_SUCCESS;\n }\n\n /** Compute the Jacobian of the residual, where each row of the matrix corresponds to a\n * point in the data.\n */\n Int jacobianDC(const gsl_vector * x, void * params, gsl_matrix * J)\n {\n // For the conventions on x and params c.f. the commentary in residual()\n //\n // The matrix J is supposed to contain the result when we return from this function.\n // Note: GSL expects the Jacobian as follows:\n // - each row corresponds to one data point\n // - each column corresponds to one parameter\n\n std::vector & positions = static_cast(params)->positions;\n std::vector & peaks = static_cast(params)->peaks;\n OptimizationFunctions::PenaltyFactorsIntensity & penalties = static_cast(params)->penalties;\n Int charge = static_cast(params)->charge;\n\n DoubleReal leftwidth = gsl_vector_get(x, 0);\n DoubleReal rightwidth = gsl_vector_get(x, 1);\n\n\n gsl_matrix_set_zero(J);\n\n\n // iterate over all points of the signal\n for (Size current_point = 0; current_point < positions.size(); current_point++)\n {\n DoubleReal current_position = positions[current_point];\n\n // iterate over all peaks\n for (Size current_peak = 0; current_peak < peaks.size(); current_peak++)\n {\n\n\n //Store the current parameters for this peak\n DoubleReal p_height = gsl_vector_get(x, 2 + 2 * current_peak);\n DoubleReal p_position = gsl_vector_get(x, 2 + 2 * current_peak + 1);\n DoubleReal p_width = (current_position <= p_position) ? leftwidth : rightwidth;\n\n //is it a Lorentz or a Sech - Peak?\n if (peaks[current_peak].type == PeakShape::LORENTZ_PEAK)\n {\n DoubleReal diff = current_position - p_position;\n DoubleReal denom_inv = 1. / (1. + pow(p_width * diff, 2));\n\n DoubleReal ddl_left = (current_position <= p_position)\n ? -2 * p_height * pow(diff, 2) * p_width * pow(denom_inv, 2) :\n 0;\n\n DoubleReal ddl_right = (current_position > p_position)\n ? -2 * p_height * pow(diff, 2) * p_width * pow(denom_inv, 2) :\n 0;\n\n // left and right width are the same for all peaks,\n // the sums of the derivations over all peaks are stored in the first two columns\n gsl_matrix_set(J, current_point, 0, gsl_matrix_get(J, current_point, 0) + ddl_left);\n gsl_matrix_set(J, current_point, 1, gsl_matrix_get(J, current_point, 1) + ddl_right);\n\n DoubleReal ddx0 = 2 * p_height * pow(p_width, 2) * diff * pow(denom_inv, 2);\n\n // partial derivation with respect to intensity\n gsl_matrix_set(J, current_point, 2 + 2 * current_peak, denom_inv);\n\n // partial derivation with respect to the mz-position\n gsl_matrix_set(J, current_point, 2 + 2 * current_peak + 1, ddx0);\n }\n else // It's a Sech - Peak\n {\n DoubleReal diff = current_position - p_position;\n DoubleReal denom_inv = 1. / cosh(p_width * diff);\n\n // The remaining computations are not stable if denom_inv == 0. In that case, we are far away from the peak\n // and can assume that all derivatives vanish\n DoubleReal sinh_term = (fabs(denom_inv) < 1e-6) ? 0.0 : sinh(p_width * diff);\n\n\n DoubleReal ddl_left = (current_position <= p_position)\n ? -2 * p_height * sinh_term * diff * pow(denom_inv, 3) :\n 0;\n DoubleReal ddl_right = (current_position > p_position)\n ? -2 * p_height * sinh_term * diff * pow(denom_inv, 3) :\n 0;\n\n gsl_matrix_set(J, current_point, 0, gsl_matrix_get(J, current_point, 0) + ddl_left);\n gsl_matrix_set(J, current_point, 1, gsl_matrix_get(J, current_point, 1) + ddl_right);\n\n DoubleReal ddx0 = 2 * p_height * p_width * sinh_term * pow(denom_inv, 3);\n\n gsl_matrix_set(J, current_point, 2 + 2 * current_peak, pow(denom_inv, 2));\n gsl_matrix_set(J, current_point, 2 + 2 * current_peak + 1, ddx0);\n }\n }\n }\n\n\n /** Now iterate over all peaks again to compute the\n * penalties.\n */\n\n for (Size current_peak = 0; current_peak < peaks.size(); current_peak++)\n {\n\n\n DoubleReal penalty_p = 0;\n DoubleReal p_position = gsl_vector_get(x, 2 + 2 * current_peak + 1);\n if (current_peak < peaks.size() - 1)\n {\n\n DoubleReal next_p_position = gsl_vector_get(x, 2 + 2 * current_peak + 3);\n // if distance between peaks does not match the peptide mass rule\n if (fabs(fabs(p_position - next_p_position) - 1.003 / charge) > 0.05)\n {\n // penalize it\n penalty_p += penalties.pos * 20000\n * fabs(fabs(p_position - next_p_position) - 1.003 / charge);\n\n }\n }\n // std::cout << \"penalty_p \"< 0.1)\n {\n penalty_p += 10000 * penalties.pos * 2 * fabs(old_position - p_position);\n }\n\n\n\n gsl_matrix_set(J, positions.size(), 2 + 2 * current_peak, 100 * penalty_h);\n gsl_matrix_set(J, positions.size(), 0, 100 * penalty_l);\n gsl_matrix_set(J, positions.size(), 1, 100 * penalty_r);\n gsl_matrix_set(J, positions.size(), 2 + 2 * current_peak + 1, 100 * penalty_p);\n }\n\n return GSL_SUCCESS;\n }\n\n // Driver function for the evaluation of function and jacobian.\n Int evaluateDC(const gsl_vector * x, void * params, gsl_vector * f, gsl_matrix * J)\n {\n residualDC(x, params, f);\n jacobianDC(x, params, J);\n\n return GSL_SUCCESS;\n }\n\n } // namespace OptimizationFunctions\n\n\n\n OptimizePeakDeconvolution::OptimizePeakDeconvolution() :\n DefaultParamHandler(\"OptimizePeakDeconvolution\"), charge_(1)\n {\n\n defaults_.setValue(\"max_iteration\", 10, \"maximal number of iterations for the fitting step\");\n defaults_.setValue(\"eps_abs\", 1e-04, \"if the absolute error gets smaller than this value the fitting is stopped\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"eps_rel\", 1e-04, \"if the relative error gets smaller than this value the fitting is stopped\", ListUtils::create(\"advanced\"));\n\n defaults_.setValue(\"penalties:left_width\", 0.0, \"penalty term for the fitting of the left width:\" \\\n \"If the left width gets too broad or negative during the fitting it can be penalized.\");\n defaults_.setValue(\"penalties:right_width\", 0.0, \"penalty term for the fitting of the right width:\" \\\n \"If the right width gets too broad or negative during the fitting it can be penalized.\");\n defaults_.setValue(\"penalties:height\", 0.0, \"penalty term for the fitting of the intensity:\" \\\n \"If it gets negative during the fitting it can be penalized.\");\n defaults_.setValue(\"penalties:position\", 0.0, \"penalty term for the fitting of the peak position:\" \\\n \"If the position changes more than 0.5Da during the fitting it can be penalized as well as \" \\\n \"discrepancies of the peptide mass rule.\");\n\n defaults_.setValue(\"fwhm_threshold\", 1.0, \"If a peaks is broader than fwhm_threshold, it is assumed that it contains another peaks and an additional peak is added.\");\n\n defaultsToParam_();\n }\n\n void OptimizePeakDeconvolution::updateMembers_()\n {\n penalties_.rWidth = (float)param_.getValue(\"penalties:right_width\");\n penalties_.lWidth = (float)param_.getValue(\"penalties:left_width\");\n penalties_.height = (float)param_.getValue(\"penalties:height\");\n penalties_.pos = (float)param_.getValue(\"penalties:position\");\n\n }\n\n bool OptimizePeakDeconvolution::optimize(std::vector & peaks, Data & data)\n {\n\n if (peaks.empty())\n return true;\n\n\n#ifdef DEBUG_DECONV\n std::cout << \"peaksanzahl:\" << peaks.size();\n std::cout << \"\\tpeaks[0].mz_position:\" << peaks[0].mz_position << std::endl;\n\n for (Size j = 0; j < peaks.size(); ++j)\n {\n std::cout << \"\\tpeaks[j].mz_position:\" << peaks[j].mz_position;\n std::cout << \"\\tpeaks[j].height:\" << peaks[j].height << std::endl;\n std::cout << \"\\tpeaks[j].left_width:\" << peaks[j].left_width;\n std::cout << \"\\tpeaks[j].right_width:\" << peaks[j].right_width << std::endl << std::endl;\n }\n\n for (Size j = 0; j < data.positions.size(); ++j)\n {\n std::cout << \"positions[\" << j << \"]=\" << data.positions[j] << std::endl;\n }\n\n#endif\n\n // the input peaks are stored in a temporary vector\n std::vector temp_shapes = peaks;\n\n\n Size global_peak_number = 0;\n\n DoubleReal min(std::numeric_limits::max());\n Int best_charge;\n Size num_peaks;\n Size best_num_peaks;\n gsl_vector * best_result = gsl_vector_alloc((Int)(2 + 2 * data.peaks.size()));\n\n\n // try three different charge states : charge-1, charge, charge +1\n // take the best solution\n Int l = (charge_ - 1 > 1) ? charge_ - 1 : charge_;\n Int start_l = l;\n#ifdef DEBUG_DECONV\n std::cout << \"charge \" << l << \" max_charge\" << charge_ + 1\n << \"\\tpeaks.size() \" << peaks.size() << std::endl;\n#endif\n best_charge = l;\n best_num_peaks = peaks.size();\n num_peaks = peaks.size();\n for (; l < charge_ + 2; ++l)\n {\n\n\n num_peaks = getNumberOfPeaks_(l, temp_shapes, data);\n#ifdef DEBUG_DECONV\n std::cout << \"charge \" << l << \" #peaks \" << num_peaks << \"\\tpeaks.size() \"\n << data.peaks.size() << std::endl;\n#endif\n gsl_vector * start_value;\n // the vector storing the start values for the parameters has to be filled\n // differently depending on the usage of the peptide mass rule\n start_value = gsl_vector_alloc(2 + 2 * data.peaks.size());\n for (Size i = 0; i < data.peaks.size(); i++)\n {\n gsl_vector_set(start_value, 2 + 2 * i, data.peaks[i].height);\n gsl_vector_set(start_value, 3 + 2 * i, data.peaks[i].mz_position);\n }\n\n\n // Initialize the parameters for the optimization\n\n // all peaks shall have the same width\n DoubleReal wl = data.peaks[0].left_width;\n DoubleReal wr = data.peaks[0].right_width;\n if (boost::math::isnan(wl))\n {\n for (Size i = 0; i < data.peaks.size(); ++i)\n {\n data.peaks[i].left_width = 1;\n }\n wl = 1.;\n }\n if (boost::math::isnan(wr))\n {\n for (Size i = 0; i < data.peaks.size(); ++i)\n {\n data.peaks[i].right_width = 1;\n }\n wr = 1.;\n }\n\n gsl_vector_set(start_value, 0, wl);\n gsl_vector_set(start_value, 1, wr);\n\n\n // The gsl algorithms require us to provide function pointers for the evaluation of\n // the target function.\n gsl_multifit_function_fdf fit_function;\n\n fit_function.f = OptimizationFunctions::residualDC;\n fit_function.df = OptimizationFunctions::jacobianDC;\n fit_function.fdf = OptimizationFunctions::evaluateDC;\n\n fit_function.n = std::max(data.positions.size() + 1,\n 2 + 2 * data.peaks.size());\n\n fit_function.p = 2 + 2 * data.peaks.size();\n data.penalties = penalties_;\n// fit_function.params = &penalties_;\n data.charge = l;\n fit_function.params = &data;\n#ifdef DEBUG_DECONV\n std::cout << \"fit_function.p \" << fit_function.p << \"\\t fit_function.n \" << fit_function.n << std::endl;\n std::cout << \"peaks.size() \" << data.peaks.size() << std::endl;\n#endif\n const gsl_multifit_fdfsolver_type * type = gsl_multifit_fdfsolver_lmsder;\n gsl_multifit_fdfsolver * fit;\n fit = gsl_multifit_fdfsolver_alloc(type,\n std::max(data.positions.size() + 1,\n 2 + 2 * data.peaks.size()),\n 2 + 2 * data.peaks.size());\n\n\n\n\n\n gsl_multifit_fdfsolver_set(fit, &fit_function, start_value);\n\n#ifdef DEBUG_DECONV\n // initial norm\n std::cout << \"Before optimization: ||f|| = \" << gsl_blas_dnrm2(fit->f) << std::endl;\n#endif\n // Iteration\n Int iteration = 0;\n Int status;\n\n do\n {\n iteration++;\n status = gsl_multifit_fdfsolver_iterate(fit);\n\n#ifdef DEBUG_DECONV\n std::cout << \"Iteration \" << iteration << \"; Status \" << gsl_strerror(status) << \"; \" << std::endl;\n std::cout << \"||f|| = \" << gsl_blas_dnrm2(fit->f) << std::endl;\n std::cout << \"Number of parms: \" << data.peaks.size() + 3 << std::endl;\n std::cout << \"Delta: \" << gsl_blas_dnrm2(fit->dx) << std::endl;\n#endif\n if (boost::math::isnan(gsl_blas_dnrm2(fit->dx)))\n {\n#ifdef DEBUG_DECONV\n std::cout << \"norm is not a number\" << std::endl;\n#endif\n break;\n }\n status = gsl_multifit_test_delta(fit->dx, fit->x, (float)param_.getValue(\"eps_abs\"),\n (float)param_.getValue(\"eps_rel\"));\n\n if (status != GSL_CONTINUE)\n {\n#ifdef DEBUG_DECONV\n std::cout << \"gsl status != GSL_CONTINUE\" << std::endl;\n#endif\n break;\n }\n // if(!checkFWHM_(peaks,fit) && failure <1)\n// {\n// #ifdef DEBUG_DECONV\n// std::cout << \"fwhm differ\"<f);\n#ifdef DEBUG_DECONV\n std::cout << \"Finished! Charge \" << l << \"\\tIterations: \" << iteration << std::endl;\n std::cout << \"Delta: \" << gsl_blas_dnrm2(fit->dx) << std::endl;\n\n std::cout << \"chisq/dof = \" << pow(chi, 2.0) / (data.positions.size()\n - (3 + data.peaks.size()));\n std::cout << \"\\nAfter optimization: ||f|| = \" << gsl_blas_dnrm2(fit->f) << std::endl;\n#endif\n if ((l == start_l) || (chi < min))\n {\n if (l != start_l)\n gsl_vector_free(best_result);\n best_result = gsl_vector_alloc(2 + 2 * data.peaks.size());\n gsl_vector_memcpy(best_result, fit->x);\n min = chi;\n best_charge = l;\n best_num_peaks = data.peaks.size();\n }\n iteration = 0;\n\n\n\n gsl_multifit_fdfsolver_free(fit);\n gsl_vector_free(start_value);\n\n }\n global_peak_number += best_num_peaks;\n // iterate over all peaks and store the optimized values in peaks\n if (best_num_peaks > 0)\n {\n peaks.resize(best_num_peaks);\n for (Size current_peak = 0; current_peak < best_num_peaks; current_peak++)\n {\n\n // Store the current parameters for this peak\n\n peaks[current_peak].left_width = gsl_vector_get(best_result, 0);\n peaks[current_peak].right_width = gsl_vector_get(best_result, 1);\n\n peaks[current_peak].height = gsl_vector_get(best_result, 2 + 2 * current_peak);\n peaks[current_peak].mz_position = gsl_vector_get(best_result, 2 + 2 * current_peak + 1);\n\n\n\n // compute the area\n // is it a Lorentz or a Sech - Peak?\n if (peaks[current_peak].type == PeakShape::LORENTZ_PEAK)\n {\n PeakShape p = peaks[current_peak];\n DoubleReal x_left_endpoint = p.mz_position + 1 / p.left_width * sqrt(p.height / 1 - 1);\n DoubleReal x_right_endpoint = p.mz_position + 1 / p.right_width * sqrt(p.height / 1 - 1);\n#ifdef DEBUG_DECONV\n std::cout << \"x_left_endpoint \" << x_left_endpoint << \" x_right_endpoint \" << x_right_endpoint << std::endl;\n std::cout << \"p.height\" << p.height << std::endl;\n#endif\n DoubleReal area_left = -p.height / p.left_width * atan(p.left_width * (x_left_endpoint - p.mz_position));\n DoubleReal area_right = -p.height / p.right_width * atan(p.right_width * (p.mz_position - x_right_endpoint));\n peaks[current_peak].area = area_left + area_right;\n\n }\n else //It's a Sech - Peak\n {\n PeakShape p = peaks[current_peak];\n DoubleReal x_left_endpoint = p.mz_position + 1 / p.left_width * boost::math::acosh(sqrt(p.height / 0.001));\n DoubleReal x_right_endpoint = p.mz_position + 1 / p.right_width * boost::math::acosh(sqrt(p.height / 0.001));\n#ifdef DEBUG_DECONV\n std::cout << \"x_left_endpoint \" << x_left_endpoint << \" x_right_endpoint \" << x_right_endpoint << std::endl;\n std::cout << \"p.height\" << p.height << std::endl;\n#endif\n DoubleReal area_left = -p.height / p.left_width * (sinh(p.left_width * (p.mz_position - x_left_endpoint))\n / cosh(p.left_width * (p.mz_position - x_left_endpoint)));\n DoubleReal area_right = -p.height / p.right_width * (sinh(p.right_width * (p.mz_position - x_right_endpoint))\n / cosh(p.right_width * (p.mz_position - x_right_endpoint)));\n peaks[current_peak].area = area_left + area_right;\n\n }\n\n }\n }\n charge_ = best_charge;\n gsl_vector_free(best_result);\n\n return true;\n }\n\n bool OptimizePeakDeconvolution::checkFWHM_(std::vector & peaks, gsl_multifit_fdfsolver * & fit)\n {\n DoubleReal fwhm_threshold = (DoubleReal)param_.getValue(\"fwhm_threshold\");\n\n PeakShape p;\n for (Size current_peak = 0; current_peak < peaks.size(); current_peak++)\n {\n p.left_width = gsl_vector_get(fit->x, 0);\n p.right_width = gsl_vector_get(fit->x, 1);\n p.type = peaks[current_peak].type;\n#ifdef DEBUG_DECONV\n std::cout << \"fwhm: \" << p.getFWHM() << \" > \" << fwhm_threshold << \" ?\" << std::endl;\n#endif\n if (p.getFWHM() > fwhm_threshold)\n return false;\n }\n\n return true;\n }\n\n Size OptimizePeakDeconvolution::getNumberOfPeaks_(Int charge, std::vector & temp_shapes, Data & data)\n {\n DoubleReal dist = dist_ / charge;\n\n data.peaks.clear();\n\n Size shape = 0;\n#ifdef DEBUG_DECONV\n std::cout << \"temp_shapes[0].mz_position \" << temp_shapes[0].mz_position\n << \"\\t dist \" << dist << \"\\tp_index \" << shape << std::endl;\n#endif\n // while the peak's position is smaller than the last considered position\n // take the peak for optimization\n while ((temp_shapes[0].mz_position + shape * dist <\n data.positions[data.positions.size() - 1]) &&\n (shape < temp_shapes.size()))\n {\n data.peaks.push_back(temp_shapes[shape]);\n#ifdef DEBUG_DECONV\n std::cout << \"temp_shapes[0].mz_position + p_index*dist = \" << temp_shapes[0].mz_position + shape * dist << std::endl;\n#endif\n ++shape;\n }\n\n return shape;\n\n }\n\n}\n", "meta": {"hexsha": "958d15e46cc883f8d2ee8f894abc716d8413eaec", "size": 28300, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/openms/source/TRANSFORMATIONS/RAW2PEAK/OptimizePeakDeconvolution.cpp", "max_stars_repo_name": "kreinert/OpenMS", "max_stars_repo_head_hexsha": "45455356482ce5ab35e32e445609b291ec78a6d6", "max_stars_repo_licenses": ["Zlib", "Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2016-12-09T01:45:03.000Z", "max_stars_repo_stars_event_max_datetime": "2016-12-09T01:45:03.000Z", "max_issues_repo_path": "src/openms/source/TRANSFORMATIONS/RAW2PEAK/OptimizePeakDeconvolution.cpp", "max_issues_repo_name": "kreinert/OpenMS", "max_issues_repo_head_hexsha": "45455356482ce5ab35e32e445609b291ec78a6d6", "max_issues_repo_licenses": ["Zlib", "Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/openms/source/TRANSFORMATIONS/RAW2PEAK/OptimizePeakDeconvolution.cpp", "max_forks_repo_name": "kreinert/OpenMS", "max_forks_repo_head_hexsha": "45455356482ce5ab35e32e445609b291ec78a6d6", "max_forks_repo_licenses": ["Zlib", "Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.3138686131, "max_line_length": 170, "alphanum_fraction": 0.5997879859, "num_tokens": 6971, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6477982315512489, "lm_q2_score": 0.34864513533394575, "lm_q1q2_score": 0.2258517021082759}} {"text": "#include \n#include \n\n#include \"Simulation.h\"\n\ntemplate \nSimulation::Simulation(\n\t\tjson settings, double lc_thickness, unsigned int N_lc_steps,\n\t\tstd::shared_ptr > &n_field) :\n\tmat_properties(settings.at(\"Material properties\")),\n\tN_lc_steps(N_lc_steps),\n\tlc_thickness(lc_thickness),\n\tn_field(n_field) {\n\n\t// We initialize the wavelength array\n\tauto &j = settings.at(\"Light source\");\n\tif(j.find(\"Wavelengths\") != j.end()) {\n\t\twavelengths = j.at(\"Wavelengths\").get >();\n\t\tN_wavelengths = int(wavelengths.size());\n\t}\n\telse {\n\t\tN_wavelengths = j.at(\"N wavelengths\");\n\t\tdouble mean_lambda = j.at(\"Mean wavelength\");\n\t\tdouble fwhm = j.at(\"Spectral FWHM\");\n\n\t\tif(N_wavelengths==1)\n\t\t\twavelengths.push_back(j.at(\"Mean wavelength\"));\n\t\telse {\n\t\t\tfor(int i=0; i >(\n\t\tstd::pow(mat_properties.n_out, 2.),\n\t\tstd::make_shared >(\n\t\t\tBasisVector(dim-1),\n\t\t\t-lower_iso_thickness-lc_thickness*1.5,\n\t\t\t-lower_iso_thickness-lc_thickness/2.)));\n\n\tdouble z_interface = -lower_iso_thickness-lc_thickness/2.;\n\tfor(int i=0; i >(\n\t\t\tstd::pow(mat_properties.n_lower_layers[i],2.),\n\t\t\tstd::make_shared >(\n\t\t\t\tBasisVector(dim-1), z_interface,\n\t\t\t\tz_interface + mat_properties.e_lower_layers[i])));\n\t\tz_interface += mat_properties.e_lower_layers[i];\n\t}\n\n\tz_interface = lc_thickness/2;\n\tfor(int i=0; i >(\n\t\t\tstd::pow(mat_properties.n_upper_layers[i],2.),\n\t\t\tstd::make_shared >(\n\t\t\t\tBasisVector(dim-1), z_interface,\n\t\t\t\tz_interface + mat_properties.e_upper_layers[i])));\n\t\tz_interface += mat_properties.e_upper_layers[i];\n\t}\n\tupper_iso_odes.push_back(std::make_shared >(\n\t\tstd::pow(mat_properties.n_out,2.),\n\t\tstd::make_shared >(\n\t\t\tBasisVector(dim-1), z_interface,\n\t\t\tz_interface+lc_thickness)));\n\n\t// We initalize the other parameters\n\tN_refinement_cycles =\n\t\tsettings.at(\"InverseScreenMap parameters\").at(\"N refinement cycles\");\n\thc_parameters = settings.at(\"InverseScreenMap parameters\");\n\n\tviz_settings = settings.at(\"Visualisation\");\n\tnumerical_aperture = viz_settings.at(\"Screen output\").at(\"Numerical aperture\");\n\n\tbulk_basename = viz_settings.at(\"Bulk output\").at(\"Base name\");\n\tscreen_basename = viz_settings.at(\"Screen output\").at(\"Base name\");\n\tbasedir = viz_settings.at(\"Results folder name\");\n\n\tbulk_ray_output = viz_settings.at(\"Bulk output\").at(\"Export ray data\");\n\tbulk_fields_output = viz_settings.at(\"Bulk output\").at(\"Export reconstructed fields\");\n\tscreen_ray_output = viz_settings.at(\"Screen output\").at(\"Export ray data\");\n\tscreen_fields_output = viz_settings.at(\"Screen output\").at(\"Export reconstructed fields\");\n\n\tauto widths = parse_Vector(viz_settings, \"Target output widths\");\n\tauto dims = parse_Vector(viz_settings, \"Target N pixels per dim\");\n\tauto origin = -widths/2;\n\n\tVector coarse_dims;\n\tfor(int i=0; i >(\n\t\torigin, widths, coarse_dims, false);\n\tfull_horiz_mesh = std::make_shared >(\n\t\torigin, widths, dims, false);\n\n\tswitch(dim) {\n\t\tcase 2:\n\t\t\ttypical_length = widths(0);\n\t\t\tbreak;\n\t\tcase 3:\n\t\t\ttypical_length = std::max(widths(0),widths(1));\n\t\t\tbreak;\n\t}\n\n\tdouble bulk_z_length = lc_thickness;\n\tif(settings.at(\"Geometry\").at(\"Sample type\")==\"Droplet\")\n\t\tbulk_z_length -= 2*settings.at(\"Geometry\").at(\"Droplet sample parameters\").at(\n\t\t\t\"Distance from upper sample plate\").get();\n\tz_step = bulk_z_length / (N_lc_steps-1);\n\n\tdouble n_above_lc =\n\t\tmat_properties.n_upper_layers.size()>0 ?\n\t\tmat_properties.n_upper_layers[0] : mat_properties.n_out;\n\tz_foc_1 =\n\t\tbulk_z_length*(1-n_above_lc/mat_properties.n_lc)/2. +\n\t\t(lc_thickness-bulk_z_length)*(1-n_above_lc/mat_properties.n_host)/2.;\n\tz_foc_2 = \n\t\tbulk_z_length*(1-mat_properties.n_out/mat_properties.n_lc)/2. +\n\t\t(lc_thickness-bulk_z_length)*(1-mat_properties.n_out/mat_properties.n_host)/2.;\n\tfor(int i=0; i(\n\t\t\t\t\t1, dims(0), N_lc_steps,\n\t\t\t\t\t0., widths(0)/(dims(0)-1), z_step);\n\t\t\t\tbulk_ordi_data = std::make_shared(\n\t\t\t\t\t1, dims(0), N_lc_steps,\n\t\t\t\t\t0., widths(0)/(dims(0)-1), z_step);\n\t\t\t}\n\t\t\tif(bulk_fields_output)\n\t\t\t\tbulk_fields_data = std::make_shared(\n\t\t\t\t\t1, dims(0), N_lc_steps,\n\t\t\t\t\t0., widths(0)/(dims(0)-1), z_step,\n\t\t\t\t\twavelengths);\n\t\t\tif(screen_ray_output) {\n\t\t\t\tscreen_extra_data = std::make_shared(\n\t\t\t\t\t1, dims(0), 1,\n\t\t\t\t\t0., widths(0)/(dims(0)-1), 0.);\n\t\t\t\tscreen_ordi_data = std::make_shared(\n\t\t\t\t\t1, dims(0), 1,\n\t\t\t\t\t0., widths(0)/(dims(0)-1), 0.);\n\t\t\t}\n\t\t\tif(screen_fields_output)\n\t\t\t\tscreen_fields_data = std::make_shared(\n\t\t\t\t\t1, dims(0), 1,\n\t\t\t\t\t0., widths(0)/(dims(0)-1), 0.,\n\t\t\t\t\twavelengths);\n\t\t\tbreak;\n\n\t\tcase 3:\n\t\t\tN_horiz_pixels = dims(0)*dims(1);\n\t\t\tif(bulk_ray_output) {\n\t\t\t\tbulk_extra_data = std::make_shared(\n\t\t\t\t\tdims(0), dims(1), N_lc_steps,\n\t\t\t\t\twidths(0)/(dims(0)-1), widths(1)/(dims(1)-1), z_step);\n\t\t\t\tbulk_ordi_data = std::make_shared(\n\t\t\t\t\tdims(0), dims(1), N_lc_steps,\n\t\t\t\t\twidths(0)/(dims(0)-1), widths(1)/(dims(1)-1), z_step);\n\t\t\t}\n\t\t\tif(bulk_fields_output)\n\t\t\t\tbulk_fields_data = std::make_shared(\n\t\t\t\t\tdims(0), dims(1), N_lc_steps,\n\t\t\t\t\twidths(0)/(dims(0)-1), widths(1)/(dims(1)-1), z_step,\n\t\t\t\t\twavelengths);\n\t\t\tif(screen_ray_output) {\n\t\t\t\tscreen_extra_data = std::make_shared(\n\t\t\t\t\tdims(0), dims(1), 1,\n\t\t\t\t\twidths(0)/(dims(0)-1), widths(1)/(dims(1)-1), 0.);\n\t\t\t\tscreen_ordi_data = std::make_shared(\n\t\t\t\t\tdims(0), dims(1), 1,\n\t\t\t\t\twidths(0)/(dims(0)-1), widths(1)/(dims(1)-1), 0.);\n\t\t\t}\n\t\t\tif(screen_fields_output)\n\t\t\t\tscreen_fields_data = std::make_shared(\n\t\t\t\t\tdims(0), dims(1), 1,\n\t\t\t\t\twidths(0)/(dims(0)-1), widths(1)/(dims(1)-1), 0.,\n\t\t\t\t\twavelengths);\n\t\t\tbreak;\n\t}\n}\n\ntemplate \nvoid Simulation::apply_fourier_iso_layer_filter() {\n\n\tint dims[3]; \t\tscreen_fields_data->vti_data->GetDimensions(dims);\n\tdouble spacings[3];\tscreen_fields_data->vti_data->GetSpacing(spacings);\n\n\tfftw_complex *in = static_cast(\n\t\tfftw_malloc(sizeof(fftw_complex) * dims[0]*dims[1]));\n\tfftw_complex *out = static_cast(\n\t\tfftw_malloc(sizeof(fftw_complex) * dims[0]*dims[1]));\n\tfftw_plan forward_plan = (dim==3) ?\n\t\tfftw_plan_dft_2d(dims[1], dims[0], in, out, FFTW_FORWARD, FFTW_ESTIMATE) :\n\t\tfftw_plan_dft_1d(dims[1], in, out, FFTW_FORWARD, FFTW_ESTIMATE);\n\tfftw_plan backward_plan = (dim==3) ?\n\t\tfftw_plan_dft_2d(dims[1], dims[0], in, out, FFTW_BACKWARD, FFTW_ESTIMATE) :\n\t\tfftw_plan_dft_1d(dims[1], in, out, FFTW_BACKWARD, FFTW_ESTIMATE);\n\n\t#pragma omp parallel for\n\tfor(int wave_idx=0; wave_idx > >(dims[0]*dims[1]);\n\t\tdouble k0 = 2*PI/wavelengths[wave_idx];\n\n\t\t// transfer matrix in column-major order\n\t\tstd::vector > tmat(4), prev_tmat(4); \n\t\tstd::complex t1, t2;\n\n\t\tfor(int iy=0; iy(0,(kp-kN)*ep));\n\t\t\t\t\t\tt2 = 0.5*(1-kp/next_kp)*std::exp(std::complex(0,(kp-kN)*ep));\n\n\t\t\t\t\t\ttmat[0] = t1*prev_tmat[0] + std::conj(t2)*prev_tmat[1];\n\t\t\t\t\t\ttmat[1] = t2*prev_tmat[0] + std::conj(t1)*prev_tmat[1];\n\t\t\t\t\t\ttmat[2] = t1*prev_tmat[2] + std::conj(t2)*prev_tmat[3];\n\t\t\t\t\t\ttmat[3] = t2*prev_tmat[2] + std::conj(t1)*prev_tmat[3];\n\n\t\t\t\t\t\tprev_tmat = tmat;\n\t\t\t\t\t}\n\t\t\t\t\tiso_filter->at(ix+dims[0]*iy) =\n\t\t\t\t\t\t(tmat[0]-tmat[1]*tmat[2]/tmat[3]) *\n\t\t\t\t\t\tstd::exp(std::complex(0,kN*z_foc_2));\n\t\t\t\t}\n\t\t\t\telse \n\t\t\t\t\tiso_filter->at(ix+dims[0]*iy) = 0;\n\t\t\t}\n\t\t}\n\n\t\t// FFTW arrays for this thread\n\t\tfftw_complex *field = static_cast(\n\t\t\tfftw_malloc(sizeof(fftw_complex) * dims[0]*dims[1]));\n\t\tfftw_complex *fft_field = static_cast(\n\t\t\tfftw_malloc(sizeof(fftw_complex) * dims[0]*dims[1]));\n\n\t\tfor(int pol_idx=0; pol_idx<2; pol_idx++) {\n\t\t\tfor(int comp=0; comp<2; comp++) {\n\t\t\t\t// First, we fill the fftw input array with our data\n\t\t\t\tfor(int i=0; iE_real[pol_idx][wave_idx]->GetComponent(i,comp);\n\t\t\t\t\tfield[i][1] = \n\t\t\t\t\t\tscreen_fields_data->E_imag[pol_idx][wave_idx]->GetComponent(i,comp);\n\t\t\t\t}\n\n\t\t\t\t// We switch to fourrier space and apply the iso filter\n\t\t\t\tfftw_execute_dft(forward_plan, field, fft_field);\n\t\t\t\tfor(int i=0; i(fft_field[i][0], fft_field[i][1]);\n\t\t\t\t\tfft_field[i][0] = std::real(val * iso_filter->at(i));\n\t\t\t\t\tfft_field[i][1] = std::imag(val * iso_filter->at(i));\n\t\t\t\t}\n\n\t\t\t\t// We come back to real space and save the transformed\n\t\t\t\t// data\n\t\t\t\tfftw_execute_dft(backward_plan, fft_field, field);\n\n\t\t\t\tfor(int i=0; iE_real[pol_idx][wave_idx]->SetComponent(\n\t\t\t\t\t\ti, comp, field[i][0]/(dims[0]*dims[1]));\n\t\t\t\t\tscreen_fields_data->E_imag[pol_idx][wave_idx]->SetComponent(\n\t\t\t\t\t\ti, comp, field[i][1]/(dims[0]*dims[1]));\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// We free the FFTW pointers\n\t\tfftw_free(field);\n\t\tfftw_free(fft_field);\n\t}\n\n\t// We free the FFTW pointers\n\tfftw_destroy_plan(forward_plan);\n\tfftw_destroy_plan(backward_plan);\n\tfftw_free(in);\n\tfftw_free(out);\n}\n\ntemplate class Simulation<2>;\ntemplate class Simulation<3>;\n", "meta": {"hexsha": "a7df01901387b506d1bd25401d06929e42d2e100", "size": 11509, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "RayTracingSolver/src/SimulationLib/Simulation.cpp", "max_stars_repo_name": "warthan07/Nemaktis", "max_stars_repo_head_hexsha": "54b1e64c1d40668e6dc22b11eac5487a09b58478", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-02-25T08:13:32.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-18T12:12:34.000Z", "max_issues_repo_path": "RayTracingSolver/src/SimulationLib/Simulation.cpp", "max_issues_repo_name": "warthan07/Nemaktis", "max_issues_repo_head_hexsha": "54b1e64c1d40668e6dc22b11eac5487a09b58478", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "RayTracingSolver/src/SimulationLib/Simulation.cpp", "max_forks_repo_name": "warthan07/Nemaktis", "max_forks_repo_head_hexsha": "54b1e64c1d40668e6dc22b11eac5487a09b58478", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2022-02-18T12:13:40.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-18T12:13:40.000Z", "avg_line_length": 35.1957186544, "max_line_length": 91, "alphanum_fraction": 0.6719958294, "num_tokens": 3670, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.603931819468636, "lm_q2_score": 0.37387582277169656, "lm_q1q2_score": 0.22579550590184402}} {"text": "/**\nBSD 3-Clause License\n\nThis file is part of the code accompanying the paper\nFrom Planes to Corners: Multi-Purpose Primitive Detection in Unorganized 3D Point Clouds\nby C. Sommer, Y. Sun, L. Guibas, D. Cremers and T. Birdal,\naccepted for Publication in IEEE Robotics and Automation Letters (RA-L) 2020.\n\nCopyright (c) 2019, Christiane Sommer.\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n\n// includes\n#include \n#include \n#include \n#include \"definitions.h\"\n// libraries\n#include \n#include \n#include \n// classes\n#include \"Timer.h\"\n#include \"Plane.h\"\n#include \"graph/PlaneGraph.h\"\n#include \"graph/ParallelPlaneGraph.h\"\n#include \"PPF/PairDetector.h\"\n// function includes\n#include \"io/load_ply_cloud.h\"\n#include \"visualize/pcshow.h\"\n\n#include \n#include \n\n\n\n#include \n\nnamespace fs = std::experimental::filesystem;\n\n/*\n * main\n */\nint main(int argc, char** argv) {\n\n int nr_volume=0;\n int nr_pointclouds=0;\n\n std::string string_pointcloud;\n std::ifstream MyReadFile2(\"../data/main_programs/config_files/input_file_plane_dataset.txt\");\n std::getline (MyReadFile2, string_pointcloud);\n\n \n\n\n std::ofstream myfile(\"/home/alex-pop/Desktop/Doctorat/Side_projects/Volume_box_batch_processing/orthogonal-planes/data/output_volumes.txt\");\n \n\n myfile<<\"Hello\"<<'\\n';\n\n\n\n \n\t\n\t//fs::path path(\"/home/alex-pop/Desktop/Doctorat/Side_projects/Volume_box_batch_processing/orthogonal-planes/data/Pointcloud_recordings\");\n fs::path path(string_pointcloud);\n\tfor (auto& p : fs::directory_iterator(path))\n\t{\n\t\t\n\tstd::stringstream ss;\n\t\n ss << p ;\n \n std::string str = ss.str();\n \n str.erase(std::remove(str.begin(), str.end(), '\"'), str.end());\n \n std::cout<::Ptr cloud(new pcl::PointCloud);\n\n if (pcl::io::loadPCDFile (str, *cloud) == -1) //* load the file\n {\n PCL_ERROR (\"Couldn't read file test_pcd.pcd \\n\");\n return (-1);\n }\n std::cout << \"Loaded \"\n << cloud->width * cloud->height\n << \" data points from \"< points;\n std::vector normals;\n\n nr_pointclouds++;\n\n\n\n for (int i = 0; i < cloud->size(); i++)\n {\n \n \n Eigen::Vector3f position_aux= Eigen::Vector3f( (float) cloud->points[i].x,(float) cloud->points[i].y,(float) cloud->points[i].z);\n points.push_back(position_aux);\n\n Eigen::Vector3f normal_aux= Eigen::Vector3f( (float) cloud->points[i].normal_x,(float) cloud->points[i].normal_y,(float) cloud->points[i].normal_z);\n normals.push_back(normal_aux);\n }\n\n std::cout << points.size() << \" points loaded.\" << std::endl;\n std::cout << normals.size() << \" normals loaded.\" << std::endl;\n std::cout<<\"\\n\";\n\n Timer T;\n \n // parse setttings from command line\n std::string ply_file;\n int min_votes = 5;\n double d_min = .1, d_max = 1.;\n int sampling = 10;\n\n\n std::ifstream MyReadFile(\"../data/main_programs/config_files/input_file_plane.txt\");\n\n std::ifstream MyReadFile2(\"../data/main_programs/config_files/input_file_plane_dataset.txt\");\n\n std::string min_votes_text;\n std::string d_min_text;\n std::string d_max_text;\n std::string sampling_text;\n std::string threshold_text;\n\n std::getline (MyReadFile, min_votes_text);\n std::getline (MyReadFile, d_min_text);\n std::getline (MyReadFile, d_max_text);\n std::getline (MyReadFile, sampling_text);\n std::getline (MyReadFile, threshold_text);\n\n\n min_votes=std::stoi(min_votes_text);\n std::cout<<\"min_votes=\"<::Triangle> triangles;\n T.tic();\n planeMap.find_triangles(triangles);\n T.toc(\"Finding triangles\");\n //planeMap.print_triangles(triangles);\n \n // reduce graph to ParallelPlaneGraph\n T.tic();\n ParallelPlaneGraph redMap = planeMap.reduce_graph();\n T.toc(\"graph reduction\");\n //redMap.print_info();\n //redMap.print_edges();\n T.tic();\n redMap.triangle_reduce();\n T.toc(\"triangle reduction\");\n //redMap.print_info();\n //redMap.print_parameters();\n //redMap.print_edges();\n \n T.tic();\n redMap.filter_outliers(points, normals, pairDet.distance_bin(), .5*sampling);\n T.toc(\"outlier filtering\");\n //redMap.print_info();\n //redMap.print_parameters();\n //redMap.print_edges();\n \n // CERES problem setup and solving\n double lambda = 0.01 * points.size();\n T.tic();\n redMap.refine_coarse_fine(points, normals, lambda, pairDet.distance_bin(), sampling);\n T.toc(\"Multi-plane parameter optimization\");\n //redMap.print_info();\n //redMap.print_parameters();\n //redMap.print_edges();\n \n T.tic();\n redMap.filter_outliers(points, normals, pairDet.distance_bin(), .5*sampling);\n T.toc(\"outlier filtering\");\n //redMap.print_info();\n //redMap.print_parameters();\n //redMap.print_edges();\n \n // From here on visualization / debugging\n\n// // get points located on planes and not-planes\n PlaneGraph finalMap = PlaneGraph::from_ppg(redMap, 2*pairDet.para_thresh()*pairDet.para_thresh()-1, pairDet.distance_bin()); // double angle\n std::vector> labeled;\n T.tic();\n finalMap.go_thr_pcl(points, normals, labeled);\n T.toc(\"labeling planes\");\n\n // visualize\n // pcshow(labeled, \"Result\");\n \n std::vector lines;\n // line extraction and visualization\n T.tic();\n lines = finalMap.extract_lines(points, normals);\n T.toc(\"get lines\");\n //std::cout << lines.size() << \" lines found.\" << std::endl;\n\n std::vector new_lines;\n\n std::vector final_lines;\n std::vector distances;\n\n std::vector Volume_edge;\n\n std::vector positions_wrong;\n std::vector Volumes;\n\n \n\n std::cout<<'\\n';\n\n \n if (lines.size()>=3){\n for(int i = 0; i != lines.size()-2; i++) {\n //std::cout<< lines[i][0]<<\" \"<1){\n for(int i = 0; i != new_lines.size(); i++) {\n //std::cout<< new_lines[i][0]<<\" \"<total_distance_j){\n positions_wrong.push_back(i - offset_i);\n }\n else\n {\n positions_wrong.push_back(j - offset_j);\n }\n\n \n }\n \n }\n }\n }\n \n /*\n std::cout<<\"Positions wrong:\"<<'\\n';\n\n\n for(int i = 0; i != positions_wrong.size(); i++) {\n std::cout<=3){\n nr_volume++;\n\n //myfile<distance1= \"<distance2= \"<distance3= \"<\n#include \n\n#include \n\nnamespace Bempp {\n\ntemplate \nPiecewisePolynomialDiscontinuousScalarSpace::\n PiecewisePolynomialDiscontinuousScalarSpace(\n const shared_ptr &grid, int polynomialOrder)\n : ScalarSpace(grid), m_polynomialOrder(polynomialOrder),\n m_flatLocalDofCount(0) {\n initialize(GridSegment::wholeGrid(*grid));\n}\n\ntemplate \nPiecewisePolynomialDiscontinuousScalarSpace::\n PiecewisePolynomialDiscontinuousScalarSpace(\n const shared_ptr &grid, int polynomialOrder,\n const GridSegment &segment, int dofMode)\n : ScalarSpace(grid), m_polynomialOrder(polynomialOrder),\n m_flatLocalDofCount(0) {\n initialize(segment, dofMode);\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace::initialize(\n const GridSegment &segment, int dofMode) {\n const int gridDim = this->grid()->dim();\n if (gridDim != 2)\n throw std::invalid_argument(\"PiecewisePolynomialDiscontinuousScalarSpace::\"\n \"initialize(): \"\n \"only 2-dimensional grids are supported\");\n if (!(dofMode & (REFERENCE_POINT_ON_SEGMENT | ELEMENT_ON_SEGMENT)))\n throw std::invalid_argument(\"PiecewisePolynomialDiscontinuousScalarSpace::\"\n \"initialize(): invalid dofMode\");\n m_view = this->grid()->leafView();\n if (m_polynomialOrder == 0)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 0>());\n else if (m_polynomialOrder == 1)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 1>());\n else if (m_polynomialOrder == 2)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 2>());\n else if (m_polynomialOrder == 3)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 3>());\n else if (m_polynomialOrder == 4)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 4>());\n else if (m_polynomialOrder == 5)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 5>());\n else if (m_polynomialOrder == 6)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 6>());\n else if (m_polynomialOrder == 7)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 7>());\n else if (m_polynomialOrder == 8)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 8>());\n else if (m_polynomialOrder == 9)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 9>());\n else if (m_polynomialOrder == 10)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarBasis<3, BasisFunctionType, 10>());\n else\n throw std::invalid_argument(\n \"PiecewisePolynomialDiscontinuousScalarSpace::\"\n \"PiecewisePolynomialDiscontinuousScalarSpace(): \"\n \"polynomialOrder must be >= 0 and <= 10\");\n assignDofsImpl(segment, dofMode);\n}\n\ntemplate \nPiecewisePolynomialDiscontinuousScalarSpace<\n BasisFunctionType>::~PiecewisePolynomialDiscontinuousScalarSpace() {}\n\ntemplate \nint PiecewisePolynomialDiscontinuousScalarSpace<\n BasisFunctionType>::domainDimension() const {\n return this->grid()->dim();\n}\n\ntemplate \nint PiecewisePolynomialDiscontinuousScalarSpace<\n BasisFunctionType>::codomainDimension() const {\n return 1;\n}\n\ntemplate \nconst Fiber::Shapeset &\nPiecewisePolynomialDiscontinuousScalarSpace::shapeset(\n const Entity<0> &element) const {\n if (elementVariant(element) == 3)\n return *m_triangleShapeset;\n throw std::logic_error(\n \"PiecewisePolynomialDiscontinuousScalarSpace::shapeset(): \"\n \"invalid element variant, this shouldn't happen!\");\n}\n\ntemplate \nbool PiecewisePolynomialDiscontinuousScalarSpace<\n BasisFunctionType>::spaceIsCompatible(const Space &other)\n const {\n\n typedef PiecewisePolynomialDiscontinuousScalarSpace\n thisSpaceType;\n\n if (other.grid().get() != this->grid().get())\n return false;\n\n if (other.spaceIdentifier() == this->spaceIdentifier()) {\n // Try to typecast the other space down.\n const thisSpaceType &temp = dynamic_cast(other);\n if (this->m_polynomialOrder == temp.m_polynomialOrder)\n return true;\n else\n return false;\n } else\n return false;\n}\n\ntemplate \nElementVariant\nPiecewisePolynomialDiscontinuousScalarSpace::elementVariant(\n const Entity<0> &element) const {\n GeometryType type = element.type();\n if (type.isLine())\n return 2;\n else if (type.isTriangle())\n return 3;\n else if (type.isQuadrilateral())\n return 4;\n else\n throw std::runtime_error(\"PiecewisePolynomialDiscontinuousScalarSpace::\"\n \"elementVariant(): invalid geometry type, \"\n \"this shouldn't happen!\");\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace<\n BasisFunctionType>::setElementVariant(const Entity<0> &element,\n ElementVariant variant) {\n if (variant != elementVariant(element))\n // for this space, the element variants are unmodifiable,\n throw std::runtime_error(\"PiecewisePolynomialDiscontinuousScalarSpace::\"\n \"setElementVariant(): invalid variant\");\n}\n\ntemplate \nshared_ptr>\nPiecewisePolynomialDiscontinuousScalarSpace::\n discontinuousSpace(const shared_ptr> &self)\n const {\n if (self.get() != this)\n throw std::invalid_argument(\n \"PiecewisePolynomialDiscontinuousScalarSpace::discontinuousSpace(): \"\n \"argument should be a shared pointer to *this\");\n return self;\n}\n\ntemplate \nbool PiecewisePolynomialDiscontinuousScalarSpace<\n BasisFunctionType>::isDiscontinuous() const {\n return true;\n}\n\ntemplate \nvoid\nPiecewisePolynomialDiscontinuousScalarSpace::assignDofsImpl(\n const GridSegment &segment, int dofMode) {\n const int gridDim = this->domainDimension();\n if (gridDim != 2)\n throw std::runtime_error(\"PiecewisePolynomialDiscontinuousScalarSpace::\"\n \"assignDofsImpl(): only 2-dimensional grids \"\n \"are supported at present\");\n const int vertexCodim = 2, edgeCodim = 1, elementCodim = 0;\n const Mapper &elementMapper = m_view->elementMapper();\n const IndexSet &indexSet = m_view->indexSet();\n\n int elementCount = m_view->entityCount(0);\n\n const int localDofCountPerTriangle =\n (m_polynomialOrder + 1) * (m_polynomialOrder + 2) / 2;\n const int localDofCountPerQuad =\n (m_polynomialOrder + 1) * (m_polynomialOrder + 1);\n\n BoundingBox model;\n model.lbound.x = std::numeric_limits::max();\n model.lbound.y = std::numeric_limits::max();\n model.lbound.z = std::numeric_limits::max();\n model.ubound.x = -std::numeric_limits::max();\n model.ubound.y = -std::numeric_limits::max();\n model.ubound.z = -std::numeric_limits::max();\n m_globalDofBoundingBoxes.reserve(localDofCountPerQuad * elementCount);\n\n // (Re)initialise DOF maps\n m_local2globalDofs.clear();\n m_local2globalDofs.resize(elementCount);\n m_global2localDofs.clear();\n // estimated number of global DOFs\n m_global2localDofs.reserve(localDofCountPerQuad * elementCount);\n\n // Fill in global<->local dof maps\n std::unique_ptr> it = m_view->entityIterator<0>();\n arma::Mat vertices;\n arma::Col dofPosition;\n GlobalDofIndex globalDofCount = 0;\n while (!it->finished()) {\n const Entity<0> &element = it->entity();\n EntityIndex elementIndex = elementMapper.entityIndex(element);\n bool elementContained = !(dofMode & ELEMENT_ON_SEGMENT) ||\n segment.contains(elementCodim, elementIndex);\n typedef arma::Col Col;\n const Geometry &geo = element.geometry();\n geo.getCorners(vertices);\n int vertexCount = vertices.n_cols;\n int localDofCount =\n vertexCount == 3 ? localDofCountPerTriangle : localDofCountPerQuad;\n\n // List of global DOF indices corresponding to the local DOFs of the\n // current element\n std::vector &globalDofs = m_local2globalDofs[elementIndex];\n globalDofs.resize(localDofCount);\n // GlobalDofIndex gdofStart = globalDofCount;\n // for (int i = 0; i < localDofCount; ++i) {\n // globalDofs.push_back(globalDofCount);\n // std::vector localDofs(1, LocalDof(elementIndex, i));\n // m_global2localDofs.push_back(localDofs);\n // ++globalDofCount;\n // }\n // GlobalDofIndex gdofEnd = globalDofCount;\n\n // Bounding boxes\n BoundingBox bbox = model;\n extendBoundingBox(bbox, vertices);\n // m_globalDofBoundingBoxes.insert(m_globalDofBoundingBoxes.end(),\n // localDofCount, bbox);\n if (vertexCount == 3) {\n int subEntityIndex;\n int ldof;\n\n if (m_polynomialOrder == 0) {\n ldof = 0;\n if (segment.contains(0, elementIndex)) {\n acc(globalDofs, ldof) = globalDofCount;\n std::vector localDofs(1, LocalDof(elementIndex, ldof));\n m_global2localDofs.push_back(localDofs);\n m_globalDofBoundingBoxes.push_back(bbox);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, globalDofCount),\n (vertices.col(0) + vertices(1) + vertices(2)) / 3);\n ++globalDofCount;\n } else\n acc(globalDofs, ldof) = -1;\n } else {\n // vertex dofs\n ldof = 0;\n subEntityIndex = indexSet.subEntityIndex(element, 0, vertexCodim);\n if (elementContained &&\n (!(dofMode & REFERENCE_POINT_ON_SEGMENT) ||\n segment.contains(vertexCodim, subEntityIndex))) {\n acc(globalDofs, ldof) = globalDofCount;\n std::vector localDofs(1, LocalDof(elementIndex, ldof));\n m_global2localDofs.push_back(localDofs);\n m_globalDofBoundingBoxes.push_back(bbox);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, globalDofCount), vertices.col(0));\n ++globalDofCount;\n } else\n acc(globalDofs, ldof) = -1;\n\n ldof = m_polynomialOrder;\n subEntityIndex = indexSet.subEntityIndex(element, 1, vertexCodim);\n if (elementContained &&\n (!(dofMode & REFERENCE_POINT_ON_SEGMENT) ||\n segment.contains(vertexCodim, subEntityIndex))) {\n acc(globalDofs, ldof) = globalDofCount;\n std::vector localDofs(1, LocalDof(elementIndex, ldof));\n m_global2localDofs.push_back(localDofs);\n m_globalDofBoundingBoxes.push_back(bbox);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, globalDofCount), vertices.col(1));\n ++globalDofCount;\n } else\n acc(globalDofs, ldof) = -1;\n\n ldof = localDofCount - 1;\n subEntityIndex = indexSet.subEntityIndex(element, 2, vertexCodim);\n if (elementContained &&\n (!(dofMode & REFERENCE_POINT_ON_SEGMENT) ||\n segment.contains(vertexCodim, subEntityIndex))) {\n acc(globalDofs, ldof) = globalDofCount;\n std::vector localDofs(1, LocalDof(elementIndex, ldof));\n m_global2localDofs.push_back(localDofs);\n m_globalDofBoundingBoxes.push_back(bbox);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, globalDofCount), vertices.col(2));\n ++globalDofCount;\n } else\n acc(globalDofs, ldof) = -1;\n\n // edge dofs\n if (m_polynomialOrder >= 2) {\n\n subEntityIndex = indexSet.subEntityIndex(element, 0, edgeCodim);\n if (elementContained &&\n (!(dofMode & REFERENCE_POINT_ON_SEGMENT) ||\n segment.contains(edgeCodim, subEntityIndex))) {\n dofPosition = 0.5 * (vertices.col(0) + vertices.col(1));\n for (int ldof = 1; ldof < m_polynomialOrder; ++ldof) {\n acc(globalDofs, ldof) = globalDofCount;\n std::vector localDofs(1, LocalDof(elementIndex, ldof));\n m_global2localDofs.push_back(localDofs);\n m_globalDofBoundingBoxes.push_back(bbox);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, globalDofCount), dofPosition);\n ++globalDofCount;\n }\n } else\n for (int ldof = 1; ldof < m_polynomialOrder; ++ldof) {\n acc(globalDofs, ldof) = -1;\n }\n\n subEntityIndex = indexSet.subEntityIndex(element, 1, edgeCodim);\n if (elementContained &&\n (!(dofMode & REFERENCE_POINT_ON_SEGMENT) ||\n segment.contains(edgeCodim, subEntityIndex))) {\n dofPosition = 0.5 * (vertices.col(0) + vertices.col(2));\n for (int ldofy = 1; ldofy < m_polynomialOrder; ++ldofy) {\n int ldof =\n ldofy * (m_polynomialOrder + 1) - ldofy * (ldofy - 1) / 2;\n acc(globalDofs, ldof) = globalDofCount;\n std::vector localDofs(1, LocalDof(elementIndex, ldof));\n m_global2localDofs.push_back(localDofs);\n m_globalDofBoundingBoxes.push_back(bbox);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, globalDofCount), dofPosition);\n ++globalDofCount;\n }\n } else\n for (int ldofy = 1; ldofy < m_polynomialOrder; ++ldofy) {\n int ldof =\n ldofy * (m_polynomialOrder + 1) - ldofy * (ldofy - 1) / 2;\n acc(globalDofs, ldof) = -1;\n }\n\n subEntityIndex = indexSet.subEntityIndex(element, 2, edgeCodim);\n if (elementContained &&\n (!(dofMode & REFERENCE_POINT_ON_SEGMENT) ||\n segment.contains(edgeCodim, subEntityIndex))) {\n dofPosition = 0.5 * (vertices.col(1) + vertices.col(2));\n for (int ldofy = 1; ldofy < m_polynomialOrder; ++ldofy) {\n int ldof = ldofy * (m_polynomialOrder + 1) -\n ldofy * (ldofy - 1) / 2 + (m_polynomialOrder - ldofy);\n acc(globalDofs, ldof) = globalDofCount;\n std::vector localDofs(1, LocalDof(elementIndex, ldof));\n m_global2localDofs.push_back(localDofs);\n m_globalDofBoundingBoxes.push_back(bbox);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, globalDofCount), dofPosition);\n ++globalDofCount;\n }\n } else\n for (int ldofy = 1; ldofy < m_polynomialOrder; ++ldofy) {\n int ldof = ldofy * (m_polynomialOrder + 1) -\n ldofy * (ldofy - 1) / 2 + (m_polynomialOrder - ldofy);\n acc(globalDofs, ldof) = -1;\n }\n\n // dofPosition = 0.5 * (vertices.col(0) + vertices.col(1));\n // for (int ldof = 1; ldof < m_polynomialOrder; ++ldof){\n // setBoundingBoxReference(\n // acc(m_globalDofBoundingBoxes, gdofStart + ldof),\n // dofPosition);\n // }\n // dofPosition = 0.5 * (vertices.col(0) + vertices.col(2));\n // for (int ldofy = 1; ldofy < m_polynomialOrder; ++ldofy) {\n // int ldof = ldofy * (m_polynomialOrder + 1) -\n // ldofy * (ldofy - 1) / 2;\n // setBoundingBoxReference(\n // acc(m_globalDofBoundingBoxes, gdofStart + ldof),\n // dofPosition);\n // }\n // dofPosition = 0.5 * (vertices.col(1) + vertices.col(2));\n // for (int ldofy = 1; ldofy < m_polynomialOrder; ++ldofy) {\n // int ldof = ldofy * (m_polynomialOrder + 1) -\n // ldofy * (ldofy - 1) / 2 + (m_polynomialOrder - ldofy);\n // setBoundingBoxReference(\n // acc(m_globalDofBoundingBoxes, gdofStart + ldof),\n // dofPosition);\n // }\n }\n // bubble dofs\n if (m_polynomialOrder >= 3) {\n if (segment.contains(elementCodim, elementIndex)) {\n dofPosition =\n (vertices.col(0) + vertices.col(1) + vertices.col(2)) / 3.;\n for (int ldofy = 1; ldofy < m_polynomialOrder; ++ldofy)\n for (int ldofx = 1; ldofx + ldofy < m_polynomialOrder; ++ldofx) {\n int ldof = ldofy * (m_polynomialOrder + 1) -\n ldofy * (ldofy - 1) / 2 + ldofx;\n acc(globalDofs, ldof) = globalDofCount;\n std::vector localDofs(1,\n LocalDof(elementIndex, ldof));\n m_global2localDofs.push_back(localDofs);\n m_globalDofBoundingBoxes.push_back(bbox);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, globalDofCount), dofPosition);\n ++globalDofCount;\n }\n } else\n for (int ldofy = 1; ldofy < m_polynomialOrder; ++ldofy)\n for (int ldofx = 1; ldofx + ldofy < m_polynomialOrder; ++ldofx) {\n int ldof = ldofy * (m_polynomialOrder + 1) -\n ldofy * (ldofy - 1) / 2 + ldofx;\n acc(globalDofs, ldof) = -1;\n }\n\n // dofPosition = (vertices.col(0) + vertices.col(1) +\n // vertices.col(2)) / 3.;\n // for (int ldofy = 1; ldofy < m_polynomialOrder; ++ldofy)\n // for (int ldofx = 1; ldofx + ldofy < m_polynomialOrder; ++ldofx)\n // {\n // int ldof = ldofy * (m_polynomialOrder + 1) -\n // ldofy * (ldofy - 1) / 2 + ldofx;\n // setBoundingBoxReference(\n // acc(m_globalDofBoundingBoxes, gdofStart + ldof),\n // dofPosition);\n // }\n }\n }\n }\n\n it->next();\n }\n\n // Initialize the container mapping the flat local dof indices to\n // local dof indices\n SpaceHelper::initializeLocal2FlatLocalDofMap(\n m_flatLocalDofCount, m_local2globalDofs, m_flatLocal2localDofs);\n\n#ifndef NDEBUG\n for (size_t i = 0; i < m_globalDofBoundingBoxes.size(); ++i) {\n const BoundingBox &bbox = acc(m_globalDofBoundingBoxes, i);\n\n assert(bbox.reference.x >= bbox.lbound.x);\n assert(bbox.reference.y >= bbox.lbound.y);\n assert(bbox.reference.z >= bbox.lbound.z);\n assert(bbox.reference.x <= bbox.ubound.x);\n assert(bbox.reference.y <= bbox.ubound.y);\n assert(bbox.reference.z <= bbox.ubound.z);\n }\n#endif // NDEBUG\n}\n\ntemplate \nsize_t\nPiecewisePolynomialDiscontinuousScalarSpace::globalDofCount()\n const {\n return m_global2localDofs.size();\n}\n\ntemplate \nsize_t PiecewisePolynomialDiscontinuousScalarSpace<\n BasisFunctionType>::flatLocalDofCount() const {\n return m_flatLocalDofCount;\n}\n\ntemplate \nvoid\nPiecewisePolynomialDiscontinuousScalarSpace::getGlobalDofs(\n const Entity<0> &element, std::vector &dofs) const {\n const Mapper &mapper = m_view->elementMapper();\n EntityIndex index = mapper.entityIndex(element);\n dofs = m_local2globalDofs[index];\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace::\n global2localDofs(const std::vector &globalDofs,\n std::vector> &localDofs) const {\n localDofs.resize(globalDofs.size());\n for (size_t i = 0; i < globalDofs.size(); ++i)\n localDofs[i] = m_global2localDofs[globalDofs[i]];\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace::\n flatLocal2localDofs(const std::vector &flatLocalDofs,\n std::vector &localDofs) const {\n localDofs.resize(flatLocalDofs.size());\n for (size_t i = 0; i < flatLocalDofs.size(); ++i)\n localDofs[i] = m_flatLocal2localDofs[flatLocalDofs[i]];\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace::\n getGlobalDofPositions(std::vector> &positions)\n const {\n positions.resize(m_globalDofBoundingBoxes.size());\n for (size_t i = 0; i < m_globalDofBoundingBoxes.size(); ++i)\n acc(positions, i) = acc(m_globalDofBoundingBoxes, i).reference;\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace::\n getFlatLocalDofPositions(std::vector> &positions)\n const {\n getGlobalDofPositions(positions);\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace::\n getGlobalDofBoundingBoxes(std::vector> &bboxes)\n const {\n bboxes = m_globalDofBoundingBoxes;\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace::\n getFlatLocalDofBoundingBoxes(\n std::vector> &bboxes) const {\n getGlobalDofBoundingBoxes(bboxes);\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace::\n getGlobalDofNormals(std::vector> &normals) const {\n SpaceHelper::getGlobalDofNormals_defaultImplementation(\n *m_view, m_global2localDofs, normals);\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace::\n getFlatLocalDofNormals(std::vector> &normals)\n const {\n getGlobalDofNormals(normals);\n}\n\ntemplate \nvoid\nPiecewisePolynomialDiscontinuousScalarSpace::dumpClusterIds(\n const char *fileName,\n const std::vector &clusterIdsOfDofs) const {\n dumpClusterIdsEx(fileName, clusterIdsOfDofs, GLOBAL_DOFS);\n}\n\ntemplate \nvoid PiecewisePolynomialDiscontinuousScalarSpace<\n BasisFunctionType>::dumpClusterIdsEx(const char *fileName,\n const std::vector &\n clusterIdsOfDofs,\n DofType dofType) const {\n throw std::runtime_error(\"PiecewisePolynomialDiscontinuousScalarSpace::\"\n \"dumpClusterIdsEx(): not implemented yet\");\n}\n\nFIBER_INSTANTIATE_CLASS_TEMPLATED_ON_BASIS(\n PiecewisePolynomialDiscontinuousScalarSpace);\n\n} // namespace Bempp\n", "meta": {"hexsha": "d0bb72c7a7a09917c81fdf3f8849bb41eac75300", "size": 25354, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/space/piecewise_polynomial_discontinuous_scalar_space.cpp", "max_stars_repo_name": "mdavezac/bempp", "max_stars_repo_head_hexsha": "bc573062405bda107d1514e40b6153a8350d5ab5", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/space/piecewise_polynomial_discontinuous_scalar_space.cpp", "max_issues_repo_name": "mdavezac/bempp", "max_issues_repo_head_hexsha": "bc573062405bda107d1514e40b6153a8350d5ab5", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/space/piecewise_polynomial_discontinuous_scalar_space.cpp", "max_forks_repo_name": "mdavezac/bempp", "max_forks_repo_head_hexsha": "bc573062405bda107d1514e40b6153a8350d5ab5", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.7693574959, "max_line_length": 80, "alphanum_fraction": 0.6658121007, "num_tokens": 6376, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.22575051571482055}} {"text": "#ifndef STAN_MATH_TORSTEN_MIXODE1CPTMODEL_RK45_HPP\n#define STAN_MATH_TORSTEN_MIXODE1CPTMODEL_RK45_HPP\n\n#include \n#include \n#include \n#include \n#include \n// #include \n#include \n#include \n\nnamespace torsten {\n\n/**\n * Compute the predicted amounts in each compartment at each event\n * of an ODEs model. The model contains a base 1 Compartment PK\n * component which gets solved analytically, while the other ODEs\n * are solved numerically using stan::math::integrate_ode_rk45. This\n * amounts to using the mixed solver method.\n *\n * Warning: This prototype does not handle steady state events.\n *\n * @tparam T0 type of scalar for time of events.\n * @tparam T1 type of scalar for amount at each event.\n * @tparam T2 type of scalar for rate at each event.\n * @tparam T3 type of scalar for inter-dose inteveral at each event.\n * @tparam T4 type of scalars for the model parameters.\n * @tparam T5 type of scalars for the bio-variability parameters.\n * @tparam T6 type of scalars for the model tlag parameters.\n * @tparam F type of ODE system function.\n * @param[in] f functor for base ordinary differential equation\n * which gets solved numerically.\n * @param[in] nOde number of ODEs we solve numerically.\n * @param[in] time times of events\n * @param[in] amt amount at each event\n * @param[in] rate rate at each event\n * @param[in] ii inter-dose interval at each event\n * @param[in] evid event identity:\n * (0) observation\n * (1) dosing\n * (2) other\n * (3) reset\n * (4) reset AND dosing\n * @param[in] cmt compartment number at each event\n * @param[in] addl additional dosing at each event\n * @param[in] ss steady state approximation at each event (0: no, 1: yes)\n * @param[in] theta vector of ODE parameters\n * @param[in] biovar bio-availability in each compartment\n * @param[in] tlag lag time in each compartment\n * @param[in] rel_tol relative tolerance for the Boost ode solver\n * @param[in] abs_tol absolute tolerance for the Boost ode solver\n * @param[in] max_num_steps maximal number of steps to take within\n * the Boost ode solver\n * @return a matrix with predicted amount in each compartment\n * at each event.\n *\n * FIX ME: msg should be passed on to functor (allows use of\n * print statement inside ODE system).\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nmixOde1CptModel_rk45(const F& f,\n const int nOde,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector >& theta,\n const std::vector >& biovar,\n const std::vector >& tlag,\n std::ostream* msgs = 0,\n double rel_tol = 1e-6,\n double abs_tol = 1e-6,\n long int max_num_steps = 1e6) { // NOLINT(runtime/int)\n using std::vector;\n using Eigen::Dynamic;\n using Eigen::Matrix;\n using boost::math::tools::promote_args;\n\n int nPK = 2;\n\n // check arguments\n static const char* function(\"mixOde1CptModel_rk45\");\n torsten::pmetricsCheck(time, amt, rate, ii, evid, cmt, addl, ss,\n theta, biovar, tlag, function);\n\n // Construct dummy array of matrix for last argument of pred\n Matrix dummy_system;\n vector > dummy_systems(1, dummy_system);\n\n typedef mix1_functor F0;\n\n return Pred(time, amt, rate, ii, evid, cmt, addl, ss,\n theta, biovar, tlag, nPK + nOde, dummy_systems,\n Pred1_mix1(F0(f), rel_tol, abs_tol, max_num_steps, msgs,\n \"rk45\"),\n PredSS_mix1(F0(f), rel_tol, abs_tol, max_num_steps, msgs,\n \"rk45\", nOde));\n}\n\n/**\n * Overload function to allow user to pass an std::vector for \n * theta.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nmixOde1CptModel_rk45(const F& f,\n const int nOde,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector& theta,\n const std::vector >& biovar,\n const std::vector >& tlag,\n std::ostream* msgs = 0,\n double rel_tol = 1e-6,\n double abs_tol = 1e-6,\n long int max_num_steps = 1e6) { // NOLINT(runtime/int)\n std::vector > vec_theta(1, theta);\n\n return mixOde1CptModel_rk45(f, nOde,\n time, amt, rate, ii, evid, cmt, addl, ss,\n vec_theta, biovar, tlag,\n msgs, rel_tol, abs_tol, max_num_steps);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for \n * theta and biovar.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nmixOde1CptModel_rk45(const F& f,\n const int nOde,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector& theta,\n const std::vector& biovar,\n const std::vector >& tlag,\n std::ostream* msgs = 0,\n double rel_tol = 1e-6,\n double abs_tol = 1e-6,\n long int max_num_steps = 1e6) { // NOLINT(runtime/int)\n std::vector > vec_theta(1, theta);\n std::vector > vec_biovar(1, biovar);\n\n return mixOde1CptModel_rk45(f, nOde,\n time, amt, rate, ii, evid, cmt, addl, ss,\n vec_theta, vec_biovar, tlag,\n msgs, rel_tol, abs_tol, max_num_steps);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for \n * theta, biovar, and tlag.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nmixOde1CptModel_rk45(const F& f,\n const int nOde,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector& theta,\n const std::vector& biovar,\n const std::vector& tlag,\n std::ostream* msgs = 0,\n double rel_tol = 1e-6,\n double abs_tol = 1e-6,\n long int max_num_steps = 1e6) { // NOLINT(runtime/int)\n std::vector > vec_theta(1, theta);\n std::vector > vec_biovar(1, biovar);\n std::vector > vec_tlag(1, tlag);\n\n return mixOde1CptModel_rk45(f, nOde,\n time, amt, rate, ii, evid, cmt, addl, ss,\n vec_theta, vec_biovar, vec_tlag,\n msgs, rel_tol, abs_tol, max_num_steps);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for \n * theta and tlag.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nmixOde1CptModel_rk45(const F& f,\n const int nOde,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector& theta,\n const std::vector >& biovar,\n const std::vector& tlag,\n std::ostream* msgs = 0,\n double rel_tol = 1e-6,\n double abs_tol = 1e-6,\n long int max_num_steps = 1e6) { // NOLINT(runtime/int)\n std::vector > vec_theta(1, theta);\n std::vector > vec_tlag(1, tlag);\n\n return mixOde1CptModel_rk45(f, nOde,\n time, amt, rate, ii, evid, cmt, addl, ss,\n vec_theta, biovar, vec_tlag,\n msgs, rel_tol, abs_tol, max_num_steps);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for \n * biovar.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nmixOde1CptModel_rk45(const F& f,\n const int nOde,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector >& theta,\n const std::vector& biovar,\n const std::vector >& tlag,\n std::ostream* msgs = 0,\n double rel_tol = 1e-6,\n double abs_tol = 1e-6,\n long int max_num_steps = 1e6) { // NOLINT(runtime/int)\n std::vector > vec_biovar(1, biovar);\n\n return mixOde1CptModel_rk45(f, nOde,\n time, amt, rate, ii, evid, cmt, addl, ss,\n theta, vec_biovar, tlag,\n msgs, rel_tol, abs_tol, max_num_steps);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for \n * biovar and tlag.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nmixOde1CptModel_rk45(const F& f,\n const int nOde,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector >& theta,\n const std::vector& biovar,\n const std::vector& tlag,\n std::ostream* msgs = 0,\n double rel_tol = 1e-6,\n double abs_tol = 1e-6,\n long int max_num_steps = 1e6) { // NOLINT(runtime/int)\n std::vector > vec_biovar(1, biovar);\n std::vector > vec_tlag(1, tlag);\n\n return mixOde1CptModel_rk45(f, nOde,\n time, amt, rate, ii, evid, cmt, addl, ss,\n theta, vec_biovar, vec_tlag,\n msgs, rel_tol, abs_tol, max_num_steps);\n}\n\n\n/**\n * Overload function to allow user to pass an std::vector for \n * tlag.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nmixOde1CptModel_rk45(const F& f,\n const int nOde,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector >& theta,\n const std::vector >& biovar,\n const std::vector& tlag,\n std::ostream* msgs = 0,\n double rel_tol = 1e-6,\n double abs_tol = 1e-6,\n long int max_num_steps = 1e6) { // NOLINT(runtime/int)\n std::vector > vec_tlag(1, tlag);\n\n return mixOde1CptModel_rk45(f, nOde,\n time, amt, rate, ii, evid, cmt, addl, ss,\n theta, biovar, vec_tlag,\n msgs, rel_tol, abs_tol, max_num_steps);\n}\n\n}\n#endif\n", "meta": {"hexsha": "d3ba83771498d2fa06c3c422f34e978509e242dc", "size": 15691, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "cmdstan/stan/lib/stan_math/stan/math/torsten/mixOde1CptModel_rk45.hpp", "max_stars_repo_name": "yizhang-cae/torsten", "max_stars_repo_head_hexsha": "dc82080ca032325040844cbabe81c9a2b5e046f9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "cmdstan/stan/lib/stan_math/stan/math/torsten/mixOde1CptModel_rk45.hpp", "max_issues_repo_name": "yizhang-cae/torsten", "max_issues_repo_head_hexsha": "dc82080ca032325040844cbabe81c9a2b5e046f9", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cmdstan/stan/lib/stan_math/stan/math/torsten/mixOde1CptModel_rk45.hpp", "max_forks_repo_name": "yizhang-cae/torsten", "max_forks_repo_head_hexsha": "dc82080ca032325040844cbabe81c9a2b5e046f9", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.2, "max_line_length": 76, "alphanum_fraction": 0.547575043, "num_tokens": 4042, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.22570049469200587}} {"text": "#pragma once\n#include \n#include \n#include \n/*\n * Algorithms for non-convex, constrained optimization\n */\n\nnamespace sco {\n\nusing std::string;\nusing std::vector;\n\n\nenum OptStatus {\n OPT_CONVERGED,\n OPT_SCO_ITERATION_LIMIT, // hit iteration limit before convergence\n OPT_PENALTY_ITERATION_LIMIT,\n OPT_FAILED,\n INVALID\n};\nstatic const char* OptStatus_strings[] = {\n \"CONVERGED\",\n \"SCO_ITERATION_LIMIT\",\n \"PENALTY_ITERATION_LIMIT\",\n \"FAILED\",\n \"INVALID\"\n};\ninline string statusToString(OptStatus status) {\n return OptStatus_strings[status];\n}\n\n\nstruct OptResults {\n DblVec x; // solution estimate\n OptStatus status;\n double total_cost;\n vector cost_vals;\n DblVec cnt_viols;\n int n_func_evals, n_qp_solves;\n void clear() {\n x.clear();\n status = INVALID;\n cost_vals.clear();\n cnt_viols.clear();\n n_func_evals = 0;\n n_qp_solves = 0;\n }\n OptResults() {clear();}\n};\nstd::ostream& operator<<(std::ostream& o, const OptResults& r);\n\nclass Optimizer {\n /*\n * Solves an optimization problem\n */\npublic:\n virtual OptStatus optimize() = 0;\n virtual ~Optimizer() {}\n virtual void setProblem(OptProbPtr prob) {prob_ = prob;}\n void initialize(const vector& x);\n vector& x() {return results_.x;}\n OptResults& results() {return results_;}\n\n typedef boost::function Callback;\n void addCallback(const Callback& f); // called before each iteration\nprotected:\n vector callbacks_;\n void callCallbacks(DblVec& x);\n OptProbPtr prob_;\n OptResults results_;\n};\n\nclass BasicTrustRegionSQP : public Optimizer {\n /*\n * Alternates between convexifying objectives and constraints and then solving convex subproblem\n * Uses a merit function to decide whether or not to accept the step\n * merit function = objective + merit_err_coeff * | constraint_error |\n * Note: sometimes the convexified objectives and constraints lead to an infeasible subproblem\n * In that case, you should turn them into penalties and solve that problem\n * (todo: implement penalty-based sqp that gracefully handles infeasible constraints)\n */\npublic:\n double improve_ratio_threshold_, // minimum ratio true_improve/approx_improve to accept step\n min_trust_box_size_, // if trust region gets any smaller, exit and report convergence\n min_approx_improve_, // if model improves less than this, exit and report convergence\n min_approx_improve_frac_, // if model improves less than this, exit and report convergence\n max_iter_,\n trust_shrink_ratio_, // if improvement is less than improve_ratio_threshold, shrink trust region by this ratio\n trust_expand_ratio_, // see above\n cnt_tolerance_, // after convergence of penalty subproblem, if constraint violation is less than this, we're done\n max_merit_coeff_increases_, // number of times that we jack up penalty coefficient\n merit_coeff_increase_ratio_, // ratio that we increate coeff each time\n max_time_ // not yet implemented\n ;\n double merit_error_coeff_, // initial penalty coefficient\n trust_box_size_ // current size of trust region (component-wise)\n ;\n\n BasicTrustRegionSQP();\n BasicTrustRegionSQP(OptProbPtr prob);\n void setProblem(OptProbPtr prob);\n OptStatus optimize();\nprotected:\n void adjustTrustRegion(double ratio);\n void setTrustBoxConstraints(const vector& x);\n void initParameters();\n ModelPtr model_;\n};\n\n\n}\n", "meta": {"hexsha": "d5eab3c79a6b10531133865f90611aa338c38f1c", "size": 3519, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "trajopt_sco/include/trajopt_sco/optimizers.hpp", "max_stars_repo_name": "Levi-Armstrong/trajopt_ros", "max_stars_repo_head_hexsha": "a90cd90478d4048af501ad503360d8dfd7460f20", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2017-12-08T14:43:43.000Z", "max_stars_repo_stars_event_max_datetime": "2017-12-09T16:41:36.000Z", "max_issues_repo_path": "trajopt_sco/include/trajopt_sco/optimizers.hpp", "max_issues_repo_name": "Levi-Armstrong/trajopt_ros", "max_issues_repo_head_hexsha": "a90cd90478d4048af501ad503360d8dfd7460f20", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2017-12-08T04:57:34.000Z", "max_issues_repo_issues_event_max_datetime": "2020-02-07T21:46:45.000Z", "max_forks_repo_path": "trajopt_sco/include/trajopt_sco/optimizers.hpp", "max_forks_repo_name": "Levi-Armstrong/trajopt_ros", "max_forks_repo_head_hexsha": "a90cd90478d4048af501ad503360d8dfd7460f20", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1415929204, "max_line_length": 122, "alphanum_fraction": 0.7289002558, "num_tokens": 826, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832058771036, "lm_q2_score": 0.4186969093556867, "lm_q1q2_score": 0.22567060249536305}} {"text": "/*\n\nCandyPoker\nhttps://github.com/sweeterthancandy/CandyPoker\n\nMIT License\n\nCopyright (c) 2019 Gerry Candy\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\n*/\n#include \"ps/eval/binary_strategy_description.h\"\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"app/pretty_printer.h\"\n#include \"ps/base/algorithm.h\"\n#include \"ps/base/board_combination_iterator.h\"\n#include \"ps/base/cards.h\"\n#include \"ps/base/cards.h\"\n#include \"ps/base/frontend.h\"\n#include \"ps/base/holdem_board_decl.h\"\n#include \"ps/base/rank_hasher.h\"\n#include \"ps/base/suit_hasher.h\"\n#include \"ps/base/tree.h\"\n\n#include \"ps/detail/tree_printer.h\"\n\n#include \"ps/eval/class_cache.h\"\n#include \"ps/eval/pass_mask_eval.h\"\n#include \"ps/eval/instruction.h\"\n\n#include \"ps/support/config.h\"\n#include \"ps/support/index_sequence.h\"\n\n#include \n\n#include \n\n#include \n\n#include \"ps/support/command.h\"\n\n#include \n#include \"ps/eval/holdem_class_vector_cache.h\"\n\nnamespace ps{\n\n\n\n struct static_event : binary_strategy_description::event_decl{\n explicit static_event(std::string const& key, Eigen::VectorXd vec):\n key_{key}, vec_{vec}\n {}\n virtual std::string key()const override{ return key_; }\n virtual void expected_value_given_event(Eigen::VectorXd& out, holdem_class_vector const& cv, double p)const override{\n for(size_t idx=0;idx!=vec_.size();++idx){\n out[idx] += vec_[idx] * p;\n }\n }\n virtual std::string to_string()const override{\n std::stringstream sstr;\n sstr << \"Static{key=\" << key_ << \", vec_=\" << vector_to_string(vec_) << \"}\";\n return sstr.str();\n }\n private:\n std::string key_;\n Eigen::VectorXd vec_;\n };\n struct eval_event : binary_strategy_description::event_decl{\n enum{ Debug = 0 };\n /*\n perm is the vector of players who are in the hand, ie for hu eval\n it will always be {0,1}, whilst for three players it colud be\n {0,1}, {0,2}, {1,2}, or {1,2,3}.\n */\n explicit\n eval_event( binary_strategy_description::eval_view* eval, \n std::string const& key,\n std::vector perm,\n Eigen::VectorXd const& dead_money,\n Eigen::VectorXd const& active)\n :eval_{eval}, key_(key), perm_{perm}, dead_money_{dead_money}, active_{active}\n ,pot_amt_{active_.sum() + dead_money_.sum()}\n {\n\n delta_proto_.resize(dead_money_.size()+1);\n delta_proto_.fill(0);\n for(size_t idx=0;idx!=active_.size();++idx){\n delta_proto_[idx] -= active_[idx];\n delta_proto_[idx] -= dead_money_[idx];\n }\n \n if( Debug ){\n std::cout << \"perm => \" << detail::to_string(perm) << \"\\n\"; // __CandyPrint__(cxx-print-scalar,perm)\n std::cout << \"dead_money_ => \" << vector_to_string(dead_money_) << \"\\n\"; // __CandyPrint__(cxx-print-scalar,dead_money_)\n std::cout << \"active_ => \" << vector_to_string(active_) << \"\\n\"; // __CandyPrint__(cxx-print-scalar,active_)\n std::cout << \"delta_proto_ => \" << vector_to_string(delta_proto_) << \"\\n\"; // __CandyPrint__(cxx-print-scalar,delta_proto_)\n std::cout << \"pot_amt_ => \" << pot_amt_ << \"\\n\"; // __CandyPrint__(cxx-print-scalar,pot_amt_)\n }\n\n }\n virtual std::string key()const override{ return key_; }\n virtual void expected_value_given_event(Eigen::VectorXd& out, holdem_class_vector const& cv, double p)const override{\n\n // short circuit for optimization purposes\n if( std::fabs(p) < 0.001 )\n return;\n\n #if 0\n struct stride_view{\n enum{ MaxSize = 9 };\n void construct(std::vector mask_, holdem_id* first, holdem_id* last){\n first_ = first;\n last_ = last;\n }\n private:\n std::array view_;\n };\n #endif\n\n /*\n We have a perm of the players involed in the hold, perhaps P = (1,2).\n What we need to do it then map this perm, so that\n CV[S[P[0]]] < CV[S[P[0]]],\n */\n\n std::array re_perm;\n std::copy(perm_.begin(), perm_.end(), re_perm.begin());\n auto re_perm_end = re_perm.begin() + perm_.size();\n std::sort(re_perm.begin(), re_perm_end, [&](size_t l, size_t r){\n return cv[l] < cv[r];\n });\n\n\n std::array tmp;\n for(size_t idx=0;idx!=perm_.size();++idx){\n tmp[idx] = cv[re_perm[idx]];\n }\n \n support::array_view tmp_view(&tmp[0], perm_.size());\n auto ev_ptr = eval_->eval_no_perm(tmp_view);\n auto const& ev = *ev_ptr;\n \n out += delta_proto_ * p;\n\n size_t ev_idx = 0;\n for(size_t idx=0;idx!=perm_.size();++idx){\n out[re_perm[idx]] += pot_amt_ * ev[ev_idx] * p;\n ++ev_idx;\n }\n }\n virtual std::string to_string()const override{\n std::stringstream sstr;\n sstr << std::fixed;\n sstr << \"Eval {key=\" << key_ << \", perm=\" << detail::to_string(perm_) \n << \", dead=\" << vector_to_string(dead_money_)\n << \", active=\" << vector_to_string(active_)\n << \", proto=\" << vector_to_string(delta_proto_)\n << \", pot=\" << pot_amt_ \n << \"}\";\n return sstr.str();\n }\n private:\n binary_strategy_description::eval_view* eval_;\n std::string key_;\n std::vector perm_;\n Eigen::VectorXd dead_money_; // not used\n Eigen::VectorXd active_; // not used\n Eigen::VectorXd delta_proto_;\n double pot_amt_;\n };\n\n\n struct heads_up_description : binary_strategy_description{\n heads_up_description(eval_view* eval, double sb, double bb, double eff)\n : sb_{sb}, bb_{bb}, eff_{eff}\n {\n eval_ = eval;\n\n\n\n Eigen::VectorXd v_f_{2};\n v_f_(0) = -sb_;\n v_f_(1) = sb_;\n auto n_f_ = std::make_shared(\"f\", v_f_);\n events_.push_back(n_f_);\n\n Eigen::VectorXd v_pf{2};\n v_pf(0) = bb_;\n v_pf(1) = -bb_;\n auto n_pf = std::make_shared(\"pf\", v_pf);\n events_.push_back(n_pf);\n\n Eigen::VectorXd dead_money = Eigen::VectorXd::Zero(2);\n Eigen::VectorXd active{2};\n active[0] = eff_;\n active[1] = eff_;\n auto n_pp = std::make_shared(eval_, \"pp\", std::vector{0,1}, dead_money, active);\n events_.push_back(n_pp);\n\n strats_.emplace_back(this, 0,0, \"SB Pushing\", \"\");\n strats_.emplace_back(this, 1,1, \"BB Calling, given a SB push\", \"p\");\n \n finish();\n }\n virtual strategy_impl_t make_inital_state()const override{\n Eigen::VectorXd proto(169);\n proto.fill(0.0);\n strategy_impl_t vec;\n vec.emplace_back(proto);\n vec.emplace_back(proto);\n return vec;\n }\n virtual double sb()const{ return sb_; }\n virtual double bb()const{ return bb_; }\n virtual double eff()const{ return eff_; }\n virtual size_t num_players()const{ return 2; }\n virtual size_t strat_vector_size()const{ return 2; }\n \n virtual double probability_of_event(std::string const& key, holdem_class_vector const& cv, strategy_impl_t const& impl)const{\n auto a = impl[0][cv[0]];\n auto b = impl[1][cv[1]];\n //if( key == \"p\" ){ return a; }\n if( key == \"f\" ){ return ( 1.0 - a); }\n if( key == \"pp\"){ return a * b; }\n if( key == \"pf\"){ return a * (1.0 - b ); }\n std::stringstream sstr;\n sstr << \"unknown key \" << key;\n throw std::domain_error(sstr.str());\n }\n virtual Eigen::VectorXd expected_value_by_class_id(size_t player_idx, strategy_impl_t const& impl)const override{\n Eigen::VectorXd result(169);\n result.fill(0);\n for(auto const& group : *Memory_TwoPlayerClassVector){\n for(auto const& _ : group.vec){\n auto const& cv = _.cv;\n auto ev = expected_value_of_vector(aux_event_set_, cv, impl);\n result(cv[player_idx]) += _.prob * ev[player_idx];\n }\n }\n return result;\n }\n virtual Eigen::VectorXd expected_value(strategy_impl_t const& impl)const override{\n Eigen::VectorXd result(2);\n result.fill(0);\n for(auto const& group : *Memory_TwoPlayerClassVector){\n for(auto const& _ : group.vec){\n auto const& cv = _.cv;\n check_probability_of_event(cv, impl);\n auto ev = expected_value_of_vector(aux_event_set_, cv, impl);\n result[0] += _.prob * ev[0];\n result[1] += _.prob * ev[1];\n }\n }\n return result;\n }\n virtual double expected_value_for_class_id_es(event_set const& es, size_t player_idx, holdem_class_id class_id, strategy_impl_t const& impl)const override{\n double result = 0.0;\n holdem_class_vector cv;\n for(auto const& group : *Memory_TwoPlayerClassVector){\n if( group.cid != class_id)\n continue;\n for(auto const& _ : group.vec){\n cv = _.cv;\n if( player_idx != 0 )\n std::swap(cv[0], cv[player_idx]);\n auto ev = expected_value_of_vector(es, cv, impl);\n result += _.prob * ev[player_idx];\n }\n }\n return result;\n }\n private:\n double sb_;\n double bb_;\n double eff_;\n };\n \n \n struct three_player_description : binary_strategy_description{\n three_player_description(eval_view* eval, double sb, double bb, double eff)\n : sb_{sb}, bb_{bb}, eff_{eff}\n {\n eval_ = eval;\n\n\n size_t num_players = 3;\n \n\n Eigen::VectorXd stacks{num_players};\n for(size_t idx=0;idx!=num_players;++idx){\n stacks[idx] = eff_;\n }\n\n\n Eigen::VectorXd v_blinds{num_players};\n v_blinds.fill(0.0);\n v_blinds[1] = sb_;\n v_blinds[2] = bb_;\n\n auto make_static = [&](std::string const& key, size_t target){\n Eigen::VectorXd sv = -v_blinds;\n sv[target] += v_blinds.sum();\n auto ptr = std::make_shared(key, sv);\n return ptr;\n };\n\n for(unsigned long long mask = ( 1 << num_players ); mask != 0;){\n --mask;\n std::bitset<32> bs = {mask};\n\n\n std::string key;\n std::vector perm;\n Eigen::VectorXd dead_money = Eigen::VectorXd::Zero(3);\n Eigen::VectorXd active = Eigen::VectorXd::Zero(3);\n\n for(size_t idx=0;idx!= num_players;++idx){\n if( bs.test(idx) ){\n active[idx] = stacks[idx];\n perm.push_back(idx);\n key += \"p\";\n } else{\n dead_money[idx] = v_blinds[idx];\n key += \"f\";\n }\n }\n\n if( bs.count() == 0 )\n continue;\n if( bs.count() == 1 && bs.test(num_players-1) ){\n // walk\n std::string degenerate_key(num_players-1, 'f');\n auto walk = make_static(degenerate_key, num_players-1);\n events_.push_back(walk);\n } else if( bs.count() == 1 ){\n // steal \n auto steal = make_static(key, perm[0]);\n events_.push_back(steal);\n } else { \n // push call\n\n auto allin = std::make_shared(eval_, key, perm, dead_money, active);\n events_.push_back(allin);\n }\n \n }\n\n strats_.emplace_back(this, 0,0, \"BTN Pushing\" , \"\" );\n strats_.emplace_back(this, 1,1, \"SB Calling, given BTN Push\" , \"p\" );\n strats_.emplace_back(this, 2,1, \"SB Pushing, given BTN Fold\" , \"f\" ); \n strats_.emplace_back(this, 3,2, \"BB Calling, given BTN Push, SB Call\", \"pp\");\n strats_.emplace_back(this, 4,2, \"BB Calling, given BTN Push, SB Fold\", \"pf\");\n strats_.emplace_back(this, 5,2, \"BB Calling, given BTN Fold, SB Push\", \"fp\");\n \n finish();\n }\n virtual strategy_impl_t make_inital_state()const override{\n Eigen::VectorXd proto(169);\n proto.fill(.5);\n strategy_impl_t vec;\n vec.emplace_back(proto);\n vec.emplace_back(proto);\n vec.emplace_back(proto);\n vec.emplace_back(proto);\n vec.emplace_back(proto);\n vec.emplace_back(proto);\n return vec;\n }\n virtual double sb()const{ return sb_; }\n virtual double bb()const{ return bb_; }\n virtual double eff()const{ return eff_; }\n virtual size_t num_players()const{ return 3; }\n virtual size_t strat_vector_size()const{ return 6; }\n \n /*\n The index of the strategy vector, for example\n for hu index 0 is for sb to push, whilst index\n 1 is for bb to call a push (given the action p),\n ie \n Index |Player| key | Given | P\n ------+------+-------+-------+------\n 0 | 0 | p | | P(p)\n 1 | 1 | pp | p | P(p|p)\n\n For three player this canonical mapping doesn't\n apply, we have\n \n Index |Player| Key | Given | P\n ------+------+-------+-------+------\n 0 | 0 | p | | P(p)\n 1 | 1 | pp | p | P(p|p)\n 2 | 1 | fp | f | P(p|f)\n 3 | 2 | ppp | pp | P(p|pp)\n 4 | 2 | pfp | pf | P(p|pf)\n 5 | 2 | fpp | fp | P(p|fp)\n */\n virtual double probability_of_event(std::string const& key, holdem_class_vector const& cv, strategy_impl_t const& impl)const{\n enum{ Debug = 0 };\n static std::unordered_map reg_alloc = {\n // Player\n { \"\" , 0 }, // 0\n { \"p\" , 1 }, // 1\n { \"f\" , 2 }, // 1\n { \"pp\", 3 }, // 2\n { \"pf\", 4 }, // 2\n { \"fp\", 5 } // 2\n };\n double result = 1.0;\n std::string sub;\n std::stringstream dbg;\n for(size_t idx=0;idx!=key.size();++idx){\n if( reg_alloc.count(sub) == 0 ){\n throw std::domain_error(\"bad\");\n }\n auto reg = reg_alloc[sub];\n switch(key[idx]){\n case 'p':\n case 'P':\n {\n dbg << \"P<\" << reg << \",\" << ( impl[reg][cv[idx]] ) << \">\";\n result *= impl[reg][cv[idx]];\n sub += 'p';\n break;\n }\n case 'f':\n case 'F':\n {\n dbg << \"F<\" << reg << \",\" << ( 1 - impl[reg][cv[idx]] )<<\">\";\n result *= ( 1 - impl[reg][cv[idx]] );\n sub += 'f';\n break;\n }}\n }\n if( Debug ) std::cout << dbg.str() << \"\\n\";\n return result;\n }\n virtual Eigen::VectorXd expected_value_by_class_id(size_t player_idx, strategy_impl_t const& impl)const override{\n Eigen::VectorXd result(169);\n result.fill(0);\n for(auto const& group : *Memory_ThreePlayerClassVector){\n for(auto const& _ : group.vec){\n auto const& cv = _.cv;\n auto ev = expected_value_of_vector(aux_event_set_, cv, impl);\n result(cv[player_idx]) += _.prob * ev[player_idx];\n }\n }\n return result;\n }\n virtual Eigen::VectorXd expected_value(strategy_impl_t const& impl)const override{\n Eigen::VectorXd result(3);\n result.fill(0);\n for(auto const& group : *Memory_ThreePlayerClassVector){\n for(auto const& _ : group.vec){\n auto const& cv = _.cv;\n auto ev = expected_value_of_vector(aux_event_set_, cv, impl);\n result[0] += _.prob * ev[0];\n result[1] += _.prob * ev[1];\n result[2] += _.prob * ev[2];\n }\n }\n return result;\n }\n virtual double expected_value_for_class_id_es(event_set const& es, size_t player_idx, holdem_class_id class_id, strategy_impl_t const& impl)const override{\n double result = 0.0;\n holdem_class_vector cv;\n for(auto const& group : *Memory_ThreePlayerClassVector){\n if( group.cid != class_id)\n continue;\n for(auto const& _ : group.vec){\n cv = _.cv;\n if( player_idx != 0 )\n std::swap(cv[0], cv[player_idx]);\n auto ev = expected_value_of_vector(es, cv, impl);\n result += _.prob * ev[player_idx];\n }\n }\n return result;\n }\n private:\n double sb_;\n double bb_;\n double eff_;\n };\n \n std::shared_ptr binary_strategy_description::make_hu_description(eval_view* eval, double sb, double bb, double eff){\n return std::make_shared(eval, sb, bb, eff);\n }\n std::shared_ptr binary_strategy_description::make_three_player_description(eval_view* eval, double sb, double bb, double eff){\n return std::make_shared(eval, sb, bb, eff);\n }\n\n} // end namespace ps\n", "meta": {"hexsha": "ce3e8b706891106d4386bbcefc1fcde7978996bc", "size": 25518, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Trash/binary_strategy_description.cpp", "max_stars_repo_name": "sweeterthancandy/CandyPoker", "max_stars_repo_head_hexsha": "53dfcc92402492739a2300847aeb298d389d546a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9.0, "max_stars_repo_stars_event_min_datetime": "2019-10-31T12:57:38.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-16T10:41:18.000Z", 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YES\n2. NO", "lm_q1_score": 0.640635841117624, "lm_q2_score": 0.3522017752483203, "lm_q1q2_score": 0.225633080529328}} {"text": "/* Siconos is a program dedicated to modeling, simulation and control\n * of non smooth dynamical systems.\n *\n * Copyright 2016 INRIA.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n*/\n\n\n#include \"NewtonEulerFrom1DLocalFrameR.hpp\"\n#include \n#include \"NewtonEulerDS.hpp\"\n#include \"Interaction.hpp\"\n#include \"BlockVector.hpp\"\n#include \n\n//#define NERI_DEBUG\n\n\n\n\n//#define NEFC3D_DEBUG\n// #define DEBUG_NOCOLOR\n// #define DEBUG_STDOUT\n// #define DEBUG_MESSAGES\n#include \"debug.h\"\n/*\nSee devNotes.pdf for details. A detailed documentation is available in DevNotes.pdf: chapter 'NewtonEulerR: computation of \\nabla q H'. Subsection 'Case FC3D: using the local frame local velocities'\n*/\nvoid NewtonEulerFrom1DLocalFrameR::NIcomputeJachqTFromContacts(SP::SiconosVector q1)\n{\n double Nx = _Nc->getValue(0);\n double Ny = _Nc->getValue(1);\n double Nz = _Nc->getValue(2);\n double Px = _Pc1->getValue(0);\n double Py = _Pc1->getValue(1);\n double Pz = _Pc1->getValue(2);\n double G1x = q1->getValue(0);\n double G1y = q1->getValue(1);\n double G1z = q1->getValue(2);\n#ifdef NEFC3D_DEBUG\n printf(\"contact normal:\\n\");\n _Nc->display();\n printf(\"point de contact :\\n\");\n _Pc1->display();\n printf(\"center of masse :\\n\");\n q1->display();\n#endif\n _RotationAbsToContactFrame->setValue(0, 0, Nx);\n _RotationAbsToContactFrame->setValue(0, 1, Ny);\n _RotationAbsToContactFrame->setValue(0, 2, Nz);\n\n _NPG1->zero();\n\n (*_NPG1)(0, 0) = 0;\n (*_NPG1)(0, 1) = -(G1z - Pz);\n (*_NPG1)(0, 2) = (G1y - Py);\n (*_NPG1)(1, 0) = (G1z - Pz);\n (*_NPG1)(1, 1) = 0;\n (*_NPG1)(1, 2) = -(G1x - Px);\n (*_NPG1)(2, 0) = -(G1y - Py);\n (*_NPG1)(2, 1) = (G1x - Px);\n (*_NPG1)(2, 2) = 0;\n\n\n computeRotationMatrix(q1,_rotationMatrixAbsToBody);\n prod(*_NPG1, *_rotationMatrixAbsToBody, *_AUX1, true);\n\n prod(*_RotationAbsToContactFrame, *_AUX1, *_AUX2, true);\n\n\n for (unsigned int jj = 0; jj < 3; jj++)\n _jachqT->setValue(0, jj, _RotationAbsToContactFrame->getValue(0, jj));\n\n for (unsigned int jj = 3; jj < 6; jj++)\n _jachqT->setValue(0, jj, _AUX2->getValue(0, jj - 3));\n\n#ifdef NEFC3D_DEBUG\n printf(\"NewtonEulerFrom1DLocalFrameR jhqt\\n\");\n _jachqT->display();\n#endif\n}\n\nvoid NewtonEulerFrom1DLocalFrameR::NIcomputeJachqTFromContacts(SP::SiconosVector q1, SP::SiconosVector q2)\n{\n double Nx = _Nc->getValue(0);\n double Ny = _Nc->getValue(1);\n double Nz = _Nc->getValue(2);\n double Px = _Pc1->getValue(0);\n double Py = _Pc1->getValue(1);\n double Pz = _Pc1->getValue(2);\n double G1x = q1->getValue(0);\n double G1y = q1->getValue(1);\n double G1z = q1->getValue(2);\n\n _RotationAbsToContactFrame->setValue(0, 0, Nx);\n _RotationAbsToContactFrame->setValue(0, 1, Ny);\n _RotationAbsToContactFrame->setValue(0, 2, Nz);\n\n _NPG1->zero();\n\n (*_NPG1)(0, 0) = 0;\n (*_NPG1)(0, 1) = -(G1z - Pz);\n (*_NPG1)(0, 2) = (G1y - Py);\n (*_NPG1)(1, 0) = (G1z - Pz);\n (*_NPG1)(1, 1) = 0;\n (*_NPG1)(1, 2) = -(G1x - Px);\n (*_NPG1)(2, 0) = -(G1y - Py);\n (*_NPG1)(2, 1) = (G1x - Px);\n (*_NPG1)(2, 2) = 0;\n\n computeRotationMatrix(q1,_rotationMatrixAbsToBody);\n prod(*_NPG1, *_rotationMatrixAbsToBody, *_AUX1, true);\n prod(*_RotationAbsToContactFrame, *_AUX1, *_AUX2, true);\n\n for (unsigned int jj = 0; jj < 3; jj++)\n _jachqT->setValue(0, jj, _RotationAbsToContactFrame->getValue(0, jj));\n\n\n for (unsigned int jj = 3; jj < 6; jj++)\n _jachqT->setValue(0, jj, _AUX2->getValue(0, jj - 3));\n\n double G2x = q2->getValue(0);\n double G2y = q2->getValue(1);\n double G2z = q2->getValue(2);\n\n _NPG2->zero();\n (*_NPG2)(0, 0) = 0;\n (*_NPG2)(0, 1) = -(G2z - Pz);\n (*_NPG2)(0, 2) = (G2y - Py);\n (*_NPG2)(1, 0) = (G2z - Pz);\n (*_NPG2)(1, 1) = 0;\n (*_NPG2)(1, 2) = -(G2x - Px);\n (*_NPG2)(2, 0) = -(G2y - Py);\n (*_NPG2)(2, 1) = (G2x - Px);\n (*_NPG2)(2, 2) = 0;\n\n\n\n computeRotationMatrix(q2,_rotationMatrixAbsToBody);\n prod(*_NPG2, *_rotationMatrixAbsToBody, *_AUX1, true);\n\n prod(*_RotationAbsToContactFrame, *_AUX1, *_AUX2, true);\n\n for (unsigned int jj = 0; jj < 3; jj++)\n _jachqT->setValue(0, jj + 6, -_RotationAbsToContactFrame->getValue(0, jj));\n\n for (unsigned int jj = 3; jj < 6; jj++)\n _jachqT->setValue(0, jj + 6, -_AUX2->getValue(0, jj - 3));\n}\n\nvoid NewtonEulerFrom1DLocalFrameR::initialize(Interaction& inter)\n{\n NewtonEulerR::initialize(inter);\n //proj_with_q _jachqProj.reset(new SimpleMatrix(_jachq->size(0),_jachq->size(1)));\n unsigned int qSize = 7 * (inter.getSizeOfDS() / 6);\n _jachq.reset(new SimpleMatrix(1, qSize));\n\n /* VA 12/04/2016 All of what follows should be put in WorkM*/\n _RotationAbsToContactFrame.reset(new SimpleMatrix(1, 3));\n _rotationMatrixAbsToBody.reset(new SimpleMatrix(3, 3));\n _AUX1.reset(new SimpleMatrix(3, 3));\n _AUX2.reset(new SimpleMatrix(1, 3));\n _NPG1.reset(new SimpleMatrix(3, 3));\n _NPG2.reset(new SimpleMatrix(3, 3));\n // _isContact=1;\n}\n\n\n\n\nvoid NewtonEulerFrom1DLocalFrameR::computeJachq(double time, Interaction& inter, SP::BlockVector q0)\n{\n\n DEBUG_BEGIN(\"NewtonEulerFrom1DLocalFrameR::computeJachq(double time, Interaction& inter, SP::BlockVector q0 ) \\n\");\n DEBUG_PRINTF(\"with time = %f\\n\",time);\n DEBUG_PRINTF(\"with inter = %p\\n\",&inter);\n\n\n _jachq->setValue(0, 0, _Nc->getValue(0));\n _jachq->setValue(0, 1, _Nc->getValue(1));\n _jachq->setValue(0, 2, _Nc->getValue(2));\n if (inter.has2Bodies())\n {\n _jachq->setValue(0, 7, -_Nc->getValue(0));\n _jachq->setValue(0, 8, -_Nc->getValue(1));\n _jachq->setValue(0, 9, -_Nc->getValue(2));\n }\n\n for (unsigned int iDS =0 ; iDS < q0->numberOfBlocks() ; iDS++)\n {\n SP::SiconosVector q = (q0->getAllVect())[iDS];\n double sign = 1.0;\n DEBUG_PRINTF(\"NewtonEulerFrom1DLocalFrameR::computeJachq : ds%d->q :\", iDS);\n DEBUG_EXPR_WE(q->display(););\n\n ::boost::math::quaternion quatGP;\n if (iDS == 0)\n {\n ::boost::math::quaternion quatAux(0, _Pc1->getValue(0) - q->getValue(0), _Pc1->getValue(1) - q->getValue(1),\n _Pc1->getValue(2) - q->getValue(2));\n quatGP = quatAux;\n }\n else\n {\n sign = -1.0;\n //cout<<\"NewtonEulerFrom1DLocalFrameR::computeJachq sign is -1 \\n\";\n ::boost::math::quaternion quatAux(0, _Pc2->getValue(0) - q->getValue(0), _Pc2->getValue(1) - q->getValue(1),\n _Pc2->getValue(2) - q->getValue(2));\n quatGP = quatAux;\n }\n DEBUG_PRINTF(\"NewtonEulerFrom1DLocalFrameR::computeJachq :GP :%lf, %lf, %lf\\n\", quatGP.R_component_2(), quatGP.R_component_3(), quatGP.R_component_4());\n DEBUG_PRINTF(\"NewtonEulerFrom1DLocalFrameR::computeJachq :Q :%e,%e, %e, %e\\n\", q->getValue(3), q->getValue(4), q->getValue(5), q->getValue(6));\n ::boost::math::quaternion quatQ(q->getValue(3), q->getValue(4), q->getValue(5), q->getValue(6));\n ::boost::math::quaternion quatcQ(q->getValue(3), -q->getValue(4), -q->getValue(5), -q->getValue(6));\n ::boost::math::quaternion quat0(1, 0, 0, 0);\n ::boost::math::quaternion quatBuff;\n ::boost::math::quaternion _2qiquatGP;\n _2qiquatGP = quatGP;\n _2qiquatGP *= 2 * (q->getValue(3));\n quatBuff = (quatGP * quatQ) + (quatcQ * quatGP) - _2qiquatGP;\n\n DEBUG_PRINTF(\"NewtonEulerFrom1DLocalFrameR::computeJachq :quattBuuf : %e,%e,%e \\n\", quatBuff.R_component_2(), quatBuff.R_component_3(), quatBuff.R_component_4());\n\n _jachq->setValue(0, 7 * iDS + 3, sign * (quatBuff.R_component_2()*_Nc->getValue(0) +\n quatBuff.R_component_3()*_Nc->getValue(1) + quatBuff.R_component_4()*_Nc->getValue(2)));\n //cout<<\"WARNING NewtonEulerFrom1DLocalFrameR set jachq \\n\";\n //_jachq->setValue(0,7*iDS+3,0);\n for (unsigned int i = 1; i < 4; i++)\n {\n ::boost::math::quaternion quatei(0, (i == 1) ? 1 : 0, (i == 2) ? 1 : 0, (i == 3) ? 1 : 0);\n _2qiquatGP = quatGP;\n _2qiquatGP *= 2 * (q->getValue(3 + i));\n quatBuff = quatei * quatcQ * quatGP - quatGP * quatQ * quatei - _2qiquatGP;\n _jachq->setValue(0, 7 * iDS + 3 + i, sign * (quatBuff.R_component_2()*_Nc->getValue(0) +\n quatBuff.R_component_3()*_Nc->getValue(1) + quatBuff.R_component_4()*_Nc->getValue(2)));\n }\n }\n\n DEBUG_EXPR(_jachq->display(););\n DEBUG_END(\"NewtonEulerFrom1DLocalFrameR::computeJachq(double time, Interaction& inter, SP::BlockVector q0 \\n\");\n\n}\n\nvoid NewtonEulerFrom1DLocalFrameR::computeJachqT(Interaction& inter, SP::BlockVector q0 )\n{\n DEBUG_BEGIN(\"NewtonEulerFrom1DLocalFrameR::computeJachqT(Interaction& inter, SP::BlockVector q0 \\n\")\n \n if (q0->numberOfBlocks()>1)\n {\n NIcomputeJachqTFromContacts((q0->getAllVect())[0], (q0->getAllVect())[1]);\n }\n else\n {\n NIcomputeJachqTFromContacts((q0->getAllVect())[0]);\n }\n\n DEBUG_END(\"NewtonEulerFrom1DLocalFrameR::computeJachqT(Interaction& inter, SP::BlockVector q0) \\n\");\n\n}\n\ndouble NewtonEulerFrom1DLocalFrameR::distance() const\n{\n SiconosVector dpc(*_Pc2 - *_Pc1);\n return dpc.norm2() * (inner_prod(*_Nc, dpc) >= 0 ? -1 : 1);\n}\n\nvoid NewtonEulerFrom1DLocalFrameR::computeh(double time, BlockVector& q0,\n SiconosVector &y)\n{\n // Contact points and normal are stored as relative to q1 and q2, if\n // no q2 then pc2 and normal are absolute.\n\n // Update pc1 based on q0 and relPc1\n SP::SiconosVector q1 = (q0.getAllVect())[0];\n ::boost::math::quaternion qq1((*q1)(3), (*q1)(4), (*q1)(5), (*q1)(6));\n ::boost::math::quaternion qpc1(0,(*_relPc1)(0),(*_relPc1)(1),(*_relPc1)(2));\n\n // apply q1 rotation and add\n qpc1 = qq1 * qpc1 / qq1;\n (*_Pc1)(0) = qpc1.R_component_2() + (*q1)(0);\n (*_Pc1)(1) = qpc1.R_component_3() + (*q1)(1);\n (*_Pc1)(2) = qpc1.R_component_4() + (*q1)(2);\n\n if (q0.numberOfBlocks() > 1)\n {\n // Update pc2 based on q0 and relPc2\n SP::SiconosVector q2 = (q0.getAllVect())[1];\n ::boost::math::quaternion qq2((*q2)(3), (*q2)(4), (*q2)(5), (*q2)(6));\n ::boost::math::quaternion qpc2(0,(*_relPc2)(0),(*_relPc2)(1),(*_relPc2)(2));\n\n // apply q2 rotation and add\n qpc2 = qq2 * qpc2 / qq2;\n (*_Pc2)(0) = qpc2.R_component_2() + (*q2)(0);\n (*_Pc2)(1) = qpc2.R_component_3() + (*q2)(1);\n (*_Pc2)(2) = qpc2.R_component_4() + (*q2)(2);\n\n // same for normal\n ::boost::math::quaternion qnc(0, (*_relNc)(0), (*_relNc)(1), (*_relNc)(2));\n qnc = qq2 * qnc / qq2;\n (*_Nc)(0) = qnc.R_component_2();\n (*_Nc)(1) = qnc.R_component_3();\n (*_Nc)(2) = qnc.R_component_4();\n }\n else\n {\n *_Pc2 = *_relPc2;\n *_Nc = *_relNc;\n }\n\n NewtonEulerR::computeh(time, q0, y);\n}\n", "meta": {"hexsha": "ed29b19c1fb9c324288677c0f6a3800d17b5f343", "size": 11169, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "kernel/src/modelingTools/NewtonEulerFrom1DLocalFrameR.cpp", "max_stars_repo_name": "bremond/siconos", "max_stars_repo_head_hexsha": "8deea56ff6779379f4f69e0376d24a81562a42d4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "kernel/src/modelingTools/NewtonEulerFrom1DLocalFrameR.cpp", "max_issues_repo_name": "bremond/siconos", "max_issues_repo_head_hexsha": "8deea56ff6779379f4f69e0376d24a81562a42d4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "kernel/src/modelingTools/NewtonEulerFrom1DLocalFrameR.cpp", "max_forks_repo_name": "bremond/siconos", "max_forks_repo_head_hexsha": "8deea56ff6779379f4f69e0376d24a81562a42d4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.5789473684, "max_line_length": 198, "alphanum_fraction": 0.6311218551, "num_tokens": 4156, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878555160664, "lm_q2_score": 0.3665897363221599, "lm_q1q2_score": 0.22548489476859757}} {"text": "#include \"CollisionHandler.h\"\n#include \"operationVector.h\"\n#include \n#include \"Turn.h\"\n#include \n#include \n#include \"VectorOfTurn.h\"\n#include \"Game.h\"\n#include \"bw.h\"\n#include \"Position.h\"\n#include \"beta.h\"\n\nusing namespace std;\nusing namespace boost::numeric::ublas;\n\nvoid game();\nvoid test();\n\nint main()\n{\n\tgame();\n// \ttest();\n\treturn 0;\n}\n\nvoid game()\n{\n\tGame game;\n\tgame.init();\n\tgame.mainLoop();\n}\n\nvoid test()\n{\n\t\n\tmatrix P{8, 8};\n\tstd::vector pie{0, 0, 0, 0, 1, 0, 0, 0};\n\t\n\tfill(P, [&](int l, int c){\n\t\tif ( abs(l - c) < 2 || (l == 0 && c == 7) || (l == 7 && c == 0)) {\n\t\t\treturn 1/3.0;\n\t\t}\n\t\treturn 0.0;\n\t});\n\t\n\tmatrix Obs(6, 8);\n\tfill(Obs, [&](int l, int c){\n\t\tif (l == 0 && (c == 0 || c == 1 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 1 && (c == 0 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 2 && (c == 5 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 3 && (c == 5 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 4 && (c == 5 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 5 && (c == 0 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\treturn 1;\n\t});\n\t\nmatrix Obs2(3, 8);\n\tfill(Obs2, [&](int l, int c){\n\t\tif (l == 0 && (c == 0 || c == 1 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 1 && (c == 0 || c == 1 || c == 2)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 2 && c != 2 ) {\n\t\t\treturn 0;\n\t\t\n\t\t}\n\n\t\treturn 1;\n\t});\n\t\n\tmatrix Obs3(5, 8);\n\tfill(Obs3, [&](int l, int c){\n\t\tif (l == 0 && (c == 0 || c == 1 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 1 && (c == 0 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 2 && (c == 5 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 3 && (c == 5 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 4 && c!= 5 ) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\treturn 1;\n\t});\n\t\n\t\tmatrix Obs4(9, 8);\n\tfill(Obs4, [&](int l, int c){\n\t\tif (l == 0 && (c == 0 || c == 1 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 1 && (c == 0 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 2 && (c == 5 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 3 && (c == 5 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 4 && (c == 5 || c == 6 || c == 4)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 5 && (c == 4 || c == 5 || c == 3)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 6 && (c == 5 || c == 6 || c == 4)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 7 && (c == 5 || c == 6 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 8 && c!= 7 ) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\t\n\t\treturn 1;\n\t});\n\t\n\tmatrix Obs5(3, 8);\n\tfill(Obs5, [&](int l, int c){\n\t\tif (l == 0 && (c == 0 || c == 1 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 1 && (c == 0 || c == 7 || c == 6)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 2 && c != 6 ) {\n\t\t\treturn 0;\n\t\t\n\t\t}\n\n\t\t\n\t\treturn 1;\n\t});\n\t\n\t\t\tmatrix Obs6(9, 8);\n\tfill(Obs6, [&](int l, int c){\n\t\tif (l == 0 && (c == 0 || c == 1 || c == 7)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 1 && (c == 0 || c == 1 || c == 2)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 2 && (c == 2 || c == 1 || c == 3)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 3 && (c == 3 || c == 2 || c == 4)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 4 && (c == 5 || c == 3 || c == 4)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 5 && (c == 4 || c == 5 || c == 3)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 6 && (c == 5 || c == 6 || c == 4)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 7 && (c == 5 || c == 4 || c == 3)) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\tif (l == 8 && c!= 3 ) {\n\t\t\treturn 0;\n\t\t}\n\t\t\n\t\t\n\t\treturn 1;\n\t});\n\t\n\t// showMat(Beta(Obs, P, pie));\n\t\n\tshowMat(BW({Obs,Obs2,Obs3,Obs4,Obs5,Obs6,Obs5,Obs5,Obs5,Obs5},P,pie,1000));\n\n\n\t//showMat(Obs6);\n\t//showMat(Position(Obs6,BW({Obs,Obs2,Obs3,Obs4,Obs5},P,pie,10),pie));\n\t//showMat(Position(Obs6,P,pie));\n}\n", "meta": {"hexsha": "7c811e530ad891169a1d680c865c86126085be73", "size": 3691, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/main.cpp", "max_stars_repo_name": "Louis-Alexandre/Captain-Markov", "max_stars_repo_head_hexsha": "386724030b33e3bf996897db6bcf8c7b7d8c5f84", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2015-04-24T17:44:11.000Z", "max_stars_repo_stars_event_max_datetime": "2017-10-29T09:47:49.000Z", "max_issues_repo_path": "src/main.cpp", "max_issues_repo_name": "Louis-Alexandre/Captain-Markov", "max_issues_repo_head_hexsha": "386724030b33e3bf996897db6bcf8c7b7d8c5f84", "max_issues_repo_licenses": 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YES\n2. NO", "lm_q1_score": 0.519521307073646, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.22546261784065685}} {"text": "//\n// Created by eliane on 04/03/19.\n//\n\n#include \"VinaLike.h\"\n\n#include \n\n#include \n\n#include \n\n#include \n\n#include \"VinaLikeCommon.h\"\n\nnamespace SmolDock::Score {\n\n double force_Instantiate_VinaLikeIntermolecularScoringFunction(const iConformer &conformer, iTransform &transform,\n const iProtein &protein,\n std::array nonDefaultCoeffs) {\n return VinaLikeIntermolecularScoringFunction(conformer, transform, protein, nonDefaultCoeffs);\n }\n\n double force_Instantiate_VinaLikeIntermolecularScoringFunction_vectorized(\n const iConformer_Vectorized &conformer, iTransform &transform,\n const iProtein_vectorized &protein,\n std::array nonDefaultCoeffs) {\n return VinaLikeIntermolecularScoringFunction_vectorized\n (conformer, transform, protein, nonDefaultCoeffs\n );\n }\n\n const std::array\n VinaLike::coefficientsNames = {\"Gauss1\", \"Gauss2\", \"RepulsionExceptCovalent\", \"Hydrophobic\", \"Hydrogen\"};\n\n template\n double VinaLikeIntermolecularScoringFunction(const iConformer &ligand_, iTransform &transform,\n const iProtein &protein,\n std::array nonDefaultCoeffs) {\n\n BOOST_ASSERT(!ligand_.x.empty());\n BOOST_ASSERT(!protein.x.empty());\n\n transform.doHousekeeping();\n if (std::abs(transform.rota.norm() - 1) > 0.1) {\n transform.rota.normalize();\n }\n\n BOOST_ASSERT(transform.bondRotationsAngles.size() == ligand_.num_rotatable_bond);\n\n double score_raw = 0;\n\n double distance_total = 0.0;\n double gauss1_total = 0.0;\n double gauss2_total = 0.0;\n double repuls_total = 0.0;\n double hydrophobic_total = 0.0;\n double hydrogen_total = 0.0;\n\n iConformer ligand = ligand_;\n //applyBondRotationInPlace(ligand, transform);\n\n Eigen::Vector3d ProtCenterPosition = {protein.center_x, protein.center_y, protein.center_z};\n\n if constexpr(!OnlyIntermolecular) // C++17\n {\n for (unsigned int idxLig = 0; idxLig < ligand.x.size(); idxLig++) {\n for (unsigned int idxLig2 = idxLig; idxLig2 < ligand.x.size(); idxLig2++) {\n Eigen::Vector3d LigDistance = {ligand.x[idxLig] - ligand.x[idxLig2],\n ligand.y[idxLig] - ligand.y[idxLig2],\n ligand.z[idxLig] - ligand.z[idxLig2]};\n\n double distance_raw = LigDistance.norm();\n const double distance = distanceFromRawDistance(distance_raw, ligand.atomicRadius[idxLig],\n ligand.atomicRadius[idxLig2]);\n\n// score_raw += VinaClassic::coeff_gauss1 * vinaGaussComponent(distance, 0.0, 0.5);\n// score_raw += VinaClassic::coeff_gauss2 * vinaGaussComponent(distance, 3.0, 2.0);\n score_raw += VinaClassic::coeff_repulsion * vinaRepulsionComponent(distance, 0.0);\n }\n }\n }\n\n\n for (unsigned int idxLig = 0; idxLig < ligand.x.size(); idxLig++) {\n for (unsigned int idxProt = 0; idxProt < protein.x.size(); idxProt++) {\n\n\n Eigen::Vector3d LigPosition = {ligand.x[idxLig], ligand.y[idxLig], ligand.z[idxLig]};\n applyRigidTransformInPlace(LigPosition, transform);\n\n Eigen::Vector3d ProtPosition = {protein.x[idxProt], protein.y[idxProt], protein.z[idxProt]};\n Eigen::Vector3d distToCenterVector = LigPosition - ProtCenterPosition;\n\n double distanceToProteinCenter = distToCenterVector.norm();\n\n if (distanceToProteinCenter > (protein.radius - 1)) {\n score_raw += std::pow((distanceToProteinCenter - protein.radius), 4) + 10;\n continue;\n }\n\n Eigen::Vector3d distVect = ProtPosition - LigPosition;\n\n const double rawDist = distVect.norm();\n\n\n if (rawDist >= VinaClassic::interaction_cutoff)\n continue;\n\n\n const double atomicRadiusLig = ligand.atomicRadius[idxLig];\n const double atomicRadiusProt = protein.atomicRadius[idxProt];\n\n const double radToRemove = (atomicRadiusLig + atomicRadiusProt);\n\n const double distance = rawDist - radToRemove;\n\n distance_total += distance;\n\n const unsigned int atom1AtomicNumber = ligand.type[idxLig];\n const unsigned int atom1AtomVariant = ligand.variant[idxLig];\n const unsigned int atom2AtomicNumber = protein.type[idxProt];\n const unsigned int atom2AtomVariant = protein.variant[idxProt];\n\n if constexpr(useNonDefaultCoefficients) {\n score_raw += nonDefaultCoeffs[0] * vinaGaussComponent(distance, 0.0, 0.5);\n score_raw += nonDefaultCoeffs[1] * vinaGaussComponent(distance, 3.0, 2.0);\n score_raw += nonDefaultCoeffs[2] * vinaRepulsionComponent(distance, 0.0);\n score_raw += nonDefaultCoeffs[3] * vinaHydrophobicComponent(distance,\n atom1AtomicNumber, atom1AtomVariant,\n atom2AtomicNumber, atom2AtomVariant);\n\n score_raw += nonDefaultCoeffs[4] * vinaHydrogenComponent(distance,\n atom1AtomicNumber, atom1AtomVariant,\n atom2AtomicNumber, atom2AtomVariant);\n } else {\n\n gauss1_total += vinaGaussComponent(distance, 0.0, 0.5);\n gauss2_total += vinaGaussComponent(distance, 3.0, 2.0);\n repuls_total += vinaRepulsionComponent(distance, 0.0);\n hydrophobic_total += vinaHydrophobicComponent(distance,\n atom1AtomicNumber, atom1AtomVariant,\n atom2AtomicNumber, atom2AtomVariant);\n hydrogen_total += vinaHydrogenComponent(distance,\n atom1AtomicNumber, atom1AtomVariant,\n atom2AtomicNumber, atom2AtomVariant);\n\n score_raw += VinaClassic::coeff_gauss1 * vinaGaussComponent(distance, 0.0, 0.5);\n score_raw += VinaClassic::coeff_gauss2 * vinaGaussComponent(distance, 3.0, 2.0);\n score_raw += VinaClassic::coeff_repulsion * vinaRepulsionComponent(distance, 0.0);\n score_raw += VinaClassic::coeff_hydrophobic * vinaHydrophobicComponent(distance,\n atom1AtomicNumber,\n atom1AtomVariant,\n atom2AtomicNumber,\n atom2AtomVariant);\n\n score_raw += VinaClassic::coeff_hydrogen * vinaHydrogenComponent(distance,\n atom1AtomicNumber,\n atom1AtomVariant,\n atom2AtomicNumber,\n atom2AtomVariant);\n }\n\n\n } // for\n } // for\n\n double final_score = score_raw / (1 + (VinaClassic::coeff_entropic * ligand.num_rotatable_bond));\n\n/*\n std::cout << \"\\n Non-Vectorized : \\n\";\n std::cout << \"dist : \" << distance_total << std::endl;\n std::cout << \"gauss1 : \" << gauss1_total << std::endl;\n std::cout << \"gauss2 : \" << gauss2_total << std::endl;\n std::cout << \"repuls : \" << repuls_total << std::endl;\n std::cout << \"hydroph : \" << hydrophobic_total << std::endl;\n std::cout << \"hydrog : \" << hydrogen_total << std::endl;\n std::cout << \"\\n\\n\";\n*/\n return final_score;\n }\n\n template\n double VinaLikeIntermolecularScoringFunction_vectorized(const iConformer_Vectorized &ligand_, iTransform &transform,\n const iProtein_vectorized &protein,\n std::array nonDefaultCoeffs) {\n\n BOOST_ASSERT(ligand_.x.entriesCount() != 0);\n BOOST_ASSERT(protein.x.entriesCount() != 0);\n\n transform.doHousekeeping();\n if (std::abs(transform.rota.norm() - 1) > 0.1) {\n transform.rota.normalize();\n }\n\n BOOST_ASSERT(transform.bondRotationsAngles.size() == ligand_.num_rotatable_bond);\n\n double score_raw = 0;\n\n double distance_total = 0.0;\n double gauss1_total = 0.0;\n double gauss2_total = 0.0;\n double repuls_total = 0.0;\n double hydrophobic_total = 0.0;\n double hydrogen_total = 0.0;\n\n iConformer_Vectorized ligand = ligand_;\n //applyBondRotationInPlace(ligand, transform);\n\n const double xProtCenter = protein.center_x;\n const double yProtCenter = protein.center_y;\n const double zProtCenter = protein.center_z;\n\n if constexpr(!OnlyIntermolecular) // C++17\n {\n\n for (unsigned int idxLig1 = 0; idxLig1 < ligand.x.vectorsCount(); ++idxLig1) {\n for (unsigned int idxLig2 = idxLig1; idxLig2 < ligand.x.vectorsCount(); ++idxLig2) {\n\n const Vc::Vector x_diff = ligand.x.vector(idxLig1) - ligand.x.vector(idxLig2);\n const Vc::Vector y_diff = ligand.y.vector(idxLig1) - ligand.y.vector(idxLig2);\n const Vc::Vector z_diff = ligand.z.vector(idxLig1) - ligand.z.vector(idxLig2);\n const Vc::Vector squared_sum = (x_diff * x_diff) + (y_diff * y_diff) + (z_diff * z_diff);\n const Vc::Vector distances_raw = Vc::sqrt(squared_sum);\n\n const Vc::Vector distances = distanceFromRawDistance(distances_raw,\n ligand.atomicRadius.vector(idxLig1),\n ligand.atomicRadius.vector(idxLig2));\n\n score_raw += VinaClassic::coeff_repulsion * vinaRepulsionComponent(distances, 0.0).sum();\n\n\n }\n\n }\n }\n\n\n int atomicNumber_VectorIdx_ligand = -1;\n int atomicNumber_VectorIdx_protein = -1;\n int atomVariant_VectorIdx_ligand = -1;\n int atomVariant_VectorIdx_protein = -1;\n\n Vc::Memory, Vc::Vector::Size> ligandAtomicNumInformation;\n Vc::Memory, Vc::Vector::Size> ligandAtomVariantInformation;\n\n Vc::Memory, Vc::Vector::Size> proteinAtomicNumInformation;\n Vc::Memory, Vc::Vector::Size> proteinAtomVariantInformation;\n\n\n for (unsigned int idxLig = 0; idxLig < ligand.x.vectorsCount(); ++idxLig) {\n const unsigned int globalIdx_Ligand = (idxLig * Vc::Vector::Size);\n const unsigned char offsetUChar_Ligand = globalIdx_Ligand % Vc::Vector::Size;\n const unsigned char offsetUInt_Ligand = globalIdx_Ligand % Vc::Vector::Size;\n\n const unsigned int offset_UChar_ligand = offsetUChar_Ligand % Vc::Vector::Size;\n if (offset_UChar_ligand == 0) {\n // we are at the start of a\n atomicNumber_VectorIdx_ligand++;\n ligandAtomicNumInformation = ligand.type.vector(atomicNumber_VectorIdx_ligand);\n }\n\n const unsigned int offset_UInt_ligand = offsetUInt_Ligand % Vc::Vector::Size;\n if (offset_UInt_ligand == 0) {\n // we are at the start of a\n atomVariant_VectorIdx_ligand++;\n ligandAtomVariantInformation = ligand.variant.vector(atomVariant_VectorIdx_ligand);\n }\n\n Vc::Vector ligandHydrophobicMaskMaker;\n for (unsigned int k = 0; k < Vc::Vector::Size; ++k) {\n ligandHydrophobicMaskMaker[k] = isHydrophobic_prepareMask(\n ligandAtomicNumInformation[offset_UChar_ligand + k],\n ligandAtomVariantInformation[offset_UChar_ligand + k]\n );\n }\n\n Vc::Mask ligandHydrophobicMask = (ligandHydrophobicMaskMaker == Vc::Vector::One());\n\n for (unsigned int idxProt = 0; idxProt < protein.x.vectorsCount(); idxProt++) {\n Vc::Vector xLig, yLig, zLig;\n xLig = ligand.x.vector(idxLig);\n yLig = ligand.y.vector(idxLig);\n zLig = ligand.z.vector(idxLig);\n\n\n applyRigidTransformInPlace(xLig, yLig, zLig, transform);\n\n\n const Vc::Vector xsquared_disttoProtCenter = (xLig - xProtCenter) * (xLig - xProtCenter);\n const Vc::Vector ysquared_disttoProtCenter = (yLig - yProtCenter) * (yLig - yProtCenter);\n const Vc::Vector zsquared_disttoProtCenter = (zLig - zProtCenter) * (zLig - zProtCenter);\n\n\n const Vc::Vector distancesToProtCenter = Vc::sqrt(xsquared_disttoProtCenter\n + ysquared_disttoProtCenter\n + zsquared_disttoProtCenter);\n\n\n const Vc::Vector tooFarFromCenterPenalty = Vc::iif(\n distancesToProtCenter > (protein.radius - 1),\n Vc::exp(4 * Vc::log(distancesToProtCenter - protein.radius)) + 10,\n Vc::Vector(Vc::Zero));\n\n\n score_raw += tooFarFromCenterPenalty.sum();\n\n\n const Vc::Vector xProt = protein.x.vector(idxProt);\n const Vc::Vector yProt = protein.y.vector(idxProt);\n const Vc::Vector zProt = protein.z.vector(idxProt);\n\n const Vc::Vector xsquared = (xLig - xProt) * (xLig - xProt);\n const Vc::Vector ysquared = (yLig - yProt) * (yLig - yProt);\n const Vc::Vector zsquared = (zLig - zProt) * (zLig - zProt);\n\n const Vc::Vector rawDistances = Vc::sqrt(xsquared + ysquared + zsquared);\n\n\n if (Vc::all_of(rawDistances >= VinaClassic::interaction_cutoff)) {\n /*\n * Theoretically, this helps because atoms which are close in idx are ~ close in position\n * So all_of will be true a non negigible fraction of the time\n * TODO : benchmark this versus just the mask\n */\n continue;\n }\n\n // If we have some higher and some lower than the cutoff, we we mask them at the end in .sum()\n auto cutOffMask = rawDistances < VinaClassic::interaction_cutoff;\n\n const Vc::Vector distances = distanceFromRawDistance(rawDistances,\n ligand.atomicRadius.vector(idxLig),\n protein.atomicRadius.vector(idxProt));\n\n //std::cout << cutOffMask << \" -> \" << distances << std::endl;\n\n\n distance_total += distances.sum(cutOffMask);\n\n const unsigned int globalIdx_Prot = (idxProt * Vc::Vector::Size);\n const unsigned char offsetUChar_Prot = globalIdx_Prot % Vc::Vector::Size;\n const unsigned char offsetUInt_Prot = globalIdx_Prot % Vc::Vector::Size;\n\n\n const unsigned int offset_UChar_protein = offsetUChar_Prot % Vc::Vector::Size;\n if (offset_UChar_protein == 0) {\n // we are at the start of a\n atomicNumber_VectorIdx_protein++;\n proteinAtomicNumInformation = protein.type.vector(atomicNumber_VectorIdx_protein);\n }\n\n const unsigned int offset_UInt_protein = offsetUInt_Prot % Vc::Vector::Size;\n if (offset_UInt_protein == 0) {\n // we are at the start of a\n atomVariant_VectorIdx_protein++;\n proteinAtomVariantInformation = protein.variant.vector(atomVariant_VectorIdx_ligand);\n }\n\n\n Vc::Vector proteinHydrophobicMaskMaker;\n for (unsigned int k = 0; k < Vc::Vector::Size; ++k) {\n proteinHydrophobicMaskMaker[k] = isHydrophobic_prepareMask(\n proteinAtomicNumInformation[offset_UChar_protein + k],\n proteinAtomVariantInformation[offset_UInt_protein + k]\n );\n }\n\n Vc::Vector HydrogenMaskMaker;\n for (unsigned int k = 0; k < Vc::Vector::Size; ++k) {\n ligandHydrophobicMaskMaker[k] = hydrogenBondingPossible_prepareMask(\n ligandAtomicNumInformation[offset_UChar_ligand + k],\n ligandAtomVariantInformation[offset_UChar_ligand + k],\n proteinAtomicNumInformation[offset_UChar_ligand + k],\n proteinAtomVariantInformation[offset_UChar_ligand + k]\n );\n }\n\n\n Vc::Mask hydrophobicMask =\n (proteinHydrophobicMaskMaker == Vc::Vector::One()) && ligandHydrophobicMask;\n Vc::Mask hydrogenMask = (HydrogenMaskMaker == Vc::Vector::One());\n\n\n if constexpr(useNonDefaultCoefficients) {\n\n score_raw += nonDefaultCoeffs[0] * vinaGaussComponent(distances, 0.0, 0.5).sum(cutOffMask);\n score_raw += nonDefaultCoeffs[1] * vinaGaussComponent(distances, 3.0, 2.0).sum(cutOffMask);\n score_raw += nonDefaultCoeffs[2] * vinaRepulsionComponent(distances, 0.0).sum(cutOffMask);\n score_raw +=\n nonDefaultCoeffs[3] * vinaHydrophobicComponent(distances, hydrophobicMask).sum(cutOffMask);\n score_raw += nonDefaultCoeffs[4] * vinaHydrogenComponent(distances, hydrogenMask).sum(cutOffMask);\n } else {\n gauss1_total += vinaGaussComponent(distances, 0.0, 0.5).sum(cutOffMask);\n gauss2_total += vinaGaussComponent(distances, 3.0, 2.0).sum(cutOffMask);\n repuls_total += vinaRepulsionComponent(distances, 0.0).sum(cutOffMask);\n hydrophobic_total += vinaHydrophobicComponent(distances, hydrophobicMask).sum(cutOffMask);\n hydrogen_total += vinaHydrogenComponent(distances, hydrogenMask).sum(cutOffMask);\n\n score_raw += VinaClassic::coeff_gauss1 * vinaGaussComponent(distances, 0.0, 0.5).sum(cutOffMask);\n score_raw += VinaClassic::coeff_gauss2 * vinaGaussComponent(distances, 3.0, 2.0).sum(cutOffMask);\n score_raw += VinaClassic::coeff_repulsion * vinaRepulsionComponent(distances, 0.0).sum(cutOffMask);\n score_raw += VinaClassic::coeff_hydrophobic *\n vinaHydrophobicComponent(distances, hydrophobicMask).sum(cutOffMask);\n score_raw += VinaClassic::coeff_hydrogen *\n vinaHydrogenComponent(distances, hydrogenMask).sum(cutOffMask);\n }\n\n }\n }\n double final_score = score_raw / (1 + (VinaClassic::coeff_entropic * ligand.num_rotatable_bond));\n\n std::cout << \"\\n Vectorized : \\n\";\n std::cout << \"dist : \" << distance_total << std::endl;\n std::cout << \"gauss1 : \" << gauss1_total << std::endl;\n std::cout << \"gauss2 : \" << gauss2_total << std::endl;\n std::cout << \"repuls : \" << repuls_total << std::endl;\n std::cout << \"hydroph : \" << hydrophobic_total << std::endl;\n std::cout << \"hydrog : \" << hydrogen_total << std::endl;\n std::cout << \"\\n\\n\";\n\n return final_score;\n }\n\n\n VinaLike::VinaLike(const iConformer &startingConformation_,\n const iProtein &p,\n const iTransform &initialTransform_,\n double differential_epsilon_,\n bool useNonDefaultCoefficient) :\n useNonDefaultCoefficient(useNonDefaultCoefficient),\n startingConformation(startingConformation_),\n prot(p),\n initialTransform(initialTransform_),\n differential_epsilon(differential_epsilon_) {\n this->numberOfRotatableBonds = this->startingConformation.num_rotatable_bond;\n this->numberOfParamInState = 7 + (this->numberOfRotatableBonds);\n\n if (this->initialTransform.bondRotationsAngles.size() != this->numberOfRotatableBonds) {\n BOOST_LOG_TRIVIAL(error)\n << \"Discrepency between the number of rotatable bonds in the iConformer and iTransform (\"\n << this->numberOfRotatableBonds << \" != \" << this->initialTransform.bondRotationsAngles.size() << \")\";\n std::terminate();\n }\n\n if (this->useNonDefaultCoefficient) {\n this->nonDefaultCoefficients[0] = VinaClassic::coeff_gauss1;\n this->nonDefaultCoefficients[1] = VinaClassic::coeff_gauss2;\n this->nonDefaultCoefficients[2] = VinaClassic::coeff_repulsion;\n this->nonDefaultCoefficients[3] = VinaClassic::coeff_hydrophobic;\n this->nonDefaultCoefficients[4] = VinaClassic::coeff_hydrogen;\n }\n\n }\n\n\n double VinaLike::Evaluate(const arma::mat &x) {\n BOOST_ASSERT(x.n_rows == this->numberOfParamInState);\n\n iTransform tr = this->internalToExternalRepr(x);\n\n normalizeQuaternionInPlace(tr.rota);\n\n double score_ = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation, tr, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, tr, this->prot);\n\n return score_;\n }\n\n double VinaLike::EvaluateWithGradient(const arma::mat &x, arma::mat &grad) {\n\n BOOST_ASSERT(!x.has_nan());\n BOOST_ASSERT(!grad.has_nan());\n BOOST_ASSERT(x.n_rows == this->numberOfParamInState);\n BOOST_ASSERT(grad.n_rows == this->numberOfParamInState);\n\n iTransform tr = this->internalToExternalRepr(x);\n tr.doHousekeeping();\n\n double score_ = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation, tr, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, tr, this->prot);\n\n\n // Translation\n {\n iTransform transform_dx = tr;\n transform_dx.transl.x() += this->differential_epsilon;\n const double gradScore = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation,\n transform_dx, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, transform_dx, this->prot);\n grad[0] = gradScore - score_;\n }\n\n {\n iTransform transform_dy = tr;\n transform_dy.transl.y() += this->differential_epsilon;\n const double gradScore = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation,\n transform_dy, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, transform_dy, this->prot);\n grad[1] = gradScore - score_;\n }\n\n {\n iTransform transform_dz = tr;\n transform_dz.transl.z() += this->differential_epsilon;\n const double gradScore = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation,\n transform_dz, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, transform_dz, this->prot);\n grad[2] = gradScore - score_;\n }\n\n // Rotation\n\n {\n iTransform transform_dqs = tr;\n transform_dqs.rota.w() += this->differential_epsilon;\n transform_dqs.doHousekeeping();\n const double gradScore = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation,\n transform_dqs, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, transform_dqs, this->prot);\n grad[3] = gradScore - score_;\n }\n\n {\n iTransform transform_dqx = tr;\n transform_dqx.rota.x() += this->differential_epsilon;\n transform_dqx.doHousekeeping();\n const double gradScore = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation,\n transform_dqx, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, transform_dqx, this->prot);\n grad[4] = gradScore - score_;\n\n }\n\n {\n iTransform transform_dqy = tr;\n transform_dqy.rota.x() += this->differential_epsilon;\n transform_dqy.doHousekeeping();\n const double gradScore = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation,\n transform_dqy, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, transform_dqy, this->prot);\n grad[5] = gradScore - score_;\n }\n\n {\n iTransform transform_dqz = tr;\n transform_dqz.rota.x() += this->differential_epsilon;\n transform_dqz.doHousekeeping();\n const double gradScore = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation,\n transform_dqz, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, transform_dqz, this->prot);\n grad[6] = gradScore - score_;\n }\n\n for (unsigned int i = 0; i < this->numberOfRotatableBonds; i++) {\n iTransform transform_dbondrot = tr;\n transform_dbondrot.bondRotationsAngles[i] += this->differential_epsilon;\n const double gradScore = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation,\n transform_dbondrot, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, transform_dbondrot, this->prot);\n grad[7 + i] = gradScore - score_;\n\n }\n\n /*\n BOOST_LOG_TRIVIAL(debug) << \"Transform: \" << x.t();\n BOOST_LOG_TRIVIAL(debug) << \"Score: \" << score_;\n BOOST_LOG_TRIVIAL(debug) << \"Gradient\";\n BOOST_LOG_TRIVIAL(debug) << \" ds: \" << grad[3];\n BOOST_LOG_TRIVIAL(debug) << \" du: \" << grad[4] << \" dx: \" << grad[0];\n BOOST_LOG_TRIVIAL(debug) << \" dv: \" << grad[5] << \" dy: \" << grad[1];\n BOOST_LOG_TRIVIAL(debug) << \" dt: \" << grad[6] << \" dx: \" << grad[2];\n //*/\n\n BOOST_ASSERT(score_ == score_); // catches NaN\n return score_;\n }\n\n\n double VinaLike::getDifferentialEpsilon() const {\n return this->differential_epsilon;\n }\n\n arma::mat VinaLike::getStartingConditions() const {\n return this->externalToInternalRepr(this->initialTransform);\n }\n\n iConformer VinaLike::getConformerForParamMatrix(const arma::mat &x) {\n BOOST_ASSERT(x.n_rows == this->numberOfParamInState);\n\n iTransform tr = this->internalToExternalRepr(x);\n tr.doHousekeeping();\n\n iConformer ret = this->startingConformation;\n applyBondRotationInPlace(ret, tr);\n applyRigidTransformInPlace(ret, tr);\n\n return ret;\n }\n\n unsigned int VinaLike::getParamVectorDimension() const {\n return this->numberOfParamInState;\n }\n\n std::vector> VinaLike::EvaluateSubcomponents(const arma::mat &x) {\n std::vector> ret;\n\n BOOST_ASSERT(x.n_rows == this->numberOfParamInState);\n\n iTransform tr = this->internalToExternalRepr(x);\n tr.doHousekeeping();\n\n BOOST_ASSERT(!this->startingConformation.x.empty());\n BOOST_ASSERT(!this->prot.x.empty());\n BOOST_ASSERT(tr.bondRotationsAngles.size() == this->startingConformation.num_rotatable_bond);\n\n if (std::abs(tr.rota.norm() - 1) > 0.1) {\n tr.rota.normalize();\n }\n\n double gauss1_total = 0.0;\n double gauss2_total = 0.0;\n double repulsion_total = 0.0;\n double hydrogen_total = 0.0;\n double hydrophobic_total = 0.0;\n double score_raw = 0.0;\n\n double intramolecular_repuls_total = 0.0;\n double intramolecular_score = 0.0;\n\n iConformer ligand = this->startingConformation;\n applyBondRotationInPlace(ligand, tr);\n\n Eigen::Vector3d ProtCenterPosition = {this->prot.center_x, this->prot.center_y, this->prot.center_z};\n\n\n for (unsigned int idxLig = 0; idxLig < ligand.x.size(); idxLig++) {\n for (unsigned int idxLig2 = idxLig; idxLig2 < ligand.x.size(); idxLig2++) {\n\n if (idxLig == idxLig2)\n continue;\n\n Eigen::Vector3d LigDistance = {ligand.x[idxLig] - ligand.x[idxLig2],\n ligand.y[idxLig] - ligand.y[idxLig2],\n ligand.z[idxLig] - ligand.z[idxLig2]};\n\n double distance_raw = LigDistance.norm();\n const double distance = distanceFromRawDistance(distance_raw, ligand.atomicRadius[idxLig],\n ligand.atomicRadius[idxLig2]);\n\n// score_raw += VinaClassic::coeff_gauss1 * vinaGaussComponent(distance, 0.0, 0.5);\n// score_raw += VinaClassic::coeff_gauss2 * vinaGaussComponent(distance, 3.0, 2.0);\n intramolecular_repuls_total += vinaRepulsionComponent(distance, 0.0);\n\n }\n }\n\n intramolecular_score = VinaClassic::coeff_repulsion * intramolecular_repuls_total;\n\n\n for (unsigned int idxLig = 0; idxLig < ligand.x.size(); idxLig++) {\n for (unsigned int idxProt = 0; idxProt < this->prot.x.size(); idxProt++) {\n\n\n Eigen::Vector3d LigPosition = {ligand.x[idxLig], ligand.y[idxLig], ligand.z[idxLig]};\n applyRigidTransformInPlace(LigPosition, tr);\n\n Eigen::Vector3d ProtPosition = {this->prot.x[idxProt], this->prot.y[idxProt], this->prot.z[idxProt]};\n Eigen::Vector3d distToCenterVector = LigPosition - ProtCenterPosition;\n\n double distanceToProteinCenter = distToCenterVector.norm();\n\n if (distanceToProteinCenter > (this->prot.radius - 1)) {\n score_raw += std::pow((distanceToProteinCenter - this->prot.radius), 4) + 10;\n continue;\n }\n\n Eigen::Vector3d distVect = ProtPosition - LigPosition;\n\n double rawDist = distVect.norm();\n\n if (rawDist >= VinaClassic::interaction_cutoff)\n continue;\n\n double distance = distanceFromRawDistance(rawDist, ligand.atomicRadius[idxLig],\n this->prot.atomicRadius[idxProt]);\n\n const unsigned int atom1AtomicNumber = ligand.type[idxLig];\n const unsigned int atom1AtomVariant = ligand.variant[idxLig];\n const unsigned int atom2AtomicNumber = this->prot.type[idxProt];\n const unsigned int atom2AtomVariant = this->prot.variant[idxProt];\n\n gauss1_total += vinaGaussComponent(distance, 0.0, 0.5);\n gauss2_total += vinaGaussComponent(distance, 3.0, 2.0);\n repulsion_total += vinaRepulsionComponent(distance, 0.0);\n hydrophobic_total += vinaHydrophobicComponent(distance,\n atom1AtomicNumber, atom1AtomVariant,\n atom2AtomicNumber, atom2AtomVariant);\n hydrogen_total += vinaHydrogenComponent(distance,\n atom1AtomicNumber, atom1AtomVariant,\n atom2AtomicNumber, atom2AtomVariant);\n\n } // for\n } // for\n\n\n double score_sum = 0.0;\n\n if (this->useNonDefaultCoefficient) {\n ret.emplace_back(std::make_tuple(\"NonDefaultCoeffs\", 1.0));\n score_sum = this->nonDefaultCoefficients[0] * gauss1_total\n + this->nonDefaultCoefficients[1] * gauss2_total\n + this->nonDefaultCoefficients[2] * repulsion_total\n + this->nonDefaultCoefficients[3] * hydrophobic_total\n + this->nonDefaultCoefficients[4] * hydrogen_total;\n } else {\n score_sum = VinaClassic::coeff_gauss1 * gauss1_total\n + VinaClassic::coeff_gauss2 * gauss2_total\n + VinaClassic::coeff_repulsion * repulsion_total\n + VinaClassic::coeff_hydrophobic * hydrophobic_total\n + VinaClassic::coeff_hydrogen * hydrogen_total;\n }\n\n\n double final_score_fromSum = score_sum / (1 + (VinaClassic::coeff_entropic * ligand.num_rotatable_bond));\n\n ret.emplace_back(std::make_tuple(\"Gauss1\", gauss1_total));\n ret.emplace_back(std::make_tuple(\"Gauss2\", gauss2_total));\n ret.emplace_back(std::make_tuple(\"Repulsion\", repulsion_total));\n ret.emplace_back(std::make_tuple(\"Hydrophobic\", hydrophobic_total));\n ret.emplace_back(std::make_tuple(\"Hydrogen\", hydrogen_total));\n ret.emplace_back(std::make_tuple(\"Intra_Repuls\", intramolecular_repuls_total));\n ret.emplace_back(std::make_tuple(\"Intra_Score\", intramolecular_score));\n ret.emplace_back(std::make_tuple(\"ScoreRawSum\", score_sum));\n ret.emplace_back(std::make_tuple(\"Score\", final_score_fromSum));\n return ret;\n }\n\n double VinaLike::EvaluateOnlyIntermolecular(const arma::mat &x) {\n BOOST_ASSERT(x.n_rows == this->numberOfParamInState);\n\n iTransform tr = this->internalToExternalRepr(x);\n\n tr.doHousekeeping();\n\n // Template parameter controls whether onlyIntermolecular interaction are taken into account. Here we want true\n double score_ = this->useNonDefaultCoefficient ?\n VinaLikeIntermolecularScoringFunction(this->startingConformation, tr, this->prot,\n this->nonDefaultCoefficients)\n : VinaLikeIntermolecularScoringFunction(\n this->startingConformation, tr, this->prot);\n\n\n return score_;\n }\n\n unsigned int VinaLike::getCoefficientsVectorWidth() {\n return this->numCoefficients;\n }\n\n std::vector VinaLike::getCoefficientsNames() {\n return std::vector(this->coefficientsNames.begin(), this->coefficientsNames.end());\n }\n\n std::vector VinaLike::getCurrentCoefficients() {\n if (this->useNonDefaultCoefficient) {\n return std::vector(this->nonDefaultCoefficients.begin(), this->nonDefaultCoefficients.end());\n }\n return {VinaClassic::coeff_gauss1, VinaClassic::coeff_gauss2,\n VinaClassic::coeff_repulsion, VinaClassic::coeff_hydrophobic,\n VinaClassic::coeff_hydrogen};\n }\n\n bool VinaLike::setNonDefaultCoefficients(std::vector coeffs) {\n if (coeffs.size() != this->numCoefficients) {\n BOOST_LOG_TRIVIAL(error) << \"Trying to set \" << this->numCoefficients << \" coefficients with vector of \"\n << coeffs.size() << \" values.\";\n return false;\n }\n if (this->useNonDefaultCoefficient == false) {\n BOOST_LOG_TRIVIAL(error)\n << \"Trying to set non default coefficient, but this scoring function was constructed with default coefficients only.\";\n BOOST_LOG_TRIVIAL(error) << \"Check the parameters passed to the scoring function constructor.\";\n return false;\n }\n for (unsigned int j = 0; j < this->nonDefaultCoefficients.size(); ++j) {\n this->nonDefaultCoefficients[j] = coeffs[j];\n }\n return true;\n }\n\n}", "meta": {"hexsha": "122028bf783b7682537ddb4ce612a9d59027a4bb", "size": 41698, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Engines/ScoringFunctions/VinaLike.cpp", "max_stars_repo_name": "ElianeBriand/SMolDock", "max_stars_repo_head_hexsha": "de0cb746ef995ae0eef0f812ebd61727311c332f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-05-30T02:33:13.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-30T02:33:13.000Z", "max_issues_repo_path": "Engines/ScoringFunctions/VinaLike.cpp", "max_issues_repo_name": "ElianeBriand/SMolDock", "max_issues_repo_head_hexsha": "de0cb746ef995ae0eef0f812ebd61727311c332f", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Engines/ScoringFunctions/VinaLike.cpp", "max_forks_repo_name": "ElianeBriand/SMolDock", "max_forks_repo_head_hexsha": "de0cb746ef995ae0eef0f812ebd61727311c332f", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2020-12-30T19:11:51.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-25T02:45:49.000Z", "avg_line_length": 50.6658566221, "max_line_length": 134, "alphanum_fraction": 0.545277951, "num_tokens": 8829, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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NO", "lm_q1_score": 0.6334102498375401, "lm_q2_score": 0.3557748935136303, "lm_q1q2_score": 0.2253514641863928}} {"text": "#pragma once\n//===----------------------------------------------------------------------===//\n#include \"bah_patterns.hpp\"\n#include \"bah_types.hpp\"\n\n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n//===----------------------------------------------------------------------===//\nnamespace dtl {\n//===----------------------------------------------------------------------===//\n/// Implementation of the Byte Aligned Hybrid bitmap compression technique as\n/// described in the paper 'BAH: A Bitmap Index Compression Algorithm for Fast\n/// Data Retrieval' by Li et al.\nclass bah {\n static constexpr std::size_t word_bitlength = sizeof(bah_word_t) * 8;\n\n /// The encoded bitmap.\n std::vector main_;\n std::vector data_;\n std::vector index_;\n std::vector counter_;\n /// The length of the encoded bitmap.\n std::size_t encoded_bitmap_length_ = 0;\n\n /// The different word kinds.\n enum class word_kind {\n ZERO,\n ENCODABLE,\n LITERAL\n };\n\n /// Determines the word kind for a single word of the plain bitmap.\n static word_kind\n get_word_kind(bah_word_t word) {\n if (word == 0) {\n return word_kind::ZERO;\n }\n if (get_oep_code(word) != dtl::bah_code_not_found) {\n return word_kind::ENCODABLE;\n }\n if (get_tep_code(word) != dtl::bah_code_not_found) {\n return word_kind::ENCODABLE;\n }\n return word_kind::LITERAL;\n }\n\npublic:\n bah() = default;\n\n explicit bah(const boost::dynamic_bitset<$u32>& in) {\n auto* bitmap_begin = in.m_bits.data();\n auto* bitmap_end = in.m_bits.data() + in.m_bits.size();\n const auto word_cnt = bitmap_end - bitmap_begin; // TODO handle the case when the bitmap size is not a multiple of the word size\n\n // The plain bitmap is segmented into words of the same kind.\n // Keep track of the first and last word of the current segment.\n std::size_t seg_begin = 0;\n std::size_t seg_end = 0;\n\n // Encode the bitmap segment by segment.\n while (seg_begin < word_cnt) {\n auto& w = bitmap_begin[seg_begin];\n auto seg_kind = get_word_kind(w);\n seg_end = seg_begin + 1;\n // Find the end of the current 'segment'.\n while (seg_end < word_cnt\n && get_word_kind(bitmap_begin[seg_end]) == seg_kind) {\n auto seg_len = seg_end - seg_begin;\n if ((seg_kind == word_kind::LITERAL && seg_len == 63)\n || (seg_kind == word_kind::ENCODABLE)) {\n break;\n }\n ++seg_end;\n }\n // Encode the segment.\n if (seg_kind == word_kind::ZERO) {\n auto len = seg_end - seg_begin;\n if (len > 252) {\n main_.push_back(bah_byte_t(0));\n counter_.push_back(len);\n }\n else {\n while (len > 0) {\n auto l = std::min(len, 63ul);\n main_.push_back(static_cast(l));\n len -= l;\n }\n }\n }\n else if (seg_kind == word_kind::LITERAL) {\n auto len = seg_end - seg_begin;\n main_.push_back(static_cast(len) | (bah_byte_t(0b01) << 6));\n for (std::size_t i = seg_begin; i < seg_end; ++i) {\n data_.push_back(bitmap_begin[i]);\n }\n }\n else { /* ENCODABLE */\n auto code = get_oep_code(w);\n if (code != dtl::bah_code_not_found) {\n main_.push_back(\n static_cast(code) | (bah_byte_t(0b10) << 6));\n }\n else {\n code = get_tep_code(w);\n bah_byte_t code_lo = code & 0b111111;\n bah_byte_t code_hi = code >> 6;\n main_.push_back(code_lo | (bah_byte_t(0b11) << 6));\n index_.push_back(code_hi);\n }\n }\n\n // Next.\n seg_begin = seg_end;\n }\n encoded_bitmap_length_ = in.size();\n\n // Try to reduce the memory consumption.\n shrink();\n }\n\n ~bah() = default;\n bah(const bah& other) = default;\n bah(bah&& other) noexcept = default;\n bah& operator=(const bah& other) = default;\n bah& operator=(bah&& other) noexcept = default;\n\n /// Return the size in bytes.\n std::size_t __forceinline__\n size_in_bytes() const {\n return main_.size() * sizeof(bah_byte_t)\n + data_.size() * sizeof(bah_word_t)\n + index_.size() * sizeof(bah_byte_t)\n + counter_.size() * sizeof(bah_word_t)\n + 3 * sizeof(void*) /* pointers/offsets to the three other arrays */\n + sizeof(encoded_bitmap_length_); /* bit-length of the original bitmap */\n }\n\n /// Returns the size of the bitmap.\n std::size_t __forceinline__\n size() const {\n return encoded_bitmap_length_;\n }\n\n /// Conversion to an plain bitmap. // TODO remove\n dtl::plain_bitmap<$u64>\n to_plain_bitmap() {\n dtl::plain_bitmap<$u64> ret(encoded_bitmap_length_, false);\n // The write position.\n std::size_t i = 0;\n\n // The read position(s).\n std::size_t data_idx = 0;\n std::size_t index_idx = 0;\n std::size_t counter_idx = 0;\n for (std::size_t main_idx = 0; main_idx < main_.size(); ++main_idx) {\n const auto& w = main_[main_idx];\n const auto type = w >> 6;\n const auto n = w & 0b111111;\n switch (type) {\n case 0b00: { /* ZERO */\n std::size_t count = 0;\n if (n == 0) {\n count = counter_[counter_idx++];\n }\n else {\n count = n;\n }\n auto l = count * word_bitlength;\n ret.clear(i, std::min(i + l, encoded_bitmap_length_));\n i += l;\n break;\n }\n case 0b01: { /* LITERAL */\n for (std::size_t j = 0; j < n; ++j) {\n auto lit = data_[data_idx++];\n ret.store_bits(i,\n std::min(i + word_bitlength, encoded_bitmap_length_), lit);\n i += word_bitlength;\n }\n break;\n }\n case 0b10: { /* ENCODABLE (one byte) */\n auto pattern = get_oep(n);\n ret.store_bits(i,\n std::min(i + word_bitlength, encoded_bitmap_length_), pattern);\n i += word_bitlength;\n break;\n }\n case 0b11: { /* ENCODABLE (two bytes) */\n auto code = bah_word_t(n) | (bah_word_t(index_[index_idx++]) << 6);\n auto pattern = get_tep(code);\n ret.store_bits(i,\n std::min(i + word_bitlength, encoded_bitmap_length_), pattern);\n i += word_bitlength;\n break;\n }\n }\n }\n if (i < encoded_bitmap_length_) {\n ret.clear(i, encoded_bitmap_length_);\n }\n return std::move(ret);\n }\n\n static std::string\n name() {\n return \"bah\";\n }\n\n /// Returns the value of the bit at the position pos.\n u1 __forceinline__\n test(const std::size_t pos) const {\n // The current range spanned by the current encoded word.\n std::size_t b = 0;\n std::size_t e = 0;\n\n // The read position(s).\n std::size_t data_idx = 0;\n std::size_t index_idx = 0;\n std::size_t counter_idx = 0;\n for (std::size_t main_idx = 0; main_idx < main_.size(); ++main_idx) {\n const auto& w = main_[main_idx];\n const auto type = w >> 6;\n const auto n = w & 0b111111;\n switch (type) {\n case 0b00: { /* ZERO */\n std::size_t count = 0;\n if (n == 0) {\n count = counter_[counter_idx++];\n }\n else {\n count = n;\n }\n auto l = count * word_bitlength;\n e = std::min(b + l, encoded_bitmap_length_);\n if (b <= pos && pos < e) {\n return false;\n }\n break;\n }\n case 0b01: { /* LITERAL */\n e = std::min(b + n * word_bitlength, encoded_bitmap_length_);\n if (b <= pos && pos < e) {\n auto offset = pos - b;\n auto lit_word = data_[data_idx + (offset / word_bitlength)];\n return dtl::bits::bit_test(lit_word, offset % word_bitlength);\n }\n data_idx += n;\n break;\n }\n case 0b10: { /* ENCODABLE (one byte) */\n e = std::min(b + word_bitlength, encoded_bitmap_length_);\n if (b <= pos && pos < e) {\n auto pattern = get_oep(n);\n return dtl::bits::bit_test(pattern, pos % word_bitlength);\n }\n break;\n }\n case 0b11: { /* ENCODABLE (two bytes) */\n e = std::min(b + word_bitlength, encoded_bitmap_length_);\n auto code = bah_word_t(n) | (bah_word_t(index_[index_idx++]) << 6);\n if (b <= pos && pos < e) {\n auto pattern = get_tep(code);\n return dtl::bits::bit_test(pattern, pos % word_bitlength);\n }\n break;\n }\n }\n // Next.\n b = e;\n }\n return false;\n }\n\n /// Try to reduce the memory consumption. This function is supposed to be\n /// called after the bitmap has been modified.\n __forceinline__ void\n shrink() {\n main_.shrink_to_fit();\n data_.shrink_to_fit();\n index_.shrink_to_fit();\n counter_.shrink_to_fit();\n }\n\n //===--------------------------------------------------------------------===//\n /// 1-run iterator\n class iter {\n const bah& outer_;\n\n /// The read positions.\n std::size_t main_idx_;\n std::size_t data_idx_;\n std::size_t index_idx_;\n std::size_t counter_idx_;\n\n // word_kind segment_kind_;\n bah_word_t literal_;\n std::size_t literal_words_remaining_;\n std::size_t literal_in_word_idx_;\n\n /// Points to the beginning of a 1-run.\n $u64 pos_;\n /// The length of the current 1-run.\n $u64 length_;\n\n public:\n explicit __forceinline__\n iter(const bah& outer)\n : outer_(outer),\n main_idx_(0),\n data_idx_(0),\n index_idx_(0),\n counter_idx_(0),\n literal_(0),\n literal_words_remaining_(0),\n literal_in_word_idx_(0),\n pos_(0), length_(0) {\n // Find the first 1-run.\n const auto main_cnt = outer_.main_.size();\n while (main_idx_ < main_cnt) {\n pos_ += length_;\n length_ = 0;\n\n const auto& w = outer_.main_[main_idx_++];\n const auto type = w >> 6;\n const auto n = w & 0b111111;\n\n switch (type) {\n case 0b00: { /* ZERO */\n std::size_t count = 0;\n if (n == 0) {\n count = outer_.counter_[counter_idx_++];\n }\n else {\n count = n;\n }\n auto l = count * word_bitlength;\n length_ = l;\n break; // Skip over zeros.\n }\n case 0b01: { /* LITERAL */\n literal_ = outer_.data_[data_idx_++];\n assert(literal_ != 0);\n literal_words_remaining_ = n - 1;\n assert(literal_words_remaining_ < n);\n const std::size_t b = dtl::bits::tz_count(literal_);\n std::size_t e = b + 1;\n for (; e < word_bitlength; ++e) {\n u1 is_set = dtl::bits::bit_test(literal_, e);\n if (!is_set) break;\n }\n literal_in_word_idx_ = e;\n pos_ += b;\n length_ = e - b;\n return;\n }\n case 0b10: { /* ENCODABLE (one byte) */\n literal_ = get_oep(n);\n assert(literal_ != 0);\n literal_words_remaining_ = 0;\n const std::size_t b = dtl::bits::tz_count(literal_);\n std::size_t e = b + 1;\n for (; e < word_bitlength; ++e) {\n u1 is_set = dtl::bits::bit_test(literal_, e);\n if (!is_set) break;\n }\n literal_in_word_idx_ = e;\n pos_ += b;\n length_ = e - b;\n return;\n }\n case 0b11: { /* ENCODABLE (two bytes) */\n auto code =\n bah_word_t(n) | (bah_word_t(outer_.index_[index_idx_++]) << 6);\n literal_ = get_tep(code);\n assert(literal_ != 0);\n literal_words_remaining_ = 0;\n const std::size_t b = dtl::bits::tz_count(literal_);\n std::size_t e = b + 1;\n for (; e < word_bitlength; ++e) {\n u1 is_set = dtl::bits::bit_test(literal_, e);\n if (!is_set) break;\n }\n literal_in_word_idx_ = e;\n pos_ += b;\n length_ = e - b;\n return;\n }\n }\n }\n pos_ = outer_.encoded_bitmap_length_;\n length_ = 0;\n }\n\n /// Forward the iterator to the next 1-run.\n void __forceinline__\n next() {\n const auto main_cnt = outer_.main_.size();\n while (main_idx_ < main_cnt || literal_ != 0) {\n pos_ += length_;\n length_ = 0;\n\n // Check, whether we are currently in a literal segment.\n if (literal_ != 0) {\n if (literal_in_word_idx_ < word_bitlength) {\n // Find the next set bit.\n std::size_t b = literal_in_word_idx_;\n for (; b < word_bitlength; b++) { // TODO optimize\n u1 is_set = dtl::bits::bit_test(literal_, b);\n if (is_set) break;\n }\n if (b < word_bitlength) {\n // Determine the length of the current 1-run and return.\n std::size_t e = b + 1;\n for (; e < word_bitlength; ++e) { // TODO optimize\n u1 is_set = dtl::bits::bit_test(literal_, e);\n if (!is_set) break;\n }\n pos_ += b - literal_in_word_idx_;\n length_ = e - b;\n literal_in_word_idx_ = e;\n return;\n }\n pos_ += b - literal_in_word_idx_;\n }\n // Fetch the next literal word, if any.\n if (literal_words_remaining_ > 0) {\n literal_ = outer_.data_[data_idx_++];\n --literal_words_remaining_;\n literal_in_word_idx_ = 0;\n continue;\n }\n else {\n // Reached the end of the literal segment.\n literal_ = 0;\n literal_in_word_idx_ = 0;\n }\n }\n\n assert(literal_ == 0);\n\n if (main_idx_ >= main_cnt) break;\n\n const auto& w = outer_.main_[main_idx_++];\n const auto type = w >> 6;\n const auto n = w & 0b111111;\n switch (type) {\n case 0b00: { /* ZERO */\n std::size_t count = 0;\n if (n == 0) {\n count = outer_.counter_[counter_idx_++];\n }\n else {\n count = n;\n }\n auto l = count * word_bitlength;\n length_ = l;\n break; // Skip over zeros.\n }\n case 0b01: { /* LITERAL */\n literal_ = outer_.data_[data_idx_++];\n assert(literal_ != 0);\n literal_words_remaining_ = n - 1;\n assert(literal_words_remaining_ < n);\n const std::size_t b = dtl::bits::tz_count(literal_);\n std::size_t e = b + 1;\n for (; e < word_bitlength; ++e) {\n u1 is_set = dtl::bits::bit_test(literal_, e);\n if (!is_set) break;\n }\n literal_in_word_idx_ = e;\n pos_ += b;\n length_ = e - b;\n return;\n }\n case 0b10: { /* ENCODABLE (one byte) */\n literal_ = get_oep(n);\n assert(literal_ != 0);\n literal_words_remaining_ = 0;\n const std::size_t b = dtl::bits::tz_count(literal_);\n std::size_t e = b + 1;\n for (; e < word_bitlength; ++e) { // TODO optimize\n u1 is_set = dtl::bits::bit_test(literal_, e);\n if (!is_set) break;\n }\n literal_in_word_idx_ = e;\n pos_ += b;\n length_ = e - b;\n return;\n }\n case 0b11: { /* ENCODABLE (two bytes) */\n auto code =\n bah_word_t(n) | (bah_word_t(outer_.index_[index_idx_++]) << 6);\n literal_ = get_tep(code);\n assert(literal_ != 0);\n literal_words_remaining_ = 0;\n const std::size_t b = dtl::bits::tz_count(literal_);\n std::size_t e = b + 1;\n for (; e < word_bitlength; ++e) { // TODO optimize\n u1 is_set = dtl::bits::bit_test(literal_, e);\n if (!is_set) break;\n }\n literal_in_word_idx_ = e;\n pos_ += b;\n length_ = e - b;\n return;\n }\n }\n }\n pos_ = outer_.encoded_bitmap_length_;\n length_ = 0;\n }\n\n /// Forward the iterator to the desired position.\n void __forceinline__\n skip_to(const std::size_t to_pos) {\n assert(pos_ <= to_pos);\n if (to_pos >= outer_.encoded_bitmap_length_) {\n pos_ = outer_.encoded_bitmap_length_;\n length_ = 0;\n return;\n }\n // Call next until the desired position has been reached.\n while (!end() && pos() + length() <= to_pos) {\n next();\n }\n // Adjust the current position and run length.\n if (!end() && pos() < to_pos) {\n length_ -= to_pos - pos_;\n pos_ = to_pos;\n }\n }\n\n u1 __forceinline__\n end() const noexcept {\n return length_ == 0;\n }\n\n u64 __forceinline__\n pos() const noexcept {\n return pos_;\n }\n\n u64 __forceinline__\n length() const noexcept {\n return length_;\n }\n };\n //===--------------------------------------------------------------------===//\n\n using skip_iter_type = iter;\n using scan_iter_type = iter;\n\n /// Returns a 1-run iterator.\n skip_iter_type __forceinline__\n it() const {\n return skip_iter_type(*this);\n }\n\n /// Returns a 1-run iterator.\n scan_iter_type __forceinline__\n scan_it() const {\n return scan_iter_type(*this);\n }\n\n /// Returns the name of the instance including the most important parameters\n /// in JSON.\n std::string\n info() const {\n return \"{\\\"name\\\":\\\"\" + name() + \"\\\"\"\n + \",\\\"n\\\":\" + std::to_string(encoded_bitmap_length_)\n + \",\\\"size\\\":\" + std::to_string(size_in_bytes())\n + \",\\\"word_size\\\":\" + std::to_string(sizeof(bah_word_t))\n + \"}\";\n }\n\n // For debugging purposes.\n void\n print(std::ostream& os) const {\n os << \"main idx | word type | content\" << std::endl;\n os << \"---------|-----------|---------------------------------\" << std::endl;\n // The read position(s).\n std::size_t data_idx = 0;\n std::size_t index_idx = 0;\n std::size_t counter_idx = 0;\n for (std::size_t main_idx = 0; main_idx < main_.size(); ++main_idx) {\n os << std::setw(8) << main_idx << \" | \";\n auto& w = main_[main_idx];\n const auto type = w >> 6;\n const auto n = w & 0b111111;\n switch (type) {\n case 0b00: { /* ZERO */\n std::size_t count = 0;\n if (n == 0) {\n count = counter_[counter_idx++];\n }\n else {\n count = n;\n }\n os << count << \" x ZERO\";\n break;\n }\n case 0b01: { /* LITERAL */\n os << n << \" x LITERAL\";\n for (std::size_t i = 0; i < n; ++i) {\n os << std::endl;\n os << \" | | \"\n << std::bitset(data_[data_idx + i]);\n }\n data_idx += n;\n break;\n }\n case 0b10: { /* ENCODABLE (one byte) */\n os << \"1\"\n << \" x ENCODABLE (one byte)\";\n os << std::endl;\n os << \" | | \"\n << std::bitset(get_oep(n));\n break;\n }\n case 0b11: { /* ENCODABLE (two bytes) */\n os << \"1\"\n << \" x ENCODABLE (two bytes)\";\n auto code = bah_word_t(n) | (bah_word_t(index_[index_idx++]) << 6);\n os << std::endl;\n os << \" | | \"\n << std::bitset(get_tep(code));\n break;\n }\n }\n os << std::endl;\n }\n }\n};\n//===----------------------------------------------------------------------===//\n} // namespace dtl\n", "meta": {"hexsha": "bd733e5e86b552a2ee46559bf67c3d2bae1dd3c3", "size": 19992, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/dtl/bitmap/bah.hpp", "max_stars_repo_name": "harald-lang/tree-encoded-bitmaps", "max_stars_repo_head_hexsha": "a4ab056f2cefa7843b27c736833b08977b56649c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 29.0, "max_stars_repo_stars_event_min_datetime": "2020-06-18T12:51:42.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-22T07:38:24.000Z", "max_issues_repo_path": "src/dtl/bitmap/bah.hpp", "max_issues_repo_name": "marcellus-saputra/Thuja", "max_issues_repo_head_hexsha": "8443320a6d0e9a20bb6b665f0befc6988978cafd", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/dtl/bitmap/bah.hpp", "max_forks_repo_name": "marcellus-saputra/Thuja", "max_forks_repo_head_hexsha": "8443320a6d0e9a20bb6b665f0befc6988978cafd", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2021-04-07T13:43:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-09T04:49:39.000Z", "avg_line_length": 31.0434782609, "max_line_length": 132, "alphanum_fraction": 0.5025510204, "num_tokens": 5150, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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NO", "lm_q1_score": 0.5583270090337582, "lm_q2_score": 0.4035668537353746, "lm_q1q2_score": 0.22532227439123587}} {"text": "/*\n * KMajority.cpp\n *\n * Created on: Aug 28, 2013\n * Author: andresf\n */\n\n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace vlr {\n\nKMajority::KMajority(vlr::Mat& data, const cvflann::IndexParams& params,\n\t\tconst cvflann::IndexParams& nnIndexParams) :\n\t\tm_dataset(data), m_dim(data.cols), m_nnIndex(NULL), m_nnIndexParams(\n\t\t\t\tnnIndexParams) {\n\n\t// Attributes initialization\n\tm_numClusters = cvflann::get_param(params, \"num.clusters\");\n\tm_maxIterations = cvflann::get_param(params, \"max.iterations\");\n\tm_centersInitMethod = cvflann::get_param(\n\t\t\tparams, \"centers.init.method\");\n\tm_nnType = cvflann::get_param(params, \"nn.type\");\n\tm_numDatapoints = m_dataset.rows;\n\n\t// Initially all transactions belong to any cluster\n\tm_belongsTo.clear();\n\tm_belongsTo.resize(m_dataset.rows, m_numClusters);\n\n\t// Initially all transactions are at the farthest possible distance\n\t// i.e. m_dim*8 the max Hamming distance\n\tm_distanceTo.clear();\n\tm_distanceTo.resize(m_dataset.rows, data.cols * 8);\n\n\t// Initially no transaction is assigned to any cluster\n\tm_clusterCounts.clear();\n\tm_clusterCounts.resize(m_numClusters, 0);\n\n}\n\n// --------------------------------------------------------------------------\n\nKMajority::~KMajority() {\n\tdelete m_nnIndex;\n}\n\n// --------------------------------------------------------------------------\n\nvoid KMajority::build() {\n\n\tif (m_dataset.type() != CV_8U) {\n\t\tthrow std::runtime_error(\n\t\t\t\t\"[KMajority::build] Descriptors matrix is not binary\");\n\t}\n\n\tif (m_dataset.empty()) {\n\t\tthrow std::runtime_error(\"[KMajority::build] Descriptors is empty\");\n\t}\n\n\t// Trivial case: less data than clusters, assign one data point per cluster\n\tif (m_numDatapoints <= m_numClusters) {\n\t\tm_centroids.create(m_numClusters, m_dim, m_dataset.type());\n\t\tfor (int i = 0; i < m_numDatapoints; ++i) {\n\t\t\tm_dataset.row(i).copyTo(\n\t\t\t\t\tm_centroids(cv::Range(i, i + 1), cv::Range(0, m_dim)));\n\t\t\tm_belongsTo[i] = i;\n\t\t}\n\t\treturn;\n\t}\n\n#if KMAJVERBOSE\n\tprintf(\"-- Bootstrapping clustering process\\n\");\n#endif\n\n\t// Randomly generate clusters\n#if KMAJVERBOSE\n\tprintf(\" Initializing clusters centers.\\n\");\n#endif\n\tinitCentroids();\n\n\t// Update nearest neighbors index upon new centers\n#if KMAJVERBOSE\n\tprintf(\" Updating nearest neighbors index.\\n\");\n#endif\n\tupdateIndex();\n\n\t// Assign data to clusters\n#if KMAJVERBOSE\n\tprintf(\" Quantizing data into clusters.\\n\");\n#endif\n\tquantize();\n\n\tbool converged = false;\n\tint iteration = 0;\n\n\twhile (converged == false && iteration < m_maxIterations) {\n\n\t\t++iteration;\n\n#if KMAJVERBOSE\n\t\tprintf(\"-- Iteration=[%d]\\n\", iteration);\n\t\tfflush(stdout);\n#endif\n\n\t\t// Compute the new clusters centers\n#if KMAJVERBOSE\n\t\tprintf(\" Computing new clusters centers.\\n\");\n#endif\n\t\tcomputeCentroids();\n\n\t\t// Update nearest neighbors index upon new centers\n#if KMAJVERBOSE\n\t\tprintf(\" Updating nearest neighbors index.\\n\");\n#endif\n\t\tupdateIndex();\n\n\t\t// Reassign data to clusters\n#if KMAJVERBOSE\n\t\tprintf(\" Quantizing data into clusters.\\n\");\n#endif\n\t\tconverged = quantize();\n\n\t\t// Handle empty clusters case\n#if KMAJVERBOSE\n\t\tprintf(\" Handling empty clusters case.\\n\");\n#endif\n\t\thandleEmptyClusters();\n\t}\n\n}\n\n// --------------------------------------------------------------------------\n\nvoid KMajority::initCentroids() {\n\n\t// Initializing variables useful for obtaining indexes of random chosen center\n\tstd::vector centers_idx(m_numClusters);\n\tint centers_length;\n\n\t// Array of indices indicating data points involved in the clustering process\n\tint* indices = new int[m_dataset.rows];\n\tfor (int i = 0; i < m_dataset.rows; ++i) {\n\t\tindices[i] = i;\n\t}\n\n\t// Randomly chose centers\n\tCentersChooser::create(\n\t\t\tm_centersInitMethod)->chooseCenters(m_numClusters, indices,\n\t\t\tm_numDatapoints, centers_idx, centers_length, m_dataset);\n\tCV_Assert(centers_length == m_numClusters);\n\n\tstd::sort(centers_idx.begin(), centers_idx.end());\n\n\tdelete[] indices;\n\n\t// Assign centers based on the chosen indexes\n\tm_centroids.create(centers_length, m_dim, m_dataset.type());\n\tfor (int i = 0; i < centers_length; ++i) {\n\t\tm_dataset.row(centers_idx[i]).copyTo(\n\t\t\t\tm_centroids(cv::Range(i, i + 1), cv::Range(0, m_dim)));\n\t}\n\n}\n\n// --------------------------------------------------------------------------\n\nbool KMajority::quantize() {\n\n\tbool converged = true;\n\n\t// Number of nearest neighbors\n\tint knn = 1;\n\n\t// The indices of the nearest neighbors found (numQueries X numNeighbors)\n\tcvflann::Matrix indices(new int[1 * knn], 1, knn);\n\n\t// Distances to the nearest neighbors found (numQueries X numNeighbors)\n\tcvflann::Matrix distances(new DistanceType[1 * knn], 1, knn);\n\n\tfor (int i = 0; i < m_numDatapoints; ++i) {\n\t\tstd::fill(indices.data, indices.data + indices.rows * indices.cols, 0);\n\t\tstd::fill(distances.data,\n\t\t\t\tdistances.data + distances.rows * distances.cols, 0.0f);\n\n\t\tcvflann::Matrix descriptor(\n\t\t\t\t(Distance::ElementType*) m_dataset.row(i).data, 1,\n\t\t\t\tm_dataset.cols);\n\n\t\t/* Get new cluster it belongs to */\n\t\tm_nnIndex->knnSearch(descriptor, indices, distances, knn,\n\t\t\t\tcvflann::SearchParams());\n\n\t\t/* Check if cluster assignment changed */\n\t\t// If it did then algorithm hasn't converged yet\n\t\tif (m_belongsTo[i] != indices[0][0]) {\n\t\t\tconverged = false;\n\t\t}\n\n\t\t/* Update cluster assignment and cluster counts */\n\t\t// Decrease cluster count in case it was assigned to some valid cluster before.\n\t\t// Recall that initially all transaction are assigned to kth cluster which\n\t\t// is not valid since valid clusters run from 0 to k-1 both inclusive.\n\t\tif (m_belongsTo[i] != m_numClusters) {\n\t\t\t--m_clusterCounts[m_belongsTo[i]];\n\t\t}\n\t\tm_belongsTo[i] = indices[0][0];\n\t\t++m_clusterCounts[indices[0][0]];\n\t\tm_distanceTo[i] = distances[0][0];\n\t}\n\n\tdelete[] indices.data;\n\tdelete[] distances.data;\n\n\treturn converged;\n}\n\n// --------------------------------------------------------------------------\n\nvoid KMajority::computeCentroids() {\n\n\t// Warning: using matrix of integers, there might be an overflow when summing too much descriptors\n\tcv::Mat bitwiseCount(m_numClusters, m_dim * 8, cv::DataType::type);\n\t// Zeroing matrix of cumulative bits\n\tbitwiseCount = cv::Scalar::all(0);\n\t// Zeroing all cluster centers dimensions\n\tm_centroids = cv::Scalar::all(0);\n\n\t// Bitwise summing the data into each center\n\tfor (int i = 0; i < m_numDatapoints; ++i) {\n\t\tcv::Mat b = bitwiseCount.row(m_belongsTo[i]);\n\t\tKMajority::cumBitSum(m_dataset.row(i), b);\n\t}\n\n\t// Bitwise majority voting\n\tfor (int j = 0; j < m_numClusters; j++) {\n\t\tcv::Mat centroid = m_centroids.row(j);\n\t\tKMajority::majorityVoting(bitwiseCount.row(j), centroid,\n\t\t\t\tm_clusterCounts[j]);\n\t}\n}\n\n// --------------------------------------------------------------------------\n\nvoid KMajority::save(const std::string& filename) const {\n\n\tif (m_centroids.empty()) {\n\t\tthrow std::runtime_error(\"[KMajority::save] Tree is empty\");\n\t}\n\n\tcv::FileStorage fs(filename.c_str(), cv::FileStorage::WRITE);\n\n\tif (fs.isOpened() == false) {\n\t\tthrow std::runtime_error(\"[KMajority::save] \"\n\t\t\t\t\"Unable to open file [\" + filename + \"] for writing\");\n\t}\n\n\tfs << \"type\" << \"AKMAJ\";\n\tfs << \"Centers\" << m_centroids;\n\n\tfs.release();\n\n}\n\n// --------------------------------------------------------------------------\n\nvoid KMajority::load(const std::string& filename) {\n\n\tenum nodeFields {\n\t\theader, start, rows, cols, dt, data\n\t};\n\tstd::string nodeFieldsNames[] = { \"YAML\", \"Centers\", \"rows:\", \"cols:\",\n\t\t\t\"dt:\", \"data:\" };\n\n\tstd::ifstream inputZippedFileStream;\n\tboost::iostreams::filtering_istream inputFileStream;\n\n\tstd::string line, field;\n\tstd::stringstream ss;\n\n\t// Open file\n\tinputZippedFileStream.open(filename.c_str(),\n\t\t\tstd::fstream::in | std::fstream::binary);\n\n\t// Check file\n\tif (inputZippedFileStream.good() == false) {\n\t\tthrow std::runtime_error(\"[KMajority::load] \"\n\t\t\t\t\"Unable to open file [\" + filename + \"] for reading\");\n\t}\n\n\tint _rows = -1;\n\tint _cols = -1;\n\tstd::string _type;\n\tint elemIdx = -1;\n\tunsigned int elem;\n\n\ttry {\n\t\tinputFileStream.push(boost::iostreams::gzip_decompressor());\n\t\tinputFileStream.push(inputZippedFileStream);\n\n\t\twhile (getline(inputFileStream, line)) {\n\t\t\tss.clear();\n\t\t\tss.str(line);\n\t\t\tss >> field;\n\t\t\tif (field.compare(nodeFieldsNames[header]) == 0) {\n\t\t\t\tcontinue;\n\t\t\t} else if (field.compare(nodeFieldsNames[start]) == 0) {\n\t\t\t\tcontinue;\n\t\t\t} else if (field.compare(nodeFieldsNames[rows]) == 0) {\n\t\t\t\tss >> _rows;\n\t\t\t} else if (field.compare(nodeFieldsNames[cols]) == 0) {\n\t\t\t\tss >> _cols;\n\t\t\t} else if (field.compare(nodeFieldsNames[dt]) == 0) {\n\t\t\t\tss >> _type;\n\t\t\t} else {\n\t\t\t\tif (field.compare(nodeFieldsNames[data]) == 0) {\n\t\t\t\t\tm_centroids = cv::Mat::zeros(_rows, _cols,\n\t\t\t\t\t\t\t_type.compare(\"f\") == 0 ? CV_32F : CV_8U);\n\t\t\t\t\tline.replace(line.find(nodeFieldsNames[data]), 5, \" \");\n\t\t\t\t}\n\n\t\t\t\tstd::replace(line.begin(), line.end(), '[', ' ');\n\t\t\t\tstd::replace(line.begin(), line.end(), ',', ' ');\n\t\t\t\tstd::replace(line.begin(), line.end(), ']', ' ');\n\n\t\t\t\tss.clear();\n\t\t\t\tss.str(line);\n\n\t\t\t\twhile ((ss >> elem).fail() == false) {\n\t\t\t\t\t++elemIdx;\n\t\t\t\t\tint row = floor(elemIdx / _cols);\n\t\t\t\t\tint col = elemIdx % _cols;\n\t\t\t\t\tm_centroids.at(row, col) = elem;\n\t\t\t\t}\n\n\t\t\t}\n\t\t}\n\n\t} catch (const boost::iostreams::gzip_error& e) {\n\t\tthrow std::runtime_error(\"[KMajority::load] \"\n\t\t\t\t\"Got error while parsing file [\" + std::string(e.what()) + \"]\");\n\t}\n\n\t// Close file\n\tinputZippedFileStream.close();\n\n}\n\n// --------------------------------------------------------------------------\n\nvoid KMajority::cumBitSum(const cv::Mat& data, cv::Mat& accVector) {\n\n\t// cumResult and data must be row vectors\n\tif (data.rows != 1 || accVector.rows != 1) {\n\t\tthrow std::runtime_error(\n\t\t\t\t\"[KMajority::cumBitSum] data and cumResult parameters must be row vectors\\n\");\n\t}\n\t// cumResult and data must be same length\n\tif (data.cols * 8 != accVector.cols) {\n\t\tthrow std::runtime_error(\n\t\t\t\t\"[KMajority::cumBitSum] number of columns in cumResult must be that of data times 8\\n\");\n\t}\n\n\tuchar byte = 0;\n\tfor (int l = 0; l < accVector.cols; l++) {\n\t\t// bit: 7-(l%8) col: (int)l/8 descriptor: i\n\t\t// Load byte every 8 bits\n\t\tif ((l % 8) == 0) {\n\t\t\tbyte = *(data.col((int) l / 8).data);\n\t\t}\n\t\t// Note: ignore maybe-uninitialized warning because loop starts with l=0 that means byte gets a value as soon as the loop start\n\t\t// bit at ith position is mod(bitleftshift(byte,i),2) where ith position is 7-mod(l,8) i.e 7, 6, 5, 4, 3, 2, 1, 0\n\t\taccVector.at(0, l) += ((int) ((byte >> (7 - (l % 8))) % 2));\n\t}\n\n}\n\n// --------------------------------------------------------------------------\n\nvoid KMajority::majorityVoting(const cv::Mat& accVector, cv::Mat& result,\n\t\tconst int& threshold) {\n\n\t// cumResult and data must be a row vectors\n\tif (accVector.rows != 1 || result.rows != 1) {\n\t\tthrow std::runtime_error(\n\t\t\t\t\"[KMajority::majorityVoting] 'accVector' and 'result' parameters must be row vectors\\n\");\n\t}\n\n\t// cumResult and data must be same length\n\tif (result.cols * 8 != accVector.cols) {\n\t\tthrow std::runtime_error(\n\t\t\t\t\"[KMajority::majorityVoting] number of columns in 'accVector' must be that of 'result' times 8\\n\");\n\t}\n\n\t// In this point I already have stored in bitwiseCount the bitwise sum of all data assigned to jth cluster\n\tfor (int l = 0; l < accVector.cols; ++l) {\n\t\t// If the bitcount for jth cluster at dimension l is greater than half of the data assigned to it\n\t\t// then set lth centroid bit to 1 otherwise set it to 0 (break ties randomly)\n\t\tbool bit;\n\t\t// There is a tie if the number of data assigned to jth cluster is even\n\t\t// AND the number of bits set to 1 in lth dimension is the half of the data assigned to jth cluster\n\t\tif (threshold % 2 == 1\n\t\t\t\t&& 2 * accVector.at(0, l) == (int) threshold) {\n\t\t\tbit = rand() % 2;\n\t\t} else {\n\t\t\tbit = 2 * accVector.at(0, l) > (int) (threshold);\n\t\t}\n\t\t// Stores the majority voting result from the LSB to the MSB\n\t\tresult.at(0, (int) (accVector.cols - 1 - l) / 8) += (bit)\n\t\t\t\t<< ((accVector.cols - 1 - l) % 8);\n\t}\n}\n\n// --------------------------------------------------------------------------\n\nvoid KMajority::handleEmptyClusters() {\n\n\t// If some cluster appeared to be empty then:\n\t// 1. Find the biggest cluster.\n\t// 2. Find farthest point in the biggest cluster\n\t// 3. Exclude the farthest point from the biggest cluster and form a new 1-point cluster.\n\n\tfor (int k = 0; k < m_numClusters; ++k) {\n\t\tif (m_clusterCounts[k] != 0) {\n\t\t\tcontinue;\n\t\t}\n\n\t\t// 1. Find the biggest cluster\n\t\tint max_k = 0;\n\t\tfor (int k1 = 1; k1 < m_numClusters; ++k1) {\n\t\t\tif (m_clusterCounts[max_k] < m_clusterCounts[k1])\n\t\t\t\tmax_k = k1;\n\t\t}\n\n\t\t// 2. Find farthest point in the biggest cluster\n\t\tDistanceType maxDist(-1);\n\t\tint idxFarthestPt = -1;\n\t\tfor (int i = 0; i < m_numDatapoints; ++i) {\n\t\t\tif (m_belongsTo[i] == max_k) {\n\t\t\t\tif (maxDist < m_distanceTo[i]) {\n\t\t\t\t\tmaxDist = m_distanceTo[i];\n\t\t\t\t\tidxFarthestPt = i;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// 3. Exclude the farthest point from the biggest cluster and form a new 1-point cluster\n\t\t--m_clusterCounts[max_k];\n\t\t++m_clusterCounts[k];\n\t\tm_belongsTo[idxFarthestPt] = k;\n\t}\n}\n\n// --------------------------------------------------------------------------\n\nvoid KMajority::updateIndex() {\n\n\tm_nnIndex = vlr::createIndexByType(\n\t\t\tcvflann::Matrix(\n\t\t\t\t\t(Distance::ElementType*) m_centroids.data, m_centroids.rows,\n\t\t\t\t\tm_centroids.cols), m_nnType, m_nnIndexParams);\n\n\tdouble mytime = cv::getTickCount();\n m_nnIndex->buildIndex();\n\tmytime = ((double) cv::getTickCount() - mytime) / cv::getTickFrequency() * 1000;\n\n\tprintf(\" Index built in [%lf] ms\\n\", mytime);\n\n}\n\n// --------------------------------------------------------------------------\n\nconst cv::Mat& KMajority::getCentroids() const {\n\treturn m_centroids;\n}\n\n// --------------------------------------------------------------------------\n\nconst std::vector& KMajority::getClusterCounts() const {\n\treturn m_clusterCounts;\n}\n\n// --------------------------------------------------------------------------\n\nconst std::vector& KMajority::getClusterAssignments() const {\n\treturn m_belongsTo;\n}\n\n// --------------------------------------------------------------------------\n\ncvflann::NNIndex* createIndexByType(\n\t\tconst cvflann::Matrix& dataset,\n\t\tvlr::indexType type, const cvflann::IndexParams& userDefParams) {\n\n\tcvflann::IndexParams::const_iterator it;\n\n\tcvflann::IndexParams params;\n\tcvflann::NNIndex* nnIndex;\n\n\tdouble mytime = cv::getTickCount();\n\n\tswitch (type) {\n\tcase vlr::LINEAR:\n\t\tprintf(\"-- Creating [Linear] index\\n\");\n\t\tparams = cvflann::LinearIndexParams();\n\t\t// Do not copy any parameters, linear index doesn't need any\n\t\tnnIndex = new cvflann::LinearIndex(dataset, params,\n\t\t\t\tDistance());\n\t\tbreak;\n\tcase vlr::HIERARCHICAL:\n\t\tprintf(\"-- Creating [HierarchicalClustering] index\\n\");\n\t\tparams = cvflann::HierarchicalClusteringIndexParams();\n\t\tfor (it = userDefParams.begin(); it != userDefParams.end(); ++it) {\n\t\t\tint value = it->second.cast();\n\t\t\tparams[it->first] = value;\n\t\t}\n\t\tnnIndex = new cvflann::HierarchicalClusteringIndex(dataset,\n\t\t\t\tparams, Distance());\n\t\tbreak;\n\tdefault:\n\t\tthrow std::runtime_error(\"Unknown index type\");\n\t}\n\tmytime = ((double) cv::getTickCount() - mytime) / cv::getTickFrequency()\n\t\t\t* 1000;\n\n\tprintf(\" Index created in [%lf] ms\\n\", mytime);\n\n\treturn nnIndex;\n}\n\n} /* namespace vlr */\n", "meta": {"hexsha": "d0e3d5db2f8e8b342dc98e3d373ae0d1098f12d2", "size": 15776, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "KMajorityLib/src/KMajority.cpp", "max_stars_repo_name": "gantzer89/VLRPipeline", "max_stars_repo_head_hexsha": "db0ab392c2586b03518ec84455cec91b6011be4e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11.0, "max_stars_repo_stars_event_min_datetime": "2015-02-21T08:32:54.000Z", "max_stars_repo_stars_event_max_datetime": "2018-10-15T12:52:20.000Z", "max_issues_repo_path": "KMajorityLib/src/KMajority.cpp", "max_issues_repo_name": "afperezm/VLRPipeline", "max_issues_repo_head_hexsha": "db0ab392c2586b03518ec84455cec91b6011be4e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "KMajorityLib/src/KMajority.cpp", "max_forks_repo_name": "afperezm/VLRPipeline", "max_forks_repo_head_hexsha": "db0ab392c2586b03518ec84455cec91b6011be4e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2016-05-29T22:33:23.000Z", "max_forks_repo_forks_event_max_datetime": "2018-09-16T02:49:03.000Z", "avg_line_length": 29.0, "max_line_length": 129, "alphanum_fraction": 0.62918357, "num_tokens": 4251, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.4035668537353746, "lm_q1q2_score": 0.22532226845945538}} {"text": "/**\n * ****************************************************************************\n * Copyright (c) 2015, Robert Lukierski.\n * All rights reserved.\n *\n * Redistribution and use in source and binary forms, with or without\n * modification, are permitted provided that the following conditions are met:\n * \n * Redistributions of source code must retain the above copyright notice, this\n * list of conditions and the following disclaimer.\n * \n * Redistributions in binary form must reproduce the above copyright notice,\n * this list of conditions and the following disclaimer in the documentation\n * and/or other materials provided with the distribution.\n * \n * Neither the name of the copyright holder nor the names of its\n * contributors may be used to endorse or promote products derived from\n * this software without specific prior written permission.\n * \n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n * \n * ****************************************************************************\n * Gaussian distribution.\n * ****************************************************************************\n */\n#ifndef VISIONCORE_TYPES_GAUSSIAN_HPP\n#define VISIONCORE_TYPES_GAUSSIAN_HPP\n\n#include \n\n#include \n\nnamespace vc\n{\nnamespace types\n{\n template class Gaussian;\n template class MultivariateGaussian;\n}\n}\n\nnamespace Eigen \n{\n namespace internal \n {\n // Gaussian of scalars or Eigens\n template\n struct traits< vc::types::Gaussian > \n {\n static constexpr int Rows = 1;\n static constexpr int Cols = 1;\n static constexpr int Dimension = Rows * Cols;\n typedef T Scalar;\n typedef Scalar MeanType;\n };\n \n template\n struct traits > > \n {\n static constexpr int Rows = _Rows;\n static constexpr int Cols = _Cols;\n static constexpr int Dimension = Rows * Cols;\n typedef _Scalar Scalar;\n typedef Matrix MeanType;\n };\n \n template\n struct traits >, _Options> > : \n traits > >\n {\n static constexpr int Rows = _Rows;\n static constexpr int Cols = _Cols;\n static constexpr int Dimension = Rows * Cols;\n typedef _Scalar Scalar;\n typedef Map, _Options> MeanType;\n };\n \n template\n struct traits >, _Options> > : \n traits > > \n {\n static constexpr int Rows = _Rows;\n static constexpr int Cols = _Cols;\n static constexpr int Dimension = Rows * Cols;\n typedef _Scalar Scalar;\n typedef Map, _Options> MeanType;\n };\n \n // Multivariate\n template\n struct traits > \n {\n static constexpr int Dimension = _Dimension;\n typedef _Scalar Scalar;\n typedef Matrix MeanType;\n typedef Matrix CovarianceType;\n };\n \n template\n struct traits, _Options> > : \n traits > \n {\n static constexpr int Dimension = _Dimension;\n typedef _Scalar Scalar;\n typedef Map, _Options> MeanType;\n typedef Map, _Options> CovarianceType;\n };\n \n template\n struct traits, _Options> > : \n traits > \n {\n static constexpr int Dimension = _Dimension;\n typedef _Scalar Scalar;\n typedef Map, _Options> MeanType;\n typedef Map, _Options> CovarianceType;\n };\n \n }\n}\n\nnamespace vc\n{\n \nnamespace types\n{\n\n/**\n * Type agnostic Gaussian operations.\n */\ntemplate\nclass GaussianBase\n{\npublic:\n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n static constexpr int Rows = Eigen::internal::traits::Rows;\n static constexpr int Cols = Eigen::internal::traits::Cols;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef typename Eigen::internal::traits::MeanType MeanType;\n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n \n EIGEN_DEVICE_FUNC MeanType& mean() \n {\n return static_cast(this)->mean_nonconst();\n }\n \n EIGEN_DEVICE_FUNC const MeanType& mean() const\n {\n return static_cast(this)->mean_const();\n }\n \n EIGEN_DEVICE_FUNC MeanType& variance() \n {\n return static_cast(this)->variance_nonconst();\n }\n \n EIGEN_DEVICE_FUNC const MeanType& variance() const\n {\n return static_cast(this)->variance_const();\n }\n \n template\n EIGEN_DEVICE_FUNC inline GaussianBase& operator=(const GaussianBase& other)\n {\n mean() = other.mean();\n variance() = other.variance();\n return *this;\n }\n \n#if 0\n EIGEN_DEVICE_FUNC inline GaussianBase operator+(const GaussianBase& other) const\n {\n // TBD\n }\n \n EIGEN_DEVICE_FUNC inline GaussianBase operator-(const GaussianBase& other) const\n {\n // TBD\n }\n \n template\n EIGEN_DEVICE_FUNC inline GaussianBase operator*(const OtherScalar& other) const\n {\n // TBD\n }\n \n template\n EIGEN_DEVICE_FUNC inline GaussianBase operator/(const OtherScalar& other) const\n {\n // TBD\n }\n#endif\n \n#ifdef VISIONCORE_ENABLE_CEREAL\n template\n void load(Archive & archive, std::uint32_t const version)\n {\n archive(cereal::make_nvp(\"Mean\", mean()));\n archive(cereal::make_nvp(\"Variance\", variance()));\n }\n \n template\n void save(Archive & archive, std::uint32_t const version) const\n {\n archive(cereal::make_nvp(\"Mean\", mean()));\n archive(cereal::make_nvp(\"Variance\", variance()));\n }\n#endif // VISIONCORE_ENABLE_CEREAL \n};\n\n/**\n * Eigen operations for Gaussian.\n */\ntemplate\nclass GaussianDispatchingBase : public GaussianBase\n{\n typedef GaussianBase Base;\n friend class GaussianBase;\npublic:\n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n static constexpr int Rows = Eigen::internal::traits::Rows;\n static constexpr int Cols = Eigen::internal::traits::Cols;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef typename Eigen::internal::traits::MeanType MeanType;\n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n \n using Base::operator=;\n \n template\n EIGEN_DEVICE_FUNC inline Gaussian > cast() const \n {\n return Gaussian>(Base::mean().template cast<_NewScalarType>(), \n Base::variance().template cast<_NewScalarType>());\n }\n \n EIGEN_DEVICE_FUNC inline MeanType evaluate(const MeanType& x)\n {\n using Eigen::numext::sqrt;\n using Eigen::numext::exp;\n using Eigen::numext::pow;\n \n const MeanType inv_sqrt_2pi_s2 = Scalar(1.0) / sqrt(Scalar(2.0 * M_PI) * Base::variance().array());\n const MeanType a = (x - Base::mean());\n return inv_sqrt_2pi_s2.array() * exp(-(pow(a.array(), 2))/(Scalar(2.0) * Base::variance().array()));\n }\n \n EIGEN_DEVICE_FUNC inline void setZero()\n {\n Base::mean() = MeanType::Zero();\n Base::variance() = MeanType::Zero();\n }\n \n EIGEN_DEVICE_FUNC MeanType stddev() const\n {\n return sqrt(Base::variance().array());\n }\n};\n\n/**\n * Scalar operations for Gaussian.\n */\ntemplate\nclass GaussianDispatchingBase : public GaussianBase\n{\n typedef GaussianBase Base;\n friend class GaussianBase;\npublic: \n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n static constexpr int Rows = Eigen::internal::traits::Rows;\n static constexpr int Cols = Eigen::internal::traits::Cols;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef typename Eigen::internal::traits::MeanType MeanType;\n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n \n using Base::operator=;\n \n template\n EIGEN_DEVICE_FUNC inline Gaussian cast() const \n {\n return Gaussian((NewScalarType)Base::mean(), (NewScalarType)Base::variance());\n }\n \n EIGEN_DEVICE_FUNC inline MeanType evaluate(const MeanType& x)\n {\n using Eigen::numext::sqrt;\n using Eigen::numext::exp;\n \n const Scalar inv_sqrt_2pi_s2 = Scalar(1.0) / sqrt(Scalar(2.0 * M_PI) * Base::variance());\n const Scalar a = (x - Base::mean());\n return inv_sqrt_2pi_s2 * exp(-(a * a)/(Scalar(2.0) * Base::variance()));\n }\n \n EIGEN_DEVICE_FUNC inline void setZero()\n {\n Base::mean() = Scalar(0.0);\n Base::variance() = Scalar(0.0);\n }\n \n EIGEN_DEVICE_FUNC MeanType stddev() const\n {\n using Eigen::numext::sqrt;\n return sqrt(Base::variance());\n }\n};\n\n/**\n * Multivariate Gaussian Base.\n */\ntemplate\nclass MultivariateGaussianBase\n{\npublic:\n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef typename Eigen::internal::traits::MeanType MeanType;\n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n typedef typename Eigen::internal::traits::CovarianceType CovarianceType;\n typedef const typename Eigen::internal::traits::CovarianceType ConstCovarianceType;\n \n template\n EIGEN_DEVICE_FUNC inline MultivariateGaussian cast() const \n {\n return MultivariateGaussian(mean().template cast(), \n covariance().template cast());\n }\n \n EIGEN_DEVICE_FUNC MeanType& mean() \n {\n return static_cast(this)->mean_nonconst();\n }\n \n EIGEN_DEVICE_FUNC const MeanType& mean() const\n {\n return static_cast(this)->mean_const();\n }\n \n EIGEN_DEVICE_FUNC CovarianceType& covariance() \n {\n return static_cast(this)->covariance_nonconst();\n }\n \n EIGEN_DEVICE_FUNC const CovarianceType& covariance() const\n {\n return static_cast(this)->covariance_const();\n }\n \n template\n EIGEN_DEVICE_FUNC inline MultivariateGaussianBase& operator=(const MultivariateGaussianBase& other)\n {\n mean() = other.mean();\n covariance() = other.covariance();\n return *this;\n }\n \n#if 0\nEIGEN_DEVICE_FUNC inline MultivariateGaussianBase operator+(const MultivariateGaussianBase& other) const\n {\n // TBD\n }\n \n EIGEN_DEVICE_FUNC inline MultivariateGaussianBase operator-(const MultivariateGaussianBase& other) const\n {\n // TBD\n }\n \n template\n EIGEN_DEVICE_FUNC inline MultivariateGaussianBase operator*(const OtherScalar& other) const\n {\n // TBD\n }\n \n template\n EIGEN_DEVICE_FUNC inline MultivariateGaussianBase operator/(const OtherScalar& other) const\n {\n // TBD\n }\n#endif\n \n EIGEN_DEVICE_FUNC inline Scalar evaluate(const MeanType& x)\n {\n using Eigen::numext::sqrt;\n using Eigen::numext::pow;\n using Eigen::numext::exp;\n \n auto term1 = x - mean();\n auto det = covariance().determinant();\n auto part1 = (Scalar(1.0) / ( sqrt(pow(Scalar(2.0 * M_PI), Scalar(Dimension)) * det) ) );\n auto part2 = exp(Scalar(-0.5) * term1.transpose() * covariance().inverse() * term1);\n \n return part1 * part2;\n }\n \n EIGEN_DEVICE_FUNC inline void setZero()\n {\n mean().setZero();\n covariance().setZero();\n }\n \n#ifdef VISIONCORE_ENABLE_CEREAL\n template\n void load(Archive & archive, std::uint32_t const version)\n {\n archive(cereal::make_nvp(\"Mean\", mean()));\n archive(cereal::make_nvp(\"Covariance\", covariance()));\n }\n \n template\n void save(Archive & archive, std::uint32_t const version) const\n {\n archive(cereal::make_nvp(\"Mean\", mean()));\n archive(cereal::make_nvp(\"Covariance\", covariance()));\n }\n#endif // VISIONCORE_ENABLE_CEREAL \n};\n\n/**\n * Gaussian distribution.\n */\ntemplate\nclass Gaussian : public GaussianDispatchingBase, Eigen::internal::traits >::Dimension>\n{\n typedef GaussianDispatchingBase, Eigen::internal::traits >::Dimension> Base;\npublic:\n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n static constexpr int Rows = Eigen::internal::traits::Rows;\n static constexpr int Cols = Eigen::internal::traits::Cols;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef typename Eigen::internal::traits::MeanType MeanType;\n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n \n friend class GaussianDispatchingBase, Eigen::internal::traits >::Dimension>;\n friend class GaussianBase >;\n \n EIGEN_MAKE_ALIGNED_OPERATOR_NEW\n \n using Base::operator=;\n#if 0\n using Base::operator*;\n using Base::operator+;\n using Base::operator-;\n using Base::operator/;\n#endif\n \n EIGEN_DEVICE_FUNC inline Gaussian()\n {\n }\n \n template \n EIGEN_DEVICE_FUNC inline Gaussian(const GaussianDispatchingBase, \n Eigen::internal::traits::Dimension>& other)\n : mean_(other.mean()), variance_(other.variance())\n {\n }\n \n EIGEN_DEVICE_FUNC inline Gaussian(ConstMeanType& mean, ConstMeanType& variance)\n : mean_(mean), variance_(variance)\n {\n }\n \n EIGEN_DEVICE_FUNC inline ~Gaussian()\n {\n }\n \nprotected:\n EIGEN_DEVICE_FUNC inline ConstMeanType& mean_const() const { return mean_; }\n EIGEN_DEVICE_FUNC inline MeanType& mean_nonconst() { return mean_; }\n \n EIGEN_DEVICE_FUNC inline ConstMeanType& variance_const() const { return variance_; }\n EIGEN_DEVICE_FUNC inline MeanType& variance_nonconst() { return variance_; }\n \n MeanType mean_;\n MeanType variance_;\n};\n\ntemplate\ninline std::ostream& operator<<(std::ostream& os, const Gaussian& p)\n{\n os << \"N(\" << p.mean() << \",\" << p.stddev() << \")\";\n return os;\n}\n\n\n/**\n * Multivariate Gaussian distribution.\n */\ntemplate\nclass MultivariateGaussian : public MultivariateGaussianBase>\n{\n typedef MultivariateGaussianBase> Base;\npublic:\n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef typename Eigen::internal::traits::MeanType MeanType;\n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n typedef typename Eigen::internal::traits::CovarianceType CovarianceType;\n typedef const typename Eigen::internal::traits::CovarianceType ConstCovarianceType;\n \n friend class vc::types::MultivariateGaussianBase>;\n \n EIGEN_MAKE_ALIGNED_OPERATOR_NEW\n \n using Base::operator=;\n#if 0\n using Base::operator*;\n using Base::operator+;\n using Base::operator-;\n using Base::operator/;\n#endif\n \n EIGEN_DEVICE_FUNC inline MultivariateGaussian()\n {\n }\n \n template \n EIGEN_DEVICE_FUNC inline MultivariateGaussian(const MultivariateGaussianBase& other)\n : mean_(other.mean()), covariance_(other.covariance())\n {\n }\n \n EIGEN_DEVICE_FUNC inline MultivariateGaussian(ConstMeanType& mean, ConstCovarianceType& covariance)\n : mean_(mean), covariance_(covariance)\n {\n }\n \n EIGEN_DEVICE_FUNC inline ~MultivariateGaussian()\n {\n }\n \nprotected:\n EIGEN_DEVICE_FUNC inline ConstMeanType& mean_const() const { return mean_; }\n EIGEN_DEVICE_FUNC inline MeanType& mean_nonconst() { return mean_; }\n \n EIGEN_DEVICE_FUNC inline ConstCovarianceType& covariance_const() const { return covariance_; }\n EIGEN_DEVICE_FUNC inline CovarianceType& covariance_nonconst() { return covariance_; }\n \n MeanType mean_;\n CovarianceType covariance_;\n};\n\ntemplate\ninline std::ostream& operator<<(std::ostream& os, const MultivariateGaussian<_Scalar,_Dimension>& p)\n{\n os << \"MultivariateGaussian(\" << p.mean() << \",\" << p.covariance() << \")\";\n return os;\n}\n\n}\n \n}\n\nnamespace Eigen \n{\n/**\n * Specialisation of Eigen::Map for Gaussian.\n */\ntemplate\nclass Map>, _Options>\n : public vc::types::GaussianDispatchingBase<\n Map>, _Options>, \n Eigen::internal::traits>, _Options>>::Dimension> \n{\n typedef vc::types::GaussianDispatchingBase<\n Map>, _Options>, \n Eigen::internal::traits>, _Options>>::Dimension> Base;\npublic:\n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef typename Eigen::internal::traits::MeanType MeanType;\n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n \n friend class vc::types::GaussianDispatchingBase<\n Map>, _Options>,\n Eigen::internal::traits>, _Options>>::Dimension>;\n friend class vc::types::GaussianBase>, _Options> >;\n \n EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Map)\n#if 0\n using Base::operator*;\n using Base::operator+;\n using Base::operator-;\n using Base::operator/;\n#endif\n \n EIGEN_DEVICE_FUNC inline Map(Scalar* coeffs) : mean_(coeffs), variance_(coeffs + Dimension)\n {\n }\n \nprotected:\n EIGEN_DEVICE_FUNC inline ConstMeanType& mean_const() const { return mean_; }\n EIGEN_DEVICE_FUNC inline MeanType& mean_nonconst() { return mean_; }\n \n EIGEN_DEVICE_FUNC inline ConstMeanType& variance_const() const { return variance_; }\n EIGEN_DEVICE_FUNC inline MeanType& variance_nonconst() { return variance_; }\n \n MeanType mean_;\n MeanType variance_;\n};\n\n/**\n * Specialisation of Eigen::Map for const Gaussian.\n */\ntemplate\nclass Map>, _Options>\n : public vc::types::GaussianDispatchingBase<\n Map>, _Options>, \n Eigen::internal::traits>>::Dimension> \n{\n typedef vc::types::GaussianDispatchingBase<\n Map>, _Options>, \n Eigen::internal::traits>>::Dimension> Base;\npublic:\n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n \n friend class vc::types::GaussianDispatchingBase<\n Map>, _Options>, \n Eigen::internal::traits>>::Dimension>;\n friend class vc::types::GaussianBase>, _Options> >;\n \n EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Map)\n#if 0\n using Base::operator*;\n using Base::operator+;\n using Base::operator-;\n using Base::operator/;\n#endif\n \n EIGEN_DEVICE_FUNC inline Map(const Scalar* coeffs) : mean_(coeffs), variance_(coeffs + Dimension)\n {\n }\n \nprotected:\n EIGEN_DEVICE_FUNC inline ConstMeanType& mean_const() const { return mean_; } \n EIGEN_DEVICE_FUNC inline ConstMeanType& variance_const() const { return variance_; }\n \n ConstMeanType mean_;\n ConstMeanType variance_;\n};\n \n/**\n * Specialisation of Eigen::Map for Multivariate Gaussian.\n */\ntemplate\nclass Map, _Options>\n : public vc::types::MultivariateGaussianBase<\n Map, _Options> > \n{\n typedef vc::types::MultivariateGaussianBase, _Options>> Base;\n \npublic:\n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef typename Eigen::internal::traits::MeanType MeanType;\n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n typedef typename Eigen::internal::traits::CovarianceType CovarianceType;\n typedef const typename Eigen::internal::traits::CovarianceType ConstCovarianceType;\n \n friend class vc::types::MultivariateGaussianBase, _Options> >;\n \n EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Map)\n#if 0\n using Base::operator*;\n using Base::operator+;\n using Base::operator-;\n using Base::operator/;\n#endif\n \n EIGEN_DEVICE_FUNC inline Map(Scalar* coeffs) : mean_(coeffs), covariance_(coeffs + _Dimension)\n {\n }\n \nprotected:\n EIGEN_DEVICE_FUNC inline ConstMeanType& mean_const() const { return mean_; }\n EIGEN_DEVICE_FUNC inline MeanType& mean_nonconst() { return mean_; }\n \n EIGEN_DEVICE_FUNC inline ConstCovarianceType& covariance_const() const { return covariance_; }\n EIGEN_DEVICE_FUNC inline CovarianceType& covariance_nonconst() { return covariance_; }\n \n MeanType mean_;\n CovarianceType covariance_;\n};\n\n/**\n * Specialisation of Eigen::Map for const Multivariate Gaussian.\n */\ntemplate\nclass Map, _Options>\n : public vc::types::MultivariateGaussianBase<\n Map, _Options> > \n{\n typedef vc::types::MultivariateGaussianBase, _Options> > Base;\n \npublic:\n static constexpr int Dimension = Eigen::internal::traits::Dimension;\n typedef typename Eigen::internal::traits::Scalar Scalar; \n typedef typename Eigen::internal::traits::MeanType MeanType;\n typedef const typename Eigen::internal::traits::MeanType ConstMeanType;\n typedef typename Eigen::internal::traits::CovarianceType CovarianceType;\n typedef const typename Eigen::internal::traits::CovarianceType ConstCovarianceType;\n \n friend class vc::types::MultivariateGaussianBase, _Options> >;\n \n EIGEN_INHERIT_ASSIGNMENT_EQUAL_OPERATOR(Map)\n#if 0\n using Base::operator*;\n using Base::operator+;\n using Base::operator-;\n using Base::operator/;\n#endif\n \n EIGEN_DEVICE_FUNC inline Map(const Scalar* coeffs) : mean_(coeffs), covariance_(coeffs + _Dimension)\n {\n }\n \nprotected:\n EIGEN_DEVICE_FUNC inline ConstMeanType& mean_const() const { return mean_; } \n EIGEN_DEVICE_FUNC inline ConstCovarianceType& covariance_const() const { return covariance_; }\n \n MeanType mean_;\n CovarianceType covariance_;\n};\n\n}\n\n#endif // VISIONCORE_TYPES_GAUSSIAN_HPP\n", "meta": {"hexsha": "35db7b8ace04f6ec8b423acd38a368d6805e65a5", "size": 26951, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/VisionCore/Types/Gaussian.hpp", "max_stars_repo_name": "lukier/vision_core", "max_stars_repo_head_hexsha": "45cb1bf7b74e1e1d5aa1078494a328b317d5a368", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 29.0, "max_stars_repo_stars_event_min_datetime": "2016-10-30T23:59:11.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-30T12:27:40.000Z", "max_issues_repo_path": "include/VisionCore/Types/Gaussian.hpp", "max_issues_repo_name": "jczarnowski/vision_core", "max_issues_repo_head_hexsha": "924c53339b1d99ebb3b1e358edfaa1a4e8d3703b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2020-05-19T04:45:41.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-07T01:32:22.000Z", "max_forks_repo_path": "include/VisionCore/Types/Gaussian.hpp", "max_forks_repo_name": "lukier/vision_core", "max_forks_repo_head_hexsha": "45cb1bf7b74e1e1d5aa1078494a328b317d5a368", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2016-11-14T00:46:26.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-01T08:55:11.000Z", "avg_line_length": 36.6182065217, "max_line_length": 128, "alphanum_fraction": 0.6780453415, "num_tokens": 6277, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.22523334961371688}} {"text": "// -------------------------------------------\n// @description: 根据彩色图像的像素产生点云代码 产生三维骨架点云 计算外积 计算方差 方差保存供svm训练\n// @author: hts\n// @data: 2020-04-20\n// @version: wpdwp\n// -------------------------------------------\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n\n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \nusing namespace Eigen;\n#define pi 3.14159265359\n\nclass Posepoint\n{\nprivate:\n std::mutex lock;\n cv::Mat color, depth;\n cv::Mat cameraMatrixColor, cameraMatrixDepth;\n cv::Mat lookupX, lookupY;\n pcl::PointCloud::Ptr cloud;\n pcl::PCDWriter writer;\n bool visualize_stop;\n double pick_points[24];\n // double pick_points2[18];\n Vector3f Lshoulder, Rshoulder, Pelv;\n Vector3f Head,Neck,Thrx,Lhip,Lknee,Lankle,Rhip,Rknee,Rankle;\n std::string pic_class, person_class, pic_name, depth_name, color_path, depth_path;\n int sp;\n int op;\n bool right;\npublic:\n Posepoint():visualize_stop(false),sp(0),op(0)\n {\n cameraMatrixColor = cv::Mat::zeros(3, 3, CV_64F);\n // cameraMatrixDepth = cv::Mat::zeros(3, 3, CV_64F);\n // static double my_points[6] = {255.67636108398438,292.16888427734375,320.4430236816406,266.26220703125,330.15802001953125,298.64556884765625};\n // static double my_points2[18] = {191.4891815185547, 215.0364227294922, 218.52777099609375, 226.16993713378906, 248.7473602294922, 234.1224822998047, 263.0619201660156, 219.8079376220703, 231.25181579589844, 221.3984375, 228.07080078125, 207.08389282226562, 259.88092041015625, 227.7604522705078, 296.4625244140625, 262.7515869140625, 301.23406982421875, 264.34210205078125, 315.548583984375, 283.42816162109375};\n for (int i=1; i<24; i++)\n {\n // fscanf(fq,\"%f\\n\",&pick_points[i]);//%lf之间应该有逗号，因为没有逗号只能读第一个数。用&是因为要把数存到对应数组元素的地址中去。\\n是换行读取\n // cout<::Ptr(new pcl::PointCloud());\n cloud->height = color.rows;\n cloud->width = color.cols;\n cloud->is_dense = false;\n cloud->points.resize(cloud->height * cloud->width);\n readCameraInfo();\n createLookup(this->color.cols, this->color.rows); //像极坐标系中的值保存在lookupX　lookupY里面\n cloudViewer();\n }\n // 3x3内参矩阵\n// 367.933 0 254.169\n// 0 367.933 204.267\n// 0 0 1\n void readCameraInfo()\n {\n double cameraInfoK[9]={367.933 , 0 , 254.169,\n 0 , 367.933 , 204.267,\n 0 , 0 , 1 }; \n double *itC = cameraMatrixColor.ptr(0, 0);\n for(size_t i = 0; i < 9; ++i, ++itC)\n {\n *itC = cameraInfoK[i];\n // cout<<*itC<(0, 0)<x　height是行->y\n // 得到相机的内参数\n // 成像模型\n// [u [ fx　0 cx [x\n// v = 1/z 0 fy cy y\n// 1] 0 0 1] z]\n// 求逆矩阵　得到像极坐标系中的坐标　\n const float fx = 1.0f / cameraMatrixColor.at(0, 0);\n const float fy = 1.0f / cameraMatrixColor.at(1, 1);\n const float cx = cameraMatrixColor.at(0, 2);\n const float cy = cameraMatrixColor.at(1, 2);\n float *it;\n // cout<<\"相机的内参:\"<();\n for(size_t r = 0; r < height; ++r, ++it)\n {\n *it = (r - cy) * fy;\n // cout<<*it<();\n for(size_t c = 0; c < width; ++c, ++it)\n {\n *it = (c - cx) * fx;\n // cout<<*it<color;\n depth = this->depth;\n // updateCloud = false;\n lock.unlock();\n\n createCloud(depth, color, cloud);\n // 将点云数据添加到视窗中，并为其定义一个唯一的字符串作为ID号，利用此ID号保证其他成员方法也能表示该点云。\n // 多次调用addPointCloud()可以实现多个点云的叠加，每调用一次就创建一个新的ID号。如果想要更新一个已经\n // 显示的点云，用户必须先调用removePointCloud()，并提供新的ID号。（在PCL1.1版本之后直接调用updatePointCloud()\n // 就可以了，不必手动调用removePointCloud()就可实现点云更新）\n\n\n // visualizer->addPointCloud(cloud, cloudName);\n // // 修改现实点云的尺寸。用户可通过该方法控制点云在视窗中的显示方式\n // visualizer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, cloudName);\n // // 设置XYZ三个坐标轴的大小和长度，该值也可以缺省\n // // 查看复杂的点云图像会让用户没有方向感，为了让用户保持正确的方向判断，需要显示坐标轴。三个坐标轴X（R，红色）\n // // Y（G，绿色）Z（B，蓝色）分别用三种不同颜色的圆柱体代替\n // visualizer->addCoordinateSystem(1.0);\n // // 通过设置相机参数是用户从默认的角度和方向观察点\n // visualizer->initCameraParameters();\n // // 设置窗口viewer的背景颜色\n // visualizer->setBackgroundColor(0, 0, 0);\n // // visualizer->setPosition(mode == BOTH ? color.cols : 0, 0);\n // visualizer->setPosition(0, 0);\n // visualizer->setSize(color.cols, color.rows);\n // visualizer->setShowFPS(true);\n // visualizer->setCameraPosition(0, 0, 0, 0, -1, 0);\n // visualizer->registerKeyboardCallback(&Posepoint::keyboardEvent, *this);\n // saveCloud(cloud);\n // // visualizer->spinOnce(300);\n // while (!visualize_stop) {\n // visualizer->spinOnce(100);\n // // boost::this_thread::sleep(boost::posix_time::microseconds(1000));\n // }\n\n // visualizer->close();\n\n }\n void createCloud(const cv::Mat &depth, const cv::Mat &color, pcl::PointCloud::Ptr &cloud)\n {\n int body_part;\n const float badPoint = std::numeric_limits::quiet_NaN();\n // #pragma omp parallel for\n for(int r = 0; r < depth.rows; ++r)\n {\n // 创建点云row行，每一行有col列\n pcl::PointXYZRGBA *itP = &cloud->points[r * depth.cols];\n // ptr函数访问任意一行像素的首地址　\n const uint16_t *itD = depth.ptr(r);\n const cv::Vec3b *itC = color.ptr(r);\n const float y = lookupY.at(0, r);\n const float *itX = lookupX.ptr();\n\n for(size_t c = 0; c < (size_t)depth.cols; ++c, ++itP, ++itD, ++itC, ++itX)\n {\n register const float depthValue = *itD / 1000.0f;\n // Check for invalid measurements\n if(*itD == 0)\n {\n // not valid\n itP->x = itP->y = itP->z = badPoint;\n itP->rgba = 0;\n continue;\n }\n itP->z = depthValue;\n itP->x = *itX * depthValue;\n itP->y = y * depthValue;\n itP->b = itC->val[0];\n itP->g = itC->val[1];\n itP->r = itC->val[2];\n itP->a = 255;\n bool picked = false;\n if(size_t(pick_points[0]) == c && int(pick_points[1]) == r)\n {\n // cout<<\"点(\"<x; Rankle(1) = itP->y; Rankle(2) = itP->z;\n break;\n case 1:\n Rknee(0) = itP->x; Rknee(1) = itP->y; Rknee(2) = itP->z;\n break;\n case 2:\n Rhip(0) = itP->x; Rhip(1) = itP->y; Rhip(2) = itP->z;\n break;\n case 3:\n Lhip(0) = itP->x; Lhip(1) = itP->y; Lhip(2) = itP->z;\n break; \n case 4:\n Lknee(0) = itP->x; Lknee(1) = itP->y; Lknee(2) = itP->z;\n break; \n case 5:\n Lankle(0) = itP->x; Lankle(1) = itP->y; Lankle(2) = itP->z;\n break; \n case 6:\n Pelv(0) = itP->x; Pelv(1) = itP->y; Pelv(2) = itP->z;\n break; \n case 7:\n Thrx(0) = itP->x; Thrx(1) = itP->y; Thrx(2) = itP->z;\n break;\n case 8:\n Neck(0) = itP->x; Neck(1) = itP->y; Neck(2) = itP->z;\n break; \n case 9:\n Head(0) = itP->x; Head(1) = itP->y; Head(2) = itP->z;\n break; \n case 12:\n Rshoulder(0) = itP->x; Rshoulder(1) = itP->y; Rshoulder(2) = itP->z;\n break;\n case 13:\n Lshoulder(0) = itP->x; Lshoulder(1) = itP->y; Lshoulder(2) = itP->z;\n break; \n }\n }\n }\n }\n }\n void saveCloud(const pcl::PointCloud::ConstPtr cloud)\n {\n std::ostringstream oss;\n oss.str(\"\");\n oss << \"/home/hts/Desktop/kinect_cloud/a/aaa/\" << std::setfill('0') << std::setw(4);\n const std::string baseName = oss.str();\n const std::string cloudName = baseName + \"_cloud.pcd\";\n OUT_INFO(\"saving cloud: \" << cloudName);\n // writer是该类的pcl::PCDWriter类型的成员变量\n writer.writeBinary(cloudName, *cloud);\n }\n void keyboardEvent(const pcl::visualization::KeyboardEvent &event, void *)\n {\n if(event.keyUp())\n {\n switch(event.getKeyCode())\n {\n case 27:\n case 'q':\n visualize_stop = true;\n break;\n }\n }\n }\n //叉积\n Vector3f ThreeCross()\n {\n Vector3f a, b,result;\n a=Lshoulder-Pelv;\n b=Rshoulder-Pelv;\n // cout<()\n\t Vector3f rotated_result = angle_axis3.matrix().cast()*result;\n\t // cout << \"绕x轴顺时针旋转250°(Rcw):\" << endl << angle_axis3.matrix() << endl;\n\t // cout << \"躯干法向量旋转后:\" << endl << rotated_result.transpose() << endl;\n if (rotated_result(2)<0)\n {\n sp+=1;\n right = true;\n }\n if (rotated_result(2)>0)\n {\n op+=1;\n right = false;\n }\n return rotated_result;\n }\n //转换到世界坐标系\n void Transformation()\n {\n // Vector3f result = ori;\n // // 旋转矩阵就是3x3的矩阵\n // Eigen::Matrix3d rotation_matrix = Eigen::Matrix3d::Identity();\n // 旋转向量(方向与旋转轴[1,0,0]相同,模为旋转的角度250°)\n Eigen::AngleAxisd rotation_vector (pi *25/ 18,Eigen::Vector3d(1,0,0));\n cout.precision(3);//指定输出的精度\n cout<<\"Rwc旋转矩阵为：\\n\"<()*Head; Neck = T.cast()*Neck; Thrx = T.cast()*Thrx; Pelv = T.cast()*Pelv; Lhip = T.cast()*Lhip; Lknee = T.cast()*Lknee; Lankle = T.cast()*Lankle; Rhip = T.cast()*Rhip; Rknee = T.cast()*Rknee; Rankle = T.cast()*Rankle; Lshoulder = T.cast()*Lshoulder; Rshoulder = T.cast()*Rshoulder;\n cout<\n#include \n\n// FRENSIE Includes\n#include \"Utility_InverseLengthConversionPolicy.hpp\"\n#include \"Utility_GaussKronrodIntegrator.hpp\"\n#include \"Utility_ContractException.hpp\"\n\nnamespace MonteCarlo{\n\n// Constructor\ntemplate\nWHIncoherentAdjointPhotonScatteringDistribution::WHIncoherentAdjointPhotonScatteringDistribution(\n const double max_energy,\n const Teuchos::RCP& scattering_function )\n : IncoherentAdjointPhotonScatteringDistribution( max_energy ),\n d_scattering_function( scattering_function )\n{\n // Make sure the unit conversion policy is valid\n testStaticPrecondition( (boost::is_same::value) );\n // Make sure the scattering function is valid\n testPrecondition( !scattering_function.is_null() );\n}\n\n// Evaluate the distribution\ntemplate\ndouble WHIncoherentAdjointPhotonScatteringDistribution::evaluate( \n\t\t\t\t const double incoming_energy,\n\t\t\t\t const double max_energy,\n\t\t\t\t const double scattering_angle_cosine ) const\n{\n // Make sure the incoming energy is valid\n testPrecondition( incoming_energy > 0.0 );\n testPrecondition( incoming_energy <= max_energy );\n // Make sure the scattering angle cosine is valid\n testPrecondition( scattering_angle_cosine >= \n\t\t calculateMinScatteringAngleCosine( incoming_energy,\n\t\t\t\t\t\t max_energy ));\n testPrecondition( scattering_angle_cosine <= 1.0 );\n\n const double scattering_function_value = \n this->evaluateScatteringFunction( incoming_energy,\n\t\t\t\t max_energy,\n\t\t\t\t scattering_angle_cosine );\n\n const double diff_kn_cross_section = \n this->evaluateAdjointKleinNishinaDist( incoming_energy,\n\t\t\t\t\t max_energy,\n\t\t\t\t\t scattering_angle_cosine );\n\n return diff_kn_cross_section*scattering_function_value;\n}\n\n// Evaluate the integrated cross section (b)\ntemplate\ndouble WHIncoherentAdjointPhotonScatteringDistribution::evaluateIntegratedCrossSection( \n\t\t\t\t\t\t const double incoming_energy,\n\t\t\t\t\t\t const double max_energy,\n\t\t\t\t\t\t const double precision ) const\n{\n // Make sure the incoming energy is valid\n testPrecondition( incoming_energy > 0.0 );\n testPrecondition( incoming_energy <= max_energy );\n\n // Evaluate the integrated cross section\n boost::function diff_cs_wrapper = \n boost::bind( &WHIncoherentAdjointPhotonScatteringDistribution::evaluate,\n\t\t\t boost::cref( *this ),\n\t\t\t incoming_energy,\n\t\t\t max_energy,\n\t\t\t _1 );\n\n double abs_error, integrated_cs;\n\n Utility::GaussKronrodIntegrator quadrature_gkq_int( precision );\n\n const double min_scattering_angle_cosine = \n calculateMinScatteringAngleCosine( incoming_energy, max_energy );\n\n quadrature_gkq_int.integrateAdaptively<15>( diff_cs_wrapper,\n\t\t\t\t\t min_scattering_angle_cosine,\n\t\t\t\t\t 1.0,\n\t\t\t\t\t integrated_cs,\n\t\t\t\t\t abs_error );\n\n // Make sure the integrated cross section is valid\n testPostcondition( integrated_cs >= 0.0 );\n\n return integrated_cs;\n}\n \n// Sample an outgoing energy and direction from the distribution\n/*! \\details This function will only sample an adjoint Compton line energy (no\n * Doppler broadening).\n */ \ntemplate\nvoid WHIncoherentAdjointPhotonScatteringDistribution::sample( \n\t\t\t\t\tconst double incoming_energy,\n\t\t\t\t\tdouble& outgoing_energy,\n\t\t\t\t\tdouble& scattering_angle_cosine ) const\n{\n // Make sure the incoming energy is valid\n testPrecondition( incoming_energy > 0.0 );\n testPrecondition( incoming_energy <= this->getMaxEnergy() );\n\n unsigned trial_dummy;\n\n return this->sampleAndRecordTrials( incoming_energy,\n\t\t\t\t outgoing_energy,\n\t\t\t\t scattering_angle_cosine,\n\t\t\t\t trial_dummy );\n}\n\n// Sample an outgoing energy and direction and record the number of trials\n/*! \\details This function will only sample an adjoint Compton line energy (no\n * Doppler broadening).\n */ \ntemplate\nvoid WHIncoherentAdjointPhotonScatteringDistribution::sampleAndRecordTrials( \n\t\t\t\t\t const double incoming_energy,\n\t\t\t\t\t double& outgoing_energy,\n\t\t\t\t\t double& scattering_angle_cosine,\n\t\t\t\t\t unsigned& trials ) const\n{\n // Make sure the incoming energy is valid\n testPrecondition( incoming_energy > 0.0 );\n testPrecondition( incoming_energy <= this->getMaxEnergy() );\n\n // Evaluate the maximum scattering function value\n const double min_scattering_angle_cosine = \n calculateMinScatteringAngleCosine( incoming_energy, this->getMaxEnergy() );\n\n const double max_scattering_function_value = \n this->evaluateScatteringFunction( incoming_energy, \n\t\t\t\t min_scattering_angle_cosine );\n\n while( true )\n {\n this->sampleAndRecordTrialsAdjointKleinNishina( incoming_energy,\n\t\t\t\t\t\t outgoing_energy,\n\t\t\t\t\t\t scattering_angle_cosine,\n\t\t\t\t\t\t trials );\n\n const double scattering_function_value = \n this->evaluateScatteringFunction( incoming_energy,\n\t\t\t\t\tscattering_angle_cosine );\n\n const double scaled_random_number = max_scattering_function_value*\n Utility::RandomNumberGenerator::getRandomNumber();\n\n if( scaled_random_number <= scattering_function_value )\n break;\n }\n\n // Make sure the scattering angle cosine is valid\n testPostcondition( scattering_angle_cosine >= min_scattering_angle_cosine );\n testPostcondition( scattering_angle_cosine <= 1.0 );\n // Make sure the adjoint Compton line energy is valid\n testPostcondition( outgoing_energy >= incoming_energy );\n}\n\n// Randomly scatter the photon and return the shell that was interacted with\ntemplate\nvoid WHIncoherentAdjointPhotonScatteringDistribution::scatterAdjointPhoton( \n\t\t\t\t AdjointPhotonState& adjoint_photon,\n\t\t\t\t ParticleBank& bank,\n\t\t\t\t SubshellType& shell_of_interaction ) const\n{\n // Make sure the adjoint photon energy is valid\n testPrecondition( adjoint_photon.getEnergy() <= this->getMaxEnergy() );\n \n // Generate probe particles\n this->createProbeParticles( adjoint_photon, bank );\n \n // Scattering the adjoint photon\n double outgoing_energy, scattering_angle_cosine;\n \n this->sample( adjoint_photon.getEnergy(),\n\t\toutgoing_energy,\n\t\tscattering_angle_cosine );\n\n shell_of_interaction = UNKNOWN_SUBSHELL;\n\n adjoint_photon.setEnergy( outgoing_energy );\n\n adjoint_photon.rotateDirection( scattering_angle_cosine,\n\t\t\t\t this->sampleAzimuthalAngle() );\n}\n\n// Check if an energy is above the scattering window\n/*! \\details Becuase of the scattering function evaluating to zero when \n * the scattering angle cosine is 1.0, the scattering window upper bound must\n * exclude the energy of interest.\n */\ntemplate\nbool WHIncoherentAdjointPhotonScatteringDistribution::isEnergyAboveScatteringWindow( \n\t\t\t\t const double energy_of_interest,\n\t\t\t\t const double initial_energy ) const\n{\n // Make sure the incoming energy is valid\n testPrecondition( initial_energy > 0.0 );\n // Make sure the energy of interest is valid\n testPrecondition( energy_of_interest >= 0.0 );\n\n return initial_energy >= energy_of_interest;\n}\n\n// Evaluate the scattering function\ntemplate\ninline double WHIncoherentAdjointPhotonScatteringDistribution::evaluateScatteringFunction(\n\t\t\t\t const double incoming_energy,\n\t\t\t\t const double max_energy,\n\t\t\t\t const double scattering_angle_cosine ) const\n{\n // Make sure the incoming energy is valid\n testPrecondition( incoming_energy > 0.0 );\n testPrecondition( incoming_energy <= max_energy );\n // Make sure the scattering angle cosine is valid\n testPrecondition( scattering_angle_cosine >= \n\t\t calculateMinScatteringAngleCosine( incoming_energy,\n\t\t\t\t\t\t max_energy ));\n testPrecondition( scattering_angle_cosine <= 1.0 );\n\n // Calculate the outgoing energy\n const double outgoing_energy = \n calculateAdjointComptonLineEnergy( incoming_energy,\n\t\t\t\t scattering_angle_cosine );\n\n // Calculate the inverse wavelength of the outgoing photon (1/cm)\n const double inverse_wavelength = outgoing_energy/\n (Utility::PhysicalConstants::planck_constant*\n Utility::PhysicalConstants::speed_of_light);\n\n // The scattering function argument (1/cm)\n double scattering_function_arg = \n sqrt( (1.0 - scattering_angle_cosine)/2.0 )*inverse_wavelength;\n\n ScatteringFunctionArgUnitConversionPolicy::convertFromNativeUnits( \n\t\t\t\t\t\t scattering_function_arg );\n\n if( scattering_function_arg >=\n d_scattering_function->getUpperBoundOfIndepVar() )\n scattering_function_arg = d_scattering_function->getUpperBoundOfIndepVar();\n\n // Make sure the scattering function arg is valid\n testPostcondition( scattering_function_arg >=\n\t\t d_scattering_function->getLowerBoundOfIndepVar() );\n testPostcondition( scattering_function_arg <=\n\t\t d_scattering_function->getUpperBoundOfIndepVar() );\n\n return d_scattering_function->evaluate( scattering_function_arg );\n}\n\n// Evaluate the scattering function\ntemplate\ninline double WHIncoherentAdjointPhotonScatteringDistribution::evaluateScatteringFunction(\n\t\t\t\t const double incoming_energy,\n\t\t\t\t const double scattering_angle_cosine ) const\n{\n // Make sure the incoming energy is valid\n testPrecondition( incoming_energy > 0.0 );\n testPrecondition( incoming_energy <= this->getMaxEnergy() );\n // Make sure the scattering angle cosine is valid\n testPrecondition( scattering_angle_cosine >= \n\t\t calculateMinScatteringAngleCosine( incoming_energy,\n\t\t\t\t\t\t this->getMaxEnergy() ));\n testPrecondition( scattering_angle_cosine <= 1.0 );\n\n return this->evaluateScatteringFunction( incoming_energy,\n\t\t\t\t\t this->getMaxEnergy(),\n\t\t\t\t\t scattering_angle_cosine );\n}\n\n} // end MonteCarlo namespace\n\n#endif // end MONTE_CARLO_WH_INCOHERENT_ADJOINT_PHOTON_SCATTERING_DISTRIBUTION_DEF_HPP\n\n//---------------------------------------------------------------------------//\n// end MonteCarlo_WHIncoherentAdjointPhotonScatteringDistribution.cpp\n//---------------------------------------------------------------------------//\n", "meta": {"hexsha": "45577788db0c84edeaa895edbef0ab4e3f648c98", "size": 11313, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "packages/monte_carlo/collision/native/src/MonteCarlo_WHIncoherentAdjointPhotonScatteringDistribution_def.hpp", "max_stars_repo_name": "lkersting/SCR-2123", "max_stars_repo_head_hexsha": "06ae3d92998664a520dc6a271809a5aeffe18f72", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, 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"num_tokens": 2415, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.3849121444839335, "lm_q1q2_score": 0.22521254000439037}} {"text": "// morpho/cdl/bgl_expansions/algorithms/ullmann.hpp header file//\n// Copyright (c) 2003-2008 Vladimir J. Sykora\n// Copyright (c) 2007-2008 Vladimir J. Sykora and NCU Studies Ltd\n// Modifications by Greg Landrum, January 2009\n//\n//*****************************************************************************\n// Permission is hereby granted, free of charge, to any person or organization\n// obtaining a copy of the software and accompanying documentation covered by\n// this license (the \"Software\") to use, reproduce, display, distribute,\n// execute, and transmit the Software, and to prepare derivative works of the\n// Software, and to permit third-parties to whom the Software is furnished to\n// do so, all subject to the following:\n//\n// The copyright notices in the Software and this entire statement, including\n// the above license grant, this restriction and the following disclaimer,\n// must be included in all copies of the Software, in whole or in part, and\n// all derivative works of the Software, unless such copies or derivative\n// works are solely in the form of machine-executable object code generated by\n// a source language processor.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT\n// SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE\n// FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,\n// ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n//*****************************************************************************\n\n//-----------------------------------------------------------------------------\n\n// Algorithm due to JR Ullmann. \"An Algorithm for Subgraph\n// Isomorphism\". Journal of the Association for\n// Computing Machinery, Vol 23, No.1, January 1976, pp 31-42.\n\n#ifndef MORPHO_CDL_BGL_EXP_ULLMANN_HPP\n#define MORPHO_CDL_BGL_EXP_ULLMANN_HPP\n\n// -------- boost\n#include \n#include \n// -------- std\n#include // for std::pair<>\n#include \n\nnamespace boost {\nnamespace detail {\n\ntemplate \nbool forward_checking(const Graph& g1, const Graph& g2, UblasMatrix& M,\n size_t count, BackInsertionSequence& F,\n size_t num_vert_g1, size_t num_vert_g2,\n EdgeLabeling& edge_labeling) {\n typedef std::pair::edge_descriptor, bool>\n edge_presence;\n typename BackInsertionSequence::iterator fi, fi_end = F.end();\n for (size_t k = count + 1; k < num_vert_g1; ++k) {\n for (size_t l = 0; l < num_vert_g2; ++l) {\n if (!M(k, l)) continue;\n // check mapping:\n fi = F.begin();\n while (fi != fi_end) {\n bool flag1(0), flag1_1(0), flag2(0), flag2_1(0);\n edge_presence ep1 = edge(k, fi->first, g1);\n if (ep1.second) {\n flag1_1 = true;\n edge_presence ep2 = edge(l, fi->second, g2);\n if (ep2.second) flag1 = edge_labeling(ep1.first, ep2.first);\n }\n if (flag1_1 && flag1) {\n M(k, l) = 1;\n ++fi;\n continue;\n }\n edge_presence ep2 = edge(l, fi->second, g2);\n if (ep2.second) {\n flag2_1 = true;\n ep1 = edge(k, fi->first, g1);\n if (ep1.second) {\n flag2 = edge_labeling(ep1.first, ep2.first);\n } else {\n // ring closed in main structure, not closed in query. This should\n // pass\n flag2 = true;\n }\n }\n if (flag2_1 && flag2) { // if one edge exists, there must be a mapping\n M(k, l) = 1;\n } else if (!flag1_1 && !flag2_1) { // or both edges are not present\n M(k, l) = 1;\n } else\n M(k, l) = 0; // if not, there's no mapping\n ++fi;\n }\n }\n }\n // TODO: change the data structure of the M matrix to sparse matrix. This\n // wouldn't be necessary\n size_t cero_row(0);\n for (size_t k = 0; k < num_vert_g1; ++k) {\n for (size_t l = 0; l < num_vert_g2; ++l) {\n if (M(k, l))\n break;\n else\n ++cero_row;\n }\n if (cero_row == num_vert_g2) return false; // if there is a cero row\n cero_row = 0;\n }\n return true;\n}\n\ntemplate \nbool backtrack(const Graph& g1, const Graph& g2, size_t count,\n const UblasMatrix& M, BackInsertionSequence& F,\n const size_t num_vert_g1, const size_t num_vert_g2,\n EdgeLabeling& edge_labeling) {\n if (count == num_vert_g1) return true;\n for (size_t i = 0; i < num_vert_g2; ++i) {\n if (M(count, i)) {\n F.push_back(std::make_pair(count, i));\n UblasMatrix M_prime(M);\n for (size_t m = count + 1; m < num_vert_g1; ++m) {\n M_prime(m, i) = 0;\n }\n\n if (forward_checking(g1, g2, M_prime, count, F, num_vert_g1, num_vert_g2,\n edge_labeling)) {\n if (backtrack(g1, g2, count + 1, M_prime, F, num_vert_g1, num_vert_g2,\n edge_labeling)) {\n return true;\n }\n }\n F.erase(std::remove(F.begin(), F.end(), std::make_pair(count, i)),\n F.end());\n }\n }\n return false;\n}\n\ntemplate \nvoid backtrack_all(const Graph& g1, const Graph& g2, size_t count,\n const UblasMatrix& M, DoubleBackInsertionSequence& FF,\n const size_t num_vert_g1, const size_t num_vert_g2,\n EdgeLabeling& edge_labeling) {\n if (count == num_vert_g1) return;\n DoubleBackInsertionSequence holdFF;\n holdFF.insert(holdFF.begin(), FF.begin(), FF.end());\n FF.clear();\n for (size_t i = 0; i < num_vert_g2; ++i) {\n if (M(count, i)) {\n DoubleBackInsertionSequence tFF;\n\n UblasMatrix M_prime(M);\n for (size_t m = count + 1; m < num_vert_g1; ++m) {\n M_prime(m, i) = 0;\n }\n\n if (holdFF.size()) {\n for (typename DoubleBackInsertionSequence::const_iterator iter =\n holdFF.begin();\n iter != holdFF.end(); ++iter) {\n typename DoubleBackInsertionSequence::value_type F = *iter;\n F.push_back(std::make_pair(count, i));\n if (forward_checking(g1, g2, M_prime, count, F, num_vert_g1,\n num_vert_g2, edge_labeling)) {\n tFF.push_back(F);\n }\n }\n } else {\n typename DoubleBackInsertionSequence::value_type F;\n F.push_back(std::make_pair(count, i));\n if (forward_checking(g1, g2, M_prime, count, F, num_vert_g1,\n num_vert_g2, edge_labeling)) {\n tFF.push_back(F);\n }\n }\n backtrack_all(g1, g2, count + 1, M_prime, tFF, num_vert_g1, num_vert_g2,\n edge_labeling);\n if (tFF.size()) {\n for (typename DoubleBackInsertionSequence::const_iterator iter =\n tFF.begin();\n iter != tFF.end(); ++iter) {\n FF.push_back(*iter);\n }\n }\n }\n }\n}\n\ntemplate \nvoid prepareM(const Graph& g1, const Graph& g2, VertexLabeling& vertex_labeling,\n UblasMatrix& M) {\n size_t rows(num_vertices(g1));\n size_t cols(num_vertices(g2));\n M.resize(rows, cols);\n // initialize the matrix:\n for (size_t i = 0; i < rows; ++i) {\n for (size_t j = 0; j < cols; ++j) {\n if (out_degree(j, g2) >= out_degree(i, g1) && vertex_labeling(i, j)) {\n M(i, j) = 1;\n } else\n M(i, j) = 0;\n }\n }\n}\n} // namespace detail\n\n// test if g1 is a subgraph of g2. mapped vertices are returned in F\n// Mapping : first: g1 vertices, second : g2 vertices\n// O( num_vertices(g1)! num_vertices(g1) ^ 3 )\n// This function doesnt Work with filtered graphs!\n// The size of F doesn't indicate match!\ntemplate <\n class Graph, class VertexLabeling // binary predicate\n ,\n class EdgeLabeling // binary predicate\n ,\n class\n BackInsertionSequence // contains\n // std::pair\n >\nbool ullmann(const Graph& g1, const Graph& g2, VertexLabeling& vertex_labeling,\n EdgeLabeling& edge_labeling, BackInsertionSequence& F) {\n typedef ::boost::numeric::ublas::matrix matrix_t;\n size_t rows(num_vertices(g1));\n size_t cols(num_vertices(g2));\n matrix_t M;\n detail::prepareM(g1, g2, vertex_labeling, M);\n size_t count(0);\n return detail::backtrack(g1, g2, count, M, F, rows, cols, edge_labeling);\n}\n\n// test if g1 is a subgraph of g2.\n// F returns all mappings of g1 in g2. mapping in separate containers\ntemplate \nbool ullmann_all(const Graph& g1, const Graph& g2,\n VertexLabeling& vertex_labeling, EdgeLabeling& edge_labeling,\n DoubleBackInsertionSequence& F) {\n typedef ::boost::numeric::ublas::matrix matrix_t;\n size_t rows(num_vertices(g1));\n size_t cols(num_vertices(g2));\n matrix_t M;\n detail::prepareM(g1, g2, vertex_labeling, M);\n size_t count(0);\n detail::backtrack_all(g1, g2, count, M, F, rows, cols, edge_labeling);\n return !F.empty();\n}\n\n} // namespace boost\n\n#endif // MORPHO_CDL_BGL_EXP_ULLMANN_HPP\n", "meta": {"hexsha": "2476c54a53aa51693f8791111ab58b5ed27887cf", "size": 9856, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Code/GraphMol/Substruct/ullmann.hpp", "max_stars_repo_name": "kazuyaujihara/rdkit", "max_stars_repo_head_hexsha": "06027dcd05674787b61f27ba46ec0d42a6037540", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1609.0, "max_stars_repo_stars_event_min_datetime": "2015-01-05T02:41:13.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T21:57:24.000Z", "max_issues_repo_path": "Code/GraphMol/Substruct/ullmann.hpp", "max_issues_repo_name": "kazuyaujihara/rdkit", "max_issues_repo_head_hexsha": "06027dcd05674787b61f27ba46ec0d42a6037540", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 3412.0, "max_issues_repo_issues_event_min_datetime": "2015-01-06T12:13:33.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T17:25:41.000Z", "max_forks_repo_path": "Code/GraphMol/Substruct/ullmann.hpp", "max_forks_repo_name": "bp-kelley/rdkit", "max_forks_repo_head_hexsha": "e0de7c9622ce73894b1e7d9568532f6d5638058a", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 811.0, "max_forks_repo_forks_event_min_datetime": "2015-01-11T03:33:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T11:57:49.000Z", "avg_line_length": 37.1924528302, "max_line_length": 80, "alphanum_fraction": 0.6035917208, "num_tokens": 2602, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.22514787529083652}} {"text": "/**\n * GeoDa TM, Copyright (C) 2011-2015 by Luc Anselin - all rights reserved\n *\n * This file is part of GeoDa.\n * \n * GeoDa is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 3 of the License, or\n * (at your option) any later version.\n *\n * GeoDa is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with this program. If not, see .\n */\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \"../DataViewer/TableInterface.h\"\n#include \"../ShapeOperations/RateSmoothing.h\"\n#include \"../ShapeOperations/Randik.h\"\n#include \"../ShapeOperations/WeightsManState.h\"\n#include \"../ShapeOperations/WeightUtils.h\"\n#include \"../VarCalc/WeightsManInterface.h\"\n#include \"../logger.h\"\n#include \"../Project.h\"\n#include \"LisaCoordinatorObserver.h\"\n#include \"LisaCoordinator.h\"\n\nLisaWorkerThread::LisaWorkerThread(const GalElement* W_,\n const std::vector& undefs_,\n int obs_start_s, int obs_end_s,\n\t\t\t\t\t\t\t\t uint64_t\tseed_start_s,\n\t\t\t\t\t\t\t\t LisaCoordinator* lisa_coord_s,\n\t\t\t\t\t\t\t\t wxMutex* worker_list_mutex_s,\n\t\t\t\t\t\t\t\t wxCondition* worker_list_empty_cond_s,\n\t\t\t\t\t\t\t\t std::list *worker_list_s,\n\t\t\t\t\t\t\t\t int thread_id_s)\n: wxThread(),\nW(W_),\nundefs(undefs_),\nobs_start(obs_start_s), obs_end(obs_end_s), seed_start(seed_start_s),\nlisa_coord(lisa_coord_s),\nworker_list_mutex(worker_list_mutex_s),\nworker_list_empty_cond(worker_list_empty_cond_s),\nworker_list(worker_list_s),\nthread_id(thread_id_s)\n{\n}\n\nLisaWorkerThread::~LisaWorkerThread()\n{\n}\n\nwxThread::ExitCode LisaWorkerThread::Entry()\n{\n\tLOG_MSG(wxString::Format(\"LisaWorkerThread %d started\", thread_id));\n\n\t// call work for assigned range of observations\n\tlisa_coord->CalcPseudoP_range(W, undefs, obs_start, obs_end, seed_start);\n\t\n\twxMutexLocker lock(*worker_list_mutex);\n \n\t// remove ourself from the list\n\tworker_list->remove(this);\n\t\n // if empty, signal on empty condition since only main thread\n\t// should be waiting on this condition\n\tif (worker_list->empty()) {\n\t\tworker_list_empty_cond->Signal();\n\t}\n\t\n\treturn NULL;\n}\n\n/** \n Since the user has the ability to synchronise either variable over time,\n we must be able to reapply weights and recalculate lisa values as needed.\n \n 1. We will have original data as complete space-time data for both variables\n \n 2. From there we will work from info in var_info for both variables. Must\n determine number of time_steps for canvas.\n \n 3. Adjust data1(2)_vecs sizes and initialize from data.\n \n 3.5. Resize localMoran, sigLocalMoran, sigCat, and cluster arrays\n \n 4. If rates, then calculate rates for working_data1\n \n 5. Standardize working_data1 (and 2 if bivariate)\n \n 6. Compute LISA for all time-stesp and save in localMoran sp/time array\n \n 7. Calc Pseudo P for all time periods. Results saved in sigLocalMoran,\n sigCat and cluster arrays\n \n 8. Notify clients that values have been updated.\n \n */\n\nLisaCoordinator::\nLisaCoordinator(boost::uuids::uuid weights_id,\n Project* project,\n const std::vector& var_info_s,\n const std::vector& col_ids,\n LisaType lisa_type_s,\n bool calc_significances_s,\n bool row_standardize_s)\n: w_man_state(project->GetWManState()),\nw_man_int(project->GetWManInt()),\nw_id(weights_id),\nnum_obs(project->GetNumRecords()),\npermutations(999),\nlisa_type(lisa_type_s),\ncalc_significances(calc_significances_s),\nisBivariate(lisa_type_s == bivariate),\nvar_info(var_info_s),\ndata(var_info_s.size()),\nundef_data(var_info_s.size()),\nlast_seed_used(0), reuse_last_seed(false),\nrow_standardize(row_standardize_s)\n{\n reuse_last_seed = GdaConst::use_gda_user_seed;\n if ( GdaConst::use_gda_user_seed) {\n last_seed_used = GdaConst::gda_user_seed;\n }\n \n\tTableInterface* table_int = project->GetTableInt();\n\tfor (int i=0; iGetColData(col_ids[i], data[i]);\n table_int->GetColUndefined(col_ids[i], undef_data[i]);\n var_info[i].is_moran = true;\n\t}\n \n undef_tms.resize(var_info_s[0].time_max - var_info_s[0].time_min + 1);\n\t\n\tweight_name = w_man_int->GetLongDispName(w_id);\n \n weights = w_man_int->GetGal(w_id);\n \n\tSetSignificanceFilter(1);\n \n\tInitFromVarInfo();\n\tw_man_state->registerObserver(this);\n}\n\n\nLisaCoordinator::\nLisaCoordinator(wxString weights_path,\n int n,\n std::vector vals_1,\n std::vector vals_2,\n int lisa_type_s,\n int permutations_s,\n bool calc_significances_s,\n bool row_standardize_s)\n{\n num_obs = n;\n num_time_vals = 1;\n permutations = permutations_s;\n calc_significances = calc_significances_s;\n row_standardize = row_standardize_s;\n last_seed_used = 0;\n reuse_last_seed = false;\n isBivariate = false;\n \n // std::vector var_info;\n int num_vars = 1;\n isBivariate = false;\n \n if (lisa_type_s == 0) {\n lisa_type = univariate;\n \n } else if (lisa_type_s == 1) {\n lisa_type = bivariate;\n isBivariate = true;\n num_vars = 2;\n \n } else if (lisa_type_s == 2) {\n lisa_type = eb_rate_standardized;\n num_vars = 2;\n \n } else if (lisa_type_s == 3) {\n lisa_type = differential;\n num_vars = 2;\n }\n \n undef_tms.resize(num_time_vals);\n data.resize(num_vars);\n undef_data.resize(num_vars);\n var_info.resize(num_vars);\n \n // don't handle time variable for now\n for (int i=0; inum_obs = num_obs;\n weights->wflnm = weights_path;\n weights->id_field = \"ogc_fid\";\n weights->gal = tempGal;\n \n SetSignificanceFilter(1);\n InitFromVarInfo();\n}\n\nLisaCoordinator::~LisaCoordinator()\n{\n if (w_man_state) {\n w_man_state->removeObserver(this);\n }\n\tDeallocateVectors();\n}\n\nvoid LisaCoordinator::DeallocateVectors()\n{\n\tfor (int i=0; i undef_res(num_obs, false);\n\t\tdouble* smoothed_results = new double[num_obs];\n\t\tdouble* E = new double[num_obs]; // E corresponds to var_info[0]\n\t\tdouble* P = new double[num_obs]; // P corresponds to var_info[1]\n\t\t// we will only fill data1 for eb_rate_standardized and\n\t\t// further lisa calcs will treat as univariate\n\t\tfor (int t=0; t undef_res(num_obs, false);\n double* smoothed_results = new double[num_obs];\n double* E = new double[num_obs]; // E corresponds to var_info[0]\n double* P = new double[num_obs]; // P corresponds to var_info[1]\n // we will only fill data1 for eb_rate_standardized and\n // further lisa calcs will treat as univariate\n for (int i=0; i t ) {\n undef_tms[t][i] = undef_tms[t][i] || undef_data[1][t][i];\n }\n }\n }\n }\n \n\tfor (int t=0; t t)\n GenUtils::StandardizeData(num_obs, data2_vecs[t], undef_tms[t]);\n }\n\t}\n}\n\n/** assumes StandardizeData already called on data1 and data2 */\nvoid LisaCoordinator::CalcLisa()\n{\n\tfor (int t=0; t undefs;\n bool has_undef = false;\n for (int i=0; i t)\n is_undef = is_undef || undef_data[1][t][i];\n }\n if (is_undef && !has_undef) {\n has_undef = true;\n }\n undefs.push_back(is_undef);\n }\n has_undefined[t] = has_undef;\n \n // local weights copy\n GalWeight* gw = NULL;\n if ( has_undef ) {\n gw = new GalWeight(*weights);\n gw->Update(undefs);\n } else {\n gw = weights;\n }\n GalElement* W = gw->gal;\n Gal_vecs.push_back(gw);\n Gal_vecs_orig.push_back(weights);\n\t\n\t\tfor (int i=0; i 0) {\n\t\t\t\tif (data1[i] > 0 && Wdata < 0) cluster[i] = 4;\n\t\t\t\telse if (data1[i] < 0 && Wdata > 0) cluster[i] = 3;\n\t\t\t\telse if (data1[i] < 0 && Wdata < 0) cluster[i] = 2;\n\t\t\t\telse cluster[i] = 1; //data1[i] > 0 && Wdata > 0\n\t\t\t} else {\n\t\t\t\thas_isolates[t] = true;\n\t\t\t\tcluster[i] = 5; // neighborless\n\t\t\t}\n\t\t}\n\t}\n}\n\nvoid LisaCoordinator::CalcPseudoP()\n{\n\tif (!calc_significances) return;\n\twxStopWatch sw;\n\tint nCPUs = wxThread::GetCPUCount();\n\t\n\t// To ensure thread safety, only work on one time slice of data\n\t// at a time. For each time period t:\n\t// 1. copy data for time period t into data1 and data2 arrays\n\t// 2. Perform multi-threaded computation\n\t// 3. copy results into results array\n\t\n\t\n\tfor (int t=0; t& undefs = undef_tms[t];\n\t\tdata1 = data1_vecs[t];\n\t\tif (isBivariate) {\n\t\t\tdata2 = data2_vecs[0];\n\t\t\tif (var_info[1].is_time_variant &&\n\t\t\t\tvar_info[1].sync_with_global_time)\n data2 = data2_vecs[t];\n\t\t}\n\t\tlags = lags_vecs[t];\n\t\tlocalMoran = local_moran_vecs[t];\n\t\tsigLocalMoran = sig_local_moran_vecs[t];\n\t\tsigCat = sig_cat_vecs[t];\n\t\tcluster = cluster_vecs[t];\n\t\t\n\t\tif (nCPUs <= 1 || num_obs <= nCPUs * 10) {\n if (!reuse_last_seed) {\n last_seed_used = time(0);\n }\n\t\t\tCalcPseudoP_range(Gal_vecs[t]->gal, undefs,\n 0, num_obs-1, last_seed_used);\n\t\t} else {\n\t\t\tCalcPseudoP_threaded(Gal_vecs[t]->gal, undefs);\n\t\t\t//CalcPseudoP_range(Gal_vecs[t]->gal, undefs,\n // 0, num_obs-1, last_seed_used);\n\t\t}\n\t}\n \n \n\t{\n\t\twxString m;\n\t\tm << \"LISA on \" << num_obs << \" obs with \" << permutations;\n\t\tm << \" perms over \" << num_time_vals << \" time periods took \";\n\t\tm << sw.Time() << \" ms. Last seed used: \" << last_seed_used;\n\t}\n\tLOG_MSG(\"Exiting LisaCoordinator::CalcPseudoP\");\n}\n\nvoid LisaCoordinator::CalcPseudoP_threaded(const GalElement* W,\n const std::vector& undefs)\n{\n\tint nCPUs = wxThread::GetCPUCount();\n\n\t// mutext protects access to the worker_list\n wxMutex worker_list_mutex;\n\t// signals that worker_list is empty\n\twxCondition worker_list_empty_cond(worker_list_mutex);\n // mutex should be initially locked\n\tworker_list_mutex.Lock();\n\t\n // List of all the threads currently alive. As soon as the thread\n\t// terminates, it removes itself from the list.\n\tstd::list worker_list;\n\n\t// divide up work according to number of observations\n\t// and number of CPUs\n\tint work_chunk = num_obs / nCPUs;\n \n if (work_chunk == 0) {\n work_chunk = 1;\n }\n \n\tint obs_start = 0;\n\tint obs_end = obs_start + work_chunk;\n\t\n\tbool is_thread_error = false;\n\tint quotient = num_obs / nCPUs;\n\tint remainder = num_obs % nCPUs;\n\tint tot_threads = (quotient > 0) ? nCPUs : remainder;\n\t\n boost::thread_group threadPool;\n \n\tif (!reuse_last_seed)\n last_seed_used = time(0);\n\tfor (int i=0; i\" << b;\n\t\tmsg << \", seed: \" << seed_start << \"->\" << seed_end;\n\t\t\n /*\n\t\tLisaWorkerThread* thread =\n\t\t\tnew LisaWorkerThread(W, undefs, a, b, seed_start, this,\n\t\t\t\t\t\t\t\t &worker_list_mutex,\n\t\t\t\t\t\t\t\t &worker_list_empty_cond,\n\t\t\t\t\t\t\t\t &worker_list, thread_id);\n\t\tif ( thread->Create() != wxTHREAD_NO_ERROR ) {\n\t\t\tdelete thread;\n\t\t\tis_thread_error = true;\n\t\t} else {\n\t\t\tworker_list.push_front(thread);\n\t\t}\n */\n boost::thread* worker = new boost::thread(boost::bind(&LisaCoordinator::CalcPseudoP_range,this, W, undefs, a, b, seed_start));\n threadPool.add_thread(worker);\n\t}\n threadPool.join_all();\n /*\n\tif (is_thread_error) {\n\t\t// fall back to single thread calculation mode\n\t\tCalcPseudoP_range(W, undefs, 0, num_obs-1, last_seed_used);\n\t} else {\n\t\tstd::list::iterator it;\n\t\tfor (it = worker_list.begin(); it != worker_list.end(); it++) {\n\t\t\t(*it)->Run();\n\t\t}\n\t\n\t\twhile (!worker_list.empty()) {\n\t\t\t// wait until thread_list might be empty\n\t\t\tworker_list_empty_cond.Wait();\n\t\t\t// We have been woken up. If this was not a false\n\t\t\t// alarm (sprious signal), the loop will exit.\n\t\t}\n\t}\n */\n}\n\nvoid LisaCoordinator::CalcPseudoP_range(const GalElement* W,\n const std::vector& undefs,\n int obs_start, int obs_end,\n\t\t\t\t\t\t\t\t\t\tuint64_t seed_start)\n{\n\tGeoDaSet workPermutation(num_obs);\n\t//Randik rng;\n\tint max_rand = num_obs-1;\n\tfor (int cnt=obs_start; cnt<=obs_end; cnt++) {\n \n if (undefs[cnt])\n continue;\n \n\t\tconst int numNeighbors = W[cnt].Size();\n\t\t\n\t\tuint64_t countLarger = 0;\n\t\tfor (int perm=0; perm= localMoran[cnt]) {\n countLarger++;\n }\n\t\t}\n\t\t// pick the smallest\n\t\tif (permutations-countLarger <= countLarger) { \n\t\t\tcountLarger = permutations-countLarger;\n\t\t}\n\t\t\n\t\tsigLocalMoran[cnt] = (countLarger+1.0)/(permutations+1);\n\t\t// 'significance' of local Moran\n\t\tif (sigLocalMoran[cnt] <= 0.0001) sigCat[cnt] = 4;\n\t\telse if (sigLocalMoran[cnt] <= 0.001) sigCat[cnt] = 3;\n\t\telse if (sigLocalMoran[cnt] <= 0.01) sigCat[cnt] = 2;\n\t\telse if (sigLocalMoran[cnt] <= 0.05) sigCat[cnt]= 1;\n\t\telse sigCat[cnt]= 0;\n\t\t\n\t\t// observations with no neighbors get marked as isolates\n // NOTE: undefined should be marked as well, however, since undefined_cat has covered undefined category, we don't need to handle here\n\t\tif (numNeighbors == 0) {\n\t\t\tsigCat[cnt] = 5;\n\t\t}\n\t}\n}\n\nvoid LisaCoordinator::SetSignificanceFilter(int filter_id)\n{\n\t// 0: >0.05 1: 0.05, 2: 0.01, 3: 0.001, 4: 0.0001\n\tif (filter_id < 1 || filter_id > 4) return;\n\tsignificance_filter = filter_id;\n\tif (filter_id == 1) significance_cutoff = 0.05;\n\tif (filter_id == 2) significance_cutoff = 0.01;\n\tif (filter_id == 3) significance_cutoff = 0.001;\n\tif (filter_id == 4) significance_cutoff = 0.0001;\n}\n\nvoid LisaCoordinator::update(WeightsManState* o)\n{\n if (w_man_int) {\n weight_name = w_man_int->GetLongDispName(w_id);\n }\n}\n\nint LisaCoordinator::numMustCloseToRemove(boost::uuids::uuid id) const\n{\n\treturn id == w_id ? observers.size() : 0;\n}\n\nvoid LisaCoordinator::closeObserver(boost::uuids::uuid id)\n{\n\tif (numMustCloseToRemove(id) == 0) return;\n\tstd::list obs_cpy = observers;\n\tfor (std::list::iterator i=obs_cpy.begin();\n\t\t i != obs_cpy.end(); ++i) {\n\t\t(*i)->closeObserver(this);\n\t}\n}\n\nvoid LisaCoordinator::registerObserver(LisaCoordinatorObserver* o)\n{\n\tobservers.push_front(o);\n}\n\nvoid LisaCoordinator::removeObserver(LisaCoordinatorObserver* o)\n{\n\tLOG_MSG(\"Entering LisaCoordinator::removeObserver\");\n\tobservers.remove(o);\n\tLOG(observers.size());\n\tif (observers.size() == 0) {\n\t\tdelete this;\n\t}\n\tLOG_MSG(\"Exiting LisaCoordinator::removeObserver\");\n}\n\nvoid LisaCoordinator::notifyObservers()\n{\n\tfor (std::list::iterator it=observers.begin();\n\t\t it != observers.end(); ++it) {\n\t\t(*it)->update(this);\n\t}\n}\n\n", "meta": {"hexsha": "648d8bf0c7e8abb5236430d6251695e34afe839e", "size": 26442, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Explore/LisaCoordinator.cpp", "max_stars_repo_name": "chenyoujie/GeoDa", "max_stars_repo_head_hexsha": "87504344512bd0da2ccadfb160ecd1e918a52f06", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Explore/LisaCoordinator.cpp", "max_issues_repo_name": "chenyoujie/GeoDa", "max_issues_repo_head_hexsha": "87504344512bd0da2ccadfb160ecd1e918a52f06", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Explore/LisaCoordinator.cpp", "max_forks_repo_name": "chenyoujie/GeoDa", "max_forks_repo_head_hexsha": "87504344512bd0da2ccadfb160ecd1e918a52f06", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.8442437923, "max_line_length": 142, "alphanum_fraction": 0.6140987822, "num_tokens": 7574, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.2251478752908365}} {"text": "#define PY_ARRAY_UNIQUE_SYMBOL pbcvt_ARRAY_API\n\n#define NUMPY_IMPORT_ARRAY_RETVAL NULL\n\n#include \n#include \n#include \n#include \n#include \"sift.simd.hpp\"\n#include \n\n#include \n\nconst float sigma = 1.6;\nconst int nOctaveLayers = 3;\n\nstd::string getImageType(int number)\n{\n // find type\n int imgTypeInt = number%8;\n std::string imgTypeString;\n\n switch (imgTypeInt)\n {\n case 0:\n imgTypeString = \"8U\";\n break;\n case 1:\n imgTypeString = \"8S\";\n break;\n case 2:\n imgTypeString = \"16U\";\n break;\n case 3:\n imgTypeString = \"16S\";\n break;\n case 4:\n imgTypeString = \"32S\";\n break;\n case 5:\n imgTypeString = \"32F\";\n break;\n case 6:\n imgTypeString = \"64F\";\n break;\n default:\n break;\n }\n\n // find channel\n int channel = (number/8) + 1;\n\n std::stringstream type;\n type<<\"CV_\"<::type, cv::SIFT_FIXPT_SCALE, 0);\n }\n else\n img.convertTo(gray_fpt, cv::DataType::type, cv::SIFT_FIXPT_SCALE, 0);\n\n float sig_diff;\n\n if( doubleImageSize )\n {\n sig_diff = sqrtf( std::max(sigma * sigma - cv::SIFT_INIT_SIGMA * cv::SIFT_INIT_SIGMA * 4, 0.01f) );\n cv::Mat dbl;\n#if DoG_TYPE_SHORT\n cv::resize(gray_fpt, dbl, cv::Size(gray_fpt.cols*2, gray_fpt.rows*2), 0, 0, cv::INTER_LINEAR_EXACT);\n#else\n cv::resize(gray_fpt, dbl, cv::Size(gray_fpt.cols*2, gray_fpt.rows*2), 0, 0, cv::INTER_LINEAR);\n#endif\n cv::Mat result;\n cv::GaussianBlur(dbl, result, cv::Size(), sig_diff, sig_diff);\n return result;\n }\n else\n {\n sig_diff = sqrtf( std::max(sigma * sigma - cv::SIFT_INIT_SIGMA * cv::SIFT_INIT_SIGMA, 0.01f) );\n cv::Mat result;\n cv::GaussianBlur(gray_fpt, result, cv::Size(), sig_diff, sig_diff);\n return result;\n }\n}\n\nstd::vector\nbuild_image_pyramid(cv::Mat im, int firstOctave, int nOctaves) {\n\n // Copyright (c) 2006-2010, Rob Hess \n // Copyright (C) 2009, Willow Garage Inc., all rights reserved.\n // Copyright (C) 2020, Intel Corporation, all rights reserved.\n cv::Mat base = createInitialImage(im, firstOctave<0, sigma);\n printf(\"%i, %i\\n\", base.rows, base.cols);\n CV_TRACE_FUNCTION();\n std::vector gauss_pyr;\n\n std::vector sig(nOctaveLayers + 3);\n gauss_pyr.resize(nOctaves*(nOctaveLayers + 3));\n\n // precompute Gaussian sigmas using the following formula:\n // \\sigma_{total}^2 = \\sigma_{i}^2 + \\sigma_{i-1}^2\n sig[0] = sigma;\n double k = std::pow( 2., 1. / nOctaveLayers );\n for( int i = 1; i < nOctaveLayers + 3; i++ )\n {\n double sig_prev = std::pow(k, (double)(i-1))*sigma;\n double sig_total = sig_prev*k;\n sig[i] = std::sqrt(sig_total*sig_total - sig_prev*sig_prev);\n }\n\n for( int o = 0; o < nOctaves; o++ )\n {\n for( int i = 0; i < nOctaveLayers + 3; i++ )\n {\n cv::Mat& dst = gauss_pyr[o*(nOctaveLayers + 3) + i];\n if( o == 0 && i == 0 )\n dst = base;\n // base of new octave is halved image from end of previous octave\n else if( i == 0 )\n {\n const cv::Mat& src = gauss_pyr[(o-1)*(nOctaveLayers + 3) + nOctaveLayers];\n cv::resize(src, dst, cv::Size(src.cols/2, src.rows/2),\n 0, 0, cv::INTER_NEAREST);\n }\n else\n {\n const cv::Mat& src = gauss_pyr[o*(nOctaveLayers + 3) + i-1];\n cv::GaussianBlur(src, dst, cv::Size(), sig[i], sig[i]);\n }\n }\n }\n return gauss_pyr;\n}\n\nnamespace pbcvt {\n\n using namespace boost::python;\n PyObject *dog_pyramid(PyObject *impy, int firstOctave, int nOctaves) {\n cv::Mat im;\n im = pbcvt::fromNDArrayToMat(impy);\n std::vector dogpyr;\n dogpyr.resize(nOctaves * (nOctaveLayers + 2));\n std::vector gauss_pyr = build_image_pyramid(im, firstOctave, nOctaves);\n for( int a=0; a < nOctaves * (nOctaveLayers + 2); a++ ) {\n const int o = a / (nOctaveLayers + 2);\n const int i = a % (nOctaveLayers + 2);\n\n const cv::Mat& src1 = gauss_pyr[o*(nOctaveLayers + 3) + i];\n const cv::Mat& src2 = gauss_pyr[o*(nOctaveLayers + 3) + i + 1];\n cv::Mat& dst = dogpyr[o*(nOctaveLayers + 2) + i];\n cv::subtract(src2, src1, dst, noArray(), DataType::type);\n }\n PyObject *ret = PyList_New(dogpyr.size());\n for (int i=0; i gauss_pyr = build_image_pyramid(im, firstOctave, nOctaves);\n // cv::Mat out_kpts_t;\n // cv::vconcat(out_kpts, out_kpts_t);\n // PyObject *ret = pbcvt::fromMatToNDArray(out_kpts_t);\n PyObject *ret = PyList_New(gauss_pyr.size());\n for (int i=0; i gauss_pyr = build_image_pyramid(im, firstOctave, nOctaves);\n\n std::vector out_kpts;\n\n // Compute orientation of keypoints\n for (int i=0; i(i, 0);\n float y = kpts.at(i, 1);\n float size = kpts.at(i, 2);\n int layer = kpts.at(i, 3);\n int octave = kpts.at(i, 4) - firstOctave;\n float c = x / (1 << octave);\n float r = y / (1 << octave);\n if (firstOctave < 0) {\n size *= (1 << -firstOctave);\n c *= (1 << -firstOctave);\n r *= (1 << -firstOctave);\n }\n\n // Init variables\n int n = cv::SIFT_ORI_HIST_BINS;\n float hist[n];\n float scl_octv = size*0.5f/(1 << octave);\n\n // std::cout << gauss_pyr[octave*(nOctaveLayers+3) + layer].depth() << std::endl;\n // cv::imshow(\"HI\", gauss_pyr[octave*(nOctaveLayers+3) + layer]);\n // cv::waitKey(0);\n float omax = cv::opt_CV_CPU_DISPATCH_MODE::calcOrientationHist(\n gauss_pyr[octave*(nOctaveLayers+3) + layer],\n cv::Point(c, r),\n cvRound(cv::SIFT_ORI_RADIUS * scl_octv),\n cv::SIFT_ORI_SIG_FCTR * scl_octv,\n hist, n);\n\n float mag_thr = (float)(omax * cv::SIFT_ORI_PEAK_RATIO);\n for( int j = 0; j < n; j++ )\n {\n int l = j > 0 ? j - 1 : n - 1;\n int r2 = j < n-1 ? j + 1 : 0;\n\n if( hist[j] > hist[l] && hist[j] > hist[r2] && hist[j] >= mag_thr )\n {\n float bin = j + 0.5f * (hist[l]-hist[r2]) / (hist[l] - 2*hist[j] + hist[r2]);\n bin = bin < 0 ? n + bin : bin >= n ? bin - n : bin;\n float angle = 360.f - (float)((360.f/n) * bin);\n if(std::abs(angle - 360.f) < FLT_EPSILON)\n angle = 0.f;\n\n cv::Mat kpt = cv::Mat::zeros(1, 6, CV_32F);\n kpt.at(0, 0) = x;\n kpt.at(0, 1) = y;\n kpt.at(0, 2) = size;\n kpt.at(0, 3) = layer;\n kpt.at(0, 4) = octave;\n kpt.at(0, 5) = angle;\n if (firstOctave < 0) {\n kpt.at(0, 2) /= (1 << -firstOctave);\n kpt.at(0, 4) += firstOctave;\n }\n out_kpts.push_back(kpt);\n }\n }\n }\n\n cv::Mat out_kpts_t;\n cv::vconcat(out_kpts, out_kpts_t);\n PyObject *ret = pbcvt::fromMatToNDArray(out_kpts_t);\n return ret;\n }\n\n#if (PY_VERSION_HEX >= 0x03000000)\n\n static void *init_ar() {\n#else\n static void init_ar(){\n#endif\n Py_Initialize();\n\n import_array();\n return NUMPY_IMPORT_ARRAY_RETVAL;\n }\n\n BOOST_PYTHON_MODULE (sift_ori) {\n //using namespace XM;\n init_ar();\n\n //initialize converters\n to_python_converter();\n matFromNDArrayBoostConverter();\n\n //expose module-level functions\n def(\"sift_desc\", sift_desc);\n def(\"image_pyramid\", image_pyramid);\n def(\"dog_pyramid\", dog_pyramid);\n }\n\n } //end namespace pbcvt\n", "meta": {"hexsha": "2841bb52eafbda544cc7d1254a5ac1299a1916a1", "size": 9167, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/python_module.cpp", "max_stars_repo_name": "half-potato/sift_orientation", "max_stars_repo_head_hexsha": "3c1db66da91468c3d0980d80884a6d682919635f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/python_module.cpp", "max_issues_repo_name": "half-potato/sift_orientation", "max_issues_repo_head_hexsha": "3c1db66da91468c3d0980d80884a6d682919635f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/python_module.cpp", "max_forks_repo_name": "half-potato/sift_orientation", "max_forks_repo_head_hexsha": "3c1db66da91468c3d0980d80884a6d682919635f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.5017182131, "max_line_length": 108, "alphanum_fraction": 0.5691065779, "num_tokens": 2866, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621765155565326, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.2247647150700382}} {"text": "// smooth_feedback: Control theory on Lie groups\n// https://github.com/pettni/smooth_feedback\n//\n// Licensed under the MIT License .\n//\n// Copyright (c) 2021 Petter Nilsson, John B. Mains\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n\n#ifndef SMOOTH__FEEDBACK__QP_SOLVER_HPP_\n#define SMOOTH__FEEDBACK__QP_SOLVER_HPP_\n\n/**\n * @file\n * @brief Quadratic Program solver.\n */\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \"qp.hpp\"\n#include \"utils/sparse.hpp\"\n\nnamespace smooth::feedback {\n\n/**\n * @brief Options for solve_qp\n */\nstruct QPSolverParams\n{\n /// print solver info to stdout\n bool verbose = false;\n\n /// relaxation parameter\n float alpha = 1.6;\n /// first dual step size\n float rho = 0.1;\n /// second dual step length\n float sigma = 1e-6;\n\n /// scale problem\n bool scaling = true;\n\n /// absolute threshold for convergence\n float eps_abs = 1e-3;\n /// relative threshold for convergence\n float eps_rel = 1e-3;\n /// threshold for primal infeasibility\n float eps_primal_inf = 1e-4;\n /// threshold for dual infeasibility\n float eps_dual_inf = 1e-4;\n\n /// max number of iterations (default no limit)\n std::optional max_iter = {};\n\n /// max solution time (default no limit)\n std::optional max_time = {};\n\n /// iterations between checking stopping criterion\n uint32_t stop_check_iter = 25;\n\n /// run solution polishing (uses dynamic memory)\n bool polish = true;\n /// number of iterations to refine polish\n uint32_t polish_iter = 5;\n /// regularization parameter for polishing\n float delta = 1e-6;\n};\n\nnamespace detail {\n\ntemplate\nusing qp_solution_t = QPSolution<\n decltype(Pbm::A)::RowsAtCompileTime,\n decltype(Pbm::A)::ColsAtCompileTime,\n typename decltype(Pbm::A)::Scalar>;\n\n/**\n * @brief Polish solution of quadratic program\n *\n * @tparam Pbm problem type\n *\n * @param[in] pbm problem formulation\n * @param[in, out] sol solution to polish\n * @param[in] prm solver options\n * @param[in] c cost scaling\n * @param[in] sx variable scaling\n * @param[in] sy constraint scaling\n *\n * @warning This function allocates dynamic memory even for static-sized problems.\n */\ntemplate\nbool polish_qp(\n const Pbm & pbm,\n qp_solution_t & sol,\n const QPSolverParams & prm,\n const typename decltype(Pbm::A)::Scalar c,\n const Eigen::MatrixBase & sx,\n const Eigen::MatrixBase & sy)\n{\n using AmatT = decltype(Pbm::A);\n using Scalar = typename AmatT::Scalar;\n static constexpr bool sparse = std::is_base_of_v, AmatT>;\n\n static constexpr Scalar inf = std::numeric_limits::infinity();\n static constexpr Scalar eps = std::numeric_limits::epsilon();\n\n static constexpr Eigen::Index N = AmatT::ColsAtCompileTime;\n const Eigen::Index n = pbm.A.cols(), m = pbm.A.rows();\n\n // FIND ACTIVE CONSTRAINT SETS\n\n Eigen::Index nl = 0, nu = 0;\n for (Eigen::Index idx = 0; idx < m; ++idx) {\n if (sol.dual[idx] < -100 * eps && pbm.l[idx] != -inf) { nl++; }\n if (sol.dual[idx] > 100 * eps && pbm.u[idx] != inf) { nu++; }\n }\n\n Eigen::VectorXi LU_idx(nl + nu);\n for (Eigen::Index idx = 0, lcntr = 0, ucntr = 0; idx < m; ++idx) {\n if (sol.dual[idx] < -100 * eps && pbm.l[idx] != -inf) { LU_idx(lcntr++) = idx; }\n if (sol.dual[idx] > 100 * eps && pbm.u[idx] != inf) { LU_idx(nl + ucntr++) = idx; }\n }\n\n // FORM REDUCED SYSTEMS (27) AND (30)\n\n // square symmetric system matrix\n using HT = std::conditional_t, Eigen::Matrix>;\n HT H(n + nl + nu, n + nl + nu), Hp(n + nl + nu, n + nl + nu);\n\n // fill up H\n if constexpr (sparse) {\n // preallocate nonzeros\n Eigen::VectorXi nnz(n + nl + nu);\n for (auto i = 0u; i != n; ++i) {\n nnz(i) = pbm.P.outerIndexPtr()[i + 1] - pbm.P.outerIndexPtr()[i];\n }\n for (auto i = 0u; i != nl + nu; ++i) {\n nnz(n + i) = pbm.A.outerIndexPtr()[LU_idx(i) + 1] - pbm.A.outerIndexPtr()[LU_idx(i)];\n }\n H.reserve(nnz);\n Hp.reserve(nnz + Eigen::VectorXi::Ones(n + nl + nu));\n\n // fill P in top left block\n for (auto k = 0u; k < n; ++k) {\n for (Eigen::InnerIterator it(pbm.P, k); it; ++it) {\n const Scalar pij = c * sx(it.col()) * sx(it.row()) * it.value();\n H.insert(it.row(), it.col()) = pij;\n Hp.insert(it.row(), it.col()) = pij;\n }\n }\n\n // fill selected rows of A in top right block\n for (auto a_row = 0u; a_row != nl + nu; ++a_row) {\n for (Eigen::InnerIterator it(pbm.A, LU_idx(a_row)); it; ++it) {\n const Scalar Aij = sy(it.row()) * sx(it.col()) * it.value();\n H.insert(it.col(), n + a_row) = Aij;\n Hp.insert(it.col(), n + a_row) = Aij;\n }\n }\n } else {\n H.setZero();\n H.topLeftCorner(n, n) = c * sx.asDiagonal() * pbm.P * sx.asDiagonal();\n for (auto i = 0u; i != nl + nu; ++i) {\n H.col(n + i).template head(n) = sy(LU_idx(i)) * pbm.A.row(LU_idx(i)) * sx.asDiagonal();\n }\n Hp = H;\n }\n\n // add perturbing diagonal elements to Hp\n if constexpr (sparse) {\n for (auto i = 0u; i != n; ++i) { Hp.coeffRef(i, i) += prm.delta; }\n for (auto i = 0u; i != nl + nu; ++i) { Hp.coeffRef(n + i, n + i) -= prm.delta; }\n H.makeCompressed();\n Hp.makeCompressed();\n } else {\n Hp.topLeftCorner(n, n) += Eigen::VectorX::Constant(n, prm.delta).asDiagonal();\n Hp.bottomRightCorner(nl + nu, nl + nu) -=\n Eigen::VectorX::Constant(nl + nu, prm.delta).asDiagonal();\n }\n\n Eigen::VectorX h(n + nl + nu);\n h.head(n) = -c * sx.cwiseProduct(pbm.q);\n for (auto i = 0u; i != nl; ++i) { h(n + i) = sy(LU_idx(i)) * pbm.l(LU_idx(i)); }\n for (auto i = 0u; i != nu; ++i) { h(n + nl + i) = sy(LU_idx(nl + i)) * pbm.u(LU_idx(nl + i)); }\n\n // ITERATIVE REFINEMENT\n\n // factorize Hp\n std::conditional_t<\n sparse,\n Eigen::SimplicialLDLT,\n Eigen::LDLT>\n ldlt(Hp);\n\n if (ldlt.info()) { return false; }\n\n Eigen::VectorX t_hat = Eigen::VectorX::Zero(n + nl + nu);\n for (auto i = 0u; i != prm.polish_iter; ++i) {\n t_hat += ldlt.solve(h - H.template selfadjointView() * t_hat);\n }\n\n // UPDATE SOLUTION\n\n sol.primal = t_hat.template head(n);\n for (auto i = 0u; i < nl; ++i) { sol.dual(LU_idx(i)) = t_hat(n + i); }\n for (auto i = 0u; i < nu; ++i) { sol.dual(LU_idx(nl + i)) = t_hat(n + nl + i); }\n\n return true;\n}\n\n/**\n * @brief Copy-able wrapper around Eigen LDLT types.\n */\ntemplate\nstruct LDLTWrapper\n{\n bool first{true}; // true if ldlt object needs factorization\n LDLTt ldlt{};\n\n LDLTWrapper() = default;\n LDLTWrapper(const LDLTWrapper &) : first{true}, ldlt{} {}\n LDLTWrapper(LDLTWrapper &&) : first{true}, ldlt{} {}\n LDLTWrapper & operator=(const LDLTWrapper &)\n {\n first = true;\n return *this;\n }\n LDLTWrapper & operator=(LDLTWrapper &)\n {\n first = true;\n return *this;\n }\n ~LDLTWrapper() = default;\n};\n\n} // namespace detail\n\n/**\n * @brief Solver for quadratic programs\n *\n * Use this class to efficiently solve many QPs with the same problem structure.\n *\n * For one-off QPs, see solve_qp().\n */\ntemplate\nclass QPSolver\n{\n using AmatT = decltype(Pbm::A);\n using Scalar = typename AmatT::Scalar;\n static constexpr bool sparse = std::is_base_of_v, AmatT>;\n\n // static sizes\n static constexpr Eigen::Index M = AmatT::RowsAtCompileTime;\n static constexpr Eigen::Index N = AmatT::ColsAtCompileTime;\n static constexpr Eigen::Index K = (N == -1 || M == -1) ? Eigen::Index(-1) : N + M;\n\n // typedefs\n using Rn = Eigen::Vector;\n using Rm = Eigen::Vector;\n using Rk = Eigen::Vector;\n using Ht = std::conditional_t, Eigen::Matrix>;\n using LDLTt = std::\n conditional_t, Eigen::LDLT>;\n\n static inline const Scalar inf = std::numeric_limits::infinity();\n\npublic:\n /**\n * @brief Default constructor.\n */\n QPSolver(const QPSolverParams & prm = {}) : prm_(prm) {}\n\n /**\n * @brief Construct and allocate working memory.\n *\n * @param pbm template problem.\n *\n * Memory is allocated for solving problems with same structure as pbm.\n */\n QPSolver(const Pbm & pbm, const QPSolverParams & prm = {}) : prm_(prm) { analyze(pbm); }\n\n /// @brief Default copy constructor\n QPSolver(const QPSolver &) = default;\n /// @brief Default move constructor\n QPSolver(QPSolver &&) = default;\n /// @brief Default copy assignment\n QPSolver & operator=(const QPSolver &) = default;\n /// @brief Default move assignment\n QPSolver & operator=(QPSolver &&) = default;\n\n /**\n * @brief Access most recent QP solution.\n */\n const QPSolution & sol() const { return sol_; }\n\n /**\n * @brief Prepare for solving problems.\n */\n void analyze(const Pbm & pbm)\n {\n const Eigen::Index n = pbm.A.cols(), m = pbm.A.rows(), k = n + m;\n\n // solution\n sol_.primal.setZero(n);\n sol_.dual.setZero(m);\n\n // scaling variables and working memory\n c_ = 1;\n sx_.setOnes(n);\n sy_.setOnes(m);\n sx_inc_.setZero(n);\n sy_inc_.setZero(m);\n\n // solve working memory\n z_.resize(m);\n z_next_.resize(m);\n rho_.resize(m);\n p_.resize(k);\n pt_.resize(k);\n\n // stopping working memory\n x_us_.resize(n);\n dx_us_.resize(n);\n y_us_.resize(m);\n dy_us_.resize(m);\n z_us_.resize(m);\n Px_.resize(n);\n Aty_.resize(n);\n Ax_.resize(m);\n\n // preallocate nonzeros in H\n H_.setZero();\n H_.resize(k, k);\n if constexpr (sparse) {\n Eigen::VectorXi nnz(k);\n for (auto i = 0u; i < n; ++i) {\n nnz(i) = pbm.P.outerIndexPtr()[i + 1] - pbm.P.outerIndexPtr()[i] + 1;\n }\n for (auto i = 0u; i < m; ++i) {\n nnz(n + i) = pbm.A.outerIndexPtr()[i + 1] - pbm.A.outerIndexPtr()[i] + 1;\n }\n H_.reserve(nnz);\n }\n }\n\n /**\n * @brief Solve quadratic program.\n */\n const QPSolution & solve(\n const Pbm & pbm,\n std::optional>> warmstart = {})\n {\n // update problem scaling\n if (prm_.scaling) { scale(pbm); }\n\n // dynamic sizes\n const Eigen::Index n = pbm.A.cols(), m = pbm.A.rows();\n\n // cast parameters to scalar type\n const Scalar rho_bar = static_cast(prm_.rho);\n const Scalar alpha = static_cast(prm_.alpha);\n const Scalar alpha_comp = Scalar(1) - alpha;\n const Scalar sigma = static_cast(prm_.sigma);\n\n // return code: when set algorithm is finished\n std::optional ret_code = std::nullopt;\n\n for (auto i = 0u; i != m; ++i) {\n if (pbm.l(i) == inf || pbm.u(i) == -inf || pbm.u(i) - pbm.l(i) < Scalar(0.)) {\n ret_code = QPSolutionStatus::PrimalInfeasible; // feasible set trivially empty\n }\n\n // set rho depending on constraint type\n if (pbm.l(i) == -inf && pbm.u(i) == inf) {\n rho_(i) = Scalar(1e-6); // unbounded\n } else if (sy_(i) * abs(pbm.l(i) - pbm.u(i)) < 1e-5) {\n rho_(i) = Scalar(1e3) * rho_bar; // equality\n } else {\n rho_(i) = rho_bar; // inequality\n }\n }\n\n const auto t0 = std::chrono::high_resolution_clock::now();\n\n // fill square symmetric system matrix H = [P A'; A 0]\n if constexpr (sparse) {\n if (H_.isCompressed()) { H_.coeffs().setZero(); }\n\n for (auto i = 0u; i < pbm.P.outerSize(); ++i) {\n for (Eigen::InnerIterator it(pbm.P, i); it; ++it) {\n if (it.col() >= it.row()) {\n H_.coeffRef(it.row(), it.col()) = c_ * sx_(it.row()) * sx_(it.col()) * it.value();\n }\n }\n }\n block_add_identity(H_, 0, 0, n, sigma);\n for (auto i = 0u; i < pbm.A.outerSize(); ++i) {\n for (Eigen::InnerIterator it(pbm.A, i); it; ++it) {\n H_.coeffRef(it.col(), n + it.row()) = sy_(it.row()) * sx_(it.col()) * it.value();\n }\n }\n for (auto row = 0u; row < m; ++row) {\n H_.coeffRef(n + row, n + row) = Scalar(-1) / rho_(row);\n }\n\n if (!H_.isCompressed()) { H_.makeCompressed(); }\n } else {\n H_.setZero();\n\n H_.template topLeftCorner(n, n) = c_ * sx_.asDiagonal() * pbm.P * sx_.asDiagonal();\n H_.template topLeftCorner(n, n) +=\n Eigen::Vector::Constant(n, sigma).asDiagonal();\n H_.template topRightCorner(n, m) =\n (sy_.asDiagonal() * pbm.A * sx_.asDiagonal()).transpose();\n H_.template bottomRightCorner(m, m) = (-rho_).cwiseInverse().asDiagonal();\n }\n\n const auto t_fill = std::chrono::high_resolution_clock::now();\n\n if (prm_.verbose) {\n using std::cout, std::left, std::setw, std::right;\n // clang-format off\n cout << \"========================= QP Solver =========================\" << '\\n';\n cout << \"Solving \" << (sparse ? \"sparse\" : \"dense\") << \" QP with n=\" << n << \", m=\" << m << '\\n';\n cout << setw(8) << right << \"ITER\"\n << setw(14) << right << \"OBJ\"\n << setw(14) << right << \"PRI_RES\"\n << setw(14) << right << \"DUA_RES\"\n << setw(10) << right << \"TIME\" << '\\n';\n // clang-format on\n }\n\n // factorize H\n if constexpr (sparse) {\n if (ldlt_.first) { ldlt_.ldlt.analyzePattern(H_); }\n ldlt_.ldlt.factorize(H_); // makes copy for permuted matrix..\n ldlt_.first = false;\n } else {\n ldlt_.ldlt.compute(H_);\n }\n\n const auto t_factor = std::chrono::high_resolution_clock::now();\n\n if (ldlt_.ldlt.info()) { ret_code = QPSolutionStatus::Unknown; }\n\n // initialize solver variables\n if (warmstart.has_value()) {\n // warmstart variables must be scaled\n sol_.primal = sx_.cwiseInverse().cwiseProduct(warmstart.value().get().primal);\n sol_.dual = c_ * sy_.cwiseInverse().cwiseProduct(warmstart.value().get().dual);\n z_.noalias() = sy_.asDiagonal() * pbm.A * warmstart.value().get().primal;\n } else {\n sol_.primal.setZero();\n sol_.dual.setZero();\n z_.setZero();\n }\n\n // main optimization loop\n auto iter = 0u;\n for (; (!prm_.max_iter || iter != prm_.max_iter.value()) && !ret_code; ++iter) {\n p_.template segment(0, n) = sigma * sol_.primal - c_ * sx_.asDiagonal() * pbm.q;\n p_.template segment(n, m) = z_ - rho_.cwiseInverse().cwiseProduct(sol_.dual);\n if constexpr (sparse) {\n // p_ = ldlt_.solve(p_);\n // manual solve because ldlt_.solve() uses temporaries...\n // Pinv L D L' P x = b <==> x = Pinv * (L' \\ Dinv(L \\ (P * b)))\n pt_.noalias() = ldlt_.ldlt.permutationP() * p_;\n ldlt_.ldlt.matrixL().solveInPlace(pt_);\n pt_.applyOnTheLeft(ldlt_.ldlt.vectorD().cwiseInverse().asDiagonal());\n ldlt_.ldlt.matrixU().solveInPlace(pt_);\n p_.noalias() = ldlt_.ldlt.permutationPinv() * pt_;\n } else {\n ldlt_.ldlt.solveInPlace(p_);\n }\n\n if (iter % prm_.stop_check_iter == 1) {\n // termination checking requires difference, store old scaled values\n dx_us_ = sol_.primal, dy_us_ = sol_.dual;\n }\n\n sol_.primal = alpha * p_.template segment(0, n) + alpha_comp * sol_.primal;\n z_next_ = (alpha * rho_.cwiseInverse().cwiseProduct(p_.template segment(n, m))\n + alpha_comp * rho_.cwiseInverse().cwiseProduct(sol_.dual) + z_)\n .cwiseMax(sy_.cwiseProduct(pbm.l))\n .cwiseMin(sy_.cwiseProduct(pbm.u));\n sol_.dual = alpha_comp * sol_.dual + alpha * p_.template segment(n, m)\n + rho_.cwiseProduct(z_) - rho_.cwiseProduct(z_next_);\n std::swap(z_, z_next_);\n\n if (iter % prm_.stop_check_iter == 1) {\n // unscale solution\n x_us_ = sx_.cwiseProduct(sol_.primal);\n y_us_ = sy_.cwiseProduct(sol_.dual) / c_;\n z_us_ = sy_.cwiseInverse().cwiseProduct(z_);\n dx_us_ = sx_.cwiseProduct(sol_.primal - dx_us_);\n dy_us_ = sy_.cwiseProduct(sol_.dual - dy_us_) / c_;\n\n // check stopping criteria for unscaled problem and unscaled variables\n ret_code = check_stopping(pbm);\n\n if (prm_.verbose) {\n using std::cout, std::setw, std::right, std::chrono::microseconds;\n // clang-format off\n cout << setw(7) << right << iter << \":\"\n << std::scientific\n << setw(14) << right << (0.5 * pbm.P * x_us_ + pbm.q).dot(x_us_)\n << setw(14) << right << (pbm.A * x_us_ - z_us_).template lpNorm()\n << setw(14) << right << (pbm.P * x_us_ + pbm.q + pbm.A.transpose() * y_us_).template lpNorm()\n << setw(10) << right << duration_cast(std::chrono::high_resolution_clock::now() - t0).count()\n << '\\n';\n // clang-format on\n }\n\n // check for timeout\n if (!ret_code) {\n if (\n prm_.max_time\n && std::chrono::high_resolution_clock::now() > t0 + prm_.max_time.value()) {\n ret_code = QPSolutionStatus::MaxTime;\n }\n }\n }\n }\n\n const auto t_iter = std::chrono::high_resolution_clock::now();\n\n // polish solution if optimal\n if (ret_code.has_value() && ret_code.value() == QPSolutionStatus::Optimal && prm_.polish) {\n if (detail::polish_qp(pbm, sol_, prm_, c_, sx_, sy_)) {\n if (prm_.verbose) {\n // unscale solution\n x_us_ = sx_.cwiseProduct(sol_.primal);\n y_us_ = sy_.cwiseProduct(sol_.dual) / c_;\n z_us_ = sy_.cwiseInverse().cwiseProduct(z_);\n\n using std::cout, std::setw, std::right, std::chrono::microseconds;\n // clang-format off\n cout << setw(8) << right << \"polish:\"\n << std::scientific\n << setw(14) << right << (0.5 * pbm.P * x_us_ + pbm.q).dot(x_us_)\n << setw(14) << right << (pbm.A * x_us_ - z_us_).template lpNorm()\n << setw(14) << right << (pbm.P * x_us_ + pbm.q + pbm.A.transpose() * y_us_).template lpNorm()\n << setw(10) << right << duration_cast(std::chrono::high_resolution_clock::now() - t0).count()\n << '\\n';\n // clang-format on\n }\n\n } else {\n if (prm_.verbose) { std::cout << \"Polish failed\" << '\\n'; }\n sol_.code = QPSolutionStatus::PolishFailed;\n }\n }\n\n const auto t_polish = std::chrono::high_resolution_clock::now();\n\n // unscale solution\n sol_.code = ret_code.value_or(QPSolutionStatus::MaxIterations);\n sol_.primal = sx_.cwiseProduct(sol_.primal);\n sol_.dual = sy_.cwiseProduct(sol_.dual) / c_;\n sol_.objective = sol_.primal.dot(0.5 * pbm.P * sol_.primal + pbm.q);\n sol_.iter = iter;\n\n if (prm_.verbose) {\n using std::cout, std::left, std::right, std::setw, std::chrono::microseconds;\n\n // clang-format off\n cout << \"QP solver summary:\" << '\\n';\n cout << \"Result \" << static_cast(sol_.code) << '\\n';\n\n cout << setw(25) << left << \"Iterations\" << setw(10) << right << iter - 1 << '\\n';\n cout << setw(26) << left << \"Total time (µs)\" << setw(10) << right << duration_cast(t_polish - t0).count() << '\\n';\n cout << setw(25) << left << \" Matrix filling\" << setw(10) << right << duration_cast(t_fill - t0).count() << '\\n';\n cout << setw(25) << left << \" Factorization\" << setw(10) << right << duration_cast(t_factor - t_fill).count() << '\\n';\n cout << setw(25) << left << \" Iteration\" << setw(10) << right << duration_cast(t_iter - t_factor).count() << '\\n';\n cout << setw(25) << left << \" Polish\" << setw(10) << right << duration_cast(t_polish - t_iter).count() << '\\n';\n cout << \"=============================================================\" << '\\n';\n // clang-format on\n }\n\n return sol_;\n }\n\nprotected:\n /**\n * @brief Check stopping criteria for solver.\n */\n std::optional check_stopping(const Pbm & pbm)\n {\n const Eigen::Index m = pbm.A.rows();\n\n // norm function\n static const auto norm = [](auto && t) -> Scalar {\n return t.template lpNorm();\n };\n\n // OPTIMALITY\n\n // check primal\n Ax_.noalias() = pbm.A * x_us_;\n const Scalar Ax_norm = norm(Ax_);\n Ax_ -= z_us_;\n if (norm(Ax_) <= prm_.eps_abs + prm_.eps_rel * std::max(Ax_norm, norm(z_us_))) {\n // primal succeeded, check dual\n Px_.noalias() = pbm.P * x_us_;\n Aty_.noalias() = pbm.A.transpose() * y_us_;\n const Scalar dual_scale = std::max({norm(Px_), norm(pbm.q), norm(Aty_)});\n Px_ += pbm.q + Aty_;\n if (norm(Px_) <= prm_.eps_abs + prm_.eps_rel * dual_scale) {\n return QPSolutionStatus::Optimal;\n }\n }\n\n // PRIMAL INFEASIBILITY\n\n Aty_.noalias() = pbm.A.transpose() * dy_us_; // NOTE new value A' * dy\n const Scalar Edy_norm = norm(dy_us_);\n\n Scalar u_dyp_plus_l_dyn = Scalar(0);\n for (auto i = 0u; i != m; ++i) {\n if (pbm.u(i) != inf) {\n u_dyp_plus_l_dyn += pbm.u(i) * std::max(Scalar(0), dy_us_(i));\n } else if (dy_us_(i) > prm_.eps_primal_inf * Edy_norm) {\n // contributes +inf to sum --> no certificate\n u_dyp_plus_l_dyn = inf;\n break;\n }\n if (pbm.l(i) != -inf) {\n u_dyp_plus_l_dyn += pbm.l(i) * std::min(Scalar(0), dy_us_(i));\n } else if (dy_us_(i) < -prm_.eps_primal_inf * Edy_norm) {\n // contributes +inf to sum --> no certificate\n u_dyp_plus_l_dyn = inf;\n break;\n }\n }\n\n if (std::max(norm(Aty_), u_dyp_plus_l_dyn) < prm_.eps_primal_inf * Edy_norm) {\n return QPSolutionStatus::PrimalInfeasible;\n }\n\n // DUAL INFEASIBILITY\n\n Ax_.noalias() = pbm.A * dx_us_; // note new value A * dx\n const Scalar dx_norm = norm(dx_us_);\n Px_.noalias() = pbm.P * dx_us_;\n\n bool dual_infeasible = (norm(Px_) <= prm_.eps_dual_inf * dx_norm)\n && (pbm.q.dot(dx_us_) <= prm_.eps_dual_inf * dx_norm);\n for (auto i = 0u; i != m && dual_infeasible; ++i) {\n if (pbm.u(i) == inf) {\n dual_infeasible &= (Ax_(i) >= -prm_.eps_dual_inf * dx_norm);\n } else if (pbm.l(i) == -inf) {\n dual_infeasible &= (Ax_(i) <= prm_.eps_dual_inf * dx_norm);\n } else {\n dual_infeasible &= std::abs(Ax_(i)) < prm_.eps_dual_inf * dx_norm;\n }\n }\n\n if (dual_infeasible) { return QPSolutionStatus::DualInfeasible; }\n\n return std::nullopt;\n }\n\n /**\n * @brief Re-scale QP.\n *\n * The scaled problem is defined as\n *\n * * \\f$ P_s = c S_x P S_x \\f$,\n * * \\f$ q_s = c q S_x \\f$,\n * * \\f$ A_s = S_y A S_x \\f$,\n * * \\f$ l_s = S_y l \\f$,\n * * \\f$ u_s = S_y u \\f$,\n *\n * where Sx = diag(sx), Sy = diag(sy).\n *\n * The relation between scaled variables and original variables are\n *\n * * Primal: \\f$ x_s = S_x^{-1} x \\f$,\n * * Dual: \\f$ y_s = c S_y^{-1} y \\f$.\n *\n * The objective of the rescaling is the make the columns of\n * \\f[\n * \\begin{bmatrix} \\bar P & \\bar A^T \\\\ \\bar A & 0 \\end{bmatrix}\n * \\f]\n * have similar \\f$ l_\\infty \\f$ norm, and similarly for the columns of\n * \\f[\n * \\begin{bmatrix} \\bar P & \\bar q \\end{bmatrix}.\n * \\f]\n */\n void scale(const Pbm & pbm)\n {\n sx_.setOnes();\n sy_.setOnes();\n\n sx_inc_.setZero();\n\n // find \"norm\" of cost function\n for (auto i = 0u; i < pbm.P.outerSize(); ++i) {\n for (Eigen::InnerIterator it(pbm.P, i); it; ++it) {\n sx_inc_(it.col()) = std::max(sx_inc_(it.col()), std::abs(it.value()));\n }\n }\n\n // if there are \"zero cols\"\n for (auto i = 0u; i != sx_.size(); ++i) {\n if (sx_inc_(i) == 0) { sx_inc_(i) = 1; }\n }\n\n // scale cost function\n c_ = Scalar(1) / std::max({1e-6, sx_inc_.mean(), pbm.q.template lpNorm()});\n\n int iter = 0;\n\n // calculate inf-norm for every column of [Ps As' ; As 0]\n do {\n sx_inc_.setZero();\n sy_inc_.setZero();\n for (auto k = 0u; k < pbm.P.outerSize(); ++k) {\n for (Eigen::InnerIterator it(pbm.P, k); it; ++it) {\n // upper left block of H\n sx_inc_(it.col()) = std::max({\n sx_inc_(it.col()),\n std::abs(c_ * sx_(it.row()) * sx_(it.col()) * it.value()),\n });\n }\n }\n for (auto k = 0u; k < pbm.A.outerSize(); ++k) {\n for (Eigen::InnerIterator it(pbm.A, k); it; ++it) {\n const Scalar Aij = std::abs(sy_(it.row()) * sx_(it.col()) * it.value());\n sx_inc_(it.col()) = std::max(sx_inc_(it.col()), Aij); // bottom left block of H\n sy_inc_(it.row()) = std::max(sy_inc_(it.row()), Aij); // upper right block of H\n }\n }\n\n // if there are \"zero cols\" we don't scale\n for (auto i = 0u; i < sx_.size(); ++i) {\n if (sx_inc_(i) == 0) { sx_inc_(i) = 1; }\n }\n for (auto i = 0u; i < sy_.size(); ++i) {\n if (sy_inc_(i) == 0) { sy_inc_(i) = 1; }\n }\n\n sx_.applyOnTheLeft(sx_inc_.cwiseMax(1e-8).cwiseInverse().cwiseSqrt().asDiagonal());\n sy_.applyOnTheLeft(sy_inc_.cwiseMax(1e-8).cwiseInverse().cwiseSqrt().asDiagonal());\n } while (\n iter++ < 10\n && std::max((sx_inc_.array() - 1).abs().maxCoeff(), (sy_inc_.array() - 1).abs().maxCoeff())\n > 0.1);\n }\n\nprivate:\n // solver parameters\n QPSolverParams prm_{};\n\n // solution\n QPSolution sol_{};\n\n // scaling variables and working memory\n Scalar c_{0};\n Rn sx_{}, sx_inc_{};\n Rm sy_{}, sy_inc_{};\n\n // solve working memory\n Rm z_{}, z_next_{}, rho_{};\n Rk p_{}, pt_{};\n\n // stopping working memory\n Rn x_us_{}, dx_us_{};\n Rn Px_{}, Aty_{};\n Rm Ax_{};\n Rm y_us_{}, z_us_{}, dy_us_{};\n\n // system matrix and decomposition\n Ht H_{};\n detail::LDLTWrapper ldlt_{};\n};\n\n/**\n * @brief Solve a quadratic program using the operator splitting approach.\n *\n * @tparam Pbm problem type (QuadraticProgram or QuadraticProgramSparse)\n *\n * @param pbm problem formulation\n * @param prm solver options\n * @param warmstart provide initial guess for primal and dual variables\n * @return solution as QuasraticProgramSolution\n *\n * @note dynamic problem sizes (`M == -1 || N == -1`) are supported\n *\n * This is a third-party implementation of the algorithm described in the following paper:\n * * Stellato, B., Banjac, G., Goulart, P. et al.\n * **OSQP: an operator splitting solver for quadratic programs.**\n * *Math. Prog. Comp.* 12, 637–672 (2020).\n * https://doi.org/10.1007/s12532-020-00179-2\n *\n * For the official C implementation, see https://osqp.org/.\n */\ntemplate\ndetail::qp_solution_t solve_qp(\n const Pbm & pbm,\n const QPSolverParams & prm,\n std::optional>> warmstart = {})\n{\n QPSolver solver(pbm, prm);\n return solver.solve(pbm, warmstart);\n}\n\n} // namespace smooth::feedback\n\n#endif // SMOOTH__FEEDBACK__QP_SOLVER_HPP_\n", "meta": {"hexsha": "a9d49d63f1a8993f9e5945168e4e86b2daf857ba", "size": 28462, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/smooth/feedback/qp_solver.hpp", "max_stars_repo_name": "tgurriet/smooth_feedback", "max_stars_repo_head_hexsha": "1f926cb4269741ddc09ba048af5bea5e0390a053", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/smooth/feedback/qp_solver.hpp", "max_issues_repo_name": "tgurriet/smooth_feedback", "max_issues_repo_head_hexsha": "1f926cb4269741ddc09ba048af5bea5e0390a053", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/smooth/feedback/qp_solver.hpp", "max_forks_repo_name": "tgurriet/smooth_feedback", "max_forks_repo_head_hexsha": "1f926cb4269741ddc09ba048af5bea5e0390a053", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0454545455, "max_line_length": 141, "alphanum_fraction": 0.5849202445, "num_tokens": 8651, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290806, "lm_q2_score": 0.4455295350395727, "lm_q1q2_score": 0.2245050818593929}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\n// This file is manually converted from PROJ4\n\n// Copyright (c) 2008-2012 Barend Gehrels, Amsterdam, the Netherlands.\n\n// This file was modified by Oracle on 2017, 2018.\n// Modifications copyright (c) 2017-2018, Oracle and/or its affiliates.\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Geometry Library by Barend Gehrels (Geodan, Amsterdam)\n\n// Original copyright notice:\n\n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#ifndef BOOST_GEOMETRY_PROJECTIONS_IMPL_PJ_ELL_SET_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_IMPL_PJ_ELL_SET_HPP\n\n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n\n\nnamespace boost { namespace geometry { namespace projections {\n\nnamespace detail {\n\n/* set ellipsoid parameters a and es */\ntemplate \ninline T SIXTH() { return .1666666666666666667; } /* 1/6 */\ntemplate \ninline T RA4() { return .04722222222222222222; } /* 17/360 */\ntemplate \ninline T RA6() { return .02215608465608465608; } /* 67/3024 */\ntemplate \ninline T RV4() { return .06944444444444444444; } /* 5/72 */\ntemplate \ninline T RV6() { return .04243827160493827160; } /* 55/1296 */\n\n/* initialize geographic shape parameters */\ntemplate \ninline void pj_ell_set(BGParams const& /*bg_params*/, std::vector >& parameters, T &a, T &es)\n{\n T b = 0.0;\n T e = 0.0;\n std::string name;\n\n /* check for varying forms of ellipsoid input */\n a = es = 0.;\n\n /* R takes precedence */\n if (pj_param(parameters, \"tR\").i)\n a = pj_param(parameters, \"dR\").f;\n else { /* probable elliptical figure */\n\n /* check if ellps present and temporarily append its values to pl */\n name = pj_param(parameters, \"sellps\").s;\n if (! name.empty())\n {\n const int n = sizeof(pj_ellps) / sizeof(pj_ellps[0]);\n int index = -1;\n for (int i = 0; i < n && index == -1; i++)\n {\n if(pj_ellps[i].id == name)\n {\n index = i;\n }\n }\n\n if (index == -1) {\n BOOST_THROW_EXCEPTION( projection_exception(-9) );\n }\n\n parameters.push_back(pj_mkparam(pj_ellps[index].major));\n parameters.push_back(pj_mkparam(pj_ellps[index].ell));\n }\n a = pj_param(parameters, \"da\").f;\n if (pj_param(parameters, \"tes\").i) /* eccentricity squared */\n es = pj_param(parameters, \"des\").f;\n else if (pj_param(parameters, \"te\").i) { /* eccentricity */\n e = pj_param(parameters, \"de\").f;\n es = e * e;\n } else if (pj_param(parameters, \"trf\").i) { /* recip flattening */\n es = pj_param(parameters, \"drf\").f;\n if (!es) {\n BOOST_THROW_EXCEPTION( projection_exception(-10) );\n }\n es = 1./ es;\n es = es * (2. - es);\n } else if (pj_param(parameters, \"tf\").i) { /* flattening */\n es = pj_param(parameters, \"df\").f;\n es = es * (2. - es);\n } else if (pj_param(parameters, \"tb\").i) { /* minor axis */\n b = pj_param(parameters, \"db\").f;\n es = 1. - (b * b) / (a * a);\n } /* else es == 0. and sphere of radius a */\n if (!b)\n b = a * sqrt(1. - es);\n /* following options turn ellipsoid into equivalent sphere */\n if (pj_param(parameters, \"bR_A\").i) { /* sphere--area of ellipsoid */\n a *= 1. - es * (SIXTH() + es * (RA4() + es * RA6()));\n es = 0.;\n } else if (pj_param(parameters, \"bR_V\").i) { /* sphere--vol. of ellipsoid */\n a *= 1. - es * (SIXTH() + es * (RV4() + es * RV6()));\n es = 0.;\n } else if (pj_param(parameters, \"bR_a\").i) { /* sphere--arithmetic mean */\n a = .5 * (a + b);\n es = 0.;\n } else if (pj_param(parameters, \"bR_g\").i) { /* sphere--geometric mean */\n a = sqrt(a * b);\n es = 0.;\n } else if (pj_param(parameters, \"bR_h\").i) { /* sphere--harmonic mean */\n a = 2. * a * b / (a + b);\n es = 0.;\n } else {\n int i = pj_param(parameters, \"tR_lat_a\").i;\n if (i || /* sphere--arith. */\n pj_param(parameters, \"tR_lat_g\").i) { /* or geom. mean at latitude */\n T tmp;\n\n tmp = sin(pj_param(parameters, i ? \"rR_lat_a\" : \"rR_lat_g\").f);\n if (geometry::math::abs(tmp) > geometry::math::half_pi()) {\n BOOST_THROW_EXCEPTION( projection_exception(-11) );\n }\n tmp = 1. - es * tmp * tmp;\n a *= i ? .5 * (1. - es + tmp) / ( tmp * sqrt(tmp)) :\n sqrt(1. - es) / tmp;\n es = 0.;\n }\n }\n }\n\n /* some remaining checks */\n if (es < 0.) {\n BOOST_THROW_EXCEPTION( projection_exception(-12) );\n }\n if (a <= 0.) {\n BOOST_THROW_EXCEPTION( projection_exception(-13) );\n }\n}\n\ntemplate \ninline void pj_ell_set(srs::static_proj4 const& bg_params,\n std::vector >& /*parameters*/, T &a, T &es)\n{\n typedef srs::static_proj4 static_parameters_type;\n typedef typename srs::par4::detail::pick_ellps\n <\n static_parameters_type\n > pick_ellps;\n\n typename pick_ellps::model_type model = pick_ellps::model(bg_params);\n\n a = geometry::get_radius<0>(model);\n T b = geometry::get_radius<2>(model);\n es = 0.;\n if (a != b)\n {\n es = formula::eccentricity_sqr(model);\n\n // Ignore all other parameters passed in string, at least for now\n }\n\n /* some remaining checks */\n if (es < 0.) {\n BOOST_THROW_EXCEPTION( projection_exception(-12) );\n }\n if (a <= 0.) {\n BOOST_THROW_EXCEPTION( projection_exception(-13) );\n }\n}\n\n} // namespace detail\n}}} // namespace boost::geometry::projections\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_IMPL_PJ_ELL_SET_HPP\n", "meta": {"hexsha": "6f5f14b78046b3f6e7fe240f8e7bed3a4a4aac41", "size": 7886, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost_1_67_0/boost/geometry/srs/projections/impl/pj_ell_set.hpp", "max_stars_repo_name": "ramcn/gemmx", "max_stars_repo_head_hexsha": "e23ab5358322a293110b642962b478bc92580636", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 354.0, "max_stars_repo_stars_event_min_datetime": "2018-08-13T18:19:21.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-20T10:37:20.000Z", "max_issues_repo_path": "boost_1_67_0/boost/geometry/srs/projections/impl/pj_ell_set.hpp", "max_issues_repo_name": "ramcn/gemmx", "max_issues_repo_head_hexsha": "e23ab5358322a293110b642962b478bc92580636", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 79.0, "max_issues_repo_issues_event_min_datetime": "2018-08-01T11:50:45.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-17T13:40:06.000Z", "max_forks_repo_path": "boost_1_67_0/boost/geometry/srs/projections/impl/pj_ell_set.hpp", "max_forks_repo_name": "ramcn/gemmx", "max_forks_repo_head_hexsha": "e23ab5358322a293110b642962b478bc92580636", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 90.0, "max_forks_repo_forks_event_min_datetime": "2018-11-15T12:37:51.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-14T11:12:39.000Z", "avg_line_length": 38.2815533981, "max_line_length": 103, "alphanum_fraction": 0.6016992138, "num_tokens": 2067, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.22449605900427408}} {"text": "#include \n\n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n\n//TODO carefull with simplices\n\nnamespace polyfem\n{\n using namespace polysolve;\n using namespace Eigen;\n\n namespace\n {\n typedef Matrix, 3, 3> SpaceMatrix;\n typedef Matrix NodeMatrix;\n\n\n void print_local_space(const SpaceMatrix &space)\n {\n std::cout<=0; --j)\n {\n for(int i=0; i < 3; ++i)\n {\n if(space(i, j).size() > 0){\n for(std::size_t l = 0; l < space(i, j).size(); ++l)\n std::cout<= 0 && mesh.is_cube(face_id))\n return mesh_nodes.node_id_from_face(face_id);\n\n return mesh_nodes.node_id_from_edge(index.edge);\n }\n\n void explore_direction(const Navigation::Index &index, const Mesh2D &mesh, MeshNodes &mesh_nodes, const int x, const int y, const bool is_x, const bool invert, LocalBoundary &lb, SpaceMatrix &space, NodeMatrix &node, std::map &poly_edge_to_data)\n {\n int node_id = node_id_from_edge_index(mesh, mesh_nodes, index);\n // bool real_boundary = mesh.node_id_from_edge_index(index, node_id);\n\n assert(std::find(space(x, y).begin(), space(x, y).end(), node_id) == space(x, y).end());\n space(x, y).push_back(node_id);\n node(x, y) = mesh_nodes.node_position(node_id);\n assert(node(x, y).size() == 2);\n\n const int x1 = is_x ? x : (invert? 2 : 0);\n const int y1 = !is_x ? y : (invert? 2 : 0);\n\n const int x2 = is_x ? x : (invert? 0 : 2);\n const int y2 = !is_x ? y : (invert? 0 : 2);\n\n const bool is_boundary = mesh_nodes.is_boundary(node_id);\n const bool is_interface = mesh_nodes.is_interface(node_id);\n\n if(is_boundary)\n {\n lb.add_boundary_primitive(index.edge, FEBasis2d::quad_edge_local_nodes(2, mesh, index)[1]-4);\n // lb.add_boundary_primitive(index.edge, FEBasis2d::quadr_quad_edge_local_nodes(mesh, index)[1]-4);\n // bounday_nodes.push_back(node_id);\n }\n else if(is_interface)\n {\n InterfaceData &data = poly_edge_to_data[index.edge];\n data.local_indices.push_back(y * 3 + x);\n }\n else\n {\n assert(!is_boundary && !is_interface);\n\n Navigation::Index start_index = mesh.switch_face(index);\n assert(start_index.vertex == index.vertex);\n assert(start_index.face >= 0);\n\n Navigation::Index edge1 = mesh.switch_edge(start_index);\n node_id = node_id_from_edge_index(mesh, mesh_nodes, edge1);\n // if(mesh_nodes.is_boundary(node_id))\n // bounday_nodes.push_back(node_id);\n\n if(std::find(space(x1, y1).begin(), space(x1, y1).end(), node_id) == space(x1, y1).end())\n {\n space(x1, y1).push_back(node_id);\n node(x1, y1) = mesh_nodes.node_position(node_id);\n }\n\n Navigation::Index edge2 = mesh.switch_edge(mesh.switch_vertex(start_index));\n node_id = node_id_from_edge_index(mesh, mesh_nodes, edge2);\n // if(mesh_nodes.is_boundary(node_id))\n // bounday_nodes.push_back(node_id);\n if(std::find(space(x2, y2).begin(), space(x2, y2).end(), node_id) == space(x2, y2).end())\n {\n space(x2, y2).push_back(node_id);\n node(x2, y2) = mesh_nodes.node_position(node_id);\n // node(x2, y2).push_back(mesh.node_from_edge_index(edge2));\n }\n }\n }\n\n void add_id_for_poly(const Navigation::Index &index, const int x1, const int y1, const int x2, const int y2, const SpaceMatrix &space, std::map &poly_edge_to_data)\n {\n auto it = poly_edge_to_data.find(index.edge);\n if(it != poly_edge_to_data.end())\n {\n InterfaceData &data = it->second;\n\n assert(space(x1, y1).size() == 1);\n data.local_indices.push_back(y1 * 3 + x1);\n\n assert(space(x2, y2).size() == 1);\n data.local_indices.push_back(y2 * 3 + x2);\n }\n }\n\n void build_local_space(const Mesh2D &mesh, MeshNodes &mesh_nodes, const int el_index, SpaceMatrix &space, NodeMatrix &node, std::vector &local_boundary, std::map &poly_edge_to_data)\n {\n assert(!mesh.is_volume());\n\n Navigation::Index index;\n // space.setConstant(-1);\n\n const int el_node_id = mesh_nodes.node_id_from_face(el_index);\n space(1, 1).push_back(el_node_id);\n node(1, 1) = mesh_nodes.node_position(el_node_id);\n // (mesh.node_from_face(el_index));\n\n LocalBoundary lb(el_index, BoundaryType::QuadLine);\n\n //////////////////////////////////////////\n index = mesh.get_index_from_face(el_index);\n explore_direction(index, mesh, mesh_nodes, 1, 0, false, false, lb, space, node, poly_edge_to_data);\n\n //////////////////////////////////////////\n index = mesh.next_around_face(index);\n explore_direction(index, mesh, mesh_nodes, 2, 1, true, false, lb, space, node, poly_edge_to_data);\n\n //////////////////////////////////////////\n index = mesh.next_around_face(index);\n explore_direction(index, mesh, mesh_nodes, 1, 2, false, true, lb, space, node, poly_edge_to_data);\n\n //////////////////////////////////////////\n index = mesh.next_around_face(index);\n explore_direction(index, mesh, mesh_nodes, 0, 1, true, true, lb, space, node, poly_edge_to_data);\n\n //////////////////////////////////////////\n if(mesh_nodes.is_boundary_or_interface(space(1, 2).front()) && mesh_nodes.is_boundary_or_interface(space(2, 1).front()))\n {\n assert(space(2,2).empty());\n\n Navigation::Index start_index = mesh.get_index_from_face(el_index);\n start_index = mesh.next_around_face(start_index);\n start_index = mesh.next_around_face(start_index);\n\n const int node_id = mesh_nodes.node_id_from_vertex(start_index.vertex);\n // mesh.vertex_node_id(start_index.vertex);\n space(2,2).push_back(node_id);\n node(2,2) = mesh_nodes.node_position(node_id);\n // node(2,2).push_back(mesh.node_from_vertex(start_index.vertex));\n\n // bounday_nodes.push_back(node_id);\n }\n\n if(mesh_nodes.is_boundary_or_interface(space(1, 0).front()) && mesh_nodes.is_boundary_or_interface(space(2, 1).front()))\n {\n assert(space(2,0).empty());\n\n Navigation::Index start_index = mesh.get_index_from_face(el_index);\n start_index = mesh.next_around_face(start_index);\n\n const int node_id = mesh_nodes.node_id_from_vertex(start_index.vertex);\n // mesh.vertex_node_id(start_index.vertex);\n space(2,0).push_back(node_id);\n node(2,0) = mesh_nodes.node_position(node_id);\n // .push_back(mesh.node_from_vertex(start_index.vertex));\n\n // bounday_nodes.push_back(node_id);\n }\n\n if(mesh_nodes.is_boundary_or_interface(space(1, 2).front()) && mesh_nodes.is_boundary_or_interface(space(0, 1).front()))\n {\n assert(space(0,2).empty());\n\n Navigation::Index start_index = mesh.get_index_from_face(el_index);\n start_index = mesh.next_around_face(start_index);\n start_index = mesh.next_around_face(start_index);\n start_index = mesh.next_around_face(start_index);\n\n // const int node_id = mesh.vertex_node_id(start_index.vertex);\n const int node_id = mesh_nodes.node_id_from_vertex(start_index.vertex);\n space(0,2).push_back(node_id);\n node(0,2) = mesh_nodes.node_position(node_id);\n // .push_back(mesh.node_from_vertex(start_index.vertex));\n\n // bounday_nodes.push_back(node_id);\n }\n\n if(mesh_nodes.is_boundary_or_interface(space(1, 0).front()) && mesh_nodes.is_boundary_or_interface(space(0, 1).front()))\n {\n Navigation::Index start_index = mesh.get_index_from_face(el_index);\n\n // const int node_id = mesh.vertex_node_id(start_index.vertex);\n const int node_id = mesh_nodes.node_id_from_vertex(start_index.vertex);\n space(0,0).push_back(node_id);\n node(0,0) = mesh_nodes.node_position(node_id);\n //.push_back(mesh.node_from_vertex(start_index.vertex));\n\n // bounday_nodes.push_back(node_id);\n }\n\n // std::cout<, 3> &h_knots, std::array, 3> &v_knots)\n {\n //left and right neigh are absent\n if(mesh_nodes.is_boundary_or_interface(space(0,1).front()) && mesh_nodes.is_boundary_or_interface(space(2,1).front()))\n {\n h_knots[0] = {{0, 0, 0, 1}};\n h_knots[1] = {{0, 0, 1, 1}};\n h_knots[2] = {{0, 1, 1, 1}};\n }\n //left neigh is absent\n else if(mesh_nodes.is_boundary_or_interface(space(0,1).front()))\n {\n h_knots[0] = {{0, 0, 0, 1}};\n h_knots[1] = {{0, 0, 1, 2}};\n h_knots[2] = {{0, 1, 2, 3}};\n }\n //right neigh is absent\n else if(mesh_nodes.is_boundary_or_interface(space(2,1).front()))\n {\n h_knots[0] = {{-2, -1, 0, 1}};\n h_knots[1] = {{-1, 0, 1, 1}};\n h_knots[2] = {{0, 1, 1, 1}};\n }\n else\n {\n h_knots[0] = {{-2, -1, 0, 1}};\n h_knots[1] = {{-1, 0, 1, 2}};\n h_knots[2] = {{0, 1, 2, 3}};\n }\n\n\n //top and bottom neigh are absent\n if(mesh_nodes.is_boundary_or_interface(space(1,0).front()) && mesh_nodes.is_boundary_or_interface(space(1,2).front()))\n {\n v_knots[0] = {{0, 0, 0, 1}};\n v_knots[1] = {{0, 0, 1, 1}};\n v_knots[2] = {{0, 1, 1, 1}};\n }\n //bottom neigh is absent\n else if(mesh_nodes.is_boundary_or_interface(space(1,0).front()))\n {\n v_knots[0] = {{0, 0, 0, 1}};\n v_knots[1] = {{0, 0, 1, 2}};\n v_knots[2] = {{0, 1, 2, 3}};\n }\n //top neigh is absent\n else if(mesh_nodes.is_boundary_or_interface(space(1,2).front()))\n {\n v_knots[0] = {{-2, -1, 0, 1}};\n v_knots[1] = {{-1, 0, 1, 1}};\n v_knots[2] = {{0, 1, 1, 1}};\n }\n else\n {\n v_knots[0] = {{-2, -1, 0, 1}};\n v_knots[1] = {{-1, 0, 1, 2}};\n v_knots[2] = {{0, 1, 2, 3}};\n }\n }\n\n void basis_for_regular_quad(const SpaceMatrix &space, const NodeMatrix &loc_nodes, const std::array, 3> &h_knots, const std::array, 3> &v_knots, ElementBases &b)\n {\n for(int y = 0; y < 3; ++y)\n {\n for(int x = 0; x < 3; ++x)\n {\n if(space(x,y).size() == 1)\n {\n const int global_index = space(x, y).front();\n const Eigen::MatrixXd &node = loc_nodes(x,y);\n assert(node.size() == 2);\n\n const int local_index = y*3 + x;\n b.bases[local_index].init(2, global_index, local_index, node);\n\n const QuadraticBSpline2d spline(h_knots[x], v_knots[y]);\n b.bases[local_index].set_basis([spline](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { spline.interpolate(uv, val); });\n b.bases[local_index].set_grad( [spline](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { spline.derivative(uv, val); });\n }\n }\n }\n }\n\n void basis_for_irregulard_quad(const int el_id, const Mesh2D &mesh, MeshNodes &mesh_nodes, const SpaceMatrix &space, const NodeMatrix &loc_nodes, const std::array, 3> &h_knots, const std::array, 3> &v_knots, ElementBases &b)\n {\n for(int y = 0; y < 3; ++y)\n {\n for(int x = 0; x < 3; ++x)\n {\n if(space(x,y).size() > 1)\n {\n const int mpx = 1;\n const int mpy = y;\n\n const int mmx = x;\n const int mmy = 1;\n\n std::vector other_indices;\n const auto ¢er = b.bases[1*3 + 1].global().front();\n\n const auto &el1 = b.bases[mpy*3 + mpx].global().front();\n const auto &el2 = b.bases[mmy*3 + mmx].global().front();\n\n\n Navigation::Index start_index = mesh.get_index_from_face(el_id);\n bool found = false;\n for(int i =0; i < 4; ++i)\n {\n other_indices.clear();\n int n_neighs = 0;\n Navigation::Index index = start_index;\n do\n {\n const int f_index = mesh_nodes.node_id_from_face(index.face);\n if(f_index != el1.index && f_index != el2.index && f_index != center.index)\n other_indices.push_back(f_index);\n\n ++n_neighs;\n index = mesh.next_around_vertex(index);\n }\n while(index.face != start_index.face);\n if(n_neighs != 4){\n found = true;\n break;\n }\n\n start_index = mesh.next_around_face(start_index);\n }\n assert(found);\n\n\n const int local_index = y*3 + x;\n auto &base = b.bases[local_index];\n\n const int k = int(other_indices.size()) + 3;\n\n\n base.global().resize(k);\n\n base.global()[0].index = center.index;\n base.global()[0].val = (4. - k) / k;\n base.global()[0].node = center.node;\n\n base.global()[1].index = el1.index;\n base.global()[1].val = (4. - k) / k;\n base.global()[1].node = el1.node;\n\n base.global()[2].index = el2.index;\n base.global()[2].val = (4. - k) / k;\n base.global()[2].node = el2.node;\n\n\n for(std::size_t n = 0; n < other_indices.size(); ++n)\n {\n base.global()[3+n].index = other_indices[n];\n base.global()[3+n].val = 4./k;\n base.global()[3+n].node = mesh_nodes.node_position(other_indices[n]);\n }\n\n\n const QuadraticBSpline2d spline(h_knots[x], v_knots[y]);\n b.bases[local_index].set_basis([spline](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { spline.interpolate(uv, val); });\n b.bases[local_index].set_grad( [spline](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { spline.derivative(uv, val); });\n }\n }\n }\n }\n\n void create_q2_nodes(const Mesh2D &mesh, const int el_index, std::set &vertex_id, std::set &edge_id, ElementBases &b, std::vector &local_boundary, int &n_bases)\n {\n b.bases.resize(9);\n\n LocalBoundary lb(el_index, BoundaryType::QuadLine);\n\n Navigation::Index index = mesh.get_index_from_face(el_index);\n for (int j = 0; j < 4; ++j)\n {\n int current_vertex_node_id = -1;\n int current_edge_node_id = -1;\n Eigen::Matrix current_edge_node;\n Eigen::MatrixXd current_vertex_node;\n\n // auto e2l = FEBasis2d::quadr_quad_edge_local_nodes(mesh, index);\n auto e2l = FEBasis2d::quad_edge_local_nodes(2, mesh, index);\n\n int vertex_basis_id = e2l[0];\n int edge_basis_id = e2l[1];\n\n const int opposite_face = mesh.switch_face(index).face;\n\n //if the edge/vertex is boundary the it is a Q2 edge\n bool is_vertex_q2 = true;\n bool is_vertex_boundary = false;\n\n Navigation::Index vindex=index;\n\n do\n {\n if(vindex.face < 0)\n {\n is_vertex_boundary = true;\n break;\n }\n if(mesh.is_spline_compatible(vindex.face))\n {\n is_vertex_q2 = false;\n break;\n }\n vindex = mesh.next_around_vertex(vindex);\n }\n while(vindex.edge != index.edge);\n\n if(is_vertex_q2)\n {\n vindex = mesh.switch_face(index);\n do\n {\n if(vindex.face < 0)\n {\n is_vertex_boundary = true;\n break;\n }\n\n if(mesh.is_spline_compatible(vindex.face))\n {\n is_vertex_q2 = false;\n break;\n }\n vindex = mesh.next_around_vertex(vindex);\n }\n while(vindex.edge != index.edge);\n }\n\n const bool is_edge_q2 = opposite_face < 0 || !mesh.is_spline_compatible(opposite_face);\n\n if (is_edge_q2)\n {\n const bool is_new_edge = edge_id.insert(index.edge).second;\n\n if(is_new_edge)\n {\n current_edge_node_id = n_bases++;\n current_edge_node = mesh.edge_barycenter(index.edge);\n\n if(opposite_face < 0)\n {\n // bounday_nodes.push_back(current_edge_node_id);\n lb.add_boundary_primitive(index.edge, edge_basis_id-4);\n }\n }\n }\n\n if(is_vertex_q2)\n {\n assert(is_edge_q2);\n const bool is_new_vertex = vertex_id.insert(index.vertex).second;\n\n if(is_new_vertex)\n {\n current_vertex_node_id = n_bases++;\n current_vertex_node = mesh.point(index.vertex);\n\n // if(is_vertex_boundary)//mesh.is_vertex_boundary(index.vertex))\n // bounday_nodes.push_back(current_vertex_node_id);\n }\n }\n\n //init new Q2 nodes\n if(current_vertex_node_id >= 0)\n b.bases[vertex_basis_id].init(2, current_vertex_node_id, vertex_basis_id, current_vertex_node);\n\n if(current_edge_node_id >= 0)\n b.bases[edge_basis_id].init(2, current_edge_node_id, edge_basis_id, current_edge_node);\n\n //set the basis functions\n b.bases[vertex_basis_id].set_basis([vertex_basis_id](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { polyfem::autogen::q_basis_value_2d (2, vertex_basis_id, uv, val); });\n b.bases[vertex_basis_id].set_grad( [vertex_basis_id](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { polyfem::autogen::q_grad_basis_value_2d(2, vertex_basis_id, uv, val); });\n\n b.bases[edge_basis_id].set_basis([edge_basis_id](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { polyfem::autogen::q_basis_value_2d (2, edge_basis_id, uv, val); });\n b.bases[edge_basis_id].set_grad( [edge_basis_id](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { polyfem::autogen::q_grad_basis_value_2d(2, edge_basis_id, uv, val); });\n\n index = mesh.next_around_face(index);\n }\n\n //central node always present\n const int face_basis_id = 8;\n b.bases[face_basis_id].init(2, n_bases++, face_basis_id, mesh.face_barycenter(el_index));\n b.bases[face_basis_id].set_basis([face_basis_id](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { polyfem::autogen::q_basis_value_2d (2, face_basis_id, uv, val); });\n b.bases[face_basis_id].set_grad( [face_basis_id](const Eigen::MatrixXd &uv, Eigen::MatrixXd &val) { polyfem::autogen::q_grad_basis_value_2d(2, face_basis_id, uv, val); });\n\n\n if(!lb.empty())\n local_boundary.emplace_back(lb);\n }\n\n void insert_into_global(const Local2Global &data, std::vector &vec)\n {\n //ignore small weights\n if(fabs(data.val) <1e-10 )\n return;\n\n bool found = false;\n\n for(std::size_t i = 0; i < vec.size(); ++i)\n {\n if(vec[i].index == data.index)\n {\n // std::cout< &bases)\n {\n // Eigen::MatrixXd eval_p;\n std::vector eval_p;\n Navigation::Index index = mesh.get_index_from_face(el_index);\n ElementBases &b = bases[el_index];\n\n for (int j = 0; j < 4; ++j)\n {\n const int opposite_face = mesh.switch_face(index).face;\n\n if(opposite_face < 0 || !mesh.is_cube(opposite_face))\n {\n index = mesh.next_around_face(index);\n continue;\n }\n\n // const auto param_p = FEBasis2d::quadr_quad_edge_local_nodes_coordinates(mesh, mesh.switch_face(index));\n // const auto indices = FEBasis2d::quadr_quad_edge_local_nodes(mesh, index);\n\n const auto indices = FEBasis2d::quad_edge_local_nodes(2, mesh, index);\n Eigen::Matrix param_p;\n\n {\n Eigen::MatrixXd quad_loc_nodes; polyfem::autogen::q_nodes_2d(2, quad_loc_nodes);\n const auto opposite_indices = FEBasis2d::quad_edge_local_nodes(2, mesh, mesh.switch_face(index));\n for(int k = 0; k < 3; ++k)\n param_p.row(k) = quad_loc_nodes.row(opposite_indices[k]);\n }\n\n // std::cout< &poly_edge_to_data)\n {\n Navigation::Index index = mesh.get_index_from_face(el_index);\n for (int j = 0; j < 4; ++j)\n {\n const int opposite_face = mesh.switch_face(index).face;\n const bool is_neigh_poly = opposite_face >= 0 && mesh.is_polytope(opposite_face);\n\n if(is_neigh_poly)\n {\n // auto e2l = FEBasis2d::quadr_quad_edge_local_nodes(mesh, index);\n auto e2l = FEBasis2d::quad_edge_local_nodes(2, mesh, index);\n const int vertex_basis_id = e2l[0];\n const int edge_basis_id = e2l[1];\n const int vertex_basis_id2 = e2l[2];\n\n InterfaceData &data = poly_edge_to_data[index.edge];\n\n data.local_indices.push_back(edge_basis_id);\n data.local_indices.push_back(vertex_basis_id);\n data.local_indices.push_back(vertex_basis_id2);\n }\n\n index = mesh.next_around_face(index);\n }\n }\n }\n\n\n int SplineBasis2d::build_bases(const Mesh2D &mesh, const int quadrature_order, std::vector< ElementBases > &bases, std::vector< LocalBoundary > &local_boundary, std::map &poly_edge_to_data)\n {\n using std::max;\n assert(!mesh.is_volume());\n\n MeshNodes mesh_nodes(mesh, true, true, 1, 1);\n\n const int n_els = mesh.n_elements();\n bases.resize(n_els);\n\n local_boundary.clear();\n\n // QuadQuadrature quad_quadrature;\n\n for(int e = 0; e < n_els; ++e)\n {\n if(!mesh.is_spline_compatible(e))\n continue;\n\n SpaceMatrix space;\n NodeMatrix loc_nodes;\n\n // const int max_local_base =\n build_local_space(mesh, mesh_nodes, e, space, loc_nodes, local_boundary, poly_edge_to_data);\n // n_bases = max(n_bases, max_local_base);\n\n ElementBases &b=bases[e];\n // quad_quadrature.get_quadrature(quadrature_order, b.quadrature);\n b.set_quadrature([quadrature_order](Quadrature &quad){\n QuadQuadrature quad_quadrature;\n quad_quadrature.get_quadrature(quadrature_order, quad);\n });\n b.bases.resize(9);\n\n b.set_local_node_from_primitive_func([e](const int primitive_id, const Mesh &mesh)\n {\n Eigen::VectorXi res(3);\n const auto &mesh2d = dynamic_cast(mesh);\n auto index = mesh2d.get_index_from_face(e);\n int le;\n for(le = 0; le < mesh2d.n_face_vertices(e); ++le)\n {\n if(index.edge == primitive_id)\n break;\n index = mesh2d.next_around_face(index);\n }\n assert(index.edge == primitive_id);\n\n switch(le)\n {\n case 3: res << (3*0 + 0), (3*1 + 0), (3*2 + 0); break;\n case 0: res << (3*0 + 0), (3*0 + 1), (3*0 + 2); break;\n case 1: res << (3*0 + 2), (3*1 + 2), (3*2 + 2); break;\n case 2: res << (3*2 + 0), (3*2 + 1), (3*2 + 2); break;\n default: assert(false);\n }\n\n\n return res;\n });\n\n std::array, 3> h_knots;\n std::array, 3> v_knots;\n\n setup_knots_vectors(mesh_nodes, space, h_knots, v_knots);\n\n // print_local_space(space);\n\n basis_for_regular_quad(space, loc_nodes, h_knots, v_knots, b);\n basis_for_irregulard_quad(e, mesh, mesh_nodes, space, loc_nodes, h_knots, v_knots, b);\n }\n\n std::set edge_id;\n std::set vertex_id;\n\n int n_bases = mesh_nodes.n_nodes();\n\n for(int e = 0; e < n_els; ++e)\n {\n if(mesh.is_polytope(e) || mesh.is_spline_compatible(e))\n continue;\n\n ElementBases &b=bases[e];\n // quad_quadrature.get_quadrature(quadrature_order, b.quadrature);\n b.set_quadrature([quadrature_order](Quadrature &quad){\n QuadQuadrature quad_quadrature;\n quad_quadrature.get_quadrature(quadrature_order, quad);\n });\n\n b.set_local_node_from_primitive_func([e](const int primitive_id, const Mesh &mesh)\n {\n const auto &mesh2d = dynamic_cast(mesh);\n auto index = mesh2d.get_index_from_face(e);\n\n for(int le = 0; le < mesh2d.n_face_vertices(e); ++le)\n {\n if(index.edge == primitive_id)\n break;\n index = mesh2d.next_around_face(index);\n }\n assert(index.edge == primitive_id);\n\n // const auto indices = FEBasis2d::quadr_quad_edge_local_nodes(mesh2d, index);\n const auto indices = FEBasis2d::quad_edge_local_nodes(2, mesh2d, index);\n Eigen::VectorXi res(indices.size());\n\n for(size_t i = 0; i< indices.size(); ++i)\n res(i)=indices[i];\n\n return res;\n });\n\n create_q2_nodes(mesh, e, vertex_id, edge_id, b, local_boundary, n_bases);\n }\n\n\n bool missing_bases = false;\n do\n {\n missing_bases = false;\n for(int e = 0; e < n_els; ++e)\n {\n if(mesh.is_polytope(e) || mesh.is_spline_compatible(e))\n continue;\n\n auto &b=bases[e];\n if(b.is_complete())\n continue;\n\n assign_q2_weights(mesh, e, bases);\n\n missing_bases = missing_bases || b.is_complete();\n }\n }\n while(missing_bases);\n\n\n for(int e = 0; e < n_els; ++e)\n {\n if(mesh.is_polytope(e) || mesh.is_spline_compatible(e))\n continue;\n const ElementBases &b=bases[e];\n setup_data_for_polygons(mesh, e, b, poly_edge_to_data);\n }\n\n return n_bases;\n }\n\n void SplineBasis2d::fit_nodes(const Mesh2D &mesh, const int n_bases, std::vector< ElementBases > &gbases)\n {\n assert(false);\n // const int dim = 2;\n // const int n_constraints = 9;\n // const int n_elements = mesh.n_elements();\n\n // std::vector< Eigen::Triplet > entries, entries_t;\n\n // MeshNodes nodes(mesh, 1, 1, 0);\n // // Eigen::MatrixXd tmp;\n // std::vector tmp_val;\n\n // Eigen::MatrixXd node_rhs(n_constraints*n_elements, dim);\n // Eigen::MatrixXd samples(n_constraints, dim);\n // polyfem::autogen::q_nodes_2d(2, samples);\n // // for(int i = 0; i < n_constraints; ++i)\n // // samples.row(i) = FEBasis2d::quadr_quad_local_node_coordinates(i);\n\n // for(int i = 0; i < n_elements; ++i)\n // {\n // auto &base = gbases[i];\n\n // if(!mesh.is_cube(i))\n // continue;\n\n // auto global_ids = FEBasis2d::quadr_quad_local_to_global(mesh, i);\n // assert(global_ids.size() == n_constraints);\n\n // for(int j = 0; j < n_constraints; ++j)\n // {\n // auto n_id = nodes.node_id_from_primitive(global_ids[j]);\n // auto n = nodes.node_position(n_id);\n // for(int d = 0; d < dim; ++d)\n // node_rhs(n_constraints*i + j, d) = n(d);\n // }\n\n // base.evaluate_bases(samples, tmp_val);\n // const auto &lbs = base.bases;\n\n // const int n_local_bases = int(lbs.size());\n // for(int j = 0; j < n_local_bases; ++j)\n // {\n // const Basis &b = lbs[j];\n // const auto &tmp = tmp_val[j].val;\n\n // for(std::size_t ii = 0; ii < b.global().size(); ++ii)\n // {\n // for (long k = 0; k < tmp.size(); ++k)\n // {\n // entries.emplace_back(n_constraints*i + k, b.global()[ii].index, tmp(k)*b.global()[ii].val);\n // entries_t.emplace_back(b.global()[ii].index, n_constraints*i + k, tmp(k)*b.global()[ii].val);\n // }\n // }\n // }\n // }\n\n // Eigen::MatrixXd new_nodes(n_bases, dim);\n\n // {\n // StiffnessMatrix mat(n_constraints*n_elements, n_bases);\n // StiffnessMatrix mat_t(n_bases, n_constraints*n_elements);\n\n // mat.setFromTriplets(entries.begin(), entries.end());\n // mat_t.setFromTriplets(entries_t.begin(), entries_t.end());\n\n // StiffnessMatrix A = mat_t * mat;\n // Eigen::MatrixXd b = mat_t * node_rhs;\n\n // json params = {\n // {\"mtype\", -2}, // matrix type for Pardiso (2 = SPD)\n // // {\"max_iter\", 0}, // for iterative solvers\n // // {\"tolerance\", 1e-9}, // for iterative solvers\n // };\n // auto solver = LinearSolver::create(\"\", \"\");\n // solver->setParameters(params);\n // solver->analyzePattern(A);\n // solver->factorize(A);\n\n // for(int d = 0; d < dim; ++d)\n // solver->solve(b.col(d), new_nodes.col(d));\n // }\n\n // for(int i = 0; i < n_elements; ++i)\n // {\n // auto &base = gbases[i];\n\n // if(!mesh.is_cube(i))\n // continue;\n\n // auto &lbs = base.bases;\n // const int n_local_bases = int(lbs.size());\n // for(int j = 0; j < n_local_bases; ++j)\n // {\n // Basis &b = lbs[j];\n\n // for(std::size_t ii = 0; ii < b.global().size(); ++ii)\n // {\n // // if(nodes.is_primitive_boundary(b.global()[ii].index))\n // // continue;\n\n // for(int d = 0; d < dim; ++d)\n // {\n // b.global()[ii].node(d) = new_nodes(b.global()[ii].index, d);\n // }\n // }\n // }\n // }\n }\n\n}\n", "meta": {"hexsha": "108ebda7b1cf894e29ce4cdf4355f3840c87b7e5", "size": 37992, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/basis/SplineBasis2d.cpp", "max_stars_repo_name": "danielepanozzo/polyfem", "max_stars_repo_head_hexsha": "34a7719c2a3874b7ecc865c28d8b3d9bbdf7d0ba", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 228.0, "max_stars_repo_stars_event_min_datetime": "2018-11-23T19:32:42.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T10:30:51.000Z", "max_issues_repo_path": "src/basis/SplineBasis2d.cpp", "max_issues_repo_name": "danielepanozzo/polyfem", "max_issues_repo_head_hexsha": "34a7719c2a3874b7ecc865c28d8b3d9bbdf7d0ba", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 14.0, 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YES\n2. NO", "lm_q1_score": 0.6370307944803831, "lm_q2_score": 0.3522017684487511, "lm_q1q2_score": 0.22436337237230386}} {"text": "#pragma once\n\n#include \n\n#include \"heattransferstate.hpp\"\n\n/*!\n * \\brief The HeatTransferBase class is an abstract class, the base class for all\n * heat transfer implementations. It mostly just defines a stencil for the\n * evaluate() function and the HeatTransferState class.\n */\nclass HeatTransferBase\n{\npublic:\n //! Have to declare virtual destructor to avoid compiler warnings.\n //! Only declared here, to avoid the inline compiler-generated default destructor.\n virtual ~HeatTransferBase();\n\n // pure virtual method\n // returns q [W/m2] = U*(gasTemperature - ambientTemperature)\n /*!\n * \\brief Evaluate heat transfer.\n * \\param current Current heat transfer state.\n * \\param timeStep Time step [s]\n * \\param ambientTemperature Ambient temperature [K]\n * \\param gasPressure Gas pressure [Pa]\n * \\param gasTemperature Gas temperature [K]\n * \\param gasReynoldsNumber Reynolds number of gas [-]\n * \\param gasHeatCapacityConstantPressure Heat capacity (\\f$c_p\\f$) [J/kg K]\n * \\param gasViscosity Gas dynamic viscosity [Pa s] = [kg/m*s]\n * \\return HeatTransferState instance with heat flux and updated temperatures.\n */\n virtual HeatTransferState evaluate(\n const HeatTransferState& current,\n const double timeStep,\n const double ambientTemperature,\n const double gasPressure,\n const double gasTemperature,\n const double gasReynoldsNumber,\n const double gasHeatCapacityConstantPressure,\n const double gasViscosity) const = 0;\n\n /*!\n * \\brief Make instance of HeatTransferState from heat flux.\n *\n * This is meant to be overriden in more advanced heat transfer\n * implementations that that use the optional temperature property.\n *\n * \\param heatFlux Heat flux [W/m2]\n * \\return HeatTransferState instance.\n */\n virtual HeatTransferState makeState(const double heatFlux) const;\n\n /*!\n * \\brief Make instance of HeatTransferState from heat flux. Overload.\n *\n * This version just calls HeatTransferBase::makeState(const double), but more\n * advanced implementations should initialize the temperature property from\n * the two temperature arguments.\n *\n * \\param heatFlux Heat flux [W/m2]\n * \\param ambientTemperature Ambient temperature [K]\n * \\param gasTemperature Gas temperature [K]\n * \\return HeatTransferState instance with temperature.\n */\n virtual HeatTransferState makeState(\n const double heatFlux,\n const double gasTemperature,\n const double ambientTemperature) const;\n};\n", "meta": {"hexsha": "efefe264b1ba4751dec0b638448dfd481e8b0681", "size": 2662, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/heattransfer/heattransferbase.hpp", "max_stars_repo_name": "kewin1983/transient-pipeline-flow", "max_stars_repo_head_hexsha": "4ffe0b61d3d40d9bcb82a3743b2c2e403521835d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-03-26T03:30:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-26T03:30:07.000Z", "max_issues_repo_path": "src/heattransfer/heattransferbase.hpp", "max_issues_repo_name": "kewin1983/transient-pipeline-flow", "max_issues_repo_head_hexsha": "4ffe0b61d3d40d9bcb82a3743b2c2e403521835d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/heattransfer/heattransferbase.hpp", "max_forks_repo_name": "kewin1983/transient-pipeline-flow", "max_forks_repo_head_hexsha": "4ffe0b61d3d40d9bcb82a3743b2c2e403521835d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.4929577465, "max_line_length": 86, "alphanum_fraction": 0.688580015, "num_tokens": 561, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.22428679240835311}} {"text": "#ifndef ATM_BRANCHING_SUBSTRINGS_HPP\n#define ATM_BRANCHING_SUBSTRINGS_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"sast/sast.hpp\"\n\n\nnamespace atm {\n\ntemplate \nstruct branching_substrings {\n using range_type = RandomAccessRange;\n using char_type = typename boost::range_value::type;\n using index_type = Index;\n\nprotected:\n using sast_type = sast::sast;\n\npublic:\n struct substr {\n using iterator = typename boost::range_iterator::type;\n using const_iterator = typename boost::range_const_iterator::type;\n\n index_type pos() const { return i_->pos(); }\n boost::sub_range> allpos() const { return i_->allpos(); }\n index_type length() const { return i_->length(); }\n index_type frequency() const { return i_->frequency(); }\n double spurity() const { return parent_->strict_purity(i_); }\n double lpurity() const { return parent_->loose_purity(i_); }\n double luniversality() const { return parent_->left_universality(i_); }\n double runiversality() const { return parent_->right_universality(i_); }\n\n iterator begin() { return boost::begin(parent_->input_) + pos(); }\n iterator end() { return boost::begin(parent_->input_) + pos() + length(); }\n const_iterator begin() const { return boost::const_begin(parent_->input_) + pos(); }\n const_iterator end() const { return boost::const_begin(parent_->input_) + pos() + length(); }\n\n substr(const branching_substrings* parent, typename sast_type::const_iterator i)\n : parent_(parent), i_(i)\n {}\n\n private:\n const branching_substrings* parent_;\n typename sast_type::const_iterator i_;\n };\n\nprivate:\n template struct substring_iterator;\n\npublic:\n using iterator = substring_iterator;\n using const_iterator = substring_iterator;\n\n branching_substrings(const sast_type& sast)\n : sast_(sast),\n input_(sast_.input()),\n count_(sast_.size(), 0), // initialize the count table with an \"undefined\" value.\n recip_(sast_.size(), 0) // 正数は計算結果、それ以外は未計算を表わす。\n {}\n\n iterator begin() { return iterator(this, sast_.begin()); }\n iterator end() { return iterator(this, sast_.end()); }\n const_iterator begin() const { return const_iterator(this, sast_.begin()); }\n const_iterator end() const { return const_iterator(this, sast_.end()); }\n\nprivate:\n template \n struct substring_iterator\n : public boost::iterator_facade<\n substring_iterator,\n Value,\n boost::random_access_traversal_tag,\n Value,\n int>\n {\n substring_iterator()\n : parent_(0), i_()\n {}\n\n substring_iterator(const branching_substrings* parent, typename sast_type::const_iterator i)\n : parent_(parent), i_(i)\n {}\n\n template \n substring_iterator(substring_iterator const& other)\n : parent_(other.parent_), i_(other.i_)\n {}\n\n private:\n friend class boost::iterator_core_access;\n template friend struct substring_iterator;\n\n void increment() { ++i_; }\n\n void decrement() { --i_; }\n\n void advance(int n) { i_ += n; }\n\n int distance_to(const substring_iterator& other) const { return other.i_ - this->i_; }\n\n template \n bool equal(const substring_iterator& other) const {\n return this->parent_ == other.parent_ && this->i_ == other.i_;\n }\n\n Value dereference() const {\n return substr(parent_, i_);\n }\n\n const branching_substrings* parent_;\n typename sast_type::const_iterator i_;\n };\n\nprotected:\n uint64_t get_count(typename sast_type::const_iterator n) const {\n const int i = n - sast_.begin();\n\n // ここではノードi（iはpost-orderでの番号）に対応する部分文字列substrを扱う。\n // substrと同じ出現回数のsub-substrの数count[i]を数える。\n if (count_[i] > 0) {\n return count_[i];\n }\n else {\n const auto freq_substr = n->frequency();\n\n // substrと同じ出現回数のsub-substrを数える。\n uint64_t count = 0;\n {\n // substrの末尾を0文字以上削って得られるsub-substrについて考える。\n // ノードiに対応する部分文字列をsubstr[i]とすると、substr[i]の末尾\n // を削って得られる部分文字列の内で、substr[i]と同じ頻度をもつもの\n // はsuffix tree上ではノードiにまとめられている\n // （分岐が無い <=> 頻度が同じ）。\n\n // ノードiの親ノードjを見つける。\n const auto p = n.parent();\n\n // substrの末尾を0文字以上削って得られるsub-substrの内で、出現\n // 回数がsubstrと同じものの数はd[i] - d[j]である。\n count += n->length() - p->length();;\n }\n {\n // substrの先頭を1文字以上削ったsub-substrを考える。\n const auto m = n.suffix();\n const auto freq_subsubstr = m->frequency();\n if (freq_subsubstr == freq_substr) {\n count += get_count(m);\n }\n }\n\n // memoize\n count_[i] = count;\n return count_[i];\n }\n }\n\n double strict_purity(typename sast_type::const_iterator n) const {\n // ここではノードi（iはpost-orderでの番号）に対応する部分文字列substrを扱う。\n const auto len_substr = n->length();\n\n // substrと同じ出現回数のsub-substrを数える。\n const uint64_t count = get_count(n);\n\n // strict purity of substr\n const uint64_t num_subsubstrs = static_cast(1 + len_substr) * len_substr / 2;\n const double spurity = static_cast(count) / num_subsubstrs;\n\n return spurity;\n }\n\n double get_reciprocal(typename sast_type::const_iterator n) const {\n const int i = n - sast_.begin();\n\n // ここではノードi（iはpost-orderでの番号）に対応する部分文字列substrを扱う。\n // substrの全部分文字列の頻度の逆数の総和recip[i]を求める。\n if (recip_[i] > 0) {\n return recip_[i];\n }\n else {\n double recip = 0;\n {\n // substrの末尾を0文字以上削って得られるsub-substrについて考える。\n for (auto m = n, p = n.parent(); m != sast_.end(); m = p, p = p.parent()) {\n const auto num_subsubstrs_of_same_frequency = m->length() - p->length();\n const auto freq_subsubstr = m->frequency();\n const double r = 1.0 / freq_subsubstr;\n recip += num_subsubstrs_of_same_frequency * r;\n }\n }\n {\n // substrの先頭を1文字以上削ったsub-substrを考える。\n const auto m = n.suffix();\n if (m != sast_.end()) {\n recip += get_reciprocal(m);\n }\n }\n\n // memoize\n recip_[i] = recip;\n return recip_[i];\n }\n }\n\n double loose_purity(typename sast_type::const_iterator n) const {\n // ここではノードi（iはpost-orderでの番号）に対応する部分文字列substrを扱う。\n const auto freq_substr = n->frequency();\n const auto len_substr = n->length();\n\n const double rel = freq_substr * get_reciprocal(n);\n\n // loose purity of substr\n const uint64_t num_subsubstrs = static_cast(1 + len_substr) * len_substr / 2;\n const double lpurity = rel / num_subsubstrs;\n\n return lpurity;\n }\n\n std::map left_extensions(typename sast_type::const_iterator n) const {\n // ここではノードi（iはpost-orderでの番号）に対応する部分文字列substrを扱う。\n\n std::map char_dist;\n for (auto pos : n->allpos()) {\n const auto& c = boost::const_begin(input_)[pos - 1];\n char_dist[c] += 1;\n }\n\n return char_dist;\n }\n\n std::map right_extensions(typename sast_type::const_iterator n) const {\n // ここではノードi（iはpost-orderでの番号）に対応する部分文字列substrを扱う。\n const auto len_substr = n->length();\n\n std::map char_dist;\n for (auto pos : n->allpos()) {\n const auto& c = boost::const_begin(input_)[pos + len_substr];\n char_dist[c] += 1;\n }\n\n return char_dist;\n }\n\n double left_universality(typename sast_type::const_iterator n) const {\n // ここではノードi（iはpost-orderでの番号）に対応する部分文字列substrを扱う。\n const auto freq_substr = n->frequency();\n\n std::map char_dist = left_extensions(n);\n\n double e = 0;\n for (const auto& kv : char_dist) {\n const double p = static_cast(kv.second) / freq_substr;\n e += -p * std::log(p);\n }\n const double u = 1 - std::exp(-e);\n\n return u;\n }\n\n double right_universality(typename sast_type::const_iterator n) const {\n // ここではノードi（iはpost-orderでの番号）に対応する部分文字列substrを扱う。\n const auto freq_substr = n->frequency();\n\n std::map char_dist = right_extensions(n);\n\n double e = 0;\n for (const auto& kv : char_dist) {\n const double p = static_cast(kv.second) / freq_substr;\n e += -p * std::log(p);\n }\n const double u = 1 - std::exp(-e);\n\n return u;\n }\n\n const sast_type& sast_;\n const RandomAccessRange& input_;\n mutable std::vector count_;\n mutable std::vector recip_;\n};\n\n} // namespace atm\n\n\n#endif /* ATM_BRANCHING_SUBSTRINGS_HPP */\n", "meta": {"hexsha": "8a6593352313d39605e078d8cbd60e7538eb01cb", "size": 9764, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/atm/branching_substrings.hpp", "max_stars_repo_name": "yuttie/atm", "max_stars_repo_head_hexsha": "b481620088e26153e5554ae6f6732efae4852eda", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/atm/branching_substrings.hpp", "max_issues_repo_name": "yuttie/atm", "max_issues_repo_head_hexsha": "b481620088e26153e5554ae6f6732efae4852eda", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/atm/branching_substrings.hpp", "max_forks_repo_name": "yuttie/atm", "max_forks_repo_head_hexsha": "b481620088e26153e5554ae6f6732efae4852eda", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4383561644, "max_line_length": 103, "alphanum_fraction": 0.5909463335, "num_tokens": 2661, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.22428679240835311}} {"text": "#include \"LinearApproximation.hpp\"\n\n#include \n#include \n#include \n\n\nLinearApproximation::LinearApproximation(const size_t t_numVars)\n : m_numVars(t_numVars)\n{\n}\n\nLinearApproximation::~LinearApproximation()\n{\n}\n\ndouble LinearApproximation::average() const\n{\n double sum = 0;\n for (std::vector >::const_iterator lhsitr = m_values.begin();\n lhsitr != m_values.end();\n ++lhsitr)\n {\n sum += (*lhsitr)[m_numVars];\n }\n return sum / m_numVars;\n}\n\nLinearApproximation LinearApproximation::operator-(const LinearApproximation &t_rhs) const\n{\n if (m_numVars != t_rhs.m_numVars)\n {\n throw std::runtime_error(\"Unable to subtract, different number of variables\");\n }\n\n LinearApproximation retval(m_numVars);\n\n for (std::vector >::const_iterator lhsitr = m_values.begin();\n lhsitr != m_values.end();\n ++lhsitr)\n {\n for (std::vector >::const_iterator rhsitr = t_rhs.m_values.begin();\n rhsitr != t_rhs.m_values.end();\n ++rhsitr)\n {\n std::vector vals(m_numVars, 0);\n\n bool match = true;\n for (size_t i = 0; i < m_numVars; ++i)\n {\n if ( (*lhsitr)[i] != (*rhsitr)[i] )\n {\n match = false;\n break;\n } else {\n vals[i] = (*lhsitr)[i];\n }\n }\n\n if (match)\n {\n double value = (*lhsitr)[m_numVars] - (*rhsitr)[m_numVars];\n retval.addVals(vals, value);\n break;\n }\n }\n }\n\n return retval;\n}\n\nvoid LinearApproximation::addVals(std::vector t_vals, const double t_result)\n{\n validateVariableSize(t_vals);\n\n t_vals.push_back(t_result);\n\n for (std::vector >::const_iterator lhsitr = m_values.begin();\n lhsitr != m_values.end();\n ++lhsitr)\n {\n bool match = true;\n for (size_t i = 0; i < m_numVars; ++i)\n {\n if ( (*lhsitr)[i] != t_vals[i] )\n {\n match = false;\n break;\n }\n }\n\n if (match)\n {\n if ((*lhsitr)[m_numVars] != t_result)\n {\n throw std::runtime_error(\"Data already exists with a different result value\");\n } else {\n // it already exists\n return;\n }\n }\n }\n\n // it didn't already exist, add it now\n m_values.push_back(t_vals);\n\n}\n\n\n\n\nstd::vector > LinearApproximation::findMinimalDifferences(\n size_t t_numDifferences,\n const std::vector > &t_vals,\n const std::vector > &t_data) const\n{\n // t_vals includes the baseline requested value, so we want one more than that\n if (t_data.empty() || t_numDifferences + 2 == t_vals.size())\n {\n // this is as good as we're going to get / can use right now\n std::vector > retval = t_vals;\n\n if (!t_vals.empty())\n {\n retval.erase(retval.begin());\n }\n return retval;\n }\n\n std::vector > candidates;\n std::vector > remainder;\n\n for (size_t i = 0; i < t_data.size(); ++i)\n {\n size_t numDifferences = 0;\n\n for (size_t j = 0; j < m_numVars; ++j)\n {\n for (size_t i2 = 0; i2 < t_vals.size(); ++i2)\n {\n if (t_data[i][j] != t_vals[i2][j])\n {\n ++numDifferences;\n break; // don't check any further input rows\n }\n }\n\n if (numDifferences > t_numDifferences)\n {\n // don't continue with this data row\n break;\n }\n }\n\n if (numDifferences <= t_numDifferences)\n {\n candidates.push_back(t_data[i]);\n } else {\n remainder.push_back(t_data[i]);\n }\n }\n\n std::vector > keptresult = t_vals;\n keptresult.erase(keptresult.begin());\n\n for (size_t i = 0; i < candidates.size(); ++i)\n {\n std::vector > newvals(t_vals);\n newvals.push_back(candidates[i]);\n\n std::vector > newdata(t_data);\n newdata.erase(std::remove(newdata.begin(), newdata.end(), candidates[i]), newdata.end());\n\n std::vector > result = findMinimalDifferences(t_numDifferences, newvals, newdata);\n if (result.size() > keptresult.size())\n {\n keptresult = result;\n }\n }\n\n return keptresult;\n}\n\nstd::vector > LinearApproximation::filterForProblemReduction(const std::vector &t_vals,\n const std::vector > &t_data) const\n{\n std::vector > testData;\n testData.push_back(t_vals);\n\n for (size_t i = 1; i < m_numVars; ++i)\n {\n std::vector > results = findMinimalDifferences(i, testData, t_data);\n if (results.size() > i)\n {\n return results;\n }\n }\n\n return t_data;\n}\n\n\ndouble LinearApproximation::approximate(const std::vector &t_vals) const\n{\n // Linear Approximation\n // A(x-x0) + B(y-y0) + C(z-z0) = 0\n validateVariableSize(t_vals);\n\n// print(\"Approximating: \", t_vals);\n\n std::vector > sortedPoints = sortByDistance(t_vals, m_values);\n\n if (!sortedPoints.empty() && distance(t_vals, sortedPoints[0]) == 0)\n {\n //return exact match\n return sortedPoints[0].back();\n }\n\n std::vector > optimalPoints = filterForProblemReduction(t_vals, sortedPoints);\n\n// print(\"Starting points: \", m_values);\n\n// print(\"Sorted Points: \", sortedPoints);\n std::vector > chosenPoints = filterForSimilarity(t_vals, optimalPoints);\n\n// print(\"Chosen points: \", chosenPoints);\n\n\n std::vector > > coefficientMatrices = buildCoefficientMatrices(chosenPoints);\n\n// print(\"Coefficients: \", coefficientMatrices);\n\n\n std::vector coefficients = solveDeterminates(coefficientMatrices);\n\n// print(\"Coefficients: \", coefficients);\n assert(coefficients.size() == m_numVars + 1);\n\n\n\n double approximation = 0;\n for (size_t i = 0; i < m_numVars; ++i)\n {\n // A(x-x0) + B(y-y0) + C(z-z0) ... \n approximation += coefficients[i] * (t_vals[i] - chosenPoints[0][i]);\n }\n\n // the last variable is the one we are solving for \n // z=(z0*C+(y0-y)*B+(x0-x)*A)/C\n if (coefficients[m_numVars] == 0)\n {\n throw std::runtime_error(\"Unabled to approximate, not enough data\");\n }\n\n approximation = (approximation + chosenPoints[0][m_numVars] * coefficients[m_numVars]) / coefficients[m_numVars];\n\n// std::cout << \"final approximation: \" << approximation << std::endl;\n\n return approximation;\n}\n\n\nvoid LinearApproximation::validateVariableSize(const std::vector &t_vals) const\n{\n if (t_vals.size() != m_numVars)\n {\n throw std::range_error(\"Unexpected number of variables supplied\");\n }\n}\n\nconst std::vector > LinearApproximation::filterForSimilarity(const std::vector &t_point, const std::vector > &t_vals) const\n{\n std::vector > goodPoints = t_vals;\n int pointsNeeded = m_numVars + 1;\n\n for (size_t i = 0; i < m_numVars; ++i)\n {\n bool diversityFound = false;\n bool allTheSame = true;\n\n int differencePosition = -1;\n\n int rowCount = 0;\n for (std::vector >::const_iterator itr = t_vals.begin();\n itr != t_vals.end();\n ++itr, ++rowCount)\n {\n if (t_vals[0][i] != (*itr)[i])\n {\n diversityFound = true; // not all of the inputs match each other\n }\n\n if (t_point[i] != (*itr)[i])\n {\n allTheSame = false; // not all of the inputs for this position match the requested approximation\n differencePosition = rowCount;\n }\n }\n\n if (!diversityFound && !allTheSame)\n {\n // There is not enough data available to calculate the value for this position\n std::stringstream ss;\n ss << \"Not enough data available for the \" << i << \" variable position\";\n throw std::runtime_error(ss.str());\n }\n\n if (allTheSame || (differencePosition >= pointsNeeded))\n {\n if (pointsNeeded > 2)\n {\n --pointsNeeded;\n goodPoints.resize(std::min(pointsNeeded, static_cast(goodPoints.size()))); // never expand it\n }\n }\n }\n\n goodPoints.resize(std::min(pointsNeeded, static_cast(goodPoints.size())));\n return goodPoints;\n}\n\nstd::vector > > LinearApproximation::buildCoefficientMatrices(\n const std::vector > &t_points) const\n{\n std::set discardedColumns;\n\n for (size_t i = 0; i <= m_numVars; ++i)\n {\n bool allthesame = true;\n for (std::vector >::const_iterator itr = t_points.begin();\n itr != t_points.end();\n ++itr)\n {\n if ((*itr)[i] != t_points[0][i])\n {\n allthesame = false;\n }\n }\n\n if (allthesame)\n {\n discardedColumns.insert(i);\n }\n }\n\n std::vector > > retvals;\n\n for (size_t i = 0; i <= m_numVars; ++i)\n {\n if (discardedColumns.count(i) == 1)\n {\n // this is a discarded column its value is 0\n std::vector v(1,0); // a vector of one element of value 0\n std::vector > m;\n m.push_back(v);\n retvals.push_back(m);\n } else {\n std::vector > m;\n for (size_t curRow = 0; curRow < m_numVars - discardedColumns.size(); ++curRow)\n {\n std::vector row;\n for (size_t j = 0; j <= m_numVars; ++j)\n {\n if (j != i && discardedColumns.count(j) == 0)\n {\n // This is a column we want to work with\n row.push_back(t_points[curRow+1][j] - t_points[0][j]);\n }\n }\n m.push_back(row);\n }\n retvals.push_back(m);\n }\n }\n\n return retvals;\n}\n\nstd::vector > LinearApproximation::removeCol(const std::vector > &t_matrix, const size_t col) const\n{\n std::vector > retval(t_matrix.size()-1, std::vector(t_matrix.size()-1, 0));\n\n for (size_t i = 1; i < t_matrix.size(); ++i)\n {\n size_t colnum = 0;\n for (size_t j = 0; j < t_matrix[i].size(); ++j)\n {\n if (j != col)\n {\n retval[i-1][colnum] = t_matrix[i][j];\n ++colnum;\n }\n }\n }\n\n return retval;\n}\n\ndouble LinearApproximation::determinate(const std::vector > &t_matrix) const\n{\n/*\n bool docache = false;\n\n if (docache)\n {\n std::map >, double>::const_iterator itr = m_cache.find(t_matrix);\n if (itr != m_cache.end())\n {\n std::cout << \"Cache hit\" << std::endl; \n return itr->second;\n }\n }\n\n if (t_matrix.size() == 1)\n {\n return t_matrix[0][0];\n } else if (t_matrix.size() == 2) {\n return t_matrix[0][0] * t_matrix[1][1] - t_matrix[0][1] * t_matrix[1][0];\n } else {\n double d = 0;\n\n for (size_t i = 0; i < t_matrix.size(); ++i)\n {\n // d += pow(-1, i) * t_matrix[0][i] * determinate(removeCol(t_matrix, i));\n d += (i%2==1?-1:1) * t_matrix[0][i] * determinate(removeCol(t_matrix, i));\n }\n\n if (docache)\n {\n m_cache[t_matrix] = d;\n }\n return d;\n }\n */\n\n namespace bnu = boost::numeric::ublas;\n\n class Determinant \n {\n\n static int determinant_sign(const bnu::permutation_matrix& pm)\n {\n int pm_sign=1;\n std::size_t size = pm.size();\n for (std::size_t i = 0; i < size; ++i)\n {\n if (i != pm(i)) {\n pm_sign *= -1; // swap_rows would swap a pair of rows here, so we change sign\n }\n }\n return pm_sign;\n }\n\n static double determinant( bnu::matrix& m ) {\n bnu::permutation_matrix pm(m.size1());\n double det = 1.0;\n if( bnu::lu_factorize(m,pm) ) {\n det = 0.0;\n } else {\n for(size_t i = 0; i < m.size1(); i++) {\n det *= m(i,i); // multiply by elements on diagonal\n }\n\n det = det * determinant_sign( pm );\n }\n return det;\n }\n\n public:\n static double determinant(const std::vector > &t_matrix)\n {\n bnu::matrix matrix(t_matrix.size(), t_matrix.size());\n\n for (size_t i = 0; i < t_matrix.size(); ++i)\n {\n for (size_t j = 0; j < t_matrix[i].size(); ++j)\n {\n matrix(i, j) = t_matrix[i][j];\n }\n }\n\n return determinant(matrix);\n }\n };\n\n double retval = Determinant::determinant(t_matrix);\n\n /*\n if (docache)\n {\n m_cache[t_matrix] = retval;\n }\n */\n\n return retval;\n}\n\nstd::vector LinearApproximation::solveDeterminates(const std::vector > > &t_matrices) const\n{\n std::vector retval;\n\n for (std::vector > >::const_iterator itr = t_matrices.begin();\n itr != t_matrices.end();\n ++itr)\n {\n retval.push_back(determinate(*itr));\n }\n\n return retval; \n}\n\nstd::vector > LinearApproximation::sortByDistance(const std::vector &t_point, \n const std::vector > &t_values) const\n{\n std::set > > distanceSorted;\n\n for (std::vector >::const_iterator itr = t_values.begin();\n itr != t_values.end();\n ++itr)\n {\n distanceSorted.insert(std::make_pair(distance(t_point, *itr), *itr));\n }\n\n std::vector > retval;\n\n for (std::set > >::const_iterator itr = distanceSorted.begin();\n itr != distanceSorted.end();\n ++itr)\n {\n retval.push_back(itr->second);\n }\n\n return retval;\n}\n\ndouble LinearApproximation::distance(const std::vector &t_p1, const std::vector &t_p2) const\n{\n double result = 0;\n for (size_t i = 0; i < m_numVars; ++i)\n {\n double part = t_p1[i] - t_p2[i];\n part *= part;\n\n result += part;\n }\n\n return sqrt(result);\n}\n\nvoid LinearApproximation::print(const std::string &t_str, const std::vector &t_vals)\n{\n if (!t_str.empty())\n {\n std::cout << t_str << std::endl;\n }\n\n std::cout << \"[ \";\n for (std::vector::const_iterator itr = t_vals.begin();\n itr != t_vals.end();\n ++itr)\n {\n std::cout << std::setw(10) << *itr << \" \";\n }\n std::cout << \"]\" << std::endl;\n}\n\nvoid LinearApproximation::print(const std::string &t_str, const std::vector > &t_vals)\n{\n if (!t_str.empty())\n {\n std::cout << t_str << std::endl;\n }\n\n for (std::vector >::const_iterator itr = t_vals.begin();\n itr != t_vals.end();\n ++itr)\n {\n print(\"\", *itr);\n }\n}\n\nstd::pair LinearApproximation::nearestFurthestNeighborDistances(const std::vector &t_vals) const\n{\n std::vector > sorted = sortByDistance(t_vals, m_values);\n\n if (sorted.size() < 1)\n {\n throw std::range_error(\"no neighbors\");\n }\n\n return std::make_pair(distance(t_vals, sorted.front()), distance(t_vals, sorted.back()));\n}\n\nvoid LinearApproximation::print(const std::string &t_str, const std::vector > > &t_vals)\n{\n if (!t_str.empty())\n {\n std::cout << t_str << std::endl;\n }\n\n for (std::vector > >::const_iterator itr = t_vals.begin();\n itr != t_vals.end();\n ++itr)\n {\n print(\"\", *itr);\n std::cout << std::endl;\n }\n}\n\n\n\n", "meta": {"hexsha": "24ff5d769abad1d68f0c22cbc7e5e74b52055cbe", "size": 15343, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "openstudiocore/src/runmanager/lib/LinearApproximation.cpp", "max_stars_repo_name": "bobzabcik/OpenStudio", "max_stars_repo_head_hexsha": "858321dc0ad8d572de15858d2ae487b029a8d847", "max_stars_repo_licenses": ["blessing"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "openstudiocore/src/runmanager/lib/LinearApproximation.cpp", "max_issues_repo_name": "bobzabcik/OpenStudio", "max_issues_repo_head_hexsha": "858321dc0ad8d572de15858d2ae487b029a8d847", "max_issues_repo_licenses": ["blessing"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "openstudiocore/src/runmanager/lib/LinearApproximation.cpp", "max_forks_repo_name": "bobzabcik/OpenStudio", "max_forks_repo_head_hexsha": "858321dc0ad8d572de15858d2ae487b029a8d847", "max_forks_repo_licenses": ["blessing"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.0702614379, "max_line_length": 171, "alphanum_fraction": 0.6031414978, "num_tokens": 4273, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.22418192331223902}} {"text": "<<<<<<< HEAD\n/* Copyright (c) 2010-2018, Delft University of Technology\n=======\n/* Copyright (c) 2010-2019, Delft University of Technology\n>>>>>>> origin/master\n * All rigths reserved\n *\n * This file is part of the Tudat. Redistribution and use in source and\n * binary forms, with or without modification, are permitted exclusively\n * under the terms of the Modified BSD license. You should have received\n * a copy of the license with this file. If not, please or visit:\n * http://tudat.tudelft.nl/LICENSE.\n *\n * References\n * Eigen. http://eigen.tuxfamily.org/dox/TopicUnalignedArrayAssert.html, last accessed: 18th\n * March, 2013.\n *\n * Notes\n * This implementation uses the KeplerianElements class, which is marked for deprecation. The\n * code will be updated to use a simple \"Vector6d\" object from the Eigen library instead.\n *\n */\n\n#include \n\n#include \n\n#include \"Tudat/Astrodynamics/BasicAstrodynamics/orbitalElementConversions.h\"\n\n#include \"Tudat/Astrodynamics/BasicAstrodynamics/convertMeanToEccentricAnomalies.h\"\n#include \"Tudat/Astrodynamics/BasicAstrodynamics/stateVectorIndices.h\"\n#include \"Tudat/Astrodynamics/Ephemerides/keplerStateExtractor.h\"\n#include \"Tudat/InputOutput/parsedDataVectorUtilities.h\"\n\nnamespace tudat\n{\nnamespace ephemerides\n{\n\n//! Extract the Keplerian Elements.\nstd::shared_ptr< Eigen::Vector6d > KeplerStateExtractor::extract(\n ParsedDataLineMapPtr dataLineMap )\n{\n // Short-hand notation.\n namespace parsed_data_vector_utilities = input_output::parsed_data_vector_utilities;\n using Eigen::Vector6d;\n\n // Create a new KeplerianElements object.\n std::shared_ptr< Vector6d > keplerianElements\n = std::allocate_shared< Vector6d >( Eigen::aligned_allocator< Vector6d >( ) );\n\n // Find and set semi-major axis.\n if ( checkOptionalFieldType( dataLineMap, 1,\n input_output::field_types::state::semiMajorAxis ) )\n {\n ( *keplerianElements )( orbital_element_conversions::semiMajorAxisIndex )\n = parsed_data_vector_utilities::getField< double >(\n dataLineMap, input_output::field_types::state::semiMajorAxis );\n }\n\n else\n {\n throw std::runtime_error( \"No semi-major axis entry found.\" );\n }\n\n // Find and set eccentricity.\n if ( checkOptionalFieldType( dataLineMap, 1,\n input_output::field_types::state::eccentricity ) )\n {\n ( *keplerianElements )( orbital_element_conversions::eccentricityIndex )\n = parsed_data_vector_utilities::getField< double >(\n dataLineMap, input_output::field_types::state::eccentricity );\n }\n\n else\n {\n throw std::runtime_error( \"No eccentricity entry found.\" );\n }\n\n // Find and set inclination.\n if ( checkOptionalFieldType( dataLineMap, 1,\n input_output::field_types::state::inclination ) )\n {\n ( *keplerianElements )( orbital_element_conversions::inclinationIndex )\n = parsed_data_vector_utilities::getField< double >(\n dataLineMap, input_output::field_types::state::inclination );\n }\n\n else\n {\n throw std::runtime_error( \"No inclination entry found.\" );\n }\n\n // Find and set longitude of ascending node.\n if ( checkOptionalFieldType( dataLineMap, 1,\n input_output::field_types::state::longitudeOfAscendingNode ) )\n {\n ( *keplerianElements )( orbital_element_conversions::longitudeOfAscendingNodeIndex )\n = parsed_data_vector_utilities::getField< double >(\n dataLineMap, input_output::field_types::state::longitudeOfAscendingNode );\n }\n\n else\n {\n throw std::runtime_error( \"No longitude of ascending node entry found.\" );\n }\n\n // Find and set argument of periapsis.\n if ( checkOptionalFieldType( dataLineMap, 1,\n input_output::field_types::state::argumentOfPeriapsis ) )\n {\n ( *keplerianElements )( orbital_element_conversions::argumentOfPeriapsisIndex )\n = parsed_data_vector_utilities::getField< double >(\n dataLineMap, input_output::field_types::state::argumentOfPeriapsis );\n }\n\n else\n {\n throw std::runtime_error( \"No argument of periapsis entry found.\" );\n }\n\n // Find and set true anomaly.\n if ( checkOptionalFieldType( dataLineMap, 1,\n input_output::field_types::state::trueAnomaly ) )\n {\n ( *keplerianElements )( orbital_element_conversions::trueAnomalyIndex )\n = parsed_data_vector_utilities::getField< double >(\n dataLineMap, input_output::field_types::state::trueAnomaly );\n }\n\n // If the true anomaly is not present, check for mean anomaly.\n else if ( checkOptionalFieldType( dataLineMap, 1,\n input_output::field_types::state::meanAnomaly ) )\n {\n // Store mean anomaly.\n const double meanAnomaly = parsed_data_vector_utilities::getField< double >(\n dataLineMap, input_output::field_types::state::meanAnomaly );\n\n // Retrieve eccentricity.\n const double eccentricity\n = ( *keplerianElements )( orbital_element_conversions::eccentricityIndex );\n\n // Convert to eccentric anomaly.\n const double eccentricAnomaly = orbital_element_conversions::\n convertMeanAnomalyToEccentricAnomaly( eccentricity, meanAnomaly );\n\n // Convert eccentric anomaly to true anomaly and set the latter.\n ( *keplerianElements )( orbital_element_conversions::trueAnomalyIndex )\n = orbital_element_conversions::\n convertEccentricAnomalyToTrueAnomaly( eccentricAnomaly, eccentricity );\n }\n\n else\n {\n throw std::runtime_error( \"No true anomaly or mean anomaly entries found.\" );\n }\n\n return keplerianElements;\n}\n\n} // namespace ephemerides\n} // namespace tudat\n", "meta": {"hexsha": "29c39f2912208664112e3f0418b820f2ac987f59", "size": 6111, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Tudat/Astrodynamics/Ephemerides/keplerStateExtractor.cpp", "max_stars_repo_name": "ViktorJordanov/tudat", "max_stars_repo_head_hexsha": "069ceeab8f12405c356e19f50d6df037914df85c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Tudat/Astrodynamics/Ephemerides/keplerStateExtractor.cpp", "max_issues_repo_name": "ViktorJordanov/tudat", "max_issues_repo_head_hexsha": "069ceeab8f12405c356e19f50d6df037914df85c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": 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YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.22418192331223902}} {"text": "// __BEGIN_LICENSE__\n// Copyright (C) 2006-2011 United States Government as represented by\n// the Administrator of the National Aeronautics and Space Administration.\n// All Rights Reserved.\n// __END_LICENSE__\n\n\n/// \\file rmax_adjust.cc\n///\n\n#include \n#include \n#include \"boost/filesystem/operations.hpp\"\n#include \"boost/filesystem/fstream.hpp\"\nnamespace po = boost::program_options;\nnamespace fs = boost::filesystem;\n\n#include \n#include \n#include \n#include \n\nusing namespace vw;\nusing namespace vw::camera;\nusing namespace vw::ba;\n\n#include \n#include \n\n#include \n\n// This sifts out from a vector of strings, a listing of GCPs. This\n// should be useful for those programs who accept their data in a mass\n// input vector.\nstd::vector\nsort_out_gcps( std::vector& image_files ) {\n std::vector gcp_files;\n std::vector::iterator it = image_files.begin();\n while ( it != image_files.end() ) {\n if ( boost::iends_with(*it, \".gcp\") ){\n gcp_files.push_back( *it );\n it = image_files.erase( it );\n } else\n it++;\n }\n\n return gcp_files;\n}\n\nstatic std::string prefix_from_filename(std::string const& filename) {\n std::string result = filename;\n int index = result.rfind(\".\");\n if (index != -1)\n result.erase(index, result.size());\n return result;\n}\n\nclass HelicopterBundleAdjustmentModel : public ba::ModelBase {\n\n typedef Vector camera_vector_t;\n typedef Vector point_vector_t;\n\n std::vector m_image_infos;\n\n boost::shared_ptr m_network;\n\n std::vector a;\n std::vector b;\n std::vector a_target;\n std::vector b_target;\n int m_num_pixel_observations;\n\npublic:\n\n HelicopterBundleAdjustmentModel(std::vector const& image_infos,\n boost::shared_ptr network) :\n m_image_infos(image_infos), m_network(network),\n a(image_infos.size()), b(network->size()),\n a_target(image_infos.size()), b_target(network->size()) {\n\n // Compute the number of observations from the bundle.\n m_num_pixel_observations = 0;\n for (unsigned i = 0; i < network->size(); ++i)\n m_num_pixel_observations += (*m_network)[i].size();\n\n // Set up the a and b vectors, storing the initial values.\n for (unsigned j = 0; j < m_image_infos.size(); ++j) {\n a[j] = camera_vector_t();\n a_target[j] = a[j];\n }\n\n for (unsigned i = 0; i < m_network->size(); ++i) {\n b[i] = (*m_network)[i].position();\n b_target[i] = b[i];\n }\n\n }\n\n // Return a reference to the camera and point parameters.\n camera_vector_t A_parameters(int j) const { return a[j]; }\n point_vector_t B_parameters(int i) const { return b[i]; }\n void set_A_parameters(int j, camera_vector_t const& a_j) {\n a[j] = a_j;\n }\n void set_B_parameters(int i, point_vector_t const& b_i) {\n b[i] = b_i;\n }\n\n // Return the initial parameters\n camera_vector_t A_target(int j) const { return a_target[j]; }\n point_vector_t B_target(int i) const { return b_target[i]; }\n\n // Return general sizes\n unsigned num_cameras() const { return a.size(); }\n unsigned num_points() const { return b.size(); }\n unsigned num_pixel_observations() const { return m_num_pixel_observations; }\n\n // Return pixel observations\n unsigned num_observations_of_point(const int& i) const { return (*m_network)[i].size(); }\n unsigned corresponding_camera_for_measure(const int& i, const int& m){\n return (*m_network)[i][m].image_id();\n }\n Vector2 pixel_observation_of_point(const int& i, const int& m) const {\n //Finding out which camera accounts for the m_th observations of point i\n unsigned int camera_id = (*m_network)[i][m].image_id();\n\n Vector a_j = a[camera_id];\n Vector3 position_correction = subvector(a_j,0,3);\n Vector3 pose_correction = subvector(a_j,3,3);\n camera::CAHVORModel cam = rmax_image_camera_model(m_image_infos[camera_id],position_correction,pose_correction);\n\n Vector4 point_estimation = B_parameters(i);\n point_estimation = point_estimation/point_estimation(3);\n return cam.point_to_pixel(subvector(point_estimation,0,3));\n }\n\n // Return the covariance of the camera parameters for camera j.\n inline Matrix A_inverse_covariance ( unsigned /*j*/ ) {\n Matrix result;\n result(0,0) = 1/1; // Position sigma = 1 meter\n result(1,1) = 1/1;\n result(2,2) = 1/1;\n result(3,3) = 1; // Pose sigma = 1 degrees\n result(4,4) = 1;\n result(5,5) = 1;\n return result;\n }\n\n // Return the covariance of the point parameters for point i.\n inline Matrix B_inverse_covariance ( unsigned /*i*/ ) {\n Matrix result;\n result(0,0) = 1/1000.0; // Point sigma = 1000 meters ( we set this to be\n result(1,1) = 1/1000.0; // so large that it essentially removes point position\n result(2,2) = 1/1000.0; // constraints from the bundle adjustment entirely. )\n return result;\n }\n\n void write_adjustment(int j, std::string const& filename) {\n std::ofstream ostr(filename.c_str());\n ostr << a[j][0] << \" \" << a[j][1] << \" \" << a[j][2] << \"\\n\";\n ostr << a[j][3] << \" \" << a[j][4] << \" \" << a[j][5] << \"\\n\";\n }\n\n void write_adjusted_camera(int j, std::string const& filename) {\n Vector a_j = a[j];\n Vector3 position_correction = subvector(a_j, 0, 3);\n Vector3 pose_correction = subvector(a_j, 3,3);\n camera::CAHVORModel cam = rmax_image_camera_model(m_image_infos[j], position_correction, pose_correction);\n cam.write(filename);\n }\n\n void write_adjusted_cameras_append(std::string const& filename) {\n std::ofstream ostr(filename.c_str(),std::ios::app);\n for (unsigned j=0; j < a.size();++j){\n Vector a_j = a[j];\n Vector3 position_correction = subvector(a_j,0,3);\n Vector3 pose_correction = subvector(a_j,3,3);\n camera::CAHVORModel cam = rmax_image_camera_model(m_image_infos[j],position_correction,pose_correction);\n ostr << j << \"\\t\" << cam.C(0) << \"\\t\" << cam.C(1) << \"\\t\" << cam.C(2) << \"\\n\";\n ostr << j << \"\\t\" << cam.A(0) << \"\\t\" << cam.A(1) << \"\\t\" << cam.A(2) << \"\\n\";\n ostr << j << \"\\t\" << cam.H(0) << \"\\t\" << cam.H(1) << \"\\t\" << cam.H(2) << \"\\n\";\n ostr << j << \"\\t\" << cam.V(0) << \"\\t\" << cam.V(1) << \"\\t\" << cam.V(2) << \"\\n\";\n ostr << j << \"\\t\" << cam.O(0) << \"\\t\" << cam.O(1) << \"\\t\" << cam.O(2) << \"\\n\";\n ostr << j << \"\\t\" << cam.R(0) << \"\\t\" << cam.R(1) << \"\\t\" << cam.R(2) << \"\\n\";\n }\n }\n\n std::vector > adjusted_cameras() {\n std::vector > result(a.size());\n for (unsigned j = 0; j < result.size(); ++j) {\n Vector3 position_correction = subvector(a[j], 0, 3);\n Vector3 pose_correction = subvector(a[j], 3,3);\n\n camera::CAHVORModel *cahvor = new camera::CAHVORModel;\n *cahvor = rmax_image_camera_model(m_image_infos[j], position_correction, pose_correction);\n\n result[j] = boost::shared_ptr( cahvor );\n }\n return result;\n }\n\n // Given the 'a' vector (camera model parameters) for the j'th\n // image, and the 'b' vector (3D point location) for the i'th\n // point, return the location of b_i on imager j in pixel\n // coordinates.\n Vector2 operator() ( unsigned /*i*/, unsigned j, Vector const& a_j, Vector const& b_i ) const {\n Vector3 position_correction = subvector(a_j, 0, 3);\n Vector3 pose_correction = subvector(a_j, 3,3);\n camera::CAHVORModel cam = rmax_image_camera_model(m_image_infos[j], position_correction, pose_correction);\n return cam.point_to_pixel(b_i);\n }\n\n // Errors on the image plane\n void image_errors( std::vector& pix_errors ) {\n pix_errors.clear();\n for (unsigned i = 0; i < m_network->size(); ++i)\n for(unsigned m = 0; m < (*m_network)[i].size(); ++m) {\n int camera_idx = (*m_network)[i][m].image_id();\n Vector2 pixel_error = (*m_network)[i][m].position() - (*this)(i, camera_idx,\n a[camera_idx],b[i]);\n pix_errors.push_back(norm_2(pixel_error));\n }\n }\n\n // Errors for camera position\n void camera_position_errors( std::vector& camera_position_errors ) {\n camera_position_errors.clear();\n for (unsigned j=0; j < this->num_cameras(); ++j) {\n Vector3 position_initial, position_now;\n position_initial = subvector(a_target[j],0,3);\n position_now = subvector(a[j],0,3);\n\n camera_position_errors.push_back(norm_2(position_initial-position_now));\n }\n }\n\n // Errors for camera pose\n void camera_pose_errors( std::vector& camera_pose_errors ) {\n camera_pose_errors.clear();\n for (unsigned j=0; j < this->num_cameras(); ++j) {\n Vector3 pose_initial, pose_now;\n pose_initial = subvector(a_target[j],3,3);\n pose_now = subvector(a[j],3,3);\n\n camera_pose_errors.push_back(norm_2(pose_initial-pose_now));\n }\n }\n\n // Errors for gcp errors\n void gcp_errors( std::vector& gcp_errors ) {\n gcp_errors.clear();\n for (unsigned i=0; i < this->num_points(); ++i)\n if ((*m_network)[i].type() == ControlPoint::GroundControlPoint) {\n point_vector_t p1 = b_target[i]/b_target[i](3);\n point_vector_t p2 = b[i]/b[i](3);\n gcp_errors.push_back(norm_2(subvector(p1,0,3) - subvector(p2,0,3)));\n }\n }\n\n // Give access to the control network\n boost::shared_ptr control_network() {\n return m_network;\n }\n};\n\nint main(int argc, char* argv[]) {\n\n std::vector image_files;\n std::vector gcp_files;\n std::string cnet_file;\n boost::shared_ptr cnet( new ControlNetwork(\"My first control network\"));\n double lambda;\n int min_matches;\n double robust_outlier_threshold;\n int max_iterations;\n int report_level;\n\n po::options_description general_options(\"Options\");\n general_options.add_options()\n (\"cnet,c\", po::value(&cnet_file), \"Load a control network from a file\")\n (\"lambda,l\", po::value(&lambda), \"Set the initial value of the LM parameter lambda\")\n (\"min-matches\", po::value(&min_matches)->default_value(5), \"Set the mininmum number of matches between images that will be considered.\")\n (\"robust-threshold\", po::value(&robust_outlier_threshold)->default_value(10.0), \"Set the threshold for robust cost functions.\")\n (\"max-iterations\", po::value(&max_iterations)->default_value(25), \"Set the maximum number of iterations.\")\n (\"save-iteration-data,s\", \"Saves all camera information between iterations to iterCameraParam.txt, it also saves point locations for all iterations in iterPointsParam.txt. Warning: This is slow as pixel observations need to be calculated on each step.\")\n (\"report-level,r\",po::value(&report_level)->default_value(10),\"Changes the detail of the Bundle Adjustment Report\")\n (\"run-match,m\", \"Run ipmatch to create .match files from overlapping images.\")\n (\"match-debug-images,d\", \"Create debug images when you run ipmatch.\")\n (\"help,h\", \"Display this help message\");\n\n po::options_description hidden_options(\"\");\n hidden_options.add_options()\n (\"input-files\", po::value >(&image_files));\n\n po::options_description options(\"Allowed Options\");\n options.add(general_options).add(hidden_options);\n\n po::positional_options_description p;\n p.add(\"input-files\", -1);\n\n po::variables_map vm;\n po::store( po::command_line_parser( argc, argv ).options(options).positional(p).run(), vm );\n po::notify( vm );\n\n std::ostringstream usage;\n usage << \"Usage: \" << argv[0] << \" [options] ...\" << std::endl << std::endl;\n usage << general_options << std::endl;\n\n if( vm.count(\"help\") ) {\n std::cout << usage << std::endl;\n return 1;\n }\n\n if( vm.count(\"input-files\") < 1 || image_files.size() < 2) {\n std::cout << \"Error: Must specify at least two input files!\" << std::endl << std::endl;\n std::cout << usage.str();\n return 1;\n }\n gcp_files = sort_out_gcps( image_files );\n\n // Read in the camera model and RMAX image info for the input\n // images.\n std::vector camera_files(image_files.size());\n std::vector image_infos(image_files.size());\n std::vector > camera_models(image_files.size());\n for (unsigned i = 0; i < image_files.size(); ++i) {\n read_image_info( image_files[i], image_infos[i] );\n CAHVORModel *cahvor = new CAHVORModel;\n *cahvor = rmax_image_camera_model(image_infos[i]);\n camera_models[i] = boost::shared_ptr(cahvor);\n }\n\n if ( vm.count(\"cnet\") ) {\n // Loading a Control Network\n\n vw_out() << \"Loading control network from file: \" << cnet_file << \"\\n\";\n cnet->read_binary(cnet_file);\n\n } else {\n // Building a Control Network\n build_control_network( *cnet, camera_models,\n image_files, min_matches );\n add_ground_control_points( *cnet, image_files,\n gcp_files.begin(), gcp_files.end() );\n\n cnet->write_binary(\"rmax_adjust\");\n }\n\n HelicopterBundleAdjustmentModel ba_model(image_infos, cnet);\n AdjustSparse bundle_adjuster(ba_model, L2Error());\n\n if (vm.count(\"lambda\"))\n bundle_adjuster.set_lambda(lambda);\n\n //Clearing the monitoring text files to be used for saving camera params\n if (vm.count(\"save-iteration-data\")){\n std::ofstream ostr(\"iterCameraParam.txt\",std::ios::out);\n ostr << \"\";\n ostr.close();\n ostr.open(\"iterPointsParam.txt\",std::ios::out);\n ostr << \"\";\n ostr.close();\n\n //Now I'm going to save the initial starting position of the cameras\n ba_model.write_adjusted_cameras_append(\"iterCameraParam.txt\");\n std::ofstream ostr_points(\"iterPointsParam.txt\",std::ios::app);\n for (unsigned i = 0; i < ba_model.num_points(); ++i){\n Vector current_point = ba_model.B_parameters(i);\n ostr_points << i << \"\\t\" << current_point(0) << \"\\t\" << current_point(1) << \"\\t\" << current_point(2) << \"\\n\";\n }\n }\n\n // Reporter\n BundleAdjustReport >\n reporter( \"RMAX Adjust\", ba_model, bundle_adjuster, report_level );\n\n // Performing Bundle Adjustment\n double abs_tol = 1e10, rel_tol=1e10;\n while(bundle_adjuster.update(abs_tol, rel_tol)) {\n reporter.loop_tie_in();\n\n // Writing Current Camera Parameters to file for later reading in MATLAB\n if (vm.count(\"save-iteration-data\")) {\n\n //Writing this iterations camera data\n ba_model.write_adjusted_cameras_append(\"iterCameraParam.txt\");\n\n //Writing this iterations point data, also saving the pixel param data\n std::ofstream ostr_points(\"iterPointsParam.txt\",std::ios::app);\n for (unsigned i = 0; i < ba_model.num_points(); ++i){\n\n Vector current_point = ba_model.B_parameters(i);\n ostr_points << i << \"\\t\" << current_point(0) << \"\\t\" << current_point(1) << \"\\t\" << current_point(2) << \"\\n\";\n }\n }\n\n if (bundle_adjuster.iterations() > max_iterations || abs_tol < 0.01 || rel_tol < 1e-10)\n break;\n }\n reporter.end_tie_in();\n\n for (unsigned int i=0; i < ba_model.num_cameras(); ++i)\n ba_model.write_adjustment(i, prefix_from_filename(image_files[i])+\".rmax_adjust\");\n\n // Compute the post-adjustment residuals\n std::vector > adjusted_cameras = ba_model.adjusted_cameras();\n}\n", "meta": {"hexsha": "a9462879a6ebe164ad4314fc3cc20696383d7b1c", "size": 15902, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/asp/Tools/rmax_adjust.cc", "max_stars_repo_name": "nasa/StereoPipeline", "max_stars_repo_head_hexsha": "8b9c0bcab258c41d10cb2973d97722765072a7bf", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count": 29.0, "max_stars_repo_stars_event_min_datetime": "2015-05-06T01:28:21.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-19T22:55:29.000Z", "max_issues_repo_path": "src/asp/Tools/rmax_adjust.cc", "max_issues_repo_name": "imagineagents/StereoPipeline", "max_issues_repo_head_hexsha": "8b9c0bcab258c41d10cb2973d97722765072a7bf", "max_issues_repo_licenses": ["NASA-1.3"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/asp/Tools/rmax_adjust.cc", "max_forks_repo_name": "imagineagents/StereoPipeline", "max_forks_repo_head_hexsha": "8b9c0bcab258c41d10cb2973d97722765072a7bf", "max_forks_repo_licenses": ["NASA-1.3"], "max_forks_count": 27.0, "max_forks_repo_forks_event_min_datetime": "2015-01-15T04:20:50.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-10T01:31:17.000Z", "avg_line_length": 38.9754901961, "max_line_length": 257, "alphanum_fraction": 0.6663941643, "num_tokens": 4323, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.22418192331223902}} {"text": "// This file is part of DM-HEOM (https://github.com/noma/dm-heom)\n//\n// Copyright (c) 2015-2019 Matthias Noack, Zuse Institute Berlin\n//\n// Licensed under the 3-clause BSD License, see accompanying LICENSE,\n// CONTRIBUTORS.md, and README.md for further information.\n\n#include \n#include \n#include \n\n#include \n#include \n\n#include \"heom/command_line.hpp\"\n#include \"heom/common.hpp\"\n#include \"heom/dipole_matrix.hpp\"\n#include \"heom/dipole_pseudo_pathway.hpp\"\n#include \"heom/handle_main_exception.hpp\"\n#include \"heom/hierarchy_norm.hpp\"\n#include \"heom/instance.hpp\"\n#include \"heom/make_file_observer_list.hpp\"\n#include \"heom/ocl_config.hpp\"\n#include \"heom/ode.hpp\"\n#include \"heom/population_dynamics_solver.hpp\"\n#include \"heom/sites_to_states.hpp\"\n#include \"heom/static_fluorescence_config.hpp\"\n#include \"heom/thermal_state_search.hpp\"\n#include \"heom/thermal_state_search_config.hpp\"\n#include \"heom/thermal_state_search_solver.hpp\"\n\nnamespace bmt = ::heom::bmt;\nnamespace num = ::heom::num;\nnamespace ocl = ::heom::ocl;\nusing num::int_t;\nusing num::real_t;\nusing num::complex_t;\n\nint main(int argc, char* argv[])\n{\n\t// start runtime measurement\n\tbmt::timer app_timer;\n\n\t// output compile time configuration\n\tstd::cout << \"-------------------- Compile-Time Configuration ------------\" << std::endl;\n\theom::write_compile_config(std::cout);\n\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\tstd::exception_ptr eptr;\n\ttry {\n\t\t// command line parsing\n\t\theom::command_line command_line { argc, argv };\n\n\t\tstd::cout << \"Parsing OpenCL configuration from file: \" << command_line.ocl_config_filename() << std::endl;\n\t\theom::ocl_config ocl_config(command_line.ocl_config_filename());\n\t\tstd::cout << \"Parsing HEOM configuration from file: \" << command_line.heom_config_filename() << std::endl;\n\t\theom::static_fluorescence_config heom_config(command_line.heom_config_filename());\n\n\t\theom::hierarchy_graph complete_graph(heom_config.baths_number(), heom_config.baths_matsubaras(), heom_config.system_ado_depth());\n\n\t\tusing tss_solver_t = heom::thermal_state_search_solver;\n\t\tusing stepper_t = num::meta_stepper; // uses solver_stepper_type from config\n\t\tusing solver_t = heom::population_dynamics_solver;\n\n\t\t// prepare output file (so we run into possible file errors before starting the computation)\n\t\tauto& observation_filename = heom_config.observations().get().front().get().second; // filename of first specified observation, guaranteed to exist by config checks\n\t\tstd::ofstream observation_file(observation_filename);\n\t\tif (observation_file.fail())\n\t\t\tthrow std::runtime_error(\"Error: could not open output file: \" + observation_filename);\n\n\t\t// linear absorption pseudo pathway with ground state\n\t\tconst auto& pathway_spec = heom::dipole_pseudo_pathway_wgs_spec;\n\t\tconst auto sts_mode = pathway_spec.sites_to_states_mode();\n\n\t\t// compute thermal state\n\t\theom::instance heom_instance_tss(heom_config, sts_mode, complete_graph);\n\n\t\t// OpenCL range\n\t\tocl::nd_range range {\n\t\t\t{}, // offset\n\t\t\t{1, static_cast(heom_instance_tss.matrices())}, // global size\n\t\t\t{1, 1} // local size\n\t\t};\n\n\t\t// create thermal state search solver (tss) from configuration and instance\n\t\ttss_solver_t tss_solver(ocl_config, range, heom_config, heom_instance_tss);\n\n\t\tstd::cout << \"-------------------- OpenCL Runtime Configuration ----------\" << std::endl;\n\t\ttss_solver.write_ocl_runtime_config(std::cout);\n\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\t\t// determine thermal state until converged or max_steps is reached\n\t\tstd::cout << \"Computing thermal state...\" << std::endl;\n\t\tint_t i = 0;\n\t\tfor (; i < heom_config.thermal_state_search_max_steps(); ++i) {\n\t\t\ttss_solver.step_forward(1);\n\t\t\t// abort if convergence is reached\n\t\t\tif (tss_solver.test_convergence(heom_config.thermal_state_search_delta()))\n\t\t\t\tbreak;\n\t\t}\n\t\tif (i < heom_config.thermal_state_search_max_steps())\n\t\t\tstd::cout << \"... thermal state search converged after \" << i << \" steps.\" << std::endl;\n\t\telse\n\t\t\tstd::cout << \"... WARNING: thermal state search stopped after max_steps were reached (maybe increase max_steps, or delta).\" << std::endl;\n\n\t\t// synchronise instance with tss solver results, i.e. transfer compute device to host memory\n\t\ttss_solver.update_instance();\n\n\t\tstd::cout << \"-------- Thermal State Search Solver Runtime Summary -------\" << std::endl;\n\t\ttss_solver.write_runtime_summary(std::cout);\n\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\t\t// run the tss solver, no observations\n\t\tint_t iterations = heom_config.solver_steps() / heom_config.program_observe_steps();\n\t\tint_t steps_per_iteration = heom_config.program_observe_steps();\n\t\tint_t total_steps = iterations * heom_config.program_observe_steps();\n\n\t\t// output data structure for traces\n\t\tconst size_t num_dipole_matrices = heom_config.dipole_tensor_prefactors().size();\n\t\tconst int_t num_observations = iterations + 1;\n\t\tstd::vector observations(num_observations);\n\n\t\t// for progress estimate\n\t\tconst size_t solver_runs = num_dipole_matrices;\n\t\tsize_t solver_run = 0;\n\n\t\t// iterate over all dipoles and compute population dynamics\n\t\tfor (size_t tensor_index = 0; tensor_index < num_dipole_matrices; ++tensor_index) {\n\n\t\t\t// copy thermal state instance\n\t\t\theom::instance heom_instance { heom_instance_tss };\n\t\t\t// overwrite pseudo-hamiltonian of tss instance\n\t\t\theom_instance.set_hamiltonian(state_hamiltonian(heom_config.system_hamiltonian(), sts_mode));\n\n\t\t\t// OpenCL range\n\t\t\tocl::nd_range range {\n\t\t\t\t{}, // offset\n\t\t\t\t{1, static_cast(heom_instance.matrices())}, // global size\n\t\t\t\t{1, 1} // local size\n\t\t\t};\n\n\t\t\t// create a solver from configuration and instance\n\t\t\tsolver_t solver(ocl_config, range, heom_config, heom_instance);\n\t\t\tstd::cout << \"-------------------- OpenCL Runtime Configuration ----------\" << std::endl;\n\t\t\tsolver.write_ocl_runtime_config(std::cout);\n\t\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\t\t\t++solver_run; // for progress estimate\n\n\t\t\t// compute dipole matrices\n\t\t\tauto get_dipole_matrix = [&](size_t spec_index) {\n\t\t\t\treturn heom::get_dipole_matrix_for_pathway(pathway_spec, spec_index, heom_config.system_sites(), heom_config, tensor_index);\n\t\t\t};\n\t\t\tauto dipole_matrix_minus = get_dipole_matrix(0);\n\t\t\tauto dipole_matrix_plus = get_dipole_matrix(1);\n\t\t\tdipole_matrix_minus.scale(heom::get_dipole_tensor_prefactor(pathway_spec, heom_config, tensor_index));\n\n\t\t\t// multiply D+ from right\n\t\t\tsolver.hierarchy_mmult_right(dipole_matrix_plus.data());\n\n\t\t\t// setup observer with pre-scaled D-\n\t\t\theom::matrix_trace_observer trace_observer(complete_graph, solver, dipole_matrix_minus.data()); // D- from left via observer\n\n\t\t\t// generate first observation with initial value\n\t\t\tobservations[0] += trace_observer.observe_trace(0.0);\n\n\t\t\t// write header once\n\t\t\tif (tensor_index == 0)\n\t\t\t\ttrace_observer.write_header(observation_file, true);\n\n\t\t\tfor (int_t i = 0; i < iterations; ++i) {\n\t\t\t\t// propagate\n\t\t\t\tsolver.step_forward(steps_per_iteration);\n\n\t\t\t\t// after propagation, we are at:\n\t\t\t\tconst auto current_step = (i + 1) * steps_per_iteration;\n\t\t\t\tconst real_t current_time = current_step * heom_config.solver_step_size();\n\n\t\t\t\t// observe\n\t\t\t\tobservations[i + 1] += trace_observer.observe_trace(current_time);\n\n\t\t\t\t// update status\n\t\t\t\theom::write_progress(current_step - 1, total_steps, solver_run, solver_runs, \"Calculation static fluorescence: \", std::cout);\n\t\t\t}\n\n\t\t\t// write_complex_matrix(result_buffer_top, heom_instance.states(), std::cout);\n\t\t\tstd::cout << \"-------------------- Solver Runtime Summary ----------------\" << std::endl;\n\t\t\tsolver.write_runtime_summary(std::cout);\n\t\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\t\t} // for tensor_index\n\n\t\t// write averaged traces\n\t\tfor (auto& obs : observations) {\n\t\t\tobs.avg(num_dipole_matrices);\n\t\t\tobservation_file << obs << '\\n';\n\t\t}\n\t\tobservation_file << std::flush; // tell the OS to write to disk\n\n\n\t\tstd::cout << \"-------------------- Memory Summary ------------------------\" << std::endl;\n\t\tstd::cout << \"local hierarchy buffer size: \"\n\t\t << boost::format(\"%11.2f MiB\\n\") % (heom_instance_tss.size_hierarchy_byte() / 1024.0 / 1024.0);\n\t\tstd::cout << \"local instance size: \"\n\t\t << boost::format(\"%11.2f MiB\\n\") % (heom_instance_tss.allocated_byte() / 1024.0 / 1024.0);\n\t\tstd::cout << \"max. heap via aligned::allocate(..): \"\n\t\t << boost::format(\"%11.2f MiB\") % (heom::memory::aligned::instance().allocated_byte_max() / 1024.0 / 1024.0)\n\t\t << \" (\"\n\t\t << boost::format(\"count: %6i\") % heom::memory::aligned::instance().allocations_max()\n\t\t << ')' << std::endl;\n\t\tstd::cout << \"max. OpenCL buffer allocations: \"\n\t\t << boost::format(\"%11.2f MiB\") % (tss_solver.ocl_helper().allocated_byte_max() / 1024.0 / 1024.0)\n\t\t << \" (\"\n\t\t << boost::format(\"count: %6i\") % tss_solver.ocl_helper().allocations_max()\n\t\t << ')' << std::endl;\n//\t\tstd::cout << \"max. OpenCL buffer allocations: \"\n//\t\t << boost::format(\"%11.2f MiB\") % (solver.ocl_helper().allocated_byte_max() / 1024.0 / 1024.0)\n//\t\t << \" (\"\n//\t\t << boost::format(\"count: %6i\") % solver.ocl_helper().allocations_max()\n//\t\t << ')' << std::endl;\n\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\t} catch (...) {\n\t\teptr = std::current_exception();\n\t}\n\tint ret = heom::handle_main_exception(eptr);\n\n\t// print application runtime\n\tstd::cout << \"time in main():\\t\" << boost::format(\"%11.2f\") % std::chrono::duration_cast(bmt::duration(app_timer.elapsed())).count() << \" s\" << std::endl;\n\n\treturn ret;\n}\n\n\n", "meta": {"hexsha": "872173bf3cdf2b3761875692f9bbc1fe6924f8a6", "size": 10075, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "dm-heom/src/app_static_fluorescence.cpp", "max_stars_repo_name": "noma/dm-heom", "max_stars_repo_head_hexsha": "85c94f947190064d21bd38544094731113c15412", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 7.0, "max_stars_repo_stars_event_min_datetime": "2019-07-28T01:02:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-09T04:01:36.000Z", "max_issues_repo_path": "dm-heom/src/app_static_fluorescence.cpp", "max_issues_repo_name": "noma/dm-heom", "max_issues_repo_head_hexsha": "85c94f947190064d21bd38544094731113c15412", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dm-heom/src/app_static_fluorescence.cpp", "max_forks_repo_name": "noma/dm-heom", "max_forks_repo_head_hexsha": "85c94f947190064d21bd38544094731113c15412", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2020-04-04T15:20:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-24T15:16:40.000Z", "avg_line_length": 43.0555555556, "max_line_length": 169, "alphanum_fraction": 0.6644168734, "num_tokens": 2588, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.22418192331223902}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"bounding_edge.hpp\"\n#include \"kd_tree_node.hpp\"\n#include \"rtc_log.hpp\"\n#include \"scoped_timer.hpp\"\n\nnamespace rtc\n{\nkd_tree::kd_tree(const rtc::scene_model& ss) : bbox{ss.points}\n{\n SCOPE_TIME_COUNTER;\n const auto& p = ss.points;\n const auto& t = ss.triangles;\n const std::uint32_t max_depth = 1.3F * std::log2(t.size()) + 8;\n\n auto make_bbox = [](auto& p, auto& tr) noexcept {\n return rtc::bounding_box{p[tr.vertex_a()], p[tr.vertex_b()], p[tr.vertex_c()]};\n };\n\n rtc::bounding_box node_bbox{p};\n std::vector v(t.size());\n std::vector primitive_bboxes;\n edge_buffer_array_t edge_buffer{\n edge_buffer_t(2 * t.size()), edge_buffer_t(2 * t.size()), edge_buffer_t(2 * t.size())};\n\n primitive_bboxes.reserve(t.size());\n\n std::for_each(t.begin(), t.end(), [&](auto& t) { primitive_bboxes.push_back(make_bbox(p, t)); });\n std::generate(v.begin(), v.end(), [i = 0]() mutable { return i++; });\n\n build_tree(root, node_bbox, std::move(v), primitive_bboxes, edge_buffer, max_depth);\n}\n\nauto kd_tree::cbegin(const rtc::math_ray& ray) const noexcept -> kd_tree::const_iterator\n{\n if (auto range = bbox.intersection_values_for(ray))\n {\n const auto [tmin, tmax] = range.value();\n return const_iterator{ray, {root.get(), tmin, tmax}};\n }\n\n return const_iterator{};\n}\n\nauto kd_tree::cend(const rtc::math_ray&) const noexcept -> kd_tree::const_iterator { return const_iterator{}; }\n\nkd_tree::~kd_tree() = default;\nkd_tree::kd_tree(kd_tree&&) noexcept = default;\nauto kd_tree::operator=(kd_tree&&) noexcept -> kd_tree& = default;\n\nauto kd_tree::compute_node_split_paramters(edge_buffer_array_t& edge_buffer,\n const std::vector& tr,\n const rtc::bounding_box& node_bbox,\n const std::vector& primitive_bboxes)\n{\n constexpr int isect_cost{80};\n constexpr int traversal_cost{1};\n constexpr rtc_float empty_bonus{0.5F};\n const auto node_diagonal{node_bbox.diagonal()};\n const rtc_float invTotalSA{1.0F / node_bbox.surface_area()};\n const rtc_float old_cost{isect_cost * static_cast(tr.size())};\n\n int retries{0};\n int best_axis{-1};\n int best_offset{-1};\n rtc::axis axis{node_bbox.maximum_extent()};\n rtc_float best_cost{std::numeric_limits::max()};\n\n do\n {\n const rtc::axis axis2{next(axis)}, axis3{next(axis2)};\n int below{}, above(tr.size());\n\n for (const auto i : boost::irange(0, tr.size()))\n {\n const rtc::bounding_box& box{primitive_bboxes[tr[i]]};\n edge_buffer[int(axis)][2 * i] = {box.min_boundary().axis(axis), tr[i], bounding_edge_point::start};\n edge_buffer[int(axis)][2 * i + 1] = {box.max_boundary().axis(axis), tr[i], bounding_edge_point::end};\n }\n\n std::sort(&edge_buffer[int(axis)][0], &edge_buffer[int(axis)][0] + 2 * tr.size());\n\n for (const auto i : boost::irange(0, 2 * tr.size()))\n {\n const rtc::bounding_edge_point& bp{edge_buffer[int(axis)][i]};\n const auto edge_t = bp.value;\n\n if (bp.type == bounding_edge_point::end)\n --above;\n if (bp.type == bounding_edge_point::start)\n ++below;\n\n if (edge_t > node_bbox.min_boundary().axis(axis) && edge_t < node_bbox.max_boundary().axis(axis))\n {\n const rtc_float belowSA{2 * (node_diagonal.axis(axis2) * node_diagonal.axis(axis3) +\n (edge_t - node_bbox.min_boundary().axis(axis)) *\n (node_diagonal.axis(axis2) + node_diagonal.axis(axis3)))};\n\n const rtc_float aboveSA{2 * (node_diagonal.axis(axis2) * node_diagonal.axis(axis3) +\n (node_bbox.max_boundary().axis(axis) - edge_t) *\n (node_diagonal.axis(axis2) + node_diagonal.axis(axis3)))};\n\n const rtc_float pBelow{belowSA * invTotalSA};\n const rtc_float pAbove{aboveSA * invTotalSA};\n const rtc_float eb{(above == 0 || below == 0) ? empty_bonus : 0};\n const rtc_float cost{traversal_cost + isect_cost * (1 - eb) * (pBelow * below + pAbove * above)};\n\n if (cost < best_cost)\n {\n best_cost = cost;\n best_axis = int(axis);\n best_offset = i;\n }\n }\n }\n } while ((best_axis == -1) && ((axis = next(axis), retries++) < 2));\n\n return std::make_tuple(best_axis, best_offset, best_cost, old_cost);\n}\n\nauto kd_tree::split_triangles(std::vector&& tr_init,\n const edge_buffer_array_t& edge_buffer,\n const std::uint32_t best_axis,\n const std::uint32_t best_offset)\n{\n std::vector tr{std::move(tr_init)}, left_set{}, right_set{};\n\n left_set.reserve(best_offset);\n right_set.reserve(2 * tr.size() - best_offset);\n\n for (auto i = 0; i < best_offset; ++i)\n {\n if (edge_buffer[best_axis][i].type == bounding_edge_point::start)\n {\n left_set.emplace_back(edge_buffer[best_axis][i].tr_index);\n }\n }\n\n for (auto i = best_offset + 1; i < 2 * tr.size(); ++i)\n {\n if (edge_buffer[best_axis][i].type == bounding_edge_point::end)\n {\n right_set.emplace_back(edge_buffer[best_axis][i].tr_index);\n }\n }\n return std::make_tuple(std::move(left_set), std::move(right_set));\n}\n\nvoid kd_tree::build_tree(std::unique_ptr& node,\n rtc::bounding_box node_bbox,\n std::vector tr,\n const std::vector& primitive_bboxes,\n edge_buffer_array_t& edge_buffer,\n const std::uint32_t depth,\n std::uint32_t bad_refines)\n{\n const auto create_leaf_node = [&node, &tr] {\n node = std::make_unique();\n node->triangles = std::make_unique();\n node->triangles->insert(node->triangles->end(), tr.begin(), tr.end());\n };\n\n if (tr.size() <= 1 || !depth)\n return create_leaf_node();\n\n const auto [axis, offset, cost, old_cost] =\n compute_node_split_paramters(edge_buffer, tr, node_bbox, primitive_bboxes);\n\n bad_refines += (cost > old_cost) ? 1 : 0;\n\n auto no_use_of_spliting_node = [old_cost = old_cost, cost = cost, axis = axis, &bad_refines, &tr] {\n return ((cost > 4 * old_cost) && (tr.size() < 16)) || (axis == -1) || (bad_refines == 3);\n };\n\n if (no_use_of_spliting_node())\n return create_leaf_node();\n\n auto [left_set, right_set] = split_triangles(std::move(tr), edge_buffer, axis, offset);\n bounding_box left_bbox{node_bbox}, right_bbox{node_bbox};\n\n node = std::make_unique();\n node->axis = {edge_buffer[axis][offset].value, static_cast(axis)};\n\n left_bbox.max_boundary().axis(node->axis.split) = right_bbox.min_boundary().axis(node->axis.split) = node->axis.value;\n\n build_tree(node->left, left_bbox, std::move(left_set), primitive_bboxes, edge_buffer, depth - 1, bad_refines);\n build_tree(node->right, right_bbox, std::move(right_set), primitive_bboxes, edge_buffer, depth - 1, bad_refines);\n}\n\n} // namespace rtc\n", "meta": {"hexsha": "934d0d8d87f3ac9efdfc899063067e8859608ea0", "size": 7335, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "kd_tree/source/kd_tree.cpp", "max_stars_repo_name": "Abergard/rtc", "max_stars_repo_head_hexsha": "e0cdbbb4a005b7d9f3b10d1a519fcb6be876e65f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2019-05-25T01:03:38.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-29T07:43:49.000Z", "max_issues_repo_path": "kd_tree/source/kd_tree.cpp", "max_issues_repo_name": "Abergard/rtc", "max_issues_repo_head_hexsha": "e0cdbbb4a005b7d9f3b10d1a519fcb6be876e65f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2019-05-24T22:16:30.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-24T22:16:30.000Z", "max_forks_repo_path": "kd_tree/source/kd_tree.cpp", "max_forks_repo_name": "Abergard/rtc", "max_forks_repo_head_hexsha": "e0cdbbb4a005b7d9f3b10d1a519fcb6be876e65f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2019-05-24T19:12:44.000Z", "max_forks_repo_forks_event_max_datetime": "2019-05-24T19:12:44.000Z", "avg_line_length": 37.2335025381, "max_line_length": 120, "alphanum_fraction": 0.627402863, "num_tokens": 1927, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6113819874558604, "lm_q2_score": 0.36658974324230986, "lm_q1q2_score": 0.22412636580441697}} {"text": "#ifndef STAN_MATH_TORSTEN_PKMODEL_MODELPARAMETERS_HPP\n#define STAN_MATH_TORSTEN_PKMODEL_MODELPARAMETERS_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace torsten {\n\n/**\n * The ModelParameters class defines objects that contain the parameters of\n * a model at a given time.\n */\ntemplate\nstruct ModelParameters {\n double time_;\n std::vector theta_;\n int nrow, ncol;\n std::vector biovar_;\n std::vector tlag_;\n\n ModelParameters() {}\n\n ModelParameters(const T_time& time,\n const Eigen::Matrix& K,\n const std::vector& biovar,\n const std::vector& tlag)\n : time_(stan::math::value_of(time)), theta_(K.size()), nrow(K.rows()), ncol(K.cols()), biovar_(biovar), tlag_(tlag)\n {\n Eigen::Matrix::Map(theta_.data(), nrow, ncol) = K;\n }\n\n ModelParameters(const T_time& time,\n const std::vector& theta,\n const std::vector& biovar,\n const std::vector& tlag)\n : time_(stan::math::value_of(time)), theta_(theta), biovar_(biovar), tlag_(tlag) {}\n\n /**\n * Adds parameters. Useful for the mixed solver, where\n * we want to augment the parameters with the intial PK\n * states when calling the numerical integrator.\n */\n template \n ModelParameters\n augment(const std::vector& thetaAdd) const {\n std::vector theta(theta_.size());\n for (size_t i = 0; i < theta.size(); i++) theta[i] = theta_[i];\n for (size_t i = 0; i < thetaAdd.size(); i++) theta.push_back(thetaAdd[i]);\n return\n ModelParameters\n (time_, theta, biovar_, tlag_);\n }\n\n template \n ModelParameters\n augment(const Eigen::Matrix& thetaAdd)\n const {\n return augment(stan::math::to_array_1d(thetaAdd));\n }\n\n /**\n * Edit time stored in parameter object.\n */\n void time(double time) {\n time_ = time;\n }\n\n int CountParameters() const {\n return theta_.size();\n }\n\n // access functions // FIX ME - name should be get_theta.\n double get_time() const { return time_; }\n std::vector get_RealParameters(bool return_matrix) const {\n if (return_matrix) {\n auto k = get_K();\n std::vector par(k.size());\n for (size_t j = 0; j < par.size(); ++j) par[j] = k(j);\n return par;\n } else {\n return theta_;\n }\n }\n std::vector get_biovar() const {\n return biovar_;\n }\n std::vector get_tlag() const {\n return tlag_;\n }\n Eigen::Matrix get_K() const {\n Eigen::Matrix res(nrow, ncol);\n res = Eigen::Matrix::Map(theta_.data(), nrow, ncol);\n return res;\n }\n\n inline ModelParameters to_value() const {\n return ModelParameters\n (time_, \n stan::math::value_of(theta_),\n stan::math::value_of(biovar_),\n stan::math::value_of(tlag_));\n }\n};\n\n template\n struct is_matrix : std::false_type {};\n \n template\n struct is_matrix > : std::true_type {};\n \n/**\n * The ModelParameterHistory class defines objects that contain a vector\n * of ModelParameters, along with a series of functions that operate on\n * them.\n */\ntemplate\nstruct ModelParameterHistory {\n using T4 = typename stan::value_type::type;\n using Param = std::pair >;\n\n static const bool has_matrix_param;\n\n std::vector index;\n const std::vector& theta_;\n const std::vector >& biovar_;\n const std::vector >& tlag_;\n\n template\n ModelParameterHistory(const std::vector& time,\n const std::vector& theta,\n const std::vector >& biovar,\n const std::vector >& tlag) :\n ModelParameterHistory(0, time.size(), time,\n 0, theta.size(), theta,\n 0, biovar.size(), biovar,\n 0, tlag.size(), tlag)\n {}\n\n /*\n * For population data in form of ragged array, we need to\n * generate individual parameter history given the entire\n * population data and the location of the\n * inidividual. However, @c theta, @c biovar and @c tlag\n * could have different lengths, so for each variable we\n * need an index that points to the range that belongs to the individual.\n * Note that if all three variables are of size 1, their\n * time is set to be the first entry of the @c time vector\n */\n template\n ModelParameterHistory(int ibegin, int isize,\n const std::vector& time,\n int ibegin_theta, int isize_theta,\n const std::vector& theta,\n int ibegin_biovar, int isize_biovar,\n const std::vector >& biovar,\n int ibegin_tlag, int isize_tlag,\n const std::vector >& tlag) :\n index(isize),\n theta_(theta),\n biovar_(biovar),\n tlag_(tlag)\n {\n for (int i = 0; i < isize; ++i) {\n int j = isize_theta > 1 ? ibegin_theta + i : ibegin_theta;\n int k = isize_biovar > 1 ? ibegin_biovar + i : ibegin_biovar;\n int l = isize_tlag > 1 ? ibegin_tlag + i : ibegin_tlag;\n index[i] = std::make_pair >(stan::math::value_of(time[ibegin + i]), {j, k, l });\n }\n Sort();\n }\n\n const T4_container& model_param(int i) const {\n return theta_[index[i].second[0]];\n }\n\n ModelParameters GetModelParameters(int i) const {\n return ModelParameters(std::get<0>(index[i]), theta_[std::get<1>(index[i])[0]], biovar_[std::get<1>(index[i])[1]], tlag_[std::get<1>(index[i])[2]]);\n }\n\n /**\n * MPV.size gives us the number of events.\n * MPV[i].RealParameters.size gives us the number of\n * ODE parameters for the ith event.\n * \n * FIX ME - rename this GetValueTheta\n */\n inline const T4& GetValue(int iEvent, int iParameter) const {\n return theta_[std::get<1>(index[iEvent])[0]][iParameter];\n }\n\n inline const T5& GetValueBio(int iEvent, int iParameter) const {\n return biovar_[std::get<1>(index[iEvent])[1]][iParameter];\n }\n\n inline const T6& GetValueTlag(int iEvent, int iParameter) const {\n return tlag_[std::get<1>(index[iEvent])[2]][iParameter];\n }\n\n inline int get_size() const {\n return index.size();\n }\n\n void Sort() {\n std::sort(index.begin(), index.end(),\n [](const Param& a, const Param& b)\n { return std::get<0>(a) < std::get<0>(b); });\n }\n\n bool Check() {\n // check that elements are in chronological order.\n int i = index.size() - 1;\n bool ordered = true;\n\n while (i > 0 && ordered) {\n ordered = (std::get<0>(index[i]) >= std::get<0>(index[i-1]));\n i--;\n }\n return ordered;\n }\n\n /**\n * COMPLETE MODEL PARAMETERS\n *\n * Completes parameters so that it contains model parameters for each event \n * in events. If parameters contains only one set of parameters (case where\n * the parameters are constant), this set is replicated for each event in\n * events. Otherwise a new parameter vector is added for each new event \n * (isnew = true). This parameter vector is identical to the parameter vector\n * at the subsequent event. If the new event occurs at a time posterior to\n * the time of the last event, than the new vector parameter equals the\n * parameter vector of the last event. This amounts to doing an LOCF\n * (Last Observation Carried Forward):\n * Three cases:\n * (a) The time of the new event is higher than the time of the last\n * parameter vector in parameters (k = len_parameters).\n * Create a parameter vector at the the time of the new event,\n * with the parameters of the last parameter vector.\n * (Last Observation Carried Forward)\n * (b) The time of the new event matches the time of a parameter vector\n * in parameters. This parameter vector gets replicated.\n * (c) (a) is not verified and no parameter vector occurs at the time\n * of the new event. A new parameter vector is created at the time\n * of the new event, and its parameters are equal to the parameters\n * of the subsequent parameter vector in parameters.\n *\n * Since both @c index and events @c index are sorted\n * in time, we always move paramtter pointer to events\n *\n * Events and Parameters are sorted at the end of the procedure.\n *\n * @param[in] parameters at each event\n * @param[in] events elements (following NONMEM convention) at each event\n * @return - modified parameters and events.\n */\n template\n void CompleteParameterHistory(torsten::EventHistory& events) {\n int nEvent = events.size();\n assert(nEvent > 0);\n int len_Parameters = index.size(); // numbers of events for which parameters are determined\n assert(len_Parameters > 0);\n\n if (!Check()) Sort();\n if (!events.Check()) events.Sort();\n index.resize(nEvent);\n\n int iEvent = 0;\n for (int i = 0; i < len_Parameters - 1; i++) {\n while (events.isnew(iEvent)) iEvent++; // skip new events\n assert(std::get<0>(index[i]) == events.time(iEvent)); // compare time of \"old' events to time of parameters.\n iEvent++;\n }\n\n if (len_Parameters == 1) {\n for (int i = 0; i < nEvent; i++) {\n index[i] = std::make_pair >(stan::math::value_of(events.time(i)) , std::array(std::get<1>(index[0])));\n events.index[i][3] = 0;\n }\n } else { // parameters are event dependent.\n std::vector times(nEvent, 0);\n for (int i = 0; i < nEvent; i++) times[i] = index[i].first;\n iEvent = 0;\n\n Param newParameter;\n int j = 0;\n std::vector::const_iterator lower = index.begin();\n std::vector::const_iterator it_param_end = index.begin() + len_Parameters;\n for (int iEvent = 0; iEvent < nEvent; ++iEvent) {\n if (events.isnew(iEvent)) {\n // Find the index corresponding to the time of the new event in the\n // times vector.\n const double t = stan::math::value_of(events.time(iEvent));\n lower = std::lower_bound(lower, it_param_end, t,\n [](const Param& t1, const double& t2) {return t1.first < t2;});\n newParameter = lower == (it_param_end) ? index[len_Parameters-1] : *lower;\n newParameter.first = t;\n index[len_Parameters + j] = newParameter;\n events.index[iEvent][3] = 0;\n j++;\n }\n }\n }\n Sort();\n }\n};\n\ntemplate\nconst bool ModelParameterHistory::has_matrix_param = is_matrix::value;\n\n}\n\n#endif\n", "meta": {"hexsha": "9ce25a307521cfdc1a400952e233ea71dc3061db", "size": 11679, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "PKModel/ModelParameters.hpp", "max_stars_repo_name": "metrumresearchgroup/torsten_math", "max_stars_repo_head_hexsha": "318cee4199342bd36d5f2b1241c6760acfcf58b7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "PKModel/ModelParameters.hpp", "max_issues_repo_name": "metrumresearchgroup/torsten_math", "max_issues_repo_head_hexsha": "318cee4199342bd36d5f2b1241c6760acfcf58b7", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2020-04-27T23:53:22.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-27T23:57:43.000Z", "max_forks_repo_path": "PKModel/ModelParameters.hpp", "max_forks_repo_name": "metrumresearchgroup/torsten_math", "max_forks_repo_head_hexsha": "318cee4199342bd36d5f2b1241c6760acfcf58b7", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8422712934, "max_line_length": 172, "alphanum_fraction": 0.6351571196, "num_tokens": 3123, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984434543457, "lm_q2_score": 0.41869690935568665, "lm_q1q2_score": 0.22404406447537317}} {"text": "#ifndef DART_NEURAL_SNAPSHOT_HPP_\n#define DART_NEURAL_SNAPSHOT_HPP_\n\n#include \n#include \n\n#include \n\n#include \"dart/neural/DifferentiableContactConstraint.hpp\"\n#include \"dart/neural/NeuralConstants.hpp\"\n#include \"dart/neural/NeuralUtils.hpp\"\n#include \"dart/neural/WithRespectTo.hpp\"\n#include \"dart/performance/PerformanceLog.hpp\"\n#include \"dart/simulation/World.hpp\"\n\nnamespace dart {\n\nusing namespace performance;\n\nnamespace neural {\n\nclass BackpropSnapshot\n{\n friend class MappedBackpropSnapshot;\n\npublic:\n /// This saves a snapshot from a forward pass, with all the info we need in\n /// order to efficiently compute a backwards pass. Crucially, the positions\n /// must all be snapshots from before the timestep, yet this constructor must\n /// be called after the timestep.\n BackpropSnapshot(\n simulation::WorldPtr world,\n Eigen::VectorXs preStepPosition,\n Eigen::VectorXs preStepVelocity,\n Eigen::VectorXs preStepTorques,\n Eigen::VectorXs preConstraintVelocities,\n Eigen::VectorXs preStepLCPCache);\n\n /// This computes the implicit backprop without forming intermediate\n /// Jacobians. It takes a LossGradient with the position and velocity vectors\n /// filled it, though the loss with respect to torque is ignored and can be\n /// null. It returns a LossGradient with all three values filled in, position,\n /// velocity, and torque.\n void backprop(\n simulation::WorldPtr world,\n LossGradient& thisTimestepLoss,\n const LossGradient& nextTimestepLoss,\n PerformanceLog* perfLog = nullptr,\n bool exploreAlternateStrategies = false);\n\n /// This computes backprop in the high-level RL API's space, use `state` and\n /// `action` as the primitives we're taking gradients wrt to.\n LossGradientHighLevelAPI backpropState(\n simulation::WorldPtr world,\n const Eigen::VectorXs& nextTimestepStateLossGrad,\n PerformanceLog* perfLog = nullptr,\n bool exploreAlternateStrategies = false);\n\n /// This zeros out any components of the gradient that would want to push us\n /// out of the box-bounds encoded in the world for pos, vel, or force.\n void clipLossGradientsToBounds(\n simulation::WorldPtr world,\n Eigen::VectorXs& lossWrtPos,\n Eigen::VectorXs& lossWrtVel,\n Eigen::VectorXs& lossWrtForce);\n\n /// This computes and returns the whole vel-vel jacobian. For backprop, you\n /// don't actually need this matrix, you can compute backprop directly. This\n /// is here if you want access to the full Jacobian for some reason.\n const Eigen::MatrixXs& getVelVelJacobian(\n simulation::WorldPtr world, PerformanceLog* perfLog = nullptr);\n\n /// This computes and returns the whole pos-vel jacobian. For backprop, you\n /// don't actually need this matrix, you can compute backprop directly. This\n /// is here if you want access to the full Jacobian for some reason.\n const Eigen::MatrixXs& getPosVelJacobian(\n simulation::WorldPtr world, PerformanceLog* perfLog = nullptr);\n\n /// This computes and returns the whole force-vel jacobian. For backprop, you\n /// don't actually need this matrix, you can compute backprop directly. This\n /// is here if you want access to the full Jacobian for some reason.\n const Eigen::MatrixXs& getControlForceVelJacobian(\n simulation::WorldPtr world, PerformanceLog* perfLog = nullptr);\n\n /// This computes and returns the whole mass-vel jacobian. For backprop, you\n /// don't actually need this matrix, you can compute backprop directly. This\n /// is here if you want access to the full Jacobian for some reason.\n const Eigen::MatrixXs& getMassVelJacobian(\n simulation::WorldPtr world, PerformanceLog* perfLog = nullptr);\n\n /// This computes and returns the whole pos-pos jacobian. For backprop, you\n /// don't actually need this matrix, you can compute backprop directly. This\n /// is here if you want access to the full Jacobian for some reason.\n const Eigen::MatrixXs& getPosPosJacobian(\n simulation::WorldPtr world, PerformanceLog* perfLog = nullptr);\n\n /// This computes and returns the whole vel-pos jacobian. For backprop, you\n /// don't actually need this matrix, you can compute backprop directly. This\n /// is here if you want access to the full Jacobian for some reason.\n const Eigen::MatrixXs& getVelPosJacobian(\n simulation::WorldPtr world, PerformanceLog* perfLog = nullptr);\n\n /// This computes and returns the component of the pos-pos and pos-vel\n /// jacobians due to bounce approximation. For backprop, you don't actually\n /// need this matrix, you can compute backprop directly. This is here if you\n /// want access to the full Jacobian for some reason.\n const Eigen::MatrixXs& getBounceApproximationJacobian(\n simulation::WorldPtr world, PerformanceLog* perfLog = nullptr);\n\n /// This returns the Jacobian for state_t -> state_{t+1}.\n Eigen::MatrixXs getStateJacobian(simulation::WorldPtr world);\n\n /// This returns the Jacobian for action_t -> state_{t+1}.\n Eigen::MatrixXs getActionJacobian(simulation::WorldPtr world);\n\n /// Returns a concatenated vector of all the Skeletons' position()'s in the\n /// World, in order in which the Skeletons appear in the World's\n /// getSkeleton(i) returns them, BEFORE the timestep.\n Eigen::VectorXs getPreStepPosition();\n\n /// Returns a concatenated vector of all the Skeletons' velocity()'s in the\n /// World, in order in which the Skeletons appear in the World's\n /// getSkeleton(i) returns them, BEFORE the timestep.\n Eigen::VectorXs getPreStepVelocity();\n\n /// Returns a concatenated vector of all the joint torques that were applied\n /// during the forward pass, BEFORE the timestep.\n Eigen::VectorXs getPreStepTorques();\n\n /// Returns a concatenated vector of all the Skeletons' velocity()'s in the\n /// World, in order in which the Skeletons appear in the World's\n /// getSkeleton(i) returns them, AFTER integrating forward dynamics but BEFORE\n /// running the LCP.\n Eigen::VectorXs getPreConstraintVelocity();\n\n /// Returns a concatenated vector of all the Skeletons' position()'s in the\n /// World, in order in which the Skeletons appear in the World's\n /// getSkeleton(i) returns them, AFTER the timestep.\n Eigen::VectorXs getPostStepPosition();\n\n /// Returns a concatenated vector of all the Skeletons' velocity()'s in the\n /// World, in order in which the Skeletons appear in the World's\n /// getSkeleton(i) returns them, AFTER the timestep.\n Eigen::VectorXs getPostStepVelocity();\n\n /// Returns a concatenated vector of all the joint torques that were applied\n /// during the forward pass, AFTER the timestep.\n Eigen::VectorXs getPostStepTorques();\n\n /// Returns the LCP's cached solution from before the step\n const Eigen::VectorXs& getPreStepLCPCache();\n\n /////////////////////////////////////////////////////////////////////////////\n /// Just public for testing\n /////////////////////////////////////////////////////////////////////////////\n\n /// This returns the A_c matrix. You shouldn't ever need this matrix, it's\n /// just here to enable testing.\n Eigen::MatrixXs getClampingConstraintMatrix(simulation::WorldPtr world);\n\n /// This returns the V_c matrix. You shouldn't ever need this matrix, it's\n /// just here to enable testing.\n Eigen::MatrixXs getMassedClampingConstraintMatrix(simulation::WorldPtr world);\n\n /// This returns the A_ub matrix. You shouldn't ever need this matrix, it's\n /// just here to enable testing.\n Eigen::MatrixXs getUpperBoundConstraintMatrix(simulation::WorldPtr world);\n\n /// This returns the V_c matrix. You shouldn't ever need this matrix, it's\n /// just here to enable testing.\n Eigen::MatrixXs getMassedUpperBoundConstraintMatrix(\n simulation::WorldPtr world);\n\n /// This returns the E matrix. You shouldn't ever need this matrix, it's\n /// just here to enable testing.\n Eigen::MatrixXs getUpperBoundMappingMatrix();\n\n /// This returns the B matrix. You shouldn't ever need this matrix, it's\n /// just here to enable testing.\n Eigen::MatrixXs getBouncingConstraintMatrix(simulation::WorldPtr world);\n\n /// This returns the mass matrix for the whole world, a block diagonal\n /// concatenation of the skeleton mass matrices.\n Eigen::MatrixXs getMassMatrix(\n simulation::WorldPtr world, bool forFiniteDifferencing = false);\n\n /// This returns the inverse mass matrix for the whole world, a block diagonal\n /// concatenation of the skeleton inverse mass matrices.\n Eigen::MatrixXs getInvMassMatrix(\n simulation::WorldPtr world, bool forFiniteDifferencing = false);\n\n /// This is the subset of the A matrix from the original LCP that corresponds\n /// to clamping indices.\n Eigen::MatrixXs getClampingAMatrix();\n\n /// This returns the pos-C(pos,vel) Jacobian for the whole world, a block\n /// diagonal concatenation of the skeleton pos-C(pos,vel) Jacobians.\n Eigen::MatrixXs getPosCJacobian(simulation::WorldPtr world);\n\n /// This returns the vel-C(pos,vel) Jacobian for the whole world, a block\n /// diagonal concatenation of the skeleton vel-C(pos,vel) Jacobians.\n Eigen::MatrixXs getVelCJacobian(simulation::WorldPtr world);\n\n /// This computes and returns the whole vel-vel jacobian by finite\n /// differences. This is SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceVelVelJacobian(\n simulation::WorldPtr world, bool useRidders = true);\n\n /// This computes and returns the whole vel-vel jacobian by Ridders\n /// extrapolated finite differences. This is SUPER DUPER SLOW,\n /// and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersVelVelJacobian(\n simulation::WorldPtr world);\n\n /// This computes and returns the whole pos-C(pos,vel) jacobian by finite\n /// differences. This is SUPER SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferencePosVelJacobian(\n simulation::WorldPtr world, bool useRidders = true);\n\n /// This computes and returns the whole pos-C(pos,vel) jacobian by Ridders\n /// extrapolated finite differences. This is SUPER DUPER SLOW,\n /// and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersPosVelJacobian(\n simulation::WorldPtr world);\n\n /// This computes and returns the whole force-vel jacobian by finite\n /// differences. This is SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceForceVelJacobian(\n simulation::WorldPtr world, bool useRidders = true);\n\n /// This computes and returns the whole force-vel jacobian by Ridders\n /// extrapolated finite differences. This is SUPER DUPER SLOW,\n /// and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersForceVelJacobian(\n simulation::WorldPtr world);\n\n /// This computes and returns the whole mass-vel jacobian by finite\n /// differences. This is SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceMassVelJacobian(\n simulation::WorldPtr world, bool useRidders = true);\n\n /// This computes and returns the whole mass-vel jacobian by Ridders\n /// extrapolated finite differences. This is SUPER DUPER SLOW,\n /// and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersMassVelJacobian(\n simulation::WorldPtr world);\n\n /// This computes and returns the whole pos-pos jacobian by finite\n /// differences. This is SUPER SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferencePosPosJacobian(\n simulation::WorldPtr world,\n std::size_t subdivisions = 20,\n bool useRidders = true);\n\n /// This computes and returns the whole pos-pos jacobian by Ridders\n /// extrapolated finite differences. This is SUPER DUPER SUPER SLOW,\n /// and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersPosPosJacobian(\n simulation::WorldPtr world, std::size_t subdivisions = 20);\n\n /// This computes and returns the whole vel-pos jacobian by finite\n /// differences. This is SUPER SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceVelPosJacobian(\n simulation::WorldPtr world,\n std::size_t subdivisions = 20,\n bool useRidders = true);\n\n /// This computes and returns the whole vel-pos jacobian by Ridders\n /// extrapolated finite differences. This is SUPER DUPER SUPER SLOW,\n /// and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersVelPosJacobian(\n simulation::WorldPtr world, std::size_t subdivisions = 20);\n\n /// This computes and returns the whole wrt-vel jacobian by finite\n /// differences. This is SUPER SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceVelJacobianWrt(\n simulation::WorldPtr world, WithRespectTo* wrt, bool useRidders = true);\n\n /// This computes and returns the whole wrt-vel jacobian by Ridders\n /// extrapolated finite differences. This is SUPER SUPER SLOW,\n /// and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersVelJacobianWrt(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This computes and returns the whole wrt-pos jacobian by finite\n /// differences. This is SUPER SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferencePosJacobianWrt(\n simulation::WorldPtr world, WithRespectTo* wrt, bool useRidders = true);\n\n /// This computes and returns the whole wrt-pos jacobian by Ridders\n /// extrapolated finite differences. This is SUPER SUPER SLOW,\n /// and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersPosJacobianWrt(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns the P_c matrix. You shouldn't ever need this matrix, it's\n /// just here to enable testing.\n Eigen::MatrixXs getProjectionIntoClampsMatrix(\n simulation::WorldPtr world, bool forFiniteDifferencing = false);\n\n /// This replaces x with the result of M*x in place, without explicitly\n /// forming M\n Eigen::VectorXs implicitMultiplyByMassMatrix(\n simulation::WorldPtr world, const Eigen::VectorXs& x);\n\n /// This replaces x with the result of Minv*x in place, without explicitly\n /// forming Minv\n Eigen::VectorXs implicitMultiplyByInvMassMatrix(\n simulation::WorldPtr world, const Eigen::VectorXs& x);\n\n /// TODO(keenon): Remove me\n Eigen::MatrixXs getScratchAnalytical(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// TODO(keenon): Remove me\n Eigen::MatrixXs getScratchFiniteDifference(\n simulation::WorldPtr world, WithRespectTo* wrt, bool useRidders = true);\n\n /// TODO(keenon): Remove me\n Eigen::MatrixXs getScratchFiniteDifferenceRidders(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This predicts what the next velocity will be using our linear algebra\n /// formula. This is only here for testing, to compare it against the actual\n /// result of a timestep.\n ///\n /// The `morePreciseButSlower` flag tells this function to do brute force\n /// steps to get constraint matrices A_c and A_ub, rather than use 1st order\n /// approximations. This is important because when we're doing\n /// finite-differencing over tiny EPS (1e-9) then tiny errors in the 1st order\n /// approximations blow up to become huge errors in gradients.\n Eigen::VectorXs getAnalyticalNextV(\n simulation::WorldPtr world, bool morePreciseButSlower = false);\n\n /// This computes and returns the whole wrt-vel jacobian. For backprop, you\n /// don't actually need this matrix, you can compute backprop directly. This\n /// is here if you want access to the full Jacobian for some reason.\n Eigen::MatrixXs getVelJacobianWrt(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This computes and returns the whole wrt-pos jacobian. For backprop, you\n /// don't actually need this matrix, you can compute backprop directly. This\n /// is here if you want access to the full Jacobian for some reason.\n Eigen::MatrixXs getPosJacobianWrt(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns the jacobian of constraint force, holding everyhing constant\n /// except the value of WithRespectTo\n Eigen::MatrixXs getJacobianOfConstraintForce(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns the analytical expression for the Jacobian of Q*b, holding b\n /// constant, if there are some upper-bound indices\n Eigen::MatrixXs dQ_WithUB(\n simulation::WorldPtr world,\n Eigen::MatrixXs& Minv,\n Eigen::MatrixXs& A_c,\n Eigen::MatrixXs& E,\n Eigen::MatrixXs& A_c_ub_E,\n Eigen::VectorXs rhs,\n WithRespectTo* wrt);\n /// This returns the analytical expression for the Jacobian of Q^T*b, holding\n /// b constant, if there are some upper-bound indices\n Eigen::MatrixXs dQT_WithUB(\n simulation::WorldPtr world,\n Eigen::MatrixXs& Minv,\n Eigen::MatrixXs& A_c,\n Eigen::MatrixXs& E,\n Eigen::MatrixXs& A_ub,\n Eigen::VectorXs rhs,\n WithRespectTo* wrt);\n /// This returns the analytical expression for the Jacobian of Q*b, holding b\n /// constant, if there are no upper-bound indices\n Eigen::MatrixXs dQ_WithoutUB(\n simulation::WorldPtr world,\n Eigen::MatrixXs& Minv,\n Eigen::MatrixXs& A_c,\n Eigen::VectorXs rhs,\n WithRespectTo* wrt);\n\n /// This returns the jacobian of Qb, holding b constant, with respect to\n /// wrt, by finite differencing\n Eigen::MatrixXs finiteDifferenceJacobianOfQb(\n simulation::WorldPtr world,\n Eigen::VectorXs b,\n WithRespectTo* wrt,\n bool useRidders = true);\n\n /// This returns the jacobian of Qb, holding b constant, with respect to\n /// wrt, by finite differencing\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfQb(\n simulation::WorldPtr world, Eigen::VectorXs b, WithRespectTo* wrt);\n\n /// This returns the vector of constants that get added to the diagonal of Q\n /// to guarantee that Q is full-rank\n Eigen::VectorXs getConstraintForceMixingDiagonal();\n\n /// This returns the jacobian of Q^{-1}b, holding b constant, with respect to\n /// wrt\n Eigen::MatrixXs getJacobianOfLCPConstraintMatrixClampingSubset(\n simulation::WorldPtr world, Eigen::VectorXs b, WithRespectTo* wrt);\n\n /// This returns the jacobian of Q^{-1}b, holding b constant, with respect to\n /// wrt, by finite differencing\n Eigen::MatrixXs finiteDifferenceJacobianOfLCPConstraintMatrixClampingSubset(\n simulation::WorldPtr world,\n Eigen::VectorXs b,\n WithRespectTo* wrt,\n bool useRidders = true);\n\n /// This returns the jacobian of Q^{-1}b, holding b constant, with respect to\n /// wrt, by Ridders extrapolated finite differencing\n Eigen::MatrixXs\n finiteDifferenceRiddersJacobianOfLCPConstraintMatrixClampingSubset(\n simulation::WorldPtr world, Eigen::VectorXs b, WithRespectTo* wrt);\n\n /// This returns the jacobian of b (from Q^{-1}b) with respect to wrt\n Eigen::MatrixXs getJacobianOfLCPOffsetClampingSubset(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns the jacobian of b (from Q^{-1}b) with respect to wrt, by\n /// finite differencing\n Eigen::MatrixXs finiteDifferenceJacobianOfLCPOffsetClampingSubset(\n simulation::WorldPtr world, WithRespectTo* wrt, bool useRidders = true);\n\n /// This returns the jacobian of b (from Q^{-1}b) with respect to wrt, by\n /// Ridders extrapolated finite differencing\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfLCPOffsetClampingSubset(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns the jacobian of b (from Q^{-1}b) with respect to wrt, by\n /// finite differencing\n Eigen::MatrixXs finiteDifferenceJacobianOfLCPEstimatedOffsetClampingSubset(\n simulation::WorldPtr world, WithRespectTo* wrt, bool useRidders = true);\n\n /// This returns the jacobian of b (from Q^{-1}b) with respect to wrt, by\n /// Ridders extrapolated finite differencing\n Eigen::MatrixXs\n finiteDifferenceRiddersJacobianOfLCPEstimatedOffsetClampingSubset(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns the subset of the A matrix used by the original LCP for just\n /// the clamping constraints. It relates constraint force to constraint\n /// acceleration. It's a mass matrix, just in a weird frame.\n void computeLCPConstraintMatrixClampingSubset(\n simulation::WorldPtr world,\n Eigen::MatrixXs& Q,\n const Eigen::MatrixXs& A_c,\n const Eigen::MatrixXs& A_ub,\n const Eigen::MatrixXs& E);\n\n /// This returns the subset of the b vector used by the original LCP for just\n /// the clamping constraints. It's just the relative velocity at the clamping\n /// contact points.\n void computeLCPOffsetClampingSubset(\n simulation::WorldPtr world,\n Eigen::VectorXs& b,\n const Eigen::MatrixXs& A_c);\n\n /// This computes and returns an estimate of the constraint impulses for the\n /// clamping constraints. This is based on a linear approximation of the\n /// constraint impulses.\n Eigen::VectorXs estimateClampingConstraintImpulses(\n simulation::WorldPtr world,\n const Eigen::MatrixXs& A_c,\n const Eigen::MatrixXs& A_ub,\n const Eigen::MatrixXs& E);\n\n /// This returns the jacobian of P_c * v, holding everyhing constant except\n /// the value of WithRespectTo\n Eigen::MatrixXs getJacobianOfProjectionIntoClampsMatrix(\n simulation::WorldPtr world, Eigen::VectorXs v, WithRespectTo* wrt);\n\n /// This computes and returns the jacobian of P_c * v by finite\n /// differences. This is SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceJacobianOfProjectionIntoClampsMatrix(\n simulation::WorldPtr world,\n Eigen::VectorXs v,\n WithRespectTo* wrt,\n bool useRidders = true);\n\n /// This computes and returns the jacobian of P_c * v by Ridders extrapolated\n /// finite differences. This is SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfProjectionIntoClampsMatrix(\n simulation::WorldPtr world, Eigen::VectorXs v, WithRespectTo* wrt);\n\n /// This returns the jacobian of M^{-1}(pos, inertia) * tau, holding\n /// everything constant except the value of WithRespectTo\n Eigen::MatrixXs getJacobianOfMinv(\n simulation::WorldPtr world, Eigen::VectorXs tau, WithRespectTo* wrt);\n\n /// This computes and returns the jacobian of M^{-1}(pos, inertia) * tau by\n /// finite differences. This is SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceJacobianOfMinv(\n simulation::WorldPtr world,\n Eigen::VectorXs tau,\n WithRespectTo* wrt,\n bool useRidders = true);\n\n /// This computes and returns the jacobian of M^{-1}(pos, inertia) * tau by\n /// Ridders extrapolated finite differences. This is SUPER SLOW, and is\n /// only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfMinv(\n simulation::WorldPtr world, Eigen::VectorXs tau, WithRespectTo* wrt);\n\n /// This returns the jacobian of M(pos, inertia) * v, holding\n /// everything constant except the value of WithRespectTo\n Eigen::MatrixXs getJacobianOfM(\n simulation::WorldPtr world, Eigen::VectorXs v, WithRespectTo* wrt);\n\n /// This computes and returns the jacobian of M(pos, inertia) * v by\n /// finite differences. This is SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceJacobianOfM(\n simulation::WorldPtr world,\n Eigen::VectorXs v,\n WithRespectTo* wrt,\n bool useRidders = true);\n\n /// This computes and returns the jacobian of M(pos, inertia) * v by\n /// Ridders extrapolated finite differences. This is SUPER SLOW, and is\n /// only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfM(\n simulation::WorldPtr world, Eigen::VectorXs v, WithRespectTo* wrt);\n\n /// This returns the jacobian of C(pos, inertia, vel), holding everything\n /// constant except the value of WithRespectTo\n Eigen::MatrixXs getJacobianOfC(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns the jacobian of C(pos, inertia, vel), holding everything\n /// constant except the value of WithRespectTo\n Eigen::MatrixXs computeJacobianOfC(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This computes and returns the jacobian of C(pos, inertia, vel) by finite\n /// differences. This is SUPER SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceJacobianOfC(\n simulation::WorldPtr world, WithRespectTo* wrt, bool useRidders = true);\n\n /// This computes and returns the jacobian of C(pos, inertia, vel) by Ridders\n /// extrapolated finite differences. This is SUPER SLOW, and is only here\n /// for testing.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfC(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns the jacobian of M^{-1}(pos, inertia) * (C(pos, inertia, vel)\n /// + mPreStepTorques), holding everything constant except the value of\n /// WithRespectTo\n Eigen::MatrixXs getJacobianOfMinvC(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This computes and returns the jacobian of M^{-1}(pos, inertia) * C(pos,\n /// inertia, vel) by finite differences. This is SUPER SLOW, and is only here\n /// for testing.\n Eigen::MatrixXs finiteDifferenceJacobianOfMinvC(\n simulation::WorldPtr world, WithRespectTo* wrt, bool useRidders = false);\n\n /// This computes and returns the jacobian of M^{-1}(pos, inertia) * C(pos,\n /// inertia, vel) by Ridders extrapolated finite differences. This is SUPER\n /// SLOW, and is only here for testing.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfMinvC(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns a fast approximation to A_c in the neighborhood of the\n /// original\n Eigen::MatrixXs estimateClampingConstraintMatrixAt(\n simulation::WorldPtr world, Eigen::VectorXs pos);\n\n /// This returns a fast approximation to A_ub in the neighborhood of the\n /// original\n Eigen::MatrixXs estimateUpperBoundConstraintMatrixAt(\n simulation::WorldPtr world, Eigen::VectorXs pos);\n\n /// Only for testing: VERY SLOW. This returns the actual value of A_c at the\n /// desired position.\n Eigen::MatrixXs getClampingConstraintMatrixAt(\n simulation::WorldPtr world, Eigen::VectorXs pos);\n\n /// Only for testing: VERY SLOW. This returns the actual value of A_ub at the\n /// desired position.\n Eigen::MatrixXs getUpperBoundConstraintMatrixAt(\n simulation::WorldPtr world, Eigen::VectorXs pos);\n\n /// Only for testing: VERY SLOW. This returns the actual value of E at the\n /// desired position.\n Eigen::MatrixXs getUpperBoundMappingMatrixAt(\n simulation::WorldPtr world, Eigen::VectorXs pos);\n\n /// Only for testing: VERY SLOW. This returns the actual value of the bounce\n /// diagonals at the desired position.\n Eigen::VectorXs getBounceDiagonalsAt(\n simulation::WorldPtr world, Eigen::VectorXs pos);\n\n /// This computes the Jacobian of A_c*f0 with respect to position using\n /// impulse tests.\n Eigen::MatrixXs getJacobianOfClampingConstraints(\n simulation::WorldPtr world, Eigen::VectorXs f0);\n\n /// This computes the finite difference Jacobian of A_c*f0 with respect to\n /// position. This is AS SLOW AS FINITE DIFFERENCING THE WHOLE ENGINE, which\n /// is way too slow to use in practice.\n Eigen::MatrixXs finiteDifferenceJacobianOfClampingConstraints(\n simulation::WorldPtr world, Eigen::VectorXs f0, bool useRidders = true);\n\n /// This computes the finite difference Jacobian of A_c*f0 with respect to\n /// position. This is AS SLOW AS FINITE DIFFERENCING THE WHOLE ENGINE, which\n /// is way too slow to use in practice. Using Ridders, so even slower.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfClampingConstraints(\n simulation::WorldPtr world, Eigen::VectorXs f0);\n\n /// This computes the Jacobian of A_c^T*v0 with respect to position using\n /// impulse tests.\n Eigen::MatrixXs getJacobianOfClampingConstraintsTranspose(\n simulation::WorldPtr world, Eigen::VectorXs v0);\n\n /// This computes the finite difference Jacobian of A_c^T*v0 with respect to\n /// position. This is AS SLOW AS FINITE DIFFERENCING THE WHOLE ENGINE, which\n /// is way too slow to use in practice.\n Eigen::MatrixXs finiteDifferenceJacobianOfClampingConstraintsTranspose(\n simulation::WorldPtr world, Eigen::VectorXs v0, bool useRidders = true);\n\n /// This computes the finite difference Jacobian of A_c^T*v0 with respect to\n /// position. This is AS SLOW AS FINITE DIFFERENCING THE WHOLE ENGINE, which\n /// is way too slow to use in practice. Using Ridders, so even slower.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfClampingConstraintsTranspose(\n simulation::WorldPtr world, Eigen::VectorXs v0);\n\n /// This computes the Jacobian of A_ub*(E*f0) with respect to position using\n /// impulse tests.\n Eigen::MatrixXs getJacobianOfUpperBoundConstraints(\n simulation::WorldPtr world, Eigen::VectorXs E_f0);\n\n /// This computes the finite difference Jacobian of A_ub*E*f0 with respect to\n /// position. This is AS SLOW AS FINITE DIFFERENCING THE WHOLE ENGINE, which\n /// is way too slow to use in practice.\n Eigen::MatrixXs finiteDifferenceJacobianOfUpperBoundConstraints(\n simulation::WorldPtr world, Eigen::VectorXs f0, bool useRidders = true);\n\n /// This computes the finite difference Jacobian of A_ub*E*f0 with respect to\n /// position. This is AS SLOW AS FINITE DIFFERENCING THE WHOLE ENGINE, which\n /// is way too slow to use in practice. Uses Ridders method.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfUpperBoundConstraints(\n simulation::WorldPtr world, Eigen::VectorXs f0);\n\n /// This computes the Jacobian of A_ub^T*v0 with respect to position using\n /// impulse tests.\n Eigen::MatrixXs getJacobianOfUpperBoundConstraintsTranspose(\n simulation::WorldPtr world, Eigen::VectorXs v0);\n\n /// This returns the jacobian of constraint force, holding everything constant\n /// except the value of WithRespectTo\n Eigen::MatrixXs finiteDifferenceJacobianOfConstraintForce(\n simulation::WorldPtr world, WithRespectTo* wrt, bool useRidders = true);\n\n /// This returns the jacobian of constraint force, holding everything constant\n /// except the value of WithRespectTo. Uses Ridders method.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfConstraintForce(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// This returns the jacobian of estimated constraint force, without actually\n /// running forward passes, holding everyhing constant except the value of\n /// WithRespectTo\n Eigen::MatrixXs finiteDifferenceJacobianOfEstimatedConstraintForce(\n simulation::WorldPtr world, WithRespectTo* wrt, bool useRidders = true);\n\n /// This returns the jacobian of estimated constraint force, without actually\n /// running forward passes, holding everyhing constant except the value of\n /// WithRespectTo. Uses Ridders method.\n Eigen::MatrixXs finiteDifferenceRiddersJacobianOfEstimatedConstraintForce(\n simulation::WorldPtr world, WithRespectTo* wrt);\n\n /// These was the mX() vector used to construct this. Pretty much only here\n /// for testing.\n Eigen::VectorXs getContactConstraintImpulses();\n\n /// These was the fIndex() vector used to construct this. Pretty much only\n /// here for testing.\n Eigen::VectorXi getContactConstraintMappings();\n\n /// Returns the vector of the coefficients on the diagonal of the bounce\n /// matrix. These are 1+restitutionCoeff[i].\n Eigen::VectorXs getBounceDiagonals();\n\n /// Returns the vector of the restitution coeffs, sized for the number of\n /// bouncing collisions.\n Eigen::VectorXs getRestitutionDiagonals();\n\n /// Returns the penetration correction hack \"bounce\" (or 0 if the contact is\n /// not inter-penetrating or is actively bouncing) at each contact point.\n Eigen::VectorXs getPenetrationCorrectionVelocities();\n\n /// Returns the constraint impulses along the clamping constraints\n Eigen::VectorXs getClampingConstraintImpulses();\n\n /// Returns the relative velocities along the clamping constraints\n Eigen::VectorXs getClampingConstraintRelativeVels();\n\n /// Returns the velocity change caused by illegal impulses in the LCP this\n /// timestep\n Eigen::VectorXs getVelocityDueToIllegalImpulses();\n\n /// Returns the velocity pre-LCP\n Eigen::VectorXs getPreLCPVelocity();\n\n /// Returns true if there were any bounces in this snapshot.\n bool hasBounces();\n\n /// Returns the number of contacts (regardless of state) in this snapshot.\n std::size_t getNumContacts();\n\n /// Returns the number of clamping contacts in this snapshot.\n std::size_t getNumClamping();\n\n /// Returns the number of upper bound contacts in this snapshot.\n std::size_t getNumUpperBound();\n\n /// These are the gradient constraint matrices from the LCP solver\n std::vector>\n mGradientMatrices;\n\n /// This is the clamping constraints from all the constrained\n /// groups, concatenated together\n std::vector>\n getDifferentiableConstraints();\n\n /// This is the clamping constraints from all the constrained\n /// groups, concatenated together\n std::vector>\n getClampingConstraints();\n\n /// This is the upper bound constraints from all the constrained\n /// groups, concatenated together\n std::vector>\n getUpperBoundConstraints();\n\n /// This verifies that the two matrices are equal to some tolerance, and if\n /// they're not it prints the information needed to replicated this scenario\n /// and it exits the program.\n void equalsOrCrash(\n std::shared_ptr world,\n Eigen::MatrixXs analytical,\n Eigen::MatrixXs bruteForce,\n std::string name);\n\n /// This compares our analytical sub-Jacobians (like dMinv), to attempt to\n /// diagnose where there are differences creeping in between our finite\n /// differencing and our analytical results.\n void diagnoseSubJacobianErrors(\n std::shared_ptr world, WithRespectTo* wrt);\n\n /// This prints code to the console to replicate a scenario for testing.\n void printReplicationInstructions(std::shared_ptr world);\n\n /// Returns true if we were able to standardize our LCP results, false if we\n /// weren't\n bool areResultsStandardized() const;\n\n /// If this is true, we use finite-differencing to compute all of the\n /// requested Jacobians. This override can be useful to verify if there's a\n /// bug in the analytical Jacobians that's causing learning to not converge.\n void setUseFDOverride(bool override);\n\n /// If this is true, we check all Jacobians against their finite-differencing\n /// counterparts at runtime. If they aren't sufficiently close, we immediately\n /// crash the program and print what went wrong and some simple replication\n /// instructions.\n void setSlowDebugResultsAgainstFD(bool slowDebug);\n\n /// This does a battery of tests comparing the speeds to compute all the\n /// different Jacobians, both with finite differencing and analytically, and\n /// prints the results to std out.\n void benchmarkJacobians(\n std::shared_ptr world, int numSamples);\n\nprotected:\n /// If this is true, we use finite-differencing to compute all of the\n /// requested Jacobians. This override can be useful to verify if there's a\n /// bug in the analytical Jacobians that's causing learning to not converge.\n bool mUseFDOverride;\n\n /// If this is true, we check all Jacobians against their finite-differencing\n /// counterparts at runtime. If they aren't sufficiently close, we immediately\n /// crash the program and print what went wrong and some simple replication\n /// instructions.\n bool mSlowDebugResultsAgainstFD;\n\n /// This is the global timestep length. This is included here because it shows\n /// up as a constant in some of the matrices.\n s_t mTimeStep;\n\n /// This is the total DOFs for this World\n std::size_t mNumDOFs;\n\n /// This is the number of total dimensions on all the constraints active in\n /// the world\n std::size_t mNumConstraintDim;\n\n /// This is the number of total constraint dimensions that are clamping\n std::size_t mNumClamping;\n\n /// This is the number of total constraint dimensions that are upper bounded\n std::size_t mNumUpperBound;\n\n /// This is the number of total constraint dimensions that are upper bounded\n std::size_t mNumBouncing;\n\n /// These are the offsets into the total degrees of freedom for each skeleton\n std::unordered_map mSkeletonOffset;\n\n /// These are the number of degrees of freedom for each skeleton\n std::unordered_map mSkeletonDofs;\n\n /// The position of all the DOFs of the world BEFORE the timestep\n Eigen::VectorXs mPreStepPosition;\n\n /// The velocities of all the DOFs of the world BEFORE the timestep\n Eigen::VectorXs mPreStepVelocity;\n\n /// The torques on all the DOFs of the world BEFORE the timestep\n Eigen::VectorXs mPreStepTorques;\n\n /// The LCP's initial cached value BEFORE the timestep\n Eigen::VectorXs mPreStepLCPCache;\n\n /// The velocities of all the DOFs of the world AFTER an unconstrained forward\n /// step, but BEFORE the LCP runs\n Eigen::VectorXs mPreConstraintVelocities;\n\n /// The position of all the DOFs of the world AFTER the timestep\n Eigen::VectorXs mPostStepPosition;\n\n /// The velocities of all the DOFs of the world AFTER the timestep\n /// created\n Eigen::VectorXs mPostStepVelocity;\n\n /// The torques on all the DOFs of the world AFTER the timestep\n Eigen::VectorXs mPostStepTorques;\n\nprivate:\n /// These are mCached versions of the various Jacobians\n bool mCachedPosPosDirty;\n Eigen::MatrixXs mCachedPosPos;\n bool mCachedPosVelDirty;\n Eigen::MatrixXs mCachedPosVel;\n bool mCachedBounceApproximationDirty;\n Eigen::MatrixXs mCachedBounceApproximation;\n bool mCachedVelPosDirty;\n Eigen::MatrixXs mCachedVelPos;\n bool mCachedVelVelDirty;\n Eigen::MatrixXs mCachedVelVel;\n bool mCachedForcePosDirty;\n Eigen::MatrixXs mCachedForcePos;\n bool mCachedForceVelDirty;\n Eigen::MatrixXs mCachedForceVel;\n bool mCachedMassVelDirty;\n Eigen::MatrixXs mCachedMassVel;\n bool mCachedPosCDirty;\n Eigen::MatrixXs mCachedPosC;\n bool mCachedVelCDirty;\n Eigen::MatrixXs mCachedVelC;\n\n Eigen::VectorXs scratch(simulation::WorldPtr world);\n\n enum MatrixToAssemble\n {\n CLAMPING,\n MASSED_CLAMPING,\n UPPER_BOUND,\n MASSED_UPPER_BOUND,\n BOUNCING\n };\n\n Eigen::MatrixXs assembleMatrix(\n simulation::WorldPtr world, MatrixToAssemble whichMatrix);\n\n enum BlockDiagonalMatrixToAssemble\n {\n MASS,\n INV_MASS,\n POS_C,\n VEL_C\n };\n\n Eigen::MatrixXs assembleBlockDiagonalMatrix(\n simulation::WorldPtr world,\n BlockDiagonalMatrixToAssemble whichMatrix,\n bool forFiniteDifferencing = false);\n\n enum VectorToAssemble\n {\n CONTACT_CONSTRAINT_IMPULSES,\n CONTACT_CONSTRAINT_MAPPINGS,\n BOUNCE_DIAGONALS,\n RESTITUTION_DIAGONALS,\n PENETRATION_VELOCITY_HACK,\n CLAMPING_CONSTRAINT_IMPULSES,\n CLAMPING_CONSTRAINT_RELATIVE_VELS,\n VEL_DUE_TO_ILLEGAL,\n PRE_STEP_VEL,\n PRE_STEP_TAU,\n PRE_LCP_VEL,\n CFM_CONSTANTS\n };\n template \n Vec assembleVector(VectorToAssemble whichVector);\n\n template \n const Vec& getVectorToAssemble(\n std::shared_ptr matrices,\n VectorToAssemble whichVector);\n};\n\nusing BackpropSnapshotPtr = std::shared_ptr;\n\n} // namespace neural\n} // namespace dart\n\n#endif", "meta": {"hexsha": "66e552b705006348347141b3cef45f95c004dd71", "size": 39514, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "dart/neural/BackpropSnapshot.hpp", "max_stars_repo_name": "jyf588/nimblephysics", "max_stars_repo_head_hexsha": "6c09228f0abcf7aa3526a8dd65cd2541aff32c4a", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2021-09-30T06:23:29.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T09:59:09.000Z", "max_issues_repo_path": "dart/neural/BackpropSnapshot.hpp", "max_issues_repo_name": "jyf588/nimblephysics", "max_issues_repo_head_hexsha": "6c09228f0abcf7aa3526a8dd65cd2541aff32c4a", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dart/neural/BackpropSnapshot.hpp", "max_forks_repo_name": "jyf588/nimblephysics", "max_forks_repo_head_hexsha": "6c09228f0abcf7aa3526a8dd65cd2541aff32c4a", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-08-20T13:56:14.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-20T13:56:14.000Z", "avg_line_length": 43.0435729847, "max_line_length": 80, "alphanum_fraction": 0.7396365845, "num_tokens": 9894, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.66192288918838, "lm_q2_score": 0.33807711081162, "lm_q1q2_score": 0.22378097795688764}} {"text": "// This file is part of the dune-gdt project:\n// http://users.dune-project.org/projects/dune-gdt\n// Copyright holders: Felix Schindler\n// License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)\n//\n// Contributors: Kirsten Weber\n\n#ifndef DUNE_GDT_EVALUATION_ELLIPTIC_HH\n#define DUNE_GDT_EVALUATION_ELLIPTIC_HH\n\n#include \n\n#include \n\n#include \n#include \n\n#include \n\n#include \"interface.hh\"\n\nnamespace Dune {\nnamespace GDT {\nnamespace LocalEvaluation {\n\n\n// forward\ntemplate< class DiffusionFactorImp, class DiffusionTensorImp = void >\nclass Elliptic;\n\n\nnamespace internal {\n\n\n/**\n * \\brief Traits for the Elliptic evaluation (variant for given diffusion factor and tensor).\n * \\sa EllipticTraits (below) for a variant if only a diffusion is given.\n */\ntemplate< class DiffusionFactorType, class DiffusionTensorType >\nclass EllipticTraits\n{\n static_assert(Stuff::is_localizable_function< DiffusionFactorType >::value,\n \"DiffusionFactorType has to be a localizable function!\");\n static_assert(Stuff::is_localizable_function< DiffusionTensorType >::value,\n \"DiffusionTensorType has to be a localizable function!\");\n static_assert(std::is_same< typename DiffusionFactorType::EntityType,\n typename DiffusionTensorType::EntityType >::value,\n \"EntityTypes have to agree!\");\n static_assert(std::is_same< typename DiffusionFactorType::DomainFieldType,\n typename DiffusionTensorType::DomainFieldType >::value,\n \"DomainFieldTypes have to agree!\");\n static_assert(DiffusionFactorType::dimDomain == DiffusionTensorType::dimDomain,\n \"Dimensions have to agree!\");\npublic:\n typedef Elliptic< DiffusionFactorType, DiffusionTensorType > derived_type;\n typedef std::tuple< std::shared_ptr< typename DiffusionFactorType::LocalfunctionType >,\n std::shared_ptr< typename DiffusionTensorType::LocalfunctionType > > LocalfunctionTupleType;\n typedef typename DiffusionFactorType::EntityType EntityType;\n typedef typename DiffusionFactorType::DomainFieldType DomainFieldType;\n static const size_t dimDomain = DiffusionFactorType::dimDomain;\n}; // class EllipticTraits\n\n\n/**\n * \\brief Traits for the Elliptic evaluation (variant for a given diffusion).\n * \\note It does not matter if that function plays the role of the diffusion factor (scalar) or the diffusion\n * tensor (matrix).\n * \\sa EllipticTraits (above) for a variant if a diffusion factor and a diffusion tensor is given.\n */\ntemplate< class DiffusionType >\nclass EllipticTraits< DiffusionType, void >\n{\n static_assert(Stuff::is_localizable_function< DiffusionType >::value,\n \"DiffusionType has to be a localizable function!\");\npublic:\n typedef Elliptic< DiffusionType, void > derived_type;\n typedef typename DiffusionType::EntityType EntityType;\n typedef typename DiffusionType::DomainFieldType DomainFieldType;\n typedef std::tuple< std::shared_ptr< typename DiffusionType::LocalfunctionType > > LocalfunctionTupleType;\n static const size_t dimDomain = DiffusionType::dimDomain;\n}; // class EllipticTraits< ..., void >\n\n\n} // namespace internal\n\n\n/**\n * \\brief Computes an elliptic evaluation (variant for given diffusion factor and tensor).\n * \\sa Elliptic (below) for a variant if only a diffusion is given.\n */\ntemplate< class DiffusionFactorImp, class DiffusionTensorImp >\nclass Elliptic\n : public LocalEvaluation::Codim0Interface< internal::EllipticTraits< DiffusionFactorImp, DiffusionTensorImp >, 2 >\n{\npublic:\n typedef DiffusionFactorImp DiffusionFactorType;\n typedef DiffusionTensorImp DiffusionTensorType;\n typedef internal::EllipticTraits< DiffusionFactorType, DiffusionTensorType > Traits;\n typedef typename Traits::LocalfunctionTupleType LocalfunctionTupleType;\n typedef typename Traits::EntityType EntityType;\n typedef typename Traits::DomainFieldType DomainFieldType;\n static const size_t dimDomain = Traits::dimDomain;\n\n Elliptic(const DiffusionFactorType& diffusion_factor,\n const DiffusionTensorType& diffusion_tensor)\n : diffusion_factor_(diffusion_factor)\n , diffusion_tensor_(diffusion_tensor)\n {}\n\n LocalfunctionTupleType localFunctions(const EntityType& entity) const\n {\n return std::make_tuple(diffusion_factor_.local_function(entity),\n diffusion_tensor_.local_function(entity));\n }\n\n /**\n * \\brief extracts the local functions and calls the correct order() method\n */\n template< class R, size_t rT, size_t rCT, size_t rA, size_t rCA >\n size_t order(const LocalfunctionTupleType& local_functions_tuple,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rT, rCT >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rA, rCA >& ansatzBase) const\n {\n const auto local_diffusion_factor = std::get< 0 >(local_functions_tuple);\n const auto local_diffusion_tensor = std::get< 1 >(local_functions_tuple);\n return order(*local_diffusion_factor, *local_diffusion_tensor, testBase, ansatzBase);\n } // ... order(...)\n\n /**\n * \\brief extracts the local functions and calls the correct evaluate() method\n */\n template< class R, size_t rT, size_t rCT, size_t rA, size_t rCA >\n void evaluate(const LocalfunctionTupleType& local_functions_tuple,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rT, rCT >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rA, rCA >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n const auto local_diffusion_factor = std::get< 0 >(local_functions_tuple);\n const auto local_diffusion_tensor = std::get< 1 >(local_functions_tuple);\n evaluate(*local_diffusion_factor, *local_diffusion_tensor, testBase, ansatzBase, localPoint, ret);\n }\n\nprivate:\n template< class R, size_t rDF, size_t rCDF, size_t rDT, size_t rCDT, size_t rT, size_t rCT, size_t rA, size_t rCA >\n size_t order(const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, rDF, rCDF >& local_diffusion_factor,\n const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, rDT, rCDT >& local_diffusion_tensor,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rT, rCT >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rA, rCA >& ansatzBase) const\n {\n return local_diffusion_factor.order()\n + local_diffusion_tensor.order()\n + std::max(ssize_t(testBase.order()) - 1, ssize_t(0))\n + std::max(ssize_t(ansatzBase.order()) - 1, ssize_t(0));\n } // ... order(...)\n\n template< class R, size_t r >\n void evaluate(const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& local_diffusion_factor,\n const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& local_diffusion_tensor,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, r, 1 >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, r, 1 >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n // evaluate local functions\n const auto local_diffusion_factor_value = local_diffusion_factor.evaluate(localPoint);\n const auto local_diffusion_tensor_value = local_diffusion_tensor.evaluate(localPoint);\n // evaluate test gradient\n const auto rows = testBase.size();\n const auto testGradients = testBase.jacobian(localPoint);\n // evaluate ansatz gradient\n const auto cols = ansatzBase.size();\n const auto ansatzGradients = ansatzBase.jacobian(localPoint);\n // compute products\n assert(ret.rows() >= rows);\n assert(ret.cols() >= cols);\n for (size_t ii = 0; ii < rows; ++ii) {\n auto& retRow = ret[ii];\n for (size_t jj = 0; jj < cols; ++jj) {\n retRow[jj] = local_diffusion_factor_value * local_diffusion_tensor_value\n * (ansatzGradients[jj][0] * testGradients[ii][0]);\n }\n }\n } // ... evaluate(...)\n\n template< class R >\n void evaluate(const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& local_diffusion_factor,\n const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, 2, 2 >& local_diffusion_tensor,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n evaluate_matrix_valued_(local_diffusion_factor, local_diffusion_tensor, testBase, ansatzBase, localPoint, ret);\n }\n\n template< class R >\n void evaluate(const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& local_diffusion_factor,\n const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, 3, 3 >& local_diffusion_tensor,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n evaluate_matrix_valued_(local_diffusion_factor, local_diffusion_tensor, testBase, ansatzBase, localPoint, ret);\n }\n\n template< class R >\n void evaluate_matrix_valued_(const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& local_diffusion_factor,\n const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, dimDomain, dimDomain >& local_diffusion_tensor,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n typedef typename Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >::JacobianRangeType JacobianRangeType;\n // evaluate local functions\n const auto local_diffusion_factor_value = local_diffusion_factor.evaluate(localPoint);\n auto local_diffusion_tensor_value = local_diffusion_tensor.evaluate(localPoint);\n local_diffusion_tensor_value *= local_diffusion_factor_value[0];\n // evaluate test gradient\n const size_t rows = testBase.size();\n std::vector< JacobianRangeType > testGradients(rows, JacobianRangeType(0));\n testBase.jacobian(localPoint, testGradients);\n // evaluate ansatz gradient\n const size_t cols = ansatzBase.size();\n std::vector< JacobianRangeType > ansatzGradients(cols, JacobianRangeType(0));\n ansatzBase.jacobian(localPoint, ansatzGradients);\n // compute products\n assert(ret.rows() >= rows);\n assert(ret.cols() >= cols);\n FieldVector< DomainFieldType, dimDomain > product(0.0);\n for (size_t ii = 0; ii < rows; ++ii) {\n auto& retRow = ret[ii];\n for (size_t jj = 0; jj < cols; ++jj) {\n local_diffusion_tensor_value.mv(ansatzGradients[jj][0], product);\n retRow[jj] = product * testGradients[ii][0];\n }\n }\n } // ... evaluate_matrix_valued_(...)\n\n const DiffusionFactorType& diffusion_factor_;\n const DiffusionTensorType& diffusion_tensor_;\n}; // class Elliptic\n\n\n/**\n * \\brief Computes an elliptic evaluation (variant for a given diffusion).\n * \\note It does not matter if that function plays the role of the diffusion factor (scalar) or the diffusion\n * tensor (matrix).\n * \\sa Elliptic (above) for a variant if a diffusion factor and a diffusion tensor is given.\n */\ntemplate< class DiffusionImp >\nclass Elliptic< DiffusionImp, void >\n : public LocalEvaluation::Codim0Interface< internal::EllipticTraits< DiffusionImp, void >, 2 >\n{\npublic:\n typedef DiffusionImp DiffusionType;\n typedef internal::EllipticTraits< DiffusionType, void > Traits;\n typedef typename Traits::LocalfunctionTupleType LocalfunctionTupleType;\n typedef typename Traits::EntityType EntityType;\n typedef typename Traits::DomainFieldType DomainFieldType;\n static const size_t dimDomain = Traits::dimDomain;\n\n explicit Elliptic(const DiffusionType& inducingFunction)\n : diffusion_(inducingFunction)\n {}\n\n /// \\name Required by LocalEvaluation::Codim0Interface< ..., 2 >\n /// \\{\n\n LocalfunctionTupleType localFunctions(const EntityType& entity) const\n {\n return std::make_tuple(diffusion_.local_function(entity));\n }\n\n /**\n * \\brief extracts the local functions and calls the correct order() method\n */\n template< class R, size_t rT, size_t rCT, size_t rA, size_t rCA >\n size_t order(const LocalfunctionTupleType& localFuncs,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rT, rCT >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rA, rCA >& ansatzBase) const\n {\n return order(*std::get< 0 >(localFuncs), testBase, ansatzBase);\n }\n\n /**\n * \\brief extracts the local functions and calls the correct evaluate() method\n */\n template< class R, size_t rT, size_t rCT, size_t rA, size_t rCA >\n void evaluate(const LocalfunctionTupleType& localFuncs,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rT, rCT >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, rA, rCA >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n evaluate(*std::get< 0 >(localFuncs), testBase, ansatzBase, localPoint, ret);\n }\n\n /// \\}\n /// \\name Actual implementations of order\n /// \\{\n\n /**\n * \\return localFunction.order() + (testBase.order() - 1) + (ansatzBase.order() - 1)\n */\n template< class R, size_t rL, size_t rCL, size_t rT, size_t rCT, size_t rA, size_t rCA >\n size_t order(const Stuff::LocalfunctionInterface< EntityType, DomainFieldType, dimDomain, R, rL, rCL >& localFunction,\n const Stuff::LocalfunctionSetInterface< EntityType, DomainFieldType, dimDomain, R, rT, rCT >& testBase,\n const Stuff::LocalfunctionSetInterface< EntityType, DomainFieldType, dimDomain, R, rA, rCA >& ansatzBase)\n const\n {\n return localFunction.order()\n + boost::numeric_cast< size_t >(std::max(ssize_t(testBase.order()) - 1, ssize_t(0)))\n + boost::numeric_cast< size_t >(std::max(ssize_t(ansatzBase.order()) - 1, ssize_t(0)));\n } // ... order( ... )\n\n /// \\}\n /// \\name Actual implementations of evaluate\n /// \\{\n\n /**\n * \\brief Computes an elliptic evaluation for a scalar local function and scalar or vector valued basefunctionsets.\n * \\tparam R RangeFieldType\n */\n template< class R, size_t r >\n void evaluate(const Stuff::LocalfunctionInterface< EntityType, DomainFieldType, dimDomain, R, 1, 1 >& localFunction,\n const Stuff::LocalfunctionSetInterface< EntityType, DomainFieldType, dimDomain, R, r, 1 >& testBase,\n const Stuff::LocalfunctionSetInterface< EntityType, DomainFieldType, dimDomain, R, r, 1 >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n typedef typename Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, r, 1 >::JacobianRangeType JacobianRangeType;\n // evaluate local function\n const auto functionValue = localFunction.evaluate(localPoint);\n // evaluate test gradient\n const size_t rows = testBase.size();\n std::vector< JacobianRangeType > testGradients(rows, JacobianRangeType(0));\n testBase.jacobian(localPoint, testGradients);\n // evaluate ansatz gradient\n const size_t cols = ansatzBase.size();\n std::vector< JacobianRangeType > ansatzGradients(cols, JacobianRangeType(0));\n ansatzBase.jacobian(localPoint, ansatzGradients);\n // compute products\n assert(ret.rows() >= rows);\n assert(ret.cols() >= cols);\n for (size_t ii = 0; ii < rows; ++ii) {\n auto& retRow = ret[ii];\n for (size_t jj = 0; jj < cols; ++jj) {\n retRow[jj] = functionValue * (ansatzGradients[jj][0] * testGradients[ii][0]);\n }\n }\n } // ... evaluate< ..., 1, ... >(...)\n\n /**\n * \\brief Computes an elliptic evaluation for a 2x2 matrix-valued local function and matrix-valued basefunctionsets.\n * \\tparam R RangeFieldType\n * \\note Unfortunately we need this explicit specialization, otherwise the compiler will complain for 1d grids.\n */\n template< class R >\n void evaluate(const Stuff::LocalfunctionInterface< EntityType, DomainFieldType, dimDomain, R, 2, 2 >& localFunction,\n const Stuff::LocalfunctionSetInterface< EntityType, DomainFieldType, dimDomain, R, 1, 1 >& testBase,\n const Stuff::LocalfunctionSetInterface< EntityType, DomainFieldType, dimDomain, R, 1, 1 >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n evaluate_matrix_valued_(localFunction, testBase, ansatzBase, localPoint, ret);\n }\n\n /**\n * \\brief Computes an elliptic evaluation for a 3x3 matrix-valued local function and matrix-valued basefunctionsets.\n * \\tparam R RangeFieldType\n * \\note Unfortunately we need this explicit specialization, otherwise the compiler will complain for 1d grids.\n */\n template< class R >\n void evaluate(const Stuff::LocalfunctionInterface< EntityType, DomainFieldType, dimDomain, R, 3, 3 >& localFunction,\n const Stuff::LocalfunctionSetInterface< EntityType, DomainFieldType, dimDomain, R, 1, 1 >& testBase,\n const Stuff::LocalfunctionSetInterface< EntityType, DomainFieldType, dimDomain, R, 1, 1 >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n evaluate_matrix_valued_(localFunction, testBase, ansatzBase, localPoint, ret);\n }\n\n /// \\}\n\nprivate:\n template< class R >\n void evaluate_matrix_valued_(const Stuff::LocalfunctionInterface\n < EntityType, DomainFieldType, dimDomain, R, dimDomain, dimDomain >& localFunction,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& testBase,\n const Stuff::LocalfunctionSetInterface\n < EntityType, DomainFieldType, dimDomain, R, 1, 1 >& ansatzBase,\n const Dune::FieldVector< DomainFieldType, dimDomain >& localPoint,\n Dune::DynamicMatrix< R >& ret) const\n {\n // evaluate local function\n const auto functionValue = localFunction.evaluate(localPoint);\n // evaluate test gradient\n const size_t rows = testBase.size();\n const auto testGradients = testBase.jacobian(localPoint);\n // evaluate ansatz gradient\n const size_t cols = ansatzBase.size();\n const auto ansatzGradients = ansatzBase.jacobian(localPoint);\n // compute products\n assert(ret.rows() >= rows);\n assert(ret.cols() >= cols);\n FieldVector< DomainFieldType, dimDomain > product(0.0);\n for (size_t ii = 0; ii < rows; ++ii) {\n auto& retRow = ret[ii];\n for (size_t jj = 0; jj < cols; ++jj) {\n functionValue.mv(ansatzGradients[jj][0], product);\n retRow[jj] = product * testGradients[ii][0];\n }\n }\n } // ... evaluate_matrix_valued_(...)\n\n const DiffusionType& diffusion_;\n}; // class Elliptic< ...., void >\n\n\n} // namespace LocalEvaluation\n} // namespace GDT\n} // namespace Dune\n\n#endif // DUNE_GDT_EVALUATION_ELLIPTIC_HH\n", "meta": {"hexsha": "ff8f2a33820b1e7af9bc7f3db6fc7b86c79f8790", "size": 21928, "ext": "hh", "lang": "C++", "max_stars_repo_path": "dune/gdt/localevaluation/elliptic.hh", "max_stars_repo_name": "ftalbrecht/dune-gdt", "max_stars_repo_head_hexsha": "574bc4a3b28d2a6a6195a6b4df6727c61f0d73c9", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, 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"lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.223769486307537}} {"text": "//---------------------------------------------------------------------------//\n// Copyright (c) 2018-2020 Mikhail Komarov \n// Copyright (c) 2020 Nikita Kaskov \n//\n// MIT License\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n//---------------------------------------------------------------------------//\n\n#ifndef CRYPTO3_BLOCK_KASUMI_HPP\n#define CRYPTO3_BLOCK_KASUMI_HPP\n\n#include \n#include \n\n#include \n\n#include \n#include \n\nnamespace nil {\n namespace crypto3 {\n namespace block {\n /*!\n * @brief Kasumi. A 64-bit cipher used in 3GPP mobile phone protocols.\n * There is no reason to use it outside of this context.\n *\n * @ingroup block\n */\n class kasumi {\n protected:\n typedef detail::kasumi_functions policy_type;\n\n constexpr static const std::size_t key_schedule_size = policy_type::key_schedule_size;\n typedef typename policy_type::key_schedule_type key_schedule_type;\n\n public:\n constexpr static const std::size_t rounds = policy_type::rounds;\n\n constexpr static const std::size_t word_bits = policy_type::word_bits;\n typedef typename policy_type::word_type word_type;\n\n constexpr static const std::size_t block_bits = policy_type::block_bits;\n constexpr static const std::size_t block_words = policy_type::block_words;\n typedef typename policy_type::block_type block_type;\n\n constexpr static const std::size_t key_bits = policy_type::key_bits;\n constexpr static const std::size_t key_words = policy_type::key_words;\n typedef typename policy_type::key_type key_type;\n\n template\n struct stream_processor {\n struct params_type {\n\n constexpr static const std::size_t value_bits = ValueBits;\n constexpr static const std::size_t length_bits = policy_type::word_bits * 2;\n };\n\n typedef block_stream_processor type;\n };\n\n typedef typename stream_endian::little_octet_big_bit endian_type;\n\n kasumi(const key_type &key) {\n schedule_key(key_schedule, key);\n }\n\n ~kasumi() {\n key_schedule.fill(0);\n }\n\n inline block_type encrypt(const block_type &plaintext) const {\n return encrypt_block(plaintext, key_schedule);\n }\n\n inline block_type decrypt(const block_type &ciphertext) const {\n return decrypt_block(ciphertext, key_schedule);\n }\n\n protected:\n inline block_type encrypt_block(const block_type &plaintext,\n const key_schedule_type &key_schedule) const {\n word_type B0 = boost::endian::native_to_big(plaintext[0]);\n word_type B1 = boost::endian::native_to_big(plaintext[1]);\n word_type B2 = boost::endian::native_to_big(plaintext[2]);\n word_type B3 = boost::endian::native_to_big(plaintext[3]);\n\n for (size_t j = 0; j != rounds; j += 2) {\n const word_type *K = &key_schedule[8 * j];\n\n word_type R = B1 ^ (policy_type::template rotl<1>(B0) & K[0]);\n word_type L = B0 ^ (policy_type::template rotl<1>(R) | K[1]);\n\n L = policy_type::FI(L ^ K[2], K[3]) ^ R;\n R = policy_type::FI(R ^ K[4], K[5]) ^ L;\n L = policy_type::FI(L ^ K[6], K[7]) ^ R;\n\n R = B2 ^= R;\n L = B3 ^= L;\n\n R = policy_type::FI(R ^ K[10], K[11]) ^ L;\n L = policy_type::FI(L ^ K[12], K[13]) ^ R;\n R = policy_type::FI(R ^ K[14], K[15]) ^ L;\n\n R ^= (policy_type::template rotl<1>(L) & K[8]);\n L ^= (policy_type::template rotl<1>(R) | K[9]);\n\n B0 ^= L;\n B1 ^= R;\n }\n\n return {boost::endian::big_to_native(B0), boost::endian::big_to_native(B1),\n boost::endian::big_to_native(B2), boost::endian::big_to_native(B3)};\n }\n\n inline block_type decrypt_block(const block_type &ciphertext,\n const key_schedule_type &key_schedule) const {\n word_type B0 = boost::endian::native_to_big(ciphertext[0]);\n word_type B1 = boost::endian::native_to_big(ciphertext[1]);\n word_type B2 = boost::endian::native_to_big(ciphertext[2]);\n word_type B3 = boost::endian::native_to_big(ciphertext[3]);\n\n for (size_t j = 0; j != rounds; j += 2) {\n const word_type *K = &key_schedule[8 * (6 - j)];\n\n word_type L = B2, R = B3;\n\n L = policy_type::FI(L ^ K[10], K[11]) ^ R;\n R = policy_type::FI(R ^ K[12], K[13]) ^ L;\n L = policy_type::FI(L ^ K[14], K[15]) ^ R;\n\n L ^= (policy_type::template rotl<1>(R) & K[8]);\n R ^= (policy_type::template rotl<1>(L) | K[9]);\n\n R = B0 ^= R;\n L = B1 ^= L;\n\n L ^= (policy_type::template rotl<1>(R) & K[0]);\n R ^= (policy_type::template rotl<1>(L) | K[1]);\n\n R = policy_type::FI(R ^ K[2], K[3]) ^ L;\n L = policy_type::FI(L ^ K[4], K[5]) ^ R;\n R = policy_type::FI(R ^ K[6], K[7]) ^ L;\n\n B2 ^= L;\n B3 ^= R;\n }\n\n return {boost::endian::big_to_native(B0), boost::endian::big_to_native(B1),\n boost::endian::big_to_native(B2), boost::endian::big_to_native(B3)};\n }\n\n key_schedule_type key_schedule;\n\n void schedule_key(key_schedule_type &key_schedule, const key_type &key) {\n std::array K = {0};\n for (size_t i = 0; i != rounds; ++i) {\n K[i] = boost::endian::native_to_big(key[i]);\n K[i + 8] = K[i] ^ policy_type::round_constants[i];\n }\n\n for (size_t i = 0; i != rounds; ++i) {\n key_schedule[8 * i] = policy_type::template rotl<2>(K[(i + 0) % 8]);\n key_schedule[8 * i + 1] = policy_type::template rotl<1>(K[(i + 2) % 8 + 8]);\n key_schedule[8 * i + 2] = policy_type::template rotl<5>(K[(i + 1) % 8]);\n key_schedule[8 * i + 3] = K[(i + 4) % 8 + 8];\n key_schedule[8 * i + 4] = policy_type::template rotl<8>(K[(i + 5) % 8]);\n key_schedule[8 * i + 5] = K[(i + 3) % 8 + 8];\n key_schedule[8 * i + 6] = policy_type::template rotl<13>(K[(i + 6) % 8]);\n key_schedule[8 * i + 7] = K[(i + 7) % 8 + 8];\n }\n\n K.fill(0);\n }\n };\n } // namespace block\n } // namespace crypto3\n} // namespace nil\n#endif\n", "meta": {"hexsha": "c6f01eb96ea94fd8d42d13fb3ea564e3e0c8bab2", "size": 9000, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/nil/crypto3/block/kasumi.hpp", "max_stars_repo_name": "NilFoundation/crypto3-block", "max_stars_repo_head_hexsha": "94f9cc42ac0fa62c5ee54e7d678abf48ffa9eec5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-09-14T18:09:38.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-14T18:09:38.000Z", "max_issues_repo_path": "include/nil/crypto3/block/kasumi.hpp", "max_issues_repo_name": "tonlabs/crypto3-block", "max_issues_repo_head_hexsha": "d7eede022f6130797d28bc39eb312bff9afebf07", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 41.0, "max_issues_repo_issues_event_min_datetime": "2019-06-07T23:11:20.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-16T18:04:20.000Z", "max_forks_repo_path": "include/nil/crypto3/block/kasumi.hpp", "max_forks_repo_name": "tonlabs/crypto3-block", "max_forks_repo_head_hexsha": "d7eede022f6130797d28bc39eb312bff9afebf07", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2020-01-11T15:37:40.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-26T21:47:05.000Z", "avg_line_length": 45.9183673469, "max_line_length": 102, "alphanum_fraction": 0.5084444444, "num_tokens": 2030, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.37754066879814546, "lm_q1q2_score": 0.22375574452098243}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n//#include \n\n\n\n\nusing std::string;\nusing std::bitset;\nusing std::max;\nusing std::ifstream;\nusing std::cout;\nusing std::cerr;\nusing std::vector;\n\n//namespace bfs = boost::filesystem;\n\nenum token_type {EMPTY, IDENTIFIER , KEYWORD, OPERATOR , PUNCTUATOR , LITERAL, NUMBER}; //token types // glob\nconst char* token_name[] = {\"EMPTY\", \"IDENTIFIER\", \"KEYWORD\", \"OPERATOR\", \"PUNCTUATOR\", \"LITERAL\", \"NUMBER\"}; //token types // glob\nconst int GAP_SCORE = -8;\n\n struct alignment\n\n {\n\n bitset<3> both;\n bitset<3> gap_x;\n bitset<3> gap_y;\n\n alignment()\n {\n both = 1;\n gap_x = 2;\n gap_y = 4;\n }\n\n }align_type;\n\n struct tokens\n {\n\n string token;\n token_type type;\n\n }token;\n\n class string_align\n{\n private:\n int nGap_score;\n int id_matchScore,id_misScore,kw_matchScore,kw_misScore,op_matchScore,op_misScore,lit_matchScore,lit_misScore,num_matchScore,num_misScore;\n int default_misScore;\n std::vector str_x,str_y;\n std::vector types_x, types_y;\n\n public:\n std::stack str_x_aligned,str_y_aligned,str_match;\n\tstd::stack token_x_aligned,token_y_aligned;\n\n public:\n string_align (std::vector x, std::vector x_types, std::vector y, std::vector y_types)\n {\n nGap_score = GAP_SCORE;\n id_matchScore = 6;\n id_misScore = 4;\n kw_matchScore = 10; //mis(match) score;\n kw_misScore = -10;\n op_matchScore = 8;\n op_misScore = -10;\n lit_matchScore = 7;\n lit_misScore = 5;\n num_matchScore = 7;\n num_misScore = 0;\n default_misScore = -10;\n str_x = x;\n types_x = x_types;\n str_y = y;\n types_y = y_types;\n }\n\n private:\n\n bool has_flag(bitset<3> event, bitset<3> flag)\n {\n bitset<3> test = event & flag;\n if (test.count() != 0) return true;\n return false;\n }\n\n public:\n\n int match_score (string x, token_type type_x , string y, token_type type_y)\n\n {\n if (type_x == type_y)\n {\n if (type_x == IDENTIFIER)\n {\n if (x==y) return id_matchScore;\n return id_misScore;\n }\n\n if (type_x == KEYWORD)\n {\n if (x==y) return kw_matchScore;\n return kw_misScore;\n }\n\n if (type_x == OPERATOR || type_x == PUNCTUATOR)\n {\n if (x==y) return op_matchScore;\n return op_misScore;\n }\n\n if (type_x == LITERAL)\n {\n if (x==y) return lit_matchScore;\n return lit_misScore;\n }\n\n if (type_x == NUMBER)\n {\n if (x==y) return num_matchScore;\n return num_misScore;\n }\n }\n return default_misScore;\n }\n\n\n\n int align()\n\n {\n int** scores = new int*[str_x.size()+1];\n for(size_t i = 0; i < str_x.size()+1; ++i)\n scores[i] = new int[str_y.size()+1];\n\n scores[0][0] = 0;\n\n bitset<3>** events = new bitset<3>*[str_x.size()+1];\n for(size_t i = 0; i < str_x.size()+1; ++i)\n events[i] = new bitset<3>[str_y.size()+1];\n\n\n\n unsigned int i,j;\n\n for (i = 1; i <= str_x.size(); i++)\n\t\t\t{\n\t\t\t\tscores[i][0] = scores[i - 1][0] + nGap_score;\n\t\t\t\tevents[i][0] = align_type.gap_y;\n\t\t\t}\n\n for (j = 1; j <= str_y.size(); j++)\n\t\t\t{\n\t\t\t\tscores[0][j] = scores[0][j - 1] + nGap_score;\n\t\t\t\tevents[0][j] = align_type.gap_x;\n\t\t\t}\n\n\n\t for (i = 1; i <= str_x.size(); i++)\n\t for (j = 1; j <= str_y.size(); j++)\n\t \t\t{\n\n\t int m = scores[i-1][j-1] + match_score(str_x.at(i-1),types_x.at(i-1),str_y.at(j-1),types_y.at(j-1));\n\t int g1 = scores[i-1][j] + nGap_score;\n\t int g2 = scores[i][j-1] + nGap_score;\n\t scores[i][j] = max(m,max(g1,g2));\n\t bitset<3> type = align_type.both;\n\t if (scores[i][j] == g1) type |= align_type.gap_y;\n\t if (scores[i][j] == g2) type |= align_type.gap_x;\n\t if (scores[i][j] > m ) type ^= align_type.both;\n\t events[i][j] = type;\n\n\n }\n\n i = str_x.size();\n j = str_y.size();\n\n while (i > 0 || j > 0)\n\t\t\t{\n\t\t\t\tif (has_flag(events[i][j],align_type.both) && i > 0 && j > 0)\n\t\t\t\t{\n\t\t\t\t\tstr_x_aligned.push(str_x[--i]);\n\t\t\t\t\tstr_y_aligned.push(str_y[--j]);\n\t\t\t\t str_match.push(str_x[i]==str_y[j] ? \"=\" : \"!\");\n\t\t\t\t\ttokens token_x = { str_x[i],types_x[i] },token_y = { str_y[j],types_y[j] };\n\t\t\t\t\ttoken_x_aligned.push(token_x);\n\t\t\t\t\ttoken_y_aligned.push(token_y);\n\t\t\t\t}\n\t\t\t\telse if (has_flag(events[i][j],align_type.gap_y) && i > 0)\n\t\t\t\t{\n\t\t\t\t\tstr_x_aligned.push(str_x[--i]);\n\t\t\t\t\tstr_y_aligned.push(\".\");\n\t\t\t\t\tstr_match.push(\"^\");\n\t\t\t\t\ttokens token_x = { str_x[i],types_x[i] },token_y = { \"\",EMPTY };\n\t\t\t\t\ttoken_x_aligned.push(token_x);\n\t\t\t\t\ttoken_y_aligned.push(token_y);\n\t\t\t\t}\n\t\t\t\telse if (has_flag(events[i][j],align_type.gap_x) && j > 0)\n\t\t\t\t{\n\t\t\t\t\tstr_x_aligned.push(\".\");\n\t\t\t\t\tstr_y_aligned.push(str_y[--j]);\n\t\t\t\t\tstr_match.push(\"v\");\n\t\t\t\t\ttokens token_x = { \"\",EMPTY },token_y = { str_y[j],types_y[j] };\n\t\t\t\t\ttoken_x_aligned.push(token_x);\n\t\t\t\t\ttoken_y_aligned.push(token_y);\n\t\t\t\t}\n\n }\n\n\n return scores[str_x.size()][str_y.size()];\n }\n };\n\n\nclass parsing\n{\n public:\n parsing (std::vector kw)\n {\n kw_list = kw;\n }\n\n\n private:\n std::vector kw_list;\n string entire;\n std::string::iterator iter;\n\n\n public:\n std::vector get_parsing()\n {\n return tokens;\n }\n\n public:\n std::vector get_types()\n {\n return types;\n }\n\n\n\n public:\n int init (ifstream &f)\n {\n string extracted;\n while (std::getline(f,extracted))\n {\n for (iter = extracted.begin(); iter != extracted.end(); iter++) // corr. testing\n {\n\n if (*iter == '\"')\n {\n iter++;\n if (iter == extracted.end()) return -1;\n while (*iter != '\"')\n {\n iter++;\n if (iter == extracted.end()) return -1;\n }\n }\n if (*iter == '\\'')\n {\n iter++;\n if (iter == extracted.end()) return -1;\n while (*iter != '\\'')\n {\n iter++;\n if (iter == extracted.end()) return -1;\n }\n }\n\n if (*iter == '/' && *(iter+1) == '/')\n {\n extracted.erase(iter, extracted.end());\n break;\n }\n\n }\n entire += extracted;\n extracted.clear();\n }\n\n for (iter = entire.begin(); iter != entire.end(); iter++)\n {\n if (*iter == '\"')\n {\n iter++;\n while (*iter != '\"') iter++;\n }\n\n if (*iter == '\\'')\n {\n iter++;\n while (*iter != '\\'') iter++;\n }\n\n if (*iter == '/' && *(iter+1) == '*')\n {\n std::string::iterator tmp = iter;\n iter += 2;\n while ((*iter != '*' || *(iter+1) != '/') && iter != entire.end())\n {\n iter++;\n }\n if (iter == entire.end())\n {\n entire.erase(tmp,entire.end());\n break;\n }\n iter += 2;\n iter = entire.erase(tmp,iter);\n }\n }\n\n return 0;\n }\n\n\n public:\n void str_format ()\n {\n\n for (iter = entire.begin(); iter != entire.end(); iter++) //deleting exc. ws\n if (isspace(*iter))\n if ((!is_idensym(*(iter+1)) || !is_idensym(*(iter-1))) && (!is_punsym((iter+1)) || !is_punsym((iter-1))) )\n {\n iter = entire.erase(iter) - 1;\n }\n for (iter = entire.begin(); iter != entire.end(); iter++) //add req.\n {\n if (is_idensym(*iter))\n {\n\n switch (*(iter-1))\n {\n case '-':\n if (*(iter-2) == '-') iter = entire.insert(iter-2,' ') + 3;\n break;\n case '+':\n if (*(iter-2) == '+') iter = entire.insert(iter-2,' ') + 3;\n break;\n }\n\n\n if (*(iter-1) == '!' || *(iter-1) == '~')\n {\n if (*(iter-2) == '+' || *(iter-2) == '-')\n if (*(iter-3) == '+' || *(iter-3) == '-')\n iter = entire.insert(iter-3,' ') + 4;\n iter = entire.insert(iter-1,' ') + 2;\n }\n\n\n if (*(iter-1) == '*')\n {\n int k = 1;\n iter--;\n while (*iter == '*')\n {\n iter--;\n k++;\n }\n iter++;\n if (*(iter-1) == '+' || *(iter-1) == '-')\n if (*(iter-2) == '+' || *(iter-2) == '-')\n iter = entire.insert(iter-2,' ') + 3;\n iter = entire.insert(iter,' ') + k;\n }\n if (*(iter-1) == '&')\n {\n int k = 1;\n iter--;\n while (*iter == '&')\n {\n iter--;\n k++;\n }\n iter++;\n if (*(iter-1) == '+' || *(iter-1) == '-')\n if (*(iter-2) == '+' || *(iter-2) == '-')\n iter = entire.insert(iter-2,' ') + 3;\n iter = entire.insert(iter,' ') + k;\n }\n\n switch (*(iter+1))\n {\n case '-':\n if (*(iter+2) == '-') iter = entire.insert(iter+3,' ') - 3;\n break;\n case '+':\n if (*(iter+2) == '+') iter = entire.insert(iter+3,' ') - 3;\n break;\n\n }\n\n if (*(iter+1) == '*')\n {\n int k = 1;\n iter++;\n while (*iter == '*')\n {\n iter++;\n k++;\n }\n iter = entire.insert(iter,' ') - k;\n }\n if (*(iter+1) == '&')\n {\n int k = 1;\n iter++;\n while (*iter == '&')\n {\n iter++;\n k++;\n }\n iter = entire.insert(iter,' ') - k;\n }\n }\n switch (*iter)\n {\n case '(':case ')': case '[': case ']': case '{': case '}': case '\"': case '\\'': case '#':\n iter = entire.insert(iter+1,' ') - 1;\n iter = entire.insert(iter,' ') + 1;\n break;\n case '.':\n if (!isdigit(*(iter-1))) iter = entire.insert(iter,' ') + 1;\n break;\n }\n\n }\n }\n\n private: //inspector functions\n\n bool is_idensym(char c)\n {\n return isalnum(c) || c == '_' || c == '$';\n }\n\n bool is_punsym(std::string::iterator iter)\n {\n if (*iter == '.' && isdigit(*(iter+1))) return false;\n static string pun_sym = \"(){}+-*/=<>.,;:[]%!&|~^#'\\\"?\";\n return pun_sym.find(*iter) != std::string::npos;\n }\n\n bool is_keyword (string token)\n\n {\n for (std::vector::iterator iter = kw_list.begin(); iter != kw_list.end(); iter ++)\n {\n if (token == *iter) return true;\n }\n\n return false;\n }\n\n bool is_numsym (std::string::iterator iter)\n\n {\n static string num_spsym = \"ex-.ABCDEF\";\n if (*iter == '.')\n {\n return isdigit(*(iter + 1)) != 0;\n }\n if (*iter == '-')\n {\n\n return *(iter - 1) == 'e' || *(iter - 1) == 'E';\n\n }\n return isdigit(*iter) || num_spsym.find(*iter) != std::string::npos;\n }\n\n\n\n private:\n vector tokens;\n vector types;\n string curr_tok;\n\n public:\n void dismember()\n {\n\n for (iter = entire.begin();iter != entire.end();)\n {\n\n if (isalpha(*iter) || (*iter) == '_' || (*iter == '$'))\n {\n\n while (is_idensym(*iter))\n {\n curr_tok += *iter;\n iter++;\n }\n\n tokens.push_back(curr_tok);\n\n if (is_keyword(curr_tok))\n {\n types.push_back(KEYWORD);\n }\n else\n {\n types.push_back(IDENTIFIER);\n }\n curr_tok.clear();\n } else\n if (is_punsym(iter))\n {\n int flag = 0;\n if (*iter == '\"')\n {\n curr_tok += *iter++;\n while (*iter != '\"')\n {\n curr_tok += *iter++;\n }\n curr_tok += *iter++;\n tokens.push_back(curr_tok);\n types.push_back(LITERAL);\n curr_tok.clear();\n flag = 1;\n }\n if (!flag)\n {\n if (*iter == '\\'')\n {\n curr_tok += *iter++;\n while (*iter != '\\'')\n {\n curr_tok += *iter++;\n }\n curr_tok += *iter++;\n tokens.push_back(curr_tok);\n types.push_back(LITERAL);\n curr_tok.clear();\n flag = 1;\n }\n }\n if (!flag)\n {\n while (is_punsym(iter))\n {\n curr_tok += *iter;\n iter++;\n }\n tokens.push_back(curr_tok);\n if (curr_tok == \"#\" || curr_tok == \";\") types.push_back(PUNCTUATOR); else types.push_back(OPERATOR);\n curr_tok.clear();\n }\n } else\n\n if (isdigit(*iter) || *iter == '.')\n {\n while (is_numsym(iter))\n {\n curr_tok += *iter;\n iter++;\n }\n tokens.push_back(curr_tok);\n types.push_back(NUMBER);\n curr_tok.clear();\n } else\n iter++;\n }\n }\n\n\n };\n\n\n\n\nint main (int argc, const char **argv)\n{\nifstream kw_list;\nstring extracted;\nkw_list.open(\"keywords_reserved.txt\");\nstd::vector kw;\nwhile (std::getline(kw_list,extracted)) kw.push_back(extracted);\n\nif (argc < 3)\n {\n cerr << \"File isn't specified\";\n return -1;\n }\n\n\nstd::vector file1_parsed,file2_parsed;\nstd::vector file1_types,file2_types;\nifstream f1,f2;\nf1.open(argv[1]);\nf2.open(argv[2]);\n\nparsing file1 (kw);\nparsing file2 (kw);\nif (file1.init(f1) || file2.init(f2))\n{\n cerr << \"Some of files are unsafe for parsing\";\n return -1;\n}\n\nfile1.str_format();\nfile2.str_format();\n\nfile1.dismember();\nfile2.dismember();\nfile1_parsed = file1.get_parsing();\nfile2_parsed = file2.get_parsing();\nfile1_types = file1.get_types();\nfile2_types = file2.get_types();\nstring_align alignment(file1_parsed,file1_types,file2_parsed, file2_types);\nint scores = alignment.align();\n\n\n\n\nint amount = 0;\n\n//std::stack *xa = &alignment.str_x_aligned,*ya = &alignment.str_y_aligned;\nstd::stack *xa = &alignment.token_x_aligned,*ya = &alignment.token_y_aligned;\n\nwhile(!xa->empty() && !ya->empty())\n{\n//if(!xa->empty())\n//{\n\tcout << xa->top().token << '\\t' << ya->top().token << '\\t'\n\t\t<< (xa->top().type != EMPTY && ya->top().type != EMPTY\n\t\t\t? alignment.match_score(xa->top().token,xa->top().type,ya->top().token,ya->top().type)\n\t\t\t: GAP_SCORE)\n\t\t<< '/' << token_name[xa->top().type] << '/' << token_name[ya->top().type]\n\t\t<< std::endl;\n\n\txa->pop();\n\tya->pop();\n\tamount++;\n}\n\ncout << \"Score: \" << scores << std::endl << \"Amount: \" << amount << std::endl;\ndouble plag = ((double)scores) / (6*amount); // - 0.25; //empirically\nif (plag < 0) plag = 0;\nif (plag > 1) plag = 1;\ndouble uniqueness = 1-plag;\ncout << \"Uniqueness:\" << uniqueness;\n\n\n}\n\n\n", "meta": {"hexsha": "3c135bbe0635154d5d94e733a2645bd11eba481e", "size": 16908, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "old/main2.cpp", "max_stars_repo_name": "razorkam/schleiermacher", "max_stars_repo_head_hexsha": "f4bfb013613e08145ee9059e17d0f826cffb807b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2018-07-05T05:51:22.000Z", "max_stars_repo_stars_event_max_datetime": "2019-12-17T18:51:25.000Z", "max_issues_repo_path": "old/main2.cpp", "max_issues_repo_name": "razorkam/schleiermacher", "max_issues_repo_head_hexsha": "f4bfb013613e08145ee9059e17d0f826cffb807b", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "old/main2.cpp", "max_forks_repo_name": "razorkam/schleiermacher", "max_forks_repo_head_hexsha": "f4bfb013613e08145ee9059e17d0f826cffb807b", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 25.3873873874, "max_line_length": 142, "alphanum_fraction": 0.4179086823, "num_tokens": 4137, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.37754065479083276, "lm_q1q2_score": 0.22375573621931605}} {"text": "// Copyright (C) 2011-2012 by the BEM++ Authors\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n#include \"piecewise_polynomial_continuous_scalar_space.hpp\"\n\n#include \"piecewise_polynomial_discontinuous_scalar_space.hpp\"\n#include \"space_helper.hpp\"\n\n#include \"../assembly/discrete_sparse_boundary_operator.hpp\"\n#include \"../common/acc.hpp\"\n#include \"../common/boost_make_shared_fwd.hpp\"\n#include \"../common/bounding_box_helpers.hpp\"\n#include \"../fiber/explicit_instantiation.hpp\"\n#include \"../fiber/lagrange_scalar_shapeset.hpp\"\n#include \"../grid/entity.hpp\"\n#include \"../grid/entity_iterator.hpp\"\n#include \"../grid/geometry.hpp\"\n#include \"../grid/grid.hpp\"\n#include \"../grid/grid_segment.hpp\"\n#include \"../grid/grid_view.hpp\"\n#include \"../grid/mapper.hpp\"\n#include \"../grid/vtk_writer.hpp\"\n\n#include \n#include \n\n#include \n\nnamespace Bempp {\n\ntemplate \nPiecewisePolynomialContinuousScalarSpace::\n PiecewisePolynomialContinuousScalarSpace(const shared_ptr &grid,\n int polynomialOrder)\n : ScalarSpace(grid), m_polynomialOrder(polynomialOrder),\n m_segment(GridSegment::wholeGrid(*grid)), m_strictlyOnSegment(false),\n m_flatLocalDofCount(0) {\n initialize();\n}\n\ntemplate \nbool\nPiecewisePolynomialContinuousScalarSpace::spaceIsCompatible(\n const Space &other) const {\n\n typedef PiecewisePolynomialContinuousScalarSpace\n thisSpaceType;\n\n if (other.grid().get() != this->grid().get())\n return false;\n\n if (other.spaceIdentifier() == this->spaceIdentifier()) {\n // Try to typecast the other space down.\n const thisSpaceType &temp = dynamic_cast(other);\n if (this->m_polynomialOrder == temp.m_polynomialOrder)\n return true;\n else\n return false;\n } else\n return false;\n}\n\ntemplate \nPiecewisePolynomialContinuousScalarSpace::\n PiecewisePolynomialContinuousScalarSpace(const shared_ptr &grid,\n int polynomialOrder,\n const GridSegment &segment,\n bool strictlyOnSegment)\n : ScalarSpace(grid), m_polynomialOrder(polynomialOrder),\n m_segment(segment), m_strictlyOnSegment(strictlyOnSegment),\n m_flatLocalDofCount(0) {\n initialize();\n}\n\ntemplate \nvoid PiecewisePolynomialContinuousScalarSpace::initialize() {\n const int gridDim = this->grid()->dim();\n if (gridDim != 2)\n throw std::invalid_argument(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"PiecewisePolynomialContinuousScalarSpace(): \"\n \"2-dimensional grids are supported\");\n m_view = this->grid()->leafView();\n if (m_polynomialOrder == 1)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 1>());\n else if (m_polynomialOrder == 2)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 2>());\n else if (m_polynomialOrder == 3)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 3>());\n else if (m_polynomialOrder == 4)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 4>());\n else if (m_polynomialOrder == 5)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 5>());\n else if (m_polynomialOrder == 6)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 6>());\n else if (m_polynomialOrder == 7)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 7>());\n else if (m_polynomialOrder == 8)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 8>());\n else if (m_polynomialOrder == 9)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 9>());\n else if (m_polynomialOrder == 10)\n m_triangleShapeset.reset(\n new Fiber::LagrangeScalarShapeset<3, BasisFunctionType, 10>());\n else\n throw std::invalid_argument(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"PiecewisePolynomialContinuousScalarSpace(): \"\n \"polynomialOrder must be >= 1 and <= 10\");\n assignDofsImpl();\n}\n\ntemplate \nPiecewisePolynomialContinuousScalarSpace<\n BasisFunctionType>::~PiecewisePolynomialContinuousScalarSpace() {}\n\ntemplate \nint\nPiecewisePolynomialContinuousScalarSpace::domainDimension()\n const {\n return this->grid()->dim();\n}\n\ntemplate \nint\nPiecewisePolynomialContinuousScalarSpace::codomainDimension()\n const {\n return 1;\n}\n\ntemplate \nconst Fiber::Shapeset &\nPiecewisePolynomialContinuousScalarSpace::shapeset(\n const Entity<0> &element) const {\n if (elementVariant(element) == 3)\n return *m_triangleShapeset;\n throw std::logic_error(\n \"PiecewisePolynomialContinuousScalarSpace::shapeset(): \"\n \"invalid element variant, this shouldn't happen!\");\n}\n\ntemplate \nElementVariant\nPiecewisePolynomialContinuousScalarSpace::elementVariant(\n const Entity<0> &element) const {\n GeometryType type = element.type();\n if (type.isLine())\n return 2;\n else if (type.isTriangle())\n return 3;\n else if (type.isQuadrilateral())\n return 4;\n else\n throw std::runtime_error(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"elementVariant(): invalid geometry type, \"\n \"this shouldn't happen!\");\n}\n\ntemplate \nvoid\nPiecewisePolynomialContinuousScalarSpace::setElementVariant(\n const Entity<0> &element, ElementVariant variant) {\n if (variant != elementVariant(element))\n // for this space, the element variants are unmodifiable,\n throw std::runtime_error(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"setElementVariant(): invalid variant\");\n}\n\ntemplate \nshared_ptr>\nPiecewisePolynomialContinuousScalarSpace::discontinuousSpace(\n const shared_ptr> &self) const {\n if (!m_discontinuousSpace) {\n tbb::mutex::scoped_lock lock(m_discontinuousSpaceMutex);\n typedef PiecewisePolynomialDiscontinuousScalarSpace\n DiscontinuousSpace;\n if (!m_discontinuousSpace)\n m_discontinuousSpace.reset(\n new DiscontinuousSpace(this->grid(), m_polynomialOrder, m_segment));\n }\n return m_discontinuousSpace;\n}\n\ntemplate \nbool\nPiecewisePolynomialContinuousScalarSpace::isDiscontinuous()\n const {\n return false;\n}\n\ntemplate \nvoid\nPiecewisePolynomialContinuousScalarSpace::assignDofsImpl() {\n // TODO: refactor this function, it's way too long!\n\n // In addition to DOF assignment, this function also precalculates bounding\n // boxes of global DOFs\n\n const int elementCount = m_view->entityCount(0);\n if (elementCount == 0)\n return;\n const int gridDim = this->domainDimension();\n if (gridDim != 2)\n throw std::runtime_error(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"assignDofsImpl(): only 2-dimensional grids \"\n \"are supported at present\");\n const int vertexCodim = gridDim;\n const int edgeCodim = vertexCodim - 1;\n\n // const Mapper& elementMapper = m_view->elementMapper();\n const IndexSet &indexSet = m_view->indexSet();\n\n // Map vertices to global dofs\n const int vertexCount = m_view->entityCount(2);\n // At first, the elements of this vector will be set to the number of\n // DOFs corresponding to a given vertex or to -1 if that vertex is to be\n // ignored\n std::vector vertexGlobalDofs(vertexCount);\n for (int i = 0; i < vertexCount; ++i)\n if (m_segment.contains(gridDim, i))\n acc(vertexGlobalDofs, (size_t)i) = 1;\n else\n acc(vertexGlobalDofs, (size_t)i) = -1;\n\n // Map edges to global dofs\n const int edgeCount = m_view->entityCount(1);\n const int internalDofCountPerEdge = m_polynomialOrder - 1;\n // At first, the elements of this vector will be set to the number of\n // DOFs corresponding to a given edge or to -1 if that edge is to be\n // ignored\n std::vector edgeStartingGlobalDofs(edgeCount);\n for (int i = 0; i < edgeCount; ++i)\n if (m_segment.contains(gridDim - 1, i))\n acc(edgeStartingGlobalDofs, i) = internalDofCountPerEdge;\n else\n acc(edgeStartingGlobalDofs, i) = -1;\n\n // Map element interiors to global dofs\n // and, if striclyOnSegment is set, detect vertices and edges not belonging\n // to any element on segment\n const int bubbleDofCountPerTriangle =\n std::max(0, (m_polynomialOrder - 1) * (m_polynomialOrder - 2) / 2);\n const int bubbleDofCountPerQuad =\n std::max(0, (m_polynomialOrder - 1) * (m_polynomialOrder - 1));\n // At first, the elements of this vector will be set to the number of\n // DOFs corresponding to a given element or to -1 if that element is to be\n // ignored\n std::vector bubbleStartingGlobalDofs(elementCount);\n std::vector noElementAdjacentToVertexIsOnSegment(vertexCount, true);\n std::vector noElementAdjacentToEdgeIsOnSegment(edgeCount, true);\n std::unique_ptr> it = m_view->entityIterator<0>();\n while (!it->finished()) {\n const Entity<0> &element = it->entity();\n EntityIndex elementIndex = indexSet.entityIndex(element);\n int vertexCount = element.template subEntityCount<2>();\n if (vertexCount != 3 && vertexCount != 4)\n throw std::runtime_error(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"assignDofsImpl(): elements must be \"\n \"triangular or quadrilateral\");\n if (m_segment.contains(0, elementIndex)) {\n acc(bubbleStartingGlobalDofs, elementIndex) =\n vertexCount == 3 ? bubbleDofCountPerTriangle : bubbleDofCountPerQuad;\n if (m_strictlyOnSegment)\n for (int i = 0; i < vertexCount; ++i) {\n int index = indexSet.subEntityIndex(element, i, gridDim);\n acc(noElementAdjacentToVertexIsOnSegment, index) = false;\n index = indexSet.subEntityIndex(element, i, gridDim - 1);\n acc(noElementAdjacentToEdgeIsOnSegment, index) = false;\n }\n } else\n acc(bubbleStartingGlobalDofs, elementIndex) = -1;\n it->next();\n }\n\n // If strictlyOnSegment is set, deactivate vertices and edges not adjacent\n // to any element in segment\n if (m_strictlyOnSegment) {\n for (int i = 0; i < vertexCount; ++i)\n if (acc(noElementAdjacentToVertexIsOnSegment, i))\n acc(vertexGlobalDofs, i) = -1;\n\n for (int i = 0; i < edgeCount; ++i)\n if (acc(noElementAdjacentToEdgeIsOnSegment, i))\n acc(edgeStartingGlobalDofs, i) = -1;\n }\n\n // Assign global dofs to entities\n int globalDofCount_ = 0;\n for (int i = 0; i < vertexCount; ++i)\n if (acc(vertexGlobalDofs, i) == 1)\n acc(vertexGlobalDofs, i) = globalDofCount_++;\n for (int i = 0; i < edgeCount; ++i) {\n int dofCount = acc(edgeStartingGlobalDofs, i);\n if (dofCount > 0) {\n acc(edgeStartingGlobalDofs, i) = globalDofCount_;\n globalDofCount_ += dofCount;\n }\n }\n for (int i = 0; i < elementCount; ++i) {\n int dofCount = acc(bubbleStartingGlobalDofs, i);\n if (dofCount > 0) {\n acc(bubbleStartingGlobalDofs, i) = globalDofCount_;\n globalDofCount_ += dofCount;\n }\n }\n\n // Initialise DOF maps\n const int localDofCountPerTriangle =\n (m_polynomialOrder + 1) * (m_polynomialOrder + 2) / 2;\n m_local2globalDofs.clear();\n std::vector prototypeGlobalDofs;\n prototypeGlobalDofs.reserve(localDofCountPerTriangle);\n m_local2globalDofs.resize(elementCount, prototypeGlobalDofs);\n m_global2localDofs.clear();\n // std::vector prototypeLocalDofs;\n // prototypeLocalDofs.reserve(localDofCountPerTriangle);\n m_global2localDofs.resize(globalDofCount_ /*, prototypeLocalDofs*/);\n\n // Initialise bounding-box caches\n BoundingBox model;\n model.lbound.x = std::numeric_limits::max();\n model.lbound.y = std::numeric_limits::max();\n model.lbound.z = std::numeric_limits::max();\n model.ubound.x = -std::numeric_limits::max();\n model.ubound.y = -std::numeric_limits::max();\n model.ubound.z = -std::numeric_limits::max();\n m_globalDofBoundingBoxes.resize(globalDofCount_, model);\n\n // Iterate over elements\n it = m_view->entityIterator<0>();\n arma::Mat vertices;\n arma::Col dofPosition;\n m_flatLocalDofCount = 0;\n std::vector gdofAccessCounts(globalDofCount_, 0);\n while (!it->finished()) {\n const Entity<0> &element = it->entity();\n const Geometry &geo = element.geometry();\n EntityIndex elementIndex = indexSet.entityIndex(element);\n bool elementContained =\n !m_strictlyOnSegment || m_segment.contains(0, elementIndex);\n\n geo.getCorners(vertices);\n int vertexCount = vertices.n_cols;\n\n // List of global DOF indices corresponding to the local DOFs of the\n // current element\n std::vector &globalDofs =\n acc(m_local2globalDofs, elementIndex);\n if (vertexCount == 3) {\n std::vector ldofAccessCounts(localDofCountPerTriangle, 0);\n boost::array vertexIndices;\n for (int i = 0; i < 3; ++i)\n acc(vertexIndices, i) =\n indexSet.subEntityIndex(element, i, vertexCodim);\n globalDofs.resize(localDofCountPerTriangle);\n // vertex dofs\n {\n int ldof, gdof;\n\n ldof = 0;\n if (elementContained)\n gdof = vertexGlobalDofs[acc(vertexIndices, 0)];\n else\n gdof = -1;\n if (gdof >= 0) {\n acc(globalDofs, ldof) = gdof;\n acc(m_global2localDofs, gdof).push_back(LocalDof(elementIndex, ldof));\n ++acc(gdofAccessCounts, gdof);\n extendBoundingBox(acc(m_globalDofBoundingBoxes, gdof), vertices);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, gdof), vertices.col(0));\n ++m_flatLocalDofCount;\n } else\n acc(globalDofs, ldof) = -1;\n ++acc(ldofAccessCounts, ldof);\n\n ldof = m_polynomialOrder;\n if (elementContained)\n gdof = vertexGlobalDofs[acc(vertexIndices, 1)];\n else\n gdof = -1;\n if (gdof >= 0) {\n acc(globalDofs, ldof) = gdof;\n acc(m_global2localDofs, gdof).push_back(LocalDof(elementIndex, ldof));\n ++acc(gdofAccessCounts, gdof);\n extendBoundingBox(acc(m_globalDofBoundingBoxes, gdof), vertices);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, gdof), vertices.col(1));\n ++m_flatLocalDofCount;\n } else\n acc(globalDofs, ldof) = -1;\n ++acc(ldofAccessCounts, ldof);\n\n ldof = localDofCountPerTriangle - 1;\n if (elementContained)\n gdof = vertexGlobalDofs[acc(vertexIndices, 2)];\n else\n gdof = -1;\n if (gdof >= 0) {\n acc(globalDofs, ldof) = gdof;\n acc(m_global2localDofs, gdof).push_back(LocalDof(elementIndex, ldof));\n ++acc(gdofAccessCounts, gdof);\n extendBoundingBox(acc(m_globalDofBoundingBoxes, gdof), vertices);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, gdof), vertices.col(2));\n ++m_flatLocalDofCount;\n } else\n acc(globalDofs, ldof) = -1;\n ++acc(ldofAccessCounts, ldof);\n }\n\n // edge dofs\n if (m_polynomialOrder >= 2) {\n int start, end, step;\n int edgeIndex;\n\n edgeIndex = indexSet.subEntityIndex(element, 0, edgeCodim);\n dofPosition = 0.5 * (vertices.col(0) + vertices.col(1));\n if (acc(edgeStartingGlobalDofs, edgeIndex) >= 0 && elementContained) {\n if (acc(vertexIndices, 0) < acc(vertexIndices, 1)) {\n start = acc(edgeStartingGlobalDofs, edgeIndex);\n end = start + internalDofCountPerEdge;\n step = 1;\n } else {\n end = acc(edgeStartingGlobalDofs, edgeIndex) - 1;\n start = end + internalDofCountPerEdge;\n step = -1;\n }\n for (int ldof = 1, gdof = start; gdof != end; ++ldof, gdof += step) {\n acc(globalDofs, ldof) = gdof;\n acc(m_global2localDofs, gdof)\n .push_back(LocalDof(elementIndex, ldof));\n ++acc(ldofAccessCounts, ldof);\n ++acc(gdofAccessCounts, gdof);\n extendBoundingBox(acc(m_globalDofBoundingBoxes, gdof), vertices);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, gdof), dofPosition);\n ++m_flatLocalDofCount;\n }\n } else\n for (int ldof = 1; ldof <= internalDofCountPerEdge; ++ldof) {\n acc(globalDofs, ldof) = -1;\n ++acc(ldofAccessCounts, ldof);\n }\n\n edgeIndex = indexSet.subEntityIndex(element, 1, edgeCodim);\n dofPosition = 0.5 * (vertices.col(0) + vertices.col(2));\n if (acc(edgeStartingGlobalDofs, edgeIndex) >= 0 && elementContained) {\n if (acc(vertexIndices, 0) < acc(vertexIndices, 2)) {\n start = acc(edgeStartingGlobalDofs, edgeIndex);\n end = start + internalDofCountPerEdge;\n step = 1;\n } else {\n end = acc(edgeStartingGlobalDofs, edgeIndex) - 1;\n start = end + internalDofCountPerEdge;\n step = -1;\n }\n for (int ldofy = 1, gdof = start; gdof != end;\n ++ldofy, gdof += step) {\n int ldof =\n ldofy * (m_polynomialOrder + 1) - ldofy * (ldofy - 1) / 2;\n acc(globalDofs, ldof) = gdof;\n acc(m_global2localDofs, gdof)\n .push_back(LocalDof(elementIndex, ldof));\n ++acc(ldofAccessCounts, ldof);\n ++acc(gdofAccessCounts, gdof);\n extendBoundingBox(acc(m_globalDofBoundingBoxes, gdof), vertices);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, gdof), dofPosition);\n ++m_flatLocalDofCount;\n }\n } else\n for (int ldofy = 1; ldofy <= internalDofCountPerEdge; ++ldofy) {\n int ldof =\n ldofy * (m_polynomialOrder + 1) - ldofy * (ldofy - 1) / 2;\n acc(globalDofs, ldof) = -1;\n ++acc(ldofAccessCounts, ldof);\n }\n\n edgeIndex = indexSet.subEntityIndex(element, 2, edgeCodim);\n dofPosition = 0.5 * (vertices.col(1) + vertices.col(2));\n if (acc(edgeStartingGlobalDofs, edgeIndex) >= 0 && elementContained) {\n if (acc(vertexIndices, 1) < acc(vertexIndices, 2)) {\n start = acc(edgeStartingGlobalDofs, edgeIndex);\n end = start + internalDofCountPerEdge;\n step = 1;\n } else {\n end = acc(edgeStartingGlobalDofs, edgeIndex) - 1;\n start = end + internalDofCountPerEdge;\n step = -1;\n }\n for (int ldofy = 1, gdof = start; gdof != end;\n ++ldofy, gdof += step) {\n int ldof = ldofy * (m_polynomialOrder + 1) -\n ldofy * (ldofy - 1) / 2 + (m_polynomialOrder - ldofy);\n acc(globalDofs, ldof) = gdof;\n acc(m_global2localDofs, gdof)\n .push_back(LocalDof(elementIndex, ldof));\n ++acc(ldofAccessCounts, ldof);\n ++acc(gdofAccessCounts, gdof);\n extendBoundingBox(acc(m_globalDofBoundingBoxes, gdof), vertices);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, gdof), dofPosition);\n ++m_flatLocalDofCount;\n }\n } else\n for (int ldofy = 1; ldofy <= internalDofCountPerEdge; ++ldofy) {\n int ldof = ldofy * (m_polynomialOrder + 1) -\n ldofy * (ldofy - 1) / 2 + (m_polynomialOrder - ldofy);\n acc(globalDofs, ldof) = -1;\n ++acc(ldofAccessCounts, ldof);\n }\n }\n\n // bubble dofs\n if (m_polynomialOrder >= 3) {\n dofPosition =\n (vertices.col(0) + vertices.col(1) + vertices.col(2)) / 3.;\n bool useDofs = acc(bubbleStartingGlobalDofs, elementIndex) >= 0;\n for (int ldofy = 1, gdof = acc(bubbleStartingGlobalDofs, elementIndex);\n ldofy < m_polynomialOrder; ++ldofy)\n for (int ldofx = 1; ldofx + ldofy < m_polynomialOrder;\n ++ldofx, ++gdof) {\n int ldof = ldofy * (m_polynomialOrder + 1) -\n ldofy * (ldofy - 1) / 2 + ldofx;\n if (useDofs) {\n acc(globalDofs, ldof) = gdof;\n acc(m_global2localDofs, gdof)\n .push_back(LocalDof(elementIndex, ldof));\n ++acc(gdofAccessCounts, gdof);\n extendBoundingBox(acc(m_globalDofBoundingBoxes, gdof), vertices);\n setBoundingBoxReference(\n acc(m_globalDofBoundingBoxes, gdof), dofPosition);\n ++m_flatLocalDofCount;\n } else\n acc(globalDofs, ldof) = -1;\n ++acc(ldofAccessCounts, ldof);\n }\n }\n for (size_t i = 0; i < ldofAccessCounts.size(); ++i)\n assert(acc(ldofAccessCounts, i) == 1);\n } else\n throw std::runtime_error(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"assignDofsImpl(): quadrilateral elements \"\n \"are not supported yet\");\n\n it->next();\n }\n// for (size_t i = 0; i < gdofAccessCounts.size(); ++i)\n// std::cout << i << \" \" << acc(gdofAccessCounts, i) << \"\\n\";\n\n#ifndef NDEBUG\n for (size_t i = 0; i < globalDofCount_; ++i) {\n const BoundingBox &bbox = acc(m_globalDofBoundingBoxes, i);\n\n assert(bbox.reference.x >= bbox.lbound.x);\n assert(bbox.reference.y >= bbox.lbound.y);\n assert(bbox.reference.z >= bbox.lbound.z);\n assert(bbox.reference.x <= bbox.ubound.x);\n assert(bbox.reference.y <= bbox.ubound.y);\n assert(bbox.reference.z <= bbox.ubound.z);\n }\n#endif // NDEBUG\n\n // Initialize the container mapping the flat local dof indices to\n // local dof indices\n SpaceHelper::initializeLocal2FlatLocalDofMap(\n m_flatLocalDofCount, m_local2globalDofs, m_flatLocal2localDofs);\n}\n\ntemplate \nsize_t\nPiecewisePolynomialContinuousScalarSpace::globalDofCount()\n const {\n return m_global2localDofs.size();\n}\n\ntemplate \nsize_t\nPiecewisePolynomialContinuousScalarSpace::flatLocalDofCount()\n const {\n return m_flatLocalDofCount;\n}\n\ntemplate \nvoid PiecewisePolynomialContinuousScalarSpace::getGlobalDofs(\n const Entity<0> &element, std::vector &dofs) const {\n const Mapper &mapper = m_view->elementMapper();\n EntityIndex index = mapper.entityIndex(element);\n dofs = m_local2globalDofs[index];\n}\n\ntemplate \nvoid\nPiecewisePolynomialContinuousScalarSpace::global2localDofs(\n const std::vector &globalDofs,\n std::vector> &localDofs) const {\n localDofs.resize(globalDofs.size());\n for (size_t i = 0; i < globalDofs.size(); ++i)\n localDofs[i] = m_global2localDofs[globalDofs[i]];\n}\n\ntemplate \nvoid PiecewisePolynomialContinuousScalarSpace::\n flatLocal2localDofs(const std::vector &flatLocalDofs,\n std::vector &localDofs) const {\n localDofs.resize(flatLocalDofs.size());\n for (size_t i = 0; i < flatLocalDofs.size(); ++i)\n localDofs[i] = m_flatLocal2localDofs[flatLocalDofs[i]];\n}\n\ntemplate \nvoid PiecewisePolynomialContinuousScalarSpace::\n getGlobalDofPositions(std::vector> &positions)\n const {\n positions.resize(m_globalDofBoundingBoxes.size());\n for (size_t i = 0; i < m_globalDofBoundingBoxes.size(); ++i)\n acc(positions, i) = acc(m_globalDofBoundingBoxes, i).reference;\n}\n\ntemplate \nvoid PiecewisePolynomialContinuousScalarSpace::\n getFlatLocalDofPositions(std::vector> &positions)\n const {\n throw std::runtime_error(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"getFlatLocalDofPositions(): not implemented yet\");\n}\n\ntemplate \nvoid PiecewisePolynomialContinuousScalarSpace::\n getGlobalDofBoundingBoxes(std::vector> &bboxes)\n const {\n bboxes = m_globalDofBoundingBoxes;\n}\n\ntemplate \nvoid PiecewisePolynomialContinuousScalarSpace::\n getFlatLocalDofBoundingBoxes(\n std::vector> &bboxes) const {\n throw std::runtime_error(\n \"PiecewisePolynomialContinuousScalarSpace::\"\n \"getFlatLocalDofBoundingBoxes(): not implemented yet\");\n}\n\ntemplate \nvoid PiecewisePolynomialContinuousScalarSpace::\n getGlobalDofNormals(std::vector> &normals) const {\n SpaceHelper::getGlobalDofNormals_defaultImplementation(\n *m_view, m_global2localDofs, normals);\n}\n\ntemplate \nvoid PiecewisePolynomialContinuousScalarSpace::\n getFlatLocalDofNormals(std::vector> &normals)\n const {\n throw std::runtime_error(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"getFlatLocalDofNormals(): not implemented yet\");\n}\n\ntemplate \nvoid\nPiecewisePolynomialContinuousScalarSpace::dumpClusterIds(\n const char *fileName,\n const std::vector &clusterIdsOfDofs) const {\n dumpClusterIdsEx(fileName, clusterIdsOfDofs, GLOBAL_DOFS);\n}\n\ntemplate \nvoid\nPiecewisePolynomialContinuousScalarSpace::dumpClusterIdsEx(\n const char *fileName, const std::vector &clusterIdsOfDofs,\n DofType dofType) const {\n throw std::runtime_error(\"PiecewisePolynomialContinuousScalarSpace::\"\n \"dumpClusterIdsEx(): not implemented yet\");\n}\n\nFIBER_INSTANTIATE_CLASS_TEMPLATED_ON_BASIS(\n PiecewisePolynomialContinuousScalarSpace);\n\n} // namespace Bempp\n", "meta": 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YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.4148988457967689, "lm_q1q2_score": 0.22362351616714532}} {"text": "// Distributed under the MIT License.\n// See LICENSE.txt for details.\n\n#pragma once\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include // IWYU pragma: keep // for std::forward\n\n#include \"DataStructures/DataBox/DataBoxTag.hpp\"\n#include \"DataStructures/DataBox/PrefixHelpers.hpp\"\n#include \"DataStructures/DataBox/Prefixes.hpp\"\n#include \"DataStructures/Matrix.hpp\"\n#include \"DataStructures/Variables.hpp\"\n#include \"Domain/Direction.hpp\"\n#include \"Domain/ElementId.hpp\"\n#include \"Domain/Mesh.hpp\"\n#include \"ErrorHandling/Assert.hpp\"\n#include \"NumericalAlgorithms/DiscontinuousGalerkin/LiftFlux.hpp\"\n#include \"NumericalAlgorithms/LinearOperators/ApplyMatrices.hpp\"\n#include \"NumericalAlgorithms/Spectral/Projection.hpp\"\n#include \"Utilities/Algorithm.hpp\"\n#include \"Utilities/Gsl.hpp\"\n#include \"Utilities/MakeArray.hpp\"\n#include \"Utilities/TMPL.hpp\"\n/// \\cond\ntemplate \nclass ElementId;\ntemplate \nclass OrientationMap;\n// IWYU pragma: no_forward_declare Variables\n/// \\endcond\n\nnamespace dg {\n\ntemplate \nusing MortarId = std::pair<::Direction, ElementId>;\ntemplate \nusing MortarSize = std::array;\ntemplate \nusing MortarMap = std::unordered_map, ValueType,\n boost::hash>>;\n\n/// \\ingroup DiscontinuousGalerkinGroup\n/// Find a mesh for a mortar capable of representing data from either\n/// of two faces.\ntemplate \nMesh mortar_mesh(const Mesh& face_mesh1,\n const Mesh& face_mesh2) noexcept;\n\n/// \\ingroup DiscontinuousGalerkinGroup\n/// Determine the size of the mortar (i.e., the part of the face it\n/// covers) for communicating with a neighbor. This is the size\n/// relative to the size of \\p self, and will not generally agree with\n/// that determined by \\p neighbor.\ntemplate \nMortarSize mortar_size(\n const ElementId& self, const ElementId& neighbor,\n size_t dimension, const OrientationMap& orientation) noexcept;\n\n/// \\ingroup DiscontinuousGalerkinGroup\n/// Determine whether data on an element face needs to be projected to a mortar.\n/// If no projection is necessary the data may be used on the mortar as-is.\ntemplate \nbool needs_projection(const Mesh& face_mesh, const Mesh& mortar_mesh,\n const MortarSize& mortar_size) noexcept {\n return mortar_mesh != face_mesh or\n alg::any_of(mortar_size, [](const Spectral::MortarSize& size) noexcept {\n return size != Spectral::MortarSize::Full;\n });\n}\n\n/// \\ingroup DiscontinuousGalerkinGroup\n/// Project variables from a face to a mortar.\ntemplate \nVariables project_to_mortar(const Variables& vars,\n const Mesh& face_mesh,\n const Mesh& mortar_mesh,\n const MortarSize& mortar_size) noexcept {\n const Matrix identity{};\n auto projection_matrices = make_array(std::cref(identity));\n\n const auto face_slice_meshes = face_mesh.slices();\n const auto mortar_slice_meshes = mortar_mesh.slices();\n for (size_t i = 0; i < Dim; ++i) {\n const auto& face_slice_mesh = gsl::at(face_slice_meshes, i);\n const auto& mortar_slice_mesh = gsl::at(mortar_slice_meshes, i);\n const auto& slice_size = gsl::at(mortar_size, i);\n if (slice_size != Spectral::MortarSize::Full or\n face_slice_mesh != mortar_slice_mesh) {\n gsl::at(projection_matrices, i) = projection_matrix_element_to_mortar(\n slice_size, mortar_slice_mesh, face_slice_mesh);\n }\n }\n return apply_matrices(projection_matrices, vars, face_mesh.extents());\n}\n\n/// \\ingroup DiscontinuousGalerkinGroup\n/// Project variables from a mortar to a face.\ntemplate \nVariables project_from_mortar(\n const Variables& vars, const Mesh& face_mesh,\n const Mesh& mortar_mesh, const MortarSize& mortar_size) noexcept {\n ASSERT(face_mesh != mortar_mesh or\n alg::any_of(mortar_size,\n [](const Spectral::MortarSize& size) noexcept {\n return size != Spectral::MortarSize::Full;\n }),\n \"project_from_mortar should not be called if the interface mesh and \"\n \"mortar mesh are identical. Please elide the copy instead.\");\n\n const Matrix identity{};\n auto projection_matrices = make_array(std::cref(identity));\n\n const auto face_slice_meshes = face_mesh.slices();\n const auto mortar_slice_meshes = mortar_mesh.slices();\n for (size_t i = 0; i < Dim; ++i) {\n const auto& face_slice_mesh = gsl::at(face_slice_meshes, i);\n const auto& mortar_slice_mesh = gsl::at(mortar_slice_meshes, i);\n const auto& slice_size = gsl::at(mortar_size, i);\n if (slice_size != Spectral::MortarSize::Full or\n face_slice_mesh != mortar_slice_mesh) {\n gsl::at(projection_matrices, i) = projection_matrix_mortar_to_element(\n slice_size, face_slice_mesh, mortar_slice_mesh);\n }\n }\n return apply_matrices(projection_matrices, vars, mortar_mesh.extents());\n}\n\nnamespace MortarHelpers_detail {\ntemplate \nvoid apply_normal_dot_numerical_flux(\n const gsl::not_null>*>\n numerical_fluxes,\n const NormalDotNumericalFluxComputer& normal_dot_numerical_flux_computer,\n const Variables>& self_packaged_data,\n const Variables>&\n neighbor_packaged_data) noexcept {\n normal_dot_numerical_flux_computer(\n make_not_null(&get(*numerical_fluxes))...,\n get(self_packaged_data)...,\n get(neighbor_packaged_data)...);\n}\n} // namespace MortarHelpers_detail\n\n/// \\ingroup DiscontinuousGalerkinGroup\n/// Compute the lifted data resulting from computing the numerical flux.\n///\n/// \\details\n/// This applies the numerical flux, projects the result to the face\n/// mesh if necessary, and then lifts it to the volume (still\n/// presented only on the face mesh as all other points are zero).\n///\n/// Projection must happen after the numerical flux calculation so\n/// that the elements on either side of the mortar calculate the same\n/// result. Projection must happen before flux lifting because we\n/// want the factor of the magnitude of the unit normal added during\n/// the lift to cancel the Jacobian factor in integrals to preserve\n/// conservation; this only happens if the two operations are done on\n/// the same grid.\ntemplate \nauto compute_boundary_flux_contribution(\n const NormalDotNumericalFluxComputer& normal_dot_numerical_flux_computer,\n LocalData&& local_data,\n const typename FluxCommTypes::PackagedData& remote_data,\n const Mesh& face_mesh, const Mesh& mortar_mesh,\n const size_t extent_perpendicular_to_boundary,\n const MortarSize& mortar_size) noexcept\n -> db::const_item_type<\n db::remove_tag_prefix> {\n static_assert(std::is_same_v,\n typename FluxCommTypes::LocalData>,\n \"Second argument must be a FluxCommTypes::LocalData\");\n using variables_tag =\n db::remove_tag_prefix;\n db::const_item_type>\n normal_dot_numerical_fluxes(mortar_mesh.number_of_grid_points(), 0.0);\n MortarHelpers_detail::apply_normal_dot_numerical_flux(\n make_not_null(&normal_dot_numerical_fluxes),\n normal_dot_numerical_flux_computer, local_data.mortar_data, remote_data);\n\n tmpl::for_each>(\n [&normal_dot_numerical_fluxes, &local_data](const auto tag) noexcept {\n using Tag = tmpl::type_from;\n auto& numerical_flux =\n get>(normal_dot_numerical_fluxes);\n const auto& local_flux =\n get>(local_data.mortar_data);\n for (size_t i = 0; i < numerical_flux.size(); ++i) {\n numerical_flux[i] -= local_flux[i];\n }\n });\n\n const bool refining =\n face_mesh != mortar_mesh or\n std::any_of(mortar_size.begin(), mortar_size.end(),\n [](const Spectral::MortarSize s) noexcept {\n return s != Spectral::MortarSize::Full;\n });\n\n return dg::lift_flux(\n refining ? project_from_mortar(normal_dot_numerical_fluxes, face_mesh,\n mortar_mesh, mortar_size)\n : std::move(normal_dot_numerical_fluxes),\n extent_perpendicular_to_boundary,\n std::forward(local_data).magnitude_of_face_normal);\n}\n} // namespace dg\n", "meta": {"hexsha": "0f6ba63764c658e86dd7e20a6c8b437fce8d069c", "size": 9407, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/NumericalAlgorithms/DiscontinuousGalerkin/MortarHelpers.hpp", "max_stars_repo_name": "desperadoshi/spectre", "max_stars_repo_head_hexsha": "b61c12dce108a98a875a1e9476e5630bea634119", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/NumericalAlgorithms/DiscontinuousGalerkin/MortarHelpers.hpp", "max_issues_repo_name": "desperadoshi/spectre", "max_issues_repo_head_hexsha": "b61c12dce108a98a875a1e9476e5630bea634119", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/NumericalAlgorithms/DiscontinuousGalerkin/MortarHelpers.hpp", "max_forks_repo_name": "desperadoshi/spectre", "max_forks_repo_head_hexsha": "b61c12dce108a98a875a1e9476e5630bea634119", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.1513761468, "max_line_length": 80, "alphanum_fraction": 0.7110662273, "num_tokens": 2217, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832058771036, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.2236235100222525}} {"text": "/************************************************************************\n *\n *\n * Copyright 2015 Arun Das (University of Waterloo)\n * [adas@uwaterloo.ca]\n * James Servos (University of Waterloo)\n * [jdservos@uwaterloo.ca]\n *\n *\n *************************************************************************/\n#include \n#include \n#include \n\nnamespace wave {\n\nbool compareSignalPoints(const signalPoint &a, const signalPoint &b) {\n return a.height < b.height;\n}\n\ngroundSegmentation::groundSegmentation(GroundSegmentationParams config) {\n this->params = config;\n this->pBG = new polarBinGrid;\n initializePolarBinGrid();\n}\n\nvoid groundSegmentation::initializePolarBinGrid(void) {\n this->pBG->aCell.resize(this->params.num_bins_a);\n for (int i = 0; i < this->params.num_bins_a; i++) {\n this->pBG->aCell[i].sigPoints.clear();\n this->pBG->aCell[i].lCell.resize(this->params.num_bins_l);\n this->pBG->aCell[i].sigPoints.resize(this->params.num_bins_l);\n this->pBG->aCell[i].rangeHeightSignal.resize(this->params.num_bins_l);\n std::vector().swap(pBG->aCell[i].sigPoints);\n for (int j = 0; j < this->params.num_bins_l; j++) {\n pBG->aCell[i].lCell[j].binPoints.clear();\n pBG->aCell[i].lCell[j].obsPoints.clear();\n pBG->aCell[i].lCell[j].drvPoints.clear();\n pBG->aCell[i].lCell[j].groundPoints.clear();\n pBG->aCell[i].lCell[j].prototypePoint = PointXYZGD();\n pBG->aCell[i].lCell[j].prototypePoint.x = NAN;\n pBG->aCell[i].lCell[j].prototypePoint.y = NAN;\n pBG->aCell[i].lCell[j].prototypePoint.z = NAN;\n pBG->aCell[i].rangeHeightSignal[j].x = NAN;\n pBG->aCell[i].rangeHeightSignal[j].y = NAN;\n pBG->aCell[i].lCell[j].cAssigned = -1;\n\n // force memory deletion of std::vectors\n std::vector().swap(pBG->aCell[i].lCell[j].binPoints);\n std::vector().swap(pBG->aCell[i].lCell[j].obsPoints);\n std::vector().swap(pBG->aCell[i].lCell[j].drvPoints);\n std::vector().swap(pBG->aCell[i].lCell[j].groundPoints);\n }\n }\n}\n\nvoid groundSegmentation::setupGroundSegmentation(\n pcl::PointCloud::Ptr inputCloud,\n pcl::PointCloud::Ptr groundCloud,\n pcl::PointCloud::Ptr obsCloud,\n pcl::PointCloud::Ptr drvCloud) {\n groundCloud->clear();\n obsCloud->clear();\n\n refCloud = inputCloud; // set the cloud datastructure;\n oCloud = obsCloud; // set obs cloud\n gCloud = groundCloud; // set ground cloud\n dCloud = drvCloud;\n\n oCloud->clear();\n gCloud->clear();\n dCloud->clear();\n\n genPolarBinGrid(refCloud);\n}\n\nvoid groundSegmentation::genPolarBinGrid(\n pcl::PointCloud::Ptr inputCloud) {\n initializePolarBinGrid();\n\n size_t nPts = inputCloud->size();\n double bsize_rad = (double) ((360.0) / this->params.num_bins_a);\n double bsize_lin = (double) this->params.rmax / this->params.num_bins_l;\n for (size_t i = 0; i < nPts; i++) {\n PointXYZGD curPt;\n float px = curPt.x = inputCloud->points[i].x;\n float py = curPt.y = inputCloud->points[i].y;\n float pz = curPt.z = inputCloud->points[i].z;\n\n if (sqrt(px * px + py * py + pz * pz) < this->params.rmax) {\n double ph = (atan2(py, px)) * (180 / M_PI); // in degrees\n if (ph < 0)\n ph = 360.0 + ph;\n\n // bin into sector\n\n unsigned int bind_rad =\n static_cast(ph / bsize_rad); // got the radial bin\n\n if (bind_rad == this->params.num_bins_a) {\n bind_rad = 0;\n }\n\n // get the linear bin\n float xyDist = sqrt(px * px + py * py);\n\n unsigned int bind_lin = static_cast(\n xyDist / bsize_lin); // got the radial bin\n\n pBG->aCell[bind_rad].lCell[bind_lin].binPoints.push_back(curPt);\n // add the point to the bin\n // check the protoype point\n\n if (isnanf(pBG->aCell[bind_rad].lCell[bind_lin].prototypePoint.z) ||\n pz < pBG->aCell[bind_rad]\n .lCell[bind_lin]\n .prototypePoint.z) // smallest by z\n {\n pBG->aCell[bind_rad].lCell[bind_lin].prototypePoint = curPt;\n pBG->aCell[bind_rad].rangeHeightSignal[bind_lin].x = xyDist;\n pBG->aCell[bind_rad].rangeHeightSignal[bind_lin].y = pz;\n }\n }\n }\n}\n\n\nMatX groundSegmentation::genGPModel(std::vector &ps1,\n std::vector &ps2,\n float sig_f,\n float p_l) {\n size_t nP1 = ps1.size();\n size_t nP2 = ps2.size();\n\n MatX CMAT;\n CMAT.resize(nP1, nP2);\n float coeff = (-1 / (2 * p_l * p_l));\n\n for (size_t i = 0; i < nP1; i++) {\n for (size_t j = 0; j < nP2; j++) {\n double diff = (ps1[i].range - ps2[j].range);\n CMAT(i, j) = sig_f * exp(coeff * (diff * diff));\n }\n }\n return CMAT;\n}\n\nvoid groundSegmentation::segmentGround() {\n for (int i = 0; i < this->params.num_bins_a; i++) {\n sectorINSAC(i);\n }\n}\n\nvoid groundSegmentation::sectorINSAC(int sectorIndex) {\n if (sectorIndex >= this->params.num_bins_a) {\n return;\n }\n int numFilled = 0;\n\n // pull out the valid points from the sector\n std::vector &sigPtr = pBG->aCell[sectorIndex].sigPoints;\n sigPtr.clear();\n for (int i = 0; i < this->params.num_bins_l; i++) {\n if (!std::isnan(pBG->aCell[sectorIndex].rangeHeightSignal[i].x) &&\n pBG->aCell[sectorIndex].lCell[i].binPoints.size() > 5) {\n // bin has a valid point, and enough points to make a good\n // guess for a protopoint\n signalPoint newPoint;\n newPoint.range = pBG->aCell[sectorIndex].rangeHeightSignal[i].x;\n newPoint.height = pBG->aCell[sectorIndex].rangeHeightSignal[i].y;\n newPoint.idx = i;\n sigPtr.push_back(newPoint);\n numFilled++;\n }\n }\n // get the seed points. Select the 3 lowest points. Sort based on height\n // values\n sort(sigPtr.begin(), sigPtr.end(), compareSignalPoints);\n\n // now that the z points are sorted by height, take the\n // this->params.num_seed_points worth\n // as the seed\n size_t npt =\n sigPtr.size() < static_cast(this->params.num_seed_points)\n ? sigPtr.size()\n : static_cast(this->params.num_seed_points);\n std::vector currentModel;\n int ptCtr = 0;\n int currIdx = 0;\n bool keepGoing = true;\n bool sufficientModel = true;\n\n while (true) {\n if (static_cast(currIdx) >= sigPtr.size()) // overflow\n {\n break;\n }\n\n if (sigPtr[currIdx].range < this->params.max_seed_range &&\n fabs(sigPtr[currIdx].height) < this->params.max_seed_height) {\n // close enough to\n // robot and height\n // makese sense in\n // robot locality\n\n sigPtr[currIdx].isGround = true;\n currentModel.push_back(sigPtr[currIdx]);\n sigPtr.erase(sigPtr.begin() + currIdx);\n ptCtr++;\n\n } else {\n currIdx++;\n }\n\n if (static_cast(ptCtr) >= npt) // done\n {\n break;\n }\n }\n\n // check size\n if (currentModel.size() < 2) // not enough for model, all obs pts\n {\n keepGoing = false;\n sufficientModel = false;\n }\n\n // got the seedpoints, start theINSAC process\n // cov matrices\n MatX C_XsX;\n MatX C_XX;\n MatX C_XsXs;\n MatX C_XXs;\n\n if (sigPtr.size() == 0)\n // no points to insac, put the seed points in as ground\n keepGoing = false;\n\n MatX temp;\n MatX f_s;\n MatX Vf_s;\n while (keepGoing) {\n // generate the covariance matrices\n\n C_XsX =\n genGPModel(sigPtr, currentModel, this->params.p_sf, this->params.p_l);\n C_XX = genGPModel(\n currentModel, currentModel, this->params.p_sf, this->params.p_l);\n C_XsXs =\n genGPModel(sigPtr, sigPtr, this->params.p_sf, this->params.p_l);\n C_XXs = C_XsX.transpose();\n\n // temporary calc\n MatX tCalc1 =\n C_XX + (this->params.p_sn * MatX::Identity(C_XX.rows(), C_XX.cols()));\n MatX tCalc2 = C_XsX * tCalc1.inverse();\n\n // test the points against the current model\n\n MatX modelZ(currentModel.size(), 1);\n for (unsigned int i = 0; i < currentModel.size(); i++) {\n modelZ(i, 0) = currentModel[i].height;\n }\n\n f_s = tCalc2 * modelZ;\n Vf_s = C_XsXs - tCalc2 * C_XXs;\n\n if (Vf_s.rows() == 0) {\n keepGoing = false;\n LOG_INFO(\"WARNING BREAKING LOOP: VF_s does not exist\");\n continue;\n }\n\n bool searchCandidatePoints = true;\n unsigned int k = 0;\n // test for inliers using INSAC algorithm\n int startSize = currentModel.size(); // beginning size of the model set\n while (searchCandidatePoints) {\n double vf = Vf_s(k, k);\n double met =\n (sigPtr[k].height - f_s(k)) / (sqrt(this->params.p_sn + vf * vf));\n\n if (vf < this->params.p_tmodel &&\n abs(met) < this->params.p_tdata) { // we have an inlier!\n // add to model set\n currentModel.push_back(sigPtr[k]);\n // remove from sample set\n sigPtr.erase(sigPtr.begin() + k);\n\n // delete row from f_s\n temp = f_s;\n f_s.resize(f_s.rows() - 1, f_s.cols());\n f_s.topRows(k) = temp.topRows(k);\n f_s.bottomRows(temp.rows() - k - 1) =\n temp.bottomRows(temp.rows() - k - 1);\n\n // delete row from Vf_s\n temp = Vf_s;\n Vf_s.resize(Vf_s.rows() - 1, Vf_s.cols());\n Vf_s.topRows(k) = temp.topRows(k);\n Vf_s.bottomRows(temp.rows() - k - 1) =\n temp.bottomRows(temp.rows() - k - 1);\n\n // delete col from Vf_s\n temp = Vf_s;\n Vf_s.resize(Vf_s.rows(), Vf_s.cols() - 1);\n Vf_s.leftCols(k) = temp.leftCols(k);\n Vf_s.rightCols(temp.cols() - k - 1) =\n temp.rightCols(temp.cols() - k - 1);\n\n } else {\n k++;\n }\n\n if (sigPtr.size() == k) {\n searchCandidatePoints = false;\n }\n }\n\n int endSize = currentModel.size(); // end size of the model set\n if (startSize == endSize || sigPtr.size() == 0) {\n keepGoing = false;\n }\n } // end INSAC\n\n // fill in the ground and obs pointclouds\n\n double numObs = 0;\n Vec3 obsSum(0, 0, 0);\n\n for (int i = 0; i < (int) currentModel.size(); i++) {\n int currIdx = currentModel[i].idx;\n linCell *curCell = &pBG->aCell[sectorIndex].lCell[currIdx];\n\n // go through all the points in this cell and assign to ground/not\n // ground\n for (unsigned int j = 0; j < curCell->binPoints.size(); j++) {\n float h = abs(currentModel[i].height - curCell->binPoints[j].z);\n if (h < this->params.p_tg) // z heights are close\n {\n gCloud->push_back(\n curCell->binPoints[j]); // add the point to ground\n curCell->groundPoints.push_back(curCell->binPoints[j]);\n } else {\n // check drivability\n if (h > this->params.robot_height) {\n // drivable\n curCell->binPoints[j].drivable = 1;\n } else {\n curCell->binPoints[j].drivable = 0;\n dCloud->push_back(curCell->binPoints[j]); // add to obs\n curCell->drvPoints.push_back(curCell->binPoints[j]);\n }\n oCloud->push_back(curCell->binPoints[j]); // add to obs\n curCell->obsPoints.push_back(curCell->binPoints[j]);\n obsSum += Vec3(curCell->binPoints[j].x,\n curCell->binPoints[j].y,\n curCell->binPoints[j].z);\n numObs++;\n }\n }\n // mean of obs points\n curCell->obsMean = obsSum / numObs;\n }\n\n // FIXME: WHY IS F_S < SIGPTR SOMETIMES?\n int i;\n if (sufficientModel) {\n // add all the obs points from the non ground classified pts\n for (i = 0; i < (int) sigPtr.size(); i++) {\n linCell *curCell = &pBG->aCell[sectorIndex].lCell[sigPtr[i].idx];\n\n for (int j = 0; j < (int) curCell->binPoints.size(); j++) {\n float h = abs(curCell->binPoints[j].z - f_s(i));\n // check drivability\n if (h > this->params.robot_height) {\n // drivable\n curCell->binPoints[j].drivable = 1;\n } else {\n curCell->binPoints[j].drivable = 0;\n dCloud->push_back(curCell->binPoints[j]); // add to obs\n curCell->drvPoints.push_back(curCell->binPoints[j]);\n }\n oCloud->push_back(curCell->binPoints[j]); // add to obs\n curCell->obsPoints.push_back(curCell->binPoints[j]);\n obsSum += Vec3(curCell->binPoints[j].x,\n curCell->binPoints[j].y,\n curCell->binPoints[j].z);\n numObs++;\n }\n // mean of obs points\n curCell->obsMean = obsSum / numObs;\n }\n } else {\n LOG_INFO(\"WARNING:Insufficnent Model for angular slice\");\n }\n}\n\n} // namespace wave\n", "meta": {"hexsha": "4043cd2065b7d69b116c02030d55ea5b77413d13", "size": 14190, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "wave_matching/src/groundSegmentation.cpp", "max_stars_repo_name": "Jebediah/libwave", "max_stars_repo_head_hexsha": "c04998c964f0dc7d414783c6e8cf989a2716ad54", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2019-06-13T13:47:18.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-13T14:54:35.000Z", "max_issues_repo_path": "wave_matching/src/groundSegmentation.cpp", "max_issues_repo_name": "Jebediah/libwave", "max_issues_repo_head_hexsha": "c04998c964f0dc7d414783c6e8cf989a2716ad54", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "wave_matching/src/groundSegmentation.cpp", "max_forks_repo_name": "Jebediah/libwave", "max_forks_repo_head_hexsha": "c04998c964f0dc7d414783c6e8cf989a2716ad54", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2020-02-13T02:27:29.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-13T02:27:29.000Z", "avg_line_length": 35.475, "max_line_length": 80, "alphanum_fraction": 0.5306553911, "num_tokens": 3707, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526514141572, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.22357418291815262}} {"text": "#include \"it/Entropy.hpp\"\n#include \"binomial.hpp\"\n#include \n#include \n#include \n\n#if BOOST_PYTHON_EXTENSIONS\n#include \n#endif\n\n#include \"algorithm/TupleSpace.hpp\"\n#include \"it/EntropyCalculator.hpp\"\n\nusing namespace mist;\nusing namespace mist::algorithm;\n\nTupleSpace::TupleSpace()\n : tuple_size(0){};\nTupleSpace::~TupleSpace(){};\n\n// default space for N variables in tuples size d\nTupleSpace::TupleSpace(int N, int d)\n : tuple_size(d)\n{\n if (N == 0 || d == 0) {\n throw TupleSpaceException(\"TupleSpace\",\n \"Number of variables and dimension cannot be zero.\");\n }\n tuple_t vars(N);\n for (int ii = 0; ii < N; ii++) {\n vars[ii] = ii;\n }\n this->addVariableGroup(\"default\", vars);\n tuple_t groupTuple(d, 0);\n this->addVariableGroupTuple(groupTuple);\n};\n\nstd::vector\nTupleSpace::names() const\n{\n return variableNames;\n}\n\nvoid\nTupleSpace::set_names(std::vector const& names)\n{\n variableNames = names;\n}\n\nint\nTupleSpace::tupleSize() const\n{\n return tuple_size;\n}\n\nvoid\nTupleSpace::addVariableGroupTuple(std::vector const& groupNames)\n{\n TupleSpace::tuple_t groupIndexes;\n for (auto& name : groupNames) {\n try {\n groupIndexes.push_back(variableGroupNames[name]);\n } catch (std::out_of_range& e) {\n throw TupleSpaceException(\"addVariableGroupTuple\",\n \"group named \" + name + \" does not exist.\");\n }\n }\n addVariableGroupTuple(groupIndexes);\n}\n\nvoid\nTupleSpace::addVariableGroupTuple(TupleSpace::tuple_t const& groupIndexes)\n{\n // validate tuple size\n if (!tuple_size) {\n tuple_size = groupIndexes.size();\n } else if (tuple_size != groupIndexes.size()) {\n throw TupleSpaceException(\n \"addVariableGroupTuple\",\n \"Could not add group tuple, all tuples must be the same size\");\n }\n // validate group indexes\n for (auto group : groupIndexes) {\n if (group >= variableGroups.size()) {\n throw TupleSpaceException(\"addVariableGroupTuple\",\n \"variable group index \" +\n std::to_string(group) + \" out of range.\");\n }\n }\n variableGroupTuples.push_back(groupIndexes);\n}\n\nint\nTupleSpace::addVariableGroup(std::string const& name,\n TupleSpace::tuple_t const& vars)\n{\n std::set unique_vars;\n tuple_t group;\n for (auto var : vars) {\n // ignore duplicates within variable group\n if (unique_vars.find(var) == unique_vars.end()) {\n unique_vars.insert(var);\n group.push_back(var);\n // check for overlap with other variable groups\n if (seen_vars.find(var) != seen_vars.end()) {\n throw TupleSpaceException(\n \"addVariableGroup\",\n \"WARNING: variable \" + std::to_string(var) +\n \" listed twice in variable group definitions. This exception can \"\n \"be caught if overlapping variable groups are desired (unusual).\");\n }\n seen_vars.emplace(var);\n }\n }\n std::sort(group.begin(), group.end());\n variableGroups.push_back(group);\n variableGroupSizes.push_back(group.size());\n int index = variableGroups.size() - 1;\n variableGroupNames.emplace(name, index);\n return index;\n}\n\nTupleSpace::tuple_t const&\nTupleSpace::getVariableGroup(int index) const\n{\n try {\n return variableGroups.at(index);\n } catch (std::out_of_range& e) {\n throw TupleSpaceException(\"getVariableGroup\",\n \"group index \" + std::to_string(index) +\n \" out of range.\");\n }\n}\n\nTupleSpace::tuple_t const&\nTupleSpace::getVariableGroup(std::string const& name) const\n{\n try {\n return variableGroups.at(variableGroupNames.at(name));\n } catch (std::out_of_range& e) {\n throw TupleSpaceException(\"getVariableGroup\",\n \"group named \" + name + \" does not exist.\");\n }\n}\n\nstd::vector const&\nTupleSpace::getVariableGroupSizes() const\n{\n return variableGroupSizes;\n}\n\nstd::vector const&\nTupleSpace::getVariableGroups() const\n{\n return variableGroups;\n}\n\nstd::vector const&\nTupleSpace::getVariableGroupTuples() const\n{\n return variableGroupTuples;\n}\n\n#if BOOST_PYTHON_EXTENSIONS\nint\nTupleSpace::pyAddVariableGroup(std::string const& name, p::list const& list)\n{\n int n = p::len(list);\n // copy list in\n algorithm::TupleSpace::tuple_t vars(n);\n for (int ii = 0; ii < n; ii++) {\n p::extract var(list[ii]);\n if (var.check()) {\n vars[ii] = var;\n } else {\n throw TupleSpaceException(\"pyAddVariableGroup\",\n \"Expected list with elements type int\");\n }\n }\n return addVariableGroup(name, vars);\n}\n\nvoid\nTupleSpace::pyAddVariableGroupTuple(p::list const& list)\n{\n int n = p::len(list); // Doesn't give the right answer ...\n algorithm::TupleSpace::tuple_t groups(n);\n for (int ii = 0; ii < n; ii++) {\n p::extract gint(list[ii]); //TODO segaults\n p::extract gname(list[ii]);\n if (gint.check()) {\n groups[ii] = p::extract(list[ii]);\n } else if (gname.check()) {\n std::string name(gname);\n try {\n groups[ii] = variableGroupNames.at(name);\n } catch (std::exception& e) {\n throw TupleSpaceException(\"pyAddVariableGroupTuple\",\n \"group named \" + name + \" does not exist.\");\n }\n } else {\n throw TupleSpaceException(\"pyAddVariableGroupTuple\",\n \"Expected list with elements type int or str\");\n }\n }\n addVariableGroupTuple(groups);\n}\n#endif\n\nstatic std::vector\ngroupSizes(std::vector const& groups)\n{\n std::vector group_sizes;\n for (auto& group : groups) {\n group_sizes.push_back(group.size());\n }\n return group_sizes;\n}\n\nstatic TupleSpace::tuple_t\ngroupAppearances(int d, TupleSpace::tuple_t const& group_tuple, int pos)\n{\n // count group appearances following the current index\n TupleSpace::tuple_t app(d, 0);\n for (int gg = pos; gg < group_tuple.size(); gg++) {\n app[group_tuple[gg]]++;\n }\n return app;\n}\n\nstatic TupleSpace::tuple_t\ngroupAppearances(int d, TupleSpace::tuple_t const& group_tuple)\n{\n return groupAppearances(d, group_tuple, 0);\n}\n\nTupleSpace::count_t\nTupleSpace::count_tuples_group_tuple(tuple_t const& group_tuple) const\n{\n count_t total = 1;\n auto const& N = this->variableGroupSizes;\n auto d = N.size();\n auto a = groupAppearances(d, group_tuple);\n for (int ii = 0; ii < d; ii++) {\n total *= binomial(N[ii], a[ii]);\n }\n return total;\n}\n\nTupleSpace::count_t\nTupleSpace::count_tuples() const\n{\n count_t total = 0;\n for (auto const& group_tuple : this->variableGroupTuples) {\n total += count_tuples_group_tuple(group_tuple);\n }\n return total;\n}\n\n// Fast-forward to the group containig the tuple at the target position.\nstatic int\nfind_group(TupleSpace::count_t* count, TupleSpace::count_t target, TupleSpace const& ts)\n{\n auto const& group_tuples = ts.getVariableGroupTuples();\n int gg = 0;\n for (auto const& group_tuple : group_tuples) {\n TupleSpace::count_t skip = ts.count_tuples_group_tuple(group_tuple);\n if ((skip + *count) > target) {\n break;\n }\n *count += skip;\n gg++;\n }\n return gg;\n}\n\n// Fast-forward the index at position pos so that the target tuple is found by\n// incrementing indexes in the following positions. E.g. if the target 3-tuple\n// is (1,4,8), then\n//\n// find_index(..., pos=0, ...) returns 1.\n// find_index(..., pos=1, ...) returns 4.\n// find_index(..., pos=2, ...) returns 8.\n//\nstatic int\nfind_index(TupleSpace::tuple_t const& group_tuple,\n int pos,\n TupleSpace::count_t* count,\n TupleSpace::count_t target,\n std::vector const& N,\n TupleSpace::tuple_t& starts)\n{\n unsigned ii = 0; // index\n unsigned gi = group_tuple[pos]; // group corresponding to index\n auto app = groupAppearances(N.size(), group_tuple, pos + 1);\n\n for (ii = starts[gi]; ii < N[gi]; ii++) {\n starts[gi] = ii + 1;\n // count the number of tuples skipped over by incrementing index\n // for last index skip = 1 (could be optimized with a special case)\n TupleSpace::count_t skip = 1;\n for (unsigned gg = 0; gg < N.size(); gg++) {\n // binomial combination reduces to linear when appearances == 1\n TupleSpace::count_t f = 1;\n for (unsigned kk = 0; kk < app[gg]; kk++) {\n skip = skip * (N[gg] - starts[gg] - kk);\n f *= (kk + 1);\n }\n skip /= f;\n }\n if ((skip + *count) > target) {\n break;\n }\n *count += skip;\n }\n if (ii >= N[gi]) {\n throw TupleSpaceException(\"find_index\", \"Failed to FFW index, out of range\");\n }\n return ii;\n}\n\nTupleSpace::tuple_t\nTupleSpace::find_tuple(count_t target) const\n{\n // The fast-forward algorithm maintains a skipped tuple count so that when\n // the count equals the target count we have found the target tuple.\n count_t count = 0;\n\n auto const& N = this->variableGroupSizes;\n auto const& group_tuples = this->variableGroupTuples;\n\n // scan ahead to the group tuple that generates the target tuple\n unsigned gg = find_group(&count, target, *this);\n auto tuple_size = group_tuples[gg].size();\n tuple_t ret(tuple_size + 1);\n ret[0] = gg;\n\n // scan to the target tuple\n tuple_t starts(N.size(), 0);\n for (unsigned ii = 0; ii < tuple_size; ii++) {\n ret[ii + 1] = find_index(group_tuples[gg], ii, &count, target, N, starts);\n }\n\n return ret;\n}\n\nstatic void\ntraverse_d1(TupleSpace const& ts, TupleSpace::count_t start, TupleSpace::count_t stop, TupleSpaceTraverser& traverser)\n{\n auto const& groups = ts.getVariableGroups();\n auto const& group_tuples = ts.getVariableGroupTuples();\n auto const& N = ts.getVariableGroupSizes();\n unsigned ngroups = groups.size();\n unsigned ngtuples = group_tuples.size();\n\n // tuple generation state\n bool init = true;\n bool work = true;\n auto count = start;\n TupleSpace::tuple_t starts(ngroups);\n starts.assign(ngroups,0);\n\n // fast-forward to starting group and tuple\n auto ffw = ts.find_tuple(start);\n\n // sub tuples on the stack\n TupleSpace::tuple_t t0(1);\n\n for (unsigned gg = ffw[0]; gg < ngtuples && work; gg++) {\n unsigned g0 = group_tuples[gg][0];\n\n // loop through all tuples generated by this group_tuple\n for (unsigned i0 = (init) ? ffw[1] : starts[g0]; i0 < N[g0] && work; i0++) {\n starts[g0] = i0 + 1;\n auto v0 = groups[g0][i0];\n t0[0] = v0;\n traverser.process_tuple(count, t0);\n count++;\n init = false;\n work = count < stop;\n }\n starts[g0] = 0;\n }\n}\n\nstatic void\ntraverse_d2(TupleSpace const& ts, TupleSpace::count_t start, TupleSpace::count_t stop, TupleSpaceTraverser& traverser)\n{\n auto const& groups = ts.getVariableGroups();\n auto const& group_tuples = ts.getVariableGroupTuples();\n auto const& N = ts.getVariableGroupSizes();\n unsigned ngroups = groups.size();\n unsigned ngtuples = group_tuples.size();\n\n // tuple generation state\n bool init = true;\n bool work = true;\n auto count = start;\n TupleSpace::tuple_t starts(ngroups);\n starts.assign(ngroups,0);\n\n // fast-forward to starting group and tuple\n auto ffw = ts.find_tuple(start);\n\n // sub tuples on the stack\n TupleSpace::tuple_t t01(2);\n\n for (unsigned gg = ffw[0]; gg < ngtuples && work; gg++) {\n unsigned g0 = group_tuples[gg][0];\n unsigned g1 = group_tuples[gg][1];\n\n // loop through all tuples generated by this group_tuple\n for (unsigned i0 = (init) ? ffw[1] : starts[g0]; i0 < N[g0] && work; i0++) {\n starts[g0] = i0 + 1;\n auto v0 = groups[g0][i0];\n t01[0] = v0;\n for (unsigned i1 = (init) ? ffw[2] : starts[g1]; i1 < N[g1] && work; i1++) {\n starts[g1] = i1 + 1;\n auto v1 = groups[g1][i1];\n t01[1] = v1;\n traverser.process_tuple(count, t01);\n count++;\n init = false;\n work = count < stop;\n }\n starts[g1] = 0;\n }\n starts[g0] = 0;\n }\n}\n\nstatic void\ntraverse_d2_entropy(TupleSpace const& ts, TupleSpace::count_t start, TupleSpace::count_t stop, TupleSpaceTraverser& traverser, it::EntropyCalculator & ecalc)\n{\n auto const& groups = ts.getVariableGroups();\n auto const& group_tuples = ts.getVariableGroupTuples();\n auto const& N = ts.getVariableGroupSizes();\n unsigned ngroups = groups.size();\n unsigned ngtuples = group_tuples.size();\n\n // tuple generation state\n bool init = true;\n bool work = true;\n auto count = start;\n TupleSpace::tuple_t starts(ngroups);\n starts.assign(ngroups,0);\n it::Entropy entropy((unsigned)it::d2::size);\n\n // fast-forward to starting group and tuple\n auto ffw = ts.find_tuple(start);\n\n // sub tuples on the stack\n TupleSpace::tuple_t t0(1);\n TupleSpace::tuple_t t1(1);\n TupleSpace::tuple_t t01(2);\n\n for (unsigned gg = ffw[0]; gg < ngtuples && work; gg++) {\n unsigned g0 = group_tuples[gg][0];\n unsigned g1 = group_tuples[gg][1];\n\n // loop through all tuples generated by this group_tuple\n for (unsigned i0 = (init) ? ffw[1] : starts[g0]; i0 < N[g0] && work; i0++) {\n starts[g0] = i0 + 1;\n auto v0 = groups[g0][i0];\n t0[0] = v0;\n t01[0] = v0;\n entropy[(unsigned)it::d2::e0] = ecalc.entropy(t0);\n for (unsigned i1 = (init) ? ffw[2] : starts[g1]; i1 < N[g1] && work; i1++) {\n starts[g1] = i1 + 1;\n auto v1 = groups[g1][i1];\n t1[0] = v1;\n t01[1] = v1;\n entropy[(unsigned)it::d2::e1] = ecalc.entropy(t1);\n entropy[(unsigned)it::d2::e01] = ecalc.entropy(t01);\n traverser.process_tuple_entropy(count, t01, entropy);\n count++;\n init = false;\n work = count < stop;\n }\n starts[g1] = 0;\n }\n starts[g0] = 0;\n }\n}\n\nstatic void\ntraverse_d3(TupleSpace const& ts, TupleSpace::count_t start, TupleSpace::count_t stop, TupleSpaceTraverser& traverser)\n{\n auto const& groups = ts.getVariableGroups();\n auto const& group_tuples = ts.getVariableGroupTuples();\n auto const& N = ts.getVariableGroupSizes();\n unsigned ngroups = groups.size();\n unsigned ngtuples = group_tuples.size();\n\n // tuple generation state\n bool init = true;\n bool work = true;\n auto count = start;\n TupleSpace::tuple_t starts(ngroups);\n starts.assign(ngroups,0);\n\n // fast-forward to starting group and tuple\n auto ffw = ts.find_tuple(start);\n\n // sub tuples on the stack\n TupleSpace::tuple_t t012(3);\n\n for (unsigned gg = ffw[0]; gg < ngtuples && work; gg++) {\n unsigned g0 = group_tuples[gg][0];\n unsigned g1 = group_tuples[gg][1];\n unsigned g2 = group_tuples[gg][2];\n\n // loop through all tuples generated by this group_tuple\n for (unsigned i0 = (init) ? ffw[1] : starts[g0]; i0 < N[g0] && work; i0++) {\n starts[g0] = i0 + 1;\n auto v0 = groups[g0][i0];\n t012[0] = v0;\n for (unsigned i1 = (init) ? ffw[2] : starts[g1]; i1 < N[g1] && work; i1++) {\n starts[g1] = i1 + 1;\n auto v1 = groups[g1][i1];\n t012[1] = v1;\n for (unsigned i2 = (init) ? ffw[3] : starts[g2]; i2 < N[g2] && work; i2++) {\n starts[g2] = i2 + 1;\n auto v2 = groups[g2][i2];\n t012[2] = v2;\n traverser.process_tuple(count, t012);\n count++;\n init = false;\n work = count < stop;\n }\n starts[g2] = 0;\n }\n starts[g1] = 0;\n }\n starts[g0] = 0;\n }\n}\n\nstatic void\ntraverse_d3_entropy(TupleSpace const& ts, TupleSpace::count_t start, TupleSpace::count_t stop, TupleSpaceTraverser& traverser, it::EntropyCalculator & ecalc)\n{\n auto const& groups = ts.getVariableGroups();\n auto const& group_tuples = ts.getVariableGroupTuples();\n auto const& N = ts.getVariableGroupSizes();\n unsigned ngroups = groups.size();\n unsigned ngtuples = group_tuples.size();\n\n // tuple generation state\n bool init = true;\n bool work = true;\n auto count = start;\n TupleSpace::tuple_t starts(ngroups);\n starts.assign(ngroups,0);\n it::Entropy entropy((unsigned)it::d3::size);\n\n // sub tuples on the stack\n TupleSpace::tuple_t t0(1);\n TupleSpace::tuple_t t1(1);\n TupleSpace::tuple_t t2(1);\n TupleSpace::tuple_t t01(2);\n TupleSpace::tuple_t t02(2);\n TupleSpace::tuple_t t12(2);\n TupleSpace::tuple_t t012(3);\n\n // fast-forward to starting group and tuple\n auto ffw = ts.find_tuple(start);\n\n for (unsigned gg = ffw[0]; gg < ngtuples && work; gg++) {\n unsigned g0 = group_tuples[gg][0];\n unsigned g1 = group_tuples[gg][1];\n unsigned g2 = group_tuples[gg][2];\n\n // loop through all tuples generated by this group_tuple\n for (unsigned i0 = (init) ? ffw[1] : starts[g0]; i0 < N[g0] && work; i0++) {\n starts[g0] = i0 + 1;\n auto v0 = groups[g0][i0];\n t0[0] = v0;\n t02[0] = v0;\n t01[0] = v0;\n t012[0] = v0;\n entropy[(unsigned)it::d3::e0] = ecalc.entropy(t0);\n for (unsigned i1 = (init) ? ffw[2] : starts[g1]; i1 < N[g1] && work; i1++) {\n starts[g1] = i1 + 1;\n auto v1 = groups[g1][i1];\n t1[0] = v1;\n t01[1] = v1;\n t12[0] = v1;\n t012[1] = v1;\n entropy[(unsigned)it::d3::e1] = ecalc.entropy(t1);\n entropy[(unsigned)it::d3::e01] = ecalc.entropy(t01);\n for (unsigned i2 = (init) ? ffw[3] : starts[g2]; i2 < N[g2] && work; i2++) {\n starts[g2] = i2 + 1;\n auto v2 = groups[g2][i2];\n t2[0] = v2;\n t02[1] = v2;\n t12[1] = v2;\n t012[2] = v2;\n entropy[(unsigned)it::d3::e2] = ecalc.entropy(t2);\n entropy[(unsigned)it::d3::e02] = ecalc.entropy(t02);\n entropy[(unsigned)it::d3::e12] = ecalc.entropy(t12);\n entropy[(unsigned)it::d3::e012] = ecalc.entropy(t012);\n traverser.process_tuple_entropy(count, t012, entropy);\n count++;\n init = false;\n work = count < stop;\n }\n starts[g2] = 0;\n }\n starts[g1] = 0;\n }\n starts[g0] = 0;\n }\n}\n\nstatic void\ntraverse_d4(TupleSpace const& ts, TupleSpace::count_t start, TupleSpace::count_t stop, TupleSpaceTraverser& traverser)\n{\n auto const& groups = ts.getVariableGroups();\n auto const& group_tuples = ts.getVariableGroupTuples();\n auto const& N = ts.getVariableGroupSizes();\n unsigned ngroups = groups.size();\n unsigned ngtuples = group_tuples.size();\n\n // tuple generation state\n bool init = true;\n bool work = true;\n auto count = start;\n TupleSpace::tuple_t starts(ngroups);\n starts.assign(ngroups,0);\n\n // fast-forward to starting group and tuple\n auto ffw = ts.find_tuple(start);\n\n // sub tuples on the stack\n TupleSpace::tuple_t t0123(4);\n\n for (unsigned gg = ffw[0]; gg < ngtuples && work; gg++) {\n unsigned g0 = group_tuples[gg][0];\n unsigned g1 = group_tuples[gg][1];\n unsigned g2 = group_tuples[gg][2];\n unsigned g3 = group_tuples[gg][3];\n\n // loop through all tuples generated by this group_tuple\n for (unsigned i0 = (init) ? ffw[1] : starts[g0]; i0 < N[g0] && work; i0++) {\n starts[g0] = i0 + 1;\n auto v0 = groups[g0][i0];\n t0123[0] = v0;\n for (unsigned i1 = (init) ? ffw[2] : starts[g1]; i1 < N[g1] && work; i1++) {\n starts[g1] = i1 + 1;\n auto v1 = groups[g1][i1];\n t0123[1] = v1;\n for (unsigned i2 = (init) ? ffw[3] : starts[g2]; i2 < N[g2] && work; i2++) {\n starts[g2] = i2 + 1;\n auto v2 = groups[g2][i2];\n t0123[2] = v2;\n for (unsigned i3 = (init) ? ffw[4] : starts[g3]; i3 < N[g3] && work; i3++) {\n starts[g3] = i3 + 1;\n auto v3 = groups[g3][i3];\n t0123[3] = v3;\n traverser.process_tuple(count, t0123);\n count++;\n init = false;\n work = count < stop;\n }\n starts[g3] = 0;\n }\n starts[g2] = 0;\n }\n starts[g1] = 0;\n }\n starts[g0] = 0;\n }\n}\n\nstatic void\ntraverse_d4_entropy(TupleSpace const& ts, TupleSpace::count_t start, TupleSpace::count_t stop, TupleSpaceTraverser& traverser, it::EntropyCalculator & ecalc)\n{\n auto const& groups = ts.getVariableGroups();\n auto const& group_tuples = ts.getVariableGroupTuples();\n auto const& N = ts.getVariableGroupSizes();\n unsigned ngroups = groups.size();\n unsigned ngtuples = group_tuples.size();\n\n // tuple generation state\n bool init = true;\n bool work = true;\n auto count = start;\n TupleSpace::tuple_t starts(ngroups);\n starts.assign(ngroups,0);\n it::Entropy entropy((unsigned)it::d4::size);\n\n // sub tuples on the stack\n TupleSpace::tuple_t t0(1);\n TupleSpace::tuple_t t1(1);\n TupleSpace::tuple_t t2(1);\n TupleSpace::tuple_t t3(1);\n TupleSpace::tuple_t t01(2);\n TupleSpace::tuple_t t02(2);\n TupleSpace::tuple_t t03(2);\n TupleSpace::tuple_t t12(2);\n TupleSpace::tuple_t t13(2);\n TupleSpace::tuple_t t23(2);\n TupleSpace::tuple_t t012(3);\n TupleSpace::tuple_t t013(3);\n TupleSpace::tuple_t t023(3);\n TupleSpace::tuple_t t123(3);\n TupleSpace::tuple_t t0123(4);\n\n // fast-forward to starting group and tuple\n auto ffw = ts.find_tuple(start);\n\n for (unsigned gg = ffw[0]; gg < ngtuples && work; gg++) {\n unsigned g0 = group_tuples[gg][0];\n unsigned g1 = group_tuples[gg][1];\n unsigned g2 = group_tuples[gg][2];\n unsigned g3 = group_tuples[gg][3];\n\n // loop through all tuples generated by this group_tuple\n for (unsigned i0 = (init) ? ffw[1] : starts[g0]; i0 < N[g0] && work; i0++) {\n starts[g0] = i0 + 1;\n auto v0 = groups[g0][i0];\n t0[0] = v0;\n t01[0] = v0;\n t02[0] = v0;\n t03[0] = v0;\n t012[0] = v0;\n t013[0] = v0;\n t023[0] = v0;\n t0123[0] = v0;\n entropy[(unsigned)it::d4::e0] = ecalc.entropy(t0);\n for (unsigned i1 = (init) ? ffw[2] : starts[g1]; i1 < N[g1] && work; i1++) {\n starts[g1] = i1 + 1;\n auto v1 = groups[g1][i1];\n t1[0] = v1;\n t01[1] = v1;\n t12[0] = v1;\n t13[0] = v1;\n t012[1] = v1;\n t013[1] = v1;\n t123[0] = v1;\n t0123[1] = v1;\n entropy[(unsigned)it::d4::e1] = ecalc.entropy(t1);\n entropy[(unsigned)it::d4::e01] = ecalc.entropy(t01);\n for (unsigned i2 = (init) ? ffw[3] : starts[g2]; i2 < N[g2] && work; i2++) {\n starts[g2] = i2 + 1;\n auto v2 = groups[g2][i2];\n t2[0] = v2;\n t02[1] = v2;\n t12[1] = v2;\n t23[0] = v2;\n t012[2] = v2;\n t023[1] = v2;\n t123[1] = v2;\n t0123[2] = v2;\n entropy[(unsigned)it::d4::e2] = ecalc.entropy(t2);\n entropy[(unsigned)it::d4::e02] = ecalc.entropy(t02);\n entropy[(unsigned)it::d4::e12] = ecalc.entropy(t12);\n entropy[(unsigned)it::d4::e012] = ecalc.entropy(t012);\n for (unsigned i3 = (init) ? ffw[4] : starts[g3]; i3 < N[g3] && work; i3++) {\n starts[g3] = i3 + 1;\n auto v3 = groups[g3][i3];\n t3[0] = v3;\n t03[1] = v3;\n t13[1] = v3;\n t23[1] = v3;\n t013[2] = v3;\n t023[2] = v3;\n t123[2] = v3;\n t0123[3] = v3;\n entropy[(unsigned)it::d4::e3] = ecalc.entropy(t3);\n entropy[(unsigned)it::d4::e03] = ecalc.entropy(t03);\n entropy[(unsigned)it::d4::e13] = ecalc.entropy(t13);\n entropy[(unsigned)it::d4::e23] = ecalc.entropy(t23);\n entropy[(unsigned)it::d4::e013] = ecalc.entropy(t013);\n entropy[(unsigned)it::d4::e023] = ecalc.entropy(t023);\n entropy[(unsigned)it::d4::e123] = ecalc.entropy(t123);\n entropy[(unsigned)it::d4::e0123] = ecalc.entropy(t0123);\n traverser.process_tuple_entropy(count, t0123, entropy);\n count++;\n init = false;\n work = count < stop;\n }\n starts[g3] = 0;\n }\n starts[g2] = 0;\n }\n starts[g1] = 0;\n }\n starts[g0] = 0;\n }\n}\n\nvoid\nTupleSpace::traverse(TupleSpaceTraverser& traverser) const\n{\n traverse(0, -1, traverser);\n}\n\nvoid\nTupleSpace::traverse_entropy(it::EntropyCalculator &ecalc, TupleSpaceTraverser& traverser) const\n{\n traverse_entropy(0, -1, ecalc, traverser);\n}\n\nvoid\nTupleSpace::traverse(count_t start, count_t stop, TupleSpaceTraverser& traverser) const\n{\n switch(tuple_size) {\n case 1: traverse_d1(*this, start, stop, traverser); break;\n case 2: traverse_d2(*this, start, stop, traverser); break;\n case 3: traverse_d3(*this, start, stop, traverser); break;\n case 4: traverse_d4(*this, start, stop, traverser); break;\n default:\n throw TupleSpaceException(\"traverse\", \"Tuple size greater than 4 unsupported.\");\n }\n}\n\nvoid\nTupleSpace::traverse_entropy(count_t start, count_t stop, it::EntropyCalculator &ecalc, TupleSpaceTraverser& traverser) const\n{\n switch(tuple_size) {\n case 1:\n throw TupleSpaceException(\"traverse_entropy\", \"Tuple size 1 unsupported.\");\n case 2: traverse_d2_entropy(*this, start, stop, traverser, ecalc); break;\n case 3: traverse_d3_entropy(*this, start, stop, traverser, ecalc); break;\n case 4: traverse_d4_entropy(*this, start, stop, traverser, ecalc); break;\n default:\n throw TupleSpaceException(\"traverse\", \"Tuple size greater than 4 unsupported.\");\n }\n}\n", "meta": {"hexsha": "0c194cba9bf8242540151f76ba498131d4f0b89b", "size": 25179, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mist/algorithm/TupleSpace.cpp", "max_stars_repo_name": "andbanman/mist", "max_stars_repo_head_hexsha": "2546fb41bccea1f89a43dbdbed7ce3a257926b54", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/mist/algorithm/TupleSpace.cpp", "max_issues_repo_name": "andbanman/mist", "max_issues_repo_head_hexsha": "2546fb41bccea1f89a43dbdbed7ce3a257926b54", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 4.0, "max_issues_repo_issues_event_min_datetime": "2021-03-30T21:40:44.000Z", "max_issues_repo_issues_event_max_datetime": "2021-11-08T18:54:34.000Z", "max_forks_repo_path": "src/mist/algorithm/TupleSpace.cpp", "max_forks_repo_name": "andbanman/mist", "max_forks_repo_head_hexsha": "2546fb41bccea1f89a43dbdbed7ce3a257926b54", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.2996389892, "max_line_length": 157, "alphanum_fraction": 0.6097541602, "num_tokens": 7513, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.22347071917150488}} {"text": "#include \"GPS_receiver.hh\"\n\n#include \"aux.hh\"\n\n#include \"global_constants.hh\"\n#include \"matrix/utility.hh\"\n#include \"stochastic.hh\"\n\n#include \"cad_utility.hh\"\n\n#include \n\n#include \n\n#include \"Earth.hh\"\n#include \"Euler.hh\"\n#include \"GPS_satellites.hh\"\n#include \"Ins.hh\"\n#include \"Newton.hh\"\n\nGPS_Receiver::GPS_Receiver(Newton &ntn, _Euler_ &elr)\n : newton(&ntn),\n euler(&elr),\n MATRIX_INIT(PP, 8, 8),\n MATRIX_INIT(FF, 8, 8),\n MATRIX_INIT(PHI, 8, 8),\n VECTOR_INIT(SXH, 3),\n VECTOR_INIT(VXH, 3),\n VECTOR_INIT(CXH, 3) {\n this->default_data();\n}\n\nGPS_Receiver::GPS_Receiver(const GPS_Receiver &other)\n : newton(other.newton),\n euler(other.euler),\n gps_sats(other.gps_sats),\n MATRIX_INIT(PP, 8, 8),\n MATRIX_INIT(FF, 8, 8),\n MATRIX_INIT(PHI, 8, 8),\n VECTOR_INIT(SXH, 3),\n VECTOR_INIT(VXH, 3),\n VECTOR_INIT(CXH, 3) {\n this->default_data();\n\n /* Input File */\n memcpy(this->slot, other.slot, sizeof(this->slot));\n\n /* GPS Device parameters */\n this->del_rearth = other.del_rearth;\n this->gps_acqtime = other.gps_acqtime;\n this->gps_step = other.gps_step;\n\n this->ucfreq_noise = other.ucfreq_noise;\n this->ucfreq_noise_sigma = other.ucfreq_noise_sigma;\n this->ucfreq_noise_bcor = other.ucfreq_noise_bcor;\n this->ucbias_error = other.ucbias_error;\n\n memcpy(this->PR_BIAS, other.PR_BIAS, sizeof(this->PR_BIAS));\n memcpy(this->PR_NOISE, other.PR_NOISE, sizeof(this->PR_NOISE));\n memcpy(this->PR_NOISE_sigma, other.PR_NOISE_sigma,\n sizeof(this->PR_NOISE_sigma));\n memcpy(this->PR_NOISE_bcor, other.PR_NOISE_bcor, sizeof(this->PR_NOISE_bcor));\n memcpy(this->DR_NOISE, other.DR_NOISE, sizeof(this->DR_NOISE));\n memcpy(this->DR_NOISE_sigma, other.DR_NOISE_sigma,\n sizeof(this->DR_NOISE_sigma));\n memcpy(this->DR_NOISE_bcor, other.DR_NOISE_bcor, sizeof(this->DR_NOISE_bcor));\n\n /* GPS EKF Parameters */\n this->ppos = other.ppos;\n this->pvel = other.pvel;\n this->qpos = other.qpos;\n this->qvel = other.qvel;\n this->rpos = other.rpos;\n this->rvel = other.rvel;\n this->factr = other.factr;\n\n /* Internal variables */\n this->gps_acq = this->gps_acq;\n this->gps_epoch = other.gps_epoch;\n this->time_gps = other.time_gps;\n\n this->FF = other.FF;\n this->PHI = other.PHI;\n this->PP = other.PP;\n\n this->factq = other.factq;\n this->qclockb = other.qclockb;\n this->qclockf = other.qclockf;\n\n this->slotsum = other.slotsum;\n\n this->SXH = other.SXH;\n this->VXH = other.VXH;\n this->CXH = other.CXH;\n\n /* GPS Outputs */\n this->gdop = other.gdop;\n memcpy(this->ssii_quad, other.ssii_quad, sizeof(this->ssii_quad));\n memcpy(this->vsii_quad, other.vsii_quad, sizeof(this->vsii_quad));\n\n this->ucfreq_error = other.ucfreq_error;\n this->ucfreqm = other.ucfreqm;\n\n this->std_pos = other.std_pos;\n this->std_vel = other.std_vel;\n this->std_ucbias = other.std_ucbias;\n\n this->lat1 = other.lat1;\n this->lat2 = other.lat2;\n this->lat3 = other.lat3;\n this->lat4 = other.lat4;\n this->lon1 = other.lon1;\n this->lon2 = other.lon2;\n this->lon3 = other.lon3;\n this->lon4 = other.lon4;\n this->alt1 = other.alt1;\n this->alt2 = other.alt2;\n this->alt3 = other.alt3;\n this->alt4 = other.alt4;\n\n this->gps_pos_meas = other.gps_pos_meas;\n this->gps_vel_meas = other.gps_vel_meas;\n this->state_pos = other.state_pos;\n this->state_vel = other.state_vel;\n}\n\nGPS_Receiver &GPS_Receiver::operator=(const GPS_Receiver &other) {\n if (&other == this) return *this;\n\n this->newton = other.newton;\n this->euler = other.euler;\n this->gps_sats = other.gps_sats;\n\n /* Input File */\n memcpy(this->slot, other.slot, sizeof(this->slot));\n\n /* GPS Device parameters */\n this->del_rearth = other.del_rearth;\n this->gps_acqtime = other.gps_acqtime;\n this->gps_step = other.gps_step;\n\n this->ucfreq_noise = other.ucfreq_noise;\n this->ucfreq_noise_sigma = other.ucfreq_noise_sigma;\n this->ucfreq_noise_bcor = other.ucfreq_noise_bcor;\n this->ucbias_error = other.ucbias_error;\n\n memcpy(this->PR_BIAS, other.PR_BIAS, sizeof(this->PR_BIAS));\n memcpy(this->PR_NOISE, other.PR_NOISE, sizeof(this->PR_NOISE));\n memcpy(this->PR_NOISE_sigma, other.PR_NOISE_sigma,\n sizeof(this->PR_NOISE_sigma));\n memcpy(this->PR_NOISE_bcor, other.PR_NOISE_bcor, sizeof(this->PR_NOISE_bcor));\n memcpy(this->DR_NOISE, other.DR_NOISE, sizeof(this->DR_NOISE));\n memcpy(this->DR_NOISE_sigma, other.DR_NOISE_sigma,\n sizeof(this->DR_NOISE_sigma));\n memcpy(this->DR_NOISE_bcor, other.DR_NOISE_bcor, sizeof(this->DR_NOISE_bcor));\n\n /* GPS EKF Parameters */\n this->ppos = other.ppos;\n this->pvel = other.pvel;\n this->qpos = other.qpos;\n this->qvel = other.qvel;\n this->rpos = other.rpos;\n this->rvel = other.rvel;\n this->factr = other.factr;\n\n /* Internal variables */\n this->gps_acq = this->gps_acq;\n this->gps_epoch = other.gps_epoch;\n this->time_gps = other.time_gps;\n\n this->FF = other.FF;\n this->PHI = other.PHI;\n this->PP = other.PP;\n\n this->factq = other.factq;\n this->qclockb = other.qclockb;\n this->qclockf = other.qclockf;\n\n this->slotsum = other.slotsum;\n\n this->SXH = other.SXH;\n this->VXH = other.VXH;\n this->CXH = other.CXH;\n\n /* GPS Outputs */\n this->gdop = other.gdop;\n memcpy(this->ssii_quad, other.ssii_quad, sizeof(this->ssii_quad));\n memcpy(this->vsii_quad, other.vsii_quad, sizeof(this->vsii_quad));\n\n this->ucfreq_error = other.ucfreq_error;\n this->ucfreqm = other.ucfreqm;\n\n this->std_pos = other.std_pos;\n this->std_vel = other.std_vel;\n this->std_ucbias = other.std_ucbias;\n\n this->lat1 = other.lat1;\n this->lat2 = other.lat2;\n this->lat3 = other.lat3;\n this->lat4 = other.lat4;\n this->lon1 = other.lon1;\n this->lon2 = other.lon2;\n this->lon3 = other.lon3;\n this->lon4 = other.lon4;\n this->alt1 = other.alt1;\n this->alt2 = other.alt2;\n this->alt3 = other.alt3;\n this->alt4 = other.alt4;\n\n this->gps_pos_meas = other.gps_pos_meas;\n this->gps_vel_meas = other.gps_vel_meas;\n this->state_pos = other.state_pos;\n this->state_vel = other.state_vel;\n\n return *this;\n}\n\nvoid GPS_Receiver::default_data() {\n gps_update = 0;\n\n // setting inital acquisition flag\n gps_acq = false;\n}\n\nvoid GPS_Receiver::setup_state_covariance_matrix(double factp, double pclockb,\n double pclockf) {\n PP = arma::mat88(arma::fill::zeros);\n for (int i = 0; i < 3; i++) {\n PP(i, i) = pow(ppos * (1 + factp), 2);\n PP(i + 3, i + 3) = pow(pvel * (1 + factp), 2);\n }\n PP(6, 6) = pow(pclockb * (1 + factp), 2);\n PP(7, 7) = pow(pclockf * (1 + factp), 2);\n\n return;\n}\n\nvoid GPS_Receiver::setup_error_covariance_matrix(double factq, double qclockb,\n double qclockf) {\n this->factq = factq;\n this->qclockb = qclockb;\n this->qclockf = qclockf;\n\n return;\n}\n\nvoid GPS_Receiver::setup_fundamental_dynamic_matrix(double uctime_cor) {\n // fundamental dynamic matrix of filter - constant throughout\n FF = arma::mat88(arma::fill::zeros);\n FF(0, 3) = 1;\n FF(1, 4) = 1;\n FF(2, 5) = 1;\n FF(6, 7) = 1;\n FF(7, 7) = -1 / uctime_cor;\n\n return;\n}\n\nvoid GPS_Receiver::initialize(GPS_Satellites *sats, double int_step) {\n gps_sats = sats;\n\n // state transition matrix - constant throughout\n PHI = arma::mat88(arma::fill::eye) + FF * int_step +\n FF * FF * (int_step * int_step / 2);\n\n // initializing update clock\n gps_epoch = get_elapsed_time();\n}\n\nvoid GPS_Receiver::update_markov(double int_step) {\n int i;\n\n ucfreq_noise = markov(ucfreq_noise_sigma, ucfreq_noise_bcor,\n get_elapsed_time(), int_step, ucfreq_noise);\n for (i = 0; i < 4; i++) {\n PR_NOISE[i] = markov(PR_NOISE_sigma[i], PR_NOISE_bcor[i],\n get_elapsed_time(), int_step, PR_NOISE[i]);\n }\n for (i = 0; i < 4; i++) {\n DR_NOISE[i] = markov(DR_NOISE_sigma[i], DR_NOISE_bcor[i],\n get_elapsed_time(), int_step, DR_NOISE[i]);\n }\n}\n\nvoid GPS_Receiver::get_quadriga() {\n int i(0);\n int j(0);\n int m(0);\n\n gdop = LARGE;\n int quad[4] = {0, 0, 0, 0}; // location of quadriga SVs\n // in 'ssii_vis[visible_count]\n\n // conversion to inertial (J2000) coordinates\n double ssii[24][4];\n double sin_incl = sin(gps_sats->inclination);\n double cos_incl = cos(gps_sats->inclination);\n for (i = 0; i < 24; i++) {\n ssii[i][0] = gps_sats->radius *\n (cos(gps_sats->sv_data[i][0]) * cos(gps_sats->sv_data[i][1]) -\n sin(gps_sats->sv_data[i][0]) * sin(gps_sats->sv_data[i][1]) *\n cos_incl);\n ssii[i][1] = gps_sats->radius *\n (sin(gps_sats->sv_data[i][0]) * cos(gps_sats->sv_data[i][1]) +\n cos(gps_sats->sv_data[i][0]) * sin(gps_sats->sv_data[i][1]) *\n cos_incl);\n ssii[i][2] = gps_sats->radius * sin(gps_sats->sv_data[i][1]) * sin_incl;\n ssii[i][3] = 0;\n // last entry is a flag with the code:\n // =0:not visible; >0:visible (not int but double!), where the\n // number\n // is the SV slot# (1,2,3,...,24)\n }\n\n // determining visible satellites\n arma::vec3 SSII;\n arma::vec3 SBII = newton->get_SBII();\n int visible_count(0);\n\n for (i = 0; i < 24; i++) {\n SSII[0] = ssii[i][0];\n SSII[1] = ssii[i][1];\n SSII[2] = ssii[i][2];\n\n // SV to user angle with vertex at Earth center\n double delta = angle(SSII, SBII);\n\n // grazing angle of SV beam with vertex at Earth center\n double epsilon = acos((Earth::radius + del_rearth) / gps_sats->radius);\n\n // min radius of user to have clear line-of-site to SV\n double rmin = (Earth::radius + del_rearth) / cos(delta - epsilon);\n\n if (delta < epsilon) {\n // SV is in the visibility cone\n ssii[i][3] = i + 1;\n visible_count++;\n } else {\n // user is outside the visibility cone but high enough to have\n // LOS to the SV\n // (rmin can go negative if delta-epsilon>90deg; this can happen\n // when the user is opposite (or nearly opposite) of the SV;\n // this is always a no-visibility case)\n double dbi = norm(SBII);\n if (rmin > 0 && rmin < dbi) {\n ssii[i][3] = i + 1;\n visible_count++;\n }\n }\n }\n if (visible_count <\n 4) { // re-acquiring GPS if not enough SVs visible (less than 4)\n gps_epoch = get_elapsed_time();\n gps_acq = false;\n } else { // selecting best 4 SVs if 4 or more are visible\n // repackage visible SVs into 'ssii_vis' single-dimensioned array\n // inertial displacement vector elements are stored sequentially in\n // 'ssii_vis[4*visible_count]'\n // 'ssii_vis' has 3 inertial coordinates and SV slot# of all visible\n // SVs\n double *ssii_vis;\n ssii_vis = new double[4 * visible_count];\n int k(0);\n for (i = 0; i < 24; i++) {\n if (ssii[i][3] > 0) {\n *(ssii_vis + k) = ssii[i][0];\n *(ssii_vis + k + 1) = ssii[i][1];\n *(ssii_vis + k + 2) = ssii[i][2];\n *(ssii_vis + k + 3) = ssii[i][3];\n k = k + 4;\n }\n }\n // selecting quadriga (four SVs) with smallest GDOP\n // i1, i2, i3, i4 are the SVs picked by the binomial combination\n int nm3 = visible_count - 3; // nm3=1\n int nm2 = visible_count - 2; // nm2=2\n int nm1 = visible_count - 1; // nm1=3\n\n for (int i1 = 0; i1 < nm3; i1++) {\n for (int i2 = i1 + 1; i2 < nm2; i2++) {\n for (int i3 = i2 + 1; i3 < nm1; i3++) {\n for (int i4 = i3 + 1; i4 < visible_count; i4++) {\n // pullling the quadriga inertial coordinates\n arma::vec3 SSII1;\n arma::vec3 SSII2;\n arma::vec3 SSII3;\n arma::vec3 SSII4;\n for (m = 0; m < 3; m++) {\n SSII1[m] = *(ssii_vis + 4 * i1 + m);\n SSII2[m] = *(ssii_vis + 4 * i2 + m);\n SSII3[m] = *(ssii_vis + 4 * i3 + m);\n SSII4[m] = *(ssii_vis + 4 * i4 + m);\n }\n // calculating user wrt the SV displacement unit\n // vectors\n arma::vec UNI1 = normalise(SBII - SSII1);\n arma::vec UNI2 = normalise(SBII - SSII2);\n arma::vec UNI3 = normalise(SBII - SSII3);\n arma::vec UNI4 = normalise(SBII - SSII4);\n\n // building the GPS 'H' matrix\n arma::mat44 HGPS(arma::fill::ones);\n HGPS.col(0) = UNI1;\n HGPS.col(1) = UNI2;\n HGPS.col(2) = UNI3;\n HGPS.col(3) = UNI4;\n // calculating GDOP\n arma::mat44 COV;\n COV = arma::inv(HGPS * arma::trans(HGPS));\n double gdop_local = sqrt(sum(COV.diag()));\n\n // save slot # of quadriga SVs if GDOP has decreased\n if (gdop_local < gdop) {\n gdop = gdop_local;\n quad[0] = i1;\n quad[1] = i2;\n quad[2] = i3;\n quad[3] = i4;\n }\n }\n }\n }\n } // end of picking quadriga amongst visible SVs\n\n // extracting \"best\" quadriga from visible SVs\n // and storing inertial coordinates of the four SVs and their slot#\n // in ssii_quad[16]\n for (m = 0; m < 4; m++) {\n for (int n = 0; n < 4; n++) {\n *(ssii_quad + 4 * m + n) = *(ssii_vis + 4 * quad[m] + n);\n }\n }\n delete[] ssii_vis;\n\n // calculating inertial velocity of quadriga SVs\n // getting slot# of quadriga\n //\n int *islot = new int[i];\n for (i = 0; i < 4; i++) {\n slot[i] = *(ssii_quad + 4 * i + 3);\n // casting into an int\n islot[i] = static_cast slot[i];\n }\n // storing inertial velocities of the four SVs in vsii_quad[12]\n double sin_incl = sin(gps_sats->inclination);\n double cos_incl = cos(gps_sats->inclination);\n double vel = gps_sats->radius * gps_sats->angular_velocity;\n for (m = 0; m < 4; m++) {\n int ii = islot[m] - 1; // reminder: slot#=1,2,3...,24\n *(vsii_quad + 3 * m + 0) =\n vel *\n (-sin(gps_sats->sv_data[ii][1]) * cos(gps_sats->sv_data[ii][0]) -\n cos(gps_sats->sv_data[ii][1]) * sin(gps_sats->sv_data[ii][0]) *\n cos_incl);\n *(vsii_quad + 3 * m + 1) =\n vel *\n (-sin(gps_sats->sv_data[ii][1]) * sin(gps_sats->sv_data[ii][0]) +\n cos(gps_sats->sv_data[ii][1]) * cos(gps_sats->sv_data[ii][0]) *\n cos_incl);\n *(vsii_quad + 3 * m + 2) =\n vel * (cos(gps_sats->sv_data[ii][1]) * sin_incl);\n }\n } // end of picking quadriga from 4 or more visible SVs\n}\n\nvoid GPS_Receiver::filter_extrapolation(double int_step) {\n arma::mat88 QQ(arma::fill::zeros); // local\n\n // *** user-clock frequency and bias error growth between updates ***\n // integrating 'ucfreq_noise' Markov process to\n // obtain user-clock bias error 'ucbias_error' (trapezoidal\n // integration)\n // user-clock bias is updated at filter update epoch\n ucfreq_error = ucfreq_noise;\n ucbias_error = ucbias_error + (ucfreq_error + ucfreqm) * (int_step / 2);\n ucfreqm = ucfreq_error;\n\n // *** filter extrapolation ***\n // dynamic error covariance matrix\n for (int i = 0; i < 3; i++) {\n QQ(i, i) = pow(qpos * (1 + factq), 2);\n QQ(i + 3, i + 3) = pow(qvel * (1 + factq), 2);\n }\n QQ(6, 6) = pow(qclockb * (1 + factq), 2);\n QQ(7, 7) = pow(qclockf * (1 + factq), 2);\n\n // covariance estimate extrapolation\n PP = PHI * (PP + QQ * (int_step / 2)) * trans(PHI) + QQ * (int_step / 2);\n\n // diagnostics: st. deviations of the diagonals of the covariance matrix\n std_pos = sqrt(PP(0, 0));\n std_vel = sqrt(PP(3, 3));\n std_ucbias = sqrt(PP(6, 6));\n}\n\nvoid GPS_Receiver::measure() {\n double dtime_gps;\n /* Testing GPS timing for update and acquire */\n if (!gps_acq)\n // saving delay-time for GPS signal acquisition\n dtime_gps = gps_acqtime;\n else\n // saving delay-time for GPS update\n dtime_gps = gps_step;\n // checking when GPS update time has occured in order to initiate update\n time_gps = get_elapsed_time() - gps_epoch;\n if (time_gps < dtime_gps) {\n return;\n }\n\n gps_acq = true;\n gps_update++;\n // resetting update clock\n time_gps = 0;\n gps_epoch = get_elapsed_time();\n\n /* GPS Update and Measurement */\n double slotm(0);\n\n arma::vec8 ZZ(arma::fill::zeros);\n arma::vec8 XH(arma::fill::zeros); // local\n arma::mat88 RR(arma::fill::zeros); // local\n arma::mat88 HH(arma::fill::zeros); // local\n\n // ***\n // SV propagation and quadriga selection 'ssii_quad'\n // (4 SVs with best GDOP)\n // ***\n // gps_quadriga(ssii_quad,vsii_quad,gdop,mgps,\n // sv_init_data,rsi,wsi,incl,almanac_time,del_rearth,time,SBII);\n\n // Pseudo-range and range-rate measurements\n for (int i = 0; i < 4; i++) {\n // unpacking i-th SV inertial position\n arma::vec3 SSII;\n for (int j = 0; j < 3; j++) {\n SSII[j] = *(ssii_quad + 4 * i + j);\n }\n // Z150126 - start\n // diagnostics: getting long, lat, alt of the four quadriga SVs for\n // plotting in GLOBE\n double lon(0), lat(0), alt(0);\n std::tie(lon, lat, alt) = cad::geo84_in(SSII, gps_sats->time);\n switch (i) {\n case 0:\n lon1 = lon * DEG;\n lat1 = lat * DEG;\n alt1 = alt;\n break;\n case 1:\n lon2 = lon * DEG;\n lat2 = lat * DEG;\n alt2 = alt;\n break;\n case 2:\n lon3 = lon * DEG;\n lat3 = lat * DEG;\n alt3 = alt;\n break;\n case 3:\n lon4 = lon * DEG;\n lat4 = lat * DEG;\n alt4 = alt;\n break;\n }\n\n arma::vec3 SBII = newton->get_SBII();\n arma::vec3 VBII = newton->get_VBII();\n arma::vec3 WBII = euler->get_WBII();\n\n arma::vec3 SBIIC = grab_SBIIC();\n arma::vec3 VBIIC = grab_VBIIC();\n arma::vec3 WBICI = grab_WBICI();\n\n // calculating true range to SV\n arma::vec3 SSBI;\n\n SSBI = SSII - SBII;\n double dsb = norm(SSBI);\n\n // measured pseudo-range\n double dsb_meas = dsb + PR_BIAS[i] + PR_NOISE[i] + ucbias_error;\n\n // unpacking i-th SV inertial velocity\n arma::vec3 VSII;\n for (int j = 0; j < 3; j++) {\n VSII[j] = *(vsii_quad + 3 * i + j);\n }\n // velocity of SV wrt user\n arma::vec3 VSBI;\n VSBI = VSII - VBII - skew_sym(WBII) * SSBI;\n\n // calculating true range-rate to SV\n arma::vec3 USSBI;\n USSBI = SSBI * (1 / dsb);\n double dvsb = dot(VSBI, USSBI);\n\n // measured delta-range rate\n double dvsb_meas = dvsb + DR_NOISE[i] + ucfreq_error;\n\n // INS derived range measurements\n arma::vec3 SSBIC;\n SSBIC = SSII - SBIIC;\n double dsbc = norm(SSBIC);\n\n // INS derived range-rate measurements\n // velocity of SV wrt user\n arma::vec3 VSBIC;\n VSBIC = VSII - VBIIC - skew_sym(WBICI) * SSBIC;\n // calculating range-rate to SV\n arma::vec3 USSBIC;\n USSBIC = SSBIC * (1 / dsb);\n double dvsbc = dot(VSBIC, USSBIC);\n\n // loading measurement residuals into measurement vector\n // ZZ[0->3] range meas resid of SV's;\n // ZZ[4->7] range-rate meas resid of SV's\n ZZ[i] = dsb_meas - dsbc;\n ZZ[i + 4] = dvsb_meas - dvsbc;\n\n // observation matrix of filter\n for (int j = 0; j < 3; j++) {\n HH(i, j) = USSBI(j, 0);\n HH(i + 4, j + 3) = USSBI(j, 0) * gps_step;\n }\n HH(i, 6) = 1;\n HH(i + 4, 7) = gps_step;\n\n // for diagnostics: loading the 4 SV slot # of the quadriga\n *(slot + i) = *(ssii_quad + 4 * i + 3);\n // accumulating sum of slots\n slotm = slotm + slot[i];\n }\n // for diagnostics displaying the SV slot# of the quadriga on the\n // console\n // but only if they have changed (i.e., sum of slot# has changed)\n\n if (slotsum != slotm) {\n slotsum = slotm;\n std::cout << \" *** GPS Quadriga slot # \" << slot[0] << \" \" << slot[1]\n << \" \" << slot[2] << \" \" << slot[3] << \" ; GDOP = \" << gdop\n << \" m ***\\n\";\n }\n // *** filter correction and update (to INS: 'SXH' and 'VXH') ***\n // filter gain\n arma::mat88 KK(arma::fill::zeros);\n\n // measurement noise covariance matrix\n for (int i = 0; i < 4; i++) {\n RR(i, i) = pow(rpos * (1 + factr), 2);\n RR(i + 4, i + 4) = pow(rvel * (1 + factr), 2);\n }\n // Kalman gain\n KK = inv(PP * trans(HH) * (HH * PP * trans(HH) + RR));\n // state correction\n XH = KK * ZZ;\n // covariance correction for next cycle\n PP = (arma::mat88(arma::fill::eye) - KK * HH) * PP;\n\n // clock error bias update\n ucbias_error = ucbias_error - XH(6, 0);\n\n // diagnostics of 1st SV of quadriga saved to plot file\n gps_pos_meas = ZZ(0, 0);\n gps_vel_meas = ZZ(4, 0);\n\n // decomposing state vector for output\n for (int m = 0; m < 3; m++) {\n SXH(m, 0) = XH(m, 0);\n VXH(m, 0) = XH(m + 3, 0);\n }\n CXH(0, 0) = XH(6, 0);\n CXH(1, 0) = XH(7, 0);\n\n // diagnostic\n state_pos = norm(SXH);\n state_vel = norm(VXH);\n}\n\narma::vec3 GPS_Receiver::get_SXH() { return SXH; }\narma::vec3 GPS_Receiver::get_VXH() { return VXH; }\narma::vec3 GPS_Receiver::get_CXH() { return CXH; }\n", "meta": {"hexsha": "d5b7055dcafb1f63bab2c66175e51a0aca2ddacd", "size": 20765, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "models/dm/src/GPS_receiver.cpp", "max_stars_repo_name": "cihuang123/Next-simulation", "max_stars_repo_head_hexsha": "e8552a5804184b30022d103d47c8728fb242b5bc", "max_stars_repo_licenses": ["BSD-3-Clause"], 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"lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.22347071917150488}} {"text": "//---------------------------------------------------------------------------//\n// Copyright (c) 2020 Alexander Sokolov \n// Copyright (c) 2020 Nikita Kaskov \n// Copyright (c) 2020 Mikhail Komarov \n//\n// Distributed under the Boost Software License, Version 1.0\n// See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt\n//---------------------------------------------------------------------------////\n\n#ifndef CRYPTO3_MERKLE_DAMGARD_PADDING_HPP\n#define CRYPTO3_MERKLE_DAMGARD_PADDING_HPP\n\n#include \n#include \n#include \n\nnamespace boost {\n namespace crypto3 {\n namespace hashes {\n namespace detail {\n template\n class merkle_damgard_padding {\n typedef Hash policy_type;\n\n typedef typename policy_type::digest_endian endian_type;\n\n constexpr static const std::size_t word_bits = policy_type::word_bits;\n typedef typename policy_type::word_type word_type;\n\n constexpr static const std::size_t state_bits = policy_type::state_bits;\n constexpr static const std::size_t state_words = policy_type::state_words;\n typedef typename policy_type::state_type state_type;\n\n constexpr static const std::size_t block_bits = policy_type::block_bits;\n constexpr static const std::size_t block_words = policy_type::block_words;\n typedef typename policy_type::block_type block_type;\n\n constexpr static const std::size_t digest_bits = policy_type::digest_bits;\n typedef typename policy_type::digest_type digest_type;\n\n typedef ::boost::crypto3::detail::injector\n injector_type;\n\n public:\n void operator()(block_type &block, std::size_t &block_seen) {\n using namespace boost::crypto3::detail;\n // Remove garbage\n block_type block_of_zeros;\n std::size_t seen_copy = block_seen;\n std::fill(block_of_zeros.begin(), block_of_zeros.end(), 0);\n injector_type::inject(block_of_zeros, block_bits - block_seen, block, seen_copy);\n\n // Get bit 1 in the endianness used by the hashes\n std::array bit_one = {{0x80}};\n std::array bit_one_word {};\n pack(\n bit_one.begin(), bit_one.end(), bit_one_word.begin());\n\n // Add 1 bit to block\n injector_type::inject(bit_one_word[0], 1, block, block_seen);\n }\n };\n } // namespace detail\n } // namespace hashes\n } // namespace crypto3\n} // namespace boost\n\n#endif // CRYPTO3_MERKLE_DAMGARD_PADDING_HPP\n", "meta": {"hexsha": "e79fa803ae506395e9cee5756de5a11882b510a8", "size": 3329, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/crypto3/hash/detail/merkle_damgard_padding.hpp", "max_stars_repo_name": "NilFoundation/boost-crypto", "max_stars_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 4.0, "max_stars_repo_stars_event_min_datetime": "2020-09-02T06:19:14.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-07T04:55:03.000Z", "max_issues_repo_path": "include/boost/crypto3/hash/detail/merkle_damgard_padding.hpp", "max_issues_repo_name": "NilFoundation/boost-crypto", "max_issues_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2020-04-06T21:49:03.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-18T04:54:51.000Z", "max_forks_repo_path": "include/boost/crypto3/hash/detail/merkle_damgard_padding.hpp", "max_forks_repo_name": "NilFoundation/boost-crypto", "max_forks_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2022-02-13T21:14:37.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T21:14:37.000Z", "avg_line_length": 47.5571428571, "max_line_length": 111, "alphanum_fraction": 0.5683388405, "num_tokens": 650, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.22347071917150488}} {"text": "#include \n\n#include \n\n#include \n#include \n\n#include \n#include \n\nusing namespace arma;\nusing namespace optparse;\n\nconst std::string USAGE = \"besiq-var [OPTIONS] plink_file\";\nconst std::string DESCRIPTION = \"Computes variance hetrogenity for single variants.\";\nconst std::string VERSION = \"besiq 0.0.1\";\nconst std::string EPILOG = \"\";\n\narma::mat\ncompute_medians(const snp_row &row, const arma::vec &pheno, const arma::uvec &missing)\n{\n std::vector> groups( 3 );\n arma::mat medians = arma::zeros( 3, 2 );\n for(int i = 0; i < row.size( ); i++)\n {\n if( row[ i ] != 3 && missing[ i ] == 0 )\n {\n groups[ row[ i ] ].push_back( pheno[ i ] );\n medians( row[ i ], 1 ) += 1.0;\n }\n }\n\n for(int i = 0; i < 3; i++)\n {\n std::sort( groups[ i ].begin( ), groups[ i ].end( ) );\n if( groups[ i ].size( ) > 0 )\n {\n medians( i, 0 ) = groups[ i ][ groups[ i ].size( ) / 2 ];\n }\n else\n {\n medians( i, 0 ) = 0.0;\n }\n }\n\n return medians;\n}\n\nbool\ncompute_brown_forsythe(const snp_row &row, const arma::vec &pheno, const arma::uvec &missing, double *W, double *p, size_t *N)\n{\n double k = 3;\n arma::mat medians = compute_medians( row, pheno, missing );\n\n if( arma::min( medians.col( 1 ) ) <= 20 )\n {\n *N = arma::accu( medians.col( 1 ) );\n return false;\n }\n \n arma::vec z_i = arma::zeros( 3 );\n double z = 0.0;\n *N = arma::accu( medians.col( 1 ) );\n for(int i = 0; i < row.size( ); i++)\n {\n if( row[ i ] != 3 && missing[ i ] == 0 )\n {\n double z_ij = std::abs( pheno[ i ] - medians( row[ i ], 0 ) );\n z_i[ row[ i ] ] += z_ij / medians( row[ i ], 1 );\n z += z_ij / *N;\n }\n }\n\n double W_sq = 0.0;\n for(int i = 0; i < row.size( ); i++)\n {\n if( row[ i ] != 3 && missing[ i ] == 0 )\n {\n double z_ij = std::abs( pheno[ i ] - medians( row[ i ], 0 ) );\n W_sq += pow( z_ij - z_i[ row[ i ] ], 2 );\n }\n }\n\n double numerator = dot( medians.col( 1 ), pow( z_i - z, 2 ) );\n\n *W = ( (*N - k) * numerator ) / ( ( k - 1 ) * W_sq );\n *p = 1 - f_cdf( *W, k - 1, *N - k );\n\n return true;\n}\n\n\nint\nmain(int argc, char *argv[])\n{\n OptionParser parser = OptionParser( ).usage( USAGE )\n .version( VERSION )\n .description( DESCRIPTION )\n .epilog( EPILOG ); \n \n parser.add_option( \"-p\", \"--pheno\" ).help( \"Read phenotypes from this file instead of a plink file.\" );\n parser.add_option( \"-e\", \"--mpheno\" ).help( \"Name of the phenotype that you want to read (if there are more than one in the phenotype file).\" );\n parser.add_option( \"-o\", \"--out\" ).help( \"The output file that will contain the results (binary).\" );\n\n Values options = parser.parse_args( argc, argv );\n if( parser.args( ).size( ) != 1 )\n {\n parser.print_help( );\n exit( 1 );\n }\n\n std::ios_base::sync_with_stdio( false );\n \n /* Read all genotypes */\n plink_file_ptr genotype_file = open_plink_file( parser.args( )[ 0 ] );\n genotype_matrix_ptr genotypes = create_genotype_matrix( genotype_file );\n std::vector order = genotype_file->get_sample_iids( );\n\n /* Parse phenotypes */\n arma::uvec missing = arma::zeros( genotype_file->get_samples( ).size( ) );\n arma::vec phenotypes;\n if( options.is_set( \"pheno\" ) )\n {\n std::ifstream phenotype_file( options[ \"pheno\" ].c_str( ) );\n phenotypes = parse_phenotypes( phenotype_file, missing, order, options[ \"mpheno\" ] );\n }\n else\n {\n phenotypes = create_phenotype_vector( genotype_file->get_samples( ), missing );\n }\n\n\n std::vector loci = genotype_file->get_loci( );\n std::cout << \"chr\\tpos\\tsnp\\tW\\tP\\tN\\n\";\n for( int i = 0; i < loci.size( ); i++)\n {\n const snp_row &row = *genotypes->get_row( loci[ i ].name );\n double W;\n double p;\n size_t N;\n if( compute_brown_forsythe( row, phenotypes, missing, &W, &p, &N ) )\n {\n std::cout << (int) loci[ i ].chromosome << \"\\t\" << loci[ i ].bp_position << \"\\t\" << loci[ i ].name << \"\\t\" << W << \"\\t\" << p << \"\\t\" << N << \"\\n\";\n }\n else\n {\n std::cout << (int) loci[ i ].chromosome << \"\\t\" << loci[ i ].bp_position << \"\\t\" << loci[ i ].name << \"\\t\" << \"NA\" << \"\\t\" << \"NA\" << \"\\t\" << N << \"\\n\";\n }\n }\n\n return 0;\n}\n", "meta": {"hexsha": "1d2c00e7313bb5e3920df3fc50f5e15034277916", "size": 4745, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/besiq_var.cpp", "max_stars_repo_name": "hoehleatsu/besiq", "max_stars_repo_head_hexsha": "94959e2819251805e19311ce377919e6bccb7bf9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2015-10-21T14:22:12.000Z", "max_stars_repo_stars_event_max_datetime": "2015-10-21T14:22:12.000Z", "max_issues_repo_path": "src/besiq_var.cpp", "max_issues_repo_name": "hoehleatsu/besiq", "max_issues_repo_head_hexsha": "94959e2819251805e19311ce377919e6bccb7bf9", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2019-05-26T20:52:31.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-28T16:19:49.000Z", "max_forks_repo_path": "src/besiq_var.cpp", "max_forks_repo_name": "hoehleatsu/besiq", "max_forks_repo_head_hexsha": "94959e2819251805e19311ce377919e6bccb7bf9", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2016-11-06T14:58:37.000Z", "max_forks_repo_forks_event_max_datetime": "2019-05-26T14:03:13.000Z", "avg_line_length": 31.0130718954, "max_line_length": 164, "alphanum_fraction": 0.514857745, "num_tokens": 1463, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.640635868562172, "lm_q2_score": 0.34864514886966624, "lm_q1q2_score": 0.2233545877661064}} {"text": "/*\n Copyright (C) 2020 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n\n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace QuantLib;\n\nusing namespace boost::accumulators;\n\nnamespace ore {\nnamespace analytics {\n\nDynamicCreditXvaCalculator::DynamicCreditXvaCalculator(\n //! Driving portfolio consistent with the cube below\n const boost::shared_ptr portfolio, const boost::shared_ptr market,\n const string& configuration, const string& baseCurrency, const string& dvaName,\n const string& fvaBorrowingCurve, const string& fvaLendingCurve,\n const bool applyDynamicInitialMargin,\n const boost::shared_ptr dimCalculator,\n const boost::shared_ptr tradeExposureCube,\n const boost::shared_ptr nettingSetExposureCube,\n const boost::shared_ptr& cptyCube,\n const Size tradeEpeIndex, const Size tradeEneIndex,\n const Size nettingSetEpeIndex, const Size nettingSetEneIndex, const Size cptySpIndex,\n const bool flipViewXVA, const string& flipViewBorrowingCurvePostfix, const string& flipViewLendingCurvePostfix)\n : ValueAdjustmentCalculator(portfolio, market, configuration, baseCurrency, dvaName,\n fvaBorrowingCurve, fvaLendingCurve, applyDynamicInitialMargin,\n dimCalculator, tradeExposureCube, nettingSetExposureCube, tradeEpeIndex, tradeEneIndex, \n nettingSetEpeIndex, nettingSetEneIndex, \n flipViewXVA, flipViewBorrowingCurvePostfix, flipViewLendingCurvePostfix),\n cptyCube_(cptyCube), cptySpIndex_(cptySpIndex) {\n // check consistency of input\n\n QL_REQUIRE(tradeExposureCube_->numDates() == cptyCube->numDates(),\n \"number of dates in tradeExposureCube and cptyCube mismatch (\"\n << tradeExposureCube_->numDates() << \" vs \" << cptyCube->numDates() << \")\");\n\n QL_REQUIRE(cptySpIndex < cptyCube->depth(), \"cptySpIndex(\"\n << cptySpIndex << \") exceeds depth of cptyCube(\"\n << cptyCube->depth() << \")\");\n \n for (Size i = 0; i < tradeExposureCube_->numDates(); i++) {\n QL_REQUIRE(tradeExposureCube_->dates()[i] == cptyCube->dates()[i],\n \"date at \" << i << \" in tradeExposureCube and cptyCube mismatch (\"\n << tradeExposureCube_->dates()[i] << \" vs \" << cptyCube->dates()[i] << \")\");\n }\n}\n\n\nconst Real DynamicCreditXvaCalculator::calculateCvaIncrement(\n const string& tid, const string& cid, const Date& d0, const Date& d1, const Real& rr) {\n Real increment = 0.0;\n for (Size k = 0; k < tradeExposureCube_->samples(); ++k) {\n Real s0 = d0 == asof() ? 1.0 : cptyCube_->get(cid, d0, k, cptySpIndex_);\n Real s1 = cptyCube_->get(cid, d1, k, cptySpIndex_);\n Real epe = tradeExposureCube_->get(tid, d1, k, tradeEpeIndex_);\n increment += (s0 - s1) * epe;\n }\n return (1.0 - rr) * increment / tradeExposureCube_->samples();\n}\n\nconst Real DynamicCreditXvaCalculator::calculateDvaIncrement(\n const string& tid, const Date& d0, const Date& d1, const Real& rr) {\n Real increment = 0.0;\n for (Size k = 0; k < tradeExposureCube_->samples(); ++k) {\n Real s0 = d0 == asof() ? 1.0 : cptyCube_->get(dvaName_, d0, k, cptySpIndex_);\n Real s1 = cptyCube_->get(dvaName_, d1, k, cptySpIndex_);\n Real ene = tradeExposureCube_->get(tid, d1, k, tradeEneIndex_);\n increment += (s0 - s1) * ene;\n }\n return (1.0 - rr) * increment / tradeExposureCube_->samples();\n}\n\nconst Real DynamicCreditXvaCalculator::calculateNettingSetCvaIncrement(\n const string& nid, const string& cid, const Date& d0, const Date& d1, const Real& rr) {\n Real increment = 0.0;\n for (Size k = 0; k < nettingSetExposureCube_->samples(); ++k) {\n Real s0 = d0 == asof() ? 1.0 : cptyCube_->get(cid, d0, k, cptySpIndex_);\n Real s1 = cptyCube_->get(cid, d1, k, cptySpIndex_);\n Real epe = nettingSetExposureCube_->get(nid, d1, k, nettingSetEpeIndex_);\n increment += (s0 - s1) * epe;\n }\n return (1.0 - rr) * increment / nettingSetExposureCube_->samples();\n}\n\nconst Real DynamicCreditXvaCalculator::calculateNettingSetDvaIncrement(\n const string& nid, const Date& d0, const Date& d1, const Real& rr) {\n Real increment = 0.0;\n for (Size k = 0; k < nettingSetExposureCube_->samples(); ++k) {\n Real s0 = d0 == asof() ? 1.0 : cptyCube_->get(dvaName_, d0, k, cptySpIndex_);\n Real s1 = cptyCube_->get(dvaName_, d1, k, cptySpIndex_);\n Real ene = nettingSetExposureCube_->get(nid, d1, k, nettingSetEneIndex_);\n increment += (s0 - s1) * ene;\n }\n return (1.0 - rr) * increment / nettingSetExposureCube_->samples();\n}\n\nconst Real DynamicCreditXvaCalculator::calculateFbaIncrement(\n const string& tid, const string& cid, const string& dvaName,\n const Date& d0, const Date& d1, const Real& dcf) {\n Real increment = 0.0;\n for (Size k = 0; k < tradeExposureCube_->samples(); ++k) {\n Real s0 = (d0 == asof() || cid == \"\") ? 1.0 : cptyCube_->get(cid, d0, k, cptySpIndex_);\n Real s1 = (d0 == asof() || dvaName == \"\") ? 1.0 : cptyCube_->get(dvaName_, d0, k, cptySpIndex_);\n Real ene = tradeExposureCube_->get(tid, d1, k, tradeEneIndex_);\n increment += s0 * s1 * ene;\n }\n return increment * dcf / tradeExposureCube_->samples();\n}\n\nconst Real DynamicCreditXvaCalculator::calculateFcaIncrement(\n const string& tid, const string& cid, const string& dvaName,\n const Date& d0, const Date& d1, const Real& dcf) {\n Real increment = 0.0;\n for (Size k = 0; k < tradeExposureCube_->samples(); ++k) {\n Real s0 = (d0 == asof() || cid == \"\") ? 1.0 : cptyCube_->get(cid, d0, k, cptySpIndex_);\n Real s1 = (d0 == asof() || dvaName == \"\") ? 1.0 : cptyCube_->get(dvaName_, d0, k, cptySpIndex_);\n Real epe = tradeExposureCube_->get(tid, d1, k, tradeEpeIndex_);\n increment += s0 * s1 * epe;\n }\n return increment * dcf / tradeExposureCube_->samples();\n}\n\nconst Real DynamicCreditXvaCalculator::calculateNettingSetFbaIncrement(\n const string& nid, const string& cid, const string& dvaName,\n const Date& d0, const Date& d1, const Real& dcf) {\n Real increment = 0.0;\n for (Size k = 0; k < nettingSetExposureCube_->samples(); ++k) {\n Real s0 = (d0 == asof() || cid == \"\") ? 1.0 : cptyCube_->get(cid, d0, k, cptySpIndex_);\n Real s1 = (d0 == asof() || dvaName == \"\") ? 1.0 : cptyCube_->get(dvaName_, d0, k, cptySpIndex_);\n Real ene = nettingSetExposureCube_->get(nid, d1, k, nettingSetEneIndex_);\n increment += s0 * s1 * ene;\n }\n return increment * dcf / nettingSetExposureCube_->samples();\n}\n\nconst Real DynamicCreditXvaCalculator::calculateNettingSetFcaIncrement(\n const string& nid, const string& cid, const string& dvaName,\n const Date& d0, const Date& d1, const Real& dcf) {\n Real increment = 0.0;\n for (Size k = 0; k < nettingSetExposureCube_->samples(); ++k) {\n Real s0 = (d0 == asof() || cid == \"\") ? 1.0 : cptyCube_->get(cid, d0, k, cptySpIndex_);\n Real s1 = (d0 == asof() || dvaName == \"\") ? 1.0 : cptyCube_->get(dvaName_, d0, k, cptySpIndex_);\n Real epe = nettingSetExposureCube_->get(nid, d1, k, nettingSetEpeIndex_);\n increment += s0 * s1 * epe;\n }\n return increment * dcf / nettingSetExposureCube_->samples();\n}\n\nconst Real DynamicCreditXvaCalculator::calculateNettingSetMvaIncrement(\n const string& nid, const string& cid, const Date& d0, const Date& d1, const Real& dcf) {\n\n Real increment = 0.0;\n for (Size k = 0; k < nettingSetExposureCube_->samples(); ++k) {\n Real s0 = (d0 == asof() || cid == \"\") ? 1.0 : cptyCube_->get(cid, d0, k, cptySpIndex_);\n Real s1 = (d0 == asof() || dvaName_ == \"\") ? 1.0 : cptyCube_->get(dvaName_, d0, k, cptySpIndex_);\n Real im = dimCalculator_->dimCube()->get(nid, d1, k);\n increment += s0 * s1 * im;\n }\n return increment * dcf / nettingSetExposureCube_->samples();\n}\n\n} // namespace analytics\n} // namespace ore\n", "meta": {"hexsha": "947c209d9655e17f99894efb4b75cedaaf5b5431", "size": 8979, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "OREAnalytics/orea/aggregation/dynamiccreditxvacalculator.cpp", "max_stars_repo_name": "mrslezak/Engine", "max_stars_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 335.0, "max_stars_repo_stars_event_min_datetime": "2016-10-07T16:31:10.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-02T07:12:03.000Z", "max_issues_repo_path": "OREAnalytics/orea/aggregation/dynamiccreditxvacalculator.cpp", "max_issues_repo_name": "mrslezak/Engine", "max_issues_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 59.0, "max_issues_repo_issues_event_min_datetime": "2016-10-31T04:20:24.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-03T16:39:57.000Z", "max_forks_repo_path": "OREAnalytics/orea/aggregation/dynamiccreditxvacalculator.cpp", "max_forks_repo_name": "mrslezak/Engine", "max_forks_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 180.0, "max_forks_repo_forks_event_min_datetime": "2016-10-08T14:23:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T10:43:05.000Z", "avg_line_length": 48.2741935484, "max_line_length": 120, "alphanum_fraction": 0.6637710213, "num_tokens": 2685, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5234203489363239, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.22314539673029674}} {"text": "// ====================================================================\n// This file is part of FlexibleSUSY.\n//\n// FlexibleSUSY is free software: you can redistribute it and/or modify\n// it under the terms of the GNU General Public License as published\n// by the Free Software Foundation, either version 3 of the License,\n// or (at your option) any later version.\n//\n// FlexibleSUSY is distributed in the hope that it will be useful, but\n// WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n// General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with FlexibleSUSY. If not, see\n// .\n// ====================================================================\n\n#ifndef GM2CALC_INTERFACE_H\n#define GM2CALC_INTERFACE_H\n\n#include \n\n/**\n * @file gm2calc_interface.hpp\n * @brief contains declarations of GM2Calc interface functions\n */\n\nnamespace flexiblesusy {\n\n/**\n * @class GM2Calc_data\n * @brief data to be passed to GM2Calc\n */\nstruct GM2Calc_data {\n GM2Calc_data(); ///< initializes members to GM2Calc default values\n void initialize(); ///< initializes members to GM2Calc default values\n\n double alpha_em_MZ; ///< alpha_em(MZ)\n double alpha_em_0; ///< alpha_em(0)\n double alpha_s_MZ; ///< alpha_s(MZ) SM MS-bar\n double MZ; ///< Z pole mass\n double MW; ///< W pole mass\n double mb_mb; ///< mb(mb) SM MS-bar\n double MT; ///< top quark pole mass\n double MTau; ///< tau lepton pole mass\n double MM; ///< muon pole mass\n double MA0; ///< CP-odd Higgs pole mass\n double MSvm; ///< muon sneutrino pole mass\n Eigen::Array MSm; ///< smuon pole masses\n Eigen::Array MCha; ///< chargino pole masses\n Eigen::Array MChi; ///< neutralino pole masses\n double scale; ///< renormalization scale\n double TB; ///< tan(beta) DR-bar\n double Mu; ///< mu parameter (initial guess)\n double M1; ///< bino mass parameter (initial guess)\n double M2; ///< wino mass parameter (initial guess)\n double M3; ///< gluino mass parameter\n Eigen::Matrix mq2; ///< left-handed squark mass parameters squared\n Eigen::Matrix mu2; ///< right-handed up-type squark mass parameters squared\n Eigen::Matrix md2; ///< right-handed down-type squark mass parameters squared\n Eigen::Matrix ml2; ///< left-handed slepton mass parameters squared\n Eigen::Matrix me2; ///< right-handed down-type slepton mass parameters squared\n Eigen::Matrix Au; ///< up-type squark trilinear coupling\n Eigen::Matrix Ad; ///< down-type squark trilinear coupling\n Eigen::Matrix Ae; ///< down-type slepton trilinear coupling\n};\n\n/// calculates amu using GM2Calc\ndouble gm2calc_calculate_amu(const GM2Calc_data&);\n\n/// calculates uncertainty of amu using GM2Calc\ndouble gm2calc_calculate_amu_uncertainty(const GM2Calc_data&);\n\n} // namespace flexiblesusy\n\n#endif\n", "meta": {"hexsha": "a8802f42a11f56b44961d044cbab5a73fbf60cb5", "size": 3407, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contrib/MassSpectra/flexiblesusy/src/gm2calc_interface.hpp", "max_stars_repo_name": "aaronvincent/gambit_aaron", "max_stars_repo_head_hexsha": "a38bd6fc10d781e71f2adafd401c76e1e3476b05", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-09-08T20:05:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-26T07:57:56.000Z", "max_issues_repo_path": "contrib/MassSpectra/flexiblesusy/src/gm2calc_interface.hpp", "max_issues_repo_name": "aaronvincent/gambit_aaron", "max_issues_repo_head_hexsha": "a38bd6fc10d781e71f2adafd401c76e1e3476b05", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": "2020-10-19T09:56:17.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-28T06:12:03.000Z", "max_forks_repo_path": "contrib/MassSpectra/flexiblesusy/src/gm2calc_interface.hpp", "max_forks_repo_name": "aaronvincent/gambit_aaron", "max_forks_repo_head_hexsha": "a38bd6fc10d781e71f2adafd401c76e1e3476b05", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2020-09-08T02:23:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-23T08:48:04.000Z", "avg_line_length": 43.6794871795, "max_line_length": 93, "alphanum_fraction": 0.6119753449, "num_tokens": 804, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.22305200822132848}} {"text": "#include \"LanguageModel.h\"\n\n#include \n#include \n#include \n#include \n\n#include \n\nusing namespace distrust;\nusing namespace Eigen;\n\nLanguageModel::LanguageModel(const ModelInfo &model_info) :\n unif_(-1.0, 1.0),\n batch_size_(0) {\n\n window_size_ = model_info.window_size;\n wordvec_dim_ = model_info.wordvec_dim;\n hidden_dim_ = model_info.hidden_dim;\n start_token_index_ = model_info.start_token_index;\n end_token_index_ = model_info.end_token_index;\n unk_token_index_ = model_info.unk_token_index;\n vocab_size_ = model_info.vocab.size();\n for (unsigned int i = 0; i < vocab_size_; i++) {\n vocab_[model_info.vocab[i]] = i;\n }\n\n wordvec_w_.reserve(vocab_size_);\n wordvec_w_buf_.reserve(vocab_size_);\n input_hidden_w_.reserve(window_size_);\n input_hidden_w_buf_.reserve(window_size_);\n\n for (unsigned int i = 0; i < window_size_; i++) {\n input_hidden_w_grad_.push_back(Matrix_t(hidden_dim_, wordvec_dim_));\n }\n input_hidden_b_grad_ = Vector_t(hidden_dim_);\n hidden_output_w_grad_ = Matrix_t(vocab_size_, hidden_dim_);\n hidden_output_b_grad_ = Vector_t(vocab_size_);\n\n // adagrad\n for (unsigned int i = 0; i < vocab_size_; i++) {\n wordvec_w_var_.push_back(ArrayXd::Zero(wordvec_dim_));\n }\n\n for (unsigned int i = 0; i < window_size_; i++) {\n input_hidden_w_var_.push_back(MArray_t::Zero(hidden_dim_, wordvec_dim_));\n }\n\n input_hidden_b_var_ = ArrayXd::Zero(hidden_dim_);\n hidden_output_w_var_ = MArray_t::Zero(vocab_size_, hidden_dim_);\n hidden_output_b_var_ = ArrayXd::Zero(vocab_size_);\n zero_grad_params();\n}\n\nLanguageModel::LanguageModel(const LanguageModel &model) :\n window_size_(model.window_size_),\n wordvec_dim_(model.wordvec_dim_),\n hidden_dim_(model.hidden_dim_),\n start_token_index_(model.start_token_index_),\n end_token_index_(model.end_token_index_),\n unk_token_index_(model.unk_token_index_),\n vocab_size_(model.vocab_size_),\n vocab_(model.vocab_),\n wordvec_w_buf_(model.wordvec_w_buf_),\n input_hidden_w_buf_(model.input_hidden_w_buf_),\n input_hidden_b_buf_(model.input_hidden_b_buf_),\n hidden_output_w_buf_(model.hidden_output_w_buf_),\n hidden_output_b_buf_(model.hidden_output_b_buf_),\n input_hidden_w_grad_(model.input_hidden_w_grad_),\n input_hidden_b_grad_(model.input_hidden_b_grad_),\n hidden_output_w_grad_(model.hidden_output_w_grad_),\n hidden_output_b_grad_(model.hidden_output_b_grad_) {\n \n zero_grad_params();\n wrap_buffers();\n}\n\ndouble\nLanguageModel::sample() {\n return unif_(re_);\n}\n\nvoid\nLanguageModel::wrap_buffers() {\n wordvec_w_.clear();\n for (unsigned int i = 0; i < vocab_size_; i++) {\n wordvec_w_.push_back(Map(&wordvec_w_buf_[i][0], wordvec_dim_));\n } \n\n input_hidden_w_.clear();\n for (unsigned int i = 0; i < window_size_; i++) {\n input_hidden_w_.push_back(Map(&input_hidden_w_buf_[i][0], hidden_dim_, wordvec_dim_));\n }\n\n input_hidden_b_ = std::unique_ptr>(\n new Map(&input_hidden_b_buf_[0], hidden_dim_));\n hidden_output_w_ = std::unique_ptr>(\n new Map(&hidden_output_w_buf_[0], vocab_size_, hidden_dim_));\n hidden_output_b_ = std::unique_ptr>(\n new Map(&hidden_output_b_buf_[0], vocab_size_));\n}\n\nvoid\nLanguageModel::random_init() {\n wordvec_w_buf_.clear();\n for (unsigned int i = 0; i < vocab_size_; i++) {\n wordvec_w_buf_.push_back(std::vector(wordvec_dim_));\n for (unsigned int j = 0; j < wordvec_dim_; j++) {\n wordvec_w_buf_[i][j] = 0.05 * sample();\n }\n }\n\n input_hidden_w_buf_.clear();\n for (unsigned int i = 0; i < window_size_; i++) {\n input_hidden_w_buf_.push_back(std::vector(hidden_dim_ * wordvec_dim_));\n for (unsigned int j = 0; j < hidden_dim_ * wordvec_dim_; j++) {\n input_hidden_w_buf_[i][j] = 0.05 * sample();\n }\n }\n\n input_hidden_b_buf_ = std::vector(hidden_dim_, 0.0);\n hidden_output_w_buf_ = std::vector(vocab_size_ * hidden_dim_, 0.0);\n hidden_output_b_buf_ = std::vector(vocab_size_, 0.0);\n wrap_buffers();\n}\n\nvoid\nLanguageModel::set_params(Params ¶ms) {\n wordvec_w_buf_ = std::move(params.wordvec_w);\n input_hidden_w_buf_ = std::move(params.input_hidden_w);\n input_hidden_b_buf_ = std::move(params.input_hidden_b);\n hidden_output_w_buf_ = std::move(params.hidden_output_w);\n hidden_output_b_buf_ = std::move(params.hidden_output_b);\n wrap_buffers();\n}\n\nvoid\nLanguageModel::update_params(const ParamUpdate &update) {\n for (auto itr = update.wordvec_w.begin(); itr != update.wordvec_w.end(); itr++) {\n uint32_t idx = itr->first;\n for (unsigned int i = 0; i < wordvec_dim_; i++) {\n wordvec_w_buf_[idx][i] += itr->second[i];\n }\n }\n\n for (unsigned int i = 0; i < window_size_; i++) {\n for (unsigned int j = 0; j < wordvec_dim_ * hidden_dim_; j++) {\n input_hidden_w_buf_[i][j] += update.input_hidden_w[i][j];\n }\n }\n\n for (unsigned int i = 0; i < hidden_dim_; i++) {\n input_hidden_b_buf_[i] += update.input_hidden_b[i];\n }\n\n for (unsigned int i = 0; i < hidden_dim_ * vocab_size_; i++) {\n hidden_output_w_buf_[i] += update.hidden_output_w[i];\n }\n\n for (unsigned int i = 0; i < vocab_size_; i++) {\n hidden_output_b_buf_[i] += update.hidden_output_b[i];\n }\n}\n\nvoid\nLanguageModel::get_params(Params &ret) {\n ret.wordvec_w = wordvec_w_buf_;\n ret.input_hidden_w = input_hidden_w_buf_;\n ret.input_hidden_b = input_hidden_b_buf_;\n ret.hidden_output_w = hidden_output_w_buf_;\n ret.hidden_output_b = hidden_output_b_buf_;\n}\n\nvoid\nLanguageModel::get_update(ParamUpdate &ret, const double learn_rate) {\n double *ptr;\n for (auto itr = wordvec_w_grad_.begin(); itr != wordvec_w_grad_.end(); itr++) {\n uint32_t idx = itr->first;\n //ArrayXd a = itr->second.array() * (-learn_rate);\n ArrayXd a = itr->second.array();\n wordvec_w_var_[idx] += a.square();\n a *= -learn_rate / (wordvec_w_var_[idx].sqrt() + 1e-4);\n ptr = a.data();\n ret.wordvec_w[idx] = std::vector(ptr, ptr + wordvec_dim_);\n }\n\n for (unsigned int i = 0; i < window_size_; i++) {\n //MArray_t a = input_hidden_w_grad_[i].array() * (-learn_rate);\n MArray_t a = input_hidden_w_grad_[i].array();\n input_hidden_w_var_[i] += a.square();\n a *= -learn_rate / (input_hidden_w_var_[i].sqrt() + 1e-4);\n ptr = a.data();\n ret.input_hidden_w.push_back(std::vector(ptr, ptr + hidden_dim_ * wordvec_dim_));\n }\n\n //ArrayXd a_ihb = input_hidden_b_grad_.array() * (-learn_rate);\n ArrayXd a_ihb = input_hidden_b_grad_.array();\n input_hidden_b_var_ += a_ihb.square();\n a_ihb *= -learn_rate / (input_hidden_b_var_.sqrt() + 1e-4);\n ptr = a_ihb.data();\n ret.input_hidden_b = std::vector(ptr, ptr + hidden_dim_);\n\n //MArray_t a_how = hidden_output_w_grad_.array() * (-learn_rate);\n MArray_t a_how = hidden_output_w_grad_.array();\n hidden_output_w_var_ += a_how.square();\n a_how *= -learn_rate / (hidden_output_w_var_.sqrt() + 1e-4);\n ptr = a_how.data();\n ret.hidden_output_w = std::vector(ptr, ptr + vocab_size_ * hidden_dim_);\n\n //ArrayXd a_hob = hidden_output_b_grad_.array() * (-learn_rate);\n ArrayXd a_hob = hidden_output_b_grad_.array();\n hidden_output_b_var_ += a_hob.square();\n a_hob *= -learn_rate / (hidden_output_b_var_.sqrt() + 1e-4);\n ptr = a_hob.data();\n ret.hidden_output_b = std::vector(ptr, ptr + vocab_size_);\n}\n\nVectorXd\nLanguageModel::tanh(const VectorXd &v) {\n ArrayXd a = (v.array() * 2).exp();\n return ((a - 1) / (a + 1)).matrix();\n}\n\ndouble\nLanguageModel::logZ(const VectorXd &v) {\n return log(v.array().exp().sum());\n}\n\nuint32_t\nLanguageModel::word_index(const std::string &word) {\n boost::regex re(\"[0-9]\");\n std::string token = boost::regex_replace(word, re, \"0\");\n uint32_t index = unk_token_index_;\n auto itr = vocab_.find(token);\n if (itr != vocab_.end()) {\n index = itr->second;\n }\n return index;\n}\n\nstd::vector\nLanguageModel::tokenize(const std::string &line) {\n std::vector tokens;\n for (unsigned int i = 0; i < window_size_; i++) {\n tokens.push_back(start_token_index_);\n }\n\n std::string word;\n std::stringstream ss(line);\n while (std::getline(ss, word, ' ')) {\n tokens.push_back(word_index(word));\n }\n tokens.push_back(end_token_index_);\n return tokens;\n}\n\nstd::vector\nLanguageModel::forward(const std::vector &input) {\n if (input.size() != window_size_) {\n throw std::invalid_argument(\"forward: input size does not equal window size\");\n }\n\n hidden_ = *input_hidden_b_;\n for (unsigned int i = 0; i < window_size_; i++) {\n hidden_ += input_hidden_w_[i] * wordvec_w_[input[i]];\n }\n hidden_tanh_ = tanh(hidden_);\n output_ = (*hidden_output_w_) * hidden_tanh_ + (*hidden_output_b_);\n logZ_ = logZ(output_);\n output_normed_ = (output_.array() - logZ_).matrix();\n double *ptr = output_normed_.data();\n return std::vector(ptr, ptr + vocab_size_);\n}\n\n// double\n// LanguageModel::forward(const std::vector &tokens) {\n// uint32_t size = tokens.size() - window_size_;\n// Matrix input(size, window_size_);\n// for (unsigned int i = 0; i < size; i++) {\n// for (unsigned int j = 0; j < window_size_; j++) {\n// input(i, j) = tokens[i + j];\n// }\n// }\n\n\n// }\n\n// double\n// LanguageModel::forward(const std::vector &input, const uint32_t target) {\n// if (input.size() != window_size_) {\n// throw std::invalid_argument(\"forward: input size does not equal window size\");\n// }\n\n// hidden_ = *input_hidden_b_;\n// for (unsigned int i = 0; i < window_size_; i++) {\n// hidden_ += input_hidden_w_[i] * wordvec_w_[input[i]];\n// }\n// hidden_tanh_ = tanh(hidden_);\n// output_ = (*hidden_output_w_) * hidden_tanh_ + (*hidden_output_b_);\n// logZ_ = logZ(output_);\n// output_normed_ = (output_.array() - logZ_).matrix();\n// return output_normed_(target);\n// }\n\n// std::vector\n// LanguageModel::forward(const std::vector &input, uint32_t target_idx) {\n// hidden_ = *input_hidden_b_;\n// for (unsigned int i = 0; i < window_size_; i++) {\n// hidden_ += input_hidden_w_[i] * wordvec_w_[input[target_idx - window_size_ + i]];\n// }\n// hidden_tanh_ = tanh(hidden_);\n// output_ = (*hidden_output_w_) * hidden_tanh_ + (*hidden_output_b_);\n// logZ_ = logZ(output_);\n// output_normed_ = (output_.array() - logZ_).matrix();\n\n// double *ptr = output_normed_.data();\n// return std::vector(ptr, ptr + vocab_size_);\n// }\n\nvoid\nLanguageModel::backward(\n const std::vector &input,\n const uint32_t target) {\n\n batch_size_++;\n\n // hidden-output gradients\n Vector_t output_grad = output_normed_.array().exp().matrix();\n output_grad(target) -= 1;\n hidden_output_w_grad_ += output_grad * hidden_tanh_.transpose();\n hidden_output_b_grad_ += output_grad;\n\n // input-hidden gradients\n Vector_t hidden_grad = output_grad.transpose() * (*hidden_output_w_);\n hidden_grad = (hidden_grad.array() * \n (1 - hidden_tanh_.array().square()))\n .matrix();\n for (unsigned int i = 0; i < window_size_; i++) {\n input_hidden_w_grad_[i] += hidden_grad * wordvec_w_[input[i]].transpose();\n }\n input_hidden_b_grad_ += hidden_grad;\n\n // word vector gradients\n for (unsigned int i = 0; i < window_size_; i++) {\n uint32_t idx = input[i];\n Vector_t input_grad = hidden_grad.transpose() * input_hidden_w_[i];\n auto itr = wordvec_w_grad_.find(idx);\n if (itr == wordvec_w_grad_.end()) {\n wordvec_w_grad_[idx] = input_grad;\n } else {\n wordvec_w_grad_[idx] += input_grad;\n }\n }\n}\n\nvoid\nLanguageModel::zero_grad_params() {\n batch_size_ = 0;\n wordvec_w_grad_.clear();\n for (unsigned int i = 0; i < window_size_; i++) {\n input_hidden_w_grad_[i].setZero();\n }\n input_hidden_b_grad_.setZero();\n hidden_output_w_grad_.setZero();\n hidden_output_b_grad_.setZero();\n}\n", "meta": {"hexsha": "b4413ed2d1b655495c05e0abad6d882c6ca62051", "size": 11852, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "LanguageModel.cpp", "max_stars_repo_name": "kaishengtai/distrust", "max_stars_repo_head_hexsha": "b237110e7d5a70c9e1c1da20a94c1f83d1577926", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2015-02-17T21:15:55.000Z", "max_stars_repo_stars_event_max_datetime": "2015-02-17T21:15:55.000Z", "max_issues_repo_path": "LanguageModel.cpp", "max_issues_repo_name": "kaishengtai/distrust", "max_issues_repo_head_hexsha": "b237110e7d5a70c9e1c1da20a94c1f83d1577926", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "LanguageModel.cpp", "max_forks_repo_name": "kaishengtai/distrust", "max_forks_repo_head_hexsha": "b237110e7d5a70c9e1c1da20a94c1f83d1577926", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.2942779292, "max_line_length": 100, "alphanum_fraction": 0.6925413432, "num_tokens": 3424, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.596433160611502, "lm_q2_score": 0.3738758227716966, "lm_q1q2_score": 0.22299193865194877}} {"text": "/**************************************************************************\\\n|\n| Copyright (C) 2009 Marc Stevens\n|\n| This program is free software: you can redistribute it and/or modify\n| it under the terms of the GNU General Public License as published by\n| the Free Software Foundation, either version 3 of the License, or\n| (at your option) any later version.\n|\n| This program is distributed in the hope that it will be useful,\n| but WITHOUT ANY WARRANTY; without even the implied warranty of\n| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n| GNU General Public License for more details.\n|\n| You should have received a copy of the GNU General Public License\n| along with this program. If not, see .\n|\n\\**************************************************************************/\n\n#include \"main.hpp\"\n#include \n#include \n#include \n#include \n\nuint32 m[80];\nuint32 m2[80];\nuint32 Q[85];\nuint32 Q2[85];\nuint32 Qr30[85];\nuint32 Q2r30[85];\nuint32 dQ[85];\nuint32 dT[85];\nuint32 dR[85];\nuint32 dF[80];\n\nuint32 Qvaluemask[85];\nuint32 Qvalue[85];\nuint32 Qprev[85];\nuint32 Qprev2[85];\n\nvoid fill_tables(const sha1differentialpath& diffpath)\n{\n\tfor (int t = -4; t <= 80; ++t)\n\t{\n\t\tdF[t] = dQ[offset+t] = dT[offset+t] = dR[offset+t] = 0;\n\t\tQvaluemask[offset+t] = Qvalue[offset+t] = 0;\n\t\tQprev[offset+t] = Qprev2[offset+t] = 0;\n\t}\n\n\t// build tables\n\tfor (int t = diffpath.tbegin(); t < diffpath.tend(); ++t)\n\t{\n\t\tdQ[offset+t] = diffpath[t].diff();\n\t\tQprev[offset+t] = diffpath[t].prev() | diffpath[t].prevn();\n\t\tQprev2[offset+t] = diffpath[t].prev2() | diffpath[t].prev2n();\n\t\tQvaluemask[offset+t] = (~diffpath[t].set0()) | diffpath[t].set1()\n\t\t\t\t\t\t\t| diffpath[t].prev() | diffpath[t].prevn()\n\t\t\t\t\t\t\t| diffpath[t].prev2() | diffpath[t].prev2n();\n\t\tQvalue[offset+t] = diffpath[t].set1() | diffpath[t].prevn() | diffpath[t].prev2n();\n\t}\n\tbooleanfunction* F[4] = { &SHA1_F1_data, &SHA1_F2_data, &SHA1_F3_data, &SHA1_F4_data };\n\tfor (int t = diffpath.tbegin()+4; t+1 < diffpath.tend(); ++t) {\n\t\tbooleanfunction& FF = *F[t/20];\n\t\tuint32 dFt = 0;\n\t\tfor (unsigned b = 0; b < 32; ++b)\n\t\t\tdFt += FF.outcome(diffpath[t-1][b],diffpath[t-2][(b+2)&31],diffpath[t-3][(b+2)&31])(0,b);\n\t\tdF[t] = dFt;\n\t}\n\t\n\tQ[0] = Qvalue[0]; Q2[0] = Q[0] + dQ[0];\n\tQ[1] = Qvalue[1]; Q2[1] = Q[1] + dQ[1];\n\tQ[2] = Qvalue[2]; Q2[2] = Q[2] + dQ[2];\n\tQ[3] = Qvalue[3]; Q2[3] = Q[3] + dQ[3];\n\tQ[4] = Qvalue[4]; Q2[4] = Q[4] + dQ[4];\n}\n\n\nunsigned analyze_tunnel(const sha1differentialpath& tunnel, bool verbose, bool deepanalyze)\n{\n#define AT_TEST_COUNT (1<<18) // 16\n#define AT_MIN_PROB 0\n#define AT_MIN_COUNT (AT_TEST_COUNT>>AT_MIN_PROB)\n\n\tvector< vector > qtbcnts(80, vector(32,0));\n\tstatic uint32 tcnts[80], qmask[80];\n\tstatic uint32 mmask[80], mset1[80];\n\tfor (unsigned t = 0; t < 40; ++t) {\n\t\ttcnts[t] = 0;\n\t\tqmask[t] = maindiffpath[t].mask();\n\t}\n\tsha1differentialpath diffpath = tunnel;\n\tdiffpath.get(-4);\n\tdiffpath.get(80);\n\tfill_tables(diffpath);\n\tfor (int t = 16; t < 80; ++t) {\n\t\tif (t-4 >= tunnel.tbegin() && t+1 < tunnel.tend()) {\n\t\t\tmmask[t] = ~tunnel.getme(t).mask;\n\t\t\tmset1[t] = tunnel.getme(t).set1conditions();\n\t\t} else {\n\t\t\tmmask[t] = ~uint32(0);\n\t\t\tmset1[t] = 0;\n\t\t}\n\t}\n\t\n\n\tfor (unsigned k = 0; k < AT_TEST_COUNT; ++k) {\n//\t\tif (k == (AT_TEST_COUNT>>6) && double(tcnts[22])/double(k) >= 0.8) return 17;\n\t\tint t = -4;\n\t\twhile (t < 17) {\n\t\t\tbool ok = false;\n\t\t\tfor (unsigned j = 0; j < 3; ++j) {\n\t\t\t\tQ[offset+t] = (xrng128()&~Qvaluemask[offset+t]) ^ Qvalue[offset+t];\n\t\t\t\tif (t > -4)\n\t\t\t\t\tQ[offset+t] ^= Qprev[offset+t]&Q[offset+t-1];\n\t\t\t\tif (t > -3)\n\t\t\t\t\tQ[offset+t] ^= Qprev2[offset+t]&Q[offset+t-2];\n\t\t\t\tQ2[offset+t] = Q[offset+t] + dQ[offset+t];\n\t\t\t\tif (t >= 1) {\n\t\t\t\t\tm[t-1] = Q[offset+t] - (\n\t\t\t\t\t\tsha1_f1(Q[offset+t-2], rotate_left(Q[offset+t-3],30), rotate_left(Q[offset+t-4],30)) + sha1_ac[0] + rotate_left(Q[offset+t-1],5) + rotate_left(Q[offset+t-5],30)\n\t\t\t\t\t\t);\n\t\t\t\t\tm2[t-1] = Q2[offset+t] - (\n\t\t\t\t\t\tsha1_f1(Q2[offset+t-2], rotate_left(Q2[offset+t-3],30), rotate_left(Q2[offset+t-4],30)) + sha1_ac[0] + rotate_left(Q2[offset+t-1],5) + rotate_left(Q2[offset+t-5],30)\n\t\t\t\t\t\t);\n\t\t\t\t\tif ((m2[t-1]^m[t-1]) == diffpath.getme(t-1).mask) {\n\t\t\t\t\t\tok = true;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t} else {\n\t\t\t\t\t\tif (verbose)\n\t\t\t\t\t\t\tcout << \"(\" << t << \":\" << naf(m2[t-1]^m[t-1]) << \"!=\" << diffpath.getme(t-1) << \") \" << flush;\n\t\t\t\t\t\tcontinue;\n\t\t\t\t\t}\n\t\t\t\t} else\n\t\t\t\t\tok = true;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t\tif (ok)\n\t\t\t\t++t;\n\t\t\telse\n\t\t\t\tt -= 4;\n\t\t\tif (t < -4) throw std::runtime_error(\"t < -4\");\n\t\t}\n\t\tfor (unsigned i = 16; i < 26; ++i) {\n\t\t\tm[i]=rotate_left(m[i-3] ^ m[i-8] ^ m[i-14] ^ m[i-16], 1);\n\t\t\tm2[i]=rotate_left(m2[i-3] ^ m2[i-8] ^ m2[i-14] ^ m2[i-16], 1);\n\t\t\tuint32 md = m[i]^m2[i];\n\t\t\tm[i] = (m[i]&mmask[i])^mset1[i];\n\t\t\tm2[i] = m[i] ^ md;\n\t\t}\n\t\tfor (int t = 16; t < 20; ++t) {\n\t\t\tsha1_step_round1(t, Q, m);\n\t\t\tsha1_step_round1(t, Q2, m2);\n\t\t}\n\t\tfor (int t = 20; t < 26; ++t) {\n\t\t\tsha1_step_round2(t, Q, m);\n\t\t\tsha1_step_round2(t, Q2, m2);\n\t\t}\n\t\tfor (int t = 17; t < 27; ++t) {\n\t\t\tfor (unsigned b = 0; b < 32; ++b)\n\t\t\t\tif ((Q2[offset+t]^Q[offset+t])&(1<= AT_MIN_COUNT) { tret = t; break; }\n\t\tif (double(tcnts[t]) >= double(AT_TEST_COUNT)*0.1) { tret = t; break; }\n\t}\n\tif (!deepanalyze || tret < mainparameters.mintunnelpov || tret == 80)\n\t\treturn tret;\n\tcout << \"Tunnel: \" << endl;\n\tshow_path(tunnel);\n\tcout << \" << \";\n\tfor (int t1 = 17; t1 <= 25; ++t1)\n\t\tcout << t1 << \":\" << ((double(100)*double(tcnts[t1]))/double(AT_TEST_COUNT)) << \"% \";\n\tcout << \">> \" << flush;\n\tcout << tret << \"b[\";\n\tfor (unsigned b = 0; b < 32; ++b)\n\t\tcout << b << \":\" << unsigned((double(100)*double(qtbcnts[tret][b]))/double(AT_TEST_COUNT)) << \"%\" << (b==31?\"]\":\",\");\n\tcout << endl;\n\treturn tret;\n}\n\n\n\n\n\n\n\n\n\n\n#define GOODBIT(i,t,b) ((goodqtb[i][t]>>b)&1)\n#define BADBIT(i,t,b) ((badqtb[i][t]>>b)&1)\n#define STAT_TEST(ba,go) ((double(ba)/double(ba+go))<0.6*(double(badqtb.size())/double(badqtb.size()+goodqtb.size())))\n#define STAT_ANA(ba,go,s) if (STAT_TEST(ba,go)) { cout << \"poss. bc: q_\" << t << \"[\" << b << \"]=\" << s << \": \\t\" << (double(100*ba)/double(ba+go)) << \"%\" << endl; }\nvoid stat_ana(vector< vector >& goodqtb, vector< vector >& badqtb\n\t, vector< vector >& goodq2tb, vector< vector >& badq2tb\n\t, int t, int b)\n{\n\tunsigned g_zero = 0, g_one = 0, g_prev = 0, g_prevn = 0, g_prevr = 0, g_prevrn = 0, g_prev2 = 0, g_prev2n = 0;\n\tunsigned b_zero = 0, b_one = 0, b_prev = 0, b_prevn = 0, b_prevr = 0, b_prevrn = 0, b_prev2 = 0, b_prev2n = 0;\n\tfor (unsigned i = 0; i < goodqtb.size(); ++i) {\n\t\tif (GOODBIT(i,t,b)==0) ++g_zero; else ++g_one;\n\t\tif (GOODBIT(i,t,b)==GOODBIT(i,t-1,b)) ++g_prev; else ++g_prevn;\n\t\tif (GOODBIT(i,t,b)==GOODBIT(i,t-1,((b+2)&31))) ++g_prevr; else ++g_prevrn;\n\t\tif (GOODBIT(i,t,b)==GOODBIT(i,t-2,((b+2)&31))) ++g_prev2; else ++g_prev2n;\n\t}\n\tfor (unsigned i = 0; i < badqtb.size(); ++i) {\n\t\tif (BADBIT(i,t,b)==0) ++b_zero; else ++b_one;\n\t\tif (BADBIT(i,t,b)==BADBIT(i,t-1,b)) ++b_prev; else ++b_prevn;\n\t\tif (BADBIT(i,t,b)==BADBIT(i,t-1,((b+2)&31))) ++b_prevr; else ++b_prevrn;\n\t\tif (BADBIT(i,t,b)==BADBIT(i,t-2,((b+2)&31))) ++b_prev2; else ++b_prev2n;\n\t}\n\tdouble baseprob = double(badqtb.size())/double(badqtb.size()+goodqtb.size());\n\tSTAT_ANA(b_zero,g_zero,\"0\");\n\tSTAT_ANA(b_one,g_one,\"1\");\n\tSTAT_ANA(b_prev,g_prev,\"^\");\n\tSTAT_ANA(b_prevn,g_prevn,\"!\");\n\tSTAT_ANA(b_prevr,g_prevr,\"^r\");\n\tSTAT_ANA(b_prevrn,g_prevrn,\"!r\");\n\tSTAT_ANA(b_prev2,g_prev2,\"M\");\n\tSTAT_ANA(b_prev2n,g_prev2n,\"#\");\n\tif (b != 0) return;\n\tif (1) {\n\t\tmap > dqtstat;\n\t\tfor (unsigned i = 0; i < goodqtb.size(); ++i)\n\t\t\t++dqtstat[sdr(goodqtb[i][t],goodq2tb[i][t])].first;\n\t\tfor (unsigned i = 0; i < badqtb.size(); ++i)\n\t\t\t++dqtstat[sdr(badqtb[i][t],badq2tb[i][t])].second;\n\t\ttypedef map >::const_iterator dqtstatcit;\n\t\tvector goodcits;\n\t\tunsigned goodcnt = 0, badcnt = 0;\t\n\t\tfor (dqtstatcit cit = dqtstat.begin(); cit != dqtstat.end(); ++cit)\n\t\t\tif ((double(cit->second.second)/double(cit->second.first+cit->second.second))<0.6*baseprob) {\n\t\t\t\tgoodcits.push_back(cit);\n\t\t\t\tgoodcnt += cit->second.first;\n\t\t\t\tbadcnt += cit->second.second;\n\t\t\t}\n\t\tif (goodcits.size() && double(goodcnt) > 0.25*double(goodqtb.size()) && goodcits.size()*2 < goodqtb.size()) {\n\t\t\tcout << \"good dQ_\" << t << \" \" << double(badcnt*100)/double(badcnt+goodcnt) << \"% bad #=\" << goodcits.size() << \":\\t\";\n\t\t\tfor (unsigned i = 0; i < goodcits.size(); ++i)\n\t\t\t\tif (double(goodcits[i]->second.first) > 0.01*double(goodqtb.size()))\n\t\t\t\tcout << (goodcits[i]->first) << \"(\" << 100.0*double(goodcits[i]->second.first)/double(goodcnt) << \"%) \";\n\t\t\tcout << endl;\n\t\t}\n\t}\n\tif (1) {\n\t\tmap > dftstat;\n\t\tif (t < 20) {\n\t\t\tfor (unsigned i = 0; i < goodqtb.size(); ++i) {\n\t\t\t\tuint32 F1 = sha1_f1(goodqtb[i][t-1], rotate_left(goodqtb[i][t-2],30), rotate_left(goodqtb[i][t-3],30));\n\t\t\t\tuint32 F2 = sha1_f1(goodq2tb[i][t-1], rotate_left(goodq2tb[i][t-2],30), rotate_left(goodq2tb[i][t-3],30));\n\t\t\t\t++dftstat[sdr(F1,F2)].first;\n\t\t\t}\n\t\t\tfor (unsigned i = 0; i < badqtb.size(); ++i) {\n\t\t\t\tuint32 F1 = sha1_f1(badqtb[i][t-1], rotate_left(badqtb[i][t-2],30), rotate_left(badqtb[i][t-3],30));\n\t\t\t\tuint32 F2 = sha1_f1(badq2tb[i][t-1], rotate_left(badq2tb[i][t-2],30), rotate_left(badq2tb[i][t-3],30));\n\t\t\t\t++dftstat[sdr(F1,F2)].second;\n\t\t\t}\n\t\t} else {\n\t\t\tfor (unsigned i = 0; i < goodqtb.size(); ++i) {\n\t\t\t\tuint32 F1 = sha1_f2(goodqtb[i][t-1], rotate_left(goodqtb[i][t-2],30), rotate_left(goodqtb[i][t-3],30));\n\t\t\t\tuint32 F2 = sha1_f2(goodq2tb[i][t-1], rotate_left(goodq2tb[i][t-2],30), rotate_left(goodq2tb[i][t-3],30));\n\t\t\t\t++dftstat[sdr(F1,F2)].first;\n\t\t\t}\n\t\t\tfor (unsigned i = 0; i < badqtb.size(); ++i) {\n\t\t\t\tuint32 F1 = sha1_f2(badqtb[i][t-1], rotate_left(badqtb[i][t-2],30), rotate_left(badqtb[i][t-3],30));\n\t\t\t\tuint32 F2 = sha1_f2(badq2tb[i][t-1], rotate_left(badq2tb[i][t-2],30), rotate_left(badq2tb[i][t-3],30));\n\t\t\t\t++dftstat[sdr(F1,F2)].second;\n\t\t\t}\n\t\t}\n\t\ttypedef map >::const_iterator dftstatcit;\n\t\tvector goodcits;\n\t\tunsigned goodcnt = 0, badcnt = 0;\t\n\t\tfor (dftstatcit cit = dftstat.begin(); cit != dftstat.end(); ++cit)\n\t\t\tif ((double(cit->second.second)/double(cit->second.first+cit->second.second))<0.6*baseprob) {\n\t\t\t\tgoodcits.push_back(cit);\n\t\t\t\tgoodcnt += cit->second.first;\n\t\t\t\tbadcnt += cit->second.second;\n\t\t\t}\n\t\tif (goodcits.size() && double(goodcnt) > 0.25*double(goodqtb.size()) && goodcits.size()*2 < goodqtb.size()) {\n\t\t\tcout << \"good dF_\" << t << \" \" << double(badcnt*100)/double(badcnt+goodcnt) << \"% bad #=\" << goodcits.size() << \":\\t\";\n\t\t\tfor (unsigned i = 0; i < goodcits.size(); ++i)\n\t\t\t\tif (double(goodcits[i]->second.first) > 0.01*double(goodqtb.size()))\n\t\t\t\tcout << (goodcits[i]->first) << \"(\" << 100.0*double(goodcits[i]->second.first)/double(goodcnt) << \"%) \";\n\t\t\tcout << endl;\n\t\t}\n\t}\n}\n\nunsigned analyze_bc_tunnel(const sha1differentialpath& tunnel)\n{\n#define ATLC_TEST_COUNT (1<<18) // 16\n#define ATLC_MIN_PROB 0\n#define ATLC_MIN_COUNT (ATLC_TEST_COUNT>>ATLC_MIN_PROB)\n\tunsigned tend = analyze_tunnel(tunnel, false, mainparameters.changebitstat);\n\tmemset(m, 0, sizeof(uint32)*16);\n\tfor (int i = 0; i < 16; ++i)\n\t\tif (i >= tunnel.tbegin() && i < tunnel.tend())\n\t\t\tm[i] = tunnel.getme(i).mask;\n\tfor (unsigned i = 16; i < 32; ++i)\n\t\tm[i]=rotate_left(m[i-3] ^ m[i-8] ^ m[i-14] ^ m[i-16], 1);\n//\tif (tend < 21)\n//\t\treturn tend;\n\tshow_path(tunnel);\n\tcout << tend << endl;\n\tvector< vector > dmes(80);\n\tunsigned tend2 = tend;\n\tuint64 mcnt = 1;\n\tfor (unsigned i = 16; i < tend; ++i) {\n\t\tsdr tmp;\n\t\ttmp.mask = m[i];\n\t\ttmp.sign = 0;\n\t\tdo {\n\t\t\tdmes[i].push_back(tmp.adddiff());\n\t\t\ttmp.sign += ~tmp.mask + 1;\n\t\t\ttmp.sign &= tmp.mask;\n\t\t} while (tmp.sign != 0);\n\t\tsort(dmes[i].begin(), dmes[i].end());\n\t\tdmes[i].erase( unique(dmes[i].begin(),dmes[i].end()), dmes[i].end());\n\t\tif ((mcnt * dmes[i].size()) > (1<<6)) {\n\t\t\t// don't let mcnt grow too big\n\t\t\ttend2 = i;\n\t\t\tbreak;\n\t\t}\n\t\tmcnt *= uint64(dmes[i].size());\n\t}\t\n\tcout << mcnt << endl;\n\tuint32 qmask[80];\n\tfor (unsigned t = 0; t < 40; ++t)\n\t\tqmask[t] = maindiffpath[t].mask();\n\n\tfor (unsigned mi = 0; mi < mcnt; ++mi) {\n\t\tvector< vector > goodqtb, badqtb, goodq2tb, badq2tb;\n\t\tvector dmesindex(80,0);\n\t\tunsigned mi2 = mi;\n\t\tcout << mi << \": \";\n\t\tfor (unsigned i = 16; i < tend2; ++i)\t\t\n\t\t\tif (dmes[i].size() > 1) {\n\t\t\t\tdmesindex[i] = mi2 % dmes[i].size();\n\t\t\t\tmi2 /= dmes[i].size();\n\t\t\t\tcout << i << naf(dmes[i][dmesindex[i]]);\n\t\t\t}\n\t\tcout << \": \";\n\t\tsha1differentialpath diffpath = tunnel;\n\t\tdiffpath.get(-4);\n\t\tdiffpath.get(80);\n\t\tfill_tables(diffpath);\n\t\tfor (unsigned k = 0; k < ATLC_TEST_COUNT; ++k) {\n\t\t\tint t = -4;\n\t\t\twhile (t < 17) {\n\t\t\t\tbool ok = false;\n\t\t\t\tfor (unsigned j = 0; j < 3; ++j) {\n\t\t\t\t\tQ[offset+t] = (xrng128()&~Qvaluemask[offset+t]) ^ Qvalue[offset+t];\n\t\t\t\t\tif (t > -4)\n\t\t\t\t\t\tQ[offset+t] ^= Qprev[offset+t]&Q[offset+t-1];\n\t\t\t\t\tif (t > -3)\n\t\t\t\t\t\tQ[offset+t] ^= Qprev2[offset+t]&Q[offset+t-2];\n\t\t\t\t\tQ2[offset+t] = Q[offset+t] + dQ[offset+t];\n\t\t\t\t\tif (t >= 1) {\n\t\t\t\t\t\tm[t-1] = Q[offset+t] - (\n\t\t\t\t\t\t\tsha1_f1(Q[offset+t-2], rotate_left(Q[offset+t-3],30), rotate_left(Q[offset+t-4],30)) + sha1_ac[0] + rotate_left(Q[offset+t-1],5) + rotate_left(Q[offset+t-5],30)\n\t\t\t\t\t\t\t);\n\t\t\t\t\t\tm2[t-1] = Q2[offset+t] - (\n\t\t\t\t\t\t\tsha1_f1(Q2[offset+t-2], rotate_left(Q2[offset+t-3],30), rotate_left(Q2[offset+t-4],30)) + sha1_ac[0] + rotate_left(Q2[offset+t-1],5) + rotate_left(Q2[offset+t-5],30)\n\t\t\t\t\t\t\t);\n\t\t\t\t\t\tif ((m2[t-1]^m[t-1]) == diffpath.getme(t-1).mask) {\n\t\t\t\t\t\t\tok = true;\n\t\t\t\t\t\t\tbreak;\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tcontinue;\n\t\t\t\t\t\t}\n\t\t\t\t\t} else\n\t\t\t\t\t\tok = true;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif (ok)\n\t\t\t\t\t++t;\n\t\t\t\telse\n\t\t\t\t\tt -= 4;\n\t\t\t\tif (t < -4) throw std::runtime_error(\"t < -4\");\n\t\t\t}\n\t\t\tfor (unsigned i = 16; i < 26; ++i) {\n\t\t\t\tm[i]=rotate_left(m[i-3] ^ m[i-8] ^ m[i-14] ^ m[i-16], 1);\n\t\t\t\tif (i < tend2)\n\t\t\t\t\tm2[i] = m[i] + dmes[i][dmesindex[i]];\n\t\t\t\telse\n\t\t\t\t\tm2[i]=rotate_left(m2[i-3] ^ m2[i-8] ^ m2[i-14] ^ m2[i-16], 1);\n\t\t\t}\n\t\t\tfor (int t = 16; t < 20; ++t) {\n\t\t\t\tsha1_step_round1(t, Q, m);\n\t\t\t\tsha1_step_round1(t, Q2, m2);\n\t\t\t}\n\t\t\tfor (int t = 20; t < 26; ++t) {\n\t\t\t\tsha1_step_round2(t, Q, m);\n\t\t\t\tsha1_step_round2(t, Q2, m2);\n\t\t\t}\n\t\t\tfor (int t = 17; t < 27; ++t) {\n\t\t\t\tif ((Q2[offset+t]^Q[offset+t])&(qmask[t]|0x00000000)) {\n\t\t\t\t\tstatic vector tmpqtb(27);\n\t\t\t\t\tfor (int t1 = 0; t1 < tmpqtb.size(); ++t1)\n\t\t\t\t\t\ttmpqtb[t1] = Q[4+t1];\n\t\t\t\t\tif (t <= tend)\n\t\t\t\t\t\tbadqtb.push_back(tmpqtb);\n\t\t\t\t\telse\n\t\t\t\t\t\tgoodqtb.push_back(tmpqtb);\n\t\t\t\t\tfor (int t1 = 0; t1 < tmpqtb.size(); ++t1)\n\t\t\t\t\t\ttmpqtb[t1] = Q2[4+t1];\n\t\t\t\t\tif (t <= tend)\n\t\t\t\t\t\tbadq2tb.push_back(tmpqtb);\n\t\t\t\t\telse\n\t\t\t\t\t\tgoodq2tb.push_back(tmpqtb);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tcout << goodqtb.size() << \" vs \" << badqtb.size() << \": \" << (double(100)*double(badqtb.size()))/double(goodqtb.size()+badqtb.size()) << \"% bad\" << endl;\n\t\tfor (unsigned t = 16-4; t < tend; ++t)\n\t\t\tfor (unsigned b = 0; b < 32; ++b) {\n\t\t\t\tstat_ana(goodqtb, badqtb, goodq2tb, badq2tb, t, b);\n\t\t\t}\n\t}\n\treturn tend;\n}\n\n\n\n\n\nvoid find_best_tunnel(vector< sha1differentialpath >& tunnels, const sha1differentialpath& mypath, const vector< vector >& bitrels, bool force_lc = false) \n{\n//#define ADD_ME_CARRIES 0\n\tunsigned ADD_ME_CARRIES = mainparameters.mecarry;\n\tif (mainparameters.tunnelfilterbitcondition)\n\t\tfilter_tunnels_bitconditions(tunnels, mypath);\n\tstatic vector sdrsvec[16];\n\tsdr metbu;\n\tstatic vector iQvec, ncvec;\n\tiQvec.assign(tunnels.size(),0);\n\tncvec.assign(tunnels.size(),1<<20);\n//\tcout << tunnels.size() << endl;\n\tfor (unsigned i = 0; i < tunnels.size(); ++i) {\n\t\tuint64 cnt = 1;\n\t\tfor (int t = 0; t < 16; ++t) {\n\t\t\tsdrsvec[t].clear();\n\t\t\tif (t < tunnels[i].tbegin() || t >= tunnels[i].tend()) continue;\n\t\t\tmetbu = tunnels[i].getme(t);\n\t\t\ttable_sdrs(sdrsvec[t], metbu.adddiff(), hw(metbu.mask)+ADD_ME_CARRIES);\n\t\t\tunsigned j = 0;\n\t\t\twhile (j < sdrsvec[t].size()) {\n\t\t\t\tif (sdrsvec[t][j].get(31) == -1) {\n\t\t\t\t\tswap(sdrsvec[t][j], sdrsvec[t][sdrsvec[t].size()-1]);\n\t\t\t\t\tsdrsvec[t].pop_back();\n\t\t\t\t} else\n\t\t\t\t\t++j;\n\t\t\t}\n\t\t\tcnt *= sdrsvec[t].size();\n\t\t}\n\t\tif (cnt == 0)\n\t\t\tthrow std::runtime_error(\"cnt == 0 ??\");\n\t\tunsigned bestiQ = 0, bestnc = 1<<20;\n\t\tuint64 bestc = cnt;\n\t\tunsigned basenc = tunnels[i].nrcond();\n\t\tfor (uint64 c = 0; c < cnt; ++c) {\n\t\t\tuint64 cc = c;\n\t\t\tfor (int t = 0; t < 16; ++t) {\n\t\t\t\tif (sdrsvec[t].size()>1) {\n\t\t\t\t\tuint64 k = cc % sdrsvec[t].size();\n\t\t\t\t\tcc /= sdrsvec[t].size();\n\t\t\t\t\ttunnels[i].getme(t) = sdrsvec[t][k];\n\t\t\t\t}\n\t\t\t}\n\t\t\tif (!filter_tunnel_bitrelations(tunnels[i],bitrels)) continue;\n\t\t\tunsigned iQ;\n\t\t\tif (force_lc)\n\t\t\t\tiQ = analyze_bc_tunnel(tunnels[i]);\n\t\t\telse\n\t\t\t\tiQ = analyze_tunnel(tunnels[i], false, mainparameters.changebitstat);\n\t\t\tif (iQ < bestiQ) continue;\n\t\t\tif (iQ > bestiQ) {\n\t\t\t\tbestiQ = iQ;\n\t\t\t\tbestnc = 1<<20;\n\t\t\t\tbestc = c;\n\t\t\t}\n\t\t\tunsigned nc = basenc;\n\t\t\tfor (int t = tunnels[i].tbegin(); t < tunnels[i].tend(); ++t)\n\t\t\t\tnc += hw(tunnels[i].getme(t).mask&0x7FFFFFFF);\n//\t\t\tif (nc >= 15) continue;\n\t\t\tif (nc < bestnc) {\n\t\t\t\tbestnc = nc;\n\t\t\t\tbestc = c;\n\t\t\t}\n\t\t}\n\t\tif (bestc == cnt) {\n\t\t\ttunnels[i].clear();\n\t\t\tcontinue;\n\t\t}\n\t\tuint64 cc = bestc;\n\t\tfor (int t = 0; t < 16; ++t) {\n\t\t\tif (sdrsvec[t].size()) {\n\t\t\t\tuint64 k = cc % sdrsvec[t].size();\n\t\t\t\tcc /= sdrsvec[t].size();\n\t\t\t\ttunnels[i].getme(t) = sdrsvec[t][k];\n\t\t\t}\n\t\t}\n\t\tiQvec[i] = bestiQ;\n\t\tncvec[i] = bestnc;\n//\t\tif (bestiQ >= 23) break;\n\t}\n\tunsigned bestiQ = 0, bestnc = 1<<20, besti = tunnels.size(), minnc = 1<<20;\n\tfor (unsigned i = 0; i < tunnels.size(); ++i) {\n\t\tif (tunnels[i].path.size()) {\n\t\t\tif (iQvec[i] > bestiQ) {\n\t\t\t\tbestiQ = iQvec[i];\n\t\t\t\tbestnc = ncvec[i];\n\t\t\t\tbesti = i;\n\t\t\t} else if (iQvec[i] == bestiQ && ncvec[i] < bestnc) {\n\t\t\t\tbestnc = ncvec[i];\n\t\t\t\tbesti = i;\n\t\t\t}\n\t\t\tif (ncvec[i] < minnc)\n\t\t\t\tminnc = ncvec[i];\n\t\t}\n\t}\n\tif (besti < tunnels.size()) {\n//\t\tif (bestiQ >= 21)\n\t\t\tcout << endl << bestiQ << \"(\" << bestnc << \",\" << minnc << \") \" << flush;\n\t\tif (bestiQ >= 16) { //21\n\t\t\t//show_path(tunnels[besti]);\n#if 0\n\t\t\tanalyze_tunnel(tunnels[besti], false, true);\n#else\n\t\t\tfor (unsigned j = 0; j < tunnels.size(); ++j)\n\t\t\t\tif (iQvec[j] == iQvec[besti])\n\t\t\t\t\tanalyze_tunnel(tunnels[j], false, true);\n#endif\n\t\t} \n\t\tsha1differentialpath bestpath;\n\t\tbestpath.swap(tunnels[besti]);\n\t\ttunnels.resize(1);\n\t\tbestpath.swap(tunnels[0]);\n\t\t\n\t\tif (bestiQ < 19) tunnels.clear();\n\t\t\n\t} else\n\t\ttunnels.clear();\n}\n\nvoid create_tunnel_stept(vector< sha1differentialpath >& tunnels, sha1differentialpath& tunnel, int t, bool force_lc = false)\n{\n\tif (t >= 0 && t+1 >= tunnel.tend()) {\n\t\tcreate_tunnel_stept(tunnels, tunnel, t-1, force_lc);\n\t\treturn;\n\t}\n\n\tif (t-4 < tunnel.tbegin() || t < 0) {\n\t\ttunnels.push_back(tunnel);\n\t\treturn;\n\t}\n\tif (tunnel[t-1].diff() == 0 && tunnel[t-2].diff() == 0 && tunnel[t-3].diff() == 0) {\n\t\t// dFt = 0\n\t\tuint32 dme = tunnel[t+1].diff() - tunnel[t].getsdr().rotate_left(5).adddiff() - tunnel[t-4].getsdr().rotate_left(30).adddiff();\n\t\ttunnel.getme(t) = naf(dme);\n\t\tcreate_tunnel_stept(tunnels, tunnel, t-1, force_lc);\n\t\treturn;\n\t}\n\n\tbf_conditions newconditions[32][2];\n\tbool bitactive[32];\n\tunsigned cnt = 1;\n\tfor (unsigned b = 0; b < 32; ++b) {\n\t\tbitactive[b] = false;\n\t\t//sha1_f1(Q[offset+t-1], rotate_left(Q[offset+t-2],30), rotate_left(Q[offset+t-3],30))\n\t\tbf_outcome bo = SHA1_F1_data.outcome(tunnel(t-1,b), tunnel(t-2,(b+2)&31), tunnel(t-3,(b+2)&31));\n\t\tif (bo.size() == 0) throw std::runtime_error(\"bo.size() == 0\");\n\t\tif (bo.size() == 1) continue;\n\t\tif (bo.size() == 2) {\n\t\t\tif (/*b == 31 &&*/ bo[0] != bc_constant && bo[1] != bc_constant)\n\t\t\t\tcontinue;\n\t\t\tif (force_lc) {\n\t\t\t\tbf_conditions bf;\n\t\t\t\tif (bo[0] != bc_constant)\n\t\t\t\t\tbf = SHA1_F1_data.backwardconditions( tunnel(t-1,b), tunnel(t-2,(b+2)&31), tunnel(t-3,(b+2)&31), bo[0] );\n\t\t\t\telse\n\t\t\t\t\tbf = SHA1_F1_data.backwardconditions( tunnel(t-1,b), tunnel(t-2,(b+2)&31), tunnel(t-3,(b+2)&31), bo[1] );\n\t\t\t\ttunnel.setbitcondition(t-1,b,bf.first);\n\t\t\t\ttunnel.setbitcondition(t-2,(b+2)&31,bf.second);\n\t\t\t\ttunnel.setbitcondition(t-3,(b+2)&31,bf.third);\n\t\t\t\tcontinue;\n\t\t\t}\n\t\t\tnewconditions[b][0] = SHA1_F1_data.backwardconditions( tunnel(t-1,b), tunnel(t-2,(b+2)&31), tunnel(t-3,(b+2)&31), bo[0]);\n\t\t\tnewconditions[b][1] = SHA1_F1_data.backwardconditions( tunnel(t-1,b), tunnel(t-2,(b+2)&31), tunnel(t-3,(b+2)&31), bo[1]);\n\t\t\tcnt *= 2;\n\t\t\tbitactive[b] = true;\n\t\t} else { // bo.size() == 3\n\t\t\tbf_conditions bf = SHA1_F1_data.backwardconditions( tunnel(t-1,b), tunnel(t-2,(b+2)&31), tunnel(t-3,(b+2)&31), bc_constant );\n#if 1\n\t\t\tif (mainparameters.simpletunnels) {\n\t\t\t\ttunnel.setbitcondition(t-1,b,bf.first);\n\t\t\t\ttunnel.setbitcondition(t-2,(b+2)&31,bf.second);\n\t\t\t\ttunnel.setbitcondition(t-3,(b+2)&31,bf.third);\n\t\t\t\tcontinue;\n\t\t\t}\n#endif\n\t\t\tif (force_lc) {\n\t\t\t\ttunnel.setbitcondition(t-1,b,bf.first);\n\t\t\t\ttunnel.setbitcondition(t-2,(b+2)&31,bc_prevn);\n\t\t\t\ttunnel.setbitcondition(t-3,(b+2)&31,bf.third);\n\t\t\t\tcontinue;\n\t\t\t}\n\t\t\tif (bf.second != bc_prev) throw std::runtime_error(\"bf.second != bc_prev\");\n\t\t\tnewconditions[b][0] = bf;\n\t\t\tbf.second = bc_prevn;\n\t\t\tnewconditions[b][1] = bf;\n\t\t\tcnt *= 2;\n\t\t\tbitactive[b] = true;\n\t\t}\n\t}\n\tfor (unsigned c = 0; c < cnt; ++c) {\n\t\tunsigned cc = c;\n\t\tuint32 dFt = 0;\n\t\tfor (unsigned b = 0; b < 32; ++b) {\n\t\t\tif (bitactive[b]) {\n\t\t\t\tunsigned k = cc % 2;\n\t\t\t\tcc /= 2;\n\t\t\t\ttunnel.setbitcondition(t-1,b,newconditions[b][k].first);\n\t\t\t\ttunnel.setbitcondition(t-2,((b+2)&31),newconditions[b][k].second);\n\t\t\t\ttunnel.setbitcondition(t-3,((b+2)&31),newconditions[b][k].third);\n\t\t\t}\n\t\t\tdFt += SHA1_F1_data.outcome(tunnel(t-1,b), tunnel(t-2,(b+2)&31), tunnel(t-3,(b+2)&31))(0,b);\n\t\t}\n\n\t\tuint32 dme = tunnel[t+1].diff() - dFt - tunnel[t].getsdr().rotate_left(5).adddiff() - tunnel[t-4].getsdr().rotate_left(30).adddiff();\n\t\ttunnel.getme(t) = naf(dme);\n\t\tcreate_tunnel_stept(tunnels, tunnel, t-1, force_lc);\n\t}\n}\n\nvoid analyze_tunnels_diffpath(const sha1differentialpath& mypath, const vector< vector >& bitrels, vector tunnelsin)\n{\n\tcout << \"Analyzing tunnels for differential path:\" << endl;\n\tsha1differentialpath path = mypath;\n\tcleanup_path(path);\n\tshow_path(path);\n\tmaindiffpath = path;\n\tcout << \"Message space determined by \" << bitrels.size() << \" bitrelations.\" << endl;\n\n\tbool deep_analysis = mainparameters.tunneldeepanalysis;\n\t\n\tif (tunnelsin.size()) {\n\t\tvector tunnelstmp;\n\t\tfor (unsigned i = 0; i < tunnelsin.size(); ++i)\n\t\t\tcreate_tunnel_stept(tunnelstmp, tunnelsin[i], 15);\n\t\tfind_best_tunnel(tunnelstmp, path, bitrels, deep_analysis);\t\n\t\treturn;\n\t}\n\n\tsha1differentialpath tunnelpath;\n\tvector< sha1differentialpath > tunnels, tunnelstmp, tunnelstmp2;\n\n\tcout << \"Generating tunnels...\" << endl;\n\tset goodbits;\n\tset< pair > good2bits;\n#if 1\n\tfor (int bit1 = 0; bit1 < 16*32; ++bit1) {\n\t\tint t1 = bit1 / 32;\n\t\tint b1 = bit1 % 32;\n\t\ttunnelpath.clear();\n\t\ttunnelpath.get(t1-4);\n\t\tif (t1 + 6 < 16)\n\t\t\ttunnelpath.get(t1+6);\n\t\telse\n\t\t\ttunnelpath.get(16);\n\t\ttunnelpath.setbitcondition(t1+1,b1,bc_plus);\n\t\ttunnelstmp.clear();\n\t\tcreate_tunnel_stept(tunnelstmp, tunnelpath, 15);\n\t\tfind_best_tunnel(tunnelstmp, path, bitrels, deep_analysis);\n\t\tif (tunnelstmp.size()) {\n\t\t\tgoodbits.insert(bit1);\n\t\t\tcontinue;\n\t\t}\n\t}\n#if 1\n\tfor (int bit1 = 0; bit1 < 16*32; ++bit1) {\n\t\tfor (int bit2 = bit1+1; bit2 < 16*32; ++bit2) {\n\t\t\tif (goodbits.find(bit1) != goodbits.end()) continue;\n\t\t\tif (goodbits.find(bit2) != goodbits.end()) continue;\n\t\t\tint t1 = bit1 / 32;\n\t\t\tint b1 = bit1 % 32;\n\t\t\tint t2 = bit2/32;\n\t\t\tint b2 = bit2%32;\n\t\t\ttunnelpath.clear();\n\t\t\ttunnelpath.get(t1-4);\n\t\t\tif (t2 + 6 < 16)\n\t\t\t\ttunnelpath.get(t2+6);\n\t\t\telse\n\t\t\t\ttunnelpath.get(16);\n\t\t\ttunnelpath.setbitcondition(t1+1,b1,bc_plus);\n\t\t\ttunnelpath.setbitcondition(t2+1,b2,bc_plus);\n\t\t\ttunnelstmp.clear();\n\t\t\tcreate_tunnel_stept(tunnelstmp, tunnelpath, 15);\n\t\t\tfind_best_tunnel(tunnelstmp, path, bitrels, deep_analysis);\n\t\t\tif (tunnelstmp.size())\n\t\t\t\tgood2bits.insert(pair(bit1,bit2));\n\t\t}\n\t}\n#endif\n//\texit(0);\n#endif\n\tfor (int bit1 = 16*32-1; bit1 >= 0; --bit1) {\n//\tfor (int bit1 = 169; bit1 < 16*32; ++bit1) {\n\t\tcout << \"[\" << bit1 << \"] \" << flush;\n\t\tfor (int bit2 = bit1; bit2 < 16*32; ++bit2) {\n\t\t\tfor (int bit2sign = 0; bit2sign < 1; ++bit2sign)\n\t\t\tfor (int bit3 = bit2; bit3 < 16*32; ++bit3)\n\t\t\tfor (int bit3sign = 0; bit3sign < 1; ++bit3sign)\n\t\t\t{\n\t\t\t\t//if (bit2 != bit1 || bit3 != bit1) continue; // force single bit difference\n\t\t\t\tif (!mainparameters.threebittunnels && bit3 != bit2) continue; // force max 2 bit difference\n\t\t\t\tif (bit1 == bit2 && bit2sign == 1) continue;\n\t\t\t\tif (bit3 == bit2 && bit3sign != bit2sign) continue;\n\t\t\t\tif (bit1 == bit2 && bit3 > bit2) continue;\n\t\t\t\tif (goodbits.find(bit1) != goodbits.end()) continue;\n\t\t\t\tif (goodbits.find(bit2) != goodbits.end()) continue;\n\t\t\t\tif (goodbits.find(bit3) != goodbits.end()) continue;\n\t\t\t\tif (good2bits.find(pair(bit1,bit2)) != good2bits.end()) continue;\n\t\t\t\tif (good2bits.find(pair(bit1,bit3)) != good2bits.end()) continue;\n\t\t\t\tif (good2bits.find(pair(bit2,bit3)) != good2bits.end()) continue;\n\t\t\t\t\n\t\t\t\t//cout << \"[\" << bit1 << \",\" << bit2 << \"] \" << flush;\n\t\t\t\tint t1 = bit1 / 32;\n\t\t\t\tint b1 = bit1 % 32;\n\t\t\t\tint t2 = bit2 / 32;\n\t\t\t\tint b2 = bit2 % 32;\n\t\t\t\tint t3 = bit3 / 32;\n\t\t\t\tint b3 = bit3 % 32;\n\t\t\t\ttunnelpath.clear();\n\t\t\t\ttunnelpath.get(t1-4);\n\t\t\t\tif (t3 + 6 < 16)\n\t\t\t\t\ttunnelpath.get(t3+6);\n\t\t\t\telse\n\t\t\t\t\ttunnelpath.get(16);\n\t\t\t\ttunnelpath.setbitcondition(t1+1,b1,bc_plus);\n\t\t\t\tif (bit2sign == 0)\n\t\t\t\t\ttunnelpath.setbitcondition(t2+1,b2,bc_plus);\n\t\t\t\telse\n\t\t\t\t\ttunnelpath.setbitcondition(t2+1,b2,bc_minus);\n\t\t\t\tif (bit3sign == 0)\n\t\t\t\t\ttunnelpath.setbitcondition(t3+1,b3,bc_plus);\n\t\t\t\telse\n\t\t\t\t\ttunnelpath.setbitcondition(t3+1,b3,bc_minus);\n\t\t\t\ttunnelstmp.clear();\n\t\t\t\tcreate_tunnel_stept(tunnelstmp, tunnelpath, 15);\n\t\t\t\tfind_best_tunnel(tunnelstmp, path, bitrels, deep_analysis);\n\t\t\t}\n\t\t}\n\t}\n\texit(0);\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nstruct ihv_type {\n\tuint32 ihv[5];\n\tbool operator<(const ihv_type& r) const {\n\t\tfor (int i = 0; i < 4; ++i) {\n\t\t\tif (ihv[i] < r.ihv[i]) return true;\n\t\t\tif (ihv[i] > r.ihv[i]) return false;\n\t\t}\n\t\tif (ihv[4] < r.ihv[4]) return true;\n\t\treturn false;\n\t}\n\tbool operator==(const ihv_type& r) const {\n\t\tfor (int i = 0; i < 5; ++i)\n\t\t\tif (ihv[i] != r.ihv[i]) return false;\n\t\treturn true;\n\t}\n};\n\nvoid analyze_indepsection_prob()\n{\n\tvector< vector< vector > > pathbitrelationsmatrix;\n\tmespace_to_pathbitrelationsmatrix(mainmespace, pathbitrelationsmatrix);\n\tsha1differentialpath path = maindiffpath;\n\tint tbegin = 16;\n\tfor (; tbegin < 80; ++tbegin)\n\t\tif (0 == (path[tbegin].diff() | path[tbegin-1].diff() | path[tbegin-2].diff() | path[tbegin-3].diff() | path[tbegin-4].diff()))\n\t\t\tbreak;\n\twhile (tbegin < 80) {\n\t\tint tend = tbegin+1;\n\t\tfor (; tend < 80; ++tend)\n\t\t\tif (0 == (path[tend].diff() | path[tend-1].diff() | path[tend-2].diff() | path[tend-3].diff() | path[tend-4].diff()))\n\t\t\t\tbreak;\n\t\tif (0 != (path[tend].diff() | path[tend-1].diff() | path[tend-2].diff() | path[tend-3].diff() | path[tend-4].diff()))\n\t\t\tbreak;\n\t\tif (tend == tbegin+1) {\n\t\t\ttbegin = tend;\n\t\t\tcontinue;\n\t\t}\n\t\tcout << \"Prob t=[\" << tbegin << \"-\" << tend << \"): \\t\" << flush;\n#if 0\n\t\ttbegin = tend; cout << endl; continue;\n#endif\n\t\tuint64 cnt = 0;\n\t\tuint32 okcnt = 0;\n\t\twhile (okcnt < (1<<15)) {\n\t\t\t++cnt; if (hw(cnt)==1) cout << cnt << \" \" << flush;\n\t\t\trandom_me(m, pathbitrelationsmatrix);\n\t\t\tfor (int t = 0; t < 16; ++t)\n\t\t\t\tm2[t] = m[t] ^ path.getme(t).mask;\n\t\t\tfor (int t = 16; t < 80; ++t)\n\t\t\t\tm2[t]=rotate_left(m2[t-3] ^ m2[t-8] ^ m2[t-14] ^ m2[t-16], 1);\n\t\t\tfor (int t = tbegin-4; t <= tbegin; ++t)\n\t\t\t\tQ2[offset+t] = Q[offset+t] = xrng64();\n\t\t\tfor (int t = tbegin; t < tend; ++t) {\n\t\t\t\tsha1_step(t, Q, m);\n\t\t\t\tsha1_step(t, Q2, m2);\n\t\t\t}\n\t\t\tbool ok = true;\n\t\t\tfor (int t = tend-4; t <= tend; ++t)\n\t\t\t\tif (Q2[offset+t] != Q[offset+t]) {\n\t\t\t\t\tok = false;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\tif (ok)\n\t\t\t\tif (hw(++okcnt)==1) cout << \"[\" << okcnt << \"] \" << flush;\n\t\t}\n\t\tcout << \" prob=\" << log(double(okcnt)/double(cnt))/log(2.0) << endl;\n\t\t\n\t\ttbegin = tend;\n\t}\t\t\n\tcout << \"Prob t=[\" << tbegin << \"-\" << 80 << \"):\" << endl;\n\tvector target_dihvs(20);\n\tvector dihvs(1<<28);\n\tprogress_display pd(dihvs.size());\n\tfor (unsigned i = 0; i < dihvs.size(); ++i,++pd) {\n\t\trandom_me(m, pathbitrelationsmatrix);\n\t\tfor (int t = 0; t < 16; ++t)\n\t\t\tm2[t] = m[t] ^ path.getme(t).mask;\n\t\tfor (int t = 16; t < 80; ++t)\n\t\t\tm2[t]=rotate_left(m2[t-3] ^ m2[t-8] ^ m2[t-14] ^ m2[t-16], 1);\n\t\tfor (int t = tbegin-4; t <= tbegin; ++t)\n\t\t\tQ2[offset+t] = Q[offset+t] = xrng64();\n\t\tfor (int t = -4; t <= 0; ++t)\n\t\t\tQ2[offset+t] = Q[offset+t] = xrng64();\n\t\tfor (int t = tbegin; t < 80; ++t) {\n\t\t\tsha1_step(t, Q, m);\n\t\t\tsha1_step(t, Q2, m2);\n\t\t}\n\t\tdihvs[i].ihv[0] = rotate_left(Q2[offset+80-4],30)-rotate_left(Q[offset+80-4],30);\n\t\tdihvs[i].ihv[1] = rotate_left(Q2[offset+80-3],30)-rotate_left(Q[offset+80-3],30);\n\t\tdihvs[i].ihv[2] = rotate_left(Q2[offset+80-2],30)-rotate_left(Q[offset+80-2],30);\n\t\tdihvs[i].ihv[3] = Q2[offset+80-1]-Q[offset+80-1];\n\t\tdihvs[i].ihv[4] = Q2[offset+80]-Q[offset+80];\n\t}\n\tsort(dihvs.begin(), dihvs.end());\n\tunsigned i = 0;\n\tvector bestcnts;\n\twhile (i < dihvs.size()) {\n\t\tunsigned j = i+1;\n\t\twhile (j < dihvs.size() && dihvs[j] == dihvs[i]) ++j;\n\t\tbestcnts.push_back(j-i);\n\t\ti = j;\n\t}\n\tcout << \"Prob t=[\" << tbegin << \"-\" << 80 << \"): \" << flush;\n\tsort(bestcnts.begin(),bestcnts.end());\n\tunsigned bestcnt = bestcnts[bestcnts.size()-1];\n#if 1\n\ttarget_dihvs.clear();\n\ti = 0;\n\twhile (i < dihvs.size()) {\n\t\tunsigned j = i+1;\n\t\twhile (j < dihvs.size() && dihvs[j] == dihvs[i]) ++j;\n\t\tif ((j-i)*2 >= bestcnt) {\n\t\t\tcout << \"\\tprob=\" << log(double(j-i)/double(dihvs.size()))/log(2.0) << \":\\t\";\n\t\t\tfor (unsigned k = 0; k < 5; ++k)\n\t\t\t\tcout << naf(dihvs[i].ihv[k]) << \" \";\n\t\t\tcout << endl;\n\t\t\ttarget_dihvs.push_back(dihvs[i]);\n\t\t}\n\t\ti = j;\n\t}\n\t{ vector tmptmp(1); dihvs.swap(tmptmp); } // free memory\n\tuint64 cnt = 0, okcnt = 0;\n\tihv_type dihv;\n\twhile (true) {\n\t\trandom_me(m, pathbitrelationsmatrix);\n\t\tfor (int t = 0; t < 16; ++t)\n\t\t\tm2[t] = m[t] ^ path.getme(t).mask;\n\t\tfor (int t = 16; t < 80; ++t)\n\t\t\tm2[t]=rotate_left(m2[t-3] ^ m2[t-8] ^ m2[t-14] ^ m2[t-16], 1);\n\t\tfor (int t = tbegin-4; t <= tbegin; ++t)\n\t\t\tQ2[offset+t] = Q[offset+t] = xrng64();\n\t\tfor (int t = -4; t <= 0; ++t)\n\t\t\tQ2[offset+t] = Q[offset+t] = xrng64();\n\t\tfor (int t = tbegin; t < 80; ++t) {\n\t\t\tsha1_step(t, Q, m);\n\t\t\tsha1_step(t, Q2, m2);\n\t\t}\n\t\tdihv.ihv[0] = rotate_left(Q2[offset+80-4],30)-rotate_left(Q[offset+80-4],30);\n\t\tdihv.ihv[1] = rotate_left(Q2[offset+80-3],30)-rotate_left(Q[offset+80-3],30);\n\t\tdihv.ihv[2] = rotate_left(Q2[offset+80-2],30)-rotate_left(Q[offset+80-2],30);\n\t\tdihv.ihv[3] = Q2[offset+80-1]-Q[offset+80-1];\n\t\tdihv.ihv[4] = Q2[offset+80]-Q[offset+80];\n\t\t++cnt;\n\t\tfor (unsigned k = 0; k < target_dihvs.size(); ++k)\n\t\t\tif (dihv == target_dihvs[k]) {\n\t\t\t\t++okcnt;\n\t\t\t\tbreak;\n\t\t\t}\n\t\tif (hw(cnt)+hw(cnt>>32)==1 && okcnt > 1) {\n\t\t\tcout << \"[\" << cnt << \":p=\" << log(double(okcnt)/double(cnt))/log(2.0) << \"]\" << flush;\n\t\t}\n\t}\n#else\t\n\tfor (unsigned i = 0; i < bestcnts.size(); ++i)\n\t\tif (bestcnts[i]*2 >= bestcnt)\n\t\t\tcout << \" prob=\" << log(double(bestcnts[i])/double(dihvs.size()))/log(2.0) << flush;\n#endif\n\tcout << endl;\n\texit(0);\n}\n", "meta": {"hexsha": "89cb9aa8b3b5b51733c412967573f72c89bcf9f8", "size": 31559, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/sha1attackgenerator/tunnel_analysis.cpp", "max_stars_repo_name": "killua4564/hashclash", "max_stars_repo_head_hexsha": "f780f17ef579e4bb246f5c47f31765f665dab74f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 398.0, "max_stars_repo_stars_event_min_datetime": "2017-10-16T19:46:02.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T23:45:05.000Z", "max_issues_repo_path": "src/sha1attackgenerator/tunnel_analysis.cpp", "max_issues_repo_name": "killua4564/hashclash", "max_issues_repo_head_hexsha": "f780f17ef579e4bb246f5c47f31765f665dab74f", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10.0, "max_issues_repo_issues_event_min_datetime": "2019-10-23T08:14:38.000Z", "max_issues_repo_issues_event_max_datetime": "2021-07-10T09:33:44.000Z", "max_forks_repo_path": "src/sha1attackgenerator/tunnel_analysis.cpp", "max_forks_repo_name": "killua4564/hashclash", "max_forks_repo_head_hexsha": "f780f17ef579e4bb246f5c47f31765f665dab74f", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 72.0, "max_forks_repo_forks_event_min_datetime": "2017-10-18T14:44:36.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-27T18:07:19.000Z", "avg_line_length": 32.2689161554, "max_line_length": 172, "alphanum_fraction": 0.5835102506, "num_tokens": 12024, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5736783928749126, "lm_q2_score": 0.3886180267058489, "lm_q1q2_score": 0.22294176500283125}} {"text": "//////////////////////////////////////////////////////////////////////////\n//\n// Copyright (c) 2013-2014, Image Engine Design Inc. All rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are\n// met:\n//\n// * Redistributions of source code must retain the above\n// copyright notice, this list of conditions and the following\n// disclaimer.\n//\n// * Redistributions in binary form must reproduce the above\n// copyright notice, this list of conditions and the following\n// disclaimer in the documentation and/or other materials provided with\n// the distribution.\n//\n// * Neither the name of John Haddon nor the names of\n// any other contributors to this software may be used to endorse or\n// promote products derived from this software without specific prior\n// written permission.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS\n// IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,\n// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\n// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR\n// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\n// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n//\n//////////////////////////////////////////////////////////////////////////\n\n#include \"GafferScene/Constraint.h\"\n\n#include \"Gaffer/StringPlug.h\"\n\n#include \"IECoreScene/CurvesPrimitive.h\"\n#include \"IECoreScene/MeshPrimitive.h\"\n#include \"IECoreScene/PointsPrimitive.h\"\n\n#include \n\n#include \n#include \n#include \n#include \n\nusing namespace Imath;\nusing namespace IECore;\nusing namespace Gaffer;\nusing namespace GafferScene;\n\nnamespace\n{\n\ntemplate< typename Indexer >\nbool convexPolygon( const Indexer vertices, const int n )\n{\n\tassert( n >= 3 );\n\n\tif( n == 3 )\n\t{\n\t\treturn true;\n\t}\n\n\tint c[ 2 ] = { 0, 0 };\n\n\tfor( int i = 0; i < n; ++i )\n\t{\n\t\tconst int ip = ( i + n - 1 ) % n;\n\t\tconst int in = ( i + 1 ) % n;\n\n\t\tconst Imath::V2f vi = vertices( i );\n\n\t\tconst float Av = ( vi - vertices( ip ) ) % ( vertices( in ) - vi );\n\n\t\t// NOTE : colinear edges are ok\n\n\t\tif( Av == 0.f )\n\t\t{\n\t\t\tc[ 0 ] += 1;\n\t\t\tc[ 1 ] += 1;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tc[ std::signbit( Av ) ? 0 : 1 ] += 1;\n\t\t}\n\t}\n\n\treturn\n\t\t( c[ 0 ] == n ) ||\n\t\t( c[ 1 ] == n );\n}\n\ntemplate< typename Indexer >\nbool ptInPolygon( const Indexer vertices, const Imath::V2f v, const int n )\n{\n\tbool result = false;\n\tfor( int i = 0; i < n; ++i )\n\t{\n\t\tconst Imath::V2f v0 = vertices( i );\n\t\tconst Imath::V2f v1 = vertices( ( i + 1 ) % n );\n\n\t\t// NOTE: Algorithm 3.5, \"Collision Detection in Interactive 3d Environments\", Gino Van Den Bergen\n\t\t// with modifications to include end points and points on axis aligned edges.\n\n\t\tif( v0 == v )\n\t\t{\n\t\t\treturn true; // NOTE : end point\n\t\t}\n\t\telse if(\n\t\t\t( ( v0.y == v1.y ) && ( v.y == v0.y ) && ( ( v0.x >= v.x ) != ( v1.x >= v.x ) ) ) ||\n\t\t\t( ( v0.x == v1.x ) && ( v.x == v0.x ) && ( ( v0.y >= v.y ) != ( v1.y >= v.y ) ) ) )\n\t\t{\n\t\t\treturn true; // NOTE : axis aligned edge\n\t\t}\n\t\telse if( ( v0.y >= v.y ) != ( v1.y >= v.y ) )\n\t\t{\n\t\t\t// Edge crosses horizontal line `y == v.y`\n\n\t\t\tif( ( v0.x >= v.x ) != ( v1.x >= v.x ) )\n\t\t\t{\n\t\t\t\t// Edge crosses vertical line `x == v.x`\n\n\t\t\t\tif( v0.x + ( v.y - v0.y ) * ( v1.x - v0.x ) / ( v1.y - v0.y ) >= v.x )\n\t\t\t\t{\n\t\t\t\t\t// Edge crosses the ray `y == v.y, x >= v.x`\n\n\t\t\t\t\tresult = !result;\n\t\t\t\t}\n\t\t\t}\n\t\t\telse if( v0.x >= v.x )\n\t\t\t{\n\t\t\t\t// Edge crosses the ray `y == v.y, x >= v.x`\n\n\t\t\t\tresult = !result;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn result;\n}\n\ntemplate< typename PIndexer, typename UVIndexer >\nImath::V3f interpolateConvexPolygon( const PIndexer points, const UVIndexer uvs, const Imath::V2f uv, const int n )\n{\n\t// NOTE : compute vertex and edge triangle areas\n\t//\n\t// Av[ i ] is 2 * area of the triangle formed by the vertices v(i-1), v(i) and v(i+1)\n\t// Ae[ i ] is 2 * area of the triangle formed by the vertices v(i), v(i+1) and uv\n\n\tstd::unique_ptr< float[] > Av( new float[ n ] );\n\tstd::unique_ptr< float[] > Ae( new float[ n ] );\n\n\tfor( int i = 0; i < n; ++i )\n\t{\n\t\tconst Imath::V2f vi = uvs( i );\n\n\t\t// NOTE : avoid zero length edges by skipping past adjacent duplicate uv vertices\n\t\t// in both directions, c is the number of times the duplicate vertex occurs\n\t\t// in a consecutive run including the current vertex (vi). It is important\n\t\t// that only adjacent duplicated vertices are included. The computed vertex\n\t\t// area is weighted by the reciprocal of c to average the influence of the\n\t\t// positions corresponding to the duplicated uv vertices.\n\n\t\tfloat c = 1.f;\n\n\t\tImath::V2f vp;\n\t\tfor( int j = 1; j < n; ++j )\n\t\t{\n\t\t\tvp = uvs( ( i + n - j ) % n );\n\t\t\tif( vp != vi ) break;\n\t\t\tc += 1.f;\n\t\t}\n\n\t\tImath::V2f vn;\n\t\tfor( int j = 1; j < n; ++j )\n\t\t{\n\t\t\tvn = uvs( ( i + j ) % n );\n\t\t\tif( vn != vi ) break;\n\t\t\tc += 1.f;\n\t\t}\n\n\t\tAv[ i ] = ( vi - vp ) % ( vn - vi );\n\t\tAe[ i ] = ( vn - vi ) % ( uv - vn );\n\n\t\t// NOTE : sign of vertex area depends on winding.\n\n\t\tif( std::signbit( Av[ i ] ) )\n\t\t{\n\t\t\tAv[ i ] = -( Av[ i ] );\n\t\t\tAe[ i ] = -( Ae[ i ] );\n\t\t}\n\n\t\t// NOTE : clamp edge area to minimum of zero to prevent negative weights\n\n\t\tAe[ i ] = std::max( Ae[ i ], 0.f );\n\n\t\t// NOTE : this clamp is done in two steps to prevent underflow to zero\n\n\t\tAv[ i ] = std::max( Av[ i ], c * std::numeric_limits< float >::min() );\n\t\tAv[ i ] /= c;\n\n\t\t// NOTE : uv is considered on an edge when the edge area is below threshold in which\n\t\t// case lerp between average of all positions corresponding to end vertices\n\n\t\tif(\n\t\t\t( Ae[ i ] < std::sqrt( std::numeric_limits< float >::min() ) ) &&\n\t\t\t( ( ( uv - vi ) ^ ( uv - vn ) ) < std::numeric_limits< float >::min() ) )\n\t\t{\n\t\t\tconst float l2 = ( vn - vi ).length2();\n\t\t\tconst float t = std::min( std::max(\n\t\t\t\t( l2 > ( 2.f * std::numeric_limits< float >::min() ) )\n\t\t\t\t\t? std::sqrt( ( uv - vn ).length2() / l2 ) : 0.f, 0.f ), 1.f );\n\n\t\t\tImath::V3f pv( 0.f );\n\t\t\tImath::V3f pn( 0.f );\n\n\t\t\tfloat pvc = 0.f;\n\t\t\tfloat pnc = 0.f;\n\n\t\t\tfor( int j = 0; j < n; ++j )\n\t\t\t{\n\t\t\t\tif( uvs( j ) == vi )\n\t\t\t\t{\n\t\t\t\t\tpv += points( j );\n\t\t\t\t\tpvc += 1.f;\n\t\t\t\t}\n\t\t\t\tif( uvs( j ) == vn )\n\t\t\t\t{\n\t\t\t\t\tpn += points( j );\n\t\t\t\t\tpnc += 1.f;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tassert( pvc != 0.f );\n\t\t\tassert( pnc != 0.f );\n\n\t\t\treturn\n\t\t\t\t( pv / pvc ) * ( t ) +\n\t\t\t\t( pn / pnc ) * ( 1.0 - t );\n\t\t}\n\t}\n\n\t// NOTE : uv is not on any edges or coincident with any vertices use wachspress coordinates\n\t// the factor of 2 in the denominator cancels out during normalisation\n\n\tfloat ws = 0.f;\n\tImath::V3f p( 0.f );\n\n\tfor( int i = 0; i < n; ++i )\n\t{\n\t\tconst Imath::V2f vi = uvs( i );\n\n\t\tint ip = 0;\n\t\tfor( int j = 1; j < n; ++j )\n\t\t{\n\t\t\tip = ( i + n - j ) % n;\n\t\t\tif( uvs( ip ) != vi )\n\t\t\t\tbreak;\n\t\t}\n\n\t\tconst float Ad = Ae[ ip ] * Ae[ i ];\n\n\t\tif( Ad != 0.f )\n\t\t{\n\t\t\tconst float w = Av[ i ] / Ad;\n\t\t\tp += points( i ) * w;\n\t\t\tws += w;\n\t\t}\n\t}\n\n\tif( ws != 0.f )\n\t{\n\t\tp /= ws;\n\t}\n\n\treturn p;\n}\n\ntemplate< typename PIndexer, typename UVIndexer >\nImath::V3f interpolateNonConvexPolygon( const PIndexer points, const UVIndexer uvs, const Imath::V2f uv, const int n )\n{\n\t// NOTE : a simple triangle fan from any vertex will cover the non convex polygon\n\t// the generated triangles may cover areas outside the polygon this is fine\n\n\tImath::V3f p( 0.f );\n\n\tfor( int i = 2; i < n; ++i )\n\t{\n\t\tconst Imath::V2f tuv[ 3 ] =\n\t\t{\n\t\t\tuvs( 0 ),\n\t\t\tuvs( i - 1 ),\n\t\t\tuvs( i )\n\t\t};\n\n\t\tif( ptInPolygon( [ & tuv ]( const int ii ){ return tuv[ ii ]; }, uv, 3 ) )\n\t\t{\n\t\t\tconst float w = ( tuv[ 2 ] - tuv[ 0 ] ) % ( tuv[ 1 ] - tuv[ 0 ] );\n\n\t\t\tif( w == 0.f )\n\t\t\t{\n\t\t\t\t// NOTE : uv triangle numerically has zero area so is effectively a line or point.\n\t\t\t\t// position in 3d space is ambiguous unless the triangle in 3d space is a point.\n\n\t\t\t\tconst Imath::V3f& vp = points( i - 1 );\n\n\t\t\t\tif(\n\t\t\t\t\t( ( points( 0 ) - vp ).length2() <= ( 2.f * std::numeric_limits< float >::min() ) ) &&\n\t\t\t\t\t( ( points( i ) - vp ).length2() <= ( 2.f * std::numeric_limits< float >::min() ) ) )\n\t\t\t\t{\n\t\t\t\t\tp = vp;\n\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\n\t\t\t\tthrow IECore::InvalidArgumentException( (\n\t\t\t\t\tboost::format(\n\t\t\t\t\t\t\"Gaffer::Constraint : UV coordinates \\\"%s\\\" map to ambiguous point(s) in 3d space.\"\n\t\t\t\t\t) % ( uv ) ).str() );\n\t\t\t}\n\n\t\t\t// NOTE : ensure that the positive barycentric coordinates sum to a maximum of one.\n\n\t\t\tconst float b0 = std::max( 0.f, ( ( tuv[ 1 ] - tuv[ 2 ] ) % ( uv - tuv[ 1 ] ) ) / w );\n\t\t\tconst float b1 = std::max( 0.f, ( ( tuv[ 2 ] - tuv[ 0 ] ) % ( uv - tuv[ 2 ] ) ) / w );\n\t\t\tconst float b2 = std::max( 0.f, ( ( tuv[ 0 ] - tuv[ 1 ] ) % ( uv - tuv[ 0 ] ) ) / w );\n\n\t\t\tconst float bs = b0 + b1 + b2;\n\n\t\t\tif( bs != 0.f )\n\t\t\t{\n\t\t\t\tp =\n\t\t\t\t\tpoints( 0 ) * ( b0 / bs ) +\n\t\t\t\t\tpoints( i - 1 ) * ( b1 / bs ) +\n\t\t\t\t\tpoints( i ) * ( b2 / bs );\n\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn p;\n}\n\nvoid constructMatrix( Imath::M44f& m, const Imath::V3f& p, const Imath::V3f& x, const Imath::V3f& y, const Imath::V3f& z )\n{\n\tm[ 0 ][ 0 ] = x[ 0 ];\n\tm[ 0 ][ 1 ] = x[ 1 ];\n\tm[ 0 ][ 2 ] = x[ 2 ];\n\tm[ 0 ][ 3 ] = 0.f;\n\n\tm[ 1 ][ 0 ] = y[ 0 ];\n\tm[ 1 ][ 1 ] = y[ 1 ];\n\tm[ 1 ][ 2 ] = y[ 2 ];\n\tm[ 1 ][ 3 ] = 0.f;\n\n\tm[ 2 ][ 0 ] = z[ 0 ];\n\tm[ 2 ][ 1 ] = z[ 1 ];\n\tm[ 2 ][ 2 ] = z[ 2 ];\n\tm[ 2 ][ 3 ] = 0.f;\n\n\tm[ 3 ][ 0 ] = p[ 0 ];\n\tm[ 3 ][ 1 ] = p[ 1 ];\n\tm[ 3 ][ 2 ] = p[ 2 ];\n\tm[ 3 ][ 3 ] = 1.f;\n}\n\nvoid constructLocalFrame( Imath::M44f& m, const Imath::V3f& p, const Imath::V3f& t, const Imath::V3f& b, const Imath::V3f& n )\n{\n\t// NOTE : use tangent and bitangent when both have non zero length and are not colinear\n\t// and either the normal has zero length or both the tangent and bitangent are\n\t// not colinear with the normal, otherwise use tangent and normal when they both\n\t// have non zero length and are not colinear, otherwise use bitangent and normal\n\t// when they both have non zero length and are not colinear\n\n\tconst Imath::V3f nt = t.normalized();\n\tconst Imath::V3f nb = b.normalized();\n\tconst Imath::V3f nn = n.normalized();\n\tconst Imath::V3f bxt = nb % nt;\n\tconst Imath::V3f txn = nt % nn;\n\tconst Imath::V3f nxb = nn % nb;\n\n\tif(\n\t\t( bxt.length2() > ( 2.f * std::numeric_limits< float >::min() ) ) &&\n\t\t( ( n.length2() < ( 2.f * std::numeric_limits< float >::min() ) ) ||\n\t\t\t( ( std::fabs( nt ^ nn ) < 0.999f ) && ( std::fabs( nb ^ nn ) < 0.999f ) ) ) )\n\t{\n\t\tImath::V3f y = bxt.normalized();\n\n\t\t// NOTE : ensure y axis of local frame points in same direction as normal\n\n\t\tif( n.dot( y ) < 0.f )\n\t\t{\n\t\t\ty *= -1.f;\n\t\t}\n\n\t\tconstructMatrix( m, p, nt, y, ( nt % y ).normalized() );\n\t}\n\telse if( txn.length2() > ( 2.f * std::numeric_limits< float >::min() ) )\n\t{\n\t\tconst Imath::V3f z = txn.normalized();\n\t\tconstructMatrix( m, p, nt, ( z % nt ).normalized(), z );\n\t}\n\telse if( nxb.length2() > ( 2.f * std::numeric_limits< float >::min() ) )\n\t{\n\t\tconst Imath::V3f x = nxb.normalized();\n\t\tconstructMatrix( m, p, x, ( nb % x ).normalized(), nb );\n\t}\n\telse\n\t{\n\t\tm.translate( p );\n\t}\n}\n\nstruct UVIndexer\n{\n\tUVIndexer( const IECoreScene::MeshPrimitive& primitive, const std::string& uvSet, const bool throwOnError )\n\t: m_indices( nullptr )\n\t, m_view()\n\t{\n\t\tconst IECoreScene::PrimitiveVariableMap::const_iterator it = primitive.variables.find( uvSet );\n\n\t\tif(\n\t\t\t( it == primitive.variables.end() ) ||\n\t\t\t( ( *it ).second.data->typeId() != IECore::V2fVectorDataTypeId ) )\n\t\t{\n\t\t\tif( throwOnError )\n\t\t\t{\n\t\t\t\tthrow IECore::InvalidArgumentException( (\n\t\t\t\t\tboost::format(\n\t\t\t\t\t\t\"Gaffer::Constraint : MeshPrimitive has no V2fVectorData primitive variable named \\\"%s\\\".\"\n\t\t\t\t\t) % ( uvSet ) ).str() );\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\treturn;\n\t\t\t}\n\t\t}\n\n\t\t// NOTE : for vertex and varying interpolation we need to redirect through the primitive indices\n\n\t\tif(\n\t\t\t( ( *it ).second.interpolation == IECoreScene::PrimitiveVariable::Vertex ) ||\n\t\t\t( ( *it ).second.interpolation == IECoreScene::PrimitiveVariable::Varying ) )\n\t\t{\n\t\t\tm_indices = &( primitive.vertexIds()->readable() );\n\t\t}\n\t\telse if( ( *it ).second.interpolation != IECoreScene::PrimitiveVariable::FaceVarying )\n\t\t{\n\t\t\tif( throwOnError )\n\t\t\t{\n\t\t\t\tthrow IECore::InvalidArgumentException( (\n\t\t\t\t\tboost::format(\n\t\t\t\t\t\t\"Gaffer::Constraint : Primitive variable named \\\"%s\\\" has incorrect interpolation, must be either Vertex, Varying or FaceVarying\"\n\t\t\t\t\t) % ( uvSet ) ).str() );\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\treturn;\n\t\t\t}\n\t\t}\n\n\t\tm_view = IECoreScene::PrimitiveVariable::IndexedView< Imath::V2f >( ( *it ).second );\n\t}\n\n\tbool valid() const\n\t{\n\t\treturn static_cast< bool >( m_view );\n\t}\n\n\tconst Imath::V2f& operator[]( const int i ) const\n\t{\n\t\tassert( valid() );\n\t\tconst int index = ( m_indices != nullptr ) ? ( ( *m_indices )[ i ] ) : i;\n\t\treturn ( *m_view )[ index ];\n\t}\n\nprivate:\n\n\tconst std::vector< int >* m_indices;\n\tstd::optional> m_view;\n};\n\nvoid computePrimitiveVertexLocalFrame( const IECoreScene::Primitive& primitive, Imath::M44f& m, const int vertexId, const bool throwOnError )\n{\n\tconst IECore::V3fVectorData* const pdata = primitive.variableData< IECore::V3fVectorData >( \"P\" );\n\tif( pdata == nullptr )\n\t{\n\t\tif( throwOnError )\n\t\t{\n\t\t\tthrow IECore::InvalidArgumentException( \"Gaffer::Contraint : Primitive has no Vertex \\\"P\\\" primitive variable.\" );\n\t\t}\n\t\telse\n\t\t{\n\t\t\treturn;\n\t\t}\n\t}\n\tconst IECore::V3fVectorData::ValueType& points = pdata->readable();\n\n\tif( ( vertexId < 0 ) || ( vertexId >= static_cast< int >( points.size() ) ) )\n\t{\n\t\tif( throwOnError )\n\t\t{\n\t\t\tthrow IECore::InvalidArgumentException( (\n\t\t\t\tboost::format(\n\t\t\t\t\t\"Gaffer::Constraint : Vertex id \\\"%d\\\" is out of range.\"\n\t\t\t\t) % ( vertexId ) ).str() );\n\t\t}\n\t\telse\n\t\t{\n\t\t\treturn;\n\t\t}\n\t}\n\n\tm.translate( points[ vertexId ] );\n}\n\nvoid computeMeshVertexLocalFrame( const IECoreScene::MeshPrimitive& primitive, Imath::M44f& m, const int vertexId, const std::string& uvSet, const bool throwOnError, const IECore::Canceller* const canceller )\n{\n\tconst IECore::V3fVectorData* const pdata = primitive.variableData< IECore::V3fVectorData >( \"P\" );\n\n\tif( pdata == nullptr )\n\t{\n\t\tif( throwOnError )\n\t\t{\n\t\t\tthrow IECore::InvalidArgumentException( \"Gaffer::Contraint : MeshPrimitive has no Vertex \\\"P\\\" primitive variable.\" );\n\t\t}\n\t\telse\n\t\t{\n\t\t\treturn;\n\t\t}\n\t}\n\n\tconst IECore::V3fVectorData::ValueType& points = pdata->readable();\n\n\tif( ( vertexId < 0 ) || ( vertexId >= static_cast< int >( points.size() ) ) )\n\t{\n\t\tif( throwOnError )\n\t\t{\n\t\t\tthrow IECore::InvalidArgumentException( (\n\t\t\t\tboost::format(\n\t\t\t\t\t\"Gaffer::Constraint : Vertex id \\\"%d\\\" is out of range.\"\n\t\t\t\t) % ( vertexId ) ).str() );\n\t\t}\n\t\telse\n\t\t{\n\t\t\treturn;\n\t\t}\n\t}\n\n\tconst IECore::IntVectorData::ValueType& indices = primitive.vertexIds()->readable();\n\tconst IECore::IntVectorData::ValueType& faces = primitive.verticesPerFace()->readable();\n\n\tconst UVIndexer uvs( primitive, uvSet, throwOnError );\n\n\tImath::V3f t( 0.f );\n\tImath::V3f b( 0.f );\n\tImath::V3f n( 0.f );\n\n\tif( uvs.valid() )\n\t{\n\t\tint js = 0;\n\t\tfor( int i = 0; i < static_cast< int >( faces.size() ); ++i )\n\t\t{\n\t\t\tconst int ni = faces[ i ];\n\n\t\t\t// canceller support\n\n\t\t\tif( ( i % 100 ) == 0 )\n\t\t\t{\n\t\t\t\tIECore::Canceller::check( canceller );\n\t\t\t}\n\n\t\t\t// find matching face vertex index\n\n\t\t\tint jm = 0;\n\t\t\tfor( ; jm < ni; ++jm )\n\t\t\t{\n\t\t\t\tif( indices[ js + jm ] == vertexId )\n\t\t\t\t{\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif( jm < ni )\n\t\t\t{\n\t\t\t\t// compute face normal, tangent and bitangent\n\n\t\t\t\tImath::V3f ft( 0.f );\n\t\t\t\tImath::V3f fb( 0.f );\n\t\t\t\tImath::V3f fn( 0.f );\n\n\t\t\t\tfloat w = 0.f;\n\n\t\t\t\tfor( int j = 0; j < ni; ++j )\n\t\t\t\t{\n\t\t\t\t\tconst int iv = js + j;\n\t\t\t\t\tconst int ip = js + ( ( j + ni - 1 ) % ni );\n\t\t\t\t\tconst int in = js + ( ( j + 1 ) % ni );\n\n\t\t\t\t\tconst Imath::V3f pv = points[ indices[ iv ] ];\n\t\t\t\t\tconst Imath::V3f v0 = points[ indices[ ip ] ] - pv;\n\t\t\t\t\tconst Imath::V3f v2 = points[ indices[ in ] ] - pv;\n\n\t\t\t\t\tconst Imath::V2f uv = uvs[ iv ];\n\t\t\t\t\tconst Imath::V2f e0 = uvs[ ip ] - uv;\n\t\t\t\t\tconst Imath::V2f e2 = uvs[ in ] - uv;\n\n\t\t\t\t\tft += ( v0 * -e2.y + v2 * e0.y ).normalized();\n\t\t\t\t\tfb += ( v0 * -e2.x + v2 * e0.x ).normalized();\n\t\t\t\t\tfn += ( -v0 % v2 ).normalized();\n\n\t\t\t\t\tif( j == jm )\n\t\t\t\t\t{\n\t\t\t\t\t\tconst float lv2 = v0.length2() * v2.length2();\n\n\t\t\t\t\t\tif( lv2 > ( 2.f * std::numeric_limits< float >::min() ) )\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tconst float lv = std::sqrt( lv2 );\n\n\t\t\t\t\t\t\tif( lv != 0.f )\n\t\t\t\t\t\t\t{\n\t\t\t\t\t\t\t\tw = std::acos( std::min( std::max( ( v0 ^ v2 ) / lv, -1.f ), 1.f ) );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// accumulate angle weighted normal, tangent and bitangent\n\n\t\t\t\tt += ft.normalized() * w;\n\t\t\t\tb += fb.normalized() * w;\n\t\t\t\tn += fn.normalized() * w;\n\t\t\t}\n\n\t\t\tjs += ni;\n\t\t}\n\t}\n\n\tconstructLocalFrame( m, points[ vertexId ], t, b, n );\n}\n\nvoid computeVertexLocalFrame( const IECore::Object& object, Imath::M44f& m, const int vertexId, const std::string& uvSet, const bool throwOnError, const IECore::Canceller* const canceller )\n{\n\tswitch( static_cast< IECoreScene::TypeId >( object.typeId() ) )\n\t{\n\t\tcase IECoreScene::CurvesPrimitiveTypeId:\n\t\tcase IECoreScene::PointsPrimitiveTypeId:\n\t\t\tcomputePrimitiveVertexLocalFrame( static_cast< const IECoreScene::Primitive& >( object ), m, vertexId, throwOnError );\n\t\t\tbreak;\n\t\tcase IECoreScene::MeshPrimitiveTypeId:\n\t\t\tcomputeMeshVertexLocalFrame( static_cast< const IECoreScene::MeshPrimitive& >( object ), m, vertexId, uvSet, throwOnError, canceller );\n\t\t\tbreak;\n\t\tdefault:\n\t\t\tif( throwOnError )\n\t\t\t{\n\t\t\t\tthrow IECore::InvalidArgumentException( (\n\t\t\t\t\tboost::format(\n\t\t\t\t\t\t\"Gaffer::Constraint : Target primitive of type \\\"%s\\\" is not supported in Vertex target mode.\"\n\t\t\t\t\t) % ( object.typeName() ) ).str() );\n\t\t\t}\n\t\t\tbreak;\n\t}\n}\n\nvoid computeMeshUVLocalFrame( const IECoreScene::MeshPrimitive& primitive, Imath::M44f& m, const Imath::V2f& uv, const std::string& uvSet, const bool throwOnError, const IECore::Canceller* const canceller )\n{\n\tconst IECore::V3fVectorData* const pdata = primitive.variableData< IECore::V3fVectorData >( \"P\" );\n\n\tif( pdata == nullptr )\n\t{\n\t\tif( throwOnError )\n\t\t{\n\t\t\tthrow IECore::InvalidArgumentException( \"Gaffer::Contraint : MeshPrimitive has no Vertex \\\"P\\\" primitive variable.\" );\n\t\t}\n\t\telse\n\t\t{\n\t\t\treturn;\n\t\t}\n\t}\n\n\tconst IECore::V3fVectorData::ValueType& points = pdata->readable();\n\tconst IECore::IntVectorData::ValueType& indices = primitive.vertexIds()->readable();\n\tconst IECore::IntVectorData::ValueType& faces = primitive.verticesPerFace()->readable();\n\n\tconst UVIndexer uvs( primitive, uvSet, throwOnError );\n\tif( ! uvs.valid() )\n\t{\n\t\treturn;\n\t}\n\n\tint js = 0;\n\tfor( int i = 0; i < static_cast< int >( faces.size() ); ++i )\n\t{\n\t\tconst int ni = faces[ i ];\n\n\t\t// canceller support\n\n\t\tif( ( i % 100 ) == 0 )\n\t\t{\n\t\t\tIECore::Canceller::check( canceller );\n\t\t}\n\n\t\t// points and uv indexers\n\n\t\tconst auto pIndexer = [ & points, & indices, js ]( const int ii )\n\t\t{\n\t\t\treturn points[ indices[ js + ii ] ];\n\t\t};\n\n\t\tconst auto uvIndexer = [ & uvs, js ]( const int ii )\n\t\t{\n\t\t\treturn uvs[ js + ii ];\n\t\t};\n\n\t\t// determine if uv coordinate is inside face\n\n\t\tif( ptInPolygon( uvIndexer, uv, ni ) )\n\t\t{\n\t\t\tImath::V3f fp( 0.f );\n\t\t\tImath::V3f ft( 0.f );\n\t\t\tImath::V3f fb( 0.f );\n\t\t\tImath::V3f fn( 0.f );\n\n\t\t\ttry\n\t\t\t{\n\t\t\t\tfp = ( convexPolygon( uvIndexer, ni ) )\n\t\t\t\t\t? interpolateConvexPolygon( pIndexer, uvIndexer, uv, ni )\n\t\t\t\t\t: interpolateNonConvexPolygon( pIndexer, uvIndexer, uv, ni );\n\n\t\t\t\t// compute face tangent and bitangent\n\n\t\t\t\tfor( int j = 0; j < ni; ++j )\n\t\t\t\t{\n\t\t\t\t\tconst int iv = js + j;\n\t\t\t\t\tconst int ip = js + ( ( j + ni - 1 ) % ni );\n\t\t\t\t\tconst int in = js + ( ( j + 1 ) % ni );\n\n\t\t\t\t\tconst Imath::V3f pv = points[ indices[ iv ] ];\n\t\t\t\t\tconst Imath::V3f v0 = points[ indices[ ip ] ] - pv;\n\t\t\t\t\tconst Imath::V3f v2 = points[ indices[ in ] ] - pv;\n\n\t\t\t\t\tconst Imath::V2f uv = uvs[ iv ];\n\t\t\t\t\tconst Imath::V2f e0 = uvs[ ip ] - uv;\n\t\t\t\t\tconst Imath::V2f e2 = uvs[ in ] - uv;\n\n\t\t\t\t\tft += ( v0 * -e2.y + v2 * e0.y ).normalized();\n\t\t\t\t\tfb += ( v0 * -e2.x + v2 * e0.x ).normalized();\n\t\t\t\t\tfn += ( -v0 % v2 ).normalized();\n\t\t\t\t}\n\t\t\t}\n\t\t\tcatch( const IECore::Exception& e )\n\t\t\t{\n\t\t\t\tif( throwOnError )\n\t\t\t\t{\n\t\t\t\t\tthrow;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tconstructLocalFrame( m, fp, ft, fb, fn );\n\n\t\t\treturn;\n\t\t}\n\n\t\tjs += ni;\n\t}\n\n\tif( throwOnError )\n\t{\n\t\tthrow IECore::InvalidArgumentException( (\n\t\t\tboost::format(\n\t\t\t\t\"Gaffer::Constraint : UV coordinates \\\"%s\\\" are out of range.\"\n\t\t\t) % ( uv ) ).str() );\n\t}\n}\n\nvoid computeUVLocalFrame( const IECore::Object& object, Imath::M44f& m, const Imath::V2f& uv, const std::string& uvSet, const bool throwOnError, const IECore::Canceller* const canceller )\n{\n\tswitch( static_cast< IECoreScene::TypeId >( object.typeId() ) )\n\t{\n\t\tcase IECoreScene::MeshPrimitiveTypeId:\n\t\t\tcomputeMeshUVLocalFrame( static_cast< const IECoreScene::MeshPrimitive& >( object ), m, uv, uvSet, throwOnError, canceller );\n\t\t\tbreak;\n\t\tdefault:\n\t\t\tif( throwOnError )\n\t\t\t{\n\t\t\t\tthrow IECore::InvalidArgumentException( (\n\t\t\t\t\tboost::format(\n\t\t\t\t\t\t\"Gaffer::Constraint : Target primitive of type \\\"%s\\\" is not supported in UV target mode.\"\n\t\t\t\t\t) % ( object.typeName() ) ).str() );\n\t\t\t}\n\t\t\tbreak;\n\t}\n}\n\n} // namespace\n\nGAFFER_NODE_DEFINE_TYPE( Constraint );\n\nsize_t Constraint::g_firstPlugIndex = 0;\n\nConstraint::Constraint( const std::string &name )\n\t:\tSceneElementProcessor( name, IECore::PathMatcher::NoMatch )\n{\n\tstoreIndexOfNextChild( g_firstPlugIndex );\n\taddChild( new ScenePlug( \"targetScene\" ) );\n\taddChild( new StringPlug( \"target\" ) );\n\taddChild( new BoolPlug( \"ignoreMissingTarget\" ) );\n\taddChild( new IntPlug( \"targetMode\", Plug::In, Constraint::Origin, Constraint::Origin, Constraint::Vertex ) );\n\taddChild( new V2fPlug( \"targetUV\" ) );\n\taddChild( new IntPlug( \"targetVertex\", Plug::In, 0, 0 ) );\n\taddChild( new V3fPlug( \"targetOffset\" ) );\n\n\t// Pass through things we don't want to modify\n\toutPlug()->attributesPlug()->setInput( inPlug()->attributesPlug() );\n\toutPlug()->objectPlug()->setInput( inPlug()->objectPlug() );\n}\n\nConstraint::~Constraint()\n{\n}\n\nScenePlug *Constraint::targetScenePlug()\n{\n\treturn getChild( g_firstPlugIndex );\n}\n\nconst ScenePlug *Constraint::targetScenePlug() const\n{\n\treturn getChild( g_firstPlugIndex );\n}\n\nGaffer::StringPlug *Constraint::targetPlug()\n{\n\treturn getChild( g_firstPlugIndex + 1 );\n}\n\nconst Gaffer::StringPlug *Constraint::targetPlug() const\n{\n\treturn getChild( g_firstPlugIndex + 1 );\n}\n\nGaffer::BoolPlug *Constraint::ignoreMissingTargetPlug()\n{\n\treturn getChild( g_firstPlugIndex + 2 );\n}\n\nconst Gaffer::BoolPlug *Constraint::ignoreMissingTargetPlug() const\n{\n\treturn getChild( g_firstPlugIndex + 2 );\n}\n\nGaffer::IntPlug *Constraint::targetModePlug()\n{\n\treturn getChild( g_firstPlugIndex + 3 );\n}\n\nconst Gaffer::IntPlug *Constraint::targetModePlug() const\n{\n\treturn getChild( g_firstPlugIndex + 3 );\n}\n\nGaffer::V2fPlug *Constraint::targetUVPlug()\n{\n\treturn getChild( g_firstPlugIndex + 4 );\n}\n\nconst Gaffer::V2fPlug *Constraint::targetUVPlug() const\n{\n\treturn getChild( g_firstPlugIndex + 4 );\n}\n\nGaffer::IntPlug *Constraint::targetVertexPlug()\n{\n\treturn getChild( g_firstPlugIndex + 5 );\n}\n\nconst Gaffer::IntPlug *Constraint::targetVertexPlug() const\n{\n\treturn getChild( g_firstPlugIndex + 5 );\n}\n\nGaffer::V3fPlug *Constraint::targetOffsetPlug()\n{\n\treturn getChild( g_firstPlugIndex + 6 );\n}\n\nconst Gaffer::V3fPlug *Constraint::targetOffsetPlug() const\n{\n\treturn getChild( g_firstPlugIndex + 6 );\n}\n\nvoid Constraint::affects( const Gaffer::Plug *input, AffectedPlugsContainer &outputs ) const\n{\n\tSceneElementProcessor::affects( input, outputs );\n\n\tif(\n\t\tinput == targetPlug() ||\n\t\tinput == ignoreMissingTargetPlug() ||\n\t\tinput == inPlug()->existsPlug() ||\n\t\tinput == inPlug()->transformPlug() ||\n\t\tinput == inPlug()->boundPlug() ||\n\t\tinput == targetScenePlug()->existsPlug() ||\n\t\tinput == targetScenePlug()->transformPlug() ||\n\t\tinput == targetScenePlug()->boundPlug() ||\n\t\tinput == targetScenePlug()->objectPlug() ||\n\t\tinput == targetModePlug() ||\n\t\tinput->parent() == targetOffsetPlug() ||\n\t\tinput->parent() == targetUVPlug() ||\n\t\tinput == targetVertexPlug() ||\n\t\t// TypeId comparison is necessary to avoid calling pure virtual\n\t\t// if we're called before being fully constructed.\n\t\t( typeId() != staticTypeId() && affectsConstraint( input ) )\n\t)\n\t{\n\t\toutputs.push_back( outPlug()->transformPlug() );\n\t\toutputs.push_back( outPlug()->boundPlug() );\n\t}\n}\n\nbool Constraint::processesTransform() const\n{\n\treturn true;\n}\n\nvoid Constraint::hashProcessedTransform( const ScenePath &path, const Gaffer::Context *context, IECore::MurmurHash &h ) const\n{\n\tauto targetOpt = target();\n\tif( !targetOpt )\n\t{\n\t\t// Pass through input unchanged\n\t\th = inPlug()->transformPlug()->hash();\n\t\treturn;\n\t}\n\n\tScenePath parentPath = path;\n\tparentPath.pop_back();\n\th.append( inPlug()->fullTransformHash( parentPath ) );\n\n\th.append( targetOpt->scene->fullTransformHash( targetOpt->path ) );\n\n\tconst TargetMode targetMode = (TargetMode)targetModePlug()->getValue();\n\th.append( targetMode );\n\tswitch( targetMode )\n\t{\n\t\tcase Constraint::BoundMin:\n\t\tcase Constraint::BoundMax:\n\t\tcase Constraint::BoundCenter:\n\t\t\th.append( targetOpt->scene->boundHash( targetOpt->path ) );\n\t\t\tbreak;\n\t\tcase Constraint::UV:\n\t\t\th.append( targetOpt->scene->objectHash( targetOpt->path ) );\n\t\t\tignoreMissingTargetPlug()->hash( h );\n\t\t\ttargetUVPlug()->hash( h );\n\t\t\tbreak;\n\t\tcase Constraint::Vertex:\n\t\t\th.append( targetOpt->scene->objectHash( targetOpt->path ) );\n\t\t\tignoreMissingTargetPlug()->hash( h );\n\t\t\ttargetVertexPlug()->hash( h );\n\t\t\tbreak;\n\t\tdefault:\n\t\t\tbreak;\n\t}\n\n\ttargetOffsetPlug()->hash( h );\n\n\thashConstraint( context, h );\n}\n\nImath::M44f Constraint::computeProcessedTransform( const ScenePath &path, const Gaffer::Context *context, const Imath::M44f &inputTransform ) const\n{\n\tauto targetOpt = target();\n\tif( !targetOpt )\n\t{\n\t\treturn inputTransform;\n\t}\n\n\tScenePath parentPath = path;\n\tparentPath.pop_back();\n\n\tconst M44f parentTransform = inPlug()->fullTransform( parentPath );\n\tconst M44f fullInputTransform = inputTransform * parentTransform;\n\n\tM44f fullTargetTransform = targetOpt->scene->fullTransform( targetOpt->path );\n\n\tconst TargetMode targetMode = (TargetMode)targetModePlug()->getValue();\n\n\tswitch( targetMode )\n\t{\n\t\tcase Constraint::BoundMin:\n\t\t{\n\t\t\tconst Box3f targetBound = targetOpt->scene->bound( targetOpt->path );\n\t\t\tif( ! targetBound.isEmpty() )\n\t\t\t{\n\t\t\t\tfullTargetTransform.translate( targetBound.min );\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n\t\tcase Constraint::BoundMax:\n\t\t{\n\t\t\tconst Box3f targetBound = targetOpt->scene->bound( targetOpt->path );\n\t\t\tif( ! targetBound.isEmpty() )\n\t\t\t{\n\t\t\t\tfullTargetTransform.translate( targetBound.max );\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n\t\tcase Constraint::BoundCenter:\n\t\t{\n\t\t\tconst Box3f targetBound = targetOpt->scene->bound( targetOpt->path );\n\t\t\tif( ! targetBound.isEmpty() )\n\t\t\t{\n\t\t\t\tfullTargetTransform.translate( targetBound.center() );\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n\t\tcase Constraint::UV:\n\t\t{\n\t\t\tconst IECore::ConstObjectPtr object = targetOpt->scene->object( targetOpt->path );\n\t\t\tImath::M44f surfaceTransform;\n\t\t\tconst Imath::V2f uv = targetUVPlug()->getValue();\n\t\t\tconst bool throwOnError = !( ignoreMissingTargetPlug()->getValue() );\n\t\t\tcomputeUVLocalFrame( *object, surfaceTransform, uv, \"uv\", throwOnError, context->canceller() );\n\t\t\tfullTargetTransform = surfaceTransform * fullTargetTransform;\n\t\t\tbreak;\n\t\t}\n\t\tcase Constraint::Vertex:\n\t\t{\n\t\t\tconst IECore::ConstObjectPtr object = targetOpt->scene->object( targetOpt->path );\n\t\t\tImath::M44f surfaceTransform;\n\t\t\tconst int vertexId = targetVertexPlug()->getValue();\n\t\t\tconst bool throwOnError = !( ignoreMissingTargetPlug()->getValue() );\n\t\t\tcomputeVertexLocalFrame( *object, surfaceTransform, vertexId, \"uv\", throwOnError, context->canceller() );\n\t\t\tfullTargetTransform = surfaceTransform * fullTargetTransform;\n\t\t\tbreak;\n\t\t}\n\t\tdefault:\n\t\t\tbreak;\n\t}\n\n\tfullTargetTransform.translate( targetOffsetPlug()->getValue() );\n\n\tconst M44f fullConstrainedTransform = computeConstraint( fullTargetTransform, fullInputTransform, inputTransform );\n\treturn fullConstrainedTransform * parentTransform.inverse();\n}\n\nstd::optional Constraint::target() const\n{\n\tstd::string targetPathAsString = targetPlug()->getValue();\n\tif( targetPathAsString == \"\" )\n\t{\n\t\treturn std::nullopt;\n\t}\n\n\tScenePath targetPath;\n\tScenePlug::stringToPath( targetPathAsString, targetPath );\n\n\tconst ScenePlug *targetScene = targetScenePlug();\n\tif( !targetScene->getInput() )\n\t{\n\t\t// Backwards compatibility for time when there was\n\t\t// no `targetScene` plug.\n\t\ttargetScene = inPlug();\n\t}\n\n\tif( !targetScene->exists( targetPath ) )\n\t{\n\t\tif( ignoreMissingTargetPlug()->getValue() )\n\t\t{\n\t\t\treturn std::nullopt;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tthrow IECore::Exception( boost::str(\n\t\t\t\tboost::format( \"Constraint target does not exist: \\\"%s\\\". Use 'ignoreMissingTarget' option if you want to just skip this constraint\" ) % targetPathAsString ) );\n\t\t}\n\t}\n\n\treturn Target( { targetPath, targetScene } );\n}\n", "meta": {"hexsha": "73ef99097768457a31bc2687faa48348f0e9e6c6", "size": 29431, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/GafferScene/Constraint.cpp", "max_stars_repo_name": "pier-robot/gaffer", "max_stars_repo_head_hexsha": "9267f2ba3822b14430d8a283c745261110b0f570", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/GafferScene/Constraint.cpp", "max_issues_repo_name": "pier-robot/gaffer", "max_issues_repo_head_hexsha": "9267f2ba3822b14430d8a283c745261110b0f570", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/GafferScene/Constraint.cpp", "max_forks_repo_name": "pier-robot/gaffer", "max_forks_repo_head_hexsha": "9267f2ba3822b14430d8a283c745261110b0f570", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.0754369825, "max_line_length": 208, "alphanum_fraction": 0.6153035915, "num_tokens": 9080, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795672, "lm_q2_score": 0.42250463481418826, "lm_q1q2_score": 0.22279367514197143}} {"text": "/*****************************************************************************\n*\n* Copyright (c) 2003-2018 by The University of Queensland\n* http://www.uq.edu.au\n*\n* Primary Business: Queensland, Australia\n* Licensed under the Apache License, version 2.0\n* http://www.apache.org/licenses/LICENSE-2.0\n*\n* Development until 2012 by Earth Systems Science Computational Center (ESSCC)\n* Development 2012-2013 by School of Earth Sciences\n* Development from 2014 by Centre for Geoscience Computing (GeoComp)\n*\n*****************************************************************************/\n\n\n/****************************************************************************/\n\n/* Paso: SystemMatrix: controls iterative linear system solvers */\n\n/****************************************************************************/\n\n/* Copyrights by ACcESS Australia 2003/04 */\n/* Author: Lutz Gross, l.gross@uq.edu.au */\n\n/****************************************************************************/\n\n#include \"Solver.h\"\n#include \"Options.h\"\n#include \"SystemMatrix.h\"\n\n#include // for isnan\n\n#include \n\nnamespace bm = boost::math;\n\nnamespace paso {\n\nvoid Solver_free(SystemMatrix* A)\n{\n A->freePreconditioner();\n}\n\n/// calls the iterative solver\nSolverResult Solver(SystemMatrix_ptr A, double* x, double* b, Options* options,\n Performance* pp)\n{\n const real_t EPSILON = escript::DataTypes::real_t_eps();\n double norm2_of_b,tol,tolerance,time_iter,net_time_start;\n double *r=NULL,norm2_of_residual,last_norm2_of_residual,norm_max_of_b;\n double norm2_of_b_local,norm_max_of_b_local,norm2_of_residual_local;\n double norm_max_of_residual_local,norm_max_of_residual;\n double last_norm_max_of_residual;\n#ifdef ESYS_MPI\n double loc_norm;\n#endif\n dim_t i,totIter=0,cntIter,method;\n bool finalizeIteration;\n SolverResult errorCode = NoError;\n const dim_t numSol = A->getTotalNumCols();\n const dim_t numEqua = A->getTotalNumRows();\n double *x0=NULL;\n\n tolerance=options->tolerance;\n if (tolerance < 100.* EPSILON) {\n throw PasoException(\"Solver: Tolerance is too small.\");\n }\n if (tolerance >1.) {\n throw PasoException(\"Solver: Tolerance must be less than one.\");\n }\n method=Options::getSolver(options->method, PASO_PASO, options->symmetric, A->mpi_info);\n /* check matrix type */\n if ((A->type & MATRIX_FORMAT_CSC) || (A->type & MATRIX_FORMAT_OFFSET1) ) {\n throw PasoException(\"Solver: Iterative solver requires CSR format with unsymmetric storage scheme and index offset 0.\");\n }\n if (A->col_block_size != A->row_block_size) {\n throw PasoException(\"Solver: Iterative solver requires row and column block sizes to be equal.\");\n }\n if (A->getGlobalNumCols() != A->getGlobalNumRows()) {\n throw PasoException(\"Solver: Iterative solver requires a square matrix.\");\n }\n time_iter=escript::gettime();\n /* this for testing only */\n if (method==PASO_NONLINEAR_GMRES) {\n LinearSystem* F = new LinearSystem(A, b, options);\n A->solvePreconditioner(x, b);\n errorCode = Solver_NewtonGMRES(F, x, options, pp);\n if (errorCode != NoError) {\n throw PasoException(\"Solver_NewtonGMRES: an error has occurred.\");\n }\n delete F;\n return errorCode;\n }\n\n r = new double[numEqua];\n x0 = new double[numEqua];\n A->balance();\n options->num_level=0;\n options->num_inner_iter=0;\n\n /* ========================= */\n Performance_startMonitor(pp, PERFORMANCE_ALL);\n A->applyBalance(r, b, true);\n /* get the norm of the right hand side */\n norm2_of_b=0.;\n norm_max_of_b=0.;\n #pragma omp parallel private(norm2_of_b_local,norm_max_of_b_local)\n {\n norm2_of_b_local=0.;\n norm_max_of_b_local=0.;\n #pragma omp for private(i) schedule(static)\n for (i = 0; i < numEqua ; ++i) {\n norm2_of_b_local += r[i] * r[i];\n norm_max_of_b_local = std::max(std::abs(r[i]),norm_max_of_b_local);\n }\n #pragma omp critical\n {\n norm2_of_b += norm2_of_b_local;\n norm_max_of_b = std::max(norm_max_of_b_local,norm_max_of_b);\n }\n }\n#ifdef ESYS_MPI\n /* TODO: use one call */\n loc_norm = norm2_of_b;\n MPI_Allreduce(&loc_norm,&norm2_of_b, 1, MPI_DOUBLE, MPI_SUM, A->mpi_info->comm);\n loc_norm = norm_max_of_b;\n MPI_Allreduce(&loc_norm,&norm_max_of_b, 1, MPI_DOUBLE, MPI_MAX, A->mpi_info->comm);\n#endif\n norm2_of_b=sqrt(norm2_of_b);\n /* if norm2_of_b==0 we are ready: x=0 */\n if (bm::isnan(norm2_of_b) || bm::isnan(norm_max_of_b)) {\n throw PasoException(\"Solver: Matrix or right hand side contains undefined values.\");\n } else if (norm2_of_b <= 0.) {\n#pragma omp parallel for private(i) schedule(static)\n for (i = 0; i < numSol; i++) x[i]=0.;\n if (options->verbose)\n std::cout << \"right hand side is identical to zero.\" << std::endl;\n } else {\n if (options->verbose) {\n std::cout << \"Solver: l2/lmax-norm of right hand side is \"\n << norm2_of_b << \"/\" << norm_max_of_b << \".\" << std::endl\n << \"Solver: l2/lmax-stopping criterion is \"\n << norm2_of_b*tolerance << \"/\" << norm_max_of_b*tolerance\n << \".\" << std::endl;\n switch (method) {\n case PASO_BICGSTAB:\n std::cout << \"Solver: Iterative method is BiCGStab.\\n\";\n break;\n case PASO_PCG:\n std::cout << \"Solver: Iterative method is PCG.\\n\";\n break;\n case PASO_TFQMR:\n std::cout << \"Solver: Iterative method is TFQMR.\\n\";\n break;\n case PASO_MINRES:\n std::cout << \"Solver: Iterative method is MINRES.\\n\";\n break;\n case PASO_PRES20:\n std::cout << \"Solver: Iterative method is PRES20.\\n\";\n break;\n case PASO_GMRES:\n if (options->restart > 0) {\n std::cout << \"Solver: Iterative method is GMRES(\"\n << options->truncation << \",\"\n << options->restart << \").\" << std::endl;\n } else {\n std::cout << \"Solver: Iterative method is GMRES(\"\n << options->truncation << \").\" << std::endl;\n }\n break;\n }\n }\n\n // construct the preconditioner\n Performance_startMonitor(pp, PERFORMANCE_PRECONDITIONER_INIT);\n A->setPreconditioner(options);\n Performance_stopMonitor(pp, PERFORMANCE_PRECONDITIONER_INIT);\n options->set_up_time=escript::gettime()-time_iter;\n // get an initial guess by evaluating the preconditioner\n A->solvePreconditioner(x, r);\n\n totIter = 1;\n finalizeIteration = false;\n last_norm2_of_residual=norm2_of_b;\n last_norm_max_of_residual=norm_max_of_b;\n net_time_start=escript::gettime();\n\n // main loop\n while (!finalizeIteration) {\n cntIter = options->iter_max - totIter;\n finalizeIteration = true;\n\n // Set initial residual\n if (totIter > 1) {\n // in the first iteration r = balance * b already\n A->applyBalance(r, b, true);\n }\n\n A->MatrixVector_CSR_OFFSET0(-1., x, 1., r);\n norm2_of_residual = 0;\n norm_max_of_residual = 0;\n #pragma omp parallel private(norm2_of_residual_local,norm_max_of_residual_local)\n {\n norm2_of_residual_local = 0;\n norm_max_of_residual_local = 0;\n #pragma omp for private(i) schedule(static)\n for (i = 0; i < numEqua; i++) {\n norm2_of_residual_local+= r[i] * r[i];\n norm_max_of_residual_local=std::max(std::abs(r[i]),norm_max_of_residual_local);\n }\n #pragma omp critical\n {\n norm2_of_residual += norm2_of_residual_local;\n norm_max_of_residual = std::max(norm_max_of_residual_local,norm_max_of_residual);\n }\n }\n#ifdef ESYS_MPI\n // TODO: use one call\n loc_norm = norm2_of_residual;\n MPI_Allreduce(&loc_norm,&norm2_of_residual, 1, MPI_DOUBLE, MPI_SUM, A->mpi_info->comm);\n loc_norm = norm_max_of_residual;\n MPI_Allreduce(&loc_norm,&norm_max_of_residual, 1, MPI_DOUBLE, MPI_MAX, A->mpi_info->comm);\n#endif\n norm2_of_residual =sqrt(norm2_of_residual);\n options->residual_norm=norm2_of_residual;\n\n if (options->verbose)\n std::cout << \"Solver: Step \" << totIter\n << \": l2/lmax-norm of residual is \"\n << norm2_of_residual << \"/\" << norm_max_of_residual;\n\n if (totIter > 1 &&\n norm2_of_residual >= last_norm2_of_residual &&\n norm_max_of_residual >= last_norm_max_of_residual) {\n\n if (options->verbose) std::cout << \" divergence!\\n\";\n throw PasoException(\"Solver: No improvement during iteration. Iterative solver gives up.\");\n\n } else {\n if (norm2_of_residual>tolerance*norm2_of_b ||\n norm_max_of_residual>tolerance*norm_max_of_b ) {\n\n tol=tolerance*std::min(norm2_of_b,0.1*norm2_of_residual/norm_max_of_residual*norm_max_of_b);\n if (options->verbose)\n std::cout << \" (new tolerance = \" << tol << \").\\n\";\n\n last_norm2_of_residual=norm2_of_residual;\n last_norm_max_of_residual=norm_max_of_residual;\n\n // call the solver\n switch (method) {\n case PASO_BICGSTAB:\n errorCode = Solver_BiCGStab(A, r, x, &cntIter, &tol, pp);\n break;\n case PASO_PCG:\n errorCode = Solver_PCG(A, r, x, &cntIter, &tol, pp);\n break;\n case PASO_TFQMR:\n tol=tolerance*norm2_of_residual/norm2_of_b;\n errorCode = Solver_TFQMR(A, r, x0, &cntIter, &tol, pp);\n #pragma omp for private(i) schedule(static)\n for (i = 0; i < numEqua; i++) {\n x[i]+= x0[i];\n }\n break;\n case PASO_MINRES:\n //tol=tolerance*norm2_of_residual/norm2_of_b;\n errorCode = Solver_MINRES(A, r, x, &cntIter, &tol, pp);\n break;\n case PASO_PRES20:\n errorCode = Solver_GMRES(A, r, x, &cntIter, &tol, 5, 20, pp);\n break;\n case PASO_GMRES:\n errorCode = Solver_GMRES(A, r, x, &cntIter, &tol, options->truncation, options->restart, pp);\n break;\n }\n\n totIter += cntIter;\n\n // error handling\n if (errorCode == NoError) {\n finalizeIteration = false;\n } else if (errorCode == MaxIterReached) {\n if (options->verbose)\n std::cout << \"Solver: Maximum number of \"\n \"iterations reached.\" << std::endl;\n break;\n } else if (errorCode == InputError) {\n if (options->verbose)\n std::cout << \"Solver: Internal error!\\n\";\n break;\n } else if (errorCode == NegativeNormError) {\n if (options->verbose)\n std::cout << \"Solver: negative energy norm\"\n \" (try other solver or preconditioner)!\\n\";\n break;\n } else if (errorCode == Breakdown) {\n if (cntIter <= 1) {\n if (options->verbose)\n std::cout << \"Solver: Uncurable break \"\n \"down!\" << std::endl;\n break;\n } else {\n if (options->verbose)\n std::cout << \"Solver: Breakdown at iter \"\n << totIter << \" (residual = \"\n << tol << \"). Restarting ...\\n\";\n finalizeIteration = false;\n errorCode = NoError;\n }\n } else {\n if (options->verbose)\n std::cout << \"Solver: Generic error in solver!\\n\";\n break;\n }\n } else {\n if (options->verbose)\n std::cout << \" convergence!\" << std::endl;\n options->converged = true;\n }\n }\n } // while\n options->net_time = escript::gettime()-net_time_start;\n options->num_iter = totIter;\n A->applyBalanceInPlace(x, false);\n }\n delete[] r;\n delete[] x0;\n options->time = escript::gettime()-time_iter;\n Performance_stopMonitor(pp, PERFORMANCE_ALL);\n return errorCode;\n}\n\nSolverResult Solver(SystemMatrix_ptr A, cplx_t* x, cplx_t* b, Options* options,\n Performance* pp)\n{\n throw PasoException(\"Solver(): complex not implemented.\");\n}\n\nvoid Solver_free(SystemMatrix* A)\n{\n throw PasoException(\"Solver_free(): complex not implemented.\");\n}\n\n} // namespace paso\n\n", "meta": {"hexsha": "7dd0212f3ad60cf663253798072460a935e6764d", "size": 14164, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "paso/src/Solver.cpp", "max_stars_repo_name": "markendr/esys-escript.github.io", "max_stars_repo_head_hexsha": "0023eab09cd71f830ab098cb3a468e6139191e8d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "paso/src/Solver.cpp", "max_issues_repo_name": "markendr/esys-escript.github.io", "max_issues_repo_head_hexsha": "0023eab09cd71f830ab098cb3a468e6139191e8d", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "paso/src/Solver.cpp", "max_forks_repo_name": "markendr/esys-escript.github.io", "max_forks_repo_head_hexsha": "0023eab09cd71f830ab098cb3a468e6139191e8d", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.7011494253, "max_line_length": 128, "alphanum_fraction": 0.5132730867, "num_tokens": 3111, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631556226292, "lm_q2_score": 0.3665897294020099, "lm_q1q2_score": 0.22276307178727106}} {"text": "/**\n * @file\n * @copyright This code is licensed under the 3-clause BSD license.\\n\n * Copyright ETH Zurich, Laboratory for Physical Chemistry, Reiher Group.\\n\n * See LICENSE.txt for details.\n */\n#include \"DavidsonDiagonalizer.h\"\n#include \"IndirectPreconditionerEvaluator.h\"\n#include \"IndirectSigmaVectorEvaluator.h\"\n#include \n#include \n\nnamespace Scine {\nnamespace Utils {\n\ntemplate\nDavidsonDiagonalizer::DavidsonDiagonalizer(int eigenvaluesToCompute, int numberGuessVectors, int totalDimension)\n : eigenvaluesToCompute_(eigenvaluesToCompute), blockSize_(numberGuessVectors), subspaceDimension_(numberGuessVectors) {\n assert(eigenvaluesToCompute > 0 &&\n \"Unintended behaviour: calculate negative amount of eigenvalues in Davidson diagonalizer.\");\n if (eigenvaluesToCompute_ > blockSize_)\n throw InvalidDavidsonInputException();\n initialize(totalDimension);\n}\n\ntemplate\nvoid DavidsonDiagonalizer::initialize(int dimension) {\n if (eigenvaluesToCompute_ > dimension) {\n eigenvaluesToCompute_ = dimension;\n Utils::Log::warning() << \"Too many eigenvalues requested (\" << eigenvaluesToCompute_ << \"), they were set to \"\n << dimension << \".\";\n }\n if (subspaceDimension_ > dimension) {\n subspaceDimension_ = eigenvaluesToCompute_;\n blockSize_ = subspaceDimension_;\n Utils::Log::warning() << \"Initial subspace dimension too big (\" << subspaceDimension_ << \"), it was reduced to \"\n << eigenvaluesToCompute_ << \".\";\n }\n assert(dimension >= blockSize_ && \"Matrix dimension cannot be smaller than number of guess vectors!\");\n guessVectors_ = Eigen::MatrixXd::Zero(dimension, subspaceDimension_);\n if (s == DavidsonBalancedType::standard) {\n guessVectors_.block(0, 0, subspaceDimension_, subspaceDimension_) =\n Eigen::MatrixXd::Identity(subspaceDimension_, subspaceDimension_);\n }\n else {\n srand(seed_);\n guessVectors_.block(0, 0, subspaceDimension_, subspaceDimension_) =\n Eigen::MatrixXd::Random(subspaceDimension_, subspaceDimension_);\n }\n maxDimension_ = dimension;\n maxIterations_ = dimension;\n sigmaVectorEvaluator_.reset();\n preconditionerEvaluator_.reset();\n}\n\ntemplate\nvoid DavidsonDiagonalizer::setMaxIterations(int maxIterations) {\n maxIterations_ = maxIterations;\n}\n\ntemplate\nvoid DavidsonDiagonalizer::setMatrixToDiagonalize(const MatrixType& matrix) {\n if (type_ == DavidsonDirectType::direct) { // if a direct method is in use, no matrix is needed.\n return;\n }\n matrixToDiagonalize_ = matrix;\n originalDiagonal_ = matrixToDiagonalize_.diagonal();\n initialize(matrixToDiagonalize_.cols());\n}\n\ntemplate\nvoid DavidsonDiagonalizer::setGuess(const Eigen::MatrixXd& guessVectors) {\n guessVectors_ = guessVectors.leftCols(subspaceDimension_);\n}\n\ntemplate\nvoid DavidsonDiagonalizer::setDavidsonType(DavidsonDirectType type) {\n type_ = type;\n}\n\ntemplate\nvoid DavidsonDiagonalizer::setSeed(unsigned seed) {\n seed_ = seed;\n}\n\ntemplate\nvoid DavidsonDiagonalizer::setSigmaVectorEvaluator(std::unique_ptr>&& evaluator) {\n sigmaVectorEvaluator_ = std::move(evaluator);\n}\n\ntemplate\nvoid DavidsonDiagonalizer::setPreconditionerEvaluator(std::unique_ptr&& evaluator) {\n preconditionerEvaluator_ = std::move(evaluator);\n}\n\ntemplate\nEigenContainer DavidsonDiagonalizer::solve() {\n checkEvaluators();\n\n // Set initial size of guess matrix\n subspaceDimension_ = blockSize_;\n for (int i = 0; i < maxIterations_; ++i) {\n performIteration();\n Log::info() << \"Iteration number: \" << i << \"\\tSubspace dimension: \" << subspaceDimension_ << \".\";\n if (converged_) { // if full matrix still calculate everything\n Log::info() << \"Converged in \" << i << \" iterations, dimensionality \" << subspaceDimension_ << \".\";\n return eigenPairs_;\n }\n }\n throw DavidsonNotConvergedException();\n}\n\ntemplate\nvoid DavidsonDiagonalizer::performIteration() {\n // Orthogonalize the projection matrix\n if (s == DavidsonBalancedType::standard) {\n orthogonalizeSubspace(guessVectors_);\n guessVectors_.leftCols(subspaceDimension_).colwise().normalize();\n }\n\n const Eigen::MatrixXd& projector = guessVectors_.leftCols(subspaceDimension_);\n if (s == DavidsonBalancedType::balanced) {\n for (int i = basisOverlap_.cols(); i < subspaceDimension_; ++i) {\n basisOverlap_.conservativeResize(subspaceDimension_, subspaceDimension_);\n basisOverlap_.col(i) = projector.transpose() * projector.col(i);\n Eigen::VectorXd symmetricRow = basisOverlap_.col(i);\n basisOverlap_.row(i) = symmetricRow;\n }\n }\n\n Eigen::MatrixXd sigmaMatrix = sigmaVectorEvaluator_->evaluateSigmaVector(projector);\n Eigen::MatrixXd projectedMatrix = projector.transpose() * sigmaMatrix;\n\n Eigen::MatrixXd subspaceEVectors;\n Eigen::VectorXd subspaceEValues;\n\n if (s == DavidsonBalancedType::standard) {\n Eigen::SelfAdjointEigenSolver subspaceDiagonalizer(projectedMatrix);\n subspaceEVectors = subspaceDiagonalizer.eigenvectors();\n subspaceEValues = subspaceDiagonalizer.eigenvalues();\n }\n else if (s == DavidsonBalancedType::balanced) {\n Eigen::GeneralizedSelfAdjointEigenSolver subspaceDiagonalizer(projectedMatrix, basisOverlap_);\n subspaceEVectors = subspaceDiagonalizer.eigenvectors();\n subspaceEValues = subspaceDiagonalizer.eigenvalues();\n }\n\n // Check convergence:\n // Get a matrix with the first *eigenvaluesToCompute_* residual vectors as column.\n // basically, it calculated r_k = H*Psi_{k, approx} - h_k*Psi_{k, approx}\n // = (sigmaMatrix - h_k * bMatrix) * v_k\n // H: matrix to diagonalize\n // Psi_{k, approx} k-th approximated Ritz eigenvectors for the subspace\n // h_k: k-th approximated Ritz eigenvalue\n // sigmaMatrix: matrix with as columns the sigma vectors\n // bMatrix: matrix with as column the tentative vectors\n // v_k: k-th subspace eigenvector\n Eigen::MatrixXd residualVectors(sigmaMatrix.rows(), eigenvaluesToCompute_);\n for (int col = 0; col < eigenvaluesToCompute_; ++col) {\n residualVectors.col(col) = (sigmaMatrix - subspaceEValues(col) * projector) * subspaceEVectors.col(col);\n }\n\n Eigen::VectorXd residualNorms = residualVectors.colwise().norm();\n converged_ = true;\n for (int i = 0; i < residualNorms.size(); ++i) {\n if (residualNorms(i) > eigenvalueTol)\n converged_ = false;\n }\n if (converged_) {\n eigenPairs_ = {subspaceEValues.head(eigenvaluesToCompute_), projector * subspaceEVectors.leftCols(eigenvaluesToCompute_)};\n return;\n }\n expandSubspace(subspaceEValues, residualVectors, projector, residualNorms);\n}\n\ntemplate\nvoid DavidsonDiagonalizer::expandSubspace(const Eigen::VectorXd& subspaceEValues,\n const Eigen::MatrixXd& residualVectors, const Eigen::MatrixXd& projector,\n const Eigen::VectorXd& residualNorms) {\n // look at each residual vector\n for (int i = 0; i < eigenvaluesToCompute_; ++i) {\n if (residualNorms(i) > eigenvalueTol) {\n Eigen::VectorXd correctionVector =\n residualVectors.col(i).cwiseProduct(-preconditionerEvaluator_->evaluate(subspaceEValues(i)));\n if (s == DavidsonBalancedType::standard) {\n Eigen::VectorXd orthogonalizedCorrectionVector(guessVectors_.rows());\n orthogonalizedCorrectionVector = orthogonalizeToSubspace(correctionVector.normalized(), projector);\n\n if (orthogonalizedCorrectionVector.norm() / correctionVector.norm() > /*correctionTol*/ 1e-3) {\n if (subspaceDimension_ < maxDimension_) {\n ++subspaceDimension_;\n guessVectors_.conservativeResize(Eigen::NoChange, subspaceDimension_);\n guessVectors_.col(subspaceDimension_ - 1) = orthogonalizedCorrectionVector.normalized();\n }\n }\n }\n else {\n if (subspaceDimension_ < maxDimension_) {\n ++subspaceDimension_;\n guessVectors_.conservativeResize(Eigen::NoChange, subspaceDimension_);\n guessVectors_.col(subspaceDimension_ - 1) = correctionVector;\n }\n }\n }\n }\n}\n\ntemplate\nvoid DavidsonDiagonalizer::checkEvaluators() {\n if (!sigmaVectorEvaluator_ || !preconditionerEvaluator_) {\n switch (type_) {\n case DavidsonDirectType::standard:\n setSigmaVectorEvaluator(std::make_unique>(matrixToDiagonalize_));\n setPreconditionerEvaluator(std::make_unique(originalDiagonal_));\n break;\n case DavidsonDirectType::direct:\n throw InvalidSigmaVectorEvaluator();\n default:\n throw InvalidDavidsonTypeException();\n }\n }\n}\n\ntemplate\nvoid DavidsonDiagonalizer::orthogonalizeSubspace(Eigen::MatrixXd& trialSpace) {\n int spaceDimension = trialSpace.rows();\n // Calculate modified Gram-Schmidt QR decomposition\n Eigen::MatrixXd Q(spaceDimension, subspaceDimension_);\n Eigen::MatrixXd R(subspaceDimension_, subspaceDimension_);\n\n for (int i = 0; i < subspaceDimension_; ++i) {\n R(i, i) = trialSpace.col(i).norm();\n Q.col(i) = trialSpace.col(i) / R(i, i);\n for (int j = i + 1; j < subspaceDimension_; ++j) {\n R(i, j) = Q.col(i).transpose() * trialSpace.col(j);\n trialSpace.col(j) -= Q.col(i) * R(i, j);\n }\n }\n}\ntemplate\nEigen::VectorXd DavidsonDiagonalizer::orthogonalizeToSubspace(const Eigen::VectorXd& vector,\n const Eigen::MatrixXd& subspace) const {\n Eigen::VectorXd orthonormalizedVector = vector;\n for (int basis = 0; basis < subspace.cols(); ++basis) {\n orthonormalizedVector -= subspace.col(basis).dot(vector) * subspace.col(basis) / subspace.col(basis).norm();\n }\n return orthonormalizedVector;\n}\n\ntemplate class DavidsonDiagonalizer;\ntemplate class DavidsonDiagonalizer>;\ntemplate class DavidsonDiagonalizer;\ntemplate class DavidsonDiagonalizer, DavidsonBalancedType::balanced>;\n\n} // namespace Utils\n} // namespace Scine\n", "meta": {"hexsha": "75fbcff9c9008e433dd3ae9d60944ad0282c1b43", "size": 11041, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Utils/Utils/Math/IterativeDiagonalizer/DavidsonDiagonalizer.cpp", "max_stars_repo_name": "DockBio/utilities", "max_stars_repo_head_hexsha": "213ed5ac2a64886b16d0fee1fcecb34d36eea9e9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/Utils/Utils/Math/IterativeDiagonalizer/DavidsonDiagonalizer.cpp", "max_issues_repo_name": "DockBio/utilities", "max_issues_repo_head_hexsha": "213ed5ac2a64886b16d0fee1fcecb34d36eea9e9", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/Utils/Utils/Math/IterativeDiagonalizer/DavidsonDiagonalizer.cpp", "max_forks_repo_name": "DockBio/utilities", "max_forks_repo_head_hexsha": "213ed5ac2a64886b16d0fee1fcecb34d36eea9e9", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.9610894942, "max_line_length": 130, "alphanum_fraction": 0.7251154787, "num_tokens": 2651, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6261241772283034, "lm_q2_score": 0.3557748935136304, "lm_q1q2_score": 0.2227592624797091}} {"text": "/*=========================================================================\n *\n * Program: Bifurcation Analysis Library\n * Module: main.cpp\n *\n * Copyright (C) 2009 Daniele Linaro\n *\n * This program is free software: you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation, either version 3 of the License, or\n * (at your option) any later version.\n * \n * This program is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n * \n * You should have received a copy of the GNU General Public License\n * along with this program. If not, see .\n *\n *=========================================================================*/\n\n#include \n#include \n#include \n\n#include \n#include \nusing boost::property_tree::ptree;\n\n#include \n\n#include \"balCommon.h\"\n#include \"balParameters.h\"\n#include \"balLogger.h\"\n#include \"balODESolver.h\"\n#include \"balBifurcationDiagram.h\"\n#include \"balBifurcationParameters.h\"\n#include \"balPLL.h\"\nusing namespace bal;\n\nconst realtype pi = 3.1415926535897931;\n\nint main(int argc, char *argv[]) {\n if(argc == 1) {\n fprintf(stderr, \"Usage: %s ConfigFile\\n\", argv[0]);\n exit(1);\n }\n ptree config;\n read_xml(argv[1], config);\n char name[100];\n \n int steps[PLL::npar];\n BifurcationParameters * bp = BifurcationParameters::Create();\n bp->SetNumber(PLL::npar);\n \n for(int i=0; i(name);\n if(steps[i] == 1) {\n sprintf(name,\"pll.%s.min\",PLL::parname[i]);\n bp->SetIthParameter(i,config.get(name));\n }\n else {\n sprintf(name,\"pll.%s.min\",PLL::parname[i]);\n bp->SetIthParameterLowerBound(i,config.get(name));\n sprintf(name,\"pll.%s.max\",PLL::parname[i]);\n bp->SetIthParameterUpperBound(i,config.get(name));\n }\n }\n \n#ifdef WITHPHIERR\n#ifdef EXTEND\n realtype x0[7] = {0.,0.,0.,0.,pi,0.,0.};\n#else\n realtype x0[5] = {0.,0.,0.,0.,pi};\n#endif\n#else\n#ifdef EXTEND\n realtype x0[6] = {0.,0.,0.,0.,0.,0.};\n#else\n realtype x0[4] = {0.,0.,0.,0.};\n#endif\n#endif\n x0[1] = config.get(\"pll.vdd.min\");\t\n \n bp->SetNumberOfSteps(steps);\n PLL * pll = PLL::Create();\n pll->SetParameters(bp);\n BifurcationDiagram * bifd = BifurcationDiagram::Create();\n bifd->RestartFromX0(true);\n bifd->SetDynamicalSystem(pll);\n bifd->SetFilename((char *) config.get(\"simulation.outputfile\").c_str());\n if(config.get(\"simulation.trajectory\"))\n bifd->GetODESolver()->SetIntegrationMode(BOTH);\n else\n bifd->GetODESolver()->SetIntegrationMode(EVENTS);\n bifd->GetODESolver()->SetTransientDuration(config.get(\"simulation.ttran\"));\n bifd->GetODESolver()->HaltAtEquilibrium(false);\n bifd->GetODESolver()->HaltAtCycle(false);\n bifd->GetODESolver()->SetFinalTime(config.get(\"simulation.tout\"));\n bifd->GetODESolver()->SetTimeStep(1e-11);\n bifd->GetODESolver()->SetMaxNumberOfIntersections((int) 1e7);\n bifd->GetODESolver()->SetX0(x0);\n bifd->GetODESolver()->SetRelativeTolerance(1e-10);\n bifd->SetNumberOfThreads(1);\n bifd->ComputeDiagram();\n bifd->Destroy();\n pll->Destroy();\n bp->Destroy();\n \n return EXIT_SUCCESS;\n}\n\n", "meta": {"hexsha": "b622036dd42e0183f4feffd33459c4f76bf84911", "size": 3584, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/pll.cpp", "max_stars_repo_name": "danielelinaro/BAL", "max_stars_repo_head_hexsha": "d735048d9962a0c424c29db93f774494c67b12a9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-02-02T22:30:37.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-02T22:30:37.000Z", "max_issues_repo_path": "examples/pll.cpp", "max_issues_repo_name": "danielelinaro/BAL", "max_issues_repo_head_hexsha": "d735048d9962a0c424c29db93f774494c67b12a9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "examples/pll.cpp", "max_forks_repo_name": "danielelinaro/BAL", "max_forks_repo_head_hexsha": "d735048d9962a0c424c29db93f774494c67b12a9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.1652173913, "max_line_length": 87, "alphanum_fraction": 0.6512276786, "num_tokens": 1011, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.40733340004593027, "lm_q1q2_score": 0.22270471038272133}} {"text": "//\n// synthetic_data_helper.cpp\n// synthetic_data_helper\n//\n// Created by Cristián Garay on 10/15/16.\n// Copyright © 2016 Cristian Garay. All rights reserved.\n//\n\n#define NPY_NO_DEPRECATED_API NPY_1_11_API_VERSION\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace Eigen;\nusing namespace std;\nusing namespace boost::python;\n\n#if PY_VERSION_HEX >= 0x03000000\nvoid *\n#else\nvoid\n#endif\ninit_numpy(){\n //Py_Initialize;\n import_array();\n}\n\nstruct double_to_python_float\n{\n static PyObject* convert(double const& d)\n {\n return boost::python::incref(\n boost::python::object(d).ptr());\n }\n};\n\n//numpy scalar converters.\ntemplate \nstruct enable_numpy_scalar_converter\n{\n enable_numpy_scalar_converter()\n {\n // Required NumPy call in order to use the NumPy C API within another\n // extension module.\n // import_array();\n init_numpy();\n\n boost::python::converter::registry::push_back(\n &convertible,\n &construct,\n boost::python::type_id());\n }\n\n static void* convertible(PyObject* object)\n {\n // The object is convertible if all of the following are true:\n // - is a valid object.\n // - is a numpy array scalar.\n // - its descriptor type matches the type for this converter.\n return (\n object && // Valid\n PyArray_CheckScalar(object) && // Scalar\n PyArray_DescrFromScalar(object)->type_num == NumPyScalarType // Match\n )\n ? object // The Python object can be converted.\n : NULL;\n }\n\n static void construct(\n PyObject* object,\n boost::python::converter::rvalue_from_python_stage1_data* data)\n {\n // Obtain a handle to the memory block that the converter has allocated\n // for the C++ type.\n namespace python = boost::python;\n typedef python::converter::rvalue_from_python_storage storage_type;\n void* storage = reinterpret_cast(data)->storage.bytes;\n\n // Extract the array scalar type directly into the storage.\n PyArray_ScalarAsCtype(object, storage);\n\n // Set convertible to indicate success.\n data->convertible = storage;\n }\n};\n\ndict create_synthetic_data(dict& model, numeric::array& starts, numeric::array& lengths, numeric::array& resources){\n //TODO: check if parameters are null.\n //TODO: check that dicts have the required members.\n //TODO: check that all parameters have the right sizes.\n //TODO: i'm not sending any error messages.\n \n numeric::array learns = extract(model[\"learns\"]);\n int num_resources = len(learns);\n\n numeric::array forgets = extract(model[\"forgets\"]);\n numeric::array guesses = extract(model[\"guesses\"]);\n \n numeric::array slips = extract(model[\"slips\"]);\n int num_subparts = len(slips);\n \n Vector2d initial_distn;\n double prior = extract(model[\"prior\"]);\n initial_distn << 1-prior, prior;\n \n MatrixXd As(2, 2*num_resources);\n for (int n=0; n(learns[n]);\n double forget = extract(forgets[n]);\n As.col(2*n) << 1-learn, learn;\n As.col(2*n+1) << forget, 1-forget;\n }\n \n int num_sequences = len(starts);\n \n int64_t bigT = 0;\n for (int k=0; k(lengths[k]); //extract this as int??\n }\n \n //// outputs\n int all_stateseqs[1][bigT]; //used to be int8_t\n int all_data[num_subparts][bigT]; //used to be int8_t\n all_data[0][0] = 0;\n dict result;\n \n /* COMPUTATION */\n \n for (int sequence_index=0; sequence_index < num_sequences; sequence_index++) {\n int64_t sequence_start = extract(starts[sequence_index]) - 1; //should i extract these as ints?\n int64_t T = extract(lengths[sequence_index]);\n \n Vector2d nextstate_distr = initial_distn;\n\n for (int t=0; t(slips[n]) : (1-extract(guesses[n]))) < (((double) rand()) / ((double) RAND_MAX));\n }\n \n nextstate_distr = As.col(2*(extract(resources[sequence_start + t])-1)+all_stateseqs[0][sequence_start + t]); //extract int is right??\n }\n }\n \n //wrapping results in numpy objects.\n npy_intp all_stateseqs_dims[2] = {1, bigT}; //just put directly this array into the PyArray_SimpleNewFromData function?\n PyObject * all_stateseqs_pyObj = PyArray_SimpleNewFromData(2, all_stateseqs_dims, NPY_INT, all_stateseqs); //this should be NPY_INT8.\n boost::python::handle<> all_stateseqs_handle( all_stateseqs_pyObj );\n boost::python::numeric::array all_stateseqs_handle_arr( all_stateseqs_handle );\n \n npy_intp all_data_dims[2] = {num_subparts, bigT}; //just put directly this array into the PyArray_SimpleNewFromData function?\n PyObject * all_data_pyObj = PyArray_SimpleNewFromData(2, all_data_dims, NPY_INT, all_data); //this should be NPY_INT8.\n boost::python::handle<> all_data_handle( all_data_pyObj );\n boost::python::numeric::array all_data_arr( all_data_handle );\n \n result[\"stateseqs\"] = all_stateseqs_handle_arr;\n result[\"data\"] = all_data_arr;\n return(result);\n \n}\n\nBOOST_PYTHON_MODULE(synthetic_data_helper){\n //import_array();\n init_numpy();\n /*if(PyArray_API == NULL)\n\t{\n\t import_array();\n\t}*/\n numeric::array::set_module_and_type(\"numpy\", \"ndarray\");\n to_python_converter();\n enable_numpy_scalar_converter();\n enable_numpy_scalar_converter();\n enable_numpy_scalar_converter();\n enable_numpy_scalar_converter();\n \n def(\"create_synthetic_data\", create_synthetic_data);\n \n}", "meta": {"hexsha": "1d3519414b0207a33498878535bb7762cdacbaca", "size": 6385, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "generate/synthetic_data_helper.cpp", "max_stars_repo_name": "rachelcusack/pyBKT", "max_stars_repo_head_hexsha": "af10442bcffcdd2dcbdd9ee7e437b81a002a55f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "generate/synthetic_data_helper.cpp", "max_issues_repo_name": "rachelcusack/pyBKT", "max_issues_repo_head_hexsha": "af10442bcffcdd2dcbdd9ee7e437b81a002a55f8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "generate/synthetic_data_helper.cpp", "max_forks_repo_name": "rachelcusack/pyBKT", "max_forks_repo_head_hexsha": "af10442bcffcdd2dcbdd9ee7e437b81a002a55f8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.5135135135, "max_line_length": 195, "alphanum_fraction": 0.6662490211, "num_tokens": 1635, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621765155565327, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.22266022163880617}} {"text": "/// @file DeconvolverEntropy.tcc\n/// @brief Class for a deconvolver based on entropy or min L1 norm\n/// @details This concrete class defines a deconvolver used to estimate an\n/// image from a residual image, psf optionally using a weights image.\n/// @ingroup Deconvolver\n///\n/// @copyright (c) 2007 CSIRO\n/// Australia Telescope National Facility (ATNF)\n/// Commonwealth Scientific and Industrial Research Organisation (CSIRO)\n/// PO Box 76, Epping NSW 1710, Australia\n/// atnf-enquiries@csiro.au\n///\n/// This file is part of the ASKAP software distribution.\n///\n/// The ASKAP software distribution is free software: you can redistribute it\n/// and/or modify it under the terms of the GNU General Public License as\n/// published by the Free Software Foundation; either version 2 of the License,\n/// or (at your option) any later version.\n///\n/// This program is distributed in the hope that it will be useful,\n/// but WITHOUT ANY WARRANTY; without even the implied warranty of\n/// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n/// GNU General Public License for more details.\n///\n/// You should have received a copy of the GNU General Public License\n/// along with this program; if not, write to the Free Software\n/// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA\n///\n/// @author Tim Cornwell \n///\n\n#include \n\n#include \n#include \n#include \n#include \n#include \nASKAP_LOGGER(decentropylogger, \".deconvolution.entropy\");\n\n#include \n#include \n#include \n#include \n\nusing namespace casa;\n\nnamespace askap {\n\n namespace synthesis {\n\n /// @brief Class for a deconvolver based on the Entropy algorithm of Cornwell and Evans\n /// @details This base class defines a deconvolver used to estimate an\n /// image from a residual image, psf optionally using a weights image.\n /// The template argument T is the type, and FT is the transform\n /// e.g. DeconvolverEntropy\n /// @ingroup Deconvolver\n\n template\n DeconvolverEntropy::~DeconvolverEntropy()\n {\n };\n\n template\n DeconvolverEntropy::DeconvolverEntropy(Vector >& dirty, Vector >& psf)\n : DeconvolverBase::DeconvolverBase(dirty, psf)\n {\n if (this->itsNumberDirtyTerms > 1) {\n throw(AskapError(\"Entropy deconvolver cannot perform multi-term deconvolutions\"));\n }\n };\n\n template\n DeconvolverEntropy::DeconvolverEntropy(Array& dirty, Array& psf)\n : DeconvolverBase::DeconvolverBase(dirty, psf)\n {\n };\n template\n void DeconvolverEntropy::finalise()\n {\n // Find residuals for current model model\n this->updateResiduals(this->itsModel);\n }\n\n template\n void DeconvolverEntropy::initialise()\n {\n DeconvolverBase::initialise();\n\n // Set the initial value of Q\n T Q;\n Q = sum(this->psf()(this->psf() > T(0.1)));\n ASKAPLOG_INFO_STR(decentropylogger, \"Initial value of Q = \" << Q << \" pixels\");\n\n this->itsEntropy->setQ(Q);\n }\n\n // This is basically the algorithm described in the Cornwell-Evans paper of 1985, with\n // some improvements as suggested by Bob Sault.\n template\n bool DeconvolverEntropy::deconvolve()\n {\n this->initialise();\n\n ASKAPLOG_INFO_STR(decentropylogger, \"Performing Entropy deconvolution for \"\n << this->control()->targetIter() << \" iterations\");\n\n ASKAPLOG_INFO_STR(decentropylogger, \"Target rms fit = \"\n << this->control()->targetObjectiveFunction() << \" Jy/beam\");\n\n uInt numberPixels = this->itsModel.shape().product();\n T targetChisq = square(this->control()->targetObjectiveFunction()) * numberPixels;\n T chisq;\n\n Array trialModel(this->model().shape());\n trialModel.set(T(0.0));\n\n this->updateResiduals(this->model());\n\n T lipschitz(10.0);\n\n this->model() = this->dirty().copy() / lipschitz;\n\n Array step(this->model().shape());\n step.set(T(0.0));\n\n T absPeakVal(max(abs(this->dirty())));\n T peakSidelobe(0.1);\n T aFit = max(peakSidelobe * absPeakVal, rms(this->dirty())) / lipschitz;\n ASKAPLOG_INFO_STR(decentropylogger, \"Scaling = \" << aFit << \" Jy/pixel\");\n this->itsEntropy->setScale(aFit);\n\n this->model().set(aFit);\n\n this->updateResiduals(this->model());\n\n do {\n // Find the current fit\n chisq = sum(square(this->dirty()));\n\n Matrix GDG(this->itsEntropy->formGDGStep(this->model(), this->dirty(), step));\n\n // Check to see if Alpha and Beta need to be initialised. If so then we need to\n // do so and recalculate the gradients and step\n if (this->itsEntropy->initialiseAlphaBeta(GDG)) {\n GDG = this->itsEntropy->formGDGStep(this->model(), this->dirty(), step);\n }\n\n T flux = sum(this->model());\n this->itsEntropy->changeAlphaBeta(GDG, targetChisq, chisq, this->control()->targetFlux(), flux);\n\n // Now find the normalised gradient - we will use this to limit the step taken\n T length(this->itsEntropy->formLength(GDG));\n if (length <= T(0.0)) {\n length = GDG(F, F);\n }\n T normGrad = GDG(J, J) / length;\n\n // relaxMin();\n\n // We limit the step to less than the tolerance e.g. 0.1 so that the\n // quadratic approximation in the Newton-Raphson is still valid.\n T scale = 1.0;\n T scalem = 1.0;\n if (normGrad > 0.0) {\n scalem = this->control()->tolerance() / normGrad;\n }\n scale = min(T(1.0), scalem);\n\n // OK - now we take the proposed step and evaluate the\n // gradient there.\n trialModel = this->model() + scale * step;\n\n // Calculate residual for this new trial image\n this->updateResiduals(trialModel);\n chisq = sum(square(this->dirty()));\n\n // Form the scalar Gradient . Step at this new location. Ideally this should be\n // zero. Once we know the value of the gradient initially and for the trial image, we\n // can determine the optimal step by interpolating the gradient to zero. Ideally the\n // step should be O(1) times the original step\n T eps = 1.0;\n T gradDotStep0 = GDG(J, J);\n T gradDotStep1(this->itsEntropy->formGDS(this->model(), this->dirty(), step));\n\n if (gradDotStep0 != gradDotStep1) {\n eps = gradDotStep0 / (gradDotStep0 - gradDotStep1);\n }\n if (scale != T(0.0)) eps = min(eps, T(scalem / scale));\n if (eps <= T(0.0)) {\n eps = T(1.0);\n }\n\n // Step to optimum point\n this->model() = this->model() + scale * eps * step;\n\n // Recalculate residual for the new image\n this->updateResiduals(this->model());\n\n chisq = sum(square(this->dirty()));\n\n // readjust beam volume\n // itsQ = itsQ*(T(1.0)/max(T(0.5), min(T(2.0),eps))+T(1.0))/T(2.0);\n\n flux = sum(this->model());\n this->itsEntropy->changeAlphaBeta(GDG, targetChisq, chisq, this->control()->targetFlux(), flux);\n\n absPeakVal = max(abs(this->dirty()));\n\n T lipschitz(10.0);\n\n aFit = max(peakSidelobe * absPeakVal, rms(this->dirty())) / lipschitz;\n ASKAPLOG_INFO_STR(decentropylogger, \"Scaling = \" << aFit << \" Jy/pixel\");\n this->itsEntropy->setScale(aFit);\n\n this->state()->setPeakResidual(absPeakVal);\n this->state()->setObjectiveFunction(sqrt(chisq / numberPixels));\n this->state()->setTotalFlux(flux);\n\n this->monitor()->monitor(*(this->state()));\n this->state()->incIter();\n } while (!this->control()->terminate(*(this->state())));\n\n ASKAPLOG_INFO_STR(decentropylogger, \"Performed Entropy deconvolution for \"\n << this->state()->currentIter() << \" iterations\");\n\n ASKAPLOG_INFO_STR(decentropylogger, this->control()->terminationString());\n\n this->finalise();\n\n return True;\n }\n\n template\n void DeconvolverEntropy::configure(const LOFAR::ParameterSet& parset)\n {\n DeconvolverBase::configure(parset);\n\n String algorithm(parset.getString(\"algorithm\", \"Emptiness\"));\n this->control()->setAlgorithm(algorithm);\n\n if (algorithm == \"EntropyI\") {\n ASKAPLOG_INFO_STR(decentropylogger, \"Maximising information entropy of model image\");\n itsEntropy = boost::shared_ptr >(new EntropyI());\n itsEntropy->setTolerance(parset.getFloat(\"tolerance\", 0.3));\n } else {\n ASKAPLOG_INFO_STR(decentropylogger, \"Maximising emptiness (negative L1 norm) of model image\");\n itsEntropy = boost::shared_ptr >(new Emptiness());\n itsEntropy->setTolerance(parset.getFloat(\"tolerance\", 0.3));\n }\n }\n\n } // namespace synthesis\n\n} // namespace askap\n", "meta": {"hexsha": "8a79ba447006e189e20c78900b96a8b1d975e796", "size": 10311, "ext": "tcc", "lang": "C++", "max_stars_repo_path": "Code/Components/Synthesis/synthesis/current/deconvolution/DeconvolverEntropy.tcc", "max_stars_repo_name": "rtobar/askapsoft", "max_stars_repo_head_hexsha": "6bae06071d7d24f41abe3f2b7f9ee06cb0a9445e", "max_stars_repo_licenses": ["BSL-1.0", "Apache-2.0", "OpenSSL"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-06-18T08:37:43.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-18T08:37:43.000Z", "max_issues_repo_path": "Code/Components/Synthesis/synthesis/current/deconvolution/DeconvolverEntropy.tcc", "max_issues_repo_name": "ATNF/askapsoft", "max_issues_repo_head_hexsha": "d839c052d5c62ad8a511e58cd4b6548491a6006f", "max_issues_repo_licenses": ["BSL-1.0", "Apache-2.0", "OpenSSL"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code/Components/Synthesis/synthesis/current/deconvolution/DeconvolverEntropy.tcc", "max_forks_repo_name": "ATNF/askapsoft", "max_forks_repo_head_hexsha": "d839c052d5c62ad8a511e58cd4b6548491a6006f", "max_forks_repo_licenses": ["BSL-1.0", "Apache-2.0", "OpenSSL"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 40.27734375, "max_line_length": 112, "alphanum_fraction": 0.5729803123, "num_tokens": 2423, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621765155565326, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.22266022163880614}} {"text": "/*\n Copyright (C) 2016 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n#include \n\n#include \n#include \n\n#include \n\nnamespace QuantExt {\n\nusing namespace CrossAssetAnalytics;\nusing namespace QuantLib;\n\nnamespace {\ninline void setValue(Matrix& m, const Real& value, const QuantExt::CrossAssetModel* model,\n const QuantExt::CrossAssetModelTypes::AssetType& t1, const Size& i1,\n const QuantExt::CrossAssetModelTypes::AssetType& t2, const Size& i2, const Size& offset1 = 0,\n const Size& offset2 = 0) {\n Size i = model->pIdx(t1, i1, offset1);\n Size j = model->pIdx(t2, i2, offset2);\n m[i][j] = m[j][i] = value;\n}\ninline void setValue(Array& a, const Real& value, const QuantExt::CrossAssetModel* model,\n const QuantExt::CrossAssetModelTypes::AssetType& t, const Size& i, const Size& offset = 0) {\n a[model->pIdx(t, i, offset)] = value;\n}\n} // anonymous namespace\n\nCrossAssetStateProcess::CrossAssetStateProcess(const CrossAssetModel* const model, discretization disc,\n SalvagingAlgorithm::Type salvaging)\n : StochasticProcess(), model_(model), disc_(disc), salvaging_(salvaging), cirppCount_(0) {\n\n updateSqrtCorrelation();\n if (disc_ == euler) {\n discretization_ = boost::make_shared();\n } else {\n discretization_ = boost::make_shared(model, salvaging);\n }\n\n // set up CR CIR++ processes, defer the euler discretisation check to evolve()\n for (Size i = 0; i < model_->components(CR); ++i) {\n if (model_->modelType(CR, i) == CIRPP) {\n crCirpp_.push_back(model->crcirppModel(i)->stateProcess());\n cirppCount_++;\n } else {\n crCirpp_.push_back(boost::shared_ptr());\n }\n }\n}\n\nSize CrossAssetStateProcess::size() const { return model_->dimension(); }\n\nvoid CrossAssetStateProcess::flushCache() const {\n cache_m_.clear();\n cache_md_.clear();\n cache_v_.clear();\n cache_d_.clear();\n boost::shared_ptr tmp =\n boost::dynamic_pointer_cast(discretization_);\n if (tmp != NULL) {\n tmp->flushCache();\n }\n updateSqrtCorrelation();\n}\n\nvoid CrossAssetStateProcess::updateSqrtCorrelation() const {\n if (disc_ != euler)\n return;\n // build sqrt corr (for correlation matrix that covers all state variables)\n // this can be simplified once we use as many brownians as model->brownians()\n // instead of the full state vector\n Matrix corr(model_->dimension(), model_->dimension(), 1.0);\n Size brownianIndex = 0;\n std::vector brownianIndices;\n for (Size t = 0; t < crossAssetModelAssetTypes; ++t) {\n AssetType assetType = AssetType(t);\n for (Size i = 0; i < model_->components(assetType); ++i) {\n\n // 3 possibilities for number of state variables vs. the number of Brownian motions for the i-th component\n // within the current asset type.\n // 1) They are equal. Make the assumption that there is a 1-1 correspondence. This is essentially what has\n // been happening until now outside of DK model i.e. always 1 state var and 1 Brownian motion.\n // 2) number of state vars > number of Brownian motions. Happens with DK model. Think the code below will\n // only work when number of Brownian motions equals 1. Not changing it as not sure what was intended.\n // 3) number of state vars < number of Brownian motions. Not covered below.\n auto brownians = model_->brownians(assetType, i);\n auto stateVars = model_->stateVariables(assetType, i);\n\n if (brownians == stateVars) {\n for (Size k = 0; k < brownians; ++k) {\n brownianIndices.push_back(brownianIndex++);\n }\n } else {\n for (Size j = 0; j < brownians; ++j) {\n for (Size k = 0; k < stateVars; ++k) {\n brownianIndices.push_back(brownianIndex);\n }\n ++brownianIndex;\n }\n }\n }\n }\n for (Size i = 0; i < corr.rows(); ++i) {\n for (Size j = 0; j < i; ++j) {\n corr[i][j] = corr[j][i] = model_->correlation()(brownianIndices[i], brownianIndices[j]);\n }\n }\n\n sqrtCorrelation_ = pseudoSqrt(corr, salvaging_);\n}\n\nDisposable CrossAssetStateProcess::initialValues() const {\n Array res(model_->dimension(), 0.0);\n /* irlgm1f processes have initial value 0 */\n for (Size i = 0; i < model_->components(FX); ++i) {\n /* fxbs processes are in log spot */\n res[model_->pIdx(FX, i, 0)] = std::log(model_->fxbs(i)->fxSpotToday()->value());\n }\n for (Size i = 0; i < model_->components(EQ); ++i) {\n /* eqbs processes are in log spot */\n res[model_->pIdx(EQ, i, 0)] = std::log(model_->eqbs(i)->eqSpotToday()->value());\n }\n // CR CIR++ components\n for (Size i = 0; i < model_->components(CR); ++i) {\n if (model_->modelType(CR, i) != CIRPP)\n continue;\n QL_REQUIRE(crCirpp_[i], \"crcirpp is null!\");\n Array r = crCirpp_[i]->initialValues();\n res[model_->pIdx(CR, i, 0)] = r[0]; // y0\n res[model_->pIdx(CR, i, 1)] = r[1]; // S(0,0) = 1\n }\n\n for (Size i = 0; i < model_->components(INF); ++i) {\n // Second component of JY model is the inflation index process.\n if (model_->modelType(INF, i) == JY) {\n res[model_->pIdx(INF, i, 1)] = std::log(model_->infjy(i)->index()->fxSpotToday()->value());\n }\n }\n /* infdk, crlgm1f, aux processes have initial value 0 */\n return res;\n}\n\nDisposable CrossAssetStateProcess::drift(Time t, const Array& x) const {\n Array res(model_->dimension(), 0.0);\n Size n = model_->components(IR);\n Size n_eq = model_->components(EQ);\n Real H0 = model_->irlgm1f(0)->H(t);\n Real Hprime0 = model_->irlgm1f(0)->Hprime(t);\n Real alpha0 = model_->irlgm1f(0)->alpha(t);\n Real zeta0 = model_->irlgm1f(0)->zeta(t);\n boost::unordered_map::const_iterator i = cache_m_.find(t);\n if (i == cache_m_.end()) {\n /* z0 has drift 0 in the LGM measure but non-zero drift in the bank account measure, so start loop at i = 0 */\n for (Size i = 0; i < n; ++i) {\n Real Hi = model_->irlgm1f(i)->H(t);\n Real alphai = model_->irlgm1f(i)->alpha(t);\n if (i == 0 && model_->measure() == Measure::BA) {\n // ADD z0 drift in the BA measure\n res[model_->pIdx(IR, i, 0)] = -Hi * alphai * alphai;\n // the auxiliary state variable is drift-free\n res[model_->pIdx(AUX, i, 0)] = 0.0;\n }\n if (i > 0) {\n Real sigmai = model_->fxbs(i - 1)->sigma(t);\n // ir-ir\n Real rhozz0i = model_->correlation(IR, 0, IR, i);\n // ir-fx\n Real rhozx0i = model_->correlation(IR, 0, FX, i - 1);\n Real rhozxii = model_->correlation(IR, i, FX, i - 1);\n // ir drifts\n res[model_->pIdx(IR, i, 0)] =\n -Hi * alphai * alphai + H0 * alpha0 * alphai * rhozz0i - sigmai * alphai * rhozxii;\n // log spot fx drifts (z0, zi independent parts)\n res[model_->pIdx(FX, i - 1, 0)] = H0 * alpha0 * sigmai * rhozx0i +\n model_->irlgm1f(0)->termStructure()->forwardRate(t, t, Continuous) -\n model_->irlgm1f(i)->termStructure()->forwardRate(t, t, Continuous) -\n 0.5 * sigmai * sigmai;\n if (model_->measure() == Measure::BA) {\n // REMOVE the LGM measure drift contributions above\n res[model_->pIdx(IR, i, 0)] -= H0 * alpha0 * alphai * rhozz0i;\n res[model_->pIdx(FX, i - 1, 0)] -= H0 * alpha0 * sigmai * rhozx0i;\n }\n }\n }\n /* log equity spot drifts (the cache-able parts) */\n for (Size k = 0; k < n_eq; ++k) {\n Size i = model_->ccyIndex(model_->eqbs(k)->currency());\n // ir params (for equity currency)\n Real eps_ccy = (i == 0) ? 0.0 : 1.0;\n // Real Hi = model_->irlgm1f(i)->H(t);\n // Real alphai = model_->irlgm1f(i)->alpha(t);\n // eq vol\n Real sigmask = model_->eqbs(k)->sigma(t);\n // fx vol (eq ccy / base ccy)\n Real sigmaxi = (i == 0) ? 0.0 : model_->fxbs(i - 1)->sigma(t);\n // ir-eq corr\n // Real rhozsik = model_->correlation(EQ, k, IR, i); // eq cur\n Real rhozs0k = model_->correlation(EQ, k, IR, 0); // base cur\n // fx-eq corr\n Real rhoxsik = (i == 0) ? 0.0 : // no fx process for base-ccy\n model_->correlation(FX, i - 1, EQ, k);\n // ir instantaneous forward rate (from curve used for eq forward projection)\n Real fr_i = model_->eqbs(k)->equityIrCurveToday()->forwardRate(t, t, Continuous);\n // div yield instantaneous forward rate\n Real fq_k = model_->eqbs(k)->equityDivYieldCurveToday()->forwardRate(t, t, Continuous);\n res[model_->pIdx(EQ, k, 0)] = fr_i - fq_k + (rhozs0k * H0 * alpha0 * sigmask) -\n (eps_ccy * rhoxsik * sigmaxi * sigmask) - (0.5 * sigmask * sigmask);\n }\n\n // State independent pieces of JY inflation model, if there is a CAM JY component.\n for (Size j = 0; j < model_->components(INF); ++j) {\n\n if (model_->modelType(INF, j) == JY) {\n\n auto p = model_->infjy(j);\n Size i_j = model_->ccyIndex(p->currency());\n\n // JY inflation parameter values.\n Real H_y_j = p->realRate()->H(t);\n Real Hp_y_j = p->realRate()->Hprime(t);\n Real zeta_y_j = p->realRate()->zeta(t);\n Real alpha_y_j = p->realRate()->alpha(t);\n Real sigma_c_j = p->index()->sigma(t);\n\n // Inflation nominal currency parameter values\n Real H_i_j = model_->irlgm1f(i_j)->H(t);\n Real Hp_i_j = model_->irlgm1f(i_j)->Hprime(t);\n Real zeta_i_j = model_->irlgm1f(i_j)->zeta(t);\n\n // Correlations\n Real rho_zy_0j = model_->correlation(IR, 0, INF, j, 0, 0);\n Real rho_yc_ij = model_->correlation(INF, j, INF, j, 0, 1);\n Real rho_zc_0j = model_->correlation(IR, 0, INF, j, 0, 1);\n\n // JY real rate drift. It is state independent\n auto rrDrift = -alpha_y_j * alpha_y_j * H_y_j + rho_zy_0j * alpha0 * alpha_y_j * H_y_j -\n rho_yc_ij * alpha_y_j * sigma_c_j;\n\n if (i_j > 0) {\n Real sigma_x_i_j = model_->fxbs(i_j - 1)->sigma(t);\n Real rho_yx_j_i_j = model_->correlation(INF, j, FX, i_j - 1, 0, 0);\n rrDrift -= rho_yx_j_i_j * alpha_y_j * sigma_x_i_j;\n }\n\n res[model_->pIdx(INF, j, 0)] = rrDrift;\n\n // JY log inflation index drift (state independent piece).\n auto indexDrift = rho_zc_0j * alpha0 * sigma_c_j * H0 - 0.5 * sigma_c_j * sigma_c_j +\n zeta_i_j * Hp_i_j * H_i_j - zeta_y_j * Hp_y_j * H_y_j;\n\n // Add on the f_n(0, t) - f_r(0, t) piece using the initial zero inflation term structure.\n // Use the same dt below that is used in yield forward rate calculations.\n auto ts = p->realRate()->termStructure();\n Time dt = 0.0001;\n Time t1 = std::max(t - dt / 2.0, 0.0);\n Time t2 = t1 + dt;\n auto z_t = ts->zeroRate(t);\n auto z_t1 = ts->zeroRate(t1);\n auto z_t2 = ts->zeroRate(t2);\n indexDrift += std::log(1 + z_t) + (t / (1 + z_t)) * ((z_t2 - z_t1) / dt);\n\n if (i_j > 0) {\n Real sigma_x_i_j = model_->fxbs(i_j - 1)->sigma(t);\n Real rho_cx_j_i_j = model_->correlation(INF, j, FX, i_j - 1, 1, 0);\n indexDrift -= rho_cx_j_i_j * sigma_c_j * sigma_x_i_j;\n }\n\n res[model_->pIdx(INF, j, 1)] = indexDrift;\n }\n }\n\n cache_m_.insert(std::make_pair(t, res));\n } else {\n res = i->second;\n }\n // non-cacheable sections of drifts\n for (Size i = 1; i < n; ++i) {\n // log spot fx drifts (z0, zi dependent parts)\n Real Hi = model_->irlgm1f(i)->H(t);\n Real Hprimei = model_->irlgm1f(i)->Hprime(t);\n Real zetai = model_->irlgm1f(i)->zeta(t);\n res[model_->pIdx(FX, i - 1, 0)] += x[model_->pIdx(IR, 0, 0)] * Hprime0 + zeta0 * Hprime0 * H0 -\n x[model_->pIdx(IR, i, 0)] * Hprimei - zetai * Hprimei * Hi;\n }\n for (Size k = 0; k < n_eq; ++k) {\n // log equity spot drifts (path-dependent parts)\n // notice the assumption in below that dividend yield curve is static\n Size i = model_->ccyIndex(model_->eqbs(k)->currency());\n // ir params (for equity currency)\n Real Hi = model_->irlgm1f(i)->H(t);\n Real Hprimei = model_->irlgm1f(i)->Hprime(t);\n Real zetai = model_->irlgm1f(i)->zeta(t);\n res[model_->pIdx(EQ, k, 0)] += (x[model_->pIdx(IR, i, 0)] * Hprimei) + (zetai * Hprimei * Hi);\n }\n\n // Non-cacheable portion of inflation JY drift, if there is a CAM JY component.\n for (Size j = 0; j < model_->components(INF); ++j) {\n if (model_->modelType(INF, j) == JY) {\n\n auto p = model_->infjy(j);\n Size i_j = model_->ccyIndex(p->currency());\n\n // JY inflation parameter values.\n Real Hp_y_j = p->realRate()->Hprime(t);\n\n // Inflation nominal currency parameter values\n Real Hp_i_j = model_->irlgm1f(i_j)->Hprime(t);\n\n res[model_->pIdx(INF, j, 1)] += x[model_->pIdx(IR, i_j, 0)] * Hp_i_j - x[model_->pIdx(INF, j, 0)] * Hp_y_j;\n }\n }\n\n /* no drift for infdk, crlgm1f components */\n return res;\n}\n\nDisposable CrossAssetStateProcess::diffusion(Time t, const Array& x) const {\n boost::unordered_map::const_iterator i = cache_d_.find(t);\n if (i == cache_d_.end()) {\n Matrix tmp = diffusionImpl(t, x);\n cache_d_.insert(std::make_pair(t, tmp));\n return tmp;\n } else {\n // we have to make a copy, otherwise we destroy the map entry\n // since a disposable is returned\n Matrix tmp = i->second;\n return tmp;\n }\n}\n\nDisposable CrossAssetStateProcess::marginalDiffusion(Time t, const Array& x) const {\n boost::unordered_map::const_iterator i = cache_md_.find(t);\n if (i == cache_md_.end()) {\n Array tmp = marginalDiffusionImpl(t, x);\n cache_md_.insert(std::make_pair(t, tmp));\n return tmp;\n } else {\n // we have to make a copy, otherwise we destroy the map entry\n // since a disposable is returned\n Array tmp = i->second;\n return tmp;\n }\n}\n\nDisposable CrossAssetStateProcess::diffusionImpl(Time t, const Array& x) const {\n Matrix res(model_->dimension(), model_->dimension(), 0.0);\n Array diag = marginalDiffusion(t, x);\n for (Size i = 0; i < x.size(); ++i) {\n res[i][i] = diag[i];\n }\n return res * sqrtCorrelation_;\n} // namespace QuantExt\n\nDisposable CrossAssetStateProcess::marginalDiffusionImpl(Time t, const Array&) const {\n Array res(model_->dimension(), 0.0);\n Size n = model_->components(IR);\n Size m = model_->components(FX);\n Size d = model_->components(INF);\n Size c = model_->components(CR);\n Size e = model_->components(EQ);\n // ir-ir\n for (Size i = 0; i < n; ++i) {\n Real alphai = model_->irlgm1f(i)->alpha(t);\n setValue(res, alphai, model_, IR, i, 0);\n }\n // fx-fx\n for (Size i = 0; i < m; ++i) {\n Real sigmai = model_->fxbs(i)->sigma(t);\n setValue(res, sigmai, model_, FX, i, 0);\n }\n // inf-inf\n for (Size i = 0; i < d; ++i) {\n if (model_->modelType(INF, i) == DK) {\n Real alphai = model_->infdk(i)->alpha(t);\n Real Hi = model_->infdk(i)->H(t);\n // DK z diffusion coefficient\n setValue(res, alphai, model_, INF, i, 0);\n // DK y diffusion coefficient\n setValue(res, alphai * Hi, model_, INF, i, 1);\n } else {\n auto p = model_->infjy(i);\n // JY z diffusion coefficient\n setValue(res, p->realRate()->alpha(t), model_, INF, i, 0);\n // JY I diffusion coefficient\n setValue(res, p->index()->sigma(t), model_, INF, i, 1);\n }\n }\n for (Size i = 0; i < c; ++i) {\n // Skip CR components that are not LGM\n if (model_->modelType(CR, i) != LGM1F)\n continue;\n Real alphai = model_->crlgm1f(i)->alpha(t);\n Real Hi = model_->crlgm1f(i)->H(t);\n // crz-crz\n setValue(res, alphai, model_, CR, i, 0);\n // cry-cry\n setValue(res, alphai * Hi, model_, CR, i, 1);\n }\n // eq-eq\n for (Size i = 0; i < e; ++i) {\n Real sigmai = model_->eqbs(i)->sigma(t);\n setValue(res, sigmai, model_, EQ, i, 0);\n }\n\n if (model_->measure() == Measure::BA) {\n // aux-aux\n Real H0 = model_->irlgm1f(0)->H(t);\n Real alpha0 = model_->irlgm1f(0)->alpha(t);\n setValue(res, alpha0 * H0, model_, AUX, 0, 0);\n }\n\n return res;\n}\n\nDisposable CrossAssetStateProcess::evolve(Time t0, const Array& x0, Time dt, const Array& dw) const {\n\n Array res;\n if (disc_ == euler) {\n const Array dz = sqrtCorrelation_ * dw;\n const Array df = marginalDiffusion(t0, x0);\n res = apply(expectation(t0, x0, dt), df * dz * std::sqrt(dt));\n\n // CR CIRPP components\n if (cirppCount_ > 0) {\n for (Size i = 0; i < model_->components(CR); ++i) {\n if (!crCirpp_[i])\n continue; // ignore non-cir cr model\n Size idx1 = model_->pIdx(CR, i, 0);\n Size idx2 = model_->pIdx(CR, i, 1);\n Array x0Tmp(2), dwTmp(2);\n x0Tmp[0] = x0[idx1];\n x0Tmp[1] = x0[idx2];\n dwTmp[0] = dz[idx1];\n dwTmp[1] = 0.0; // not used\n // evolve original process\n auto r = crCirpp_[i]->evolve(t0, x0Tmp, dt, dwTmp);\n\n // set result\n res[idx1] = r[0]; // y\n res[idx2] = r[1]; // S(0,T)\n }\n }\n } else {\n QL_REQUIRE(cirppCount_ == 0, \"only euler discretization is supported for CIR++\");\n res = StochasticProcess::evolve(t0, x0, dt, dw);\n }\n\n return res;\n}\n\nCrossAssetStateProcess::ExactDiscretization::ExactDiscretization(const CrossAssetModel* const model,\n SalvagingAlgorithm::Type salvaging)\n : model_(model), salvaging_(salvaging) {}\n\nDisposable CrossAssetStateProcess::ExactDiscretization::drift(const StochasticProcess& p, Time t0,\n const Array& x0, Time dt) const {\n Array res;\n cache_key k = {t0, dt};\n boost::unordered_map::const_iterator i = cache_m_.find(k);\n if (i == cache_m_.end()) {\n res = driftImpl1(p, t0, x0, dt);\n cache_m_.insert(std::make_pair(k, res));\n } else {\n res = i->second;\n }\n Array res2 = driftImpl2(p, t0, x0, dt);\n for (Size i = 0; i < res.size(); ++i) {\n res[i] += res2[i];\n }\n return res - x0;\n}\n\nDisposable CrossAssetStateProcess::ExactDiscretization::diffusion(const StochasticProcess& p, Time t0,\n const Array& x0, Time dt) const {\n cache_key k = {t0, dt};\n boost::unordered_map::const_iterator i = cache_d_.find(k);\n if (i == cache_d_.end()) {\n Matrix res = pseudoSqrt(covariance(p, t0, x0, dt), salvaging_);\n // note that covariance actually does not depend on x0\n cache_d_.insert(std::make_pair(k, res));\n return res;\n } else {\n // see above about the copy\n Matrix tmp = i->second;\n return tmp;\n }\n}\n\nDisposable CrossAssetStateProcess::ExactDiscretization::covariance(const StochasticProcess& p, Time t0,\n const Array& x0, Time dt) const {\n cache_key k = {t0, dt};\n boost::unordered_map::const_iterator i = cache_v_.find(k);\n if (i == cache_v_.end()) {\n Matrix res = covarianceImpl(p, t0, x0, dt);\n cache_v_.insert(std::make_pair(k, res));\n return res;\n } else {\n // see above about the copy\n Matrix tmp = i->second;\n return tmp;\n }\n}\n\nDisposable CrossAssetStateProcess::ExactDiscretization::driftImpl1(const StochasticProcess&, Time t0,\n const Array&, Time dt) const {\n Size n = model_->components(IR);\n Size m = model_->components(FX);\n Size e = model_->components(EQ);\n Array res(model_->dimension(), 0.0);\n for (Size i = 0; i < n; ++i) {\n res[model_->pIdx(IR, i, 0)] = ir_expectation_1(model_, i, t0, dt);\n }\n for (Size j = 0; j < m; ++j) {\n res[model_->pIdx(FX, j, 0)] = fx_expectation_1(model_, j, t0, dt);\n }\n for (Size k = 0; k < e; ++k) {\n res[model_->pIdx(EQ, k, 0)] = eq_expectation_1(model_, k, t0, dt);\n }\n\n // If inflation is JY, need to take account of the drift.\n for (Size i = 0; i < model_->components(INF); ++i) {\n if (model_->modelType(INF, i) == JY) {\n std::tie(res[model_->pIdx(INF, i, 0)], res[model_->pIdx(INF, i, 1)]) =\n inf_jy_expectation_1(model_, i, t0, dt);\n }\n }\n\n return res;\n}\n\nDisposable CrossAssetStateProcess::ExactDiscretization::driftImpl2(const StochasticProcess&, Time t0,\n const Array& x0, Time dt) const {\n Size n = model_->components(IR);\n Size m = model_->components(FX);\n Size e = model_->components(EQ);\n Array res(model_->dimension(), 0.0);\n\n if (model_->measure() == Measure::BA) {\n // zero AUX state drift, i.e. conditional expectation equal to the previous level as for z_i\n res[model_->pIdx(AUX, 0, 0)] += x0[model_->pIdx(AUX, 0, 0)];\n }\n\n for (Size i = 0; i < n; ++i) {\n res[model_->pIdx(IR, i, 0)] += ir_expectation_2(model_, i, x0[model_->pIdx(IR, i, 0)]);\n }\n for (Size j = 0; j < m; ++j) {\n res[model_->pIdx(FX, j, 0)] += fx_expectation_2(model_, j, t0, x0[model_->pIdx(FX, j, 0)],\n x0[model_->pIdx(IR, j + 1, 0)], x0[model_->pIdx(IR, 0, 0)], dt);\n }\n for (Size k = 0; k < e; ++k) {\n Size eqCcyIdx = model_->ccyIndex(model_->eqbs(k)->currency());\n res[model_->pIdx(EQ, k, 0)] +=\n eq_expectation_2(model_, k, t0, x0[model_->pIdx(EQ, k, 0)], x0[model_->pIdx(IR, eqCcyIdx, 0)], dt);\n }\n\n // Inflation: JY is state dependent. DK is not. Even for DK, still need to return the conditional expected value.\n for (Size i = 0; i < model_->components(INF); ++i) {\n if (model_->modelType(INF, i) == JY) {\n auto i_i = model_->ccyIndex(model_->infjy(i)->currency());\n auto zi_i_0 = x0[model_->pIdx(IR, i_i, 0)];\n auto state_0 = std::make_pair(x0[model_->pIdx(INF, i, 0)], x0[model_->pIdx(INF, i, 1)]);\n std::tie(res[model_->pIdx(INF, i, 0)], res[model_->pIdx(INF, i, 1)]) =\n inf_jy_expectation_2(model_, i, t0, state_0, zi_i_0, dt);\n } else {\n res[model_->pIdx(INF, i, 0)] = x0[model_->pIdx(INF, i, 0)];\n res[model_->pIdx(INF, i, 1)] = x0[model_->pIdx(INF, i, 1)];\n }\n }\n\n /*! cr components have integrated drift 0, we have to return the conditional\n expected value though, since x0 is subtracted later */\n Size c = model_->components(CR);\n for (Size i = 0; i < c; ++i) {\n res[model_->pIdx(CR, i, 0)] = x0[model_->pIdx(CR, i, 0)];\n res[model_->pIdx(CR, i, 1)] = x0[model_->pIdx(CR, i, 1)];\n }\n return res;\n}\n\nDisposable CrossAssetStateProcess::ExactDiscretization::covarianceImpl(const StochasticProcess&, Time t0,\n const Array&, Time dt) const {\n Matrix res(model_->dimension(), model_->dimension());\n Size n = model_->components(IR);\n Size m = model_->components(FX);\n Size d = model_->components(INF);\n Size c = model_->components(CR);\n Size e = model_->components(EQ);\n\n if (model_->measure() == Measure::BA) {\n // aux-aux\n setValue(res, aux_aux_covariance(model_, t0, dt), model_, AUX, 0, AUX, 0, 0, 0);\n // aux-ir\n for (Size j = 0; j < n; ++j) {\n setValue(res, aux_ir_covariance(model_, j, t0, dt), model_, AUX, 0, IR, j, 0, 0);\n }\n // aux-fx\n for (Size j = 0; j < m; ++j) {\n setValue(res, aux_fx_covariance(model_, j, t0, dt), model_, AUX, 0, FX, j, 0, 0);\n }\n }\n // ir-ir\n for (Size i = 0; i < n; ++i) {\n for (Size j = 0; j <= i; ++j) {\n setValue(res, ir_ir_covariance(model_, i, j, t0, dt), model_, IR, i, IR, j, 0, 0);\n }\n }\n // ir-fx\n for (Size i = 0; i < n; ++i) {\n for (Size j = 0; j < m; ++j) {\n setValue(res, ir_fx_covariance(model_, i, j, t0, dt), model_, IR, i, FX, j, 0, 0);\n }\n }\n // fx-fx\n for (Size i = 0; i < m; ++i) {\n for (Size j = 0; j <= i; ++j) {\n setValue(res, fx_fx_covariance(model_, i, j, t0, dt), model_, FX, i, FX, j);\n }\n }\n // ir,fx,inf - inf\n for (Size j = 0; j < d; ++j) {\n for (Size i = 0; i <= j; ++i) {\n // infz-infz\n setValue(res, infz_infz_covariance(model_, i, j, t0, dt), model_, INF, i, INF, j, 0, 0);\n // infz-infy\n setValue(res, infz_infy_covariance(model_, i, j, t0, dt), model_, INF, i, INF, j, 0, 1);\n setValue(res, infz_infy_covariance(model_, j, i, t0, dt), model_, INF, i, INF, j, 1, 0);\n // infy-infy\n setValue(res, infy_infy_covariance(model_, i, j, t0, dt), model_, INF, i, INF, j, 1, 1);\n }\n for (Size i = 0; i < n; ++i) {\n // ir-inf\n setValue(res, ir_infz_covariance(model_, i, j, t0, dt), model_, IR, i, INF, j, 0, 0);\n setValue(res, ir_infy_covariance(model_, i, j, t0, dt), model_, IR, i, INF, j, 0, 1);\n }\n for (Size i = 0; i < (n - 1); ++i) {\n // fx-inf\n setValue(res, fx_infz_covariance(model_, i, j, t0, dt), model_, FX, i, INF, j, 0, 0);\n setValue(res, fx_infy_covariance(model_, i, j, t0, dt), model_, FX, i, INF, j, 0, 1);\n }\n }\n // ir,fx,inf,cr - cr\n for (Size j = 0; j < c; ++j) {\n // Skip CR components that are not LGM\n if (model_->modelType(CR, j) != LGM1F)\n continue;\n for (Size i = 0; i <= j; ++i) {\n // Skip CR components that are not LGM\n if (model_->modelType(CR, i) != LGM1F)\n continue;\n // crz-crz\n setValue(res, crz_crz_covariance(model_, i, j, t0, dt), model_, CR, i, CR, j, 0, 0);\n // crz-cry\n setValue(res, crz_cry_covariance(model_, i, j, t0, dt), model_, CR, i, CR, j, 0, 1);\n setValue(res, crz_cry_covariance(model_, j, i, t0, dt), model_, CR, i, CR, j, 1, 0);\n // cry-cry\n setValue(res, cry_cry_covariance(model_, i, j, t0, dt), model_, CR, i, CR, j, 1, 1);\n }\n for (Size i = 0; i < n; ++i) {\n // ir-cr\n setValue(res, ir_crz_covariance(model_, i, j, t0, dt), model_, IR, i, CR, j, 0, 0);\n setValue(res, ir_cry_covariance(model_, i, j, t0, dt), model_, IR, i, CR, j, 0, 1);\n }\n for (Size i = 0; i < (n - 1); ++i) {\n // fx-cr\n setValue(res, fx_crz_covariance(model_, i, j, t0, dt), model_, FX, i, CR, j, 0, 0);\n setValue(res, fx_cry_covariance(model_, i, j, t0, dt), model_, FX, i, CR, j, 0, 1);\n }\n for (Size i = 0; i < d; ++i) {\n // inf-cr\n setValue(res, infz_crz_covariance(model_, i, j, t0, dt), model_, INF, i, CR, j, 0, 0);\n setValue(res, infy_crz_covariance(model_, i, j, t0, dt), model_, INF, i, CR, j, 1, 0);\n setValue(res, infz_cry_covariance(model_, i, j, t0, dt), model_, INF, i, CR, j, 0, 1);\n setValue(res, infy_cry_covariance(model_, i, j, t0, dt), model_, INF, i, CR, j, 1, 1);\n }\n }\n // ir,fx,inf,cr,eq - eq\n for (Size j = 0; j < e; ++j) {\n for (Size i = 0; i <= j; ++i) {\n // eq-eq\n setValue(res, eq_eq_covariance(model_, i, j, t0, dt), model_, EQ, i, EQ, j, 0, 0);\n }\n for (Size i = 0; i < n; ++i) {\n // ir-eq\n setValue(res, ir_eq_covariance(model_, i, j, t0, dt), model_, IR, i, EQ, j, 0, 0);\n }\n for (Size i = 0; i < (n - 1); ++i) {\n // fx-eq\n setValue(res, fx_eq_covariance(model_, i, j, t0, dt), model_, FX, i, EQ, j, 0, 0);\n }\n for (Size i = 0; i < d; ++i) {\n // inf-eq\n setValue(res, infz_eq_covariance(model_, i, j, t0, dt), model_, INF, i, EQ, j, 0, 0);\n setValue(res, infy_eq_covariance(model_, i, j, t0, dt), model_, INF, i, EQ, j, 1, 0);\n }\n for (Size i = 0; i < c; ++i) {\n // Skip CR components that are not LGM\n if (model_->modelType(CR, i) != LGM1F)\n continue;\n // cr-eq\n setValue(res, crz_eq_covariance(model_, i, j, t0, dt), model_, CR, i, EQ, j, 0, 0);\n setValue(res, cry_eq_covariance(model_, i, j, t0, dt), model_, CR, i, EQ, j, 1, 0);\n }\n }\n return res;\n}\n\nvoid CrossAssetStateProcess::ExactDiscretization::flushCache() const {\n cache_m_.clear();\n cache_v_.clear();\n cache_d_.clear();\n}\n\n} // namespace QuantExt\n", "meta": {"hexsha": "808ddcddb5ab0fe78f652e212d13d7f9c7054ae6", "size": 31567, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "QuantExt/qle/processes/crossassetstateprocess.cpp", "max_stars_repo_name": "mrslezak/Engine", "max_stars_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 335.0, "max_stars_repo_stars_event_min_datetime": "2016-10-07T16:31:10.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-02T07:12:03.000Z", "max_issues_repo_path": "QuantExt/qle/processes/crossassetstateprocess.cpp", "max_issues_repo_name": "mrslezak/Engine", "max_issues_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 59.0, "max_issues_repo_issues_event_min_datetime": "2016-10-31T04:20:24.000Z", "max_issues_repo_issues_event_max_datetime": "2022-01-03T16:39:57.000Z", "max_forks_repo_path": "QuantExt/qle/processes/crossassetstateprocess.cpp", "max_forks_repo_name": "mrslezak/Engine", "max_forks_repo_head_hexsha": "c46ff278a2c5f4162db91a7ab500a0bb8cef7657", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 180.0, "max_forks_repo_forks_event_min_datetime": "2016-10-08T14:23:50.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-28T10:43:05.000Z", "avg_line_length": 43.1243169399, "max_line_length": 120, "alphanum_fraction": 0.5366363608, "num_tokens": 9302, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.33807712415000585, "lm_q1q2_score": 0.22259589507992866}} {"text": "// Copyright (c) 2006, Stephan Diederich\r\n//\r\n// This code may be used under either of the following two licences:\r\n//\r\n// Permission is hereby granted, free of charge, to any person\r\n// obtaining a copy of this software and associated documentation\r\n// files (the \"Software\"), to deal in the Software without\r\n// restriction, including without limitation the rights to use,\r\n// copy, modify, merge, publish, distribute, sublicense, and/or\r\n// sell copies of the Software, and to permit persons to whom the\r\n// Software is furnished to do so, subject to the following\r\n// conditions:\r\n//\r\n// The above copyright notice and this permission notice shall be\r\n// included in all copies or substantial portions of the Software.\r\n//\r\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\r\n// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\r\n// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\r\n// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\r\n// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\r\n// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\r\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\r\n// OTHER DEALINGS IN THE SOFTWARE. OF SUCH DAMAGE.\r\n//\r\n// Or:\r\n//\r\n// Distributed under the Boost Software License, Version 1.0.\r\n// (See accompanying file LICENSE_1_0.txt or copy at\r\n// http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n#ifndef BOOST_KOLMOGOROV_MAX_FLOW_HPP\r\n#define BOOST_KOLMOGOROV_MAX_FLOW_HPP\r\n\r\n#if defined(_MSC_VER) || defined(__BORLANDC__) || defined(__DMC__)\r\n# pragma message (\"The kolmogorov_max_flow.hpp header is deprecated and will be removed in Boost 1.46. Use boykov_kolmogorov_max_flow.hpp instead.\")\r\n#elif defined(__GNUC__) || defined(__HP_aCC) || defined(__SUNPRO_CC) || defined(__IBMCPP__)\r\n# warning \"The kolmogorov_max_flow.hpp header is deprecated and will be removed in Boost 1.46. Use boykov_kolmogorov_max_flow.hpp instead.\"\r\n#endif\r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include // for std::min and std::max\r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\nnamespace boost {\r\n namespace detail {\r\n\r\n template \r\n class kolmogorov{\r\n typedef typename property_traits::value_type tEdgeVal;\r\n typedef graph_traits tGraphTraits;\r\n typedef typename tGraphTraits::vertex_iterator vertex_iterator;\r\n typedef typename tGraphTraits::vertex_descriptor vertex_descriptor;\r\n typedef typename tGraphTraits::edge_descriptor edge_descriptor;\r\n typedef typename tGraphTraits::edge_iterator edge_iterator;\r\n typedef typename tGraphTraits::out_edge_iterator out_edge_iterator;\r\n typedef boost::queue tQueue; //queue of vertices, used in adoption-stage\r\n typedef typename property_traits::value_type tColorValue;\r\n typedef color_traits tColorTraits;\r\n typedef typename property_traits::value_type tDistanceVal;\r\n\r\n public:\r\n kolmogorov(Graph& g,\r\n EdgeCapacityMap cap,\r\n ResidualCapacityEdgeMap res,\r\n ReverseEdgeMap rev,\r\n PredecessorMap pre,\r\n ColorMap color,\r\n DistanceMap dist,\r\n IndexMap idx,\r\n vertex_descriptor src,\r\n vertex_descriptor sink):\r\n m_g(g),\r\n m_index_map(idx),\r\n m_cap_map(cap),\r\n m_res_cap_map(res),\r\n m_rev_edge_map(rev),\r\n m_pre_map(pre),\r\n m_tree_map(color),\r\n m_dist_map(dist),\r\n m_source(src),\r\n m_sink(sink),\r\n m_active_nodes(),\r\n m_in_active_list_vec(num_vertices(g), false),\r\n m_in_active_list_map(make_iterator_property_map(m_in_active_list_vec.begin(), m_index_map)),\r\n m_has_parent_vec(num_vertices(g), false),\r\n m_has_parent_map(make_iterator_property_map(m_has_parent_vec.begin(), m_index_map)),\r\n m_time_vec(num_vertices(g), 0),\r\n m_time_map(make_iterator_property_map(m_time_vec.begin(), m_index_map)),\r\n m_flow(0),\r\n m_time(1),\r\n m_last_grow_vertex(graph_traits::null_vertex()){\r\n // initialize the color-map with gray-values\r\n vertex_iterator vi, v_end;\r\n for(boost::tie(vi, v_end) = vertices(m_g); vi != v_end; ++vi){\r\n set_tree(*vi, tColorTraits::gray());\r\n }\r\n // Initialize flow to zero which means initializing\r\n // the residual capacity equal to the capacity\r\n edge_iterator ei, e_end;\r\n for(boost::tie(ei, e_end) = edges(m_g); ei != e_end; ++ei) {\r\n m_res_cap_map[*ei] = m_cap_map[*ei];\r\n assert(m_rev_edge_map[m_rev_edge_map[*ei]] == *ei); //check if the reverse edge map is build up properly\r\n }\r\n //init the search trees with the two terminals\r\n set_tree(m_source, tColorTraits::black());\r\n set_tree(m_sink, tColorTraits::white());\r\n m_time_map[m_source] = 1;\r\n m_time_map[m_sink] = 1;\r\n }\r\n\r\n ~kolmogorov(){}\r\n\r\n tEdgeVal max_flow(){\r\n //augment direct paths from SOURCE->SINK and SOURCE->VERTEX->SINK\r\n augment_direct_paths();\r\n //start the main-loop\r\n while(true){\r\n bool path_found;\r\n edge_descriptor connecting_edge;\r\n boost::tie(connecting_edge, path_found) = grow(); //find a path from source to sink\r\n if(!path_found){\r\n //we're finished, no more paths were found\r\n break;\r\n }\r\n ++m_time;\r\n augment(connecting_edge); //augment that path\r\n adopt(); //rebuild search tree structure\r\n }\r\n return m_flow;\r\n }\r\n\r\n //the complete class is protected, as we want access to members in derived test-class (see $(BOOST_ROOT)/libs/graph/test/kolmogorov_max_flow_test.cpp)\r\n protected:\r\n void augment_direct_paths(){\r\n //in a first step, we augment all direct paths from source->NODE->sink\r\n //and additionally paths from source->sink\r\n //this improves especially graphcuts for segmentation, as most of the nodes have source/sink connects\r\n //but shouldn't have an impact on other maxflow problems (this is done in grow() anyway)\r\n out_edge_iterator ei, e_end;\r\n for(boost::tie(ei, e_end) = out_edges(m_source, m_g); ei != e_end; ++ei){\r\n edge_descriptor from_source = *ei;\r\n vertex_descriptor current_node = target(from_source, m_g);\r\n if(current_node == m_sink){\r\n tEdgeVal cap = m_res_cap_map[from_source];\r\n m_res_cap_map[from_source] = 0;\r\n m_flow += cap;\r\n continue;\r\n }\r\n edge_descriptor to_sink;\r\n bool is_there;\r\n boost::tie(to_sink, is_there) = lookup_edge(current_node, m_sink, m_g);\r\n if(is_there){\r\n tEdgeVal cap_from_source = m_res_cap_map[from_source];\r\n tEdgeVal cap_to_sink = m_res_cap_map[to_sink];\r\n if(cap_from_source > cap_to_sink){\r\n set_tree(current_node, tColorTraits::black());\r\n add_active_node(current_node);\r\n set_edge_to_parent(current_node, from_source);\r\n m_dist_map[current_node] = 1;\r\n m_time_map[current_node] = 1;\r\n //add stuff to flow and update residuals\r\n //we dont need to update reverse_edges, as incoming/outgoing edges to/from source/sink don't count for max-flow\r\n m_res_cap_map[from_source] -= cap_to_sink;\r\n m_res_cap_map[to_sink] = 0;\r\n m_flow += cap_to_sink;\r\n } else if(cap_to_sink > 0){\r\n set_tree(current_node, tColorTraits::white());\r\n add_active_node(current_node);\r\n set_edge_to_parent(current_node, to_sink);\r\n m_dist_map[current_node] = 1;\r\n m_time_map[current_node] = 1;\r\n //add stuff to flow and update residuals\r\n //we dont need to update reverse_edges, as incoming/outgoing edges to/from source/sink don't count for max-flow\r\n m_res_cap_map[to_sink] -= cap_from_source;\r\n m_res_cap_map[from_source] = 0;\r\n m_flow += cap_from_source;\r\n }\r\n } else if(m_res_cap_map[from_source]){\r\n //there is no sink connect, so we can't augment this path\r\n //but to avoid adding m_source to the active nodes, we just activate this node and set the approciate things\r\n set_tree(current_node, tColorTraits::black());\r\n set_edge_to_parent(current_node, from_source);\r\n m_dist_map[current_node] = 1;\r\n m_time_map[current_node] = 1;\r\n add_active_node(current_node);\r\n }\r\n }\r\n for(boost::tie(ei, e_end) = out_edges(m_sink, m_g); ei != e_end; ++ei){\r\n edge_descriptor to_sink = m_rev_edge_map[*ei];\r\n vertex_descriptor current_node = source(to_sink, m_g);\r\n if(m_res_cap_map[to_sink]){\r\n set_tree(current_node, tColorTraits::white());\r\n set_edge_to_parent(current_node, to_sink);\r\n m_dist_map[current_node] = 1;\r\n m_time_map[current_node] = 1;\r\n add_active_node(current_node);\r\n }\r\n }\r\n }\r\n\r\n /**\r\n * returns a pair of an edge and a boolean. if the bool is true, the edge is a connection of a found path from s->t , read \"the link\" and\r\n * source(returnVal, m_g) is the end of the path found in the source-tree\r\n * target(returnVal, m_g) is the beginning of the path found in the sink-tree\r\n */\r\n std::pair grow(){\r\n assert(m_orphans.empty());\r\n vertex_descriptor current_node;\r\n while((current_node = get_next_active_node()) != graph_traits::null_vertex()){ //if there is one\r\n assert(get_tree(current_node) != tColorTraits::gray() && (has_parent(current_node) || current_node==m_source || current_node==m_sink));\r\n if(get_tree(current_node) == tColorTraits::black()){\r\n //source tree growing\r\n out_edge_iterator ei, e_end;\r\n if(current_node != m_last_grow_vertex){\r\n m_last_grow_vertex = current_node;\r\n boost::tie(m_last_grow_edge_it, m_last_grow_edge_end) = out_edges(current_node, m_g);\r\n }\r\n for(; m_last_grow_edge_it != m_last_grow_edge_end; ++m_last_grow_edge_it){\r\n edge_descriptor out_edge = *m_last_grow_edge_it;\r\n if(m_res_cap_map[out_edge] > 0){ //check if we have capacity left on this edge\r\n vertex_descriptor other_node = target(out_edge, m_g);\r\n if(get_tree(other_node) == tColorTraits::gray()){ //it's a free node\r\n set_tree(other_node, tColorTraits::black()); //aquire other node to our search tree\r\n set_edge_to_parent(other_node, out_edge); //set us as parent\r\n m_dist_map[other_node] = m_dist_map[current_node] + 1; //and update the distance-heuristic\r\n m_time_map[other_node] = m_time_map[current_node];\r\n add_active_node(other_node);\r\n } else if(get_tree(other_node) == tColorTraits::black()){\r\n if(is_closer_to_terminal(current_node, other_node)){ //we do this to get shorter paths. check if we are nearer to the source as its parent is\r\n set_edge_to_parent(other_node, out_edge);\r\n m_dist_map[other_node] = m_dist_map[current_node] + 1;\r\n m_time_map[other_node] = m_time_map[current_node];\r\n }\r\n } else{\r\n assert(get_tree(other_node)==tColorTraits::white());\r\n //kewl, found a path from one to the other search tree, return the connecting edge in src->sink dir\r\n return std::make_pair(out_edge, true);\r\n }\r\n }\r\n } //for all out-edges\r\n } //source-tree-growing\r\n else{\r\n assert(get_tree(current_node) == tColorTraits::white());\r\n out_edge_iterator ei, e_end;\r\n if(current_node != m_last_grow_vertex){\r\n m_last_grow_vertex = current_node;\r\n boost::tie(m_last_grow_edge_it, m_last_grow_edge_end) = out_edges(current_node, m_g);\r\n }\r\n for(; m_last_grow_edge_it != m_last_grow_edge_end; ++m_last_grow_edge_it){\r\n edge_descriptor in_edge = m_rev_edge_map[*m_last_grow_edge_it];\r\n if(m_res_cap_map[in_edge] > 0){ //check if there is capacity left\r\n vertex_descriptor other_node = source(in_edge, m_g);\r\n if(get_tree(other_node) == tColorTraits::gray()){ //it's a free node\r\n set_tree(other_node, tColorTraits::white()); //aquire that node to our search tree\r\n set_edge_to_parent(other_node, in_edge); //set us as parent\r\n add_active_node(other_node); //activate that node\r\n m_dist_map[other_node] = m_dist_map[current_node] + 1; //set its distance\r\n m_time_map[other_node] = m_time_map[current_node]; //and time\r\n } else if(get_tree(other_node) == tColorTraits::white()){\r\n if(is_closer_to_terminal(current_node, other_node)){\r\n //we are closer to the sink than its parent is, so we \"adopt\" him\r\n set_edge_to_parent(other_node, in_edge);\r\n m_dist_map[other_node] = m_dist_map[current_node] + 1;\r\n m_time_map[other_node] = m_time_map[current_node];\r\n }\r\n } else{\r\n assert(get_tree(other_node)==tColorTraits::black());\r\n //kewl, found a path from one to the other search tree, return the connecting edge in src->sink dir\r\n return std::make_pair(in_edge, true);\r\n }\r\n }\r\n } //for all out-edges\r\n } //sink-tree growing\r\n //all edges of that node are processed, and no more paths were found. so remove if from the front of the active queue\r\n finish_node(current_node);\r\n } //while active_nodes not empty\r\n return std::make_pair(edge_descriptor(), false); //no active nodes anymore and no path found, we're done\r\n }\r\n\r\n /**\r\n * augments path from s->t and updates residual graph\r\n * source(e, m_g) is the end of the path found in the source-tree\r\n * target(e, m_g) is the beginning of the path found in the sink-tree\r\n * this phase generates orphans on satured edges, if the attached verts are from different search-trees\r\n * orphans are ordered in distance to sink/source. first the farest from the source are front_inserted into the orphans list,\r\n * and after that the sink-tree-orphans are front_inserted. when going to adoption stage the orphans are popped_front, and so we process the nearest\r\n * verts to the terminals first\r\n */\r\n void augment(edge_descriptor e){\r\n assert(get_tree(target(e, m_g)) == tColorTraits::white());\r\n assert(get_tree(source(e, m_g)) == tColorTraits::black());\r\n assert(m_orphans.empty());\r\n\r\n const tEdgeVal bottleneck = find_bottleneck(e);\r\n //now we push the found flow through the path\r\n //for each edge we saturate we have to look for the verts that belong to that edge, one of them becomes an orphans\r\n //now process the connecting edge\r\n m_res_cap_map[e] -= bottleneck;\r\n assert(m_res_cap_map[e] >= 0);\r\n m_res_cap_map[m_rev_edge_map[e]] += bottleneck;\r\n\r\n //now we follow the path back to the source\r\n vertex_descriptor current_node = source(e, m_g);\r\n while(current_node != m_source){\r\n edge_descriptor pred = get_edge_to_parent(current_node);\r\n m_res_cap_map[pred] -= bottleneck;\r\n assert(m_res_cap_map[pred] >= 0);\r\n m_res_cap_map[m_rev_edge_map[pred]] += bottleneck;\r\n if(m_res_cap_map[pred] == 0){\r\n set_no_parent(current_node);\r\n m_orphans.push_front(current_node);\r\n }\r\n current_node = source(pred, m_g);\r\n }\r\n //then go forward in the sink-tree\r\n current_node = target(e, m_g);\r\n while(current_node != m_sink){\r\n edge_descriptor pred = get_edge_to_parent(current_node);\r\n m_res_cap_map[pred] -= bottleneck;\r\n assert(m_res_cap_map[pred] >= 0);\r\n m_res_cap_map[m_rev_edge_map[pred]] += bottleneck;\r\n if(m_res_cap_map[pred] == 0){\r\n set_no_parent(current_node);\r\n m_orphans.push_front(current_node);\r\n }\r\n current_node = target(pred, m_g);\r\n }\r\n //and add it to the max-flow\r\n m_flow += bottleneck;\r\n }\r\n\r\n /**\r\n * returns the bottleneck of a s->t path (end_of_path is last vertex in source-tree, begin_of_path is first vertex in sink-tree)\r\n */\r\n inline tEdgeVal find_bottleneck(edge_descriptor e){\r\n BOOST_USING_STD_MIN();\r\n tEdgeVal minimum_cap = m_res_cap_map[e];\r\n vertex_descriptor current_node = source(e, m_g);\r\n //first go back in the source tree\r\n while(current_node != m_source){\r\n edge_descriptor pred = get_edge_to_parent(current_node);\r\n minimum_cap = min BOOST_PREVENT_MACRO_SUBSTITUTION(minimum_cap, m_res_cap_map[pred]);\r\n current_node = source(pred, m_g);\r\n }\r\n //then go forward in the sink-tree\r\n current_node = target(e, m_g);\r\n while(current_node != m_sink){\r\n edge_descriptor pred = get_edge_to_parent(current_node);\r\n minimum_cap = min BOOST_PREVENT_MACRO_SUBSTITUTION(minimum_cap, m_res_cap_map[pred]);\r\n current_node = target(pred, m_g);\r\n }\r\n return minimum_cap;\r\n }\r\n\r\n /**\r\n * rebuild search trees\r\n * empty the queue of orphans, and find new parents for them or just drop them from the search trees\r\n */\r\n void adopt(){\r\n while(!m_orphans.empty() || !m_child_orphans.empty()){\r\n vertex_descriptor current_node;\r\n if(m_child_orphans.empty()){\r\n //get the next orphan from the main-queue and remove it\r\n current_node = m_orphans.front();\r\n m_orphans.pop_front();\r\n } else{\r\n current_node = m_child_orphans.front();\r\n m_child_orphans.pop();\r\n }\r\n if(get_tree(current_node) == tColorTraits::black()){\r\n //we're in the source-tree\r\n tDistanceVal min_distance = (std::numeric_limits::max)();\r\n edge_descriptor new_parent_edge;\r\n out_edge_iterator ei, e_end;\r\n for(boost::tie(ei, e_end) = out_edges(current_node, m_g); ei != e_end; ++ei){\r\n const edge_descriptor in_edge = m_rev_edge_map[*ei];\r\n assert(target(in_edge, m_g) == current_node); //we should be the target of this edge\r\n if(m_res_cap_map[in_edge] > 0){\r\n vertex_descriptor other_node = source(in_edge, m_g);\r\n if(get_tree(other_node) == tColorTraits::black() && has_source_connect(other_node)){\r\n if(m_dist_map[other_node] < min_distance){\r\n min_distance = m_dist_map[other_node];\r\n new_parent_edge = in_edge;\r\n }\r\n }\r\n }\r\n }\r\n if(min_distance != (std::numeric_limits::max)()){\r\n set_edge_to_parent(current_node, new_parent_edge);\r\n m_dist_map[current_node] = min_distance + 1;\r\n m_time_map[current_node] = m_time;\r\n } else{\r\n m_time_map[current_node] = 0;\r\n for(boost::tie(ei, e_end) = out_edges(current_node, m_g); ei != e_end; ++ei){\r\n edge_descriptor in_edge = m_rev_edge_map[*ei];\r\n vertex_descriptor other_node = source(in_edge, m_g);\r\n if(get_tree(other_node) == tColorTraits::black() && has_parent(other_node)){\r\n if(m_res_cap_map[in_edge] > 0){\r\n add_active_node(other_node);\r\n }\r\n if(source(get_edge_to_parent(other_node), m_g) == current_node){\r\n //we are the parent of that node\r\n //it has to find a new parent, too\r\n set_no_parent(other_node);\r\n m_child_orphans.push(other_node);\r\n }\r\n }\r\n }\r\n set_tree(current_node, tColorTraits::gray());\r\n } //no parent found\r\n } //source-tree-adoption\r\n else{\r\n //now we should be in the sink-tree, check that...\r\n assert(get_tree(current_node) == tColorTraits::white());\r\n out_edge_iterator ei, e_end;\r\n edge_descriptor new_parent_edge;\r\n tDistanceVal min_distance = (std::numeric_limits::max)();\r\n for(boost::tie(ei, e_end) = out_edges(current_node, m_g); ei != e_end; ++ei){\r\n const edge_descriptor out_edge = *ei;\r\n if(m_res_cap_map[out_edge] > 0){\r\n const vertex_descriptor other_node = target(out_edge, m_g);\r\n if(get_tree(other_node) == tColorTraits::white() && has_sink_connect(other_node))\r\n if(m_dist_map[other_node] < min_distance){\r\n min_distance = m_dist_map[other_node];\r\n new_parent_edge = out_edge;\r\n }\r\n }\r\n }\r\n if(min_distance != (std::numeric_limits::max)()){\r\n set_edge_to_parent(current_node, new_parent_edge);\r\n m_dist_map[current_node] = min_distance + 1;\r\n m_time_map[current_node] = m_time;\r\n } else{\r\n m_time_map[current_node] = 0;\r\n for(boost::tie(ei, e_end) = out_edges(current_node, m_g); ei != e_end; ++ei){\r\n const edge_descriptor out_edge = *ei;\r\n const vertex_descriptor other_node = target(out_edge, m_g);\r\n if(get_tree(other_node) == tColorTraits::white() && has_parent(other_node)){\r\n if(m_res_cap_map[out_edge] > 0){\r\n add_active_node(other_node);\r\n }\r\n if(target(get_edge_to_parent(other_node), m_g) == current_node){\r\n //we were it's parent, so it has to find a new one, too\r\n set_no_parent(other_node);\r\n m_child_orphans.push(other_node);\r\n }\r\n }\r\n }\r\n set_tree(current_node, tColorTraits::gray());\r\n } //no parent found\r\n } //sink-tree adoption\r\n } //while !orphans.empty()\r\n } //adopt\r\n\r\n /**\r\n * return next active vertex if there is one, otherwise a null_vertex\r\n */\r\n inline vertex_descriptor get_next_active_node(){\r\n while(true){\r\n if(m_active_nodes.empty())\r\n return graph_traits::null_vertex();\r\n vertex_descriptor v = m_active_nodes.front();\r\n\r\n if(!has_parent(v) && v != m_source && v != m_sink){ //if it has no parent, this node can't be active(if its not source or sink)\r\n m_active_nodes.pop();\r\n m_in_active_list_map[v] = false;\r\n } else{\r\n assert(get_tree(v) == tColorTraits::black() || get_tree(v) == tColorTraits::white());\r\n return v;\r\n }\r\n }\r\n }\r\n\r\n /**\r\n * adds v as an active vertex, but only if its not in the list already\r\n */\r\n inline void add_active_node(vertex_descriptor v){\r\n assert(get_tree(v) != tColorTraits::gray());\r\n if(m_in_active_list_map[v]){\r\n return;\r\n } else{\r\n m_in_active_list_map[v] = true;\r\n m_active_nodes.push(v);\r\n }\r\n }\r\n\r\n /**\r\n * finish_node removes a node from the front of the active queue (its called in grow phase, if no more paths can be found using this node)\r\n */\r\n inline void finish_node(vertex_descriptor v){\r\n assert(m_active_nodes.front() == v);\r\n m_active_nodes.pop();\r\n m_in_active_list_map[v] = false;\r\n m_last_grow_vertex = graph_traits::null_vertex();\r\n }\r\n\r\n /**\r\n * removes a vertex from the queue of active nodes (actually this does nothing,\r\n * but checks if this node has no parent edge, as this is the criteria for beeing no more active)\r\n */\r\n inline void remove_active_node(vertex_descriptor v){\r\n assert(!has_parent(v));\r\n }\r\n\r\n /**\r\n * returns the search tree of v; tColorValue::black() for source tree, white() for sink tree, gray() for no tree\r\n */\r\n inline tColorValue get_tree(vertex_descriptor v) const {\r\n return m_tree_map[v];\r\n }\r\n\r\n /**\r\n * sets search tree of v; tColorValue::black() for source tree, white() for sink tree, gray() for no tree\r\n */\r\n inline void set_tree(vertex_descriptor v, tColorValue t){\r\n m_tree_map[v] = t;\r\n }\r\n\r\n /**\r\n * returns edge to parent vertex of v;\r\n */\r\n inline edge_descriptor get_edge_to_parent(vertex_descriptor v) const{\r\n return m_pre_map[v];\r\n }\r\n\r\n /**\r\n * returns true if the edge stored in m_pre_map[v] is a valid entry\r\n */\r\n inline bool has_parent(vertex_descriptor v) const{\r\n return m_has_parent_map[v];\r\n }\r\n\r\n /**\r\n * sets edge to parent vertex of v;\r\n */\r\n inline void set_edge_to_parent(vertex_descriptor v, edge_descriptor f_edge_to_parent){\r\n assert(m_res_cap_map[f_edge_to_parent] > 0);\r\n m_pre_map[v] = f_edge_to_parent;\r\n m_has_parent_map[v] = true;\r\n }\r\n\r\n /**\r\n * removes the edge to parent of v (this is done by invalidating the entry an additional map)\r\n */\r\n inline void set_no_parent(vertex_descriptor v){\r\n m_has_parent_map[v] = false;\r\n }\r\n\r\n /**\r\n * checks if vertex v has a connect to the sink-vertex (@var m_sink)\r\n * @param v the vertex which is checked\r\n * @return true if a path to the sink was found, false if not\r\n */\r\n inline bool has_sink_connect(vertex_descriptor v){\r\n tDistanceVal current_distance = 0;\r\n vertex_descriptor current_vertex = v;\r\n while(true){\r\n if(m_time_map[current_vertex] == m_time){\r\n //we found a node which was already checked this round. use it for distance calculations\r\n current_distance += m_dist_map[current_vertex];\r\n break;\r\n }\r\n if(current_vertex == m_sink){\r\n m_time_map[m_sink] = m_time;\r\n break;\r\n }\r\n if(has_parent(current_vertex)){\r\n //it has a parent, so get it\r\n current_vertex = target(get_edge_to_parent(current_vertex), m_g);\r\n ++current_distance;\r\n } else{\r\n //no path found\r\n return false;\r\n }\r\n }\r\n current_vertex=v;\r\n while(m_time_map[current_vertex] != m_time){\r\n m_dist_map[current_vertex] = current_distance--;\r\n m_time_map[current_vertex] = m_time;\r\n current_vertex = target(get_edge_to_parent(current_vertex), m_g);\r\n }\r\n return true;\r\n }\r\n\r\n /**\r\n * checks if vertex v has a connect to the source-vertex (@var m_source)\r\n * @param v the vertex which is checked\r\n * @return true if a path to the source was found, false if not\r\n */\r\n inline bool has_source_connect(vertex_descriptor v){\r\n tDistanceVal current_distance = 0;\r\n vertex_descriptor current_vertex = v;\r\n while(true){\r\n if(m_time_map[current_vertex] == m_time){\r\n //we found a node which was already checked this round. use it for distance calculations\r\n current_distance += m_dist_map[current_vertex];\r\n break;\r\n }\r\n if(current_vertex == m_source){\r\n m_time_map[m_source] = m_time;\r\n break;\r\n }\r\n if(has_parent(current_vertex)){\r\n //it has a parent, so get it\r\n current_vertex = source(get_edge_to_parent(current_vertex), m_g);\r\n ++current_distance;\r\n } else{\r\n //no path found\r\n return false;\r\n }\r\n }\r\n current_vertex=v;\r\n while(m_time_map[current_vertex] != m_time){\r\n m_dist_map[current_vertex] = current_distance-- ;\r\n m_time_map[current_vertex] = m_time;\r\n current_vertex = source(get_edge_to_parent(current_vertex), m_g);\r\n }\r\n return true;\r\n }\r\n\r\n /**\r\n * returns true, if p is closer to a terminal than q\r\n */\r\n inline bool is_closer_to_terminal(vertex_descriptor p, vertex_descriptor q){\r\n //checks the timestamps first, to build no cycles, and after that the real distance\r\n return (m_time_map[q] <= m_time_map[p] && m_dist_map[q] > m_dist_map[p]+1);\r\n }\r\n\r\n ////////\r\n // member vars\r\n ////////\r\n Graph& m_g;\r\n IndexMap m_index_map;\r\n EdgeCapacityMap m_cap_map;\r\n ResidualCapacityEdgeMap m_res_cap_map;\r\n ReverseEdgeMap m_rev_edge_map;\r\n PredecessorMap m_pre_map; //stores paths found in the growth stage\r\n ColorMap m_tree_map; //maps each vertex into one of the two search tree or none (gray())\r\n DistanceMap m_dist_map; //stores distance to source/sink nodes\r\n vertex_descriptor m_source;\r\n vertex_descriptor m_sink;\r\n\r\n tQueue m_active_nodes;\r\n std::vector m_in_active_list_vec;\r\n iterator_property_map::iterator, IndexMap> m_in_active_list_map;\r\n\r\n std::list m_orphans;\r\n tQueue m_child_orphans; // we use a second queuqe for child orphans, as they are FIFO processed\r\n\r\n std::vector m_has_parent_vec;\r\n iterator_property_map::iterator, IndexMap> m_has_parent_map;\r\n\r\n std::vector m_time_vec; //timestamp of each node, used for sink/source-path calculations\r\n iterator_property_map::iterator, IndexMap> m_time_map;\r\n tEdgeVal m_flow;\r\n long m_time;\r\n vertex_descriptor m_last_grow_vertex;\r\n out_edge_iterator m_last_grow_edge_it;\r\n out_edge_iterator m_last_grow_edge_end;\r\n };\r\n } //namespace detail\r\n\r\n /**\r\n * non-named-parameter version, given everything\r\n * this is the catch all version\r\n */\r\n template \r\n typename property_traits::value_type\r\n kolmogorov_max_flow\r\n (Graph& g,\r\n CapacityEdgeMap cap,\r\n ResidualCapacityEdgeMap res_cap,\r\n ReverseEdgeMap rev_map,\r\n PredecessorMap pre_map,\r\n ColorMap color,\r\n DistanceMap dist,\r\n IndexMap idx,\r\n typename graph_traits::vertex_descriptor src,\r\n typename graph_traits::vertex_descriptor sink\r\n )\r\n {\r\n typedef typename graph_traits::vertex_descriptor vertex_descriptor;\r\n typedef typename graph_traits::edge_descriptor edge_descriptor;\r\n //as this method is the last one before we instantiate the solver, we do the concept checks here\r\n function_requires >(); //to have vertices(), num_vertices(),\r\n function_requires >(); //to have edges()\r\n function_requires >(); //to have source(), target() and out_edges()\r\n function_requires >(); //read flow-values from edges\r\n function_requires >(); //write flow-values to residuals\r\n function_requires >(); //read out reverse edges\r\n function_requires >(); //store predecessor there\r\n function_requires >(); //write corresponding tree\r\n function_requires >(); //write distance to source/sink\r\n function_requires >(); //get index 0...|V|-1\r\n assert(num_vertices(g) >= 2 && src != sink);\r\n detail::kolmogorov\r\n algo(g, cap, res_cap, rev_map, pre_map, color, dist, idx, src, sink);\r\n return algo.max_flow();\r\n }\r\n\r\n /**\r\n * non-named-parameter version, given: capacity, residucal_capacity, reverse_edges, and an index map.\r\n */\r\n template \r\n typename property_traits::value_type\r\n kolmogorov_max_flow\r\n (Graph& g,\r\n CapacityEdgeMap cap,\r\n ResidualCapacityEdgeMap res_cap,\r\n ReverseEdgeMap rev,\r\n IndexMap idx,\r\n typename graph_traits::vertex_descriptor src,\r\n typename graph_traits::vertex_descriptor sink)\r\n {\r\n typename graph_traits::vertices_size_type n_verts = num_vertices(g);\r\n std::vector::edge_descriptor> predecessor_vec(n_verts);\r\n std::vector color_vec(n_verts);\r\n std::vector::vertices_size_type> distance_vec(n_verts);\r\n return kolmogorov_max_flow\r\n (g, cap, res_cap, rev,\r\n make_iterator_property_map(predecessor_vec.begin(), idx),\r\n make_iterator_property_map(color_vec.begin(), idx),\r\n make_iterator_property_map(distance_vec.begin(), idx),\r\n idx, src, sink);\r\n }\r\n\r\n /**\r\n * non-named-parameter version, some given: capacity, residual_capacity, reverse_edges, color_map and an index map.\r\n * Use this if you are interested in the minimum cut, as the color map provides that info\r\n */\r\n template \r\n typename property_traits::value_type\r\n kolmogorov_max_flow\r\n (Graph& g,\r\n CapacityEdgeMap cap,\r\n ResidualCapacityEdgeMap res_cap,\r\n ReverseEdgeMap rev,\r\n ColorMap color,\r\n IndexMap idx,\r\n typename graph_traits::vertex_descriptor src,\r\n typename graph_traits::vertex_descriptor sink)\r\n {\r\n typename graph_traits::vertices_size_type n_verts = num_vertices(g);\r\n std::vector::edge_descriptor> predecessor_vec(n_verts);\r\n std::vector::vertices_size_type> distance_vec(n_verts);\r\n\r\n return kolmogorov_max_flow\r\n (g, cap, res_cap, rev,\r\n make_iterator_property_map(predecessor_vec.begin(), idx),\r\n color,\r\n make_iterator_property_map(distance_vec.begin(), idx),\r\n idx, src, sink);\r\n }\r\n\r\n /**\r\n * named-parameter version, some given\r\n */\r\n template \r\n typename property_traits::const_type>::value_type\r\n kolmogorov_max_flow\r\n (Graph& g,\r\n typename graph_traits::vertex_descriptor src,\r\n typename graph_traits::vertex_descriptor sink,\r\n const bgl_named_params& params)\r\n {\r\n return kolmogorov_max_flow(g,\r\n choose_const_pmap(get_param(params, edge_capacity), g, edge_capacity),\r\n choose_pmap(get_param(params, edge_residual_capacity), g, edge_residual_capacity),\r\n choose_const_pmap(get_param(params, edge_reverse), g, edge_reverse),\r\n choose_pmap(get_param(params, vertex_predecessor), g, vertex_predecessor),\r\n choose_pmap(get_param(params, vertex_color), g, vertex_color),\r\n choose_pmap(get_param(params, vertex_distance), g, vertex_distance),\r\n choose_const_pmap(get_param(params, vertex_index), g, vertex_index),\r\n src, sink);\r\n }\r\n\r\n /**\r\n * named-parameter version, none given\r\n */\r\n template \r\n typename property_traits::const_type>::value_type\r\n kolmogorov_max_flow\r\n (Graph& g,\r\n typename graph_traits::vertex_descriptor src,\r\n typename graph_traits::vertex_descriptor sink)\r\n {\r\n bgl_named_params params(0); // bogus empty param\r\n return kolmogorov_max_flow(g, src, sink, params);\r\n }\r\n} // namespace boost\r\n\r\n#endif // BOOST_KOLMOGOROV_MAX_FLOW_HPP\r\n\r\n", "meta": {"hexsha": "edb4b7eceb71d5ee231b4853a3485a975b3fa54c", "size": 40320, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Compiler/boost/boost/graph/kolmogorov_max_flow.hpp", "max_stars_repo_name": "davidov541/MiniC", "max_stars_repo_head_hexsha": "d3b16a1568b97a4d801880b110a8be04fe848adb", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2015-04-16T01:05:53.000Z", "max_stars_repo_stars_event_max_datetime": "2019-08-26T07:38:43.000Z", "max_issues_repo_path": "LibsExternes/Includes/boost/graph/kolmogorov_max_flow.hpp", "max_issues_repo_name": "benkaraban/anima-games-engine", "max_issues_repo_head_hexsha": "8aa7a5368933f1b82c90f24814f1447119346c3b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2017-04-05T01:56:28.000Z", "max_issues_repo_issues_event_max_datetime": "2017-04-05T01:56:28.000Z", "max_forks_repo_path": "LibsExternes/Includes/boost/graph/kolmogorov_max_flow.hpp", "max_forks_repo_name": "benkaraban/anima-games-engine", "max_forks_repo_head_hexsha": "8aa7a5368933f1b82c90f24814f1447119346c3b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.472392638, "max_line_length": 164, "alphanum_fraction": 0.5826140873, "num_tokens": 8269, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.22256175291053032}} {"text": "/**\n * @file dens_matrix.cpp\n * @brief full matrix\n * @date 2009-12-08\n */\n#ifdef BSPY_EXPORTING_PLUGIN\n#include \n#endif\n#include \n\n#include \"dens_matrix.h\"\n#include \"bcsr_matrix_iface.h\"\n\n#include \"pyublas/numpy.hpp\"\n\nusing namespace std;\nusing namespace boost::python;\n\n\nnamespace blue_sky\n{\n dens_matrix::dens_matrix (bs_type_ctor_param)\n : dens_matrix_iface (),\n values (BS_KERNEL.create_object (v_float::bs_type ()))\n {\n }\n\n dens_matrix ::dens_matrix (const this_t & /*src*/) : bs_refcounter (),\n values (BS_KERNEL.create_object (v_float::bs_type ()))\n {\n }\n\n // TODO:\n int\n dens_matrix::matrix_vector_product (spv_double v_, spv_double r_) const\n {\n t_long block_rows;\n t_long block_cols;\n t_long clb = calc_block_size < 1 ? (n_rows + n_cols) : calc_block_size;\n\n if (n_cols != (t_long) v_->size () || n_rows != (t_long) r_->size ())\n return -1;\n\n t_double *v = &(*v_)[0];\n t_double *r = &(*r_)[0];\n\n block_rows = n_rows / clb;\n if (n_rows % clb)\n ++block_rows;\n\n block_cols = n_cols / clb;\n if (n_cols % clb)\n ++block_cols;\n\n for (t_long i = 0; i < block_rows; ++i)\n {\n for (t_long j = 0; j < block_cols; ++j)\n {\n block_mv_product (i, j, v, r);\n }\n }\n return 0;\n }\n\n // TODO:\n int\n dens_matrix::matrix_vector_product_t (spv_double v_,\n spv_double r_) const\n {\n t_long block_rows;\n t_long block_cols;\n t_long clb = calc_block_size < 1 ? (n_rows + n_cols) : calc_block_size;\n\n if (n_cols != (t_long)r_->size () || n_rows != (t_long)v_->size ())\n return -1;\n\n t_double *v = &(*v_)[0];\n t_double *r = &(*r_)[0];\n\n block_rows = n_rows / clb;\n if (n_rows % clb)\n ++block_rows;\n\n block_cols = n_cols / clb;\n if (n_cols % clb)\n ++block_cols;\n\n for (t_long i = 0; i < block_rows; ++i)\n {\n for (t_long j = 0; j < block_cols; ++j)\n {\n block_mv_product_t (i, j, v, r);\n }\n }\n return 0;\n }\n\n int\n dens_matrix::calc_lin_comb (t_double alpha,\n t_double beta,\n spv_double u_,\n spv_double v_,\n spv_double r_) const\n {\n static const t_double eps = t_double (1.0e-12);\n t_long i;\n int r_code = 0;\n\n t_double *v = &(*v_)[0];\n t_double *r = &(*r_)[0];\n\n memset (r, 0, sizeof (t_double) * n_rows);\n\n if (fabs (alpha) > eps)\n {\n r_code = matrix_vector_product (u_, r_);\n for (i = 0; i < n_rows; ++i)\n r[i] *= alpha;\n }\n if (fabs (beta) > eps)\n {\n for (i = 0; i < n_rows; ++i)\n r[i] += v[i] * beta;\n }\n return r_code;\n }\n\n int\n dens_matrix::init_by_matrix (sp_base_t matrix_)\n {\n BS_ASSERT (matrix_);\n\n typedef smart_ptr sp_bcsr_matrix_iface;\n\n sp_dens_matrix_iface_t dense_matrix_ (matrix_, bs_dynamic_cast ());\n sp_bcsr_matrix_iface bcsr_matrix_ (matrix_, bs_dynamic_cast ());\n\n if (dense_matrix_)\n {\n init (dense_matrix_->get_n_rows (), dense_matrix_->get_n_cols (),\n dense_matrix_->get_calc_block_size ());\n //TODO: copy values\n }\n else if (bcsr_matrix_)\n {\n t_long i, j, j1, j2, cl;\n if (bcsr_matrix_->get_n_block_size () > 1)\n {\n bs_throw_exception (\"dens_matrix::init_by_matrix (bcsr) n_block_size must be = 1\");\n }\n\n if (init (bcsr_matrix_->get_n_rows (), bcsr_matrix_->get_n_cols (),\n bcsr_matrix_->get_n_block_size ()))\n return -3;\n\n t_long *rows = &(*bcsr_matrix_->get_rows_ptr ())[0];\n t_long *cols = &(*bcsr_matrix_->get_cols_ind ())[0];\n t_float *vals = &(*bcsr_matrix_->get_values ())[0];\n t_float *values_ptr = &(*values)[0];\n\n // set values zero. then copy matrix values to dense\n memset (values_ptr, 0, n_rows * n_cols * sizeof (t_float));\n for (i = 0; i < n_rows; ++i)\n {\n j1 = rows[i];\n j2 = rows[i + 1];\n for (j = j1; j < j2; ++j)\n {\n cl = cols[j];\n values_ptr[i * n_rows + cl] = vals[j];\n }\n }\n }\n else\n {\n bs_throw_exception (\"dens_matrix::init_by_matrix: wrong type\");\n }\n return 0;\n }\n\n int\n dens_matrix::init (const t_long new_n_rows,\n const t_long new_n_cols,\n const t_long block_size)\n {\n npy_intp dims[2];\n if (new_n_rows < 1 || new_n_cols < 1)\n return -1;\n dims[0] = n_rows = new_n_rows;\n dims[1] = n_cols = new_n_cols;\n calc_block_size = block_size;\n values->resize (n_rows * n_cols);\n // TODO: add this method\n //values->reshape (2, dims);\n //std::fill (values->numpy.as_ublas ().data ().begin (), values->numpy.as_ublas ().data ().end (), 0);\n if (values->size ())\n std::fill (values->begin (), values->end (), 0);\n return 0;\n }\n\n int\n dens_matrix::copy (sp_dens_matrix_iface_t matrix)\n {\n if (init (matrix->get_n_rows (), matrix->get_n_cols (), matrix->get_calc_block_size ()))\n return -3;\n values = matrix->get_values ()->clone ();\n //values->numpy.resize (matrix->get_values ()->numpy.size1 (), matrix->get_values ()->numpy.size2 ());\n //values->numpy.as_ublas () = matrix->get_values ()->numpy.as_ublas ();\n // matrix->get_values ()->numpy.as_ublas ().data ().end ());\n\n\n //memcpy (&values[0], &(matrix->get_values_const ())[0], n_rows * n_cols * sizeof (t_float));\n return 0;\n }\n\n void\n dens_matrix::block_mv_product (t_long row_block,\n t_long col_block,\n const t_double *v,\n t_double *r) const\n {\n t_long block_n_rows;\n t_long block_n_cols;\n const t_float *block;\n const t_double *block_v;\n t_double *block_r;\n t_long clb = calc_block_size < 1 ? (n_rows + n_cols) : calc_block_size;\n\n // calculate block start position\n //block = &values[row_block * clb * n_cols + col_block * clb];\n block = &(*values)[0] + row_block * clb * n_cols + col_block * clb;\n block_v = v + col_block * clb;\n block_r = r + row_block * clb;\n\n if ((row_block + 1) * clb <= n_rows)\n block_n_rows = clb;\n else\n block_n_rows = n_rows - row_block * clb;\n\n if ((col_block + 1) * clb <= n_cols)\n block_n_cols = clb;\n else\n block_n_cols = n_cols - col_block * clb;\n\n for (t_long i = 0; i < block_n_rows; ++i)\n {\n for (t_long j = 0; j < block_n_cols; ++j)\n {\n block_r[i] += block[i * n_rows + j] * block_v[j];\n }\n }\n }\n\n void\n dens_matrix::block_mv_product_t (t_long row_block,\n t_long col_block,\n const t_double *v,\n t_double *r) const\n {\n t_long block_n_rows;\n t_long block_n_cols;\n const t_float *block;\n const t_double *block_v;\n t_double *block_r;\n t_long clb = calc_block_size < 1 ? (n_rows + n_cols) : calc_block_size;\n\n // calculate block start position\n block = &(*values)[0] + row_block * clb * n_cols + col_block * clb;\n block_v = v + row_block * clb;\n block_r = r + col_block * clb;\n\n if ((row_block + 1) * clb <= n_rows)\n block_n_rows = clb;\n else\n block_n_rows = n_rows - row_block * clb;\n\n if ((col_block + 1) * clb <= n_cols)\n block_n_cols = clb;\n else\n block_n_cols = n_cols - col_block * clb;\n\n for (t_long i = 0; i < block_n_rows; ++i)\n {\n for (t_long j = 0; j < block_n_cols; ++j)\n {\n block_r[j] += block[i * n_rows + j] * block_v[i];\n }\n }\n }\n\n#ifdef BSPY_EXPORTING_PLUGIN\n std::string\n dens_matrix::py_str () const\n {\n stringstream s;\n\n s << \"Dens matrix\\n--------------------------------\\n\";\n s << \"\\tRows: \" << n_rows << \"\\n\";\n s << \"\\tColumns: \" << n_cols << \"\\n\";\n s << \"\\tCalc Block: \" << calc_block_size << \"\\n\";\n s << \"\\tAllocated memory (Mb): \" << get_allocated_memory_in_mbytes () << \"\\n\";\n s << \"--------------------------------\\n\";\n return s.str ();\n }\n#endif //BSPY_EXPORTING_PLUGIN\n/////////////////////////////////BS Register\n/////////////////////////////////Stuff//////////////////////////\n\n BLUE_SKY_TYPE_STD_CREATE (dens_matrix);\n BLUE_SKY_TYPE_STD_COPY (dens_matrix);\n\n BLUE_SKY_TYPE_IMPL (dens_matrix, dens_matrix_iface, \"dens_matrix\", \"Dens Matrix class\", \"Realization of Dens Matricies\");\n} // blue_sky namespace\n", "meta": {"hexsha": "871d8e2d0086c3d2446faba9725d9a4d294b2653", "size": 9274, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "bs_mtx/src/dens_matrix.cpp", "max_stars_repo_name": "bs-eagle/bs-eagle", "max_stars_repo_head_hexsha": "b1017a4f6ac2dcafba2deafec84052ddde792671", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 7.0, "max_stars_repo_stars_event_min_datetime": "2015-07-16T22:30:36.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-06T10:16:42.000Z", "max_issues_repo_path": "bs_mtx/src/dens_matrix.cpp", "max_issues_repo_name": "bs-eagle/bs-eagle", "max_issues_repo_head_hexsha": "b1017a4f6ac2dcafba2deafec84052ddde792671", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "bs_mtx/src/dens_matrix.cpp", "max_forks_repo_name": "bs-eagle/bs-eagle", "max_forks_repo_head_hexsha": "b1017a4f6ac2dcafba2deafec84052ddde792671", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2017-01-05T20:06:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-20T16:19:10.000Z", "avg_line_length": 29.5350318471, "max_line_length": 124, "alphanum_fraction": 0.5053914169, "num_tokens": 2472, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.44167300566462553, "lm_q1q2_score": 0.22256175291053032}} {"text": "// Boost.Geometry - gis-projections (based on PROJ4)\r\n\r\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\r\n\r\n// This file was modified by Oracle on 2017, 2018.\r\n// Modifications copyright (c) 2017-2018, Oracle and/or its affiliates.\r\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle.\r\n\r\n// Use, modification and distribution is subject to the Boost Software License,\r\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\r\n// http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\r\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\r\n// PROJ4 is maintained by Frank Warmerdam\r\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\r\n\r\n// Last updated version of proj: 5.0.0\r\n\r\n// Original copyright notice:\r\n\r\n// Permission is hereby granted, free of charge, to any person obtaining a\r\n// copy of this software and associated documentation files (the \"Software\"),\r\n// to deal in the Software without restriction, including without limitation\r\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\r\n// and/or sell copies of the Software, and to permit persons to whom the\r\n// Software is furnished to do so, subject to the following conditions:\r\n\r\n// The above copyright notice and this permission notice shall be included\r\n// in all copies or substantial portions of the Software.\r\n\r\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\r\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\r\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\r\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\r\n// DEALINGS IN THE SOFTWARE.\r\n\r\n#ifndef BOOST_GEOMETRY_PROJECTIONS_CHAMB_HPP\r\n#define BOOST_GEOMETRY_PROJECTIONS_CHAMB_HPP\r\n\r\n#include \r\n#include \r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\nnamespace boost { namespace geometry\r\n{\r\n\r\nnamespace projections\r\n{\r\n #ifndef DOXYGEN_NO_DETAIL\r\n namespace detail { namespace chamb\r\n {\r\n\r\n //static const double third = 0.333333333333333333;\r\n static const double tolerance = 1e-9;\r\n\r\n // specific for 'chamb'\r\n template \r\n struct vect_ra { T r, Az; };\r\n template \r\n struct point_xy { T x, y; };\r\n\r\n template \r\n struct par_chamb\r\n {\r\n struct { /* control point data */\r\n T phi, lam;\r\n T cosphi, sinphi;\r\n vect_ra v;\r\n point_xy p;\r\n T Az;\r\n } c[3];\r\n point_xy p;\r\n T beta_0, beta_1, beta_2;\r\n };\r\n\r\n /* distance and azimuth from point 1 to point 2 */\r\n template \r\n inline vect_ra vect(T const& dphi, T const& c1, T const& s1, T const& c2, T const& s2, T const& dlam)\r\n {\r\n vect_ra v;\r\n T cdl, dp, dl;\r\n\r\n cdl = cos(dlam);\r\n if (fabs(dphi) > 1. || fabs(dlam) > 1.)\r\n v.r = aacos(s1 * s2 + c1 * c2 * cdl);\r\n else { /* more accurate for smaller distances */\r\n dp = sin(.5 * dphi);\r\n dl = sin(.5 * dlam);\r\n v.r = 2. * aasin(sqrt(dp * dp + c1 * c2 * dl * dl));\r\n }\r\n if (fabs(v.r) > tolerance)\r\n v.Az = atan2(c2 * sin(dlam), c1 * s2 - s1 * c2 * cdl);\r\n else\r\n v.r = v.Az = 0.;\r\n return v;\r\n }\r\n\r\n /* law of cosines */\r\n template \r\n inline T lc(T const& b, T const& c, T const& a)\r\n {\r\n return aacos(.5 * (b * b + c * c - a * a) / (b * c));\r\n }\r\n\r\n // template class, using CRTP to implement forward/inverse\r\n template \r\n struct base_chamb_spheroid\r\n : public base_t_f, T, Parameters>\r\n {\r\n par_chamb m_proj_parm;\r\n\r\n inline base_chamb_spheroid(const Parameters& par)\r\n : base_t_f, T, Parameters>(*this, par)\r\n {}\r\n\r\n // FORWARD(s_forward) spheroid\r\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\r\n inline void fwd(T const& lp_lon, T const& lp_lat, T& xy_x, T& xy_y) const\r\n {\r\n static const T third = detail::third();\r\n\r\n T sinphi, cosphi, a;\r\n vect_ra v[3];\r\n int i, j;\r\n\r\n sinphi = sin(lp_lat);\r\n cosphi = cos(lp_lat);\r\n for (i = 0; i < 3; ++i) { /* dist/azimiths from control */\r\n v[i] = vect(lp_lat - this->m_proj_parm.c[i].phi, this->m_proj_parm.c[i].cosphi, this->m_proj_parm.c[i].sinphi,\r\n cosphi, sinphi, lp_lon - this->m_proj_parm.c[i].lam);\r\n if (v[i].r == 0.0)\r\n break;\r\n v[i].Az = adjlon(v[i].Az - this->m_proj_parm.c[i].v.Az);\r\n }\r\n if (i < 3) /* current point at control point */\r\n { xy_x = this->m_proj_parm.c[i].p.x; xy_y = this->m_proj_parm.c[i].p.y; }\r\n else { /* point mean of intersepts */\r\n { xy_x = this->m_proj_parm.p.x; xy_y = this->m_proj_parm.p.y; }\r\n for (i = 0; i < 3; ++i) {\r\n j = i == 2 ? 0 : i + 1;\r\n a = lc(this->m_proj_parm.c[i].v.r, v[i].r, v[j].r);\r\n if (v[i].Az < 0.)\r\n a = -a;\r\n if (! i) { /* coord comp unique to each arc */\r\n xy_x += v[i].r * cos(a);\r\n xy_y -= v[i].r * sin(a);\r\n } else if (i == 1) {\r\n a = this->m_proj_parm.beta_1 - a;\r\n xy_x -= v[i].r * cos(a);\r\n xy_y -= v[i].r * sin(a);\r\n } else {\r\n a = this->m_proj_parm.beta_2 - a;\r\n xy_x += v[i].r * cos(a);\r\n xy_y += v[i].r * sin(a);\r\n }\r\n }\r\n xy_x *= third; /* mean of arc intercepts */\r\n xy_y *= third;\r\n }\r\n }\r\n\r\n static inline std::string get_name()\r\n {\r\n return \"chamb_spheroid\";\r\n }\r\n\r\n };\r\n\r\n template \r\n inline T chamb_init_lat(srs::detail::proj4_parameters const& params, int i)\r\n {\r\n static const std::string lat[3] = {\"lat_1\", \"lat_2\", \"lat_3\"};\r\n return _pj_get_param_r(params, lat[i]);\r\n }\r\n template \r\n inline T chamb_init_lat(srs::dpar::parameters const& params, int i)\r\n {\r\n static const srs::dpar::name_r lat[3] = {srs::dpar::lat_1, srs::dpar::lat_2, srs::dpar::lat_3};\r\n return _pj_get_param_r(params, lat[i]);\r\n }\r\n\r\n template \r\n inline T chamb_init_lon(srs::detail::proj4_parameters const& params, int i)\r\n {\r\n static const std::string lon[3] = {\"lon_1\", \"lon_2\", \"lon_3\"};\r\n return _pj_get_param_r(params, lon[i]);\r\n }\r\n template \r\n inline T chamb_init_lon(srs::dpar::parameters const& params, int i)\r\n {\r\n static const srs::dpar::name_r lon[3] = {srs::dpar::lon_1, srs::dpar::lon_2, srs::dpar::lon_3};\r\n return _pj_get_param_r(params, lon[i]);\r\n }\r\n\r\n // Chamberlin Trimetric\r\n template \r\n inline void setup_chamb(Params const& params, Parameters& par, par_chamb& proj_parm)\r\n {\r\n static const T pi = detail::pi();\r\n\r\n int i, j;\r\n\r\n for (i = 0; i < 3; ++i) { /* get control point locations */\r\n proj_parm.c[i].phi = chamb_init_lat(params, i);\r\n proj_parm.c[i].lam = chamb_init_lon(params, i);\r\n proj_parm.c[i].lam = adjlon(proj_parm.c[i].lam - par.lam0);\r\n proj_parm.c[i].cosphi = cos(proj_parm.c[i].phi);\r\n proj_parm.c[i].sinphi = sin(proj_parm.c[i].phi);\r\n }\r\n for (i = 0; i < 3; ++i) { /* inter ctl pt. distances and azimuths */\r\n j = i == 2 ? 0 : i + 1;\r\n proj_parm.c[i].v = vect(proj_parm.c[j].phi - proj_parm.c[i].phi, proj_parm.c[i].cosphi, proj_parm.c[i].sinphi,\r\n proj_parm.c[j].cosphi, proj_parm.c[j].sinphi, proj_parm.c[j].lam - proj_parm.c[i].lam);\r\n if (proj_parm.c[i].v.r == 0.0)\r\n BOOST_THROW_EXCEPTION( projection_exception(error_control_point_no_dist) );\r\n /* co-linearity problem ignored for now */\r\n }\r\n proj_parm.beta_0 = lc(proj_parm.c[0].v.r, proj_parm.c[2].v.r, proj_parm.c[1].v.r);\r\n proj_parm.beta_1 = lc(proj_parm.c[0].v.r, proj_parm.c[1].v.r, proj_parm.c[2].v.r);\r\n proj_parm.beta_2 = pi - proj_parm.beta_0;\r\n proj_parm.p.y = 2. * (proj_parm.c[0].p.y = proj_parm.c[1].p.y = proj_parm.c[2].v.r * sin(proj_parm.beta_0));\r\n proj_parm.c[2].p.y = 0.;\r\n proj_parm.c[0].p.x = - (proj_parm.c[1].p.x = 0.5 * proj_parm.c[0].v.r);\r\n proj_parm.p.x = proj_parm.c[2].p.x = proj_parm.c[0].p.x + proj_parm.c[2].v.r * cos(proj_parm.beta_0);\r\n\r\n par.es = 0.;\r\n }\r\n\r\n }} // namespace detail::chamb\r\n #endif // doxygen\r\n\r\n /*!\r\n \\brief Chamberlin Trimetric projection\r\n \\ingroup projections\r\n \\tparam Geographic latlong point type\r\n \\tparam Cartesian xy point type\r\n \\tparam Parameters parameter type\r\n \\par Projection characteristics\r\n - Miscellaneous\r\n - Spheroid\r\n - no inverse\r\n \\par Projection parameters\r\n - lat_1: Latitude of control point 1 (degrees)\r\n - lon_1: Longitude of control point 1 (degrees)\r\n - lat_2: Latitude of control point 2 (degrees)\r\n - lon_2: Longitude of control point 2 (degrees)\r\n - lat_3: Latitude of control point 3 (degrees)\r\n - lon_3: Longitude of control point 3 (degrees)\r\n \\par Example\r\n \\image html ex_chamb.gif\r\n */\r\n template \r\n struct chamb_spheroid : public detail::chamb::base_chamb_spheroid\r\n {\r\n template \r\n inline chamb_spheroid(Params const& params, Parameters const& par)\r\n : detail::chamb::base_chamb_spheroid(par)\r\n {\r\n detail::chamb::setup_chamb(params, this->m_par, this->m_proj_parm);\r\n }\r\n };\r\n\r\n #ifndef DOXYGEN_NO_DETAIL\r\n namespace detail\r\n {\r\n\r\n // Static projection\r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_STATIC_PROJECTION(srs::spar::proj_chamb, chamb_spheroid, chamb_spheroid)\r\n\r\n // Factory entry(s)\r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_ENTRY_F(chamb_entry, chamb_spheroid)\r\n \r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_BEGIN(chamb_init)\r\n {\r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_ENTRY(chamb, chamb_entry);\r\n }\r\n\r\n } // namespace detail\r\n #endif // doxygen\r\n\r\n} // namespace projections\r\n\r\n}} // namespace boost::geometry\r\n\r\n#endif // BOOST_GEOMETRY_PROJECTIONS_CHAMB_HPP\r\n\r\n", "meta": {"hexsha": "cde301497b47b292738012db322e864e00ac9b7a", "size": 12784, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "externals/boost/boost/geometry/srs/projections/proj/chamb.hpp", "max_stars_repo_name": "YuukiTsuchida/v8_embeded", "max_stars_repo_head_hexsha": "c6e18f4e91fcc50607f8e3edc745a3afa30b2871", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 32.0, "max_stars_repo_stars_event_min_datetime": "2019-02-27T06:57:07.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-29T10:56:19.000Z", "max_issues_repo_path": "externals/boost/boost/geometry/srs/projections/proj/chamb.hpp", "max_issues_repo_name": "YuukiTsuchida/v8_embeded", "max_issues_repo_head_hexsha": "c6e18f4e91fcc50607f8e3edc745a3afa30b2871", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2019-04-04T18:00:00.000Z", "max_issues_repo_issues_event_max_datetime": "2019-04-04T18:00:00.000Z", "max_forks_repo_path": "externals/boost/boost/geometry/srs/projections/proj/chamb.hpp", "max_forks_repo_name": "YuukiTsuchida/v8_embeded", "max_forks_repo_head_hexsha": "c6e18f4e91fcc50607f8e3edc745a3afa30b2871", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2019-06-19T05:06:03.000Z", "max_forks_repo_forks_event_max_datetime": "2021-07-30T03:29:19.000Z", "avg_line_length": 43.3355932203, "max_line_length": 135, "alphanum_fraction": 0.5191645807, "num_tokens": 3072, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.679178686187839, "lm_q2_score": 0.3276683139517237, "lm_q1q2_score": 0.22254533497511605}} {"text": "//---------------------------------------------------------------------------//\n// MIT License\n//\n// Copyright (c) 2020-2021 Mikhail Komarov \n// Copyright (c) 2020-2021 Nikita Kaskov \n// Copyright (c) 2021-2022 Aleksei Moskvin \n// Copyright (c) 2021 Ilias Khairullin \n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n//---------------------------------------------------------------------------//\n\n#ifndef CRYPTO3_MERKLE_PROOF_HPP\n#define CRYPTO3_MERKLE_PROOF_HPP\n\n#include \n#include \n\n#include \n\n#include \n\nnamespace nil {\n namespace crypto3 {\n namespace zk {\n namespace components {\n template\n struct merkle_proof;\n } // namespace components\n } // namespace zk\n namespace marshalling {\n namespace types {\n template\n struct merkle_proof_marshalling;\n }\n } // namespace marshalling\n namespace containers {\n namespace detail {\n template\n struct merkle_proof_impl {\n typedef NodeType node_type;\n typedef typename node_type::hash_type hash_type;\n\n constexpr static const std::size_t arity = Arity;\n\n constexpr static const std::size_t value_bits = node_type::value_bits;\n typedef typename node_type::value_type value_type;\n\n merkle_proof_impl() : _li(0) {};\n\n merkle_proof_impl(const merkle_tree &tree, const std::size_t leaf_idx) {\n _root = tree.root();\n _path.resize(tree.row_count() - 1);\n _li = leaf_idx;\n\n typename std::vector::iterator v_itr = _path.begin();\n std::size_t cur_leaf = leaf_idx;\n std::size_t row_len = tree.leaves();\n std::size_t row_begin_idx = 0;\n while (cur_leaf != tree.size() - 1) { // while it's not _root\n std::size_t cur_leaf_pos = cur_leaf % arity;\n std::size_t cur_leaf_arity_pos = (cur_leaf - row_begin_idx) / arity;\n std::size_t begin_this_arity = cur_leaf - cur_leaf_pos;\n typename layer_type::iterator a_itr = v_itr->begin();\n for (size_t i = 0; i < cur_leaf_pos; ++i, ++begin_this_arity, ++a_itr) {\n *a_itr = path_element_type(tree[begin_this_arity], i);\n }\n for (size_t i = cur_leaf_pos + 1; i < arity; ++i, ++begin_this_arity, ++a_itr) {\n *a_itr = path_element_type(tree[begin_this_arity + 1], i);\n }\n v_itr++;\n cur_leaf = row_len + row_begin_idx + cur_leaf_arity_pos;\n row_begin_idx += row_len;\n row_len /= arity;\n }\n }\n\n template\n bool validate(const Hashable &a) const {\n value_type d = crypto3::hash(a);\n for (auto &it : _path) {\n accumulator_set acc;\n size_t i = 0;\n for (; (i < arity - 1) && i == it[i]._position; ++i) {\n crypto3::hash(it[i]._hash.begin(), it[i]._hash.end(), acc);\n }\n crypto3::hash(d.begin(), d.end(), acc);\n for (; i < arity - 1; ++i) {\n crypto3::hash(it[i]._hash.begin(), it[i]._hash.end(), acc);\n }\n d = accumulators::extract::hash(acc);\n }\n return (d == _root);\n }\n\n std::size_t leaf_index() const {\n return _li;\n }\n\n bool operator==(const merkle_proof_impl &rhs) const {\n return _li == rhs._li && _root == rhs._root && _path == rhs._path;\n }\n bool operator!=(const merkle_proof_impl &rhs) const {\n return !(rhs == *this);\n }\n\n struct path_element_type {\n path_element_type(value_type x, size_t pos) : _hash(x), _position(pos) {\n }\n path_element_type() {\n }\n\n bool operator==(const path_element_type &rhs) const {\n return _hash == rhs._hash && _position == rhs._position;\n }\n bool operator!=(const path_element_type &rhs) const {\n return !(rhs == *this);\n }\n\n const value_type &hash() const {\n return _hash;\n }\n\n std::size_t position() const {\n return _position;\n }\n\n value_type _hash;\n std::size_t _position;\n\n template\n friend class nil::crypto3::marshalling::types::merkle_proof_marshalling;\n };\n\n typedef std::array layer_type;\n typedef std::vector path_type;\n\n const value_type &root() const {\n return _root;\n }\n\n const path_type &path() const {\n return _path;\n }\n\n private:\n std::size_t _li;\n value_type _root;\n path_type _path;\n\n template\n friend class nil::crypto3::zk::components::merkle_proof;\n\n template\n friend class nil::crypto3::marshalling::types::merkle_proof_marshalling;\n };\n } // namespace detail\n\n template\n using merkle_proof =\n typename std::conditional::value,\n detail::merkle_proof_impl, Arity>,\n detail::merkle_proof_impl>::type;\n } // namespace containers\n } // namespace crypto3\n} // namespace nil\n\n#endif\n", "meta": {"hexsha": "7ae010a907b1aabab85f64024223d71a836c3cd7", "size": 8279, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/nil/crypto3/container/merkle/proof.hpp", "max_stars_repo_name": "NilFoundation/crypto3-containers", "max_stars_repo_head_hexsha": "5dd5be6bcec6f2f32a71a058052119e98da1ceff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/nil/crypto3/container/merkle/proof.hpp", "max_issues_repo_name": "NilFoundation/crypto3-containers", "max_issues_repo_head_hexsha": "5dd5be6bcec6f2f32a71a058052119e98da1ceff", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2021-11-25T22:02:14.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-14T13:03:14.000Z", "max_forks_repo_path": "include/nil/crypto3/container/merkle/proof.hpp", "max_forks_repo_name": "NilFoundation/crypto3-containers", "max_forks_repo_head_hexsha": "5dd5be6bcec6f2f32a71a058052119e98da1ceff", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.9945652174, "max_line_length": 110, "alphanum_fraction": 0.4864114023, "num_tokens": 1589, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093733737562, "lm_q2_score": 0.4186969093556867, "lm_q1q2_score": 0.22241572285236272}} {"text": "// Copyright 2019-2020 Josh Pieper, jjp@pobox.com.\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n// http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\n#include \"moteus/tool/calibrate.h\"\n\n#include \n#include \n#include \n\n#include \n\n#include \n\n#include \n\n#include \"mjlib/base/system_error.h\"\n\nnamespace moteus {\nnamespace tool {\n\nnamespace {\n\nconstexpr double kPi = 3.14159265358979323846;\n\nstruct Entry {\n int phase = 0;\n int encoder = 0;\n};\n\nstruct File {\n std::vector phase_up;\n std::vector phase_down;\n};\n\nFile ParseFile(const std::vector& lines_in) {\n std::deque lines;\n std::copy(lines_in.begin(), lines_in.end(), std::back_inserter(lines));\n if (lines.front() != \"CAL start\") {\n mjlib::base::system_error::throw_if(\n true, \"Does not start with magic line: \" + lines.front());\n }\n\n lines.pop_front();\n\n File result;\n\n while (!lines.empty()) {\n const auto line = lines.front();\n lines.pop_front();\n\n if (line == \"CAL done\") {\n return result;\n }\n\n std::vector fields;\n boost::split(fields, line, boost::is_any_of(\" \"));\n\n if (fields.size() != 3) {\n mjlib::base::system_error::throw_if(\n true, \"malformed line: \" + line);\n }\n\n const auto phase = std::stoi(fields[1]);\n const auto encoder = std::stoi(fields[2]);\n const auto entry = Entry{phase, encoder};\n\n if (fields[0] == \"1\") {\n result.phase_up.push_back(entry);\n } else if (fields[0] == \"2\") {\n result.phase_down.push_back(entry);\n } else {\n mjlib::base::system_error::throw_if(\n true, \"unknown phase code: \" + line);\n }\n }\n\n mjlib::base::system_error::throw_if(\n true, \"Does not end with magic line\");\n return {};\n}\n\nint WrapInt16(int value) {\n if (value > 32767) {\n return value - 65536;\n } else if (value < -32768) {\n return 65536 + value;\n }\n return value;\n}\n\ndouble WrapNegPiToPi(double value) {\n if (value >= -kPi && value <= kPi) { return value; }\n if (value > 0.0) {\n return std::fmod(value + kPi, 2 * kPi) - kPi;\n } else {\n return std::fmod(value - kPi, 2 * kPi) + kPi;\n }\n}\n\nEigen::VectorXd Unwrap(const Eigen::VectorXd& value) {\n Eigen::VectorXd result(value.size());\n for (int i = 0; i < value.size(); i++) {\n if (i == 0) {\n result(i) = value(i);\n } else {\n result(i) = (result(i - 1) + WrapNegPiToPi(value(i) - result(i - 1)));\n }\n }\n\n return result;\n}\n\nEigen::VectorXd Linspace(double start, double end, size_t count) {\n Eigen::VectorXd result(count);\n for (size_t i = 0; i < count; i++) {\n result(i) = (\n (i + 1) == count ?\n end :\n i * (end - start) / (count - 1) + start);\n }\n return result;\n}\n\nEigen::VectorXd Range(double start, double end, size_t count) {\n Eigen::VectorXd result(count);\n for (size_t i = 0; i < count; i++) {\n result(i) = (i * (end - start) / count + start);\n }\n return result;\n}\n\nEigen::VectorXd Interpolate(const Eigen::VectorXd& sample_points,\n const Eigen::VectorXd& x,\n const Eigen::VectorXd& y) {\n BOOST_ASSERT(x.size() > 1 && x.size() == y.size());\n\n int xindex = 0;\n\n Eigen::VectorXd result(sample_points.size());\n for (int i = 0; i < sample_points.size(); i++) {\n const auto point = sample_points[i];\n result(i) = [&]() {\n if (point < x[xindex]) {\n // Whoops? This can only legimately happen at the very beginning.\n return y[xindex];\n }\n while ((xindex + 2) < x.size() &&\n point >= x[xindex + 1]) {\n xindex++;\n }\n\n if (point > x[xindex + 1]) {\n // We're past the end.\n return y[xindex + 1];\n }\n\n // Linearly interpolate.\n const double length = x[xindex + 1] - x[xindex];\n if (length == 0.0) {\n return y[xindex + 1];\n }\n double ratio = (point - x[xindex]) / length;\n return (y[xindex + 1] - y[xindex]) * ratio + y[xindex];\n }();\n }\n\n return result;\n}\n\nEigen::VectorXd WindowAverage(const Eigen::VectorXd& values, int window_size) {\n Eigen::VectorXd result(values.size());\n for (int i = 0; i < values.size(); i++) {\n int start = std::max(0, i - window_size / 2);\n int end = std::min(values.size(), i + window_size / 2);\n Eigen::VectorXd errs(end - start);\n for (int i = start; i < end; i++) {\n errs[i - start] = WrapNegPiToPi(values[i] - values[start]);\n }\n result[i] = values[start] + errs.mean();\n }\n\n return result;\n}\n\n}\n\nCalibrationResult Calibrate(const std::vector& lines) {\n auto file = ParseFile(lines);\n\n if (file.phase_up.empty() ||\n file.phase_down.empty()) {\n mjlib::base::system_error::throw_if(\n true, \"one or more phases was empty\");\n }\n\n double total_delta = 0;\n for (size_t i = 0; i < file.phase_up.size() - 1; i++) {\n total_delta += WrapInt16(file.phase_up[i + 1].encoder -\n file.phase_up[i].encoder);\n }\n\n if (std::abs(std::abs(total_delta) - 65536) > 5000) {\n mjlib::base::system_error::throw_if(\n true, \"phase_up did not traverse appropriate encoder distance\");\n }\n\n CalibrationResult result;\n\n // Figure out inversion.\n if (total_delta < 0) {\n result.invert = true;\n for (auto& item : file.phase_up) { item.encoder = 65535 - item.encoder; }\n for (auto& item : file.phase_down) { item.encoder = 65535 - item.encoder; }\n total_delta *= -1;\n }\n\n // Next, figure out the number of poles. We compare the total\n // encoder delta to the total phase delta.\n double total_phase = 0;\n for (size_t i = 0; i < file.phase_up.size() - 1; i++) {\n total_phase += WrapInt16(file.phase_up[i + 1].phase -\n file.phase_up[i].phase);\n }\n\n const double ratio = total_phase / total_delta;\n const double remainder = std::abs(std::round(ratio) - ratio);\n\n const double kMaxRemainderError = 0.1;\n if (remainder > kMaxRemainderError) {\n mjlib::base::system_error::throw_if(\n true, fmt::format(\n \"encoder not an integral multiple of phase, {} > {}\",\n remainder, kMaxRemainderError));\n }\n\n result.total_phase = total_phase;\n result.total_delta = total_delta;\n result.ratio = ratio;\n\n result.poles = static_cast(std::round(ratio) * 2);\n\n // Now we need to figure out the phase offset at select points. We\n // interpolate and average the phase up and phase down sections.\n std::vector phase_up_by_encoder = file.phase_up;\n const auto encoder_sort = [](const auto& lhs, const auto& rhs) {\n return lhs.encoder < rhs.encoder;\n };\n std::sort(phase_up_by_encoder.begin(), phase_up_by_encoder.end(),\n encoder_sort);\n std::vector phase_down_by_encoder = file.phase_down;\n std::sort(phase_down_by_encoder.begin(), phase_down_by_encoder.end(),\n encoder_sort);\n\n double offset = phase_down_by_encoder[0].phase - phase_up_by_encoder[0].phase;\n if (std::abs(offset) > 32767) {\n // We need to shift it so that they start from the same place.\n auto change = static_cast(-65536 * std::round(offset / 65536.0));\n for (auto& item : phase_down_by_encoder) { item.phase += change; }\n }\n\n Eigen::VectorXd phase_up_encoder(phase_up_by_encoder.size());\n for (size_t i = 0; i < phase_up_by_encoder.size(); i++) {\n phase_up_encoder[i] = phase_up_by_encoder[i].encoder;\n }\n\n Eigen::VectorXd phase_up_phase(phase_up_by_encoder.size());\n for (size_t i = 0; i < phase_up_by_encoder.size(); i++) {\n phase_up_phase[i] =\n 2.0 * kPi / 65536.0 * phase_up_by_encoder[i].phase;\n }\n phase_up_phase = Unwrap(phase_up_phase);\n\n Eigen::VectorXd phase_down_encoder(phase_down_by_encoder.size());\n for (size_t i = 0; i < phase_down_by_encoder.size(); i++) {\n phase_down_encoder[i] = phase_down_by_encoder[i].encoder;\n }\n\n Eigen::VectorXd phase_down_phase(phase_down_by_encoder.size());\n for (size_t i = 0; i < phase_down_by_encoder.size(); i++) {\n phase_down_phase[i] =\n 2.0 * kPi / 65536.0 * phase_down_by_encoder[i].phase;\n }\n phase_down_phase = Unwrap(phase_down_phase);\n\n Eigen::VectorXd xpos = Linspace(0, 65535.0, 10000);\n\n const Eigen::VectorXd pu_interp =\n Interpolate(xpos, phase_up_encoder, phase_up_phase);\n const Eigen::VectorXd pd_interp =\n Interpolate(xpos, phase_down_encoder, phase_down_phase);\n const Eigen::VectorXd avg_interp = 0.5 * (pu_interp + pd_interp);\n\n const Eigen::VectorXd expected =\n (2.0 * kPi / 65536) * (result.poles / 2) * xpos;\n\n Eigen::VectorXd err(expected.size());\n for (int i = 0; i < expected.size(); i++) {\n err[i] = WrapNegPiToPi(avg_interp[i] - expected[i]);\n }\n\n // Make the error seem reasonable, so unwrap if we happen to span\n // the pi boundary.\n if ((err.maxCoeff() - err.minCoeff()) > 1.5 * kPi) {\n for (int i = 0; i < err.size(); i++) {\n err[i] = (err[i] > 0) ? err[i] : (err[i] + 2 * kPi);\n }\n }\n\n auto avg_window = static_cast(err.size() / result.poles);\n const Eigen::VectorXd avg_err = WindowAverage(err, avg_window);\n\n const Eigen::VectorXd offset_x = Range(0, 65536, 64);\n const Eigen::VectorXd offsets = Interpolate(offset_x, xpos, avg_err);\n\n for (int i = 0; i < offsets.size(); i++) {\n result.offset.push_back(offsets[i]);\n }\n\n return result;\n}\n\n}\n}\n", "meta": {"hexsha": "9c7bd88145c8841c0c78929c68134ce445a37cf1", "size": 9799, "ext": "cc", "lang": "C++", "max_stars_repo_path": "moteus/tool/calibrate.cc", "max_stars_repo_name": "annhan/moteus", "max_stars_repo_head_hexsha": "03cafe4472da1fe018ed90e24a2b1f00667c688e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "moteus/tool/calibrate.cc", "max_issues_repo_name": "annhan/moteus", "max_issues_repo_head_hexsha": "03cafe4472da1fe018ed90e24a2b1f00667c688e", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "moteus/tool/calibrate.cc", "max_forks_repo_name": "annhan/moteus", "max_forks_repo_head_hexsha": "03cafe4472da1fe018ed90e24a2b1f00667c688e", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.2507462687, "max_line_length": 80, "alphanum_fraction": 0.624145321, "num_tokens": 2730, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.3593641588823761, "lm_q1q2_score": 0.22236750505249436}} {"text": "//\n// Copyright (c) 2000-2002\n// Joerg Walter, Mathias Koch\n//\n// Distributed under the Boost Software License, Version 1.0. (See\n// accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n//\n// The authors gratefully acknowledge the support of\n// GeNeSys mbH & Co. KG in producing this work.\n//\n\n#ifndef _BOOST_UBLAS_MATRIX_EXPRESSION_\n#define _BOOST_UBLAS_MATRIX_EXPRESSION_\n\n#include \n\n// Expression templates based on ideas of Todd Veldhuizen and Geoffrey Furnish\n// Iterators based on ideas of Jeremy Siek\n//\n// Classes that model the Matrix Expression concept\n\nnamespace boost { namespace numeric { namespace ublas {\n\n template\n class matrix_reference:\n public matrix_expression > {\n\n typedef matrix_reference self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using matrix_expression::operator ();\n#endif\n typedef typename E::size_type size_type;\n typedef typename E::difference_type difference_type;\n typedef typename E::value_type value_type;\n typedef typename E::const_reference const_reference;\n typedef typename boost::mpl::if_,\n typename E::const_reference,\n typename E::reference>::type reference;\n typedef E referred_type;\n typedef const self_type const_closure_type;\n typedef self_type closure_type;\n typedef typename E::orientation_category orientation_category;\n typedef typename E::storage_category storage_category;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n explicit matrix_reference (referred_type &e):\n e_ (e) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size1 () const {\n return e_.size1 ();\n }\n BOOST_UBLAS_INLINE\n size_type size2 () const {\n return e_.size2 ();\n }\n\n public:\n // Expression accessors - const correct\n BOOST_UBLAS_INLINE\n const referred_type &expression () const {\n return e_;\n }\n BOOST_UBLAS_INLINE\n referred_type &expression () {\n return e_;\n }\n\n public:\n // Element access\n#ifndef BOOST_UBLAS_REFERENCE_CONST_MEMBER\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type i, size_type j) const {\n return expression () (i, j);\n }\n BOOST_UBLAS_INLINE\n reference operator () (size_type i, size_type j) {\n return expression () (i, j);\n }\n#else\n BOOST_UBLAS_INLINE\n reference operator () (size_type i, size_type j) const {\n return expression () (i, j);\n }\n#endif\n\n // Assignment\n BOOST_UBLAS_INLINE\n matrix_reference &operator = (const matrix_reference &m) {\n expression ().operator = (m);\n return *this;\n }\n template\n BOOST_UBLAS_INLINE\n matrix_reference &operator = (const matrix_expression &ae) {\n expression ().operator = (ae);\n return *this;\n }\n template\n BOOST_UBLAS_INLINE\n matrix_reference &assign (const matrix_expression &ae) {\n expression ().assign (ae);\n return *this;\n }\n template\n BOOST_UBLAS_INLINE\n matrix_reference &operator += (const matrix_expression &ae) {\n expression ().operator += (ae);\n return *this;\n }\n template\n BOOST_UBLAS_INLINE\n matrix_reference &plus_assign (const matrix_expression &ae) {\n expression ().plus_assign (ae);\n return *this;\n }\n template\n BOOST_UBLAS_INLINE\n matrix_reference &operator -= (const matrix_expression &ae) {\n expression ().operator -= (ae);\n return *this;\n }\n template\n BOOST_UBLAS_INLINE\n matrix_reference &minus_assign (const matrix_expression &ae) {\n expression ().minus_assign (ae);\n return *this;\n }\n template\n BOOST_UBLAS_INLINE\n matrix_reference &operator *= (const AT &at) {\n expression ().operator *= (at);\n return *this;\n }\n template\n BOOST_UBLAS_INLINE\n matrix_reference &operator /= (const AT &at) {\n expression ().operator /= (at);\n return *this;\n }\n\n // Swapping\n BOOST_UBLAS_INLINE\n void swap (matrix_reference &m) {\n expression ().swap (m.expression ());\n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const matrix_reference &mr) const {\n return &(*this).e_ == &mr.e_;\n }\n\n // Iterator types\n typedef typename E::const_iterator1 const_iterator1;\n typedef typename boost::mpl::if_,\n typename E::const_iterator1,\n typename E::iterator1>::type iterator1;\n typedef typename E::const_iterator2 const_iterator2;\n typedef typename boost::mpl::if_,\n typename E::const_iterator2,\n typename E::iterator2>::type iterator2;\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator1 find1 (int rank, size_type i, size_type j) const {\n return expression ().find1 (rank, i, j);\n }\n BOOST_UBLAS_INLINE\n iterator1 find1 (int rank, size_type i, size_type j) {\n return expression ().find1 (rank, i, j);\n }\n BOOST_UBLAS_INLINE\n const_iterator2 find2 (int rank, size_type i, size_type j) const {\n return expression ().find2 (rank, i, j);\n }\n BOOST_UBLAS_INLINE\n iterator2 find2 (int rank, size_type i, size_type j) {\n return expression ().find2 (rank, i, j);\n }\n\n // Iterators are the iterators of the referenced expression.\n\n BOOST_UBLAS_INLINE\n const_iterator1 begin1 () const {\n return expression ().begin1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cbegin1 () const {\n return begin1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 end1 () const {\n return expression ().end1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cend1 () const {\n return end1 ();\n }\n\n BOOST_UBLAS_INLINE\n iterator1 begin1 () {\n return expression ().begin1 ();\n }\n BOOST_UBLAS_INLINE\n iterator1 end1 () {\n return expression ().end1 ();\n }\n\n BOOST_UBLAS_INLINE\n const_iterator2 begin2 () const {\n return expression ().begin2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cbegin2 () const {\n return begin2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 end2 () const {\n return expression ().end2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cend2 () const {\n return end2 ();\n }\n\n BOOST_UBLAS_INLINE\n iterator2 begin2 () {\n return expression ().begin2 ();\n }\n BOOST_UBLAS_INLINE\n iterator2 end2 () {\n return expression ().end2 ();\n }\n\n // Reverse iterators\n typedef reverse_iterator_base1 const_reverse_iterator1;\n typedef reverse_iterator_base1 reverse_iterator1;\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rbegin1 () const {\n return const_reverse_iterator1 (end1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crbegin1 () const {\n return rbegin1 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rend1 () const {\n return const_reverse_iterator1 (begin1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crend1 () const {\n return rend1 ();\n }\n\n BOOST_UBLAS_INLINE\n reverse_iterator1 rbegin1 () {\n return reverse_iterator1 (end1 ());\n }\n BOOST_UBLAS_INLINE\n reverse_iterator1 rend1 () {\n return reverse_iterator1 (begin1 ());\n }\n\n typedef reverse_iterator_base2 const_reverse_iterator2;\n typedef reverse_iterator_base2 reverse_iterator2;\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rbegin2 () const {\n return const_reverse_iterator2 (end2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crbegin2 () const {\n return rbegin2 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rend2 () const {\n return const_reverse_iterator2 (begin2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crend2 () const {\n return rend2 ();\n }\n\n BOOST_UBLAS_INLINE\n reverse_iterator2 rbegin2 () {\n return reverse_iterator2 (end2 ());\n }\n BOOST_UBLAS_INLINE\n reverse_iterator2 rend2 () {\n return reverse_iterator2 (begin2 ());\n }\n\n private:\n referred_type &e_;\n };\n\n\n template\n class vector_matrix_binary:\n public matrix_expression > {\n\n typedef E1 expression1_type;\n typedef E2 expression2_type;\n public:\n typedef typename E1::const_closure_type expression1_closure_type;\n typedef typename E2::const_closure_type expression2_closure_type;\n private:\n typedef vector_matrix_binary self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using matrix_expression::operator ();\n#endif\n typedef F functor_type;\n typedef typename promote_traits::promote_type size_type;\n typedef typename promote_traits::promote_type difference_type;\n typedef typename F::result_type value_type;\n typedef value_type const_reference;\n typedef const_reference reference;\n typedef const self_type const_closure_type;\n typedef const_closure_type closure_type;\n typedef unknown_orientation_tag orientation_category;\n typedef unknown_storage_tag storage_category;\n\n // Construction and destruction \n BOOST_UBLAS_INLINE\n vector_matrix_binary (const expression1_type &e1, const expression2_type &e2): \n e1_ (e1), e2_ (e2) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size1 () const {\n return e1_.size ();\n }\n BOOST_UBLAS_INLINE\n size_type size2 () const { \n return e2_.size ();\n }\n\n public:\n // Expression accessors\n BOOST_UBLAS_INLINE\n const expression1_closure_type &expression1 () const {\n return e1_;\n }\n BOOST_UBLAS_INLINE\n const expression2_closure_type &expression2 () const {\n return e2_;\n }\n\n public:\n // Element access\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type i, size_type j) const {\n return functor_type::apply (e1_ (i), e2_ (j));\n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const vector_matrix_binary &vmb) const {\n return (*this).expression1 ().same_closure (vmb.expression1 ()) &&\n (*this).expression2 ().same_closure (vmb.expression2 ());\n }\n\n // Iterator types\n private:\n typedef typename E1::const_iterator const_subiterator1_type;\n typedef typename E2::const_iterator const_subiterator2_type;\n typedef const value_type *const_pointer;\n\n public:\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef indexed_const_iterator1 const_iterator1;\n typedef const_iterator1 iterator1;\n typedef indexed_const_iterator2 const_iterator2;\n typedef const_iterator2 iterator2;\n#else\n class const_iterator1;\n typedef const_iterator1 iterator1;\n class const_iterator2;\n typedef const_iterator2 iterator2;\n#endif\n typedef reverse_iterator_base1 const_reverse_iterator1;\n typedef reverse_iterator_base2 const_reverse_iterator2;\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator1 find1 (int rank, size_type i, size_type j) const {\n const_subiterator1_type it1 (e1_.find (i));\n const_subiterator1_type it1_end (e1_.find (size1 ()));\n const_subiterator2_type it2 (e2_.find (j));\n const_subiterator2_type it2_end (e2_.find (size2 ()));\n if (it2 == it2_end || (rank == 1 && (it2.index () != j || *it2 == value_type/*zero*/()))) {\n it1 = it1_end;\n it2 = it2_end;\n }\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator1 (*this, it1.index (), it2.index ());\n#else\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return const_iterator1 (*this, it1, it2, it2 != it2_end ? *it2 : value_type/*zero*/());\n#else\n return const_iterator1 (*this, it1, it2);\n#endif\n#endif\n }\n BOOST_UBLAS_INLINE\n const_iterator2 find2 (int rank, size_type i, size_type j) const {\n const_subiterator2_type it2 (e2_.find (j));\n const_subiterator2_type it2_end (e2_.find (size2 ()));\n const_subiterator1_type it1 (e1_.find (i));\n const_subiterator1_type it1_end (e1_.find (size1 ()));\n if (it1 == it1_end || (rank == 1 && (it1.index () != i || *it1 == value_type/*zero*/()))) {\n it2 = it2_end;\n it1 = it1_end;\n }\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator2 (*this, it1.index (), it2.index ());\n#else\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return const_iterator2 (*this, it1, it2, it1 != it1_end ? *it1 : value_type/*zero*/());\n#else\n return const_iterator2 (*this, it1, it2);\n#endif\n#endif\n }\n\n // Iterators enhance the iterators of the referenced expressions\n // with the binary functor.\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator1:\n public container_const_reference,\n public iterator_base_traits::iterator_category>::template\n iterator_base::type {\n public:\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef typename vector_matrix_binary::difference_type difference_type;\n typedef typename vector_matrix_binary::value_type value_type;\n typedef typename vector_matrix_binary::const_reference reference;\n typedef typename vector_matrix_binary::const_pointer pointer;\n\n typedef const_iterator2 dual_iterator_type;\n typedef const_reverse_iterator2 dual_reverse_iterator_type;\n\n // Construction and destruction\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n BOOST_UBLAS_INLINE\n const_iterator1 ():\n container_const_reference (), it1_ (), it2_ (), t2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator1 (const self_type &vmb, const const_subiterator1_type &it1, const const_subiterator2_type &it2, value_type t2):\n container_const_reference (vmb), it1_ (it1), it2_ (it2), t2_ (t2) {}\n#else\n BOOST_UBLAS_INLINE\n const_iterator1 ():\n container_const_reference (), it1_ (), it2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator1 (const self_type &vmb, const const_subiterator1_type &it1, const const_subiterator2_type &it2):\n container_const_reference (vmb), it1_ (it1), it2_ (it2) {}\n#endif\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator1 &operator ++ () {\n ++ it1_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -- () {\n -- it1_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator += (difference_type n) {\n it1_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -= (difference_type n) {\n it1_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ - it.it1_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (*it1_, t2_);\n#else\n return functor_type::apply (*it1_, *it2_);\n#endif\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 begin () const {\n return (*this) ().find2 (1, index1 (), 0);\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 end () const {\n return (*this) ().find2 (1, index1 (), (*this) ().size2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rbegin () const {\n return const_reverse_iterator2 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rend () const {\n return const_reverse_iterator2 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it1_.index ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it2_.index ();\n }\n\n // Assignment\n BOOST_UBLAS_INLINE\n const_iterator1 &operator = (const const_iterator1 &it) {\n container_const_reference::assign (&it ());\n it1_ = it.it1_;\n it2_ = it.it2_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n t2_ = it.t2_;\n#endif\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ == it.it1_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ < it.it1_;\n }\n\n private:\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n const_subiterator1_type it1_;\n // Mutable due to assignment\n /* const */ const_subiterator2_type it2_;\n value_type t2_;\n#else\n const_subiterator1_type it1_;\n const_subiterator2_type it2_;\n#endif\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator1 begin1 () const {\n return find1 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cbegin1 () const {\n return begin1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 end1 () const {\n return find1 (0, size1 (), 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cend1 () const {\n return end1 ();\n }\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator2:\n public container_const_reference,\n public iterator_base_traits::iterator_category>::template\n iterator_base::type {\n public:\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef typename vector_matrix_binary::difference_type difference_type;\n typedef typename vector_matrix_binary::value_type value_type;\n typedef typename vector_matrix_binary::const_reference reference;\n typedef typename vector_matrix_binary::const_pointer pointer;\n\n typedef const_iterator1 dual_iterator_type;\n typedef const_reverse_iterator1 dual_reverse_iterator_type;\n\n // Construction and destruction\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n BOOST_UBLAS_INLINE\n const_iterator2 ():\n container_const_reference (), it1_ (), it2_ (), t1_ () {}\n BOOST_UBLAS_INLINE\n const_iterator2 (const self_type &vmb, const const_subiterator1_type &it1, const const_subiterator2_type &it2, value_type t1):\n container_const_reference (vmb), it1_ (it1), it2_ (it2), t1_ (t1) {}\n#else\n BOOST_UBLAS_INLINE\n const_iterator2 ():\n container_const_reference (), it1_ (), it2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator2 (const self_type &vmb, const const_subiterator1_type &it1, const const_subiterator2_type &it2):\n container_const_reference (vmb), it1_ (it1), it2_ (it2) {}\n#endif\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator2 &operator ++ () {\n ++ it2_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -- () {\n -- it2_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator += (difference_type n) {\n it2_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -= (difference_type n) {\n it2_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure(it ()), external_logic ());\n BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ - it.it2_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (t1_, *it2_);\n#else\n return functor_type::apply (*it1_, *it2_);\n#endif\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 begin () const {\n return (*this) ().find1 (1, 0, index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 end () const {\n return (*this) ().find1 (1, (*this) ().size1 (), index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rbegin () const {\n return const_reverse_iterator1 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rend () const {\n return const_reverse_iterator1 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it1_.index ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it2_.index ();\n }\n\n // Assignment\n BOOST_UBLAS_INLINE\n const_iterator2 &operator = (const const_iterator2 &it) {\n container_const_reference::assign (&it ());\n it1_ = it.it1_;\n it2_ = it.it2_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n t1_ = it.t1_;\n#endif\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure( it ()), external_logic ());\n BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ == it.it2_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ < it.it2_;\n }\n\n private:\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n // Mutable due to assignment\n /* const */ const_subiterator1_type it1_;\n const_subiterator2_type it2_;\n value_type t1_;\n#else\n const_subiterator1_type it1_;\n const_subiterator2_type it2_;\n#endif\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator2 begin2 () const {\n return find2 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cbegin2 () const {\n return begin2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 end2 () const {\n return find2 (0, 0, size2 ());\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cend2 () const {\n return end2 ();\n }\n\n // Reverse iterators\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rbegin1 () const {\n return const_reverse_iterator1 (end1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crbegin1 () const {\n return rbegin1 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rend1 () const {\n return const_reverse_iterator1 (begin1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crend1 () const {\n return rend1 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rbegin2 () const {\n return const_reverse_iterator2 (end2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crbegin2 () const {\n return rbegin2 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rend2 () const {\n return const_reverse_iterator2 (begin2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crend2 () const {\n return rend2 ();\n }\n\n private:\n expression1_closure_type e1_;\n expression2_closure_type e2_;\n };\n\n template\n struct vector_matrix_binary_traits {\n typedef vector_matrix_binary expression_type;\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type; \n#else\n // ISSUE matrix is arbitary temporary type\n typedef matrix result_type;\n#endif\n };\n\n // (outer_prod (v1, v2)) [i] [j] = v1 [i] * v2 [j]\n template\n BOOST_UBLAS_INLINE\n typename vector_matrix_binary_traits >::result_type\n outer_prod (const vector_expression &e1,\n const vector_expression &e2) {\n BOOST_STATIC_ASSERT (E1::complexity == 0 && E2::complexity == 0);\n typedef typename vector_matrix_binary_traits >::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n template\n class matrix_unary1:\n public matrix_expression > {\n\n typedef E expression_type;\n typedef F functor_type;\n public:\n typedef typename E::const_closure_type expression_closure_type;\n private:\n typedef matrix_unary1 self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using matrix_expression::operator ();\n#endif\n typedef typename E::size_type size_type;\n typedef typename E::difference_type difference_type;\n typedef typename F::result_type value_type;\n typedef value_type const_reference;\n typedef const_reference reference;\n typedef const self_type const_closure_type;\n typedef const_closure_type closure_type;\n typedef typename E::orientation_category orientation_category;\n typedef unknown_storage_tag storage_category;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n explicit matrix_unary1 (const expression_type &e):\n e_ (e) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size1 () const {\n return e_.size1 ();\n }\n BOOST_UBLAS_INLINE\n size_type size2 () const {\n return e_.size2 ();\n }\n\n public:\n // Expression accessors\n BOOST_UBLAS_INLINE\n const expression_closure_type &expression () const {\n return e_;\n }\n\n public:\n // Element access\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type i, size_type j) const {\n return functor_type::apply (e_ (i, j));\n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const matrix_unary1 &mu1) const {\n return (*this).expression ().same_closure (mu1.expression ());\n }\n\n // Iterator types\n private:\n typedef typename E::const_iterator1 const_subiterator1_type;\n typedef typename E::const_iterator2 const_subiterator2_type;\n typedef const value_type *const_pointer;\n\n public:\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n typedef indexed_const_iterator1 const_iterator1;\n typedef const_iterator1 iterator1;\n typedef indexed_const_iterator2 const_iterator2;\n typedef const_iterator2 iterator2;\n#else\n class const_iterator1;\n typedef const_iterator1 iterator1;\n class const_iterator2;\n typedef const_iterator2 iterator2;\n#endif\n typedef reverse_iterator_base1 const_reverse_iterator1;\n typedef reverse_iterator_base2 const_reverse_iterator2;\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator1 find1 (int rank, size_type i, size_type j) const {\n const_subiterator1_type it1 (e_.find1 (rank, i, j));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator1 (*this, it1.index1 (), it1.index2 ());\n#else\n return const_iterator1 (*this, it1);\n#endif\n }\n BOOST_UBLAS_INLINE\n const_iterator2 find2 (int rank, size_type i, size_type j) const {\n const_subiterator2_type it2 (e_.find2 (rank, i, j));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator2 (*this, it2.index1 (), it2.index2 ());\n#else\n return const_iterator2 (*this, it2);\n#endif\n }\n\n // Iterators enhance the iterators of the referenced expression\n // with the unary functor.\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator1:\n public container_const_reference,\n public iterator_base_traits::template\n iterator_base::type {\n public:\n typedef typename E::const_iterator1::iterator_category iterator_category;\n typedef typename matrix_unary1::difference_type difference_type;\n typedef typename matrix_unary1::value_type value_type;\n typedef typename matrix_unary1::const_reference reference;\n typedef typename matrix_unary1::const_pointer pointer;\n\n typedef const_iterator2 dual_iterator_type;\n typedef const_reverse_iterator2 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator1 ():\n container_const_reference (), it_ () {}\n BOOST_UBLAS_INLINE\n const_iterator1 (const self_type &mu, const const_subiterator1_type &it):\n container_const_reference (mu), it_ (it) {}\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator1 &operator ++ () {\n ++ it_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -- () {\n -- it_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator += (difference_type n) {\n it_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -= (difference_type n) {\n it_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ - it.it_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return functor_type::apply (*it_);\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 begin () const {\n return (*this) ().find2 (1, index1 (), 0);\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 end () const {\n return (*this) ().find2 (1, index1 (), (*this) ().size2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rbegin () const {\n return const_reverse_iterator2 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rend () const {\n return const_reverse_iterator2 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it_.index1 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it_.index2 ();\n }\n\n // Assignment \n BOOST_UBLAS_INLINE\n const_iterator1 &operator = (const const_iterator1 &it) {\n container_const_reference::assign (&it ());\n it_ = it.it_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ == it.it_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ < it.it_;\n }\n\n private:\n const_subiterator1_type it_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator1 begin1 () const {\n return find1 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cbegin1 () const {\n return begin1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 end1 () const {\n return find1 (0, size1 (), 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cend1 () const {\n return end1 ();\n }\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator2:\n public container_const_reference,\n public iterator_base_traits::template\n iterator_base::type {\n public:\n typedef typename E::const_iterator2::iterator_category iterator_category;\n typedef typename matrix_unary1::difference_type difference_type;\n typedef typename matrix_unary1::value_type value_type;\n typedef typename matrix_unary1::const_reference reference;\n typedef typename matrix_unary1::const_pointer pointer;\n\n typedef const_iterator1 dual_iterator_type;\n typedef const_reverse_iterator1 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator2 ():\n container_const_reference (), it_ () {}\n BOOST_UBLAS_INLINE\n const_iterator2 (const self_type &mu, const const_subiterator2_type &it):\n container_const_reference (mu), it_ (it) {}\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator2 &operator ++ () {\n ++ it_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -- () {\n -- it_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator += (difference_type n) {\n it_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -= (difference_type n) {\n it_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ - it.it_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return functor_type::apply (*it_);\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 begin () const {\n return (*this) ().find1 (1, 0, index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 end () const {\n return (*this) ().find1 (1, (*this) ().size1 (), index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rbegin () const {\n return const_reverse_iterator1 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rend () const {\n return const_reverse_iterator1 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it_.index1 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it_.index2 ();\n }\n\n // Assignment \n BOOST_UBLAS_INLINE\n const_iterator2 &operator = (const const_iterator2 &it) {\n container_const_reference::assign (&it ());\n it_ = it.it_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ == it.it_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ < it.it_;\n }\n\n private:\n const_subiterator2_type it_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator2 begin2 () const {\n return find2 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cbegin2 () const {\n return begin2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 end2 () const {\n return find2 (0, 0, size2 ());\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cend2 () const {\n return end2 ();\n }\n\n // Reverse iterators\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rbegin1 () const {\n return const_reverse_iterator1 (end1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crbegin1 () const {\n return rbegin1 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rend1 () const {\n return const_reverse_iterator1 (begin1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crend1 () const {\n return rend1 ();\n }\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rbegin2 () const {\n return const_reverse_iterator2 (end2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crbegin2 () const {\n return rbegin2 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rend2 () const {\n return const_reverse_iterator2 (begin2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crend2 () const {\n return rend2 ();\n }\n\n private:\n expression_closure_type e_;\n };\n\n template\n struct matrix_unary1_traits {\n typedef matrix_unary1 expression_type;\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type; \n#else\n typedef typename E::matrix_temporary_type result_type;\n#endif\n };\n\n // (- m) [i] [j] = - m [i] [j]\n template\n BOOST_UBLAS_INLINE\n typename matrix_unary1_traits >::result_type\n operator - (const matrix_expression &e) {\n typedef typename matrix_unary1_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n\n // (conj m) [i] [j] = conj (m [i] [j])\n template \n BOOST_UBLAS_INLINE\n typename matrix_unary1_traits >::result_type\n conj (const matrix_expression &e) {\n typedef typename matrix_unary1_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n\n // (real m) [i] [j] = real (m [i] [j])\n template \n BOOST_UBLAS_INLINE\n typename matrix_unary1_traits >::result_type\n real (const matrix_expression &e) {\n typedef typename matrix_unary1_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n\n // (imag m) [i] [j] = imag (m [i] [j])\n template \n BOOST_UBLAS_INLINE\n typename matrix_unary1_traits >::result_type\n imag (const matrix_expression &e) {\n typedef typename matrix_unary1_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n\n template\n class matrix_unary2:\n public matrix_expression > {\n\n typedef typename boost::mpl::if_ >,\n E,\n const E>::type expression_type;\n typedef F functor_type;\n public:\n typedef typename boost::mpl::if_,\n typename E::const_closure_type,\n typename E::closure_type>::type expression_closure_type;\n private:\n typedef matrix_unary2 self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using matrix_expression::operator ();\n#endif\n typedef typename E::size_type size_type;\n typedef typename E::difference_type difference_type;\n typedef typename F::result_type value_type;\n typedef value_type const_reference;\n typedef typename boost::mpl::if_ >,\n typename E::reference,\n value_type>::type reference;\n\n typedef const self_type const_closure_type;\n typedef self_type closure_type;\n typedef typename boost::mpl::if_,\n column_major_tag,\n typename boost::mpl::if_,\n row_major_tag,\n typename E::orientation_category>::type>::type orientation_category;\n typedef typename E::storage_category storage_category;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n // matrix_unary2 may be used as mutable expression -\n // this is the only non const expression constructor\n explicit matrix_unary2 (expression_type &e):\n e_ (e) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size1 () const {\n return e_.size2 ();\n }\n BOOST_UBLAS_INLINE\n size_type size2 () const {\n return e_.size1 ();\n }\n\n public:\n // Expression accessors\n BOOST_UBLAS_INLINE\n const expression_closure_type &expression () const {\n return e_;\n }\n\n public:\n // Element access\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type i, size_type j) const {\n return functor_type::apply (e_ (j, i));\n }\n BOOST_UBLAS_INLINE\n reference operator () (size_type i, size_type j) {\n BOOST_STATIC_ASSERT ((boost::is_same >::value));\n return e_ (j, i);\n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const matrix_unary2 &mu2) const {\n return (*this).expression ().same_closure (mu2.expression ());\n }\n\n // Iterator types\n private:\n typedef typename E::const_iterator1 const_subiterator2_type;\n typedef typename E::const_iterator2 const_subiterator1_type;\n typedef const value_type *const_pointer;\n\n public:\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n typedef indexed_const_iterator1 const_iterator1;\n typedef const_iterator1 iterator1;\n typedef indexed_const_iterator2 const_iterator2;\n typedef const_iterator2 iterator2;\n#else\n class const_iterator1;\n typedef const_iterator1 iterator1;\n class const_iterator2;\n typedef const_iterator2 iterator2;\n#endif\n typedef reverse_iterator_base1 const_reverse_iterator1;\n typedef reverse_iterator_base2 const_reverse_iterator2;\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator1 find1 (int rank, size_type i, size_type j) const {\n const_subiterator1_type it1 (e_.find2 (rank, j, i));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator1 (*this, it1.index2 (), it1.index1 ());\n#else\n return const_iterator1 (*this, it1);\n#endif\n }\n BOOST_UBLAS_INLINE\n const_iterator2 find2 (int rank, size_type i, size_type j) const {\n const_subiterator2_type it2 (e_.find1 (rank, j, i));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator2 (*this, it2.index2 (), it2.index1 ());\n#else\n return const_iterator2 (*this, it2);\n#endif\n }\n\n // Iterators enhance the iterators of the referenced expression\n // with the unary functor.\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator1:\n public container_const_reference,\n public iterator_base_traits::template\n iterator_base::type {\n public:\n typedef typename E::const_iterator2::iterator_category iterator_category;\n typedef typename matrix_unary2::difference_type difference_type;\n typedef typename matrix_unary2::value_type value_type;\n typedef typename matrix_unary2::const_reference reference;\n typedef typename matrix_unary2::const_pointer pointer;\n\n typedef const_iterator2 dual_iterator_type;\n typedef const_reverse_iterator2 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator1 ():\n container_const_reference (), it_ () {}\n BOOST_UBLAS_INLINE\n const_iterator1 (const self_type &mu, const const_subiterator1_type &it):\n container_const_reference (mu), it_ (it) {}\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator1 &operator ++ () {\n ++ it_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -- () {\n -- it_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator += (difference_type n) {\n it_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -= (difference_type n) {\n it_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ - it.it_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return functor_type::apply (*it_);\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 begin () const {\n return (*this) ().find2 (1, index1 (), 0);\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 end () const {\n return (*this) ().find2 (1, index1 (), (*this) ().size2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rbegin () const {\n return const_reverse_iterator2 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rend () const {\n return const_reverse_iterator2 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it_.index2 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it_.index1 ();\n }\n\n // Assignment \n BOOST_UBLAS_INLINE\n const_iterator1 &operator = (const const_iterator1 &it) {\n container_const_reference::assign (&it ());\n it_ = it.it_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ == it.it_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ < it.it_;\n }\n\n private:\n const_subiterator1_type it_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator1 begin1 () const {\n return find1 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cbegin1 () const {\n return begin1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 end1 () const {\n return find1 (0, size1 (), 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cend1 () const {\n return end1 ();\n }\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator2:\n public container_const_reference,\n public iterator_base_traits::template\n iterator_base::type {\n public:\n typedef typename E::const_iterator1::iterator_category iterator_category;\n typedef typename matrix_unary2::difference_type difference_type;\n typedef typename matrix_unary2::value_type value_type;\n typedef typename matrix_unary2::const_reference reference;\n typedef typename matrix_unary2::const_pointer pointer;\n\n typedef const_iterator1 dual_iterator_type;\n typedef const_reverse_iterator1 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator2 ():\n container_const_reference (), it_ () {}\n BOOST_UBLAS_INLINE\n const_iterator2 (const self_type &mu, const const_subiterator2_type &it):\n container_const_reference (mu), it_ (it) {}\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator2 &operator ++ () {\n ++ it_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -- () {\n -- it_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator += (difference_type n) {\n it_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -= (difference_type n) {\n it_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ - it.it_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return functor_type::apply (*it_);\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 begin () const {\n return (*this) ().find1 (1, 0, index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 end () const {\n return (*this) ().find1 (1, (*this) ().size1 (), index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rbegin () const {\n return const_reverse_iterator1 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rend () const {\n return const_reverse_iterator1 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it_.index2 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it_.index1 ();\n }\n\n // Assignment\n BOOST_UBLAS_INLINE\n const_iterator2 &operator = (const const_iterator2 &it) {\n container_const_reference::assign (&it ());\n it_ = it.it_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ == it.it_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it_ < it.it_;\n }\n\n private:\n const_subiterator2_type it_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator2 begin2 () const {\n return find2 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cbegin2 () const {\n return begin2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 end2 () const {\n return find2 (0, 0, size2 ());\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cend2 () const {\n return end2 ();\n }\n\n // Reverse iterators\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rbegin1 () const {\n return const_reverse_iterator1 (end1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crbegin1 () const {\n return rbegin1 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rend1 () const {\n return const_reverse_iterator1 (begin1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crend1 () const {\n return rend1 ();\n }\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rbegin2 () const {\n return const_reverse_iterator2 (end2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crbegin2 () const {\n return rbegin2 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rend2 () const {\n return const_reverse_iterator2 (begin2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crend2 () const {\n return rend2 ();\n }\n\n private:\n expression_closure_type e_;\n };\n\n template\n struct matrix_unary2_traits {\n typedef matrix_unary2 expression_type;\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type; \n#else\n typedef typename E::matrix_temporary_type result_type;\n#endif\n };\n\n // (trans m) [i] [j] = m [j] [i]\n template\n BOOST_UBLAS_INLINE\n typename matrix_unary2_traits >::result_type\n trans (const matrix_expression &e) {\n typedef typename matrix_unary2_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n template\n BOOST_UBLAS_INLINE\n typename matrix_unary2_traits >::result_type\n trans (matrix_expression &e) {\n typedef typename matrix_unary2_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n\n // (herm m) [i] [j] = conj (m [j] [i])\n template\n BOOST_UBLAS_INLINE\n typename matrix_unary2_traits >::result_type\n herm (const matrix_expression &e) {\n typedef typename matrix_unary2_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n\n template\n class matrix_binary:\n public matrix_expression > {\n\n typedef E1 expression1_type;\n typedef E2 expression2_type;\n typedef F functor_type;\n public:\n typedef typename E1::const_closure_type expression1_closure_type;\n typedef typename E2::const_closure_type expression2_closure_type;\n private:\n typedef matrix_binary self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using matrix_expression::operator ();\n#endif\n typedef typename promote_traits::promote_type size_type;\n typedef typename promote_traits::promote_type difference_type;\n typedef typename F::result_type value_type;\n typedef value_type const_reference;\n typedef const_reference reference;\n typedef const self_type const_closure_type;\n typedef const_closure_type closure_type;\n typedef unknown_orientation_tag orientation_category;\n typedef unknown_storage_tag storage_category;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n matrix_binary (const E1 &e1, const E2 &e2): \n e1_ (e1), e2_ (e2) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size1 () const { \n return BOOST_UBLAS_SAME (e1_.size1 (), e2_.size1 ());\n }\n BOOST_UBLAS_INLINE\n size_type size2 () const {\n return BOOST_UBLAS_SAME (e1_.size2 (), e2_.size2 ());\n }\n\n public:\n // Expression accessors\n BOOST_UBLAS_INLINE\n const expression1_closure_type &expression1 () const {\n return e1_;\n }\n BOOST_UBLAS_INLINE\n const expression2_closure_type &expression2 () const {\n return e2_;\n }\n\n public:\n // Element access\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type i, size_type j) const {\n return functor_type::apply (e1_ (i, j), e2_ (i, j));\n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const matrix_binary &mb) const {\n return (*this).expression1 ().same_closure (mb.expression1 ()) &&\n (*this).expression2 ().same_closure (mb.expression2 ());\n }\n\n // Iterator types\n private:\n typedef typename E1::const_iterator1 const_iterator11_type;\n typedef typename E1::const_iterator2 const_iterator12_type;\n typedef typename E2::const_iterator1 const_iterator21_type;\n typedef typename E2::const_iterator2 const_iterator22_type;\n typedef const value_type *const_pointer;\n\n public:\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n typedef typename iterator_restrict_traits::iterator_category iterator_category1;\n typedef indexed_const_iterator1 const_iterator1;\n typedef const_iterator1 iterator1;\n typedef typename iterator_restrict_traits::iterator_category iterator_category2;\n typedef indexed_const_iterator2 const_iterator2;\n typedef const_iterator2 iterator2;\n#else\n class const_iterator1;\n typedef const_iterator1 iterator1;\n class const_iterator2;\n typedef const_iterator2 iterator2;\n#endif\n typedef reverse_iterator_base1 const_reverse_iterator1;\n typedef reverse_iterator_base2 const_reverse_iterator2;\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator1 find1 (int rank, size_type i, size_type j) const {\n const_iterator11_type it11 (e1_.find1 (rank, i, j));\n const_iterator11_type it11_end (e1_.find1 (rank, size1 (), j));\n const_iterator21_type it21 (e2_.find1 (rank, i, j));\n const_iterator21_type it21_end (e2_.find1 (rank, size1 (), j));\n BOOST_UBLAS_CHECK (rank == 0 || it11 == it11_end || it11.index2 () == j, internal_logic ())\n BOOST_UBLAS_CHECK (rank == 0 || it21 == it21_end || it21.index2 () == j, internal_logic ())\n i = (std::min) (it11 != it11_end ? it11.index1 () : size1 (),\n it21 != it21_end ? it21.index1 () : size1 ());\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator1 (*this, i, j);\n#else\n return const_iterator1 (*this, i, j, it11, it11_end, it21, it21_end);\n#endif\n }\n BOOST_UBLAS_INLINE\n const_iterator2 find2 (int rank, size_type i, size_type j) const {\n const_iterator12_type it12 (e1_.find2 (rank, i, j));\n const_iterator12_type it12_end (e1_.find2 (rank, i, size2 ()));\n const_iterator22_type it22 (e2_.find2 (rank, i, j));\n const_iterator22_type it22_end (e2_.find2 (rank, i, size2 ()));\n BOOST_UBLAS_CHECK (rank == 0 || it12 == it12_end || it12.index1 () == i, internal_logic ())\n BOOST_UBLAS_CHECK (rank == 0 || it22 == it22_end || it22.index1 () == i, internal_logic ())\n j = (std::min) (it12 != it12_end ? it12.index2 () : size2 (),\n it22 != it22_end ? it22.index2 () : size2 ());\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator2 (*this, i, j);\n#else\n return const_iterator2 (*this, i, j, it12, it12_end, it22, it22_end);\n#endif\n }\n\n // Iterators enhance the iterators of the referenced expression\n // with the binary functor.\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator1:\n public container_const_reference,\n public iterator_base_traits::iterator_category>::template\n iterator_base::type {\n public:\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef typename matrix_binary::difference_type difference_type;\n typedef typename matrix_binary::value_type value_type;\n typedef typename matrix_binary::const_reference reference;\n typedef typename matrix_binary::const_pointer pointer;\n\n typedef const_iterator2 dual_iterator_type;\n typedef const_reverse_iterator2 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator1 ():\n container_const_reference (), i_ (), j_ (), it1_ (), it1_end_ (), it2_ (), it2_end_ () {}\n BOOST_UBLAS_INLINE\n const_iterator1 (const self_type &mb, size_type i, size_type j,\n const const_iterator11_type &it1, const const_iterator11_type &it1_end,\n const const_iterator21_type &it2, const const_iterator21_type &it2_end):\n container_const_reference (mb), i_ (i), j_ (j), it1_ (it1), it1_end_ (it1_end), it2_ (it2), it2_end_ (it2_end) {}\n\n private:\n // Dense specializations\n BOOST_UBLAS_INLINE\n void increment (dense_random_access_iterator_tag) {\n ++ i_; ++ it1_; ++ it2_;\n }\n BOOST_UBLAS_INLINE\n void decrement (dense_random_access_iterator_tag) {\n -- i_; -- it1_; -- it2_;\n }\n BOOST_UBLAS_INLINE\n void increment (dense_random_access_iterator_tag, difference_type n) {\n i_ += n; it1_ += n; it2_ += n;\n }\n BOOST_UBLAS_INLINE\n void decrement (dense_random_access_iterator_tag, difference_type n) {\n i_ -= n; it1_ -= n; it2_ -= n;\n }\n BOOST_UBLAS_INLINE\n value_type dereference (dense_random_access_iterator_tag) const {\n return functor_type::apply (*it1_, *it2_);\n }\n\n // Packed specializations\n BOOST_UBLAS_INLINE\n void increment (packed_random_access_iterator_tag) {\n if (it1_ != it1_end_)\n if (it1_.index1 () <= i_)\n ++ it1_;\n if (it2_ != it2_end_)\n if (it2_.index1 () <= i_)\n ++ it2_;\n ++ i_;\n }\n BOOST_UBLAS_INLINE\n void decrement (packed_random_access_iterator_tag) {\n if (it1_ != it1_end_)\n if (i_ <= it1_.index1 ())\n -- it1_;\n if (it2_ != it2_end_)\n if (i_ <= it2_.index1 ())\n -- it2_;\n -- i_;\n }\n BOOST_UBLAS_INLINE\n void increment (packed_random_access_iterator_tag, difference_type n) {\n while (n > 0) {\n increment (packed_random_access_iterator_tag ());\n --n;\n }\n while (n < 0) {\n decrement (packed_random_access_iterator_tag ());\n ++n;\n }\n }\n BOOST_UBLAS_INLINE\n void decrement (packed_random_access_iterator_tag, difference_type n) {\n while (n > 0) {\n decrement (packed_random_access_iterator_tag ());\n --n;\n }\n while (n < 0) {\n increment (packed_random_access_iterator_tag ());\n ++n;\n }\n }\n BOOST_UBLAS_INLINE\n value_type dereference (packed_random_access_iterator_tag) const {\n value_type t1 = value_type/*zero*/();\n if (it1_ != it1_end_) {\n BOOST_UBLAS_CHECK (it1_.index2 () == j_, internal_logic ());\n if (it1_.index1 () == i_)\n t1 = *it1_;\n }\n value_type t2 = value_type/*zero*/();\n if (it2_ != it2_end_) {\n BOOST_UBLAS_CHECK (it2_.index2 () == j_, internal_logic ());\n if (it2_.index1 () == i_)\n t2 = *it2_;\n }\n return functor_type::apply (t1, t2);\n }\n\n // Sparse specializations\n BOOST_UBLAS_INLINE\n void increment (sparse_bidirectional_iterator_tag) {\n size_type index1 = (*this) ().size1 ();\n if (it1_ != it1_end_) {\n if (it1_.index1 () <= i_)\n ++ it1_;\n if (it1_ != it1_end_)\n index1 = it1_.index1 ();\n }\n size_type index2 = (*this) ().size1 ();\n if (it2_ != it2_end_)\n if (it2_.index1 () <= i_)\n ++ it2_;\n if (it2_ != it2_end_) {\n index2 = it2_.index1 ();\n }\n i_ = (std::min) (index1, index2);\n }\n BOOST_UBLAS_INLINE\n void decrement (sparse_bidirectional_iterator_tag) {\n size_type index1 = (*this) ().size1 ();\n if (it1_ != it1_end_) {\n if (i_ <= it1_.index1 ())\n -- it1_;\n if (it1_ != it1_end_)\n index1 = it1_.index1 ();\n }\n size_type index2 = (*this) ().size1 ();\n if (it2_ != it2_end_) {\n if (i_ <= it2_.index1 ())\n -- it2_;\n if (it2_ != it2_end_)\n index2 = it2_.index1 ();\n }\n i_ = (std::max) (index1, index2);\n }\n BOOST_UBLAS_INLINE\n void increment (sparse_bidirectional_iterator_tag, difference_type n) {\n while (n > 0) {\n increment (sparse_bidirectional_iterator_tag ());\n --n;\n }\n while (n < 0) {\n decrement (sparse_bidirectional_iterator_tag ());\n ++n;\n }\n }\n BOOST_UBLAS_INLINE\n void decrement (sparse_bidirectional_iterator_tag, difference_type n) {\n while (n > 0) {\n decrement (sparse_bidirectional_iterator_tag ());\n --n;\n }\n while (n < 0) {\n increment (sparse_bidirectional_iterator_tag ());\n ++n;\n }\n }\n BOOST_UBLAS_INLINE\n value_type dereference (sparse_bidirectional_iterator_tag) const {\n value_type t1 = value_type/*zero*/();\n if (it1_ != it1_end_) {\n BOOST_UBLAS_CHECK (it1_.index2 () == j_, internal_logic ());\n if (it1_.index1 () == i_)\n t1 = *it1_;\n }\n value_type t2 = value_type/*zero*/();\n if (it2_ != it2_end_) {\n BOOST_UBLAS_CHECK (it2_.index2 () == j_, internal_logic ());\n if (it2_.index1 () == i_)\n t2 = *it2_;\n }\n return functor_type::apply (t1, t2);\n }\n\n public:\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator1 &operator ++ () {\n increment (iterator_category ());\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -- () {\n decrement (iterator_category ());\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator += (difference_type n) {\n increment (iterator_category (), n);\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -= (difference_type n) {\n decrement (iterator_category (), n);\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (index2 () == it.index2 (), external_logic ());\n return index1 () - it.index1 ();\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return dereference (iterator_category ());\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 begin () const {\n return (*this) ().find2 (1, index1 (), 0);\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 end () const {\n return (*this) ().find2 (1, index1 (), (*this) ().size2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rbegin () const {\n return const_reverse_iterator2 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rend () const {\n return const_reverse_iterator2 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return i_;\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n // if (it1_ != it1_end_ && it2_ != it2_end_)\n // return BOOST_UBLAS_SAME (it1_.index2 (), it2_.index2 ());\n // else\n return j_;\n }\n\n // Assignment\n BOOST_UBLAS_INLINE\n const_iterator1 &operator = (const const_iterator1 &it) {\n container_const_reference::assign (&it ());\n i_ = it.i_;\n j_ = it.j_;\n it1_ = it.it1_;\n it1_end_ = it.it1_end_;\n it2_ = it.it2_;\n it2_end_ = it.it2_end_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (index2 () == it.index2 (), external_logic ());\n return index1 () == it.index1 ();\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (index2 () == it.index2 (), external_logic ());\n return index1 () < it.index1 ();\n }\n\n private:\n size_type i_;\n size_type j_;\n const_iterator11_type it1_;\n const_iterator11_type it1_end_;\n const_iterator21_type it2_;\n const_iterator21_type it2_end_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator1 begin1 () const {\n return find1 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cbegin1 () const {\n return begin1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 end1 () const {\n return find1 (0, size1 (), 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cend1 () const {\n return end1 ();\n }\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator2:\n public container_const_reference,\n public iterator_base_traits::iterator_category>::template\n iterator_base::type {\n public:\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef typename matrix_binary::difference_type difference_type;\n typedef typename matrix_binary::value_type value_type;\n typedef typename matrix_binary::const_reference reference;\n typedef typename matrix_binary::const_pointer pointer;\n\n typedef const_iterator1 dual_iterator_type;\n typedef const_reverse_iterator1 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator2 ():\n container_const_reference (), i_ (), j_ (), it1_ (), it1_end_ (), it2_ (), it2_end_ () {}\n BOOST_UBLAS_INLINE\n const_iterator2 (const self_type &mb, size_type i, size_type j,\n const const_iterator12_type &it1, const const_iterator12_type &it1_end,\n const const_iterator22_type &it2, const const_iterator22_type &it2_end):\n container_const_reference (mb), i_ (i), j_ (j), it1_ (it1), it1_end_ (it1_end), it2_ (it2), it2_end_ (it2_end) {}\n\n private:\n // Dense access specializations\n BOOST_UBLAS_INLINE\n void increment (dense_random_access_iterator_tag) {\n ++ j_; ++ it1_; ++ it2_;\n }\n BOOST_UBLAS_INLINE\n void decrement (dense_random_access_iterator_tag) {\n -- j_; -- it1_; -- it2_;\n }\n BOOST_UBLAS_INLINE\n void increment (dense_random_access_iterator_tag, difference_type n) {\n j_ += n; it1_ += n; it2_ += n;\n }\n BOOST_UBLAS_INLINE\n void decrement (dense_random_access_iterator_tag, difference_type n) {\n j_ -= n; it1_ -= n; it2_ -= n;\n }\n BOOST_UBLAS_INLINE\n value_type dereference (dense_random_access_iterator_tag) const {\n return functor_type::apply (*it1_, *it2_);\n }\n\n // Packed specializations\n BOOST_UBLAS_INLINE\n void increment (packed_random_access_iterator_tag) {\n if (it1_ != it1_end_)\n if (it1_.index2 () <= j_)\n ++ it1_;\n if (it2_ != it2_end_)\n if (it2_.index2 () <= j_)\n ++ it2_;\n ++ j_;\n }\n BOOST_UBLAS_INLINE\n void decrement (packed_random_access_iterator_tag) {\n if (it1_ != it1_end_)\n if (j_ <= it1_.index2 ())\n -- it1_;\n if (it2_ != it2_end_)\n if (j_ <= it2_.index2 ())\n -- it2_;\n -- j_;\n }\n BOOST_UBLAS_INLINE\n void increment (packed_random_access_iterator_tag, difference_type n) {\n while (n > 0) {\n increment (packed_random_access_iterator_tag ());\n --n;\n }\n while (n < 0) {\n decrement (packed_random_access_iterator_tag ());\n ++n;\n }\n }\n BOOST_UBLAS_INLINE\n void decrement (packed_random_access_iterator_tag, difference_type n) {\n while (n > 0) {\n decrement (packed_random_access_iterator_tag ());\n --n;\n }\n while (n < 0) {\n increment (packed_random_access_iterator_tag ());\n ++n;\n }\n }\n BOOST_UBLAS_INLINE\n value_type dereference (packed_random_access_iterator_tag) const {\n value_type t1 = value_type/*zero*/();\n if (it1_ != it1_end_) {\n BOOST_UBLAS_CHECK (it1_.index1 () == i_, internal_logic ());\n if (it1_.index2 () == j_)\n t1 = *it1_;\n }\n value_type t2 = value_type/*zero*/();\n if (it2_ != it2_end_) {\n BOOST_UBLAS_CHECK (it2_.index1 () == i_, internal_logic ());\n if (it2_.index2 () == j_)\n t2 = *it2_;\n }\n return functor_type::apply (t1, t2);\n }\n\n // Sparse specializations\n BOOST_UBLAS_INLINE\n void increment (sparse_bidirectional_iterator_tag) {\n size_type index1 = (*this) ().size2 ();\n if (it1_ != it1_end_) {\n if (it1_.index2 () <= j_)\n ++ it1_;\n if (it1_ != it1_end_)\n index1 = it1_.index2 ();\n }\n size_type index2 = (*this) ().size2 ();\n if (it2_ != it2_end_) {\n if (it2_.index2 () <= j_)\n ++ it2_;\n if (it2_ != it2_end_)\n index2 = it2_.index2 ();\n }\n j_ = (std::min) (index1, index2);\n }\n BOOST_UBLAS_INLINE\n void decrement (sparse_bidirectional_iterator_tag) {\n size_type index1 = (*this) ().size2 ();\n if (it1_ != it1_end_) {\n if (j_ <= it1_.index2 ())\n -- it1_;\n if (it1_ != it1_end_)\n index1 = it1_.index2 ();\n }\n size_type index2 = (*this) ().size2 ();\n if (it2_ != it2_end_) {\n if (j_ <= it2_.index2 ())\n -- it2_;\n if (it2_ != it2_end_)\n index2 = it2_.index2 ();\n }\n j_ = (std::max) (index1, index2);\n }\n BOOST_UBLAS_INLINE\n void increment (sparse_bidirectional_iterator_tag, difference_type n) {\n while (n > 0) {\n increment (sparse_bidirectional_iterator_tag ());\n --n;\n }\n while (n < 0) {\n decrement (sparse_bidirectional_iterator_tag ());\n ++n;\n }\n }\n BOOST_UBLAS_INLINE\n void decrement (sparse_bidirectional_iterator_tag, difference_type n) {\n while (n > 0) {\n decrement (sparse_bidirectional_iterator_tag ());\n --n;\n }\n while (n < 0) {\n increment (sparse_bidirectional_iterator_tag ());\n ++n;\n }\n }\n BOOST_UBLAS_INLINE\n value_type dereference (sparse_bidirectional_iterator_tag) const {\n value_type t1 = value_type/*zero*/();\n if (it1_ != it1_end_) {\n BOOST_UBLAS_CHECK (it1_.index1 () == i_, internal_logic ());\n if (it1_.index2 () == j_)\n t1 = *it1_;\n }\n value_type t2 = value_type/*zero*/();\n if (it2_ != it2_end_) {\n BOOST_UBLAS_CHECK (it2_.index1 () == i_, internal_logic ());\n if (it2_.index2 () == j_)\n t2 = *it2_;\n }\n return functor_type::apply (t1, t2);\n }\n\n public:\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator2 &operator ++ () {\n increment (iterator_category ());\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -- () {\n decrement (iterator_category ());\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator += (difference_type n) {\n increment (iterator_category (), n);\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -= (difference_type n) {\n decrement (iterator_category (), n);\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (index1 () == it.index1 (), external_logic ());\n return index2 () - it.index2 ();\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return dereference (iterator_category ());\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 begin () const {\n return (*this) ().find1 (1, 0, index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 end () const {\n return (*this) ().find1 (1, (*this) ().size1 (), index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rbegin () const {\n return const_reverse_iterator1 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rend () const {\n return const_reverse_iterator1 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n // if (it1_ != it1_end_ && it2_ != it2_end_)\n // return BOOST_UBLAS_SAME (it1_.index1 (), it2_.index1 ());\n // else\n return i_;\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return j_;\n }\n\n // Assignment\n BOOST_UBLAS_INLINE\n const_iterator2 &operator = (const const_iterator2 &it) {\n container_const_reference::assign (&it ());\n i_ = it.i_;\n j_ = it.j_;\n it1_ = it.it1_;\n it1_end_ = it.it1_end_;\n it2_ = it.it2_;\n it2_end_ = it.it2_end_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (index1 () == it.index1 (), external_logic ());\n return index2 () == it.index2 ();\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (index1 () == it.index1 (), external_logic ());\n return index2 () < it.index2 ();\n }\n\n private:\n size_type i_;\n size_type j_;\n const_iterator12_type it1_;\n const_iterator12_type it1_end_;\n const_iterator22_type it2_;\n const_iterator22_type it2_end_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator2 begin2 () const {\n return find2 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cbegin2 () const {\n return begin2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 end2 () const {\n return find2 (0, 0, size2 ());\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cend2 () const {\n return end2 ();\n }\n\n // Reverse iterators\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rbegin1 () const {\n return const_reverse_iterator1 (end1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crbegin1 () const {\n return rbegin1 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rend1 () const {\n return const_reverse_iterator1 (begin1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crend1 () const {\n return rend1 ();\n }\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rbegin2 () const {\n return const_reverse_iterator2 (end2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crbegin2 () const {\n return rbegin2 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rend2 () const {\n return const_reverse_iterator2 (begin2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crend2 () const {\n return rend2 ();\n }\n\n private:\n expression1_closure_type e1_;\n expression2_closure_type e2_;\n };\n\n template\n struct matrix_binary_traits {\n typedef matrix_binary expression_type;\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type; \n#else\n typedef typename E1::matrix_temporary_type result_type;\n#endif\n };\n\n // (m1 + m2) [i] [j] = m1 [i] [j] + m2 [i] [j]\n template\n BOOST_UBLAS_INLINE\n typename matrix_binary_traits >::result_type\n operator + (const matrix_expression &e1,\n const matrix_expression &e2) {\n typedef typename matrix_binary_traits >::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n // (m1 - m2) [i] [j] = m1 [i] [j] - m2 [i] [j]\n template\n BOOST_UBLAS_INLINE\n typename matrix_binary_traits >::result_type\n operator - (const matrix_expression &e1,\n const matrix_expression &e2) {\n typedef typename matrix_binary_traits >::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n // (m1 * m2) [i] [j] = m1 [i] [j] * m2 [i] [j]\n template\n BOOST_UBLAS_INLINE\n typename matrix_binary_traits >::result_type\n element_prod (const matrix_expression &e1,\n const matrix_expression &e2) {\n typedef typename matrix_binary_traits >::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n // (m1 / m2) [i] [j] = m1 [i] [j] / m2 [i] [j]\n template\n BOOST_UBLAS_INLINE\n typename matrix_binary_traits >::result_type\n element_div (const matrix_expression &e1,\n const matrix_expression &e2) {\n typedef typename matrix_binary_traits >::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n template\n class matrix_binary_scalar1:\n public matrix_expression > {\n\n typedef E1 expression1_type;\n typedef E2 expression2_type;\n typedef F functor_type;\n typedef const E1& expression1_closure_type;\n typedef typename E2::const_closure_type expression2_closure_type;\n typedef matrix_binary_scalar1 self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using matrix_expression::operator ();\n#endif\n typedef typename E2::size_type size_type;\n typedef typename E2::difference_type difference_type;\n typedef typename F::result_type value_type;\n typedef value_type const_reference;\n typedef const_reference reference;\n typedef const self_type const_closure_type;\n typedef const_closure_type closure_type;\n typedef typename E2::orientation_category orientation_category;\n typedef unknown_storage_tag storage_category;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n matrix_binary_scalar1 (const expression1_type &e1, const expression2_type &e2):\n e1_ (e1), e2_ (e2) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size1 () const {\n return e2_.size1 ();\n }\n BOOST_UBLAS_INLINE\n size_type size2 () const {\n return e2_.size2 ();\n }\n\n public:\n // Element access\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type i, size_type j) const {\n return functor_type::apply (expression1_type (e1_), e2_ (i, j));\n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const matrix_binary_scalar1 &mbs1) const {\n return &e1_ == &(mbs1.e1_) &&\n (*this).e2_.same_closure (mbs1.e2_);\n }\n\n // Iterator types\n private:\n typedef expression1_type const_subiterator1_type;\n typedef typename E2::const_iterator1 const_iterator21_type;\n typedef typename E2::const_iterator2 const_iterator22_type;\n typedef const value_type *const_pointer;\n\n public:\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n typedef indexed_const_iterator1 const_iterator1;\n typedef const_iterator1 iterator1;\n typedef indexed_const_iterator2 const_iterator2;\n typedef const_iterator2 iterator2;\n#else\n class const_iterator1;\n typedef const_iterator1 iterator1;\n class const_iterator2;\n typedef const_iterator2 iterator2;\n#endif\n typedef reverse_iterator_base1 const_reverse_iterator1;\n typedef reverse_iterator_base2 const_reverse_iterator2;\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator1 find1 (int rank, size_type i, size_type j) const {\n const_iterator21_type it21 (e2_.find1 (rank, i, j));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator1 (*this, it21.index1 (), it21.index2 ());\n#else\n return const_iterator1 (*this, const_subiterator1_type (e1_), it21);\n#endif\n }\n BOOST_UBLAS_INLINE\n const_iterator2 find2 (int rank, size_type i, size_type j) const {\n const_iterator22_type it22 (e2_.find2 (rank, i, j));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator2 (*this, it22.index1 (), it22.index2 ());\n#else\n return const_iterator2 (*this, const_subiterator1_type (e1_), it22);\n#endif\n }\n\n // Iterators enhance the iterators of the referenced expression\n // with the binary functor.\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator1:\n public container_const_reference,\n public iterator_base_traits::template\n iterator_base::type {\n public:\n typedef typename E2::const_iterator1::iterator_category iterator_category;\n typedef typename matrix_binary_scalar1::difference_type difference_type;\n typedef typename matrix_binary_scalar1::value_type value_type;\n typedef typename matrix_binary_scalar1::const_reference reference;\n typedef typename matrix_binary_scalar1::const_pointer pointer;\n\n typedef const_iterator2 dual_iterator_type;\n typedef const_reverse_iterator2 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator1 ():\n container_const_reference (), it1_ (), it2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator1 (const self_type &mbs, const const_subiterator1_type &it1, const const_iterator21_type &it2):\n container_const_reference (mbs), it1_ (it1), it2_ (it2) {}\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator1 &operator ++ () {\n ++ it2_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -- () {\n -- it2_ ;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator += (difference_type n) {\n it2_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -= (difference_type n) {\n it2_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ - it.it2_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return functor_type::apply (it1_, *it2_);\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 begin () const {\n return (*this) ().find2 (1, index1 (), 0);\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 end () const {\n return (*this) ().find2 (1, index1 (), (*this) ().size2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rbegin () const {\n return const_reverse_iterator2 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rend () const {\n return const_reverse_iterator2 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it2_.index1 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it2_.index2 ();\n }\n\n // Assignment \n BOOST_UBLAS_INLINE\n const_iterator1 &operator = (const const_iterator1 &it) {\n container_const_reference::assign (&it ());\n it1_ = it.it1_;\n it2_ = it.it2_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ == it.it2_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ < it.it2_;\n }\n\n private:\n const_subiterator1_type it1_;\n const_iterator21_type it2_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator1 begin1 () const {\n return find1 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cbegin1 () const {\n return begin1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 end1 () const {\n return find1 (0, size1 (), 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cend1 () const {\n return end1 ();\n }\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator2:\n public container_const_reference,\n public iterator_base_traits::template\n iterator_base::type {\n public:\n typedef typename E2::const_iterator2::iterator_category iterator_category;\n typedef typename matrix_binary_scalar1::difference_type difference_type;\n typedef typename matrix_binary_scalar1::value_type value_type;\n typedef typename matrix_binary_scalar1::const_reference reference;\n typedef typename matrix_binary_scalar1::const_pointer pointer;\n\n typedef const_iterator1 dual_iterator_type;\n typedef const_reverse_iterator1 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator2 ():\n container_const_reference (), it1_ (), it2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator2 (const self_type &mbs, const const_subiterator1_type &it1, const const_iterator22_type &it2):\n container_const_reference (mbs), it1_ (it1), it2_ (it2) {}\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator2 &operator ++ () {\n ++ it2_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -- () {\n -- it2_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator += (difference_type n) {\n it2_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -= (difference_type n) {\n it2_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ - it.it2_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return functor_type::apply (it1_, *it2_);\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 begin () const {\n return (*this) ().find1 (1, 0, index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 end () const {\n return (*this) ().find1 (1, (*this) ().size1 (), index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rbegin () const {\n return const_reverse_iterator1 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rend () const {\n return const_reverse_iterator1 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it2_.index1 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it2_.index2 ();\n }\n\n // Assignment \n BOOST_UBLAS_INLINE\n const_iterator2 &operator = (const const_iterator2 &it) {\n container_const_reference::assign (&it ());\n it1_ = it.it1_;\n it2_ = it.it2_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ == it.it2_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ < it.it2_;\n }\n\n private:\n const_subiterator1_type it1_;\n const_iterator22_type it2_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator2 begin2 () const {\n return find2 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cbegin2 () const {\n return begin2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 end2 () const {\n return find2 (0, 0, size2 ());\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cend2 () const {\n return end2 ();\n }\n\n // Reverse iterators\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rbegin1 () const {\n return const_reverse_iterator1 (end1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crbegin1 () const {\n return rbegin1 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rend1 () const {\n return const_reverse_iterator1 (begin1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crend1 () const {\n return rend1 ();\n }\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rbegin2 () const {\n return const_reverse_iterator2 (end2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crbegin2 () const {\n return rbegin2 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rend2 () const {\n return const_reverse_iterator2 (begin2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crend2 () const {\n return rend2 ();\n }\n\n private:\n expression1_closure_type e1_;\n expression2_closure_type e2_;\n };\n\n template\n struct matrix_binary_scalar1_traits {\n typedef matrix_binary_scalar1 expression_type; // allow E1 to be builtin type\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type;\n#else\n typedef typename E2::matrix_temporary_type result_type;\n#endif\n };\n\n // (t * m) [i] [j] = t * m [i] [j]\n template\n BOOST_UBLAS_INLINE\n typename enable_if< is_convertible,\n typename matrix_binary_scalar1_traits >::result_type\n >::type\n operator * (const T1 &e1,\n const matrix_expression &e2) {\n typedef typename matrix_binary_scalar1_traits >::expression_type expression_type;\n return expression_type (e1, e2 ());\n }\n\n\n template\n class matrix_binary_scalar2:\n public matrix_expression > {\n\n typedef E1 expression1_type;\n typedef E2 expression2_type;\n typedef F functor_type;\n public:\n typedef typename E1::const_closure_type expression1_closure_type;\n typedef const E2& expression2_closure_type;\n private:\n typedef matrix_binary_scalar2 self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using matrix_expression::operator ();\n#endif\n typedef typename E1::size_type size_type;\n typedef typename E1::difference_type difference_type;\n typedef typename F::result_type value_type;\n typedef value_type const_reference;\n typedef const_reference reference;\n\n typedef const self_type const_closure_type;\n typedef const_closure_type closure_type;\n typedef typename E1::orientation_category orientation_category;\n typedef unknown_storage_tag storage_category;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n matrix_binary_scalar2 (const expression1_type &e1, const expression2_type &e2): \n e1_ (e1), e2_ (e2) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size1 () const {\n return e1_.size1 ();\n }\n BOOST_UBLAS_INLINE\n size_type size2 () const {\n return e1_.size2 ();\n }\n\n public:\n // Element access\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type i, size_type j) const {\n return functor_type::apply (e1_ (i, j), expression2_type (e2_));\n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const matrix_binary_scalar2 &mbs2) const {\n return (*this).e1_.same_closure (mbs2.e1_) &&\n &e2_ == &(mbs2.e2_);\n }\n\n // Iterator types\n private:\n typedef typename E1::const_iterator1 const_iterator11_type;\n typedef typename E1::const_iterator2 const_iterator12_type;\n typedef expression2_type const_subiterator2_type;\n typedef const value_type *const_pointer;\n\n public:\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n typedef indexed_const_iterator1 const_iterator1;\n typedef const_iterator1 iterator1;\n typedef indexed_const_iterator2 const_iterator2;\n typedef const_iterator2 iterator2;\n#else\n class const_iterator1;\n typedef const_iterator1 iterator1;\n class const_iterator2;\n typedef const_iterator2 iterator2;\n#endif\n typedef reverse_iterator_base1 const_reverse_iterator1;\n typedef reverse_iterator_base2 const_reverse_iterator2;\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator1 find1 (int rank, size_type i, size_type j) const {\n const_iterator11_type it11 (e1_.find1 (rank, i, j));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator1 (*this, it11.index1 (), it11.index2 ());\n#else\n return const_iterator1 (*this, it11, const_subiterator2_type (e2_));\n#endif\n }\n BOOST_UBLAS_INLINE\n const_iterator2 find2 (int rank, size_type i, size_type j) const {\n const_iterator12_type it12 (e1_.find2 (rank, i, j));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator2 (*this, it12.index1 (), it12.index2 ());\n#else\n return const_iterator2 (*this, it12, const_subiterator2_type (e2_));\n#endif\n }\n\n // Iterators enhance the iterators of the referenced expression\n // with the binary functor.\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator1:\n public container_const_reference,\n public iterator_base_traits::template\n iterator_base::type {\n public:\n typedef typename E1::const_iterator1::iterator_category iterator_category;\n typedef typename matrix_binary_scalar2::difference_type difference_type;\n typedef typename matrix_binary_scalar2::value_type value_type;\n typedef typename matrix_binary_scalar2::const_reference reference;\n typedef typename matrix_binary_scalar2::const_pointer pointer;\n\n typedef const_iterator2 dual_iterator_type;\n typedef const_reverse_iterator2 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator1 ():\n container_const_reference (), it1_ (), it2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator1 (const self_type &mbs, const const_iterator11_type &it1, const const_subiterator2_type &it2):\n container_const_reference (mbs), it1_ (it1), it2_ (it2) {}\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator1 &operator ++ () {\n ++ it1_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -- () {\n -- it1_ ;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator += (difference_type n) {\n it1_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -= (difference_type n) {\n it1_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ - it.it1_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return functor_type::apply (*it1_, it2_);\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 begin () const {\n return (*this) ().find2 (1, index1 (), 0);\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 end () const {\n return (*this) ().find2 (1, index1 (), (*this) ().size2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rbegin () const {\n return const_reverse_iterator2 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rend () const {\n return const_reverse_iterator2 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it1_.index1 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it1_.index2 ();\n }\n\n // Assignment \n BOOST_UBLAS_INLINE\n const_iterator1 &operator = (const const_iterator1 &it) {\n container_const_reference::assign (&it ());\n it1_ = it.it1_;\n it2_ = it.it2_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ == it.it1_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ < it.it1_;\n }\n\n private:\n const_iterator11_type it1_;\n const_subiterator2_type it2_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator1 begin1 () const {\n return find1 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cbegin1 () const {\n return begin1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 end1 () const {\n return find1 (0, size1 (), 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cend1 () const {\n return end1 ();\n }\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator2:\n public container_const_reference,\n public iterator_base_traits::template\n iterator_base::type {\n public:\n typedef typename E1::const_iterator2::iterator_category iterator_category;\n typedef typename matrix_binary_scalar2::difference_type difference_type;\n typedef typename matrix_binary_scalar2::value_type value_type;\n typedef typename matrix_binary_scalar2::const_reference reference;\n typedef typename matrix_binary_scalar2::const_pointer pointer;\n\n typedef const_iterator1 dual_iterator_type;\n typedef const_reverse_iterator1 dual_reverse_iterator_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n const_iterator2 ():\n container_const_reference (), it1_ (), it2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator2 (const self_type &mbs, const const_iterator12_type &it1, const const_subiterator2_type &it2):\n container_const_reference (mbs), it1_ (it1), it2_ (it2) {}\n\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator2 &operator ++ () {\n ++ it1_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -- () {\n -- it1_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator += (difference_type n) {\n it1_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -= (difference_type n) {\n it1_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ - it.it1_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return functor_type::apply (*it1_, it2_);\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 begin () const {\n return (*this) ().find1 (1, 0, index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 end () const {\n return (*this) ().find1 (1, (*this) ().size1 (), index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rbegin () const {\n return const_reverse_iterator1 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rend () const {\n return const_reverse_iterator1 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it1_.index1 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it1_.index2 ();\n }\n\n // Assignment \n BOOST_UBLAS_INLINE\n const_iterator2 &operator = (const const_iterator2 &it) {\n container_const_reference::assign (&it ());\n it1_ = it.it1_;\n it2_ = it.it2_;\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ == it.it1_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n // FIXME we shouldn't compare floats\n // BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ < it.it1_;\n }\n\n private:\n const_iterator12_type it1_;\n const_subiterator2_type it2_;\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator2 begin2 () const {\n return find2 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cbegin2 () const {\n return begin2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 end2 () const {\n return find2 (0, 0, size2 ());\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cend2 () const {\n return end2 ();\n }\n\n // Reverse iterators\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rbegin1 () const {\n return const_reverse_iterator1 (end1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crbegin1 () const {\n return rbegin1 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rend1 () const {\n return const_reverse_iterator1 (begin1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crend1 () const {\n return rend1 ();\n }\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rbegin2 () const {\n return const_reverse_iterator2 (end2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crbegin2 () const {\n return rbegin2 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rend2 () const {\n return const_reverse_iterator2 (begin2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crend2 () const {\n return rend2 ();\n }\n\n private:\n expression1_closure_type e1_;\n expression2_closure_type e2_;\n };\n\n template\n struct matrix_binary_scalar2_traits {\n typedef matrix_binary_scalar2 expression_type; // allow E2 to be builtin type\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type; \n#else\n typedef typename E1::matrix_temporary_type result_type;\n#endif\n };\n\n // (m * t) [i] [j] = m [i] [j] * t\n template\n BOOST_UBLAS_INLINE\n typename enable_if< is_convertible,\n typename matrix_binary_scalar2_traits >::result_type\n >::type\n operator * (const matrix_expression &e1,\n const T2 &e2) {\n typedef typename matrix_binary_scalar2_traits >::expression_type expression_type;\n return expression_type (e1 (), e2);\n }\n\n // (m / t) [i] [j] = m [i] [j] / t\n template\n BOOST_UBLAS_INLINE\n typename enable_if< is_convertible,\n typename matrix_binary_scalar2_traits >::result_type\n >::type\n operator / (const matrix_expression &e1,\n const T2 &e2) {\n typedef typename matrix_binary_scalar2_traits >::expression_type expression_type;\n return expression_type (e1 (), e2);\n }\n\n\n template\n class matrix_vector_binary1:\n public vector_expression > {\n\n public:\n typedef E1 expression1_type;\n typedef E2 expression2_type;\n private:\n typedef F functor_type;\n public:\n typedef typename E1::const_closure_type expression1_closure_type;\n typedef typename E2::const_closure_type expression2_closure_type;\n private:\n typedef matrix_vector_binary1 self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using vector_expression::operator ();\n#endif\n static const unsigned complexity = 1;\n typedef typename promote_traits::promote_type size_type;\n typedef typename promote_traits::promote_type difference_type;\n typedef typename F::result_type value_type;\n typedef value_type const_reference;\n typedef const_reference reference;\n typedef const self_type const_closure_type;\n typedef const_closure_type closure_type;\n typedef unknown_storage_tag storage_category;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n matrix_vector_binary1 (const expression1_type &e1, const expression2_type &e2):\n e1_ (e1), e2_ (e2) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size () const {\n return e1_.size1 ();\n }\n\n public:\n // Expression accessors\n BOOST_UBLAS_INLINE\n const expression1_closure_type &expression1 () const {\n return e1_;\n }\n BOOST_UBLAS_INLINE\n const expression2_closure_type &expression2 () const {\n return e2_;\n }\n\n public:\n // Element access\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type i) const {\n return functor_type::apply (e1_, e2_, i);\n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const matrix_vector_binary1 &mvb1) const {\n return (*this).expression1 ().same_closure (mvb1.expression1 ()) &&\n (*this).expression2 ().same_closure (mvb1.expression2 ());\n }\n\n // Iterator types\n private:\n typedef typename E1::const_iterator1 const_subiterator1_type;\n typedef typename E2::const_iterator const_subiterator2_type;\n typedef const value_type *const_pointer;\n\n public:\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n typedef indexed_const_iterator const_iterator;\n typedef const_iterator iterator;\n#else\n class const_iterator;\n typedef const_iterator iterator;\n#endif\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator find (size_type i) const {\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n const_subiterator1_type it1 (e1_.find1 (0, i, 0));\n return const_iterator (*this, it1.index1 ());\n#else\n return const_iterator (*this, e1_.find1 (0, i, 0));\n#endif\n }\n\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator:\n public container_const_reference,\n public iterator_base_traits::iterator_category>::template\n iterator_base::type {\n public:\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef typename matrix_vector_binary1::difference_type difference_type;\n typedef typename matrix_vector_binary1::value_type value_type;\n typedef typename matrix_vector_binary1::const_reference reference;\n typedef typename matrix_vector_binary1::const_pointer pointer;\n\n // Construction and destruction\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n BOOST_UBLAS_INLINE\n const_iterator ():\n container_const_reference (), it1_ (), e2_begin_ (), e2_end_ () {}\n BOOST_UBLAS_INLINE\n const_iterator (const self_type &mvb, const const_subiterator1_type &it1):\n container_const_reference (mvb), it1_ (it1), e2_begin_ (mvb.expression2 ().begin ()), e2_end_ (mvb.expression2 ().end ()) {}\n#else\n BOOST_UBLAS_INLINE\n const_iterator ():\n container_const_reference (), it1_ () {}\n BOOST_UBLAS_INLINE\n const_iterator (const self_type &mvb, const const_subiterator1_type &it1):\n container_const_reference (mvb), it1_ (it1) {}\n#endif\n\n private:\n // Dense random access specialization\n BOOST_UBLAS_INLINE\n value_type dereference (dense_random_access_iterator_tag) const {\n const self_type &mvb = (*this) ();\n#ifdef BOOST_UBLAS_USE_INDEXING\n return mvb (index ());\n#elif BOOST_UBLAS_USE_ITERATING\n difference_type size = BOOST_UBLAS_SAME (mvb.expression1 ().size2 (), mvb.expression2 ().size ());\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (size, it1_.begin (), e2_begin_);\n#else\n return functor_type::apply (size, it1_.begin (), mvb.expression2 ().begin ());\n#endif\n#else\n difference_type size = BOOST_UBLAS_SAME (mvb.expression1 ().size2 (), mvb.expression2 ().size ());\n if (size >= BOOST_UBLAS_ITERATOR_THRESHOLD)\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (size, it1_.begin (), e2_begin_);\n#else\n return functor_type::apply (size, it1_.begin (), mvb.expression2 ().begin ());\n#endif\n else\n return mvb (index ());\n#endif\n }\n\n // Packed bidirectional specialization\n BOOST_UBLAS_INLINE\n value_type dereference (packed_random_access_iterator_tag) const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (it1_.begin (), it1_.end (), e2_begin_, e2_end_);\n#else\n const self_type &mvb = (*this) ();\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n return functor_type::apply (it1_.begin (), it1_.end (),\n mvb.expression2 ().begin (), mvb.expression2 ().end ());\n#else\n return functor_type::apply (boost::numeric::ublas::begin (it1_, iterator1_tag ()),\n boost::numeric::ublas::end (it1_, iterator1_tag ()),\n mvb.expression2 ().begin (), mvb.expression2 ().end ());\n#endif\n#endif\n }\n\n // Sparse bidirectional specialization\n BOOST_UBLAS_INLINE\n value_type dereference (sparse_bidirectional_iterator_tag) const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (it1_.begin (), it1_.end (), e2_begin_, e2_end_, sparse_bidirectional_iterator_tag ());\n#else\n const self_type &mvb = (*this) ();\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n return functor_type::apply (it1_.begin (), it1_.end (),\n mvb.expression2 ().begin (), mvb.expression2 ().end (), sparse_bidirectional_iterator_tag ());\n#else\n return functor_type::apply (boost::numeric::ublas::begin (it1_, iterator1_tag ()),\n boost::numeric::ublas::end (it1_, iterator1_tag ()),\n mvb.expression2 ().begin (), mvb.expression2 ().end (), sparse_bidirectional_iterator_tag ());\n#endif\n#endif\n }\n\n public:\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator &operator ++ () {\n ++ it1_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator &operator -- () {\n -- it1_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator &operator += (difference_type n) {\n it1_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator &operator -= (difference_type n) {\n it1_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it1_ - it.it1_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return dereference (iterator_category ());\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n // Index\n BOOST_UBLAS_INLINE\n size_type index () const {\n return it1_.index1 ();\n }\n\n // Assignment\n BOOST_UBLAS_INLINE\n const_iterator &operator = (const const_iterator &it) {\n container_const_reference::assign (&it ());\n it1_ = it.it1_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n e2_begin_ = it.e2_begin_;\n e2_end_ = it.e2_end_;\n#endif\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it1_ == it.it1_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it1_ < it.it1_;\n }\n\n private:\n const_subiterator1_type it1_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n // Mutable due to assignment\n /* const */ const_subiterator2_type e2_begin_;\n /* const */ const_subiterator2_type e2_end_;\n#endif\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator begin () const {\n return find (0);\n }\n BOOST_UBLAS_INLINE\n const_iterator cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n const_iterator end () const {\n return find (size ()); \n }\n BOOST_UBLAS_INLINE\n const_iterator cend () const {\n return end ();\n }\n\n // Reverse iterator\n typedef reverse_iterator_base const_reverse_iterator;\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator rbegin () const {\n return const_reverse_iterator (end ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator rend () const {\n return const_reverse_iterator (begin ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator crend () const {\n return rend ();\n }\n\n private:\n expression1_closure_type e1_;\n expression2_closure_type e2_;\n };\n\n template\n struct matrix_vector_binary1_traits {\n typedef unknown_storage_tag storage_category;\n typedef row_major_tag orientation_category;\n typedef typename promote_traits::promote_type promote_type;\n typedef matrix_vector_binary1 > expression_type;\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type;\n#else\n typedef typename E1::vector_temporary_type result_type;\n#endif\n };\n\n template\n BOOST_UBLAS_INLINE\n typename matrix_vector_binary1_traits::result_type\n prod (const matrix_expression &e1,\n const vector_expression &e2,\n unknown_storage_tag,\n row_major_tag) {\n typedef typename matrix_vector_binary1_traits::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n // Dispatcher\n template\n BOOST_UBLAS_INLINE\n typename matrix_vector_binary1_traits::result_type\n prod (const matrix_expression &e1,\n const vector_expression &e2) {\n BOOST_STATIC_ASSERT (E2::complexity == 0);\n typedef typename matrix_vector_binary1_traits::storage_category storage_category;\n typedef typename matrix_vector_binary1_traits::orientation_category orientation_category;\n return prod (e1, e2, storage_category (), orientation_category ());\n }\n\n template\n BOOST_UBLAS_INLINE\n typename matrix_vector_binary1_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::result_type\n prec_prod (const matrix_expression &e1,\n const vector_expression &e2,\n unknown_storage_tag,\n row_major_tag) {\n typedef typename matrix_vector_binary1_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n // Dispatcher\n template\n BOOST_UBLAS_INLINE\n typename matrix_vector_binary1_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::result_type\n prec_prod (const matrix_expression &e1,\n const vector_expression &e2) {\n BOOST_STATIC_ASSERT (E2::complexity == 0);\n typedef typename matrix_vector_binary1_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::storage_category storage_category;\n typedef typename matrix_vector_binary1_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::orientation_category orientation_category;\n return prec_prod (e1, e2, storage_category (), orientation_category ());\n }\n\n template\n BOOST_UBLAS_INLINE\n V &\n prod (const matrix_expression &e1,\n const vector_expression &e2,\n V &v) {\n return v.assign (prod (e1, e2));\n }\n\n template\n BOOST_UBLAS_INLINE\n V &\n prec_prod (const matrix_expression &e1,\n const vector_expression &e2,\n V &v) {\n return v.assign (prec_prod (e1, e2));\n }\n\n template\n BOOST_UBLAS_INLINE\n V\n prod (const matrix_expression &e1,\n const vector_expression &e2) {\n return V (prod (e1, e2));\n }\n\n template\n BOOST_UBLAS_INLINE\n V\n prec_prod (const matrix_expression &e1,\n const vector_expression &e2) {\n return V (prec_prod (e1, e2));\n }\n\n template\n class matrix_vector_binary2:\n public vector_expression > {\n\n typedef E1 expression1_type;\n typedef E2 expression2_type;\n typedef F functor_type;\n public:\n typedef typename E1::const_closure_type expression1_closure_type;\n typedef typename E2::const_closure_type expression2_closure_type;\n private:\n typedef matrix_vector_binary2 self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using vector_expression::operator ();\n#endif\n static const unsigned complexity = 1;\n typedef typename promote_traits::promote_type size_type;\n typedef typename promote_traits::promote_type difference_type;\n typedef typename F::result_type value_type;\n typedef value_type const_reference;\n typedef const_reference reference;\n typedef const self_type const_closure_type;\n typedef const_closure_type closure_type;\n typedef unknown_storage_tag storage_category;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n matrix_vector_binary2 (const expression1_type &e1, const expression2_type &e2): \n e1_ (e1), e2_ (e2) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size () const { \n return e2_.size2 (); \n }\n\n public:\n // Expression accessors\n BOOST_UBLAS_INLINE\n const expression1_closure_type &expression1 () const {\n return e1_;\n }\n BOOST_UBLAS_INLINE\n const expression2_closure_type &expression2 () const {\n return e2_;\n }\n public:\n\n // Element access\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type j) const { \n return functor_type::apply (e1_, e2_, j); \n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const matrix_vector_binary2 &mvb2) const {\n return (*this).expression1 ().same_closure (mvb2.expression1 ()) &&\n (*this).expression2 ().same_closure (mvb2.expression2 ());\n }\n\n // Iterator types\n private:\n typedef typename E1::const_iterator const_subiterator1_type;\n typedef typename E2::const_iterator2 const_subiterator2_type;\n typedef const value_type *const_pointer;\n\n public:\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n typedef indexed_const_iterator const_iterator;\n typedef const_iterator iterator;\n#else\n class const_iterator;\n typedef const_iterator iterator;\n#endif\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator find (size_type j) const {\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n const_subiterator2_type it2 (e2_.find2 (0, 0, j));\n return const_iterator (*this, it2.index2 ());\n#else\n return const_iterator (*this, e2_.find2 (0, 0, j));\n#endif\n }\n\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator:\n public container_const_reference,\n public iterator_base_traits::iterator_category>::template\n iterator_base::type {\n public:\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef typename matrix_vector_binary2::difference_type difference_type;\n typedef typename matrix_vector_binary2::value_type value_type;\n typedef typename matrix_vector_binary2::const_reference reference;\n typedef typename matrix_vector_binary2::const_pointer pointer;\n\n // Construction and destruction\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n BOOST_UBLAS_INLINE\n const_iterator ():\n container_const_reference (), it2_ (), e1_begin_ (), e1_end_ () {}\n BOOST_UBLAS_INLINE\n const_iterator (const self_type &mvb, const const_subiterator2_type &it2):\n container_const_reference (mvb), it2_ (it2), e1_begin_ (mvb.expression1 ().begin ()), e1_end_ (mvb.expression1 ().end ()) {}\n#else\n BOOST_UBLAS_INLINE\n const_iterator ():\n container_const_reference (), it2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator (const self_type &mvb, const const_subiterator2_type &it2):\n container_const_reference (mvb), it2_ (it2) {}\n#endif\n\n private:\n // Dense random access specialization\n BOOST_UBLAS_INLINE\n value_type dereference (dense_random_access_iterator_tag) const {\n const self_type &mvb = (*this) ();\n#ifdef BOOST_UBLAS_USE_INDEXING\n return mvb (index ());\n#elif BOOST_UBLAS_USE_ITERATING\n difference_type size = BOOST_UBLAS_SAME (mvb.expression2 ().size1 (), mvb.expression1 ().size ());\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (size, e1_begin_, it2_.begin ());\n#else\n return functor_type::apply (size, mvb.expression1 ().begin (), it2_.begin ());\n#endif\n#else\n difference_type size = BOOST_UBLAS_SAME (mvb.expression2 ().size1 (), mvb.expression1 ().size ());\n if (size >= BOOST_UBLAS_ITERATOR_THRESHOLD)\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (size, e1_begin_, it2_.begin ());\n#else\n return functor_type::apply (size, mvb.expression1 ().begin (), it2_.begin ());\n#endif\n else\n return mvb (index ());\n#endif\n }\n\n // Packed bidirectional specialization\n BOOST_UBLAS_INLINE\n value_type dereference (packed_random_access_iterator_tag) const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (e1_begin_, e1_end_, it2_.begin (), it2_.end ());\n#else\n const self_type &mvb = (*this) ();\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n return functor_type::apply (mvb.expression1 ().begin (), mvb.expression1 ().end (),\n it2_.begin (), it2_.end ());\n#else\n return functor_type::apply (mvb.expression1 ().begin (), mvb.expression1 ().end (),\n boost::numeric::ublas::begin (it2_, iterator2_tag ()),\n boost::numeric::ublas::end (it2_, iterator2_tag ()));\n#endif\n#endif\n }\n\n // Sparse bidirectional specialization\n BOOST_UBLAS_INLINE\n value_type dereference (sparse_bidirectional_iterator_tag) const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (e1_begin_, e1_end_, it2_.begin (), it2_.end (), sparse_bidirectional_iterator_tag ());\n#else\n const self_type &mvb = (*this) ();\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n return functor_type::apply (mvb.expression1 ().begin (), mvb.expression1 ().end (),\n it2_.begin (), it2_.end (), sparse_bidirectional_iterator_tag ());\n#else\n return functor_type::apply (mvb.expression1 ().begin (), mvb.expression1 ().end (),\n boost::numeric::ublas::begin (it2_, iterator2_tag ()),\n boost::numeric::ublas::end (it2_, iterator2_tag ()), sparse_bidirectional_iterator_tag ());\n#endif\n#endif\n }\n\n public:\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator &operator ++ () {\n ++ it2_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator &operator -- () {\n -- it2_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator &operator += (difference_type n) {\n it2_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator &operator -= (difference_type n) {\n it2_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it2_ - it.it2_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return dereference (iterator_category ());\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n // Index\n BOOST_UBLAS_INLINE\n size_type index () const {\n return it2_.index2 ();\n }\n\n // Assignment \n BOOST_UBLAS_INLINE\n const_iterator &operator = (const const_iterator &it) {\n container_const_reference::assign (&it ());\n it2_ = it.it2_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n e1_begin_ = it.e1_begin_;\n e1_end_ = it.e1_end_;\n#endif\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it2_ == it.it2_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n return it2_ < it.it2_;\n }\n\n private:\n const_subiterator2_type it2_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n // Mutable due to assignment \n /* const */ const_subiterator1_type e1_begin_;\n /* const */ const_subiterator1_type e1_end_;\n#endif\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator begin () const {\n return find (0);\n }\n BOOST_UBLAS_INLINE\n const_iterator cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n const_iterator end () const {\n return find (size ()); \n }\n BOOST_UBLAS_INLINE\n const_iterator cend () const {\n return end ();\n }\n\n // Reverse iterator\n typedef reverse_iterator_base const_reverse_iterator;\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator rbegin () const {\n return const_reverse_iterator (end ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator rend () const {\n return const_reverse_iterator (begin ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator crend () const {\n return rend ();\n }\n\n private:\n expression1_closure_type e1_;\n expression2_closure_type e2_;\n };\n\n template\n struct matrix_vector_binary2_traits {\n typedef unknown_storage_tag storage_category;\n typedef column_major_tag orientation_category;\n typedef typename promote_traits::promote_type promote_type;\n typedef matrix_vector_binary2 > expression_type;\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type;\n#else\n typedef typename E2::vector_temporary_type result_type;\n#endif\n };\n\n template\n BOOST_UBLAS_INLINE\n typename matrix_vector_binary2_traits::result_type\n prod (const vector_expression &e1,\n const matrix_expression &e2,\n unknown_storage_tag,\n column_major_tag) {\n typedef typename matrix_vector_binary2_traits::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n // Dispatcher\n template\n BOOST_UBLAS_INLINE\n typename matrix_vector_binary2_traits::result_type\n prod (const vector_expression &e1,\n const matrix_expression &e2) {\n BOOST_STATIC_ASSERT (E1::complexity == 0);\n typedef typename matrix_vector_binary2_traits::storage_category storage_category;\n typedef typename matrix_vector_binary2_traits::orientation_category orientation_category;\n return prod (e1, e2, storage_category (), orientation_category ());\n }\n\n template\n BOOST_UBLAS_INLINE\n typename matrix_vector_binary2_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::result_type\n prec_prod (const vector_expression &e1,\n const matrix_expression &e2,\n unknown_storage_tag,\n column_major_tag) {\n typedef typename matrix_vector_binary2_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n // Dispatcher\n template\n BOOST_UBLAS_INLINE\n typename matrix_vector_binary2_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::result_type\n prec_prod (const vector_expression &e1,\n const matrix_expression &e2) {\n BOOST_STATIC_ASSERT (E1::complexity == 0);\n typedef typename matrix_vector_binary2_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::storage_category storage_category;\n typedef typename matrix_vector_binary2_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::orientation_category orientation_category;\n return prec_prod (e1, e2, storage_category (), orientation_category ());\n }\n\n template\n BOOST_UBLAS_INLINE\n V &\n prod (const vector_expression &e1,\n const matrix_expression &e2,\n V &v) {\n return v.assign (prod (e1, e2));\n }\n\n template\n BOOST_UBLAS_INLINE\n V &\n prec_prod (const vector_expression &e1,\n const matrix_expression &e2,\n V &v) {\n return v.assign (prec_prod (e1, e2));\n }\n\n template\n BOOST_UBLAS_INLINE\n V\n prod (const vector_expression &e1,\n const matrix_expression &e2) {\n return V (prod (e1, e2));\n }\n\n template\n BOOST_UBLAS_INLINE\n V\n prec_prod (const vector_expression &e1,\n const matrix_expression &e2) {\n return V (prec_prod (e1, e2));\n }\n\n template\n class matrix_matrix_binary:\n public matrix_expression > {\n\n public:\n typedef E1 expression1_type;\n typedef E2 expression2_type;\n private:\n typedef F functor_type;\n public:\n typedef typename E1::const_closure_type expression1_closure_type;\n typedef typename E2::const_closure_type expression2_closure_type;\n private:\n typedef matrix_matrix_binary self_type;\n public:\n#ifdef BOOST_UBLAS_ENABLE_PROXY_SHORTCUTS\n using matrix_expression::operator ();\n#endif\n static const unsigned complexity = 1;\n typedef typename promote_traits::promote_type size_type;\n typedef typename promote_traits::promote_type difference_type;\n typedef typename F::result_type value_type;\n typedef value_type const_reference;\n typedef const_reference reference;\n typedef const self_type const_closure_type;\n typedef const_closure_type closure_type;\n typedef unknown_orientation_tag orientation_category;\n typedef unknown_storage_tag storage_category;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n matrix_matrix_binary (const expression1_type &e1, const expression2_type &e2):\n e1_ (e1), e2_ (e2) {}\n\n // Accessors\n BOOST_UBLAS_INLINE\n size_type size1 () const {\n return e1_.size1 ();\n }\n BOOST_UBLAS_INLINE\n size_type size2 () const {\n return e2_.size2 ();\n }\n\n public:\n // Expression accessors\n BOOST_UBLAS_INLINE\n const expression1_closure_type &expression1 () const {\n return e1_;\n }\n BOOST_UBLAS_INLINE\n const expression2_closure_type &expression2 () const {\n return e2_;\n }\n\n public:\n // Element access\n BOOST_UBLAS_INLINE\n const_reference operator () (size_type i, size_type j) const {\n return functor_type::apply (e1_, e2_, i, j);\n }\n\n // Closure comparison\n BOOST_UBLAS_INLINE\n bool same_closure (const matrix_matrix_binary &mmb) const {\n return (*this).expression1 ().same_closure (mmb.expression1 ()) &&\n (*this).expression2 ().same_closure (mmb.expression2 ());\n }\n\n // Iterator types\n private:\n typedef typename E1::const_iterator1 const_iterator11_type;\n typedef typename E1::const_iterator2 const_iterator12_type;\n typedef typename E2::const_iterator1 const_iterator21_type;\n typedef typename E2::const_iterator2 const_iterator22_type;\n typedef const value_type *const_pointer;\n\n public:\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef indexed_const_iterator1 const_iterator1;\n typedef const_iterator1 iterator1;\n typedef indexed_const_iterator2 const_iterator2;\n typedef const_iterator2 iterator2;\n#else\n class const_iterator1;\n typedef const_iterator1 iterator1;\n class const_iterator2;\n typedef const_iterator2 iterator2;\n#endif\n typedef reverse_iterator_base1 const_reverse_iterator1;\n typedef reverse_iterator_base2 const_reverse_iterator2;\n\n // Element lookup\n BOOST_UBLAS_INLINE\n const_iterator1 find1 (int /* rank */, size_type i, size_type j) const {\n // FIXME sparse matrix tests fail!\n // const_iterator11_type it11 (e1_.find1 (rank, i, 0));\n const_iterator11_type it11 (e1_.find1 (0, i, 0));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator1 (*this, it11.index1 (), j);\n#else\n // FIXME sparse matrix tests fail!\n // const_iterator22_type it22 (e2_.find2 (rank, 0, j));\n const_iterator22_type it22 (e2_.find2 (0, 0, j));\n return const_iterator1 (*this, it11, it22);\n#endif\n }\n BOOST_UBLAS_INLINE\n const_iterator2 find2 (int /* rank */, size_type i, size_type j) const {\n // FIXME sparse matrix tests fail!\n // const_iterator22_type it22 (e2_.find2 (rank, 0, j));\n const_iterator22_type it22 (e2_.find2 (0, 0, j));\n#ifdef BOOST_UBLAS_USE_INDEXED_ITERATOR\n return const_iterator2 (*this, i, it22.index2 ());\n#else\n // FIXME sparse matrix tests fail!\n // const_iterator11_type it11 (e1_.find1 (rank, i, 0));\n const_iterator11_type it11 (e1_.find1 (0, i, 0));\n return const_iterator2 (*this, it11, it22);\n#endif\n }\n\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator1:\n public container_const_reference,\n public iterator_base_traits::iterator_category>::template\n iterator_base::type {\n public:\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef typename matrix_matrix_binary::difference_type difference_type;\n typedef typename matrix_matrix_binary::value_type value_type;\n typedef typename matrix_matrix_binary::const_reference reference;\n typedef typename matrix_matrix_binary::const_pointer pointer;\n\n typedef const_iterator2 dual_iterator_type;\n typedef const_reverse_iterator2 dual_reverse_iterator_type;\n\n // Construction and destruction\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n BOOST_UBLAS_INLINE\n const_iterator1 ():\n container_const_reference (), it1_ (), it2_ (), it2_begin_ (), it2_end_ () {}\n BOOST_UBLAS_INLINE\n const_iterator1 (const self_type &mmb, const const_iterator11_type &it1, const const_iterator22_type &it2):\n container_const_reference (mmb), it1_ (it1), it2_ (it2), it2_begin_ (it2.begin ()), it2_end_ (it2.end ()) {}\n#else\n BOOST_UBLAS_INLINE\n const_iterator1 ():\n container_const_reference (), it1_ (), it2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator1 (const self_type &mmb, const const_iterator11_type &it1, const const_iterator22_type &it2):\n container_const_reference (mmb), it1_ (it1), it2_ (it2) {}\n#endif\n\n private:\n // Random access specialization\n BOOST_UBLAS_INLINE\n value_type dereference (dense_random_access_iterator_tag) const {\n const self_type &mmb = (*this) ();\n#ifdef BOOST_UBLAS_USE_INDEXING\n return mmb (index1 (), index2 ());\n#elif BOOST_UBLAS_USE_ITERATING\n difference_type size = BOOST_UBLAS_SAME (mmb.expression1 ().size2 (), mmb.expression2 ().size1 ());\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (size, it1_.begin (), it2_begin_);\n#else\n return functor_type::apply (size, it1_.begin (), it2_.begin ());\n#endif\n#else\n difference_type size = BOOST_UBLAS_SAME (mmb.expression1 ().size2 (), mmb.expression2 ().size1 ());\n if (size >= BOOST_UBLAS_ITERATOR_THRESHOLD)\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (size, it1_.begin (), it2_begin_);\n#else\n return functor_type::apply (size, it1_.begin (), it2_.begin ());\n#endif\n else\n return mmb (index1 (), index2 ());\n#endif\n }\n\n // Packed bidirectional specialization\n BOOST_UBLAS_INLINE\n value_type dereference (packed_random_access_iterator_tag) const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (it1_.begin (), it1_.end (),\n it2_begin_, it2_end_, packed_random_access_iterator_tag ());\n#else\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n return functor_type::apply (it1_.begin (), it1_.end (),\n it2_.begin (), it2_.end (), packed_random_access_iterator_tag ());\n#else\n return functor_type::apply (boost::numeric::ublas::begin (it1_, iterator1_tag ()),\n boost::numeric::ublas::end (it1_, iterator1_tag ()),\n boost::numeric::ublas::begin (it2_, iterator2_tag ()),\n boost::numeric::ublas::end (it2_, iterator2_tag ()), packed_random_access_iterator_tag ());\n#endif\n#endif\n }\n\n // Sparse bidirectional specialization\n BOOST_UBLAS_INLINE\n value_type dereference (sparse_bidirectional_iterator_tag) const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (it1_.begin (), it1_.end (),\n it2_begin_, it2_end_, sparse_bidirectional_iterator_tag ());\n#else\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n return functor_type::apply (it1_.begin (), it1_.end (),\n it2_.begin (), it2_.end (), sparse_bidirectional_iterator_tag ());\n#else\n return functor_type::apply (boost::numeric::ublas::begin (it1_, iterator1_tag ()),\n boost::numeric::ublas::end (it1_, iterator1_tag ()),\n boost::numeric::ublas::begin (it2_, iterator2_tag ()),\n boost::numeric::ublas::end (it2_, iterator2_tag ()), sparse_bidirectional_iterator_tag ());\n#endif\n#endif\n }\n\n public:\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator1 &operator ++ () {\n ++ it1_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -- () {\n -- it1_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator += (difference_type n) {\n it1_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator1 &operator -= (difference_type n) {\n it1_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ - it.it1_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return dereference (iterator_category ());\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 begin () const {\n return (*this) ().find2 (1, index1 (), 0);\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 end () const {\n return (*this) ().find2 (1, index1 (), (*this) ().size2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator2 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rbegin () const {\n return const_reverse_iterator2 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 rend () const {\n return const_reverse_iterator2 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator2 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it1_.index1 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it2_.index2 ();\n }\n\n // Assignment\n BOOST_UBLAS_INLINE\n const_iterator1 &operator = (const const_iterator1 &it) {\n container_const_reference::assign (&it ());\n it1_ = it.it1_;\n it2_ = it.it2_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n it2_begin_ = it.it2_begin_;\n it2_end_ = it.it2_end_;\n#endif\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ == it.it1_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator1 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it2_ == it.it2_, external_logic ());\n return it1_ < it.it1_;\n }\n\n private:\n const_iterator11_type it1_;\n // Mutable due to assignment\n /* const */ const_iterator22_type it2_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n /* const */ const_iterator21_type it2_begin_;\n /* const */ const_iterator21_type it2_end_;\n#endif\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator1 begin1 () const {\n return find1 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cbegin1 () const {\n return begin1 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator1 end1 () const {\n return find1 (0, size1 (), 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator1 cend1 () const {\n return end1 ();\n }\n\n#ifndef BOOST_UBLAS_USE_INDEXED_ITERATOR\n class const_iterator2:\n public container_const_reference,\n public iterator_base_traits::iterator_category>::template\n iterator_base::type {\n public:\n typedef typename iterator_restrict_traits::iterator_category iterator_category;\n typedef typename matrix_matrix_binary::difference_type difference_type;\n typedef typename matrix_matrix_binary::value_type value_type;\n typedef typename matrix_matrix_binary::const_reference reference;\n typedef typename matrix_matrix_binary::const_pointer pointer;\n\n typedef const_iterator1 dual_iterator_type;\n typedef const_reverse_iterator1 dual_reverse_iterator_type;\n\n // Construction and destruction\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n BOOST_UBLAS_INLINE\n const_iterator2 ():\n container_const_reference (), it1_ (), it2_ (), it1_begin_ (), it1_end_ () {}\n BOOST_UBLAS_INLINE\n const_iterator2 (const self_type &mmb, const const_iterator11_type &it1, const const_iterator22_type &it2):\n container_const_reference (mmb), it1_ (it1), it2_ (it2), it1_begin_ (it1.begin ()), it1_end_ (it1.end ()) {}\n#else\n BOOST_UBLAS_INLINE\n const_iterator2 ():\n container_const_reference (), it1_ (), it2_ () {}\n BOOST_UBLAS_INLINE\n const_iterator2 (const self_type &mmb, const const_iterator11_type &it1, const const_iterator22_type &it2):\n container_const_reference (mmb), it1_ (it1), it2_ (it2) {}\n#endif\n\n private:\n // Random access specialization\n BOOST_UBLAS_INLINE\n value_type dereference (dense_random_access_iterator_tag) const {\n const self_type &mmb = (*this) ();\n#ifdef BOOST_UBLAS_USE_INDEXING\n return mmb (index1 (), index2 ());\n#elif BOOST_UBLAS_USE_ITERATING\n difference_type size = BOOST_UBLAS_SAME (mmb.expression1 ().size2 (), mmb.expression2 ().size1 ());\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (size, it1_begin_, it2_.begin ());\n#else\n return functor_type::apply (size, it1_.begin (), it2_.begin ());\n#endif\n#else\n difference_type size = BOOST_UBLAS_SAME (mmb.expression1 ().size2 (), mmb.expression2 ().size1 ());\n if (size >= BOOST_UBLAS_ITERATOR_THRESHOLD)\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (size, it1_begin_, it2_.begin ());\n#else\n return functor_type::apply (size, it1_.begin (), it2_.begin ());\n#endif\n else\n return mmb (index1 (), index2 ());\n#endif\n }\n\n // Packed bidirectional specialization\n BOOST_UBLAS_INLINE\n value_type dereference (packed_random_access_iterator_tag) const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (it1_begin_, it1_end_,\n it2_.begin (), it2_.end (), packed_random_access_iterator_tag ());\n#else\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n return functor_type::apply (it1_.begin (), it1_.end (),\n it2_.begin (), it2_.end (), packed_random_access_iterator_tag ());\n#else\n return functor_type::apply (boost::numeric::ublas::begin (it1_, iterator1_tag ()),\n boost::numeric::ublas::end (it1_, iterator1_tag ()),\n boost::numeric::ublas::begin (it2_, iterator2_tag ()),\n boost::numeric::ublas::end (it2_, iterator2_tag ()), packed_random_access_iterator_tag ());\n#endif\n#endif\n }\n\n // Sparse bidirectional specialization\n BOOST_UBLAS_INLINE\n value_type dereference (sparse_bidirectional_iterator_tag) const {\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n return functor_type::apply (it1_begin_, it1_end_,\n it2_.begin (), it2_.end (), sparse_bidirectional_iterator_tag ());\n#else\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n return functor_type::apply (it1_.begin (), it1_.end (),\n it2_.begin (), it2_.end (), sparse_bidirectional_iterator_tag ());\n#else\n return functor_type::apply (boost::numeric::ublas::begin (it1_, iterator1_tag ()),\n boost::numeric::ublas::end (it1_, iterator1_tag ()),\n boost::numeric::ublas::begin (it2_, iterator2_tag ()),\n boost::numeric::ublas::end (it2_, iterator2_tag ()), sparse_bidirectional_iterator_tag ());\n#endif\n#endif\n }\n\n public:\n // Arithmetic\n BOOST_UBLAS_INLINE\n const_iterator2 &operator ++ () {\n ++ it2_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -- () {\n -- it2_;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator += (difference_type n) {\n it2_ += n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n const_iterator2 &operator -= (difference_type n) {\n it2_ -= n;\n return *this;\n }\n BOOST_UBLAS_INLINE\n difference_type operator - (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ - it.it2_;\n }\n\n // Dereference\n BOOST_UBLAS_INLINE\n const_reference operator * () const {\n return dereference (iterator_category ());\n }\n BOOST_UBLAS_INLINE\n const_reference operator [] (difference_type n) const {\n return *(*this + n);\n }\n\n#ifndef BOOST_UBLAS_NO_NESTED_CLASS_RELATION\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 begin () const {\n return (*this) ().find1 (1, 0, index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cbegin () const {\n return begin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 end () const {\n return (*this) ().find1 (1, (*this) ().size1 (), index2 ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_iterator1 cend () const {\n return end ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rbegin () const {\n return const_reverse_iterator1 (end ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crbegin () const {\n return rbegin ();\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 rend () const {\n return const_reverse_iterator1 (begin ());\n }\n BOOST_UBLAS_INLINE\n#ifdef BOOST_UBLAS_MSVC_NESTED_CLASS_RELATION\n typename self_type::\n#endif\n const_reverse_iterator1 crend () const {\n return rend ();\n }\n#endif\n\n // Indices\n BOOST_UBLAS_INLINE\n size_type index1 () const {\n return it1_.index1 ();\n }\n BOOST_UBLAS_INLINE\n size_type index2 () const {\n return it2_.index2 ();\n }\n\n // Assignment\n BOOST_UBLAS_INLINE\n const_iterator2 &operator = (const const_iterator2 &it) {\n container_const_reference::assign (&it ());\n it1_ = it.it1_;\n it2_ = it.it2_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n it1_begin_ = it.it1_begin_;\n it1_end_ = it.it1_end_;\n#endif\n return *this;\n }\n\n // Comparison\n BOOST_UBLAS_INLINE\n bool operator == (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ == it.it2_;\n }\n BOOST_UBLAS_INLINE\n bool operator < (const const_iterator2 &it) const {\n BOOST_UBLAS_CHECK ((*this) ().same_closure (it ()), external_logic ());\n BOOST_UBLAS_CHECK (it1_ == it.it1_, external_logic ());\n return it2_ < it.it2_;\n }\n\n private:\n // Mutable due to assignment\n /* const */ const_iterator11_type it1_;\n const_iterator22_type it2_;\n#ifdef BOOST_UBLAS_USE_INVARIANT_HOISTING\n /* const */ const_iterator12_type it1_begin_;\n /* const */ const_iterator12_type it1_end_;\n#endif\n };\n#endif\n\n BOOST_UBLAS_INLINE\n const_iterator2 begin2 () const {\n return find2 (0, 0, 0);\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cbegin2 () const {\n return begin2 ();\n }\n BOOST_UBLAS_INLINE\n const_iterator2 end2 () const {\n return find2 (0, 0, size2 ());\n }\n BOOST_UBLAS_INLINE\n const_iterator2 cend2 () const {\n return end2 ();\n }\n\n // Reverse iterators\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rbegin1 () const {\n return const_reverse_iterator1 (end1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crbegin1 () const {\n return rbegin1 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 rend1 () const {\n return const_reverse_iterator1 (begin1 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator1 crend1 () const {\n return rend1 ();\n }\n\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rbegin2 () const {\n return const_reverse_iterator2 (end2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crbegin2 () const {\n return rbegin2 ();\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 rend2 () const {\n return const_reverse_iterator2 (begin2 ());\n }\n BOOST_UBLAS_INLINE\n const_reverse_iterator2 crend2 () const {\n return rend2 ();\n }\n\n private:\n expression1_closure_type e1_;\n expression2_closure_type e2_;\n };\n\n template\n struct matrix_matrix_binary_traits {\n typedef unknown_storage_tag storage_category;\n typedef unknown_orientation_tag orientation_category;\n typedef typename promote_traits::promote_type promote_type;\n typedef matrix_matrix_binary > expression_type;\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type;\n#else\n typedef typename E1::matrix_temporary_type result_type;\n#endif\n };\n\n template\n BOOST_UBLAS_INLINE\n typename matrix_matrix_binary_traits::result_type\n prod (const matrix_expression &e1,\n const matrix_expression &e2,\n unknown_storage_tag,\n unknown_orientation_tag) {\n typedef typename matrix_matrix_binary_traits::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n // Dispatcher\n template\n BOOST_UBLAS_INLINE\n typename matrix_matrix_binary_traits::result_type\n prod (const matrix_expression &e1,\n const matrix_expression &e2) {\n BOOST_STATIC_ASSERT (E1::complexity == 0 && E2::complexity == 0);\n typedef typename matrix_matrix_binary_traits::storage_category storage_category;\n typedef typename matrix_matrix_binary_traits::orientation_category orientation_category;\n return prod (e1, e2, storage_category (), orientation_category ());\n }\n\n template\n BOOST_UBLAS_INLINE\n typename matrix_matrix_binary_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::result_type\n prec_prod (const matrix_expression &e1,\n const matrix_expression &e2,\n unknown_storage_tag,\n unknown_orientation_tag) {\n typedef typename matrix_matrix_binary_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::expression_type expression_type;\n return expression_type (e1 (), e2 ());\n }\n\n // Dispatcher\n template\n BOOST_UBLAS_INLINE\n typename matrix_matrix_binary_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::result_type\n prec_prod (const matrix_expression &e1,\n const matrix_expression &e2) {\n BOOST_STATIC_ASSERT (E1::complexity == 0 && E2::complexity == 0);\n typedef typename matrix_matrix_binary_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::storage_category storage_category;\n typedef typename matrix_matrix_binary_traits::precision_type, E1,\n typename type_traits::precision_type, E2>::orientation_category orientation_category;\n return prec_prod (e1, e2, storage_category (), orientation_category ());\n }\n\n template\n BOOST_UBLAS_INLINE\n M &\n prod (const matrix_expression &e1,\n const matrix_expression &e2,\n M &m) {\n return m.assign (prod (e1, e2));\n }\n\n template\n BOOST_UBLAS_INLINE\n M &\n prec_prod (const matrix_expression &e1,\n const matrix_expression &e2,\n M &m) {\n return m.assign (prec_prod (e1, e2));\n }\n\n template\n BOOST_UBLAS_INLINE\n M\n prod (const matrix_expression &e1,\n const matrix_expression &e2) {\n return M (prod (e1, e2));\n }\n\n template\n BOOST_UBLAS_INLINE\n M\n prec_prod (const matrix_expression &e1,\n const matrix_expression &e2) {\n return M (prec_prod (e1, e2));\n }\n\n template\n class matrix_scalar_unary:\n public scalar_expression > {\n public:\n typedef E expression_type;\n typedef F functor_type;\n typedef typename F::result_type value_type;\n typedef typename E::const_closure_type expression_closure_type;\n\n // Construction and destruction\n BOOST_UBLAS_INLINE\n explicit matrix_scalar_unary (const expression_type &e):\n e_ (e) {}\n\n private:\n // Expression accessors\n BOOST_UBLAS_INLINE\n const expression_closure_type &expression () const {\n return e_;\n }\n\n public:\n BOOST_UBLAS_INLINE\n operator value_type () const {\n return functor_type::apply (e_);\n }\n\n private:\n expression_closure_type e_;\n };\n\n template\n struct matrix_scalar_unary_traits {\n typedef matrix_scalar_unary expression_type;\n#ifndef BOOST_UBLAS_SIMPLE_ET_DEBUG\n typedef expression_type result_type;\n#else\n typedef typename F::result_type result_type;\n#endif\n };\n\n template\n BOOST_UBLAS_INLINE\n typename matrix_scalar_unary_traits >::result_type\n norm_1 (const matrix_expression &e) {\n typedef typename matrix_scalar_unary_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n\n template\n BOOST_UBLAS_INLINE\n typename matrix_scalar_unary_traits >::result_type\n norm_frobenius (const matrix_expression &e) {\n typedef typename matrix_scalar_unary_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n\n template\n BOOST_UBLAS_INLINE\n typename matrix_scalar_unary_traits >::result_type\n norm_inf (const matrix_expression &e) {\n typedef typename matrix_scalar_unary_traits >::expression_type expression_type;\n return expression_type (e ());\n }\n\n}}}\n\n#endif\n", "meta": {"hexsha": "a36313096e45d47f0e3e9ea2d1c7ee2e7c1d8532", "size": 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YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.22233196731042748}} {"text": "// Boost.Units - A C++ library for zero-overhead dimensional analysis and \n// unit/quantity manipulation and conversion\n//\n// Copyright (C) 2003-2008 Matthias Christian Schabel\n// Copyright (C) 2007-2008 Steven Watanabe\n//\n// Distributed under the Boost Software License, Version 1.0. (See\n// accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_UNITS_DIM_HPP\n#define BOOST_UNITS_DIM_HPP\n\n#include \n\n#include \n\n#include \n\n#include \n#include \n#include \n\n/// \\file dim.hpp\n/// \\brief Handling of fundamental dimension/exponent pairs.\n\nnamespace boost {\n\nnamespace units {\n\nnamespace detail {\n\nstruct dim_tag { };\n\n}\n\n/// \\brief Dimension tag/exponent pair for a single fundamental dimension.\n///\n/// \\details \n/// The dim class represents a single dimension tag/dimension exponent pair.\n/// That is, @c dim is a pair where @c tag_type represents the\n/// fundamental dimension being represented and @c value_type represents the \n/// exponent of that fundamental dimension as a @c static_rational. @c tag_type must \n/// be a derived from a specialization of @c base_dimension.\n/// Specialization of the following Boost.MPL metafunctions are provided\n///\n/// - @c mpl::plus for two @c dims\n/// - @c mpl::minus for two @c dims\n/// - @c mpl::negate for a @c dim\n///\n/// These metafunctions all operate on the exponent, and require\n/// that the @c dim operands have the same base dimension tag.\n/// In addition, multiplication and division by @c static_rational\n/// is supported.\n///\n/// - @c mpl::times for a @c static_rational and a @c dim in either order\n/// - @c mpl::divides for a @c static_rational and a @c dim in either order\n///\n/// These metafunctions likewise operate on the exponent only.\ntemplate \nstruct dim\n{\n typedef dim type;\n typedef detail::dim_tag tag;\n typedef T tag_type;\n typedef V value_type;\n};\n\n} // namespace units\n\n} // namespace boost\n\n#if BOOST_UNITS_HAS_BOOST_TYPEOF\n\n#include BOOST_TYPEOF_INCREMENT_REGISTRATION_GROUP()\n\nBOOST_TYPEOF_REGISTER_TEMPLATE(boost::units::dim, 2)\n\n#endif\n\n#ifndef BOOST_UNITS_DOXYGEN\n\nnamespace boost {\n\nnamespace mpl {\n\n// define MPL operators acting on dim\n\ntemplate<>\nstruct plus_impl\n{\n template\n struct apply\n {\n BOOST_STATIC_ASSERT((boost::is_same::value == true));\n typedef boost::units::dim::type> type;\n };\n};\n\ntemplate<>\nstruct minus_impl\n{\n template\n struct apply\n {\n BOOST_STATIC_ASSERT((boost::is_same::value == true));\n typedef boost::units::dim::type> type;\n };\n};\n\ntemplate<>\nstruct times_impl\n{\n template\n struct apply\n {\n typedef boost::units::dim::type> type;\n };\n};\n\ntemplate<>\nstruct times_impl\n{\n template\n struct apply\n {\n typedef boost::units::dim::type> type;\n };\n};\n\ntemplate<>\nstruct divides_impl\n{\n template\n struct apply\n {\n typedef boost::units::dim::type> type;\n };\n};\n\ntemplate<>\nstruct divides_impl\n{\n template\n struct apply\n {\n typedef boost::units::dim::type> type;\n };\n};\n\ntemplate<>\nstruct negate_impl\n{\n template\n struct apply\n {\n typedef boost::units::dim::type> type;\n };\n};\n\n} // namespace mpl\n\n} // namespace boost\n\n#endif\n\n#endif // BOOST_UNITS_DIM_HPP\n", "meta": {"hexsha": "eb2813139e5eb59877231c15d49b93872be0bad9", "size": 4745, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/boost/units/dim.hpp", "max_stars_repo_name": "randolphwong/mcsema", "max_stars_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 12278.0, "max_stars_repo_stars_event_min_datetime": "2015-01-29T17:11:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T21:12:00.000Z", "max_issues_repo_path": "boost/boost/units/dim.hpp", "max_issues_repo_name": "randolphwong/mcsema", "max_issues_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 9469.0, "max_issues_repo_issues_event_min_datetime": "2015-01-30T05:33:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T16:17:21.000Z", "max_forks_repo_path": "boost/boost/units/dim.hpp", "max_forks_repo_name": "randolphwong/mcsema", "max_forks_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 892.0, "max_forks_repo_forks_event_min_datetime": "2015-01-29T16:26:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T07:44:30.000Z", "avg_line_length": 28.244047619, "max_line_length": 139, "alphanum_fraction": 0.7051633298, "num_tokens": 1177, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.22233196731042748}} {"text": "#include \"calibrator.hpp\"\n\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include // memset\n\nnamespace{\n\n template \n inline std::string\n toString(T value)\n {\n std::ostringstream stream;\n stream << value;\n return stream.str();\n }\n\n\n float\n gauss(float x, float sigma, float mean){\n return (1.0f/(sigma*sqrt(2.0f * M_PI))) * exp( -0.5f * ((x-mean)/sigma) * ((x-mean)/sigma) );\n }\n\n\n}\n\n/*static*/ bool Calibrator::using_nni = false;\n\nCalibrator::Calibrator()\n : m_nni_possible(0)\n{}\n\n\nCalibrator::~Calibrator(){\n if(m_nni_possible){\n delete [] m_nni_possible;\n }\n}\n\nvoid\nCalibrator::postFilterSamples(CalibVolume* cv, std::vector& sps, const RGBDConfig& cfg, const float post_filter_error_sd, const float post_filter_percentile){\n\n\n std::vector errors_3D;\n std::vector errors_2D;\n\n std::vector> errors_3D_VK;\n std::vector> errors_2D_VK;\n\n\n const unsigned cv_width = cv->width;\n const unsigned cv_height = cv->height;\n const unsigned cv_depth = cv->depth;\n\n for(unsigned i = 0; i < sps.size(); ++i){\n \n const float x = cv_width * ( sps[i].tex_depth.u) / cfg.size_d.x;\n const float y = cv_height * ( sps[i].tex_depth.v)/ cfg.size_d.y;\n const float z = cv_depth * ( sps[i].depth - cv->min_d)/(cv->max_d - cv->min_d);\n\n xyz pos = getTrilinear(cv->cv_xyz, cv_width, cv_height, cv_depth, x , y , z );\n uv tex = getTrilinear(cv->cv_uv, cv_width, cv_height, cv_depth, x , y , z );\n\n const float d_x = sps[i].pos_real[0] - pos.x;\n const float d_y = sps[i].pos_real[1] - pos.y;\n const float d_z = sps[i].pos_real[2] - pos.z;\n \n const float d_u = sps[i].tex_color.u/cfg.size_rgb.x - tex.u;\n const float d_v = sps[i].tex_color.v/cfg.size_rgb.y - tex.v;\n\n const float err_3D(glm::length(glm::vec3(d_x, d_y, d_z)));\n const float err_2D(glm::length(glm::vec2(d_u * cfg.size_rgb.x, d_v * cfg.size_rgb.y)));\n errors_3D.push_back(err_3D);\n errors_2D.push_back(err_2D);\n\n errors_3D_VK.push_back(std::pair(err_3D, i));\n errors_2D_VK.push_back(std::pair(err_2D, i));\n }\n\n if(post_filter_error_sd > 0.0){\n double mean3D, mean2D, sd3D, sd2D;\n calcMeanSD(errors_3D, mean3D, sd3D);\n calcMeanSD(errors_2D, mean2D, sd2D);\n\n unsigned out_3D = 0;\n const float out_3D_thresh = mean3D + post_filter_error_sd * sd3D;\n for(const auto& s_id_VK : errors_3D_VK){\n if(s_id_VK.first > out_3D_thresh){\n\tsps[s_id_VK.second].quality = 0.0;\n\t++out_3D;\n }\n }\n std::cout << \"INFO: Calibrator::postFilterSamples 3D post filter samples and discard samples with << \"\n\t << post_filter_error_sd << \" << more error than standard deviation: \" << out_3D << std::endl;\n\n unsigned out_2D = 0;\n const float out_2D_thresh = mean2D + post_filter_error_sd * sd2D;\n for(const auto& s_id_VK : errors_2D_VK){\n if(s_id_VK.first > out_2D_thresh){\n\tsps[s_id_VK.second].quality = 0.0;\n\t++out_2D;\n }\n }\n std::cout << \"INFO: Calibrator::postFilterSamples 2D post filter samples and discard samples with << \"\n\t << post_filter_error_sd << \" << more error than standard deviation: \" << out_2D << std::endl; \n }\n\n if(post_filter_percentile > 0.0){\n std::sort(errors_3D_VK.begin(), errors_3D_VK.end());\n std::reverse(errors_3D_VK.begin(),errors_3D_VK.end());\n std::sort(errors_2D_VK.begin(), errors_2D_VK.end());\n std::reverse(errors_2D_VK.begin(),errors_2D_VK.end());\n const unsigned last_outlier = std::min(sps.size(), size_t(sps.size() * post_filter_percentile));\n std::cout << \"INFO: Calibrator::postFilterSamples post filter samples, order by error (both, 3D and 2D) and discard \" << post_filter_percentile << \" percentile: \" << last_outlier << std::endl;\n for(unsigned o_id = 0; o_id < last_outlier; ++o_id){\n sps[errors_3D_VK[o_id].second].quality = 0.0;\n sps[errors_2D_VK[o_id].second].quality = 0.0;\n }\n }\n}\n\nvoid\nCalibrator::applySamples(CalibVolume* cv, const std::vector& sps, const RGBDConfig& cfg, unsigned idwneighbours, const char* basefilename, RGBDSensor* sensor, const glm::mat4* eye_d_to_world){\n\n auto start_time = std::chrono::system_clock::now();\n //CGAL : build Tree, search 100 neighbors, try NNI of neighbourhood, fallback to IDW small neighborhood\n std::cout << \"INFO: Calibrator::applySamples applying \" << sps.size()\n\t << \" for volume using on-the-fly initial calibration: \" << (sensor != 0 ? \"yes\" : \"no\") << std::endl;\n\n std::vector nnisamples;\n\n for(unsigned s = 0; s < sps.size(); ++s){\n \n if( !(sps[s].quality > 0.0) ){\n std::cout << \"INFO: calibrator.cpp: skipping sample because quality is zero: \" << sps[s] << std::endl;\n continue;\n }\n nniSample nnis;\n nnis.quality = sps[s].quality;\n nnis.s_pos_cs = sps[s].pos_real;\n\n nnis.s_pos.x = cv->width * ( sps[s].tex_depth.u)/ cfg.size_d.x;\n nnis.s_pos.y = cv->height * ( sps[s].tex_depth.v)/ cfg.size_d.y;\n nnis.s_pos.z = cv->depth * (sps[s].depth - cv->min_d) / (cv->max_d - cv->min_d);\n if( !((sps[s].depth > cv->min_d) && (sps[s].depth < cv->max_d)) ){\n std::cerr << \"ERROR: calibrator.cpp: skipping sample because invalid depth: \" << sps[s] << std::endl;\n continue;\n }\n\n\n // here we will introduce a small error because we have a tri-linear interpolation already in the first stage\n nnis.s_pos_off = sps[s/*sample point*/].pos_offset; // in world coordinates (metric) \n nnis.s_tex_off = sps[s/*sample point*/].tex_offset; // normalized between 0...1\n\n if(0 && sensor && eye_d_to_world /*use on the fly offset computation from initial calibration to avoid above error*/){\n\n const float depth = sps[s].depth;\n const float xd = sps[s].tex_depth.u;\n const float yd = sps[s].tex_depth.v;\n\n glm::vec3 pos3D_local = sensor->calc_pos_d(xd, yd, depth);\n glm::vec2 pos2D_rgb = sensor->calc_pos_rgb(pos3D_local);\n pos2D_rgb.x /= sensor->config.size_rgb.x;\n pos2D_rgb.y /= sensor->config.size_rgb.y;\n\n glm::vec4 pos3D_world = (*eye_d_to_world) * glm::vec4(pos3D_local.x, pos3D_local.y, pos3D_local.z, 1.0);\n\n xyz pos3D;\n pos3D.x = pos3D_world.x;\n pos3D.y = pos3D_world.y;\n pos3D.z = pos3D_world.z;\n \n \n uv posUV;\n posUV.u = pos2D_rgb.x;\n posUV.v = pos2D_rgb.y;\n std::cout << \"INFO: s_pos_off before: \" << nnis.s_pos_off << std::endl;\n#if 0\n xyz pos_tmp;\n xyz pos_tmp_tri = getTrilinear(cv->cv_xyz, cv->width, cv->height, cv->depth,\n\t\t\t\t nnis.s_pos.x ,nnis.s_pos.y , nnis.s_pos.z );\n pos_tmp.x = sps[s/*sample point*/].pos_real[0] - pos_tmp_tri.x;\n pos_tmp.y = sps[s/*sample point*/].pos_real[1] - pos_tmp_tri.y;\n pos_tmp.z = sps[s/*sample point*/].pos_real[2] - pos_tmp_tri.z;\n#endif\n //std::cout << \"INFO: s_pos_off with Trilinear: \" << pos_tmp << std::endl;\n //std::cout << \"INFO: pos_tmp_tri: \" << pos_tmp_tri << std::endl;\n //std::cout << \"INFO: pos3D_onthefly: \" << pos3D << std::endl;\n \n nnis.s_pos_off.x = sps[s/*sample point*/].pos_real[0] - pos3D.x;\n nnis.s_pos_off.y = sps[s/*sample point*/].pos_real[1] - pos3D.y;\n nnis.s_pos_off.z = sps[s/*sample point*/].pos_real[2] - pos3D.z;\n std::cout << \"INFO: s_pos_off after: \" << nnis.s_pos_off << std::endl;\n uv sps_tex_color_norm = sps[s/*sample point*/].tex_color;\n sps_tex_color_norm.u /= sensor->config.size_rgb.x;\n sps_tex_color_norm.v /= sensor->config.size_rgb.y;\n std::cout << \"INFO: s_tex_off before: \" << nnis.s_tex_off << std::endl;\n nnis.s_tex_off = sps_tex_color_norm - posUV;\n std::cout << \"INFO: s_tex_off after: \" << nnis.s_tex_off << std::endl;\n }\n\n \n \n\n\n //std::cerr << s << \" \" << nnis << std::endl;\n nnisamples.push_back(nnis);\n }\n\n \n Resampler rsa;\n rsa.resampleGridBased(nnisamples, cv,\tbasefilename);\n\n#if 0\n std::cout << \"INFO: initializing nearest neighbor search for border fill \" << nnisamples.size() << \" samples.\" << std::endl;\n NearestNeighbourSearch nns_before_border_fill(nnisamples);\n rsa.fillBorder(nnisamples, cv, &nns_before_border_fill, idwneighbours, basefilename);\n //exit(0);\n#endif\n\n std::cout << \"INFO: initializing nearest neighbor search for interpolation of calibvolume using \" << nnisamples.size() << \" samples.\" << std::endl;\n NearestNeighbourSearch nns(nnisamples);\n\n // init calib volume for natural neighbor interpolation\n NaturalNeighbourInterpolator* nnip = 0;\n CalibVolume* cv_nni = 0;\n if(using_nni){\n if(m_nni_possible == 0){\n m_nni_possible = new unsigned char [cv->width * cv->height * cv->depth];\n }\n memset(m_nni_possible, 0, cv->width * cv->height * cv->depth);\n cv_nni = new CalibVolume(cv->width, cv->height, cv->depth, cv->min_d, cv->max_d);\n std::cout << \"INFO: initializing natural neighbor interpolation for interpolation of calibvolume using \" << nnisamples.size() << \" samples.\" << std::endl;\n std::shuffle(std::begin(nnisamples), std::end(nnisamples), std::default_random_engine());\n nnip = new NaturalNeighbourInterpolator(nnisamples);\n }\n\n const unsigned numthreads = 32;\n std::cout << \"INFO: start interpolation per thread for \" << numthreads << \" threads.\" << std::endl;\n boost::thread_group threadGroup;\n for (unsigned tid = 0; tid < numthreads; ++tid){\n threadGroup.create_thread(boost::bind(&Calibrator::applySamplesPerThread, this, cv, &nns, tid, numthreads, idwneighbours, cv_nni, nnip));\n }\n threadGroup.join_all();\n\n \n if(using_nni){\n blendIDW2NNI(cv, cv_nni, basefilename);\n }\n\n auto end_time = std::chrono::system_clock::now();\n std::cout << \"INFO: finished interpolation in \"\n\t << std::chrono::duration_cast>(end_time - start_time).count()\n\t << \" seconds\" << std::endl;\n\n}\n\n\nvoid\nCalibrator::blendIDW2NNI(CalibVolume* cv, CalibVolume* cv_nni, const char* basefilename){\n\n std::cerr << \"USING NNI therefore blending IDW into NNI\" << std::endl;\n\n // print stats for nni_possible and write to volume file\n size_t possible = 0;\n for(size_t idx = 0; idx < (cv->width * cv->height * cv->depth); ++idx){\n possible += m_nni_possible[idx] > 0 ? 1 : 0;\n }\n std::cerr << \"natural_neighbor_interpolation_was_possible for: \"\n\t << possible << \" out of: \" << (cv->width * cv->height * cv->depth)\n\t << std::endl;\n\n // 1st pass detect border\n unsigned char* nni_border = new unsigned char[cv->width * cv->height * cv->depth];\n memset(nni_border, 0, cv->width * cv->height * cv->depth);\n const int border_size = 1;\n for(unsigned z = 0; z < cv->depth; ++z){\n for(unsigned y = 0; y < cv->height; ++y){\n for(unsigned x = 0; x < cv->width; ++x){\n\tconst unsigned cv_index = (z * cv->width * cv->height) + (y * cv->width) + x;\n\tif(m_nni_possible[cv_index]){\n\t bool border = false;\n\t for(int z_l = std::max(0 , (int(z) - border_size));\n\t z_l < std::min(int(cv->depth), (int(z) + border_size + 1));\n\t ++z_l){\n\t for(int y_l = std::max(0 , (int(y) - border_size));\n\t\ty_l < std::min(int(cv->height), (int(y) + border_size + 1));\n\t\t++y_l){\n\t for(int x_l = std::max(0 , (int(x) - border_size));\n\t\t x_l < std::min(int(cv->width), (int(x) + border_size + 1));\n\t\t ++x_l){\n\t\tconst unsigned cv_index_l = (z_l * cv->width * cv->height) + (y_l * cv->width) + x_l;\n\t\tif(0 == m_nni_possible[cv_index_l]){\n\t\t border = true;\n\t\t}\n\t }\n\t }\n\t }\n\t nni_border[cv_index] = border ? 255 : 0;\n\t}\n }\n }\n }\n\n // 2nd pass blend between NNI and IDW\n unsigned char* nni_percentage = new unsigned char [cv->width * cv->height * cv->depth];\n memset(nni_percentage, 0, cv->width * cv->height * cv->depth);\n const int kernel_size = 8;\n const float min_dist = 1.0f;\n const float max_dist = 6.0f;\n\n for(unsigned z = 0; z < cv->depth; ++z){\n for(unsigned y = 0; y < cv->height; ++y){\n for(unsigned x = 0; x < cv->width; ++x){\n\tconst unsigned cv_index = (z * cv->width * cv->height) + (y * cv->width) + x;\n\tif(m_nni_possible[cv_index]){\n\t float distance_to_border = std::numeric_limits::max();\n\t glm::vec3 voxel_curr(1.0f * x, 1.0f * y, 1.0f * z);\n\t for(int z_l = std::max(0 , (int(z) - kernel_size));\n\t z_l < std::min(int(cv->depth), (int(z) + kernel_size + 1));\n\t ++z_l){\n\t for(int y_l = std::max(0 , (int(y) - kernel_size));\n\t\ty_l < std::min(int(cv->height), (int(y) + kernel_size + 1));\n\t\t++y_l){\n\t for(int x_l = std::max(0 , (int(x) - kernel_size));\n\t\t x_l < std::min(int(cv->width), (int(x) + kernel_size + 1));\n\t\t ++x_l){\n\t\tconst unsigned cv_index_l = (z_l * cv->width * cv->height) + (y_l * cv->width) + x_l;\n\t\tif(nni_border[cv_index_l] > 0){\n\t\t distance_to_border = std::min(distance_to_border,\n\t\t\t\t\t\tglm::length(voxel_curr - glm::vec3(1.0f * x_l, 1.0f * y_l, 1.0f * z_l)));\n\t\t}\n\t }\n\t }\n\t }\n \n\t const float t_blend = std::min( max_dist, std::max(min_dist, distance_to_border)) / max_dist;\n\t if(t_blend > 1.0f || t_blend < 0.0f){\n\t std::cerr << \"ERROR: invalid t_blend: \" << t_blend << std::endl;\n\t exit(0);\n\t }\n\n\t nni_percentage[cv_index] = (unsigned char) std::max(0.0f, std::min(255.0f, 255.0f * t_blend));\n\t const xyz xyz_idw = cv->cv_xyz[cv_index];\n\t const xyz xyz_nni = cv_nni->cv_xyz[cv_index];\n\t \n\t const uv uv_idw = cv->cv_uv[cv_index];\n\t const uv uv_nni = cv_nni->cv_uv[cv_index];\n\t \n\t cv->cv_xyz[cv_index] = interpolate(xyz_idw, xyz_nni, t_blend);\n\t cv->cv_uv[cv_index] = interpolate(uv_idw , uv_nni , t_blend);\n\n\t}\n }\n }\n }\n\n\n std::cerr << \"writing \" << (std::string(basefilename) + \"_nnistats\").c_str() << std::endl;\n FILE* f_nni_stats = fopen((std::string(basefilename) + \"_nnistats\").c_str(), \"wb\");\n fwrite(m_nni_possible, sizeof(unsigned char), (cv->width * cv->height * cv->depth), f_nni_stats);\n fclose(f_nni_stats);\n\n\n std::cerr << \"writing \" << (std::string(basefilename) + \"_nniborder\").c_str() << std::endl;\n FILE* f_nni_border = fopen((std::string(basefilename) + \"_nniborder\").c_str(), \"wb\");\n fwrite(nni_border, sizeof(unsigned char), (cv->width * cv->height * cv->depth), f_nni_border);\n fclose(f_nni_border);\n \n std::cerr << \"writing \" << (std::string(basefilename) + \"_percentage\").c_str() << std::endl;\n FILE* f_nni_percentage = fopen((std::string(basefilename) + \"_percentage\").c_str(), \"wb\");\n fwrite(nni_percentage, sizeof(unsigned char), (cv->width * cv->height * cv->depth), f_nni_percentage);\n fclose(f_nni_percentage);\n\n\n delete [] nni_border;\n delete [] nni_percentage;\n\n}\n\nvoid\nCalibrator::evaluateSamples(CalibVolume* cv, std::vector& sps, const RGBDConfig& cfg, const char* basefilename, bool isnni, bool create_error_vis){\n\n unsigned char* cv_nnistats = 0;\n unsigned nni_valids = 0;\n if(isnni){\n cv_nnistats = new unsigned char [cv->width * cv->height * cv->depth];\n // load .cv_nnistats if nni has to be evaluated\n FILE* f_nni_stats = fopen((std::string(basefilename) + \"_nnistats\").c_str(), \"rb\");\n fread(cv_nnistats, sizeof(unsigned char), (cv->width * cv->height * cv->depth), f_nni_stats);\n fclose(f_nni_stats);\n }\n\n std::vector nnisamples_error_vol;\n\n std::vector errors_3D;\n std::vector errors_2D;\n float max_3D = std::numeric_limits::lowest();\n float max_2D = std::numeric_limits::lowest();\n\n\n // ranges in depth 0.5-1.25 1.25-2.0 2.0-3.0\n const float range_A_start = 0.5;\n const float range_A_end = 1.25;\n float range_A_max_3D = std::numeric_limits::lowest();\n float range_A_max_2D = std::numeric_limits::lowest();\n std::vector range_A_errors_3D;\n std::vector range_A_errors_2D;\n\n const float range_B_start = 1.25;\n const float range_B_end = 2.0;\n float range_B_max_3D = std::numeric_limits::lowest();\n float range_B_max_2D = std::numeric_limits::lowest();\n std::vector range_B_errors_3D;\n std::vector range_B_errors_2D;\n\n const float range_C_start = 2.0;\n const float range_C_end = 3.0;\n float range_C_max_3D = std::numeric_limits::lowest();\n float range_C_max_2D = std::numeric_limits::lowest();\n std::vector range_C_errors_3D;\n std::vector range_C_errors_2D;\n\n unsigned num_skipped_due_quality = 0;\n for(unsigned i = 0; i < sps.size(); ++i){\n\n if(!(sps[i].quality > 0.0)){\n ++num_skipped_due_quality;\n continue;\n }\n\n const unsigned cv_width = cv->width;\n const unsigned cv_height = cv->height;\n const unsigned cv_depth = cv->depth;\n\n const float x = cv_width * ( sps[i].tex_depth.u) / cfg.size_d.x;\n const float y = cv_height * ( sps[i].tex_depth.v)/ cfg.size_d.y;\n const float z = cv_depth * ( sps[i].depth - cv->min_d)/(cv->max_d - cv->min_d);\n\n\n if(isnni){\n const unsigned cv_index000 = (std::floor(z) * cv_width * cv_height) + (std::floor(y) * cv_width) + std::floor(x);\n const unsigned cv_index001 = (std::floor(z) * cv_width * cv_height) + (std::floor(y) * cv_width) + std::ceil(x);\n const unsigned cv_index010 = (std::floor(z) * cv_width * cv_height) + (std::ceil(y) * cv_width) + std::floor(x);\n const unsigned cv_index011 = (std::floor(z) * cv_width * cv_height) + (std::ceil(y) * cv_width) + std::ceil(x);\n const unsigned cv_index100 = (std::ceil(z) * cv_width * cv_height) + (std::floor(y) * cv_width) + std::floor(x);\n const unsigned cv_index101 = (std::ceil(z) * cv_width * cv_height) + (std::floor(y) * cv_width) + std::ceil(x);\n const unsigned cv_index110 = (std::ceil(z) * cv_width * cv_height) + (std::ceil(y) * cv_width) + std::floor(x);\n const unsigned cv_index111 = (std::ceil(z) * cv_width * cv_height) + (std::ceil(y) * cv_width) + std::ceil(x);\n bool nni_valid = ( cv_nnistats[cv_index000]\n\t\t\t && cv_nnistats[cv_index001]\n\t\t\t && cv_nnistats[cv_index010]\n\t\t\t && cv_nnistats[cv_index011]\n\t\t\t && cv_nnistats[cv_index100]\n\t\t\t && cv_nnistats[cv_index101]\n\t\t\t && cv_nnistats[cv_index110]\n\t\t\t && cv_nnistats[cv_index111]);\n if(!nni_valid){\n\tcontinue;\n }\n else{\n\t++nni_valids;\n }\n }\n\n\n xyz pos = getTrilinear(cv->cv_xyz, cv_width, cv_height, cv_depth, x , y , z );\n uv tex = getTrilinear(cv->cv_uv, cv_width, cv_height, cv_depth, x , y , z );\n\n sps[i].pos_offset.x = sps[i].pos_real[0] - pos.x;\n sps[i].pos_offset.y = sps[i].pos_real[1] - pos.y;\n sps[i].pos_offset.z = sps[i].pos_real[2] - pos.z;\n \n sps[i].tex_offset.u = sps[i].tex_color.u/cfg.size_rgb.x - tex.u;\n sps[i].tex_offset.v = sps[i].tex_color.v/cfg.size_rgb.y - tex.v;\n\n sps[i].quality = 1.0f;\n\n const float err_3D(glm::length(glm::vec3(sps[i].pos_offset.x,sps[i].pos_offset.y,sps[i].pos_offset.z)));\n const float err_2D(glm::length(glm::vec3(sps[i].tex_offset.u * cfg.size_rgb.x,\n\t\t\t\t\t sps[i].tex_offset.v * cfg.size_rgb.y,0.0)));\n\n\n // only track if sample is inside sweet bbx \n const glm::vec3 sweet_min(-0.6, 0.0, -0.6);\n const glm::vec3 sweet_max( 0.6, 1.9, 0.6);\n if(sps[i].pos_real[0] > sweet_min[0] &&\n sps[i].pos_real[1] > sweet_min[1] &&\n sps[i].pos_real[2] > sweet_min[2] &&\n\n sps[i].pos_real[0] < sweet_max[0] &&\n sps[i].pos_real[1] < sweet_max[1] &&\n sps[i].pos_real[2] < sweet_max[2]){\n\n errors_3D.push_back(err_3D);\n errors_2D.push_back(err_2D);\n \n max_3D = std::max(max_3D, err_3D);\n max_2D = std::max(max_2D, err_2D);\n\n // track local error for volume vis\n nniSample nnis;\n nnis.s_tex_off.u = err_3D;\n nnis.s_tex_off.v = err_2D;\n \n nnis.s_pos.x = x;\n nnis.s_pos.y = y;\n nnis.s_pos.z = z;\n \n nnisamples_error_vol.push_back(nnis);\n }\n\n\n if(range_A_start < sps[i].depth &&\n range_A_end >= sps[i].depth){\n range_A_errors_3D.push_back(err_3D);\n range_A_errors_2D.push_back(err_2D);\n range_A_max_3D = std::max(range_A_max_3D, err_3D);\n range_A_max_2D = std::max(range_A_max_2D, err_2D);\n }\n else if(range_B_start < sps[i].depth &&\n range_B_end >= sps[i].depth){\n range_B_errors_3D.push_back(err_3D);\n range_B_errors_2D.push_back(err_2D);\n range_B_max_3D = std::max(range_B_max_3D, err_3D);\n range_B_max_2D = std::max(range_B_max_2D, err_2D);\n }\n else if(range_C_start < sps[i].depth &&\n range_C_end >= sps[i].depth){\n range_C_errors_3D.push_back(err_3D);\n range_C_errors_2D.push_back(err_2D);\n range_C_max_3D = std::max(range_C_max_3D, err_3D);\n range_C_max_2D = std::max(range_C_max_2D, err_2D);\n }\n else{\n ;//std::cout << \"ERROR: not in range: \" << sps[i].depth << std::endl;\n }\n\n }\n\n std::cout << \"Calibrator::evaluateSamples INFO: skipped due to quality is zero: \" << num_skipped_due_quality << std::endl;\n\n double mean3D, mean2D, sd3D, sd2D;\n calcMeanSD(errors_3D, mean3D, sd3D);\n calcMeanSD(errors_2D, mean2D, sd2D);\n std::cout << \"---------------------------------------------------------\" << std::endl;\n std::cout << \"Evaluation of ground truth samples: \" << errors_3D.size() << std::endl;\n std::cout << \"mean_error_3D: \" << mean3D * 1000 << \" (\" << sd3D * 1000 << \") [\" << max_3D * 1000 << \"] (in millimeter)\" << std::endl;\n std::cout << \"mean_error_2D: \" << mean2D << \" (\" << sd2D << \") [\" << max_2D << \"] (in pixels)\" << std::endl;\n if(isnni){\n std::cout << \"Calibrator::evaluateSamples INFO: could evaluate based on natural neighbour interpolation: \" << nni_valids << \" samples from \" << sps.size() << std::endl;\n }\n else{\n std::cout << \"Calibrator::evaluateSamples INFO: natural neighbour based evalution is turned off\" << std::endl;\n }\n\n if(create_error_vis){\n createErrorVis(nnisamples_error_vol, cv->width, cv->height, cv->depth, basefilename, isnni);\n }\n\n\n double range_A_mean3D, range_A_mean2D, range_A_sd3D, range_A_sd2D;\n calcMeanSD(range_A_errors_3D, range_A_mean3D, range_A_sd3D);\n calcMeanSD(range_A_errors_2D, range_A_mean2D, range_A_sd2D);\n std::cout << \"---------------------------------------------------------\" << std::endl;\n std::cout << \"(\" << range_A_start << \"m, \" << range_A_end << \"m]\" << std::endl;\n std::cout << \"Evaluation of ground truth samples range_A: \" << range_A_errors_3D.size() << std::endl;\n std::cout << \"range_A_mean_error_3D: \" << range_A_mean3D * 1000 << \" (\" << range_A_sd3D * 1000 << \") [\" << range_A_max_3D * 1000 << \"] (in millimeter)\" << std::endl;\n std::cout << \"range_A_mean_error_2D: \" << range_A_mean2D << \" (\" << range_A_sd2D << \") [\" << range_A_max_2D << \"] (in pixels)\" << std::endl;\n\n double range_B_mean3D, range_B_mean2D, range_B_sd3D, range_B_sd2D;\n calcMeanSD(range_B_errors_3D, range_B_mean3D, range_B_sd3D);\n calcMeanSD(range_B_errors_2D, range_B_mean2D, range_B_sd2D);\n std::cout << \"---------------------------------------------------------\" << std::endl;\n std::cout << \"(\" << range_B_start << \"m, \" << range_B_end << \"m]\" << std::endl;\n std::cout << \"Evaluation of ground truth samples range_B: \" << range_B_errors_3D.size() << std::endl;\n std::cout << \"range_B_mean_error_3D: \" << range_B_mean3D * 1000 << \" (\" << range_B_sd3D * 1000 << \") [\" << range_B_max_3D * 1000 << \"] (in millimeter)\" << std::endl;\n std::cout << \"range_B_mean_error_2D: \" << range_B_mean2D << \" (\" << range_B_sd2D << \") [\" << range_B_max_2D << \"] (in pixels)\" << std::endl;\n\n double range_C_mean3D, range_C_mean2D, range_C_sd3D, range_C_sd2D;\n calcMeanSD(range_C_errors_3D, range_C_mean3D, range_C_sd3D);\n calcMeanSD(range_C_errors_2D, range_C_mean2D, range_C_sd2D);\n std::cout << \"---------------------------------------------------------\" << std::endl;\n std::cout << \"(\" << range_C_start << \"m, \" << range_C_end << \"m]\" << std::endl;\n std::cout << \"Evaluation of ground truth samples range_C: \" << range_C_errors_3D.size() << std::endl;\n std::cout << \"range_C_mean_error_3D: \" << range_C_mean3D * 1000 << \" (\" << range_C_sd3D * 1000 << \") [\" << range_C_max_3D * 1000 << \"] (in millimeter)\" << std::endl;\n std::cout << \"range_C_mean_error_2D: \" << range_C_mean2D << \" (\" << range_C_sd2D << \") [\" << range_C_max_2D << \"] (in pixels)\" << std::endl;\n\n std::cout << \"---------------------------------------------------------\" << std::endl;\n\n#if 0 // this is used to print for automatic gnu plot stuff related to paper evaluation\n // 0.0 (0.0)[0.0] & 0.0 (0.0)[0.0]\n std::cout << std::setprecision(3) << mean3D * 1000 << \" (\" << sd3D * 1000 << \") [\" << max_3D * 1000 << \"] & \" << mean2D << \" (\" << sd2D << \") [\" << max_2D << \"]\" << std::endl;\n std::cout << (range_A_errors_3D.size() > 0 ? range_A_mean3D * 1000 : 0.0) << std::endl;\n std::cout << (range_A_errors_3D.size() > 0 ? range_A_sd3D * 1000 : 0.0) << std::endl;\n std::cout << (range_A_errors_2D.size() > 0 ? range_A_mean2D : 0.0) << std::endl;\n std::cout << (range_A_errors_2D.size() > 0 ? range_A_sd2D : 0.0) << std::endl;\n\n std::cout << (range_B_errors_3D.size() > 0 ? range_B_mean3D * 1000 : 0.0) << std::endl;\n std::cout << (range_B_errors_3D.size() > 0 ? range_B_sd3D * 1000 : 0.0) << std::endl;\n std::cout << (range_B_errors_2D.size() > 0 ? range_B_mean2D : 0.0) << std::endl;\n std::cout << (range_B_errors_2D.size() > 0 ? range_B_sd2D : 0.0) << std::endl;\n\n std::cout << (range_C_errors_3D.size() > 0 ? range_C_mean3D * 1000 : 0.0) << std::endl;\n std::cout << (range_C_errors_3D.size() > 0 ? range_C_sd3D * 1000 : 0.0) << std::endl;\n std::cout << (range_C_errors_2D.size() > 0 ? range_C_mean2D : 0.0) << std::endl;\n std::cout << (range_C_errors_2D.size() > 0 ? range_C_sd2D : 0.0) << std::endl;\n#endif\n\n}\n\nvoid\nCalibrator::createErrorVis(const std::vector& sps, const unsigned width, const unsigned height, const unsigned depth, const char* basefilename, bool isnni){\n\n // create volumes for world and texture coordinates\n unsigned char* error_vol_3D = new unsigned char [width * height * depth];\n memset(error_vol_3D, 0, width * height * depth);\n \n unsigned char* error_vol_2D = new unsigned char [width * height * depth];\n memset(error_vol_2D, 0, width * height * depth);\n\n unsigned char* error_vol_3D_nni = new unsigned char [width * height * depth];\n memset(error_vol_3D_nni, 0, width * height * depth);\n \n unsigned char* error_vol_2D_nni = new unsigned char [width * height * depth];\n memset(error_vol_2D_nni, 0, width * height * depth);\n\n \n NearestNeighbourSearch nns(sps);\n std::vector sps_tmp(sps);\n std::shuffle(std::begin(sps_tmp), std::end(sps_tmp), std::default_random_engine());\n NaturalNeighbourInterpolator nnip(sps_tmp);\n\n\n // boost threads here\n const unsigned numthreads = 32;\n //std::cerr << \"start interpolation of error visualization volumes using \" << numthreads << \" threads.\" << std::endl;\n boost::thread_group threadGroup;\n for (unsigned tid = 0; tid < numthreads; ++tid){\n //threadGroup.create_thread(boost::bind(&Calibrator::applyErrorVisPerThread, this, width, height, depth,\n //\t\t\t\t\t error_vol_3D, error_vol_2D, error_vol_3D_nni, error_vol_2D_nni, &nns, &nnip, tid, numthreads));\n threadGroup.create_thread([width, height, depth,\n\t\t\t error_vol_3D, error_vol_2D, error_vol_3D_nni, error_vol_2D_nni, &nns, &nnip, tid, numthreads, this](){\n\t\t\t\tthis->applyErrorVisPerThread(width, height, depth,\n\t\t\t\t\t\t\t error_vol_3D, error_vol_2D, error_vol_3D_nni, error_vol_2D_nni, &nns, &nnip, tid, numthreads);\n\t\t\t });\n }\n threadGroup.join_all();\n\n \n //std::cout << \"INFO: writing \" << (std::string(basefilename) + \"_error3D_idw\").c_str()<< std::endl;\n FILE* f_error_vol_3D = fopen((std::string(basefilename) + \"_error3D_idw\").c_str(), \"wb\");\n fwrite(error_vol_3D, sizeof(unsigned char), (width * height * depth), f_error_vol_3D);\n fclose(f_error_vol_3D);\n\n //std::cout << \"INFO: writing \" << (std::string(basefilename) + \"_error2D_idw\").c_str()<< std::endl;\n FILE* f_error_vol_2D = fopen((std::string(basefilename) + \"_error2D_idw\").c_str(), \"wb\");\n fwrite(error_vol_2D, sizeof(unsigned char), (width * height * depth), f_error_vol_2D);\n fclose(f_error_vol_2D);\n\n if(isnni){\n //std::cout << \"INFO: writing \" << (std::string(basefilename) + \"_error3D_nni\").c_str()<< std::endl;\n FILE* f_error_vol_3Dnni = fopen((std::string(basefilename) + \"_error3D_nni\").c_str(), \"wb\");\n fwrite(error_vol_3D_nni, sizeof(unsigned char), (width * height * depth), f_error_vol_3Dnni);\n fclose(f_error_vol_3Dnni);\n\n //std::cout << \"INFO: writing \" << (std::string(basefilename) + \"_error2D_nni\").c_str()<< std::endl;\n FILE* f_error_vol_2Dnni = fopen((std::string(basefilename) + \"_error2D_nni\").c_str(), \"wb\");\n fwrite(error_vol_2D_nni, sizeof(unsigned char), (width * height * depth), f_error_vol_2Dnni);\n fclose(f_error_vol_2Dnni);\n }\n}\n\n\nvoid\nCalibrator::applyErrorVisPerThread(const unsigned width, const unsigned height, const unsigned depth,\n\t\t\t\t unsigned char* error_vol_3D, unsigned char* error_vol_2D,\n\t\t\t\t unsigned char* error_vol_3D_nni, unsigned char* error_vol_2D_nni,\n\t\t\t\t const NearestNeighbourSearch* nns, const NaturalNeighbourInterpolator* nnip,\n\t\t\t\t const unsigned tid, const unsigned numthreads){\n\n const unsigned idwneighbours = 20;\n const glm::vec3 diameter(width, height, depth);\n const float max_influence_dist = glm::length(diameter);\n const float max_error_3D_vol = 0.01; // 5cm\n const float max_error_2D_vol = 2.0; // 5 pixel\n\n for(unsigned z = tid; z < depth; z += numthreads){\n for(unsigned y = 0; y < height; ++y){\n for(unsigned x = 0; x < width; ++x){\n\t\n\tconst unsigned cv_index = (z * width * height) + (y * width) + x;\n\t\n\tnniSample ipolant;\n\tipolant.s_pos.x = x;\n\tipolant.s_pos.y = y;\n\tipolant.s_pos.z = z;\n\t\n\tipolant.s_pos_off.x = 0.0;\n\tipolant.s_pos_off.y = 0.0;\n\tipolant.s_pos_off.z = 0.0;\n\t\n\tipolant.s_tex_off.u = 0.0;\n\tipolant.s_tex_off.v = 0.0;\n\t\n\tstd::vector neighbours = nns->search(ipolant,idwneighbours);\n\tif(neighbours.empty()){\n\t //std::cerr << \"ERROR in Calibrator::applySamplesPerThread -> no neighbours found, skipping voxel at pos \" << ipolant.s_pos << std::endl;\n\t continue;\n\t}\n\tidw_interpolate(neighbours, idwneighbours, ipolant, max_influence_dist);\n\n\t// s_tex_off.u -> 3D error\n\terror_vol_3D[cv_index] = (unsigned char) (std::max(0.0f,\n\t\t\t\t\t\t\t std::min(ipolant.s_tex_off.u, max_error_3D_vol))\n\t\t\t\t\t\t * 255.0f/max_error_3D_vol);\n\t// s_tex_off.v -> 2D error\n\terror_vol_2D[cv_index] = (unsigned char) (std::max(0.0f,\n\t\t\t\t\t\t\t std::min(ipolant.s_tex_off.v, max_error_2D_vol))\n\t\t\t\t\t\t * 255.0f/max_error_2D_vol);\n\n\n\t{\n\t nniSample ipolant_nni;\n\t ipolant_nni.s_pos.x = x;\n\t ipolant_nni.s_pos.y = y;\n\t ipolant_nni.s_pos.z = z;\n\t \n\t ipolant_nni.s_pos_off.x = 0.0;\n\t ipolant_nni.s_pos_off.y = 0.0;\n\t ipolant_nni.s_pos_off.z = 0.0;\n\t \n\t ipolant_nni.s_tex_off.u = 0.0;\n\t ipolant_nni.s_tex_off.v = 0.0;\n\n\t bool nni_valid = nnip->interpolate(ipolant_nni);\n\t if(nni_valid){\n\t // s_tex_off.u -> 3D error\n\t error_vol_3D_nni[cv_index] = (unsigned char) (std::max(0.0f,\n\t\t\t\t\t\t\t\t std::min(ipolant_nni.s_tex_off.u, max_error_3D_vol))\n\t\t\t\t\t\t\t * 255.0f/max_error_3D_vol);\n\t // s_tex_off.v -> 2D error\n\t error_vol_2D_nni[cv_index] = (unsigned char) (std::max(0.0f,\n\t\t\t\t\t\t\t\t std::min(ipolant_nni.s_tex_off.v, max_error_2D_vol))\n\t\t\t\t\t\t\t * 255.0f/max_error_2D_vol);\n\t }\n\n\t}\n\n\n }\n }\n }\n \n}\n\n\n\n\n\ndouble\nCalibrator::evaluatePlanes(CalibVolume* cv, ChessboardSampling* cbs, const RGBDConfig& cfg, unsigned stride){\n\n const unsigned cv_width = cv->width;\n const unsigned cv_height = cv->height;\n const unsigned cv_depth = cv->depth;\n\n\n std::vector plane_qualities;\n const std::vector& valid_ranges = cbs->getValidRanges();\n const std::vector& cb_irs = cbs->getIRs();\n for(const auto& r : valid_ranges){\n for(unsigned cb_id = r.start; cb_id != r.end; ++cb_id){\n if((cb_id % stride) == 0){\n\tstd::vector world_space_corners;\n\tfor(unsigned idx = 0; idx < (CB_WIDTH * CB_HEIGHT); ++idx){\n\t if(cb_irs[cb_id].quality[idx] > 0.0){\n\t const xyz corner = cb_irs[cb_id].corners[idx];\n\t const float x = cv_width * ( corner.x)/ cfg.size_d.x;\n\t const float y = cv_height * ( corner.y)/ cfg.size_d.y;\n\t const float z = cv_depth * ( corner.z - cv->min_d)/(cv->max_d - cv->min_d);\n\t xyz pos = getTrilinear(cv->cv_xyz, cv_width, cv_height, cv_depth, x , y , z );\n\t \n\t pos.x *= 10;\n\t pos.y *= 10;\n\t pos.z *= 10;\n\t \n\t world_space_corners.push_back(pos);\n\t }\n\t}\n\t\n\tconst auto pq = detectPlaneQuality(world_space_corners);\n\t//std::cout << \"cb_id: \" << cb_id << \" -> \" << pq << std::endl;\n\tplane_qualities.push_back(pq);\n }\n\n }\n }\n \n double mean;\n double sd;\n calcMeanSD(plane_qualities, mean, sd);\n\n return mean;\n}\n\n\ndouble\nCalibrator::evaluate3DError(CalibVolume* cv, ChessboardSampling* cbs, const Checkerboard* cb, const RGBDConfig& cfg, float delta_t_pose, unsigned stride){\n\n const unsigned cv_width = cv->width;\n const unsigned cv_height = cv->height;\n const unsigned cv_depth = cv->depth;\n\n\n std::vector errors_3D;\n const std::vector& valid_ranges = cbs->getValidRanges();\n const std::vector& cb_irs = cbs->getIRs();\n\n for(const auto& r : valid_ranges){\n for(unsigned cb_id = r.start; cb_id != r.end; ++cb_id){\n\n if((cb_id % stride) == 0){\n\n\tconst double time = cb_irs[cb_id].time;\n\t// retrieve chessboards_pose\n\tbool valid_pose = false;\n\tglm::mat4 cb_transform = cbs->interpolatePose(time + delta_t_pose, valid_pose);\n\tif(!valid_pose){\n\t continue;\n\t}\n\n\n\tstd::vector cb_errors_3D;\n\tfor(unsigned idx = 0; idx < (CB_WIDTH * CB_HEIGHT); ++idx){\n\n\t // calculate calibrated position\n\t if(cb_irs[cb_id].quality[idx] > 0.0){\n\t const xyz corner = cb_irs[cb_id].corners[idx];\n\t const float x = cv_width * ( corner.x)/ cfg.size_d.x;\n\t const float y = cv_height * ( corner.y)/ cfg.size_d.y;\n\t const float z = cv_depth * ( corner.z - cv->min_d)/(cv->max_d - cv->min_d);\n\t xyz pos = getTrilinear(cv->cv_xyz, cv_width, cv_height, cv_depth, x , y , z );\n\t glm::vec3 pos_calib(pos.x,pos.y,pos.z);\n\t // calculate ground truth position\n\t glm::vec4 pos_realH = (cb->pose_offset * cb_transform) * glm::vec4(cb->points_local[idx].x,\n\t\t\t\t\t\t\t\t\t cb->points_local[idx].y,\n\t\t\t\t\t\t\t\t\t cb->points_local[idx].z, 1.0f);\n\t glm::vec3 pos_gt = glm::vec3(pos_realH.x, pos_realH.y, pos_realH.z);\n\n\t cb_errors_3D.push_back(glm::length(pos_calib - pos_gt));\n\t }\n\t}\n\n\tdouble cb_mean;\n\tdouble cb_sd;\n\tcalcMeanSD(cb_errors_3D, cb_mean, cb_sd);\n\terrors_3D.push_back(cb_mean);\n }\n\n }\n }\n\n\n double mean;\n double sd;\n calcMeanSD(errors_3D, mean, sd);\n\n return mean;\n\n}\n\n\ndouble\nCalibrator::evaluate2DError(CalibVolume* cv, ChessboardSampling* cbs, const RGBDConfig& cfg, float delta_t_color, unsigned stride){\n\n\n const unsigned cv_width = cv->width;\n const unsigned cv_height = cv->height;\n const unsigned cv_depth = cv->depth;\n\n\n std::vector errors_2D;\n const std::vector& valid_ranges = cbs->getValidRanges();\n const std::vector& cb_irs = cbs->getIRs();\n\n for(const auto& r : valid_ranges){\n for(unsigned cb_id = r.start; cb_id != r.end; ++cb_id){\n\n if((cb_id % stride) == 0){\n\n\tconst double time = cb_irs[cb_id].time;\n\t// retrieve color chessboard view\n\tbool valid_color = false;\n\tChessboardViewRGB cb_rgb_i = cbs->interpolateRGB(time + delta_t_color, valid_color);\n\tif(!valid_color){\n\t continue;\n\t}\n\n\n\tstd::vector cb_errors_2D;\n\tfor(unsigned idx = 0; idx < (CB_WIDTH * CB_HEIGHT); ++idx){\n\n\t // look up calibrated color coordinate\n\t if(cb_irs[cb_id].quality[idx] > 0.0){\n\t const xyz corner = cb_irs[cb_id].corners[idx];\n\t const float x = cv_width * ( corner.x)/ cfg.size_d.x;\n\t const float y = cv_height * ( corner.y)/ cfg.size_d.y;\n\t const float z = cv_depth * ( corner.z - cv->min_d)/(cv->max_d - cv->min_d);\n\n\t // normalized\n\t uv cc_calib = getTrilinear(cv->cv_uv, cv_width, cv_height, cv_depth, x , y , z );\n\t // renormalize\n\t glm::vec2 cc_calib_pixel(cc_calib.u * cfg.size_rgb.x, cc_calib.v * cfg.size_rgb.y);\n\n\t // retrieve ground truth color coordinate, already in pixels\n\t uv cc_gt(cb_rgb_i.corners[idx]);\n\t glm::vec2 cc_gt_pixel(cc_gt.u, cc_gt.v);\n#if 0\n\t std::cerr << \"cb_id: \" << cb_id << \" corner idx: \" << idx\n\t\t << \" cc_calib_pixel -> cc_gt_pixel: \"\n\t\t << cc_calib_pixel[0] << \", \" << cc_calib_pixel[1] << \" -> \"\n\t\t << cc_gt_pixel[0] << \", \" << cc_gt_pixel[1] << std::endl;\n#endif\n\t cb_errors_2D.push_back(glm::length(cc_calib_pixel - cc_gt_pixel));\n\t }\n\t}\n\n\tdouble cb_mean;\n\tdouble cb_sd;\n\tcalcMeanSD(cb_errors_2D, cb_mean, cb_sd);\n\terrors_2D.push_back(cb_mean);\n }\n\n }\n }\n\n\n double mean;\n double sd;\n calcMeanSD(errors_2D, mean, sd);\n\n return mean;\n\n}\n\n\nvoid\nCalibrator::applySamplesPerThread(CalibVolume* cv, const NearestNeighbourSearch* nns, unsigned tid, unsigned numthreads, unsigned idwneighbours, CalibVolume* cv_nni, const NaturalNeighbourInterpolator* nnip){\n\n\n const glm::vec3 diameter(cv->width, cv->height, cv->depth);\n const float max_influence_dist = glm::length(diameter);\n\n const unsigned cv_width = cv->width;\n const unsigned cv_height = cv->height;\n const unsigned cv_depth = cv->depth;\n //unsigned having = 0; \n for(unsigned z = tid; z < cv_depth; z += numthreads){\n //std::cerr << \"tid: having \" << ++having << \" from \" << cv_depth / numthreads << std::endl;\n for(unsigned y = 0; y < cv_height; ++y){\n\tfor(unsigned x = 0; x < cv_width; ++x){\n\t \n\t const unsigned cv_index = (z * cv_width * cv_height) + (y * cv_width) + x;\n\n\t nniSample ipolant;\n\t ipolant.s_pos.x = x;\n\t ipolant.s_pos.y = y;\n\t ipolant.s_pos.z = z;\n\t \n\t ipolant.s_pos_off.x = 0.0;\n\t ipolant.s_pos_off.y = 0.0;\n\t ipolant.s_pos_off.z = 0.0;\n\n\t ipolant.s_tex_off.u = 0.0;\n\t ipolant.s_tex_off.v = 0.0;\n\t \n\t std::vector neighbours = nns->search(ipolant,idwneighbours);\n\t if(neighbours.empty()){\n\t std::cerr << \"ERROR in Calibrator::applySamplesPerThread -> no neighbours found, skipping voxel at pos \" << ipolant.s_pos << std::endl;\n\t continue;\n\t }\n\t idw_interpolate(neighbours, idwneighbours, ipolant, max_influence_dist);\n\n\t const xyz xyz_curr = cv->cv_xyz[cv_index];\n\t const uv uv_curr = cv->cv_uv[cv_index];\n\n\t cv->cv_xyz[cv_index] = cv->cv_xyz[cv_index] + ipolant.s_pos_off;\n\t cv->cv_uv[cv_index] = cv->cv_uv[cv_index] + ipolant.s_tex_off;\n\t \n\t if(using_nni){\n\t nniSample ipolant_nni;\n\t ipolant_nni.s_pos.x = x;\n\t ipolant_nni.s_pos.y = y;\n\t ipolant_nni.s_pos.z = z;\n\t \n\t ipolant_nni.s_pos_off.x = 0.0;\n\t ipolant_nni.s_pos_off.y = 0.0;\n\t ipolant_nni.s_pos_off.z = 0.0;\n\t \n\t ipolant_nni.s_tex_off.u = 0.0;\n\t ipolant_nni.s_tex_off.v = 0.0;\n\t bool nni_valid = nnip->interpolate(ipolant_nni);\n\t m_nni_possible[cv_index] = nni_valid ? 255 : 0;\n\t if(nni_valid){\n\t cv_nni->cv_xyz[cv_index] = xyz_curr + ipolant_nni.s_pos_off;\n\t cv_nni->cv_uv[cv_index] = uv_curr + ipolant_nni.s_tex_off;\n\t // STEPPO REMOVE\n\t //std::cerr << \"IDW: \" << ipolant << std::endl;\n\t //std::cerr << \"NNI: \" << ipolant_nni << std::endl;\n\t //glm::vec3 pos_glm(ipolant_nni.s_pos_off.x,ipolant_nni.s_pos_off.y,ipolant_nni.s_pos_off.z);\n\t //const float od = glm::length(pos_glm);\n\t //if(od > 0.1){\n\t //\tstd::cerr << tid << \" ERROR: outside offset_too_large at \" << ipolant_nni.s_pos_off << \" -> \" << od << std::endl;\n\t //}\n\n\n\t }\n\t }\n\n\t}\n }\n }\n\n\n}\n\n\n/*static*/ void\nCalibrator::idw_interpolate(const std::vector& neighbours, const unsigned idw_neigbours, nniSample& ipolant, const float max_influence_dist){\n \n\n const float sigma = max_influence_dist * 1.0/3.3;\n const float mean = 0;\n const float norm = 1.0f/gauss(0.0f, sigma, mean);\n\n\n double weight_d = 0.0;\n xyz_d pos_offset;\n pos_offset.x = 0.0;pos_offset.y = 0.0;pos_offset.z = 0.0;\n uv_d tex_offset;\n tex_offset.u = 0.0;tex_offset.v = 0.0;\n glm::vec3 ipolant_pos(ipolant.s_pos.x,ipolant.s_pos.y,ipolant.s_pos.z);\n\n for(unsigned i = 0; i < neighbours.size() && i < idw_neigbours; ++i){\n nniSample s = neighbours[i];\n //std::cerr << \"s.quality: \" << s.quality << std::endl;\n glm::vec3 s_pos(s.s_pos.x,s.s_pos.y,s.s_pos.z);\n const float influence_dist = std::min(max_influence_dist, glm::length(ipolant_pos - s_pos));\n //std::cerr << \"influence dist: \" << influence_dist << \" of \" << max_influence_dist << std::endl;\n const double s_weight = /*double(s.quality) * */gauss(influence_dist, sigma, mean) * norm;\n //std::cerr << \"s_weight: \" << s_weight << std::endl;\n \n weight_d += s_weight;\n pos_offset = pos_offset + s_weight * s.s_pos_off;\n tex_offset = tex_offset + s_weight * s.s_tex_off;\n \n }\n\n if(weight_d > 0.00001){\n \n ipolant.s_pos_off.x = pos_offset.x/weight_d;\n ipolant.s_pos_off.y = pos_offset.y/weight_d;\n ipolant.s_pos_off.z = pos_offset.z/weight_d;\n\n ipolant.s_tex_off.u = tex_offset.u/weight_d;\n ipolant.s_tex_off.v = tex_offset.v/weight_d;\n }\n else{\n std::cerr << \"ERROR in Calibrator::idw_interpolate!!!! weight to low (weight_d < 0.0001) \" << std::endl;\n }\n \n}\n", "meta": {"hexsha": "9b091e6a9ae1e12c4c0da8d0dfa5dfee0f771d58", "size": 41363, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "framework/calibrator.cpp", "max_stars_repo_name": "aosterthun/rgbdri", "max_stars_repo_head_hexsha": "8e513172f512c902f7d6d8631c7580b5b62277c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "framework/calibrator.cpp", "max_issues_repo_name": "aosterthun/rgbdri", "max_issues_repo_head_hexsha": "8e513172f512c902f7d6d8631c7580b5b62277c4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.2220755216739912}} {"text": "/* -----------------------------------------------------------------\n * File: Recipe.cpp\n * Author: Michael Gharbi \n * Created: 2015-07-24\n * -----------------------------------------------------------------\n * \n * \n * \n * ---------------------------------------------------------------*/\n\n\n#include \n#include \n\n#ifndef __ANDROID__\n #include \n #include \"utils/image_io.h\"\n#endif\n\n// HALIDE\n#include \"utils/static_image.h\"\n\n#include \"recipe/hl_reconstruct.h\"\n#include \"recipe/hl_lowpass.h\"\n#include \"recipe/hl_highpass.h\"\n#include \"recipe/hl_compute_features.h\"\n#include \"recipe/hl_precompute_pyramid.h\"\n\n#include \"print_helper.h\"\n#include \"perf_measure.h\"\n\n\n#include \"recipe/Recipe.h\"\n\nnamespace xform {\n\nRecipe::Recipe( Image& unprocessed ) {\n m_model_width = static_cast(ceil(2.0f*unprocessed.width()/XFORM_WSIZE));\n m_model_height = static_cast(ceil(2.0f*unprocessed.height()/XFORM_WSIZE));\n m_lp_width = unprocessed.width()/XFORM_WSIZE;\n m_lp_height = unprocessed.height()/XFORM_WSIZE;\n m_unprocessed_channels = 3;\n m_processed_channels = 3;\n}\n\n#ifndef __ANDROID__\nRecipe::Recipe( Image& unprocessed, Image& processed ):\n Recipe(unprocessed)\n{\n fit(unprocessed, processed);\n}\n#endif\n\n#ifndef __ANDROID__\nvoid Recipe::fit( Image& unprocessed, Image& processed )\n{\n PRINT(\"Fit recipe\\n\");\n m_lp_residual = std::make_shared >(m_lp_width,m_lp_height,m_processed_channels);\n m_hp_coefs = std::make_shared >(m_model_width,m_model_height,nCoefMaps());\n m_qtable = std::vector(2*nCoefMaps());\n\n int width = unprocessed.width();\n int height = unprocessed.height();\n\n Image processed_hp(width, height, 3);\n\n // Compute and store lowpass of the processed image\n auto start = get_time();\n hl_lowpass(processed, *m_lp_residual);\n PRINT(\" - lowpass:\\t %ldms\\n\", get_duration(start,get_time()));\n\n // Compute highpass of the output\n start = get_time();\n hl_highpass(processed, *m_lp_residual, processed_hp);\n PRINT(\" - highpass:\\t %ldms\\n\", get_duration(start,get_time()));\n\n // Features\n start = get_time();\n Image features(width, height, nFeatureChannels());\n hl_compute_features(unprocessed, features);\n PRINT(\" - features:\\t %ldms\\n\", get_duration(start,get_time()));\n\n start = get_time();\n regression(features, processed_hp);\n PRINT(\" - regression:\\t %ldms\\n\", get_duration(start,get_time()));\n\n quantize();\n}\n#endif\n\n\nvoid Recipe::precompute_features( const Image& unprocessed)\n{\n m_lp_unprocessed = Image(m_lp_width, m_lp_height);\n m_hp_unprocessed = Image(unprocessed.width(), unprocessed.height(), 3);\n\n auto start = get_time();\n hl_lowpass(unprocessed, m_lp_unprocessed);\n PRINT(\" - lowpass : %ldms\\n\", get_duration(start,get_time()));\n\n start = get_time();\n hl_highpass(unprocessed, m_lp_unprocessed, m_hp_unprocessed);\n PRINT(\" - highpass : %ldms\\n\", get_duration(start,get_time()));\n\n start = get_time();\n m_pyramid_unprocessed = Image(unprocessed.width(), unprocessed.height(), nPyrLevels()-1);\n hl_precompute_pyramid(unprocessed,m_pyramid_unprocessed);\n PRINT(\" - precompute pyramid : %ldms\\n\", get_duration(start,get_time()));\n}\n\n\nvoid Recipe::reconstruct_with_features( Image &output )\n{\n dequantize();\n\n output = Image(m_pyramid_unprocessed.width(),\n m_pyramid_unprocessed.height(), m_processed_channels);\n\n auto start = get_time();\n hl_reconstruct(\n m_pyramid_unprocessed,\n m_hp_unprocessed,\n *m_hp_coefs,\n *m_lp_residual,\n output\n );\n PRINT(\" - reconstruct precomputed: %ldms\\n\", get_duration(start,get_time()));\n}\n\n\nvoid Recipe::reconstruct_image( const Image& unprocessed, Image &output )\n{\n precompute_features(unprocessed);\n reconstruct_with_features(output);\n}\n\n\n#ifndef __ANDROID__\nvoid Recipe::regression( Image& features, Image& target) \n{\n const int h_mdl = m_model_height;\n const int w_mdl = m_model_width;\n const int h = target.height();\n const int w = target.width();\n const int n_targets = target.channels();\n const int n_lumCoefs = nLumCoefs();\n const int n_chromCoefs = nChromCoefs();\n const int n_features = features.channels();\n\n float* pDataFeat = features.data();\n float* pDataTarget = target.data();\n float* pLuminCoef = m_hp_coefs->data();\n float* pChromCoef = pLuminCoef + h_mdl*w_mdl*n_lumCoefs;\n\n int wSize = XFORM_WSIZE;\n int step = XFORM_WSIZE/2;\n\n #pragma omp parallel for\n for (int patch_idx = 0; patch_idx < h_mdl*w_mdl; ++patch_idx)\n {\n // temporary storage for the current patch\n float* X = new float[wSize*wSize*n_lumCoefs]();\n float* Y = new float[wSize*wSize*n_targets]();\n\n // Patch indices (in the recipe)\n int patch_x = patch_idx % w_mdl;\n int patch_y = patch_idx / w_mdl;\n\n // Pixel indices (in the image)\n int x_min = patch_x*step;\n int x_max = min(x_min+wSize, w);\n int y_min = patch_y*step;\n int y_max = min(y_min+wSize, h);\n int n_samples = (y_max-y_min)*(x_max-x_min);\n\n // Copy patch features\n int idx = 0;\n for (int f = 0; f < n_features; ++f)\n for (int y = y_min; y < y_max; ++y)\n for (int x = x_min; x < x_max; ++x)\n {\n X[idx] = pDataFeat[x+y*w+f*h*w];\n ++idx;\n }\n\n // Make features for the non-linear luminance curve\n int first_curve = idx;\n auto minmax = std::minmax_element(X,X + n_samples);\n if(!XFORM_ADAPTIVE_LUMA_RANGE) {\n *minmax.first = 0.0f;\n *minmax.second = 1.0f;\n }\n float step_sz = (*minmax.second-*minmax.first)/XFORM_LUMA_BANDS;\n float mini = *minmax.first;\n for (int s = 0; s < XFORM_LUMA_BANDS-1; ++s){\n float thresh = mini+(s+1)*step_sz;\n for (idx = 0; idx < n_samples; ++idx)\n {\n X[first_curve + n_samples*s + idx] = std::max(X[idx]-thresh, 0.0f);\n }\n }\n\n // Copy target variables\n idx = 0;\n for (int f = 0; f < n_targets; ++f)\n for (int y = y_min; y < y_max; ++y)\n for (int x = x_min; x < x_max; ++x)\n {\n Y[idx] = pDataTarget[x+y*w+f*h*w];\n ++idx;\n }\n\n // Solve least-square regression\n arma::Mat matX(X, n_samples,n_lumCoefs, false);\n arma::Mat matY(Y, n_samples,n_targets, false);\n\n // For the luminance\n arma::Mat result_lumin;\n arma::Mat Xview = matX.cols(0,n_lumCoefs-1);\n arma::Mat Yview = matY.cols(0,0);\n arma::Mat regularizer = arma::eye>(n_lumCoefs, n_lumCoefs);\n regularizer(3,3) = 0.0f; //do not regularize affine offset\n arma::Mat lhs = arma::trans(Xview) * Xview + XFORM_EPSILON*regularizer;\n arma::Mat rhs = arma::trans(Xview) * Yview;\n arma::solve(result_lumin,lhs,rhs);\n\n // For the chrominance\n arma::Mat result_chrom;\n Xview = matX.cols(0,n_chromCoefs-1);\n Yview = matY.cols(1,2);\n regularizer = arma::eye>(n_chromCoefs, n_chromCoefs);\n regularizer(3,3) = 0.0f; //do not regularize affine offset\n lhs = arma::trans(Xview) * Xview + XFORM_EPSILON*regularizer;\n rhs = arma::trans(Xview) * Yview;\n arma::solve(result_chrom,lhs,rhs);\n\n // Fill-in the luminance regression coefficients\n float* coefs = result_lumin.memptr();\n idx = 0;\n for (int f = 0; f < n_lumCoefs; ++f)\n {\n pLuminCoef[patch_x+patch_y*w_mdl+f*h_mdl*w_mdl] = coefs[idx];\n ++idx;\n }\n\n // Fill in the chrominance regression coefficients\n coefs = result_chrom.memptr();\n idx = 0;\n for (int c = 0; c < 2; ++c)\n for (int f = 0; f < n_chromCoefs; ++f)\n {\n pChromCoef[patch_x + patch_y*w_mdl + f*h_mdl*w_mdl + c*n_chromCoefs*h_mdl*w_mdl] = coefs[idx];\n ++idx;\n }\n coefs = nullptr;\n\n delete[] X; X = nullptr;\n delete[] Y; Y = nullptr;\n }\n pDataTarget = nullptr;\n pDataFeat = nullptr;\n}\n#endif \n\n\nvoid Recipe::quantize() \n{\n const int h_mdl = m_model_height;\n const int w_mdl = m_model_width;\n const int n_feats = nCoefMaps();\n\n // Get values range per regression channel\n float* pCoef = m_hp_coefs->data();\n\n // Compute min and max for each channel\n for (int f = 0; f < n_feats; ++f){\n m_qtable[2*f] = pCoef[f*h_mdl*w_mdl];\n m_qtable[2*f+1] = pCoef[f*h_mdl*w_mdl];\n for (int y = 0; y < h_mdl; ++y)\n for (int x = 0; x < w_mdl; ++x)\n {\n float val = pCoef[x + y*w_mdl + f*h_mdl*w_mdl];\n // min\n m_qtable[2*f] = min(m_qtable[2*f],val);\n // max\n m_qtable[2*f+1] = max(m_qtable[2*f+1],val);\n }\n }\n\n // Quantize\n for (int f = 0; f < n_feats; ++f)\n {\n float min = (m_qtable)[2*f];\n float max = (m_qtable)[2*f+1];\n float inv_rng = 1.0f/(max-min);\n for (int y = 0; y < h_mdl; ++y)\n for (int x = 0; x < w_mdl; ++x)\n {\n float *val = pCoef + (x+y*w_mdl+f*h_mdl*w_mdl);\n *val -= min;\n *val *= inv_rng;\n *val = floor(255.0f*(*val)+0.5f);\n }\n }\n}\n\n\nvoid Recipe::dequantize() \n{\n const int h_mdl = m_model_height;\n const int w_mdl = m_model_width;\n const int n_feats = nCoefMaps();\n\n float* pCoef = m_hp_coefs->data();\n\n // Dequantize\n for (int f = 0; f < n_feats; ++f)\n {\n float min = m_qtable[2*f];\n float max = m_qtable[2*f+1];\n float rng = (max-min)/255.0f;\n for (int y = 0; y < h_mdl; ++y)\n for (int x = 0; x < w_mdl; ++x) {\n float *val = pCoef + (x+y*w_mdl+f*h_mdl*w_mdl);\n *val *= rng;\n *val += min;\n }\n }\n}\n\n} //namespace xform\n", "meta": {"hexsha": "4a90fff71418cb78419425a4bd01b1d388ebe03d", "size": 10341, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "xform_cpp/src/recipe/Recipe.cpp", "max_stars_repo_name": "mgharbi/xform_recipes", "max_stars_repo_head_hexsha": "69404155f07860a7c754670dcb61c5ce03d88809", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9.0, "max_stars_repo_stars_event_min_datetime": "2016-01-11T16:58:42.000Z", "max_stars_repo_stars_event_max_datetime": "2020-06-11T02:05:57.000Z", "max_issues_repo_path": "xform_cpp/src/recipe/Recipe.cpp", "max_issues_repo_name": "mgharbi/xform_recipes", "max_issues_repo_head_hexsha": "69404155f07860a7c754670dcb61c5ce03d88809", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2018-05-04T07:09:03.000Z", "max_issues_repo_issues_event_max_datetime": "2018-05-04T07:09:03.000Z", "max_forks_repo_path": "xform_cpp/src/recipe/Recipe.cpp", "max_forks_repo_name": "mgharbi/xform_recipes", "max_forks_repo_head_hexsha": "69404155f07860a7c754670dcb61c5ce03d88809", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2016-04-03T09:09:57.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-04T10:09:08.000Z", "avg_line_length": 31.1475903614, "max_line_length": 106, "alphanum_fraction": 0.5805047868, "num_tokens": 2945, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.22206991791466832}} {"text": "/// \\file python_module.cpp\n///\n/// \\brief\n///\n/// \\authors Maarten P. Scholl\n/// \\date 2020-08-30\n/// \\copyright Copyright 2017-2020 The Institute for New Economic Thinking,\n/// Oxford Martin School, University of Oxford\n///\n/// Licensed under the Apache License, Version 2.0 (the \"License\");\n/// you may not use this file except in compliance with the License.\n/// You may obtain a copy of the License at\n///\n/// http://www.apache.org/licenses/LICENSE-2.0\n///\n/// Unless required by applicable law or agreed to in writing,\n/// software distributed under the License is distributed on an \"AS\n/// IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either\n/// express or implied. See the License for the specific language\n/// governing permissions and limitations under the License.\n///\n/// You may obtain instructions to fulfill the attribution\n/// requirements in CITATION.cff\n///\n#include \n\n\n#ifdef WITH_PYTHON\n\n#include \n\n#define BOOST_BIND_GLOBAL_PLACEHOLDERS\n#include \nusing namespace boost::python;\n\n#include \n\n\ntypedef adept::internal::BinaryOperation, adept::internal::Multiply, adept::Active > binary_operation_t;\n\nusing namespace esl;\n\n\nBOOST_PYTHON_MODULE(_mathematics)\n{\n class_( \"variable\", init<>())\n .def(init())\n .def(self == self)\n .def(self != self)\n .def(self < self)\n .def(self <= self)\n .def(self > self)\n .def(self >= self)\n .def(self + self)\n .def(self += self)\n .def(self - self)\n .def(self -= self)\n .def(self * self)\n .def(self *= self)\n .def(self / self)\n .def(self /= self)\n //.def(\"value\", &variable::value)\n ;\n\n class_(\"stack\", init<>())\n .def(\"pause_recording\", &adept::Stack::pause_recording)\n .def(\"continue_recording\", &adept::Stack::continue_recording)\n .def(\"new_recording\", &adept::Stack::new_recording)\n .def(\"compute_adjoint\", &adept::Stack::compute_adjoint)\n\n ;\n\n // TODO: verify that all opeartions on this are exported\n class_(\"binary_operation_t\", no_init)\n .def(self == self)\n .def(self != self)\n .def(self < self)\n .def(self <= self)\n .def(self > self)\n .def(self >= self)\n .def(self + self)\n // .def(self + variable())\n .def(self - self)\n // .def(self - variable())\n .def(self * self)\n // .def(self * variable())\n .def(self / self)\n // .def(self / variable())\n ;\n\n\n// TODO: export these\n// def(\"get_gradients\", &adept::get_gradients);\n// stack_.independent(&active_[0], active_.size());\n// stack_.dependent(&values_[0], values_.size());\n// stack_.jacobian(jacobian);\n\n\n\n}\n\n#endif", "meta": {"hexsha": "8f27b4232d537d31704f285125a90a72283c8edf", "size": 3066, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "esl/mathematics/python_module_mathematics.cpp", "max_stars_repo_name": "rht/ESL", "max_stars_repo_head_hexsha": "f883155a167d3c48e5ecdca91c8302fefc901c22", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "esl/mathematics/python_module_mathematics.cpp", "max_issues_repo_name": "rht/ESL", "max_issues_repo_head_hexsha": "f883155a167d3c48e5ecdca91c8302fefc901c22", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "esl/mathematics/python_module_mathematics.cpp", "max_forks_repo_name": "rht/ESL", "max_forks_repo_head_hexsha": "f883155a167d3c48e5ecdca91c8302fefc901c22", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-01-27T12:11:48.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-27T12:11:48.000Z", "avg_line_length": 30.0588235294, "max_line_length": 142, "alphanum_fraction": 0.5945857795, "num_tokens": 727, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.2220117204248458}} {"text": "/* Siconos is a program dedicated to modeling, simulation and control\n * of non smooth dynamical systems.\n *\n * Copyright 2021 INRIA.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n*/\n/*! \\file BlockCSRMatrix.hpp\nDefinition of a compressed row sparse block matrix of SiconosMatrix*\n*/\n\n#ifndef BLOCKCSRMATRIX_H\n#define BLOCKCSRMATRIX_H\n\n#include \"NumericsFwd.h\" // Numerics forward declarations\n#include \"SimulationTypeDef.hpp\"\n#include \"SiconosSerialization.hpp\" // for ACCEPT_SERIALIZATION\n#include // Boost forward declarations \n\n/* with signed int typedef boost::numeric::ublas::compressed_matrix CompressedRowMat; */\n/* cf http://boost.2283326.n4.nabble.com/LU-decomposition-of-compressed-matrix-td3417929.html */\ntypedef boost::numeric::ublas::compressed_matrix <\ndouble*, boost::numeric::ublas::basic_row_major, 0, boost::numeric::ublas::unbounded_array > CompressedRowMat;\nTYPEDEF_SPTR(CompressedRowMat)\nTYPEDEF_SPTR(SparseBlockStructuredMatrix)\n\n\n/** Definition of a compressed sparse row matrix of SiconosMatrix,\n * used in OneStepNSProblem to store the M matrix.\n *\n * This class defines a specific compressed row sparse storage for\n * blocks matrices, each block being a SiconosMatrix*.\n *\n * It handles:\n * - a SparseMat (boost-ublas) of SiconosMatrix*\n * - a vector which handles the non-null blocks\n\n * - three vector (IndexInt) to save non-null blocks position in\n row, columns and the list of the sizes of diagonal blocks.\n\n * - two int, the number of blocks in a row and the number of non null blocks.\n *\n * Each block of the current object represents the connection between\n * two coupled Interactions, \\n (for example for Lagrangian\n * systems, a single \\f$ H W^{-1} H^t \\f$ block or for first order\n * systems \\f$ hCW^{-1}B \\f$ ...) \\n\n *\n * This objects is built using an index set of SP::Interaction,\n * that represents the \"active\" constraints in the OSNS problem and a\n * map >, block being the link\n * between u1 and u2. Only Interaction present in the index set are picked out\n * in the map.\n *\n * A convert method is also implemented to create a\n * SparseBlockStructuredMatrix which is Numerics-readable.\n *\n * As an example, consider the index set I={u1, u3, u5, u8} and the\n * map where non null blocks are (ui,ui), (u1,u3), (u1,u8), (u3,u1),\n * (u8,u1).\\n Each block being a pointer to a 3x3 matrix.\\n Then the\n * resulting matrix has 4 X 4 blocks, with 8 non-null blocks and looks\n * like:\n *\n \\rst\n\n .. math::\n :nowrap:\n\n M=\\left\\lbrace\\begin{array}{cccc}\n b11 & b13 & 0 & b18 \\\\\n b31 & b22 & 0 & 0 \\\\\n 0 & 0 & b33&0 \\\\\n b81 & 0 & 0 & b44\n \\end{array}\\right.\n\n \\endrst\n *\n * with nc = 4, nbNonNullBlocks = 8, RowPos = [0 0 0 1 1 2 3 3],\n * RowCol = [0 1 3 0 1 2 0 3]\\n and _diagsize0 = [3 6 9 12].\n *\n * We use stl::vector (which may seems redundent with the double* of\n * the numerics SparseBlockStructuredMatrix) because memory can be\n * reserved during construction or initialized and then vectors are\n * resized when the object is filled in. This avoid some call to\n * malloc/free at each iteration.\n *\n */\nclass BlockCSRMatrix\n{\nprivate:\n /** serialization hooks\n */\n ACCEPT_SERIALIZATION(BlockCSRMatrix);\n\n /** Number of blocks rows (first dimension of the block matrix)*/\n unsigned int _nr;\n\n /** Number of blocks columns (second dimension of the block matrix)*/\n unsigned int _nc;\n\n /** Sparse-Block Boost Matrix. Each block is a SiconosMatrix**/\n SP::CompressedRowMat _blockCSR;\n\n /** Specific structure required when a (Numerics) solver block is used */\n SP::SparseBlockStructuredMatrix _sparseBlockStructuredMatrix;\n\n /** Vector used to save the sum of rows of diagonal blocks of M:\n _diagsize0[i] = _diagsize0[i-1] + ni, ni being the size of the\n diagonal block at row(block) i */\n SP::IndexInt _diagsize0;\n\n /** Vector used to save the sum of dim of diagonal blocks of M:\n _diagsize0[i] = _diagsize0[i-1] + ni, ni being the size of the\n diagonal block at row(block) i */\n SP::IndexInt _diagsize1;\n\n /** List of non null blocks positions (in row) */\n SP::IndexInt rowPos;\n\n /** List of non null blocks positions (in col) */\n SP::IndexInt colPos;\n\n /** Private copy constructor => no copy nor pass by value */\n BlockCSRMatrix(const BlockCSRMatrix&);\n\n /** Private assignment -> forbidden \n * \\return BlockCSRMatrix&\n */\n BlockCSRMatrix& operator=(const BlockCSRMatrix&);\n\npublic:\n\n /** Default constructor -> empty matrix\n */\n BlockCSRMatrix();\n\n /** Constructor with dimension (number of blocks)\n \\param n number of blocks in a row/column (only square matrices allowed)\n */\n BlockCSRMatrix(unsigned int n);\n\n /** Constructor from index set\n \\param indexSet the index set of the active constraints\n */\n BlockCSRMatrix(InteractionsGraph& indexSet);\n\n /** destructor\n */\n ~BlockCSRMatrix();\n\n /** get size (in block-components) \n * \\return unsigned int NumberOfBlocksInARow\n */\n inline unsigned int numberOfBlocksInARow() const\n {\n return _nr;\n };\n\n /** get total number of non-null blocks\n * \\return unsigned int\n */\n unsigned int getNbNonNullBlocks() const;\n\n /** get the numerics-readable structure\n * \\return SP::SparseBlockStructuredMatrix\n */\n inline SP::SparseBlockStructuredMatrix getNumericsMatSparse()\n {\n return _sparseBlockStructuredMatrix;\n };\n\n /** get the ublas sparse mat\n * \\return SP::CompressedRowMat\n */\n inline SP::CompressedRowMat getMSparse()\n {\n return _blockCSR;\n };\n\n /** get the dimension of the square-diagonal block number num\n * \\param i block position\n * \\return unsigned int\n */\n IndexInt::value_type getSizeOfDiagonalBlock(int i) const\n {\n if (i == 0) return _diagsize0->at(0);\n else return (_diagsize0->at(i) - _diagsize0->at(i - 1));\n };\n\n /** get the index of blocks position (i=0 -> rows, i=1 -> columns)\n * \\param i unsigned int, 0 for rows, 1 for columns\n * \\return SP::IndexInt\n */\n inline SP::IndexInt getPositionsIndex(bool i)\n {\n if (i) return rowPos;\n else return colPos;\n };\n\n /** fill the current class using an index set\n * \\param indexSet set of the active constraints\n */\n void fill(InteractionsGraph& indexSet);\n\n\n /** fill the matrix with the Mass matrix \n * \\warning only for NewtonEulerDS\n * \\param indexSet of the active constraints\n */\n void fillM(InteractionsGraph& indexSet);\n\n /** fill the matrix with the H matrix \n * \\warning only for NewtonEuler3DR\n * \\param indexSet of the active constraints\n */\n void fillH(InteractionsGraph& indexSet);\n\n /** fill the numerics structure _sparseBlockStructuredMatrix using _blockCSR */\n void convert();\n\n /** display the current matrix\n */\n void display() const;\n};\n\n#endif\n", "meta": {"hexsha": "ef2e3a4d87fe2e058e3eb214db90a8f9a43311a5", "size": 7380, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "kernel/src/simulationTools/BlockCSRMatrix.hpp", "max_stars_repo_name": "BuildJet/siconos", "max_stars_repo_head_hexsha": "5e9c95806f0a01d62ab564ffb1d9d50c2dc32ef0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2018-12-11T11:21:09.000Z", "max_stars_repo_stars_event_max_datetime": "2018-12-11T11:21:09.000Z", "max_issues_repo_path": "kernel/src/simulationTools/BlockCSRMatrix.hpp", "max_issues_repo_name": "BuildJet/siconos", "max_issues_repo_head_hexsha": "5e9c95806f0a01d62ab564ffb1d9d50c2dc32ef0", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "kernel/src/simulationTools/BlockCSRMatrix.hpp", "max_forks_repo_name": "BuildJet/siconos", "max_forks_repo_head_hexsha": "5e9c95806f0a01d62ab564ffb1d9d50c2dc32ef0", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.2711864407, "max_line_length": 138, "alphanum_fraction": 0.7031165312, "num_tokens": 1984, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.22179253962631093}} {"text": "#include \n#include \n#include \n#include \"Sundials.h\"\n\nnamespace Upp {\n\nusing namespace Eigen;\n\n#include \t\t /* access to KINSOL func., consts. */\n#include /* prototypes for IDA fcts., consts. */\n#include /* access to serial N_Vector */\n#include /* access to dense SUNMatrix */\n#include /* access to dense SUNLinearSolver */\n#include /* access to Newton SUNNonlinearSolver */\n#include /* defs. of realtype, sunindextype */\n#include /* defs. of SUNRabs, SUNRexp, etc. */\n\n\nstatic void CheckMem(void *returnvalue, const char *funcname) {\n\tif (returnvalue == NULL) \n\t\tthrow Exc(Format(t_(\"SUNDIALS_ERROR: %s() failed - returned NULL pointer\"), funcname));\n}\n\nstatic void CheckRet(int returnvalue, const char *funcname) {\n\tif (returnvalue < 0) \n\t \tthrow Exc(Format(t_(\"SUNDIALS_ERROR: %s() failed with retval = %d\"), funcname, returnvalue));\n}\n\nstatic void PrintFinalStats(void *mem) {\n\tint retval;\n\tlong int nst, nni, nje, nre, nreLS, netf, ncfn, nge;\n\t\n\tretval = IDAGetNumSteps(mem, &nst);\n\tCheckRet(retval, \"IDAGetNumSteps\");\n\tretval = IDAGetNumResEvals(mem, &nre);\n\tCheckRet(retval, \"IDAGetNumResEvals\");\n\tretval = IDAGetNumJacEvals(mem, &nje);\n\tCheckRet(retval, \"IDAGetNumJacEvals\");\n\tretval = IDAGetNumNonlinSolvIters(mem, &nni);\n\tCheckRet(retval, \"IDAGetNumNonlinSolvIters\");\n\tretval = IDAGetNumErrTestFails(mem, &netf);\n\tCheckRet(retval, \"IDAGetNumErrTestFails\");\n\tretval = IDAGetNumNonlinSolvConvFails(mem, &ncfn);\n\tCheckRet(retval, \"IDAGetNumNonlinSolvConvFails\");\n\tretval = IDAGetNumLinResEvals(mem, &nreLS);\n\tCheckRet(retval, \"IDAGetNumLinResEvals\");\n\tretval = IDAGetNumGEvals(mem, &nge);\n\tCheckRet(retval, \"IDAGetNumGEvals\");\n\t\n\tprintf(\"\\nFinal Run Statistics: \\n\\n\");\n\tprintf(\"Number of steps = %ld\\n\", nst);\n\tprintf(\"Number of residual evaluations = %ld\\n\", nre+nreLS);\n\tprintf(\"Number of Jacobian evaluations = %ld\\n\", nje);\n\tprintf(\"Number of nonlinear iterations = %ld\\n\", nni);\n\tprintf(\"Number of error test failures = %ld\\n\", netf);\n\tprintf(\"Number of nonlinear conv. failures = %ld\\n\", ncfn);\n\tprintf(\"Number of root fn. evaluations = %ld\\n\", nge);\n}\n\nstatic void PrintIteration(realtype t, realtype *y, int neq) {\n\tCout() << \"\\n\" << Format(\"%7.3f \", t);\n\tfor (int i = 0; i < neq; ++i)\n\t\tCout() << Format(\"%12.4e \", y[i]);\n}\n\nstatic void PrintRootInfo(int *roots, int nroots) {\n\tCout() << \"\\nRoots found[] = \";\n\tfor (int i = 0; i < nroots; ++i)\n\t\tCout() << roots[i] << \" \";\n}\n\nstatic String GetIdaErrorMsg(int ret) {\n\tswitch (ret) {\n\tcase IDA_TOO_MUCH_WORK:\treturn \"IDASolve: IDA_TOO_MUCH_WORK\";\n\tcase IDA_TOO_MUCH_ACC: return \"IDASolve: IDA_TOO_MUCH_ACC\";\n\tcase IDA_ERR_FAIL: return \"IDASolve: IDA_ERR_FAIL\";\n\tcase IDA_MEM_NULL:\t\treturn \"ida_mem is NULL\";\n\tcase IDA_NO_MALLOC:\t\treturn \"ida_mem was not allocated\";\t\n\tcase IDA_ILL_INPUT:\t\treturn \"bad value for icopt, tout1, or id\";\n\tcase IDA_LINIT_FAIL:\treturn \"the linear solver linit routine failed\";\n \tcase IDA_BAD_EWT:\t\treturn \"zero value of some component of ewt\";\t\n \tcase IDA_RES_FAIL:\t\treturn \"res had a non-recoverable error\";\t\n \tcase IDA_FIRST_RES_FAIL:return \"res failed recoverably on the first call\";\t\n \tcase IDA_LSETUP_FAIL:\treturn \"lsetup had a non-recoverable error\";\t\n \tcase IDA_LSOLVE_FAIL:\treturn \"lsolve had a non-recoverable error\";\t\n \tcase IDA_NO_RECOVERY:\treturn \"res, lsetup, or lsolve had a recoverable error, but IDACalcIC could not recover\";\t\n \tcase IDA_CONSTR_FAIL:\treturn \"the inequality constraints could not be met\";\t\n \tcase IDA_LINESEARCH_FAIL:return\"the linesearch failed (either on steptol test or on the maxbacks test)\";\n \tcase IDA_CONV_FAIL:\t\treturn \"the Newton iterations failed to converge\";\n\tdefault:\t\t\t\treturn Format(\"Unknown Sundials error %d\", ret);\n\t}\n}\n \t\nvoid SolveDAE(const VectorXd &y, const VectorXd &dy, double dt, double maxt, Upp::Array &res, \n\t\tFunction Residual, int numZero,\n\t\tFunction ResidualZero) {\n\tASSERT(y.size() == dy.size());\n\tEigen::Index numEq = y.size();\n\tres.SetCount(int(numEq));\n\tfor (int i = 0; i < numEq; ++i) {\n\t\tres[i].resize(int(maxt/dt)+1);\n\t\tres[i](0) = y[i];\n\t}\n\tSolveDAE(y.data(), dy.data(), int(numEq), dt, maxt, Residual, numZero, ResidualZero, [&](double t, Eigen::Index iiter, const double y[], const double dy[], bool isZero, int *whichZero)->int {\n\t\tfor (int i = 0; i < int(numEq); ++i)\n\t\t\tres[i](iiter) = y[i]; \n\t\treturn true;\n\t});\t\t\n}\n\nvoid SolveDAE(const VectorXd &y, const VectorXd &dy, double dt, double maxt, \n\t\tUpp::Array &res, Upp::Array &dres, \n\t\tFunction Residual, int numZero,\n\t\tFunction ResidualZero) {\n\tASSERT(y.size() == dy.size());\n\tEigen::Index numEq = y.size();\n\tres.SetCount(int(numEq));\n\tdres.SetCount(int(numEq));\n\tfor (int i = 0; i < numEq; ++i) {\n\t\tres[i].resize(int(maxt/dt)+1);\n\t\tres[i](0) = y[i];\n\t\tdres[i].resize(int(maxt/dt)+1);\n\t\tdres[i](0) = dy[i];\n\t}\n\tSolveDAE(y.data(), dy.data(), int(numEq), dt, maxt, Residual, numZero, ResidualZero, [&](double t, Eigen::Index iiter, const double y[], const double dy[], bool isZero, int *whichZero)->int {\n\t\tfor (int i = 0; i < int(numEq); ++i) {\n\t\t\tres[i](iiter) = y[i]; \n\t\t\tdres[i](iiter) = dy[i];\n\t\t}\n\t\treturn true;\n\t});\t\t\n}\n\nvoid SolveDAE(const double y[], const double dy[], int numEq, double dt, double maxt, \n\t\tFunction Residual, int numZero, \n\t\tFunction ResidualZero,\n\t\tFunction OnIteration) {\n\tint retval, retvalr;\n\tBuffer rootsfound(numEq);\n\tSUNNonlinearSolver NLS = NULL;\n\tSUNLinearSolver LS = NULL;\n\tSUNMatrix A = NULL;\n\tvoid *mem = NULL;\n\tN_Vector avtol = NULL, yy = NULL, yp = NULL;\n\t\n\tExc error;\n\ttry {\n\t\tyy = N_VNew_Serial(numEq);\n\t\tCheckMem((void *)yy, \"N_VNew_Serial\");\n\t\typ = N_VNew_Serial(numEq);\n\t\tCheckMem((void *)yp, \"N_VNew_Serial\");\n\t\tavtol = N_VNew_Serial(numEq);\n\t\tCheckMem((void *)avtol, \"N_VNew_Serial\");\n\t\n\t \t/* Create and initialize y, y', and absolute tolerance vectors. */\n\t\tmemcpy(N_VGetArrayPointer(yy), y, numEq*sizeof(double));\n\t\tmemcpy(N_VGetArrayPointer(yp), dy, numEq*sizeof(double));\n\t\t\n\t\trealtype rtol = 1.0e-5;\n\t\t\n\t\trealtype *atval = N_VGetArrayPointer(avtol);\n\t\tfor (int i = 0; i < numEq; ++i)\n\t\t\tatval[i] = 1.0e-8;\n\t\n\t \tdouble t0 = 0;\n\t\n\t\tstruct UserData {\n\t\t\tFunction Residual;\n\t\t\tFunction ResidualZero;\n\t\t\tint iiter = 0;\n\t\t} userData;\n\t\t\n\t\tuserData.Residual = Residual;\n\t\tuserData.ResidualZero = ResidualZero;\n\t\t\n\t auto ResFun = [](realtype t, N_Vector _y, N_Vector _dy, N_Vector _res, void *user_data) { \n\t\t\trealtype *y = N_VGetArrayPointer(_y);\n\t\t\trealtype *dy = N_VGetArrayPointer(_dy);\n\t\t\trealtype *res = N_VGetArrayPointer(_res);\n\t\t\t\n\t\t\tUserData &userData = *(UserData *)user_data;\n\t\t\t\n\t\t\treturn userData.Residual(t, userData.iiter, y, dy, res) ? 0 : -1;\n\t\t};\n\t auto ResZeroFun = [](realtype t, N_Vector _y, N_Vector _dy, realtype *res, void *user_data) { \n\t\t\trealtype *y = N_VGetArrayPointer(_y);\n\t\t\trealtype *dy = N_VGetArrayPointer(_dy);\n\t\t\t\n\t\t\tUserData &userData = *(UserData *)user_data;\n\t\t\t\n\t\t\treturn userData.ResidualZero(t, userData.iiter, y, dy, res) ? 0 : -1;\n\t\t};\t\n\t\t\n\t\t/* Call IDACreate and IDAInit to initialize IDA memory */\n\t\tmem = IDACreate();\n\t\tCheckMem((void *)mem, \"IDACreate\");\t\n\t\t\n\t\tretval = IDASetUserData(mem, (void *)&userData);\n\t\tCheckRet(retval, \"IDASetUserData\");\n\t\n\t\tretval = IDAInit(mem, ResFun, t0, yy, yp);\n\t\tCheckRet(retval, \"IDAInit\");\n\t\t\n\t\tIDASetErrHandlerFn(mem, [](int error_code, const char *module, const char *function,\n char *msg, void *user_data) {\n \tthrow Exc(Format(t_(\"Sundials %s error (%s): %s\"), module, function, msg));\n }, nullptr);\n \n\t\t/* Call IDASVtolerances to set tolerances */\n\t\tretval = IDASVtolerances(mem, rtol, avtol);\n\t\tCheckRet(retval, \"IDASVtolerances\");\n\t\t\n\t\t/* Call IDARootInit to specify the root function grob with 2 components */\n\t\tif (ResidualZero && numZero > 0) {\n\t\t\tretval = IDARootInit(mem, numZero, ResZeroFun);\n\t\t\tCheckRet(retval, \"IDARootInit\");\n\t\t}\n\t\t\n\t\t/* Create dense SUNMatrix for use in linear solver */\n\t\tA = SUNDenseMatrix(numEq, numEq);\n\t\tCheckMem((void *)A, \"SUNDenseMatrix\");\n\t\t\n\t\t/* Create dense SUNLinearSolver object */\n\t\tLS = SUNLinSol_Dense(yy, A);\n\t\tCheckMem((void *)LS, \"SUNLinSol_Dense\");\n\t\t\n\t\t/* Attach the matrix and linear solver */\n\t\tretval = IDASetLinearSolver(mem, LS, A);\n\t\tCheckRet(retval, \"IDASetLinearSolver\");\n\t\t\n\t\tretval = IDASetMaxNumSteps(mem, 20000);\n\t\tCheckRet(retval, \"IDASetLinearSolver\");\n\t\t\n\t\t/* Create Newton SUNNonlinearSolver object. IDA uses a\n\t\t* Newton SUNNonlinearSolver by default, so it is unnecessary\n\t\t* to create it and attach it. It is done in this example code\n\t\t* solely for demonstration purposes. */\n\t\tNLS = SUNNonlinSol_Newton(yy);\n\t\tCheckMem((void *)NLS, \"SUNNonlinSol_Newton\");\n\t\t\n\t\t/* Attach the nonlinear solver */\n\t\tretval = IDASetNonlinearSolver(mem, NLS);\n\t\tCheckRet(retval, \"IDASetNonlinearSolver\");\n\t\t\n\t\tuserData.iiter = 0; \n\t\tdouble tnext = 0.000001;\n\t\tdouble titer;\n\t\twhile(tnext <= maxt && userData.iiter < int(maxt/dt)+1) {\n\t\t\tretval = IDASolve(mem, tnext, &titer, yy, yp, IDA_NORMAL);\n\t\t\tCheckRet(retval, \"IDASolve\");\n\t\t\n\t\t\trealtype *y = N_VGetArrayPointer(yy);\n\t\t\trealtype *dy = N_VGetArrayPointer(yp);\n\t\t\t\n\t\t\t//PrintIteration(titer, y, numEq);\n\t\t\t\n\t\t\tif (retval == IDA_ROOT_RETURN) {\n\t\t\t\tretvalr = IDAGetRootInfo(mem, rootsfound);\n\t\t\t\tCheckRet(retvalr, \"IDAGetRootInfo\");\n\t\t\t\t//PrintRootInfo(rootsfound, numEq);\n\t\t\t} else if (retval == IDA_SUCCESS) \n\t\t\t\t;\n\t\t\telse\t\t\n\t\t\t\tthrow Exc(Format(t_(\"Sundials IDA error: %s\"), GetIdaErrorMsg(retval)));\n\t\t\t\n\t\t\tif(OnIteration && !OnIteration(titer, userData.iiter, y, dy, retval == IDA_ROOT_RETURN, rootsfound))\n\t\t\t\tbreak;\n\t\t\t\n\t\t\tif (retval == IDA_SUCCESS) {\n\t\t\t\tuserData.iiter++;\n\t\t\t\ttnext = userData.iiter*dt;\n\t\t\t}\n\t \t}\n\t \t//PrintFinalStats(mem);\n\t} catch(Exc err) {\n\t\terror = err;\n\t}\n\tIDAFree(&mem);\n\tSUNNonlinSolFree(NLS);\n\tSUNLinSolFree(LS);\n\tSUNMatDestroy(A);\n\tN_VDestroy(avtol);\n\tN_VDestroy(yy);\n\tN_VDestroy(yp);\n\t\n\tif (!error.IsEmpty())\n\t\tthrow Exc(error);\n}\n\n/*\n0 \tthen no constraint is imposed on ui.\n1 \tthen ui will be constrained to be ui 0:0.\n-1 \tthen ui will be constrained to be ui 0:0.\n2 \tthen ui will be constrained to be ui > 0:0.\n-2 \tthen ui will be constrained to be ui < 0:0.\n*/\nvoid SolveNonLinearEquationsSun(double y[], int numEq, \n\t\t\tFunction Residual, int constraints[]) {\n\tN_Vector s = NULL;\n\tvoid *kmem = NULL;\n\t\n \tExc error;\n \ttry {\n \t\tN_Vector u = N_VNew_Serial(numEq);\t\n\t\tCheckMem((void *)u, \"N_VNew_Serial\");\n \t\trealtype *udata = NV_DATA_S(u);\n \t\tmemcpy(udata, y, numEq*sizeof(double));\t\t\n \t\t\n\t\ts = N_VNew_Serial(numEq);\n\t \tCheckMem((void *)s, \"N_VNew_Serial\");\n\t\n\t\tN_VConst_Serial(1. ,s); /* no scaling */\n\t\t\n\t\tdouble fnormtol = 1.e-6;\n\t\tdouble scsteptol = 1.e-6;\n\t\tint numIterations = 500;\n\n\t \tkmem = KINCreate();\n\t \tCheckMem((void *)kmem, \"KINCreate\");\n\t \t\n\t \tauto ErrorFun = [](int error_code, const char *module, const char *function, char *msg, void *data) {\n\t\t \tString serror = Format(\"%d, module %s, function %s\", error_code, module, function);\n\t\t \tif (error_code == KIN_WARNING)\n\t\t \tCout() << Format(t_(\"Kinsol Warning (%s): %s\"), serror, msg);\n\t\t \telse {\n\t\t \t\tchar *str = KINGetReturnFlagName(error_code);\n\t\t \t\tString cerror(str);\n\t\t \t\tfree(str);\n\t\t \tthrow Exc(Format(t_(\"Kinsol Error %s (%s): %s\"), cerror, serror, msg));\n\t\t \t}\n\t\t};\n\t \t\n\t \tint flag;\n\t \t\n\t\tflag = KINSetErrHandlerFn(kmem, ErrorFun, NULL); \n\t\tCheckRet(flag, \"KINSetErrHandlerFn\");\n\t \t\n\t \tstruct UserData {\n\t\t\tFunction Residual;\n\t\t} userData;\n\t\t\n\t\tuserData.Residual = Residual;\t \t\n\t \t\n\t \tflag = KINSetUserData(kmem, &userData);\n\t \tCheckRet(flag, \"KINSetUserData\");\n\t\tflag = KINSetFuncNormTol(kmem, fnormtol);\n\t\tCheckRet(flag, \"KINSetFuncNormTol\");\n\t\tflag = KINSetScaledStepTol(kmem, scsteptol);\n\t\tCheckRet(flag, \"KINSetScaledStepTol\");\n\t\tflag = KINSetNumMaxIters(kmem, numIterations);\n\t \tCheckRet(flag, \"KINSetNumMaxIters\");\n\t\t\n\t \tauto ResFun = [](N_Vector u, N_Vector f, void *user_data) { \n\t\t\trealtype *y = NV_DATA_S(u);\n\t\t\trealtype *res = NV_DATA_S(f);\n\t\t\t\n\t\t\tUserData &userData = *(UserData *)user_data;\n\t\t\t\n\t\t\treturn userData.Residual(y, res) ? 0 : 1;\n\t\t};\n\t\t\n\t\tflag = KINInit(kmem, ResFun, u);\n\t\tCheckRet(flag, \"KINInit\");\n\t\n\t\tif (constraints != nullptr) {\n\t\t\tN_Vector constr = N_VNew_Serial(numEq);\t\n\t\t\tCheckMem((void *)constr, \"N_VNew_Serial\");\n\t\t\trealtype *constrdata = NV_DATA_S(constr);\n\t\t\tfor (int i = 0; i < numEq; ++i)\n\t\t\t\tconstrdata[i] = constraints[i];\n \t\t\n\t\t \tflag = KINSetConstraints(kmem, constr);\n\t\t \tCheckRet(flag, \"KINSetConstraints\");\n\t\t \t\n\t\t\tN_VDestroy_Serial(constr);\n\t\t}\n\t\t\n\t\t/* Create dense SUNMatrix */\n\t\tSUNMatrix J = SUNDenseMatrix(numEq, numEq);\n\t\tCheckMem((void *)J, \"SUNDenseMatrix\");\n\t\t\n\t\t/* Create dense SUNLinearSolver object */\n\t\tSUNLinearSolver LS = SUNLinSol_Dense(u, J);\n\t\tCheckMem((void *)LS, \"SUNLinSol_Dense\");\n\t\t\n\t\t/* Attach the matrix and linear solver to KINSOL */\n\t\tflag = KINSetLinearSolver(kmem, LS, J);\n\t\tCheckRet(flag, \"KINSetLinearSolver\");\n\t\n\t\tint glstr = KIN_NONE;\t// KIN_NONE \t\tKIN_LINESEARCH\tUsing line search\n\t\tint mset = 1;\t\t\t// 1 Exact Newton\t0 Modified Newton\n\t\n\t\tflag = KINSetMaxSetupCalls(kmem, mset);\n\t\tCheckRet(flag, \"KINSetMaxSetupCalls\");\n\t\t\n\t\tflag = KINSol(kmem, u, glstr, s, s);\n\t\tCheckRet(flag, \"KINSol\");\n\t\t\n\t\tfor (int i = 0; i < numEq; ++i) \n\t\t\tif (udata[i] != udata[i]) \n\t\t\t\tthrow Exc(Format(t_(\"Obtained NaN in value %d\"), i));\n\t\t memcpy(y, udata, numEq*sizeof(double));\t\t\n\t\t\n \t} catch(Exc err) {\n \t\terror = err;\n \t}\n \tN_VDestroy_Serial(s);\n \tKINFree(&kmem);\n \tif (!error.IsEmpty())\n \t\tthrow Exc(error);\n}\n\n}\n", "meta": {"hexsha": "edb5d7ef54e9c3e2f6ab4612f478fa6de66ec09b", "size": 14817, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "STEM4U/Sundials.cpp", "max_stars_repo_name": "XOULID/Anboto", "max_stars_repo_head_hexsha": "2743b066f23bf2db9cc062d3adedfd044bc69ec1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "STEM4U/Sundials.cpp", "max_issues_repo_name": "XOULID/Anboto", "max_issues_repo_head_hexsha": "2743b066f23bf2db9cc062d3adedfd044bc69ec1", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "STEM4U/Sundials.cpp", "max_forks_repo_name": "XOULID/Anboto", "max_forks_repo_head_hexsha": "2743b066f23bf2db9cc062d3adedfd044bc69ec1", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.7036144578, "max_line_length": 192, "alphanum_fraction": 0.6607275427, "num_tokens": 4593, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.39981164073979497, "lm_q1q2_score": 0.22168374633758542}} {"text": "#include \"farm_ng/calibration/multi_view_apriltag_rig_calibrator.h\"\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include // TODO remove.\n#include \n#include \n#include \n\n#include \"farm_ng/calibration/apriltag_rig_calibrator.h\"\n#include \"farm_ng/calibration/calibrator.pb.h\"\n#include \"farm_ng/calibration/camera_rig_apriltag_rig_cost_functor.h\"\n#include \"farm_ng/calibration/local_parameterization.h\"\n#include \"farm_ng/core/blobstore.h\"\n#include \"farm_ng/core/event_log_reader.h\"\n#include \"farm_ng/core/ipc.h\"\n#include \"farm_ng/perception/apriltag.h\"\n#include \"farm_ng/perception/apriltag.pb.h\"\n#include \"farm_ng/perception/camera_model.h\"\n#include \"farm_ng/perception/capture_video_dataset.pb.h\"\n#include \"farm_ng/perception/image_loader.h\"\n#include \"farm_ng/perception/image_utils.h\"\n#include \"farm_ng/perception/pose_graph.h\"\n#include \"farm_ng/perception/pose_utils.h\"\n#include \"farm_ng/perception/sophus_protobuf.h\"\n#include \"farm_ng/perception/time_series.h\"\n\nnamespace farm_ng {\nnamespace calibration {\ntypedef farm_ng::core::Event EventPb;\nusing farm_ng::core::EventLogReader;\nusing farm_ng::core::GetUniqueArchiveResource;\nusing farm_ng::core::ReadProtobufFromResource;\nusing farm_ng::core::Resource;\nusing farm_ng::perception::ApriltagDetections;\nusing farm_ng::perception::ApriltagRig;\nusing farm_ng::perception::ApriltagsFilter;\nusing farm_ng::perception::CameraModel;\nusing farm_ng::perception::PoseEdge;\n\nusing farm_ng::perception::ConstructGridImage;\nusing farm_ng::perception::FrameNameNumber;\nusing farm_ng::perception::FrameRigTag;\nusing farm_ng::perception::Image;\nusing farm_ng::perception::ImageLoader;\nusing farm_ng::perception::NamedSE3Pose;\nusing farm_ng::perception::PoseGraph;\nusing farm_ng::perception::SE3Map;\nusing farm_ng::perception::SophusToProto;\nusing farm_ng::perception::TimeSeries;\nusing Sophus::SE3d;\n\nusing farm_ng::core::Event;\nusing farm_ng::perception::CaptureVideoDatasetResult;\nusing farm_ng::perception::MultiViewApriltagDetections;\nusing Sophus::SE3d;\n\nvoid GetCameraRigPosesTagRig(const MultiViewApriltagRigModel& model,\n PoseGraph* pose_graph) {\n std::string tag_rig_view_frame = model.apriltag_rig().name() + \"/view/\";\n std::map camera_rig_poses_tag_rig;\n for (const NamedSE3Pose& camera_rig_pose_tag_rig :\n model.camera_rig_poses_apriltag_rig()) {\n CHECK(camera_rig_pose_tag_rig.frame_b().rfind(tag_rig_view_frame) == 0)\n << camera_rig_pose_tag_rig.frame_b()\n << \" does not start with: \" << tag_rig_view_frame;\n CHECK_EQ(camera_rig_pose_tag_rig.frame_a(), model.camera_rig().name());\n int frame_n = std::stoi(\n camera_rig_pose_tag_rig.frame_b().substr(tag_rig_view_frame.size()));\n CHECK_GE(frame_n, 0);\n CHECK_LT(frame_n, model.multi_view_detections_size());\n pose_graph->AddPose(camera_rig_pose_tag_rig);\n }\n}\n\nPoseGraph PoseGraphFromModel(const MultiViewApriltagRigModel& model) {\n PoseGraph pose_graph;\n for (const ApriltagRig::Node& node : model.apriltag_rig().nodes()) {\n pose_graph.AddPose(node.pose());\n }\n pose_graph.AddPoses(model.camera_rig().camera_pose_rig());\n GetCameraRigPosesTagRig(model, &pose_graph);\n return pose_graph;\n}\nvoid UpdateModelFromPoseGraph(const PoseGraph& pose_graph,\n MultiViewApriltagRigModel* model) {\n for (ApriltagRig::Node& node :\n *model->mutable_apriltag_rig()->mutable_nodes()) {\n pose_graph.UpdateNamedSE3Pose(node.mutable_pose());\n }\n pose_graph.UpdateNamedSE3Poses(\n model->mutable_camera_rig()->mutable_camera_pose_rig());\n pose_graph.UpdateNamedSE3Poses(\n model->mutable_camera_rig_poses_apriltag_rig());\n}\n\nvoid ModelError(MultiViewApriltagRigModel* model,\n bool output_reprojection_images) {\n model->set_rmse(0.0);\n model->clear_reprojection_images();\n model->clear_tag_stats();\n PoseGraph pose_graph = PoseGraphFromModel(*model);\n std::string root_tag_frame = FrameRigTag(model->apriltag_rig().name(),\n model->apriltag_rig().root_tag_id());\n std::string tag_rig_frame = model->apriltag_rig().name();\n std::string root_camera_frame = model->camera_rig().root_camera_name();\n std::string camera_rig_frame = model->camera_rig().name();\n int frame_num = -1;\n double total_rmse = 0.0;\n double total_count = 0.0;\n\n double total_depth_error = 0.0;\n double total_depth_count = 0.0;\n\n std::map tag_stats;\n\n ImageLoader image_loader;\n\n for (const auto& mv_detections : model->multi_view_detections()) {\n frame_num++;\n std::string tag_rig_view_frame =\n tag_rig_frame + \"/view/\" + std::to_string(frame_num);\n\n if (!pose_graph.HasEdge(camera_rig_frame, tag_rig_view_frame)) {\n continue;\n }\n PoseEdge* camera_rig_to_tag_rig_view =\n pose_graph.MutablePoseEdge(camera_rig_frame, tag_rig_view_frame);\n std::vector images;\n\n for (const auto& detections_per_view :\n mv_detections.detections_per_view()) {\n const auto& camera_model = detections_per_view.image().camera_model();\n std::string camera_frame = camera_model.frame_name();\n\n PoseEdge* camera_to_camera_rig =\n pose_graph.MutablePoseEdge(camera_frame, camera_rig_frame);\n\n if (output_reprojection_images) {\n cv::Mat image = image_loader.LoadImage(detections_per_view.image());\n if (image.channels() == 1) {\n cv::Mat color;\n cv::cvtColor(image, color, cv::COLOR_GRAY2BGR);\n image = color;\n }\n\n for (const auto& node : model->apriltag_rig().nodes()) {\n PoseEdge* tag_to_tag_rig =\n pose_graph.MutablePoseEdge(node.frame_name(), tag_rig_frame);\n\n auto camera_pose_tag =\n camera_to_camera_rig->GetAPoseBMapped(camera_frame,\n camera_rig_frame) *\n camera_rig_to_tag_rig_view->GetAPoseBMapped(camera_rig_frame,\n tag_rig_view_frame) *\n tag_to_tag_rig->GetAPoseBMapped(tag_rig_frame, node.frame_name());\n for (int i = 0; i < 4; ++i) {\n Eigen::Vector3d point_tag(node.points_tag().Get(i).x(),\n node.points_tag().Get(i).y(),\n node.points_tag().Get(i).z());\n Eigen::Vector3d point_camera = camera_pose_tag * point_tag;\n if (point_camera.z() > 0.001) {\n Eigen::Vector2d rp =\n ProjectPointToPixel(camera_model, point_camera);\n cv::circle(image, cv::Point(rp.x(), rp.y()), 3,\n cv::Scalar(0, 0, 255), -1);\n }\n }\n for (const auto& detection : detections_per_view.detections()) {\n auto points_image = PointsImage(detection);\n for (size_t i = 0; i < points_image.size(); ++i) {\n cv::circle(image,\n cv::Point(points_image[i].x(), points_image[i].y()), 5,\n cv::Scalar(255, 0, 0));\n }\n }\n }\n images.push_back(image);\n }\n\n for (const auto& detection : detections_per_view.detections()) {\n std::string tag_frame = FrameRigTag(tag_rig_frame, detection.id());\n PoseEdge* tag_to_tag_rig =\n pose_graph.MutablePoseEdge(tag_frame, tag_rig_frame);\n auto points_image = PointsImage(detection);\n CameraRigApriltagRigCostFunctor cost(\n detections_per_view.image().camera_model(), detection,\n camera_to_camera_rig->GetAPoseBMap(camera_frame, camera_rig_frame),\n tag_to_tag_rig->GetAPoseBMap(tag_rig_frame, tag_frame),\n camera_rig_to_tag_rig_view->GetAPoseBMap(camera_rig_frame,\n tag_rig_view_frame),\n 1.0);\n Eigen::Matrix residuals;\n CHECK(cost(camera_to_camera_rig->GetAPoseB().data(),\n tag_to_tag_rig->GetAPoseB().data(),\n camera_rig_to_tag_rig_view->GetAPoseB().data(),\n residuals.data()));\n double depth_error = 0;\n int depth_count = 0;\n for (int i = 0; i < 4; ++i) {\n double d2 = residuals(i, 2) * residuals(i, 2);\n if (d2 > 0.0) {\n depth_error += residuals(i, 2);\n depth_count += 1;\n }\n }\n total_depth_count += depth_count;\n total_depth_error += depth_error;\n total_rmse += residuals.block<4, 2>(0, 0).squaredNorm();\n total_count += 8;\n\n ApriltagRigTagStats& stats = tag_stats[detection.id()];\n stats.set_tag_id(detection.id());\n stats.set_n_frames(stats.n_frames() + 1);\n stats.set_tag_rig_rmse(stats.tag_rig_rmse() +\n residuals.block<4, 2>(0, 0).squaredNorm() / 8);\n stats.set_tag_rig_depth_error(stats.tag_rig_depth_error() +\n depth_error);\n stats.set_tag_rig_depth_count(stats.tag_rig_depth_count() +\n depth_count);\n\n PerImageRmse* image_rmse = stats.add_per_image_rmse();\n image_rmse->set_rmse(\n std::sqrt(residuals.block<4, 2>(0, 0).squaredNorm() / 8));\n image_rmse->set_frame_number(frame_num);\n image_rmse->set_camera_name(\n detections_per_view.image().camera_model().frame_name());\n if (depth_count > 0) {\n image_rmse->set_depth_error( // std::sqrt\n (depth_error / depth_count));\n }\n }\n }\n if (output_reprojection_images && !images.empty()) {\n Image& reprojection_image = *model->add_reprojection_images();\n int n_cols = 3;\n int n_rows = std::ceil(float(images.size()) / n_cols);\n if (n_cols > int(images.size())) {\n n_cols = images.size();\n n_rows = 1;\n }\n\n int image_width = images[0].size().width * n_cols;\n int image_height = images[0].size().height * n_rows;\n reprojection_image.mutable_camera_model()->set_image_width(image_width);\n reprojection_image.mutable_camera_model()->set_image_height(image_height);\n auto resource_path = GetUniqueArchiveResource(\n FrameNameNumber(\n \"reprojection-\" + SolverStatus_Name(model->solver_status()),\n frame_num),\n \"jpg\", \"image/jpeg\");\n reprojection_image.mutable_resource()->CopyFrom(resource_path.first);\n LOG(INFO) << resource_path.second.string();\n CHECK(\n cv::imwrite(resource_path.second.string(),\n ConstructGridImage(\n images, cv::Size(image_width, image_height), n_cols)))\n << \"Could not write: \" << resource_path.second;\n }\n }\n std::stringstream summary;\n summary << \"# tag_id frame_number camera_name rmse depth_error\\n\";\n for (auto& stats : tag_stats) {\n for (auto& per_image : stats.second.per_image_rmse()) {\n summary << stats.second.tag_id() << \" \" << per_image.frame_number() << \" \"\n << per_image.camera_name() << \" \" << per_image.rmse() << \" \"\n << per_image.depth_error() << \"\\n\";\n }\n }\n for (auto& stats : tag_stats) {\n stats.second.set_tag_rig_rmse(\n std::sqrt(stats.second.tag_rig_rmse() / stats.second.n_frames()));\n if (stats.second.tag_rig_depth_count() > 0) {\n stats.second.set_tag_rig_depth_error(\n // std::sqrt\n (stats.second.tag_rig_depth_error() /\n stats.second.tag_rig_depth_count()));\n }\n\n auto debug_stats = stats.second;\n debug_stats.clear_per_image_rmse();\n summary << debug_stats.DebugString() << \"\\n\\n\";\n model->add_tag_stats()->CopyFrom(stats.second);\n }\n model->set_rmse(std::sqrt(total_rmse / total_count));\n summary << \"model rmse (pixels): \" << model->rmse() << \"\\n\";\n if (total_depth_count > 0) {\n model->set_depth_error( // std::sqrt\n (total_depth_error / total_depth_count));\n summary << \"model depth error (meters): \" << model->depth_error() << \"\\n\";\n }\n LOG(INFO) << \"Error Summary:\\n\" << summary.str();\n}\n\nstd::vector LoadMultiViewApriltagDetections(\n const std::string& root_camera_name, const Resource& event_log,\n const CalibrateMultiViewApriltagRigConfiguration& config) {\n EventLogReader log_reader(event_log);\n\n std::set allowed_cameras;\n\n for (auto camera_name : config.include_cameras()) {\n allowed_cameras.insert(camera_name);\n LOG(INFO) << \"allowed camera: \" << camera_name;\n }\n std::unordered_set allowed_ids;\n\n for (auto id : config.tag_ids()) {\n allowed_ids.insert(id);\n LOG(INFO) << \"allowed tag id: \" << id;\n }\n\n CHECK(allowed_ids.count(config.root_tag_id()) == 1)\n << \"Please ensure root_tag_id is in the tag_ids list\";\n\n std::map> apriltag_series;\n\n while (true) {\n EventPb event;\n try {\n event = log_reader.ReadNext();\n } catch (std::runtime_error& e) {\n break;\n }\n ApriltagDetections unfiltered_detections;\n if (event.data().UnpackTo(&unfiltered_detections)) {\n auto camera_name =\n unfiltered_detections.image().camera_model().frame_name();\n CHECK(!camera_name.empty()) << \" camera_name is not set.\";\n if (!allowed_cameras.empty() && !allowed_cameras.count(camera_name)) {\n LOG(INFO) << \"skipping data from camera: \" << camera_name;\n continue;\n }\n ApriltagDetections detections = unfiltered_detections;\n detections.clear_detections();\n for (const auto& detection : unfiltered_detections.detections()) {\n if (allowed_ids.count(detection.id())) {\n detections.add_detections()->CopyFrom(detection);\n }\n }\n event.mutable_data()->PackFrom(detections);\n\n event.set_name(detections.image().camera_model().frame_name() +\n \"/apriltags\");\n apriltag_series[event.name()].insert(event);\n }\n }\n {\n std::stringstream ss;\n ss << \"Raw detections\\n\";\n for (const auto& series : apriltag_series) {\n ss << series.first << \" \" << series.second.size() << \" detections\\n\";\n }\n LOG(INFO) << ss.str();\n }\n\n ApriltagsFilter tag_filter;\n std::vector mv_detections_series;\n auto time_window =\n google::protobuf::util::TimeUtil::MillisecondsToDuration(1000.0 / 2);\n\n std::map detection_counts;\n int steady_count = 5;\n if (config.has_steady_count()) {\n steady_count = config.steady_count().value();\n }\n LOG(INFO) << \"Steady count is: \" << steady_count;\n for (const Event& event : apriltag_series[root_camera_name + \"/apriltags\"]) {\n ApriltagDetections detections;\n CHECK(event.data().UnpackTo(&detections));\n if (!config.filter_stable_tags() ||\n tag_filter.AddApriltags(detections, steady_count, 7)) {\n MultiViewApriltagDetections mv_detections;\n for (auto name_series : apriltag_series) {\n auto nearest_event =\n name_series.second.FindNearest(event.stamp(), time_window);\n if (nearest_event) {\n CHECK(nearest_event->data().UnpackTo(\n mv_detections.add_detections_per_view()));\n detection_counts[nearest_event->name()]++;\n }\n }\n mv_detections_series.push_back(mv_detections);\n }\n }\n {\n std::stringstream ss;\n ss << \"Stable multi-view detections: \" << mv_detections_series.size()\n << \"\\n\";\n for (const auto& name_count : detection_counts) {\n ss << name_count.first << \" \" << name_count.second\n << \" stable detections\\n\";\n }\n LOG(INFO) << ss.str();\n }\n\n return mv_detections_series;\n}\n\nPoseGraph TagRigFromMultiViewDetections(std::string tag_rig_name,\n int root_tag_id,\n MultiViewApriltagRigModel* model) {\n model->mutable_apriltag_rig()->set_name(tag_rig_name);\n model->mutable_apriltag_rig()->set_root_tag_id(root_tag_id);\n\n PoseGraph tag_rig;\n std::map tag_rig_nodes;\n\n for (const auto& mv_detections : model->multi_view_detections()) {\n for (const auto& detections_per_view :\n mv_detections.detections_per_view()) {\n if (detections_per_view.detections_size() <= 1) {\n continue;\n }\n for (const auto& detection : detections_per_view.detections()) {\n if (tag_rig_nodes.count(detection.id())) {\n continue;\n }\n ApriltagRig::Node node;\n node.set_id(detection.id());\n node.set_frame_name(FrameRigTag(tag_rig_name, detection.id()));\n node.set_tag_size(detection.tag_size());\n for (const auto& v : PointsTag(detection)) {\n EigenToProto(v, node.add_points_tag());\n }\n tag_rig_nodes.emplace(detection.id(), std::move(node));\n }\n for (int i = 0; i < detections_per_view.detections_size() - 1; ++i) {\n for (int j = i + 1; j < detections_per_view.detections_size(); ++j) {\n const auto& detection_i = detections_per_view.detections().Get(i);\n const auto& detection_j = detections_per_view.detections().Get(j);\n Sophus::SE3d c_pose_i, c_pose_j;\n CHECK_EQ(detection_i.pose().frame_a(), detection_j.pose().frame_a());\n ProtoToSophus(detection_i.pose().a_pose_b(), &c_pose_i);\n ProtoToSophus(detection_j.pose().a_pose_b(), &c_pose_j);\n tag_rig.AddPose(detection_i.pose().frame_b(),\n detection_j.pose().frame_b(),\n c_pose_i.inverse() * c_pose_j);\n }\n }\n }\n }\n std::string root_tag = \"tag/\" + std::to_string(root_tag_id);\n\n auto tag_rig_small = tag_rig.AveragePoseGraph(root_tag);\n\n for (auto& node : tag_rig_nodes) {\n std::string tag = \"tag/\" + std::to_string(node.first);\n auto root_pose_tag = tag_rig_small.AverageAPoseB(root_tag, tag);\n if (!root_pose_tag) {\n continue;\n }\n SophusToProto(*root_pose_tag, tag_rig_name, node.second.frame_name(),\n node.second.mutable_pose());\n model->mutable_apriltag_rig()->add_nodes()->CopyFrom(node.second);\n }\n return tag_rig_small;\n}\n\nvoid CameraRigFromMultiViewDetections(std::string camera_rig_name,\n std::string root_camera_name,\n const PoseGraph& tag_rig,\n MultiViewApriltagRigModel* model) {\n model->mutable_camera_rig()->set_root_camera_name(root_camera_name);\n model->mutable_camera_rig()->set_name(camera_rig_name);\n\n std::string root_tag_name =\n \"tag/\" + std::to_string(model->apriltag_rig().root_tag_id());\n PoseGraph camera_rig_inter;\n PoseGraph camera_rig_tags;\n\n int frame_num = -1;\n\n std::map camera_models;\n for (const auto& mv_detections : model->multi_view_detections()) {\n frame_num++;\n PoseGraph camera_rig_step_i;\n\n camera_rig_step_i.AddPose(camera_rig_name, root_camera_name, SE3d::rotX(0));\n\n for (const auto& detections_per_view :\n mv_detections.detections_per_view()) {\n if (detections_per_view.detections_size() < 1) {\n continue;\n }\n camera_models.emplace(\n detections_per_view.image().camera_model().frame_name(),\n detections_per_view.image().camera_model());\n\n for (const auto& detection : detections_per_view.detections()) {\n Sophus::SE3d c_pose_tag;\n ProtoToSophus(detection.pose().a_pose_b(), &c_pose_tag);\n auto o_tag_pose_root_tag =\n tag_rig.AverageAPoseB(detection.pose().frame_b(), root_tag_name);\n if (!o_tag_pose_root_tag) {\n LOG(WARNING) << \"Unable to compute pose for: \"\n << detection.pose().frame_b() << \" <- \" << root_tag_name;\n continue;\n }\n auto c_pose_root_tag = c_pose_tag * (*o_tag_pose_root_tag);\n camera_rig_step_i.AddPose(detection.pose().frame_a(), root_tag_name,\n c_pose_root_tag);\n }\n }\n if (!camera_rig_step_i.HasName(root_camera_name)) {\n continue;\n }\n auto camera_rig_i = camera_rig_step_i.AveragePoseGraph(camera_rig_name);\n\n for (auto es = camera_rig_i.Edges(); es.first != es.second; es.first++) {\n auto edge = camera_rig_i.PoseEdgeMap()[*es.first];\n std::string frame_a = edge.frame_a;\n std::string frame_b = edge.frame_b;\n if (frame_a == root_tag_name || frame_b == root_tag_name) {\n std::string rig_frame_name =\n model->apriltag_rig().name() + \"/view/\" + std::to_string(frame_num);\n if (frame_a == root_tag_name) {\n frame_a = rig_frame_name;\n }\n if (frame_b == root_tag_name) {\n frame_b = rig_frame_name;\n }\n camera_rig_tags.AddPose(frame_a, frame_b, edge.GetAPoseB());\n } else {\n camera_rig_inter.AddPose(frame_a, frame_b, edge.GetAPoseB());\n }\n }\n }\n for (auto camera : camera_models) {\n model->mutable_camera_rig()->add_cameras()->CopyFrom(camera.second);\n }\n\n model->mutable_camera_rig()->mutable_camera_pose_rig()->CopyFrom(\n camera_rig_inter.AveragePoseGraph(camera_rig_name).ToNamedSE3Poses());\n\n model->mutable_camera_rig_poses_apriltag_rig()->CopyFrom(\n camera_rig_tags.AveragePoseGraph(camera_rig_name).ToNamedSE3Poses());\n}\n\nMultiViewApriltagRigModel InitialMultiViewApriltagModelFromConfig(\n const CalibrateMultiViewApriltagRigConfiguration& config) {\n core::Resource event_log;\n switch (config.input_case()) {\n case CalibrateMultiViewApriltagRigConfiguration::InputCase::kVideoDataset: {\n auto dataset_result = ReadProtobufFromResource(\n config.video_dataset());\n event_log = dataset_result.dataset();\n break;\n }\n case CalibrateMultiViewApriltagRigConfiguration::InputCase::kEventLog: {\n event_log = config.event_log();\n break;\n }\n default:\n LOG(FATAL) << \"No input provided in config.\";\n }\n MultiViewApriltagRigModel model;\n for (const auto& mv_detections : LoadMultiViewApriltagDetections(\n config.root_camera_name(), event_log, config)) {\n model.add_multi_view_detections()->CopyFrom(mv_detections);\n }\n auto tag_rig = TagRigFromMultiViewDetections(config.tag_rig_name(),\n config.root_tag_id(), &model);\n CameraRigFromMultiViewDetections(config.name(), config.root_camera_name(),\n tag_rig, &model);\n\n model.set_solver_status(SolverStatus::SOLVER_STATUS_INITIAL);\n return model;\n}\n\nMultiViewApriltagRigModel SolveMultiViewApriltagModel(\n MultiViewApriltagRigModel model) {\n PoseGraph pose_graph = PoseGraphFromModel(model);\n double depth_weight = 100.0;\n ceres::Problem problem;\n for (PoseEdge* pose_edge : pose_graph.MutablePoseEdges()) {\n LOG(INFO) << *pose_edge;\n problem.AddParameterBlock(pose_edge->GetAPoseB().data(),\n SE3d::num_parameters,\n new LocalParameterizationSE3);\n }\n std::string root_tag_frame = FrameRigTag(model.apriltag_rig().name(),\n model.apriltag_rig().root_tag_id());\n std::string tag_rig_frame = model.apriltag_rig().name();\n problem.SetParameterBlockConstant(\n pose_graph.MutablePoseEdge(root_tag_frame, tag_rig_frame)\n ->GetAPoseB()\n .data());\n\n std::string root_camera_frame = model.camera_rig().root_camera_name();\n std::string camera_rig_frame = model.camera_rig().name();\n\n problem.SetParameterBlockConstant(\n pose_graph.MutablePoseEdge(root_camera_frame, camera_rig_frame)\n ->GetAPoseB()\n .data());\n\n int frame_num = -1;\n for (const auto& mv_detections : model.multi_view_detections()) {\n frame_num++;\n\n std::string tag_rig_view_frame =\n tag_rig_frame + \"/view/\" + std::to_string(frame_num);\n\n if (!pose_graph.HasEdge(camera_rig_frame, tag_rig_view_frame)) {\n continue;\n }\n\n PoseEdge* camera_rig_to_tag_rig_view =\n pose_graph.MutablePoseEdge(camera_rig_frame, tag_rig_view_frame);\n\n for (const auto& detections_per_view :\n mv_detections.detections_per_view()) {\n if (detections_per_view.detections_size() < 1) {\n continue;\n }\n std::string camera_frame =\n detections_per_view.image().camera_model().frame_name();\n\n PoseEdge* camera_to_camera_rig =\n pose_graph.MutablePoseEdge(camera_frame, camera_rig_frame);\n\n for (const auto& detection : detections_per_view.detections()) {\n std::string tag_frame = FrameRigTag(tag_rig_frame, detection.id());\n PoseEdge* tag_to_tag_rig =\n pose_graph.MutablePoseEdge(tag_frame, tag_rig_frame);\n\n ceres::CostFunction* cost_function1 = new ceres::AutoDiffCostFunction<\n CameraRigApriltagRigCostFunctor, 12, Sophus::SE3d::num_parameters,\n Sophus::SE3d::num_parameters, Sophus::SE3d::num_parameters>(\n new CameraRigApriltagRigCostFunctor(\n detections_per_view.image().camera_model(), detection,\n camera_to_camera_rig->GetAPoseBMap(camera_frame,\n camera_rig_frame),\n tag_to_tag_rig->GetAPoseBMap(tag_rig_frame, tag_frame),\n camera_rig_to_tag_rig_view->GetAPoseBMap(camera_rig_frame,\n tag_rig_view_frame),\n depth_weight));\n problem.AddResidualBlock(\n cost_function1, new ceres::CauchyLoss(1.0),\n camera_to_camera_rig->GetAPoseB().data(),\n tag_to_tag_rig->GetAPoseB().data(),\n camera_rig_to_tag_rig_view->GetAPoseB().data());\n }\n }\n }\n\n // Set solver options (precision / method)\n ceres::Solver::Options options;\n options.linear_solver_type = ceres::SPARSE_SCHUR;\n options.gradient_tolerance = 1e-18;\n options.function_tolerance = 1e-18;\n options.parameter_tolerance = 1e-18;\n options.max_num_iterations = 2000;\n\n // Solve\n ceres::Solver::Summary summary;\n options.logging_type = ceres::PER_MINIMIZER_ITERATION;\n options.minimizer_progress_to_stdout = true;\n ceres::Solve(options, &problem, &summary);\n LOG(INFO) << summary.FullReport() << std::endl;\n if (summary.termination_type == ceres::CONVERGENCE) {\n model.set_solver_status(SolverStatus::SOLVER_STATUS_CONVERGED);\n } else {\n model.set_solver_status(SolverStatus::SOLVER_STATUS_FAILED);\n }\n UpdateModelFromPoseGraph(pose_graph, &model);\n return model;\n}\n\nstd::optional CameraRigPoseApriltagRigEstimateAverage(\n const perception::MultiViewCameraRig& camera_rig,\n const perception::ApriltagRig& apriltag_rig,\n const perception::MultiViewApriltagDetections& mv_detections,\n PoseGraph* pose_graph) {\n perception::ApriltagRigIdMap tag_id_map;\n tag_id_map.AddRig(apriltag_rig);\n\n *pose_graph = PoseGraph();\n\n for (const ApriltagRig::Node& node : apriltag_rig.nodes()) {\n pose_graph->AddPose(node.pose());\n }\n pose_graph->AddPoses(camera_rig.camera_pose_rig());\n for (const auto& detections_per_view : mv_detections.detections_per_view()) {\n for (const auto& detection : detections_per_view.detections()) {\n auto frame_names = tag_id_map.GetFrameNames(detection.id());\n if (!frame_names) {\n continue;\n }\n CHECK_EQ(detections_per_view.image().camera_model().frame_name(),\n detection.pose().frame_a());\n\n Sophus::SE3d c_pose_tag;\n ProtoToSophus(detection.pose().a_pose_b(), &c_pose_tag);\n\n auto tag_pose_tag_rig = pose_graph->CheckAverageAPoseB(\n frame_names->tag_frame, frame_names->rig_frame);\n\n auto camera_rig_pose_camera = pose_graph->CheckAverageAPoseB(\n camera_rig.name(),\n detections_per_view.image().camera_model().frame_name());\n\n auto camera_rig_pose_tag_rig =\n camera_rig_pose_camera * c_pose_tag * tag_pose_tag_rig;\n pose_graph->AddPose(camera_rig.name(), apriltag_rig.name(),\n camera_rig_pose_tag_rig);\n }\n }\n if (pose_graph->HasEdge(camera_rig.name(), apriltag_rig.name())) {\n auto edge =\n pose_graph->MutablePoseEdge(camera_rig.name(), apriltag_rig.name());\n\n LOG(INFO) << camera_rig.name() << \" <- \" << apriltag_rig.name()\n << \" has: \" << edge->a_poses_b.size() << \" estimates.\";\n edge->Collapse();\n }\n return pose_graph->AverageNamedSE3Pose(camera_rig.name(),\n apriltag_rig.name());\n}\n\nstd::optional>>\nEstimateMultiViewCameraRigPoseApriltagRig(\n const perception::MultiViewCameraRig& camera_rig,\n const perception::ApriltagRig& apriltag_rig,\n const perception::MultiViewApriltagDetections& mv_detections) {\n PoseGraph pose_graph;\n auto o_camera_rig_pose_tag_rig = CameraRigPoseApriltagRigEstimateAverage(\n camera_rig, apriltag_rig, mv_detections, &pose_graph);\n if (!o_camera_rig_pose_tag_rig) {\n return std::nullopt;\n }\n double depth_weight = 100.0;\n ceres::Problem problem;\n for (PoseEdge* pose_edge : pose_graph.MutablePoseEdges()) {\n VLOG(2) << *pose_edge;\n problem.AddParameterBlock(pose_edge->GetAPoseB().data(),\n SE3d::num_parameters,\n new LocalParameterizationSE3);\n if (!pose_edge->HasFrames(camera_rig.name(), apriltag_rig.name())) {\n problem.SetParameterBlockConstant(pose_edge->GetAPoseB().data());\n }\n }\n\n PoseEdge* camera_rig_to_tag_rig =\n pose_graph.MutablePoseEdge(camera_rig.name(), apriltag_rig.name());\n\n std::map tag_stats;\n std::map>>\n tag_id_to_per_image_rmse_block_id;\n\n for (const auto& detections_per_view : mv_detections.detections_per_view()) {\n if (detections_per_view.detections_size() < 1) {\n continue;\n }\n std::string camera_frame =\n detections_per_view.image().camera_model().frame_name();\n\n PoseEdge* camera_to_camera_rig =\n pose_graph.MutablePoseEdge(camera_frame, camera_rig.name());\n\n for (const auto& detection : detections_per_view.detections()) {\n std::string tag_frame = FrameRigTag(apriltag_rig.name(), detection.id());\n if (!pose_graph.HasEdge(tag_frame, apriltag_rig.name())) {\n continue;\n }\n ApriltagRigTagStats& stats = tag_stats[detection.id()];\n stats.set_tag_id(detection.id());\n stats.set_n_frames(stats.n_frames() + 1);\n\n PoseEdge* tag_to_tag_rig =\n pose_graph.MutablePoseEdge(tag_frame, apriltag_rig.name());\n\n ceres::CostFunction* cost_function1 = new ceres::AutoDiffCostFunction<\n CameraRigApriltagRigCostFunctor, 12, Sophus::SE3d::num_parameters,\n Sophus::SE3d::num_parameters, Sophus::SE3d::num_parameters>(\n new CameraRigApriltagRigCostFunctor(\n detections_per_view.image().camera_model(), detection,\n camera_to_camera_rig->GetAPoseBMap(camera_frame,\n camera_rig.name()),\n tag_to_tag_rig->GetAPoseBMap(apriltag_rig.name(), tag_frame),\n camera_rig_to_tag_rig->GetAPoseBMap(camera_rig.name(),\n apriltag_rig.name()),\n depth_weight));\n auto block_id =\n problem.AddResidualBlock(cost_function1, new ceres::CauchyLoss(1.0),\n camera_to_camera_rig->GetAPoseB().data(),\n tag_to_tag_rig->GetAPoseB().data(),\n camera_rig_to_tag_rig->GetAPoseB().data());\n PerImageRmse per_image_rmse;\n per_image_rmse.set_frame_number(0);\n per_image_rmse.set_camera_name(camera_frame);\n\n tag_id_to_per_image_rmse_block_id[detection.id()].push_back(\n std::make_tuple(per_image_rmse, block_id));\n }\n }\n\n // Set solver options (precision / method)\n ceres::Solver::Options options;\n options.linear_solver_type = ceres::SPARSE_SCHUR;\n options.gradient_tolerance = 1e-18;\n options.function_tolerance = 1e-18;\n options.parameter_tolerance = 1e-18;\n options.max_num_iterations = 2000;\n\n // Solve\n ceres::Solver::Summary summary;\n // options.logging_type = ceres::PER_MINIMIZER_ITERATION;\n // options.minimizer_progress_to_stdout = true;\n ceres::Solve(options, &problem, &summary);\n VLOG(2) << summary.FullReport() << std::endl;\n if (!summary.IsSolutionUsable()) {\n return std::nullopt;\n }\n double total_depth_count = 0;\n double total_depth_error = 0;\n double total_rmse = 0.0;\n double total_count = 0;\n std::vector out_tag_stats;\n for (auto it : tag_id_to_per_image_rmse_block_id) {\n int tag_id = it.first;\n auto& stats = tag_stats[tag_id];\n for (auto per_image_rmse_block : it.second) {\n auto [image_rmse, block] = per_image_rmse_block;\n double cost;\n Eigen::Matrix residuals;\n problem.EvaluateResidualBlockAssumingParametersUnchanged(\n block, false, &cost, residuals.data(), nullptr);\n double depth_error = 0;\n int depth_count = 0;\n for (int i = 0; i < 4; ++i) {\n double d2 = residuals(i, 2) * residuals(i, 2);\n if (d2 > 0.0) {\n depth_error += residuals(i, 2) / depth_weight;\n depth_count += 1;\n }\n }\n total_depth_count += depth_count;\n total_depth_error += depth_error;\n total_rmse += residuals.block<4, 2>(0, 0).squaredNorm();\n total_count += 8;\n\n stats.set_tag_rig_rmse(stats.tag_rig_rmse() +\n residuals.block<4, 2>(0, 0).squaredNorm() / 8);\n stats.set_tag_rig_depth_error(stats.tag_rig_depth_error() + depth_error);\n stats.set_tag_rig_depth_count(stats.tag_rig_depth_count() + depth_count);\n\n image_rmse.set_rmse(\n std::sqrt(residuals.block<4, 2>(0, 0).squaredNorm() / 8));\n\n if (depth_count > 0) {\n image_rmse.set_depth_error( // std::sqrt\n (depth_error / depth_count));\n }\n stats.add_per_image_rmse()->CopyFrom(image_rmse);\n }\n stats.set_tag_rig_rmse(std::sqrt(stats.tag_rig_rmse() / stats.n_frames()));\n if (stats.tag_rig_depth_count() > 0) {\n stats.set_tag_rig_depth_error(\n // std::sqrt\n (stats.tag_rig_depth_error() / stats.tag_rig_depth_count()));\n }\n out_tag_stats.push_back(stats);\n }\n double rmse = std::sqrt(total_rmse / total_count);\n std::stringstream ss;\n ss << \"model rmse (pixels): \" << rmse << \"\\n\";\n double depth_error = 0;\n if (total_depth_count > 0) {\n depth_error = (total_depth_error / total_depth_count);\n ss << \"model depth error (meters): \" << depth_error << \"\\n\";\n }\n\n auto pose = pose_graph.CheckAverageNamedSE3Pose(camera_rig.name(),\n apriltag_rig.name());\n\n LOG(INFO) << \"Solved pose:\" << pose.ShortDebugString() << \"\\n\" << ss.str();\n return {{pose, rmse, out_tag_stats}};\n}\n} // namespace calibration\n} // namespace farm_ng\n", "meta": {"hexsha": "c0a76455f377063e7d070a452444f5e369747ece", "size": 35386, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "modules/calibration/cpp/farm_ng/calibration/multi_view_apriltag_rig_calibrator.cpp", "max_stars_repo_name": "ethanrublee/tractor", "max_stars_repo_head_hexsha": "f76da65ccb34d2d09d90559b53df4874f5770c09", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "modules/calibration/cpp/farm_ng/calibration/multi_view_apriltag_rig_calibrator.cpp", "max_issues_repo_name": "ethanrublee/tractor", 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YES\n2. NO", "lm_q1_score": 0.5813031051514762, "lm_q2_score": 0.38121955219593834, "lm_q1q2_score": 0.2216041094359542}} {"text": "#pragma once\n#include \n#include \n#include \n#include \n#include \n#include \"../../container_cast.hh\"\n#include \"geodesic/geodesic_algorithm_dijkstra.h\"\n#include \"geodesic/geodesic_algorithm_exact.h\"\n#include \"geodesic/geodesic_algorithm_subdivision.h\"\n\nnamespace kt84 {\n\n struct GeodesicPoint {\n OpenMesh::PolyConnectivity::VHandle vhandle;\n OpenMesh::PolyConnectivity::EHandle ehandle;\n OpenMesh::PolyConnectivity::FHandle fhandle;\n Eigen::Vector2d ecoord; // barycentric coord for edge\n Eigen::Vector3d fcoord; // barycentric coord for face\n GeodesicPoint(){}\n GeodesicPoint(OpenMesh::PolyConnectivity::VHandle vhandle) : vhandle(vhandle) {}\n GeodesicPoint(OpenMesh::PolyConnectivity::EHandle ehandle, const Eigen::Vector2d& ecoord) : ehandle(ehandle), ecoord(ecoord) {}\n GeodesicPoint(OpenMesh::PolyConnectivity::FHandle fhandle, const Eigen::Vector3d& fcoord) : fhandle(fhandle), fcoord(fcoord) {}\n GeodesicPoint(const geodesic::SurfacePoint& p) {\n switch (p.type()) {\n case geodesic::VERTEX:\n vhandle = OpenMesh::PolyConnectivity::VHandle(p.base_element()->id());\n break;\n case geodesic::EDGE:\n ehandle = OpenMesh::PolyConnectivity::EHandle(p.base_element()->id());\n break;\n }\n }\n operator geodesic::SurfacePoint() const {\n geodesic::SurfacePoint p;\n if (is_vertex()) {\n \n }\n //...\n return p;\n }\n bool is_vertex() const { return vhandle.is_valid(); }\n bool is_edge () const { return ehandle.is_valid(); }\n bool is_face () const { return fhandle.is_valid(); }\n };\n \n template \n struct Geodesic : public DerivedPtrHolder> {\n void geodesic_init() {\n auto mesh = get_mesh();\n std::vector points;\n points.reserve(mesh->n_vertices() * 3);\n for (auto v : mesh->vertices()) {\n auto p = mesh->point(v);\n points.push_back(p[0]);\n points.push_back(p[1]);\n points.push_back(p[2]);\n }\n std::vector faces;\n faces.reserve(mesh->n_faces() * 3);\n for (auto f : mesh->faces()) {\n if (mesh->valence(f) != 3) {\n std::cerr << \"Error: geodesic algorithm does not work on polygonal meshes!\\n\";\n assert(false);\n }\n for (auto v : mesh->fv_range(f))\n faces.push_back(v.idx());\n }\n geodesic.mesh.initialize_mesh_data(points, faces);\n geodesic_set_algorithm_exact(); // default setting\n }\n void geodesic_set_algorithm_exact() {\n geodesic.algorithm = std::make_shared(&geodesic.mesh);\n }\n void geodesic_set_algorithm_dijkstra() {\n geodesic.algorithm = std::make_shared(&geodesic.mesh);\n }\n void geodesic_set_algorithm_subdivision(int subdivision_level) {\n geodesic.algorithm = std::make_shared(&geodesic.mesh, subdivision_level);\n }\n std::vector geodesic_compute(const GeodesicPoint& source, const GeodesicPoint& target) {\n assert(geodesic.algorithm);\n std::vector path;\n geodesic::SurfacePoint source_(source), target_(target);\n geodesic.algorithm->geodesic(source_, target_, path);\n return container_cast(path);\n }\n struct Data {\n geodesic::Mesh mesh;\n std::shared_ptr algorithm;\n Data() {}\n Data(const Data& src)\n : mesh(src.mesh)\n {\n switch (src.algorithm->type()) {\n case geodesic::GeodesicAlgorithmBase::EXACT:\n algorithm = std::make_shared(&mesh);\n break;\n case geodesic::GeodesicAlgorithmBase::DIJKSTRA:\n algorithm = std::make_shared(&mesh);\n break;\n case geodesic::GeodesicAlgorithmBase::SUBDIVISION:\n {\n auto subdivision_level = static_cast(algorithm.get())->subdivision_level();\n algorithm = std::make_shared(&mesh, subdivision_level);\n break;\n }\n default:\n assert(false);\n }\n }\n Data& Data::operator=(const Data& src) {\n mesh = src.mesh;\n switch (src.algorithm->type()) {\n case geodesic::GeodesicAlgorithmBase::EXACT:\n algorithm = std::make_shared(&mesh);\n break;\n case geodesic::GeodesicAlgorithmBase::DIJKSTRA:\n algorithm = std::make_shared(&mesh);\n break;\n case geodesic::GeodesicAlgorithmBase::SUBDIVISION:\n {\n auto subdivision_level = static_cast(algorithm.get())->subdivision_level();\n algorithm = std::make_shared(&mesh, subdivision_level);\n break;\n }\n default:\n assert(false);\n }\n return *this;\n }\n } geodesic;\n private:\n const TMesh* get_mesh() const { return DerivedPtrHolder>::derived_ptr; }\n };\n}\n", "meta": {"hexsha": "009c95ccccbd01e97dadb0f14a0ea65dfcb88e45", "size": 6159, "ext": "hh", "lang": "C++", "max_stars_repo_path": "src/kt84/openmesh/base/TODO_Geodesic.hh", "max_stars_repo_name": "honoriocassiano/skbar", "max_stars_repo_head_hexsha": "e2a8fca752f5a2f9d9a32f19cbe1f9032a0e1ac4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/kt84/openmesh/base/TODO_Geodesic.hh", "max_issues_repo_name": "honoriocassiano/skbar", "max_issues_repo_head_hexsha": "e2a8fca752f5a2f9d9a32f19cbe1f9032a0e1ac4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5.0, "max_issues_repo_issues_event_min_datetime": "2020-09-01T12:16:28.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-01T12:21:41.000Z", "max_forks_repo_path": "src/kt84/openmesh/base/TODO_Geodesic.hh", "max_forks_repo_name": "honoriocassiano/skbar", "max_forks_repo_head_hexsha": "e2a8fca752f5a2f9d9a32f19cbe1f9032a0e1ac4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.9562043796, "max_line_length": 136, "alphanum_fraction": 0.572495535, "num_tokens": 1370, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.3812195521959384, "lm_q1q2_score": 0.2216041039055401}} {"text": "#include \"abstract_sph.hpp\"\n#include \"generic/is_finite.hpp\"\n#include \"generic/eigen.hpp\"\n\n#include \n\nnamespace GooBalls {\n\nnamespace d2 {\n\nnamespace Physics {\n\n#define INLINE_KERNELS 1\n\nvoid AbstractSph::initFluid(Scene& scene) {\n BOOST_LOG_TRIVIAL(trace) << \"AbstractSph: initializing\";\n const auto& pos = scene.fluid->particles_position();\n int PN = pos.rows();\n BOOST_LOG_TRIVIAL(trace) << \"Found \" << PN << \" fluid particles.\";\n if(PN != scene.fluid->particles_velocity().rows()){\n // by default, give the particles no speed\n BOOST_LOG_TRIVIAL(warning) << \"No proper fluid particle velocity set, setting to zero.\";\n scene.fluid->particles_velocity().setOnes(PN, Eigen::NoChange);\n }\n if(PN != scene.fluid->particles_mass().rows()){\n BOOST_LOG_TRIVIAL(warning) << \"No proper fluid particle mass set, setting to ones.\";\n scene.fluid->particles_mass().setOnes(PN);\n }\n if(PN != scene.fluid->particles_velocity_correction().rows()){\n BOOST_LOG_TRIVIAL(warning) << \"No proper fluid particle velocity correction coefficients set, setting to 1.\";\n scene.fluid->particles_velocity_correction().resize(PN, Eigen::NoChange);\n scene.fluid->particles_velocity_correction().array() = 0.001;\n }\n if(PN != scene.fluid->particles_external_force().rows()){\n BOOST_LOG_TRIVIAL(warning) << \"No proper external particle force set, setting zero.\";\n scene.fluid->particles_external_force().setZero(PN, 2);\n }\n\n\n scene.fluid->particles_density().setZero(PN, 1);\n scene.fluid->particles_total_force().setZero(PN, 2);\n scene.fluid->particles_pressure().setZero(PN, 1);\n\n scene.fluid->boundary_force().setZero(scene.fluid->boundary_position().rows(), 2);\n\n FGravity.setZero(PN, 2);\n FPressure.setZero(PN, 2);\n FSurface.setZero(PN, 2);\n FViscosity.setZero(PN, 2);\n\n\n // initialize with some rough values such that the first frame looks ok\n assert(scene.fluid->rest_density() > 0);\n scene.fluid->particles_density().array() = scene.fluid->rest_density();\n computeFluidPressure(scene);\n BOOST_LOG_TRIVIAL(trace) << \"Abstract fluid initialized\";\n}\n\nvoid AbstractSph::prepareFluid(Scene& scene) const {\n assert(scene.fluid.get() != nullptr);\n assert(scene.fluid->sanity_check());\n const auto& pos = scene.fluid->particles_position();\n assert(is_finite(pos));\n scene.fluid->fluid_neighborhood->inRange(pos, scene.fluid->h());\n}\n\nvoid AbstractSph::prepareBoundary(Scene& scene) const {\n Coordinates1d& psi = scene.fluid->boundary_psi();\n Float rho0 = scene.fluid->rest_density();\n psi = rho0 * scene.fluid->boundary_volume();\n auto h = scene.fluid->h();\n scene.fluid->boundary_neighborhood->inRange(scene.fluid->particles_position(), scene.fluid->boundary_position(), h);\n}\n\nvoid AbstractSph::advance(Scene& scene, TimeStep dt){\n if(scene.fluid.get() == nullptr){\n return;\n }\n computeTotalForce(scene, dt);\n // a_i = f_i / rho_i\n const auto& rho = scene.fluid->particles_density();\n const auto& Ftotal = scene.fluid->particles_total_force();\n Coordinates2d a(rho.rows(), 2);\n a.col(0) = Ftotal.col(0).array() / rho.array();\n a.col(1) = Ftotal.col(1).array() / rho.array();\n auto& pos = scene.fluid->particles_position();\n auto& vs = scene.fluid->particles_velocity();\n vs = vs + dt * a;\n scene.room.restrictFluid(* scene.fluid);\n pos = pos + dt * vs;\n limitVelocity(scene);\n assert(is_finite(pos));\n assert(is_finite(vs));\n}\n\nvoid AbstractSph::computeGravityForce(const Scene& scene) {\n const auto& rho = scene.fluid->particles_density();\n FGravity.col(0) = scene.gravity[0] * rho;\n FGravity.col(1) = scene.gravity[1] * rho;\n}\n\nvoid AbstractSph::computeFluidPressure(Scene& scene) const {\n auto rho0 = scene.fluid->rest_density();\n auto pressure_gamma = scene.fluid->pressure_gamma();\n const auto& rho = scene.fluid->particles_density();\n auto& ps = scene.fluid->particles_pressure();\n auto K = scene.fluid->stiffnessConstant();\n //ps = K * (rho.array() - rho0);\n ps = K * rho0 / pressure_gamma * ((rho.array()/rho0).pow(pressure_gamma) - 1.0);\n ps = ps.array().max(0);\n assert(is_finite(ps));\n}\n\nvoid AbstractSph::computeStandardPressureForce(const Scene& scene, const Kernel& pressureKernel){\n const auto& pos = scene.fluid->particles_position();\n const auto& ms = scene.fluid->particles_mass();\n const auto& rho = scene.fluid->particles_density();\n const auto& ps = scene.fluid->particles_pressure();\n int PN = pos.rows();\n const auto& fluid_index = scene.fluid->fluid_neighborhood->indexes();\n Coordinates2d jpos, jGrad, jPress;\n for(int i = 0; i < PN; ++i){\n const auto& index = fluid_index[i];\n minSize(jPress, index.size());\n pickRows(pos, index, jpos);\n auto xij = -(jpos.rowwise() - pos.row(i));\n pressureKernel.compute(xij, nullptr, &jGrad, nullptr);\n for(size_t j = 0; j < index.size(); ++j){\n int jj = index[j];\n jPress.row(j) = ms[jj] / rho[jj] * (ps[jj] + ps[i]) * jGrad.row(j);\n }\n // f_i^pressure = - sum_j m_j*(p_i + p_j) / (2 rho_j) \\nabla W(r_i - r_j, h)\n FPressure.row(i) = (- 1.0/2.0) * jPress.colwise().sum();\n }\n}\n\nvoid AbstractSph::computeMomentumPreservingPressureForce(const Scene& scene, const Kernel& pressureKernel) {\n const auto& pos = scene.fluid->particles_position();\n const auto& ms = scene.fluid->particles_mass();\n const auto& rho = scene.fluid->particles_density();\n const auto& ps = scene.fluid->particles_pressure();\n assert(is_finite(pos));\n assert(is_finite(ms));\n assert(is_finite(rho));\n assert(is_finite(ps));\n int PN = pos.rows();\n const auto& fluid_index = scene.fluid->fluid_neighborhood->indexes();\n#if INLINE_KERNELS\n Float h = scene.fluid->h();\n Float kernelScale = -3*pressureKernel.scale2d();\n Float epsilon = 0.000001;\n#endif\n for(int i = 0; i < PN; ++i){\n const auto& index = fluid_index[i];\n int N = index.size();\n Float A = ps[i]/(rho[i] * rho[i]);\n TranslationVector vi = pos.row(i);\n TranslationVector jPress;\n jPress.setZero();\n for(int j = 0; j < N; ++j){\n int jj = index[j];\n TranslationVector xij = vi - pos.row(jj);\n#if INLINE_KERNELS\n Float r1 = xij.norm();\n Float hr = h - r1;\n Float hr2 = hr * hr;\n TranslationVector jGrad = kernelScale * (xij * ( hr2 / (r1 + h*epsilon)));\n#else\n TranslationVector jGrad = pressureKernel.computeGradient(xij);\n#endif\n jPress += ms[jj] * (A + ps[jj] / (rho[jj] * rho[jj])) * jGrad;\n }\n // f_i^pressure = - m_i * sum_j m_j*(p_i/rho_i^2 + p_j/rho_j^2) \\nabla W(r_i - r_j, h)\n FPressure.row(i) = - ms[i] * jPress;\n }\n assert(is_finite(FPressure));\n}\n\n\nvoid AbstractSph::computeStandardViscosityForce(const Scene& scene, const Kernel& viscosityKernel) {\n const auto& pos = scene.fluid->particles_position();\n const auto& ms = scene.fluid->particles_mass();\n const auto& vs = scene.fluid->particles_velocity();\n const auto mu = scene.fluid->fluid_viscosity();\n const auto& rho = scene.fluid->particles_density();\n int PN = pos.rows();\n const auto& fluid_index = scene.fluid->fluid_neighborhood->indexes();\n#if INLINE_KERNELS\n Float h = scene.fluid->h();\n Float kernelScale = viscosityKernel.scale2d();\n#endif\n for(int i = 0; i < PN; ++i){\n const auto& index = fluid_index[i];\n int N = index.size();\n TranslationVector vi = pos.row(i);\n TranslationVector vvi = vs.row(i);\n TranslationVector jVisc;\n jVisc.setZero();\n for(int j = 0; j < N; ++j){\n int jj = index[j];\n auto xij = vi - pos.row(jj);\n#if INLINE_KERNELS\n Float jLap = kernelScale * (h - xij.norm());\n#else\n Float jLap = viscosityKernel.computeLaplacian(xij);\n#endif\n jVisc += ms[jj] / rho[jj] * (vs.row(jj) - vvi) * jLap;\n }\n // f_i^viscosity = mu * sum_j m_j (v_j - v_i) / rho_j \\nabla^2 W(r_i - r_j, h)\n FViscosity.row(i) = mu * jVisc;\n }\n}\n\nvoid AbstractSph::computeStandardSurfaceTensionForce(const Scene& scene, const Kernel& kernel, Float color_relevant_normal_size) {\n const auto& pos = scene.fluid->particles_position();\n const auto& ms = scene.fluid->particles_mass();\n const auto& rho = scene.fluid->particles_density();\n auto color_sigma = scene.fluid->surface_tension();\n int PN = pos.rows();\n const auto& fluid_index = scene.fluid->fluid_neighborhood->indexes();\n for(int i = 0; i < PN; ++i){\n const auto& index = fluid_index[i];\n Coordinates2d jpos;\n Coordinates2d jGrad;\n Coordinates1d jCLap;\n Coordinates2d jColGrad(index.size(), 2);\n Coordinates1d jColLap(index.size());\n pickRows(pos, index, jpos);\n // c_s(r_i) = sum_j m_j/rho-j W(r_i - r_j, h)\n auto xij = -(jpos.rowwise() - pos.row(i));\n kernel.compute(xij, nullptr, &jGrad, &jCLap);\n for(size_t j = 0; j < index.size(); ++j){\n int jj = index[j];\n Float a = ms[jj] / rho[jj];\n jColGrad.row(j) = a * jGrad.row(j);\n jColLap[j] = a * jCLap[j];\n }\n TranslationVector colorN = jColGrad.colwise().sum();\n Float colorLap = jColLap.sum();\n Float colorNNorm = colorN.norm();\n if(colorNNorm > color_relevant_normal_size){\n Float aa = (-color_sigma * colorLap / colorNNorm);\n FSurface.row(i) = aa * colorN;\n }\n }\n}\n\n/// computes rho_i += sum_j m_j W_ij\nvoid AbstractSph::computeFluidDensity(Scene& scene, const Kernel& densityKernel) const {\n const auto& pos = scene.fluid->particles_position();\n int PN = pos.rows();\n const auto& ms = scene.fluid->particles_mass();\n assert(ms.minCoeff() > 0.0);\n auto& rho = scene.fluid->particles_density();\n const auto& fluid_index = scene.fluid->fluid_neighborhood->indexes();\n#if INLINE_KERNELS\n Float h = scene.fluid->h();\n Float kernelScale = densityKernel.scale2d();\n Float h2 = h * h;\n#endif\n for(int i = 0; i < PN; ++i){\n // density from fluid<->fluid\n const auto& index = fluid_index[i];\n int N = index.size();\n TranslationVector vi = pos.row(i);\n Float jW = 0.0;\n for(int j = 0; j < N; ++j){\n int jj = index[j];\n TranslationVector xij = vi - pos.row(jj);\n#if INLINE_KERNELS\n Float d = h2 - xij.squaredNorm();\n Float v = kernelScale * d*d*d;\n#else\n Float v = densityKernel.computeValue(xij);\n#endif\n jW += ms[jj] * v;\n }\n rho[i] = jW;\n }\n assert(rho.minCoeff() > 0.0);\n assert(is_finite(rho));\n}\n\n/// based on CFL condition: dt = lambda * h /max(sqrt(K), v_max)\nvoid AbstractSph::limitVelocity(const Scene& scene) const {\n const Float vMax = std::sqrt(scene.fluid->stiffnessConstant());\n auto& vs = scene.fluid->particles_velocity();\n\n Coordinates1d vsMag2 = vs.rowwise().squaredNorm();\n Float maxV2 = vsMag2.maxCoeff();\n if(maxV2 > vMax*vMax){\n BOOST_LOG_TRIVIAL(info) << \"Limiting particle speed from \" << std::sqrt(maxV2) << \" to \" << vMax;\n for(int i = 0; i < vs.rows(); ++i){\n if(vsMag2[i] > vMax*vMax){\n vs.row(i) = vs.row(i) / std::sqrt(vsMag2[i]) * vMax;\n }\n }\n vs.array().min(vMax).max(-vMax);\n }\n}\n\n\n} // Physics\n\n} // d2\n\n} // GooBalls\n\n", "meta": {"hexsha": "2d6ea1ee1852503c1b525f3220561b352b5c2488", "size": 11599, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/lib/physics/2d/abstract_sph.cpp", "max_stars_repo_name": "Fluci/GooBalls", "max_stars_repo_head_hexsha": "4b68084303e66af368fd6bbf94aaec0950c9c6e3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/lib/physics/2d/abstract_sph.cpp", "max_issues_repo_name": "Fluci/GooBalls", "max_issues_repo_head_hexsha": "4b68084303e66af368fd6bbf94aaec0950c9c6e3", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/lib/physics/2d/abstract_sph.cpp", "max_forks_repo_name": "Fluci/GooBalls", "max_forks_repo_head_hexsha": "4b68084303e66af368fd6bbf94aaec0950c9c6e3", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.5372168285, "max_line_length": 130, "alphanum_fraction": 0.6203120959, "num_tokens": 3166, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.41111086923216805, "lm_q1q2_score": 0.22158186035846728}} {"text": "// SPDX-License-Identifier: MIT\n// SPDX-FileCopyrightText: Copyright 2019-2022 Heal Research\n\n#ifndef OPERON_EVAL_DETAIL\n#define OPERON_EVAL_DETAIL\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"operon/core/node.hpp\"\n#include \"operon/core/range.hpp\"\n#include \"operon/core/types.hpp\"\n#include \"functions.hpp\"\n\nnamespace Operon {\n\nnamespace detail {\n // this should be good enough - tests show 512 is about optimal\n template\n struct BatchSize {\n static const size_t Value = 512 / sizeof(T);\n };\n\n template\n using Array = Eigen::Array::Value, 1>;\n\n template\n using Ref = Eigen::Ref>;\n\n // dispatching mechanism\n // compared to the simple/naive way of evaluating n-ary symbols, this method has the following advantages:\n // 1) improved performance: the naive method accumulates into the result for each argument, leading to unnecessary assignments\n // 2) minimizing the number of intermediate steps which might improve floating point accuracy of some operations\n // if arity > 4, one accumulation is performed every 4 args\n template\n inline void DispatchOpNary(Operon::Vector>& m, Operon::Vector const& nodes, size_t parentIndex, Operon::Range /* not used here - provided for dynamic symbols */)\n {\n static_assert(Type < NodeType::Aq);\n auto result = Ref(m[parentIndex]);\n const auto f = [](bool cont, decltype(result) res, auto&&... args) {\n if (cont) {\n ContinuedFunction{}(res, std::forward(args)...);\n } else {\n Function{}(res, std::forward(args)...);\n }\n };\n const auto nextArg = [&](size_t i) { return i - (nodes[i].Length + 1); };\n\n auto arg1 = parentIndex - 1;\n\n bool continued = false;\n\n using R = Ref;\n\n int arity = nodes[parentIndex].Arity;\n while (arity > 0) {\n switch (arity) {\n case 1: {\n f(continued, result, R(m[arg1]));\n arity = 0;\n break;\n }\n case 2: {\n auto arg2 = nextArg(arg1);\n f(continued, result, R(m[arg1]), R(m[arg2]));\n arity = 0;\n break;\n }\n case 3: {\n auto arg2 = nextArg(arg1);\n auto arg3 = nextArg(arg2);\n f(continued, result, R(m[arg1]), R(m[arg2]), R(m[arg3]));\n arity = 0;\n break;\n }\n default: {\n auto arg2 = nextArg(arg1);\n auto arg3 = nextArg(arg2);\n auto arg4 = nextArg(arg3);\n f(continued, result, R(m[arg1]), R(m[arg2]), R(m[arg3]), R(m[arg4]));\n arity -= 4;\n arg1 = nextArg(arg4);\n break;\n }\n }\n continued = true;\n }\n }\n\n template\n inline void DispatchOpUnary(Operon::Vector>& m, Operon::Vector const& /*unused*/, size_t i, Operon::Range /* not used here - provided for dynamic symbols */)\n {\n static_assert(Type < NodeType::Dynamic && Type > NodeType::Pow);\n Function{}(Ref(m[i]), Ref(m[i-1]));\n }\n\n template\n inline void DispatchOpBinary(Operon::Vector>& m, Operon::Vector const& nodes, size_t i, Operon::Range /* not used here - provided for dynamic symbols */)\n {\n static_assert(Type < NodeType::Abs && Type > NodeType::Fmax);\n auto j = i - 1;\n auto k = j - nodes[j].Length - 1;\n Function{}(Ref(m[i]), Ref(m[j]), Ref(m[k]));\n }\n\n template\n inline void DispatchOpSimpleUnaryOrBinary(Operon::Vector>& m, Operon::Vector const& nodes, size_t parentIndex, Operon::Range /* not used here - provided for dynamic symbols */)\n {\n auto r = Ref(m[parentIndex]);\n size_t i = parentIndex - 1;\n size_t arity = nodes[parentIndex].Arity;\n\n Function f{};\n\n if (arity == 1) {\n f(r, Ref(m[i]));\n } else {\n auto j = i - (nodes[i].Length + 1);\n f(r, Ref(m[j]));\n }\n }\n\n template\n inline void DispatchOpSimpleNary(Operon::Vector>& m, Operon::Vector const& nodes, size_t parentIndex, Operon::Range /* not used here - provided for dynamic symbols */)\n {\n auto r = Ref(m[parentIndex]);\n size_t arity = nodes[parentIndex].Arity;\n\n auto i = parentIndex - 1;\n\n Function f{};\n\n if (arity == 1) {\n f(r, Ref(m[i]));\n } else {\n r = m[i];\n\n for (size_t k = 1; k < arity; ++k) {\n i -= nodes[i].Length + 1;\n f(r, Ref(m[i]));\n }\n }\n }\n\n struct Noop {\n template\n void operator()(Args&&... /*unused*/) {}\n };\n\n template\n class tuple_index;\n\n template\n class tuple_index> {\n template\n static constexpr auto FindIdx(std::index_sequence /*unused*/) -> int64_t\n {\n return -1 + ((std::is_same::value ? Idx + 1 : 0) + ...);\n }\n\n public:\n static constexpr int64_t value = FindIdx(std::index_sequence_for{});\n };\n\n template\n using Callable = typename std::function>&, Operon::Vector const&, size_t, Operon::Range)>;\n\n template\n static constexpr auto MakeCall() -> Callable\n {\n if constexpr (Type < NodeType::Aq) { // nary: add, sub, mul, div, fmin, fmax\n return Callable(detail::DispatchOpNary);\n } else if constexpr (Type < NodeType::Abs) { // binary: aq, pow\n return Callable(detail::DispatchOpBinary);\n } else if constexpr (Type < NodeType::Dynamic) { // unary: exp, log, sin, cos, tan, tanh, sqrt, cbrt, square\n return Callable(detail::DispatchOpUnary);\n }\n }\n\n template = true>\n static constexpr auto MakeTuple()\n {\n return std::make_tuple(MakeCall()...);\n };\n\n template&, Vector const&, size_t, Operon::Range> && ...), bool> = true>\n static constexpr auto MakeTuple(F&& f)\n {\n return std::make_tuple(Callable(std::forward(f))...);\n }\n} // namespace detail\n\ntemplate\nstruct DispatchTable {\n template\n using Callable = detail::Callable;\n\n using Tuple = std::tuple...>;\n using Map = robin_hood::unordered_flat_map;\n\nprivate:\n Map map_;\n\n template\n void InitMap(std::index_sequence /*unused*/)\n {\n auto f = [](auto i) { return static_cast(1U << i); };\n (map_.insert({ Node(f(Is)).HashValue, detail::MakeTuple() }), ...);\n }\n\npublic:\n DispatchTable()\n {\n InitMap(std::make_index_sequence{}); // exclude constant, variable, dynamic\n }\n\n ~DispatchTable() = default;\n\n auto operator=(DispatchTable const& other) -> DispatchTable& {\n if (this != &other) {\n map_ = other.map_;\n }\n return *this;\n }\n\n auto operator=(DispatchTable&& other) noexcept -> DispatchTable& {\n map_ = std::move(other.map_);\n return *this;\n }\n\n DispatchTable(DispatchTable const& other) : map_(other.map_) { }\n DispatchTable(DispatchTable &&other) noexcept : map_(std::move(other.map_)) { }\n\n template\n inline auto Get(Operon::Hash const h) -> Callable&\n {\n return const_cast&>(const_cast const*>(*this)->Get(h)); // NOLINT\n }\n\n template\n [[nodiscard]] inline auto Get(Operon::Hash const h) const -> Callable const&\n {\n constexpr int64_t idx = detail::tuple_index, Tuple>::value;\n static_assert(idx >= 0, \"Tuple does not contain type T\");\n if (auto it = map_.find(h); it != map_.end()) {\n return std::get(idx)>(it->second);\n }\n throw std::runtime_error(fmt::format(\"Hash value {} is not in the map\\n\", h));\n }\n\n template\n void RegisterCallable(Operon::Hash hash, F&& f) {\n map_[hash] = detail::MakeTuple(std::forward(f));\n }\n\n template\n [[nodiscard]] inline auto TryGet(Operon::Hash const h) const noexcept -> std::optional>\n {\n constexpr int64_t idx = detail::tuple_index, Tuple>::value;\n static_assert(idx >= 0, \"Tuple does not contain type T\");\n if (auto it = map_.find(h); it != map_.end()) {\n return { std::get(idx)>(it->second) };\n }\n return {};\n }\n\n [[nodiscard]] auto Contains(Operon::Hash hash) const noexcept -> bool { return map_.contains(hash); }\n};\n\n} // namespace Operon\n\n#endif\n", "meta": {"hexsha": "4ce7f05b65dc129a2faa74a8cfcedbf73201e3ec", "size": 9588, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/operon/interpreter/dispatch_table.hpp", "max_stars_repo_name": "foolnotion/operon", "max_stars_repo_head_hexsha": "5faa938b98e0ee40e224eca8793db5f6a9934673", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2019-10-29T09:36:18.000Z", "max_stars_repo_stars_event_max_datetime": "2020-08-17T08:31:37.000Z", "max_issues_repo_path": "include/operon/interpreter/dispatch_table.hpp", "max_issues_repo_name": "foolnotion/operon", "max_issues_repo_head_hexsha": "5faa938b98e0ee40e224eca8793db5f6a9934673", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2020-04-24T20:02:56.000Z", "max_issues_repo_issues_event_max_datetime": "2020-10-14T10:07:18.000Z", "max_forks_repo_path": "include/operon/interpreter/dispatch_table.hpp", "max_forks_repo_name": "foolnotion/operon", "max_forks_repo_head_hexsha": "5faa938b98e0ee40e224eca8793db5f6a9934673", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2020-01-29T05:36:03.000Z", "max_forks_repo_forks_event_max_datetime": "2020-05-31T06:48:52.000Z", "avg_line_length": 34.6137184116, "max_line_length": 198, "alphanum_fraction": 0.5729036295, "num_tokens": 2443, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.22145501290566572}} {"text": "#ifndef GUNERO_MERKLE_TREE_GADGET_H_\n#define GUNERO_MERKLE_TREE_GADGET_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"uint256.h\"\n#include \"serialize.h\"\n#include \"gunero_merkle_tree.hpp\"\n\nusing namespace libsnark;\n\nnamespace gunero {\n\ntemplate\nclass gunero_merkle_tree_gadget : gadget {\nprivate:\n pb_variable_array positions;\n std::shared_ptr> authvars;\n std::shared_ptr> auth;\n\npublic:\n gunero_merkle_tree_gadget(\n protoboard& pb,\n digest_variable& leaf,\n digest_variable& root,\n const pb_variable& enforce,\n const std::string &annotation_prefix\n ) : gadget(pb, annotation_prefix) {\n positions.allocate(pb, tree_depth);\n authvars.reset(new gunero_merkle_authentication_path_variable(\n pb, tree_depth, \"auth\"\n ));\n auth.reset(new gunero_merkle_tree_check_read_gadget(\n pb,\n tree_depth,\n positions,\n leaf,\n root,\n *authvars,\n enforce,\n \"path\"\n ));\n }\n\n void generate_r1cs_constraints() {\n for (size_t i = 0; i < tree_depth; i++) {\n // TODO: This might not be necessary, and doesn't\n // appear to be done in libsnark's tests, but there\n // is no documentation, so let's do it anyway to\n // be safe.\n generate_boolean_r1cs_constraint(\n this->pb,\n positions[i],\n \"boolean_positions\"\n );\n }\n\n authvars->generate_r1cs_constraints();\n auth->generate_r1cs_constraints();\n }\n\n void generate_r1cs_witness(const std::vector& M_account, const libff::bit_vector& A_account)\n {\n // size_t path_index_account = convertVectorToInt(path.index_account);\n // uint256 path_A_account = bool_vector_to_uint256(A_account);\n // size_t path_index_account = convertVectorToInt(A_account);\n\n // positions.fill_with_bits_of_ulong(this->pb, path_index_account);\n libff::bit_vector A_account_LSB(tree_depth);\n for(size_t i = 0; i < tree_depth; i++)\n {\n A_account_LSB.at(i) = A_account.at(tree_depth - 1 - i);\n }\n positions.fill_with_bits(this->pb, A_account_LSB);\n\n // authvars->generate_r1cs_witness(path_index_account, path.authentication_path);\n // auth->generate_r1cs_witness();\n\n // positions.fill_with_bits(this->pb, A_account);\n\n authvars->generate_r1cs_witness(A_account, M_account);\n auth->generate_r1cs_witness();\n }\n};\n\n} // end namespace `gunero`\n\n#endif /* GUNERO_MERKLE_TREE_GADGET_H_ */", "meta": {"hexsha": "340c556a4e91f4ee60383aeee8282f5d577f8eea", "size": 4091, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/gunero_merkle_tree_gadget.hpp", "max_stars_repo_name": "GunClear/Silencer", "max_stars_repo_head_hexsha": "625b5ce0860af98763aa2ce14d6b406d5ef368e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2018-10-17T22:27:54.000Z", "max_stars_repo_stars_event_max_datetime": "2018-10-17T22:27:54.000Z", "max_issues_repo_path": "src/gunero_merkle_tree_gadget.hpp", "max_issues_repo_name": "GunClear/Silencer", "max_issues_repo_head_hexsha": "625b5ce0860af98763aa2ce14d6b406d5ef368e4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": "2018-11-09T19:57:52.000Z", "max_issues_repo_issues_event_max_datetime": "2019-01-04T15:49:02.000Z", "max_forks_repo_path": "src/gunero_merkle_tree_gadget.hpp", "max_forks_repo_name": "GunClear/Silencer", "max_forks_repo_head_hexsha": "625b5ce0860af98763aa2ce14d6b406d5ef368e4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.8796296296, "max_line_length": 131, "alphanum_fraction": 0.7088731362, "num_tokens": 1076, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6548947425132315, "lm_q2_score": 0.33807711081162, "lm_q1q2_score": 0.22140492243459314}} {"text": "#include \"tile/lib/lib.h\"\n\n#include \n#include \n\n#include \n\n#include \"base/util/stream_container.h\"\n#include \"plaidml2/edsl/autodiff.h\"\n#include \"tile/util/tile_file.h\"\n\nnamespace vertexai {\nnamespace tile {\nnamespace lib {\n\nusing namespace plaidml::edsl; // NOLINT\n\nnamespace {\n\nlang::RunInfo Evaluate(const std::string& name, const std::vector& vars) {\n Program program(name, vars, {});\n return *static_cast(program.runinfo());\n}\n\nstd::shared_ptr MakeBuffer(const LogicalShape& shape) {\n std::vector sizes;\n for (const auto& dim : shape.int_dims()) {\n sizes.push_back(dim);\n }\n auto tensor_shape = tile::SimpleShape(static_cast(shape.dtype()), sizes);\n auto buffer = std::make_shared();\n buffer->bytes.resize(tensor_shape.byte_size());\n return buffer;\n}\n\nTensor MatMul(const Tensor& A, const Tensor& B) {\n TensorDim M, N, K;\n A.bind_dims(M, K);\n B.bind_dims(K, N);\n TensorIndex k(\"k\"), m(\"m\"), n(\"n\");\n auto C = NamedTensorOutput(\"C\", M, N);\n C(m, n) += A(m, k) * B(k, n);\n return C;\n}\n\nTensor DilatedConvolution2(const Tensor& I, const Tensor& K) {\n TensorDim N, Lx, Ly, LKx, LKy, CI, CO;\n I.bind_dims(N, Lx, Ly, CI);\n K.bind_dims(LKx, LKy, CI, CO);\n auto O = NamedTensorOutput(\"O\", N, Lx - 2 * (LKx - 1), Ly - 3 * (LKy - 1), CO);\n TensorIndex n, x, y, kx, ky, ci, co;\n O(n, x, y, co) += I(n, x + 2 * kx, y + 3 * ky, ci) * K(kx, ky, ci, co);\n return O;\n}\n\nTensor Relu(const Tensor& X) { return Call(\"relu\", X); }\n\nTensor Sin(const Tensor& X) { return Call(\"sin\", X); }\n\nTensor Tanh(const Tensor& X) { return Call(\"tanh\", X); }\n\n} // namespace\n\nTensor Convolution(const Tensor& I, //\n const Tensor& K, //\n const std::vector& O_sizes, //\n std::vector strides, //\n ConvolutionFormat I_format, //\n ConvolutionFormat K_format) {\n TensorDim N, CI, CO;\n auto I_shape = I.shape();\n auto K_shape = K.shape();\n IVLOG(1, \"I.shape(): \" << I_shape);\n IVLOG(1, \"K.shape(): \" << K_shape);\n auto ndims = I_shape.ndims() - 2;\n if (strides.empty()) {\n for (size_t i = 0; i < ndims; i++) {\n strides.push_back(1);\n }\n } else if (strides.size() != ndims) {\n throw std::runtime_error(str(\n boost::format(\"Convolution strides length inconsistent with input shape: %1% (ndims %2%) v %3% (ndims %4%)\") %\n StreamContainer(strides) % strides.size() % I_shape % ndims));\n }\n TensorIndex n(\"n\"), co(\"co\"), ci(\"ci\");\n std::vector I_dims = {N};\n std::vector I_spatial_dims(ndims);\n std::vector K_dims;\n std::vector K_spatial_dims(ndims);\n std::vector O_dims;\n for (const auto& size : O_sizes) {\n O_dims.emplace_back(size);\n }\n std::vector K_idxs;\n std::vector I_idxs = {n};\n std::vector O_idxs = {n};\n size_t K_spatial_dims_offset = 0;\n if (K_format == ConvolutionFormat::ChannelsFirst) {\n K_spatial_dims_offset = 2;\n K_idxs.push_back(co);\n K_idxs.push_back(ci);\n K_dims.push_back(CO);\n K_dims.push_back(CI);\n }\n if (I_format == ConvolutionFormat::ChannelsFirst) {\n I_idxs.push_back(ci);\n O_idxs.push_back(co);\n I_dims.push_back(CI);\n }\n K_dims.insert(std::end(K_dims), std::begin(K_spatial_dims), std::end(K_spatial_dims));\n I_dims.insert(std::end(I_dims), std::begin(I_spatial_dims), std::end(I_spatial_dims));\n if (I_format == ConvolutionFormat::ChannelsLast) {\n I_dims.push_back(CI);\n }\n if (K_format == ConvolutionFormat::ChannelsLast) {\n K_dims.push_back(CI);\n K_dims.push_back(CO);\n }\n I.bind_dims(I_dims);\n K.bind_dims(K_dims);\n for (size_t i = 0; i < ndims; i++) {\n TensorIndex x(str(boost::format(\"x%1%\") % i));\n TensorIndex k(str(boost::format(\"k%1%\") % i));\n IVLOG(1, \"Adding \" << i);\n I_idxs.emplace_back(strides[i] * x + k - K_dims[K_spatial_dims_offset + i] / 2);\n K_idxs.push_back(k);\n O_idxs.push_back(x);\n }\n if (I_format == ConvolutionFormat::ChannelsLast) {\n I_idxs.push_back(ci);\n O_idxs.push_back(co);\n }\n if (K_format == ConvolutionFormat::ChannelsLast) {\n K_idxs.push_back(ci);\n K_idxs.push_back(co);\n }\n Tensor O(\"O\", O_dims);\n O(O_idxs) += I(I_idxs) * K(K_idxs);\n return O;\n}\n\nlang::RunInfo LoadMatMul(const std::string& name, const LogicalShape& i1, const LogicalShape& i2) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n return Evaluate(name, {MatMul(A, B)});\n}\n\nlang::RunInfo LoadMatMulIntermediate(const std::string& name, const LogicalShape& i1, const LogicalShape& i2,\n const LogicalShape& i3) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n auto C = Placeholder(i3, \"C\");\n Tensor D = MatMul(A, B);\n Tensor E = D + C;\n return Evaluate(name, {D, E});\n}\n\nlang::RunInfo LoadEltwiseMulFlip(const std::string& name, const LogicalShape& i1, const LogicalShape& i2) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n return Evaluate(name, {~(A * B)});\n}\n\nlang::RunInfo LoadMatMulAmongEltwise(const std::string& name, const LogicalShape& i1, const LogicalShape& i2,\n const LogicalShape& i3) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n auto C = Placeholder(i3, \"C\");\n Tensor NegA = -A;\n Tensor NegB = -B;\n Tensor P = MatMul(NegA, NegB);\n return Evaluate(name, {P + C});\n}\n\nlang::RunInfo LoadEltwiseAdd(const std::string& name, const LogicalShape& i1, const LogicalShape& i2) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n return Evaluate(name, {A + B});\n}\n\nlang::RunInfo LoadEltwiseMultiAdd(const std::string& name, const LogicalShape& i1, const LogicalShape& i2,\n const LogicalShape& i3, const LogicalShape& i4) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n auto C = Placeholder(i3, \"C\");\n auto D = Placeholder(i4, \"D\");\n return Evaluate(name, {A + B + C + D});\n}\n\nlang::RunInfo LoadEltwiseDiv(const std::string& name, const LogicalShape& i1, const LogicalShape& i2) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n return Evaluate(name, {A / B});\n}\n\nlang::RunInfo LoadConstScalarMul(const std::string& name, const double s, const LogicalShape& i1) {\n Tensor scalar(s);\n auto A = Placeholder(i1, \"A\");\n return Evaluate(name, {scalar * A});\n}\n\nlang::RunInfo LoadEltwiseMul(const std::string& name, const LogicalShape& i1, const LogicalShape& i2) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n return Evaluate(name, {A * B});\n}\n\nlang::RunInfo LoadEltwiseMultiMul(const std::string& name, const LogicalShape& i1, const LogicalShape& i2,\n const LogicalShape& i3, const LogicalShape& i4) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n auto C = Placeholder(i3, \"C\");\n auto D = Placeholder(i4, \"D\");\n return Evaluate(name, {A * B * C * D});\n}\n\nlang::RunInfo LoadSin(const std::string& name, const LogicalShape& i1) {\n auto A = Placeholder(i1, \"A\");\n return Evaluate(name, {Sin(A)});\n}\n\nlang::RunInfo LoadTanh(const std::string& name, const LogicalShape& i1) {\n auto A = Placeholder(i1, \"A\");\n return Evaluate(name, {Tanh(A)});\n}\n\nlang::RunInfo LoadMulThenNeg(const std::string& name, const LogicalShape& i1, const LogicalShape& i2) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n Tensor C = A * B;\n return Evaluate(name, {-C});\n}\n\nlang::RunInfo LoadNegThenMul(const std::string& name, const LogicalShape& i1, const LogicalShape& i2) {\n auto A = Placeholder(i1, \"A\");\n auto B = Placeholder(i2, \"B\");\n Tensor NegA = -A;\n Tensor NegB = -B;\n return Evaluate(name, {NegA * NegB});\n}\n\nlang::RunInfo LoadConstCalc(const std::string& name) {\n Tensor N(1);\n Tensor F(0.0);\n Tensor F2(3.7);\n TensorIndex i;\n auto Simple = TensorOutput(1);\n Simple(i) = F();\n auto DoubleN = TensorOutput(1);\n DoubleN(i) = N();\n Tensor Partial = Simple + DoubleN;\n Tensor O = Partial + F2;\n return Evaluate(name, {O});\n}\n\nlang::RunInfo LoadConv1d(const std::string& name, //\n const LogicalShape& input, //\n const LogicalShape& kernel, //\n const std::vector& output) {\n auto I = Placeholder(input, \"I\");\n auto K = Placeholder(kernel, \"K\");\n auto runinfo = Evaluate(name, {Convolution(I, K, output)});\n runinfo.const_inputs = {\"K\"};\n runinfo.input_buffers = {{\"K\", MakeBuffer(kernel)}};\n return runinfo;\n}\n\nlang::RunInfo LoadConv2d(const std::string& name, //\n const LogicalShape& input, //\n const LogicalShape& kernel, //\n const std::vector& output) {\n auto I = Placeholder(input, \"I\");\n auto K = Placeholder(kernel, \"K\");\n auto runinfo = Evaluate(name, {Convolution(I, K, output)});\n runinfo.const_inputs = {\"K\"};\n runinfo.input_buffers = {{\"K\", MakeBuffer(kernel)}};\n return runinfo;\n}\n\nlang::RunInfo LoadConv2dRelu(const std::string& name, //\n const LogicalShape& input, //\n const LogicalShape& kernel, //\n const std::vector& output) {\n auto I = Placeholder(input, \"I\");\n auto K = Placeholder(kernel, \"K\");\n auto runinfo = Evaluate(name, {Relu(Convolution(I, K, output))});\n runinfo.const_inputs = {\"K\"};\n runinfo.input_buffers = {{\"K\", MakeBuffer(kernel)}};\n return runinfo;\n}\n\nlang::RunInfo LoadConv2dBnRelu(const std::string& name, //\n const LogicalShape& input, //\n const LogicalShape& kernel, //\n const LogicalShape& channels, //\n const std::vector& output) {\n auto I = Placeholder(input, \"I\");\n auto K = Placeholder(kernel, \"K\");\n auto B = Placeholder(channels, \"B\");\n auto S = Placeholder(channels, \"S\");\n auto O = Convolution(I, K, output);\n auto R = Relu((O + B) * S);\n auto runinfo = Evaluate(name, {R});\n runinfo.const_inputs = {\"K\"};\n runinfo.input_buffers = {\n {\"K\", MakeBuffer(kernel)},\n {\"B\", MakeBuffer(channels)},\n {\"S\", MakeBuffer(channels)},\n };\n return runinfo;\n}\n\nlang::RunInfo LoadConv2d3Deep(const std::string& name, //\n const LogicalShape& input, //\n const LogicalShape& kernel1, //\n const LogicalShape& kernel2, //\n const LogicalShape& kernel3) {\n auto I = Placeholder(input, \"I\");\n auto K1 = Placeholder(kernel1, \"K1\");\n auto K2 = Placeholder(kernel2, \"K2\");\n auto K3 = Placeholder(kernel3, \"K3\");\n auto dims = input.int_dims();\n auto O1 = Convolution(I, K1, {dims[0], dims[1], dims[2], kernel1.int_dims()[3]});\n auto O2 = Convolution(O1, K2, {dims[0], dims[1], dims[2], kernel2.int_dims()[3]});\n auto O3 = Convolution(O2, K3, {dims[0], dims[1], dims[2], kernel3.int_dims()[3]});\n auto runinfo = Evaluate(name, {O3});\n runinfo.const_inputs = {\"K1\", \"K2\", \"K3\"};\n runinfo.input_buffers = {\n {\"K1\", MakeBuffer(kernel1)},\n {\"K2\", MakeBuffer(kernel2)},\n {\"K3\", MakeBuffer(kernel3)},\n };\n return runinfo;\n}\n\nlang::RunInfo LoadDilatedConv2d(const std::string& name, //\n const LogicalShape& input, //\n const LogicalShape& kernel) {\n auto I = Placeholder(input, \"I\");\n auto K = Placeholder(kernel, \"K\");\n return Evaluate(name, {DilatedConvolution2(I, K)});\n}\n\nTensor Normalize(const Tensor& X) {\n auto XSqr = X * X;\n auto X_MS = TensorOutput();\n std::vector idxs(X.shape().ndims());\n X_MS() += XSqr(idxs);\n return sqrt(X_MS);\n}\n\nstd::tuple LarsMomentum(const Tensor& X, //\n const Tensor& Grad, //\n const Tensor& Veloc, //\n const Tensor& LR, //\n double lars_coeff, //\n double lars_weight_decay, //\n double momentum) {\n auto XNorm = Normalize(X);\n auto GradNorm = Normalize(Grad);\n auto LocLR = LR * lars_coeff * XNorm / (GradNorm + lars_weight_decay * XNorm);\n auto NewVeloc = momentum * Veloc + LocLR * (Grad + lars_weight_decay * X);\n return std::make_tuple(X - NewVeloc, NewVeloc);\n}\n\nlang::RunInfo LoadLarsMomentum4d(const std::string& name, //\n const LogicalShape& x_shape, //\n const LogicalShape& lr_shape) {\n // Note: X/Grad/Veloc/NewX/NewVeloc should all have the same shape for the\n // semantics of this operation to be correct, so we only pass in 1 shape for\n // all of them.\n double lars_coeff = 1. / 1024.;\n double lars_weight_decay = 1. / 2048.;\n double momentum = 1. / 8.;\n auto X = Placeholder(x_shape);\n auto Grad = Placeholder(x_shape);\n auto Veloc = Placeholder(x_shape);\n auto LR = Placeholder(lr_shape);\n auto R = LarsMomentum(X, Grad, Veloc, LR, lars_coeff, lars_weight_decay, momentum);\n return Evaluate(\"lars_momentum4d\", {std::get<0>(R), std::get<1>(R)});\n}\n\nlang::RunInfo LoadPow(const std::string& name, //\n const LogicalShape& i1, //\n const LogicalShape& i2) {\n auto X = Placeholder(i1, \"X\");\n auto Y = Placeholder(i2, \"Y\");\n auto runinfo = Evaluate(name, {pow(X, Y)});\n runinfo.input_buffers = {\n {\"X\", MakeBuffer(i1)},\n {\"Y\", MakeBuffer(i2)},\n };\n return runinfo;\n}\n\nTensor Norm4dAx2(const Tensor& I, const Tensor& G, const Tensor& B, const Tensor& Epsilon) {\n TensorDim I0, I1, I2, I3;\n I.bind_dims(I0, I1, I2, I3);\n auto H = I2 * I3;\n auto Sum = TensorOutput(I0, I1, 1, 1);\n TensorIndex i0, i1, i2, i3;\n Sum(i0, i1, 0, 0) += I(i0, i1, i2, i3);\n auto Mu = Sum / H;\n auto Diff = I - Mu;\n auto SqDiff = Diff * Diff;\n auto SumSqDiff = TensorOutput(I0, I1, 1, 1);\n SumSqDiff(i0, i1, 0, 0) += SqDiff(i0, i1, i2, i3);\n auto Stdev = sqrt(SumSqDiff + Epsilon) / H;\n return (G / Stdev) * (I - Mu) + B;\n}\n\nlang::RunInfo LoadLayerNorm4dAx2(const std::string& name, //\n const LogicalShape& input) {\n // Note: I/G/B/O should all have the same shape, so pass in one shape to share\n auto I = Placeholder(input);\n auto G = Placeholder(input);\n auto B = Placeholder(input);\n auto Epsilon = Placeholder(PLAIDML_DATA_FLOAT32, {});\n return Evaluate(name, {Norm4dAx2(I, G, B, Epsilon)});\n}\n\nTensor PolygonBoxTransform(const Tensor& I) {\n TensorDim N, C, H, W;\n I.bind_dims(N, C, H, W);\n auto TEpartial = TensorOutput(N, C, H, W);\n auto TOpartial = TensorOutput(N, C, H, W);\n TensorIndex n, c, h, w;\n auto Widx = index(I, 3);\n TEpartial(2 * n, c, h, w) = I(2 * n, c, h, w);\n auto TE = 4 * Widx - TEpartial;\n TOpartial(2 * n + 1, c, h, w) = I(2 * n + 1, c, h, w);\n auto Hidx = index(I, 2);\n auto TO = 4 * Hidx - TOpartial;\n return TE + TO;\n}\n\nlang::RunInfo LoadPolygonBoxTransform(const std::string& name, //\n const LogicalShape& input) {\n // Note: I and O have the same shape\n auto I = Placeholder(input);\n return Evaluate(name, {PolygonBoxTransform(I)});\n}\n\nlang::RunInfo LoadSoftmax(const std::string& name, //\n const LogicalShape& input) { //\n auto X1 = Placeholder(input);\n TensorDim I, J;\n X1.bind_dims(I, J);\n TensorIndex i(\"i\"), j(\"j\");\n auto M = NamedTensorOutput(\"M\", I, 1);\n M(i, 0) >= X1(i, j);\n auto E = exp(X1 - M);\n auto N = NamedTensorOutput(\"N\", I, 1);\n N(i, 0) += E(i, j);\n return Evaluate(name, {E / N});\n}\n\n} // namespace lib\n} // namespace tile\n} // namespace vertexai\n", "meta": {"hexsha": "8b36a76ffe90322645061ab5606bf20f366c3844", "size": 16017, "ext": "cc", "lang": "C++", "max_stars_repo_path": "tile/lib/lib.cc", "max_stars_repo_name": "TolyaTalamanov/plaidml", 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"avg_line_length": 34.7440347072, "max_line_length": 118, "alphanum_fraction": 0.5959293251, "num_tokens": 4666, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.4225046348141883, "lm_q1q2_score": 0.2211475233816566}} {"text": "/*\n* LEGAL NOTICE\n* This computer software was prepared by Battelle Memorial Institute,\n* hereinafter the Contractor, under Contract No. DE-AC05-76RL0 1830\n* with the Department of Energy (DOE). NEITHER THE GOVERNMENT NOR THE\n* CONTRACTOR MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY\n* LIABILITY FOR THE USE OF THIS SOFTWARE. This notice including this\n* sentence must appear on any copies of this computer software.\n* \n* EXPORT CONTROL\n* User agrees that the Software will not be shipped, transferred or\n* exported into any country or used in any manner prohibited by the\n* United States Export Administration Act or any other applicable\n* export laws, restrictions or regulations (collectively the \"Export Laws\").\n* Export of the Software may require some form of license or other\n* authority from the U.S. Government, and failure to obtain such\n* export control license may result in criminal liability under\n* U.S. laws. In addition, if the Software is identified as export controlled\n* items under the Export Laws, User represents and warrants that User\n* is not a citizen, or otherwise located within, an embargoed nation\n* (including without limitation Iran, Syria, Sudan, Cuba, and North Korea)\n* and that User is not otherwise prohibited\n* under the Export Laws from receiving the Software.\n* \n* Copyright 2011 Battelle Memorial Institute. All Rights Reserved.\n* Distributed as open-source under the terms of the Educational Community \n* License version 2.0 (ECL 2.0). http://www.opensource.org/licenses/ecl2.php\n* \n* For further details, see: http://www.globalchange.umd.edu/models/gcam/\n*\n*/\n\n\n/*!\n * wind_technology.cpp\n * Created: 03/20/2007\n * Version: 04/16/2007\n *\n * This software, which is provided in confidence, was prepared by employees\n * of Pacific Northwest National Laboratory operated by Battelle Memorial\n * Institute. Battelle has certain unperfected rights in the software\n * which should not be copied or otherwise disseminated outside your\n * organization without the express written authorization from Battelle.\n * All rights to the software are reserved by Battelle. Battelle makes no\n * warranty, express or implied, and assumes no liability or responsibility\n * for the use of this software.\n */\n\n// include files ***********************************************************\n\n#include \"util/base/include/definitions.h\"\n#include \"technologies/include/wind_technology.h\"\n#include \"technologies/include/marginal_profit_calculator.h\"\n#include \"technologies/include/iproduction_state.h\"\n#include \"marketplace/include/marketplace.h\"\n#include \"containers/include/iinfo.h\"\n#include \"util/base/include/TValidatorInfo.h\"\n#include \"util/base/include/util.h\"\n#include \"util/base/include/xml_helper.h\"\n#include \"technologies/include/ioutput.h\"\n#include \"functions/include/non_energy_input.h\"\n\n#include \n#include \n#include \n\n#include \nusing boost::math::erf;\n\n// namespaces **************************************************************\n\n// WindTechnology::kWhrtoGJ ************************************************\n\nconst double WindTechnology::kWhrtoGJ = 0.0036;\n\n// WindTechnology::sXMLTagNames ********************************************\n\nstd::string WindTechnology::sXMLTagNames[] =\n{\n std::string( \"air-density\" ),\n std::string( \"average-wind-speed\" ),\n std::string( \"capital-cost\" ),\n std::string( \"cutout-speed\" ),\n std::string( \"fcr\" ),\n std::string( \"grid-connection-cost\" ),\n std::string( \"om\" ),\n std::string( \"reference-height\" ),\n std::string( \"rotor-diameter\" ),\n std::string( \"turbine-density\" ),\n std::string( \"turbine-derating\" ),\n std::string( \"turbine-hub-height\" ),\n std::string( \"turbine-rating\" ),\n std::string( \"wind-farm-loss\" ),\n std::string( \"wind-velocity-exponent\" )\n};\n\n// Constructor: WindTechnology *********************************************\n\n/*! Default constructor\n * \\param aName the name of the technology\n * \\param aYear the year\n */\nWindTechnology::WindTechnology(\n const std::string& aName,\n const int aYear )\n : IntermittentTechnology( aName, aYear )\n{\n mCapitalCost = 261.83;\n mConnectCost = -1;\n mCutOutSpeed = -1;\n mFCR = 0.0856;\n mGenerationCost = -1;\n mGridConnectionCost = 392.75;\n mOM = 2.62;\n mRealizedTurbineOutput = -1;\n mRotorDiameter = -1;\n mTurbineDensity = -1;\n mTurbineDerating = -1;\n mTurbineHubHeight = -1;\n mTurbineRating = -1;\n mWindCapacityFactor = -1;\n mWindFarmLoss = -1;\n}\n\n// Destructor: WindTechnology **********************************************\n\nWindTechnology::~WindTechnology(void)\n{\n}\n\nvoid WindTechnology::copy( const WindTechnology& aOther ) {\n IntermittentTechnology::copy( aOther );\n mCapitalCost = aOther.mCapitalCost;\n mConnectCost = aOther.mConnectCost;\n mCutOutSpeed = aOther.mCutOutSpeed;\n mFCR = aOther.mFCR;\n mGenerationCost = aOther.mGenerationCost;\n mGridConnectionCost = aOther.mGridConnectionCost;\n mOM = aOther.mOM;\n mRealizedTurbineOutput = aOther.mRealizedTurbineOutput;\n mRotorDiameter = aOther.mRotorDiameter;\n mTurbineDensity = aOther.mTurbineDensity;\n mTurbineDerating = aOther.mTurbineDerating;\n mTurbineHubHeight = aOther.mTurbineHubHeight;\n mTurbineRating = aOther.mTurbineRating;\n mWindCapacityFactor = aOther.mWindCapacityFactor;\n mWindFarmLoss = aOther.mWindFarmLoss;\n}\n\n// WindTechnology::calcCost ************************************************\n\n// Documentation is inherited\nvoid WindTechnology::calcCost(\n const std::string& aRegionName,\n const std::string& aSectorName,\n const int aPeriod )\n{\n if( !mProductionState[ aPeriod ]->isOperating() ){\n return;\n }\n\n // Var to hold capital and operating costs\n double totalTechCapOMCost = 0;\n\n // Get marketplace and calculate costs\n Marketplace* pMarketplace = scenario->getMarketplace();\n const IInfo* pInfo = pMarketplace->getMarketInfo( ( *mResourceInput )->getName(), aRegionName, aPeriod, true );\n\n // Equation 4:\n mRealizedTurbineOutput = calcRealizedTurbineOutput( pInfo );\n\n // Equation 3:\n // WindCapacityFactor = RealizedTurbineOutput / TurbineRating\n mWindCapacityFactor = mRealizedTurbineOutput / mTurbineRating;\n\n // Equation 6:\n // CConnect = FCR * DConnect * ( gridConnectionCost / ( WindCapacityFactor * 1000.0 * kWhrtoGJ * 24 * 365 * 1 ) )\n double dConnect = pMarketplace->getPrice( ( *mResourceInput )->getName(), aRegionName, aPeriod );\n mConnectCost = mFCR * dConnect * ( mGridConnectionCost / ( mWindCapacityFactor * 1000.0 * kWhrtoGJ * 24.0 * 365.0 ) );\n\n // Equation 2:\n // CGeneration = ( FCR * CapitalCost + OM ) / ( 24 * 365 * WindCapacityFactor * kWhrtoGJ )\n mGenerationCost = ( mFCR * mCapitalCost + mOM ) / ( 24.0 * 365.0 * mWindCapacityFactor * kWhrtoGJ );\n\n if( !mProductionState[ aPeriod ]->isNewInvestment() ||\n mFixedOutput != IProductionState::fixedOutputDefault() )\n {\n // If an existing stock, only have O&M costs. \n totalTechCapOMCost = mOM / ( 24.0 * 365.0 * mWindCapacityFactor * kWhrtoGJ );\n }\n else {\n // Generation plus connection costs. \n totalTechCapOMCost = std::max( mGenerationCost + mConnectCost, util::getSmallNumber() );\n }\n\n // Set tech capital and operating costs to input object\n ( *mTechCostInput )->setPrice( aRegionName, totalTechCapOMCost, aPeriod );\n \n // Call parent function to set costs and coefficients\n IntermittentTechnology::calcCost( aRegionName, aSectorName, aPeriod );\n \n}\n\n// WindTechnology::calcIdealTurbineOutput **********************************\n\n/*! \n * \\param aAveWindSpeed the average Wind Speed\n * \\param aDiameter the turbine blade diameter (in meters)\n * \\param aAirDensity the average Air density (in g/m^3)\n */\ndouble WindTechnology::calcIdealTurbineOutput(\n double aAveWindSpeed,\n double aDiameter,\n double aAirDensity )\n{\n /*\n * The ideal maximum extractable power, known as the Betz limit, has a\n * coefficient of Cp = 16/27. At this limit, the integral evaluates to\n * (Equation A-6):\n * Prb = p * ( 2 / 3 * D )^2 * V^3\n * where:\n * p = the average Air density (in g/m^3)\n * D = the turbine blade diameter (in meters)\n * V = the average Wind Speed\n */\n return aAirDensity * std::pow( 2.0 / 3.0 * aDiameter, 2.0 ) * std::pow( aAveWindSpeed, 3.0 );\n}\n\n// WindTechnology::calcRealizedTurbineOutput *******************************\n/*! Compute the realized turbine output\n * \\param apInfo pointer to the market info\n */\ndouble WindTechnology::calcRealizedTurbineOutput( const IInfo* apInfo ) const\n{\n // Equation 5:\n // aveWindSpeedAtHub = aveWindSpeed * ( turbineHubHeight / referenceHeight ) ^ windVelocityExponent\n double aveWindSpeed = apInfo->getDouble( sXMLTagNames[ AVERAGE_WIND_SPEED_KEY ], true );\n double referenceHeight = apInfo->getDouble( sXMLTagNames[ REFERENCE_HEIGHT_KEY ], true );\n double windVelocityExponent = apInfo->getDouble( sXMLTagNames[ WIND_VELOCITY_EXPONENT_KEY ], true );\n double aveWindSpeedAtHub = aveWindSpeed * std::pow( mTurbineHubHeight / referenceHeight, windVelocityExponent );\n\n // Equation 4:\n // RealizedTurbineOutput = ( IdealTurbineOutput / 10^6 ) * TurbineCoefficient * ( 1 - Derating ) * ( 1 - WindFarmLoss )\n double airDensity = apInfo->getDouble( sXMLTagNames[ AIR_DENSITY_KEY ], true );\n double realizedTurbineOutput = ( calcIdealTurbineOutput( aveWindSpeedAtHub, mRotorDiameter, airDensity ) / 1.0e6 ) * calcTurbineCoefficient( aveWindSpeedAtHub, mTurbineRating, mRotorDiameter, airDensity, mCutOutSpeed ) * ( 1.0 - mTurbineDerating ) * ( 1.0 - mWindFarmLoss );\n mWindPowerVariance = computeWindPowerVariance( aveWindSpeedAtHub, mTurbineRating, mRotorDiameter, airDensity, mCutOutSpeed );\n return realizedTurbineOutput;\n}\n\n// WindTechnology::calcResourceArea ****************************************\n\n/*! Calculate the resource area in km^2\n * NOTE: Unlike solar techs, wind does not exclusively use this land. This need to be \n * accounted for when setting land costs.\n * \\param aRegionName the region name\n * \\param aSectorName the sector name\n * \\param aVariableDemand the variable demand\n * \\param aPeriod the period\n * \\return the resource area\n */\ndouble WindTechnology::calcResourceArea(\n const std::string& aRegionName,\n const std::string& aSectorName,\n double aVariableDemand,\n const int aPeriod )\n{\n // Get info object for this resource \n Marketplace* pMarketplace = scenario->getMarketplace();\n const IInfo* pInfo = pMarketplace->getMarketInfo( ( *mResourceInput )->getName(), aRegionName, aPeriod, true );\n\n // Equation 4:\n mRealizedTurbineOutput = calcRealizedTurbineOutput( pInfo );\n\n // Equation 3:\n // WindCapacityFactor = RealizedTurbineOutput / TurbineRating\n mWindCapacityFactor = mRealizedTurbineOutput / mTurbineRating;\n\n // Equation 7:\n // WindGeneration / ( kWhrtoGJ * 10^-9 ) = TurbineDensity * ResourceArea * WindCapacityFactor * 1000 * 24 * 365\n // ResourceArea = ( WindGeneration / ( kWhrtoGJ * 10^-9 ) ) / ( TurbineDensity * WindCapacityFactor * 1000 * 24 * 365 )\n double windGeneration = aVariableDemand;\n double resourceArea = ( windGeneration / ( kWhrtoGJ * 1.0e-9 ) ) / ( mTurbineDensity * mWindCapacityFactor * 1000.0 * 24.0 * 365.0 );\n\n return resourceArea;\n}\n\n/*! \\brief Return amount of resource needed per unit of energy output.\n* This method should be used when a technology uses a resource that is\n* not in energy units. \n* NOTE: Unlike solar techs, wind does not exclusively use this land. This need to be \n* accounted for when setting land costs.\n* \\author Steve Smith\n* \\param aPeriod Model period.\n*/\ndouble WindTechnology::getResourceToEnergyRatio( const std::string& aRegionName,\n const std::string& aSectorName,\n const int aPeriod )\n{\n // Default assumpion is that resource is in energy units\n // Need to return the amount of resource (in km^2) needed per EJ of energy output\n return calcResourceArea( aRegionName, aSectorName, 1.0, aPeriod );\n}\n\n// WindTechnology::calcTurbineCoefficient **********************************\n\n/*! Compute the capture coefficient for a turbine with a finite power rating.\n * \\param aAveWindSpeed the average Wind Speed\n * \\param aRating the turbine Rating (in MW)\n * \\param aDiameter the turbine Blade Diameter (in meters)\n * \\param aAirDensity the average Air density (in g/m^3)\n * \\param aCutoutSpeed the cut-out Speed (m/s)\n */\ndouble WindTechnology::calcTurbineCoefficient(\n double aAveWindSpeed,\n double aRating,\n double aDiameter,\n double aAirDensity,\n double aCutoutSpeed )\n{\n static const double PI = 2.0 * std::asin( 1.0 );\n static const double sqrtPI = std::sqrt( PI );\n\n /*\n * Equation A-8\n * Calculate the final cutout speed (Xf)\n * Xs = ( sqrt( pi ) / 2) * ( Vs / Vave )\n */\n double Xf = ( sqrtPI / 2.0 ) * ( aCutoutSpeed / aAveWindSpeed );\n\n /*\n * Equation A-10\n * Calculate the velocity (solve for v)\n * 1/8pD^2(16/27)v^3 = Prated\n * v = cube-root( Prated / ( 1/8pD^2(16/27) )\n */\n double aRatingInWatts = aRating * 1.0e6;\n double velocity = std::pow( aRatingInWatts / ( 1.0 / 8.0 * aAirDensity * PI * std::pow( aDiameter, 2.0 ) * 16.0 / 27.0 ), 1.0 / 3.0 );\n\n /*\n * Equation A-11\n * Calculate the finite power rating (Xr)\n * Xr = ( sqrt( pi ) / 2 ) * V / Vave\n */\n double Xr = ( sqrtPI / 2.0 ) * velocity / aAveWindSpeed;\n\n /*\n * Equation A-7:\n * Compute the power up to a finite cutout for Xr\n * speed is ():\n *\n * CCfinite_cutout( Xs ) =\n * Erf( Xs ) - ( 4 / ( 3 * sqrt( pi ) ) ) * Xs * ( Xs^2 + 3 / 2 ) * e^-Xs^2\n */\n double CCfiniteCutout = erf( Xr ) - ( 4.0 / ( 3.0 * sqrtPI ) ) * Xr * ( std::pow( Xr, 2.0 ) + 3.0 / 2.0 ) * std::exp( -std::pow( Xr, 2.0 ) );\n\n /*\n * Equation A-9:\n * Finally, compute the capture coefficient\n * CCfinite_power( Xr, Xf ) = \n * CCfinite_cutout( Xr ) + ( Xr^3 / ( 3 / 4 * sqrt( pi ) ) ) * ( e ^-Xr^2 - e^-Xf^2 )\n */\n double CCfinitePower = CCfiniteCutout + ( std::pow( Xr, 3.0 ) / ( 3.0 / 4.0 * sqrtPI ) ) * ( std::exp( -std::pow( Xr, 2.0 ) ) - std::exp( -std::pow( Xf, 2.0 ) ) );\n\n return CCfinitePower;\n}\n\n// WindTechnology::clone ***************************************************\n\n// Documentation is inherited\nWindTechnology* WindTechnology::clone( void ) const\n{\n WindTechnology* clone = new WindTechnology( mName, mYear );\n clone->copy( *this );\n return clone;\n}\n\n// WindTechnology::completeInit ********************************************\n\n// Documentation is inherited\nvoid WindTechnology::completeInit(\n const std::string& aRegionName,\n const std::string& aSectorName,\n const std::string& aSubsectorName,\n const IInfo* aSubsectorIInfo,\n ILandAllocator* aLandAllocator )\n{\n // Initialize a non-energy input to hold technology costs\n if( util::searchForValue( mInputs, getTechCostName() ) == mInputs.end() ){\n mInputs.push_back( new NonEnergyInput( getTechCostName() ) );\n }\n\n IntermittentTechnology::completeInit( aRegionName, aSectorName, aSubsectorName, aSubsectorIInfo, aLandAllocator );\n\n // Validate input parameters\n typedef ObjECTS::TValidatorInfo<> validator_type;\n validator_type validator[] =\n {\n validator_type( mCapitalCost, sXMLTagNames[ CAPITAL_COST_KEY ], mCapitalCost > 0 ),\n validator_type( mCutOutSpeed, sXMLTagNames[ CUTOUT_SPEED_KEY ], mCutOutSpeed > 0 ),\n validator_type( mFCR, sXMLTagNames[ FCR_KEY ], mFCR > 0 ),\n validator_type( mRotorDiameter, sXMLTagNames[ ROTOR_DIAMETER_KEY ], mRotorDiameter > 0 ),\n validator_type( mTurbineDensity, sXMLTagNames[ TURBINE_DENSITY_KEY ], mTurbineDensity > 0 ),\n validator_type( mTurbineDerating, sXMLTagNames[ TURBINE_DERATING_KEY ], mTurbineDerating > 0 ),\n validator_type( mTurbineHubHeight, sXMLTagNames[ TURBINE_HUB_HEIGHT_KEY ], mTurbineHubHeight > 0 ),\n validator_type( mTurbineRating, sXMLTagNames[ TURBINE_RATING_KEY ], mTurbineRating > 0 ),\n validator_type( mWindFarmLoss, sXMLTagNames[ WIND_FARM_LOSS_KEY ], mWindFarmLoss > 0 )\n };\n\n unsigned short numParams = sizeof( validator ) / sizeof( validator[0] );\n std::string msg = ObjECTS::getInvalidNames(\n &validator[0],\n &validator[numParams] );\n\n if ( msg.length() )\n // Invalid input parameter\n {\n ILogger& mainLog = ILogger::getLogger( \"main_log\" );\n mainLog.setLevel( ILogger::ERROR );\n mainLog << \"Invalid input parameter(s) to \"\n << getXMLNameStatic()\n << \" in sector \" << aSectorName\n << \": \" << msg << std::endl;\n exit( -1 );\n }\n}\n\n// WindTechnology::getXMLName1D ********************************************\n\n// Documentation is inherited\nconst std::string& WindTechnology::getXMLName1D( void ) const\n{\n return getXMLNameStatic();\n}\n\n// WindTechnology::getXMLNameStatic1D **************************************\n\n// Documentation is inherited\nconst std::string& WindTechnology::getXMLNameStatic1D( void )\n{\n static const std::string XML_NAME1D = \"wind-technology\";\n\n return XML_NAME1D;\n}\n\n/*! \\brief Return name to be used for input object containing technology costs.\n*\n* This input object will contain technology capital, operation, and any other costs\n* exclusive of backup or fuel costs. Setting to blank indicates that this object does\n* not use this cost.\n*\n* \\author Steve Smith\n* \\return The constant XML_NAME as a static.\n*/\nconst std::string& WindTechnology::getTechCostName( ) const {\n const static std::string TECH_COST_NAME = \"turbine-and-connect-costs\";\n return TECH_COST_NAME;\n}\n\n// WindTechnology::initCalc ************************************************\n\n// Documentation is inherited\nvoid WindTechnology::initCalc(\n const std::string& aRegionName,\n const std::string& aSectorName,\n const IInfo* aSubsectorIInfo,\n const Demographic* aDemographics,\n PreviousPeriodInfo& aPrevPeriodInfo,\n const int aPeriod )\n{\n IntermittentTechnology::initCalc( aRegionName, aSectorName, aSubsectorIInfo, aDemographics, \n aPrevPeriodInfo, aPeriod );\n\n // Get marketplace and make sure we have the correct type of resource\n Marketplace* pMarketplace = scenario->getMarketplace();\n const IInfo* pInfo = pMarketplace->getMarketInfo( ( *mResourceInput )->getName(), aRegionName, aPeriod, true );\n std::string msg = \"\";\n\n if ( !pInfo )\n {\n std::ostringstream ostr;\n ostr << \"Error getting marketplace info for ( \"\n << ( *mResourceInput )->getName() << \", \"\n << aRegionName << \", \"\n << aPeriod << \" )\"\n << std::ends;\n msg = ostr.str();\n }\n else\n {\n typedef ObjECTS::TValidatorInfo<> validator_type;\n double notUsed=0.0;\n validator_type validator[] =\n {\n validator_type( notUsed, sXMLTagNames[ AVERAGE_WIND_SPEED_KEY ], pInfo->hasValue( sXMLTagNames[ AVERAGE_WIND_SPEED_KEY ] ) ),\n validator_type( notUsed, sXMLTagNames[ AIR_DENSITY_KEY ], pInfo->hasValue( sXMLTagNames[ AIR_DENSITY_KEY ] ) ),\n validator_type( notUsed, sXMLTagNames[ REFERENCE_HEIGHT_KEY ], pInfo->hasValue( sXMLTagNames[ REFERENCE_HEIGHT_KEY ] ) ),\n validator_type( notUsed, sXMLTagNames[ WIND_VELOCITY_EXPONENT_KEY ], pInfo->hasValue( sXMLTagNames[ WIND_VELOCITY_EXPONENT_KEY ] ) )\n };\n\n unsigned short numParams = sizeof( validator ) / sizeof( validator[0] );\n msg = ObjECTS::getInvalidNames(\n &validator[0],\n &validator[numParams] );\n if ( msg.length() )\n {\n std::ostringstream ostr;\n ostr << \"Invalid input parameter(s) to \"\n << getXMLNameStatic()\n << \" in sector \" << aSectorName\n << \": \" << msg << std::ends;\n msg = ostr.str();\n }\n }\n\n if ( msg.length() )\n // Invalid input parameter\n {\n ILogger& mainLog = ILogger::getLogger( \"main_log\" );\n mainLog.setLevel( ILogger::ERROR );\n mainLog << msg << std::endl;\n }\n}\n\n// WindTechnology::production **********************************************\n\ndouble WindTechnology::getCalibrationOutput( const bool aHasRequiredInput,\n const std::string& aRequiredInput,\n const int aPeriod ) const\n{\n return IntermittentTechnology::getCalibrationOutput( aHasRequiredInput, aRequiredInput, aPeriod );\n}\n\n// WindTechnology::toDebugXMLDerived ***************************************\n\n// Documentation is inherited\nvoid WindTechnology::toDebugXMLDerived(\n const int period,\n std::ostream& out,\n Tabs* tabs ) const\n{\n IntermittentTechnology::toDebugXMLDerived( period, out, tabs );\n XMLWriteElement( mCapitalCost, sXMLTagNames[ CAPITAL_COST_KEY ], out, tabs );\n XMLWriteElement( mConnectCost, \"connection-cost\", out, tabs );\n XMLWriteElement( mCutOutSpeed, sXMLTagNames[ CUTOUT_SPEED_KEY ], out, tabs );\n XMLWriteElement( mFCR, sXMLTagNames[ FCR_KEY ], out, tabs );\n XMLWriteElement( mGenerationCost, \"generation-cost\", out, tabs );\n XMLWriteElement( mGridConnectionCost, sXMLTagNames[ GRID_CONNECTION_COST_KEY ], out, tabs );\n XMLWriteElement( mOM, sXMLTagNames[ OM_KEY ], out, tabs );\n XMLWriteElement( mRealizedTurbineOutput, \"realized-turbine-output\", out, tabs );\n XMLWriteElement( mRotorDiameter, sXMLTagNames[ ROTOR_DIAMETER_KEY ], out, tabs );\n XMLWriteElement( mTurbineDensity, sXMLTagNames[ TURBINE_DENSITY_KEY ], out, tabs );\n XMLWriteElement( mTurbineDerating, sXMLTagNames[ TURBINE_DERATING_KEY ], out, tabs );\n XMLWriteElement( mTurbineHubHeight, sXMLTagNames[ TURBINE_HUB_HEIGHT_KEY ], out, tabs );\n XMLWriteElement( mTurbineRating, sXMLTagNames[ TURBINE_RATING_KEY ], out, tabs );\n XMLWriteElement( mWindCapacityFactor, \"wind-capacity-factor\", out, tabs );\n XMLWriteElement( mWindFarmLoss, sXMLTagNames[ WIND_FARM_LOSS_KEY ], out, tabs );\n XMLWriteElement( mWindPowerVariance, \"wind-power-variance\", out, tabs );\n}\n\n// WindTechnology::toInputXMLDerived ***************************************\n\n// Documentation is inherited\nvoid WindTechnology::toInputXMLDerived(\n std::ostream& out,\n Tabs* tabs ) const\n{\n IntermittentTechnology::toInputXMLDerived( out, tabs );\n XMLWriteElementCheckDefault( mCapitalCost, sXMLTagNames[ CAPITAL_COST_KEY ], out, tabs, double( 1000.0 ) );\n XMLWriteElement( mCutOutSpeed, sXMLTagNames[ CUTOUT_SPEED_KEY ], out, tabs );\n XMLWriteElementCheckDefault( mFCR, sXMLTagNames[ FCR_KEY ], out, tabs, double( 0.0856 ) );\n XMLWriteElementCheckDefault( mGridConnectionCost, sXMLTagNames[ GRID_CONNECTION_COST_KEY ], out, tabs, double( 1500.0 ) );\n XMLWriteElementCheckDefault( mOM, sXMLTagNames[ OM_KEY ], out, tabs, double( 10.0 ) );\n XMLWriteElement( mRotorDiameter, sXMLTagNames[ ROTOR_DIAMETER_KEY ], out, tabs );\n XMLWriteElement( mTurbineDensity, sXMLTagNames[ TURBINE_DENSITY_KEY ], out, tabs );\n XMLWriteElement( mTurbineDerating, sXMLTagNames[ TURBINE_DERATING_KEY ], out, tabs );\n XMLWriteElement( mTurbineHubHeight, sXMLTagNames[ TURBINE_HUB_HEIGHT_KEY ], out, tabs );\n XMLWriteElement( mTurbineRating, sXMLTagNames[ TURBINE_RATING_KEY ], out, tabs );\n XMLWriteElement( mWindFarmLoss, sXMLTagNames[ WIND_FARM_LOSS_KEY ], out, tabs );\n}\n\n// WindTechnology::XMLDerivedClassParse ************************************\n\n// Documentation is inherited\nbool WindTechnology::XMLDerivedClassParse(\n const std::string& nodeName,\n const xercesc::DOMNode* curr )\n{\n if ( nodeName == sXMLTagNames[ CAPITAL_COST_KEY ] )\n {\n mCapitalCost = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ CUTOUT_SPEED_KEY ] )\n {\n mCutOutSpeed = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ FCR_KEY ] )\n {\n mFCR = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ GRID_CONNECTION_COST_KEY ] )\n {\n mGridConnectionCost = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ OM_KEY ] )\n {\n mOM = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ ROTOR_DIAMETER_KEY ] )\n {\n mRotorDiameter = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ TURBINE_DENSITY_KEY ] )\n {\n mTurbineDensity = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ TURBINE_DERATING_KEY ] )\n {\n mTurbineDerating = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ TURBINE_HUB_HEIGHT_KEY ] )\n {\n mTurbineHubHeight = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ TURBINE_RATING_KEY ] )\n {\n mTurbineRating = XMLHelper::getValue( curr );\n }\n else if ( nodeName == sXMLTagNames[ WIND_FARM_LOSS_KEY ] )\n {\n mWindFarmLoss = XMLHelper::getValue( curr );\n }\n else if ( IntermittentTechnology::XMLDerivedClassParse( nodeName, curr ) ) {\n }\n else\n {\n return false;\n }\n\n return true;\n}\n\n\n/*\n The one parameter Weibull distribution is (Equation A-1):\n f( x ) = ( B / c ) * ( x / c )^(B - 1) * e^-( x / c )^B\n\n The mean of the distribution is (Equation A-2):\n X = c * gamma * ( 1 / B + 1)\n\n The mean wind speed is (Equation A-3):\n X = c * gamma * 1.5 = ( sqrt( pi ) / 2 ) * c\n or\n c = X / a = 2X / sqrt( pi ) * a = sqrt( pi ) / 2 = 0.88623\n\n The wind distribution for a mean wind speed of is (Equation A-4):\n f( v ) = ( pi * v / 2X^2 ) * e^( -pi * ( v / 2X )^2\n\n Equation A-8:\n Xf = ( sqrt( pi ) / 2 ) * ( Vf / Vave )\n\n Equation A-10:\n 1/2 * p * A * Cp * v^3 = 1 / 8 * p * D^2 * ( 16 / 27 ) * v^3 = Prated\n\n Equation A-18:\n (16 / 9 * pi) * Prb^2 * { 6 - [ Xr^6 + 3 Xr^4 + 6Xr^2 + 6 ]e^(-(Xr)^2) + Xr^6[ e^(-(Xr)^2) - e^(-(Xf)^2)]} = (16 / 9 * pi) * G( Xr, Xf ) * Prb^2\n\n Equation A-19:\n Var(P^2) = [ ( 16 / 9 * pi ) * G( Xr, Xf ) - CCfinite-power^2( Xr, Xf ) ] * Prb^2\n*/\ndouble WindTechnology::computeWindPowerVariance(\n double aAveWindSpeed,\n double aRating,\n double aDiameter,\n double aAirDensity,\n double aCutoutSpeed ) const\n{\n static const double PI = 2.0 * std::asin( 1.0 );\n\n // Equation A-18:\n // (16 / 9 * pi) * Prb^2 * { 6 - [ Xr^6 + 3 Xr^4 + 6Xr^2 + 6 ]e^(-(Xr)^2) + Xr^6[ e^(-(Xr)^2) - e^(-(Xf)^2)]} = (16 / 9 * pi) * G( Xr, Xf ) * Prb^2\n // Xr = rating\n // Xf = cutout speed\n double G = ( 6.0 - ( std::pow( aRating, 6.0 ) + 3.0 * std::pow( aRating, 4.0 ) + 6.0 * std::pow( aRating, 2.0 ) + 6.0 ) * std::exp( -std::pow( aRating, 2.0) ) + std::pow( aRating, 6.0 ) * ( std::exp( -std::pow( aRating, 2.0) ) - std::exp( -std::pow( aCutoutSpeed, 2.0 ) ) ) );\n\n // Equation A-19:\n // Var(P^2) = [ ( 16 / 9 * pi ) * G( Xr, Xf ) - CCfinite-power^2( Xr, Xf ) ] * Prb^2\n // Xr = rating\n // Xf = cutout speed\n double windPowerVariance = ( 16.0 / 9.0 * PI * G - std::pow( WindTechnology::calcTurbineCoefficient( aAveWindSpeed, aRating, aDiameter, aAirDensity, aCutoutSpeed ), 2.0 ) ) * std::pow( WindTechnology::calcIdealTurbineOutput( aAveWindSpeed, aDiameter, aAirDensity ), 2.0 );\n\n return windPowerVariance;\n}\n\n// end of wind_technology.cpp **********************************************\n\n", "meta": {"hexsha": "a19de175f5a710b0795b09970cefb48d8bf1f24c", "size": 27535, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cvs/objects/technologies/source/wind_technology.cpp", "max_stars_repo_name": "crvernon/gcam-core", "max_stars_repo_head_hexsha": "bbfb78aeb0cde4d75f307fc3967526d70157c2f8", "max_stars_repo_licenses": ["ECL-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5234203340678567, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.22114751709966018}} {"text": "/*\n Copyright (C) 2018 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n#include \n#ifdef QL_USE_INDEXED_COUPON\n#include \n#endif\n\n#include \n\n#include \n\nnamespace QuantExt {\n\nnamespace {\nvoid no_deletion(YieldTermStructure*) {}\n} // namespace\n\nCrossCcyBasisMtMResetSwapHelper::CrossCcyBasisMtMResetSwapHelper(\n const Handle& spreadQuote, const Handle& spotFX, Natural settlementDays,\n const Calendar& settlementCalendar, const Period& swapTenor, BusinessDayConvention rollConvention,\n const boost::shared_ptr& foreignCcyIndex,\n const boost::shared_ptr& domesticCcyIndex,\n const Handle& foreignCcyDiscountCurve,\n const Handle& domesticCcyDiscountCurve,\n const Handle& foreignCcyFxFwdRateCurve,\n const Handle& domesticCcyFxFwdRateCurve, bool eom, bool spreadOnForeignCcy, bool invertFxIndex,\n boost::optional foreignTenor, boost::optional domesticTenor)\n : RelativeDateRateHelper(spreadQuote), spotFX_(spotFX), settlementDays_(settlementDays),\n settlementCalendar_(settlementCalendar), swapTenor_(swapTenor), rollConvention_(rollConvention),\n foreignCcyIndex_(foreignCcyIndex), domesticCcyIndex_(domesticCcyIndex),\n foreignCcyDiscountCurve_(foreignCcyDiscountCurve), domesticCcyDiscountCurve_(domesticCcyDiscountCurve),\n foreignCcyFxFwdRateCurve_(foreignCcyFxFwdRateCurve), domesticCcyFxFwdRateCurve_(domesticCcyFxFwdRateCurve),\n eom_(eom), spreadOnForeignCcy_(spreadOnForeignCcy), invertFxIndex_(invertFxIndex),\n foreignTenor_(foreignTenor ? *foreignTenor : foreignCcyIndex_->tenor()),\n domesticTenor_(domesticTenor ? *domesticTenor : domesticCcyIndex_->tenor()) {\n\n foreignCurrency_ = foreignCcyIndex_->currency();\n domesticCurrency_ = domesticCcyIndex_->currency();\n QL_REQUIRE(foreignCurrency_ != domesticCurrency_,\n \"matching currencies not allowed on CrossCcyBasisMtMResetSwapHelper\");\n\n bool foreignIndexHasCurve = !foreignCcyIndex_->forwardingTermStructure().empty();\n bool domesticIndexHasCurve = !domesticCcyIndex_->forwardingTermStructure().empty();\n bool haveForeignDiscountCurve = !foreignCcyDiscountCurve_.empty();\n bool haveDomesticDiscountCurve = !domesticCcyDiscountCurve_.empty();\n\n QL_REQUIRE(\n !(foreignIndexHasCurve && domesticIndexHasCurve && haveForeignDiscountCurve && haveDomesticDiscountCurve),\n \"CrossCcyBasisMtMResetSwapHelper - Have all curves, nothing to solve for.\");\n\n /* Link the curve being bootstrapped to the index if the index has\n no projection curve */\n if (foreignIndexHasCurve && haveForeignDiscountCurve) {\n if (!domesticIndexHasCurve) {\n domesticCcyIndex_ = domesticCcyIndex_->clone(termStructureHandle_);\n domesticCcyIndex_->unregisterWith(termStructureHandle_);\n }\n // if we have both index and discounting curve on foreign leg,\n // check foreignCcyFxFwdRateCurve and link it to foreign discount curve if empty\n // (we are bootstrapping on domestic leg in this instance, so foreign leg needs to be fully determined\n if (foreignCcyFxFwdRateCurve_.empty())\n foreignCcyFxFwdRateCurve_ = foreignCcyDiscountCurve_;\n } else if (domesticIndexHasCurve && haveDomesticDiscountCurve) {\n if (!foreignIndexHasCurve) {\n foreignCcyIndex_ = foreignCcyIndex_->clone(termStructureHandle_);\n foreignCcyIndex_->unregisterWith(termStructureHandle_);\n }\n // if we have both index and discounting curve on domestic leg,\n // check domesticCcyFxFwdRateCurve and link it to domestic discount curve if empty\n // (we are bootstrapping on foreign leg in this instance, so domestic leg needs to be fully determined\n if (domesticCcyFxFwdRateCurve_.empty())\n domesticCcyFxFwdRateCurve_ = domesticCcyDiscountCurve_;\n } else {\n QL_FAIL(\"Need one leg of the cross currency basis swap to \"\n \"have all of its curves.\");\n }\n\n registerWith(spotFX_);\n registerWith(domesticCcyIndex_);\n registerWith(foreignCcyIndex_);\n registerWith(foreignCcyDiscountCurve_);\n registerWith(domesticCcyDiscountCurve_);\n registerWith(foreignCcyFxFwdRateCurve_);\n registerWith(domesticCcyFxFwdRateCurve_);\n\n initializeDates();\n}\n\nvoid CrossCcyBasisMtMResetSwapHelper::initializeDates() {\n\n Date refDate = evaluationDate_;\n // if the evaluation date is not a business day\n // then move to the next business day\n refDate = settlementCalendar_.adjust(refDate);\n\n Date settlementDate = settlementCalendar_.advance(refDate, settlementDays_, Days);\n Date maturityDate = settlementDate + swapTenor_;\n\n Schedule foreignLegSchedule = MakeSchedule()\n .from(settlementDate)\n .to(maturityDate)\n .withTenor(foreignTenor_)\n .withCalendar(settlementCalendar_)\n .withConvention(rollConvention_)\n .endOfMonth(eom_);\n\n Schedule domesticLegSchedule = MakeSchedule()\n .from(settlementDate)\n .to(maturityDate)\n .withTenor(domesticTenor_)\n .withCalendar(settlementCalendar_)\n .withConvention(rollConvention_)\n .endOfMonth(eom_);\n\n Real foreignNominal = 1.0;\n // build an FX index for forward rate projection (TODO - review settlement and calendar)\n boost::shared_ptr fxIdx = boost::make_shared(\n \"dummy\", settlementDays_, foreignCurrency_, domesticCurrency_, settlementCalendar_, spotFX_,\n foreignCcyFxFwdRateCurveRLH_, domesticCcyFxFwdRateCurveRLH_, invertFxIndex_);\n\n swap_ = boost::shared_ptr(\n new CrossCcyBasisMtMResetSwap(foreignNominal, foreignCurrency_, foreignLegSchedule, foreignCcyIndex_, 0.0,\n domesticCurrency_, domesticLegSchedule, domesticCcyIndex_, 0.0, fxIdx));\n\n boost::shared_ptr engine;\n if (invertFxIndex_) {\n engine.reset(new CrossCcySwapEngine(foreignCurrency_, foreignDiscountRLH_, domesticCurrency_,\n domesticDiscountRLH_, spotFX_));\n } else {\n engine.reset(new CrossCcySwapEngine(domesticCurrency_, domesticDiscountRLH_, foreignCurrency_,\n foreignDiscountRLH_, spotFX_));\n }\n swap_->setPricingEngine(engine);\n\n earliestDate_ = swap_->startDate();\n latestDate_ = swap_->maturityDate();\n\n/* May need to adjust latestDate_ if you are projecting libor based\n on tenor length rather than from accrual date to accrual date. */\n#ifdef QL_USE_INDEXED_COUPON\n if (termStructureHandle_ == foreignCcyIndex_->forwardingTermStructure()) {\n Size numCashflows = swap_->leg(0).size();\n Date endDate = latestDate_;\n if (numCashflows > 0) {\n for (Size i = numCashflows - 1; i >= 0; i--) {\n boost::shared_ptr lastFloating =\n boost::dynamic_pointer_cast(swap_->leg(0)[i]);\n if (!lastFloating)\n continue;\n else {\n Date fixingValueDate = foreignCcyIndex_->valueDate(lastFloating->fixingDate());\n endDate = domesticCcyIndex_->maturityDate(fixingValueDate);\n Date endValueDate = foreignCcyIndex_->maturityDate(fixingValueDate);\n latestDate_ = std::max(latestDate_, endValueDate);\n break;\n }\n }\n }\n }\n if (termStructureHandle_ == domesticCcyIndex_->forwardingTermStructure()) {\n Size numCashflows = swap_->leg(1).size();\n Date endDate = latestDate_;\n if (numCashflows > 0) {\n for (Size i = numCashflows - 1; i >= 0; i--) {\n boost::shared_ptr lastFloating =\n boost::dynamic_pointer_cast(swap_->leg(1)[i]);\n if (!lastFloating)\n continue;\n else {\n Date fixingValueDate = domesticCcyIndex_->valueDate(lastFloating->fixingDate());\n endDate = domesticCcyIndex_->maturityDate(fixingValueDate);\n Date endValueDate = domesticCcyIndex_->maturityDate(fixingValueDate);\n latestDate_ = std::max(latestDate_, endValueDate);\n break;\n }\n }\n }\n }\n#endif\n}\n\nvoid CrossCcyBasisMtMResetSwapHelper::setTermStructure(YieldTermStructure* t) {\n\n bool observer = false;\n boost::shared_ptr temp(t, no_deletion);\n\n termStructureHandle_.linkTo(temp, observer);\n\n if (foreignCcyDiscountCurve_.empty())\n foreignDiscountRLH_.linkTo(temp, observer);\n else\n foreignDiscountRLH_.linkTo(*foreignCcyDiscountCurve_, observer);\n\n if (domesticCcyDiscountCurve_.empty())\n domesticDiscountRLH_.linkTo(temp, observer);\n else\n domesticDiscountRLH_.linkTo(*domesticCcyDiscountCurve_, observer);\n\n // the below are the curves used for FX forward rate projection (for the resetting cashflows)\n if (foreignCcyFxFwdRateCurve_.empty())\n foreignCcyFxFwdRateCurveRLH_.linkTo(temp, observer);\n else\n foreignCcyFxFwdRateCurveRLH_.linkTo(*foreignCcyFxFwdRateCurve_, observer);\n\n if (domesticCcyFxFwdRateCurve_.empty())\n domesticCcyFxFwdRateCurveRLH_.linkTo(temp, observer);\n else\n domesticCcyFxFwdRateCurveRLH_.linkTo(*domesticCcyFxFwdRateCurve_, observer);\n\n RelativeDateRateHelper::setTermStructure(t);\n}\n\nReal CrossCcyBasisMtMResetSwapHelper::impliedQuote() const {\n QL_REQUIRE(termStructure_, \"Term structure needs to be set\");\n swap_->recalculate();\n if (spreadOnForeignCcy_)\n return swap_->fairForeignSpread();\n else\n return swap_->fairDomesticSpread();\n}\n\nvoid CrossCcyBasisMtMResetSwapHelper::accept(AcyclicVisitor& v) {\n Visitor* v1 = dynamic_cast*>(&v);\n if (v1)\n v1->visit(*this);\n else\n RateHelper::accept(v);\n}\n} // namespace QuantExt\n", "meta": {"hexsha": "86ce997c0dbee977af59b360a37983a6bd2179ee", "size": 11417, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "QuantExt/qle/termstructures/crossccybasismtmresetswaphelper.cpp", "max_stars_repo_name": "nvolfango/Engine", "max_stars_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-03-30T17:24:17.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-30T17:24:17.000Z", "max_issues_repo_path": "QuantExt/qle/termstructures/crossccybasismtmresetswaphelper.cpp", "max_issues_repo_name": "zhangjiayin/Engine", "max_issues_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "QuantExt/qle/termstructures/crossccybasismtmresetswaphelper.cpp", "max_forks_repo_name": "zhangjiayin/Engine", "max_forks_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.6, "max_line_length": 119, "alphanum_fraction": 0.6884470526, "num_tokens": 2651, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.40733340004593027, "lm_q1q2_score": 0.2211263468947532}} {"text": "/** \\file itasc/kdl/inertia.cpp\n * \\ingroup itasc\n */\n// Copyright (C) 2007 Ruben Smits \n\n// Version: 1.0\n// Author: Ruben Smits \n// Maintainer: Ruben Smits \n// URL: http://www.orocos.org/kdl\n\n// This library is free software; you can redistribute it and/or\n// modify it under the terms of the GNU Lesser General Public\n// License as published by the Free Software Foundation; either\n// version 2.1 of the License, or (at your option) any later version.\n\n// This library is distributed in the hope that it will be useful,\n// but WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n// Lesser General Public License for more details.\n\n// You should have received a copy of the GNU Lesser General Public\n// License along with this library; if not, write to the Free Software\n// Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA\n\n#include \"inertia.hpp\"\n\n#include \n\nnamespace KDL {\nusing namespace Eigen;\n\nInertia::Inertia(double m,double Ixx,double Iyy,double Izz,double Ixy,double Ixz,double Iyz):\ndata(Matrix::Zero())\n{\n data(0,0)=Ixx;\n data(1,1)=Iyy;\n data(2,2)=Izz;\n data(2,1)=data(1,2)=Ixy;\n data(3,1)=data(1,3)=Ixz;\n data(3,2)=data(2,3)=Iyz;\n\n\tdata.block(3,3,3,3)=m*Matrix::Identity();\n}\n\nInertia::~Inertia()\n{\n}\n\n\n\n}\n", "meta": {"hexsha": "783bb04db70f02b147369b7983804a8a85bb41de", "size": 1514, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "intern/itasc/kdl/inertia.cpp", "max_stars_repo_name": "1-MillionParanoidTterabytes/Blender-2.79b-blackened", "max_stars_repo_head_hexsha": "e8d767324e69015aa66850d13bee7db1dc7d084b", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2018-06-18T01:50:25.000Z", "max_stars_repo_stars_event_max_datetime": "2018-06-18T01:50:32.000Z", "max_issues_repo_path": "intern/itasc/kdl/inertia.cpp", "max_issues_repo_name": "1-MillionParanoidTterabytes/Blender-2.79b-blackened", "max_issues_repo_head_hexsha": "e8d767324e69015aa66850d13bee7db1dc7d084b", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "intern/itasc/kdl/inertia.cpp", "max_forks_repo_name": "1-MillionParanoidTterabytes/Blender-2.79b-blackened", "max_forks_repo_head_hexsha": "e8d767324e69015aa66850d13bee7db1dc7d084b", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.1153846154, "max_line_length": 93, "alphanum_fraction": 0.7073976222, "num_tokens": 448, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.3775406547908328, "lm_q1q2_score": 0.22089947287675993}} {"text": "// ************************\n// ************************\n// Edited by CCJ:\n// evaldisp_lib.cpp, \n// which can be called\n// by python code;\n// ************************\n// ************************\n\n// evaluate disparity map\n// simple version for SDK\n// supports upsampling of disp map if GT has higher resolution\n\n// DS 7/2/2014\n// 10/14/2014 changed computation of average error\n// 1/27/2015 added clipping of valid (non-INF) disparities to [0 .. maxdisp]\n// in fairness to those methods that do not utilize the given disparity range\n// (maxdisp is specified at disp resolution, NOT GT resolution)\n\n#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\n\n\n#include \n#include \n#include \n#include \n#include \n#include \"imageLib/imageLib.h\"\n#include /*for int32_t*/\n\n//added by CCJ for Python + CPP coding;\n#include \n#include \"boost/python/extract.hpp\"\n//#include \"boost/python/numeric.hpp\"\n/* Updated by CCJ for Boost version 1.65:\n * Boost 1.65 removes boost/python/numeric.hpp \n */\n// BOOST_LIB_VERSION and BOOST_VERSION are defined in this header file;\n#include \n#if BOOST_VERSION >= 106300 // >= 1.63.0\n #include \"boost/python/numpy.hpp\"\n namespace np = boost::python::numpy;\n#else\n #include \"boost/python/numeric.hpp\"\n\t namespace np = boost::python::numeric;\n#endif\n\n#include \nusing namespace boost::python;\n\n// see Fastest way to check if a file exist using standard C++/C++11/C?\n// at https://stackoverflow.com/questions/12774207/fastest-way-to-check-if-a-file-exist-using-standard-c-c11-c;\n#include \ninline bool exists_file_test(const std::string & name){\n\tstruct stat buffer;\n\treturn (stat (name.c_str(), &buffer) == 0);\n}\n\n\n\nconst int verbose = 0;\n\n/* The `occlusion mask` for the left image is given as a file \"mask0nocc.png\":\n * - Pixels without ground truth have the color (0, 0, 0). \n * - Pixels which are only observed by the left image have the color (128, 128, 128).\n * - Pixels which are observed by both images have the color (255, 255, 255). \n * - For the \"non-occluded\" evaluation, the evaluation is limited to the pixels observed by both images.\t\t\n*/\n\n\nvoid evaldisp(\n\t\tCFloatImage disp, \n\t\tCFloatImage gtdisp, \n\t\tCByteImage mask, \n\t\tfloat badthresh, \n\t\tint maxdisp, \n\t\tint rounddisp,\n\t\tfloat * statistics\n\t\t){\n\n CShape sh = gtdisp.Shape();\n CShape sh2 = disp.Shape();\n CShape msh = mask.Shape();\n\n int width = sh.width, height = sh.height;\n int width2 = sh2.width, height2 = sh2.height;\n int scale = width / width2;\n\t\t//std::cout << \"scale = \" << scale << \"\\n\";\n\n if ((!(scale == 1 || scale == 2 || scale == 4)) \n\t\t\t\t|| (scale * width2 != width)\n\t\t\t\t|| (scale * height2 != height)){\n\t\t\tprintf(\" disp size = %4d x %4d\\n\", width2, height2);\n\t\t\tprintf(\"GT disp size = %4d x %4d\\n\", width, height);\n\t\t\tthrow CError(\"GT disp size must be exactly 1, 2, or 4 * disp size\");\n }\n\n int usemask = (msh.width > 0 && msh.height > 0);\n if (!usemask)\n\t\t\tthrow CError(\"No mask image's been read yet!\\n\");\n if (usemask && (msh != sh))\n\t\t\tthrow CError(\"mask image must have same size as GT!\\n\");\n\n\t\t// all region;\n int n_all = 0;\n int bad_all = 0;\n int invalid_all = 0;\n float err_all = 0;\n\t\t// non-occluded region;\n int invalid_noc = 0;\n\t\tint n_noc = 0;\n int bad_noc = 0;\n float err_noc = 0;\n\t\t// updated for mae and rmse error metric, on 2019/08/31\n float err2_all = 0; // square \n float err2_noc = 0;\n\t\t\n\n for (int y = 0; y < height; y++) {\n\t\t\tfor (int x = 0; x < width; x++) {\n\t\t\t\tfloat gt = gtdisp.Pixel(x, y, 0);\n\t\t\t\tif (gt == INFINITY) // unknown\n\t\t\t\t\tcontinue;\n\t\t\t\tfloat d = scale * disp.Pixel(x / scale, y / scale, 0);\n\t int valid = (d != INFINITY);\n\t\t\t\tif (valid) {\n\t\t\t\t\tfloat maxd = scale * maxdisp; // max disp range\n\t\t d = __max(0, __min(maxd, d)); // clip disps to max disp range\n\t\t\t\t}\n\t if (valid && rounddisp){\n\t\t\t\t\td = round(d);\n\t\t\t\t} \n\t\t\t\tfloat err = fabs(d - gt);\n \n\t if (mask.Pixel(x,y,0) == 0){ // no ground truth;\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\tif (mask.Pixel(x, y, 0) == 255){ // non-occluded region;\n\t\t\t\t\tn_noc++;\n\t\t\t\t\tif (valid) {\n\t\t\t\t\t\terr_noc += err;\n\t\t\t\t\t\terr2_noc += err*err;\n\n\t\t\t\t\t\tif (err > badthresh) {\n\t\t\t\t\t\t\tbad_noc++;\n\t\t\t\t\t\t}\n\t\t\t\t\t} \n\t\t\t\t\telse {// invalid (i.e. hole in sparse disp map)\n\t\t\t\t\t\tinvalid_noc++;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\tn_all++;\n\t\t\t\tif(valid){\n\t\t\t\t\terr_all += err;\n\t\t\t\t\terr2_all += err * err;\n\n\t\t\t\t\tif (err > badthresh){\n\t\t\t\t\t\tbad_all++;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse {// invalid (i.e. hole in sparse disp map)\n\t\t\t\t\tinvalid_all++;\n\t\t\t\t} \n\t\t\t}/*end of width x*/\n\t\t}/*end of height y*/\n \n\t\tfloat badpercent_all = (float)bad_all / (float)n_all;\n float avgErr_all = err_all / (float)(n_all - invalid_all); // CHANGED 10/14/2014 -- was: serr / n\n\t\tfloat badpercent_noc = (float) bad_noc / (float) n_noc;\n\t\tfloat avgErr_noc = err_noc /(float)(n_noc - invalid_noc);\n\t\tfloat rmse_all = sqrt(err2_all /(float)(n_all - invalid_all));\n\t\tfloat rmse_noc = sqrt(err2_noc /(float)(n_noc - invalid_noc));\n#if 0\n float invalidpercent_all = (float)invalid_all/(float)n_all;\n float totalbadpercent_all = (float)(bad_all+invalid_all)/(float)n_all;\n\t\tprintf(\"n_all = %d, n_noc = %d\\n\", n_all, n_noc);\n printf(\"%4.1f%(N-rate-all) %6.2f%(bad-%2.1f-all) %6.2f%(invalid-all) %6.2f%(totalBad-all) %6.2f(avgErr-all)\\n\", \n\t\t\t\t100.0*n_all/(width * height), 100.0*badpercent_all, badthresh, 100.0*invalidpercent_all, \n\t\t\t\t100.0*totalbadpercent_all, avgErr_all);\n float invalidpercent_noc = (float)invalid_noc / (float)n_noc;\n float totalbadpercent_noc = (float)(bad_noc+invalid_noc)/ (float)n_noc;\n printf(\"%4.1f%(N-rate-noc) %6.2f%(bad-%2.1f-noc) %6.2f%(invalid-noc) %6.2f%(totalBad-noc) %6.2f(avgErr-noc)\\n\", \n\t\t\t\t100.0*n_noc/(width * height), 100.0*badpercent_noc, badthresh, 100.0*invalidpercent_noc, \n\t\t\t\t100.0*totalbadpercent_noc, avgErr_noc);\n#endif\n\n\t\tstatistics[0] = badpercent_all;\n\t\tstatistics[1] = avgErr_all;\n\n\t\tstatistics[2] = badpercent_noc;\n\t\tstatistics[3] = avgErr_noc;\n\t\t\n\t\tstatistics[4] = rmse_all;\n\t\tstatistics[5] = rmse_noc;\n}\n\n// C++ code\ntypedef std::vector StringList;\nclass IMG_NAMES{\n\tprivate:\n\t\tStringList myvec;\n\tpublic:\n\t\t// constructor\n\t\tIMG_NAMES(){\n\t\t\tthis -> myvec = StringList(0);\n\t\t}\n\tvoid set_img_names(boost::python::list & ns){\n\t\tfor(int i = 0; i < len(ns); ++i){\n\t\t\tthis -> myvec.push_back(boost::python::extract(ns[i]));\n\t\t}\n\t}\n\n\tvoid show_img_names(){\n\t\tstd:: cout << \"Images :\\n\";\n\t\tfor(auto i: this-> myvec){\n\t\t\tstd::cout << i << \", \";\n\t\t}\n\t\tstd:: cout << \"\\n\";\n\t}\n\n\tStringList get_img_names(){\n\t\treturn this -> myvec;\n\t}\n};\n\n\nPyObject * eval_disp_mbv3_1_img(\n\t\tconst string & dispname, // your predicted disparity results dir;\n\t const string & disp_gt_trainingF_dir, \t// ground truth disparity dir to trainingF;\n\t\tfloat badthresh, // e.g., == 1.0 for Half resolution;\n\t\tconst int & ndisp, // max_disp in groud truth disparity;\n\t\tconst int & rounddisp,\n const std::string & img_name // 1 image to evaluate;\n\t\t){\n\n\t\tnpy_intp * dims = new npy_intp[1];\n\t\tdims[0] = 4;\n\t\tPyObject * errs_A = PyArray_SimpleNew(1, dims, NPY_FLOAT32);\n\t //std::cout << \"new PyObject errs_A, img_N = \" << img_N << std::endl;\n\t\tfloat * err_result = static_cast(PyArray_DATA(reinterpret_cast(errs_A)));\n\t\t\n\t\t//std::string dispname = disp_dir + img_name + \"/\" + disp_name; //\"disp0.pfm\"\n\t\tstd::string gtdispname = disp_gt_trainingF_dir + img_name + \"/disp0GT.pfm\";\n\t\t// updated for middlebury additional data, due to the lack of mask0nocc.png file;\n\t\tstd::string maskname = disp_gt_trainingF_dir + img_name + \"/mask0nocc.png\";\n\t\tbool readMask = true;\n\t\tif (!exists_file_test(maskname)){\n\t\t\tstd::cout << \"No mask0noc.png exists!\\n\";\n\t\t\treadMask = false;\n\t\t}\n\t\t\n\t\t//std::cout << \"maxdisp = \" << ndisp << \", \"<< dispname << \", \" << gtdispname << \", \" << maskname << \"\\n\";\n\n\t\tCFloatImage disp, gtdisp, gtdisp1;\n\t\tReadImageVerb(disp, dispname.c_str(), verbose);\n\t\tReadImageVerb(gtdisp, gtdispname.c_str(), verbose);\n\t\tCByteImage mask;\n\t\tif (readMask){\n\t\t\t//std::cout << \"read mask0noc.png!\\n\";\n\t\t\tReadImageVerb(mask, maskname.c_str(), verbose);\n\t\t}\n\n\t\tfloat statistics[4]= {-1.0, -1.0, -1.0, -1.0 };\n\t\tevaldisp(disp, gtdisp, mask, badthresh, ndisp, rounddisp, statistics);\n\t\terr_result[0] = statistics[0];\n\t\terr_result[1] = statistics[1];\n\t\terr_result[2] = statistics[2];\n\t\terr_result[3] = statistics[3];\n\t\t\n\t\t// release the memory\n\t\tdisp.DeAllocate();\n\t\tgtdisp.DeAllocate();\n\t\tgtdisp1.DeAllocate();\n\t\tif (maskname.c_str())\n\t\t\tmask.DeAllocate();\n\t\t//exit(0);\n\t\treturn errs_A;\n}\n\nPyObject * eval_disp_mbv3(\n\t\tconst string & disp_dir, // your prediction disparity results dir;\n\t const string & disp_gt_trainingF_dir, \t// ground truth disparity dir to trainingF;\n\t\tfloat badthresh, // e.g., == 1.0 for Half resolution;\n\t\tconst string & disp_name, // e.g., == \"_disp0\" or \"_disp0_post\", or \"_rf_disp0PKLS\", and so on;\n\t\tPyObject * ndisps, // max_disp for each groud truth disparity \n\t\tPyObject * rounddisps, // disparity is int or float, specified in calib.txt files;\n boost::python::list img_names // img_names to evaluate;\n\t\t){\n\n\t\t/*vector mb_v3_imgs_1 = {\n\t\t\t\"Adirondack\",\n\t\t\t\"ArtL\", \n\t\t\t\"Jadeplant\", \n\t\t\t\"Motorcycle\",\n\t\t\t\"MotorcycleE\", \n\t\t\t\"Piano\", \n\t\t\t\"PianoL\", \n\t\t\t\"Pipes\", \n\t\t\t\"Playroom\", \n\t\t\t\"Playtable\", \n\t\t \t\"PlaytableP\", \n\t\t\t\"Recycle\", \n\t\t\t\"Shelves\",\n\t\t\t\"Teddy\", \n\t\t\t\"Vintage\", \n\t\t};\n\t\t*/\n IMG_NAMES mb_v3; \n\t\tmb_v3.set_img_names(img_names);\n\t\t//mb_v3.show_img_names();\n\t\tvector mb_v3_imgs = mb_v3.get_img_names();\n\n PyArrayObject* ndispsA = reinterpret_cast(ndisps);\n PyArrayObject* rounddispsA = reinterpret_cast(rounddisps);\n\t\t//int32_t * p_ndisps = reinterpret_cast(PyArray_DATA(ndispsA));\n\t\tint * p_ndisps = reinterpret_cast(PyArray_DATA(ndispsA));\n\t\tint * p_rounddisps = reinterpret_cast(PyArray_DATA(rounddispsA));\n\n\t\tconst int img_N = mb_v3_imgs.size();\n\t\tnpy_intp * dims = new npy_intp[1];\n\t\tdims[0] = (4+2)*img_N;\n\t\tPyObject * errs_A = PyArray_SimpleNew(1, dims, NPY_FLOAT32);\n\t //std::cout << \"new PyObject errs_A, img_N = \" << img_N << std::endl;\n\t\tfloat * err_result = static_cast(PyArray_DATA(reinterpret_cast(errs_A)));\n\t\tfor (int i = 0; i < img_N; i++){\n\t\t\tstd::string dispname = disp_dir + mb_v3_imgs[i] + \"/\" + disp_name + \".pfm\";\n\t\t\t// check if a file exists or not;\n\t\t\t// > see: https://stackoverflow.com/questions/12774207/fastest-way-to-check-if-a-file-exist-using-standard-c-c11-c;\n\t\t if (!exists_file_test(dispname)){\n\t\t\t\t//std::cout << \"Not exist : \" << dispname << \", changed it to : \";\n\t\t\t\t\n\t\t\t\t//dispname = disp_dir + mb_v3_imgs[i] + \"_\" + disp_name;\n\t\t\t\t//Updated: delete \"_\" here;\n\t\t\t\tdispname = disp_dir + mb_v3_imgs[i] + disp_name + \".pfm\";\n\t\t\t\t//std::cout << dispname << \" \";\n\t\t\t\tif (!exists_file_test(dispname)){\n\t\t\t\t\tstd::cout << \"loading \" << dispname << \" ... but failed!\\n\"; \n\t\t\t\t}\n\t\t\t}\n \n\t\t\tstd::string gtdispname = disp_gt_trainingF_dir + mb_v3_imgs[i] + \"/disp0GT.pfm\";\n\t\t\tstd::string maskname = disp_gt_trainingF_dir + mb_v3_imgs[i] + \"/mask0nocc.png\";\n\t\t\tbool readMask = true;\n\t\t\t// updated for middlebury additional data, due to the lack of mask0nocc.png file;\n\t\t\tif (!exists_file_test(maskname)){\n\t\t\t std::cout << \"No mask0noc.png exists!\\n\";\n\t\t\t\treadMask = false;\n\t\t\t}\n\t int maxdisp = p_ndisps[i];\n\t int rounddisp = p_rounddisps[i];\n //std::cout << \"maxdisp = \" << maxdisp << \", rounddisp = \" << rounddisp << \", \"<< dispname << \", \" << gtdispname << \", \" << maskname << \"\\n\";\n\n\t CFloatImage disp, gtdisp, gtdisp1;\n\t ReadImageVerb(disp, dispname.c_str(), verbose);\n\t ReadImageVerb(gtdisp, gtdispname.c_str(), verbose);\n\t CByteImage mask;\n\t if (readMask){\n\t\t\t\t//std::cout << \"read mask0noc.png!\\n\";\n\t\t\t\tReadImageVerb(mask, maskname.c_str(), verbose);\n\t\t\t}\n\t \n //float statistics[4]= {-1.0, -1.0, -1.0, -1.0 };\n\t\t\t// updated for rmse metric on 2019/08/31;\n\t float statistics[4 + 2]= {-1.0, -1.0, -1.0, -1.0, -1.0, -1.0 };\n\t\t\tevaldisp(disp, gtdisp, mask, badthresh, maxdisp, rounddisp, statistics);\n#if 0\n\t\t\tprintf(\"Processing: %10s, %6.2f%(bad-%2.1f-all), %6.2f(avgErr-all), %6.2f%(bad-%2.1f-noc), %6.2f(avgErr-nov)\\n\", \n\t\t\t\t\tmb_v3_imgs[i].c_str(), statistics[0]*100.0, badthresh, \n\t\t\t\t\tstatistics[1], statistics[2]*100.0, badthresh, statistics[3]);\n#endif\n\t\t\terr_result[6*i] = statistics[0];\n\t\t\terr_result[6*i + 1] = statistics[1];\n\t\t\terr_result[6*i + 2] = statistics[2];\n\t\t\terr_result[6*i + 3] = statistics[3];\n\t\t\terr_result[6*i + 4] = statistics[4];\n\t\t\terr_result[6*i + 5] = statistics[5];\n \n\t\t\t// release the memory\n\t\t\tdisp.DeAllocate();\n\t\t\tgtdisp.DeAllocate();\n\t\t\tgtdisp1.DeAllocate();\n\t if (maskname.c_str())\n\t\t\t\tmask.DeAllocate();\n\n\t\t\t//exit(0);\n\t\t\n\t\t}/*end of each image*/\n\t\treturn errs_A;\n}\n\nBOOST_PYTHON_MODULE(libevaldisp_mbv3){\n\t/* for Boost <= version 1.63*/\n\t//numeric::array::set_module_and_type(\"numpy\", \"ndarray\");\n /* for Boost > version 1.63*/\n\t//np::initialize();\n#if BOOST_VERSION >= 106300 // >= 1.63.0\n\tnp::initialize();\n#else\n\tnp::array::set_module_and_type(\"numpy\", \"ndarray\");\n#endif\n\tdef(\"evaluate_mbv3\", eval_disp_mbv3);\n\tdef(\"evaluate_mbv3_1_img\", eval_disp_mbv3_1_img);\n\t/* Error: return-statement with a value, in function returning 'void' [-fpermissive]\n\t * #define NUMPY_IMPORT_ARRAY_RETVAL NULL\n\t * > See solution: https://github.com/numpy/numpy/issues/10486\n\t * > 1) Solution1: Okay, so the issue occurs only on py2 + py3c (the initialization function is nonstandard, and has py3 semantics). Solution appears to be to use `import_array1()`;\n\t * > 2) Solution2: Or call _import_array(), which allows you more control than just `return`;\n\t */ \n //import_array(); // work well for Python2.7;\n\timport_array1(); // work well for python3.7;\n\t//_import_array(); // work well for python3.7;\n}\n", "meta": {"hexsha": "a62d1ea6b1ece46724a487973c0a73a07c269665", "size": 14027, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cpp/MiddEval3/code/evaldisp_lib.cpp", "max_stars_repo_name": "ccj5351/DAFStereoNets", "max_stars_repo_head_hexsha": "66b720a4abbac9097a794eacef034bab641771d9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 19.0, "max_stars_repo_stars_event_min_datetime": "2020-10-16T02:21:13.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T10:49:47.000Z", "max_issues_repo_path": "src/cpp/MiddEval3/code/evaldisp_lib.cpp", "max_issues_repo_name": "ccj5351/DAFStereoNets", "max_issues_repo_head_hexsha": "66b720a4abbac9097a794eacef034bab641771d9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/cpp/MiddEval3/code/evaldisp_lib.cpp", "max_forks_repo_name": "ccj5351/DAFStereoNets", "max_forks_repo_head_hexsha": "66b720a4abbac9097a794eacef034bab641771d9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2020-10-21T06:29:34.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-07T05:02:42.000Z", "avg_line_length": 33.8, "max_line_length": 182, "alphanum_fraction": 0.629927996, "num_tokens": 4442, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.22062196280627974}} {"text": "/*\n Copyright (c) 2012,2013,2014 Matthew H. Reilly (kb1vc)\n All rights reserved.\n\n Redistribution and use in source and binary forms, with or without\n modification, are permitted provided that the following conditions are\n met:\n\n Redistributions of source code must retain the above copyright\n notice, this list of conditions and the following disclaimer.\n Redistributions in binary form must reproduce the above copyright\n notice, this list of conditions and the following disclaimer in\n the documentation and/or other materials provided with the\n distribution.\n\n THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR\n A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT\n LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,\n DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY\n THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n\n#include \"ReSampler.hxx\"\n#include \n#include \n#include \n#include \n#include \n#include \n\nstatic void bindump(char * fn, std::complex * buf, unsigned int num_elts, int append = 0) __attribute__ ((unused));\nstatic void bindump(char * fn, std::complex * buf, unsigned int num_elts, int append)\n{\n FILE * of;\n if(append != 0) {\n of = fopen(fn, \"a\");\n }\n else {\n of = fopen(fn, \"w\");\n }\n unsigned int i;\n for(i = 0; i < num_elts; i++) {\n fprintf(of, \"%d %g %g\\n\", i + (append * num_elts), buf[i].real(), buf[i].imag()); \n }\n fclose(of); \n}\n\nstatic void bindump_2(char * fn, std::complex * buf, unsigned int num_elts, unsigned int num_cols) __attribute__ ((unused));\nstatic void bindump_2(char * fn, std::complex * buf, unsigned int num_elts, unsigned int num_cols)\n{\n FILE * of;\n of = fopen(fn, \"w\");\n unsigned int i, j;\n for(i = 0; i < num_elts; i++) {\n fprintf(of, \"%d \", i);\n for(j = 0; j < num_cols; j++) {\n fprintf(of, \"%g %g \", buf[i * num_cols + j].real(), buf[i * num_cols + j].imag());\n }\n fprintf(of, \"\\n\");\n }\n fclose(of); \n}\n\nstatic unsigned int ipow(unsigned int a, unsigned int b)\n{\n unsigned int ret = 1;\n unsigned int i ;\n\n for(i = 0; i < b; i++) {\n ret *= a; \n }\n\n return ret; \n}\n\nSoDa::ReSampler::ReSampler(unsigned int interpolate_ratio,\n\t\t\t unsigned int decimate_ratio,\n\t\t\t unsigned int _inlen, \n\t\t\t unsigned int _filter_len)\n{\n // save the parameters\n inlen = _inlen;\n iM = interpolate_ratio;\n dN = decimate_ratio;\n filter_len = _filter_len; \n Q = filter_len;\n M = _inlen;\n\n\n // give preferences to convenient powers of two.\n // but let's search for the nearest solution that is\n // a multiple of 2^a * 3^b * 5^c where b and c are in the range 0..3\n // and a is in the range 1..16\n unsigned int N_guess, N_best, E_best;\n N_best = 2; \n E_best = 0x80000000;\n unsigned int i; \n for(i = 1; i <= 0xff; i++) {\n unsigned int a = i & 0xf;\n unsigned int b = (i >> 4) & 0x3;\n unsigned int c = (i >> 6) & 0x3; \n N_guess = ipow(2, a) * ipow(3, b) * ipow(5, c);\n if(N_guess >= (M + Q - 1)) {\n unsigned slop = N_guess - (M + Q - 1);\n if(slop < E_best) {\n\tN_best = N_guess;\n\tE_best = slop; \n }\n }\n }\n N = N_best; \n\n \n // now that we have N, we can back-calculate Q.\n Q = N - M;\n\n filter_len = Q; \n \n tail_index = M - (Q - 1);\n \n // create the buffers.\n // a copy of the input sequence\n inbuf = (std::complex *) fftwf_alloc_complex(N);\n // the transformed input\n in_fft = (std::complex *) fftwf_alloc_complex(N);\n\n // zero out the whole of the input buffer\n for(i = 0; i < N; i++) inbuf[i] = std::complex(0.0,0.0);\n \n // the filter bank is really iM interleaved filters\n // but we can't seem to make that work right now, so we're\n // backing off to a simpler way. \n c_filt = new std::complex*[iM];\n for(i = 0; i < iM; i++) {\n c_filt[i] = (std::complex *) fftwf_alloc_complex(N);\n }\n\n // the upsampled result\n filt_fft = (std::complex *) fftwf_alloc_complex(iM*N);\n // the inverse transform of the upsampled result\n interp_res = (std::complex *) fftwf_alloc_complex(iM*N);\n\n // the first plan is pretty simple. \n in_fft_plan = fftwf_plan_dft_1d(N,\n\t\t\t\t (fftwf_complex *) inbuf,\n\t\t\t\t (fftwf_complex *) in_fft,\n\t\t\t\t FFTW_FORWARD, FFTW_ESTIMATE);\n\n\n // now create the inverting plans\n int n[1];\n n[0] = N; \n mid_ifft_plan = fftwf_plan_many_dft(1, n, iM, \n\t\t\t\t (fftwf_complex *) filt_fft, NULL, iM, 1,\n\t\t\t\t (fftwf_complex *) interp_res, NULL, iM, 1,\n\t\t\t\t FFTW_BACKWARD, FFTW_ESTIMATE);\n \n\n // now create the polyphase filter banks.\n CreateFilter(filter_len);\n\n transform_gain = 1.0 / ((float) N); \n}\n\nvoid SoDa::ReSampler::CreateFilter(unsigned int filter_len)\n{\n // filter_len must be odd\n int fN = filter_len;\n if((fN & 1) == 0) fN--; \n \n unsigned int i, j, k;\n\n // Use the plan of fischer in \"The mkfilter Digital Filter Generation Program\" mkshape...\n // This is borrowed quite heavily and with tremendous gratitude from \n // http://www-users.cs.york.ac.uk/~fisher Thanks, Prof. Fisher...\n float alpha, beta;\n float cutdown = ((float) ((iM > dN) ? iM : dN));\n beta = 1.0 / (2.2 * cutdown); // This is the transition freq -- a little shy of 0.5/cutdown\n alpha = 0.1; // this is a shape factor\n\n // create a frequency domain image of the filter.\n float f1 = (1.0 - alpha) * beta;\n float f2 = (1.0 + alpha) * beta;\n float tau = 0.5 / alpha; \n\n // now create the filter banks.\n std::complex * filt_FD = (std::complex*) fftwf_alloc_complex(N); \n std::complex * filt_td = (std::complex*) fftwf_alloc_complex(N); \n std::complex * filt_td2 = (std::complex*) fftwf_alloc_complex(N); \n\n float f;\n float f_incr = 1.0 / ((float) (N));\n unsigned int hlim = (N);\n float gain_corr = 1.0 / ((float) hlim);\n // gain_corr = gain_corr * gain_corr; \n for(i = 0, f = 0.0; i <= hlim / 2; i++, f += f_incr) {\n if(f <= f1) {\n filt_FD[i] = std::complex(1.0, 0.0);\n }\n else if (f <= f2) {\n float h = 0.5 * (1.0 + cos(M_PI * tau * (f - f1) / beta));\n filt_FD[i] = std::complex(h, 0.0); \n }\n else {\n filt_FD[i] = std::complex(0.0, 0.0); \n }\n\n filt_FD[i] *= tau * gain_corr;\n\n if(i != 0) filt_FD[hlim - i] = filt_FD[i]; \n }\n\n // then ifft it to time domain\n fftwf_plan filt_ifft_plan = fftwf_plan_dft_1d(hlim,\n\t\t\t\t\t\t(fftwf_complex *) filt_FD,\n\t\t\t\t\t\t(fftwf_complex *) filt_td,\n\t\t\t\t\t\tFFTW_BACKWARD, FFTW_ESTIMATE);\n fftwf_execute(filt_ifft_plan);\n\n float sum = 0.0;\n for(i = 0; i < hlim; i++) sum += filt_td[i].real();\n\n float sgain = 1.0 / sum; \n for(i = 0; i < hlim; i++) filt_td[i] *= sgain; \n \n fftwf_destroy_plan(filt_ifft_plan); \n\n // now truncate the filter\n // We've got an image that is symmetric around 0.\n for(i = 0; i < N; i++) {\n filt_td2[i] = std::complex(0.0,0.0);\n }\n\n // shift it up to be symmetric around filt_len / 2\n for(i = 0, j = N - (filter_len/2); j < N; i++, j++) {\n filt_td2[i] = filt_td[j]; \n }\n for(j = 0; j < filter_len / 2; j++, i++) {\n filt_td2[i] = filt_td[j]; \n }\n\n // bindump(\"filt_td2.dat\", filt_td2, N);\n \n // and fft back to frequency domain for each of the iM filters.\n for(i = 0; i < iM; i++) {\n // grab the slice of the filter that we need to transform.\n\n for(j = i, k = 0; j < filter_len; k++, j += iM) {\n filt_td[k] = filt_td2[j]; \n }\n for(; k < N; k++) filt_td[k] = std::complex(0.0,0.0);\n fftwf_plan filt_fft_plan = fftwf_plan_dft_1d(N,\n\t\t\t\t\t\t (fftwf_complex *) filt_td,\n\t\t\t\t\t\t (fftwf_complex *) c_filt[i],\n\t\t\t\t\t\t FFTW_FORWARD, FFTW_ESTIMATE);\n fftwf_execute(filt_fft_plan);\n fftwf_destroy_plan(filt_fft_plan); \n }\n\n // now free up buffers\n fftwf_free(filt_td2);\n fftwf_free(filt_td);\n fftwf_free(filt_FD); \n}\n\nunsigned int SoDa::ReSampler::apply(std::complex * in,\n\t\t\t\t std::complex * out,\n\t\t\t\t float gain)\n{\n unsigned int i, j;\n // copy the input buffer\n memcpy(&(inbuf[Q-1]), in, sizeof(std::complex) * M);\n \n // transform the input\n fftwf_execute(in_fft_plan); // , (fftwf_complex *) in, (fftwf_complex *) in_fft);\n\n // now multiply by the M filters\n for(i = 0; i < N; i++) {\n for(j = 0; j < iM; j++) {\n filt_fft[j + i*iM] = in_fft[i] * c_filt[j][i]; \n }\n }\n // do M inverse transforms on the filter result ...\n // to produce M interleaved time domain vectors.\n fftwf_execute(mid_ifft_plan);\n\t \n // save the last bits of the input buffer to the Q-1 side of the FFT input vector\n // Do this now incase inbuf and outbuf are the same buffers. (we may\n // over-write the outbuf with the downsample at the bottom.... )\n memcpy(inbuf, &(in[tail_index]), sizeof(std::complex) * (Q-1));\n\n // now build the output buffer. \n\n // now downsample by a factor of dN.\n int idx = 0; \n unsigned int mctr = 0;\n unsigned int out_lim = (M * iM) / dN;\n std::complex * ir_clean = &(interp_res[(Q-1)]); \n // std::cerr << \" iM, dN, M, N, out_lim = \"\n // \t << iM << \" \" << dN << \" \" << M << \" \" << N << \" \" << out_lim << std::endl; \n for(j = 0; j < out_lim; j++) {\n out[j] = ir_clean[mctr + idx * iM] * gain * transform_gain * ((float) iM);\n mctr += dN;\n while(mctr >= iM) {\n mctr -= iM;\n idx += 1; \n }\n }\n // bindump(\"out.dat\", out, j); \n\n return 0; \n}\n\n\n\nunsigned int SoDa::ReSampler::apply(float * in,\n\t\t\t\t float * out,\n\t\t\t\t float gain)\n{\n unsigned int i, j;\n \n for(i = 0; i < M; i++) {\n inbuf[i+(Q-1)] = std::complex(in[i], 0.0);\n }\n\n // transform the input\n fftwf_execute(in_fft_plan); // , (fftwf_complex *) in, (fftwf_complex *) in_fft);\n \n // now multiply by the M filters\n for(i = 0; i < N; i++) {\n for(j = 0; j < iM; j++) {\n filt_fft[j + i*iM] = in_fft[i] * c_filt[j][i]; \n }\n }\n // do M inverse transforms on the filter result ...\n // to produce M interleaved time domain vectors.\n fftwf_execute(mid_ifft_plan);\n\t \n // save the last bits of the input buffer to the Q-1 side of the FFT input vector\n // Do this now incase inbuf and outbuf are the same buffers. (we may\n // over-write the outbuf with the downsample at the bottom.... )\n for(i = 0; i < (Q-1); i++) {\n inbuf[i] = std::complex(in[i + tail_index], 0.0);\n }\n\n // now build the output buffer. \n\n // now downsample by a factor of dN.\n int idx = 0; \n unsigned int mctr = 0;\n unsigned int out_lim = (M * iM) / dN;\n std::complex * ir_clean = &(interp_res[(Q-1)]); \n for(j = 0; j < out_lim; j++) {\n out[j] = ir_clean[mctr + idx * iM].real() * gain * transform_gain * ((float) iM);\n mctr += dN;\n while(mctr >= iM) {\n mctr -= iM;\n idx += 1; \n }\n }\n\n return 0; \n}\n", "meta": {"hexsha": "5b1365dab3d166928f02f5d69e863a6e11c204d8", "size": 11211, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "src/ReSampler.cxx", "max_stars_repo_name": "kb1vc/SoDaRadio", "max_stars_repo_head_hexsha": "0a41fa3d795b1c93795ad62ad17bf2de5f60a752", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 14.0, "max_stars_repo_stars_event_min_datetime": "2017-10-27T16:01:05.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-16T08:12:42.000Z", "max_issues_repo_path": "src/ReSampler.cxx", "max_issues_repo_name": "dd0vs/SoDaRadio", "max_issues_repo_head_hexsha": "0a41fa3d795b1c93795ad62ad17bf2de5f60a752", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": 11.0, "max_issues_repo_issues_event_min_datetime": "2017-09-16T03:13:11.000Z", "max_issues_repo_issues_event_max_datetime": "2020-12-11T09:11:35.000Z", "max_forks_repo_path": "src/ReSampler.cxx", "max_forks_repo_name": "dd0vs/SoDaRadio", "max_forks_repo_head_hexsha": "0a41fa3d795b1c93795ad62ad17bf2de5f60a752", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 6.0, "max_forks_repo_forks_event_min_datetime": "2017-09-13T12:47:43.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-02T20:54:25.000Z", "avg_line_length": 30.3, "max_line_length": 131, "alphanum_fraction": 0.6151993578, "num_tokens": 3578, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.22062196280627974}} {"text": "// *****************************************************************************\n/*!\n \\file src/RNG/RNGSSE.hpp\n \\copyright 2012-2015 J. Bakosi,\n 2016-2018 Los Alamos National Security, LLC.,\n 2019 Triad National Security, LLC.\n All rights reserved. See the LICENSE file for details.\n \\brief Interface to RNGSSE random number generators\n \\details Interface to RNGSSE random number generators\n*/\n// *****************************************************************************\n#ifndef RNGSSE_h\n#define RNGSSE_h\n\n#include \n#include \n\n#include \"NoWarning/beta_distribution.hpp\"\n#include \n\n#include \"Make_unique.hpp\"\n#include \"Exception.hpp\"\n#include \"Macro.hpp\"\n#include \"Options/RNGSSESeqLen.hpp\"\n\nnamespace tk {\n\n//! RNGSSE-based random number generator used polymorphically with tk::RNG\ntemplate< class State, typename SeqNumType, unsigned int (*Generate)(State*) >\nclass RNGSSE {\n\n private:\n using InitFn = void (*)( State*, SeqNumType );\n using ncomp_t = kw::ncomp::info::expect::type; \n\n //! Adaptor to use a std distribution with the RNGSSE generator\n //! \\see C++ concepts: UniformRandomNumberGenerator\n struct Adaptor {\n using result_type = unsigned int;\n Adaptor( const std::unique_ptr< State[] >& s, int t ) : str(s), tid(t) {}\n static constexpr result_type min() { return 0u; }\n static constexpr result_type max() { return 4294967295u; }\n result_type operator()()\n { return Generate( &str[ static_cast(tid) ] ); }\n const std::unique_ptr< State[] >& str;\n int tid;\n };\n\n public:\n //! Constructor\n //! \\param[in] n Initialize RNG using this many independent streams\n //! \\param[in] fnShort RNG initializer function for short streams\n //! \\param[in] seqlen Sequence length enum: short, medium or long\n //! \\param[in] fnLong RNG initializer function for long streams\n //! \\param[in] fnMed RNG initializer function for medium streams\n explicit RNGSSE( SeqNumType n,\n InitFn fnShort,\n ctr::RNGSSESeqLenType seqlen = ctr::RNGSSESeqLenType::SHORT,\n InitFn fnLong = nullptr,\n InitFn fnMed = nullptr) :\n m_nthreads( n ),\n m_init( seqlen == ctr::RNGSSESeqLenType::LONG ? fnLong :\n seqlen == ctr::RNGSSESeqLenType::MEDIUM ? fnMed : fnShort ),\n m_stream()\n {\n Assert( m_init != nullptr, \"nullptr passed to RNGSSE constructor\" );\n Assert( n > 0, \"Need at least one thread\" );\n // Allocate array of stream-pointers for threads\n m_stream = tk::make_unique< State[] >( n );\n // Initialize thread-streams\n for (SeqNumType i=0; i(\n Generate( &m_stream[ static_cast(tid) ] ) )\n / 4294967296.0;\n }\n\n //! Gaussian RNG: Generate Gaussian random numbers\n //! \\param[in] tid Thread (or more precisely stream) ID\n //! \\param[in] num Number of RNGs to generate\n //! \\param[in,out] r Pointer to memory to write the random numbers to\n //! \\details Generating Gaussian random numbers is implemented via an\n //! adaptor, modeling std::UniformRandomNumberGenerator, outsourcing the\n //! transformation of uniform random numbers to Gaussian ones, to the\n //! standard library. The adaptor is instantiated here because a standard\n //! distribution, such as e.g., std::normal_distribution, generates\n //! numbers using operator() with no arguments, thus the RNG state and the\n //! thread ID (this latter only known here) must be stored in the adaptor\n //! functor's state. Even though creating the adaptor seems like a\n //! potentially costly operation for every call, using the standard\n //! library implementation is still faster than a hand-coded\n //! implementation of the Box-Muller algorithm. Note that libc++ uses a\n //! cache, as Box-Muller, implemented using the polar algorithm generates\n //! 2 Gaussian numbers for each pair of uniform ones, caching every 2nd.\n void gaussian( int tid, ncomp_t num, double* r ) const {\n Adaptor generator( m_stream, tid );\n std::normal_distribution<> gauss_dist( 0.0, 1.0 );\n for (ncomp_t i=0; i beta_dist( p, q );\n for (ncomp_t i=0; i gamma_dist( a, b );\n for (ncomp_t i=0; i( x.m_nthreads );\n for (SeqNumType i=0; i( x.m_nthreads );\n for (SeqNumType i=0; i m_stream; //!< Random number stream for threads\n};\n\n} // tk::\n\n#endif // RNGSSE_h\n", "meta": {"hexsha": "8a87ba9a3b4a40948b97bd8e6d939f73e6ed875a", "size": 9482, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/RNG/RNGSSE.hpp", "max_stars_repo_name": "franjgonzalez/Quinoa", "max_stars_repo_head_hexsha": "411eb8815e92618c563881b784e287e2dd916f89", "max_stars_repo_licenses": ["RSA-MD"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/RNG/RNGSSE.hpp", "max_issues_repo_name": "franjgonzalez/Quinoa", "max_issues_repo_head_hexsha": "411eb8815e92618c563881b784e287e2dd916f89", "max_issues_repo_licenses": ["RSA-MD"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/RNG/RNGSSE.hpp", "max_forks_repo_name": "franjgonzalez/Quinoa", "max_forks_repo_head_hexsha": "411eb8815e92618c563881b784e287e2dd916f89", "max_forks_repo_licenses": ["RSA-MD"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.495412844, "max_line_length": 81, "alphanum_fraction": 0.6309850243, "num_tokens": 2371, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.4378234991142019, "lm_q1q2_score": 0.22062196280627974}} {"text": "////////////////////////////////////////////////////////////////////////////\r\n//! \\file robot.cpp\r\n//! \\date February 2018\r\n//! \\author Eric Barnett\r\n//! Marc-Antoine Lacasse\r\n//!\r\n//! Copyright (C) 2018 Eric Barnett \r\n//! Copyright (C) 2018 Marc-Antoine Lacasse \r\n//! Copyright (C) 2018 Clément Gosselin \r\n//!\r\n//! \\brief Robot-specific kinematics and dynamics\r\n//!\r\n//! This file is part of BA, a bisection algorithm for time-optimal trajectory\r\n//! planning\r\n//!\r\n//! This Source Code Form is subject to the terms of the Berkeley Software\r\n//! Distribution (BSD) 3-clause license . If a copy of the BSD license was not\r\n//! distributed with this file, you can obtain one at\r\n//! https://opensource.org/licenses/BSD-3-Clause.\r\n////////////////////////////////////////////////////////////////////////////////\r\n\r\n////////////////////////////////////////////////////////////////////////////////\r\n// Includes\r\n////////////////////////////////////////////////////////////////////////////////\r\n#include \"util.h\"\r\n#include \"robot.h\"\r\n#include \"config.h\"\r\n#include \r\n#include // for linear Algebra in fwdKinKuka()\r\n#ifndef isWin\r\n #include \r\n #include \"string.h\"\r\n#endif\r\n\r\nusing namespace Eigen;\r\n\r\nnamespace BATOTP{\r\n\r\n//////////////////////////////////////////////////////////////////////////////\r\n//!\r\n//! \\brief set_robotType\r\n//!\r\n//! Set the robot type (define new robots here)\r\n//!\r\n//////////////////////////////////////////////////////////////////////////////\r\nint Robot::call_set_robotType(const std::string& robotTypeStr)\r\n{\r\n return set_robotType(robotTypeStr);\r\n}\r\n\r\nint Robot::set_robotType(const std::string &robotTypeStr)\r\n{\r\n _robotType=0;\r\n _robotTypeStr=robotTypeStr;\r\n if(_robotTypeStr==\"KUKA\") _robotType=KUKA;\r\n if(_robotTypeStr==\"UR\") _robotType=UR;\r\n if(_robotTypeStr==\"RR\") _robotType=RR;\r\n if(_robotTypeStr==\"CSPR3DOF\") _robotType=CSPR3DOF;\r\n if(_robotTypeStr==\"GENJNT\") _robotType=GENJNT;\r\n\r\n return(_robotType);\r\n}\r\n\r\n//! FORWARD KINEMATICS\r\n\r\n//////////////////////////////////////////////////////////////////////////////\r\n//!\r\n//! \\brief fwdKin\r\n//!\r\n//! Forward displacement problem\r\n//!\r\n//////////////////////////////////////////////////////////////////////////////\r\nint Robot::call_fwdKin(const std::vector >& theta,\r\n std::vector> &cart)\r\n{return(fwdKin(theta,cart));}\r\nint Robot::fwdKin(const std::vector> &theta,\r\n std::vector> &cart)\r\n{\r\n\r\n switch(_robotType)\r\n {\r\n case KUKA: //KUKA-LWR IV+\r\n fwdKinKuka(theta,cart);\r\n break;\r\n case RR: // planar RR positioning manipulator\r\n fwdKinRR(theta,cart);\r\n break;\r\n default:\r\n printf (\"No forward Kinematics model provided for robotType=%s.\\n\",\r\n _robotTypeStr.c_str());\r\n return -1;\r\n assert(!\"Unhandled case\"); // Always assert unhandled cases\r\n break;\r\n }\r\n return 0;\r\n}\r\n\r\n//////////////////////////////////////////////////////////////////////////////\r\n//!\r\n//! \\brief fwdKinKuka\r\n//!\r\n//! Forward displacement problem for the Kuka LWR IV+\r\n//!\r\n//////////////////////////////////////////////////////////////////////////////\r\nint Robot::fwdKinKuka(const std::vector >& theta,\r\n std::vector> &cart)\r\n{\r\n int i;\r\n Matrix3d Q12,Q34,Q567,Q1234,Q;\r\n //op=[0; 0; .282]; .204+78 Robotiq hand with Robotiq fingers (center of pads)\r\n //op=[0; 0; .3145]; Robotiq hand with Robotiq fingers (tips of pads)\r\n Vector3d ToolPointVector(0,-.08,.545); // .468+.078 Robotiq hand with long fingers\r\n\r\n int nPts=(int)theta[0].size();\r\n\r\n double a0=.3105,a1=.4,a2=.39; // link lengths\r\n\r\n double t1,t2,t3,t4,t5,t6,t7;\r\n double c1,c2,c3,c4,c5,c6,c7;\r\n double s1,s2,s3,s4,s5,s6,s7;\r\n double x1,y1,z1;\r\n double x2,y2,z2;\r\n double x3,y3,z3;\r\n\r\n cart.resize(3);\r\n for(i=0;i<3;i++) cart[i].resize(nPts);\r\n\r\n for(i=0; i >& theta,\r\n std::vector> &cart)\r\n{\r\n int i,nPts=(int)theta[0].size();\r\n double a1=.4,a2=.6;\r\n double th1,th2;\r\n cart.resize(3);\r\n for(i=0;i<3;i++) cart[i].resize(nPts);\r\n\r\n for(i=0;i> &theta,\r\n const std::vector> &cart)\r\n{return(invKin(theta,cart));}\r\nint Robot::invKin(std::vector> &theta,\r\n const std::vector> &cart)\r\n{\r\n switch(_robotType)\r\n {\r\n case CSPR3DOF:\r\n invKinCSPR3DOF(theta,cart);\r\n break;\r\n default:\r\n printf (\"No inverse Kinematics model provided for robotType=%s.\\n\",\r\n _robotTypeStr.c_str());\r\n assert(!\"Unhandled case\"); // Always assert unhandled cases\r\n return -1;\r\n break;\r\n }\r\n return 0;\r\n}\r\n\r\n//////////////////////////////////////////////////////////////////////////////\r\n//!\r\n//! \\brief invKinCSPR3DOF\r\n//!\r\n//! Inverse kinematics for the large 3-DOF point-mass CSPR in PLT-00370:\r\n//! finds cable lengths (theta) from Cartesian EE positions (cart) and\r\n//! cable attacment points (pmat)\r\n//!\r\n//////////////////////////////////////////////////////////////////////////////\r\nint Robot::invKinCSPR3DOF(std::vector> &theta,\r\n const std::vector> &cart)\r\n{\r\n\r\n int i;\r\n int nDim=(int)cart.size();\r\n\r\n int nPts=(int)cart[0].size();\r\n theta.resize(3);\r\n\r\n for(i=0;i rv1,rv2,rv3;\r\n\r\n if(_pmat.size()==0)\r\n {\r\n findCSPR3DOFpmat(_pmat);\r\n }\r\n\r\n for(i=0;i> &pmat)\r\n{\r\n int i,j;\r\n std::array cible1={1.0941, -4.9074, 2.5542};\r\n std::array delta1={-0.765, 0.112, 3.74};\r\n std::array cible3={0.2098, 5.3409, 2.6236};\r\n std::array delta2={0.43, 0.125, 3.615};\r\n\r\n std::array p1=cible1+delta1;\r\n std::array p2=cible3+delta2;\r\n std::array p3={-5.9751, 0.1399, 6.1543};\r\n\r\n pmat.resize(3); for(i=0;i<3;i++) pmat[i].resize(3);\r\n\r\n std::array ind={1,0,2};\r\n for(i=0;i<3;i++)\r\n {\r\n int it=ind[i];\r\n pmat[i][0]=-p1[it];\r\n pmat[i][1]=-p2[it];\r\n pmat[i][2]=-p3[it];\r\n }\r\n\r\n std::array centroide;\r\n for(i=0;i<3;i++) centroide[i]=1/3.0*(pmat[i][0]+pmat[i][1]+pmat[i][2]);\r\n\r\n for(i=0;i<3;i++)\r\n {\r\n for(j=0;j<3;j++) pmat[i][j]-=centroide[i];\r\n }\r\n return 0;\r\n}\r\n\r\n//! DYNAMICS\r\n\r\n//////////////////////////////////////////////////////////////////////////////\r\n//!\r\n//! \\brief dynSerial\r\n//!\r\n//! Dynamics model for a serial robot\r\n//!\r\n//////////////////////////////////////////////////////////////////////////////\r\nint Robot::call_dynSerial(std::vector> &a1,\r\n std::vector> &a2,\r\n std::vector> &a3,\r\n std::vector> &a4,\r\n const std::vector> &theta,\r\n const std::vector> &thetaD,\r\n const std::vector> &thetaD2)\r\n{return(dynSerial(a1,a2,a3,a4,theta,thetaD,thetaD2));}\r\nint Robot::dynSerial(std::vector> &a1,\r\n std::vector> &a2,\r\n std::vector> &a3,\r\n std::vector> &a4,\r\n const std::vector> &theta,\r\n const std::vector> &thetaD,\r\n const std::vector> &thetaD2)\r\n{\r\n switch(_robotType)\r\n {\r\n case RR:\r\n dynRR(a1,a2,a3,a4,theta,thetaD,thetaD2);\r\n break;\r\n default:\r\n printf (\"No dynamics model provided for serial robotType=%s.\\n\",\r\n _robotTypeStr.c_str());\r\n assert(!\"Unhandled case\"); // Always assert unhandled cases\r\n return -1;\r\n break;\r\n }\r\n return 0;\r\n}\r\n\r\n//////////////////////////////////////////////////////////////////////////////\r\n//!\r\n//! \\brief dynRR\r\n//!\r\n//! Dynamics model for an RR planar robot\r\n//! - links are approximated as point masses\r\n//! - tau = a1*sddot + a2*sdot^2 + a3*sdot + a4\r\n//! - thetaD and thetaD2 are partial derivatives wrt s\r\n//!\r\n//! \\param\r\n//! \\return\r\n//!\r\n//////////////////////////////////////////////////////////////////////////////\r\nint Robot::dynRR(\r\n std::vector> &a1,\r\n std::vector> &a2,\r\n std::vector> &a3,\r\n std::vector> &a4,\r\n const std::vector> &theta,\r\n const std::vector> &thetaD,\r\n const std::vector> &thetaD2)\r\n{\r\n int i;\r\n int nPts=(int)theta[0].size();\r\n double A1=.4,A2=.6,m1=4,m2=8;\r\n double th1,th2,dth1,dth2,ddth1,ddth2,c1,c2,c12,ccFact;\r\n double A11,A12,A22;\r\n for(i=0;i<2;i++)\r\n {\r\n a1[i].resize(nPts);\r\n a2[i].resize(nPts);\r\n a3[i].resize(nPts);\r\n a4[i].resize(nPts);\r\n }\r\n\r\n for(i=0;i> &a1,\r\n std::vector> &a2,\r\n std::vector> &a3,\r\n std::vector> &a4,\r\n const std::vector> &cart,\r\n const std::vector> &cartD,\r\n const std::vector> &cartD2)\r\n{\r\n return dynParallel(a1,a2,a3,a4,cart,cartD,cartD2);\r\n}\r\n\r\nint Robot::dynParallel(std::vector> &a1,\r\n std::vector> &a2,\r\n std::vector> &a3,\r\n std::vector> &a4,\r\n const std::vector> &cart,\r\n const std::vector> &cartD,\r\n const std::vector> &cartD2)\r\n{\r\n (void)cart;\r\n\r\n switch(_robotType)\r\n {\r\n case CSPR3DOF: //CSPR3DOF\r\n dynCSPR3DOF(a1,a2,a3,a4,cartD,cartD2);\r\n break;\r\n default:\r\n printf (\"No dynamics model provided for parallel robotType=%s.\\n\",\r\n _robotTypeStr.c_str());\r\n assert(!\"Unhandled case\"); // Always assert unhandled cases\r\n return -1;\r\n break;\r\n }\r\n return 0;\r\n}\r\n\r\n//////////////////////////////////////////////////////////////////////////////\r\n//!\r\n//! \\brief dynCSPR3DOF\r\n//!\r\n//! Dynamics model for the large 3-DOF point-mass CSPR in PLT-00370\r\n//! - A*tau = a1*sddot + a2*sdot^2 + a3*sdot + a4\r\n//! - cartD and cartD2 are partial derivatives wrt s\r\n//!\r\n//////////////////////////////////////////////////////////////////////////////\r\nint Robot::dynCSPR3DOF(\r\n std::vector> &a1,\r\n std::vector> &a2,\r\n std::vector> &a3,\r\n std::vector> &a4,\r\n const std::vector> &cartD,\r\n const std::vector> &cartD2)\r\n{\r\n int i,j;\r\n int nCart=3;\r\n int nPts =(int)cartD[0].size();\r\n\r\n for(i=0;i &theta,\r\n const std::vector &cart,\r\n std::vector> &A)\r\n{return(setA(theta,cart,A));}\r\n\r\nint Robot::setA(const std::vector &theta,\r\n const std::vector &cart,\r\n std::vector> &A)\r\n{\r\n switch(_robotType)\r\n {\r\n case CSPR3DOF:\r\n for(int i=0;i<3;i++)\r\n {\r\n for(int j=0;j<3;j++)\r\n {\r\n A[i][j]=(cart[i]-_pmat[i][j])/theta[j];\r\n }\r\n }\r\n break;\r\n\r\n default:\r\n printf (\"isParallel=True and no code was provided to find the A matrix\");\r\n printf(\"for robotType=%s.\\n\",_robotTypeStr.c_str());\r\n assert(!\"Unhandled case\"); // Always assert unhandled cases\r\n return -1;\r\n break;\r\n }\r\n return 0;\r\n}\r\n\r\n}\r\n", "meta": {"hexsha": "635e7a3fc7b6d92973ad5ef609757503a75da766", "size": 17050, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "batotp/robot.cpp", "max_stars_repo_name": "lpdon/batotp", "max_stars_repo_head_hexsha": "f6b807dc10a8a7756adeb48223f426614af5aed6", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 15.0, "max_stars_repo_stars_event_min_datetime": "2021-05-17T02:58:25.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T03:30:18.000Z", "max_issues_repo_path": "batotp/robot.cpp", "max_issues_repo_name": "lpdon/batotp", "max_issues_repo_head_hexsha": "f6b807dc10a8a7756adeb48223f426614af5aed6", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2021-08-02T07:13:55.000Z", "max_issues_repo_issues_event_max_datetime": "2021-08-02T07:31:29.000Z", "max_forks_repo_path": "batotp/robot.cpp", "max_forks_repo_name": "lpdon/batotp", "max_forks_repo_head_hexsha": "f6b807dc10a8a7756adeb48223f426614af5aed6", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2021-08-01T19:30:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-21T10:44:36.000Z", "avg_line_length": 30.3921568627, "max_line_length": 86, "alphanum_fraction": 0.4766568915, "num_tokens": 4940, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.3923368443773709, "lm_q1q2_score": 0.2205625543406463}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\r\n\r\n// Copyright (c) 2007-2012 Barend Gehrels, Amsterdam, the Netherlands.\r\n\r\n// Use, modification and distribution is subject to the Boost Software License,\r\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\r\n// http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n#ifndef BOOST_GEOMETRY_ALGORITHMS_DETAIL_OVERLAY_CLIP_LINESTRING_HPP\r\n#define BOOST_GEOMETRY_ALGORITHMS_DETAIL_OVERLAY_CLIP_LINESTRING_HPP\r\n\r\n#include \r\n\r\n#include \r\n#include \r\n\r\n#include \r\n\r\n#include \r\n#include \r\n\r\nnamespace boost { namespace geometry\r\n{\r\n\r\nnamespace strategy { namespace intersection\r\n{\r\n\r\n/*!\r\n \\brief Strategy: line clipping algorithm after Liang Barsky\r\n \\ingroup overlay\r\n \\details The Liang-Barsky line clipping algorithm clips a line with a clipping box.\r\n It is slightly adapted in the sense that it returns which points are clipped\r\n \\tparam B input box type of clipping box\r\n \\tparam P input/output point-type of segments to be clipped\r\n \\note The algorithm is currently only implemented for 2D Cartesian points\r\n \\note Though it is implemented in namespace strategy, and theoretically another\r\n strategy could be used, it is not (yet) updated to the general strategy concepts,\r\n and not (yet) splitted into a file in folder strategies\r\n \\author Barend Gehrels, and the following recourses\r\n - A tutorial: http://www.skytopia.com/project/articles/compsci/clipping.html\r\n - a German applet (link broken): http://ls7-www.cs.uni-dortmund.de/students/projectgroups/acit/lineclip.shtml\r\n*/\r\ntemplate\r\nclass liang_barsky\r\n{\r\nprivate:\r\n typedef model::referring_segment segment_type;\r\n\r\n template \r\n inline bool check_edge(T const& p, T const& q, T& t1, T& t2) const\r\n {\r\n bool visible = true;\r\n\r\n if(p < 0)\r\n {\r\n T const r = q / p;\r\n if (r > t2)\r\n visible = false;\r\n else if (r > t1)\r\n t1 = r;\r\n }\r\n else if(p > 0)\r\n {\r\n T const r = q / p;\r\n if (r < t1)\r\n visible = false;\r\n else if (r < t2)\r\n t2 = r;\r\n }\r\n else\r\n {\r\n if (q < 0)\r\n visible = false;\r\n }\r\n\r\n return visible;\r\n }\r\n\r\npublic:\r\n\r\n inline bool clip_segment(Box const& b, segment_type& s, bool& sp1_clipped, bool& sp2_clipped) const\r\n {\r\n typedef typename select_coordinate_type::type coordinate_type;\r\n\r\n coordinate_type t1 = 0;\r\n coordinate_type t2 = 1;\r\n\r\n coordinate_type const dx = get<1, 0>(s) - get<0, 0>(s);\r\n coordinate_type const dy = get<1, 1>(s) - get<0, 1>(s);\r\n\r\n coordinate_type const p1 = -dx;\r\n coordinate_type const p2 = dx;\r\n coordinate_type const p3 = -dy;\r\n coordinate_type const p4 = dy;\r\n\r\n coordinate_type const q1 = get<0, 0>(s) - get(b);\r\n coordinate_type const q2 = get(b) - get<0, 0>(s);\r\n coordinate_type const q3 = get<0, 1>(s) - get(b);\r\n coordinate_type const q4 = get(b) - get<0, 1>(s);\r\n\r\n if (check_edge(p1, q1, t1, t2) // left\r\n && check_edge(p2, q2, t1, t2) // right\r\n && check_edge(p3, q3, t1, t2) // bottom\r\n && check_edge(p4, q4, t1, t2)) // top\r\n {\r\n sp1_clipped = t1 > 0;\r\n sp2_clipped = t2 < 1;\r\n\r\n if (sp2_clipped)\r\n {\r\n set<1, 0>(s, get<0, 0>(s) + t2 * dx);\r\n set<1, 1>(s, get<0, 1>(s) + t2 * dy);\r\n }\r\n\r\n if(sp1_clipped)\r\n {\r\n set<0, 0>(s, get<0, 0>(s) + t1 * dx);\r\n set<0, 1>(s, get<0, 1>(s) + t1 * dy);\r\n }\r\n\r\n return true;\r\n }\r\n\r\n return false;\r\n }\r\n\r\n template\r\n inline void apply(Linestring& line_out, OutputIterator out) const\r\n {\r\n if (!boost::empty(line_out))\r\n {\r\n *out = line_out;\r\n ++out;\r\n geometry::clear(line_out);\r\n }\r\n }\r\n};\r\n\r\n\r\n}} // namespace strategy::intersection\r\n\r\n\r\n#ifndef DOXYGEN_NO_DETAIL\r\nnamespace detail { namespace intersection\r\n{\r\n\r\n/*!\r\n \\brief Clips a linestring with a box\r\n \\details A linestring is intersected (clipped) by the specified box\r\n and the resulting linestring, or pieces of linestrings, are sent to the specified output operator.\r\n \\tparam OutputLinestring type of the output linestrings\r\n \\tparam OutputIterator an output iterator which outputs linestrings\r\n \\tparam Linestring linestring-type, for example a vector of points, matching the output-iterator type,\r\n the points should also match the input-iterator type\r\n \\tparam Box box type\r\n \\tparam Strategy strategy, a clipping strategy which should implement the methods \"clip_segment\" and \"apply\"\r\n*/\r\ntemplate\r\n<\r\n typename OutputLinestring,\r\n typename OutputIterator,\r\n typename Range,\r\n typename Box,\r\n typename Strategy\r\n>\r\nOutputIterator clip_range_with_box(Box const& b, Range const& range,\r\n OutputIterator out, Strategy const& strategy)\r\n{\r\n if (boost::begin(range) == boost::end(range))\r\n {\r\n return out;\r\n }\r\n\r\n typedef typename point_type::type point_type;\r\n\r\n OutputLinestring line_out;\r\n\r\n typedef typename boost::range_iterator::type iterator_type;\r\n iterator_type vertex = boost::begin(range);\r\n for(iterator_type previous = vertex++;\r\n vertex != boost::end(range);\r\n ++previous, ++vertex)\r\n {\r\n point_type p1, p2;\r\n geometry::convert(*previous, p1);\r\n geometry::convert(*vertex, p2);\r\n\r\n // Clip the segment. Five situations:\r\n // 1. Segment is invisible, finish line if any (shouldn't occur)\r\n // 2. Segment is completely visible. Add (p1)-p2 to line\r\n // 3. Point 1 is invisible (clipped), point 2 is visible. Start new line from p1-p2...\r\n // 4. Point 1 is visible, point 2 is invisible (clipped). End the line with ...p2\r\n // 5. Point 1 and point 2 are both invisible (clipped). Start/finish an independant line p1-p2\r\n //\r\n // This results in:\r\n // a. if p1 is clipped, start new line\r\n // b. if segment is partly or completely visible, add the segment\r\n // c. if p2 is clipped, end the line\r\n\r\n bool c1 = false;\r\n bool c2 = false;\r\n model::referring_segment s(p1, p2);\r\n\r\n if (!strategy.clip_segment(b, s, c1, c2))\r\n {\r\n strategy.apply(line_out, out);\r\n }\r\n else\r\n {\r\n // a. If necessary, finish the line and add a start a new one\r\n if (c1)\r\n {\r\n strategy.apply(line_out, out);\r\n }\r\n\r\n // b. Add p1 only if it is the first point, then add p2\r\n if (boost::empty(line_out))\r\n {\r\n detail::overlay::append_no_duplicates(line_out, p1, true);\r\n }\r\n detail::overlay::append_no_duplicates(line_out, p2);\r\n\r\n // c. If c2 is clipped, finish the line\r\n if (c2)\r\n {\r\n strategy.apply(line_out, out);\r\n }\r\n }\r\n\r\n }\r\n\r\n // Add last part\r\n strategy.apply(line_out, out);\r\n return out;\r\n}\r\n\r\n}} // namespace detail::intersection\r\n#endif // DOXYGEN_NO_DETAIL\r\n\r\n}} // namespace boost::geometry\r\n\r\n#endif // BOOST_GEOMETRY_ALGORITHMS_DETAIL_OVERLAY_CLIP_LINESTRING_HPP\r\n", "meta": {"hexsha": "9c6b01e7b2b867a312ed3441a21fc68f0e8d9a65", "size": 7958, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/geometry/algorithms/detail/overlay/clip_linestring.hpp", "max_stars_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_stars_repo_head_hexsha": "4fb2d44945a6305ae3ca87be3872f9432d16f1fb", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 198.0, "max_stars_repo_stars_event_min_datetime": "2015-01-13T05:47:18.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T04:46:46.000Z", "max_issues_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/geometry/algorithms/detail/overlay/clip_linestring.hpp", "max_issues_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_issues_repo_head_hexsha": "4fb2d44945a6305ae3ca87be3872f9432d16f1fb", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 197.0, "max_issues_repo_issues_event_min_datetime": "2017-07-06T16:53:59.000Z", "max_issues_repo_issues_event_max_datetime": "2019-05-31T17:57:51.000Z", "max_forks_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/geometry/algorithms/detail/overlay/clip_linestring.hpp", "max_forks_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_forks_repo_head_hexsha": "4fb2d44945a6305ae3ca87be3872f9432d16f1fb", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 139.0, "max_forks_repo_forks_event_min_datetime": "2015-01-15T20:09:31.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-31T15:21:16.000Z", "avg_line_length": 32.7489711934, "max_line_length": 114, "alphanum_fraction": 0.5917315909, "num_tokens": 1956, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093585306514, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.22039814973080923}} {"text": "//==============================================================================\n// Copyright 2003 - 2011 LASMEA UMR 6602 CNRS/Univ. Clermont II\n// Copyright 2009 - 2011 LRI UMR 8623 CNRS/Univ Paris Sud XI\n//\n// Distributed under the Boost Software License, Version 1.0.\n// See accompanying file LICENSE.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt\n//==============================================================================\n#ifndef NT2_LINALG_FUNCTIONS_GENERAL_MTIMES_HPP_INCLUDED\n#define NT2_LINALG_FUNCTIONS_GENERAL_MTIMES_HPP_INCLUDED\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace nt2 { namespace tag\n{\n struct blas_normal_\n {\n BOOST_FORCEINLINE static char call()\n {\n return 'N';\n }\n };\n\n struct blas_transpose_\n {\n BOOST_FORCEINLINE static char call()\n {\n return 'T';\n }\n };\n\n struct blas_ctranspose_\n {\n BOOST_FORCEINLINE static char call()\n {\n return 'C';\n }\n };\n} }\n\nnamespace nt2 { namespace ext\n{\n template\n struct size_of\n {\n typedef typename boost::proto::result_of::child_c::value_type child0;\n typedef typename boost::proto::result_of::child_c::value_type child1;\n\n typedef typename boost::proto::result_of::value< typename boost::proto::result_of::child_c::value_type >::value_type trs0;\n typedef typename boost::proto::result_of::value< typename boost::proto::result_of::child_c::value_type >::value_type trs1;\n\n typedef typename child0::extent_type sz0;\n typedef typename child1::extent_type sz1;\n\n static const std::size_t sz0_0 = boost::is_same::value ? 0 : 1;\n static const std::size_t sz0_1 = boost::is_same::value ? 1 : 0;\n static const std::size_t sz1_0 = boost::is_same::value ? 0 : 1;\n static const std::size_t sz1_1 = boost::is_same::value ? 1 : 0;\n\n typedef of_size_< mpl_value< typename boost::fusion::result_of::at_c::type>::value\n , mpl_value< typename boost::fusion::result_of::at_c::type>::value\n > result_type;\n\n result_type operator()(Expr& e) const\n {\n sz0 const& size0 = boost::proto::child_c<0>(e).extent();\n sz1 const& size1 = boost::proto::child_c<1>(e).extent();\n\n BOOST_ASSERT_MSG( ndims(size0) <= 2 && ndims(size1) <= 2\n , \"Inputs must be 2-D, or at least one input must be scalar\"\n );\n\n BOOST_ASSERT_MSG( boost::fusion::at_c(size0) == boost::fusion::at_c(size1)\n , \"Inner dimensions must agree\"\n );\n\n return result_type( boost::fusion::at_c(size0)\n , boost::fusion::at_c(size1)\n );\n }\n };\n\n template\n struct value_type\n {\n typedef typename boost::proto::result_of::child_c::value_type child0;\n typedef typename boost::proto::result_of::child_c::value_type child1;\n typedef typename meta::call::type type;\n };\n} }\n\nnamespace nt2 { namespace ext\n{\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)\n , ((ast_))\n ((ast_))\n )\n {\n typedef typename meta::call::type T;\n BOOST_DISPATCH_RETURNS(2, (A0 const& a0, A1 const& a1),\n mtimes(a0, a1, Zero(), One(), tag::blas_normal_(), tag::blas_normal_())\n )\n };\n\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)(A2)\n , ((ast_))\n ((ast_))\n (scalar_< unspecified_ >)\n )\n {\n typedef typename meta::call::type T;\n BOOST_DISPATCH_RETURNS(3, (A0 const& a0, A1 const& a1, A2 const& a2),\n mtimes(a0, a1, a2, One(), tag::blas_normal_(), tag::blas_normal_())\n )\n };\n\n // Recognize scalar/matrix, matrix/scalar and scalar/scalar\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)(T1)(N1)\n , (scalar_< unspecified_ >)\n ((expr_< generic_< unspecified_ >, T1, N1 >))\n )\n {\n typedef typename meta::call::type result_type;\n BOOST_FORCEINLINE result_type operator()(A0 const& a0, A1 const& a1) const\n {\n return nt2::multiplies(a0, a1);\n }\n };\n\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(T0)(N0)(A1)\n , ((expr_< generic_< unspecified_ >, T0, N0 >))\n (scalar_< unspecified_ >)\n )\n {\n typedef typename meta::call::type result_type;\n BOOST_FORCEINLINE result_type operator()(A0 const& a0, A1 const& a1) const\n {\n return nt2::multiplies(a0, a1);\n }\n };\n\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)\n , (scalar_< unspecified_ >)\n (scalar_< unspecified_ >)\n )\n {\n typedef typename meta::call::type result_type;\n BOOST_FORCEINLINE result_type operator()(A0 const& a0, A1 const& a1) const\n {\n return nt2::multiplies(a0, a1);\n }\n };\n\n // Recognize transpose\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)(A2)(A3)(A4)(A5)\n , ((node_< A0, nt2::tag::transpose_, boost::mpl::long_<1> , nt2::container::domain>))\n ((ast_< A1, nt2::container::domain>))\n (scalar_< unspecified_ >)\n (scalar_< unspecified_ >)\n (unspecified_)\n (unspecified_)\n )\n {\n BOOST_DISPATCH_RETURNS_ARGS(6, (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const& a4, A5 const& a5), (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const&, A5 const& a5),\n mtimes(boost::proto::child_c<0>(a0), a1, a2, a3, tag::blas_transpose_(), a5)\n )\n };\n\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)(A2)(A3)(A4)(A5)\n , ((ast_< A0, nt2::container::domain>))\n ((node_< A1, nt2::tag::transpose_, boost::mpl::long_<1> , nt2::container::domain>))\n (scalar_< unspecified_ >)\n (scalar_< unspecified_ >)\n (unspecified_)\n (unspecified_)\n )\n {\n BOOST_DISPATCH_RETURNS_ARGS(6, (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const& a4, A5 const& a5), (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const& a4, A5 const&),\n mtimes(a0, boost::proto::child_c<0>(a1), a2, a3, a4, tag::blas_transpose_())\n )\n };\n\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)(A2)(A3)(A4)(A5)\n , ((node_< A0, nt2::tag::transpose_, boost::mpl::long_<1> , nt2::container::domain>))\n ((node_< A1, nt2::tag::transpose_, boost::mpl::long_<1> , nt2::container::domain>))\n (scalar_< unspecified_ >)\n (scalar_< unspecified_ >)\n (unspecified_)\n (unspecified_)\n )\n {\n BOOST_DISPATCH_RETURNS_ARGS(6, (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const& a4, A5 const& a5), (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const&, A5 const&),\n mtimes(boost::proto::child_c<0>(a0), boost::proto::child_c<0>(a1), a2, a3, tag::blas_transpose_(), tag::blas_transpose_())\n )\n };\n\n // Recognize ctranspose\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)(A2)(A3)(A4)(A5)\n , ((node_< A0, nt2::tag::ctranspose_, boost::mpl::long_<1> , nt2::container::domain>))\n ((ast_< A1, nt2::container::domain>))\n (scalar_< unspecified_ >)\n (scalar_< unspecified_ >)\n (unspecified_)\n (unspecified_)\n )\n {\n BOOST_DISPATCH_RETURNS_ARGS(6, (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const& a4, A5 const& a5), (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const&, A5 const& a5),\n mtimes(boost::proto::child_c<0>(a0), a1, a2, a3, tag::blas_ctranspose_(), a5)\n )\n };\n\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)(A2)(A3)(A4)(A5)\n , ((ast_< A0, nt2::container::domain>))\n ((node_< A1, nt2::tag::ctranspose_, boost::mpl::long_<1> , nt2::container::domain>))\n (scalar_< unspecified_ >)\n (scalar_< unspecified_ >)\n (unspecified_)\n (unspecified_)\n )\n {\n BOOST_DISPATCH_RETURNS_ARGS(6, (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const& a4, A5 const& a5), (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const& a4, A5 const&),\n mtimes(a0, boost::proto::child_c<0>(a1), a2, a3, a4, tag::blas_ctranspose_())\n )\n };\n\n BOOST_DISPATCH_IMPLEMENT ( mtimes_, tag::cpu_\n , (A0)(A1)(A2)(A3)(A4)(A5)\n , ((node_< A0, nt2::tag::ctranspose_, boost::mpl::long_<1> , nt2::container::domain>))\n ((node_< A1, nt2::tag::ctranspose_, boost::mpl::long_<1> , nt2::container::domain>))\n (scalar_< unspecified_ >)\n (scalar_< unspecified_ >)\n (unspecified_)\n (unspecified_)\n )\n {\n BOOST_DISPATCH_RETURNS_ARGS(6, (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const& a4, A5 const& a5), (A0 const& a0, A1 const& a1, A2 const& a2, A3 const& a3, A4 const&, A5 const&),\n mtimes(boost::proto::child_c<0>(a0), boost::proto::child_c<0>(a1), a2, a3, tag::blas_ctranspose_(), tag::blas_ctranspose_())\n )\n };\n} }\n\nnamespace boost { namespace simd { namespace ext\n{\n // Recognize alpha\n BOOST_DISPATCH_IMPLEMENT ( multiplies_, tag::cpu_\n , (A0)(A1)\n , (scalar_< unspecified_ >)\n ((node_< A1, nt2::tag::mtimes_, boost::mpl::long_<6> , nt2::container::domain>))\n )\n {\n BOOST_DISPATCH_RETURNS(2, (A0 const& a0, A1 const& a1),\n mtimes(boost::proto::child_c<0>(a1), boost::proto::child_c<1>(a1), boost::proto::child_c<2>(a1), a0 * boost::proto::value(boost::proto::child_c<3>(a1)), boost::proto::child_c<4>(a1), boost::proto::child_c<5>(a1))\n )\n };\n\n BOOST_DISPATCH_IMPLEMENT ( multiplies_, tag::cpu_\n , (A0)(A1)\n , ((node_< A0, nt2::tag::mtimes_, boost::mpl::long_<6> , nt2::container::domain>))\n (scalar_< unspecified_ >)\n )\n {\n BOOST_DISPATCH_RETURNS(2, (A0 const& a0, A1 const& a1),\n mtimes(boost::proto::child_c<0>(a0), boost::proto::child_c<1>(a0), boost::proto::child_c<2>(a0), a1 * boost::proto::value(boost::proto::child_c<3>(a0)), boost::proto::child_c<4>(a0), boost::proto::child_c<5>(a0))\n )\n };\n} } }\n\nnamespace nt2 { namespace ext\n{\n // run_assign\n BOOST_DISPATCH_IMPLEMENT ( run_assign_, tag::cpu_\n , (A0)(A1)\n , ((ast_))\n ((node_< A1, nt2::tag::mtimes_, boost::mpl::long_<6> , nt2::container::domain>))\n )\n {\n typedef A0& result_type;\n\n result_type operator()(A0& a0, A1& a1) const\n {\n using boost::fusion::at_c;\n typedef typename A1::value_type value_type;\n\n if(at_c<1>(boost::proto::child_c<0>(a1).extent()) == 0u)\n {\n a0 = zeros(a1.extent(), meta::as_());\n return a0;\n }\n\n typedef nt2::memory::container desired_semantic;\n NT2_AS_TERMINAL_IN(desired_semantic, child0, boost::proto::child_c<0>(a1));\n NT2_AS_TERMINAL_IN(desired_semantic, child1, boost::proto::child_c<1>(a1));\n NT2_AS_TERMINAL_OUT(desired_semantic, result, a0);\n\n value_type alpha = boost::proto::value(boost::proto::child_c<3>(a1));\n value_type beta = boost::proto::value(boost::proto::child_c<2>(a1));\n\n char transA = A1::proto_child4::proto_child0::call();\n char transB = A1::proto_child5::proto_child0::call();\n\n nt2_la_int m = transA == 'N' ? nt2_la_int(at_c<0>(child0.extent())) : nt2_la_int(at_c<1>(child0.extent()));\n nt2_la_int n = transB == 'N' ? nt2_la_int(at_c<1>(child1.extent())) : nt2_la_int(at_c<0>(child1.extent()));\n nt2_la_int k = transA == 'N' ? nt2_la_int(at_c<1>(child0.extent())) : nt2_la_int(at_c<0>(child0.extent()));\n nt2_la_int lda = at_c<0>(child0.extent());\n nt2_la_int ldb = at_c<0>(child1.extent());\n nt2_la_int ldc = at_c<0>(a1.extent());\n\n typedef typename container::as_terminal::type dummy_type;\n typedef typename container::as_view_impl::type view_type;\n\n dummy_type dummy;\n view_type result_view;\n bool swap = (void*)&result != (void*)&a0;\n\n if( swap || ( container::alias(result, child0) || container::alias(result, child1) ) )\n {\n // overlapping of input and output data\n // so we provide dummy space and put it back in result later\n dummy.resize(a1.extent());\n result_view.reset(dummy);\n swap = true;\n }\n else\n {\n result.resize(a1.extent());\n result_view.reset(result);\n }\n\n nt2::details::\n gemm( &transA, &transB\n , &m, &n, &k\n , &alpha\n , child0.data(), &lda\n , child1.data(), &ldb\n , &beta\n , result_view.data(), &ldc\n );\n\n if(swap)\n container::assign_swap(a0, dummy);\n return a0;\n }\n };\n\n} }\n\n#endif\n\n", "meta": {"hexsha": "68f115efb389df53a108e1760c1a7f8c64102aa7", "size": 15976, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "modules/core/linalg/include/nt2/linalg/functions/general/mtimes.hpp", "max_stars_repo_name": "psiha/nt2", "max_stars_repo_head_hexsha": "5e829807f6b57b339ca1be918a6b60a2507c54d0", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 34.0, "max_stars_repo_stars_event_min_datetime": "2017-05-19T18:10:17.000Z", 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"lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118791767282, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.22038094316993687}} {"text": "/**\nBSD 3-Clause License\n\nThis file is part of the code accompanying the paper\nGradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction\nby Christiane Sommer*, Lu Sang*, David Schubert, and Daniel Cremers (* denotes equal contribution).\n\nCopyright (c) 2021, Christiane Sommer and Lu Sang.\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n\n\n#include \"ColorUpsampler.h\"\n#include \"mesh/HrLayeredMarchingCubes.h\"\n\n#include \n// #include \n\nusing Vec8f = Vec8f;\nusing Arr8f = Eigen::Array;\n\n// ========== non-class functions ==========\n\n//! from pose matrix to 6-DoF\nEigen::Matrix MatrixTo7DoF(Mat4f& pose){\n\tEigen::Matrix xi;\n\tVec3f t(pose.topRightCorner(3,1));\n\tEigen::Quaternion q(pose.topLeftCorner<3,3>());\n\txi << t[0], t[1], t[2], q.x(), q.y(), q.z(), q.w();\n\treturn xi;\n}\n\ntemplate\nstatic void printVec(std::vector vec){\n\tfor (const auto& i: vec)\n \t\tstd::cout << i << \" \";\n}\n\n\n//! check for NaN values inside Eigen object\ntemplate \nbool checkNan(S vector)\n{\n return vector.array().isNaN().sum(); \n}\n\n//! interpolation for RGB image\nstatic Vec3f interpolateImage(const float m, const float n, const cv::Mat& img)\n{\n\tint x = std::floor(m);\n\tint y = std::floor(n);\n\tcv::Vec3f tmp;\n\tif ((x+1) < img.rows && (y+1) < img.cols){\n\t\ttmp = (y+1.0-n)*(m-x)*img.at(x+1,y) + (y+1.0-n)*(x+1.0-m)*img.at(x,y) + (n-y)*(m-x)*img.at(x+1,y+1) + (n-y)*(x+1.0-m)*img.at(x,y+1);\n\t}\n\telse if ((y+1) < img.cols && x >= img.rows){\n\t\ttmp = (y+1.0-n)*img.at(x,y) + (n-y)*img.at(x,y+1);\n\t}\n\telse if ( y >= img.cols && (x+1) < img.rows){\n\t\ttmp = (m-x)*img.at(x+1,y) + (x+1.0-m)*img.at(x,y);\n\t}\n\telse{\n\t\ttmp = img.at(x,y);\n\t}\n\n\tVec3f intensity(tmp[2],tmp[1],tmp[0]); // OpenCV stores image colors as BGR.\n\treturn intensity;\n}\n\n\n//! corners of cube [-1, 1]^3\nEigen::Matrix centeredCubeCorners()\n{\n\tEigen::Matrix corners;\n\t\n\tcorners.col(0) << -1.0, -1.0, -1.0;\n\tcorners.col(1) << 1.0, -1.0, -1.0;\n\tcorners.col(2) << -1.0, 1.0, -1.0;\n\tcorners.col(3) << 1.0, 1.0, -1.0;\n\tcorners.col(4) << -1.0, -1.0, 1.0;\n\tcorners.col(5) << 1.0, -1.0, 1.0;\n\tcorners.col(6) << -1.0, 1.0, 1.0;\n\tcorners.col(7) << 1.0, 1.0, 1.0;\n\n\treturn corners;\n}\n\n\n// ========== initialization ==========\n\n//! constructor\nColorUpsampler::ColorUpsampler(const SdfLrMap& sdf_lr,\n phmap::parallel_flat_hash_map>& vis_map,\n std::vector>& images,\n std::vector>& poses,\n std::vector& frame_idx,\n const float voxel_size,\n const Mat3f& K) :\n images_(images),\n poses_(poses),\n frame_idx_(frame_idx),\n voxel_size_(voxel_size),\n voxel_size_inv_(1.f / voxel_size),\n K_(K)\n{\n num_frames_ = frame_idx_.size(); // does not work inside colon initializer\n init(sdf_lr, vis_map);\n // selectPoses();\n}\n\n//! init\nvoid ColorUpsampler::init(const SdfLrMap& sdf_lr, phmap::parallel_flat_hash_map>& vis_map)\n{\n\tindices_.clear();\n\tvis_.clear();\n sdf_.clear();\n\t// convert low-res voxels to high-res voxels\n const float voxel_diameter = std::sqrt(3.) * voxel_size_;\n\tfor (const auto& voxel: sdf_lr) {\n if (std::fabs(voxel.second.dist) < voxel_diameter) { // only accept voxels close to surface\n sdf_.emplace(voxel.first, SdfVoxelHr(voxel.second, voxel_size_));\n }\n\t}\n\n\tnum_voxels_ = sdf_.size();\n\tindices_.resize(num_voxels_);\n\tvis_.resize(num_voxels_);\n\n\t// store indices and visibility vectors in correct order (that of high-res voxel map)\n\tint count = 0;\n\tfor (const auto& voxel: sdf_) {\n\t\tindices_[count] = voxel.first;\n\t\tvis_[count] = vis_map[voxel.first];\n\t\tvis_[count].resize(frame_idx_.back()+1);\n\t\t++count;\n }\n}\n\n\n// ========== helper functions for Jacobians ==========\n\n//! get I_i(R_iv+t) RGB/gray value for 8 subvoxels \nbool ColorUpsampler::getIntensity(const int frame, const Vec3i& idx, const Mat3f& R, const Vec3f& t, Mat3x8f& intensity)\n{\n\tconst float fx = K_(0,0);\n\tconst float fy = K_(1,1);\n\tconst float cx = K_(0,2);\n\tconst float cy = K_(1,2);\n\n\tSdfVoxelHr voxel(getSdf(idx));\n\n\t\n\tMat3x8f point = R.transpose()*(getSubvoxelFloat(idx) - voxel.grad * voxel.d.transpose() - t.replicate(1,8));\n\n auto m = fx * point.row(0).array()/point.row(2).array() + cx;\n auto n = fy * point.row(1).array()/point.row(2).array() + cy;\n\n\t//DEBUG\n\tif (m.hasNaN()|| n.hasNaN()){\n\t\tstd::cout << \"invalid pixel coordinate at voxel \"<< idx.transpose() << \" at frame \" << frame << std::endl; \n\t\treturn false;\n\t}\n\n const cv::Mat& img = getFrame(frame);\n\n\t// Mat3x8f intensity;\n\tfor (size_t i = 0; i < 8; i++){\n if (m(i)<0 || m(i)>=img.cols || n(i)<0 || n(i)>=img.rows) {\n\t\t\t\n\t\t\treturn false;\n\t\t}\n else {\n intensity.col(i) = interpolateImage(n(i), m(i), img);\n\t\t}\n\t}\n\n\treturn true;\n}\n\n\n\n//! coordinates of subvoxel centers\nMat3x8f ColorUpsampler::getSubvoxelFloat(const Vec3i& voxel_in)\n{\n\tVec3f voxel_float = voxel_in.cast();\n return voxel_size_ * (.25 * centeredCubeCorners() + voxel_float.replicate<1,8>());\n}\n\n// ========== set values for individual voxels ==========\n\n//! set albedo\nvoid ColorUpsampler::setAlbedo(const Vec3i& idx, const Vec8f& r, const Vec8f& g, const Vec8f& b)\n{\n\tVec8f rr, gg, bb;\n\trr = r;\n\trr = rr.cwiseMax(0.0);\n\trr = rr.cwiseMin(1.0);\n\n\tgg = g;\n\tgg = gg.cwiseMax(0.0);\n\tgg = gg.cwiseMin(1.0);\n\n\tbb = b;\n\tbb = bb.cwiseMax(0.0);\n\tbb = bb.cwiseMin(1.0);\n\n\tsdf_.at(idx).r = rr;\n\tsdf_.at(idx).g = gg;\n\tsdf_.at(idx).b = bb;\n\n}\n\n\n//! extract mesh from SDF to debug geometry\nbool ColorUpsampler::extractMesh(std::string filename)\n{\n HrLayeredMarchingCubes lmc(Vec3f(voxel_size_, voxel_size_, voxel_size_));\n lmc.computeIsoSurface(&sdf_);\n bool success = lmc.savePly(filename + \".ply\");\n\tif (success)\n\t\tstd::cout << \"Mesh \" << filename << \".ply successfully saved.\" << std::endl;\n\n\treturn success;\n}\n\nbool ColorUpsampler::extractCloud(std::string filename)\n{\n\n\tconst float voxel_size_4 = .25 * voxel_size_;\n\tfilename += \".ply\";\n\n\tint voxel_id = 0;\n std::vector> points_normals_colors;\n for (const auto& el : sdf_) {\n\n\t\tstd::vector vis = vis_[voxel_id];\n\t\tbool visible = false;\n\t\tfor (size_t i = 0; i < num_frames_; i++){\n\t\t\tif (vis[frame_idx_[i]]){\n\t\t\t\tvisible = true;\n\t\t\t\tbreak;\n\t\t\t}\t\n\t\t}\n\n\t\tif(!visible){\n\t\t\tvoxel_id++;\n\t\t\tcontinue;\n\t\t}\n\n const SdfVoxelHr& v = el.second;\n if (v.weight < 5){\n\t\t\tvoxel_id++;\n continue;\n\t\t}\n\n\t\tMat3x8f voxel_normal = -v.grad.normalized().replicate(1,8);\n\t\tMat3x8f voxel_float = getSubvoxelFloat(el.first);\n\t\tMat3x8f distances = voxel_normal * v.d.asDiagonal();\n\t\tfor (int i=0; i<8; ++i)\n\t\t{\n\t\t\tVec3f d = distances.col(i);\n\t\t\tif (std::fabs(d[0]) < voxel_size_4 && std::fabs(d[1]) < voxel_size_4 && std::fabs(d[2]) < voxel_size_4 && !std::isnan(v.r[i]) && !std::isnan(v.g[i]) && !std::isnan(v.b[i]))\n\t\t\t{\n\t\t\t\tEigen::Matrix pnc;\n\t\t\t\tpnc.segment<3>(0) = voxel_float.col(i) + d;\n\t\t\t\tpnc.segment<3>(3) = voxel_normal.col(i);\n\t\t\t\tpnc.segment<3>(6) = Vec3f(v.r[i], v.g[i], v.b[i]);\n\t\t\t\tpoints_normals_colors.push_back(pnc);\n\t\t\t}\n\t\t}\n\t\tvoxel_id++;\n } \n\n std::ofstream plyFile;\n plyFile.open(filename.c_str());\n if (!plyFile.is_open())\n return false;\n \n plyFile << \"ply\" << std::endl;\n plyFile << \"format ascii 1.0\" << std::endl;\n plyFile << \"element vertex \" << points_normals_colors.size() << std::endl;\n plyFile << \"property float x\" << std::endl;\n plyFile << \"property float y\" << std::endl;\n plyFile << \"property float z\" << std::endl;\n plyFile << \"property float nx\" << std::endl;\n plyFile << \"property float ny\" << std::endl;\n plyFile << \"property float nz\" << std::endl;\n plyFile << \"property uchar red\" << std::endl;\n plyFile << \"property uchar green\" << std::endl;\n plyFile << \"property uchar blue\" << std::endl;\n plyFile << \"end_header\" << std::endl;\n \n for (const Eigen::Matrix& p : points_normals_colors)\n\t{\n plyFile << p[0] << \" \" << p[1] << \" \" << p[2] << \" \" << p[3] << \" \" << p[4] << \" \" << p[5] << \" \" << int(255 * p[6]) << \" \" << int(255 * p[7]) << \" \" << int(255 * p[8]) << std::endl;\n }\n \n plyFile.close();\n\n\tstd::cout << \"Cloud \" << filename << \" successfully saved.\" << std::endl;\n return true;\n}\n\n\n // ------------------- solvers -----------------------------------------------------------------------------------------------------------------\n\n\n//! compute albedo from ambient light assumption, i.e. average over all observations\nvoid ColorUpsampler::computeColor()\n{\n\t// size_t num_tot_frames = getVis(0).size();\n\n\tsize_t count = 0; // counter (same for all three color channels)\n Vec8f br, bb, bg;\n int voxel_id = 0;\n\n\tfor (const auto& idx : indices_) {\n\n\t\tbr.setZero();\n\t\tbb.setZero();\n\t\tbg.setZero();\n\n\t\tstd::vector vis = getVis(voxel_id);\n\t\tSdfVoxelHr voxel(getSdf(idx));\n\t\t\n\t\tfor(size_t i = 0; i < num_frames_; i++){\n\t\t\tMat3f R = getRotation(i);\n\t\t\tVec3f t = getTranslation(i);\n\t\t\t\n if(vis[frame_idx_[i]]) {\n\t\t\t\t// r(v) = \\sum_i (I(pi(Rv+t)) - pho(v)())\n\t\t\t\tMat3x8f intensity;\n\t\t\t\tif(!getIntensity(i, idx, R, t, intensity)){\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t\tbr += intensity.row(0);\n\t\t\t\tbg += intensity.row(1);\n\t\t\t\tbb += intensity.row(2);\n\t\t\t\t++count;\n\t\t\t}\n\t\t}\n\n\t\tVec8f r,g,b;\n\t\tfloat inv_count = 1.f / float(count);\n\t\tr = inv_count * br;\n\t\tg = inv_count * bg;\n\t\tb = inv_count * bb;\n\t\tsetAlbedo(idx, r, g, b); // cut-off happens here\n\t\tcount = 0;\n\t\tvoxel_id++;\n\t}\n}\n\n", "meta": {"hexsha": "5452d0d63d8b96d2ae3d010602dea5f6b8c4d70e", "size": 11211, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cpp/include/ps_optimizer/ColorUpsampler.cpp", "max_stars_repo_name": "c-sommer/gradient-sdf", "max_stars_repo_head_hexsha": "8e777231e04ca138fc3118bd5851caa0280e503b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2022-03-27T11:06:45.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-27T11:06:45.000Z", "max_issues_repo_path": "cpp/include/ps_optimizer/ColorUpsampler.cpp", "max_issues_repo_name": "c-sommer/gradient-sdf", "max_issues_repo_head_hexsha": "8e777231e04ca138fc3118bd5851caa0280e503b", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cpp/include/ps_optimizer/ColorUpsampler.cpp", "max_forks_repo_name": "c-sommer/gradient-sdf", "max_forks_repo_head_hexsha": "8e777231e04ca138fc3118bd5851caa0280e503b", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 29.580474934, "max_line_length": 190, "alphanum_fraction": 0.6187672821, "num_tokens": 3448, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.22038093670468767}} {"text": "#ifndef DART_CONSTRAINT_LCPUTILS_HPP_\n#define DART_CONSTRAINT_LCPUTILS_HPP_\n\n#include \n\n#include \n\n#include \"dart/constraint/BoxedLcpSolver.hpp\"\n\nnamespace dart {\nnamespace constraint {\n\nclass LCPUtils\n{\npublic:\n static bool isLCPSolutionValid(\n const Eigen::MatrixXs& mA,\n const Eigen::VectorXs& mX,\n const Eigen::VectorXs& mB,\n const Eigen::VectorXs& mHi,\n const Eigen::VectorXs& mLo,\n const Eigen::VectorXi& mFIndex,\n bool ignoreFrictionIndices);\n\n /// This applies a simple algorithm to guess the solution to the LCP problem.\n /// It's not guaranteed to be correct, but it often can be if there is no\n /// sliding friction on this timestep.\n static Eigen::VectorXs guessSolution(\n const Eigen::MatrixXs& mA,\n const Eigen::VectorXs& mB,\n const Eigen::VectorXs& mHi,\n const Eigen::VectorXs& mLo,\n const Eigen::VectorXi& mFIndex);\n\n /// This reduces an LCP problem by merging any near-identical contact points.\n /// It returns a mapOut matrix, such that if you solve this LCP and then\n /// multiply the resulting x as mapOut*x, you'll get the solution to the\n /// original LCP.\n static Eigen::MatrixXs reduce(\n Eigen::MatrixXs& A,\n Eigen::VectorXs& X,\n Eigen::VectorXs& b,\n Eigen::VectorXs& hi,\n Eigen::VectorXs& lo,\n Eigen::VectorXi& fIndex);\n\n /// This cuts a problem down to just the normal forces, ignoring friction.\n /// It returns a mapOut matrix, such that if you solve this LCP and then\n /// multiply the resulting x as mapOut*x, you'll get the solution to the\n /// original LCP, but with friction forces all 0.\n static Eigen::MatrixXs removeFriction(\n Eigen::MatrixXs& A,\n Eigen::VectorXs& X,\n Eigen::VectorXs& b,\n Eigen::VectorXs& hi,\n Eigen::VectorXs& lo,\n Eigen::VectorXi& fIndex);\n\n /// This solves the LCP problem by first automatically de-duplicating columns\n /// to create a reduced version of an equivalent problem, ideally with a\n /// full-rank A. Then the solution to the original LCP is recovered by\n /// re-inflating the solution to the reduced problem.\n static bool solveDeduplicated(\n std::shared_ptr& solver,\n const Eigen::MatrixXs& A,\n Eigen::VectorXs& X,\n const Eigen::VectorXs& b,\n const Eigen::VectorXs& hi,\n const Eigen::VectorXs& lo,\n const Eigen::VectorXi& fIndex);\n\n /// This will modify the LCP problem formulation to merge two columns\n /// together, and rewrite and resize all the matrices appropriately. It will\n /// also yell and scream (throw asserts) if the columns shouldn't be merged.\n /// It'll also update the mapOut matrix, so that it's possible to simply\n /// multiply mapOut*x on the reduced problem to get a valid solution to the\n /// larger problem.\n static void mergeLCPColumns(\n int colA,\n int colB,\n Eigen::MatrixXs& A,\n Eigen::VectorXs& X,\n Eigen::VectorXs& b,\n Eigen::VectorXs& hi,\n Eigen::VectorXs& lo,\n Eigen::VectorXi& fIndex,\n Eigen::MatrixXs& mapOut);\n\n /// This will modify the LCP problem formulation to drop a column\n /// and rewrite and resize all the matrices appropriately.\n /// It'll also update the mapOut matrix, so that it's possible to simply\n /// multiply mapOut*x on the reduced problem to get a valid solution to the\n /// larger problem.\n static void dropLCPColumn(\n int col,\n Eigen::MatrixXs& A,\n Eigen::VectorXs& X,\n Eigen::VectorXs& b,\n Eigen::VectorXs& hi,\n Eigen::VectorXs& lo,\n Eigen::VectorXi& fIndex,\n Eigen::MatrixXs& mapOut);\n\n /// Print replication code info\n static void printReplicationCode(\n Eigen::MatrixXs A,\n Eigen::VectorXs x,\n Eigen::VectorXs lo,\n Eigen::VectorXs hi,\n Eigen::VectorXs b,\n Eigen::VectorXi fIndex);\n};\n\n} // namespace constraint\n} // namespace dart\n\n#endif", "meta": {"hexsha": "1cfe3ec97f4d1bdfc312ab6c2aef46487605d6bc", "size": 3904, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "dart/constraint/LCPUtils.hpp", "max_stars_repo_name": "jyf588/nimblephysics", "max_stars_repo_head_hexsha": "6c09228f0abcf7aa3526a8dd65cd2541aff32c4a", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2021-09-30T06:23:29.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-09T09:59:09.000Z", "max_issues_repo_path": "dart/constraint/LCPUtils.hpp", "max_issues_repo_name": "jyf588/nimblephysics", "max_issues_repo_head_hexsha": "6c09228f0abcf7aa3526a8dd65cd2541aff32c4a", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dart/constraint/LCPUtils.hpp", "max_forks_repo_name": "jyf588/nimblephysics", "max_forks_repo_head_hexsha": "6c09228f0abcf7aa3526a8dd65cd2541aff32c4a", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-08-20T13:56:14.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-20T13:56:14.000Z", "avg_line_length": 33.3675213675, "max_line_length": 79, "alphanum_fraction": 0.6785348361, "num_tokens": 1048, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.22038093670468767}} {"text": "#ifndef AMGCL_BACKEND_VEXCL_HPP\n#define AMGCL_BACKEND_VEXCL_HPP\n\n/*\nThe MIT License\n\nCopyright (c) 2012-2020 Denis Demidov \n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n*/\n\n/**\n * \\file amgcl/backend/vexcl.hpp\n * \\author Denis Demidov \n * \\brief VexCL backend.\n */\n\n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\nnamespace amgcl {\n\nnamespace solver {\n\n/** Wrapper around solver::skyline_lu for use with the VexCL backend.\n * Copies the rhs to the host memory, solves the problem using the host CPU,\n * then copies the solution back to the compute device(s).\n */\ntemplate \nstruct vexcl_skyline_lu : solver::skyline_lu {\n typedef solver::skyline_lu Base;\n typedef typename math::rhs_of::type rhs_type;\n\n mutable std::vector _rhs, _x;\n\n template \n vexcl_skyline_lu(const Matrix &A, const Params&)\n : Base(*A), _rhs(backend::rows(*A)), _x(backend::rows(*A))\n { }\n\n template \n void operator()(const Vec1 &rhs, Vec2 &x) const {\n vex::copy(rhs, _rhs);\n static_cast(this)->operator()(_rhs, _x);\n vex::copy(_x, x);\n }\n\n size_t bytes() const {\n return\n backend::bytes(*static_cast(this)) +\n backend::bytes(_rhs) +\n backend::bytes(_x);\n }\n};\n\n}\n\nnamespace backend {\n\n/// The VexCL backend parameters.\nstruct vexcl_params {\n\n std::vector< vex::backend::command_queue > q; ///< Command queues that identify compute devices to use with VexCL.\n\n /// Do CSR to ELL conversion on the GPU side.\n /** This will result in faster setup, but will require more GPU memory. */\n bool fast_matrix_setup;\n\n vexcl_params() : fast_matrix_setup(true) {}\n\n#ifndef AMGCL_NO_BOOST\n vexcl_params(const boost::property_tree::ptree &p)\n : fast_matrix_setup(p.get(\"fast_matrix_setup\", vexcl_params().fast_matrix_setup))\n {\n std::vector *ptr = 0;\n ptr = p.get(\"q\", ptr);\n if (ptr) q = *ptr;\n check_params(p, {\"q\", \"fast_matrix_setup\"});\n }\n\n void get(boost::property_tree::ptree &p, const std::string &path) const {\n p.put(path + \"q\", &q);\n p.put(path + \"fast_matrix_setup\", fast_matrix_setup);\n }\n#endif\n\n const std::vector& context() const {\n if (q.empty())\n return vex::current_context().queue();\n else\n return q;\n }\n};\n\n\n/**\n * The backend uses the VexCL\n * library for accelerating solution on the modern GPUs and multicore\n * processors with the help of OpenCL or CUDA technologies.\n * The VexCL backend stores the system matrix as ``vex::SpMat`` and\n * expects the right hand side and the solution vectors to be instances of the\n * ``vex::vector`` type.\n */\ntemplate >\nstruct vexcl {\n typedef real value_type;\n typedef ptrdiff_t index_type;\n\n typedef vex::sparse::distributed<\n vex::sparse::matrix\n > matrix;\n typedef typename math::rhs_of::type rhs_type;\n typedef vex::vector vector;\n typedef vex::vector matrix_diagonal;\n typedef DirectSolver direct_solver;\n\n struct provides_row_iterator : std::false_type {};\n\n typedef vexcl_params params;\n\n static std::string name() { return \"vexcl\"; }\n\n // Copy matrix from builtin backend.\n static std::shared_ptr\n copy_matrix(std::shared_ptr< typename builtin::matrix > A, const params &prm)\n {\n precondition(!prm.context().empty(), \"Empty VexCL context!\");\n\n const typename builtin::matrix &a = *A;\n\n const size_t n = rows(*A);\n const size_t m = cols(*A);\n const size_t nnz = a.ptr[n];\n\n return std::make_shared(prm.context(), n, m,\n boost::make_iterator_range(a.ptr, a.ptr + n+1),\n boost::make_iterator_range(a.col, a.col + nnz),\n boost::make_iterator_range(a.val, a.val + nnz),\n prm.fast_matrix_setup\n );\n }\n\n // Copy vector from builtin backend.\n template \n static std::shared_ptr< vex::vector >\n copy_vector(const std::vector &x, const params &prm)\n {\n precondition(!prm.context().empty(), \"Empty VexCL context!\");\n return std::make_shared< vex::vector >(prm.context(), x);\n }\n\n template \n static std::shared_ptr< vex::vector >\n copy_vector(const numa_vector &x, const params &prm)\n {\n precondition(!prm.context().empty(), \"Empty VexCL context!\");\n return std::make_shared< vex::vector >(prm.context(), x.size(), x.data());\n }\n\n // Copy vector from builtin backend.\n template \n static std::shared_ptr< vex::vector >\n copy_vector(std::shared_ptr< numa_vector > x, const params &prm)\n {\n return copy_vector(*x, prm);\n }\n\n // Create vector of the specified size.\n static std::shared_ptr\n create_vector(size_t size, const params &prm)\n {\n precondition(!prm.context().empty(), \"Empty VexCL context!\");\n\n return std::make_shared(prm.context(), size);\n }\n\n struct gather {\n size_t n;\n mutable vex::gather G;\n mutable std::vector buf;\n\n gather(size_t src_size, const std::vector &I, const params &prm)\n : n(I.size()), G(prm.context(), src_size, std::vector(I.begin(), I.end()))\n { }\n\n template \n void operator()(const vex::vector &src, vex::vector &dst) const {\n if (buf.size() < sizeof(D) * n) buf.resize(sizeof(D) * n);\n auto t = reinterpret_cast(buf.data());\n G(src, t);\n vex::copy(t, t + n, dst.begin());\n }\n\n template \n void operator()(const vex::vector &vec, std::vector &vals) const {\n G(vec, vals);\n }\n };\n\n struct scatter {\n size_t n;\n mutable vex::scatter S;\n mutable std::vector buf;\n\n scatter(size_t size, const std::vector &I, const params &prm)\n : n(I.size()), S(prm.context(), size, std::vector(I.begin(), I.end()))\n { }\n\n template \n void operator()(const vex::vector &src, vex::vector &dst) const {\n if (buf.size() < sizeof(D) * n) buf.resize(sizeof(D) * n);\n auto t = reinterpret_cast(buf.data());\n vex::copy(src.begin(), src.end(), t);\n S(t, dst);\n }\n };\n\n\n // Create direct solver for coarse level\n static std::shared_ptr\n create_solver(std::shared_ptr< typename builtin::matrix > A, const params &prm)\n {\n return std::make_shared(A, prm);\n }\n};\n\n//---------------------------------------------------------------------------\n// Backend interface implementation\n//---------------------------------------------------------------------------\ntemplate \nstruct backends_compatible< vexcl, vexcl > : std::true_type {};\n\ntemplate < typename V, typename C, typename P >\nstruct bytes_impl< vex::sparse::distributed > > {\n static size_t get(const vex::sparse::distributed > &A) {\n return\n sizeof(P) * (A.rows() + 1) +\n sizeof(C) * A.nonzeros() +\n sizeof(V) * A.nonzeros();\n }\n};\n\ntemplate < typename V >\nstruct bytes_impl< vex::vector > {\n static size_t get(const vex::vector &v) {\n return v.size() * sizeof(V);\n }\n};\n\ntemplate < typename Alpha, typename Beta, typename Va, typename Vx, typename Vy, typename C, typename P >\nstruct spmv_impl<\n Alpha, vex::sparse::distributed>, vex::vector,\n Beta, vex::vector,\n typename std::enable_if<\n math::static_rows::value == 1 &&\n math::static_rows::value == 1 &&\n math::static_rows::value == 1\n >::type\n >\n{\n typedef vex::sparse::distributed> matrix;\n\n static void apply(Alpha alpha, const matrix &A, const vex::vector &x,\n Beta beta, vex::vector &y)\n {\n if (beta)\n y = alpha * (A * x) + beta * y;\n else\n y = alpha * (A * x);\n }\n};\n\ntemplate < typename Va, typename Vf, typename Vx, typename Vr, typename C, typename P >\nstruct residual_impl<\n vex::sparse::distributed>,\n vex::vector,\n vex::vector,\n vex::vector\n >\n{\n typedef vex::sparse::distributed> matrix;\n\n static void apply(const vex::vector &rhs, const matrix &A, const vex::vector &x,\n vex::vector &r)\n {\n r = rhs - A * x;\n }\n};\n\ntemplate < typename V >\nstruct clear_impl< vex::vector >\n{\n static void apply(vex::vector &x)\n {\n x = 0;\n }\n};\n\ntemplate < class V, class T >\nstruct copy_impl >\n{\n static void apply(const V &x, vex::vector &y)\n {\n vex::copy(x, y);\n }\n};\n\ntemplate < class T, class V >\nstruct copy_impl, V>\n{\n static void apply(const vex::vector &x, V &y)\n {\n vex::copy(x, y);\n }\n};\n\ntemplate < class T1, class T2 >\nstruct copy_impl, vex::vector>\n{\n static void apply(const vex::vector &x, vex::vector &y)\n {\n vex::copy(x, y);\n }\n};\n\ntemplate < typename V >\nstruct inner_product_impl<\n vex::vector,\n vex::vector\n >\n{\n static V get(const vex::vector &x, const vex::vector &y)\n {\n vex::Reductor sum( x.queue_list() );\n return sum(x * y);\n }\n};\n\ntemplate < typename A, typename B, typename V1, typename V2 >\nstruct axpby_impl<\n A, vex::vector,\n B, vex::vector\n > {\n static void apply(A a, const vex::vector &x, B b, vex::vector &y)\n {\n if (b)\n y = a * x + b * y;\n else\n y = a * x;\n }\n};\n\ntemplate < typename A, typename B, typename C, typename V1, typename V2, typename V3 >\nstruct axpbypcz_impl<\n A, vex::vector,\n B, vex::vector,\n C, vex::vector\n >\n{\n static void apply(\n A a, const vex::vector &x,\n B b, const vex::vector &y,\n C c, vex::vector &z\n )\n {\n if (c)\n z = a * x + b * y + c * z;\n else\n z = a * x + b * y;\n }\n};\n\ntemplate < typename A, typename B, typename Vx, typename Vy, typename Vz >\nstruct vmul_impl<\n A, vex::vector, vex::vector,\n B, vex::vector\n >\n{\n static void apply(A a, const vex::vector &x, const vex::vector &y,\n B b, vex::vector &z)\n {\n if (b)\n z = a * x * y + b * z;\n else\n z = a * x * y;\n }\n};\n\ntemplate \nstruct reinterpret_impl>\n{\n typedef vex::vector::type> return_type;\n\n static return_type get(const vex::vector &x) {\n return x.template reinterpret::type>();\n }\n};\n\n\n} // namespace backend\n} // namespace amgcl\n\n#endif\n", "meta": {"hexsha": "a8b333425db4c406fbbc4d85dd9f21034710ed94", "size": 12901, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external_libraries/amgcl/backend/vexcl.hpp", "max_stars_repo_name": "lkusch/Kratos", "max_stars_repo_head_hexsha": "e8072d8e24ab6f312765185b19d439f01ab7b27b", "max_stars_repo_licenses": ["BSD-4-Clause"], "max_stars_count": 778.0, 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118, "alphanum_fraction": 0.6018913263, "num_tokens": 3363, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.22038093670468767}} {"text": "#ifndef BOOST_GEOMETRY_PROJECTIONS_OCEA_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_OCEA_HPP\n\n// Boost.Geometry - extensions-gis-projections (based on PROJ4)\n// This file is automatically generated. DO NOT EDIT.\n\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\n\n// Last updated version of proj: 4.9.1\n\n// Original copyright notice:\n\n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#include \n\n#include \n#include \n#include \n#include \n\nnamespace boost { namespace geometry { namespace projections\n{\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail { namespace ocea\n {\n\n struct par_ocea\n {\n double rok;\n double rtk;\n double sinphi;\n double cosphi;\n double singam;\n double cosgam;\n };\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_ocea_spheroid : public base_t_fi,\n Geographic, Cartesian, Parameters>\n {\n\n typedef double geographic_type;\n typedef double cartesian_type;\n\n par_ocea m_proj_parm;\n\n inline base_ocea_spheroid(const Parameters& par)\n : base_t_fi,\n Geographic, Cartesian, Parameters>(*this, par) {}\n\n // FORWARD(s_forward) spheroid\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\n inline void fwd(geographic_type& lp_lon, geographic_type& lp_lat, cartesian_type& xy_x, cartesian_type& xy_y) const\n {\n double t;\n\n xy_y = sin(lp_lon);\n /*\n xy_x = atan2((tan(lp_lat) * this->m_proj_parm.cosphi + this->m_proj_parm.sinphi * xy_y) , cos(lp_lon));\n */\n t = cos(lp_lon);\n xy_x = atan((tan(lp_lat) * this->m_proj_parm.cosphi + this->m_proj_parm.sinphi * xy_y) / t);\n if (t < 0.)\n xy_x += geometry::math::pi();\n xy_x *= this->m_proj_parm.rtk;\n xy_y = this->m_proj_parm.rok * (this->m_proj_parm.sinphi * sin(lp_lat) - this->m_proj_parm.cosphi * cos(lp_lat) * xy_y);\n }\n\n // INVERSE(s_inverse) spheroid\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\n inline void inv(cartesian_type& xy_x, cartesian_type& xy_y, geographic_type& lp_lon, geographic_type& lp_lat) const\n {\n double t, s;\n\n xy_y /= this->m_proj_parm.rok;\n xy_x /= this->m_proj_parm.rtk;\n t = sqrt(1. - xy_y * xy_y);\n lp_lat = asin(xy_y * this->m_proj_parm.sinphi + t * this->m_proj_parm.cosphi * (s = sin(xy_x)));\n lp_lon = atan2(t * this->m_proj_parm.sinphi * s - xy_y * this->m_proj_parm.cosphi,\n t * cos(xy_x));\n }\n\n static inline std::string get_name()\n {\n return \"ocea_spheroid\";\n }\n\n };\n\n // Oblique Cylindrical Equal Area\n template \n void setup_ocea(Parameters& par, par_ocea& proj_parm)\n {\n double phi_0=0.0, phi_1, phi_2, lam_1, lam_2, lonz, alpha;\n\n proj_parm.rok = par.a / par.k0;\n proj_parm.rtk = par.a * par.k0;\n if ( pj_param(par.params, \"talpha\").i) {\n alpha = pj_param(par.params, \"ralpha\").f;\n lonz = pj_param(par.params, \"rlonc\").f;\n proj_parm.singam = atan(-cos(alpha)/(-sin(phi_0) * sin(alpha))) + lonz;\n proj_parm.sinphi = asin(cos(phi_0) * sin(alpha));\n } else {\n phi_1 = pj_param(par.params, \"rlat_1\").f;\n phi_2 = pj_param(par.params, \"rlat_2\").f;\n lam_1 = pj_param(par.params, \"rlon_1\").f;\n lam_2 = pj_param(par.params, \"rlon_2\").f;\n proj_parm.singam = atan2(cos(phi_1) * sin(phi_2) * cos(lam_1) -\n sin(phi_1) * cos(phi_2) * cos(lam_2),\n sin(phi_1) * cos(phi_2) * sin(lam_2) -\n cos(phi_1) * sin(phi_2) * sin(lam_1) );\n proj_parm.sinphi = atan(-cos(proj_parm.singam - lam_1) / tan(phi_1));\n }\n par.lam0 = proj_parm.singam + geometry::math::half_pi();\n proj_parm.cosphi = cos(proj_parm.sinphi);\n proj_parm.sinphi = sin(proj_parm.sinphi);\n proj_parm.cosgam = cos(proj_parm.singam);\n proj_parm.singam = sin(proj_parm.singam);\n par.es = 0.;\n }\n\n }} // namespace detail::ocea\n #endif // doxygen\n\n /*!\n \\brief Oblique Cylindrical Equal Area projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Cylindrical\n - Spheroid\n \\par Projection parameters\n - lonc: Longitude (only used if alpha (or gamma) is specified) (degrees)\n - alpha: Alpha (degrees)\n - lat_1: Latitude of first standard parallel (degrees)\n - lat_2: Latitude of second standard parallel (degrees)\n - lon_1 (degrees)\n - lon_2 (degrees)\n \\par Example\n \\image html ex_ocea.gif\n */\n template \n struct ocea_spheroid : public detail::ocea::base_ocea_spheroid\n {\n inline ocea_spheroid(const Parameters& par) : detail::ocea::base_ocea_spheroid(par)\n {\n detail::ocea::setup_ocea(this->m_par, this->m_proj_parm);\n }\n };\n\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail\n {\n\n // Factory entry(s)\n template \n class ocea_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n inline void ocea_init(detail::base_factory& factory)\n {\n factory.add_to_factory(\"ocea\", new ocea_entry);\n }\n\n } // namespace detail\n #endif // doxygen\n\n}}} // namespace boost::geometry::projections\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_OCEA_HPP\n\n", "meta": {"hexsha": "67f21ef857e22a89d1cb78b8d32fe7bb5b109317", "size": 9021, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/ocea.hpp", "max_stars_repo_name": "bowlofstew/omim", "max_stars_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-01-11T05:02:05.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-11T05:02:05.000Z", "max_issues_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/ocea.hpp", "max_issues_repo_name": "bowlofstew/omim", "max_issues_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/ocea.hpp", "max_forks_repo_name": "bowlofstew/omim", "max_forks_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2018-04-04T10:55:01.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-23T18:52:06.000Z", "avg_line_length": 43.3701923077, "max_line_length": 140, "alphanum_fraction": 0.601263718, "num_tokens": 2061, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6513548646660543, "lm_q2_score": 0.3380771174808128, "lm_q1q2_score": 0.22020817510340457}} {"text": "#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"caffe/layer.hpp\"\n#include \"caffe/layer_factory.hpp\"\n#include \"caffe/layers/malis_loss_layer.hpp\"\n#include \"caffe/util/math_functions.hpp\"\n\n\nnamespace caffe {\n\ntemplate\nclass MalisAffinityGraphCompare {\n private:\n const Dtype * mEdgeWeightArray;\n public:\n explicit MalisAffinityGraphCompare(const Dtype * EdgeWeightArray) {\n mEdgeWeightArray = EdgeWeightArray;\n }\n bool operator()(const uint64_t& ind1, const uint64_t& ind2) const {\n return (mEdgeWeightArray[ind1] > mEdgeWeightArray[ind2]);\n }\n};\n\n// Derived from https://github.com/srinituraga/malis/blob/master/matlab/malis_loss_mex.cpp\ntemplate\nvoid MalisLossLayer::Malis(const Dtype* conn_data,\n const int_tp conn_num_dims,\n const int_tp* conn_dims,\n const int_tp* nhood_data,\n const int_tp* nhood_dims,\n const Dtype* seg_data, const bool pos,\n Dtype* dloss_data, Dtype* loss_out,\n Dtype *classerr_out, Dtype *rand_index_out) {\n if ((nhood_dims[1] != (conn_num_dims - 1))\n || (nhood_dims[0] != conn_dims[0])) {\n LOG(FATAL) << \"nhood and conn dimensions don't match\"\n << \" (\" << nhood_dims[1] << \" vs. \" << (conn_num_dims - 1)\n << \" and \" << nhood_dims[0] << \" vs. \"\n << conn_dims[conn_num_dims - 1] <<\")\";\n }\n\n /* Cache for speed to access neighbors */\n // nVert stores (X * Y * z)\n uint64_t nVert = 1;\n for (uint64_t i = 1; i < conn_num_dims; ++i) {\n nVert *= conn_dims[i];\n // std::cout << i << \" nVert: \" << nVert << std::endl;\n }\n\n // prodDims stores X, X*Y, X*Y*z offsets\n vector prodDims(conn_num_dims - 1);\n prodDims[conn_num_dims - 2] = 1;\n for (uint64_t i = 1; i < conn_num_dims - 1; ++i) {\n prodDims[conn_num_dims - 2 - i] = prodDims[conn_num_dims - 1 - i]\n * conn_dims[conn_num_dims - i];\n // std::cout << conn_num_dims - 2 - i << \" dims: \"\n // << prodDims[conn_num_dims - 2 - i] << std::endl;\n }\n\n /* convert n-d offset vectors into linear array offset scalars */\n // nHood is a vector of size #edges\n\n vector nHood(nhood_dims[0]);\n for (uint64_t i = 0; i < nhood_dims[0]; ++i) {\n nHood[i] = 0;\n for (uint64_t j = 0; j < nhood_dims[1]; ++j) {\n nHood[i] += (uint32_t) nhood_data[j + i * nhood_dims[1]] * prodDims[j];\n }\n // std::cout << i << \" nHood: \" << nHood[i] << std::endl;\n }\n\n /* Disjoint sets and sparse overlap vectors */\n vector > overlap(nVert);\n vector rank(nVert);\n vector parent(nVert);\n std::map segSizes;\n uint64_t nLabeledVert = 0;\n uint64_t nPairPos = 0;\n boost::disjoint_sets dsets(&rank[0], &parent[0]);\n // Loop over all seg data items\n for (uint64_t i = 0; i < nVert; ++i) {\n dsets.make_set(i);\n if (0 != seg_data[i]) {\n overlap[i].insert(std::pair(seg_data[i], 1));\n ++nLabeledVert;\n ++segSizes[seg_data[i]];\n nPairPos += (segSizes[seg_data[i]] - 1);\n }\n }\n\n uint64_t nPairTot = (nLabeledVert * (nLabeledVert - 1)) / 2;\n uint64_t nPairNeg = nPairTot - nPairPos;\n uint64_t nPairNorm;\n\n if (pos) {\n nPairNorm = nPairPos;\n } else {\n nPairNorm = nPairNeg;\n }\n\n uint64_t edgeCount = 0;\n // Loop over #edges\n for (uint64_t d = 0, i = 0; d < conn_dims[0]; ++d) {\n // Loop over Z\n for (uint64_t z = 0; z < conn_dims[1]; ++z) {\n // Loop over Y\n for (uint64_t Y = 0; Y < conn_dims[2]; ++Y) {\n // Loop over X\n for (uint64_t X = 0; X < conn_dims[3]; ++X, ++i) {\n // Out-of-bounds check:\n if (!((z + nhood_data[d * nhood_dims[1] + 0] < 0)\n ||(z + nhood_data[d * nhood_dims[1] + 0] >= conn_dims[1])\n ||(Y + nhood_data[d * nhood_dims[1] + 1] < 0)\n ||(Y + nhood_data[d * nhood_dims[1] + 1] >= conn_dims[2])\n ||(X + nhood_data[d * nhood_dims[1] + 2] < 0)\n ||(X + nhood_data[d * nhood_dims[1] + 2] >= conn_dims[3]))) {\n ++edgeCount;\n }\n }\n }\n }\n }\n\n /* Sort all the edges in increasing order of weight */\n vector pqueue(edgeCount);\n uint64_t j = 0;\n // Loop over #edges\n for (uint64_t d = 0, i = 0; d < conn_dims[0]; ++d) {\n // Loop over Z\n for (uint64_t z = 0; z < conn_dims[1]; ++z) {\n // Loop over Y\n for (uint64_t Y = 0; Y < conn_dims[2]; ++Y) {\n // Loop over X\n for (uint64_t X = 0; X < conn_dims[3]; ++X, ++i) {\n // Out-of-bounds check:\n if (!((z + nhood_data[d * nhood_dims[1] + 0] < 0)\n ||(z + nhood_data[d * nhood_dims[1] + 0] >= conn_dims[1])\n ||(Y + nhood_data[d * nhood_dims[1] + 1] < 0)\n ||(Y + nhood_data[d * nhood_dims[1] + 1] >= conn_dims[2])\n ||(X + nhood_data[d * nhood_dims[1] + 2] < 0)\n ||(X + nhood_data[d * nhood_dims[1] + 2] >= conn_dims[3]))) {\n pqueue[j++] = i;\n }\n }\n }\n }\n }\n\n pqueue.resize(j);\n\n std::sort(pqueue.begin(), pqueue.end(),\n MalisAffinityGraphCompare(conn_data));\n\n /* Start MST */\n uint64_t minEdge;\n uint64_t e, v1, v2;\n uint64_t set1, set2;\n uint64_t nPair = 0;\n double loss = 0, dl = 0;\n uint64_t nPairIncorrect = 0;\n std::map::iterator it1, it2;\n\n /* Start Kruskal's */\n for (uint64_t i = 0; i < pqueue.size(); ++i) {\n minEdge = pqueue[i];\n // nVert = X * Y * z, minEdge in [0, X * Y * z * #edges]\n\n // e: edge dimension\n e = minEdge / nVert;\n\n // v1: node at edge beginning\n v1 = minEdge % nVert;\n\n // v2: neighborhood node at edge e\n v2 = v1 + nHood[e];\n\n // std::cout << \"V1: \" << v1 << \", V2: \" << v2 << std::endl;\n\n set1 = dsets.find_set(v1);\n set2 = dsets.find_set(v2);\n\n\n if (set1 != set2) {\n dsets.link(set1, set2);\n\n /* compute the dloss for this MST edge */\n for (it1 = overlap[set1].begin(); it1 != overlap[set1].end(); ++it1) {\n for (it2 = overlap[set2].begin(); it2 != overlap[set2].end(); ++it2) {\n nPair = it1->second * it2->second;\n\n if (pos && (it1->first == it2->first)) {\n // +ve example pairs\n dl = (Dtype(1.0) - conn_data[minEdge]);\n loss += dl * dl * nPair;\n // Use hinge loss\n dloss_data[minEdge] += dl * nPair;\n if (conn_data[minEdge] <= Dtype(0.5)) { // an error\n nPairIncorrect += nPair;\n }\n\n } else if ((!pos) && (it1->first != it2->first)) {\n // -ve example pairs\n dl = (-conn_data[minEdge]);\n loss += dl * dl * nPair;\n // Use hinge loss\n dloss_data[minEdge] += dl * nPair;\n if (conn_data[minEdge] > Dtype(0.5)) { // an error\n nPairIncorrect += nPair;\n }\n }\n }\n }\n\n if (nPairNorm > 0) {\n dloss_data[minEdge] /= nPairNorm;\n } else {\n dloss_data[minEdge] = 0;\n }\n\n if (dsets.find_set(set1) == set2) {\n std::swap(set1, set2);\n }\n\n for (it2 = overlap[set2].begin();\n it2 != overlap[set2].end(); ++it2) {\n it1 = overlap[set1].find(it2->first);\n if (it1 == overlap[set1].end()) {\n overlap[set1].insert(pair\n (it2->first, it2->second));\n } else {\n it1->second += it2->second;\n }\n }\n overlap[set2].clear();\n } // end link\n } // end while\n\n /* Return items */\n double classerr, randIndex;\n if (nPairNorm > 0) {\n loss /= nPairNorm;\n } else {\n loss = 0;\n }\n\n // std::cout << \"nPairIncorrect: \" << nPairIncorrect << std::endl;\n // std::cout << \"nPairNorm: \" << nPairNorm << std::endl;\n\n *loss_out = loss;\n classerr = static_cast(nPairIncorrect)\n / static_cast(nPairNorm);\n *classerr_out = classerr;\n randIndex = 1.0 - static_cast(nPairIncorrect)\n / static_cast(nPairNorm);\n *rand_index_out = randIndex;\n}\n\n\ntemplate\nvoid MalisLossLayer::LayerSetUp(\n const vector*>& bottom, const vector*>& top) {\n LossLayer::LayerSetUp(bottom, top);\n\n // Expected inputs:\n // Required (bottom 0 to 2):\n // Bottom 0: Predicted affinity, shaped (batch size, #edges, (Z), (Y), X)\n // Bottom 1: Ground truth affinity, shaped (batch size, #edges, (Z), (Y), X)\n // Bottom 2: Segmented ground truth, shaped (batch size, 1, (Z), (Y), X)\n\n // Optional (bottom 3):\n // Bottom 3: Edge connectivity, size #edges * 3, shaped (Z,Y,X);(Z,Y,X);...\n // (this means pairs of 3 per edge)\n}\n\ntemplate\nvoid MalisLossLayer::Reshape(\n const vector*>& bottom, const vector*>& top) {\n LossLayer::Reshape(bottom, top);\n\n if (top.size() >= 2) {\n top[1]->ReshapeLike(*bottom[0]);\n }\n\n // Up to 5 dimensional; supported modes:\n // batch, channels (edges), Z, Y, X => 3D affinity\n // batch, channels (edges), Y, X => 2D affinity\n // batch, channels (edges), X => 1D affinity\n vector shape = bottom[0]->shape();\n\n conn_dims_.clear();\n nhood_dims_.clear();\n\n // #edges, Z, Y, X specification (4 dimensions)\n conn_num_dims_ = 4;\n\n // Channel axis equals number of edges\n nedges_ = shape[1];\n\n // #edges\n conn_dims_.push_back(nedges_);\n // Z-axis\n conn_dims_.push_back(shape.size() >= 5 ? shape[shape.size() - 3] : 1);\n // Y-axis\n conn_dims_.push_back(shape.size() >= 4 ? shape[shape.size() - 2] : 1);\n // X-axis\n conn_dims_.push_back(shape.size() >= 3 ? shape[shape.size() - 1] : 1);\n\n // #edges\n nhood_dims_.push_back(nedges_);\n // 3 dimensional (always, to simplify things;\n // can just set unused spatials to 0)\n nhood_dims_.push_back(3);\n\n affinity_pos_.Reshape(shape);\n affinity_neg_.Reshape(shape);\n dloss_pos_.Reshape(shape);\n dloss_neg_.Reshape(shape);\n}\n\ntemplate\nvoid MalisLossLayer::Forward_cpu(const vector*>& bottom,\n const vector*>& top) {\n // Set up the neighborhood\n nhood_data_.clear();\n if (bottom.size() == 4) {\n // Custom edges\n for (int_tp i = 0; i < nedges_; ++i) {\n // Z edge direction\n nhood_data_.push_back(bottom[3]->cpu_data()[i * 3 + 0]);\n // Y edge direction\n nhood_data_.push_back(bottom[3]->cpu_data()[i * 3 + 1]);\n // X edge direction\n nhood_data_.push_back(bottom[3]->cpu_data()[i * 3 + 2]);\n }\n } else {\n // Dimension primary edges (+Z, +Y, +X) only:\n // 1 edge: +X (0,0,1)\n // 2 edges: +Y, +X (0,1,0); (0,0,1)\n // 3 edges: +Z, +Y, +X (1,0,0); (0,1,0); (0,0,1)\n for (int_tp i = 3 - nedges_; i < 3; ++i) {\n nhood_data_.push_back((i + 3) % 3 == 0 ? 1 : 0);\n nhood_data_.push_back((i + 2) % 3 == 0 ? 1 : 0);\n nhood_data_.push_back((i + 1) % 3 == 0 ? 1 : 0);\n }\n }\n\n // Predicted affinity\n const Dtype* affinity_prob = bottom[0]->cpu_data();\n\n // Effective affinity\n const Dtype* affinity = bottom[1]->cpu_data();\n\n Dtype* affinity_data_pos = affinity_pos_.mutable_cpu_data();\n Dtype* affinity_data_neg = affinity_neg_.mutable_cpu_data();\n\n// Affinity graph must be in the range (0,1)\n// square loss (euclidean) is used by MALIS\n for (int_tp i = 0; i < bottom[0]->count(); ++i) {\n affinity_data_pos[i] = std::min(affinity_prob[i], affinity[i]);\n affinity_data_neg[i] = std::max(affinity_prob[i], affinity[i]);\n }\n\n uint_tp batch_offset = 1;\n for (int_tp i = 1; i < bottom[0]->shape().size(); ++i) {\n batch_offset *= bottom[0]->shape()[i];\n }\n\n uint_tp components_batch_offset = 1;\n uint_tp components_channel_offset = bottom[2]->shape()[1] == 2 ? 1 : 0;\n for (int_tp i = 1; i < bottom[2]->shape().size(); ++i) {\n components_batch_offset *= bottom[2]->shape()[i];\n if (i > 1) {\n components_channel_offset *= bottom[2]->shape()[i];\n }\n }\n\n float loss = 0;\n\n#pragma omp parallel for reduction(+:loss)\n for (int_tp batch = 0; batch < bottom[0]->shape()[0]; ++batch) {\n Dtype loss_out = 0;\n Dtype classerr_out = 0;\n Dtype rand_index_out = 0;\n\n caffe_set(dloss_neg_.count(), Dtype(0.0), dloss_neg_.mutable_cpu_data());\n caffe_set(dloss_pos_.count(), Dtype(0.0), dloss_pos_.mutable_cpu_data());\n\n Malis(&affinity_data_neg[batch_offset * batch], conn_num_dims_,\n &conn_dims_[0], &nhood_data_[0], &nhood_dims_[0],\n bottom[2]->cpu_data() + components_batch_offset * batch, false,\n dloss_neg_.mutable_cpu_data() + batch_offset * batch, &loss_out,\n &classerr_out, &rand_index_out);\n\n loss += 0.5 * loss_out;\n // std::cout << \"NEG: \" << loss_out << std::endl;\n\n Malis(&affinity_data_pos[batch_offset * batch], conn_num_dims_,\n &conn_dims_[0], &nhood_data_[0], &nhood_dims_[0],\n bottom[2]->cpu_data() + components_batch_offset * batch\n + components_channel_offset, true,\n dloss_pos_.mutable_cpu_data() + batch_offset * batch, &loss_out,\n &classerr_out, &rand_index_out);\n\n loss += 0.5 * loss_out;\n // std::cout << \"POS: \" << loss_out << std::endl;\n }\n\n // Normalized loss over batch size\n top[0]->mutable_cpu_data()[0] = loss\n / (static_cast(bottom[0]->shape()[0]));\n\n if (top.size() == 2) {\n top[1]->ShareData(*(bottom[0]));\n }\n}\n\ntemplate\nvoid MalisLossLayer::Backward_cpu(const vector*>& top,\n const vector& propagate_down,\n const vector*>& bottom) {\n if (propagate_down[0]) {\n Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();\n const Dtype* dloss_pos_data = dloss_pos_.cpu_data();\n const Dtype* dloss_neg_data = dloss_neg_.cpu_data();\n\n // Clear the diff\n caffe_set(bottom[0]->count(), Dtype(0.0), bottom_diff);\n\n for (int_tp i = 0; i < bottom[0]->count(); ++i) {\n bottom_diff[i] = -(dloss_neg_data[i] + dloss_pos_data[i]) / 2.0;\n }\n }\n}\n\nINSTANTIATE_CLASS_3T_GUARDED(MalisLossLayer, (half_fp), (half_fp), (half_fp));\nINSTANTIATE_CLASS_3T_GUARDED(MalisLossLayer, (float), (float), (float));\nINSTANTIATE_CLASS_3T_GUARDED(MalisLossLayer, (double), (double), (double));\n\nREGISTER_LAYER_CLASS(MalisLoss);\nREGISTER_LAYER_CLASS_INST(MalisLoss, (half_fp), (half_fp), (half_fp));\nREGISTER_LAYER_CLASS_INST(MalisLoss, (float), (float), (float));\nREGISTER_LAYER_CLASS_INST(MalisLoss, (double), (double), (double));\n\n} // namespace caffe\n", "meta": {"hexsha": "17b4d8040e6446ee9c2eb65fe3a5fd31bab68b39", "size": 15332, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/caffe/layers/malis_loss_layer.cpp", "max_stars_repo_name": "naibaf7/caffe", "max_stars_repo_head_hexsha": "29960153c828820b1abb55a5792283742f57caa2", "max_stars_repo_licenses": ["Intel", "BSD-2-Clause"], "max_stars_count": 89.0, "max_stars_repo_stars_event_min_datetime": "2015-04-20T01:25:01.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-07T17:03:28.000Z", "max_issues_repo_path": "src/caffe/layers/malis_loss_layer.cpp", "max_issues_repo_name": "Miaomz/caffe-opencl", "max_issues_repo_head_hexsha": "505693d54298b89cf83b54778479087cff2f3bd6", "max_issues_repo_licenses": ["Intel", "BSD-2-Clause"], "max_issues_count": 62.0, "max_issues_repo_issues_event_min_datetime": "2015-06-18T13:11:20.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-19T05:00:10.000Z", "max_forks_repo_path": "src/caffe/layers/malis_loss_layer.cpp", "max_forks_repo_name": "Miaomz/caffe-opencl", "max_forks_repo_head_hexsha": "505693d54298b89cf83b54778479087cff2f3bd6", "max_forks_repo_licenses": ["Intel", "BSD-2-Clause"], "max_forks_count": 30.0, "max_forks_repo_forks_event_min_datetime": "2015-07-05T17:08:09.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-10T13:16:02.000Z", "avg_line_length": 33.3304347826, "max_line_length": 92, "alphanum_fraction": 0.5806157057, "num_tokens": 4803, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.3738758367247085, "lm_q1q2_score": 0.22017141650390945}} {"text": "/*\n * cuConebeamProjectionOperator.cpp\n *\n * Created on: Feb 18, 2015\n * Author: u051747\n */\n\n#include \"cuConebeamProjectionOperator.h\"\n#include \"conebeam_projection.h\"\n#include \n#include \"hoNDArray_math.h\"\nusing boost::math::constants::pi;\nnamespace Gadgetron {\n\ncuConebeamProjectionOperator::cuConebeamProjectionOperator() {\n\t// TODO Auto-generated constructor stub\n\n\tsamples_per_pixel_ = 1.5f;\n\tallow_offset_correction_override_=true;\n\n}\nvoid cuConebeamProjectionOperator\n::offset_correct(cuNDArray* projections){\n\n\tif( !preprocessed_ ){\n\t\tthrow std::runtime_error( \"Error: cuConebeamProjectionOperator::offset_correct: setup not performed\");\n\t}\n\tfloat SDD = acquisition_->get_geometry()->get_SDD();\n\tfloat SAD = acquisition_->get_geometry()->get_SAD();\n\tfloatd2 ps_dims_in_mm = acquisition_->get_geometry()->get_FOV();\n\tapply_offset_correct( projections,acquisition_->get_geometry()->get_offsets(),ps_dims_in_mm, SDD, SAD);\n}\n\ncuConebeamProjectionOperator::~cuConebeamProjectionOperator() {\n\t// TODO Auto-generated destructor stub\n}\n\nvoid cuConebeamProjectionOperator::mult_M(cuNDArray* input,\n\t\tcuNDArray* output, bool accumulate) {\n\n\tauto dims = *input->get_dimensions();\n\tstd::vector dims3d(dims);\n\n\tif (dims3d.size() ==4) dims3d.pop_back();\n\tauto outdims = *output->get_dimensions();\n\tstd::vector outbindims(outdims);\n\tfloat* input_ptr = input->get_data_ptr();\n\tfloat* output_ptr = output->get_data_ptr();\n\tfor (int bin = 0; bin < binning_->get_number_of_bins(); bin++){\n\t\t//Check for empty bins\n\t\tif (binning_->get_bin(bin).size() == 0)\n\t\t\tcontinue;\n\t\tcuNDArray input_view(dims3d,input_ptr);\n\t\toutbindims.back() = angles[bin].size();\n\t\tauto output_view = boost::make_shared>(outbindims,output_ptr);\n\t\tauto output_view2 = output_view;\n\t\t/*\n\t\tif (use_offset_correction_ && accumulate){\n\t\t\toutput_view2 = boost::make_shared>(outbindims);\n\t\t\tclear(output_view2.get());\n\t\t}\n\t\t */\n\n\t\tconebeam_forwards_projection(output_view2.get(),&input_view,angles[bin],offsets[bin],samples_per_pixel_,is_dims_in_mm_,acquisition_->get_geometry()->get_FOV(),acquisition_->get_geometry()->get_SDD(),acquisition_->get_geometry()->get_SAD(),accumulate);\n\n\t\t/*\n\t\tif (use_offset_correction_){\n\t\t\tapply_offset_correct(output_view2.get(),offsets[bin],acquisition_->get_geometry()->get_FOV(),acquisition_->get_geometry()->get_SDD(),acquisition_->get_geometry()->get_SAD());\n\t\t\tif (accumulate)\n\t\t\t\t*output_view += *output_view2;\n\t\t}\n\t\t */\n\n\t\tinput_ptr += input_view.get_number_of_elements();\n\t\toutput_ptr += output_view->get_number_of_elements();\n\n\n\t}\n\n}\n\nvoid cuConebeamProjectionOperator::mult_MH(cuNDArray* input,\n\t\tcuNDArray* output, bool accumulate) {\n\tauto dims = *output->get_dimensions();\n\tstd::vector dims3d = dims;\n\tif (dims3d.size() ==4) dims3d.pop_back();\n\n\tauto indims = *input->get_dimensions();\n\tstd::vector inbindims(indims);\n\tfloat* input_ptr = input->get_data_ptr();\n\tfloat* output_ptr = output->get_data_ptr();\n\tfor (int bin = 0; bin < binning_->get_number_of_bins(); bin++){\n\t\t//Check for empty bins\n\t\tif (binning_->get_bin(bin).size() == 0)\n\t\t\tcontinue;\n\n\t\tcuNDArray output_view(dims3d,output_ptr);\n\t\tinbindims.back() = angles[bin].size();\n\t\tauto input_view = boost::make_shared>(inbindims,input_ptr);\n\t\tauto input_view2 = input_view;\n\t\tif(use_offset_correction_)\n\t\t\tinput_view2 = boost::make_shared>(*input_view);\n\n\t\tvector_td is_dims_in_pixels{dims3d[0], dims3d[1],dims3d[2]};\n\n\t\tconebeam_backwards_projection(input_view2.get(),&output_view, angles[bin],offsets[bin],is_dims_in_pixels, is_dims_in_mm_,acquisition_->get_geometry()->get_FOV(),acquisition_->get_geometry()->get_SDD(),acquisition_->get_geometry()->get_SAD(),use_offset_correction_,accumulate);\n\n\t\tinput_ptr += input_view->get_number_of_elements();\n\t\toutput_ptr += output_view.get_number_of_elements();\n\n\t}\n\n\tif (mask)\n\t\tapply_mask(output,mask.get());\n\n}\n\nboost::shared_ptr> cuConebeamProjectionOperator::calculate_mask( cuNDArray* projections,float limit)\n{\n\tauto dims = *this->get_domain_dimensions();\n\tstd::vector dims3d(dims.begin(),dims.end()-1);\n\tauto indims = *projections->get_dimensions();\n\tstd::vector inbindims(indims);\n\tfloat* input_ptr = projections->get_data_ptr();\n\tauto mask = boost::make_shared>(dims);\n\tbool* mask_ptr = mask->get_data_ptr();\n\tfor (int bin = 0; bin < binning_->get_number_of_bins(); bin++){\n\t\t//Check for empty bins\n\t\tif (binning_->get_bin(bin).size() == 0)\n\t\t\tcontinue;\n\n\t\tcuNDArray mask_view(dims3d,mask_ptr);\n\t\tinbindims.back() = angles[bin].size();\n\t\tauto input_view = boost::make_shared>(inbindims,input_ptr);\n\n\t\tvector_td is_dims_in_pixels{dims3d[0], dims3d[1],dims3d[2]};\n\n\t\tconebeam_spacecarver(input_view.get(),&mask_view, angles[bin],offsets[bin],is_dims_in_pixels, is_dims_in_mm_,acquisition_->get_geometry()->get_FOV(),acquisition_->get_geometry()->get_SDD(),acquisition_->get_geometry()->get_SAD(),limit);\n\n\t\tinput_ptr += input_view->get_number_of_elements();\n\t\tmask_ptr += mask_view.get_number_of_elements();\n\n\t}\n\n\treturn mask;\n\n}\nvoid Gadgetron::cuConebeamProjectionOperator::setup( boost::shared_ptr acquisition,\n\t\tfloatd3 is_dims_in_mm, bool transform_angles )\n{\n\tacquisition_ = acquisition;\n\tis_dims_in_mm_ = is_dims_in_mm;\n\n\t// Determine the minimum and maximum angles scanned and transform array angles from [0;max_angle_].\n\t//\n\n\tstd::vector &all_angles = acquisition->get_geometry()->get_angles();\n\tif (transform_angles){\n\t\tfloat min_value = *std::min_element(all_angles.begin(), all_angles.end() );\n\t\ttransform(all_angles.begin(), all_angles.end(), all_angles.begin(), bind2nd(std::minus(), min_value));\n\t}\n\t// Are we in a short scan setup?\n\t// - we say yes if we have covered less than PI+3*delta radians\n\t//\n\n\tfloat angle_span = *std::max_element(all_angles.begin(), all_angles.end() );\n\tfloatd2 ps_dims_in_mm = acquisition_->get_geometry()->get_FOV();\n\tfloat SDD = acquisition_->get_geometry()->get_SDD();\n\tfloat delta = std::atan(ps_dims_in_mm[0]/(2.0f*SDD)); // Fan angle\n\n\tif( angle_span*pi()/180.0f > pi()+3.0f*delta )\n\t\tshort_scan_ = false;\n\telse\n\t\tshort_scan_ = true;\n\n\tstd::vector all_offsets = acquisition_->get_geometry()->get_offsets();\n\tfloatd2 mean_offset = std::accumulate(all_offsets.begin(),all_offsets.end(),floatd2(0,0))/float(all_offsets.size());\n\tif( allow_offset_correction_override_ && std::abs(mean_offset[0]) > ps_dims_in_mm[0]*0.1f )\n\t\tuse_offset_correction_ = true;\n\n\tstd::cout << \"Mean offset \" << mean_offset << \" Use offset correct: \" << (use_offset_correction_ ? \"true\" : \"false\") << std::endl;\n\tpreprocessed_ = true;\n\tif (!binning_){\n\t\tstd::vector bins(angles.size());\n\t\tstd::iota(bins.begin(),bins.end(),0);\n\t\tbinning_ = boost::make_shared(std::vector>(1,bins));\n\t}\n\n\tangles = std::vector>();\n\toffsets = std::vector>();\n\tfor (int bin =0; bin < binning_->get_number_of_bins(); bin++){\n\t\tauto binvec = binning_->get_bin(bin);\n\t\tangles.push_back(std::vector());\n\t\toffsets.push_back(std::vector());\n\t\tfor (auto index : binvec){\n\t\t\tangles.back().push_back(all_angles[index]);\n\t\t\toffsets.back().push_back(all_offsets[index]);\n\t\t}\n\n\t}\n\tauto permutations = new_order(binning_->get_bins());\n\tauto proj = acquisition->get_projections();\n\tif (proj)\n\t\t*proj =\t*permute_projections(proj,permutations);\n\n}\n\nvoid Gadgetron::cuConebeamProjectionOperator::setup( boost::shared_ptr acquisition,\n\t\tboost::shared_ptr binning,floatd3 is_dims_in_mm, \t\tbool transform_angles)\n\n{\n\n\tbinning_ = binning;\n\tsetup( acquisition, is_dims_in_mm,transform_angles );\n}\n\nstd::vector Gadgetron::cuConebeamProjectionOperator::new_order(std::vector> bins){\n\tstd::vector result;\n\tfor (auto & b : bins)\n\t\tfor (auto p : b)\n\t\t\tresult.push_back(p);\n\treturn result;\n}\n\nboost::shared_ptr > Gadgetron::cuConebeamProjectionOperator::permute_projections(\n\t\tboost::shared_ptr > projections,\n\t\tstd::vector & permutations) {\n\n\tstd::vector new_proj_dims = {projections->get_size(0),projections->get_size(1),permutations.size()};\n\n\tauto result = boost::make_shared>(new_proj_dims);\n\n\tsize_t nproj = permutations.size();\n\tsize_t proj_size = projections->get_size(0)*projections->get_size(1);\n\n\tfloat * res_ptr = result->get_data_ptr();\n\tfloat * proj_ptr = projections->get_data_ptr();\n\n\tfor (unsigned int i = 0; i < nproj; i++){\n\t\tcudaMemcpy(res_ptr+i*proj_size,proj_ptr+proj_size*permutations[i],proj_size*sizeof(float),cudaMemcpyHostToHost);\n\t}\n\treturn result;\n\n\n}\n\n\n\n} /* namespace Gadgetron */\n", "meta": {"hexsha": "6789aa75bb1b5325fd78249a1a66c4d599366f9f", "size": 8913, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "xray/cuConebeamProjectionOperator.cpp", "max_stars_repo_name": "ahsanjav/gt-tomography", "max_stars_repo_head_hexsha": "1f53d72672ccda417bc8966d8497af6d786e8935", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6.0, "max_stars_repo_stars_event_min_datetime": "2017-06-26T13:41:55.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-10T11:06:27.000Z", "max_issues_repo_path": "xray/cuConebeamProjectionOperator.cpp", "max_issues_repo_name": "ahsanjav/gt-tomography", "max_issues_repo_head_hexsha": "1f53d72672ccda417bc8966d8497af6d786e8935", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "xray/cuConebeamProjectionOperator.cpp", "max_forks_repo_name": "ahsanjav/gt-tomography", "max_forks_repo_head_hexsha": "1f53d72672ccda417bc8966d8497af6d786e8935", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-09-27T14:37:29.000Z", "max_forks_repo_forks_event_max_datetime": "2021-09-27T14:37:29.000Z", "avg_line_length": 35.652, "max_line_length": 278, "alphanum_fraction": 0.7373499383, "num_tokens": 2418, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5888891163376235, "lm_q2_score": 0.3738758227716966, "lm_q1q2_score": 0.22017140289202636}} {"text": "// Copyright (C) 2011-2012 by the BEM++ Authors\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n#include \"laplace_3d_hypersingular_boundary_operator.hpp\"\n\n#include \"blas_quadrature_helper.hpp\"\n#include \"context.hpp\"\n#include \"general_elementary_local_operator_imp.hpp\"\n#include \"general_hypersingular_integral_operator_imp.hpp\"\n#include \"laplace_3d_single_layer_boundary_operator.hpp\"\n#include \"synthetic_integral_operator.hpp\"\n\n#include \"../common/boost_make_shared_fwd.hpp\"\n\n#include \"../fiber/explicit_instantiation.hpp\"\n\n#include \"../fiber/laplace_3d_single_layer_potential_kernel_functor.hpp\"\n#include \"../fiber/laplace_3d_hypersingular_off_diagonal_kernel_functor.hpp\"\n#include \"../fiber/surface_curl_3d_functor.hpp\"\n#include \"../fiber/scalar_function_value_functor.hpp\"\n#include \"../fiber/simple_test_scalar_kernel_trial_integrand_functor.hpp\"\n#include \"../fiber/single_component_test_trial_integrand_functor.hpp\"\n\n#include \"../fiber/default_collection_of_kernels.hpp\"\n#include \"../fiber/default_collection_of_basis_transformations.hpp\"\n#include \"../fiber/default_test_kernel_trial_integral.hpp\"\n\n#include \"general_elementary_singular_integral_operator_imp.hpp\"\n#include \"../fiber/typical_test_scalar_kernel_trial_integral.hpp\"\n\n#include \n\nnamespace Bempp {\n\ntemplate \nBoundaryOperator\nlaplace3dSyntheticHypersingularBoundaryOperator(\n const shared_ptr> &context,\n const shared_ptr> &domain,\n const shared_ptr> &range,\n const shared_ptr> &dualToRange,\n std::string label, int internalSymmetry,\n const BoundaryOperator &externalSlp) {\n typedef typename ScalarTraits::RealType CoordinateType;\n\n typedef Fiber::ScalarFunctionValueFunctor ValueFunctor;\n typedef Fiber::SurfaceCurl3dFunctor CurlFunctor;\n typedef Fiber::SingleComponentTestTrialIntegrandFunctor<\n BasisFunctionType, ResultType> IntegrandFunctor;\n\n typedef GeneralElementaryLocalOperator LocalOp;\n typedef SyntheticIntegralOperator SyntheticOp;\n\n if (!domain || !range || !dualToRange)\n throw std::invalid_argument(\n \"laplace3dSyntheticHypersingularBoundaryOperator(): \"\n \"domain, range and dualToRange must not be null\");\n\n shared_ptr> newDomain = domain;\n shared_ptr> newDualToRange = dualToRange;\n\n bool isBarycentric =\n (domain->isBarycentric() || dualToRange->isBarycentric());\n\n if (isBarycentric) {\n newDomain = domain->barycentricSpace(domain);\n newDualToRange = dualToRange->barycentricSpace(dualToRange);\n }\n\n shared_ptr> internalContext,\n auxContext;\n SyntheticOp::getContextsForInternalAndAuxiliaryOperators(\n context, internalContext, auxContext);\n shared_ptr> internalTrialSpace =\n newDomain->discontinuousSpace(newDomain);\n shared_ptr> internalTestSpace =\n newDualToRange->discontinuousSpace(newDualToRange);\n\n // Note: we don't really need to care about ranges and duals to domains of\n // the internal operator. The only range space that matters is that of the\n // leftmost operator in the product.\n\n const char xyz[] = \"xyz\";\n const size_t dimWorld = 3;\n\n if (label.empty())\n label =\n AbstractBoundaryOperator::uniqueLabel();\n\n BoundaryOperator slp;\n\n if (!externalSlp.isInitialized()) {\n\n slp = laplace3dSingleLayerBoundaryOperator(\n internalContext, internalTrialSpace,\n internalTestSpace /* or whatever */, internalTestSpace,\n \"(\" + label + \")_internal_SLP\", internalSymmetry);\n\n } else {\n\n slp = externalSlp;\n }\n\n std::vector>\n trialCurlComponents;\n std::vector>\n testCurlComponents;\n testCurlComponents.resize(3);\n for (size_t i = 0; i < dimWorld; ++i)\n testCurlComponents[i] = BoundaryOperator(\n auxContext, boost::make_shared(\n internalTestSpace, range, newDualToRange,\n (\"(\" + label + \")_test_curl_\") + xyz[i], NO_SYMMETRY,\n CurlFunctor(), ValueFunctor(), IntegrandFunctor(i, 0)));\n int syntheseSymmetry = 0; // symmetry of the decomposition\n if (newDomain == newDualToRange && internalTrialSpace == internalTestSpace)\n syntheseSymmetry =\n HERMITIAN | (boost::is_complex() ? 0 : SYMMETRIC);\n else {\n trialCurlComponents.resize(3);\n for (size_t i = 0; i < dimWorld; ++i)\n trialCurlComponents[i] = BoundaryOperator(\n auxContext,\n boost::make_shared(\n newDomain, internalTrialSpace /* or whatever */,\n internalTrialSpace, (\"(\" + label + \")_trial_curl_\") + xyz[i],\n NO_SYMMETRY, ValueFunctor(), CurlFunctor(),\n IntegrandFunctor(0, i)));\n }\n\n return BoundaryOperator(\n context, boost::make_shared(testCurlComponents, slp,\n trialCurlComponents, label,\n syntheseSymmetry));\n}\n\ntemplate \nBoundaryOperator\nlaplace3dHypersingularBoundaryOperator(\n const shared_ptr> &context,\n const shared_ptr> &domain,\n const shared_ptr> &range,\n const shared_ptr> &dualToRange,\n const std::string &label, int symmetry,\n const BoundaryOperator &externalSlp) {\n const AssemblyOptions &assemblyOptions = context->assemblyOptions();\n if ((assemblyOptions.assemblyMode() == AssemblyOptions::ACA &&\n assemblyOptions.acaOptions().mode == AcaOptions::LOCAL_ASSEMBLY) ||\n (externalSlp.isInitialized()))\n return laplace3dSyntheticHypersingularBoundaryOperator(\n context, domain, range, dualToRange, label, symmetry, externalSlp);\n\n typedef typename ScalarTraits::RealType KernelType;\n typedef typename ScalarTraits::RealType CoordinateType;\n\n typedef Fiber::Laplace3dSingleLayerPotentialKernelFunctor\n KernelFunctor;\n typedef Fiber::SurfaceCurl3dFunctor TransformationFunctor;\n typedef Fiber::SimpleTestScalarKernelTrialIntegrandFunctorExt<\n BasisFunctionType, KernelType, ResultType, 3> IntegrandFunctor;\n\n typedef Fiber::Laplace3dHypersingularOffDiagonalKernelFunctor\n OffDiagonalKernelFunctor;\n typedef Fiber::ScalarFunctionValueFunctor\n OffDiagonalTransformationFunctor;\n typedef Fiber::SimpleTestScalarKernelTrialIntegrandFunctorExt<\n BasisFunctionType, KernelType, ResultType, 1> OffDiagonalIntegrandFunctor;\n\n typedef GeneralHypersingularIntegralOperator Op;\n\n shared_ptr> integral,\n offDiagonalIntegral;\n if (shouldUseBlasInQuadrature(assemblyOptions, *domain, *dualToRange)) {\n integral.reset(new Fiber::TypicalTestScalarKernelTrialIntegral<\n BasisFunctionType, KernelType, ResultType>());\n offDiagonalIntegral = integral;\n } else {\n integral.reset(new Fiber::DefaultTestKernelTrialIntegral(\n IntegrandFunctor()));\n offDiagonalIntegral.reset(\n new Fiber::DefaultTestKernelTrialIntegral(\n OffDiagonalIntegrandFunctor()));\n }\n\n shared_ptr newOp(\n new Op(domain, range, dualToRange, label, symmetry, KernelFunctor(),\n TransformationFunctor(), TransformationFunctor(), integral,\n OffDiagonalKernelFunctor(), OffDiagonalTransformationFunctor(),\n OffDiagonalTransformationFunctor(), offDiagonalIntegral));\n\n return BoundaryOperator(context, newOp);\n}\n\ntemplate \nBoundaryOperator\nlaplace3dHypersingularBoundaryOperator(\n const ParameterList& parameterList,\n const shared_ptr> &domain,\n const shared_ptr> &range,\n const shared_ptr> &dualToRange,\n const std::string &label, int symmetry,\n const BoundaryOperator &externalSlp){\n\n\n shared_ptr> context(\n new Context(parameterList));\n return laplace3dHypersingularBoundaryOperator(context, domain, range,\n dualToRange, label, symmetry,\n externalSlp);\n\n\n}\n\n\n#define INSTANTIATE_NONMEMBER_CONSTRUCTOR(BASIS, RESULT) \\\n template BoundaryOperator \\\n laplace3dHypersingularBoundaryOperator( \\\n const ParameterList&, \\\n const shared_ptr> &, \\\n const shared_ptr> &, \\\n const shared_ptr> &, const std::string &, int, \\\n const BoundaryOperator &); \\\n template BoundaryOperator \\\n laplace3dHypersingularBoundaryOperator( \\\n const shared_ptr> &, \\\n const shared_ptr> &, \\\n const shared_ptr> &, \\\n const shared_ptr> &, const std::string &, int, \\\n const BoundaryOperator &)\nFIBER_ITERATE_OVER_BASIS_AND_RESULT_TYPES(INSTANTIATE_NONMEMBER_CONSTRUCTOR);\n\n} // namespace Bempp\n", "meta": {"hexsha": "a99908695a2d3b396b29969c801d27d6e8f0cd0d", "size": 11767, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/assembly/laplace_3d_hypersingular_boundary_operator.cpp", "max_stars_repo_name": "mdavezac/bempp", "max_stars_repo_head_hexsha": "bc573062405bda107d1514e40b6153a8350d5ab5", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/assembly/laplace_3d_hypersingular_boundary_operator.cpp", "max_issues_repo_name": "mdavezac/bempp", "max_issues_repo_head_hexsha": "bc573062405bda107d1514e40b6153a8350d5ab5", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "lib/assembly/laplace_3d_hypersingular_boundary_operator.cpp", "max_forks_repo_name": "mdavezac/bempp", "max_forks_repo_head_hexsha": "bc573062405bda107d1514e40b6153a8350d5ab5", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 46.6944444444, "max_line_length": 80, "alphanum_fraction": 0.7208294383, "num_tokens": 2639, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.2201525326310721}} {"text": "#pragma once\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\nnamespace basalt {\n\ntemplate \nclass LandmarkBlockAbsDynamic : public LandmarkBlock {\n public:\n using Options = typename LandmarkBlock::Options;\n using State = typename LandmarkBlock::State;\n\n inline bool isNumericalFailure() const override {\n return state == State::NumericalFailure;\n }\n\n using Vec2 = Eigen::Matrix;\n using Vec3 = Eigen::Matrix;\n using Vec4 = Eigen::Matrix;\n\n using VecX = Eigen::Matrix;\n\n using Mat36 = Eigen::Matrix;\n\n using MatX = Eigen::Matrix;\n using RowMatX =\n Eigen::Matrix;\n\n virtual inline void allocateLandmark(\n Keypoint& lm,\n const Eigen::aligned_unordered_map,\n RelPoseLin>& relative_pose_lin,\n const Calibration& calib, const AbsOrderMap& aom,\n const Options& options,\n const std::map* rel_order = nullptr) override {\n // some of the logic assumes the members are at their initial values\n BASALT_ASSERT(state == State::Uninitialized);\n\n UNUSED(rel_order);\n\n lm_ptr = &lm;\n options_ = &options;\n calib_ = &calib;\n\n // TODO: consider for VIO that we have a lot of 0 columns if we just use aom\n // --> add option to use different AOM with reduced size and/or just\n // involved poses --> when accumulating results, check which case we have;\n // if both aom are identical, we don't have to do block-wise operations.\n aom_ = &aom;\n\n pose_lin_vec.clear();\n pose_lin_vec.reserve(lm.obs.size());\n pose_tcid_vec.clear();\n pose_tcid_vec.reserve(lm.obs.size());\n\n // LMBs without host frame should not be created\n BASALT_ASSERT(aom.abs_order_map.count(lm.host_kf_id.frame_id) > 0);\n\n for (const auto& [tcid_t, pos] : lm.obs) {\n size_t i = pose_lin_vec.size();\n\n auto it = relative_pose_lin.find(std::make_pair(lm.host_kf_id, tcid_t));\n BASALT_ASSERT(it != relative_pose_lin.end());\n\n if (aom.abs_order_map.count(tcid_t.frame_id) > 0) {\n pose_lin_vec.push_back(&it->second);\n } else {\n // Observation droped for marginalization\n pose_lin_vec.push_back(nullptr);\n }\n pose_tcid_vec.push_back(&it->first);\n\n res_idx_by_abs_pose_[it->first.first.frame_id].insert(i); // host\n res_idx_by_abs_pose_[it->first.second.frame_id].insert(i); // target\n }\n\n // number of pose-jacobian columns is determined by oam\n padding_idx = aom_->total_size;\n\n num_rows = pose_lin_vec.size() * 2 + 3; // residuals and lm damping\n\n size_t pad = padding_idx % 4;\n if (pad != 0) {\n padding_size = 4 - pad;\n }\n\n lm_idx = padding_idx + padding_size;\n res_idx = lm_idx + 3;\n num_cols = res_idx + 1;\n\n // number of columns should now be multiple of 4 for good memory alignment\n // TODO: test extending this to 8 --> 32byte alignment for float?\n BASALT_ASSERT(num_cols % 4 == 0);\n\n storage.resize(num_rows, num_cols);\n\n damping_rotations.clear();\n damping_rotations.reserve(6);\n\n state = State::Allocated;\n }\n\n // may set state to NumericalFailure --> linearization at this state is\n // unusable. Numeric check is only performed for residuals that were\n // considered to be used (valid), which depends on\n // use_valid_projections_only setting.\n virtual inline Scalar linearizeLandmark() override {\n BASALT_ASSERT(state == State::Allocated ||\n state == State::NumericalFailure ||\n state == State::Linearized || state == State::Marginalized);\n\n // storage.setZero(num_rows, num_cols);\n storage.setZero();\n damping_rotations.clear();\n damping_rotations.reserve(6);\n\n bool numerically_valid = true;\n\n Scalar error_sum = 0;\n\n size_t i = 0;\n for (const auto& [tcid_t, obs] : lm_ptr->obs) {\n std::visit(\n [&, obs = obs](const auto& cam) {\n // TODO: The pose_lin_vec[i] == nullptr is intended to deal with\n // dropped measurements during marginalization. However, dropped\n // measurements should only occur for the remaining frames, not for\n // the marginalized frames. Maybe these are observations bewtween\n // two marginalized frames, if more than one is marginalized at the\n // same time? But those we would not have to drop... Double check if\n // and when this happens and possibly resolve by fixing handling\n // here, or else updating the measurements in lmdb before calling\n // linearization. Otherwise, check where else we need a `if\n // (pose_lin_vec[i])` check or `pose_lin_vec[i] != nullptr` assert\n // in this class.\n\n if (pose_lin_vec[i]) {\n size_t obs_idx = i * 2;\n size_t abs_h_idx =\n aom_->abs_order_map.at(pose_tcid_vec[i]->first.frame_id)\n .first;\n size_t abs_t_idx =\n aom_->abs_order_map.at(pose_tcid_vec[i]->second.frame_id)\n .first;\n\n Vec2 res;\n Eigen::Matrix d_res_d_xi;\n Eigen::Matrix d_res_d_p;\n\n using CamT = std::decay_t;\n bool valid = linearizePoint(\n obs, *lm_ptr, pose_lin_vec[i]->T_t_h, cam, res, &d_res_d_xi,\n &d_res_d_p);\n\n if (!options_->use_valid_projections_only || valid) {\n numerically_valid = numerically_valid &&\n d_res_d_xi.array().isFinite().all() &&\n d_res_d_p.array().isFinite().all();\n\n const Scalar res_squared = res.squaredNorm();\n const auto [weighted_error, weight] =\n compute_error_weight(res_squared);\n const Scalar sqrt_weight =\n std::sqrt(weight) / options_->obs_std_dev;\n\n error_sum += weighted_error /\n (options_->obs_std_dev * options_->obs_std_dev);\n\n storage.template block<2, 3>(obs_idx, lm_idx) =\n sqrt_weight * d_res_d_p;\n storage.template block<2, 1>(obs_idx, res_idx) =\n sqrt_weight * res;\n\n d_res_d_xi *= sqrt_weight;\n storage.template block<2, 6>(obs_idx, abs_h_idx) +=\n d_res_d_xi * pose_lin_vec[i]->d_rel_d_h;\n storage.template block<2, 6>(obs_idx, abs_t_idx) +=\n d_res_d_xi * pose_lin_vec[i]->d_rel_d_t;\n }\n }\n\n i++;\n },\n calib_->intrinsics[tcid_t.cam_id].variant);\n }\n\n if (numerically_valid) {\n state = State::Linearized;\n } else {\n state = State::NumericalFailure;\n }\n\n return error_sum;\n }\n\n virtual inline void performQR() override {\n BASALT_ASSERT(state == State::Linearized);\n\n // Since we use dense matrices Householder QR might be better:\n // https://mathoverflow.net/questions/227543/why-householder-reflection-is-better-than-givens-rotation-in-dense-linear-algebr\n\n if (options_->use_householder) {\n performQRHouseholder();\n } else {\n performQRGivens();\n }\n\n state = State::Marginalized;\n }\n\n // Sets damping and maintains upper triangular matrix for landmarks.\n virtual inline void setLandmarkDamping(Scalar lambda) override {\n BASALT_ASSERT(state == State::Marginalized);\n BASALT_ASSERT(lambda >= 0);\n\n if (hasLandmarkDamping()) {\n BASALT_ASSERT(damping_rotations.size() == 6);\n\n // undo dampening\n for (int n = 2; n >= 0; n--) {\n for (int m = n; m >= 0; m--) {\n storage.applyOnTheLeft(num_rows - 3 + n - m, n,\n damping_rotations.back().adjoint());\n damping_rotations.pop_back();\n }\n }\n }\n\n if (lambda == 0) {\n storage.template block<3, 3>(num_rows - 3, lm_idx).diagonal().setZero();\n } else {\n BASALT_ASSERT(Jl_col_scale.array().isFinite().all());\n\n storage.template block<3, 3>(num_rows - 3, lm_idx)\n .diagonal()\n .setConstant(sqrt(lambda));\n\n BASALT_ASSERT(damping_rotations.empty());\n\n // apply dampening and remember rotations to undo\n for (int n = 0; n < 3; n++) {\n for (int m = 0; m <= n; m++) {\n damping_rotations.emplace_back();\n damping_rotations.back().makeGivens(\n storage(n, lm_idx + n),\n storage(num_rows - 3 + n - m, lm_idx + n));\n storage.applyOnTheLeft(num_rows - 3 + n - m, n,\n damping_rotations.back());\n }\n }\n }\n }\n\n // lambda < 0 means computing exact model cost change\n virtual inline void backSubstitute(const VecX& pose_inc,\n Scalar& l_diff) override {\n BASALT_ASSERT(state == State::Marginalized);\n\n // For now we include all columns in LMB\n BASALT_ASSERT(pose_inc.size() == signed_cast(padding_idx));\n\n const auto Q1Jl = storage.template block<3, 3>(0, lm_idx)\n .template triangularView();\n\n const auto Q1Jr = storage.col(res_idx).template head<3>();\n const auto Q1Jp = storage.topLeftCorner(3, padding_idx);\n\n Vec3 inc = -Q1Jl.solve(Q1Jr + Q1Jp * pose_inc);\n\n // We want to compute the model cost change. The model function is\n //\n // L(inc) = F(x) + inc^T J^T r + 0.5 inc^T J^T J inc\n //\n // and thus the expected decrease in cost for the computed increment is\n //\n // l_diff = L(0) - L(inc)\n // = - inc^T J^T r - 0.5 inc^T J^T J inc\n // = - inc^T J^T (r + 0.5 J inc)\n // = - (J inc)^T (r + 0.5 (J inc)).\n //\n // Here we have J = [Jp, Jl] under the orthogonal projection Q = [Q1, Q2],\n // i.e. the linearized system (model cost) is\n //\n // L(inc) = 0.5 || J inc + r ||^2 = 0.5 || Q^T J inc + Q^T r ||^2\n //\n // and below we thus compute\n //\n // l_diff = - (Q^T J inc)^T (Q^T r + 0.5 (Q^T J inc)).\n //\n // We have\n // | Q1^T | | Q1^T Jp Q1^T Jl |\n // Q^T J = | | [Jp, Jl] = | |\n // | Q2^T | | Q2^T Jp 0 |.\n //\n // Note that Q2 is the nullspace of Jl, and Q1^T Jl == R. So with inc =\n // [incp^T, incl^T]^T we have\n //\n // | Q1^T Jp incp + Q1^T Jl incl |\n // Q^T J inc = | |\n // | Q2^T Jp incp |\n //\n\n // undo damping before we compute the model cost difference\n setLandmarkDamping(0);\n\n // compute \"Q^T J incp\"\n VecX QJinc = storage.topLeftCorner(num_rows - 3, padding_idx) * pose_inc;\n\n // add \"Q1^T Jl incl\" to the first 3 rows\n QJinc.template head<3>() += Q1Jl * inc;\n\n auto Qr = storage.col(res_idx).head(num_rows - 3);\n l_diff -= QJinc.transpose() * (Scalar(0.5) * QJinc + Qr);\n\n // TODO: detect and handle case like ceres, allowing a few iterations but\n // stopping eventually\n if (!inc.array().isFinite().all() ||\n !lm_ptr->direction.array().isFinite().all() ||\n !std::isfinite(lm_ptr->inv_dist)) {\n std::cerr << \"Numerical failure in backsubstitution\\n\";\n }\n\n // Note: scale only after computing model cost change\n inc.array() *= Jl_col_scale.array();\n\n lm_ptr->direction += inc.template head<2>();\n lm_ptr->inv_dist = std::max(Scalar(0), lm_ptr->inv_dist + inc[2]);\n }\n\n virtual inline size_t numReducedCams() const override {\n BASALT_LOG_FATAL(\"check what we mean by numReducedCams for absolute poses\");\n return pose_lin_vec.size();\n }\n\n inline void addQ2JpTQ2Jp_mult_x(VecX& res,\n const VecX& x_pose) const override {\n UNUSED(res);\n UNUSED(x_pose);\n BASALT_LOG_FATAL(\"not implemented\");\n }\n\n virtual inline void addQ2JpTQ2r(VecX& res) const override {\n UNUSED(res);\n BASALT_LOG_FATAL(\"not implemented\");\n }\n\n virtual inline void addJp_diag2(VecX& res) const override {\n BASALT_ASSERT(state == State::Linearized);\n\n for (const auto& [frame_id, idx_set] : res_idx_by_abs_pose_) {\n const int pose_idx = aom_->abs_order_map.at(frame_id).first;\n for (const int i : idx_set) {\n const auto block = storage.block(2 * i, pose_idx, 2, POSE_SIZE);\n\n res.template segment(pose_idx) +=\n block.colwise().squaredNorm();\n }\n }\n }\n\n virtual inline void addQ2JpTQ2Jp_blockdiag(\n BlockDiagonalAccumulator& accu) const override {\n UNUSED(accu);\n BASALT_LOG_FATAL(\"not implemented\");\n }\n\n virtual inline void scaleJl_cols() override {\n BASALT_ASSERT(state == State::Linearized);\n\n // ceres uses 1.0 / (1.0 + sqrt(SquaredColumnNorm))\n // we use 1.0 / (eps + sqrt(SquaredColumnNorm))\n Jl_col_scale =\n (options_->jacobi_scaling_eps +\n storage.block(0, lm_idx, num_rows - 3, 3).colwise().norm().array())\n .inverse();\n\n storage.block(0, lm_idx, num_rows - 3, 3) *= Jl_col_scale.asDiagonal();\n }\n\n virtual inline void scaleJp_cols(const VecX& jacobian_scaling) override {\n BASALT_ASSERT(state == State::Marginalized);\n\n // we assume we apply scaling before damping (we exclude the last 3 rows)\n BASALT_ASSERT(!hasLandmarkDamping());\n\n storage.topLeftCorner(num_rows - 3, padding_idx) *=\n jacobian_scaling.asDiagonal();\n }\n\n inline bool hasLandmarkDamping() const { return !damping_rotations.empty(); }\n\n virtual inline void printStorage(const std::string& filename) const override {\n std::ofstream f(filename);\n\n Eigen::IOFormat CleanFmt(4, 0, \" \", \"\\n\", \"\", \"\");\n\n f << \"Storage (state: \" << state\n << \", damping: \" << (hasLandmarkDamping() ? \"yes\" : \"no\")\n << \" Jl_col_scale: \" << Jl_col_scale.transpose() << \"):\\n\"\n << storage.format(CleanFmt) << std::endl;\n\n f.close();\n }\n#if 0\n virtual inline void stage2(\n Scalar lambda, const VecX* jacobian_scaling, VecX* precond_diagonal2,\n BlockDiagonalAccumulator* precond_block_diagonal,\n VecX& bref) override {\n // 1. scale jacobian\n if (jacobian_scaling) {\n scaleJp_cols(*jacobian_scaling);\n }\n\n // 2. dampen landmarks\n setLandmarkDamping(lambda);\n\n // 3a. compute diagonal preconditioner (SCHUR_JACOBI_DIAGONAL)\n if (precond_diagonal2) {\n addQ2Jp_diag2(*precond_diagonal2);\n }\n\n // 3b. compute block diagonal preconditioner (SCHUR_JACOBI)\n if (precond_block_diagonal) {\n addQ2JpTQ2Jp_blockdiag(*precond_block_diagonal);\n }\n\n // 4. compute rhs of reduced camera normal equations\n addQ2JpTQ2r(bref);\n }\n#endif\n\n inline State getState() const override { return state; }\n\n virtual inline size_t numQ2rows() const override { return num_rows - 3; }\n\n protected:\n inline void performQRGivens() {\n // Based on \"Matrix Computations 4th Edition by Golub and Van Loan\"\n // See page 252, Algorithm 5.2.4 for how these two loops work\n Eigen::JacobiRotation gr;\n for (size_t n = 0; n < 3; n++) {\n for (size_t m = num_rows - 4; m > n; m--) {\n gr.makeGivens(storage(m - 1, lm_idx + n), storage(m, lm_idx + n));\n storage.applyOnTheLeft(m, m - 1, gr);\n }\n }\n }\n\n inline void performQRHouseholder() {\n VecX tempVector1(num_cols);\n VecX tempVector2(num_rows - 3);\n\n for (size_t k = 0; k < 3; ++k) {\n size_t remainingRows = num_rows - k - 3;\n\n Scalar beta;\n Scalar tau;\n storage.col(lm_idx + k)\n .segment(k, remainingRows)\n .makeHouseholder(tempVector2, tau, beta);\n\n storage.block(k, 0, remainingRows, num_cols)\n .applyHouseholderOnTheLeft(tempVector2, tau, tempVector1.data());\n }\n }\n\n inline std::tuple compute_error_weight(\n Scalar res_squared) const {\n // Note: Definition of cost is 0.5 ||r(x)||^2 to be in line with ceres\n\n if (options_->huber_parameter > 0) {\n // use huber norm\n const Scalar huber_weight =\n res_squared <= options_->huber_parameter * options_->huber_parameter\n ? Scalar(1)\n : options_->huber_parameter / std::sqrt(res_squared);\n const Scalar error =\n Scalar(0.5) * (2 - huber_weight) * huber_weight * res_squared;\n return {error, huber_weight};\n } else {\n // use squared norm\n return {Scalar(0.5) * res_squared, Scalar(1)};\n }\n }\n\n void get_dense_Q2Jp_Q2r(MatX& Q2Jp, VecX& Q2r,\n size_t start_idx) const override {\n Q2r.segment(start_idx, num_rows - 3) =\n storage.col(res_idx).tail(num_rows - 3);\n\n BASALT_ASSERT(Q2Jp.cols() == signed_cast(padding_idx));\n\n Q2Jp.block(start_idx, 0, num_rows - 3, padding_idx) =\n storage.block(3, 0, num_rows - 3, padding_idx);\n }\n\n void get_dense_Q2Jp_Q2r_rel(\n MatX& Q2Jp, VecX& Q2r, size_t start_idx,\n const std::map& rel_order) const override {\n UNUSED(Q2Jp);\n UNUSED(Q2r);\n UNUSED(start_idx);\n UNUSED(rel_order);\n BASALT_LOG_FATAL(\"not implemented\");\n }\n\n void add_dense_H_b(DenseAccumulator& accum) const override {\n UNUSED(accum);\n BASALT_LOG_FATAL(\"not implemented\");\n }\n\n void add_dense_H_b(MatX& H, VecX& b) const override {\n const auto r = storage.col(res_idx).tail(num_rows - 3);\n const auto J = storage.block(3, 0, num_rows - 3, padding_idx);\n\n H.noalias() += J.transpose() * J;\n b.noalias() += J.transpose() * r;\n }\n\n void add_dense_H_b_rel(\n MatX& H_rel, VecX& b_rel,\n const std::map& rel_order) const override {\n UNUSED(H_rel);\n UNUSED(b_rel);\n UNUSED(rel_order);\n BASALT_LOG_FATAL(\"not implemented\");\n }\n\n const Eigen::PermutationMatrix& get_rel_permutation()\n const override {\n BASALT_LOG_FATAL(\"not implemented\");\n }\n\n Eigen::PermutationMatrix compute_rel_permutation(\n const std::map& rel_order) const override {\n UNUSED(rel_order);\n BASALT_LOG_FATAL(\"not implemented\");\n }\n\n void add_dense_H_b_rel_2(MatX& H_rel, VecX& b_rel) const override {\n UNUSED(H_rel);\n UNUSED(b_rel);\n BASALT_LOG_FATAL(\"not implemented\");\n }\n\n virtual TimeCamId getHostKf() const override { return lm_ptr->host_kf_id; }\n\n private:\n // Dense storage for pose Jacobians, padding, landmark Jacobians and\n // residuals [J_p | pad | J_l | res]\n Eigen::Matrix\n storage;\n\n Vec3 Jl_col_scale = Vec3::Ones();\n std::vector> damping_rotations;\n\n std::vector*> pose_lin_vec;\n std::vector*> pose_tcid_vec;\n size_t padding_idx = 0;\n size_t padding_size = 0;\n size_t lm_idx = 0;\n size_t res_idx = 0;\n\n size_t num_cols = 0;\n size_t num_rows = 0;\n\n const Options* options_ = nullptr;\n\n State state = State::Uninitialized;\n\n Keypoint* lm_ptr = nullptr;\n const Calibration* calib_ = nullptr;\n const AbsOrderMap* aom_ = nullptr;\n\n std::map> res_idx_by_abs_pose_;\n};\n\n} // namespace basalt\n", "meta": {"hexsha": "cc10c99bc7522896ba1b73a2d58026ae820b8a4c", "size": 19639, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/basalt/linearization/landmark_block_abs_dynamic.hpp", "max_stars_repo_name": "kwang-12/basalt-mirror", "max_stars_repo_head_hexsha": "9dd7b2c8031283ec033211bc90ad70aa70323eaa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 445.0, "max_stars_repo_stars_event_min_datetime": "2019-04-18T01:13:58.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T20:54:45.000Z", "max_issues_repo_path": "include/basalt/linearization/landmark_block_abs_dynamic.hpp", "max_issues_repo_name": "kwang-12/basalt-mirror", "max_issues_repo_head_hexsha": "9dd7b2c8031283ec033211bc90ad70aa70323eaa", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/basalt/linearization/landmark_block_abs_dynamic.hpp", "max_forks_repo_name": "kwang-12/basalt-mirror", "max_forks_repo_head_hexsha": "9dd7b2c8031283ec033211bc90ad70aa70323eaa", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 162.0, "max_forks_repo_forks_event_min_datetime": "2019-04-18T09:10:48.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T08:35:39.000Z", "avg_line_length": 33.5136518771, "max_line_length": 129, "alphanum_fraction": 0.6140333011, "num_tokens": 5319, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.22015253263107204}} {"text": "\n// Distributed under the Boost Software License, Version 1.0.\n// (See accompanying file LICENSE_1_0.txt or copy at\n// https://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_ASTRONOMY_COORDINATE_BASE_COORDINATE_SYSTEM_HPP\n#define BOOST_ASTRONOMY_COORDINATE_BASE_COORDINATE_SYSTEM_HPP\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\n\nnamespace boost { namespace astronomy { namespace coordinate {\n\ntypedef boost::geometry::degree degree;\ntypedef boost::geometry::radian radian;\n\nnamespace bu = boost::units;\nnamespace bg = boost::geometry;\n\ntemplate \nstruct base_frame\n{\n ///@cond INTERNAL\n BOOST_STATIC_ASSERT_MSG((boost::astronomy::detail::is_base_template_of\n ::value),\n \"First template argument is expected to be a representation class\");\n BOOST_STATIC_ASSERT_MSG((boost::astronomy::detail::is_base_template_of\n ::value),\n \"Second template argument is expected to be a differential class\");\n ///@endcond\n\nprotected:\n Representation data;\n Differential motion;\n\npublic:\n typedef Representation representation;\n typedef Differential differential;\n\n //!returns 2d propermotion\n std::tuple\n <\n typename Differential::quantity1,\n typename Differential::quantity2\n > get_proper_motion() const\n {\n return std::make_tuple\n (\n Differential::quantity1::from_value(bg::get<0>(motion.get_differential())),\n Differential::quantity2::from_value(bg::get<1>(motion.get_differential()))\n );\n }\n\n //!returns radial velocity (line of sight velocity) of the object\n typename Differential::quantity3 get_radial_velocity() const\n {\n return Differential::quantity3::from_value(bg::get<2>(motion.get_differential()));\n }\n\n /*!returns differential data in form of boost::geometry::model::point \n having components (pm_lat, pm_lon, radial_velocity) including cos(lat) component*/\n Differential get_differential() const\n {\n return motion;\n }\n\n //!set differential for the motion of the object\n void set_differential(Differential const& other)\n {\n this->motion = other;\n }\n\n /*!returns coordinate data in form of boost::geometry::model::point\n having components (lat, lon, distance)*/\n Representation get_data() const\n {\n return data;\n }\n\n //!returns data in specified subclass of base_representation\n //template \n //ReturnType represent_as() const\n //{\n // BOOST_STATIC_ASSERT_MSG((boost::astronomy::detail::is_base_template_of\n // ::value), \n // \"return type is expected to be a representation class\");\n\n // return ReturnType(data);\n //}\n\n //!angular separation between two coordinates in radians\n bu::quantity get_angular_separation(base_frame const& other) const\n {\n return bu::quantity::from_value(\n std::acos((dot(this->data, other.get_data()) / \n (magnitude(this->data) * magnitude(other.get_data()))).value())\n );\n }\n \n};\n}}} //namespace boost::astronomy::coordinate\n#endif // !BOOST_ASTRONOMY_COORDINATE_BASE_COORDINATE_SYSTEM_HPP\n", "meta": {"hexsha": "29cb888103cf71e8a05aded7e6835e09f1474b8a", "size": 3886, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/astronomy/coordinate/base_frame.hpp", "max_stars_repo_name": "Solariii/astronomy", "max_stars_repo_head_hexsha": "6adad8e3f10c318b61b61d0b1836f2be19da01e3", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/boost/astronomy/coordinate/base_frame.hpp", "max_issues_repo_name": "Solariii/astronomy", "max_issues_repo_head_hexsha": "6adad8e3f10c318b61b61d0b1836f2be19da01e3", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/boost/astronomy/coordinate/base_frame.hpp", "max_forks_repo_name": "Solariii/astronomy", "max_forks_repo_head_hexsha": "6adad8e3f10c318b61b61d0b1836f2be19da01e3", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.7913043478, "max_line_length": 91, "alphanum_fraction": 0.7071538857, "num_tokens": 849, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804337438501, "lm_q2_score": 0.3557749071749625, "lm_q1q2_score": 0.2201465513769013}} {"text": "/*! \\file hydrodynamics_2d.hpp\n \\brief Various manipulations of hydrodynamic variables\n \\author Almog Yalinewich\n*/\n\n#ifndef HYDRODYNAMICS_2D_HPP\n#define HYDRODYNAMICS_2D_HPP 1\n\n#include \"spatial_distribution2d.hpp\"\n#include \"../common/equation_of_state.hpp\"\n#include \"point_motion.hpp\"\n#include \"spatial_reconstruction.hpp\"\n#include \"SourceTerm.hpp\"\n#include \"../../misc/utils.hpp\"\n#include \"../../misc/lazy_list.hpp\"\n#include \"../common/hydrodynamics.hpp\"\n#include \"../../misc/universal_error.hpp\"\n#include \"../../tessellation/ConvexHull.hpp\"\n#include \"../../tessellation/polygon_overlap_area.hpp\"\n#include \n#include \"../../tessellation/EdgeLengthCorrect.hpp\"\n#include \"physical_geometry.hpp\"\n#include \"time_step_function.hpp\"\n#include \"cache_data.hpp\"\n#include \"../common/riemann_solver.hpp\"\n\n/*! \\brief Rotates primitive variables to align with edge\n \\param n Normal directions\n \\param p Parallel direction\n \\param cell Primitive variables\n \\return Rotated cell\n */\nPrimitive RotatePrimitive(const Vector2D& n,\n\tconst Vector2D& p,\n\tconst Primitive& cell);\n\n/*! \\brief Rotates flux from the edge frame back to the lab frame\n \\param c Flux\n \\param n Normal direction\n \\param p Parallel direction\n \\return Rotated flux\n */\nConserved RotateFluxBack(const Conserved& c,\n\tconst Vector2D& n,\n\tconst Vector2D& p);\n\n/*! \\brief Given an edge and an index of one neighbor, returns the index of another neighbor\n \\param edge Voronoi edge\n \\param index Index of one neighbor\n \\return Index of the other neighbor\n */\nint get_other_index(const Edge& edge,\n\tconst int index);\n\n/*! \\brief Initialize computational cells\n \\param density Density distribution\n \\param pressure Pressure distribution\n \\param xvelocity Distribution of the x component of the velocity\n \\param yvelocity Distribution of the y component of the velocity\n \\param eos Equation of state\n \\param tess Tessellation\n \\param CMvalue Determines whether to evaluate the spatial distributions in the mesh generating points or the center of mass\n \\return List of primitive variables\n*/\nvector InitialiseCells\n(SpatialDistribution const& density,\n\tSpatialDistribution const& pressure,\n\tSpatialDistribution const& xvelocity,\n\tSpatialDistribution const& yvelocity,\n\tEquationOfState const& eos,\n\tTessellation const& tess, bool CMvalue = true);\n\n/*! \\brief Calculates the intensive conserved variables\n \\param cells Hydrodynamical cells\n \\return List of conserved variables\n*/\nvector CalcConservedIntensive\n(vector const& cells);\n\n/*! \\brief Calculates the extensive conserved variables\n \\param cons_int Conserved intensive variables\n \\param tess Tessellation\n \\param pg Physical geometry\n \\return List of conserved variables\n*/\nvector CalcConservedExtensive\n(const vector& cons_int,\n\tconst Tessellation& tess,\n\tconst PhysicalGeometry& pg);\n\n/*! \\brief Calculates the time step for a cell\n \\param cell Computational cell\n \\param width Cell width\n \\param face_velocites Velocities of the edges of the cell\n \\return Time step\n*/\ndouble TimeStepForCell(Primitive const& cell,\n\tdouble width, vector const& face_velocites);\n\n/*! \\brief Move mesh points\n \\param pointvelocity Velocities of all mesh points\n \\param dt Time step\n \\param tessellation Tessellation\n \\param oldpoints Possible input for the location of the old points\n \\param reorder Should we reorder the points\n \\return The indeces of the hilbert order of the points\n*/\nvector MoveMeshPoints(vector const& pointvelocity,\n\tdouble dt, Tessellation& tessellation, bool reorder,\n\tvector oldpoints = vector());\n/*! \\brief Move mesh points\n \\param pointvelocity Velocities of all mesh points\n \\param dt Time step\n \\param tessellation The local tessellation\n \\param vproc The processor tessellation\n \\param oldpoints Possible input for the location of the old points\n \\param reorder Should we reorder the points\n \\return The indeces of the hilbert order of the points\n*/\nvector MoveMeshPoints(vector const& pointvelocity,\n\tdouble dt, Tessellation& tessellation, Tessellation const& vproc, bool reorder,\n\tvector oldpoints = vector());\n\n/*! \\brief Calculates the intensive conserved variables\n \\param tess Tessellation\n \\param extensive Extensive conserved variables\n \\param pg Physical geometry\n \\return List of intensive conserved variables\n */\nvector calc_conserved_intensive\n(const Tessellation& tess,\n\tconst vector& extensive,\n\tconst PhysicalGeometry& pg);\n\n/*! \\brief Updates the intensive conserved variables\n \\param tessellation Tessellation\n \\param conservedextensive Extensive conserved variables\n \\param conservedintensive Intensive conserved variables\n*/\nvoid UpdateConservedIntensive(Tessellation const& tessellation,\n\tvector const& conservedextensive,\n\tvector& conservedintensive);\n\n/*! \\brief Calculates the flux in the bulk of the fluid\n \\param normaldir A unit vector normal to the interface\n \\param paraldir A unit vector parallel to the interface\n \\param left Primitive variables on the left side of the interface\n \\param right Primitive variables on the right side of the interface\n \\param edge_velocity Velocity of the interface\n \\param rs Riemann solver\n \\return Flux\n */\nConserved FluxInBulk(Vector2D const& normaldir,\n\tVector2D const& paraldir,\n\tPrimitive const& left,\n\tPrimitive const& right,\n\tVector2D const& edge_velocity,\n\tRiemannSolver const& rs);\n\n/*! \\brief Adds force contribution to the extensive conserved variables\n \\param tess Tessellation\n \\param pg Physical geometry\n \\param cd Cache data\n \\param cells Computational cells\n \\param fluxes Fluxes\n \\param point_velocities Velocities of the mesh generating points\n \\param source Source term\n \\param t Time\n \\param dt Time step\n \\param extensives Extensive variables\n \\param tracerstickernames The names of the tracers and stickers\n */\nvoid ExternalForceContribution\n(const Tessellation& tess,\n\tconst PhysicalGeometry& pg,\n\tconst CacheData& cd,\n\tconst vector& cells,\n\tconst vector& fluxes,\n\tconst vector& point_velocities,\n\tconst SourceTerm& source,\n\tdouble t,\n\tdouble dt,\n\tvector& extensives,\n\tTracerStickerNames const& tracerstickernames);\n\n/*! \\brief Returns the position of all mesh generating points\n \\param tess Tessellation\n \\return Position of all mesh generating points\n */\nvector get_all_mesh_points\n(Tessellation const& tess);\n\n/*! \\brief Changes the energy and sound speed so they would satisfy the equation of state\n \\param vp Primitive variables\n \\param eos Equation of state\n \\return Primitive variable with corrected sound speed and energy\n */\nvector make_eos_consistent\n(vector const& vp,\n\tEquationOfState const& eos);\n\n/*! \\brief Returns the energies due to external potentials\n \\param tess Tessellation\n \\param g TBA\n \\return TBA\n \\todo Add documentation\n */\nvector GetForceEnergy(Tessellation const& tess,\n\tvector const& g);\n\n/*! \\brief Calculates extensive tracers\n \\param intensive_tracer Intensive tracers\n \\param tess Tessellation\n \\param cells List of primitive variables\n \\param pg Physical geometry\n \\return List of extensive tracers\n */\nvector > calc_extensive_tracer\n(const vector > & intensive_tracer,\n\tconst Tessellation& tess,\n\tconst vector& cells,\n\tconst PhysicalGeometry& pg);\n\n/*! \\brief Calculates the extensive tracer\n \\param tracer Intensive tracer\n \\param tess Tessellation\n \\param cells Fluid elements\n \\param result List of extensive tracers\n */\nvoid MakeTracerExtensive\n(vector > const&tracer,\n\tTessellation const& tess, vector const& cells,\n\tvector > &result);\n\n/*! \\brief Makes a list of points to remove\n \\param tess Tessellation\n \\param point TBA\n \\param R Radius?\n \\param PointToRemove output\n \\param Inner TBA\n \\todo Add documentation\n */\nvoid GetPointToRemove(Tessellation const& tess, Vector2D const& point,\n\tdouble R, vector & PointToRemove, int Inner);\n\n/*! \\brief Applies a correction to the extensive variables due to the change in volume during time step.\n \\details This method calculates the change in extensive by calculating the volume swept by an edge and multiplying it by the intensive variables of the respective cell.\n \\param extensive Extensive variables\n \\param intensive Intensive variables\n \\param tessold Old tessellation\n \\param tessnew New tessellation\n \\param facevelocity Face velocity\n \\param dt Time step\n \\param pointvelocity Velocities of mesh generating points\n */\nvoid FixAdvection(vector& extensive,\n\tvector const& intensive,\n\tTessellation const& tessold,\n\tTessellation const& tessnew,\n\tvector const& facevelocity,\n\tdouble dt, vector const& pointvelocity);\n\n/*! \\brief Determines the time step\n \\param hydro_time_step Time step derived from hydrodynamics\n \\param external_dt Time step suggested by user\n \\param current_time Current simulation time\n \\param end_time Termination time\n \\return Time step\n */\ndouble determine_time_step(double hydro_time_step,\n\tdouble external_dt,\n\tdouble current_time,\n\tdouble end_time);\n\n#endif // HYDRODYNAMICS_2D_HPP\n", "meta": {"hexsha": "f1868d4f79205b95a634b4e35492d749e507632a", "size": 9341, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lib/SurfacingAlgorithms/huji-rich-Elad3DFast/source/newtonian/two_dimensional/hydrodynamics_2d.hpp", "max_stars_repo_name": "GalaxyHunters/Vivid", "max_stars_repo_head_hexsha": "f724e5671b650433d0c26319c86231bd3b246e4e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/SurfacingAlgorithms/huji-rich-Elad3DFast/source/newtonian/two_dimensional/hydrodynamics_2d.hpp", "max_issues_repo_name": "GalaxyHunters/Vivid", "max_issues_repo_head_hexsha": "f724e5671b650433d0c26319c86231bd3b246e4e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 222.0, "max_issues_repo_issues_event_min_datetime": "2018-07-25T18:13:57.000Z", "max_issues_repo_issues_event_max_datetime": "2021-10-10T14:54:04.000Z", "max_forks_repo_path": "lib/SurfacingAlgorithms/huji-rich-Elad3DFast/source/newtonian/two_dimensional/hydrodynamics_2d.hpp", "max_forks_repo_name": "GalaxyHunters/Vivid", "max_forks_repo_head_hexsha": "f724e5671b650433d0c26319c86231bd3b246e4e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2018-07-29T09:39:40.000Z", "max_forks_repo_forks_event_max_datetime": "2018-08-25T19:17:49.000Z", "avg_line_length": 33.8442028986, "max_line_length": 170, "alphanum_fraction": 0.7804303608, "num_tokens": 2192, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073802837477, "lm_q2_score": 0.39981164073979497, "lm_q1q2_score": 0.22013924011468539}} {"text": "/*\n * @file pppBayesTree.cpp\n * @brief Iterative GPS Range/Phase Estimator with collected data\n * @author Ryan Watson & Jason Gross\n */\n\n\n#include \"nonlinear/ISAM2.h\"\n#include \"nonlinear/NonlinearFactorGraph.h\"\n#include \"inference/Symbol.h\"\n\n#include \"slam/PriorFactor.h\"\n#include \"slam/BetweenFactor.h\"\n#include \"pppbayestree/gnssNavigation/GnssData.h\"\n#include \"pppbayestree/gnssNavigation/GnssTools.h\"\n#include \"pppbayestree/gnssNavigation/PhaseFactor.h\"\n#include \"pppbayestree/gnssNavigation/nonBiasStates.h\"\n#include \"pppbayestree/configReader/ConfDataReader.hpp\"\n\n#include \"pppbayestree/gnssNavigation/PseudorangeFactor.h\"\n\n\n// GPSTK\n#include \"pppbayestree/gpstk/MJD.hpp\"\n#include \"pppbayestree/gpstk/PowerSum.hpp\"\n#include \"pppbayestree/gpstk/Decimate.hpp\"\n#include \"pppbayestree/gpstk/SolidTides.hpp\"\n#include \"pppbayestree/gpstk/PoleTides.hpp\"\n#include \"pppbayestree/gpstk/TropModel.hpp\"\n#include \"pppbayestree/gpstk/BasicModel.hpp\"\n#include \"pppbayestree/gpstk/CommonTime.hpp\"\n#include \"pppbayestree/gpstk/PCSmoother.hpp\"\n#include \"pppbayestree/gpstk/OceanLoading.hpp\"\n#include \"pppbayestree/gpstk/CodeSmoother.hpp\"\n#include \"pppbayestree/gpstk/SimpleFilter.hpp\"\n#include \"pppbayestree/gpstk/MWCSDetector.hpp\"\n#include \"pppbayestree/gpstk/SatArcMarker.hpp\"\n#include \"pppbayestree/gpstk/DCBDataReader.hpp\"\n#include \"pppbayestree/gpstk/ComputeWindUp.hpp\"\n#include \"pppbayestree/gpstk/Rinex3NavData.hpp\"\n#include \"pppbayestree/gpstk/GNSSconstants.hpp\"\n#include \"pppbayestree/gpstk/ComputeLinear.hpp\"\n#include \"pppbayestree/gpstk/GPSWeekSecond.hpp\"\n#include \"pppbayestree/gpstk/LICSDetector2.hpp\"\n#include \"pppbayestree/gpstk/DataStructures.hpp\"\n#include \"pppbayestree/gpstk/RinexObsStream.hpp\"\n#include \"pppbayestree/gpstk/Rinex3ObsStream.hpp\"\n#include \"pppbayestree/gpstk/Rinex3NavStream.hpp\"\n#include \"pppbayestree/gpstk/ComputeTropModel.hpp\"\n#include \"pppbayestree/gpstk/SP3EphemerisStore.hpp\"\n#include \"pppbayestree/gpstk/ComputeSatPCenter.hpp\"\n#include \"pppbayestree/gpstk/EclipsedSatFilter.hpp\"\n#include \"pppbayestree/gpstk/GPSEphemerisStore.hpp\"\n#include \"pppbayestree/gpstk/CorrectCodeBiases.hpp\"\n#include \"pppbayestree/gpstk/ComputeSatPCenter.hpp\"\n#include \"pppbayestree/gpstk/RequireObservables.hpp\"\n#include \"pppbayestree/gpstk/CorrectObservables.hpp\"\n#include \"pppbayestree/gpstk/LinearCombinations.hpp\"\n#include \"pppbayestree/gpstk/GravitationalDelay.hpp\"\n#include \"pppbayestree/gpstk/PhaseCodeAlignment.hpp\"\n#include \"pppbayestree/slam/dataset.h\"\n\n// BOOST\n#include \n#include \n#include \n#include \n#include \n\n// STD\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace gpstk;\nusing namespace boost;\nusing namespace std::chrono;\nusing namespace minisam;\nnamespace po = boost::program_options;\n\n\nint main(int argc, char* argv[])\n{\n // define std out print color\n vector prn_vec;\n vector data;\n const string red(\"\\033[0;31m\");\n const string green(\"\\033[0;32m\");\n string confFile, gnssFile, station, sp3File, p1p2File, p1c1File, antennaModel;\n string rnx_file, nav_file, sp3_file, out_file, antexFile;\n double xn, yn, zn, xp, yp, range, phase, rho, minElev, weightFactor;\n double antennaOffSetH, antennaOffSetE, antennaOffSetN;\n int startKey(0), currKey, startEpoch(0), svn, doy;\n int nThreads(-1), phase_break, break_count(0), nextKey, dec_int, itsBelowThree=0, count=0;\n bool printECEF, printENU, printAmb, printUpdateRate, first_ob(true), usingP1(false);\n\n FILE *fprealtime=fopen(\"examples_tuning/gpsdata/pppgpsposbackcount.txt\",\"w+\");\n\n cout.precision(12);\n\n /*po::options_description desc(\"Available options\");\n desc.add_options()\n (\"help,h\", \"Print help message\")\n (\"confFile,c\", po::value(&confFile)->default_value(\"\"),\n \"Input config file\" )\n (\"out\", po::value(&out_file)->default_value(\"\"),\n \"output file.\")\n (\"usingP1\", \"Are you using P1 instead of C1?\");\n po::variables_map vm;\n po::store(po::command_line_parser(argc, argv).options(desc).run(), vm);\n po::notify(vm);*/\n\n ConfDataReader confReader;\n confReader.open(\"examples_tuning/gpsdata/phastball.conf\");\n ISAM2Data isam2data;\n\n /*if (confFile.empty() ) {\n cout << red << \"\\n\\n Currently, you need to provide a conf file \\n\"\n << \"\\n\\n\" << green << desc << endl;\n }*/\n\n while ( (station = confReader.getEachSection()) != \"\" )\n {\n // Fetch nominal station location [m]\n xn = confReader.fetchListValueAsDouble(\"nominalECEF\",station);\n yn = confReader.fetchListValueAsDouble(\"nominalECEF\",station);\n zn = confReader.fetchListValueAsDouble(\"nominalECEF\",station);\n // day of year ( used for Niell Trop model)\n doy = confReader.getValueAsInt(\"DOY\", station);\n // Elevation cut-off\n minElev = confReader.getValueAsDouble(\"minElev\", station);\n // Code/carrier ratio\n weightFactor = confReader.getValueAsDouble(\"weightFactor\", station);\n // Data file names\n gnssFile = confReader(\"rnxFile\", station);\n sp3File = confReader(\"sp3File\", station);\n p1p2File = confReader(\"p1p2\", station);\n p1c1File = confReader(\"p1c1\", station);\n // Print statements\n printENU = confReader.getValueAsBoolean(\"printENU\", station);\n printAmb = confReader.getValueAsBoolean(\"printAmb\", station);\n printECEF = confReader.getValueAsBoolean(\"printECEF\", station);\n printUpdateRate = confReader.getValueAsBoolean(\"printUpdateRate\", station);\n }\n\n //usingP1 = (vm.count(\"usingP1\")>0);\n\n minivector nomXYZ(xn, yn, zn);\n minivector prop_xyz = nomXYZ;\n\n\n /*#ifdef USE_TBB\n std::auto_ptr init;\n if(nThreads > 0) {\n init.reset(new tbb::task_scheduler_init(nThreads));\n }\n else\n cout << green << \" \\n\\n Using threads for all processors\" << endl;\n #else\n */\n if(nThreads > 0)\n {\n cout << red <<\" \\n\\n not compiled with TBB, so threading is\"\n << \" disabled and the --threads option cannot be used.\"\n << endl;\n exit(1);\n }\n //#endif\n\n //ISAM2DoglegParams doglegParams;\n ISAM2Params parameters;\n //parameters.optimizationParamsDogleg=new ISAM2DoglegParams;\n parameters.optimizationParamsGaussNewton=new ISAM2GaussNewtonParams;\n parameters.relinearizeThresholdDouble = 0.1;\n parameters.relinearizeSkip = 10;\n ISAM2 isam(parameters);\n\n double output_time = 0.0;\n double rw = 2.5;\n double rangeWeight = pow(rw,2);\n double phaseWeight = pow(rw*1/weightFactor,2);\n\n string value;\n\n minivector prior_nonBias(5,0.0);\n //prior_nonBias<<0.0, 0.0, 0.0, 0.0, 0.0;\n\n double bias_state(0.0);\n minivector phase_arc(34,0.0);\n //phase_arc.setZero();\n minivector bias_counter(34,0.0);\n // bias_counter.setZero();\n\n for (int i=1; i<34; i++)\n {\n bias_counter.data[i] = bias_counter.data[i-1] + 10000;\n }\n\n minivector* initEst5=new minivector(5);\n minivector_set_zero(initEst5);\n // initEst5<<0.0, 0.0, 0.0, 0.0, 0.0;\n\n minivector* between_nonBias_State=new minivector(5);\n minivector_set_zero(between_nonBias_State);\n //between_nonBias_State<<0.0, 0.0, 0.0, 0.0, 0.0;\n\n //Values initial_values;\n std::map initial_values;\n // std::map initial_valuesP;\n //Values result;\n // std::map result;\n\n minivector BiasinitNoise(5);\n BiasinitNoise.data[0]=10.0;BiasinitNoise.data[1]=10.0;BiasinitNoise.data[2]=10.0;\n BiasinitNoise.data[3]=3e8;BiasinitNoise.data[4]=1e-1;\n //BiasinitNoise<<10.0, 10.0, 10.0, 3e8, 1e-1;\n minivector_cwisesqrt(&BiasinitNoise);\n // GaussianNoiseModel* nonBias_InitNoise=new GaussianNoiseModel(BiasinitNoise.cwiseSqrt());\n GaussianNoiseModel* nonBias_InitNoise=new GaussianNoiseModel(BiasinitNoise);\n\n minivector nonBiasProcessNoise(5);\n nonBiasProcessNoise.data[0]=0.1;nonBiasProcessNoise.data[1]=0.1;nonBiasProcessNoise.data[2]=0.1;\n nonBiasProcessNoise.data[3]=3e6;nonBiasProcessNoise.data[4]=3e-5;\n minivector_cwisesqrt(&nonBiasProcessNoise);\n\n GaussianNoiseModel* nonBias_ProcessNoise=new GaussianNoiseModel(nonBiasProcessNoise);\n\n\n\n //0.1, 0.1, 0.1, 3e6, 3e-5;\n minivector init_Noise(1,100);//init_Noise[0]=100;\n //init_Noise<<100;\n minivector_cwisesqrt(&init_Noise);\n\n GaussianNoiseModel* initNoise=new GaussianNoiseModel(init_Noise);\n\n string obs_path = findExampleDataFile(gnssFile,\"examples_tuning/gpsdata\");\n string p1p2_path = findExampleDataFile(p1p2File,\"examples_tuning/gpsdata\");\n string p1c1_path = findExampleDataFile(p1c1File,\"examples_tuning/gpsdata\");\n\n\n\n if ( obs_path.empty() )\n {\n // cout << \" Must pass in obs file !!! \" << desc << endl;\n exit(1);\n }\n if ( sp3File.empty() )\n {\n //cout << \" Must pass in ephemeris file !!! \" << desc << endl;\n exit(1);\n }\n // Declare a \"SP3EphemerisStore\" object to handle precise ephemeris\n SP3EphemerisStore SP3EphList;\n\n size_t pos = 0;\n string path, token;\n string delimiter = \" \";\n while ((pos = sp3File.find(delimiter)) != string::npos)\n {\n path = findExampleDataFile(sp3File.substr(0,pos),\"examples_tuning/gpsdata\");\n SP3EphList.loadFile(path);\n sp3File.erase(0, pos + delimiter.length());\n }\n path = findExampleDataFile(sp3File,\"examples_tuning/gpsdata\");\n SP3EphList.loadFile(path);\n\n // Set flags to reject satellites with bad or absent positional\n // values or clocks\n SP3EphList.rejectBadPositions(true);\n SP3EphList.rejectBadClocks(true);\n\n // Create the input observation file stream\n Rinex3ObsStream rin(obs_path);\n\n // station nominal position\n Position nominalPos(xn, yn, zn);\n\n CorrectCodeBiases corrCode;\n corrCode.setDCBFile(p1p2_path, p1c1_path);\n\n if (!usingP1)\n {\n corrCode.setUsingC1(true);\n }\n\n\n // This is the GNSS data structure that will hold all the\n // GNSS-related information\n gnssRinex gRin;\n\n RequireObservables requireObs;\n requireObs.addRequiredType(TypeID::L1);\n requireObs.addRequiredType(TypeID::L2);\n\n SimpleFilter pObsFilter;\n pObsFilter.setFilteredType(TypeID::C1);\n\n if ( usingP1 )\n {\n requireObs.addRequiredType(TypeID::P1);\n pObsFilter.addFilteredType(TypeID::P1);\n requireObs.addRequiredType(TypeID::P2);\n pObsFilter.addFilteredType(TypeID::P2);\n }\n else\n {\n requireObs.addRequiredType(TypeID::C1);\n pObsFilter.addFilteredType(TypeID::C1);\n requireObs.addRequiredType(TypeID::P2);\n pObsFilter.addFilteredType(TypeID::P2);\n }\n\n // Declare a couple of basic modelers\n BasicModel basic(nominalPos, SP3EphList);\n basic.setMinElev(minElev);\n\n // Object to correct for SP3 Sat Phase-center offset\n ComputeSatPCenter svPcenter(SP3EphList, nominalPos);\n\n // Objects to mark cycle slips\n MWCSDetector markCSMW; // Checks Merbourne-Wubbena cycle slip\n\n // object def several linear combinations\n LinearCombinations comb;\n\n // Object to compute linear combinations for cycle slip detection\n ComputeLinear linear1;\n if ( usingP1 )\n {\n linear1.addLinear(comb.pdeltaCombination);\n linear1.addLinear(comb.mwubbenaCombination);\n }\n else\n {\n linear1.addLinear(comb.pdeltaCombWithC1);\n linear1.addLinear(comb.mwubbenaCombWithC1);\n }\n linear1.addLinear(comb.ldeltaCombination);\n linear1.addLinear(comb.liCombination);\n\n ComputeLinear linear2;\n\n // Read if we should use C1 instead of P1\n if ( usingP1 )\n {\n linear2.addLinear(comb.pcCombination);\n }\n else\n {\n linear2.addLinear(comb.pcCombWithC1);\n }\n linear2.addLinear(comb.lcCombination);\n\n LICSDetector2 markCSLI2; // Checks LI cycle slips\n\n // Object to keep track of satellite arcs\n SatArcMarker markArc;\n markArc.setDeleteUnstableSats(true);\n\n // Objects to compute gravitational delay effects\n GravitationalDelay grDelay(nominalPos);\n\n // Object to remove eclipsed satellites\n EclipsedSatFilter eclipsedSV;\n\n //Object to compute wind-up effect\n ComputeWindUp windup( SP3EphList, nominalPos );\n\n // Object to compute prefit-residuals\n ComputeLinear linear3(comb.pcPrefit);\n linear3.addLinear(comb.lcPrefit);\n\n TypeIDSet tset;\n tset.insert(TypeID::prefitC);\n tset.insert(TypeID::prefitL);\n\n // Declare a NeillTropModel object, setting the defaults\n NeillTropModel neillTM( nominalPos.getAltitude(),\n nominalPos.getGeodeticLatitude(),\n doy);\n\n // Objects to compute the tropospheric data\n ComputeTropModel computeTropo(neillTM);\n\n // initialize factor graph\n //NonlinearFactorGraph *graph = new NonlinearFactorGraph();\n NonlinearFactorGraph graph;\n\n auto start = std::chrono::steady_clock::now();\n auto end = std::chrono::steady_clock::now();\n // Loop over all data epochs\n while(rin >> gRin)\n {\n TimeSystem sys;\n sys.fromString(\"GPS\");\n CommonTime time(gRin.header.epoch);\n time.setTimeSystem(sys);\n GPSWeekSecond gpstime( time );\n\n // update nominal ECEF with propogated pos.\n NeillTropModel neillTM( nominalPos.getAltitude(),\n nominalPos.getGeodeticLatitude(),\n doy);\n try\n {\n gRin >> requireObs // Check if required observations are present\n >> pObsFilter // Filter out spurious data\n >> linear1 // Compute linear combinations to detect CS\n >> markCSLI2 // Mark cycle slips\n >> markArc // Keep track of satellite arcs\n >> basic // Compute the basic components of model\n >> eclipsedSV // Remove satellites in eclipse\n >> grDelay // Compute gravitational delay\n >> svPcenter // Computer delta for sat. phase center\n >> corrCode // Correct for differential code biases\n >> windup // phase windup correction\n >> computeTropo // neill trop function\n >> linear2 // Compute ionosphere-free combinations\n >> linear3; // Compute prefit residuals\n }\n catch(Exception& e)\n {\n continue;\n }\n catch(...)\n {\n cerr << \"Unknown exception at epoch: \" << time << endl;\n continue;\n }\n\n // Iterate through the GNSS Data Structure\n satTypeValueMap::const_iterator it;\n typeValueMap::const_iterator itObs;\n if ( itsBelowThree > 0 )\n {\n itsBelowThree = 0;\n continue;\n }\n\n if (gRin.body.size() == 0)\n {\n continue;\n }\n\n // Loop over all observed sats at current epoch\n for (it = gRin.body.begin(); it!= gRin.body.end(); it++)\n {\n\n start = std::chrono::steady_clock::now();\n svn = ((*it).first).id;\n double satX, satY, satZ;\n satX = (*it).second.getValue(TypeID::satX);\n satY = (*it).second.getValue(TypeID::satY);\n satZ = (*it).second.getValue(TypeID::satZ);\n minivector satXYZ(satX,satY,satZ);\n double range, rangeRes;\n range = (*it).second.getValue(TypeID::PC);\n rangeRes = (*it).second.getValue(TypeID::prefitC);\n double phase, phaseRes;\n phase = (*it).second.getValue(TypeID::LC);\n phaseRes = (*it).second.getValue(TypeID::prefitL);\n int phase_break;\n phase_break = (*it).second.getValue(TypeID::satArc);\n\n if (first_ob)\n {\n startKey = count;\n first_ob=false;\n PriorFactor* npn=new PriorFactor(Symbol('X',count).key(), initEst5, nonBias_InitNoise);\n\n graph.push_back(npn);\n initial_values.insert(std::make_pair(Symbol('X',count).key(), new minivector(initEst5)));\n\n }\n\n if (phase_arc.data[svn]!=phase_break)\n {\n bias_state = phase - range;\n if (count > startKey)\n {\n bias_counter.data[svn] = bias_counter.data[svn] +1;\n }\n minivector *biasb=new minivector(1);\n biasb->data[0]=bias_state;\n // biasb< startKey )\n {\n // GaussianNoiseModel* nonBias_ProcessNoise=\n // new GaussianNoiseModel(nonBiasProcessNoise.cwiseSqrt());\n\n BetweenFactor* nbfnbs=new BetweenFactor(Symbol('X',count).key(),\n Symbol('X',count-1).key(), new minivector(between_nonBias_State), nonBias_ProcessNoise);\n graph.push_back(nbfnbs);\n }\n\n isam.update(graph, initial_values,isam2data);\n //result = isam.calculateEstimate(isam2data);\n isam.calculateEstimate(isam2data);\n\n end = std::chrono::steady_clock::now();\n\n\n //prior_nonBias =result.at(Symbol('X',count).key());\n //prior_nonBias =isam2data.result.at(Symbol('X',count).key());\n minimatrix_memcpy(&prior_nonBias,isam2data.resulttheta_.at(Symbol('X',count).key()));\n\n minivector delta_xyz(prior_nonBias.data[0], prior_nonBias.data[1], prior_nonBias.data[2]);\n Position deltaPos(prior_nonBias.data[0], prior_nonBias.data[1], prior_nonBias.data[2]);\n //prop_xyz = nomXYZ - delta_xyz;\n minivector_sub(&prop_xyz,nomXYZ,delta_xyz);\n nominalPos -= deltaPos;\n\n if (printECEF)\n {\n cout << \"xyz \" << gpstime.week << \" \" << gpstime.sow << \" \" << prop_xyz.x() <<\n \" \" << prop_xyz.y() << \" \" << prop_xyz.data[2] << endl;\n }\n\n if (printENU)\n {\n minivector enu = xyz2enu(prop_xyz, nomXYZ);\n cout << \"enu \" << gpstime.week << \" \" << gpstime.sow << \" \" <<\n enu.x() << \" \" << enu.y() << \" \" << enu.data[2] << endl;\n fprintf(fprealtime,\"%d %.15f %.15f %.15f %.15f %d\\n\",\n gpstime.week,gpstime.sow,enu.x(),enu.y(),enu.data[2],\n isam.lastBacksubVariableCount);\n }\n\n if (printAmb)\n {\n for (int k=0; k(end - start).count()\n << \" µs\" << endl;\n }\n\n output_time = output_time +1;\n graph.resize(0);\n initial_values.clear();\n prn_vec.clear();\n count++;\n initial_values.insert(std::make_pair(Symbol('X',count).key(),\n new minivector(prior_nonBias)));\n if(count>20000)\n {\n isam.clearall();\n isam2data.clearvalues();\n isam2data.clearfactors();\n delete parameters.optimizationParamsGaussNewton;\n delete between_nonBias_State;\n delete nonBias_InitNoise;\n delete initNoise;\n delete nonBias_ProcessNoise;\n for(auto& dlt:initial_values)\n {\n if(dlt.second!=NULL)\n delete dlt.second;\n }\n return 0;\n }\n }\n // ofstream bs(\"examples_tuning/data/gnsstree.dot\");\n //isam.saveGraph(bs);\n\n isam.clearall();\n isam2data.clearvalues();\n isam2data.clearfactors();\n delete parameters.optimizationParamsGaussNewton;\n delete between_nonBias_State;\n delete nonBias_InitNoise;\n delete initNoise;\n delete nonBias_ProcessNoise;\n for(auto& dlt:initial_values)\n {\n 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YES\n2. NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.22013923421806053}} {"text": "#pragma once\n/** @file KDTree\n * @brief A binary tree constructed by splitting each box by a median.\n */\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n\n#include \"fmmtl/tree/util/CountedProxyIterator.hpp\"\n\n#include \"fmmtl/util/Logger.hpp\"\n#include \"fmmtl/numeric/Vec.hpp\"\n#include \"fmmtl/tree/BoundingBox.hpp\"\n\n#include \"fmmtl/numeric/bits.hpp\"\n\nnamespace fmmtl {\nusing boost::has_range_iterator;\n\n\n//! Class for tree structure\ntemplate \nclass KDTree {\n // Predeclarations\n struct Box;\n struct Body;\n struct BoxData;\n\n // The type of this tree\n typedef KDTree tree_type;\n\n public:\n //! The type of indices and integers in this tree\n typedef unsigned size_type;\n\n //! The spacial point type used for centers and extents\n typedef Vec point_type;\n\n //! Public type declarations\n typedef Box box_type;\n typedef Body body_type;\n using box_iterator = CountedProxyIterator;\n using body_iterator = CountedProxyIterator;\n\n private:\n // Tree representation\n\n // Permutation: permute_[i] is the current idx of originally ith point\n std::vector permute_;\n // Vector of data describing a box\n std::vector box_data_;\n\n struct BoxData {\n // Index of the first body in this box\n size_type body_begin_;\n // Index of one-past-last body in this box\n size_type body_end_;\n\n // Bounding Box of this box\n BoundingBox bounding_box_;\n\n BoxData(size_type bb, size_type be,\n const point_type& min, const point_type& max)\n : body_begin_(bb), body_end_(be),\n bounding_box_(min,max) {\n }\n };\n\n struct Body {\n /** Construct an invalid Body */\n Body() {}\n //! The original order this body was seen\n size_type number() const {\n return tree_->permute_[idx_];\n }\n //! The current order of this body\n size_type index() const {\n return idx_;\n }\n private:\n size_type idx_;\n tree_type* tree_;\n friend body_iterator;\n Body(size_type idx, const tree_type* tree)\n : idx_(idx), tree_(const_cast(tree)) {\n FMMTL_ASSERT(idx_ < tree_->size());\n }\n friend class KDTree;\n };\n\n // A tree-aligned box\n struct Box {\n typedef typename tree_type::box_iterator box_iterator;\n typedef typename tree_type::body_iterator body_iterator;\n\n //! Construct an invalid Box\n Box() {}\n //! The index of this box\n size_type index() const {\n return idx_;\n }\n //! The level of this box (root level is 0)\n size_type level() const {\n return std::log2(idx_+1); // XXX: Slow? do this with bits\n }\n\n //! The dimension of each side of this box\n point_type extents() const {\n return data().bounding_box_.max() - data().bounding_box_.min();\n }\n //! The squared radius of this box\n double radius_sq() const {\n return norm_2_sq(extents()) / 4.0;\n }\n //! The center of this box\n point_type center() const {\n return (data().bounding_box_.max() + data().bounding_box_.min()) / 2.0;\n }\n\n //! The parent box of this box\n Box parent() const {\n FMMTL_ASSERT(!(*this == tree_->root()));\n return Box((idx_-1)/2, tree_);\n }\n\n //! True if this box is a leaf and has no children\n bool is_leaf() const {\n return 2*idx_+1 >= tree_->box_data_.size();\n }\n //! The begin iterator to the child boxes contained in this box\n box_iterator child_begin() const {\n FMMTL_ASSERT(!is_leaf());\n return box_iterator(2*idx_+1, tree_);\n }\n //! The end iterator to the child boxes contained in this box\n box_iterator child_end() const {\n FMMTL_ASSERT(!is_leaf());\n return box_iterator(2*idx_+3, tree_);\n }\n //! The number of children this box has\n static constexpr size_type num_children() {\n return 2;\n }\n\n //! The begin iterator to the bodies contained in this box\n body_iterator body_begin() const {\n return body_iterator(data().body_begin_, tree_);\n }\n //! The end iterator to the bodies contained in this box\n body_iterator body_end() const {\n return body_iterator(data().body_end_, tree_);\n }\n //! The number of bodies this box contains\n size_type num_bodies() const {\n return std::distance(body_begin(), body_end());\n }\n\n //! Equality comparison operator\n bool operator==(const Box& b) const {\n FMMTL_ASSERT(tree_ == b.tree_);\n return idx_ == b.idx_;\n }\n //! Comparison operator for std:: containers and algorithms\n bool operator<(const Box& b) const {\n FMMTL_ASSERT(tree_ == b.tree_);\n return idx_ < b.idx_;\n }\n\n //! Write a Box to an output stream\n inline friend std::ostream& operator<<(std::ostream& s,\n const box_type& b) {\n size_type num_bodies = b.num_bodies();\n size_type first_body = b.body_begin()->index();\n size_type last_body = first_body + num_bodies - 1;\n size_type parent_idx = b.index()==0 ? 0 : b.parent().index();\n\n return s << \"Box \" << b.index()\n << \" (L\" << b.level() << \", P\" << parent_idx\n << \", \" << num_bodies << (num_bodies == 1 ? \" body\" : \" bodies\")\n << \" \" << first_body << \"-\" << last_body\n << \"): \" << b.center() << \" - \" << b.extents();\n }\n private:\n size_type idx_;\n tree_type* tree_;\n friend box_iterator;\n Box(size_type idx, const tree_type* tree)\n : idx_(idx), tree_(const_cast(tree)) {\n FMMTL_ASSERT(idx_ < tree_->boxes());\n }\n inline BoxData& data() const {\n return tree_->box_data_[idx_];\n }\n friend class KDTree;\n };\n\n public:\n\n /** Construct a tree encompassing a bounding box\n * and insert a range of points */\n template \n KDTree(const Range& rng, size_type n_crit = 256,\n typename std::enable_if::value>::type* = 0)\n : KDTree(rng.begin(), rng.end(), n_crit) {\n }\n\n /** Construct an tree encompassing a bounding box\n * and insert a range of points */\n template \n KDTree(PointIter first, PointIter last, size_type n_crit = 256) {\n insert(first, last, n_crit);\n }\n\n /** Return the Bounding Box that this KDTree encompasses */\n BoundingBox bounding_box() const {\n return box_data_[0].bounding_box_;\n }\n\n /** Return the center of this KDTree */\n point_type center() const {\n return root().center();\n }\n\n /** The number of bodies contained in this tree */\n inline size_type size() const {\n return permute_.size();\n }\n /** The number of bodies contained in this tree */\n inline size_type bodies() const {\n return size();\n }\n\n /** The number of boxes contained in this tree */\n inline size_type boxes() const {\n return box_data_.size();\n }\n\n /** The number of boxes contained in level L of this tree */\n inline size_type boxes(size_type L) const {\n return (1 << L);\n }\n\n /** The maximum level of any box in this tree */\n inline size_type levels() const {\n return std::log2(boxes()); // XXX: Slow?\n }\n\n /** Returns true if the box is contained in this tree, false otherwise */\n inline bool contains(const box_type& box) const {\n return this == box.tree_;\n }\n /** Returns true if the body is contained in this tree, false otherwise */\n inline bool contains(const body_type& body) const {\n return this == body.tree_;\n }\n\n /** Return the root box of this tree */\n box_type root() const {\n return Box(0, this);\n }\n /** Return a box given its index */\n box_type box(const size_type idx) const {\n FMMTL_ASSERT(idx < box_data_.size());\n return Box(idx, this);\n }\n /** Return a body given its index */\n body_type body(const size_type idx) const {\n FMMTL_ASSERT(idx < size());\n return Body(idx, this);\n }\n /** Return an iterator to the first body in this tree */\n body_iterator body_begin() const {\n return body_iterator(0, this);\n }\n /** Return an iterator one past the last body in this tree */\n body_iterator body_end() const {\n return body_iterator(bodies(), this);\n }\n /** Return an iterator to the first box in this tree */\n box_iterator box_begin() const {\n return box_iterator(0, this);\n }\n /** Return an iterator one past the last box in this tree */\n box_iterator box_end() const {\n return box_iterator(boxes(), this);\n }\n /** Return an iterator to the first box at level L in this tree\n * @pre L < levels()\n */\n box_iterator box_begin(size_type L) const {\n FMMTL_ASSERT(L < levels());\n return box_iterator((1 << L) - 1, this);\n }\n /** Return an iterator one past the last box at level L in this tree\n * @pre L < levels()\n */\n box_iterator box_end(size_type L) const {\n FMMTL_ASSERT(L < levels());\n return box_iterator((1 << (L+1)) - 1, this);\n }\n\n template \n struct body_permuted_iterator {\n typedef typename std::vector::const_iterator permute_iter;\n typedef boost::permutation_iterator type;\n };\n\n /** Tranform (permute) an iterator so its traversal follows the same order as\n * the bodies contained in this tree\n */\n template \n typename body_permuted_iterator::type\n body_permute(RandomAccessIter it, const body_iterator& bi) const {\n return boost::make_permutation_iterator(it, permute_.cbegin() + bi.index());\n }\n\n /** Tranform (permute) an iterator so its traversal follows the same order as\n * the bodies contained in this tree\n *\n * Specialized for bi = body_begin().\n */\n template \n typename body_permuted_iterator::type\n body_permute(RandomAccessIter it) const {\n return body_permute(it, body_begin());\n }\n\n /** Write an KDTree to an output stream */\n inline friend std::ostream& operator<<(std::ostream& s,\n const tree_type& t) {\n struct {\n inline std::ostream& print(std::ostream& s,\n const box_type& box) {\n s << std::string(2*box.level(), ' ') << box;\n if (!box.is_leaf())\n for (auto ci = box.child_begin(); ci != box.child_end(); ++ci)\n print(s << \"\\n\", *ci);\n return s;\n }\n } recursive_box;\n\n return recursive_box.print(s, t.root());\n }\n\n private:\n //! TODO: Make dynamic and public?\n template \n void insert(PointIter p_first, PointIter p_last, size_type NCRIT) {\n FMMTL_LOG(\"KDTree Insert\");\n\n FMMTL_ASSERT(p_first != p_last);\n\n // Create a point-idx pair vector\n typedef typename std::iterator_traits::value_type point_i_type;\n\n // XXX: Generalize?\n static_assert(std::is_same::value,\n \"PointIter value_type must be point_type\");\n\n typedef std::pair point_t;\n\n std::vector point;\n // If iterators are random access, we can reserve space efficiently\n // Compile-time predicate!\n if (std::is_same::iterator_category,\n std::random_access_iterator_tag>::value)\n point.reserve(std::distance(p_first, p_last));\n\n unsigned idx = 0;\n BoundingBox root_bb(*p_first);\n for (PointIter pi = p_first; pi != p_last; ++pi, ++idx) {\n point.emplace_back(*pi, idx);\n root_bb |= point.back().first;\n }\n\n permute_.reserve(point.size());\n\n // The number of leaf boxes that will be created\n // (Smallest power of two greater than or equal to ceil(N/NCRIT))\n unsigned leaves = ceil_pow_2((point.size() + NCRIT - 1) / NCRIT);\n unsigned levels = std::log2(leaves);\n\n // Reserve the number of boxes that will be added\n box_data_.reserve(2*leaves - 1);\n // Push the root box which contains all points\n box_data_.emplace_back(0, size_type(point.size()),\n root_bb.min(), root_bb.max());\n\n // For every box that is created\n unsigned end_k = (1 << levels) - 1;\n for (unsigned k = 0; k < end_k; ++k) {\n\n // Get the bounding box of the current box\n auto& bb = box_data_[k].bounding_box_;\n\n // Make a comparator for the largest dimension\n const unsigned dim = max_dim(bb.max() - bb.min());\n auto comp = [=] (const point_t& a, const point_t& b) {\n return a.first[dim] < b.first[dim];\n };\n\n // Partition the points on the median of this dimension\n auto p_begin = point.begin() + box_data_[k].body_begin_;\n auto p_end = point.begin() + box_data_[k].body_end_;\n auto p_mid = p_begin + (p_end - p_begin + 1) / 2;\n std::nth_element(p_begin, p_mid, p_end, comp);\n\n // Record the child boxes\n unsigned mid = p_mid - point.begin();\n point_type split = bb.max();\n split[dim] = (*p_mid).first[dim];\n box_data_.emplace_back(box_data_[k].body_begin_, mid, bb.min(), split);\n split = bb.min();\n split[dim] = (*p_mid).first[dim];\n box_data_.emplace_back(mid, box_data_[k].body_end_, split, bb.max());\n }\n\n // Assert no re-allocation\n FMMTL_ASSERT(box_data_.size() <= 2*leaves-1);\n\n // Extract the permutation idx\n for (auto&& p : point)\n permute_.push_back(p.second);\n }\n\n static unsigned max_dim(const point_type& p) {\n return std::max_element(p.begin(), p.end()) - p.begin();\n }\n\n // Just making sure for now\n KDTree(const KDTree&) {};\n void operator=(const KDTree&) {};\n};\n\n} // end namespace fmmtl\n", "meta": {"hexsha": "0f536cccb9348263db8c144ad338ed5ebfb088a7", "size": 13701, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "source/fmmtl/fmmtl/tree/KDTree.hpp", "max_stars_repo_name": "sergeneren/BubbleH", "max_stars_repo_head_hexsha": "e018e5a008e101221a4f8a3bbb25e7ec03fc9662", "max_stars_repo_licenses": ["BSD-3-Clause-Clear"], "max_stars_count": 52.0, "max_stars_repo_stars_event_min_datetime": "2019-10-06T17:25:39.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-20T23:01:13.000Z", "max_issues_repo_path": "source/fmmtl/fmmtl/tree/KDTree.hpp", "max_issues_repo_name": "sergeneren/BubbleH", "max_issues_repo_head_hexsha": "e018e5a008e101221a4f8a3bbb25e7ec03fc9662", "max_issues_repo_licenses": ["BSD-3-Clause-Clear"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2019-10-08T18:44:41.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-09T07:48:31.000Z", "max_forks_repo_path": "source/fmmtl/fmmtl/tree/KDTree.hpp", "max_forks_repo_name": "sergeneren/BubbleH", "max_forks_repo_head_hexsha": "e018e5a008e101221a4f8a3bbb25e7ec03fc9662", "max_forks_repo_licenses": ["BSD-3-Clause-Clear"], "max_forks_count": 10.0, "max_forks_repo_forks_event_min_datetime": "2019-10-07T16:33:24.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-21T01:09:47.000Z", "avg_line_length": 30.8581081081, "max_line_length": 81, "alphanum_fraction": 0.639734326, "num_tokens": 3442, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.21998478011745345}} {"text": "#ifndef BOOST_GEOMETRY_PROJECTIONS_CHAMB_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_CHAMB_HPP\n\n// Boost.Geometry - extensions-gis-projections (based on PROJ4)\n// This file is automatically generated. DO NOT EDIT.\n\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\n\n// Last updated version of proj: 4.9.1\n\n// Original copyright notice:\n\n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\nnamespace boost { namespace geometry { namespace projections\n{\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail { namespace chamb\n {\n\n static const double THIRD = 0.333333333333333333;\n static const double TOL = 1e-9;\n\n // specific for 'chamb'\n struct VECT { double r, Az; };\n struct XY { double x, y; };\n\n struct par_chamb\n {\n struct { /* control point data */\n double phi, lam;\n double cosphi, sinphi;\n VECT v;\n XY p;\n double Az;\n } c[3];\n XY p;\n double beta_0, beta_1, beta_2;\n };\n\n static VECT /* distance and azimuth from point 1 to point 2 */\n vect(double dphi, double c1, double s1, double c2, double s2, double dlam) {\n VECT v;\n double cdl, dp, dl;\n\n cdl = cos(dlam);\n if (fabs(dphi) > 1. || fabs(dlam) > 1.)\n v.r = aacos(s1 * s2 + c1 * c2 * cdl);\n else { /* more accurate for smaller distances */\n dp = sin(.5 * dphi);\n dl = sin(.5 * dlam);\n v.r = 2. * aasin(sqrt(dp * dp + c1 * c2 * dl * dl));\n }\n if (fabs(v.r) > TOL)\n v.Az = atan2(c2 * sin(dlam), c1 * s2 - s1 * c2 * cdl);\n else\n v.r = v.Az = 0.;\n return v;\n }\n static double /* law of cosines */\n lc(double b,double c,double a) {\n return aacos(.5 * (b * b + c * c - a * a) / (b * c));\n }\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_chamb_spheroid : public base_t_f,\n Geographic, Cartesian, Parameters>\n {\n\n typedef double geographic_type;\n typedef double cartesian_type;\n\n par_chamb m_proj_parm;\n\n inline base_chamb_spheroid(const Parameters& par)\n : base_t_f,\n Geographic, Cartesian, Parameters>(*this, par) {}\n\n // FORWARD(s_forward) spheroid\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\n inline void fwd(geographic_type& lp_lon, geographic_type& lp_lat, cartesian_type& xy_x, cartesian_type& xy_y) const\n {\n double sinphi, cosphi, a;\n VECT v[3];\n int i, j;\n\n sinphi = sin(lp_lat);\n cosphi = cos(lp_lat);\n for (i = 0; i < 3; ++i) { /* dist/azimiths from control */\n v[i] = vect(lp_lat - this->m_proj_parm.c[i].phi, this->m_proj_parm.c[i].cosphi, this->m_proj_parm.c[i].sinphi,\n cosphi, sinphi, lp_lon - this->m_proj_parm.c[i].lam);\n if ( ! v[i].r)\n break;\n v[i].Az = adjlon(v[i].Az - this->m_proj_parm.c[i].v.Az);\n }\n if (i < 3) /* current point at control point */\n { xy_x = this->m_proj_parm.c[i].p.x; xy_y = this->m_proj_parm.c[i].p.y; }\n else { /* point mean of intersepts */\n { xy_x = this->m_proj_parm.p.x; xy_y = this->m_proj_parm.p.y; }\n for (i = 0; i < 3; ++i) {\n j = i == 2 ? 0 : i + 1;\n a = lc(this->m_proj_parm.c[i].v.r, v[i].r, v[j].r);\n if (v[i].Az < 0.)\n a = -a;\n if (! i) { /* coord comp unique to each arc */\n xy_x += v[i].r * cos(a);\n xy_y -= v[i].r * sin(a);\n } else if (i == 1) {\n a = this->m_proj_parm.beta_1 - a;\n xy_x -= v[i].r * cos(a);\n xy_y -= v[i].r * sin(a);\n } else {\n a = this->m_proj_parm.beta_2 - a;\n xy_x += v[i].r * cos(a);\n xy_y += v[i].r * sin(a);\n }\n }\n xy_x *= THIRD; /* mean of arc intercepts */\n xy_y *= THIRD;\n }\n }\n\n static inline std::string get_name()\n {\n return \"chamb_spheroid\";\n }\n\n };\n\n // Chamberlin Trimetric\n template \n void setup_chamb(Parameters& par, par_chamb& proj_parm)\n {\n int i, j;\n char line[10];\n\n for (i = 0; i < 3; ++i) { /* get control point locations */\n (void)sprintf(line, \"rlat_%d\", i+1);\n proj_parm.c[i].phi = pj_param(par.params, line).f;\n (void)sprintf(line, \"rlon_%d\", i+1);\n proj_parm.c[i].lam = pj_param(par.params, line).f;\n proj_parm.c[i].lam = adjlon(proj_parm.c[i].lam - par.lam0);\n proj_parm.c[i].cosphi = cos(proj_parm.c[i].phi);\n proj_parm.c[i].sinphi = sin(proj_parm.c[i].phi);\n }\n for (i = 0; i < 3; ++i) { /* inter ctl pt. distances and azimuths */\n j = i == 2 ? 0 : i + 1;\n proj_parm.c[i].v = vect(proj_parm.c[j].phi - proj_parm.c[i].phi, proj_parm.c[i].cosphi, proj_parm.c[i].sinphi,\n proj_parm.c[j].cosphi, proj_parm.c[j].sinphi, proj_parm.c[j].lam - proj_parm.c[i].lam);\n if (! proj_parm.c[i].v.r) throw proj_exception(-25);\n /* co-linearity problem ignored for now */\n }\n proj_parm.beta_0 = lc(proj_parm.c[0].v.r, proj_parm.c[2].v.r, proj_parm.c[1].v.r);\n proj_parm.beta_1 = lc(proj_parm.c[0].v.r, proj_parm.c[1].v.r, proj_parm.c[2].v.r);\n proj_parm.beta_2 = geometry::math::pi() - proj_parm.beta_0;\n proj_parm.p.y = 2. * (proj_parm.c[0].p.y = proj_parm.c[1].p.y = proj_parm.c[2].v.r * sin(proj_parm.beta_0));\n proj_parm.c[2].p.y = 0.;\n proj_parm.c[0].p.x = - (proj_parm.c[1].p.x = 0.5 * proj_parm.c[0].v.r);\n proj_parm.p.x = proj_parm.c[2].p.x = proj_parm.c[0].p.x + proj_parm.c[2].v.r * cos(proj_parm.beta_0);\n par.es = 0.;\n }\n\n }} // namespace detail::chamb\n #endif // doxygen\n\n /*!\n \\brief Chamberlin Trimetric projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Miscellaneous\n - Spheroid\n - no inverse\n \\par Projection parameters\n - lat_1: Latitude of control point 1 (degrees)\n - lon_1: Longitude of control point 1 (degrees)\n - lat_2: Latitude of control point 2 (degrees)\n - lon_2: Longitude of control point 2 (degrees)\n - lat_3: Latitude of control point 3 (degrees)\n - lon_3: Longitude of control point 3 (degrees)\n \\par Example\n \\image html ex_chamb.gif\n */\n template \n struct chamb_spheroid : public detail::chamb::base_chamb_spheroid\n {\n inline chamb_spheroid(const Parameters& par) : detail::chamb::base_chamb_spheroid(par)\n {\n detail::chamb::setup_chamb(this->m_par, this->m_proj_parm);\n }\n };\n\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail\n {\n\n // Factory entry(s)\n template \n class chamb_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_f, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n inline void chamb_init(detail::base_factory& factory)\n {\n factory.add_to_factory(\"chamb\", new chamb_entry);\n }\n\n } // namespace detail\n #endif // doxygen\n\n}}} // namespace boost::geometry::projections\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_CHAMB_HPP\n\n", "meta": {"hexsha": "8b11fde28c857b6cf8704e10e60bdac0cbd43d5f", "size": 11476, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/chamb.hpp", "max_stars_repo_name": "bowlofstew/omim", "max_stars_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-01-11T05:02:05.000Z", "max_stars_repo_stars_event_max_datetime": "2019-01-11T05:02:05.000Z", "max_issues_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/chamb.hpp", "max_issues_repo_name": "bowlofstew/omim", "max_issues_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/chamb.hpp", "max_forks_repo_name": "bowlofstew/omim", "max_forks_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2018-04-04T10:55:01.000Z", "max_forks_repo_forks_event_max_datetime": "2020-04-23T18:52:06.000Z", "avg_line_length": 44.480620155, "max_line_length": 134, "alphanum_fraction": 0.5394736842, "num_tokens": 2697, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.3451052642223204, "lm_q1q2_score": 0.21984268064690735}} {"text": "#ifndef DCM_PLANNER_H\n#define DCM_PLANNER_H\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nclass DCMPlanner{\npublic:\n DCMPlanner();\n ~DCMPlanner(); \n\n static int const DCM_RL_SWING_VRP_TYPE;\n static int const DCM_LL_SWING_VRP_TYPE;\n static int const DCM_MIDSTEP_TRANSFER_VRP_TYPE;\n static int const DCM_TRANSFER_VRP_TYPE;\n static int const DCM_END_VRP_TYPE;\n\n std::vector rvrp_type_list; // List of type of virtual repelant point\n\n std::vector footstep_list; // Footstep list to generate pattern\n\n std::vector rvrp_list; // List of virtual repelant points.\n std::vector dcm_ini_list; // List of initial DCM states \n std::vector dcm_eos_list; // List of end-of-step DCM states\n\n // Initial DCM states\n Eigen::Vector3d ini_dcm_pos;\n Eigen::Vector3d ini_dcm_vel;\n\n // map containing the rvrp index that have a corresponding footstep swing\n std::map rvrp_index_to_footstep_index;\n // Vector containing hermite quaternion curve objects for the pelvis orientation.\n // one curve per RVRP.\n std::vector pelvis_ori_quat_curves;\n\n // Initial and Boundary Conditions for the continuous DS trajectory\n std::vector dcm_ini_DS_list; \n std::vector dcm_vel_ini_DS_list; \n std::vector dcm_acc_ini_DS_list; \n std::vector dcm_end_DS_list; \n std::vector dcm_vel_end_DS_list; \n std::vector dcm_acc_end_DS_list; \n std::vector dcm_P; // polynomial matrices for polynomial interpolation\n std::vector dcm_minjerk; // minjerk curves for interpolation\n\n // DCM walking parameters\n double t_transfer = 0.1; //0.125 ; //0.1; // exponential interpolation transfer time during initial transfer or same step transfer\n double t_ds = 0.05; // double support polynomial transfer time\n double t_ss = 0.3; // single support exponential interpolation time\n double percentage_settle = 0.99;//0.999; // percent to converge at the end of the trajectory\n double alpha_ds = 0.5; // value between 0.0 and 1.0 for double support DCM interpolation\n\n\n void paramInitialization(const YAML::Node& node);\n void setRobotMass(double mass);\n void setCoMHeight(double z_vrp_in); // Sets the desired CoM Height\n void setInitialTime(double t_start_in); // Sets the initial offset time.\n void setInitialOri(const Eigen::Quaterniond initial_ori_in);\n\n double getInitialTime(); // Returns t_start;\n\n\n // DCM trajectory calculation\n // input: input_footstep_list - a list of footsteps to take not including the current stance configuration.\n // initial_footstance - a footstep object describing the stance leg. \n void initialize_footsteps_rvrp(const std::vector & input_footstep_list, const Footstep & initial_footstance, bool clear_list=false);\n\n // input: input_footstep_list - a list of footsteps to take not including the current stance configuration.\n // initial_footstance - a footstep object describing the stance leg. \n // initial_rvrp - an initial virtual repelant point (eg: average of the stance feet's rvrp). \n void initialize_footsteps_rvrp(const std::vector & input_footstep_list, const Footstep & initial_footstance, const Eigen::Vector3d & initial_rvrp);\n\n\n // input: input_footstep_list - a list of footsteps to take not including the current stance configuration.\n // left_footstance - a footstep object describing the left stance feet\n // right_footstance - a footstep object describing the right stance feet\n void initialize_footsteps_rvrp(const std::vector & input_footstep_list, const Footstep & left_footstance, const Footstep & right_footstance);\n\n // input: input_footstep_list - a list of footsteps to take not including the current stance configuration.\n // left_footstance - a footstep object describing the left stance feet\n // right_footstance - a footstep object describing the right stance feet\n // initial_com - the initial location of the com \n void initialize_footsteps_rvrp(const std::vector & input_footstep_list, const Footstep & left_footstance, const Footstep & right_footstance, const Eigen::Vector3d & initial_com);\n\n // input: input_footstep_list - a list of footsteps to take not including the current stance configuration.\n // left_footstance - a footstep object describing the left stance feet\n // right_footstance - a footstep object describing the right stance feet\n // initial_dcm - the initial starting position of the dcm\n // initial_dcm_vel - the initial starting velocity of the dcm\n void initialize_footsteps_rvrp(const std::vector & input_footstep_list, \n const Footstep & left_footstance, const Footstep & right_footstance, \n const Eigen::Vector3d & initial_dcm, const Eigen::Vector3d & initial_dcm_vel);\n\n\n // Compute: DCM, DCM vel, CoM, CoM Vel, given time, t.\n // t is a global time.\n void get_ref_dcm(const double t, Eigen::Vector3d & dcm_out);\n void get_ref_dcm_vel(const double t, Eigen::Vector3d & dcm_vel_out);\n void get_ref_com(const double t, Eigen::Vector3d & com_out);\n void get_ref_com_vel(const double t, Eigen::Vector3d & com_vel_out);\n void get_ref_r_vrp(const double t, Eigen::Vector3d & r_vrvp_out);\n void get_ref_reaction_force(const double t, Eigen::Vector3d & f_out);\n\n // Global reference quat, ang vel and ang acc\n void get_ref_ori_ang_vel_acc(const double t, Eigen::Quaterniond & quat_out,\n Eigen::Vector3d & ang_vel_out,\n Eigen::Vector3d & ang_acc_out);\n\n // computes the CoM velocity given the current CoM position and DCM velocity state.\n void get_com_vel(const Eigen::Vector3d & com_pos, const Eigen::Vector3d & dcm, Eigen::Vector3d & com_vel_out);\n // computes the CoM reaction force / leg reaction force given mass, CoM position and the r_vrp\n void get_reaction_force(const double mass, const Eigen::Vector3d & com_pos, const Eigen::Vector3d & r_vrp, Eigen::Vector3d fr_out);\n // computes the current r_vrp given \n // -the DCM dynamics time constant b,\n // - the current dcm \n // - and the current dcm_vel\n void get_r_vrp(const double b_in, const Eigen::Vector3d & dcm, const Eigen::Vector3d & dcm_vel, Eigen::Vector3d & r_vrp_out);\n\n // prints the boundary conditions of the DCM\n void printBoundaryConditions();\n\n // Helper Functions\n // Returns the exponential step index the current time falls in.\n int which_step_index(const double t);\n // Returns the polynomial step index to use given the input time from t_start.\n int which_step_index_to_use(const double t);\n\n // returns the starting and ending time of the step_index from t_start.\n double get_t_step_start(const int step_index);\n double get_t_step_end(const int step_index);\n\n // returns the double support starting and ending time of the step_index from t_start.\n double get_double_support_t_start(const int step_index);\n double get_double_support_t_end(const int step_index);\n\n // if the step_index is a swing type, returns true an populates the swing start time and end\n bool get_t_swing_start_end(const int step_index, double & swing_start_time, double & swing_end_time);\n\n // returns the polynomial duration for the given step index\n double get_polynomial_duration(const int step_index);\n\n // Get the initial and end of double support transition times\n double get_eoDS_transition_time();\n double get_iniDS_transition_time(); \n\n // Returns the type of VRP. Clamps the index to valid values.\n int get_r_vrp_type(const int step_index);\n\n // Returns t_end\n double get_total_trajectory_time();\n\n // Returns t_settle;\n double get_settle_time(){\n double t_settle = -b*log(1.0 - percentage_settle);\n return t_settle;\n }\n\nprivate:\n // DCM parameters:\n double robot_mass = 50; // kg\n double gravity = 9.81;\n double z_vrp = 0.75; // desired VRP height / CoM height\n double b = std::sqrt(z_vrp/gravity); // time constant of DCM dynamics \n\n double t_start = 0.0; // the starting time for the DCM Walking reference\n\n // Outputs the average r_vrp location given two footstances\n void get_average_rvrp(const Footstep & footstance_1, const Footstep & footstance_2, Eigen::Vector3d & average_rvrp);\n\n // computes all the dcm states. Computation properly populates the dcm_ini_list and dcm_eos_list\n void computeDCM_states();\n\n // Sums up the total trajectory time and stores the result in t_end\n void compute_total_trajectory_time();\n double t_end = 0.0;\n\n // computes the reference com trajectories by integration\n void compute_reference_com();\n double dt_local = 1e-3; // discretization factor used \n // containers for the integrated reference CoM position and velocites\n std::vector ref_com_pos;\n std::vector ref_com_vel;\n\n // computes the reference pelvis orientation.\n void compute_reference_pelvis_ori();\n Footstep initial_leftfoot_stance;\n Footstep initial_rightfoot_stance;\n Eigen::Quaterniond initial_ori;\n\n\n // input: r_vrp_d_i - the desired virtual repelant point for the i-th step.\n // t_step - the time interval to use for backwards integration\n // dcm_eos_i - the DCM state at the end of the i-th step. \n // computes the step i's initial DCM state and the end-of-step i-1's dcm state. \n // The computation is stored in the dcm_ini_list and dcm_eos_list. \n Eigen::Vector3d computeDCM_ini_i(const Eigen::Vector3d & r_vrp_d_i, const double & t_step, const Eigen::Vector3d & dcm_eos_i);\n\n // Computes the double support DCM boundary conditions for continuous double support trajectories\n // step_index the boundary condition for the step index\n // t_DS_{ini, end} the interpolation duration away from a Virtual repelant point. \n Eigen::Vector3d computeDCM_iniDS_i(const int & step_index, const double t_DS_ini);\n Eigen::Vector3d computeDCM_eoDS_i(const int & step_index, const double t_DS_end);\n Eigen::Vector3d computeDCMvel_iniDS_i(const int & step_index, const double t_DS_ini);\n Eigen::Vector3d computeDCMvel_eoDS_i(const int & step_index, const double t_DS_end);\n Eigen::Vector3d computeDCMacc_iniDS_i(const int & step_index, const double t_DS_ini);\n Eigen::Vector3d computeDCMacc_eoDS_i(const int & step_index, const double t_DS_end);\n\n // Returns the DCM exponential interpolation for the requested step_index.\n // time, t, is clamped between 0.0 and t_step.\n Eigen::Vector3d get_DCM_exp(const int & step_index, const double & t);\n\n // Returns the DCM velocity exponential interpolation for the requested step_index.\n // time, t, is clamped between 0.0 and t_step.\n Eigen::Vector3d get_DCM_vel_exp(const int & step_index, const double & t);\n\n // Returns the DCM acceleration exponential interpolation for the requested step_index.\n // time, t, is clamped between 0.0 and t_step.\n Eigen::Vector3d get_DCM_acc_exp(const int & step_index, const double & t);\n\n // Returns the DCM double support polynomial interpolation for the requested step_index.\n // time, t, is clamped between 0.0 and t_step.\n Eigen::Vector3d get_DCM_DS_poly(const int & step_index, const double & t);\n\n // Returns the DCM double support velocity polynomial interpolation for the requested step_index.\n // time, t, is clamped between 0.0 and t_step.\n Eigen::Vector3d get_DCM_DS_vel_poly(const int & step_index, const double & t);\n\n\n // Returns the DCM double support min jerk interpolation for the requested step_index.\n // time, t, is clamped between 0.0 and t_step.\n Eigen::Vector3d get_DCM_DS_minjerk(const int & step_index, const double & t);\n\n // Returns the DCM double support velocity min jerk interpolation for the requested step_index.\n // time, t, is clamped between 0.0 and t_step.\n Eigen::Vector3d get_DCM_DS_vel_minjerk(const int & step_index, const double & t);\n\n\n // Get the t_step for step i.\n double get_t_step(const int & step_i);\n\n // returns matrix P \\in R^{4x3}\n // P = mat_coeff * [dcm_ini^T; \\dot{dcm}_ini^T; dcm_end^T; \\dot{dcm}_end]\n // where mat_coeff \\in R^{4x4} and ^T denotes a vector transpose transpose.\n // Ts is the duration in seconds\n Eigen::MatrixXd polynomialMatrix(const double Ts,\n const Eigen::Vector3d & dcm_ini, const Eigen::Vector3d & dcm_vel_ini,\n const Eigen::Vector3d & dcm_end, const Eigen::Vector3d & dcm_vel_end);\n\n\n int clampINT(int input, int low_bound, int upper_bound);\n double clampDOUBLE(double input, double low_bound, double upper_bound);\n\n};\n\n#endif\n", "meta": {"hexsha": "16fa2efb39e7ef2539fabf873080ae84711fd6e0", "size": 13007, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "PnC/PlannerSet/DCMPlanner/DCMPlanner.hpp", "max_stars_repo_name": "stevenjj/PnC", "max_stars_repo_head_hexsha": "e1e417dbd507f174bb2661247cb4360b6ee0ada7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-05-04T22:36:54.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-04T22:36:54.000Z", "max_issues_repo_path": "PnC/PlannerSet/DCMPlanner/DCMPlanner.hpp", "max_issues_repo_name": "stevenjj/PnC", "max_issues_repo_head_hexsha": "e1e417dbd507f174bb2661247cb4360b6ee0ada7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "PnC/PlannerSet/DCMPlanner/DCMPlanner.hpp", "max_forks_repo_name": "stevenjj/PnC", "max_forks_repo_head_hexsha": "e1e417dbd507f174bb2661247cb4360b6ee0ada7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 49.4562737643, "max_line_length": 190, "alphanum_fraction": 0.7395248712, "num_tokens": 3353, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6113819732941511, "lm_q2_score": 0.35936415888237616, "lm_q1q2_score": 0.21970876858869998}} {"text": "/*\n * Sparse linear solver.\n * Copyright (C) 2004 Bruno Levy\n * Copyright (C) 2005-2015 Blender Foundation\n *\n * This program is free software; you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation; either version 2 of the License, or\n * (at your option) any later version.\n *\n * This program is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with this program; if not, write to the Free Software\n * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.\n *\n * If you modify this software, you should include a notice giving the\n * name of the person performing the modification, the date of modification,\n * and the reason for such modification.\n */\n\n#include \"linear_solver.h\"\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n/* Eigen data structures */\n\ntypedef Eigen::SparseMatrix EigenSparseMatrix;\ntypedef Eigen::SparseLU EigenSparseLU;\ntypedef Eigen::VectorXd EigenVectorX;\ntypedef Eigen::Triplet EigenTriplet;\n\n/* Linear Solver data structure */\n\nstruct LinearSolver\n{\n\tstruct Coeff\n\t{\n\t\tCoeff()\n\t\t{\n\t\t\tindex = 0;\n\t\t\tvalue = 0.0;\n\t\t}\n\n\t\tint index;\n\t\tdouble value;\n\t};\n\n\tstruct Variable\n\t{\n\t\tVariable()\n\t\t{\n\t\t\tmemset(value, 0, sizeof(value));\n\t\t\tlocked = false;\n\t\t\tindex = 0;\n\t\t}\n\n\t\tdouble value[4];\n\t\tbool locked;\n\t\tint index;\n\t\tstd::vector a;\n\t};\n\n\tenum State\n\t{\n\t\tSTATE_VARIABLES_CONSTRUCT,\n\t\tSTATE_MATRIX_CONSTRUCT,\n\t\tSTATE_MATRIX_SOLVED\n\t};\n\n\tLinearSolver(int num_rows_, int num_variables_, int num_rhs_, bool lsq_)\n\t{\n\t\tassert(num_variables_ > 0);\n\t\tassert(num_rhs_ <= 4);\n\n\t\tstate = STATE_VARIABLES_CONSTRUCT;\n\t\tm = 0;\n\t\tn = 0;\n\t\tsparseLU = NULL;\n\t\tnum_variables = num_variables_;\n\t\tnum_rhs = num_rhs_;\n\t\tnum_rows = num_rows_;\n\t\tleast_squares = lsq_;\n\n\t\tvariable.resize(num_variables);\n\t}\n\n\t~LinearSolver()\n\t{\n\t\tdelete sparseLU;\n\t}\n\n\tState state;\n\n\tint n;\n\tint m;\n\n\tstd::vector Mtriplets;\n\tEigenSparseMatrix M;\n\tEigenSparseMatrix MtM;\n\tstd::vector b;\n\tstd::vector x;\n\n\tEigenSparseLU *sparseLU;\n\n\tint num_variables;\n\tstd::vector variable;\n\n\tint num_rows;\n\tint num_rhs;\n\n\tbool least_squares;\n};\n\nLinearSolver *EIG_linear_solver_new(int num_rows, int num_columns, int num_rhs)\n{\n\treturn new LinearSolver(num_rows, num_columns, num_rhs, false);\n}\n\nLinearSolver *EIG_linear_least_squares_solver_new(int num_rows, int num_columns, int num_rhs)\n{\n\treturn new LinearSolver(num_rows, num_columns, num_rhs, true);\n}\n\nvoid EIG_linear_solver_delete(LinearSolver *solver)\n{\n\tdelete solver;\n}\n\n/* Variables */\n\nvoid EIG_linear_solver_variable_set(LinearSolver *solver, int rhs, int index, double value)\n{\n\tsolver->variable[index].value[rhs] = value;\n}\n\ndouble EIG_linear_solver_variable_get(LinearSolver *solver, int rhs, int index)\n{\n\treturn solver->variable[index].value[rhs];\n}\n\nvoid EIG_linear_solver_variable_lock(LinearSolver *solver, int index)\n{\n\tif (!solver->variable[index].locked) {\n\t\tassert(solver->state == LinearSolver::STATE_VARIABLES_CONSTRUCT);\n\t\tsolver->variable[index].locked = true;\n\t}\n}\n\nvoid EIG_linear_solver_variable_unlock(LinearSolver *solver, int index)\n{\n\tif (solver->variable[index].locked) {\n\t\tassert(solver->state == LinearSolver::STATE_VARIABLES_CONSTRUCT);\n\t\tsolver->variable[index].locked = false;\n\t}\n}\n\nstatic void linear_solver_variables_to_vector(LinearSolver *solver)\n{\n\tint num_rhs = solver->num_rhs;\n\n\tfor (int i = 0; i < solver->num_variables; i++) {\n\t\tLinearSolver::Variable* v = &solver->variable[i];\n\t\tif (!v->locked) {\n\t\t\tfor (int j = 0; j < num_rhs; j++)\n\t\t\t\tsolver->x[j][v->index] = v->value[j];\n\t\t}\n\t}\n}\n\nstatic void linear_solver_vector_to_variables(LinearSolver *solver)\n{\n\tint num_rhs = solver->num_rhs;\n\n\tfor (int i = 0; i < solver->num_variables; i++) {\n\t\tLinearSolver::Variable* v = &solver->variable[i];\n\t\tif (!v->locked) {\n\t\t\tfor (int j = 0; j < num_rhs; j++)\n\t\t\t\tv->value[j] = solver->x[j][v->index];\n\t\t}\n\t}\n}\n\n/* Matrix */\n\nstatic void linear_solver_ensure_matrix_construct(LinearSolver *solver)\n{\n\t/* transition to matrix construction if necessary */\n\tif (solver->state == LinearSolver::STATE_VARIABLES_CONSTRUCT) {\n\t\tint n = 0;\n\n\t\tfor (int i = 0; i < solver->num_variables; i++) {\n\t\t\tif (solver->variable[i].locked)\n\t\t\t\tsolver->variable[i].index = ~0;\n\t\t\telse\n\t\t\t\tsolver->variable[i].index = n++;\n\t\t}\n\n\t\tint m = (solver->num_rows == 0)? n: solver->num_rows;\n\n\t\tsolver->m = m;\n\t\tsolver->n = n;\n\n\t\tassert(solver->least_squares || m == n);\n\n\t\t/* reserve reasonable estimate */\n\t\tsolver->Mtriplets.clear();\n\t\tsolver->Mtriplets.reserve(std::max(m, n)*3);\n\n\t\tsolver->b.resize(solver->num_rhs);\n\t\tsolver->x.resize(solver->num_rhs);\n\n\t\tfor (int i = 0; i < solver->num_rhs; i++) {\n\t\t\tsolver->b[i].setZero(m);\n\t\t\tsolver->x[i].setZero(n);\n\t\t}\n\n\t\tlinear_solver_variables_to_vector(solver);\n\n\t\tsolver->state = LinearSolver::STATE_MATRIX_CONSTRUCT;\n\t}\n}\n\nvoid EIG_linear_solver_matrix_add(LinearSolver *solver, int row, int col, double value)\n{\n\tif (solver->state == LinearSolver::STATE_MATRIX_SOLVED)\n\t\treturn;\n\n\tlinear_solver_ensure_matrix_construct(solver);\n\n\tif (!solver->least_squares && solver->variable[row].locked);\n\telse if (solver->variable[col].locked) {\n\t\tif (!solver->least_squares)\n\t\t\trow = solver->variable[row].index;\n\n\t\tLinearSolver::Coeff coeff;\n\t\tcoeff.index = row;\n\t\tcoeff.value = value;\n\t\tsolver->variable[col].a.push_back(coeff);\n\t}\n\telse {\n\t\tif (!solver->least_squares)\n\t\t\trow = solver->variable[row].index;\n\t\tcol = solver->variable[col].index;\n\n\t\t/* direct insert into matrix is too slow, so use triplets */\n\t\tEigenTriplet triplet(row, col, value);\n\t\tsolver->Mtriplets.push_back(triplet);\n\t}\n}\n\n/* Right hand side */\n\nvoid EIG_linear_solver_right_hand_side_add(LinearSolver *solver, int rhs, int index, double value)\n{\n\tlinear_solver_ensure_matrix_construct(solver);\n\n\tif (solver->least_squares) {\n\t\tsolver->b[rhs][index] += value;\n\t}\n\telse if (!solver->variable[index].locked) {\n\t\tindex = solver->variable[index].index;\n\t\tsolver->b[rhs][index] += value;\n\t}\n}\n\n/* Solve */\n\nbool EIG_linear_solver_solve(LinearSolver *solver)\n{\n\t/* nothing to solve, perhaps all variables were locked */\n\tif (solver->m == 0 || solver->n == 0)\n\t\treturn true;\n\n\tbool result = true;\n\n\tassert(solver->state != LinearSolver::STATE_VARIABLES_CONSTRUCT);\n\n\tif (solver->state == LinearSolver::STATE_MATRIX_CONSTRUCT) {\n\t\t/* create matrix from triplets */\n\t\tsolver->M.resize(solver->m, solver->n);\n\t\tsolver->M.setFromTriplets(solver->Mtriplets.begin(), solver->Mtriplets.end());\n\t\tsolver->Mtriplets.clear();\n\n\t\t/* create least squares matrix */\n\t\tif (solver->least_squares)\n\t\t\tsolver->MtM = solver->M.transpose() * solver->M;\n\n\t\t/* convert M to compressed column format */\n\t\tEigenSparseMatrix& M = (solver->least_squares)? solver->MtM: solver->M;\n\t\tM.makeCompressed();\n\n\t\t/* perform sparse LU factorization */\n\t\tEigenSparseLU *sparseLU = new EigenSparseLU();\n\t\tsolver->sparseLU = sparseLU;\n\n\t\tsparseLU->compute(M);\n\t\tresult = (sparseLU->info() == Eigen::Success);\n\n\t\tsolver->state = LinearSolver::STATE_MATRIX_SOLVED;\n\t}\n\n\tif (result) {\n\t\t/* solve for each right hand side */\n\t\tfor (int rhs = 0; rhs < solver->num_rhs; rhs++) {\n\t\t\t/* modify for locked variables */\n\t\t\tEigenVectorX& b = solver->b[rhs];\n\n\t\t\tfor (int i = 0; i < solver->num_variables; i++) {\n\t\t\t\tLinearSolver::Variable *variable = &solver->variable[i];\n\n\t\t\t\tif (variable->locked) {\n\t\t\t\t\tstd::vector& a = variable->a;\n\n\t\t\t\t\tfor (int j = 0; j < a.size(); j++)\n\t\t\t\t\t\tb[a[j].index] -= a[j].value*variable->value[rhs];\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t/* solve */\n\t\t\tif (solver->least_squares) {\n\t\t\t\tEigenVectorX Mtb = solver->M.transpose() * b;\n\t\t\t\tsolver->x[rhs] = solver->sparseLU->solve(Mtb);\n\t\t\t}\n\t\t\telse {\n\t\t\t\tEigenVectorX& b = solver->b[rhs];\n\t\t\t\tsolver->x[rhs] = solver->sparseLU->solve(b);\n\t\t\t}\n\n\t\t\tif (solver->sparseLU->info() != Eigen::Success)\n\t\t\t\tresult = false;\n\t\t}\n\n\t\tif (result)\n\t\t\tlinear_solver_vector_to_variables(solver);\n\t}\n\n\t/* clear for next solve */\n\tfor (int rhs = 0; rhs < solver->num_rhs; rhs++)\n\t\tsolver->b[rhs].setZero(solver->m);\n\n\treturn result;\n}\n\n/* Debugging */\n\nvoid EIG_linear_solver_print_matrix(LinearSolver *solver)\n{\n\tstd::cout << \"A:\" << solver->M << std::endl;\n\n\tfor (int rhs = 0; rhs < solver->num_rhs; rhs++)\n\t\tstd::cout << \"b \" << rhs << \":\" << solver->b[rhs] << std::endl;\n\n\tif (solver->MtM.rows() && solver->MtM.cols())\n\t\tstd::cout << \"AtA:\" << solver->MtM << std::endl;\n}\n\n", "meta": {"hexsha": "0fc4d39309b4c53e915b1d8ed87bcb5f7d462297", "size": 8780, "ext": "cc", "lang": "C++", "max_stars_repo_path": "intern/eigen/intern/linear_solver.cc", "max_stars_repo_name": "1-MillionParanoidTterabytes/Blender-2.79b-blackened", "max_stars_repo_head_hexsha": "e8d767324e69015aa66850d13bee7db1dc7d084b", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2018-06-18T01:50:25.000Z", "max_stars_repo_stars_event_max_datetime": "2018-06-18T01:50:32.000Z", "max_issues_repo_path": "intern/eigen/intern/linear_solver.cc", "max_issues_repo_name": "1-MillionParanoidTterabytes/Blender-2.79b-blackened", "max_issues_repo_head_hexsha": "e8d767324e69015aa66850d13bee7db1dc7d084b", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "intern/eigen/intern/linear_solver.cc", "max_forks_repo_name": "1-MillionParanoidTterabytes/Blender-2.79b-blackened", "max_forks_repo_head_hexsha": "e8d767324e69015aa66850d13bee7db1dc7d084b", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.9237057221, "max_line_length": 98, "alphanum_fraction": 0.6864464692, "num_tokens": 2476, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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NO", "lm_q1_score": 0.554470450236115, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.21960809447816032}} {"text": "#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include // Also includes \n#include \n\n#include \n\nnamespace C = Utility::Constants;\n\n#ifndef SPIRIT_USE_CUDA\n\nnamespace Engine\n{\n namespace Manifoldmath\n {\n void project_parallel(vectorfield & vf1, const vectorfield & vf2)\n {\n vectorfield vf3 = vf1;\n project_orthogonal(vf3, vf2);\n // TODO: replace the loop with Vectormath Kernel\n #pragma omp parallel for\n for (unsigned int i = 0; i < vf1.size(); ++i)\n vf1[i] -= vf3[i];\n }\n\n void project_orthogonal(vectorfield & vf1, const vectorfield & vf2)\n {\n scalar x = Vectormath::dot(vf1, vf2);\n // TODO: replace the loop with Vectormath Kernel\n #pragma omp parallel for\n for (unsigned int i=0; i> configurations, const std::vector & energies, std::vector & tangents)\n {\n int noi = configurations.size();\n int nos = (*configurations[0]).size();\n\n for (int idx_img = 0; idx_img < noi; ++idx_img)\n {\n auto& image = *configurations[idx_img];\n\n // First Image\n if (idx_img == 0)\n {\n auto& image_plus = *configurations[idx_img + 1];\n Vectormath::set_c_a( 1, image_plus, tangents[idx_img]);\n Vectormath::add_c_a(-1, image, tangents[idx_img]);\n }\n // Last Image\n else if (idx_img == noi - 1)\n {\n auto& image_minus = *configurations[idx_img - 1];\n Vectormath::set_c_a( 1, image, tangents[idx_img]);\n Vectormath::add_c_a(-1, image_minus, tangents[idx_img]);\n }\n // Images Inbetween\n else\n {\n auto& image_plus = *configurations[idx_img + 1];\n auto& image_minus = *configurations[idx_img - 1];\n\n // Energies\n scalar E_mid = 0, E_plus = 0, E_minus = 0;\n E_mid = energies[idx_img];\n E_plus = energies[idx_img + 1];\n E_minus = energies[idx_img - 1];\n\n // Vectors to neighbouring images\n vectorfield t_plus(nos), t_minus(nos);\n\n Vectormath::set_c_a( 1, image_plus, t_plus);\n Vectormath::add_c_a(-1, image, t_plus);\n\n Vectormath::set_c_a( 1, image, t_minus);\n Vectormath::add_c_a(-1, image_minus, t_minus);\n\n // Near maximum or minimum\n if ((E_plus < E_mid && E_mid > E_minus) || (E_plus > E_mid && E_mid < E_minus))\n {\n // Get a smooth transition between forward and backward tangent\n scalar E_max = std::max(std::abs(E_plus - E_mid), std::abs(E_minus - E_mid));\n scalar E_min = std::min(std::abs(E_plus - E_mid), std::abs(E_minus - E_mid));\n\n if (E_plus > E_minus)\n {\n Vectormath::set_c_a(E_max, t_plus, tangents[idx_img]);\n Vectormath::add_c_a(E_min, t_minus, tangents[idx_img]);\n }\n else\n {\n Vectormath::set_c_a(E_min, t_plus, tangents[idx_img]);\n Vectormath::add_c_a(E_max, t_minus, tangents[idx_img]);\n }\n }\n // Rising slope\n else if (E_plus > E_mid && E_mid > E_minus)\n {\n Vectormath::set_c_a(1, t_plus, tangents[idx_img]);\n }\n // Falling slope\n else if (E_plus < E_mid && E_mid < E_minus)\n {\n Vectormath::set_c_a(1, t_minus, tangents[idx_img]);\n //tangents = t_minus;\n for (int i = 0; i < nos; ++i)\n {\n tangents[idx_img][i] = t_minus[i];\n }\n }\n // No slope(constant energy)\n else\n {\n Vectormath::set_c_a(1, t_plus, tangents[idx_img]);\n Vectormath::add_c_a(1, t_minus, tangents[idx_img]);\n }\n }\n\n // Project tangents into tangent planes of spin vectors to make them actual tangents\n project_tangential(tangents[idx_img], image);\n\n // Normalise in 3N - dimensional space\n Manifoldmath::normalize(tangents[idx_img]);\n\n }// end for idx_img\n }// end Tangents\n }\n}\n\n#endif\n\nnamespace Engine\n{\n namespace Manifoldmath\n {\n scalar norm(const vectorfield & vf)\n {\n scalar x = Vectormath::dot(vf, vf);\n return std::sqrt(x);\n }\n\n void normalize(vectorfield & vf)\n {\n scalar sc = 1.0/norm(vf);\n Vectormath::scale(vf, sc);\n }\n\n\n MatrixX tangential_projector(const vectorfield & image)\n {\n int nos = image.size();\n int size = 3*nos;\n\n // Get projection matrix M=1-S, blockwise S=x*x^T\n MatrixX proj = MatrixX::Identity(size, size);\n for (int i = 0; i < nos; ++i)\n {\n proj.block<3, 3>(3*i, 3*i) -= image[i] * image[i].transpose();\n }\n\n return proj;\n }\n\n // This gives an orthogonal matrix of shape (3N, 2N), meaning M^T=M^-1 or M^T*M=1.\n // This assumes that the vectors of vf are normalized and that basis is 3N x 2N\n // It can be used to transform a vector into or back from the tangent space of a\n // sphere w.r.t. euclidean 3N space.\n // It is generated by column-wise normalization of the Jacobi matrix for the\n // transformation from (unit-)spherical coordinates to euclidean.\n // It therefore consists of the local basis vectors of the spherical coordinates\n // of a unit sphere, represented in 3N, as the two columns of the matrix.\n void tangent_basis_spherical(const vectorfield & vf, MatrixX & basis)\n {\n Vector3 tmp, etheta, ephi;\n basis.setZero();\n for (unsigned int i=0; i < vf.size(); ++i)\n {\n if (vf[i][2] > 1-1e-8)\n {\n tmp = Vector3{1, 0, 0};\n basis.block<3,1>(3*i,2*i) = (tmp - tmp.dot(vf[i])*vf[i]).normalized();\n tmp = Vector3{0, 1, 0};\n basis.block<3,1>(3*i,2*i+1) = (tmp - tmp.dot(vf[i])*vf[i]).normalized();\n }\n else if (vf[i][2] < -1+1e-8)\n {\n tmp = Vector3{1, 0, 0};\n basis.block<3,1>(3*i,2*i) = (tmp - tmp.dot(vf[i])*vf[i]).normalized();\n tmp = Vector3{0, -1, 0};\n basis.block<3,1>(3*i,2*i+1) = (tmp - tmp.dot(vf[i])*vf[i]).normalized();\n }\n else\n {\n scalar rxy = std::sqrt( 1 - vf[i][2]*vf[i][2] );\n scalar z_rxy = vf[i][2] / rxy;\n\n // Note: these are not unit vectors, but derivatives!\n etheta = Vector3{ vf[i][0]*z_rxy, vf[i][1]*z_rxy, -rxy };\n ephi = Vector3{ -vf[i][1]/rxy, vf[i][0]/rxy, 0 };\n\n basis.block<3,1>(3*i,2*i) = (etheta - etheta.dot(vf[i])*vf[i]).normalized();\n basis.block<3,1>(3*i,2*i+1) = (ephi - ephi.dot(vf[i])*vf[i]).normalized();\n }\n }\n }\n\n // This calculates the basis via calculation of cross products\n // This assumes that the vectors of vf are normalized and that basis is 3N x 2N\n void tangent_basis_cross(const vectorfield & vf, MatrixX & basis)\n {\n basis.setZero();\n for(int i=0; i < vf.size(); ++i)\n {\n if (std::abs(vf[i].z()) > 1-1e-8)\n {\n basis.block<3,1>(3*i,2*i) = Vector3{0,1,0}.cross(vf[i]).normalized();\n basis.block<3,1>(3*i,2*i+1) = vf[i].cross(basis.block<3,1>(3*i,2*i));\n }\n else\n {\n basis.block<3,1>(3*i,2*i) = Vector3{0,0,1}.cross(vf[i]).normalized();\n basis.block<3,1>(3*i,2*i+1) = vf[i].cross(basis.block<3,1>(3*i,2*i));\n }\n }\n }\n\n // This calculates the basis via orthonormalization to a random vector\n // This assumes that the vectors of vf are normalized and that basis is 3N x 2N\n void tangent_basis_righthanded(const vectorfield & vf, MatrixX & basis)\n {\n int size = vf.size();\n basis.setZero();\n\n // vf should be 3N\n // basis should be 3N x 2N\n\n // e1 and e2 will form a righthanded vectorset with the axis (though not orthonormal!)\n Vector3 e1, e2, v1;\n Vector3 ex{1,0,0}, ey{0,1,0}, ez{0,0,1};\n\n for(int i=0; i < size; ++i)\n {\n auto& axis = vf[i];\n\n // Choose orthogonalisation basis for Grahm-Schmidt\n // We will need two vectors with which the axis always forms the\n // same orientation (händigkeit des vektor-dreibeins)\n // If axis_z=0 its in the xy-plane\n // the vectors should be: axis, ez, (axis x ez)\n if (axis[2] == 0)\n {\n e1 = ez;\n e2 = axis.cross(ez);\n }\n // Else its either above or below the xy-plane.\n // if its above the xy-plane, it points in z-direction\n // the vectors should be: axis, ex, -ey\n else if (axis[2] > 0)\n {\n e1 = ex;\n e2 = -ey;\n }\n // if its below the xy-plane, it points in -z-direction\n // the vectors should be: axis, ex, ey\n else if (axis[2] < 0)\n {\n e1 = ex;\n e2 = ey;\n }\n\n // First vector: orthogonalize e1 w.r.t. axis\n v1 = ( e1 - e1.dot(axis) * axis ).normalized();\n basis.block<3,1>(3*i,2*i) = v1;\n\n // Second vector: orthogonalize e2 w.r.t. axis and v1\n basis.block<3,1>(3*i,2*i+1) = ( e2 - e2.dot(axis)*axis - e2.dot(v1)*v1 ).normalized();\n }\n }\n\n\n // This gives the Jacobian matrix for the transformation from (unit-)spherical\n // to euclidean coordinates. It consists of the derivative vectors d/d_theta\n // and d/d_phi as the two columns of the matrix.\n void spherical_to_cartesian_jacobian(const vectorfield & vf, MatrixX & jacobian)\n {\n Vector3 tmp, etheta, ephi;\n jacobian.setZero();\n for (unsigned int i=0; i < vf.size(); ++i)\n {\n if (vf[i][2] > 1-1e-8)\n {\n tmp = Vector3{1, 0, 0};\n jacobian.block<3,1>(3*i,2*i) = (tmp - tmp.dot(vf[i])*vf[i]).normalized();\n tmp = Vector3{0, 1, 0};\n jacobian.block<3,1>(3*i,2*i+1) = (tmp - tmp.dot(vf[i])*vf[i]).normalized();\n }\n else if (vf[i][2] < -1+1e-8)\n {\n tmp = Vector3{1, 0, 0};\n jacobian.block<3,1>(3*i,2*i) = (tmp - tmp.dot(vf[i])*vf[i]).normalized();\n tmp = Vector3{0, -1, 0};\n jacobian.block<3,1>(3*i,2*i+1) = (tmp - tmp.dot(vf[i])*vf[i]).normalized();\n }\n else\n {\n scalar rxy = std::sqrt( 1 - vf[i][2]*vf[i][2] );\n scalar z_rxy = vf[i][2] / rxy;\n\n // Note: these are not unit vectors, but derivatives!\n etheta = Vector3{ vf[i][0]*z_rxy, vf[i][1]*z_rxy, -rxy };\n ephi = Vector3{ -vf[i][1], vf[i][0], 0 };\n\n jacobian.block<3,1>(3*i,2*i) = etheta - etheta.dot(vf[i])*vf[i];\n jacobian.block<3,1>(3*i,2*i+1) = ephi - ephi.dot(vf[i])*vf[i];\n }\n }\n }\n\n // The Hessian matrix of the transformation from spherical to euclidean coordinates\n void spherical_to_cartesian_hessian(const vectorfield & vf, MatrixX & gamma_x, MatrixX & gamma_y, MatrixX & gamma_z)\n {\n int nos = vf.size();\n gamma_x.setZero();\n gamma_y.setZero();\n gamma_z.setZero();\n\n for (unsigned int i=0; i < nos; ++i)\n {\n scalar z_rxy = vf[i][2] / std::sqrt(1+1e-6-vf[i][2]*vf[i][2]);\n\n gamma_x.block<2,2>(2*i,2*i) << -vf[i][0], -vf[i][1]*z_rxy,\n -vf[i][1]*z_rxy, -vf[i][0];\n\n gamma_y.block<2,2>(2*i,2*i) << -vf[i][1], vf[i][0]*z_rxy,\n vf[i][0]*z_rxy, -vf[i][1];\n\n gamma_z.block<2,2>(2*i,2*i) << -vf[i][2], 0,\n 0, 0;\n }\n }\n\n // The (2Nx2N) Christoffel symbols of the transformation from (unit-)spherical coordinates to euclidean\n void spherical_to_cartesian_christoffel_symbols(const vectorfield & vf, MatrixX & gamma_theta, MatrixX & gamma_phi)\n {\n using std::cos;\n using std::sin;\n using std::acos;\n using std::tan;\n using std::atan2;\n\n int nos = vf.size();\n gamma_theta = MatrixX::Zero(2*nos, 2*nos);\n gamma_phi = MatrixX::Zero(2*nos, 2*nos);\n\n for (unsigned int i=0; i < nos; ++i)\n {\n scalar theta = acos(vf[i][2]);\n scalar phi = atan2(vf[i][1], vf[i][0]);\n scalar cot = 0;\n if (std::abs(theta) > 1e-4)\n cot = -tan(C::Pi_2 + theta);\n\n gamma_theta(2*i+1,2*i+1) = -sin(theta)*cos(theta);\n\n gamma_phi(2*i+1,2*i) = cot;\n gamma_phi(2*i,2*i+1) = cot;\n }\n }\n\n\n\n void hessian_bordered(const vectorfield & image, const vectorfield & gradient, const MatrixX & hessian, MatrixX & tangent_basis, MatrixX & hessian_out)\n {\n // Calculates a 3Nx3N matrix in the bordered Hessian approach and transforms it into the tangent basis,\n // making the result a 2Nx2N matrix. The bordered Hessian's Lagrange multipliers assume a local extremum.\n\n int nos = image.size();\n MatrixX tmp_3N = hessian;\n\n VectorX lambda(nos);\n for (int i=0; i(3*i, 3*i) -= P.block<3, 3>(3*i, 3*i) * ( image[i].dot(gradient[i]) )\n + ( P.block<3, 3>(3*i, 3*i) * gradient[i] ) * image[i].transpose();\n }\n\n // Calculate the basis transformation matrix\n tangent_basis = MatrixX::Zero(3*nos, 2*nos);\n tangent_basis_spherical(image, tangent_basis);\n\n // Result is a 2Nx2N matrix\n hessian_out = tangent_basis.transpose() * hessian_out * tangent_basis;\n }\n\n\n void hessian_weingarten(const vectorfield & image, const vectorfield & gradient, const MatrixX & hessian, MatrixX & tangent_basis, MatrixX & hessian_out)\n {\n // Calculates a 3Nx3N matrix in the Weingarten map approach and transforms it into the tangent basis,\n // making the result a 2Nx2N matrix\n\n int nos = image.size();\n hessian_out.setZero();\n\n // Calculate projector matrix\n auto P = tangential_projector(image);\n \n // Calculate tangential projection of Hessian\n hessian_out = P * hessian;\n\n // Add the Weingarten map\n for (unsigned int i = 0; i < nos; ++i)\n {\n MatrixX proj = MatrixX::Identity(3, 3);\n hessian_out.block<3, 3>(3*i, 3*i) -= MatrixX::Identity(3, 3) * image[i].dot(gradient[i]);\n }\n\n // Calculate the basis transformation matrix\n tangent_basis = MatrixX::Zero(3*nos, 2*nos);\n tangent_basis_spherical(image, tangent_basis);\n\n // Result is a 2Nx2N matrix\n hessian_out = tangent_basis.transpose() * hessian_out * tangent_basis;\n }\n\n\n void hessian_spherical(const vectorfield & image, const vectorfield & gradient, const MatrixX & hessian, MatrixX & hessian_out)\n {\n // Calculates a 2Nx2N hessian matrix containing second order spherical derivatives\n\n int nos = image.size();\n\n MatrixX jacobian = MatrixX::Zero(3*nos, 2*nos);\n MatrixX sph_hess_x = MatrixX::Zero(2*nos, 2*nos);\n MatrixX sph_hess_y = MatrixX::Zero(2*nos, 2*nos);\n MatrixX sph_hess_z = MatrixX::Zero(2*nos, 2*nos);\n\n // Calculate coordinate transformation jacobian\n Engine::Manifoldmath::spherical_to_cartesian_jacobian(image, jacobian);\n \n // Calculate coordinate transformation Hessian\n Engine::Manifoldmath::spherical_to_cartesian_hessian(image, sph_hess_x, sph_hess_y, sph_hess_z);\n \n // Calculate transformed Hessian\n hessian_out = jacobian.transpose() * hessian * jacobian;\n for (int i=0; i < nos; ++i)\n {\n hessian_out.block<2,2>(2*i, 2*i) += gradient[i][0] * sph_hess_x.block<2,2>(2*i, 2*i)\n + gradient[i][1] * sph_hess_y.block<2,2>(2*i, 2*i)\n + gradient[i][2] * sph_hess_z.block<2,2>(2*i, 2*i);\n }\n\n }\n\n\n void hessian_covariant(const vectorfield & image, const vectorfield & gradient, const MatrixX & hessian, MatrixX & hessian_out)\n {\n // Calculates a 2Nx2N covariant hessian matrix containing second order spherical derivatives\n // and correction terms (containing Christoffel symbols)\n \n int nos = image.size();\n\n // Calculate coordinate transformation jacobian\n MatrixX jacobian(3*nos, 2*nos);\n Engine::Manifoldmath::spherical_to_cartesian_jacobian(image, jacobian);\n\n // Calculate the gradient in spherical coordinates\n Eigen::Ref grad = Eigen::Map(gradient[0].data(), 3 * nos);\n VectorX gradient_spherical = jacobian.transpose() * grad;\n\n // Calculate the Hessian in spherical coordinates\n hessian_spherical(image, gradient, hessian, hessian_out);\n\n // Calculate the Christoffel symbols for spherical coordinates\n MatrixX christoffel_theta = MatrixX::Zero(2*nos, 2*nos);\n MatrixX christoffel_phi = MatrixX::Zero(2*nos, 2*nos);\n Engine::Manifoldmath::spherical_to_cartesian_christoffel_symbols(image, christoffel_theta, christoffel_phi);\n\n // Calculate the covariant Hessian\n for (int i=0; i < nos; ++i)\n {\n hessian_out.block<2,2>(2*i, 2*i) -= gradient_spherical[2*i] * christoffel_theta.block<2,2>(2*i, 2*i)\n + gradient_spherical[2*i+1] * christoffel_phi.block<2,2>(2*i, 2*i);\n }\n }\n\n }\n}", "meta": {"hexsha": "8674022d4f52c1239c37bd7d10c447d44b1b7629", "size": 23113, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "core/src/engine/Manifoldmath.cpp", "max_stars_repo_name": "ddkn/spirit", "max_stars_repo_head_hexsha": "8e51bcdd78ee05d433d000c7e389fe1e6c3716bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 46.0, "max_stars_repo_stars_event_min_datetime": "2020-08-24T22:40:15.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-28T06:54:54.000Z", "max_issues_repo_path": "core/src/engine/Manifoldmath.cpp", "max_issues_repo_name": "ddkn/spirit", "max_issues_repo_head_hexsha": "8e51bcdd78ee05d433d000c7e389fe1e6c3716bc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "core/src/engine/Manifoldmath.cpp", "max_forks_repo_name": "ddkn/spirit", "max_forks_repo_head_hexsha": "8e51bcdd78ee05d433d000c7e389fe1e6c3716bc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2020-09-05T13:24:41.000Z", "max_forks_repo_forks_event_max_datetime": "2021-11-06T07:46:47.000Z", "avg_line_length": 40.6919014085, "max_line_length": 161, "alphanum_fraction": 0.4904599143, "num_tokens": 5771, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813031051514762, "lm_q2_score": 0.3775406828054583, "lm_q1q2_score": 0.21946557123582147}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace po = boost::program_options;\n\nusing namespace std;\nusing namespace boost;\n\nclass Corpus {\npublic:\n string text;\n dynamic_bitset<> utt_boundaries;\n dynamic_bitset<> boundaries;\n map p_phonemes;\n\n Corpus(string path)\n {\n load_file({path});\n compute_p_phonemes();\n }\n\n Corpus(string path, string trained_path,\n dynamic_bitset<> &trained_boundaries)\n {\n load_file({path, trained_path});\n compute_p_phonemes();\n\n // set the trained boundaries as unchangable utterance boundaries\n trained_boundaries.resize(text.size() + 1, false);\n utt_boundaries |= trained_boundaries;\n }\n\n double p0(string word, double p_hash=0.5)\n {\n double p = 1;\n for (char ph : word)\n p *= p_phonemes[ph];\n\n return p_hash * pow(1 - p_hash, word.size() - 1) * p;\n }\n\n vector get_words()\n {\n vector words;\n dynamic_bitset<> bounds = utt_boundaries | boundaries;\n\n for (int i = 0; i < text.size(); ++i) {\n if (bounds[i])\n words.push_back(string());\n\n words.back().append(1, text[i]);\n }\n\n return words;\n }\n\nprivate:\n virtual void load_file(const vector &paths)\n {\n string line;\n stringstream text_stream;\n vector utt_indices = {0};\n\n for (string path : paths) {\n fstream in(path);\n\n while (getline(in, line)) {\n text_stream << line;\n utt_indices.push_back(utt_indices.back() + line.size());\n }\n\n in.close();\n }\n\n\n text = text_stream.str();\n\n utt_boundaries = dynamic_bitset<>(text.size() + 1);\n boundaries = dynamic_bitset<>(text.size() + 1);\n\n for (int index : utt_indices) {\n utt_boundaries[index] = 1;\n }\n }\n\n void compute_p_phonemes()\n {\n for (char phoneme : text) {\n // only compute if the char isn't in the map yet\n if (p_phonemes.find(phoneme) == p_phonemes.end())\n p_phonemes[phoneme] = count(text.begin(), text.end(), phoneme) /\n (double)text.size();\n }\n }\n};\n\nmap histogram(const vector &vec)\n{\n map counts;\n vector unique_words;\n\n unique_copy(vec.begin(), vec.end(), back_inserter(unique_words));\n\n for (const string &word : unique_words) {\n if (counts.find(word) == counts.end())\n counts[word] = 1;\n else\n counts[word] += 1;\n }\n\n return counts;\n}\n\nvoid find_enclosing_boundaries(dynamic_bitset<> bounds, int i,\n int *lower, int *upper)\n{\n *lower = i - 1;\n while (!bounds[*lower])\n *lower -= 1;\n\n *upper = i + 1;\n while (!bounds[*upper])\n *upper += 1;\n}\n\nvoid gibbs_iteration(Corpus &corpus, double rho=2.0, double alpha=0.5,\n double p_hash=0.5)\n{\n dynamic_bitset<> bounds = corpus.utt_boundaries | corpus.boundaries;\n vector words = corpus.get_words();\n map word_counts = histogram(words);\n\n for (int i = 0; i < corpus.text.size(); ++i) {\n char phoneme = corpus.text[i];\n\n if (corpus.utt_boundaries[i])\n continue;\n\n int lower, upper;\n find_enclosing_boundaries(bounds, i, &lower, &upper);\n\n string w1 = corpus.text.substr(lower, (upper - lower));\n string w2 = corpus.text.substr(lower, (i - lower));\n string w3 = corpus.text.substr(i, (upper - i));\n\n double n_;\n if (!bounds[i])\n n_ = words.size();\n else\n n_ = words.size() - 1;\n\n double n_dollar = corpus.utt_boundaries.count() - 1;\n double nu = corpus.utt_boundaries[upper] ? n_dollar : n_ - n_dollar;\n\n double p_h1_factor1;\n if (!bounds[i])\n p_h1_factor1 = (word_counts[w1] - 1 + alpha * corpus.p0(w1, p_hash)) / (n_ + alpha);\n else\n p_h1_factor1 = (word_counts[w1] + alpha * corpus.p0(w1, p_hash)) / (n_ + alpha);\n\n double p_h1_factor2 = (nu + rho/2) / (n_ + rho);\n\n double p_h2_factor1, p_h2_factor3;\n if (!bounds[i]) {\n p_h2_factor1 = (word_counts[w2] + alpha * corpus.p0(w2, p_hash)) / (n_ + alpha);\n p_h2_factor3 = ((word_counts[w3] + (w2 == w3 ? 1 : 0) + alpha *\n corpus.p0(w3, p_hash)) / (n_ + 1 + alpha));\n } else {\n p_h2_factor1 = (word_counts[w2] - 1 + alpha * corpus.p0(w2, p_hash)) / (n_ + alpha);\n p_h2_factor3 = ((word_counts[w3] - 1 + (w2 == w3 ? 1 : 0) + alpha *\n corpus.p0(w3, p_hash)) / (n_ + 1 + alpha));\n }\n\n double p_h2_factor2 = (n_ - n_dollar + rho/2) / (n_ + rho);\n double p_h2_factor4 = ((nu + (w2 == w3 ? 1 : 0) + rho/2) / (n_ + 1 + rho));\n\n double p_h1 = p_h1_factor1 * p_h1_factor2;\n double p_h2 = p_h2_factor1 * p_h2_factor2 * p_h2_factor3 * p_h2_factor4;\n\n if (p_h2 > p_h1)\n corpus.boundaries[i] = 1;\n else\n corpus.boundaries[i] = 0;\n }\n}\n\nvoid write_boundaries(const Corpus &corpus, string filename)\n{\n ofstream out(filename);\n out << \"[\";\n\n for (int i = 0; i < corpus.text.size(); ++i) {\n if (i != 0)\n out << \", \";\n out << corpus.boundaries[i];\n }\n\n out << \"]\";\n out.close();\n}\n\ndynamic_bitset<> read_boundaries(string path)\n{\n fstream in(path);\n string line;\n getline(in, line);\n in.close();\n\n auto end_iter = remove_if(line.begin(), line.end(),\n [](char ch) {return !(ch == '1' || ch == '0');});\n line.erase(end_iter, line.end());\n\n return dynamic_bitset<>(line);\n}\n\nstring generate_filepath(const string &out_dir, double alpha, double p_hash, int i=-1)\n{\n stringstream s;\n s << out_dir << \"/\";\n if (i >= 0)\n s << \"iter_\" << i << \"_\";\n s << alpha << \"_\" << p_hash << \".txt\";\n\n return s.str();\n}\n\nint main(int argc, char *argv[])\n{\n string out_dir;\n string train_path;\n string test_path;\n string boundaries;\n double alpha;\n double p_hash;\n vector eval_points;\n\n po::options_description desc(\"Usage\");\n desc.add_options()\n (\"alpha,a\", po::value(&alpha)->default_value(0.5), \"alpha parameter\")\n (\"p_hash,ph\", po::value(&p_hash)->default_value(0.5), \"p# parameter\")\n (\"out_dir\", po::value(&out_dir), \"path to write output to\")\n (\"train_path\", po::value(&train_path), \"path to load training data\")\n (\"test_path\", po::value(&test_path), \"path to load test data\")\n (\"test\", \"apply a learned model to test data (requires test_path and boundaries)\")\n (\"help\", \"print usage information\")\n (\"boundaries\", po::value(&boundaries), \"path to a learned set of boundaries\")\n (\",n\", po::value >(&eval_points)->multitoken(), \"points to evaluate\");\n\n po::variables_map opts;\n po::store(po::parse_command_line(argc, argv, desc), opts);\n po::notify(opts);\n\n if (opts.count(\"help\")) {\n cout << desc << endl;\n return 0;\n }\n\n string out_path = generate_filepath(out_dir, alpha, p_hash);\n\n Corpus *corpus;\n if (opts.count(\"test\")) {\n cout << \"Testing\" << endl;\n auto bounds = read_boundaries(boundaries);\n corpus = new Corpus(train_path, test_path, bounds);\n } else {\n corpus = new Corpus(train_path);\n }\n\n // calculate the maximum iteration\n int i_max = *max_element(eval_points.begin(), eval_points.end());\n\n for (int i = 0; i < i_max; ++i) {\n cout << \"Iteration \" << i << endl;\n gibbs_iteration(*corpus, 2, alpha, p_hash);\n\n if (find(eval_points.begin(), eval_points.end(), i) != eval_points.end()) {\n string iterpath = generate_filepath(out_dir, alpha, p_hash, i);\n write_boundaries(*corpus, iterpath);\n }\n }\n\n write_boundaries(*corpus, out_path);\n\n return 0;\n}\n", "meta": {"hexsha": "6fb857fd2663448e40a8f71861fc58d7205787e6", "size": 8348, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "goldwater/segment.cpp", "max_stars_repo_name": "wenkokke/UoE-MT3-NMT", "max_stars_repo_head_hexsha": "242a4d8221f8a0462c5c23c057d954e18c88fc34", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "goldwater/segment.cpp", "max_issues_repo_name": "wenkokke/UoE-MT3-NMT", "max_issues_repo_head_hexsha": "242a4d8221f8a0462c5c23c057d954e18c88fc34", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "goldwater/segment.cpp", "max_forks_repo_name": "wenkokke/UoE-MT3-NMT", "max_forks_repo_head_hexsha": "242a4d8221f8a0462c5c23c057d954e18c88fc34", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.2027027027, "max_line_length": 96, "alphanum_fraction": 0.5609726881, "num_tokens": 2209, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926665855647395, "lm_q2_score": 0.3702254064929193, "lm_q1q2_score": 0.21942022755547622}} {"text": "/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */\n\n/*\n Copyright (C) 2010 Roland Lichters\n Copyright (C) 2014 Jose Aparicio\n\n This file is part of QuantLib, a free-software/open-source library\n for financial quantitative analysts and developers - http://quantlib.org/\n\n QuantLib is free software: you can redistribute it and/or modify it\n under the terms of the QuantLib license. You should have received a\n copy of the license along with this program; if not, please email\n . The license is also available online at\n .\n\n This program is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n\nusing boost::algorithm::to_upper_copy;\n\nnamespace QuantLibAddin {\n\n Issuer::Issuer(\n const boost::shared_ptr& properties,\n const boost::shared_ptr& dfts,\n const boost::shared_ptr& evtSet,\n bool permanent\n )\n : ObjectHandler::LibraryObject(properties, permanent) {\n\n std::vector curves(1, std::make_pair(\n QuantLib::NorthAmericaCorpDefaultKey(QuantLib::EURCurrency(),\n QuantLib::SeniorSec, \n QuantLib::Period(),\n 1. // amount threshold\n ),\n QuantLib::Handle(dfts)\n ));\n libraryObject_ = boost::shared_ptr(new QuantLib::Issuer(curves, *evtSet));\n }\n\n DefaultEventSet::DefaultEventSet(\n const boost::shared_ptr& properties,\n const std::string& eventType,\n const QuantLib::Date& eventDate,\n const QuantLib::Currency& cur,\n QuantLib::Seniority sen,\n const QuantLib::Date& settlementDate,\n QuantLib::Real settledRecovery,\n bool permanent\n ) \n : ObjectHandler::LibraryObject(properties, permanent) {\n // if no match return empty set\n libraryObject_ = boost::shared_ptr(new QuantLib::DefaultEventSet());\n\n // only one recovery parsed by now; bankruptcy events need the whole\n // set or they fail to construct.\n std::map rrs;\n rrs.insert(std::pair(sen, settledRecovery));\n if(eventType==std::string(\"FailureToPayEvent\")) {\n libraryObject_->insert(boost::shared_ptr (\n new QuantLib::FailureToPayEvent(eventDate, cur, sen, 1.e7, \n //implSettlemt, \n settlementDate,\n rrs)));\n }else if(eventType==std::string(\"BankruptcyEvent\")){\n libraryObject_->insert(boost::shared_ptr (\n new QuantLib::BankruptcyEvent(eventDate, cur, sen, \n settlementDate,\n rrs)));\n }\n }\n\n /* Code essentially copied from QuantLibAddIn::SimpleQuote. \n Same considerations mentioned there apply. Here the\n Seniority of the quote is considered a fixed \n property, not a market value.\n */\n RecoveryRateQuote::RecoveryRateQuote(\n const boost::shared_ptr& properties,\n QuantLib::Seniority sen,\n QuantLib::Real value,\n bool permanent) : Quote(properties, permanent) {\n libraryObject_ = recoveryQuote_ = boost::shared_ptr(\n new QuantLib::RecoveryRateQuote(value, sen));\n }\n\n\n // CreditDefaultSwap::CreditDefaultSwap(\n // const boost::shared_ptr& properties,\n // QuantLib::Protection::Side side,\n // QuantLib::Real notional,\n // QuantLib::Rate upfront,\n // QuantLib::Rate spread,\n // const boost::shared_ptr& schedule,\n // QuantLib::BusinessDayConvention paymentConvention,\n // const QuantLib::DayCounter& dayCounter,\n // bool settlesAccrual,\n // bool paysAtDefaultTime,\n // const QuantLib::Date& protectionStart,\n // const QuantLib::Date& upfrontDate,\n // bool permanent)\n // : Instrument(properties, permanent) {\n\t\t\t//// dirty way to decide if this is constructed through a run only version\n\t\t\t//if(upfrontDate == QuantLib::Null() && upfront == 0.) {\n\t\t\t//\tlibraryObject_ = boost::shared_ptr(\n\t\t\t//\t\t\t\tnew QuantLib::CreditDefaultSwap(side,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tnotional,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tspread,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\t*schedule,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tpaymentConvention,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tdayCounter,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tsettlesAccrual,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tpaysAtDefaultTime,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tprotectionStart,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tboost::shared_ptr()));\n\t\t\t//}else{\n\t\t\t//\tlibraryObject_ = boost::shared_ptr(\n\t\t\t//\t\t\t\tnew QuantLib::CreditDefaultSwap(side,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tnotional,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tupfront,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tspread,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\t*schedule,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tpaymentConvention,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tdayCounter,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tsettlesAccrual,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tpaysAtDefaultTime,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tprotectionStart,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tupfrontDate,\n\t\t\t//\t\t\t\t\t\t\t\t\t\t\t\tboost::shared_ptr()));\n // }\n // }\n // \n MidPointCdsEngine::MidPointCdsEngine(\n const boost::shared_ptr& properties,\n const QuantLib::Handle& defaultTS,\n QuantLib::Real recoveryRate,\n const QuantLib::Handle& yieldTS,\n bool permanent) \n : PricingEngine(properties, permanent) {\n libraryObject_ = boost::shared_ptr(new\n QuantLib::MidPointCdsEngine(defaultTS, recoveryRate, yieldTS));\n }\n\n\n\n SpreadCdsHelper::SpreadCdsHelper(\n const boost::shared_ptr& properties,\n const QuantLib::Handle& quote,\n const QuantLib::Period& period,\n QuantLib::Natural settlementDays,\n const QuantLib::Calendar& calendar,\n QuantLib::Frequency frequency,\n QuantLib::BusinessDayConvention paymentConvention,\n QuantLib::DateGeneration::Rule rule,\n const QuantLib::DayCounter& dayCounter,\n QuantLib::Real recoveryRate,\n const QuantLib::Handle& yieldTS,\n bool settlesAccrual,\n bool paysAtDefaultTime,\n bool permanent) : DefaultProbabilityHelper(properties, permanent) {\n\n libraryObject_ = boost::shared_ptr(new\n\t\t QuantLib::SpreadCdsHelper(quote,\n\t\t\t\t\t\t period,\n\t\t\t\t\t\t settlementDays,\n\t\t\t\t\t\t calendar,\n\t\t\t\t\t\t frequency,\n\t\t\t\t\t\t paymentConvention,\n\t\t\t\t\t\t rule,\n\t\t\t\t\t\t dayCounter,\n\t\t\t\t\t\t recoveryRate,\n\t\t\t\t\t\t yieldTS,\n\t\t\t\t\t\t settlesAccrual,\n\t\t\t\t\t\t paysAtDefaultTime));\n }\n\n UpfrontCdsHelper::UpfrontCdsHelper(\n const boost::shared_ptr& properties,\n const QuantLib::Handle& quote,\n QuantLib::Rate runningSpread,\n const QuantLib::Period& period,\n QuantLib::Natural settlementDays,\n const QuantLib::Calendar& calendar,\n QuantLib::Frequency frequency,\n QuantLib::BusinessDayConvention paymentConvention,\n QuantLib::DateGeneration::Rule rule,\n const QuantLib::DayCounter& dayCounter,\n QuantLib::Real recoveryRate,\n const QuantLib::Handle& yieldTS,\n QuantLib::Natural upfrontSettlementDays,\n bool settlesAccrual,\n bool paysAtDefaultTime,\n bool permanent) : DefaultProbabilityHelper(properties, permanent) {\n libraryObject_ = boost::shared_ptr(new\n\t\t QuantLib::UpfrontCdsHelper(quote,\n runningSpread,\n period,\n settlementDays,\n calendar,\n frequency,\n paymentConvention,\n rule,\n dayCounter,\n recoveryRate,\n yieldTS,\n upfrontSettlementDays,\n settlesAccrual,\n paysAtDefaultTime));\n }\n\n HazardRateCurve::HazardRateCurve(\n const boost::shared_ptr& properties,\n const std::vector& dates,\n const std::vector& hazardRates,\n const QuantLib::DayCounter& dayCounter,\n bool permanent) \n : DefaultProbabilityTermStructure(properties, permanent) {\n QL_REQUIRE(!dates.empty(), \"no input dates given\");\n QL_REQUIRE(dates.size() == hazardRates.size(), \n \"vector sizes differ\");\n libraryObject_ = boost::shared_ptr(\n new QuantLib::InterpolatedHazardRateCurve(\n \t\t\t\t dates, hazardRates, dayCounter));\n }\n\n PiecewiseHazardRateCurve::PiecewiseHazardRateCurve(\n const boost::shared_ptr& properties,\n const std::vector >& helpers,\n const QuantLib::DayCounter& dayCounter,\n const QuantLib::Calendar& calendar,\n const std::string& interpolator,\n QuantLib::Real accuracy,\n bool permanent) \n : DefaultProbabilityTermStructure(properties, permanent) {\n\n if(interpolator == std::string(\"LINEAR\")){\n libraryObject_ = boost::shared_ptr(new\n QuantLib::PiecewiseDefaultCurve(\n 0, \n calendar,\n helpers, \n dayCounter));\n }else if(interpolator == std::string(\"BACKWARDFLAT\")) {\n libraryObject_ = boost::shared_ptr(new\n QuantLib::PiecewiseDefaultCurve(\n 0, \n calendar,\n helpers, \n dayCounter));\n }else{\n QL_FAIL(\"Unrecognised interpolator\");\n }\n\n libraryObject_->enableExtrapolation();\n }\n\n // ptr type check here would correspond to template spez in the \n // subscribers factory solution.\n const std::vector& PiecewiseHazardRateCurve::dates() const {\n typedef QuantLib::PiecewiseDefaultCurve flat_curve;\n typedef QuantLib::PiecewiseDefaultCurve lin_curve;\n boost::shared_ptr ptrBF =\n boost::dynamic_pointer_cast(libraryObject_);\n if(ptrBF) return ptrBF->dates();\n boost::shared_ptr ptrLIN =\n boost::dynamic_pointer_cast(libraryObject_);\n if(ptrLIN) return ptrLIN->dates();\n QL_FAIL(\"Unable to cast default probability term structure.\");\n }\n\n const std::vector& PiecewiseHazardRateCurve::data() const {\n typedef QuantLib::PiecewiseDefaultCurve flat_curve;\n typedef QuantLib::PiecewiseDefaultCurve lin_curve;\n boost::shared_ptr ptrBF =\n boost::dynamic_pointer_cast(libraryObject_);\n if(ptrBF) return ptrBF->data();\n boost::shared_ptr ptrLIN =\n boost::dynamic_pointer_cast(libraryObject_);\n if(ptrLIN) return ptrLIN->data();\n QL_FAIL(\"Unable to cast default probability term structure.\");\n } \n\n\n PiecewiseFlatForwardCurve::PiecewiseFlatForwardCurve(\n const boost::shared_ptr& properties,\n const QuantLib::Date& referenceDate,\n const std::vector >& helpers,\n const QuantLib::DayCounter& dayCounter,\n QuantLib::Real accuracy,\n bool permanent)\n : YieldTermStructure(properties, permanent) {\n libraryObject_ = boost::shared_ptr(new\n QuantLib::PiecewiseYieldCurve(referenceDate, helpers, dayCounter));\n }\n\n\n\n RiskyFixedBond::RiskyFixedBond(\n const boost::shared_ptr& properties,\n std::string name,\n QuantLib::Currency ccy,\n QuantLib::Real recoveryRate,\n QuantLib::Handle defaultTS,\n const boost::shared_ptr& schedule,\n QuantLib::Real rate,\n QuantLib::DayCounter dayCounter,\n QuantLib::BusinessDayConvention paymentConvention,\n QuantLib::Real notional,\n QuantLib::Handle yieldTS,\n QuantLib::Date npvDate, // unused by now\n bool permanent)\n : Instrument(properties, permanent) {\n\n std::vector notionals(1,notional);\n\n libraryObject_ = boost::shared_ptr(\n new QuantLib::RiskyFixedBond(\n name,ccy,recoveryRate,defaultTS,*schedule,rate,dayCounter,\n paymentConvention,notionals,yieldTS///, npvDate\n ));\n\n }\n\n\n SyntheticCDO::SyntheticCDO(\n const boost::shared_ptr& properties,\n const boost::shared_ptr& bskt,\n QuantLib::Protection::Side side,\n const boost::shared_ptr& schedule,\n QuantLib::Rate upfront,\n QuantLib::Rate spread,\n const QuantLib::DayCounter& dayCounter,\n QuantLib::BusinessDayConvention paymentConvention,\n bool permanent)\n : Instrument(properties, permanent) {\n\t\tlibraryObject_ = boost::make_shared(\n bskt,side,*schedule,upfront,spread,dayCounter,paymentConvention);\n }\n\n MidPointCDOEngine::MidPointCDOEngine(\n const boost::shared_ptr& properties,\n const QuantLib::Handle& discTS,\n bool permanent)\n : PricingEngine(properties, permanent) {\n\t\tlibraryObject_ = \n boost::make_shared(discTS);\n }\n\n NthToDefault::NthToDefault(\n const boost::shared_ptr& properties,\n const boost::shared_ptr& bskt,\n QuantLib::Size order,\n QuantLib::Protection::Side side,\n const boost::shared_ptr& schedule,\n QuantLib::Rate upfront,\n QuantLib::Rate spread,\n const QuantLib::DayCounter& dayCounter,\n QuantLib::Real notional,\n bool paysAccrual,\n bool permanent)\n : Instrument(properties, permanent) {\n\t\tlibraryObject_ = boost::make_shared(\n bskt,order,side,*schedule,upfront,spread,dayCounter,\n notional,paysAccrual);\n }\n\n IntegralNtdEngine::IntegralNtdEngine(\n const boost::shared_ptr& properties,\n const QuantLib::Period& step,\n const QuantLib::Handle& discTS,\n bool permanent)\n : PricingEngine(properties, permanent) {\n\t\tlibraryObject_ = \n boost::make_shared(step, discTS);\n }\n\n\n}\n", "meta": {"hexsha": "66101e30767f29bc2b426d7d84786e26f179556e", "size": 17840, "ext": "cpp", 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"max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 43.4063260341, "max_line_length": 122, "alphanum_fraction": 0.6224215247, "num_tokens": 3888, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6224593171945416, "lm_q2_score": 0.3522017684487511, "lm_q1q2_score": 0.21923127230331968}} {"text": "#include \n#include \n#include \"energy.h\"\n#include \"mcpdft.h\"\n#include \"libMem.h\"\n#include \"openrdmConfig.h\"\n#include \n//#include \"HDF5_Read_Contiguous.h\"\n//#include \"HDF5_Write_Contiguous.h\"\n//#include \"HDF5_Read_Compact.h\"\n//#include \"HDF5_Write_Compact.h\"\n//#include \"HDF5_Read_Chunked.h\"\n//#include \"HDF5_Write_Chunked.h\"\n//#include \"HDF5ContiguousFactory.h\"\n//#include \"HDF5CompactFactory.h\"\n//#include \"HDF5ChunkedFactory.h\"\n#include \"HDF5Client.h\"\n#ifdef WITH_LIBXC\n #include \n#else\n #include \"functional.h\"\n#endif\n\nnamespace mcpdft {\n\n // calculates the MCPDFT energy\n double mcpdft_energy(MCPDFT *mc,\n\t\t std::string &functional,\n const arma::mat &D1a,\n const arma::mat &D1b,\n const arma::mat &D2ab) {\n\n // Query memory information from linux\n struct sysinfo info;\n sysinfo(&info);\n\n // Calculating the amount of available memory\n LibMem *libmem;\n libmem = new LibMem();\n libmem->query_system_memory(&info);\n\n double tot_energy = 0.0;\n\n // getting the value of the classical energy\n double eclass = mc->get_eclass();\n // printf(\"eclass = %-20.15lf\\n\",eclass);\n\n // building the one electron densities rho_a(r) and rho_b(r)\n mc->build_rho();\n\n // building the on-top pair density pi(r,r)\n mc->build_pi(D2ab);\n\n // building the R(r) factor for density translation\n mc->build_R();\n\n // translate the one-electron densities\n mc->translate();\n\n size_t npts = mc->get_npts();\n arma::vec tr_rhoa(mc->get_tr_rhoa());\n arma::vec tr_rhob(mc->get_tr_rhob());\n arma::vec tr_sigma_aa(npts, arma::fill::zeros);\n arma::vec tr_sigma_ab(npts, arma::fill::zeros);\n arma::vec tr_sigma_bb(npts, arma::fill::zeros);\n if(mc->is_gga()) {\n tr_sigma_aa = mc->get_tr_sigma_aa();\n tr_sigma_ab = mc->get_tr_sigma_ab();\n tr_sigma_bb = mc->get_tr_sigma_bb();\n }\n#ifdef WITH_LIBXC\n arma::vec tr_rho(tr_rhoa);\n arma::vec ex(npts);\n arma::vec ec(npts);\n arma::vec W(mc->get_w());\n tr_rho = tr_rho + tr_rhob;\n double * rhop = tr_rho.memptr();\n double * exp = ex.memptr();\n double * ecp = ec.memptr();\n\n double Ex = 0.0;\n double Ec = 0.0;\n\n xc_func_type func_x;\n if(xc_func_init(&func_x,XC_LDA_X, XC_UNPOLARIZED) != 0){\n xc_lda_exc(&func_x, npts, rhop, exp);\n }\n\n double ans = 0.0;\n for (int p = 0; p < npts; p++) {\n if (tr_rho(p) > 1.0e-20)\n ans += W(p) * ex(p) * tr_rho(p);\n }\n Ex=ans;\n\n xc_func_type func_c;\n if(xc_func_init(&func_c,XC_LDA_C_VWN_RPA, XC_UNPOLARIZED) != 0){\n xc_lda_exc(&func_c, npts, rhop, ecp);\n }\n\n ans = 0.0;\n for (int p = 0; p < npts; p++) {\n if (tr_rho(p) > 1.0e-20)\n ans += W(p) * ec(p) * tr_rho(p);\n }\n Ec=ans;\n xc_func_end(&func_x);\n xc_func_end(&func_c);\n#else\n Functional* func = new Functional;\n\n double Ex = 0.0;\n double Ec = 0.0;\n if (functional == \"SVWN\") {\n Ex = func->EX_LSDA(mc, tr_rhoa, tr_rhob);\n Ec = func->EC_VWN3(mc, tr_rhoa, tr_rhob);\n }else{\n Ex = func->EX_PBE(mc, tr_rhoa, tr_rhob, tr_sigma_aa, tr_sigma_bb);\n Ec = func->EC_PBE(mc, tr_rhoa, tr_rhob, tr_sigma_aa, tr_sigma_ab, tr_sigma_bb);\n }\n\n delete func;\n#endif\n\n size_t nbfs = mc->get_nbfs();\n size_t nbfs2 = nbfs * nbfs;\n\n arma::mat d1a(nbfs, nbfs, arma::fill::zeros);\n arma::mat d1b(nbfs, nbfs, arma::fill::zeros);\n arma::mat d2ab(nbfs2, nbfs2, arma::fill::zeros);\n\n HDF5Client* h5client = new HDF5Client();\n\n HDF5Client::factory_mode mode(HDF5Client::factory_mode::WRITE);\n h5client->factory_client(H5D_COMPACT,mode,D1a,D1b,D2ab);\n\n mode = HDF5Client::factory_mode::READ;\n h5client->factory_client(H5D_COMPACT,mode,D1a,D1b,D2ab);\n\n delete h5client;\n\n // IOFactory* iof;\n // IRead* ird;\n // IWrite* iwt;\n\n// // iof = new HDF5ContiguousFactory;\n// // iof = new HDF5CompactFactory;\n // iof = new HDF5ChunkedFactory;\n // iwt = iof->create_IWrite();\n // iwt->write_rdms(D1a,D1b,D2ab);\n // ird = iof->create_IRead();\n // ird->read_rdms(d1a,d1b,d2ab);\n // delete iof;\n // IWrite* h5w = iof->create_IWrite();\n // h5w->write_opdm(D1a,D1b);\n // DiskRW dskrw;\n // dskrw.write_opdm(D1a,D1b);\n // dskrw.write_tpdm(D2ab);\n // size_t nbfs = mc->get_nbfs();\n // size_t nbfs2 = nbfs * nbfs;\n // try{\n // arma::mat d1a(nbfs, nbfs, arma::fill::zeros);\n // arma::mat d1b(nbfs, nbfs, arma::fill::zeros);\n // arma::mat d2ab(nbfs2, nbfs2, arma::fill::zeros);\n // dskrw.read_opdm(d1a,d1b);\n // // d1a.print(\"D1a =\");\n // // d1b.print(\"D1b =\");\n // dskrw.read_tpdm(d2ab);\n // // d2ab.print(\"D2ab =\");\n // } catch(const char* err_msg) {\n // printf(\"%s\\n\",err_msg);\n // }\n\n\n printf(\"------------------------------------------\\n\");\n printf(\" Classical energy = %-20.12lf\\n\", eclass);\n printf(\" Ex = %-20.12lf\\n\", Ex);\n printf(\" Ec = %-20.12lf\\n\", Ec);\n printf(\"------------------------------------------\\n\\n\");\n\n tot_energy += eclass;\n tot_energy += Ex;\n tot_energy += Ec;\n\n delete libmem;\n\n return tot_energy;\n }\n\n}\n", "meta": {"hexsha": "786432a69dc589216612ae5379d4184f502746ba", "size": 5520, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/libmcpdft/energy.cc", "max_stars_repo_name": "SinaMostafanejad/libRDMInoles", "max_stars_repo_head_hexsha": "0cc9fba75755cfa046f352a6aca80e77af261ca3", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 11.0, "max_stars_repo_stars_event_min_datetime": "2019-11-19T14:23:35.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-14T08:41:55.000Z", "max_issues_repo_path": "src/libmcpdft/energy.cc", "max_issues_repo_name": "SinaMostafanejad/libRDMInoles", "max_issues_repo_head_hexsha": "0cc9fba75755cfa046f352a6aca80e77af261ca3", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/libmcpdft/energy.cc", "max_forks_repo_name": "SinaMostafanejad/libRDMInoles", "max_forks_repo_head_hexsha": "0cc9fba75755cfa046f352a6aca80e77af261ca3", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2020-11-13T05:00:51.000Z", "max_forks_repo_forks_event_max_datetime": "2020-11-29T02:39:04.000Z", "avg_line_length": 28.9005235602, "max_line_length": 89, "alphanum_fraction": 0.556884058, "num_tokens": 1729, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6548947290421275, "lm_q2_score": 0.3345894545235253, "lm_q1q2_score": 0.21912087016053736}} {"text": "#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n#include \"exciton_transfer.h\"\r\n#include \"cnt.h\"\r\n#include \"constants.h\"\r\n#include \"../helper/progress.hpp\"\r\n\r\n// calculate and plot Q matrix element between two exciton bands\r\nvoid exciton_transfer::save_Q_matrix_element(const int i_n_principal, const int f_n_principal)\r\n{\r\n // set the donor and accdeptor cnts\r\n const cnt& init_cnt = *_cnts[0];\r\n const cnt& final_cnt = *_cnts[1];\r\n \r\n // set the donor and acceptor exciton structs\r\n const cnt::exciton_struct& i_exciton = init_cnt.A2_singlet();\r\n const cnt::exciton_struct& f_exciton = final_cnt.A2_singlet();\r\n\r\n std::cout << \"\\n...calculating Q matrix element\\n\";\r\n\r\n // some error checking\r\n if ((i_n_principal >= i_exciton.n_principal) and (f_n_principal >= f_exciton.n_principal)){\r\n throw std::invalid_argument(\"i_n_principal or f_n_principla are two large.\");\r\n }\r\n\r\n // find lists of relevant states in donor and acceptor excitons\r\n auto get_exciton_band = [](const auto& exciton, const int i_principal){\r\n std::vector states;\r\n for (int ik_cm_idx=0; ik_cm_idx state_pairs = match_all_states(i_relevant_states, f_relevant_states);\r\n\r\n // get min of ik_cm_idx from relevant states indices\r\n auto get_min_ik_cm_idx = [](const auto& relevant_states){\r\n int min_idx = 1.e9;\r\n for (const auto& state: relevant_states)\r\n {\r\n min_idx = (min_idx < state.ik_cm_idx) ? min_idx : state.ik_cm_idx;\r\n }\r\n return min_idx;\r\n };\r\n // get max of ik_cm_idx from relevant states indices\r\n auto get_max_ik_cm_idx = [](const auto& relevant_states){\r\n int max_idx = -1.e9;\r\n for (const auto& state: relevant_states)\r\n {\r\n max_idx = (max_idx > state.ik_cm_idx) ? max_idx : state.ik_cm_idx;\r\n }\r\n return max_idx;\r\n };\r\n int i_min_idx = get_min_ik_cm_idx(i_relevant_states);\r\n int i_max_idx = get_max_ik_cm_idx(i_relevant_states);\r\n int f_min_idx = get_min_ik_cm_idx(f_relevant_states);\r\n int f_max_idx = get_max_ik_cm_idx(f_relevant_states);\r\n\r\n arma::cx_mat Q_mat(i_max_idx-i_min_idx+1, f_max_idx-f_min_idx+1, arma::fill::zeros);\r\n arma::vec init_ik_cm(i_max_idx-i_min_idx+1, arma::fill::zeros);\r\n arma::vec final_ik_cm(f_max_idx-f_min_idx+1, arma::fill::zeros);\r\n\r\n progress_bar prog(state_pairs.size(), \"calculate Q\");\r\n\r\n for (const auto& pair:state_pairs)\r\n { \r\n Q_mat(pair.i.ik_cm_idx-i_min_idx, pair.f.ik_cm_idx-f_min_idx) = calculate_Q(pair);\r\n init_ik_cm(pair.i.ik_cm_idx-i_min_idx) = pair.i.ik_cm;\r\n final_ik_cm(pair.f.ik_cm_idx-f_min_idx) = pair.f.ik_cm;\r\n prog.step();\r\n }\r\n\r\n arma::mat tmp;\r\n \r\n // save the real part matrix element Q to a file\r\n tmp= arma::real(Q_mat);\r\n std::string filename = _directory.path() / \"matrix_element_q.real.dat\";\r\n tmp.save(filename,arma::arma_ascii);\r\n\r\n // save the imaginary part matrix element Q to a file\r\n tmp= arma::imag(Q_mat);\r\n filename = _directory.path() / \"matrix_element_q.imag.dat\";\r\n tmp.save(filename,arma::arma_ascii);\r\n\r\n // save ik_cm of the inital states\r\n filename = _directory.path() / \"matrix_element_q.init_ik_cm.dat\";\r\n init_ik_cm.save(filename,arma::arma_ascii);\r\n\r\n // save ik_cm of the final states\r\n filename = _directory.path() / \"matrix_element_q.final_ik_cm.dat\";\r\n final_ik_cm.save(filename,arma::arma_ascii);\r\n\r\n std::cout << \"\\n...calculated and saved Q matrix element\\n\";\r\n\r\n}\r\n\r\n// calculate and plot J matrix element between two exciton bands\r\nvoid exciton_transfer::save_J_matrix_element(const int i_n_principal, const int f_n_principal)\r\n{\r\n // set geometrical properties\r\n double z_shift = 1.5e-9;\r\n double theta = 0;\r\n std::array axis_shifts = {0., 0.};\r\n\r\n // set the donor and accdeptor cnts\r\n const cnt& init_cnt = *_cnts[0];\r\n const cnt& final_cnt = *_cnts[1];\r\n \r\n // set the donor and acceptor exciton structs\r\n const cnt::exciton_struct& i_exciton = init_cnt.A2_singlet();\r\n const cnt::exciton_struct& f_exciton = final_cnt.A2_singlet();\r\n\r\n std::cout << \"\\n...calculating J matrix element\\n\";\r\n // std::cout << \"initial exciton ik_cm_range: [\" << i_exciton.ik_cm_range[0] << \",\" << i_exciton.ik_cm_range[1] << \"]\\n\";\r\n // std::cout << \"final exciton ik_cm_range: [\" << f_exciton.ik_cm_range[0] << \",\" << f_exciton.ik_cm_range[1] << \"]\\n\";\r\n\r\n // some error checking\r\n if ((i_n_principal >= i_exciton.n_principal) and (f_n_principal >= f_exciton.n_principal)){\r\n throw std::invalid_argument(\"i_n_principal or f_n_principla are two large.\");\r\n }\r\n\r\n // find lists of relevant states in donor and acceptor excitons\r\n auto get_exciton_band = [](const auto& exciton, const int i_principal){\r\n std::vector states;\r\n // int ik_cm_min = exciton.ik_cm_range[0];\r\n // int ik_cm_max = exciton.ik_cm_range[1];\r\n int ik_cm_min = -50;\r\n int ik_cm_max = -ik_cm_min+1;\r\n for (int ik_cm=ik_cm_min; ik_cmlength_in_meter()*1e9 << \" [nm]\\n\";\r\n std::cout << \"final exciton length: \" << f_exciton.cnt_obj->length_in_meter()*1e9 << \" [nm]\\n\";\r\n\r\n\r\n // match the states based on their energy\r\n std::vector state_pairs = match_all_states(i_relevant_states, f_relevant_states);\r\n\r\n // get min of ik_cm_idx from relevant states indices\r\n auto get_min_ik_cm_idx = [](const auto& relevant_states){\r\n int min_idx = 1.e9;\r\n for (const auto& state: relevant_states)\r\n {\r\n min_idx = (min_idx < state.ik_cm_idx) ? min_idx : state.ik_cm_idx;\r\n }\r\n return min_idx;\r\n };\r\n // get max of ik_cm_idx from relevant states indices\r\n auto get_max_ik_cm_idx = [](const auto& relevant_states){\r\n int max_idx = -1.e9;\r\n for (const auto& state: relevant_states)\r\n {\r\n max_idx = (max_idx > state.ik_cm_idx) ? max_idx : state.ik_cm_idx;\r\n }\r\n return max_idx;\r\n };\r\n int i_min_idx = get_min_ik_cm_idx(i_relevant_states);\r\n int i_max_idx = get_max_ik_cm_idx(i_relevant_states);\r\n int f_min_idx = get_min_ik_cm_idx(f_relevant_states);\r\n int f_max_idx = get_max_ik_cm_idx(f_relevant_states);\r\n\r\n arma::cx_mat J_mat(i_max_idx-i_min_idx+1, f_max_idx-f_min_idx+1, arma::fill::zeros);\r\n arma::vec init_ik_cm(i_max_idx-i_min_idx+1, arma::fill::zeros);\r\n arma::vec final_ik_cm(f_max_idx-f_min_idx+1, arma::fill::zeros);\r\n\r\n // int count = 0;\r\n // int number_of_pairs = state_pairs.size();\r\n progress_bar prog(state_pairs.size(),\"calculate J\");\r\n\r\n for (const auto& pair:state_pairs)\r\n { \r\n // count++;\r\n // prog.step(count, number_of_pairs, \"calculate J\");\r\n prog.step();\r\n // std::cout << \"calculate J: \" << count << \"/\" << number_of_pairs << \"\\n\";\r\n\r\n J_mat(pair.i.ik_cm_idx-i_min_idx, pair.f.ik_cm_idx-f_min_idx) = calculate_J(pair, axis_shifts, z_shift, theta);\r\n init_ik_cm(pair.i.ik_cm_idx-i_min_idx) = pair.i.ik_cm;\r\n final_ik_cm(pair.f.ik_cm_idx-f_min_idx) = pair.f.ik_cm;\r\n }\r\n\r\n arma::mat tmp;\r\n \r\n\r\n // save the real part matrix element Q to a file\r\n tmp= arma::real(J_mat);\r\n std::string filename = _directory.path() / \"matrix_element_j.real.dat\";\r\n tmp.save(filename,arma::arma_ascii);\r\n\r\n // save the imaginary part matrix element Q to a file\r\n tmp= arma::imag(J_mat);\r\n filename = _directory.path() / \"matrix_element_j.imag.dat\";\r\n tmp.save(filename,arma::arma_ascii);\r\n\r\n // save ik_cm of the inital states\r\n filename = _directory.path() / \"matrix_element_j.init_ik_cm.dat\";\r\n init_ik_cm.save(filename,arma::arma_ascii);\r\n\r\n // save ik_cm of the final states\r\n filename = _directory.path() / \"matrix_element_j.final_ik_cm.dat\";\r\n final_ik_cm.save(filename,arma::arma_ascii);\r\n\r\n std::cout << \"\\n...calculated and saved J matrix element\\n\";\r\n\r\n}\r\n\r\n// get the energetically relevant states in the form a vector of ex_state structs\r\nstd::vector exciton_transfer::get_relevant_states(const cnt::exciton_struct& exciton, const double min_energy)\r\n{\r\n const double threshold_population = 1.e-3;\r\n const double threshold_energy = min_energy+std::abs(std::log(threshold_population) * constants::kb*_temperature);\r\n\r\n std::vector relevant_states;\r\n \r\n for (int i_n=0; i_n energy:\" << delta_e/constants::eV \r\n // << \" , population:\" << std::exp(-delta_e/(constants::kb*_temperature))/normalization_factor << \"\\n\";\r\n // }\r\n \r\n return relevant_states;\r\n}\r\n\r\n// calculate Q()\r\nstd::complex exciton_transfer::calculate_Q(const matching_states& pair) const\r\n{\r\n // lambda function to calculate part of the Q matrix element that relates to each pair.\r\n auto Q_partial = [](const ex_state& state)\r\n {\r\n const int ic = 1;\r\n const int iv = 0;\r\n\r\n const arma::vec dA = {0,0};\r\n const arma::vec& dB = *(state.exciton->aCC_vec);\r\n const arma::cx_vec exp_factor({std::exp(std::complex(0.,+1.)*arma::dot(state.ik_cm*state.dk_l(),dA)),\\\r\n std::exp(std::complex(0.,+1.)*arma::dot(state.ik_cm*state.dk_l(),dB))});\r\n\r\n std::complex Q_partial = 0;\r\n for (int ik_c_idx=0; ik_c_idxnk_c; ik_c_idx++)\r\n {\r\n const arma::cx_vec& Cc = state.elec_struct->wavefunc(state.ik_idx(1,ik_c_idx)).slice(state.ik_idx(0,ik_c_idx)).col(ic);\r\n const arma::cx_vec& Cv = state.elec_struct->wavefunc(state.ik_idx(3,ik_c_idx)).slice(state.ik_idx(2,ik_c_idx)).col(iv);\r\n Q_partial += state.psi(ik_c_idx)*arma::accu(Cc%arma::conj(Cv)%exp_factor);\r\n }\r\n\r\n return Q_partial;\r\n\r\n };\r\n\r\n double coeff = (std::pow(constants::q0,2)*pair.i.cnt_obj->Au()*pair.f.cnt_obj->Au())/\\\r\n (16*std::pow(constants::pi,3)*constants::eps0*pair.i.cnt_obj->radius()*pair.f.cnt_obj->radius()*\\\r\n std::sqrt(pair.i.cnt_obj->length_in_meter()*pair.f.cnt_obj->length_in_meter()));\r\n\r\n return std::complex(coeff)*std::conj(Q_partial(pair.i))*Q_partial(pair.f);\r\n\r\n}\r\n\r\n// calculate J()\r\nstd::complex exciton_transfer::calculate_J(const matching_states& pair, const std::array& shifts_along_axis, const double& z_shift, const double& angle) const\r\n{\r\n // make position of all atoms in the entire cnt length in 3d space\r\n auto make_Ru_3d = [](const cnt &m_cnt, const double shift_along_axis, const double z_shift, const double angle) {\r\n int n_atoms_in_cnt_unit_cell = m_cnt.pos_u_3d().n_rows;\r\n int total_number_of_atoms = m_cnt.pos_u_3d().n_rows * m_cnt.length_in_cnt_unit_cell();\r\n arma::mat all_atoms(total_number_of_atoms, 3);\r\n\r\n for (int i = 0; i < m_cnt.length_in_cnt_unit_cell(); i++) {\r\n all_atoms.rows(i * n_atoms_in_cnt_unit_cell, (i + 1) * n_atoms_in_cnt_unit_cell - 1) = m_cnt.pos_u_3d().each_row() + i * m_cnt.t_vec_3d().t();\r\n }\r\n\r\n // make the cnt center at the middle\r\n double y_max = all_atoms.col(1).max();\r\n double y_min = all_atoms.col(1).min();\r\n all_atoms.col(1) -= ((y_max + y_min) / 2.);\r\n\r\n // shift the center of the cnt axis along it's axis\r\n all_atoms.col(1) += shift_along_axis;\r\n\r\n // shift the atoms along the z axis\r\n all_atoms.col(2) += z_shift;\r\n\r\n // rotate by angle around the z axis\r\n for (unsigned int i = 0; i < all_atoms.n_rows; i++) {\r\n double x = all_atoms(i, 0) * std::cos(angle) - all_atoms(i, 1) * std::sin(angle);\r\n double y = all_atoms(i, 0) * std::sin(angle) + all_atoms(i, 1) * std::cos(angle);\r\n all_atoms(i, 0) = x;\r\n all_atoms(i, 1) = y;\r\n }\r\n return all_atoms;\r\n };\r\n\r\n // make position of all atoms in the entire cnt length in 2d space of unrolled cnt\r\n auto make_Ru_2d = [](const cnt& m_cnt) {\r\n int n_atoms_in_cnt_unit_cell = m_cnt.pos_u_2d().n_rows;\r\n int total_number_of_atoms = m_cnt.pos_u_2d().n_rows * m_cnt.length_in_cnt_unit_cell();\r\n arma::mat all_atoms(total_number_of_atoms,2);\r\n\r\n for (int i=0; i J = 0;\r\n const std::complex i1(0,1);\r\n\r\n // prebuild exponential factor for the inner loop\r\n arma::cx_vec f_exp(f_Ru_2d.n_rows);\r\n for (unsigned int j=0; j i_exp = std::exp(-i1*arma::dot(pair.i.ik_cm*pair.i.dk_l(),i_Ru_2d.row(i))); \r\n for (unsigned int j=0; j axis_shifts, const double& theta, const bool& show_results)\r\n{\r\n const cnt& donor = *_cnts[0];\r\n const cnt& acceptor = *_cnts[1];\r\n\r\n double min_energy = donor.A2_singlet().energy.min();\r\n\r\n const cnt::exciton_struct& d_exciton = donor.A2_singlet();\r\n const cnt::exciton_struct& a_exciton = acceptor.A2_singlet();\r\n\r\n // find lists of relevant states in donor and acceptor excitons\r\n std::vector d_relevant_states = get_relevant_states(d_exciton,min_energy);\r\n std::vector a_relevant_states = get_relevant_states(a_exciton,min_energy);\r\n\r\n double Z = 0;\r\n for (const auto& state:d_relevant_states) {\r\n Z += std::exp(-state.energy/(constants::kb*_temperature));\r\n }\r\n\r\n // match the states based on their energy\r\n std::vector state_pairs = match_states(d_relevant_states, a_relevant_states);\r\n\r\n\r\n // std::cout << \"number of matching states: \" << state_pairs.size() << std::endl;\r\n\r\n // int i=0;\r\n\r\n\r\n double transfer_rate = 0;\r\n\r\n progress_bar prog(state_pairs.size(),\"calculate first-order exciton transfer rate\", !show_results);\r\n for (const auto& pair:state_pairs) { \r\n prog.step();\r\n std::complex Q = calculate_Q(pair);\r\n std::complex J = calculate_J(pair, axis_shifts, z_shift, theta);\r\n double M = std::abs(Q * J) / std::sqrt(pair.i.cnt_obj->length_in_meter() * pair.f.cnt_obj->length_in_meter());\r\n // double M = std::abs(Q * J);\r\n\r\n // std::cout << std::showpos;\r\n // std::cout << i << \"---------> \";\r\n // std::cout << \"(\" << pair.i.ik_cm << \",\" << pair.f.ik_cm << \")\" << \" E(i)=\" << pair.i.energy << \" , E(f)=\" << pair.f.energy;\r\n // std::cout << \" , Q = \" << std::abs(Q);\r\n // std::cout << \" , J = \" << std::abs(J);\r\n // std::cout << \" , M = \" << std::abs(M) << std::endl;\r\n // i++;\r\n\r\n transfer_rate += (2*constants::pi/constants::hb)*(std::exp(-pair.i.energy/(constants::kb*_temperature))/Z)*std::pow(M,2)*lorentzian(pair.i.energy-pair.f.energy);\r\n }\r\n\r\n if (show_results) {\r\n std::cout << \"\\n\\n\";\r\n std::cout << \"cnt lengths: \" << _cnts[0]->length_in_meter()*1.e9 << \" [nm], \" << _cnts[1]->length_in_meter()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"center to center distance: \" << z_shift*1.e9 << \" [nm]\\n\";\r\n std::cout << \"cnt1 radius: \" << _cnts[0]->radius()*1.e9 << \" [nm], cnt2 radius: \" << _cnts[1]->radius()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"wall to wall distance: \" << (z_shift - _cnts[0]->radius() - _cnts[1]->radius())*1.e9 << \" [nm]\\n\";\r\n std::cout << \"theta: \" << theta/constants::pi*180 << \" [degrees]\\n\";\r\n std::cout << \"axis shifts: \" << axis_shifts[0]*1e9 << \" [nm] and \" << axis_shifts[1]*1e9 << \" [nm]\\n\";\r\n std::cout << \"exciton transfer rate: \" << transfer_rate << \"\\n\";\r\n }\r\n\r\n return transfer_rate;\r\n}\r\n\r\n// calculate first order transfer rate for varying angle\r\nvoid exciton_transfer::calculate_first_order_vs_angle(const arma::vec& angle_vec ,const double& z_shift, const std::array axis_shifts) {\r\n int n_theta = angle_vec.n_elem;\r\n arma::vec transfer_rate(arma::size(angle_vec), arma::fill::zeros);\r\n\r\n progress_bar prog(n_theta, \"first order transfer rate versus angle\");\r\n for (int i=0; ilength_in_meter()*1.e9 << \" [nm], \" << _cnts[1]->length_in_meter()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"center to center distance: \" << z_shift*1.e9 << \" [nm]\\n\";\r\n std::cout << \"cnt1 radius: \" << _cnts[0]->radius()*1.e9 << \" [nm], cnt2 radius: \" << _cnts[1]->radius()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"wall to wall distance: \" << (z_shift - _cnts[0]->radius() - _cnts[1]->radius())*1.e9 << \" [nm]\\n\";\r\n std::cout << \"axis shifts: \" << axis_shifts[0]*1e9 << \" [nm] and \" << axis_shifts[1]*1e9 << \" [nm]\\n\";\r\n std::cout << \"max transfer rate: \" << transfer_rate.max()/1e12 << \" [1/ps] at \" << angle_vec(transfer_rate.index_max())*180/constants::pi << \" [degrees]\\n\";\r\n std::cout << \"min transfer rate: \" << transfer_rate.min()/1e12 << \" [1/ps] at \" << angle_vec(transfer_rate.index_min())*180/constants::pi << \" [degrees]\\n\";\r\n}\r\n\r\n// calculate first order transfer rate for center to center distance\r\nvoid exciton_transfer::calculate_first_order_vs_zshift(const arma::vec& z_shift_vec, const std::array axis_shifts, const double& theta) {\r\n int n = z_shift_vec.n_elem;\r\n arma::vec transfer_rate(arma::size(z_shift_vec), arma::fill::zeros);\r\n\r\n progress_bar prog(n, \"first order transfer rate versus z_shift\");\r\n for (int i=0; ilength_in_meter()*1.e9 << \" [nm], \" << _cnts[1]->length_in_meter()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"cnt1 radius: \" << _cnts[0]->radius()*1.e9 << \" [nm], cnt2 radius: \" << _cnts[1]->radius()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"theta: \" << theta*180/constants::pi << \" [degrees]\\n\";\r\n std::cout << \"axis shifts: \" << axis_shifts[0]*1e9 << \" [nm] and \" << axis_shifts[1]*1e9 << \" [nm]\\n\";\r\n std::cout << \"max transfer rate: \" << transfer_rate.max() << \" [1/s] at \" << z_shift_vec(transfer_rate.index_max())*1e9 << \" [nm]\\n\";\r\n std::cout << \"min transfer rate: \" << transfer_rate.min() << \" [1/s] at \" << z_shift_vec(transfer_rate.index_min())*1e9 << \" [nm]\\n\";\r\n}\r\n\r\n// calculate first order transfer rate for varying axis shift for initial cnt\r\nvoid exciton_transfer::calculate_first_order_vs_axis_shift_1(const arma::vec& axis_shift_vec_1, const double axis_shift_2, const double z_shift, const double& theta) {\r\n int n = axis_shift_vec_1.n_elem;\r\n arma::vec transfer_rate(arma::size(axis_shift_vec_1), arma::fill::zeros);\r\n\r\n progress_bar prog(n, \"first order transfer rate versus axis shift of initial cnt\");\r\n for (int i=0; i axis_shifts = {axis_shift_vec_1(i),axis_shift_2};\r\n transfer_rate(i) = first_order(z_shift, axis_shifts, theta);\r\n }\r\n\r\n // save the transfer rate\r\n std::string filename = _directory.path() / \"first_order_transfer_rate_vs_axis_shift_1.dat\";\r\n transfer_rate.save(filename,arma::arma_ascii);\r\n\r\n // save theta vector\r\n filename = _directory.path() / \"first_order_transfer_rate_vs_axis_shift_1.shift.dat\";\r\n axis_shift_vec_1.save(filename,arma::arma_ascii);\r\n\r\n std::cout << \"\\n\\n\";\r\n std::cout << \"cnt lengths: \" << _cnts[0]->length_in_meter()*1.e9 << \" [nm], \" << _cnts[1]->length_in_meter()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"cnt1 radius: \" << _cnts[0]->radius()*1.e9 << \" [nm], cnt2 radius: \" << _cnts[1]->radius()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"theta: \" << theta*180/constants::pi << \" [degrees]\\n\";\r\n std::cout << \"center to center distance: \" << z_shift*1.e9 << \" [nm]\\n\";\r\n std::cout << \"max transfer rate: \" << transfer_rate.max() << \" [1/s] at \" << axis_shift_vec_1(transfer_rate.index_max())*1e9 << \" [nm]\\n\";\r\n std::cout << \"min transfer rate: \" << transfer_rate.min() << \" [1/s] at \" << axis_shift_vec_1(transfer_rate.index_min())*1e9 << \" [nm]\\n\";\r\n}\r\n\r\n// calculate first order transfer rate for varying axis shift for final cnt\r\nvoid exciton_transfer::calculate_first_order_vs_axis_shift_2(const arma::vec& axis_shift_vec_2, const double axis_shift_1, const double z_shift, const double& theta) {\r\n int n = axis_shift_vec_2.n_elem;\r\n arma::vec transfer_rate(arma::size(axis_shift_vec_2), arma::fill::zeros);\r\n\r\n progress_bar prog(n, \"first order transfer rate versus axis shift of final cnt\");\r\n for (int i=0; i axis_shifts = {axis_shift_1, axis_shift_vec_2(i)};\r\n transfer_rate(i) = first_order(z_shift, axis_shifts, theta);\r\n }\r\n\r\n // save the transfer rate\r\n std::string filename = _directory.path() / \"first_order_transfer_rate_vs_axis_shift_2.dat\";\r\n transfer_rate.save(filename,arma::arma_ascii);\r\n\r\n // save theta vector\r\n filename = _directory.path() / \"first_order_transfer_rate_vs_axis_shift_2.shift.dat\";\r\n axis_shift_vec_2.save(filename,arma::arma_ascii);\r\n\r\n std::cout << \"\\n\\n\";\r\n std::cout << \"cnt lengths: \" << _cnts[0]->length_in_meter()*1.e9 << \" [nm], \" << _cnts[1]->length_in_meter()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"cnt1 radius: \" << _cnts[0]->radius()*1.e9 << \" [nm], cnt2 radius: \" << _cnts[1]->radius()*1.e9 << \" [nm]\\n\";\r\n std::cout << \"theta: \" << theta*180/constants::pi << \" [degrees]\\n\";\r\n std::cout << \"center to center distance: \" << z_shift*1.e9 << \" [nm]\\n\";\r\n std::cout << \"max transfer rate: \" << transfer_rate.max() << \" [1/s] at \" << axis_shift_vec_2(transfer_rate.index_max())*1e9 << \" [nm]\\n\";\r\n std::cout << \"min transfer rate: \" << transfer_rate.min() << \" [1/s] at \" << axis_shift_vec_2(transfer_rate.index_min())*1e9 << \" [nm]\\n\";\r\n}\r\n\r\ntypedef std::experimental::filesystem::path path_t;\r\nvoid exciton_transfer::save_atom_locations(path_t path, const std::array &shifts_along_axis, const double &z_shift, const double &angle, std::string prefix) {\r\n \r\n std::cout << \"saving 3d atom locations for donor and acceptor cnts...\";\r\n\r\n // make position of all atoms in the entire cnt length in 3d space\r\n auto make_Ru_3d = [](const cnt &m_cnt, const double shift_along_axis, const double z_shift, const double angle) {\r\n int n_atoms_in_cnt_unit_cell = m_cnt.pos_u_3d().n_rows;\r\n int total_number_of_atoms = m_cnt.pos_u_3d().n_rows * m_cnt.length_in_cnt_unit_cell();\r\n arma::mat all_atoms(total_number_of_atoms, 3);\r\n\r\n for (int i = 0; i < m_cnt.length_in_cnt_unit_cell(); i++) {\r\n all_atoms.rows(i * n_atoms_in_cnt_unit_cell, (i + 1) * n_atoms_in_cnt_unit_cell - 1) = m_cnt.pos_u_3d().each_row() + i*m_cnt.t_vec_3d().t();\r\n }\r\n\r\n // make the cnt center at the middle\r\n double y_max = all_atoms.col(1).max();\r\n double y_min = all_atoms.col(1).min();\r\n all_atoms.col(1) -= ((y_max + y_min) / 2.);\r\n\r\n // shift the center of the cnt axis along it's axis\r\n all_atoms.col(1) += shift_along_axis;\r\n\r\n // shift the atoms along the z axis\r\n all_atoms.col(2) += z_shift;\r\n\r\n // rotate by angle around the z axis\r\n for (unsigned int i = 0; i < all_atoms.n_rows; i++)\r\n {\r\n double x = all_atoms(i, 0) * std::cos(angle) - all_atoms(i, 1) * std::sin(angle);\r\n double y = all_atoms(i, 0) * std::sin(angle) + all_atoms(i, 1) * std::cos(angle);\r\n all_atoms(i, 0) = x;\r\n all_atoms(i, 1) = y;\r\n }\r\n return all_atoms;\r\n };\r\n\r\n const cnt* donor = _cnts[0];\r\n const cnt* acceptor = _cnts[1];\r\n\r\n arma::mat i_Ru_3d = make_Ru_3d(*donor, shifts_along_axis[0], 0, 0);\r\n arma::mat f_Ru_3d = make_Ru_3d(*acceptor, shifts_along_axis[1], z_shift, angle);\r\n\r\n path /= \"3d_coord\"+prefix;\r\n\r\n std::ofstream file;\r\n\r\n file.open(std::string(path) + \".cnt1.dat\");\r\n file << i_Ru_3d;\r\n file.close();\r\n\r\n file.open(std::string(path) + \".cnt2.dat\");\r\n file << f_Ru_3d;\r\n file.close();\r\n\r\n std::cout << \"done!!!\" << std::endl;\r\n}", "meta": {"hexsha": "25d3e34489fc08083cf9c8cbef1f1908dd84105b", "size": 26573, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "montecarlo/src/exciton_transfer/exciton_transfer.cpp", "max_stars_repo_name": "li779/DECaNT", "max_stars_repo_head_hexsha": "8fe0faedd372a8214f1bd475eb7451d2eee1ca56", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8.0, "max_stars_repo_stars_event_min_datetime": "2020-10-21T19:21:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-30T16:41:21.000Z", "max_issues_repo_path": "montecarlo/src/exciton_transfer/exciton_transfer.cpp", "max_issues_repo_name": "li779/DECaNT", "max_issues_repo_head_hexsha": "8fe0faedd372a8214f1bd475eb7451d2eee1ca56", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "montecarlo/src/exciton_transfer/exciton_transfer.cpp", "max_forks_repo_name": "li779/DECaNT", "max_forks_repo_head_hexsha": "8fe0faedd372a8214f1bd475eb7451d2eee1ca56", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2020-10-22T15:02:52.000Z", "max_forks_repo_forks_event_max_datetime": "2020-10-22T15:02:52.000Z", "avg_line_length": 42.998381877, "max_line_length": 177, "alphanum_fraction": 0.6546870884, "num_tokens": 7823, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.21908162719838362}} {"text": "// Distributed under the MIT License.\n// See LICENSE.txt for details.\n\n#include \"Evolution/Systems/ScalarAdvection/FiniteDifference/MonotisedCentral.hpp\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"DataStructures/DataBox/Prefixes.hpp\"\n#include \"DataStructures/FixedHashMap.hpp\"\n#include \"DataStructures/Index.hpp\"\n#include \"DataStructures/Tensor/IndexType.hpp\"\n#include \"DataStructures/Variables.hpp\"\n#include \"Domain/Structure/Direction.hpp\"\n#include \"Domain/Structure/Element.hpp\"\n#include \"Domain/Structure/ElementId.hpp\"\n#include \"Domain/Structure/MaxNumberOfNeighbors.hpp\"\n#include \"Domain/Structure/Side.hpp\"\n#include \"Evolution/Systems/ScalarAdvection/FiniteDifference/ReconstructWork.tpp\"\n#include \"Evolution/Systems/ScalarAdvection/FiniteDifference/Reconstructor.hpp\"\n#include \"Evolution/Systems/ScalarAdvection/Tags.hpp\"\n#include \"NumericalAlgorithms/FiniteDifference/MonotisedCentral.hpp\"\n#include \"NumericalAlgorithms/Spectral/Mesh.hpp\"\n#include \"Utilities/GenerateInstantiations.hpp\"\n#include \"Utilities/Gsl.hpp\"\n#include \"Utilities/TMPL.hpp\"\n\nnamespace ScalarAdvection::fd {\ntemplate \nMonotisedCentral::MonotisedCentral(CkMigrateMessage* const msg)\n : Reconstructor(msg) {}\n\ntemplate \nstd::unique_ptr> MonotisedCentral::get_clone() const {\n return std::make_unique(*this);\n}\n\ntemplate \nvoid MonotisedCentral::pup(PUP::er& p) {\n Reconstructor::pup(p);\n}\n\ntemplate \nPUP::able::PUP_ID MonotisedCentral::my_PUP_ID = 0;\n\ntemplate \ntemplate \nvoid MonotisedCentral::reconstruct(\n const gsl::not_null, Dim>*>\n vars_on_lower_face,\n const gsl::not_null, Dim>*>\n vars_on_upper_face,\n const Variables>& volume_vars,\n const Element& element,\n const FixedHashMap<\n maximum_number_of_neighbors(Dim),\n std::pair, ElementId>, std::vector,\n boost::hash, ElementId>>>& neighbor_data,\n const Mesh& subcell_mesh) const {\n reconstruct_work(\n vars_on_lower_face, vars_on_upper_face,\n [](auto upper_face_vars_ptr, auto lower_face_vars_ptr,\n const auto& volume_variables, const auto& ghost_cell_vars,\n const auto& subcell_extents, const size_t number_of_variables) {\n ::fd::reconstruction::monotised_central(\n upper_face_vars_ptr, lower_face_vars_ptr, volume_variables,\n ghost_cell_vars, subcell_extents, number_of_variables);\n },\n volume_vars, element, neighbor_data, subcell_mesh, ghost_zone_size());\n}\n\ntemplate \ntemplate \nvoid MonotisedCentral::reconstruct_fd_neighbor(\n const gsl::not_null*> vars_on_face,\n const Variables>& volume_vars,\n const Element& element,\n const FixedHashMap<\n maximum_number_of_neighbors(Dim),\n std::pair, ElementId>, std::vector,\n boost::hash, ElementId>>>& neighbor_data,\n const Mesh& subcell_mesh,\n const Direction direction_to_reconstruct) const {\n reconstruct_fd_neighbor_work(\n vars_on_face,\n [](const auto tensor_component_on_face_ptr,\n const auto& tensor_component_volume,\n const auto& tensor_component_neighbor, const auto& subcell_extents,\n const auto& ghost_data_extents,\n const auto& local_direction_to_reconstruct) {\n ::fd::reconstruction::reconstruct_neighbor<\n Side::Lower,\n ::fd::reconstruction::detail::MonotisedCentralReconstructor>(\n tensor_component_on_face_ptr, tensor_component_volume,\n tensor_component_neighbor, subcell_extents, ghost_data_extents,\n local_direction_to_reconstruct);\n },\n [](const auto tensor_component_on_face_ptr,\n const auto& tensor_component_volume,\n const auto& tensor_component_neighbor, const auto& subcell_extents,\n const auto& ghost_data_extents,\n const auto& local_direction_to_reconstruct) {\n ::fd::reconstruction::reconstruct_neighbor<\n Side::Upper,\n ::fd::reconstruction::detail::MonotisedCentralReconstructor>(\n tensor_component_on_face_ptr, tensor_component_volume,\n tensor_component_neighbor, subcell_extents, ghost_data_extents,\n local_direction_to_reconstruct);\n },\n volume_vars, element, neighbor_data, subcell_mesh,\n direction_to_reconstruct, ghost_zone_size());\n}\n\ntemplate \nbool operator==(const MonotisedCentral& /*lhs*/,\n const MonotisedCentral& /*rhs*/) {\n return true;\n}\n\ntemplate \nbool operator!=(const MonotisedCentral& lhs,\n const MonotisedCentral& rhs) {\n return not(lhs == rhs);\n}\n\n#define DIM(data) BOOST_PP_TUPLE_ELEM(0, data)\n#define TAGS_LIST(data) \\\n tmpl::list, Frame::Inertial>, \\\n Tags::VelocityField>\n\n#define INSTANTIATION(r, data) \\\n template class MonotisedCentral; \\\n template bool operator== \\\n (const MonotisedCentral& /*lhs*/, \\\n const MonotisedCentral& /*rhs*/); \\\n template bool operator!=(const MonotisedCentral& lhs, \\\n const MonotisedCentral& rhs);\n\nGENERATE_INSTANTIATIONS(INSTANTIATION, (1, 2))\n#undef INSTANTIATION\n\n#define INSTANTIATION(r, data) \\\n template void MonotisedCentral::reconstruct( \\\n gsl::not_null, DIM(data)>*> \\\n vars_on_lower_face, \\\n gsl::not_null, DIM(data)>*> \\\n vars_on_upper_face, \\\n const Variables>& volume_vars, \\\n const Element& element, \\\n const FixedHashMap< \\\n maximum_number_of_neighbors(DIM(data)), \\\n std::pair, ElementId>, \\\n std::vector, \\\n boost::hash, ElementId>>>& \\\n neighbor_data, \\\n const Mesh& subcell_mesh) const; \\\n template void MonotisedCentral::reconstruct_fd_neighbor( \\\n gsl::not_null*> vars_on_face, \\\n const Variables>& volume_vars, \\\n const Element& element, \\\n const FixedHashMap< \\\n maximum_number_of_neighbors(DIM(data)), \\\n std::pair, ElementId>, \\\n std::vector, \\\n boost::hash, ElementId>>>& \\\n neighbor_data, \\\n const Mesh& subcell_mesh, \\\n const Direction direction_to_reconstruct) const;\n\nGENERATE_INSTANTIATIONS(INSTANTIATION, (1, 2))\n\n#undef INSTANTIATION\n#undef TAGS_LIST\n#undef DIM\n} // namespace ScalarAdvection::fd\n", "meta": {"hexsha": "2e06ee82a7f671f2870b64e30c491fe27224b1d8", "size": 8366, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Evolution/Systems/ScalarAdvection/FiniteDifference/MonotisedCentral.cpp", "max_stars_repo_name": "nilsvu/spectre", "max_stars_repo_head_hexsha": "1455b9a8d7e92db8ad600c66f54795c29c3052ee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 117.0, "max_stars_repo_stars_event_min_datetime": "2017-04-08T22:52:48.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T07:23:36.000Z", "max_issues_repo_path": "src/Evolution/Systems/ScalarAdvection/FiniteDifference/MonotisedCentral.cpp", "max_issues_repo_name": "nilsvu/spectre", "max_issues_repo_head_hexsha": "1455b9a8d7e92db8ad600c66f54795c29c3052ee", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3177.0, "max_issues_repo_issues_event_min_datetime": "2017-04-07T21:10:18.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T23:55:59.000Z", "max_forks_repo_path": "src/Evolution/Systems/ScalarAdvection/FiniteDifference/MonotisedCentral.cpp", "max_forks_repo_name": "nilsvu/spectre", "max_forks_repo_head_hexsha": "1455b9a8d7e92db8ad600c66f54795c29c3052ee", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 85.0, "max_forks_repo_forks_event_min_datetime": "2017-04-07T19:36:13.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-01T10:21:00.000Z", "avg_line_length": 45.967032967, "max_line_length": 82, "alphanum_fraction": 0.614391585, "num_tokens": 1811, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5583269943353745, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.2190522431542699}} {"text": "#pragma once\n\n#ifdef USE_FIBONACCI_HEAP\n#include \n#endif\n\n#include \n#include \n\n#include \n#include \n\n// #include \"neighbor.hpp\"\n// #include \"planresult.hpp\"\n#include \"a_star.hpp\"\n\nnamespace libMultiRobotPlanning {\n\n/*!\n \\example sipp.cpp Simple example using a 2D grid world and\n up/down/left/right\n actions\n*/\n\n/*! \\brief SIPP Algorithm to find the shortest path with dynamic obstacles\n\nThis class implements the SIPP algorithm. SIPP is an informed search algorithm\nthat finds the shortest path for a given map and dynamic a-priori known\nobstacles.\nIt can use a heuristic that needs to be admissible.\n\nDetails of the algorithm can be found in the following paper:\\n\nMike Phillips and Maxim Likhachev:\\n\n\"SIPP: Safe Interval Path Planning for Dynamic Environments\". IEEE\nInternational Conference on Robotics and Automation (ICRA), 2011\\n\nhttps://doi.org/10.1109/ICRA.2011.5980306\n\nThis class can either use a fibonacci heap, or a d-ary heap. The latter is the\ndefault. Define \"USE_FIBONACCI_HEAP\" to use the fibonacci heap instead.\n\n\\tparam State Custom state for the search. Needs to be copy'able\n\\tparam Location Custom location type for the search. Needs to be copy'able\n\\tparam Cost Custom Cost type (integer or floating point types)\n\\tparam Environment This class needs to provide the custom A* logic. In\n particular, it needs to support the following functions:\n - `Cost admissibleHeuristic(const State& s)`\\n\n This function can return 0 if no suitable heuristic is available.\n\n - `bool isSolution(const State& s)`\\n\n Return true if the given state is a goal state.\n\n - `void getNeighbors(const State& s, std::vector >& neighbors)`\\n\n Fill the list of neighboring state for the given state s.\n\n - `void onExpandNode(const State& s, int fScore, int gScore)`\\n\n This function is called on every expansion and can be used for statistical\npurposes.\n\n - `void onDiscover(const State& s, int fScore, int gScore)`\\n\n This function is called on every node discovery and can be used for\n statistical purposes.\n*/\ntemplate \nclass SIPP {\n public:\n struct interval {\n interval(Cost start, Cost end) : start(start), end(end) {}\n\n Cost start;\n Cost end;\n\n friend bool operator<(const interval& a, const interval& b) {\n return a.start < b.start;\n }\n };\n\n public:\n struct edgeCollision{\n \t edgeCollision(Cost t, Action action) : t(t), action(action){}\n\n \tCost t;\n \tAction action;\n \tfriend bool operator==(const edgeCollision& a, const edgeCollision& b){\n \t\treturn (a.t == b.t) && (a.action == b.action);\n \t}\n };\n\n public:\n SIPP(Environment& environment) : m_env(environment), m_astar(m_env) {}\n\n void setCollisionIntervals(const Location& location,\n const std::vector& intervals) {\n m_env.setCollisionIntervals(location, intervals);\n }\n\n void setEdgeCollisions(const Location& location,\n const std::vector& ec) {\n m_env.setEdgeCollisions(location, ec);\n }\n\n bool mightHaveSolution(const State& goal) {\n return m_env.mightHaveSolution(goal);\n }\n\n bool search(const State& startState, const Action& waitAction,\n PlanResult& solution, Cost startTime = 0) {\n PlanResult astarsolution;\n solution.cost = 0;\n solution.fmin = 0;\n solution.actions.clear();\n solution.states.clear();\n size_t interval;\n if (!m_env.findSafeInterval(startState, startTime, interval)) {\n return false;\n }\n bool success = m_astar.search(SIPPState(startState, interval),\n astarsolution, startTime);\n solution.cost = astarsolution.cost - startTime;\n solution.fmin = astarsolution.fmin;\n for (size_t i = 0; i < astarsolution.actions.size(); ++i) {\n Cost waitTime =\n astarsolution.actions[i].second - astarsolution.actions[i].first.time;\n if (waitTime == 0) {\n solution.states.push_back(\n std::make_pair<>(astarsolution.states[i].first.state,\n astarsolution.states[i].second));\n solution.actions.push_back(\n std::make_pair<>(astarsolution.actions[i].first.action,\n astarsolution.actions[i].second));\n } else {\n // additional wait action before\n solution.states.push_back(\n std::make_pair<>(astarsolution.states[i].first.state,\n astarsolution.states[i].second));\n solution.actions.push_back(std::make_pair<>(waitAction, waitTime));\n solution.states.push_back(\n std::make_pair<>(astarsolution.states[i].first.state,\n astarsolution.states[i].second + waitTime));\n solution.actions.push_back(\n std::make_pair<>(astarsolution.actions[i].first.action,\n astarsolution.actions[i].first.time));\n }\n }\n solution.states.push_back(\n std::make_pair<>(astarsolution.states.back().first.state,\n astarsolution.states.back().second));\n\n return success;\n }\n\n private:\n // public:\n struct SIPPState {\n SIPPState(const State& state, size_t interval)\n : state(state), interval(interval) {}\n SIPPState(const State& state, size_t interval, unsigned int dir)\n : state(state), interval(interval), dir(dir) {}\n\n bool operator==(const SIPPState& other) const {\n return std::tie(state, interval) == std::tie(other.state, other.interval);\n }\n\n friend std::ostream& operator<<(std::ostream& os, const SIPPState& s) {\n return os << \"(\" << s.state << \",\" << s.interval << \")\";\n }\n\n State state;\n unsigned int dir;\n size_t interval;\n };\n\n struct SIPPStateHasher {\n size_t operator()(const SIPPState& s) const {\n size_t seed = 0;\n boost::hash_combine(seed, std::hash()(s.state));\n boost::hash_combine(seed, s.interval);\n return seed;\n }\n };\n\n struct SIPPAction {\n SIPPAction(const Action& action, Cost time) : action(action), time(time) {}\n\n Action action;\n Cost time;\n };\n\n // private:\n struct SIPPEnvironment {\n SIPPEnvironment(Environment& env) : m_env(env) {}\n\n Cost admissibleHeuristic(const SIPPState& s) {\n return m_env.admissibleHeuristic(s.state);\n }\n\n bool mightHaveSolution(const State& goal) {\n const auto& si = safeIntervals(m_env.getLocation(goal));\n return m_env.isSolution(goal) && !si.empty() &&\n si.back().end == std::numeric_limits::max();\n }\n\n bool isSolution(const SIPPState& s) {\n return m_env.isSolution(s.state) &&\n safeIntervals(m_env.getLocation(s.state)).at(s.interval).end ==\n std::numeric_limits::max();\n }\n\n void getNeighbors(\n const SIPPState& s,\n std::vector >& neighbors) {\n std::vector > motions;\n// \t std::cout << \"Current state ---------------------------------------GScore \" << m_lastGScore << \" \" << s.state.x << \" \" << s.state.y << \" dir \" << s.dir << \" \\n\";\n\n\n m_env.getNeighbors(s.state, motions);\n for (const auto& m : motions) {\n \t m_env.num_generation++;\n // std::cout << \"gN \" << m.state << std::endl;\n Cost m_time = m.cost;\n // std::cout << m_lastGScore;\n Cost start_t = m_lastGScore + m_time;\n Cost end_t =\n safeIntervals(m_env.getLocation(s.state)).at(s.interval).end;\n\n const auto& sis = safeIntervals(m_env.getLocation(m.state));\n for (size_t i = 0; i < sis.size(); ++i) {\n const interval& si = sis[i];\n// std::cout << m.state.x << \" \" << m.state.y << \" i \" << i << \": \" << si.start << \" , \" << si.end << \" \" << start_t << \" \" << end_t<< \" \\n\";\n // std::endl;\n if (si.start - m_time > end_t || si.end < start_t) {\n continue;\n }\n// std::cout << \" --------------\\n\";\n int t;\n unsigned int dir_1 = 0x00;\n Action a_temp;\n if(m.action == Action::Left) {a_temp = Action::Right;dir_1 = 0x01;}\n else if(m.action == Action::Right) {a_temp = Action::Left; dir_1 = 0x02;}\n else if(m.action == Action::Up) {a_temp = Action::Down;dir_1 = 0x04;}\n else if(m.action == Action::Down){ a_temp = Action::Up; dir_1 = 0x08;}\n\n if (m_env.isCommandValid(s.state, m.state, m.action, m_lastGScore,\n end_t, si.start, si.end, t)\n \t\t && !IsEdgeCollisions(m.state, edgeCollision(t - 1, a_temp))) {\n // std::cout << \" gN: \" << m.state << \",\" << i << \",\" << t << \",\"\n // << m_lastGScore << std::endl;\n neighbors.emplace_back(Neighbor(\n SIPPState(m.state, i, dir_1), SIPPAction(m.action, m.cost),\n t - m_lastGScore));\n// std::cout << \"Successor : \" << m.state.x << \" \" << m.state.y <<\" Cost \" << m.cost << \" dir \" << dir_1 << \" Gscore \" << t << \" \\n\";\n }\n }\n }\n }\n\n void onExpandNode(const SIPPState& s, Cost fScore, Cost gScore) {\n // const auto& interval =\n // safeIntervals(m_env.getLocation(s.state)).at(s.interval);\n // std::cout << \"expand: \" << s.state << \",\" << interval.start << \" to \"\n // << interval.end << \"(g: \" << gScore << \" f: \" << fScore << \")\" <<\n // std::endl;\n // This is called before getNeighbors(). We use the callback to find the\n // current cost (=time) of the expanded node\n m_env.num_expansion++;\n m_lastGScore = gScore;\n m_env.onExpandNode(s.state, fScore, gScore);\n }\n\n void onDiscover(const SIPPState& s, Cost fScore, Cost gScore) {\n // const auto& interval =\n // safeIntervals(m_env.getLocation(s.state)).at(s.interval);\n // std::cout << \"discover: \" << s.state << \",\" << interval.start << \" to \"\n // << interval.end << std::endl;\n m_env.onDiscover(s.state, fScore, gScore);\n }\n\n void setCollisionIntervals(const Location& location,\n const std::vector& intervals) {\n m_safeIntervals.erase(location);\n std::vector sortedIntervals(intervals);\n std::sort(sortedIntervals.begin(), sortedIntervals.end());\n\n // std::cout << location << \": \" << std::endl;\n if (intervals.size() > 0) {\n m_safeIntervals[location]; // create empty safe interval\n int start = 0;\n int lastEnd = 0;\n for (const auto& interval : sortedIntervals) {\n // std::cout << \" ci: \" << interval.start << \" - \" << interval.end <<\n // std::endl;\n assert(interval.start <= interval.end);\n assert(start <= interval.start);\n // if (start + 1 != interval.start - 1) {\n // std::cout << start << \",\" << interval.start << std::endl;\n // assert(start + 1 < interval.start - 1);\n if (start <= interval.start - 1) {\n m_safeIntervals[location].push_back({start, interval.start - 1});\n }\n // }\n start = interval.end + 1;\n lastEnd = interval.end;\n }\n if (lastEnd < std::numeric_limits::max()) {\n // assert(start < std::numeric_limits::max());\n m_safeIntervals[location].push_back(\n {start, std::numeric_limits::max()});\n }\n }\n\n // auto iter = m_safeIntervals.find(location);\n // if (iter != m_safeIntervals.end()) {\n // for (const auto& si : iter->second) {\n // std::cout << \" si: \" << si.start << \" - \" << si.end << std::endl;\n // }\n // }\n }\n\n bool findSafeInterval(const State& state, Cost time, size_t& interval) {\n const auto& si = safeIntervals(m_env.getLocation(state));\n for (size_t idx = 0; idx < si.size(); ++idx) {\n if (si[idx].start <= time && si[idx].end >= time) {\n interval = idx;\n return true;\n }\n }\n return false;\n }\n\n void setEdgeCollisions(const Location& location,\n \t\t \t \t \t \tconst std::vector& edge_collision) {\n \t m_edgeCollision.erase(location);\n \t if(edge_collision.size() > 0){\n \t\t m_edgeCollision[location];\n \t\t for(const auto& ec : edge_collision){\n \t\t\t m_edgeCollision[location].push_back(ec);\n \t\t }\n \t }\n }\n\n bool IsEdgeCollisions(const Location& location, const edgeCollision& ec){\n// \t\tstd::cout << location.x << \" \" << location.y << \" \" << ec.t << \" \" << ec.action << std::endl;\n// \t\treturn false;\n \t\tconst auto iter = m_edgeCollision.find(location);\n \t\tif(iter == m_edgeCollision.end()) return false;\n \t\tif((iter->second).size() == 0) return false;\n \t\tfor (auto& cec : (iter->second)){\n \t\t\tif( cec == ec ){\n \t\t\t\treturn true;\n \t\t\t}\n \t\t}\n \t\treturn false;\n }\n\n private:\n const std::vector& safeIntervals(const Location& location) {\n static std::vector defaultInterval(\n 1, {0, std::numeric_limits::max()});\n const auto iter = m_safeIntervals.find(location);\n if (iter == m_safeIntervals.end()) {\n return defaultInterval;\n }\n return iter->second;\n }\n\n private:\n Environment& m_env;\n Cost m_lastGScore;\n std::unordered_map > m_safeIntervals;\n std::unordered_map> m_edgeCollision;\n };\n\n private:\n SIPPEnvironment m_env;\n AStar m_astar;\n};\n\n} // namespace libMultiRobotPlanning\n", "meta": {"hexsha": "03324ee9b9d226b695cb607f5e8fe9723117d7ec", "size": 13836, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/libMultiRobotPlanning/sipp.hpp", "max_stars_repo_name": "husl903/Multi-pathfinding", "max_stars_repo_head_hexsha": "5629e9295904995e3dee075431802f435c0e12f2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/libMultiRobotPlanning/sipp.hpp", "max_issues_repo_name": "husl903/Multi-pathfinding", "max_issues_repo_head_hexsha": "5629e9295904995e3dee075431802f435c0e12f2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/libMultiRobotPlanning/sipp.hpp", "max_forks_repo_name": "husl903/Multi-pathfinding", "max_forks_repo_head_hexsha": "5629e9295904995e3dee075431802f435c0e12f2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4105263158, "max_line_length": 167, "alphanum_fraction": 0.6012575889, "num_tokens": 3437, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.21878301691974728}} {"text": "/* Copyright (c) 2016 - 2020, the adamantine authors.\n *\n * This file is subject to the Modified BSD License and may not be distributed\n * without copyright and license information. Please refer to the file LICENSE\n * for the text and further information on this license.\n */\n\n#ifndef THERMAL_PHYSICS_TEMPLATES_HH\n#define THERMAL_PHYSICS_TEMPLATES_HH\n\n#include \n#ifdef ADAMANTINE_HAVE_CUDA\n#include \n#endif\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n\nnamespace adamantine\n{\nnamespace\n{\ntemplate ::value,\n int> = 0>\ndealii::LA::distributed::Vector vmult_and_scale(\n std::shared_ptr> thermal_operator,\n dealii::LA::distributed::Vector const &y,\n dealii::LA::distributed::Vector &value,\n std::vector &timers)\n{\n // Apply the Thermal Operator.\n thermal_operator->vmult_add(value, y);\n\n // Multiply by the inverse of the mass matrix.\n value.scale(*thermal_operator->get_inverse_mass_matrix());\n\n timers[evol_time_eval_th_ph].stop();\n\n return value;\n}\n\ntemplate ::value,\n int> = 0>\nvoid init_dof_vector(\n dealii::DoFHandler const &dof_handler, dealii::FE_Q const &fe,\n double const value,\n dealii::LinearAlgebra::distributed::Vector &vector)\n{\n dealii::LA::distributed::Vector dummy;\n dealii::QGauss quadrature(1);\n dealii::FEValues fe_values(fe, quadrature, dealii::update_values);\n unsigned int const dofs_per_cell = fe.dofs_per_cell;\n std::vector local_dof_indices(dofs_per_cell);\n dealii::IndexSet local_elements = vector.locally_owned_elements();\n for (auto cell :\n dealii::filter_iterators(dof_handler.active_cell_iterators(),\n dealii::IteratorFilters::LocallyOwnedCell()))\n {\n fe_values.reinit(cell);\n cell->get_dof_indices(local_dof_indices);\n for (auto const dof_index : local_dof_indices)\n {\n if (local_elements.is_element(dof_index) == true)\n vector[dof_index] = value;\n }\n }\n}\n\n#ifdef ADAMANTINE_HAVE_CUDA\ntemplate ::value,\n int> = 0>\ndealii::LA::distributed::Vector vmult_and_scale(\n std::shared_ptr> thermal_operator,\n dealii::LA::distributed::Vector const &y,\n dealii::LA::distributed::Vector &value,\n std::vector &timers)\n{\n dealii::LA::distributed::Vector value_dev(\n value.get_partitioner());\n value_dev.import(value, dealii::VectorOperation::insert);\n\n // Apply the Thermal Operator.\n thermal_operator->vmult_add(value_dev, y);\n\n // Multiply by the inverse of the mass matrix.\n value_dev.scale(*thermal_operator->get_inverse_mass_matrix());\n\n timers[evol_time_eval_th_ph].stop();\n\n return value_dev;\n}\n\ntemplate ::value,\n int> = 0>\nvoid init_dof_vector(\n dealii::DoFHandler const &dof_handler, dealii::FE_Q const &fe,\n double const value,\n dealii::LinearAlgebra::distributed::Vector &vector)\n{\n dealii::LA::distributed::Vector\n vector_host(vector.get_partitioner());\n dealii::LA::distributed::Vector dummy;\n\n dealii::QGauss quadrature(1);\n dealii::FEValues fe_values(fe, quadrature, dealii::update_values);\n unsigned int const dofs_per_cell = fe.dofs_per_cell;\n std::vector local_dof_indices(dofs_per_cell);\n dealii::IndexSet local_elements = vector.locally_owned_elements();\n for (auto cell :\n dealii::filter_iterators(dof_handler.active_cell_iterators(),\n dealii::IteratorFilters::LocallyOwnedCell()))\n {\n fe_values.reinit(cell);\n cell->get_dof_indices(local_dof_indices);\n for (auto const dof_index : local_dof_indices)\n {\n if (local_elements.is_element(dof_index) == true)\n vector_host[dof_index] = value;\n }\n }\n vector.import(vector_host, dealii::VectorOperation::insert);\n}\n#endif\n} // namespace\n\ntemplate \nThermalPhysics::ThermalPhysics(\n MPI_Comm const &communicator, boost::property_tree::ptree const &database,\n Geometry &geometry)\n : _embedded_method(false), _implicit_method(false), _geometry(geometry),\n _fe(fe_degree), _dof_handler(_geometry.get_triangulation()),\n _quadrature(fe_degree + 1)\n{\n // Create the material properties\n boost::property_tree::ptree const &material_database =\n database.get_child(\"materials\");\n _material_properties.reset(new MaterialProperty(\n communicator, _geometry.get_triangulation(), material_database));\n\n // Create the electron beams\n boost::property_tree::ptree const &source_database =\n database.get_child(\"sources\");\n unsigned int const n_beams = source_database.get(\"n_beams\");\n _electron_beams.resize(n_beams);\n for (unsigned int i = 0; i < n_beams; ++i)\n {\n boost::property_tree::ptree const &beam_database =\n source_database.get_child(\"beam_\" + std::to_string(i));\n _electron_beams[i] = std::make_unique>(beam_database);\n _electron_beams[i]->set_max_height(_geometry.get_max_height());\n }\n\n // Create the thermal operator\n if (std::is_same::value)\n _thermal_operator =\n std::make_shared>(\n communicator, _material_properties);\n#ifdef ADAMANTINE_HAVE_CUDA\n else\n _thermal_operator = std::make_shared<\n ThermalOperatorDevice>(\n communicator, _material_properties);\n#endif\n\n // Create the time stepping scheme\n boost::property_tree::ptree const &time_stepping_database =\n database.get_child(\"time_stepping\");\n std::string method = time_stepping_database.get(\"method\");\n std::transform(method.begin(), method.end(), method.begin(),\n [](unsigned char c) { return std::tolower(c); });\n if (method.compare(\"forward_euler\") == 0)\n _time_stepping =\n std::make_unique>(\n dealii::TimeStepping::FORWARD_EULER);\n else if (method.compare(\"rk_third_order\") == 0)\n _time_stepping =\n std::make_unique>(\n dealii::TimeStepping::RK_THIRD_ORDER);\n else if (method.compare(\"rk_fourth_order\") == 0)\n _time_stepping =\n std::make_unique>(\n dealii::TimeStepping::RK_CLASSIC_FOURTH_ORDER);\n else if (method.compare(\"heun_euler\") == 0)\n {\n _time_stepping = std::make_unique<\n dealii::TimeStepping::EmbeddedExplicitRungeKutta>(\n dealii::TimeStepping::HEUN_EULER);\n _embedded_method = true;\n }\n else if (method.compare(\"bogacki_shampine\") == 0)\n {\n _time_stepping = std::make_unique<\n dealii::TimeStepping::EmbeddedExplicitRungeKutta>(\n dealii::TimeStepping::BOGACKI_SHAMPINE);\n _embedded_method = true;\n }\n else if (method.compare(\"dopri\") == 0)\n {\n _time_stepping = std::make_unique<\n dealii::TimeStepping::EmbeddedExplicitRungeKutta>(\n dealii::TimeStepping::DOPRI);\n _embedded_method = true;\n }\n else if (method.compare(\"fehlberg\") == 0)\n {\n _time_stepping = std::make_unique<\n dealii::TimeStepping::EmbeddedExplicitRungeKutta>(\n dealii::TimeStepping::FEHLBERG);\n _embedded_method = true;\n }\n else if (method.compare(\"cash_karp\") == 0)\n {\n _time_stepping = std::make_unique<\n dealii::TimeStepping::EmbeddedExplicitRungeKutta>(\n dealii::TimeStepping::CASH_KARP);\n _embedded_method = true;\n }\n else if (method.compare(\"backward_euler\") == 0)\n {\n _time_stepping =\n std::make_unique>(\n dealii::TimeStepping::BACKWARD_EULER);\n _implicit_method = true;\n }\n else if (method.compare(\"implicit_midpoint\") == 0)\n {\n _time_stepping =\n std::make_unique>(\n dealii::TimeStepping::IMPLICIT_MIDPOINT);\n _implicit_method = true;\n }\n else if (method.compare(\"crank_nicolson\") == 0)\n {\n _time_stepping =\n std::make_unique>(\n dealii::TimeStepping::CRANK_NICOLSON);\n _implicit_method = true;\n }\n else if (method.compare(\"sdirk2\") == 0)\n {\n _time_stepping =\n std::make_unique>(\n dealii::TimeStepping::SDIRK_TWO_STAGES);\n _implicit_method = true;\n }\n\n if (_embedded_method == true)\n {\n double coarsen_param =\n time_stepping_database.get(\"coarsening_parameter\", 1.2);\n double refine_param = time_stepping_database.get(\"refining_parameter\", 0.8);\n double min_delta = time_stepping_database.get(\"min_time_step\", 1e-14);\n double max_delta = time_stepping_database.get(\"max_time_step\", 1e100);\n double refine_tol = time_stepping_database.get(\"refining_tolerance\", 1e-8);\n double coarsen_tol =\n time_stepping_database.get(\"coarsening_tolerance\", 1e-12);\n dealii::TimeStepping::EmbeddedExplicitRungeKutta *embedded_rk =\n static_cast<\n dealii::TimeStepping::EmbeddedExplicitRungeKutta *>(\n _time_stepping.get());\n embedded_rk->set_time_adaptation_parameters(coarsen_param, refine_param,\n min_delta, max_delta,\n refine_tol, coarsen_tol);\n }\n\n // If the time stepping scheme is implicit, set the parameters for the solver\n // and create the implicit operator.\n if (_implicit_method == true)\n {\n _max_iter = time_stepping_database.get(\"max_iteration\", 1000);\n _tolerance = time_stepping_database.get(\"tolerance\", 1e-12);\n _right_preconditioning =\n time_stepping_database.get(\"right_preconditioning\", false);\n _max_n_tmp_vectors = time_stepping_database.get(\"n_tmp_vectors\", 30);\n unsigned int newton_max_iter =\n time_stepping_database.get(\"newton_max_iteration\", 100);\n double newton_tolerance =\n time_stepping_database.get(\"newton_tolerance\", 1e-6);\n dealii::TimeStepping::ImplicitRungeKutta *implicit_rk =\n static_cast *>(\n _time_stepping.get());\n implicit_rk->set_newton_solver_parameters(newton_max_iter,\n newton_tolerance);\n\n bool jfnk = time_stepping_database.get(\"jfnk\", false);\n _implicit_operator = std::make_unique>(\n _thermal_operator, jfnk);\n }\n}\n\ntemplate \nvoid ThermalPhysics::setup_dofs()\n{\n _dof_handler.distribute_dofs(_fe);\n dealii::IndexSet locally_relevant_dofs;\n dealii::DoFTools::extract_locally_relevant_dofs(_dof_handler,\n locally_relevant_dofs);\n _affine_constraints.clear();\n _affine_constraints.reinit(locally_relevant_dofs);\n dealii::DoFTools::make_hanging_node_constraints(_dof_handler,\n _affine_constraints);\n _affine_constraints.close();\n\n _thermal_operator->reinit(_dof_handler, _affine_constraints, _quadrature);\n}\n\ntemplate \nvoid ThermalPhysics::compute_inverse_mass_matrix()\n{\n _thermal_operator->compute_inverse_mass_matrix(_dof_handler,\n _affine_constraints);\n if (_implicit_method == true)\n _implicit_operator->set_inverse_mass_matrix(\n _thermal_operator->get_inverse_mass_matrix());\n}\n\ntemplate \ndouble ThermalPhysics::\n evolve_one_time_step(\n double t, double delta_t,\n dealii::LA::distributed::Vector &solution,\n std::vector &timers)\n{\n auto eval = [&](double const t, LA_Vector const &y) {\n return evaluate_thermal_physics(t, y, timers);\n };\n auto id_m_Jinv = [&](double const t, double const tau, LA_Vector const &y) {\n return id_minus_tau_J_inverse(t, tau, y, timers);\n };\n\n double time = _time_stepping->evolve_one_time_step(eval, id_m_Jinv, t,\n delta_t, solution);\n\n // If the method is embedded, get the next time step. Otherwise, just use the\n // current time step.\n if (_embedded_method == false)\n _delta_t_guess = delta_t;\n else\n {\n dealii::TimeStepping::EmbeddedExplicitRungeKutta *embedded_rk =\n static_cast<\n dealii::TimeStepping::EmbeddedExplicitRungeKutta *>(\n _time_stepping.get());\n _delta_t_guess = embedded_rk->get_status().delta_t_guess;\n }\n\n // Return the time at the end of the time step. This may be different than\n // t+delta_t for embedded methods.\n return time;\n}\n\ntemplate \nvoid ThermalPhysics::\n initialize_dof_vector(\n dealii::LA::distributed::Vector &vector) const\n{\n _thermal_operator->initialize_dof_vector(vector);\n}\n\ntemplate \nvoid ThermalPhysics::\n initialize_dof_vector(\n double const value,\n dealii::LA::distributed::Vector &vector) const\n{\n // Resize the vector\n _thermal_operator->initialize_dof_vector(vector);\n\n init_dof_vector(_dof_handler, _fe, value,\n vector);\n}\n\ntemplate \ndealii::LA::distributed::Vector\nThermalPhysics::\n evaluate_thermal_physics(\n double const t,\n dealii::LA::distributed::Vector const &y,\n std::vector &timers) const\n{\n timers[evol_time_eval_mat_prop].start();\n if (std::is_same::value)\n _thermal_operator->evaluate_material_properties(y);\n else\n {\n // TODO do this on the GPU\n dealii::LA::distributed::Vector y_host(\n y.get_partitioner());\n y_host.import(y, dealii::VectorOperation::insert);\n _thermal_operator->evaluate_material_properties(y_host);\n }\n timers[evol_time_eval_mat_prop].stop();\n\n timers[evol_time_eval_th_ph].start();\n dealii::LA::distributed::Vector source(\n y.get_partitioner());\n source = 0.;\n\n // Compute the source term.\n for (auto &beam : _electron_beams)\n beam->set_time(t);\n dealii::QGauss source_quadrature(fe_degree + 1);\n dealii::FEValues fe_values(_fe, source_quadrature,\n dealii::update_quadrature_points |\n dealii::update_values |\n dealii::update_JxW_values);\n unsigned int const dofs_per_cell = _fe.dofs_per_cell;\n unsigned int const n_q_points = source_quadrature.size();\n std::vector local_dof_indices(dofs_per_cell);\n dealii::Vector cell_source(dofs_per_cell);\n\n for (auto cell :\n dealii::filter_iterators(_dof_handler.active_cell_iterators(),\n dealii::IteratorFilters::LocallyOwnedCell()))\n {\n cell_source = 0.;\n fe_values.reinit(cell);\n double const inv_rho_cp = _thermal_operator->get_inv_rho_cp(cell);\n\n for (unsigned int i = 0; i < dofs_per_cell; ++i)\n {\n for (unsigned int q = 0; q < n_q_points; ++q)\n {\n double quad_pt_source = 0.;\n dealii::Point const &q_point = fe_values.quadrature_point(q);\n for (auto &beam : _electron_beams)\n quad_pt_source += beam->value(q_point);\n\n cell_source[i] += inv_rho_cp * quad_pt_source *\n fe_values.shape_value(i, q) * fe_values.JxW(q);\n }\n }\n cell->get_dof_indices(local_dof_indices);\n _affine_constraints.distribute_local_to_global(cell_source,\n local_dof_indices, source);\n }\n\n return vmult_and_scale(_thermal_operator, y,\n source, timers);\n}\n\ntemplate \ndealii::LA::distributed::Vector\nThermalPhysics::\n id_minus_tau_J_inverse(\n double const /*t*/, double const tau,\n dealii::LA::distributed::Vector const &y,\n std::vector &timers) const\n{\n timers[evol_time_J_inv].start();\n _implicit_operator->set_tau(tau);\n dealii::LA::distributed::Vector solution(\n y.get_partitioner());\n\n // TODO Add a geometric multigrid preconditioner.\n dealii::PreconditionIdentity preconditioner;\n\n dealii::SolverControl solver_control(_max_iter, _tolerance * y.l2_norm());\n // We need to inverse (I - tau M^{-1} J). While M^{-1} and J are SPD,\n // (I - tau M^{-1} J) is symmetric indefinite in the general case.\n typename dealii::SolverGMRES<\n dealii::LA::distributed::Vector>::AdditionalData\n additional_data(_max_n_tmp_vectors, _right_preconditioning);\n dealii::SolverGMRES>\n solver(solver_control, additional_data);\n solver.solve(*_implicit_operator, solution, y, preconditioner);\n\n timers[evol_time_J_inv].stop();\n\n return solution;\n}\n} // namespace adamantine\n\n#endif\n", "meta": {"hexsha": "4e9c254d58d08bf252b89c62d3d88f13f413fc9e", "size": 19563, "ext": "hh", "lang": "C++", "max_stars_repo_path": "source/ThermalPhysics.templates.hh", "max_stars_repo_name": "stvdwtt/adamantine", "max_stars_repo_head_hexsha": "af396f02089a488a35146ab83234974ae465ada2", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "source/ThermalPhysics.templates.hh", "max_issues_repo_name": "stvdwtt/adamantine", "max_issues_repo_head_hexsha": "af396f02089a488a35146ab83234974ae465ada2", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "source/ThermalPhysics.templates.hh", "max_forks_repo_name": "stvdwtt/adamantine", "max_forks_repo_head_hexsha": "af396f02089a488a35146ab83234974ae465ada2", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.2044088176, "max_line_length": 80, "alphanum_fraction": 0.6955988345, "num_tokens": 4909, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5851011397337391, "lm_q2_score": 0.37387582277169656, "lm_q1q2_score": 0.2187551700226091}} {"text": "// Copyright 2015-2022 The ALMA Project Developers\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n// http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n// implied. See the License for the specific language governing\n// permissions and limitations under the License.\n\n#pragma once\n\n/// @file\n/// Classes and functions used to manipulate grids in reciprocal space.\n\n#include \n#include \n#include \n\nnamespace alma {\n/// Convenient shorthand for an array of four indices.\nusing Tetrahedron = std::array;\n/// Convenient shorthand for an array of three indices.\nusing Triangle = std::array;\n\n/// Objects of this class represent a regular grid with the Gamma\n/// point in one corner.\nclass Gamma_grid {\npublic:\n /// Size of the grid along the first reciprocal axis.\n const int na;\n /// Size of the grid along the second reciprocal axis.\n const int nb;\n /// Size of the grid along the third reciprocal axis.\n const int nc;\n /// Total number of q points in the grid.\n const std::size_t nqpoints;\n /// Reciprocal lattice basis vectors.\n const Eigen::MatrixXd rlattvec;\n /// Side vectors of each element in reciprocal space.\n const Eigen::MatrixXd dq;\n /// Constructor: initialize all internal variables and compute\n /// the spectrum at each q point. Do not correct the dynamical\n /// matrix for the effect of long-range interactions.\n Gamma_grid(const Crystal_structure& poscar,\n const Symmetry_operations& symms,\n const Harmonic_ifcs& force_constants,\n int _na,\n int _nb,\n int _nc);\n\n\n /// Constructor: initialize all internal variables and compute\n /// the spectrum at each q point. Correct the dynamical\n /// matrix for the effect of long-range interactions.\n Gamma_grid(const Crystal_structure& poscar,\n const Symmetry_operations& symms,\n const Harmonic_ifcs& force_constants,\n const Dielectric_parameters& born,\n int _na,\n int _nb,\n int _nc);\n\n\n /// Set the three lowest frequencies at Gamma to zero.\n void enforce_asr() {\n this->spectrum[0].omega.head(3).fill(0.);\n }\n\n\n /// Return the index of a q point identified by its position\n /// along the three axes.\n ///\n /// Indices are interpreted modulo-{na, nb, nc}, so even\n /// negative values are accepted.\n /// @param[in] indices - positions of the q point\n /// along the three axes\n /// @return the index of the q point in this grid\n std::size_t three_to_one(const std::array& indices) const {\n auto ia = python_mod(indices[0], this->na);\n auto ib = python_mod(indices[1], this->nb);\n auto ic = python_mod(indices[2], this->nc);\n\n return ic + nc * (ib + nb * ia);\n }\n\n\n /// Return the coordinates of a q point identified by its\n /// index.\n ///\n /// @param[in] index - index of the q point\n /// @return - an array with the positions of the q point\n /// along the three axes.\n std::array one_to_three(std::size_t iq) const {\n std::array nruter;\n nruter[2] = iq % nc;\n iq /= nc;\n nruter[1] = iq % nb;\n nruter[0] = iq / nb;\n return nruter;\n }\n\n\n /// @return the number of q-point equivalence classes in the grid.\n std::size_t get_nequivalences() const {\n return this->equivalences.size();\n }\n\n\n /// Access the harmonic properties at a point in the grid.\n ///\n /// The index is interpreted using modular arithmetic,\n /// so even negative values are accepted.\n /// @param[in] iq - index of the q point\n /// @return the harmonic properties at the q point.\n const Spectrum_at_point& get_spectrum_at_q(int iq) const {\n std::size_t index = python_mod(iq, this->nqpoints);\n\n return this->spectrum[index];\n }\n\n\n /// Return the cardinal of an equivalence class.\n ///\n /// @param[in] ic - index of the equivalence class.\n /// @return the number of points in an equivalence class.\n std::size_t get_cardinal(std::size_t ic) const {\n if (ic > this->equivalences.size())\n throw value_error(\"wrong equivalence class index\");\n return this->equivalences[ic].size();\n }\n\n\n /// Return a representative of an equivalence class.\n ///\n /// The method is guaranteed to always return the same point.\n /// @param[in] ic - index of the equivalence class.\n /// @return a representative of the equivalence class.\n std::size_t get_representative(std::size_t ic) const {\n if (ic > this->equivalences.size())\n throw value_error(\"wrong equivalence class index\");\n return this->equivalences[ic][0];\n }\n\n\n /// Return the elements in an equivalence class.\n ///\n /// @param[in] ic - index of the equivalence class.\n /// @return a vector with the elements in the equivalence\n /// class.\n std::vector get_equivalence(std::size_t ic) const {\n if (ic > this->equivalences.size())\n throw value_error(\"wrong equivalence class index\");\n return this->equivalences[ic];\n }\n\n\n /// Return the Cartesian coordinates of a q point.\n ///\n /// @param[in] iq - a q point index\n /// @return three Cartesian coordinates\n Eigen::VectorXd get_q(std::size_t iq) const {\n std::size_t index = python_mod(iq, this->nqpoints);\n\n return this->cpos.col(index);\n }\n\n\n /// Return the base broadening (without any prefactor) for a\n /// mode.\n ///\n /// @param[in] v - group velocity, or difference\n /// of group velocities in the case of three-phonon processes\n /// @return - the standard deviation of a Gaussian\n double base_sigma(const Eigen::VectorXd& v) const {\n return (v.transpose() * this->dq).norm() / std::sqrt(12.);\n }\n\n\n /// Very basic constructor that builds a stub object. Useful\n /// for deserialization or for obtaining an equivalences vector\n /// without computing the spectrum.\n Gamma_grid(const Crystal_structure& poscar,\n const Symmetry_operations& symms,\n int _na,\n int _nb,\n int _nc);\n\n\n /// Find the index of the polar opposite q point.\n ///\n /// @param[in] q - a q point\n /// @return the index of the polar opposite of q\n std::size_t polar_opposite(std::size_t q) {\n auto indices = this->one_to_three(q);\n\n return this->three_to_one({{-indices[0], -indices[1], -indices[2]}});\n }\n\n\n /// Return the images of a q point through all the symmetry\n /// operations, including inversions.\n ///\n /// @param[in] q - a q point\n /// @return a vector of q point indices. Elements with even indices\n /// correspond to operations in the space group,\n /// while elements with odd indices compound them with time\n /// reversal.\n std::vector equivalent_qpoints(std::size_t original) const {\n std::size_t index = python_mod(original, this->nqpoints);\n return this->symmetry_map[index];\n }\n\n\n /// Find all q-point pairs equivalent to the input.\n ///\n /// Given a pair of indices, obtain all equivalent pairs\n /// after looking the up in the symmetry_map.\n /// @param[in] pair a pair of q point indices\n /// @return a vector of pairs, including the input\n std::vector> equivalent_qpairs(\n const std::array& original) const;\n\n\n /// Find all q-point triplets equivalent to the input.\n ///\n /// Given a triplet of indices, obtain all equivalent triplets\n /// after looking the up in the symmetry_map.\n /// @param[in] a triplet of q point indices\n /// @return a vector of triplets, including the input\n std::vector> equivalent_qtriplets(\n const std::array& original) const;\n\n\n /// Decompose the q-th microcell in five tetrahedra.\n ///\n /// @param[in] q - the index of the q point\n /// @return a vector of five Tetrahedron objects\n std::vector get_tetrahedra(std::size_t q) const {\n // Find out the indices of the eight corners of the\n // microcell.\n std::size_t i000 = q;\n auto indices = this->one_to_three(i000);\n std::size_t i001 =\n this->three_to_one({{indices[0], indices[1], indices[2] + 1}});\n std::size_t i010 =\n this->three_to_one({{indices[0], indices[1] + 1, indices[2]}});\n std::size_t i011 =\n this->three_to_one({{indices[0], indices[1] + 1, indices[2] + 1}});\n std::size_t i100 =\n this->three_to_one({{indices[0] + 1, indices[1], indices[2]}});\n std::size_t i101 =\n this->three_to_one({{indices[0] + 1, indices[1], indices[2] + 1}});\n std::size_t i110 =\n this->three_to_one({{indices[0] + 1, indices[1] + 1, indices[2]}});\n std::size_t i111 = this->three_to_one(\n {{indices[0] + 1, indices[1] + 1, indices[2] + 1}});\n\n // Build the four equivalent tetrahedra.\n std::vector nruter;\n nruter.reserve(5);\n nruter.emplace_back(Tetrahedron({{i000, i001, i010, i100}}));\n nruter.emplace_back(Tetrahedron({{i110, i111, i100, i010}}));\n nruter.emplace_back(Tetrahedron({{i101, i100, i111, i001}}));\n nruter.emplace_back(Tetrahedron({{i011, i010, i001, i111}}));\n // And the central, inequivalent one.\n nruter.emplace_back(Tetrahedron({{i010, i100, i111, i001}}));\n return nruter;\n }\n\n\n /// @return a vector of triangle objects with each containing three\n /// indices\n std::vector get_triangles(std::size_t ia) const;\n\n\n /// @return the index of the representative of the equivalence\n /// class\n /// of the provided q-point\n std::size_t getParentIdx(std::size_t iq) const;\n\n\n /// @return the index of the symmetry operation that maps the\n /// provided q-point\n /// to the representative of the equivalence class to which it\n /// belongs\n std::size_t getSymIdxToParent(std::size_t iq) const;\n\n\n /// @return the grid's symmetry map\n std::vector> getSymmetryMap() {\n return this->symmetry_map;\n }\n\n\n /// Remove the component of a Cartesian vector which does transform as a\n /// q-point in the grid.\n ///\n /// @param[in] iq - index of the q point\n /// @param[in] symms - set of symmetry operations of the crystal\n /// @param[in] x - vector in Cartesian coordinates. Several vectors can\n /// be provided by making v a matrix and each vector a column\n /// @return the symmetrized version of x\n template \n auto copy_symmetry(std::size_t iq,\n const Symmetry_operations& symms,\n const Eigen::MatrixBase& x) const\n -> Eigen::Matrix {\n std::size_t index = python_mod(iq, this->nqpoints);\n std::size_t nops = symms.get_nsym();\n\n typename T::PlainObject nruter(x);\n nruter.fill(0.);\n std::size_t nfound = 0;\n\n for (std::size_t iop = 0; iop < nops; ++iop) {\n if (this->symmetry_map[index][2 * iop] == index) {\n nruter += symms.rotate_v(x, iop, true);\n ++nfound;\n }\n }\n nruter /= nfound;\n return nruter;\n }\n\nprivate:\n friend void save_bulk_hdf5(const char* filename,\n const std::string& description,\n const Crystal_structure& cell,\n const Symmetry_operations& symmetries,\n const Gamma_grid& grid,\n const std::vector& processes,\n const boost::mpi::communicator& comm);\n\n friend std::tuple,\n std::unique_ptr,\n std::unique_ptr,\n std::unique_ptr>>\n load_bulk_hdf5(const char* filename, const boost::mpi::communicator& comm);\n\n /// Cartesian coordinates of each q point.\n Eigen::MatrixXd cpos;\n /// Harmonic properties at each q point.\n std::vector spectrum;\n /// Vector of equivalence classes. Two q points in the same\n /// equivalence class are related by a symmetry operation.\n /// Note that both space group symmetries and time reversal\n /// symmetry are taken into account.\n std::vector> equivalences;\n /// Detailed map between q points through the symmetry\n /// operations.\n std::vector> symmetry_map;\n /// Initialize the Cartesian coordinates of the q points.\n /// @param[in] poscar - a description of the crystal structure\n void initialize_cpos();\n\n /// Compute the spectrum at a subset of the q points.\n ///\n /// That subset is selected on the basis of this process'\n /// position in an MPI communicator.\n /// @param[in] factory - object in charge of building the\n /// dynamical matrix at each point.\n /// @param[in] symms - set of symmetry operations of the crystal\n /// @param[in] communicator - MPI communicator to use\n std::vector compute_my_spectrum(\n const Dynamical_matrix_builder& factory,\n const Symmetry_operations& symms,\n const boost::mpi::communicator& communicator);\n\n /// Fill the equivalences vector.\n ///\n /// @param[in] symms - the set of symmetry operations to try.\n void fill_equivalences(const Symmetry_operations& symms);\n\n /// Parentlookup table: stores for each q-point the index of\n /// the representative of the equivalence class to which it\n /// belongs.\n std::vector parentlookup;\n\n /// Fill the parentlookup table\n void fill_parentlookup();\n\n /// Fill the symmetry_map vector.\n ///\n /// @param[in] symms - the set of symmetry operations to try.\n void fill_map(const Symmetry_operations& symms);\n};\n} // namespace alma\n", "meta": {"hexsha": "1a82d232f38ef5ac63f6617c5da8ff7a7045a5cb", "size": 14635, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/qpoint_grid.hpp", "max_stars_repo_name": "sousaw/BTE-Barna", "max_stars_repo_head_hexsha": "029ca43ef096c4b725d3aeb2955bc0df9ca544a9", "max_stars_repo_licenses": ["MIT", "BSD-3-Clause"], "max_stars_count": 5.0, "max_stars_repo_stars_event_min_datetime": "2022-02-07T03:36:38.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T13:11:20.000Z", "max_issues_repo_path": "include/qpoint_grid.hpp", "max_issues_repo_name": "sousaw/BTE-Barna", "max_issues_repo_head_hexsha": "029ca43ef096c4b725d3aeb2955bc0df9ca544a9", "max_issues_repo_licenses": ["MIT", "BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/qpoint_grid.hpp", "max_forks_repo_name": "sousaw/BTE-Barna", "max_forks_repo_head_hexsha": "029ca43ef096c4b725d3aeb2955bc0df9ca544a9", "max_forks_repo_licenses": ["MIT", "BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.1446700508, "max_line_length": 79, "alphanum_fraction": 0.6271950803, "num_tokens": 3537, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5736784074525098, "lm_q2_score": 0.3812195521959384, "lm_q1q2_score": 0.21869742559352484}} {"text": "/*\n Copyright (c) 2014, Philipp Krähenbühl\n All rights reserved.\n\t\n Redistribution and use in source and binary forms, with or without\n modification, are permitted provided that the following conditions are met:\n * Redistributions of source code must retain the above copyright\n notice, this list of conditions and the following disclaimer.\n * Redistributions in binary form must reproduce the above copyright\n notice, this list of conditions and the following disclaimer in the\n documentation and/or other materials provided with the distribution.\n * Neither the name of the Stanford University nor the\n names of its contributors may be used to endorse or promote products\n derived from this software without specific prior written permission.\n\t\n THIS SOFTWARE IS PROVIDED BY Philipp Krähenbühl ''AS IS'' AND ANY\n EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n DISCLAIMED. IN NO EVENT SHALL Philipp Krähenbühl BE LIABLE FOR ANY\n DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n\t LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\n\t ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n\t (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n*/\n#include \"util/win_util.h\"\n#include \"gradient.h\"\n#include \"filter.h\"\n#include \n#include \n#include \nusing namespace Eigen;\n\nstatic const float * acosTable(){\n\tstatic float table[2001];\n\tfloat * r = table+1000;\n\tfor( int i=-1000; i<1001; i++ )\n\t\tr[i] = acos( i / 1000. );\n\treturn r;\n}\nstatic void computeGradientOriAndMag( float * g, float * o, const float * gx, const float * gy, int N, int C ) {\n\tconst float * acost = acosTable();\n\tMap mgx( gx, C, N ), mgy( gy, C, N );\n\tArrayXXf mag = (mgx*mgx+mgy*mgy);\n\tfor( int i=0; i 1 ) cm = 1;\n\t\tif( cm < -1 ) cm = -1;\n\t\t\n\t\to[i] = acost[(int)(cm*1000)];\n\t\tg[i] = gm;\n\t}\n}\nstatic void computeGradientMag( float * g, const float * gx, const float * gy, int N, int C ) {\n\tMap mgx( gx, C, N ), mgy( gy, C, N );\n\tMap(g,N) = (mgx*mgx+mgy*mgy).colwise().maxCoeff().sqrt();\n}\ntemplate \nstatic void computeGradHist( Image & hist, const RMatrixXf & gm, const RMatrixXf & go, int nori ) {\n\tconst int W = gm.cols(), H = gm.rows();\n\tconst int Wb = W/BINS, Hb = H/BINS;\n\tconst int W0 = Wb*BINS, H0 = Hb*BINS;\n\thist.create( Wb, Hb, nori );\n\thist = 0;\n\tfor(int j = 0; j < H0; j++){\n\t\tfloat * phist = hist.data() + (j/BINS)*Wb*nori;\n\t\tfor(int i = 0; i < W0; phist+=nori){\n\t\t\tfor(int k = 0; k < BINS && i= nori )\n\t\t\t\t\to0 = 0;\n\t\t\t\tunsigned int o1 = o0+1;\n\t\t\t\tif( o1 >= nori )\n\t\t\t\t\to1 = 0;\n\t\t\t\tphist[o0] += (1-w)*gm(j,i) / (BINS*BINS);\n\t\t\t\tphist[o1] += w*gm(j,i) / (BINS*BINS);\n\t\t\t}\n\t\t}\n\t}\n}\nvoid gradientHist( Image & hist, const RMatrixXf & gm, const RMatrixXf & go, int nori, int nbins) {\n\tswitch(nbins){\n\t\tcase 1: return computeGradHist<1>(hist, gm, go, nori);\n\t\tcase 2: return computeGradHist<2>(hist, gm, go, nori);\n\t\tcase 3: return computeGradHist<3>(hist, gm, go, nori);\n\t\tcase 4: return computeGradHist<4>(hist, gm, go, nori);\n\t\tcase 5: return computeGradHist<5>(hist, gm, go, nori);\n\t\tcase 6: return computeGradHist<6>(hist, gm, go, nori);\n\t\tcase 7: return computeGradHist<7>(hist, gm, go, nori);\n\t\tcase 8: return computeGradHist<8>(hist, gm, go, nori);\n\t\tdefault: throw std::invalid_argument(\"Bin size too large!\");\n\t}\n}\nstatic void diff( float * r, const float * a, const float * b, int N, float w=1.0 ) {\n\tMap( r, N ) = w*(Map( (float*)a, N ) - Map( (float*)b, N ));\n}\nvoid gradient( Image & gx, Image & gy, const Image & im ) {\n\tconst int W = im.W(), H = im.H(), C = im.C();\n\tgx.create( W, H, C );\n\tgy.create( W, H, C );\n\tfor( int j=0; j0 ) {\n\t\tfloat * tmp = gx.data();\n\t\ttentFilter( tmp, gm.data(), W, H, 1, norm_rad );\n\t\tfor( int i=0; i0 ) {\n\t\tfloat * tmp = gx.data();\n\t\ttentFilter( tmp, gm.data(), W, H, 1, norm_rad );\n\t\tfor( int i=0; i\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \"Scene_points_with_normal_item.h\"\n\n#include \"Messages_interface.h\"\n#include \"Scene_surface_mesh_item.h\"\n#include \"Color_ramp.h\"\n#include \n#include \"ui_Display_property.h\"\n#include \"id_printing.h\"\n#include \"Scene.h\"\n#include \"triangulate_primitive.h\"\n#include \n#include \n#include \n\n#define ARBITRARY_DBL_MIN 1.0E-30\n#define ARBITRARY_DBL_MAX 1.0E+30\n\n\n//Item for heat values\ntypedef CGAL::Three::Triangle_container Tri;\ntypedef CGAL::Three::Viewer_interface VI;\n\nclass Scene_heat_item\n : public CGAL::Three::Scene_item_rendering_helper\n{\n Q_OBJECT\n \npublic: \n Scene_heat_item(Scene_surface_mesh_item* item)\n :sm(item->face_graph()), parent(item)\n {\n setTriangleContainer(0, new Triangle_container(VI::PROGRAM_HEAT_INTENSITY,\n true));\n setRenderingMode(Gouraud);\n }\n Scene_item* clone() const Q_DECL_OVERRIDE {return nullptr;}\n QString toolTip() const Q_DECL_OVERRIDE{return QString(); }\n void select(double orig_x,\n double orig_y,\n double orig_z,\n double dir_x,\n double dir_y,\n double dir_z) Q_DECL_OVERRIDE\n {\n parent->select( orig_x, orig_y, orig_z, \n dir_x, dir_y, dir_z);\n }\n \n void initializeBuffers(CGAL::Three::Viewer_interface *viewer) const Q_DECL_OVERRIDE\n {\n getTriangleContainer(0)->initializeBuffers(viewer); \n getTriangleContainer(0)->setIdxSize(nb_idx);\n verts.resize(0);\n normals .resize(0);\n colors.resize(0);\n idx.clear();\n idx.shrink_to_fit();\n colors.shrink_to_fit();\n verts.shrink_to_fit();\n normals.shrink_to_fit();\n }\n \n void draw(CGAL::Three::Viewer_interface *viewer) const Q_DECL_OVERRIDE\n {\n if(!visible())\n return;\n if(!isInit(viewer))\n initGL(viewer);\n if ( getBuffersFilled() &&\n ! getBuffersInit(viewer))\n {\n initializeBuffers(viewer);\n setBuffersInit(viewer, true);\n }\n if(!getBuffersFilled())\n {\n computeElements();\n initializeBuffers(viewer);\n }\n \n getTriangleContainer(0)->setAlpha(1.0f);\n getTriangleContainer(0)->draw(viewer, false);\n }\n void compute_bbox() const Q_DECL_OVERRIDE\n {\n SMesh::Property_map pprop = sm->points();\n CGAL::Bbox_3 bbox ;\n \n for(vertex_descriptor vd :vertices(*sm))\n {\n bbox = bbox + pprop[vd].bbox();\n }\n _bbox = Bbox(bbox.xmin(),bbox.ymin(),bbox.zmin(),\n bbox.xmax(),bbox.ymax(),bbox.zmax());\n is_bbox_computed = true;\n }\n Scene_item::Bbox bbox() const Q_DECL_OVERRIDE {\n if(!is_bbox_computed)\n compute_bbox();\n is_bbox_computed = true;\n return _bbox;\n }\n\n ~Scene_heat_item(){}\n virtual bool supportsRenderingMode(RenderingMode m) const Q_DECL_OVERRIDE { return m==Gouraud; }\n virtual void invalidateOpenGLBuffers() Q_DECL_OVERRIDE\n {\n \n setBuffersFilled(false);\n compute_bbox();\n getTriangleContainer(0)->reset_vbos(NOT_INSTANCED);\n is_bbox_computed = false;\n }\n void triangulate_convex_facet(face_descriptor fd,\n boost::property_map< SMesh, boost::vertex_index_t >::type *im) const\n {\n const CGAL::qglviewer::Vec v_offset = static_cast(CGAL::QGLViewer::QGLViewerPool().first())->offset();\n EPICK::Vector_3 offset = EPICK::Vector_3(v_offset.x, v_offset.y, v_offset.z);\n \n EPICK::Point_3 p0,p1,p2;\n SMesh::Halfedge_around_face_circulator he(halfedge(fd, *sm), *sm);\n SMesh::Halfedge_around_face_circulator he_end = he;\n \n while(next(*he, *sm) != prev(*he_end, *sm))\n {\n ++he;\n vertex_descriptor v0(target(*he_end, *sm)),\n v1(target(*he, *sm)),\n v2(target(next(*he, *sm), *sm));\n p0 = sm->point(v0) + offset;\n p1 = sm->point(v1) + offset;\n p2 = sm->point(v2) + offset;\n idx.push_back((*im)[v0]);\n idx.push_back((*im)[v1]);\n idx.push_back((*im)[v2]);\n }\n }\n void triangulate_facet(face_descriptor fd,\n SMesh::Property_map *fnormals,\n boost::property_map< SMesh, boost::vertex_index_t >::type *im) const\n {\n //Computes the normal of the facet\n EPICK::Vector_3 normal = get(*fnormals, fd);\n \n //check if normal contains NaN values\n if (normal.x() != normal.x() || normal.y() != normal.y() || normal.z() != normal.z())\n {\n qDebug()<<\"Warning : normal is not valid. Facet not displayed\";\n return;\n }\n \n typedef FacetTriangulator::vertex_descriptor> FT;\n const CGAL::qglviewer::Vec off = static_cast(CGAL::QGLViewer::QGLViewerPool().first())->offset();\n EPICK::Vector_3 offset(off.x,off.y,off.z);\n FT triangulation(fd,normal,sm, offset);\n //iterates on the internal faces\n for(FT::CDT::Finite_faces_iterator\n ffit = triangulation.cdt->finite_faces_begin(),\n end = triangulation.cdt->finite_faces_end();\n ffit != end; ++ffit)\n {\n if(ffit->info().is_external)\n continue;\n //add the vertices to the positions\n //adds the vertices, normals and colors to the appropriate vectors\n //adds the indices to the appropriate vector\n idx.push_back((*im)[triangulation.v2v[ffit->vertex(0)]]);\n idx.push_back((*im)[triangulation.v2v[ffit->vertex(1)]]);\n idx.push_back((*im)[triangulation.v2v[ffit->vertex(2)]]);\n }\n }\n \n void computeElements() const Q_DECL_OVERRIDE\n {\n typedef EPICK::Point_3 Point;\n QApplication::setOverrideCursor(Qt::WaitCursor);\n const CGAL::qglviewer::Vec o = static_cast(CGAL::QGLViewer::QGLViewerPool().first())->offset();\n EPICK::Vector_3 offset(o.x, o.y, o.z);\n SMesh::Property_map positions =\n sm->points();\n SMesh::Property_map vnormals =\n sm->property_map(\"v:normal\").first;\n SMesh::Property_map fnormals =\n sm->property_map(\"f:normal\").first;\n typedef boost::graph_traits::face_descriptor face_descriptor;\n typedef boost::graph_traits::halfedge_descriptor halfedge_descriptor;\n typedef boost::graph_traits::vertex_descriptor vertex_descriptor;\n SMesh::Property_map vcolors =\n sm->property_map(\"v:color\").first;\n SMesh::Property_map vdist=\n sm->property_map(\"v:dist\").first; \n typedef CGAL::Buffer_for_vao CPF;\n verts.clear();\n normals.clear();\n idx.clear();\n colors.clear();\n boost::property_map< SMesh, boost::vertex_index_t >::type\n im = get(boost::vertex_index, *sm);\n \n idx.reserve(num_faces(*sm) * 3);\n for(face_descriptor fd : faces(*sm))\n {\n if(is_triangle(halfedge(fd,*sm),*sm))\n {\n for(halfedge_descriptor hd : halfedges_around_face(halfedge(fd, *sm),*sm))\n {\n idx.push_back(source(hd, *sm));\n }\n }\n else\n {\n std::vector facet_points;\n for(halfedge_descriptor hd : halfedges_around_face(halfedge(fd, *sm),*sm))\n {\n facet_points.push_back(positions[target(hd, *sm)]);\n }\n bool is_convex = CPF::is_facet_convex(facet_points, fnormals[fd]);\n \n if(is_convex && is_quad(halfedge(fd,*sm),*sm) )\n {\n halfedge_descriptor hd = halfedge(fd,*sm);\n //1st half\n idx.push_back(source(hd, *sm));\n idx.push_back(source(next(hd, *sm), *sm));\n idx.push_back(source(next(next(hd, *sm), *sm), *sm));\n \n //2nd half\n idx.push_back(source(hd, *sm));\n idx.push_back(source(next(next(hd, *sm), *sm), *sm));\n idx.push_back(source(prev(hd, *sm), *sm));\n } \n else if(is_convex)\n {\n triangulate_convex_facet(fd, &im);\n }\n else\n {\n triangulate_facet(fd, &fnormals, &im);\n }\n }\n }\n for(vertex_descriptor vd : vertices(*sm))\n {\n CGAL::Color c = vcolors[vd];\n colors.push_back((float)c.red()/255);\n colors.push_back((float)c.green()/255);\n colors.push_back((float)c.blue()/255);\n \n \n Point p = positions[vd] + offset;\n CPF::add_point_in_buffer(p, verts);\n EPICK::Vector_3 n = vnormals[vd];\n CPF::add_normal_in_buffer(n, normals);\n heat_values.push_back(vdist[vd]);\n }\n nb_idx = idx.size();\n getTriangleContainer(0)->allocate(Tri::Vertex_indices, idx.data(),\n static_cast(idx.size()*sizeof(unsigned int)));\n getTriangleContainer(0)->allocate(Tri::Smooth_vertices, verts.data(),\n static_cast(num_vertices(*sm)*3*sizeof(float)));\n \n getTriangleContainer(0)->allocate(Tri::Smooth_normals, normals.data(),\n static_cast(num_vertices(*sm)*3*sizeof(float)));\n getTriangleContainer(0)->allocate(Tri::VColors, colors.data(),\n static_cast(colors.size()*sizeof(float)));\n getTriangleContainer(0)->allocate(Tri::Distances, heat_values.data(),\n static_cast(heat_values.size()*sizeof(float)));\n compute_bbox();\n setBuffersFilled(true);\n QApplication::restoreOverrideCursor();\n }\n \n bool isEmpty() const Q_DECL_OVERRIDE {return false;}\n SMesh *face_graph() { return sm;}\n Scene_surface_mesh_item* getParent() { return parent; }\n\nprivate:\n SMesh* sm;\n Scene_surface_mesh_item* parent;\n mutable std::vector normals;\n mutable std::vector idx;\n mutable std::vector verts;\n mutable std::vector colors;\n mutable std::vector heat_values;\n mutable std::size_t nb_idx;\n}; // end class Scene_heat_item\n\nclass DockWidget :\n public QDockWidget,\n public Ui::DisplayPropertyWidget\n{\npublic:\n DockWidget(QString name, QWidget *parent)\n :QDockWidget(name,parent)\n {\n setupUi(this);\n }\n};\n\ntypedef boost::graph_traits::halfedge_descriptor halfedge_descriptor;\ntypedef boost::graph_traits::face_descriptor face_descriptor;\nCGAL::Three::Viewer_interface* (&getActiveViewer)() = CGAL::Three::Three::activeViewer;\nclass DisplayPropertyPlugin :\n public QObject,\n public CGAL::Three::Polyhedron_demo_plugin_helper\n{\n Q_OBJECT\n Q_INTERFACES(CGAL::Three::Polyhedron_demo_plugin_interface)\n Q_PLUGIN_METADATA(IID \"com.geometryfactory.PolyhedronDemo.PluginInterface/1.0\")\n typedef SMesh::Property_map::vertex_descriptor, double> Vertex_distance_map;\n typedef CGAL::Heat_method_3::Surface_mesh_geodesic_distances_3 Heat_method;\n typedef CGAL::Heat_method_3::Surface_mesh_geodesic_distances_3 Heat_method_idt;\n typedef CGAL::dynamic_vertex_property_t Vertex_source_tag;\n typedef boost::property_map::type Vertex_source_map;\n \npublic:\n\n bool applicable(QAction*) const Q_DECL_OVERRIDE\n {\n CGAL::Three::Scene_item* item = scene->item(scene->mainSelectionIndex());\n return qobject_cast(item);\n }\n\n QList actions() const Q_DECL_OVERRIDE\n {\n return _actions;\n }\n\n QColor textColor(const QColor& color)\n {\n QColor text_color (255, 255, 255);\n if (color.red() * 0.299 + color.green() * 0.587 + color.blue() * 0.114 > 128)\n text_color = QColor (0, 0, 0);\n return text_color;\n }\n\n void init(QMainWindow* mw, CGAL::Three::Scene_interface* sc, Messages_interface*) Q_DECL_OVERRIDE\n {\n this->scene = sc;\n this->mw = mw;\n this->current_item = NULL;\n\n QAction *actionDisplayAngles= new QAction(QString(\"Display Properties\"), mw);\n QAction *actionHeatMethod= new QAction(QString(\"Heat Method\"), mw);\n actionHeatMethod->setProperty(\"submenuName\", \"Color\");\n\n rm = 1.0;\n rM = 0.0;\n gm = 0.0;\n gM = 1.0;\n bm = 0.0;\n bM = 0.0;\n actionDisplayAngles->setProperty(\"submenuName\", \"Color\");\n\n if(actionDisplayAngles) {\n connect(actionDisplayAngles, SIGNAL(triggered()),\n this, SLOT(openDialog()));\n if(actionHeatMethod)\n {\n connect(actionHeatMethod, &QAction::triggered,\n this, [this](){\n this->dock_widget->propertyBox->setCurrentIndex(2);\n this->dock_widget->show();\n });\n }\n _actions << actionDisplayAngles;\n _actions << actionHeatMethod;\n\n }\n dock_widget = new DockWidget(\"Property Displaying\", mw);\n dock_widget->setVisible(false);\n addDockWidget(dock_widget);\n QPalette palette(Qt::red);\n dock_widget->minColorButton->setPalette(palette);\n dock_widget->minColorButton->setStyle(QStyleFactory::create(\"Fusion\"));\n dock_widget->minColorButton->update();\n\n palette = QPalette(Qt::green);\n dock_widget->maxColorButton->setPalette(palette);\n dock_widget->maxColorButton->setStyle(QStyleFactory::create(\"Fusion\"));\n dock_widget->maxColorButton->update();\n connect(dock_widget->colorizeButton, SIGNAL(clicked(bool)),\n this, SLOT(colorize()));\n\n connect(dock_widget->propertyBox, SIGNAL(currentIndexChanged(int)),\n this, SLOT(on_propertyBox_currentIndexChanged(int)));\n connect(dock_widget->zoomToMinButton, &QPushButton::pressed,\n this, &DisplayPropertyPlugin::on_zoomToMinButton_pressed);\n connect(dock_widget->zoomToMaxButton, &QPushButton::pressed,\n this, &DisplayPropertyPlugin::on_zoomToMaxButton_pressed);\n connect(dock_widget->minColorButton, &QPushButton::pressed,\n this, [this]()\n {\n QColor minColor = QColorDialog::getColor();\n if (!minColor.isValid())\n {\n return;\n }\n \n rm = minColor.redF();\n gm = minColor.greenF();\n bm = minColor.blueF();\n QPalette palette(minColor);\n dock_widget->minColorButton->setPalette(palette);\n dock_widget->minColorButton->update();\n replaceRamp();\n });\n connect(dock_widget->maxColorButton, &QPushButton::pressed,\n this, [this]()\n {\n QColor maxColor = QColorDialog::getColor();\n if(!maxColor.isValid())\n return;\n QPalette palette(maxColor);\n rM = maxColor.redF();\n gM = maxColor.greenF();\n bM = maxColor.blueF();\n\n dock_widget->maxColorButton->setPalette(palette);\n dock_widget->maxColorButton->update();\n replaceRamp();\n });\n\n connect(dock_widget->sourcePointsButton, SIGNAL(toggled(bool)),\n this, SLOT(on_sourcePointsButton_toggled(bool)));\n connect(dock_widget->deleteButton, &QPushButton::clicked,\n this, &DisplayPropertyPlugin::delete_group);\n\n connect(dock_widget->resetButton, &QPushButton::pressed,\n this, &DisplayPropertyPlugin::resetRampExtremas);\n\n dock_widget->zoomToMaxButton->setEnabled(false);\n dock_widget->zoomToMinButton->setEnabled(false);\n Scene* scene_obj =static_cast(scene);\n connect(scene_obj, &Scene::itemIndexSelected,\n this, &DisplayPropertyPlugin::enableButtons);\n on_propertyBox_currentIndexChanged(0);\n\n }\nprivate Q_SLOTS:\n void openDialog()\n {\n if(dock_widget->isVisible()) { dock_widget->hide(); }\n else{\n replaceRamp(); \n dock_widget->show();\n dock_widget->raise(); }\n }\n\n void resetRampExtremas()\n {\n Scene_surface_mesh_item* item =\n qobject_cast(scene->item(scene->mainSelectionIndex()));\n if(!item)\n return;\n QApplication::setOverrideCursor(Qt::WaitCursor);\n item->face_graph()->collect_garbage();\n bool ok;\n switch(dock_widget->propertyBox->currentIndex())\n {\n case 0:\n ok = resetAngles(item);\n break;\n default:\n ok = resetScaledJacobian(item);\n break;\n }\n QApplication::restoreOverrideCursor();\n if(!ok)\n QMessageBox::warning(mw, \"Error\", \"You must first run colorize once to initialize the values.\");\n }\n \n void colorize()\n {\n Scene_heat_item* h_item = nullptr;\n Scene_surface_mesh_item* item =\n qobject_cast(scene->item(scene->mainSelectionIndex()));\n if(!item)\n {\n h_item = qobject_cast(scene->item(scene->mainSelectionIndex()));\n if(!h_item)\n return;\n item = h_item->getParent();\n }\n QApplication::setOverrideCursor(Qt::WaitCursor);\n\n replaceRamp();\n item->face_graph()->collect_garbage();\n\n switch(dock_widget->propertyBox->currentIndex()){\n case 0:\n displayAngles(item);\n break;\n case 1:\n displayScaledJacobian(item);\n break;\n case 2:\n if(!displayHeatIntensity(item))\n return;\n item->setRenderingMode(Gouraud);\n break;\n default: // Heat Method (Intrinsic Delaunay)\n if(!displayHeatIntensity(item, true))\n return;\n item->setRenderingMode(Gouraud);\n break;\n }\n\n connect(item, &Scene_surface_mesh_item::itemChanged,\n this, [item](){\n bool does_exist;\n SMesh::Property_map pmap;\n boost::tie(pmap, does_exist) = \n item->face_graph()->property_map(\"f:jacobian\");\n if(does_exist)\n item->face_graph()->remove_property_map(pmap);\n boost::tie(pmap, does_exist) = \n item->face_graph()->property_map(\"f:angle\");\n if(does_exist)\n item->face_graph()->remove_property_map(pmap);\n });\n QApplication::restoreOverrideCursor();\n item->invalidateOpenGLBuffers();\n item->redraw();\n if(dock_widget->propertyBox->currentIndex() != 2){\n dock_widget->zoomToMinButton->setEnabled(true);\n dock_widget->zoomToMaxButton->setEnabled(true);}\n }\n\n void enableButtons()\n {\n Scene_surface_mesh_item* item =\n qobject_cast(scene->item(scene->mainSelectionIndex()));\n if(! item )\n {\n dock_widget->zoomToMinButton->setEnabled(false);\n dock_widget->zoomToMaxButton->setEnabled(false);\n }\n\n switch(dock_widget->propertyBox->currentIndex())\n {\n case 0:\n dock_widget->zoomToMinButton->setEnabled(angles_max.count(item)>0 );\n dock_widget->zoomToMaxButton->setEnabled(angles_max.count(item)>0 );\n break;\n case 1:\n dock_widget->zoomToMinButton->setEnabled(jacobian_max.count(item)>0);\n dock_widget->zoomToMaxButton->setEnabled(jacobian_max.count(item)>0);\n break;\n default:\n break;\n }\n }\n\n void resetProperty()\n {\n Scene_surface_mesh_item* item =\n qobject_cast(sender());\n if(!item)\n return;\n SMesh& smesh = *item->face_graph();\n SMesh::Property_map jacobians;\n bool found;\n boost::tie(jacobians, found) = smesh.property_map(\"f:jacobian\");\n if(found)\n {\n smesh.remove_property_map(jacobians);\n }\n SMesh::Property_map angles;\n boost::tie(angles, found) = smesh.property_map(\"f:angle\");\n if(found)\n {\n smesh.remove_property_map(angles);\n }\n }\n\n void displayScaledJacobian(Scene_surface_mesh_item* item)\n {\n\n SMesh& smesh = *item->face_graph();\n //compute and store the jacobian per face\n bool non_init;\n SMesh::Property_map fjacobian;\n boost::tie(fjacobian, non_init) = smesh.add_property_map(\"f:jacobian\", 0);\n if(non_init)\n {\n double res_min = ARBITRARY_DBL_MAX,\n res_max = -ARBITRARY_DBL_MAX;\n SMesh::Face_index min_index, max_index;\n for(boost::graph_traits::face_iterator fit = faces(smesh).begin();\n fit != faces(smesh).end();\n ++fit)\n {\n fjacobian[*fit] = scaled_jacobian(*fit, smesh);\n if(fjacobian[*fit] > res_max)\n {\n res_max = fjacobian[*fit];\n max_index = *fit;\n }\n if(fjacobian[*fit] < res_min)\n {\n res_min = fjacobian[*fit];\n min_index = *fit;\n }\n }\n jacobian_min.erase(item);\n jacobian_min.insert(std::make_pair(item, std::make_pair(res_min, min_index)));\n jacobian_max.erase(item);\n jacobian_max.insert(std::make_pair(item, std::make_pair(res_max, max_index)));\n connect(item, &Scene_surface_mesh_item::itemChanged,\n this, &DisplayPropertyPlugin::resetProperty);\n }\n //scale a color ramp between min and max\n double max = maxBox;\n double min = minBox;\n //fill f:color pmap\n SMesh::Property_map fcolors =\n smesh.add_property_map(\"f:color\", CGAL::Color()).first;\n for(boost::graph_traits::face_iterator fit = faces(smesh).begin();\n fit != faces(smesh).end();\n ++fit)\n {\n if(min == max)\n --min;\n double f = (fjacobian[*fit]-min)/(max-min);\n if(fmax)\n f = max;\n CGAL::Color color(\n 255*color_ramp.r(f),\n 255*color_ramp.g(f),\n 255*color_ramp.b(f));\n fcolors[*fit] = color;\n }\n }\n\n bool resetScaledJacobian(Scene_surface_mesh_item* item)\n {\n SMesh& smesh = *item->face_graph();\n if(!smesh.property_map(\"f:jacobian\").second)\n {\n return false;\n }\n dock_widget->minBox->setValue(jacobian_min[item].first-0.01);\n dock_widget->maxBox->setValue(jacobian_max[item].first);\n return true;\n }\n \n void displayAngles(Scene_surface_mesh_item* item)\n {\n SMesh& smesh = *item->face_graph();\n typedef boost::property_map::type PMap;\n PMap pmap = get(boost::vertex_point, smesh);\n //compute and store smallest angle per face\n bool non_init;\n SMesh::Property_map fangle;\n boost::tie(fangle, non_init) = smesh.add_property_map(\"f:angle\", 0);\n if(non_init)\n {\n double res_min = ARBITRARY_DBL_MAX,\n res_max = -ARBITRARY_DBL_MAX;\n SMesh::Face_index index_min, index_max;\n for(boost::graph_traits::face_iterator fit = faces(smesh).begin();\n fit != faces(smesh).end();\n ++fit)\n {\n bool is_face_triangle = is_triangle(halfedge(*fit, smesh), smesh);\n bool normal_is_ok = true;\n EPICK::Vector_3 normal(0,0,0);\n\n EPICK::Orientation orientation = CGAL::POSITIVE;\n if(!is_face_triangle)\n {\n face_descriptor f = *fit;\n CGAL::Halfedge_around_face_circulator\n he(halfedge(f, smesh), smesh),\n he_end(he);\n do{\n normal_is_ok = true;\n\n //Initializes the facet orientation\n\n EPICK::Point_3 S,T;\n T = get(pmap, source(*he, smesh));\n S = get(pmap, target(*he, smesh));\n EPICK::Vector_3 V1((T-S).x(), (T-S).y(), (T-S).z());\n S = get(pmap,source(next(*he,smesh), smesh));\n T = get(pmap, target(next(*he,smesh), smesh));\n EPICK::Vector_3 V2((T-S).x(), (T-S).y(), (T-S).z());\n\n if(normal == EPICK::Vector_3(0,0,0))\n normal_is_ok = false;\n {\n normal = CGAL::cross_product(V1, V2);\n }\n if(normal_is_ok)\n {\n orientation = EPICK::Orientation_3()(V1, V2, normal);\n if( orientation == CGAL::COPLANAR )\n normal_is_ok = false;\n }\n }while( ++he != he_end && !normal_is_ok);\n }\n\n std::vector local_angles;\n local_angles.reserve(degree(*fit, smesh));\n for(halfedge_descriptor hd :\n halfedges_around_face(halfedge(*fit, smesh),smesh))\n {\n halfedge_descriptor hdn = next(hd, smesh);\n EPICK::Vector_3 v1(get(pmap, source(hd, smesh)), get(pmap, target(hd, smesh))),\n v2(get(pmap, target(hdn, smesh)), get(pmap, source(hdn, smesh)));\n float norm1(CGAL::approximate_sqrt(v1.squared_length())), norm2(CGAL::approximate_sqrt(v2.squared_length()));\n float dot_prod = v1*v2;\n float angle = std::acos(dot_prod/(norm1*norm2));\n if(is_face_triangle || !normal_is_ok)\n local_angles.push_back(angle * 180/CGAL_PI);\n else\n {\n bool is_convex = true;\n EPICK::Orientation res = EPICK::Orientation_3()(v1, v2, normal) ;\n if(res!= orientation && res != CGAL::ZERO)\n is_convex = false;\n local_angles.push_back(is_convex ? angle * 180/CGAL_PI : 360 - angle * 180/CGAL_PI );\n }\n }\n std::sort(local_angles.begin(), local_angles.end());\n fangle[*fit]=local_angles.front();\n\n if(fangle[*fit] > res_max)\n {\n res_max = fangle[*fit];\n index_max = *fit;\n }\n if(fangle[*fit] < res_min)\n {\n res_min = fangle[*fit];\n index_min = *fit;\n }\n }\n angles_min.erase(item);\n angles_min.insert(std::make_pair(item, std::make_pair(res_min, index_min)));\n angles_max.erase(item);\n angles_max.insert(std::make_pair(item, std::make_pair(res_max, index_max)));\n\n connect(item, &Scene_surface_mesh_item::itemChanged,\n this, &DisplayPropertyPlugin::resetProperty);\n }\n //scale a color ramp between min and max\n\n float max = maxBox;\n float min = minBox;\n\n //fill f:color pmap\n SMesh::Property_map fcolors =\n smesh.add_property_map(\"f:color\", CGAL::Color()).first;\n for(boost::graph_traits::face_iterator fit = faces(smesh).begin();\n fit != faces(smesh).end();\n ++fit)\n {\n if(min == max)\n --min;\n float f = (fangle[*fit]-min)/(max-min);\n if(f<0)\n f = 0;\n if(f>1)\n f = 1;\n CGAL::Color color(\n 255*color_ramp.r(f),\n 255*color_ramp.g(f),\n 255*color_ramp.b(f));\n fcolors[*fit] = color;\n }\n }\n\n bool resetAngles(Scene_surface_mesh_item* item)\n {\n SMesh& smesh = *item->face_graph();\n if(!smesh.property_map(\"f:angle\").second)\n {\n return false;\n }\n dock_widget->minBox->setValue(angles_min[item].first);\n dock_widget->maxBox->setValue(angles_max[item].first);\n return true;\n }\n\n // AF: This function gets called when we click on the button \"Colorize\"\n bool displayHeatIntensity(Scene_surface_mesh_item* item, bool iDT = false)\n {\n SMesh& mesh = *item->face_graph();\n bool found = is_source.find(item) != is_source.end();\n if(!found\n || ! source_points\n || source_points->point_set()->is_empty())\n {\n QApplication::restoreOverrideCursor();\n QMessageBox::warning(mw, \"Warning\",\"Source vertices are needed for this property.\");\n return false;\n }\n if(!is_triangle_mesh(mesh))\n {\n QApplication::restoreOverrideCursor();\n QMessageBox::warning(mw,\"Error\",\"The mesh must be triangulated.\");\n return false;\n }\n Heat_method * hm = NULL;\n Heat_method_idt * hm_idt = NULL;\n SMesh::Property_map heat_intensity =\n mesh.add_property_map(\"v:heat_intensity\", 0).first;\n if(! iDT){\n if(mesh_heat_method_map.find(item) != mesh_heat_method_map.end()){\n hm = mesh_heat_method_map[item];\n }else {\n hm = new Heat_method(mesh);\n mesh_heat_method_map[item] = hm;\n }\n connect(item, &Scene_surface_mesh_item::aboutToBeDestroyed,\n [this,item](){\n auto it = mesh_heat_method_map.find(item);\n delete it->second;\n mesh_heat_method_map.erase(it);\n }\n );\n } else {\n if(mesh_heat_method_idt_map.find(item) != mesh_heat_method_idt_map.end()){\n hm_idt = mesh_heat_method_idt_map[item];\n }else {\n hm_idt = new Heat_method_idt(mesh);\n mesh_heat_method_idt_map[item] = hm_idt;\n }\n connect(item, &Scene_surface_mesh_item::aboutToBeDestroyed,\n [this,item](){\n auto it = mesh_heat_method_idt_map.find(item);\n if(it == mesh_heat_method_idt_map.end())\n return;\n Heat_method_idt *hm_idt = it->second;\n delete hm_idt;\n mesh_heat_method_idt_map.erase(it);\n }\n );\n }\n\n for(vertex_descriptor vd : vertices(mesh)){\n if(get(is_source[item], vd)){\n if(iDT){\n hm_idt->add_source(vd);\n } else\n hm->add_source(vd);\n }\n else\n {\n if(iDT){\n hm_idt->remove_source(vd);\n } else\n hm->remove_source(vd);\n }\n }\n\n if(iDT){\n hm_idt->estimate_geodesic_distances(heat_intensity);\n }else{\n hm->estimate_geodesic_distances(heat_intensity);\n }\n\n double max = 0;\n double min = (std::numeric_limits::max)();\n\n for(vertex_descriptor vd : vertices(mesh)){\n double hi = heat_intensity[vd];\n if(hi < min)\n min = hi;\n if(hi > max)\n max = hi;\n }\n color_ramp = Color_ramp(rm, rM, gm, gM, bm, bM);\n dock_widget->minBox->setValue(min);\n dock_widget->maxBox->setValue(max);\n\n //}\n SMesh::Property_map vcolors =\n mesh.add_property_map(\"v:color\", CGAL::Color()).first;\n SMesh::Property_map vdist=\n mesh.add_property_map(\"v:dist\", 0.0).first;\n for(boost::graph_traits::vertex_iterator vit = vertices(mesh).begin();\n vit != vertices(mesh).end();\n ++vit)\n {\n double h =(heat_intensity[*vit]-min)/(max-min);\n CGAL::Color color(\n 255*color_ramp.r(h),\n 255*color_ramp.g(h),\n 255*color_ramp.b(h));\n vcolors[*vit] = color;\n vdist[*vit]=h;\n }\n Scene_group_item* group;\n if(mesh_heat_item_map.find(item) != mesh_heat_item_map.end())\n {\n group = mesh_heat_item_map[item]->parentGroup();\n group->unlockChild(mesh_heat_item_map[item]);\n scene->erase(scene->item_id(mesh_heat_item_map[item]));\n }\n else\n {\n group = new Scene_group_item(\"Heat Visualization\");\n group->setProperty(\"heat_group\", true);\n scene->addItem(group);\n scene->changeGroup(item, group);\n scene->changeGroup(source_points, group);\n group->lockChild(item);\n group->lockChild(source_points);\n dock_widget->deleteButton->setEnabled(true);\n connect(group, &Scene_group_item::aboutToBeDestroyed,\n this, [this](){\n this->dock_widget->deleteButton->setEnabled(false);\n });\n }\n mesh_heat_item_map[item] = new Scene_heat_item(item);\n mesh_heat_item_map[item]->setName(tr(\"%1 heat\").arg(item->name()));\n scene->addItem(mesh_heat_item_map[item]);\n scene->changeGroup(mesh_heat_item_map[item], group);\n group->lockChild(mesh_heat_item_map[item]);\n item->setVisible(false);\n displayLegend();\n if(dock_widget->sourcePointsButton->isChecked())\n dock_widget->sourcePointsButton->toggle();\n return true;\n }\n\n void replaceRamp()\n {\n color_ramp = Color_ramp(rm, rM, gm, gM, bm, bM);\n displayLegend();\n minBox = dock_widget->minBox->value();\n maxBox = dock_widget->maxBox->value();\n }\n\n void on_propertyBox_currentIndexChanged(int)\n {\n switch(dock_widget->propertyBox->currentIndex())\n {\n case 0:\n {\n dock_widget->groupBox-> setEnabled(true);\n dock_widget->groupBox_3->setEnabled(true);\n\n dock_widget->sourcePointsButton->setEnabled(false);\n\n dock_widget->minBox->setMinimum(0);\n dock_widget->minBox->setMaximum(360);\n dock_widget->minBox->setValue(0);\n\n dock_widget->maxBox->setMinimum(0);\n dock_widget->maxBox->setMaximum(360);\n Scene_surface_mesh_item* item =\n qobject_cast(scene->item(scene->mainSelectionIndex()));\n if(! item )\n dock_widget->maxBox->setValue(180);\n else if(is_triangle_mesh(*item->face_graph()))\n dock_widget->maxBox->setValue(60);\n else if(is_quad_mesh(*item->face_graph()))\n dock_widget->maxBox->setValue(90);\n break;\n }\n case 1:\n dock_widget->groupBox-> setEnabled(true);\n dock_widget->groupBox_3->setEnabled(true);\n dock_widget->sourcePointsButton->setEnabled(false);\n\n dock_widget->minBox->setMinimum(-1000);\n dock_widget->minBox->setMaximum(1000);\n dock_widget->minBox->setValue(0);\n\n dock_widget->maxBox->setMinimum(-1000);\n dock_widget->maxBox->setMaximum(1000);\n dock_widget->maxBox->setValue(2);\n break;\n default:\n dock_widget->maxBox->setMinimum(0);\n dock_widget->maxBox->setMaximum(99999999);\n dock_widget->groupBox-> setEnabled(false);\n dock_widget->groupBox_3->setEnabled(false);\n dock_widget->sourcePointsButton->setEnabled(true);\n\n }\n replaceRamp();\n enableButtons();\n }\n\n void closure()Q_DECL_OVERRIDE\n {\n dock_widget->hide();\n }\n\n void on_zoomToMinButton_pressed()\n {\n Scene_surface_mesh_item* item =\n qobject_cast(scene->item(scene->mainSelectionIndex()));\n if(!item)\n return;\n face_descriptor dummy_fd;\n Point_3 dummy_p;\n switch(dock_widget->propertyBox->currentIndex())\n {\n case 0:\n {\n ::zoomToId(*item->face_graph(),\n QString(\"f%1\").arg(angles_min[item].second),\n getActiveViewer(),\n dummy_fd,\n dummy_p);\n }\n break;\n case 1:\n {\n ::zoomToId(*item->face_graph(),\n QString(\"f%1\").arg(jacobian_min[item].second),\n getActiveViewer(),\n dummy_fd,\n dummy_p);\n }\n break;\n default:\n break;\n }\n }\n\n void on_zoomToMaxButton_pressed()\n {\n Scene_surface_mesh_item* item =\n qobject_cast(scene->item(scene->mainSelectionIndex()));\n if(!item)\n return;\n face_descriptor dummy_fd;\n Point_3 dummy_p;\n switch(dock_widget->propertyBox->currentIndex())\n {\n case 0:\n {\n ::zoomToId(*item->face_graph(),\n QString(\"f%1\").arg(angles_max[item].second),\n getActiveViewer(),\n dummy_fd,\n dummy_p);\n }\n break;\n case 1:\n {\n ::zoomToId(*item->face_graph(),\n QString(\"f%1\").arg(jacobian_max[item].second),\n getActiveViewer(),\n dummy_fd,\n dummy_p);\n }\n break;\n default:\n break;\n }\n }\n\n void delete_group()\n {\n Scene_item* item = scene->item(scene->selectionIndices().first());\n Scene_group_item* group = qobject_cast(item);\n if(!group || !group->property(\"heat_group\").toBool())\n return;\n for(auto child_id : group->getChildren())\n {\n if(Scene_surface_mesh_item* child = qobject_cast(scene->item(child_id))){\n group->unlockChild(child);\n group->removeChild(child);\n scene->addChild(child);\n child->setVisible(true);\n child->resetColors();\n break;\n }\n }\n scene->erase(scene->item_id(group));\n\n }\n\n void on_sourcePointsButton_toggled(bool b)\n {\n if(b)\n {\n Scene_heat_item* h_item = nullptr;\n Scene_surface_mesh_item* item =\n qobject_cast(scene->item(scene->mainSelectionIndex()));\n if(!item)\n {\n h_item = qobject_cast(scene->item(scene->mainSelectionIndex()));\n if(h_item)\n item = h_item->getParent();\n }\n if(!item)\n {\n QMessageBox::warning(mw, \"Warning\", \"You must select a Surface_mesh_item to make this work. Aborting.\");\n dock_widget->sourcePointsButton->setChecked(false);\n return;\n }\n current_item = item;\n connect(current_item, &Scene_surface_mesh_item::aboutToBeDestroyed,\n this, [this]()\n {\n dock_widget->sourcePointsButton->setChecked(false);\n });\n if(mesh_sources_map.find(item) == mesh_sources_map.end())\n {\n source_points = new Scene_points_with_normal_item();\n source_points->setName(QString(\"Source vertices for %1\").arg(current_item->name()));\n source_points->setColor(QColor(Qt::red));\n source_points->setPointSize(5);\n scene->addItem(source_points);\n connect(source_points, &Scene_points_with_normal_item::aboutToBeDestroyed,\n [this](){\n boost::unordered_map::iterator it;\n for(it = mesh_sources_map.begin();\n it != mesh_sources_map.end();\n ++it)\n {\n if(it->second == source_points)\n {\n mesh_sources_map.erase(it);\n break;\n }\n }\n });\n mesh_sources_map[current_item] = source_points;\n }\n else\n {\n source_points=mesh_sources_map[current_item];\n }\n connect(item, SIGNAL(selected_vertex(void*)), this, SLOT(on_vertex_selected(void*)));\n bool non_init = is_source.find(item) == is_source.end();\n if(non_init)\n {\n Vertex_source_map map = get(Vertex_source_tag(), *item->face_graph());\n is_source.insert(std::make_pair(item, map));\n connect(item, &Scene_surface_mesh_item::itemChanged,\n this, &DisplayPropertyPlugin::resetProperty);\n connect(item, &Scene_surface_mesh_item::aboutToBeDestroyed,\n [this, item](){\n if(is_source.find(item) != is_source.end())\n {\n is_source.erase(item);\n }\n });\n }\n }\n else\n {\n if(!current_item)\n return;\n disconnect(current_item, SIGNAL(selected_vertex(void*)), this, SLOT(on_vertex_selected(void*)));\n current_item = NULL;\n }\n }\n\n void on_vertex_selected(void* void_ptr)\n {\n typedef boost::graph_traits::vertices_size_type size_type;\n size_type h = static_cast(reinterpret_cast(void_ptr));\n vertex_descriptor vd = static_cast(h) ;\n bool found = is_source.find(current_item) != is_source.end();\n if(found)\n {\n if(!get(is_source[current_item], vd))\n {\n put(is_source[current_item], vd, true);\n source_points->point_set()->insert(current_item->face_graph()->point(vd));\n }\n else\n {\n put(is_source[current_item], vd, false);\n Point_set::iterator it;\n for(it = source_points->point_set()->begin(); it != source_points->point_set()->end(); ++it)\n if(source_points->point_set()->point(*it) == current_item->face_graph()->point(vd))\n {\n source_points->point_set()->remove(it);\n source_points->point_set()->collect_garbage();\n break;\n }\n }\n }\n\n source_points->invalidateOpenGLBuffers();\n source_points->itemChanged();\n }\nprivate:\n void displayLegend()\n {\n // Create an legend_ and display it\n const int height = 256;\n const int width = 90;\n const int cell_width = width/3;\n const int top_margin = 5;\n const int left_margin = 5;\n const int drawing_height = height - top_margin * 2;\n const int text_height = 20;\n\n legend_ = QPixmap(width, height + text_height);\n legend_.fill(QColor(200, 200, 200));\n\n QPainter painter(&legend_);\n painter.setPen(Qt::black);\n painter.setBrush(QColor(200, 200, 200));\n\n // Build legend_ data\n double min_value(dock_widget->minBox->value()),\n max_value(dock_widget->maxBox->value());\n std::vector graduations(100);\n for(int i=0; i<100; ++i)\n graduations[i] = i/100.0;\n\n // draw\n int i=0;\n for (std::vector::iterator it = graduations.begin(), end = graduations.end();\n it != end; ++it, i+=2)\n {\n QColor color(255*color_ramp.r(*it),\n 255*color_ramp.g(*it),\n 255*color_ramp.b(*it));\n painter.fillRect(left_margin,\n drawing_height - top_margin - i,\n cell_width,\n 2,\n color);\n }\n\n // draw right vertical line\n painter.setPen(Qt::blue);\n\n painter.drawLine(QPoint(left_margin + cell_width+10, drawing_height - top_margin),\n QPoint(left_margin + cell_width+10,\n drawing_height - top_margin - static_cast(graduations.size())*2));\n\n\n // draw min value and max value\n painter.setPen(Qt::blue);\n QRect min_text_rect(left_margin + cell_width+10,drawing_height - top_margin,\n 50, text_height);\n painter.drawText(min_text_rect, Qt::AlignCenter, tr(\"%1\").arg(min_value, 0, 'f', 1));\n\n QRect max_text_rect(left_margin + cell_width+10, drawing_height - top_margin - 200,\n 50, text_height);\n painter.drawText(max_text_rect, Qt::AlignCenter, tr(\"%1\").arg(max_value, 0, 'f', 1));\n\n dock_widget->legendLabel->setPixmap(legend_);\n }\n double scaled_jacobian(const face_descriptor& f , const SMesh &mesh);\n QList _actions;\n Color_ramp color_ramp;\n DockWidget* dock_widget;\n double rm;\n double rM;\n double gm;\n double gM;\n double bm;\n double bM;\n boost::unordered_map > jacobian_min;\n boost::unordered_map > jacobian_max;\n\n boost::unordered_map > angles_min;\n boost::unordered_map > angles_max;\n boost::unordered_map is_source;\n\n\n double minBox;\n double maxBox;\n QPixmap legend_;\n\n Scene_surface_mesh_item* current_item;\n Scene_points_with_normal_item* source_points;\n boost::unordered_map mesh_sources_map;\n boost::unordered_map mesh_heat_item_map;\n\n boost::unordered_map mesh_heat_method_map;\n boost::unordered_map mesh_heat_method_idt_map;\n};\n\n /// Code based on the verdict module of vtk\n\n /*=========================================================================\n Copyright (c) 2006 Sandia Corporation.\n All rights reserved.\n See Copyright.txt or http://www.kitware.com/Copyright.htm for details.\n\n This software is distributed WITHOUT ANY WARRANTY; without even\n the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR\n PURPOSE. See the above copyright notice for more information.\n=========================================================================*/\n\n double DisplayPropertyPlugin::scaled_jacobian( const face_descriptor& f , const SMesh& mesh)\n {\n boost::property_map::type\n pmap = get(boost::vertex_point, mesh);\n std::vector corner_areas(degree(f, mesh));\n std::vector edges;\n for(halfedge_descriptor hd : CGAL::halfedges_around_face(halfedge(f, mesh), mesh))\n {\n edges.push_back(EPICK::Vector_3(get(pmap, source(hd, mesh)), get(pmap, target(hd, mesh))));\n }\n std::vector corner_normals;\n for(std::size_t i = 0; i < edges.size(); ++i)\n {\n corner_normals.push_back(CGAL::cross_product(edges[i], edges[(i+1)%(edges.size())]));\n }\n\n\n EPICK::Vector_3 unit_center_normal = CGAL::Polygon_mesh_processing::compute_face_normal(f, mesh);\n unit_center_normal *= 1.0/CGAL::approximate_sqrt(unit_center_normal.squared_length());\n\n for(std::size_t i = 0; i < corner_areas.size(); ++i)\n {\n corner_areas[i] = unit_center_normal*corner_normals[i];\n }\n std::vector length;\n for(std::size_t i=0; i 0 )\n return (double) (std::min)( min_scaled_jac, ARBITRARY_DBL_MAX );\n return (double) (std::max)( min_scaled_jac, -ARBITRARY_DBL_MAX );\n\n }\n\n\n#include \"Display_property_plugin.moc\"\n", "meta": {"hexsha": "71ffd20d3dff7d9a62150ade96b8a736c84b9262", "size": 45694, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "CoreSystem/lib/CGAL/demo/Polyhedron/Plugins/Display/Display_property_plugin.cpp", "max_stars_repo_name": "josuehfa/DAASystem", "max_stars_repo_head_hexsha": "a1fe61ffc19f0781eeeddcd589137eefde078a45", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-03-17T01:13:02.000Z", "max_stars_repo_stars_event_max_datetime": "2020-03-17T01:13:02.000Z", "max_issues_repo_path": "CoreSystem/lib/CGAL/demo/Polyhedron/Plugins/Display/Display_property_plugin.cpp", "max_issues_repo_name": "josuehfa/DAASystem", "max_issues_repo_head_hexsha": "a1fe61ffc19f0781eeeddcd589137eefde078a45", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "CoreSystem/lib/CGAL/demo/Polyhedron/Plugins/Display/Display_property_plugin.cpp", "max_forks_repo_name": "josuehfa/DAASystem", "max_forks_repo_head_hexsha": "a1fe61ffc19f0781eeeddcd589137eefde078a45", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-12-02T11:11:36.000Z", "max_forks_repo_forks_event_max_datetime": "2021-12-02T11:11:36.000Z", "avg_line_length": 33.6232523915, "max_line_length": 138, "alphanum_fraction": 0.6289009498, "num_tokens": 11414, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.43398146480389854, "lm_q1q2_score": 0.21868593800993344}} {"text": "#include \"framework/builder/cfem_framework_builder.h\"\n\n#include \n#include \n#include \n#include \n\n#include \"calculator/cell/integrated_fission_source.h\"\n#include \"calculator/cell/total_aggregated_fission_source.h\"\n#include \"convergence/moments/convergence_checker_l1_norm.hpp\"\n#include \"convergence/reporter/mpi_noisy.h\"\n#include \"domain/domain.hpp\"\n#include \"domain/finite_element/finite_element_gaussian.h\"\n#include \"domain/mesh/mesh_cartesian.hpp\"\n#include \"eigenvalue/k_effective/updater_via_fission_source.h\"\n#include \"formulation/cfem_diffusion_stamper.h\"\n#include \"formulation/scalar/diffusion.hpp\"\n#include \"formulation/cfem_saaf_stamper.h\"\n#include \"framework/framework.h\"\n#include \"iteration/updater/source_updater_gauss_seidel.h\"\n#include \"iteration/updater/angular_source_updater_gauss_seidel.h\"\n#include \"iteration/updater/fixed_updater.h\"\n#include \"iteration/updater/angular_fixed_updater.h\"\n#include \"iteration/initializer/initialize_fixed_terms_once.h\"\n#include \"iteration/initializer/set_fixed_terms_once.h\" // to be removed\n#include \"iteration/group/group_source_iteration.hpp\"\n#include \"iteration/outer/outer_power_iteration.h\"\n#include \"material/material_protobuf.h\"\n#include \"problem/parameter_types.h\"\n#include \"convergence/parameters/single_parameter_checker.h\"\n#include \"convergence/final_checker_or_n.h\"\n#include \"quadrature/utility/quadrature_utilities.h\"\n#include \"quadrature/factory/quadrature_factories.h\"\n#include \"results/output_dealii_vtu.h\"\n#include \"solver/group/single_group_solver.h\"\n#include \"solver/gmres.h\"\n#include \"system/system.h\"\n#include \"system/solution/mpi_group_angular_solution.h\"\n#include \"system/terms/term.h\"\n#include \"system/terms/term_types.h\"\n#include \"system/moments/spherical_harmonic.h\"\n#include \"system/system_functions.h\"\n\nnamespace bart {\n\nnamespace framework {\n\nnamespace builder {\n\ntemplate \nstd::unique_ptr CFEM_FrameworkBuilder::BuildFramework(\n problem::ParametersI &prm,\n dealii::ParameterHandler &d2_prm) {\n\n std::cout << \"Setting up materials\" << std::endl;\n\n std::ifstream mapping_file(prm.MaterialMapFilename());\n std::string material_mapping(\n (std::istreambuf_iterator(mapping_file)),\n std::istreambuf_iterator());\n\n const int n_groups = prm.NEnergyGroups();\n int n_angles;\n\n MaterialProtobuf materials(d2_prm);\n auto cross_sections_ptr = std::make_shared(materials);\n\n std::cout << \"Building Finite Element\"<< std::endl;\n std::shared_ptrfinite_element_ptr(std::move(BuildFiniteElement(\n &prm)));\n\n std::cout << \"Building Domain\" << std::endl;\n std::shared_ptr domain_ptr(std::move(BuildDomain(\n &prm,finite_element_ptr, material_mapping)));\n\n std::cout << \"Setting up domain\" << std::endl;\n domain_ptr->SetUpMesh().SetUpDOF();\n\n std::shared_ptr source_updater_ptr;\n std::unique_ptr initializer_ptr;\n\n std::shared_ptr quadrature_ptr = nullptr;\n\n if (prm.TransportModel() == problem::EquationType::kSelfAdjointAngularFlux) {\n printf(\"Building Quadrature Set\\n\");\n quadrature_ptr = BuildAngularQuadratureSet(&prm);\n n_angles = quadrature_ptr->size();\n } else {\n printf(\"Scalar solve\\n\");\n n_angles = 1;\n }\n\n if (prm.TransportModel() == problem::EquationType::kSelfAdjointAngularFlux) {\n printf(\"Building Stamper\\n\");\n std::shared_ptr stamper_ptr(std::move(BuildAngularStamper(\n &prm, domain_ptr, finite_element_ptr, cross_sections_ptr, quadrature_ptr)));\n printf(\"Building Source Updater\\n\");\n source_updater_ptr = std::move(BuildSourceUpdater(&prm, stamper_ptr, quadrature_ptr));\n printf(\"Building Initializer\\n\");\n initializer_ptr = BuildInitializer(&prm, stamper_ptr, quadrature_ptr);\n } else {\n printf(\"Building Scalar Stamper\\n\");\n std::shared_ptr stamper_ptr(std::move(BuildStamper(\n &prm, domain_ptr, finite_element_ptr, cross_sections_ptr)));\n printf(\"Building Source Updater\\n\");\n source_updater_ptr = std::move(BuildSourceUpdater(&prm, stamper_ptr));\n printf(\"Building Initializer\\n\");\n initializer_ptr = BuildInitializer(&prm, stamper_ptr);\n }\n\n std::cout << \"Building single group solver\" << std::endl;\n auto single_group_solver_ptr = BuildSingleGroupSolver();\n\n\n std::cout << \"Building inner iteration objects\" << std::endl;\n\n auto in_group_final_checker = BuildMomentConvergenceChecker(1e-10, 100);\n\n // Build reporter\n std::shared_ptr reporter(std::move(BuildConvergenceReporter()));\n\n auto moment_calculator_type = quadrature::MomentCalculatorImpl::kScalarMoment;\n\n // Moment calculator\n if (prm.TransportModel() == problem::EquationType::kSelfAdjointAngularFlux) {\n moment_calculator_type = quadrature::MomentCalculatorImpl::kZerothMomentOnly;\n }\n\n auto moment_calculator_ptr = quadrature::factory::MakeMomentCalculator(\n moment_calculator_type,\n quadrature_ptr);\n\n std::cout << \"Building and initializing system object\" << std::endl;\n\n // Solution group\n auto solution_ptr =\n std::make_shared(n_angles);\n\n system::SetUpMPIAngularSolution(*solution_ptr, *domain_ptr);\n\n std::cout << \"Building in-group iteration\" << std::endl;\n\n auto in_group_iteration =\n std::make_unique>(\n std::move(single_group_solver_ptr),\n std::move(in_group_final_checker),\n std::move(moment_calculator_ptr),\n solution_ptr,\n source_updater_ptr,\n reporter);\n\n std::cout << \"Building K_Effective updater\" << std::endl;\n\n // KEffectiveUpdater\n using FissionSourceCalulator = calculator::cell::TotalAggregatedFissionSource;\n using IntegratedFissionSourceCalc = calculator::cell::IntegratedFissionSource;\n auto int_fission_ptr = std::make_unique(\n finite_element_ptr, cross_sections_ptr);\n auto fission_source_calc_ptr = std::make_unique(\n std::move(int_fission_ptr), domain_ptr);\n using KEffectiveUpdater = eigenvalue::k_effective::UpdaterViaFissionSource;\n\n // KEffective convergence checker\n using KEffConvChecker = bart::convergence::parameters::SingleParameterChecker;\n auto k_eff_conv_checker = std::make_unique();\n using KEffectiveFinalCovergence = bart::convergence::FinalCheckerOrN;\n auto k_eff_final_checker = std::make_unique(\n std::move(k_eff_conv_checker)\n );\n\n // KEffective updater\n auto k_effective_updater = std::make_unique(\n std::move(fission_source_calc_ptr), 2.0, 10);\n\n std::cout << \"Building outer-iteration\" << std::endl;\n\n using PowerIteration = iteration::outer::OuterPowerIteration;\n auto power_iteration_ptr = std::make_unique(\n std::move(in_group_iteration),\n std::move(k_eff_final_checker),\n std::move(k_effective_updater),\n source_updater_ptr,\n reporter\n );\n\n std::cout << \"Building System\" << std::endl;\n\n auto system = std::make_unique();\n\n system->total_groups = n_groups;\n std::unordered_set\n source_terms{bart::system::terms::VariableLinearTerms::kScatteringSource,\n bart::system::terms::VariableLinearTerms::kFissionSource};\n system->right_hand_side_ptr_ =\n std::make_unique(source_terms);\n system->left_hand_side_ptr_ =\n std::make_unique();\n\n std::cout << \"Filling system\" << std::endl;\n\n // Fill system with objects\n for (int group = 0; group < n_groups; ++group) {\n for (int angle = 0; angle < n_angles; ++angle) {\n system::Index index{group, angle};\n // LHS\n auto fixed_matrix_ptr = domain_ptr->MakeSystemMatrix();\n system->left_hand_side_ptr_->SetFixedTermPtr(index, fixed_matrix_ptr);\n\n // RHS\n auto fixed_vector_ptr =\n std::make_shared(domain_ptr->locally_owned_dofs(),\n MPI_COMM_WORLD);\n system->right_hand_side_ptr_->SetFixedTermPtr(index, fixed_vector_ptr);\n\n for (auto term : source_terms) {\n auto variable_vector_ptr =\n std::make_shared(domain_ptr->locally_owned_dofs(),\n MPI_COMM_WORLD);\n system->right_hand_side_ptr_->SetVariableTermPtr(\n index, term, variable_vector_ptr);\n }\n }\n }\n\n std::cout << \"Fill system moments\" << std::endl;\n\n // Moments\n system->current_moments =\n std::make_unique(n_groups, 0);\n system->previous_moments =\n std::make_unique(n_groups, 0);\n\n for (auto& moment_pair : system->current_moments->moments()) {\n auto index = moment_pair.first;\n auto& current_moment = system->current_moments->operator[](index);\n current_moment.reinit(solution_ptr->operator[](0).size());\n auto& previous_moment = system->previous_moments->operator[](index);\n previous_moment.reinit(solution_ptr->operator[](0).size());\n current_moment = 1;\n previous_moment = 1;\n }\n\n // Initialize System\n system->k_effective = 1.0;\n system->total_groups = n_groups;\n system->total_angles = n_angles;\n\n std::cout << \"Build Results Output\" << std::endl;\n auto results_output_ptr =\n std::make_unique>(domain_ptr);\n\n\n return std::make_unique(\n std::move(system),\n std::move(initializer_ptr),\n std::move(power_iteration_ptr),\n std::move(results_output_ptr));\n}\n\ntemplate \nauto CFEM_FrameworkBuilder::BuildAngularQuadratureSet(\n problem::ParametersI* problem_parameters)\n-> std::shared_ptr {\n\n std::shared_ptr return_ptr = nullptr;\n\n std::shared_ptr>\n quadrature_generator_ptr = nullptr;\n\n const int order_value = problem_parameters->AngularQuadOrder();\n switch (problem_parameters->AngularQuad()) {\n default: {\n if (dim == 3) {\n quadrature_generator_ptr =\n quadrature::factory::MakeAngularQuadratureGeneratorPtr(\n quadrature::Order(order_value),\n quadrature::AngularQuadratureSetType::kLevelSymmetricGaussian);\n } else {\n AssertThrow(false,\n dealii::ExcMessage(\"No supported quadratures for this dimension \"\n \"and transport model\"))\n }\n }\n }\n\n return_ptr = quadrature::factory::MakeQuadratureSetPtr();\n\n auto quadrature_points = quadrature::utility::GenerateAllPositiveX(\n quadrature_generator_ptr->GenerateSet());\n\n quadrature::factory::FillQuadratureSet(return_ptr.get(),\n quadrature_points);\n\n return std::move(return_ptr);\n}\n\ntemplate \nauto CFEM_FrameworkBuilder::BuildConvergenceReporter()\n-> std::unique_ptr {\n std::unique_ptr return_ptr = nullptr;\n\n using Reporter = bart::convergence::reporter::MpiNoisy;\n int this_process = dealii::Utilities::MPI::this_mpi_process(MPI_COMM_WORLD);\n auto pout_ptr = std::make_unique(std::cout, this_process == 0);\n return_ptr = std::make_unique(std::move(pout_ptr));\n\n return std::move(return_ptr);\n}\n\ntemplate\nauto CFEM_FrameworkBuilder::BuildDomain(\n problem::ParametersI *problem_parameters,\n const std::shared_ptr &finite_element_ptr,\n std::string material_mapping)-> std::unique_ptr {\n\n // Build mesh\n auto mesh_ptr = std::make_unique>(\n problem_parameters->SpatialMax(),\n problem_parameters->NCells(),\n material_mapping);\n\n return std::make_unique>(\n std::move(mesh_ptr), finite_element_ptr);\n}\n\ntemplate\nauto CFEM_FrameworkBuilder::BuildFiniteElement(\n problem::ParametersI *problem_parameters)-> std::unique_ptr {\n return std::make_unique>(\n problem::DiscretizationType::kContinuousFEM,\n problem_parameters->FEPolynomialDegree());\n}\n\ntemplate \nauto CFEM_FrameworkBuilder::BuildInitializer(\n const problem::ParametersI *problem_parameters,\n const std::shared_ptr &stamper_ptr)\n-> std::unique_ptr {\n\n std::unique_ptr return_ptr = nullptr;\n\n using FixedUpdaterType = iteration::updater::FixedUpdater;\n auto fixed_updater_ptr = std::make_unique(stamper_ptr);\n\n if (problem_parameters->TransportModel() == problem::EquationType::kDiffusion) {\n return_ptr = std::make_unique(\n std::move(fixed_updater_ptr), problem_parameters->NEnergyGroups(), 1);\n }\n\n return std::move(return_ptr);\n}\n\ntemplate \nauto CFEM_FrameworkBuilder::BuildInitializer(\n const problem::ParametersI *problem_parameters,\n const std::shared_ptr &stamper_ptr,\n const std::shared_ptr& quadrature_set_ptr)\n-> std::unique_ptr {\n\n std::unique_ptr return_ptr = nullptr;\n\n using FixedUpdaterType = iteration::updater::AngularFixedUpdater;\n auto fixed_updater_ptr = std::make_unique(\n stamper_ptr, quadrature_set_ptr);\n\n if (problem_parameters->TransportModel() == problem::EquationType::kSelfAdjointAngularFlux) {\n return_ptr = std::make_unique(\n std::move(fixed_updater_ptr),\n problem_parameters->NEnergyGroups(),\n quadrature_set_ptr->size());\n }\n\n return std::move(return_ptr);\n}\n\n\ntemplate\nauto CFEM_FrameworkBuilder::BuildMomentConvergenceChecker(\n double max_delta, int max_iterations)\n-> std::unique_ptr{\n //TODO(Josh): Add option for using other than L1Norm\n\n using CheckerType = convergence::moments::SingleMomentCheckerL1Norm;\n using FinalCheckerType = convergence::FinalCheckerOrN<\n system::moments::MomentVector,\n convergence::moments::SingleMomentCheckerI>;\n\n auto single_checker_ptr = std::make_unique(max_delta);\n auto return_ptr = std::make_unique(\n std::move(single_checker_ptr));\n\n return_ptr->SetMaxIterations(max_iterations);\n\n return std::move(return_ptr);\n}\n\ntemplate\nauto CFEM_FrameworkBuilder::BuildParameterConvergenceChecker(\n double max_delta, int max_iterations)\n-> std::unique_ptr{\n\n using CheckerType = convergence::parameters::SingleParameterChecker;\n using FinalCheckerType = convergence::FinalCheckerOrN;\n\n auto single_checker_ptr = std::make_unique(max_delta);\n auto return_ptr = std::make_unique(\n std::move(single_checker_ptr));\n\n return_ptr->SetMaxIterations(max_iterations);\n\n return std::move(return_ptr);\n}\n\ntemplate\nauto CFEM_FrameworkBuilder::BuildSingleGroupSolver(\n const int max_iterations,\n const double convergence_tolerance) -> std::unique_ptr {\n std::unique_ptr return_ptr = nullptr;\n\n auto linear_solver_ptr = std::make_unique(max_iterations,\n convergence_tolerance);\n\n return_ptr = std::move(\n std::make_unique(\n std::move(linear_solver_ptr)));\n\n return return_ptr;\n}\n\ntemplate\nauto CFEM_FrameworkBuilder::BuildSourceUpdater(\n problem::ParametersI *,\n const std::shared_ptr stamper_ptr)\n -> std::unique_ptr {\n // TODO(Josh): Add option for non-gauss-seidel updating\n using SourceUpdater = iteration::updater::SourceUpdaterGaussSeidel;\n return std::make_unique(stamper_ptr);\n}\n\ntemplate\nauto CFEM_FrameworkBuilder::BuildSourceUpdater(\n problem::ParametersI *,\n const std::shared_ptr stamper_ptr,\n const std::shared_ptr& quadrature_set_ptr)\n-> std::unique_ptr {\n // TODO(Josh): Add option for non-gauss-seidel updating\n using SourceUpdater = iteration::updater::AngularSourceUpdaterGaussSeidel;\n return std::make_unique(stamper_ptr, quadrature_set_ptr);\n}\n\ntemplate\nauto CFEM_FrameworkBuilder::BuildStamper(\n problem::ParametersI *problem_parameters,\n const std::shared_ptr &domain_ptr,\n const std::shared_ptr &finite_element_ptr,\n const std::shared_ptr &cross_sections_ptr)\n-> std::unique_ptr {\n\n std::unique_ptr return_ptr = nullptr;\n\n // Diffusion Stamper\n if (problem_parameters->TransportModel() == problem::EquationType::kDiffusion) {\n\n auto diffusion_ptr = std::make_unique>(\n finite_element_ptr, cross_sections_ptr);\n\n return_ptr = std::move(\n std::make_unique>(\n std::move(diffusion_ptr),\n domain_ptr,\n problem_parameters->ReflectiveBoundary()));\n\n } else {\n AssertThrow(false, dealii::ExcMessage(\"Unsuppored equation type passed\"\n \" to BuildScalarFormulation\"));\n }\n\n return return_ptr;\n}\n\ntemplate\nauto CFEM_FrameworkBuilder::BuildAngularStamper(\n problem::ParametersI *problem_parameters,\n const std::shared_ptr &domain_ptr,\n const std::shared_ptr &finite_element_ptr,\n const std::shared_ptr &cross_sections_ptr,\n const std::shared_ptr& quadrature_set_ptr)\n-> std::unique_ptr {\n\n std::unique_ptr return_ptr = nullptr;\n\n if (problem_parameters->TransportModel() == problem::EquationType::kSelfAdjointAngularFlux) {\n\n auto saaf_formulation_ptr =\n std::make_unique>(\n finite_element_ptr, cross_sections_ptr, quadrature_set_ptr);\n\n return_ptr = std::move(\n std::make_unique>(\n std::move(saaf_formulation_ptr),\n domain_ptr));\n } else {\n AssertThrow(false, dealii::ExcMessage(\"Unsuppored equation type passed\"\n \"to BuildScalarFormulation\"));\n }\n\n return return_ptr;\n}\n\ntemplate class CFEM_FrameworkBuilder<1>;\ntemplate class CFEM_FrameworkBuilder<2>;\ntemplate class CFEM_FrameworkBuilder<3>;\n\n\n\n} // namespace builder\n\n} // namespace framework\n\n} // namespace bart", "meta": {"hexsha": "f32dffb739421ad78d4165226edb62ded8be93ce", "size": 19127, "ext": "cc", "lang": "C++", "max_stars_repo_path": "legacy_code/builder/cfem_framework_builder.cc", "max_stars_repo_name": "SlaybaughLab/Transport", "max_stars_repo_head_hexsha": "8eb32cb8ae50c92875526a7540350ef9a85bc050", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12.0, "max_stars_repo_stars_event_min_datetime": "2018-03-14T12:30:53.000Z", "max_stars_repo_stars_event_max_datetime": 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YES\n2. NO", "lm_q1_score": 0.546738151984614, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.21859227760001193}} {"text": "// Boost.Geometry\n\n// Copyright (c) 2021, Oracle and/or its affiliates.\n\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle\n\n// Licensed under the Boost Software License version 1.0.\n// http://www.boost.org/users/license.html\n\n#ifndef BOOST_GEOMETRY_STRATEGIES_DISTANCE_GEOGRAPHIC_HPP\n#define BOOST_GEOMETRY_STRATEGIES_DISTANCE_GEOGRAPHIC_HPP\n\n\n#include \n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n// TODO - for backwards compatibility, remove?\n#include \n#include \n#include \n\n#include \n#include \n\n\nnamespace boost { namespace geometry\n{\n\nnamespace strategies { namespace distance\n{\n\n// TODO: azimuth and normalize getters would not be needed if distance_segment_box was implemented differently\n// right now it calls disjoint algorithm details.\n\ntemplate\n<\n typename FormulaPolicy = strategy::andoyer,\n std::size_t SeriesOrder = strategy::default_order::value,\n typename Spheroid = srs::spheroid,\n typename CalculationType = void\n>\nclass geographic\n : public strategies::relate::geographic\n{\n using base_t = strategies::relate::geographic;\n\npublic:\n geographic() = default;\n\n explicit geographic(Spheroid const& spheroid)\n : base_t(spheroid)\n {}\n\n // azimuth\n\n auto azimuth() const\n {\n return strategy::azimuth::geographic\n <\n FormulaPolicy, Spheroid, CalculationType\n >(base_t::m_spheroid);\n }\n\n // distance\n\n template \n auto distance(Geometry1 const&, Geometry2 const&,\n detail::enable_if_pp_t * = nullptr) const\n {\n return strategy::distance::geographic\n <\n FormulaPolicy, Spheroid, CalculationType\n >(base_t::m_spheroid);\n }\n\n template \n auto distance(Geometry1 const&, Geometry2 const&,\n detail::enable_if_ps_t * = nullptr) const\n {\n return strategy::distance::geographic_cross_track\n <\n FormulaPolicy, Spheroid, CalculationType\n >(base_t::m_spheroid);\n }\n\n template \n auto distance(Geometry1 const&, Geometry2 const&,\n detail::enable_if_pb_t * = nullptr) const\n {\n return strategy::distance::geographic_cross_track_point_box\n <\n FormulaPolicy, Spheroid, CalculationType\n >(base_t::m_spheroid);\n }\n\n template \n auto distance(Geometry1 const&, Geometry2 const&,\n detail::enable_if_sb_t * = nullptr) const\n {\n return strategy::distance::geographic_segment_box\n <\n FormulaPolicy, Spheroid, CalculationType\n >(base_t::m_spheroid);\n }\n\n template \n auto distance(Geometry1 const&, Geometry2 const&,\n detail::enable_if_bb_t * = nullptr) const\n {\n return strategy::distance::geographic_cross_track_box_box\n <\n FormulaPolicy, Spheroid, CalculationType\n >(base_t::m_spheroid);\n }\n\n // normalize\n\n template \n static auto normalize(Geometry const&,\n std::enable_if_t\n <\n util::is_point::value\n > * = nullptr)\n {\n return strategy::normalize::spherical_point();\n }\n};\n\n\nnamespace services\n{\n\ntemplate \nstruct default_strategy\n{\n using type = strategies::distance::geographic<>;\n};\n\n\ntemplate \nstruct strategy_converter >\n{\n static auto get(strategy::distance::geographic const& s)\n {\n return strategies::distance::geographic::value, S, CT>(s.model());\n }\n};\n// TODO - for backwards compatibility, remove?\ntemplate \nstruct strategy_converter >\n{\n static auto get(strategy::distance::andoyer const& s)\n {\n return strategies::distance::geographic::value, S, CT>(s.model());\n }\n};\n// TODO - for backwards compatibility, remove?\ntemplate \nstruct strategy_converter >\n{\n static auto get(strategy::distance::thomas const& s)\n {\n return strategies::distance::geographic::value, S, CT>(s.model());\n }\n};\n// TODO - for backwards compatibility, remove?\ntemplate \nstruct strategy_converter >\n{\n static auto get(strategy::distance::vincenty const& s)\n {\n return strategies::distance::geographic::value, S, CT>(s.model());\n }\n};\n\ntemplate \nstruct strategy_converter >\n{\n static auto get(strategy::distance::geographic_cross_track const& s)\n {\n return strategies::distance::geographic::value, S, CT>(s.model());\n }\n};\n\ntemplate \nstruct strategy_converter >\n{\n static auto get(strategy::distance::geographic_cross_track_point_box const& s)\n {\n return strategies::distance::geographic::value, S, CT>(s.model());\n }\n};\n\ntemplate \nstruct strategy_converter >\n{\n static auto get(strategy::distance::geographic_segment_box const& s)\n {\n return strategies::distance::geographic::value, S, CT>(s.model());\n }\n};\n\ntemplate \nstruct strategy_converter >\n{\n static auto get(strategy::distance::geographic_cross_track_box_box const& s)\n {\n return strategies::distance::geographic::value, S, CT>(s.model());\n }\n};\n\n\n// details\n\n// TODO: This specialization wouldn't be needed if strategy::distance::geographic_cross_track was implemented as an alias\ntemplate \nstruct strategy_converter >\n{\n struct altered_strategy\n : strategies::distance::geographic::value, S, CT>\n {\n typedef strategies::distance::geographic::value, S, CT> base_t;\n\n explicit altered_strategy(S const& s) : base_t(s) {}\n\n using base_t::distance;\n\n template \n auto distance(Geometry1 const&, Geometry2 const&,\n std::enable_if_t\n <\n util::is_pointlike::value\n && util::is_segmental::value\n || util::is_segmental::value\n && util::is_pointlike::value\n || util::is_segmental::value\n && util::is_segmental::value\n > * = nullptr) const\n {\n return strategy::distance::detail::geographic_cross_track\n <\n FP, S, CT, B, ECP\n >(base_t::m_spheroid);\n }\n };\n\n static auto get(strategy::distance::detail::geographic_cross_track const& s)\n {\n return altered_strategy(s.model());\n }\n};\n\n\n} // namespace services\n\n}} // namespace strategies::distance\n\n}} // namespace boost::geometry\n\n#endif // BOOST_GEOMETRY_STRATEGIES_DISTANCE_GEOGRAPHIC_HPP\n", "meta": {"hexsha": "4c9aadd91e210beb6464a4c8df4c6e5f95aa6d61", "size": 9409, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/geometry/strategies/distance/geographic.hpp", "max_stars_repo_name": "pranavgo/RRT", "max_stars_repo_head_hexsha": "87148c3ddb91600f4e74f00ffa8af14b54689aa4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "boost/geometry/strategies/distance/geographic.hpp", "max_issues_repo_name": "pranavgo/RRT", "max_issues_repo_head_hexsha": "87148c3ddb91600f4e74f00ffa8af14b54689aa4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "boost/geometry/strategies/distance/geographic.hpp", "max_forks_repo_name": "pranavgo/RRT", "max_forks_repo_head_hexsha": "87148c3ddb91600f4e74f00ffa8af14b54689aa4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.8481481481, "max_line_length": 138, "alphanum_fraction": 0.674566904, "num_tokens": 2175, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.4301473485858429, "lm_q1q2_score": 0.2184339270001337}} {"text": "#include \"../checkpoint/checkpoint.h\"\n#include \"goexit/goexit.h\"\n#ifdef HAVE_SSE2\n\t#include \"myrandom/myrandsfmt.h\"\n#else\n\t#include \"myrandom/myrand.h\"\n#endif\n#include \t// for std::array\n#include \t \t// for std::uint32_t\n#include // for std::hash\n#include \t\t \t// for std::setiosflags, std::setprecision\n#include \t \t// for std::cout\n#include \t// for std::string\n#include // for std::move\n#ifdef _CHECK_PARALELL_PERFORM\n #include \t // for std::vector\n#endif\n#include \t// for boost::container::flat_map\n#include // for tbb::concurrent_hash_map\n#include \t// for tbb::concurrent_vector\n#include // for tbb::parallel_for\n\nnamespace {\n //! A global variable (constant expression).\n /*!\n モンテカルロシミュレーションの試行回数\n */\n static auto constexpr MCMAX = 1000000U;\n\n //! A global variable (constant expression).\n /*!\n UかDの文字列の長さ\n */\n static auto constexpr RANDNUMTABLELEN = 100U;\n\n //! A global variable (constant).\n /*!\n UとDの文字列の可能な集合の配列\n */\n static std::array const udarray = { \"DDD\", \"DDU\", \"DUD\", \"DUU\", \"UDD\", \"UDU\", \"UUD\", \"UUU\" };\n\n //! A typedef.\n /*!\n 文字列とその文字列に対応する出現回数のstd::map\n */\n using mymap = boost::container::flat_map;\n\n //! A typedef.\n /*!\n 文字列のペア\n */\n using strpair = std::pair;\n \n //! A struct.\n /*!\n strpairを扱うハッシュと比較操作を定義する構造体\n */\n struct MyHashCompare final {\n //! A public static member function.\n /*!\n strpairからハッシュを生成する\n \\param sp 文字列のペア\n \\return 与えられたstrpairのハッシュ\n */\n static std::size_t hash(strpair const & sp)\n {\n auto const h1 = std::hash()(sp.first);\n auto const h2 = std::hash()(sp.second);\n return h1 ^ (h2 << 1);\n }\n\n //! A public static member function.\n /*!\n 二つのstrpairを比較する\n \\param lhs 左辺のstrpair\n \\param rhs 右辺のstrpair\n \\return 文字列のペアが等しい場合にはtrue\n */\n static bool equal(strpair const & lhs, strpair const & rhs)\n {\n return lhs == rhs;\n }\n };\n\n //! A typedef.\n /*!\n 各文字列の順列に対応する勝利回数の結果を格納するtbb::concurrent_hash_map\n */\n using myhashmap = tbb::concurrent_hash_map;\n \n //! A typedef.\n /*!\n 文字列のペアと、どちらの文字列が勝ったかのstd::pair\n */\n using mymap2 = boost::container::flat_map;\n\n //! A typedef.\n /*!\n 文字列のペアと、文字列の勝利数のstd::pair\n */\n using mymap3 = boost::container::flat_map;\n \n //! A function.\n /*!\n 文字列の可能な順列を列挙する\n \\return 文字列の可能な順列を列挙したstd::array\n */\n std::array makecombination();\n\n //! A global variable (constant).\n /*!\n udarrayから二つを抽出したときの可能な順列の配列\n */\n static std::array const cbarray = makecombination();\n\n#ifdef _CHECK_PARALELL_PERFORM\n //! A function.\n /*!\n 文字列のペアの、前者が勝利した回数を集計する\n \\param mcresultwinningavg 文字列のペアと、どちらの文字列が勝ったかの結果が格納された連想配列の可変長配列\n \\return 文字列のペアの、前者が勝利した回数が格納された連想配列\n */\n mymap3 aggregateWinningAvg(std::vector const & mcresultwinningavg);\n#endif\n\n //! A function.\n /*!\n 文字列のペアの、前者が勝利した回数を集計する\n \\param mcresultwinningavg 文字列のペアと、どちらの文字列が勝ったかの結果が格納された連想配列の可変長配列\n \\return 文字列のペアの、前者が勝利した回数が格納された連想配列\n */\n mymap3 aggregateWinningAvg(tbb::concurrent_vector const & mcresultwinningavg);\n\n template \n //! A template function.\n /*!\n UDのランダム文字列を生成する\n \\param mr 自作乱数クラスのオブジェクト\n \\return UDのランダム文字列を格納したstd::string\n */\n inline auto makerandomudstr(T & mr);\n\n#ifdef _CHECK_PARALELL_PERFORM\n //! A function.\n /*!\n モンテカルロ・シミュレーションを行う\n \\return 期待値と、どちらの文字列が先に出現したかどうかのモンテカルロ・シミュレーションの結果のstd::pair \n */\n std::pair, std::vector > montecarlo();\n#endif\n\n //! A function.\n /*!\n モンテカルロ・シミュレーションをTBBで並列化して行う\n \\return 期待値と、どちらの文字列が先に出現したかどうかのモンテカルロ・シミュレーションの結果のstd::pair\n */\n std::pair< tbb::concurrent_vector, tbb::concurrent_vector > montecarloTBB();\n\n template \n //! A template function.\n /*!\n 期待値に対するモンテカルロ・シミュレーションの実装\n \\param mr 自作乱数クラスのオブジェクト\n \\return 期待値に対するモンテカルロ・シミュレーションの結果が格納された連想配列\n */\n mymap montecarloImplAvg(T & mr);\n\n template \n //! A template function.\n /*!\n 文字列のペアのうち、どちらの文字列が先に出現したかのモンテカルロ・シミュレーションの実装\n \\param mr 自作乱数クラスのオブジェクト\n \\return 文字列のペアのうち、どちらの文字列が先に出現したかのモンテカルロ・シミュレーションの結果が格納された連想配列\n */\n mymap2 montecarloImplWinningAvg(T & mr);\n\n //! A function.\n /*!\n UとDのランダム文字列から与えられた文字列の位置を検索し、文字列の末尾の位置を与える\n \\param str 検索する文字列\n \\param udstr UとDのランダム文字列\n \\return 検索された文字列の末尾の位置\n */\n inline std::uint32_t myfind(std::string const & str, std::string const & udstr);\n\n //! A template function.\n /*!\n 期待値に対するモンテカルロ・シミュレーションの和を計算する\n \\return 期待値に対するモンテカルロ・シミュレーションの結果の和の連想配列\n */\n template \n mymap sumMontecarloAvg(T const & mcresultavg);\n}\n\nint main()\n{\n checkpoint::CheckPoint cp;\n\n cp.checkpoint(\"処理開始\", __LINE__);\n\n#ifdef _CHECK_PARALELL_PERFORM\n {\n // モンテカルロ・シミュレーションの結果を代入\n auto const mcresult(montecarlo());\n\n // 各文字列のペアに対する勝率を計算する\n auto const trialwinningavg(aggregateWinningAvg(mcresult.second));\n }\n\n cp.checkpoint(\"並列化無効\", __LINE__);\n#endif\n\n // モンテカルロ・シミュレーションの結果を代入\n auto const mcresultTBB(montecarloTBB());\n \n // 各文字列のペアに対する勝率を計算する\n auto const trialwinningavg(aggregateWinningAvg(mcresultTBB.second));\n\n cp.checkpoint(\"並列化有効\", __LINE__);\n\n // 期待値に対するモンテカルロ・シミュレーションの結果の和を計算する\n auto const trialavg(sumMontecarloAvg(mcresultTBB.first));\n \n // 各文字列に対する期待値の表示\n std::cout << std::setprecision(1) << std::setiosflags(std::ios::fixed);\n for (auto && itr = trialavg.begin(); itr != trialavg.end(); ++itr) {\n std::cout << itr->first\n << \" が出るまでの期待値: \"\n << static_cast(itr->second) / static_cast(MCMAX)\n << \"回\\n\";\n }\n \n // 各文字列のペアに対する勝率の表示\n std::cout << \"\\n \";\n for (auto i = 0; i < 8; i++) {\n std::cout << udarray[i] << \" \";\n }\n std::cout << '\\n';\n\n auto && itr = trialwinningavg.begin();\n auto const len = udarray.size();\n for (auto i = 0U; i < len; i++) {\n std::cout << itr->first.first << ' ';\n for (auto j = 0U; j < len; j++) {\n if (i == j) {\n std::cout << \" \";\n }\n else {\n std::cout << static_cast(itr->second) / static_cast(MCMAX) * 100.0\n << ' ';\n ++itr;\n }\n }\n std::cout << '\\n';\n }\n\n cp.checkpoint(\"それ以外の処理\", __LINE__);\n\n cp.checkpoint_print();\n\n\tgoexit::goexit();\n\n return 0;\n}\n\nnamespace {\n#ifdef _CHECK_PARALELL_PERFORM\n mymap3 aggregateWinningAvg(std::vector const & mcresultwinningavg)\n {\n // 各文字列の順列に対応する勝利回数の結果を格納するboost::container::flat_map\n mymap3 trialwinningavg;\n\n // tbb::concurrent_hash_mapの初期化\n for (auto const & sp : cbarray) {\n trialwinningavg[sp] = 0U;\n }\n\n // 試行回数分繰り返す\n for (auto const & mcr : mcresultwinningavg) {\n for (auto && itr = mcr.begin(); itr != mcr.end(); ++itr) {\n if (itr->second) {\n trialwinningavg[itr->first]++;\n }\n }\n }\n\n return trialwinningavg;\n }\n#endif\n\n mymap3 aggregateWinningAvg(tbb::concurrent_vector const & mcresultwinningavg)\n {\n // 各文字列の順列に対応する勝利回数の結果を格納するtbb::concurrent_hash_map\n myhashmap trial;\n\n // tbb::concurrent_hash_mapの初期化\n for (auto const & sp : cbarray) {\n myhashmap::accessor a;\n trial.insert(a, sp);\n a->second = 0U;\n }\n\n // MCMAX回のループを並列化して実行\n tbb::parallel_for(\n tbb::blocked_range(0U, MCMAX),\n [&mcresultwinningavg, &trial](auto const & range) {\n for (auto && i = range.begin(); i != range.end(); ++i) {\n auto const mcr = mcresultwinningavg[i];\n for (auto && itr = mcr.begin(); itr != mcr.end(); ++itr) {\n if (itr->second) {\n myhashmap::accessor a;\n trial.insert(a, itr->first);\n a->second++;\n }\n }\n }\n });\n\n // boost::container::flat_mapに計算結果を複写\n boost::container::flat_map trialwinningavg;\n for (auto && res : trial) {\n trialwinningavg[res.first] = res.second;\n }\n\n return trialwinningavg;\n }\n\n std::array makecombination()\n {\n // 全ての可能な順列を収納する配列\n std::array cb;\n\n // カウンタ\n auto cnt = 0;\n\n // 全ての可能な順列を列挙\n auto const len = udarray.size();\n for (auto i = 0U; i < len; i++) {\n for (auto j = 0U; j < len; j++) {\n if (i != j) {\n cb[cnt++] = std::make_pair(udarray[i], udarray[j]);\n }\n }\n }\n\n return cb;\n }\n\n template \n auto makerandomudstr(T & mr)\n {\n // UDのランダム文字列を格納するstd::string\n std::string udstring(RANDNUMTABLELEN, '\\0');\n\n // UDのランダム文字列を格納\n for (auto && c : udstring) {\n c = mr.myrand() > 3 ? 'U' : 'D';\n }\n\n\t\t// UDのランダム文字列を返す\n return udstring;\n }\n\n#ifdef _CHECK_PARALELL_PERFORM\n std::pair, std::vector > montecarlo()\n {\n // 期待値に対するモンテカルロ・シミュレーションの結果を格納するための可変長配列\n std::vector mcresultavg;\n\n // どちらの文字列が先に出現したかどうかのモンテカルロ・シミュレーションの結果を格納するための可変長配列\n std::vector mcresultwinningavg;\n\n // MCMAX個の容量を確保\n mcresultavg.reserve(MCMAX);\n mcresultwinningavg.reserve(MCMAX);\n\n#ifdef HAVE_SSE2\n\t\t// 自作乱数クラスを初期化\n\t\tmyrandom::MyRandSfmt mr(1, 6);\n#else\n\t\t// 自作乱数クラスを初期化\n\t\tmyrandom::MyRand mr(1, 6);\n#endif\n\n // 試行回数分繰り返す\n for (auto i = 0U; i < MCMAX; i++) {\n // 期待値に対するモンテカルロ・シミュレーションの結果を代入\n mcresultavg.emplace_back(montecarloImplAvg(mr));\n\n // どちらの文字列が先に出現したかどうかのモンテカルロ・シミュレーションの結果を代入\n mcresultwinningavg.emplace_back(montecarloImplWinningAvg(mr));\n }\n\n return std::make_pair(std::move(mcresultavg), std::move(mcresultwinningavg));\n }\n#endif\n\n std::pair, tbb::concurrent_vector > montecarloTBB()\n {\n // 期待値に対するモンテカルロ・シミュレーションの結果を格納するための可変長配列\n tbb::concurrent_vector mcresultavg;\n\n // どちらの文字列が先に出現したかどうかのモンテカルロ・シミュレーションの結果を格納するための可変長配列\n tbb::concurrent_vector mcresultwinningavg;\n\n // MCMAX個の容量を確保\n mcresultavg.reserve(MCMAX);\n mcresultwinningavg.reserve(MCMAX);\n \n // MCMAX回のループを並列化して実行\n tbb::parallel_for(\n 0U,\n MCMAX,\n 1U,\n [&](auto) {\n\n#ifdef HAVE_SSE2\n\t\t // 自作乱数クラスを初期化\n\t\t myrandom::MyRandSfmt mr(1, 6);\n#else\n\t\t // 自作乱数クラスを初期化\n\t\t myrandom::MyRand mr(1, 6);\n#endif\n\n // 期待値に対するモンテカルロ・シミュレーションの結果を代入\n mcresultavg.emplace_back(montecarloImplAvg(mr));\n\n // どちらの文字列が先に出現したかどうかのモンテカルロ・シミュレーションの結果を代入\n mcresultwinningavg.emplace_back(montecarloImplWinningAvg(mr));\n });\n\n return std::make_pair(std::move(mcresultavg), std::move(mcresultwinningavg));\n }\n\n template \n mymap montecarloImplAvg(T & mr)\n {\n // UDのランダム文字列\n auto const udstr(makerandomudstr(mr));\n\n // 検索結果のstd::map\n mymap result;\n\n // 文字列が最初に出現するのは何文字目かを検索し結果を代入\n for (auto const & str : udarray) {\n result.insert(std::make_pair(str, myfind(str, udstr)));\n }\n\n return result;\n }\n \n template \n mymap2 montecarloImplWinningAvg(T & mr)\n {\n // UDのランダム文字列\n auto const udstr(makerandomudstr(mr));\n\n // 検索結果のstd::map\n mymap2 result;\n\n // どちらの文字列が先に出現したかの結果を代入\n for (auto const & sp : cbarray) {\n result.insert(std::make_pair(sp, myfind(sp.first, udstr) < myfind(sp.second, udstr)));\n }\n\n // 検索結果を返す\n return result;\n }\n \n std::uint32_t myfind(std::string const & str, std::string const & udstr)\n {\n // 文字列の位置を検索\n auto const pos = udstr.find(str);\n \n // posをその文字列の末尾の位置に変換\n // もし文字列が見つかっていなかった場合はRANDNUMTABLELENに変換\n return pos != std::string::npos ? static_cast(pos + 3) : RANDNUMTABLELEN;\n }\n \n template \n mymap sumMontecarloAvg(T const & mcresultavg)\n {\n // 各文字列に対して、期待値に対するモンテカルロ・シミュレーションの結果の和を格納するstd::map\n mymap trial;\n\n // std::mapの初期化\n for (auto const & str : udarray) {\n trial[str] = 0;\n }\n\n // 試行回数分繰り返す\n for (auto const & mcr : mcresultavg) {\n for (auto && itr = mcr.begin(); itr != mcr.end(); ++itr) {\n trial[itr->first] += itr->second;\n }\n }\n\n return trial;\n }\n}\n\n", "meta": {"hexsha": "09ff89052dda52d9d81ea6892309110ba2ac7c4f", "size": 13818, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/kakeguruitwin_MC/kakeguruitwin_mc.cpp", "max_stars_repo_name": "dc1394/kakeguruitwin_MC", "max_stars_repo_head_hexsha": "dffcf2da01c4e2a0afe7464d61c7c17e2079af50", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/kakeguruitwin_MC/kakeguruitwin_mc.cpp", "max_issues_repo_name": "dc1394/kakeguruitwin_MC", "max_issues_repo_head_hexsha": "dffcf2da01c4e2a0afe7464d61c7c17e2079af50", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/kakeguruitwin_MC/kakeguruitwin_mc.cpp", "max_forks_repo_name": "dc1394/kakeguruitwin_MC", "max_forks_repo_head_hexsha": "dffcf2da01c4e2a0afe7464d61c7c17e2079af50", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 27.5258964143, "max_line_length": 114, "alphanum_fraction": 0.5672311478, "num_tokens": 5194, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.21843392700013367}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"ros/ros.h\"\n\n#include \"snoptProblem.hpp\"\n\nusing namespace std;\nusing namespace Eigen;\n\nconst string prefix = \"j2s7s300\";\nconst double pi = 3.1415926535897;\n\nconst int T = 10.0; // planning horizon\nconst double dt = 0.5; // timestep\nconst int dofRobot = 3; // num dofs of robot\nconst int dofEnv = 1; // num dofs of environment\nconst int dofLamb = 1; // dimension of contact forces \n\nconst double goal[] = {-0.4, 0.5};\nconst double obsLow[] = {-1.0, 0.3};\nconst double obsHigh[] = {1.0, 0.3};\n\n// --------- HELPER FUNCTIONS --------- //\n\nVectorXf config2DTo3D(VectorXf& q2D){\n VectorXf q3D(10);\n q3D << q2D(0), pi/2.0, pi/2.0, q2D(1), pi, q2D(2), pi, 0, 0, 0;\n return q3D;\n}\n\nVectorXf config3DTo2D(VectorXf& q3D){\n VectorXf q2D(3);\n q2D << q3D(0), q3D(3), q3D(5);\n return q2D;\n}\n\nVectorXf vel2DTo3D(VectorXf& dq2D){\n VectorXf dq3D(10);\n dq3D << dq2D(0), 0, 0, dq2D(1), 0, dq2D(2), 0, 0, 0, 0;\n return dq3D;\n}\n\nVectorXf vel3DTo2D(VectorXf& dq3D){\n VectorXf dq2D(3);\n dq2D << dq3D(0), dq3D(3), dq3D(5);\n return dq2D;\n}\n\nVectorXf control2DTo3D(VectorXf& u2D){\n VectorXf u3D(10);\n u3D << u2D(0), 0, 0, u2D(1), 0, u2D(2), 0, 0, 0, 0;\n return u3D;\n}\n\nVectorXf control3DTo2D(VectorXf& u3D){\n VectorXf u2D(3);\n u2D << u3D(0), u3D(3), u3D(5);\n return u2D;\n}\n\nVectorXf acc2DTo3D(VectorXf& ddq2D){\n VectorXf ddq3D(10);\n ddq3D << ddq2D(0), 0, 0, ddq2D(1), 0, ddq2D(2), 0, 0, 0, 0;\n return ddq3D;\n}\n\nVectorXf acc3DTo2D(VectorXf& ddq3D){\n VectorXf ddq2D(3);\n ddq2D << ddq3D(0), ddq3D(3), ddq3D(5);\n return ddq2D;\n}\n\n// --------- GETTER FUNCTIONS --------- //\n\ndouble getFinalCost(VectorXf& q, VectorXf& dq){\n VectorXf q3D = config2DTo3D(q);\n //string linkName = strcat(prefix, \"_link_7\");\n //vector eePose = getTransform(robot, q3d, linkName);\n // VectorXf goal(2);\n // goalPose << goal[0], goal[1];\n //double gf = 30*(goalPose - eePose).norm();\n double gf = 0.0;\n return gf;\n}\n\ndouble getRunningCost(VectorXf& q, VectorXf& dq, VectorXf& u){\n return 0.0;\n}\n\nVectorXf getConfigRobot(double *x, int t){\n VectorXf qRobot(dofRobot); \n int lowOffset = 2*(dofRobot + dofEnv)*t + 1;\n int upOffset = 2*(dofRobot + dofEnv)*t + dofRobot;\n for(size_t ii=lowOffset; ii <= upOffset; ii++) // TODO check if should be inclusive\n qRobot(ii-lowOffset) = x[ii];\n return qRobot;\n}\n\nVectorXf getVelRobot(double *x, int t){\n VectorXf dqRobot(dofRobot); \n int lowOffset = 2*(dofRobot + dofEnv)*t + (dofRobot + dofEnv) + 1;\n int upOffset = 2*(dofRobot + dofEnv)*t + (dofRobot + dofEnv) + dofRobot;\n for(size_t ii=lowOffset; ii <= upOffset; ii++) // TODO check if should be inclusive\n dqRobot(ii-lowOffset) = x[ii];\n return dqRobot;\n}\n\nVectorXf getConfigEnv(double *x, int t){\n VectorXf qEnv(dofEnv); //= new double[dofEnv];\n int lowOffset = 2*(dofRobot + dofEnv)*t + dofRobot + 1;\n int upOffset = 2*(dofRobot + dofEnv)*t + dofRobot + dofEnv;\n for(size_t ii=lowOffset; ii <= upOffset; ii++) // TODO check if should be inclusive\n qEnv(ii-lowOffset) = x[ii];\n return qEnv;\n}\n\nVectorXf getVelEnv(double *x, int t){\n VectorXf dqEnv(dofEnv); \n int lowOffset = 2*(dofRobot + dofEnv)*t + (dofRobot + dofEnv) + dofRobot + 1;\n int upOffset = 2*(dofRobot + dofEnv)*t + 2*(dofRobot + dofEnv);\n for(size_t ii=lowOffset; ii <= upOffset; ii++) // TODO check if should be inclusive\n dqEnv(ii-lowOffset) = x[ii];\n return dqEnv;\n}\n\nVectorXf getU(double *x, int t){\n VectorXf u(dofRobot); \n int nx = 2*(dofRobot+dofEnv)*(T+1);\n int lowOffset = nx + 1 + dofRobot*(t - 1);\n int upOffset = nx + 1 + dofRobot*t - 1;\n for(size_t ii=lowOffset; ii <= upOffset; ii++) // TODO check if should be inclusive\n u(ii-lowOffset) = x[ii];\n return u;\n}\n// ------------------------------------ //\n\n// --------- SETTER FUNCTIONS --------- //\n\nvoid setConfigRobot(double *x, int t, double *q){\n int lowOffset = 2*(dofRobot + dofEnv)*t + 1;\n int upOffset = 2*(dofRobot + dofEnv)*t + dofRobot;\n copy(x + lowOffset, x + upOffset, q);\n}\n\nvoid setConfigEnv(double *x, int t, double *q){\n int lowOffset = 2*(dofRobot + dofEnv)*t + dofRobot + 1;\n int upOffset = 2*(dofRobot + dofEnv)*t + dofRobot + dofEnv;\n copy(x + lowOffset, x + upOffset, q);\n}\n\nvoid setVelRobot(double *x, int t, double *dq){\n int lowOffset = 2*(dofRobot + dofEnv)*t + (dofRobot + dofEnv) + 1;\n int upOffset = 2*(dofRobot + dofEnv)*t + (dofRobot + dofEnv) + dofRobot;\n copy(x + lowOffset, x + upOffset, dq);\n}\n\nvoid setVelEnv(double *x, int t, double *dq){\n int lowOffset = 2*(dofRobot + dofEnv)*t + (dofRobot + dofEnv) + dofRobot + 1;\n int upOffset = 2*(dofRobot + dofEnv)*t + 2*(dofRobot + dofEnv);\n copy(x + lowOffset, x + upOffset, dq);\n}\n\nvoid setU(double *x, int t, double *u, int nx){\n int uStart = nx + 1;\n int lowOffset = uStart + dofRobot*(t-1);\n int upOffset = uStart + dofRobot*t - 1;\n copy(x + lowOffset, x + upOffset, u);\n}\n\nvoid setLambda(double *x, int t, double *lambda, int nx, int nu){\n int lambStart = nx + nu + 1;\n int lowOffset = lambStart + dofLamb*(t-1);\n int upOffset = lambStart + dofLamb*t - 1;\n copy(x + lowOffset, x + upOffset, lambda);\n}\n\n// ------------------------------------ //\n\nvoid userfun(int *Status, int *n, double x[],\n int *needF, int *neF, double F[],\n int *needG, int *neG, double G[],\n char *cu, int *lencu,\n int iu[], int *leniu,\n double ru[], int *lenru) {\n \n // TODO IMPLEMENT ME!\n VectorXf qRobot_T = getConfigRobot(x, T);\n VectorXf dqRobot_T = getVelRobot(x, T);\n double obj = getFinalCost(qRobot_T, dqRobot_T); \n\n int kinLen = (dofRobot + dofEnv)*T;\n int dynLen = (dofRobot + dofEnv)*T;\n int collLen = T;\n\n // Compute kinematic, dynamics, and collision constraints\n double *kinConstraints = new double[kinLen];\n double *dynConstraints = new double[dynLen];\n double *collConstraints = new double[collLen];\n\n for(int t=0; t 0){\n // Set torque limits\n setU(xlow, t, uMin, nx);\n setU(xupp, t, uMax, nx);\n\n // Set lambda constraint forces\n setLambda(xlow, t, lambMin, nx, nu);\n setLambda(xupp, t, lambMax, nx, nu);\n }\n }\n\n // ----------------------------------------------- //\n\n // Set bounds on the objective\n Flow[0] = -1e3; \n Fupp[0] = 1e3; \n fill(Fupp + signedDistLinkStart, Fupp + signedDistLinkStart + T, 1000.0);\n\n // Load the data for planContact ...\n ToyProb.initialize (\"\", 1); // no print file; summary on\n ToyProb.setPrintFile (\"Contact.out\"); // oh wait, i want a print file\n ToyProb.setProbName (\"Contact\");\n\n // snopta will compute the Jacobian by finite-differences.\n // snJac will be called to define the\n // coordinate arrays (iAfun,jAvar,A) and (iGfun, jGvar).\n ToyProb.setIntParameter(\"Derivative option\", 0);\n ToyProb.setIntParameter(\"Verify level \", 3);\n\n // Solve the problem.\n // snJac is called implicitly in this case to compute the Jacobian.\n ToyProb.solve(Cold, neF, n, ObjAdd, ObjRow, userfun,\n\t\txlow, xupp, Flow, Fupp,\n\t\tx, xstate, xmul, F, Fstate, Fmul,\n\t\tnS, nInf, sInf);\n\n for (int i = 0; i < n; i++){\n cout << \"x = \" << x[i] << \" xstate = \" << xstate[i] << endl;\n }\n\n delete []x; delete []xlow; delete []xupp;\n delete []xmul; delete []xstate;\n\n delete []F; delete []Flow; delete []Fupp;\n delete []Fmul; delete []Fstate;\n}\n", "meta": {"hexsha": "83962426c655ffda4ea03107b4ae610f61b82703", "size": 12466, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ros/src/contact_planner/src/planContact.cpp", "max_stars_repo_name": "abajcsy/jaco_contact_planning", "max_stars_repo_head_hexsha": "c47e71962346d9252a812d86da9c7fc2ca78acef", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-10-22T03:12:21.000Z", "max_stars_repo_stars_event_max_datetime": "2019-10-22T03:12:21.000Z", "max_issues_repo_path": "ros/src/contact_planner/src/planContact.cpp", "max_issues_repo_name": "abajcsy/jaco_contact_planning", "max_issues_repo_head_hexsha": "c47e71962346d9252a812d86da9c7fc2ca78acef", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "ros/src/contact_planner/src/planContact.cpp", "max_forks_repo_name": "abajcsy/jaco_contact_planning", "max_forks_repo_head_hexsha": "c47e71962346d9252a812d86da9c7fc2ca78acef", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.3309002433, "max_line_length": 94, "alphanum_fraction": 0.6124659073, "num_tokens": 4340, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.21843392700013367}} {"text": "/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */\n\n/*\n Copyright (C) 2001, 2002, 2003 Sadruddin Rejeb\n Copyright (C) 2006 Cristina Duminuco\n Copyright (C) 2006 Marco Bianchetti\n Copyright (C) 2007 StatPro Italia srl\n Copyright (C) 2014 Ferdinando Ametrano\n Copyright (C) 2016 Peter Caspers\n\n This file is part of QuantLib, a free-software/open-source library\n for financial quantitative analysts and developers - http://quantlib.org/\n\n QuantLib is free software: you can redistribute it and/or modify it\n under the terms of the QuantLib license. You should have received a\n copy of the license along with this program; if not, please email\n . The license is also available online at\n .\n\n This program is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n/*! \\file swaption.hpp\n \\brief Swaption class\n*/\n\n#ifndef quantlib_instruments_swaption_hpp\n#define quantlib_instruments_swaption_hpp\n\n#include \n#include \n#include \n#include \n\nnamespace QuantLib {\n\n //! %settlement information\n struct Settlement {\n enum Type { Physical, Cash };\n };\n\n std::ostream& operator<<(std::ostream& out,\n Settlement::Type type);\n\n //! %Swaption class\n /*! \\ingroup instruments\n\n \\test\n - the correctness of the returned value is tested by checking\n that the price of a payer (resp. receiver) swaption\n decreases (resp. increases) with the strike.\n - the correctness of the returned value is tested by checking\n that the price of a payer (resp. receiver) swaption\n increases (resp. decreases) with the spread.\n - the correctness of the returned value is tested by checking\n it against that of a swaption on a swap with no spread and a\n correspondingly adjusted fixed rate.\n - the correctness of the returned value is tested by checking\n it against a known good value.\n - the correctness of the returned value of cash settled swaptions\n is tested by checking the modified annuity against a value\n calculated without using the Swaption class.\n\n\n \\todo add greeks and explicit exercise lag\n */\n class Swaption : public Option {\n public:\n class arguments;\n class engine;\n Swaption(const boost::shared_ptr& swap,\n const boost::shared_ptr& exercise,\n Settlement::Type delivery = Settlement::Physical);\n //! \\name Instrument interface\n //@{\n bool isExpired() const;\n void setupArguments(PricingEngine::arguments*) const;\n //@}\n //! \\name Inspectors\n //@{\n Settlement::Type settlementType() const { return settlementType_; }\n VanillaSwap::Type type() const { return swap_->type(); }\n const boost::shared_ptr& underlyingSwap() const {\n return swap_;\n }\n //@}\n //! implied volatility\n Volatility impliedVolatility(\n Real price,\n const Handle& discountCurve,\n Volatility guess,\n Real accuracy = 1.0e-4,\n Natural maxEvaluations = 100,\n Volatility minVol = 1.0e-7,\n Volatility maxVol = 4.0,\n VolatilityType type = ShiftedLognormal,\n Real displacement = 0.0) const;\n private:\n // arguments\n boost::shared_ptr swap_;\n //Handle termStructure_;\n Settlement::Type settlementType_;\n };\n\n //! %Arguments for swaption calculation\n class Swaption::arguments : public VanillaSwap::arguments,\n public Option::arguments {\n public:\n arguments() : settlementType(Settlement::Physical) {}\n boost::shared_ptr swap;\n Settlement::Type settlementType;\n void validate() const;\n };\n\n //! base class for swaption engines\n class Swaption::engine\n : public GenericEngine {};\n\n}\n\n\n/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */\n\n/*\n Copyright (C) 2001, 2002, 2003 Sadruddin Rejeb\n Copyright (C) 2006 Cristina Duminuco\n Copyright (C) 2006 Marco Bianchetti\n Copyright (C) 2007 StatPro Italia srl\n Copyright (C) 2014 Ferdinando Ametrano\n Copyright (C) 2016 Peter Caspers\n\n This file is part of QuantLib, a free-software/open-source library\n for financial quantitative analysts and developers - http://quantlib.org/\n\n QuantLib is free software: you can redistribute it and/or modify it\n under the terms of the QuantLib license. You should have received a\n copy of the license along with this program; if not, please email\n . The license is also available online at\n .\n\n This program is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n#include \n#include \n#include \n\n#include \n\nnamespace QuantLib {\n\n namespace {\n\n class ImpliedSwaptionVolHelper {\n public:\n ImpliedSwaptionVolHelper(const Swaption&,\n const Handle& discountCurve,\n Real targetValue,\n Real displacement,\n VolatilityType type);\n Real operator()(Volatility x) const;\n Real derivative(Volatility x) const;\n private:\n boost::shared_ptr engine_;\n Handle discountCurve_;\n Real targetValue_;\n boost::shared_ptr vol_;\n const Instrument::results* results_;\n };\n\n inline ImpliedSwaptionVolHelper::ImpliedSwaptionVolHelper(\n const Swaption& swaption,\n const Handle& discountCurve,\n Real targetValue,\n Real displacement,\n VolatilityType type)\n : discountCurve_(discountCurve), targetValue_(targetValue) {\n\n // set an implausible value, so that calculation is forced\n // at first ImpliedSwaptionVolHelper::operator()(Volatility x) call\n vol_ = boost::shared_ptr(new SimpleQuote(-1.0));\n Handle h(vol_);\n\n switch (type) {\n case ShiftedLognormal:\n engine_ = boost::make_shared(\n discountCurve_, h, Actual365Fixed(), displacement);\n break;\n case Normal:\n engine_ = boost::make_shared(\n discountCurve_, h, Actual365Fixed());\n break;\n default:\n QL_FAIL(\"unknown VolatilityType (\" << type << \")\");\n break;\n }\n swaption.setupArguments(engine_->getArguments());\n results_ = dynamic_cast(\n engine_->getResults());\n }\n\n inline Real ImpliedSwaptionVolHelper::operator()(Volatility x) const {\n if (x!=vol_->value()) {\n vol_->setValue(x);\n engine_->calculate();\n }\n return results_->value-targetValue_;\n }\n\n inline Real ImpliedSwaptionVolHelper::derivative(Volatility x) const {\n if (x!=vol_->value()) {\n vol_->setValue(x);\n engine_->calculate();\n }\n std::map::const_iterator vega_ =\n results_->additionalResults.find(\"vega\");\n QL_REQUIRE(vega_ != results_->additionalResults.end(),\n \"vega not provided\");\n return boost::any_cast(vega_->second);\n }\n }\n\n inline std::ostream& operator<<(std::ostream& out,\n Settlement::Type t) {\n switch (t) {\n case Settlement::Physical:\n return out << \"Delivery\";\n case Settlement::Cash:\n return out << \"Cash\";\n default:\n QL_FAIL(\"unknown Settlement::Type(\" << Integer(t) << \")\");\n }\n }\n\n inline Swaption::Swaption(const boost::shared_ptr& swap,\n const boost::shared_ptr& exercise,\n Settlement::Type delivery)\n : Option(boost::shared_ptr(), exercise), swap_(swap),\n settlementType_(delivery) {\n registerWith(swap_);\n registerWithObservables(swap_);\n }\n\n inline bool Swaption::isExpired() const {\n return detail::simple_event(exercise_->dates().back()).hasOccurred();\n }\n\n inline void Swaption::setupArguments(PricingEngine::arguments* args) const {\n\n swap_->setupArguments(args);\n\n Swaption::arguments* arguments =\n dynamic_cast(args);\n\n QL_REQUIRE(arguments != 0, \"wrong argument type\");\n\n arguments->swap = swap_;\n arguments->settlementType = settlementType_;\n arguments->exercise = exercise_;\n }\n\n inline void Swaption::arguments::validate() const {\n VanillaSwap::arguments::validate();\n QL_REQUIRE(swap, \"vanilla swap not set\");\n QL_REQUIRE(exercise, \"exercise not set\");\n }\n\n inline Volatility Swaption::impliedVolatility(Real targetValue,\n const Handle& d,\n Volatility guess,\n Real accuracy,\n Natural maxEvaluations,\n Volatility minVol,\n Volatility maxVol,\n VolatilityType type,\n Real displacement) const {\n //calculate();\n QL_REQUIRE(!isExpired(), \"instrument expired\");\n\n ImpliedSwaptionVolHelper f(*this, d, targetValue, displacement, type);\n //Brent solver;\n NewtonSafe solver;\n solver.setMaxEvaluations(maxEvaluations);\n return solver.solve(f, accuracy, guess, minVol, maxVol);\n }\n\n}\n\n#endif\n", "meta": {"hexsha": "4df864119fd1bda7e753f89f8c07f985f4c8c9d6", "size": 11164, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ql/instruments/swaption.hpp", "max_stars_repo_name": "markxio/Quantuccia", "max_stars_repo_head_hexsha": "ebe71a1b9c2a9ee7fc4ea918a9602f100316869d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 29.0, "max_stars_repo_stars_event_min_datetime": "2017-03-20T14:17:39.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-22T08:00:52.000Z", "max_issues_repo_path": "ql/instruments/swaption.hpp", "max_issues_repo_name": "markxio/Quantuccia", "max_issues_repo_head_hexsha": "ebe71a1b9c2a9ee7fc4ea918a9602f100316869d", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 10.0, "max_issues_repo_issues_event_min_datetime": "2017-04-02T14:34:07.000Z", "max_issues_repo_issues_event_max_datetime": "2021-01-13T05:31:12.000Z", "max_forks_repo_path": "ql/instruments/swaption.hpp", "max_forks_repo_name": "markxio/Quantuccia", "max_forks_repo_head_hexsha": "ebe71a1b9c2a9ee7fc4ea918a9602f100316869d", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 22.0, "max_forks_repo_forks_event_min_datetime": "2017-03-19T05:56:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-16T13:30:20.000Z", "avg_line_length": 37.8440677966, "max_line_length": 85, "alphanum_fraction": 0.5966499463, "num_tokens": 2292, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.21843392700013367}} {"text": "// $Id$\n//\n// Copyright (C) 2007-2013 Greg Landrum\n//\n// @@ All Rights Reserved @@\n// This file is part of the RDKit.\n// The contents are covered by the terms of the BSD license\n// which is included in the file license.txt, found at the root\n// of the RDKit source tree.\n//\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace RDKit {\nnamespace AtomPairs {\nunsigned int numPiElectrons(const Atom *atom) {\n PRECONDITION(atom, \"no atom\");\n unsigned int res = 0;\n if (atom->getIsAromatic()) {\n res = 1;\n } else if (atom->getHybridization() != Atom::SP3) {\n unsigned int val = static_cast(atom->getExplicitValence());\n val -= atom->getNumExplicitHs();\n CHECK_INVARIANT(val >= atom->getDegree(),\n \"explicit valence exceeds atom degree\");\n res = val - atom->getDegree();\n }\n return res;\n}\n\nboost::uint32_t getAtomCode(const Atom *atom, unsigned int branchSubtract,\n bool includeChirality) {\n PRECONDITION(atom, \"no atom\");\n boost::uint32_t code;\n\n unsigned int numBranches = 0;\n if (atom->getDegree() > branchSubtract) {\n numBranches = atom->getDegree() - branchSubtract;\n }\n\n code = numBranches % maxNumBranches;\n unsigned int nPi = numPiElectrons(atom) % maxNumPi;\n code |= nPi << numBranchBits;\n\n unsigned int typeIdx = 0;\n unsigned int nTypes = 1 << numTypeBits;\n while (typeIdx < nTypes) {\n if (atomNumberTypes[typeIdx] ==\n static_cast(atom->getAtomicNum())) {\n break;\n } else if (atomNumberTypes[typeIdx] >\n static_cast(atom->getAtomicNum())) {\n typeIdx = nTypes;\n break;\n }\n ++typeIdx;\n }\n if (typeIdx == nTypes) --typeIdx;\n code |= typeIdx << (numBranchBits + numPiBits);\n if (includeChirality) {\n std::string cipCode;\n if (atom->getPropIfPresent(common_properties::_CIPCode, cipCode)) {\n boost::uint32_t offset = numBranchBits + numPiBits + numTypeBits;\n if (cipCode == \"R\") {\n code |= 1 << offset;\n } else if (cipCode == \"S\") {\n code |= 2 << offset;\n }\n }\n }\n POSTCONDITION(code < static_cast(1 << (codeSize + (includeChirality ? 2 : 0))),\n \"code exceeds number of bits\");\n return code;\n};\n\nboost::uint32_t getAtomPairCode(boost::uint32_t codeI, boost::uint32_t codeJ,\n unsigned int dist, bool includeChirality) {\n PRECONDITION(dist < maxPathLen, \"dist too long\");\n boost::uint32_t res = dist;\n res |= std::min(codeI, codeJ) << numPathBits;\n res |= std::max(codeI, codeJ)\n << (numPathBits + codeSize + (includeChirality ? numChiralBits : 0));\n return res;\n}\n\ntemplate \nvoid updateElement(SparseIntVect &v, T2 elem) {\n v.setVal(elem, v.getVal(elem) + 1);\n}\n\ntemplate \nvoid updateElement(ExplicitBitVect &v, T1 elem) {\n v.setBit(elem % v.getNumBits());\n}\n\ntemplate \nvoid setAtomPairBit(boost::uint32_t i, boost::uint32_t j,\n boost::uint32_t nAtoms,\n const std::vector &atomCodes,\n const double *dm, T *bv, unsigned int minLength,\n unsigned int maxLength, bool includeChirality) {\n unsigned int dist = static_cast(floor(dm[i * nAtoms + j]));\n if (dist >= minLength && dist <= maxLength) {\n boost::uint32_t bitId =\n getAtomPairCode(atomCodes[i], atomCodes[j], dist, includeChirality);\n updateElement(*bv, static_cast(bitId));\n }\n}\n\nSparseIntVect *getAtomPairFingerprint(\n const ROMol &mol, const std::vector *fromAtoms,\n const std::vector *ignoreAtoms,\n const std::vector *atomInvariants, bool includeChirality,\n bool use2D, int confId) {\n return getAtomPairFingerprint(mol, 1, maxPathLen - 1, fromAtoms, ignoreAtoms,\n atomInvariants, includeChirality, use2D,\n confId);\n};\n\nSparseIntVect *getAtomPairFingerprint(\n const ROMol &mol, unsigned int minLength, unsigned int maxLength,\n const std::vector *fromAtoms,\n const std::vector *ignoreAtoms,\n const std::vector *atomInvariants, bool includeChirality,\n bool use2D, int confId) {\n PRECONDITION(minLength <= maxLength, \"bad lengths provided\");\n PRECONDITION(!atomInvariants || atomInvariants->size() >= mol.getNumAtoms(),\n \"bad atomInvariants size\");\n SparseIntVect *res = new SparseIntVect(\n 1 << (numAtomPairFingerprintBits + 2 * (includeChirality ? 2 : 0)));\n const double *dm;\n if (use2D) {\n dm = MolOps::getDistanceMat(mol);\n } else {\n dm = MolOps::get3DDistanceMat(mol, confId);\n }\n const unsigned int nAtoms = mol.getNumAtoms();\n\n std::vector atomCodes;\n for (ROMol::ConstAtomIterator atomItI = mol.beginAtoms();\n atomItI != mol.endAtoms(); ++atomItI) {\n if (!atomInvariants) {\n atomCodes.push_back(getAtomCode(*atomItI, 0, includeChirality));\n } else {\n atomCodes.push_back((*atomInvariants)[(*atomItI)->getIdx()] %\n ((1 << codeSize) - 1));\n }\n }\n\n for (ROMol::ConstAtomIterator atomItI = mol.beginAtoms();\n atomItI != mol.endAtoms(); ++atomItI) {\n unsigned int i = (*atomItI)->getIdx();\n if (ignoreAtoms &&\n std::find(ignoreAtoms->begin(), ignoreAtoms->end(), i) !=\n ignoreAtoms->end()) {\n continue;\n }\n if (!fromAtoms) {\n for (ROMol::ConstAtomIterator atomItJ = atomItI + 1;\n atomItJ != mol.endAtoms(); ++atomItJ) {\n unsigned int j = (*atomItJ)->getIdx();\n if (ignoreAtoms &&\n std::find(ignoreAtoms->begin(), ignoreAtoms->end(), j) !=\n ignoreAtoms->end()) {\n continue;\n }\n setAtomPairBit(i, j, nAtoms, atomCodes, dm, res, minLength, maxLength,\n includeChirality);\n }\n } else {\n BOOST_FOREACH (boost::uint32_t j, *fromAtoms) {\n if (j != i) {\n if (ignoreAtoms &&\n std::find(ignoreAtoms->begin(), ignoreAtoms->end(), j) !=\n ignoreAtoms->end()) {\n continue;\n }\n setAtomPairBit(i, j, nAtoms, atomCodes, dm, res, minLength, maxLength,\n includeChirality);\n }\n }\n }\n }\n return res;\n}\n\nSparseIntVect *getHashedAtomPairFingerprint(\n const ROMol &mol, unsigned int nBits, unsigned int minLength,\n unsigned int maxLength, const std::vector *fromAtoms,\n const std::vector *ignoreAtoms,\n const std::vector *atomInvariants, bool includeChirality,\n bool use2D, int confId) {\n PRECONDITION(minLength <= maxLength, \"bad lengths provided\");\n PRECONDITION(!atomInvariants || atomInvariants->size() >= mol.getNumAtoms(),\n \"bad atomInvariants size\");\n SparseIntVect *res = new SparseIntVect(nBits);\n const double *dm;\n if (use2D) {\n dm = MolOps::getDistanceMat(mol);\n } else {\n dm = MolOps::get3DDistanceMat(mol, confId);\n }\n\n const unsigned int nAtoms = mol.getNumAtoms();\n\n std::vector atomCodes;\n atomCodes.reserve(nAtoms);\n for (ROMol::ConstAtomIterator atomItI = mol.beginAtoms();\n atomItI != mol.endAtoms(); ++atomItI) {\n if (!atomInvariants) {\n atomCodes.push_back(getAtomCode(*atomItI, 0, includeChirality));\n } else {\n atomCodes.push_back((*atomInvariants)[(*atomItI)->getIdx()]);\n }\n }\n\n for (ROMol::ConstAtomIterator atomItI = mol.beginAtoms();\n atomItI != mol.endAtoms(); ++atomItI) {\n unsigned int i = (*atomItI)->getIdx();\n if (ignoreAtoms &&\n std::find(ignoreAtoms->begin(), ignoreAtoms->end(), i) !=\n ignoreAtoms->end()) {\n continue;\n }\n if (!fromAtoms) {\n for (ROMol::ConstAtomIterator atomItJ = atomItI + 1;\n atomItJ != mol.endAtoms(); ++atomItJ) {\n unsigned int j = (*atomItJ)->getIdx();\n if (ignoreAtoms &&\n std::find(ignoreAtoms->begin(), ignoreAtoms->end(), j) !=\n ignoreAtoms->end()) {\n continue;\n }\n unsigned int dist =\n static_cast(floor(dm[i * nAtoms + j]));\n if (dist >= minLength && dist <= maxLength) {\n boost::uint32_t bit = 0;\n gboost::hash_combine(bit, std::min(atomCodes[i], atomCodes[j]));\n gboost::hash_combine(bit, dist);\n gboost::hash_combine(bit, std::max(atomCodes[i], atomCodes[j]));\n updateElement(*res, static_cast(bit % nBits));\n }\n }\n } else {\n BOOST_FOREACH (boost::uint32_t j, *fromAtoms) {\n if (j != i) {\n if (ignoreAtoms &&\n std::find(ignoreAtoms->begin(), ignoreAtoms->end(), j) !=\n ignoreAtoms->end()) {\n continue;\n }\n unsigned int dist =\n static_cast(floor(dm[i * nAtoms + j]));\n if (dist >= minLength && dist <= maxLength) {\n boost::uint32_t bit = 0;\n gboost::hash_combine(bit, std::min(atomCodes[i], atomCodes[j]));\n gboost::hash_combine(bit, dist);\n gboost::hash_combine(bit, std::max(atomCodes[i], atomCodes[j]));\n updateElement(*res, static_cast(bit % nBits));\n }\n }\n }\n }\n }\n return res;\n}\n\nExplicitBitVect *getHashedAtomPairFingerprintAsBitVect(\n const ROMol &mol, unsigned int nBits, unsigned int minLength,\n unsigned int maxLength, const std::vector *fromAtoms,\n const std::vector *ignoreAtoms,\n const std::vector *atomInvariants,\n unsigned int nBitsPerEntry, bool includeChirality, bool use2D, int confId) {\n PRECONDITION(minLength <= maxLength, \"bad lengths provided\");\n PRECONDITION(!atomInvariants || atomInvariants->size() >= mol.getNumAtoms(),\n \"bad atomInvariants size\");\n static int bounds[4] = {1, 2, 4, 8};\n\n unsigned int blockLength = nBits / nBitsPerEntry;\n SparseIntVect *sres = getHashedAtomPairFingerprint(\n mol, blockLength, minLength, maxLength, fromAtoms, ignoreAtoms,\n atomInvariants, includeChirality, use2D, confId);\n ExplicitBitVect *res = new ExplicitBitVect(nBits);\n if (nBitsPerEntry != 4) {\n BOOST_FOREACH (SparseIntVect::StorageType::value_type val,\n sres->getNonzeroElements()) {\n for (unsigned int i = 0; i < nBitsPerEntry; ++i) {\n if (val.second > static_cast(i))\n res->setBit(val.first * nBitsPerEntry + i);\n }\n }\n } else {\n BOOST_FOREACH (SparseIntVect::StorageType::value_type val,\n sres->getNonzeroElements()) {\n for (unsigned int i = 0; i < nBitsPerEntry; ++i) {\n if (val.second >= bounds[i]) {\n res->setBit(val.first * nBitsPerEntry + i);\n }\n }\n }\n }\n delete sres;\n return res;\n}\n\nboost::uint64_t getTopologicalTorsionCode(\n const std::vector &pathCodes, bool includeChirality) {\n bool reverseIt = false;\n unsigned int i = 0;\n unsigned int j = pathCodes.size() - 1;\n while (i < j) {\n if (pathCodes[i] > pathCodes[j]) {\n reverseIt = true;\n break;\n } else if (pathCodes[i] < pathCodes[j]) {\n break;\n }\n ++i;\n --j;\n }\n\n int shiftSize = codeSize + (includeChirality ? numChiralBits : 0);\n boost::uint64_t res = 0;\n if (reverseIt) {\n for (unsigned int i = 0; i < pathCodes.size(); ++i) {\n res |= static_cast(pathCodes[pathCodes.size() - i - 1])\n << (shiftSize * i);\n }\n } else {\n for (unsigned int i = 0; i < pathCodes.size(); ++i) {\n res |= static_cast(pathCodes[i]) << (shiftSize * i);\n }\n }\n return res;\n}\n\nsize_t getTopologicalTorsionHash(\n const std::vector &pathCodes) {\n bool reverseIt = false;\n unsigned int i = 0;\n unsigned int j = pathCodes.size() - 1;\n while (i < j) {\n if (pathCodes[i] > pathCodes[j]) {\n reverseIt = true;\n break;\n } else if (pathCodes[i] < pathCodes[j]) {\n break;\n }\n ++i;\n --j;\n }\n\n boost::uint32_t res = 0;\n if (reverseIt) {\n for (unsigned int i = 0; i < pathCodes.size(); ++i) {\n gboost::hash_combine(res, pathCodes[pathCodes.size() - i - 1]);\n }\n } else {\n for (unsigned int i = 0; i < pathCodes.size(); ++i) {\n gboost::hash_combine(res, pathCodes[i]);\n }\n }\n return res;\n}\n\nSparseIntVect *getTopologicalTorsionFingerprint(\n const ROMol &mol, unsigned int targetSize,\n const std::vector *fromAtoms,\n const std::vector *ignoreAtoms,\n const std::vector *atomInvariants, bool includeChirality) {\n PRECONDITION(!atomInvariants || atomInvariants->size() >= mol.getNumAtoms(),\n \"bad atomInvariants size\");\n boost::uint64_t sz = 1;\n sz = (sz << (targetSize *\n (codeSize + (includeChirality ? numChiralBits : 0))));\n // NOTE: this -1 is incorrect but it's needed for backwards compatibility.\n // hopefully we'll never have a case with a torsion that hits this.\n //\n // mmm, bug compatible.\n sz -= 1;\n SparseIntVect *res = new SparseIntVect(sz);\n\n std::vector atomCodes;\n atomCodes.reserve(mol.getNumAtoms());\n for (ROMol::ConstAtomIterator atomItI = mol.beginAtoms();\n atomItI != mol.endAtoms(); ++atomItI) {\n if (!atomInvariants) {\n atomCodes.push_back(getAtomCode(*atomItI, 0, includeChirality));\n } else {\n // need to add to the atomCode here because we subtract off up to 2 below\n // as part of the branch correction\n atomCodes.push_back(\n (*atomInvariants)[(*atomItI)->getIdx()] % ((1 << codeSize) - 1) + 2);\n }\n }\n\n boost::dynamic_bitset<> *fromAtomsBV = 0;\n if (fromAtoms) {\n fromAtomsBV = new boost::dynamic_bitset<>(mol.getNumAtoms());\n BOOST_FOREACH (boost::uint32_t fAt, *fromAtoms) { fromAtomsBV->set(fAt); }\n }\n boost::dynamic_bitset<> *ignoreAtomsBV = 0;\n if (ignoreAtoms) {\n ignoreAtomsBV = new boost::dynamic_bitset<>(mol.getNumAtoms());\n BOOST_FOREACH (boost::uint32_t fAt, *ignoreAtoms) {\n ignoreAtomsBV->set(fAt);\n }\n }\n boost::dynamic_bitset<> pAtoms(mol.getNumAtoms());\n PATH_LIST paths = findAllPathsOfLengthN(mol, targetSize, false);\n for (PATH_LIST::const_iterator pathIt = paths.begin(); pathIt != paths.end();\n ++pathIt) {\n bool keepIt = true;\n if (fromAtomsBV) {\n keepIt = false;\n }\n std::vector pathCodes;\n const PATH_TYPE &path = *pathIt;\n if (fromAtomsBV) {\n if (fromAtomsBV->test(static_cast(path.front())) ||\n fromAtomsBV->test(static_cast(path.back()))) {\n keepIt = true;\n }\n }\n if (keepIt && ignoreAtomsBV) {\n BOOST_FOREACH (int pElem, path) {\n if (ignoreAtomsBV->test(pElem)) {\n keepIt = false;\n break;\n }\n }\n }\n if (keepIt) {\n pAtoms.reset();\n for (PATH_TYPE::const_iterator pIt = path.begin(); pIt < path.end();\n ++pIt) {\n // look for a cycle that doesn't start at the first atom\n // we can't effectively canonicalize these at the moment\n // (was github #811)\n if (pIt != path.begin() && *pIt != *(path.begin()) && pAtoms[*pIt]) {\n pathCodes.clear();\n break;\n }\n pAtoms.set(*pIt);\n unsigned int code = atomCodes[*pIt] - 1;\n // subtract off the branching number:\n if (pIt != path.begin() && pIt + 1 != path.end()) {\n --code;\n }\n pathCodes.push_back(code);\n }\n if (pathCodes.size()) {\n boost::int64_t code =\n getTopologicalTorsionCode(pathCodes, includeChirality);\n updateElement(*res, code);\n }\n }\n }\n delete fromAtomsBV;\n delete ignoreAtomsBV;\n\n return res;\n}\n\nnamespace {\ntemplate \nvoid TorsionFpCalc(T *res, const ROMol &mol, unsigned int nBits,\n unsigned int targetSize,\n const std::vector *fromAtoms,\n const std::vector *ignoreAtoms,\n const std::vector *atomInvariants,\n bool includeChirality) {\n PRECONDITION(!atomInvariants || atomInvariants->size() >= mol.getNumAtoms(),\n \"bad atomInvariants size\");\n std::vector atomCodes;\n atomCodes.reserve(mol.getNumAtoms());\n for (ROMol::ConstAtomIterator atomItI = mol.beginAtoms();\n atomItI != mol.endAtoms(); ++atomItI) {\n if (!atomInvariants) {\n atomCodes.push_back(getAtomCode(*atomItI, 0, includeChirality));\n } else {\n // need to add to the atomCode here because we subtract off up to 2 below\n // as part of the branch correction\n atomCodes.push_back(((*atomInvariants)[(*atomItI)->getIdx()] << 1) + 1);\n }\n }\n\n boost::dynamic_bitset<> *fromAtomsBV = 0;\n if (fromAtoms) {\n fromAtomsBV = new boost::dynamic_bitset<>(mol.getNumAtoms());\n BOOST_FOREACH (boost::uint32_t fAt, *fromAtoms) { fromAtomsBV->set(fAt); }\n }\n boost::dynamic_bitset<> *ignoreAtomsBV = 0;\n if (ignoreAtoms) {\n ignoreAtomsBV = new boost::dynamic_bitset<>(mol.getNumAtoms());\n BOOST_FOREACH (boost::uint32_t fAt, *ignoreAtoms) {\n ignoreAtomsBV->set(fAt);\n }\n }\n\n PATH_LIST paths = findAllPathsOfLengthN(mol, targetSize, false);\n for (PATH_LIST::const_iterator pathIt = paths.begin(); pathIt != paths.end();\n ++pathIt) {\n bool keepIt = true;\n if (fromAtomsBV) {\n keepIt = false;\n }\n const PATH_TYPE &path = *pathIt;\n if (fromAtomsBV) {\n if (fromAtomsBV->test(static_cast(path.front())) ||\n fromAtomsBV->test(static_cast(path.back()))) {\n keepIt = true;\n }\n }\n if (keepIt && ignoreAtomsBV) {\n BOOST_FOREACH (int pElem, path) {\n if (ignoreAtomsBV->test(pElem)) {\n keepIt = false;\n break;\n }\n }\n }\n if (keepIt) {\n std::vector pathCodes(targetSize);\n for (unsigned int i = 0; i < targetSize; ++i) {\n unsigned int code = atomCodes[path[i]] - 1;\n // subtract off the branching number:\n if (i > 0 && i < targetSize - 1) {\n --code;\n }\n pathCodes[i] = code;\n }\n size_t bit = getTopologicalTorsionHash(pathCodes);\n updateElement(*res, bit % nBits);\n }\n }\n delete fromAtomsBV;\n delete ignoreAtomsBV;\n}\n} // end of local namespace\nSparseIntVect *getHashedTopologicalTorsionFingerprint(\n const ROMol &mol, unsigned int nBits, unsigned int targetSize,\n const std::vector *fromAtoms,\n const std::vector *ignoreAtoms,\n const std::vector *atomInvariants, bool includeChirality) {\n PRECONDITION(!atomInvariants || atomInvariants->size() >= mol.getNumAtoms(),\n \"bad atomInvariants size\");\n SparseIntVect *res = new SparseIntVect(nBits);\n TorsionFpCalc(res, mol, nBits, targetSize, fromAtoms, ignoreAtoms,\n atomInvariants, includeChirality);\n return res;\n}\n\nExplicitBitVect *getHashedTopologicalTorsionFingerprintAsBitVect(\n const ROMol &mol, unsigned int nBits, unsigned int targetSize,\n const std::vector *fromAtoms,\n const std::vector *ignoreAtoms,\n const std::vector *atomInvariants,\n unsigned int nBitsPerEntry, bool includeChirality) {\n PRECONDITION(!atomInvariants || atomInvariants->size() >= mol.getNumAtoms(),\n \"bad atomInvariants size\");\n static int bounds[4] = {1, 2, 4, 8};\n unsigned int blockLength = nBits / nBitsPerEntry;\n SparseIntVect *sres =\n new SparseIntVect(blockLength);\n TorsionFpCalc(sres, mol, blockLength, targetSize, fromAtoms, ignoreAtoms,\n atomInvariants, includeChirality);\n ExplicitBitVect *res = new ExplicitBitVect(nBits);\n\n if (nBitsPerEntry != 4) {\n BOOST_FOREACH (SparseIntVect::StorageType::value_type val,\n sres->getNonzeroElements()) {\n for (unsigned int i = 0; i < nBitsPerEntry; ++i) {\n if (val.second > static_cast(i))\n res->setBit(val.first * nBitsPerEntry + i);\n }\n }\n } else {\n BOOST_FOREACH (SparseIntVect::StorageType::value_type val,\n sres->getNonzeroElements()) {\n for (unsigned int i = 0; i < nBitsPerEntry; ++i) {\n if (val.second >= bounds[i]) {\n res->setBit(val.first * nBitsPerEntry + i);\n }\n }\n }\n }\n delete sres;\n return res;\n}\n} // end of namespace AtomPairs\n} // end of namespace RDKit\n", "meta": {"hexsha": "0937fbeac6690da6bb35834876c92d0c27355e6b", "size": 21305, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Code/GraphMol/Fingerprints/AtomPairs.cpp", "max_stars_repo_name": "docking-org/rdk", "max_stars_repo_head_hexsha": "6eb710254f027b348a8e3089e6a92c3d40de0949", "max_stars_repo_licenses": ["PostgreSQL"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Code/GraphMol/Fingerprints/AtomPairs.cpp", "max_issues_repo_name": "docking-org/rdk", "max_issues_repo_head_hexsha": "6eb710254f027b348a8e3089e6a92c3d40de0949", "max_issues_repo_licenses": ["PostgreSQL"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Code/GraphMol/Fingerprints/AtomPairs.cpp", "max_forks_repo_name": "docking-org/rdk", "max_forks_repo_head_hexsha": "6eb710254f027b348a8e3089e6a92c3d40de0949", "max_forks_repo_licenses": ["PostgreSQL"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2017-12-04T02:28:18.000Z", "max_forks_repo_forks_event_max_datetime": "2018-11-29T01:18:46.000Z", "avg_line_length": 35.5083333333, "max_line_length": 98, "alphanum_fraction": 0.6196198076, "num_tokens": 5887, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.21838594723196023}} {"text": "/*\n * Copyright 2009-2018 The VOTCA Development Team\n * (http://www.votca.org)\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\")\n *\n * You may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\n\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n\nnamespace votca {\n namespace xtp {\n\n NumericalIntegration::~NumericalIntegration(){\n if (_setXC) {\n xc_func_end(&xfunc);\n if (_use_separate) {\n xc_func_end(&cfunc);\n }\n }\n }\n\n \n\n double NumericalIntegration::getExactExchange(const std::string& functional) { \n\n double exactexchange = 0.0;\n Vxc_Functionals map;\n std::vector strs;\n\n boost::split(strs, functional, boost::is_any_of(\" \"));\n if (strs.size() > 2) {\n throw std::runtime_error(\"Too many functional names\");\n } else if (strs.size() < 1) {\n throw std::runtime_error(\"Specify at least one functional\");\n }\n\n for (unsigned i = 0; i < strs.size(); i++) {\n\n int func_id = map.getID(strs[i]);\n if (func_id < 0) {\n exactexchange = 0.0;\n break;\n }\n xc_func_type func;\n if (xc_func_init(&func, func_id, XC_UNPOLARIZED) != 0) {\n throw std::runtime_error((boost::format(\"Functional %s not found\\n\") %strs[i]).str());\n }\n if (exactexchange > 0 && func.cam_alpha > 0) {\n throw std::runtime_error(\"You have specified two functionals with exact exchange\");\n }\n exactexchange += func.cam_alpha;\n xc_func_end(&func);\n }\n \n return exactexchange;\n\n }\n \n \n void NumericalIntegration::setXCfunctional(const std::string& functional) {\n\n Vxc_Functionals map;\n std::vector strs;\n tools::Tokenizer tok(functional,\" ,\\n\\t\");\n tok.ToVector(strs);\n xfunc_id = 0;\n _use_separate = false;\n cfunc_id = 0;\n if (strs.size() == 1) {\n xfunc_id = map.getID(strs[0]);\n } else if (strs.size() == 2) {\n xfunc_id = map.getID(strs[0]);\n cfunc_id = map.getID(strs[1]);\n _use_separate = true;\n } else {\n std::cout << \"LIBXC \" << strs.size() << std::endl;\n throw std::runtime_error(\"LIBXC. Please specify one combined or an exchange and a correlation functionals\");\n }\n\n if (xc_func_init(&xfunc, xfunc_id, XC_UNPOLARIZED) != 0) {\n throw std::runtime_error((boost::format(\"Functional %s not found\\n\") %strs[0]).str());\n }\n if (xfunc.info->kind != 2 && !_use_separate) {\n throw std::runtime_error(\"Your functional misses either correlation or exchange, please specify another functional, separated by whitespace\");\n }\n if (_use_separate) {\n if (xc_func_init(&cfunc, cfunc_id, XC_UNPOLARIZED) != 0) {\n throw std::runtime_error((boost::format(\"Functional %s not found\\n\") %strs[1]).str());\n }\n if ((xfunc.info->kind + cfunc.info->kind) != 1) {\n throw std::runtime_error(\"Your functionals are not one exchange and one correlation\");\n }\n }\n _setXC = true;\n return;\n }\n\n \n void NumericalIntegration::EvaluateXC(const double rho, const double sigma, double& f_xc, double& df_drho, double& df_dsigma) {\n\n double exc[1];\n double vsigma[1]; // libxc \n double vrho[1]; // libxc df/drho\n switch (xfunc.info->family) {\n case XC_FAMILY_LDA:\n xc_lda_exc_vxc(&xfunc, 1, &rho, exc, vrho);\n break;\n case XC_FAMILY_GGA:\n case XC_FAMILY_HYB_GGA:\n xc_gga_exc_vxc(&xfunc, 1, &rho, &sigma, exc, vrho, vsigma);\n break;\n }\n f_xc = exc[0];\n df_drho = vrho[0];\n df_dsigma = vsigma[0];\n if (_use_separate) {\n // via libxc correlation part only\n switch (cfunc.info->family) {\n case XC_FAMILY_LDA:\n xc_lda_exc_vxc(&cfunc, 1, &rho, exc, vrho);\n break;\n case XC_FAMILY_GGA:\n case XC_FAMILY_HYB_GGA:\n xc_gga_exc_vxc(&cfunc, 1, &rho, &sigma, exc, vrho, vsigma);\n break;\n }\n\n f_xc += exc[0];\n df_drho += vrho[0];\n df_dsigma += vsigma[0];\n }\n \n return;\n }\n \n \n double NumericalIntegration::IntegratePotential(const tools::vec& rvector) {\n\n double result = 0.0;\n assert(_density_set && \"Density not calculated\");\n for (unsigned i = 0; i < _grid_boxes.size(); i++) {\n const std::vector& points = _grid_boxes[i].getGridPoints();\n const std::vector& weights = _grid_boxes[i].getGridWeights();\n const std::vector& densities = _grid_boxes[i].getGridDensities();\n for (unsigned j = 0; j < points.size(); j++) {\n double dist = abs(points[j] - rvector);\n result -= weights[j] * densities[j] / dist;\n }\n }\n return result;\n }\n \n \n \n \n void NumericalIntegration::SortGridpointsintoBlocks(std::vector< std::vector< GridContainers::Cartesian_gridpoint > >& grid) {\n const double boxsize = 1;//1 bohr\n\n std::vector< std::vector< std::vector< std::vector< GridContainers::Cartesian_gridpoint* > > > > boxes;\n tools::vec min = tools::vec(std::numeric_limits::max());\n tools::vec max = tools::vec(std::numeric_limits::min());\n\n for (unsigned i = 0; i < grid.size(); i++) {\n for (unsigned j = 0; j < grid[i].size(); j++) {\n const tools::vec& pos = grid[i][j].grid_pos;\n if (pos.getX() > max.getX()) {\n max.x() = pos.getX();\n } else if (pos.getX() < min.getX()) {\n min.x() = pos.getX();\n }\n if (pos.getY() > max.getY()) {\n max.y() = pos.getY();\n } else if (pos.getY() < min.getY()) {\n min.y() = pos.getY();\n }\n if (pos.getZ() > max.getZ()) {\n max.z() = pos.getZ();\n } else if (pos.getZ() < min.getZ()) {\n min.z() = pos.getZ();\n }\n }\n }\n\n tools::vec molextension = (max - min);\n tools::vec numberofboxes = molextension / boxsize;\n tools::vec roundednumofbox = tools::vec(std::ceil(numberofboxes.getX()), std::ceil(numberofboxes.getY()), std::ceil(numberofboxes.getZ()));\n\n //creating temparray\n for (unsigned i = 0; i > > boxes_yz;\n for (unsigned j = 0; j > boxes_z;\n for (unsigned k = 0; k box;\n box.reserve(100);\n boxes_z.push_back(box);\n }\n boxes_yz.push_back(boxes_z);\n }\n boxes.push_back(boxes_yz);\n }\n\n for (auto & atomgrid : grid) {\n for (auto & gridpoint : atomgrid) {\n tools::vec pos = gridpoint.grid_pos - min;\n tools::vec index = pos / boxsize;\n int i_x = int(index.getX());\n int i_y = int(index.getY());\n int i_z = int(index.getZ());\n boxes[i_x][i_y][i_z].push_back(&gridpoint);\n }\n }\n\n for (auto& boxes_xy : boxes) {\n for (auto& boxes_z : boxes_xy) {\n for (auto& box : boxes_z) {\n if (box.size() < 1) {\n continue;\n }\n GridBox gridbox;\n\n for (const auto&point : box) {\n gridbox.addGridPoint(*point);\n }\n _grid_boxes.push_back(gridbox);\n }\n }\n }\n return;\n }\n \n void NumericalIntegration::FindSignificantShells(const AOBasis& basis) {\n for (unsigned i = 0; i < _grid_boxes.size(); ++i) {\n GridBox & box = _grid_boxes[i];\n for (const AOShell* store:basis) {\n const double decay = store->getMinDecay();\n const tools::vec& shellpos =store->getPos();\n for (const auto& point : box.getGridPoints()) {\n tools::vec dist = shellpos - point;\n double distsq = dist*dist;\n // if contribution is smaller than -ln(1e-10), add shell to list\n if ((decay * distsq) < 20.7) {\n box.addShell(store);\n break;\n }\n }\n }\n }\n\n std::vector< GridBox > grid_boxes_copy;\n int combined = 0;\n //use vecot of bool to indicate if a gridbox has already been merged into another\n std::vector Merged = std::vector(_grid_boxes.size(), false);\n for (unsigned i = 0; i < _grid_boxes.size(); i++) {\n if (Merged[i]) {\n continue;\n }\n GridBox box = _grid_boxes[i];\n if (box.Shellsize() < 1) {\n continue;\n }\n Merged[i] = true;\n for (unsigned j = i + 1; j < _grid_boxes.size(); j++) {\n if (GridBox::compareGridboxes(_grid_boxes[i], _grid_boxes[j])) {\n Merged[j] = true;\n box.addGridBox(_grid_boxes[j]);\n combined++;\n }\n }\n grid_boxes_copy.push_back(box);\n }\n std::vector sizes;\n sizes.reserve(grid_boxes_copy.size());\n for (auto& box : grid_boxes_copy) {\n sizes.push_back(box.size() * box.Matrixsize());\n }\n std::vector indexes = std::vector(sizes.size());\n std::iota(indexes.begin(), indexes.end(), 0);\n std::sort(indexes.begin(), indexes.end(), [&sizes](unsigned i1, unsigned i2) {\n return sizes[i1] > sizes[i2];\n });\n\n unsigned nthreads = 1;\n#ifdef _OPENMP\n nthreads = omp_get_max_threads();\n#endif\n std::vector scores = std::vector(nthreads, 0);\n std::vector< std::vector > indices;\n for (unsigned i = 0; i < nthreads; ++i) {\n std::vector thread_box_indices;\n indices.push_back(thread_box_indices);\n }\n for (const auto index : indexes) {\n unsigned thread = 0;\n unsigned minimum = std::numeric_limits::max();\n for (unsigned i = 0; i < scores.size(); ++i) {\n if (scores[i] < minimum) {\n minimum = scores[i];\n thread = i;\n }\n }\n indices[thread].push_back(index);\n scores[thread] += sizes[index];\n }\n thread_start = std::vector(0);\n thread_stop = std::vector(0);\n unsigned start = 0;\n unsigned stop = 0;\n unsigned indexoffirstgridpoint = 0;\n _grid_boxes.resize(0);\n for (const std::vector& thread_index : indices) {\n thread_start.push_back(start);\n stop = start + thread_index.size();\n thread_stop.push_back(stop);\n start = stop;\n for (const unsigned index : thread_index) {\n GridBox newbox = grid_boxes_copy[index];\n newbox.setIndexoffirstgridpoint(indexoffirstgridpoint);\n indexoffirstgridpoint += newbox.size();\n newbox.PrepareForIntegration();\n _grid_boxes.push_back(newbox);\n }\n }\n return;\n }\n \n \n \n Eigen::MatrixXd NumericalIntegration::IntegrateVXC(const Eigen::MatrixXd& density_matrix) {\n Eigen::MatrixXd Vxc = Eigen::MatrixXd::Zero(density_matrix.rows(), density_matrix.cols());\n _EXC = 0;\n unsigned nthreads = 1;\n#ifdef _OPENMP\n nthreads = omp_get_max_threads();\n#endif\n std::vectorvxc_thread;\n std::vector Exc_thread = std::vector(nthreads, 0.0);\n for (unsigned i = 0; i < nthreads; ++i) {\n Eigen::MatrixXd Vxc_thread = Eigen::MatrixXd::Zero(density_matrix.rows(), density_matrix.cols());\n vxc_thread.push_back(Vxc_thread);\n }\n \n#pragma omp parallel for\n for (unsigned thread = 0; thread < nthreads; ++thread) {\n for (unsigned i = thread_start[thread]; i < thread_stop[thread]; ++i) {\n\n double EXC_box = 0.0;\n const GridBox& box = _grid_boxes[i];\n const Eigen::MatrixXd DMAT_here = box.ReadFromBigMatrix(density_matrix);\n const Eigen::MatrixXd DMAT_symm= DMAT_here+DMAT_here.transpose();\n double cutoff=1.e-40/density_matrix.rows()/density_matrix.rows();\n if (DMAT_here.cwiseAbs2().maxCoeff()& points = box.getGridPoints();\n const std::vector& weights = box.getGridWeights();\n const std::vector& aoranges = box.getAOranges();\n const std::vector& shells = box.getShells();\n \n //iterate over gridpoints\n for (unsigned p = 0; p < box.size(); p++) {\n Eigen::VectorXd ao = Eigen::VectorXd::Zero(box.Matrixsize());\n Eigen::MatrixX3d ao_grad= Eigen::MatrixX3d::Zero(box.Matrixsize(),3);\n for (unsigned j = 0; j < box.Shellsize(); ++j) {\n Eigen::Block grad_block=ao_grad.block(aoranges[j].start,0,aoranges[j].size,3);\n Eigen::VectorBlock ao_block=ao.segment(aoranges[j].start,aoranges[j].size);\n shells[j]->EvalAOspace(ao_block,grad_block,points[p]); \n }\n const double rho =0.5* (ao.transpose()*DMAT_symm*ao).value();\n const double weight = weights[p];\n if (rho*weight < 1.e-20) continue; // skip the rest, if density is very small\n const Eigen::Vector3d rho_grad = ao.transpose()*DMAT_symm*ao_grad;\n const double sigma = (rho_grad.transpose()*rho_grad).value();\n const Eigen::VectorXd grad =ao_grad*rho_grad;\n double f_xc; // E_xc[n] = int{n(r)*eps_xc[n(r)] d3r} = int{ f_xc(r) d3r }\n double df_drho; // v_xc_rho(r) = df/drho\n double df_dsigma; // df/dsigma ( df/dgrad(rho) = df/dsigma * dsigma/dgrad(rho) = df/dsigma * 2*grad(rho))\n EvaluateXC(rho, sigma, f_xc, df_drho, df_dsigma); \n EXC_box += weight * rho * f_xc;\n auto addXC = weight * (0.5*df_drho * ao+ 2.0 * df_dsigma *grad);\n // Exchange correlation energy\n Vxc_here.noalias() += addXC* ao.transpose();\n }\n box.AddtoBigMatrix(vxc_thread[thread], Vxc_here);\n Exc_thread[thread] += EXC_box;\n }\n }\n for (unsigned i = 0; i < nthreads; ++i) {\n Vxc += vxc_thread[i];\n _EXC += Exc_thread[i];\n } \n return Vxc+Vxc.transpose();\n }\n \n \n Eigen::MatrixXd NumericalIntegration::IntegrateExternalPotential(const std::vector& Potentialvalues) {\n\n Eigen::MatrixXd ExternalMat = Eigen::MatrixXd::Zero(_AOBasisSize, _AOBasisSize);\n unsigned nthreads = 1;\n#ifdef _OPENMP\n nthreads = omp_get_max_threads();\n#endif\n std::vectorvex_thread;\n std::vector Exc_thread = std::vector(nthreads, 0.0);\n for (unsigned i = 0; i < nthreads; ++i) {\n Eigen::MatrixXd Vex_thread = Eigen::MatrixXd::Zero(ExternalMat.rows(), ExternalMat.cols());\n vex_thread.push_back(Vex_thread);\n }\n\n#pragma omp parallel for\n for (unsigned thread = 0; thread < nthreads; ++thread) {\n for (unsigned i = thread_start[thread]; i < thread_stop[thread]; ++i) {\n\n const GridBox& box = _grid_boxes[i];\n Eigen::MatrixXd Vex_here = Eigen::MatrixXd::Zero(box.Matrixsize(), box.Matrixsize());\n const std::vector& points = box.getGridPoints();\n const std::vector& weights = box.getGridWeights();\n\n //iterate over gridpoints\n for (unsigned p = 0; p < box.size(); p++) {\n Eigen::VectorXd ao = Eigen::VectorXd::Zero(box.Matrixsize());\n const std::vector& aoranges = box.getAOranges();\n const std::vector shells = box.getShells();\n for (unsigned j = 0; j < box.Shellsize(); ++j) {\n Eigen::VectorBlock ao_block=ao.segment(aoranges[j].start,aoranges[j].size);\n shells[j]->EvalAOspace(ao_block, points[p]);\n }\n Eigen::VectorXd addEX = weights[p] * Potentialvalues[box.getIndexoffirstgridpoint() + p] * ao;\n Vex_here += addEX.transpose() * ao;\n }\n box.AddtoBigMatrix(vex_thread[thread], Vex_here);\n }\n }\n for (unsigned i = 0; i < nthreads; ++i) {\n ExternalMat += vex_thread[i];\n }\n ExternalMat += ExternalMat.transpose();\n return ExternalMat;\n }\n \n double NumericalIntegration::IntegrateDensity(const Eigen::MatrixXd& density_matrix) {\n double N = 0;\n unsigned nthreads = 1;\n#ifdef _OPENMP\n nthreads = omp_get_max_threads();\n#endif\n std::vector N_thread = std::vector(nthreads, 0.0);\n\n#pragma omp parallel for\n for (unsigned thread = 0; thread < nthreads; ++thread) {\n for (unsigned i = thread_start[thread]; i < thread_stop[thread]; ++i) {\n\n double N_box = 0.0;\n GridBox& box = _grid_boxes[i];\n const Eigen::MatrixXd DMAT_here = box.ReadFromBigMatrix(density_matrix);\n const std::vector& points = box.getGridPoints();\n const std::vector& weights = box.getGridWeights();\n box.prepareDensity();\n //iterate over gridpoints\n for (unsigned p = 0; p < box.size(); p++) {\n Eigen::VectorXd ao = Eigen::VectorXd::Zero(box.Matrixsize());\n const std::vector& aoranges = box.getAOranges();\n const std::vector shells = box.getShells();\n for (unsigned j = 0; j < box.Shellsize(); ++j) {\n Eigen::VectorBlock ao_block=ao.segment(aoranges[j].start,aoranges[j].size);\n shells[j]->EvalAOspace(ao_block, points[p]);\n }\n double rho =(ao.transpose()*DMAT_here*ao)(0, 0);\n box.addDensity(rho);\n N_box += rho * weights[p];\n }\n N_thread[thread] += N_box;\n }\n }\n for (unsigned i = 0; i < nthreads; ++i) {\n N += N_thread[i];\n }\n _density_set = true;\n return N;\n }\n \n Gyrationtensor NumericalIntegration::IntegrateGyrationTensor(const Eigen::MatrixXd& density_matrix) {\n double N = 0;\n tools::vec centroid = tools::vec(0.0);\n tools::matrix gyration = tools::matrix(0.0);\n unsigned nthreads = 1;\n#ifdef _OPENMP\n nthreads = omp_get_max_threads();\n#endif\n std::vector N_thread = std::vector(nthreads, 0.0);\n // centroid\n std::vector centroid_thread;\n std::vector gyration_thread;\n for (unsigned thread = 0; thread < nthreads; ++thread) {\n tools::vec tempvec = tools::vec(0.0);\n centroid_thread.push_back(tempvec);\n tools::matrix tempmatrix = tools::matrix(0.0);\n gyration_thread.push_back(tempmatrix);\n }\n\n#pragma omp parallel for\n for (unsigned thread = 0; thread < nthreads; ++thread) {\n for (unsigned i = thread_start[thread]; i < thread_stop[thread]; ++i) {\n double N_box = 0.0;\n tools::vec centroid_box = tools::vec(0.0);\n tools::matrix gyration_box = tools::matrix(0.0);\n GridBox& box = _grid_boxes[i];\n const Eigen::MatrixXd DMAT_here = box.ReadFromBigMatrix(density_matrix);\n const std::vector& points = box.getGridPoints();\n const std::vector& weights = box.getGridWeights();\n box.prepareDensity();\n //iterate over gridpoints\n for (unsigned p = 0; p < box.size(); p++) {\n Eigen::VectorXd ao = Eigen::VectorXd::Zero(box.Matrixsize());\n const std::vector& aoranges = box.getAOranges();\n const std::vector shells = box.getShells();\n for (unsigned j = 0; j < box.Shellsize(); ++j) {\n Eigen::VectorBlock ao_block=ao.segment(aoranges[j].start,aoranges[j].size);\n shells[j]->EvalAOspace(ao_block, points[p]);\n }\n double rho =(ao.transpose()*DMAT_here*ao)(0, 0);\n box.addDensity(rho);\n N_box += rho * weights[p];\n centroid_box+=rho * weights[p] * points[p];\n gyration_box += rho * weights[p] * (points[p]|points[p]); \n }\n N_thread[thread] += N_box;\n centroid_thread[thread] += centroid_box;\n gyration_thread[thread] += gyration_box;\n }\n }\n for (unsigned i = 0; i < nthreads; ++i) {\n N += N_thread[i];\n centroid += centroid_thread[i];\n gyration += gyration_thread[i];\n }\n _density_set = true;\n // Normalize\n centroid = centroid / N;\n gyration = gyration / N;\n gyration=gyration-(centroid|centroid);\n Gyrationtensor gyro;\n gyro.mass=N;\n gyro.centroid=centroid;\n gyro.gyration=gyration;\n \n return gyro;\n }\n\n \nstd::vector NumericalIntegration::getGridpoints() const{\n std::vector gridpoints;\n for (unsigned i = 0; i < _grid_boxes.size(); i++) {\n const std::vector& points = _grid_boxes[i].getGridPoints();\n for (unsigned j = 0; j < points.size(); j++) {\n gridpoints.push_back(&points[j]);\n }\n }\n return gridpoints;\n }\n\n\nEigen::MatrixXd NumericalIntegration::IntegratePotential(const AOBasis& externalbasis){\n Eigen::MatrixXd Potential=Eigen::MatrixXd::Zero(externalbasis.AOBasisSize(),externalbasis.AOBasisSize());\n \n assert(_density_set && \"Density not calculated\");\n for (unsigned i = 0; i < _grid_boxes.size(); i++) {\n const std::vector& points = _grid_boxes[i].getGridPoints();\n const std::vector& weights = _grid_boxes[i].getGridWeights();\n const std::vector& densities = _grid_boxes[i].getGridDensities();\n for (unsigned j = 0; j < points.size(); j++) {\n double weighteddensity=weights[j]*densities[j];\n if (weighteddensity<1e-12){\n continue;\n }\n AOESP esp;\n esp.setPosition(points[j]);\n esp.Fill(externalbasis);\n Potential+=weighteddensity*esp.Matrix();\n }\n }\n return Potential; \n}\n \n\nEigen::MatrixXd NumericalIntegration::CalcInverseAtomDist(std::vector& atoms){\n Eigen::MatrixXd result=Eigen::MatrixXd::Zero(atoms.size(),atoms.size());\n#pragma omp parallel for\n for (unsigned i=0;igetPos();\n for (unsigned j=0;jgetPos();\n result(j,i)=1/tools::abs(pos_a-pos_b);\n } \n }\n return result+result.transpose();\n}\n\nint NumericalIntegration::UpdateOrder(LebedevGrid& sphericalgridofElement, int maxorder, std::vector& PruningIntervals, double r){\n int order;\n int maxindex=sphericalgridofElement.getIndexFromOrder(maxorder);\n if (maxindex == 1) {\n // smallest possible grid anyway, nothing to do\n order = maxorder;\n } else if (maxindex == 2) {\n // only three intervals\n if (r < PruningIntervals[0]) {\n order = sphericalgridofElement.getOrderFromIndex(1); //1;\n } else if ((r >= PruningIntervals[0]) && (r < PruningIntervals[3])) {\n order = sphericalgridofElement.getOrderFromIndex(2);\n } else {\n order = sphericalgridofElement.getOrderFromIndex(1);\n } // maxorder == 2\n } else {\n // five intervals\n if (r < PruningIntervals[0]) {\n order = sphericalgridofElement.getOrderFromIndex(2);\n } else if ((r >= PruningIntervals[0]) && (r < PruningIntervals[1])) {\n order = sphericalgridofElement.getOrderFromIndex(4);\n } else if ((r >= PruningIntervals[1]) && (r < PruningIntervals[2])) {\n order = sphericalgridofElement.getOrderFromIndex(std::max(maxindex - 1, 4));\n } else if ((r >= PruningIntervals[2]) && (r < PruningIntervals[3])) {\n order = maxorder;\n } else {\n order = sphericalgridofElement.getOrderFromIndex(std::max(maxindex - 1, 1));\n }\n }\n return order;\n }\n\n GridContainers::Cartesian_gridpoint NumericalIntegration::CreateCartesianGridpoint(const tools::vec& atomA_pos,\n GridContainers::radial_grid& radial_grid, GridContainers::spherical_grid& spherical_grid,\n unsigned i_rad, unsigned i_sph) {\n GridContainers::Cartesian_gridpoint gridpoint;\n double p = spherical_grid.phi[i_sph];\n double t = spherical_grid.theta[i_sph];\n const tools::vec s = tools::vec(sin(p) * cos(t), sin(p) * sin(t), cos(p));\n double r = radial_grid.radius[i_rad];\n gridpoint.grid_pos = atomA_pos + r*s;\n gridpoint.grid_weight = radial_grid.weight[i_rad] * spherical_grid.weight[i_sph];\n return gridpoint;\n }\n\n Eigen::MatrixXd NumericalIntegration::CalcDistanceAtomsGridpoints(std::vector& atoms, std::vector& atomgrid){\n Eigen::MatrixXd result=Eigen::MatrixXd::Zero(atoms.size(),atomgrid.size());\n #pragma omp parallel for\n for (unsigned i=0;igetPos();\n for (unsigned j=0;j& atoms, std::vector& atomgrid\n , unsigned i_atom, const Eigen::MatrixXd& Rij){\n Eigen::MatrixXd AtomGridDist=CalcDistanceAtomsGridpoints(atoms, atomgrid);\n \n#pragma omp parallel for schedule(guided)\n for (unsigned i_grid = 0; i_grid < atomgrid.size(); i_grid++) {\n Eigen::VectorXd p = SSWpartition(i_grid, AtomGridDist,Rij);\n // check weight sum\n double wsum = p.sum();\n if (wsum != 0.0) {\n // update the weight of this grid point\n atomgrid[i_grid].grid_weight *= p[i_atom] / wsum;\n } else {\n std::cerr << \"\\nSum of partition weights of grid point \" << i_grid << \" of atom \" << i_atom << \" is zero! \";\n throw std::runtime_error(\"\\nThis should never happen!\");\n }\n } // partition weight for each gridpoint\n }\n \nvoid NumericalIntegration::GridSetup(const std::string& type, std::vector atoms,const AOBasis& basis) {\n _AOBasisSize=basis.AOBasisSize();\n GridContainers initialgrids;\n // get radial grid per element\n EulerMaclaurinGrid radialgridofElement;\n initialgrids.radial_grids=radialgridofElement.CalculateAtomicRadialGrids(basis, atoms, type); // this checks out 1:1 with NWChem results! AWESOME\n LebedevGrid sphericalgridofElement;\n initialgrids.spherical_grids=sphericalgridofElement.CalculateSphericalGrids(atoms,type);\n \n // for the partitioning, we need all inter-center distances later, stored in matrix\n Eigen::MatrixXd Rij=CalcInverseAtomDist(atoms);\n _totalgridsize = 0;\n std::vector< std::vector< GridContainers::Cartesian_gridpoint > > grid;\n \n for (unsigned i_atom=0;i_atomgetPos();\n const std::string & name = atom->getType();\n GridContainers::radial_grid radial_grid = initialgrids.radial_grids.at(name);\n GridContainers::spherical_grid spherical_grid = initialgrids.spherical_grids.at(name);\n \n // maximum order (= number of points) in spherical integration grid\n int maxorder = sphericalgridofElement.Type2MaxOrder(name, type);\n // for pruning of integration grid, get interval boundaries for this element\n std::vector PruningIntervals = radialgridofElement.CalculatePruningIntervals(name);\n int current_order = 0;\n // for each radial value\n std::vector< GridContainers::Cartesian_gridpoint > atomgrid;\n for (unsigned i_rad = 0; i_rad < radial_grid.radius.size(); i_rad++) {\n double r = radial_grid.radius[i_rad];\n\n // which Lebedev order for this point?\n int order=UpdateOrder(sphericalgridofElement, maxorder, PruningIntervals, r);\n // get new spherical grid, if order changed\n if (order != current_order) {\n spherical_grid=sphericalgridofElement.CalculateUnitSphereGrid(order);\n current_order = order;\n }\n\n for (unsigned i_sph = 0; i_sph < spherical_grid.phi.size(); i_sph++) {\n GridContainers::Cartesian_gridpoint gridpoint=CreateCartesianGridpoint(atomA_pos, radial_grid, spherical_grid, i_rad,i_sph);\n atomgrid.push_back(gridpoint);\n } // spherical gridpoints\n } // radial gridpoint\n\n SSWpartitionAtom(atoms, atomgrid, i_atom,Rij);\n\n // now remove points from the grid with negligible weights\n for (std::vector::iterator git = atomgrid.begin(); git != atomgrid.end();) {\n if (git->grid_weight < 1e-13) {\n git = atomgrid.erase(git);\n } else {\n ++git;\n }\n }\n _totalgridsize += atomgrid.size();\n grid.push_back(atomgrid);\n } // atoms\n SortGridpointsintoBlocks(grid);\n FindSignificantShells(basis);\n return;\n }\n\n Eigen::VectorXd NumericalIntegration::SSWpartition(int igrid, const Eigen::MatrixXd & rq,const Eigen::MatrixXd& Rij) {\n const double ass = 0.725;\n // initialize partition vector to 1.0\n Eigen::VectorXd p=Eigen::VectorXd::Ones(rq.rows());\n const double tol_scr = 1e-10;\n const double leps = 1e-6;\n // go through centers\n for (int i = 1; i < rq.rows(); i++) {\n double rag = rq(i,igrid);\n // through all other centers (one-directional)\n for (int j = 0; j < i; j++) {\n if ((std::abs(p[i]) > tol_scr) || (std::abs(p[j]) > tol_scr)) {\n double mu = (rag - rq(j,igrid)) * Rij(j,i);\n if (mu > ass) {\n p[i] = 0.0;\n } else if (mu < -ass) {\n p[j] = 0.0;\n } else {\n double sk;\n if (std::abs(mu) < leps) {\n sk = -1.88603178008 * mu + 0.5;\n } else {\n sk =erf1c(mu);\n }\n if (mu > 0.0) sk = 1.0 - sk;\n p[j] = p[j] * sk;\n p[i] = p[i] * (1.0 - sk);\n }\n }\n }\n }\n return p;\n }\n\n double NumericalIntegration::erf1c(double x){ \n const static double alpha_erf1=1.0/0.30;\n return 0.5*std::erfc(std::abs(x/(1.0-x*x))*alpha_erf1); \n }\n \n \n \n }\n}\n", "meta": {"hexsha": "4e040104de64c664a75123814304638e252c2b86", "size": 32085, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/libxtp/numerical_integration/numerical_integrations.cc", "max_stars_repo_name": "mbarbry/xtp", "max_stars_repo_head_hexsha": "e79828209d11ec25bf1750ab75499ecf50f584ef", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/libxtp/numerical_integration/numerical_integrations.cc", "max_issues_repo_name": "mbarbry/xtp", "max_issues_repo_head_hexsha": "e79828209d11ec25bf1750ab75499ecf50f584ef", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/libxtp/numerical_integration/numerical_integrations.cc", "max_forks_repo_name": "mbarbry/xtp", "max_forks_repo_head_hexsha": "e79828209d11ec25bf1750ab75499ecf50f584ef", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.4164619165, "max_line_length": 159, "alphanum_fraction": 0.5887174692, "num_tokens": 8212, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.4263215925474903, "lm_q1q2_score": 0.21815583785047435}} {"text": "// Boost.Geometry - gis-projections (based on PROJ4)\n\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\n\n// This file was modified by Oracle on 2017, 2018.\n// Modifications copyright (c) 2017-2018, Oracle and/or its affiliates.\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\n\n// Last updated version of proj: 5.0.0\n\n// Original copyright notice:\n\n// This code was entirely written by Nathan Wagner\n// and is in the public domain.\n\n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#ifndef BOOST_GEOMETRY_PROJECTIONS_ISEA_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_ISEA_HPP\n\n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace boost { namespace geometry\n{\n\nnamespace projections\n{\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail { namespace isea\n {\n static const double epsilon = std::numeric_limits::epsilon();\n\n /* sqrt(5)/M_PI */\n static const double isea_scale = 0.8301572857837594396028083;\n /* 26.565051177 degrees */\n static const double v_lat = 0.46364760899944494524;\n /* 52.62263186 */\n static const double e_rad = 0.91843818702186776133;\n /* 10.81231696 */\n static const double f_rad = 0.18871053072122403508;\n /* R tan(g) sin(60) */\n static const double table_g = 0.6615845383;\n /* H = 0.25 R tan g = */\n static const double table_h = 0.1909830056;\n //static const double RPRIME = 0.91038328153090290025;\n static const double isea_std_lat = 1.01722196792335072101;\n static const double isea_std_lon = .19634954084936207740;\n\n template \n inline T deg30_rad() { return T(30) * geometry::math::d2r(); }\n template \n inline T deg120_rad() { return T(120) * geometry::math::d2r(); }\n template \n inline T deg72_rad() { return T(72) * geometry::math::d2r(); }\n template \n inline T deg90_rad() { return geometry::math::half_pi(); }\n template \n inline T deg144_rad() { return T(144) * geometry::math::d2r(); }\n template \n inline T deg36_rad() { return T(36) * geometry::math::d2r(); }\n template \n inline T deg108_rad() { return T(108) * geometry::math::d2r(); }\n template \n inline T deg180_rad() { return geometry::math::pi(); }\n\n inline bool downtri(int tri) { return (((tri - 1) / 5) % 2 == 1); }\n\n /*\n * Proj 4 provides its own entry points into\n * the code, so none of the library functions\n * need to be global\n */\n\n struct hex {\n int iso;\n int x, y, z;\n };\n\n /* y *must* be positive down as the xy /iso conversion assumes this */\n inline\n int hex_xy(struct hex *h) {\n if (!h->iso) return 1;\n if (h->x >= 0) {\n h->y = -h->y - (h->x+1)/2;\n } else {\n /* need to round toward -inf, not toward zero, so x-1 */\n h->y = -h->y - h->x/2;\n }\n h->iso = 0;\n\n return 1;\n }\n\n inline\n int hex_iso(struct hex *h) {\n if (h->iso) return 1;\n\n if (h->x >= 0) {\n h->y = (-h->y - (h->x+1)/2);\n } else {\n /* need to round toward -inf, not toward zero, so x-1 */\n h->y = (-h->y - (h->x)/2);\n }\n\n h->z = -h->x - h->y;\n h->iso = 1;\n return 1;\n }\n\n template \n inline\n int hexbin2(T const& width, T x, T y, int *i, int *j)\n {\n T z, rx, ry, rz;\n T abs_dx, abs_dy, abs_dz;\n int ix, iy, iz, s;\n struct hex h;\n\n static const T cos_deg30 = cos(deg30_rad());\n\n x = x / cos_deg30; /* rotated X coord */\n y = y - x / 2.0; /* adjustment for rotated X */\n\n /* adjust for actual hexwidth */\n x /= width;\n y /= width;\n\n z = -x - y;\n\n rx = floor(x + 0.5);\n ix = (int)rx;\n ry = floor(y + 0.5);\n iy = (int)ry;\n rz = floor(z + 0.5);\n iz = (int)rz;\n\n s = ix + iy + iz;\n\n if (s) {\n abs_dx = fabs(rx - x);\n abs_dy = fabs(ry - y);\n abs_dz = fabs(rz - z);\n\n if (abs_dx >= abs_dy && abs_dx >= abs_dz) {\n ix -= s;\n } else if (abs_dy >= abs_dx && abs_dy >= abs_dz) {\n iy -= s;\n } else {\n iz -= s;\n }\n }\n h.x = ix;\n h.y = iy;\n h.z = iz;\n h.iso = 1;\n\n hex_xy(&h);\n *i = h.x;\n *j = h.y;\n return ix * 100 + iy;\n }\n\n //enum isea_poly { isea_none = 0, isea_icosahedron = 20 };\n //enum isea_topology { isea_hexagon=6, isea_triangle=3, isea_diamond=4 };\n enum isea_address_form {\n isea_addr_geo, isea_addr_q2di, isea_addr_seqnum,\n isea_addr_interleave, isea_addr_plane, isea_addr_q2dd,\n isea_addr_projtri, isea_addr_vertex2dd, isea_addr_hex\n };\n\n template \n struct isea_dgg {\n //isea_poly polyhedron; /* ignored, icosahedron */\n T o_lat, o_lon, o_az; /* orientation, radians */\n int pole; /* true if standard snyder */\n //isea_topology topology; /* ignored, hexagon */\n int aperture; /* valid values depend on partitioning method */\n int resolution;\n T radius; /* radius of the earth in meters, ignored 1.0 */\n isea_address_form output; /* an isea_address_form */\n int triangle; /* triangle of last transformed point */\n int quad; /* quad of last transformed point */\n unsigned long serial;\n };\n\n template \n struct isea_pt {\n T x, y;\n };\n\n template \n struct isea_geo {\n T lon, lat;\n };\n\n template \n struct isea_address {\n int type; /* enum isea_address_form */\n int number;\n T x,y; /* or i,j or lon,lat depending on type */\n };\n\n /* ENDINC */\n\n enum snyder_polyhedron {\n snyder_poly_hexagon = 0, snyder_poly_pentagon = 1,\n snyder_poly_tetrahedron = 2, snyder_poly_cube = 3,\n snyder_poly_octahedron = 4, snyder_poly_dodecahedron = 5,\n snyder_poly_icosahedron = 6\n };\n\n template \n struct snyder_constants {\n T g, G, theta, ea_w, ea_a, ea_b, g_w, g_a, g_b;\n };\n\n template \n inline const snyder_constants * constants()\n {\n /* TODO put these in radians to avoid a later conversion */\n static snyder_constants result[] = {\n {23.80018260, 62.15458023, 60.0, 3.75, 1.033, 0.968, 5.09, 1.195, 1.0},\n {20.07675127, 55.69063953, 54.0, 2.65, 1.030, 0.983, 3.59, 1.141, 1.027},\n {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0},\n {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0},\n {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0},\n {0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0},\n {37.37736814, 36.0, 30.0, 17.27, 1.163, 0.860, 13.14, 1.584, 1.0}\n };\n return result;\n }\n \n template \n inline const isea_geo * vertex()\n {\n static isea_geo result[] = {\n { 0.0, deg90_rad()},\n { deg180_rad(), v_lat},\n {-deg108_rad(), v_lat},\n {-deg36_rad(), v_lat},\n { deg36_rad(), v_lat},\n { deg108_rad(), v_lat},\n {-deg144_rad(), -v_lat},\n {-deg72_rad(), -v_lat},\n { 0.0, -v_lat},\n { deg72_rad(), -v_lat},\n { deg144_rad(), -v_lat},\n { 0.0, -deg90_rad()}\n };\n return result;\n }\n\n /* TODO make an isea_pt array of the vertices as well */\n\n static int tri_v1[] = {0, 0, 0, 0, 0, 0, 6, 7, 8, 9, 10, 2, 3, 4, 5, 1, 11, 11, 11, 11, 11};\n\n /* triangle Centers */\n template \n inline const isea_geo * icostriangles()\n {\n static isea_geo result[] = {\n { 0.0, 0.0},\n {-deg144_rad(), e_rad},\n {-deg72_rad(), e_rad},\n { 0.0, e_rad},\n { deg72_rad(), e_rad},\n { deg144_rad(), e_rad},\n {-deg144_rad(), f_rad},\n {-deg72_rad(), f_rad},\n { 0.0, f_rad},\n { deg72_rad(), f_rad},\n { deg144_rad(), f_rad},\n {-deg108_rad(), -f_rad},\n {-deg36_rad(), -f_rad},\n { deg36_rad(), -f_rad},\n { deg108_rad(), -f_rad},\n { deg180_rad(), -f_rad},\n {-deg108_rad(), -e_rad},\n {-deg36_rad(), -e_rad},\n { deg36_rad(), -e_rad},\n { deg108_rad(), -e_rad},\n { deg180_rad(), -e_rad},\n };\n return result;\n }\n\n template \n inline T az_adjustment(int triangle)\n {\n T adj;\n\n isea_geo v;\n isea_geo c;\n\n v = vertex()[tri_v1[triangle]];\n c = icostriangles()[triangle];\n\n /* TODO looks like the adjustment is always either 0 or 180 */\n /* at least if you pick your vertex carefully */\n adj = atan2(cos(v.lat) * sin(v.lon - c.lon),\n cos(c.lat) * sin(v.lat)\n - sin(c.lat) * cos(v.lat) * cos(v.lon - c.lon));\n return adj;\n }\n\n template \n inline isea_pt isea_triangle_xy(int triangle)\n {\n isea_pt c;\n T Rprime = 0.91038328153090290025;\n\n triangle = (triangle - 1) % 20;\n\n c.x = table_g * ((triangle % 5) - 2) * 2.0;\n if (triangle > 9) {\n c.x += table_g;\n }\n switch (triangle / 5) {\n case 0:\n c.y = 5.0 * table_h;\n break;\n case 1:\n c.y = table_h;\n break;\n case 2:\n c.y = -table_h;\n break;\n case 3:\n c.y = -5.0 * table_h;\n break;\n default:\n /* should be impossible */\n BOOST_THROW_EXCEPTION( projection_exception() );\n };\n c.x *= Rprime;\n c.y *= Rprime;\n\n return c;\n }\n\n /* snyder eq 14 */\n template \n inline T sph_azimuth(T const& f_lon, T const& f_lat, T const& t_lon, T const& t_lat)\n {\n T az;\n\n az = atan2(cos(t_lat) * sin(t_lon - f_lon),\n cos(f_lat) * sin(t_lat)\n - sin(f_lat) * cos(t_lat) * cos(t_lon - f_lon)\n );\n return az;\n }\n\n /* coord needs to be in radians */\n template \n inline int isea_snyder_forward(isea_geo * ll, isea_pt * out)\n {\n static T const two_pi = detail::two_pi();\n static T const d2r = geometry::math::d2r();\n\n int i;\n\n /*\n * spherical distance from center of polygon face to any of its\n * vertexes on the globe\n */\n T g;\n\n /*\n * spherical angle between radius vector to center and adjacent edge\n * of spherical polygon on the globe\n */\n T G;\n\n /*\n * plane angle between radius vector to center and adjacent edge of\n * plane polygon\n */\n T theta;\n\n /* additional variables from snyder */\n T q, Rprime, H, Ag, Azprime, Az, dprime, f, rho,\n x, y;\n\n /* variables used to store intermediate results */\n T cot_theta, tan_g, az_offset;\n\n /* how many multiples of 60 degrees we adjust the azimuth */\n int Az_adjust_multiples;\n\n snyder_constants c;\n\n /*\n * TODO by locality of reference, start by trying the same triangle\n * as last time\n */\n\n /* TODO put these constants in as radians to begin with */\n c = constants()[snyder_poly_icosahedron];\n theta = c.theta * d2r;\n g = c.g * d2r;\n G = c.G * d2r;\n\n for (i = 1; i <= 20; i++) {\n T z;\n isea_geo center;\n\n center = icostriangles()[i];\n\n /* step 1 */\n z = acos(sin(center.lat) * sin(ll->lat)\n + cos(center.lat) * cos(ll->lat) * cos(ll->lon - center.lon));\n\n /* not on this triangle */\n if (z > g + 0.000005) { /* TODO DBL_EPSILON */\n continue;\n }\n\n Az = sph_azimuth(center.lon, center.lat, ll->lon, ll->lat);\n\n /* step 2 */\n\n /* This calculates \"some\" vertex coordinate */\n az_offset = az_adjustment(i);\n\n Az -= az_offset;\n\n /* TODO I don't know why we do this. It's not in snyder */\n /* maybe because we should have picked a better vertex */\n if (Az < 0.0) {\n Az += two_pi;\n }\n /*\n * adjust Az for the point to fall within the range of 0 to\n * 2(90 - theta) or 60 degrees for the hexagon, by\n * and therefore 120 degrees for the triangle\n * of the icosahedron\n * subtracting or adding multiples of 60 degrees to Az and\n * recording the amount of adjustment\n */\n\n Az_adjust_multiples = 0;\n while (Az < 0.0) {\n Az += deg120_rad();\n Az_adjust_multiples--;\n }\n while (Az > deg120_rad() + epsilon) {\n Az -= deg120_rad();\n Az_adjust_multiples++;\n }\n\n /* step 3 */\n cot_theta = 1.0 / tan(theta);\n tan_g = tan(g); /* TODO this is a constant */\n\n /* Calculate q from eq 9. */\n /* TODO cot_theta is cot(30) */\n q = atan2(tan_g, cos(Az) + sin(Az) * cot_theta);\n\n /* not in this triangle */\n if (z > q + 0.000005) {\n continue;\n }\n /* step 4 */\n\n /* Apply equations 5-8 and 10-12 in order */\n\n /* eq 5 */\n /* Rprime = 0.9449322893 * R; */\n /* R' in the paper is for the truncated */\n Rprime = 0.91038328153090290025;\n\n /* eq 6 */\n H = acos(sin(Az) * sin(G) * cos(g) - cos(Az) * cos(G));\n\n /* eq 7 */\n /* Ag = (Az + G + H - deg180_rad) * M_PI * R * R / deg180_rad; */\n Ag = Az + G + H - deg180_rad();\n\n /* eq 8 */\n Azprime = atan2(2.0 * Ag, Rprime * Rprime * tan_g * tan_g - 2.0 * Ag * cot_theta);\n\n /* eq 10 */\n /* cot(theta) = 1.73205080756887729355 */\n dprime = Rprime * tan_g / (cos(Azprime) + sin(Azprime) * cot_theta);\n\n /* eq 11 */\n f = dprime / (2.0 * Rprime * sin(q / 2.0));\n\n /* eq 12 */\n rho = 2.0 * Rprime * f * sin(z / 2.0);\n\n /*\n * add back the same 60 degree multiple adjustment from step\n * 2 to Azprime\n */\n\n Azprime += deg120_rad() * Az_adjust_multiples;\n\n /* calculate rectangular coordinates */\n\n x = rho * sin(Azprime);\n y = rho * cos(Azprime);\n\n /*\n * TODO\n * translate coordinates to the origin for the particular\n * hexagon on the flattened polyhedral map plot\n */\n\n out->x = x;\n out->y = y;\n\n return i;\n }\n\n /*\n * should be impossible, this implies that the coordinate is not on\n * any triangle\n */\n\n //fprintf(stderr, \"impossible transform: %f %f is not on any triangle\\n\",\n // ll->lon * geometry::math::r2d(), ll->lat * geometry::math::r2d());\n std::stringstream ss;\n ss << \"impossible transform: \" << ll->lon * geometry::math::r2d()\n << \" \" << ll->lat * geometry::math::r2d() << \" is not on any triangle.\";\n\n BOOST_THROW_EXCEPTION( projection_exception(ss.str()) );\n\n /* not reached */\n return 0; /* supresses a warning */\n }\n\n /*\n * return the new coordinates of any point in orginal coordinate system.\n * Define a point (newNPold) in orginal coordinate system as the North Pole in\n * new coordinate system, and the great circle connect the original and new\n * North Pole as the lon0 longitude in new coordinate system, given any point\n * in orginal coordinate system, this function return the new coordinates.\n */\n\n\n /* formula from Snyder, Map Projections: A working manual, p31 */\n /*\n * old north pole at np in new coordinates\n * could be simplified a bit with fewer intermediates\n *\n * TODO take a result pointer\n */\n template \n inline isea_geo snyder_ctran(isea_geo * np, isea_geo * pt)\n {\n static T const pi = detail::pi();\n static T const two_pi = detail::two_pi();\n\n isea_geo npt;\n T alpha, phi, lambda, lambda0, beta, lambdap, phip;\n T sin_phip;\n T lp_b; /* lambda prime minus beta */\n T cos_p, sin_a;\n\n phi = pt->lat;\n lambda = pt->lon;\n alpha = np->lat;\n beta = np->lon;\n lambda0 = beta;\n\n cos_p = cos(phi);\n sin_a = sin(alpha);\n\n /* mpawm 5-7 */\n sin_phip = sin_a * sin(phi) - cos(alpha) * cos_p * cos(lambda - lambda0);\n\n /* mpawm 5-8b */\n\n /* use the two argument form so we end up in the right quadrant */\n lp_b = atan2(cos_p * sin(lambda - lambda0),\n (sin_a * cos_p * cos(lambda - lambda0) + cos(alpha) * sin(phi)));\n\n lambdap = lp_b + beta;\n\n /* normalize longitude */\n /* TODO can we just do a modulus ? */\n lambdap = fmod(lambdap, two_pi);\n while (lambdap > pi)\n lambdap -= two_pi;\n while (lambdap < -pi)\n lambdap += two_pi;\n\n phip = asin(sin_phip);\n\n npt.lat = phip;\n npt.lon = lambdap;\n\n return npt;\n }\n\n template \n inline isea_geo isea_ctran(isea_geo * np, isea_geo * pt, T const& lon0)\n {\n static T const pi = detail::pi();\n static T const two_pi = detail::two_pi();\n\n isea_geo npt;\n\n np->lon += pi;\n npt = snyder_ctran(np, pt);\n np->lon -= pi;\n\n npt.lon -= (pi - lon0 + np->lon);\n\n /*\n * snyder is down tri 3, isea is along side of tri1 from vertex 0 to\n * vertex 1 these are 180 degrees apart\n */\n npt.lon += pi;\n /* normalize longitude */\n npt.lon = fmod(npt.lon, two_pi);\n while (npt.lon > pi)\n npt.lon -= two_pi;\n while (npt.lon < -pi)\n npt.lon += two_pi;\n\n return npt;\n }\n\n /* in radians */\n\n /* fuller's at 5.2454 west, 2.3009 N, adjacent at 7.46658 deg */\n\n template \n inline int isea_grid_init(isea_dgg * g)\n {\n if (!g)\n return 0;\n\n //g->polyhedron = isea_icosahedron;\n g->o_lat = isea_std_lat;\n g->o_lon = isea_std_lon;\n g->o_az = 0.0;\n g->aperture = 4;\n g->resolution = 6;\n g->radius = 1.0;\n //g->topology = isea_hexagon;\n\n return 1;\n }\n\n template \n inline int isea_orient_isea(isea_dgg * g)\n {\n if (!g)\n return 0;\n g->o_lat = isea_std_lat;\n g->o_lon = isea_std_lon;\n g->o_az = 0.0;\n return 1;\n }\n\n template \n inline int isea_orient_pole(isea_dgg * g)\n {\n static T const half_pi = detail::half_pi();\n\n if (!g)\n return 0;\n g->o_lat = half_pi;\n g->o_lon = 0.0;\n g->o_az = 0;\n return 1;\n }\n\n template \n inline int isea_transform(isea_dgg * g, isea_geo * in,\n isea_pt * out)\n {\n isea_geo i, pole;\n int tri;\n\n pole.lat = g->o_lat;\n pole.lon = g->o_lon;\n\n i = isea_ctran(&pole, in, g->o_az);\n\n tri = isea_snyder_forward(&i, out);\n out->x *= g->radius;\n out->y *= g->radius;\n g->triangle = tri;\n\n return tri;\n }\n\n\n template \n inline void isea_rotate(isea_pt * pt, T const& degrees)\n {\n static T const d2r = geometry::math::d2r();\n static T const two_pi = detail::two_pi();\n\n T rad;\n\n T x, y;\n\n rad = -degrees * d2r;\n while (rad >= two_pi) rad -= two_pi;\n while (rad <= -two_pi) rad += two_pi;\n\n x = pt->x * cos(rad) + pt->y * sin(rad);\n y = -pt->x * sin(rad) + pt->y * cos(rad);\n\n pt->x = x;\n pt->y = y;\n }\n\n template \n inline int isea_tri_plane(int tri, isea_pt *pt, T const& radius)\n {\n isea_pt tc; /* center of triangle */\n\n if (downtri(tri)) {\n isea_rotate(pt, 180.0);\n }\n tc = isea_triangle_xy(tri);\n tc.x *= radius;\n tc.y *= radius;\n pt->x += tc.x;\n pt->y += tc.y;\n\n return tri;\n }\n\n /* convert projected triangle coords to quad xy coords, return quad number */\n template \n inline int isea_ptdd(int tri, isea_pt *pt)\n {\n int downtri, quad;\n\n downtri = (((tri - 1) / 5) % 2 == 1);\n quad = ((tri - 1) % 5) + ((tri - 1) / 10) * 5 + 1;\n\n isea_rotate(pt, downtri ? 240.0 : 60.0);\n if (downtri) {\n pt->x += 0.5;\n /* pt->y += cos(30.0 * M_PI / 180.0); */\n pt->y += .86602540378443864672;\n }\n return quad;\n }\n\n template \n inline int isea_dddi_ap3odd(isea_dgg *g, int quad, isea_pt *pt, isea_pt *di)\n {\n static T const pi = detail::pi();\n\n isea_pt v;\n T hexwidth;\n T sidelength; /* in hexes */\n int d, i;\n int maxcoord;\n hex h;\n\n /* This is the number of hexes from apex to base of a triangle */\n sidelength = (math::pow(T(2), g->resolution) + T(1)) / T(2);\n\n /* apex to base is cos(30deg) */\n hexwidth = cos(pi / 6.0) / sidelength;\n\n /* TODO I think sidelength is always x.5, so\n * (int)sidelength * 2 + 1 might be just as good\n */\n maxcoord = (int) (sidelength * 2.0 + 0.5);\n\n v = *pt;\n hexbin2(hexwidth, v.x, v.y, &h.x, &h.y);\n h.iso = 0;\n hex_iso(&h);\n\n d = h.x - h.z;\n i = h.x + h.y + h.y;\n\n /*\n * you want to test for max coords for the next quad in the same\n * \"row\" first to get the case where both are max\n */\n if (quad <= 5) {\n if (d == 0 && i == maxcoord) {\n /* north pole */\n quad = 0;\n d = 0;\n i = 0;\n } else if (i == maxcoord) {\n /* upper right in next quad */\n quad += 1;\n if (quad == 6)\n quad = 1;\n i = maxcoord - d;\n d = 0;\n } else if (d == maxcoord) {\n /* lower right in quad to lower right */\n quad += 5;\n d = 0;\n }\n } else if (quad >= 6) {\n if (i == 0 && d == maxcoord) {\n /* south pole */\n quad = 11;\n d = 0;\n i = 0;\n } else if (d == maxcoord) {\n /* lower right in next quad */\n quad += 1;\n if (quad == 11)\n quad = 6;\n d = maxcoord - i;\n i = 0;\n } else if (i == maxcoord) {\n /* upper right in quad to upper right */\n quad = (quad - 4) % 5;\n i = 0;\n }\n }\n\n di->x = d;\n di->y = i;\n\n g->quad = quad;\n return quad;\n }\n\n template \n inline int isea_dddi(isea_dgg *g, int quad, isea_pt *pt, isea_pt *di)\n {\n isea_pt v;\n T hexwidth;\n int sidelength; /* in hexes */\n hex h;\n\n if (g->aperture == 3 && g->resolution % 2 != 0) {\n return isea_dddi_ap3odd(g, quad, pt, di);\n }\n /* todo might want to do this as an iterated loop */\n if (g->aperture >0) {\n sidelength = (int) (math::pow(T(g->aperture), T(g->resolution / T(2))) + T(0.5));\n } else {\n sidelength = g->resolution;\n }\n\n hexwidth = 1.0 / sidelength;\n\n v = *pt;\n isea_rotate(&v, -30.0);\n hexbin2(hexwidth, v.x, v.y, &h.x, &h.y);\n h.iso = 0;\n hex_iso(&h);\n\n /* we may actually be on another quad */\n if (quad <= 5) {\n if (h.x == 0 && h.z == -sidelength) {\n /* north pole */\n quad = 0;\n h.z = 0;\n h.y = 0;\n h.x = 0;\n } else if (h.z == -sidelength) {\n quad = quad + 1;\n if (quad == 6)\n quad = 1;\n h.y = sidelength - h.x;\n h.z = h.x - sidelength;\n h.x = 0;\n } else if (h.x == sidelength) {\n quad += 5;\n h.y = -h.z;\n h.x = 0;\n }\n } else if (quad >= 6) {\n if (h.z == 0 && h.x == sidelength) {\n /* south pole */\n quad = 11;\n h.x = 0;\n h.y = 0;\n h.z = 0;\n } else if (h.x == sidelength) {\n quad = quad + 1;\n if (quad == 11)\n quad = 6;\n h.x = h.y + sidelength;\n h.y = 0;\n h.z = -h.x;\n } else if (h.y == -sidelength) {\n quad -= 4;\n h.y = 0;\n h.z = -h.x;\n }\n }\n di->x = h.x;\n di->y = -h.z;\n\n g->quad = quad;\n return quad;\n }\n\n template \n inline int isea_ptdi(isea_dgg *g, int tri, isea_pt *pt,\n isea_pt *di)\n {\n isea_pt v;\n int quad;\n\n v = *pt;\n quad = isea_ptdd(tri, &v);\n quad = isea_dddi(g, quad, &v, di);\n return quad;\n }\n\n /* q2di to seqnum */\n template \n inline int isea_disn(isea_dgg *g, int quad, isea_pt *di)\n {\n int sidelength;\n int sn, height;\n int hexes;\n\n if (quad == 0) {\n g->serial = 1;\n return g->serial;\n }\n /* hexes in a quad */\n hexes = (int) (math::pow(T(g->aperture), T(g->resolution)) + T(0.5));\n if (quad == 11) {\n g->serial = 1 + 10 * hexes + 1;\n return g->serial;\n }\n if (g->aperture == 3 && g->resolution % 2 == 1) {\n height = (int) (math::pow(T(g->aperture), T((g->resolution - 1) / T(2))));\n sn = ((int) di->x) * height;\n sn += ((int) di->y) / height;\n sn += (quad - 1) * hexes;\n sn += 2;\n } else {\n sidelength = (int) (math::pow(T(g->aperture), T(g->resolution / T(2))) + T(0.5));\n sn = (int) ((quad - 1) * hexes + sidelength * di->x + di->y + 2);\n }\n\n g->serial = sn;\n return sn;\n }\n\n /* TODO just encode the quad in the d or i coordinate\n * quad is 0-11, which can be four bits.\n * d' = d << 4 + q, d = d' >> 4, q = d' & 0xf\n */\n /* convert a q2di to global hex coord */\n template \n inline int isea_hex(isea_dgg *g, int tri, isea_pt *pt,\n isea_pt *hex)\n {\n isea_pt v;\n#ifdef BOOST_GEOMETRY_PROJECTIONS_FIXME\n int sidelength;\n int d, i, x, y;\n#endif // BOOST_GEOMETRY_PROJECTIONS_FIXME\n int quad;\n\n quad = isea_ptdi(g, tri, pt, &v);\n\n hex->x = ((int)v.x << 4) + quad;\n hex->y = v.y;\n\n return 1;\n#ifdef BOOST_GEOMETRY_PROJECTIONS_FIXME\n d = (int)v.x;\n i = (int)v.y;\n\n /* Aperture 3 odd resolutions */\n if (g->aperture == 3 && g->resolution % 2 != 0) {\n int offset = (int)(pow(T(3.0), T(g->resolution - 1)) + 0.5);\n\n d += offset * ((g->quad-1) % 5);\n i += offset * ((g->quad-1) % 5);\n\n if (quad == 0) {\n d = 0;\n i = offset;\n } else if (quad == 11) {\n d = 2 * offset;\n i = 0;\n } else if (quad > 5) {\n d += offset;\n }\n\n x = (2*d - i) /3;\n y = (2*i - d) /3;\n\n hex->x = x + offset / 3;\n hex->y = y + 2 * offset / 3;\n return 1;\n }\n\n /* aperture 3 even resolutions and aperture 4 */\n sidelength = (int) (pow(T(g->aperture), T(g->resolution / 2.0)) + 0.5);\n if (g->quad == 0) {\n hex->x = 0;\n hex->y = sidelength;\n } else if (g->quad == 11) {\n hex->x = sidelength * 2;\n hex->y = 0;\n } else {\n hex->x = d + sidelength * ((g->quad-1) % 5);\n if (g->quad > 5) hex->x += sidelength;\n hex->y = i + sidelength * ((g->quad-1) % 5);\n }\n\n return 1;\n#endif // BOOST_GEOMETRY_PROJECTIONS_FIXME\n }\n\n template \n inline isea_pt isea_forward(isea_dgg *g, isea_geo *in)\n {\n int tri;\n isea_pt out, coord;\n\n tri = isea_transform(g, in, &out);\n\n if (g->output == isea_addr_plane) {\n isea_tri_plane(tri, &out, g->radius);\n return out;\n }\n\n /* convert to isea standard triangle size */\n out.x = out.x / g->radius * isea_scale;\n out.y = out.y / g->radius * isea_scale;\n out.x += 0.5;\n out.y += 2.0 * .14433756729740644112;\n\n switch (g->output) {\n case isea_addr_projtri:\n /* nothing to do, already in projected triangle */\n break;\n case isea_addr_vertex2dd:\n g->quad = isea_ptdd(tri, &out);\n break;\n case isea_addr_q2dd:\n /* Same as above, we just don't print as much */\n g->quad = isea_ptdd(tri, &out);\n break;\n case isea_addr_q2di:\n g->quad = isea_ptdi(g, tri, &out, &coord);\n return coord;\n break;\n case isea_addr_seqnum:\n isea_ptdi(g, tri, &out, &coord);\n /* disn will set g->serial */\n isea_disn(g, g->quad, &coord);\n return coord;\n break;\n case isea_addr_hex:\n isea_hex(g, tri, &out, &coord);\n return coord;\n break;\n }\n\n return out;\n }\n /*\n * Proj 4 integration code follows\n */\n\n template \n struct par_isea\n {\n isea_dgg dgg;\n };\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_isea_spheroid\n : public base_t_f, T, Parameters>\n {\n par_isea m_proj_parm;\n\n inline base_isea_spheroid(const Parameters& par)\n : base_t_f, T, Parameters>(*this, par)\n {}\n\n // FORWARD(s_forward)\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\n inline void fwd(T const& lp_lon, T const& lp_lat, T& xy_x, T& xy_y) const\n {\n isea_pt out;\n isea_geo in;\n\n in.lon = lp_lon;\n in.lat = lp_lat;\n\n isea_dgg copy = this->m_proj_parm.dgg;\n out = isea_forward(©, &in);\n\n xy_x = out.x;\n xy_y = out.y;\n }\n\n static inline std::string get_name()\n {\n return \"isea_spheroid\";\n }\n\n };\n\n template \n inline void isea_orient_init(srs::detail::proj4_parameters const& params,\n par_isea& proj_parm)\n {\n std::string opt = pj_get_param_s(params, \"orient\");\n if (! opt.empty()) {\n if (opt == std::string(\"isea\")) {\n isea_orient_isea(&proj_parm.dgg);\n } else if (opt == std::string(\"pole\")) {\n isea_orient_pole(&proj_parm.dgg);\n } else {\n BOOST_THROW_EXCEPTION( projection_exception(error_ellipsoid_use_required) );\n }\n }\n }\n\n template \n inline void isea_orient_init(srs::dpar::parameters const& params,\n par_isea& proj_parm)\n {\n typename srs::dpar::parameters::const_iterator\n it = pj_param_find(params, srs::dpar::orient);\n if (it != params.end()) {\n srs::dpar::value_orient o = static_cast(it->template get_value());\n if (o == srs::dpar::orient_isea) {\n isea_orient_isea(&proj_parm.dgg);\n } else if (o == srs::dpar::orient_pole) {\n isea_orient_pole(&proj_parm.dgg);\n } else {\n BOOST_THROW_EXCEPTION( projection_exception(error_ellipsoid_use_required) );\n }\n }\n }\n\n template \n inline void isea_mode_init(srs::detail::proj4_parameters const& params,\n par_isea& proj_parm)\n {\n std::string opt = pj_get_param_s(params, \"mode\");\n if (! opt.empty()) {\n if (opt == std::string(\"plane\")) {\n proj_parm.dgg.output = isea_addr_plane;\n } else if (opt == std::string(\"di\")) {\n proj_parm.dgg.output = isea_addr_q2di;\n } else if (opt == std::string(\"dd\")) {\n proj_parm.dgg.output = isea_addr_q2dd;\n } else if (opt == std::string(\"hex\")) {\n proj_parm.dgg.output = isea_addr_hex;\n } else {\n BOOST_THROW_EXCEPTION( projection_exception(error_ellipsoid_use_required) );\n }\n }\n }\n\n template \n inline void isea_mode_init(srs::dpar::parameters const& params,\n par_isea& proj_parm)\n {\n typename srs::dpar::parameters::const_iterator\n it = pj_param_find(params, srs::dpar::mode);\n if (it != params.end()) {\n srs::dpar::value_mode m = static_cast(it->template get_value());\n if (m == srs::dpar::mode_plane) {\n proj_parm.dgg.output = isea_addr_plane;\n } else if (m == srs::dpar::mode_di) {\n proj_parm.dgg.output = isea_addr_q2di;\n } else if (m == srs::dpar::mode_dd) {\n proj_parm.dgg.output = isea_addr_q2dd;\n } else if (m == srs::dpar::mode_hex) {\n proj_parm.dgg.output = isea_addr_hex;\n } else {\n BOOST_THROW_EXCEPTION( projection_exception(error_ellipsoid_use_required) );\n }\n }\n }\n\n // Icosahedral Snyder Equal Area\n template \n inline void setup_isea(Params const& params, par_isea& proj_parm)\n {\n std::string opt;\n\n isea_grid_init(&proj_parm.dgg);\n\n proj_parm.dgg.output = isea_addr_plane;\n /* proj_parm.dgg.radius = par.a; / * otherwise defaults to 1 */\n /* calling library will scale, I think */\n\n isea_orient_init(params, proj_parm);\n\n pj_param_r(params, \"azi\", srs::dpar::azi, proj_parm.dgg.o_az);\n pj_param_r(params, \"lon_0\", srs::dpar::lon_0, proj_parm.dgg.o_lon);\n pj_param_r(params, \"lat_0\", srs::dpar::lat_0, proj_parm.dgg.o_lat);\n // TODO: this parameter is set below second time\n pj_param_i(params, \"aperture\", srs::dpar::aperture, proj_parm.dgg.aperture);\n // TODO: this parameter is set below second time\n pj_param_i(params, \"resolution\", srs::dpar::resolution, proj_parm.dgg.resolution);\n \n isea_mode_init(params, proj_parm);\n\n // TODO: pj_param_exists -> pj_get_param_b ?\n if (pj_param_exists(params, \"rescale\", srs::dpar::rescale)) {\n proj_parm.dgg.radius = isea_scale;\n }\n\n if (pj_param_i(params, \"resolution\", srs::dpar::resolution, proj_parm.dgg.resolution)) {\n /* empty */\n } else {\n proj_parm.dgg.resolution = 4;\n }\n\n if (pj_param_i(params, \"aperture\", srs::dpar::aperture, proj_parm.dgg.aperture)) {\n /* empty */\n } else {\n proj_parm.dgg.aperture = 3;\n }\n }\n\n }} // namespace detail::isea\n #endif // doxygen\n\n /*!\n \\brief Icosahedral Snyder Equal Area projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Spheroid\n \\par Projection parameters\n - orient (string)\n - azi: Azimuth (or Gamma) (degrees)\n - lon_0: Central meridian (degrees)\n - lat_0: Latitude of origin (degrees)\n - aperture (integer)\n - resolution (integer)\n - mode (string)\n - rescale\n \\par Example\n \\image html ex_isea.gif\n */\n template \n struct isea_spheroid : public detail::isea::base_isea_spheroid\n {\n template \n inline isea_spheroid(Params const& params, Parameters const& par)\n : detail::isea::base_isea_spheroid(par)\n {\n detail::isea::setup_isea(params, this->m_proj_parm);\n }\n };\n\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail\n {\n\n // Static projection\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_STATIC_PROJECTION(srs::spar::proj_isea, isea_spheroid, isea_spheroid)\n\n // Factory entry(s)\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_ENTRY_F(isea_entry, isea_spheroid)\n \n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_BEGIN(isea_init)\n {\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_ENTRY(isea, isea_entry)\n }\n\n } // namespace detail\n #endif // doxygen\n\n} // namespace projections\n\n}} // namespace boost::geometry\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_ISEA_HPP\n\n", "meta": {"hexsha": "2320ac8206ee28f9d9909600b0a406682a1ffb51", "size": 48720, "ext": "hpp", "lang": 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"max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.9090909091, "max_line_length": 127, "alphanum_fraction": 0.4174466338, "num_tokens": 11352, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.665410572017153, "lm_q2_score": 0.32766830738621877, "lm_q1q2_score": 0.21803395584975616}} {"text": "// __BEGIN_LICENSE__\n// Copyright (C) 2006-2011 United States Government as represented by\n// the Administrator of the National Aeronautics and Space Administration.\n// All Rights Reserved.\n// __END_LICENSE__\n\n\n#include \n#include \n#include \nusing namespace vw;\nusing namespace vw::platefile;\n\n#include \n#include \nnamespace po = boost::program_options;\n\n#include \n\nusing namespace std;\n\n// --- Functions to Apply --------------------------\n\n// Reduce Base class\n// defines interface\ntemplate \nstruct ReduceBase {\n inline ImplT& impl() { return static_cast(*this); }\n inline ImplT const& impl() const { return static_cast(*this); }\n\n template \n inline void operator()( list > const& input,\n list const& input_header,\n ImageView & output) {\n impl()(input,input_header,output);\n }\n};\n\n// Weighted Average Implementation\n// mimick this with own functions\nstruct WeightedAverage : public ReduceBase {\n\n template \n inline void operator()( list > const& input,\n list const& /*input_header*/,\n ImageView & output) {\n // Input Images will always have an alpha channel. That is a requirement of PlateFiles.\n int num_channels = CompoundNumChannels::value;\n std::vector > sum_weighted_data;\n for ( uint8 i = 0; i < num_channels-1; i++ )\n sum_weighted_data.push_back(ImageView(input.front().cols(),\n input.front().rows()));\n ImageView summed_weights(input.front().cols(),\n input.front().rows());\n\n // Summing multiple images\n typedef typename list< ImageView >::const_iterator image_iter;\n for ( image_iter image = input.begin();\n image != input.end(); image++ ) {\n summed_weights += channel_cast(select_channel(*image,num_channels-1));\n // Iterating over non alpha channels\n for ( uint8 i = 0; i < num_channels-1; i++ )\n sum_weighted_data[i] += channel_cast(select_channel(*image,num_channels-1))*channel_cast(select_channel(*image,i));\n }\n\n // Normalizing\n for ( uint8 i = 0; i < num_channels-1; i++ )\n sum_weighted_data[i] /= summed_weights;\n output.set_size(input.front().cols(),\n input.front().rows());\n for ( uint8 i = 0; i < num_channels-1; i++ )\n select_channel(output,i) = sum_weighted_data[i];\n // Setting output alpha\n select_channel(output,num_channels-1) = threshold(summed_weights,0,\n ChannelRange::type>::min(),\n ChannelRange::type>::max());\n }\n};\n\n// Weighted Variance Implementation\nstruct WeightedVar2 : public ReduceBase {\n\n template \n inline void operator()( list > const& input,\n list const& /*input_header*/,\n ImageView & var2) {\n // Input Images will always have an alpha channel. That is a requirement of PlateFiles.\n int num_channels = CompoundNumChannels::value;\n std::vector > sum_weighted_data; //store the mean\n std::vector > sum_weighted_data2; //store the variance\n std::vector > sum_weighted_stdv; //store the standard deviation\n\n for ( uint8 i = 0; i < num_channels-1; i++ ){\n sum_weighted_data.push_back(ImageView(input.front().cols(),\n input.front().rows()));\n sum_weighted_data2.push_back(ImageView(input.front().cols(),\n input.front().rows()));\n sum_weighted_stdv.push_back(ImageView(input.front().cols(),\n input.front().rows()));\n }\n\n ImageView summed_weights(input.front().cols(),\n input.front().rows());\n\n // Summing multiple images\n typedef typename list< ImageView >::const_iterator image_iter;\n for ( image_iter image = input.begin();image != input.end(); image++ ) {\n summed_weights += channel_cast(select_channel(*image,num_channels-1));\n\n // Iterating over non alpha channels\n for ( uint8 i = 0; i < num_channels-1; i++ ){\n sum_weighted_data[i] += channel_cast(select_channel(*image,num_channels-1))*channel_cast(select_channel(*image,i));\n\n sum_weighted_data2[i] += channel_cast(select_channel(*image,num_channels-1))\n *channel_cast(select_channel(*image,i))*channel_cast(select_channel(*image,i));\n }\n }\n\n // Normalizing\n for ( uint8 i = 0; i < num_channels-1; i++ ){\n sum_weighted_data[i] /= summed_weights;\n sum_weighted_data2[i] = sum_weighted_data2[i]/summed_weights - sum_weighted_data[i]*sum_weighted_data[i];\n sum_weighted_stdv[i]=sqrt(clamp(sum_weighted_data2[i],0, 999999999));\n }\n\n var2.set_size(input.front().cols(),\n input.front().rows());\n for ( uint8 i = 0; i < num_channels-1; i++ )\n select_channel(var2,i) = sum_weighted_stdv[i];\n // Setting output alpha\n select_channel(var2,num_channels-1) =\n threshold(summed_weights,0,\n ChannelRange::type>::min(),\n ChannelRange::type>::max());\n }\n};\n\n// Robust Mean Implementation\nstruct RobustMean : public ReduceBase {\nprivate:\n float smart_weighted_mean( vector & weights,\n vector const& samples,\n float const sign_level=0.3f,\n float const learn_rate=0.1f,\n float const error_tol=1e-5f,\n int32 const max_iter=1000 ) {\n namespace bm = boost::math;\n switch ( samples.size() ) {\n case 1:\n weights[0]=1;\n return samples[0];\n case 2:\n weights[0]=1;\n weights[1]=1;\n return (samples[0]+samples[1])/2;\n default:\n\n float weighted_mean = 0;\n std::vector prev_wt = weights; // the previous weights\n for (int i = 0; i < max_iter; ++i) {\n\n // accumulation of all sums\n float SW1 = 0; // sum of weights\n float SW2 = 0; // sum of squared weights\n float SWX = 0; // weighted sum of samples\n float WX2 = 0; // weighted sum of squared data\n for (size_t j = 0; j < samples.size(); ++j) {\n SW1 += weights[j];\n SW2 += weights[j]*weights[j];\n SWX += weights[j]*samples[j];\n WX2 += weights[j]*samples[j]*samples[j];\n }\n\n // weighted mean\n weighted_mean = SWX/SW1;\n\n float DN = SW1*WX2-SWX*SWX; // denominator\n float v2 = SW1-SW2/SW1; // the second degree of freedom\n float sse_wt = 0; // squared sum of weight differences\n for (size_t j = 0; j < samples.size(); ++j) {\n\n // F statistic\n float DF = samples[j]-weighted_mean; // difference from the mean\n float FS = (SW1*SW1-SW2)*DF*DF/DN; // F statistic\n\n // degree of freedom\n float v1 = 1-weights[j]/SW1; // the first degree of freedom\n\n // p-value calculation\n float p_value = 1; // p-value\n if (SW1 > 1 && DN > 0) { // basic assumptions\n bm::fisher_f dist(v1,v2);\n p_value = boost::numeric_cast(1-cdf(dist, FS));\n }\n\n // gradient decent update\n weights[j] += learn_rate*(p_value-sign_level);\n if ( weights[j] < 0 ) weights[j] = 0; // 0.0 is lower bound of weight\n if ( weights[j] > 1 ) weights[j] = 1; // 1.0 is upper bound of weight\n\n // squared sum of weight differences\n sse_wt += (weights[j]-prev_wt[j])*(weights[j]-prev_wt[j]);\n prev_wt[j] = weights[j]; // update previous weight\n\n }\n\n // terminal condition: mean squared difference of weights is\n // smaller than the tolerance\n if ( sse_wt/float(weights.size()) < error_tol ) break;\n }\n\n return weighted_mean;\n }\n }\n\npublic:\n template \n inline void operator()( list > const& input,\n list const& /*input_header*/,\n ImageView & output ) {\n uint8 num_v_channels = CompoundNumChannels::value-1;\n output.set_size( input.front().cols(),\n input.front().rows() );\n\n // Iterating through every pixel within a tile\n for ( int32 ix = 0; ix < input.front().cols(); ix++ ) {\n for ( int32 iy = 0; iy < input.front().rows(); iy++ ) {\n vector > t_samples;\n t_samples.resize(num_v_channels);\n vector t_weight;\n\n // Seperating data out into vectors, only loading up data that\n // is not empty.\n typedef typename list >::const_iterator iter_type;\n for ( iter_type iter = input.begin();\n iter != input.end(); iter++ ) {\n if ( (*iter)(ix,iy)[num_v_channels] == 0 )\n continue;\n t_weight.push_back( float((*iter)(ix,iy)[num_v_channels])/float(ChannelRange::type>::max()) );\n for ( uint8 iz = 0; iz < num_v_channels; iz++ )\n t_samples[iz].push_back( (*iter)(ix,iy)[iz] );\n }\n\n if ( t_weight.empty() ) {\n output(ix,iy) = PixelT();\n continue;\n }\n\n typedef typename PixelChannelType::type ChannelT;\n output(ix,iy)[num_v_channels] = ChannelRange::max();\n for ( uint8 iz = 0; iz < num_v_channels; iz++ ) {\n vector copy = t_weight;\n output(ix,iy)[iz] =\n boost::numeric_cast(smart_weighted_mean( copy,\n t_samples[iz]));\n }\n } // iy - end loop\n } // ix - first loop\n } // end operator()\n};\n\n// --- Standard Terminal Argument ------------------\n\n// Standard Arguments\nstruct Options {\n // Input\n Url url;\n int32 level;\n TransactionOrNeg start_trans_id, end_trans_id;\n std::string start_description;\n bool finish;\n\n // Output\n string function;\n TransactionOrNeg transaction_id;\n\n // For spawning multiple jobs\n int32 job_id, num_jobs;\n};\n\nvoid handle_arguments(int argc, char *argv[], Options& opt) {\n po::options_description general_options(\"Perform weighted averages of all layers within a tile inside a plate file\");\n general_options.add_options()\n (\"job_id,j\", po::value(&opt.job_id)->default_value(0), \"\")\n (\"num_jobs,n\", po::value(&opt.num_jobs)->default_value(1), \"\")\n (\"begin_transaction\", po::value(&opt.start_trans_id)->default_value(0), \"Input starting transaction ID range.\")\n (\"end_transaction\", po::value(&opt.end_trans_id), \"Input ending transaction ID range.\")\n (\"level,l\", po::value(&opt.level)->default_value(-1), \"Level inside the plate in which to process. -1 will error out and show the number of levels available.\")\n (\"function,f\", po::value(&opt.function)->default_value(\"WeightedAvg\"), \"Functions that are available are [WeightedAvg RobustMean WeightedVar]\")\n (\"transaction-id,t\",po::value(&opt.transaction_id)->default_value(2000), \"Transaction id to write to\")\n (\"start\", po::value(&opt.start_description), \"Starts a multi-part plate reduce.\")\n (\"finish\", po::bool_switch(&opt.finish)->default_value(false), \"Finish a multi-part plate reduce\")\n (\"help,h\", \"Display this help message\");\n\n po::options_description hidden_options(\"\");\n hidden_options.add_options()\n (\"input-file\", po::value(&opt.url), \"\");\n\n po::options_description options(\"\");\n options.add(general_options).add(hidden_options);\n\n po::positional_options_description p;\n p.add(\"input-file\",-1);\n\n po::variables_map vm;\n try {\n po::store( po::command_line_parser( argc, argv ).options(options).positional(p).run(), vm );\n po::notify( vm );\n } catch (const po::error& e) {\n vw_throw( ArgumentErr() << \"Error parsing input:\\n\\t\"\n << e.what() << options );\n }\n\n std::ostringstream usage;\n usage << \"Usage: \" << argv[0] << \" [options]\\n\";\n\n if ( vm.count(\"help\") || vm.count(\"input-file\") != 1 || opt.transaction_id.newest())\n vw_throw( ArgumentErr() << usage.str() << general_options );\n}\n\n// --- Meta Application of Above Functions ----------\n\n// apply_reduce\ntemplate \nvoid apply_reduce( boost::shared_ptr platefile,\n std::list const& workunits,\n Options& opt, ReduceBase& reduce) {\n\n TerminalProgressCallback tpc(\"plate.platereduce\", \"Processing\");\n double inc_tpc = 1.0/float(workunits.size());\n BOOST_FOREACH( const BBox2i& workunit, workunits) {\n tpc.report_incremental_progress(inc_tpc);\n for ( int ix = 0; ix < workunit.width(); ix++ ) {\n for ( int iy = 0; iy < workunit.height(); iy++ ) {\n Vector2i location(ix,iy);\n location += workunit.min();\n\n // Polling for Tiles\n std::list tile_records;\n tile_records = platefile->search_by_location(location[0],\n location[1],\n opt.level,\n TransactionRange(opt.start_trans_id, opt.end_trans_id));\n\n // No Tiles? No Problem!\n if (tile_records.empty())\n continue;\n\n // Loading images\n std::list > tiles;\n BOOST_FOREACH( const TileHeader& tile, tile_records ) {\n ImageView new_tile;\n platefile->read( new_tile, location[0],\n location[1], opt.level,\n tile.transaction_id(), true );\n tiles.push_back(new_tile);\n }\n\n // Calling function\n ImageView result;\n reduce(tiles, tile_records, result);\n\n platefile->write_update(result, location[0], location[1], opt.level);\n }\n }\n }\n tpc.report_finished();\n}\n\n// Function that runs the apply_reduce over the plate file\ntemplate \nvoid do_run( Options& opt, ReduceBase& reduce ) {\n boost::shared_ptr platefile =\n boost::shared_ptr( new PlateFile(opt.url) );\n\n if ( !opt.start_description.empty() ) {\n platefile->transaction_begin(opt.start_description, opt.transaction_id );\n vw_out() << \"Transaction started with ID = \" << platefile->transaction_id() << \"\\n\";\n vw_out() << \"Plate has \" << platefile->num_levels() << \" levels.\\n\";\n exit(0);\n }\n\n platefile->transaction_resume(opt.transaction_id.promote());\n\n if ( opt.finish ) {\n // Update the read cursor when the snapshot is complete!\n platefile->transaction_end(true);\n vw_out() << \"Transaction \" << opt.transaction_id << \" complete.\\n\";\n exit(0);\n }\n\n if ( opt.level < 0 || opt.level >= boost::numeric_cast(platefile->num_levels()) ) {\n vw_throw( ArgumentErr() << \"Incorrect level selection, \"\n << opt.level << \".\\n\\nPlatefile \" << opt.url.string() << \" has \"\n << platefile->num_levels() << \" levels internally.\\n\" );\n }\n\n // This is arbitrary, just needed to divide up jobs\n int32 region_size = 1 << opt.level;\n BBox2i full_region(0,0,region_size,region_size);\n std::list workunits = bbox_tiles(full_region,4,4);\n std::list mworkunits;\n int32 count = 0;\n BOOST_FOREACH(const BBox2i& c, workunits) {\n if (count==opt.num_jobs)\n count=0;\n if (count==opt.job_id)\n mworkunits.push_back(c);\n count++;\n }\n vw_out() << \"Job \" << opt.job_id << \"/\" << opt.num_jobs << \" has \"\n << mworkunits.size() << \" work units.\\n\";\n\n platefile->audit_log()\n << \"Started multi-part reduce (t_id = \" << opt.transaction_id\n << \") -- level:\" << opt.level\n << \" jobid:\" << opt.job_id << \"/\" << opt.num_jobs << \"\\n\";\n\n platefile->write_request();\n\n switch(platefile->pixel_format()) {\n case VW_PIXEL_GRAYA:\n switch(platefile->channel_type()) {\n case VW_CHANNEL_UINT8:\n apply_reduce >(platefile, mworkunits,\n opt, reduce);\n break;\n case VW_CHANNEL_INT16:\n apply_reduce >(platefile, mworkunits,\n opt, reduce);\n break;\n case VW_CHANNEL_FLOAT32:\n apply_reduce >(platefile, mworkunits,\n opt, reduce);\n break;\n default:\n vw_throw(InputErr() << \"Platefile contains unsupported channel type.\\n\" );\n }\n break;\n case VW_PIXEL_RGBA:\n switch(platefile->channel_type()) {\n case VW_CHANNEL_UINT8:\n apply_reduce >(platefile, mworkunits,\n opt, reduce);\n break;\n default:\n vw_throw(InputErr() << \"Platefile contains unsupported channel type.\\n\" );\n }\n break;\n default:\n vw_throw(InputErr() << \"Platefile contains a pixel type thats unsupported.\\n\" );\n }\n\n platefile->write_complete();\n platefile->audit_log()\n << \"Finished multi-part reduce (t_id = \" << opt.transaction_id\n << \") -- level:\" << opt.level\n << \" jobid:\" << opt.job_id << \"/\" << opt.num_jobs << \"\\n\";\n}\n\nint main( int argc, char *argv[] ) {\n\n Options opt;\n try {\n handle_arguments( argc, argv, opt );\n\n // Handing out jobs now\n boost::to_lower(opt.function);\n if ( opt.function == \"weightedavg\" ) {\n WeightedAverage f;\n do_run( opt, f );\n } else if ( opt.function == \"robustmean\" ) {\n RobustMean f;\n do_run( opt, f );\n }\n else if ( opt.function == \"weightedvar\" ) {\n WeightedVar2 f;\n do_run( opt, f );\n } else {\n vw_throw( ArgumentErr() << \"Unknown function, \" << opt.function << \"\\n\" );\n }\n\n } catch ( const ArgumentErr& e ) {\n vw_out() << e.what() << std::endl;\n return 1;\n } catch ( const Exception& e ) {\n std::cerr << \"Error: \" << e.what() << std::endl;\n return 1;\n }\n\n return 0;\n}\n", "meta": {"hexsha": "36ef35c48889d3787150cf075808fa0460cdd1a1", "size": 18923, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/vw/Plate/platereduce.cc", "max_stars_repo_name": "digimatronics/ComputerVision", "max_stars_repo_head_hexsha": "2af5da17dfd277f0cb3f19a97e3d49ba19cc9d24", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-05-16T23:57:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-16T23:57:32.000Z", "max_issues_repo_path": "src/vw/Plate/platereduce.cc", "max_issues_repo_name": "rkrishnasanka/visionworkbench", "max_issues_repo_head_hexsha": "2af5da17dfd277f0cb3f19a97e3d49ba19cc9d24", "max_issues_repo_licenses": ["NASA-1.3"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.3775406828054584, "lm_q1q2_score": 0.21802799448923887}} {"text": "// --------------------------------------------------------------------------\n// OpenMS -- Open-Source Mass Spectrometry\n// --------------------------------------------------------------------------\n// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,\n// ETH Zurich, and Freie Universitaet Berlin 2002-2021.\n//\n// This software is released under a three-clause BSD license:\n// * Redistributions of source code must retain the above copyright\n// notice, this list of conditions and the following disclaimer.\n// * Redistributions in binary form must reproduce the above copyright\n// notice, this list of conditions and the following disclaimer in the\n// documentation and/or other materials provided with the distribution.\n// * Neither the name of any author or any participating institution\n// may be used to endorse or promote products derived from this software\n// without specific prior written permission.\n// For a full list of authors, refer to the file AUTHORS.\n// --------------------------------------------------------------------------\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n// ARE DISCLAIMED. IN NO EVENT SHALL ANY OF THE AUTHORS OR THE CONTRIBUTING\n// INSTITUTIONS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\n// OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\n// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\n// OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF\n// ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n//\n// --------------------------------------------------------------------------\n// $Maintainer: Timo Sachsenberg $\n// $Authors: Eva Lange $\n// --------------------------------------------------------------------------\n#include \n#include \n\n#include \n\n#include \n\n\nusing std::max;\n\n\nnamespace OpenMS\n{\n\n OptimizePick::OptimizePick(\n const struct OptimizationFunctions::PenaltyFactors & penalties,\n const int max_iteration)\n {\n\n penalties_ = penalties;\n\n max_iteration_ = max_iteration;\n\n#ifdef DEBUG_PEAK_PICKING\n std::cout << \"max iteration \" << max_iteration_\n << \"\\n penalty factor pos \" << penalties.pos\n << \"\\n penalty factor left width \" << penalties.lWidth\n << \"\\n penalty factor right width \" << penalties.rWidth\n << std::endl;\n#endif\n\n }\n\n OptimizePick::~OptimizePick()\n {\n }\n\n void OptimizePick::optimize(std::vector & peaks, Data & data)\n {\n if (peaks.empty())\n return;\n\n size_t global_peak_number = 0;\n data.peaks.assign(peaks.begin(), peaks.end());\n\n size_t num_dimensions = 4 * data.peaks.size();\n Eigen::VectorXd x_init (num_dimensions);\n x_init.setZero();\n // We have to initialize the parameters for the optimization\n for (size_t i = 0; i < data.peaks.size(); i++)\n {\n PeakShape current_peak = data.peaks[i];\n double h = current_peak.height;\n double wl = current_peak.left_width;\n double wr = current_peak.right_width;\n double p = current_peak.mz_position;\n if (boost::math::isnan(wl))\n {\n data.peaks[i].left_width = 1;\n wl = 1.;\n }\n if (boost::math::isnan(wr))\n {\n data.peaks[i].right_width = 1;\n wr = 1.;\n }\n x_init(4 * i) = h;\n x_init(4 * i + 1) = wl;\n x_init(4 * i + 2) = wr;\n x_init(4 * i + 3) = p;\n }\n\n data.penalties = penalties_;\n\n unsigned num_data_points = std::max(data.positions.size() + 1, num_dimensions);\n OptPeakFunctor functor (num_dimensions, num_data_points, &data);\n Eigen::LevenbergMarquardt lmSolver (functor);\n lmSolver.parameters.maxfev = max_iteration_;\n Eigen::LevenbergMarquardtSpace::Status status = lmSolver.minimize(x_init);\n //the states are poorly documented. after checking the source, we believe that\n //all states except NotStarted, Running and ImproperInputParameters are good\n //termination states.\n if (status <= Eigen::LevenbergMarquardtSpace::ImproperInputParameters)\n {\n throw Exception::UnableToFit(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, \"UnableToFit-OptimizePeak:\", \"Could not fit the data: Error \" + String(status));\n }\n\n // iterate over all peaks and store the optimized values in peaks\n for (size_t current_peak = 0; current_peak < data.peaks.size(); current_peak++)\n {\n // Store the current parameters for this peak\n peaks[global_peak_number + current_peak].height = x_init(4 * current_peak);\n peaks[global_peak_number + current_peak].mz_position = x_init(4 * current_peak + 3);\n peaks[global_peak_number + current_peak].left_width = x_init(4 * current_peak + 1);\n peaks[global_peak_number + current_peak].right_width = x_init(4 * current_peak + 2);\n\n // compute the area\n // is it a Lorentz or a Sech - Peak?\n if (peaks[global_peak_number + current_peak].type == PeakShape::LORENTZ_PEAK)\n {\n PeakShape p = peaks[global_peak_number + current_peak];\n double x_left_endpoint = p.mz_position - 1 / p.left_width * sqrt(p.height / 1 - 1);\n double x_right_endpoint = p.mz_position + 1 / p.right_width * sqrt(p.height / 1 - 1);\n double area_left = -p.height / p.left_width * atan(p.left_width * (x_left_endpoint - p.mz_position));\n double area_right = -p.height / p.right_width * atan(p.right_width * (p.mz_position - x_right_endpoint));\n peaks[global_peak_number + current_peak].area = area_left + area_right;\n#ifdef DEBUG_PEAK_PICKING\n std::cout << \"Lorentz \" << area_left << \" \" << area_right\n << \" \" << peaks[global_peak_number + current_peak].area << std::endl;\n#endif\n }\n else //It's a Sech - Peak\n {\n PeakShape p = peaks[global_peak_number + current_peak];\n double x_left_endpoint = p.mz_position - 1 / p.left_width * boost::math::acosh(sqrt(p.height / 0.001));\n double x_right_endpoint = p.mz_position + 1 / p.right_width * boost::math::acosh(sqrt(p.height / 0.001));\n double area_left = p.height / p.left_width * (sinh(p.left_width * (p.mz_position - x_left_endpoint)) / cosh(p.left_width * (p.mz_position - x_left_endpoint)));\n double area_right = -p.height / p.right_width * (sinh(p.right_width * (p.mz_position - x_right_endpoint)) / cosh(p.right_width * (p.mz_position - x_right_endpoint)));\n peaks[global_peak_number + current_peak].area = area_left + area_right;\n#ifdef DEBUG_PEAK_PICKING\n std::cout << \"Sech \" << area_left << \" \" << area_right\n << \" \" << peaks[global_peak_number + current_peak].area << std::endl;\n std::cout << p.mz_position << \" \" << x_left_endpoint << \" \" << x_right_endpoint << std::endl;\n#endif\n }\n }\n //global_peak_number += data.peaks.size();\n\n }\n\n int OptimizePick::OptPeakFunctor::operator()(const Eigen::VectorXd &x, Eigen::VectorXd &fvec)\n {\n const std::vector & signal = m_data->signal;\n const std::vector & positions = m_data->positions;\n const std::vector & peaks = m_data->peaks;\n const OptimizationFunctions::PenaltyFactors & penalties = m_data->penalties;\n // iterate over all points of the signal\n for (size_t current_point = 0; current_point < positions.size(); current_point++)\n {\n double computed_signal = 0.;\n double current_position = positions[current_point];\n double experimental_signal = signal[current_point];\n\n // iterate over all peaks\n for (size_t current_peak = 0; current_peak < peaks.size(); current_peak++)\n {\n // Store the current parameters for this peak\n double p_height = x(4 * current_peak);\n double p_position = x(4 * current_peak + 3);\n double p_width\n = (current_position <= p_position) ? x(4 * current_peak + 1)\n : x(4 * current_peak + 2);\n\n // is it a Lorentz or a Sech - Peak?\n if (peaks[current_peak].type == PeakShape::LORENTZ_PEAK)\n {\n computed_signal += p_height / (1. + pow(p_width * (current_position - p_position), 2));\n }\n else // It's a Sech - Peak\n {\n computed_signal += p_height / pow(cosh(p_width * (current_position - p_position)), 2);\n }\n }\n fvec(current_point) = computed_signal - experimental_signal;\n }\n\n double penalty = 0.;\n double penalty_pos = penalties.pos;\n double penalty_lwidth = penalties.lWidth;\n double penalty_rwidth = penalties.rWidth;\n\n // iterate over all peaks again to compute the penalties\n for (size_t current_peak = 0; current_peak < peaks.size(); current_peak++)\n {\n double old_position = peaks[current_peak].mz_position;\n double old_width_l = peaks[current_peak].left_width;\n double old_width_r = peaks[current_peak].right_width;\n double p_position = x(4 * current_peak + 3);\n double p_width_l = x(4 * current_peak + 1);\n double p_width_r = x(4 * current_peak + 2);\n\n //penalty += pow(p_position - old_position, 2) + pow(p_width_l - old_width_l, 2) + pow(p_width_r - old_width_r, 2);\n penalty += penalty_pos * pow(p_position - old_position, 2)\n + penalty_lwidth * pow(p_width_l - old_width_l, 2)\n + penalty_rwidth * pow(p_width_r - old_width_r, 2);\n }\n\n fvec(positions.size()) = 100 * penalty;\n\n return 0;\n }\n // compute Jacobian matrix for the different parameters\n int OptimizePick::OptPeakFunctor::df(const Eigen::VectorXd &x, Eigen::MatrixXd &J)\n {\n std::cout << \"rows: \" << J.rows() << \" columns: \" << J.cols() << std::endl;//DEBUG\n const std::vector & positions = m_data->positions;\n const std::vector & peaks = m_data->peaks;\n const OptimizationFunctions::PenaltyFactors & penalties = m_data->penalties;\n // iterate over all points of the signal\n for (size_t current_point = 0; current_point < positions.size(); current_point++)\n {\n double current_position = positions[current_point];\n\n // iterate over all peaks\n for (size_t current_peak = 0; current_peak < peaks.size(); current_peak++)\n {\n // Store the current parameters for this peak\n double p_height = x(4 * current_peak);\n double p_position = x(4 * current_peak + 3);\n double p_width = (current_position <= p_position) ? x(4 * current_peak + 1)\n : x(4 * current_peak + 2);\n\n // is it a Lorentz or a Sech - Peak?\n if (peaks[current_peak].type == PeakShape::LORENTZ_PEAK)\n {\n double diff = current_position - p_position;\n double denom_inv = 1. / (1. + pow(p_width * diff, 2));\n\n double ddl_left = (current_position <= p_position)\n ? -2 * p_height * pow(diff, 2) * p_width * pow(denom_inv, 2) : 0;\n\n double ddl_right = (current_position > p_position)\n ? -2 * p_height * pow(diff, 2) * p_width * pow(denom_inv, 2) : 0;\n\n double ddx0 = -2 * p_height * pow(p_width, 2) * diff * pow(denom_inv, 2);\n\n J(current_point, 4 * current_peak) = denom_inv;\n J(current_point, 4 * current_peak + 1) = ddl_left;\n J(current_point, 4 * current_peak + 2) = ddl_right;\n J(current_point, 4 * current_peak + 3) = ddx0;\n }\n else // It's a Sech - Peak\n {\n double diff = current_position - p_position;\n double denom_inv = 1. / cosh(p_width * diff);\n\n // The remaining computations are not stable if denom_inv == 0. In that case, we are far away from the peak\n // and can assume that all derivatives vanish\n double sinh_term = (fabs(denom_inv) < 1e-6) ? 0.0 : sinh(p_width * diff);\n double ddl_left = (current_position <= p_position)\n ? -2 * p_height * sinh_term * diff * pow(denom_inv, 3) : 0;\n double ddl_right = (current_position > p_position)\n ? -2 * p_height * sinh_term * diff * pow(denom_inv, 3) : 0;\n double ddx0 = 2 * p_height * p_width * sinh_term * pow(denom_inv, 3);\n\n J(current_point, 4 * current_peak) = pow(denom_inv, 2);\n J(current_point, 4 * current_peak + 1) = ddl_left;\n J(current_point, 4 * current_peak + 2) = ddl_right;\n J(current_point, 4 * current_peak + 3) = ddx0;\n }\n }\n }\n\n // Now iterate over all peaks again to compute the penalties.\n for (size_t current_peak = 0; current_peak < peaks.size(); current_peak++)\n {\n double p_width_left = x(4 * current_peak + 1);\n double p_width_right = x(4 * current_peak + 2);\n double p_position = x(4 * current_peak + 3);\n\n double old_width_left = peaks[current_peak].left_width;\n double old_width_right = peaks[current_peak].right_width;\n double old_position = peaks[current_peak].mz_position;\n\n\n double penalty_l = 2. * penalties.lWidth * (p_width_left - old_width_left);\n double penalty_r = 2. * penalties.rWidth * (p_width_right - old_width_right);\n double penalty_p = 0;\n if (fabs(p_position - old_position) < 0.2)\n {\n penalty_p = 2. * penalties.pos * (p_position - old_position);\n }\n\n J(positions.size(), 4 * current_peak) = 0.;\n J(positions.size(), 4 * current_peak + 1) = 100 * penalty_l;\n J(positions.size(), 4 * current_peak + 2) = 100 * penalty_r;\n J(positions.size(), 4 * current_peak + 3) = 100 * penalty_p;\n }\n return 0;\n }\n}//namespace\n", "meta": {"hexsha": "dc28d5285ab9f140481d25ecd6aa796e3642cf74", "size": 13888, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/openms/source/TRANSFORMATIONS/RAW2PEAK/OptimizePick.cpp", "max_stars_repo_name": "rahul799/OpenMS", "max_stars_repo_head_hexsha": "962916f588789b6fab185d7f2d5fbac27226a99e", "max_stars_repo_licenses": ["BSL-1.0", "Apache-2.0", "Zlib"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/openms/source/TRANSFORMATIONS/RAW2PEAK/OptimizePick.cpp", "max_issues_repo_name": "rahul799/OpenMS", "max_issues_repo_head_hexsha": "962916f588789b6fab185d7f2d5fbac27226a99e", "max_issues_repo_licenses": ["BSL-1.0", "Apache-2.0", "Zlib"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/openms/source/TRANSFORMATIONS/RAW2PEAK/OptimizePick.cpp", "max_forks_repo_name": "rahul799/OpenMS", "max_forks_repo_head_hexsha": "962916f588789b6fab185d7f2d5fbac27226a99e", "max_forks_repo_licenses": ["BSL-1.0", "Apache-2.0", "Zlib"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 44.0888888889, "max_line_length": 174, "alphanum_fraction": 0.6301123272, "num_tokens": 3483, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5660185351961015, "lm_q2_score": 0.3849121444839335, "lm_q1q2_score": 0.2178674081999862}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n# if __cplusplus >= 201703L\n# include \n# else\n# include \n# endif\n\n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#ifndef BRA_NO_MPI\n# include \n# include \n#endif // BRA_NO_MPI\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n# if __cplusplus >= 201703L\n# define BRA_is_nothrow_swappable std::is_nothrow_swappable\n# else\n# define BRA_is_nothrow_swappable boost::is_nothrow_swappable\n# endif\n\n\nnamespace bra\n{\n unsupported_mnemonic_error::unsupported_mnemonic_error(std::string const& mnemonic)\n : std::runtime_error{(mnemonic + \" is not supported\").c_str()}\n { }\n\n wrong_mnemonics_error::wrong_mnemonics_error(::bra::gates::columns_type const& columns)\n : std::runtime_error{generate_what_string(columns).c_str()}\n { }\n\n std::string wrong_mnemonics_error::generate_what_string(::bra::gates::columns_type const& columns)\n {\n auto result = std::string{};\n\n auto const last = columns.end();\n for (auto iter = columns.begin(); iter != last; ++iter)\n {\n result += *iter;\n result += \" \";\n }\n\n return result;\n }\n\n#ifndef BRA_NO_MPI\n wrong_mpi_communicator_size_error::wrong_mpi_communicator_size_error()\n : std::runtime_error{\"communicator size is wrong\"}\n { }\n#endif // BRA_NO_MPI\n\n#ifndef BRA_NO_MPI\n gates::gates()\n : data_{}, num_qubits_{}, num_lqubits_{}, num_uqubits_{}, num_processes_per_unit_{1u},\n initial_state_value_{}, initial_permutation_{}, phase_coefficients_{}, root_{}\n { }\n\n gates::gates(gates::allocator_type const& allocator)\n : data_{allocator}, num_qubits_{}, num_lqubits_{}, num_uqubits_{}, num_processes_per_unit_{1u},\n initial_state_value_{}, initial_permutation_{}, phase_coefficients_{}, root_{}\n { }\n\n gates::gates(gates&& other, gates::allocator_type const& allocator)\n : data_{std::move(other.data_), allocator},\n num_qubits_{std::move(other.num_qubits_)},\n num_lqubits_{std::move(other.num_lqubits_)},\n num_uqubits_{std::move(other.num_uqubits_)},\n num_processes_per_unit_{std::move(other.num_processes_per_unit_)},\n initial_state_value_{std::move(other.initial_state_value_)},\n initial_permutation_{std::move(other.initial_permutation_)},\n phase_coefficients_{std::move(other.phase_coefficients_)},\n root_{std::move(other.root_)}\n { }\n#else // BRA_NO_MPI\n gates::gates()\n : data_{}, num_qubits_{},\n initial_state_value_{}, phase_coefficients_{}\n { }\n\n gates::gates(gates::allocator_type const& allocator)\n : data_{allocator}, num_qubits_{},\n initial_state_value_{}, phase_coefficients_{}\n { }\n\n gates::gates(gates&& other, gates::allocator_type const& allocator)\n : data_{std::move(other.data_), allocator},\n num_qubits_{std::move(other.num_qubits_)},\n initial_state_value_{std::move(other.initial_state_value_)},\n phase_coefficients_{std::move(other.phase_coefficients_)}\n { }\n#endif // BRA_NO_MPI\n\n#ifndef BRA_NO_MPI\n gates::gates(\n std::istream& input_stream,\n bit_integer_type num_uqubits, unsigned int num_processes_per_unit,\n yampi::environment const& environment,\n yampi::rank const root, yampi::communicator const& communicator,\n size_type const num_reserved_gates)\n : data_{}, num_qubits_{}, num_lqubits_{},\n num_uqubits_{num_uqubits}, num_processes_per_unit_{num_processes_per_unit},\n initial_state_value_{}, initial_permutation_{}, phase_coefficients_{}, root_{root}\n {\n assert(num_processes_per_unit >= 1u);\n assign(input_stream, environment, communicator, num_reserved_gates);\n }\n#else // BRA_NO_MPI\n gates::gates(std::istream& input_stream)\n : data_{}, num_qubits_{},\n initial_state_value_{}, phase_coefficients_{}\n { assign(input_stream, size_type{0u}); }\n\n gates::gates(std::istream& input_stream, size_type const num_reserved_gates)\n : data_{}, num_qubits_{},\n initial_state_value_{}, phase_coefficients_{}\n { assign(input_stream, num_reserved_gates); }\n#endif // BRA_NO_MPI\n\n bool gates::operator==(gates const& other) const\n {\n#ifndef BRA_NO_MPI\n return data_ == other.data_\n and num_qubits_ == other.num_qubits_\n and num_lqubits_ == other.num_lqubits_\n and num_uqubits_ == other.num_uqubits_\n and num_processes_per_unit_ == other.num_processes_per_unit_\n and initial_state_value_ == other.initial_state_value_\n and initial_permutation_ == other.initial_permutation_\n and phase_coefficients_ == other.phase_coefficients_\n and root_ == other.root_;\n#else // BRA_NO_MPI\n return data_ == other.data_\n and num_qubits_ == other.num_qubits_\n and initial_state_value_ == other.initial_state_value_\n and phase_coefficients_ == other.phase_coefficients_;\n#endif // BRA_NO_MPI\n }\n\n#ifndef BRA_NO_MPI\n void gates::num_qubits(\n bit_integer_type const new_num_qubits,\n yampi::communicator const& communicator, yampi::environment const& environment)\n {\n auto const num_gqubits\n = ket::utility::integer_log2(\n communicator.size(environment) / num_processes_per_unit_);\n set_num_qubits_params(new_num_qubits - num_gqubits - num_uqubits_, num_gqubits, communicator, environment);\n }\n\n void gates::num_lqubits(\n bit_integer_type const new_num_lqubits,\n yampi::communicator const& communicator, yampi::environment const& environment)\n {\n set_num_qubits_params(\n new_num_lqubits,\n ket::utility::integer_log2(\n communicator.size(environment) / num_processes_per_unit_),\n communicator, environment);\n }\n#else // BRA_NO_MPI\n void gates::num_qubits(bit_integer_type const new_num_qubits)\n { set_num_qubits_params(new_num_qubits); }\n#endif // BRA_NO_MPI\n\n#ifndef BRA_NO_MPI\n void gates::set_num_qubits_params(\n bit_integer_type const new_num_lqubits, bit_integer_type const num_gqubits,\n yampi::communicator const& communicator, yampi::environment const& environment)\n {\n if (ket::utility::integer_exp2(num_gqubits) * num_processes_per_unit_\n != static_cast(communicator.size(environment)))\n throw wrong_mpi_communicator_size_error{};\n\n num_lqubits_ = new_num_lqubits;\n num_qubits_ = new_num_lqubits + num_uqubits_ + num_gqubits;\n ket::utility::generate_phase_coefficients(phase_coefficients_, num_qubits_);\n\n initial_permutation_.clear();\n initial_permutation_.reserve(num_qubits_);\n for (auto bit = bit_integer_type{0u}; bit < num_qubits_; ++bit)\n initial_permutation_.push_back(permutated_qubit_type{bit});\n }\n#else // BRA_NO_MPI\n void gates::set_num_qubits_params(bit_integer_type const new_num_qubits)\n {\n num_qubits_ = new_num_qubits;\n ket::utility::generate_phase_coefficients(phase_coefficients_, num_qubits_);\n }\n#endif // BRA_NO_MPI\n\n#ifndef BRA_NO_MPI\n void gates::assign(\n std::istream& input_stream, yampi::environment const& environment,\n yampi::communicator const& communicator, size_type const num_reserved_gates)\n#else // BRA_NO_MPI\n void gates::assign(std::istream& input_stream, size_type const num_reserved_gates)\n#endif // BRA_NO_MPI\n {\n data_.clear();\n data_.reserve(num_reserved_gates);\n\n auto line = std::string{};\n auto columns = columns_type{};\n columns.reserve(10u);\n\n while (std::getline(input_stream, line))\n {\n if (line.empty())\n continue;\n\n line.erase(std::find(line.begin(), line.end(), '!'), line.end());\n boost::algorithm::trim(line);\n if (line.empty())\n continue;\n\n boost::algorithm::split(\n columns, line, boost::algorithm::is_space(),\n boost::algorithm::token_compress_on);\n\n if (boost::empty(columns))\n continue;\n\n boost::algorithm::to_upper(columns.front());\n auto const& first_mnemonic = columns.front();\n if (first_mnemonic == \"QUBITS\")\n {\n#ifndef BRA_NO_MPI\n num_qubits(\n static_cast< ::bra::state::bit_integer_type >(read_num_qubits(columns)),\n communicator, environment);\n#else // BRA_NO_MPI\n num_qubits(\n static_cast< ::bra::state::bit_integer_type >(read_num_qubits(columns)));\n#endif // BRA_NO_MPI\n }\n else if (first_mnemonic == \"INITIAL\") // INITIAL STATE\n initial_state_value_\n = static_cast< ::bra::state::state_integer_type >(read_initial_state_value(columns));\n else if (first_mnemonic == \"MPIPROCESSES\")\n {\n read_num_mpi_processes(columns);\n // ignore this statement\n }\n else if (first_mnemonic == \"MPISWAPBUFFER\")\n {\n read_mpi_buffer_size(columns);\n // ignore this statement\n }\n else if (first_mnemonic == \"BIT\") // BIT ASSIGNMENT\n {\n auto const statement = read_bit_statement(columns);\n\n if (statement == ::bra::bit_statement::assignment)\n {\n#ifndef BRA_NO_MPI\n initial_permutation_ = read_initial_permutation(columns);\n#endif\n }\n }\n else if (first_mnemonic == \"PERMUTATION\")\n throw unsupported_mnemonic_error{first_mnemonic};\n else if (first_mnemonic == \"RANDOM\") // RANDOM PERMUTATION\n throw unsupported_mnemonic_error{first_mnemonic};\n else if (first_mnemonic == \"I\")\n continue;\n else if (first_mnemonic == \"H\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::hadamard{read_hadamard(columns)}});\n else if (first_mnemonic == \"X\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::pauli_x{read_pauli_x(columns)}});\n else if (first_mnemonic == \"Y\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::pauli_y{read_pauli_y(columns)}});\n else if (first_mnemonic == \"Z\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::pauli_z{read_pauli_z(columns)}});\n else if (first_mnemonic == \"S\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::s_gate{\n phase_coefficients_[2u], read_s_gate(columns)}});\n else if (first_mnemonic == \"S+\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::adj_s_gate{\n phase_coefficients_[2u], read_adj_s_gate(columns)}});\n else if (first_mnemonic == \"T\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::t_gate{\n phase_coefficients_[3u], read_t_gate(columns)}});\n else if (first_mnemonic == \"T+\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::adj_t_gate{\n phase_coefficients_[3u], read_adj_t_gate(columns)}});\n else if (first_mnemonic == \"U1\")\n {\n auto target = qubit_type{};\n auto phase = real_type{};\n std::tie(target, phase) = read_u1(columns);\n\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::u1{phase, target}});\n }\n else if (first_mnemonic == \"U2\")\n {\n auto target = qubit_type{};\n auto phase1 = real_type{};\n auto phase2 = real_type{};\n std::tie(target, phase1, phase2) = read_u2(columns);\n\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::u2{phase1, phase2, target}});\n }\n else if (first_mnemonic == \"U3\")\n {\n auto target = qubit_type{};\n auto phase1 = real_type{};\n auto phase2 = real_type{};\n auto phase3 = real_type{};\n std::tie(target, phase1, phase2, phase3) = read_u3(columns);\n\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::u3{phase1, phase2, phase3, target}});\n }\n else if (first_mnemonic == \"R\" or first_mnemonic == \"+R\")\n {\n auto target = qubit_type{};\n auto phase_exponent = int{};\n std::tie(target, phase_exponent) = read_phase_shift(columns);\n\n if (phase_exponent >= 0)\n data_.push_back(std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::phase_shift{\n phase_exponent, phase_coefficients_[phase_exponent], target}});\n else\n {\n phase_exponent *= -1;\n data_.push_back(std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::adj_phase_shift{\n phase_exponent, phase_coefficients_[phase_exponent], target}});\n }\n }\n else if (first_mnemonic == \"-R\")\n {\n auto target = qubit_type{};\n auto phase_exponent = int{};\n std::tie(target, phase_exponent) = read_phase_shift(columns);\n\n if (phase_exponent >= 0)\n data_.push_back(std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::adj_phase_shift{\n phase_exponent, phase_coefficients_[phase_exponent], target}});\n else\n {\n phase_exponent *= -1;\n data_.push_back(std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::phase_shift{\n phase_exponent, phase_coefficients_[phase_exponent], target}});\n }\n }\n else if (first_mnemonic == \"+X\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::x_rotation_half_pi{read_x_rotation_half_pi(columns)}});\n else if (first_mnemonic == \"-X\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::adj_x_rotation_half_pi{read_adj_x_rotation_half_pi(columns)}});\n else if (first_mnemonic == \"+Y\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::y_rotation_half_pi{read_y_rotation_half_pi(columns)}});\n else if (first_mnemonic == \"-Y\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::adj_y_rotation_half_pi{read_adj_y_rotation_half_pi(columns)}});\n else if (first_mnemonic == \"CNOT\")\n {\n auto control = control_qubit_type{};\n auto target = qubit_type{};\n std::tie(control, target) = read_controlled_not(columns);\n\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::controlled_not{target, control}});\n }\n else if (first_mnemonic == \"U\")\n {\n auto control = control_qubit_type{};\n auto target = qubit_type{};\n auto phase_exponent = int{};\n std::tie(control, target, phase_exponent) = read_controlled_phase_shift(columns);\n\n if (phase_exponent >= 0)\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::controlled_phase_shift{\n phase_exponent, phase_coefficients_[phase_exponent], target, control}});\n else\n {\n phase_exponent *= -1;\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::adj_controlled_phase_shift{\n phase_exponent, phase_coefficients_[phase_exponent], target, control}});\n }\n }\n else if (first_mnemonic == \"V\")\n {\n auto control = control_qubit_type{};\n auto target = qubit_type{};\n auto phase_exponent = int{};\n std::tie(control, target, phase_exponent) = read_controlled_v(columns);\n\n if (phase_exponent >= 0)\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::controlled_v{\n phase_exponent, phase_coefficients_[phase_exponent], target, control}});\n else\n {\n phase_exponent *= -1;\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::adj_controlled_v{\n phase_exponent, phase_coefficients_[phase_exponent], target, control}});\n }\n }\n else if (first_mnemonic == \"TOFFOLI\")\n {\n auto control1 = control_qubit_type{};\n auto control2 = control_qubit_type{};\n auto target = qubit_type{};\n std::tie(control1, control2, target) = read_toffoli(columns);\n\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::toffoli{target, control1, control2}});\n }\n else if (first_mnemonic == \"M\")\n {\n#ifndef BRA_NO_MPI\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::projective_measurement{read_projective_measurement(columns), root_}});\n#else // BRA_NO_MPI\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::projective_measurement{read_projective_measurement(columns)}});\n#endif // BRA_NO_MPI\n }\n else if (first_mnemonic == \"SHORBOX\")\n {\n auto num_exponent_qubits = bit_integer_type{};\n auto divisor = state_integer_type{};\n auto base = state_integer_type{};\n std::tie(num_exponent_qubits, divisor, base) = read_shor_box(columns);\n\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::shor_box{num_exponent_qubits, divisor, base}});\n }\n else if (first_mnemonic == \"BEGIN\") // BEGIN MEASUREMENT/LEARNING MACHINE\n {\n auto const statement = read_begin_statement(columns);\n\n if (statement == ::bra::begin_statement::measurement)\n {\n#ifndef BRA_NO_MPI\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{new ::bra::gate::measurement{root_}});\n#else // BRA_NO_MPI\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{new ::bra::gate::measurement{}});\n#endif // BRA_NO_MPI\n }\n else if (statement == ::bra::begin_statement::learning_machine)\n throw unsupported_mnemonic_error{first_mnemonic};\n }\n else if (first_mnemonic == \"DO\") // DO MEASUREMENT\n {\n /*\n auto const statement = read_do_statement(columns);\n\n if (statement == do_statement::error)\n throw wrong_mnemonics_error{columns};\n else if (statement == do_statement::measurement)\n throw unsupported_mnemonic_error{first_mnemonic};\n */\n throw unsupported_mnemonic_error{first_mnemonic};\n }\n else if (first_mnemonic == \"END\") // END MEASUREMENT/LEARNING MACHINE\n {\n /*\n auto const statement = read_end_statement(columns);\n\n if (statement == ::bra::end_statement::measurement)\n {\n#ifndef BRA_NO_MPI\n data_.push_back(std::make_unique< ::bra::gate::measurement >(root_));\n#else // BRA_NO_MPI\n data_.push_back(std::make_unique< ::bra::gate::measurement >());\n#endif // BRA_NO_MPI\n }\n else if (statement == ::bra::end_statement::learning_machine)\n throw unsupported_mnemonic_error{first_mnemonic};\n*/\n throw unsupported_mnemonic_error{first_mnemonic};\n }\n else if (first_mnemonic == \"GENERATE\") // GENERATE EVENTS\n {\n auto statement = ::bra::generate_statement{};\n auto num_events = int{};\n auto seed = int{};\n std::tie(statement, num_events, seed) = read_generate_statement(columns);\n\n if (statement == ::bra::generate_statement::events)\n {\n#ifndef BRA_NO_MPI\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::generate_events{root_, num_events, seed}});\n#else // BRA_NO_MPI\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::generate_events{num_events, seed}});\n#endif // BRA_NO_MPI\n break;\n }\n }\n else if (first_mnemonic == \"CLEAR\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::clear{read_clear(columns)}});\n else if (first_mnemonic == \"SET\")\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::set{read_set(columns)}});\n else if (first_mnemonic == \"DEPOLARIZING\")\n {\n auto statement = ::bra::depolarizing_statement{};\n auto px = real_type{};\n auto py = real_type{};\n auto pz = real_type{};\n auto seed = int{};\n std::tie(statement, px, py, pz, seed) = read_depolarizing_statement(columns);\n\n if (statement == ::bra::depolarizing_statement::channel)\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{\n new ::bra::gate::depolarizing_channel{px, py, pz, seed}});\n else\n throw unsupported_mnemonic_error{first_mnemonic};\n }\n else if (first_mnemonic == \"EXIT\")\n {\n if (boost::size(columns) != 1u)\n throw wrong_mnemonics_error{columns};\n\n#ifndef BRA_NO_MPI\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{new ::bra::gate::exit{root_}});\n#else // BRA_NO_MPI\n data_.push_back(\n std::unique_ptr< ::bra::gate::gate >{new ::bra::gate::exit{}});\n#endif // BRA_NO_MPI\n break;\n }\n else\n throw unsupported_mnemonic_error{first_mnemonic};\n }\n }\n\n void gates::swap(gates& other)\n noexcept(\n BRA_is_nothrow_swappable::value\n and BRA_is_nothrow_swappable::value\n and BRA_is_nothrow_swappable::value\n and BRA_is_nothrow_swappable::value)\n {\n using std::swap;\n#ifndef BRA_NO_MPI\n swap(data_, other.data_);\n swap(num_qubits_, other.num_qubits_);\n swap(num_lqubits_, other.num_lqubits_);\n swap(initial_state_value_, other.initial_state_value_);\n swap(initial_permutation_, other.initial_permutation_);\n swap(phase_coefficients_, other.phase_coefficients_);\n swap(root_, other.root_);\n#else // BRA_NO_MPI\n swap(data_, other.data_);\n swap(num_qubits_, other.num_qubits_);\n swap(initial_state_value_, other.initial_state_value_);\n swap(phase_coefficients_, other.phase_coefficients_);\n#endif // BRA_NO_MPI\n }\n\n gates::bit_integer_type gates::read_num_qubits(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 2u)\n throw ::bra::wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n return boost::lexical_cast(*++iter);\n }\n\n gates::state_integer_type gates::read_initial_state_value(gates::columns_type& columns) const\n {\n if (boost::size(columns) != 3u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n boost::algorithm::to_upper(*++iter);\n if (columns[1] != \"STATE\")\n throw wrong_mnemonics_error{columns};\n\n return boost::lexical_cast(*++iter);\n }\n\n gates::bit_integer_type gates::read_num_mpi_processes(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 2u)\n throw ::bra::wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n return boost::lexical_cast(*++iter);\n }\n\n gates::state_integer_type gates::read_mpi_buffer_size(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 2u)\n throw ::bra::wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n return boost::lexical_cast(*++iter);\n }\n\n#ifndef BRA_NO_MPI\n std::vector\n gates::read_initial_permutation(gates::columns_type const& columns) const\n {\n auto result = std::vector{};\n result.reserve(boost::size(columns)-2u);\n\n auto iter = std::begin(columns);\n ++iter;\n ++iter;\n\n auto const last = std::end(columns);\n for (; iter != last; ++iter)\n result.push_back(static_cast(boost::lexical_cast(*iter)));\n\n return result;\n }\n#endif\n\n gates::qubit_type gates::read_target(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 2u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n auto const target = boost::lexical_cast(*++iter);\n\n return ket::make_qubit(target);\n }\n\n std::tuple\n gates::read_target_phase(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 3u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n auto const target = boost::lexical_cast(*++iter);\n auto const phase = boost::lexical_cast(*++iter);\n\n return std::make_tuple(ket::make_qubit(target), phase);\n }\n\n std::tuple\n gates::read_target_2phases(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 4u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n auto const target = boost::lexical_cast(*++iter);\n auto const phase1 = boost::lexical_cast(*++iter);\n auto const phase2 = boost::lexical_cast(*++iter);\n\n return std::make_tuple(ket::make_qubit(target), phase1, phase2);\n }\n\n std::tuple\n gates::read_target_3phases(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 5u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n auto const target = boost::lexical_cast(*++iter);\n auto const phase1 = boost::lexical_cast(*++iter);\n auto const phase2 = boost::lexical_cast(*++iter);\n auto const phase3 = boost::lexical_cast(*++iter);\n\n return std::make_tuple(ket::make_qubit(target), phase1, phase2, phase3);\n }\n\n std::tuple gates::read_target_phaseexp(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 3u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n auto const target = boost::lexical_cast(*++iter);\n auto const phase_exponent = boost::lexical_cast(*++iter);\n\n return std::make_tuple(ket::make_qubit(target), phase_exponent);\n }\n\n std::tuple gates::read_control_target(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 3u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n auto const control = boost::lexical_cast(*++iter);\n auto const target = boost::lexical_cast(*++iter);\n\n return std::make_tuple(\n ket::make_control(ket::make_qubit(control)),\n ket::make_qubit(target));\n }\n\n std::tuple\n gates::read_control_target_phaseexp(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 4u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n auto const control = boost::lexical_cast(*++iter);\n auto const target = boost::lexical_cast(*++iter);\n auto const phase_exponent = boost::lexical_cast(*++iter);\n\n return std::make_tuple(\n ket::make_control(ket::make_qubit(control)),\n ket::make_qubit(target),\n phase_exponent);\n }\n\n std::tuple\n gates::read_2controls_target(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 4u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n auto const control1 = boost::lexical_cast(*++iter);\n auto const control2 = boost::lexical_cast(*++iter);\n auto const target = boost::lexical_cast(*++iter);\n\n return std::make_tuple(\n ket::make_control(ket::make_qubit(control1)),\n ket::make_control(ket::make_qubit(control2)),\n ket::make_qubit(target));\n }\n\n ::bra::begin_statement gates::read_begin_statement(gates::columns_type& columns) const\n {\n auto const column_size = boost::size(columns);\n\n if (column_size <= 1u or column_size >= 4u)\n throw wrong_mnemonics_error{columns};\n\n if (column_size == 3u)\n {\n auto iter = std::begin(columns);\n boost::algorithm::to_upper(*++iter);\n if (*iter == \"LEARNING\")\n {\n boost::algorithm::to_upper(*++iter);\n\n if (*iter == \"MACHINE\")\n return ::bra::begin_statement::learning_machine;\n else\n throw wrong_mnemonics_error{columns};\n }\n else\n throw wrong_mnemonics_error{columns};\n }\n\n // if (column_size == 2u)\n auto iter = std::begin(columns);\n boost::algorithm::to_upper(*++iter);\n\n if (*iter != \"MEASUREMENT\")\n throw wrong_mnemonics_error{columns};\n\n return ::bra::begin_statement::measurement;\n }\n\n ::bra::bit_statement gates::read_bit_statement(gates::columns_type& columns) const\n {\n if (boost::size(columns) <= 1u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n boost::algorithm::to_upper(*++iter);\n\n if (*iter != \"ASSIGNMENT\")\n throw wrong_mnemonics_error{columns};\n\n return ::bra::bit_statement::assignment;\n }\n\n std::tuple\n gates::read_shor_box(gates::columns_type const& columns) const\n {\n if (boost::size(columns) != 4u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n auto const num_exponent_qubits = boost::lexical_cast(*++iter);\n auto const divisor = boost::lexical_cast(*++iter);\n auto const base = boost::lexical_cast(*++iter);\n\n return std::make_tuple(num_exponent_qubits, divisor, base);\n }\n\n std::tuple< ::bra::generate_statement, int, int > gates::read_generate_statement(gates::columns_type& columns) const\n {\n if (boost::size(columns) != 4u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = std::begin(columns);\n boost::algorithm::to_upper(*++iter);\n\n if (*iter != \"EVENTS\")\n throw wrong_mnemonics_error{columns};\n\n auto const num_events = boost::lexical_cast(*++iter);\n auto const seed = boost::lexical_cast(*++iter);\n return std::make_tuple(::bra::generate_statement::events, num_events, seed);\n }\n\n std::tuple< ::bra::depolarizing_statement, gates::real_type, gates::real_type, gates::real_type, int >\n gates::read_depolarizing_statement(gates::columns_type& columns) const\n {\n if (boost::size(columns) <= 2u)\n throw wrong_mnemonics_error{columns};\n\n auto iter = columns.cbegin();\n auto present_string = std::string{*++iter}; // present_string == \"CHANNEL\"\n boost::algorithm::to_upper(present_string);\n\n if (present_string != \"CHANNEL\")\n throw wrong_mnemonics_error{columns};\n\n auto px = real_type{};\n auto py = real_type{};\n auto pz = real_type{};\n auto seed = -1;\n auto is_px_checked = false;\n auto is_py_checked = false;\n auto is_pz_checked = false;\n auto is_seed_checked = false;\n\n present_string = *++iter; // present_string == \"P_*\" or \"P_*=\" or \"P_*=0.xxx\" or \"P_*=0.xxx,\" or \"P_*=0.xxx,...\"\n auto probability_string = std::string{\" \"};\n auto const last = columns.cend();\n while (iter != last)\n {\n auto string_found = std::find(present_string.cbegin(), present_string.cend(), '=');\n probability_string.assign(present_string.cbegin(), string_found);\n boost::algorithm::to_upper(probability_string);\n if (probability_string == \"P_X\")\n {\n if (is_px_checked)\n throw wrong_mnemonics_error{columns};\n\n ::bra::gates_detail::read_depolarizing_statement(px, present_string, iter, last, string_found, columns);\n if (px < 0.0 or px > 1.0)\n throw wrong_mnemonics_error{columns};\n\n is_px_checked = true;\n }\n else if (probability_string == \"P_Y\")\n {\n if (is_py_checked)\n throw wrong_mnemonics_error{columns};\n\n ::bra::gates_detail::read_depolarizing_statement(py, present_string, iter, last, string_found, columns);\n if (py < 0.0 or py > 1.0)\n throw wrong_mnemonics_error{columns};\n\n is_py_checked = true;\n }\n else if (probability_string == \"P_Z\")\n {\n if (is_pz_checked)\n throw wrong_mnemonics_error{columns};\n\n ::bra::gates_detail::read_depolarizing_statement(pz, present_string, iter, last, string_found, columns);\n if (pz < 0.0 or pz > 1.0)\n throw wrong_mnemonics_error{columns};\n\n is_pz_checked = true;\n }\n else if (probability_string == \"SEED\")\n {\n if (is_seed_checked)\n throw wrong_mnemonics_error{columns};\n\n ::bra::gates_detail::read_depolarizing_statement(seed, present_string, iter, last, string_found, columns);\n is_seed_checked = true;\n }\n else\n throw wrong_mnemonics_error{columns};\n }\n\n if (is_px_checked and is_py_checked and is_pz_checked and px + py + pz > 1.0)\n throw wrong_mnemonics_error{columns};\n else if (is_px_checked and is_py_checked and not is_pz_checked and px + py < 1.0)\n pz = 1.0 - px - py;\n else if (is_px_checked and not is_py_checked and is_pz_checked and px + pz < 1.0)\n py = 1.0 - px - pz;\n else if (not is_px_checked and is_py_checked and is_pz_checked and py + pz < 1.0)\n px = 1.0 - py - pz;\n else\n throw wrong_mnemonics_error{columns};\n\n return std::make_tuple(::bra::depolarizing_statement::channel, px, py, pz, seed);\n }\n} // namespace bra\n\n\n# undef BRA_is_nothrow_swappable\n", "meta": {"hexsha": "a81b274211098729decf9b27cf8c037ffc7913e6", "size": 35743, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "bra/src/gates.cpp", "max_stars_repo_name": "naoki-yoshioka/bracket", "max_stars_repo_head_hexsha": "6eff40e6ee5768744974e9d79bf6ff892bd67390", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2021-02-26T21:50:37.000Z", "max_stars_repo_stars_event_max_datetime": 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YES\n2. NO", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.42250463481418826, "lm_q1q2_score": 0.21785180419547528}} {"text": "//\r\n// $Id: IsotopeCalculator.cpp 2051 2010-06-15 18:39:13Z chambm $ \r\n//\r\n//\r\n// Original author: Darren Kessner \r\n//\r\n// Copyright 2006 Louis Warschaw Prostate Cancer Center\r\n// Cedars Sinai Medical Center, Los Angeles, California 90048\r\n//\r\n// Licensed under the Apache License, Version 2.0 (the \"License\"); \r\n// you may not use this file except in compliance with the License. \r\n// You may obtain a copy of the License at \r\n//\r\n// http://www.apache.org/licenses/LICENSE-2.0\r\n//\r\n// Unless required by applicable law or agreed to in writing, software \r\n// distributed under the License is distributed on an \"AS IS\" BASIS, \r\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \r\n// See the License for the specific language governing permissions and \r\n// limitations under the License.\r\n//\r\n\r\n\r\n#define PWIZ_SOURCE\r\n\r\n#include \"IsotopeCalculator.hpp\"\r\n#include \"IsotopeTable.hpp\"\r\n#include \"Ion.hpp\"\r\n#include \"pwiz/utility/misc/Std.hpp\"\r\n#include \r\n\r\n\r\nnamespace pwiz {\r\nnamespace chemistry {\r\n\r\n\r\nusing boost::ptr_vector;\r\n\r\n\r\nclass IsotopeCalculator::Impl\r\n{\r\n public:\r\n\r\n Impl(double abundanceCutoff, double massPrecision);\r\n\r\n MassDistribution distribution(const Formula& formula,\r\n int chargeState,\r\n int normalization) const;\r\n private:\r\n\r\n double abundanceCutoff_;\r\n double massPrecision_; \r\n\r\n ptr_vector tableStorage_;\r\n typedef map TableMap;\r\n TableMap tableMap_;\r\n\r\n void initializeIsotopeTables();\r\n MassDistribution distributionManually(Element::Type e, int atomCount) const;\r\n};\r\n\r\n\r\nIsotopeCalculator::Impl::Impl(double abundanceCutoff, double massPrecision)\r\n: abundanceCutoff_(abundanceCutoff),\r\n massPrecision_(massPrecision)\r\n{\r\n initializeIsotopeTables();\r\n}\r\n\r\n\r\nnamespace {\r\n\r\nbool hasLessMass(const MassAbundance& a, const MassAbundance& b)\r\n{\r\n return a.mass < b.mass;\r\n}\r\n\r\nMassAbundance coalesce(const MassAbundance& a, const MassAbundance& b)\r\n{\r\n double abundance = a.abundance + b.abundance;\r\n double mass = (a.abundance*a.mass + b.abundance*b.mass) / abundance;\r\n return MassAbundance(mass, abundance);\r\n}\r\n\r\nclass Distinct\r\n{\r\n public:\r\n\r\n Distinct(double mass, double precision) \r\n : mass_(mass), precision_(precision) \r\n {}\r\n\r\n bool operator()(const MassAbundance& ma)\r\n {\r\n return fabs(ma.mass - mass_) > precision_;\r\n }\r\n \r\n private:\r\n double mass_;\r\n double precision_;\r\n};\r\n\r\nMassDistribution coalesceDistribution(const MassDistribution& md, double precision)\r\n{\r\n // assumes MassDistribution md is sorted by mass!\r\n\r\n MassDistribution result;\r\n\r\n for (MassDistribution::const_iterator it=md.begin(); it!=md.end();)\r\n {\r\n Distinct distinct(it->mass, precision);\r\n MassDistribution::const_iterator next = find_if(it, md.end(), distinct); \r\n result.push_back(accumulate(it, next, MassAbundance(), coalesce));\r\n it = next;\r\n }\r\n\r\n return result;\r\n}\r\n\r\nclass Convolve\r\n{\r\n public:\r\n\r\n Convolve(double cutoff = 0)\r\n : cutoff_(cutoff)\r\n {}\r\n\r\n MassDistribution operator()(const MassDistribution& m, const MassDistribution& n)\r\n {\r\n if (m.empty()) return n;\r\n if (n.empty()) return m;\r\n\r\n MassDistribution result;\r\n\r\n // we could break out of these loops early if the mass distributions \r\n // are sorted by abundance\r\n\r\n for (MassDistribution::const_iterator i=m.begin(); i!=m.end(); ++i)\r\n for (MassDistribution::const_iterator j=n.begin(); j!=n.end(); ++j)\r\n {\r\n double mass = i->mass + j->mass;\r\n double abundance = i->abundance * j->abundance;\r\n if (abundance > cutoff_)\r\n result.push_back(MassAbundance(mass, abundance));\r\n }\r\n\r\n return result;\r\n }\r\n\r\n private:\r\n double cutoff_;\r\n};\r\n\r\nclass Ionize\r\n{\r\n public:\r\n\r\n Ionize(int chargeState)\r\n : chargeState_(chargeState)\r\n {}\r\n\r\n void operator()(MassAbundance& ma) const\r\n {\r\n ma.mass = Ion::mz(ma.mass, chargeState_);\r\n }\r\n\r\n private:\r\n int chargeState_;\r\n};\r\n\r\nvoid normalize(MassDistribution& md, int normalization)\r\n{\r\n if (md.empty()) \r\n return;\r\n\r\n double abundanceScale = 1;\r\n\r\n if (normalization & IsotopeCalculator::NormalizeAbundance)\r\n {\r\n double sumSquaredAbundances = 0;\r\n for (MassDistribution::iterator it=md.begin(); it!=md.end(); ++it)\r\n {\r\n double a = it->abundance;\r\n sumSquaredAbundances += a*a;\r\n }\r\n\r\n abundanceScale = sqrt(sumSquaredAbundances);\r\n }\r\n\r\n double massShift = (normalization & IsotopeCalculator::NormalizeMass) ? md[0].mass : 0;\r\n\r\n for (MassDistribution::iterator it=md.begin(); it!=md.end(); ++it)\r\n {\r\n it->mass -= massShift; \r\n it->abundance /= abundanceScale; \r\n }\r\n}\r\n\r\n} // namespace \r\n\r\n\r\nMassDistribution IsotopeCalculator::Impl::distribution(const Formula& formula,\r\n int chargeState,\r\n int normalization) const\r\n{\r\n // collect the distributions for each element in the formula\r\n\r\n vector distributions; \r\n\r\n Formula::Map formulaData = formula.data();\r\n for (Formula::Map::const_iterator it=formulaData.begin(); it!=formulaData.end(); ++it)\r\n {\r\n Element::Type e = it->first;\r\n int atomCount = it->second;\r\n\r\n TableMap::const_iterator table = tableMap_.find(e); \r\n if (table != tableMap_.end())\r\n distributions.push_back(table->second->distribution(atomCount));\r\n else\r\n distributions.push_back(distributionManually(e, atomCount));\r\n }\r\n\r\n // coalesce each elemental distribution \r\n\r\n vector coalescedDistributions;\r\n for (vector::iterator it=distributions.begin(); it!=distributions.end(); ++it)\r\n coalescedDistributions.push_back(coalesceDistribution(*it, massPrecision_)); \r\n\r\n // combine the distributions and sort by mass\r\n\r\n MassDistribution combined = accumulate(coalescedDistributions.begin(), coalescedDistributions.end(), \r\n MassDistribution(), Convolve(abundanceCutoff_));\r\n\r\n sort(combined.begin(), combined.end(), hasLessMass);\r\n\r\n MassDistribution result = coalesceDistribution(combined, massPrecision_);\r\n\r\n // adjust for charge state\r\n \r\n if (chargeState)\r\n for_each(result.begin(), result.end(), Ionize(chargeState));\r\n\r\n // normalize if requested\r\n\r\n if (normalization)\r\n normalize(result, normalization);\r\n \r\n return result;\r\n}\r\n\r\n\r\nnamespace {\r\n\r\nstruct TableInfo\r\n{\r\n Element::Type element;\r\n int maxAtomCount;\r\n};\r\n\r\nTableInfo tableInfo_[] = \r\n{\r\n {Element::C, 5000}, \r\n {Element::H, 8000}, \r\n {Element::N, 1500}, \r\n {Element::O, 1500}, \r\n {Element::S, 50}, \r\n};\r\n\r\nconst int tableInfoSize_ = sizeof(tableInfo_)/sizeof(TableInfo);\r\n\r\n} // namespace\r\n\r\n\r\nvoid IsotopeCalculator::Impl::initializeIsotopeTables()\r\n{\r\n for (TableInfo* it=tableInfo_; it!=tableInfo_+tableInfoSize_; ++it)\r\n {\r\n IsotopeTable* temp(new IsotopeTable(Element::Info::record(it->element).isotopes, \r\n it->maxAtomCount, \r\n abundanceCutoff_));\r\n tableMap_[it->element] = temp; // store pointer in the map\r\n tableStorage_.push_back(temp); // maintain ownership in the ptr_vector\r\n }\r\n}\r\n\r\n\r\nMassDistribution IsotopeCalculator::Impl::distributionManually(Element::Type e, \r\n int atomCount) const\r\n{\r\n throw runtime_error(\"[IsotopeCalculator::distribution()] No table for element \" \r\n + Element::Info::record(e).symbol); \r\n}\r\n\r\n\r\nPWIZ_API_DECL IsotopeCalculator::IsotopeCalculator(double abundanceCutoff, double massPrecision)\r\n: impl_(new Impl(abundanceCutoff, massPrecision))\r\n{}\r\n\r\n\r\nPWIZ_API_DECL IsotopeCalculator::~IsotopeCalculator(){} // auto destruction of impl_\r\n\r\n\r\nPWIZ_API_DECL\r\nMassDistribution IsotopeCalculator::distribution(const Formula& formula,\r\n int chargeState,\r\n int normalization) const\r\n{\r\n return impl_->distribution(formula, chargeState, normalization);\r\n}\r\n\r\n\r\n} // namespace chemistry\r\n} // namespace pwiz\r\n", "meta": {"hexsha": "be2136b7cfb6ed6381c2378fd379a88882a066c7", "size": 8659, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "pwiz/utility/chemistry/IsotopeCalculator.cpp", "max_stars_repo_name": "edyp-lab/pwiz-mzdb", "max_stars_repo_head_hexsha": "d13ce17f4061596c7e3daf9cf5671167b5996831", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 11.0, "max_stars_repo_stars_event_min_datetime": "2015-01-08T08:33:44.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-12T06:14:54.000Z", "max_issues_repo_path": "pwiz/utility/chemistry/IsotopeCalculator.cpp", "max_issues_repo_name": "shze/pwizard-deb", "max_issues_repo_head_hexsha": "4822829196e915525029a808470f02d24b8b8043", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 61.0, "max_issues_repo_issues_event_min_datetime": "2015-05-27T11:20:11.000Z", "max_issues_repo_issues_event_max_datetime": "2019-12-20T15:06:21.000Z", "max_forks_repo_path": "pwiz/utility/chemistry/IsotopeCalculator.cpp", "max_forks_repo_name": "shze/pwizard-deb", "max_forks_repo_head_hexsha": "4822829196e915525029a808470f02d24b8b8043", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2016-02-03T09:41:16.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-01T18:42:36.000Z", "avg_line_length": 27.576433121, "max_line_length": 106, "alphanum_fraction": 0.620279478, "num_tokens": 1874, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.21785180419547523}} {"text": "/*\nCopyright 2020 Dennis Rohde\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n*/\n\n#include \n#include \n\n#include \n\n#include \"frechet.hpp\"\n\nnamespace Frechet {\n\nnamespace Continuous {\n \ndistance_t epsilon = 0.001;\nbool round = true;\n \nstd::string Distance::repr() const {\n std::stringstream ss;\n ss << value;\n return ss.str();\n}\n\nDistance distance(const Curve &curve1, const Curve &curve2) {\n if ((curve1.complexity() < 2) or (curve2.complexity() < 2)) {\n std::cerr << \"WARNING: comparison possible only for curves of at least two points\" << std::endl;\n Distance result;\n result.value = std::numeric_limits::signaling_NaN();\n return result;\n }\n if (curve1.dimensions() != curve2.dimensions()) {\n std::cerr << \"WARNING: comparison possible only for curves of equal number of dimensions\" << std::endl;\n Distance result;\n result.value = std::numeric_limits::signaling_NaN();\n return result;\n }\n \n auto start = boost::chrono::process_real_cpu_clock::now();\n const auto lb = std::sqrt(std::max(curve1[0].dist_sqr(curve2[0]), curve1[curve1.complexity()-1].dist_sqr(curve2[curve2.complexity()-1])));\n const auto ub = _greedy_upper_bound(curve1, curve2);\n auto end = boost::chrono::process_real_cpu_clock::now();\n\n #if DEBUG\n std::cout << \"narrowed to [\" << lb << \", \" << ub.value << \"]\" << std::endl;\n #endif\n\n auto dist = _distance(curve1, curve2, ub, lb);\n dist.time_bounds = (end-start).count() / 1000000000.0;\n if (round) dist.value = std::round(dist.value * 1e3) / 1e3;\n\n return dist;\n}\n\nDistance _distance(const Curve &curve1, const Curve &curve2, distance_t ub, distance_t lb) {\n Distance result;\n auto start = boost::chrono::process_real_cpu_clock::now();\n \n distance_t split = (ub + lb)/2;\n std::size_t number_searches = 0;\n \n if (ub - lb > epsilon) {\n auto infty = std::numeric_limits::infinity();\n std::vector> reachable1(curve1.complexity()-1, std::vector(curve2.complexity(), infty));\n std::vector> reachable2(curve1.complexity(), std::vector(curve2.complexity()-1, infty));\n \n std::vector> free_intervals1(curve2.complexity(), std::vector(curve1.complexity(), Interval()));\n std::vector> free_intervals2(curve1.complexity(), std::vector(curve2.complexity(), Interval()));\n\n if (std::isnan(lb) or std::isnan(ub)) {\n result.value = std::numeric_limits::signaling_NaN();\n return result;\n }\n\n //Binary search over the feasible distances\n while (ub - lb > epsilon) {\n ++number_searches;\n split = (ub + lb)/2;\n auto isLessThan = _less_than_or_equal(split, curve1, curve2, reachable1, reachable2, free_intervals1, free_intervals2);\n if (isLessThan) {\n ub = split;\n }\n else {\n lb = split;\n }\n #if DEBUG\n std::cout << \"narrowed to [\" << lb << \", \" << ub << \"]\" << std::endl;\n #endif\n }\n }\n \n distance_t value = (ub + lb)/2.;\n auto end = boost::chrono::process_real_cpu_clock::now();\n result.value = value;\n result.time_searches = (end-start).count() / 1000000000.0;\n result.number_searches = number_searches;\n return result;\n}\n\nbool _less_than_or_equal(const distance_t distance, Curve const& curve1, Curve const& curve2, \n std::vector> &reachable1, std::vector> &reachable2,\n std::vector> &free_intervals1, std::vector> &free_intervals2) {\n assert(curve1.complexity() >= 2);\n assert(curve2.complexity() >= 2);\n \n distance_t dist_sqr = distance * distance;\n auto infty = std::numeric_limits::infinity();\n\n if (curve1[0].dist_sqr(curve2[0]) > dist_sqr or curve1.back().dist_sqr(curve2.back()) > dist_sqr) return false;\n\n for (auto &elem: reachable1) {\n #pragma omp parallel for\n for (curve_size_t i = 0; i < elem.size(); ++i) {\n elem[i] = infty;\n }\n }\n \n for (auto &elem: reachable2) {\n #pragma omp parallel for\n for (curve_size_t i = 0; i < elem.size(); ++i) {\n elem[i] = infty;\n }\n }\n \n for (auto &elem: free_intervals1) {\n #pragma omp parallel for\n for (curve_size_t i = 0; i < elem.size(); ++i) {\n elem[i] = Interval();\n }\n }\n \n for (auto &elem: free_intervals2) {\n #pragma omp parallel for\n for (curve_size_t i = 0; i < elem.size(); ++i) {\n elem[i] = Interval();\n }\n }\n \n for (curve_size_t i = 0; i < curve1.complexity() - 1; ++i) {\n reachable1[i][0] = 0.;\n if (curve2[0].dist_sqr(curve1[i+1]) > dist_sqr) break;\n }\n \n for (curve_size_t j = 0; j < curve2.complexity() - 1; ++j) {\n reachable2[0][j] = 0.;\n if (curve1[0].dist_sqr(curve2[j+1]) > dist_sqr) break;\n }\n \n #pragma omp parallel for collapse(2)\n for (curve_size_t i = 0; i < curve1.complexity(); ++i) {\n for (curve_size_t j = 0; j < curve2.complexity(); ++j) {\n if ((i < curve1.complexity() - 1) and (j > 0)) {\n free_intervals1[j][i] = curve2[j].intersection_interval(dist_sqr, curve1[i], curve1[i+1]);\n }\n if ((j < curve2.complexity() - 1) and (i > 0)) {\n free_intervals2[i][j] = curve1[i].intersection_interval(dist_sqr, curve2[j], curve2[j+1]);\n }\n }\n }\n \n for (curve_size_t i = 0; i < curve1.complexity(); ++i) {\n for (curve_size_t j = 0; j < curve2.complexity(); ++j) {\n if ((i < curve1.complexity() - 1) and (j > 0)) {\n if (not free_intervals1[j][i].is_empty()) {\n if (reachable2[i][j-1] != infty) {\n reachable1[i][j] = free_intervals1[j][i].begin();\n }\n else if (reachable1[i][j-1] <= free_intervals1[j][i].end()) {\n reachable1[i][j] = std::max(free_intervals1[j][i].begin(), reachable1[i][j-1]);\n }\n }\n }\n if ((j < curve2.complexity() - 1) and (i > 0)) {\n if (not free_intervals2[i][j].is_empty()) {\n if (reachable1[i-1][j] != infty) {\n reachable2[i][j] = free_intervals2[i][j].begin();\n }\n else if (reachable2[i-1][j] <= free_intervals2[i][j].end()) {\n reachable2[i][j] = std::max(free_intervals2[i][j].begin(), reachable2[i-1][j]);\n }\n }\n }\n }\n }\n\n assert((reachable1.back().back() < infty) == (reachable2.back().back() < infty));\n\n return reachable1.back().back() < infty;\n}\n\ndistance_t _greedy_upper_bound(const Curve &curve1, const Curve &curve2) {\n distance_t result = 0;\n \n const curve_size_t len1 = curve1.complexity(), len2 = curve2.complexity();\n curve_size_t i = 0, j = 0;\n \n while ((i < len1 - 1) and (j < len2 - 1)) {\n result = std::max(result, curve1[i].dist_sqr(curve2[j]));\n \n distance_t dist1 = curve1[i+1].dist_sqr(curve2[j]),\n dist2 = curve1[i].dist_sqr(curve2[j+1]),\n dist3 = curve1[i+1].dist_sqr(curve2[j+1]);\n \n if ((dist1 <= dist2) and (dist1 <= dist3)) ++i;\n else if ((dist2 <= dist1) and (dist2 <= dist3)) ++j;\n else {\n ++i;\n ++j;\n }\n }\n \n while (i < len1) result = std::max(result, curve1[i++].dist_sqr(curve2[j]));\n \n --i;\n \n while (j < len2) result = std::max(result, curve1[i].dist_sqr(curve2[j++]));\n \n return std::sqrt(result);\n}\n\n} // end namespace Continuous\n\nnamespace Discrete {\n \nstd::string Distance::repr() const {\n std::stringstream ss;\n ss << value;\n return ss.str();\n}\n \nDistance distance(const Curve &curve1, const Curve &curve2) {\n Distance result;\n auto start = boost::chrono::process_real_cpu_clock::now();\n std::vector> a(curve1.complexity(), std::vector(curve2.complexity(), -1));\n auto value = std::sqrt(_dp(a, curve1.complexity() - 1, curve2.complexity() - 1, curve1, curve2));\n auto end = boost::chrono::process_real_cpu_clock::now();\n result.time = (end-start).count() / 1000000000.0;\n result.value = value;\n return result;\n \n}\n\ndistance_t _dp(std::vector> &a, const curve_size_t i, const curve_size_t j, const Curve &curve1, const Curve &curve2) {\n if (a[i][j] > -1) return a[i][j];\n else if (i == 0 and j == 0) return curve1[i].dist_sqr(curve2[j]);\n else if (i > 0 and j == 0) return std::max(_dp(a, i-1, 0, curve1, curve2), curve1[i].dist_sqr(curve2[j]));\n else if (i == 0 and j > 0) return std::max(_dp(a, 0, j-1, curve1, curve2), curve1[i].dist_sqr(curve2[j]));\n else {\n a[i][j] = std::max(\n std::min(\n std::min(_dp(a, i-1, j, curve1, curve2), \n _dp(a, i-1, j-1, curve1, curve2)), \n _dp(a, i, j-1, curve1, curve2)), \n curve1[i].dist_sqr(curve2[j]));\n }\n return a[i][j];\n}\n\n} // end namespace Discrete\n\n} // end namespace Frechet\n", "meta": {"hexsha": "c17c40fab90af7593405fcbc70913f84e951c449", "size": 10564, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/frechet.cpp", "max_stars_repo_name": "hairbeRt/Fred", "max_stars_repo_head_hexsha": "ae3770e2ad62f83a7d2070e8e48087d89aa09bfc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/frechet.cpp", "max_issues_repo_name": "hairbeRt/Fred", "max_issues_repo_head_hexsha": "ae3770e2ad62f83a7d2070e8e48087d89aa09bfc", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/frechet.cpp", "max_forks_repo_name": "hairbeRt/Fred", "max_forks_repo_head_hexsha": "ae3770e2ad62f83a7d2070e8e48087d89aa09bfc", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 39.4179104478, "max_line_length": 460, "alphanum_fraction": 0.5841537296, "num_tokens": 2929, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.21751576258908772}} {"text": "/*\n Copyright (C) 2016 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n\nusing boost::timer::cpu_timer;\nusing boost::timer::default_places;\nusing namespace QuantLib;\nusing std::lower_bound;\nusing std::sort;\n\nnamespace ore {\nnamespace data {\n\nvoid Swaption::build(const boost::shared_ptr& engineFactory) {\n\n QL_REQUIRE(swap_.size() == 2, \"underlying swap must have 2 legs\");\n\n // we can assume 2 legs now\n bool isCrossCcy = swap_[0].currency() != swap_[1].currency();\n\n bool isFixedFloating = (swap_[0].legType() == \"Fixed\" && swap_[1].legType() == \"Floating\") ||\n (swap_[1].legType() == \"Fixed\" && swap_[0].legType() == \"Floating\");\n\n // First elimate these\n QL_REQUIRE(!isCrossCcy, \"Cross Currency Swaptions not supported\");\n QL_REQUIRE(isFixedFloating, \"Basis Swaptions not supported\");\n\n // check at least one exercise date is given\n QL_REQUIRE(!option_.exerciseDates().empty(), \"No exercise dates given\");\n\n // check if all exercise dates (adjusted by the notice period) are in the past and if so, build an expired\n // instrument\n Period noticePeriod = option_.noticePeriod().empty() ? 0 * Days : parsePeriod(option_.noticePeriod());\n Calendar noticeCal = option_.noticeCalendar().empty() ? NullCalendar() : parseCalendar(option_.noticeCalendar());\n BusinessDayConvention noticeBdc =\n option_.noticeConvention().empty() ? Unadjusted : parseBusinessDayConvention(option_.noticeConvention());\n std::vector exerciseDates(option_.exerciseDates().size());\n for (Size i = 0; i < option_.exerciseDates().size(); ++i) {\n exerciseDates.push_back(noticeCal.advance(parseDate(option_.exerciseDates()[i]), -noticePeriod, noticeBdc));\n }\n\n Date latestExerciseDate = *std::max_element(exerciseDates.begin(), exerciseDates.end());\n if (QuantLib::detail::simple_event(latestExerciseDate).hasOccurred()) {\n exercise_ = boost::make_shared(exerciseDates);\n legs_ = {{boost::make_shared(0.0, latestExerciseDate)}};\n instrument_ = boost::shared_ptr(\n new VanillaInstrument(boost::make_shared(legs_, std::vector(1, false)), 1.0, {}, {}));\n legCurrencies_ = {swap_[0].currency()};\n legPayers_ = {false};\n npvCurrency_ = swap_[0].currency();\n notional_ = 0.0;\n notionalCurrency_ = npvCurrency_;\n maturity_ = latestExerciseDate;\n return;\n }\n\n // build swaption with at least one alive exercise date\n bool fixedFirst = swap_[0].legType() == \"Fixed\";\n boost::shared_ptr fixedLegData =\n boost::dynamic_pointer_cast(swap_[fixedFirst ? 0 : 1].concreteLegData());\n boost::shared_ptr floatingLegData =\n boost::dynamic_pointer_cast(swap_[fixedFirst ? 1 : 0].concreteLegData());\n underlyingIndex_ = floatingLegData->index();\n\n bool isNonStandard =\n (swap_[0].notionals().size() > 1 || swap_[1].notionals().size() > 1 || floatingLegData->spreads().size() > 1 ||\n floatingLegData->gearings().size() > 1 || fixedLegData->rates().size() > 1);\n\n Exercise::Type exerciseType = parseExerciseType(option_.style());\n\n // treat non standard europeans as bermudans, also if exercise fees are present\n QL_REQUIRE(exerciseType == Exercise::Bermudan || exerciseType == Exercise::European,\n \"Exercise type \" << option_.style() << \" not implemented for Swaptions\");\n if (exerciseType == Exercise::Bermudan || (isNonStandard && exerciseType == Exercise::European) ||\n !option_.exerciseFees().empty())\n buildBermudan(engineFactory);\n else\n buildEuropean(engineFactory);\n\n // add required fixings, we add the required fixing for the underlying swap, which might be more\n // than actually required, i.e. we are conservative here\n addToRequiredFixings(underlyingLeg_,\n boost::make_shared(\n requiredFixings_, std::map{{underlyingIndexQlName_, underlyingIndex_}}));\n}\n\nnamespace {\nQuantLib::Settlement::Method defaultMethod(const QuantLib::Settlement::Type t) {\n if (t == QuantLib::Settlement::Physical)\n return QuantLib::Settlement::PhysicalOTC;\n else\n return QuantLib::Settlement::ParYieldCurve; // ql < 1.14 behaviour\n}\n} // namespace\n\nvoid Swaption::buildEuropean(const boost::shared_ptr& engineFactory) {\n\n LOG(\"Building European Swaption \" << id());\n\n // Swaption details\n Settlement::Type settleType = parseSettlementType(option_.settlement());\n Settlement::Method settleMethod = option_.settlementMethod() == \"\"\n ? defaultMethod(settleType)\n : parseSettlementMethod(option_.settlementMethod());\n QL_REQUIRE(option_.exerciseDates().size() == 1, \"Only one exercise date expected for European Option\");\n\n Period noticePeriod = option_.noticePeriod().empty() ? 0 * Days : parsePeriod(option_.noticePeriod());\n Calendar noticeCal = option_.noticeCalendar().empty() ? NullCalendar() : parseCalendar(option_.noticeCalendar());\n BusinessDayConvention noticeBdc =\n option_.noticeConvention().empty() ? Unadjusted : parseBusinessDayConvention(option_.noticeConvention());\n Date exDate = noticeCal.advance(parseDate(option_.exerciseDates().front()), -noticePeriod, noticeBdc);\n QL_REQUIRE(exDate >= Settings::instance().evaluationDate(), \"Exercise date expected in the future.\");\n\n boost::shared_ptr swap = buildVanillaSwap(engineFactory, exDate);\n underlyingLeg_ = swap->floatingLeg();\n\n string ccy = swap_[0].currency();\n Currency currency = parseCurrency(ccy);\n\n exercise_ = boost::make_shared(exDate);\n\n // Build Swaption\n boost::shared_ptr swaption(new QuantLib::Swaption(swap, exercise_, settleType, settleMethod));\n\n // Add Engine\n string tt(\"EuropeanSwaption\");\n boost::shared_ptr builder = engineFactory->builder(tt);\n QL_REQUIRE(builder, \"No builder found for \" << tt);\n boost::shared_ptr swaptionBuilder =\n boost::dynamic_pointer_cast(builder);\n swaption->setPricingEngine(swaptionBuilder->engine(currency));\n\n Position::Type positionType = parsePositionType(option_.longShort());\n Real multiplier = (positionType == QuantLib::Position::Long ? 1.0 : -1.0);\n\n // If premium data is provided\n // 1) build the fee trade and pass it to the instrument wrapper for pricing\n // 2) add fee payment as additional trade leg for cash flow reporting\n std::vector> additionalInstruments;\n std::vector additionalMultipliers;\n if (option_.premiumPayDate() != \"\" && option_.premiumCcy() != \"\") {\n Real premiumAmount = -multiplier * option_.premium(); // pay if long, receive if short\n Currency premiumCurrency = parseCurrency(option_.premiumCcy());\n Date premiumDate = parseDate(option_.premiumPayDate());\n addPayment(additionalInstruments, additionalMultipliers, premiumDate, premiumAmount, premiumCurrency, currency,\n engineFactory, swaptionBuilder->configuration(MarketContext::pricing));\n DLOG(\"option premium added for european swaption \" << id());\n }\n\n // Now set the instrument wrapper, depending on delivery\n if (settleType == Settlement::Physical) {\n // tracks state for any option flavour, including physical delivery\n instrument_ = boost::shared_ptr(\n new EuropeanOptionWrapper(swaption, positionType == Position::Long ? true : false, exDate,\n settleType == Settlement::Physical ? true : false, swap, 1.0, 1.0,\n additionalInstruments, additionalMultipliers));\n // maturity of underlying\n maturity_ = std::max(swap->fixedSchedule().dates().back(), swap->floatingSchedule().dates().back());\n } else {\n instrument_ = boost::shared_ptr(\n new VanillaInstrument(swaption, multiplier, additionalInstruments, additionalMultipliers));\n maturity_ = exDate;\n }\n\n DLOG(\"Building European Swaption done\");\n}\n\nvoid Swaption::buildBermudan(const boost::shared_ptr& engineFactory) {\n DLOG(\"Building Bermudan Swaption \" << id());\n\n QL_REQUIRE(swap_.size() >= 1, \"underlying swap must have at least 1 leg\");\n\n string ccy_str = swap_[0].currency();\n Currency currency = parseCurrency(ccy_str);\n\n bool fixedFirst = swap_[0].legType() == \"Fixed\";\n boost::shared_ptr fixedLegData =\n boost::dynamic_pointer_cast(swap_[fixedFirst ? 0 : 1].concreteLegData());\n boost::shared_ptr floatingLegData =\n boost::dynamic_pointer_cast(swap_[fixedFirst ? 1 : 0].concreteLegData());\n underlyingIndex_ = floatingLegData->index();\n\n bool isNonStandard =\n (swap_[0].notionals().size() > 1 || swap_[1].notionals().size() > 1 || floatingLegData->spreads().size() > 1 ||\n floatingLegData->gearings().size() > 1 || fixedLegData->rates().size() > 1);\n\n boost::shared_ptr vanillaSwap;\n boost::shared_ptr nonstandardSwap;\n boost::shared_ptr swap;\n if (!isNonStandard) {\n vanillaSwap = buildVanillaSwap(engineFactory);\n underlyingLeg_ = vanillaSwap->floatingLeg();\n swap = vanillaSwap;\n } else {\n nonstandardSwap = buildNonStandardSwap(engineFactory);\n underlyingLeg_ = nonstandardSwap->floatingLeg();\n swap = nonstandardSwap;\n }\n\n DLOG(\"Swaption::Build(): Underlying Start = \" << QuantLib::io::iso_date(swap->startDate()));\n\n // build exercise: only keep a) future exercise dates and b) exercise dates that exercise into a whole\n // accrual period of the underlying; TODO handle exercises into broken periods?\n Date lastAccrualStartDate = Date::minDate();\n for (Size i = 0; i < 2; ++i) {\n for (auto const& c : swap->leg(i)) {\n if (auto cpn = boost::dynamic_pointer_cast(c))\n lastAccrualStartDate = std::max(lastAccrualStartDate, cpn->accrualStartDate());\n }\n }\n Period noticePeriod = option_.noticePeriod().empty() ? 0 * Days : parsePeriod(option_.noticePeriod());\n Calendar noticeCal = option_.noticeCalendar().empty() ? NullCalendar() : parseCalendar(option_.noticeCalendar());\n BusinessDayConvention noticeBdc =\n option_.noticeConvention().empty() ? Unadjusted : parseBusinessDayConvention(option_.noticeConvention());\n std::vector sortedExerciseDates;\n for (auto const& d : option_.exerciseDates())\n sortedExerciseDates.push_back(parseDate(d));\n std::sort(sortedExerciseDates.begin(), sortedExerciseDates.end());\n std::vector noticeDates, exerciseDates;\n std::vector isExerciseDateAlive(sortedExerciseDates.size(), false);\n for (Size i = 0; i < sortedExerciseDates.size(); i++) {\n Date noticeDate = noticeCal.advance(sortedExerciseDates[i], -noticePeriod, noticeBdc);\n if (noticeDate > Settings::instance().evaluationDate() && noticeDate <= lastAccrualStartDate) {\n isExerciseDateAlive[i] = true;\n noticeDates.push_back(noticeDate);\n exerciseDates.push_back(sortedExerciseDates[i]);\n DLOG(\"Got notice date \" << QuantLib::io::iso_date(noticeDate) << \" using notice period \" << noticePeriod\n << \", convention \" << noticeBdc << \", calendar \" << noticeCal.name()\n << \" from exercise date \" << exerciseDates.back());\n }\n if (noticeDate > lastAccrualStartDate)\n WLOG(\"Remove notice date \" << ore::data::to_string(noticeDate) << \" (exercise date \"\n << sortedExerciseDates[i] << \") after last accrual start date \"\n << ore ::data::to_string(lastAccrualStartDate));\n }\n QL_REQUIRE(noticeDates.size() > 0, \"Bermudan Swaption does not have any alive exercise dates\");\n maturity_ = noticeDates.back();\n exercise_ = boost::make_shared(noticeDates);\n\n // check for exercise fees, if present build a rebated exercise with rebates = -fee\n if (!option_.exerciseFees().empty()) {\n // build an exercise date \"schedule\" by adding the maximum possible date at the end\n std::vector exDatesPlusInf(sortedExerciseDates);\n exDatesPlusInf.push_back(Date::maxDate());\n vector allRebates =\n buildScheduledVectorNormalised(option_.exerciseFees(), option_.exerciseFeeDates(), exDatesPlusInf, 0.0);\n // filter on alive rebates, so that we can a vector of rebates corresponding to the exerciseDates vector\n vector rebates;\n for (Size i = 0; i < sortedExerciseDates.size(); ++i) {\n if (isExerciseDateAlive[i])\n rebates.push_back(allRebates[i]);\n }\n // flip the sign of the fee to get a rebate\n for (auto& r : rebates)\n r = -r;\n vector feeType = buildScheduledVectorNormalised(option_.exerciseFeeTypes(),\n option_.exerciseFeeDates(), exDatesPlusInf, \"\");\n // convert relative to absolute fees if required\n for (Size i = 0; i < rebates.size(); ++i) {\n // default to Absolute\n if (feeType[i].empty())\n feeType[i] = \"Absolute\";\n if (feeType[i] == \"Percentage\") {\n // get next float coupon after exercise to determine relevant notional\n Real feeNotional = Null();\n for (auto const& c : swap->leg(1)) {\n if (auto cpn = boost::dynamic_pointer_cast(c)) {\n if (feeNotional == Null() && cpn->accrualStartDate() >= exerciseDates[i])\n feeNotional = cpn->nominal();\n }\n }\n if (feeNotional == Null())\n rebates[i] = 0.0; // notional is zero\n else {\n DLOG(\"Convert percentage rebate \"\n << rebates[i] << \" to absolute reabte \" << rebates[i] * feeNotional << \" using nominal \"\n << feeNotional << \" for exercise date \" << QuantLib::io::iso_date(exerciseDates[i]));\n rebates[i] *= feeNotional; // multiply percentage fee by relevant notional\n }\n } else {\n QL_REQUIRE(feeType[i] == \"Absolute\", \"fee type must be Absolute or Relative\");\n }\n }\n Period feeSettlPeriod = option_.exerciseFeeSettlementPeriod().empty()\n ? 0 * Days\n : parsePeriod(option_.exerciseFeeSettlementPeriod());\n Calendar feeSettlCal = option_.exerciseFeeSettlementCalendar().empty()\n ? NullCalendar()\n : parseCalendar(option_.exerciseFeeSettlementCalendar());\n BusinessDayConvention feeSettlBdc = option_.exerciseFeeSettlementConvention().empty()\n ? Unadjusted\n : parseBusinessDayConvention(option_.exerciseFeeSettlementConvention());\n exercise_ = boost::make_shared(*exercise_, exerciseDates, rebates, feeSettlPeriod,\n feeSettlCal, feeSettlBdc);\n // log rebates\n auto dbgEx = boost::static_pointer_cast(exercise_);\n for (Size i = 0; i < exerciseDates.size(); ++i) {\n DLOG(\"Got rebate \" << dbgEx->rebate(i) << \" with payment date \"\n << QuantLib::io::iso_date(dbgEx->rebatePaymentDate(i)) << \" (exercise date=\"\n << QuantLib::io::iso_date(exerciseDates[i]) << \") using rebate settl period \"\n << feeSettlPeriod << \", calendar \" << feeSettlCal << \", convention \" << feeSettlBdc);\n }\n }\n\n Settlement::Type delivery = parseSettlementType(option_.settlement());\n Settlement::Method deliveryMethod =\n option_.settlementMethod() == \"\" ? defaultMethod(delivery) : parseSettlementMethod(option_.settlementMethod());\n\n if (delivery == Settlement::Cash && deliveryMethod == Settlement::ParYieldCurve)\n WLOG(\"Cash-settled Bermudan Swaption (id = \"\n << id() << \") with ParYieldCurve settlement method not supported by Lgm engine. \"\n << \"Approximate pricing using CollateralizedCashPrice pricing methodology\");\n\n // Build swaption\n DLOG(\"Build Swaption instrument\");\n boost::shared_ptr swaption;\n if (isNonStandard) {\n swaption = boost::shared_ptr(\n new QuantLib::NonstandardSwaption(nonstandardSwap, exercise_, delivery, deliveryMethod));\n // workaround for missing registration in QL versions < 1.13\n swaption->registerWithObservables(nonstandardSwap);\n } else\n swaption = boost::shared_ptr(\n new QuantLib::Swaption(vanillaSwap, exercise_, delivery, deliveryMethod));\n\n QuantLib::Position::Type positionType = parsePositionType(option_.longShort());\n if (delivery == Settlement::Physical)\n maturity_ = swap->maturityDate();\n\n DLOG(\"Get/Build Bermudan Swaption engine\");\n cpu_timer timer;\n\n string tt(\"BermudanSwaption\");\n boost::shared_ptr builder = engineFactory->builder(tt);\n QL_REQUIRE(builder, \"No builder found for \" << tt);\n boost::shared_ptr swaptionBuilder =\n boost::dynamic_pointer_cast(builder);\n\n // determine strikes for calibration basket (simple approach, a la summit)\n std::vector strikes(noticeDates.size(), Null());\n if (!isNonStandard) {\n Real tmp = vanillaSwap->fixedRate() - vanillaSwap->spread();\n std::fill(strikes.begin(), strikes.end(), tmp);\n DLOG(\"calibration strike is \" << tmp << \"(fixed rate \" << vanillaSwap->fixedRate() << \", spread \"\n << vanillaSwap->spread() << \")\");\n } else {\n for (Size i = 0; i < noticeDates.size(); ++i) {\n const Schedule& fix = nonstandardSwap->fixedSchedule();\n const Schedule& flt = nonstandardSwap->floatingSchedule();\n Size fixIdx =\n std::lower_bound(fix.dates().begin(), fix.dates().end(), noticeDates[i]) - fix.dates().begin();\n Size fltIdx =\n std::lower_bound(flt.dates().begin(), flt.dates().end(), noticeDates[i]) - flt.dates().begin();\n // play safe\n fixIdx = std::min(fixIdx, nonstandardSwap->fixedRate().size() - 1);\n fltIdx = std::min(fltIdx, nonstandardSwap->spreads().size() - 1);\n strikes[i] = nonstandardSwap->fixedRate()[fixIdx] - nonstandardSwap->spreads()[fltIdx];\n DLOG(\"calibration strike for ex date \" << QuantLib::io::iso_date(noticeDates[i]) << \" is \" << strikes[i]\n << \" (fixed rate \" << nonstandardSwap->fixedRate()[fixIdx]\n << \", spread \" << nonstandardSwap->spreads()[fltIdx] << \")\");\n }\n }\n\n boost::shared_ptr engine =\n swaptionBuilder->engine(id(), isNonStandard, ccy_str, noticeDates, swap->maturityDate(), strikes);\n\n timer.stop();\n DLOG(\"Swaption model calibration time: \" << timer.format(default_places, \"%w\") << \" s\");\n\n swaption->setPricingEngine(engine);\n\n // timer.restart();\n // Real npv = swaption->NPV();\n // Real pt = timer.elapsed();\n // DLOG(\"Swaption pricing time: \" << pt*1000 << \" ms\");\n\n boost::shared_ptr tmp = engineFactory->builder(\"Swap\");\n boost::shared_ptr swapBuilder = boost::dynamic_pointer_cast(tmp);\n QL_REQUIRE(swapBuilder, \"No Swap Builder found for Swaption \" << id());\n boost::shared_ptr swapEngine = swapBuilder->engine(currency);\n\n std::vector> underlyingSwaps = buildUnderlyingSwaps(swapEngine, swap, noticeDates);\n\n // If premium data is provided\n // 1) build the fee trade and pass it to the instrument wrapper for pricing\n // 2) add fee payment as additional trade leg for cash flow reporting\n std::vector> additionalInstruments;\n std::vector additionalMultipliers;\n if (option_.premiumPayDate() != \"\" && option_.premiumCcy() != \"\") {\n Real multiplier = positionType == Position::Long ? 1.0 : -1.0;\n Real premiumAmount = -multiplier * option_.premium(); // pay if long, receive if short\n Currency premiumCurrency = parseCurrency(option_.premiumCcy());\n Date premiumDate = parseDate(option_.premiumPayDate());\n addPayment(additionalInstruments, additionalMultipliers, premiumDate, premiumAmount, premiumCurrency, currency,\n engineFactory, swaptionBuilder->configuration(MarketContext::pricing));\n DLOG(\"option premium added for bermudan swaption \" << id());\n }\n\n // instrument_ = boost::shared_ptr (new VanillaInstrument (swaption, multiplier));\n instrument_ = boost::shared_ptr(\n new BermudanOptionWrapper(swaption, positionType == Position::Long ? true : false, noticeDates,\n delivery == Settlement::Physical ? true : false, underlyingSwaps, 1.0, 1.0,\n additionalInstruments, additionalMultipliers));\n\n DLOG(\"Building Bermudan Swaption done\");\n}\n\nboost::shared_ptr Swaption::buildVanillaSwap(const boost::shared_ptr& engineFactory,\n const Date& firstExerciseDate) {\n // Limitation to vanilla for now\n QL_REQUIRE(swap_.size() == 2 && swap_[0].currency() == swap_[1].currency() &&\n swap_[0].isPayer() != swap_[1].isPayer() && swap_[0].notionals() == swap_[1].notionals() &&\n swap_[0].notionals().size() == 1,\n \"Not a Vanilla Swap\");\n\n Size fixedLegIndex, floatingLegIndex;\n if (swap_[0].legType() == \"Floating\" && swap_[1].legType() == \"Fixed\") {\n floatingLegIndex = 0;\n fixedLegIndex = 1;\n } else if (swap_[1].legType() == \"Floating\" && swap_[0].legType() == \"Fixed\") {\n floatingLegIndex = 1;\n fixedLegIndex = 0;\n } else {\n QL_FAIL(\"Invalid leg types \" << swap_[0].legType() << \" + \" << swap_[1].legType());\n }\n boost::shared_ptr fixedLegData =\n boost::dynamic_pointer_cast(swap_[fixedLegIndex].concreteLegData());\n boost::shared_ptr floatingLegData =\n boost::dynamic_pointer_cast(swap_[floatingLegIndex].concreteLegData());\n\n QL_REQUIRE(fixedLegData->rates().size() == 1, \"Vanilla Swaption: constant rate required\");\n QL_REQUIRE(floatingLegData->spreads().size() <= 1, \"Vanilla Swaption: constant spread required\");\n\n boost::shared_ptr tmp = engineFactory->builder(\"Swap\");\n boost::shared_ptr swapBuilder = boost::dynamic_pointer_cast(tmp);\n QL_REQUIRE(swapBuilder, \"No Swap Builder found for Swaption \" << id());\n\n // Get Trade details\n string ccy = swap_[0].currency();\n Currency currency = parseCurrency(ccy);\n Real nominal = swap_[0].notionals().back();\n\n Real rate = fixedLegData->rates().front();\n Real spread = floatingLegData->spreads().empty() ? 0.0 : floatingLegData->spreads().front();\n string indexName = floatingLegData->index();\n\n Schedule fixedSchedule = makeSchedule(swap_[fixedLegIndex].schedule());\n DayCounter fixedDayCounter = parseDayCounter(swap_[fixedLegIndex].dayCounter());\n\n Schedule floatingSchedule = makeSchedule(swap_[floatingLegIndex].schedule());\n Handle index =\n engineFactory->market()->iborIndex(indexName, swapBuilder->configuration(MarketContext::pricing));\n underlyingIndexQlName_ = index->name();\n DayCounter floatingDayCounter = parseDayCounter(swap_[floatingLegIndex].dayCounter());\n\n BusinessDayConvention paymentConvention = parseBusinessDayConvention(swap_[floatingLegIndex].paymentConvention());\n\n VanillaSwap::Type type = swap_[fixedLegIndex].isPayer() ? VanillaSwap::Payer : VanillaSwap::Receiver;\n\n // We treat overnight and bma indices approximately as ibor indices and warn about this in the log\n if (boost::dynamic_pointer_cast(*index) ||\n boost ::dynamic_pointer_cast(*index))\n ALOG(\"Swaption trade \" << id() << \" on ON or BMA index '\" << underlyingIndex_\n << \"' built, will treat the index approximately as an ibor index\");\n\n // only take into account accrual periods with start date on or after first exercise date (if given)\n if (firstExerciseDate != Null()) {\n std::vector fixDates = fixedSchedule.dates();\n auto it1 = std::lower_bound(fixDates.begin(), fixDates.end(), firstExerciseDate);\n fixDates.erase(fixDates.begin(), it1);\n // check we have at least 1 to stop set fault on vector(fixDates.size() - 1, true) but maybe check should be 2\n QL_REQUIRE(fixDates.size() >= 2,\n \"Not enough schedule dates are left in Swaption fixed leg (check exercise dates)\");\n fixedSchedule = Schedule(fixDates, fixedSchedule.calendar(), Unadjusted, boost::none, boost::none, boost::none,\n boost::none, std::vector(fixDates.size() - 1, true));\n std::vector floatingDates = floatingSchedule.dates();\n auto it2 = std::lower_bound(floatingDates.begin(), floatingDates.end(), firstExerciseDate);\n floatingDates.erase(floatingDates.begin(), it2);\n QL_REQUIRE(floatingDates.size() >= 2,\n \"Not enough schedule dates are left in Swaption floating leg (check exercise dates)\");\n floatingSchedule = Schedule(floatingDates, floatingSchedule.calendar(), Unadjusted, boost::none, boost::none,\n boost::none, boost::none, std::vector(floatingDates.size() - 1, true));\n }\n\n // Build a vanilla (bullet) swap underlying\n boost::shared_ptr swap(new VanillaSwap(type, nominal, fixedSchedule, rate, fixedDayCounter,\n floatingSchedule, *index, spread, floatingDayCounter,\n paymentConvention));\n\n swap->setPricingEngine(swapBuilder->engine(currency));\n\n // Set other ore::data::Trade details\n npvCurrency_ = ccy;\n notional_ = nominal;\n notionalCurrency_ = ccy;\n legCurrencies_ = vector(2, ccy);\n legs_.push_back(swap->fixedLeg());\n legs_.push_back(swap->floatingLeg());\n legPayers_.push_back(swap_[fixedLegIndex].isPayer());\n legPayers_.push_back(swap_[floatingLegIndex].isPayer());\n\n return swap;\n}\n\nboost::shared_ptr\nSwaption::buildNonStandardSwap(const boost::shared_ptr& engineFactory) {\n QL_REQUIRE(swap_.size() == 2, \"Two swap legs expected\");\n QL_REQUIRE(swap_[0].currency() == swap_[1].currency(), \"single currency swap expected\");\n QL_REQUIRE(swap_[0].isPayer() != swap_[1].isPayer(), \"pay and receive leg expected\");\n\n Size fixedLegIndex, floatingLegIndex;\n if (swap_[0].legType() == \"Floating\" && swap_[1].legType() == \"Fixed\") {\n floatingLegIndex = 0;\n fixedLegIndex = 1;\n } else if (swap_[1].legType() == \"Floating\" && swap_[0].legType() == \"Fixed\") {\n floatingLegIndex = 1;\n fixedLegIndex = 0;\n } else {\n QL_FAIL(\"Invalid leg types \" << swap_[0].legType() << \" + \" << swap_[1].legType());\n }\n boost::shared_ptr fixedLegData =\n boost::dynamic_pointer_cast(swap_[fixedLegIndex].concreteLegData());\n boost::shared_ptr floatingLegData =\n boost::dynamic_pointer_cast(swap_[floatingLegIndex].concreteLegData());\n\n boost::shared_ptr tmp = engineFactory->builder(\"Swap\");\n boost::shared_ptr swapBuilder = boost::dynamic_pointer_cast(tmp);\n QL_REQUIRE(swapBuilder, \"No Swap Builder found for Swaption \" << id());\n\n // Get Trade details\n string ccy = swap_[0].currency();\n Currency currency = parseCurrency(ccy);\n Schedule fixedSchedule = makeSchedule(swap_[fixedLegIndex].schedule());\n Schedule floatingSchedule = makeSchedule(swap_[floatingLegIndex].schedule());\n vector fixedNominal = buildScheduledVectorNormalised(\n swap_[fixedLegIndex].notionals(), swap_[fixedLegIndex].notionalDates(), fixedSchedule, (Real)Null());\n vector floatNominal =\n buildScheduledVectorNormalised(swap_[floatingLegIndex].notionals(), swap_[floatingLegIndex].notionalDates(),\n floatingSchedule, (Real)Null());\n vector fixedRate = buildScheduledVectorNormalised(fixedLegData->rates(), fixedLegData->rateDates(),\n fixedSchedule, (Real)Null());\n vector spreads = buildScheduledVectorNormalised(floatingLegData->spreads(), floatingLegData->spreadDates(),\n floatingSchedule, (Real)Null());\n // gearings are optional, i.e. may be empty\n vector gearings = buildScheduledVectorNormalised(floatingLegData->gearings(), floatingLegData->gearingDates(),\n floatingSchedule, 1.0);\n string indexName = floatingLegData->index();\n DayCounter fixedDayCounter = parseDayCounter(swap_[fixedLegIndex].dayCounter());\n Handle index =\n engineFactory->market()->iborIndex(indexName, swapBuilder->configuration(MarketContext::pricing));\n underlyingIndexQlName_ = index->name();\n DayCounter floatingDayCounter = parseDayCounter(swap_[floatingLegIndex].dayCounter());\n BusinessDayConvention paymentConvention = parseBusinessDayConvention(swap_[floatingLegIndex].paymentConvention());\n\n // We treat overnight and bma indices approximately as ibor indices and warn about this in the log\n if (boost::dynamic_pointer_cast(*index) ||\n boost ::dynamic_pointer_cast(*index))\n ALOG(\"Swaption trade \" << id() << \" on ON or BMA index '\" << underlyingIndex_\n << \"' built, will treat the index approximately as an ibor index\");\n\n VanillaSwap::Type type = swap_[fixedLegIndex].isPayer() ? VanillaSwap::Payer : VanillaSwap::Receiver;\n\n // Build a vanilla (bullet) swap underlying\n boost::shared_ptr swap(new NonstandardSwap(type, fixedNominal, floatNominal, fixedSchedule,\n fixedRate, fixedDayCounter, floatingSchedule, *index,\n gearings, spreads, floatingDayCounter,\n false, // no intermediate notional exchanges\n false, // no final notional exchanges\n paymentConvention));\n\n swap->setPricingEngine(swapBuilder->engine(currency));\n\n // Set other ore::data::Trade details\n npvCurrency_ = ccy;\n notional_ = std::max(currentNotional(swap->fixedLeg()), currentNotional(swap->floatingLeg()));\n notionalCurrency_ = npvCurrency_;\n legCurrencies_ = vector(2, ccy);\n legs_.push_back(swap->fixedLeg());\n legs_.push_back(swap->floatingLeg());\n legPayers_.push_back(swap_[fixedLegIndex].isPayer());\n legPayers_.push_back(swap_[floatingLegIndex].isPayer());\n\n return swap;\n}\n\nstd::vector>\nSwaption::buildUnderlyingSwaps(const boost::shared_ptr& swapEngine,\n const boost::shared_ptr& underlyingSwap, const std::vector& exerciseDates) {\n std::vector> swaps;\n for (Size i = 0; i < exerciseDates.size(); ++i) {\n std::vector legs(2);\n std::vector payer(2);\n for (Size j = 0; j < legs.size(); ++j) {\n legs[j] = underlyingSwap->leg(j);\n // if in the swap data the fixed leg is the first component, the legs in the underlying\n // swap match the order of the legs in the swap data, otherwise they are swapped\n payer[j] = swap_[0].legType() == \"Fixed\" ? swap_[j].isPayer() : swap_[1 - j].isPayer();\n boost::shared_ptr coupon = boost::dynamic_pointer_cast(legs[j].front());\n while (legs[j].size() > 0 && coupon->accrualStartDate() < exerciseDates[i]) {\n legs[j].erase(legs[j].begin());\n coupon = boost::dynamic_pointer_cast(legs[j].front());\n }\n }\n boost::shared_ptr newSwap(new Swap(legs, payer));\n newSwap->setPricingEngine(swapEngine);\n swaps.push_back(newSwap);\n if (legs[0].size() > 0 && legs[1].size() > 0) {\n boost::shared_ptr coupon1 = boost::dynamic_pointer_cast(legs[0].front());\n boost::shared_ptr coupon2 = boost::dynamic_pointer_cast(legs[1].front());\n DLOG(\"Added underlying Swap start \" << QuantLib::io::iso_date(coupon1->accrualStartDate()) << \" \"\n << QuantLib::io::iso_date(coupon2->accrualStartDate()) << \" \"\n << \"exercise \" << QuantLib::io::iso_date(exerciseDates[i]));\n } else {\n WLOG(\"Added underlying Swap with at least one empty leg for exercise \"\n << QuantLib::io::iso_date(exerciseDates[i]) << \"!\");\n }\n }\n return swaps;\n}\n\nvoid Swaption::fromXML(XMLNode* node) {\n Trade::fromXML(node);\n XMLNode* swapNode = XMLUtils::getChildNode(node, \"SwaptionData\");\n option_.fromXML(XMLUtils::getChildNode(swapNode, \"OptionData\"));\n swap_.clear();\n vector nodes = XMLUtils::getChildrenNodes(swapNode, \"LegData\");\n for (Size i = 0; i < nodes.size(); i++) {\n LegData ld;\n ld.fromXML(nodes[i]);\n swap_.push_back(ld);\n }\n}\n\nXMLNode* Swaption::toXML(XMLDocument& doc) {\n XMLNode* node = Trade::toXML(doc);\n XMLNode* swaptionNode = doc.allocNode(\"SwaptionData\");\n XMLUtils::appendNode(node, swaptionNode);\n\n XMLUtils::appendNode(swaptionNode, option_.toXML(doc));\n for (Size i = 0; i < swap_.size(); i++)\n XMLUtils::appendNode(swaptionNode, swap_[i].toXML(doc));\n\n return node;\n}\n} // namespace data\n} // namespace ore\n", "meta": {"hexsha": "f6dc29952c6507a3db1d2fde92a29018fa24cb64", "size": 36956, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "OREData/ored/portfolio/swaption.cpp", "max_stars_repo_name": "nvolfango/Engine", "max_stars_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-03-30T17:24:17.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-30T17:24:17.000Z", "max_issues_repo_path": "OREData/ored/portfolio/swaption.cpp", "max_issues_repo_name": "zhangjiayin/Engine", "max_issues_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "OREData/ored/portfolio/swaption.cpp", "max_forks_repo_name": "zhangjiayin/Engine", "max_forks_repo_head_hexsha": "a5ee0fc09d5a50ab36e50d55893b6e484d6e7004", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 54.1083455344, "max_line_length": 120, "alphanum_fraction": 0.6469314861, "num_tokens": 8576, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.538983220687684, "lm_q2_score": 0.4035668537353746, "lm_q1q2_score": 0.2175157625890877}} {"text": "#ifndef BOOST_GEOMETRY_PROJECTIONS_LSAT_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_LSAT_HPP\n\n// Boost.Geometry - extensions-gis-projections (based on PROJ4)\n// This file is automatically generated. DO NOT EDIT.\n\n// Copyright (c) 2008-2012 Barend Gehrels, Amsterdam, the Netherlands.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\n\n// Original copyright notice:\n \n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#include \n\n#include \n#include \n#include \n#include \n\nnamespace boost { namespace geometry { namespace projections\n{\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail { namespace lsat{ \n static const double TOL = 1e-7;\n static const double PI_HALFPI = 4.71238898038468985766;\n static const double TWOPI_HALFPI = 7.85398163397448309610;\n\n struct par_lsat\n {\n double a2, a4, b, c1, c3;\n double q, t, u, w, p22, sa, ca, xj, rlm, rlm2;\n };\n /* based upon Snyder and Linck, USGS-NMD */\n template \n inline void\n seraz0(double lam, double mult, Parameters& par, par_lsat& proj_parm) {\n double sdsq, h, s, fc, sd, sq, d__1;\n \n lam *= DEG_TO_RAD;\n sd = sin(lam);\n sdsq = sd * sd;\n s = proj_parm.p22 * proj_parm.sa * cos(lam) * sqrt((1. + proj_parm.t * sdsq) / ((\n 1. + proj_parm.w * sdsq) * (1. + proj_parm.q * sdsq)));\n d__1 = 1. + proj_parm.q * sdsq;\n h = sqrt((1. + proj_parm.q * sdsq) / (1. + proj_parm.w * sdsq)) * ((1. + \n proj_parm.w * sdsq) / (d__1 * d__1) - proj_parm.p22 * proj_parm.ca);\n sq = sqrt(proj_parm.xj * proj_parm.xj + s * s);\n proj_parm.b += fc = mult * (h * proj_parm.xj - s * s) / sq;\n proj_parm.a2 += fc * cos(lam + lam);\n proj_parm.a4 += fc * cos(lam * 4.);\n fc = mult * s * (h + proj_parm.xj) / sq;\n proj_parm.c1 += fc * cos(lam);\n proj_parm.c3 += fc * cos(lam * 3.);\n }\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_lsat_ellipsoid : public base_t_fi,\n Geographic, Cartesian, Parameters>\n {\n\n typedef double geographic_type;\n typedef double cartesian_type;\n\n par_lsat m_proj_parm;\n\n inline base_lsat_ellipsoid(const Parameters& par)\n : base_t_fi,\n Geographic, Cartesian, Parameters>(*this, par) {}\n\n inline void fwd(geographic_type& lp_lon, geographic_type& lp_lat, cartesian_type& xy_x, cartesian_type& xy_y) const\n {\n int l, nn;\n double lamt, xlam, sdsq, c, d, s, lamdp, phidp, lampp, tanph,\n lamtp, cl, sd, sp, fac, sav, tanphi;\n \n if (lp_lat > HALFPI)\n lp_lat = HALFPI;\n else if (lp_lat < -HALFPI)\n lp_lat = -HALFPI;\n lampp = lp_lat >= 0. ? HALFPI : PI_HALFPI;\n tanphi = tan(lp_lat);\n for (nn = 0;;) {\n sav = lampp;\n lamtp = lp_lon + this->m_proj_parm.p22 * lampp;\n cl = cos(lamtp);\n if (fabs(cl) < TOL)\n lamtp -= TOL;\n fac = lampp - sin(lampp) * (cl < 0. ? -HALFPI : HALFPI);\n for (l = 50; l; --l) {\n lamt = lp_lon + this->m_proj_parm.p22 * sav;\n if (fabs(c = cos(lamt)) < TOL)\n lamt -= TOL;\n xlam = (this->m_par.one_es * tanphi * this->m_proj_parm.sa + sin(lamt) * this->m_proj_parm.ca) / c;\n lamdp = atan(xlam) + fac;\n if (fabs(fabs(sav) - fabs(lamdp)) < TOL)\n break;\n sav = lamdp;\n }\n if (!l || ++nn >= 3 || (lamdp > this->m_proj_parm.rlm && lamdp < this->m_proj_parm.rlm2))\n break;\n if (lamdp <= this->m_proj_parm.rlm)\n lampp = TWOPI_HALFPI;\n else if (lamdp >= this->m_proj_parm.rlm2)\n lampp = HALFPI;\n }\n if (l) {\n sp = sin(lp_lat);\n phidp = aasin((this->m_par.one_es * this->m_proj_parm.ca * sp - this->m_proj_parm.sa * cos(lp_lat) * \n sin(lamt)) / sqrt(1. - this->m_par.es * sp * sp));\n tanph = log(tan(FORTPI + .5 * phidp));\n sd = sin(lamdp);\n sdsq = sd * sd;\n s = this->m_proj_parm.p22 * this->m_proj_parm.sa * cos(lamdp) * sqrt((1. + this->m_proj_parm.t * sdsq)\n / ((1. + this->m_proj_parm.w * sdsq) * (1. + this->m_proj_parm.q * sdsq)));\n d = sqrt(this->m_proj_parm.xj * this->m_proj_parm.xj + s * s);\n xy_x = this->m_proj_parm.b * lamdp + this->m_proj_parm.a2 * sin(2. * lamdp) + this->m_proj_parm.a4 *\n sin(lamdp * 4.) - tanph * s / d;\n xy_y = this->m_proj_parm.c1 * sd + this->m_proj_parm.c3 * sin(lamdp * 3.) + tanph * this->m_proj_parm.xj / d;\n } else\n xy_x = xy_y = HUGE_VAL;\n }\n\n inline void inv(cartesian_type& xy_x, cartesian_type& xy_y, geographic_type& lp_lon, geographic_type& lp_lat) const\n {\n int nn;\n double lamt, sdsq, s, lamdp, phidp, sppsq, dd, sd, sl, fac, scl, sav, spp;\n \n lamdp = xy_x / this->m_proj_parm.b;\n nn = 50;\n do {\n sav = lamdp;\n sd = sin(lamdp);\n sdsq = sd * sd;\n s = this->m_proj_parm.p22 * this->m_proj_parm.sa * cos(lamdp) * sqrt((1. + this->m_proj_parm.t * sdsq)\n / ((1. + this->m_proj_parm.w * sdsq) * (1. + this->m_proj_parm.q * sdsq)));\n lamdp = xy_x + xy_y * s / this->m_proj_parm.xj - this->m_proj_parm.a2 * sin(\n 2. * lamdp) - this->m_proj_parm.a4 * sin(lamdp * 4.) - s / this->m_proj_parm.xj * (\n this->m_proj_parm.c1 * sin(lamdp) + this->m_proj_parm.c3 * sin(lamdp * 3.));\n lamdp /= this->m_proj_parm.b;\n } while (fabs(lamdp - sav) >= TOL && --nn);\n sl = sin(lamdp);\n fac = exp(sqrt(1. + s * s / this->m_proj_parm.xj / this->m_proj_parm.xj) * (xy_y - \n this->m_proj_parm.c1 * sl - this->m_proj_parm.c3 * sin(lamdp * 3.)));\n phidp = 2. * (atan(fac) - FORTPI);\n dd = sl * sl;\n if (fabs(cos(lamdp)) < TOL)\n lamdp -= TOL;\n spp = sin(phidp);\n sppsq = spp * spp;\n lamt = atan(((1. - sppsq * this->m_par.rone_es) * tan(lamdp) * \n this->m_proj_parm.ca - spp * this->m_proj_parm.sa * sqrt((1. + this->m_proj_parm.q * dd) * (\n 1. - sppsq) - sppsq * this->m_proj_parm.u) / cos(lamdp)) / (1. - sppsq \n * (1. + this->m_proj_parm.u)));\n sl = lamt >= 0. ? 1. : -1.;\n scl = cos(lamdp) >= 0. ? 1. : -1;\n lamt -= HALFPI * (1. - scl) * sl;\n lp_lon = lamt - this->m_proj_parm.p22 * lamdp;\n if (fabs(this->m_proj_parm.sa) < TOL)\n lp_lat = aasin(spp / sqrt(this->m_par.one_es * this->m_par.one_es + this->m_par.es * sppsq));\n else\n lp_lat = atan((tan(lamdp) * cos(lamt) - this->m_proj_parm.ca * sin(lamt)) /\n (this->m_par.one_es * this->m_proj_parm.sa));\n }\n };\n\n // Space oblique for LANDSAT\n template \n void setup_lsat(Parameters& par, par_lsat& proj_parm)\n {\n int land, path;\n double lam, alf, esc, ess;\n land = pj_param(par.params, \"ilsat\").i;\n if (land <= 0 || land > 5) throw proj_exception(-28);\n path = pj_param(par.params, \"ipath\").i;\n if (path <= 0 || path > (land <= 3 ? 251 : 233)) throw proj_exception(-29);\n if (land <= 3) {\n par.lam0 = DEG_TO_RAD * 128.87 - TWOPI / 251. * path;\n proj_parm.p22 = 103.2669323;\n alf = DEG_TO_RAD * 99.092;\n } else {\n par.lam0 = DEG_TO_RAD * 129.3 - TWOPI / 233. * path;\n proj_parm.p22 = 98.8841202;\n alf = DEG_TO_RAD * 98.2;\n }\n proj_parm.p22 /= 1440.;\n proj_parm.sa = sin(alf);\n proj_parm.ca = cos(alf);\n if (fabs(proj_parm.ca) < 1e-9)\n proj_parm.ca = 1e-9;\n esc = par.es * proj_parm.ca * proj_parm.ca;\n ess = par.es * proj_parm.sa * proj_parm.sa;\n proj_parm.w = (1. - esc) * par.rone_es;\n proj_parm.w = proj_parm.w * proj_parm.w - 1.;\n proj_parm.q = ess * par.rone_es;\n proj_parm.t = ess * (2. - par.es) * par.rone_es * par.rone_es;\n proj_parm.u = esc * par.rone_es;\n proj_parm.xj = par.one_es * par.one_es * par.one_es;\n proj_parm.rlm = PI * (1. / 248. + .5161290322580645);\n proj_parm.rlm2 = proj_parm.rlm + TWOPI;\n proj_parm.a2 = proj_parm.a4 = proj_parm.b = proj_parm.c1 = proj_parm.c3 = 0.;\n seraz0(0., 1., par, proj_parm);\n for (lam = 9.;\n lam <= 81.0001;\n lam += 18.)\n seraz0(lam, 4., par, proj_parm);\n for (lam = 18;\n lam <= 72.0001;\n lam += 18.)\n seraz0(lam, 2., par, proj_parm);\n seraz0(90., 1., par, proj_parm);\n proj_parm.a2 /= 30.;\n proj_parm.a4 /= 60.;\n proj_parm.b /= 30.;\n proj_parm.c1 /= 15.;\n proj_parm.c3 /= 45.;\n // par.inv = e_inverse;\n // par.fwd = e_forward;\n }\n\n }} // namespace detail::lsat\n #endif // doxygen \n\n /*!\n \\brief Space oblique for LANDSAT projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Cylindrical\n - Spheroid\n - Ellipsoid\n - lsat= path=\n \\par Example\n \\image html ex_lsat.gif\n */\n template \n struct lsat_ellipsoid : public detail::lsat::base_lsat_ellipsoid\n {\n inline lsat_ellipsoid(const Parameters& par) : detail::lsat::base_lsat_ellipsoid(par)\n {\n detail::lsat::setup_lsat(this->m_par, this->m_proj_parm);\n }\n };\n\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail\n {\n\n // Factory entry(s)\n template \n class lsat_entry : public detail::factory_entry\n {\n public :\n virtual projection* create_new(const Parameters& par) const\n {\n return new base_v_fi, Geographic, Cartesian, Parameters>(par);\n }\n };\n\n template \n inline void lsat_init(detail::base_factory& factory)\n {\n factory.add_to_factory(\"lsat\", new lsat_entry);\n }\n\n } // namespace detail \n #endif // doxygen\n\n}}} // namespace boost::geometry::projections\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_LSAT_HPP\n\n", "meta": {"hexsha": "816c63fbbebe0fca49a1a4fa6f370b3bf16e95b4", "size": 14665, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/geometry/extensions/gis/projections/proj/lsat.hpp", "max_stars_repo_name": "ballisticwhisper/boost", "max_stars_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2015-01-02T14:24:56.000Z", "max_stars_repo_stars_event_max_datetime": "2015-01-02T14:25:17.000Z", "max_issues_repo_path": "boost/geometry/extensions/gis/projections/proj/lsat.hpp", "max_issues_repo_name": "ballisticwhisper/boost", "max_issues_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2019-01-13T23:45:51.000Z", "max_issues_repo_issues_event_max_datetime": "2019-02-03T08:13:26.000Z", "max_forks_repo_path": "boost/geometry/extensions/gis/projections/proj/lsat.hpp", "max_forks_repo_name": "ballisticwhisper/boost", "max_forks_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2016-05-29T13:41:15.000Z", "max_forks_repo_forks_event_max_datetime": "2016-05-29T13:41:15.000Z", "avg_line_length": 48.8833333333, "max_line_length": 133, "alphanum_fraction": 0.5091033072, "num_tokens": 3665, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297745935070806, "lm_q2_score": 0.34510525748676846, "lm_q1q2_score": 0.217338523250886}} {"text": "// Copyright (c) 2020 Vitaly Chipounov\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\nnamespace pt = boost::property_tree;\n\nnamespace gpsmap {\n\nstd::ostream &operator<<(std::ostream &os, TrackItem const &m) {\n os << \"TrackItem \" << m.OriginalTimestamp << \" lat=\" << m.Latitude << \" lon=\" << m.Longitude << \" speed=\" << m.Speed\n << \" alt=\" << m.Elevation;\n return os;\n}\n\ntime_t parse_time(const std::string &iso) {\n std::stringstream ss(iso);\n std::tm t = {};\n if (ss >> std::get_time(&t, \"%Y-%m-%dT%H:%M:%SZ\")) {\n return std::mktime(&t) - timezone;\n }\n return 0;\n}\n\nstd::string time_to_str(time_t t) {\n char buff[64];\n struct tm *timeinfo;\n timeinfo = localtime(&t);\n strftime(buff, sizeof(buff), \"%Y-%m-%d %H:%M:%S\", timeinfo);\n return buff;\n}\n\n// Returns the distance in meters between the given coordinates (in degrees)\nstatic double distance(double lat1, double lon1, double lat2, double lon2) {\n // Radius of Earth in meters, R = 6371 * 1000\n long double R = 6371 * 1000;\n\n lat1 = to_rad(lat1);\n lon1 = to_rad(lon1);\n lat2 = to_rad(lat2);\n lon2 = to_rad(lon2);\n\n // Haversine Formula\n long double dlon = lon2 - lon1;\n long double dlat = lat2 - lat1;\n\n long double ans = pow(sin(dlat / 2), 2) + cos(lat1) * cos(lat2) * pow(sin(dlon / 2), 2);\n\n return R * 2 * asin(sqrt(ans));\n}\n\nstatic double bearing(double lat1, double lon1, double lat2, double lon2) {\n lat1 = to_rad(lat1);\n lon1 = to_rad(lon1);\n lat2 = to_rad(lat2);\n lon2 = to_rad(lon2);\n\n auto x = cos(lat2) * sin(lon2 - lon1);\n auto y = cos(lat1) * sin(lat2) - sin(lat1) * cos(lat2) * cos(lon2 - lon1);\n auto bearing = atan2(x, y);\n return to_deg(bearing);\n}\n\nstatic double bearing(const TrackItem &i1, const TrackItem &i2) {\n return bearing(i1.Latitude, i1.Longitude, i2.Latitude, i2.Longitude);\n}\n\nstatic double GetGrade(const std::vector &items, int i) {\n const double maxDist = 50.0;\n\n // Get altitude and cumulative distance on the left\n double ldist = 0.0;\n double lelev = 0.0;\n for (auto l = i; l >= 0; --l) {\n ldist += items[l].DistanceDelta;\n lelev = items[l].Elevation;\n if (ldist >= maxDist) {\n break;\n }\n }\n\n // Get altitude and cumulative distance on the right\n double rdist = 0.0;\n double relev = 0.0;\n for (size_t l = i; l < items.size(); ++l) {\n rdist += items[l].DistanceDelta;\n relev = items[l].Elevation;\n if (rdist >= maxDist) {\n break;\n }\n }\n\n auto td = ldist + rdist;\n if (td < 0.01) {\n return 0.0;\n }\n\n return (relev - lelev) / td * 100;\n}\n\nstd::shared_ptr GPX::Create() {\n return std::shared_ptr(new GPX());\n}\n\nvoid GPX::LoadFromFile(const std::string &path) {\n pt::ptree tree;\n pt::read_xml(path, tree);\n\n bool first = true;\n\n BOOST_FOREACH (pt::ptree::value_type &trkseg, tree.get_child(\"gpx.trk\")) {\n BOOST_FOREACH (pt::ptree::value_type &trkpt, trkseg.second) {\n TrackItem item;\n auto lat = trkpt.second.get_child(\".lat\").get_value(\"\");\n auto lon = trkpt.second.get_child(\".lon\").get_value(\"\");\n auto speed = trkpt.second.get_child(\"speed\").get_value(\"\");\n auto elevation = trkpt.second.get_child(\"ele\").get_value(\"\");\n auto time = trkpt.second.get_child(\"time\").get_value(\"\");\n\n item.Speed = strtod(speed.c_str(), nullptr);\n item.Latitude = strtod(lat.c_str(), nullptr);\n item.Longitude = strtod(lon.c_str(), nullptr);\n item.Elevation = strtod(elevation.c_str(), nullptr);\n item.OriginalTimestamp = time;\n item.Timestamp = parse_time(time);\n item.Grade = 0.0;\n item.DistanceDelta = 0.0;\n item.TotalDistance = 0.0;\n item.Bearing = 0.0;\n item.IsTrackStart = first;\n first = false;\n\n m_trackItems.push_back(item);\n }\n }\n\n std::sort(m_trackItems.begin(), m_trackItems.end());\n\n for (size_t i = 1; i < m_trackItems.size(); ++i) {\n auto &ti0 = m_trackItems[i - 1];\n if (i == 1) {\n ti0.TotalDistance = m_initialDistance;\n }\n\n auto &ti1 = m_trackItems[i];\n ti1.DistanceDelta = distance(ti0.Latitude, ti0.Longitude, ti1.Latitude, ti1.Longitude);\n ti1.TotalDistance = ti0.TotalDistance + ti1.DistanceDelta;\n }\n\n for (size_t i = 0; i < m_trackItems.size() - 1; ++i) {\n m_trackItems[i].Grade = GetGrade(m_trackItems, i);\n auto b = bearing(m_trackItems[i], m_trackItems[i + 1]);\n if (b == 0) {\n if (i > 1) {\n m_trackItems[i].Bearing = m_trackItems[i - 1].Bearing;\n }\n } else {\n m_trackItems[i].Bearing = b;\n }\n }\n}\n\nvoid GPX::CreateSegments() {\n std::vector boundaries;\n\n if (m_trackItems.size() == 0) {\n return;\n }\n\n boundaries.push_back(0);\n\n for (size_t i = 1; i < m_trackItems.size(); ++i) {\n const auto &prevItem = m_trackItems[i - 1];\n const auto &item = m_trackItems[i];\n\n if (item.Latitude == prevItem.Latitude && item.Longitude == prevItem.Longitude) {\n boundaries.push_back(i);\n }\n }\n\n auto bs = boundaries.size();\n for (size_t i = 0; i < bs; ++i) {\n int start = boundaries[i];\n int end = i == bs - 1 ? m_trackItems.size() - 1 : boundaries[i + 1];\n\n if (i == bs - 1) {\n if (start < end) {\n m_segments.push_back(Segment(start, end));\n }\n } else {\n assert(start < end);\n m_segments.push_back(Segment(start, end));\n }\n }\n}\n\nbool GPX::GetItem(size_t index, TrackItem &item) {\n if (index >= m_trackItems.size() || index < 0) {\n return false;\n }\n\n item = m_trackItems[index];\n return true;\n}\n\n// Return the first element i such that i.timestamp <= timestamp < (i+1).timestamp\nbool GPX::GetClosestItem(time_t timestamp, size_t &nextItem, TrackItem &item) {\n if (nextItem >= m_trackItems.size() || nextItem < 0) {\n return false;\n }\n\n if (timestamp < m_trackItems[nextItem].Timestamp) {\n return false;\n }\n\n for (auto i = nextItem; i + 1 < m_trackItems.size(); ++i) {\n const auto &i1 = m_trackItems[i];\n const auto &i2 = m_trackItems[i + 1];\n if (i1.Timestamp <= timestamp && timestamp < i2.Timestamp) {\n item = i1;\n nextItem = i;\n return true;\n }\n }\n\n return false;\n}\n\n} // namespace gpsmap\n", "meta": {"hexsha": "a4cdf9b0cbc06fb8624c6df8a4b4ac839858c4d5", "size": 7854, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/gpx.cpp", "max_stars_repo_name": "vitalych/gpsmap", "max_stars_repo_head_hexsha": "4716a6c9e52eda08beeaf6e19448a8fff5e00fb8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-04-20T08:13:32.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-20T08:13:32.000Z", "max_issues_repo_path": "src/gpx.cpp", "max_issues_repo_name": "vitalych/gpsmap", "max_issues_repo_head_hexsha": "4716a6c9e52eda08beeaf6e19448a8fff5e00fb8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2020-10-11T12:23:33.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-23T14:40:31.000Z", "max_forks_repo_path": "src/gpx.cpp", "max_forks_repo_name": "vitalych/gpsmap", "max_forks_repo_head_hexsha": "4716a6c9e52eda08beeaf6e19448a8fff5e00fb8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.9212598425, "max_line_length": 120, "alphanum_fraction": 0.5972752737, "num_tokens": 2144, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526660244837, "lm_q2_score": 0.3812195803163617, "lm_q1q2_score": 0.21723899418401352}} {"text": "//---------------------------------------------------------------------------//\n// Copyright (c) 2018-2020 Mikhail Komarov \n// Copyright (c) 2020 Nikita Kaskov \n//\n// Distributed under the Boost Software License, Version 1.0\n// See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt\n//---------------------------------------------------------------------------//\n\n#ifndef CRYPTO3_RIPEMD_FUNCTIONS_HPP\n#define CRYPTO3_RIPEMD_FUNCTIONS_HPP\n\n#include \n\nnamespace boost {\n namespace crypto3 {\n namespace hashes {\n namespace detail {\n template\n struct ripemd_functions : public ripemd_policy {\n typedef ripemd_policy policy_type;\n\n typedef typename policy_type::word_type word_type;\n\n struct f1 {\n inline word_type operator()(word_type x, word_type y, word_type z) const {\n return x ^ y ^ z;\n }\n };\n\n struct f2 {\n inline word_type operator()(word_type x, word_type y, word_type z) const {\n return (x & y) | (~x & z);\n }\n };\n\n struct f3 {\n inline word_type operator()(word_type x, word_type y, word_type z) const {\n return (x | ~y) ^ z;\n }\n };\n\n struct f4 {\n inline word_type operator()(word_type x, word_type y, word_type z) const {\n return (x & z) | (y & ~z);\n }\n };\n\n struct f5 {\n inline word_type operator()(word_type x, word_type y, word_type z) const {\n return x ^ (y | ~z);\n }\n };\n\n template\n inline static void transform(word_type &a, word_type &b, word_type &c, word_type &d, word_type x,\n word_type k, word_type s) {\n word_type T = policy_type::rotl(a + F()(b, c, d) + x + k, s);\n a = d;\n d = c;\n c = b;\n b = T;\n }\n\n template\n inline static void transform(word_type &a, word_type &b, word_type &c, word_type &d, word_type &e,\n word_type x, word_type k, word_type s) {\n word_type T = policy_type::rotl(a + Functor()(b, c, d) + x + k, s) + e;\n a = e;\n e = d;\n d = policy_type::template rotl<10>(c);\n c = b;\n b = T;\n }\n };\n } // namespace detail\n } // namespace hashes\n } // namespace crypto3\n} // namespace boost\n\n#endif // CRYPTO3_RIPEMD_FUNCTIONS_HPP\n", "meta": {"hexsha": "3ef69eb482416a51318e17f846aa073d03a56711", "size": 3334, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/crypto3/hash/detail/ripemd/ripemd_functions.hpp", "max_stars_repo_name": "NilFoundation/boost-crypto", "max_stars_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 4.0, "max_stars_repo_stars_event_min_datetime": "2020-09-02T06:19:14.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-07T04:55:03.000Z", "max_issues_repo_path": "include/boost/crypto3/hash/detail/ripemd/ripemd_functions.hpp", "max_issues_repo_name": "NilFoundation/boost-crypto", "max_issues_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2020-04-06T21:49:03.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-18T04:54:51.000Z", "max_forks_repo_path": "include/boost/crypto3/hash/detail/ripemd/ripemd_functions.hpp", "max_forks_repo_name": "NilFoundation/boost-crypto", "max_forks_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2022-02-13T21:14:37.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T21:14:37.000Z", "avg_line_length": 40.6585365854, "max_line_length": 118, "alphanum_fraction": 0.4142171566, "num_tokens": 643, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.21723898060202199}} {"text": "/*\n This file is part of Mitsuba, a physically based rendering system.\n\n Copyright (c) 2007-2014 by Wenzel Jakob and others.\n\n Mitsuba is free software; you can redistribute it and/or modify\n it under the terms of the GNU General Public License Version 3\n as published by the Free Software Foundation.\n\n Mitsuba is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n*/\n\n#include \n#include \n#include \n#define EIGEN_DONT_PARALLELIZE\n#define EIGEN_NO_DEBUG\n#include \n#include \n\nMTS_NAMESPACE_BEGIN\n\n/* Some statistics counters */\nstatic StatsCounter statsStepFailed(\n\t\t\"Specular manifold\", \"Retries (step failed)\");\nstatic StatsCounter statsStepTooFar(\n\t\t\"Specular manifold\", \"Retries (step increased distance)\");\nstatic StatsCounter statsStepSuccess(\n\t\t\"Specular manifold\", \"Successful steps\");\nstatic StatsCounter statsAvgIterations(\n\t\t\"Specular manifold\", \"Avg. iterations per walk\", EAverage);\nstatic StatsCounter statsAvgIterationsSuccess(\n\t\t\"Specular manifold\", \"Avg. iterations per successful walk\", EAverage);\nstatic StatsCounter statsAvgManifoldSize(\n\t\t\"Specular manifold\", \"Avg. manifold size\", EAverage);\nstatic StatsCounter statsSuccessfulWalks(\n\t\t\"Specular manifold\", \"Successful walks\", EPercentage);\nstatic StatsCounter statsMediumSuccess(\n\t\t\"Specular manifold\", \"Successful walks w/ media\", EPercentage);\nstatic StatsCounter statsNonManifold(\n\t\t\"Specular manifold\", \"Non-manifold\", EPercentage);\nstatic StatsCounter statsUpdateFailed(\n\t\t\"Specular manifold\", \"Update failed\");\nstatic StatsCounter statsMaxManifold(\n\t\t\"Specular manifold\", \"Max. manifold size\", EMaximumValue);\n\nSpecularManifold::SpecularManifold(const Scene *scene, int maxIterations)\n : m_scene(scene) {\n\tm_maxIterations = maxIterations > 0 ? maxIterations :\n\t\tMTS_MANIFOLD_MAX_ITERATIONS;\n}\n\nbool SpecularManifold::init(const Path &path, int start, int end) {\n\tint step = start < end ? 1 : -1;\n\tif (path.vertex(start)->isSupernode())\n\t\tstart += step;\n\tif (path.vertex(end)->isSupernode())\n\t\tend -= step;\n\n\tconst PathVertex\n\t\t*vs = path.vertex(start),\n\t\t*ve = path.vertex(end);\n\n\t/* Create the initial vertex that is pinned in position by default */\n\tSimpleVertex v(EPinnedPosition, vs->getPosition());\n\n\t/* When the endpoint is on an orthographic camera or directional light\n\t source, switch to a directionally pinned vertex instead */\n\tif (vs->getType() & (PathVertex::ESensorSample | PathVertex::EEmitterSample)) {\n\t\tconst PositionSamplingRecord &pRec\n\t\t\t= vs->getPositionSamplingRecord();\n\t\tuint32_t type = static_cast(pRec.object)->getType()\n\t\t\t& (AbstractEmitter::EDeltaDirection | AbstractEmitter::EDeltaPosition);\n\t\tif (type == AbstractEmitter::EDeltaDirection) {\n\t\t\tv.type = EPinnedDirection;\n\t\t\tv.gn = v.n = pRec.n;\n\t\t\tcoordinateSystem(pRec.n, v.dpdu, v.dpdv);\n\t\t}\n\t}\n\n\tm_time = vs->getTime();\n\tm_vertices.clear();\n\tm_vertices.push_back(v);\n\n\tfor (int i=start + step; i != end; i += step) {\n\t\tconst PathVertex\n\t\t\t*pred = path.vertex(i-step),\n\t\t\t*vertex = path.vertex(i),\n\t\t\t*succ = path.vertex(i+step);\n\n\t\tif (vertex->isSurfaceInteraction()) {\n\t\t\tconst Intersection &its = vertex->getIntersection();\n\t\t\tconst BSDF *bsdf = its.getBSDF();\n\n\t\t\tv.p = its.p;\n\t\t\tv.gn = its.geoFrame.n;\n\t\t\tv.n = its.shFrame.n;\n\t\t\tv.dpdu = its.dpdu;\n\t\t\tv.dpdv = its.dpdv;\n\t\t\tv.object = bsdf;\n\t\t\tv.degenerate = !vertex->isConnectable();\n\t\t\tconst Shape *shape = its.instance != NULL ? its.instance : its.shape;\n\t\t\tshape->getNormalDerivative(its, v.dndu, v.dndv);\n\n\t\t\t/* Turn into an orthonormal parameterization at 'p' */\n\t\t\tFloat invLen = 1 / v.dpdu.length();\n\t\t\tv.dpdu *= invLen;\n\t\t\tv.dndu *= invLen;\n\t\t\tFloat dp = dot(v.dpdu, v.dpdv);\n\t\t\tVector dpdv = v.dpdv - dp * v.dpdu;\n\t\t\tVector dndv = v.dndv - dp * v.dndu;\n\t\t\tinvLen = 1 / dpdv.length();\n\t\t\tv.dpdv = dpdv * invLen;\n\t\t\tv.dndv = dndv * invLen;\n\n\t\t\tVector wPred = pred->getPosition() - v.p;\n\t\t\tVector wSucc = succ->getPosition() - v.p;\n\n\t\t\tif (dot(v.gn, wPred) * dot(v.gn, wSucc) < 0) {\n\t\t\t\tv.type = ERefraction;\n\t\t\t\tv.eta = bsdf->getEta();\n\t\t\t} else {\n\t\t\t\tv.type = EReflection;\n\t\t\t\tv.eta = 1.0f;\n\t\t\t}\n\t\t} else if (vertex->isMediumInteraction()) {\n\t\t\tconst MediumSamplingRecord &mRec = vertex->getMediumSamplingRecord();\n\n\t\t\tVector wi = pred->getPosition() - mRec.p;\n\t\t\tFloat invLength = 1.0f / wi.length();\n\t\t\twi *= invLength;\n\n\t\t\tv.p = mRec.p;\n\t\t\tv.gn = v.n = Normal(0.0f);\n\n\t\t\tVector s, t;\n\t\t\tcoordinateSystem(wi, s, t);\n\n\t\t \tv.dpdu = s;\n\t\t\tv.dpdv = t;\n\t\t \tv.dndu = s * invLength;\n\t\t\tv.dndv = t * invLength;\n\n\t\t\tv.object = mRec.getPhaseFunction();\n\t\t\tv.eta = 1.0f;\n\t\t\tv.degenerate = false;\n\t\t\tv.type = EMedium;\n\t\t} else {\n\t\t\tLog(EError, \"Unknown vertex type! : %s\", vertex->toString().c_str());\n\t\t}\n\n\t\tm_vertices.push_back(v);\n\t}\n\n\tv = SimpleVertex(EMovable, ve->getPosition());\n\tm_vertices.push_back(v);\n\n\t#if MTS_MANIFOLD_DEBUG == 1\n\t\tcout << \"==========================================\" << endl;\n\t\tcout << \"Initialized specular manifold: \" << toString() << endl;\n\t#endif\n\n\treturn true;\n}\n\nbool SpecularManifold::computeTangents() {\n\tconst int n = static_cast(m_vertices.size() - 1);\n\n\tm_vertices[0].Tp.setZero();\n\tm_vertices[m_vertices.size()-1].Tp.setIdentity();\n\n\tif (m_vertices.size() == 2) /* Nothing to do */\n\t\treturn true;\n\n\t/* Matrix assembly stage */\n\tfor (int i=0; i std::abs(wi.y)) {\n\t\t\t\tFloat tl = 1.0f / std::sqrt(wi.x * wi.x + wi.z * wi.z);\n\t\t\t\tt = Vector(wi.z * tl, 0.0f, -wi.x * tl);\n\n\t\t\t\tdt_dpred_u = Vector(dwi_dpred_u.z*tl, 0.0f, -dwi_dpred_u.x*tl);\n\t\t\t\tdt_dpred_v = Vector(dwi_dpred_v.z*tl, 0.0f, -dwi_dpred_v.x*tl);\n\t\t\t\tdt_dcur_u = Vector(dwi_dcur_u.z*tl, 0.0f, -dwi_dcur_u.x*tl);\n\t\t\t\tdt_dcur_v = Vector(dwi_dcur_v.z*tl, 0.0f, -dwi_dcur_v.x*tl);\n\t\t\t} else {\n\t\t\t\tFloat tl = 1.0f / std::sqrt(wi.y * wi.y + wi.z * wi.z);\n\t\t\t\tt = Vector(0.0f, wi.z * tl, -wi.y * tl);\n\n\t\t\t\tdt_dpred_u = Vector(0.0f, dwi_dpred_u.z*tl, -dwi_dpred_u.y*tl);\n\t\t\t\tdt_dpred_v = Vector(0.0f, dwi_dpred_v.z*tl, -dwi_dpred_v.y*tl);\n\t\t\t\tdt_dcur_u = Vector(0.0f, dwi_dcur_u.z*tl, -dwi_dcur_u.y*tl);\n\t\t\t\tdt_dcur_v = Vector(0.0f, dwi_dcur_v.z*tl, -dwi_dcur_v.y*tl);\n\t\t\t}\n\n\t\t\tdt_dpred_u -= t * dot(t, dt_dpred_u);\n\t\t\tdt_dpred_v -= t * dot(t, dt_dpred_v);\n\t\t\tdt_dcur_u -= t * dot(t, dt_dcur_u);\n\t\t\tdt_dcur_v -= t * dot(t, dt_dcur_v);\n\n\t\t\tVector s = cross(t, wi);\n\t\t\tVector ds_dpred_u = cross(dt_dpred_u, wi) + cross(t, dwi_dpred_u);\n\t\t\tVector ds_dpred_v = cross(dt_dpred_v, wi) + cross(t, dwi_dpred_v);\n\t\t\tVector ds_dcur_u = cross(dt_dcur_u, wi) + cross(t, dwi_dcur_u);\n\t\t\tVector ds_dcur_v = cross(dt_dcur_v, wi) + cross(t, dwi_dcur_v);\n\n\t\t\t/* Some tangential projections */\n\t\t\tVector2\n\t\t\t\tt_cur_dpdu (dot(v[ 0].dpdu, s), dot(v[ 0].dpdu, t)),\n\t\t\t\tt_cur_dpdv (dot(v[ 0].dpdv, s), dot(v[ 0].dpdv, t)),\n\t\t\t\tt_next_dpdu(dot(v[ 1].dpdu, s), dot(v[ 1].dpdu, t)),\n\t\t\t\tt_next_dpdv(dot(v[ 1].dpdv, s), dot(v[ 1].dpdv, t)),\n\t\t\t\tt_wo = Vector2(dot(wo, s), dot(wo, t));\n\n\t\t\tv[0].a = Matrix2x2(\n\t\t\t\tVector2(dot(ds_dpred_u, wo), dot(dt_dpred_u, wo)),\n\t\t\t\tVector2(dot(ds_dpred_v, wo), dot(dt_dpred_v, wo))\n\t\t\t);\n\n\t\t\tv[0].b = Matrix2x2(\n\t\t\t\t(t_wo * dot(wo, v[0].dpdu) - t_cur_dpdu) * ilo +\n\t\t\t\tVector2(dot(ds_dcur_u, wo), dot(dt_dcur_u, wo)),\n\t\t\t\t(t_wo * dot(wo, v[0].dpdv) - t_cur_dpdv) * ilo +\n\t\t\t\tVector2(dot(ds_dcur_v, wo), dot(dt_dcur_v, wo)));\n\n\t\t\tv[0].c = Matrix2x2(\n\t\t\t\t(t_next_dpdu - t_wo * dot(wo, v[1].dpdu)) * ilo,\n\t\t\t\t(t_next_dpdv - t_wo * dot(wo, v[1].dpdv)) * ilo);\n\n\t\t\tv[0].m = Vector(dot(s, wo), dot(t, wo), dot(wi, wo));\n\t\t} else {\n\t\t\tLog(EError, \"Unknown vertex type!\");\n\t\t}\n\t}\n\n\t/* Find the tangent space with respect to translation of the last\n\t vertex. For this, we must solve a tridiagonal system. The following is\n\t simplified version of the block tridiagonal LU factorization algorithm\n\t for this specific problem */\n\tMatrix2x2 Li;\n\tif (!m_vertices[0].b.invert(Li))\n\t\treturn false;\n\n\tfor (int i=0; i < n - 1; ++i) {\n\t\tm_vertices[i].u = Li * m_vertices[i].c;\n\t\tMatrix2x2 temp = m_vertices[i+1].b - m_vertices[i+1].a * m_vertices[i].u;\n\t\tif (!temp.invert(Li))\n\t\t\treturn false;\n\t}\n\n\tm_vertices[n-1].Tp = -Li * m_vertices[n-1].c;\n\n\tfor (int i=n-2; i>=0; --i)\n\t\tm_vertices[i].Tp = -m_vertices[i].u * m_vertices[i+1].Tp;\n\treturn true;\n}\n\nbool SpecularManifold::project(const Vector &d) {\n\tconst SimpleVertex &last = m_vertices[m_vertices.size()-1];\n\tFloat du = dot(d, last.dpdu), dv = dot(d, last.dpdv);\n\n\tRay ray(Point(0.0f), Vector(1.0f), 0); // make gcc happy\n\tIntersection its;\n\n\tm_proposal.clear();\n\tfor (size_t i=0; irayIntersect(ray, its))\n\t\t\t\treturn false;\n\n\t\t\tVector n = its.shFrame.n,\n\t\t\t\t s = its.dpdu, t;\n\t\t\ts = normalize(s - n * dot(n, s));\n\t\t\tt = cross(n, s);\n\n\t\t\tVector m = s * vertex.m[0] + t * vertex.m[1] + n * vertex.m[2];\n\n\t\t\tray.setOrigin(its.p);\n\t\t\tray.setDirection(reflect(-ray.d, m));\n\t\t} else if (vertex.type == ERefraction) {\n\t\t\tif (!m_scene->rayIntersect(ray, its))\n\t\t\t\treturn false;\n\n\t\t\tVector n = its.shFrame.n,\n\t\t\t\t s = its.dpdu, t;\n\t\t\ts = normalize(s - n * dot(n, s));\n\t\t\tt = cross(n, s);\n\n\t\t\tVector m = s * vertex.m[0] + t * vertex.m[1] + n * vertex.m[2];\n\t\t\tVector refracted = refract(-ray.d, m, its.shape->getBSDF()->getEta());\n\n\t\t\tif (refracted.isZero())\n\t\t\t\treturn false;\n\n\t\t\tray.setOrigin(its.p);\n\t\t\tray.setDirection(refracted);\n\t\t} else if (vertex.type == EMedium) {\n\t\t\tFloat length = (m_vertices[i].p - m_vertices[i-1].p).length(),\n\t\t\t\t invLength = 1.0f / length;\n\n\t\t\t/* Check for occlusion */\n\t\t\tif (m_scene->rayIntersect(Ray(ray, Epsilon, length)))\n\t\t\t\treturn false;\n\n\t\t\tvertex.p = ray(length);\n\t\t\tvertex.n = Vector(0.0f);\n\n\t\t\tVector wi = -ray.d, s, t;\n\t\t\tcoordinateSystem(wi, s, t);\n\n\t\t \tvertex.dpdu = s;\n\t\t\tvertex.dpdv = t;\n\t\t \tvertex.dndu = s * invLength;\n\t\t\tvertex.dndv = t * invLength;\n\n\t\t\tray.setOrigin(vertex.p);\n\t\t\tray.setDirection(s * vertex.m[0] + t * vertex.m[1] + wi * vertex.m[2]);\n\t\t} else {\n\t\t\tLog(EError, \"Unsupported vertex type!\");\n\t\t}\n\n\t\tif (vertex.type != EMedium) {\n\t\t\tif (vertex.object != its.shape->getBSDF())\n\t\t\t\treturn false;\n\n\t\t\tvertex.p = its.p;\n\t\t\tvertex.n = its.shFrame.n;\n\t\t\tvertex.gn = its.geoFrame.n;\n\t\t\tvertex.dpdu = its.dpdu;\n\t\t\tvertex.dpdv = its.dpdv;\n\n\t\t\tconst Shape *shape = its.instance != NULL ? its.instance : its.shape;\n\t\t\tshape->getNormalDerivative(its,\n\t\t\t\tvertex.dndu, vertex.dndv);\n\n\t\t\t/* Turn into an orthonormal parameterization at 'p' */\n\t\t\tFloat invLen = 1 / vertex.dpdu.length();\n\t\t\tvertex.dpdu *= invLen; vertex.dndu *= invLen;\n\t\t\tFloat dp = dot(vertex.dpdu, vertex.dpdv);\n\t\t\tVector dpdv = vertex.dpdv - dp * vertex.dpdu;\n\t\t\tVector dndv = vertex.dndv - dp * vertex.dndu;\n\t\t\tinvLen = 1 / dpdv.length();\n\t\t\tvertex.dpdv = dpdv * invLen;\n\t\t\tvertex.dndv = dndv * invLen;\n\t\t}\n\t}\n\treturn true;\n}\n\nbool SpecularManifold::move(const Point &target, const Normal &n) {\n\tSimpleVertex &last = m_vertices[m_vertices.size()-1];\n\n\t#if MTS_MANIFOLD_DEBUG == 1\n\t\tcout << \"moveTo(\" << last.p.toString() << \" => \" << target.toString() << \", n=\" << n.toString() << \")\" << endl;\n\t#endif\n\n\tif (m_vertices.size() == 2 && m_vertices[0].type == EPinnedPosition) {\n\t\t/* Nothing to do */\n\t\treturn true;\n\t}\n\n\tbool medium = false;\n\tfor (size_t i=0; i \" << newDist << endl;\n\t\t#endif\n\t\tif (newDist > dist) {\n\t\t\t++statsStepTooFar;\n\t\t\t#if MTS_MANIFOLD_DEBUG == 1\n\t\t\t\tcout << \"-> Rejecting!\" << endl;\n\t\t\t#endif\n\t\t\tgoto failure;\n\t\t}\n\t\t#if MTS_MANIFOLD_DEBUG == 1\n\t\t\tcout << \"-> Accepting!\" << endl;\n\t\t#endif\n\t\t++statsStepSuccess;\n\n\t\tm_proposal.swap(m_vertices);\n\n\t\t/* Increase the step size */\n\t\tstepSize = std::min((Float) 1.0f, stepSize * 2.0f);\n\t\tcontinue;\n\tfailure:\n\t\t/* Reduce the step size */\n\t\tstepSize /= 2.0f;\n\t}\n\t#if MTS_MANIFOLD_DEBUG == 1\n\t\tcout << \"Exceeded the max. iteration count!\" << endl;\n\t#endif\n\n\treturn false;\n}\n\nbool SpecularManifold::update(Path &path, int start, int end) {\n\tint step;\n\tETransportMode mode;\n\n\tif (start < end) {\n\t\tstep = 1; mode = EImportance;\n\t} else {\n\t\tstep = -1; mode = ERadiance;\n\t}\n\n\tint last = (int) m_vertices.size() - 2;\n\tif (m_vertices[0].type == EPinnedDirection)\n\t\tlast = std::max(last, 1);\n\n\tfor (int j=0, i=start; j < last; ++j, i += step) {\n\t\tconst SimpleVertex\n\t\t\t&v = m_vertices[j],\n\t\t\t&vn = m_vertices[j+1];\n\n\t\tPathVertex\n\t\t\t*pred = path.vertexOrNull(i-step),\n\t\t\t*vertex = path.vertex(i),\n\t\t\t*succ = path.vertex(i+step);\n\n\t\tint predEdgeIdx = (mode == EImportance) ? i-step : i-step-1;\n\t\tPathEdge *predEdge = path.edgeOrNull(predEdgeIdx),\n\t\t *succEdge = path.edge(predEdgeIdx + step);\n\n\t\tVector d = vn.p - v.p;\n\t\tFloat length = d.length();\n\t\td /= length;\n\t\tPathVertex::EVertexType desiredType = vn.type == EMedium ?\n\t\t\tPathVertex::EMediumInteraction : PathVertex::ESurfaceInteraction;\n\n\t\tif (v.type == EPinnedDirection) {\n\t\t\t/* Create a fake vertex and use it to call sampleDirect(). This is\n\t\t\t kind of terrible -- a nicer API is needed to cleanly support this */\n\t\t\tPathVertex temp;\n\t\t\ttemp.type = PathVertex::EMediumInteraction;\n\t\t\ttemp.degenerate = false;\n\t\t\ttemp.measure = EArea;\n\t\t\tMediumSamplingRecord &mRec = temp.getMediumSamplingRecord();\n\t\t\tmRec.time = m_time;\n\t\t\tmRec.p = vn.p;\n\n\t\t\tif (temp.sampleDirect(m_scene, NULL, vertex, succEdge, succ, mode).isZero()) {\n\t\t\t\t#if MTS_MANIFOLD_DEBUG == 1\n\t\t\t\t\tcout << \"update(): failed in sampleDirect()!\" << endl;\n\t\t\t\t#endif\n\t\t\t\t++statsUpdateFailed;\n\t\t\t\treturn false;\n\t\t\t}\n\n\t\t\tif (m_vertices.size() >= 3) {\n\t\t\t\tPathVertex *succ2 = path.vertex(i+2*step);\n\t\t\t\tPathEdge *succ2Edge = path.edge(predEdgeIdx + 2*step);\n\t\t\t\tif (!succ->sampleNext(m_scene, NULL, vertex, succEdge, succ2Edge, succ2, mode)) {\n\t\t\t\t\t#if MTS_MANIFOLD_DEBUG == 1\n\t\t\t\t\t\tcout << \"update(): failed in sampleNext() / pinned direction!\" << endl;\n\t\t\t\t\t#endif\n\t\t\t\t\t++statsUpdateFailed;\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t\ti += step;\n\t\t} else if (!v.degenerate) {\n\t\t\tif (!vertex->perturbDirection(m_scene,\n\t\t\t\t\tpred, predEdge, succEdge, succ, d,\n\t\t\t\t\tlength, desiredType, mode)) {\n\t\t\t\t#if MTS_MANIFOLD_DEBUG == 1\n\t\t\t\t\tcout << \"update(): failed in perturbDirection()\" << endl;\n\t\t\t\t#endif\n\t\t\t\t++statsUpdateFailed;\n\t\t\t\treturn false;\n\t\t\t}\n\n\t\t\tFloat relerr = (vn.p - succ->getPosition()).length() /\n\t\t\t\tstd::max(std::max(std::abs(vn.p.x),\n\t\t\t\t\tstd::abs(vn.p.y)), std::abs(vn.p.z));\n\n\t\t\tif (relerr > 1e-3f) {\n\t\t\t\t// be extra-cautious\n\t\t\t\t#if MTS_MANIFOLD_DEBUG == 1\n\t\t\t\t\tcout << \"update(): failed, relative error of perturbDirection() too high:\" << relerr << endl;\n\t\t\t\t#endif\n\t\t\t\t++statsUpdateFailed;\n\t\t\t\treturn false;\n\t\t\t}\n\t\t} else {\n\t\t\tunsigned int compType;\n\t\t\tif (v.type == ERefraction)\n\t\t\t\tcompType = v.eta != 1 ? BSDF::EDeltaTransmission : (BSDF::ENull | BSDF::EDeltaTransmission);\n\t\t\telse\n\t\t\t\tcompType = BSDF::EDeltaReflection;\n\n\t\t\tif (!vertex->propagatePerturbation(m_scene,\n\t\t\t\t\tpred, predEdge, succEdge, succ, compType,\n\t\t\t\t\tlength, desiredType, mode)) {\n\t\t\t\t#if MTS_MANIFOLD_DEBUG == 1\n\t\t\t\t\tcout << \"update(): failed in propagatePerturbation()\" << endl;\n\t\t\t\t#endif\n\t\t\t\t++statsUpdateFailed;\n\t\t\t\treturn false;\n\t\t\t}\n\n\t\t\tFloat relerr = (vn.p - succ->getPosition()).length() /\n\t\t\t\tstd::max(std::max(std::abs(vn.p.x),\n\t\t\t\t\tstd::abs(vn.p.y)), std::abs(vn.p.z));\n\t\t\tif (relerr > 1e-3f) {\n\t\t\t\t// be extra-cautious\n\t\t\t\t#if MTS_MANIFOLD_DEBUG == 1\n\t\t\t\t\tcout << \"update(): failed, relative error of propagatePerturbation() too high:\" << relerr << endl;\n\t\t\t\t#endif\n\t\t\t\t++statsUpdateFailed;\n\t\t\t\treturn false;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn true;\n}\n\nFloat SpecularManifold::det(const Path &path, int a, int b, int c) {\n\tint k = path.length();\n\n\tif (a == 0 || a == k)\n\t\tstd::swap(a, c);\n\n\tint step = b > a ? 1 : -1, nGlossy = 0, nSpecular = 0;\n\n\tfor (int i=a + step; i != c; i += step) {\n\t\tif (path.vertex(i)->isConnectable())\n\t\t\t++nGlossy;\n\t\telse\n\t\t\t++nSpecular;\n\t}\n\n\tif (nGlossy <= 1) /* No glossy materials -- we don't need this derivative */\n\t\treturn 1.0f;\n\n\tbool success = init(path, a, c);\n\tBDAssert(success);\n\n\tint b_idx = std::abs(b-a);\n\tSimpleVertex &vb = m_vertices[b_idx];\n\tconst PathVertex *pb = path.vertex(b);\n\n\tif (pb->isMediumInteraction()) {\n\t\tvb.n = Vector(path.edge(a < b ? (b-1) : b)->d);\n\t} else {\n\t\tvb.n = pb->getShadingNormal();\n\t}\n\tcoordinateSystem(vb.n, vb.dpdu, vb.dpdv);\n\n\tif (!computeTangents()) {\n\t\tLog(EWarn, \"Could not compute tangents!\");\n\t\treturn 0.0f;\n\t}\n\n\tm_vertices[b_idx].a.setZero();\n\tm_vertices[b_idx].b.setIdentity();\n\tm_vertices[b_idx].c.setZero();\n\n\tif (nSpecular == 0) {\n\t\t/* The chain only consists of glossy vertices -- simply compute the\n\t\t determinant of the block tridiagonal matrix A.\n\n\t\t See D.K. Salkuyeh, Comments on \"A note on a three-term recurrence for a\n\t\t tridiagonal matrix\", Appl. Math. Comput. 176 (2006) 442-444. */\n\n\t\tMatrix2x2 Di(0.0f), D = m_vertices[1].b;\n\n\t\tFloat det = D.det();\n\t\tfor (size_t i=2; i A(2*(nGlossy + nSpecular), 2*(nGlossy + nSpecular));\n\t\tA.setZero();\n\n\t\tfor (int j=0, i=0; j= 0) {\n\t\t\t\tA(2*i, 2*(j-1)) = m_vertices[j+1].a(0,0);\n\t\t\t\tA(2*i, 2*(j-1)+1) = m_vertices[j+1].a(0,1);\n\t\t\t\tA(2*i+1, 2*(j-1)) = m_vertices[j+1].a(1,0);\n\t\t\t\tA(2*i+1, 2*(j-1)+1) = m_vertices[j+1].a(1,1);\n\t\t\t}\n\t\t\tA(2*i, 2*j) = m_vertices[j+1].b(0,0);\n\t\t\tA(2*i, 2*j+1) = m_vertices[j+1].b(0,1);\n\t\t\tA(2*i+1, 2*j) = m_vertices[j+1].b(1,0);\n\t\t\tA(2*i+1, 2*j+1) = m_vertices[j+1].b(1,1);\n\n\t\t\tif (j+1 < nGlossy + nSpecular) {\n\t\t\t\tA(2*i, 2*(j+1)) = m_vertices[j+1].c(0,0);\n\t\t\t\tA(2*i, 2*(j+1)+1) = m_vertices[j+1].c(0,1);\n\t\t\t\tA(2*i+1, 2*(j+1)) = m_vertices[j+1].c(1,0);\n\t\t\t\tA(2*i+1, 2*(j+1)+1) = m_vertices[j+1].c(1,1);\n\t\t\t}\n\t\t\t++i;\n\t\t}\n\n\t\t/* Compute the inverse and \"cross out\" irrelevant columns and rows */\n\t\tEigen::Matrix Ai = A.inverse();\n\n\t\tfor (int i=0; i(2*i, 2*i).setIdentity();\n\t\t}\n\n\t\treturn std::abs(Ai.determinant());\n\t}\n}\n\nFloat SpecularManifold::multiG(const Path &path, int a, int b) {\n\tif (a == 0)\n\t\t++a;\n\telse if (a == path.length())\n\t\t--a;\n\tif (b == 0)\n\t\t++b;\n\telse if (b == path.length())\n\t\t--b;\n\n\tint step = b > a ? 1 : -1;\n\twhile (!path.vertex(b)->isConnectable())\n\t\tb -= step;\n\twhile (!path.vertex(a)->isConnectable())\n\t\ta += step;\n\n\tFloat result = 1;\n\n\tBDAssert(path.vertex(a)->isConnectable() && path.vertex(b)->isConnectable());\n\tfor (int i = a + step, start = a; i != b + step; i += step) {\n\t\tif (path.vertex(i)->isConnectable()) {\n\t\t\tresult *= G(path, start, i);\n\t\t\tstart = i;\n\t\t}\n\t}\n\n\treturn result;\n}\n\nFloat SpecularManifold::G(const Path &path, int a, int b) {\n\tif (std::abs(a-b) == 1) {\n\t\tif (a > b)\n\t\t\tstd::swap(a, b);\n\t\treturn path.edge(a)->evalCached(path.vertex(a),\n\t\t\tpath.vertex(b), PathEdge::EGeometricTerm)[0];\n\t}\n\n\tAssert(path.vertex(a)->isConnectable());\n\tAssert(path.vertex(b)->isConnectable());\n\tint step = b > a ? 1 : -1;\n\n\tbool success = init(path, a, b);\n\tBDAssert(success);\n\n\tSimpleVertex &last = m_vertices[m_vertices.size()-1];\n\tconst PathVertex *vb = path.vertex(b);\n\tif (!vb->isOnSurface()) {\n\t\tlast.n = Vector(path.edge(a < b ? (b-1) : b)->d);\n\t} else {\n\t\tlast.n = vb->getShadingNormal();\n\t}\n\tcoordinateSystem(last.n, last.dpdu, last.dpdv);\n\n\tstatsNonManifold.incrementBase();\n\tif (!computeTangents()) {\n\t\t++statsNonManifold;\n\t\tLog(EWarn, \"SpecularManifold::evalG(): non-manifold configuration!\");\n\t\treturn 0;\n\t}\n\n\tFloat result;\n\tif (m_vertices[0].type == EPinnedDirection) {\n\t\tresult = cross(m_vertices[0].map(1, 0), m_vertices[0].map(0, 1)).length();\n\t} else if (m_vertices[0].type == EPinnedPosition) {\n\t\tVector d = m_vertices[1].p - m_vertices[0].p;\n\t\tFloat lengthSqr = d.lengthSquared(), invLength = 1/std::sqrt(lengthSqr);\n\n\t\tresult = cross(m_vertices[1].map(1, 0), m_vertices[1].map(0, 1)).length() / lengthSqr;\n\n\t\tif (path.vertex(a)->isOnSurface())\n\t\t\tresult *= absDot(d, path.vertex(a)->getShadingNormal()) * invLength;\n\n\t\tif (path.vertex(a+step)->isOnSurface())\n\t\t\tresult *= absDot(d, path.vertex(a+step)->getShadingNormal()) * invLength;\n\t} else {\n\t\tLog(EError, \"Invalid vertex type!\");\n\t\treturn 0;\n\t}\n\n\treturn result;\n}\n\nstd::string SpecularManifold::SimpleVertex::toString() const {\n\tstd::ostringstream oss;\n\n\toss << \"SimpleVertex[\" << endl\n\t\t<< \" type = \";\n\n\tswitch (type) {\n\t\tcase EPinnedPosition: oss << \"pinnedPosition\"; break;\n\t\tcase EPinnedDirection: oss << \"pinnedDirection\"; break;\n\t\tcase EReflection: oss << \"reflection\"; break;\n\t\tcase ERefraction: oss << \"refraction\"; break;\n\t\tcase EMedium: oss << \"medium\"; break;\n\t\tcase EMovable: oss << \"movable\"; break;\n\t\tdefault: SLog(EError, \"Unknown vertex type!\");\n\t}\n\n\toss << \",\" << endl\n\t\t<< \" p = \" << p.toString() << \",\" << endl\n\t\t<< \" n = \" << n.toString() << \",\" << endl\n\t\t<< \" m = \" << m.toString() << \",\" << endl\n\t\t<< \" dpdu = \" << dpdu.toString() << \",\" << endl\n\t\t<< \" dpdv = \" << dpdv.toString() << \",\" << endl\n\t\t<< \" dndu = \" << dndu.toString() << \",\" << endl\n\t\t<< \" dndv = \" << dndv.toString() << \",\" << endl\n\t\t<< \" eta = \" << eta << \",\" << endl\n\t\t<< \" object = \" << (object ? indent(object->toString()).c_str() : \"null\") << endl\n\t\t<< \"]\";\n\n\treturn oss.str();\n}\n\nstd::string SpecularManifold::toString() const {\n\tstd::ostringstream oss;\n\n\toss << \"SpecularManifold[\" << endl;\n\tfor (size_t i=0; i \" << indent(m_vertices[i].toString());\n\t\tif (i+1 < m_vertices.size())\n\t\t\toss << \",\";\n\t\toss << endl;\n\t}\n\toss << \"]\";\n\n\treturn oss.str();\n}\n\nMTS_IMPLEMENT_CLASS(SpecularManifold, false, Object)\nMTS_NAMESPACE_END\n", "meta": {"hexsha": "17b9384b7659758ca9dd4ce5f0c76c574c086944", "size": 29883, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "mitsuba-af602c6fd98a/src/libbidir/manifold.cpp", "max_stars_repo_name": "NTForked-ML/pbrs", "max_stars_repo_head_hexsha": "0b405d92c12d257e2581366542762c9f0c3facce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 139.0, "max_stars_repo_stars_event_min_datetime": "2017-04-21T00:22:34.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-16T20:33:10.000Z", "max_issues_repo_path": "mitsuba-af602c6fd98a/src/libbidir/manifold.cpp", "max_issues_repo_name": "NTForked-ML/pbrs", "max_issues_repo_head_hexsha": "0b405d92c12d257e2581366542762c9f0c3facce", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 11.0, "max_issues_repo_issues_event_min_datetime": "2017-08-15T18:22:59.000Z", "max_issues_repo_issues_event_max_datetime": "2019-07-01T05:44:41.000Z", "max_forks_repo_path": "mitsuba-af602c6fd98a/src/libbidir/manifold.cpp", "max_forks_repo_name": "NTForked-ML/pbrs", "max_forks_repo_head_hexsha": "0b405d92c12d257e2581366542762c9f0c3facce", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 30.0, "max_forks_repo_forks_event_min_datetime": "2017-07-21T03:56:45.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-11T06:55:34.000Z", "avg_line_length": 29.8531468531, "max_line_length": 113, "alphanum_fraction": 0.6151992772, "num_tokens": 10085, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526368038304, "lm_q2_score": 0.38121956625614994, "lm_q1q2_score": 0.21723897503227957}} {"text": "#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"filelib.h\"\n#include \"weights.h\"\n#include \"sparse_vector.h\"\n#include \"optimize.h\"\n\nusing namespace std;\nnamespace po = boost::program_options;\n\n// since this is a ranking model, there should be equal numbers of\n// positive and negative examples, so the bias should be 0\nstatic const double MAX_BIAS = 1e-10;\n\nvoid InitCommandLine(int argc, char** argv, po::variables_map* conf) {\n po::options_description opts(\"Configuration options\");\n opts.add_options()\n (\"weights,w\", po::value(), \"Weights from previous iteration (used as initialization and interpolation\")\n (\"interpolation,p\",po::value()->default_value(0.9), \"Output weights are p*w + (1-p)*w_prev\")\n (\"memory_buffers,m\",po::value()->default_value(200), \"Number of memory buffers (LBFGS)\")\n (\"sigma_squared,s\",po::value()->default_value(0.1), \"Sigma squared for Gaussian prior\")\n (\"min_reg,r\",po::value()->default_value(1e-8), \"When tuning (-T) regularization strength, minimum regularization strenght\")\n (\"max_reg,R\",po::value()->default_value(10.0), \"When tuning (-T) regularization strength, maximum regularization strenght\")\n (\"testset,t\",po::value(), \"Optional held-out test set\")\n (\"tune_regularizer,T\", \"Use the held out test set (-t) to tune the regularization strength\")\n (\"help,h\", \"Help\");\n po::options_description dcmdline_options;\n dcmdline_options.add(opts);\n po::store(parse_command_line(argc, argv, dcmdline_options), *conf);\n if (conf->count(\"help\")) {\n cerr << dcmdline_options << endl;\n exit(1);\n }\n}\n\nvoid ParseSparseVector(string& line, size_t cur, SparseVector* out) {\n SparseVector& x = *out;\n size_t last_start = cur;\n size_t last_comma = string::npos;\n while(cur <= line.size()) {\n if (line[cur] == ' ' || cur == line.size()) {\n if (!(cur > last_start && last_comma != string::npos && cur > last_comma)) {\n cerr << \"[ERROR] \" << line << endl << \" position = \" << cur << endl;\n exit(1);\n }\n const int fid = FD::Convert(line.substr(last_start, last_comma - last_start));\n if (cur < line.size()) line[cur] = 0;\n const double val = strtod(&line[last_comma + 1], NULL);\n x.set_value(fid, val);\n\n last_comma = string::npos;\n last_start = cur+1;\n } else {\n if (line[cur] == '=')\n last_comma = cur;\n }\n ++cur;\n }\n}\n\nvoid ReadCorpus(istream* pin, vector > >* corpus) {\n istream& in = *pin;\n corpus->clear();\n bool flag = false;\n int lc = 0;\n string line;\n SparseVector x;\n while(getline(in, line)) {\n ++lc;\n if (lc % 1000 == 0) { cerr << '.'; flag = true; }\n if (lc % 40000 == 0) { cerr << \" [\" << lc << \"]\\n\"; flag = false; }\n if (line.empty()) continue;\n const size_t ks = line.find(\"\\t\");\n assert(string::npos != ks);\n assert(ks == 1);\n const bool y = line[0] == '1';\n x.clear();\n ParseSparseVector(line, ks + 1, &x);\n corpus->push_back(make_pair(y, x));\n }\n if (flag) cerr << endl;\n}\n\nvoid GradAdd(const SparseVector& v, const double scale, vector* acc) {\n for (SparseVector::const_iterator it = v.begin();\n it != v.end(); ++it) {\n (*acc)[it->first] += it->second * scale;\n }\n}\n\ndouble TrainingInference(const vector& x,\n const vector > >& corpus,\n vector* g = NULL) {\n double cll = 0;\n for (int i = 0; i < corpus.size(); ++i) {\n const double dotprod = corpus[i].second.dot(x) + x[0]; // x[0] is bias\n double lp_false = dotprod;\n double lp_true = -dotprod;\n if (0 < lp_true) {\n lp_true += log1p(exp(-lp_true));\n lp_false = log1p(exp(lp_false));\n } else {\n lp_true = log1p(exp(lp_true));\n lp_false += log1p(exp(-lp_false));\n }\n lp_true*=-1;\n lp_false*=-1;\n if (corpus[i].first) { // true label\n cll -= lp_true;\n if (g) {\n // g -= corpus[i].second * exp(lp_false);\n GradAdd(corpus[i].second, -exp(lp_false), g);\n (*g)[0] -= exp(lp_false); // bias\n }\n } else { // false label\n cll -= lp_false;\n if (g) {\n // g += corpus[i].second * exp(lp_true);\n GradAdd(corpus[i].second, exp(lp_true), g);\n (*g)[0] += exp(lp_true); // bias\n }\n }\n }\n return cll;\n}\n\n// return held-out log likelihood\ndouble LearnParameters(const vector > >& training,\n const vector > >& testing,\n const double sigsq,\n const unsigned memory_buffers,\n vector* px) {\n vector& x = *px;\n vector vg(FD::NumFeats(), 0.0);\n bool converged = false;\n LBFGSOptimizer opt(FD::NumFeats(), memory_buffers);\n double tppl = 0.0;\n while(!converged) {\n fill(vg.begin(), vg.end(), 0.0);\n double cll = TrainingInference(x, training, &vg);\n double ppl = cll / log(2);\n ppl /= training.size();\n ppl = pow(2.0, ppl);\n\n // evaluate optional held-out test set\n if (testing.size()) {\n tppl = TrainingInference(x, testing) / log(2);\n tppl /= testing.size();\n tppl = pow(2.0, tppl);\n }\n\n // handle regularizer\n#if 1\n double norm = 0;\n for (int i = 1; i < x.size(); ++i) {\n const double mean_i = 0.0;\n const double param = (x[i] - mean_i);\n norm += param * param;\n vg[i] += param / sigsq;\n } \n const double reg = norm / (2.0 * sigsq);\n#else\n double reg = 0;\n#endif\n cll += reg;\n cerr << cll << \" (REG=\" << reg << \")\\tPPL=\" << ppl << \"\\t TEST_PPL=\" << tppl << \"\\t\";\n try {\n vector old_x = x;\n do {\n opt.Optimize(cll, vg, &x);\n converged = opt.HasConverged();\n } while (!converged && x == old_x);\n } catch (...) {\n cerr << \"Exception caught, assuming convergence is close enough...\\n\";\n converged = true;\n }\n if (fabs(x[0]) > MAX_BIAS) {\n cerr << \"Biased model learned. Are your training instances wrong?\\n\";\n cerr << \" BIAS: \" << x[0] << endl;\n }\n }\n return tppl;\n}\n\nint main(int argc, char** argv) {\n po::variables_map conf;\n InitCommandLine(argc, argv, &conf);\n string line;\n vector > > training, testing;\n SparseVector old_weights;\n const bool tune_regularizer = conf.count(\"tune_regularizer\");\n if (tune_regularizer && !conf.count(\"testset\")) {\n cerr << \"--tune_regularizer requires --testset to be set\\n\";\n return 1;\n }\n const double min_reg = conf[\"min_reg\"].as();\n const double max_reg = conf[\"max_reg\"].as();\n double sigsq = conf[\"sigma_squared\"].as();\n assert(sigsq > 0.0);\n assert(min_reg > 0.0);\n assert(max_reg > 0.0);\n assert(max_reg > min_reg);\n const double psi = conf[\"interpolation\"].as();\n if (psi < 0.0 || psi > 1.0) { cerr << \"Invalid interpolation weight: \" << psi << endl; }\n if (conf.count(\"weights\")) {\n Weights w;\n w.InitFromFile(conf[\"weights\"].as());\n w.InitSparseVector(&old_weights);\n }\n ReadCorpus(&cin, &training);\n if (conf.count(\"testset\")) {\n ReadFile rf(conf[\"testset\"].as());\n ReadCorpus(rf.stream(), &testing);\n }\n cerr << \"Number of features: \" << FD::NumFeats() << endl;\n vector x(FD::NumFeats(), 0.0); // x[0] is bias\n for (SparseVector::const_iterator it = old_weights.begin();\n it != old_weights.end(); ++it)\n x[it->first] = it->second;\n double tppl = 0.0;\n vector > sp;\n vector smoothed;\n if (tune_regularizer) {\n cerr << \"Tuning regularizer...\" << endl;\n\n sigsq = min_reg;\n const double steps = 18;\n double sweep_factor = exp((log(max_reg) - log(min_reg)) / steps);\n cerr << \"SWEEP FACTOR: \" << sweep_factor << endl;\n while(sigsq < max_reg) {\n tppl = LearnParameters(training, testing, sigsq, conf[\"memory_buffers\"].as(), &x);\n sp.push_back(make_pair(sigsq, tppl));\n sigsq *= sweep_factor;\n }\n smoothed.resize(sp.size(), 0);\n smoothed[0] = sp[0].second;\n smoothed.back() = sp.back().second; \n for (int i = 1; i < sp.size()-1; ++i) {\n double prev = sp[i-1].second;\n double next = sp[i+1].second;\n double cur = sp[i].second;\n smoothed[i] = (prev*0.2) + cur * 0.6 + (0.2*next);\n }\n double best_ppl = 9999999;\n unsigned best_i = 0;\n for (unsigned i = 0; i < sp.size(); ++i) {\n if (smoothed[i] < best_ppl) {\n best_ppl = smoothed[i];\n best_i = i;\n }\n }\n sigsq = sp[best_i].first;\n }\n cerr << \"Learning parameters...\" << endl;\n tppl = LearnParameters(training, testing, sigsq, conf[\"memory_buffers\"].as(), &x);\n\n Weights w;\n if (conf.count(\"weights\")) {\n cerr << \"Interpolating with previous weight vectors...\" << endl;\n for (int i = 1; i < x.size(); ++i)\n x[i] = (x[i] * psi) + old_weights.get(i) * (1.0 - psi);\n }\n cout.precision(15);\n cout << \"# sigma^2=\" << sigsq << \"\\theld out perplexity=\";\n if (tppl) { cout << tppl << endl; } else { cout << \"N/A\\n\"; }\n if (sp.size()) {\n cout << \"# Parameter sweep:\\n\";\n for (int i = 0; i < sp.size(); ++i) {\n cout << \"# \" << sp[i].first << \"\\t\" << sp[i].second << \"\\t\" << smoothed[i] << endl;\n }\n }\n w.InitFromVector(x);\n w.WriteToFile(\"-\");\n return 0;\n}\n", "meta": {"hexsha": "1264f535c4e5d02e8c643d3b3ae5626e942dd2ee", "size": 9632, "ext": "cc", "lang": "C++", "max_stars_repo_path": "pro-train/mr_pro_reduce.cc", "max_stars_repo_name": "jhclark/cdec", "max_stars_repo_head_hexsha": "237ddc67ffa61da310e19710f902d4771dc323c2", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL", "Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-06-03T00:44:01.000Z", "max_stars_repo_stars_event_max_datetime": "2019-06-03T00:44:01.000Z", "max_issues_repo_path": "pro-train/mr_pro_reduce.cc", "max_issues_repo_name": "jhclark/cdec", "max_issues_repo_head_hexsha": "237ddc67ffa61da310e19710f902d4771dc323c2", "max_issues_repo_licenses": ["BSD-3-Clause-LBNL", "Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "pro-train/mr_pro_reduce.cc", "max_forks_repo_name": "jhclark/cdec", "max_forks_repo_head_hexsha": "237ddc67ffa61da310e19710f902d4771dc323c2", "max_forks_repo_licenses": ["BSD-3-Clause-LBNL", "Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.035335689, "max_line_length": 139, "alphanum_fraction": 0.5849252492, "num_tokens": 2762, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5583269943353744, "lm_q2_score": 0.3886180267058489, "lm_q1q2_score": 0.21697593479522084}} {"text": "// Copyright John Maddock 2006.\n// Use, modification and distribution are subject to the\n// Boost Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_STATS_EXTREME_VALUE_HPP\n#define BOOST_STATS_EXTREME_VALUE_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n//\n// This is the maximum extreme value distribution, see\n// http://www.itl.nist.gov/div898/handbook/eda/section3/eda366g.htm\n// and http://mathworld.wolfram.com/ExtremeValueDistribution.html\n// Also known as a Fisher-Tippett distribution, a log-Weibull\n// distribution or a Gumbel distribution.\n\n#include \n#include \n\n#ifdef _MSC_VER\n# pragma warning(push)\n# pragma warning(disable: 4702) // unreachable code (return after domain_error throw).\n#endif\n\nnamespace boost{ namespace math{\n\nnamespace detail{\n//\n// Error check:\n//\ntemplate \ninline bool verify_scale_b(const char* function, RealType b, RealType* presult, const Policy& pol)\n{\n if((b <= 0) || !(boost::math::isfinite)(b))\n {\n *presult = policies::raise_domain_error(\n function,\n \"The scale parameter \\\"b\\\" must be finite and > 0, but was: %1%.\", b, pol);\n return false;\n }\n return true;\n}\n\n} // namespace detail\n\ntemplate >\nclass extreme_value_distribution\n{\npublic:\n typedef RealType value_type;\n typedef Policy policy_type;\n\n extreme_value_distribution(RealType a = 0, RealType b = 1)\n : m_a(a), m_b(b)\n {\n RealType err;\n detail::verify_scale_b(\"boost::math::extreme_value_distribution<%1%>::extreme_value_distribution\", b, &err, Policy());\n detail::check_finite(\"boost::math::extreme_value_distribution<%1%>::extreme_value_distribution\", a, &err, Policy());\n } // extreme_value_distribution\n\n RealType location()const { return m_a; }\n RealType scale()const { return m_b; }\n\nprivate:\n RealType m_a, m_b;\n};\n\ntypedef extreme_value_distribution extreme_value;\n\n#ifdef __cpp_deduction_guides\ntemplate \nextreme_value_distribution(RealType)->extreme_value_distribution::type>;\ntemplate \nextreme_value_distribution(RealType,RealType)->extreme_value_distribution::type>;\n#endif\n\ntemplate \ninline const std::pair range(const extreme_value_distribution& /*dist*/)\n{ // Range of permissible values for random variable x.\n using boost::math::tools::max_value;\n return std::pair(\n std::numeric_limits::has_infinity ? -std::numeric_limits::infinity() : -max_value(), \n std::numeric_limits::has_infinity ? std::numeric_limits::infinity() : max_value());\n}\n\ntemplate \ninline const std::pair support(const extreme_value_distribution& /*dist*/)\n{ // Range of supported values for random variable x.\n // This is range where cdf rises from 0 to 1, and outside it, the pdf is zero.\n using boost::math::tools::max_value;\n return std::pair(-max_value(), max_value());\n}\n\ntemplate \ninline RealType pdf(const extreme_value_distribution& dist, const RealType& x)\n{\n BOOST_MATH_STD_USING // for ADL of std functions\n\n static const char* function = \"boost::math::pdf(const extreme_value_distribution<%1%>&, %1%)\";\n\n RealType a = dist.location();\n RealType b = dist.scale();\n RealType result = 0;\n if(0 == detail::verify_scale_b(function, b, &result, Policy()))\n return result;\n if(0 == detail::check_finite(function, a, &result, Policy()))\n return result;\n if((boost::math::isinf)(x))\n return 0.0f;\n if(0 == detail::check_x(function, x, &result, Policy()))\n return result;\n RealType e = (a - x) / b;\n if(e < tools::log_max_value())\n result = exp(e) * exp(-exp(e)) / b;\n // else.... result *must* be zero since exp(e) is infinite...\n return result;\n} // pdf\n\ntemplate \ninline RealType cdf(const extreme_value_distribution& dist, const RealType& x)\n{\n BOOST_MATH_STD_USING // for ADL of std functions\n\n static const char* function = \"boost::math::cdf(const extreme_value_distribution<%1%>&, %1%)\";\n\n if((boost::math::isinf)(x))\n return x < 0 ? 0.0f : 1.0f;\n RealType a = dist.location();\n RealType b = dist.scale();\n RealType result = 0;\n if(0 == detail::verify_scale_b(function, b, &result, Policy()))\n return result;\n if(0 == detail::check_finite(function, a, &result, Policy()))\n return result;\n if(0 == detail::check_finite(function, a, &result, Policy()))\n return result;\n if(0 == detail::check_x(\"boost::math::cdf(const extreme_value_distribution<%1%>&, %1%)\", x, &result, Policy()))\n return result;\n\n result = exp(-exp((a-x)/b));\n\n return result;\n} // cdf\n\ntemplate \nRealType quantile(const extreme_value_distribution& dist, const RealType& p)\n{\n BOOST_MATH_STD_USING // for ADL of std functions\n\n static const char* function = \"boost::math::quantile(const extreme_value_distribution<%1%>&, %1%)\";\n\n RealType a = dist.location();\n RealType b = dist.scale();\n RealType result = 0;\n if(0 == detail::verify_scale_b(function, b, &result, Policy()))\n return result;\n if(0 == detail::check_finite(function, a, &result, Policy()))\n return result;\n if(0 == detail::check_probability(function, p, &result, Policy()))\n return result;\n\n if(p == 0)\n return -policies::raise_overflow_error(function, 0, Policy());\n if(p == 1)\n return policies::raise_overflow_error(function, 0, Policy());\n\n result = a - log(-log(p)) * b;\n\n return result;\n} // quantile\n\ntemplate \ninline RealType cdf(const complemented2_type, RealType>& c)\n{\n BOOST_MATH_STD_USING // for ADL of std functions\n\n static const char* function = \"boost::math::cdf(const extreme_value_distribution<%1%>&, %1%)\";\n\n if((boost::math::isinf)(c.param))\n return c.param < 0 ? 1.0f : 0.0f;\n RealType a = c.dist.location();\n RealType b = c.dist.scale();\n RealType result = 0;\n if(0 == detail::verify_scale_b(function, b, &result, Policy()))\n return result;\n if(0 == detail::check_finite(function, a, &result, Policy()))\n return result;\n if(0 == detail::check_x(function, c.param, &result, Policy()))\n return result;\n\n result = -boost::math::expm1(-exp((a-c.param)/b), Policy());\n\n return result;\n}\n\ntemplate \nRealType quantile(const complemented2_type, RealType>& c)\n{\n BOOST_MATH_STD_USING // for ADL of std functions\n\n static const char* function = \"boost::math::quantile(const extreme_value_distribution<%1%>&, %1%)\";\n\n RealType a = c.dist.location();\n RealType b = c.dist.scale();\n RealType q = c.param;\n RealType result = 0;\n if(0 == detail::verify_scale_b(function, b, &result, Policy()))\n return result;\n if(0 == detail::check_finite(function, a, &result, Policy()))\n return result;\n if(0 == detail::check_probability(function, q, &result, Policy()))\n return result;\n\n if(q == 0)\n return policies::raise_overflow_error(function, 0, Policy());\n if(q == 1)\n return -policies::raise_overflow_error(function, 0, Policy());\n\n result = a - log(-boost::math::log1p(-q, Policy())) * b;\n\n return result;\n}\n\ntemplate \ninline RealType mean(const extreme_value_distribution& dist)\n{\n RealType a = dist.location();\n RealType b = dist.scale();\n RealType result = 0;\n if(0 == detail::verify_scale_b(\"boost::math::mean(const extreme_value_distribution<%1%>&)\", b, &result, Policy()))\n return result;\n if (0 == detail::check_finite(\"boost::math::mean(const extreme_value_distribution<%1%>&)\", a, &result, Policy()))\n return result;\n return a + constants::euler() * b;\n}\n\ntemplate \ninline RealType standard_deviation(const extreme_value_distribution& dist)\n{\n BOOST_MATH_STD_USING // for ADL of std functions.\n\n RealType b = dist.scale();\n RealType result = 0;\n if(0 == detail::verify_scale_b(\"boost::math::standard_deviation(const extreme_value_distribution<%1%>&)\", b, &result, Policy()))\n return result;\n if(0 == detail::check_finite(\"boost::math::standard_deviation(const extreme_value_distribution<%1%>&)\", dist.location(), &result, Policy()))\n return result;\n return constants::pi() * b / sqrt(static_cast(6));\n}\n\ntemplate \ninline RealType mode(const extreme_value_distribution& dist)\n{\n return dist.location();\n}\n\ntemplate \ninline RealType median(const extreme_value_distribution& dist)\n{\n using constants::ln_ln_two;\n return dist.location() - dist.scale() * ln_ln_two();\n}\n\ntemplate \ninline RealType skewness(const extreme_value_distribution& /*dist*/)\n{\n //\n // This is 12 * sqrt(6) * zeta(3) / pi^3:\n // See http://mathworld.wolfram.com/ExtremeValueDistribution.html\n //\n return static_cast(1.1395470994046486574927930193898461120875997958366L);\n}\n\ntemplate \ninline RealType kurtosis(const extreme_value_distribution& /*dist*/)\n{\n // See http://mathworld.wolfram.com/ExtremeValueDistribution.html\n return RealType(27) / 5;\n}\n\ntemplate \ninline RealType kurtosis_excess(const extreme_value_distribution& /*dist*/)\n{\n // See http://mathworld.wolfram.com/ExtremeValueDistribution.html\n return RealType(12) / 5;\n}\n\n\n} // namespace math\n} // namespace boost\n\n#ifdef _MSC_VER\n# pragma warning(pop)\n#endif\n\n// This include must be at the end, *after* the accessors\n// for this distribution have been defined, in order to\n// keep compilers that support two-phase lookup happy.\n#include \n\n#endif // BOOST_STATS_EXTREME_VALUE_HPP\n", "meta": {"hexsha": "d503b31bc9097dfd071565b678554e10354b51a4", "size": 10812, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "AqooleEngine/src/main/cpp/boost/boost/math/distributions/extreme_value.hpp", "max_stars_repo_name": "kodai731/Aqoole-Engine-Android-Vulkan-Rendering-Engine-", "max_stars_repo_head_hexsha": "72c8f34b6b6d507319069e681ff8c5008337b7c6", "max_stars_repo_licenses": ["Apache-2.0", "MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "AqooleEngine/src/main/cpp/boost/boost/math/distributions/extreme_value.hpp", "max_issues_repo_name": "kodai731/Aqoole-Engine-Android-Vulkan-Rendering-Engine-", "max_issues_repo_head_hexsha": "72c8f34b6b6d507319069e681ff8c5008337b7c6", "max_issues_repo_licenses": ["Apache-2.0", "MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "AqooleEngine/src/main/cpp/boost/boost/math/distributions/extreme_value.hpp", "max_forks_repo_name": "kodai731/Aqoole-Engine-Android-Vulkan-Rendering-Engine-", "max_forks_repo_head_hexsha": "72c8f34b6b6d507319069e681ff8c5008337b7c6", "max_forks_repo_licenses": ["Apache-2.0", "MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.1038961039, "max_line_length": 143, "alphanum_fraction": 0.7066222716, "num_tokens": 2794, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795672, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.21678555428705873}} {"text": "/*\nCopyright (c) 2020 Naomasa Matsubayashi\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n*/\n\n#include \n#include \n#include \n#include \n#include \n#include \"ifm/bessel.h\"\nint main( int argc, char* argv[] ) {\n boost::program_options::options_description options(\"オプション\");\n options.add_options()\n (\"help,h\", \"ヘルプを表示\")\n (\"bessel,b\", boost::program_options::value()->default_value( \"bessel.mp\" ), \"ベッセル関数近似係数\");\n boost::program_options::variables_map params;\n boost::program_options::store( boost::program_options::parse_command_line( argc, argv, options ), params );\n boost::program_options::notify( params );\n if( params.count(\"help\") ) {\n std::cout << options << std::endl;\n return 0;\n }\n std::vector< std::pair< float, float > > pre;\n {\n std::ifstream bessel_file( params[\"bessel\"].as(), std::ofstream::binary );\n nlohmann::json bessel = nlohmann::json::from_msgpack( bessel_file );\n for( const auto &v: bessel )\n pre.emplace_back( v.at( 0 ), v.at( 1 ) );\n }\n const auto t0 = std::chrono::steady_clock::now();\n for( unsigned int b_ = 0.f; b_ != 400; ++b_ ) {\n float b = b_ * 0.1f;\n for( unsigned int n = 0; n != 60; ++n ) {\n ifm::bessel_kind1( b, n );\n }\n }\n const auto t1 = std::chrono::steady_clock::now();\n for( unsigned int b_ = 0.f; b_ != 400; ++b_ ) {\n float b = b_ * 0.1f;\n float l1 = 0;\n float l2 = 0;\n for( unsigned int n = 0; n != 60; ++n ) {\n float y = ifm::bessel_kind1_approx_2019( b, n, l1, l2, pre );\n l2 = l1;\n l1 = y;\n }\n }\n const auto t2 = std::chrono::steady_clock::now();\n float e0 = float( std::chrono::duration_cast< std::chrono::microseconds >( t1 - t0 ).count() );\n float e1 = float( std::chrono::duration_cast< std::chrono::microseconds >( t2 - t1 ).count() );\n std::cout << \"台形公式: \" << (400*60)/e0 << \"Mbps(\" << 400*60 << \" samples in \" << e0 << \"microseconds)\" << std::endl;\n std::cout << \"近似式: \" << (400*60)/e1 << \"Mbps(\" << 400*60 << \" samples in \" << e1 << \"microseconds)\" << std::endl;\n}\n\n", "meta": {"hexsha": "27289a7a1fabd7e998b0af8481147616f77521e2", "size": 3087, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/bessel_benchmark.cpp", "max_stars_repo_name": "Fadis/ifm", "max_stars_repo_head_hexsha": "8e538c67e756cfc4056bf406e578809dc2b462f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20.0, "max_stars_repo_stars_event_min_datetime": "2020-02-08T08:51:42.000Z", "max_stars_repo_stars_event_max_datetime": "2021-05-28T04:44:24.000Z", "max_issues_repo_path": "src/bessel_benchmark.cpp", "max_issues_repo_name": "Fadis/ifm", "max_issues_repo_head_hexsha": "8e538c67e756cfc4056bf406e578809dc2b462f9", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2020-04-01T18:31:50.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-01T18:31:50.000Z", "max_forks_repo_path": "src/bessel_benchmark.cpp", "max_forks_repo_name": "Fadis/ifm", "max_forks_repo_head_hexsha": "8e538c67e756cfc4056bf406e578809dc2b462f9", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.2876712329, "max_line_length": 116, "alphanum_fraction": 0.6705539359, "num_tokens": 902, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.21678555428705867}} {"text": "// Copyright John Maddock 2012 - 2021.\n// Copyright Christopher Kormanyos 2016 - 2021.\n// Use, modification and distribution are subject to the\n// Boost Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)\n//\n\n// Wrapper that works with e_float-2021, see also\n// https://github.com/ckormanyos/e_float-2021\n\n#ifndef E_FLOAT_2017_08_18_HPP_\n #define E_FLOAT_2017_08_18_HPP_\n\n #include \n #include \n #include \n #include \n\n #include \n #include \n\n #include \n #include \n\n namespace boost { namespace math { namespace ef {\n\n // Forward declaration of the e_float multiple precision class.\n // This class binds native ::e_float to boost::multiprecsion::e_float.\n class e_float;\n\n } } }\n\n // Define the number category as a floating-point kind\n // for the e_float. This is needed for properly\n // interacting as a backend with boost::muliprecision.\n #if (BOOST_VERSION <= 107200)\n template<>\n struct boost::multiprecision::number_category\n : public boost::integral_constant { };\n #elif (BOOST_VERSION <= 107500)\n template<>\n struct boost::multiprecision::number_category\n : public boost::integral_constant { };\n #else\n template<>\n struct boost::multiprecision::number_category\n : public std::integral_constant { };\n #endif\n\n namespace boost { namespace math { namespace ef {\n\n // This is the e_float multiple precision class.\n class e_float\n {\n public:\n #if (BOOST_VERSION <= 107500)\n using signed_types = mpl::list;\n using unsigned_types = mpl::list;\n using float_types = mpl::list;\n #else\n using signed_types = std::tuple< signed char, signed short, signed int, signed long, signed long long, std::intmax_t>;\n using unsigned_types = std::tuple;\n using float_types = std::tuple;\n #endif\n using exponent_type = std::int64_t;\n\n e_float() : m_value() { }\n\n explicit e_float(const ::e_float& rep) : m_value(rep) { }\n\n e_float(const e_float& other) : m_value(other.m_value) { }\n e_float(e_float&& other) : m_value(static_cast<::e_float&&>(other.m_value)) { }\n\n template::value == true)\n && (std::is_unsigned::value == true))>::type const* = nullptr>\n e_float(UnsignedIntegralType u) : m_value(::e_float(std::uint64_t(u))) { }\n\n template::value == true)\n && (std::is_signed ::value == true))>::type const* = nullptr>\n e_float(SignedIntegralType n) : m_value(::e_float(std::int64_t(n))) { }\n\n template::value == true>::type const* = nullptr>\n e_float(FloatingPointType f) : m_value(::e_float(static_cast(f))) { }\n\n e_float(const char* c) : m_value(c) { }\n\n e_float(const std::string& str) : m_value(str) { }\n\n ~e_float() { }\n\n e_float& operator=(const e_float& other)\n {\n if(this != &other)\n {\n m_value = other.m_value;\n }\n\n return *this;\n }\n\n e_float& operator=(e_float&& other)\n {\n m_value.operator=(static_cast<::e_float&&>(other.m_value));\n\n return *this;\n }\n\n template::value == true>::type const* = nullptr>\n e_float& operator=(const ArithmeticType& x)\n {\n m_value = ::e_float(x);\n\n return *this;\n }\n\n e_float& operator=(const std::string& str_rep) { m_value = ::e_float(str_rep); return *this; }\n e_float& operator=(const char* char_ptr) { m_value = ::e_float(char_ptr); return *this; }\n\n void swap(e_float& other_mp_cpp_backend)\n {\n m_value.swap(other_mp_cpp_backend.m_value);\n }\n\n ::e_float& representation() { return m_value; }\n const ::e_float& representation() const { return m_value; }\n const ::e_float& crepresentation() const { return m_value; }\n\n std::string str(std::streamsize number_of_digits, const std::ios::fmtflags format_flags) const\n {\n std::string my_result_str;\n std::stringstream my_stream_str;\n\n my_stream_str.flags(format_flags);\n\n static_cast(my_stream_str.precision(number_of_digits));\n\n m_value.wr_string(my_result_str, my_stream_str);\n\n return my_result_str;\n }\n\n void negate()\n {\n m_value.negate();\n }\n\n int compare(const e_float& other_mp_cpp_backend) const\n {\n return static_cast(m_value.compare(other_mp_cpp_backend.m_value));\n }\n\n template::value == true>::type const* = nullptr>\n int compare(ArithmeticType x)\n {\n return static_cast(m_value.compare(::e_float(x)));\n }\n\n private:\n ::e_float m_value;\n\n e_float& operator=(const ::e_float&) = delete;\n };\n\n inline void eval_add(e_float& result, const e_float& x)\n {\n result.representation() += x.crepresentation();\n }\n\n inline void eval_subtract(e_float& result, const e_float& x)\n {\n result.representation() -= x.crepresentation();\n }\n\n inline void eval_multiply(e_float& result, const e_float& x)\n {\n result.representation() *= x.crepresentation();\n }\n\n inline void eval_multiply(e_float& result, const e_float& x, const e_float& y)\n {\n result.representation() = x.crepresentation() * y.crepresentation();\n }\n\n template::value == true)\n && (std::is_signed ::value == true))>::type const* = nullptr>\n void eval_multiply(e_float& result, const SignedIntegralType& n)\n {\n result.representation().mul_signed_long_long(static_cast(n));\n }\n\n inline void eval_divide(e_float& result, const e_float& x)\n {\n result.representation() /= x.crepresentation();\n }\n\n template::value == true)\n && (std::is_signed ::value == true))>::type const* = nullptr>\n void eval_divide(e_float& result, const SignedIntegralType& n)\n {\n result.representation().div_signed_long_long(static_cast(n));\n }\n\n inline bool eval_eq(const e_float& a, const e_float& b)\n {\n return (a.compare(b) == 0);\n }\n\n template::value == true>::type const* = nullptr>\n bool eval_eq(const e_float& a, const ArithmeticType& b)\n {\n return (a.compare(b) == 0);\n }\n\n template::value == true>::type const* = nullptr>\n bool eval_eq(const ArithmeticType& a, const e_float& b)\n {\n return (e_float(a).compare(b) == 0);\n }\n\n inline bool eval_gt(const e_float& a, const e_float& b)\n {\n return (a.compare(b) == 1);\n }\n\n template::value == true>::type const* = nullptr>\n bool eval_gt(const e_float& a, const ArithmeticType& b)\n {\n return (a.compare(b) == 1);\n }\n\n template::value>::type const* = nullptr>\n bool eval_gt(const ArithmeticType& a, const e_float& b)\n {\n return (e_float(a).compare(b) == 1);\n }\n\n inline bool eval_lt(const e_float& a, const e_float& b)\n {\n return (a.compare(b) == -1);\n }\n\n template::value == true>::type const* = nullptr>\n bool eval_lt(const e_float& a, const ArithmeticType& b)\n {\n return (a.compare(b) == -1);\n }\n\n template::value == true>::type const* = nullptr>\n bool eval_lt(const ArithmeticType& a, const e_float& b)\n {\n return (e_float(a).compare(b) == -1);\n }\n\n inline bool eval_is_zero(const e_float& x)\n {\n return x.crepresentation().iszero();\n }\n\n inline int eval_get_sign(const e_float& x)\n {\n if (x.crepresentation().iszero()) { return 0; }\n else if(x.crepresentation().isneg ()) { return -1; }\n else { return 1; }\n }\n\n inline void eval_convert_to(unsigned long long* result,\n const e_float& val)\n {\n *result = (val.crepresentation()).extract_unsigned_long_long();\n }\n\n inline void eval_convert_to(signed long long* result,\n const e_float& val)\n {\n *result = (val.crepresentation()).extract_signed_long_long();\n }\n\n inline void eval_convert_to(long double* result,\n const e_float& val)\n {\n *result = (val.crepresentation()).extract_long_double();\n }\n\n inline void eval_frexp( e_float& result,\n const e_float& x,\n typename e_float::exponent_type* expptr)\n {\n using local_exponent_type = int;\n\n local_exponent_type exp2;\n\n result.representation() = ::ef::frexp(x.crepresentation(), &exp2);\n\n *expptr = static_cast(exp2);\n }\n\n inline void eval_frexp( e_float& result,\n const e_float& x,\n int* expptr,\n typename std::enable_if::value == false>::type const* = nullptr)\n {\n result.representation() = ::ef::frexp(x.crepresentation(), expptr);\n }\n\n inline void eval_ldexp( e_float& result,\n const e_float& x,\n const typename e_float::exponent_type exp_value)\n {\n using local_exponent_type = int;\n\n result.representation() = ::ef::ldexp(x.crepresentation(), local_exponent_type(exp_value));\n }\n\n inline void eval_ldexp( e_float& result,\n const e_float& x,\n int exp_value,\n typename std::enable_if::value == false>::type const* = nullptr)\n {\n using local_exponent_type = int;\n\n result.representation() = ::ef::ldexp(x.crepresentation(), local_exponent_type(exp_value));\n }\n\n inline e_float::exponent_type eval_ilogb(const e_float& val)\n {\n if(val.crepresentation().iszero())\n {\n return (std::numeric_limits::min)();\n }\n\n if ((val.crepresentation().isinf)())\n {\n return (std::numeric_limits::max)();\n }\n\n if ((val.crepresentation().isnan)())\n {\n #if defined(FP_ILOGBNAN)\n return FP_ILOGBNAN;\n #else\n return (std::numeric_limits::max)();\n #endif\n }\n\n // Set the result to base-10 exponent (order) of the value.\n return val.crepresentation().order();\n }\n\n template::value == true)\n && (std::is_integral ::value == true))>::type const* = nullptr>\n void eval_scalbn(e_float& result, const e_float& val, IntegralType e)\n {\n const std::int64_t my_e = static_cast(e);\n\n const e_float t(::e_float(1.0, my_e));\n\n eval_multiply(result, val, t);\n }\n\n inline void eval_floor( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::floor(x.crepresentation());\n }\n\n inline void eval_ceil( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::ceil(x.crepresentation());\n }\n\n inline int eval_fpclassify(const e_float& x)\n {\n if ((x.crepresentation().isinf)()) { return FP_INFINITE; }\n else if((x.crepresentation().isnan)()) { return FP_NAN; }\n else if( x.crepresentation().iszero()) { return FP_ZERO; }\n else { return FP_NORMAL; }\n }\n\n inline void eval_trunc( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::integer_part(x.crepresentation());\n }\n\n inline void eval_abs( e_float& result,\n const e_float& x)\n {\n result.representation() = x.crepresentation();\n\n if(result.crepresentation().isneg())\n {\n result.representation().negate();\n }\n }\n\n inline void eval_fabs( e_float& result,\n const e_float& x)\n {\n result.representation() = x.crepresentation();\n\n if(result.crepresentation().isneg())\n {\n result.representation().negate();\n }\n }\n\n inline void eval_sqrt( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::sqrt(x.crepresentation());\n }\n\n inline void eval_sin( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::sin(x.crepresentation());\n }\n\n inline void eval_cos( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::cos(x.crepresentation());\n }\n\n inline void eval_tan( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::tan(x.crepresentation());\n }\n\n inline void eval_asin( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::asin(x.crepresentation());\n }\n\n inline void eval_acos( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::acos(x.crepresentation());\n }\n\n inline void eval_atan( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::atan(x.crepresentation());\n }\n\n inline void eval_atan2( e_float& result,\n const e_float& y,\n const e_float& x)\n {\n result.representation() = ::ef::atan2(y.crepresentation(), x.crepresentation());\n }\n\n inline void eval_log( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::log(x.crepresentation());\n }\n\n inline void eval_log10( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::log10(x.crepresentation());\n }\n\n inline void eval_exp( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::exp(x.crepresentation());\n }\n\n inline void eval_sinh( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::sinh(x.crepresentation());\n }\n\n inline void eval_cosh( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::cosh(x.crepresentation());\n }\n\n inline void eval_tanh( e_float& result,\n const e_float& x)\n {\n result.representation() = ::ef::tanh(x.crepresentation());\n }\n\n inline void eval_fmod( e_float& result,\n const e_float& x,\n const e_float& y)\n {\n if(y.crepresentation().iszero())\n {\n result.representation() = ::ef::zero();\n }\n else\n {\n // Calculate the fractional part of x such that:\n // x = (integer_part * y) + fractional_part,\n // where\n // |fractional_part| < |y|,\n // and fractional_part has the same sign as x.\n\n const ::e_float integer_part = ::ef::floor(x.crepresentation() / y.crepresentation());\n\n result.representation() =\n x.crepresentation() - (integer_part * y.crepresentation());\n\n if(x.crepresentation().isneg() != y.crepresentation().isneg())\n {\n result.representation() -= y.crepresentation();\n }\n }\n }\n\n inline void eval_pow( e_float& result,\n const e_float& x,\n const e_float& a)\n {\n result.representation() = ::ef::pow(x.crepresentation(), a.crepresentation());\n }\n\n } } } // namespace boost::math::ef\n\n namespace boost { namespace math { namespace policies {\n\n // Specialization of the precision structure.\n template\n struct precision,\n ThisPolicy>\n {\n using precision_type = typename ThisPolicy::precision_type;\n\n using local_digits_2 = digits2<((::e_float::ef_digits10 + 1LL) * 1000LL) / 301LL>;\n\n #if (BOOST_VERSION <= 107500)\n using type = typename mpl::if_c <((local_digits_2::value <= precision_type::value) || (precision_type::value <= 0)),\n local_digits_2,\n precision_type>::type;\n #else\n using type = typename std::conditional<((local_digits_2::value <= precision_type::value) || (precision_type::value <= 0)),\n local_digits_2,\n precision_type>::type;\n #endif\n };\n\n } } } // namespaces boost::math::policies\n\n namespace std\n {\n template\n class numeric_limits>\n {\n public:\n static constexpr bool is_specialized = true;\n static constexpr bool is_signed = true;\n static constexpr bool is_integer = false;\n static constexpr bool is_exact = false;\n static constexpr bool is_bounded = true;\n static constexpr bool is_modulo = false;\n static constexpr bool is_iec559 = false;\n static constexpr int digits = ::e_float::ef_digits10;\n static constexpr int digits10 = ::e_float::ef_digits10;\n static constexpr int max_digits10 = ::e_float::ef_digits10 + 1;\n\n static constexpr std::int64_t max_exponent = ::e_float::ef_max_exp;\n static constexpr std::int64_t max_exponent10 = ::e_float::ef_max_exp10;\n static constexpr std::int64_t min_exponent = ::e_float::ef_min_exp;\n static constexpr std::int64_t min_exponent10 = ::e_float::ef_min_exp10;\n\n static constexpr int radix = 10;\n static constexpr std::float_round_style round_style = std::round_indeterminate;\n static constexpr bool has_infinity = true;\n static constexpr bool has_quiet_NaN = true;\n static constexpr bool has_signaling_NaN = false;\n static constexpr std::float_denorm_style has_denorm = std::denorm_absent;\n static constexpr bool has_denorm_loss = false;\n static constexpr bool traps = false;\n static constexpr bool tinyness_before = false;\n\n static constexpr boost::multiprecision::number (min) () { return boost::math::ef::e_float(ef::value_min()); }\n static constexpr boost::multiprecision::number (max) () { return boost::math::ef::e_float(ef::value_max()); }\n static constexpr boost::multiprecision::number lowest () { return boost::math::ef::e_float(ef::zero()); }\n static constexpr boost::multiprecision::number epsilon () { return boost::math::ef::e_float(ef::value_eps()); }\n static constexpr boost::multiprecision::number round_error () { return boost::math::ef::e_float(ef::half()); }\n static constexpr boost::multiprecision::number infinity () { return boost::math::ef::e_float(ef::value_inf()); }\n static constexpr boost::multiprecision::number quiet_NaN () { return boost::math::ef::e_float(ef::value_nan()); }\n static constexpr boost::multiprecision::number signaling_NaN() { return boost::math::ef::e_float(ef::zero()); }\n static constexpr boost::multiprecision::number denorm_min () { return boost::math::ef::e_float(ef::zero()); }\n };\n } // namespace std\n\n#endif // E_FLOAT_2017_08_18_HPP_\n", "meta": {"hexsha": "4810f0ba832ec70780fadb85384df46db773d39d", "size": 21711, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/math/include/boost/math/bindings/e_float.hpp", "max_stars_repo_name": "ckormanyos/e_float-2021", "max_stars_repo_head_hexsha": "fac3eef3aa15cc5b74fb19135d6474396cbc6fa8", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.43014734858584286, "lm_q1q2_score": 0.21675390318912957}} {"text": "// Boost.Geometry\n\n// Copyright (c) 2007-2012 Barend Gehrels, Amsterdam, the Netherlands.\n\n// This file was modified by Oracle on 2014-2021.\n// Modifications copyright (c) 2014-2021, Oracle and/or its affiliates.\n// Contributed and/or modified by Vissarion Fysikopoulos, on behalf of Oracle\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_GEOMETRY_ALGORITHMS_AZIMUTH_HPP\n#define BOOST_GEOMETRY_ALGORITHMS_AZIMUTH_HPP\n\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \n\n\nnamespace boost { namespace geometry\n{\n\n\n#ifndef DOXYGEN_NO_DETAIL\nnamespace detail\n{\n \n} // namespace detail\n#endif // DOXYGEN_NO_DETAIL\n\n\n#ifndef DOXYGEN_NO_DISPATCH\nnamespace dispatch\n{\n\ntemplate\n<\n typename Geometry1, typename Geometry2,\n typename Tag1 = typename tag::type,\n typename Tag2 = typename tag::type\n>\nstruct azimuth : not_implemented\n{};\n\ntemplate \nstruct azimuth\n{\n template \n static auto apply(Point1 const& p1, Point2 const& p2, Strategy const& strategy)\n {\n typedef typename decltype(strategy.azimuth())::template result_type\n <\n typename coordinate_type::type,\n typename coordinate_type::type\n >::type calc_t;\n\n calc_t result = 0;\n calc_t const x1 = geometry::get_as_radian<0>(p1);\n calc_t const y1 = geometry::get_as_radian<1>(p1);\n calc_t const x2 = geometry::get_as_radian<0>(p2);\n calc_t const y2 = geometry::get_as_radian<1>(p2);\n\n strategy.azimuth().apply(x1, y1, x2, y2, result);\n\n // NOTE: It is not clear which units we should use for the result.\n // For now radians are always returned but a user could expect\n // e.g. something like this:\n /*\n bool const both_degree = std::is_same\n <\n typename detail::cs_angular_units::type,\n geometry::degree\n >::value\n && std::is_same\n <\n typename detail::cs_angular_units::type,\n geometry::degree\n >::value;\n if (both_degree)\n {\n result *= math::r2d();\n }\n */\n\n return result;\n }\n};\n\n} // namespace dispatch\n#endif // DOXYGEN_NO_DISPATCH\n\n\nnamespace resolve_strategy\n{\n\ntemplate\n<\n typename Strategy,\n bool IsUmbrella = strategies::detail::is_umbrella_strategy::value\n>\nstruct azimuth\n{\n template \n static auto apply(P1 const& p1, P2 const& p2, Strategy const& strategy)\n {\n return dispatch::azimuth::apply(p1, p2, strategy);\n }\n};\n\ntemplate \nstruct azimuth\n{\n template \n static auto apply(P1 const& p1, P2 const& p2, Strategy const& strategy)\n {\n using strategies::azimuth::services::strategy_converter;\n return dispatch::azimuth\n <\n P1, P2\n >::apply(p1, p2, strategy_converter::get(strategy));\n }\n};\n\ntemplate <>\nstruct azimuth\n{\n template \n static auto apply(P1 const& p1, P2 const& p2, default_strategy)\n {\n typedef typename strategies::azimuth::services::default_strategy\n <\n P1, P2\n >::type strategy_type;\n\n return dispatch::azimuth::apply(p1, p2, strategy_type());\n }\n};\n\n\n} // namespace resolve_strategy\n\n\nnamespace resolve_variant\n{\n} // namespace resolve_variant\n\n\n/*!\n\\brief Calculate azimuth of a segment defined by a pair of points.\n\\ingroup azimuth\n\\tparam Point1 Type of the first point of a segment.\n\\tparam Point2 Type of the second point of a segment.\n\\param point1 First point of a segment.\n\\param point2 Second point of a segment.\n\\return Azimuth in radians.\n\n\\qbk{[include reference/algorithms/azimuth.qbk]}\n\n\\qbk{\n[heading Example]\n[azimuth]\n[azimuth_output]\n}\n*/\ntemplate \ninline auto azimuth(Point1 const& point1, Point2 const& point2)\n{\n concepts::check();\n concepts::check();\n \n return resolve_strategy::azimuth\n <\n default_strategy\n >::apply(point1, point2, default_strategy());\n}\n\n\n/*!\n\\brief Calculate azimuth of a segment defined by a pair of points.\n\\ingroup azimuth\n\\tparam Point1 Type of the first point of a segment.\n\\tparam Point2 Type of the second point of a segment.\n\\tparam Strategy Type of an umbrella strategy defining azimuth strategy.\n\\param point1 First point of a segment.\n\\param point2 Second point of a segment.\n\\param strategy Umbrella strategy defining azimuth strategy.\n\\return Azimuth in radians.\n\n\\qbk{distinguish,with strategy}\n\\qbk{[include reference/algorithms/azimuth.qbk]}\n\n\\qbk{\n[heading Example]\n[azimuth_strategy]\n[azimuth_strategy_output]\n}\n*/\ntemplate \ninline auto azimuth(Point1 const& point1, Point2 const& point2, Strategy const& strategy)\n{\n concepts::check();\n concepts::check();\n\n return resolve_strategy::azimuth::apply(point1, point2, strategy);\n}\n\n\n}} // namespace boost::geometry\n\n\n#endif // BOOST_GEOMETRY_ALGORITHMS_AZIMUTH_HPP\n", "meta": {"hexsha": "28e5491e7a4ce423a201640e987d8383e460954b", "size": 6125, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/geometry/algorithms/azimuth.hpp", "max_stars_repo_name": "angryDuck2/PopcornTorrent", "max_stars_repo_head_hexsha": "63bed793cbd59117fc296f887ed91b937a85ce77", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 106.0, "max_stars_repo_stars_event_min_datetime": "2015-08-07T04:23:50.000Z", "max_stars_repo_stars_event_max_datetime": "2020-12-27T18:25:15.000Z", "max_issues_repo_path": 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YES\n2. NO", "lm_q1_score": 0.6477982179521103, "lm_q2_score": 0.3345894545235253, "lm_q1q2_score": 0.21674645238590834}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\n// This file is manually converted from PROJ4 (projects.h)\n\n// Copyright (c) 2008-2012 Barend Gehrels, Amsterdam, the Netherlands.\n\n// This file was modified by Oracle on 2017.\n// Modifications copyright (c) 2017, Oracle and/or its affiliates.\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Geometry Library by Barend Gehrels (Geodan, Amsterdam)\n\n// Original copyright notice:\n\n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#ifndef BOOST_GEOMETRY_PROJECTIONS_IMPL_PROJECTS_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_IMPL_PROJECTS_HPP\n\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\n\nnamespace boost { namespace geometry { namespace projections\n{\n\n#ifndef DOXYGEN_NO_DETAIL\nnamespace detail\n{\n\n/* some useful constants */\ntemplate \ninline T ONEPI() { return boost::math::constants::pi(); }\ntemplate \ninline T HALFPI() { return boost::math::constants::half_pi(); }\ntemplate \ninline T FORTPI() { return boost::math::constants::pi() / T(4); }\ntemplate \ninline T TWOPI() { return boost::math::constants::two_pi(); }\ntemplate \ninline T TWO_D_PI() { return boost::math::constants::two_div_pi(); }\ntemplate \ninline T HALFPI_SQR() { return 2.4674011002723396547086227499689; }\ntemplate \ninline T PI_SQR() { return boost::math::constants::pi_sqr(); }\ntemplate \ninline T THIRD() { return 0.3333333333333333333333333333333; }\ntemplate \ninline T TWOTHIRD() { return 0.6666666666666666666666666666666; }\ntemplate \ninline T PI_HALFPI() { return 4.7123889803846898576939650749193; }\ntemplate \ninline T TWOPI_HALFPI() { return 7.8539816339744830961566084581988; }\ntemplate \ninline T PI_DIV_3() { return 1.0471975511965977461542144610932; }\n\n/* datum_type values */\nstatic const int PJD_UNKNOWN = 0;\nstatic const int PJD_3PARAM = 1;\nstatic const int PJD_7PARAM = 2;\nstatic const int PJD_GRIDSHIFT = 3;\nstatic const int PJD_WGS84 = 4; /* WGS84 (or anything considered equivelent) */\n\n/* library errors */\nstatic const int PJD_ERR_GEOCENTRIC = -45;\nstatic const int PJD_ERR_AXIS = -47;\nstatic const int PJD_ERR_GRID_AREA = -48;\nstatic const int PJD_ERR_CATALOG = -49;\n\ntemplate \nstruct pvalue\n{\n std::string param;\n int used;\n\n int i;\n T f;\n std::string s;\n};\n\ntemplate \nstruct pj_const_pod\n{\n int over; /* over-range flag */\n int geoc; /* geocentric latitude flag */\n int is_latlong; /* proj=latlong ... not really a projection at all */\n int is_geocent; /* proj=geocent ... not really a projection at all */\n T\n a, /* major axis or radius if es==0 */\n a_orig, /* major axis before any +proj related adjustment */\n es, /* e ^ 2 */\n es_orig, /* es before any +proj related adjustment */\n e, /* eccentricity */\n ra, /* 1/A */\n one_es, /* 1 - e^2 */\n rone_es, /* 1/one_es */\n lam0, phi0, /* central longitude, latitude */\n x0, y0, /* easting and northing */\n k0, /* general scaling factor */\n to_meter, fr_meter, /* cartesian scaling */\n vto_meter, vfr_meter; /* Vertical scaling. Internal unit [m] */\n\n int datum_type; /* PJD_UNKNOWN/3PARAM/7PARAM/GRIDSHIFT/WGS84 */\n T datum_params[7];\n T from_greenwich; /* prime meridian offset (in radians) */\n T long_wrap_center; /* 0.0 for -180 to 180, actually in radians*/\n bool is_long_wrap_set;\n\n // Initialize all variables to zero\n pj_const_pod()\n {\n std::memset(this, 0, sizeof(pj_const_pod));\n }\n};\n\ntemplate \nstruct pj_const_non_pod\n{\n int over; /* over-range flag */\n int geoc; /* geocentric latitude flag */\n int is_latlong; /* proj=latlong ... not really a projection at all */\n int is_geocent; /* proj=geocent ... not really a projection at all */\n T\n a, /* major axis or radius if es==0 */\n a_orig, /* major axis before any +proj related adjustment */\n es, /* e ^ 2 */\n es_orig, /* es before any +proj related adjustment */\n e, /* eccentricity */\n ra, /* 1/A */\n one_es, /* 1 - e^2 */\n rone_es, /* 1/one_es */\n lam0, phi0, /* central longitude, latitude */\n x0, y0, /* easting and northing */\n k0, /* general scaling factor */\n to_meter, fr_meter, /* cartesian scaling */\n vto_meter, vfr_meter; /* Vertical scaling. Internal unit [m] */\n\n int datum_type; /* PJD_UNKNOWN/3PARAM/7PARAM/GRIDSHIFT/WGS84 */\n T datum_params[7];\n T from_greenwich; /* prime meridian offset (in radians) */\n T long_wrap_center; /* 0.0 for -180 to 180, actually in radians*/\n bool is_long_wrap_set;\n\n // Initialize all variables to zero\n pj_const_non_pod()\n : over(0), geoc(0), is_latlong(0), is_geocent(0)\n , a(0), a_orig(0), es(0), es_orig(0), e(0), ra(0)\n , one_es(0), rone_es(0), lam0(0), phi0(0), x0(0), y0(0), k0(0)\n , to_meter(0), fr_meter(0), vto_meter(0), vfr_meter(0)\n , datum_type(PJD_UNKNOWN)\n , from_greenwich(0), long_wrap_center(0), is_long_wrap_set(false)\n {\n datum_params[0] = 0;\n datum_params[1] = 0;\n datum_params[2] = 0;\n datum_params[3] = 0;\n datum_params[4] = 0;\n datum_params[5] = 0;\n datum_params[6] = 0;\n }\n};\n\ntemplate \nstruct pj_const\n : boost::mpl::if_c\n <\n boost::is_pod::value,\n pj_const_pod,\n pj_const_non_pod\n >::type\n{};\n\n// PROJ4 complex. Might be replaced with std::complex\ntemplate \nstruct COMPLEX { T r, i; };\n\nstruct PJ_ELLPS\n{\n std::string id; /* ellipse keyword name */\n std::string major; /* a= value */\n std::string ell; /* elliptical parameter */\n std::string name; /* comments */\n};\n\nstruct PJ_DATUMS\n{\n std::string id; /* datum keyword */\n std::string defn; /* ie. \"to_wgs84=...\" */\n std::string ellipse_id; /* ie from ellipse table */\n std::string comments; /* EPSG code, etc */\n};\n\nstruct PJ_PRIME_MERIDIANS\n{\n std::string id; /* prime meridian keyword */\n std::string defn; /* offset from greenwich in DMS format. */\n};\n\nstruct PJ_UNITS\n{\n std::string id; /* units keyword */\n std::string to_meter; /* multiply by value to get meters */\n std::string name; /* comments */\n};\n\ntemplate \nstruct DERIVS\n{\n T x_l, x_p; /* derivatives of x for lambda-phi */\n T y_l, y_p; /* derivatives of y for lambda-phi */\n};\n\ntemplate \nstruct FACTORS\n{\n DERIVS der;\n T h, k; /* meridinal, parallel scales */\n T omega, thetap; /* angular distortion, theta prime */\n T conv; /* convergence */\n T s; /* areal scale factor */\n T a, b; /* max-min scale error */\n int code; /* info as to analytics, see following */\n};\n\n} // namespace detail\n#endif // DOXYGEN_NO_DETAIL\n\n/*!\n \\brief parameters, projection parameters\n \\details This structure initializes all projections\n \\ingroup projection\n*/\ntemplate \nstruct parameters : public detail::pj_const\n{\n typedef T type;\n\n std::string name;\n std::vector > params;\n};\n\n}}} // namespace boost::geometry::projections\n#endif // BOOST_GEOMETRY_PROJECTIONS_IMPL_PROJECTS_HPP\n", "meta": {"hexsha": "232ae67ae953d9be741fa5416645f30702315d78", "size": 9017, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost_1_67_0/boost/geometry/srs/projections/impl/projects.hpp", "max_stars_repo_name": "ramcn/gemmx", "max_stars_repo_head_hexsha": "e23ab5358322a293110b642962b478bc92580636", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 354.0, "max_stars_repo_stars_event_min_datetime": "2018-08-13T18:19:21.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-20T10:37:20.000Z", "max_issues_repo_path": "boost_1_67_0/boost/geometry/srs/projections/impl/projects.hpp", "max_issues_repo_name": "ramcn/gemmx", "max_issues_repo_head_hexsha": "e23ab5358322a293110b642962b478bc92580636", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 79.0, "max_issues_repo_issues_event_min_datetime": "2018-08-01T11:50:45.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-17T13:40:06.000Z", "max_forks_repo_path": "boost_1_67_0/boost/geometry/srs/projections/impl/projects.hpp", "max_forks_repo_name": "ramcn/gemmx", "max_forks_repo_head_hexsha": "e23ab5358322a293110b642962b478bc92580636", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 90.0, "max_forks_repo_forks_event_min_datetime": "2018-11-15T12:37:51.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-14T11:12:39.000Z", "avg_line_length": 33.0293040293, "max_line_length": 81, "alphanum_fraction": 0.6641898636, "num_tokens": 2431, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.3522017684487511, "lm_q1q2_score": 0.21663503046410854}} {"text": "#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\nchar *outFile;\r\n\r\nchar* combine(char *a,char *b)\r\n{\r\n char *c=(char*)malloc(strlen(a) + strlen(b) + 1);\r\n strcpy(c,a);\r\n strcat(c,b);\r\n return c;\r\n}\r\n\r\n#define FOR(i,n) for(i=0;i Run the Low Density Separation algorithm as described in\r\n --> \"Semi-supervised classification by Low Density Separation\" by O. Chapelle and A. Zien\r\n\t --> Xl: d0 x m0 matrix with the labeled points\r\n\t --> Xu: d1 x m1 matrix with the unlabeled points\r\n\t --> Yl: column vector of length n containing the labels (+1 or -1 for binary)\r\n\t --> rho: constant \r\n\t --> opt: optional structure containing the (optional) fields,\r\n\t -->\t C: the soft margin parameter relative to 1/var^2\r\n\t --> [default = 1]\r\n\t --> nofNn: number of NN in the graph construction \r\n\t --> [default = 0, i.e. fully connected graph]\r\n\t --> sigma: the width of the RBF kernel\r\n\t --> delta: threshold\r\n\t --> nofIter: number of iterations for C* to reach C [default = 10]\r\n\t --> Cinit: initial value for C* relative to C [default = 0.01] \r\n\t --> Cfinal: final value of C* relative to C [default = 1]\r\n\t --> maxiter: maximum number of iterations in each gradient\r\n\t --> descent (multiplied by nb of variables) [default = 3]\r\n\t --> tolfun: stopping criterion on the function value\r\n\t --> (relative to C) [default = 1e-5]\r\n */\r\n\tLL i, j, k, K;\r\n\tParam param;\r\n\tMAT D2, NN;\r\n\topt.delta = 0.1;\r\n\topt.nofIter = 10;\r\n\topt.C = 0.390;\r\n\topt.Cinit = 0.01;\r\n\topt.Cfinal = 1;\r\n\topt.nofNn = 100;\r\n\topt.sigma = INFINITY;\r\n\topt.opt_tb = 1;\r\n\topt.maxiter = 3;\r\n\topt.tolfun = exp(-5);\r\n\tif((1>opt.nofIter)|(opt.C<0)|((opt.Cinit<0)&&(opt.Cinit>opt.Cfinal))|(opt.sigma<0))error(\"Assertion error\");\r\n\tLL m = m0 + m1;\r\n\tif((opt.nofNn<0)|(opt.nofNn>m))error(\"Assertion error\");\r\n\tDD** mat = (DD**)calloc(m, sizeof(DD*));\r\n\tFOR(i, m)mat[i] = (DD*)calloc(d0, sizeof(DD));\r\n\tMAT X;\r\n\tX.matrix = mat; X.rows = m; X.columns = d0;\r\n\tFOR(i, X.rows)\r\n\t{\r\n\t\tFOR(j, X.columns)\r\n\t\t{\r\n\t\t\tif (i < m0)\r\n\t\t\t{\r\n\t\t\t\tX.matrix[i][j] = Xl[j][i];\r\n\t\t\t}\r\n\t\t\telse{\r\n\t\t\t\tX.matrix[i][j] = Xu[j][i - m0];\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\tK = classes;\r\n\t//===================================================\r\n\t// ****** Compute the full distance graph ******\r\n\t//===================================================\r\n\t\r\n\toutput t = CalcNnDists(X.matrix, X.rows, X.columns, opt.nofNn);\r\n\tD2 = t.D2; NN = t.NN;\r\n\tparam.pathNorm = rho;\r\n\tparam.sigma = INFINITY;\r\n\tparam.nofNn = opt.nofNn;\r\n\tDD* idx = (DD*)calloc(m, sizeof(DD));\r\n\tFOR(i, m)\r\n\t{\r\n\t\tidx[i] = i + 1;\r\n\t}\r\n\tMAT E2 = graphDistKernelC(X, D2, NN, param, idx, m);\r\n FOR(i,X.rows)free(X.matrix[i]);free(X.matrix); \r\n FOR(i,opt.nofNn){free(D2.matrix[i]);free(NN.matrix[i]);}free(D2.matrix);free(NN.matrix);\r\n D2.rows=0;D2.columns=0;NN.rows=0;NN.columns=0; \r\n\tif (opt.nofNn != 0)\r\n\t{\r\n\t\tDD** new_E2 = (DD**)calloc(E2.rows, sizeof(DD*));\r\n\t\tDD max1 = E2.matrix[0][0];\r\n\t\tFOR(i, E2.rows)\r\n\t\t{\r\n\t\t\tnew_E2[i] = (DD*)calloc(E2.columns, sizeof(DD));\r\n\t\t\tFOR(j, E2.columns)\r\n\t\t\t{\r\n\t\t\t\tnew_E2[i][j] = min(E2.matrix[i][j], E2.matrix[j][i]);\r\n\t\t\t\tif ((new_E2[i][j] > max1) && (!isinf(new_E2[i][j])))\r\n\t\t\t\t{\r\n\t\t\t\t\tmax1 = new_E2[i][j];\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t\tFOR(i, E2.rows)free(E2.matrix[i]);free(E2.matrix);\r\n\t\tE2.matrix = new_E2;\r\n\t\tFOR(i, E2.rows)\r\n\t\t{\r\n\t\t\tFOR(j, E2.columns)\r\n\t\t\t{\r\n\t\t\t\tif (isinf(E2.matrix[i][j]))E2.matrix[i][j] = 2 * max1;\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\tDD* E2_array = (DD*)calloc(E2.rows*E2.columns, sizeof(DD));\r\n\tbool isinf_E2 = false;\r\n\tFOR(i, E2.columns)\r\n\t{\r\n\t\tFOR(j, E2.rows)\r\n\t\t{\r\n\t\t\tif (isinf(E2.matrix[j][i]))isinf_E2 = true;\r\n\t\t\tE2_array[i*E2.rows + j] = E2.matrix[j][i];\r\n\t\t}\r\n\t}\r\n\t//===========================================================================\r\n\t// ********* Compute the new kernel, do the MDS reduction **********\r\n\t//===========================================================================\r\n\t\r\n\tdouble defaultSigma = calcDefaultSigma(E2_array, 2, E2.columns*E2.rows);\r\n\tdouble sigma = opt.sigma * defaultSigma;\r\n\tOutput_mds mds;\r\n\tif (isinf(sigma))\r\n\t{\r\n\t\tif (isinf_E2)\r\n\t\t{\r\n\t\t\terror(\"Cannot do the MDS: The graph is not connected\");\r\n\t\t}\r\n\t\tmds = MDS(E2.matrix, E2.rows, E2.columns, opt.delta);\r\n\t}\r\n\telse\r\n\t{\r\n\t\tparam.rbf = 1;// set RBF name equal to Gauss\r\n\t\t//=================================================\r\n\t\t// ****** Calculate RBF Kernel *******\r\n\t\t//=================================================\r\n\t\tdouble **K = calcRbfKernel(E2.matrix, E2.rows, E2.columns, param.rbf, sigma, 1);\r\n\t\tFOR(i, E2.rows)\r\n\t\t{\r\n\t\t\tFOR(j, E2.columns)\r\n\t\t\t{\r\n\t\t\t\tK[i][j] = 2 - 2 * K[i][j];\r\n\t\t\t}\r\n\t\t}\r\n\t\tmds = MDS(K, E2.rows, E2.columns, opt.delta);\r\n\t}\r\n\tdouble **cumsum = 0, sum = 0;\r\n\tcumsum = (DD**)calloc(E2.rows, sizeof(DD*));\r\n\tFOR(i, E2.rows){\r\n\t\tcumsum[i] = (DD*)calloc(1, sizeof(DD));\r\n\t\tif (i == 0)cumsum[i][0] = mds.eigen[i];\r\n\t\telse cumsum[i][0] = mds.eigen[i] + cumsum[i - 1][0];\r\n\t}\r\n\tsum = cumsum[E2.rows - 1][0];\r\n\tLL nb_comp;\r\n\tif (opt.delta >= 0)\r\n\t{\r\n\t\tFOR(i, E2.rows)\r\n\t\t{\r\n\t\t\tif ((1 - cumsum[i][0] < opt.delta)&(mds.eigen[i] < opt.delta*mds.eigen[0]))\r\n\t\t\t{\r\n\t\t\t\tnb_comp = i;\r\n\t\t\t\tbreak;\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\telse\r\n\t{\r\n\t\tnb_comp = E2.rows;\r\n\t}\r\n\tdouble **Xnldr = (DD**)calloc(mds.Y.rows, sizeof(DD*));\r\n\tdouble *mean_Xnldr = (DD*)calloc(nb_comp, sizeof(DD));\r\n\tFOR(i, mds.Y.rows)\r\n\t{\r\n\t\tXnldr[i] = (DD*)calloc(nb_comp, sizeof(DD));\r\n\t\t// Keep only the first components\r\n\t\tFOR(j, nb_comp){\r\n\t\t\tXnldr[i][j] = mds.Y.matrix[i][j];\r\n\t\t\tmean_Xnldr[j] += mds.Y.matrix[i][j];\r\n\t\t\tif (i == mds.Y.rows - 1){\r\n\t\t\t\tmean_Xnldr[j] = (mean_Xnldr[j])/ mds.Y.rows;\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\tmds.Y.columns = nb_comp;\r\n\tmds.Y.matrix = Xnldr;\r\n\tdouble sum_var = 0;\r\n\tFOR(i, nb_comp)\r\n\t{\r\n\t\tdouble var = 0;\r\n\t\tFOR(j, mds.Y.rows)\r\n\t\t{\r\n\t\t\tvar += pow(Xnldr[j][i] - mean_Xnldr[i], 2);\r\n\t\t}\r\n\t\tsum_var += var / (mds.Y.rows - 1);\r\n\t}\r\n\t// Default value of C = invert of the variance\r\n\tdouble defaultC = 1 / sum_var;\r\n\tdouble C = opt.C*defaultC; opt.Cinit = C*opt.Cinit; opt.Cfinal = C*opt.Cfinal; opt.C = C;\r\n\topt.s = 3;\r\n\t//============================================\r\n\t// ******* Train the TSVM ********\r\n\t//============================================\r\n\tdouble **Yu = train_one_split(mds.Y, Yl,Yl_rows,m0, opt, classes);\r\n\treturn Yu;\r\n}\r\ndouble ** train_one_split(MAT Xnldr, double *Yl,long Yl_rows,long m0, Opt opt,long classes){\r\n\tMAT Y0,w0;\r\n\tw0.matrix = NULL;\r\n\tY0.matrix = (DD**)calloc(Xnldr.rows, sizeof(DD*)); Y0.rows = Xnldr.rows; Y0.columns = 1;\r\n\tlong i, j;\r\n\tif (classes == 2){\r\n\t\tif (Yl_rows > m0)\r\n\t\t{\r\n\t\t\topt.Cinit = opt.Cfinal; opt.nofIter = 1;\r\n\t\t}\r\n\t\telse{\r\n\t\t\tFOR(i, Y0.rows)\r\n\t\t\t{\r\n\t\t\t\tY0.matrix[i] = (DD*)calloc(1, sizeof(DD));\r\n\t\t\t\tif (i < Yl_rows)Y0.matrix[i][0] = Yl[i];\r\n\t\t\t}\r\n\t\t\tOutput_Primal pr = primal_tsvm(Xnldr, Y0, w0, opt);\r\n\t\t\tint count=0;\r\n FOR(i,pr.Yu.rows)\r\n\t\t\t{\r\n\t\t\t\tdouble boo=(pr.Yu.matrix[i][0]>0)?1:-1;\r\n if(boo==1){\r\n printf(\"%ld Kareena \\n\",i);\r\n }\r\n else{\r\n printf(\"%ld Amir \\n\",i);\r\n } \r\n }\r\n printf(\"\\n total %d\\n\\n\",count);\r\n return pr.Yu.matrix;\r\n }\r\n\t}\r\n\telse{\r\n\t\tMAT new_Y;\r\n\t\tDD final_obj=0;\r\n\t\tOutput_Primal pr;\r\n\t\tnew_Y.rows = Xnldr.rows - Yl_rows; new_Y.columns = classes;\r\n\t\tnew_Y.matrix = (DD**)calloc(new_Y.rows, sizeof(DD*));\r\n\t\tFOR(i, new_Y.rows)new_Y.matrix[i] = (DD*)calloc(new_Y.columns, sizeof(DD));\r\n\t\tFOR(i, Y0.rows)\r\n\t\t{\r\n\t\t\tY0.matrix[i] = (DD*)calloc(1, sizeof(DD));\r\n\t\t}\r\n\t\tFOR(i, classes)\r\n\t\t{\r\n\t\t\tFOR(j, Yl_rows)\r\n\t\t\t{\r\n\t\t\t\tif (Yl[j] == i+1)\r\n\t\t\t\t\tY0.matrix[j][0] = 1;\r\n\t\t\t\telse Y0.matrix[j][0] = -1;\r\n\t\t\t}\r\n\t\t\tpr = primal_tsvm(Xnldr, Y0, w0, opt);\r\n\t\t\tFOR(j, new_Y.rows)new_Y.matrix[j][i] = pr.Yu.matrix[j][0];\r\n\t\t\tfinal_obj += pr.obj;\r\n\t\t}\r\n int count=0;\r\n FOR(i,new_Y.rows){\r\n LL index=0;\r\n FOR(j,new_Y.columns)\r\n {\r\n if(new_Y.matrix[i][j]>new_Y.matrix[i][index])index=j;\r\n }\r\n new_Y.matrix[i][0]=index+1;\r\n } \r\n FILE *fp;\r\n char *new_string;\r\n new_string=combine(outFile,\"/../results.txt\");\r\n fp=fopen(new_string,\"w\");\r\n FOR(i,new_Y.rows)\r\n\t\t{ \r\n\t\t\tfprintf(fp,\"%lf\\n\",new_Y.matrix[i][0]);\r\n }\r\n fclose(fp);\r\n\t}\r\n\t\t\treturn NULL;\r\n}\r\nvoid SVD(double*** return_mat,MAT inp)\r\n{\t\r\n // Calculate Singular Value Decomposition using Armadillo library\t \r\n long i ,j;\r\n fmat A=randu(inp.columns,inp.rows);\r\n double **my_mat=(double**)calloc(inp.columns,sizeof(double*));\r\n for(i=0;i 0)\r\n\t{\r\n\t\tfor (i = 0; i < Y.rows; i++)\r\n\t\t{\r\n\t\t\tif (Y.matrix[i][0] != 0)\r\n\t\t\t{\r\n\t\t\t\tvbal.matrix[0][i] = 0;\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\tvbal.matrix = Multiply_Matrices(vbal.matrix, new_X.matrix, vbal.rows, new_X.columns, vbal.columns); vbal.columns = new_X.columns;\r\n\tj = 0;\r\n\tfor (i = 0; i < Y.rows; i++)\r\n\t{\r\n\t\tif (Y.matrix[i][0] != 0)\r\n\t\t{\r\n\t\t\tj += 1;\r\n\t\t\tcbal += Y.matrix[i][0];\r\n\t\t}\r\n\t}\r\n\tfor (i = 0; i < vbal.columns; i++)sum_vbal += vbal.matrix[0][i];\r\n\tcbal = (cbal / j)*sum_vbal;\r\n R.rows = vbal.columns; R.columns = vbal.columns;\r\n\tSVD(&R.matrix,vbal);\t\r\n\tDD** newR = (double**)calloc(R.rows, sizeof(double*));\r\n\tfor (i = 0; i < R.rows; i++)\r\n\t{\r\n\t\tnewR[i] = (double*)calloc(1, sizeof(double));\r\n\t\tnewR[i][0] = R.matrix[i][0];\r\n\t}\r\n\tMAT w;\r\n\tw.matrix = Multiply_Matrices(vbal.matrix, newR, 1, 1, R.rows);\r\n\tDD w1 = cbal / w.matrix[0][0];\r\n\tif (w0.matrix == NULL)\r\n\t{\r\n\t\tw.matrix = (double**)calloc(n - 1, sizeof(double*));\r\n\t\tfor (i = 0; i < n - 1; i++)\r\n\t\t{\r\n\t\t\tw.matrix[i] = (double*)calloc(1, sizeof(double));\r\n\t\t\tfor (j = 0; j < 1; j++)w.matrix[i][j] = 0;\r\n\t\t}\r\n\t\tw.rows = n - 1; w.columns = 1;\r\n\t}\r\n\telse\r\n\t{\r\n\t\tDD** new_newR = (double**)calloc(R.rows, sizeof(double*));\r\n\t\tfor (i = 0; i < R.rows; i++)\r\n\t\t{\r\n\t\t\tnew_newR[i] = (double*)calloc(R.columns - 1, sizeof(double));\r\n\t\t\tfor (j = 0; j < R.columns - 1; j++)\r\n\t\t\t{\r\n\t\t\t\tnew_newR[i][j] = R.matrix[i][j + 1];\r\n\t\t\t}\r\n\t\t}\r\n\t\tw.matrix = Multiply_Matrices(Transpose(new_newR, R.rows, R.columns - 1), w0.matrix, R.columns - 1, w0.rows, R.rows);\r\n\t\tw.rows = R.columns - 1; w.columns = w0.rows;\r\n\t}\r\n\tmaxiter2 = opt.maxiter*new_X.rows;\r\n\ttolfun2 = opt.tolfun*C;\r\n\tOpt2 opt2;\r\n\topt2.length = maxiter2;\r\n\topt2.tolX = 0;\r\n\topt2.tolFun = tolfun2;\r\n\tDD* y_temp = (DD*)calloc(Y.rows, sizeof(DD));\r\n\tFOR(i, Y.rows)\r\n\t{\r\n\t\ty_temp[i] = Y.matrix[i][0];\r\n\t}\r\n\tbool FLAG;\r\n\tOutput_Obj oo;\r\n\tFOR(i, opt.nofIter)\r\n\t{\r\n\t\tC2 = C2*exponent;\r\n\t\tOutput_mini tt = minimize(w, w1, y_temp, Y.rows, C, C2, opt.s, new_X, Xu, R, opt2);\r\n\t\too = obj_fun(w, w1, y_temp, Y.rows, C, C2, opt.s,new_X,Xu,R);\r\n\t\tFLAG = (tt.FX < opt2.length) ? 1 : 0;\r\n\t}\r\n\tDD **Temp=(DD**)calloc(w.rows+1,sizeof(DD*));\r\n\tFOR(i, w.rows + 1)\r\n\t{\r\n\t\tTemp[i] = (DD*)calloc(1, sizeof(DD));\r\n\t\tif (i !=0)Temp[i][0] = w.matrix[i-1][0];\r\n\t\telse Temp[i][0] = w1;\r\n\t}\r\n\tw.matrix = Multiply_Matrices(R.matrix, Temp, R.rows, 1, w.rows + 1); w.rows = R.rows; w.columns = 1;\r\n\tMAT Yu;\r\n\tYu.matrix = Multiply_Matrices(Transpose(Xu.matrix, Xu.rows, Xu.columns), w.matrix, Xu.columns, w.columns, w.rows); Yu.rows = Xu.columns; Yu.columns = w.columns;\r\n\tOutput_Primal pr;\r\n\tpr.obj = oo.obj;\r\n\tpr.b = w.matrix[w.rows - 1][0];\r\n\tpr.Yu = Yu;\r\n\tfree(w.matrix[w.rows - 1]);\r\n\tw.rows = w.rows - 1;\r\n\tpr.w = w;\r\n\treturn pr;\r\n}\r\n\r\n//==============================\r\n// ****** MAIN *******\r\n//==============================\r\n\r\nint main(int argc,char * argv[]){\r\n long rows=atoi(argv[3]),columns=atoi(argv[2]),columns2=atoi(argv[1])-columns,i,j;\t\r\n FILE *fp;\r\n char *new_string;\r\n new_string=combine(argv[4],\"/../data/final_file_train.txt\");\r\n fp=fopen(new_string,\"r\");\r\n DD** k=(DD**)calloc(rows,sizeof(DD*));\r\n FOR(i,rows)\r\n {\r\n\tk[i]=(DD*)calloc(columns,sizeof(DD));\t\r\n }\r\n FOR(j,columns)\r\n {\r\n\t FOR(i,rows)\r\n\t {\r\n\t fscanf(fp,\"%lf \",&k[i][j]);\r\n\t }\r\n }\r\n fclose(fp);\r\n new_string=combine(argv[4],\"/../data/final_file_test.txt\");\r\n fp=fopen(new_string,\"r\");\r\n DD** k2=(DD**)calloc(rows,sizeof(DD*));\r\n FOR(i,rows)\r\n {\r\n\tk2[i]=(DD*)calloc(columns2,sizeof(DD));\t\r\n }\r\n FOR(j,columns2)\r\n {\r\n\t FOR(i,rows)\r\n\t {\r\n\t fscanf(fp,\"%lf \",&k2[i][j]);\r\n\t }\r\n } \r\n fclose(fp);\r\n DD* Yl=(DD*)calloc(columns,sizeof(DD*));\r\n FOR(i,columns)\r\n {\r\n\t if(i\n\n#include \n\n\nnamespace openstudio{\n /// convert degrees to radians\n double degToRad(double degrees)\n {\n return degrees*boost::math::constants::pi()/180.0;\n }\n\n /// convert radians to degrees\n double radToDeg(double radians)\n {\n return radians*180.0/boost::math::constants::pi();\n }\n\n /// compute area from surface as Point3dVector\n boost::optional getArea(const Point3dVector& points)\n {\n boost::optional result;\n OptionalVector3d newall = getNewallVector(points);\n if (newall){\n result = newall->length() / 2.0;\n }\n return result;\n }\n\n // compute Newall vector from Point3dVector, direction is same as outward normal\n // magnitude is twice the area\n OptionalVector3d getNewallVector(const Point3dVector& points)\n {\n OptionalVector3d result;\n size_t N = points.size();\n if (N >= 3){\n Vector3d vec;\n for (unsigned i = 1; i < N-1; ++i){\n Vector3d v1 = points[i] - points[0];\n Vector3d v2 = points[i+1] - points[0];\n vec += v1.cross(v2);\n }\n result = vec;\n }\n return result;\n }\n\n // compute outward normal from Point3dVector\n OptionalVector3d getOutwardNormal(const Point3dVector& points)\n {\n OptionalVector3d result = getNewallVector(points);\n if (result){\n if (!result->normalize()){\n result.reset();\n }\n }\n return result;\n }\n\n /// compute centroid from surface as Point3dVector\n OptionalPoint3d getCentroid(const Point3dVector& points)\n {\n OptionalPoint3d result;\n\n if (points.size() >= 3){\n // convert to face coordinates\n Transformation alignFace = Transformation::alignFace(points);\n Point3dVector surfacePoints = alignFace.inverse()*points;\n\n size_t N = surfacePoints.size();\n double A = 0;\n double cx = 0;\n double cy = 0;\n for (size_t i = 0; i < N; ++i){\n double x1, x2, y1, y2;\n if (i == N-1){\n x1 = surfacePoints[i].x();\n x2 = surfacePoints[0].x();\n y1 = surfacePoints[i].y();\n y2 = surfacePoints[0].y();\n }else{\n x1 = surfacePoints[i].x();\n x2 = surfacePoints[i+1].x();\n y1 = surfacePoints[i].y();\n y2 = surfacePoints[i+1].y();\n }\n\n double dA = (x1*y2-x2*y1);\n A += 0.5*dA;\n cx += (x1+x2)*dA;\n cy += (y1+y2)*dA;\n }\n\n if (A > 0){\n // centroid in face coordinates\n Point3d surfaceCentroid(cx/(6.0*A), cy/(6.0*A), 0.0);\n\n // centroid\n result = alignFace*surfaceCentroid;\n }\n }\n return result;\n }\n\n /// reorder points to upper-left-corner convention\n Point3dVector reorderULC(const Point3dVector& points)\n {\n size_t N = points.size();\n if (N < 3){\n return Point3dVector();\n }\n\n // transformation to align face\n Transformation t = Transformation::alignFace(points);\n Point3dVector facePoints = t.inverse()*points;\n\n // find ulc index in face coordinates\n double maxY = std::numeric_limits::min();\n double minX = std::numeric_limits::max();\n unsigned ulcIndex = 0;\n for(unsigned i = 0; i < N; ++i){\n OS_ASSERT(std::abs(facePoints[i].z()) < 0.001);\n if ((maxY < facePoints[i].y()) || ((maxY < facePoints[i].y() + 0.00001) && (minX > facePoints[i].x()))){\n ulcIndex = i;\n maxY = facePoints[i].y();\n minX = facePoints[i].x();\n }\n }\n\n // no-op\n if (ulcIndex == 0){\n return points;\n }\n\n // create result\n Point3dVector result;\n std::copy (points.begin() + ulcIndex, points.end(), std::back_inserter(result));\n std::copy (points.begin(), points.begin() + ulcIndex, std::back_inserter(result));\n OS_ASSERT(result.size() == N);\n return result;\n }\n\n std::vector removeCollinear(const Point3dVector& points, double tol)\n {\n Transformation t = Transformation::alignFace(points);\n std::vector result = t*simplify(t.inverse()*points, true, tol);\n return result;\n }\n\n\n std::vector removeCollinearLegacy(const Point3dVector& points, double tol)\n {\n size_t N = points.size();\n if (N < 3){\n return points;\n }\n\n std::vector result;\n Point3d lastPoint = points[0];\n result.push_back(lastPoint);\n\n for (unsigned i = 1; i < N; ++i){\n Point3d currentPoint = points[i];\n Point3d nextPoint = points[0];\n if (i < N-1){\n nextPoint = points[i+1];\n }\n\n Vector3d a = (currentPoint - lastPoint);\n Vector3d b = (nextPoint - currentPoint);\n\n // if these fail to normalize we have zero length vectors (e.g. adjacent points)\n if (a.normalize()){\n if (b.normalize()){\n\n Vector3d c = a.cross(b);\n if (c.length() >= tol){\n // cross product is significant\n result.push_back(currentPoint);\n lastPoint = currentPoint;\n }else{\n // see if dot product is near -1\n double d = a.dot(b);\n if (d <= -1.0 + tol){\n // this is a line reversal\n result.push_back(currentPoint);\n lastPoint = currentPoint;\n }\n }\n }\n }\n }\n\n size_t iBegin = 0;\n size_t iEnd = result.size();\n\n bool resizeBegin = true;\n while (resizeBegin){\n resizeBegin = false;\n unsigned N = iEnd - iBegin;\n if (N > 3){\n Vector3d a = (result[iBegin] - result[iEnd - 1]);\n Vector3d b = (result[iBegin + 1] - result[iBegin]);\n if (a.normalize()){\n if (b.normalize()){\n double d = a.dot(b);\n if (d >= 1.0 - tol){\n iBegin++;\n resizeBegin = true;\n }\n } else{\n iBegin++;\n resizeBegin = true;\n }\n } else{\n iBegin++;\n resizeBegin = true;\n }\n }\n }\n\n bool resizeEnd = true;\n while (resizeEnd){\n resizeEnd = false;\n unsigned N = iEnd - iBegin;\n if (N > 3){\n Vector3d a = (result[iEnd - 1] - result[iEnd - 2]);\n Vector3d b = (result[iBegin] - result[iEnd - 1]);\n if (a.normalize()){\n if (b.normalize()){\n double d = a.dot(b);\n if (d >= 1.0 - tol){\n iEnd--;\n resizeEnd = true;\n }\n } else{\n iEnd--;\n resizeEnd = true;\n }\n } else{\n iEnd--;\n resizeEnd = true;\n }\n }\n }\n\n result = std::vector(result.begin() + iBegin, result.begin() + iEnd);\n return result;\n }\n\n double getDistance(const Point3d& point1, const Point3d& point2) {\n double dx = point1.x() - point2.x();\n double dy = point1.y() - point2.y();\n double dz = point1.z() - point2.z();\n double result = std::sqrt(dx*dx + dy*dy + dz*dz);\n return result;\n }\n\n double getDistancePointToLineSegment(const Point3d& point, const std::vector& lineSegment)\n {\n if (lineSegment.size() != 2){\n return 0;\n }\n\n // http://paulbourke.net/geometry/pointlineplane/\n\n Point3d point1 = lineSegment[0];\n Point3d point2 = lineSegment[1];\n\n Vector3d p2p1 = point2-point1;\n Vector3d p3p1 = point-point1;\n\n double d12 = p2p1.length();\n if (d12 < 1.0e-12){\n return p3p1.length();\n }\n\n Point3d closestPoint;\n double u = p3p1.dot(p2p1) / (d12*d12);\n if (u < 0){\n closestPoint = point1;\n }else if (u > 1){\n closestPoint = point2;\n }else{\n closestPoint = point1 + u*p2p1;\n }\n\n Vector3d diff = point - closestPoint;\n\n return diff.length();\n }\n\n double getDistancePointToTriangle(const Point3d& point, const std::vector& triangle)\n {\n if (triangle.size() != 3){\n return 0;\n }\n\n //Distance Between Point and Triangle in 3D\n //David Eberly\n //Geometric Tools, LLC\n //http://www.geometrictools.com/\n\n //T(s; t) = B+sE0+tE1\n\n Point3d B = triangle[0];\n Vector3d E0 = triangle[1] - triangle[0];\n Vector3d E1 = triangle[2] - triangle[0];\n Vector3d BminusP = B - point;\n\n double b = E0.dot(E1);\n\n if (std::abs(b) > 1.0-1.0E-12){\n // triangle is collinear\n return 0;\n }\n\n double a = E0.dot(E0);\n double c = E1.dot(E1);\n double d = E0.dot(BminusP);\n double e = E1.dot(BminusP);\n // double f = BminusP.dot(BminusP); // unused\n\n double det = a*c-b*b;\n double s = b*e-c*d;\n double t = b*d-a*e;\n\n Point3d closestPoint;\n\n if ( s+t <= det ) {\n if ( s < 0 ) {\n if ( t < 0 ) {\n //region 4, closest to point triangle[0]\n return getDistance(point, triangle[0]);\n } else {\n //region 3, closest to line triangle[0] to triangle[2]\n std::vector line;\n line.push_back(triangle[0]);\n line.push_back(triangle[2]);\n return getDistancePointToLineSegment(point, line);\n }\n } else if ( t < 0 ) {\n //region 5, closest to line triangle[0] to triangle[1]\n std::vector line;\n line.push_back(triangle[0]);\n line.push_back(triangle[1]);\n return getDistancePointToLineSegment(point, line);\n } else {\n //region 0, closest point is inside triangle\n double invDet = 1.0/det;\n closestPoint = B + invDet*s*E0 + invDet*t*E1;\n }\n } else {\n if ( s < 0 ) {\n //region 2, closest to point triangle[2]\n return getDistance(point, triangle[2]);\n } else if ( t < 0 ) {\n //region 6, closest to point triangle[1]\n return getDistance(point, triangle[1]);\n } else {\n //region 1, closest to line triangle[1] to triangle[2]\n std::vector line;\n line.push_back(triangle[1]);\n line.push_back(triangle[2]);\n return getDistancePointToLineSegment(point, line);\n }\n }\n\n Vector3d diff = point-closestPoint;\n return diff.length();\n }\n\n double getAngle(const Vector3d& vector1, const Vector3d& vector2) {\n Vector3d working1(vector1);\n working1.normalize();\n Vector3d working2(vector2);\n working2.normalize();\n return acos(working1.dot(working2));\n }\n\n bool circularEqual(const Point3dVector& points1, const Point3dVector& points2, double tol)\n {\n size_t N = points1.size();\n if (N != points2.size()){\n return false;\n }\n\n if (N == 0){\n return true;\n }\n\n bool result = false;\n\n // look for a common starting point\n for (size_t i = 0; i < N; ++i){\n if (getDistance(points1[0], points2[i]) <= tol){\n\n result = true;\n\n // check all other points\n for (unsigned j = 0; j < N; ++j){\n if (getDistance(points1[j], points2[(i + j) % N]) > tol){\n result = false;\n break;\n }\n }\n }\n\n if (result){\n return result;\n }\n }\n\n return result;\n }\n\n Point3d getCombinedPoint(const Point3d& point3d, std::vector& allPoints, double tol)\n {\n for (const Point3d& otherPoint : allPoints){\n if (std::sqrt(std::pow(point3d.x()-otherPoint.x(), 2) + std::pow(point3d.y()-otherPoint.y(), 2) + std::pow(point3d.z()-otherPoint.z(), 2)) < tol){\n return otherPoint;\n }\n }\n allPoints.push_back(point3d);\n return point3d;\n }\n\n std::vector > computeTriangulation(const Point3dVector& vertices, const std::vector >& holes, double tol)\n {\n std::vector > result;\n\n // check input\n if (vertices.size () < 3){\n return result;\n }\n\n boost::optional normal = getOutwardNormal(vertices);\n if (!normal || normal->z() > -0.999){\n return result;\n }\n\n for (const auto& hole : holes){\n normal = getOutwardNormal(hole);\n if (!normal || normal->z() > -0.999){\n return result;\n }\n }\n\n std::vector allPoints;\n\n // PolyPartition does not support holes which intersect the polygon or share an edge\n // if any hole is not fully contained we will use boost to remove all the holes\n bool polyPartitionHoles = true;\n for (const std::vector& hole : holes){\n if (!within(hole, vertices, tol)){\n // PolyPartition can't handle this\n polyPartitionHoles = false;\n break;\n }\n }\n\n if (!polyPartitionHoles){\n // use boost to do all the intersections\n std::vector > allFaces = subtract(vertices, holes, tol);\n std::vector > noHoles;\n for (const std::vector& face : allFaces){\n std::vector > temp = computeTriangulation(face, noHoles);\n result.insert(result.end(), temp.begin(), temp.end());\n }\n return result;\n }\n\n // convert input to vector of TPPLPoly\n std::list polys;\n\n TPPLPoly outerPoly; // must be counter-clockwise, input vertices are clockwise\n outerPoly.Init(vertices.size());\n outerPoly.SetHole(false);\n size_t n = vertices.size();\n for(size_t i = 0; i < n; ++i){\n\n // should all have zero z coordinate now\n double z = vertices[n-i-1].z();\n if (abs(z) > tol){\n LOG_FREE(Error, \"utilities.geometry.computeTriangulation\", \"All points must be on z = 0 plane for triangulation methods\");\n return result;\n }\n\n Point3d point = getCombinedPoint(vertices[n-i-1], allPoints, tol);\n outerPoly[i].x = point.x();\n outerPoly[i].y = point.y();\n }\n outerPoly.SetOrientation(TPPL_CCW);\n polys.push_back(outerPoly);\n\n\n for (const std::vector& holeVertices : holes){\n\n if (holeVertices.size () < 3){\n LOG_FREE(Error, \"utilities.geometry.computeTriangulation\", \"Hole has fewer than 3 points, ignoring\");\n continue;\n }\n\n TPPLPoly innerPoly; // must be clockwise, input vertices are clockwise\n innerPoly.Init(holeVertices.size());\n innerPoly.SetHole(true);\n //std::cout << \"inner :\";\n for(unsigned i = 0; i < holeVertices.size(); ++i){\n\n // should all have zero z coordinate now\n double z = holeVertices[i].z();\n if (abs(z) > tol){\n LOG_FREE(Error, \"utilities.geometry.computeTriangulation\", \"All points must be on z = 0 plane for triangulation methods\");\n return result;\n }\n\n Point3d point = getCombinedPoint(holeVertices[i], allPoints, tol);\n innerPoly[i].x = point.x();\n innerPoly[i].y = point.y();\n }\n innerPoly.SetOrientation(TPPL_CW);\n polys.push_back(innerPoly);\n }\n\n // do partitioning\n TPPLPartition pp;\n std::list resultPolys;\n int test = pp.Triangulate_EC(&polys,&resultPolys);\n if (test == 0){\n test = pp.Triangulate_MONO(&polys, &resultPolys);\n }\n if (test == 0){\n //std::stringstream ss;\n //ss << \"Vertices: \" << vertices << std::endl;\n //for (const auto& hole : holes){ ss << \"Hole:\" << hole << std::endl; }\n //std::string testStr = ss.str();\n LOG_FREE(Error, \"utilities.geometry.computeTriangulation\", \"Failed to partition polygon\");\n return result;\n }\n\n // convert back to vertices\n std::list::iterator it, itend;\n //std::cout << \"Start\" << std::endl;\n for(it = resultPolys.begin(), itend = resultPolys.end(); it != itend; ++it){\n\n it->SetOrientation(TPPL_CW);\n\n std::vector triangle;\n for (long i = 0; i < it->GetNumPoints(); ++i){\n TPPLPoint point = it->GetPoint(i);\n triangle.push_back(Point3d(point.x, point.y, 0));\n }\n //std::cout << triangle << std::endl;\n result.push_back(triangle);\n }\n //std::cout << \"End\" << std::endl;\n\n return result;\n }\n\n std::vector moveVerticesTowardsPoint(const Point3dVector& vertices, const Point3d& point, double distance)\n {\n Point3dVector result;\n for (const Point3d& vertex : vertices){\n Vector3d vector = point-vertex;\n vector.setLength(distance);\n result.push_back(vertex+vector);\n }\n return result;\n }\n\n std::vector reverse(const Point3dVector& vertices)\n {\n std::vector result(vertices);\n std::reverse(result.begin(), result.end());\n return result;\n }\n\n bool applyViewAndDaylightingGlassRatios(double viewGlassToWallRatio, double daylightingGlassToWallRatio,\n double desiredViewGlassSillHeight, double desiredDaylightingGlassHeaderHeight,\n double exteriorShadingProjectionFactor, double interiorShelfProjectionFactor,\n const Point3dVector& vertices, Point3dVector& viewVertices,\n Point3dVector& daylightingVertices, Point3dVector& exteriorShadingVertices,\n Point3dVector& interiorShelfVertices)\n {\n // check inputs for reasonableness\n double totalWWR = viewGlassToWallRatio + daylightingGlassToWallRatio;\n if (totalWWR == 0){\n // requesting no glass? remove existing windows?\n return false;\n }else if (totalWWR < 0.0 || totalWWR >= 1.0){\n return false;\n }\n\n boost::optional grossArea = getArea(vertices);\n if (!grossArea){\n return false;\n }\n\n Transformation transformation = Transformation::alignFace(vertices);\n Point3dVector faceVertices = transformation.inverse() * vertices;\n\n if (faceVertices.empty()){\n return false;\n }\n\n bool doViewGlass = (viewGlassToWallRatio > 0);\n bool doDaylightGlass = (daylightingGlassToWallRatio > 0);\n bool doExteriorShading = (doViewGlass && (exteriorShadingProjectionFactor > 0));\n bool doInteriorShelf = (doDaylightGlass && (interiorShelfProjectionFactor > 0));\n bool doViewAndDaylightGlass = (doViewGlass && doDaylightGlass);\n\n // ignore these user arguments?\n if (!doViewGlass){\n desiredViewGlassSillHeight = 0.0;\n }\n if (!doDaylightGlass){\n desiredDaylightingGlassHeaderHeight = 0.0;\n }\n\n // new coordinate system has z' in direction of outward normal, y' is up\n double xmin = std::numeric_limits::max();\n double xmax = std::numeric_limits::min();\n double ymin = std::numeric_limits::max();\n double ymax = std::numeric_limits::min();\n for (const Point3d& faceVertex : faceVertices){\n xmin = std::min(xmin, faceVertex.x());\n xmax = std::max(xmax, faceVertex.x());\n ymin = std::min(ymin, faceVertex.y());\n ymax = std::max(ymax, faceVertex.y());\n }\n if ((xmin > xmax) || (ymin > ymax)){\n return false;\n }\n\n double oneInch = 0.0254;\n\n // DLM: preserve a 1\" gap between window and edge to keep SketchUp happy\n double minGlassToEdgeDistance = oneInch;\n double minViewToDaylightDistance = 0;\n if (doViewAndDaylightGlass){\n minViewToDaylightDistance = oneInch;\n }\n\n // wall parameters\n double wallWidth = xmax - xmin;\n double wallHeight = ymax - ymin;\n double wallArea = wallWidth*wallHeight;\n\n if (wallWidth < 2*minGlassToEdgeDistance){\n return false;\n }\n\n if (wallHeight < 2*minGlassToEdgeDistance + minViewToDaylightDistance){\n return false;\n }\n\n // check against actual surface area to ensure this is a rectangle?\n if (std::abs(wallArea - grossArea.get()) > oneInch*oneInch){\n return false;\n }\n\n double maxWindowArea = wallArea - 2*wallHeight*minGlassToEdgeDistance - (wallWidth-2*minGlassToEdgeDistance)*(2*minGlassToEdgeDistance + minViewToDaylightDistance);\n double requestedViewArea = viewGlassToWallRatio*wallArea;\n double requestedDaylightingArea = daylightingGlassToWallRatio*wallArea;\n double requestedTotalWindowArea = totalWWR*wallArea;\n\n if (requestedTotalWindowArea > maxWindowArea){\n return false;\n }\n\n // view glass parameters\n double viewMinX = 0;\n double viewMinY = 0;\n double viewWidth = 0;\n double viewHeight = 0;\n\n // daylighting glass parameters\n double daylightingWidth = 0;\n double daylightingHeight = 0;\n double daylightingMinX = 0;\n double daylightingMinY = 0;\n\n // initial free parameters\n double viewWidthInset = minGlassToEdgeDistance;\n double viewSillHeight = std::max(desiredViewGlassSillHeight, minGlassToEdgeDistance);\n double daylightingWidthInset = minGlassToEdgeDistance;\n double daylightingHeaderHeight = std::max(desiredDaylightingGlassHeaderHeight, minGlassToEdgeDistance);\n\n bool converged = false;\n for (unsigned i = 0; i < 100; ++i){\n\n // view glass parameters\n viewMinX = viewWidthInset;\n viewMinY = viewSillHeight;\n viewWidth = wallWidth - 2*viewWidthInset;\n viewHeight = requestedViewArea/viewWidth;\n\n // daylighting glass parameters\n daylightingWidth = wallWidth - 2*daylightingWidthInset;\n daylightingHeight = requestedDaylightingArea/daylightingWidth;\n daylightingMinX = viewWidthInset;\n daylightingMinY = wallHeight - daylightingHeaderHeight - daylightingHeight;\n\n if (viewMinY + viewHeight + minViewToDaylightDistance > daylightingMinY){\n // windows overlap or exceed maximum size\n\n if (doViewAndDaylightGlass){\n\n // try shrinking vertical offsets\n viewSillHeight = std::max(viewSillHeight - oneInch, minGlassToEdgeDistance);\n daylightingHeaderHeight = std::max(daylightingHeaderHeight - oneInch, minGlassToEdgeDistance);\n\n }else if (doViewGlass){\n\n // solve directly\n viewSillHeight = wallHeight - minGlassToEdgeDistance - viewHeight;\n\n if (viewSillHeight < minGlassToEdgeDistance){\n // cannot make window this large\n return false;\n }\n\n }else if (doDaylightGlass){\n\n // solve directly\n daylightingHeaderHeight = wallHeight - minGlassToEdgeDistance - daylightingHeight;\n\n if (daylightingHeaderHeight < minGlassToEdgeDistance){\n // cannot make window this large\n return false;\n }\n\n }\n\n }else{\n\n converged = true;\n break;\n\n }\n }\n\n if (!converged){\n return false;\n }\n\n Point3dVector surfacePolygon;\n for (const Point3d& point : faceVertices){\n if (std::abs(point.z()) > 0.001){\n LOG_FREE(Warn, \"utilities.geometry.applyViewAndDaylightingGlassRatios\", \"Surface point z not on plane, z =\" << point.z());\n }\n surfacePolygon.push_back(Point3d(point.x(),point.y(), 0.0));\n }\n std::reverse(surfacePolygon.begin(), surfacePolygon.end());\n\n if (doViewGlass){\n viewVertices.push_back(Point3d(viewMinX, viewMinY + viewHeight, 0));\n viewVertices.push_back(Point3d(viewMinX, viewMinY, 0));\n viewVertices.push_back(Point3d(viewMinX + viewWidth, viewMinY, 0));\n viewVertices.push_back(Point3d(viewMinX + viewWidth, viewMinY + viewHeight, 0));\n\n Point3dVector windowPolygon;\n for (const Point3d& point : viewVertices){\n if (std::abs(point.z()) > 0.001){\n LOG_FREE(Warn, \"utilities.geometry.applyViewAndDaylightingGlassRatios\", \"Surface point z not on plane, z =\" << point.z());\n }\n windowPolygon.push_back(Point3d(point.x(),point.y(), 0.0));\n }\n\n // sub surface must be fully contained by base surface\n for (const Point3d& point : windowPolygon){\n if (!within(point, surfacePolygon, 0.001)){\n std::cout << \"point: \" << point << std::endl;\n std::cout << \"surfacePolygon: \" << surfacePolygon << std::endl;\n LOG_FREE(Debug, \"utilities.geometry.applyViewAndDaylightingGlassRatios\", \"Surface does not fully contain SubSurface\");\n return false;\n }\n }\n }\n\n if (doDaylightGlass){\n daylightingVertices.push_back(Point3d(daylightingMinX, daylightingMinY + daylightingHeight, 0));\n daylightingVertices.push_back(Point3d(daylightingMinX, daylightingMinY, 0));\n daylightingVertices.push_back(Point3d(daylightingMinX + daylightingWidth, daylightingMinY, 0));\n daylightingVertices.push_back(Point3d(daylightingMinX + daylightingWidth, daylightingMinY + daylightingHeight, 0));\n\n Point3dVector windowPolygon;\n for (const Point3d& point : daylightingVertices){\n if (std::abs(point.z()) > 0.001){\n LOG_FREE(Warn, \"utilities.geometry.applyViewAndDaylightingGlassRatios\", \"Surface point z not on plane, z =\" << point.z());\n }\n windowPolygon.push_back(Point3d(point.x(),point.y(), 0.0));\n }\n\n // sub surface must be fully contained by base surface\n for (const Point3d& point : windowPolygon){\n if (!within(point, surfacePolygon, 0.001)){\n LOG_FREE(Debug, \"utilities.geometry.applyViewAndDaylightingGlassRatios\", \"Surface does not fully contain SubSurface\");\n return false;\n }\n }\n }\n\n if (doExteriorShading) {\n exteriorShadingVertices.push_back(Point3d(viewMinX, viewMinY + viewHeight, 0));\n exteriorShadingVertices.push_back(Point3d(viewMinX, viewMinY + viewHeight, exteriorShadingProjectionFactor*viewHeight));\n exteriorShadingVertices.push_back(Point3d(viewMinX + viewWidth, viewMinY + viewHeight, exteriorShadingProjectionFactor*viewHeight));\n exteriorShadingVertices.push_back(Point3d(viewMinX + viewWidth, viewMinY + viewHeight, 0));\n }\n\n if (doInteriorShelf) {\n interiorShelfVertices.push_back(Point3d(daylightingMinX + daylightingWidth, daylightingMinY, 0));\n interiorShelfVertices.push_back(Point3d(daylightingMinX + daylightingWidth, daylightingMinY, -interiorShelfProjectionFactor*daylightingHeight));\n interiorShelfVertices.push_back(Point3d(daylightingMinX, daylightingMinY, -interiorShelfProjectionFactor*daylightingHeight));\n interiorShelfVertices.push_back(Point3d(daylightingMinX, daylightingMinY, 0));\n }\n\n // put all vertices back into input coordinate system\n viewVertices = transformation*viewVertices;\n daylightingVertices = transformation*daylightingVertices;\n exteriorShadingVertices = transformation*exteriorShadingVertices;\n interiorShelfVertices = transformation*interiorShelfVertices;\n\n return true;\n }\n\n} // openstudio\n", "meta": {"hexsha": "b4ceb68d0e9e304a0c6a06333fb0902594ad904e", "size": 28411, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "openstudiocore/src/utilities/geometry/Geometry.cpp", "max_stars_repo_name": "hellok-coder/OS-Testing", "max_stars_repo_head_hexsha": "e9e18ad9e99f709a3f992601ed8d2e0662175af4", "max_stars_repo_licenses": ["blessing"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-11-12T02:07:03.000Z", "max_stars_repo_stars_event_max_datetime": "2019-11-12T02:07:03.000Z", "max_issues_repo_path": "openstudiocore/src/utilities/geometry/Geometry.cpp", "max_issues_repo_name": "hellok-coder/OS-Testing", "max_issues_repo_head_hexsha": 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NO", "lm_q1_score": 0.546738151984614, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.21654557728860435}} {"text": "#include \"BlocksEngine/pch.h\"\n#include \"BlocksEngine/Core/Math/Vector3.h\"\n\n#include \n\nusing namespace BlocksEngine;\nusing namespace DirectX;\n\n//------------------------------------------------------------------------------\n// Constructors\n//------------------------------------------------------------------------------\n\ntemplate \nVector3::Vector3() noexcept\n : Vector3Base::Base{0, 0, 0}\n{\n}\n\ntemplate \nconstexpr Vector3::Vector3(const T a) noexcept\n : Vector3Base::Base{a, a, a}\n{\n}\n\n/*template \nconstexpr Vector3::Vector3(const T x, const T y, const T z) noexcept\n : Vector3Base::Base{x, y, z}\n{\n}*/\n\ntemplate \nVector3::Vector3(const physx::PxVec3& vec3) noexcept\n : Vector3Base::Base{\n static_cast(vec3.x),\n static_cast(vec3.y),\n static_cast(vec3.z)\n }\n{\n}\n\ntemplate \nVector3::Vector3(const physx::PxExtendedVec3& vec3) noexcept\n : Vector3Base::Base{\n static_cast(vec3.x),\n static_cast(vec3.y),\n static_cast(vec3.z)\n }\n{\n}\n\ntemplate \nVector3::Vector3(_In_reads_(3) const T* pArray) noexcept\n : Vector3Base::Base{pArray}\n{\n}\n\ntemplate \nVector3::Vector3(FXMVECTOR v) noexcept // NOLINT(cppcoreguidelines-pro-type-member-init)\n{\n Store(this, v);\n}\n\ntemplate \nVector3::Vector3(const typename Vector3Base::Base& v) noexcept\n : Vector3Base::Base{v.x, v.y, v.z}\n{\n}\n\ntemplate <>\nVector3::Vector3(const Vector3Base::Vector& v) noexcept\n : Vector3Base::Base{v.f[0], v.f[1], v.f[2]}\n{\n}\n\ntemplate <>\nVector3::Vector3(const Vector3Base::Vector& v) noexcept\n : Vector3Base::Base{v.i[0], v.i[1], v.i[2]}\n{\n}\n\ntemplate <>\nVector3::Vector3(const Vector3Base::Vector& v) noexcept\n : Vector3Base::Base{v.u[0], v.u[1], v.u[2]}\n{\n}\n\n//------------------------------------------------------------------------------\n// Converter operators\n//------------------------------------------------------------------------------\n\ntemplate \nVector3::operator XMVECTOR() const noexcept\n{\n return Load(this);\n}\n\n//------------------------------------------------------------------------------\n// Comparision operators\n//------------------------------------------------------------------------------\n\ntemplate \nbool Vector3::operator==(const Vector3& v) const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR v2 = Load(&v);\n return XMVector3Equal(v1, v2);\n}\n\ntemplate \nbool Vector3::operator!=(const Vector3& v) const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR v2 = Load(&v);\n return XMVector3NotEqual(v1, v2);\n}\n\n//------------------------------------------------------------------------------\n// Assignment operators\n//------------------------------------------------------------------------------\n\ntemplate <>\nVector3& Vector3::operator=(const Vector3Base::Vector& v) noexcept\n{\n this->x = v.f[0];\n this->y = v.f[1];\n this->z = v.f[2];\n\n return *this;\n}\n\ntemplate <>\nVector3& Vector3::operator=(const Vector3Base::Vector& v) noexcept\n{\n this->x = v.i[0];\n this->y = v.i[1];\n this->z = v.i[2];\n\n return *this;\n}\n\ntemplate <>\nVector3& Vector3::operator=(const Vector3Base::Vector& v) noexcept\n{\n this->x = v.u[0];\n this->y = v.u[1];\n this->z = v.u[2];\n\n return *this;\n}\n\ntemplate \nVector3& Vector3::operator/=(const float s) noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR x = XMVectorScale(v1, 1.0f / s);\n Store(this, x);\n\n return *this;\n}\n\n//------------------------------------------------------------------------------\n// Unary operators\n//------------------------------------------------------------------------------\n\ntemplate \nVector3 Vector3::operator+() const noexcept\n{\n return *this;\n}\n\ntemplate \nVector3 Vector3::operator-() const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR x = XMVectorNegate(v1);\n\n Vector3 r;\n Store(&r, x);\n return r;\n}\n\n//------------------------------------------------------------------------------\n// Hashing\n//------------------------------------------------------------------------------\n\ntemplate \nstd::size_t hash_value(const Vector3& v)\n{\n std::size_t seed = 0;\n boost::hash_combine(seed, v.x);\n boost::hash_combine(seed, v.y);\n boost::hash_combine(seed, v.z);\n\n return seed;\n}\n\n//------------------------------------------------------------------------------\n// Vector operations\n//------------------------------------------------------------------------------\n\ntemplate \ntemplate \nbool Vector3::InBounds(const Vector3& bounds) const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR v2 = Load(&bounds);\n return XMVector3InBounds(v1, v2);\n}\n\ntemplate \nfloat Vector3::Length() const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR x = XMVector3Length(v1);\n return XMVectorGetX(x);\n}\n\ntemplate \nfloat Vector3::LengthSquared() const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR x = XMVector3LengthSq(v1);\n return XMVectorGetX(x);\n}\n\ntemplate \ntemplate \nfloat Vector3::Dot(const Vector3& v) const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR v2 = Load(&v);\n const XMVECTOR x = XMVector3Dot(v1, v2);\n return XMVectorGetX(x);\n}\n\ntemplate \ntemplate \nvoid Vector3::Cross(const Vector3& v, Vector3& result) const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR v2 = Load(&v);\n const XMVECTOR x = XMVector3Cross(v1, v2);\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::Cross(const Vector3& v) const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR v2 = Load(&v);\n const XMVECTOR x = XMVector3Cross(v1, v2);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \nvoid Vector3::Normalize() noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR x = XMVector3Normalize(v1);\n Store(this, x);\n}\n\ntemplate \ntemplate \nvoid Vector3::Normalize(Vector3& result) const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR x = XMVector3Normalize(v1);\n Store(&result, x);\n}\n\n\n//------------------------------------------------------------------------------\n// Static functions\n//------------------------------------------------------------------------------\n\ntemplate \nvoid Vector3::Abs() noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR x = XMVectorAbs(v1);\n Store(this, x);\n}\n\ntemplate \nvoid Vector3::Abs(Vector3& result) const noexcept\n{\n const XMVECTOR v1 = Load(this);\n const XMVECTOR x = XMVectorAbs(v1);\n Store(&result, x);\n}\n\ntemplate \ntemplate \nfloat Vector3::Distance(const Vector3& v1, const Vector3& v2) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR v = XMVectorSubtract(x2, x1);\n const XMVECTOR x = XMVector3Length(v);\n\n return XMVectorGetX(x);\n}\n\ntemplate \ntemplate \nfloat Vector3::DistanceSquared(const Vector3& v1, const Vector3& v2) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR v = XMVectorSubtract(x2, x1);\n const XMVECTOR x = XMVector3LengthSq(v);\n return XMVectorGetX(x);\n}\n\ntemplate \ntemplate \nvoid Vector3::Min(const Vector3& v1, const Vector3& v2, Vector3& result) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x = XMVectorMin(x1, x2);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::Min(const Vector3& v1, const Vector3& v2) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x = XMVectorMin(x2, x1);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \ntemplate \nvoid Vector3::Max(const Vector3& v1, const Vector3& v2, Vector3& result) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x = XMVectorMax(x2, x1);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::Max(const Vector3& v1, const Vector3& v2) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x = XMVectorMax(x2, x1);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \ntemplate \nvoid Vector3::Lerp(const Vector3& v1, const Vector3& v2, const float t, Vector3& result) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x = XMVectorLerp(x2, x1, t);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::Lerp(const Vector3& v1, const Vector3& v2, const float t) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x = XMVectorLerp(x2, x1, t);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \ntemplate \nvoid Vector3::SmoothStep(const Vector3& v1, const Vector3& v2, float t, Vector3& result) noexcept\n{\n t = t > 1.0f ? 1.0f : t < 0.0f ? 0.0f : t; // Clamp value between 0 and 1\n t = t * t * (3.0f - 2.0f * t);\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x = XMVectorLerp(x1, x2, t);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::SmoothStep(const Vector3& v1, const Vector3& v2, float t) noexcept\n{\n t = t > 1.0f ? 1.0f : t < 0.0f ? 0.0f : t; // Clamp value between 0 and 1\n t = t * t * (3.0f - 2.0f * t);\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x = XMVectorLerp(x1, x2, t);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \ntemplate \nvoid Vector3::Barycentric(const Vector3& v1, const Vector3& v2, const Vector3& v3, const float f,\n const float g, Vector3& result) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x3 = Load(&v3);\n const XMVECTOR x = XMVectorBaryCentric(x1, x2, x3, f, g);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::Barycentric(const Vector3& v1, const Vector3& v2, const Vector3& v3,\n const float f, const float g) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x3 = Load(&v3);\n const XMVECTOR x = XMVectorBaryCentric(x1, x2, x3, f, g);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \ntemplate \nvoid Vector3::CatmullRom(const Vector3& v1, const Vector3& v2, const Vector3& v3, const Vector3& v4,\n const float t, Vector3& result) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x3 = Load(&v3);\n const XMVECTOR x4 = Load(&v4);\n const XMVECTOR x = XMVectorCatmullRom(x1, x2, x3, x4, t);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::CatmullRom(const Vector3& v1, const Vector3& v2, const Vector3& v3,\n const Vector3& v4, const float t) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&v2);\n const XMVECTOR x3 = Load(&v3);\n const XMVECTOR x4 = Load(&v4);\n const XMVECTOR x = XMVectorCatmullRom(x1, x2, x3, x4, t);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \ntemplate \nvoid Vector3::Hermite(const Vector3& v1, const Vector3& t1, const Vector3& v2, const Vector3& t2,\n const float t, Vector3& result) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&t1);\n const XMVECTOR x3 = Load(&v2);\n const XMVECTOR x4 = Load(&t2);\n const XMVECTOR x = XMVectorHermite(x1, x2, x3, x4, t);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::Hermite(const Vector3& v1, const Vector3& t1, const Vector3& v2,\n const Vector3& t2, const float t) noexcept\n{\n const XMVECTOR x1 = Load(&v1);\n const XMVECTOR x2 = Load(&t1);\n const XMVECTOR x3 = Load(&v2);\n const XMVECTOR x4 = Load(&t2);\n const XMVECTOR x = XMVectorHermite(x1, x2, x3, x4, t);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \ntemplate \nvoid Vector3::Reflect(const Vector3& iVec, const Vector3& nVec, Vector3& result) noexcept\n{\n const XMVECTOR x1 = Load(&iVec);\n const XMVECTOR x2 = Load(&nVec);\n const XMVECTOR x = XMVector3Reflect(x1, x2);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::Reflect(const Vector3& iVec, const Vector3& nVec) noexcept\n{\n const XMVECTOR x1 = Load(&iVec);\n const XMVECTOR x2 = Load(&nVec);\n const XMVECTOR x = XMVector3Reflect(x1, x2);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \ntemplate \nvoid Vector3::Refract(const Vector3& iVec, const Vector3& nVec, const float refractionIndex,\n Vector3& result) noexcept\n{\n const XMVECTOR x1 = Load(&iVec);\n const XMVECTOR x2 = Load(&nVec);\n const XMVECTOR x = XMVector3Refract(x1, x2, refractionIndex);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::Refract(const Vector3& iVec, const Vector3& nVec,\n const float refractionIndex) noexcept\n{\n const XMVECTOR x1 = Load(&iVec);\n const XMVECTOR x2 = Load(&nVec);\n const XMVECTOR x = XMVector3Refract(x1, x2, refractionIndex);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\n\ntemplate \ntemplate \n_Use_decl_annotations_\n\nvoid Vector3::Transform(const Vector3* varray, const size_t count, const Matrix& m,\n Vector3* resultArray) noexcept\n{\n const XMMATRIX m1 = XMLoadFloat4x4(&m);\n XMVector3TransformCoordStream(resultArray, sizeof Vector3Base::Vector, varray, sizeof Vector3Base::Vector,\n count, m1);\n}\n\ntemplate \ntemplate \nvoid Vector3::TransformNormal(const Vector3& v, const Matrix& m, Vector3& result) noexcept\n{\n const XMVECTOR v1 = Load(&v);\n const XMMATRIX m1 = XMLoadFloat4x4(&m);\n const XMVECTOR x = XMVector3TransformNormal(v1, m1);\n\n Store(&result, x);\n}\n\ntemplate \ntemplate \nVector3 Vector3::TransformNormal(const Vector3& v, const Matrix& m) noexcept\n{\n const XMVECTOR v1 = Load(&v);\n const XMMATRIX m1 = XMLoadFloat4x4(&m);\n const XMVECTOR x = XMVector3TransformNormal(v1, m1);\n\n Vector3 result;\n Store(&result, x);\n return result;\n}\n\ntemplate \ntemplate \n_Use_decl_annotations_\n\nvoid Vector3::TransformNormal(const Vector3* varray, const size_t count, const Matrix& m,\n Vector3* resultArray) noexcept\n{\n const XMMATRIX m1 = XMLoadFloat4x4(&m);\n XMVector3TransformNormalStream(resultArray, sizeof Vector3Base::Vector, varray, sizeof Vector3Base::Vector,\n count, m1);\n}\n\ntemplate struct Vector3;\ntemplate struct Vector3;\ntemplate struct Vector3;\n", "meta": {"hexsha": "750929f5c58584d6c48f885cfcbeaf8f8eb76671", "size": 16380, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "BlocksEngine/src/Core/Math/Vector3.cpp", "max_stars_repo_name": "jorgeparavicini/Blocks", "max_stars_repo_head_hexsha": "dd1654d5643ca3707d39ae6ef21667e9b72130ef", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, 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NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.4263215925474903, "lm_q1q2_score": 0.21649116269402044}} {"text": "//---------------------------------------------------------------------------//\n//!\n//! \\file Utility_InterpolationPolicy_def.hpp\n//! \\author Alex Robinson\n//! \\brief Interpolation policy struct definitions\n//!\n//---------------------------------------------------------------------------//\n\n#ifndef UTILITY_INTERPOLATION_POLICY_DEF_HPP\n#define UTILITY_INTERPOLATION_POLICY_DEF_HPP\n\n// Std Lib Includes\n#include \n\n// Boost Includes\n#include \n\n// FRENSIE Includes\n#include \"Utility_QuantityTraits.hpp\"\n#include \"Utility_IsFloatingPoint.hpp\"\n#include \"Utility_DesignByContract.hpp\"\n\nnamespace Utility{\n\n// Interpolate between two processed points\ntemplate\ntemplate\nT InterpolationHelper::interpolate(\n const T processed_indep_var_0,\n const T processed_indep_var,\n const T processed_dep_var_0,\n const T processed_slope )\n{\n // T must be a floating point type\n testStaticPrecondition( (std::is_floating_point::value) );\n // Make sure the processed independent variables are valid\n testPrecondition( !Utility::QuantityTraits::isnaninf(\n\t\t\t\t\t\t processed_indep_var_0 ) );\n testPrecondition( !Utility::QuantityTraits::isnaninf(\n\t\t\t\t\t\t processed_indep_var ) );\n testPrecondition( processed_indep_var_0 <= processed_indep_var );\n // Make sure the processed dependent variable is valid\n testPrecondition( !Utility::QuantityTraits::isnaninf(\n\t\t\t\t\t\t processed_dep_var_0 ) );\n // Make sure that the slope is valid\n testPrecondition( !Utility::QuantityTraits::isnaninf( processed_slope ) );\n\n return ParentInterpolationType::recoverProcessedDepVar(\n processed_dep_var_0 +\n processed_slope*(processed_indep_var - processed_indep_var_0) );\n}\n\n// Interpolate between two processed points and return the processed value\ntemplate\ntemplate\nT InterpolationHelper::interpolateAndProcess(\n const T processed_indep_var_0,\n const T processed_indep_var,\n const T processed_dep_var_0,\n const T processed_slope )\n{\n // T must be a floating point type\n testStaticPrecondition( (std::is_floating_point::value) );\n // Make sure the processed independent variables are valid\n testPrecondition( !Utility::QuantityTraits::isnaninf(\n\t\t\t\t\t\t processed_indep_var_0 ) );\n testPrecondition( !Utility::QuantityTraits::isnaninf(\n\t\t\t\t\t\t processed_indep_var ) );\n testPrecondition( processed_indep_var_0 <= processed_indep_var );\n // Make sure the processed dependent variable is valid\n testPrecondition( !Utility::QuantityTraits::isnaninf(\n\t\t\t\t\t\t processed_dep_var_0 ) );\n // Make sure that the slope is valid\n testPrecondition( !Utility::QuantityTraits::isnaninf( processed_slope ) );\n\n return processed_dep_var_0 +\n processed_slope*(processed_indep_var - processed_indep_var_0);\n}\n\n// Calculate the unit base grid length (L)\n/*! \\details For LinLog and LogLog interpolation types the grid length\n * that is calculated is not a traditional length. It is the distance between\n * the processed upper independent value and the processed lower\n * independent value. This is why any units associated with the independent\n * grid limits are stripped away.\n */\ntemplate\ntemplate\ninline typename QuantityTraits::RawType\nInterpolationHelper::calculateUnitBaseGridLength(\n const IndepType grid_lower_indep_value,\n const IndepType grid_upper_indep_value )\n{\n // Make sure the grid is valid\n testPrecondition( grid_lower_indep_value <= grid_upper_indep_value );\n testPrecondition( ParentInterpolationType::isIndepVarInValidRange(\n grid_lower_indep_value ) );\n\n return ThisType::calculateUnitBaseGridLengthProcessed(\n ParentInterpolationType::processIndepVar( grid_lower_indep_value ),\n ParentInterpolationType::processIndepVar( grid_upper_indep_value ) );\n}\n\n// Calculate the unit base grid length from a processed grid (L)\n/*! \\details For LinLog and LogLog interpolation types the grid length\n * that is calculated is not a traditional length. It is the distance between\n * the processed upper independent value and the processed lower\n * independent value. This is why any units associated with the independent\n * grid limits are stripped away. Due to conversion of the independent\n * values from a cosine (mu) to a delta cosine ( 1 - mu ) + nudge for LogLogCos\n * and LinLogCos, it is assumed the processed grids are inverted to ensure they\n * are in ascending order.\n */\ntemplate\ntemplate\ninline T InterpolationHelper::calculateUnitBaseGridLengthProcessed(\n const T processed_grid_lower_indep_value,\n const T processed_grid_upper_indep_value )\n{\n // Make sure the grid is valid\n testPrecondition( processed_grid_lower_indep_value <=\n processed_grid_upper_indep_value );\n\n const T processed_grid_length =\n processed_grid_upper_indep_value - processed_grid_lower_indep_value;\n\n // Make sure the grid length is valid\n testPrecondition( processed_grid_length >= 0.0 );\n\n return processed_grid_length;\n}\n\n// Calculate the unit base independent variable (eta)\n/*! \\details The independent grid length is calculated using the\n * processed independent grid limits. It has been found that a tolerance of\n * 1e-3 works best for most applications.\n */\ntemplate\ntemplate\ninline typename QuantityTraits::RawType\nInterpolationHelper::calculateUnitBaseIndepVar(\n const IndepType indep_var,\n const IndepType indep_var_min,\n const typename QuantityTraits::RawType indep_grid_length,\n const double tol )\n{\n // Make sure the intermediate grid min indep var is valid\n testPrecondition( !QuantityTraits::isnaninf( indep_var_min ) );\n testPrecondition( ParentInterpolationType::isIndepVarInValidRange( indep_var_min ) );\n // Make sure the intermediate grid length is valid\n testPrecondition( !QuantityTraits::RawType>::isnaninf( indep_grid_length ) );\n testPrecondition( indep_grid_length > 0.0 );\n // Make sure the independent variable is valid\n testPrecondition( !QuantityTraits::isnaninf( indep_var ) );\n testPrecondition( ParentInterpolationType::isIndepVarInValidRange( indep_var ) );\n testPrecondition( indep_var >=\n ThisType::calculateFuzzyLowerBound( indep_var_min, tol ) );\n remember( typename QuantityTraits::RawType test_difference =\n ParentInterpolationType::processIndepVar(indep_var) -\n ParentInterpolationType::processIndepVar(indep_var_min) );\n\n testPrecondition( test_difference <= ThisType::calculateFuzzyUpperBound(\n indep_grid_length, tol ) );\n\n return calculateUnitBaseIndepVarProcessed(\n ParentInterpolationType::processIndepVar(indep_var),\n ParentInterpolationType::processIndepVar(indep_var_min),\n indep_grid_length,\n tol );\n}\n\n// Calculate the unit base independent variable (eta)\n/*! \\details It has been found that a tolerance of 1e-3 works best for most\n * applications.\n */\ntemplate\ntemplate\ninline T InterpolationHelper::calculateUnitBaseIndepVarProcessed(\n const T processed_indep_var,\n const T processed_indep_var_min,\n const T indep_grid_length,\n const double tol )\n{\n // Make sure the intermediate grid min indep var is valid\n testPrecondition( !QuantityTraits::isnaninf( processed_indep_var_min ) );\n // Make sure the independent y variable is valid\n testPrecondition( !QuantityTraits::isnaninf(processed_indep_var) );\n testPrecondition( processed_indep_var >= ThisType::calculateFuzzyLowerBound(\n processed_indep_var_min, tol ) );\n remember( T test_difference = processed_indep_var - processed_indep_var_min);\n testPrecondition( test_difference <= ThisType::calculateFuzzyUpperBound(\n indep_grid_length, tol ) );\n // Make sure the intermediate grid length is valid\n testPrecondition( !QuantityTraits::isnaninf( indep_grid_length ) );\n testPrecondition( indep_grid_length > 0.0 );\n\n T eta = (processed_indep_var - processed_indep_var_min)/indep_grid_length;\n\n // Check for rounding errors and correct\n if( eta > 1.0 )\n {\n if( eta - 1.0 < tol )\n eta = 1.0;\n }\n else if( eta < 0.0 )\n {\n if( eta > -tol )\n eta = 0.0;\n }\n\n // Make sure eta is valid\n testPostcondition( eta >= 0.0 );\n testPostcondition( eta <= 1.0 );\n\n return eta;\n}\n\n// Calculate the independent variable (from eta)\n/*! \\details It has been found that a tolerance of 1e-3 works best for most\n * applications.\n */\ntemplate\ntemplate\ninline IndepType\nInterpolationHelper::calculateIndepVar(\n const typename QuantityTraits::RawType eta,\n const IndepType indep_var_min,\n const typename QuantityTraits::RawType indep_grid_length,\n const double tol )\n{\n // Make sure the eta value is valid\n testPrecondition( eta >= 0.0 );\n testPrecondition( eta <= 1.0 );\n // Make sure the grid min indep var is valid\n testPrecondition( !QuantityTraits::isnaninf( indep_var_min ) );\n testPrecondition( ParentInterpolationType::isIndepVarInValidRange( indep_var_min ) );\n // Make sure the grid length is valid\n testPrecondition( !QuantityTraits::RawType>::isnaninf( indep_grid_length ) );\n testPrecondition( indep_grid_length >= 0.0 );\n\n IndepType grid_indep_var( QuantityTraits::initializeQuantity(\n ParentInterpolationType::recoverProcessedIndepVar(\n ParentInterpolationType::processIndepVar( indep_var_min ) +\n indep_grid_length*eta ) ) );\n\n // Check for rounding errors\n if( grid_indep_var < indep_var_min &&\n grid_indep_var >= ThisType::calculateFuzzyLowerBound(indep_var_min, tol))\n grid_indep_var = indep_var_min;\n\n // Make sure the calculated independent variable is valid\n testPostcondition( grid_indep_var >= indep_var_min );\n\n return grid_indep_var;\n}\n\n// Calculate the processed independent variable (from eta)\n/*! \\details A tolerance is not required with this method because no variable\n * processing is done. Due to conversion of the independent values from a cosine\n * (mu) to a delta cosine ( 1 - mu ) + nudge for LogLogCos and LinLogCos, it is\n * assumed the processed grids are inverted to ensure they are in ascending\n * order.\n */\ntemplate\ntemplate\ninline T InterpolationHelper::calculateProcessedIndepVar(\n const T eta,\n const T processed_indep_var_min,\n const T indep_grid_length )\n{\n // Make sure the eta value is valid\n testPrecondition( eta >= 0.0 );\n testPrecondition( eta <= 1.0 );\n // Make sure the grid min indep var is valid\n testPrecondition( !QuantityTraits::isnaninf( processed_indep_var_min ) );\n // Make sure the grid length is valid\n testPrecondition( !QuantityTraits::isnaninf( indep_grid_length ) );\n testPrecondition( indep_grid_length >= 0.0 );\n\n return processed_indep_var_min + indep_grid_length*eta;\n}\n\n// Calculate the \"fuzzy\" lower bound (lower bound with roundoff tolerance)\n/*! \\details It has been found that a tolerance of 1e-3 works best for most\n * applications.\n */\ntemplate\ntemplate\ninline T InterpolationHelper::calculateFuzzyLowerBound(\n const T value,\n const double tol )\n{\n if( value < QuantityTraits::zero() )\n return value*(1+tol);\n else\n return value*(1-tol);\n}\n\n// Calculate the \"fuzzy\" upper bound (upper bound with roundoff tolerance)\n/*! \\details It has been found that a tolerance of 1e-3 works best for most\n * applications\n */\ntemplate\ntemplate\ninline T InterpolationHelper::calculateFuzzyUpperBound(\n const T value,\n const double tol )\n{\n if( value > QuantityTraits::zero() )\n return value*(1+tol);\n else\n return value*(1-tol);\n}\n\n// The name of the policy\ntemplate\ninline std::string InterpolationHelper::name()\n{\n return Utility::typeName();\n}\n\n// Get the interpolation type\ninline InterpolationType LogLog::getInterpolationType()\n{\n return LOGLOG_INTERPOLATION;\n}\n\n// Interpolate between two points\ntemplate\ninline DepType LogLog::interpolate( const IndepType indep_var_0,\n\t\t\t\t const IndepType indep_var_1,\n\t\t\t\t const IndepType indep_var,\n\t\t\t\t const DepType dep_var_0,\n\t\t\t\t const DepType dep_var_1 )\n{\n // The IndepType must be a floating point type\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n // Make sure the independent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( indep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var_1 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var ) );\n testPrecondition( LogLog::isIndepVarInValidRange( indep_var_0 ) );\n testPrecondition( LogLog::isIndepVarInValidRange( indep_var_1 ) );\n testPrecondition( LogLog::isIndepVarInValidRange( indep_var ) );\n testPrecondition( indep_var_0 < indep_var_1 );\n testPrecondition( indep_var >= indep_var_0 );\n testPrecondition( indep_var <= indep_var_1 );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LogLog::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LogLog::isDepVarInValidRange( dep_var_1 ) );\n\n return dep_var_0*\n pow((dep_var_1/dep_var_0),\n\tlog(indep_var/indep_var_0)/log(indep_var_1/indep_var_0));\n}\n\n// Interpolate between two points using the indep variable ratio (beta)\ntemplate\ninline DepType LogLog::interpolate( const T beta,\n const DepType dep_var_0,\n const DepType dep_var_1 )\n{\n // The IndepType must be a floating point type\n testStaticPrecondition( (std::is_floating_point::RawType>::value) );\n testStaticPrecondition( (std::is_floating_point::RawType>::value ) );\n // Make sure the independent variables are valid\n testPrecondition( beta >= QuantityTraits::zero() );\n testPrecondition( beta <= QuantityTraits::one() );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LogLog::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LogLog::isDepVarInValidRange( dep_var_1 ) );\n\n return dep_var_0*pow((dep_var_1/dep_var_0),beta);\n}\n\n// Interpolate between two points and return the processed value\ntemplate\ninline typename QuantityTraits::RawType\nLogLog::interpolateAndProcess( const IndepType indep_var_0,\n\t\t\t const IndepType indep_var_1,\n\t\t\t const IndepType indep_var,\n\t\t\t const DepType dep_var_0,\n\t\t\t const DepType dep_var_1 )\n{\n // T must be a floating point type\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n // Make sure the independent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( indep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var_1 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var ) );\n testPrecondition( LogLog::isIndepVarInValidRange( indep_var_0 ) );\n testPrecondition( LogLog::isIndepVarInValidRange( indep_var_1 ) );\n testPrecondition( LogLog::isIndepVarInValidRange( indep_var ) );\n testPrecondition( indep_var_0 < indep_var_1 );\n testPrecondition( indep_var >= indep_var_0 );\n testPrecondition( indep_var <= indep_var_1 );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LogLog::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LogLog::isDepVarInValidRange( dep_var_1 ) );\n\n return log( getRawQuantity(dep_var_0) ) + log ( dep_var_1/dep_var_0 )*\n log( indep_var/indep_var_0 )/log( indep_var_1/indep_var_0 );\n}\n\n// Process the independent value\ntemplate\ninline typename QuantityTraits::RawType\nLogLog::processIndepVar( const T indep_var )\n{\n // Make sure the indep var value is valid\n testPrecondition( LogLog::isIndepVarInValidRange( indep_var ) );\n\n return log( getRawQuantity(indep_var) );\n}\n\n// Process the dependent value\ntemplate\ninline typename QuantityTraits::RawType\nLogLog::processDepVar( const T dep_var )\n{\n // Make sure the indep var value is valid\n testPrecondition( LogLog::isDepVarInValidRange( dep_var ) );\n\n return log( getRawQuantity(dep_var) );\n}\n\n// Recover the processed independent value\ntemplate\ninline T LogLog::recoverProcessedIndepVar( const T processed_indep_var )\n{\n return exp( processed_indep_var );\n}\n\n// Recover the processed dependent value\ntemplate\ninline T LogLog::recoverProcessedDepVar( const T processed_dep_var )\n{\n return exp( processed_dep_var );\n}\n\n// Test if the independent value is in a valid range (doesn't check nan/inf)\ntemplate\ninline bool LogLog::isIndepVarInValidRange( const T indep_var )\n{\n return indep_var > QuantityTraits::zero();\n}\n\n// Test if the dependent value is in a valid range (doesn't check nan/inf)\ntemplate\ninline bool LogLog::isDepVarInValidRange( const T dep_var )\n{\n // Make sure the indep var is not inf or nan\n testPrecondition( !QuantityTraits::isnaninf( dep_var ) );\n\n return dep_var > QuantityTraits::zero();\n}\n\n// Get the interpolation type\ninline InterpolationType LogLin::getInterpolationType()\n{\n return LOGLIN_INTERPOLATION;\n}\n\n// Interpolate between two points\ntemplate\ninline DepType LogLin::interpolate( const IndepType indep_var_0,\n\t\t\t\t const IndepType indep_var_1,\n\t\t\t\t const IndepType indep_var,\n\t\t\t\t const DepType dep_var_0,\n\t\t\t\t const DepType dep_var_1 )\n{\n // T must be a floating point type\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n // Make sure the independent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( indep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var_1 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var ) );\n testPrecondition( LogLin::isIndepVarInValidRange( indep_var_0 ) );\n testPrecondition( LogLin::isIndepVarInValidRange( indep_var_1 ) );\n testPrecondition( LogLin::isIndepVarInValidRange( indep_var ) );\n testPrecondition( indep_var_0 < indep_var_1 );\n testPrecondition( indep_var >= indep_var_0 );\n testPrecondition( indep_var <= indep_var_1 );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LogLin::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LogLin::isDepVarInValidRange( dep_var_1 ) );\n\n return dep_var_0*pow((dep_var_1/dep_var_0), (indep_var-indep_var_0)/(indep_var_1-indep_var_0));\n}\n\n// Interpolate between two points using the indep variable ratio (beta)\ntemplate\ninline DepType LogLin::interpolate( const T beta,\n const DepType dep_var_0,\n const DepType dep_var_1 )\n{\n // The IndepType must be a floating point type\n testStaticPrecondition( (std::is_floating_point::RawType>::value) );\n testStaticPrecondition( (std::is_floating_point::RawType>::value) );\n // Make sure the independent variables are valid\n testPrecondition( beta >= QuantityTraits::zero() );\n testPrecondition( beta <= QuantityTraits::one() );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LogLin::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LogLin::isDepVarInValidRange( dep_var_1 ) );\n\n return dep_var_0*pow((dep_var_1/dep_var_0),beta);\n}\n\n// Interpolate between two points and return the processed value\ntemplate\ninline typename QuantityTraits::RawType\nLogLin::interpolateAndProcess( const IndepType indep_var_0,\n\t\t\t const IndepType indep_var_1,\n\t\t\t const IndepType indep_var,\n\t\t\t const DepType dep_var_0,\n\t\t\t const DepType dep_var_1 )\n{\n // T must be a floating point type\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n // Make sure the independent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( indep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var_1 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var ) );\n testPrecondition( LogLin::isIndepVarInValidRange( indep_var_0 ) );\n testPrecondition( LogLin::isIndepVarInValidRange( indep_var_1 ) );\n testPrecondition( LogLin::isIndepVarInValidRange( indep_var ) );\n testPrecondition( indep_var_0 < indep_var_1 );\n testPrecondition( indep_var >= indep_var_0 );\n testPrecondition( indep_var <= indep_var_1 );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LogLin::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LogLin::isDepVarInValidRange( dep_var_1 ) );\n\n return log( getRawQuantity(dep_var_0) ) + log( dep_var_1/dep_var_0 )*\n (indep_var-indep_var_0)/(indep_var_1-indep_var_0);\n}\n\n// Process the independent value\ntemplate\ninline typename QuantityTraits::RawType\nLogLin::processIndepVar( const T indep_var )\n{\n // Make sure the indep var value is valid\n testPrecondition( LogLin::isIndepVarInValidRange( indep_var ) );\n\n return getRawQuantity(indep_var);\n}\n\n// Process the dependent value\ntemplate\ninline typename QuantityTraits::RawType\nLogLin::processDepVar( const T dep_var )\n{\n // Make sure the indep var value is valid\n testPrecondition( LogLin::isDepVarInValidRange( dep_var ) );\n\n return log( getRawQuantity(dep_var) );\n}\n\n// Recover the processed independent value\ntemplate\ninline T LogLin::recoverProcessedIndepVar( const T processed_indep_var )\n{\n return processed_indep_var;\n}\n\n// Recover the processed dependent value\ntemplate\ninline T LogLin::recoverProcessedDepVar( const T processed_dep_var )\n{\n return exp( processed_dep_var );\n}\n\n// Test if the independent value is in a valid range (doesn't check nan/inf)\ntemplate\ninline bool LogLin::isIndepVarInValidRange( const T indep_var )\n{\n return true;\n}\n\n// Test if the dependent value is in a valid range (doesn't check nan/inf)\ntemplate\ninline bool LogLin::isDepVarInValidRange( const T dep_var )\n{\n // Make sure the indep var is not inf or nan\n testPrecondition( !QuantityTraits::isnaninf( dep_var ) );\n\n return dep_var > QuantityTraits::zero();\n}\n\n// Get the interpolation type\ninline InterpolationType LinLog::getInterpolationType()\n{\n return LINLOG_INTERPOLATION;\n}\n\n// Interpolate between two points\ntemplate\ninline DepType LinLog::interpolate( const IndepType indep_var_0,\n\t\t\t\t const IndepType indep_var_1,\n\t\t\t\t const IndepType indep_var,\n\t\t\t\t const DepType dep_var_0,\n\t\t\t\t const DepType dep_var_1 )\n{\n // T must be a floating point type\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n // Make sure the independent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( indep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var_1 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var ) );\n testPrecondition( LinLog::isIndepVarInValidRange( indep_var_0 ) );\n testPrecondition( LinLog::isIndepVarInValidRange( indep_var_1 ) );\n testPrecondition( LinLog::isIndepVarInValidRange( indep_var ) );\n testPrecondition( indep_var_0 < indep_var_1 );\n testPrecondition( indep_var >= indep_var_0 );\n testPrecondition( indep_var <= indep_var_1 );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LinLog::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LinLog::isDepVarInValidRange( dep_var_1 ) );\n\n DepType term_2( (dep_var_1 - dep_var_0)*log(indep_var/indep_var_0)/\n\t\t log(indep_var_1/indep_var_0) );\n\n return dep_var_0 + term_2;\n}\n\n// Interpolate between two points using the indep variable ratio (beta)\ntemplate\ninline DepType LinLog::interpolate( const T beta,\n const DepType dep_var_0,\n const DepType dep_var_1 )\n{\n // The IndepType must be a floating point type\n testStaticPrecondition( (std::is_floating_point::RawType>::value) );\n testStaticPrecondition( (std::is_floating_point::RawType>::value) );\n // Make sure the independent variables are valid\n testPrecondition( beta >= QuantityTraits::zero() );\n testPrecondition( beta <= QuantityTraits::one() );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LinLog::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LinLog::isDepVarInValidRange( dep_var_1 ) );\n\n return dep_var_0 + (dep_var_1 - dep_var_0)*beta;\n}\n\n// Interpolate between two points and return the processed value\ntemplate\ninline typename QuantityTraits::RawType\nLinLog::interpolateAndProcess( const IndepType indep_var_0,\n\t\t\t const IndepType indep_var_1,\n\t\t\t const IndepType indep_var,\n\t\t\t const DepType dep_var_0,\n\t\t\t const DepType dep_var_1 )\n{\n return getRawQuantity( interpolate( indep_var_0,\n\t\t\t\t indep_var_1,\n\t\t\t\t indep_var,\n\t\t\t\t dep_var_0,\n\t\t\t\t dep_var_1 ) );\n}\n\n// Process the independent value\ntemplate\ninline typename QuantityTraits::RawType\nLinLog::processIndepVar( const T indep_var )\n{\n // Make sure the indep var value is valid\n testPrecondition( LinLog::isIndepVarInValidRange( indep_var ) );\n\n return log( getRawQuantity(indep_var) );\n}\n\n// Process the dependent value\ntemplate\ninline typename QuantityTraits::RawType\nLinLog::processDepVar( const T dep_var )\n{\n // Make sure the indep var value is valid\n testPrecondition( LinLog::isDepVarInValidRange( dep_var ) );\n\n return getRawQuantity(dep_var);\n}\n\n// Recover the processed independent value\ntemplate\ninline T LinLog::recoverProcessedIndepVar( const T processed_indep_var )\n{\n return exp( processed_indep_var );\n}\n\n// Recover the processed dependent value\ntemplate\ninline T LinLog::recoverProcessedDepVar( const T processed_dep_var )\n{\n return processed_dep_var;\n}\n\n// Test if the independent value is in a valid range (doesn't check nan/inf)\ntemplate\ninline bool LinLog::isIndepVarInValidRange( const T indep_var )\n{\n return indep_var > QuantityTraits::zero();\n}\n\n// Test if the dependent value is in a valid range (doesn't check nan/inf)\ntemplate\ninline bool LinLog::isDepVarInValidRange( const T dep_var )\n{\n // Make sure the indep var is not inf or nan\n testPrecondition( !QuantityTraits::isnaninf( dep_var ) );\n\n return true;\n}\n\n// Get the interpolation type\ninline InterpolationType LinLin::getInterpolationType()\n{\n return LINLIN_INTERPOLATION;\n}\n\n// Interpolate between two points\ntemplate\ninline DepType LinLin::interpolate( const IndepType indep_var_0,\n\t\t\t\t const IndepType indep_var_1,\n\t\t\t\t const IndepType indep_var,\n\t\t\t\t const DepType dep_var_0,\n\t\t\t\t const DepType dep_var_1 )\n{\n // T must be a floating point type\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n testStaticPrecondition( (Utility::IsFloatingPoint::value) );\n // Make sure the independent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( indep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var_1 ) );\n testPrecondition( !QuantityTraits::isnaninf( indep_var ) );\n testPrecondition( LinLin::isIndepVarInValidRange( indep_var_0 ) );\n testPrecondition( LinLin::isIndepVarInValidRange( indep_var_1 ) );\n testPrecondition( LinLin::isIndepVarInValidRange( indep_var ) );\n testPrecondition( indep_var_0 < indep_var_1 );\n testPrecondition( indep_var >= indep_var_0 );\n testPrecondition( indep_var <= indep_var_1 );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LinLin::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LinLin::isDepVarInValidRange( dep_var_1 ) );\n\n DepType term_2( (dep_var_1 - dep_var_0)/(indep_var_1 - indep_var_0)*\n\t\t (indep_var - indep_var_0) );\n\n return dep_var_0 + term_2;\n}\n\n// Interpolate between two points using the indep variable ratio (beta)\ntemplate\ninline DepType LinLin::interpolate( const T beta,\n const DepType dep_var_0,\n const DepType dep_var_1 )\n{\n // The IndepType must be a floating point type\n testStaticPrecondition( (std::is_floating_point::RawType>::value) );\n testStaticPrecondition( (std::is_floating_point::RawType>::value) );\n // Make sure the independent variables are valid\n testPrecondition( beta >= QuantityTraits::zero() );\n testPrecondition( beta <= QuantityTraits::one() );\n // Make sure the dependent variables are valid\n testPrecondition( !QuantityTraits::isnaninf( dep_var_0 ) );\n testPrecondition( !QuantityTraits::isnaninf( dep_var_1 ) );\n testPrecondition( LinLin::isDepVarInValidRange( dep_var_0 ) );\n testPrecondition( LinLin::isDepVarInValidRange( dep_var_1 ) );\n\n return dep_var_0 + (dep_var_1 - dep_var_0)*beta;\n}\n\n// Interpolate between two points and return the processed value\ntemplate\ninline typename QuantityTraits::RawType\nLinLin::interpolateAndProcess( const IndepType indep_var_0,\n\t\t\t const IndepType indep_var_1,\n\t\t\t const IndepType indep_var,\n\t\t\t const DepType dep_var_0,\n\t\t\t const DepType dep_var_1 )\n{\n return getRawQuantity( interpolate( indep_var_0,\n\t\t\t\t indep_var_1,\n\t\t\t\t indep_var,\n\t\t\t\t dep_var_0,\n\t\t\t\t dep_var_1 ) );\n}\n\n// Process the independent value\ntemplate\ninline typename QuantityTraits::RawType\nLinLin::processIndepVar( const T indep_var )\n{\n // Make sure the indep var value is valid\n testPrecondition( LinLin::isIndepVarInValidRange( indep_var ) );\n\n return getRawQuantity(indep_var);\n}\n\n// Process the dependent value\ntemplate\ninline typename QuantityTraits::RawType\nLinLin::processDepVar( const T dep_var )\n{\n // Make sure the indep var value is valid\n testPrecondition( LinLin::isDepVarInValidRange( dep_var ) );\n\n return getRawQuantity(dep_var);\n}\n\n// Recover the processed independent value\ntemplate\ninline T LinLin::recoverProcessedIndepVar( const T processed_indep_var )\n{\n return processed_indep_var;\n}\n\n// Recover the processed dependent value\ntemplate\ninline T LinLin::recoverProcessedDepVar( const T processed_dep_var )\n{\n return processed_dep_var;\n}\n\n// Test if the independent value is in a valid range (doesn't check nan/inf)\ntemplate\ninline bool LinLin::isIndepVarInValidRange( const T indep_var )\n{\n return true;\n}\n\n// Test if the dependent value is in a valid range (doesn't check nan/inf)\ntemplate\ninline bool LinLin::isDepVarInValidRange( const T dep_var )\n{\n // Make sure the indep var is not inf or nan\n testPrecondition( !QuantityTraits::isnaninf( dep_var ) );\n\n return true;\n}\n\n} // end Utility namespace\n\n#endif // end UTILITY_INTERPOLATION_POLICY_DEF_HPP\n\n//---------------------------------------------------------------------------//\n// end Utility_InterpolationPolicy_def.hpp\n//---------------------------------------------------------------------------//\n", "meta": {"hexsha": "42e0c14abec47e9649ecb4055a357c9df22ba43c", "size": 35126, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "packages/utility/interpolation/src/Utility_InterpolationPolicy_def.hpp", "max_stars_repo_name": "bam241/FRENSIE", "max_stars_repo_head_hexsha": "e1760cd792928699c84f2bdce70ff54228e88094", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 10.0, 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"avg_line_length": 40.2359679267, "max_line_length": 114, "alphanum_fraction": 0.7186129932, "num_tokens": 8662, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.4263215925474903, "lm_q1q2_score": 0.21649116269402044}} {"text": "/* CirKit: A circuit toolkit\n * Copyright (C) 2009-2015 University of Bremen\n * Copyright (C) 2015-2017 EPFL\n *\n * Permission is hereby granted, free of charge, to any person\n * obtaining a copy of this software and associated documentation\n * files (the \"Software\"), to deal in the Software without\n * restriction, including without limitation the rights to use,\n * copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the\n * Software is furnished to do so, subject to the following\n * conditions:\n *\n * The above copyright notice and this permission notice shall be\n * included in all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES\n * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT\n * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,\n * WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR\n * OTHER DEALINGS IN THE SOFTWARE.\n */\n\n#include \"error_metrics.hpp\"\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace cirkit\n{\n\n/******************************************************************************\n * Types *\n ******************************************************************************/\n\n/******************************************************************************\n * Private functions *\n ******************************************************************************/\n\ninline void assert_valid( const std::vector& f, const std::vector& fhat )\n{\n assert( f.size() == fhat.size() );\n assert( !f.empty() );\n}\n\nstd::vector compute_diff( const std::vector& f, const std::vector& fhat, bool print_truthtables = false )\n{\n assert_valid( f, fhat );\n\n const auto to = f.size() + 1u;\n\n const auto zf = zero_extend( f, to );\n const auto zfhat = zero_extend( fhat, to );\n const auto subtr = bdd_subtract( zf, zfhat );\n const auto diff = bdd_abs( subtr );\n\n#if ( BOOST_VERSION / 100000 ) >= 1 && ( BOOST_VERSION / 100 % 1000 ) >= 56\n if ( print_truthtables )\n {\n using boost::adaptors::transformed;\n using boost::format;\n using boost::str;\n\n auto valid = true;\n auto max = 0u;\n auto sum = 0u;\n\n std::cout << std::endl << \" f zf fhat zfhat subtr diff valid\" << std::endl\n << boost::join( boost::combine( bdds_to_truth_table_unsigned( f ),\n bdds_to_truth_table_unsigned( zf ),\n bdds_to_truth_table_unsigned( fhat ),\n bdds_to_truth_table_unsigned( zfhat ),\n bdds_to_truth_table_signed( subtr ),\n bdds_to_truth_table_unsigned( diff ) )\n | transformed( [&]( const boost::tuple& p ) {\n const auto vf = boost::get<0>( p );\n const auto vzf = boost::get<1>( p );\n const auto vfhat = boost::get<2>( p );\n const auto vzfhat = boost::get<3>( p );\n const auto vsubtr = boost::get<4>( p );\n const auto vdiff = boost::get<5>( p );\n\n const auto row_valid = ( vf == vzf ) && ( vfhat == vzfhat ) && ( vsubtr == static_cast( vzf - vzfhat ) ) && ( abs( vsubtr ) == static_cast( vdiff ) );\n valid = valid && row_valid;\n max = std::max( max, vdiff );\n sum += vdiff;\n\n return str( format( \"%5d %5d %5d %5d %5d %5d %d\") % vf % vzf % vfhat % vzfhat % vsubtr % vdiff % row_valid );\n } ), \"\\n\" ) << std::endl\n << format( \"valid: %d, max: %d, avg: %.2f\" ) % valid % max % ( sum / pow( 2.0, f.front().manager->num_vars() ) ) << std::endl;\n }\n#endif\n\n assert( diff.size() == to );\n assert( diff.back().index == 0u );\n\n return diff;\n}\n\nboost::multiprecision::uint256_t get_max_value( const std::vector& f )\n{\n assert( !f.empty() );\n\n boost::dynamic_bitset<> bs( f.size() );\n auto mask = f.front().manager->bdd_top();\n\n for ( int k = f.size() - 1; k >= 0; --k )\n {\n auto r = mask && f[k];\n if ( r.index != 0 )\n {\n bs.set( k );\n mask = r;\n }\n }\n\n return to_multiprecision( bs );\n}\n\nboost::multiprecision::uint256_t get_max_value_with_chi( const std::vector& f )\n{\n bdd_manager mgr_chi( f.front().manager->num_vars() + f.size(), 10u ); /* can we approximate the number of used nodes? */\n\n auto fr = f; boost::reverse( fr );\n auto chi = characteristic_function( fr, mgr_chi );\n\n boost::dynamic_bitset<> bs( f.size() );\n\n while ( chi.var() < f.size() )\n {\n if ( chi.high().is_bot() )\n {\n chi = chi.low();\n }\n else\n {\n bs.set( f.size() - 1u - chi.var() );\n chi = chi.high();\n }\n }\n\n return to_multiprecision( bs );\n}\n\nboost::multiprecision::uint256_t get_weighted_sum( const std::vector& f )\n{\n auto level = f.size();\n bdd_manager mgr_chi( f.front().manager->num_vars() + level, 10u ); /* can we approximate the number of used nodes? */\n\n auto fr = f; boost::reverse( fr );\n auto chi = characteristic_function( fr, mgr_chi );\n\n boost::multiprecision::uint256_t sum = 0;\n const boost::multiprecision::uint256_t one = 1;\n std::stack>> stack;\n\n stack.push( {chi, boost::dynamic_bitset<>( level )} );\n\n while ( !stack.empty() )\n {\n auto p = stack.top(); stack.pop();\n\n if ( p.first.var() >= level )\n {\n sum += to_multiprecision( p.second ) * ( count_solutions( p.first ) / ( one << level ) );\n }\n else\n {\n stack.push( {p.first.low(), p.second} );\n p.second.set( p.first.var() );\n stack.push( {p.first.high(), p.second} );\n }\n }\n\n return sum;\n}\n\n/******************************************************************************\n * Public functions *\n ******************************************************************************/\n\nboost::multiprecision::uint256_t error_rate( const std::vector& f, const std::vector& fhat,\n const properties::ptr& settings,\n const properties::ptr& statistics )\n{\n properties_timer t( statistics );\n\n assert_valid( f, fhat );\n\n auto h = f.front().manager->bdd_bot();\n\n for ( auto i = 0u; i < f.size(); ++i )\n {\n h = h || ( f[i] ^ fhat[i] );\n }\n\n return count_solutions( h );\n}\n\nboost::multiprecision::uint256_t worst_case( const std::vector& f, const std::vector& fhat,\n const properties::ptr& settings,\n const properties::ptr& statistics )\n{\n auto print_truthtables = get( settings, \"print_truthtables\", false );\n auto maximum_method = get( settings, \"maximum_method\", worst_case_maximum_method::shift );\n\n properties_timer t( statistics );\n\n auto diff = compute_diff( f, fhat, print_truthtables );\n\n switch ( maximum_method )\n {\n case worst_case_maximum_method::shift:\n return get_max_value( diff );\n case worst_case_maximum_method::chi:\n return get_max_value_with_chi( diff );\n default:\n assert( false );\n }\n}\n\nboost::multiprecision::cpp_dec_float_100 average_case( const std::vector& f, const std::vector& fhat,\n const properties::ptr& settings, const properties::ptr& statistics )\n{\n auto print_truthtables = get( settings, \"print_truthtables\", false );\n\n properties_timer t( statistics );\n\n auto diff = compute_diff( f, fhat, print_truthtables );\n\n const boost::multiprecision::uint256_t one = 1;\n return boost::multiprecision::cpp_dec_float_100( get_weighted_sum( diff ) ) /\n boost::multiprecision::cpp_dec_float_100( one << f.front().manager->num_vars() );\n}\n\n}\n\n// Local Variables:\n// c-basic-offset: 2\n// eval: (c-set-offset 'substatement-open 0)\n// eval: (c-set-offset 'innamespace 0)\n// End:\n", "meta": {"hexsha": "05646913c182d0abf9c46b68f390135ca905ccb6", "size": 9282, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/classical/approximate/error_metrics.cpp", "max_stars_repo_name": "eletesta/cirkit", "max_stars_repo_head_hexsha": "6d0939798ea25cecf92306ce796be154139b94f5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/classical/approximate/error_metrics.cpp", "max_issues_repo_name": "eletesta/cirkit", "max_issues_repo_head_hexsha": "6d0939798ea25cecf92306ce796be154139b94f5", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/classical/approximate/error_metrics.cpp", "max_forks_repo_name": "eletesta/cirkit", "max_forks_repo_head_hexsha": "6d0939798ea25cecf92306ce796be154139b94f5", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.427480916, "max_line_length": 194, "alphanum_fraction": 0.5498814911, "num_tokens": 2188, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621765008857982, "lm_q2_score": 0.3849121585956185, "lm_q1q2_score": 0.21638857046768423}} {"text": "/* Copyright (c) 2016 - 2021, the adamantine authors.\n *\n * This file is subject to the Modified BSD License and may not be distributed\n * without copyright and license information. Please refer to the file LICENSE\n * for the text and further information on this license.\n */\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n\nnamespace adamantine\n{\n\ntemplate \nMaterialProperty::MaterialProperty(\n MPI_Comm const &communicator,\n dealii::parallel::distributed::Triangulation const &tria,\n boost::property_tree::ptree const &database)\n : _communicator(communicator), _fe(0), _mp_dof_handler(tria)\n{\n // Because deal.II cannot easily attach data to a cell. We store the state\n // of the material in distributed::Vector. This allows to use deal.II to\n // compute the new state after refinement of the mesh. However, this\n // requires to use another DoFHandler.\n reinit_dofs();\n\n // Set the material state to the state defined in the geometry.\n set_initial_state();\n\n // Fill the _properties map\n fill_properties(database);\n}\n\ntemplate \ndouble MaterialProperty::get_cell_value(\n typename dealii::Triangulation::active_cell_iterator const &cell,\n StateProperty prop) const\n{\n unsigned int property = static_cast(prop);\n auto const mp_dof_index = get_dof_index(cell);\n\n return _property_values[property][mp_dof_index];\n}\n\ntemplate \ndouble MaterialProperty::get_cell_value(\n typename dealii::Triangulation::active_cell_iterator const &cell,\n Property prop) const\n{\n dealii::types::material_id material_id = cell->material_id();\n unsigned int property = static_cast(prop);\n\n return _properties(material_id, property);\n}\n\ntemplate \nvoid MaterialProperty::reinit_dofs()\n{\n _mp_dof_handler.distribute_dofs(_fe);\n // Initialize the state vectors\n for (auto &vec : _state)\n vec.reinit(_mp_dof_handler.locally_owned_dofs(), _communicator);\n}\n\ntemplate \nvoid MaterialProperty::update(\n dealii::DoFHandler const &temperature_dof_handler,\n dealii::LA::distributed::Vector const &temperature)\n{\n auto temperature_average =\n compute_average_temperature(temperature_dof_handler, temperature);\n for (auto &val : _property_values)\n val.reinit(temperature_average.get_partitioner());\n\n std::vector mp_dof(1);\n for (auto cell :\n dealii::filter_iterators(_mp_dof_handler.active_cell_iterators(),\n dealii::IteratorFilters::LocallyOwnedCell()))\n {\n dealii::types::material_id material_id = cell->material_id();\n\n unsigned int constexpr liquid =\n static_cast(MaterialState::liquid);\n unsigned int constexpr powder =\n static_cast(MaterialState::powder);\n unsigned int constexpr solid =\n static_cast(MaterialState::solid);\n unsigned int constexpr prop_solidus =\n static_cast(Property::solidus);\n unsigned int constexpr prop_liquidus =\n static_cast(Property::liquidus);\n\n double const solidus = _properties(material_id, prop_solidus);\n double const liquidus = _properties(material_id, prop_liquidus);\n cell->get_dof_indices(mp_dof);\n unsigned int const dof = mp_dof[0];\n\n // First determine the ratio of liquid.\n double liquid_ratio = -1.;\n double powder_ratio = -1.;\n double solid_ratio = -1.;\n if (temperature_average[dof] < solidus)\n liquid_ratio = 0.;\n else if (temperature_average[dof] > liquidus)\n liquid_ratio = 1.;\n else\n liquid_ratio =\n (temperature_average[dof] - solidus) / (liquidus - solidus);\n // Because the powder can only become liquid, the solid can only become\n // liquid, and the liquid can only become solid, the ratio of powder can\n // only decrease.\n powder_ratio = std::min(1. - liquid_ratio, _state[powder][dof]);\n // Use max to make sure that we don't create matter because of round-off.\n solid_ratio = std::max(1 - liquid_ratio - powder_ratio, 0.);\n\n // Update the value\n _state[liquid][dof] = liquid_ratio;\n _state[powder][dof] = powder_ratio;\n _state[solid][dof] = solid_ratio;\n\n if (_use_table)\n {\n for (unsigned int property = 0; property < _n_state_properties;\n ++property)\n {\n for (unsigned int material_state = 0;\n material_state < _n_material_states; ++material_state)\n {\n _property_values[property][dof] +=\n _state[material_state][dof] *\n compute_property_from_table(material_id, material_state, property,\n temperature_average[dof]);\n }\n }\n }\n else\n {\n for (unsigned int property = 0; property < _n_state_properties;\n ++property)\n {\n for (unsigned int material_state = 0;\n material_state < _n_material_states; ++material_state)\n {\n for (unsigned int i = 0; i <= _polynomial_order; ++i)\n {\n _property_values[property][dof] +=\n _state[material_state][dof] *\n _state_property_polynomials(material_id, material_state,\n property, i) *\n std::pow(temperature_average[dof], i);\n }\n }\n }\n }\n\n // If we are in the mushy state, i.e., part liquid part solid, we need to\n // modify the rho C_p to take into account the latent heat.\n if ((liquid_ratio > 0.) && (liquid_ratio < 1.))\n {\n unsigned int const specific_heat_prop =\n static_cast(StateProperty::specific_heat);\n unsigned int const latent_heat_prop =\n static_cast(Property::latent_heat);\n for (unsigned int material_state = 0; material_state < _n_material_states;\n ++material_state)\n {\n _property_values[specific_heat_prop][dof] +=\n _state[material_state][dof] *\n _properties(material_id, latent_heat_prop) / (liquidus - solidus);\n }\n }\n\n // The radiation heat transfer coefficient is not a real material property\n // but it is derived from other material properties:\n // h_rad = emissitivity * stefan-boltzmann constant * (T + T_infty) (T^2 +\n // T^2_infty).\n unsigned int const emissivity_prop =\n static_cast(StateProperty::emissivity);\n unsigned int const radiation_heat_transfer_coef_prop =\n static_cast(StateProperty::radiation_heat_transfer_coef);\n unsigned int const radiation_temperature_infty_prop =\n static_cast(Property::radiation_temperature_infty);\n double const T = temperature_average[dof];\n double const T_infty =\n _properties(material_id, radiation_temperature_infty_prop);\n double const emissivity = _property_values[emissivity_prop][dof];\n _property_values[radiation_heat_transfer_coef_prop][dof] =\n emissivity * Constant::stefan_boltzmann * (T + T_infty) *\n (T * T + T_infty * T_infty);\n }\n}\n\ntemplate \nvoid MaterialProperty::update_boundary_material_properties(\n dealii::DoFHandler const &temperature_dof_handler,\n dealii::LA::distributed::Vector const &temperature)\n{\n auto temperature_average =\n compute_average_temperature(temperature_dof_handler, temperature);\n for (auto &val : _property_values)\n val.reinit(temperature_average.get_partitioner());\n\n std::vector mp_dof(1);\n // We don't need to loop over all the active cells. We only need to loop over\n // the cells at the boundary and at the interface with FE_Nothing. However, to\n // do this we need to use the temperature_dof_handler instead of the\n // _mp_dof_handler.\n for (auto cell :\n dealii::filter_iterators(_mp_dof_handler.active_cell_iterators(),\n dealii::IteratorFilters::LocallyOwnedCell()))\n {\n dealii::types::material_id material_id = cell->material_id();\n\n cell->get_dof_indices(mp_dof);\n unsigned int const dof = mp_dof[0];\n if (_use_table)\n {\n // We only care about properties that are used to compute the boundary\n // condition. So we start at 3.\n for (unsigned int property = 3; property < _n_state_properties;\n ++property)\n {\n for (unsigned int material_state = 0;\n material_state < _n_material_states; ++material_state)\n {\n _property_values[property][dof] +=\n _state[material_state][dof] *\n compute_property_from_table(material_id, material_state, property,\n temperature_average[dof]);\n }\n }\n }\n else\n {\n // We only care about properties that are used to compute the boundary\n // condition. So we start at 3.\n for (unsigned int property = 3; property < _n_state_properties;\n ++property)\n {\n for (unsigned int material_state = 0;\n material_state < _n_material_states; ++material_state)\n {\n for (unsigned int i = 0; i <= _polynomial_order; ++i)\n {\n _property_values[property][dof] +=\n _state[material_state][dof] *\n _state_property_polynomials(material_id, material_state,\n property, i) *\n std::pow(temperature_average[dof], i);\n }\n }\n }\n }\n\n // The radiation heat transfer coefficient is not a real material property\n // but it is derived from other material properties:\n // h_rad = emissitivity * stefan-boltzmann constant * (T + T_infty) (T^2 +\n // T^2_infty).\n unsigned int const emissivity_prop =\n static_cast(StateProperty::emissivity);\n unsigned int const radiation_heat_transfer_coef_prop =\n static_cast(StateProperty::radiation_heat_transfer_coef);\n unsigned int const radiation_temperature_infty_prop =\n static_cast(Property::radiation_temperature_infty);\n double const T = temperature_average[dof];\n double const T_infty =\n _properties(material_id, radiation_temperature_infty_prop);\n double const emissivity = _property_values[emissivity_prop][dof];\n _property_values[radiation_heat_transfer_coef_prop][dof] =\n emissivity * Constant::stefan_boltzmann * (T + T_infty) *\n (T * T + T_infty * T_infty);\n }\n}\n\ntemplate \nADAMANTINE_HOST_DEV dealii::VectorizedArray\nMaterialProperty::compute_material_property(\n StateProperty state_property, dealii::types::material_id const *material_id,\n dealii::VectorizedArray const *state_ratios,\n dealii::VectorizedArray temperature) const\n{\n dealii::VectorizedArray value = 0.0;\n unsigned int const property_index = static_cast(state_property);\n\n if (_use_table)\n {\n for (unsigned int material_state = 0; material_state < _n_material_states;\n ++material_state)\n {\n for (unsigned int n = 0; n < dealii::VectorizedArray::size(); ++n)\n {\n\n const dealii::types::material_id m_id = material_id[n];\n\n value[n] += state_ratios[material_state][n] *\n compute_property_from_table(m_id, material_state,\n property_index, temperature[n]);\n }\n }\n }\n else\n {\n for (unsigned int material_state = 0; material_state < _n_material_states;\n ++material_state)\n {\n for (unsigned int n = 0; n < dealii::VectorizedArray::size(); ++n)\n {\n\n dealii::types::material_id m_id = material_id[n];\n\n for (unsigned int i = 0; i <= _polynomial_order; ++i)\n {\n value[n] += state_ratios[material_state][n] *\n _state_property_polynomials(m_id, material_state,\n property_index, i) *\n std::pow(temperature[n], i);\n }\n }\n }\n }\n\n return value;\n}\n\ntemplate \nvoid MaterialProperty::set_state(\n dealii::Table<2, dealii::VectorizedArray> const &liquid_ratio,\n dealii::Table<2, dealii::VectorizedArray> const &powder_ratio,\n std::map::cell_iterator,\n std::pair> &cell_it_to_mf_cell_map,\n dealii::DoFHandler const &dof_handler)\n{\n auto const powder_state = static_cast(MaterialState::powder);\n auto const liquid_state = static_cast(MaterialState::liquid);\n auto const solid_state = static_cast(MaterialState::solid);\n std::vector mp_dof(1.);\n\n for (auto cell :\n dealii::filter_iterators(dof_handler.active_cell_iterators(),\n dealii::IteratorFilters::LocallyOwnedCell()))\n {\n typename dealii::Triangulation::active_cell_iterator cell_tria(cell);\n auto mp_dof_index = get_dof_index(cell_tria);\n auto const &mf_cell_vector = cell_it_to_mf_cell_map[cell];\n unsigned int const n_q_points = dof_handler.get_fe().tensor_degree() + 1;\n double liquid_ratio_sum = 0.;\n double powder_ratio_sum = 0.;\n for (unsigned int q = 0; q < n_q_points; ++q)\n {\n liquid_ratio_sum +=\n liquid_ratio(mf_cell_vector.first, q)[mf_cell_vector.second];\n powder_ratio_sum +=\n powder_ratio(mf_cell_vector.first, q)[mf_cell_vector.second];\n }\n _state[liquid_state][mp_dof_index] = liquid_ratio_sum / n_q_points;\n _state[powder_state][mp_dof_index] = powder_ratio_sum / n_q_points;\n _state[solid_state][mp_dof_index] =\n std::max(1. - _state[liquid_state][mp_dof_index] -\n _state[powder_state][mp_dof_index],\n 0.);\n }\n}\n\ntemplate \nvoid MaterialProperty::set_initial_state()\n{\n // Set the material state to the one defined by the user_index\n std::vector mp_dof(1);\n for (auto cell :\n dealii::filter_iterators(_mp_dof_handler.active_cell_iterators(),\n dealii::IteratorFilters::LocallyOwnedCell()))\n {\n cell->get_dof_indices(mp_dof);\n _state[cell->user_index()][mp_dof[0]] = 1.;\n }\n}\n\ntemplate \nvoid MaterialProperty::fill_properties(\n boost::property_tree::ptree const &database)\n{\n std::array material_state = {\n {\"powder\", \"solid\", \"liquid\"}};\n std::array properties = {\n {\"liquidus\", \"solidus\", \"latent_heat\", \"radiation_temperature_infty\",\n \"convection_temperature_infty\"}};\n std::array state_properties = {\n {\"density\", \"specific_heat\", \"thermal_conductivity\", \"emissivity\",\n \"radiation_heat_transfer_coef\", \"convection_heat_transfer_coef\"}};\n\n // PropertyTreeInput materials.property_format\n std::string property_format = database.get(\"property_format\");\n ASSERT_THROW((property_format == \"table\") ||\n (property_format == \"polynomial\"),\n \"property_format should be table or polynomial.\");\n _use_table = (property_format == \"table\");\n // PropertyTreeInput materials.n_materials\n unsigned int const n_materials = database.get(\"n_materials\");\n // Find all the material_ids being used.\n std::vector material_ids;\n for (dealii::types::material_id id = 0;\n id < dealii::numbers::invalid_material_id; ++id)\n {\n if (database.count(\"material_\" + std::to_string(id)) != 0)\n material_ids.push_back(id);\n if (material_ids.size() == n_materials)\n break;\n }\n ASSERT_THROW(material_ids.size() == n_materials,\n \"Could not find all the material_ids.\");\n\n // When using the polynomial format we allocate one contiguous block of\n // memory. Thus, the largest material_id should be as small as possible\n unsigned int const n_material_ids =\n *std::max_element(material_ids.begin(), material_ids.end()) + 1;\n _properties.reinit(n_material_ids);\n if (_use_table)\n {\n // TODO read table size from input file\n _table_size = 4;\n _state_property_tables.reinit(n_material_ids, _table_size);\n _state_property_tables.set_zero();\n }\n else\n {\n // TODO read polynomial_order from input file\n _polynomial_order = 4;\n _state_property_polynomials.reinit(n_material_ids + 1,\n _polynomial_order + 1);\n _state_property_polynomials.set_zero();\n }\n\n for (auto const material_id : material_ids)\n {\n // Get the material property tree.\n boost::property_tree::ptree const &material_database =\n database.get_child(\"material_\" + std::to_string(material_id));\n // For each material, loop over the possible states.\n bool valid_state = false;\n for (unsigned int state = 0; state < _n_material_states; ++state)\n {\n // The state may or may not exist for the material.\n boost::optional state_database =\n material_database.get_child_optional(material_state[state]);\n if (state_database)\n {\n valid_state = true;\n // For each state, loop over the possible properties.\n for (unsigned int p = 0; p < _n_state_properties; ++p)\n {\n // The property may or may not exist for that state\n boost::optional const property =\n state_database.get().get_optional(\n state_properties[p]);\n // If the property exists, put it in the map. If the property does not\n // exist, we have a nullptr.\n if (property)\n {\n // Remove blank spaces\n std::string property_string = property.get();\n property_string.erase(\n std::remove_if(property_string.begin(), property_string.end(),\n [](unsigned char x) { return std::isspace(x); }),\n property_string.end());\n if (_use_table)\n {\n std::vector parsed_property;\n boost::split(parsed_property, property_string,\n [](char c) { return c == ';'; });\n unsigned int const parsed_property_size = parsed_property.size();\n ASSERT_THROW(parsed_property_size <= _table_size,\n \"Too many coefficients, increase the table size\");\n for (unsigned int i = 0; i < parsed_property_size; ++i)\n {\n std::vector t_v;\n boost::split(t_v, parsed_property[i],\n [](char c) { return c == ','; });\n ASSERT(t_v.size() == 2, \"Error reading material property.\");\n _state_property_tables(material_id, state, p, i, 0) =\n std::stod(t_v[0]);\n _state_property_tables(material_id, state, p, i, 1) =\n std::stod(t_v[1]);\n }\n // fill the rest with the last value\n for (unsigned int i = parsed_property_size; i < _table_size; ++i)\n {\n _state_property_tables(material_id, state, p, i, 0) =\n _state_property_tables(material_id, state, p, i - 1, 0);\n _state_property_tables(material_id, state, p, i, 1) =\n _state_property_tables(material_id, state, p, i - 1, 1);\n }\n }\n else\n {\n std::vector parsed_property;\n boost::split(parsed_property, property_string,\n [](char c) { return c == ','; });\n unsigned int const parsed_property_size = parsed_property.size();\n ASSERT_THROW(\n parsed_property_size <= _polynomial_order,\n \"Too many coefficients, increase the polynomial order\");\n for (unsigned int i = 0; i < parsed_property_size; ++i)\n {\n _state_property_polynomials(material_id, state, p, i) =\n std::stod(parsed_property[i]);\n }\n }\n }\n }\n }\n }\n // Check that there is at least one valid MaterialState\n ASSERT_THROW(\n valid_state == true,\n \"Material without any valid state (solid, powder, or liquid).\");\n\n // Check for the properties that are associated to a material but that\n // are independent of an individual state. These properties are duplicated\n // for every state.\n for (unsigned int p = 0; p < _n_properties; ++p)\n {\n // The property may or may not exist for that state\n boost::optional const property =\n material_database.get_optional(properties[p]);\n // If the property exists, put it in the map. If the property does not\n // exist, we use the largest possible value. This is useful if the\n // liquidus and the solidus are not set.\n _properties(material_id, p) =\n property ? property.get() : std::numeric_limits::max();\n }\n }\n}\n\n// We need to compute the average temperature on the cell because we need the\n// material properties to be uniform over the cell. If there aren't then we have\n// problems with the weak form discretization.\ntemplate \ndealii::LA::distributed::Vector\nMaterialProperty::compute_average_temperature(\n dealii::DoFHandler const &temperature_dof_handler,\n dealii::LA::distributed::Vector const &temperature) const\n{\n // TODO: this should probably done in a matrix-free fashion.\n // The triangulation is the same for both DoFHandler\n dealii::LA::distributed::Vector temperature_average(\n _mp_dof_handler.locally_owned_dofs(), temperature.get_mpi_communicator());\n temperature.update_ghost_values();\n temperature_average = 0.;\n auto mp_cell = _mp_dof_handler.begin_active();\n auto mp_end_cell = _mp_dof_handler.end();\n auto enth_cell = temperature_dof_handler.begin_active();\n dealii::hp::FECollection const &fe_collection =\n temperature_dof_handler.get_fe_collection();\n dealii::hp::QCollection q_collection;\n q_collection.push_back(dealii::QGauss(fe_collection.max_degree() + 1));\n q_collection.push_back(dealii::QGauss(1));\n dealii::hp::FEValues hp_fe_values(\n fe_collection, q_collection,\n dealii::UpdateFlags::update_values |\n dealii::UpdateFlags::update_quadrature_points |\n dealii::UpdateFlags::update_JxW_values);\n std::vector mp_dof_indices(1);\n unsigned int const n_q_points = q_collection.max_n_quadrature_points();\n unsigned int const dofs_per_cell = fe_collection.max_dofs_per_cell();\n std::vector enth_dof_indices(dofs_per_cell);\n for (; mp_cell != mp_end_cell; ++enth_cell, ++mp_cell)\n {\n ASSERT(mp_cell->is_locally_owned() == enth_cell->is_locally_owned(),\n \"Internal Error\");\n if ((mp_cell->is_locally_owned()) && (enth_cell->active_fe_index() == 0))\n {\n hp_fe_values.reinit(enth_cell);\n dealii::FEValues const &fe_values =\n hp_fe_values.get_present_fe_values();\n mp_cell->get_dof_indices(mp_dof_indices);\n dealii::types::global_dof_index const mp_dof_index = mp_dof_indices[0];\n enth_cell->get_dof_indices(enth_dof_indices);\n double volume = 0.;\n for (unsigned int q = 0; q < n_q_points; ++q)\n for (unsigned int i = 0; i < dofs_per_cell; ++i)\n {\n volume += fe_values.shape_value(i, q) * fe_values.JxW(q);\n temperature_average[mp_dof_index] +=\n fe_values.shape_value(i, q) * temperature[enth_dof_indices[i]] *\n fe_values.JxW(q);\n }\n temperature_average[mp_dof_index] /= volume;\n }\n }\n\n return temperature_average;\n}\n\ntemplate \ndouble MaterialProperty::compute_property_from_table(\n unsigned int const material_id, unsigned int const material_state,\n unsigned int const property, double const temperature) const\n{\n if (temperature <=\n _state_property_tables(material_id, material_state, property, 0, 0))\n {\n return _state_property_tables(material_id, material_state, property, 0, 1);\n }\n else\n {\n unsigned int i = 0;\n unsigned int const size = _state_property_tables.extent(3);\n for (; i < size; ++i)\n {\n if (temperature <\n _state_property_tables(material_id, material_state, property, i, 0))\n {\n break;\n }\n }\n\n if (i >= size - 1)\n {\n return _state_property_tables(material_id, material_state, property,\n size - 1, 1);\n }\n else\n {\n auto tempertature_i =\n _state_property_tables(material_id, material_state, property, i, 0);\n auto tempertature_im1 = _state_property_tables(\n material_id, material_state, property, i - 1, 0);\n auto property_i =\n _state_property_tables(material_id, material_state, property, i, 1);\n auto property_im1 = _state_property_tables(material_id, material_state,\n property, i - 1, 1);\n return property_im1 + (temperature - tempertature_im1) *\n (property_i - property_im1) /\n (tempertature_i - tempertature_im1);\n }\n }\n}\n\n} // namespace adamantine\n", "meta": {"hexsha": "9705d60539fb656e7762653bb106ac0e2175392c", "size": 25847, "ext": "hh", "lang": "C++", "max_stars_repo_path": "source/MaterialProperty.templates.hh", "max_stars_repo_name": "Rombur/adamantine", "max_stars_repo_head_hexsha": "45dd37397680fad1eaa64dbb311724c4f727a675", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 4.0, "max_stars_repo_stars_event_min_datetime": "2016-09-03T02:08:26.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-03T01:26:41.000Z", "max_issues_repo_path": "source/MaterialProperty.templates.hh", "max_issues_repo_name": "Rombur/adamantine", "max_issues_repo_head_hexsha": "45dd37397680fad1eaa64dbb311724c4f727a675", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 74.0, "max_issues_repo_issues_event_min_datetime": "2016-08-31T18:10:46.000Z", "max_issues_repo_issues_event_max_datetime": "2021-04-08T01:51:44.000Z", "max_forks_repo_path": "source/MaterialProperty.templates.hh", "max_forks_repo_name": "Rombur/adamantine", "max_forks_repo_head_hexsha": "45dd37397680fad1eaa64dbb311724c4f727a675", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2019-11-12T15:43:38.000Z", "max_forks_repo_forks_event_max_datetime": "2021-01-19T02:58:56.000Z", "avg_line_length": 39.7035330261, "max_line_length": 80, "alphanum_fraction": 0.652764344, "num_tokens": 6043, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.40733340004593027, "lm_q1q2_score": 0.2163793201926002}} {"text": "\n\n#include \"UGCPopularity.hpp\"\n#include \"ContentElement.hpp\"\n#include \n#include \n#include \n#include \"boost/random/uniform_real_distribution.hpp\"\n#include \n#include \"boost/random/uniform_int.hpp\"\n#include \n\nextern boost::mt19937 gen;\n\nUGCPopularity::UGCPopularity(unsigned int totalRounds,\n bool perturbations) {\n this->totalRounds = totalRounds;\n this->ttpDist = new boost::math::ttpDistribution(totalRounds);\n this->perturbations = perturbations;\n}\n\nUGCPopularity::~UGCPopularity() {\n delete ttpDist;\n}\n\n// generate views for all the content elements at a given phase for this round only\nunsigned int UGCPopularity::generateViews(std::list contentList, \n peakingPhase phase) {\n unsigned int bodyCount, tailCount;\n tailCount = std::floor(contentList.size() / 10);\n bodyCount = contentList.size() - tailCount;\n std::list views;\n if (phase == BEFORE_PEAK) {\n for (unsigned int i = 0; i < tailCount; i++) {\n views.push_back(this->getBeforePeakTailViews());\n }\n for (unsigned int i = 0; i < bodyCount; i++) {\n views.push_back(this->getBeforePeakViews());\n }\n } else if (phase == AT_PEAK) {\n for (unsigned int i = 0; i < tailCount; i++) {\n views.push_back(this->getAtPeakTailViews());\n }\n for (unsigned int i = 0; i < bodyCount; i++) {\n views.push_back(this->getAtPeakViews());\n }\n } else { // phase == AFTER_PEAK\n for (unsigned int i = 0; i < tailCount; i++) {\n views.push_back(this->getAfterPeakTailViews());\n }\n for (unsigned int i = 0; i < bodyCount; i++) {\n views.push_back(this->getAfterPeakViews());\n }\n }\n // sort the views in descending order and assign them to the contentList\n // (which is ordered by ranking) so that relative ranking remains unaltered\n views.sort(std::greater());\n std::list::iterator viewsIt, mIt;\n \n // introduce perturbations in the number of views, by switching views between\n // videos in the same phase and at a maximum distance g\n if (this->perturbations && phase != AT_PEAK) {\n uint g = 12; // suggested value from Borghol et al.\n std::vector > winLimits;\n uint minWin, maxWin;\n uint maxViews = views.front();\n // compute valid switching windows for each content\n BOOST_FOREACH(uint v, views) {\n minWin = std::floor(v / g);\n maxWin = std::min(maxViews, v * g);\n winLimits.push_back(std::make_pair(minWin,maxWin));\n }\n // switch views a \"sufficiently large number of times\" (!) between contents\n // with compatible switching windows\n \n // NOTE: the following code is so ugly it makes me want to kill kittens. \n // try to rewrite it in a half-decent way, ok?\n boost::random::uniform_int_distribution<> uniDist(0, contentList.size()-1);\n for (uint i = 0; i < contentList.size() / 3; i++) {\n uint randC = uniDist(gen);\n viewsIt = views.begin();\n for (uint j = 0; j < randC; j++)\n viewsIt++;\n mIt = viewsIt;\n int j = 0;\n while (mIt != views.begin() && *mIt >= winLimits[randC].first) {\n j--;\n mIt--;\n }\n if (j != 0) j++;\n mIt = viewsIt;\n int k = 0;\n while (mIt != views.end() && *mIt <= winLimits[randC].second) {\n mIt++;\n k++;\n }\n if (k != 0) k--;\n if (j != 0 || k != 0) {\n boost::random::uniform_int_distribution<> switchDist(0,k-j);\n j = switchDist(gen) + j;\n if (j < 0) {\n std::swap(winLimits[randC], winLimits[randC+j]);\n mIt = viewsIt;\n while (j < 0) {\n mIt--;\n j++;\n }\n std::iter_swap(viewsIt, mIt); \n }\n else if (j > 0) {\n std::swap(winLimits[randC], winLimits[randC+j]);\n mIt = viewsIt;\n while (j > 0) {\n mIt++;\n j--;\n }\n std::iter_swap(viewsIt, mIt); \n }\n } \n }\n }\n // assign views in the intended order (sorted or permuted)\n uint totalViews = 0;\n viewsIt = views.begin();\n BOOST_FOREACH (ContentElement* content, contentList) {\n content->setViewsThisRound(*viewsIt);\n totalViews += *viewsIt;\n viewsIt++;\n }\n return totalViews;\n}\n\n// Returns the round at which a content will peak in terms of views\nunsigned int UGCPopularity::generatePeakRound() {\n boost::uniform_01<> uni;\n double randVal = uni(gen);\n// std::cout << \"Uniform Value: \" << randVal << std::endl;\n return boost::math::quantile(*ttpDist, randVal);\n}\n\nunsigned int UGCPopularity::getBeforePeakTailViews() {\n boost::math::lognormal logDist(2.000, 2.135);\n boost::random::uniform_real_distribution<> uniDist(0.903, 1);\n double randValue = uniDist(gen);\n double retValue = boost::math::quantile(logDist, randValue); \n return static_cast(0.5+retValue);\n}\n\nunsigned int UGCPopularity::getAtPeakTailViews() {\n boost::math::lognormal logDist(-3.826, 3.477);\n boost::random::uniform_real_distribution<> uniDist(0.997, 1);\n double randValue = uniDist(gen);\n double retValue = boost::math::quantile(logDist, randValue); \n return static_cast(0.5+retValue);\n}\n\nunsigned int UGCPopularity::getAfterPeakTailViews() {\n boost::math::lognormal logDist(-0.356, 2.533);\n boost::random::uniform_real_distribution<> uniDist(0.931, 1);\n double randValue = uniDist(gen);\n double retValue = boost::math::quantile(logDist, randValue); \n return static_cast(0.5+retValue);\n}\n\nunsigned int UGCPopularity::getBeforePeakViews() {\n const unsigned int xMin = 0, xThresh = 119;\n unsigned int retValue = 0;\n boost::uniform_01<> uniDist;\n double randVal = uniDist(gen);\n // std::cout << \"Uniform Value: \" << randVal << std::endl;\n boost::math::beta_distribution<> betaDist(0.191, 1.330);\n retValue = std::floor(0.5+xMin+(xThresh-xMin)*\n boost::math::quantile(betaDist, randVal)); \n return retValue;\n}\n\nunsigned int UGCPopularity::getAtPeakViews() {\n const unsigned int xMin = 4, xThresh = 297;\n unsigned int retValue = 0;\n boost::uniform_01<> uniDist;\n double randVal = uniDist(gen);\n // std::cout << \"Uniform Value: \" << randVal << std::endl; \n boost::math::beta_distribution<> betaDist(0.543, 2.259);\n retValue = std::floor(0.5+xMin+(xThresh-xMin)*\n boost::math::quantile(betaDist, randVal));\n return retValue;\n}\n\nunsigned int UGCPopularity::getAfterPeakViews() {\n const unsigned int xMin = 0, xThresh = 30;\n unsigned int retValue = 0;\n boost::uniform_01<> uniDist;\n double randVal = uniDist(gen);\n // std::cout << \"Uniform Value: \" << randVal << std::endl;\n boost::math::beta_distribution<> betaDist(0.077, 0.968);\n retValue = std::floor(0.5+xMin+(xThresh-xMin)*\n boost::math::quantile(betaDist, randVal));\n return retValue;\n}\n", "meta": {"hexsha": "e8735ef178714a9e6c78f033e9c9ab76d99069a6", "size": 6932, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/UGCPopularity.cpp", "max_stars_repo_name": "manuhalo/PLACeS", "max_stars_repo_head_hexsha": "1574a34a2a98468e72d072cc9d1f2b32fcee38f2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/UGCPopularity.cpp", "max_issues_repo_name": "manuhalo/PLACeS", "max_issues_repo_head_hexsha": "1574a34a2a98468e72d072cc9d1f2b32fcee38f2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2015-04-24T10:10:51.000Z", "max_issues_repo_issues_event_max_datetime": "2015-06-18T08:32:16.000Z", "max_forks_repo_path": "src/UGCPopularity.cpp", "max_forks_repo_name": "manuhalo/PLACeS", "max_forks_repo_head_hexsha": "1574a34a2a98468e72d072cc9d1f2b32fcee38f2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.66, "max_line_length": 83, "alphanum_fraction": 0.6419503751, "num_tokens": 1971, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6334102636778403, "lm_q2_score": 0.341582499438317, "lm_q1q2_score": 0.2163618610369601}} {"text": "/*\n * =====================================================================================\n *\n * Filename: EnvelopFinder.cpp\n *\n * Description: \n *\n * Version: 1.0\n * Created: 9/6/2012 4:48:20 PM\n * Revision: none\n * Compiler: msvc\n *\n * Author: Han Hu (hh1985@bu.edu), \n * Organization: Boston University\n *\n * =====================================================================================\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n//#include \n\nnamespace gag\n{\n\tEnvelopFinder::EnvelopFinder(RichList& spec)\n\t\t: spectrum(spec), param(Param::instance())\t\n\t{\n\t\t/* \n\t\tGuess the range of average formula. Usually we assume the difference between composition estimated from the base peak and the one estimated from monoisotopic peak can be safely ignored.\n\t\t\tNo Sulfur (100D)\t\tHigh Sulfur (100D) \n\t\t\tC: 3.7238523 1.9961864\n\t\t\tH: 5.4425534 2.9942796\n\t\t\tO: 2.8645018 3.1938983\n\t\t\tN: 0.2864502 0.1996186\n\t\t\tS: 0 0.5988559\n\t\t*/\n\t\tave1.insert(std::make_pair(\"C\", 3.7238523));\n\t\tave1.insert(std::make_pair(\"H\", 5.4425534));\n\t\tave1.insert(std::make_pair(\"O\", 2.8645018));\n\t\tave1.insert(std::make_pair(\"N\", 0.2864502));\n\t\tave1.insert(std::make_pair(\"S\", 0.0));\n\n\t\tave2.insert(std::make_pair(\"C\", 1.9961864));\n\t\tave2.insert(std::make_pair(\"H\", 2.9942796));\n\t\tave2.insert(std::make_pair(\"O\", 3.1938983));\n\t\tave2.insert(std::make_pair(\"N\", 0.1996186));\n\t\tave2.insert(std::make_pair(\"S\", 0.5988559));\n\n\t\trun();\n\t}\n\n\tvoid EnvelopFinder::run()\n\t{\n\t\t// Get/Set parameters.\n\t\tunsigned int precursor_charge = param.getParameter(\"precursor_charge\").first;\n\n\t\t// Divide the spectrum into several independent islands.\n\t\tIslandList is_list = this->getPeakIslands(spectrum);\n\n\t\t// Iterate over all independent islands.\n\t\tfor(IslandList::iterator iter = is_list.begin(); iter != is_list.end(); iter++)\n\t\t{\n\t\t\tstd::set temp_env_set;\n\n\t\t\tRichList island_pks = *iter;\n\t\t\tstd::cout << \"Found island: \" << std::endl;\n\t\t\tisland_pks.printPeakList();\n\t\t\t// Starts from the peak with highest intensity. Assuming it is the base peak.\n\t\t\tRichPeakListByResolution& pks_area = island_pks.getPeakListByType();\n\t\t\t\n\t\t\tRichPeakListByResolution::iterator iter_area = pks_area.begin();\n\t\t\tRichPeakListByResolution::iterator end_iter = pks_area.end();\n\t\t\t// The last peak cannot be considered as a base peak.\n\t\t\tend_iter--;\n\n\t\t\t/* Step 1. Envelop identification */\n\t\t\tfor(; iter_area != end_iter; iter_area++)\n\t\t\t{\n\t\t\t\t// Iterate over all possible charge states.\n\t\t\t\t// Notice that the sign of the charge is not used here.\n\t\t\t\tfor(unsigned int z=precursor_charge; z>=1; z--)\n\t\t\t\t{\n\t\t\t\t\t// All candidate envelops should be stored for records.\n\t\t\t\t\tthis->findNextEnvelops(island_pks, iter_area, z, temp_env_set);\n\t\t\t\t}\n\t\t\t\t\n\t\t\t}\t\n\n\t\t\tstd::cout << \"Before optimizing, output all the envelop information...\" << std::endl;\n\t\t\tBOOST_FOREACH(EnvelopPtr env, temp_env_set)\n\t\t\t\tenv_ref.printEnvelopInformation(env);\n\n\t\t\tstd::cout << std::endl;\n\n\t\t\t// Adjust the fitting scores and splitting scores for envelop set.\n\t\t\tthis->optimizeEnvelopSet(temp_env_set);\n\n\t\t} \n\t}\n\n\tIslandList EnvelopFinder::getPeakIslands(RichList& pks)\n\t{\n\t\tdouble island_space = param.getParameter(\"island_space\").first;\n\n\t\tIslandList is_list;\n\n\t\tRichList* temp_spec = new RichList();\n\n\t\tRichPeakListByMZ& pks_mz = pks.getPeakListByType();\n\n\t\tfor(RichPeakListByMZ::const_iterator iter = pks_mz.begin(); iter != pks_mz.end(); iter++)\n\t\t{\n\t\t\tif(iter == pks_mz.begin()) {\n\t\t\t\ttemp_spec->addPeak(*iter);\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tif((*iter)->mz - (*boost::prior(iter))->mz < island_space) {\n\t\t\t\ttemp_spec->addPeak(*iter);\n\t\t\t} else { // \n\t\t\t\tis_list.push_back(*temp_spec);\n\t\t\t\t\n\t\t\t\t// Delete the container. Be careful of this section.\n\t\t\t\tdelete temp_spec;\n\n\t\t\t\ttemp_spec = new RichList();\n\t\t\t\ttemp_spec->addPeak(*iter);\n\t\t\t}\n\t\t}\n\t\tis_list.push_back(*temp_spec);\n\t\tdelete temp_spec;\n\t\t\n\t\treturn is_list;\n\t}\n\n\tvoid EnvelopFinder::findNextEnvelops(RichList& island, RichPeakListByResolution::iterator iter_area, unsigned int charge, std::set& env_set)\n\t{\n\t\t// 1. Find candidate envelops which extend from current peak.\n\t\t\n\t\t// Project the area iterator into corresponding mz iterator.\n\t\tRichPeakListByMZ::iterator iter_mz = island.getPeakContainer().project(iter_area);\n\n\t\tstd::cout << \"Identify base peak: \" << (*iter_mz)->mz << \" Charge: \" << charge << std::endl;\n\t\t\n\t\tint mode = param.getParameter(\"mode\").first;\n\t\tdouble pre_mz = param.getParameter(\"precursor_mz\").first;\n\t\tint pre_z = param.getParameter(\"precursor_charge\").first;\n\t\t\n\t\t// Convert m/z value to corresponding mass. \n\t\tdouble pre_mass = calculateMass(pre_mz, pre_z * mode);\n\n\t\t// If the calculated mass is larger than precursor mass, there is no need to continue identification.\n\t\tdouble mass = calculateMass((*iter_mz)->mz, charge * mode);\n\t\t\n\t\tif(mass > pre_mass)\n\t\t\treturn;\n\n\t\tstd::pair pk_set = this->extendPeakSet(island, iter_mz, charge);\n\t\t\n\t\tif(pk_set.first.getSize()==1)\n\t\t\treturn;\n\n // Container for temporary storage of envelops.\n\t\tstd::vector env_store;\n\t\tfor(size_t i=0; i < pk_set.second; i++) {\n\t\t\tEnvelopPtr env = createEnvelop(charge, pk_set.first.getBoundary());\n\t\t\tenv_store.push_back(env);\n\t\t}\n\n\t\t// 2. TBD: Estimate the validity of the pk_set.\n\t\t// e.g. the overall shifts and the continuity of the shifts.\n\n\t\t// 2.a If the base peak has been reported as a distal peak of other envelops.\n\t\t// 2.b If envelops which share the base peak have the same charge \n\t\t// 2.c No event report for the base peak.\n\t\t// 2.d no contribution of new peaks.\n\t\t// 2.e fitting score is worse than expected.\n\n\t\t// Get the maximum number of envelops.\n\n\t\t// Dynamically convert pk_set into vector of envelops.\n\t\tProtoEnv& peak_information = pk_set.first.getPeakInformation();\n\n\t\tProtoEnv::iterator pk_iter = peak_information.begin(); \n\t\t// Iterate over all shift, decide if all envelop will share.\n\t\tfor(; pk_iter != peak_information.end(); )\n\t\t{\n\t\t\tint count = peak_information.count(pk_iter->first);\n\t\t\tstd::pair shift_pair = peak_information.equal_range(pk_iter->first);\n\t\t\tProtoEnv::iterator shift_iter = shift_pair.first;\n\t\t\tProtoEnv::iterator last_iter = shift_pair.second;\n\t\t\tlast_iter--;\n\n\t\t\tfor(size_t i=0; ifirst;\n\t\t\t\tRichPeakPtr pk = shift_iter->second.first;\t\t\t\n\t\t\t\tInfoPeakPtr info = shift_iter->second.second;\n\t\t\t\t// Register the (peak, env) pair, shift and peak info into the envelop reference table.\n\t\t\t\tInfoPeakPtr info_copy(new InfoPeak(*info));\n\t\t\t\tstd::cout << \"Register peak \" << pk->mz << \"for envelop \" << env_store[i]->id << std::endl;\n\t\t\t\tenv_ref.addDictionaryReference(pk, env_store.at(i), shift, info_copy);\n\n\t\t\t\t// Non_unique_key.\n\t\t\t\tif(shift_iter != last_iter)\n\t\t\t\t\tshift_iter++;\t\t\n\n\t\t\t}\n\n\t\t\tpk_iter = shift_pair.second;\n\t\t}\n\n\t\t// Update envelop lambda information for each envelop.\n\t\tthis->updateEnvelopParameter(env_store);\n\n\t\t// Store the envelops.\n\t\tenv_set.insert(env_store.begin(), env_store.end());\n\t}\n\t\nstd::pair EnvelopFinder::extendPeakSet(RichList& island, RichPeakListByMZ::iterator iter_mz, unsigned int charge)\n\t{\n\t\t\n\t\t// Candidate base peak. Notice that this might not be the monoisotopic peak for molecules with high mass values.\n\t\tRichPeakPtr expr_base_pk = *iter_mz;\n\t\tint mode = param.getParameter(\"mode\").first;\n\t\tdouble mass = calculateMass(expr_base_pk->mz, charge * mode);\n\t\tEnvelopBoundary bound = this->createBoundary(mass, charge);\n\n\t\t// The reason of creating an object called envelop is because during the extension of peakset towards different directions, there might be multiple matching for given shift. \n\t\tPeakSet pk_set(bound);\n\n\t\tInfoPeakPtr pk_infor = boost::make_shared(expr_base_pk->resolution, 0.0, 0.5, 0.0);\n\t\t\n\t\tpk_set.addPeak(0, expr_base_pk, pk_infor);\n\n\t\tRichPeakListByMZ& pks_mz = island.getPeakListByType();\n\n\t\t// Shift towards right along the mz axis.\n\t\tsize_t max_env = this->extendPeak(pk_set, pks_mz, iter_mz, charge, 1);\n\t\t// TBD: Shift towards left along the mz axis.\n\t\t//this->extendPeak(pk_set, pks_mz, iter_mz, charge, -1);\n\n\t\treturn std::make_pair(pk_set,max_env);\n\t}\n\n\n\tEnvelopBoundary EnvelopFinder::createBoundary(const double mass, unsigned int charge)\n\t{\n\t\t// 1. Estimate the rounded version of compositions.\n\t\tdouble coef = mass / 100.0;\n\t\tComposition compo1, compo2;\n\n\t\tAveragineFormulae::iterator iter1 = ave1.begin();\n\t\tfor(; iter1 != ave1.end(); iter1++)\n\t\t\tcompo1.addElement(iter1->first, (int)floor(iter1->second * coef + 0.5));\n\n\t\tAveragineFormulae::iterator iter2 = ave2.begin();\n\t\tfor(; iter2 != ave2.end(); iter2++)\n\t\t\tcompo2.addElement(iter2->first, (int)floor(iter2->second * coef + 0.5));\n\t\t\n\t\tint mode = param.getParameter(\"mode\").first;\n\n\t\tint z = mode > 0 ? charge : (-1)*charge;\n\t\t// 2. Get the range of the isotopic distribution\n\t\tIsotopicDistribution iso_up(compo1);\n\t\tAggregatedIsotopicVariants peakset1 = iso_up.getAggregatedIsotopicVariants(z);\n\t\tIsotopicDistribution iso_down(compo2);\n\t\tAggregatedIsotopicVariants peakset2 = iso_down.getAggregatedIsotopicVariants(z);\n\n\t\treturn EnvelopBoundary(peakset1, peakset2);\n\t}\n\n\tsize_t EnvelopFinder::extendPeak(PeakSet& pk_set, RichPeakListByMZ& pks_mz,RichPeakListByMZ::iterator iter_mz, unsigned int charge, int direction)\n\t{\n\t\t//int dist = direction > 0 ? std::distance(iter_mz, pks_mz.end())-1 : std::distance(pks_mz.begin(), iter_mz);\n\t\t\n\t\tunsigned int shift = 1;\n\t\t//int prev_dist = 0;\n\n\t\t// Experimental candidate base peak.\n\t\tRichPeakPtr expr_base_pk = *iter_mz;\n\n\t\tint mode = param.getParameter(\"mode\").first;\n\t\t// For confidence estimation, using internal_accuracy.\n\t\tdouble internal_accuracy = param.getParameter(\"internal_accuracy\").first;\n\n\t\t// For peak finding and lambda estimation, using external_accuracy.\n\t\tdouble external_accuracy = param.getParameter(\"external_accuracy\").first;\n\n\t\t// Calculate the mass of the candidate base peak.\n\t\t// Notice that there will be mechanism where there is electron capture.\n\t\tdouble mass = calculateMass(expr_base_pk->mz, charge * mode);\n\n\t\t// Create theoretical boundary. The boundary has been adjusted to fit the charge.\n\t\t//EnvelopBoundary env_bound = this->createBoundary(mass, charge);\n\t\tEnvelopBoundary& env_bound = pk_set.getBoundary();\n\n\t\t//std::cout << \"Up Boundary: \" << std::endl;\n\t\t//env_bound.first.printPeakList();\n\t\t//std::cout << \"Down Boundary: \" << std::endl;\n\t\t//env_bound.second.printPeakList();\n\n\t\t// Get a copy of the base peak iterator for further move operation.\n\t\t//RichPeakListByMZ::iterator iter = iter_mz;\n\t\tint stop_flag = 1;\n\n\t\t// Dynamically convert pk_set into vector of envelops.\n\t\tstd::pair shift_count(0,1);\n\t\tsize_t max_env = 0;\n\n\t\t//std::advance(iter_mz, direction);\n\t\tif(direction < 0 && iter_mz == pks_mz.begin())\n\t\t\treturn 0;\n\t\tstd::advance(iter_mz, direction);\n\t\tif(direction > 0 && iter_mz == pks_mz.end())\n\t\t\treturn 0;\n\n\t\t// TBD: the program should efficiently location the position of most likely peak. instead of sequentially search for it.\n\t\twhile(1)\n\t\t{\n\n\n\t\t\tstd::cout << \"Shift: \" << shift << std::endl;\n\n\t\t\t// Get the boundary peaks at current shift.\n\t\t\tPeakPtr theo_pk1 = env_bound.getUpperBound().getPeakByShift(direction * shift);\n\t\t\tPeakPtr theo_pk2 = env_bound.getLowerBound().getPeakByShift(direction * shift);\n\n\t\t\t// No extension any more.\n\t\t\tif(theo_pk1->mz == 0.0 && theo_pk2->mz == 0.0)\n\t\t\t\tbreak;\n\n\t\t\tRichPeakPtr current_pk = *iter_mz;\n\t\t\tstd::cout << \"Examine peak: \" << current_pk->mz << std::endl;\n\n\t\t\t// Experimental distance from current peak to base peak.\n\t\t\t// Notice that the value might be negative.\n\t\t\tdouble expr_dist = abs(current_pk->mz - expr_base_pk->mz);\n\n\t\t\t// Theoretical distance from current peak to base peak. Adjusted for charged peak.\n\t\t\tdouble theo_dist1 = abs(env_bound.getUpperBound().getMassDifferenceByShift(0, shift));\n\t\t\tdouble theo_dist2 = abs(env_bound.getLowerBound().getMassDifferenceByShift(0, shift));\n\n\t\t\tdouble max_dist = theo_dist1;\n\t\t\tdouble min_dist = theo_dist2;\n\n\t\t\tif(max_dist < min_dist) {\n\t\t\t\tswap(min_dist, max_dist);\n\t\t\t}\n\n\t\t\tdouble error1 = 1e6 * (expr_dist - min_dist)/expr_base_pk->mz;\n\t\t\tdouble error2 = 1e6 * (expr_dist - max_dist)/expr_base_pk->mz;\n\t\t\t\n\t\t\tif(error1 < -1 * external_accuracy) {\n\t\t\t\t// Before the shift region: keep moving the iterator.\n\t\t\t\t// std::advance(iter_mz, direction); continue;\n\t\t\t} else if(error2 > external_accuracy) { \n\t\t\t\t// Beyond the shift region: update shift.\n\t\t\t\tif(stop_flag == 0) {\n\t\t\t\t\tshift++; stop_flag = 1; continue;\n\t\t\t\t} else {\n\t\t\t\t\t// break means no missing peak is allowed in the middle. This might be controlled by some parameter.\n\t\t\t\t\tstop_flag = 1;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t// A matching peak. Notice the effect of charge state.\n\t\t\t\tstd::cout << \"A matching peak!\" << std::endl;\n\t\t\t\t\n\t\t\t\tif(shift == shift_count.first) {\n\t\t\t\t\tshift_count.second++;\n\t\t\t\t} else {\n\t\t\t\t\t// Reset shift_count.\n\t\t\t\t\tshift_count = std::make_pair(shift, 1);\n\t\t\t\t}\n\n\t\t\t\t// Update maximum number of envelops.\n\t\t\t\tif(shift_count.second > max_env)\n\t\t\t\t\tmax_env = shift_count.second;\n\n\t\t\t\tstop_flag = 0;\n\t\t\t\t// Estimate the confidence of the mz in terms of estimating lambda.\n\t\t\t\tdouble diff = max_dist - min_dist;\n\n\t\t\t\t// Error window is used for confidence estimation.\n\t\t\t\tdouble err_win = internal_accuracy * expr_base_pk->mz * 1e-6;\n\t\t\t\tdouble prob = diff/(diff + 2*err_win);\n\n\t\t\t\t// Indicate the sign of the error window.\n\t\t\t\tint err_sign = (theo_dist1 < theo_dist2 ? -1 : 1);\n\n\t\t\t\t//: lambda_mz should always be in (0, 1). If lambda is closed to 1, the pattern is closed to no-sulfate boundary, and 0 for high-sulfate boundary.\n\t\t\t\tdouble lambda_mz = (expr_dist - theo_dist2+err_sign*err_win)/(theo_dist1 - theo_dist2 + 2*err_sign*err_win);\n\n\t\t\t\t// Round the abnormal lambda_mz to the boundary.\n\t\t\t\tif(lambda_mz > 1) {\n\t\t\t\t\tlambda_mz = 1.0;\n\t\t\t\t\tprob = 1.0;\n\t\t\t\t} else if(lambda_mz < 0) {\n\t\t\t\t\tlambda_mz = 0.0;\n\t\t\t\t\tprob = 1.0;\n\t\t\t\t}\n\n\t\t\t\t// TBD: estimate lambda_abd (within 5%) and delta_lambda.\n\t\t\t\t// Notice: lambda_abd can be < 0 or > 1.\n\t\t\t\tdouble lambda_abd = (current_pk->resolution/expr_base_pk->resolution - theo_pk2->intensity)/(theo_pk1->intensity - theo_pk2->intensity);\n\n\t\t\t\tInfoPeakPtr pk_infor = boost::make_shared(current_pk->resolution, lambda_mz, lambda_abd, prob);\n\t\t\t\t// Notice: The abundance information should be initialized during the establish of global lambda value.\n\t\t\t\t//pk_infor.adjusted_abundance = env_bound.getTheoreticalPeak(lambda_abd, shift).intensity;\n\t\t\t\t\n\t\t\t\tpk_set.addPeak(shift, current_pk, pk_infor);\n\t\t\t\t\n\t\t\t}\n\t\t\tif(direction < 0 && iter_mz == pks_mz.begin())\n\t\t\t\tbreak;\n\t\t\tstd::advance(iter_mz, direction);\n\t\t\tif(direction > 0 && iter_mz == pks_mz.end())\n\t\t\t\tbreak;\n\t\t}\n\t\treturn max_env;\n\t}\n\n\tvoid EnvelopFinder::updateEnvelopParameter(EnvelopPtr env)\n\t{\n\t\tEnvEntry base_entry = env_ref.getEntryByShift(env, 0);\n\n\t\t// 1.a Find lambda value for each of the peak.\n\t\tstd::vector peak_entries = env_ref.getEntryByEnvelop(env);\n\n\t\tdouble current_prob = 0.0;\n\t\tBOOST_FOREACH(EnvEntry& entry, peak_entries)\n\t\t{\n\t\t\tif(entry.info->prob > current_prob) {\n\t\t\t\tcurrent_prob = entry.info->prob;\n\t\t\t\tenv->lambda_mz = entry.info->lambda_mz;\n\t\t\t}\n\n\t\t}\n\t\t//// 1.b Normalize the confidence value.\n\t\t//double sum_confidence = 0.0;\n\t\t//for(std::vector::iterator iter = peak_entries.begin(); \n\t\t//\titer != peak_entries.end(); iter++)\n\t\t//\tsum_confidence += pow(iter->info->prob,2);\n\n\t\t//double lambda_mz = 0.0;\n\t\t//for(std::vector::iterator iter = peak_entries.begin(); \n\t\t//\titer != peak_entries.end(); iter++) {\n\t\t//\t\t// Pass base peak.\n\t\t//\t\tif(iter->getShift() == 0) \n\t\t//\t\t\tcontinue;\n\n\t\t//\t\tlambda_mz += pow(iter->info->prob,2)/sum_confidence * iter->info->lambda_mz;\n\t\t//}\n\t\t//env->lambda_mz = lambda_mz;\n\n\t\t// 2. Update theoretical abundance from the lambda.\n\t\tfor(std::vector::iterator iter = peak_entries.begin(); \n\t\t\titer != peak_entries.end(); iter++) {\n\t\t\t// The adjusted abundance is initialized as the exp data.\n\t\t\titer->info->adjusted_abundance = iter->pk->area;\n\t\t}\n\t}\n\n\tvoid EnvelopFinder::updateEnvelopParameter( std::vector env_list )\n\t{\n\t\tBOOST_FOREACH(EnvelopPtr env, env_list) {\n\t\t\tthis->updateEnvelopParameter(env);\n\n\t\t\t//this->updateSplittingPotential(env);\n\t\t}\n\t}\n\n\tvoid EnvelopFinder::optimizeEnvelopSet(std::set& env_set, double last_score)\n\t{\n\t\t/* New version */\n\t\tthis->updateSplittingPotential(env_set);\n\t\tthis->updateFittingScore(env_set);\n\n\t\t// Get all the peaks. \n\t\tRichList pk_list = env_ref.getRichPeakList(env_set);\n\n\t\tRichPeakListByMZ& pks_mz = pk_list.getPeakListByType();\n\n\t\tfor(RichPeakListByMZ::iterator iter = pks_mz.begin(); iter != pks_mz.end(); iter++)\n\t\t{\n\t\t\t// Split the peak based on fitting score & estimated abundance.\n\t\t\t// Notice that the identification of FP will also happen here.\n\t\t\tthis->splitPeak(*iter);\n\t\t}\n\t\t// Calculate the overall fitting scores.\n\t\tdouble next_score = this->updateFittingScore(env_set);\n\t\tstd::cout << \"Old score: \" << last_score << \"\\t\" << \"New score: \" << next_score << std::endl;\n\t\tif(abs(next_score - last_score)/last_score < 1e-5)\n\t\t\treturn;\n\t\telse\n\t\t\tthis->optimizeEnvelopSet(env_set, next_score);\n\t\t/* Old version */\n\n\t\t//std::set processed_peak_set;\n\t\t//std::set::iterator peak_iter;\n\n\t\t//// Generate fitting score for each envelop.\n\t\t//// Estimate scaling factor for each envelop.\n\t\t//this->updateSplittingPotential(env_pool);\n\t\t//this->updateFittingScore(env_pool);\n\n\t\t//sort(env_pool.begin(), env_pool.end(), Envelop::scoreLarger);\n\n\t\t//// Iterate over all envelops with intensity from high to low.\n\t\t//BOOST_FOREACH(EnvelopPtr& env, env_pool)\n\t\t//{\n\t\t//\t// If the base peak has been processed.\n\t\t//\tEnvEntry base_entry = env_ref.getBasePeakForEnvelop(env);\n\t\t//\t\n\t\t//\tpeak_iter = processed_peak_set.find(base_entry->pk);\n\t\t//\tif(peak_iter != processed_peak_set.end())\n\t\t//\t\tcontinue;\n\n\t\t//\t// Split the base peak.\n\t\t//\tbool status = this->splitPeak(base_entry->pk);\n\t\t//\tif(status) {\n\t\t//\t\tthis->updateSplittingPotential(env);\n\t\t//\t\tthis->updateFittingScore(env);\n\t\t//\t}\n\n\t\t//\t// Get all belonging peaks.\n\t\t//\tstd::vector entry_vec = env_ref.getEntryByEnvelop(env);\n\n\t\t//\t// Iterate over all peaks with intensity from high to low.\n\t\t//\tBOOST_FOREACH(EnvEntry& entry, entry_vec)\n\t\t//\t{\n\t\t//\t\tif(entry->getShift()==0)\n\t\t//\t\t\tcontinue;\n\t\t//\t\t// Split the peak based on 1. splitting potential and 2. scaling factor.\n\t\t//\t\tthis->splitPeak(entry->pk);\n\t\t//\t\t// Update the records.\n\t\t//\t\tprocessed_peak_set.insert(entry->pk);\n\t\t//\t}\n\t\t\t\n\t\t//}\t\t\n\t}\n\n\tvoid EnvelopFinder::updateSplittingPotential(EnvelopPtr env)\n\t{\n\t\t\n\t\t//double scaling_factor = env->scaling_factor;\n\n\t\t// Entry for base peak.\n\t\tEnvEntry& base_entry = env_ref.getEntryByShift(env, 0);\n\t\tstd::cout << \"The base peak is: \" << base_entry.pk->mz << std::endl;\n\n\t\t// Get all peaks included by the envelop.\n\t\tstd::vector entry_vec = env_ref.getEntryByEnvelop(env);\n\n\t\t// Update the scaling factor. The value for base peak should be 1.\n\t\tBOOST_FOREACH(EnvEntry& entry, entry_vec)\n\t\t{\n\t\t\tdouble expected_abundance = env_ref.getTheoreticalAbundance(entry);\n\t\t\t// Adjusted abunance / Theoretical abundance.\n\t\t\tdouble scaling_factor = entry.info->adjusted_abundance/expected_abundance;\n\t\t\tif(scaling_factor < env->scaling_factor)\n\t\t\t\tenv->scaling_factor = scaling_factor;\n\t\t}\n\n\t\t// Update the adjusted abundance.\n\t\t// 1. Update base entry.\n\t\tbase_entry.info->adjusted_abundance *= env->scaling_factor;\n\t\t// 2. Update all the rest of the peaks.\n\t\tBOOST_FOREACH(EnvEntry& entry, entry_vec)\n\t\t{\n\t\t\tif(entry.getShift()==0)\n\t\t\t\tcontinue;\n\n\t\t\tentry.info->adjusted_abundance = env_ref.getTheoreticalAbundance(entry);\n\t\t}\n\t\t\n\n\n\t\t//// Iterate over all theoretical peaks (except base peak), calculate their relative shift.\n\t\t//double total_share = 0.0;\n\t\t//std::map pk_map = env->getTheoreticalPeaks();\n\t\t//for(std::map::iterator iter = pk_map.begin(); \n\t\t//\titer != pk_map.end(); iter++) \n\t\t//{\n\t\t//\ttotal_share += iter->second->intensity;\n\t\t//}\n\t\t//\t\n\t\t//double base_adjusted_abd = 0.0;\n\t\t//for(std::map::iterator iter = pk_map.begin(); \n\t\t//\titer != pk_map.end(); iter++)\n\t\t//{\n\t\t//\tint shift = iter->first; PeakPtr pk = iter->second;\n\n\t\t//\t// Get entry corresponding to the shift. Notice the problem if shift does not exist for the envelop..\n\t\t//\tEnvEntry entry = env_ref.getEntryByShift(env, shift);\n\t\n\t\t//\t// If EnvEntry is empty. Which means the experimental peak corresponds to the shift doesn't show up.\n\t\t//\tif(entry.isEmpty()) continue;\n\n\t\t//\tentry.info->relative_share = pk->intensity / total_share;\n\n\t\t//\tdouble expected_abundance = env_ref.getTheoreticalAbundance(entry);\n\n\t\t//\tif(expected_abundance > entry.info->adjusted_abundance) {\n\t\t//\t\t// Potential overlapping event for base peak. Scale the base peak.\n\t\t//\t\tbase_adjusted_abd += base_entry.info->adjusted_abundance * (entry.info->adjusted_abundance / expected_abundance) * entry.info->relative_share;\n\t\t//\t}\n\t\t//\t// Update adjusted abundance.\n\t\t//\tentry.info->adjusted_abundance = expected_abundance;\n\t\t//}\n\n\t\t//// Update base peak information.\n\t\t//base_entry.info->adjusted_abundance = base_adjusted_abd;\n\t}\n\n\tvoid EnvelopFinder::updateSplittingPotential( std::vector entry_vec )\n\t{ \n\t\tBOOST_FOREACH(EnvEntry entry, entry_vec)\n\t\t\tthis->updateSplittingPotential(entry.env);\n\t}\n\tvoid EnvelopFinder::updateSplittingPotential(std::set& env_set)\n\t{\n\t\tBOOST_FOREACH(EnvelopPtr env, env_set)\n\t\t\tthis->updateSplittingPotential(env);\n\t}\n\n\tdouble EnvelopFinder::updateFittingScore(EnvelopPtr env)\n\t{\n\t\tstd::vector entry_vec = env_ref.getEntryByEnvelop(env);\n\t\t//RichPeakPtr base_pk = env_ref.getBasePeakForEnvelop(env);\n\t\tEnvEntry base_entry = env_ref.getEntryByShift(env, 0);\n\t\t\n\t\tdouble fitting_score = 0.0;\n\t\tBOOST_FOREACH(EnvEntry& entry, entry_vec)\n\t\t{\n\t\t\t// For base peak, intensity_shift and mz_shift is always 1.\n\t\t\tif(entry.getShift()==0) {\t\t\t\n\t\t\t\tfitting_score += sqrt(base_entry.info->adjusted_abundance);\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\tdouble expected_abundance = env_ref.getTheoreticalAbundance(entry);\n\t\t\t// The fitting score comes from 3 parts: \n\t\t\t// 1. the shift of intensity: abs(E-T)/T.\n\t\t\tdouble intensity_shift = 1.0 - abs(expected_abundance - entry.info->adjusted_abundance)/expected_abundance;\n\t\t\tif(intensity_shift < 0)\n\t\t\t\tintensity_shift = 0.0;\n\t\t\t//std::cout << \"Intensity shift: \" << intensity_shift << std::endl;\n\t\t\t\t\n\t\t\t// 2. the relative shift of mz.\n\t\t\tdouble internal_accuracy = param.getParameter(\"internal_accuracy\").first;\n\t\t\t\n\t\t\tdouble mz_shift = 1.0 - abs(entry.pk->mz - base_entry.pk->mz - entry.env->getTheoreticalMZDistance(0, entry.getShift()))/(entry.env->getWindowSizeByShift(entry.getShift()));\n\t\t\t//std::cout << \"MZ shift: \" << mz_shift << std::endl;\n\n\t\t\t// 3. the square root of expected_abundance.\n\t\t\tdouble weight = sqrt(expected_abundance);\n\t\t\t//std::cout << \"Weight: \" << weight << std::endl;\n\t\t\tfitting_score += weight * intensity_shift * mz_shift;\n\t\t}\n\t\t//std::cout << \"Fitting score: \" << fitting_score << std::endl;\n\t\tenv->fitting_score = fitting_score;\n\t\treturn fitting_score;\n\t} \n\n\tdouble EnvelopFinder::updateFittingScore( std::vector entry_vec)\n\t{\n\t\tdouble sum_score = 0.0;\n\t\tBOOST_FOREACH(EnvEntry& entry, entry_vec)\n\t\t\tsum_score += this->updateFittingScore(entry.env);\n\t\treturn sum_score;\n\t}\n\n\tdouble EnvelopFinder::updateFittingScore(std::set& env_set)\n\t{\n\t\tdouble sum_score = 0.0;\n\t\tBOOST_FOREACH(EnvelopPtr env, env_set)\n\t\t\tsum_score += this->updateFittingScore(env);\n\t\treturn sum_score;\n\t}\n\n\tvoid EnvelopFinder::adjustLambdaFromProfile( EnvelopPtr env )\n\t{\n\t\tdouble last_score = this->updateFittingScore(env);\n\t\t// The value of lambda_bound has to be in between 0.0 and 1.0\n\t\tdouble lambda_bound = 0.0;\n\n\t\twhile(1)\n\t\t{\n\t\t\t// Adjust lambda. Notice the direction.\n\t\t\tenv->lambda_mz = (lambda_bound + env->lambda_mz)/2;\n\n\t\t\t// Update splitting score.\n\t\t\tthis->updateSplittingPotential(env);\n\n\t\t\tdouble new_score = this->updateFittingScore(env);\n\n\t\t\tdouble score_diff = new_score - last_score;\n\t\t\t\n\t\t\t// Converge.\n\t\t\tif(abs(score_diff) < 1e-6)\n\t\t\t\tbreak;\n\t\t\t\n\t\t\tif(score_diff > 0.0) {\n\t\t\t\t\tenv->lambda_mz = (lambda_bound + env->lambda_mz)/2;\n\t\t\t} else {\n\t\t\t\t\t// Change the bound.\n\t\t\t\t\tlambda_bound = 1.0-lambda_bound;\n\t\t\t\t\tenv->lambda_mz = (lambda_bound + env->lambda_mz)/2;\n\t\t\t}\n\n\t\t}\n\n\t}\n\n\tvoid EnvelopFinder::splitPeak(RichPeakPtr pk)\n\t{\n\t\t// Get all connecting envelops.\n\t\tstd::vector entry_vec = env_ref.getEntryByPeak(pk);\n\n\t\tif(entry_vec.size() == 1) // No need to split for single envelop.\n\t\t\treturn;\n\t\telse if(entry_vec.size() == 0)\n\t\t\tthrow std::runtime_error(\"Undefined peak in the dictionary\");\n\t\telse\n\t\t\t;\n\n\t\tdouble splitting_score = 0.0;\n\t\t\n\t\tBOOST_FOREACH(EnvEntry& entry, entry_vec)\n\t\t{\n\t\t\t// splitting_score += entry.info->adjusted_abundance;\n\t\t\tsplitting_score += sqrt(entry.env->fitting_score * entry.info->adjusted_abundance);\n\t\t}\n\t\t\t\n\t\t// Split the peak.\n\t\tBOOST_FOREACH(EnvEntry& entry, entry_vec)\n\t\t{\n\t\t\tdouble scale_coef = sqrt(entry.env->fitting_score * entry.info->adjusted_abundance)/ splitting_score;\n\n\t\t\t// Update the estimated abundance.\n\t\t\tentry.info->adjusted_abundance = pk->resolution * scale_coef;\n\n\t\t\tenv_ref.printEnvelopInformation(entry.env);\n\t\t}\n\n\t}\n\n\tvoid EnvelopFinder::printEnvelop( EnvelopPtr env )\n\t{\n\t\tstd::cout << \"Envelop ID: \" << env->id << std::endl;\n\t\tstd::cout << \"Charge: \" << env->charge_state << std::endl;\n\t\tstd::cout << \"Lambda: \" << env->lambda_mz << std::endl;\n\t\tstd::cout << \"Score: \" << env->fitting_score << std::endl;\n\t\tstd::cout << std::endl;\n\t\t// Peak information.\n\t\tstd::cout << \"Covered peaks:\" << std::endl;\n\t\tstd::cout << \"Shift\\tMZ\\tEXP_ABD\\tFIT_ABD\" << std::endl;\n\n\t\t// Get all peaks.\n\t\tstd::vector pk_entries = env_ref.getEntryByEnvelop(env);\n\t\tBOOST_FOREACH(EnvEntry& pk_entry, pk_entries)\n\t\t{\n\t\t\tstd::cout << pk_entry.getShift() << \"\\t\" << pk_entry.pk->mz << \"\\t\"\n\t\t\t\t<< pk_entry.pk->resolution << \"\\t\" << pk_entry.info->adjusted_abundance\n\t\t\t\t<< std::endl;\n\t\t}\n\t\tstd::cout << std::endl;\n\t}\n\n\t//void EnvelopFinder::optimizeEnvelopSet(RichList& pk_list)\n\t//{\n\t//\t// 1. Iterate over peaks with m/z from low to high\n\t//\tRichPeakListByMZ& pks_by_mz = pk_list.getPeakListSortedByType();\n\n\t//\tRichPeakListByMZ::iterator iter_mz = pks_by_mz.begin();\n\n\t//\t// Envelop set and corresponding fitting score.\n\t//\tEnvelopSetCollection last_frontier;\n\n\t//\t// TBD: how to decide the cluster group will end.\n\t//\tfor(; iter_mz != pks_by_mz.end(); iter_mz++)\n\t//\t{\n\t//\t\t// 2. For each peak, find all connecting envelops.\n\t//\t\tstd::vector entry_vec = env_ref.getEntryByPeak(*iter_mz);\n\n\t//\t\t// Empty information. Consider storing the information with a separate container.\n\t//\t\tif(entry_vec.size() == 0)\n\t//\t\t\tcontinue;\n\n\t//\t\tstd::vector > env_set_collection;\n\t//\t\tfor(size_t i = 0; i < entry_vec.size()-1; i++)\n\t//\t\t\tfor(size_t j=i; j temp_env;\n\t//\t\t\t\ttemp_env.insert(entry_vec[i]->env);\n\n\t//\t\t\t\tif(i != j) // Not just pairs.\n\t//\t\t\t\t\ttemp_env.insert(entry_vec[j]->env);\n\n\t//\t\t\t\tenv_set_collection.insert(temp_env);\n\t//\t\t\t}\n\n\t//\t\t// New frontier. \n\t//\t\tif(last_frontier.size() == 0)\n\t//\t\t{\n\t//\t\t\tstd::vector >::iterator iter = env_set_collection.begin();\n\t//\t\t\tfor(; iter != env_set_collection.end(); iter++)\n\t//\t\t\t{\n\t//\t\t\t\tdouble temp_score = this->calculateFittingScore(*iter);\n\t//\t\t\t\tlast_frontier.insert(std::make_pair(temp_score, *iter));\n\t//\t\t\t}\n\t//\t\t\tcontinue;\n\t//\t\t}\n\n\t//\t\tEnvelopSetCollection next_frontier;\n\t//\t\t// Update current frontier.\n\t//\t\tfor(EnvelopSetCollection::iterator fron_iter = last_frontier.begin(); fron_iter != last_frontier.end(); fron_iter++)\n\t//\t\t{\n\t//\t\t\tBOOST_FOREACH(std::set& env_set, env_set_collection)\n\t//\t\t\t{\n\t//\t\t\t\tstd::set temp_set(fron_iter->second);\n\t//\t\t\t\t// Append the set.\n\t//\t\t\t\tset_union(temp_set.begin(), temp_set.end(), \n\t//\t\t\t\t\tenv_set.begin(), env_set.end(),\n\t//\t\t\t\t\tinserter(temp_set, temp_set.begin()));\n\n\t//\t\t\t\t// Compare the fitting score between std::vector with and without new current envelops.\n\t//\t\t\t\tdouble score1 = fron_iter->first;\n\t//\t\t\t\tdouble score2 = this->calculateFittingScore(new_set);\n\n\t//\t\t\t\tif(score2 > score1)\n\t//\t\t\t\t\tnext_frontier.insert(std::make_pair(score2, temp_set));\n\t//\t\t\t\telse\n\t//\t\t\t\t\tnext_frontier.insert(std::make_pair(score1, fron_iter->second));\n\t//\t\t\t}\n\t//\t\t}\n\n\t//\t\tlast_frontier = next_frontier;\n\t//\t\n\t//\t}\n\n\t//\t// Select the set with maximum score. \n\t//\tstd::set& winner_set = last_frontier.rbegin()->second;\n\t//\t\n\t//\tenv_pool.insert(env_pool.end(), winner_set.begin(), winner_set.end());\n\t//\t\n\t//}\n\n\t//double EnvelopFinder::calculateFittingScore( std::set env_set )\n\t//{\n\t//\t/* Estimate the abundance based on current set selection. */\n\t//\t\n\n\n\t//\tdouble total_score = 0.0;\n\t//\tBOOST_FOREACH(EnvelopPtr& env, env_set)\n\t//\t{\n\t//\t\ttotal_score += this->calculateFittingScore(env);\n\t//\t}\n\t//\treturn total_score;\n\t//}\n\n\t//double EnvelopFinder::calculateFittingScore(EnvelopPtr env)\n\t//{\n\t//\tstd::vector entry_vec = env_ref.getEntryByEnvelop(env);\n\t//\t\n\t//\tif(entry_vec.size()==0)\n\t//\t\treturn 0.0;\n\n\t//\t// Get base entry.\n\t//\tEnvEntry base_entry = env_ref.getEntryByShift(env, 0);\n\n\t//\tdouble fitting_score = 0.0;\n\t//\tBOOST_FOREACH(EnvEntry& entry, entry_vec)\n\t//\t{\n\t//\t\t// For base peak, intensity_shift and mz_shift is always 1.\n\t//\t\tif(entry.getShift()==0) {\n\t//\t\t\tfitting_score += sqrt(base_entry.info->adjusted_abundance);\n\t//\t\t\tcontinue;\n\t//\t\t}\n\n\t//\t\tdouble expected_abundance = env_ref.getTheoreticalAbundance(entry);\n\t//\t\t\n\t//\t\t// The fitting score comes from 3 parts: \n\t//\t\t// 1. the shift of intensity: abs(E-T)/T.\n\t//\t\tdouble intensity_shift = 1.0 - abs(expected_abundance - entry.info->adjusted_abundance)/expected_abundance;\n\t//\t\tif(intensity_shift < 0)\n\t//\t\t\tintensity_shift = 0.0;\n\n\t//\t\t// 2. the relative shift of mz. Here the accuracy depends on the position of the peak in the envelop.\n\t//\t\tdouble mz_shift = 1.0 - abs(entry.pk->mz - base_entry.pk->mz - entry.env->getTheoreticalMZDistance(0, entry.getShift()))/(entry.env->getWindowSizeByShift(entry.getShift()));\n\n\t//\t\t// 3. the square root of expected_abundance.\n\t//\t\tdouble weight = sqrt(expected_abundance);\n\n\t//\t\tfitting_score += weight * intensity_shift * mz_shift;\n\t//\t}\n\n\t//\tenv->fitting_score = fitting_score;\n\t//\treturn fitting_score;\n\t//}\n}", "meta": {"hexsha": "34f38064d9e6a3872a58c484f7e4de36bdcf9a0f", "size": 30970, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "GAG/src/GAGPL/SPECTRUM/EnvelopFinder.cpp", "max_stars_repo_name": "hh1985/multi_hs_seq", "max_stars_repo_head_hexsha": "9cf4e70fb59283da30339499952c43a0684f7e77", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2015-04-03T14:44:45.000Z", "max_stars_repo_stars_event_max_datetime": "2015-04-15T13:38:39.000Z", "max_issues_repo_path": "GAG/src/GAGPL/SPECTRUM/EnvelopFinder.cpp", "max_issues_repo_name": "hh1985/multi_hs_seq", "max_issues_repo_head_hexsha": "9cf4e70fb59283da30339499952c43a0684f7e77", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "GAG/src/GAGPL/SPECTRUM/EnvelopFinder.cpp", "max_forks_repo_name": "hh1985/multi_hs_seq", "max_forks_repo_head_hexsha": "9cf4e70fb59283da30339499952c43a0684f7e77", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.0704070407, "max_line_length": 187, "alphanum_fraction": 0.684178237, "num_tokens": 8958, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041652, "lm_q2_score": 0.42250463481418826, "lm_q1q2_score": 0.21620263719888966}} {"text": "/** kmeans.cc\n Jeremy Barnes, 16 December 2014\n Copyright (c) 2014 mldb.ai inc. All rights reserved.\n\n This file is part of MLDB. Copyright 2015 mldb.ai inc. All rights reserved.\n Implementation of an KMEANS algorithm for embedding of a dataset.\n*/\n\n#include \"mldb/utils/distribution_simd.h\"\n#include \"kmeans_interface.h\"\n#include \"mldb/builtin/matrix.h\"\n#include \"mldb/core/mldb_engine.h\"\n#include \"mldb/core/dataset.h\"\n#include \"mldb/utils/distribution.h\"\n#include \n#include \"mldb/base/scope.h\"\n#include \"mldb/base/parallel.h\"\n#include \"mldb/utils/pair_utils.h\"\n#include \"mldb/arch/timers.h\"\n#include \"mldb/arch/simd_vector.h\"\n#include \"mldb/utils/vector_utils.h\"\n#include \"mldb/types/basic_value_descriptions.h\"\n#include \"mldb/plugins/jml/kmeans.h\"\n#include \"mldb/sql/sql_expression.h\"\n#include \"mldb/builtin/sql_config_validator.h\"\n#include \"mldb/core/analytics.h\"\n#include \"mldb/types/any_impl.h\"\n#include \"mldb/types/optional_description.h\"\n#include \"mldb/vfs/fs_utils.h\"\n#include \"mldb/vfs/filter_streams.h\"\n\nusing namespace std;\n\n\n\nnamespace MLDB {\n\nDEFINE_STRUCTURE_DESCRIPTION(KmeansConfig);\n\n\nKmeansConfigDescription::\nKmeansConfigDescription()\n{\n Optional > optional;\n optional.emplace(PolyConfigT().\n withType(KmeansConfig::defaultOutputDatasetType));\n\n addField(\"trainingData\", &KmeansConfig::trainingData,\n \"Specification of the data for input to the k-means procedure. This should be \"\n \"organized as an embedding, with each selected row containing the same \"\n \"set of columns with numeric values to be used as coordinates. The select statement \"\n \"does not support groupby and having clauses.\");\n addField(\"outputDataset\", &KmeansConfig::output,\n \"Dataset for cluster assignment. This dataset will contain the same \"\n \"row names as the input dataset, but the coordinates will be replaced \"\n \"by a single column giving the cluster number that the row was assigned to.\",\n optional);\n addField(\"centroidsDataset\", &KmeansConfig::centroids,\n \"Dataset in which the centroids will be recorded. This dataset will \"\n \"have the same coordinates (columns) as those selected from the input \"\n \"dataset, but will have one row per cluster, providing the centroid of \"\n \"the cluster.\",\n optional);\n addField(\"numInputDimensions\", &KmeansConfig::numInputDimensions,\n \"Number of dimensions from the input to use (-1 = all). This limits \"\n \"the number of columns used. Columns will be ordered alphabetically \"\n \"and the lowest ones kept.\",\n -1);\n addField(\"numClusters\", &KmeansConfig::numClusters,\n \"Number of clusters to create. This will provide the total number of \"\n \"centroids created. There must be at least as many rows selected as \"\n \"clusters.\", 10);\n addField(\"maxIterations\", &KmeansConfig::maxIterations,\n \"Maximum number of iterations to perform. If no convergeance is \"\n \"reached within this number of iterations, the current clustering \"\n \"will be returned.\", 100);\n addField(\"metric\", &KmeansConfig::metric,\n \"Metric space in which the k-means distances will be calculated. \"\n \"Normally this will be Cosine for an orthonormal basis, and \"\n \"Euclidian for another basis\",\n METRIC_COSINE);\n addField(\"modelFileUrl\", &KmeansConfig::modelFileUrl,\n \"URL where the model file (with extension '.kms') should be saved. \"\n \"This file can be loaded by the ![](%%doclink kmeans function). \"\n \"This parameter is optional unless the `functionName` parameter is used.\");\n addField(\"functionName\", &KmeansConfig::functionName,\n \"If specified, an instance of the ![](%%doclink kmeans function) of this name will be created using \"\n \"the trained model. Note that to use this parameter, the `modelFileUrl` must \"\n \"also be provided.\");\n addParent();\n\n onPostValidate = chain(validateQuery(&KmeansConfig::trainingData,\n MustContainFrom(),\n NoGroupByHaving()),\n validateFunction());\n}\n\n// TODO: see http://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf\n\nnamespace {\n\nML::KMeansMetric * makeMetric(MetricSpace metric)\n{\n switch (metric) {\n case METRIC_EUCLIDEAN:\n return new ML::KMeansEuclideanMetric();\n break;\n case METRIC_COSINE:\n return new ML::KMeansCosineMetric();\n break;\n default:\n throw MLDB::Exception(\"Unknown kmeans metric type\");\n }\n}\n\n} // file scope\n\n/*****************************************************************************/\n/* KMEANS PROCEDURE */\n/*****************************************************************************/\n\nKmeansProcedure::\nKmeansProcedure(MldbEngine * owner,\n PolyConfig config,\n const std::function & onProgress)\n : Procedure(owner)\n{\n kmeansConfig = config.params.convert();\n}\n\nAny\nKmeansProcedure::\ngetStatus() const\n{\n return Any();\n}\n\nRunOutput\nKmeansProcedure::\nrun(const ProcedureRunConfig & run,\n const std::function & onProgress) const\n{\n auto runProcConf = applyRunConfOverProcConf(kmeansConfig, run);\n\n // an empty url is allowed but other invalid urls are not\n if(!runProcConf.modelFileUrl.empty() && !runProcConf.modelFileUrl.valid()) {\n throw AnnotatedException(400, \"modelFileUrl \\\"\" +\n runProcConf.modelFileUrl.toUtf8String()\n + \"\\\" is not valid\");\n }\n\n if (!runProcConf.modelFileUrl.empty()) {\n checkWritability(runProcConf.modelFileUrl.toDecodedString(), \"modelFileUrl\");\n }\n\n auto onProgress2 = [&] (const Json::Value & progress)\n {\n Json::Value value;\n value[\"dataset\"] = progress;\n return onProgress(value);\n };\n\n SqlExpressionMldbScope context(engine);\n\n ConvertProgressToJson convertProgressToJson(onProgress);\n auto embeddingOutput = getEmbedding(*runProcConf.trainingData.stm,\n context,\n runProcConf.numInputDimensions,\n convertProgressToJson);\n\n std::vector,\n std::vector > > & rows\n = embeddingOutput.first;\n std::vector & vars = embeddingOutput.second;\n\n std::vector columnNames;\n for (auto & v: vars) {\n columnNames.push_back(v.columnName);\n }\n\n std::vector > vecs;\n\n for (unsigned i = 0; i < rows.size(); ++i) {\n vecs.emplace_back(distribution(std::get<2>(rows[i]).begin(),\n std::get<2>(rows[i]).end()));\n }\n\n if (vecs.size() == 0)\n throw AnnotatedException(400, \"Kmeans training requires at least 1 datapoint. \"\n \"Make sure your dataset is not empty and that your WHERE expression \"\n \"does not filter all the rows\");\n\n ML::KMeans kmeans;\n kmeans.metric.reset(makeMetric(runProcConf.metric));\n\n vector inCluster;\n\n int numClusters = runProcConf.numClusters;\n int numIterations = runProcConf.maxIterations;\n\n kmeans.train(vecs, inCluster, numClusters, numIterations);\n\n bool saved = false;\n if (!runProcConf.modelFileUrl.empty()) {\n try {\n makeUriDirectory(runProcConf.modelFileUrl.toDecodedString());\n Json::Value md;\n md[\"algorithm\"] = \"MLDB k-Means model\";\n md[\"version\"] = 1;\n md[\"columnNames\"] = jsonEncode(columnNames);\n\n filter_ostream stream(runProcConf.modelFileUrl);\n stream << md.toString();\n MLDB::DB::Store_Writer writer(stream);\n kmeans.serialize(writer);\n saved = true;\n }\n catch (const std::exception & exc) {\n throw AnnotatedException(400, \"Error saving kmeans centroids at location'\" +\n runProcConf.modelFileUrl.toString() + \"': \" +\n exc.what());\n }\n }\n\n if (runProcConf.output.get()) {\n\n PolyConfigT outputDataset = *runProcConf.output;\n if (outputDataset.type.empty())\n outputDataset.type = KmeansConfig::defaultOutputDatasetType;\n\n auto output = createDataset(engine, outputDataset, onProgress2, true /*overwrite*/);\n\n Date applyDate = Date::now();\n\n for (unsigned i = 0; i < rows.size(); ++i) {\n std::vector > cols;\n cols.emplace_back(ColumnPath(\"cluster\"), inCluster[i], applyDate);\n output->recordRow(std::get<1>(rows[i]), cols);\n }\n\n output->commit();\n }\n\n if (runProcConf.centroids.get()) {\n\n PolyConfigT centroidsDataset = *runProcConf.centroids;\n if (centroidsDataset.type.empty())\n centroidsDataset.type = KmeansConfig::defaultOutputDatasetType;\n\n auto centroids = createDataset(engine, centroidsDataset, onProgress2, true /*overwrite*/);\n\n Date applyDate = Date::now();\n\n for (unsigned i = 0; i < kmeans.clusters.size(); ++i) {\n auto & cluster = kmeans.clusters[i];\n\n std::vector > cols;\n\n for (unsigned j = 0; j < cluster.centroid.size(); ++j) {\n cols.emplace_back(columnNames[j], cluster.centroid[j], applyDate);\n }\n\n centroids->recordRow(RowPath(MLDB::format(\"%i\", i)), cols);\n }\n\n centroids->commit();\n }\n\n if(!runProcConf.functionName.empty()) {\n if (saved) {\n KmeansFunctionConfig funcConf;\n funcConf.modelFileUrl = runProcConf.modelFileUrl;\n\n PolyConfig kmeansFuncPC;\n kmeansFuncPC.type = \"kmeans\";\n kmeansFuncPC.id = runProcConf.functionName;\n kmeansFuncPC.params = funcConf;\n\n createFunction(engine, kmeansFuncPC, onProgress, true);\n } else {\n throw AnnotatedException(400, \"Can't create kmeans function '\" +\n runProcConf.functionName.rawString() +\n \"'. Have you provided a valid modelFileUrl?\",\n \"modelFileUrl\", runProcConf.modelFileUrl.toString());\n }\n }\n\n return Any();\n}\n\nDEFINE_STRUCTURE_DESCRIPTION(KmeansFunctionConfig);\n\nKmeansFunctionConfigDescription::\nKmeansFunctionConfigDescription()\n{\n addField(\"modelFileUrl\", &KmeansFunctionConfig::modelFileUrl,\n \"URL of the model file (with extension '.kms') to load. \"\n \"This file is created by the ![](%%doclink kmeans.train procedure).\");\n\n onPostValidate = [] (KmeansFunctionConfig * cfg,\n JsonParsingContext & context) {\n // this includes empty url\n if(!cfg->modelFileUrl.valid()) {\n throw MLDB::Exception(\"modelFileUrl \\\"\" + cfg->modelFileUrl.toString()\n + \"\\\" is not valid\");\n }\n };\n}\n\n\n/*****************************************************************************/\n/* KMEANS FUNCTION */\n/*****************************************************************************/\n\nDEFINE_STRUCTURE_DESCRIPTION(KmeansFunctionArgs);\n\nKmeansFunctionArgsDescription::\nKmeansFunctionArgsDescription()\n{\n addField(\"embedding\", &KmeansFunctionArgs::embedding,\n \"Embedding values for the k-means function. The column names in \"\n \"this embedding must match those used in the original kmeans \"\n \"dataset.\");\n}\n\nDEFINE_STRUCTURE_DESCRIPTION(KmeansExpressionValue);\n\nKmeansExpressionValueDescription::KmeansExpressionValueDescription()\n{\n addField(\"cluster\", &KmeansExpressionValue::cluster,\n \"Index of the row in the `centroids` dataset whose columns describe \"\n \"the point which is closest to the input according to the `metric` \"\n \"specified in training.\");\n}\n\n\nstruct KmeansFunction::Impl {\n ML::KMeans kmeans;\n std::vector columnNames;\n\n Impl(const Url & modelFileUrl)\n {\n filter_istream stream(modelFileUrl);\n std::string firstLine;\n std::getline(stream, firstLine);\n Json::Value md = Json::parse(firstLine);\n if (md[\"algorithm\"] != \"MLDB k-Means model\") {\n throw AnnotatedException(400, \"Model file is not a k-means model\");\n }\n if (md[\"version\"].asInt() != 1) {\n throw AnnotatedException(400, \"k-Means model version is wrong\");\n }\n columnNames = jsonDecode >(md[\"columnNames\"]);\n \n MLDB::DB::Store_Reader store(stream);\n kmeans.reconstitute(store);\n }\n};\n\nKmeansFunction::\nKmeansFunction(MldbEngine * owner,\n PolyConfig config,\n const std::function & onProgress)\n : BaseT(owner, config)\n{\n functionConfig = config.params.convert();\n\n impl.reset(new Impl(functionConfig.modelFileUrl));\n\n dimension = impl->kmeans.clusters[0].centroid.size();\n}\n\nKmeansExpressionValue \nKmeansFunction::\ncall(KmeansFunctionArgs input) const\n{\n Date ts = input.embedding.getEffectiveTimestamp();\n\n return {ExpressionValue\n (impl->kmeans.assign\n (input.embedding.getEmbedding(impl->columnNames.data(),\n impl->columnNames.size())\n .cast()),\n ts)};\n}\n\nnamespace {\n\nRegisterProcedureType\nregKmeans(builtinPackage(),\n \"Simple clustering algorithm based on cluster centroids in embedding space\",\n \"procedures/KmeansProcedure.md.html\");\n\nRegisterFunctionType\nregKmeansFunction(builtinPackage(),\n \"kmeans\",\n \"Apply a k-means clustering to new data\",\n \"functions/Kmeans.md.html\");\n\n} // file scope\n\n} // namespace MLDB\n\n", "meta": {"hexsha": "76aec64f63e3919c1608815742a64971fc405b16", "size": 14692, "ext": "cc", "lang": "C++", "max_stars_repo_path": "plugins/jml/kmeans_interface.cc", "max_stars_repo_name": "mldbai/mldb", "max_stars_repo_head_hexsha": "0554aa390a563a6294ecc841f8026a88139c3041", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 665.0, "max_stars_repo_stars_event_min_datetime": "2015-12-09T17:00:14.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T07:46:46.000Z", "max_issues_repo_path": "plugins/jml/kmeans_interface.cc", "max_issues_repo_name": "mldbai/mldb", "max_issues_repo_head_hexsha": "0554aa390a563a6294ecc841f8026a88139c3041", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 797.0, "max_issues_repo_issues_event_min_datetime": "2015-12-09T19:48:19.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-07T02:19:47.000Z", "max_forks_repo_path": "plugins/jml/kmeans_interface.cc", "max_forks_repo_name": "mldbai/mldb", "max_forks_repo_head_hexsha": "0554aa390a563a6294ecc841f8026a88139c3041", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 103.0, "max_forks_repo_forks_event_min_datetime": "2015-12-25T04:39:29.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-03T02:55:22.000Z", "avg_line_length": 36.3663366337, "max_line_length": 114, "alphanum_fraction": 0.6025728288, "num_tokens": 3145, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.3557748935136304, "lm_q1q2_score": 0.2161912995394521}} {"text": "// ====================================================================\n// This file is part of FlexibleSUSY.\n//\n// FlexibleSUSY is free software: you can redistribute it and/or modify\n// it under the terms of the GNU General Public License as published\n// by the Free Software Foundation, either version 3 of the License,\n// or (at your option) any later version.\n//\n// FlexibleSUSY is distributed in the hope that it will be useful, but\n// WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n// General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with FlexibleSUSY. If not, see\n// .\n// ====================================================================\n\n#ifndef SUM_H\n#define SUM_H\n\n#include \n#include \n#include \n\nnamespace flexiblesusy {\n\n#define SUM(...) (get_sum(__VA_ARGS__)(__VA_ARGS__))\n\n#define get_sum(...) get_sum_macro(__VA_ARGS__, sum_user_t, sum_ptrdiff_t,)\n\n#define get_sum_macro(_1, _2, _3, _4, _5, name, ...) name\n\n#define sum_ptrdiff_t(idx, ini, fin, expr) \\\n sum((ini), (fin), [&](std::ptrdiff_t (idx)) { return (expr); })\n\n#define sum_user_t(type, idx, ini, fin, expr)\t\\\n sum((ini), (fin), [&](type (idx)) { return (expr); })\n\ntemplate\nstruct is_eigen_type\n{\n static constexpr auto value =\n\tstd::is_base_of, T>::value;\n};\n\ntemplate\nstruct EvalEigenXprImpl {\n static auto eval(Idx i, Function f) -> decltype(f(i)) {\n\treturn f(i);\n }\n};\n\ntemplate\nstruct EvalEigenXprImpl {\n static auto eval(Idx i, Function f) ->\n\ttypename std::remove_reference::type\n {\n\treturn f(i).eval();\n }\n};\n\ntemplate\nauto EvalEigenXpr(Idx i, Function f) ->\n decltype(\n\tEvalEigenXprImpl::value>::\n\teval(i, f))\n{\n return\n\tEvalEigenXprImpl::value>::\n\teval(i, f);\n}\n\ntemplate\nstruct create_zero {\n static const T zero() {\n\treturn T();\n }\n};\n\ntemplate\nstruct create_zero {\n static const T zero() {\n\tT z;\n\tz.setZero();\n\treturn z;\n }\n};\n\ntemplate\nauto sum(Idx ini, Idx fin, Function f) -> decltype(EvalEigenXpr(ini, f))\n{\n using Evaled = decltype(EvalEigenXpr(ini, f));\n using Acc = typename std::remove_cv::type;\n Acc s = create_zero::value>::zero();\n for (Idx i = ini; i <= fin; i++) s += f(i);\n return s;\n}\n\n} // namespace flexiblesusy\n\n#endif // sum_hpp\n", "meta": {"hexsha": "60ec2d67878e7770e4a0c4ae37c1dfc74100103a", "size": 2846, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contrib/MassSpectra/flexiblesusy/src/sum.hpp", "max_stars_repo_name": "sebhoof/gambit_1.5", "max_stars_repo_head_hexsha": "f9a3f788e3331067c555ae1a030420e903c6fdcd", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-09-08T20:05:27.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-26T07:57:56.000Z", "max_issues_repo_path": "contrib/MassSpectra/flexiblesusy/src/sum.hpp", "max_issues_repo_name": "sebhoof/gambit_1.5", "max_issues_repo_head_hexsha": "f9a3f788e3331067c555ae1a030420e903c6fdcd", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": "2020-10-19T09:56:17.000Z", "max_issues_repo_issues_event_max_datetime": "2021-05-28T06:12:03.000Z", "max_forks_repo_path": "contrib/MassSpectra/flexiblesusy/src/sum.hpp", "max_forks_repo_name": "patscott/gambit_1.4", "max_forks_repo_head_hexsha": "a50537419918089effc207e8b206489a5cfd2258", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2020-09-08T02:23:34.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-23T08:48:04.000Z", "avg_line_length": 27.6310679612, "max_line_length": 83, "alphanum_fraction": 0.6531974701, "num_tokens": 742, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073802837477, "lm_q2_score": 0.3923368301671083, "lm_q1q2_score": 0.21602355424714112}} {"text": "/*\n * This file is part of the M. xanthus polarity simulator\n * Copyright (c) 2020 Filipe Tostevin\n *\n * This file contains the main routine that creates all simulator\n * objects and calls the propagation routines.\n */\n\n#include \n#include \n#include \n#include \n#include \"state.h\"\n#include \"propagator.h\"\n\nusing namespace std;\nnamespace po = boost::program_options;\nnamespace pt = boost::property_tree;\n\nint main(\n\tint argc,\n\tchar* argv[]\n){\n\t// Command line options and help\n\tpo::options_description desc(\"Allowed options\");\n\tdesc.add_options()\n\t\t(\"help,h\", \"print help message\")\n\t\t(\"conf,c\", po::value(), \"parameter configuration file\")\n\t\t(\"mutants,m\", \"simulate deletion mutants\")\n\t;\n\tpo::variables_map vm;\n\tpo::store(po::parse_command_line(argc, argv, desc), vm);\n\tpo::notify(vm);\n\n\tif(vm.count(\"help\")) {\n\t\tcout << desc << \"\\n\";\n\t\treturn(EXIT_SUCCESS);\n\t}\n\n\t// Read the input file given by the \"--conf\" option, or otherwise from\n\t// \"params.in\"\n\tpt::ptree ptree;\n\tstring defaultconfig=\"params.in\";\n\tif(vm.count(\"conf\")) {\n\t\tcout << \"Reading parameters from: \" << vm[\"conf\"].as() << endl;\n\t\tpt::read_ini(vm[\"conf\"].as(), ptree);\n\t} else {\n\t\tcout << \"No parameter file specified. Reading parameters from default file: \" << defaultconfig << endl;\n\t\tpt::read_ini(defaultconfig, ptree);\n\t}\n\n\t// Get total simulation time from input file\n\tconst double t_total=ptree.get(\"t_total\");\n\tconst double sampling_interval=ptree.get(\"sampling_interval\");\n\n\t// Define the system state variable and initialize all elements to zero\n\tstate conc(NSPEC,NPOOL,0);\n\n\t// Set up simulation infrastructure\n\tpropagator P;\n\t// Read in parameter values from input file\n\tfor(auto p: ptree.get_child(\"model_parameters\")) {\n\t\tP.params.emplace(p.first, ptree.get(\"model_parameters.\"+p.first));\n\t}\n\t//Run a test call of the propagator to check that all required parameters\n\t//exist\n\ttry{ P(conc, conc, 0); }\n\tcatch(...) {\n\t\tcout << \"Error in model definition, check that all parameters are specified in the input file.\" << endl;\n\t\texit(EXIT_FAILURE);\n\t}\n\n\t// Deletion mutants are defined by an integer that when represented as a\n\t// binary string defines the presence/absence of each protein,\n\t// i.e. if the number of proteins in NSPEC=3, then\n\t// mutant_number=1==b100 indicates only the first-listed protein is present,\n\t// mutant_number=7==b111 indicates that all proteins are present\n\t// Here we define the first simulation to run depending on whether we are\n\t// simulating all mutants, or just the wild-type\n\tint mutant_number;\n\tif(vm.count(\"mutants\")) {\n\t\tcout << \"Simulating deletion mutants\" << endl;\n\t\tmutant_number=1;\n\t} else {\n\t\tcout << \"Simulating only wild-type\" << endl;\n\t\tmutant_number=(1<> s) & 1) ) {\n\t\t\t\tmutant_string+=\".d\" + species_names[s];\n\t\t\t}\n\t\t}\n\t\tofstream datafile(\"data.txt\"+mutant_string);\n\n\t\t//Headings for output file\n\t\tdatafile << \"t \";\n\t\tfor(int s=0; s> s) & 1 ) {\n\t\t\t\tconc(s,TOTAL)=1;\n\t\t\t} else {\n\t\t\t\tconc(s,TOTAL)=0;\n\t\t\t}\n\t\t\t// Initialize polar fractions\n\t\t\tconc(s,POLE1)=ptree.get(\"initial_fractions_\"+species_names[s]+\".pole1\")*conc(s,TOTAL);\n\t\t\tconc(s,POLE2)=ptree.get(\"initial_fractions_\"+species_names[s]+\".pole2\")*conc(s,TOTAL);\n\t\t}\n\n\t\t// Start at t=0\n\t\tdouble t=0;\n\t\twrite_state(conc, t, datafile);\n\n\t\t// Run until the end of the simulation time t_total\n\t\twhile(t\n#include \n#include \n#include \n#include \n\n#include \n\n#define WG_SIZE 256\n#define MAX_ACTIVE_THREADS (64 * 4 * 64)\n#define MAX_LOCAL_MEM 65536\n\nnamespace miopen {\n\nfloat Im2d2ColGPU(Handle& handle,\n ConstData_t im,\n const int im_offset,\n const int c,\n const int in_h,\n const int in_w,\n const int wei_h,\n const int wei_w,\n const int out_h,\n const int out_w,\n const int pad_h,\n const int pad_w,\n const int stride_h,\n const int stride_w,\n const int dilation_h,\n const int dilation_w,\n Data_t col,\n miopenDataType_t type)\n{\n std::string program_name = \"MIOpenIm2d2Col.cl\";\n std::string kernel_name = \"Im2d2Col\";\n\n // clang-format off\n std::string network_config =\n \"c\" + std::to_string(c) + \n \"i\" + std::to_string(in_h) + \n \"_\" + std::to_string(in_w) + \n \"w\" + std::to_string(wei_h) + \n \"_\" + std::to_string(wei_w) + \n \"p\" + std::to_string(pad_h) + \n \"_\" + std::to_string(pad_w) + \n \"s\" + std::to_string(stride_h) + \n \"_\" + std::to_string(stride_w) + \n \"d\" + std::to_string(dilation_h) + \n \"_\" + std::to_string(dilation_w) + \n \"t\" + std::to_string(type);\n // clang-format on\n\n auto&& kernels = handle.GetKernels(\"miopenIm2d2Col\", network_config);\n\n int data_size_bound = c * in_h * in_w;\n\n int data_size_bound_pack = type == miopenInt8x4 ? data_size_bound * 4 : data_size_bound;\n int im_offset_pack = type == miopenInt8x4 ? im_offset / 4 : im_offset;\n\n if(!kernels.empty())\n {\n auto kernel = kernels.front();\n kernel(data_size_bound_pack,\n im,\n im_offset_pack,\n in_h,\n in_w,\n wei_h,\n wei_w,\n out_h,\n out_w,\n pad_h,\n pad_w,\n stride_h,\n stride_w,\n dilation_h,\n dilation_w,\n col);\n }\n else\n {\n const int c_pack = type == miopenInt8x4 ? c / 4 : c;\n\n std::string params;\n int num_ch_per_wg;\n if((out_h <= 8 && out_w <= 8) && (stride_h == 1 && stride_w == 1) && (c_pack % 4 == 0))\n num_ch_per_wg = 4;\n else\n num_ch_per_wg = 1;\n\n int tile_sz_x = 32;\n int tile_sz_y = 8;\n int num_blks_x = std::ceil(static_cast(out_w) / static_cast(tile_sz_x));\n int num_blks =\n num_blks_x *\n static_cast(std::ceil(static_cast(out_h) / static_cast(tile_sz_y)));\n int local_mem_sz;\n if(num_ch_per_wg == 1)\n {\n local_mem_sz = ((tile_sz_x - 1) * stride_w + (wei_w - 1) * dilation_w + 1) *\n ((tile_sz_y - 1) * stride_h + (wei_h - 1) * dilation_h + 1);\n }\n else\n {\n auto uprdTileX = static_cast(\n std::ceil(static_cast(tile_sz_x) / static_cast(num_ch_per_wg)) - 1);\n auto uprdTileY = static_cast(\n std::ceil(static_cast(tile_sz_y) / static_cast(num_ch_per_wg)) - 1);\n auto memXsize = (num_ch_per_wg * uprdTileX * stride_w + (wei_w - 1) * dilation_w + 1) *\n ((tile_sz_y - 1) * stride_h + (wei_h - 1) * dilation_h + 1);\n auto memYsize = num_ch_per_wg *\n ((tile_sz_x - 1) * stride_w + (wei_w - 1) * dilation_w + 1) *\n (uprdTileY * stride_h + (wei_h - 1) * dilation_h + 1);\n local_mem_sz = static_cast(std::max(memXsize, memYsize));\n }\n\n // adjust mapping for large kernel\n int type_size = 4; // Need to adjust for fp16, int8\n int extreme_case = num_ch_per_wg * ((wei_w - 1) * dilation_w + 1) *\n ((wei_h - 1) * dilation_h + 1) * type_size;\n if(extreme_case > MAX_LOCAL_MEM)\n {\n params += \" -DEXTREME_LARGE\";\n params += \" -DNUM_CH_TOTAL=\" + std::to_string(c_pack);\n }\n else\n {\n while(local_mem_sz * type_size > MAX_LOCAL_MEM)\n {\n tile_sz_x = tile_sz_x == 1 ? 1 : (tile_sz_y == 1 ? (tile_sz_x / 2) : tile_sz_x);\n tile_sz_y = tile_sz_y == 1 ? 1 : (tile_sz_y / 2);\n num_blks_x = std::ceil(static_cast(out_w) / static_cast(tile_sz_x));\n num_blks = num_blks_x * static_cast(std::ceil(static_cast(out_h) /\n static_cast(tile_sz_y)));\n if(num_ch_per_wg == 1)\n {\n local_mem_sz = ((tile_sz_x - 1) * stride_w + (wei_w - 1) * dilation_w + 1) *\n ((tile_sz_y - 1) * stride_h + (wei_h - 1) * dilation_h + 1);\n }\n else\n {\n auto uprdTileX = static_cast(std::ceil(static_cast(tile_sz_x) /\n static_cast(num_ch_per_wg)) -\n 1);\n auto uprdTileY = static_cast(std::ceil(static_cast(tile_sz_y) /\n static_cast(num_ch_per_wg)) -\n 1);\n auto memXsize =\n (num_ch_per_wg * uprdTileX * stride_w + (wei_w - 1) * dilation_w + 1) *\n ((tile_sz_y - 1) * stride_h + (wei_h - 1) * dilation_h + 1);\n auto memYsize = num_ch_per_wg *\n ((tile_sz_x - 1) * stride_w + (wei_w - 1) * dilation_w + 1) *\n (uprdTileY * stride_h + (wei_h - 1) * dilation_h + 1);\n local_mem_sz = static_cast(std::max(memXsize, memYsize));\n }\n }\n }\n\n params += \" -DNUM_CH_PER_WG=\" + std::to_string(num_ch_per_wg);\n params += \" -DNUM_IM_BLKS_X=\" + std::to_string(num_blks_x);\n params += \" -DNUM_IM_BLKS=\" + std::to_string(num_blks);\n params += \" -DLOCAL_MEM_SIZE=\" + std::to_string(local_mem_sz);\n params += \" -DSTRIDE_GT_1=\" + std::to_string(static_cast(stride_h * stride_w > 1));\n params += \" -DTILE_SZ_X=\" + std::to_string(tile_sz_x);\n params += \" -DTILE_SZ_Y=\" + std::to_string(tile_sz_y);\n params += \" -DUSE_IM_OFF_GUARD=1\";\n\n params += GetDataTypeKernelParams(type);\n\n const std::vector vld{256, 1, 1};\n size_t global_threads = 256 * std::max(1, (c_pack / num_ch_per_wg)) * num_blks;\n const std::vector vgd{global_threads, 1, 1};\n handle.AddKernel(\n \"miopenIm2Col\", network_config, program_name, kernel_name, vld, vgd, params)(\n data_size_bound_pack,\n im,\n im_offset_pack,\n in_h,\n in_w,\n wei_h,\n wei_w,\n out_h,\n out_w,\n pad_h,\n pad_w,\n stride_h,\n stride_w,\n dilation_h,\n dilation_w,\n col);\n }\n\n return handle.GetKernelTime();\n}\n\nfloat Im3d2ColGPU(Handle& handle,\n ConstData_t im,\n const int im_offset,\n const int im_c,\n const int im_d,\n const int im_h,\n const int im_w,\n const int wei_d,\n const int wei_h,\n const int wei_w,\n const int out_d,\n const int out_h,\n const int out_w,\n const int pad_d,\n const int pad_h,\n const int pad_w,\n const int stride_d,\n const int stride_h,\n const int stride_w,\n const int dilation_d,\n const int dilation_h,\n const int dilation_w,\n Data_t col,\n miopenDataType_t type)\n{\n std::string program_name = \"MIOpenIm3d2Col.cl\";\n std::string kernel_name = \"Im3d2Col\";\n\n // clang-format off\n std::string network_config =\n \"c\" + std::to_string(im_c) + \n \"i\" + std::to_string(im_d) + \n \"_\" + std::to_string(im_h) + \n \"_\" + std::to_string(im_w) + \n \"w\" + std::to_string(wei_d) + \n \"_\" + std::to_string(wei_h) + \n \"_\" + std::to_string(wei_w) + \n \"p\" + std::to_string(pad_d) + \n \"_\" + std::to_string(pad_h) + \n \"_\" + std::to_string(pad_w) + \n \"s\" + std::to_string(stride_d) + \n \"_\" + std::to_string(stride_h) +\n \"_\" + std::to_string(stride_w) + \n \"d\" + std::to_string(dilation_d) + \n \"_\" + std::to_string(dilation_h) + \n \"_\" + std::to_string(dilation_w) + \n \"t\" + std::to_string(type);\n // clang-format on\n\n auto&& kernels = handle.GetKernels(\"miopenIm3d2Col\", network_config);\n\n // int8x4 vectorize-c format\n int im_offset_pack = type == miopenInt8x4 ? im_offset / 4 : im_offset;\n int im_c_pack = type == miopenInt8x4 ? im_c / 4 : im_c;\n\n if(!kernels.empty())\n {\n auto kernel = kernels.front();\n kernel(im,\n im_offset_pack,\n im_c_pack,\n im_d,\n im_h,\n im_w,\n wei_d,\n wei_h,\n wei_w,\n out_d,\n out_h,\n out_w,\n pad_d,\n pad_h,\n pad_w,\n stride_d,\n stride_h,\n stride_w,\n dilation_d,\n dilation_h,\n dilation_w,\n col);\n }\n else\n {\n std::string params = GetDataTypeKernelParams(type);\n\n const std::vector vld{256, 1, 1};\n size_t global_threads = std::min(\n 256 * static_cast(out_d * out_h * out_w * im_c * wei_d * wei_h * wei_w) /\n 8,\n static_cast(256) * 1024);\n const std::vector vgd{global_threads, 1, 1};\n\n handle.AddKernel(\n \"miopenIm3d2Col\", network_config, program_name, kernel_name, vld, vgd, params)(\n im,\n im_offset_pack,\n im_c_pack,\n im_d,\n im_h,\n im_w,\n wei_d,\n wei_h,\n wei_w,\n out_d,\n out_h,\n out_w,\n pad_d,\n pad_h,\n pad_w,\n stride_d,\n stride_h,\n stride_w,\n dilation_d,\n dilation_h,\n dilation_w,\n col);\n }\n\n return handle.GetKernelTime();\n}\n\nfloat Col2Im2dGPU(Handle& handle,\n ConstData_t col,\n const int out_h,\n const int out_w,\n const int wei_h,\n const int wei_w,\n const int pad_h,\n const int pad_w,\n const int stride_h,\n const int stride_w,\n const int dilation_h,\n const int dilation_w,\n const int in_c,\n const int in_h,\n const int in_w,\n Data_t im,\n int im_offset,\n miopenDataType_t type)\n{\n std::string program_name = \"MIOpenCol2Im2d.cl\";\n std::string kernel_name = \"Col2Im2d\";\n\n // clang-format off\n std::string network_config =\n \"c\" + std::to_string(in_c) + \n \"in_h\" + std::to_string(in_h) +\n \"in_w\" + std::to_string(in_w) + \n \"y\" + std::to_string(wei_h) + \n \"x\" + std::to_string(wei_w) + \n \"p\" + std::to_string(pad_h) + \n \"q\" + std::to_string(pad_w) + \n \"u\" + std::to_string(stride_h) + \n \"v\" + std::to_string(stride_w) + \n \"l\" + std::to_string(dilation_h) + \n \"j\" + std::to_string(dilation_w) + \n \"t\" + std::to_string(type);\n // clang-format on\n\n auto&& kernels = handle.GetKernels(\"miopenCol2Im2d\", network_config);\n\n if(!kernels.empty())\n {\n auto kernel = kernels.front();\n kernel(col,\n out_h,\n out_w,\n wei_h,\n wei_w,\n pad_h,\n pad_w,\n stride_h,\n stride_w,\n dilation_h,\n dilation_w,\n in_h,\n in_w,\n im,\n im_offset);\n }\n else\n {\n std::string params = GetDataTypeKernelParams(type);\n\n const std::vector vld{256, 1, 1};\n size_t global_threads = in_c * in_h * in_w;\n const std::vector vgd{global_threads, 1, 1};\n\n handle.AddKernel(\n \"miopenCol2Im2d\", network_config, program_name, kernel_name, vld, vgd, params)(\n col,\n out_h,\n out_w,\n wei_h,\n wei_w,\n pad_h,\n pad_w,\n stride_h,\n stride_w,\n dilation_h,\n dilation_w,\n in_h,\n in_w,\n im,\n im_offset);\n }\n return handle.GetKernelTime();\n}\n\nfloat Col2Im3dGPU(Handle& handle,\n ConstData_t col,\n const int out_d,\n const int out_h,\n const int out_w,\n const int wei_d,\n const int wei_h,\n const int wei_w,\n const int pad_d,\n const int pad_h,\n const int pad_w,\n const int stride_d,\n const int stride_h,\n const int stride_w,\n const int dilation_d,\n const int dilation_h,\n const int dilation_w,\n const int in_c,\n const int in_d,\n const int in_h,\n const int in_w,\n Data_t im,\n int im_offset,\n miopenDataType_t type)\n{\n std::string program_name = \"MIOpenCol2Im3d.cl\";\n std::string kernel_name = \"Col2Im3d\";\n\n // clang-format off\n std::string network_config =\n \"c\" + std::to_string(in_c) + \n \"i\" + std::to_string(in_d) + \n \"_\" + std::to_string(in_h) + \n \"_\" + std::to_string(in_w) + \n \"w\" + std::to_string(wei_d) + \n \"_\" + std::to_string(wei_h) + \n \"_\" + std::to_string(wei_w) + \n \"p\" + std::to_string(pad_d) + \n \"_\" + std::to_string(pad_h) + \n \"_\" + std::to_string(pad_w) + \n \"s\" + std::to_string(stride_d) + \n \"_\" + std::to_string(stride_h) +\n \"_\" + std::to_string(stride_w) + \n \"d\" + std::to_string(dilation_d) + \n \"_\" + std::to_string(dilation_h) + \n \"_\" + std::to_string(dilation_w) + \n \"t\" + std::to_string(type);\n // clang-format on\n\n auto&& kernels = handle.GetKernels(\"miopenCol2Im3d\", network_config);\n\n if(!kernels.empty())\n {\n auto kernel = kernels.front();\n kernel(col,\n out_d,\n out_h,\n out_w,\n wei_d,\n wei_h,\n wei_w,\n pad_d,\n pad_h,\n pad_w,\n stride_d,\n stride_h,\n stride_w,\n dilation_d,\n dilation_h,\n dilation_w,\n in_d,\n in_h,\n in_w,\n im,\n im_offset);\n }\n else\n {\n std::string params = GetDataTypeKernelParams(type);\n\n const std::vector vld{256, 1, 1};\n size_t global_threads = in_c * in_d * in_h * in_w;\n const std::vector vgd{global_threads, 1, 1};\n\n handle.AddKernel(\n \"miopenCol2Im3d\", network_config, program_name, kernel_name, vld, vgd, params)(\n col,\n out_d,\n out_h,\n out_w,\n wei_d,\n wei_h,\n wei_w,\n pad_d,\n pad_h,\n pad_w,\n stride_d,\n stride_h,\n stride_w,\n dilation_d,\n dilation_h,\n dilation_w,\n in_d,\n in_h,\n in_w,\n im,\n im_offset);\n }\n return handle.GetKernelTime();\n}\n\nfloat Im2ColGPU(\n Handle& handle,\n std::size_t spatial_dim,\n ConstData_t im,\n std::size_t im_offset,\n std::size_t in_c,\n const decltype(boost::adaptors::slice(std::vector(), 0, 1))& in_spatial,\n const decltype(boost::adaptors::slice(std::vector(), 0, 1))& wei_spatial,\n const decltype(boost::adaptors::slice(std::vector(), 0, 1))& out_spatial,\n const std::vector& pad_spatial,\n const std::vector& stride_spatial,\n const std::vector& dilation_spatial,\n Data_t col,\n miopenDataType_t type)\n{\n switch(spatial_dim)\n {\n case 2:\n {\n return Im2d2ColGPU(handle,\n im,\n im_offset,\n in_c,\n in_spatial[0],\n in_spatial[1],\n wei_spatial[0],\n wei_spatial[1],\n out_spatial[0],\n out_spatial[1],\n pad_spatial[0],\n pad_spatial[1],\n stride_spatial[0],\n stride_spatial[1],\n dilation_spatial[0],\n dilation_spatial[1],\n col,\n type);\n }\n case 3:\n {\n return Im3d2ColGPU(handle,\n im,\n im_offset,\n in_c,\n in_spatial[0],\n in_spatial[1],\n in_spatial[2],\n wei_spatial[0],\n wei_spatial[1],\n wei_spatial[2],\n out_spatial[0],\n out_spatial[1],\n out_spatial[2],\n pad_spatial[0],\n pad_spatial[1],\n pad_spatial[2],\n stride_spatial[0],\n stride_spatial[1],\n stride_spatial[2],\n dilation_spatial[0],\n dilation_spatial[1],\n dilation_spatial[2],\n col,\n type);\n }\n default: { MIOPEN_THROW(\"unsupported convolution dimension\");\n }\n }\n}\n\nfloat Col2ImGPU(\n Handle& handle,\n std::size_t spatial_dim,\n ConstData_t col,\n const decltype(boost::adaptors::slice(std::vector(), 0, 1))& out_spatial,\n const decltype(boost::adaptors::slice(std::vector(), 0, 1))& wei_spatial,\n const std::vector& pad_spatial,\n const std::vector& stride_spatial,\n const std::vector& dilation_spatial,\n std::size_t in_c,\n const decltype(boost::adaptors::slice(std::vector(), 0, 1))& in_spatial,\n Data_t im,\n std::size_t im_offset,\n miopenDataType_t type)\n{\n switch(spatial_dim)\n {\n case 2:\n {\n return Col2Im2dGPU(handle,\n col,\n out_spatial[0],\n out_spatial[1],\n wei_spatial[0],\n wei_spatial[1],\n pad_spatial[0],\n pad_spatial[1],\n stride_spatial[0],\n stride_spatial[1],\n dilation_spatial[0],\n dilation_spatial[1],\n in_c,\n in_spatial[0],\n in_spatial[1],\n im,\n im_offset,\n type);\n }\n case 3:\n {\n return Col2Im3dGPU(handle,\n col,\n out_spatial[0],\n out_spatial[1],\n out_spatial[2],\n wei_spatial[0],\n wei_spatial[1],\n wei_spatial[2],\n pad_spatial[0],\n pad_spatial[1],\n pad_spatial[2],\n stride_spatial[0],\n stride_spatial[1],\n stride_spatial[2],\n dilation_spatial[0],\n dilation_spatial[1],\n dilation_spatial[2],\n in_c,\n in_spatial[0],\n in_spatial[1],\n in_spatial[2],\n im,\n im_offset,\n type);\n }\n default: { MIOPEN_THROW(\"unsupported convolution dimension\");\n }\n }\n\n MIOPEN_THROW(\"unsupported convolution dimension\");\n}\n\nfloat transpose_NCHW2CNHW(Handle& handle,\n int n,\n int c,\n int h_in,\n int w_in,\n int h_out,\n int w_out,\n ConstData_t in,\n Data_t out,\n int in_offset,\n int out_offset,\n int h_stride,\n int w_stride,\n miopenDataType_t type)\n{\n\n std::string program_name = \"MIOpenUtilKernels4.cl\";\n\n std::string network_config = \"n\" + std::to_string(n) + \"c\" + std::to_string(c) + \"h\" +\n std::to_string(h_in) + \"w\" + std::to_string(w_in) + \"inoff\" +\n std::to_string(in_offset) + \"otoff\" + std::to_string(out_offset) +\n \"u\" + std::to_string(h_stride) + \"v\" + std::to_string(w_stride) +\n \"t\" + std::to_string(type);\n\n std::string kernel_name = \"transpose_NCHW2CNHW\";\n\n if(h_stride == 1 && w_stride == 1 && type == miopenFloat)\n kernel_name += \"_opt\";\n\n auto&& kernels = handle.GetKernels(kernel_name, network_config);\n\n if(!kernels.empty())\n {\n auto kernel = kernels.front();\n kernel(in, out);\n }\n else\n {\n std::string params = GetDataTypeKernelParams(type);\n\n if(type == miopenInt8x4)\n {\n c /= 4;\n in_offset /= 4;\n out_offset /= 4;\n }\n\n if(h_stride == 1 && w_stride == 1 && type == miopenFloat)\n {\n params +=\n \" -DNC_TRANS_NCHW_OPT=1 -DNC_TRANS_CNHW_OPT=0 -DNC_TRANS_NCHW=0 -DNC_TRANS_CNHW=0\";\n\n int RD_BLCK = ((h_in * w_in) % 4 == 0) ? 4 : ((h_in * w_in) % 2 == 0) ? 2 : 1;\n int HW_RD = (h_in * w_in) / RD_BLCK;\n size_t MAP_RD = HW_RD * c;\n size_t lcl_size0 = WG_SIZE; //((MAP_RD + 63)/64 < 4) ? ((MAP_RD + 63)/64)*64 : 256;\n\n std::string READ_TYPE = (RD_BLCK == 1) ? \"float\" : \"float\" + std::to_string(RD_BLCK);\n\n params += \" -DIN_OFF=\" + std::to_string(in_offset);\n params += \" -DOUT_OFF=\" + std::to_string(out_offset);\n params += \" -DH=\" + std::to_string(h_in);\n params += \" -DW=\" + std::to_string(w_in);\n params += \" -DN=\" + std::to_string(n);\n params += \" -DC=\" + std::to_string(c);\n params += \" -DRD_BLCK=\" + std::to_string(RD_BLCK);\n params += \" -DHW_RD=\" + std::to_string(HW_RD);\n params += \" -DMAP_RD=\" + std::to_string(MAP_RD);\n params += \" -DREAD_TYPE=\" + READ_TYPE;\n\n const std::vector vld{lcl_size0, 1, 1};\n std::vector vgd{MAP_RD, 1, 1};\n\n if(MAP_RD < MAX_ACTIVE_THREADS)\n {\n vgd = {MAP_RD, static_cast(n), 1};\n params += \" -DIS_2D_WG=1\";\n }\n else\n {\n params += \" -DIS_2D_WG=0\";\n }\n\n handle.AddKernel(\n kernel_name, network_config, program_name, kernel_name, vld, vgd, params)(in, out);\n }\n else\n {\n params +=\n \" -DNC_TRANS_NCHW_OPT=0 -DNC_TRANS_CNHW_OPT=0 -DNC_TRANS_NCHW=1 -DNC_TRANS_CNHW=0\";\n\n params += \" -DN=\" + std::to_string(n);\n params += \" -DC=\" + std::to_string(c);\n params += \" -DHW_IN=\" + std::to_string(h_in * w_in);\n params += \" -DHW_OUT=\" + std::to_string(h_out * w_out);\n params += \" -DW_IN=\" + std::to_string(w_in);\n params += \" -DW_OUT=\" + std::to_string(w_out);\n params += \" -DH_STRIDE=\" + std::to_string(h_stride);\n params += \" -DW_STRIDE=\" + std::to_string(w_stride);\n params += \" -DIN_OFF=\" + std::to_string(in_offset);\n params += \" -DOUT_OFF=\" + std::to_string(out_offset);\n\n size_t ld0 = WG_SIZE;\n size_t gd0 = c * h_out * w_out;\n const std::vector vld{ld0, 1, 1};\n std::vector vgd{gd0, 1, 1};\n\n if(gd0 < MAX_ACTIVE_THREADS)\n {\n vgd = {gd0, static_cast(n), 1};\n params += \" -DIS_2D_WG=1\";\n }\n else\n {\n params += \" -DIS_2D_WG=0\";\n }\n\n handle.AddKernel(\n kernel_name, network_config, program_name, kernel_name, vld, vgd, params)(in, out);\n }\n }\n\n return handle.GetKernelTime();\n}\n\nfloat transpose_CNHW2NCHW(Handle& handle,\n int n,\n int c,\n int h_out,\n int w_out,\n int h_in,\n int w_in,\n ConstData_t in,\n Data_t out,\n int in_offset,\n int out_offset,\n int h_stride,\n int w_stride,\n miopenDataType_t type)\n{\n\n std::string program_name = \"MIOpenUtilKernels4.cl\";\n\n std::string network_config = \"n\" + std::to_string(n) + \"c\" + std::to_string(c) + \"h\" +\n std::to_string(h_in) + \"w\" + std::to_string(w_in) + \"inoff\" +\n std::to_string(in_offset) + \"otoff\" + std::to_string(out_offset) +\n \"h_stride\" + std::to_string(h_stride) + \"w_stride\" +\n std::to_string(w_stride) + \"t\" + std::to_string(type);\n\n std::string kernel_name = \"transpose_CNHW2NCHW\";\n\n if(h_stride == 1 && w_stride == 1 && type == miopenFloat)\n kernel_name += \"_opt\";\n\n auto&& kernels = handle.GetKernels(kernel_name, network_config);\n\n if(!kernels.empty())\n {\n auto kernel = kernels.front();\n kernel(in, out);\n }\n else\n {\n std::string params = GetDataTypeKernelParams(type);\n\n if(type == miopenInt8x4)\n {\n c /= 4;\n in_offset /= 4;\n out_offset /= 4;\n }\n\n if(h_stride == 1 && w_stride == 1 && type == miopenFloat)\n {\n params +=\n \" -DNC_TRANS_NCHW_OPT=0 -DNC_TRANS_CNHW_OPT=1 -DNC_TRANS_NCHW=0 -DNC_TRANS_CNHW=0\";\n\n int RD_BLCK = ((h_out * w_out) % 4 == 0) ? 4 : ((h_out * w_out) % 2 == 0) ? 2 : 1;\n int HW_RD = (h_out * w_out) / RD_BLCK;\n size_t MAP_RD = HW_RD * c;\n size_t lcl_size0 = WG_SIZE; //((MAP_RD + 63)/64 < 4) ? ((MAP_RD + 63)/64)*64 : 256;\n\n std::string READ_TYPE = (RD_BLCK == 1) ? \"float\" : \"float\" + std::to_string(RD_BLCK);\n\n params += \" -DIN_OFF=\" + std::to_string(in_offset);\n params += \" -DOUT_OFF=\" + std::to_string(out_offset);\n params += \" -DH=\" + std::to_string(h_out);\n params += \" -DW=\" + std::to_string(w_out);\n params += \" -DN=\" + std::to_string(n);\n params += \" -DC=\" + std::to_string(c);\n params += \" -DRD_BLCK=\" + std::to_string(RD_BLCK);\n params += \" -DHW_RD=\" + std::to_string(HW_RD);\n params += \" -DMAP_RD=\" + std::to_string(MAP_RD);\n params += \" -DREAD_TYPE=\" + READ_TYPE;\n\n const std::vector vld{lcl_size0, 1, 1};\n std::vector vgd{MAP_RD, 1, 1};\n\n if(MAP_RD < MAX_ACTIVE_THREADS)\n {\n vgd = {MAP_RD, static_cast(n), 1};\n params += \" -DIS_2D_WG=1\";\n }\n else\n {\n params += \" -DIS_2D_WG=0\";\n }\n\n handle.AddKernel(\n kernel_name, network_config, program_name, kernel_name, vld, vgd, params)(in, out);\n }\n else\n {\n params +=\n \" -DNC_TRANS_NCHW_OPT=0 -DNC_TRANS_CNHW_OPT=0 -DNC_TRANS_NCHW=0 -DNC_TRANS_CNHW=1\";\n\n params += \" -DN=\" + std::to_string(n);\n params += \" -DC=\" + std::to_string(c);\n params += \" -DHW_IN=\" + std::to_string(h_in * w_in);\n params += \" -DHW_OUT=\" + std::to_string(h_out * w_out);\n params += \" -DW_IN=\" + std::to_string(w_in);\n params += \" -DW_OUT=\" + std::to_string(w_out);\n params += \" -DH_STRIDE=\" + std::to_string(h_stride);\n params += \" -DW_STRIDE=\" + std::to_string(w_stride);\n params += \" -DIN_OFF=\" + std::to_string(in_offset);\n params += \" -DOUT_OFF=\" + std::to_string(out_offset);\n\n size_t ld0 = WG_SIZE;\n size_t gd0 = c * h_out * w_out;\n const std::vector vld{ld0, 1, 1};\n std::vector vgd{gd0, 1, 1};\n\n if(gd0 < MAX_ACTIVE_THREADS)\n {\n vgd = {gd0, static_cast(n), 1};\n params += \" -DIS_2D_WG=1\";\n }\n else\n {\n params += \" -DIS_2D_WG=0\";\n }\n\n handle.AddKernel(\n kernel_name, network_config, program_name, kernel_name, vld, vgd, params)(in, out);\n }\n }\n\n return handle.GetKernelTime();\n}\n\n// NCHW (or NCDHW) to NCHW_C4 (or NCDHW_C4)\nfloat transpose_NCHW2Vec(Handle& handle,\n const std::vector& lens,\n ConstData_t in,\n Data_t out,\n std::size_t vec_size,\n bool trans,\n bool forward)\n{\n std::string program_name = \"MIOpenUtilKernels5.cl\";\n\n if(!(vec_size == 2 || vec_size == 4))\n {\n MIOPEN_THROW(\"Only support type half and int8!\");\n }\n\n const auto n = lens[0];\n const auto c = lens[1];\n\n // \"hw\" is for any-D spatial data\n const auto hw = std::accumulate(\n lens.begin() + 2, lens.end(), std::size_t(1), std::multiplies());\n\n // clang-format off\n std::string network_config = \n \"n\" + std::to_string(n) +\n \"c\" + std::to_string(c) +\n \"hw\" + std::to_string(hw) +\n \"t\" + std::to_string(static_cast(trans)) +\n \"v\" + std::to_string(vec_size) +\n \"f\" + std::to_string(static_cast(forward));\n // clang-format on\n\n std::string algo_name = \"transpose_NCHWVecForward\";\n\n auto&& kernels = handle.GetKernels(algo_name, network_config);\n\n if(!kernels.empty())\n {\n auto kernel = kernels.front();\n kernel(in, out);\n }\n else\n {\n auto n_vec = (trans && (n % vec_size != 0)) ? (n + (vec_size - n % vec_size)) : n;\n auto c_vec = (!trans && (c % vec_size != 0)) ? (c + (vec_size - c % vec_size)) : c;\n\n std::string kernel_name = \"transpose_NCHW2Vec\";\n\n const std::vector vld{WG_SIZE, 1, 1};\n std::vector vgd{1, 1, 1};\n\n int RD_BLCK = ((hw) % (vec_size * 2) == 0) ? static_cast(vec_size) * 2\n : static_cast(vec_size);\n int HW_RD = (static_cast(hw) + RD_BLCK - 1) / RD_BLCK;\n size_t MAP_RD = HW_RD * (trans ? c : (c_vec / vec_size));\n\n std::string READ_TYPE =\n (RD_BLCK == vec_size) ? \"uint\" : \"uint\" + std::to_string(RD_BLCK / vec_size);\n int WR_BLCK = RD_BLCK * static_cast(vec_size);\n std::string WRITE_TYPE = \"uint\" + std::to_string(WR_BLCK / vec_size);\n\n std::string params;\n params += \" -DFORWARD=\" + std::to_string(static_cast(forward));\n params += \" -DN=\" + std::to_string(n);\n params += \" -DC=\" + std::to_string(c);\n params += \" -DHW=\" + std::to_string(hw);\n params += \" -DCHW=\" + std::to_string(c * hw);\n params += \" -DVEC_SIZE=\" + std::to_string(vec_size);\n params += vec_size == 4 ? \" -DDATA_TYPE=char\" : \" -DDATA_TYPE=ushort\";\n\n params += \" -DTRANS=\" + std::to_string(static_cast(trans));\n if(trans)\n {\n params += \" -DNHW_OUT=\" + std::to_string(n_vec * hw);\n params += \" -DN_OUT=\" + std::to_string(n_vec);\n params += \" -DIS_N_ODD=\" + std::to_string(static_cast((n % vec_size) != 0));\n }\n else\n {\n params += \" -DCHW_OUT=\" + std::to_string(c_vec * hw);\n params += \" -DIS_C_ODD=\" + std::to_string(static_cast((c % vec_size) != 0));\n }\n\n params += \" -DIS_HW_ODD=\" + std::to_string(static_cast(((hw) % vec_size) != 0));\n params += \" -DRD_BLCK=\" + std::to_string(RD_BLCK);\n params += \" -DWR_BLCK=\" + std::to_string(WR_BLCK);\n params += \" -DHW_RD=\" + std::to_string(HW_RD);\n params += \" -DMAP_RD=\" + std::to_string(MAP_RD);\n params += \" -DREAD_TYPE=\" + READ_TYPE;\n params += \" -DWRITE_TYPE=\" + WRITE_TYPE;\n\n vgd[0] = MAP_RD;\n\n size_t gd1 = trans ? static_cast(n_vec / vec_size) : static_cast(n);\n\n /// disable iteration of n due to perf degrade\n /// \\to-do fix the perf issue\n // if(vgd[0] < MAX_ACTIVE_THREADS)\n {\n vgd[1] = gd1;\n params += \" -DIS_2D_WG=1\";\n }\n // else\n //{\n // params += \" -DIS_2D_WG=0\";\n // params += \" -DGD_1=\" + std::to_string(gd1);\n //}\n\n handle.AddKernel(algo_name, network_config, program_name, kernel_name, vld, vgd, params)(\n in, out);\n }\n\n return handle.GetKernelTime();\n}\n\nfloat transpose_packed_MN2NM(Handle& handle,\n int m,\n int n,\n int in_offset,\n int out_offset,\n ConstData_t in,\n Data_t out,\n miopenDataType_t type)\n{\n\n std::string program_name = \"MIOpenUtilKernels4.cl\";\n\n std::string network_config = \"n\" + std::to_string(n) + \"m\" + std::to_string(m) + \"inoff\" +\n std::to_string(in_offset) + \"otoff\" + std::to_string(out_offset) +\n \"t\" + std::to_string(type);\n\n std::string kernel_name = \"transpose_packed_MN2NM\";\n\n auto&& kernels = handle.GetKernels(kernel_name, network_config);\n\n if(!kernels.empty())\n {\n auto kernel = kernels.front();\n kernel(in, out);\n }\n else\n {\n std::string params = GetDataTypeKernelParams(type);\n if(type == miopenInt8x4)\n {\n m /= 4;\n in_offset /= 4;\n out_offset /= 4;\n }\n\n if(!(type == miopenInt8x4 || type == miopenInt8))\n {\n MIOPEN_THROW(\"transpose_packed_MN2NM only meant for int8 variants.\");\n }\n\n params += \" -DNC_TRANS_MN2NM=1\";\n\n params += \" -DN=\" + std::to_string(n);\n params += \" -DM=\" + std::to_string(m);\n params += \" -DIN_OFF=\" + std::to_string(in_offset);\n params += \" -DOUT_OFF=\" + std::to_string(out_offset);\n\n size_t ld0 = WG_SIZE;\n size_t gd0 = m * n;\n const std::vector vld{ld0, 1, 1};\n std::vector vgd{gd0, 1, 1};\n\n handle.AddKernel(kernel_name, network_config, program_name, kernel_name, vld, vgd, params)(\n in, out);\n }\n\n return handle.GetKernelTime();\n}\n} // namespace miopen\n", "meta": {"hexsha": "81e544af70b61ecaafbbde6cbfa715375766eb2e", "size": 38657, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/ocl/utilocl.cpp", "max_stars_repo_name": "luxteam/MIOpen", "max_stars_repo_head_hexsha": "f7b73815c1f9c3ccb95ed759590277de0be74cef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-01-13T10:34:34.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-21T13:35:47.000Z", "max_issues_repo_path": "src/ocl/utilocl.cpp", "max_issues_repo_name": "luxteam/MIOpen", "max_issues_repo_head_hexsha": "f7b73815c1f9c3ccb95ed759590277de0be74cef", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5.0, "max_issues_repo_issues_event_min_datetime": "2019-07-22T09:45:51.000Z", "max_issues_repo_issues_event_max_datetime": "2019-10-21T08:35:47.000Z", "max_forks_repo_path": "src/ocl/utilocl.cpp", "max_forks_repo_name": "luxteam/MIOpen", "max_forks_repo_head_hexsha": "f7b73815c1f9c3ccb95ed759590277de0be74cef", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2021-04-12T08:27:15.000Z", "max_forks_repo_forks_event_max_datetime": "2021-05-11T12:01:20.000Z", "avg_line_length": 34.8889891697, "max_line_length": 100, "alphanum_fraction": 0.4686602685, "num_tokens": 9582, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.577495350642608, "lm_q2_score": 0.3738758367247084, "lm_q1q2_score": 0.21591155742613397}} {"text": "#pragma once\n#ifndef ALGORITHM_CALC_FIXPOINT_HPP\n#define ALGORITHM_CALC_FIXPOINT_HPP\n\n#include \"devavxprng.h\"\n#include \"dSFMTAVX2search.hpp\"\n//#include \"Annihilate.hpp\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace MTToolBox {\n template\n U calc_fixpoint(const G& dsfmt, const NTL::GF2X& irreducible,\n const NTL::GF2X& quotient)\n {\n using namespace std;\n GF2X a, b, d;\n G dsfmt_const(dsfmt);\n /* a*irreducible + b*quotient = d */\n XGCD(d, a, b, irreducible, quotient);\n if (deg(d) != 0) {\n cout << \"failure d != 1\" << endl;\n throw new logic_error(\"failure d != 1\");\n }\n b *= quotient;\n a *= irreducible;\n dsfmt_const.setConst();\n //Annihilate annihilate;\n annihilate(&dsfmt_const, b);\n\n GF2X t1(1, 1);\n SetCoeff(t1, 0);\n /* a*irreducible + b*t1 = d */\n XGCD(d, a, b, irreducible, t1);\n if (deg(d) != 0) {\n cout << \"failure d != 1\" << endl;\n cout << \"deg(d) = \" << dec << deg(d) << endl;\n cout << \"deg(irreducible) = \" << dec << deg(irreducible) << endl;\n cout << \"deg(t1) = \" << dec << deg(t1) << endl;\n throw new logic_error(\"failure d != 1\");\n }\n annihilate(&dsfmt_const, b);\n return dsfmt_const.getParityValue();\n }\n}\n#endif // ALGORITHM_CALC_FIXPOINT_HPP\n", "meta": {"hexsha": "88393751be078076a400228f1f0a74e07da8219a", "size": 1631, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/AlgorithmCalcFixPoint.hpp", "max_stars_repo_name": "MSaito/DevAVXPRNG", "max_stars_repo_head_hexsha": "3860d6c89437a0aa6bf48f4b0baa2f4756683363", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "include/AlgorithmCalcFixPoint.hpp", "max_issues_repo_name": "MSaito/DevAVXPRNG", "max_issues_repo_head_hexsha": "3860d6c89437a0aa6bf48f4b0baa2f4756683363", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/AlgorithmCalcFixPoint.hpp", "max_forks_repo_name": "MSaito/DevAVXPRNG", "max_forks_repo_head_hexsha": "3860d6c89437a0aa6bf48f4b0baa2f4756683363", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.7735849057, "max_line_length": 77, "alphanum_fraction": 0.5628448804, "num_tokens": 473, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6187804196836383, "lm_q2_score": 0.3486451285660856, "lm_q1q2_score": 0.21573477897477847}} {"text": "// Copyright (c) 2017-2018 Steven Watanabe\n//\n// Distributed under the Boost Software License Version 1.0. (See\n// accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_UNITS2_ABSOLUTE_HPP_INCLUDED\n#define BOOST_UNITS2_ABSOlUTE_HPP_INCLUDED\n\n#include \n#include \n\nnamespace boost {\nnamespace units2 {\n\n// celsius - celsius = kelvin\n// celsius + kelvin = celsius\n// fahrenheit\ntemplate>\nstruct absolute_unit {\n absolute_unit() = default;\n constexpr absolute_unit(Unit, Offset) {}\n /// INTERNAL ONLY\n template\n using _boost_units2_apply = typename F::template apply_absolute;\n};\n\ntemplate\noperator+(absolute_unit, Offset2) -> absolute_unit;\n\ntemplate>\nauto operator*(absolute_unit, Scale) -> absolute_unit, Factor> >\n{ return {}; }\ntemplate\nauto operator*(absolute_unit>, std::ratio) -> absolute_unit), std::ratio_divides, std::ratio>>\n{ return {}; }\n\nnamespace detail {\n\nstruct requires_absolute_impl\n{\n template\n using apply_absolute = void;\n};\ntemplate\nusing requires_absolute = visit;\n\nstruct unitdiff_visitor;\n\ntemplate\nusing unitdiff_visit = visit;\n\ntemplate\nusing unitdiff_t = detected_or;\n\nstruct unitdiff_visitor\n{\n template\n using apply_scaled = scaled_unit, Scale>;\n template\n using apply_absolute = unitdiff_t;\n};\n\n}\n\ntemplate\nconstexpr auto operator-(absolute_unit, abosolute_unit) -> detail::unitdiff_t\n{ return {}; }\n\ntemplate\nconstexpr auto operator+(absolute_unit, detail::unitdiff_t) -> absolute_unit\n{ return {}; }\n\ntemplate\nconstexpr auto operator+(detail::unitdiff_t, absolute_unit) -> absolute_unit\n{ return {}; }\n\ntemplate\nconstexpr auto operator-(absolute_unit, detail::unitdiff_t) -> absolute_unit\n{ return {}; }\n\ntemplate\nconstexpr auto operator+(absolute_unit) -> absolute_unit\n{ return {}; }\n\nnamespace detail {\n\nstruct offset_base_case\n{\n template\n using apply_base = void;\n template\n using apply_composite = void;\n};\n\ntemplate>\nconstexpr auto base_offset(Unit) -> double { return 0; }\ntemplate\nconstexpr auto base_offset(absolute_unit) -> double\n{ return detail::base_offset(Unit{}) + detail::get_value(Offset{}); }\ntemplate\nconstexpr auto base_offset(scaled_unit) -> double\n{ return detail::base_offset(Unit{}) * detail::get_value(Scale{}); }\n\n}\n\ntemplate\nauto conversion_offset(From, To)\n{\n if constexpr(is_absolute)\n}\n\ntemplate, class=requires_absolute>\nconstexpr auto convert(From, To, T&& value)\n{\n return (value - detail::base_offset(From)) * conversion_factor(detail::unitdiff_t{}, detail::unitdiff_t{}) + detail::base_offset(To{});\n}\n\n}\n}\n", "meta": {"hexsha": "7250702b43b5610a8f80c224b410ce51c6f3bc62", "size": 3789, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/units2/absolute.hpp", "max_stars_repo_name": "swatanabe/cppnow17-units", "max_stars_repo_head_hexsha": "e317aff5255afd11e3ebcd759ae3c824f6c95260", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 5.0, "max_stars_repo_stars_event_min_datetime": "2017-05-19T20:46:07.000Z", "max_stars_repo_stars_event_max_datetime": "2018-08-21T21:21:46.000Z", "max_issues_repo_path": "include/boost/units2/absolute.hpp", "max_issues_repo_name": "swatanabe/cppnow17-units", "max_issues_repo_head_hexsha": "e317aff5255afd11e3ebcd759ae3c824f6c95260", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/boost/units2/absolute.hpp", "max_forks_repo_name": "swatanabe/cppnow17-units", "max_forks_repo_head_hexsha": "e317aff5255afd11e3ebcd759ae3c824f6c95260", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 31.3140495868, "max_line_length": 187, "alphanum_fraction": 0.7397730272, "num_tokens": 919, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6001883592602051, "lm_q2_score": 0.359364152021239, "lm_q1q2_score": 0.21568618077856236}} {"text": "#include \n#include \n\n#include \n\n#include \n#include \n#include \n\n#include \n\nusing namespace std;\n\n#define LOG_TAG\t\"MatrixOp\"\n#define LOGI(...)\t__android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__)\n#define LOGE(...)\t__android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__)\n\ntemplate void c_mulVector(T1 matrix[], T2 vector[], int rows, int cols, T1 result[]);\n//void arm_neon_mulVector3F(float matrix[], float vector[], float result[]);\ntemplate void c_mulMatrix(T1 matrix1[], T2 matrix2[], int rows1, int cols1, int cols2, T1 result[]);\n//void arm_neon_mulMatrix3F(float matrix1[], float matrix2[], float result[]);\n\n//bool neon = (android_getCpuFamily() == ANDROID_CPU_FAMILY_ARM) && ((android_getCpuFeatures() & ANDROID_CPU_ARM_FEATURE_NEON) != 0);\n\nextern \"C\"\n{\n\tJNIEXPORT jfloatArray JNICALL Java_it_unibo_slam_datatypes_eigen_typefloat_EigenMatrixF_matrixMulVectorF(JNIEnv *env, jobject obj,\n\t\t\tjfloatArray m, jfloatArray v, jint numRowsMatrix, jint numColsMatrix);\n\tJNIEXPORT jfloatArray JNICALL Java_it_unibo_slam_datatypes_eigen_typefloat_EigenMatrixF_matrixMulMatrixF(JNIEnv *env, jobject obj,\n\t\t\tjfloatArray m1, jfloatArray m2, jint numRowsMatrix1, jint numColsMatrix1, jint numColsMatrix2);\n\tJNIEXPORT jfloatArray JNICALL Java_it_unibo_slam_datatypes_eigen_typefloat_EigenMatrixF_matrixMulVectorFD(JNIEnv *env, jobject obj,\n\t\t\tjfloatArray m, jdoubleArray v, jint numRowsMatrix, jint numColsMatrix);\n\tJNIEXPORT jfloatArray JNICALL Java_it_unibo_slam_datatypes_eigen_typefloat_EigenMatrixF_matrixMulMatrixFD(JNIEnv *env, jobject obj,\n\t\t\tjfloatArray m1, jdoubleArray m2, jint numRowsMatrix1, jint numColsMatrix1, jint numColsMatrix2);\n\tJNIEXPORT jdoubleArray JNICALL Java_it_unibo_slam_datatypes_eigen_typedouble_EigenMatrixD_matrixMulVectorD(JNIEnv *env, jobject obj,\n\t\t\tjdoubleArray m, jdoubleArray v, jint numRowsMatrix, jint numColsMatrix);\n\tJNIEXPORT jdoubleArray JNICALL Java_it_unibo_slam_datatypes_eigen_typedouble_EigenMatrixD_matrixMulMatrixD(JNIEnv *env, jobject obj,\n\t\t\tjdoubleArray m1, jdoubleArray m2, jint numRowsMatrix1, jint numColsMatrix1, jint numColsMatrix2);\n\tJNIEXPORT jdoubleArray JNICALL Java_it_unibo_slam_datatypes_eigen_typedouble_EigenMatrixD_matrixMulVectorDF(JNIEnv *env, jobject obj,\n\t\t\tjdoubleArray m, jfloatArray v, jint numRowsMatrix, jint numColsMatrix);\n\tJNIEXPORT jdoubleArray JNICALL Java_it_unibo_slam_datatypes_eigen_typedouble_EigenMatrixD_matrixMulMatrixDF(JNIEnv *env, jobject obj,\n\t\t\tjdoubleArray m1, jfloatArray m2, jint numRowsMatrix1, jint numColsMatrix1, jint numColsMatrix2);\n}\n\nJNIEXPORT jfloatArray JNICALL Java_it_unibo_slam_datatypes_eigen_typefloat_EigenMatrixF_matrixMulVectorF(JNIEnv *env, jobject obj,\n\t\tjfloatArray m, jfloatArray v, jint numRowsMatrix, jint numColsMatrix)\n{\n\tjfloatArray result;\n\tint rows = numRowsMatrix;\n\tint cols = numColsMatrix;\n\tint sizeMatrix = rows * cols;\n\tresult = env->NewFloatArray(rows);\n\n\tfloat *tempResult = new float[rows];\n\tfloat *tempM = new float[sizeMatrix];\n\tfloat *tempV = new float[cols];\n\n\tenv->GetFloatArrayRegion(m, 0, sizeMatrix, tempM);\n\tenv->GetFloatArrayRegion(v, 0, cols, tempV);\n\n\tc_mulVector(tempM, tempV, rows, cols, tempResult);\n\n\tenv->SetFloatArrayRegion(result, 0, rows, tempResult);\n\n\tdelete [] tempResult;\n\tdelete [] tempM;\n\tdelete [] tempV;\n\n\treturn result;\n}\n\nJNIEXPORT jfloatArray JNICALL Java_it_unibo_slam_datatypes_eigen_typefloat_EigenMatrixF_matrixMulMatrixF(JNIEnv *env, jobject obj,\n\t\tjfloatArray m1, jfloatArray m2, jint numRowsMatrix1, jint numColsMatrix1, jint numColsMatrix2)\n{\n\tjfloatArray result;\n\tint rows1 = numRowsMatrix1;\n\tint cols1 = numColsMatrix1;\n\tint cols2 = numColsMatrix2;\n\tint sizeMatrix1 = rows1 * cols1;\n\tint sizeMatrix2 = cols1 * cols2;\n\tint sizeResult = rows1 * cols2;\n\tresult = env->NewFloatArray(sizeResult);\n\n\tfloat *tempResult = new float[sizeResult];\n\tfloat *tempM1 = new float[sizeMatrix1];\n\tfloat *tempM2 = new float[sizeMatrix2];\n\n\tenv->GetFloatArrayRegion(m1, 0, sizeMatrix1, tempM1);\n\tenv->GetFloatArrayRegion(m2, 0, sizeMatrix2, tempM2);\n\n\tc_mulMatrix(tempM1, tempM2, rows1, cols1, cols2, tempResult);\n\n\tenv->SetFloatArrayRegion(result, 0, sizeResult, tempResult);\n\n\tdelete [] tempResult;\n\tdelete [] tempM1;\n\tdelete [] tempM2;\n\n\treturn result;\n}\n\nJNIEXPORT jfloatArray JNICALL Java_it_unibo_slam_datatypes_eigen_typefloat_EigenMatrixF_matrixMulVectorFD(JNIEnv *env, jobject obj,\n\t\tjfloatArray m, jdoubleArray v, jint numRowsMatrix, jint numColsMatrix)\n{\n\tjfloatArray result;\n\tint rows = numRowsMatrix;\n\tint cols = numColsMatrix;\n\tint sizeMatrix = rows * cols;\n\tresult = env->NewFloatArray(rows);\n\n\tfloat *tempResult = new float[rows];\n\tfloat *tempM = new float[sizeMatrix];\n\tdouble *tempV = new double[cols];\n\n\tenv->GetFloatArrayRegion(m, 0, sizeMatrix, tempM);\n\tenv->GetDoubleArrayRegion(v, 0, cols, tempV);\n\n\tc_mulVector(tempM, tempV, rows, cols, tempResult);\n\n\tenv->SetFloatArrayRegion(result, 0, rows, tempResult);\n\n\tdelete [] tempResult;\n\tdelete [] tempM;\n\tdelete [] tempV;\n\n\treturn result;\n}\n\nJNIEXPORT jfloatArray JNICALL Java_it_unibo_slam_datatypes_eigen_typefloat_EigenMatrixF_matrixMulMatrixFD(JNIEnv *env, jobject obj,\n\t\tjfloatArray m1, jdoubleArray m2, jint numRowsMatrix1, jint numColsMatrix1, jint numColsMatrix2)\n{\n\tjfloatArray result;\n\tint rows1 = numRowsMatrix1;\n\tint cols1 = numColsMatrix1;\n\tint cols2 = numColsMatrix2;\n\tint sizeMatrix1 = rows1 * cols1;\n\tint sizeMatrix2 = cols1 * cols2;\n\tint sizeResult = rows1 * cols2;\n\tresult = env->NewFloatArray(sizeResult);\n\n\tfloat *tempResult = new float[sizeResult];\n\tfloat *tempM1 = new float[sizeMatrix1];\n\tdouble *tempM2 = new double[sizeMatrix2];\n\n\tenv->GetFloatArrayRegion(m1, 0, sizeMatrix1, tempM1);\n\tenv->GetDoubleArrayRegion(m2, 0, sizeMatrix2, tempM2);\n\n\tc_mulMatrix(tempM1, tempM2, rows1, cols1, cols2, tempResult);\n\n\tenv->SetFloatArrayRegion(result, 0, sizeResult, tempResult);\n\n\tdelete [] tempResult;\n\tdelete [] tempM1;\n\tdelete [] tempM2;\n\n\treturn result;\n}\n\nJNIEXPORT jdoubleArray JNICALL Java_it_unibo_slam_datatypes_eigen_typedouble_EigenMatrixD_matrixMulVectorD(JNIEnv *env, jobject obj,\n\t\tjdoubleArray m, jdoubleArray v, jint numRowsMatrix, jint numColsMatrix)\n{\n\tjdoubleArray result;\n\tint rows = numRowsMatrix;\n\tint cols = numColsMatrix;\n\tint sizeMatrix = rows * cols;\n\tresult = env->NewDoubleArray(rows);\n\n\tdouble *tempResult = new double[rows];\n\tdouble *tempM = new double[sizeMatrix];\n\tdouble *tempV = new double[cols];\n\n\tenv->GetDoubleArrayRegion(m, 0, sizeMatrix, tempM);\n\tenv->GetDoubleArrayRegion(v, 0, cols, tempV);\n\n\tc_mulVector(tempM, tempV, rows, cols, tempResult);\n\n\tenv->SetDoubleArrayRegion(result, 0, rows, tempResult);\n\n\tdelete [] tempResult;\n\tdelete [] tempM;\n\tdelete [] tempV;\n\n\treturn result;\n}\n\nJNIEXPORT jdoubleArray JNICALL Java_it_unibo_slam_datatypes_eigen_typedouble_EigenMatrixD_matrixMulMatrixD(JNIEnv *env, jobject obj,\n\t\tjdoubleArray m1, jdoubleArray m2, jint numRowsMatrix1, jint numColsMatrix1, jint numColsMatrix2)\n{\n\tjdoubleArray result;\n\tint rows1 = numRowsMatrix1;\n\tint cols1 = numColsMatrix1;\n\tint cols2 = numColsMatrix2;\n\tint sizeMatrix1 = rows1 * cols1;\n\tint sizeMatrix2 = cols1 * cols2;\n\tint sizeResult = rows1 * cols2;\n\tresult = env->NewDoubleArray(sizeResult);\n\n\tdouble *tempResult = new double[sizeResult];\n\tdouble *tempM1 = new double[sizeMatrix1];\n\tdouble *tempM2 = new double[sizeMatrix2];\n\n\tenv->GetDoubleArrayRegion(m1, 0, sizeMatrix1, tempM1);\n\tenv->GetDoubleArrayRegion(m2, 0, sizeMatrix2, tempM2);\n\n\tc_mulMatrix(tempM1, tempM2, rows1, cols1, cols2, tempResult);\n\n\tenv->SetDoubleArrayRegion(result, 0, sizeResult, tempResult);\n\n\tdelete [] tempResult;\n\tdelete [] tempM1;\n\tdelete [] tempM2;\n\n\treturn result;\n}\n\nJNIEXPORT jdoubleArray JNICALL Java_it_unibo_slam_datatypes_eigen_typedouble_EigenMatrixD_matrixMulVectorDF(JNIEnv *env, jobject obj,\n\t\tjdoubleArray m, jfloatArray v, jint numRowsMatrix, jint numColsMatrix)\n{\n\tjdoubleArray result;\n\tint rows = numRowsMatrix;\n\tint cols = numColsMatrix;\n\tint sizeMatrix = rows * cols;\n\tresult = env->NewDoubleArray(rows);\n\n\tdouble *tempResult = new double[rows];\n\tdouble *tempM = new double[sizeMatrix];\n\tfloat *tempV = new float[cols];\n\n\tenv->GetDoubleArrayRegion(m, 0, sizeMatrix, tempM);\n\tenv->GetFloatArrayRegion(v, 0, cols, tempV);\n\n\tc_mulVector(tempM, tempV, rows, cols, tempResult);\n\n\tenv->SetDoubleArrayRegion(result, 0, rows, tempResult);\n\n\tdelete [] tempResult;\n\tdelete [] tempM;\n\tdelete [] tempV;\n\n\treturn result;\n}\n\nJNIEXPORT jdoubleArray JNICALL Java_it_unibo_slam_datatypes_eigen_typedouble_EigenMatrixD_matrixMulMatrixDF(JNIEnv *env, jobject obj,\n\t\tjdoubleArray m1, jfloatArray m2, jint numRowsMatrix1, jint numColsMatrix1, jint numColsMatrix2)\n{\n\tjdoubleArray result;\n\tint rows1 = numRowsMatrix1;\n\tint cols1 = numColsMatrix1;\n\tint cols2 = numColsMatrix2;\n\tint sizeMatrix1 = rows1 * cols1;\n\tint sizeMatrix2 = cols1 * cols2;\n\tint sizeResult = rows1 * cols2;\n\tresult = env->NewDoubleArray(sizeResult);\n\n\tdouble *tempResult = new double[sizeResult];\n\tdouble *tempM1 = new double[sizeMatrix1];\n\tfloat *tempM2 = new float[sizeMatrix2];\n\n\tenv->GetDoubleArrayRegion(m1, 0, sizeMatrix1, tempM1);\n\tenv->GetFloatArrayRegion(m2, 0, sizeMatrix2, tempM2);\n\n\tc_mulMatrix(tempM1, tempM2, rows1, cols1, cols2, tempResult);\n\n\tenv->SetDoubleArrayRegion(result, 0, sizeResult, tempResult);\n\n\tdelete [] tempResult;\n\tdelete [] tempM1;\n\tdelete [] tempM2;\n\n\treturn result;\n}\n\ntemplate \nvoid c_mulMatrix(T1 matrix1[], T2 matrix2[], int rows1, int cols1, int cols2, T1 result[])\n{\n\tT1 tempVal;\n\n\tfor (int x = 0, y = 0; x < cols2; x++, y += cols1)\n\t{\n\t\tfor (int i = 0; i < rows1; i++, result++)\n\t\t{\n\t\t\ttempVal = 0;\n\t\t\tfor (int j = 0, k = 0; j < cols1; j++, k += rows1)\n\t\t\t\ttempVal += matrix1[i + k] * matrix2[j + y];\n\t\t\t*result = tempVal;\n\t\t}\n\t}\n}\n\ntemplate \nvoid c_mulVector(T1 matrix[], T2 vector[], int rows, int cols, T1 result[])\n{\n\tT1 tempVal;\n\n\tfor (int i = 0; i < rows; i++)\n\t{\n\t\ttempVal = 0;\n\t\tfor (int j = 0, k = 0; j < cols; j++, k += rows)\n\t\t\ttempVal += matrix[i + k] * (T1)vector[j];\n\t\tresult[i] = tempVal;\n\t}\n}\n\n/*void arm_neon_mulVector3F(float matrix[], float vector[], float result[])\n{\n\tint temp;\n\tasm volatile\n\t(\n\t\t\"mov %3, #12\t\t\t\t\t\t\\n\\t\" //r3 = 12\n\t\t\"vld1.32 {d0, d1}, [%1] \t\\n\\t\" //Q0 = v\n\t\t\"vld1.32 {d2, d3}, [%0], %3 \t\t\\n\\t\" //Q1 = m\n\t\t\"vld1.32 {d4, d5}, [%0], %3 \t\\n\\t\" //Q2 = m+12\n\t\t\"vld1.32 {d6, d7}, [%0], %3 \t\\n\\t\" //Q3 = m+24\n\n\t\t\"vmul.f32 q9, q1, d0[0] \t\\n\\t\" //Q9 = Q1*Q0[0]\n\t\t\"vmla.f32 q9, q2, d0[1] \t\\n\\t\" //Q9 += Q2*Q0[1]\n\t\t\"vmla.f32 q9, q3, d1[0] \t\\n\\t\" //Q9 += Q3*Q0[2]\n\t\t\"vmov.f32 q0, q9 \t\t\t\\n\\t\" //Q0 = q9\n\n\t\t\"vst1.32 d0, [%2]! \t\\n\\t\" //r2 = D24\n\t\t\"fsts s2, [%2] \t\\n\\t\" //r2 = D25[0]\n\n\t\t:\n\t\t: \"r\" (matrix), \"r\" (vector), \"r\" (result), \"r\" (temp)\n\t\t: \"q0\", \"q9\", \"q10\", \"q11\", \"q12\", \"q13\", \"memory\"\n\t);\n}\n\nvoid arm_neon_mulMatrix3F(float matrix1[], float matrix2[], float result[])\n{\n\tasm volatile\n\t(\n\t\t\"vld1.32 {d0, d1}, [%1]!\t\t\t\\n\\t\" //q0 = m1\n\t\t\"vld1.32 {d2, d3}, [%1]!\t\t\t\\n\\t\" //q1 = m1+4\n\t\t\"flds s8, [%1]\t\t\t\t\\n\\t\" //q2 = m1+8\n\n\t\t\"vld1.32 {d6, d7}, [%0]\t\t\t\\n\\t\" //q3[0] = m0\n\t\t\"add %0, %0, #12\t\t\t\t\\n\\t\" //q3[0] = m0\n\t\t\"vld1.32 {d8, d9}, [%0]\t\t\t\\n\\t\" //q4[0] = m0+12\n\t\t\"add %0, %0, #12\t\t\t\t\\n\\t\" //q3[0] = m0\n\t\t\"vld1.32 {d10}, [%0]\t\t\t\t\\n\\t\" //q5[0] = m0+24\n\t\t\"add %0, %0, #8\t\t\t\t\\n\\t\" //q3[0] = m0\n\t\t\"flds s22, [%0]\t\t\t\t\\n\\t\" //q2 = m1+8\n\n\t\t\"vmul.f32 q6, q3, d0[0]\t\t\t\\n\\t\" //q12 = q3 * d0[0]\n\t\t\"vmul.f32 q7, q3, d1[1]\t\t\t\\n\\t\" //q13 = q3 * d2[0]\n\t\t\"vmul.f32 q8, q3, d3[0]\t\t\t\\n\\t\" //q14 = q3 * d4[0]\n\t\t\"vmla.f32 q6, q4, d0[1] \t\t\t\\n\\t\" //q12 = q9 * d0[1]\n\t\t\"vmla.f32 q7, q4, d2[0] \t\t\\n\\t\" //q13 = q9 * d2[1]\n\t\t\"vmla.f32 q8, q4, d3[1] \t\t\t\\n\\t\" //q14 = q9 * d4[1]\n\t\t\"vmla.f32 q6, q5, d1[0]\t\t\t\\n\\t\" //q12 = q10 * d0[0]\n\t\t\"vmla.f32 q7, q5, d2[1]\t\t\t\\n\\t\" //q13 = q10 * d2[0]\n\t\t\"vmla.f32 q8, q5, d4[0]\t\t\t\\n\\t\" //q14 = q10 * d4[0]\n\n\t\t\"vmov.f32 q0, q8\t\t\t\t\t\\n\\t\" //q14 = q10 * d4[0]\n\t\t\"vst1.32 {d12, d13}, [%2]\t\t\\n\\t\" //d = q12\n\t\t\"add %2, %2, #12\t\t\t\t\\n\\t\" //q3[0] = m0\n\t\t\"vst1.32 {d14, d15}, [%2]\t\t\\n\\t\" //d+4 = q13\n\t\t\"add %2, %2, #12\t\t\t\t\\n\\t\" //q3[0] = m0\n\t\t\"vst1.32 {d0}, [%2]\t\t\t\t\\n\\t\" //d+8 = q14\n\t\t\"add %2, %2, #8\t\t\t\t\\n\\t\" //q3[0] = m0\n\t\t\"fsts s2, [%2]\t\t\t\t\\n\\t\" //d = q12\n\n\t\t:\n\t\t: \"r\"(matrix1), \"r\"(matrix2), \"r\"(result)\n\t\t: \"d8\", \"d9\", \"d10\", \"d11\", \"d12\", \"d13\", \"d14\", \"d15\", \"memory\"\n\t);\n}*/\n", "meta": {"hexsha": "11d747a1eb97a139de8a67ef7512c38a0c95f516", "size": 13245, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "jni/eigen/MatrixOp.cpp", "max_stars_repo_name": "CVLAB-Unibo/Slam-Dunk-Android", "max_stars_repo_head_hexsha": "28343eb7d92cfd884e025dbaf510f4115199f58f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 27.0, "max_stars_repo_stars_event_min_datetime": "2018-01-18T15:50:36.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-11T23:07:15.000Z", "max_issues_repo_path": "jni/eigen/MatrixOp.cpp", "max_issues_repo_name": "CVLAB-Unibo/Slam-Dunk-Android", 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YES\n2. NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.21554879684473494}} {"text": "// This file is part of LatNet Builder.\n//\n// Copyright (C) 2012-2021 The LatNet Builder author's, supervised by Pierre L'Ecuyer, Universite de Montreal.\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n// http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\n#include \"netbuilder/NetConstructionTraits.h\"\n#include \"netbuilder/Helpers/JoeKuo.h\"\n\n#include \n#include \n\n\nnamespace NetBuilder {\n\n const std::string NetConstructionTraits::name = \"LMS\";\n \n typedef typename NetConstructionTraits::GenValue GenValue;\n\n typedef std::vector DirectionNumber;\n typedef std::pair PrimitivePolynomial; \n typedef std::vector> BinaryMatrix;\n\n typedef NetConstructionTraits::SizeParameter SizeParameter;\n\n bool NetConstructionTraits::checkGenValue(const GenValue& genValue, const SizeParameter& sizeParameter)\n {\n return genValue.nRows() == nRows(sizeParameter) && genValue.nCols() == nCols(sizeParameter);\n }\n\n unsigned int NetConstructionTraits::nRows(const SizeParameter& sizeParameter) {return (unsigned int) sizeParameter.first.first; }\n\n unsigned int NetConstructionTraits::nCols(const SizeParameter& sizeParameter) {return (unsigned int) sizeParameter.first.second; }\n\n GeneratingMatrix* NetConstructionTraits::createGeneratingMatrix(const GenValue& genValue, const SizeParameter& sizeParameter, const Dimension& dimension_j, const unsigned int nRows)\n {\n GeneratingMatrix* result = new GeneratingMatrix(genValue * *sizeParameter.second[dimension_j]);\n unsigned int finalnRows = (nRows == 0)? NetConstructionTraits::nRows(sizeParameter) : nRows;\n result->resize(finalnRows, nCols(sizeParameter));\n return result;\n }\n\n std::vector NetConstructionTraits::genValueSpaceCoord(Dimension coord, const SizeParameter& sizeParameter)\n {\n throw std::logic_error(\"The space of all matrices is far too big to be exhautively explored.\");\n return std::vector{};\n }\n\n std::vector> NetConstructionTraits::genValueSpace(Dimension dimension, const SizeParameter& sizeParameter)\n {\n throw std::logic_error(\"The space of all matrices is far too big to be exhautively explored.\");\n return std::vector>{};\n }\n\n std::string NetConstructionTraits::format(const std::vector>& genMatrices, const std::vector>& genVals, const SizeParameter& sizeParameter, OutputStyle outputStyle, unsigned int interlacingFactor)\n {\n std::ostringstream stream;\n \n if (outputStyle == OutputStyle::TERMINAL){\n stream << \"Left-Matrix-Scrambled Digital Net - Matrix size = \" << sizeParameter.first.first << \"x\" << sizeParameter.first.second << std::endl;\n stream << \"Printing only the scrambled generating matrices - see output.txt file for original matrices and scrambling matrices.\\n\";\n for (Dimension dim = 0; dim < genMatrices.size(); ++dim)\n {\n stream << \"Coordinate \" << dim << std::endl;\n stream << *genMatrices[dim] << std::endl;\n }\n }\n\n else if (outputStyle == OutputStyle::RANDOMIZED_NET){\n unsigned long dimension = genMatrices.size();\n unsigned int nb_col = sizeParameter.first.second;\n stream << \"# Parameters for a digital net in base 2\\n\";\n stream << dimension / interlacingFactor << \" # s = \" << dimension / interlacingFactor << \" dimensions\\n\";\n if (interlacingFactor > 1){\n stream << interlacingFactor << \" # Interlacing factor\" << \"\\n\";\n stream << dimension << \" # Number of components = interlacing factor x dimension\" << \"\\n\";\n }\n stream << nb_col << \" # k = \" << nb_col << \", n = 2^\" << nb_col << \" = \"; \n stream << (int)pow(2,nb_col) << \" points\"<< std::endl;\n stream << \"31 # r = 31 binary output digits\\n\";\n\n if (interlacingFactor == 1){\n stream << \"# Columns of original gen. matrices C_1,...,C_s, one matrix per line\\n\";\n }\n else {\n stream << \"# Columns of original gen. matrices C_1,...,C_{ds}, one matrix per line\\n\";\n }\n for(unsigned int coord = 0; coord < dimension; coord++)\n {\n stream << sizeParameter.second[coord]->formatToColumnsReverse();\n stream << \"\\n\";\n }\n\n if (interlacingFactor == 1){\n stream << \"# Columns of scrambling matrices C_1,...,C_s, one matrix per line\\n\";\n }\n else {\n stream << \"# Columns of scrambling matrices C_1,...,C_{ds}, one matrix per line\\n\";\n }\n for(unsigned int coord = 0; coord < dimension; coord++)\n {\n stream << genVals[coord]->formatToColumnsReverse();\n stream << \"\\n\";\n }\n\n if (interlacingFactor == 1){\n stream << \"# Columns of scrambled gen. matrices C_1,...,C_s, one matrix per line\\n\";\n }\n else {\n stream << \"# Columns of scrambled gen. matrices C_1,...,C_{ds}, one matrix per line\\n\";\n }\n for(unsigned int coord = 0; coord < dimension; coord++)\n {\n stream << genMatrices[coord]->formatToColumnsReverse();\n if (coord < dimension - 1){\n stream << \"\\n\";\n }\n }\n }\n return stream.str();\n } \n}\n", "meta": {"hexsha": "0b75dccfe3bbae806c89a82016434b64e0365ed1", "size": 6384, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/NetBuilder/NetConstructionTraits-LMS.cc", "max_stars_repo_name": "YochevedDarmon/latnetbuilder", "max_stars_repo_head_hexsha": "7df3a7ba89e10ca51d4af347b75cdb0987e5151c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 10.0, "max_stars_repo_stars_event_min_datetime": "2020-01-21T06:08:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-24T16:11:31.000Z", "max_issues_repo_path": "src/NetBuilder/NetConstructionTraits-LMS.cc", "max_issues_repo_name": "YochevedDarmon/latnetbuilder", "max_issues_repo_head_hexsha": "7df3a7ba89e10ca51d4af347b75cdb0987e5151c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2019-10-31T07:49:28.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-03T12:07:49.000Z", "max_forks_repo_path": "src/NetBuilder/NetConstructionTraits-LMS.cc", "max_forks_repo_name": "YochevedDarmon/latnetbuilder", "max_forks_repo_head_hexsha": "7df3a7ba89e10ca51d4af347b75cdb0987e5151c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2016-07-27T19:25:26.000Z", "max_forks_repo_forks_event_max_datetime": "2018-05-22T20:00:47.000Z", "avg_line_length": 48.0, "max_line_length": 282, "alphanum_fraction": 0.6296992481, "num_tokens": 1429, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832354982645, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.21549177171580447}} {"text": "//---------------------------------------------------------------------------//\n// Copyright (c) 2018-2019 Mikhail Komarov \n// Copyright (c) 2020 Alexander Sokolov \n//\n// Distributed under the Boost Software License, Version 1.0\n// See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt\n//---------------------------------------------------------------------------//\n\n#ifndef CRYPTO3_HASH_SPONGE_CONSTRUCTION_HPP\n#define CRYPTO3_HASH_SPONGE_CONSTRUCTION_HPP\n\n#include \n#include \n\n#include \n\nnamespace boost {\n namespace crypto3 {\n namespace hashes {\n /*!\n * @brief\n * @tparam DigestEndian\n * @tparam DigestBits\n * @tparam IV\n * @tparam Compressor\n * @tparam Finalizer\n *\n * The Sponge construction builds a block hashes from a\n * one-way compressor. As this version operated on the block\n * level, it doesn't contain any padding or other strengthening.\n * For a Wide Pipe construction, use a digest that will\n * truncate the internal state.\n */\n template\n class sponge_construction {\n public:\n typedef IV iv_generator;\n typedef Compressor compressor_functor;\n typedef Padding padding_functor;\n typedef Finalizer finalizer_functor;\n\n typedef typename Params::digest_endian endian_type;\n\n constexpr static const std::size_t word_bits = compressor_functor::word_bits;\n typedef typename compressor_functor::word_type word_type;\n\n constexpr static const std::size_t state_bits = compressor_functor::state_bits;\n constexpr static const std::size_t state_words = compressor_functor::state_words;\n typedef typename compressor_functor::state_type state_type;\n\n constexpr static const std::size_t block_bits = compressor_functor::block_bits;\n constexpr static const std::size_t block_words = compressor_functor::block_words;\n typedef typename compressor_functor::block_type block_type;\n\n constexpr static const std::size_t digest_bits = Params::digest_bits;\n constexpr static const std::size_t digest_bytes = digest_bits / octet_bits;\n constexpr static const std::size_t digest_words = digest_bits / word_bits;\n typedef static_digest digest_type;\n\n template\n inline sponge_construction &process_block(const block_type &block, Integer seen = Integer()) {\n compressor_functor::process_block(state_, block);\n return *this;\n }\n\n inline digest_type digest(const block_type &block = block_type(),\n std::size_t total_seen = std::size_t()) {\n using namespace boost::crypto3::detail;\n\n block_type b = block;\n std::size_t block_seen = total_seen % block_bits;\n // Process block if it is full\n if (total_seen && !block_seen)\n process_block(b);\n\n std::size_t copy_seen = block_seen;\n // Pad last message block\n padding_functor padding;\n padding(b, block_seen);\n process_block(b);\n\n // Process additional block if not all bits were padded\n if (!padding.is_last_block()) {\n std::fill(b.begin(), b.end(), 0);\n padding.process_last(b, copy_seen);\n process_block(b);\n }\n\n // Apply finalizer\n finalizer_functor()(state_);\n\n // Convert digest to byte representation\n std::array d_full;\n pack_from(state_.begin(), state_.end(), d_full.begin());\n\n digest_type d;\n std::copy(d_full.begin(), d_full.begin() + digest_bytes, d.begin());\n\n return d;\n }\n\n sponge_construction() {\n reset();\n }\n\n void reset(state_type const &s) {\n state_ = s;\n }\n\n void reset() {\n iv_generator iv;\n reset(iv());\n }\n\n state_type const &state() const {\n return state_;\n }\n\n private:\n state_type state_;\n };\n\n } // namespace hashes\n } // namespace crypto3\n} // namespace boost\n\n#endif // CRYPTO3_HASH_SPONGE_CONSTRUCTION_HPP\n", "meta": {"hexsha": "673ee28b117e1d5783e2818d6fb9301a4ad43d56", "size": 5367, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/crypto3/hash/detail/sponge_construction.hpp", "max_stars_repo_name": "NilFoundation/boost-crypto", "max_stars_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 4.0, "max_stars_repo_stars_event_min_datetime": "2020-09-02T06:19:14.000Z", "max_stars_repo_stars_event_max_datetime": "2020-09-07T04:55:03.000Z", "max_issues_repo_path": "include/boost/crypto3/hash/detail/sponge_construction.hpp", "max_issues_repo_name": "NilFoundation/boost-crypto", "max_issues_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2020-04-06T21:49:03.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-18T04:54:51.000Z", "max_forks_repo_path": "include/boost/crypto3/hash/detail/sponge_construction.hpp", "max_forks_repo_name": "NilFoundation/boost-crypto", "max_forks_repo_head_hexsha": "a3e599b780bbbbc063b7c8da0e498125769e08be", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2022-02-13T21:14:37.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-13T21:14:37.000Z", "avg_line_length": 40.3533834586, "max_line_length": 112, "alphanum_fraction": 0.5321408608, "num_tokens": 952, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.3886180267058489, "lm_q1q2_score": 0.2154772179595958}} {"text": "\n#ifndef OFFPOLSETACFITTED_HPP\n#define OFFPOLSETACFITTED_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"arch/AACAgent.hpp\"\n#include \"bib/Seed.hpp\"\n#include \"bib/Utils.hpp\"\n#include \n#include \n#include \"MLP.hpp\"\n#include \"LinMLP.hpp\"\n#include \"kde.hpp\"\n#include \"kdtree++/kdtree.hpp\"\n\n#define DOUBLE_COMPARE_PRECISION 1e-9\n\ntypedef struct _sample {\n std::vector s;\n std::vector pure_a;\n std::vector a;\n std::vector next_s;\n double r;\n bool goal_reached;\n double p0;\n\n friend class boost::serialization::access;\n template \n void serialize(Archive& ar, const unsigned int) {\n ar& BOOST_SERIALIZATION_NVP(s);\n ar& BOOST_SERIALIZATION_NVP(pure_a);\n ar& BOOST_SERIALIZATION_NVP(a);\n ar& BOOST_SERIALIZATION_NVP(next_s);\n ar& BOOST_SERIALIZATION_NVP(r);\n ar& BOOST_SERIALIZATION_NVP(goal_reached);\n }\n\n //Used to store all sample into a tree, might be stochastic\n //only pure_a is negligate\n bool operator< (const _sample& b) const {\n for (uint i = 0; i < s.size(); i++) {\n if(fabs(s[i] - b.s[i])>=DOUBLE_COMPARE_PRECISION)\n return s[i] < b.s[i];\n }\n \n for (uint i = 0; i < a.size(); i++) {\n if(fabs(a[i] - b.a[i])>=DOUBLE_COMPARE_PRECISION)\n return a[i] < b.a[i];\n }\n \n for (uint i = 0; i < next_s.size(); i++) {\n if(fabs(next_s[i] - b.next_s[i])>=DOUBLE_COMPARE_PRECISION)\n return next_s[i] < b.next_s[i];\n }\n \n if(fabs(r - b.r)>=DOUBLE_COMPARE_PRECISION)\n return r < b.r;\n\n return goal_reached < b.goal_reached;\n }\n \n typedef double value_type;\n\n inline double operator[](size_t const N) const{\n return s[N];\n }\n \n bool same_state(const _sample& b) const{\n for (uint i = 0; i < s.size(); i++) {\n if(fabs(s[i] - b.s[i])>=DOUBLE_COMPARE_PRECISION)\n return false;\n }\n \n return true;\n }\n\n} sample;\n\n\nclass OffPolSetACFitted : public arch::AACAgent {\n public:\n typedef MLP PolicyImpl;\n \n OffPolSetACFitted(unsigned int _nb_motors, unsigned int _nb_sensors)\n : arch::AACAgent(_nb_motors), nb_sensors(_nb_sensors) {\n\n }\n\n virtual ~OffPolSetACFitted() {\n delete kdtree_s;\n\n delete vnn;\n delete ann;\n }\n\n const std::vector& _run(double reward, const std::vector& sensors,\n bool learning, bool goal_reached, bool) {\n\n vector* next_action = ann->computeOut(sensors);\n \n if (last_action.get() != nullptr && learning){\n double p0 = 1.f;\n for(uint i=0;i < nb_motors;i++)\n p0 *= exp(-(last_pure_action->at(i)-last_action->at(i))*(last_pure_action->at(i)-last_action->at(i))/(2.f*noise*noise));\n \n trajectory.insert({last_state, *last_pure_action, *last_action, sensors, reward, goal_reached, p0});\n last_trajectory.insert( {last_state, *last_pure_action, *last_action, sensors, reward, goal_reached, p0});\n proba_s.add_data(last_state);\n \n sample sa = {last_state, *last_pure_action, *last_action, sensors, reward, goal_reached, p0};\n \n kdtree_s->insert(sa);\n \n std::vector psa;\n std::vector psas;\n mergeSA(psa, last_state, *last_action);\n mergeSAS(psas, last_state, *last_action, sensors);\n \n proba_sa.add_data(psa);\n proba_sas.add_data(psas);\n }\n\n last_pure_action.reset(new vector(*next_action));\n if(learning) {\n if(gaussian_policy){\n vector* randomized_action = bib::Proba::multidimentionnalGaussianWReject(*next_action, noise);\n delete next_action;\n next_action = randomized_action;\n } else if(bib::Utils::rand01() < noise){ //e-greedy\n for (uint i = 0; i < next_action->size(); i++)\n next_action->at(i) = bib::Utils::randin(-1.f, 1.f);\n }\n }\n last_action.reset(next_action);\n\n\n last_state.clear();\n for (uint i = 0; i < sensors.size(); i++)\n last_state.push_back(sensors[i]);\n\n return *next_action;\n }\n\n\n void _unique_invoke(boost::property_tree::ptree* pt, boost::program_options::variables_map*) override {\n hidden_unit_v = pt->get(\"agent.hidden_unit_v\");\n hidden_unit_a = pt->get(\"agent.hidden_unit_a\");\n noise = pt->get(\"agent.noise\");\n gaussian_policy = pt->get(\"agent.gaussian_policy\");\n lecun_activation = pt->get(\"agent.lecun_activation\");\n determinist_vnn_update = pt->get(\"agent.determinist_vnn_update\");\n vnn_from_scratch = pt->get(\"agent.vnn_from_scratch\");\n change_policy_each = pt->get(\"agent.change_policy_each\");\n strategy_w = pt->get(\"agent.strategy_w\");\n current_loaded_policy = pt->get(\"agent.index_starting_loaded_policy\");\n converge_precision = pt->get(\"agent.converge_precision\");\n number_fitted_iteration = pt->get(\"agent.number_fitted_iteration\");\n\n if(hidden_unit_v == 0)\n vnn = new LinMLP(nb_sensors + nb_motors , 1, 0.0, lecun_activation);\n else\n vnn = new MLP(nb_sensors + nb_motors, hidden_unit_v, nb_sensors, 0.0, lecun_activation);\n\n if(hidden_unit_a == 0)\n ann = new LinMLP(nb_sensors , nb_motors, 0.0, lecun_activation);\n else\n ann = new MLP(nb_sensors, hidden_unit_a, nb_motors, lecun_activation);\n \n kdtree_s = new kdtree_sample(nb_sensors);\n }\n\n bool learning;\n void _start_episode(const std::vector& sensors, bool _learning) override {\n learning = _learning;\n last_state.clear();\n for (uint i = 0; i < sensors.size(); i++)\n last_state.push_back(sensors[i]);\n\n last_action = nullptr;\n last_pure_action = nullptr;\n \n //trajectory.clear();\n last_trajectory.clear();\n \n fann_reset_MSE(vnn->getNeuralNet());\n \n if(episode == 0 || episode % change_policy_each == 0){\n if (! boost::filesystem::exists( \"polset.\"+std::to_string(current_loaded_policy) ) ){\n LOG_ERROR(\"file doesn't exists \"<< \"polset.\"+std::to_string(current_loaded_policy));\n exit(1);\n }\n \n ann->load(\"polset.\"+std::to_string(current_loaded_policy));\n current_loaded_policy++;\n end_episode();\n }\n }\n \n void computePTheta(vector< sample >& vtraj, double *ptheta){\n uint i=0;\n for(auto it = vtraj.begin(); it != vtraj.end() ; ++it) {\n sample sm = *it;\n vector* next_action = ann->computeOut(sm.s);\n \n double p0 = 1.f;\n for(uint i=0;i < nb_motors;i++)\n p0 *= exp(-(next_action->at(i)-sm.a[i])*(next_action->at(i)-sm.a[i])/(2.f*noise*noise));\n\n ptheta[i] = p0;\n i++;\n delete next_action;\n }\n }\n\n struct ParraVtoVNext {\n ParraVtoVNext(const std::vector& _vtraj, const OffPolSetACFitted* _ptr) : vtraj(_vtraj), ptr(_ptr) {\n data = fann_create_train(vtraj.size(), ptr->nb_sensors + ptr->nb_motors, 1);\n for (uint n = 0; n < vtraj.size(); n++) {\n sample sm = vtraj[n];\n for (uint i = 0; i < ptr->nb_sensors ; i++)\n data->input[n][i] = sm.s[i];\n for (uint i= ptr->nb_sensors ; i < ptr->nb_sensors + ptr->nb_motors; i++)\n data->input[n][i] = sm.a[i - ptr->nb_sensors];\n }\n }\n\n ~ParraVtoVNext() { //must be empty cause of tbb\n\n }\n\n void free() {\n fann_destroy_train(data);\n }\n\n void operator()(const tbb::blocked_range& range) const {\n\n struct fann* local_nn = fann_copy(ptr->vnn->getNeuralNet());\n struct fann* local_pol = fann_copy(ptr->ann->getNeuralNet());\n\n for (size_t n = range.begin(); n < range.end(); n++) {\n sample sm = vtraj[n];\n\n double delta = sm.r;\n if (!sm.goal_reached) {\n std::vector * next_action = MLP::computeOut(local_pol, sm.next_s);\n double nextQA = MLP::computeOutVF(local_nn, sm.next_s, *next_action);\n delta += ptr->gamma * nextQA;\n delete next_action;\n }\n\n data->output[n][0] = delta;\n }\n\n fann_destroy(local_nn);\n fann_destroy(local_pol);\n }\n\n struct fann_train_data* data;\n const std::vector& vtraj;\n const OffPolSetACFitted* ptr;\n };\n \n void update_critic(){\n if (trajectory.size() > 2) {\n\n std::vector *vtraj; \n double *importance_sample = nullptr;\n if(strategy_w == 0){//only on the last data\n vtraj = new std::vector(last_trajectory.size());\n std::copy(last_trajectory.begin(), last_trajectory.end(), vtraj->begin());\n } else if(strategy_w >= 1 && strategy_w <= 5){\n vtraj = new std::vector(trajectory.size());\n std::copy(trajectory.begin(), trajectory.end(), vtraj->begin());\n \n if(strategy_w > 1)//on every data\n importance_sample = new double [trajectory.size()];\n \n if(strategy_w == 2) {//on every data 1/p(s)\n uint i=0;\n for(auto it = vtraj->begin(); it != vtraj->end() ; ++it) {\n importance_sample[i] = 1.f / proba_s.pdf(it->s);\n i++;\n }\n } else if(strategy_w == 3) {//on every data 1/p(s) normalized\n uint i=0;\n double sum_ps = 0.00f;\n for(auto it = vtraj->begin(); it != vtraj->end() ; ++it) {\n sum_ps += 1.f / proba_s.pdf(it->s);\n i++;\n }\n \n i=0;\n for(auto it = vtraj->begin(); it != vtraj->end() ; ++it) {\n importance_sample[i] = (1.f / proba_s.pdf(it->s)) / sum_ps;\n i++;\n } \n } else if(strategy_w >= 4 && strategy_w <= 5) {\n \n double * ptheta = new double [trajectory.size()];\n computePTheta(*vtraj, ptheta);\n \n if(strategy_w == 4){\n uint i=0;\n for(auto it = vtraj->begin(); it != vtraj->end() ; ++it) {\n importance_sample[i] = ptheta[i];\n i++;\n }\n } else {\n uint i=0;\n for(auto it = vtraj->begin(); it != vtraj->end() ; ++it) {\n importance_sample[i] = ptheta[i] / it->p0;\n i++;\n }\n }\n \n delete[] ptheta;\n \n } else if(strategy_w != 1) {\n LOG_ERROR(\"to be implemented\");\n exit(1);\n }\n \n } else if(strategy_w != 0) {\n LOG_ERROR(\"to be implemented\");\n exit(1);\n }\n\t\n ParraVtoVNext dq(*vtraj, this);\n\n auto iter = [&]() {\n tbb::parallel_for(tbb::blocked_range(0, vtraj->size()), dq);\n\n if(vnn_from_scratch)\n fann_randomize_weights(vnn->getNeuralNet(), -0.025, 0.025);\n \n if(strategy_w <= 1)\n vnn->learn_stoch(dq.data, 30000, 0, converge_precision);\n else\n vnn->learn_stoch_lw(dq.data, importance_sample, 30000, 0, converge_precision);\n };\n\n auto eval = [&]() {\n return fann_get_MSE(vnn->getNeuralNet());\n };\n\n if(determinist_vnn_update)\n bib::Converger::determinist<>(iter, eval, number_fitted_iteration, converge_precision, 0);\n else {\n NN best_nn = nullptr;\n auto save_best = [&]() {\n if(best_nn != nullptr)\n fann_destroy(best_nn);\n best_nn = fann_copy(vnn->getNeuralNet());\n };\n\n bib::Converger::min_stochastic<>(iter, eval, save_best, 30, converge_precision, 0, 10);\n vnn->copy(best_nn);\n fann_destroy(best_nn);\n }\n\n dq.free(); \n if(strategy_w != 0)\n delete[] importance_sample;\n delete vtraj;\n }\n }\n\n inline void mergeSA(std::vector& AB, const std::vector& A, const std::vector& B) {\n AB.reserve( A.size() + B.size() ); // preallocate memory\n AB.insert( AB.end(), A.begin(), A.end() );\n AB.insert( AB.end(), B.begin(), B.end() );\n }\n \n inline void mergeSAS(std::vector& AB, const std::vector& A, const std::vector& B, const std::vector& C) {\n AB.reserve( A.size() + B.size() + C.size() ); // preallocate memory\n AB.insert( AB.end(), A.begin(), A.end() );\n AB.insert( AB.end(), B.begin(), B.end() );\n AB.insert( AB.end(), C.begin(), C.end() );\n }\n\n void end_episode() override {\n if(learning)\n update_critic();\n }\n \n void learn_V(std::map, double>& bvf) override {\n if((episode+1) % change_policy_each == 0){\n trajectory.clear();\n last_trajectory.clear();\n \n \n struct fann_train_data* data = fann_create_train(bvf.size(), nb_sensors + nb_motors, 1);\n \n auto it = bvf.cbegin();\n for (uint n = 0; n < bvf.size(); n++) {\n \n for (uint i = 0; i < nb_sensors + nb_motors ; i++){\n data->input[n][i] = it->first[i];\n LOG_FILE_NNL(\"vset.data.\"+std::to_string(current_loaded_policy-1), it->first[i] << \" \");\n }\n \n data->output[n][0] = it->second;\n LOG_FILE_NNL(\"vset.data.\"+std::to_string(current_loaded_policy-1), it->second << \"\\n\");\n \n ++it;\n }\n //clear all; close all ;X=load('vset.data.904');[xx,yy] = meshgrid (linspace (-pi,pi,300));griddata(X(:,1),X(:,3),X(:,end),xx,yy);\n bib::Logger::getInstance()->closeFile(\"vset.data.\"+std::to_string(current_loaded_policy-1));\n \n fann_randomize_weights(vnn->getNeuralNet(), -0.025, 0.025);\n vnn->learn_stoch(data, 10000, 200, 0.0000001, 200);\n \n fann_destroy_train(data);\n \n vnn->save(\"vset.\"+std::to_string(current_loaded_policy-1));\n LOG_DEBUG(\"vset.\"+std::to_string(current_loaded_policy-1) << \" saved \" << bvf.size() );\n bvf.clear();\n }\n }\n \n void end_instance(bool) override {\n episode++;\n }\n \n double criticEval(const std::vector& perceptions, const std::vector& actions) override {\n return vnn->computeOutVF(perceptions, actions);\n }\n \n arch::Policy* getCopyCurrentPolicy() override {\n return new arch::Policy(new MLP(*ann) , gaussian_policy ? arch::policy_type::GAUSSIAN : arch::policy_type::GREEDY, noise, decision_each);\n }\n\n void save(const std::string& path) override {\n ann->save(path+\".actor\");\n vnn->save(path+\".critic\");\n bib::XMLEngine::save<>(trajectory, \"trajectory\", \"trajectory.data\");\n }\n\n void load(const std::string& path) override {\n ann->load(path+\".actor\");\n vnn->load(path+\".critic\");\n }\n\n protected:\n void _display(std::ostream& out) const override {\n out << std::setw(12) << std::fixed << std::setprecision(10) << sum_weighted_reward << \" \" << std::setw(\n 8) << std::fixed << std::setprecision(8) << vnn->error() << \" \" << noise << \" \" << trajectory.size() ;\n }\n\n void _dump(std::ostream& out) const override {\n out <<\" \" << std::setw(25) << std::fixed << std::setprecision(22) <<\n sum_weighted_reward << \" \" << std::setw(8) << std::fixed <<\n std::setprecision(8) << vnn->error() << \" \" << trajectory.size() ;\n }\n \n\n private:\n uint nb_sensors;\n \n uint episode = 0;\n uint current_loaded_policy = 0;\n uint change_policy_each;\n uint strategy_w;\n\n double noise, converge_precision;\n bool gaussian_policy, vnn_from_scratch, lecun_activation, \n determinist_vnn_update;\n uint hidden_unit_v;\n uint hidden_unit_a;\n uint number_fitted_iteration;\n\n std::shared_ptr> last_action;\n std::shared_ptr> last_pure_action;\n std::vector last_state;\n\n std::set trajectory;\n std::set last_trajectory;\n// std::list trajectory;\n KDE proba_s;\n KDE proba_sa, proba_sas;\n \n struct L1_distance\n {\n typedef double distance_type;\n \n double operator() (const double& __a, const double& __b, const size_t) const\n {\n double d = fabs(__a - __b);\n return d;\n }\n };\n typedef KDTree::KDTree, L1_distance> kdtree_sample;\n kdtree_sample* kdtree_s;\n\n MLP* ann;\n MLP* vnn;\n};\n\n#endif\n\n", "meta": {"hexsha": "a2becc7711b2912198dec1e5f57b8f1c8877afeb", "size": 16446, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "agent/cacla/include/old/OffPolSetACFitted.hpp", "max_stars_repo_name": "matthieu637/ddrl", "max_stars_repo_head_hexsha": "a454d09a3ac9be5db960ff180b3d075c2f9e4a70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 27.0, "max_stars_repo_stars_event_min_datetime": "2017-11-27T09:32:41.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-02T13:50:23.000Z", "max_issues_repo_path": "agent/cacla/include/old/OffPolSetACFitted.hpp", "max_issues_repo_name": "matthieu637/ddrl", "max_issues_repo_head_hexsha": "a454d09a3ac9be5db960ff180b3d075c2f9e4a70", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10.0, "max_issues_repo_issues_event_min_datetime": "2018-10-09T14:39:14.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-10T15:01:00.000Z", "max_forks_repo_path": "agent/cacla/include/old/OffPolSetACFitted.hpp", "max_forks_repo_name": "matthieu637/ddrl", "max_forks_repo_head_hexsha": "a454d09a3ac9be5db960ff180b3d075c2f9e4a70", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2019-05-16T09:14:15.000Z", "max_forks_repo_forks_event_max_datetime": "2019-08-15T14:35:40.000Z", "avg_line_length": 32.3104125737, "max_line_length": 146, "alphanum_fraction": 0.5907819531, "num_tokens": 4538, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604272, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.21547721795959576}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n//#include \n\nenum KernelType {CubicSpline = 0, WendlandC2 = 1, WendlandC4 = 2};\n\n#include \"particle_simulator.hpp\"\n#include \"hdr_time.hpp\"\n#include \"hdr_run.hpp\"\n#include \"vector_x86.hpp\"\n#include \"hdr_dimension.hpp\"\n#include \"hdr_kernel.hpp\"\n#include \"hdr_sph.hpp\"\n#include \"hdr_hgas.hpp\"\n#include \"hdr_bhns.hpp\"\n\nclass SPHAnalysis : public HelmholtzGas {\npublic:\n SPHAnalysis() {\n this->id = 0;\n this->istar = 0;\n this->mass = 0.;\n this->pos = 0.;\n this->vel = 0.;\n this->uene = 0.;\n this->alph = 0.;\n this->alphu = 0.;\n this->ksr = 0.;\n for(PS::S32 k = 0; k < NR::NumberOfNucleon; k++) {\n this->cmps[k] = 0.;\n } \n }\n\n void readAscii(FILE * fp) {\n fscanf(fp, \"%lld%lld%lf\", &this->id, &this->istar, &this->mass); // 3\n fscanf(fp, \"%lf%lf%lf\", &this->pos[0], &this->pos[1], &this->pos[2]); // 6\n fscanf(fp, \"%lf%lf%lf\", &this->vel[0], &this->vel[1], &this->vel[2]); // 9\n fscanf(fp, \"%lf%lf%lf\", &this->acc[0], &this->acc[1], &this->acc[2]); // 12\n fscanf(fp, \"%lf%lf%lf\", &this->uene, &this->alph, &this->alphu); // 15\n fscanf(fp, \"%lf%lf%6d\", &this->dens, &this->ksr, &this->np); // 18\n fscanf(fp, \"%lf%lf%lf\", &this->vsnd, &this->pres, &this->temp); // 21\n fscanf(fp, \"%lf%lf%lf\", &this->divv, &this->rotv, &this->bswt); // 24\n fscanf(fp, \"%lf%lf%lf\", &this->pot, &this->abar, &this->zbar); // 27\n fscanf(fp, \"%lf\", &this->enuc); // 28\n fscanf(fp, \"%lf%lf%lf\", &this->vsmx, &this->udot, &this->dnuc); // 31\n for(PS::S32 k = 0; k < NuclearReaction::NumberOfNucleon; k++) { // 32 -- 44\n fscanf(fp, \"%lf\", &this->cmps[k]);\n }\n fscanf(fp, \"%lf\", &this->pot3);\n fscanf(fp, \"%lf%lf%lf\", &this->tempmax[0], &this->tempmax[1], &this->tempmax[2]);\n fscanf(fp, \"%lf\", &this->entr);\n }\n\n /*\n void write(FILE * fp = stdout) {\n fprintf(fp, \"%8d %2d %+e\", this->id, this->istar, this->mass);\n fprintf(fp, \" %+e %+e %+e\", this->pos[0], this->pos[1], this->pos[2]);\n fprintf(fp, \" %+e %+e %+e\", this->vel[0], this->vel[1], this->vel[2]);\n fprintf(fp, \" %+e %+e %+e\", this->uene, this->alph, this->alphu);\n fprintf(fp, \" %+e\", this->ksr);\n for(PS::S32 k = 0; k < NR::NumberOfNucleon; k++) {\n fprintf(fp, \" %+.3e\", this->cmps[k]);\n }\n fprintf(fp, \"\\n\");\n }\n */\n\n inline PS::F64mat calcMomentOfInertia(PS::F64vec xc) {\n PS::F64mat qq = 0.;\n PS::F64vec dx = this->pos - xc;\n PS::F64 r2 = dx * dx;\n qq.xx = this->mass * (r2 - dx[0] * dx[0]);\n qq.xy = this->mass * ( - dx[0] * dx[1]);\n qq.xz = this->mass * ( - dx[0] * dx[2]);\n qq.yy = this->mass * (r2 - dx[1] * dx[1]);\n qq.yz = this->mass * ( - dx[1] * dx[2]);\n qq.zz = this->mass * (r2 - dx[2] * dx[2]);\n return qq;\n }\n};\n\n\nclass BlackHoleNeutronStarAnalysis : public BlackHoleNeutronStar {\npublic:\n BlackHoleNeutronStarAnalysis() {\n this->id = 0;\n this->istar = 0;\n this->mass = 0.;\n this->pos = 0.;\n this->vel = 0.;\n this->acc = 0.;\n this->eps = 0.;\n this->pot = 0.;\n }\n\n void readAscii(FILE * fp) {\n fscanf(fp, \"%lld%lld%lf%lf%lf%lf%lf%lf%lf%lf%lf%lf%lf%lf\",\n &this->id, &this->istar, &this->mass,\n &this->pos[0], &this->pos[1], &this->pos[2],\n &this->vel[0], &this->vel[1], &this->vel[2],\n &this->acc[0], &this->acc[1], &this->acc[2],\n &this->eps, &this->pot);\n }\n\n inline PS::F64mat calcMomentOfInertia(PS::F64vec xc) {\n PS::F64mat qq = 0.;\n PS::F64vec dx = this->pos - xc;\n PS::F64 r2 = dx * dx;\n qq.xx = this->mass * (r2 - dx[0] * dx[0]);\n qq.xy = this->mass * ( - dx[0] * dx[1]);\n qq.xz = this->mass * ( - dx[0] * dx[2]);\n qq.yy = this->mass * (r2 - dx[1] * dx[1]);\n qq.yz = this->mass * ( - dx[1] * dx[2]);\n qq.zz = this->mass * (r2 - dx[2] * dx[2]);\n return qq;\n }\n};\n\ntemplate \nvoid calcDiskParameter(char * ofile,\n Tsph & sph,\n Tbhns & bhns) {\n\n for(PS::S64 i = 0; i < sph.getNumberOfParticleLocal(); i++) {\n sph[i].pos -= bhns[0].pos;\n }\n \n PS::F64 dz = 1e6;\n PS::F64 rbinmin = 1e7;\n PS::F64 rbinmax = 1e10;\n PS::S64 npd = 10;\n PS::F64 drbin = pow(10., 1 / (PS::F64)npd);\n\n FILE * fp = fopen(ofile, \"w\"); \n for(PS::F64 rbin = rbinmin; rbin < rbinmax; rbin *= drbin) {\n PS::F64 rbin02 = rbin * rbin;\n PS::F64 rbin12 = (rbin * drbin) * (rbin * drbin);\n PS::F64 mdisk = 0.;\n PS::F64 mcol = 0.;\n PS::F64 udisk = 0.;\n PS::S64 ncol = 0;\n PS::S64 ndisk = 0;\n for(PS::S64 i = 0; i < sph.getNumberOfParticleLocal(); i++) {\n PS::F64 r2 = sph[i].pos[0] * sph[i].pos[0] + sph[i].pos[1] * sph[i].pos[1];\n if(r2 < rbin02 || rbin12 <= r2) {\n continue;\n }\n mcol += sph[i].mass;\n ncol += 1;\n if(fabs(sph[i].pos[2]) > dz) {\n continue;\n }\n mdisk += sph[i].mass;\n udisk += sph[i].mass * sph[i].uene;\n ndisk += 1;\n }\n PS::F64 surface = M_PI * (rbin12 - rbin02);\n PS::F64 volume = surface * (2. * dz);\n PS::F64 sigma = mcol / surface;\n PS::F64 dens = mdisk / volume;\n PS::F64 uene = udisk / mdisk;\n PS::F64 tout = 0.;\n if(dens != 0.) {\n NR::Nucleon cmps;\n cmps[1] = cmps[2] = 0.5;\n PS::F64 temp = 1e9;\n PS::F64 pout, cout, sout;\n flash_helmholtz_(&dens, &uene, &temp, cmps.getPointer(),\n &pout, &cout, &tout, &sout);\n }\n fprintf(fp, \"%+e %+e %+e %+e %6d %6d\\n\", rbin, sigma, dens, tout, ncol, ndisk);\n }\n fclose(fp);\n\n}\n\nint main(int argc, char ** argv) {\n MPI_Init(&argc, &argv);\n\n init_flash_helmholtz_(&CodeUnit::FractionOfCoulombCorrection);\n\n PS::ParticleSystem sph;\n sph.initialize();\n sph.createParticle(0);\n sph.setNumberOfParticleLocal(0);\n PS::ParticleSystem bhns;\n bhns.initialize();\n bhns.createParticle(0);\n bhns.setNumberOfParticleLocal(0);\n\n char idir[1024], otype[1024];\n PS::S64 ibgn, iend;\n FILE * fp = fopen(argv[1], \"r\");\n fscanf(fp, \"%s\", idir);\n fscanf(fp, \"%s\", otype);\n fscanf(fp, \"%lld%lld\", &ibgn, &iend);\n fclose(fp);\n\n for(PS::S64 itime = ibgn; itime <= iend; itime++) {\n char sfile[1024], bfile[1024];\n sprintf(sfile, \"%s/sph_t%04d.dat\", idir, itime);\n sprintf(bfile, \"%s/bhns_t%04d.dat\", idir, itime);\n fp = fopen(sfile, \"r\");\n if(fp == NULL) {\n continue;\n }\n sph.readParticleAscii(sfile);\n fclose(fp);\n fp = fopen(bfile, \"r\");\n if(fp == NULL) {\n continue;\n }\n bhns.readParticleAscii(bfile);\n fclose(fp);\n\n char ofile[1024];\n sprintf(ofile, \"%s_t%04d.dat\", otype, itime);\n printf(\"Output: %s\\n\", ofile);\n calcDiskParameter(ofile, sph, bhns);\n\n }\n\n MPI_Finalize();\n\n return 0;\n}\n", "meta": {"hexsha": "9925b0c056c2c7de0a894baf6510d77054069aea", "size": 7656, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tool.nswd/calc_disk/main.cpp", "max_stars_repo_name": "atrtnkw/sph", "max_stars_repo_head_hexsha": "c6bb3d7fd18f57c51fe60197706741e5a26e6ce4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2018-07-11T00:43:01.000Z", "max_stars_repo_stars_event_max_datetime": "2020-01-11T13:41:50.000Z", "max_issues_repo_path": "tool.nswd/calc_disk/main.cpp", "max_issues_repo_name": "atrtnkw/sph", "max_issues_repo_head_hexsha": "c6bb3d7fd18f57c51fe60197706741e5a26e6ce4", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "tool.nswd/calc_disk/main.cpp", "max_forks_repo_name": "atrtnkw/sph", "max_forks_repo_head_hexsha": "c6bb3d7fd18f57c51fe60197706741e5a26e6ce4", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2020-01-06T14:22:25.000Z", "max_forks_repo_forks_event_max_datetime": "2020-01-06T14:22:25.000Z", "avg_line_length": 33.0, "max_line_length": 89, "alphanum_fraction": 0.4772727273, "num_tokens": 2635, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6370307944803832, "lm_q2_score": 0.3380771374883919, "lm_q1q2_score": 0.21536554748988407}} {"text": "//---------------------------------------------------------------------------//\n// Copyright (c) 2018-2020 Mikhail Komarov \n// Copyright (c) 2020 Nikita Kaskov \n//\n// MIT License\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n//---------------------------------------------------------------------------//\n\n#ifndef CRYPTO3_R1CS_GG_PPZKSNARK_IPP2_VERIFY_HPP\n#define CRYPTO3_R1CS_GG_PPZKSNARK_IPP2_VERIFY_HPP\n\n#include \n#include \n#include \n\n#include \n\n#include \n\n#include \n#include \n\n#include \n#include \n#include \n\nnamespace nil {\n namespace crypto3 {\n namespace zk {\n namespace snark {\n template\n struct Op {\n typedef CurveType curve_type;\n\n typedef typename curve_type::pairing::fqk_type::value_type TAB;\n typedef typename curve_type::pairing::fqk_type::value_type UAB;\n typedef typename curve_type::pairing::fqk_type::value_type ZAB;\n typedef typename curve_type::pairing::fqk_type::value_type TC;\n typedef typename curve_type::pairing::fqk_type::value_type UC;\n typedef typename curve_type::pairing::g1_type::value_type ZC;\n };\n\n /// Keeps track of the variables that have been sent by the prover and must\n /// be multiplied together by the verifier. Both MIPP and TIPP are merged\n /// together.\n template\n struct gipa_tuz {\n typedef CurveType curve_type;\n typedef typename curve_type::scalar_field_type::value_type fr_type;\n\n std::pair tab;\n std::pair uab;\n std::pair zab;\n std::pair tc;\n std::pair uc;\n std::pair zc;\n };\n\n /// gipa_verify_tipp_mipp recurse on the proof and statement and produces the final\n /// values to be checked by TIPP and MIPP verifier, namely, for TIPP for example:\n /// * T,U: the final commitment values of A and B\n /// * Z the final product between A and B.\n /// * Challenges are returned in inverse order as well to avoid\n /// repeating the operation multiple times later on.\n /// * There are T,U,Z vectors as well for the MIPP relationship. Both TIPP and\n /// MIPP share the same challenges however, enabling to re-use common operations\n /// between them, such as the KZG proof for commitment keys.\n template>\n std::tuple, std::vector,\n std::vector>\n gipa_verify_tipp_mipp(const r1cs_gg_ppzksnark_aggregate_proof &proof) {\n std::vector challenges, challenges_inv;\n\n typename CurveType::scalar_field_type::value_type default_transcript =\n typename CurveType::scalar_field_type::value_type::zero();\n\n // We first generate all challenges as this is the only consecutive process\n // that can not be parallelized then we scale the commitments in a\n // parallelized way\n std::for_each(\n boost::make_zip_iterator(\n std::make_tuple(proof.tmipp.gipa.comms_ab.begin(), proof.tmipp.gipa.z_ab.begin(),\n proof.tmipp.gipa.comms_c.begin(), proof.tmipp.gipa.z_c.begin())),\n boost::make_zip_iterator(\n std::make_tuple(proof.tmipp.gipa.comms_ab.end(), proof.tmipp.gipa.z_ab.end(),\n proof.tmipp.gipa.comms_c.end(), proof.tmipp.gipa.z_c.end())),\n [&](const std::tuple,\n r1cs_gg_ppzksnark_ipp2_commitment_output> &,\n const std::pair &,\n const std::pair,\n r1cs_gg_ppzksnark_ipp2_commitment_output> &,\n const std::pair &> &t) {\n auto tab_l = std::get<0>(std::get<0>(t));\n auto tab_r = std::get<1>(std::get<0>(t));\n\n auto zab_l = std::get<0>(std::get<1>(t));\n auto zab_r = std::get<1>(std::get<1>(t));\n\n auto tc_l = std::get<0>(std::get<2>(t));\n auto tc_r = std::get<1>(std::get<2>(t));\n\n auto zc_l = std::get<0>(std::get<3>(t));\n auto zc_r = std::get<1>(std::get<3>(t));\n\n // Fiat-Shamir challenge\n auto transcript = challenges.empty() ? *(challenges.end() - 1) : default_transcript;\n\n std::size_t counter_nonce = 1;\n std::array counter_nonce_bytes;\n crypto3::detail::pack({counter_nonce},\n counter_nonce_bytes);\n accumulator_set acc;\n\n hash(counter_nonce_bytes, acc);\n hash(transcript, acc);\n hash(std::get<0>(tab_l), acc);\n hash(std::get<1>(tab_l), acc);\n hash(std::get<0>(tab_r), acc);\n hash(std::get<1>(tab_r), acc);\n hash(zab_l, acc);\n hash(zab_r, acc);\n hash(zc_l, acc);\n hash(zc_r, acc);\n hash(std::get<0>(tc_l), acc);\n hash(std::get<1>(tc_l), acc);\n hash(std::get<0>(tc_r), acc);\n hash(std::get<1>(tc_r), acc);\n\n typename Hash::digest_type d = accumulators::extract::hash(acc);\n typename CurveType::scalar_field_type::value_type c;\n multiprecision::import_bits(c.data, d);\n\n challenges.emplace_back(c);\n challenges_inv.emplace_back(c.inversed());\n });\n\n gipa_tuz final_res = {std::get<0>(proof.com_ab), std::get<1>(proof.com_ab), proof.ip_ab,\n std::get<0>(proof.com_c), std::get<1>(proof.com_c), proof.agg_c};\n\n // we first multiply each entry of the Z U and L vectors by the respective\n // challenges independently\n // Since at the end we want to multiple all \"t\" values together, we do\n // multiply all of them in parrallel and then merge then back at the end.\n // same for u and z.\n std::for_each(\n boost::make_zip_iterator(\n std::make_tuple(proof.tmipp.gipa.comms_ab.begin(), proof.tmipp.gipa.z_ab.begin(),\n proof.tmipp.gipa.comms_c.begin(), proof.tmipp.gipa.z_c.begin(),\n challenges.begin(), challenges_inv.begin())),\n boost::make_zip_iterator(\n std::make_tuple(proof.tmipp.gipa.comms_ab.end(), proof.tmipp.gipa.z_ab.end(),\n proof.tmipp.gipa.comms_c.end(), proof.tmipp.gipa.z_c.end(),\n challenges.end(), challenges_inv.end())),\n [&](const std::tuple,\n r1cs_gg_ppzksnark_ipp2_commitment_output> &,\n const std::pair &,\n const std::pair,\n r1cs_gg_ppzksnark_ipp2_commitment_output> &,\n const std::pair &,\n const typename CurveType::scalar_field_type::value_type &,\n const typename CurveType::scalar_field_type::value_type &> &t) {\n // T and U values for right and left for AB part\n auto tab_l = std::get<0>(std::get<0>(std::get<0>(t)));\n auto uab_l = std::get<1>(std::get<0>(std::get<0>(t)));\n auto tab_r = std::get<0>(std::get<1>(std::get<0>(t)));\n auto uab_r = std::get<1>(std::get<1>(std::get<0>(t)));\n\n auto zab_l = std::get<0>(std::get<1>(t));\n auto zab_r = std::get<1>(std::get<1>(t));\n\n // T and U values for right and left for C part\n auto tc_l = std::get<0>(std::get<0>(std::get<2>(t)));\n auto uc_l = std::get<1>(std::get<0>(std::get<2>(t)));\n auto tc_r = std::get<0>(std::get<1>(std::get<2>(t)));\n auto uc_r = std::get<1>(std::get<1>(std::get<2>(t)));\n\n auto zc_l = std::get<0>(std::get<3>(t));\n auto zc_r = std::get<1>(std::get<3>(t));\n\n // we multiple left side by x and right side by x^-1\n vec ![\n Op::TAB(tab_l, std::get<4>(t)),\n Op::tab(tab_r, c_inv_repr),\n Op::UAB(uab_l, c_repr),\n Op::UAB(uab_r, c_inv_repr),\n Op::ZAB(zab_l, c_repr),\n Op::ZAB(zab_r, c_inv_repr),\n Op::TC::(tc_l, c_repr),\n Op::TC(tc_r, c_inv_repr),\n Op::UC(uc_l, c_repr),\n Op::UC(uc_r, c_inv_repr),\n Op::ZC(zc_l, c_repr),\n Op::ZC(zc_r, c_inv_repr),\n ]\n });\n }\n\n /// verify_tipp_mipp returns a pairing equation to check the tipp proof. $r$ is\n /// the randomness used to produce a random linear combination of A and B and\n /// used in the MIPP part with C\n template\n PairingCheck verify_tipp_mipp(const r1cs_gg_ppzksnark_verifying_srs &v_srs,\n const r1cs_gg_ppzksnark_aggregate_proof &proof,\n const typename CurveType::scalar_field_type::value_type &r_shift) {\n // (T,U), Z for TIPP and MIPP and all challenges\n std::tuple, std::vector,\n std::vector>\n gtmp = gipa_verify_tipp_mipp(proof);\n auto &final_res = std::get<0>(gtmp);\n auto &challenges = std::get<1>(gtmp);\n auto &challenges_inv = std::get<2>(gtmp);\n\n // we reverse the order so the KZG polynomial have them in the expected\n // order to construct them in logn time.\n std::reverse(challenges.begin(), challenges.end());\n std::reverse(challenges_inv.begin(), challenges_inv.end());\n\n // Verify commitment keys wellformed\n auto fvkey = proof.tmipp.gipa.final_vkey;\n auto fwkey = proof.tmipp.gipa.final_wkey;\n }\n\n template\n bool verify_aggregate_proof(const r1cs_gg_ppzksnark_verifying_srs &ip_verifier_srs,\n const r1cs_gg_ppzksnark_processed_verification_key &pvk,\n InputPublicInputsIterator public_inputs_first,\n InputPublicInputsIterator public_inputs_last,\n const r1cs_gg_ppzksnark_aggregate_proof &proof) {\n\n // Random linear combination of proofs\n std::size_t counter_nonce = 1;\n std::array counter_nonce_bytes;\n crypto3::detail::pack({counter_nonce}, counter_nonce_bytes);\n accumulator_set> acc;\n\n hash>(counter_nonce_bytes, acc);\n hash>(std::get<0>(proof.com_ab), acc);\n hash>(std::get<1>(proof.com_ab), acc);\n hash>(std::get<0>(proof.com_c), acc);\n hash>(std::get<1>(proof.com_c), acc);\n\n typename hashes::sha2<256>::digest_type d = accumulators::extract::hash>(acc);\n typename CurveType::scalar_field_type::value_type r;\n crypto3::detail::pack(d, r.data);\n r = r.inversed();\n\n InputPublicInputsIterator vpitr = public_inputs_first;\n\n while (vpitr != public_inputs_last) {\n BOOST_ASSERT_MSG(vpitr->size() + 1 == pvk.ic.size(), \"malformed verification key!\");\n ++vpitr;\n }\n\n // 1.Check TIPA proof ab\n // 2.Check TIPA proof c\n auto tipa_ab =\n verify_tipp_mipp(ip_verifier_srs,\n proof,\n r // we give the extra r as it's not part of the proof itself\n // - it is simply used on top for the groth16 aggregation\n );\n }\n\n /// verify_kzg_opening_g2 takes a KZG opening, the final commitment key, SRS and\n /// any shift (in TIPP we shift the v commitment by r^-1) and returns a pairing\n /// tuple to check if the opening is correct or not.\n template\n PairingCheck verify_kzg_opening_g2(\n const r1cs_gg_ppzksnark_verifying_srs &v_srs,\n const r1cs_gg_ppzksnark_ipp2_vkey &final_vkey,\n const kzg_opening &vkey_opening,\n InputScalarIterator challenges_first,\n InputScalarIterator challenges_last,\n const typename std::iterator_traits::value_type &r_shift,\n const typename std::iterator_traits::value_type &kzg_challenge) {\n }\n\n /// Similar to verify_kzg_opening_g2 but for g1.\n template\n PairingCheck verify_kzg_opening_g1(\n const r1cs_gg_ppzksnark_verifier_srs &v_srs,\n const r1cs_gg_ppzksnark_ipp2_wkey &final_wkey,\n const kzg_opening &wkey_opening, InputScalarIterator challenges_first,\n InputScalarIterator challenges_last, ,\n const typename std::iterator_traits::value_type &r_shift,\n const typename std::iterator_traits::value_type &kzg_challenge) {\n }\n } // namespace snark\n } // namespace zk\n } // namespace crypto3\n} // namespace nil\n\n#endif // CRYPTO3_R1CS_GG_PPZKSNARK_TYPES_POLICY_HPP\n", "meta": {"hexsha": "5e366833b8dc9fe51eece37c28d6e732d7035367", "size": 19781, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/nil/crypto3/zk/snark/schemes/ppzksnark/r1cs_gg_ppzksnark/ipp2/verify.hpp", "max_stars_repo_name": "NoamDev/crypto3-zk", "max_stars_repo_head_hexsha": "5f03e49b737994a3cecf673b029a4e32a2a8aaa5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.37022537869825406, "lm_q1q2_score": 0.2152131568722564}} {"text": "///////////////////////////////////////////////////////////////////////////////\r\n// Copyright Christopher Kormanyos 2014.\r\n// Copyright John Maddock 2014.\r\n// Copyright Paul Bristow 2014.\r\n// Distributed under the Boost Software License,\r\n// Version 1.0. (See accompanying file LICENSE_1_0.txt\r\n// or copy at http://www.boost.org/LICENSE_1_0.txt)\r\n//\r\n\r\n// Implement a specialization of std::complex<> for *anything* that\r\n// is defined as BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE.\r\n\r\n#ifndef _BOOST_CSTDFLOAT_COMPLEX_STD_2014_02_15_HPP_\r\n #define _BOOST_CSTDFLOAT_COMPLEX_STD_2014_02_15_HPP_\r\n\r\n #if defined(__GNUC__)\r\n #pragma GCC system_header\r\n #endif\r\n\r\n #include \r\n #include \r\n\r\n namespace std\r\n {\r\n // Forward declarations.\r\n template\r\n class complex;\r\n\r\n template<>\r\n class complex;\r\n\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE real(const complex&);\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE imag(const complex&);\r\n\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE abs (const complex&);\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE arg (const complex&);\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE norm(const complex&);\r\n\r\n inline complex conj (const complex&);\r\n inline complex proj (const complex&);\r\n\r\n inline complex polar(const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE&,\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& = 0);\r\n\r\n inline complex sqrt (const complex&);\r\n\r\n inline complex sin (const complex&);\r\n inline complex cos (const complex&);\r\n inline complex tan (const complex&);\r\n inline complex asin (const complex&);\r\n inline complex acos (const complex&);\r\n inline complex atan (const complex&);\r\n\r\n inline complex exp (const complex&);\r\n inline complex log (const complex&);\r\n inline complex log10(const complex&);\r\n\r\n inline complex pow (const complex&,\r\n int);\r\n inline complex pow (const complex&,\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE&);\r\n inline complex pow (const complex&,\r\n const complex&);\r\n inline complex pow (const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE&,\r\n const complex&);\r\n\r\n inline complex sinh (const complex&);\r\n inline complex cosh (const complex&);\r\n inline complex tanh (const complex&);\r\n\r\n inline complex asinh(const complex&);\r\n inline complex acosh(const complex&);\r\n inline complex atanh(const complex&);\r\n\r\n template\r\n inline std::basic_ostream& operator<<(std::basic_ostream&, const std::complex&);\r\n\r\n template\r\n inline std::basic_istream& operator>>(std::basic_istream&, std::complex&);\r\n\r\n // Template specialization of the complex class.\r\n template<>\r\n class complex\r\n {\r\n public:\r\n typedef BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE value_type;\r\n\r\n explicit complex(const complex&);\r\n explicit complex(const complex&);\r\n explicit complex(const complex&);\r\n\r\n #if defined(BOOST_NO_CXX11_CONSTEXPR)\r\n complex(const value_type& r = value_type(),\r\n const value_type& i = value_type()) : re(r),\r\n im(i) { }\r\n\r\n template\r\n complex(const complex& x) : re(x.real()),\r\n im(x.imag()) { }\r\n\r\n const value_type& real() const { return re; }\r\n const value_type& imag() const { return im; }\r\n\r\n value_type& real() { return re; }\r\n value_type& imag() { return im; }\r\n #else\r\n BOOST_CONSTEXPR complex(const value_type& r = value_type(),\r\n const value_type& i = value_type()) : re(r),\r\n im(i) { }\r\n\r\n template\r\n BOOST_CONSTEXPR complex(const complex& x) : re(x.real()),\r\n im(x.imag()) { }\r\n\r\n value_type real() const { return re; }\r\n value_type imag() const { return im; }\r\n #endif\r\n\r\n void real(value_type r) { re = r; }\r\n void imag(value_type i) { im = i; }\r\n\r\n complex& operator=(const value_type& v)\r\n {\r\n re = v;\r\n im = value_type(0);\r\n return *this;\r\n }\r\n\r\n complex& operator+=(const value_type& v)\r\n {\r\n re += v;\r\n return *this;\r\n }\r\n\r\n complex& operator-=(const value_type& v)\r\n {\r\n re -= v;\r\n return *this;\r\n }\r\n\r\n complex& operator*=(const value_type& v)\r\n {\r\n re *= v;\r\n im *= v;\r\n return *this;\r\n }\r\n\r\n complex& operator/=(const value_type& v)\r\n {\r\n re /= v;\r\n im /= v;\r\n return *this;\r\n }\r\n\r\n template\r\n complex& operator=(const complex& x)\r\n {\r\n re = x.real();\r\n im = x.imag();\r\n return *this;\r\n }\r\n\r\n template\r\n complex& operator+=(const complex& x)\r\n {\r\n re += x.real();\r\n im += x.imag();\r\n return *this;\r\n }\r\n\r\n template\r\n complex& operator-=(const complex& x)\r\n {\r\n re -= x.real();\r\n im -= x.imag();\r\n return *this;\r\n }\r\n\r\n template\r\n complex& operator*=(const complex& x)\r\n {\r\n const value_type tmp_real = (re * x.real()) - (im * x.imag());\r\n im = (re * x.imag()) + (im * x.real());\r\n re = tmp_real;\r\n return *this;\r\n }\r\n\r\n template\r\n complex& operator/=(const complex& x)\r\n {\r\n const value_type tmp_real = (re * x.real()) + (im * x.imag());\r\n const value_type the_norm = std::norm(x);\r\n im = ((im * x.real()) - (re * x.imag())) / the_norm;\r\n re = tmp_real / the_norm;\r\n return *this;\r\n }\r\n\r\n private:\r\n value_type re;\r\n value_type im;\r\n };\r\n\r\n // Constructors from built-in complex representation of floating-point types.\r\n complex::complex(const complex& f) : re(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE( f.real())), im(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE( f.imag())) { }\r\n complex::complex(const complex& d) : re(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE( d.real())), im(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE( d.imag())) { }\r\n complex::complex(const complex& ld) : re(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(ld.real())), im(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(ld.imag())) { }\r\n } // namespace std\r\n\r\n namespace boost { namespace math { namespace cstdfloat { namespace detail {\r\n template std::complex multiply_by_i(const std::complex& x)\r\n {\r\n // Multiply x (in C) by I (the imaginary component), and return the result.\r\n return std::complex(-x.imag(), x.real());\r\n }\r\n } } } } // boost::math::cstdfloat::detail\r\n\r\n namespace std\r\n {\r\n // ISO/IEC 14882:2011, Section 26.4.7, specific values.\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE real(const complex& x) { return x.real(); }\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE imag(const complex& x) { return x.imag(); }\r\n\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE abs (const complex& x) { using std::sqrt; return sqrt ((real(x) * real(x)) + (imag(x) * imag(x))); }\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE arg (const complex& x) { using std::atan2; return atan2(x.imag(), x.real()); }\r\n inline BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE norm(const complex& x) { return (real(x) * real(x)) + (imag(x) * imag(x)); }\r\n\r\n inline complex conj (const complex& x) { return complex(x.real(), -x.imag()); }\r\n\r\n inline complex proj (const complex& x)\r\n {\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE m = (std::numeric_limits::max)();\r\n if ((x.real() > m)\r\n || (x.real() < -m)\r\n || (x.imag() > m)\r\n || (x.imag() < -m))\r\n {\r\n // We have an infinity, return a normalized infinity, respecting the sign of the imaginary part:\r\n return complex(std::numeric_limits::infinity(), x.imag() < 0 ? -0 : 0);\r\n }\r\n return x;\r\n }\r\n\r\n inline complex polar(const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& rho,\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& theta)\r\n {\r\n using std::sin;\r\n using std::cos;\r\n\r\n return complex(rho * cos(theta), rho * sin(theta));\r\n }\r\n\r\n // Global add, sub, mul, div.\r\n inline complex operator+(const complex& u, const complex& v) { return complex(u.real() + v.real(), u.imag() + v.imag()); }\r\n inline complex operator-(const complex& u, const complex& v) { return complex(u.real() - v.real(), u.imag() - v.imag()); }\r\n\r\n inline complex operator*(const complex& u, const complex& v)\r\n {\r\n return complex((u.real() * v.real()) - (u.imag() * v.imag()),\r\n (u.real() * v.imag()) + (u.imag() * v.real()));\r\n }\r\n\r\n inline complex operator/(const complex& u, const complex& v)\r\n {\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE the_norm = std::norm(v);\r\n\r\n return complex(((u.real() * v.real()) + (u.imag() * v.imag())) / the_norm,\r\n ((u.imag() * v.real()) - (u.real() * v.imag())) / the_norm);\r\n }\r\n\r\n inline complex operator+(const complex& u, const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& v) { return complex(u.real() + v, u.imag()); }\r\n inline complex operator-(const complex& u, const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& v) { return complex(u.real() - v, u.imag()); }\r\n inline complex operator*(const complex& u, const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& v) { return complex(u.real() * v, u.imag() * v); }\r\n inline complex operator/(const complex& u, const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& v) { return complex(u.real() / v, u.imag() / v); }\r\n\r\n inline complex operator+(const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& u, const complex& v) { return complex(u + v.real(), v.imag()); }\r\n inline complex operator-(const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& u, const complex& v) { return complex(u - v.real(), -v.imag()); }\r\n inline complex operator*(const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& u, const complex& v) { return complex(u * v.real(), u * v.imag()); }\r\n inline complex operator/(const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& u, const complex& v) { const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE v_norm = norm(v); return complex((u * v.real()) / v_norm, (-u * v.imag()) / v_norm); }\r\n\r\n // Unary plus / minus.\r\n inline complex operator+(const complex& u) { return u; }\r\n inline complex operator-(const complex& u) { return complex(-u.real(), -u.imag()); }\r\n\r\n // Equality and inequality.\r\n inline bool operator==(const complex& x, const complex& y) { return ((x.real() == y.real()) && (x.imag() == y.imag())); }\r\n inline bool operator==(const complex& x, const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& y) { return ((x.real() == y) && (x.imag() == BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(0))); }\r\n inline bool operator==(const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& x, const complex& y) { return ((x == y.real()) && (y.imag() == BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(0))); }\r\n inline bool operator!=(const complex& x, const complex& y) { return ((x.real() != y.real()) || (x.imag() != y.imag())); }\r\n inline bool operator!=(const complex& x, const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& y) { return ((x.real() != y) || (x.imag() != BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(0))); }\r\n inline bool operator!=(const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& x, const complex& y) { return ((x != y.real()) || (y.imag() != BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(0))); }\r\n\r\n // ISO/IEC 14882:2011, Section 26.4.8, transcendentals.\r\n inline complex sqrt(const complex& x)\r\n {\r\n using std::fabs;\r\n using std::sqrt;\r\n\r\n // Compute sqrt(x) for x in C:\r\n // sqrt(x) = (s , xi / 2s) : for xr > 0,\r\n // (|xi| / 2s, +-s) : for xr < 0,\r\n // (sqrt(xi), sqrt(xi) : for xr = 0,\r\n // where s = sqrt{ [ |xr| + sqrt(xr^2 + xi^2) ] / 2 },\r\n // and the +- sign is the same as the sign of xi.\r\n\r\n if(x.real() > 0)\r\n {\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE s = sqrt((fabs(x.real()) + std::abs(x)) / 2);\r\n\r\n return complex(s, x.imag() / (s * 2));\r\n }\r\n else if(x.real() < 0)\r\n {\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE s = sqrt((fabs(x.real()) + std::abs(x)) / 2);\r\n\r\n const bool imag_is_neg = (x.imag() < 0);\r\n\r\n return complex(fabs(x.imag()) / (s * 2), (imag_is_neg ? -s : s));\r\n }\r\n else\r\n {\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sqrt_xi_half = sqrt(x.imag() / 2);\r\n\r\n return complex(sqrt_xi_half, sqrt_xi_half);\r\n }\r\n }\r\n\r\n inline complex sin(const complex& x)\r\n {\r\n using std::sin;\r\n using std::cos;\r\n using std::exp;\r\n\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sin_x = sin (x.real());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cos_x = cos (x.real());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_yp = exp (x.imag());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_ym = BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) / exp_yp;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sinh_y = (exp_yp - exp_ym) / 2;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cosh_y = (exp_yp + exp_ym) / 2;\r\n\r\n return complex(sin_x * cosh_y, cos_x * sinh_y);\r\n }\r\n\r\n inline complex cos(const complex& x)\r\n {\r\n using std::sin;\r\n using std::cos;\r\n using std::exp;\r\n\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sin_x = sin (x.real());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cos_x = cos (x.real());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_yp = exp (x.imag());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_ym = BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) / exp_yp;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sinh_y = (exp_yp - exp_ym) / 2;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cosh_y = (exp_yp + exp_ym) / 2;\r\n\r\n return complex(cos_x * cosh_y, -(sin_x * sinh_y));\r\n }\r\n\r\n inline complex tan(const complex& x)\r\n {\r\n using std::sin;\r\n using std::cos;\r\n using std::exp;\r\n\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sin_x = sin (x.real());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cos_x = cos (x.real());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_yp = exp (x.imag());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_ym = BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) / exp_yp;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sinh_y = (exp_yp - exp_ym) / 2;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cosh_y = (exp_yp + exp_ym) / 2;\r\n\r\n return ( complex(sin_x * cosh_y, cos_x * sinh_y)\r\n / complex(cos_x * cosh_y, -sin_x * sinh_y));\r\n }\r\n\r\n inline complex asin(const complex& x)\r\n {\r\n return -boost::math::cstdfloat::detail::multiply_by_i(std::log(boost::math::cstdfloat::detail::multiply_by_i(x) + std::sqrt(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) - (x * x))));\r\n }\r\n\r\n inline complex acos(const complex& x)\r\n {\r\n return boost::math::constants::half_pi() - std::asin(x);\r\n }\r\n\r\n inline complex atan(const complex& x)\r\n {\r\n const complex izz = boost::math::cstdfloat::detail::multiply_by_i(x);\r\n\r\n return boost::math::cstdfloat::detail::multiply_by_i(std::log(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) - izz) - std::log(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) + izz)) / 2;\r\n }\r\n\r\n inline complex exp(const complex& x)\r\n {\r\n using std::exp;\r\n\r\n return std::polar(exp(x.real()), x.imag());\r\n }\r\n\r\n inline complex log(const complex& x)\r\n {\r\n using std::atan2;\r\n using std::log;\r\n\r\n return complex(log(std::norm(x)) / 2, atan2(x.imag(), x.real()));\r\n }\r\n\r\n inline complex log10(const complex& x)\r\n {\r\n return std::log(x) / boost::math::constants::ln_ten();\r\n }\r\n\r\n inline complex pow(const complex& x,\r\n int p)\r\n {\r\n const bool re_isneg = (x.real() < 0);\r\n const bool re_isnan = (x.real() != x.real());\r\n const bool re_isinf = ((!re_isneg) ? bool(+x.real() > (std::numeric_limits::max)())\r\n : bool(-x.real() > (std::numeric_limits::max)()));\r\n\r\n const bool im_isneg = (x.imag() < 0);\r\n const bool im_isnan = (x.imag() != x.imag());\r\n const bool im_isinf = ((!im_isneg) ? bool(+x.imag() > (std::numeric_limits::max)())\r\n : bool(-x.imag() > (std::numeric_limits::max)()));\r\n\r\n if(re_isnan || im_isnan) { return x; }\r\n\r\n if(re_isinf || im_isinf)\r\n {\r\n return complex(std::numeric_limits::quiet_NaN(),\r\n std::numeric_limits::quiet_NaN());\r\n }\r\n\r\n if(p < 0)\r\n {\r\n if(std::abs(x) < (std::numeric_limits::min)())\r\n {\r\n return complex(std::numeric_limits::infinity(),\r\n std::numeric_limits::infinity());\r\n }\r\n else\r\n {\r\n return BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) / std::pow(x, -p);\r\n }\r\n }\r\n\r\n if(p == 0)\r\n {\r\n return complex(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1));\r\n }\r\n else\r\n {\r\n if(p == 1) { return x; }\r\n\r\n if(std::abs(x) > (std::numeric_limits::max)())\r\n {\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE re = (re_isneg ? -std::numeric_limits::infinity()\r\n : +std::numeric_limits::infinity());\r\n\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE im = (im_isneg ? -std::numeric_limits::infinity()\r\n : +std::numeric_limits::infinity());\r\n\r\n return complex(re, im);\r\n }\r\n\r\n if (p == 2) { return (x * x); }\r\n else if(p == 3) { return ((x * x) * x); }\r\n else if(p == 4) { const complex x2 = (x * x); return (x2 * x2); }\r\n else\r\n {\r\n // The variable xn stores the binary powers of x.\r\n complex result(((p % 2) != 0) ? x : complex(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1)));\r\n complex xn (x);\r\n\r\n int p2 = p;\r\n\r\n while((p2 /= 2) != 0)\r\n {\r\n // Square xn for each binary power.\r\n xn *= xn;\r\n\r\n const bool has_binary_power = ((p2 % 2) != 0);\r\n\r\n if(has_binary_power)\r\n {\r\n // Multiply the result with each binary power contained in the exponent.\r\n result *= xn;\r\n }\r\n }\r\n\r\n return result;\r\n }\r\n }\r\n }\r\n\r\n inline complex pow(const complex& x,\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& a)\r\n {\r\n return std::exp(a * std::log(x));\r\n }\r\n\r\n inline complex pow(const complex& x,\r\n const complex& a)\r\n {\r\n return std::exp(a * std::log(x));\r\n }\r\n\r\n inline complex pow(const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE& x,\r\n const complex& a)\r\n {\r\n return std::exp(a * std::log(x));\r\n }\r\n\r\n inline complex sinh(const complex& x)\r\n {\r\n using std::sin;\r\n using std::cos;\r\n using std::exp;\r\n\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sin_y = sin (x.imag());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cos_y = cos (x.imag());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_xp = exp (x.real());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_xm = BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) / exp_xp;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sinh_x = (exp_xp - exp_xm) / 2;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cosh_x = (exp_xp + exp_xm) / 2;\r\n\r\n return complex(cos_y * sinh_x, cosh_x * sin_y);\r\n }\r\n\r\n inline complex cosh(const complex& x)\r\n {\r\n using std::sin;\r\n using std::cos;\r\n using std::exp;\r\n\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sin_y = sin (x.imag());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cos_y = cos (x.imag());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_xp = exp (x.real());\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE exp_xm = BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) / exp_xp;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE sinh_x = (exp_xp - exp_xm) / 2;\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE cosh_x = (exp_xp + exp_xm) / 2;\r\n\r\n return complex(cos_y * cosh_x, sin_y * sinh_x);\r\n }\r\n\r\n inline complex tanh(const complex& x)\r\n {\r\n const complex ex_plus = std::exp(x);\r\n const complex ex_minus = BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) / ex_plus;\r\n\r\n return (ex_plus - ex_minus) / (ex_plus + ex_minus);\r\n }\r\n\r\n inline complex asinh(const complex& x)\r\n {\r\n return std::log(x + std::sqrt((x * x) + BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1)));\r\n }\r\n\r\n inline complex acosh(const complex& x)\r\n {\r\n const BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE my_one(1);\r\n\r\n const complex zp(x.real() + my_one, x.imag());\r\n const complex zm(x.real() - my_one, x.imag());\r\n\r\n return std::log(x + (zp * std::sqrt(zm / zp)));\r\n }\r\n\r\n inline complex atanh(const complex& x)\r\n {\r\n return (std::log(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) + x) - std::log(BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE(1) - x)) / 2.0;\r\n }\r\n\r\n template\r\n inline std::basic_ostream& operator<<(std::basic_ostream& os, const std::complex& x)\r\n {\r\n std::basic_ostringstream ostr;\r\n\r\n ostr.flags(os.flags());\r\n ostr.imbue(os.getloc());\r\n ostr.precision(os.precision());\r\n\r\n ostr << char_type('(')\r\n << x.real()\r\n << char_type(',')\r\n << x.imag()\r\n << char_type(')');\r\n\r\n return (os << ostr.str());\r\n }\r\n\r\n template\r\n inline std::basic_istream& operator>>(std::basic_istream& is, std::complex& x)\r\n {\r\n BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE rx;\r\n BOOST_CSTDFLOAT_EXTENDED_COMPLEX_FLOAT_TYPE ix;\r\n\r\n char_type the_char;\r\n\r\n static_cast(is >> the_char);\r\n\r\n if(the_char == static_cast('('))\r\n {\r\n static_cast(is >> rx >> the_char);\r\n\r\n if(the_char == static_cast(','))\r\n {\r\n static_cast(is >> ix >> the_char);\r\n\r\n if(the_char == static_cast(')'))\r\n {\r\n x = complex(rx, ix);\r\n }\r\n else\r\n {\r\n is.setstate(ios_base::failbit);\r\n }\r\n }\r\n else if(the_char == static_cast(')'))\r\n {\r\n x = rx;\r\n }\r\n else\r\n {\r\n is.setstate(ios_base::failbit);\r\n }\r\n }\r\n else\r\n {\r\n static_cast(is.putback(the_char));\r\n\r\n static_cast(is >> rx);\r\n\r\n x = rx;\r\n }\r\n\r\n return is;\r\n }\r\n } // namespace std\r\n\r\n#endif // _BOOST_CSTDFLOAT_COMPLEX_STD_2014_02_15_HPP_\r\n", "meta": {"hexsha": "81659ab5aa9d1b83e28845e17c3a139fcf9ca627", "size": 34507, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/math/cstdfloat/cstdfloat_complex_std.hpp", "max_stars_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_stars_repo_head_hexsha": "4fb2d44945a6305ae3ca87be3872f9432d16f1fb", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 85.0, "max_stars_repo_stars_event_min_datetime": "2015-02-08T20:36:17.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-14T20:38:31.000Z", "max_issues_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/math/cstdfloat/cstdfloat_complex_std.hpp", "max_issues_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_issues_repo_head_hexsha": "4fb2d44945a6305ae3ca87be3872f9432d16f1fb", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": "2015-01-28T16:33:19.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-12T23:03:28.000Z", "max_forks_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/math/cstdfloat/cstdfloat_complex_std.hpp", "max_forks_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_forks_repo_head_hexsha": "4fb2d44945a6305ae3ca87be3872f9432d16f1fb", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 27.0, "max_forks_repo_forks_event_min_datetime": "2015-01-28T16:33:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-12T05:04:39.000Z", "avg_line_length": 53.7492211838, "max_line_length": 375, "alphanum_fraction": 0.6813979772, "num_tokens": 8184, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.3775406687981454, "lm_q1q2_score": 0.2151425511312973}} {"text": "// Boost.Geometry\n\n// Copyright (c) 2018, Oracle and/or its affiliates.\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n\n#ifndef BOOST_GEOMETRY_PROJECTIONS_CONSTANTS_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_CONSTANTS_HPP\n\n\n#include \n#include \n\n\nnamespace boost { namespace geometry { namespace projections\n{\n\n#ifndef DOXYGEN_NO_DETAIL\nnamespace detail\n{\n\n\ntemplate \ninline T fourth_pi() { return T(0.78539816339744830961566084581988); }\ntemplate \ninline T third_pi() { return boost::math::constants::third_pi(); }\ntemplate \ninline T half_pi() { return boost::math::constants::half_pi(); }\ntemplate \ninline T pi() { return boost::math::constants::pi(); }\ntemplate \ninline T one_and_half_pi() { return T(4.7123889803846898576939650749193); }\ntemplate \ninline T two_pi() { return boost::math::constants::two_pi(); }\ntemplate \ninline T two_and_half_pi() { return T(7.8539816339744830961566084581988); }\n\ntemplate \ninline T two_div_pi() { return boost::math::constants::two_div_pi(); }\ntemplate \ninline T half_pi_sqr() { return T(2.4674011002723396547086227499689); }\ntemplate \ninline T pi_sqr() { return boost::math::constants::pi_sqr(); }\n\ntemplate \ninline T sixth() { return boost::math::constants::sixth(); }\ntemplate \ninline T third() { return boost::math::constants::third(); }\ntemplate \ninline T two_thirds() { return boost::math::constants::two_thirds(); }\n\n\n} // namespace detail\n#endif // DOXYGEN_NO_DETAIL\n\n\n}}} // namespace boost::geometry::projections\n#endif // BOOST_GEOMETRY_PROJECTIONS_IMPL_PROJECTS_HPP\n", "meta": {"hexsha": "a8ebf2bca063a52d13832619c9dd8b98b8351b92", "size": 1974, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactNativeFrontend/ios/Pods/boost/boost/geometry/srs/projections/constants.hpp", "max_stars_repo_name": "Harshitha91/Tmdb-react-native-node", "max_stars_repo_head_hexsha": "e06e3f25a7ee6946ef07a1f524fdf62e48424293", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 12278.0, "max_stars_repo_stars_event_min_datetime": "2015-01-29T17:11:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T21:12:00.000Z", "max_issues_repo_path": "ReactNativeFrontend/ios/Pods/boost/boost/geometry/srs/projections/constants.hpp", "max_issues_repo_name": "Harshitha91/Tmdb-react-native-node", "max_issues_repo_head_hexsha": "e06e3f25a7ee6946ef07a1f524fdf62e48424293", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 9469.0, "max_issues_repo_issues_event_min_datetime": "2015-01-30T05:33:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T16:17:21.000Z", "max_forks_repo_path": "ReactNativeFrontend/ios/Pods/boost/boost/geometry/srs/projections/constants.hpp", "max_forks_repo_name": "Harshitha91/Tmdb-react-native-node", "max_forks_repo_head_hexsha": "e06e3f25a7ee6946ef07a1f524fdf62e48424293", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1343.0, "max_forks_repo_forks_event_min_datetime": "2017-12-08T19:47:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-26T11:31:36.000Z", "avg_line_length": 31.3333333333, "max_line_length": 79, "alphanum_fraction": 0.7497467072, "num_tokens": 511, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.3415825128436339, "lm_q1q2_score": 0.2151199976693983}} {"text": "/*\n\nSoftware License Agreement (BSD License)\n\nCopyright (c) 2016--, Liana Bertoni (liana.bertoni@gmail.com)\n All rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder(s) nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\nContact GitHub API Training Shop Blog About\n*/\n\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\n#include \"quality.h\"\n#include \"PGR_5.h\"\n#include \"normal_component_box_surface.h\"\n\n\nusing namespace std;\nusing namespace Eigen;\nusing namespace KDL;\nconst double PPI_ = 3.141592653589793238463;\n\nconst double PI_ = 3.141592653589793238463;\n\n////////////////////////////////// BOX\n\nstd::vector box;\n//////////////////////////////////////////////\n\n\n/////////////////////////////////// CONTACT POINTS\n\nstd::vector contact_points;\n///////////////////////////////////////\n\n\n//////////////////////////////////// CONTACT FORCE\n\nstd::vector contact_forces;\n/////////////////////////////////////////////\n\n\n////////////////////////////////////// JOINTS\n\nstd::vector joints;\n////////////////////////////////////////////\n\n\nint n_c = 0;\nint n_q = 0;\ndouble quality_i = 0;\ndouble contact_stiffness = 100;\ndouble joint_stiffness = 1; \ndouble mu = 1.5;\ndouble f_i_max = 200;\n\n\n//////////////////////////////////////////// ROBOT SCTRUCTURE\n\nKDL::Tree hand_tree(\"RootName\");\n\n\n KDL::Chain chain_palm;\n\n KDL::Chain chain_finger_right_0;\n KDL::Chain chain_finger_left_0;\n KDL::Chain chain_finger_right_1;\n KDL::Chain chain_finger_left_1;\n KDL::Chain chain_finger_right_2;\n KDL::Chain chain_finger_left_2;\n\n KDL::Chain chain_palm_finger_left_0; // return the result\n KDL::Chain chain_palm_finger_left_1;\n KDL::Chain chain_palm_finger_left_2; \n KDL::Chain chain_palm_finger_right_0;\n KDL::Chain chain_palm_finger_right_1;\n KDL::Chain chain_palm_finger_right_2;\n\n KDL::JntArray jointpositions_right_0 = JntArray(8); \n KDL::JntArray jointpositions_right_1 = JntArray(8); \n KDL::JntArray jointpositions_right_2 = JntArray(8); \n KDL::JntArray jointpositions_left_0 = JntArray(8);\n KDL::JntArray jointpositions_left_1 = JntArray(8);\n KDL::JntArray jointpositions_left_2 = JntArray(8);\n\n KDL::Jacobian jacobian_right_0;\n KDL::Jacobian jacobian_left_0;\n KDL::Jacobian jacobian_right_1;\n KDL::Jacobian jacobian_left_1;\n KDL::Jacobian jacobian_right_2;\n KDL::Jacobian jacobian_left_2;\n\n boost::scoped_ptr jnt_to_jac_right_0;\n boost::scoped_ptr jnt_to_jac_left_0;\n boost::scoped_ptr jnt_to_jac_right_1;\n boost::scoped_ptr jnt_to_jac_left_1; \n boost::scoped_ptr jnt_to_jac_right_2;\n boost::scoped_ptr jnt_to_jac_left_2;\n\n///////////////////////////////////////////////////////////\n\n\n\n//------------------------------------------------------------------------------------------\nvoid createStructureKDL()\n{\n\n chain_palm.addSegment(Segment(\"palm_trasl_x_link\",Joint(Joint::TransX),Frame(Vector(0.0,0.0,0.0)))); \n chain_palm.addSegment(Segment(\"palm_trasl_y_link\",Joint(Joint::TransY),Frame(Vector(0.0,0.0,0.0))));\n chain_palm.addSegment(Segment(\"palm_trasl_z_link\",Joint(Joint::TransZ),Frame(Vector(0.0,0.0,0.0))));\n chain_palm.addSegment(Segment(\"palm_rot_x_link\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.0))));\n chain_palm.addSegment(Segment(\"palm_rot_y_link\",Joint(Joint::RotY),Frame(Vector(0.0,0.0,0.0))));\n chain_palm.addSegment(Segment(\"palm_rot_z_link\",Joint(Joint::RotZ),Frame(Vector(0.0,0.0,0.0))));\n\n chain_finger_right_0.addSegment(Segment(\"finger_right_proximal_link_0\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.03))));\n chain_finger_right_0.addSegment(Segment(\"finger_right_distal_link_0\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.03))));\n\n chain_finger_left_0.addSegment(Segment(\"finger_left_proximal_link_0\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.3))));\n chain_finger_left_0.addSegment(Segment(\"finger_left_distal_link_0\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.03))));\n\n chain_finger_right_1.addSegment(Segment(\"finger_right_proximal_link_1\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.03))));\n chain_finger_right_1.addSegment(Segment(\"finger_right_distal_link_1\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.03))));\n\n chain_finger_left_1.addSegment(Segment(\"finger_left_proximal_link_1\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.3))));\n chain_finger_left_1.addSegment(Segment(\"finger_left_distal_link_1\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.03))));\n\n chain_finger_right_2.addSegment(Segment(\"finger_right_proximal_link_2\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.03))));\n chain_finger_right_2.addSegment(Segment(\"finger_right_distal_link_2\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.03))));\n\n chain_finger_left_2.addSegment(Segment(\"finger_left_proximal_link_2\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.3))));\n chain_finger_left_2.addSegment(Segment(\"finger_left_distal_link_2\",Joint(Joint::RotX),Frame(Vector(0.0,0.0,0.03))));\n \n \n\n hand_tree.addChain(chain_palm,\"RootName\"); \n hand_tree.addChain(chain_finger_right_0,\"palm_rot_z_link\");\n hand_tree.addChain(chain_finger_left_0, \"palm_rot_z_link\"); \n hand_tree.addChain(chain_finger_right_1,\"palm_rot_z_link\");\n hand_tree.addChain(chain_finger_left_1, \"palm_rot_z_link\"); \n hand_tree.addChain(chain_finger_right_2,\"palm_rot_z_link\");\n hand_tree.addChain(chain_finger_left_2, \"palm_rot_z_link\"); \n\n\n\n hand_tree.getChain(\"RootName\", \"finger_right_distal_link_0\", chain_palm_finger_right_0);\n hand_tree.getChain(\"RootName\", \"finger_left_distal_link_0\", chain_palm_finger_left_0);\n hand_tree.getChain(\"RootName\", \"finger_right_distal_link_1\", chain_palm_finger_right_1);\n hand_tree.getChain(\"RootName\", \"finger_left_distal_link_1\", chain_palm_finger_left_1); \n hand_tree.getChain(\"RootName\", \"finger_right_distal_link_2\", chain_palm_finger_right_2);\n hand_tree.getChain(\"RootName\", \"finger_left_distal_link_2\", chain_palm_finger_left_2);\n\n}\n//-------------------------------------------------------------------------------------------\n\n\n//-------------------------------------------------------------------------------------------\nvoid setJoints(std::vector joints_in)\n{\n for(int i=0 ; i < 6 ; i++)\n jointpositions_right_0(i) = jointpositions_left_0(i) = jointpositions_right_1(i) = jointpositions_left_1(i) = jointpositions_right_2(i) = jointpositions_left_2(i) = joints[i];\n \n jointpositions_right_0(6) = joints_in[6];\n jointpositions_right_0(7) = joints_in[7];\n\n jointpositions_left_0(6) = joints_in[8];\n jointpositions_left_0(7) = joints_in[9];\n\n jointpositions_right_1(6) = joints_in[10];\n jointpositions_right_1(7) = joints_in[11];\n\n jointpositions_left_1(6) = joints_in[12];\n jointpositions_left_1(7) = joints_in[13];\n\n jointpositions_right_2(6) = joints_in[14];\n jointpositions_right_2(7) = joints_in[15];\n\n jointpositions_left_2(6) = joints_in[16];\n jointpositions_left_2(7) = joints_in[17];\n\n jointpositions_right_0.resize(chain_palm_finger_right_0.getNrOfJoints());\n jointpositions_left_0.resize(chain_palm_finger_left_0.getNrOfJoints());\n\n jointpositions_right_1.resize(chain_palm_finger_right_1.getNrOfJoints());\n jointpositions_left_1.resize(chain_palm_finger_left_1.getNrOfJoints());\n\n jointpositions_right_2.resize(chain_palm_finger_right_2.getNrOfJoints());\n jointpositions_left_2.resize(chain_palm_finger_left_2.getNrOfJoints());\n}\n\n\n\nvoid initJacobian()\n{\n jacobian_right_0.resize(chain_palm_finger_right_0.getNrOfJoints());\n jacobian_left_0.resize(chain_palm_finger_left_0.getNrOfJoints());\n\n jnt_to_jac_right_0.reset(new KDL::ChainJntToJacSolver(chain_palm_finger_right_0));\n jnt_to_jac_left_0.reset(new KDL::ChainJntToJacSolver(chain_palm_finger_left_0));\n\n jacobian_right_1.resize(chain_palm_finger_right_1.getNrOfJoints());\n jacobian_left_1.resize(chain_palm_finger_left_1.getNrOfJoints());\n\n jnt_to_jac_right_1.reset(new KDL::ChainJntToJacSolver(chain_palm_finger_right_1));\n jnt_to_jac_left_1.reset(new KDL::ChainJntToJacSolver(chain_palm_finger_left_1));\n\n jacobian_right_2.resize(chain_palm_finger_right_2.getNrOfJoints());\n jacobian_left_2.resize(chain_palm_finger_left_2.getNrOfJoints());\n\n jnt_to_jac_right_2.reset(new KDL::ChainJntToJacSolver(chain_palm_finger_right_2));\n jnt_to_jac_left_2.reset(new KDL::ChainJntToJacSolver(chain_palm_finger_left_2));\n}\n\n\n\nint main (int argc, char **argv)\n{\n\n ros::init(argc, argv, \"Test_file_4_finger\"); \n ros::NodeHandle nh;\n\n \n\n\n nh.param>(\"box\", box, std::vector{1, 1, 1});\n nh.param>(\"contact_points\", contact_points, std::vector{1,1,1,1,1,1});\n nh.param>(\"contact_forces\", contact_forces, std::vector{1,1,1,1,1,1});\n nh.param>(\"joints\", joints, std::vector{0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0});\n nh.param(\"mu\", mu, 0.5);\n nh.param(\"f_i_max\", f_i_max, 1);\n nh.param(\"n_c\", n_c, 0);\n nh.param(\"n_q\", n_q, 0);\n\n\n if(n_c <= 0) \n return 0;\n\n createStructureKDL();\n setJoints(joints);\n initJacobian();\n\n Eigen::MatrixXd Skew_Matrix1(3,3);\n Eigen::MatrixXd Skew_Matrix2(3,3);\n Eigen::MatrixXd Skew_Matrix3(3,3);\n Eigen::MatrixXd Skew_Matrix4(3,3);\n Eigen::MatrixXd Skew_Matrix5(3,3);\n Eigen::MatrixXd Skew_Matrix6(3,3);\n\n Skew_Matrix1(0,0) = Skew_Matrix1(1,1) = Skew_Matrix1(2,2) = 0;\n Skew_Matrix1(0,1) = - contact_points[2]; // -rz \n Skew_Matrix1(0,2) = contact_points[1]; // ry\n Skew_Matrix1(1,0) = contact_points[2]; // rz\n Skew_Matrix1(2,0) = - contact_points[1]; // -ry\n Skew_Matrix1(1,2) = - contact_points[0]; // -rx\n Skew_Matrix1(2,1) = contact_points[0]; // rx\n // left\n\n\n Skew_Matrix2(0,0) = Skew_Matrix2(1,1) = Skew_Matrix2(2,2) = 0;\n Skew_Matrix2(0,1) = - contact_points[5]; // -rz \n Skew_Matrix2(0,2) = contact_points[4]; // ry\n Skew_Matrix2(1,0) = contact_points[5]; // rz\n Skew_Matrix2(2,0) = - contact_points[4]; // -ry\n Skew_Matrix2(1,2) = - contact_points[3]; // -rx\n Skew_Matrix2(2,1) = contact_points[3]; // rx\n // right\n\n\n Skew_Matrix3(0,0) = Skew_Matrix3(1,1) = Skew_Matrix3(2,2) = 0;\n Skew_Matrix3(0,1) = - contact_points[8]; // -rz \n Skew_Matrix3(0,2) = contact_points[7]; // ry\n Skew_Matrix3(1,0) = contact_points[8]; // rz\n Skew_Matrix3(2,0) = - contact_points[7]; // -ry\n Skew_Matrix3(1,2) = - contact_points[6]; // -rx\n Skew_Matrix3(2,1) = contact_points[6]; // rx\n // up\n\n\n Skew_Matrix4(0,0) = Skew_Matrix4(1,1) = Skew_Matrix4(2,2) = 0;\n Skew_Matrix4(0,1) = - contact_points[11]; // -rz \n Skew_Matrix4(0,2) = contact_points[10]; // ry\n Skew_Matrix4(1,0) = contact_points[11]; // rz\n Skew_Matrix4(2,0) = - contact_points[10]; // -ry\n Skew_Matrix4(1,2) = - contact_points[9]; // -rx\n Skew_Matrix4(2,1) = contact_points[9]; // rx\n // down\n\n Skew_Matrix5(0,0) = Skew_Matrix5(1,1) = Skew_Matrix5(2,2) = 0;\n Skew_Matrix5(0,1) = - contact_points[14]; // -rz \n Skew_Matrix5(0,2) = contact_points[13]; // ry\n Skew_Matrix5(1,0) = contact_points[14]; // rz\n Skew_Matrix5(2,0) = - contact_points[13]; // -ry\n Skew_Matrix5(1,2) = - contact_points[12]; // -rx\n Skew_Matrix5(2,1) = contact_points[12]; // rx\n // front of\n\n\n Skew_Matrix6(0,0) = Skew_Matrix6(1,1) = Skew_Matrix6(2,2) = 0;\n Skew_Matrix6(0,1) = - contact_points[17]; // -rz \n Skew_Matrix6(0,2) = contact_points[16]; // ry\n Skew_Matrix6(1,0) = contact_points[17]; // rz\n Skew_Matrix6(2,0) = - contact_points[16]; // -ry\n Skew_Matrix6(1,2) = - contact_points[15]; // -rx\n Skew_Matrix6(2,1) = contact_points[15]; // rx\n // behind\n\n\n\nEigen::MatrixXd G_c_t = MatrixXd::Zero(6, 6*n_c);\nEigen::MatrixXd G1 = MatrixXd::Zero(6,6);\nEigen::MatrixXd G2 = MatrixXd::Zero(6,6);\nEigen::MatrixXd G3 = MatrixXd::Zero(6,6);\nEigen::MatrixXd G4 = MatrixXd::Zero(6,6);\nEigen::MatrixXd G5 = MatrixXd::Zero(6,6);\nEigen::MatrixXd G6 = MatrixXd::Zero(6,6);\n\nEigen::MatrixXd R1(3,3);\nEigen::MatrixXd R2(3,3);\nEigen::MatrixXd R3(3,3);\nEigen::MatrixXd R4(3,3);\nEigen::MatrixXd R5(3,3);\nEigen::MatrixXd R6(3,3);\n\nR1 << 1, 0, 0,\n 0, cos(-90*PPI_/180), -sin(-90*PPI_/180),\n 0, sin(-90*PPI_/180), cos(-90*PPI_/180);\n// left\n\nR2 << 1, 0, 0,\n 0, cos(90*PPI_/180), -sin(90*PPI_/180),\n 0, sin(90*PPI_/180), cos(90*PPI_/180);\n// right\n\nR3 << 1, 0, 0,\n 0, cos(180*PPI_/180), -sin(180*PPI_/180),\n 0, sin(180*PPI_/180), cos(180*PPI_/180);\n// up\n\nR4 << 1, 0, 0,\n 0, 1, 0,\n 0, 0, 1;\n// down\n\nR5 << cos(-90*PPI/180), 0, sin(-90*PPI/180),\n 0 , 1, 0 ,\n -sin(-90*PPI/180), 0, cos(-90*PPI/180);\n//front of\n\nR6 << cos(90*PPI/180), 0, sin(90*PPI/180),\n 0 , 1, 0 ,\n -sin(90*PPI/180), 0, cos(90*PPI/180);\n//behind\n\n\n\n\nG1.block<3,3>(0,0) = R1.transpose();\nG1.block<3,3>(3,3) = R1.transpose();\nG1.block<3,3>(3,0) = Skew_Matrix1 * R1.transpose();\n\nG2.block<3,3>(0,0) = R2.transpose();\nG2.block<3,3>(3,3) = R2.transpose();\nG2.block<3,3>(3,0) = Skew_Matrix2 * R2.transpose();\n\nG3.block<3,3>(0,0) = R3.transpose();\nG3.block<3,3>(3,3) = R3.transpose();\nG3.block<3,3>(3,0) = Skew_Matrix3 * R3.transpose();\n\nG4.block<3,3>(0,0) = R4.transpose();\nG4.block<3,3>(3,3) = R4.transpose();\nG4.block<3,3>(3,0) = Skew_Matrix4 * R4.transpose();\n\nG5.block<3,3>(0,0) = R5.transpose();\nG5.block<3,3>(3,3) = R5.transpose();\nG5.block<3,3>(3,0) = Skew_Matrix5 * R5.transpose();\n\nG6.block<3,3>(0,0) = R6.transpose();\nG6.block<3,3>(3,3) = R6.transpose();\nG6.block<3,3>(3,0) = Skew_Matrix6 * R6.transpose();\n\nG_c_t.block<6,6>(0,0) = G1;\nG_c_t.block<6,6>(0,6) = G2;\nG_c_t.block<6,6>(0,12) = G3;\nG_c_t.block<6,6>(0,18) = G4;\nG_c_t.block<6,6>(0,24) = G5;\nG_c_t.block<6,6>(0,30) = G6;\n\n\n\nEigen::MatrixXd Jacobian = MatrixXd::Zero(6*n_c,n_q);\n\njnt_to_jac_right_0->JntToJac(jointpositions_right_0, jacobian_right_0);\njnt_to_jac_left_0->JntToJac(jointpositions_left_0, jacobian_left_0);\n\njnt_to_jac_right_1->JntToJac(jointpositions_right_1, jacobian_right_1);\njnt_to_jac_left_1->JntToJac(jointpositions_left_1, jacobian_left_1);\n\njnt_to_jac_right_2->JntToJac(jointpositions_right_2, jacobian_right_2);\njnt_to_jac_left_2->JntToJac(jointpositions_left_2, jacobian_left_2);\n\n\n \nJacobian.block<6,8>(0,0) = jacobian_right_0.data; // first contact - first finger\n\nJacobian.block<6,6>(6,0) = jacobian_left_0.data.block<6,6>(0,0); // second contact - second finger\nJacobian.block<6,2>(6,8) = jacobian_left_0.data.block<6,2>(0,6);\n\nJacobian.block<6,6>(12,0) = jacobian_right_1.data.block<6,6>(0,0); // third contact - third finger\nJacobian.block<6,2>(12,10) = jacobian_right_1.data.block<6,2>(0,6);\n\nJacobian.block<6,6>(18,0) = jacobian_left_1.data.block<6,6>(0,0); // four-th contact - four-th finger\nJacobian.block<6,2>(18,12) = jacobian_left_1.data.block<6,2>(0,6);\n \nJacobian.block<6,6>(24,0) = jacobian_right_2.data.block<6,6>(0,0); // five-th contact - five-th finger\nJacobian.block<6,2>(24,14) = jacobian_right_2.data.block<6,2>(0,6);\n \nJacobian.block<6,6>(30,0) = jacobian_left_2.data.block<6,6>(0,0); // six-th contact - six-th finger\nJacobian.block<6,2>(30,16) = jacobian_left_2.data.block<6,2>(0,6);\n \n\n\n\n\n Eigen::MatrixXd R_c = MatrixXd::Zero(3*n_c,3*n_c);\n int k = 0; \n for(int i = 0 ; i < n_c ; i++)\n { \n R_c.block<3,3>(k,k) = MatrixXd::Identity(3,3);\n k += 3;\n }\n\n Eigen::VectorXd f_c_(contact_forces.size());\n for(int i = 0 ; i < contact_forces.size() ; i++ )\n f_c_(i) = contact_forces[i];\n\n Eigen::MatrixXd Contact_Stiffness_Matrix = MatrixXd::Zero(3,3); // Kis\n for(int i = 0 ; i < Contact_Stiffness_Matrix.rows() ; i++) //Kis\n Contact_Stiffness_Matrix(i,i) = contact_stiffness;\n\n\n Eigen::MatrixXd Joint_Stiffness_Matrix = MatrixXd::Zero(n_q,n_q); // Kp \n for(int j = 0 ; j < Joint_Stiffness_Matrix.rows() ; j++) //Kp\n Joint_Stiffness_Matrix(j,j) = joint_stiffness;\n \n //quality_i = quality_pcr_pgr_5(f_c_, G_c, Jacobian, R_c, Contact_Stiffness_Matrix, Joint_Stiffness_Matrix, mu, f_i_max);\n // quality_i = quality_pcr_pgr_5(f_c_, G_b, Jacobian, R_c, Contact_Stiffness_Matrix, Joint_Stiffness_Matrix, mu, f_i_max);\n\n\n\n quality_i = quality_pcr_pgr_5(f_c_, G_c_t, Jacobian, R_c, Contact_Stiffness_Matrix, Joint_Stiffness_Matrix, mu, f_i_max);\n\n\n cout << \" quality : \" << quality_i << endl;\n\n ros::spinOnce();\n return 0;\n}", "meta": {"hexsha": "bae9ed9b7fdd1bd37609b925658b83ac7d86d9da", "size": 18565, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test_file_6_fingers.cpp", "max_stars_repo_name": "lia2790/grasp-learning", "max_stars_repo_head_hexsha": "e32c58eff37c1a951e914705a916452044c1d019", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 5.0, "max_stars_repo_stars_event_min_datetime": "2017-11-08T12:51:50.000Z", "max_stars_repo_stars_event_max_datetime": "2021-01-26T12:17:22.000Z", "max_issues_repo_path": "src/test_file_6_fingers.cpp", "max_issues_repo_name": "lia2790/grasp-learning", "max_issues_repo_head_hexsha": "e32c58eff37c1a951e914705a916452044c1d019", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/test_file_6_fingers.cpp", "max_forks_repo_name": "lia2790/grasp-learning", "max_forks_repo_head_hexsha": "e32c58eff37c1a951e914705a916452044c1d019", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2017-05-30T14:59:28.000Z", "max_forks_repo_forks_event_max_datetime": "2019-12-11T13:34:27.000Z", "avg_line_length": 36.259765625, "max_line_length": 181, "alphanum_fraction": 0.6844600054, "num_tokens": 6197, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5926665999540698, "lm_q2_score": 0.36296921241058616, "lm_q1q2_score": 0.21511972900738865}} {"text": "/* Copyright (C) 2012-2020 IBM Corp.\n * This program is Licensed under the Apache License, Version 2.0\n * (the \"License\"); you may not use this file except in compliance\n * with the License. You may obtain a copy of the License at\n * http://www.apache.org/licenses/LICENSE-2.0\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License. See accompanying LICENSE file.\n */\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef HELIB_DEBUG\n\n#include \n\nnamespace helib {\n\nlong printFlag = FLAG_PRINT_VEC;\n/************************ Some local functions ***********************/\n/*********************************************************************/\n\nstatic void checkCriticalValue(const std::vector& zzParts,\n const DoubleCRT& sKey,\n const RecryptData& rcData,\n long q);\n\nstatic void checkRecryptBounds(const std::vector& zzParts,\n const DoubleCRT& sKey,\n const Context& context,\n long q);\n\nstatic void checkRecryptBounds_v(const std::vector& v,\n const DoubleCRT& sKey,\n const Context& context,\n long q);\n} // namespace helib\n\n#endif // HELIB_DEBUG\n\nnamespace helib {\n\n// Return in poly a polynomial with X^i encoded in all the slots\nstatic void x2iInSlots(NTL::ZZX& poly,\n long i,\n std::vector& xVec,\n const EncryptedArray& ea)\n{\n xVec.resize(ea.size());\n NTL::ZZX x2i = NTL::ZZX(i, 1);\n for (long j = 0; j < (long)xVec.size(); j++)\n xVec[j] = x2i;\n ea.encode(poly, xVec);\n}\n\n// Make every entry of vec divisible by p2e by adding/subtracting q, while\n// keeping the added multiples small. Specifically, for q = 1 mod p2e and any\n// integer z can be made divisible by p2e via z' = z + v*q, with |v| <= p2e/2.\n\nstatic void newMakeDivisible(NTL::ZZX& poly,\n long p2e,\n long q,\n const Context& context,\n NTL::ZZX& vpoly)\n{\n if (p2e == 1) {\n vpoly = 0;\n return;\n }\n\n assertTrue(q > 0l, \"q must be positive\");\n assertTrue(p2e > 0l, \"p2e must be positive\");\n\n assertEq(q % p2e, 1l, \"q must equal 1 modulo p2e\");\n\n long p = context.getP();\n\n const RecryptData& rcData = context.getRcData();\n const PowerfulDCRT& p2d_conv = *rcData.p2dConv;\n\n NTL::Vec pwrfl;\n p2d_conv.ZZXtoPowerful(pwrfl, poly);\n\n#ifdef HELIB_DEBUG\n NTL::Vec vvec(NTL::INIT_SIZE, pwrfl.length());\n#endif\n\n for (long i : range(pwrfl.length())) {\n NTL::ZZ& z = pwrfl[i];\n long v;\n\n // What to add to z to make it divisible by p2e?\n long zMod = rem(z, p2e); // zMod is in [0,p2e-1]\n // NOTE: this makes sure we get a truly balanced remainder\n if (zMod > p2e / 2 || (p == 2 && zMod == p2e / 2 && NTL::RandomBnd(2))) {\n // randomize so that v has expected value 0\n zMod = p2e - zMod;\n } else {\n // need to add a negative number\n zMod = -zMod;\n }\n v = zMod;\n z += NTL::to_ZZ(q) * v; // make z divisible by p2e\n\n if (rem(z, p2e) != 0) { // sanity check\n std::cerr << \"**error: original z[\" << i\n << \"]=\" << (z - (NTL::to_ZZ(q) * v)) << std::dec\n << \", p^e=\" << p2e << std::endl;\n std::cerr << \"z' = z + \" << v << \"*q = \" << z << std::endl;\n exit(1);\n }\n\n#ifdef HELIB_DEBUG\n vvec[i] = v;\n#endif\n }\n\n p2d_conv.powerfulToZZX(poly, pwrfl);\n\n#ifdef HELIB_DEBUG\n p2d_conv.powerfulToZZX(vpoly, vvec);\n#endif\n}\n\n/*********************************************************************/\n/*********************************************************************/\n\n/**\n * Summary of Appendix A from https://ia.cr/2014/873 (version from 2019):\n * Assume that we already chosen e, e' and t.\n *\n * Based in this analysis, we need\n * (1) (f*p^{e'} + 2*p^r+2))*B <= p^e/2\n * where B is a certain high-probability bound and f is a certain\n * fudge factor.\n *\n **/\n\n// the routine compute_fudge is used to correct for the fact that\n// the v-coeffs are not quite uniform\n\nstatic double compute_fudge(long p2ePrime, long p2e)\n{\n double eps = 0;\n\n if (p2ePrime > 1) {\n\n if (p2ePrime % 2 == 0) {\n eps = 1 / fsquare(p2ePrime);\n\n // The exact variance in this case is at most the variance\n // of a random variable that is distributed over\n // -N..+N\n // where N = 2^{e'}/2.\n // Each endpoint occurs with probability 1/(4*N),\n // and the remaining values each occur with the same probability\n // 1/(2*N)\n\n // This variance is exactly computed as\n // (N^2)/3 + 1/6 = ((N^2)/3)*(1 + 1/(2*N^2)), where N = 2^{e'}/2\n // So the std dev is at most\n // N/sqrt(3)*(1 + 1/(4*N^2))\n\n } else {\n eps = 1 / double(p2e);\n\n // We are computing X + Y mod p^{e'}, where\n // X and Y are independent.\n // Y is uniformly distributed over\n // -floor(p^{r}/2)..floor(p^{r}/2)\n // X is distributed over\n // -floor(p^e/2)-1..floor(p^e/2)+1,\n // where each endpoint occurs with probability 1 / (2*(p^e+1)),\n // and the remaining p^e values are equally likely\n\n // The variance in this case is bounded by\n // (N^2)/3*(1-eps) + (N^2)*eps = (N^2)/3*(1+2*eps),\n // where N = p^{e'}/2 and eps < 1/p^e\n // So the std dev is bounded by\n // N/sqrt(3)*sqrt(1+2*eps) <= N/sqrt(3)*(1+eps)\n }\n }\n\n return 1 + eps;\n}\n\nvoid RecryptData::setAE(long& e, long& ePrime, const Context& context)\n{\n double coeff_bound = context.boundForRecryption();\n // coeff_bound is ultimately a high prob bound on |w0+w1*s|,\n // the coeffs of w0, w1 are chosen uniformly on [-1/2,1/2]\n\n long p = context.getP();\n long p2r = context.getAlMod().getPPowR();\n long r = context.getAlMod().getR();\n long frstTerm = 2 * p2r + 2;\n\n long e_bnd = 0;\n long p2e_bnd = 1;\n while (p2e_bnd <= ((1L << 30) - 2) / p) { // NOTE: this avoids overflow\n e_bnd++;\n p2e_bnd *= p;\n }\n // e_bnd is largest e such that p^e+1 < 2^30\n\n // Start with the smallest e s.t. p^e/2 >= frstTerm*coeff_bound\n ePrime = 0;\n e = r + 1;\n while (e <= e_bnd && NTL::power_long(p, e) < frstTerm * coeff_bound * 2)\n e++;\n\n // if (e > e_bnd) Error(\"setAE: cannot find suitable e\");\n assertFalse(e > e_bnd, \"setAE: cannot find suitable e\");\n\n // long ePrimeTry = r+1;\n long ePrimeTry = 1;\n\n while (ePrimeTry <= e_bnd) {\n long p2ePrimeTry = NTL::power_long(p, ePrimeTry);\n // long eTry = ePrimeTry+1;\n long eTry = std::max(r + 1, ePrimeTry + 1);\n while (eTry <= e_bnd && eTry - ePrimeTry < e - ePrime) {\n long p2eTry = NTL::power_long(p, eTry);\n double fudge = compute_fudge(p2ePrimeTry, p2eTry);\n if (p2eTry >= (p2ePrimeTry * fudge + frstTerm) * coeff_bound * 2)\n break;\n\n eTry++;\n }\n\n if (eTry <= e_bnd && eTry - ePrimeTry < e - ePrime) {\n e = eTry;\n ePrime = ePrimeTry;\n }\n\n ePrimeTry++;\n }\n\n#ifdef HELIB_DEBUG\n std::cerr << \"RecryptData::setAE(): e=\" << e << \", e'=\" << ePrime\n << std::endl;\n#endif\n}\n\nbool RecryptData::operator==(const RecryptData& other) const\n{\n if (mvec != other.mvec)\n return false;\n\n if (skHwt != other.skHwt)\n return false;\n\n return true;\n}\n\n// The main method\nvoid RecryptData::init(const Context& context,\n const NTL::Vec& mvec_,\n bool enableThick,\n bool build_cache_,\n bool minimal)\n{\n if (alMod != nullptr) { // were we called for a second time?\n std::cerr << \"@Warning: multiple calls to RecryptData::init\\n\";\n return;\n }\n\n // sanity check\n assertEq(computeProd(mvec_),\n context.getM(),\n \"Cyclotomic polynomial mismatch\");\n\n // Record the arguments to this function\n mvec = mvec_;\n build_cache = build_cache_;\n alsoThick = enableThick;\n\n bool mvec_ok = true;\n for (long i : range(mvec.length())) {\n NTL::Vec> factors;\n factorize(factors, mvec[i]);\n if (factors.length() > 1)\n mvec_ok = false;\n }\n\n if (!mvec_ok) {\n Warning(\"prime power factorization recommended for bootstrapping\");\n }\n\n skHwt = context.getHwt();\n e = context.getE();\n ePrime = context.getEPrime();\n\n long r = context.getAlMod().getR();\n\n // First part of Bootstrapping works wrt plaintext space p^{r'}\n alMod = std::make_shared(context.getZMStar(), e - ePrime + r);\n ea = std::make_shared(context, *alMod);\n // Polynomial defaults to F0, PAlgebraMod explicitly given\n\n p2dConv = std::make_shared(context, mvec);\n\n if (!enableThick)\n return;\n\n // Initialize the linear polynomial for unpacking the slots\n NTL::zz_pBak bak;\n bak.save();\n ea->getAlMod().restoreContext();\n long nslots = ea->size();\n long d = ea->getDegree();\n\n const NTL::Mat& CBi =\n ea->getDerived(PA_zz_p()).getNormalBasisMatrixInverse();\n\n std::vector LM;\n LM.resize(d);\n for (long i = 0; i < d; i++) // prepare the linear polynomial\n LM[i] = rep(CBi[i][0]);\n\n std::vector C;\n ea->buildLinPolyCoeffs(C, LM); // \"build\" the linear polynomial\n\n unpackSlotEncoding.resize(d); // encode the coefficients\n\n for (long j = 0; j < d; j++) {\n std::vector v(nslots);\n for (long k = 0; k < nslots; k++)\n v[k] = C[j];\n ea->encode(unpackSlotEncoding[j], v);\n }\n firstMap = std::make_shared(*ea, minimal, mvec, true, build_cache);\n secondMap = std::make_shared(context.getEA(),\n minimal,\n mvec,\n false,\n build_cache);\n}\n\n/********************************************************************/\n/********************************************************************/\n\n// Extract digits from fully packed slots\nvoid extractDigitsPacked(Ctxt& ctxt,\n long botHigh,\n long r,\n long ePrime,\n const std::vector& unpackSlotEncoding);\n\n// Extract digits from unpacked slots\nvoid extractDigitsThin(Ctxt& ctxt, long botHigh, long r, long ePrime);\n\n// bootstrap a ciphertext to reduce noise\nvoid PubKey::reCrypt(Ctxt& ctxt) const\n{\n HELIB_TIMER_START;\n\n // Some sanity checks for dummy ciphertext\n long ptxtSpace = ctxt.getPtxtSpace();\n if (ctxt.isEmpty())\n return;\n if (ctxt.parts.size() == 1 && ctxt.parts[0].skHandle.isOne()) {\n // Dummy encryption, just ensure that it is reduced mod p\n NTL::ZZX poly = to_ZZX(ctxt.parts[0]);\n for (long i = 0; i < poly.rep.length(); i++)\n poly[i] = NTL::to_ZZ(rem(poly[i], ptxtSpace));\n poly.normalize();\n ctxt.DummyEncrypt(poly);\n return;\n }\n\n // check that we have bootstrapping data\n assertTrue(recryptKeyID >= 0l, \"No bootstrapping data\");\n\n long p = getContext().getP();\n long r = getContext().getAlMod().getR();\n long p2r = getContext().getAlMod().getPPowR();\n\n long intFactor = ctxt.intFactor;\n\n // the bootstrapping key is encrypted relative to plaintext space p^{e-e'+r}.\n const RecryptData& rcData = getContext().getRcData();\n long e = rcData.e;\n long ePrime = rcData.ePrime;\n long p2ePrime = NTL::power_long(p, ePrime);\n long q = NTL::power_long(p, e) + 1;\n assertTrue(e >= r, \"rcData.e must be at least alMod.r\");\n\n#ifdef HELIB_DEBUG\n std::cerr << \"reCrypt: p=\" << p << \", r=\" << r << \", e=\" << e\n << \" ePrime=\" << ePrime << \", q=\" << q << std::endl;\n CheckCtxt(ctxt, \"init\");\n#endif\n\n // can only bootstrap ciphertext with plaintext-space dividing p^r\n assertEq(p2r % ptxtSpace, 0l, \"ptxtSpace must divide p^r when bootstrapping\");\n\n ctxt.dropSmallAndSpecialPrimes();\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after mod down\");\n#endif\n\n HELIB_NTIMER_START(AAA_preProcess);\n\n // Make sure that this ciphertext is in canonical form\n if (!ctxt.inCanonicalForm())\n ctxt.reLinearize();\n\n // Mod-switch down if needed\n IndexSet s = ctxt.getPrimeSet() / context.getSpecialPrimes();\n assertTrue(s <= context.getCtxtPrimes(), \"prime set is messed up\");\n if (s.card() > 3) { // leave only first three ciphertext primes\n long first = s.first();\n IndexSet s3(first, first + 2);\n s.retain(s3);\n }\n ctxt.modDownToSet(s);\n\n // key-switch to the bootstrapping key\n ctxt.reLinearize(recryptKeyID);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after key switching\");\n#endif\n\n // \"raw mod-switch\" to the bootstrapping modulus q=p^e+1.\n std::vector zzParts; // the mod-switched parts, in ZZX format\n\n double mfac = ctxt.getContext().getZMStar().getNormBnd();\n double noise_est = ctxt.rawModSwitch(zzParts, q) * mfac;\n // noise_est is an upper bound on the L-infty norm of the scaled noise\n // in the pwrfl basis\n double noise_bnd =\n HELIB_MIN_CAP_FRAC * p2r * ctxt.getContext().boundForRecryption();\n // noise_bnd is the bound assumed in selecting the parameters\n double noise_rat = noise_est / noise_bnd;\n\n HELIB_STATS_UPDATE(\"raw-mod-switch-noise\", noise_rat);\n\n if (noise_rat > 1) {\n // TODO: Turn the following preprocessor logics into a warnOrThrow function\n std::string message =\n \"rawModSwitch scaled noise exceeds bound: \" + std::to_string(noise_rat);\n#ifdef HELIB_DEBUG\n Warning(message);\n#else\n throw LogicError(message);\n#endif\n }\n\n assertEq(zzParts.size(),\n (std::size_t)2,\n \"Exactly 2 parts required for mod-switching in thin bootstrapping\");\n\n#ifdef HELIB_DEBUG\n if (dbgKey) {\n checkRecryptBounds(zzParts, dbgKey->getRecryptKey(), ctxt.getContext(), q);\n }\n#endif\n\n std::vector v;\n v.resize(2);\n\n // Add multiples of q to make the zzParts divisible by p^{e'}\n for (long i : range(2)) {\n // make divisible by p^{e'}\n\n newMakeDivisible(zzParts[i], p2ePrime, q, ctxt.getContext(), v[i]);\n }\n\n#ifdef HELIB_DEBUG\n if (dbgKey) {\n checkRecryptBounds_v(v, dbgKey->getRecryptKey(), ctxt.getContext(), q);\n checkCriticalValue(zzParts,\n dbgKey->getRecryptKey(),\n ctxt.getContext().getRcData(),\n q);\n }\n#endif\n\n for (long i : range(zzParts.size())) {\n zzParts[i] /= p2ePrime; // divide by p^{e'}\n }\n\n // NOTE: here we lose the intFactor associated with ctxt.\n // We will restore it below.\n ctxt = recryptEkey;\n\n ctxt.multByConstant(zzParts[1]);\n ctxt.addConstant(zzParts[0]);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after preProcess\");\n#endif\n HELIB_NTIMER_STOP(AAA_preProcess);\n\n // Move the powerful-basis coefficients to the plaintext slots\n HELIB_NTIMER_START(AAA_LinearTransform1);\n ctxt.getContext().getRcData().firstMap->apply(ctxt);\n HELIB_NTIMER_STOP(AAA_LinearTransform1);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after LinearTransform1\");\n#endif\n\n // Extract the digits e-e'+r-1,...,e-e' (from fully packed slots)\n HELIB_NTIMER_START(AAA_extractDigitsPacked);\n extractDigitsPacked(ctxt,\n e - ePrime,\n r,\n ePrime,\n context.getRcData().unpackSlotEncoding);\n HELIB_NTIMER_STOP(AAA_extractDigitsPacked);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after extractDigitsPacked\");\n#endif\n\n // Move the slots back to powerful-basis coefficients\n HELIB_NTIMER_START(AAA_LinearTransform2);\n ctxt.getContext().getRcData().secondMap->apply(ctxt);\n HELIB_NTIMER_STOP(AAA_LinearTransform2);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after linearTransform2\");\n#endif\n\n // restore intFactor\n if (intFactor != 1)\n ctxt.intFactor = NTL::MulMod(ctxt.intFactor, intFactor, ptxtSpace);\n}\n\n#ifdef HELIB_BOOT_THREADS\n\n// Extract digits from fully packed slots, multithreaded version\nvoid extractDigitsPacked(Ctxt& ctxt,\n long botHigh,\n long r,\n long ePrime,\n const std::vector& unpackSlotEncoding)\n{\n HELIB_TIMER_START;\n\n // Step 1: unpack the slots of ctxt\n HELIB_NTIMER_START(unpack);\n ctxt.cleanUp();\n\n // Apply the d automorphisms and store them in scratch area\n long d = ctxt.getContext().getOrdP();\n\n std::vector unpacked(d, Ctxt(ZeroCtxtLike, ctxt));\n { // explicit scope to force all temporaries to be released\n std::vector> coeff_vector;\n std::vector coeff_vector_sz;\n coeff_vector.resize(d);\n coeff_vector_sz.resize(d);\n\n HELIB_NTIMER_START(unpack1);\n for (long i = 0; i < d; i++) {\n coeff_vector[i] = std::make_shared(unpackSlotEncoding[i],\n ctxt.getContext(),\n ctxt.getPrimeSet());\n coeff_vector_sz[i] = NTL::conv(\n embeddingLargestCoeff(unpackSlotEncoding[i],\n ctxt.getContext().getZMStar()));\n }\n HELIB_NTIMER_STOP(unpack1);\n\n HELIB_NTIMER_START(unpack2);\n std::vector frob(d, Ctxt(ZeroCtxtLike, ctxt));\n\n NTL_EXEC_RANGE(d, first, last)\n // FIXME: implement using hoisting!\n for (long j = first; j < last; j++) { // process jth Frobenius\n frob[j] = ctxt;\n frob[j].frobeniusAutomorph(j);\n frob[j].cleanUp();\n // FIXME: not clear if we should call cleanUp here\n }\n NTL_EXEC_RANGE_END\n\n HELIB_NTIMER_STOP(unpack2);\n\n HELIB_NTIMER_START(unpack3);\n Ctxt tmp1(ZeroCtxtLike, ctxt);\n for (long i = 0; i < d; i++) {\n for (long j = 0; j < d; j++) {\n tmp1 = frob[j];\n tmp1.multByConstant(*coeff_vector[mcMod(i + j, d)],\n coeff_vector_sz[mcMod(i + j, d)]);\n unpacked[i] += tmp1;\n }\n }\n HELIB_NTIMER_STOP(unpack3);\n }\n HELIB_NTIMER_STOP(unpack);\n\n //#ifdef HELIB_DEBUG\n // CheckCtxt(unpacked[0], \"after unpack\");\n //#endif\n\n NTL_EXEC_RANGE(d, first, last)\n for (long i = first; i < last; i++) {\n extractDigitsThin(unpacked[i], botHigh, r, ePrime);\n }\n NTL_EXEC_RANGE_END\n\n //#ifdef HELIB_DEBUG\n // CheckCtxt(unpacked[0], \"before repack\");\n //#endif\n\n // Step 3: re-pack the slots\n HELIB_NTIMER_START(repack);\n const EncryptedArray& ea2 = ctxt.getContext().getEA();\n NTL::ZZX xInSlots;\n std::vector xVec(ea2.size());\n ctxt = unpacked[0];\n for (long i = 1; i < d; i++) {\n x2iInSlots(xInSlots, i, xVec, ea2);\n unpacked[i].multByConstant(xInSlots);\n ctxt += unpacked[i];\n }\n HELIB_NTIMER_STOP(repack);\n //#ifdef HELIB_DEBUG\n // CheckCtxt(ctxt, \"after repack\");\n //#endif\n}\n\n#else\n\n// Extract digits from fully packed slots\nvoid extractDigitsPacked(Ctxt& ctxt,\n long botHigh,\n long r,\n long ePrime,\n const std::vector& unpackSlotEncoding)\n{\n HELIB_TIMER_START;\n\n // Step 1: unpack the slots of ctxt\n HELIB_NTIMER_START(unpack);\n ctxt.cleanUp();\n\n // Apply the d automorphisms and store them in scratch area\n long d = ctxt.getContext().getOrdP();\n\n std::vector unpacked(d, Ctxt(ZeroCtxtLike, ctxt));\n { // explicit scope to force all temporaries to be released\n std::vector> coeff_vector;\n std::vector coeff_vector_sz;\n coeff_vector.resize(d);\n coeff_vector_sz.resize(d);\n for (long i = 0; i < d; i++) {\n coeff_vector[i] = std::make_shared(unpackSlotEncoding[i],\n ctxt.getContext(),\n ctxt.getPrimeSet());\n coeff_vector_sz[i] = NTL::conv(\n embeddingLargestCoeff(unpackSlotEncoding[i],\n ctxt.getContext().getZMStar()));\n }\n\n Ctxt tmp1(ZeroCtxtLike, ctxt);\n Ctxt tmp2(ZeroCtxtLike, ctxt);\n\n // FIXME: implement using hoisting!\n for (long j = 0; j < d; j++) { // process jth Frobenius\n tmp1 = ctxt;\n tmp1.frobeniusAutomorph(j);\n tmp1.cleanUp();\n // FIXME: not clear if we should call cleanUp here\n\n for (long i = 0; i < d; i++) {\n tmp2 = tmp1;\n tmp2.multByConstant(*coeff_vector[mcMod(i + j, d)],\n coeff_vector_sz[mcMod(i + j, d)]);\n unpacked[i] += tmp2;\n }\n }\n }\n HELIB_NTIMER_STOP(unpack);\n\n //#ifdef HELIB_DEBUG\n // CheckCtxt(unpacked[0], \"after unpack\");\n //#endif\n\n for (long i = 0; i < (long)unpacked.size(); i++) {\n extractDigitsThin(unpacked[i], botHigh, r, ePrime);\n }\n\n //#ifdef HELIB_DEBUG\n // CheckCtxt(unpacked[0], \"before repack\");\n //#endif\n\n // Step 3: re-pack the slots\n HELIB_NTIMER_START(repack);\n const EncryptedArray& ea2 = *ctxt.getContext().ea;\n NTL::ZZX xInSlots;\n std::vector xVec(ea2.size());\n ctxt = unpacked[0];\n for (long i = 1; i < d; i++) {\n x2iInSlots(xInSlots, i, xVec, ea2);\n unpacked[i].multByConstant(xInSlots);\n ctxt += unpacked[i];\n }\n HELIB_NTIMER_STOP(repack);\n}\n\n#endif\n\n// Use packed bootstrapping, so we can bootstrap all in just one go.\nvoid packedRecrypt(const CtPtrs& cPtrs,\n const std::vector& unpackConsts,\n const EncryptedArray& ea)\n{\n PubKey& pKey = (PubKey&)cPtrs[0]->getPubKey();\n\n // Allocate temporary ciphertexts for the recryption\n int nPacked = divc(cPtrs.size(), ea.getDegree()); // ceil(totalNum/d)\n std::vector cts(nPacked, Ctxt(pKey));\n\n repack(CtPtrs_vectorCt(cts), cPtrs, ea); // pack ciphertexts\n // cout << \"@\"<< lsize(cts)<& unpackConsts,\n const EncryptedArray& ea,\n long belowLvl)\n{\n std::vector v;\n for (long i = 0; i < array.size(); i++)\n if (array.isSet(i) && !array[i]->isEmpty() &&\n array[i]->bitCapacity() < belowLvl * (array[i]->getContext().BPL()))\n v.push_back(array[i]);\n packedRecrypt(CtPtrs_vectorPt(v), unpackConsts, ea);\n}\nvoid packedRecrypt(const CtPtrMat& m,\n const std::vector& unpackConsts,\n const EncryptedArray& ea,\n long belowLvl)\n{\n std::vector v;\n for (long i = 0; i < m.size(); i++)\n for (long j = 0; j < m[i].size(); j++)\n if (m[i].isSet(j) && !m[i][j]->isEmpty() &&\n m[i][j]->bitCapacity() < belowLvl * (m[i][j]->getContext().BPL()))\n v.push_back(m[i][j]);\n packedRecrypt(CtPtrs_vectorPt(v), unpackConsts, ea);\n}\n\n//===================== Thin Bootstrapping stuff ==================\n\nvoid ThinRecryptData::init(const Context& context,\n const NTL::Vec& mvec_,\n bool alsoThick,\n bool build_cache_,\n bool minimal)\n{\n RecryptData::init(context, mvec_, alsoThick, build_cache_, minimal);\n coeffToSlot =\n std::make_shared(*ea, minimal, mvec, true, build_cache);\n slotToCoeff = std::make_shared(context.getEA(),\n minimal,\n mvec,\n false,\n build_cache);\n}\n\n// Extract digits from thinly packed slots\n\nlong fhe_force_chen_han = 0;\n\nvoid extractDigitsThin(Ctxt& ctxt, long botHigh, long r, long ePrime)\n{\n HELIB_TIMER_START;\n\n Ctxt unpacked(ctxt);\n unpacked.cleanUp();\n\n std::vector scratch;\n\n long p = ctxt.getContext().getP();\n long p2r = NTL::power_long(p, r);\n long topHigh = botHigh + r - 1;\n\n // degree Chen/Han technique is p^{bot-1}(p-1)r\n // degree of basic technique is p^{bot-1}p^r,\n // or p^{bot-1}p^{r-1} if p==2, r > 1, and bot+r > 2\n\n bool use_chen_han = false;\n if (r > 1) {\n double chen_han_cost = log(p - 1) + log(r);\n double basic_cost;\n if (p == 2 && r > 2 && botHigh + r > 2)\n basic_cost = (r - 1) * log(p);\n else\n basic_cost = r * log(p);\n\n // std::cerr << \"*** basic: \" << basic_cost << \"\\n\";\n // std::cerr << \"*** chen/han: \" << chen_han_cost << \"\\n\";\n\n double thresh = 1.5;\n if (p == 2)\n thresh = 1.75;\n // increasing thresh makes chen_han less likely to be chosen.\n // For p == 2, the basic algorithm is just squaring,\n // and so is a bit cheaper, so we raise thresh a bit.\n // This is all a bit heuristic.\n\n if (basic_cost > thresh * chen_han_cost)\n use_chen_han = true;\n }\n\n if (fhe_force_chen_han > 0)\n use_chen_han = true;\n else if (fhe_force_chen_han < 0)\n use_chen_han = false;\n\n if (use_chen_han) {\n // use Chen and Han technique\n\n extendExtractDigits(scratch, unpacked, botHigh, r);\n\n#if 0\n for (long i: range(scratch.size())) {\n CheckCtxt(scratch[i], \"**\");\n }\n#endif\n\n for (long j = 0; j < botHigh; j++) {\n unpacked -= scratch[j];\n unpacked.divideByP();\n }\n\n if (p == 2 && botHigh > 0) // For p==2, subtract also the previous bit\n unpacked += scratch[botHigh - 1];\n unpacked.negate();\n\n if (r > ePrime) { // Add in digits from the bottom part, if any\n long topLow = r - 1 - ePrime;\n Ctxt tmp = scratch[topLow];\n for (long j = topLow - 1; j >= 0; --j) {\n tmp.multByP();\n tmp += scratch[j];\n }\n if (ePrime > 0)\n tmp.multByP(ePrime); // multiply by p^e'\n unpacked += tmp;\n }\n unpacked.reducePtxtSpace(p2r); // Our plaintext space is now mod p^r\n\n ctxt = unpacked;\n } else {\n\n if (p == 2 && r > 2 && topHigh + 1 > 2)\n topHigh--; // For p==2 we sometime get a bit for free\n\n extractDigits(scratch, unpacked, topHigh + 1);\n\n // set unpacked = -\\sum_{j=botHigh}^{topHigh} scratch[j] * p^{j-botHigh}\n if (topHigh >= LONG(scratch.size())) {\n topHigh = scratch.size() - 1;\n std::cerr << \" @ suspect: not enough digits in extractDigitsPacked\\n\";\n }\n\n unpacked = scratch[topHigh];\n for (long j = topHigh - 1; j >= botHigh; --j) {\n unpacked.multByP();\n unpacked += scratch[j];\n }\n if (p == 2 && botHigh > 0) // For p==2, subtract also the previous bit\n unpacked += scratch[botHigh - 1];\n unpacked.negate();\n\n if (r > ePrime) { // Add in digits from the bottom part, if any\n long topLow = r - 1 - ePrime;\n Ctxt tmp = scratch[topLow];\n for (long j = topLow - 1; j >= 0; --j) {\n tmp.multByP();\n tmp += scratch[j];\n }\n if (ePrime > 0)\n tmp.multByP(ePrime); // multiply by p^e'\n unpacked += tmp;\n }\n unpacked.reducePtxtSpace(p2r); // Our plaintext space is now mod p^r\n ctxt = unpacked;\n }\n}\n\n// Hack to get at private fields of public key\nstruct PubKeyHack\n{ // The public key\n const Context& context; // The context\n\n //! @var Ctxt pubEncrKey\n //! The public encryption key is an encryption of 0,\n //! relative to the first secret key\n Ctxt pubEncrKey;\n\n std::vector skHwts; // The Hamming weight of the secret keys\n std::vector keySwitching; // The key-switching matrices\n\n // The keySwitchMap structure contains pointers to key-switching matrices\n // for re-linearizing automorphisms. The entry keySwitchMap[i][n] contains\n // the index j such that keySwitching[j] is the first matrix one needs to\n // use when re-linearizing s_i(X^n).\n std::vector> keySwitchMap;\n\n NTL::Vec KS_strategy; // NTL Vec's support I/O, which is\n // more convenient\n\n // bootstrapping data\n\n long recryptKeyID; // index of the bootstrapping key\n Ctxt recryptEkey; // the key itself, encrypted under key #0\n};\n\n// bootstrap a ciphertext to reduce noise\nvoid PubKey::thinReCrypt(Ctxt& ctxt) const\n{\n HELIB_TIMER_START;\n\n // Some sanity checks for dummy ciphertext\n long ptxtSpace = ctxt.getPtxtSpace();\n if (ctxt.isEmpty())\n return;\n\n if (ctxt.parts.size() == 1 && ctxt.parts[0].skHandle.isOne()) {\n // Dummy encryption, just ensure that it is reduced mod p\n NTL::ZZX poly = to_ZZX(ctxt.parts[0]);\n for (long i = 0; i < poly.rep.length(); i++)\n poly[i] = NTL::to_ZZ(rem(poly[i], ptxtSpace));\n poly.normalize();\n ctxt.DummyEncrypt(poly);\n return;\n }\n\n // check that we have bootstrapping data\n assertTrue(recryptKeyID >= 0l, \"Bootstrapping data not present\");\n\n long p = ctxt.getContext().getP();\n long r = ctxt.getContext().getAlMod().getR();\n long p2r = ctxt.getContext().getAlMod().getPPowR();\n\n long intFactor = ctxt.intFactor;\n\n const ThinRecryptData& trcData = ctxt.getContext().getRcData();\n\n // the bootstrapping key is encrypted relative to plaintext space p^{e-e'+r}.\n long e = trcData.e;\n long ePrime = trcData.ePrime;\n long p2ePrime = NTL::power_long(p, ePrime);\n long q = NTL::power_long(p, e) + 1;\n assertTrue(e >= r, \"trcData.e must be at least alMod.r\");\n\n // can only bootstrap ciphertext with plaintext-space dividing p^r\n assertEq(p2r % ptxtSpace,\n 0l,\n \"ptxtSpace must divide p^r when thin bootstrapping\");\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"init\");\n#endif\n\n ctxt.dropSmallAndSpecialPrimes();\n\n#define DROP_BEFORE_THIN_RECRYPT\n#define THIN_RECRYPT_NLEVELS (3)\n#ifdef DROP_BEFORE_THIN_RECRYPT\n // experimental code...we should drop down to a reasonably low level\n // before doing the first linear map.\n long first = context.getCtxtPrimes().first();\n long last = std::min(context.getCtxtPrimes().last(),\n first + THIN_RECRYPT_NLEVELS - 1);\n ctxt.bringToSet(IndexSet(first, last));\n#endif\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after mod down\");\n#endif\n\n // Move the slots to powerful-basis coefficients\n HELIB_NTIMER_START(AAA_slotToCoeff);\n trcData.slotToCoeff->apply(ctxt);\n HELIB_NTIMER_STOP(AAA_slotToCoeff);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after slotToCoeff\");\n#endif\n\n HELIB_NTIMER_START(AAA_bootKeySwitch);\n\n // Make sure that this ciphertext is in canonical form\n if (!ctxt.inCanonicalForm())\n ctxt.reLinearize();\n\n // Mod-switch down if needed\n IndexSet s = ctxt.getPrimeSet() / context.getSpecialPrimes();\n assertTrue(s <= context.getCtxtPrimes(), \"prime set is messed up\");\n if (s.card() > 3) { // leave only first three ciphertext primes\n long first = s.first();\n IndexSet s3(first, first + 2);\n s.retain(s3);\n }\n ctxt.modDownToSet(s);\n\n // key-switch to the bootstrapping key\n ctxt.reLinearize(recryptKeyID);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after key switching\");\n#endif\n\n // \"raw mod-switch\" to the bootstrapping mosulus q=p^e+1.\n std::vector zzParts; // the mod-switched parts, in ZZX format\n\n double mfac = ctxt.getContext().getZMStar().getNormBnd();\n double noise_est = ctxt.rawModSwitch(zzParts, q) * mfac;\n // noise_est is an upper bound on the L-infty norm of the scaled noise\n // in the pwrfl basis\n double noise_bnd =\n HELIB_MIN_CAP_FRAC * p2r * ctxt.getContext().boundForRecryption();\n // noise_bnd is the bound assumed in selecting the parameters\n double noise_rat = noise_est / noise_bnd;\n\n HELIB_STATS_UPDATE(\"raw-mod-switch-noise\", noise_rat);\n\n if (noise_rat > 1) {\n // TODO: Turn the following preprocessor logics into a warnOrThrow function\n std::string message =\n \"rawModSwitch scaled noise exceeds bound: \" + std::to_string(noise_rat);\n#ifdef HELIB_DEBUG\n Warning(message);\n#else\n throw LogicError(message);\n#endif\n }\n\n assertEq(zzParts.size(),\n (std::size_t)2,\n \"Exactly 2 parts required for mod-switching in thin bootstrapping\");\n\n#ifdef HELIB_DEBUG\n if (dbgKey) {\n checkRecryptBounds(zzParts, dbgKey->getRecryptKey(), ctxt.getContext(), q);\n }\n#endif\n\n std::vector v;\n v.resize(2);\n\n // Add multiples of q to make the zzParts divisible by p^{e'}\n for (long i : range(2)) {\n // make divisible by p^{e'}\n\n newMakeDivisible(zzParts[i], p2ePrime, q, ctxt.getContext(), v[i]);\n }\n\n#ifdef HELIB_DEBUG\n if (dbgKey) {\n checkRecryptBounds_v(v, dbgKey->getRecryptKey(), ctxt.getContext(), q);\n checkCriticalValue(zzParts,\n dbgKey->getRecryptKey(),\n ctxt.getContext().getRcData(),\n q);\n }\n#endif\n\n for (long i : range(zzParts.size())) {\n zzParts[i] /= p2ePrime; // divide by p^{e'}\n }\n\n // NOTE: here we lose the intFactor associated with ctxt.\n // We will restore it below.\n ctxt = recryptEkey;\n\n ctxt.multByConstant(zzParts[1]);\n ctxt.addConstant(zzParts[0]);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after bootKeySwitch\");\n#endif\n\n HELIB_NTIMER_STOP(AAA_bootKeySwitch);\n\n // Move the powerful-basis coefficients to the plaintext slots\n HELIB_NTIMER_START(AAA_coeffToSlot);\n trcData.coeffToSlot->apply(ctxt);\n HELIB_NTIMER_STOP(AAA_coeffToSlot);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after coeffToSlot\");\n#endif\n\n // Extract the digits e-e'+r-1,...,e-e' (from fully packed slots)\n HELIB_NTIMER_START(AAA_extractDigitsThin);\n extractDigitsThin(ctxt, e - ePrime, r, ePrime);\n HELIB_NTIMER_STOP(AAA_extractDigitsThin);\n\n#ifdef HELIB_DEBUG\n CheckCtxt(ctxt, \"after extractDigitsThin\");\n#endif\n\n // restore intFactor\n if (intFactor != 1)\n ctxt.intFactor = NTL::MulMod(ctxt.intFactor, intFactor, ptxtSpace);\n}\n\n#ifdef HELIB_DEBUG\n\nstatic void checkCriticalValue(const std::vector& zzParts,\n const DoubleCRT& sKey,\n const RecryptData& rcData,\n long q)\n{\n NTL::ZZX ptxt;\n rawDecrypt(ptxt, zzParts, sKey); // no mod q\n\n NTL::Vec powerful;\n rcData.p2dConv->ZZXtoPowerful(powerful, ptxt);\n NTL::xdouble max_pwrfl = NTL::conv(largestCoeff(powerful));\n double critical_value = NTL::conv((max_pwrfl / q) / q);\n\n vecRed(powerful, powerful, q, false);\n max_pwrfl = NTL::conv(largestCoeff(powerful));\n critical_value += NTL::conv(max_pwrfl / q);\n\n HELIB_STATS_UPDATE(\"critical-value\", critical_value);\n\n std::cerr << \"=== critical_value=\" << critical_value;\n if (critical_value > 0.5)\n std::cerr << \" BAD-BOUND\";\n\n std::cerr << \"\\n\";\n}\n\nstatic void checkRecryptBounds(const std::vector& zzParts,\n const DoubleCRT& sKey,\n const Context& context,\n long q)\n{\n const RecryptData& rcData = context.getRcData();\n double coeff_bound = context.boundForRecryption();\n long p2r = context.getAlMod().getPPowR();\n\n NTL::ZZX ptxt;\n rawDecrypt(ptxt, zzParts, sKey); // no mod q\n\n NTL::Vec powerful;\n rcData.p2dConv->ZZXtoPowerful(powerful, ptxt);\n double max_pwrfl = NTL::conv(largestCoeff(powerful));\n double ratio = max_pwrfl / (2 * q * coeff_bound);\n\n HELIB_STATS_UPDATE(\"|x|/bound\", ratio);\n\n std::cerr << \"=== |x|/bound=\" << ratio;\n if (ratio > 1.0)\n std::cerr << \" BAD-BOUND\";\n\n vecRed(powerful, powerful, q, false);\n max_pwrfl = NTL::conv(largestCoeff(powerful));\n ratio = max_pwrfl / (2 * p2r * coeff_bound);\n\n HELIB_STATS_UPDATE(\"|x%q|/bound\", ratio);\n\n std::cerr << \", (|x%q|)/bound=\" << ratio;\n if (ratio > 1.0)\n std::cerr << \" BAD-BOUND\";\n\n std::cerr << \"\\n\";\n}\n\nstatic void checkRecryptBounds_v(const std::vector& v,\n const DoubleCRT& sKey,\n const Context& context,\n UNUSED long q)\n{\n const RecryptData& rcData = context.getRcData();\n\n long p = context.getP();\n long e = rcData.e;\n long p2e = NTL::power_long(p, e);\n long ePrime = rcData.ePrime;\n long p2ePrime = NTL::power_long(p, ePrime);\n long phim = context.getPhiM();\n\n double fudge = compute_fudge(p2ePrime, p2e);\n\n double coeff_bound = context.boundForRecryption() * fudge;\n\n double sigma = context.stdDevForRecryption() * fudge;\n\n NTL::ZZX ptxt;\n rawDecrypt(ptxt, v, sKey); // no mod q\n\n NTL::Vec powerful;\n rcData.p2dConv->ZZXtoPowerful(powerful, ptxt);\n double max_pwrfl = NTL::conv(largestCoeff(powerful));\n\n double denom = p2ePrime * coeff_bound;\n double ratio = max_pwrfl / denom;\n\n HELIB_STATS_UPDATE(\"|v|/bound\", ratio);\n\n std::cerr << \"=== |v|/bound=\" << ratio;\n if (ratio > 1.0)\n std::cerr << \" BAD-BOUND\";\n std::cerr << \"\\n\";\n\n ptxt -= v[0]; // so now ptxt is just sKey * v[1]\n rcData.p2dConv->ZZXtoPowerful(powerful, ptxt);\n\n assertEq(powerful.length(), phim, \"length should be phim\");\n\n double ran_pwrfl = NTL::conv(powerful[NTL::RandomBnd(phim)]);\n // pick a random coefficient in the poweful basis\n\n double std_devs = fabs(ran_pwrfl) / (p2ePrime * sigma);\n // number of standard deviations away from mean\n\n // update various indicator variables\n HELIB_STATS_UPDATE(\"sigma_0_5\", double(std_devs <= 0.5)); // 0.383\n HELIB_STATS_UPDATE(\"sigma_1_0\", double(std_devs <= 1.0)); // 0.683\n HELIB_STATS_UPDATE(\"sigma_1_5\", double(std_devs <= 1.5)); // 0.866\n HELIB_STATS_UPDATE(\"sigma_2_0\", double(std_devs <= 2.0)); // 0.954\n HELIB_STATS_UPDATE(\"sigma_2_5\", double(std_devs <= 2.5)); // 0.988\n HELIB_STATS_UPDATE(\"sigma_3_0\", double(std_devs <= 3.0)); // 0.997, 1 in 370\n HELIB_STATS_UPDATE(\"sigma_3_5\",\n double(std_devs <= 3.5)); // 0.999535, 1 in 2149\n HELIB_STATS_UPDATE(\"sigma_4_0\",\n double(std_devs <= 4.0)); // 0.999937, 1 in 15787\n\n // compute sample variance, and scale by the variance we expect\n HELIB_STATS_UPDATE(\"sigma_calc\",\n fsquare(ran_pwrfl) / fsquare(p2ePrime * sigma));\n\n // save the scaled value for application of other tests\n HELIB_STATS_SAVE(\"v_values\", ran_pwrfl / (p2ePrime * sigma));\n}\n\n#endif\n\n#if 0\nvoid fhe_stats_print(long iter, const Context& context)\n{\n long phim = context.getPhiM();\n\n std::cerr << \"||||| recryption stats ||||\\n\";\n std::cerr << \"**** averages ****\\n\";\n std::cerr << \"=== critical_value=\" << (fhe_stats_cv_sum/iter) << \"\\n\";\n std::cerr << \"=== |x|/bound=\" << (fhe_stats_x_sum/iter) << \"\\n\";\n std::cerr << \"=== |x%q|/bound=\" << (fhe_stats_xmod_sum/iter) << \"\\n\";\n std::cerr << \"=== |u|/bound=\" << (fhe_stats_u_sum/iter) << \"\\n\";\n std::cerr << \"=== |v|/bound=\" << (fhe_stats_v_sum/iter) << \"\\n\";\n std::cerr << \"**** maxima ****\\n\";\n std::cerr << \"=== critical_value=\" << (fhe_stats_cv_max) << \"\\n\";\n std::cerr << \"=== |x|/bound=\" << (fhe_stats_x_max) << \"\\n\";\n std::cerr << \"=== |x%q|/bound=\" << (fhe_stats_xmod_max) << \"\\n\";\n std::cerr << \"=== |u|/bound=\" << (fhe_stats_u_max) << \"\\n\";\n std::cerr << \"=== |v|/bound=\" << (fhe_stats_v_max) << \"\\n\";\n std::cerr << \"**** theoretical bounds ***\\n\";\n std::cerr << \"=== single-max=\" << (sqrt(2.0*log(phim))/context.scale) << \"\\n\";\n std::cerr << \"=== global-max=\" << (sqrt(2.0*(log(iter)+log(phim)))/context.scale) << \"\\n\";\n\n\n}\n#endif\n\n} // namespace helib\n", "meta": {"hexsha": "0215fba3c8faad2299ce2b2e3cf524d7e1c9394f", "size": 39814, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/recryption.cpp", "max_stars_repo_name": "msh086/HElib", "max_stars_repo_head_hexsha": "7b919ce4ff22a04f1fb394d875172b91ae2c4c11", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-04-29T16:10:50.000Z", "max_stars_repo_stars_event_max_datetime": "2020-04-29T16:11:10.000Z", "max_issues_repo_path": "src/recryption.cpp", "max_issues_repo_name": "maliasadi/HElib", "max_issues_repo_head_hexsha": "7b919ce4ff22a04f1fb394d875172b91ae2c4c11", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/recryption.cpp", "max_forks_repo_name": "maliasadi/HElib", "max_forks_repo_head_hexsha": "7b919ce4ff22a04f1fb394d875172b91ae2c4c11", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.8396591789, "max_line_length": 93, "alphanum_fraction": 0.6046616768, "num_tokens": 11619, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632979641571, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.21501087096214241}} {"text": "// Boost.Geometry (aka GGL, Generic Geometry Library)\n\n// Copyright (c) 2007-2015 Barend Gehrels, Amsterdam, the Netherlands.\n// Copyright (c) 2008-2015 Bruno Lalande, Paris, France.\n// Copyright (c) 2009-2015 Mateusz Loskot, London, UK.\n// Copyright (c) 2014-2015 Adam Wulkiewicz, Lodz, Poland.\n\n// This file was modified by Oracle on 2014, 2015.\n// Modifications copyright (c) 2014-2015 Oracle and/or its affiliates.\n\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle\n// Contributed and/or modified by Menelaos Karavelas, on behalf of Oracle\n\n// Parts of Boost.Geometry are redesigned from Geodan's Geographic Library\n// (geolib/GGL), copyright (c) 1995-2010 Geodan, Amsterdam, the Netherlands.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_GEOMETRY_ALGORITHMS_EQUALS_HPP\n#define BOOST_GEOMETRY_ALGORITHMS_EQUALS_HPP\n\n\n#include \n#include \n\n#include \n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n\n// For trivial checks\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \n\n\nnamespace boost { namespace geometry\n{\n\n#ifndef DOXYGEN_NO_DETAIL\nnamespace detail { namespace equals\n{\n\n\ntemplate\n<\n std::size_t Dimension,\n std::size_t DimensionCount\n>\nstruct box_box\n{\n template \n static inline bool apply(Box1 const& box1, Box2 const& box2)\n {\n if (!geometry::math::equals(get(box1), get(box2))\n || !geometry::math::equals(get(box1), get(box2)))\n {\n return false;\n }\n return box_box::apply(box1, box2);\n }\n};\n\ntemplate \nstruct box_box\n{\n template \n static inline bool apply(Box1 const& , Box2 const& )\n {\n return true;\n }\n};\n\n\nstruct segment_segment\n{\n template \n static inline bool apply(Segment1 const& segment1, Segment2 const& segment2)\n {\n return equals::equals_point_point(\n indexed_point_view(segment1),\n indexed_point_view(segment2) )\n ? equals::equals_point_point(\n indexed_point_view(segment1),\n indexed_point_view(segment2) )\n : ( equals::equals_point_point(\n indexed_point_view(segment1),\n indexed_point_view(segment2) )\n && equals::equals_point_point(\n indexed_point_view(segment1),\n indexed_point_view(segment2) )\n );\n }\n};\n\n\nstruct area_check\n{\n template \n static inline bool apply(Geometry1 const& geometry1, Geometry2 const& geometry2)\n {\n return geometry::math::equals(\n geometry::area(geometry1),\n geometry::area(geometry2));\n }\n};\n\n\nstruct length_check\n{\n template \n static inline bool apply(Geometry1 const& geometry1, Geometry2 const& geometry2)\n {\n return geometry::math::equals(\n geometry::length(geometry1),\n geometry::length(geometry2));\n }\n};\n\n\ntemplate \nstruct equals_by_collection\n{\n template \n static inline bool apply(Geometry1 const& geometry1, Geometry2 const& geometry2)\n {\n if (! TrivialCheck::apply(geometry1, geometry2))\n {\n return false;\n }\n\n typedef typename geometry::select_most_precise\n <\n typename select_coordinate_type\n <\n Geometry1, Geometry2\n >::type,\n double\n >::type calculation_type;\n\n typedef std::vector > v;\n v c1, c2;\n\n geometry::collect_vectors(c1, geometry1);\n geometry::collect_vectors(c2, geometry2);\n\n if (boost::size(c1) != boost::size(c2))\n {\n return false;\n }\n\n std::sort(c1.begin(), c1.end());\n std::sort(c2.begin(), c2.end());\n\n // Just check if these vectors are equal.\n return std::equal(c1.begin(), c1.end(), c2.begin());\n }\n};\n\ntemplate\nstruct equals_by_relate\n : detail::relate::relate_impl\n <\n detail::de9im::static_mask_equals_type,\n Geometry1,\n Geometry2\n >\n{};\n\n}} // namespace detail::equals\n#endif // DOXYGEN_NO_DETAIL\n\n\n#ifndef DOXYGEN_NO_DISPATCH\nnamespace dispatch\n{\n\ntemplate\n<\n typename Geometry1,\n typename Geometry2,\n typename Tag1 = typename tag::type,\n typename Tag2 = typename tag::type,\n std::size_t DimensionCount = dimension::type::value,\n bool Reverse = reverse_dispatch::type::value\n>\nstruct equals: not_implemented\n{};\n\n\n// If reversal is needed, perform it\ntemplate\n<\n typename Geometry1, typename Geometry2,\n typename Tag1, typename Tag2,\n std::size_t DimensionCount\n>\nstruct equals\n : equals\n{\n static inline bool apply(Geometry1 const& g1, Geometry2 const& g2)\n {\n return equals\n <\n Geometry2, Geometry1,\n Tag2, Tag1,\n DimensionCount,\n false\n >::apply(g2, g1);\n }\n};\n\n\ntemplate \nstruct equals\n : geometry::detail::not_\n <\n detail::disjoint::point_point\n >\n{};\n\n\ntemplate \nstruct equals\n : detail::equals::box_box<0, DimensionCount>\n{};\n\n\ntemplate \nstruct equals\n : detail::equals::equals_by_collection\n{};\n\n\ntemplate \nstruct equals\n : detail::equals::equals_by_collection\n{};\n\n\ntemplate \nstruct equals\n : detail::equals::equals_by_collection\n{};\n\n\ntemplate \nstruct equals\n : detail::equals::equals_by_collection\n{};\n\n\ntemplate \nstruct equals\n : detail::equals::equals_by_collection\n{};\n\ntemplate \nstruct equals\n : detail::equals::segment_segment\n{};\n\ntemplate \nstruct equals\n //: detail::equals::equals_by_collection\n : detail::equals::equals_by_relate\n{};\n\ntemplate \nstruct equals\n : detail::equals::equals_by_relate\n{};\n\ntemplate \nstruct equals\n : detail::equals::equals_by_relate\n{};\n\n\ntemplate \nstruct equals\n <\n MultiPolygon1, MultiPolygon2,\n multi_polygon_tag, multi_polygon_tag,\n 2,\n Reverse\n >\n : detail::equals::equals_by_collection\n{};\n\n\ntemplate \nstruct equals\n <\n Polygon, MultiPolygon,\n polygon_tag, multi_polygon_tag,\n 2,\n Reverse\n >\n : detail::equals::equals_by_collection\n{};\n\n\n} // namespace dispatch\n#endif // DOXYGEN_NO_DISPATCH\n\n\nnamespace resolve_variant {\n\ntemplate \nstruct equals\n{\n static inline bool apply(Geometry1 const& geometry1,\n Geometry2 const& geometry2)\n {\n concept::check_concepts_and_equal_dimensions\n <\n Geometry1 const,\n Geometry2 const\n >();\n\n return dispatch::equals\n ::apply(geometry1, geometry2);\n }\n};\n\ntemplate \nstruct equals, Geometry2>\n{\n struct visitor: static_visitor\n {\n Geometry2 const& m_geometry2;\n\n visitor(Geometry2 const& geometry2)\n : m_geometry2(geometry2)\n {}\n\n template \n inline bool operator()(Geometry1 const& geometry1) const\n {\n return equals\n ::apply(geometry1, m_geometry2);\n }\n\n };\n\n static inline bool apply(\n boost::variant const& geometry1,\n Geometry2 const& geometry2\n )\n {\n return boost::apply_visitor(visitor(geometry2), geometry1);\n }\n};\n\ntemplate \nstruct equals >\n{\n struct visitor: static_visitor\n {\n Geometry1 const& m_geometry1;\n\n visitor(Geometry1 const& geometry1)\n : m_geometry1(geometry1)\n {}\n\n template \n inline bool operator()(Geometry2 const& geometry2) const\n {\n return equals\n ::apply(m_geometry1, geometry2);\n }\n\n };\n\n static inline bool apply(\n Geometry1 const& geometry1,\n boost::variant const& geometry2\n )\n {\n return boost::apply_visitor(visitor(geometry1), geometry2);\n }\n};\n\ntemplate <\n BOOST_VARIANT_ENUM_PARAMS(typename T1),\n BOOST_VARIANT_ENUM_PARAMS(typename T2)\n>\nstruct equals<\n boost::variant,\n boost::variant\n>\n{\n struct visitor: static_visitor\n {\n template \n inline bool operator()(Geometry1 const& geometry1,\n Geometry2 const& geometry2) const\n {\n return equals\n ::apply(geometry1, geometry2);\n }\n\n };\n\n static inline bool apply(\n boost::variant const& geometry1,\n boost::variant const& geometry2\n )\n {\n return boost::apply_visitor(visitor(), geometry1, geometry2);\n }\n};\n\n} // namespace resolve_variant\n\n\n/*!\n\\brief \\brief_check{are spatially equal}\n\\details \\details_check12{equals, is spatially equal}. Spatially equal means\n that the same point set is included. A box can therefore be spatially equal\n to a ring or a polygon, or a linestring can be spatially equal to a\n multi-linestring or a segment. This only works theoretically, not all\n combinations are implemented yet.\n\\ingroup equals\n\\tparam Geometry1 \\tparam_geometry\n\\tparam Geometry2 \\tparam_geometry\n\\param geometry1 \\param_geometry\n\\param geometry2 \\param_geometry\n\\return \\return_check2{are spatially equal}\n\n\\qbk{[include reference/algorithms/equals.qbk]}\n\n */\ntemplate \ninline bool equals(Geometry1 const& geometry1, Geometry2 const& geometry2)\n{\n return resolve_variant::equals\n ::apply(geometry1, geometry2);\n}\n\n\n}} // namespace boost::geometry\n\n\n#endif // BOOST_GEOMETRY_ALGORITHMS_EQUALS_HPP\n\n", "meta": {"hexsha": "0f0bdde5848654ea4ba0c0cc411229ba35ed77a0", "size": 13914, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "clm/src/main/clm/jni/boost/armv7a/include/boost/geometry/algorithms/equals.hpp", "max_stars_repo_name": "BruceNUAA/gaze-detection-android-app", "max_stars_repo_head_hexsha": "5daa2c8a0e51eb506fe435a6f8d03758162d0579", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1210.0, "max_stars_repo_stars_event_min_datetime": "2020-08-18T07:57:36.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T15:06:05.000Z", "max_issues_repo_path": "libs/boost/include/boost/geometry/algorithms/equals.hpp", "max_issues_repo_name": "dnevera/ofxiOSBoost", "max_issues_repo_head_hexsha": "aae717bf8c5229f644057a17ccc971abf1c68bc3", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 39.0, "max_issues_repo_issues_event_min_datetime": "2019-07-06T02:51:39.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-18T11:48:33.000Z", "max_forks_repo_path": "libs/boost/include/boost/geometry/algorithms/equals.hpp", "max_forks_repo_name": "dnevera/ofxiOSBoost", "max_forks_repo_head_hexsha": "aae717bf8c5229f644057a17ccc971abf1c68bc3", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 275.0, "max_forks_repo_forks_event_min_datetime": "2020-08-18T08:35:16.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T15:06:07.000Z", "avg_line_length": 29.5414012739, "max_line_length": 107, "alphanum_fraction": 0.6840592209, "num_tokens": 3250, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.21501086510363995}} {"text": "#include \n#include \n#include \n\n#include \"george.h\"\n\n#if PY_MAJOR_VERSION >= 3\n#define PyInt_AsLong PyLong_AsLong\n#endif\n\nusing namespace george;\n\nusing george::HODLRSolver;\n\n// Eigen is column major and numpy is row major. Barf.\ntypedef Map > RowMajorMap;\n\n#define PARSE_ARRAY(o) (PyArrayObject*) PyArray_FROM_OTF(o, NPY_DOUBLE, \\\n NPY_IN_ARRAY)\n\nextern \"C\" {\n\n// Type definition.\ntypedef struct {\n\n PyObject_HEAD\n kernels::Kernel* kernel;\n HODLRSolver* solver;\n\n} _george_object;\n\n// Allocation/deallocation.\nstatic void _george_dealloc (_george_object* self);\nstatic PyObject* _george_new (PyTypeObject* type, PyObject* args, PyObject* kwds);\nstatic int _george_init (_george_object* self, PyObject* args, PyObject* kwds);\n\n// Methods.\nstatic PyObject* _george_compute (_george_object* self, PyObject* args);\nstatic PyObject* _george_lnlikelihood (_george_object* self, PyObject* args);\nstatic PyObject* _george_computed (_george_object* self, PyObject* args);\nstatic PyObject* _george_predict (_george_object* self, PyObject* args);\nstatic PyObject* _george_get_matrix (_george_object* self, PyObject* args);\n\n// Python 3.\n\n#if PY_MAJOR_VERSION >= 3\n\nPyObject *PyInit__george (void);\nstatic int _george_traverse(PyObject* m, visitproc visit, void* arg);\nstatic int _george_clear(PyObject* m);\n\n#else\n\nvoid init_george (void);\n\n#endif\n\n}\n\nstatic void _george_dealloc (_george_object* self)\n{\n if (self->kernel != NULL) delete self->kernel;\n if (self->solver != NULL) delete self->solver;\n Py_TYPE(self)->tp_free((PyObject*)self);\n}\n\nstatic PyObject *_george_new (PyTypeObject* type, PyObject* args, PyObject* kwds)\n{\n _george_object* self = (_george_object*)type->tp_alloc(type, 0);\n self->kernel = NULL;\n self->solver = NULL;\n return (PyObject*)self;\n}\n\nkernels::Kernel* parse_kernel (PyObject* kernel)\n{\n // Check the kernel.\n if (!PyObject_HasAttrString(kernel, \"is_kernel\")) {\n PyErr_SetString(PyExc_TypeError, \"Invalid kernel\");\n return NULL;\n }\n\n // Deal with operators.\n PyObject* is_kernel_obj = PyObject_GetAttrString(kernel, \"is_kernel\");\n if (is_kernel_obj == Py_False) {\n Py_DECREF(is_kernel_obj);\n\n // Get the Python kernel objects and the type of operator.\n PyObject* k1 = PyObject_GetAttrString(kernel, \"k1\"),\n * k2 = PyObject_GetAttrString(kernel, \"k2\"),\n * otype_obj = PyObject_GetAttrString(kernel, \"operator_type\");\n long otype = PyInt_AsLong(otype_obj);\n Py_XDECREF(otype_obj);\n\n // Validate the kernels.\n if (k1 == NULL || k2 == NULL || PyErr_Occurred() != NULL) {\n PyErr_SetString(PyExc_TypeError, \"Invalid operator\");\n Py_XDECREF(k1);\n Py_XDECREF(k2);\n return NULL;\n }\n\n // Parse the combined kernels.\n kernels::Kernel* kernel1 = parse_kernel(k1);\n kernels::Kernel* kernel2 = parse_kernel(k2);\n Py_DECREF(k1);\n Py_DECREF(k2);\n\n // Check that these were successfully parsed.\n if (kernel1 == NULL || kernel2 == NULL) {\n if (kernel1 != NULL) delete kernel1;\n if (kernel2 != NULL) delete kernel2;\n return NULL;\n }\n\n // Combine the kernels.\n if (otype == 0)\n return new kernels::Sum (kernel1, kernel2);\n else if (otype == 1)\n return new kernels::Product (kernel1, kernel2);\n\n // If we get here then the operator type was unknown.\n if (kernel1 != NULL) delete kernel1;\n if (kernel2 != NULL) delete kernel2;\n PyErr_SetString(PyExc_TypeError, \"Unknown operator\");\n return NULL;\n }\n\n // If we get here then the object claims to be a kernel.\n Py_DECREF(is_kernel_obj);\n\n // Get the kernel type.\n PyObject* ktype_obj = PyObject_GetAttrString(kernel, \"kernel_type\");\n long ktype = PyInt_AsLong(ktype_obj);\n Py_XDECREF(ktype_obj);\n if (PyErr_Occurred() != NULL) {\n PyErr_SetString(PyExc_TypeError, \"Invalid kernel type\");\n return NULL;\n }\n\n // Get the number of dimensions.\n PyObject* ndim_obj = PyObject_GetAttrString(kernel, \"ndim\");\n long ndim = PyInt_AsLong(ndim_obj);\n Py_XDECREF(ndim_obj);\n if (PyErr_Occurred() != NULL) {\n PyErr_SetString(PyExc_TypeError,\n \"Couldn't parse number of kernel dimensions\");\n return NULL;\n }\n\n // Get the parameter vector.\n PyObject* pars_obj = PyObject_GetAttrString(kernel, \"pars\");\n PyArrayObject* pars_array = PARSE_ARRAY(pars_obj);\n Py_XDECREF(pars_obj);\n if (pars_array == NULL) {\n PyErr_SetString(PyExc_ValueError, \"Invalid parameter vector\");\n Py_XDECREF(pars_array);\n return NULL;\n }\n\n // Get the number of parameters and a pointer to the parameter data.\n int npars = PyArray_DIM(pars_array, 0);\n double* pars = (double*)PyArray_DATA(pars_array);\n\n // Build the kernel.\n kernels::Kernel* k = NULL;\n\n#define GEORGE_DEFINE_KERNEL(N,NAME) { \\\n if (npars != N) { \\\n PyErr_SetString(PyExc_ValueError, \"The NAME requires N parameters.\"); \\\n Py_DECREF(pars_array); \\\n return NULL; \\\n } \\\n k = new kernels::NAME(ndim, pars); \\\n}\n\n if (ktype == 0) GEORGE_DEFINE_KERNEL (1, ConstantKernel)\n else if (ktype == 1) GEORGE_DEFINE_KERNEL (0, DotProductKernel)\n else if (ktype == 2) GEORGE_DEFINE_KERNEL ((ndim*ndim+ndim)/2, ExpKernel)\n else if (ktype == 3) GEORGE_DEFINE_KERNEL ((ndim*ndim+ndim)/2, RBFKernel)\n else if (ktype == 4) GEORGE_DEFINE_KERNEL (1, CosineKernel)\n else if (ktype == 5) GEORGE_DEFINE_KERNEL (2, ExpSine2Kernel)\n else if (ktype == 6) GEORGE_DEFINE_KERNEL ((ndim*ndim+ndim)/2, Matern32Kernel)\n else if (ktype == 7) GEORGE_DEFINE_KERNEL ((ndim*ndim+ndim)/2, Matern52Kernel)\n else if (ktype == 8) GEORGE_DEFINE_KERNEL (1, WhiteKernel)\n else if (ktype == 9) GEORGE_DEFINE_KERNEL (1+(ndim*ndim+ndim)/2, RationalQuadraticKernel)\n else PyErr_SetString(PyExc_TypeError, \"Unknown kernel\");\n\n#undef GEORGE_DEFINE_KERNEL\n\n Py_DECREF(pars_array);\n return k;\n}\n\nstatic int _george_init(_george_object* self, PyObject* args, PyObject* kwds)\n{\n int nleaf;\n double tol;\n PyObject* kernel = NULL;\n if (!PyArg_ParseTuple(args, \"Oid\", &kernel, &nleaf, &tol))\n return -1;\n\n // Parse the kernel.\n self->kernel = parse_kernel (kernel);\n if (self->kernel == NULL) return -2;\n\n // Set up the solver.\n self->solver = new HODLRSolver (self->kernel, nleaf, tol);\n\n return 0;\n}\n\nstatic PyObject* _george_compute (_george_object* self, PyObject* args)\n{\n int seed;\n PyObject* x_obj, * yerr_obj;\n\n // Parse the input arguments.\n if (!PyArg_ParseTuple(args, \"OOi\", &x_obj, &yerr_obj, &seed))\n return NULL;\n\n // Decode the numpy arrays.\n PyArrayObject * x_array = PARSE_ARRAY(x_obj),\n * yerr_array = PARSE_ARRAY(yerr_obj);\n if (x_array == NULL || yerr_array == NULL) {\n Py_XDECREF(x_array);\n Py_XDECREF(yerr_array);\n PyErr_SetString(PyExc_ValueError,\n \"Failed to parse input objects as numpy arrays\");\n return NULL;\n }\n\n // Get the dimensions.\n int nsamples = (int)PyArray_DIM(x_array, 0),\n ndim = (int)PyArray_DIM(x_array, 1);\n if ((int)PyArray_NDIM(x_array) != 2 ||\n (int)PyArray_DIM(yerr_array, 0) != nsamples) {\n Py_DECREF(x_array);\n Py_DECREF(yerr_array);\n PyErr_SetString(PyExc_ValueError, \"Dimension mismatch\");\n return NULL;\n }\n\n // Access the data.\n double * x = (double*)PyArray_DATA(x_array),\n * yerr = (double*)PyArray_DATA(yerr_array);\n\n // Map to vectors.\n MatrixXd x_vec = RowMajorMap(x, nsamples, ndim);\n VectorXd yerr_vec = VectorXd::Map(yerr, nsamples);\n\n // Pre-compute the factorization.\n int info = self->solver->compute (x_vec, yerr_vec, seed);\n\n // Clean up.\n Py_DECREF(x_array);\n Py_DECREF(yerr_array);\n\n // Check success.\n if (info != george::SOLVER_OK) {\n PyErr_SetString(PyExc_RuntimeError, \"Failed to compute model\");\n return NULL;\n }\n\n Py_INCREF(Py_None);\n return Py_None;\n}\n\nstatic PyObject* _george_lnlikelihood (_george_object* self, PyObject* args)\n{\n PyObject *y_obj;\n if (!PyArg_ParseTuple(args, \"O\", &y_obj)) return NULL;\n\n if (!self->solver->get_computed()) {\n PyErr_SetString(PyExc_RuntimeError,\n \"You need to compute the model first\");\n return NULL;\n }\n\n PyArrayObject *y_array = PARSE_ARRAY(y_obj);\n if (y_array == NULL) {\n Py_XDECREF(y_array);\n PyErr_SetString(PyExc_ValueError,\n \"Failed to parse input object as a numpy array\");\n return NULL;\n }\n\n // Get the data.\n int nsamples = (int)PyArray_DIM(y_array, 0);\n double* y = (double*)PyArray_DATA(y_array);\n VectorXd y_vec = VectorXd::Map(y, nsamples);\n\n // Compute the likelihood.\n double lnlike = self->solver->log_likelihood(y_vec);\n Py_DECREF(y_array);\n\n // Check success.\n if (self->solver->get_status() != george::SOLVER_OK) {\n PyErr_SetString(PyExc_RuntimeError, \"Failed to compute likelihood\");\n return NULL;\n }\n\n return Py_BuildValue(\"d\", lnlike);\n}\n\nstatic PyObject* _george_predict (_george_object* self, PyObject* args)\n{\n PyObject* y_obj, * x_obj;\n if (!PyArg_ParseTuple(args, \"OO\", &y_obj, &x_obj)) return NULL;\n\n if (!self->solver->get_computed()) {\n PyErr_SetString(PyExc_RuntimeError,\n \"You need to compute the model first\");\n return NULL;\n }\n\n PyArrayObject *y_array = PARSE_ARRAY(y_obj),\n *x_array = PARSE_ARRAY(x_obj);\n if (y_array == NULL || x_array == NULL) {\n Py_XDECREF(y_array);\n Py_XDECREF(x_array);\n PyErr_SetString(PyExc_ValueError,\n \"Failed to parse input objects as numpy arrays\");\n return NULL;\n }\n\n // Get the dimensions.\n int nsamples = (int)PyArray_DIM(y_array, 0),\n ntest = (int)PyArray_DIM(x_array, 0),\n ndim = (int)PyArray_DIM(x_array, 1);\n if ((int)PyArray_NDIM(x_array) != 2 ||\n self->solver->get_dimension() != nsamples) {\n Py_DECREF(x_array);\n Py_DECREF(y_array);\n PyErr_SetString(PyExc_ValueError, \"Dimension mismatch\");\n return NULL;\n }\n\n // Access the data.\n double* y = (double*) PyArray_DATA(y_array),\n * x = (double*) PyArray_DATA(x_array);\n\n // Compute the mean.\n VectorXd y_vec = VectorXd::Map(y, nsamples),\n mu_vec;\n MatrixXd x_vec = RowMajorMap(x, ntest, ndim);\n MatrixXd cov_mat;\n self->solver->predict(y_vec, x_vec, mu_vec, cov_mat);\n\n // Clean up.\n Py_DECREF(y_array);\n Py_DECREF(x_array);\n\n // Check success.\n if (self->solver->get_status() != george::SOLVER_OK) {\n PyErr_SetString(PyExc_RuntimeError, \"Failed to compute model\");\n return NULL;\n }\n\n // Allocate the output arrays.\n npy_intp dim[] = {ntest}, dim2[] = {ntest, ntest};\n PyArrayObject* mu_array = (PyArrayObject*)PyArray_SimpleNew(1, dim, NPY_DOUBLE),\n * cov_array = (PyArrayObject*)PyArray_SimpleNew(2, dim2, NPY_DOUBLE);\n if (mu_array == NULL || cov_array == NULL) {\n Py_XDECREF(mu_array);\n Py_XDECREF(cov_array);\n return NULL;\n }\n\n // Copy over the result.\n double *mu = (double*)PyArray_DATA(mu_array),\n *cov = (double*)PyArray_DATA(cov_array);\n for (int i = 0; i < ntest; ++i) {\n mu[i] = mu_vec[i];\n for (int j = 0; j < ntest; ++j) cov[i*ntest+j] = cov_mat(i, j);\n }\n\n // Build the result.\n PyObject *ret = Py_BuildValue(\"OO\", mu_array, cov_array);\n Py_DECREF(mu_array);\n Py_DECREF(cov_array);\n\n if (ret == NULL) {\n PyErr_SetString(PyExc_RuntimeError, \"Couldn't build output tuple\");\n Py_XDECREF(ret);\n return NULL;\n }\n\n return ret;\n}\n\nstatic PyObject* _george_get_matrix (_george_object* self, PyObject* args)\n{\n PyObject* t_obj;\n if (!PyArg_ParseTuple(args, \"O\", &t_obj)) return NULL;\n\n PyArrayObject* t_array = PARSE_ARRAY(t_obj);\n if (t_array == NULL) {\n Py_XDECREF(t_array);\n PyErr_SetString(PyExc_ValueError,\n \"Failed to parse input object as numpy array\");\n return NULL;\n }\n\n // Check the shape of the input array.\n if ((int)PyArray_NDIM(t_array) != 2) {\n Py_DECREF(t_array);\n PyErr_SetString(PyExc_ValueError, \"Dimension mismatch\");\n return NULL;\n }\n\n // Get the dimensions of the problem.\n int n = (int)PyArray_DIM(t_array, 0),\n ndim = (int)PyArray_DIM(t_array, 1);\n\n // Access the data.\n double* t = (double*) PyArray_DATA(t_array);\n MatrixXd tm = RowMajorMap(t, n, ndim);\n\n // Allocate the output arrays.\n npy_intp dim[] = {n, n};\n PyArrayObject* out_array = (PyArrayObject*)PyArray_SimpleNew(2, dim, NPY_DOUBLE);\n if (out_array == NULL) {\n Py_DECREF(t_array);\n Py_XDECREF(out_array);\n return NULL;\n }\n\n // Copy over the result.\n double* matrix = (double*)PyArray_DATA(out_array), value;\n kernels::Kernel* kernel = self->kernel;\n for (int i = 0; i < n; ++i) {\n matrix[i*n+i] = kernel->evaluate(tm.row(i), tm.row(i));\n for (int j = 0; j < i; ++j) {\n value = kernel->evaluate(tm.row(i), tm.row(j));\n matrix[i*n+j] = value;\n matrix[j*n+i] = value;\n }\n }\n\n Py_DECREF(t_array);\n return (PyObject*)out_array;\n}\n\nstatic PyObject* _george_computed(_george_object* self, PyObject* args)\n{\n if (self->solver->get_computed()) Py_RETURN_TRUE;\n Py_RETURN_FALSE;\n}\n\nstatic PyMethodDef _george_methods[] = {\n {\"compute\",\n (PyCFunction)_george_compute,\n METH_VARARGS,\n \"Fit the GP.\"},\n {\"lnlikelihood\",\n (PyCFunction)_george_lnlikelihood,\n METH_VARARGS,\n \"Get the marginalized ln likelihood of some values.\"\n },\n {\"predict\",\n (PyCFunction)_george_predict,\n METH_VARARGS,\n \"Predict the mean function\"\n },\n {\"get_matrix\",\n (PyCFunction)_george_get_matrix,\n METH_VARARGS,\n \"Get the covariance matrix for a set of times.\"\n },\n {\"computed\",\n (PyCFunction)_george_computed,\n METH_NOARGS,\n \"Has the GP been computed?\"\n },\n {NULL} /* Sentinel */\n};\n\nstatic char _george_doc[] = \"This is the ``_george`` object. \"\n \"There is some black magic.\";\nstatic PyTypeObject _george_type = {\n PyVarObject_HEAD_INIT(NULL, 0)\n \"_george._george\", /*tp_name*/\n sizeof(_george_object), /*tp_basicsize*/\n 0, /*tp_itemsize*/\n (destructor)_george_dealloc, /*tp_dealloc*/\n 0, /*tp_print*/\n 0, /*tp_getattr*/\n 0, /*tp_setattr*/\n 0, /*tp_compare*/\n 0, /*tp_repr*/\n 0, /*tp_as_number*/\n 0, /*tp_as_sequence*/\n 0, /*tp_as_mapping*/\n 0, /*tp_hash */\n 0, /*tp_call*/\n 0, /*tp_str*/\n 0, /*tp_getattro*/\n 0, /*tp_setattro*/\n 0, /*tp_as_buffer*/\n Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE,\n /*tp_flags*/\n _george_doc, /* tp_doc */\n 0, /* tp_traverse */\n 0, /* tp_clear */\n 0, /* tp_richcompare */\n 0, /* tp_weaklistoffset */\n 0, /* tp_iter */\n 0, /* tp_iternext */\n _george_methods, /* tp_methods */\n 0, /* tp_members */\n 0, /* tp_getset */\n 0, /* tp_base */\n 0, /* tp_dict */\n 0, /* tp_descr_get */\n 0, /* tp_descr_set */\n 0, /* tp_dictoffset */\n (initproc)_george_init, /* tp_init */\n 0, /* tp_alloc */\n _george_new, /* tp_new */\n};\n\n\n//\n// Initialize the module.\n//\n\nstatic char module_doc[] = \"GP Module\";\n\n#if PY_MAJOR_VERSION >= 3\n\nstruct george_state {\n PyObject *error;\n};\n#define GETSTATE(m) ((struct george_state*)PyModule_GetState(m))\n\nstatic int _george_traverse(PyObject* m, visitproc visit, void* arg) {\n Py_VISIT(GETSTATE(m)->error);\n return 0;\n}\n\nstatic int _george_clear(PyObject* m) {\n Py_CLEAR(GETSTATE(m)->error);\n return 0;\n}\n\nextern \"C\" {\nstatic struct PyModuleDef moduledef = {\n PyModuleDef_HEAD_INIT,\n \"_george\",\n module_doc,\n sizeof(struct george_state),\n _george_methods,\n NULL,\n _george_traverse,\n _george_clear,\n NULL\n};\n}\n\n#define INITERROR return NULL\n\nPyObject *PyInit__george (void)\n\n#else\n#define INITERROR return\n\nvoid init_george (void)\n\n#endif\n{\n PyObject *m;\n if (PyType_Ready(&_george_type) < 0) INITERROR;\n\n#if PY_MAJOR_VERSION >= 3\n m = PyModule_Create(&moduledef);\n#else\n m = Py_InitModule3(\"_george\", _george_methods, module_doc);\n#endif\n\n if (m == NULL) INITERROR;\n\n // Add the _george type.\n Py_INCREF(&_george_type);\n PyModule_AddObject(m, \"_george\", (PyObject *)&_george_type);\n\n // Numpy.\n import_array();\n\n#if PY_MAJOR_VERSION >= 3\n return m;\n#endif\n}\n", "meta": {"hexsha": "e80afbcc651c43bd8047a35576f19c86255ccfb6", "size": 17787, "ext": "cc", "lang": "C++", "max_stars_repo_path": "george/_george.cc", "max_stars_repo_name": "kastnerkyle/george", "max_stars_repo_head_hexsha": "8c33a837e8922be142bf7adbe80726dc611c9b25", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-05-24T02:30:22.000Z", "max_stars_repo_stars_event_max_datetime": "2019-05-24T02:30:22.000Z", "max_issues_repo_path": "george/_george.cc", "max_issues_repo_name": "kastnerkyle/george", 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YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.3960681662740416, "lm_q1q2_score": 0.21501086510363993}} {"text": "#pragma once\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"argcheck.hpp\"\n\nnamespace irspack {\nnamespace sparse_util {\n\ntemplate \nusing CSRMatrix = Eigen::SparseMatrix;\n\ntemplate \nusing RowMajorMatrix =\n Eigen::Matrix;\n\ntemplate \nusing ColMajorMatrix =\n Eigen::Matrix;\n\ntemplate \nusing DenseVector = Eigen::Matrix;\n\ntemplate \nusing DenseColVector = Eigen::Matrix;\n\ntemplate \nusing CSCMatrix = Eigen::SparseMatrix;\n\ntemplate \ninline RowMajorMatrix\nparallel_sparse_product(const CSRMatrix &left,\n const CSCMatrix &right, const size_t n_threads) {\n RowMajorMatrix result(left.rows(), right.cols());\n result.array() = 0;\n check_arg(n_threads > 0, \"n_thraed must be > 0\");\n const int64_t n_row = left.rows();\n std::atomic cursor(0);\n std::vector workers;\n for (int i = 0; i < static_cast(n_threads); i++) {\n workers.emplace_back([&left, &right, &cursor, n_row, &result]() {\n const int64_t chunk_size = 16;\n while (true) {\n auto current_position = cursor.fetch_add(chunk_size);\n if (current_position >= n_row) {\n break;\n }\n auto block_size =\n std::min(current_position + chunk_size, n_row) - current_position;\n result.middleRows(current_position, block_size) +=\n left.middleRows(current_position, block_size) * right;\n }\n });\n }\n for (auto &worker : workers) {\n worker.join();\n }\n return result;\n}\n\ntemplate \nstruct SplitFunction {\n template \n static std::pair, CSRMatrix>\n split_imple(const CSRMatrix &X, std::int64_t random_seed,\n Args... args) {\n Derived::check_args(args...);\n using Triplet = Eigen::Triplet;\n std::mt19937 random_state(random_seed);\n std::vector col_buffer;\n std::vector data_buffer;\n std::vector index_;\n std::vector train_data, test_data;\n for (int row = 0; row < X.outerSize(); ++row) {\n col_buffer.clear(); // does not change capacity\n data_buffer.clear();\n index_.clear();\n Integer cnt = 0;\n for (typename CSRMatrix::InnerIterator it(X, row); it; ++it) {\n index_.push_back(cnt);\n col_buffer.push_back(it.col());\n data_buffer.push_back(it.value());\n cnt += 1;\n }\n std::shuffle(index_.begin(), index_.end(), random_state);\n size_t n_test = Derived::get_n_test(cnt, args...);\n for (size_t i = 0; i < n_test; i++) {\n test_data.emplace_back(row, col_buffer[index_[i]],\n data_buffer[index_[i]]);\n }\n for (size_t i = n_test; i < col_buffer.size(); i++) {\n train_data.emplace_back(row, col_buffer[index_[i]],\n data_buffer[index_[i]]);\n }\n }\n CSRMatrix X_train(X.rows(), X.cols()), X_test(X.rows(), X.cols());\n auto dupfunction = [](const Integer &a, const Integer &b) { return a + b; };\n X_train.setFromTriplets(train_data.begin(), train_data.end(), dupfunction);\n X_test.setFromTriplets(test_data.begin(), test_data.end(), dupfunction);\n X_train.makeCompressed();\n X_test.makeCompressed();\n return {X_train, X_test};\n }\n};\n\ntemplate \nstruct SplitByRatioFunction\n : SplitFunction, Integer> {\n using Base =\n SplitFunction, Integer>;\n static void check_args(double test_ratio, bool n_test_ceil) {\n check_arg(((test_ratio <= 1.0 && (test_ratio >= 0.0))),\n \"test_ratio must be within [0.0, 1.0]\");\n }\n\n static size_t get_n_test(size_t nnz_row, double test_ratio,\n bool n_test_ceil) {\n if (n_test_ceil) {\n return std::ceil(nnz_row * test_ratio);\n } else {\n return std::floor(nnz_row * test_ratio);\n }\n };\n\n static std::pair, CSRMatrix>\n split(const CSRMatrix &X, std::int64_t random_seed, Real heldout_ratio,\n bool n_test_ceil) {\n return Base::split_imple(X, random_seed, heldout_ratio, n_test_ceil);\n }\n};\n\ntemplate \nstruct SplitFixedN : SplitFunction, Integer> {\n using Base = SplitFunction, Integer>;\n static void check_args(size_t n_held_out) {}\n\n static size_t get_n_test(size_t nnz_row, size_t n_held_out) {\n return std::min(nnz_row, n_held_out);\n };\n\n static std::pair, CSRMatrix>\n split(const CSRMatrix &X, std::int64_t random_seed, size_t n_held_out) {\n return Base::split_imple(X, random_seed, n_held_out);\n }\n};\n\ntemplate \nCSRMatrix okapi_BM_25_weight(const CSRMatrix &X, Real k1, Real b) {\n CSRMatrix result(X);\n using itertype = typename CSRMatrix::InnerIterator;\n const int N = X.rows();\n result.makeCompressed();\n DenseVector idf(X.cols());\n DenseVector doc_length(N);\n idf.array() = 0;\n doc_length.array() = 0;\n\n for (int i = 0; i < N; i++) {\n for (itertype iter(X, i); iter; ++iter) {\n idf(iter.col()) += 1;\n doc_length(i) += iter.value();\n }\n }\n Real avgdl = doc_length.sum() / N;\n idf.array() =\n (N / (idf.array() + static_cast(1)) + static_cast(1)).log();\n for (int i = 0; i < N; i++) {\n Real regularizer = k1 * (1 - b + b * doc_length(i) / avgdl);\n for (itertype iter(result, i); iter; ++iter) {\n iter.valueRef() = idf(iter.col()) * (iter.valueRef() * (k1 + 1)) /\n (iter.valueRef() + regularizer);\n }\n }\n return result;\n}\n\ntemplate \nCSRMatrix tf_idf_weight(const CSRMatrix &X, bool smooth) {\n CSRMatrix result(X);\n using itertype = typename CSRMatrix::InnerIterator;\n const int N = X.rows();\n result.makeCompressed();\n DenseVector idf(X.cols());\n idf.array() = 0;\n\n for (int i = 0; i < N; i++) {\n for (itertype iter(X, i); iter; ++iter) {\n idf(iter.col()) += 1;\n }\n }\n idf.array() = (N / (idf.array() + static_cast(smooth))).log();\n for (int i = 0; i < N; i++) {\n for (itertype iter(result, i); iter; ++iter) {\n iter.valueRef() *= idf(iter.col());\n }\n }\n return result;\n}\n\ntemplate \nCSRMatrix remove_diagonal(const CSRMatrix &X) {\n check_arg(X.rows() == X.cols(), \"X must be square\");\n CSRMatrix result(X);\n using itertype = typename CSRMatrix::InnerIterator;\n const int N = X.rows();\n result.makeCompressed();\n for (int i = 0; i < N; i++) {\n for (itertype iter(result, i); iter; ++iter) {\n if (i == iter.col()) {\n iter.valueRef() = static_cast(0);\n }\n }\n }\n return result;\n}\n\ntemplate ::size>\ninline CSCMatrix SLIM(const CSRMatrix &X, size_t n_threads,\n size_t n_iter, Real l2_coeff, Real l1_coeff,\n Real tol, int64_t top_k) {\n check_arg(n_threads > 0, \"n_threads must be > 0.\");\n check_arg(n_iter > 0, \"n_iter must be > 0.\");\n check_arg(l2_coeff >= 0, \"l2_coeff must be > 0.\");\n check_arg(l1_coeff >= 0, \"l1_coeff must be > 0.\");\n using MatrixType =\n Eigen::Matrix;\n using VectorType = Eigen::Matrix;\n\n CSCMatrix X_csc(X);\n X_csc.makeCompressed();\n using TripletType = Eigen::Triplet;\n using CSCIter = typename CSCMatrix::InnerIterator;\n using RealAndIndex = std::pair;\n std::vector>> workers;\n std::atomic cursor(0);\n for (size_t th = 0; th < n_threads; th++) {\n workers.emplace_back(std::async(std::launch::async, [&cursor, &X_csc,\n l2_coeff, l1_coeff,\n n_iter, tol, top_k] {\n const int64_t F = X_csc.cols();\n std::mt19937 gen(0);\n std::vector indices(F);\n for (int64_t i = 0; i < F; i++) {\n indices[i] = i;\n }\n MatrixType remnants(block_size, X_csc.rows());\n MatrixType coeffs(block_size, F);\n VectorType coeff_temp(block_size);\n VectorType diff(block_size);\n VectorType linear(block_size);\n VectorType linear_plus(block_size);\n VectorType linear_minus(block_size);\n\n std::vector argsort_buffer;\n if (top_k >= 0) {\n argsort_buffer.resize(F);\n }\n\n std::vector local_results;\n while (true) {\n int64_t current_cursor = cursor.fetch_add(block_size);\n if (current_cursor >= F) {\n break;\n }\n\n int64_t block_begin = current_cursor;\n int64_t block_end = std::min(block_begin + block_size, F);\n int64_t valid_block_size = block_end - block_begin;\n remnants.setZero();\n coeffs.setZero();\n\n for (int64_t f_cursor = block_begin; f_cursor < block_end; f_cursor++) {\n const int64_t internal_col_position = f_cursor - block_begin;\n for (CSCIter iter(X_csc, f_cursor); iter; ++iter) {\n remnants(internal_col_position, iter.row()) = -iter.value();\n }\n }\n\n for (size_t cd_iteration = 0; cd_iteration < n_iter; cd_iteration++) {\n std::shuffle(indices.begin(), indices.end(), gen);\n Real delta = 0;\n for (int64_t feature_index = 0; feature_index < F; feature_index++) {\n int64_t shuffled_feature_index = indices[feature_index];\n coeff_temp = coeffs.col(shuffled_feature_index);\n diff = coeff_temp;\n linear.setZero();\n Real x2_sum = 0.0;\n for (CSCIter nnz_iter(X_csc, shuffled_feature_index); nnz_iter;\n ++nnz_iter) {\n Real x = nnz_iter.value();\n x2_sum += x * x;\n linear.noalias() += x * remnants.col(nnz_iter.row());\n }\n linear.noalias() -= x2_sum * coeff_temp;\n\n Real quadratic = x2_sum + l2_coeff;\n linear_plus.array() = (-linear.array() - l1_coeff) / quadratic;\n if (!positive_only) {\n linear_minus.array() = (-linear.array() + l1_coeff) / quadratic;\n }\n\n Real *ptr_location =\n coeffs.data() + shuffled_feature_index * block_size;\n Real *lp_ptr = linear_plus.data();\n Real *lm_ptr = linear_minus.data();\n\n for (int64_t inner_cursor_position = 0;\n inner_cursor_position < block_size; inner_cursor_position++) {\n Real lplus = *(lp_ptr++);\n Real lminus = *(lm_ptr++);\n int64_t original_cursor_position =\n inner_cursor_position + block_begin;\n if (original_cursor_position == shuffled_feature_index) {\n *(ptr_location++) = 0.0;\n continue;\n }\n if (positive_only) {\n if (lplus > 0) {\n *(ptr_location++) = lplus;\n } else {\n *(ptr_location++) = static_cast(0.0);\n }\n\n } else {\n if (lplus > 0) {\n *(ptr_location++) = lplus;\n } else {\n if (lminus < 0) {\n *(ptr_location++) = lminus;\n } else {\n *(ptr_location++) = static_cast(0.0);\n }\n }\n } // allow nagative block\n }\n coeff_temp.noalias() =\n coeffs.col(shuffled_feature_index) - coeff_temp;\n\n if (!coeff_temp.isZero()) {\n for (CSCIter nnz_iter(X_csc, shuffled_feature_index); nnz_iter;\n ++nnz_iter) {\n const int64_t row = nnz_iter.row();\n remnants.col(row).noalias() += nnz_iter.valueRef() * coeff_temp;\n }\n delta = std::max(delta, coeff_temp.cwiseAbs().array().maxCoeff());\n }\n }\n if (delta < tol) {\n break;\n }\n }\n\n if (top_k < 0) {\n for (int64_t f = 0; f < F; f++) {\n for (int64_t inner_cursor_position = 0;\n inner_cursor_position < valid_block_size;\n inner_cursor_position++) {\n int64_t original_location = inner_cursor_position + block_begin;\n Real c = coeffs(inner_cursor_position, f);\n if (c != 0.0) {\n local_results.emplace_back(f, original_location, c);\n }\n }\n }\n } else {\n for (int64_t inner_cursor_position = 0;\n inner_cursor_position < valid_block_size;\n inner_cursor_position++) {\n int64_t original_location = inner_cursor_position + block_begin;\n auto iter = argsort_buffer.begin();\n int64_t nnz = 0;\n for (int64_t f = 0; f < F; f++) {\n Real c = coeffs(inner_cursor_position, f);\n if (c != 0.0) {\n iter->first = c;\n iter->second = f;\n iter++;\n nnz++;\n }\n }\n int64_t n_taken_coeffs = nnz;\n if (nnz > top_k) {\n std::sort(argsort_buffer.begin(), argsort_buffer.end(),\n [](RealAndIndex &val1, RealAndIndex &val2) {\n return val1.first > val2.first;\n });\n n_taken_coeffs = top_k;\n }\n for (int64_t i = 0; i < n_taken_coeffs; i++) {\n local_results.emplace_back(argsort_buffer[i].second,\n original_location,\n argsort_buffer[i].first);\n }\n }\n }\n }\n return local_results;\n }));\n }\n std::vector nnzs;\n for (auto &fres : workers) {\n auto result = fres.get();\n for (const auto &w : result) {\n nnzs.emplace_back(w);\n }\n }\n\n CSCMatrix result(X.cols(), X.cols());\n result.setFromTriplets(nnzs.begin(), nnzs.end());\n result.makeCompressed();\n return result;\n}\n\ntemplate \ninline std::vector>>\nretrieve_recommend_from_score(\n const RowMajorMatrix &score,\n const std::vector> &allowed_indices,\n const size_t cutoff, size_t n_threads) {\n using score_and_index = std::pair;\n check_arg(n_threads > 0, \"n_threads must not be 0.\");\n check_arg(\n (score.rows() == static_cast(allowed_indices.size())) ||\n allowed_indices.empty(),\n \"allowed_indices, if not empty, must have a size equal to X.rows()\");\n std::vector> result(score.rows());\n std::vector> workers;\n std::atomic cursor(0);\n const size_t n_users = static_cast(score.rows());\n for (size_t thread = 0; thread < std::min(n_threads, n_users); thread++) {\n workers.emplace_back(\n std::async([&score, cutoff, &allowed_indices, &cursor, &result]() {\n const int64_t n_rows = score.rows();\n const int64_t n_items = score.cols();\n std::vector index_holder;\n index_holder.reserve(n_items);\n\n while (true) {\n int64_t current = cursor.fetch_add(1);\n if (current >= n_rows) {\n break;\n }\n\n std::vector inserted;\n const Real *score_ptr = score.data() + n_items * current;\n\n index_holder.clear();\n if (!allowed_indices.empty()) {\n for (auto item_index : allowed_indices.at(current)) {\n if ((item_index < n_items) && (item_index >= 0)) {\n index_holder.emplace_back(item_index, score_ptr[item_index]);\n }\n }\n } else {\n for (int64_t i = 0; i < n_items; i++) {\n index_holder.emplace_back(i, *(score_ptr++));\n }\n }\n std::partial_sort(\n index_holder.begin(),\n index_holder.begin() + std::min(cutoff, index_holder.size()),\n index_holder.end(), [](score_and_index i1, score_and_index i2) {\n return i1.second > i2.second;\n });\n\n size_t items_recommended = 0;\n for (auto item_index : index_holder) {\n if (items_recommended >= cutoff) {\n break;\n }\n\n if (item_index.second == -std::numeric_limits::infinity()) {\n break;\n }\n result[current].emplace_back(item_index);\n items_recommended++;\n }\n }\n }));\n }\n workers.clear();\n return result;\n}\n\n} // namespace sparse_util\n} // namespace irspack\n", "meta": {"hexsha": "ba9fb8fe4ae1d33d069655b0f848f9843528a180", "size": 17701, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "cpp_source/util.hpp", "max_stars_repo_name": "Random1992/irspack", "max_stars_repo_head_hexsha": "c49b05841318049c72a4b09c3edefdd90bc314d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 30.0, "max_stars_repo_stars_event_min_datetime": "2020-12-23T08:08:42.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T13:48:55.000Z", "max_issues_repo_path": "cpp_source/util.hpp", "max_issues_repo_name": "Random1992/irspack", "max_issues_repo_head_hexsha": "c49b05841318049c72a4b09c3edefdd90bc314d6", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 38.0, "max_issues_repo_issues_event_min_datetime": "2021-01-03T12:29:40.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-11T12:58:05.000Z", "max_forks_repo_path": "cpp_source/util.hpp", "max_forks_repo_name": "Random1992/irspack", "max_forks_repo_head_hexsha": "c49b05841318049c72a4b09c3edefdd90bc314d6", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 8.0, "max_forks_repo_forks_event_min_datetime": "2020-12-24T10:23:00.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-04T12:53:20.000Z", "avg_line_length": 35.4729458918, "max_line_length": 80, "alphanum_fraction": 0.5764646065, "num_tokens": 4325, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5583269943353744, "lm_q2_score": 0.3849121444839335, "lm_q1q2_score": 0.21490684071289795}} {"text": "/**\n * @file environment.hpp\n * @author Licheng Wen (wenlc@zju.edu.cn)\n * @brief Environment class header\n * @date 2020-11-12\n *\n * @copyright Copyright (c) 2020\n *\n */\n\n#pragma once\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"neighbor.hpp\"\n#include \"planresult.hpp\"\n\nnamespace Constants {\n// [m] --- The minimum turning radius of the vehicle\nstatic float r = 3;\nstatic float deltat = 6.75 * 6 / 180.0 * M_PI;\n// [#] --- A movement cost penalty for turning (choosing non straight motion\n// primitives)\nstatic float penaltyTurning = 1.5;\n// [#] --- A movement cost penalty for reversing (choosing motion primitives >\n// 2)\nstatic float penaltyReversing = 2.0;\n// [#] --- A movement cost penalty for change of direction (changing from\n// primitives < 3 to primitives > 2)\nstatic float penaltyCOD = 2.0;\n// map resolution\nstatic float mapResolution = 2.0;\n// change to set calcIndex resolution\nstatic float xyResolution = r * deltat;\nstatic float yawResolution = deltat;\n\n// width of car\nstatic float carWidth = 2.0;\n// distance from rear to vehicle front end\nstatic float LF = 2.0;\n// distance from rear to vehicle back end\nstatic float LB = 1.0;\n// obstacle default radius\nstatic float obsRadius = 1;\n// least time to wait for constraint\nstatic int constraintWaitTime = 2;\n\n// R = 3, 6.75 DEG\nstd::vector dyaw = {0, deltat, -deltat, 0, -deltat, deltat};\nstd::vector dx = {r * deltat, r *sin(deltat), r *sin(deltat),\n -r *deltat, -r *sin(deltat), -r *sin(deltat)};\nstd::vector dy = {0, -r *(1 - cos(deltat)), r *(1 - cos(deltat)),\n 0, -r *(1 - cos(deltat)), r *(1 - cos(deltat))};\n\nstatic inline float normalizeHeadingRad(float t) {\n if (t < 0) {\n t = t - 2.f * M_PI * (int)(t / (2.f * M_PI));\n return 2.f * M_PI + t;\n }\n\n return t - 2.f * M_PI * (int)(t / (2.f * M_PI));\n}\n} // namespace Constants\n\nnamespace libMultiRobotPlanning {\n\nusing libMultiRobotPlanning::Neighbor;\nusing libMultiRobotPlanning::PlanResult;\nusing namespace libMultiRobotPlanning;\ntypedef ompl::base::SE2StateSpace::StateType OmplState;\ntypedef boost::geometry::model::d2::point_xy Point;\ntypedef boost::geometry::model::segment Segment;\n/**\n * @brief Environment class\n *\n * @tparam Location\n * @tparam State\n * @tparam Action\n * @tparam Cost\n * @tparam Conflict\n * @tparam Constraint\n * @tparam Constraints\n */\ntemplate \nclass Environment {\n public:\n Environment(size_t maxx, size_t maxy, std::unordered_set obstacles,\n std::multimap dynamic_obstacles,\n std::vector goals)\n : m_obstacles(std::move(obstacles)),\n m_dynamic_obstacles(std::move(dynamic_obstacles)),\n m_agentIdx(0),\n m_constraints(nullptr),\n m_lastGoalConstraint(-1),\n m_highLevelExpanded(0),\n m_lowLevelExpanded(0) {\n m_dimx = (int)maxx / Constants::mapResolution;\n m_dimy = (int)maxy / Constants::mapResolution;\n // std::cout << \"env build \" << m_dimx << \" \" << m_dimy << \" \"\n // << m_obstacles.size() << std::endl;\n holonomic_cost_maps = std::vector>>(\n goals.size(), std::vector>(\n m_dimx, std::vector(m_dimy, 0)));\n m_goals.clear();\n for (const auto &g : goals) {\n if (g.x < 0 || g.x > maxx || g.y < 0 || g.y > maxy) {\n std::cout << \"\\033[1m\\033[31m Goal out of boundary, Fail to build \"\n \"environment \\033[0m\\n\";\n return;\n }\n m_goals.emplace_back(\n State(g.x, g.y, Constants::normalizeHeadingRad(g.yaw)));\n }\n updateCostmap();\n }\n\n Environment(const Environment &) = delete;\n Environment &operator=(const Environment &) = delete;\n\n /// High Level Environment functions\n bool getFirstConflict(\n const std::vector> &solution,\n Conflict &result) {\n int max_t = 0;\n for (const auto &sol : solution) {\n max_t = std::max(max_t, sol.states.size() - 1);\n }\n for (int t = 0; t < max_t; ++t) {\n // check drive-drive collisions\n for (size_t i = 0; i < solution.size(); ++i) {\n State state1 = getState(i, solution, t);\n for (size_t j = i + 1; j < solution.size(); ++j) {\n State state2 = getState(j, solution, t);\n if (state1.agentCollision(state2)) {\n result.time = t;\n result.agent1 = i;\n result.agent2 = j;\n result.s1 = state1;\n result.s2 = state2;\n return true;\n }\n }\n }\n }\n return false;\n }\n\n void createConstraintsFromConflict(\n const Conflict &conflict, std::map &constraints) {\n Constraints c1;\n c1.constraints.emplace(\n Constraint(conflict.time, conflict.s2, conflict.agent2));\n constraints[conflict.agent1] = c1;\n Constraints c2;\n c2.constraints.emplace(\n Constraint(conflict.time, conflict.s1, conflict.agent1));\n constraints[conflict.agent2] = c2;\n }\n\n void onExpandHighLevelNode(int /*cost*/) {\n m_highLevelExpanded++;\n if (m_highLevelExpanded % 50 == 0)\n std::cout << \"Now expand \" << m_highLevelExpanded\n << \" high level nodes.\\n\";\n }\n\n int highLevelExpanded() { return m_highLevelExpanded; }\n\n /// Low Level Environment functions\n void setLowLevelContext(size_t agentIdx, const Constraints *constraints) {\n assert(constraints); // NOLINT\n m_agentIdx = agentIdx;\n m_constraints = constraints;\n m_lastGoalConstraint = -1;\n for (const auto &c : constraints->constraints) {\n if (m_goals[m_agentIdx].agentCollision(c.s)) {\n m_lastGoalConstraint = std::max(m_lastGoalConstraint, c.time);\n }\n }\n\n // std::cout << \"Setting Lowlevel agent idx:\" << agentIdx\n // << \" Constraints:\" << constraints->constraints.size()\n // << \" lastGoalConstraints:\" << m_lastGoalConstraint <<\n // std::endl;\n }\n\n int admissibleHeuristic(const State &s) {\n // non-holonomic-without-obstacles heuristic: use a Reeds-Shepp\n ompl::base::ReedsSheppStateSpace reedsSheppPath(Constants::r);\n OmplState *rsStart = (OmplState *)reedsSheppPath.allocState();\n OmplState *rsEnd = (OmplState *)reedsSheppPath.allocState();\n rsStart->setXY(s.x, s.y);\n rsStart->setYaw(s.yaw);\n rsEnd->setXY(m_goals[m_agentIdx].x, m_goals[m_agentIdx].y);\n rsEnd->setYaw(m_goals[m_agentIdx].yaw);\n double reedsSheppCost = reedsSheppPath.distance(rsStart, rsEnd);\n // std::cout << \"ReedsShepps cost:\" << reedsSheppCost << std::endl;\n // Euclidean distance\n double euclideanCost = sqrt(pow(m_goals[m_agentIdx].x - s.x, 2) +\n pow(m_goals[m_agentIdx].y - s.y, 2));\n // std::cout << \"Euclidean cost:\" << euclideanCost << std::endl;\n // holonomic-with-obstacles heuristic\n double twoDoffset =\n sqrt(pow((s.x - (int)s.x) -\n (m_goals[m_agentIdx].x - (int)m_goals[m_agentIdx].x),\n 2) +\n pow((s.y - (int)s.y) -\n (m_goals[m_agentIdx].y - (int)m_goals[m_agentIdx].y),\n 2));\n double twoDCost =\n holonomic_cost_maps[m_agentIdx][(int)s.x / Constants::mapResolution]\n [(int)s.y / Constants::mapResolution] -\n twoDoffset;\n // std::cout << \"holonomic cost:\" << twoDCost << std::endl;\n\n return std::max({reedsSheppCost, euclideanCost, twoDCost});\n return 0;\n }\n\n bool isSolution(\n const State &state, double gscore,\n std::unordered_map,\n std::hash> &_camefrom) {\n double goal_distance =\n sqrt(pow(state.x - getGoal().x, 2) + pow(state.y - getGoal().y, 2));\n if (goal_distance > 3 * (Constants::LB + Constants::LF)) return false;\n ompl::base::ReedsSheppStateSpace reedsSheppSpace(Constants::r);\n OmplState *rsStart = (OmplState *)reedsSheppSpace.allocState();\n OmplState *rsEnd = (OmplState *)reedsSheppSpace.allocState();\n rsStart->setXY(state.x, state.y);\n rsStart->setYaw(-state.yaw);\n rsEnd->setXY(getGoal().x, getGoal().y);\n rsEnd->setYaw(-getGoal().yaw);\n ompl::base::ReedsSheppStateSpace::ReedsSheppPath reedsShepppath =\n reedsSheppSpace.reedsShepp(rsStart, rsEnd);\n\n std::vector path;\n std::unordered_map,\n std::hash>\n cameFrom;\n cameFrom.clear();\n path.emplace_back(state);\n for (auto pathidx = 0; pathidx < 5; pathidx++) {\n if (fabs(reedsShepppath.length_[pathidx]) < 1e-6) continue;\n double deltat = 0, dx = 0, act = 0, cost = 0;\n switch (reedsShepppath.type_[pathidx]) {\n case 0: // RS_NOP\n continue;\n break;\n case 1: // RS_LEFT\n deltat = -reedsShepppath.length_[pathidx];\n dx = Constants::r * sin(-deltat);\n // dy = Constants::r * (1 - cos(-deltat));\n act = 2;\n cost = reedsShepppath.length_[pathidx] * Constants::r *\n Constants::penaltyTurning;\n break;\n case 2: // RS_STRAIGHT\n deltat = 0;\n dx = reedsShepppath.length_[pathidx] * Constants::r;\n // dy = 0;\n act = 0;\n cost = dx;\n break;\n case 3: // RS_RIGHT\n deltat = reedsShepppath.length_[pathidx];\n dx = Constants::r * sin(deltat);\n // dy = -Constants::r * (1 - cos(deltat));\n act = 1;\n cost = reedsShepppath.length_[pathidx] * Constants::r *\n Constants::penaltyTurning;\n break;\n default:\n std::cout << \"\\033[1m\\033[31m\"\n << \"Warning: Receive unknown ReedsSheppPath type\"\n << \"\\033[0m\\n\";\n break;\n }\n if (cost < 0) {\n cost = -cost * Constants::penaltyReversing;\n act = act + 3;\n }\n State s = path.back();\n std::vector> next_path;\n if (generatePath(s, act, deltat, dx, next_path)) {\n for (auto iter = next_path.begin(); iter != next_path.end(); iter++) {\n State next_s = iter->first;\n gscore += iter->second;\n if (!(next_s == path.back())) {\n cameFrom.insert(std::make_pair<>(\n next_s,\n std::make_tuple<>(path.back(), act, iter->second, gscore)));\n }\n path.emplace_back(next_s);\n }\n } else {\n return false;\n }\n }\n\n if (path.back().time <= m_lastGoalConstraint) {\n return false;\n }\n\n m_goals[m_agentIdx] = path.back();\n\n _camefrom.insert(cameFrom.begin(), cameFrom.end());\n return true;\n }\n\n void getNeighbors(const State &s, Action action,\n std::vector> &neighbors) {\n neighbors.clear();\n double g = Constants::dx[0];\n for (Action act = 0; act < 6; act++) { // has 6 directions for Reeds-Shepp\n double xSucc, ySucc, yawSucc;\n g = Constants::dx[0];\n xSucc = s.x + Constants::dx[act] * cos(-s.yaw) -\n Constants::dy[act] * sin(-s.yaw);\n ySucc = s.y + Constants::dx[act] * sin(-s.yaw) +\n Constants::dy[act] * cos(-s.yaw);\n yawSucc = Constants::normalizeHeadingRad(s.yaw + Constants::dyaw[act]);\n // if (act != action) { // penalize turning\n // g = g * Constants::penaltyTurning;\n // if (act >= 3) // penalize change of direction\n // g = g * Constants::penaltyCOD;\n // }\n // if (act > 3) { // backwards\n // g = g * Constants::penaltyReversing;\n // }\n if (act % 3 != 0) { // penalize turning\n g = g * Constants::penaltyTurning;\n }\n if ((act < 3 && action >= 3) || (action < 3 && act >= 3)) {\n // penalize change of direction\n g = g * Constants::penaltyCOD;\n }\n if (act >= 3) { // backwards\n g = g * Constants::penaltyReversing;\n }\n State tempState(xSucc, ySucc, yawSucc, s.time + 1);\n if (stateValid(tempState)) {\n neighbors.emplace_back(\n Neighbor(tempState, act, g));\n }\n }\n // wait\n g = Constants::dx[0];\n State tempState(s.x, s.y, s.yaw, s.time + 1);\n if (stateValid(tempState)) {\n neighbors.emplace_back(Neighbor(tempState, 6, g));\n }\n }\n State getGoal() { return m_goals[m_agentIdx]; }\n uint64_t calcIndex(const State &s) {\n return (uint64_t)s.time * (2 * M_PI / Constants::deltat) *\n (m_dimx / Constants::xyResolution) *\n (m_dimy / Constants::xyResolution) +\n (uint64_t)(Constants::normalizeHeadingRad(s.yaw) /\n Constants::yawResolution) *\n (m_dimx / Constants::xyResolution) *\n (m_dimy / Constants::xyResolution) +\n (uint64_t)(s.y / Constants::xyResolution) *\n (m_dimx / Constants::xyResolution) +\n (uint64_t)(s.x / Constants::xyResolution);\n }\n\n void onExpandLowLevelNode(const State & /*s*/, int /*fScore*/,\n int /*gScore*/) {\n m_lowLevelExpanded++;\n }\n\n int lowLevelExpanded() const { return m_lowLevelExpanded; }\n\n bool startAndGoalValid(const std::vector &m_starts, const size_t iter,\n const int batchsize) {\n assert(m_goals.size() == m_starts.size());\n for (size_t i = 0; i < m_goals.size(); i++)\n for (size_t j = i + 1; j < m_goals.size(); j++) {\n if (m_goals[i].agentCollision(m_goals[j])) {\n std::cout << \"ERROR: Goal point of \" << i + iter * batchsize << \" & \"\n << j + iter * batchsize << \" collide!\\n\";\n return false;\n }\n if (m_starts[i].agentCollision(m_starts[j])) {\n std::cout << \"ERROR: Start point of \" << i + iter * batchsize << \" & \"\n << j + iter * batchsize << \" collide!\\n\";\n return false;\n }\n }\n return true;\n }\n\n private:\n State getState(size_t agentIdx,\n const std::vector> &solution,\n size_t t) {\n assert(agentIdx < solution.size());\n if (t < solution[agentIdx].states.size()) {\n return solution[agentIdx].states[t].first;\n }\n assert(!solution[agentIdx].states.empty());\n return solution[agentIdx].states.back().first;\n }\n\n bool stateValid(const State &s) {\n double x_ind = s.x / Constants::mapResolution;\n double y_ind = s.y / Constants::mapResolution;\n if (x_ind < 0 || x_ind >= m_dimx || y_ind < 0 || y_ind >= m_dimy)\n return false;\n\n for (auto it = m_obstacles.begin(); it != m_obstacles.end(); it++) {\n if (s.obsCollision(*it)) return false;\n }\n\n auto it = m_dynamic_obstacles.equal_range(s.time);\n for (auto itr = it.first; itr != it.second; ++itr) {\n if (s.agentCollision(itr->second)) return false;\n }\n auto itlow = m_dynamic_obstacles.lower_bound(-s.time);\n auto itup = m_dynamic_obstacles.upper_bound(-1);\n for (auto it = itlow; it != itup; ++it)\n if (s.agentCollision(it->second)) return false;\n\n for (auto it = m_constraints->constraints.begin();\n it != m_constraints->constraints.end(); it++) {\n if (!it->satisfyConstraint(s)) return false;\n }\n\n return true;\n }\n\n private:\n struct compare_node {\n bool operator()(const std::pair &n1,\n const std::pair &n2) const {\n return (n1.second > n2.second);\n }\n };\n void updateCostmap() {\n boost::heap::fibonacci_heap,\n boost::heap::compare>\n heap;\n\n std::set> temp_obs_set;\n for (auto it = m_obstacles.begin(); it != m_obstacles.end(); it++) {\n temp_obs_set.insert(\n std::make_pair((int)it->x / Constants::mapResolution,\n (int)it->y / Constants::mapResolution));\n }\n\n for (size_t idx = 0; idx < m_goals.size(); idx++) {\n heap.clear();\n int goal_x = (int)m_goals[idx].x / Constants::mapResolution;\n int goal_y = (int)m_goals[idx].y / Constants::mapResolution;\n heap.push(std::make_pair(State(goal_x, goal_y, 0), 0));\n\n while (!heap.empty()) {\n std::pair node = heap.top();\n heap.pop();\n\n int x = node.first.x;\n int y = node.first.y;\n for (int dx = -1; dx <= 1; dx++)\n for (int dy = -1; dy <= 1; dy++) {\n if (dx == 0 && dy == 0) continue;\n int new_x = x + dx;\n int new_y = y + dy;\n if (new_x == goal_x && new_y == goal_y) continue;\n if (new_x >= 0 && new_x < m_dimx && new_y >= 0 && new_y < m_dimy &&\n holonomic_cost_maps[idx][new_x][new_y] == 0 &&\n temp_obs_set.find(std::make_pair(new_x, new_y)) ==\n temp_obs_set.end()) {\n holonomic_cost_maps[idx][new_x][new_y] =\n holonomic_cost_maps[idx][x][y] +\n sqrt(pow(dx * Constants::mapResolution, 2) +\n pow(dy * Constants::mapResolution, 2));\n heap.push(std::make_pair(State(new_x, new_y, 0),\n holonomic_cost_maps[idx][new_x][new_y]));\n }\n }\n }\n }\n\n // for (size_t idx = 0; idx < m_goals.size(); idx++) {\n // std::cout << \"---------Cost Map -------Agent: \" << idx\n // << \"------------\\n\";\n // for (size_t i = 0; i < m_dimx; i++) {\n // for (size_t j = 0; j < m_dimy; j++)\n // std::cout << holonomic_cost_maps[idx][i][j] << \"\\t\";\n // std::cout << std::endl;\n // }\n // }\n }\n\n bool generatePath(State startState, int act, double deltaSteer,\n double deltaLength,\n std::vector> &result) {\n double xSucc, ySucc, yawSucc, dx, dy, dyaw, ratio;\n result.emplace_back(std::make_pair<>(startState, 0));\n if (act == 0 || act == 3) {\n for (size_t i = 0; i < (size_t)(deltaLength / Constants::dx[act]); i++) {\n State s = result.back().first;\n xSucc = s.x + Constants::dx[act] * cos(-s.yaw) -\n Constants::dy[act] * sin(-s.yaw);\n ySucc = s.y + Constants::dx[act] * sin(-s.yaw) +\n Constants::dy[act] * cos(-s.yaw);\n yawSucc = Constants::normalizeHeadingRad(s.yaw + Constants::dyaw[act]);\n State nextState(xSucc, ySucc, yawSucc, result.back().first.time + 1);\n if (!stateValid(nextState)) return false;\n result.emplace_back(std::make_pair<>(nextState, Constants::dx[0]));\n }\n ratio = (deltaLength -\n (int)(deltaLength / Constants::dx[act]) * Constants::dx[act]) /\n Constants::dx[act];\n dyaw = 0;\n dx = ratio * Constants::dx[act];\n dy = 0;\n } else {\n for (size_t i = 0; i < (size_t)(deltaSteer / Constants::dyaw[act]); i++) {\n State s = result.back().first;\n xSucc = s.x + Constants::dx[act] * cos(-s.yaw) -\n Constants::dy[act] * sin(-s.yaw);\n ySucc = s.y + Constants::dx[act] * sin(-s.yaw) +\n Constants::dy[act] * cos(-s.yaw);\n yawSucc = Constants::normalizeHeadingRad(s.yaw + Constants::dyaw[act]);\n State nextState(xSucc, ySucc, yawSucc, result.back().first.time + 1);\n if (!stateValid(nextState)) return false;\n result.emplace_back(std::make_pair<>(\n nextState, Constants::dx[0] * Constants::penaltyTurning));\n }\n ratio = (deltaSteer - (int)(deltaSteer / Constants::dyaw[act]) *\n Constants::dyaw[act]) /\n Constants::dyaw[act];\n dyaw = ratio * Constants::dyaw[act];\n dx = Constants::r * sin(dyaw);\n dy = -Constants::r * (1 - cos(dyaw));\n if (act == 2 || act == 5) {\n dx = -dx;\n dy = -dy;\n }\n }\n State s = result.back().first;\n xSucc = s.x + dx * cos(-s.yaw) - dy * sin(-s.yaw);\n ySucc = s.y + dx * sin(-s.yaw) + dy * cos(-s.yaw);\n yawSucc = Constants::normalizeHeadingRad(s.yaw + dyaw);\n // std::cout << m_agentIdx << \" ratio::\" << ratio << std::endl;\n State nextState(xSucc, ySucc, yawSucc, result.back().first.time + 1);\n if (!stateValid(nextState)) return false;\n result.emplace_back(std::make_pair<>(nextState, ratio * Constants::dx[0]));\n\n // std::cout << \"Have generate \" << result.size() << \" path segments:\\n\\t\";\n // for (auto iter = result.begin(); iter != result.end(); iter++)\n // std::cout << iter->first << \":\" << iter->second << \"->\";\n // std::cout << std::endl;\n\n return true;\n }\n\n private:\n int m_dimx;\n int m_dimy;\n std::vector>> holonomic_cost_maps;\n std::unordered_set m_obstacles;\n std::multimap m_dynamic_obstacles;\n std::vector m_goals;\n // std::vector< std::vector > m_heuristic;\n std::vector m_vel_limit;\n size_t m_agentIdx;\n const Constraints *m_constraints;\n int m_lastGoalConstraint;\n int m_highLevelExpanded;\n int m_lowLevelExpanded;\n};\n\n} // namespace libMultiRobotPlanning", "meta": {"hexsha": "ffa469e0b2b0a42c35e4503852e74ba59dfe0cf4", "size": 21839, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/environment.hpp", "max_stars_repo_name": "LIJUNCHENG001/CL-CBS", "max_stars_repo_head_hexsha": "b353759a3e34962ee57475a031026416fbbc2382", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 101.0, "max_stars_repo_stars_event_min_datetime": "2020-10-29T05:01:37.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-25T09:17:08.000Z", "max_issues_repo_path": "include/environment.hpp", "max_issues_repo_name": "mieximiemie/CL-CBS", "max_issues_repo_head_hexsha": "b353759a3e34962ee57475a031026416fbbc2382", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2021-06-25T17:48:40.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-21T02:57:54.000Z", "max_forks_repo_path": "include/environment.hpp", "max_forks_repo_name": "mieximiemie/CL-CBS", "max_forks_repo_head_hexsha": "b353759a3e34962ee57475a031026416fbbc2382", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 29.0, "max_forks_repo_forks_event_min_datetime": "2020-11-23T05:09:07.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-25T09:17:10.000Z", "avg_line_length": 37.1411564626, "max_line_length": 80, "alphanum_fraction": 0.5767663355, "num_tokens": 6019, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6039318337259584, "lm_q2_score": 0.35577489351363034, "lm_q1q2_score": 0.21486378383334434}} {"text": "// Copyright (c) 2019, The Monero And Italo Project\n// \n// All rights reserved.\n// \n// Redistribution and use in source and binary forms, with or without modification, are\n// permitted provided that the following conditions are met:\n// \n// 1. Redistributions of source code must retain the above copyright notice, this list of\n// conditions and the following disclaimer.\n// \n// 2. Redistributions in binary form must reproduce the above copyright notice, this list\n// of conditions and the following disclaimer in the documentation and/or other\n// materials provided with the distribution.\n// \n// 3. Neither the name of the copyright holder nor the names of its contributors may be\n// used to endorse or promote products derived from this software without specific\n// prior written permission.\n// \n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY\n// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF\n// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL\n// THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,\n// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF\n// THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n#include \n\nvoid div128_64(uint64_t dividend_hi, uint64_t dividend_lo, uint64_t divisor, uint64_t* quotient_hi, uint64_t *quotient_lo, uint64_t *remainder_hi, uint64_t *remainder_lo)\n{\n typedef boost::multiprecision::uint128_t uint128_t;\n\n uint128_t dividend = dividend_hi;\n dividend <<= 64;\n dividend |= dividend_lo;\n\n uint128_t q, r;\n divide_qr(dividend, uint128_t(divisor), q, r);\n\n *quotient_hi = ((q >> 64) & 0xffffffffffffffffull).convert_to();\n *quotient_lo = (q & 0xffffffffffffffffull).convert_to();\n if (remainder_hi)\n *remainder_hi = ((r >> 64) & 0xffffffffffffffffull).convert_to();\n if (remainder_lo)\n *remainder_lo = (r & 0xffffffffffffffffull).convert_to();\n}\n", "meta": {"hexsha": "b84919020dc6fbe08db61b0bf63f41765232edd6", "size": 2361, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "contrib/epee/src/int-util.cpp", "max_stars_repo_name": "blackrangersoftware/brs-italo-core", "max_stars_repo_head_hexsha": "0837150243470bb45b388aea376cc1c12479fd91", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "contrib/epee/src/int-util.cpp", "max_issues_repo_name": "blackrangersoftware/brs-italo-core", "max_issues_repo_head_hexsha": "0837150243470bb45b388aea376cc1c12479fd91", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "contrib/epee/src/int-util.cpp", "max_forks_repo_name": "blackrangersoftware/brs-italo-core", "max_forks_repo_head_hexsha": "0837150243470bb45b388aea376cc1c12479fd91", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 48.1836734694, "max_line_length": 170, "alphanum_fraction": 0.7581533249, "num_tokens": 551, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.42632159254749036, "lm_q1q2_score": 0.21482608111446486}} {"text": "// (C) Copyright Jeremy William Murphy 2016.\n\n// Use, modification and distribution are subject to the\n// Boost Software License, Version 1.0. (See accompanying file\n// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_INTEGER_COMMON_FACTOR_RT_HPP\n#define BOOST_INTEGER_COMMON_FACTOR_RT_HPP\n\n#include \n#include \n\n#include // for BOOST_NESTED_TEMPLATE, etc.\n#include // for std::numeric_limits\n#include // for CHAR_MIN\n#include \n#include \n#include \n#include \n#ifndef BOOST_NO_CXX11_HDR_TYPE_TRAITS\n#include \n#endif\n#ifdef BOOST_NO_CXX11_HDR_FUNCTIONAL\n#include \n#endif\n\n#if ((defined(BOOST_MSVC) && (BOOST_MSVC >= 1600)) || (defined(__clang__) && defined(__c2__)) || (defined(BOOST_INTEL) && defined(_MSC_VER))) && (defined(_M_IX86) || defined(_M_X64))\n#include \n#endif\n\n#ifdef BOOST_MSVC\n#pragma warning(push)\n#pragma warning(disable:4127 4244) // Conditional expression is constant\n#endif\n\n#if !defined(BOOST_NO_CXX11_HDR_TYPE_TRAITS) && !defined(BOOST_NO_CXX11_NOEXCEPT)\n#define BOOST_GCD_NOEXCEPT(T) noexcept(std::is_arithmetic::value)\n#else\n#define BOOST_GCD_NOEXCEPT(T)\n#endif\n\nnamespace boost {\n\n template \n class rational;\n\n namespace integer {\n\n namespace gcd_detail{\n\n //\n // some helper functions which really should be constexpr already, but sadly aren't:\n //\n#ifndef BOOST_NO_CXX14_CONSTEXPR\n template \n inline constexpr T constexpr_min(T const& a, T const& b) BOOST_GCD_NOEXCEPT(T)\n {\n return a < b ? a : b;\n }\n template \n inline constexpr auto constexpr_swap(T&a, T& b) BOOST_GCD_NOEXCEPT(T) -> decltype(a.swap(b))\n {\n return a.swap(b);\n }\n template \n inline constexpr void constexpr_swap(T&a, U& b...) BOOST_GCD_NOEXCEPT(T)\n {\n T t(static_cast(a));\n a = static_cast(b);\n b = static_cast(t);\n }\n#else\n template \n inline T constexpr_min(T const& a, T const& b) BOOST_GCD_NOEXCEPT(T)\n {\n return a < b ? a : b;\n }\n template \n inline void constexpr_swap(T&a, T& b) BOOST_GCD_NOEXCEPT(T)\n {\n using std::swap;\n swap(a, b);\n }\n#endif\n\n template ::value ||\n#endif\n (std::numeric_limits::is_specialized && !std::numeric_limits::is_signed)>\n struct gcd_traits_abs_defaults\n {\n inline static BOOST_CXX14_CONSTEXPR const T& abs(const T& val) BOOST_GCD_NOEXCEPT(T) { return val; }\n };\n template \n struct gcd_traits_abs_defaults\n {\n inline static T BOOST_CXX14_CONSTEXPR abs(const T& val) BOOST_GCD_NOEXCEPT(T)\n {\n // This sucks, but std::abs is not constexpr :(\n return val < T(0) ? -val : val;\n }\n };\n\n enum method_type\n {\n method_euclid = 0,\n method_binary = 1,\n method_mixed = 2\n };\n\n struct any_convert\n {\n template \n any_convert(const T&);\n };\n\n struct unlikely_size\n {\n char buf[9973];\n };\n\n unlikely_size operator <<= (any_convert, any_convert);\n unlikely_size operator >>= (any_convert, any_convert);\n\n template \n struct gcd_traits_defaults : public gcd_traits_abs_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(T& val) BOOST_GCD_NOEXCEPT(T)\n {\n unsigned r = 0;\n while(0 == (val & 1u))\n {\n#ifdef _MSC_VER // VC++ can't handle operator >>= in constexpr code for some reason\n val = val >> 1;\n#else\n val >>= 1;\n#endif\n ++r;\n }\n return r;\n }\n inline static BOOST_CXX14_CONSTEXPR bool less(const T& a, const T& b) BOOST_GCD_NOEXCEPT(T)\n {\n return a < b;\n }\n\n static T& get_value();\n\n#ifndef BOOST_NO_SFINAE\n static const bool has_operator_left_shift_equal = sizeof(get_value() <<= 2) != sizeof(unlikely_size);\n static const bool has_operator_right_shift_equal = sizeof(get_value() >>= 2) != sizeof(unlikely_size);\n#else\n static const bool has_operator_left_shift_equal = true;\n static const bool has_operator_right_shift_equal = true;\n#endif\n static const method_type method = std::numeric_limits::is_specialized && std::numeric_limits::is_integer && has_operator_left_shift_equal && has_operator_right_shift_equal ? method_mixed : method_euclid;\n };\n //\n // Default gcd_traits just inherits from defaults:\n //\n template \n struct gcd_traits : public gcd_traits_defaults {};\n\n //\n // Some platforms have fast bitscan operations, that allow us to implement\n // make_odd much more efficiently, unfortunately we can't use these if we want\n // the functions to be constexpr as the compiler intrinsics aren't constexpr.\n //\n#if defined(BOOST_NO_CXX14_CONSTEXPR) && ((defined(BOOST_MSVC) && (BOOST_MSVC >= 1600)) || (defined(__clang__) && defined(__c2__)) || (defined(BOOST_INTEL) && defined(_MSC_VER))) && (defined(_M_IX86) || defined(_M_X64))\n#pragma intrinsic(_BitScanForward,)\n template <>\n struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static unsigned find_lsb(unsigned long val) BOOST_NOEXCEPT\n {\n unsigned long result;\n _BitScanForward(&result, val);\n return result;\n }\n BOOST_FORCEINLINE static unsigned make_odd(unsigned long& val) BOOST_NOEXCEPT\n {\n unsigned result = find_lsb(val);\n val >>= result;\n return result;\n }\n };\n\n#ifdef _M_X64\n#pragma intrinsic(_BitScanForward64)\n template <>\n struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static unsigned find_lsb(unsigned __int64 mask) BOOST_NOEXCEPT\n {\n unsigned long result;\n _BitScanForward64(&result, mask);\n return result;\n }\n BOOST_FORCEINLINE static unsigned make_odd(unsigned __int64& val) BOOST_NOEXCEPT\n {\n unsigned result = find_lsb(val);\n val >>= result;\n return result;\n }\n };\n#endif\n //\n // Other integer type are trivial adaptations of the above,\n // this works for signed types too, as by the time these functions\n // are called, all values are > 0.\n //\n template <> struct gcd_traits : public gcd_traits_defaults\n { BOOST_FORCEINLINE static unsigned make_odd(long& val)BOOST_NOEXCEPT{ unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n template <> struct gcd_traits : public gcd_traits_defaults\n { BOOST_FORCEINLINE static unsigned make_odd(unsigned int& val)BOOST_NOEXCEPT{ unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n template <> struct gcd_traits : public gcd_traits_defaults\n { BOOST_FORCEINLINE static unsigned make_odd(int& val)BOOST_NOEXCEPT{ unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n template <> struct gcd_traits : public gcd_traits_defaults\n { BOOST_FORCEINLINE static unsigned make_odd(unsigned short& val)BOOST_NOEXCEPT{ unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n template <> struct gcd_traits : public gcd_traits_defaults\n { BOOST_FORCEINLINE static unsigned make_odd(short& val)BOOST_NOEXCEPT{ unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n template <> struct gcd_traits : public gcd_traits_defaults\n { BOOST_FORCEINLINE static unsigned make_odd(unsigned char& val)BOOST_NOEXCEPT{ unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n template <> struct gcd_traits : public gcd_traits_defaults\n { BOOST_FORCEINLINE static signed make_odd(signed char& val)BOOST_NOEXCEPT{ signed result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n template <> struct gcd_traits : public gcd_traits_defaults\n { BOOST_FORCEINLINE static unsigned make_odd(char& val)BOOST_NOEXCEPT{ unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n#ifndef BOOST_NO_INTRINSIC_WCHAR_T\n template <> struct gcd_traits : public gcd_traits_defaults\n { BOOST_FORCEINLINE static unsigned make_odd(wchar_t& val)BOOST_NOEXCEPT{ unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n#endif\n#ifdef _M_X64\n template <> struct gcd_traits<__int64> : public gcd_traits_defaults<__int64>\n { BOOST_FORCEINLINE static unsigned make_odd(__int64& val)BOOST_NOEXCEPT{ unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; } };\n#endif\n\n#elif defined(BOOST_GCC) || defined(__clang__) || (defined(BOOST_INTEL) && defined(__GNUC__))\n\n template <>\n struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned find_lsb(unsigned mask)BOOST_NOEXCEPT\n {\n return __builtin_ctz(mask);\n }\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(unsigned& val)BOOST_NOEXCEPT\n {\n unsigned result = find_lsb(val);\n val >>= result;\n return result;\n }\n };\n template <>\n struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned find_lsb(unsigned long mask)BOOST_NOEXCEPT\n {\n return __builtin_ctzl(mask);\n }\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(unsigned long& val)BOOST_NOEXCEPT\n {\n unsigned result = find_lsb(val);\n val >>= result;\n return result;\n }\n };\n template <>\n struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned find_lsb(boost::ulong_long_type mask)BOOST_NOEXCEPT\n {\n return __builtin_ctzll(mask);\n }\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(boost::ulong_long_type& val)BOOST_NOEXCEPT\n {\n unsigned result = find_lsb(val);\n val >>= result;\n return result;\n }\n };\n //\n // Other integer type are trivial adaptations of the above,\n // this works for signed types too, as by the time these functions\n // are called, all values are > 0.\n //\n template <> struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(boost::long_long_type& val)BOOST_NOEXCEPT { unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; }\n };\n template <> struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(long& val)BOOST_NOEXCEPT { unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; }\n };\n template <> struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(int& val)BOOST_NOEXCEPT { unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; }\n };\n template <> struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(unsigned short& val)BOOST_NOEXCEPT { unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; }\n };\n template <> struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(short& val)BOOST_NOEXCEPT { unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; }\n };\n template <> struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(unsigned char& val)BOOST_NOEXCEPT { unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; }\n };\n template <> struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR signed make_odd(signed char& val)BOOST_NOEXCEPT { signed result = gcd_traits::find_lsb(val); val >>= result; return result; }\n };\n template <> struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(char& val)BOOST_NOEXCEPT { unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; }\n };\n#ifndef BOOST_NO_INTRINSIC_WCHAR_T\n template <> struct gcd_traits : public gcd_traits_defaults\n {\n BOOST_FORCEINLINE static BOOST_CXX14_CONSTEXPR unsigned make_odd(wchar_t& val)BOOST_NOEXCEPT { unsigned result = gcd_traits::find_lsb(val); val >>= result; return result; }\n };\n#endif\n#endif\n //\n // The Mixed Binary Euclid Algorithm\n // Sidi Mohamed Sedjelmaci\n // Electronic Notes in Discrete Mathematics 35 (2009) 169-176\n //\n template \n BOOST_CXX14_CONSTEXPR T mixed_binary_gcd(T u, T v) BOOST_GCD_NOEXCEPT(T)\n {\n if(gcd_traits::less(u, v))\n constexpr_swap(u, v);\n\n unsigned shifts = 0;\n\n if(u == T(0))\n return v;\n if(v == T(0))\n return u;\n\n shifts = constexpr_min(gcd_traits::make_odd(u), gcd_traits::make_odd(v));\n\n while(gcd_traits::less(1, v))\n {\n u %= v;\n v -= u;\n if(u == T(0))\n return v << shifts;\n if(v == T(0))\n return u << shifts;\n gcd_traits::make_odd(u);\n gcd_traits::make_odd(v);\n if(gcd_traits::less(u, v))\n constexpr_swap(u, v);\n }\n return (v == 1 ? v : u) << shifts;\n }\n\n /** Stein gcd (aka 'binary gcd')\n *\n * From Mathematics to Generic Programming, Alexander Stepanov, Daniel Rose\n */\n template \n BOOST_CXX14_CONSTEXPR SteinDomain Stein_gcd(SteinDomain m, SteinDomain n) BOOST_GCD_NOEXCEPT(SteinDomain)\n {\n BOOST_ASSERT(m >= 0);\n BOOST_ASSERT(n >= 0);\n if (m == SteinDomain(0))\n return n;\n if (n == SteinDomain(0))\n return m;\n // m > 0 && n > 0\n int d_m = gcd_traits::make_odd(m);\n int d_n = gcd_traits::make_odd(n);\n // odd(m) && odd(n)\n while (m != n)\n {\n if (n > m)\n constexpr_swap(n, m);\n m -= n;\n gcd_traits::make_odd(m);\n }\n // m == n\n m <<= constexpr_min(d_m, d_n);\n return m;\n }\n\n\n /** Euclidean algorithm\n *\n * From Mathematics to Generic Programming, Alexander Stepanov, Daniel Rose\n *\n */\n template \n inline BOOST_CXX14_CONSTEXPR EuclideanDomain Euclid_gcd(EuclideanDomain a, EuclideanDomain b) BOOST_GCD_NOEXCEPT(EuclideanDomain)\n {\n while (b != EuclideanDomain(0))\n {\n a %= b;\n constexpr_swap(a, b);\n }\n return a;\n }\n\n\n template \n inline BOOST_CXX14_CONSTEXPR BOOST_DEDUCED_TYPENAME enable_if_c::method == method_mixed, T>::type\n optimal_gcd_select(T const &a, T const &b) BOOST_GCD_NOEXCEPT(T)\n {\n return gcd_detail::mixed_binary_gcd(a, b);\n }\n\n template \n inline BOOST_CXX14_CONSTEXPR BOOST_DEDUCED_TYPENAME enable_if_c::method == method_binary, T>::type\n optimal_gcd_select(T const &a, T const &b) BOOST_GCD_NOEXCEPT(T)\n {\n return gcd_detail::Stein_gcd(a, b);\n }\n\n template \n inline BOOST_CXX14_CONSTEXPR BOOST_DEDUCED_TYPENAME enable_if_c::method == method_euclid, T>::type\n optimal_gcd_select(T const &a, T const &b) BOOST_GCD_NOEXCEPT(T)\n {\n return gcd_detail::Euclid_gcd(a, b);\n }\n\n template \n inline BOOST_CXX14_CONSTEXPR T lcm_imp(const T& a, const T& b) BOOST_GCD_NOEXCEPT(T)\n {\n T temp = boost::integer::gcd_detail::optimal_gcd_select(a, b);\n#if BOOST_WORKAROUND(BOOST_GCC_VERSION, < 40500)\n return (temp != T(0)) ? T(a / temp * b) : T(0);\n#else\n return temp != T(0) ? T(a / temp * b) : T(0);\n#endif\n }\n\n} // namespace detail\n\n\ntemplate \ninline BOOST_CXX14_CONSTEXPR Integer gcd(Integer const &a, Integer const &b) BOOST_GCD_NOEXCEPT(Integer)\n{\n if(a == (std::numeric_limits::min)())\n return a == static_cast(0) ? gcd_detail::gcd_traits::abs(b) : boost::integer::gcd(static_cast(a % b), b);\n else if (b == (std::numeric_limits::min)())\n return b == static_cast(0) ? gcd_detail::gcd_traits::abs(a) : boost::integer::gcd(a, static_cast(b % a));\n return gcd_detail::optimal_gcd_select(static_cast(gcd_detail::gcd_traits::abs(a)), static_cast(gcd_detail::gcd_traits::abs(b)));\n}\n\ntemplate \ninline BOOST_CXX14_CONSTEXPR Integer lcm(Integer const &a, Integer const &b) BOOST_GCD_NOEXCEPT(Integer)\n{\n return gcd_detail::lcm_imp(static_cast(gcd_detail::gcd_traits::abs(a)), static_cast(gcd_detail::gcd_traits::abs(b)));\n}\n#ifndef BOOST_NO_CXX11_VARIADIC_TEMPLATES\n//\n// This looks slightly odd, but the variadic forms must have 3 or more arguments, and the variadic argument pack may be empty.\n// This matters not at all for most compilers, but Oracle C++ selects the wrong overload in the 2-arg case unless we do this.\n//\ntemplate \ninline BOOST_CXX14_CONSTEXPR Integer gcd(Integer const &a, Integer const &b, const Integer& c, Args const&... args) BOOST_GCD_NOEXCEPT(Integer)\n{\n Integer t = gcd(b, c, args...);\n return t == 1 ? 1 : gcd(a, t);\n}\n\ntemplate \ninline BOOST_CXX14_CONSTEXPR Integer lcm(Integer const &a, Integer const &b, Integer const& c, Args const&... args) BOOST_GCD_NOEXCEPT(Integer)\n{\n return lcm(a, lcm(b, c, args...));\n}\n#endif\n//\n// Special handling for rationals:\n//\ntemplate \ninline typename boost::enable_if_c::is_specialized, boost::rational >::type gcd(boost::rational const &a, boost::rational const &b)\n{\n return boost::rational(static_cast(gcd(a.numerator(), b.numerator())), static_cast(lcm(a.denominator(), b.denominator())));\n}\n\ntemplate \ninline typename boost::enable_if_c::is_specialized, boost::rational >::type lcm(boost::rational const &a, boost::rational const &b)\n{\n return boost::rational(static_cast(lcm(a.numerator(), b.numerator())), static_cast(gcd(a.denominator(), b.denominator())));\n}\n/**\n * Knuth, The Art of Computer Programming: Volume 2, Third edition, 1998\n * Chapter 4.5.2, Algorithm C: Greatest common divisor of n integers.\n *\n * Knuth counts down from n to zero but we naturally go from first to last.\n * We also return the termination position because it might be useful to know.\n *\n * Partly by quirk, partly by design, this algorithm is defined for n = 1,\n * because the gcd of {x} is x. It is not defined for n = 0.\n *\n * @tparam I Input iterator.\n * @return The gcd of the range and the iterator position at termination.\n */\ntemplate \nstd::pair::value_type, I>\ngcd_range(I first, I last) BOOST_GCD_NOEXCEPT(I)\n{\n BOOST_ASSERT(first != last);\n typedef typename std::iterator_traits::value_type T;\n\n T d = *first++;\n while (d != T(1) && first != last)\n {\n d = gcd(d, *first);\n first++;\n }\n return std::make_pair(d, first);\n}\ntemplate \nstd::pair::value_type, I>\nlcm_range(I first, I last) BOOST_GCD_NOEXCEPT(I)\n{\n BOOST_ASSERT(first != last);\n typedef typename std::iterator_traits::value_type T;\n\n T d = *first++;\n while (d != T(1) && first != last)\n {\n d = lcm(d, *first);\n first++;\n }\n return std::make_pair(d, first);\n}\n\ntemplate < typename IntegerType >\nclass gcd_evaluator\n#ifdef BOOST_NO_CXX11_HDR_FUNCTIONAL\n : public std::binary_function\n#endif\n{\npublic:\n#ifndef BOOST_NO_CXX11_HDR_FUNCTIONAL\n typedef IntegerType first_argument_type;\n typedef IntegerType second_argument_type;\n typedef IntegerType result_type;\n#endif\n IntegerType operator()(IntegerType const &a, IntegerType const &b)const\n {\n return boost::integer::gcd(a, b);\n }\n};\n\ntemplate < typename IntegerType >\nclass lcm_evaluator\n#ifdef BOOST_NO_CXX11_HDR_FUNCTIONAL\n : public std::binary_function\n#endif\n{\npublic:\n#ifndef BOOST_NO_CXX11_HDR_FUNCTIONAL\n typedef IntegerType first_argument_type;\n typedef IntegerType second_argument_type;\n typedef IntegerType result_type;\n#endif\n IntegerType operator()(IntegerType const &a, IntegerType const &b)const\n {\n return boost::integer::lcm(a, b);\n }\n};\n\n} // namespace integer\n} // namespace boost\n\n#ifdef BOOST_MSVC\n#pragma warning(pop)\n#endif\n\n#endif // BOOST_INTEGER_COMMON_FACTOR_RT_HPP\n", "meta": {"hexsha": "f6e02bfff3e76a59a5d6f355a6b6c78c2069e4d8", "size": 22731, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/src/boost_1_65_1/boost/integer/common_factor_rt.hpp", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 11356.0, "max_stars_repo_stars_event_min_datetime": "2017-12-08T19:42:32.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T16:55:25.000Z", "max_issues_repo_path": "deps/src/boost_1_65_1/boost/integer/common_factor_rt.hpp", "max_issues_repo_name": "shreyasvj25/turicreate", "max_issues_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 2402.0, "max_issues_repo_issues_event_min_datetime": "2017-12-08T22:31:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-28T19:25:52.000Z", "max_forks_repo_path": "deps/src/boost_1_65_1/boost/integer/common_factor_rt.hpp", "max_forks_repo_name": "shreyasvj25/turicreate", "max_forks_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 1343.0, "max_forks_repo_forks_event_min_datetime": "2017-12-08T19:47:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-26T11:31:36.000Z", "avg_line_length": 39.2590673575, "max_line_length": 219, "alphanum_fraction": 0.6659187893, "num_tokens": 5469, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.4263215925474903, "lm_q1q2_score": 0.21482608111446483}} {"text": "/* Copyright (c) 2010-2019, Delft University of Technology\n * All rigths reserved\n *\n * This file is part of the Tudat. Redistribution and use in source and\n * binary forms, with or without modification, are permitted exclusively\n * under the terms of the Modified BSD license. You should have received\n * a copy of the license with this file. If not, please or visit:\n * http://tudat.tudelft.nl/LICENSE.\n */\n\n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \"tudat/astro/basic_astro/unitConversions.h\"\n#include \n\n#include \n#include \n\n#include \"tudat/simulation/propagation_setup/propagationPatchedConicFullProblem.h\"\n#include \"tudat/simulation/propagation_setup/propagationLambertTargeterFullProblem.h\"\n\n\n#include \"tudat/astro/ephemerides/approximatePlanetPositions.h\"\n#include \"tudat/astro/trajectory_design/trajectory.h\"\n#include \"tudat/astro/trajectory_design/exportTrajectory.h\"\n#include \"tudat/astro/trajectory_design/planetTrajectory.h\"\n#include \n\nint main( )\n{\n using namespace tudat;\n using namespace tudat::input_output;\n using namespace tudat::input_output::parsed_data_vector_utilities;\n using namespace tudat::transfer_trajectories;\n\n\n /// Characteristics of the interplanetary trajectory\n\n // Define central body of the trajectory and body to be propagated.\n std::vector< std::string > centralBody;\n centralBody.push_back( \"Sun\" );\n std::string bodyToPropagate = \"spacecraft\";\n\n // Specify the number and types of legs and type of legs.\n int numberOfLegs = 5;\n std::vector< TransferLegType > legTypeVector( numberOfLegs );\n legTypeVector[ 0 ] = mga1DsmVelocity_Departure;\n legTypeVector[ 1 ] = mga1DsmVelocity_Swingby;\n legTypeVector[ 2 ] = mga1DsmVelocity_Swingby;\n legTypeVector[ 3 ] = mga1DsmVelocity_Swingby;\n legTypeVector[ 4 ] = capture;\n\n\n // Name of the bodies involved in the trajectory\n std::vector< std::string > transferBodyTrajectory;\n transferBodyTrajectory.push_back(\"Earth\");\n transferBodyTrajectory.push_back(\"Earth\");\n transferBodyTrajectory.push_back(\"Venus\");\n transferBodyTrajectory.push_back(\"Venus\");\n transferBodyTrajectory.push_back(\"Mercury\");\n\n\n // Create variable vector.\n std::vector< double > variableVector;\n\n // Add the time of flight and start epoch.\n variableVector.push_back( 1171.64503236 * physical_constants::JULIAN_DAY);\n variableVector.push_back( 399.999999715 * physical_constants::JULIAN_DAY);\n variableVector.push_back( 178.372255301 * physical_constants::JULIAN_DAY);\n variableVector.push_back( 299.223139512 * physical_constants::JULIAN_DAY);\n variableVector.push_back( 180.510754824 * physical_constants::JULIAN_DAY);\n variableVector.push_back( 1.0 ); // The capture time is irrelevant for the final leg.\n\n // Add the additional variables.\n // 1st leg.\n variableVector.push_back( 0.234594654679 );\n variableVector.push_back( 1408.99421278 );\n variableVector.push_back( 0.37992647165 * 2.0 * 3.14159265358979 );\n variableVector.push_back( std::acos( 2.0 * 0.498004040298 - 1. ) - 3.14159265358979 / 2.0 );\n // 2nd leg.\n variableVector.push_back( 0.0964769387134 );\n variableVector.push_back( 1.35077257078 );\n variableVector.push_back( 1.80629232251 * 6.378e6 );\n variableVector.push_back( 0.0 );\n // 3rd leg.\n variableVector.push_back( 0.829948744508);\n variableVector.push_back( 1.09554368115 );\n variableVector.push_back( 3.04129845698 * 6.052e6 );\n variableVector.push_back( 0.0 );\n // 4th leg.\n variableVector.push_back( 0.317174785637 );\n variableVector.push_back( 1.34317576594 );\n variableVector.push_back( 1.10000000891 * 6.052e6 );\n variableVector.push_back( 0.0 );\n\n\n // Create minimum pericenter radii vector\n std::vector< double > minimumPericenterRadii;\n minimumPericenterRadii.push_back( TUDAT_NAN ); minimumPericenterRadii.push_back( TUDAT_NAN ); minimumPericenterRadii.push_back( TUDAT_NAN );\n minimumPericenterRadii.push_back( TUDAT_NAN ); minimumPericenterRadii.push_back( TUDAT_NAN );\n\n // Create departure and capture variables.\n std::vector< double > semiMajorAxes;\n semiMajorAxes.push_back( std::numeric_limits< double >::infinity( ) ); semiMajorAxes.push_back( std::numeric_limits< double >::infinity( ) );\n std::vector< double > eccentricities;\n eccentricities.push_back( 0.0 ); eccentricities.push_back( 0.0 );\n\n // Define integrator settings.\n double initialTime = 0.0;\n double fixedStepSize = 1000.0;\n std::shared_ptr< numerical_integrators::IntegratorSettings< double > > integratorSettings =\n std::make_shared < numerical_integrators::IntegratorSettings < > > (\n numerical_integrators::rungeKutta4, initialTime, fixedStepSize);\n\n\n\n /// Ideal case\n\n // Create system of bodies.\n std::vector< double > gravitationalParametersTransferBodies;\n gravitationalParametersTransferBodies.push_back( 3.9860119e14 );\n gravitationalParametersTransferBodies.push_back( 3.9860119e14 );\n gravitationalParametersTransferBodies.push_back( 3.24860e14 );\n gravitationalParametersTransferBodies.push_back( 3.24860e14 );\n gravitationalParametersTransferBodies.push_back( 2.2321e13 );\n\n std::vector< ephemerides::EphemerisPointer > ephemerisVectorTransferBodies;\n ephemerisVectorTransferBodies.push_back( std::make_shared< ephemerides::ApproximatePlanetPositions>(\n ephemerides::ApproximatePlanetPositionsBase::BodiesWithEphemerisData::earthMoonBarycenter) ) ;\n ephemerisVectorTransferBodies.push_back( std::make_shared< ephemerides::ApproximatePlanetPositions>(\n ephemerides::ApproximatePlanetPositionsBase::BodiesWithEphemerisData::earthMoonBarycenter) ) ;\n ephemerisVectorTransferBodies.push_back( std::make_shared< ephemerides::ApproximatePlanetPositions>(\n ephemerides::ApproximatePlanetPositionsBase::BodiesWithEphemerisData::venus) );\n ephemerisVectorTransferBodies.push_back( std::make_shared< ephemerides::ApproximatePlanetPositions>(\n ephemerides::ApproximatePlanetPositionsBase::BodiesWithEphemerisData::venus) );\n ephemerisVectorTransferBodies.push_back( std::make_shared< ephemerides::ApproximatePlanetPositions>(\n ephemerides::ApproximatePlanetPositionsBase::BodiesWithEphemerisData::mercury) );\n\n simulation_setup::SystemOfBodies bodies = propagators::setupBodyMapFromUserDefinedEphemeridesForPatchedConicsTrajectory(\n centralBody[0], bodyToPropagate, transferBodyTrajectory,\n ephemerisVectorTransferBodies, gravitationalParametersTransferBodies, \"ECLIPJ2000\" );\n\n\n // Create acceleration map.\n std::vector< basic_astrodynamics::AccelerationMap > accelerationMap = propagators::setupAccelerationMapPatchedConicsTrajectory(\n transferBodyTrajectory.size(), centralBody[0], bodyToPropagate, bodies);\n\n // Calculate the patched conics solution and the propagation results of the associated full dynamics problem for each leg.\n std::map< int, std::map< double, Eigen::Vector6d > > patchedConicsTrajectory;\n std::map< int, std::map< double, Eigen::Vector6d > > fullProblemTrajectory;\n std::map< int, std::map< double, Eigen::VectorXd > > dependentVariablesTrajectory;\n\n propagators::fullPropagationPatchedConicsTrajectory(\n bodies, accelerationMap, transferBodyTrajectory, centralBody[0], bodyToPropagate,\n legTypeVector, variableVector, minimumPericenterRadii, semiMajorAxes, eccentricities, integratorSettings,\n patchedConicsTrajectory, fullProblemTrajectory, dependentVariablesTrajectory, false );\n\n\n for( auto itr : patchedConicsTrajectory )\n {\n std::cout << \"Leg \" << itr.first << \"\\n\\n\";\n std::cout << \"Departure: \" << fullProblemTrajectory[ itr.first ].begin( )->second -\n patchedConicsTrajectory[ itr.first ].begin( )->second<< \"\\n\\n\";\n std::cout << \"Arrival: \" << fullProblemTrajectory[ itr.first ].rbegin( )->second -\n patchedConicsTrajectory[ itr.first ].rbegin( )->second << \"\\n\\n\";\n }\n\n\n\n /// Perturbed case\n\n // Define accelerations acting on the spacecraft.\n std::map< std::string, std::vector< std::shared_ptr< simulation_setup::AccelerationSettings > > > bodyToPropagateAccelerations;\n bodyToPropagateAccelerations[ \"Sun\" ].push_back(std::make_shared< simulation_setup::AccelerationSettings >(\n basic_astrodynamics::central_gravity ) );\n bodyToPropagateAccelerations[ \"Earth\" ].push_back(std::make_shared< simulation_setup::AccelerationSettings >(\n basic_astrodynamics::central_gravity ) );\n bodyToPropagateAccelerations[ \"Venus\" ].push_back(std::make_shared< simulation_setup::AccelerationSettings >(\n basic_astrodynamics::central_gravity ) );\n bodyToPropagateAccelerations[ \"Mercury\" ].push_back(std::make_shared< simulation_setup::AccelerationSettings >(\n basic_astrodynamics::central_gravity ) );\n simulation_setup::SelectedAccelerationMap accelerationMapPerturbedCase;\n accelerationMapPerturbedCase[ \"spacecraft\" ] = bodyToPropagateAccelerations;\n\n\n // Create the acceleration map.\n basic_astrodynamics::AccelerationMap accelerationModelMapPerturbedCase = simulation_setup::createAccelerationModelsMap(\n bodies, accelerationMapPerturbedCase, {bodyToPropagate}, {centralBody} );\n std::vector< basic_astrodynamics::AccelerationMap > accelerationMapVectorPerturbedCase;\n for (int i = 0 ; i < numberOfLegs ; i++){\n accelerationMapVectorPerturbedCase.push_back( accelerationModelMapPerturbedCase );\n }\n\n\n // Calculate the patched conics trajectory and propagate the full dynamics problem jointly.\n std::map< int, std::map< double, Eigen::Vector6d > > patchedConicsTrajectoryPerturbedCase;\n std::map< int, std::map< double, Eigen::Vector6d > > fullProblemTrajectoryPerturbedCase;\n std::map< int, std::map< double, Eigen::VectorXd > > dependentVariablesPerturbedCase;\n\n propagators::fullPropagationPatchedConicsTrajectory( bodies, accelerationMapVectorPerturbedCase,\n transferBodyTrajectory, centralBody[0], bodyToPropagate, legTypeVector, variableVector, minimumPericenterRadii,\n semiMajorAxes, eccentricities, integratorSettings, patchedConicsTrajectoryPerturbedCase,\n fullProblemTrajectoryPerturbedCase, dependentVariablesPerturbedCase, true );\n\n for( auto itr : patchedConicsTrajectoryPerturbedCase )\n {\n std::cout << \"Leg \" << itr.first << \"\\n\\n\";\n std::cout << \"Departure: \" << fullProblemTrajectoryPerturbedCase[ itr.first ].begin( )->second -\n patchedConicsTrajectoryPerturbedCase[ itr.first ].begin( )->second<< \"\\n\\n\";\n std::cout << \"Arrival: \" << fullProblemTrajectoryPerturbedCase[ itr.first ].rbegin( )->second -\n patchedConicsTrajectoryPerturbedCase[ itr.first ].rbegin( )->second << \"\\n\\n\";\n }\n\n\n\n\n /// Outputs\n\n for( std::map< int, std::map< double, Eigen::Vector6d > >::iterator itr = patchedConicsTrajectory.begin( );\n itr != patchedConicsTrajectory.end( ); itr++ ){\n\n input_output::writeDataMapToTextFile( fullProblemTrajectory[itr->first],\n \"fullProblemInterplanetaryTrajectory_0_leg_\" + std::to_string(itr->first) + \".dat\",\n tudat_applications::getOutputPath( ),\n \"\",\n std::numeric_limits< double >::digits10,\n std::numeric_limits< double >::digits10,\n \",\" );\n\n input_output::writeDataMapToTextFile( fullProblemTrajectoryPerturbedCase[itr->first],\n \"fullProblemInterplanetaryTrajectory_1_leg_\" + std::to_string(itr->first) + \".dat\",\n tudat_applications::getOutputPath( ),\n \"\",\n std::numeric_limits< double >::digits10,\n std::numeric_limits< double >::digits10,\n \",\" );\n\n input_output::writeDataMapToTextFile( patchedConicsTrajectory[itr->first],\n \"patchedConicsInterplanetaryTrajectory_0_leg_\" + std::to_string(itr->first) + \".dat\",\n tudat_applications::getOutputPath( ),\n \"\",\n std::numeric_limits< double >::digits10,\n std::numeric_limits< double >::digits10,\n \",\" );\n\n input_output::writeDataMapToTextFile( patchedConicsTrajectoryPerturbedCase[itr->first],\n \"patchedConicsInterplanetaryTrajectory_1_leg_\" + std::to_string(itr->first) + \".dat\",\n tudat_applications::getOutputPath( ),\n \"\",\n std::numeric_limits< double >::digits10,\n std::numeric_limits< double >::digits10,\n \",\" );\n\n }\n\n\n // Final statement.\n // The exit code EXIT_SUCCESS indicates that the program was successfully executed.\n return EXIT_SUCCESS;\n}\n", "meta": {"hexsha": 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YES\n2. NO", "lm_q1_score": 0.6224593171945417, "lm_q2_score": 0.34510527095787247, "lm_q1q2_score": 0.2148139913206746}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\n\ntemplate \nclass AffinityGraphCompare{\n private:\n const T * mEdgeWeightArray;\n public:\n AffinityGraphCompare(const T * EdgeWeightArray){\n mEdgeWeightArray = EdgeWeightArray;\n }\n bool operator() (const int& ind1, const int& ind2) const {\n return (mEdgeWeightArray[ind1] > mEdgeWeightArray[ind2]);\n }\n};\n\n/*\n * Compute the MALIS loss function and its derivative wrt the affinity graph\n * MAXIMUM spanning tree\n * Author: Srini Turaga (sturaga@mit.edu)\n * All rights reserved\n */\nvoid preCompute(const uint64_t* conn_dims, const int32_t* nhood_data, const uint64_t* nhood_dims,\n uint64_t* pre_ve, uint64_t* pre_prodDims, int32_t* pre_nHood){\n\n uint64_t conn_num_dims = nhood_dims[1]+1; //4\n // nVert stores (x * y * z)\n uint64_t nVert = 1;\n for (int64_t i = 1; i < conn_num_dims; ++i) {\n nVert *= conn_dims[i];\n }\n pre_ve[0] = nVert;\n /* get number of edges */\n uint64_t nEdge = 0;\n // Loop over #edges\n for (int d = 0, i = 0; d < conn_dims[0]; ++d) {\n // Loop over Z\n for (int z = 0; z < conn_dims[1]; ++z) {\n // Loop over Y\n for (int y = 0; y < conn_dims[2]; ++y) {\n // Loop over X\n for (int x = 0; x < conn_dims[3]; ++x, ++i) {\n // Out-of-bounds check:\n if (!((z + nhood_data[d * nhood_dims[1] + 0] < 0)\n ||(z + nhood_data[d * nhood_dims[1] + 0] >= conn_dims[1])\n ||(y + nhood_data[d * nhood_dims[1] + 1] < 0)\n ||(y + nhood_data[d * nhood_dims[1] + 1] >= conn_dims[2])\n ||(x + nhood_data[d * nhood_dims[1] + 2] < 0)\n ||(x + nhood_data[d * nhood_dims[1] + 2] >= conn_dims[3]))) {\n ++nEdge;\n }\n }\n }\n }\n }\n pre_ve[1] = nEdge;\n\n // prodDims stores x, x*y, x*y*z offsets\n pre_prodDims[conn_num_dims - 2] = 1;\n for (int64_t i = 1; i < conn_num_dims - 1; ++i) {\n pre_prodDims[conn_num_dims - 2 - i] = pre_prodDims[conn_num_dims - 1 - i]\n * conn_dims[conn_num_dims - i];\n }\n\n /* convert n-d offset vectors into linear array offset scalars */\n // nHood is a vector of size #edges\n for (int64_t i = 0; i < nhood_dims[0]; ++i) {\n pre_nHood[i] = 0;\n for (int64_t j = 0; j < nhood_dims[1]; ++j) {\n pre_nHood[i] += nhood_data[j + i * nhood_dims[1]] * pre_prodDims[j];\n }\n }\n}\n\nconst std::vector & initPqueue(const int64_t nEdge,\n const uint64_t* conn_dims, const int32_t* nhood_data, const uint64_t* nhood_dims ){\n static std::vector pqueue;\n if(pqueue.empty()){\n // initialization\n pqueue.resize(nEdge);\n int j = 0;\n // Loop over #edges\n for (int d = 0, i = 0; d < conn_dims[0]; ++d) {\n // Loop over Z\n for (int z = 0; z < conn_dims[1]; ++z) {\n // Loop over Y\n for (int y = 0; y < conn_dims[2]; ++y) {\n // Loop over X\n for (int x = 0; x < conn_dims[3]; ++x, ++i) {\n // Out-of-bounds check:\n if (!((z + nhood_data[d * nhood_dims[1] + 0] < 0)\n ||(z + nhood_data[d * nhood_dims[1] + 0] >= conn_dims[1])\n ||(y + nhood_data[d * nhood_dims[1] + 1] < 0)\n ||(y + nhood_data[d * nhood_dims[1] + 1] >= conn_dims[2])\n ||(x + nhood_data[d * nhood_dims[1] + 2] < 0)\n ||(x + nhood_data[d * nhood_dims[1] + 2] >= conn_dims[3]))) {\n pqueue[j++] = i;\n }\n }\n }\n }\n }\n }\n return pqueue;\n}\n\nvoid malis_loss_weights_cpp_both(const uint64_t* seg,\n const uint64_t* conn_dims, const int32_t* nhood_data, const uint64_t* nhood_dims,\n const float* edgeWeight,\n float* nPairPerEdge,\n const uint64_t* pre_ve, const uint64_t* pre_prodDims, const int32_t* pre_nHood, \n const int pos, const float weight_opt){\n\n /* Disjoint sets and sparse overlap vectors */\n const uint64_t nVert = pre_ve[0];\n const uint64_t nEdge = pre_ve[1];\n uint64_t conn_num_dims = nhood_dims[1]+1;\n \n vector > overlap(nVert);\n vector rank(nVert);\n vector parent(nVert);\n boost::disjoint_sets dsets(&rank[0],&parent[0]);\n std::map segSizes;\n uint64_t nLabeledVert = 0;\n uint64_t nPairPos = 0;\n // int mm=0;\n for (int i=0; i(seg[i],1));\n ++nLabeledVert;\n ++segSizes[seg[i]];\n nPairPos += (segSizes[seg[i]] - 1); //add pair with previous seg label\n //if(seg[i]>mm){mm=seg[i];}\n }\n }\n\n // for (int i=0; i<=mm; ++i){std::cout< Pqueue1 = initPqueue(nEdge, conn_dims, nhood_data, nhood_dims);\n static std::vector Pqueue2(nEdge);\n std::copy(Pqueue1.begin(), Pqueue1.end(), Pqueue2.begin());\n \n sort( Pqueue2.begin(), Pqueue2.end(), AffinityGraphCompare( edgeWeight ) );\n\n /* Start MST */\n uint64_t minEdge, e, v1, v2;\n uint64_t set1, set2;\n float nPair = 0;\n map::iterator it1, it2;\n\n //std::ofstream db;\n //if(pos==1){ db.open(\"/n/coxfs01/donglai/malis_trans/db/mst-pt-pos.txt\");}\n float ll=0;\n /* Start Kruskal's */\n for (uint64_t i = 0; i < Pqueue2.size(); ++i ) {\n minEdge = Pqueue2[i];\n // std::cout <<\"do:\"<second * it2->second);\n if ((pos==1 && it1->first == it2->first) || (pos==0 && it1->first != it2->first)) { //pos\n nPairPerEdge[minEdge] += nPairPosNormInv * nPair;\n //if (pos==1){db <first);\n if (it1 == overlap[set1].end()) {\n overlap[set1].insert(pair(it2->first,it2->second));\n } else {\n it1->second += it2->second;\n }\n overlap[set2].erase(it2++);\n }\n } // end link\n } // end while\n // if(pos==1){ db.close();}\n}\n\n// for a given segmentation map (if not all 0), there are two cases:\n// 1. only positive edges: single segmentation label\n// 2. both positive and negative edges\nvoid malis_loss_weights_cpp_pre(const uint64_t* seg,\n const uint64_t* conn_dims, const int32_t* nhood_data, const uint64_t* nhood_dims,\n const float* edgeWeight,\n float* nPairPerEdge,\n const uint64_t* pre_ve, const uint64_t* pre_prodDims, const int32_t* pre_nHood, const int pos){\n\n /* Disjoint sets and sparse overlap vectors */\n const int64_t nVert = pre_ve[0];\n const int64_t nEdge = pre_ve[1];\n uint64_t conn_num_dims = nhood_dims[1]+1;\n \n vector > overlap(nVert);\n vector rank(nVert);\n vector parent(nVert);\n boost::disjoint_sets dsets(&rank[0],&parent[0]);\n std::map segSizes;\n int nLabeledVert = 0;\n int nPairPos = 0;\n for (int i=0; i(seg[i],1));\n ++nLabeledVert;\n ++segSizes[seg[i]];\n nPairPos += (segSizes[seg[i]] - 1); //add pair with previous seg label\n }\n }\n\n float nPairPosNormInv;\n if (pos==1){\n nPairPosNormInv = 1.0/(float)nPairPos;\n }else{\n nPairPosNormInv = 1.0/(nLabeledVert*(nLabeledVert-1)*0.5-nPairPos);\n }\n //std::cout<<\"norm:\"< Pqueue1 = initPqueue(nEdge, conn_dims, nhood_data, nhood_dims);\n static std::vector Pqueue2(nEdge);\n std::copy(Pqueue1.begin(), Pqueue1.end(), Pqueue2.begin());\n \n sort( Pqueue2.begin(), Pqueue2.end(), AffinityGraphCompare( edgeWeight ) );\n\n /* Start MST */\n int minEdge, e, v1, v2;\n uint64_t set1, set2;\n float nPair = 0;\n map::iterator it1, it2;\n\n //std::ofstream db;\n //if(pos==1){ db.open(\"/n/coxfs01/donglai/malis_trans/db/mst-pt-pos.txt\");}\n float ll=0;\n /* Start Kruskal's */\n for (unsigned int i = 0; i < Pqueue2.size(); ++i ) {\n minEdge = Pqueue2[i];\n // std::cout <<\"do:\"<second * it2->second);\n if ((pos==1 && it1->first == it2->first) || (pos==0 && it1->first != it2->first)) { //pos\n nPairPerEdge[minEdge] += nPairPosNormInv * nPair;\n //if (pos==1){db <first);\n if (it1 == overlap[set1].end()) {\n overlap[set1].insert(pair(it2->first,it2->second));\n } else {\n it1->second += it2->second;\n }\n overlap[set2].erase(it2++);\n }\n } // end link\n } // end while\n // if(pos==1){ db.close();}\n}\n\nvoid malis_loss_weights_cpp(const uint64_t* seg,\n const uint64_t* conn_dims, const int32_t* nhood_data, const uint64_t* nhood_dims,\n const float* edgeWeight,\n const int pos,\n float* nPairPerEdge){\n\n uint64_t conn_num_dims = nhood_dims[1]+1;\n /* Cache for speed to access neighbors */\n // nVert stores (x * y * z)\n int64_t nVert = 1;\n for (int64_t i = 1; i < conn_num_dims; ++i) {\n nVert *= conn_dims[i];\n }\n\n // prodDims stores x, x*y, x*y*z offsets\n std::vector prodDims(conn_num_dims - 1);\n prodDims[conn_num_dims - 2] = 1;\n for (int64_t i = 1; i < conn_num_dims - 1; ++i) {\n prodDims[conn_num_dims - 2 - i] = prodDims[conn_num_dims - 1 - i]\n * conn_dims[conn_num_dims - i];\n }\n\n /* convert n-d offset vectors into linear array offset scalars */\n // nHood is a vector of size #edges\n std::vector nHood(nhood_dims[0]);\n for (int64_t i = 0; i < nhood_dims[0]; ++i) {\n nHood[i] = 0;\n for (int64_t j = 0; j < nhood_dims[1]; ++j) {\n nHood[i] += (int32_t) nhood_data[j + i * nhood_dims[1]] * prodDims[j];\n }\n }\n\n /* Disjoint sets and sparse overlap vectors */\n vector > overlap(nVert);\n vector rank(nVert);\n vector parent(nVert);\n boost::disjoint_sets dsets(&rank[0],&parent[0]);\n std::map segSizes;\n int nLabeledVert = 0;\n int nPairPos = 0;\n for (int i=0; i(seg[i],1));\n ++nLabeledVert;\n ++segSizes[seg[i]];\n nPairPos += (segSizes[seg[i]] - 1); //add pair with previous seg label\n }\n }\n\n int nPairTot = (nLabeledVert * (nLabeledVert - 1)) / 2;\n int nPairNeg = nPairTot - nPairPos;\n float nPairNormR;\n if(pos==1){\n nPairNormR = (float)nPairPos;\n }else{\n nPairNormR = (float)nPairNeg;\n }\n\n if (nPairNormR ==0){\n return;\n }else{\n nPairNormR = 1.0/nPairNormR;\n }\n /* get number of edges */\n int nEdge = 0;\n // Loop over #edges\n for (int d = 0, i = 0; d < conn_dims[0]; ++d) {\n // Loop over Z\n for (int z = 0; z < conn_dims[1]; ++z) {\n // Loop over Y\n for (int y = 0; y < conn_dims[2]; ++y) {\n // Loop over X\n for (int x = 0; x < conn_dims[3]; ++x, ++i) {\n // Out-of-bounds check:\n if (!((z + nhood_data[d * nhood_dims[1] + 0] < 0)\n ||(z + nhood_data[d * nhood_dims[1] + 0] >= conn_dims[1])\n ||(y + nhood_data[d * nhood_dims[1] + 1] < 0)\n ||(y + nhood_data[d * nhood_dims[1] + 1] >= conn_dims[2])\n ||(x + nhood_data[d * nhood_dims[1] + 2] < 0)\n ||(x + nhood_data[d * nhood_dims[1] + 2] >= conn_dims[3]))) {\n ++nEdge;\n }\n }\n }\n }\n }\n /* Sort all the edges in increasing order of weight */\n std::vector< int > pqueue( nEdge );\n int j = 0;\n // Loop over #edges\n for (int d = 0, i = 0; d < conn_dims[0]; ++d) {\n // Loop over Z\n for (int z = 0; z < conn_dims[1]; ++z) {\n // Loop over Y\n for (int y = 0; y < conn_dims[2]; ++y) {\n // Loop over X\n for (int x = 0; x < conn_dims[3]; ++x, ++i) {\n // Out-of-bounds check:\n if (!((z + nhood_data[d * nhood_dims[1] + 0] < 0)\n ||(z + nhood_data[d * nhood_dims[1] + 0] >= conn_dims[1])\n ||(y + nhood_data[d * nhood_dims[1] + 1] < 0)\n ||(y + nhood_data[d * nhood_dims[1] + 1] >= conn_dims[2])\n ||(x + nhood_data[d * nhood_dims[1] + 2] < 0)\n ||(x + nhood_data[d * nhood_dims[1] + 2] >= conn_dims[3]))) {\n pqueue[j++] = i;\n }\n }\n }\n }\n }\n pqueue.resize(j);\n sort( pqueue.begin(), pqueue.end(), AffinityGraphCompare( edgeWeight ) );\n\n /* Start MST */\n int minEdge, e, v1, v2;\n uint64_t set1, set2;\n float nPair = 0;\n map::iterator it1, it2;\n\n /* Start Kruskal's */\n // std::ofstream db;\n // db.open(\"/n/coxfs01/donglai/malis_trans/db/mst-pt.txt\");\n \n //std::cout<<\"hi:\"<second * it2->second);\n\n if (pos>0 && (it1->first == it2->first)) {\n nPairPerEdge[minEdge] += nPairNormR * nPair;\n //db <first != it2->first)) {\n nPairPerEdge[minEdge] += nPairNormR * nPair;\n }\n }\n }\n\n /* move the pixel bags of the non-representative to the representative */\n if (dsets.find_set(set1) == set2) // make set1 the rep to keep and set2 the rep to empty\n swap(set1,set2);\n\n it2 = overlap[set2].begin();\n while (it2 != overlap[set2].end()) {\n it1 = overlap[set1].find(it2->first);\n if (it1 == overlap[set1].end()) {\n overlap[set1].insert(pair(it2->first,it2->second));\n } else {\n it1->second += it2->second;\n }\n overlap[set2].erase(it2++);\n }\n } // end link\n\n } // end while\n //db.close();\n}\n\n\n", "meta": {"hexsha": "69fa6f28dd122ff5cb18836be5ca6923fff53f70", "size": 19042, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "torch_connectomics/utils/seg/cpp/seg_malis/cpp-malis_core.cpp", "max_stars_repo_name": "aarushgupta/pytorch_connectomics", "max_stars_repo_head_hexsha": "eb90ada14dbd425a741f481761d1ed9ea633e67c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5.0, "max_stars_repo_stars_event_min_datetime": "2019-09-28T02:20:58.000Z", "max_stars_repo_stars_event_max_datetime": "2020-05-17T18:18:18.000Z", "max_issues_repo_path": "torch_connectomics/utils/seg/cpp/seg_malis/cpp-malis_core.cpp", "max_issues_repo_name": "HoraceKem/pytorch_connectomics", "max_issues_repo_head_hexsha": "2cd4e17b6fa83005a13c1347a01b8b6964e746c3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2020-09-22T08:49:04.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-22T08:49:04.000Z", "max_forks_repo_path": "torch_connectomics/utils/seg/cpp/seg_malis/cpp-malis_core.cpp", "max_forks_repo_name": "HoraceKem/pytorch_connectomics", "max_forks_repo_head_hexsha": "2cd4e17b6fa83005a13c1347a01b8b6964e746c3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 37.8568588469, "max_line_length": 196, "alphanum_fraction": 0.516752442, "num_tokens": 5785, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5736784220301065, "lm_q2_score": 0.37387581579519075, "lm_q1q2_score": 0.21448448804060383}} {"text": "/*************************************************************************\nCopyright (c) 2019 Cognitics, Inc.\n\nPermission is hereby granted, free of charge, to any person obtaining a\ncopy of this software and associated documentation files (the \"Software\"),\nto deal in the Software without restriction, including without limitation\nthe rights to use, copy, modify, merge, publish, distribute, sublicense,\nand/or sell copies of the Software, and to permit persons to whom the\nSoftware is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\nTHE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\nFROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\nDEALINGS IN THE SOFTWARE.\n****************************************************************************/\n\n//#pragma optimize( \"\", off )\n#include \n#include \"sfa/Buffer.h\"\n#include \"sfa/PointMath.h\"\n#include \"sfa/LineString.h\"\n#include \"sfa/Polygon.h\"\n#include \"sfa/PolyhedralSurface.h\"\n#include \"sfa/GeometryCollection.h\"\n#include \"sfa/RingMath.h\"\n#include \"sfa/PointMath.h\"\n#include \"sfa/SegmentIntersector.h\"\n#include \"sfa/BSP.h\"\n#include \"sfa/File.h\"\n#include \n#include \n#include \n\n#undef min\n#undef max\n\nnamespace sfa {\n\n const double Buffer::pi = atan(1.0)*4.0;\n\n void Buffer::addPoint(const Point& point)\n {\n if (points.empty())\n points.push_back(point);\n else if (points.back() != point)\n points.push_back(point);\n }\n\n void Buffer::closePoints(void)\n {\n if (points.size() < 3)\n return;\n else if (points.back() != points.front())\n points.push_back(points.front());\n }\n\n void Buffer::addTurn(const Point& p, const Point& p0, const Point& p1, int direction)\n {\n double x = p.X();\n double y = p.Y();\n double dx0 = p0.X() - p.X();\n double dy0 = p0.Y() - p.Y();\n double dx1 = p1.X() - p.X();\n double dy1 = p1.Y() - p.Y();\n\n double startAngle = atan2(dy0, dx0);\n double endAngle = atan2(dy1, dx1);\n // Check for bad geometry that creates indeterminates.\n if (startAngle != startAngle)\n {\n return;\n }\n if (endAngle != endAngle)\n {\n return;\n }\n if (direction == COUNTERCLOCKWISE)\n {\n if (startAngle >= endAngle) startAngle -= 2.0*pi;\n }\n else //if (direction == CLOCKWISE)\n {\n if (startAngle <= endAngle) startAngle += 2.0*pi;\n }\n\n double totalAngle = abs(startAngle - endAngle);\n int nsegs = int((totalAngle / angle) + 0.5);\n //Detect overflow caused by bad math (previous div by 0)\n if (nsegs < (nsegs - 1))\n return;\n addPoint(p0);\n for (int i = 0; i < nsegs - 1; i++)\n {\n // User rotational matrix on initial vector. This is much faster than computing the angle and transforming every time.\n // We just need to know which direction to rotate.\n if (direction == COUNTERCLOCKWISE)\n {\n dx1 = cos_angle*dx0 - sin_angle*dy0;\n dy1 = sin_angle*dx0 + cos_angle*dy0;\n }\n else\n {\n dx1 = cos_angle*dx0 + sin_angle*dy0;\n dy1 = -sin_angle*dx0 + cos_angle*dy0;\n }\n addPoint(Point(x + dx1, y + dy1));\n dx0 = dx1;\n dy0 = dy1;\n }\n addPoint(p1);\n }\n\n void Buffer::computeOffset(const Point& p0, const Point& p1, Point& o0, Point& o1)\n {\n if (p0 == p1)\n {\n o0 = p0;\n o1 = p1;\n return;\n }\n double dx = p1.X() - p0.X();\n double dy = p1.Y() - p0.Y();\n double len = sqrt(dx*dx + dy*dy);\n len = distance / len;\n // Compute offset vector\n double ux = dy * len;\n double uy = -dx * len;\n // Offset points\n o0.setX(p0.X() + ux);\n o0.setY(p0.Y() + uy);\n o1.setX(p1.X() + ux);\n o1.setY(p1.Y() + uy);\n }\n\n void Buffer::addSegment(const Point& p)\n {\n s0 = s1;\n s1 = s2;\n s2 = p;\n offset00 = offset10;\n offset01 = offset11;\n computeOffset(s1, s2, offset10, offset11);\n\n if (s1 == s2) return;\n\n int orientation = CrossProduct(s0, s1, s1, s2);\n\n if (orientation == 1) // COUNTERCLOCKWISE turn\n addTurn(s1, offset01, offset10, COUNTERCLOCKWISE);\n else if (orientation == -1) // CLOCKWISE turn\n {\n // Compute intersection\n Geometry* intersection = NULL;\n SegmentIntersector::Intersection(&offset00, &offset01, &offset10, &offset11, intersection);\n\n Point* intPoint = dynamic_cast(intersection);\n if (intPoint)\n addPoint(*intPoint);\n else\n {\n // If there is no intersection ,it means the angle is so small and/or the offset so large the segments won't intersect,\n // so a temporary inner loop must be created which will be fixed later.\n if (offset01.distance(&offset10) < distance / 1000.0)\n addPoint(offset01);\n else\n {\n addPoint(offset01);\n addPoint(s1);\n addPoint(offset10);\n }\n }\n\n if (intersection) delete intersection;\n }\n // else do nothing, the points are collinear\n }\n\n void Buffer::addLineEnd(const Point& p0, const Point& p1)\n {\n Point right0, right1;\n Point left0, left1;\n\n computeOffset(p1, p0, left1, left0);\n computeOffset(p0, p1, right0, right1);\n\n switch (end_policy)\n {\n case ROUND_ENDS:\n addTurn(p1, right1, left1, COUNTERCLOCKWISE);\n break;\n case FLATTEN_ENDS:\n addPoint(right1);\n addPoint(left1);\n break;\n //case SQUARE_ENDS:\n // TODO\n // break;\n default:\n break;\n }\n }\n\n#define DEBUG_NODE_WITH_ID 0\n#if DEBUG_NODE_WITH_ID\n static int dbgid = 0;\n#endif\n class LineDeconvolute\n {\n double position_epsilon;\n class NodeSegment;\n //Multiple nodes may exist with the same point value, but each unique Node is a different position in the linestring\n struct Node\n {\n Point pt;\n#if DEBUG_NODE_WITH_ID\n int id;\n#endif\n std::set segments;//weak, for quick removal from the BSP since it works on pointers\n std::set neighbors;//Weak\n std::set reverse_neighbors;//Weak, to allow quick changes\n virtual ~Node()\n {\n }\n\n Node(Point _pt)\n {\n pt = _pt;\n#if DEBUG_NODE_WITH_ID\n id = dbgid++;\n#endif\n }\n\n bool hasNeighbor(Node *neighbor)\n {\n if (this == neighbor)\n return false;\n std::set::iterator iter = neighbors.find(neighbor);\n if (neighbors.end() != iter)\n {\n return true;\n }\n return false;\n }\n\n void addNeighbor(Node *neighbor)\n {\n if (neighbor != this)\n {\n neighbors.insert(neighbor);\n //Add myself to the neighbors reverse list\n neighbor->reverse_neighbors.insert(this);\n }\n }\n void removeNeighbor(Node *neighbor)\n {\n if (neighbor == this)\n return;\n std::set::iterator iter = neighbors.find(neighbor);\n if (neighbors.end() != iter)\n {\n neighbors.erase(iter);\n }\n //Update the reverse map\n iter = neighbor->reverse_neighbors.find(this);\n if (neighbor->reverse_neighbors.end() != iter)\n {\n neighbor->reverse_neighbors.erase(iter);\n }\n\n }\n\n bool alreadyTraversed(const std::set &traversed, Node *n)\n {\n return (traversed.find(n) != traversed.end());\n }\n\n //Returns true if c is to the right of the line between a and b\n bool isRight(Point a, Point b, Point c)\n {\n double cpval = CrossProduct(a, b, a, c);\n if (cpval < 0)\n return true;\n /*\n double val = (b.X() - a.X())*(c.Y() - a.Y()) - (b.Y() - a.Y())*(c.X() - a.X());\n if (val < 0)\n return true;\n */\n return false;\n }\n\n //Return the neighboring edge that is the most to the right side of all the other edges\n //if no neighbors, return NULL\n Node *findRightMostNeighbor(const std::set &traversed)\n {\n std::set::iterator iter = neighbors.begin();\n Node *right_most = NULL;\n bool found_right = false;\n while (iter != neighbors.end())\n {\n if (alreadyTraversed(traversed, *iter))\n {\n iter++;\n continue;\n }\n if (!right_most)\n right_most = *iter;\n else\n {\n double val = (right_most->pt.X() - pt.X())*((*iter)->pt.Y() - pt.Y()) - (right_most->pt.Y() - pt.Y())*((*iter)->pt.X() - pt.X());\n if (val<0)\n {\n right_most = *iter;\n found_right = true;\n }\n else if (val == 0)\n {\n double dist = pt.distance2D2(&(*iter)->pt);\n double right_most_dist = pt.distance2D2(&right_most->pt);\n if (dist < right_most_dist)\n {\n right_most = *iter;\n found_right = true;\n }\n }\n\n }\n iter++;\n }\n \n return right_most; \n } \n };\n\n class NodeSegment : public sfa::LineString\n {\n public:\n Node *first;//weak\n Node *second;//weak\n \n virtual ~NodeSegment()\n {\n }\n\n NodeSegment(Node *a, Node *b)\n {\n first = a;\n second = b;\n addPoint(a->pt);\n addPoint(b->pt);\n }\n\n };\n\n std::set frontier; \n std::vector exteriorRings;\n std::vector interiorRings;\n \n std::list segments;\n BSP bsp;\n \n bool Intersection(const Point& p1, const Point& p2, const Point& p3, const Point& p4, Point* &result)\n {\n Point u = p2 - p1;\n Point v = p4 - p3;\n Point w = p1 - p3;\n\n double denom = v.X()*u.Y() - v.Y()*u.X();\n\n if (abs(denom) < SFA_EPSILON)\n return false;\n\n double s = (v.Y()*w.X() - v.X()*w.Y()) / denom;\n if (s < -SFA_EPSILON || s > 1 + SFA_EPSILON) \n return false;\n double t = (u.Y()*w.X() - u.X()*w.Y()) / denom;\n if (t < -SFA_EPSILON || t > 1 + SFA_EPSILON) \n return false;\n\n result = new Point(p1 + u*s);\n return true;\n }\n\n\n bool isCounterClockwise(LineString *line)\n {\n /**\n * Sum over the edges, (x2 - x1)(y2 + y1). If the result is positive the curve is clockwise, \n * if it's negative the curve is counter-clockwise. (The result is twice the enclosed area, with a +/- convention.)\n **/\n int numPoints = line->getNumPoints();\n double sum = 0;\n for (int i = 1; i < numPoints; i++)\n {\n Point p1 = line->getPointN(i - 1);\n Point p2 = line->getPointN(i);\n sum += ((p2.X() - p1.X())*(p2.Y() + p1.Y()));\n }\n if (sum <= 0)\n return true;\n return false;\n }\n public:\n\n sfa::Geometry *getPolygon()\n {\n sfa::Polygon *ret = new sfa::Polygon();\n if (!extractRings())\n {\n return ret;\n }\n\n std::vector::iterator ext_iter = exteriorRings.begin();\n while (ext_iter != exteriorRings.end())\n {\n ret->addRing(*ext_iter++);\n }\n\n std::vector::iterator int_iter = interiorRings.begin();\n while (int_iter != interiorRings.end())\n {\n ret->addRing(*int_iter++);\n }\n return ret;\n }\n\n \n\n struct{\n bool operator()(const Node *n1, const Node *n2)\n {\n return n1->pt.X() < n2->pt.X();\n }\n } customLess;\n\n\n bool extractRings()\n {\n std::set traversed;\n std::set nodes = frontier;\n std::vector sorted_nodes;\n sorted_nodes.insert(sorted_nodes.begin(), frontier.begin(), frontier.end());\n std::sort(sorted_nodes.begin(), sorted_nodes.end(), customLess);\n\n //We need to start with an outside point, so we'll use the left most edge\n\n //pick some random point, and iterate along the left edge until either:\n // a) we've iterated over frontier.size() points and found no cycle (wtf?)\n // -Return an empty LineString\n // b) we come across a node we've already visited\n // -Return all the points in the cycle, removing them from the frontier as we collect them\n\n std::vector::iterator iter = sorted_nodes.begin();\n size_t numNodes = sorted_nodes.size();\n while (iter != sorted_nodes.end())\n {\n Node *n = *iter++;\n if (traversed.find(n) != traversed.end())\n continue;\n std::vector path;\n path.push_back(n);\n size_t i = 0;\n bool found_cycle = false;\n while (i < numNodes && !found_cycle)\n {\n Node *right = n->findRightMostNeighbor(traversed);\n if (right)\n {\n n = right;\n }\n else\n {\n break;\n }\n // Look to see if we've completed a cycle\n for (size_t j = 0, jc = path.size(); j < jc; j++)\n {\n if (path.at(j) == n)\n {\n found_cycle = true;\n //We have a cycle! from j to (jc-1) + n\n LineString ring;\n for (size_t k = j; k < jc; k++)\n {\n ring.addPoint(path.at(k)->pt);\n traversed.insert(path.at(k));\n }\n ring.addPoint(n->pt);//Close the ring\n //Only add a true polygon\n if (ring.getNumPoints() > 3)\n {\n bool CCW = isCounterClockwise(&ring);\n\n //Add the ring\n if (CCW)\n exteriorRings.push_back(ring);\n else\n interiorRings.push_back(ring);\n }\n }\n if (found_cycle)\n break;\n }\n if (!found_cycle)\n path.push_back(n);\n i++;\n }\n\n }\n return exteriorRings.size() > 0;//No exterior ring means failure\n }\n\n ~LineDeconvolute()\n {\n std::set::iterator iter = frontier.begin();\n while (iter != frontier.end())\n {\n Node *n = *iter++;\n delete n;\n }\n frontier.clear();\n std::list::iterator segiter = segments.begin();\n while (segiter != segments.end())\n {\n delete *segiter++;\n }\n segments.clear();\n }\n\n typedef std::pair node_pair_t;\n\n void removePair(std::list &plist, node_pair_t &thepair)\n {\n std::list::iterator iter = plist.begin();\n while (iter != plist.end())\n {\n if (thepair == *iter)\n {\n plist.erase(iter);\n return;\n }\n iter++;\n }\n }\n\n bool addNeighbor(Node *node, Node *neighbor, std::list &segment_pair)\n {\n if (node == neighbor)\n return false;\n if (node->hasNeighbor(neighbor))\n return false;\n //Add \n node->addNeighbor(neighbor);\n NodeSegment *seg1 = new NodeSegment(node, neighbor);\n node->segments.insert(seg1);\n segments.push_back(seg1);\n //Add the segment to the BSP\n sfa::BSPEditVisitor insertEditor;\n insertEditor.setVisitMode(sfa::INSERT);\n insertEditor.setEditElement(seg1);\n insertEditor.visiting(&bsp);\n segment_pair.push_back(std::make_pair(node, neighbor));\n return true;\n }\n\n bool removeNeighbor(Node *node, Node *neighbor, std::list &segment_pair)\n {\n //Remove the segment from the BSP\n std::set::iterator segment_iter = node->segments.begin();\n while (segment_iter != node->segments.end())\n {\n NodeSegment *remove_seg = *segment_iter++;\n if (remove_seg->second == neighbor)\n {\n sfa::BSPEditVisitor removeEditor;\n removeEditor.setVisitMode(sfa::REMOVE);\n removeEditor.setEditElement(remove_seg);\n removeEditor.visiting(&bsp);\n }\n } \n node->removeNeighbor(neighbor);\n node_pair_t np = std::make_pair(node, neighbor);\n removePair(segment_pair, np);\n\n return true;\n }\n\n // If node has neighbor 'oldNeighbor', remove oldNeighbor and add newNeighbor\n bool replaceNeighbor(Node *node, Node *oldNeighbor, Node *newNeighbor, NodeSegment *ns, std::list &segment_pair)\n {\n if (node == newNeighbor)\n return false;\n if (node->hasNeighbor(oldNeighbor))\n {\n#if DEBUG_NODE_WITH_ID\n printf(\"Node %d: replacing neighbor of %d with %d\\n\", node->id, oldNeighbor->id, newNeighbor->id);\n#endif\n addNeighbor(node, newNeighbor,segment_pair);\n removeNeighbor(node, oldNeighbor, segment_pair);\n return true;\n }\n\n return false;\n\n }\n\n void consolidateDuplicateNodes(Node *keepNode, Node *delNode, NodeSegment *ns, std::list &segment_pair)\n {\n#if DEBUG_NODE_WITH_ID\n printf(\"Consolidating %d into %d\\n\", delNode->id, keepNode->id);\n#endif\n \n //Update any node that points to delNode to instead point to keepNode \n std::set::iterator f_neighbor_iter = delNode->neighbors.begin();\n while (f_neighbor_iter != delNode->neighbors.end())\n {\n Node *neighbor = *f_neighbor_iter++;\n removeNeighbor(delNode, neighbor, segment_pair);\n addNeighbor(keepNode, neighbor, segment_pair);\n }\n\n //Update any other node that points to delNode to point to keepNode instead\n std::set::iterator front_iter = delNode->reverse_neighbors.begin();\n while (front_iter != delNode->reverse_neighbors.end())\n {\n Node *f = *front_iter++;\n removeNeighbor(f, delNode, segment_pair);//Remove delNode from being a neighbor of f \n addNeighbor(f, keepNode, segment_pair);//Add keepNode as a neighbor to f\n }\n\n }\n\n bool PointEqualsEpsilon(sfa::Point *p1, sfa::Point *p2, double epsilon = sfa::SFA_EPSILON)\n {\n double deltax = p1->X() - p2->X();\n double deltay = p1->Y() - p2->Y();\n double deltaz = p1->Z() - p2->Z();\n if (deltax == 0 && deltay == 0 )//&& deltaz == 0\n return true;\n\n double dist2 = (deltax * deltax) + (deltay*deltay);// +(deltaz*deltaz);\n if (dist2 < (epsilon*epsilon))\n return true;\n return false;\n }\n \n LineDeconvolute(std::vector& points, const sfa::LineString *refLine, double bufferDist)\n {\n //Use a larger epsilon for positional duplication\n position_epsilon = sfa::SFA_EPSILON * 10;\n#if DEBUG_NODE_WITH_ID\n dbgid = 0;\n#endif\n if (points.size() < 3)\n return;\n if (points.at(points.size() - 1) != points.at(0))\n points.push_back(points.at(0)); \n \n\n std::list segment_pair;\n //Build the initial 'graph'\n std::vector::const_iterator prev = points.begin();\n if (prev != points.end())\n {\n Node *prevNode = new Node(*prev++);\n Node *firstNode = prevNode;\n frontier.insert(prevNode);\n std::vector::const_iterator iter = prev;\n while (iter != points.end())\n {\n Node *n = new Node(*iter); \n frontier.insert(n);\n prevNode->addNeighbor(n);\n segment_pair.push_back(std::make_pair(prevNode, n));\n NodeSegment *seg = new NodeSegment(prevNode, n);\n segments.push_back(seg);\n prevNode->segments.insert(seg);\n bsp.addGeometry(seg);\n //printf(\"Node %d connects to %d\\n\", prevNode->id, n->id);\n prevNode = n;\n iter++;\n }\n prevNode->addNeighbor(firstNode);\n segment_pair.push_back(std::make_pair(prevNode, firstNode));\n NodeSegment *seg = new NodeSegment(prevNode, firstNode);\n segments.push_back(seg);\n prevNode->segments.insert(seg);\n bsp.addGeometry(seg);\n }\n\n {\n \n std::set traversed;\n std::set::iterator iter = frontier.begin();\n size_t numNodes = frontier.size();\n while (iter != frontier.end())\n {\n Node *n = *iter++;\n \n BSPCollectGeometriesVisitor collector;\n collector.setBounds(n->pt.X() - position_epsilon, n->pt.Y() - position_epsilon, n->pt.X() + position_epsilon, n->pt.Y() + position_epsilon);\n collector.visiting(&bsp);\n std::list results = collector.results;\n std::list::iterator isect_iter = results.begin();\n while (!results.empty())\n {\n sfa::Geometry *result = results.back();\n results.pop_back();\n NodeSegment *ns = dynamic_cast(result);\n\n if (n == ns->first || n == ns->second)\n continue;\n if (PointEqualsEpsilon(&n->pt, &ns->first->pt, position_epsilon))\n {\n //If we do this we need to update the geometry of n and all of its nodes in the BSP\n //n->pt = (n->pt + ns->first->pt) / 2;\n\n consolidateDuplicateNodes(n, ns->first, ns, segment_pair);\n \n //Requery the bsp now\n collector.results.clear();\n collector.visiting(&bsp);\n results = collector.results;\n }\n if (PointEqualsEpsilon(&n->pt, &ns->second->pt, position_epsilon))\n {\n //If we do this we need to update the geometry of n and all of its nodes in the BSP\n //n->pt = (n->pt + ns->second->pt) / 2;\n consolidateDuplicateNodes(n, ns->second, ns, segment_pair);\n //Requery the bsp now\n collector.results.clear();\n collector.visiting(&bsp);\n results = collector.results;\n }\n }\n }\n\n }\n\n std::map envelopes;\n bsp.generate(envelopes);\n //When a pair is broken up, remove that pair from the segment_pair list!\n while (!segment_pair.empty())\n {\n node_pair_t pair = segment_pair.back();\n segment_pair.pop_back();\n Node *n = pair.first;\n Node *neighbor = pair.second; \n\n NodeSegment seg1(n, neighbor);\n BSPCollectGeometriesVisitor collector;\n sfa::LineString *env = dynamic_cast(seg1.getEnvelope());\n sfa::Point *lsMin = dynamic_cast(env->getPointN(0));\n sfa::Point *lsMax = dynamic_cast(env->getPointN(1));\n collector.setBounds(lsMin->X(), lsMin->Y(), lsMax->X(), lsMax->Y());\n delete env;\n collector.visiting(&bsp);\n std::list results = collector.results;\n\n std::list::iterator isect_iter = results.begin();\n while (!results.empty())\n {\n sfa::Geometry *result = results.back();\n results.pop_back();\n NodeSegment *ns = dynamic_cast(result);\n Node *f = ns->first;\n Node *f_neighbor = ns->second;\n\n //seg2 is f and f_neighbor\n if (n == f)\n continue;\n if (n == f_neighbor)\n continue;\n if (neighbor == f)\n continue;\n if (f_neighbor == neighbor)\n continue;\n //We don't want to compare f_neighbor with f or neighbor\n if (f->hasNeighbor(n))\n continue;\n if (f_neighbor->hasNeighbor(neighbor))\n continue;\n if (f->hasNeighbor(neighbor))\n continue;\n#if DEBUG_NODE_WITH_ID\n printf(\"testing %d -> %d against %d -> %d\\n\", n->id, neighbor->id, f->id, f_neighbor->id);\n#endif\n if (PointEqualsEpsilon(&n->pt, &f_neighbor->pt, position_epsilon))\n {\n consolidateDuplicateNodes(n, f_neighbor, ns, segment_pair);\n //Requery the bsp now\n collector.results.clear();\n collector.visiting(&bsp);\n results = collector.results;\n continue;\n }\n if (PointEqualsEpsilon(&f->pt, &neighbor->pt, position_epsilon))\n {\n consolidateDuplicateNodes(f, neighbor, ns, segment_pair);\n //Neighbor is now f!\n neighbor = f;\n \n //Requery the bsp now\n collector.results.clear();\n collector.visiting(&bsp);\n results = collector.results;\n continue;\n }\n\n // look for an intersection between seg1 and seg2\n sfa::Point *ipoint = NULL;\n if (Intersection(n->pt, neighbor->pt, f->pt, f_neighbor->pt, ipoint))\n {\n if (ipoint)\n {\n#if DEBUG_NODE_WITH_ID\n printf(\"Intersection between %d<-->%d and %d<-->%d\\n\", n->id, neighbor->id, f->id, f_neighbor->id);\n#endif\n if (PointEqualsEpsilon(ipoint, &n->pt) || \n PointEqualsEpsilon(ipoint, &neighbor->pt) ||\n PointEqualsEpsilon(ipoint, &f->pt) ||\n PointEqualsEpsilon(ipoint, &f_neighbor->pt))\n {\n delete ipoint;\n continue;\n }\n // Add the intersection point (int_node) as a node\n Node *int_node = new Node(*ipoint);\n delete ipoint;\n#if DEBUG_NODE_WITH_ID\n if (int_node->id == 39)\n printf(\"\");\n printf(\"Adding node %d\\n\", int_node->id);\n#endif \n //Add it to the master list of nodes\n frontier.insert(int_node);\n // Add a neighbor relationship between int_node and (n,neighbor,f,f_neighbor)\n if (replaceNeighbor(f, f_neighbor, int_node, ns, segment_pair))\n {\n //Above we added a node from f -> int_pair, so now we complete it by adding from int_pair to f\n addNeighbor(int_node, f_neighbor, segment_pair); \n } \n else\n {\n replaceNeighbor(f_neighbor, f, int_node, ns, segment_pair);\n addNeighbor(int_node, f, segment_pair);\n }\n\n if (replaceNeighbor(n, neighbor, int_node, ns, segment_pair))\n {\n addNeighbor(int_node, neighbor, segment_pair); \n }\n else\n {\n replaceNeighbor(neighbor, n, int_node, ns, segment_pair);\n addNeighbor(int_node, n, segment_pair);\n }\n \n //Requery the bsp now\n collector.results.clear();\n collector.visiting(&bsp);\n results = collector.results;\n break;\n }\n else\n {\n //Something broke in Intersection\n }\n }\n }\n }\n //Iterate over the frontier, and remove any neighbors for any nodes closer than the buffer length from the refLine\n std::set::iterator iter = frontier.begin();\n while (iter != frontier.end())\n {\n Node *n = *iter++;\n double dist = n->pt.distance(refLine);\n //This is a magic number (just a number that seems to work reliably), but it's trying to account for the approximation of a circle\n //that we form with facets of a turn\n if (dist < (bufferDist *.8))\n {\n#if DEBUG_NODE_WITH_ID\n printf(\"Removing node %d\\n\", n->id);\n#endif\n //This node should be removed from the graph (by breaking the links)\n //First remove any neighbors I have\n std::set::iterator niter = n->neighbors.begin();\n while (niter != n->neighbors.end())\n {\n Node *neighbor = *niter++;\n n->removeNeighbor(neighbor);\n }\n //Next update any nodes that have me as a neighbor, to not have me as a neighbor\n niter = n->reverse_neighbors.begin();\n while (niter != n->reverse_neighbors.end())\n {\n Node *neighbor = *niter++;\n neighbor->removeNeighbor(n);\n }\n }\n }\n\n#if DEBUG_NODE_WITH_ID\n if (1)\n {\n std::string gvfilename(\"dot.gv\");\n std::remove(gvfilename.c_str());\n std::ofstream of(gvfilename.c_str());\n of << \"digraph {\\n\";\n std::set traversed;\n std::set::iterator iter = frontier.begin();\n size_t numNodes = frontier.size();\n while (iter != frontier.end())\n {\n Node *n = *iter++;\n //of << \"\\t\\\"\" << n->id << \"\\\"\\n\";\n Node *rightmost = n->findRightMostNeighbor(traversed);\n //if (rightmost)\n // of << \"\\t\\\"\" << n->id << \"\\\" -> \\\"\" << rightmost->id << \"\\\"\\n\";\n std::set::iterator niter = n->neighbors.begin();\n while (niter != n->neighbors.end())\n {\n Node *neighbor = *niter++;\n if (neighbor == rightmost)\n {\n of << \"\\t\" << n->id << \" -> \" << neighbor->id << \"[color=red,penwidth=3.0]\\n\";\n }\n else\n {\n of << \"\\t\" << n->id << \" -> \" << neighbor->id << \"\\n\";\n }\n }\n }\n of << \"}\\n\";\n }\n\n\n if (1)\n {\n std::set nodes = frontier;\n FeatureList features;\n std::set traversed;\n //Write a feature (segment) for each node connection.\n std::set::iterator iter = frontier.begin();\n size_t numNodes = frontier.size();\n while (iter != frontier.end())\n {\n Node *n = *iter++;\n\n {\n sfa::Feature *f = new sfa::Feature;\n f->geometry = new sfa::Point(n->pt.X(), n->pt.Y(), 0);\n f->attributes.setAttribute(\"nodeid\", n->id);\n features.push_back(f);\n }\n Node *rightmost = n->findRightMostNeighbor(traversed);\n if (rightmost)\n {\n LineString *seg = new LineString;\n seg->addPoint(sfa::Point(n->pt.X(), n->pt.Y(), 0));\n seg->addPoint(sfa::Point(rightmost->pt.X(),rightmost->pt.Y(),0));\n sfa::Feature *f = new sfa::Feature;\n f->geometry = seg;\n f->attributes.setAttribute(\"nodeid\", n->id);\n f->attributes.setAttribute(\"rightmost\", rightmost->id);\n features.push_back(f);\n } \n }\n sfa::Feature origFeature;\n origFeature.attributes.setAttribute(\"name\", \"original\");\n origFeature.geometry = refLine->copy();\n features.push_back(&origFeature);\n sfa::writeSFAFile(features, \"features.sfa\");\n }\n#endif\n\n }\n\n \n\n\n };\n\n /*\n DE_CONVOLUTE\n ===============================================================================================\n */\n Geometry* Buffer::de_convolute(const std::vector& points)\n {\n /*\n We can detect loops in the convolution by running a ray-tracing backwards when we attemp to add a new Point to the result.\n If it intersects a previous edge, then the first intersection point acts as the point to \"pinch\" the loop closed.\n If the loop is found to be counter-clockwise, the loop is excessive and can be discarded, else if it is clockwise, then we\n should keep the loop and use it as a hole.\n\n When detecting if an edge intersects the ray, we do not count any intersections that occur at an endpoint of an edge; although,\n we do count intersections that occur at the start point of an edge.\n\n Use brute force for now, with queries in linear time.\n Later implement MonotonicChain to allow optimal log(n) query time.\n */\n\n // Holds the currently processed points to the list\n std::vector processed;\n std::vector< std::vector > holes;\n\n // Find the first point to start on. This must be on the exterior or else problems may ocur, so we just choose the point further in one direction.\n int start = 0;\n int rev_start = 0;\n for (int i = 0; i < int(points.size()); i++)\n {\n if (points[start].X() < points[i].X())\n start = i;\n if (points[rev_start].X() > points[i].X())\n rev_start = i;\n }\n\n // we want to use a start that is the furthest distance possible from the endpoint\n int start_dist = std::min(start, int(points.size()) - start);\n int rev_dist = std::min(rev_start, int(points.size()) - rev_start);\n if (rev_dist > start_dist)\n start = rev_start;\n\n\n processed.push_back(points[start]);\n\n /* Itterate through the points and try to add the next point. Shoot a ray back and try to intersect all the edges already added.\n If there is an intersection, attempt to handle the loop.\n */\n for (size_t offset = 1; offset < int(points.size()); offset++)\n {\n size_t i = (offset + start) % points.size();\n\n Point next = points[i];\n bool intersection = false;\n\n // Shoot ray backwards\n double U[2] = { next.X() - processed.back().X(), next.Y() - processed.back().Y() };\n\n // Find s such that next + s*dnext = intersection\n // find t such that p1 + t*(p2-p1) = intersection\n double s;\n double t;\n\n // Index of point after intersection\n int j;\n // CCW or CW\n bool CCW;\n\n // Don't start at the end, start just before the end...we already know that the segment will be intersecting the processed list at the\n // previous point by definition.\n for (j = int(processed.size() - 2); j > 0; j--)\n {\n Point p1 = processed[j - 1];\n Point p2 = processed[j];\n\n double V[2] = { p2.X() - p1.X(), p2.Y() - p1.Y() };\n double W[2] = { next.X() - p1.X(), next.Y() - p1.Y() };\n\n double denom = V[0] * U[1] - V[1] * U[0];\n\n if (denom == 0)\n continue;\n else\n {\n s = (V[1] * W[0] - V[0] * W[1]) / denom;\n t = (U[1] * W[0] - U[0] * W[1]) / denom;\n }\n\n if (s > 0 || s < -1)\n continue;\n else if (t < 0 || t > 1)\n continue;\n else\n {\n CCW = (denom < 0);\n intersection = true;\n //printf(\"Intersection at i=%d j=%d\\n\", i, j);\n break;\n }\n }\n\n if (intersection)\n {\n Point intPoint(next.X() + s*U[0], next.Y() + s*U[1]);\n\n if (!CCW) // Save loop as a hole\n {\n // Note that hole will NOT be closed yet\n std::vector hole;\n hole.push_back(intPoint);\n for (int k = j; k < int(processed.size()); k++)\n hole.push_back(processed[k]);\n\n holes.push_back(hole);\n }\n\n // Cut away loop from main processed values\n\n size_t d = processed.size() - j;\n //printf(\"Cutting %d points from the end\\n\", d);\n for (size_t k = 0; k < d; k++)\n processed.pop_back();\n //printf(\"Pushing back point at %f %f\\n\", intPoint.X(), intPoint.Y());\n processed.push_back(intPoint);\n offset--;\n continue;\n\n }\n else processed.push_back(next);\n }\n\n Polygon* result = new Polygon;\n\n // Add boundary\n LineString* boundary = new LineString;\n for (unsigned int i = 0; i < processed.size(); i++)\n boundary->addPoint(processed[i]); // create new pointer\n\n boundary->addPoint(processed[0]); // close\n result->addRing(boundary);\n\n\n // Add holes\n for (std::vector< std::vector >::iterator it = holes.begin(); it != holes.end(); ++it)\n {\n LineString* hole = new LineString;\n for (unsigned int i = 0; i < it->size(); i++)\n hole->addPoint((*it)[i]); // create new pointer\n\n hole->addPoint((*it)[0]);\n\n // Check if interior ring hasn't collapsed\n double area = GetRingArea(hole);\n if (area <-SFA_EPSILON)\n result->addRing(hole);\n }\n\n return result;\n }\n\n /*\n Point buffer\n ===============================================================================================\n */\n Geometry* Buffer::bufferPoint(const Geometry* point)\n {\n // You cannot buffer a Point with a negative distance. It results in nothing, so wer return nothing.\n if (distance < 0) return NULL;\n\n const Point* p = dynamic_cast(point);\n LineString* ring = new LineString;\n\n double dx0, dy0, dx1, dy1;\n dx0 = distance;\n dy0 = 0;\n\n for (unsigned int i = 0; i < 4 * quad_segments; i++)\n {\n dx1 = cos_angle*dx0 - sin_angle*dy0;\n dy1 = sin_angle*dx0 + cos_angle*dy0;\n\n ring->addPoint(Point(p->X() + dx1, p->Y() + dy1));\n\n dx0 = dx1;\n dy0 = dy1;\n }\n\n ring->addPoint(*ring->getPointN(0));\n\n Polygon* poly = new Polygon;\n poly->addRing(ring);\n return poly;\n }\n\n\n\n /*\n LineString buffer\n ===============================================================================================\n */\n Geometry* Buffer::bufferLineString(const Geometry* origLine)\n {\n sfa::Geometry *geomcopy = origLine->copy();\n sfa::LineString *line = dynamic_cast(geomcopy);\n \n if (!line)\n {\n if (geomcopy)\n delete geomcopy;\n return NULL;\n }\n line->removeColinearPoints(sfa::SFA_EPSILON);\n\n if (distance < 0)\n {\n if (geomcopy)\n delete geomcopy;\n return NULL;\n }\n\n points.clear();\n const LineString* linestring = line;\n\n int num = linestring->getNumPoints();\n if (num == 0)\n {\n if (geomcopy)\n delete geomcopy;\n return NULL;\n }\n else if (num == 1)\n return bufferPoint(linestring->getPointN(0));\n else if (num == 2)\n {\n addLineEnd(*linestring->getPointN(0), *linestring->getPointN(1));\n addLineEnd(*linestring->getPointN(1), *linestring->getPointN(0));\n }\n else\n {\n Point next, first;\n\n // Move down the line\n s1 = *linestring->getPointN(0);\n s2 = *linestring->getPointN(1);\n computeOffset(s1, s2, offset10, offset11);\n for (int i = 2; i < num; i++)\n addSegment(*linestring->getPointN(i));\n\n // Add end cap\n first = *linestring->getPointN(num - 1);\n for (int i = 2; i <= num; i++)\n {\n next = *linestring->getPointN(num - i);\n if (next != first) break;\n else if (i == num)\n {\n if (geomcopy)\n delete geomcopy;\n // ERROR!! All points of the line are equal!\n return NULL;\n }\n }\n addLineEnd(next, first);\n\n // Move back up the line\n s1 = *linestring->getPointN(num - 1);\n s2 = *linestring->getPointN(num - 2);\n computeOffset(s1, s2, offset10, offset11);\n for (int i = num - 3; i >= 0; i--)\n addSegment(*linestring->getPointN(i));\n\n // Add end cap\n first = *linestring->getPointN(0);\n for (int i = 1; i <= num; i++)\n {\n next = *linestring->getPointN(i);\n if (next != first) break;\n else if (i == num)\n {\n if (geomcopy)\n delete geomcopy;\n // ERROR!! All point of the line are equal!\n return NULL;\n }\n }\n\n addLineEnd(next, first);\n }\n \n LineDeconvolute dc(points, linestring, distance);\n if (geomcopy)\n delete geomcopy;\n sfa::Polygon *ret = dynamic_cast(dc.getPolygon());\n //Return NULL on failure rather than an empty polygon\n if (ret->isEmpty())\n {\n delete ret;\n ret = NULL;\n }\n return ret;\n }\n\n\n /*\n Polygon buffer\n ===============================================================================================\n */\n Geometry* Buffer::bufferPolygon(const Geometry* poly)\n {\n bool negativeDistance = distance < 0;\n distance = abs(distance);\n\n // Only supports rounded end policies\n int backupEndPolicy = end_policy;\n end_policy = ROUND_ENDS;\n\n const Polygon* polygon = dynamic_cast(poly);\n Geometry* result = NULL;\n\n // Buffer exterior ring\n Geometry* outerRingBuffer = bufferLineString(polygon->getExteriorRing());\n\n if (outerRingBuffer)\n {\n if (outerRingBuffer->isEmpty())\n return NULL;\n Polygon* outerRingBufferPolygon = dynamic_cast(outerRingBuffer);\n\n if (negativeDistance) // Use interior rings\n {\n int n = outerRingBufferPolygon->getNumInteriorRing();\n if (n == 1) // Result is a single polygon\n {\n Polygon* polygonResult = new Polygon();\n polygonResult->addRing(*outerRingBufferPolygon->getInteriorRingN(0));\n polygonResult->reverse();\n result = polygonResult;\n }\n else if (n>1) // Result is a multi polygon\n {\n MultiPolygon* multiPolygonResult = new MultiPolygon();\n for (int i = 0; iaddRing(*outerRingBufferPolygon->getInteriorRingN(i));\n next->reverse();\n multiPolygonResult->addGeometry(next);\n }\n result = multiPolygonResult;\n }\n }\n else // Use exterior rings\n {\n Polygon* polygonResult = new Polygon();\n polygonResult->addRing(*outerRingBufferPolygon->getExteriorRing());\n result = polygonResult;\n }\n\n delete outerRingBuffer;\n }\n\n // Buffer all interior rings and use them as a difference\n for (int i = 0; igetNumInteriorRing(); i++)\n {\n if (!result) break;\n\n Geometry* nextBuffer = bufferLineString(polygon->getInteriorRingN(i));\n if (nextBuffer)\n {\n Polygon* polygonBuffer = dynamic_cast(nextBuffer);\n\n if (negativeDistance) // Use exterior ring\n {\n Geometry* temp = result->difference(polygonBuffer->getExteriorRing());\n delete result;\n result = temp;\n }\n else // Use Interior rings\n {\n for (int j = 0; jgetNumInteriorRing(); j++)\n {\n if (!result) break;\n\n Polygon ring;\n ring.addRing(*polygonBuffer->getInteriorRingN(j));\n ring.reverse();\n Geometry* temp = result->difference(&ring);\n delete result;\n result = temp;\n }\n }\n\n delete nextBuffer;\n }\n }\n\n // Restore original settings\n end_policy = backupEndPolicy;\n if (negativeDistance) distance *= -1;\n\n return result;\n }\n\n /*\n PolyhedralSurface buffer\n ===============================================================================================\n */\n Geometry* Buffer::bufferPolyhedralSurface(const Geometry* surface)\n {\n const PolyhedralSurface* polyhedralSurface = dynamic_cast(surface);\n\n if (union_policy == DISJOINT_RESULTS)\n {\n GeometryCollection* result = new GeometryCollection;\n\n for (int i = 0; igetNumPatches(); i++)\n {\n Geometry* temp = polyhedralSurface->getPatchN(i)->buffer(distance, end_policy, union_policy, quad_segments);\n if (temp)\n result->addGeometry(temp);\n }\n\n return result;\n }\n else\n {\n Geometry* result = NULL;\n\n for (int i = 0; igetNumPatches(); i++)\n {\n if (result)\n {\n Geometry* geometryBuffer = polyhedralSurface->getPatchN(i)->buffer(distance, end_policy, union_policy, quad_segments);\n Geometry* temp = result->Union(geometryBuffer);\n delete geometryBuffer;\n delete result;\n result = temp;\n }\n else\n result = polyhedralSurface->getPatchN(i)->buffer(distance, end_policy, union_policy, quad_segments);\n }\n\n return result;\n }\n }\n\n /*\n Collection buffer\n ===============================================================================================\n */\n Geometry* Buffer::bufferCollection(const Geometry* collection)\n {\n const GeometryCollection* geometryCollection = dynamic_cast(collection);\n\n if (union_policy == DISJOINT_RESULTS)\n {\n GeometryCollection* result = new GeometryCollection;\n\n for (int i = 1; i <= geometryCollection->getNumGeometries(); i++)\n {\n Geometry* temp = geometryCollection->getGeometryN(i)->buffer(distance, end_policy, union_policy, quad_segments);\n if (temp)\n result->addGeometry(temp);\n }\n\n return result;\n }\n else\n {\n Geometry* result = NULL;\n\n for (int i = 1; i <= geometryCollection->getNumGeometries(); i++)\n {\n if (result)\n {\n Geometry* geometryBuffer = geometryCollection->getGeometryN(i)->buffer(distance, end_policy, union_policy, quad_segments);\n Geometry* temp = result->Union(geometryBuffer);\n delete geometryBuffer;\n delete result;\n result = temp;\n }\n else\n result = geometryCollection->getGeometryN(i)->buffer(distance, end_policy, union_policy, quad_segments);\n }\n\n return result;\n }\n }\n\n /*\n Geometry buffer\n ===============================================================================================\n */\n Geometry* Buffer::bufferGeometry(const Geometry* geom)\n {\n int type = geom->getWKBGeometryType(false, false);\n\n switch (type)\n {\n case (wkbPoint) :\n return bufferPoint(geom);\n break;\n case (wkbLineString) :\n return bufferLineString(geom);\n break;\n case (wkbPolygon) :\n case (wkbTriangle) :\n return bufferPolygon(geom);\n break;\n case (wkbMultiPoint) :\n case (wkbMultiLineString) :\n case (wkbMultiPolygon) :\n case (wkbGeometryCollection) :\n return bufferCollection(geom);\n break;\n case (wkbPolyhedralSurface) :\n case (wkbTIN) :\n return bufferPolyhedralSurface(geom);\n break;\n default:\n throw std::runtime_error(\"Buffer::apply() Unknown Geometry!\");\n }\n }\n\n // PUBLIC METHODS ================================================================================\n // ===============================================================================================\n\n Buffer::Buffer(unsigned int quadrantSegments, int endPolicy, int unionPolicy)\n {\n end_policy = endPolicy;\n union_policy = unionPolicy;\n\n distance = 0;\n\n if (quadrantSegments < 1) quadrantSegments = 1;\n\n // Compute quadrant segment information\n quad_segments = quadrantSegments;\n angle = (pi / 2) / quad_segments;\n sin_angle = sin(angle);\n cos_angle = cos(angle);\n }\n\n int Buffer::getEndPolicy(void) const\n {\n return end_policy;\n }\n\n void Buffer::setEndPolicy(int policy)\n {\n switch (policy)\n {\n case ROUND_ENDS:\n case FLATTEN_ENDS:\n end_policy = policy;\n break;\n default:\n end_policy = ROUND_ENDS;\n }\n }\n\n int Buffer::getUnionPolicy(void) const\n {\n return union_policy;\n }\n\n void Buffer::setUnionPolicy(int policy)\n {\n switch (policy)\n {\n case UNION_RESULTS:\n case DISJOINT_RESULTS:\n union_policy = policy;\n break;\n default:\n union_policy = UNION_RESULTS;\n }\n }\n\n Geometry* Buffer::apply(const Geometry* geom, double d)\n {\n if (!geom) return NULL;\n if (d == 0 || geom->isEmpty()) return geom->copy();\n\n distance = d;\n return bufferGeometry(geom);\n }\n\n}\n", "meta": {"hexsha": "eb4f80cd2c2dcb26e98b78f7a25fe551c15c8717", "size": 57394, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cognitics/src/sfa/Buffer.cpp", "max_stars_repo_name": "mikedig/cdb-productivity-api", "max_stars_repo_head_hexsha": "e2bedaa550a8afa780c01f864d72e0aebd87dd5a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "cognitics/src/sfa/Buffer.cpp", "max_issues_repo_name": "mikedig/cdb-productivity-api", "max_issues_repo_head_hexsha": "e2bedaa550a8afa780c01f864d72e0aebd87dd5a", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "cognitics/src/sfa/Buffer.cpp", "max_forks_repo_name": "mikedig/cdb-productivity-api", "max_forks_repo_head_hexsha": "e2bedaa550a8afa780c01f864d72e0aebd87dd5a", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.4175126904, "max_line_length": 160, "alphanum_fraction": 0.4528870614, "num_tokens": 11598, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.3486451488696663, "lm_q1q2_score": 0.2144473969543228}} {"text": "/*******************************************************************************\n *\n * Generic implementation of non-relational domains.\n *\n * Author: Arnaud J. Venet (arnaud.j.venet@nasa.gov)\n *\n * Notices:\n *\n * Copyright (c) 2011 United States Government as represented by the\n * Administrator of the National Aeronautics and Space Administration.\n * All Rights Reserved.\n *\n * Disclaimers:\n *\n * No Warranty: THE SUBJECT SOFTWARE IS PROVIDED \"AS IS\" WITHOUT ANY WARRANTY OF\n * ANY KIND, EITHER EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED\n * TO, ANY WARRANTY THAT THE SUBJECT SOFTWARE WILL CONFORM TO SPECIFICATIONS,\n * ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE,\n * OR FREEDOM FROM INFRINGEMENT, ANY WARRANTY THAT THE SUBJECT SOFTWARE WILL BE\n * ERROR FREE, OR ANY WARRANTY THAT DOCUMENTATION, IF PROVIDED, WILL CONFORM TO\n * THE SUBJECT SOFTWARE. THIS AGREEMENT DOES NOT, IN ANY MANNER, CONSTITUTE AN\n * ENDORSEMENT BY GOVERNMENT AGENCY OR ANY PRIOR RECIPIENT OF ANY RESULTS,\n * RESULTING DESIGNS, HARDWARE, SOFTWARE PRODUCTS OR ANY OTHER APPLICATIONS\n * RESULTING FROM USE OF THE SUBJECT SOFTWARE. FURTHER, GOVERNMENT AGENCY\n * DISCLAIMS ALL WARRANTIES AND LIABILITIES REGARDING THIRD-PARTY SOFTWARE,\n * IF PRESENT IN THE ORIGINAL SOFTWARE, AND DISTRIBUTES IT \"AS IS.\"\n *\n * Waiver and Indemnity: RECIPIENT AGREES TO WAIVE ANY AND ALL CLAIMS AGAINST\n * THE UNITED STATES GOVERNMENT, ITS CONTRACTORS AND SUBCONTRACTORS, AS WELL\n * AS ANY PRIOR RECIPIENT. IF RECIPIENT'S USE OF THE SUBJECT SOFTWARE RESULTS\n * IN ANY LIABILITIES, DEMANDS, DAMAGES, EXPENSES OR LOSSES ARISING FROM SUCH\n * USE, INCLUDING ANY DAMAGES FROM PRODUCTS BASED ON, OR RESULTING FROM,\n * RECIPIENT'S USE OF THE SUBJECT SOFTWARE, RECIPIENT SHALL INDEMNIFY AND HOLD\n * HARMLESS THE UNITED STATES GOVERNMENT, ITS CONTRACTORS AND SUBCONTRACTORS,\n * AS WELL AS ANY PRIOR RECIPIENT, TO THE EXTENT PERMITTED BY LAW.\n * RECIPIENT'S SOLE REMEDY FOR ANY SUCH MATTER SHALL BE THE IMMEDIATE,\n * UNILATERAL TERMINATION OF THIS AGREEMENT.\n *\n ******************************************************************************/\n\n#pragma once\n\n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n\nnamespace ikos {\n\n/* Environment from Key to Value with all lattice operations */ \ntemplate class separate_domain {\n\nprivate:\n using patricia_tree_t = patricia_tree;\n using unary_op_t = typename patricia_tree_t::unary_op_t;\n using binary_op_t = typename patricia_tree_t::binary_op_t;\n using partial_order_t = typename patricia_tree_t::partial_order_t;\n\npublic:\n using separate_domain_t = separate_domain;\n using iterator = typename patricia_tree_t::iterator;\n using key_type = Key;\n using value_type = Value;\n\nprivate:\n bool _is_bottom;\n patricia_tree_t _tree;\n\npublic:\n class join_op : public binary_op_t {\n std::pair> apply(Value x, Value y) {\n Value z = x.operator|(y);\n if (z.is_top()) {\n return {false, boost::optional()};\n } else {\n return {false, boost::optional(z)};\n }\n };\n\n bool default_is_absorbing() { return true; }\n }; // class join_op\n\n class widening_op : public binary_op_t {\n std::pair> apply(Value x, Value y) {\n Value z = x.operator||(y);\n if (z.is_top()) {\n return {false, boost::optional()};\n } else {\n return {false, boost::optional(z)};\n }\n };\n\n bool default_is_absorbing() { return true; }\n\n }; // class widening_op\n\n template \n class widening_thresholds_op : public binary_op_t {\n const Thresholds &m_ts;\n\n public:\n widening_thresholds_op(const Thresholds &ts) : m_ts(ts) {}\n\n std::pair> apply(Value x, Value y) {\n Value z = x.widening_thresholds(y, m_ts);\n if (z.is_top()) {\n return {false, boost::optional()};\n } else {\n return {false, boost::optional(z)};\n }\n };\n\n bool default_is_absorbing() { return true; }\n\n }; // class widening_thresholds_op\n\n class meet_op : public binary_op_t {\n std::pair> apply(Value x, Value y) {\n Value z = x.operator&(y);\n if (z.is_bottom()) {\n return {true, boost::optional()};\n } else {\n return {false, boost::optional(z)};\n }\n };\n\n bool default_is_absorbing() { return false; }\n\n }; // class meet_op\n\n class narrowing_op : public binary_op_t {\n std::pair> apply(Value x, Value y) {\n Value z = x.operator&&(y);\n if (z.is_bottom()) {\n return {true, boost::optional()};\n } else {\n return {false, boost::optional(z)};\n }\n };\n\n bool default_is_absorbing() { return false; }\n\n }; // class narrowing_op\n\n class domain_po : public partial_order_t {\n bool leq(Value x, Value y) { return x.operator<=(y); }\n\n bool default_is_top() { return true; }\n\n }; // class domain_po\n\npublic:\n static separate_domain_t top() { return separate_domain_t(); }\n\n static separate_domain_t bottom() { return separate_domain_t(false); }\n\nprivate:\n static patricia_tree_t apply_operation(binary_op_t &o, patricia_tree_t t1,\n const patricia_tree_t &t2,\n bool &is_bottom) {\n is_bottom = t1.merge_with(t2, o);\n return t1;\n }\n\n separate_domain(patricia_tree_t t) : _is_bottom(false), _tree(t) {}\n\n separate_domain(bool b) : _is_bottom(!b) {}\n\npublic:\n separate_domain() : _is_bottom(false) {}\n\n separate_domain(const separate_domain_t &e)\n : _is_bottom(e._is_bottom), _tree(e._tree) {}\n\n separate_domain(const separate_domain_t &&e)\n : _is_bottom(e._is_bottom), _tree(std::move(e._tree)) {}\n\n separate_domain_t &operator=(separate_domain_t e) {\n this->_is_bottom = e._is_bottom;\n this->_tree = e._tree;\n return *this;\n }\n\n iterator begin() const {\n if (this->is_bottom()) {\n CRAB_ERROR(\"Separate domain: trying to invoke iterator on bottom\");\n } else {\n return this->_tree.begin();\n }\n }\n\n iterator end() const {\n if (this->is_bottom()) {\n CRAB_ERROR(\"Separate domain: trying to invoke iterator on bottom\");\n } else {\n return this->_tree.end();\n }\n }\n\n bool is_bottom() const { return this->_is_bottom; }\n\n bool is_top() const {\n return (!this->is_bottom() && this->_tree.size() == 0);\n }\n\n bool operator<=(const separate_domain_t &e) const {\n if (this->is_bottom()) {\n return true;\n } else if (e.is_bottom()) {\n return false;\n } else {\n domain_po po;\n return this->_tree.leq(e._tree, po);\n }\n }\n\n bool operator==(const separate_domain_t &e) const {\n return (this->operator<=(e) && e.operator<=(*this));\n }\n\n // Join\n separate_domain_t operator|(const separate_domain_t &e) const {\n if (this->is_bottom()) {\n return e;\n } else if (e.is_bottom()) {\n return *this;\n } else {\n join_op o;\n bool is_bottom /*unused*/;\n patricia_tree_t res = apply_operation(o, this->_tree, e._tree, is_bottom);\n return separate_domain_t(std::move(res));\n }\n }\n\n // Meet\n separate_domain_t operator&(const separate_domain_t &e) const {\n if (this->is_bottom() || e.is_bottom()) {\n return this->bottom();\n } else {\n meet_op o;\n bool is_bottom;\n patricia_tree_t res = apply_operation(o, this->_tree, e._tree, is_bottom);\n if (is_bottom) {\n return this->bottom();\n } else {\n return separate_domain_t(std::move(res));\n }\n }\n }\n\n // Widening\n separate_domain_t operator||(const separate_domain_t &e) const {\n if (this->is_bottom()) {\n return e;\n } else if (e.is_bottom()) {\n return *this;\n } else {\n widening_op o;\n bool is_bottom /*unused*/;\n patricia_tree_t res = apply_operation(o, this->_tree, e._tree, is_bottom);\n return separate_domain_t(std::move(res));\n }\n }\n\n // Widening with thresholds\n template \n separate_domain_t widening_thresholds(const separate_domain_t &e,\n const Thresholds &ts) const {\n if (this->is_bottom()) {\n return e;\n } else if (e.is_bottom()) {\n return *this;\n } else {\n widening_thresholds_op o(ts);\n bool is_bottom /*unused*/;\n patricia_tree_t res = apply_operation(o, this->_tree, e._tree, is_bottom);\n return separate_domain_t(std::move(res));\n }\n }\n\n // Narrowing\n separate_domain_t operator&&(const separate_domain_t &e) const {\n if (this->is_bottom() || e.is_bottom()) {\n return separate_domain_t(false);\n } else {\n narrowing_op o;\n bool is_bottom;\n patricia_tree_t res = apply_operation(o, this->_tree, e._tree, is_bottom);\n if (is_bottom) {\n return this->bottom();\n } else {\n return separate_domain_t(std::move(res));\n }\n }\n }\n\n void set(Key k, Value v) {\n if (!this->is_bottom()) {\n if (v.is_bottom()) {\n set_to_bottom();\n } else if (v.is_top()) {\n this->_tree.remove(k);\n } else {\n this->_tree.insert(k, v);\n }\n }\n }\n\n void set_to_bottom() {\n this->_is_bottom = true;\n this->_tree = patricia_tree_t();\n }\n\n separate_domain_t &operator-=(const Key &k) {\n if (!this->is_bottom()) {\n this->_tree.remove(k);\n }\n return *this;\n }\n\n Value operator[](const Key &k) const {\n if (this->is_bottom()) {\n return Value::bottom();\n } else {\n boost::optional v = this->_tree.lookup(k);\n if (v) {\n return *v;\n } else {\n return Value::top();\n }\n }\n }\n\n std::size_t size() const {\n if (is_bottom()) {\n return 0;\n } else if (is_top()) {\n CRAB_ERROR(\"separate_domains::size() is undefined if top\");\n } else {\n return this->_tree.size();\n }\n }\n\n void write(crab::crab_os &o) const {\n if (this->is_bottom()) {\n o << \"_|_\";\n } else {\n o << \"{\";\n for (typename patricia_tree_t::iterator it = this->_tree.begin();\n it != this->_tree.end();) {\n Key k = it->first;\n k.write(o);\n o << \" -> \";\n Value v = it->second;\n v.write(o);\n ++it;\n if (it != this->_tree.end()) {\n o << \"; \";\n }\n }\n o << \"}\";\n }\n }\n\n void project(const std::vector &keys) {\n if (is_bottom() || is_top()) {\n return;\n }\n const int factor = 60; // between 0 and 100\n const int small_env = 5; // size of a small environment\n int num_total_keys = (int)this->_tree.size();\n int num_keys = (int)keys.size();\n if (num_total_keys <= small_env ||\n num_keys < num_total_keys * factor / 100) {\n // project on less than factor% of keys: we copy\n separate_domain_t env;\n for (auto key : keys) {\n env.set(key, operator[](key));\n }\n std::swap(*this, env);\n } else {\n // project on more or equal then factor% of keys: that\n // might be too many copies so we remove instead.\n std::vector sorted_keys(keys);\n std::sort(sorted_keys.begin(), sorted_keys.end());\n std::vector project_out_keys;\n project_out_keys.reserve(num_total_keys);\n for (auto it = this->_tree.begin(); it != this->_tree.end(); ++it) {\n Key key = it->first;\n if (!std::binary_search(sorted_keys.begin(), sorted_keys.end(), key)) {\n project_out_keys.push_back(key);\n }\n }\n for (auto const &key : project_out_keys) {\n this->operator-=(key);\n }\n }\n }\n\n // Assume that from does not have duplicates.\n void rename(const std::vector &from, const std::vector &to) {\n if (is_top() || is_bottom()) {\n // nothing to rename\n return;\n }\n if (from.size() != to.size()) {\n CRAB_ERROR(\n \"separate_domain::rename with input vectors of different sizes\");\n }\n\n if (::crab::CrabSanityCheckFlag) {\n std::set s1, s2;\n s1.insert(from.begin(), from.end());\n if (s1.size() != from.size()) {\n CRAB_ERROR(\"separate_domain::rename expects no duplicates\");\n }\n s2.insert(to.begin(), to.end());\n if (s2.size() != to.size()) {\n CRAB_ERROR(\"separate_domain::rename expects no duplicates\");\n }\n }\n\n for (unsigned i = 0, sz = from.size(); i < sz; ++i) {\n Key k = from[i];\n Key new_k = to[i];\n if (k == new_k) { // nothing to rename\n continue;\n }\n if (::crab::CrabSanityCheckFlag) {\n if (_tree.lookup(new_k)) {\n CRAB_ERROR(\"separate_domain::rename assumes that \", new_k,\n \" does not exist in \", *this);\n }\n }\n if (boost::optional k_val_opt = _tree.lookup(k)) {\n if (!(*k_val_opt).is_top()) {\n _tree.insert(new_k, *k_val_opt);\n }\n _tree.remove(k);\n }\n }\n }\n\n friend crab::crab_os &operator<<(crab::crab_os &o,\n const separate_domain &d) {\n d.write(o);\n return o;\n }\n}; // class separate_domain\n\n} // namespace ikos\n\n\n\n\nnamespace crab {\nnamespace domains {\n//===================================================================// \n// The NOSA license does not apply to this code \n//===================================================================// \n\n/* \n * Environment from Key to discrete_domain\n *\n * We cannot use separate_domain because if the value of a key-value\n * pair is bottom then the whole environment becomes bottom. Here, if\n * the value is bottom then it means that the value is simply the\n * \"empty set\" so we want to allow entries whose values are\n * bottom. But, similar to separate_domain, the whole environment can\n * still be bottom meaning failure/undefined/unreachable.\n */ \ntemplate class separate_discrete_domain {\n\nprivate:\n using discrete_domain_t = ikos::discrete_domain;\n using patricia_tree_t = ikos::patricia_tree;\n using unary_op_t = typename patricia_tree_t::unary_op_t;\n using binary_op_t = typename patricia_tree_t::binary_op_t;\n using partial_order_t = typename patricia_tree_t::partial_order_t;\n\npublic:\n using key_type = Key;\n using value_type = discrete_domain_t; \n using separate_discrete_domain_t = separate_discrete_domain;\n using iterator = typename patricia_tree_t::iterator;\n\nprivate:\n bool m_is_bottom;\n patricia_tree_t m_tree;\n\n static patricia_tree_t apply_operation(binary_op_t &o,\n const patricia_tree_t &t1,\n const patricia_tree_t &t2) {\n patricia_tree_t res(t1);\n res.merge_with(t2, o);\n return res;\n }\n /* begin patricia_tree API */\n class join_op : public binary_op_t {\n std::pair> apply(value_type x, value_type y) override {\n value_type z = x.operator|(y);\n if (z.is_top()) {\n\t// special encoding for top: the patricia tree will not keep\n\t// top values.\n return {false, boost::optional()};\n } else {\n return {false, boost::optional(z)};\n }\n }\n bool default_is_absorbing() override { return true; } \n }; // class join_op\n\n class meet_op : public binary_op_t {\n std::pair> apply(value_type x, value_type y) override {\n value_type z = x.operator&(y);\n // Returning this pair means that if z is bottom do not treat it\n // special and just update the patricia tree with z.\n return {false, boost::optional(z)};\n };\n bool default_is_absorbing() override { return false; }\n }; // class meet_op\n\n class domain_po : public partial_order_t {\n bool leq(value_type x, value_type y) { return x.operator<=(y); }\n bool default_is_top() { return true; }\n }; // class domain_po\n /* end patricia_tree API */\n\n \n separate_discrete_domain(patricia_tree_t &&t)\n : m_is_bottom(false), m_tree(std::move(t)) {}\n\npublic:\n \n static separate_discrete_domain_t top() {\n return separate_discrete_domain_t();\n }\n\n static separate_discrete_domain_t bottom() {\n return separate_discrete_domain_t(true);\n }\n\n // Default constructor returns a top environment\n separate_discrete_domain(bool is_bottom = false)\n : m_is_bottom(is_bottom), m_tree(patricia_tree_t()) {}\n \n separate_discrete_domain(const separate_discrete_domain_t &o) = default;\n separate_discrete_domain(separate_discrete_domain_t &&o) = default;\n separate_discrete_domain_t &operator=(const separate_discrete_domain_t &o) = default;\n separate_discrete_domain_t &operator=(separate_discrete_domain_t &&o) = default;\n \n iterator begin() const {\n if (is_bottom()) {\n CRAB_ERROR(\"Separate discrete domain: trying to invoke iterator on bottom\");\n } else {\n return m_tree.begin();\n }\n }\n\n iterator end() const {\n if (is_bottom()) {\n CRAB_ERROR(\"Separate discrete domain: trying to invoke iterator on bottom\");\n } else {\n return m_tree.end();\n }\n }\n\n bool is_bottom() const { return m_is_bottom; }\n\n bool is_top() const {\n return (!is_bottom()&& m_tree.size() == 0);\n }\n\n bool operator<=(const separate_discrete_domain_t &other) const {\n if (is_bottom() || other.is_top()) {\n return true;\n } else if (other.is_bottom() || is_top()) {\n return false;\n } else {\n domain_po po;\n return m_tree.leq(other.m_tree, po);\n }\n }\n \n separate_discrete_domain_t\n operator|(const separate_discrete_domain_t &o) const {\n CRAB_LOG(\"separate-domain\",\n\t crab::outs() << \"Join \" << *this << \" and \" << o << \"=\";); \n \n if (is_bottom() || o.is_top()) {\n CRAB_LOG(\"separate-domain\",\n\t crab::outs() << \"Res=\" << o << \"\\n\";);\n return o;\n } else if (o.is_bottom() || is_top()) {\n CRAB_LOG(\"separate-domain\",\n\t crab::outs() << \"Res=\" << *this << \"\\n\";); \n return *this;\n } else {\n join_op op;\n patricia_tree_t res = apply_operation(op, m_tree, o.m_tree);\n auto out = separate_discrete_domain_t(std::move(res));\n CRAB_LOG(\"separate-domain\",\n\t crab::outs() << \"Res=\" << out << \"\\n\";);\n return out;\n }\n }\n\n separate_discrete_domain_t\n operator&(const separate_discrete_domain_t &o) const {\n CRAB_LOG(\"separate-domain\",\n\t crab::outs() << \"Meet \" << *this << \" and \" << o << \"=\";); \n \n if (is_top() || o.is_bottom()) {\n CRAB_LOG(\"separate-domain\",\n\t crab::outs() << \"Res=\" << o << \"\\n\";);\n \n return o;\n } else if (o.is_top() || is_bottom()) {\n CRAB_LOG(\"separate-domain\",\n\t crab::outs() << \"Res=\" << *this << \"\\n\";);\n \n return *this;\n } else {\n meet_op op;\n patricia_tree_t res = apply_operation(op, m_tree, o.m_tree);\n auto out = separate_discrete_domain_t(std::move(res));\n CRAB_LOG(\"separate-domain\",\n\t crab::outs() << \"Res=\" << out << \"\\n\";);\n return out;\n }\n }\n\n void set(const Key &k, value_type v) {\n // Note that we can store a key-value pair where the value is\n // bottom because bottom means empty set.\n if (!is_bottom()) {\n if (v.is_top()) {\n\tm_tree.remove(k);\n } else {\n\tm_tree.insert(k, v);\n }\n }\n }\n\n separate_discrete_domain_t &operator-=(const Key &k) {\n if (!is_bottom()) {\n m_tree.remove(k);\n }\n return *this;\n }\n\n value_type operator[](const Key &k) {\n if (is_bottom()) {\n CRAB_ERROR(\"separate_discrete_domain::operator[] is undefined on bottom\");\n } else if (is_top()) {\n return value_type::top();\n } else {\n if (boost::optional v = m_tree.lookup(k)) {\n return *v;\n } else {\n return value_type::top();\n }\n }\n }\n\n void project(const std::vector &keys) {\n if (is_bottom() || is_top()) {\n return;\n }\n const int factor = 60; // between 0 and 100\n const int small_env = 5; // size of a small environment\n int num_total_keys = (int)m_tree.size();\n int num_keys = (int)keys.size();\n if (num_total_keys <= small_env ||\n num_keys < num_total_keys * factor / 100) {\n // project on less than factor% of keys: we copy\n separate_discrete_domain_t env;\n for (auto key : keys) {\n env.set(key, operator[](key));\n }\n std::swap(*this, env);\n } else {\n // project on more or equal then factor% of keys: that\n // might be too many copies so we remove instead.\n std::vector sorted_keys(keys);\n std::sort(sorted_keys.begin(), sorted_keys.end());\n std::vector project_out_keys;\n project_out_keys.reserve(num_total_keys);\n for (auto it = m_tree.begin(); it != m_tree.end(); ++it) {\n Key key = it->first;\n if (!std::binary_search(sorted_keys.begin(), sorted_keys.end(), key)) {\n project_out_keys.push_back(key);\n }\n }\n for (auto const &key : project_out_keys) {\n this->operator-=(key);\n }\n }\n }\n\n // Assume that from does not have duplicates.\n void rename(const std::vector &from, const std::vector &to) {\n if (is_top() || is_bottom()) {\n // nothing to rename\n return;\n }\n if (from.size() != to.size()) {\n CRAB_ERROR(\n \"separate_discrete_domain::rename with input vectors of different sizes\");\n }\n\n if (::crab::CrabSanityCheckFlag) {\n std::set s1, s2;\n s1.insert(from.begin(), from.end());\n if (s1.size() != from.size()) {\n CRAB_ERROR(\"separate_discrete_domain::rename expects no duplicates\");\n }\n s2.insert(to.begin(), to.end());\n if (s2.size() != to.size()) {\n CRAB_ERROR(\"separate_discrete_domain::rename expects no duplicates\");\n }\n }\n\n for (unsigned i = 0, sz = from.size(); i < sz; ++i) {\n Key k = from[i];\n Key new_k = to[i];\n if (k == new_k) { // nothing to rename\n continue;\n }\n if (::crab::CrabSanityCheckFlag) {\n if (m_tree.lookup(new_k)) {\n CRAB_ERROR(\"separate_discrete_domain::rename assumes that \",\n\t\t new_k, \" does not exist in \", *this);\n }\n }\n if (boost::optional val_opt = m_tree.lookup(k)) {\n if (!(*val_opt).is_top()) {\n m_tree.insert(new_k, *val_opt);\n }\n m_tree.remove(k);\n }\n }\n }\n\n \n void write(crab::crab_os &o) const {\n if (is_top()) {\n o << \"{}\";\n } else if (is_bottom()) {\n o << \"_|_\";\n } else {\n o << \"{\";\n for (typename patricia_tree_t::iterator it = m_tree.begin();\n it != m_tree.end();) {\n Key k = it->first;\n k.write(o);\n o << \" -> \";\n value_type v = it->second;\n v.write(o);\n ++it;\n if (it != m_tree.end()) {\n o << \"; \";\n }\n }\n o << \"}\";\n }\n }\n}; // class separate_discrete_domain\n\ntemplate \ninline crab_os &operator<<(crab_os &o,\n const separate_discrete_domain &env) {\n env.write(o);\n return o;\n}\n\n} //end namespace domains \n} //end namespace crab\n", "meta": {"hexsha": "79f00e58e005dcc36805e69f9e975744e2f9f6e3", "size": 23430, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/crab/domains/separate_domains.hpp", "max_stars_repo_name": "LinerSu/crab", "max_stars_repo_head_hexsha": "8f3516f4b4765f4a093bb3c3a94ac2daa174130c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 152.0, "max_stars_repo_stars_event_min_datetime": "2016-02-28T06:04:02.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T10:44:56.000Z", "max_issues_repo_path": "include/crab/domains/separate_domains.hpp", "max_issues_repo_name": "LinerSu/crab", "max_issues_repo_head_hexsha": "8f3516f4b4765f4a093bb3c3a94ac2daa174130c", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 43.0, "max_issues_repo_issues_event_min_datetime": "2017-07-03T06:25:19.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-23T21:09:32.000Z", "max_forks_repo_path": "include/crab/domains/separate_domains.hpp", "max_forks_repo_name": "LinerSu/crab", "max_forks_repo_head_hexsha": "8f3516f4b4765f4a093bb3c3a94ac2daa174130c", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 28.0, "max_forks_repo_forks_event_min_datetime": "2015-11-22T15:51:52.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-30T00:46:57.000Z", "avg_line_length": 29.8852040816, "max_line_length": 94, "alphanum_fraction": 0.6015364917, "num_tokens": 5993, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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NO", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.21437849548718665}} {"text": "// Copyright (c) Microsoft Corporation.\n// Licensed under the MIT License.\n\n#include \"Tracking\\ComputeEssential.h\"\n#include \"Tracking\\MapInitialization.h\"\n#include \"Mapping\\NewMapPointsCreation.h\"\n#include \"Tracking\\Triangulation.h\"\n#include \"Tracking\\Reprojection.h\"\n#include \"MageSettings.h\"\n#include \"Utils\\Epipolar.h\"\n#include \"Map\\Map.h\"\n#include \"Map\\MapPoint.h\"\n#include \"KeyframeBuilder.h\"\n#include \"Utils\\format.h\"\n#include \"Utils\\cv.h\"\n#include \"FeatureMatcher.h\"\n#include \"BundleAdjustment\\BundleAdjust.h\"\n#include \"Debugging\\SkeletonLogger.h\"\n#include \"arcana\\analysis\\determinator.h\"\n#include \"Analysis\\binary_iterators.h\"\n\n#include \n#include \n\n#include \"Utils\\Logging.h\"\n\n#include \n\n#include \n#include \n\nusing namespace std;\n\nnamespace mage\n{\n namespace\n {\n float ComputePerpDistanceSquared(\n const cv::Matx31f& line,\n const cv::Matx31f& point)\n {\n auto A = line(0, 0);\n auto B = line(1, 0);\n auto C = line(2, 0);\n auto X = point(0, 0);\n auto Y = point(1, 0);\n\n auto numerator = (A * X) + (B*Y) + C;\n auto denominatorSquared = (A*A) + (B*B);\n\n return numerator * numerator / denominatorSquared;\n }\n\n long long ComputeFrameTimeDifference(const shared_ptr& frame1, const shared_ptr& frame2)\n {\n return std::chrono::duration_cast(frame2->GetTimeStamp() - frame1->GetTimeStamp()).count();\n }\n\n void CreateNewMapPointsHelper(InitializationData& initializationData, mage::temp::vector& Kcs, const KeyframeProxy& Ki, BaseBow& bagOfWords, const MonoMapInitializationSettings& settings, const PerCameraSettings& cameraSettings, thread_memory memory)\n {\n // compute the current scale for the initialization pair\n float scale = static_cast(cv::norm(initializationData.Frames.back()->GetPose().GetViewSpacePosition(), cv::NORM_L2));\n float minParallaxScale = settings.MapInitializationNewPointsCreationMinDistance * scale;\n\n vector newMapPoints;\n NewMapPointsCreation(bagOfWords, Ki, cameraSettings,\n settings.NewMapPointsCreationSettings,\n minParallaxScale, memory, Kcs, newMapPoints);\n\n //associate new map points with the keyframes it is visible in\n for (const auto& curMapPoint : newMapPoints)\n {\n initializationData.MapPoints.emplace_back(curMapPoint.MapPoint);\n for (const KeyframeAssociation& curKeyframeAssoc : curMapPoint.Keyframes)\n {\n auto found = std::find_if(initializationData.Frames.begin(), initializationData.Frames.end(),\n [curKeyframeAssoc](const auto& keyframe) { return keyframe->GetId() == curKeyframeAssoc.KeyframeId; });\n assert(found != initializationData.Frames.end());\n (*found)->AddAssociation(curMapPoint.MapPoint.As(), curKeyframeAssoc.KeypointDescriptorIndex);\n }\n }\n }\n }\n\n vector MapInitialization::FindEssentialPotientialPoses(\n const cv::Matx33f& essentialMatrix)\n {\n cv::Mat R1, R2;\n cv::Mat T;\n\n //This gives us back 2 possible rotations, and a translation that can be negative\n // From the docs:\n // Generally 4 possible poses exists for a given E.\n // They are[R1, t], [R1, -t], [R2, t], [R2, -t].\n cv::decomposeEssentialMat(essentialMatrix, R1, R2, T);\n\n const cv::Vec3f normalizedTranslationViewSpace = Normalize(cv::Vec3f{ T });\n const cv::Vec3f negT = -1 * normalizedTranslationViewSpace;\n\n vector poses;\n poses.reserve(4);\n\n //pose of R1, T\n Pose p1 = { normalizedTranslationViewSpace, R1 };\n poses.emplace_back(p1);\n poses.emplace_back(negT, R1);\n\n //pose of R2, T\n Pose p2 = { T, R2 };\n poses.emplace_back(p2);\n poses.emplace_back(negT, R2);\n\n return poses;\n }\n\n MapInitialization::MapInitialization(\n const MonoMapInitializationSettings& settings,\n const PerCameraSettings& cameraSettings,\n const device::IMUCharacterization& imuCharacterization,\n mira::determinator& determinator)\n : m_settings{ settings },\n m_cameraSettings{ cameraSettings },\n m_imuCharacterization{ imuCharacterization },\n m_determinator{ determinator }\n {\n ResetMapInitialization();\n }\n\n bool HasConsistentCheirality(const vector& frame2Poses, /*cv::Vec3f& dominantDirection,*/\n const CameraCalibration& frame1Calibration, const CameraCalibration& frame2Calibration,\n const vector& frame1Points, const vector& frame2Points)\n {\n assert(frame1Points.size() == frame2Points.size());\n\n // Find any of the poses that have the expected orientation, to simplify cheirality computations.\n auto found = find_if(frame2Poses.begin(), frame2Poses.end(), [](const Pose& p) { return p.GetWorldSpaceRight()[0] > 0; });\n if (found == frame2Poses.end())\n return false;\n const Pose& frame2Pose = *found;\n\n // First frame is always at origin and identity.\n const Pose frame1Pose;\n\n const cv::Matx33f& frame1InvCam = frame1Calibration.GetInverseCameraMatrix();\n const cv::Matx33f& frame2InvCam = frame2Calibration.GetInverseCameraMatrix();\n const cv::Matx44f& frame1World = frame1Pose.GetInverseViewMatrix();\n const cv::Matx44f& frame2World = frame2Pose.GetInverseViewMatrix();\n\n const cv::Vec3f frame1Forward = frame1Pose.GetWorldSpaceForward();\n const cv::Vec3f frame2Forward = frame2Pose.GetWorldSpaceForward();\n\n int concensus = 1;\n {\n const cv::Point3f current3DPoint = TriangulatePointWorldSpace(frame1InvCam, frame1World, frame2InvCam, frame2World,\n frame1Points[0], frame2Points[0]);\n\n const cv::Vec3f fromFrame1 = current3DPoint - frame1Pose.GetWorldSpacePosition();\n const cv::Vec3f fromFrame2 = current3DPoint - frame2Pose.GetWorldSpacePosition();\n\n if (frame1Forward.dot(fromFrame1) <= 0.f || frame2Forward.dot(fromFrame2) <= 0.f)\n concensus = -1;\n }\n\n for (size_t pointIterator = 1; pointIterator < frame1Points.size(); pointIterator++)\n {\n const cv::Point3f current3DPoint = TriangulatePointWorldSpace(frame1InvCam, frame1World, frame2InvCam, frame2World,\n frame1Points[pointIterator], frame2Points[pointIterator]);\n\n const cv::Vec3f fromFrame1 = current3DPoint - frame1Pose.GetWorldSpacePosition();\n const cv::Vec3f fromFrame2 = current3DPoint - frame2Pose.GetWorldSpacePosition();\n\n if (concensus * frame1Forward.dot(fromFrame1) <= 0.f || concensus * frame2Forward.dot(fromFrame2) <= 0.f)\n return false;\n }\n\n //dominantDirection = frame2Pose.GetWorldSpacePosition() * concensus;\n \n return true;\n }\n\n vector MapInitialization::FindPossiblePoses(\n gsl::span frame1Points,\n gsl::span frame2Points,\n const CameraCalibration& frame1Calibration,\n const CameraCalibration& frame2Calibration)\n {\n SCOPE_TIMER(MapInitialization::FindPossiblePoses);\n struct InitialPoseEstimate\n {\n float Score;\n std::vector Candidates;\n };\n\n InitialPoseEstimate bestFive{ 0.0f, {} };\n\n cv::RNG rng(12345);\n\n const size_t pointCount = 5;\n\n std::vector frame1Points_subset;\n std::vector frame2Points_subset;\n frame1Points_subset.reserve(pointCount);\n frame2Points_subset.reserve(pointCount);\n\n const cv::Point2f principalPoint{ frame1Calibration.GetPrincipalPointX(),frame1Calibration.GetPrincipalPointY() };\n const cv::Matx33f inverseCalibrationCamera2Transpose = frame2Calibration.GetInverseCameraMatrix().t();\n const cv::Matx33f inverseCalibrationCamera1 = frame1Calibration.GetInverseCameraMatrix();\n \n const float minPixelDiffSquared = m_settings.MinPixelSpread * m_settings.MinPixelSpread;\n for (unsigned int i = 0; i < m_settings.RansacIterationsForModels; i++)\n {\n frame1Points_subset.clear();\n frame2Points_subset.clear();\n \n size_t tryCount = 0;\n std::set randomIndices;\n while (randomIndices.size() < pointCount && tryCount < 100)\n {\n tryCount++;\n int candidateIndex = rng.uniform(0, frame1Points.size());\n\n bool tooClose = std::any_of(randomIndices.cbegin(), randomIndices.cend(), [&](int value)\n {\n const auto pixelDiff1 = frame1Points[value] - frame1Points[candidateIndex];\n const float dist2_1 = pixelDiff1.dot(pixelDiff1);\n\n const auto pixelDiff2 = frame2Points[value] - frame2Points[candidateIndex];\n const float dist2_2 = pixelDiff2.dot(pixelDiff2);\n\n return dist2_1 < minPixelDiffSquared || dist2_2 < minPixelDiffSquared;\n });\n\n if (!tooClose)\n {\n randomIndices.insert(candidateIndex);\n }\n }\n\n // if we weren't able to find a set of points, reset and try again\n if (randomIndices.size() < pointCount)\n {\n continue;\n }\n\n for (auto index : randomIndices)\n {\n frame1Points_subset.push_back(frame1Points[index]);\n frame2Points_subset.push_back(frame2Points[index]);\n }\n\n // Essential Matrix estimation (5 point)\n cv::Mat essentialMat1To2 = mira::FindEssentialMat(frame1Points_subset, frame2Points_subset, frame1Calibration.GetFocalLengthX(), frame1Calibration.GetFocalLengthY(), principalPoint, 1e-6f);\n\n // openCV may actually compute several essential matrixes\n int rowIterator = 0;\n while (essentialMat1To2.rows - rowIterator >= 3)\n {\n cv::Matx33f ess = cv::Matx33f{ essentialMat1To2.rowRange(rowIterator, rowIterator + 3) };\n cv::Matx33f fundamental1to2 = inverseCalibrationCamera2Transpose * ess * inverseCalibrationCamera1;\n cv::Matx33f fundamental2to1 = fundamental1to2.t();\n float fiveScore = ScoreFundamentalMatrix(frame1Points, frame2Points, fundamental1to2, fundamental2to1);\n\n if (fiveScore > bestFive.Score)\n {\n auto possiblePoses = FindEssentialPotientialPoses(ess);\n if (possiblePoses.size() > 0 && HasConsistentCheirality(possiblePoses, frame1Calibration, frame2Calibration, frame1Points_subset, frame2Points_subset))\n {\n bestFive = { fiveScore, possiblePoses };\n }\n }\n\n rowIterator += 3;\n }\n }\n\n return bestFive.Candidates;\n }\n\n float MapInitialization::ScoreFundamentalMatrix(\n gsl::span frame1Points,\n gsl::span frame2Points,\n const cv::Matx33f& fundamentalMat1To2,\n const cv::Matx33f& fundamentalMat2To1)\n {\n float Total_F_sum = 0;\n size_t inlierCount = 0;\n\n //compute the difference between:\n // point1 * (Fundamental Matrix) vs actual points 2\n // point2 * (Fundamental Matrix Transpose) vs actual points 1\n for (auto frame1 = frame1Points.begin(), frame2 = frame2Points.begin();\n frame1 != frame1Points.end() && frame2 != frame2Points.end(); ++frame1, ++frame2)\n {\n cv::Matx31f m1{ frame1->x, frame1->y, 1 };\n cv::Matx31f m2{ frame2->x, frame2->y, 1 };\n\n cv::Matx31f result12 = fundamentalMat1To2 * m1;\n cv::Matx31f result21 = fundamentalMat2To1 * m2;\n\n float diff12Squared = ComputePerpDistanceSquared(result12, m2);\n float diff21Squared = ComputePerpDistanceSquared(result21, m1);\n\n //only add to the sum if the squared difference is less than the inlier threshold\n if (diff12Squared < m_settings.FundamentalTransferErrorThreshold\n && diff21Squared < m_settings.FundamentalTransferErrorThreshold)\n {\n Total_F_sum += (m_settings.FundamentalTransferErrorThreshold - diff12Squared);\n Total_F_sum += (m_settings.FundamentalTransferErrorThreshold - diff21Squared);\n inlierCount++;\n }\n }\n\n if (inlierCount >= m_settings.MinScoringInliers\n && (float)inlierCount / (float)frame1Points.size() > m_settings.MinInlierPercentage)\n {\n return Total_F_sum;\n }\n else\n {\n return 0;\n }\n }\n\n bool MapInitialization::FindCorrectPose(\n gsl::span frame1Points,\n gsl::span frame2Points,\n gsl::span matches,\n const CameraCalibration& frame1Calibration,\n const CameraCalibration& frame2Calibration,\n const Pose& referencePose,\n const vector& poses,\n Pose& correctPose,\n std::vector>& triangulated3Dpoints)\n {\n SCOPE_TIMER(MapInitialization::FindCorrectPose);\n assert(frame1Points.size() == frame2Points.size());\n assert(triangulated3Dpoints.size() == 0);\n\n cv::Matx34f firstPoseMatrix = referencePose.GetViewMatrix();\n float maxEpipolarError = 2 * m_settings.MaxEpipolarError;\n\n float nextBestPoseScore = 0;\n float selectedPoseScore = 0;\n int selectedPoseIndex = -1;\n\n vector> selectedPosePoints;\n vector> candidatePosePoints;\n candidatePosePoints.reserve(frame1Points.size());\n selectedPosePoints.reserve(frame1Points.size());\n for (uint poseIterator = 0; poseIterator < poses.size(); poseIterator++)\n {\n candidatePosePoints.clear();\n\n const Pose& currentPose = poses.at(poseIterator);\n\n // From a given essential matrix, there are four possible solutions for the relative\n // camera pose; two of these will have a sensible rotation, and two will be \"twisted\"\n // a half-turn around the first camera's forward vector (see Nister's five-point paper,\n // section 3.1). Because we know that such a twist will never occur in the brief \n // interval between our initialization frames, we can early-out if we detect that we're\n // dealing with one of the twisted poses.\n if (currentPose.GetWorldSpaceRight().dot(referencePose.GetWorldSpaceRight()) <= 0.f)\n {\n continue;\n }\n\n const cv::Matx33f fundamentalMat = ComputeFundamentalMatrix(referencePose, frame1Calibration, currentPose, frame2Calibration);\n const cv::Matx33f fundamentalMat2 = ComputeFundamentalMatrix(currentPose, frame2Calibration, referencePose, frame1Calibration);\n\n //Triangulate the points from the first and second frame with the first (identity) matrix and one of the possible poses.\n const cv::Matx33f& frame1InvCam = frame1Calibration.GetInverseCameraMatrix();\n const cv::Matx33f& frame2InvCam = frame2Calibration.GetInverseCameraMatrix();\n const cv::Matx44f& frame1World = referencePose.GetInverseViewMatrix();\n const cv::Matx44f& frame2World = currentPose.GetInverseViewMatrix();\n\n // compute a scale factor so that the distance between the computed poses can be \"1\" for calculations\n assert(referencePose.GetWorldSpacePosition().x == 0 && \"referencePose should be identity\");\n assert(referencePose.GetWorldSpacePosition().y == 0 && \"referencePose should be identity\");\n assert(referencePose.GetWorldSpacePosition().z == 0 && \"referencePose should be identity\");\n const float scale = 1.0f / currentPose.GetWorldSpacePosition().dot(currentPose.GetWorldSpacePosition());\n\n float score = 0;\n for (int pointIterator = 0; pointIterator < frame1Points.size(); pointIterator++)\n {\n const cv::Point3f current3DPoint = TriangulatePointWorldSpace(frame1InvCam, frame1World, frame2InvCam, frame2World,\n frame1Points[pointIterator], frame2Points[pointIterator]);\n\n // we only want to accept points which don't project out too far in the Z direction\n // we can get away with just testing the Z direction because the first pose is identity\n // but we do need to compute a normalization for the frame pair for this test to be reasonable\n\n // test that the point is in front\n if (current3DPoint.z < 0)\n {\n continue;\n }\n\n // parallax test\n if (current3DPoint.z * scale > m_settings.MaxParallax3dDistance)\n {\n continue;\n }\n\n const float KcToKiEpipolarError = DistanceFromEpipolarLine(fundamentalMat, frame1Points[pointIterator], frame2Points[pointIterator]);\n const float KiToKcEpiploarError = DistanceFromEpipolarLine(fundamentalMat2, frame2Points[pointIterator], frame1Points[pointIterator]);\n\n float temp = KiToKcEpiploarError + KcToKiEpipolarError;\n\n if (temp < maxEpipolarError)\n {\n score += maxEpipolarError - temp;\n\n candidatePosePoints.emplace_back(matches[pointIterator], current3DPoint);\n }\n }\n \n assert(score >= 0);\n\n if (candidatePosePoints.size() >= m_settings.MinScoringInliers\n && (float)candidatePosePoints.size() / (float)frame1Points.size() > m_settings.MinInlierPercentage)\n {\n //better parallax test\n {\n size_t middleIndex = candidatePosePoints.size() / 2;\n std::nth_element(candidatePosePoints.begin(), candidatePosePoints.begin() + middleIndex, candidatePosePoints.end(),\n [](const pair& matchAndPoint1, const pair& matchAndPoint2)\n {\n return matchAndPoint1.second.z < matchAndPoint2.second.z;\n });\n auto medianDepth = candidatePosePoints[middleIndex].second.z;\n\n // ensure that the depths of the traingulated points match the expected 'good' range\n if (medianDepth <= m_settings.MaxParallax3dMedianDistance)\n {\n if (selectedPoseIndex == -1 || score > selectedPoseScore)\n {\n nextBestPoseScore = selectedPoseScore;\n selectedPoseScore = score;\n selectedPoseIndex = poseIterator;\n std::swap(selectedPosePoints, candidatePosePoints);\n }\n else\n {\n // handle the case that this score isn't best, but is better than next best score\n nextBestPoseScore = max(nextBestPoseScore, score);\n }\n }\n }\n }\n }\n\n // None of the potiential poses are valid\n if (selectedPoseScore <= 0)\n {\n LogMessage(L\"MapInitialization No pose with valid score\");\n return false;\n }\n\n // compute the difference between the two best pose scores, if they are too similar, reject the pairing\n float scoreRatio = (selectedPoseScore - nextBestPoseScore) / selectedPoseScore;\n if (scoreRatio < m_settings.MinCandidatePoseDisimilarity)\n {\n LogMessage(L\"MapInitialization Best poses too similar\");\n return false;\n }\n\n // validate for z-direction constraint\n if (abs(poses.at(selectedPoseIndex).GetWorldSpacePosition().z) > m_settings.MaxPoseContributionZ)\n {\n LogMessage(L\"MapInitialization Best pose too biased in Z direction\");\n return false;\n }\n\n // At least one of the poses is valid, and the most accurate is chosen\n {\n correctPose = poses.at(selectedPoseIndex);\n triangulated3Dpoints = selectedPosePoints;\n return true;\n }\n }\n\n void MapInitialization::ResetMapInitialization()\n {\n LogMessage<>(L\"MapInitialization ResetMapInitialization\");\n m_initializationFrames.clear();\n m_initializationDescriptorsCounters.clear();\n }\n\n MapInitialization::InitializationAttemptState MapInitialization::TryInitializeMap(\n const shared_ptr& frame,\n thread_memory memory,\n InitializationData& initializationData,\n BaseBow& bagOfWords)\n {\n SCOPE_TIMER(MapInitialization::TryInitializeMap);\n\n // ensure that too much time hasn't passed, if so then re-init\n if (m_initializationFrames.size() != 0)\n {\n long long frameTimeDeltaMillisecondsFront = ComputeFrameTimeDifference(m_initializationFrames.front().Image, frame);\n // reset initialization\n if (frameTimeDeltaMillisecondsFront > m_settings.MaxInitializationIntervalMilliseconds)\n {\n ResetMapInitialization();\n }\n else\n {\n // deterministic skipping of frames during initialization period\n // if we are trying to compute poses, then only accept frames which are beyond the interval time delta\n if (frameTimeDeltaMillisecondsFront > m_settings.MinInitializationIntervalMilliseconds)\n {\n long long frameTimeDeltaMillisecondsBack = ComputeFrameTimeDifference(m_initializationFrames.back().Image, frame);\n if (frameTimeDeltaMillisecondsBack < m_settings.MapInitFrameIntervalMilliseconds)\n {\n return InitializationAttemptState::Skipped;\n }\n }\n }\n }\n\n // add the new frame to our list of frames\n m_initializationFrames.emplace_back(frame);\n\n // bootstrap just the call and set things up\n if (m_initializationFrames.size() == 1)\n {\n unsigned int numDescriptors = m_initializationFrames.front().Image->GetDescriptors().size();\n\n if (numDescriptors < m_settings.MinFeatureMatches)\n {\n // not enough matches, reset and try again\n ResetMapInitialization();\n return InitializationAttemptState::ResetInit;\n }\n m_initializationDescriptorsCounters.clear();\n m_initializationDescriptorsCounters.resize(numDescriptors, 1); // each descriptor has been seen in one frame\n\n return InitializationAttemptState::NoPose;\n }\n LogMessage((boost::wformat(L\"MapInitialization frame %d Initializing has %d frames\") % frame->GetFrameId().CorrelationId % m_initializationFrames.size()).str());\n\n if (!TryIntializeMapWithProvidedFrames(m_initializationFrames.front(), m_initializationFrames.back(), memory, initializationData, bagOfWords))\n {\n return InitializationAttemptState::NoPose;\n }\n\n return InitializationAttemptState::FinishInit;\n }\n\n bool MapInitialization::TryIntializeMapWithProvidedFrames(\n const MatchedImage& referenceFrame,\n MatchedImage& currentFrame,\n thread_memory memory,\n InitializationData& initializationData,\n BaseBow& bagOfWords)\n {\n SCOPE_TIMER(MapInitialization::TryIntializeMapWithProvidedFrames);\n\n // match points with the descriptors that have been valid over the set of initialization frames\n //QUERY IMAGE: referenceFrame\n //TRAIN IMAGE: current frame\n int descriptorSizeFrame1 = gsl::narrow_cast(referenceFrame.Image->GetDescriptorsCount());\n int trueCountFrame1 = 0;\n vector flagsFrame1(descriptorSizeFrame1, false);\n for (int i = 0; i < descriptorSizeFrame1; i++)\n {\n if (referenceFrame.Image->GetKeyPoint(i).octave == 0)\n {\n flagsFrame1[i] = true;\n trueCountFrame1 += 1;\n }\n }\n int descriptorSizeFrame2 = gsl::narrow_cast(currentFrame.Image->GetDescriptorsCount());\n int trueCountFrame2 = 0;\n vector flagsFrame2(descriptorSizeFrame2, false);\n for (int i = 0; i < descriptorSizeFrame2; i++)\n {\n if (currentFrame.Image->GetKeyPoint(i).octave == 0)\n {\n flagsFrame2[i] = true;\n trueCountFrame2 += 1;\n }\n }\n\n unsigned int numMatches = Match(referenceFrame.Image,\n currentFrame.Image, flagsFrame1, flagsFrame2, trueCountFrame1, trueCountFrame2,\n m_settings.FivePointMatchingSettings.MaxHammingDistance,\n m_settings.FivePointMatchingSettings.MinHammingDifference, currentFrame.Matches);\n\n SkeletonLogger::MapInitialization::LogMatches(currentFrame.Matches, *referenceFrame.Image, *currentFrame.Image);\n\n if (numMatches < m_settings.MinFeatureMatches)\n {\n SkeletonLogger::MapInitialization::LogFailure(\"numMatches < m_mapInitializationSettings.MinFeatureMatches\");\n\n ResetMapInitialization();\n return false;\n }\n // increment the counter for each feature that we matched\n for (unsigned int row = 0; row < numMatches; row++)\n {\n m_initializationDescriptorsCounters[currentFrame.Matches[row].queryIdx]++;\n }\n\n long long frameTimeDeltaMilliseconds = ComputeFrameTimeDifference(referenceFrame.Image, currentFrame.Image);\n size_t frameCount = m_initializationFrames.size();\n // if the frame count crosses the threshold, then we have a candidate match to try and initialize with\n if (frameTimeDeltaMilliseconds >= m_settings.MinInitializationIntervalMilliseconds)\n {\n // check to make sure that enough of the common features were seen in many of the frames which we have matched\n vector bestMatches;\n\n assert(frameCount * m_settings.FeatureCovisibilityThreshold < 256);\n uint8_t goodFeatureThreshold = (uint8_t)(frameCount * m_settings.FeatureCovisibilityThreshold);\n \n for (unsigned int row = 0; row < numMatches; row++)\n {\n const auto& currentMatch = currentFrame.Matches[row];\n if (m_initializationDescriptorsCounters[currentMatch.queryIdx] > goodFeatureThreshold)\n {\n bestMatches.push_back(currentMatch);\n }\n }\n\n // ensure that we still have enough matches\n if (bestMatches.size() < m_settings.MinFeatureMatches)\n {\n ResetMapInitialization();\n SkeletonLogger::MapInitialization::LogFailure(\"bestMatches.size() < m_mapInitializationSettings.MinFeatureMatches\");\n LogMessage((boost::wformat(L\"MapInitialization Not enough matches : %d\") % bestMatches.size()).str());\n return false;\n }\n\n // try to compute the pose for the frame pairing\n bool initializeSucceeded = InitializeWithFrames(bestMatches, referenceFrame.Image, currentFrame.Image, initializationData, memory);\n LogMessage((boost::wformat(L\"MapInitialization Initializing attempt success? %s\") % (initializeSucceeded ? \"true\" : \"false\")).str());\n SkeletonLogger::MapInitialization::LogFailure(initializeSucceeded ? \"Initialize succeeded\" : \"Initialize failed\");\n\n if (!initializeSucceeded)\n {\n return false;\n }\n\n DETERMINISTIC_CHECK(m_determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n DETERMINISTIC_CHECK(m_determinator, initializationData.MapPoints.begin(), initializationData.MapPoints.end());\n\n // perform new map points creation before bundling this solution to add additional datapoints\n // clear out any existing map points, because we're going to add new ones\n initializationData.MapPoints.clear();\n for (auto& keyframe : initializationData.Frames)\n {\n keyframe->ClearAssociations();\n }\n std::unique_ptr tempBagOfWords = bagOfWords.CreateTemporaryBow();\n\n for (const auto& keyframe : initializationData.Frames)\n {\n tempBagOfWords->AddImage(keyframe->GetId(), *keyframe->GetAnalyzedImage());\n }\n \n // Create a list of connected keyframes which in this case is just thhe first frame\n mage::temp::vector Kcs = memory.stack_vector(initializationData.Frames.size());\n Kcs.emplace_back(*initializationData.Frames.front());\n\n CreateNewMapPointsHelper(initializationData, Kcs, *initializationData.Frames.back(), *tempBagOfWords, m_settings, m_cameraSettings, memory);\n\n // Validate that we were able to create a minimal set of map points using the NewMapPointsCreation technique\n if ((float)initializationData.MapPoints.size() < m_settings.MinInitialMapPoints)\n {\n return false;\n }\n\n // perform an initial bundle adjust of the map before creating new map points\n BundlerSettings bundlerSettings1{\n m_settings.BundleAdjustmentHuberWidth,\n m_settings.MaxOutlierError,\n 1.0f,\n 0.0f,\n false,\n m_settings.BundleAdjustmentG2OSteps,\n m_settings.BundleAdjustmentG2OSteps,\n 0 };\n\n BundleAdjustInitializationData(initializationData, m_determinator, true, bundlerSettings1, memory);\n\n DETERMINISTIC_CHECK(m_determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n DETERMINISTIC_CHECK(m_determinator, initializationData.MapPoints.begin(), initializationData.MapPoints.end());\n\n // re-validate that we have enough map points following post-BA outlier culling\n if ((float)initializationData.MapPoints.size() < m_settings.MinInitialMapPoints)\n {\n return false;\n }\n\n // now we'll add in a third frame and its associations to the existing set of map points\n bool addThirdFrame = m_initializationFrames.size() > 2;\n if (addThirdFrame)\n {\n SCOPE_TIMER(MapInitialization::InitializeWithFrames::LocateThirdFrame);\n \n const auto& frame0 = initializationData.Frames.front();\n const auto& frame1 = initializationData.Frames.back();\n auto& thirdFrame = initializationData.Frames[1];\n const auto& image0 = frame0->GetAnalyzedImage();\n const auto& image1 = frame1->GetAnalyzedImage();\n const auto& image_thirdFrame = thirdFrame->GetAnalyzedImage();\n const Pose& pose0 = frame0->GetPose();\n const Pose& pose1 = frame1->GetPose();\n\n // need to estimate a pose for this new frame. For now just assume in the middle of the initialization pair\n const mage::Quaternion worldRotationQuat = ToQuat(Rotation(pose1.GetInverseViewMatrix()));\n const cv::Vec3f halfPosition = (pose0.GetWorldSpacePosition() + pose1.GetWorldSpacePosition()) / 2;\n const mage::Quaternion halfRotation = ToQuat(Rotation(pose0.GetInverseViewMatrix())).slerp(0.5f, worldRotationQuat);\n Pose additionalPose{ To4x4(ToMat(halfRotation), halfPosition) };\n\n const cv::Matx33f& calibration = image_thirdFrame->GetUndistortedCalibration().GetCameraMatrix();\n std::vector unassociatedKeypointsMask = std::vector(image_thirdFrame->GetDescriptorsCount(), true);\n auto mapPoints = memory.stack_vector(initializationData.MapPoints.size());\n auto mapPointIds = memory.stack_vector>(initializationData.MapPoints.size());\n\n // project the triangulated point back into the third frame and\n // perform a radius match to find the best matching point\n for (const auto& mapPoint : initializationData.MapPoints)\n {\n const mage::Projection projection = mage::ProjectUndistorted(additionalPose.GetViewMatrix(), calibration, mapPoint.GetPosition());\n\n // skip point projected behind the image\n if (projection.Distance < 0) continue;\n\n auto keypointIndex = frame0->GetAssociatedIndex(mapPoint);\n\n cv::DMatch referenceMatch;\n const cv::KeyPoint mapPointKeypoint(projection.Point, -1.0f, 0.0f, 0.0f, 0, -1);\n bool referenceMatched = RadiusMatch(\n mapPointKeypoint,\n nullptr,\n ORBDescriptor::Ref{ image0->GetDescriptor(keypointIndex) },\n image_thirdFrame->GetKeyPoints(),\n image_thirdFrame->GetKeypointSpatialIndex(),\n &unassociatedKeypointsMask,\n image_thirdFrame->GetDescriptors(),\n m_settings.ExtraFrame_SearchRadius,\n m_settings.ExtraFrameMatchingSettings.MaxHammingDistance,\n m_settings.ExtraFrameMatchingSettings.MinHammingDifference,\n memory,\n referenceMatch);\n referenceMatch.queryIdx = gsl::narrow_cast(keypointIndex);\n\n keypointIndex = frame1->GetAssociatedIndex(mapPoint);\n cv::DMatch currentMatch;\n bool currentMatched = RadiusMatch(\n mapPointKeypoint,\n nullptr,\n ORBDescriptor::Ref{ image1->GetDescriptor(keypointIndex) },\n image_thirdFrame->GetKeyPoints(),\n image_thirdFrame->GetKeypointSpatialIndex(),\n &unassociatedKeypointsMask,\n image_thirdFrame->GetDescriptors(),\n m_settings.ExtraFrame_SearchRadius,\n m_settings.FivePointMatchingSettings.MaxHammingDistance,\n m_settings.FivePointMatchingSettings.MinHammingDifference,\n memory,\n currentMatch);\n currentMatch.queryIdx = gsl::narrow_cast(keypointIndex);\n\n // if only one match, use that match\n // if both matched, but to different keypoints then choose the closest match\n if (currentMatched && referenceMatched)\n {\n if (currentMatch.distance < referenceMatch.distance)\n {\n unassociatedKeypointsMask[currentMatch.trainIdx] = false;\n mapPoints.emplace_back(mapPoint.GetPosition(), image_thirdFrame->GetKeyPoint(currentMatch.trainIdx).pt, 1);\n mapPointIds.emplace_back(mapPoint.GetId());\n thirdFrame->AddAssociation(mapPoint, currentMatch.trainIdx);\n }\n else\n {\n unassociatedKeypointsMask[referenceMatch.trainIdx] = false;\n mapPoints.emplace_back(mapPoint.GetPosition(), image_thirdFrame->GetKeyPoint(referenceMatch.trainIdx).pt, 1);\n mapPointIds.emplace_back(mapPoint.GetId());\n thirdFrame->AddAssociation(mapPoint, referenceMatch.trainIdx);\n }\n }\n else if (currentMatched)\n {\n unassociatedKeypointsMask[currentMatch.trainIdx] = false;\n mapPoints.emplace_back(mapPoint.GetPosition(), image_thirdFrame->GetKeyPoint(currentMatch.trainIdx).pt, 1);\n mapPointIds.emplace_back(mapPoint.GetId());\n thirdFrame->AddAssociation(mapPoint, currentMatch.trainIdx);\n }\n else if (referenceMatched)\n {\n unassociatedKeypointsMask[referenceMatch.trainIdx] = false;\n mapPoints.emplace_back(mapPoint.GetPosition(), image_thirdFrame->GetKeyPoint(referenceMatch.trainIdx).pt, 1);\n mapPointIds.emplace_back(mapPoint.GetId());\n thirdFrame->AddAssociation(mapPoint, referenceMatch.trainIdx);\n }\n }\n\n // if the middle initialization frame isn't adding enough value then abort this init\n // and look for a better trio of frames\n if ((float)thirdFrame->GetAssociatedKeypointCount() / (float)initializationData.MapPoints.size() < m_settings.MinThirdFrameMatchPercentage)\n {\n return false;\n }\n\n // perform a track local map like bundle adjust of only this third frame to better align it\n // with the map points that were match above without adjusting the points and culling outliers\n std::vector outliers;\n outliers.reserve(mapPoints.size());\n const float outlierErrorSquared = m_settings.ExtraFrame_MaxOutlierError * m_settings.ExtraFrame_MaxOutlierError;\n Pose updatedPose;\n {\n SCOPE_TIMER(MapInitialization::OptimizeThirdCameraPose);\n updatedPose = TrackLocalMap::OptimizeCameraPose(\n additionalPose,\n image_thirdFrame->GetUndistortedCalibration(),\n mapPoints,\n m_settings.ExtraFrame_BundleAdjustmentSteps,\n outlierErrorSquared,\n m_settings.ExtraFrame_HuberWidth,\n m_determinator,\n memory,\n outliers);\n }\n\n // Cull outliers\n for (const size_t outlierIndex : outliers)\n {\n thirdFrame->RemoveAssociation(mapPointIds[outlierIndex]);\n }\n\n // recheck that outlier culling hasn't dropped our match percentage too low\n if ((float)thirdFrame->GetAssociatedKeypointCount() / (float)initializationData.MapPoints.size() < m_settings.MinThirdFrameMatchPercentage)\n {\n return false;\n }\n\n initializationData.Frames[1]->SetPose(additionalPose);\n // Create a list of connected keyframes of all of the previous inputed frames\n // note that Kcs is already expected to contain the 'front' element\n assert(Kcs.size() == 1 && \"Kcs should contain only one element\");\n assert(Kcs.front().GetId() == initializationData.Frames.front()->GetId() && \"Kcs should contain the first element\");\n Kcs.emplace_back(*initializationData.Frames.back());\n\n // use the middle frame as the seed for creating new map points\n assert(initializationData.Frames.size() == 3 && \"Expecting three frame initialization\");\n CreateNewMapPointsHelper(initializationData, Kcs, *initializationData.Frames[1], *tempBagOfWords, m_settings, m_cameraSettings, memory);\n \n DETERMINISTIC_CHECK(m_determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n DETERMINISTIC_CHECK(m_determinator, initializationData.MapPoints.begin(), initializationData.MapPoints.end());\n\n BundlerSettings finalBundlerSettings{\n m_settings.FinalBA_HuberWidth,\n m_settings.FinalBA_MaxOutlierError,\n m_settings.FinalBA_MaxOutlierErrorScaleFactor,\n m_settings.FinalBA_MinMeanSquareError,\n false,\n m_settings.FinalBA_NumStepsPerRun,\n m_settings.FinalBA_NumSteps,\n 0 };\n\n BundleAdjustInitializationData(initializationData, m_determinator, true, finalBundlerSettings, memory);\n\n DETERMINISTIC_CHECK(m_determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n DETERMINISTIC_CHECK(m_determinator, initializationData.MapPoints.begin(), initializationData.MapPoints.end());\n\n }\n\n if (!ValidateInitializationData(initializationData,\n m_settings.MaxPoseContributionZ,\n m_settings.AmountBACanChangePose,\n m_settings.MinMapPoints))\n {\n DETERMINISTIC_CHECK(m_determinator, 8794548);\n return false;\n }\n\n LogMessage((boost::wformat(L\"MapInitialization Initializing attempt success? %s\") % (initializeSucceeded ? \"true\" : \"false\")).str());\n\n SkeletonLogger::MapInitialization::LogFailure(initializeSucceeded ? \"Initialize succeeded\" : \"Initialize failed\");\n\n return initializeSucceeded;\n }\n return false;\n }\n\n void MapInitialization::CollectMatchPoints(\n const shared_ptr& referenceFrame,\n const shared_ptr& currentFrame,\n gsl::span matches,\n vector& frame1_points,\n vector& frame2_points)\n {\n frame1_points.reserve(matches.size());\n frame2_points.reserve(matches.size());\n\n const auto& current_kp = currentFrame->GetKeyPoints();\n const auto& ref_kp = referenceFrame->GetKeyPoints();\n for (const auto& match : matches)\n {\n cv::Point2f point1 = ref_kp[match.queryIdx].pt;\n cv::Point2f point2 = current_kp[match.trainIdx].pt;\n\n frame1_points.push_back(point1);\n frame2_points.push_back(point2);\n }\n }\n\n void MapInitialization::TriangulatePoints(const shared_ptr& referenceFrame, \n const shared_ptr& currentFrame,\n const Pose& referencePose,\n const Pose& currentPose,\n gsl::span matches,\n gsl::span frame1_points,\n gsl::span frame2_points,\n float pixelMaxEpipolarDistance,\n float minAcceptanceDistanceRatio,\n std::vector>& initial3DPoints)\n {\n const cv::Matx33f& frame1InvCam = referenceFrame->GetUndistortedCalibration().GetInverseCameraMatrix();\n const cv::Matx33f& frame2InvCam = currentFrame->GetUndistortedCalibration().GetInverseCameraMatrix();\n const cv::Matx44f& frame1World = referencePose.GetInverseViewMatrix();\n const cv::Matx44f& frame2World = currentPose.GetInverseViewMatrix();\n\n // computed frame elements used for match filtering\n const auto fundamentalMatrixKcToKi = ComputeFundamentalMatrix(currentPose, currentFrame->GetUndistortedCalibration(), referencePose, referenceFrame->GetUndistortedCalibration());\n const auto fundamentalMatrixKiToKc = ComputeFundamentalMatrix(referencePose, referenceFrame->GetUndistortedCalibration(), currentPose, currentFrame->GetUndistortedCalibration());\n\n for (ptrdiff_t pointIterator = 0; pointIterator < frame1_points.size(); pointIterator++)\n {\n//#define DGBEPI\n#ifdef DBGEPI\n cv::Mat dbgImg2;\n cv::cvtColor( referenceFrame->GetDebugImage().clone(), dbgImg2, CV_GRAY2RGB);\n DebugRenderEpipolarLine(fundamentalMatrixKcToKi, currentFrame->GetKeyPoint(matches[pointIterator].trainIdx).pt, referenceFrame->GetKeyPoint(matches[pointIterator].queryIdx).pt, dbgImg2);\n\n cv::Mat dbgImg1;\n cv::cvtColor(currentFrame->GetDebugImage().clone(), dbgImg1, CV_GRAY2RGB);\n DebugRenderEpipolarLine(fundamentalMatrixKiToKc, referenceFrame->GetKeyPoint(matches[pointIterator].queryIdx).pt, currentFrame->GetKeyPoint(matches[pointIterator].trainIdx).pt, dbgImg1);\n#endif\n\n // epipolar line test\n const float KcToKiEpipolarDistance = DistanceFromEpipolarLine(fundamentalMatrixKcToKi, currentFrame->GetKeyPoint(matches[pointIterator].trainIdx).pt, referenceFrame->GetKeyPoint(matches[pointIterator].queryIdx).pt);\n const float KiToKcEpipolarDistance = DistanceFromEpipolarLine(fundamentalMatrixKiToKc, referenceFrame->GetKeyPoint(matches[pointIterator].queryIdx).pt, currentFrame->GetKeyPoint(matches[pointIterator].trainIdx).pt);\n if (KiToKcEpipolarDistance + KcToKiEpipolarDistance > 2 * pixelMaxEpipolarDistance)\n {\n continue;\n }\n\n const cv::Point3f current3DPoint = TriangulatePointWorldSpace(frame1InvCam, frame1World, frame2InvCam, frame2World,\n frame1_points[pointIterator], frame2_points[pointIterator]);\n \n // Validate that the points projects in front of the camera\n if (!VerifyProjectInFront(referencePose.GetViewMatrix(), referenceFrame->GetUndistortedCalibration().GetCameraMatrix(), current3DPoint)\n || !VerifyProjectInFront(currentPose.GetViewMatrix(), currentFrame->GetUndistortedCalibration().GetCameraMatrix(), current3DPoint))\n {\n continue;\n }\n\n // Distance Ratio Test\n // To avoid creating points near the camera that will be culled later, we do a distance ratio test, where \n // points that are near the keyframe, relative to the distance between the keyframes that see the point, are\n // not added.\n cv::Vec3f KiToTriangulatedPoint = current3DPoint - referencePose.GetWorldSpacePosition();\n float KiToTriangulatedPointDistance = sqrtf(KiToTriangulatedPoint.dot(KiToTriangulatedPoint));\n cv::Vec3f KiToKc = referencePose.GetWorldSpacePosition() - currentPose.GetWorldSpacePosition();\n float KiToKcDistance = sqrtf(KiToKc.dot(KiToKc));\n float distanceRatio = KiToTriangulatedPointDistance / KiToKcDistance;\n if (distanceRatio < minAcceptanceDistanceRatio)\n {\n continue;\n }\n\n // enforce the octaves to match\n if (referenceFrame->GetKeyPoint(matches[pointIterator].queryIdx).octave != currentFrame->GetKeyPoint(matches[pointIterator].trainIdx).octave)\n {\n continue;\n }\n\n // TODO enforce gridding?\n\n initial3DPoints.emplace_back(matches[pointIterator], current3DPoint);\n }\n }\n\n bool MapInitialization::InitializeWithFrames(\n gsl::span matches,\n const shared_ptr& referenceFrame, //query\n const shared_ptr& currentFrame, //train\n InitializationData& initializationData,\n thread_memory memory)\n {\n SCOPE_TIMER(MapInitialization::InitializeWithFrames);\n\n // TODO try to refactor so that we use KeyframeBuilders throughout instead?\n mage::temp::vector initializationPoses = memory.stack_vector(3);\n\n std::vector> initial3DPoints;\n // ensure that enough matches have been accepted during the collection step\n if (matches.size() < (int)m_settings.MinFeatureMatches)\n {\n LogMessage(L\"MapInitialization Not enough accepted matches\");\n return false;\n }\n\n vector frame1_points;\n vector frame2_points;\n CollectMatchPoints(referenceFrame, currentFrame, matches, frame1_points, frame2_points);\n\n Pose referencePose{};\n Pose currentPose{};\n\n vector possiblePoses = FindPossiblePoses(frame1_points, frame2_points, referenceFrame->GetUndistortedCalibration(), currentFrame->GetUndistortedCalibration());\n DETERMINISTIC_CHECK(m_determinator, possiblePoses.begin(), possiblePoses.end());\n\n bool foundPose = FindCorrectPose(frame1_points, frame2_points, matches, referenceFrame->GetUndistortedCalibration(), currentFrame->GetUndistortedCalibration(), referencePose, possiblePoses, currentPose, initial3DPoints);\n DETERMINISTIC_CHECK(m_determinator, foundPose);\n\n if (!foundPose)\n {\n LogMessage(L\"MapInitialization didn't find correct pose\");\n return false;\n }\n\n assert(m_initializationFrames.size() >= 2);\n DETERMINISTIC_CHECK(m_determinator, initial3DPoints.data(), sizeof(initial3DPoints.front()) * initial3DPoints.size());\n\n // compute the associations with the resulting map points\n std::vector frontAssociations;\n std::vector backAssociations;\n frontAssociations.reserve(initial3DPoints.size());\n backAssociations.reserve(initial3DPoints.size());\n\n for (size_t iterator = 0; iterator < initial3DPoints.size(); iterator++)\n {\n frontAssociations.emplace_back(static_cast(initial3DPoints[iterator].first.queryIdx), iterator);\n backAssociations.emplace_back(static_cast(initial3DPoints[iterator].first.trainIdx), iterator);\n }\n\n initializationPoses.clear();\n initializationPoses.emplace_back(m_initializationFrames.front().Image, referencePose, frontAssociations);\n\n // add a placeholder for the third frame\n bool addThirdFrame = m_initializationFrames.size() > 2;\n if (addThirdFrame)\n {\n MatchedImage additionalImage = m_initializationFrames[m_initializationFrames.size() / 2];\n initializationPoses.emplace_back(additionalImage.Image, Pose(), std::vector{});\n }\n\n // add the back frame\n initializationPoses.emplace_back(m_initializationFrames.back().Image, currentPose, backAssociations);\n\n // create the initialization map\n {\n // create the mappoint proxies to link the keyframe builders to\n initializationData.Clear();\n\n std::transform(initial3DPoints.cbegin(), initial3DPoints.cend(), std::back_inserter(initializationData.MapPoints),\n [](const auto& point3D)\n {\n // MapInitialization map points are special and get treated as though they have been adjusted once already (which is technically true)\n return MapPointTrackingProxy::CreateNew(point3D.second, 1);\n });\n\n DETERMINISTIC_CHECK(m_determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n\n // populate up keyframebuilders with the information we have created thus far\n for (size_t i = 0; i < initializationPoses.size(); i++)\n {\n const InitializationPose& initializationPose = initializationPoses[i];\n const std::shared_ptr& keyframeBuilder = initializationData.AddKeyframeBuilder(initializationPose.Image, initializationPose.Pose);\n\n DETERMINISTIC_CHECK(m_determinator, *keyframeBuilder);\n\n // add all the point associations to the first and last frames\n if (i == 0 || i == initializationPoses.size() - 1)\n {\n for (const auto& assoc : initializationPose.Associations)\n {\n keyframeBuilder->AddAssociation(initializationData.MapPoints[assoc.PointIndex3d], assoc.PointIndex2d);\n }\n }\n\n DETERMINISTIC_CHECK(m_determinator, *keyframeBuilder);\n }\n\n DETERMINISTIC_CHECK(m_determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n }\n\n return true;\n }\n\n void MapInitialization::BundleAdjustInitializationData(InitializationData& initializationData, mira::determinator& determinator, bool cullOutliers, const BundlerSettings& bundlerSettings, thread_memory memory)\n {\n // now bundle adjust the whole system to get a better positions and map point locations\n AdjustableData baData{};\n\n baData.Keyframes.reserve(initializationData.Frames.size());\n for (const auto& builder : initializationData.Frames)\n {\n baData.Keyframes.emplace_back(Proxy{*builder});\n\n builder->IterateAssociations([&](const MapPointTrackingProxy& mp, KeypointDescriptorIndex idx)\n {\n baData.MapPointAssociations.emplace_back(builder->GetAnalyzedImage()->GetKeyPoint(idx).pt, mp.GetId(), builder->GetId());\n });\n }\n\n // only the first keyframe is fixed, all others are allowed to float\n baData.Keyframes.front().SetFixed(true);\n\n std::swap(baData.MapPoints, initializationData.MapPoints);\n\n BundleAdjust ba{ baData, determinator, memory };\n ba.RunBundleAdjustment(\n BundleAdjust::NoOpScheduler{},\n bundlerSettings.HuberWidth,\n bundlerSettings.MaxOutlierError,\n bundlerSettings.MaxOutlierErrorScaleFactor,\n bundlerSettings.MinMeanSquareError,\n bundlerSettings.FixMapPoints,\n bundlerSettings.NumStepsPerRun,\n bundlerSettings.NumSteps,\n bundlerSettings.MinSteps,\n memory);\n\n auto outliers = ba.GetOutliers();\n\n DETERMINISTIC_CHECK(determinator, baData.Keyframes);\n DETERMINISTIC_CHECK(determinator, baData.MapPoints);\n DETERMINISTIC_CHECK(determinator, baData.MapPointAssociations);\n DETERMINISTIC_CHECK(determinator, outliers.data(), sizeof(outliers[0]) * outliers.size());\n\n std::swap(baData.MapPoints, initializationData.MapPoints);\n\n // update the keyframes\n for (size_t keyframeIterator = 0; keyframeIterator < baData.Keyframes.size(); keyframeIterator++)\n {\n // skip keyframes that were marked as fixed\n if (!baData.Keyframes[keyframeIterator].IsFixed())\n {\n initializationData.Frames[keyframeIterator]->SetPose(baData.Keyframes[keyframeIterator].GetPose());\n }\n\n // update the position of the mappoints in the keyframe builders by iterating over the associations\n initializationData.Frames[keyframeIterator]->IterateAssociations([&initializationData](MapPointTrackingProxy& mp, KeypointDescriptorIndex /*idx*/)\n {\n auto foundMapPoint = std::find_if(initializationData.MapPoints.begin(), initializationData.MapPoints.end(), [&mp](const MapPointTrackingProxy& adjustedMapPoint)\n {\n return mp.GetId() == adjustedMapPoint.GetId();\n });\n assert(foundMapPoint != initializationData.MapPoints.end());\n\n mp.SetPosition(foundMapPoint->GetPosition());\n });\n }\n\n DETERMINISTIC_CHECK(determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n\n if (cullOutliers)\n {\n\n // compute initial association counts for map points\n mage::temp::vector, int>> mapPointAssociationCounts = memory.stack_vector, int>>(initializationData.MapPoints.size());\n for (const MapPointTrackingProxy& proxy : initializationData.MapPoints)\n {\n mapPointAssociationCounts.emplace_back(proxy.GetId(), 0);\n };\n for (const auto& keyframe : initializationData.Frames)\n {\n std::vector::Association> associations;\n keyframe->GetAssociations(associations);\n for (const MapPointAssociations::Association& assoc : associations)\n {\n auto pair = std::find_if(mapPointAssociationCounts.begin(), mapPointAssociationCounts.end(), [assoc](auto& candidatePair) { return assoc.MapPoint.GetId() == candidatePair.first; });\n pair->second++;\n }\n }\n\n DETERMINISTIC_CHECK(determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n\n // cull the outlier associations\n for (const auto& outlierAssociation : outliers)\n {\n // find the associated keyframe\n auto keyframe = std::find_if(initializationData.Frames.begin(), initializationData.Frames.end(),\n [outlierAssociation](auto& kb) { return kb->GetId() == outlierAssociation.second; });\n assert(keyframe != initializationData.Frames.end());\n\n // find the associated map point\n auto mapPoint = std::find_if(initializationData.MapPoints.cbegin(), initializationData.MapPoints.cend(),\n [outlierAssociation](auto& mp) { return mp.GetId() == outlierAssociation.first; });\n assert(mapPoint != initializationData.MapPoints.end());\n\n keyframe->get()->RemoveAssociation(mapPoint->GetId());\n auto pair = std::find_if(mapPointAssociationCounts.begin(), mapPointAssociationCounts.end(), [mapPoint](auto& candidatePair) { return mapPoint->GetId() == candidatePair.first; });\n pair->second--;\n }\n\n DETERMINISTIC_CHECK(determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n\n // now we need to go through and remove any map points which no longer have enough connections and disassociate them\n for (const auto& associationCount : mapPointAssociationCounts)\n {\n if (associationCount.second < 2)\n {\n // remove this from any keyframes, and then remove the map point all together\n for (auto& keyframe : initializationData.Frames)\n {\n keyframe->TryRemoveAssociation(associationCount.first);\n }\n\n // remove the element from the vector by swapping the back (order doesn't matter for us)\n auto iter = std::find_if(initializationData.MapPoints.begin(), initializationData.MapPoints.end(), [associationCount](const MapPointTrackingProxy& proxy) { return associationCount.first == proxy.GetId(); });\n assert(iter != initializationData.MapPoints.end());\n std::swap(*iter, initializationData.MapPoints.back());\n initializationData.MapPoints.pop_back();\n }\n }\n\n DETERMINISTIC_CHECK(determinator, initializationData.Frames.begin(), initializationData.Frames.end());\n }\n }\n\n bool MapInitialization::ValidateInitializationData(const InitializationData& initializationData,\n float maxPoseContributionZ,\n float amountBACanChangePose,\n size_t minMapPoints)\n {\n if (initializationData.MapPoints.size() < minMapPoints)\n {\n return false;\n }\n\n //TODO: check for reversed init after bundle adjustment\n\n // make sure that normalized the bundled system isn't too Z biassed\n const cv::Point3f& world = initializationData.Frames.back()->GetPose().GetWorldSpacePosition();\n const cv::Point3f normalizedWorld = Normalize(world);\n if (abs(normalizedWorld.z) > maxPoseContributionZ)\n {\n return false;\n }\n\n // enforce that the 'back' frame is close to initial scale (1.0)\n float scale = sqrt(world.dot(world));\n if ((amountBACanChangePose != 0) && ((scale < 1.0f / amountBACanChangePose) || (scale > 1.0f * amountBACanChangePose)))\n {\n return false;\n }\n\n // enforce that any extra frames are at a reasonable scale 2x initial scale\n // note that pose[0] should be identity, and pose[size-1] should be near to 1 as verified above\n for (size_t iterator = 1; iterator < initializationData.Frames.size() - 1; iterator++)\n {\n const cv::Point3f& position = initializationData.Frames[iterator]->GetPose().GetWorldSpacePosition();\n float norm = sqrt(position.dot(position));\n if (norm > 2.0f * scale)\n {\n return false;\n }\n }\n\n return true;\n }\n}\n", "meta": {"hexsha": "2c22c3401d05bda3acb0250afa9ddddbd2e64cc6", "size": 62621, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Core/MAGESLAM/Source/Tracking/MapInitialization.cpp", "max_stars_repo_name": 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NO", "lm_q1_score": 0.6406358548398982, "lm_q2_score": 0.33458944125318596, "lm_q1q2_score": 0.21434999271763866}} {"text": "////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n\n/// @project Open Space Toolkit ▸ Physics\n/// @file OpenSpaceToolkit/Physics/Coordinate/Frame/Providers/IERS/Finals2000A.cpp\n/// @author Lucas Brémond \n/// @license Apache License 2.0\n\n////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n\n#include \n\n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n\n////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n\nnamespace ostk\n{\nnamespace physics\n{\nnamespace coord\n{\nnamespace frame\n{\nnamespace provider\n{\nnamespace iers\n{\n\n////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n\nstd::ostream& operator << ( std::ostream& anOutputStream,\n const Finals2000A& aFinals2000A )\n{\n\n using ostk::core::types::String ;\n\n using ostk::physics::time::Scale ;\n\n ostk::core::utils::Print::Header(anOutputStream, \"Finals 2000A\") ;\n\n ostk::core::utils::Print::Line(anOutputStream) << \"Interval:\" << (aFinals2000A.getInterval().isDefined() ? aFinals2000A.getInterval().toString(Scale::UTC) : \"Undefined\") ;\n\n ostk::core::utils::Print::Footer(anOutputStream) ;\n\n return anOutputStream ;\n\n}\n\nbool Finals2000A::isDefined ( ) const\n{\n return !data_.empty() ;\n}\n\nInterval Finals2000A::getInterval ( ) const\n{\n\n if (!this->isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Finals 2000A\") ;\n }\n\n return span_ ;\n\n}\n\nVector2d Finals2000A::getPolarMotionAt ( const Instant& anInstant ) const\n{\n\n using ostk::physics::time::Scale ;\n\n if (!anInstant.isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Instant\") ;\n }\n\n if (!this->isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Finals 2000A\") ;\n }\n\n const Pair dataRange = this->accessDataRange(anInstant) ;\n\n if ((dataRange.first != nullptr) && (dataRange.second != nullptr))\n {\n\n const Finals2000A::Data& previousData = *(dataRange.first) ;\n const Finals2000A::Data& nextData = *(dataRange.second) ;\n\n if (previousData.x_A.isDefined() && previousData.y_A.isDefined() && nextData.x_A.isDefined() && nextData.y_A.isDefined())\n {\n\n const Real instantMjd_UTC = anInstant.getModifiedJulianDate(Scale::UTC) ;\n\n const Real ratio = (instantMjd_UTC - previousData.mjd) / (nextData.mjd - previousData.mjd) ;\n\n const Real x_A = previousData.x_A + ratio * (nextData.x_A - previousData.x_A) ;\n const Real y_A = previousData.y_A + ratio * (nextData.y_A - previousData.y_A) ;\n\n return { x_A, y_A } ;\n\n }\n else\n {\n return Vector2d::Undefined() ;\n }\n\n }\n\n throw ostk::core::error::RuntimeError(\"Cannot get polar motion at [{}].\", anInstant.toString(Scale::UTC)) ;\n\n}\n\nReal Finals2000A::getUt1MinusUtcAt ( const Instant& anInstant ) const\n{\n\n using ostk::physics::time::Scale ;\n\n if (!anInstant.isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Instant\") ;\n }\n\n if (!this->isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Finals 2000A\") ;\n }\n\n const Pair dataRange = this->accessDataRange(anInstant) ;\n\n if ((dataRange.first != nullptr) && (dataRange.second != nullptr))\n {\n\n const Finals2000A::Data& previousData = *(dataRange.first) ;\n const Finals2000A::Data& nextData = *(dataRange.second) ;\n\n if (previousData.ut1MinusUtc_A.isDefined() && nextData.ut1MinusUtc_A.isDefined())\n {\n\n const Real instantMjd_UTC = anInstant.getModifiedJulianDate(Scale::UTC) ;\n\n const Real ratio = (instantMjd_UTC - previousData.mjd) / (nextData.mjd - previousData.mjd) ;\n\n const Real ut1MinusUtc_A = previousData.ut1MinusUtc_A + ratio * (nextData.ut1MinusUtc_A - previousData.ut1MinusUtc_A) ;\n // const Real ut1MinusUtc_B = previousData.ut1MinusUtc_B + ratio * (nextData.ut1MinusUtc_B - previousData.ut1MinusUtc_B) ;\n\n return ut1MinusUtc_A ;\n\n }\n else\n {\n return Real::Undefined() ;\n }\n\n }\n\n throw ostk::core::error::RuntimeError(\"Cannot get UT1 - UTC at [{}].\", anInstant.toString(Scale::UTC)) ;\n\n}\n\nReal Finals2000A::getLodAt ( const Instant& anInstant ) const\n{\n\n using ostk::physics::time::Scale ;\n\n if (!anInstant.isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Instant\") ;\n }\n\n if (!this->isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Finals 2000A\") ;\n }\n\n const Pair dataRange = this->accessDataRange(anInstant) ;\n\n if ((dataRange.first != nullptr) && (dataRange.second != nullptr))\n {\n\n const Finals2000A::Data& previousData = *(dataRange.first) ;\n const Finals2000A::Data& nextData = *(dataRange.second) ;\n\n if (previousData.lod_A.isDefined() && nextData.lod_A.isDefined())\n {\n\n const Real instantMjd_UTC = anInstant.getModifiedJulianDate(Scale::UTC) ;\n\n const Real ratio = (instantMjd_UTC - previousData.mjd) / (nextData.mjd - previousData.mjd) ;\n\n const Real lod_A = previousData.lod_A + ratio * (nextData.lod_A - previousData.lod_A) ;\n\n return lod_A ;\n\n }\n else\n {\n return Real::Undefined() ;\n }\n\n }\n\n throw ostk::core::error::RuntimeError(\"Cannot get length of day at [{}].\", anInstant.toString(Scale::UTC)) ;\n\n}\n\nFinals2000A::Data Finals2000A::getDataAt ( const Instant& anInstant ) const\n{\n\n using ostk::physics::time::Scale ;\n\n if (!anInstant.isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Instant\") ;\n }\n\n if (!this->isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Finals 2000A\") ;\n }\n\n const Pair dataRange = this->accessDataRange(anInstant) ;\n\n if ((dataRange.first != nullptr) && (dataRange.second != nullptr))\n {\n\n auto interpolate = [] (const Real& aPreviousValue, const Real& aNextValue, const Real& aRatio) -> Real\n {\n\n if (!aRatio.isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"Ratio\") ;\n }\n\n if (aPreviousValue.isDefined() && aNextValue.isDefined())\n {\n return aPreviousValue + aRatio * (aNextValue - aPreviousValue) ;\n }\n\n return Real::Undefined() ;\n\n } ;\n\n const Finals2000A::Data& previousData = *(dataRange.first) ;\n const Finals2000A::Data& nextData = *(dataRange.second) ;\n\n const Real instantMjd_UTC = anInstant.getModifiedJulianDate(Scale::UTC) ;\n\n const Real ratio = (instantMjd_UTC - previousData.mjd) / (nextData.mjd - previousData.mjd) ;\n\n const Integer year = previousData.year ;\n const Integer month = previousData.month ;\n const Integer day = previousData.day ;\n\n const Real mjd = interpolate(previousData.mjd, nextData.mjd, ratio) ;\n\n const char polarMotionflag = (previousData.polarMotionflag == nextData.polarMotionflag) ? previousData.polarMotionflag : '?' ;\n\n const Real x_A = interpolate(previousData.x_A, nextData.x_A, ratio) ;\n const Real xError_A = interpolate(previousData.xError_A, nextData.xError_A, ratio) ;\n const Real y_A = interpolate(previousData.y_A, nextData.y_A, ratio) ;\n const Real yError_A = interpolate(previousData.yError_A, nextData.yError_A, ratio) ;\n\n const char ut1MinusUtcFlag = (previousData.ut1MinusUtcFlag == nextData.ut1MinusUtcFlag) ? previousData.ut1MinusUtcFlag : '?' ;\n\n const Real ut1MinusUtc_A = interpolate(previousData.ut1MinusUtc_A, nextData.ut1MinusUtc_A, ratio) ;\n const Real ut1MinusUtcError_A = interpolate(previousData.ut1MinusUtcError_A, nextData.ut1MinusUtcError_A, ratio) ;\n const Real lod_A = interpolate(previousData.lod_A, nextData.lod_A, ratio) ;\n const Real lodError_A = interpolate(previousData.lodError_A, nextData.lodError_A, ratio) ;\n\n const char nutationFlag = (previousData.nutationFlag == nextData.nutationFlag) ? previousData.nutationFlag : '?' ;\n\n const Real dx_A = interpolate(previousData.dx_A, nextData.dx_A, ratio) ;\n const Real dxError_A = interpolate(previousData.dxError_A, nextData.dxError_A, ratio) ;\n const Real dy_A = interpolate(previousData.dy_A, nextData.dy_A, ratio) ;\n const Real dyError_A = interpolate(previousData.dyError_A, nextData.dyError_A, ratio) ;\n const Real x_B = interpolate(previousData.x_B, nextData.x_B, ratio) ;\n const Real y_B = interpolate(previousData.y_B, nextData.y_B, ratio) ;\n const Real ut1MinusUtc_B = interpolate(previousData.ut1MinusUtc_B, nextData.ut1MinusUtc_B, ratio) ;\n const Real dx_B = interpolate(previousData.dx_B, nextData.dx_B, ratio) ;\n const Real dy_B = interpolate(previousData.dy_B, nextData.dy_B, ratio) ;\n\n const Finals2000A::Data data =\n {\n year,\n month,\n day,\n mjd,\n polarMotionflag,\n x_A,\n xError_A,\n y_A,\n yError_A,\n ut1MinusUtcFlag,\n ut1MinusUtc_A,\n ut1MinusUtcError_A,\n lod_A,\n lodError_A,\n nutationFlag,\n dx_A,\n dxError_A,\n dy_A,\n dyError_A,\n x_B,\n y_B,\n ut1MinusUtc_B,\n dx_B,\n dy_B\n } ;\n\n return data ;\n\n }\n\n throw ostk::core::error::RuntimeError(\"Cannot get data at [{}].\", anInstant.toString(Scale::UTC)) ;\n\n}\n\nFinals2000A Finals2000A::Undefined ( )\n{\n return Finals2000A() ;\n}\n\nFinals2000A Finals2000A::Load ( const fs::File& aFile )\n{\n\n using ostk::core::types::Index ;\n using ostk::core::types::Uint8 ;\n using ostk::core::types::Uint16 ;\n using ostk::core::types::Real ;\n using ostk::core::types::String ;\n using ostk::core::ctnr::Array ;\n\n using ostk::physics::time::Scale ;\n using ostk::physics::time::Time ;\n using ostk::physics::time::DateTime ;\n\n if (!aFile.isDefined())\n {\n throw ostk::core::error::runtime::Undefined(\"File\") ;\n }\n\n if (!aFile.exists())\n {\n throw ostk::core::error::RuntimeError(\"File [{}] does not exist.\", aFile.toString()) ;\n }\n\n Finals2000A finals2000a ;\n\n std::ifstream fileStream(aFile.getPath().toString()) ;\n\n Index lineIndex = 0 ;\n String line ;\n\n // auto splitString = [] (const String& aString, char aDelimiter) -> Array\n // {\n\n // Array aStringArray = Array::Empty() ;\n\n // std::stringstream stringStream(aString) ;\n\n // String item ;\n\n // while (std::getline(stringStream, item, aDelimiter))\n // {\n\n // if (!item.empty())\n // {\n // aStringArray.add(item) ;\n // }\n\n // }\n\n // return aStringArray ;\n\n // } ;\n\n auto parseReal = [] (const String& aLine, const Index& aStartColumnNumber, const Index& anEndColumnNumber) -> Real\n {\n\n String string = aLine.getSubstring(aStartColumnNumber - 1, anEndColumnNumber - aStartColumnNumber + 1).trim() ;\n\n if (string.isEmpty())\n {\n return Real::Undefined() ;\n }\n\n if ((string.getLength() > 2) && (string.getHead(1) == \".\"))\n {\n string = \"0.\" + string.getTail(string.getLength() - 1) ;\n }\n\n if ((string.getLength() > 2) && (string.getHead(2) == \"-.\"))\n {\n string = \"-0.\" + string.getTail(string.getLength() - 2) ;\n }\n\n return Real::Parse(string) ;\n\n } ;\n\n while (std::getline(fileStream, line))\n {\n\n const Integer year = Integer::Parse(line.getSubstring(0, 2).trim()) ;\n const Integer month = Integer::Parse(line.getSubstring(2, 2).trim()) ;\n const Integer day = Integer::Parse(line.getSubstring(4, 2).trim()) ;\n\n const Real mjd = parseReal(line, 8, 15) ;\n\n const char polarMotionflag = line.at(16) ;\n\n const Real x_A = parseReal(line, 19, 27) ;\n const Real xError_A = parseReal(line, 28, 36) ;\n const Real y_A = parseReal(line, 38, 46) ;\n const Real yError_A = parseReal(line, 47, 55) ;\n\n const char ut1MinusUtcFlag = line.at(57) ;\n\n const Real ut1MinusUtc_A = parseReal(line, 59, 68) ;\n const Real ut1MinusUtcError_A = parseReal(line, 69, 78) ;\n const Real lod_A = parseReal(line, 80, 86) ;\n const Real lodError_A = parseReal(line, 87, 93) ;\n\n const char nutationFlag = line.at(95) ;\n\n const Real dx_A = parseReal(line, 98, 106) ;\n const Real dxError_A = parseReal(line, 107, 115) ;\n const Real dy_A = parseReal(line, 117, 125) ;\n const Real dyError_A = parseReal(line, 126, 134) ;\n const Real x_B = parseReal(line, 135, 144) ;\n const Real y_B = parseReal(line, 145, 154) ;\n const Real ut1MinusUtc_B = parseReal(line, 155, 165) ;\n const Real dx_B = parseReal(line, 166, 175) ;\n const Real dy_B = parseReal(line, 176, 185) ;\n\n const Finals2000A::Data data =\n {\n year,\n month,\n day,\n mjd,\n polarMotionflag,\n x_A,\n xError_A,\n y_A,\n yError_A,\n ut1MinusUtcFlag,\n ut1MinusUtc_A,\n ut1MinusUtcError_A,\n lod_A,\n lodError_A,\n nutationFlag,\n dx_A,\n dxError_A,\n dy_A,\n dyError_A,\n x_B,\n y_B,\n ut1MinusUtc_B,\n dx_B,\n dy_B\n } ;\n\n finals2000a.data_.insert({ mjd, data }) ;\n\n lineIndex++ ;\n\n }\n\n if (!finals2000a.data_.empty())\n {\n\n const Instant startInstant = Instant::ModifiedJulianDate(finals2000a.data_.begin()->first, Scale::UTC) ;\n const Instant endInstant = Instant::ModifiedJulianDate(finals2000a.data_.rbegin()->first, Scale::UTC) ;\n\n finals2000a.span_ = Interval::Closed(startInstant, endInstant) ;\n\n }\n\n return finals2000a ;\n\n}\n\n////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n\n Finals2000A::Finals2000A ( )\n : span_(Interval::Undefined()),\n data_(Map())\n{\n\n}\n\nPair Finals2000A::accessDataRange ( const Instant& anInstant ) const\n{\n\n using ostk::physics::time::Scale ;\n\n const Real instantMjd_UTC = anInstant.getModifiedJulianDate(Scale::UTC) ;\n\n const auto nextDataIt = data_.lower_bound(instantMjd_UTC) ;\n\n if (nextDataIt == data_.end())\n {\n return { &(data_.rbegin()->second), nullptr } ;\n }\n else if (nextDataIt == data_.begin())\n {\n return { nullptr, &(nextDataIt->second) } ;\n }\n else\n {\n\n const auto previousDataIt = std::prev(nextDataIt) ;\n\n return { &(previousDataIt->second), &(nextDataIt->second) } ;\n\n }\n\n return { nullptr, nullptr } ;\n\n}\n\n////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n\n}\n}\n}\n}\n}\n}\n\n////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////\n", "meta": {"hexsha": "3d80321564b269886018d4b5408ec88488f037b9", "size": 17320, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/OpenSpaceToolkit/Physics/Coordinate/Frame/Providers/IERS/Finals2000A.cpp", "max_stars_repo_name": "robinpdm/open-space-toolkit-physics", "max_stars_repo_head_hexsha": "b53e5d4287fa6568d700cb8942c9a56d57b8d7cf", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 7.0, "max_stars_repo_stars_event_min_datetime": "2020-03-30T11:51:11.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-02T15:20:44.000Z", 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"num_tokens": 4055, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.3812195662561499, "lm_q1q2_score": 0.21431268182708402}} {"text": "/**\n * @file ev_record.hpp\n * @author Yi Zhang \n * @date Wed Aug 26 23:43:13 2020\n * \n * @brief raw event record\n * \n * \n */\n#ifndef STAN_MATH_TORSTEN_NONMEN_EVENTS_RECORD_HPP\n#define STAN_MATH_TORSTEN_NONMEN_EVENTS_RECORD_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace torsten {\n\n /**\n * Raw events record in the form of NONMEN, except that\n * for the population input @c len consisting of data record\n * length for each individual.\n * @tparam T0 type of scalar for time of events. \n * @tparam T1 type of scalar for amount at each event.\n * @tparam T2 type of scalar for rate at each event.\n * @tparam T3 type of scalar for inter-dose inteveral at each event.\n * @tparam theta_container type of container for parameter. linode model uses\n * matrix as parameter, other than std::vector used by others.\n */\n template \n struct NONMENEventsRecord {\n using T_scalar = typename stan::return_type_t;\n using T_time = typename stan::return_type_t;\n using T_rate = T2;\n using T_amt = T1;\n using T_par_rate = T2;\n using T_par_ii = T3;\n\n private:\n /// len needs actual variable to ref to.\n const std::vector len_1_;\n\n public:\n static const double lag_time_min; /**< minimum lag time allowed */\n\n /// nb. of compartments\n const int ncmt;\n /// begin id for each subject in concat vector time_, etc\n std::vector begin_;\n /// data length for each subject\n const std::vector& len_;\n /// nb. of events in the entire population\n const int total_num_event_times;\n /// event time\n const std::vector& time_;\n /// dosing amount\n const std::vector& amt_;\n /// dosing rate\n const std::vector& rate_;\n /// dosing interval\n const std::vector& ii_;\n /// dosing event id\\n\n /// (0) observation\\n\n /// (1) dosing\\n\n /// (2) other\\n\n /// (3) reset\\n\n /// (4) reset AND dosing\n const std::vector& evid_;\n /// event compartment\n const std::vector& cmt_;\n /// nb. of additional doses\n const std::vector& addl_;\n /// steady-states flag\n const std::vector& ss_;\n\n /**\n * Constructor using population data with parameter give as\n * matrix, such as in linear ODE models\n * @param[in] n number of compartments in model\n * @param[in] len record length for each individual.\n * @param[in] time times of events \n * @param[in] amt amount at each event\n * @param[in] rate rate at each event\n * @param[in] ii inter-dose interval at each event\n * @param[in] evid event identity: \\n\n * (0) observation\\n \n * (1) dosing\\n\n * (2) other \\n\n * (3) reset \\n\n * (4) reset AND dosing \n * @param[in] cmt compartment number at each event \n * @param[in] addl additional dosing at each event \n * @param[in] ss steady state approximation at each event (0: no, 1: yes)\n */\n template \n NONMENEventsRecord(int n,\n const std::vector& len,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss) :\n len_1_(),\n ncmt(n),\n begin_(len.size()),\n len_(len),\n total_num_event_times(std::accumulate(len_.begin(), len_.end(), 0)),\n time_ (time ),\n amt_ (amt ),\n rate_ (rate ),\n ii_ (ii ),\n evid_ (evid ),\n cmt_ (cmt ),\n addl_ (addl ),\n ss_ (ss )\n {\n begin_[0] = 0;\n std::partial_sum(len.begin(), len.end() - 1, begin_.begin() + 1);\n }\n\n /**\n * Constructor using individual data with parameter give as\n * matrix, such as in linear ODE models\n * @param[in] n number of compartments in model\n * @param[in] time times of events \n * @param[in] amt amount at each event\n * @param[in] rate rate at each event\n * @param[in] ii inter-dose interval at each event\n * @param[in] evid event identity: \\n\n * (0) observation \\n\n * (1) dosing\\n\n * (2) other \\n\n * (3) reset \\n\n * (4) reset AND dosing \n * @param[in] cmt compartment number at each event \n * @param[in] addl additional dosing at each event \n * @param[in] ss steady state approximation at each event (0: no, 1: yes)\n */\n template \n NONMENEventsRecord(int n,\n const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss) :\n len_1_(1, time.size()),\n ncmt(n),\n begin_{0},\n len_(len_1_),\n total_num_event_times(std::accumulate(len_.begin(), len_.end(), 0)),\n time_ (time ),\n amt_ (amt ),\n rate_ (rate ),\n ii_ (ii ),\n evid_ (evid ),\n cmt_ (cmt ),\n addl_ (addl ),\n ss_ (ss )\n {}\n \n /** \n * begin of parameters for a subject @c id\n * in @c pMatrix. It is assumed that all the paramter are\n * either constant or time dependent.\n *\n * @param id subject id\n * \n * @return begin index in @c pMatrix for the subject\n */\n template\n int begin_param(int id, const std::vector& param) const {\n return param.size() == len_.size() ? id : begin_[id];\n }\n\n /**\n * length of parameters for a subject @c id\n * in @c pMatrix. It is assumed that all the paramter are\n * either constant or time dependent.\n *\n * @param id subject id\n * \n * @return len in @c pMatrix for the subject\n */\n template\n int len_param(int id, const std::vector& param) const {\n return param.size() == len_.size() ? 1 : len_[id]; \n }\n\n /** \n * check the exisitence of steady state dosing events\n * for the 1st subject, used for single-individual data.\n * \n * @return if the subject has any SS dosing event.\n */\n inline bool has_ss_dosing() const {\n return has_ss_dosing(0);\n }\n\n /** \n * check the exisitence of steady state dosing events\n * \n * @param id subject id\n * \n * @return if the subject has any SS dosing event.\n */\n inline bool has_ss_dosing(int id) const {\n bool res = false;\n int begin = begin_[id];\n int end = size_t(id + 1) == len_.size() ? time_.size() : begin_[id + 1];\n for (int i = begin; i < end; ++i) {\n if ((evid_[i] == 1 || evid_[i] == 4) && ss_[i] != 0) {\n res = true;\n break;\n }\n }\n return res;\n }\n\n /** \n * check the exisitence of time-lagged events\n * \n * @param id subject id\n * \n * @return if the subject has any time-lagged dosing event.\n */\n template\n inline bool has_positive_param(int id, const std::vector>& params) const {\n using stan::math::value_of;\n return std::any_of(params.begin() + begin_param(id, params),\n params.begin() + begin_param(id, params) + len_param(id, params),\n [](const std::vector& v) {\n return std::any_of(v.begin(), v.end(), [](const T& x) { return std::abs(value_of(x)) > lag_time_min; });\n });\n }\n\n /** \n * check the exisitence of time-lagged dosing events\n * for the 1st subject, used for single-individual data.\n * \n * @return if the subject has any time-lagged dosing event.\n */\n template\n inline bool has_positive_param(const std::vector>& params) const {\n return has_positive_param(0, params);\n }\n\n /** \n * @param id subject id\n * \n * @return nb. of events for the subject\n */\n inline int num_event_times(int id) const {\n return len_.at(id);\n }\n\n /** \n * @return nb. of events for subject 0.\n */\n inline int num_event_times() const {\n return len_.at(0);\n }\n\n /** \n * @return population size.\n */\n inline int num_subjects() const {\n return len_.size();\n }\n };\n\n template \n const double NONMENEventsRecord::lag_time_min = 1.e-12;\n\n}\n\n#endif\n", "meta": {"hexsha": "847f3091214ace5f9292b3e91b2815558d701d4a", "size": 9382, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ev_record.hpp", "max_stars_repo_name": "metrumresearchgroup/torsten_math", "max_stars_repo_head_hexsha": "318cee4199342bd36d5f2b1241c6760acfcf58b7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "ev_record.hpp", "max_issues_repo_name": "metrumresearchgroup/torsten_math", "max_issues_repo_head_hexsha": "318cee4199342bd36d5f2b1241c6760acfcf58b7", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1.0, "max_issues_repo_issues_event_min_datetime": "2020-04-27T23:53:22.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-27T23:57:43.000Z", "max_forks_repo_path": "ev_record.hpp", "max_forks_repo_name": "metrumresearchgroup/torsten_math", "max_forks_repo_head_hexsha": "318cee4199342bd36d5f2b1241c6760acfcf58b7", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.6898954704, "max_line_length": 131, "alphanum_fraction": 0.5571306758, "num_tokens": 2520, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041652, "lm_q2_score": 0.41869690935568665, "lm_q1q2_score": 0.2142541608556196}} {"text": "#include \"tuw_marker_slam/ekf_slam.h\"\n#include \"tuw_marker_slam/munkre.h\"\n#include \n\nusing namespace tuw;\n\nEKFSLAM::EKFSLAM( const std::vector beta ) :\n SLAMTechnique ( EKF ),\n beta_ ( beta ) {\n}\n\nvoid EKFSLAM::init() {\n reset_ = false;\n\n // initial mean\n y = cv::Mat_ ( 3, 1, 0.0 );\n\n // initial covariance\n C_Y = cv::Mat_ ( 3, 3, 0.0 );\n\n // initialize covariance submatrices pointing to the same data (no copy!)\n x = cv::Mat_ ( y, cv::Range ( 0, 3 ), cv::Range ( 0, 1 ) );\n C_X = cv::Mat_ ( C_Y, cv::Range ( 0, 3 ), cv::Range ( 0, 3 ) );\n\n // reset marker landmark correspondences\n f_kj.clear();\n}\n\nvoid EKFSLAM::cycle ( std::vector &yt, cv::Mat_ &C_Yt, const Command &ut, const MeasurementConstPtr &zt ) {\n if ( reset_ ) init();\n\n // invariance check\n assert ( y.rows % 3 == 0 && y.cols == 1 );\n assert ( x.rows == 3 && x.cols == 1 );\n assert ( C_Y.rows == C_Y.cols && C_Y.rows == y.rows );\n assert ( C_X.rows == C_X.cols && C_X.rows == x.rows );\n\n prediction ( ut );\n if ( zt->getType() == tuw::Measurement::Type::MARKER && updateTimestamp ( zt->stamp() ) ) {\n MeasurementMarkerConstPtr z = std::static_pointer_cast ( zt );\n\n data_association ( z );\n update();\n integration ( z );\n }\n\n // implicit return\n yt.resize ( y.rows/3 );\n for ( size_t i = 0; i < yt.size(); i++ ) {\n yt[i].x() = y[3*i + 0][0];\n yt[i].y() = y[3*i + 1][0];\n yt[i].theta() = y[3*i + 2][0];\n }\n C_Yt = C_Y;\n}\n\nvoid EKFSLAM::setConfig ( const void *config ) {\n config_ = * ( ( tuw_marker_slam::EKFSLAMConfig* ) config );\n}\n\nvoid EKFSLAM::prediction ( const Command &ut ) {\n if ( !config_.enable_prediction ) return;\n\n // control noise\n cv::Matx M = cv::Matx ( config_.alpha_1*ut.v() *ut.v() + config_.alpha_2*ut.w() *ut.w(), 0,\n 0, config_.alpha_3*ut.v() *ut.v() + config_.alpha_4*ut.w() *ut.w() );\n\n // pre-calculate needed data\n double dt = duration_last_update_.total_microseconds() /1000000.0;\n double r = ut.v() /ut.w();\n double s = sin ( x[2][0] );\n double c = cos ( x[2][0] );\n double s_dt = sin ( x[2][0] + ut.w() *dt );\n double c_dt = cos ( x[2][0] + ut.w() *dt );\n\n // calculate update and its derivatives (= jacobian matrices)\n cv::Vec dx_;\n cv::Matx G_x_;\n cv::Matx G_u;\n if ( std::abs ( ut.w() ) > __DBL_MIN__ ) {\n dx_ = cv::Vec ( -r*s + r*s_dt,\n r*c - r*c_dt,\n ut.w() *dt );\n G_x_ = cv::Matx ( 1, 0, -r*c + r*c_dt,\n 0, 1, -r*s + r*s_dt,\n 0, 0, 1 );\n G_u = cv::Matx ( ( -s + s_dt ) /ut.w(), r * ( s - s_dt ) / ut.w() + r*c_dt*dt,\n ( c - c_dt ) /ut.w(), -r * ( c - c_dt ) / ut.w() + r*s_dt*dt,\n 0, dt );\n } else {\n dx_ = cv::Vec ( ut.v() *dt*c,\n ut.v() *dt*s,\n 0 );\n G_x_ = cv::Matx ( 1, 0, -ut.v() *dt*s,\n 0, 1, ut.v() *dt*c,\n 0, 0, 1 );\n G_u = cv::Matx ( dt*c, -0.5*dt*dt*ut.v() *s,\n dt*s, 0.5*dt*dt*ut.v() *c,\n 0, dt );\n }\n\n // convert from Matx/Vec to Mat_ in order to use +/* operations on submatrices\n cv::Mat_ dx = cv::Mat_ ( dx_ );\n cv::Mat_ G_x = cv::Mat_ ( G_x_ );\n cv::Mat_ R = cv::Mat_ ( G_u * M * G_u.t() );\n\n // update mean and covariance\n x = x + dx;\n C_X = G_x*C_X*G_x.t() + R;\n for ( size_t i = 3; i < C_Y.cols; i += 3 ) {\n // references to submatrices\n cv::Mat_ C_XM_i = cv::Mat_ ( C_Y, cv::Range ( 0, 3 ), cv::Range ( i, i + 3 ) );\n cv::Mat_ C_MX_i = cv::Mat_ ( C_Y, cv::Range ( i, i + 3 ), cv::Range ( 0, 3 ) );\n\n // calculation & matrix update\n C_XM_i = G_x*C_XM_i;\n C_MX_i = C_XM_i.t();\n }\n\n // normalize angle\n x[2][0] = angle_normalize ( x[2][0] );\n}\n\nvoid EKFSLAM::data_association ( const MeasurementMarkerConstPtr &zt ) {\n // initialization\n c_ij = std::vector ( zt->size() );\n c_ji = std::vector ( y.rows/3 );\n z_known.clear();\n z_new.clear();\n\n // gamma is a threshold such that 100*(1-alpha)% of true measurements are rejected\n boost::math::chi_squared chi_squared = boost::math::chi_squared ( 3 );\n double gamma = boost::math::quantile(chi_squared, config_.alpha);\n\n // find correspondences based on measurement marker IDs\n for ( size_t i = 0; i < zt->size(); i++ ) {\n if ( zt->operator[] ( i ).ids.size() == 0 ) {\n // invalid measurement\n } else {\n // valid measurement\n std::map::iterator it = f_kj.find ( zt->operator[] ( i ).ids[0] );\n\n if ( it != f_kj.end() ) {\n // known landmark j\n size_t j = it->second;\n assert ( 0 < j && j < c_ji.size() );\n\n // create correspondence data\n CorrDataPtr c = std::make_shared();\n c->ij = std::pair ( i, j );\n measurement ( zt, c );\n\n // document founded correspondence\n assert ( c_ij[i] == nullptr && c_ji[j] == nullptr );\n c_ij[i] = c;\n c_ji[j] = c;\n z_known.push_back(i);\n } else {\n // new landmark\n z_new.push_back( i );\n }\n }\n }\n\n // complement correspondences based on probability\n switch (config_.data_association_mode) {\n case ID:\n // Nothing to do anymore\n break;\n case NNSF_LOCAL:\n NNSF_local ( zt, gamma );\n break;\n case NNSF_GLOBAL:\n NNSF_global ( zt, gamma );\n break;\n default:\n assert ( false );\n }\n}\n\nvoid EKFSLAM::NNSF_local ( const MeasurementMarkerConstPtr &zt, const double gamma ) {\n std::vector m_reject;\n\n // find correspondences based on Mahalanobis distance\n for ( size_t i = 0; i < c_ij.size(); i++ ) {\n assert ( c_ij[i] == nullptr || c_ij[i]->ij.first == i );\n\n // consider only measurements without marker IDs\n if ( zt->operator[] ( i ).ids.size() > 0) continue;\n assert ( c_ij[i] == nullptr );\n\n double min_d_2 = std::numeric_limits::infinity();\n CorrDataPtr min_c = nullptr;\n for ( size_t j = 1; j < c_ji.size(); j++ ) {\n assert ( c_ji[j] == nullptr || c_ji[j]->ij.second == j );\n\n // consider only landmarks without correspondences based on measurement marker IDs\n if ( c_ji[j] != nullptr && zt->operator[] ( c_ji[j]->ij.first ).ids.size() > 0 ) continue;\n\n // create correspondence data\n CorrDataPtr c = std::make_shared();\n c->ij = std::pair ( i, j );\n measurement ( zt, c );\n\n // calculate Mahalanobis distance: d = sqrt(v^T S^(-1) v)\n double d_2 = ( c->v.t() * c->S_inv * c->v ) [0];\n if (d_2 < min_d_2) {\n min_d_2 = d_2;\n min_c = c;\n }\n }\n\n // update correspondences\n if ( min_c == nullptr || min_d_2 > gamma ) continue;\n\n // new (possible) correspondence found\n size_t min_i = min_c->ij.first;\n size_t min_j = min_c->ij.second;\n assert ( i == min_i && 0 < min_j && min_j < c_ji.size() );\n\n if ( c_ji[min_j] != nullptr ) {\n // landmark already correspond to another measurement\n size_t old_i = c_ji[min_j]->ij.first;\n ROS_INFO ( \"measurements %lu and %lu correspond both to landmark %lu\", old_i, min_i, min_j );\n if ( zt->operator[] ( old_i ).ids.size() > 0 ) {\n // old correspondence is fix -> reject current measurement\n ROS_INFO ( \"reject measurement %lu\", min_i );\n } else {\n // old correspondence is just probably -> reject both measurements\n ROS_INFO ( \"reject measurement %lu (conservative approach)\", i );\n\n if ( c_ij[old_i] != nullptr ) {\n ROS_INFO ( \"reject measurement %lu (conservative approach)\", old_i );\n\n // remove already added old measurement from known measurements\n std::vector::iterator it = std::find ( z_known.begin(), z_known.end(), old_i );\n assert ( it != z_known.end() );\n z_known.erase( it );\n\n // reset old correspondence c_ij at the first time and\n // remember old correspondence c_ji for later reset\n c_ij[old_i] = nullptr;\n m_reject.push_back( min_j );\n }\n }\n } else {\n // document founded correspondence\n assert ( c_ij[min_i] == nullptr && c_ji[min_j] == nullptr );\n c_ij[min_i] = min_c;\n c_ji[min_j] = min_c;\n z_known.push_back( min_i );\n }\n }\n\n // reset correspondences c_ji found but invalidated again during corresponde update\n for (auto j: m_reject) c_ji[j] = nullptr;\n}\n\nvoid EKFSLAM::NNSF_global ( const MeasurementMarkerConstPtr &zt, const double gamma ) {\n // book keeping: measurements\n std::vector map_i;\n for ( size_t i = 0; i < c_ij.size(); i++ ) {\n assert ( c_ij[i] == nullptr || c_ij[i]->ij.first == i );\n\n // consider only measurements without marker IDs\n if ( zt->operator[] ( i ).ids.size() > 0 ) continue;\n assert ( c_ij[i] == nullptr );\n\n map_i.push_back(i);\n }\n\n // no further correspondences possible\n if (map_i.size() == 0) return;\n\n // book keeping: landmarks\n std::vector map_j;\n for ( size_t j = 1; j < c_ji.size(); j++ ) {\n assert ( c_ji[j] == nullptr || c_ji[j]->ij.second == j );\n\n // consider only landmarks without correspondences based on measurement marker IDs\n assert ( c_ji[j] == nullptr || zt->operator[] ( c_ji[j]->ij.first ).ids.size() > 0);\n if ( c_ji[j] != nullptr ) continue;\n\n map_j.push_back(j);\n }\n\n // no further correspondences possible\n if (map_j.size() == 0) return;\n\n // build assignment matrix (Mahalanobis distance)\n std::vector> C = std::vector> ( map_i.size() );\n cv::Mat_ D_2 = cv::Mat_ ( map_i.size(), map_j.size() );\n for ( size_t x = 0; x < map_i.size(); x++ ) {\n C[x] = std::vector ( map_j.size() );\n for ( size_t y = 0; y < map_j.size(); y++ ) {\n // create correspondence data\n CorrDataPtr c = std::make_shared();\n c->ij = std::pair ( map_i[x], map_j[y] );\n measurement ( zt, c );\n\n // calculate Mahalanobis distance: d = sqrt(v^T S^(-1) v)\n double d_2 = ( c->v.t() * c->S_inv * c->v ) [0];\n assert ( d_2 >= 0 );\n\n // book keeping: correspondence data\n C[x][y] = c;\n D_2[x][y] = d_2;\n }\n }\n\n // update correspondences\n std::vector> assignment = Munkre::find_minimum_assignment(D_2);\n for (size_t k = 0; k < assignment.size(); k++) {\n // book keeping measurement and landmark\n size_t x = assignment[k].first;\n size_t y = assignment[k].second;\n\n if ( D_2[x][y] > gamma ) continue;\n\n // new correspondence found\n assert ( x < C.size() && y < C[x].size() );\n size_t i = C[x][y]->ij.first;\n size_t j = C[x][y]->ij.second;\n assert ( i < c_ij.size() && 0 < j && j < c_ji.size() );\n\n // document founded correspondence\n assert ( c_ij[i] == nullptr && c_ji[j] == nullptr );\n c_ij[i] = C[x][y];\n c_ji[j] = C[x][y];\n z_known.push_back( i );\n }\n}\n\nvoid EKFSLAM::measurement ( const MeasurementMarkerConstPtr &zt, const CorrDataPtr &corr ) {\n const size_t i = corr->ij.first;\n const size_t j = corr->ij.second;\n assert ( i < zt->size() && 0 < j && j < y.rows/3 );\n\n // transformation matrix from robot coordinates in global coordinates\n Pose2D p(x[0][0], x[1][0], x[2][0] );\n cv::Matx T_x = cv::Matx44d ( p.theta_cos(), -p.theta_sin(), 0, p.x(), p.theta_sin(), p.theta_cos(), 0, p.y(), 0, 0, 1, p.theta(), 0, 0, 0, 1 );\n\n // measurement prediction of landmark j\n cv::Vec homogenious_stage_vector = append(zt->pose2d().state_vector(), 1);\n cv::Vec sensor = T_x * homogenious_stage_vector;\n cv::Vec delta = cv::Vec ( y[3*j + 0][0] - sensor[0],\n y[3*j + 1][0] - sensor[1] );\n double q = ( delta.t() * delta ) [0];\n\n // predicted measurement\n cv::Vec z_ = cv::Vec ( sqrt ( q ),\n angle_difference ( atan2 ( delta[1], delta[0] ), sensor[2] ),\n angle_difference ( y[3*j + 2][0], sensor[2] ) );\n\n // obtained measurement\n cv::Vec z = cv::Vec ( zt->operator[] ( i ).length,\n zt->operator[] ( i ).angle ,\n zt->operator[] ( i ).orientation );\n\n // compare predicted and obtainend measurement\n corr->v[0] = z[0] - z_[0];\n corr->v[1] = angle_difference ( z[1], z_[1] );\n corr->v[2] = angle_difference ( z[2], z_[2] );\n //corr->v[2] = 0;\n\n // pre-calculate needed data\n assert ( q > 0 );\n double s = sin ( x[2][0] );\n double c = cos ( x[2][0] );\n double ddeltax = zt->pose2d().x() *s + zt->pose2d().y() *c;\n double ddeltay = -zt->pose2d().x() *c + zt->pose2d().y() *s;\n double dx_sq = delta[0]/sqrt ( q );\n double dy_sq = delta[1]/sqrt ( q );\n double dx_q = delta[0]/q;\n double dy_q = delta[1]/q;\n\n //H_ij = (dx 0 .. 0 dm 0 .. 0)\n corr->dx = cv::Matx ( -dx_sq, -dy_sq, dx_sq*ddeltax + dy_sq*ddeltay,\n dy_q, -dx_q, ddeltay*dx_q - ddeltax*dy_q - 1,\n 0, 0, -1 );\n\n corr->dm = cv::Matx ( dx_sq, dy_sq, 0,\n -dy_q, dx_q, 0,\n 0, 0, 1 );\n\n // measurement noise\n corr->Q = measurement_noise( zt->operator[]( i ) );\n\n // S_i = H_i*C_Y*H_i^T + Q\n cv::Matx C_X_ = cv::Mat_ ( C_Y, cv::Range ( 0, 3 ), cv::Range ( 0, 3 ) );\n cv::Matx C_M = cv::Mat_ ( C_Y, cv::Range ( 3*j, 3*j + 3 ), cv::Range ( 3*j, 3*j + 3 ) );\n cv::Matx C_XM = cv::Mat_ ( C_Y, cv::Range ( 0, 3 ), cv::Range ( 3*j, 3*j + 3 ) );\n\n cv::Matx tmp = corr->dx*C_XM*corr->dm.t();\n cv::Matx S = corr->dx*C_X_*corr->dx.t() + tmp + tmp.t() + corr->dm*C_M*corr->dm.t() + corr->Q;\n corr->S_inv = S.inv();\n}\n\ncv::Matx EKFSLAM::measurement_noise ( MeasurementMarker::Marker zi ) {\n double l = zi.length;\n double a = zi.angle;\n double o = zi.orientation;\n\n double var_l = std::max ( beta_[0]*l*l + beta_[1], 0 ) + std::max ( std::min ( beta_[2]*a*a + beta_[3], M_PI*M_PI ), 0 ) + std::max ( std::min ( beta_[4]*o*o + beta_[5], M_PI*M_PI ), 0 );\n double var_a = std::min ( std::max ( beta_[6]/l/l + beta_[7], 0 ) + std::max ( std::min ( beta_[8]*a*a + beta_[9], M_PI*M_PI ), 0 ) + std::max ( std::min ( beta_[10]*o*o + beta_[11], M_PI*M_PI ), 0 ), M_PI*M_PI );\n double var_o = std::min ( std::max ( beta_[12]*l*l + beta_[13], 0 ) + std::max ( std::min ( beta_[14]*a*a + beta_[15], M_PI*M_PI ), 0 ) + std::max ( std::min ( beta_[16]*o*o + beta_[17], M_PI*M_PI ), 0 ), M_PI*M_PI );\n\n return cv::Matx ( var_l, 0, 0,\n 0, var_a, 0,\n 0, 0, var_o );\n}\n\nvoid EKFSLAM::update() {\n switch (config_.update_mode) {\n case None:\n // Nothing to do\n break;\n case Single:\n update_single();\n break;\n case Combined:\n update_combined();\n break;\n default:\n assert ( false );\n }\n}\n\nvoid EKFSLAM::update_single() {\n if ( z_known.size() == 0 ) return;\n\n for ( auto i: z_known ) {\n assert ( i < c_ij.size() && c_ij[i] != nullptr );\n\n // obtained measurement i corresponds to landmark j\n int j = c_ij[i]->ij.second;\n assert ( 0 < j && j < y.rows/3 && c_ji[j] == c_ij[i] );\n\n // fetch needed data\n cv::Mat_ v = cv::Mat_ ( c_ij[i]->v );\n cv::Mat_ S_inv = cv::Mat_ ( c_ij[i]->S_inv );\n\n // K_i = C_Y*H_i^T*S_i^(-1)\n cv::Mat_ K_i = cv::Mat_ ( C_Y.rows, 3, 0.0 );\n for ( size_t k = 0; k < K_i.rows; k += 3 ) {\n // reference to submatrix\n cv::Mat_ K_i_k = cv::Mat_ ( K_i, cv::Range ( k, k + 3 ), cv::Range ( 0, 3 ) );\n\n // calculation & matrix update\n cv::Matx C_MX_k = cv::Mat_ ( C_Y, cv::Range ( k, k + 3 ), cv::Range ( 0, 3 ) );\n cv::Matx C_M_kj = cv::Mat_ ( C_Y, cv::Range ( k, k + 3 ), cv::Range ( 3*j, 3*j + 3 ) );\n cv::Mat_ tmp = cv::Mat_ ( C_MX_k*c_ij[i]->dx.t() + C_M_kj*c_ij[i]->dm.t() );\n K_i_k = tmp * S_inv;\n }\n\n // I - K_i*H_i\n cv::Mat_ I_K_i_H_i = cv::Mat_::eye ( C_Y.rows, C_Y.cols );\n for ( size_t k = 0; k < C_Y.rows; k += 3 ) {\n // references to submatrices\n cv::Mat_ I_K_i_H_i_k0 = cv::Mat_ ( I_K_i_H_i, cv::Range ( k, k + 3 ), cv::Range ( 0, 3 ) );\n cv::Mat_ I_K_i_H_i_kj = cv::Mat_ ( I_K_i_H_i, cv::Range ( k, k + 3 ), cv::Range ( 3*j, 3*j + 3 ) );\n\n // calculation & matrix update\n cv::Matx K_i_k = cv::Mat_ ( K_i, cv::Range ( k, k + 3 ), cv::Range ( 0, 3 ) );\n I_K_i_H_i_k0 -= cv::Mat_ ( K_i_k * c_ij[i]->dx );\n I_K_i_H_i_kj -= cv::Mat_ ( K_i_k * c_ij[i]->dm );\n }\n\n // update mean and covariance\n y = y + K_i*v;\n C_Y = I_K_i_H_i*C_Y;\n\n // normalize angles\n for ( size_t i = 2; i < y.rows; i += 3 )\n y[i][0] = angle_normalize ( y[i][0] );\n\n // re-establish diagonalization\n C_Y = 0.5 * ( C_Y + C_Y.t() );\n }\n}\n\nvoid EKFSLAM::update_combined() {\n if ( z_known.size() == 0 ) return;\n\n // initialization\n cv::Mat_ v = cv::Mat_ ( 3*z_known.size(), 1, 0.0 );\n cv::Mat_ H = cv::Mat_ ( 3*z_known.size(), C_Y.cols, 0.0 );\n cv::Mat_ R = cv::Mat_ ( 3*z_known.size(), 3*z_known.size(), 0.0 );\n size_t k = 0;\n\n for ( auto i: z_known ) {\n assert ( i < c_ij.size() && c_ij[i] != nullptr );\n\n // obtained measurement i corresponds to landmark j\n int j = c_ij[i]->ij.second;\n assert ( 0 < j && j < y.rows/3 && c_ji[j] == c_ij[i] );\n\n // references to submatrices\n cv::Mat_ v_k = cv::Mat_ ( v, cv::Range ( k, k + 3 ), cv::Range ( 0, 1 ) );\n cv::Mat_ Hx_kj = cv::Mat_ ( H, cv::Range ( k, k + 3 ), cv::Range ( 0, 3 ) );\n cv::Mat_ Hm_kj = cv::Mat_ ( H, cv::Range ( k, k + 3 ), cv::Range ( 3*j, 3*j + 3 ) );\n cv::Mat_ R_k = cv::Mat_ ( R, cv::Range ( k, k + 3 ), cv::Range ( k, k + 3 ) );\n\n // calculation & update\n // H = (H_1j H_2j' ... H_Nj'')^T\n // H_kj = (dx_kj 0 .. 0 dm_kj 0 .. 0)\n v_k += cv::Mat_ ( c_ij[i]->v );\n Hx_kj += cv::Mat_ ( c_ij[i]->dx );\n Hm_kj += cv::Mat_ ( c_ij[i]->dm );\n R_k += cv::Mat_ ( c_ij[i]->Q );\n k += 3;\n }\n\n // calculation\n cv::Mat_ S = R + H*C_Y*H.t();\n cv::Mat_ K = C_Y*H.t() *S.inv();\n\n // update mean and covariance\n y = y + K*v;\n C_Y = ( cv::Mat_::eye ( C_Y.rows, C_Y.cols ) - K*H ) *C_Y;\n\n // normalize angles\n for ( size_t i = 2; i < y.rows; i += 3 )\n y[i][0] = angle_normalize ( y[i][0] );\n\n // re-establish diagonalization\n C_Y = 0.5 * ( C_Y + C_Y.t() );\n}\n\nvoid EKFSLAM::integration ( const MeasurementMarkerConstPtr &zt ) {\n if ( !config_.enable_integration || z_new.size() == 0 ) return;\n\n for ( auto i: z_new ) {\n // consider only measurements with marker IDs for new landmarks\n assert ( zt->operator[] ( i ).ids.size() > 0 );\n\n // measurement noise\n cv::Matx Q = measurement_noise( zt->operator[]( i ) );\n\n // number landmarks found in ascending order\n int j = y.rows/3;\n f_kj.insert ( std::pair ( zt->operator[] ( i ).ids[0], j ) );\n ROS_INFO ( \"new landmark found: marker %d corresponds to landmark %d\", zt->operator[] ( i ).ids[0], j );\n\n // pre-calculate needed data\n double r = zt->operator[] ( i ).length;\n double T_s = sin ( x[2][0] );\n double T_c = cos ( x[2][0] );\n double F_s = sin ( zt->operator[] ( i ).angle );\n double F_c = cos ( zt->operator[] ( i ).angle );\n\n // transformation matrix from robot coordinates in global coordinates\n Pose2D px(x[0][0], x[1][0], x[2][0] );\n cv::Matx T_x = cv::Matx44d ( px.theta_cos(), -px.theta_sin(), 0, px.x(), px.theta_sin(), px.theta_cos(), 0, px.y(), 0, 0, 1, px.theta(), 0, 0, 0, 1 );\n\n // transformation matrix from sensor coordinates in robot coordinates\n Pose2D pz = zt->pose2d();\n cv::Matx T_z = cv::Matx44d ( pz.theta_cos(), -pz.theta_sin(), 0, pz.x(), pz.theta_sin(), pz.theta_cos(), 0, pz.y(), 0, 0, 1, pz.theta(), 0, 0, 0, 1 );\n\n // transformation matrix from sensor coordinates in global coordinates\n cv::Matx T_x_T_z = T_x * T_z;\n\n // derivation of T_x by theta\n cv::Matx T_x_theta = cv::Matx ( -T_s, -T_c, 0, 0,\n T_c, -T_s, 0, 0,\n 0, 0, 0, 1,\n 0, 0, 0, 0 );\n\n // adapt size of global mean\n y.resize ( y.rows + 3 );\n\n // update gloabl mean with mean of landmark j: m_j = g(x, z) = T_x(x) * T_z * f(z)\n // with f(z) = (r*cos(a), r*sin(a), o), r..radius, a..alpha, o..orientation\n // (function f transforms measurements from spheric coordinates into cartesian coordinates)\n cv::Vec homogenious_stage_vector_i = append(zt->operator[] ( i ).pose.state_vector(), 1);\n cv::Vec m_j = T_x_T_z * homogenious_stage_vector_i;\n y[3*j + 0][0] = m_j[0];\n y[3*j + 1][0] = m_j[1];\n y[3*j + 2][0] = m_j[2];\n\n // jacobian matrix of f\n cv::Matx F_z = cv::Matx ( F_c, -r*F_s, 0,\n F_s, r*F_c, 0,\n 0, 0, 1,\n 0, 0, 0 );\n\n // jacobian matrix of G in respect to x\n cv::Mat_ G_x = cv::Mat_::eye ( 3, 3 );\n cv::Vec G_theta = T_x_theta * T_z * homogenious_stage_vector_i;\n G_x[0][2] = G_theta[0];\n G_x[1][2] = G_theta[1];\n\n // jacobian matrix of G in respect to z\n cv::Matx G_z = cv::Mat_ ( cv::Mat_ ( T_x_T_z * F_z ), cv::Range ( 0, 3 ), cv::Range ( 0, 3 ) );\n\n // create horizontal covariance of landmark j\n cv::Mat_ C_M_j = cv::Mat_ ( 3, C_Y.cols, 0.0 );\n for ( size_t k = 0; k < C_M_j.cols; k += 3 ) {\n // references to submatrices\n cv::Mat_ C_M_jk = cv::Mat_ ( C_M_j, cv::Range ( 0, 3 ), cv::Range ( k, k + 3 ) );\n cv::Mat_ C_XM_k = cv::Mat_ ( C_Y, cv::Range ( 0, 3 ), cv::Range ( k, k + 3 ) );\n\n // calculation & matrix update\n C_M_jk = G_x*C_XM_k;\n }\n\n // append horizontal covariance of landmark j to global covariance\n cv::Mat tmp_v[] = { C_Y, C_M_j };\n cv::vconcat ( tmp_v, 2, C_Y );\n\n // create vertical covariance of landmark j\n C_M_j = C_M_j.t();\n C_M_j.resize ( C_M_j.rows + 3 );\n\n // reference to submatrix\n cv::Mat_ C_M_jj = cv::Mat_ ( C_M_j, cv::Range ( 3*j, 3*j + 3 ), cv::Range ( 0, 3 ) );\n\n // calculation & matrix update\n cv::Mat_ R = cv::Mat_ ( G_z*Q*G_z.t() );\n C_M_jj = G_x*C_X*G_x.t() + R;\n\n // append vertical covariance of landmark j to global covariance\n cv::Mat tmp_h[] = { C_Y, C_M_j };\n cv::hconcat ( tmp_h, 2, C_Y );\n\n // reinitialize covariance submatrices pointing to the same data (no copy!)\n x = cv::Mat_ ( y, cv::Range ( 0, 3 ), cv::Range ( 0, 1 ) );\n C_X = cv::Mat_ ( C_Y, cv::Range ( 0, 3 ), cv::Range ( 0, 3 ) );\n }\n}\n", "meta": {"hexsha": "054d32c1467522279e4d4a74bb5c18a1f4293958", "size": 25822, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tuw_marker_slam/src/tuw_marker_slam/ekf_slam.cpp", "max_stars_repo_name": "tuw-robotics/tuw_marker_slam", "max_stars_repo_head_hexsha": "3ebbc5385f1fe882d94814bf717890a05f4c883e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 11.0, "max_stars_repo_stars_event_min_datetime": "2016-12-20T16:42:51.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-01T09:47:38.000Z", "max_issues_repo_path": "tuw_marker_slam/src/tuw_marker_slam/ekf_slam.cpp", "max_issues_repo_name": "tuw-robotics/tuw_marker_slam", "max_issues_repo_head_hexsha": "3ebbc5385f1fe882d94814bf717890a05f4c883e", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 8.0, "max_issues_repo_issues_event_min_datetime": "2017-08-21T02:50:58.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-04T08:14:49.000Z", "max_forks_repo_path": "tuw_marker_slam/src/tuw_marker_slam/ekf_slam.cpp", "max_forks_repo_name": "tuw-robotics/tuw_marker_slam", "max_forks_repo_head_hexsha": "3ebbc5385f1fe882d94814bf717890a05f4c883e", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 9.0, "max_forks_repo_forks_event_min_datetime": "2017-02-11T12:06:53.000Z", "max_forks_repo_forks_event_max_datetime": "2022-01-12T12:08:48.000Z", "avg_line_length": 40.9223454834, "max_line_length": 269, "alphanum_fraction": 0.5086360468, "num_tokens": 8169, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631698328917, "lm_q2_score": 0.3522017752483203, "lm_q1q2_score": 0.21402004716816603}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"kinect_view.h\"\n\nnamespace kinect_merge {\n\n// This is used for deciding whether two points are similar enough to be merged.\nstatic const float SIMILARITY_THRESHOLD = 3.0f; // in units of standard deviations\n\n// Measurement variance constants\nstatic const float ALPHA_0 = 0.0032225f;\nstatic const float ALPHA_1 = -0.0020925f;\nstatic const float ALPHA_2 = 0.0022078f;\nstatic const float BETA_X = 0.0017228f;\nstatic const float BETA_Y = 0.0017092f;\n\nCPoint::CPoint() {}\n\nCPoint::CPoint(const cv::Matx31f& position, const cv::Matx33f& covariance, const cv::Matx& color) :\n pos(position),\n cov(covariance),\n col_sum(color),\n num_merged(1) {}\n\ncv::Matx CPoint::get_color() const {\n // NOTE: The following line would segfault on cv::Mat(col_sum). An alternate method follows.\n // return cv::Mat(col_sum) / num_merged;\n cv::Matx m;\n for(int i = 0; i < 3; i++)\n m(i) = col_sum(i) / num_merged;\n return m;\n}\n\nCView::CView(const std::string &extrinsic_file,\n const std::string &disparity_file,\n const std::string &color_file,\n float covariance_scale_xy,\n float covariance_scale_z,\n CKinectCalibration &calibration_) :\n calibration(calibration_),\n original_pointmap(boost::extents[IMAGE_HEIGHT][IMAGE_WIDTH]),\n pointmap(boost::extents[IMAGE_HEIGHT][IMAGE_WIDTH]),\n num_missing(0),\n#ifdef DEBUG // The measurement_used vector is used for outputting debug images.\n num_added(0),\n measurement_used(IMAGE_HEIGHT) {\n#else\n num_added(0) {\n#endif\n\n // Initialize the depth map.\n for(int v = 0; v < IMAGE_HEIGHT; v++) {\n for(int u = 0; u < IMAGE_WIDTH; u++) {\n depthmap(v, u) = -1;\n }\n }\n\n // Load extrinsic data.\n\n cv::Mat inv_transformation_tmp = cv::Mat_::eye(4, 4);\n cv::Mat inv_rotation(inv_transformation_tmp, cv::Rect(0, 0, 3, 3));\n cv::Mat inv_translation(inv_transformation_tmp, cv::Rect(3, 0, 1, 3));\n\n try {\n cv::FileStorage fs(extrinsic_file, cv::FileStorage::READ);\n fs[\"R\"] >> inv_rotation;\n fs[\"T\"] >> inv_translation;\n } catch(cv::Exception &e) {\n std::cerr << std::endl << \"Error reading extrinsic data: \" << e.what() << std::endl;\n exit(4);\n }\n\n inv_transformation = inv_transformation_tmp;\n cv::Mat transformation = inv_transformation_tmp.inv();\n cv::Mat rotation = inv_rotation.t();\n\n // Load disparity map.\n int flags = -1; // Load as-is (greyscale). Using 0 (force grayscale) results in a segfault.\n cv::Mat1s disparitymap = cv::imread(disparity_file, flags);\n\n if(disparitymap.cols != IMAGE_WIDTH || disparitymap.rows != IMAGE_HEIGHT) {\n std::cerr << std::endl << \"Disparity map has dimensions \" << disparitymap.cols << \"x\" << disparitymap.rows <<\n \" instead of \" << IMAGE_WIDTH << \"x\" << IMAGE_HEIGHT << std::endl;\n exit(5);\n }\n\n // Load color map.\n cv::Mat3b colormap = cv::imread(color_file, flags);\n\n if(colormap.cols != IMAGE_WIDTH || colormap.rows != IMAGE_HEIGHT) {\n std::cerr << std::endl << \"Color map has dimensions \" << colormap.cols << \"x\" << colormap.rows <<\n \" instead of \" << IMAGE_WIDTH << \"x\" << IMAGE_HEIGHT << std::endl;\n exit(6);\n }\n\n // Convert to a point cloud in global coordinates and calculate the covariance matrix.\n cv::Matx31f local_point;\n cv::Mat local_point_tmp = (cv::Mat_(4, 1) << 0, 0, 0, 1);\n cv::Mat global_point;\n cv::Mat covariance;\n for(int v = 0; v < disparitymap.rows; v++) {\n for(int u = 0; u < disparitymap.cols; u++) {\n const short disparity = disparitymap(v, u);\n if(disparity == 2047) {\n continue;\n }\n\n // Convert to local and then global coordinates.\n calibration.disparity2point(u, v, disparity, local_point);\n local_point_tmp.at(0) = local_point(0);\n local_point_tmp.at(1) = local_point(1);\n local_point_tmp.at(2) = local_point(2);\n global_point = transformation * local_point_tmp;\n global_point.pop_back(); // Turn into a 3 by 1 matrix.\n\n float depth = local_point(2);\n if(depth < 0) {\n // The point projects behind the camera. Ignore it.\n continue;\n }\n\n // Calculate the covariance matrix.\n covariance = cv::Mat_::eye(3, 3);\n covariance.at(0, 0) = BETA_X * BETA_X * depth * depth / 12;\n covariance.at(1, 1) = BETA_Y * BETA_Y * depth * depth / 12;\n covariance.at(2, 2) = pow(ALPHA_2 * depth * depth + ALPHA_1 * depth + ALPHA_0, 2);\n covariance.at(0, 0) *= covariance_scale_xy;\n covariance.at(1, 1) *= covariance_scale_xy;\n covariance.at(2, 2) *= covariance_scale_z;\n\n // Convert the covariance matrix to the global reference frame.\n covariance = rotation * covariance * inv_rotation;\n assert(covariance.at(0, 0) >= 0);\n assert(covariance.at(1, 1) >= 0);\n assert(covariance.at(2, 2) >= 0);\n\n // Get the pixel coordinates for the measurement.\n int uc, vc;\n to_image_plane(local_point_tmp, uc, vc);\n if(uc < 0 || uc >= IMAGE_WIDTH || vc < 0 || vc >= IMAGE_HEIGHT) {\n continue;\n }\n\n if(!original_pointmap[vc][uc] || depthmap(vc, uc) > depth) {\n depthmap(vc, uc) = depth;\n original_pointmap[vc][uc] = boost::make_shared(global_point,\n covariance,\n colormap(vc, uc));\n }\n }\n }\n}\n\nvoid CView::to_image_plane(const cv::Matx41f &local_pos, int &uc, int &vc) const {\n cv::Matx31f local_pos_tmp(local_pos(0), local_pos(1), local_pos(2));\n cv::Matx21f cf;\n calibration.point2rgb(local_pos_tmp, cf);\n uc = cf(0) + 0.5;\n vc = cf(1) + 0.5;\n}\n\nstatic float calculate_mahalanobis_distance(const CPoint &refined_point, const CPoint &p) {\n cv::Matx operand = (p.pos - refined_point.pos).t() * p.cov.inv() * (p.pos - refined_point.pos);\n return sqrt(operand(0));\n}\n\n// Calculate a new position estimate, covariance matrix and color for an existing point given a new measurement.\nstatic void refine_point(const CPoint &existing_point, const CPoint &new_measurement, CPoint &refined_point) {\n cv::Matx33f new_measurement_cov_inv = new_measurement.cov.inv();\n\n // Combine the covariance matrices.\n refined_point.cov = (existing_point.cov.inv() + new_measurement_cov_inv).inv();\n\n // Calculate the refined position estimate.\n refined_point.pos = existing_point.pos + refined_point.cov * new_measurement_cov_inv * (new_measurement.pos - existing_point.pos);\n\n // Add the color values.\n refined_point.col_sum = existing_point.col_sum + new_measurement.col_sum;\n refined_point.num_merged = existing_point.num_merged + new_measurement.num_merged;\n}\n\nvoid CView::refine_points(CView &connected_view, std::vector > &measurement_used) const {\n for(int v = 0; v < IMAGE_HEIGHT; v++) {\n for(int u = 0; u < IMAGE_WIDTH; u++) {\n if(!connected_view.has_point(u, v)) {\n continue;\n }\n\n // Project the point onto the current view image plane.\n // NOTE: Only points which were added to cloud from the connected view are projected.\n // Each point in the cloud is pointed to by exactly one view. This means that a single\n // point in the cloud will NOT be refined multiple times with the same new measurement\n // through different connected views.\n int proj_u, proj_v;\n float proj_depth;\n CPoint &connected_view_point = connected_view.get_point(u, v);\n project(connected_view_point, proj_u, proj_v, proj_depth);\n\n if(proj_depth < 0) {\n // The point projects outside the image plane.\n continue;\n }\n\n if(!has_measurement(proj_u, proj_v)) {\n // There is nothing to merge with at this pixel.\n continue;\n }\n\n const CPoint ¤t_view_point = get_original_point(proj_u, proj_v);\n\n // Calculate the refined point and use it if it's close enough to the existing point and\n // new measurement.\n CPoint refined_point;\n refine_point(connected_view_point, current_view_point, refined_point);\n if(calculate_mahalanobis_distance(refined_point, connected_view_point) < SIMILARITY_THRESHOLD &&\n calculate_mahalanobis_distance(refined_point, current_view_point) < SIMILARITY_THRESHOLD) {\n connected_view_point = refined_point;\n measurement_used[proj_v][proj_u] = true;\n }\n }\n }\n}\n\nvoid CView::insert_into_point_cloud(std::vector &point_cloud) {\n for(int v = 0; v < IMAGE_HEIGHT; v++) {\n for(int u = 0; u < IMAGE_WIDTH; u++) {\n if(has_measurement(u, v)) {\n point_cloud.push_back(original_pointmap[v][u]);\n }\n }\n }\n}\n\nvoid CView::project(const CPoint &global_point, int &proj_u, int &proj_v, float &proj_depth) const {\n cv::Matx41f global_pos(global_point.pos(0),\n global_point.pos(1),\n global_point.pos(2),\n 1);\n cv::Matx41f local_pos = inv_transformation * global_pos;\n to_image_plane(local_pos, proj_u, proj_v);\n\n if(proj_u < 0 || proj_u >= IMAGE_WIDTH ||\n proj_v < 0 || proj_v >= IMAGE_HEIGHT) {\n // The point projects outside the image plane.\n proj_depth = -1;\n } else {\n proj_depth = local_pos(2);\n }\n}\n\nvoid CView::merge(boost::ptr_vector &views,\n unsigned int view_idx,\n const cv::Mat view_connectivity,\n std::vector &global_point_cloud) {\n // Initialize vectors.\n // A measurement is marked as used if an existing point is refined with it.\n#ifndef DEBUG\n // Use a local variable instead of a member variable.\n std::vector > measurement_used(IMAGE_HEIGHT);\n#endif\n for(int v = 0; v < IMAGE_HEIGHT; v++) {\n measurement_used[v].resize(IMAGE_WIDTH);\n for(int u = 0; u < IMAGE_WIDTH; u++) {\n measurement_used[v][u] = false;\n }\n }\n\n // Project all connected views into the current view to find points to be refined.\n for(unsigned int connected_idx = 0; connected_idx < view_idx; connected_idx++) {\n if(!view_connectivity.at(view_idx, connected_idx)) {\n // The view is not connected. Skip it.\n continue;\n }\n\n CView &connected_view = views[connected_idx];\n\n refine_points(connected_view, measurement_used);\n }\n\n // Add all the points that weren't used to refine existing points to the point cloud.\n for(int v = 0; v < IMAGE_HEIGHT; v++) {\n for(int u = 0; u < IMAGE_WIDTH; u++) {\n if(has_measurement(u, v)) {\n if(!measurement_used[v][u]) {\n pointmap[v][u] = original_pointmap[v][u];\n global_point_cloud.push_back(pointmap[v][u]);\n num_added++;\n }\n } else {\n num_missing++;\n }\n }\n }\n}\n\n}\n", "meta": {"hexsha": "b327759a577daf3f60be9dec1618ba6adbdde5dd", "size": 11947, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/kinect_view.cpp", "max_stars_repo_name": "tomikyos/kinect-merge", "max_stars_repo_head_hexsha": "28e66a2e3cd720eb48bb2470611ed7e8f1fd89e3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 20.0, "max_stars_repo_stars_event_min_datetime": "2015-07-16T01:15:20.000Z", "max_stars_repo_stars_event_max_datetime": "2021-06-24T05:28:12.000Z", "max_issues_repo_path": "src/kinect_view.cpp", "max_issues_repo_name": "tomikyos/kinect-merge", "max_issues_repo_head_hexsha": "28e66a2e3cd720eb48bb2470611ed7e8f1fd89e3", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/kinect_view.cpp", "max_forks_repo_name": "tomikyos/kinect-merge", "max_forks_repo_head_hexsha": "28e66a2e3cd720eb48bb2470611ed7e8f1fd89e3", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2015-07-16T21:57:06.000Z", "max_forks_repo_forks_event_max_datetime": "2020-12-28T12:16:09.000Z", "avg_line_length": 39.4290429043, "max_line_length": 134, "alphanum_fraction": 0.6013225077, "num_tokens": 2907, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984434543458, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.2139385866347924}} {"text": "/*\n\tSatellite Attitude Dynamics Stepper\n\n\t@author\t\t:\tsiddharth deore\n\t@licence\t:\tMIT\n*/\n\n#define _CRT_SECURE_NO_WARNINGS\n#include \n// reading a text file\n#include \n#include \n#include \n\n#include \n#include \n#include \"Satellite.h\"\n#include \nusing namespace std;\nusing namespace boost::numeric::odeint;\n\n\nint main(int argc, char** argv)\n{\n\tsystem(\"cls\");\n\t// Initial condition\n\tdouble IC[7] = { 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0 };\n\t// Read Configration file for initial condiion Each line is state, TODO:: some kind of parser!\n\tstring line;\n\tifstream myfile(\"conf.txt\");\n\tint i = 0;\n\tif (myfile.is_open())\n\t{\n\t\twhile (getline(myfile, line))\n\t\t{\n\t\t\tIC[i++] = std::stod(line);\n\t\t\t//cout << IC[i-1] <\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\n\nnamespace boost { namespace geometry\n{\n\n#ifndef DOXYGEN_NO_DETAIL\nnamespace detail { namespace intersection\n{\n\n\ntemplate\n<\n typename MultiLinestring1, typename MultiLinestring2,\n typename OutputIterator, typename PointOut,\n typename Strategy\n>\nstruct intersection_multi_linestring_multi_linestring_point\n{\n static inline OutputIterator apply(MultiLinestring1 const& ml1,\n MultiLinestring2 const& ml2, OutputIterator out,\n Strategy const& strategy)\n {\n // Note, this loop is quadratic w.r.t. number of linestrings per input.\n // Future Enhancement: first do the sections of each, then intersect.\n for (typename boost::range_iterator\n <\n MultiLinestring1 const\n >::type it1 = boost::begin(ml1);\n it1 != boost::end(ml1);\n ++it1)\n {\n for (typename boost::range_iterator\n <\n MultiLinestring2 const\n >::type it2 = boost::begin(ml2);\n it2 != boost::end(ml2);\n ++it2)\n {\n out = intersection_linestring_linestring_point\n <\n typename boost::range_value::type,\n typename boost::range_value::type,\n OutputIterator, PointOut, Strategy\n >::apply(*it1, *it2, out, strategy);\n }\n }\n\n return out;\n }\n};\n\n\ntemplate\n<\n typename Linestring, typename MultiLinestring,\n typename OutputIterator, typename PointOut,\n typename Strategy\n>\nstruct intersection_linestring_multi_linestring_point\n{\n static inline OutputIterator apply(Linestring const& linestring,\n MultiLinestring const& ml, OutputIterator out,\n Strategy const& strategy)\n {\n for (typename boost::range_iterator\n <\n MultiLinestring const\n >::type it = boost::begin(ml);\n it != boost::end(ml);\n ++it)\n {\n out = intersection_linestring_linestring_point\n <\n Linestring,\n typename boost::range_value::type,\n OutputIterator, PointOut, Strategy\n >::apply(linestring, *it, out, strategy);\n }\n\n return out;\n }\n};\n\n\ntemplate\n<\n typename MultiLinestring, typename Box,\n typename OutputIterator, typename LinestringOut,\n typename Strategy\n>\nstruct clip_multi_linestring\n{\n static inline OutputIterator apply(MultiLinestring const& multi_linestring,\n Box const& box, OutputIterator out, Strategy const& strategy)\n {\n typedef typename point_type::type point_type;\n strategy::intersection::liang_barsky lb_strategy;\n for (typename boost::range_iterator::type it\n = boost::begin(multi_linestring);\n it != boost::end(multi_linestring); ++it)\n {\n out = detail::intersection::clip_range_with_box\n (box, *it, out, lb_strategy);\n }\n return out;\n }\n};\n\n\n}} // namespace detail::intersection\n#endif // DOXYGEN_NO_DETAIL\n\n\n#ifndef DOXYGEN_NO_DISPATCH\nnamespace dispatch\n{\n\n\n// Linear\ntemplate\n<\n typename MultiLinestring1, typename MultiLinestring2,\n bool Reverse1, bool Reverse2, bool ReverseOut,\n typename OutputIterator, typename GeometryOut,\n overlay_type OverlayType,\n typename Strategy\n>\nstruct intersection_insert\n <\n multi_linestring_tag, multi_linestring_tag, point_tag,\n false, false, false,\n MultiLinestring1, MultiLinestring2,\n Reverse1, Reverse2, ReverseOut,\n OutputIterator, GeometryOut,\n OverlayType,\n Strategy\n > : detail::intersection::intersection_multi_linestring_multi_linestring_point\n <\n MultiLinestring1, MultiLinestring2,\n OutputIterator, GeometryOut,\n Strategy\n >\n{};\n\n\ntemplate\n<\n typename Linestring, typename MultiLinestring,\n typename OutputIterator, typename GeometryOut,\n bool Reverse1, bool Reverse2, bool ReverseOut,\n overlay_type OverlayType,\n typename Strategy\n>\nstruct intersection_insert\n <\n linestring_tag, multi_linestring_tag, point_tag,\n false, false, false,\n Linestring, MultiLinestring,\n Reverse1, Reverse2, ReverseOut,\n OutputIterator, GeometryOut,\n OverlayType,\n Strategy\n > : detail::intersection::intersection_linestring_multi_linestring_point\n <\n Linestring, MultiLinestring,\n OutputIterator, GeometryOut,\n Strategy\n >\n{};\n\n\ntemplate\n<\n typename MultiLinestring, typename Box,\n bool Reverse1, bool Reverse2, bool ReverseOut,\n typename OutputIterator, typename GeometryOut,\n overlay_type OverlayType,\n typename Strategy\n>\nstruct intersection_insert\n <\n multi_linestring_tag, box_tag, linestring_tag,\n false, true, false,\n MultiLinestring, Box,\n Reverse1, Reverse2, ReverseOut,\n OutputIterator, GeometryOut,\n OverlayType,\n Strategy\n > : detail::intersection::clip_multi_linestring\n <\n MultiLinestring, Box,\n OutputIterator, GeometryOut,\n Strategy\n >\n{};\n\n\n} // namespace dispatch\n#endif\n\n}} // namespace boost::geometry\n\n\n#endif // BOOST_GEOMETRY_MULTI_ALGORITHMS_INTERSECTION_HPP\n\n", "meta": {"hexsha": "870a1b2ab4eaa4670ec62f94b802d533c653c1a4", "size": 6970, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external/boost_1_47_0/boost/geometry/multi/algorithms/intersection.hpp", "max_stars_repo_name": "zigaosolin/Raytracer", "max_stars_repo_head_hexsha": "df17f77e814b2e4b90c4a194e18cc81fa84dcb27", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 47.0, "max_stars_repo_stars_event_min_datetime": "2015-01-01T14:37:36.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-25T07:38:07.000Z", "max_issues_repo_path": "external/boost_1_47_0/boost/geometry/multi/algorithms/intersection.hpp", "max_issues_repo_name": "zigaosolin/Raytracer", "max_issues_repo_head_hexsha": "df17f77e814b2e4b90c4a194e18cc81fa84dcb27", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 6.0, "max_issues_repo_issues_event_min_datetime": "2016-01-11T05:20:05.000Z", "max_issues_repo_issues_event_max_datetime": "2021-02-06T11:37:24.000Z", "max_forks_repo_path": "external/boost_1_47_0/boost/geometry/multi/algorithms/intersection.hpp", "max_forks_repo_name": "zigaosolin/Raytracer", "max_forks_repo_head_hexsha": "df17f77e814b2e4b90c4a194e18cc81fa84dcb27", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 17.0, "max_forks_repo_forks_event_min_datetime": "2015-01-05T15:10:43.000Z", "max_forks_repo_forks_event_max_datetime": "2021-06-22T04:59:16.000Z", "avg_line_length": 29.9141630901, "max_line_length": 82, "alphanum_fraction": 0.6578192253, "num_tokens": 1461, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266116, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.2139385807064917}} {"text": "// Copyright (C) 2004-2006 The Trustees of Indiana University.\n\n// Use, modification and distribution is subject to the Boost Software\n// License, Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// Authors: Douglas Gregor\n// Andrew Lumsdaine\n\n/**\n * This header implements four distributed algorithms to compute\n * the minimum spanning tree (actually, minimum spanning forest) of a\n * graph. All of the algorithms were implemented as specified in the\n * paper by Dehne and Gotz:\n *\n * Frank Dehne and Silvia Gotz. Practical Parallel Algorithms for Minimum\n * Spanning Trees. In Symposium on Reliable Distributed Systems,\n * pages 366--371, 1998.\n *\n * There are four algorithm variants implemented.\n */\n\n#ifndef BOOST_DEHNE_GOTZ_MIN_SPANNING_TREE_HPP\n#define BOOST_DEHNE_GOTZ_MIN_SPANNING_TREE_HPP\n\n#ifndef BOOST_GRAPH_USE_MPI\n#error \"Parallel BGL files should not be included unless has been included\"\n#endif\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace boost { namespace graph { namespace distributed {\n\nnamespace detail {\n /**\n * Binary function object type that selects the (edge, weight) pair\n * with the minimum weight. Used within a Boruvka merge step to select\n * the candidate edges incident to each supervertex.\n */\n struct smaller_weighted_edge\n {\n template\n std::pair\n operator()(const std::pair& x,\n const std::pair& y) const\n { return x.second < y.second? x : y; }\n };\n\n /**\n * Unary predicate that determines if the source and target vertices\n * of the given edge have the same representative within a disjoint\n * sets data structure. Used to indicate when an edge is now a\n * self-loop because of supervertex merging in Boruvka's algorithm.\n */\n template\n class do_has_same_supervertex\n {\n public:\n typedef typename graph_traits::edge_descriptor edge_descriptor;\n\n do_has_same_supervertex(DisjointSets& dset, const Graph& g)\n : dset(dset), g(g) { }\n\n bool operator()(edge_descriptor e)\n { return dset.find_set(source(e, g)) == dset.find_set(target(e, g)); }\n\n private:\n DisjointSets& dset;\n const Graph& g;\n };\n\n /**\n * Build a @ref do_has_same_supervertex object.\n */\n template\n inline do_has_same_supervertex\n has_same_supervertex(DisjointSets& dset, const Graph& g)\n { return do_has_same_supervertex(dset, g); }\n\n /** \\brief A single distributed Boruvka merge step.\n *\n * A distributed Boruvka merge step involves computing (globally)\n * the minimum weight edges incident on each supervertex and then\n * merging supervertices along these edges. Once supervertices are\n * merged, self-loops are eliminated.\n *\n * The set of parameters passed to this algorithm is large, and\n * considering this algorithm in isolation there are several\n * redundancies. However, the more asymptotically efficient\n * distributed MSF algorithms require mixing Boruvka steps with the\n * merging of local MSFs (implemented in\n * merge_local_minimum_spanning_trees_step): the interaction of the\n * two algorithms mandates the addition of these parameters.\n *\n * \\param pg The process group over which communication should be\n * performed. Within the distributed Boruvka algorithm, this will be\n * equivalent to \\code process_group(g); however, in the context of\n * the mixed MSF algorithms, the process group @p pg will be a\n * (non-strict) process subgroup of \\code process_group(g).\n *\n * \\param g The underlying graph on which the MSF is being\n * computed. The type of @p g must model DistributedGraph, but there\n * are no other requirements because the edge and (super)vertex\n * lists are passed separately.\n *\n * \\param weight_map Property map containing the weights of each\n * edge. The type of this property map must model\n * ReadablePropertyMap and must support caching.\n *\n * \\param out An output iterator that will be written with the set\n * of edges selected to build the MSF. Every process within the\n * process group @p pg will receive all edges in the MSF.\n *\n * \\param dset Disjoint sets data structure mapping from vertices in\n * the graph @p g to their representative supervertex.\n *\n * \\param supervertex_map Mapping from supervertex descriptors to\n * indices.\n *\n * \\param supervertices A vector containing all of the\n * supervertices. Will be modified to include only the remaining\n * supervertices after merging occurs.\n *\n * \\param edge_list The list of edges that remain in the graph. This\n * list will be pruned to remove self-loops once MSF edges have been\n * found.\n */\n template\n OutputIterator\n boruvka_merge_step(ProcessGroup pg, const Graph& g, WeightMap weight_map,\n OutputIterator out,\n disjoint_sets& dset,\n SupervertexMap supervertex_map,\n std::vector& supervertices,\n EdgeList& edge_list)\n {\n typedef typename graph_traits::vertex_descriptor\n vertex_descriptor;\n typedef typename graph_traits::vertices_size_type\n vertices_size_type;\n typedef typename graph_traits::edge_descriptor edge_descriptor;\n typedef typename EdgeList::iterator edge_iterator;\n typedef typename property_traits::value_type\n weight_type;\n typedef boost::parallel::detail::untracked_pair w_edge;\n typedef typename property_traits::value_type\n supervertex_index;\n\n smaller_weighted_edge min_edge;\n weight_type inf = (std::numeric_limits::max)();\n\n // Renumber the supervertices\n for (std::size_t i = 0; i < supervertices.size(); ++i)\n put(supervertex_map, supervertices[i], i);\n\n // BSP-B1: Find local minimum-weight edges for each supervertex\n std::vector candidate_edges(supervertices.size(),\n w_edge(edge_descriptor(), inf));\n for (edge_iterator ei = edge_list.begin(); ei != edge_list.end(); ++ei) {\n weight_type w = get(weight_map, *ei);\n supervertex_index u =\n get(supervertex_map, dset.find_set(source(*ei, g)));\n supervertex_index v =\n get(supervertex_map, dset.find_set(target(*ei, g)));\n\n if (u != v) {\n candidate_edges[u] = min_edge(candidate_edges[u], w_edge(*ei, w));\n candidate_edges[v] = min_edge(candidate_edges[v], w_edge(*ei, w));\n }\n }\n\n // BSP-B2 (a): Compute global minimum edges for each supervertex\n all_reduce(pg,\n &candidate_edges[0],\n &candidate_edges[0] + candidate_edges.size(),\n &candidate_edges[0], min_edge);\n\n // BSP-B2 (b): Use the edges to compute sequentially the new\n // connected components and emit the edges.\n for (vertices_size_type i = 0; i < candidate_edges.size(); ++i) {\n if (candidate_edges[i].second != inf) {\n edge_descriptor e = candidate_edges[i].first;\n vertex_descriptor u = dset.find_set(source(e, g));\n vertex_descriptor v = dset.find_set(target(e, g));\n if (u != v) {\n // Emit the edge, but cache the weight so everyone knows it\n cache(weight_map, e, candidate_edges[i].second);\n *out++ = e;\n\n // Link the two supervertices\n dset.link(u, v);\n\n // Whichever vertex was reparented will be removed from the\n // list of supervertices.\n vertex_descriptor victim = u;\n if (dset.find_set(u) == u) victim = v;\n supervertices[get(supervertex_map, victim)] =\n graph_traits::null_vertex();\n }\n }\n }\n\n // BSP-B3: Eliminate self-loops\n edge_list.erase(std::remove_if(edge_list.begin(), edge_list.end(),\n has_same_supervertex(dset, g)),\n edge_list.end());\n\n // TBD: might also eliminate multiple edges between supervertices\n // when the edges do not have the best weight, but this is not\n // strictly necessary.\n\n // Eliminate supervertices that have been absorbed\n supervertices.erase(std::remove(supervertices.begin(),\n supervertices.end(),\n graph_traits::null_vertex()),\n supervertices.end());\n\n return out;\n }\n\n /**\n * An edge descriptor adaptor that reroutes the source and target\n * edges to different vertices, but retains the original edge\n * descriptor for, e.g., property maps. This is used when we want to\n * turn a set of edges in the overall graph into a set of edges\n * between supervertices.\n */\n template\n struct supervertex_edge_descriptor\n {\n typedef supervertex_edge_descriptor self_type;\n typedef typename graph_traits::vertex_descriptor Vertex;\n typedef typename graph_traits::edge_descriptor Edge;\n\n Vertex source;\n Vertex target;\n Edge e;\n\n operator Edge() const { return e; }\n\n friend inline bool operator==(const self_type& x, const self_type& y)\n { return x.e == y.e; }\n\n friend inline bool operator!=(const self_type& x, const self_type& y)\n { return x.e != y.e; }\n };\n\n template\n inline typename supervertex_edge_descriptor::Vertex\n source(supervertex_edge_descriptor se, const Graph&)\n { return se.source; }\n\n template\n inline typename supervertex_edge_descriptor::Vertex\n target(supervertex_edge_descriptor se, const Graph&)\n { return se.target; }\n\n /**\n * Build a supervertex edge descriptor from a normal edge descriptor\n * using the given disjoint sets data structure to identify\n * supervertex representatives.\n */\n template\n struct build_supervertex_edge_descriptor\n {\n typedef typename graph_traits::vertex_descriptor Vertex;\n typedef typename graph_traits::edge_descriptor Edge;\n\n typedef Edge argument_type;\n typedef supervertex_edge_descriptor result_type;\n\n build_supervertex_edge_descriptor() : g(0), dsets(0) { }\n\n build_supervertex_edge_descriptor(const Graph& g, DisjointSets& dsets)\n : g(&g), dsets(&dsets) { }\n\n result_type operator()(argument_type e) const\n {\n result_type result;\n result.source = dsets->find_set(source(e, *g));\n result.target = dsets->find_set(target(e, *g));\n result.e = e;\n return result;\n }\n\n private:\n const Graph* g;\n DisjointSets* dsets;\n };\n\n template\n inline build_supervertex_edge_descriptor\n make_supervertex_edge_descriptor(const Graph& g, DisjointSets& dsets)\n { return build_supervertex_edge_descriptor(g, dsets); }\n\n template\n struct identity_function\n {\n typedef T argument_type;\n typedef T result_type;\n\n result_type operator()(argument_type x) const { return x; }\n };\n\n template\n class is_not_msf_edge\n {\n typedef typename graph_traits::vertex_descriptor Vertex;\n typedef typename graph_traits::edge_descriptor Edge;\n\n public:\n is_not_msf_edge(const Graph& g, DisjointSets dset, EdgeMapper edge_mapper)\n : g(g), dset(dset), edge_mapper(edge_mapper) { }\n\n bool operator()(Edge e)\n {\n Vertex u = dset.find_set(source(edge_mapper(e), g));\n Vertex v = dset.find_set(target(edge_mapper(e), g));\n if (u == v) return true;\n else {\n dset.link(u, v);\n return false;\n }\n }\n\n private:\n const Graph& g;\n DisjointSets dset;\n EdgeMapper edge_mapper;\n };\n\n template\n void\n sorted_mutating_kruskal(const Graph& g,\n ForwardIterator first_vertex,\n ForwardIterator last_vertex,\n EdgeList& edge_list, EdgeMapper edge_mapper,\n RankMap rank_map, ParentMap parent_map)\n {\n typedef disjoint_sets DisjointSets;\n\n // Build and initialize disjoint-sets data structure\n DisjointSets dset(rank_map, parent_map);\n for (ForwardIterator v = first_vertex; v != last_vertex; ++v)\n dset.make_set(*v);\n\n is_not_msf_edge\n remove_non_msf_edges(g, dset, edge_mapper);\n edge_list.erase(std::remove_if(edge_list.begin(), edge_list.end(),\n remove_non_msf_edges),\n edge_list.end());\n }\n\n /**\n * Merge local minimum spanning forests from p processes into\n * minimum spanning forests on p/D processes (where D is the tree\n * factor, currently fixed at 3), eliminating unnecessary edges in\n * the process.\n *\n * As with @ref boruvka_merge_step, this routine has many\n * parameters, not all of which make sense within the limited\n * context of this routine. The parameters are required for the\n * Boruvka and local MSF merging steps to interoperate.\n *\n * \\param pg The process group on which local minimum spanning\n * forests should be merged. The top (D-1)p/D processes will be\n * eliminated, and a new process subgroup containing p/D processors\n * will be returned. The value D is a constant factor that is\n * currently fixed to 3.\n *\n * \\param g The underlying graph whose MSF is being computed. It must model\n * the DistributedGraph concept.\n *\n * \\param first_vertex Iterator to the first vertex in the graph\n * that should be considered. While the local MSF merging algorithm\n * typically operates on the entire vertex set, within the hybrid\n * distributed MSF algorithms this will refer to the first\n * supervertex.\n *\n * \\param last_vertex The past-the-end iterator for the vertex list.\n *\n * \\param edge_list The list of local edges that will be\n * considered. For the p/D processes that remain, this list will\n * contain edges in the MSF known to the vertex after other\n * processes' edge lists have been merged. The edge list must be\n * sorted in order of increasing weight.\n *\n * \\param weight Property map containing the weights of each\n * edge. The type of this property map must model\n * ReadablePropertyMap and must support caching.\n *\n * \\param global_index Mapping from vertex descriptors to a global\n * index. The type must model ReadablePropertyMap.\n *\n * \\param edge_mapper A function object that can remap edge descriptors\n * in the edge list to any alternative edge descriptor. This\n * function object will be the identity function when a pure merging\n * of local MSFs is required, but may be a mapping to a supervertex\n * edge when the local MSF merging occurs on a supervertex\n * graph. This function object saves us the trouble of having to\n * build a supervertex graph adaptor.\n *\n * \\param already_local_msf True when the edge list already\n * constitutes a local MSF. If false, Kruskal's algorithm will first\n * be applied to the local edge list to select MSF edges.\n *\n * \\returns The process subgroup containing the remaining p/D\n * processes. If the size of this process group is greater than one,\n * the MSF edges contained in the edge list do not constitute an MSF\n * for the entire graph.\n */\n template\n ProcessGroup\n merge_local_minimum_spanning_trees_step(ProcessGroup pg,\n const Graph& g,\n ForwardIterator first_vertex,\n ForwardIterator last_vertex,\n EdgeList& edge_list,\n WeightMap weight,\n GlobalIndexMap global_index,\n EdgeMapper edge_mapper,\n bool already_local_msf)\n {\n typedef typename ProcessGroup::process_id_type process_id_type;\n typedef typename EdgeList::value_type edge_descriptor;\n typedef typename property_traits::value_type weight_type;\n typedef typename graph_traits::vertex_descriptor vertex_descriptor;\n\n // The tree factor, often called \"D\"\n process_id_type const tree_factor = 3;\n process_id_type num_procs = num_processes(pg);\n process_id_type id = process_id(pg);\n process_id_type procs_left = (num_procs + tree_factor - 1) / tree_factor;\n std::size_t n = std::size_t(last_vertex - first_vertex);\n\n if (!already_local_msf) {\n // Compute local minimum spanning forest. We only care about the\n // edges in the MSF, because only edges in the local MSF can be in\n // the global MSF.\n std::vector ranks(n);\n std::vector parents(n);\n detail::sorted_mutating_kruskal\n (g, first_vertex, last_vertex,\n edge_list, edge_mapper,\n make_iterator_property_map(ranks.begin(), global_index),\n make_iterator_property_map(parents.begin(), global_index));\n }\n\n typedef std::pair w_edge;\n\n // Order edges based on their weights.\n indirect_cmp > cmp_edge_weight(weight);\n\n if (id < procs_left) {\n // The p/D processes that remain will receive local MSF edges from\n // D-1 other processes.\n synchronize(pg);\n for (process_id_type from_id = procs_left + id; from_id < num_procs;\n from_id += procs_left) {\n std::size_t num_incoming_edges;\n receive(pg, from_id, 0, num_incoming_edges);\n if (num_incoming_edges > 0) {\n std::vector incoming_edges(num_incoming_edges);\n receive(pg, from_id, 1, &incoming_edges[0], num_incoming_edges);\n\n edge_list.reserve(edge_list.size() + num_incoming_edges);\n for (std::size_t i = 0; i < num_incoming_edges; ++i) {\n cache(weight, incoming_edges[i].first, incoming_edges[i].second);\n edge_list.push_back(incoming_edges[i].first);\n }\n std::inplace_merge(edge_list.begin(),\n edge_list.end() - num_incoming_edges,\n edge_list.end(),\n cmp_edge_weight);\n }\n }\n\n // Compute the local MSF from union of the edges in the MSFs of\n // all children.\n std::vector ranks(n);\n std::vector parents(n);\n detail::sorted_mutating_kruskal\n (g, first_vertex, last_vertex,\n edge_list, edge_mapper,\n make_iterator_property_map(ranks.begin(), global_index),\n make_iterator_property_map(parents.begin(), global_index));\n } else {\n // The (D-1)p/D processes that are dropping out of further\n // computations merely send their MSF edges to their parent\n // process in the process tree.\n send(pg, id % procs_left, 0, edge_list.size());\n if (edge_list.size() > 0) {\n std::vector outgoing_edges;\n outgoing_edges.reserve(edge_list.size());\n for (std::size_t i = 0; i < edge_list.size(); ++i) {\n outgoing_edges.push_back(std::make_pair(edge_list[i],\n get(weight, edge_list[i])));\n }\n send(pg, id % procs_left, 1, &outgoing_edges[0],\n outgoing_edges.size());\n }\n synchronize(pg);\n }\n\n // Return a process subgroup containing the p/D parent processes\n return process_subgroup(pg,\n make_counting_iterator(process_id_type(0)),\n make_counting_iterator(procs_left));\n }\n} // end namespace detail\n\n// ---------------------------------------------------------------------\n// Dense Boruvka MSF algorithm\n// ---------------------------------------------------------------------\ntemplate\nOutputIterator\ndense_boruvka_minimum_spanning_tree(const Graph& g, WeightMap weight_map,\n OutputIterator out,\n VertexIndexMap index_map,\n RankMap rank_map, ParentMap parent_map,\n SupervertexMap supervertex_map)\n{\n using boost::graph::parallel::process_group;\n\n typedef typename graph_traits::traversal_category traversal_category;\n\n BOOST_STATIC_ASSERT((is_convertible::value));\n\n typedef typename graph_traits::vertices_size_type vertices_size_type;\n typedef typename graph_traits::vertex_descriptor vertex_descriptor;\n typedef typename graph_traits::vertex_iterator vertex_iterator;\n typedef typename graph_traits::edge_descriptor edge_descriptor;\n\n // Don't throw away cached edge weights\n weight_map.set_max_ghost_cells(0);\n\n // Initialize the disjoint sets structures\n disjoint_sets dset(rank_map, parent_map);\n vertex_iterator vi, vi_end;\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\n dset.make_set(*vi);\n\n std::vector supervertices;\n supervertices.assign(vertices(g).first, vertices(g).second);\n\n // Use Kruskal's algorithm to find the minimum spanning forest\n // considering only the local edges. The resulting edges are not\n // necessarily going to be in the final minimum spanning\n // forest. However, any edge not part of the local MSF cannot be a\n // part of the global MSF, so we should have eliminated some edges\n // from consideration.\n std::vector edge_list;\n kruskal_minimum_spanning_tree\n (make_vertex_and_edge_range(g, vertices(g).first, vertices(g).second,\n edges(g).first, edges(g).second),\n std::back_inserter(edge_list),\n boost::weight_map(weight_map).\n vertex_index_map(index_map));\n\n // While the number of supervertices is decreasing, keep executing\n // Boruvka steps to identify additional MSF edges. This loop will\n // execute log |V| times.\n vertices_size_type old_num_supervertices;\n do {\n old_num_supervertices = supervertices.size();\n out = detail::boruvka_merge_step(process_group(g), g,\n weight_map, out,\n dset, supervertex_map, supervertices,\n edge_list);\n } while (supervertices.size() < old_num_supervertices);\n\n return out;\n}\n\ntemplate\nOutputIterator\ndense_boruvka_minimum_spanning_tree(const Graph& g, WeightMap weight_map,\n OutputIterator out, VertexIndex i_map)\n{\n typedef typename graph_traits::vertex_descriptor vertex_descriptor;\n\n std::vector ranks(num_vertices(g));\n std::vector parents(num_vertices(g));\n std::vector supervertices(num_vertices(g));\n\n return dense_boruvka_minimum_spanning_tree\n (g, weight_map, out, i_map,\n make_iterator_property_map(ranks.begin(), i_map),\n make_iterator_property_map(parents.begin(), i_map),\n make_iterator_property_map(supervertices.begin(), i_map));\n}\n\ntemplate\nOutputIterator\ndense_boruvka_minimum_spanning_tree(const Graph& g, WeightMap weight_map,\n OutputIterator out)\n{\n return dense_boruvka_minimum_spanning_tree(g, weight_map, out,\n get(vertex_index, g));\n}\n\n// ---------------------------------------------------------------------\n// Merge local MSFs MSF algorithm\n// ---------------------------------------------------------------------\ntemplate\nOutputIterator\nmerge_local_minimum_spanning_trees(const Graph& g, WeightMap weight,\n OutputIterator out,\n GlobalIndexMap global_index)\n{\n using boost::graph::parallel::process_group_type;\n using boost::graph::parallel::process_group;\n\n typedef typename graph_traits::traversal_category traversal_category;\n\n BOOST_STATIC_ASSERT((is_convertible::value));\n\n typedef typename graph_traits::edge_descriptor edge_descriptor;\n\n // Don't throw away cached edge weights\n weight.set_max_ghost_cells(0);\n\n // Compute the initial local minimum spanning forests\n std::vector edge_list;\n kruskal_minimum_spanning_tree\n (make_vertex_and_edge_range(g, vertices(g).first, vertices(g).second,\n edges(g).first, edges(g).second),\n std::back_inserter(edge_list),\n boost::weight_map(weight).vertex_index_map(global_index));\n\n // Merge the local MSFs from p processes into p/D processes,\n // reducing the number of processes in each step. Continue looping\n // until either (a) the current process drops out or (b) only one\n // process remains in the group. This loop will execute log_D p\n // times.\n typename process_group_type::type pg = process_group(g);\n while (pg && num_processes(pg) > 1) {\n pg = detail::merge_local_minimum_spanning_trees_step\n (pg, g, vertices(g).first, vertices(g).second,\n edge_list, weight, global_index,\n detail::identity_function(), true);\n }\n\n // Only process 0 has the entire edge list, so emit it to the output\n // iterator.\n if (pg && process_id(pg) == 0) {\n out = std::copy(edge_list.begin(), edge_list.end(), out);\n }\n\n synchronize(process_group(g));\n return out;\n}\n\ntemplate\ninline OutputIterator\nmerge_local_minimum_spanning_trees(const Graph& g, WeightMap weight,\n OutputIterator out)\n{\n return merge_local_minimum_spanning_trees(g, weight, out,\n get(vertex_index, g));\n}\n\n// ---------------------------------------------------------------------\n// Boruvka-then-merge MSF algorithm\n// ---------------------------------------------------------------------\ntemplate\nOutputIterator\nboruvka_then_merge(const Graph& g, WeightMap weight, OutputIterator out,\n GlobalIndexMap index, RankMap rank_map,\n ParentMap parent_map, SupervertexMap supervertex_map)\n{\n using std::log;\n using boost::graph::parallel::process_group_type;\n using boost::graph::parallel::process_group;\n\n typedef typename graph_traits::traversal_category traversal_category;\n\n BOOST_STATIC_ASSERT((is_convertible::value));\n\n typedef typename graph_traits::vertices_size_type vertices_size_type;\n typedef typename graph_traits::vertex_descriptor vertex_descriptor;\n typedef typename graph_traits::vertex_iterator vertex_iterator;\n typedef typename graph_traits::edge_descriptor edge_descriptor;\n\n // Don't throw away cached edge weights\n weight.set_max_ghost_cells(0);\n\n // Compute the initial local minimum spanning forests\n std::vector edge_list;\n kruskal_minimum_spanning_tree\n (make_vertex_and_edge_range(g, vertices(g).first, vertices(g).second,\n edges(g).first, edges(g).second),\n std::back_inserter(edge_list),\n boost::weight_map(weight).\n vertex_index_map(index));\n\n // Initialize the disjoint sets structures for Boruvka steps\n disjoint_sets dset(rank_map, parent_map);\n vertex_iterator vi, vi_end;\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\n dset.make_set(*vi);\n\n // Construct the initial set of supervertices (all vertices)\n std::vector supervertices;\n supervertices.assign(vertices(g).first, vertices(g).second);\n\n // Continue performing Boruvka merge steps until the number of\n // supervertices reaches |V| / (log_D p)^2.\n const std::size_t tree_factor = 3; // TBD: same as above! should be param\n double log_d_p = log((double)num_processes(process_group(g)))\n / log((double)tree_factor);\n vertices_size_type target_supervertices =\n vertices_size_type(num_vertices(g) / (log_d_p * log_d_p));\n vertices_size_type old_num_supervertices;\n while (supervertices.size() > target_supervertices) {\n old_num_supervertices = supervertices.size();\n out = detail::boruvka_merge_step(process_group(g), g,\n weight, out, dset,\n supervertex_map, supervertices,\n edge_list);\n if (supervertices.size() == old_num_supervertices)\n return out;\n }\n\n // Renumber the supervertices\n for (std::size_t i = 0; i < supervertices.size(); ++i)\n put(supervertex_map, supervertices[i], i);\n\n // Merge local MSFs on the supervertices. (D-1)p/D processors drop\n // out each iteration, so this loop executes log_D p times.\n typename process_group_type::type pg = process_group(g);\n bool have_msf = false;\n while (pg && num_processes(pg) > 1) {\n pg = detail::merge_local_minimum_spanning_trees_step\n (pg, g, supervertices.begin(), supervertices.end(),\n edge_list, weight, supervertex_map,\n detail::make_supervertex_edge_descriptor(g, dset),\n have_msf);\n have_msf = true;\n }\n\n // Only process 0 has the complete list of _supervertex_ MST edges,\n // so emit those to the output iterator. This is not the complete\n // list of edges in the MSF, however: the Boruvka steps in the\n // beginning of the algorithm emitted any edges used to merge\n // supervertices.\n if (pg && process_id(pg) == 0)\n out = std::copy(edge_list.begin(), edge_list.end(), out);\n\n synchronize(process_group(g));\n return out;\n}\n\ntemplate\ninline OutputIterator\nboruvka_then_merge(const Graph& g, WeightMap weight, OutputIterator out,\n GlobalIndexMap index)\n{\n typedef typename graph_traits::vertex_descriptor vertex_descriptor;\n typedef typename graph_traits::vertices_size_type vertices_size_type;\n std::vector ranks(num_vertices(g));\n std::vector parents(num_vertices(g));\n std::vector supervertex_indices(num_vertices(g));\n\n return boruvka_then_merge\n (g, weight, out, index,\n make_iterator_property_map(ranks.begin(), index),\n make_iterator_property_map(parents.begin(), index),\n make_iterator_property_map(supervertex_indices.begin(), index));\n}\n\ntemplate\ninline OutputIterator\nboruvka_then_merge(const Graph& g, WeightMap weight, OutputIterator out)\n{ return boruvka_then_merge(g, weight, out, get(vertex_index, g)); }\n\n// ---------------------------------------------------------------------\n// Boruvka-mixed-merge MSF algorithm\n// ---------------------------------------------------------------------\ntemplate\nOutputIterator\nboruvka_mixed_merge(const Graph& g, WeightMap weight, OutputIterator out,\n GlobalIndexMap index, RankMap rank_map,\n ParentMap parent_map, SupervertexMap supervertex_map)\n{\n using boost::graph::parallel::process_group_type;\n using boost::graph::parallel::process_group;\n\n typedef typename graph_traits::traversal_category traversal_category;\n\n BOOST_STATIC_ASSERT((is_convertible::value));\n\n typedef typename graph_traits::vertices_size_type vertices_size_type;\n typedef typename graph_traits::vertex_descriptor vertex_descriptor;\n typedef typename graph_traits::vertex_iterator vertex_iterator;\n typedef typename graph_traits::edge_descriptor edge_descriptor;\n\n // Don't throw away cached edge weights\n weight.set_max_ghost_cells(0);\n\n // Initialize the disjoint sets structures for Boruvka steps\n disjoint_sets dset(rank_map, parent_map);\n vertex_iterator vi, vi_end;\n for (boost::tie(vi, vi_end) = vertices(g); vi != vi_end; ++vi)\n dset.make_set(*vi);\n\n // Construct the initial set of supervertices (all vertices)\n std::vector supervertices;\n supervertices.assign(vertices(g).first, vertices(g).second);\n\n // Compute the initial local minimum spanning forests\n std::vector edge_list;\n kruskal_minimum_spanning_tree\n (make_vertex_and_edge_range(g, vertices(g).first, vertices(g).second,\n edges(g).first, edges(g).second),\n std::back_inserter(edge_list),\n boost::weight_map(weight).\n vertex_index_map(index));\n\n if (num_processes(process_group(g)) == 1) {\n return std::copy(edge_list.begin(), edge_list.end(), out);\n }\n\n // Like the merging local MSFs algorithm and the Boruvka-then-merge\n // algorithm, each iteration of this loop reduces the number of\n // processes by a constant factor D, and therefore we require log_D\n // p iterations. Note also that the number of edges in the edge list\n // decreases geometrically, giving us an efficient distributed MSF\n // algorithm.\n typename process_group_type::type pg = process_group(g);\n vertices_size_type old_num_supervertices;\n while (pg && num_processes(pg) > 1) {\n // A single Boruvka step. If this doesn't change anything, we're done\n old_num_supervertices = supervertices.size();\n out = detail::boruvka_merge_step(pg, g, weight, out, dset,\n supervertex_map, supervertices,\n edge_list);\n if (old_num_supervertices == supervertices.size()) {\n edge_list.clear();\n break;\n }\n\n // Renumber the supervertices\n for (std::size_t i = 0; i < supervertices.size(); ++i)\n put(supervertex_map, supervertices[i], i);\n\n // A single merging of local MSTs, which reduces the number of\n // processes we're using by a constant factor D.\n pg = detail::merge_local_minimum_spanning_trees_step\n (pg, g, supervertices.begin(), supervertices.end(),\n edge_list, weight, supervertex_map,\n detail::make_supervertex_edge_descriptor(g, dset),\n true);\n\n }\n\n // Only process 0 has the complete edge list, so emit it for the\n // user. Note that list edge list only contains the MSF edges in the\n // final supervertex graph: all of the other edges were used to\n // merge supervertices and have been emitted by the Boruvka steps,\n // although only process 0 has received the complete set.\n if (pg && process_id(pg) == 0)\n out = std::copy(edge_list.begin(), edge_list.end(), out);\n\n synchronize(process_group(g));\n return out;\n}\n\ntemplate\ninline OutputIterator\nboruvka_mixed_merge(const Graph& g, WeightMap weight, OutputIterator out,\n GlobalIndexMap index)\n{\n typedef typename graph_traits::vertex_descriptor vertex_descriptor;\n typedef typename graph_traits::vertices_size_type vertices_size_type;\n std::vector ranks(num_vertices(g));\n std::vector parents(num_vertices(g));\n std::vector supervertex_indices(num_vertices(g));\n\n return boruvka_mixed_merge\n (g, weight, out, index,\n make_iterator_property_map(ranks.begin(), index),\n make_iterator_property_map(parents.begin(), index),\n make_iterator_property_map(supervertex_indices.begin(), index));\n}\n\ntemplate\ninline OutputIterator\nboruvka_mixed_merge(const Graph& g, WeightMap weight, OutputIterator out)\n{ return boruvka_mixed_merge(g, weight, out, get(vertex_index, g)); }\n\n} // end namespace distributed\n\nusing distributed::dense_boruvka_minimum_spanning_tree;\nusing distributed::merge_local_minimum_spanning_trees;\nusing distributed::boruvka_then_merge;\nusing distributed::boruvka_mixed_merge;\n\n} } // end namespace boost::graph\n\n\n#endif // BOOST_DEHNE_GOTZ_MIN_SPANNING_TREE_HPP\n", "meta": {"hexsha": "0e7c1cf23bf9383f32f944a74ffdb9643b6d8544", "size": 38687, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactNativeFrontend/ios/Pods/boost/boost/graph/distributed/dehne_gotz_min_spanning_tree.hpp", "max_stars_repo_name": "Harshitha91/Tmdb-react-native-node", "max_stars_repo_head_hexsha": "e06e3f25a7ee6946ef07a1f524fdf62e48424293", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 12278.0, "max_stars_repo_stars_event_min_datetime": "2015-01-29T17:11:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T21:12:00.000Z", "max_issues_repo_path": "ios/Pods/boost-for-react-native/boost/graph/distributed/dehne_gotz_min_spanning_tree.hpp", "max_issues_repo_name": "c7yrus/alyson-v3", "max_issues_repo_head_hexsha": "5ad95a8f782f5f5d2fd543d44ca6a8b093395965", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 9469.0, "max_issues_repo_issues_event_min_datetime": "2015-01-30T05:33:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T16:17:21.000Z", "max_forks_repo_path": "ios/Pods/boost-for-react-native/boost/graph/distributed/dehne_gotz_min_spanning_tree.hpp", "max_forks_repo_name": "c7yrus/alyson-v3", "max_forks_repo_head_hexsha": "5ad95a8f782f5f5d2fd543d44ca6a8b093395965", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 892.0, "max_forks_repo_forks_event_min_datetime": "2015-01-29T16:26:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T07:44:30.000Z", "avg_line_length": 41.2441364606, "max_line_length": 101, "alphanum_fraction": 0.674283351, "num_tokens": 8439, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.21393010790330644}} {"text": "// graph-tool -- a general graph modification and manipulation thingy\n//\n// Copyright (C) 2006-2015 Tiago de Paula Peixoto \n//\n// This program is free software; you can redistribute it and/or\n// modify it under the terms of the GNU General Public License\n// as published by the Free Software Foundation; either version 3\n// of the License, or (at your option) any later version.\n//\n// This program is distributed in the hope that it will be useful,\n// but WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n// GNU General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with this program. If not, see .\n\n#include \"graph_filtering.hh\"\n#include \"graph.hh\"\n#include \"graph_properties.hh\"\n\n#include \n\nusing namespace std;\nusing namespace boost;\nusing namespace graph_tool;\n\nstruct get_planar_embedding\n{\n template \n class edge_inserter\n {\n public:\n edge_inserter(EdgeMap edge_map): _edge_map(edge_map) {}\n\n edge_inserter& operator++() { return *this; }\n edge_inserter& operator++(int) { return *this; }\n edge_inserter& operator*() { return *this; }\n\n template \n edge_inserter& operator=(const Key& e)\n {\n _edge_map[e] = 1;\n return *this;\n }\n\n private:\n EdgeMap _edge_map;\n };\n\n template \n void operator()(Graph& g, VertexIndex vertex_index, EdgeIndex edge_index,\n EmbedMap embed_map, KurMap kur_map, bool& is_planar) const\n {\n edge_inserter kur_insert(kur_map);\n unchecked_vector_property_map\n ::edge_descriptor>, VertexIndex>\n embedding(vertex_index, num_vertices(g));\n is_planar = boyer_myrvold_planarity_test\n (boyer_myrvold_params::graph = g,\n boyer_myrvold_params::edge_index_map = edge_index,\n boyer_myrvold_params::embedding = embedding,\n boyer_myrvold_params::kuratowski_subgraph = kur_insert);\n\n int i, N = num_vertices(g);\n #pragma omp parallel for default(shared) private(i) schedule(runtime) if (N > 100)\n for (i = 0; i < N; ++i)\n {\n typename graph_traits::vertex_descriptor v = vertex(i, g);\n if (v == graph_traits::null_vertex())\n continue;\n embed_map[v].resize(embedding[v].size());\n for (size_t j = 0; j < embedding[v].size(); ++j)\n embed_map[v][j] = edge_index[embedding[v][j]];\n }\n }\n\n template \n void operator()(Graph& g, VertexIndex, EdgeIndex edge_index,\n dummy_property_map, KurMap kur_map,\n bool& is_planar) const\n {\n edge_inserter kur_insert(kur_map);\n is_planar = boyer_myrvold_planarity_test\n (boyer_myrvold_params::graph = g,\n boyer_myrvold_params::edge_index_map = edge_index,\n boyer_myrvold_params::kuratowski_subgraph = kur_insert);\n }\n\n};\n\nbool is_planar(GraphInterface& gi, boost::any embed_map, boost::any kur_map)\n{\n bool is_planar;\n\n if (embed_map.empty())\n embed_map = dummy_property_map();\n if (kur_map.empty())\n kur_map = dummy_property_map();\n\n typedef mpl::push_back::type edge_map_types;\n typedef mpl::push_back::type vertex_map_types;\n\n run_action()\n (gi, std::bind(get_planar_embedding(), placeholders::_1, gi.GetVertexIndex(),\n gi.GetEdgeIndex(), placeholders::_2, placeholders::_3,\n std::ref(is_planar)),\n vertex_map_types(), edge_map_types())\n (embed_map, kur_map);\n return is_planar;\n}\n", "meta": {"hexsha": "93859946c45170fe72aa5c1c4b44c1f1cc20592f", "size": 4235, "ext": "cc", "lang": "C++", "max_stars_repo_path": "graph-tool/src/graph/topology/graph_planar.cc", "max_stars_repo_name": "johankaito/fufuka", "max_stars_repo_head_hexsha": "32a96ecf98ce305c2206c38443e58fdec88c788d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2015-08-04T19:41:53.000Z", "max_stars_repo_stars_event_max_datetime": "2015-08-04T19:41:53.000Z", "max_issues_repo_path": "graph-tool/src/graph/topology/graph_planar.cc", "max_issues_repo_name": "johankaito/fufuka", "max_issues_repo_head_hexsha": "32a96ecf98ce305c2206c38443e58fdec88c788d", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "graph-tool/src/graph/topology/graph_planar.cc", "max_forks_repo_name": "johankaito/fufuka", "max_forks_repo_head_hexsha": "32a96ecf98ce305c2206c38443e58fdec88c788d", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 36.8260869565, "max_line_length": 90, "alphanum_fraction": 0.6498229044, "num_tokens": 984, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.21393010790330638}} {"text": "/*** \n HiCapTools.\n Copyright (c) 2017 Pelin Sahlén \n\n\tPermission is hereby granted, free of charge, to any person obtaining a \n\tcopy of this software and associated documentation files (the \"Software\"), \n\tto deal in the Software with some restriction, including without limitation \n\tthe rights to use, copy, modify, merge, publish, distribute the Software, \n\tand to permit persons to whom the Software is furnished to do so, subject to\n\tthe following conditions:\n\n\tThe above copyright notice and this permission notice shall be included in all \n\tcopies or substantial portions of the Software. The Software shall not be used \n\tfor commercial purposes.\n\n\tTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, \n\tINCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A \n\tPARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT \n\tHOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF \n\tCONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE \n\tOR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n***/\n\n//\n// BackgroundInteractionFrequency.cpp\n// HiCapTools\n//\n// Created by Pelin Sahlen and Anandashankar Anil.\n//\n\n#include \n#include \n#include \nusing namespace boost::accumulators;\n\n#include \"BackgroundInteractionFrequency.h\"\n#include \"Global.h\"\n#include \"linear.h\"\n#include \n#include \n\n\n///For Probe-Distal and Probe-Probe\nvoid DetermineBackgroundLevels::CalculateMeanandStdRegress(std::string eName, int ExperimentNo, std::string DesignName, BG_signals& bglevelsloc, int binsize, std::string whichProx, int MinimumJunctionDistance, OutStream& log, int WindowSizeloc){\n\n\n\texpName= eName; //set experiment name\n\t\n\tstd::map< int, int > nofentries_perBin; // required for calculating mean\n\tstd::map< int, int > signal_square; // required for calculating stdev\n double sum_square, variance;\n\tint distance;\n std::string feature_id;\n\n\t\n std::map< int, Junction >::const_iterator iter;\n\tfor(int i = 0; i < Design_NegCtrl[DesignName].Probes.size(); i++){\n feature_id = Design_NegCtrl[DesignName].Probes[i].feature_id;\n //////////Probe-distal\n if(whichProx==\"ProbeDistal\"){\n\t\t\tfor (iter = Features[feature_id].proximities.junctions.begin(); iter != Features[feature_id].proximities.junctions.end(); ++iter){\n\t\t\t\t\n\t\t\t\tdistance = iter->first - Design_NegCtrl[DesignName].Probes[i].end;\n \n\t\t\t\tint bin = abs(distance) / binsize;\n\n\t\t\t\tif(iter->second.paircount[ExperimentNo] > 0){\n \n\t\t\t\t\tif(bglevelsloc.mean.find(bin) == bglevelsloc.mean.end())\n\t\t\t\t\t\tbglevelsloc.mean[bin] = iter->second.paircount[ExperimentNo];\n\t\t\t\t\telse\n\t\t\t\t\t\tbglevelsloc.mean[bin] = bglevelsloc.mean[bin] + iter->second.paircount[ExperimentNo];\n \n\t\t\t\t\tif(nofentries_perBin.find(bin) == nofentries_perBin.end()){\n\t\t\t\t\t\tnofentries_perBin[bin] = 1;\n\t\t\t\t\t\tsignal_square[bin] = (iter->second.paircount[ExperimentNo])*(iter->second.paircount[ExperimentNo]);\n\t\t\t\t\t}\n\t\t\t\t\telse{\n\t\t\t\t\t\tnofentries_perBin[bin] = nofentries_perBin[bin] + 1;\n\t\t\t\t\t\tsignal_square[bin] = (signal_square[bin] + ((iter->second.paircount[ExperimentNo])*(iter->second.paircount[ExperimentNo])));\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t///////////Probe-Probe\n else if(whichProx==\"ProbeProbe\"){\n\t\t\tfor (auto iter = Features[feature_id].Inter_feature_ints.begin(); iter != Features[feature_id].Inter_feature_ints.end(); ++iter){\n\t\t\t\t\t\t\t\t\n\t\t\t\tdistance = Features[feature_id].start - Features[(*iter).interacting_feature_id].start;\n\t\t\t\t\n\t\t\t\tif((Features[feature_id].FeatureType == 3 && Features[(*iter).interacting_feature_id].FeatureType == 3) && Features[feature_id].TranscriptName != Features[(*iter).interacting_feature_id].TranscriptName && (abs(distance) >= MinimumJunctionDistance)){\n \n\t\t\t\t\tint bin = abs(distance) / binsize; \n\t\t\t\t\tif((*iter).signal[ExperimentNo] > 0){\n\t\t\t\t\t\tif(bglevelsloc.mean.find(bin) == bglevelsloc.mean.end())\n\t\t\t\t\t\t\tbglevelsloc.mean[bin] = (*iter).signal[ExperimentNo];\n\t\t\t\t\t\telse\n\t\t\t\t\t\t\tbglevelsloc.mean[bin] = bglevelsloc.mean[bin] + (*iter).signal[ExperimentNo];\n \n\t\t\t\t\t\tif(nofentries_perBin.find(bin) == nofentries_perBin.end()){\n\t\t\t\t\t\t\tnofentries_perBin[bin] = 1;\n\t\t\t\t\t\t\tsignal_square[bin] = ((*iter).signal[ExperimentNo])*((*iter).signal[ExperimentNo]);\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse{\n\t\t\t\t\t\t\tnofentries_perBin[bin] = nofentries_perBin[bin] + 1;\n\t\t\t\t\t\t\tsignal_square[bin] = (signal_square[bin] + (((*iter).signal[ExperimentNo])*((*iter).signal[ExperimentNo])));\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t} \n }\n \n// Calculate Mean and stdev\n\tstd::map< int, double>::iterator it; // iterator for bin signals\n\tfor (it = bglevelsloc.mean.begin(); it != bglevelsloc.mean.end(); ++it){\n bglevelsloc.samplesize[it->first] = nofentries_perBin[it->first];\n if(bglevelsloc.samplesize[it->first] == 0){\n it->second = 1; //Mean\n }\n else{\n it->second /= nofentries_perBin[it->first]; //Mean\n }\n if(bglevelsloc.samplesize[it->first] <= 1){\n bglevelsloc.stdev[it->first] = 0;\n }\n else{\n sum_square = ((it->second)*(it->second));\n sum_square /= (double(nofentries_perBin[it->first])); // Average Sum Squared\n variance = signal_square[it->first] - sum_square;\n variance /= (nofentries_perBin[it->first] -1);\n bglevelsloc.stdev[it->first] = sqrt(variance); //stdev\n }\n\t}\n \n int first25kb = ceil(25000/(double)binsize); // to exclude bins of first 25 kb from smoothing \n \n boost::accumulators::accumulator_set > acc(tag::rolling_window::window_size = (WindowSizeloc*2));\n boost::accumulators::accumulator_set > acc2(tag::rolling_window::window_size = (WindowSizeloc*2));\n \n int w = 0, z = 0, s = 0, a=0;\n std::deque< double > dm, ds, db, dwm, dws;\n\n \n // Put values into a queue\n for (it = bglevelsloc.mean.begin(); it != bglevelsloc.mean.end(); ++it){\n\t\n dm.push_back(it->second);\n ds.push_back(bglevelsloc.stdev[it->first]);\n db.push_back(it->first);\n\t\n }\n //Push the first WindowSize-1 elements into a queue\n\tif(dm.empty())\n\t\tlog<<\"No Element in dm Outloop\"< > acc(tag::rolling_window::window_size = (WindowSizeloc));\n\t\t\tboost::accumulators::accumulator_set > acc2(tag::rolling_window::window_size = (WindowSizeloc));\n\t\t\t\n\t\t\tif(w >= first25kb && w < first25kb + WindowSizeloc - 1){\n\t\t\t\t\n\t\n\t\t\t\t/**\n\t\t\t\tif(dwm.size()==WindowSizeloc){\n\t\t\t\t\tfor(z = 0; z < WindowSizeloc;++z){\n\t\t\t\t\t\tstd::cout<first) << '\\t'<< (it->first)*bSize << '\\t' << bglevels.mean[it->first] << '\\t' << bglevels.stdev[it->first] << '\\t' << bglevels.samplesize[it->first] << '\\t' << it->second << '\\t' << bglevels.smoothed_stdev[it->first] << std::endl;\t\n\t}\n\t\n\toutfPP << \"Distance Bin\" << '\\t' << \"Interaction Distance\" << '\\t' << \"Mean \" << '\\t' << \"Stdev\" << '\\t' << \"Sample Size\" << '\\t' << \"Mean (Smoothed)\" << '\\t' << \"StDev (Smoothed)\" << std::endl;\n\t\n\tfor (auto it = bglevelsProbeProbe.smoothed.begin(); it != bglevelsProbeProbe.smoothed.end(); ++it){\n outfPP << (it->first) << '\\t' << (it->first)*bSizePP << '\\t' << bglevelsProbeProbe.mean[it->first] << '\\t' << bglevelsProbeProbe.stdev[it->first] << '\\t' << bglevelsProbeProbe.samplesize[it->first] << '\\t' << it->second << '\\t' << bglevelsProbeProbe.smoothed_stdev[it->first] << std::endl;\t\n\t}\n\t\n}\n\n", "meta": {"hexsha": "be1901808230496c1e472dc5ad062127e18f7934", "size": 10193, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/BackgroundInteractionFrequency.cpp", "max_stars_repo_name": "aanil/HiCapTools", "max_stars_repo_head_hexsha": "21783926679f1045dcda36f806baa6608b7cf67c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7.0, "max_stars_repo_stars_event_min_datetime": "2018-02-13T16:19:30.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-02T09:15:42.000Z", "max_issues_repo_path": "src/BackgroundInteractionFrequency.cpp", "max_issues_repo_name": "aanil/HiCapTools", "max_issues_repo_head_hexsha": "21783926679f1045dcda36f806baa6608b7cf67c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 3.0, "max_issues_repo_issues_event_min_datetime": "2018-10-31T11:13:39.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-06T18:28:42.000Z", "max_forks_repo_path": "src/BackgroundInteractionFrequency.cpp", "max_forks_repo_name": "aanil/HiCapTools", "max_forks_repo_head_hexsha": "21783926679f1045dcda36f806baa6608b7cf67c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 5.0, "max_forks_repo_forks_event_min_datetime": "2017-06-27T15:03:55.000Z", "max_forks_repo_forks_event_max_datetime": "2019-08-03T10:54:20.000Z", "avg_line_length": 39.0536398467, "max_line_length": 298, "alphanum_fraction": 0.6477975081, "num_tokens": 2835, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.21393010790330638}} {"text": "/* Copyright (c) 2016 - 2019, the adamantine authors.\n *\n * This file is subject to the Modified BSD License and may not be distributed\n * without copyright and license information. Please refer to the file LICENSE\n * for the text and further information on this license.\n */\n\n#ifndef THERMAL_OPERATOR_HH\n#define THERMAL_OPERATOR_HH\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace adamantine\n{\n template \n class ThermalOperatorQuad\n{\npublic:\n__device__ ThermalOperatorQuad(NumberType thermal_conductivity, NumberType alpha, NumberType beta):\n_thermal_conductivity(thermal_conductivity),\n_alpha(alpha),\n_beta(beta)\n{}\n\n__device__ void \noperator()(dealii::CUDAWrappers::FEEvaluation *fe_eval) const;\n\nprivate:\nNumberType _thermal_conductivity;\nNumberType _alpha;\nNumberType _beta;\n};\n\ntemplate \nclass LocalThermalOperator\n{\npublic:\nLocalThermalOperator(NumberType *thermal_conductivity, NumberType *alpha, NumberType *beta)\n: _thermal_conductivity(thermal_conductivity),\n_alpha(alpha),\n_beta(beta)\n{}\n\n__device__ void operator()(\n const unsigned int cell,\n const typename dealii::CUDAWrappers::MatrixFree::Data *gpu_data,\n dealii::CUDAWrappers::SharedData * shared_data,\n const NumberType *src,\n NumberType* dst) const;\n\n static const unsigned int n_dofs_1d = fe_degree + 1;\n static const unsigned int n_local_dofs = dealii::Utilities::pow(fe_degree + 1, dim);\n static const unsigned int n_q_points = dealii::Utilities::pow(fe_degree + 1, dim);\n private:\n NumberType *_thermal_conductivity;\nNumberType *_alpha;\nNumberType *_beta;\n };\n\n/**\n * This class is the operator associated with the heat equation, i.e., vmult\n * performs \\f$ dst = -\\nabla k \\nabla src \\f$.\n */\ntemplate \nclass ThermalOperator : public Operator\n{\npublic:\n ThermalOperator(MPI_Comm const &communicator,\n std::shared_ptr> material_properties);\n\n /**\n * Associate the AffineConstraints and the MatrixFree objects to the\n * underlying Triangulation.\n */\n template \n void setup_dofs(dealii::DoFHandler const &dof_handler,\n dealii::AffineConstraints const &affine_constraints,\n QuadratureType const &quad);\n\n /**\n * Compute the inverse of the mass matrix and update the material properties.\n */\n void reinit(dealii::DoFHandler const &dof_handler,\n dealii::AffineConstraints const &affine_constraints);\n\n /**\n * Clear the MatrixFree object and resize the inverse of the mass matrix to\n * zero.\n */\n void clear();\n\n dealii::types::global_dof_index m() const override;\n\n dealii::types::global_dof_index n() const override;\n\n /**\n * Return a shared pointer to the inverse of the mass matrix.\n */\n std::shared_ptr>\n get_inverse_mass_matrix() const;\n\n /**\n * Return a shared pointer to the underlying MatrixFree object.\n */\n dealii::CUDAWrappers::MatrixFree const &get_matrix_free() const;\n\n void\n vmult(dealii::LA::distributed::Vector &dst,\n dealii::LA::distributed::Vector const &src) const override;\n\n void\n Tvmult(dealii::LA::distributed::Vector &dst,\n dealii::LA::distributed::Vector const &src) const override;\n\n void vmult_add(\n dealii::LA::distributed::Vector &dst,\n dealii::LA::distributed::Vector const &src) const override;\n\n void Tvmult_add(\n dealii::LA::distributed::Vector &dst,\n dealii::LA::distributed::Vector const &src) const override;\n\n void jacobian_vmult(\n dealii::LA::distributed::Vector &dst,\n dealii::LA::distributed::Vector const &src) const override;\n\n /**\n * Evaluate the material properties for a given state field.\n */\n void evaluate_material_properties(\n dealii::LA::distributed::Vector const &state);\n\nprivate:\n /**\n * Apply the operator on a given set of quadrature points.\n */\n void\n local_apply(dealii::CUDAWrappers::MatrixFree const &data,\n dealii::LA::distributed::Vector &dst,\n dealii::LA::distributed::Vector const &src,\n std::pair const &cell_range) const;\n\n /**\n * MPI communicator.\n */\n MPI_Comm const &_communicator;\n /**\n * Data to configure the MatrixFree object.\n */\n typename dealii::CUDAWrappers::MatrixFree::AdditionalData\n _matrix_free_data;\n /**\n * Store the \\f$ \\alpha \\f$ coefficient described in\n * MaterialProperty::compute_constants()\n */\n dealii::LinearAlgebra::CUDAWrappers::Vector _alpha;\n /**\n * Store the \\f$ \\beta \\f$ coefficient described in\n * MaterialProperty::compute_constants()\n */\n dealii::LinearAlgebra::CUDAWrappers::Vector _beta;\n /**\n * Table of thermal conductivity coefficient.\n */\n dealii::LinearAlgebra::CUDAWrappers::Vector _thermal_conductivity;\n /**\n * Material properties associated with the domain.\n */\n std::shared_ptr> _material_properties;\n /**\n * Underlying MatrixFree object.\n */\n dealii::CUDAWrappers::MatrixFree _matrix_free;\n /**\n * The inverse of the mass matrix is computed using an inexact Gauss-Lobatto\n * quadrature. This inexact quadrature makes the mass matrix and therefore\n * also its inverse, a diagonal matrix.\n */\n std::shared_ptr>\n _inverse_mass_matrix;\n\n dealii::types::global_dof_index _m;\n\n dealii::DoFHandlerconst * _dof_handler;\n};\n\ntemplate \ninline dealii::types::global_dof_index\nThermalOperator::m() const\n{\n return _m;\n}\n\ntemplate \ninline dealii::types::global_dof_index\nThermalOperator::n() const\n{\n return _m;\n}\n\ntemplate \ninline std::shared_ptr>\nThermalOperator::get_inverse_mass_matrix() const\n{\n return _inverse_mass_matrix;\n}\n\ntemplate \ninline dealii::CUDAWrappers::MatrixFree const &\nThermalOperator::get_matrix_free() const\n{\n return _matrix_free;\n}\n\ntemplate \ninline void ThermalOperator::jacobian_vmult(\n dealii::LA::distributed::Vector &dst,\n dealii::LA::distributed::Vector const &src) const\n{\n vmult(dst, src);\n}\n} // namespace adamantine\n\n#endif\n", "meta": {"hexsha": "a0e0ccad80ade8e7867b59e85336133c5f91e190", "size": 7717, "ext": "hh", "lang": "C++", "max_stars_repo_path": "source/ThermalOperator.hh", "max_stars_repo_name": "masterleinad/adamantine", "max_stars_repo_head_hexsha": "f5de64d869bf419273946d4f25fb0a8ddc016eaf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "source/ThermalOperator.hh", "max_issues_repo_name": "masterleinad/adamantine", "max_issues_repo_head_hexsha": "f5de64d869bf419273946d4f25fb0a8ddc016eaf", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "source/ThermalOperator.hh", "max_forks_repo_name": "masterleinad/adamantine", "max_forks_repo_head_hexsha": "f5de64d869bf419273946d4f25fb0a8ddc016eaf", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4069264069, "max_line_length": 107, "alphanum_fraction": 0.7343527277, "num_tokens": 2012, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.3702254064929193, "lm_q1q2_score": 0.213803456323694}} {"text": "/*=============================================================================\nCopyright 2018 Pranam Lashkari \n\nDistributed under the Boost Software License, Version 1.0. (See accompanying\nfile License.txt or copy at https://www.boost.org/LICENSE_1_0.txt)\n=============================================================================*/\n\n#ifndef BOOST_ASTRONOMY_COORDINATE_SUPERGALACTIC_HPP\n#define BOOST_ASTRONOMY_COORDINATE_SUPERGALACTIC_HPP\n\n#include \n#include \n#include \n\nnamespace boost { namespace astronomy { namespace coordinate {\n\ntemplate \nstruct supergalactic : public base_frame\n{\n ///@cond INTERNAL\n BOOST_STATIC_ASSERT_MSG((std::is_base_of\n , Representation>::value),\n \"argument type is expected to be a spherical_representation class\");\n BOOST_STATIC_ASSERT_MSG((std::is_base_of\n , Differential>::value),\n \"argument type is expected to be a spherical_coslat_differential class\");\n ///@endcond\n\npublic:\n //default constructor no initialization\n supergalactic() {}\n\n //!creates coordinate in supergalactic frame using any subclass of base_representation\n template \n supergalactic(OtherRepresentation const& representation_data)\n {\n BOOST_STATIC_ASSERT_MSG((boost::astronomy::detail::is_base_template_of\n ::value),\n \"Invalid representation class\");\n\n auto temp = make_spherical_representation\n <\n typename Representation::type,\n typename Representation::quantity1,\n typename Representation::quantity2,\n typename Representation::quantity3,\n typename OtherRepresentation::type,\n typename OtherRepresentation::quantity1,\n typename OtherRepresentation::quantity2,\n typename OtherRepresentation::quantity3\n >(representation_data);\n\n this->data = temp;\n }\n\n //!creates coordinate from given values\n //!sgb -> latitude, sgl -> longitude\n supergalactic\n (\n typename Representation::quantity1 const& sgb,\n typename Representation::quantity2 const& sgl,\n typename Representation::quantity3 const& distance\n )\n {\n this->data.set_lat_lon_dist(sgb, sgl, distance);\n }\n\n //!creates coordinate with motion from given values\n //!sgb -> latitude, sgl -> longitude\n //!pm_sgb -> proper motion in sgb, pm_sgl_cossgb -> proper motion in sgl including cos(sgb)\n supergalactic\n (\n typename Representation::quantity1 const& sgb,\n typename Representation::quantity2 const& sgl,\n typename Representation::quantity3 const& distance,\n typename Differential::quantity1 const& pm_sgb,\n typename Differential::quantity2 const& pm_sgl_cossgb,\n typename Differential::quantity3 const& radial_velocity\n ) : supergalactic(sgb, sgl, distance)\n {\n this->motion.set_dlat_dlon_coslat_ddist(pm_sgb, pm_sgl_cossgb, radial_velocity);\n }\n\n //!creates coordinate with motion\n //!representation class is used for coordinate data\n //!differential class is used for motion data\n template \n supergalactic\n (\n OtherRepresentation const& representation_data,\n OtherDifferential const& differential_data\n )\n {\n BOOST_STATIC_ASSERT_MSG((boost::astronomy::detail::is_base_template_of\n ::value),\n \"argument type is expected to be a differential class\");\n\n BOOST_STATIC_ASSERT_MSG((boost::astronomy::detail::is_base_template_of\n ::value),\n \"argument type is expected to be a differential class\");\n\n auto rep_temp = make_spherical_representation\n <\n typename Representation::type,\n typename Representation::quantity1,\n typename Representation::quantity2,\n typename Representation::quantity3,\n typename OtherRepresentation::type,\n typename OtherRepresentation::quantity1,\n typename OtherRepresentation::quantity2,\n typename OtherRepresentation::quantity3\n >(representation_data);\n this->data = rep_temp;\n\n auto dif_temp = make_spherical_coslat_differential\n <\n typename Differential::type,\n typename Differential::quantity1,\n typename Differential::quantity2,\n typename Differential::quantity3,\n typename OtherDifferential::type,\n typename OtherDifferential::quantity1,\n typename OtherDifferential::quantity2,\n typename OtherDifferential::quantity3\n >(differential_data);\n this->motion = dif_temp;\n }\n\n //copy constructor\n supergalactic(supergalactic const& other)\n {\n this->data = other.get_data();\n this->motion = other.get_differential();\n }\n\n //!returns component sgb of the supergalactic coordinate\n typename Representation::quantity1 get_sgb() const\n {\n return this->data.get_lat();\n }\n\n //!returns component sgl of the supergalactic coordinate\n typename Representation::quantity2 get_sgl() const\n {\n return this->data.get_lon();\n }\n\n //!returns distance component of the supergalactic coordinate\n typename Representation::quantity3 get_distance() const\n {\n return this->data.get_dist();\n }\n\n //!returns the (sgb, sgl, dist) in the form of tuple\n std::tuple\n <\n typename Representation::quantity1,\n typename Representation::quantity2,\n typename Representation::quantity3\n > get_sgb_sgl_dist() const\n {\n return this->data.get_lat_lon_dist();\n }\n\n //!returns proper motion in supergalactic latitude\n typename Differential::quantity1 get_pm_sgb() const\n {\n return this->motion.get_dlat();\n }\n\n //!returns proper motion in supergalactic longitude including cos(b)\n typename Differential::quantity2 get_pm_sgl_cossgb() const\n {\n return this->motion.get_dlon_coslat();\n }\n\n //!returns radial_velocity\n typename Differential::quantity3 get_radial_velocity() const\n {\n return this->motion.get_ddist();\n }\n\n //!returns the proper motion in form of tuple including cos(b)\n std::tuple\n <\n typename Differential::quantity1,\n typename Differential::quantity2,\n typename Differential::quantity3\n > get_pm_sgb_sgl_radial() const\n {\n return this->motion.get_dlat_dlon_coslat_ddist();\n }\n\n //!sets value of component b of the supergalactic coordinate\n void set_sgb(typename Representation::quantity1 const& sgb)\n {\n this->data.set_lat(sgb);\n }\n\n //!sets value of component sgl of the supergalactic coordinate\n void set_sgl(typename Representation::quantity2 const& sgl) const\n {\n this->data.set_lon(sgl);\n }\n\n //!sets value of distance component of the supergalactic coordinate\n void set_distance(typename Representation::quantity3 const& distance)\n {\n this->data.set_dist(distance);\n }\n\n //!sets value of all component of the coordinate\n void set_sgb_sgl_dist\n (\n typename Representation::quantity1 const& sgb,\n typename Representation::quantity2 const& sgl,\n typename Representation::quantity3 const& dist\n )\n {\n this->data.set_lat_lon_dist(sgb, sgl, dist);\n }\n\n //!sets the proper motion in supergalactic latitude\n void set_pm_sgb(typename Differential::quantity1 const& pm_sgb)\n {\n this->motion.set_dlat(pm_sgb);\n }\n\n //!sets the proper motion in supergalactic longitude including cos(b)\n void set_pm_sgl_cossgb(typename Differential::quantity2 const& pm_sgl_cossgb)\n {\n this->motion.set_dlon_coslat(pm_sgl_cossgb);\n }\n\n //!sets the radial_velocity\n void set_radial_velocity(typename Differential::quantity3 const& radial_velocity)\n {\n this->motion.set_ddist(radial_velocity);\n }\n\n //!set value of motion including cos(b)\n void set_pm_sgb_sgl_radial\n (\n typename Differential::quantity1 const& pm_sgb,\n typename Differential::quantity2 const& pm_sgl_cosb,\n typename Differential::quantity3 const& radial_velocity\n )\n {\n this->motion.set_dlat_dlon_coslat_ddist(pm_sgb, pm_sgl_cosb, radial_velocity);\n }\n\n};\n\n}}} //namespace boost::astronomy::coordinate\n\n#endif // !BOOST_ASTRONOMY_COORDINATE_SUPERGALACTIC_HPP\n\n", "meta": {"hexsha": "66047bde8ae9f73e8ee446508e48de38c0abf84e", "size": 9402, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/astronomy/coordinate/coord_sys/supergalactic.hpp", "max_stars_repo_name": "lpranam/Astronomy", "max_stars_repo_head_hexsha": "63aa055a3ce849210680451d81db4cc1ddc8d402", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2020-05-14T08:23:58.000Z", "max_stars_repo_stars_event_max_datetime": "2021-08-23T05:26:19.000Z", "max_issues_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.3702253925955866, "lm_q1q2_score": 0.21380344829804884}} {"text": "#ifndef BOOST_GEOMETRY_PROJECTIONS_CASS_HPP\r\n#define BOOST_GEOMETRY_PROJECTIONS_CASS_HPP\r\n\r\n// Boost.Geometry - extensions-gis-projections (based on PROJ4)\r\n// This file is automatically generated. DO NOT EDIT.\r\n\r\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\r\n\r\n// This file was modified by Oracle on 2017.\r\n// Modifications copyright (c) 2017, Oracle and/or its affiliates.\r\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle.\r\n\r\n// Use, modification and distribution is subject to the Boost Software License,\r\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\r\n// http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\r\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\r\n// PROJ4 is maintained by Frank Warmerdam\r\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\r\n\r\n// Last updated version of proj: 4.9.1\r\n\r\n// Original copyright notice:\r\n\r\n// Permission is hereby granted, free of charge, to any person obtaining a\r\n// copy of this software and associated documentation files (the \"Software\"),\r\n// to deal in the Software without restriction, including without limitation\r\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\r\n// and/or sell copies of the Software, and to permit persons to whom the\r\n// Software is furnished to do so, subject to the following conditions:\r\n\r\n// The above copyright notice and this permission notice shall be included\r\n// in all copies or substantial portions of the Software.\r\n\r\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\r\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\r\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\r\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\r\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\r\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\r\n// DEALINGS IN THE SOFTWARE.\r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n\r\nnamespace boost { namespace geometry\r\n{\r\n\r\nnamespace srs { namespace par4\r\n{\r\n struct cass {};\r\n\r\n}} //namespace srs::par4\r\n\r\nnamespace projections\r\n{\r\n #ifndef DOXYGEN_NO_DETAIL\r\n namespace detail { namespace cass\r\n {\r\n\r\n //static const double EPS10 = 1e-10;\r\n //static const double C1 = .16666666666666666666;\r\n //static const double C2 = .00833333333333333333;\r\n //static const double C3 = .04166666666666666666;\r\n //static const double C4 = .33333333333333333333;\r\n //static const double C5 = .06666666666666666666;\r\n\r\n template \r\n inline T C1() { return .16666666666666666666666666666666666666; }\r\n template \r\n inline T C2() { return .00833333333333333333333333333333333333; }\r\n template \r\n inline T C3() { return .04166666666666666666666666666666666666; }\r\n template \r\n inline T C4() { return .33333333333333333333333333333333333333; }\r\n template \r\n inline T C5() { return .06666666666666666666666666666666666666; }\r\n\r\n template \r\n struct par_cass\r\n {\r\n T m0;\r\n T en[EN_SIZE];\r\n };\r\n\r\n // template class, using CRTP to implement forward/inverse\r\n template \r\n struct base_cass_ellipsoid : public base_t_fi,\r\n CalculationType, Parameters>\r\n {\r\n\r\n typedef CalculationType geographic_type;\r\n typedef CalculationType cartesian_type;\r\n\r\n par_cass m_proj_parm;\r\n\r\n inline base_cass_ellipsoid(const Parameters& par)\r\n : base_t_fi,\r\n CalculationType, Parameters>(*this, par) {}\r\n\r\n // FORWARD(e_forward) ellipsoid\r\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\r\n inline void fwd(geographic_type& lp_lon, geographic_type& lp_lat, cartesian_type& xy_x, cartesian_type& xy_y) const\r\n {\r\n static const CalculationType C1 = cass::C1();\r\n static const CalculationType C2 = cass::C2();\r\n static const CalculationType C3 = cass::C3();\r\n\r\n CalculationType n = sin(lp_lat);\r\n CalculationType c = cos(lp_lat);\r\n xy_y = pj_mlfn(lp_lat, n, c, this->m_proj_parm.en);\r\n n = 1./sqrt(1. - this->m_par.es * n * n);\r\n CalculationType tn = tan(lp_lat); CalculationType t = tn * tn;\r\n CalculationType a1 = lp_lon * c;\r\n c *= this->m_par.es * c / (1 - this->m_par.es);\r\n CalculationType a2 = a1 * a1;\r\n xy_x = n * a1 * (1. - a2 * t *\r\n (C1 - (8. - t + 8. * c) * a2 * C2));\r\n xy_y -= this->m_proj_parm.m0 - n * tn * a2 *\r\n (.5 + (5. - t + 6. * c) * a2 * C3);\r\n }\r\n\r\n // INVERSE(e_inverse) ellipsoid\r\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\r\n inline void inv(cartesian_type& xy_x, cartesian_type& xy_y, geographic_type& lp_lon, geographic_type& lp_lat) const\r\n {\r\n static const CalculationType C3 = cass::C3();\r\n static const CalculationType C4 = cass::C4();\r\n static const CalculationType C5 = cass::C5();\r\n\r\n CalculationType ph1;\r\n\r\n ph1 = pj_inv_mlfn(this->m_proj_parm.m0 + xy_y, this->m_par.es, this->m_proj_parm.en);\r\n CalculationType tn = tan(ph1); CalculationType t = tn * tn;\r\n CalculationType n = sin(ph1);\r\n CalculationType r = 1. / (1. - this->m_par.es * n * n);\r\n n = sqrt(r);\r\n r *= (1. - this->m_par.es) * n;\r\n CalculationType dd = xy_x / n;\r\n CalculationType d2 = dd * dd;\r\n lp_lat = ph1 - (n * tn / r) * d2 *\r\n (.5 - (1. + 3. * t) * d2 * C3);\r\n lp_lon = dd * (1. + t * d2 *\r\n (-C4 + (1. + 3. * t) * d2 * C5)) / cos(ph1);\r\n }\r\n\r\n static inline std::string get_name()\r\n {\r\n return \"cass_ellipsoid\";\r\n }\r\n\r\n };\r\n\r\n // template class, using CRTP to implement forward/inverse\r\n template \r\n struct base_cass_spheroid : public base_t_fi,\r\n CalculationType, Parameters>\r\n {\r\n\r\n typedef CalculationType geographic_type;\r\n typedef CalculationType cartesian_type;\r\n\r\n par_cass m_proj_parm;\r\n\r\n inline base_cass_spheroid(const Parameters& par)\r\n : base_t_fi,\r\n CalculationType, Parameters>(*this, par) {}\r\n\r\n // FORWARD(s_forward) spheroid\r\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\r\n inline void fwd(geographic_type& lp_lon, geographic_type& lp_lat, cartesian_type& xy_x, cartesian_type& xy_y) const\r\n {\r\n xy_x = asin(cos(lp_lat) * sin(lp_lon));\r\n xy_y = atan2(tan(lp_lat) , cos(lp_lon)) - this->m_par.phi0;\r\n }\r\n\r\n // INVERSE(s_inverse) spheroid\r\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\r\n inline void inv(cartesian_type& xy_x, cartesian_type& xy_y, geographic_type& lp_lon, geographic_type& lp_lat) const\r\n {\r\n CalculationType dd = xy_y + this->m_par.phi0;\r\n lp_lat = asin(sin(dd) * cos(xy_x));\r\n lp_lon = atan2(tan(xy_x), cos(dd));\r\n }\r\n\r\n static inline std::string get_name()\r\n {\r\n return \"cass_spheroid\";\r\n }\r\n\r\n };\r\n\r\n // Cassini\r\n template \r\n inline void setup_cass(Parameters& par, par_cass& proj_parm)\r\n {\r\n if (par.es) {\r\n if (!pj_enfn(par.es, proj_parm.en))\r\n BOOST_THROW_EXCEPTION( projection_exception(0) );\r\n proj_parm.m0 = pj_mlfn(par.phi0, sin(par.phi0), cos(par.phi0), proj_parm.en);\r\n } else {\r\n }\r\n }\r\n\r\n }} // namespace detail::cass\r\n #endif // doxygen\r\n\r\n /*!\r\n \\brief Cassini projection\r\n \\ingroup projections\r\n \\tparam Geographic latlong point type\r\n \\tparam Cartesian xy point type\r\n \\tparam Parameters parameter type\r\n \\par Projection characteristics\r\n - Cylindrical\r\n - Spheroid\r\n - Ellipsoid\r\n \\par Example\r\n \\image html ex_cass.gif\r\n */\r\n template \r\n struct cass_ellipsoid : public detail::cass::base_cass_ellipsoid\r\n {\r\n inline cass_ellipsoid(const Parameters& par) : detail::cass::base_cass_ellipsoid(par)\r\n {\r\n detail::cass::setup_cass(this->m_par, this->m_proj_parm);\r\n }\r\n };\r\n\r\n /*!\r\n \\brief Cassini projection\r\n \\ingroup projections\r\n \\tparam Geographic latlong point type\r\n \\tparam Cartesian xy point type\r\n \\tparam Parameters parameter type\r\n \\par Projection characteristics\r\n - Cylindrical\r\n - Spheroid\r\n - Ellipsoid\r\n \\par Example\r\n \\image html ex_cass.gif\r\n */\r\n template \r\n struct cass_spheroid : public detail::cass::base_cass_spheroid\r\n {\r\n inline cass_spheroid(const Parameters& par) : detail::cass::base_cass_spheroid(par)\r\n {\r\n detail::cass::setup_cass(this->m_par, this->m_proj_parm);\r\n }\r\n };\r\n\r\n #ifndef DOXYGEN_NO_DETAIL\r\n namespace detail\r\n {\r\n\r\n // Static projection\r\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_STATIC_PROJECTION(srs::par4::cass, cass_spheroid, cass_ellipsoid)\r\n\r\n // Factory entry(s)\r\n template \r\n class cass_entry : public detail::factory_entry\r\n {\r\n public :\r\n virtual base_v* create_new(const Parameters& par) const\r\n {\r\n if (par.es)\r\n return new base_v_fi, CalculationType, Parameters>(par);\r\n else\r\n return new base_v_fi, CalculationType, Parameters>(par);\r\n }\r\n };\r\n\r\n template \r\n inline void cass_init(detail::base_factory& factory)\r\n {\r\n factory.add_to_factory(\"cass\", new cass_entry);\r\n }\r\n\r\n } // namespace detail\r\n #endif // doxygen\r\n\r\n} // namespace projections\r\n\r\n}} // namespace boost::geometry\r\n\r\n#endif // BOOST_GEOMETRY_PROJECTIONS_CASS_HPP\r\n\r\n", "meta": {"hexsha": "8d653ea23b39f567c263c5c204a3c318c8e635d2", "size": 12456, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/boost/geometry/srs/projections/proj/cass.hpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo_head_hexsha": "eb0f7ef64e188fe871f47c2ef9cdef36d8a66bc8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "REDSI_1160929_1161573/boost_1_67_0/boost/geometry/srs/projections/proj/cass.hpp", "max_issues_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_issues_repo_head_hexsha": "eb0f7ef64e188fe871f47c2ef9cdef36d8a66bc8", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "REDSI_1160929_1161573/boost_1_67_0/boost/geometry/srs/projections/proj/cass.hpp", "max_forks_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_forks_repo_head_hexsha": "eb0f7ef64e188fe871f47c2ef9cdef36d8a66bc8", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 42.5119453925, "max_line_length": 132, "alphanum_fraction": 0.5848587026, "num_tokens": 2769, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5660185205547238, "lm_q2_score": 0.37754066879814546, "lm_q1q2_score": 0.21369501080236727}} {"text": "// Copyright (c) 2014-2018, The Enro Project Copyright (c) 2014-2018, The Monero Project\n// \n// All rights reserved.\n// \n// Redistribution and use in source and binary forms, with or without modification, are\n// permitted provided that the following conditions are met:\n// \n// 1. Redistributions of source code must retain the above copyright notice, this list of\n// conditions and the following disclaimer.\n// \n// 2. Redistributions in binary form must reproduce the above copyright notice, this list\n// of conditions and the following disclaimer in the documentation and/or other\n// materials provided with the distribution.\n// \n// 3. Neither the name of the copyright holder nor the names of its contributors may be\n// used to endorse or promote products derived from this software without specific\n// prior written permission.\n// \n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY\n// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF\n// MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL\n// THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\n// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,\n// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF\n// THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n// \n// Parts of this file are originally copyright (c) 2012-2013 The Cryptonote developers\n\n#include \"include_base_utils.h\"\nusing namespace epee;\n\n#include \n#include \n#include \"wipeable_string.h\"\n#include \"string_tools.h\"\n#include \"serialization/string.h\"\n#include \"cryptonote_format_utils.h\"\n#include \"cryptonote_config.h\"\n#include \"crypto/crypto.h\"\n#include \"crypto/hash.h\"\n#include \"ringct/rctSigs.h\"\n\n#undef MONERO_DEFAULT_LOG_CATEGORY\n#define MONERO_DEFAULT_LOG_CATEGORY \"cn\"\n\n#define ENCRYPTED_PAYMENT_ID_TAIL 0x8d\n\n// #define ENABLE_HASH_CASH_INTEGRITY_CHECK\n\nusing namespace crypto;\n\nstatic const uint64_t valid_decomposed_outputs[] = {\n (uint64_t)1, (uint64_t)2, (uint64_t)3, (uint64_t)4, (uint64_t)5, (uint64_t)6, (uint64_t)7, (uint64_t)8, (uint64_t)9, // 1 piconero\n (uint64_t)10, (uint64_t)20, (uint64_t)30, (uint64_t)40, (uint64_t)50, (uint64_t)60, (uint64_t)70, (uint64_t)80, (uint64_t)90,\n (uint64_t)100, (uint64_t)200, (uint64_t)300, (uint64_t)400, (uint64_t)500, (uint64_t)600, (uint64_t)700, (uint64_t)800, (uint64_t)900,\n (uint64_t)1000, (uint64_t)2000, (uint64_t)3000, (uint64_t)4000, (uint64_t)5000, (uint64_t)6000, (uint64_t)7000, (uint64_t)8000, (uint64_t)9000,\n (uint64_t)10000, (uint64_t)20000, (uint64_t)30000, (uint64_t)40000, (uint64_t)50000, (uint64_t)60000, (uint64_t)70000, (uint64_t)80000, (uint64_t)90000,\n (uint64_t)100000, (uint64_t)200000, (uint64_t)300000, (uint64_t)400000, (uint64_t)500000, (uint64_t)600000, (uint64_t)700000, (uint64_t)800000, (uint64_t)900000,\n (uint64_t)1000000, (uint64_t)2000000, (uint64_t)3000000, (uint64_t)4000000, (uint64_t)5000000, (uint64_t)6000000, (uint64_t)7000000, (uint64_t)8000000, (uint64_t)9000000, // 1 micronero\n (uint64_t)10000000, (uint64_t)20000000, (uint64_t)30000000, (uint64_t)40000000, (uint64_t)50000000, (uint64_t)60000000, (uint64_t)70000000, (uint64_t)80000000, (uint64_t)90000000,\n (uint64_t)100000000, (uint64_t)200000000, (uint64_t)300000000, (uint64_t)400000000, (uint64_t)500000000, (uint64_t)600000000, (uint64_t)700000000, (uint64_t)800000000, (uint64_t)900000000,\n (uint64_t)1000000000, (uint64_t)2000000000, (uint64_t)3000000000, (uint64_t)4000000000, (uint64_t)5000000000, (uint64_t)6000000000, (uint64_t)7000000000, (uint64_t)8000000000, (uint64_t)9000000000,\n (uint64_t)10000000000, (uint64_t)20000000000, (uint64_t)30000000000, (uint64_t)40000000000, (uint64_t)50000000000, (uint64_t)60000000000, (uint64_t)70000000000, (uint64_t)80000000000, (uint64_t)90000000000,\n (uint64_t)100000000000, (uint64_t)200000000000, (uint64_t)300000000000, (uint64_t)400000000000, (uint64_t)500000000000, (uint64_t)600000000000, (uint64_t)700000000000, (uint64_t)800000000000, (uint64_t)900000000000,\n (uint64_t)1000000000000, (uint64_t)2000000000000, (uint64_t)3000000000000, (uint64_t)4000000000000, (uint64_t)5000000000000, (uint64_t)6000000000000, (uint64_t)7000000000000, (uint64_t)8000000000000, (uint64_t)9000000000000, // 1 enro\n (uint64_t)10000000000000, (uint64_t)20000000000000, (uint64_t)30000000000000, (uint64_t)40000000000000, (uint64_t)50000000000000, (uint64_t)60000000000000, (uint64_t)70000000000000, (uint64_t)80000000000000, (uint64_t)90000000000000,\n (uint64_t)100000000000000, (uint64_t)200000000000000, (uint64_t)300000000000000, (uint64_t)400000000000000, (uint64_t)500000000000000, (uint64_t)600000000000000, (uint64_t)700000000000000, (uint64_t)800000000000000, (uint64_t)900000000000000,\n (uint64_t)1000000000000000, (uint64_t)2000000000000000, (uint64_t)3000000000000000, (uint64_t)4000000000000000, (uint64_t)5000000000000000, (uint64_t)6000000000000000, (uint64_t)7000000000000000, (uint64_t)8000000000000000, (uint64_t)9000000000000000,\n (uint64_t)10000000000000000, (uint64_t)20000000000000000, (uint64_t)30000000000000000, (uint64_t)40000000000000000, (uint64_t)50000000000000000, (uint64_t)60000000000000000, (uint64_t)70000000000000000, (uint64_t)80000000000000000, (uint64_t)90000000000000000,\n (uint64_t)100000000000000000, (uint64_t)200000000000000000, (uint64_t)300000000000000000, (uint64_t)400000000000000000, (uint64_t)500000000000000000, (uint64_t)600000000000000000, (uint64_t)700000000000000000, (uint64_t)800000000000000000, (uint64_t)900000000000000000,\n (uint64_t)1000000000000000000, (uint64_t)2000000000000000000, (uint64_t)3000000000000000000, (uint64_t)4000000000000000000, (uint64_t)5000000000000000000, (uint64_t)6000000000000000000, (uint64_t)7000000000000000000, (uint64_t)8000000000000000000, (uint64_t)9000000000000000000, // 1 meganero\n (uint64_t)10000000000000000000ull\n};\n\nstatic std::atomic default_decimal_point(CRYPTONOTE_DISPLAY_DECIMAL_POINT);\n\nstatic std::atomic tx_hashes_calculated_count(0);\nstatic std::atomic tx_hashes_cached_count(0);\nstatic std::atomic block_hashes_calculated_count(0);\nstatic std::atomic block_hashes_cached_count(0);\n\n#define CHECK_AND_ASSERT_THROW_MES_L1(expr, message) {if(!(expr)) {MWARNING(message); throw std::runtime_error(message);}}\n\nnamespace cryptonote\n{\n static inline unsigned char *operator &(ec_point &point) {\n return &reinterpret_cast(point);\n }\n static inline const unsigned char *operator &(const ec_point &point) {\n return &reinterpret_cast(point);\n }\n\n // a copy of rct::addKeys, since we can't link to libringct to avoid circular dependencies\n static void add_public_key(crypto::public_key &AB, const crypto::public_key &A, const crypto::public_key &B) {\n ge_p3 B2, A2;\n CHECK_AND_ASSERT_THROW_MES_L1(ge_frombytes_vartime(&B2, &B) == 0, \"ge_frombytes_vartime failed at \"+boost::lexical_cast(__LINE__));\n CHECK_AND_ASSERT_THROW_MES_L1(ge_frombytes_vartime(&A2, &A) == 0, \"ge_frombytes_vartime failed at \"+boost::lexical_cast(__LINE__));\n ge_cached tmp2;\n ge_p3_to_cached(&tmp2, &B2);\n ge_p1p1 tmp3;\n ge_add(&tmp3, &A2, &tmp2);\n ge_p1p1_to_p3(&A2, &tmp3);\n ge_p3_tobytes(&AB, &A2);\n }\n}\n\nnamespace cryptonote\n{\n //---------------------------------------------------------------\n void get_transaction_prefix_hash(const transaction_prefix& tx, crypto::hash& h)\n {\n std::ostringstream s;\n binary_archive a(s);\n ::serialization::serialize(a, const_cast(tx));\n crypto::cn_fast_hash(s.str().data(), s.str().size(), h);\n }\n //---------------------------------------------------------------\n crypto::hash get_transaction_prefix_hash(const transaction_prefix& tx)\n {\n crypto::hash h = null_hash;\n get_transaction_prefix_hash(tx, h);\n return h;\n }\n //---------------------------------------------------------------\n bool expand_transaction_1(transaction &tx, bool base_only)\n {\n if (tx.version >= 2 && !is_coinbase(tx))\n {\n rct::rctSig &rv = tx.rct_signatures;\n if (rv.outPk.size() != tx.vout.size())\n {\n LOG_PRINT_L1(\"Failed to parse transaction from blob, bad outPk size in tx \" << get_transaction_hash(tx));\n return false;\n }\n for (size_t n = 0; n < tx.rct_signatures.outPk.size(); ++n)\n {\n if (tx.vout[n].target.type() != typeid(txout_to_key))\n {\n LOG_PRINT_L1(\"Unsupported output type in tx \" << get_transaction_hash(tx));\n return false;\n }\n rv.outPk[n].dest = rct::pk2rct(boost::get(tx.vout[n].target).key);\n }\n\n if (!base_only)\n {\n const bool bulletproof = rct::is_rct_bulletproof(rv.type);\n if (bulletproof)\n {\n if (rv.p.bulletproofs.size() != 1)\n {\n LOG_PRINT_L1(\"Failed to parse transaction from blob, bad bulletproofs size in tx \" << get_transaction_hash(tx));\n return false;\n }\n if (rv.p.bulletproofs[0].L.size() < 6)\n {\n LOG_PRINT_L1(\"Failed to parse transaction from blob, bad bulletproofs L size in tx \" << get_transaction_hash(tx));\n return false;\n }\n const size_t max_outputs = 1 << (rv.p.bulletproofs[0].L.size() - 6);\n if (max_outputs < tx.vout.size())\n {\n LOG_PRINT_L1(\"Failed to parse transaction from blob, bad bulletproofs max outputs in tx \" << get_transaction_hash(tx));\n return false;\n }\n const size_t n_amounts = tx.vout.size();\n CHECK_AND_ASSERT_MES(n_amounts == rv.outPk.size(), false, \"Internal error filling out V\");\n rv.p.bulletproofs[0].V.resize(n_amounts);\n for (size_t i = 0; i < n_amounts; ++i)\n rv.p.bulletproofs[0].V[i] = rct::scalarmultKey(rv.outPk[i].mask, rct::INV_EIGHT);\n }\n }\n }\n return true;\n }\n //---------------------------------------------------------------\n bool parse_and_validate_tx_from_blob(const blobdata& tx_blob, transaction& tx)\n {\n std::stringstream ss;\n ss << tx_blob;\n binary_archive ba(ss);\n bool r = ::serialization::serialize(ba, tx);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to parse transaction from blob\");\n CHECK_AND_ASSERT_MES(expand_transaction_1(tx, false), false, \"Failed to expand transaction data\");\n tx.invalidate_hashes();\n return true;\n }\n //---------------------------------------------------------------\n bool parse_and_validate_tx_base_from_blob(const blobdata& tx_blob, transaction& tx)\n {\n std::stringstream ss;\n ss << tx_blob;\n binary_archive ba(ss);\n bool r = tx.serialize_base(ba);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to parse transaction from blob\");\n CHECK_AND_ASSERT_MES(expand_transaction_1(tx, true), false, \"Failed to expand transaction data\");\n return true;\n }\n //---------------------------------------------------------------\n bool parse_and_validate_tx_from_blob(const blobdata& tx_blob, transaction& tx, crypto::hash& tx_hash, crypto::hash& tx_prefix_hash)\n {\n std::stringstream ss;\n ss << tx_blob;\n binary_archive ba(ss);\n bool r = ::serialization::serialize(ba, tx);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to parse transaction from blob\");\n CHECK_AND_ASSERT_MES(expand_transaction_1(tx, false), false, \"Failed to expand transaction data\");\n tx.invalidate_hashes();\n //TODO: validate tx\n\n get_transaction_hash(tx, tx_hash);\n get_transaction_prefix_hash(tx, tx_prefix_hash);\n return true;\n }\n //---------------------------------------------------------------\n bool generate_key_image_helper(const account_keys& ack, const std::unordered_map& subaddresses, const crypto::public_key& out_key, const crypto::public_key& tx_public_key, const std::vector& additional_tx_public_keys, size_t real_output_index, keypair& in_ephemeral, crypto::key_image& ki, hw::device &hwdev)\n {\n crypto::key_derivation recv_derivation = AUTO_VAL_INIT(recv_derivation);\n bool r = hwdev.generate_key_derivation(tx_public_key, ack.m_view_secret_key, recv_derivation);\n if (!r)\n {\n MWARNING(\"key image helper: failed to generate_key_derivation(\" << tx_public_key << \", \" << ack.m_view_secret_key << \")\");\n memcpy(&recv_derivation, rct::identity().bytes, sizeof(recv_derivation));\n }\n\n std::vector additional_recv_derivations;\n for (size_t i = 0; i < additional_tx_public_keys.size(); ++i)\n {\n crypto::key_derivation additional_recv_derivation = AUTO_VAL_INIT(additional_recv_derivation);\n r = hwdev.generate_key_derivation(additional_tx_public_keys[i], ack.m_view_secret_key, additional_recv_derivation);\n if (!r)\n {\n MWARNING(\"key image helper: failed to generate_key_derivation(\" << additional_tx_public_keys[i] << \", \" << ack.m_view_secret_key << \")\");\n }\n else\n {\n additional_recv_derivations.push_back(additional_recv_derivation);\n }\n }\n\n boost::optional subaddr_recv_info = is_out_to_acc_precomp(subaddresses, out_key, recv_derivation, additional_recv_derivations, real_output_index,hwdev);\n CHECK_AND_ASSERT_MES(subaddr_recv_info, false, \"key image helper: given output pubkey doesn't seem to belong to this address\");\n\n return generate_key_image_helper_precomp(ack, out_key, subaddr_recv_info->derivation, real_output_index, subaddr_recv_info->index, in_ephemeral, ki, hwdev);\n }\n //---------------------------------------------------------------\n bool generate_key_image_helper_precomp(const account_keys& ack, const crypto::public_key& out_key, const crypto::key_derivation& recv_derivation, size_t real_output_index, const subaddress_index& received_index, keypair& in_ephemeral, crypto::key_image& ki, hw::device &hwdev)\n {\n if (ack.m_spend_secret_key == crypto::null_skey)\n {\n // for watch-only wallet, simply copy the known output pubkey\n in_ephemeral.pub = out_key;\n in_ephemeral.sec = crypto::null_skey;\n }\n else\n {\n // derive secret key with subaddress - step 1: original CN derivation\n crypto::secret_key scalar_step1;\n hwdev.derive_secret_key(recv_derivation, real_output_index, ack.m_spend_secret_key, scalar_step1); // computes Hs(a*R || idx) + b\n\n // step 2: add Hs(a || index_major || index_minor)\n crypto::secret_key subaddr_sk;\n crypto::secret_key scalar_step2;\n if (received_index.is_zero())\n {\n scalar_step2 = scalar_step1; // treat index=(0,0) as a special case representing the main address\n }\n else\n {\n subaddr_sk = hwdev.get_subaddress_secret_key(ack.m_view_secret_key, received_index);\n hwdev.sc_secret_add(scalar_step2, scalar_step1,subaddr_sk);\n }\n\n in_ephemeral.sec = scalar_step2;\n\n if (ack.m_multisig_keys.empty())\n {\n // when not in multisig, we know the full spend secret key, so the output pubkey can be obtained by scalarmultBase\n CHECK_AND_ASSERT_MES(hwdev.secret_key_to_public_key(in_ephemeral.sec, in_ephemeral.pub), false, \"Failed to derive public key\");\n }\n else\n {\n // when in multisig, we only know the partial spend secret key. but we do know the full spend public key, so the output pubkey can be obtained by using the standard CN key derivation\n CHECK_AND_ASSERT_MES(hwdev.derive_public_key(recv_derivation, real_output_index, ack.m_account_address.m_spend_public_key, in_ephemeral.pub), false, \"Failed to derive public key\");\n // and don't forget to add the contribution from the subaddress part\n if (!received_index.is_zero())\n {\n crypto::public_key subaddr_pk;\n CHECK_AND_ASSERT_MES(hwdev.secret_key_to_public_key(subaddr_sk, subaddr_pk), false, \"Failed to derive public key\");\n add_public_key(in_ephemeral.pub, in_ephemeral.pub, subaddr_pk);\n }\n }\n\n CHECK_AND_ASSERT_MES(in_ephemeral.pub == out_key,\n false, \"key image helper precomp: given output pubkey doesn't match the derived one\");\n }\n\n hwdev.generate_key_image(in_ephemeral.pub, in_ephemeral.sec, ki);\n return true;\n }\n //---------------------------------------------------------------\n uint64_t power_integral(uint64_t a, uint64_t b)\n {\n if(b == 0)\n return 1;\n uint64_t total = a;\n for(uint64_t i = 1; i != b; i++)\n total *= a;\n return total;\n }\n //---------------------------------------------------------------\n bool parse_amount(uint64_t& amount, const std::string& str_amount_)\n {\n std::string str_amount = str_amount_;\n boost::algorithm::trim(str_amount);\n\n size_t point_index = str_amount.find_first_of('.');\n size_t fraction_size;\n if (std::string::npos != point_index)\n {\n fraction_size = str_amount.size() - point_index - 1;\n while (default_decimal_point < fraction_size && '0' == str_amount.back())\n {\n str_amount.erase(str_amount.size() - 1, 1);\n --fraction_size;\n }\n if (default_decimal_point < fraction_size)\n return false;\n str_amount.erase(point_index, 1);\n }\n else\n {\n fraction_size = 0;\n }\n\n if (str_amount.empty())\n return false;\n\n if (fraction_size < default_decimal_point)\n {\n str_amount.append(default_decimal_point - fraction_size, '0');\n }\n\n return string_tools::get_xtype_from_string(amount, str_amount);\n }\n //---------------------------------------------------------------\n uint64_t get_transaction_weight(const transaction &tx, size_t blob_size)\n {\n if (tx.version < 2)\n return blob_size;\n const rct::rctSig &rv = tx.rct_signatures;\n if (!rct::is_rct_bulletproof(rv.type))\n return blob_size;\n const size_t n_outputs = tx.vout.size();\n if (n_outputs <= 2)\n return blob_size;\n const uint64_t bp_base = 368;\n const size_t n_padded_outputs = rct::n_bulletproof_max_amounts(rv.p.bulletproofs);\n size_t nlr = 0;\n for (const auto &bp: rv.p.bulletproofs)\n nlr += bp.L.size() * 2;\n const size_t bp_size = 32 * (9 + nlr);\n CHECK_AND_ASSERT_THROW_MES_L1(bp_base * n_padded_outputs >= bp_size, \"Invalid bulletproof clawback\");\n const uint64_t bp_clawback = (bp_base * n_padded_outputs - bp_size) * 4 / 5;\n CHECK_AND_ASSERT_THROW_MES_L1(bp_clawback <= std::numeric_limits::max() - blob_size, \"Weight overflow\");\n return blob_size + bp_clawback;\n }\n //---------------------------------------------------------------\n uint64_t get_transaction_weight(const transaction &tx)\n {\n std::ostringstream s;\n binary_archive a(s);\n ::serialization::serialize(a, const_cast(tx));\n const cryptonote::blobdata blob = s.str();\n return get_transaction_weight(tx, blob.size());\n }\n //---------------------------------------------------------------\n bool get_tx_fee(const transaction& tx, uint64_t & fee)\n {\n if (tx.version > 1)\n {\n fee = tx.rct_signatures.txnFee;\n return true;\n }\n uint64_t amount_in = 0;\n uint64_t amount_out = 0;\n for(auto& in: tx.vin)\n {\n CHECK_AND_ASSERT_MES(in.type() == typeid(txin_to_key), 0, \"unexpected type id in transaction\");\n amount_in += boost::get(in).amount;\n }\n for(auto& o: tx.vout)\n amount_out += o.amount;\n\n CHECK_AND_ASSERT_MES(amount_in >= amount_out, false, \"transaction spend (\" <& tx_extra, std::vector& tx_extra_fields)\n {\n tx_extra_fields.clear();\n\n if(tx_extra.empty())\n return true;\n\n std::string extra_str(reinterpret_cast(tx_extra.data()), tx_extra.size());\n std::istringstream iss(extra_str);\n binary_archive ar(iss);\n\n bool eof = false;\n while (!eof)\n {\n tx_extra_field field;\n bool r = ::do_serialize(ar, field);\n CHECK_AND_NO_ASSERT_MES_L1(r, false, \"failed to deserialize extra field. extra = \" << string_tools::buff_to_hex_nodelimer(std::string(reinterpret_cast(tx_extra.data()), tx_extra.size())));\n tx_extra_fields.push_back(field);\n\n std::ios_base::iostate state = iss.rdstate();\n eof = (EOF == iss.peek());\n iss.clear(state);\n }\n CHECK_AND_NO_ASSERT_MES_L1(::serialization::check_stream_state(ar), false, \"failed to deserialize extra field. extra = \" << string_tools::buff_to_hex_nodelimer(std::string(reinterpret_cast(tx_extra.data()), tx_extra.size())));\n\n return true;\n }\n //---------------------------------------------------------------\n crypto::public_key get_tx_pub_key_from_extra(const std::vector& tx_extra, size_t pk_index)\n {\n std::vector tx_extra_fields;\n parse_tx_extra(tx_extra, tx_extra_fields);\n\n tx_extra_pub_key pub_key_field;\n if(!find_tx_extra_field_by_type(tx_extra_fields, pub_key_field, pk_index))\n return null_pkey;\n\n return pub_key_field.pub_key;\n }\n //---------------------------------------------------------------\n crypto::public_key get_tx_pub_key_from_extra(const transaction_prefix& tx_prefix, size_t pk_index)\n {\n return get_tx_pub_key_from_extra(tx_prefix.extra, pk_index);\n }\n //---------------------------------------------------------------\n crypto::public_key get_tx_pub_key_from_extra(const transaction& tx, size_t pk_index)\n {\n return get_tx_pub_key_from_extra(tx.extra, pk_index);\n }\n //---------------------------------------------------------------\n bool add_tx_pub_key_to_extra(transaction& tx, const crypto::public_key& tx_pub_key)\n {\n return add_tx_pub_key_to_extra(tx.extra, tx_pub_key);\n }\n //---------------------------------------------------------------\n bool add_tx_pub_key_to_extra(transaction_prefix& tx, const crypto::public_key& tx_pub_key)\n {\n return add_tx_pub_key_to_extra(tx.extra, tx_pub_key);\n }\n //---------------------------------------------------------------\n bool add_tx_pub_key_to_extra(std::vector& tx_extra, const crypto::public_key& tx_pub_key)\n {\n tx_extra.resize(tx_extra.size() + 1 + sizeof(crypto::public_key));\n tx_extra[tx_extra.size() - 1 - sizeof(crypto::public_key)] = TX_EXTRA_TAG_PUBKEY;\n *reinterpret_cast(&tx_extra[tx_extra.size() - sizeof(crypto::public_key)]) = tx_pub_key;\n return true;\n }\n //---------------------------------------------------------------\n std::vector get_additional_tx_pub_keys_from_extra(const std::vector& tx_extra)\n {\n // parse\n std::vector tx_extra_fields;\n parse_tx_extra(tx_extra, tx_extra_fields);\n // find corresponding field\n tx_extra_additional_pub_keys additional_pub_keys;\n if(!find_tx_extra_field_by_type(tx_extra_fields, additional_pub_keys))\n return {};\n return additional_pub_keys.data;\n }\n //---------------------------------------------------------------\n std::vector get_additional_tx_pub_keys_from_extra(const transaction_prefix& tx)\n {\n return get_additional_tx_pub_keys_from_extra(tx.extra);\n }\n //---------------------------------------------------------------\n bool add_additional_tx_pub_keys_to_extra(std::vector& tx_extra, const std::vector& additional_pub_keys)\n {\n // convert to variant\n tx_extra_field field = tx_extra_additional_pub_keys{ additional_pub_keys };\n // serialize\n std::ostringstream oss;\n binary_archive ar(oss);\n bool r = ::do_serialize(ar, field);\n CHECK_AND_NO_ASSERT_MES_L1(r, false, \"failed to serialize tx extra additional tx pub keys\");\n // append\n std::string tx_extra_str = oss.str();\n size_t pos = tx_extra.size();\n tx_extra.resize(tx_extra.size() + tx_extra_str.size());\n memcpy(&tx_extra[pos], tx_extra_str.data(), tx_extra_str.size());\n return true;\n }\n //---------------------------------------------------------------\n bool add_extra_nonce_to_tx_extra(std::vector& tx_extra, const blobdata& extra_nonce)\n {\n CHECK_AND_ASSERT_MES(extra_nonce.size() <= TX_EXTRA_NONCE_MAX_COUNT, false, \"extra nonce could be 255 bytes max\");\n size_t start_pos = tx_extra.size();\n tx_extra.resize(tx_extra.size() + 2 + extra_nonce.size());\n //write tag\n tx_extra[start_pos] = TX_EXTRA_NONCE;\n //write len\n ++start_pos;\n tx_extra[start_pos] = static_cast(extra_nonce.size());\n //write data\n ++start_pos;\n memcpy(&tx_extra[start_pos], extra_nonce.data(), extra_nonce.size());\n return true;\n }\n //---------------------------------------------------------------\n bool remove_field_from_tx_extra(std::vector& tx_extra, const std::type_info &type)\n {\n if (tx_extra.empty())\n return true;\n std::string extra_str(reinterpret_cast(tx_extra.data()), tx_extra.size());\n std::istringstream iss(extra_str);\n binary_archive ar(iss);\n std::ostringstream oss;\n binary_archive newar(oss);\n\n bool eof = false;\n while (!eof)\n {\n tx_extra_field field;\n bool r = ::do_serialize(ar, field);\n CHECK_AND_NO_ASSERT_MES_L1(r, false, \"failed to deserialize extra field. extra = \" << string_tools::buff_to_hex_nodelimer(std::string(reinterpret_cast(tx_extra.data()), tx_extra.size())));\n if (field.type() != type)\n ::do_serialize(newar, field);\n\n std::ios_base::iostate state = iss.rdstate();\n eof = (EOF == iss.peek());\n iss.clear(state);\n }\n CHECK_AND_NO_ASSERT_MES_L1(::serialization::check_stream_state(ar), false, \"failed to deserialize extra field. extra = \" << string_tools::buff_to_hex_nodelimer(std::string(reinterpret_cast(tx_extra.data()), tx_extra.size())));\n tx_extra.clear();\n std::string s = oss.str();\n tx_extra.reserve(s.size());\n std::copy(s.begin(), s.end(), std::back_inserter(tx_extra));\n return true;\n }\n //---------------------------------------------------------------\n void set_payment_id_to_tx_extra_nonce(blobdata& extra_nonce, const crypto::hash& payment_id)\n {\n extra_nonce.clear();\n extra_nonce.push_back(TX_EXTRA_NONCE_PAYMENT_ID);\n const uint8_t* payment_id_ptr = reinterpret_cast(&payment_id);\n std::copy(payment_id_ptr, payment_id_ptr + sizeof(payment_id), std::back_inserter(extra_nonce));\n }\n //---------------------------------------------------------------\n void set_encrypted_payment_id_to_tx_extra_nonce(blobdata& extra_nonce, const crypto::hash8& payment_id)\n {\n extra_nonce.clear();\n extra_nonce.push_back(TX_EXTRA_NONCE_ENCRYPTED_PAYMENT_ID);\n const uint8_t* payment_id_ptr = reinterpret_cast(&payment_id);\n std::copy(payment_id_ptr, payment_id_ptr + sizeof(payment_id), std::back_inserter(extra_nonce));\n }\n //---------------------------------------------------------------\n bool get_payment_id_from_tx_extra_nonce(const blobdata& extra_nonce, crypto::hash& payment_id)\n {\n if(sizeof(crypto::hash) + 1 != extra_nonce.size())\n return false;\n if(TX_EXTRA_NONCE_PAYMENT_ID != extra_nonce[0])\n return false;\n payment_id = *reinterpret_cast(extra_nonce.data() + 1);\n return true;\n }\n //---------------------------------------------------------------\n bool get_encrypted_payment_id_from_tx_extra_nonce(const blobdata& extra_nonce, crypto::hash8& payment_id)\n {\n if(sizeof(crypto::hash8) + 1 != extra_nonce.size())\n return false;\n if (TX_EXTRA_NONCE_ENCRYPTED_PAYMENT_ID != extra_nonce[0])\n return false;\n payment_id = *reinterpret_cast(extra_nonce.data() + 1);\n return true;\n }\n //---------------------------------------------------------------\n bool get_inputs_money_amount(const transaction& tx, uint64_t& money)\n {\n money = 0;\n for(const auto& in: tx.vin)\n {\n CHECKED_GET_SPECIFIC_VARIANT(in, const txin_to_key, tokey_in, false);\n money += tokey_in.amount;\n }\n return true;\n }\n //---------------------------------------------------------------\n uint64_t get_block_height(const block& b)\n {\n CHECK_AND_ASSERT_MES(b.miner_tx.vin.size() == 1, 0, \"wrong miner tx in block: \" << get_block_hash(b) << \", b.miner_tx.vin.size() != 1\");\n CHECKED_GET_SPECIFIC_VARIANT(b.miner_tx.vin[0], const txin_gen, coinbase_in, 0);\n return coinbase_in.height;\n }\n //---------------------------------------------------------------\n bool check_inputs_types_supported(const transaction& tx)\n {\n for(const auto& in: tx.vin)\n {\n CHECK_AND_ASSERT_MES(in.type() == typeid(txin_to_key), false, \"wrong variant type: \"\n << in.type().name() << \", expected \" << typeid(txin_to_key).name()\n << \", in transaction id=\" << get_transaction_hash(tx));\n\n }\n return true;\n }\n //-----------------------------------------------------------------------------------------------\n bool check_outs_valid(const transaction& tx)\n {\n for(const tx_out& out: tx.vout)\n {\n CHECK_AND_ASSERT_MES(out.target.type() == typeid(txout_to_key), false, \"wrong variant type: \"\n << out.target.type().name() << \", expected \" << typeid(txout_to_key).name()\n << \", in transaction id=\" << get_transaction_hash(tx));\n\n if (tx.version == 1)\n {\n CHECK_AND_NO_ASSERT_MES(0 < out.amount, false, \"zero amount output in transaction id=\" << get_transaction_hash(tx));\n }\n\n if(!check_key(boost::get(out.target).key))\n return false;\n }\n return true;\n }\n //-----------------------------------------------------------------------------------------------\n bool check_money_overflow(const transaction& tx)\n {\n return check_inputs_overflow(tx) && check_outs_overflow(tx);\n }\n //---------------------------------------------------------------\n bool check_inputs_overflow(const transaction& tx)\n {\n uint64_t money = 0;\n for(const auto& in: tx.vin)\n {\n CHECKED_GET_SPECIFIC_VARIANT(in, const txin_to_key, tokey_in, false);\n if(money > tokey_in.amount + money)\n return false;\n money += tokey_in.amount;\n }\n return true;\n }\n //---------------------------------------------------------------\n bool check_outs_overflow(const transaction& tx)\n {\n uint64_t money = 0;\n for(const auto& o: tx.vout)\n {\n if(money > o.amount + money)\n return false;\n money += o.amount;\n }\n return true;\n }\n //---------------------------------------------------------------\n uint64_t get_outs_money_amount(const transaction& tx)\n {\n uint64_t outputs_amount = 0;\n for(const auto& o: tx.vout)\n outputs_amount += o.amount;\n return outputs_amount;\n }\n //---------------------------------------------------------------\n std::string short_hash_str(const crypto::hash& h)\n {\n std::string res = string_tools::pod_to_hex(h);\n CHECK_AND_ASSERT_MES(res.size() == 64, res, \"wrong hash256 with string_tools::pod_to_hex conversion\");\n auto erased_pos = res.erase(8, 48);\n res.insert(8, \"....\");\n return res;\n }\n //---------------------------------------------------------------\n bool is_out_to_acc(const account_keys& acc, const txout_to_key& out_key, const crypto::public_key& tx_pub_key, const std::vector& additional_tx_pub_keys, size_t output_index)\n {\n crypto::key_derivation derivation;\n bool r = acc.get_device().generate_key_derivation(tx_pub_key, acc.m_view_secret_key, derivation);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to generate key derivation\");\n crypto::public_key pk;\n r = acc.get_device().derive_public_key(derivation, output_index, acc.m_account_address.m_spend_public_key, pk);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to derive public key\");\n if (pk == out_key.key)\n return true;\n // try additional tx pubkeys if available\n if (!additional_tx_pub_keys.empty())\n {\n CHECK_AND_ASSERT_MES(output_index < additional_tx_pub_keys.size(), false, \"wrong number of additional tx pubkeys\");\n r = acc.get_device().generate_key_derivation(additional_tx_pub_keys[output_index], acc.m_view_secret_key, derivation);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to generate key derivation\");\n r = acc.get_device().derive_public_key(derivation, output_index, acc.m_account_address.m_spend_public_key, pk);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to derive public key\");\n return pk == out_key.key;\n }\n return false;\n }\n //---------------------------------------------------------------\n boost::optional is_out_to_acc_precomp(const std::unordered_map& subaddresses, const crypto::public_key& out_key, const crypto::key_derivation& derivation, const std::vector& additional_derivations, size_t output_index, hw::device &hwdev)\n {\n // try the shared tx pubkey\n crypto::public_key subaddress_spendkey;\n hwdev.derive_subaddress_public_key(out_key, derivation, output_index, subaddress_spendkey);\n auto found = subaddresses.find(subaddress_spendkey);\n if (found != subaddresses.end())\n return subaddress_receive_info{ found->second, derivation };\n // try additional tx pubkeys if available\n if (!additional_derivations.empty())\n {\n CHECK_AND_ASSERT_MES(output_index < additional_derivations.size(), boost::none, \"wrong number of additional derivations\");\n hwdev.derive_subaddress_public_key(out_key, additional_derivations[output_index], output_index, subaddress_spendkey);\n found = subaddresses.find(subaddress_spendkey);\n if (found != subaddresses.end())\n return subaddress_receive_info{ found->second, additional_derivations[output_index] };\n }\n return boost::none;\n }\n //---------------------------------------------------------------\n bool lookup_acc_outs(const account_keys& acc, const transaction& tx, std::vector& outs, uint64_t& money_transfered)\n {\n crypto::public_key tx_pub_key = get_tx_pub_key_from_extra(tx);\n if(null_pkey == tx_pub_key)\n return false;\n std::vector additional_tx_pub_keys = get_additional_tx_pub_keys_from_extra(tx);\n return lookup_acc_outs(acc, tx, tx_pub_key, additional_tx_pub_keys, outs, money_transfered);\n }\n //---------------------------------------------------------------\n bool lookup_acc_outs(const account_keys& acc, const transaction& tx, const crypto::public_key& tx_pub_key, const std::vector& additional_tx_pub_keys, std::vector& outs, uint64_t& money_transfered)\n {\n CHECK_AND_ASSERT_MES(additional_tx_pub_keys.empty() || additional_tx_pub_keys.size() == tx.vout.size(), false, \"wrong number of additional pubkeys\" );\n money_transfered = 0;\n size_t i = 0;\n for(const tx_out& o: tx.vout)\n {\n CHECK_AND_ASSERT_MES(o.target.type() == typeid(txout_to_key), false, \"wrong type id in transaction out\" );\n if(is_out_to_acc(acc, boost::get(o.target), tx_pub_key, additional_tx_pub_keys, i))\n {\n outs.push_back(i);\n money_transfered += o.amount;\n }\n i++;\n }\n return true;\n }\n //---------------------------------------------------------------\n void get_blob_hash(const blobdata& blob, crypto::hash& res)\n {\n cn_fast_hash(blob.data(), blob.size(), res);\n }\n //---------------------------------------------------------------\n void set_default_decimal_point(unsigned int decimal_point)\n {\n switch (decimal_point)\n {\n case 12:\n case 9:\n case 6:\n case 3:\n case 0:\n default_decimal_point = decimal_point;\n break;\n default:\n ASSERT_MES_AND_THROW(\"Invalid decimal point specification: \" << decimal_point);\n }\n }\n //---------------------------------------------------------------\n unsigned int get_default_decimal_point()\n {\n return default_decimal_point;\n }\n //---------------------------------------------------------------\n std::string get_unit(unsigned int decimal_point)\n {\n if (decimal_point == (unsigned int)-1)\n decimal_point = default_decimal_point;\n switch (std::atomic_load(&default_decimal_point))\n {\n case 12:\n return \"enro\";\n case 9:\n return \"millinero\";\n case 6:\n return \"micronero\";\n case 3:\n return \"nanonero\";\n case 0:\n return \"piconero\";\n default:\n ASSERT_MES_AND_THROW(\"Invalid decimal point specification: \" << default_decimal_point);\n }\n }\n //---------------------------------------------------------------\n std::string print_money(uint64_t amount, unsigned int decimal_point)\n {\n if (decimal_point == (unsigned int)-1)\n decimal_point = default_decimal_point;\n std::string s = std::to_string(amount);\n if(s.size() < decimal_point+1)\n {\n s.insert(0, decimal_point+1 - s.size(), '0');\n }\n if (decimal_point > 0)\n s.insert(s.size() - decimal_point, \".\");\n return s;\n }\n //---------------------------------------------------------------\n crypto::hash get_blob_hash(const blobdata& blob)\n {\n crypto::hash h = null_hash;\n get_blob_hash(blob, h);\n return h;\n }\n //---------------------------------------------------------------\n crypto::hash get_transaction_hash(const transaction& t)\n {\n crypto::hash h = null_hash;\n get_transaction_hash(t, h, NULL);\n return h;\n }\n //---------------------------------------------------------------\n bool get_transaction_hash(const transaction& t, crypto::hash& res)\n {\n return get_transaction_hash(t, res, NULL);\n }\n //---------------------------------------------------------------\n bool calculate_transaction_prunable_hash(const transaction& t, crypto::hash& res)\n {\n if (t.version == 1)\n return false;\n transaction &tt = const_cast(t);\n std::stringstream ss;\n binary_archive ba(ss);\n const size_t inputs = t.vin.size();\n const size_t outputs = t.vout.size();\n const size_t mixin = t.vin.empty() ? 0 : t.vin[0].type() == typeid(txin_to_key) ? boost::get(t.vin[0]).key_offsets.size() - 1 : 0;\n bool r = tt.rct_signatures.p.serialize_rctsig_prunable(ba, t.rct_signatures.type, inputs, outputs, mixin);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to serialize rct signatures prunable\");\n cryptonote::get_blob_hash(ss.str(), res);\n return true;\n }\n //---------------------------------------------------------------\n crypto::hash get_transaction_prunable_hash(const transaction& t)\n {\n crypto::hash res;\n CHECK_AND_ASSERT_THROW_MES(calculate_transaction_prunable_hash(t, res), \"Failed to calculate tx prunable hash\");\n return res;\n }\n //---------------------------------------------------------------\n crypto::hash get_pruned_transaction_hash(const transaction& t, const crypto::hash &pruned_data_hash)\n {\n // v1 transactions hash the entire blob\n CHECK_AND_ASSERT_THROW_MES(t.version > 1, \"Hash for pruned v1 tx cannot be calculated\");\n\n // v2 transactions hash different parts together, than hash the set of those hashes\n crypto::hash hashes[3];\n\n // prefix\n get_transaction_prefix_hash(t, hashes[0]);\n\n transaction &tt = const_cast(t);\n\n // base rct\n {\n std::stringstream ss;\n binary_archive ba(ss);\n const size_t inputs = t.vin.size();\n const size_t outputs = t.vout.size();\n bool r = tt.rct_signatures.serialize_rctsig_base(ba, inputs, outputs);\n CHECK_AND_ASSERT_THROW_MES(r, \"Failed to serialize rct signatures base\");\n cryptonote::get_blob_hash(ss.str(), hashes[1]);\n }\n\n // prunable rct\n hashes[2] = pruned_data_hash;\n\n // the tx hash is the hash of the 3 hashes\n crypto::hash res = cn_fast_hash(hashes, sizeof(hashes));\n return res;\n }\n //---------------------------------------------------------------\n bool calculate_transaction_hash(const transaction& t, crypto::hash& res, size_t* blob_size)\n {\n // v1 transactions hash the entire blob\n if (t.version == 1)\n {\n size_t ignored_blob_size, &blob_size_ref = blob_size ? *blob_size : ignored_blob_size;\n return get_object_hash(t, res, blob_size_ref);\n }\n\n // v2 transactions hash different parts together, than hash the set of those hashes\n crypto::hash hashes[3];\n\n // prefix\n get_transaction_prefix_hash(t, hashes[0]);\n\n transaction &tt = const_cast(t);\n\n // base rct\n {\n std::stringstream ss;\n binary_archive ba(ss);\n const size_t inputs = t.vin.size();\n const size_t outputs = t.vout.size();\n bool r = tt.rct_signatures.serialize_rctsig_base(ba, inputs, outputs);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to serialize rct signatures base\");\n cryptonote::get_blob_hash(ss.str(), hashes[1]);\n }\n\n // prunable rct\n if (t.rct_signatures.type == rct::RCTTypeNull)\n {\n hashes[2] = crypto::null_hash;\n }\n else\n {\n CHECK_AND_ASSERT_MES(calculate_transaction_prunable_hash(t, hashes[2]), false, \"Failed to get tx prunable hash\");\n }\n\n // the tx hash is the hash of the 3 hashes\n res = cn_fast_hash(hashes, sizeof(hashes));\n\n // we still need the size\n if (blob_size)\n *blob_size = get_object_blobsize(t);\n\n return true;\n }\n //---------------------------------------------------------------\n bool get_transaction_hash(const transaction& t, crypto::hash& res, size_t* blob_size)\n {\n if (t.is_hash_valid())\n {\n#ifdef ENABLE_HASH_CASH_INTEGRITY_CHECK\n CHECK_AND_ASSERT_THROW_MES(!calculate_transaction_hash(t, res, blob_size) || t.hash == res, \"tx hash cash integrity failure\");\n#endif\n res = t.hash;\n if (blob_size)\n {\n if (!t.is_blob_size_valid())\n {\n t.blob_size = get_object_blobsize(t);\n t.set_blob_size_valid(true);\n }\n *blob_size = t.blob_size;\n }\n ++tx_hashes_cached_count;\n return true;\n }\n ++tx_hashes_calculated_count;\n bool ret = calculate_transaction_hash(t, res, blob_size);\n if (!ret)\n return false;\n t.hash = res;\n t.set_hash_valid(true);\n if (blob_size)\n {\n t.blob_size = *blob_size;\n t.set_blob_size_valid(true);\n }\n return true;\n }\n //---------------------------------------------------------------\n bool get_transaction_hash(const transaction& t, crypto::hash& res, size_t& blob_size)\n {\n return get_transaction_hash(t, res, &blob_size);\n }\n //---------------------------------------------------------------\n blobdata get_block_hashing_blob(const block& b)\n {\n blobdata blob = t_serializable_object_to_blob(static_cast(b));\n crypto::hash tree_root_hash = get_tx_tree_hash(b);\n blob.append(reinterpret_cast(&tree_root_hash), sizeof(tree_root_hash));\n blob.append(tools::get_varint_data(b.tx_hashes.size()+1));\n return blob;\n }\n //---------------------------------------------------------------\n bool calculate_block_hash(const block& b, crypto::hash& res)\n {\n // EXCEPTION FOR BLOCK 202612\n const std::string correct_blob_hash_202612 = \"3a8a2b3a29b50fc86ff73dd087ea43c6f0d6b8f936c849194d5c84c737903966\";\n const std::string existing_block_id_202612 = \"bbd604d2ba11ba27935e006ed39c9bfdd99b76bf4a50654bc1e1e61217962698\";\n crypto::hash block_blob_hash = get_blob_hash(block_to_blob(b));\n\n if (string_tools::pod_to_hex(block_blob_hash) == correct_blob_hash_202612)\n {\n string_tools::hex_to_pod(existing_block_id_202612, res);\n return true;\n }\n bool hash_result = get_object_hash(get_block_hashing_blob(b), res);\n\n if (hash_result)\n {\n // make sure that we aren't looking at a block with the 202612 block id but not the correct blobdata\n if (string_tools::pod_to_hex(res) == existing_block_id_202612)\n {\n LOG_ERROR(\"Block with block id for 202612 but incorrect block blob hash found!\");\n res = null_hash;\n return false;\n }\n }\n return hash_result;\n }\n //---------------------------------------------------------------\n bool get_block_hash(const block& b, crypto::hash& res)\n {\n if (b.is_hash_valid())\n {\n#ifdef ENABLE_HASH_CASH_INTEGRITY_CHECK\n CHECK_AND_ASSERT_THROW_MES(!calculate_block_hash(b, res) || b.hash == res, \"block hash cash integrity failure\");\n#endif\n res = b.hash;\n ++block_hashes_cached_count;\n return true;\n }\n ++block_hashes_calculated_count;\n bool ret = calculate_block_hash(b, res);\n if (!ret)\n return false;\n b.hash = res;\n b.set_hash_valid(true);\n return true;\n }\n //---------------------------------------------------------------\n crypto::hash get_block_hash(const block& b)\n {\n crypto::hash p = null_hash;\n get_block_hash(b, p);\n return p;\n }\n //---------------------------------------------------------------\n bool get_block_longhash(const block& b, crypto::hash& res, uint64_t height)\n {\n // block 202612 bug workaround\n const std::string longhash_202612 = \"84f64766475d51837ac9efbef1926486e58563c95a19fef4aec3254f03000000\";\n if (height == 202612)\n {\n string_tools::hex_to_pod(longhash_202612, res);\n return true;\n }\n blobdata bd = get_block_hashing_blob(b);\n const int cn_variant = b.major_version >= 7 ? b.major_version - 6 : 0;\n crypto::cn_slow_hash(bd.data(), bd.size(), res, cn_variant);\n return true;\n }\n //---------------------------------------------------------------\n std::vector relative_output_offsets_to_absolute(const std::vector& off)\n {\n std::vector res = off;\n for(size_t i = 1; i < res.size(); i++)\n res[i] += res[i-1];\n return res;\n }\n //---------------------------------------------------------------\n std::vector absolute_output_offsets_to_relative(const std::vector& off)\n {\n std::vector res = off;\n if(!off.size())\n return res;\n std::sort(res.begin(), res.end());//just to be sure, actually it is already should be sorted\n for(size_t i = res.size()-1; i != 0; i--)\n res[i] -= res[i-1];\n\n return res;\n }\n //---------------------------------------------------------------\n crypto::hash get_block_longhash(const block& b, uint64_t height)\n {\n crypto::hash p = null_hash;\n get_block_longhash(b, p, height);\n return p;\n }\n //---------------------------------------------------------------\n bool parse_and_validate_block_from_blob(const blobdata& b_blob, block& b)\n {\n std::stringstream ss;\n ss << b_blob;\n binary_archive ba(ss);\n bool r = ::serialization::serialize(ba, b);\n CHECK_AND_ASSERT_MES(r, false, \"Failed to parse block from blob\");\n b.invalidate_hashes();\n b.miner_tx.invalidate_hashes();\n return true;\n }\n //---------------------------------------------------------------\n blobdata block_to_blob(const block& b)\n {\n return t_serializable_object_to_blob(b);\n }\n //---------------------------------------------------------------\n bool block_to_blob(const block& b, blobdata& b_blob)\n {\n return t_serializable_object_to_blob(b, b_blob);\n }\n //---------------------------------------------------------------\n blobdata tx_to_blob(const transaction& tx)\n {\n return t_serializable_object_to_blob(tx);\n }\n //---------------------------------------------------------------\n bool tx_to_blob(const transaction& tx, blobdata& b_blob)\n {\n return t_serializable_object_to_blob(tx, b_blob);\n }\n //---------------------------------------------------------------\n void get_tx_tree_hash(const std::vector& tx_hashes, crypto::hash& h)\n {\n tree_hash(tx_hashes.data(), tx_hashes.size(), h);\n }\n //---------------------------------------------------------------\n crypto::hash get_tx_tree_hash(const std::vector& tx_hashes)\n {\n crypto::hash h = null_hash;\n get_tx_tree_hash(tx_hashes, h);\n return h;\n }\n //---------------------------------------------------------------\n crypto::hash get_tx_tree_hash(const block& b)\n {\n std::vector txs_ids;\n crypto::hash h = null_hash;\n size_t bl_sz = 0;\n get_transaction_hash(b.miner_tx, h, bl_sz);\n txs_ids.push_back(h);\n for(auto& th: b.tx_hashes)\n txs_ids.push_back(th);\n return get_tx_tree_hash(txs_ids);\n }\n //---------------------------------------------------------------\n bool is_valid_decomposed_amount(uint64_t amount)\n {\n const uint64_t *begin = valid_decomposed_outputs;\n const uint64_t *end = valid_decomposed_outputs + sizeof(valid_decomposed_outputs) / sizeof(valid_decomposed_outputs[0]);\n return std::binary_search(begin, end, amount);\n }\n //---------------------------------------------------------------\n void get_hash_stats(uint64_t &tx_hashes_calculated, uint64_t &tx_hashes_cached, uint64_t &block_hashes_calculated, uint64_t & block_hashes_cached)\n {\n tx_hashes_calculated = tx_hashes_calculated_count;\n tx_hashes_cached = tx_hashes_cached_count;\n block_hashes_calculated = block_hashes_calculated_count;\n block_hashes_cached = block_hashes_cached_count;\n }\n //---------------------------------------------------------------\n crypto::secret_key encrypt_key(crypto::secret_key key, const epee::wipeable_string &passphrase)\n {\n crypto::hash hash;\n crypto::cn_slow_hash(passphrase.data(), passphrase.size(), hash);\n sc_add((unsigned char*)key.data, (const unsigned char*)key.data, (const unsigned char*)hash.data);\n return key;\n }\n //---------------------------------------------------------------\n crypto::secret_key decrypt_key(crypto::secret_key key, const epee::wipeable_string &passphrase)\n {\n crypto::hash hash;\n crypto::cn_slow_hash(passphrase.data(), passphrase.size(), hash);\n sc_sub((unsigned char*)key.data, (const unsigned char*)key.data, (const unsigned char*)hash.data);\n return key;\n }\n}\n", "meta": {"hexsha": "4f4546c037d9f765813c2621ffb904dee2799836", "size": 50719, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cryptonote_basic/cryptonote_format_utils.cpp", "max_stars_repo_name": "enrocoin/enro", "max_stars_repo_head_hexsha": "c58592b6bda2ebd7495fb9438a7ac270746b58b4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, 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YES\n2. NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.411110869232168, "lm_q1q2_score": 0.21358086225236994}} {"text": "/*\n Copyright 2012 Google Inc. All Rights Reserved.\n Author: blakely@google.com (Tim Blakely)\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n */\n\n#include \n#include \n\n#include \"base_types.h\"\n#include \"boost/random/uniform_01.hpp\"\n#include \"network/objects/connections/ampa.h\"\n#include \"network/objects/neuron.h\"\n#include \"protos/basics.pb.h\"\n#include \"protos/neuron.pb.h\"\n\nnamespace google {\nnamespace protobuf {\nclass Message;\n} // namespace protobuf\n} // namespace google\n\nnamespace bigbrain {\n\nusing boost::random::uniform_01;\nusing std::string;\n\nSynapseAmpa::SynapseAmpa()\n : params_(NULL) {\n}\n\nSynapseAmpa::~SynapseAmpa() {\n}\n\nbool SynapseAmpa::CalculateDerivatives(\n const float& time, const google::protobuf::Message* input_state,\n google::protobuf::Message* output_state) {\n return false;\n}\n\nbool SynapseAmpa::UpdateState(const float& time, const float& dt) {\n double decay;\n double newdGdt;\n double noiseScalar = 1.0;\n\n AccumulateDelayedInput(time);\n\n // note: ignoring Synthesis' noise cutofftable here\n\n uniform_01<> random;\n\n if (params_->noise_rate() > 0) {\n if (params_->noise_mode() == \"Poisson\") {\n if (random(rand_num_gen_) <= params_->noise_rate() * dt) {\n double input = state_.input()\n + noiseScalar * params_->noise_amplitude();\n state_.set_input(input);\n }\n }\n }\n\n decay = expx(-dt / params_->time_constant_1());\n\n newdGdt = state_.dg_dt() * decay\n + state_.input() / dt * params_->time_constant_1() * (1.0 - decay);\n decay = expx(-dt / params_->time_constant_2());\n double new_g = state_.g() * decay\n + state_.dg_dt() * params_->time_constant_2() * (1.0 - decay);\n double new_g_lower = state_.g() / params_->peak_response()\n * params_->g_peak();\n\n state_.set_g(new_g);\n state_.set_dg_dt(newdGdt);\n state_.set_g_lower(new_g_lower);\n state_.set_input(0);\n\n return true;\n}\n\nvoid SynapseAmpa::add_delayed_input(const double& value) {\n state_.set_input(state_.input() + value);\n}\n\nbool SynapseAmpa::UpdateConnection(const float& time, const float& dt) {\n // The following functions from Synthesis are not implemented:\n // > Short term plasticity\n // > Probabilistic modeling\n // > Connection strength (hardcoded to \"5\" to be in line with Synthesis)\n // > Mini spiking\n\n double delay = 0;\n\n uniform_01<> random;\n\n if (random(rand_num_gen_) <= params_->probability()) {\n DelayInput(time, state_.input() + 5);\n }\n\n return true;\n}\n\ndouble SynapseAmpa::get_current() {\n // The following functions from Synthesis are not implemented:\n // > Conductance tables\n double mp = get_parent()->get_membrane_potential();\n state_.set_output(\n (params_->reverse_potential() - get_parent()->get_membrane_potential())\n * state_.g_lower());\n return state_.output();\n}\n\nvoid SynapseAmpa::set_params(google::protobuf::Message* params) {\n // The following functions from Synthesis are not implemented:\n // > Conductance tables\n\n // Need to downcast. Kludgy, but extendable\n params_ = static_cast(params);\n\n if (params_->time_constant_1() == params_->time_constant_2()) {\n params_->set_peak_response(params_->time_constant_1() / 2.718281828);\n } else {\n double tPeak = params_->time_constant_1() * params_->time_constant_2()\n / (params_->time_constant_1() - params_->time_constant_2())\n * log(params_->time_constant_1() / params_->time_constant_2());\n double peak_response = params_->time_constant_1()\n * params_->time_constant_2()\n / (params_->time_constant_1() - params_->time_constant_2())\n * (expx(-tPeak / params_->time_constant_1())\n - expx(-tPeak / params_->time_constant_2()));\n params_->set_peak_response(peak_response);\n }\n\n get_common_params()->set_diff_eq_solver(NONE);\n}\n\ngoogle::protobuf::Message* SynapseAmpa::get_state() {\n return &state_;\n}\n\nstring SynapseAmpa::get_description() {\n string description = \"AMPA connection\";\n return description;\n}\n\ngoogle::protobuf::Message* SynapseAmpa::get_unique_params() {\n return params_;\n}\n\n} // namespace bigbrain\n", "meta": {"hexsha": "0c9f982d7793f46190a6116f6f80d5cc3898e4d5", "size": 4585, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/network/objects/connections/ampa.cc", "max_stars_repo_name": "slhill/bigbrain", "max_stars_repo_head_hexsha": "69dcf775c450c867c8c65c1b05e06cb6d3a7e5d4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/network/objects/connections/ampa.cc", "max_issues_repo_name": "slhill/bigbrain", "max_issues_repo_head_hexsha": "69dcf775c450c867c8c65c1b05e06cb6d3a7e5d4", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/network/objects/connections/ampa.cc", "max_forks_repo_name": "slhill/bigbrain", "max_forks_repo_head_hexsha": "69dcf775c450c867c8c65c1b05e06cb6d3a7e5d4", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.4782608696, "max_line_length": 77, "alphanum_fraction": 0.7011995638, "num_tokens": 1206, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6001883735630721, "lm_q2_score": 0.35577488668296436, "lm_q1q2_score": 0.21353195059283467}} {"text": "#include \"generalized_eigensolver.h\"\n\n#include \n#include \n\n#include \"gefast/math/cayley.h\"\n#include \"gefast/util/macros.h\"\n\nnamespace gefast {\n\nnamespace {\n\nEigen::Matrix4d ComposeG(const Matrix38dRowMajor &ray_directions_matrix1,\n const Matrix38dRowMajor &ray_directions_matrix2,\n const Matrix38dRowMajor &ray_centers_matrix1,\n const Matrix38dRowMajor &rcm2_cross_rdm2,\n const cayley_t &cayley) {\n rotation_t rotation = CayleyToRotationMatrixUnscaled(cayley);\n\n Matrix38dRowMajor rot_ray_directions_matrix2 =\n rotation * ray_directions_matrix2;\n Matrix38dRowMajor rot_rcm2_cross_rdm2 = rotation * rcm2_cross_rdm2;\n\n Eigen::Matrix g;\n#ifdef GEFAST_INTRINSICS_AVAILABLE\n for (auto i = 0; i < 2; ++i) {\n __m256d _a_1 = _mm256_load_pd(ray_centers_matrix1.data() + i * 4);\n __m256d _a_2 = _mm256_load_pd(ray_centers_matrix1.data() + 8 + i * 4);\n __m256d _a_3 = _mm256_load_pd(ray_centers_matrix1.data() + 16 + i * 4);\n\n __m256d _b_1 = _mm256_load_pd(rot_ray_directions_matrix2.data() + i * 4);\n __m256d _b_2 =\n _mm256_load_pd(rot_ray_directions_matrix2.data() + 8 + i * 4);\n __m256d _b_3 =\n _mm256_load_pd(rot_ray_directions_matrix2.data() + 16 + i * 4);\n\n __m256d _rdm1_1 = _mm256_load_pd(ray_directions_matrix1.data() + i * 4);\n __m256d _rdm1_2 = _mm256_load_pd(ray_directions_matrix1.data() + 8 + i * 4);\n __m256d _rdm1_3 =\n _mm256_load_pd(ray_directions_matrix1.data() + 16 + i * 4);\n\n // ray_centers_matrix1 cross rot_ray_directions_matrix2\n __m256d _res1 = _mm256_mul_pd(_a_3, _b_2);\n _res1 = _mm256_fmsub_pd(_a_2, _b_3, _res1);\n\n __m256d _res2 = _mm256_mul_pd(_a_1, _b_3);\n _res2 = _mm256_fmsub_pd(_a_3, _b_1, _res2);\n\n __m256d _res3 = _mm256_mul_pd(_a_2, _b_1);\n _res3 = _mm256_fmsub_pd(_a_1, _b_2, _res3);\n\n // rdm1 dot rcm1_cross_rot_rdm2\n\n __m256d _rdm1_dot_rcm1_cross_rot_rdm2 = _mm256_mul_pd(_rdm1_1, _res1);\n _rdm1_dot_rcm1_cross_rot_rdm2 =\n _mm256_fmadd_pd(_rdm1_2, _res2, _rdm1_dot_rcm1_cross_rot_rdm2);\n _rdm1_dot_rcm1_cross_rot_rdm2 =\n _mm256_fmadd_pd(_rdm1_3, _res3, _rdm1_dot_rcm1_cross_rot_rdm2);\n\n // rdm1 cross rot_rdm2\n\n _res1 = _mm256_mul_pd(_rdm1_3, _b_2);\n _res1 = _mm256_fmsub_pd(_rdm1_2, _b_3, _res1);\n\n _res2 = _mm256_mul_pd(_rdm1_1, _b_3);\n _res2 = _mm256_fmsub_pd(_rdm1_3, _b_1, _res2);\n\n _res3 = _mm256_mul_pd(_rdm1_2, _b_1);\n _res3 = _mm256_fmsub_pd(_rdm1_1, _b_2, _res3);\n\n _mm256_store_pd(g.data() + i * 4, _res1);\n _mm256_store_pd(g.data() + 8 + i * 4, _res2);\n _mm256_store_pd(g.data() + 16 + i * 4, _res3);\n\n // rdm1 dot rot_rcm2_cross_rdm2\n\n _a_1 = _mm256_load_pd(rot_rcm2_cross_rdm2.data() + i * 4);\n _a_2 = _mm256_load_pd(rot_rcm2_cross_rdm2.data() + 8 + i * 4);\n _a_3 = _mm256_load_pd(rot_rcm2_cross_rdm2.data() + 16 + i * 4);\n\n __m256d _rdm1_dot_rot_rcm2_cross_rdm2 = _mm256_mul_pd(_rdm1_1, _a_1);\n _rdm1_dot_rot_rcm2_cross_rdm2 =\n _mm256_fmadd_pd(_rdm1_2, _a_2, _rdm1_dot_rot_rcm2_cross_rdm2);\n _rdm1_dot_rot_rcm2_cross_rdm2 =\n _mm256_fmadd_pd(_rdm1_3, _a_3, _rdm1_dot_rot_rcm2_cross_rdm2);\n\n _mm256_store_pd(g.data() + 24 + i * 4,\n _mm256_sub_pd(_rdm1_dot_rcm1_cross_rot_rdm2,\n _rdm1_dot_rot_rcm2_cross_rdm2));\n }\n#else\n for (auto i = 0; i != ray_directions_matrix1.cols(); ++i) {\n g.block<3, 1>(0, i) =\n ray_directions_matrix1.col(i).cross(rot_ray_directions_matrix2.col(i));\n g(3, i) =\n ray_directions_matrix1.col(i).transpose() *\n (ray_centers_matrix1.col(i).cross(rot_ray_directions_matrix2.col(i)) -\n rot_rcm2_cross_rdm2.col(i));\n }\n#endif\n return g * g.transpose();\n}\n\neigenvalues_t GetEigenvalues(const Matrix38dRowMajor &ray_directions_matrix1,\n const Matrix38dRowMajor &ray_directions_matrix2,\n const Matrix38dRowMajor &ray_centers_matrix1,\n const Matrix38dRowMajor &rcm2_cross_rdm2,\n const cayley_t &cayley) {\n Eigen::Matrix4d G = ComposeG(ray_directions_matrix1, ray_directions_matrix2,\n ray_centers_matrix1, rcm2_cross_rdm2, cayley);\n\n // now compute the roots in closed-form\n const auto G33 = G(3, 3);\n const auto G22 = G(2, 2);\n const auto G11 = G(1, 1);\n const auto G00 = G(0, 0);\n const auto G12 = G(1, 2);\n const auto G13 = G(1, 3);\n const auto G23 = G(2, 3);\n const auto G01 = G(0, 1);\n const auto G02 = G(0, 2);\n const auto G03 = G(0, 3);\n const auto G11_plus_G22 = G11 + G22;\n const auto G22_plus_G33 = G22 + G33;\n const auto G00_plus_G11 = G00 + G11;\n const auto G01_2 = G01 * G01;\n const auto G02_2 = G02 * G02;\n const auto G03_2 = G03 * G03;\n const auto G12_2 = G12 * G12;\n const auto G13_2 = G13 * G13;\n const auto G23_2 = G23 * G23;\n const auto G12_2_plus_G13_2_plus_G23_3 = G12_2 + G13_2 + G23_2;\n\n const double B = -(G00_plus_G11 + G22_plus_G33);\n const double C = -G12_2_plus_G13_2_plus_G23_3 + G33 * (G00_plus_G11 + G22) +\n G11 * G22 - (G03_2 + G02_2 + G01_2) + G00 * (G11_plus_G22);\n const double D =\n G13_2 * G22 -\n 2.0 * (G12 * (G13 * G23 + G01 * G02) + G03 * (G02 * G23 + G01 * G13)) +\n G11 * G23_2 - (G22 * G33 * (G00_plus_G11) + G00 * G11 * (G22_plus_G33)) +\n G03_2 * G11_plus_G22 + G02_2 * (G33 + G11) + G01_2 * G22_plus_G33 +\n G00 * (G12_2_plus_G13_2_plus_G23_3) + G12_2 * G33;\n\n const double E = G.determinant();\n\n double B_pw2 = B * B;\n double B_pw3 = B_pw2 * B;\n double B_pw4 = B_pw3 * B;\n double alpha = -0.375 * B_pw2 + C;\n double beta = B_pw3 / 8.0 - B * C / 2.0 + D;\n double gamma = -0.01171875 * B_pw4 + B_pw2 * C / 16.0 - B * D / 4.0 + E;\n double alpha_pw2 = alpha * alpha;\n double alpha_pw3 = alpha_pw2 * alpha;\n double p = -alpha_pw2 / 12.0 - gamma;\n double q = -alpha_pw3 / 108.0 + alpha * gamma / 3.0 - beta * beta / 8.0;\n\n double theta2 = -p / 3.0;\n double theta1 = sqrt(theta2) *\n cos((1.0 / 3.0) * acos((-q / 2.0) / sqrt(-p * p * p / 27.0)));\n double y = -(5.0 / 6.0) * alpha -\n ((1.0 / 3.0) * p * theta1 - theta1 * theta2) / theta2;\n double w = sqrt(alpha + 2.0 * y);\n\n // we currently disable the computation of all other roots, they are not used\n double temp1 = -B / 4.0 - 0.5 * w;\n double temp2 = 0.5 * sqrt(-3.0 * alpha - 2.0 * y + 2.0 * beta / w);\n\n eigenvalues_t roots;\n roots[0] = temp1 + temp2;\n roots[1] = temp1 - temp2;\n return roots;\n}\n\ndouble GetCost(const Matrix38dRowMajor &ray_directions_matrix1,\n const Matrix38dRowMajor &ray_directions_matrix2,\n const Matrix38dRowMajor &ray_centers_matrix1,\n const Matrix38dRowMajor &rcm2_cross_rdm2, const cayley_t &cayley,\n bool use_smallest_ev) {\n Eigen::Vector2d roots =\n GetEigenvalues(ray_directions_matrix1, ray_directions_matrix2,\n ray_centers_matrix1, rcm2_cross_rdm2, cayley);\n\n if (use_smallest_ev) return roots[1];\n return roots[0];\n}\n\njacobian_t GetJacobian(const Matrix38dRowMajor &ray_directions_matrix1,\n const Matrix38dRowMajor &ray_directions_matrix2,\n const Matrix38dRowMajor &ray_centers_matrix1,\n const Matrix38dRowMajor &rcm2_cross_rdm2,\n const cayley_t &cayley, double current_eigenvalue,\n bool use_smallest_ev) {\n jacobian_t jacobian;\n const double kEpsilon = 0.00000001;\n\n for (auto j = 0; j != 3; ++j) {\n cayley_t cayley_j = cayley;\n cayley_j(j) += kEpsilon;\n auto cost_j = GetCost(ray_directions_matrix1, ray_directions_matrix2,\n ray_centers_matrix1, rcm2_cross_rdm2, cayley_j,\n use_smallest_ev);\n jacobian(j) =\n cost_j - current_eigenvalue; // division by eps can be ommited\n }\n return jacobian;\n}\n\nvoid FindModel(const Matrix38dRowMajor &ray_directions_matrix1,\n const Matrix38dRowMajor &ray_directions_matrix2,\n const Matrix38dRowMajor &ray_centers_matrix1,\n const Matrix38dRowMajor &rcm2_cross_rdm2,\n const cayley_t &starting_point, RelativePose &model) {\n double lambda = 0.017;\n double lambda_modifier = 2.0;\n const double kMaxLambda = 0.07;\n const double kMinLambda = 0.00001;\n const int kMaxIterations = 11;\n const bool kDisableIncrements = false;\n\n double disturbance_amplitude = 0.3;\n bool found = false;\n int random_trials = 0;\n const int kMaxRandomTrials = 5;\n cayley_t cayley;\n\n while (!found && random_trials < kMaxRandomTrials) {\n int iterations = 0;\n if (random_trials > 2) disturbance_amplitude = 0.6;\n cayley.noalias() = starting_point;\n if (random_trials != 0)\n cayley.noalias() += disturbance_amplitude * Eigen::Vector3d::Random();\n\n double smallestEV =\n GetCost(ray_directions_matrix1, ray_directions_matrix2,\n ray_centers_matrix1, rcm2_cross_rdm2, cayley, true);\n\n jacobian_t jacobian = GetJacobian(\n ray_directions_matrix1, ray_directions_matrix2, ray_centers_matrix1,\n rcm2_cross_rdm2, cayley, smallestEV, true);\n jacobian.normalize();\n Eigen::Matrix3d inverse_hessian = Eigen::Matrix3d::Identity();\n\n while (iterations < kMaxIterations) {\n Eigen::Vector3d searchDirection = -inverse_hessian * jacobian;\n searchDirection.normalize();\n\n if (jacobian.dot(searchDirection) > 0) {\n inverse_hessian = Eigen::Matrix3d::Identity();\n searchDirection = -jacobian;\n }\n\n lambda = 0.017;\n cayley_t next_cayley = cayley + lambda * searchDirection;\n\n double nextEV =\n GetCost(ray_directions_matrix1, ray_directions_matrix2,\n ray_centers_matrix1, rcm2_cross_rdm2, next_cayley, true);\n\n if (iterations == 0 || !kDisableIncrements) {\n while (nextEV < smallestEV) {\n smallestEV = nextEV;\n if (lambda * lambda_modifier > kMaxLambda) break;\n lambda *= lambda_modifier;\n next_cayley.noalias() = cayley + lambda * searchDirection;\n nextEV =\n GetCost(ray_directions_matrix1, ray_directions_matrix2,\n ray_centers_matrix1, rcm2_cross_rdm2, next_cayley, true);\n }\n }\n\n while (nextEV > smallestEV) {\n lambda /= lambda_modifier;\n if (lambda < kMinLambda) break;\n next_cayley = cayley + lambda * searchDirection;\n nextEV =\n GetCost(ray_directions_matrix1, ray_directions_matrix2,\n ray_centers_matrix1, rcm2_cross_rdm2, next_cayley, true);\n }\n\n jacobian_t next_jacobian = GetJacobian(\n ray_directions_matrix1, ray_directions_matrix2, ray_centers_matrix1,\n rcm2_cross_rdm2, next_cayley, nextEV, true);\n next_jacobian.normalize();\n\n Eigen::Vector3d s = lambda * searchDirection;\n Eigen::Vector3d y = next_jacobian - jacobian;\n double rho = 1.0 / (y.dot(s));\n\n inverse_hessian =\n inverse_hessian -\n rho * (s * (y.transpose() * inverse_hessian) +\n (inverse_hessian * y) * s.transpose()) +\n rho * (rho * (y).dot(inverse_hessian * y) + 1) * (s * s.transpose());\n\n cayley = next_cayley;\n smallestEV = nextEV;\n jacobian = next_jacobian;\n\n if (lambda < kMinLambda) break;\n ++iterations;\n }\n\n if (cayley.norm() < 0.01) {\n // we are close to the origin, test the EV 2\n double ev2 = GetCost(ray_directions_matrix1, ray_directions_matrix2,\n ray_centers_matrix1, rcm2_cross_rdm2, cayley, false);\n if (ev2 > 0.001)\n ++random_trials;\n else\n found = true;\n } else\n found = true;\n }\n\n Eigen::Matrix4d G = ComposeG(ray_directions_matrix1, ray_directions_matrix2,\n ray_centers_matrix1, rcm2_cross_rdm2, cayley);\n\n Eigen::EigenSolver eigensolver_G(G, true);\n Eigen::Vector4d D = eigensolver_G.eigenvalues().real();\n Eigen::Matrix4d V = eigensolver_G.eigenvectors().real();\n\n auto min_eigenvalue_idx = 0;\n D.minCoeff(&min_eigenvalue_idx);\n\n model.rotation = CayleyToRotationMatrix(cayley);\n model.translation = V.col(min_eigenvalue_idx).hnormalized();\n}\n\n} // namespace\n\nvoid SolveGE(std::vector &ray_centers1,\n std::vector &ray_directions1,\n std::vector &ray_centers2,\n std::vector &ray_directions2,\n RelativePose &output) {\n const int kCorrespondencesNumber = static_cast(ray_centers1.size());\n GEFAST_ASSERT(kCorrespondencesNumber == 8,\n \"the solver only works with 8 correspondences\");\n GEFAST_ASSERT(ray_centers2.size() == kCorrespondencesNumber,\n \"the number of second camera ray centers must be 8\");\n GEFAST_ASSERT(ray_directions1.size() == kCorrespondencesNumber,\n \"the number of first camera ray directions must be 8\");\n GEFAST_ASSERT(ray_directions2.size() == kCorrespondencesNumber,\n \"the number of second camera ray directions must be 8\");\n\n Matrix38dRowMajor ray_directions_matrix1 = Eigen::Map(\n reinterpret_cast(ray_directions1.data()));\n Matrix38dRowMajor ray_directions_matrix2 = Eigen::Map(\n reinterpret_cast(ray_directions2.data()));\n Matrix38dRowMajor ray_centers_matrix1 = Eigen::Map(\n reinterpret_cast(ray_centers1.data()));\n Matrix38dRowMajor ray_centers_matrix2 = Eigen::Map(\n reinterpret_cast(ray_centers2.data()));\n Matrix38dRowMajor rcm2_cross_rdm2;\n\n Eigen::Vector3d points_center1 = ray_directions_matrix1.rowwise().sum();\n Eigen::Vector3d points_center2 = ray_directions_matrix2.rowwise().sum();\n\n#ifdef GEFAST_INTRINSICS_AVAILABLE\n for (auto i = 0; i < kCorrespondencesNumber / 4; ++i) {\n __m256d _rcm2_1 = _mm256_load_pd(ray_centers_matrix2.data() + i * 4);\n __m256d _rcm2_2 = _mm256_load_pd(ray_centers_matrix2.data() + 8 + i * 4);\n __m256d _rcm2_3 = _mm256_load_pd(ray_centers_matrix2.data() + 16 + i * 4);\n\n __m256d _rdm2_1 = _mm256_load_pd(ray_directions_matrix2.data() + i * 4);\n __m256d _rdm2_2 = _mm256_load_pd(ray_directions_matrix2.data() + 8 + i * 4);\n __m256d _rdm2_3 =\n _mm256_load_pd(ray_directions_matrix2.data() + 16 + i * 4);\n\n __m256d _res1 = _mm256_mul_pd(_rcm2_3, _rdm2_2);\n _res1 = _mm256_fmsub_pd(_rcm2_2, _rdm2_3, _res1);\n\n __m256d _res2 = _mm256_mul_pd(_rcm2_1, _rdm2_3);\n _res2 = _mm256_fmsub_pd(_rcm2_3, _rdm2_1, _res2);\n\n __m256d _res3 = _mm256_mul_pd(_rcm2_2, _rdm2_1);\n _res3 = _mm256_fmsub_pd(_rcm2_1, _rdm2_2, _res3);\n\n _mm256_store_pd(rcm2_cross_rdm2.data() + i * 4, _res1);\n _mm256_store_pd(rcm2_cross_rdm2.data() + 8 + i * 4, _res2);\n _mm256_store_pd(rcm2_cross_rdm2.data() + 16 + i * 4, _res3);\n }\n#else\n for (auto i = 0; i < kCorrespondencesNumber; ++i) {\n rcm2_cross_rdm2.col(i).noalias() =\n ray_centers_matrix2.col(i).cross(ray_directions_matrix2.col(i));\n }\n#endif\n\n points_center1 /= kCorrespondencesNumber;\n points_center2 /= kCorrespondencesNumber;\n\n Eigen::Matrix3d Hcross =\n (ray_directions_matrix2.colwise() - points_center2) *\n (ray_directions_matrix1.colwise() - points_center1).transpose();\n\n // SVD decomposition of matrix Hcross to obtain initial rotation\n Eigen::JacobiSVD svd(\n Hcross, Eigen::ComputeFullU | Eigen::ComputeFullV);\n\n Eigen::Matrix3d V = svd.matrixV();\n Eigen::Matrix3d U = svd.matrixU();\n rotation_t starting_rotation = V * U.transpose();\n\n if (starting_rotation.determinant() < 0) {\n Eigen::Matrix3d V_prime;\n V_prime.col(0) = V.col(0);\n V_prime.col(1) = V.col(1);\n V_prime.col(2) = -V.col(2);\n starting_rotation.noalias() = V_prime * U.transpose();\n }\n\n FindModel(ray_directions_matrix1, ray_directions_matrix2, ray_centers_matrix1,\n rcm2_cross_rdm2, RotationMatrixToCayley(starting_rotation), output);\n}\n\n} // namespace gefast\n", "meta": {"hexsha": "831c41dbe52a82a160315e91acbe890f76780d0c", "size": 16215, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "gefast/solver/generalized_eigensolver.cpp", "max_stars_repo_name": "kirill-ivanov-a/GEFast", "max_stars_repo_head_hexsha": "074369cc79f23290586c4cf1df20552224db4134", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2022-03-26T18:15:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-26T18:15:23.000Z", "max_issues_repo_path": "gefast/solver/generalized_eigensolver.cpp", "max_issues_repo_name": "kirill-ivanov-a/gefast", "max_issues_repo_head_hexsha": "074369cc79f23290586c4cf1df20552224db4134", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2022-03-24T20:38:01.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-26T13:34:00.000Z", "max_forks_repo_path": "gefast/solver/generalized_eigensolver.cpp", "max_forks_repo_name": "kirill-ivanov-a/GEFast", "max_forks_repo_head_hexsha": "074369cc79f23290586c4cf1df20552224db4134", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.7918660287, "max_line_length": 80, "alphanum_fraction": 0.6618563059, "num_tokens": 5278, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.658417500561683, "lm_q2_score": 0.3242353924510608, "lm_q1q2_score": 0.21348225669126383}} {"text": "#include \"kondo.h\"\n#include \"iostream_util.h\"\n#include \n#include \n#include \n#include \n#include \n\nusing boost::property_tree::ptree;\nusing boost::property_tree::read_json;\n//using boost::property_tree::write_json;\n\ntemplate \nstd::vector as_vector(ptree const& pt, ptree::key_type const& key)\n{\n std::vector r;\n for (auto& item : pt.get_child(key))\n r.push_back(item.second.get_value());\n return r;\n}\n\n\nvoid triangular(int argc, char *argv[]) {\n auto engine = fkpm::mk_engine();\n if (engine == nullptr) std::exit(EXIT_FAILURE);\n if (argc != 2) {\n cout << \"Usage: \" << argv[0] << \" \\n\";\n std::exit(EXIT_SUCCESS);\n }\n \n std::string base_dir(argv[1]);\n auto input_name = base_dir + \"/config.toml\";\n std::cout << \"using toml file `\" << input_name << \"`!\\n\";\n toml_ptr g = toml_from_file(input_name);\n \n auto m = SimpleModel::mk_triangular(toml_get(g, \"model.w\"), toml_get(g, \"model.h\"));\n m->J = toml_get(g, \"model.J\");\n m->t1 = toml_get(g, \"model.t1\", 0.0);\n m->t2 = toml_get(g, \"model.t2\", 0.0);\n m->t3 = toml_get(g, \"model.t3\", 0.0);\n m->kT_init = toml_get(g, \"model.kT\");\n m->kT_decay = toml_get(g, \"model.kT_decay\", 0.0);\n m->zeeman = {toml_get(g, \"model.zeeman_x\", 0.0), toml_get(g, \"model.zeeman_y\", 0.0), toml_get(g, \"model.zeeman_z\", 0.0)};\n m->easy_z = toml_get(g, \"model.easy_z\", 0.0);\n m->s1 = toml_get(g, \"model.s1\", 0.0);\n m->s2 = toml_get(g, \"model.s2\", 0.0);\n m->s3 = toml_get(g, \"model.s3\", 0.0);\n \n std::string json_name;\n std::cout << \"Input dumpfile name:\" << std::endl;\n std::cin >> json_name;\n std::cout << json_name << std::endl;\n json_name = base_dir + \"/dump/\" + json_name;\n std::ifstream json_file(json_name);\n if (!json_file.is_open()) {\n cerr << \"Unable to open file `\" << json_name << \"`!\\n\";\n std::exit(EXIT_FAILURE);\n }\n cout << \"Using json file `\" << json_name << \"`!\\n\";\n ptree pt_json; // used for reading the json file\n std::string json_eachline, json_contents;\n while (std::getline(json_file, json_eachline)) {\n json_contents += json_eachline;\n }\n std::istringstream is (json_contents);\n read_json (is, pt_json);\n auto time = pt_json.get (\"time\");\n auto action = pt_json.get (\"action\");\n auto filling = pt_json.get (\"filling\");\n auto mu = pt_json.get (\"mu\");\n auto spin = as_vector(pt_json, \"spin\");\n \n std::cout << \"lattice: \" << toml_get(g, \"model.lattice\") << std::endl;\n std::cout << \"time: \" << time << std::endl;\n std::cout << \"action: \" << action << std::endl;\n std::cout << \"filling: \" << filling << std::endl;\n std::cout << \"mu: \" << mu << std::endl;\n std::cout << \"J: \" << m->J << std::endl;\n std::cout << \"t: \" << m->t1 << std::endl;\n std::cout << \"kT: \" << m->kT() << std::endl;\n \n assert(spin.size() == m->n_sites * 3); // build spin configuration from dump file\n for (int i = 0; i < m->n_sites; i++) {\n m->spin[i] = vec3(spin[3*i],spin[3*i+1],spin[3*i+2]);\n }\n m->set_hamiltonian(m->spin);\n \n int purpose, M, Mq, use_correlated, n_colors, seed;\n std::cout << \"Inut 0/1/2 for calculating sigma_xx/sigma_xy/both: \" << std::endl;\n std::cin >> purpose;\n fkpm::EnergyScale es;\n es = engine->energy_scale(m->H, 0.1);\n std::cout << \"energyscale: [\" << es.lo << \", \" << es.hi << \"]\" << std::endl;\n std::cout << \"Input M:\" << std::endl;\n std::cin >> M;\n std::cout << M << std::endl;\n std::cout << \"Input Mq:\" << std::endl;\n std::cin >> Mq;\n std::cout << Mq << std::endl;\n std::cout << \"Input 0/1 for using uncorrelated/correlated random numbers:\" << std::endl;\n std::cin >> use_correlated;\n std::cout << use_correlated << std::endl;\n std::cout << \"Input n_colors:\" << std::endl;\n std::cin >> n_colors;\n std::cout << n_colors << std::endl;\n std::cout << \"Input seed:\" << std::endl;\n std::cin >> seed;\n std::cout << seed << std::endl;\n \n fkpm::RNG rng(seed);\n if (use_correlated) {\n engine->set_R_correlated(m->groups(n_colors), rng);\n } else {\n engine->set_R_uncorrelated(m->H.n_rows, 2*n_colors, rng);\n }\n \n auto kernel = fkpm::jackson_kernel(M);\n engine->set_H(m->H, es);\n \n auto jx = m->electric_current_operator(m->spin, {1,0,0});\n auto jy = m->electric_current_operator(m->spin, {0,1,0});\n \n json_file.close();\n \n auto mu_dos = engine->moments(M);\n auto gamma = fkpm::moment_transform(mu_dos, Mq);\n fkpm::Vec mu_list, rho;\n fkpm::density_function(gamma, es, mu_list, rho);\n for (int i = 0; i < rho.size(); i++) {\n rho[i]/=m->n_sites;\n }\n \n std::cout << \"calculating moments2... \" << std::flush;\n fkpm::timer[0].reset();\n Vec> mu_xx, mu_xy;\n if (purpose == 0) {\n mu_xx = engine->moments2_v1(M, jx, jx, 10, 16);\n mu_xy = mu_xx; // invalid\n } else if (purpose == 1){\n mu_xy = engine->moments2_v1(M, jx, jy, 10, 16);\n mu_xx = mu_xy; // invalid\n } else {\n mu_xx = engine->moments2_v1(M, jx, jx, 10, 16);\n mu_xy = engine->moments2_v1(M, jx, jy, 10, 16);\n }\n cout << \" done. \" << fkpm::timer[0].measure() << \"s.\\n\";\n \n cout << \"calculating dc conductivities... \" << std::flush;\n std::ofstream fout2(\"full_time\"+std::to_string(time)+\"_M\"+std::to_string(M)+\"_corr\"+std::to_string(use_correlated)+\n \"_color\"+std::to_string(n_colors)+\"_seed\"+std::to_string(seed)+\".dat\", std::ios::out);\n fout2 << std::scientific << std::right;\n fout2 << std::setw(20) << \"#(1)\" << std::setw(20) << \"(2)\" << std::setw(20) << \"(3)\"\n << std::setw(20) << \"(4)\" << std::setw(20) << \"(5)\" << std::setw(20) << \"(6)\" << std::endl;\n fout2 << std::setw(20) << \"M\" << std::setw(20) << \"kT\" << std::setw(20) << \"mu\"\n << std::setw(20) << \"rho\" << std::setw(20) << \"sigma_xx\" << std::setw(20) << \"sigma_xy\" << std::endl;\n int interval = std::max(Mq/400,5);\n for (int i = 0; i < Mq; i+=interval) {\n auto cmn = electrical_conductivity_coefficients(M, Mq, m->kT(), mu_list[i], 0.0, es, kernel);\n auto sigma_xx = std::real(fkpm::moment_product(cmn, mu_xx));\n auto sigma_xy = std::real(fkpm::moment_product(cmn, mu_xy));\n fout2 << std::setw(20) << M << std::setw(20) << m->kT() << std::setw(20) << mu_list[i]\n << std::setw(20) << rho[i] << std::setw(20) << sigma_xx << std::setw(20) << sigma_xy << std::endl;\n }\n fout2.close();\n cout << \" done. \" << fkpm::timer[0].measure() << \"s.\\n\";\n \n std::ifstream fin0(\"result_time\"+std::to_string(time)+\".dat\");\n if (! fin0.good()) {\n fin0.close();\n std::ofstream fout3(\"result_time\"+std::to_string(time)+\".dat\", std::ios::out | std::ios::app );\n fout3 << std::setw(20) << \"#(1)\" << std::setw(20) << \"(2)\" << std::setw(20) << \"(3)\"\n << std::setw(20) << \"(4)\" << std::setw(20) << \"(5)\" << std::setw(20) << \"(6)\"\n << std::setw(20) << \"(7)\" << std::setw(20) << \"(8)\" << std::endl;\n fout3 << std::setw(20) << \"M\" << std::setw(20) << \"correlated\" << std::setw(20) << \"colors\"\n << std::setw(20) << \"seed\" << std::setw(20) << \"F\" << std::setw(20) << \"mu\"\n << std::setw(20) << \"sigma_xx\" << std::setw(20) << \"sigma_xy\" << std::endl;\n fout3.close();\n }\n std::ofstream fout3(\"result_time\"+std::to_string(time)+\".dat\", std::ios::out | std::ios::app );\n fout3 << std::scientific << std::right;\n auto cmn = electrical_conductivity_coefficients(M, Mq, m->kT(), mu, 0.0, es, kernel);\n\n fout3 << std::setw(20) << M << std::setw(20) << use_correlated << std::setw(20) << n_colors\n << std::setw(20) << seed\n << std::setw(20) << fkpm::electronic_energy(gamma, es, m->kT(), filling, mu)/m->n_sites\n << std::setw(20) << mu\n << std::setw(20) << std::real(fkpm::moment_product(cmn, mu_xx))\n << std::setw(20) << std::real(fkpm::moment_product(cmn, mu_xy))\n << std::endl;\n fout3.close();\n std::cout << std::endl;\n std::remove(\"dump_device0.dat\");\n std::remove(\"dump_device1.dat\");\n}\n\n\n\nint main(int argc, char *argv[]) {\n triangular(argc, argv);\n}\n", "meta": {"hexsha": "9d582306b0ab3927f6330d737e6ab91100565b5c", "size": 8546, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/extra/conductivity.cpp", "max_stars_repo_name": "wztzjhn/Kondo", "max_stars_repo_head_hexsha": "aced2a7c9ec7be07f4de26fdf72d24bbbb82a005", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2019-03-12T01:22:45.000Z", "max_stars_repo_stars_event_max_datetime": "2019-03-12T03:20:05.000Z", "max_issues_repo_path": "src/extra/conductivity.cpp", "max_issues_repo_name": "wztzjhn/Kondo", "max_issues_repo_head_hexsha": "aced2a7c9ec7be07f4de26fdf72d24bbbb82a005", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/extra/conductivity.cpp", "max_forks_repo_name": "wztzjhn/Kondo", "max_forks_repo_head_hexsha": "aced2a7c9ec7be07f4de26fdf72d24bbbb82a005", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2019-03-12T01:22:45.000Z", "max_forks_repo_forks_event_max_datetime": "2019-03-12T02:47:18.000Z", "avg_line_length": 42.5174129353, "max_line_length": 127, "alphanum_fraction": 0.5483267026, "num_tokens": 2803, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5544704649604274, "lm_q2_score": 0.3849121444839335, "lm_q1q2_score": 0.21342241572092183}} {"text": "// --------------------------------------------------------------------------\n// OpenMS -- Open-Source Mass Spectrometry\n// --------------------------------------------------------------------------\n// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,\n// ETH Zurich, and Freie Universitaet Berlin 2002-2017.\n//\n// This software is released under a three-clause BSD license:\n// * Redistributions of source code must retain the above copyright\n// notice, this list of conditions and the following disclaimer.\n// * Redistributions in binary form must reproduce the above copyright\n// notice, this list of conditions and the following disclaimer in the\n// documentation and/or other materials provided with the distribution.\n// * Neither the name of any author or any participating institution\n// may be used to endorse or promote products derived from this software\n// without specific prior written permission.\n// For a full list of authors, refer to the file AUTHORS.\n// --------------------------------------------------------------------------\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\n// ARE DISCLAIMED. IN NO EVENT SHALL ANY OF THE AUTHORS OR THE CONTRIBUTING\n// INSTITUTIONS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,\n// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,\n// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;\n// OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,\n// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR\n// OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF\n// ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n//\n// --------------------------------------------------------------------------\n// $Maintainer: Timo Sachsenberg $\n// $Authors: Clemens Groepl, Chris Bielow $\n// --------------------------------------------------------------------------\n\n#include \n#include \n#include \n#include \n\nnamespace OpenMS\n{\n IsotopeModel::IsotopeModel() :\n InterpolationModel(),\n charge_(0),\n monoisotopic_mz_(0.0)\n {\n setName(getProductName());\n\n defaults_.setValue(\"averagines:C\", 0.04443989f, \"Number of C atoms per Dalton of mass.\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"averagines:H\", 0.06981572f, \"Number of H atoms per Dalton of mass.\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"averagines:N\", 0.01221773f, \"Number of N atoms per Dalton of mass.\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"averagines:O\", 0.01329399f, \"Number of O atoms per Dalton of mass.\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"averagines:S\", 0.00037525f, \"Number of S atoms per Dalton of mass.\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"isotope:trim_right_cutoff\", 0.001, \"Cutoff in averagine distribution, trailing isotopes below this relative intensity are not considered.\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"isotope:maximum\", 100, \"Maximum isotopic rank to be considered.\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"isotope:distance\", 1.000495, \"Distance between consecutive isotopic peaks.\", ListUtils::create(\"advanced\"));\n\n\n defaults_.setValue(\"isotope:mode:mode\", \"Gaussian\", \"Peak Shape used around each isotope peak.\", ListUtils::create(\"advanced\"));\n defaults_.setValidStrings(\"isotope:mode:mode\", ListUtils::create(\"Gaussian,Lorentzian\"));\n defaults_.setValue(\"isotope:mode:LorentzFWHM\", 0.3, \"Full width of the Lorentzian (Cauchy) function applied to the averagine isotopic pattern to simulate the inaccuracy of the mass spectrometer.\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"isotope:mode:GaussianSD\", 0.1, \"Standard deviation of Gaussian applied to the averagine isotopic pattern to simulate the inaccuracy of the mass spectrometer.\", ListUtils::create(\"advanced\"));\n\n\n defaults_.setValue(\"charge\", 1, \"Charge state of the model.\", ListUtils::create(\"advanced\"));\n defaults_.setValue(\"statistics:mean\", 0.0, \"Centroid m/z (as opposed to monoisotopic m/z).\", ListUtils::create(\"advanced\"));\n\n defaultsToParam_();\n }\n\n IsotopeModel::IsotopeModel(const IsotopeModel & source) :\n InterpolationModel(source)\n {\n setParameters(source.getParameters());\n updateMembers_();\n }\n\n IsotopeModel::~IsotopeModel()\n {\n }\n\n IsotopeModel & IsotopeModel::operator=(const IsotopeModel & source)\n {\n if (&source == this)\n return *this;\n\n InterpolationModel::operator=(source);\n setParameters(source.getParameters());\n updateMembers_();\n\n return *this;\n }\n\n EmpiricalFormula IsotopeModel::getFormula()\n {\n CoordinateType mass = mean_ * charge_;\n\n Int C_num = Int(0.5 + mass * averagine_[C]);\n Int N_num = Int(0.5 + mass * averagine_[N]);\n Int O_num = Int(0.5 + mass * averagine_[O]);\n Int H_num = Int(0.5 + mass * averagine_[H]);\n Int S_num = Int(0.5 + mass * averagine_[S]);\n\n String form;\n if (C_num)\n form.append(\"C\").append(String(C_num));\n if (H_num)\n form.append(\"H\").append(String(H_num));\n if (N_num)\n form.append(\"N\").append(String(N_num));\n if (O_num)\n form.append(\"O\").append(String(O_num));\n if (S_num)\n form.append(\"S\").append(String(S_num));\n\n return EmpiricalFormula(form);\n }\n\n const IsotopeDistribution & IsotopeModel::getIsotopeDistribution() const\n {\n return isotope_distribution_;\n }\n\n void IsotopeModel::setSamples(const EmpiricalFormula & formula)\n {\n typedef std::vector ContainerType;\n ContainerType isotopes_exact;\n\n isotope_distribution_ = formula.getIsotopeDistribution(max_isotope_);\n\n isotope_distribution_.trimRight(trim_right_cutoff_);\n isotope_distribution_.renormalize();\n\n // compute the average mass (-offset)\n CoordinateType isotopes_mean = 0;\n {\n Int cnt = 0;\n for (IsotopeDistribution::iterator iter = isotope_distribution_.begin();\n iter != isotope_distribution_.end(); ++iter, ++cnt)\n {\n isotopes_exact.push_back(iter->second);\n isotopes_mean += iter->second * cnt;\n }\n isotopes_mean *= isotope_distance_ / charge_;\n }\n // (Need not divide by sum of probabilities, which is 1.)\n\n ///\n // \"stretch\" the averagine isotope distribution (so we can add datapoints between isotope peaks)\n ///\n size_t isotopes_exact_size = isotopes_exact.size();\n isotopes_exact.resize(size_t((isotopes_exact_size - 1) * isotope_distance_ / interpolation_step_ + 1.6)); // round up a bit more\n\n for (Size i = isotopes_exact_size - 1; i; --i)\n {\n // we don't need to move the 0-th entry\n isotopes_exact[size_t(CoordinateType(i) * isotope_distance_ / interpolation_step_ / charge_ + 0.5)]\n = isotopes_exact[i];\n isotopes_exact[i] = 0;\n }\n\n ////\n // compute the Gaussian/Cauchy distribution (to be added for widening the averagine isotope distribution)\n ////\n ContainerType peak_shape_values_y;\n // fill a container with CoordinateType points (x values)\n CoordinateType peak_width = 0.0;\n if (param_.getValue(\"isotope:mode:mode\") == \"Gaussian\")\n {\n // Actual width for values in the smooth table for normal distribution\n peak_width = isotope_stdev_ * 4.0; // MAGIC alert, num stdev for smooth table for normal distribution\n ContainerType peak_shape_values_x;\n for (double coord = -peak_width; coord <= peak_width;\n coord += interpolation_step_)\n {\n peak_shape_values_x.push_back(coord);\n }\n // compute normal approximation at these CoordinateType points (y values)\n Math::BasicStatistics<> normal_widening_model;\n normal_widening_model.setSum(1);\n normal_widening_model.setMean(0);\n normal_widening_model.setVariance(isotope_stdev_ * isotope_stdev_);\n normal_widening_model.normalApproximation(peak_shape_values_y, peak_shape_values_x);\n }\n else if (param_.getValue(\"isotope:mode:mode\") == \"Lorentzian\")\n {\n peak_width = isotope_lorentz_fwhm_ * 8.0; // MAGIC alert: Lorentzian has infinite support, but we need to stop sampling at some point: 8*FWHM\n for (double coord = -peak_width; coord <= peak_width;\n coord += interpolation_step_)\n {\n boost::math::cauchy_distribution cauchy(0., isotope_lorentz_fwhm_ / 2.0);\n double x = boost::math::pdf(cauchy, coord);\n peak_shape_values_y.push_back(x); //cauchy is using HWHM not FWHM\n }\n }\n\n ///\n // fold the Gaussian/Lorentzian at each averagine peak, i.e. fill linear interpolation\n ///\n const ContainerType & left = isotopes_exact;\n const ContainerType & right = peak_shape_values_y;\n ContainerType & result = interpolation_.getData();\n result.clear();\n\n SignedSize r_max = std::min(SignedSize(left.size() + right.size() - 1),\n SignedSize(2 * peak_width / interpolation_step_ * max_isotope_ + 1));\n result.resize(r_max, 0);\n\n // we loop backwards because then the small products tend to come first\n // (for better numerics)\n for (SignedSize i = left.size() - 1; i >= 0; --i)\n {\n if (left[i] == 0)\n continue;\n for (SignedSize j = std::min(r_max - i, SignedSize(right.size())) - 1; j >= 0; --j)\n {\n result[i + j] += left[i] * right[j];\n }\n }\n\n monoisotopic_mz_ = mean_ - isotopes_mean;\n interpolation_.setMapping(interpolation_step_, peak_width / interpolation_step_, monoisotopic_mz_);\n\n //std::cerr << \"mono now: \" << monoisotopic_mz_ << \" mono easy: \" << formula.getMonoWeight()/formula.getCharge() << \"\\n\";\n\n // scale data so that integral over distribution equals one\n // multiply sum by interpolation_step_ -> rectangular approximation of integral\n IntensityType factor = scaling_ / (interpolation_step_ * std::accumulate(result.begin(), result.end(), IntensityType(0)));\n for (ContainerType::iterator iter = result.begin(); iter != result.end(); ++iter)\n {\n *iter *= factor;\n }\n }\n\n void IsotopeModel::setOffset(CoordinateType offset)\n {\n double diff = offset - getInterpolation().getOffset();\n mean_ += diff;\n monoisotopic_mz_ += diff;\n\n InterpolationModel::setOffset(offset);\n\n param_.setValue(\"statistics:mean\", mean_);\n }\n\n IsotopeModel::CoordinateType IsotopeModel::getOffset()\n {\n return getInterpolation().getOffset();\n }\n\n UInt IsotopeModel::getCharge()\n {\n return charge_;\n }\n\n IsotopeModel::CoordinateType IsotopeModel::getCenter() const\n {\n return monoisotopic_mz_;\n }\n\n void IsotopeModel::updateMembers_()\n {\n InterpolationModel::updateMembers_();\n\n charge_ = param_.getValue(\"charge\");\n isotope_stdev_ = param_.getValue(\"isotope:mode:GaussianSD\");\n isotope_lorentz_fwhm_ = param_.getValue(\"isotope:mode:LorentzFWHM\");\n mean_ = param_.getValue(\"statistics:mean\");\n max_isotope_ = param_.getValue(\"isotope:maximum\");\n trim_right_cutoff_ = param_.getValue(\"isotope:trim_right_cutoff\");\n isotope_distance_ = param_.getValue(\"isotope:distance\");\n\n averagine_[C] = param_.getValue(\"averagines:C\");\n averagine_[H] = param_.getValue(\"averagines:H\");\n averagine_[N] = param_.getValue(\"averagines:N\");\n averagine_[O] = param_.getValue(\"averagines:O\");\n averagine_[S] = param_.getValue(\"averagines:S\");\n\n }\n\n}\n", "meta": {"hexsha": "606f2ce030229287f95f9202fe9ff35f842c84f6", "size": 11809, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/openms/source/TRANSFORMATIONS/FEATUREFINDER/IsotopeModel.cpp", "max_stars_repo_name": "raghav17083/OpenMS", "max_stars_repo_head_hexsha": "ddcdd3068a93a7c415675c39bac43d796a845f1d", "max_stars_repo_licenses": ["BSL-1.0", "Apache-2.0", "Zlib"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/openms/source/TRANSFORMATIONS/FEATUREFINDER/IsotopeModel.cpp", "max_issues_repo_name": "raghav17083/OpenMS", "max_issues_repo_head_hexsha": "ddcdd3068a93a7c415675c39bac43d796a845f1d", "max_issues_repo_licenses": ["BSL-1.0", "Apache-2.0", "Zlib"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/openms/source/TRANSFORMATIONS/FEATUREFINDER/IsotopeModel.cpp", "max_forks_repo_name": "raghav17083/OpenMS", "max_forks_repo_head_hexsha": "ddcdd3068a93a7c415675c39bac43d796a845f1d", "max_forks_repo_licenses": ["BSL-1.0", "Apache-2.0", "Zlib"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 41.8758865248, "max_line_length": 240, "alphanum_fraction": 0.6759251418, "num_tokens": 2889, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5813030906443133, "lm_q2_score": 0.36658972248186, "lm_q1q2_score": 0.21309973867714632}} {"text": "#ifndef COSMO_PROFILES\n#define COSMO_PROFILES\n\n#include \"COSMO_SAC/util.hpp\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"nlohmann/json.hpp\"\n\nnamespace COSMOSAC {\n\n/**\nA single sigma profile. In this data structure (for consistency with the 2005 Virginia Tech\ndatabase of COSMO-SAC parameters), the first column is the electron density in [e/A^2], and the second column\nis the probability of finding a segment with this electron density, multiplied by the segment area, in A^2\n*/\nclass SigmaProfile {\npublic:\n Eigen::ArrayXd m_sigma, m_psigmaA;\n /// Default constructor\n SigmaProfile() {};\n /// Copy-by-reference constructor\n SigmaProfile(const Eigen::ArrayXd &sigma, const Eigen::ArrayXd &psigmaA) : m_sigma(sigma), m_psigmaA(psigmaA) {};\n /// Move constructor\n SigmaProfile(Eigen::ArrayXd &&sigma, Eigen::ArrayXd &&psigmaA) : m_sigma(sigma), m_psigmaA(psigmaA) {};\n const Eigen::ArrayXd &psigmaA() const { return m_psigmaA; }\n const Eigen::ArrayXd &sigma() const { return m_sigma; }\n const Eigen::ArrayXd psigma(double A_i) const { return m_psigmaA/A_i; }\n\n};\n\nstruct SigmaProfileSet {\n SigmaProfile nhb, ///< The profile for the non-hydrogen-bonding segments\n oh, ///< The profile for the OH-bonding segments\n ot; ///< The profile for the \"other\" segments\n};\n\n/// The sigma profiles (and some more metadata) associated with the fluid\nstruct FluidProfiles {\n enum class dispersion_classes{DISP_WATER, DISP_COOH, DISP_NHB, DISP_ONLY_ACCEPTOR, DISP_DONOR_ACCEPTOR};\n SigmaProfileSet profiles;\n std::string name;\n std::size_t VTnumber;\n double A_COSMO_A2; ///< The surface area of the molecule as calculated by COSMO-SAC, in A^2\n double V_COSMO_A3; ///< The volume of the molecule as calculated by COSMO-SAC, in A^3\n dispersion_classes dispersion_flag;\n double dispersion_eoverkB;\n nlohmann::json meta; ///< Any additional metadata, stored in JSON format\n};\n\n///\nclass ProfileDatabase {\nprivate:\n std::map datadb;\npublic:\n FluidProfiles get_profile(const std::string & identifier) const{\n if (datadb.find(identifier) != datadb.cend()) {\n return datadb.find(identifier)->second;\n }\n else {\n throw std::invalid_argument(\"Unable to load the profile for the identifier [\" + identifier + \"]\");\n }\n }\n virtual std::string normalize_identifier(const std::string &identifier) const { return identifier; };\n std::size_t length(const std::string & identifier) {\n return datadb.size();\n }\n void clear() {\n datadb.clear();\n }\n void add_to_db(const std::string & identifier, FluidProfiles &&value) {\n datadb[identifier] = value;\n }\n};\n\nstruct VTFluidInfo {\n int VTIndex;\n std::string numstring;\n std::string name;\n std::string CAS;\n double V_COSMO_A3;\n};\nvoid to_json(nlohmann::json& j, const VTFluidInfo& p) {\n j = nlohmann::json{{\"VTIndex\", p.VTIndex}, {\"numstring\", p.numstring}, {\"name\", p.name}, {\"CAS\", p.CAS}, {\"V_COSMO_A3\", p.V_COSMO_A3}};\n}\nvoid from_json(const nlohmann::json& j, VTFluidInfo& p) {\n j.at(\"VTIndex\").get_to(p.VTIndex);\n j.at(\"numstring\").get_to(p.numstring);\n j.at(\"name\").get_to(p.name);\n j.at(\"CAS\").get_to(p.CAS);\n j.at(\"V_COSMO_A3\").get_to(p.V_COSMO_A3);\n}\n\n/// The format of the VirginiaTech database of profiles\nclass VirginiaTechProfileDatabase : public ProfileDatabase{\nprivate:\n \n std::map m_VTdata;\n const std::string m_dbpath;\npublic: \n VirginiaTechProfileDatabase(const std::string &tab_delimited_summary, const std::string &dbpath) : m_dbpath(dbpath) {\n // First, read in the file of summary information\n auto lines = str_split(get_file_contents(tab_delimited_summary), \"\\n\");\n if (lines.size() == 1){\n lines = str_split(get_file_contents(tab_delimited_summary), \"\\r\");\n }\n if (lines.size() == 1){\n throw std::invalid_argument(\"Unable to match the line endings in file \" + tab_delimited_summary);\n }\n // Iterate over the lines, storing an entry for each one\n for (auto &&line : lines) {\n VTFluidInfo fluid;\n if (line.empty()) { continue; }\n auto elements = str_split(strstrip(line), \"\\t\");\n if (elements.size() < 6) {\n throw std::invalid_argument(\"Need at least 6 entries in line\");\n }\n if (elements[0] == \"Index No.\") {\n continue;\n }\n fluid.VTIndex = mystrtoi(elements[0]);\n // Index 1 is a chemical formula, but non-standard form\n fluid.name = elements[2];\n fluid.CAS = elements[3];\n fluid.V_COSMO_A3 = mystrtod(elements[5]);\n char num[5];\n snprintf(num, sizeof(num), \"%04d\", static_cast(fluid.VTIndex));\n fluid.numstring = num;\n if (m_VTdata.find(fluid.numstring) != m_VTdata.end()) {\n throw std::invalid_argument(\"Cannot have duplicate key: \"+ fluid.numstring);\n }\n else{\n m_VTdata[fluid.numstring] = fluid;\n }\n \n }\n }\n std::string normalize_identifier(const std::string &identifier) const override {\n // The key is in the database, return it\n if (m_VTdata.find(identifier) != m_VTdata.end()) {\n return identifier;\n }\n for (auto &el : m_VTdata) {\n auto &fluid = el.second;\n if (fluid.name == identifier)\n { \n // Construct the fluid identifier from the identifier that is provided\n char num[5];\n snprintf(num, sizeof(num), \"%04d\", static_cast(fluid.VTIndex));\n return num;\n }\n if (fluid.CAS == identifier)\n { \n // Construct the fluid identifier from the identifier that is provided\n char num[5];\n snprintf(num, sizeof(num), \"%04d\", static_cast(fluid.VTIndex));\n return num;\n }\n }\n throw std::invalid_argument(\"Unable to match identifier in secondary lookup\");\n }\n void add_profile(std::string identifier) {\n auto info = m_VTdata[identifier];\n // Now we load the sigma profile(s) from the file\n auto lines = str_split(get_file_contents(m_dbpath + \"/VT2005-\" + info.numstring + \"-PROF.txt\"));\n std::vector sigma, psigmaA;\n FluidProfiles fluid;\n for (auto &&line : lines) {\n if (line.size() > 10) {\n auto _sigma = mystrtod(line.substr(0, 25));\n auto _psigmaA = mystrtod(line.substr(25, 25));\n sigma.push_back(_sigma);\n psigmaA.push_back(_psigmaA);\n }\n }\n fluid.name = info.name;\n fluid.VTnumber = info.VTIndex;\n fluid.V_COSMO_A3 = info.V_COSMO_A3;\n if (sigma.size() == 51 && psigmaA.size() == 51) {\n fluid.profiles.nhb = SigmaProfile(Eigen::Map(&(sigma[0]), sigma.size()),\n Eigen::Map(&(psigmaA[0]), psigmaA.size()));\n fluid.A_COSMO_A2 = fluid.profiles.nhb.psigmaA().sum();\n }\n else {\n throw std::invalid_argument(\"Don't support 2 & 3 sigma profiles yet\");\n }\n add_to_db(info.numstring, std::move(fluid));\n }\n std::string to_JSON(){\n auto nspaces = 2;\n nlohmann::json j = m_VTdata;\n return j.dump(nspaces);\n }\n};\n\nstruct DelawareFluidInfo {\n std::size_t DelIndex;\n std::string CAS;\n std::string formula;\n std::string name;\n std::string SMILES;\n std::string InChIString; ///< Standard InChI string\n std::string InChIKey; ///< Standard InChI key\n double V_COSMO_A3;\n nlohmann::json meta;\n};\nvoid to_json(nlohmann::json& j, const DelawareFluidInfo& p) {\n j = nlohmann::json{\n {\"DelIndex\", p.DelIndex}, {\"CAS\", p.CAS}, {\"formula\", p.formula}, {\"name\", p.name},\n {\"SMILES\", p.SMILES}, {\"InChIString\", p.InChIString}, {\"InChIKey\", p.InChIKey}, {\"V_COSMO_A3\", p.V_COSMO_A3}\n};\n}\nvoid from_json(const nlohmann::json& j, DelawareFluidInfo& p) {\n j.at(\"DelIndex\").get_to(p.DelIndex);\n j.at(\"CAS\").get_to(p.CAS);\n j.at(\"formula\").get_to(p.formula);\n j.at(\"name\").get_to(p.name);\n j.at(\"SMILES\").get_to(p.SMILES);\n j.at(\"InChIString\").get_to(p.InChIString);\n j.at(\"InChIKey\").get_to(p.InChIKey);\n j.at(\"V_COSMO_A3\").get_to(p.V_COSMO_A3);\n j.at(\"name\").get_to(p.name);\n}\n \n/// The format of the Delaware database of profiles\nclass DelawareProfileDatabase : public ProfileDatabase {\nprivate:\n \n std::map m_Deldata;\n const std::string m_dbpath;\npublic:\n DelawareProfileDatabase(const std::string &space_delimited_summary, const std::string &dbpath) : m_dbpath(dbpath) {\n // First, read in the file of summary information\n auto lines = str_split(get_file_contents(space_delimited_summary), \"\\n\");\n // Iterate over the lines, storing an entry for each one\n for (auto &&line : lines) {\n DelawareFluidInfo fluid;\n if (line.empty()) { continue; }\n auto elements = str_split(strrstrip(line), \" \");\n if (elements[0] == \"ID\") {\n continue;\n }\n fluid.DelIndex = mystrtoi(elements[0]);\n fluid.formula = elements[1];\n fluid.CAS = elements[2];\n fluid.name = elements[3];\n fluid.SMILES = elements[4];\n fluid.InChIString = elements[5];\n fluid.InChIKey = elements[6];\n m_Deldata[fluid.InChIKey] = fluid;\n }\n }\n std::string normalize_identifier(const std::string &identifier) const override {\n // The key is in the database, return it\n if (m_Deldata.find(identifier) != m_Deldata.end()) {\n return identifier;\n }\n for (auto &el : m_Deldata) {\n auto &fluid = el.second;\n if (fluid.name == identifier)\n {\n return fluid.InChIKey;\n }\n if (fluid.CAS == identifier)\n {\n return fluid.InChIKey;\n }\n }\n throw std::invalid_argument(\"Unable to match identifier in secondary lookup\");\n }\n void add_profile(std::string identifier) {\n auto info = m_Deldata[identifier];\n // Now we load the sigma profile(s) from the file\n auto lines = str_split(get_file_contents(m_dbpath + \"/\" + info.InChIKey + \".sigma\"));\n\n std::vector _sigma, _psigmaA;\n FluidProfiles fluid;\n std::string meta;\n for (auto &&line : lines) {\n if (line.substr(0,8) == \"# Name: \"){ std::string check_name = line.substr(8,line.size()-8); continue;}\n if (line.substr(0,8) == \"# CASn: \") { std::string check_CAS = line.substr(8, line.size() - 8); continue; }\n if (line.substr(0, 8) == \"# meta: \") { meta = line.substr(8, line.size() - 8); continue; }\n if (line[0] == '#'){ continue; }\n auto v = str_split(strrstrip(line), \" \");\n if (v.empty() || (v.size() ==1 && v[0].empty())){ continue; }\n _sigma.push_back(mystrtod(v[0]));\n _psigmaA.push_back(mystrtod(v[1]));\n }\n Eigen::Map sigma(&(_sigma[0]), _sigma.size());\n Eigen::Map psigmaA(&(_psigmaA[0]), _psigmaA.size());\n fluid.name = info.name;\n \n fluid.VTnumber = info.DelIndex;\n if (sigma.size() == 51 && psigmaA.size() == 51) {\n fluid.profiles.nhb = SigmaProfile(sigma, psigmaA);\n fluid.A_COSMO_A2 = fluid.profiles.nhb.psigmaA().sum();\n }\n else if (sigma.size() == 51*3 && psigmaA.size() == 51*3){\n fluid.profiles.nhb = SigmaProfile(sigma.segment(0*51, 51), psigmaA.segment(0*51, 51));\n fluid.profiles.oh = SigmaProfile(sigma.segment(1*51, 51), psigmaA.segment(1*51, 51));\n fluid.profiles.ot = SigmaProfile(sigma.segment(2*51, 51), psigmaA.segment(2*51, 51));\n double check_Area2 = fluid.profiles.nhb.psigmaA().sum() + fluid.profiles.oh.psigmaA().sum() + fluid.profiles.ot.psigmaA().sum();\n fluid.A_COSMO_A2 = check_Area2;\n }\n else{\n throw std::invalid_argument(\"Length of sigma profile [\"+std::to_string(sigma.size())+\"] is neither 51 nor 51*3\");\n }\n fluid.meta = nlohmann::json::parse(meta);\n fluid.V_COSMO_A3 = fluid.meta[\"volume [A^3]\"];\n std::string flag = fluid.meta[\"disp. flag\"];\n if (flag == \"COOH\") {\n fluid.dispersion_flag = FluidProfiles::dispersion_classes::DISP_COOH;\n }\n else if (flag == \"H2O\") {\n fluid.dispersion_flag = FluidProfiles::dispersion_classes::DISP_WATER;\n }\n else if (flag == \"NHB\") {\n fluid.dispersion_flag = FluidProfiles::dispersion_classes::DISP_NHB;\n }\n else if (flag == \"HB-ACCEPTOR\") {\n fluid.dispersion_flag = FluidProfiles::dispersion_classes::DISP_ONLY_ACCEPTOR;\n }\n else if (flag == \"HB-DONOR-ACCEPTOR\") {\n fluid.dispersion_flag = FluidProfiles::dispersion_classes::DISP_DONOR_ACCEPTOR;\n }\n else {\n throw std::invalid_argument(\"Unable to match dispersion flag: \\\"\"+flag+\"\\\"\");\n }\n if (fluid.meta[\"disp. e/kB [K]\"].is_null()){\n // The null from JSON is mapped to a NaN of C++\n fluid.dispersion_eoverkB = std::numeric_limits::quiet_NaN();\n }\n else {\n fluid.dispersion_eoverkB = fluid.meta[\"disp. e/kB [K]\"];\n }\n add_to_db(info.InChIKey, std::move(fluid));\n }\n std::string to_JSON(){\n auto nspaces = 2;\n nlohmann::json j = m_Deldata;\n return j.dump(nspaces);\n }\n};\n\n} /* namespace COSMOSAC */\n\n#endif\n", "meta": {"hexsha": "1bbfe84c212ef21c6e6b15decc3abd33351176d4", "size": 14070, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/COSMO_SAC/profile_db.hpp", "max_stars_repo_name": "kypss93034/COSMOSAC", "max_stars_repo_head_hexsha": "baba5063ca3495e59d21ce7e03885fe31c0d0018", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 24.0, "max_stars_repo_stars_event_min_datetime": "2019-09-30T21:18:15.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-18T15:28:36.000Z", "max_issues_repo_path": "include/COSMO_SAC/profile_db.hpp", "max_issues_repo_name": "kypss93034/COSMOSAC", "max_issues_repo_head_hexsha": "baba5063ca3495e59d21ce7e03885fe31c0d0018", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 17.0, "max_issues_repo_issues_event_min_datetime": "2020-06-25T18:28:04.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-28T15:51:08.000Z", "max_forks_repo_path": "include/COSMO_SAC/profile_db.hpp", "max_forks_repo_name": "kypss93034/COSMOSAC", "max_forks_repo_head_hexsha": "baba5063ca3495e59d21ce7e03885fe31c0d0018", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 27.0, "max_forks_repo_forks_event_min_datetime": "2019-09-14T01:23:06.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-07T16:45:41.000Z", "avg_line_length": 39.9715909091, "max_line_length": 140, "alphanum_fraction": 0.5958777541, "num_tokens": 3644, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5698526514141571, "lm_q2_score": 0.3738758227716966, "lm_q1q2_score": 0.21305412890610081}} {"text": "// Last update: April 5, 2013\n// This is main.\n// This is MD simulation of an electrolyte confined within planar walls\n// Problem : Compute density profiles of ions trapped within planar walls\n/* Useful studies :\t\n\t\t 1. Role of dielectric contrast\n\t\t 2. Role of valency of ions\n\t\t 3. Role of varying salt concentration\n// To implement:\n\t\t 1. Charged walls\n\t\t \n*/\n/* lessons learned :\n\t\t 1. do not use exit(1) to exit the program. this leads to memory leaks.\n\n*/\n\n#include \n#include \n#include \"utility.h\"\n#include \"interface.h\"\n#include \"particle.h\"\n#include \"vertex.h\"\n#include \"BIN.h\"\n#include \"control.h\"\n#include \"functions.h\"\n#include \"precalculations.h\"\n#include \"thermostat.h\"\n#include \"fmd.h\"\n\n//MPI boundary parameters\nunsigned int lowerBoundIons;\nunsigned int upperBoundIons;\nunsigned int sizFVecIons;\nunsigned int lowerBoundMesh;\nunsigned int upperBoundMesh;\nunsigned int sizFVecMesh;\n\nmpi::environment env;\nmpi::communicator world;\n\nvoid\ncpmd(vector &, INTERFACE &, vector &, vector &, vector &, CONTROL &, CONTROL &);\n// removed particle colloid, fake thermostat and fmdremote\n\nint main(int argc, char *argv[]) {\n\n // Electrostatic system variables\n double bx, by, bz; // lengths of the box\n double ein; // permittivity of inside medium\n double e_left; // permittivity of leftside medium\n double e_right; // permittivity of rightside medium\n int pz_in; // positive valency of ions inside\n int nz_in; // negative valency of ions inside\n double salt_conc_in; // salt concentration outside\t(enter in M)\n double saltion_diameter_in; // inside salt ion diameter\t(positive and negative ions assumed to have same diameter at this point)\n double T, fake_T; // temperature at which the system of ions is\n\n // Simulation related variables\n double fraction_diameter; // fraction that multiplies the diameter to generate the discretization width for the interface\n double Q, fake_Q; // thermostat mass required to generate canonical ensemble\n unsigned int chain_length_real, chain_length_fake;\n double bin_width; // width of the bins used to compute density profiles\n CONTROL fmdremote, cpmdremote; // remote control for cpmd\n\n // Different parts of the system\n vector saltion_in; // salt ions inside\n vector ion; // all ions in the system\n INTERFACE box; // interface, z planes hard walls; rest periodic boundaries\n\n // Analysis\n vector bin; // bins\n\n // Fake system\n double fakemass;\n\n // Get input values from the user\n options_description desc(\"Usage:\\nrandom_mesh \");\n desc.add_options()\n (\"help,h\", \"print usage message\")\n (\"bx,X\", value(&bx)->default_value(22.848),\n \"box length in x direction in nanometers\") // enter in nanometers\n (\"by,Y\", value(&by)->default_value(22.848),\n \"box length in y direction in nanometers\") // enter in nanometers\n (\"bz,Z\", value(&bz)->default_value(3),\n \"box length in z direction in nanometers\") // enter in nanometers\n (\"epsilon_in,e\", value(&ein)->default_value(80.1), \"dielectric const inside\")\n (\"epsilon_left,l\", value(&e_left)->default_value(20), \"dielectric const leftside\")\n (\"epsilon_right,r\", value(&e_right)->default_value(80.1), \"dielectric const rightside\")\n (\"pz_in,p\", value(&pz_in)->default_value(1), \"positive valency inside\")\n (\"nz_in,n\", value(&nz_in)->default_value(-1), \"negative valency inside\")\n (\"salt_conc_in,c\", value(&salt_conc_in)->default_value(0.1), \"salt concentration inside\")\n (\"saltion_diameter_in,d\", value(&saltion_diameter_in)->default_value(0.714),\n \"salt ion diameter inside\") // enter in nanometers\n (\"fraction_diameter,g\", value(&fraction_diameter)->default_value(1.4),\n \"for interface discretization width\")\n (\"thermostat_mass,Q\", value(&Q)->default_value(1.0), \"thermostat mass\")\n (\"chain_length_real,L\", value(&chain_length_real)->default_value(5),\n \"chain length for real system: enter L+1 if you want L thermostats\")\n (\"fake_temperature,k\", value(&fake_T)->default_value(0.002), \"fake temperature\")\n (\"fake_thermostat_mass,q\", value(&fake_Q)->default_value(1.0), \"fake thermostat mass\")\n (\"chain_length_fake,D\", value(&chain_length_fake)->default_value(5),\n \"chain length for fake system: enter L+1 if you want L thermostats\")\n (\"fakemass,f\", value(&fakemass)->default_value(100.0), \"fake mass\")\n (\"cpmd_fake_mass,M\", value(&cpmdremote.fakemass)->default_value(1.0), \"cpmd fake mass\")\n (\"bin_width,B\", value(&bin_width)->default_value(0.1), \"bin width\")\n (\"cpmd_timestep,T\", value(&cpmdremote.timestep)->default_value(0.0005), \"time step used in cpmd\")\n (\"fmd_steps,s\", value(&fmdremote.steps)->default_value(200), \"steps used in fmd\")\n (\"cpmd_steps,S\", value(&cpmdremote.steps)->default_value(1000), \"steps used in cpmd\")\n (\"cpmd_eqm,P\", value(&cpmdremote.hiteqm)->default_value(10), \"production begin (cpmd)\")\n (\"cpmd_freq,F\", value(&cpmdremote.freq)->default_value(10), \"sample frequency (cpmd)\")\n (\"cpmd_verify,y\", value(&cpmdremote.verify)->default_value(1000), \"verify (cpmd)\")\n (\"cpmd_extra_compute,x\", value(&cpmdremote.extra_compute)->default_value(10),\n \"compute additional (cpmd)\")\n (\"cpmd_writedensity,w\", value(&cpmdremote.writedensity)->default_value(100), \"write density files\")\n (\"cpmd_movie_freq,m\", value(&cpmdremote.moviefreq)->default_value(100), \"compute additional (cpmd)\");\n\n variables_map vm;\n store(parse_command_line(argc, argv, desc), vm);\n notify(vm);\n if (vm.count(\"help\")) {\n std::cout << desc << \"\\n\";\n return 0;\n }\n\n // Set up the system\n T = 1; // set temperature\n box.ein = ein;\n box.e_left = e_left;\n box.e_right = e_right;\n box.set_up(salt_conc_in, 0, 0, pz_in, 0, 0, bx / unitlength, by / unitlength, bz / unitlength);\n box.put_saltions_inside(saltion_in, pz_in, nz_in, salt_conc_in, saltion_diameter_in, ion);\n // box.put_one_saltion_in_center(saltion_in, pz_in, nz_in, saltion_diameter_in, ion);\t\t\t\t\t\t//put one ion in center\n\n make_bins(bin, box, bin_width); // set up bins to be used for computing density profiles\n vector initial_density;\n bin_ions(ion, box, initial_density, bin); // bin the ions to get initial density profile\n\n// box.number_of_vertices = total_gridpoints;\n box.discretize(saltion_diameter_in / unitlength, fraction_diameter);\n\n fmdremote.fakemass = fakemass;\n\n // output to screen the parameters of the problem\n if (world.rank() == 0) {\n cout << \"\\nProgram starts\\n\";\n cout << \"Dielectric constant of water is \" << epsilon_water << endl;\n cout << \"Reduced units: scalefactor entering in Coloumb interaction is \" << scalefactor << endl;\n cout << \"Box dimensions x | y | z \" << setw(15) << box.lx << setw(15) << box.ly << setw(15) << box.lz << endl;\n cout << \"Permittivity inside \" << box.ein << endl;\n cout << \"Permittivity leftside \" << box.e_left << endl;\n cout << \"Permittivity rightside \" << box.e_right << endl;\n cout << \"Contrast strength left \" << 2 * (box.e_left - box.ein) / (box.e_left + box.ein) << endl;\n cout << \"Contrast strength right \" << 2 * (box.e_right - box.ein) / (box.e_right + box.ein) << endl;\n cout << \"Positive ion valency inside \" << pz_in << endl;\n cout << \"Negative ion valency inside \" << nz_in << endl;\n cout << \"Salt ion diameter inside \" << saltion_diameter_in / unitlength << endl;\n cout << \"Salt concentration inside \" << salt_conc_in << endl;\n cout << \"Debye length inside \" << box.inv_kappa_in << endl;\n cout << \"Mean separation inside \" << box.mean_sep_in << endl;\n cout << \"Number of salt ions inside \" << saltion_in.size() << endl;\n cout << \"Temperature \" << T << endl;\n cout << \"Binning width (uniform) \" << bin[0].width << endl;\n cout << \"Number of bins \" << bin.size() << endl;\n cout << \"Number of points discretizing the left and right z planes \" << box.leftplane.size() << setw(10)\n << box.rightplane.size() << endl;\n }\n\n int numOfNodes = world.size();\n if (world.rank() == 0) {\n#pragma omp parallel default(shared)\n {\n if (omp_get_thread_num() == 0) {\n printf(\"The app comes with MPI and OpenMP (Hybrid) parallelization)\\n\");\n printf(\"Number of MPI processes used %d\\n\", numOfNodes);\n printf(\"Number of OpenMP threads per MPI process %d\\n\", omp_get_num_threads());\n printf(\"Make sure that number of grid points / ions is greater than %d\\n\",\n omp_get_num_threads() * numOfNodes);\n }\n }\n }\n\n // write to files\n\n // initial configuration\n if (world.rank() == 0) {\n ofstream initial_configuration(\"outfiles/initialconfig.dat\");\n for (unsigned int i = 0; i < ion.size(); i++)\n initial_configuration << \"ion\" << setw(5) << ion[i].id << setw(15) << \"charge\" << setw(5) << ion[i].q\n << setw(15) << \"position\" << setw(15) << ion[i].posvec << endl;\n initial_configuration.close();\n\n // initial density\n ofstream density_profile(\"outfiles/initial_density_profile.dat\", ios::out);\n for (unsigned int b = 0; b < initial_density.size(); b++)\n density_profile << bin[b].lower << setw(15) << initial_density.at(b) << endl;\n density_profile.close();\n }\n // check point\n double totalions = 0;\n for (unsigned int b = 0; b < initial_density.size(); b++)\n totalions += initial_density.at(b) * bin[b].volume;\n if (world.rank() == 0)\n cout << \"total ions \" << totalions << \" should be \" << ion.size() << endl;\n int totalpions = 0, totalnions = 0;\n for (unsigned int i = 0; i < ion.size(); i++) {\n if (ion[i].valency > 0)\n totalpions += 1;\n else if (ion[i].valency < 0)\n totalnions += 1;\n }\n\n // some calculations before simulation begins\n double charge = box.total_charge_inside(ion);\n\n if (world.rank() == 0) {\n cout << \"Total positive ions \" << totalpions << endl;\n cout << \"Total negative ions \" << totalnions << endl;\n cout << \"Total charge inside\" << charge << endl;\n }\n // prepare for cpmd : make real baths\n vector real_bath;\n if (chain_length_real == 1)\n real_bath.push_back(THERMOSTAT(0, T, 3 * ion.size(), 0.0, 0, 0));\n else {\n real_bath.push_back(THERMOSTAT(Q, T, 3 * ion.size(), 0, 0, 0));\n while (real_bath.size() != chain_length_real - 1)\n real_bath.push_back(THERMOSTAT(Q / (3 * ion.size()), T, 1, 0, 0, 0));\n real_bath.push_back(THERMOSTAT(0, T, 3 * ion.size(), 0.0, 0, 0));\n // final bath is dummy bath (dummy bath always has zero mass)\n }\n\n vector fake_bath;\n if (chain_length_fake == 1)\n fake_bath.push_back(THERMOSTAT(0, fake_T, box.leftplane.size(), 0.0, 0, 0));\n else {\n fake_bath.push_back(THERMOSTAT(fake_Q, fake_T, box.leftplane.size(), 0, 0, 0));\n while (fake_bath.size() != chain_length_fake - 1)\n fake_bath.push_back(THERMOSTAT(fake_Q / box.leftplane.size(), fake_T, 1, 0, 0, 0));\n fake_bath.push_back(THERMOSTAT(0, fake_T, box.leftplane.size(), 0.0, 0,\n 0)); // finally, the coding trick: dummy bath (dummy bath always has zero mass)\n }\n\n // Induced charge system\n // fake positions initialized\n for (unsigned int i = 0; i < box.leftplane.size(); i++)\n box.leftplane[i].w = 0.0;\n double total_ind_charge_left = 0;\n for (unsigned int i = 0; i < box.leftplane.size(); i++)\n total_ind_charge_left += box.leftplane[i].w * box.leftplane[i].a;\n\n for (unsigned int i = 0; i < box.rightplane.size(); i++)\n box.rightplane[i].w = 0.0;\n double total_ind_charge_right = 0;\n for (unsigned int i = 0; i < box.rightplane.size(); i++)\n total_ind_charge_right += box.rightplane[i].w * box.rightplane[i].a;\n\n if (world.rank() == 0) {\n cout << \"total induced left interface\" << total_ind_charge_left << endl;\n cout << \"total induced right interface\" << total_ind_charge_right << endl;\n }\n\n clock_t t;\n t = clock();\n precalculate(box); // calculate the Greens function within interface or between interface\n t = clock() - t;\n //exact incuded charge density calculation\n //vector exact_induced_density_left;\n //vector exact_induced_density_right;\n\n //exact_induced_density_left = box.compute_exact_induced_density(ion,'l');\n //exact_induced_density_right = box.compute_exact_induced_density(ion,'r');\n //double total_energy = box.exact_total_energy(exact_induced_density_left, exact_induced_density_right, ion);\n //cout << \"total energy should be \" << total_energy << endl;\n if (world.rank() == 0)\n cout << \"pre_calculation finished, time taken: \" << ((double) t) / CLOCKS_PER_SEC << \" seconds\" << endl;\n\n // Fictitious Molecular Dynamics\n\n t = clock();\n fmdremote.timestep = 0.0005;\n fmdremote.verify = 0;\n fmdremote.anneal = 'y';\n fmd(ion, box, fmdremote, cpmdremote);\n t = clock() - t;\n if (world.rank() == 0)\n cout << \"fmd calculation finished, time taken: \" << ((double) t) / CLOCKS_PER_SEC << \" seconds\" << endl;\n/*\n ofstream induced_density_left (\"outfiles_windows/induced_density_left.dat\");\n for (unsigned int k = 0; k < box.leftplane.size(); k++)\n induced_density_left << k+1 <\n#include \n\nnamespace boost{ namespace math{ namespace detail{\n\ntemplate \ninline unsigned round_x_from_p(unsigned x, T p, T cum, T fudge_factor, unsigned lbound, unsigned /*ubound*/, const policies::discrete_quantile&)\n{\n if((p < cum * fudge_factor) && (x != lbound))\n {\n BOOST_MATH_INSTRUMENT_VARIABLE(x-1);\n return --x;\n }\n return x;\n}\n\ntemplate \ninline unsigned round_x_from_p(unsigned x, T p, T cum, T fudge_factor, unsigned /*lbound*/, unsigned ubound, const policies::discrete_quantile&)\n{\n if((cum < p * fudge_factor) && (x != ubound))\n {\n BOOST_MATH_INSTRUMENT_VARIABLE(x+1);\n return ++x;\n }\n return x;\n}\n\ntemplate \ninline unsigned round_x_from_p(unsigned x, T p, T cum, T fudge_factor, unsigned lbound, unsigned ubound, const policies::discrete_quantile&)\n{\n if(p >= 0.5)\n return round_x_from_p(x, p, cum, fudge_factor, lbound, ubound, policies::discrete_quantile());\n return round_x_from_p(x, p, cum, fudge_factor, lbound, ubound, policies::discrete_quantile());\n}\n\ntemplate \ninline unsigned round_x_from_p(unsigned x, T p, T cum, T fudge_factor, unsigned lbound, unsigned ubound, const policies::discrete_quantile&)\n{\n if(p >= 0.5)\n return round_x_from_p(x, p, cum, fudge_factor, lbound, ubound, policies::discrete_quantile());\n return round_x_from_p(x, p, cum, fudge_factor, lbound, ubound, policies::discrete_quantile());\n}\n\ntemplate \ninline unsigned round_x_from_p(unsigned x, T /*p*/, T /*cum*/, T /*fudge_factor*/, unsigned /*lbound*/, unsigned /*ubound*/, const policies::discrete_quantile&)\n{\n return x;\n}\n\ntemplate \ninline unsigned round_x_from_q(unsigned x, T q, T cum, T fudge_factor, unsigned lbound, unsigned /*ubound*/, const policies::discrete_quantile&)\n{\n if((q * fudge_factor > cum) && (x != lbound))\n {\n BOOST_MATH_INSTRUMENT_VARIABLE(x-1);\n return --x;\n }\n return x;\n}\n\ntemplate \ninline unsigned round_x_from_q(unsigned x, T q, T cum, T fudge_factor, unsigned /*lbound*/, unsigned ubound, const policies::discrete_quantile&)\n{\n if((q < cum * fudge_factor) && (x != ubound))\n {\n BOOST_MATH_INSTRUMENT_VARIABLE(x+1);\n return ++x;\n }\n return x;\n}\n\ntemplate \ninline unsigned round_x_from_q(unsigned x, T q, T cum, T fudge_factor, unsigned lbound, unsigned ubound, const policies::discrete_quantile&)\n{\n if(q < 0.5)\n return round_x_from_q(x, q, cum, fudge_factor, lbound, ubound, policies::discrete_quantile());\n return round_x_from_q(x, q, cum, fudge_factor, lbound, ubound, policies::discrete_quantile());\n}\n\ntemplate \ninline unsigned round_x_from_q(unsigned x, T q, T cum, T fudge_factor, unsigned lbound, unsigned ubound, const policies::discrete_quantile&)\n{\n if(q >= 0.5)\n return round_x_from_q(x, q, cum, fudge_factor, lbound, ubound, policies::discrete_quantile());\n return round_x_from_q(x, q, cum, fudge_factor, lbound, ubound, policies::discrete_quantile());\n}\n\ntemplate \ninline unsigned round_x_from_q(unsigned x, T /*q*/, T /*cum*/, T /*fudge_factor*/, unsigned /*lbound*/, unsigned /*ubound*/, const policies::discrete_quantile&)\n{\n return x;\n}\n\ntemplate \nunsigned hypergeometric_quantile_imp(T p, T q, unsigned r, unsigned n, unsigned N, const Policy& pol)\n{\n#ifdef BOOST_MSVC\n# pragma warning(push)\n# pragma warning(disable:4267)\n#endif\n typedef typename Policy::discrete_quantile_type discrete_quantile_type;\n BOOST_MATH_STD_USING\n BOOST_FPU_EXCEPTION_GUARD\n T result;\n T fudge_factor = 1 + tools::epsilon() * ((N <= boost::math::prime(boost::math::max_prime - 1)) ? 50 : 2 * N);\n unsigned base = static_cast((std::max)(0, (int)(n + r) - (int)(N)));\n unsigned lim = (std::min)(r, n);\n\n BOOST_MATH_INSTRUMENT_VARIABLE(p);\n BOOST_MATH_INSTRUMENT_VARIABLE(q);\n BOOST_MATH_INSTRUMENT_VARIABLE(r);\n BOOST_MATH_INSTRUMENT_VARIABLE(n);\n BOOST_MATH_INSTRUMENT_VARIABLE(N);\n BOOST_MATH_INSTRUMENT_VARIABLE(fudge_factor);\n BOOST_MATH_INSTRUMENT_VARIABLE(base);\n BOOST_MATH_INSTRUMENT_VARIABLE(lim);\n\n if(p <= 0.5)\n {\n unsigned x = base;\n result = hypergeometric_pdf(x, r, n, N, pol);\n T diff = result;\n while(result < p)\n {\n diff = (diff > tools::min_value() * 8) \n ? T(n - x) * T(r - x) * diff / (T(x + 1) * T(N + x + 1 - n - r))\n : hypergeometric_pdf(x + 1, r, n, N, pol);\n if(result + diff / 2 > p)\n break;\n ++x;\n result += diff;\n#ifdef BOOST_MATH_INSTRUMENT\n if(diff != 0)\n {\n BOOST_MATH_INSTRUMENT_VARIABLE(x);\n BOOST_MATH_INSTRUMENT_VARIABLE(diff);\n BOOST_MATH_INSTRUMENT_VARIABLE(result);\n }\n#endif\n }\n return round_x_from_p(x, p, result, fudge_factor, base, lim, discrete_quantile_type());\n }\n else\n {\n unsigned x = lim;\n result = 0;\n T diff = hypergeometric_pdf(x, r, n, N, pol);\n while(result + diff / 2 < q)\n {\n result += diff;\n diff = (diff > tools::min_value() * 8)\n ? x * T(N + x - n - r) * diff / (T(1 + n - x) * T(1 + r - x))\n : hypergeometric_pdf(x - 1, r, n, N, pol);\n --x;\n#ifdef BOOST_MATH_INSTRUMENT\n if(diff != 0)\n {\n BOOST_MATH_INSTRUMENT_VARIABLE(x);\n BOOST_MATH_INSTRUMENT_VARIABLE(diff);\n BOOST_MATH_INSTRUMENT_VARIABLE(result);\n }\n#endif\n }\n return round_x_from_q(x, q, result, fudge_factor, base, lim, discrete_quantile_type());\n }\n#ifdef BOOST_MSVC\n# pragma warning(pop)\n#endif\n}\n\ntemplate \ninline unsigned hypergeometric_quantile(T p, T q, unsigned r, unsigned n, unsigned N, const Policy&)\n{\n BOOST_FPU_EXCEPTION_GUARD\n typedef typename tools::promote_args::type result_type;\n typedef typename policies::evaluation::type value_type;\n typedef typename policies::normalise<\n Policy, \n policies::promote_float, \n policies::promote_double, \n policies::assert_undefined<> >::type forwarding_policy;\n\n return detail::hypergeometric_quantile_imp(p, q, r, n, N, forwarding_policy());\n}\n\n}}} // namespaces\n\n#endif\n\n", "meta": {"hexsha": "a855a4a777a45a57fa66ad196ce2245c5fc49218", "size": 7260, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/boost/math/distributions/detail/hypergeometric_quantile.hpp", "max_stars_repo_name": "randolphwong/mcsema", "max_stars_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1210.0, "max_stars_repo_stars_event_min_datetime": "2020-08-18T07:57:36.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T15:06:05.000Z", "max_issues_repo_path": "boost/boost/math/distributions/detail/hypergeometric_quantile.hpp", "max_issues_repo_name": "randolphwong/mcsema", "max_issues_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 1667.0, "max_issues_repo_issues_event_min_datetime": "2017-03-27T14:41:22.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T19:50:06.000Z", "max_forks_repo_path": "boost/boost/math/distributions/detail/hypergeometric_quantile.hpp", "max_forks_repo_name": "randolphwong/mcsema", "max_forks_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 275.0, "max_forks_repo_forks_event_min_datetime": "2020-08-18T08:35:16.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-31T15:06:07.000Z", "avg_line_length": 36.3, "max_line_length": 193, "alphanum_fraction": 0.68815427, "num_tokens": 1976, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.4225046348141882, "lm_q1q2_score": 0.21290269256000519}} {"text": "#include \"solver.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"../injector.h\"\n#include \"../omp_hash_map/src/omp_hash_map.h\"\n#include \"../omp_hash_map/src/reducer.h\"\n#include \"../vector_stats.h\"\n#include \"davidson_util.h\"\n#include \"spin_det_util.h\"\n\n#define ENERGY_FORMAT \"%.12f\"\n#define TABLE_FORMAT_ITEM_NAME \"%20s\"\n#define TABLE_FORMAT_F \"%17.10f\"\n#define TABLE_FORMAT_D \"%'17d\"\n#define TABLE_FORMAT_LL \"%'17llu\"\n#define MAX_HASH_LOAD 1.618\n#define MAX_DETS_PER_FILE 20000000\n\nclass UncertainResult {\n public:\n double value = 0.0;\n double uncertainty = 0.0;\n};\n\nclass SolverImpl : public Solver {\n public:\n ~SolverImpl();\n\n SolverImpl(\n Session* const session,\n Connections* const connections,\n AbstractSystem* const abstrct_system);\n\n void setup_hf() override;\n\n void variation(const double eps_var) override;\n\n void save_variation_result(const std::string& filename) override;\n\n bool load_variation_result(const std::string& filename) override;\n\n std::vector apply_hamiltonian(\n const std::vector& vec, const bool first_iteration) override;\n\n void perturbation(\n const int n_orbs_var,\n const double eps_var,\n const std::vector& n_orbs_pts,\n const std::vector& eps_pts) override;\n\n private:\n int n_up = 0;\n\n int n_dn = 0;\n\n double energy_hf = 0.0;\n\n double energy_var = 0.0;\n\n std::unordered_set var_dets_set;\n\n bool verbose = false;\n\n void print_var_result() const;\n\n std::vector tmp_dets;\n\n std::vector get_energy_pts_pre_dtm(\n const std::vector& n_orbs_pts);\n\n std::vector get_energy_pts_dtm(\n const std::vector& n_orbs_pts,\n const std::vector& energy_pts_pre_dtm);\n\n std::vector> get_energy_pts_stc(\n const std::vector& n_orbs_pts,\n const std::vector& eps_pts,\n const std::vector& energy_pts_dtm);\n\n double get_weight(const int i) const;\n};\n\nSolverImpl::SolverImpl(\n Session* const session,\n Connections* const connections,\n AbstractSystem* const abstract_system)\n : Solver(session, connections, abstract_system) {\n Parallel* const parallel = session->get_parallel();\n Config* const config = session->get_config();\n verbose = parallel->is_master();\n tmp_dets.resize(parallel->get_n_threads());\n n_up = config->get_int(\"n_up\");\n n_dn = config->get_int(\"n_dn\");\n}\n\nSolverImpl::~SolverImpl() {}\n\nvoid SolverImpl::setup_hf() {\n // Create or clear wavefunction.\n abstract_system->dets.clear();\n abstract_system->coefs.clear();\n\n // Add a single term with coef 1.0 and no diffs.\n auto& det_hf = tmp_dets[0];\n det_hf.Clear();\n det_hf.mutable_up()->set_n_hf_elecs(n_up);\n det_hf.mutable_dn()->set_n_hf_elecs(n_dn);\n abstract_system->dets.push_back(det_hf.SerializeAsString());\n abstract_system->coefs.push_back(1.0);\n\n // Update HF and variational energy.\n energy_hf = energy_var = abstract_system->hamiltonian(&det_hf, &det_hf);\n\n // Clear connections.\n connections->clear();\n\n if (verbose) printf(\"HF det setup.\\n\");\n};\n\nvoid SolverImpl::variation(const double eps_var) {\n Timer* const timer = session->get_timer();\n\n // Contruct variational determinant hash set.\n const int n_start_dets = abstract_system->dets.size();\n var_dets_set.clear();\n std::vector prev_coefs;\n var_dets_set.reserve(n_start_dets);\n prev_coefs.resize(n_start_dets, 0.0);\n for (int i = 0; i < n_start_dets; i++) {\n var_dets_set.insert(abstract_system->dets[i]);\n }\n\n // Variation iterations.\n bool converged = false;\n int iteration = 0;\n int n_new_dets = 0;\n\n // Add to the var wf if not exists and set its coef to zero.\n const auto& connected_det_handler =\n [&](const data::Determinant* const connected_det) {\n std::string det_code = connected_det->SerializeAsString();\n if (var_dets_set.count(det_code) == 0) {\n var_dets_set.insert(det_code);\n abstract_system->dets.push_back(det_code);\n abstract_system->coefs.push_back(0.0);\n n_new_dets++;\n }\n };\n\n // Apply hamiltonian to a vec (with partially cached hamiltonian).\n const auto& apply_hamiltonian_func = std::bind(\n &SolverImpl::apply_hamiltonian,\n this,\n std::placeholders::_1,\n std::placeholders::_2);\n\n while (!converged) {\n timer->start(\"loop \" + std::to_string(iteration));\n\n // Find new dets.\n n_new_dets = 0;\n const int n_old_dets = abstract_system->dets.size();\n for (int i = 0; i < n_old_dets; i++) {\n const double coef = abstract_system->coefs[i];\n auto& det = tmp_dets[0];\n det.ParseFromString(abstract_system->dets[i]);\n if (std::abs(coef) <= std::abs(prev_coefs[i])) continue;\n abstract_system->find_connected_dets(\n &det, eps_var / std::abs(coef), connected_det_handler);\n }\n const int n_total_dets = abstract_system->dets.size();\n if (verbose) {\n printf(\"New / total dets: %'d / %'d\\n\", n_new_dets, n_total_dets);\n }\n timer->checkpoint(\"found new dets\");\n\n // Update prev coefs.\n prev_coefs.resize(n_total_dets);\n for (int i = 0; i < n_total_dets; i++) {\n prev_coefs[i] = abstract_system->coefs[i];\n }\n\n // Diagonalize.\n connections->update();\n timer->checkpoint(\"updated connections\");\n std::vector diagonal(n_total_dets, 0.0);\n#pragma omp parallel for schedule(static, 1)\n for (int i = 0; i < n_total_dets; i++) {\n const int thread_id = omp_get_thread_num();\n auto& tmp_det = tmp_dets[thread_id];\n tmp_det.ParseFromString(abstract_system->dets[i]);\n diagonal[i] = abstract_system->hamiltonian(&tmp_det, &tmp_det);\n }\n const auto& diagonalization_result = DavidsonUtil::diagonalize(\n prev_coefs, diagonal, apply_hamiltonian_func, 5, verbose);\n const double energy_var_new = diagonalization_result.first;\n const auto& new_coefs = diagonalization_result.second;\n for (int i = 0; i < n_total_dets; i++) {\n abstract_system->coefs[i] = new_coefs[i];\n }\n\n // Determine convergence.\n if (std::abs(energy_var_new - energy_var) < 1.0e-7) {\n converged = true;\n }\n\n energy_var = energy_var_new;\n iteration++;\n\n if (verbose) print_var_result();\n\n timer->end(); // iteration.\n }\n};\n\nvoid SolverImpl::save_variation_result(const std::string& filename) {\n // Write summary.\n std::fstream var_file(\n filename, std::ios::out | std::ios::trunc | std::ios::binary);\n data::VariationResult res;\n data::Wavefunction trunk_wf;\n res.set_energy_hf(energy_hf);\n res.set_energy_var(energy_var);\n const int n_dets = abstract_system->dets.size();\n res.set_n_dets(n_dets);\n res.SerializeToOstream(&var_file);\n var_file.close();\n if (verbose) {\n printf(\"Saved summary to: %s\\n\", filename.c_str());\n }\n\n // Write wavefunction trunks. (Due to the 2GB serialization limit of Protobuf)\n int trunk_n_dets = 0;\n int trunk_id = 0;\n for (int i = 0; i < n_dets; i++) {\n data::Term* const new_term = trunk_wf.add_terms();\n new_term->set_coef(abstract_system->coefs[i]);\n new_term->mutable_det()->ParseFromString(abstract_system->dets[i]);\n trunk_n_dets++;\n if (trunk_n_dets >= MAX_DETS_PER_FILE || i == n_dets - 1) {\n const auto& wf_filename = filename + \".part\" + std::to_string(trunk_id);\n std::fstream wf_file(\n wf_filename, std::ios::out | std::ios::trunc | std::ios::binary);\n trunk_wf.SerializeToOstream(&wf_file);\n wf_file.close();\n if (verbose) {\n printf(\"Saved %'d dets to: %s\\n\", trunk_n_dets, wf_filename.c_str());\n }\n trunk_wf.Clear();\n trunk_n_dets = 0;\n trunk_id++;\n }\n }\n}\n\nbool SolverImpl::load_variation_result(const std::string& filename) {\n // Load summary.\n std::fstream var_file(filename, std::ios::in | std::ios::binary);\n data::VariationResult res;\n if (!res.ParseFromIstream(&var_file)) {\n return false;\n }\n var_file.close();\n energy_hf = res.energy_hf();\n energy_var = res.energy_var();\n const int n_dets_total = res.n_dets();\n if (verbose) {\n printf(\"Loaded summary from: %s\\n\", filename.c_str());\n }\n\n // Load wavefunctions from trunks.\n abstract_system->dets.clear();\n abstract_system->coefs.clear();\n int n_dets_read = 0;\n int trunk_id = 0;\n data::Wavefunction trunk_wf;\n while (n_dets_read < n_dets_total) {\n const auto& wf_filename = filename + \".part\" + std::to_string(trunk_id);\n trunk_id++;\n std::fstream var_file(wf_filename, std::ios::in | std::ios::binary);\n if (!trunk_wf.ParseFromIstream(&var_file)) {\n throw std::runtime_error(\"variational results corrupted\");\n }\n const int trunk_n_dets = trunk_wf.terms_size();\n for (int i = 0; i < trunk_n_dets; i++) {\n const auto& term = trunk_wf.terms(i);\n abstract_system->coefs.push_back(term.coef());\n abstract_system->dets.push_back(term.det().SerializeAsString());\n n_dets_read++;\n }\n if (verbose) {\n printf(\"Loaded %'d dets from: %s\\n\", trunk_n_dets, wf_filename.c_str());\n }\n }\n\n if (verbose) {\n print_var_result();\n }\n\n return true;\n}\n\nvoid SolverImpl::print_var_result() const {\n printf(\"Number of dets: %'zu\\n\", abstract_system->dets.size());\n printf(\"Variation energy: \" ENERGY_FORMAT \" Ha\\n\", energy_var);\n const double energy_corr = energy_var - energy_hf;\n printf(\"Correlation energy (variation): \" ENERGY_FORMAT \" Ha\\n\", energy_corr);\n}\n\nstd::vector SolverImpl::apply_hamiltonian(\n const std::vector& vec, const bool first_iteration) {\n const int n_dets = vec.size();\n Parallel* const parallel = session->get_parallel();\n const int proc_id = parallel->get_proc_id();\n const int n_procs = parallel->get_n_procs();\n const int n_threads = parallel->get_n_threads();\n std::vector> res(n_threads);\n for (int i = 0; i < n_threads; i++) res[i].resize(n_dets, 0.0);\n std::vector n_nonzero_elems(n_threads, 0);\n\n#pragma omp parallel for schedule(static, 1)\n for (int i = proc_id; i < n_dets; i += n_procs) {\n const int thread_id = omp_get_thread_num();\n const auto& conns = connections->get_connections(i);\n for (const auto conn : conns) {\n const int j = conn.first;\n const double H_ij = conn.second;\n res[thread_id][i] += H_ij * vec[j];\n if (i != j) {\n res[thread_id][j] += H_ij * vec[i];\n n_nonzero_elems[thread_id] += 2;\n } else {\n n_nonzero_elems[thread_id]++;\n }\n }\n }\n\n for (int i = 1; i < n_threads; i++) {\n n_nonzero_elems[0] += n_nonzero_elems[i];\n for (int j = 0; j < n_dets; j++) {\n res[0][j] += res[i][j];\n }\n }\n parallel->reduce_to_sum(res[0]);\n parallel->reduce_to_sum(n_nonzero_elems[0]);\n if (verbose && first_iteration) {\n printf(\"Number of non-zero elements: %'llu\\n\", n_nonzero_elems[0]);\n }\n\n session->get_timer()->checkpoint(\"hamiltonian applied\");\n\n return res[0];\n};\n\nvoid SolverImpl::perturbation(\n const int n_orbs_var,\n const double eps_var,\n const std::vector& n_orbs_pts,\n const std::vector& eps_pts) {\n // Check if the results already exists.\n const auto result_filename =\n str(boost::format(\"pt_%d_%#.4g.csv\") % n_orbs_var % eps_var);\n if (std::ifstream(result_filename)) {\n if (verbose) printf(\"PT results found in: %s\\n\", result_filename.c_str());\n return;\n }\n\n // Clean variation variables.\n connections->clear();\n\n // Construct var dets set.\n var_dets_set.clear();\n var_dets_set.reserve(abstract_system->dets.size());\n for (const auto& det_string : abstract_system->dets) {\n var_dets_set.insert(det_string);\n }\n\n // Get Deterministic PT correction.\n const auto& energy_pts_pre_dtm = get_energy_pts_pre_dtm(n_orbs_pts);\n const auto& energy_pts_dtm =\n get_energy_pts_dtm(n_orbs_pts, energy_pts_pre_dtm);\n\n // Check eps_pts validity.\n const double eps_dtm_pt = config->get_double(\"eps_dtm_pt\");\n const int n_eps_pts = eps_pts.size();\n for (int i = 1; i < n_eps_pts; i++) {\n assert(eps_pts[i] <= eps_dtm_pt);\n assert(eps_pts[i] < eps_pts[i - 1]);\n }\n\n // Get Stochastic PT correction.\n const auto& energy_pts_stc =\n get_energy_pts_stc(n_orbs_pts, eps_pts, energy_pts_dtm);\n\n // Record results.\n std::ofstream result_file(result_filename);\n result_file << \"n_orbs_var,eps_var,n_orbs_pt,eps_pt,energy_corr,uncert,\"\n << \"energy_hf,energy_var,energy_pt\" << std::endl;\n\n const int n_n_orbs_pts = n_orbs_pts.size();\n for (int i = 0; i < n_eps_pts; i++) {\n for (int j = 0; j < n_n_orbs_pts; j++) {\n const double eps_pt = eps_pts[i];\n const int n_orbs_pt = n_orbs_pts[j];\n const double energy_pt_dtm = energy_pts_dtm[j].value;\n const double energy_pt_stc = energy_pts_stc[i][j].value;\n const double energy_pt = energy_pt_dtm + energy_pt_stc;\n const double uncert = std::sqrt(\n std::pow(energy_pts_stc[i][j].uncertainty, 2) +\n std::pow(energy_pts_dtm[j].uncertainty, 2));\n const double energy_corr = energy_var + energy_pt - energy_hf;\n result_file << str(boost::format(\"%d, %#.4g, %d, %#.4g, %#.15g, %#.15g, \"\n \"%#.15g, %#.15g, %#.15g\") %\n n_orbs_var % eps_var % n_orbs_pt % eps_pt %\n energy_corr % uncert % energy_hf % energy_var %\n energy_pt)\n << std::endl;\n }\n }\n\n if (verbose) {\n printf(\"Results saved to: %s\\n\", result_filename.c_str());\n }\n\n result_file.close();\n}\n\nstd::vector SolverImpl::get_energy_pts_pre_dtm(\n const std::vector& n_orbs_pts) {\n // Cache commonly used variables.\n const int n_n_orbs_pts = n_orbs_pts.size();\n const int n_var_dets = var_dets_set.size();\n const double eps_pre_dtm_pt = config->get_double(\"eps_pre_dtm_pt\");\n std::hash string_hasher;\n\n // Construct partial sums store and pt results store.\n omp_hash_map partial_sums_pre;\n partial_sums_pre.set_max_load_factor(MAX_HASH_LOAD);\n std::vector energy_pts_pre_dtm(n_n_orbs_pts, 0.0);\n std::vector n_pt_dets_pre_dtm(n_n_orbs_pts, 0);\n\n timer->start(\"pre_dtm\");\n if (verbose) printf(\">>> eps_pre_dtm_pt %#.4g\\n\", eps_pre_dtm_pt);\n timer->start(\"search\");\n double target_progress = 0.25;\n#pragma omp parallel for schedule(dynamic, 5)\n for (int i = 0; i < n_var_dets; i++) {\n auto& var_det = tmp_dets[omp_get_thread_num()];\n var_det.ParseFromString(abstract_system->dets[i]);\n const double coef = abstract_system->coefs[i];\n\n const auto& pt_det_handler = [&](const auto& det_a) {\n const auto& det_a_code = det_a->SerializeAsString();\n const size_t det_a_hash = string_hasher(det_a_code);\n if (var_dets_set.count(det_a_code) == 1) return;\n if (det_a_hash % n_procs != proc_id) return;\n const double H_ai = abstract_system->hamiltonian(&var_det, det_a);\n const double partial_sum_term = H_ai * coef;\n partial_sums_pre.set(\n det_a_code, [&](double& value) { value += partial_sum_term; }, 0.0);\n };\n\n abstract_system->find_connected_dets(\n &var_det, eps_pre_dtm_pt / std::abs(coef), pt_det_handler);\n\n // Report progress. (every time progress reaches 2^n %).\n const double current_progress = i * 100.0 / n_var_dets;\n if (target_progress <= current_progress) {\n if (omp_get_thread_num() == 0) {\n if (verbose) {\n printf(\"Master node PT dets: %'zu\\n\", partial_sums_pre.get_n_keys());\n }\n std::string event =\n str(boost::format(\"Progress: %.2f %%\") % target_progress);\n timer->checkpoint(event);\n target_progress *= 2.0;\n }\n }\n }\n while (target_progress <= 100) {\n std::string event =\n str(boost::format(\"Progress: %.2f %%\") % target_progress);\n timer->checkpoint(event);\n target_progress *= 2.0;\n }\n timer->end(); // Search.\n\n timer->start(\"accumulate\");\n partial_sums_pre.apply([&](const std::string& det_code, const double value) {\n const int thread_id = omp_get_thread_num();\n auto& det = tmp_dets[thread_id];\n det.ParseFromString(det_code);\n const int n_orbs_used = SpinDetUtil::get_n_orbs_used(det);\n const double H_aa = abstract_system->hamiltonian(&det, &det);\n const double contribution = value * value / (energy_var - H_aa);\n for (int i = 0; i < n_n_orbs_pts; i++) {\n if (n_orbs_used > n_orbs_pts[i]) continue;\n#pragma omp atomic\n n_pt_dets_pre_dtm[i]++;\n#pragma omp atomic\n energy_pts_pre_dtm[i] += contribution;\n }\n });\n partial_sums_pre.clear();\n parallel->reduce_to_sum(n_pt_dets_pre_dtm);\n parallel->reduce_to_sum(energy_pts_pre_dtm);\n\n if (verbose) {\n printf(TABLE_FORMAT_ITEM_NAME, \"# orbitals PT:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n printf(TABLE_FORMAT_D, n_orbs_pts[i]);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"Pre DTM energy PT:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n printf(TABLE_FORMAT_F, energy_pts_pre_dtm[i]);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"EST. CORR. energy:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n const double energy_corr = energy_pts_pre_dtm[i] + energy_var - energy_hf;\n printf(TABLE_FORMAT_F, energy_corr);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"# DTM PT dets:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n printf(TABLE_FORMAT_LL, n_pt_dets_pre_dtm[i]);\n }\n printf(\"\\n\");\n }\n timer->end(); // Accumulate.\n timer->end(); // pre dtm.\n\n return energy_pts_pre_dtm;\n}\n\nstd::vector SolverImpl::get_energy_pts_dtm(\n const std::vector& n_orbs_pts,\n const std::vector& energy_pts_pre_dtm) {\n // Cache commonly used variables.\n const int n_n_orbs_pts = n_orbs_pts.size();\n const int n_var_dets = var_dets_set.size();\n const size_t n_pt_batches_dtm = config->get_int(\"n_batches_dtm_pt\");\n const double eps_pre_dtm_pt = config->get_double(\"eps_pre_dtm_pt\");\n const double eps_dtm_pt = config->get_double(\"eps_dtm_pt\");\n std::hash string_hasher;\n\n // Construct partial sums store and pt results store.\n omp_hash_map partial_sums_pre;\n omp_hash_map partial_sums;\n partial_sums.set_max_load_factor(MAX_HASH_LOAD);\n partial_sums_pre.set_max_load_factor(MAX_HASH_LOAD);\n std::vector energy_pts_dtm(n_n_orbs_pts);\n std::vector> energy_pts_dtm_batches(n_n_orbs_pts);\n std::vector n_pt_dets_dtm(n_n_orbs_pts, 0);\n const double target_error = config->get_double(\"target_error\");\n\n timer->start(\"dtm\");\n if (verbose) printf(\">>> eps_dtm_pt %#.4g\\n\", eps_dtm_pt);\n // Process batch by batch to achieve a larger run in a constrained mem env.\n for (size_t b = 0; b < n_pt_batches_dtm; b++) {\n timer->start(str(boost::format(\"%d/%d\") % (b + 1) % n_pt_batches_dtm));\n\n double target_progress = 0.25;\n timer->start(\"search\");\n#pragma omp parallel for schedule(dynamic, 5)\n for (int i = 0; i < n_var_dets; i++) {\n auto& var_det = tmp_dets[omp_get_thread_num()];\n var_det.ParseFromString(abstract_system->dets[i]);\n const double coef = abstract_system->coefs[i];\n\n // Process only if:\n // 1. is not a var det, and\n // 2. belongs to the current proc_id, and\n // 3. belongs to the current batch.\n const auto& pt_det_handler = [&](const auto& det_a) {\n const auto& det_a_code = det_a->SerializeAsString();\n const size_t det_a_hash = string_hasher(det_a_code);\n if (var_dets_set.count(det_a_code) == 1) return;\n if (det_a_hash % n_procs != proc_id) return;\n if ((det_a_hash / n_procs) % n_pt_batches_dtm != b) return;\n const double H_ai = abstract_system->hamiltonian(&var_det, det_a);\n const double partial_sum_term = H_ai * coef;\n if (std::abs(partial_sum_term) >= eps_pre_dtm_pt) {\n partial_sums_pre.set(\n det_a_code,\n [&](double& value) { value += partial_sum_term; },\n 0.0);\n }\n partial_sums.set(\n det_a_code, [&](double& value) { value += partial_sum_term; }, 0.0);\n };\n\n abstract_system->find_connected_dets(\n &var_det, eps_dtm_pt / std::abs(coef), pt_det_handler);\n\n // Report progress. (every time progress reaches 2^n %).\n const double current_progress = i * 100.0 / n_var_dets;\n if (target_progress <= current_progress) {\n if (omp_get_thread_num() == 0) {\n if (verbose) {\n printf(\"Master node PT dets: %'zu\\n\", partial_sums.get_n_keys());\n }\n std::string event =\n str(boost::format(\"Progress: %.2f %%\") % target_progress);\n timer->checkpoint(event);\n target_progress *= 2.0;\n }\n }\n }\n while (target_progress <= 100) {\n std::string event =\n str(boost::format(\"Progress: %.2f %%\") % target_progress);\n timer->checkpoint(event);\n target_progress *= 2.0;\n }\n timer->end(); // Search.\n\n timer->start(\"accumulate\");\n\n // For reduction after each batch.\n std::vector energy_pts_dtm_batch(n_n_orbs_pts, 0.0);\n\n // Apply to each key, value pair:\n // 1. Calculate the contribution.\n // 2. For each n_orbs_pt interested:\n // If the n_orbs used by the key is less than n_orbs_pt:\n // Increase n_pt_dets by one and add contribution to energy_pt.\n partial_sums.apply([&](const std::string& det_code, const double value) {\n const int thread_id = omp_get_thread_num();\n auto& det = tmp_dets[thread_id];\n det.ParseFromString(det_code);\n const int n_orbs_used = SpinDetUtil::get_n_orbs_used(det);\n const double H_aa = abstract_system->hamiltonian(&det, &det);\n const double value_pre =\n partial_sums_pre.get_copy_or_default(det_code, 0.0);\n partial_sums_pre.unset(det_code);\n const double contribution =\n (value * value - value_pre * value_pre) / (energy_var - H_aa);\n for (int i = 0; i < n_n_orbs_pts; i++) {\n if (n_orbs_used > n_orbs_pts[i]) continue;\n#pragma omp atomic\n n_pt_dets_dtm[i]++;\n#pragma omp atomic\n energy_pts_dtm_batch[i] += contribution;\n }\n });\n\n // Aggregate the results from each proc.\n parallel->reduce_to_sum(energy_pts_dtm_batch);\n double max_uncert = 0.0;\n for (int i = 0; i < n_n_orbs_pts; i++) {\n energy_pts_dtm_batches[i].push_back(energy_pts_dtm_batch[i]);\n }\n for (int i = 0; i < n_n_orbs_pts; i++) {\n energy_pts_dtm[i].value =\n get_avg(energy_pts_dtm_batches[i]) * n_pt_batches_dtm +\n energy_pts_pre_dtm[i];\n if (b == n_pt_batches_dtm - 1) {\n energy_pts_dtm[i].uncertainty = 0.0; // All batches finished.\n } else {\n energy_pts_dtm[i].uncertainty =\n get_stdev(energy_pts_dtm_batches[i]) * n_pt_batches_dtm / b;\n }\n max_uncert = std::max(max_uncert, energy_pts_dtm[i].uncertainty);\n }\n\n // Print batch result and estimated total correction.\n if (verbose) {\n printf(TABLE_FORMAT_ITEM_NAME, \"# orbitals PT:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n printf(TABLE_FORMAT_D, n_orbs_pts[i]);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"Batch DTM energy PT:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n printf(TABLE_FORMAT_F, energy_pts_dtm_batch[i]);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"EST. DTM energy PT:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n printf(TABLE_FORMAT_F, energy_pts_dtm[i].value - energy_pts_pre_dtm[i]);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"(including pre dtm):\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n printf(TABLE_FORMAT_F, energy_pts_dtm[i].value);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"Uncertainty:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n printf(TABLE_FORMAT_F, energy_pts_dtm[i].uncertainty);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"EST. CORR. energy:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n const double energy_corr =\n energy_pts_dtm[i].value + energy_var - energy_hf;\n printf(TABLE_FORMAT_F, energy_corr);\n }\n printf(\"\\n\");\n }\n\n timer->end(); // Accumulation.\n\n partial_sums.clear();\n partial_sums_pre.clear();\n timer->end(); // Batch.\n\n if (b > 0 && b < n_pt_batches_dtm - 2 && max_uncert < 0.2 * target_error) {\n if (verbose) {\n printf(\n \"\\n>>> Skip remaining batches since uncertainty is significantly \"\n \"smaller than target error.\\n\");\n }\n break;\n }\n }\n\n parallel->reduce_to_sum(n_pt_dets_dtm);\n\n // Print total PT dets.\n if (verbose) {\n printf(TABLE_FORMAT_ITEM_NAME, \"# DTM PT dets:\");\n for (int i = 0; i < n_n_orbs_pts; i++) {\n printf(TABLE_FORMAT_LL, n_pt_dets_dtm[i]);\n }\n printf(\"\\n\");\n }\n\n timer->end();\n\n return energy_pts_dtm;\n}\n\nstd::vector> SolverImpl::get_energy_pts_stc(\n const std::vector& n_orbs_pts,\n const std::vector& eps_pts,\n const std::vector& energy_pts_dtm) {\n const int n_n_orbs_pts = n_orbs_pts.size();\n const int n_eps_pts = eps_pts.size();\n\n // Construct results store.\n std::vector> energy_pts_stc(n_eps_pts);\n std::vector>> energy_pts_loops(n_eps_pts);\n std::vector> partial_sums(n_eps_pts);\n omp_hash_map partial_sums_dtm;\n for (int i = 0; i < n_eps_pts; i++) {\n energy_pts_stc[i].resize(n_n_orbs_pts);\n energy_pts_loops[i].resize(n_n_orbs_pts);\n partial_sums[i].set_max_load_factor(MAX_HASH_LOAD);\n }\n partial_sums_dtm.set_max_load_factor(MAX_HASH_LOAD);\n\n // Construct probabilities from normalized weights.\n const int n_var_dets = var_dets_set.size();\n double sum_weights = 0.0;\n for (int i = 0; i < n_var_dets; i++) {\n sum_weights += get_weight(i);\n }\n std::vector probs(n_var_dets);\n std::vector cum_probs(n_var_dets); // For sampling.\n for (int i = 0; i < n_var_dets; i++) {\n probs[i] = get_weight(i) / sum_weights;\n if (i == 0) {\n cum_probs[i] = probs[i];\n } else {\n cum_probs[i] = probs[i] + cum_probs[i - 1];\n }\n }\n\n // Stochastic iterations.\n int iteration = 0;\n const int max_n_iterations = config->get_int(\"max_n_iterations_stc_pt\");\n std::unordered_map stc_pt_sample_dets;\n std::vector stc_pt_sample_dets_list;\n const int n_samples = config->get_int(\"n_samples_stc_pt\");\n srand(time(NULL));\n const double eps_dtm_pt = config->get_double(\"eps_dtm_pt\");\n const double eps_pts_min = eps_pts.back();\n const size_t n_batches = config->get_int(\"n_batches_stc_pt\");\n std::hash string_hasher;\n const double target_error = config->get_double(\"target_error\");\n\n timer->start(\"stc\");\n while (iteration < max_n_iterations) {\n timer->start(str(boost::format(\"loop %d\") % iteration));\n\n // Cleanup.\n for (int i = 0; i < n_eps_pts; i++) {\n partial_sums[i].clear();\n }\n partial_sums_dtm.clear();\n stc_pt_sample_dets.clear();\n stc_pt_sample_dets_list.clear();\n\n std::vector> energy_pts_loop(n_eps_pts);\n std::vector> n_pt_dets_loop(n_eps_pts);\n for (int i = 0; i < n_eps_pts; i++) {\n energy_pts_loop[i].resize(n_n_orbs_pts, 0.0);\n n_pt_dets_loop[i].resize(n_n_orbs_pts, 0);\n }\n\n // Generate random samples.\n for (int i = 0; i < n_samples; i++) {\n const double rand_01 = ((double)rand() / (RAND_MAX));\n const int sample_det_id =\n std::lower_bound(cum_probs.begin(), cum_probs.end(), rand_01) -\n cum_probs.begin();\n if (stc_pt_sample_dets.count(sample_det_id) == 0) {\n stc_pt_sample_dets_list.push_back(sample_det_id);\n }\n stc_pt_sample_dets[sample_det_id]++;\n }\n\n // Use one batch to estimate contribution from all batches.\n const size_t selected_batch = rand() % n_batches;\n\n // Search PT dets from selected sample var dets.\n const int n_stc_pt_sample_dets = stc_pt_sample_dets_list.size();\n\n#pragma omp parallel for schedule(dynamic, 2)\n for (int s = 0; s < n_stc_pt_sample_dets; s++) {\n const int var_det_id = stc_pt_sample_dets_list[s];\n const double prob = probs[var_det_id];\n const double cnt = static_cast(stc_pt_sample_dets[var_det_id]);\n auto& var_det = tmp_dets[omp_get_thread_num()];\n var_det.ParseFromString(abstract_system->dets[var_det_id]);\n const double coef = abstract_system->coefs[var_det_id];\n\n const auto& pt_det_handler = [&](const auto& det_a) {\n const auto& det_a_code = det_a->SerializeAsString();\n const size_t det_a_hash = string_hasher(det_a_code);\n if (var_dets_set.count(det_a_code) == 1) return;\n if (det_a_hash % n_procs != proc_id) return;\n if ((det_a_hash / n_procs) % n_batches != selected_batch) {\n return; // Only use one batch.\n }\n const double H_ai = abstract_system->hamiltonian(&var_det, det_a);\n const double H_aa = abstract_system->hamiltonian(det_a, det_a);\n const double factor =\n n_batches / ((energy_var - H_aa) * n_samples * (n_samples - 1));\n const double partial_sum_term = H_ai * coef;\n const double contrib_1 = cnt * partial_sum_term / prob * sqrt(-factor);\n\n // Add to dtm partial sums.\n if (std::abs(partial_sum_term) >= eps_dtm_pt) {\n partial_sums_dtm.set(\n det_a_code, [&](double& value) { value += contrib_1; }, 0.0);\n }\n\n // Add to partial sums.\n for (int i = 0; i < n_eps_pts; i++) {\n if (std::abs(partial_sum_term) >= eps_pts[i]) {\n partial_sums[i].set(\n det_a_code, [&](double& value) { value += contrib_1; }, 0.0);\n break;\n }\n }\n\n // Calculate 2nd order contribution if not belong to dtm pt .\n if (std::abs(partial_sum_term) >= eps_dtm_pt) return;\n\n const double contrib_2 =\n (cnt * (n_samples - 1) / prob - (cnt * cnt) / (prob * prob)) *\n partial_sum_term * partial_sum_term * factor;\n const int n_orbs_used = SpinDetUtil::get_n_orbs_used(*det_a);\n for (int i = 0; i < n_eps_pts; i++) {\n if (std::abs(partial_sum_term) < eps_pts[i]) continue;\n for (int j = 0; j < n_n_orbs_pts; j++) {\n if (n_orbs_pts[j] < n_orbs_used) continue;\n#pragma omp atomic\n energy_pts_loop[i][j] += contrib_2;\n }\n }\n };\n\n abstract_system->find_connected_dets(\n &var_det, eps_pts_min / std::abs(coef), pt_det_handler);\n }\n\n // Accumulate and get PT correction from batch.\n for (int i = 0; i < n_eps_pts; i++) {\n partial_sums[i].apply(\n [&](const std::string& det_code, const double value) {\n const int thread_id = omp_get_thread_num();\n auto& det = tmp_dets[thread_id];\n det.ParseFromString(det_code);\n\n double cum_value = value;\n const double value_dtm =\n partial_sums_dtm.get_copy_or_default(det_code, 0.0);\n partial_sums_dtm.unset(det_code);\n\n const int n_orbs_used = SpinDetUtil::get_n_orbs_used(det);\n for (int j = i; j < n_eps_pts; j++) {\n if (j > i) {\n const double value_j =\n partial_sums[j].get_copy_or_default(det_code, 0.0);\n partial_sums[j].unset(det_code);\n cum_value += value_j;\n }\n\n const double contrib_1 =\n -cum_value * cum_value + value_dtm * value_dtm;\n\n for (int k = 0; k < n_n_orbs_pts; k++) {\n if (n_orbs_pts[k] < n_orbs_used) continue;\n#pragma omp atomic\n energy_pts_loop[j][k] += contrib_1;\n#pragma omp atomic\n n_pt_dets_loop[j][k]++;\n }\n }\n });\n }\n\n // Append loop results store.\n for (int i = 0; i < n_eps_pts; i++) {\n parallel->reduce_to_sum(energy_pts_loop[i]);\n parallel->reduce_to_sum(n_pt_dets_loop[i]);\n for (int j = 0; j < n_n_orbs_pts; j++) {\n energy_pts_loops[i][j].push_back(energy_pts_loop[i][j]);\n }\n }\n\n // Calculate statistics.\n double max_uncert = 0.0;\n for (int i = 0; i < n_eps_pts; i++) {\n for (int j = 0; j < n_n_orbs_pts; j++) {\n const double avg = get_avg(energy_pts_loops[i][j]);\n const double stdev = get_stdev(energy_pts_loops[i][j]);\n energy_pts_stc[i][j].value = avg;\n energy_pts_stc[i][j].uncertainty = stdev / sqrt(iteration);\n max_uncert = std::max(energy_pts_stc[i][j].uncertainty, max_uncert);\n }\n }\n\n // Report results.\n if (verbose) {\n printf(TABLE_FORMAT_ITEM_NAME, \"# orbitals PT:\");\n for (int j = 0; j < n_n_orbs_pts; j++) {\n printf(TABLE_FORMAT_D, n_orbs_pts[j]);\n }\n printf(\"\\n\");\n for (int i = 0; i < n_eps_pts; i++) {\n printf(\">>> eps_stc_pt %#.4g\\n\", eps_pts[i]);\n printf(TABLE_FORMAT_ITEM_NAME, \"Loop # STC PT dets:\");\n for (int j = 0; j < n_n_orbs_pts; j++) {\n printf(TABLE_FORMAT_LL, n_pt_dets_loop[i][j]);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"Loop STC energy PT:\");\n for (int j = 0; j < n_n_orbs_pts; j++) {\n printf(TABLE_FORMAT_F, energy_pts_loops[i][j][iteration]);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"EST. STC energy PT:\");\n for (int j = 0; j < n_n_orbs_pts; j++) {\n printf(TABLE_FORMAT_F, energy_pts_stc[i][j].value);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"Uncertainty:\");\n for (int j = 0; j < n_n_orbs_pts; j++) {\n printf(TABLE_FORMAT_F, energy_pts_stc[i][j].uncertainty);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"EST. energy PT:\");\n for (int j = 0; j < n_n_orbs_pts; j++) {\n printf(\n TABLE_FORMAT_F,\n energy_pts_stc[i][j].value + energy_pts_dtm[j].value);\n }\n printf(\"\\n\");\n printf(TABLE_FORMAT_ITEM_NAME, \"EST. CORR. energy:\");\n for (int j = 0; j < n_n_orbs_pts; j++) {\n const double energy_corr = energy_pts_stc[i][j].value +\n energy_pts_dtm[j].value + energy_var -\n energy_hf;\n printf(TABLE_FORMAT_F, energy_corr);\n }\n printf(\"\\n\");\n }\n }\n\n timer->end();\n\n if (iteration >= 10 && max_uncert <= target_error) break;\n\n iteration++;\n }\n\n timer->end();\n\n return energy_pts_stc;\n}\n\ndouble SolverImpl::get_weight(const int i) const {\n const double coef = abstract_system->coefs[i];\n const double abs_coef = std::abs(coef);\n return abs_coef;\n}\n\nSolver* Injector::new_solver(\n Session* const session,\n Connections* const connections,\n AbstractSystem* const abstract_system) {\n return new SolverImpl(session, connections, abstract_system);\n}\n", "meta": {"hexsha": "4804ddf88092a03708675144057a3deb06ee8dc6", "size": 35381, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/solver/solver.cc", "max_stars_repo_name": "jl2922/hci", "max_stars_repo_head_hexsha": "2806ad1f2cc0e100632eaaaf491670f928c9feec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2017-09-23T17:52:52.000Z", "max_stars_repo_stars_event_max_datetime": "2017-11-01T20:15:01.000Z", "max_issues_repo_path": "src/solver/solver.cc", "max_issues_repo_name": "jl2922/hci", "max_issues_repo_head_hexsha": "2806ad1f2cc0e100632eaaaf491670f928c9feec", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2017-09-24T14:29:03.000Z", "max_issues_repo_issues_event_max_datetime": "2017-09-24T14:44:12.000Z", "max_forks_repo_path": "src/solver/solver.cc", "max_forks_repo_name": "jl2922/hci", "max_forks_repo_head_hexsha": "2806ad1f2cc0e100632eaaaf491670f928c9feec", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7895771878, "max_line_length": 80, "alphanum_fraction": 0.6389304994, "num_tokens": 9748, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165233795671, "lm_q2_score": 0.4035668537353745, "lm_q1q2_score": 0.21280747026296795}} {"text": "//Copyright (c) 2015 Zachary Kann\n//\n//Permission is hereby granted, free of charge, to any person obtaining a copy\n//of this software and associated documentation files (the \"Software\"), to deal\n//in the Software without restriction, including without limitation the rights\n//to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n//copies of the Software, and to permit persons to whom the Software is\n//furnished to do so, subject to the following conditions:\n//\n//The above copyright notice and this permission notice shall be included in all\n//copies or substantial portions of the Software.\n//\n//THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n//IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n//FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n//AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n//LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n//OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n//SOFTWARE.\n\n// ---\n// Author: Zachary Kann\n\n// A set of functions for calculation of electric field / frequency maps\n// as outlined in the papers of Skinner et al. See, for example, S.M. Gruenbaum,\n// et al. J. Chem. Theory Comput. 2013 9 (7), 3109. These maps are necessary\n// for calculation of vibrational spectra using the methodology outline in the\n// aforementioned papers.\n\n// Abbreviations:\n// FTIR = fourier-transform infrared\n// SFG = sum-frequency generation\n\n#include \n#include \"z_frequency_map.hpp\"\n#include \"z_histogram.hpp\"\n#include \"z_molecule_group.hpp\"\n#include \"z_cx_tcf.hpp\"\n\n#ifndef _Z_INFRARED_MAP_HPP_\n#define _Z_INFRARED_MAP_HPP_\n\nclass InfraredMap:\n public FrequencyMap {\n public:\n inline InfraredMap(const MoleculeGroup& group, const Chromophore chromophore,\n const double timestep, const int correlation_length = 200)\n : FrequencyMap(group, chromophore, timestep, correlation_length) {\n mu_01_ = arma::zeros(DIMS, steps_guess_, num_chromophores());\n }\n\n private:\n arma::cube mu_01_;\n\n inline void ResizeArrays() {\n omega_01_.resize(num_chromophores(), steps_guess_);\n mu_01_.resize(DIMS, steps_guess_, num_chromophores());\n }\n\n inline double MuOrAlphaProduct(const int chromophore, const int corr_start,\n const int i_corr) {\n return arma::dot(mu_01_.slice(chromophore).col(corr_start),\n mu_01_.slice(chromophore).col(corr_start+i_corr));\n }\n\n // Calculates omega and mu/alpha using the frequency maps.\r\n void UseMapping(const int chromophore,\n const arma::rowvec& box);\n};\n#endif\n", "meta": {"hexsha": "a0e7e00d9917644cce5b1b84e038e0f4b0e23777", "size": 2735, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/z_infrared_map.hpp", "max_stars_repo_name": "thekannman/z_infrared", "max_stars_repo_head_hexsha": "a091041ad744d8591e4d9cb98eb7b650950bacaf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2015-06-12T04:49:11.000Z", "max_stars_repo_stars_event_max_datetime": "2015-06-12T04:49:11.000Z", "max_issues_repo_path": "include/z_infrared_map.hpp", "max_issues_repo_name": "thekannman/z_infrared", "max_issues_repo_head_hexsha": "a091041ad744d8591e4d9cb98eb7b650950bacaf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "include/z_infrared_map.hpp", "max_forks_repo_name": "thekannman/z_infrared", "max_forks_repo_head_hexsha": "a091041ad744d8591e4d9cb98eb7b650950bacaf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 38.5211267606, "max_line_length": 80, "alphanum_fraction": 0.7341864717, "num_tokens": 649, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5078118642792044, "lm_q2_score": 0.4186969093556867, "lm_q1q2_score": 0.21261925810785234}} {"text": "#define NPY_NO_DEPRECATED_API NPY_1_11_API_VERSION\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n//#include \n\nusing namespace Eigen;\nusing namespace std;\nusing namespace boost::python;\n\n#if PY_VERSION_HEX >= 0x03000000\nvoid *\n#else\nvoid\n#endif\ninit_numpy(){\n //Py_Initialize;\n import_array();\n}\n\n//original comment:\n//\"TODO if we aren't outputting gamma, don't need to write it to memory (just\n//need t and t+1), so we can save the stack array for each HMM at the cost of\n//a branch\"\n\n/*struct double_to_python_float\n{\n static PyObject* convert(double const& d)\n {\n return boost::python::incref(\n boost::python::object(d).ptr());\n }\n};*/\n\n//numpy scalar converters.\ntemplate \nstruct enable_numpy_scalar_converter\n{\n enable_numpy_scalar_converter()\n {\n // Required NumPy call in order to use the NumPy C API within another\n // extension module.\n // import_array();\n init_numpy();\n\n boost::python::converter::registry::push_back(\n &convertible,\n &construct,\n boost::python::type_id());\n }\n\n static void* convertible(PyObject* object)\n {\n // The object is convertible if all of the following are true:\n // - is a valid object.\n // - is a numpy array scalar.\n // - its descriptor type matches the type for this converter.\n return (\n object && // Valid\n PyArray_CheckScalar(object) && // Scalar\n PyArray_DescrFromScalar(object)->type_num == NumPyScalarType // Match\n )\n ? object // The Python object can be converted.\n : NULL;\n }\n\n static void construct(\n PyObject* object,\n boost::python::converter::rvalue_from_python_stage1_data* data)\n {\n // Obtain a handle to the memory block that the converter has allocated\n // for the C++ type.\n namespace python = boost::python;\n typedef python::converter::rvalue_from_python_storage storage_type;\n void* storage = reinterpret_cast(data)->storage.bytes;\n\n // Extract the array scalar type directly into the storage.\n PyArray_ScalarAsCtype(object, storage);\n\n // Set convertible to indicate success.\n data->convertible = storage;\n }\n};\n\n// TODO openmp version\n\nnumeric::array run(dict& data, dict& model, numeric::array& forward_messages){\n //TODO: check if parameters are null.\n //TODO: check that dicts have the required members.\n //TODO: check that all parameters have the right sizes.\n //TODO: i'm not sending any error messages.\n\n numeric::array alldata = extract(data[\"data\"]); //multidimensional array, so i need to keep extracting arrays.\n int bigT = len(alldata[0]); //this should be the number of columns in the alldata object. i'm assuming is 2d array.\n int num_subparts = len(alldata);\n\n numeric::array allresources = extract(data[\"resources\"]);\n\n numeric::array starts = extract(data[\"starts\"]);\n int num_sequences = len(starts);\n\n numeric::array lengths = extract(data[\"lengths\"]);\n\n numeric::array learns = extract(model[\"learns\"]);\n int num_resources = len(learns);\n\n numeric::array forgets = extract(model[\"forgets\"]);\n\n numeric::array guesses = extract(model[\"guesses\"]);\n\n numeric::array slips = extract(model[\"slips\"]);\n\n double prior = extract(model[\"prior\"]);\n\n Array2d initial_distn;\n initial_distn << 1-prior, prior;\n\n MatrixXd As(2,2*num_resources);\n for (int n=0; n(learns[n]);\n double forget = extract(forgets[n]);\n As.col(2*n) << 1-learn, learn;\n As.col(2*n+1) << forget, 1-forget;\n }\n\n // forward messages\n //numeric::array all_forward_messages = extract(forward_messages);\n double * forward_messages_temp = new double[2*bigT];\n for (int i=0; i<2; i++) {\n for (int j=0; j(forward_messages[i][j]);\n }\n }\n\n //// outputs\n\n double * all_predictions = new double[2*bigT];\n Map predictions(all_predictions,2,bigT);\n\n /* COMPUTATION */\n\n for (int sequence_index=0; sequence_index < num_sequences; sequence_index++) {\n // NOTE: -1 because Matlab indexing starts at 1\n int64_t sequence_start = extract(starts[sequence_index]) - 1;\n int64_t T = extract(lengths[sequence_index]);\n\n //int16_t *resources = allresources + sequence_start;\n Map forward_messages(forward_messages_temp + 2*sequence_start,2,T);\n Map predictions(all_predictions + 2*sequence_start,2,T);\n\n predictions.col(0) = initial_distn;\n for (int t=0; t(allresources[sequence_start+t]);\n predictions.col(t+1) = As.block(0,2*(resources_temp-1),2,2) * forward_messages.col(t);\n }\n }\n\n npy_intp all_predictions_dims[2] = {2,bigT}; //TODO: just put directly this array into the PyArray_SimpleNewFromData function?\n PyObject * all_predictions_pyObj = PyArray_New(&PyArray_Type, 2, all_predictions_dims, NPY_DOUBLE, NULL, all_predictions, 0, NPY_ARRAY_CARRAY, NULL);\n boost::python::handle<> all_predictions_handle( all_predictions_pyObj );\n boost::python::numeric::array all_predictions_arr( all_predictions_handle );\n\n delete all_predictions;\n delete forward_messages_temp;\n\n return(all_predictions_arr);\n}\n\nBOOST_PYTHON_MODULE(predict_onestep_states){\n //import_array();\n init_numpy();\n numeric::array::set_module_and_type(\"numpy\", \"ndarray\");\n //to_python_converter();\n enable_numpy_scalar_converter();\n enable_numpy_scalar_converter();\n enable_numpy_scalar_converter();\n enable_numpy_scalar_converter();\n\n def(\"run\", run);\n\n}\n\n", "meta": {"hexsha": "884a3cbbea555a1b7deabfe439098913178f34ee", "size": 6356, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "source-cpp/.DEPRECATED/predict_onestep_states.cpp", "max_stars_repo_name": "themrzmaster/pyBKT", "max_stars_repo_head_hexsha": "f004ae72e0e09c59de7c8d63040e7f90c0b43c0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-07-13T02:10:34.000Z", "max_stars_repo_stars_event_max_datetime": "2021-07-13T02:10:34.000Z", "max_issues_repo_path": "source-cpp/.DEPRECATED/predict_onestep_states.cpp", "max_issues_repo_name": "themrzmaster/pyBKT", "max_issues_repo_head_hexsha": "f004ae72e0e09c59de7c8d63040e7f90c0b43c0c", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "source-cpp/.DEPRECATED/predict_onestep_states.cpp", "max_forks_repo_name": "themrzmaster/pyBKT", "max_forks_repo_head_hexsha": "f004ae72e0e09c59de7c8d63040e7f90c0b43c0c", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.4526315789, "max_line_length": 153, "alphanum_fraction": 0.6787287602, "num_tokens": 1553, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5736784074525096, "lm_q2_score": 0.3702253925955866, "lm_q1q2_score": 0.2123903136227163}} {"text": "/*\n\nCopyright (c) 2005-2021, University of Oxford.\nAll rights reserved.\n\nUniversity of Oxford means the Chancellor, Masters and Scholars of the\nUniversity of Oxford, having an administrative office at Wellington\nSquare, Oxford OX1 2JD, UK.\n\nThis file is part of Chaste.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n * Redistributions of source code must retain the above copyright notice,\n this list of conditions and the following disclaimer.\n * Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n * Neither the name of the University of Oxford nor the names of its\n contributors may be used to endorse or promote products derived from this\n software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\nARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE\nLIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\nCONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE\nGOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)\nHOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\nLIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT\nOF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n*/\n\n#ifndef CorriasBuistICCModified_HPP_\n#define CorriasBuistICCModified_HPP_\n\n#include \"ChasteSerialization.hpp\"\n#include \n#include \"AbstractCardiacCell.hpp\"\n#include \"AbstractStimulusFunction.hpp\"\n\n/**\n * This class is a modified version of the model of a gastric\n * Interstitial Cell of Cajal.\n *\n * Reference publication is:\n *\n * Corrias A, Buist ML.\n * \"Quantitative cellular description of gastric slow wave activity.\"\n * Am J Physiol Gastrointest Liver Physiol. 2008 Apr;294(4):G989-95. Epub 2008 Feb 14.\n *\n * Modifications include:\n * - simplified mitochondria dynamics (assumed mitochondrial potential is almost constant\n * - ability to set K+ channels-affecting CO concentrations\n * - ability to deflect a fraction of VDDR channels into the pacemaker unit.\n */\nclass CorriasBuistICCModified : public AbstractCardiacCell\n{\n friend class boost::serialization::access;\n /**\n * Boost Serialization method for archiving/checkpointing.\n * Archives the object and its member variables.\n *\n * @param archive The boost archive.\n * @param version The current version of this class.\n */\n template\n void serialize(Archive & archive, const unsigned int version)\n {\n archive & boost::serialization::base_object(*this);\n archive & mFractionOfVDDRInPU;\n archive & mIP3Concentration;\n archive & mScaleFactorSerca;\n archive & mScaleFactorCarbonMonoxide;\n }\n\nprivate:\n\n /**fraction of VDDR channel in the PU, initialised to zero*/\n double mFractionOfVDDRInPU;\n /**the IP3 concentration, defaults to 0.0006 mM*/\n double mIP3Concentration;\n /**scales the flux through the SERCA pump.1.0-> control. 0.0-> blocked*/\n double mScaleFactorSerca;\n /**\n * Scale factor for CO-affected currents\n * Note that this the number that multiply the currents, hence it is not [CO],\n * but a function of [CO] (for example, 2.8*[CO] - 0.1)\n */\n double mScaleFactorCarbonMonoxide;\n\n /* Concentrations */\n double Ca_o;/**< mM */\n double Cl_o;/**< mM */\n double K_o;/**< mM */\n double Na_o;/**< mM */\n\n /* Nernst parameters */\n double R;/**< pJ/nmol/K */\n double T;/**< degK */\n double F;/**< nC/nmol */\n double FoRT;/**< 1/mV */\n double RToF;/**< mV */\n\n double Cm ;/**< pF */\n double Asurf_in_cm_square;/**< cm2 */\n double Asurf ;/**< mm2 */\n double Cl_i ;/**< mM */\n double K_i ;/**< mM */\n double Na_i ;/**< mM */\n double P_cyto;/**< dim */\n double Vol ;/**< mm3 */\n double fc ;/**< dim */\n double fe ;/**< dim */\n double fm ;/**< dim */\n double Q10Ca ;/**< dim */\n double Q10K ;/**< dim */\n double Q10Na ;/**< dim */\n double T_exp ;/**< degK */\n\n double G_max_BK ;/**< nS */\n double G_max_CaCl ;/**< nS */\n double G_max_ERG ;/**< nS */\n double G_max_Ltype ;/**< nS */\n double G_max_NSCC ;/**< nS */\n double G_max_Na ;/**< nS */\n double G_max_VDDR ;/**< nS */\n double G_max_bk ;/**< nS */\n double G_max_kv11 ;/**< nS */\n\n\n double J_max_PMCA ;/**< mM/ms (mM/s) * 1/1000 (s/ms) = mM/ms */\n double J_max_PMCA_PU ;/**< mM/ms (mM/s) * 1/1000 (s/ms) = mM/ms */\n double J_ERleak ;/**< 1/ms (1/s) * 1/1000 (ms/s) = 1/ms */\n double J_max_leak ;/**< 1/ms (1/s) * 1/1000 (ms/s) = 1/ms */\n double Jmax_IP3 ;/**< 1/ms (1/s) * 1/1000 (ms/s) = 1/ms */\n double Jmax_NaCa ;/**< mM/ms (mM/s) * 1/1000 (s/ms) = mM/ms */\n double Jmax_serca ;/**< mM/ms (mM/s) * 1/1000 (s/ms) = mM/ms */\n double Jmax_uni ;/**< 1/ms (1/s) * 1/1000 (ms/s) = 1/ms */\n\n double NaPerm_o_Kperm ;/**< dim */\n double L ;/**< dim */\n double P_ER ;/**< dim */\n double P_PU ;/**< dim */\n double P_mito ;/**< dim */\n double b ;/**< dim */\n double na ;/**< dim */\n\n double K_Ca ;/**< mM */\n double K_Na ;/**< mM */\n double K_act ;/**< mM */\n double K_trans ;/**< mM */\n double k_serca ;/**< mM */\n double conc ;/**< mM */\n double d_ACT ;/**< mM */\n double d_IP3 ;/**< mM */\n double d_INH ;/**< mM */\n\n double tau_d_CaCl;/**< ms(s) * 1000 (ms/s) = ms */\n double tau_d_NSCC ;/**< ms(s) * 1000 (ms/s) = ms */\n double tauh;/**< ms(s ) * 1000 (ms/s) = ms */\n\n double deltaPsi_B;/**< mV */\n double deltaPsi_star;/**< mV */\n double deltaPsi;/**< mV */\n\n\n /////////////////////\n //Calculated constants\n ////////////////////\n /* Volumes */\n double V_cyto; /**< mm3 */\n double V_ER; /**< mm3 */\n double V_MITO; /**< mm3 */\n double V_PU; /**< mm3 */\n\n /* Temperature corrections */\n double T_correction_Ca; /**< dim */\n double T_correction_K; /**< dim */\n double T_correction_Na; /**< dim */\n double T_correction_BK; /**< uA/mm2 */\n\n /* Nernst potentials */\n double E_Na; /**< mV */\n double E_K; /**< mV */\n double E_Cl; /**< mV */\n double E_NSCC; /**< mV */\n\n /* Activation gate time constants */\n double tau_d_ERG; /**< ms */\n double tau_d_Ltype; /**< ms */\n double tau_d_Na; /**< ms */\n double tau_d_VDDR; /**< ms */\n double tau_d_kv11; /**< ms */\n\n /* Inactivation gate time constants */\n double tau_f_Ltype; /**< ms */\n double tau_f_Na; /**< ms */\n double tau_f_VDDR; /**< ms */\n double tau_f_ca_Ltype; /**< ms */\n double tau_f_kv11; /**< ms */\n\n /* Speed ups */\n double e2FoRTdPsiMdPsiS;/**< speed-up constant*/\n double ebFoRTdPsiMdPsiS;/**< speed-up constant*/\n\n\npublic:\n /**\n * Constructor\n *\n * @param pSolver is a pointer to the ODE solver\n * @param pIntracellularStimulus is a pointer to the intracellular stimulus\n */\n CorriasBuistICCModified(boost::shared_ptr pSolver, boost::shared_ptr pIntracellularStimulus);\n\n /**\n * Destructor\n */\n ~CorriasBuistICCModified();\n\n /**\n * Now empty\n */\n void VerifyStateVariables();\n\n /**\n * Calculates the ionic current\n *\n * @param pStateVariables the state variables of this model\n * @return the total ionic current\n */\n double GetIIonic(const std::vector* pStateVariables=NULL);\n\n /**\n * Compute the RHS of the FitHugh-Nagumo system of ODEs\n *\n * @param time the current time, in milliseconds\n * @param rY current values of the state variables\n * @param rDY to be filled in with derivatives\n */\n void EvaluateYDerivatives(double time, const std::vector& rY, std::vector& rDY);\n\n /**\n * Sets the fraction of VDDR channels in the Pacemaker unit\n *\n * @param fraction the fraction of VDDr channels in the PU\n */\n void SetFractionOfVDDRInPU(double fraction);\n\n /**\n * Set the value of IP3 concentration in the cell\n *\n * @param concentration the concentration of IP3\n */\n void SetIP3Concentration(double concentration);\n\n /**\n * Set a multiplying factor for the influx of Ca2+ into the Er via the SERCA pump (set to zero will block the SW generation)\n *\n * @param scaleFactor the scale factor (=0 --> no Ca2+ uptake into the ER and, consequently, no SW)\n */\n void SetSercaPumpScaleFactor(double scaleFactor);\n\n /**\n * Set the carbon monoxide scale factor.\n * This will multiply the following currents: I_kv11, I_ERG, Ibk\n *\n * @param scaleFactor the scale factor that multiply the currents.\n */\n void SetCarbonMonoxideScaleFactor(double scaleFactor);\n\n /**\n * @return the Carbon Monoxide scale factor\n */\n double GetCarbonMonoxideScaleFactor();\n};\n\n\n// Needs to be included last\n#include \"SerializationExportWrapper.hpp\"\nCHASTE_CLASS_EXPORT(CorriasBuistICCModified)\n\nnamespace boost\n{\n namespace serialization\n {\n template\n inline void save_construct_data(\n Archive & ar, const CorriasBuistICCModified * t, const unsigned int fileVersion)\n {\n const boost::shared_ptr p_solver = t->GetSolver();\n const boost::shared_ptr p_stimulus = t->GetStimulusFunction();\n ar << p_solver;\n ar << p_stimulus;\n }\n\n template\n inline void load_construct_data(\n Archive & ar, CorriasBuistICCModified * t, const unsigned int fileVersion)\n {\n boost::shared_ptr p_solver;\n boost::shared_ptr p_stimulus;\n ar >> p_solver;\n ar >> p_stimulus;\n ::new(t)CorriasBuistICCModified(p_solver, p_stimulus);\n }\n }\n}\n\n#endif // CorriasBuistICCModified_HPP_\n", "meta": {"hexsha": "f511d5b188212a33037b89d1c21b21cf2a092c75", "size": 10841, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "heart/src/odes/ionicmodels/noncardiac/CorriasBuistICCModified.hpp", "max_stars_repo_name": "mdp19pn/Chaste", "max_stars_repo_head_hexsha": "f7b6bafa64287d567125b587b29af6d8bd7aeb90", "max_stars_repo_licenses": ["Apache-2.0", "BSD-3-Clause"], "max_stars_count": 100.0, "max_stars_repo_stars_event_min_datetime": "2015-02-23T08:32:23.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-25T11:39:26.000Z", "max_issues_repo_path": "heart/src/odes/ionicmodels/noncardiac/CorriasBuistICCModified.hpp", 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YES\n2. NO\n\n", "lm_q1_score": 0.5312093733737563, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.2123836911449199}} {"text": "// ====================================================================\n// This file is part of FlexibleSUSY.\n//\n// FlexibleSUSY is free software: you can redistribute it and/or modify\n// it under the terms of the GNU General Public License as published\n// by the Free Software Foundation, either version 3 of the License,\n// or (at your option) any later version.\n//\n// FlexibleSUSY is distributed in the hope that it will be useful, but\n// WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n// General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with FlexibleSUSY. If not, see\n// .\n// ====================================================================\n\n// File generated at Sat 27 Aug 2016 12:48:12\n\n#ifndef MSSMatMGUT_TWO_SCALE_LOW_SCALE_CONSTRAINT_H\n#define MSSMatMGUT_TWO_SCALE_LOW_SCALE_CONSTRAINT_H\n\n#include \"MSSMatMGUT_low_scale_constraint.hpp\"\n#include \"MSSMatMGUT_input_parameters.hpp\"\n#include \"two_scale_constraint.hpp\"\n#include \"lowe.h\"\n#include \n\nnamespace flexiblesusy {\n\ntemplate \nclass MSSMatMGUT;\n\nclass Two_scale;\n\ntemplate<>\nclass MSSMatMGUT_low_scale_constraint : public Constraint {\npublic:\n MSSMatMGUT_low_scale_constraint();\n MSSMatMGUT_low_scale_constraint(MSSMatMGUT*, const softsusy::QedQcd&);\n virtual ~MSSMatMGUT_low_scale_constraint();\n virtual void apply();\n virtual double get_scale() const;\n virtual void set_model(Two_scale_model*);\n\n void clear();\n const Eigen::Matrix,3,3>& get_ckm();\n const Eigen::Matrix,3,3>& get_pmns();\n double get_initial_scale_guess() const;\n void initialize();\n const softsusy::QedQcd& get_sm_parameters() const;\n void set_sm_parameters(const softsusy::QedQcd&);\n void set_threshold_corrections_loop_order(unsigned); ///< threshold corrections loop order\n\nprivate:\n double scale;\n double initial_scale_guess;\n MSSMatMGUT* model;\n softsusy::QedQcd qedqcd;\n Eigen::Matrix,3,3> ckm;\n Eigen::Matrix,3,3> pmns;\n Eigen::Matrix neutrinoDRbar;\n double MWDRbar;\n double MZDRbar;\n double AlphaS;\n double EDRbar;\n double ThetaWDRbar;\n double new_g1, new_g2, new_g3;\n double self_energy_w_at_mw;\n unsigned threshold_corrections_loop_order; ///< threshold corrections loop order\n\n double calculate_theta_w(double);\n void calculate_threshold_corrections();\n void calculate_DRbar_gauge_couplings();\n void calculate_DRbar_yukawa_couplings();\n void calculate_Yu_DRbar();\n void calculate_Yd_DRbar();\n void calculate_Ye_DRbar();\n void calculate_MNeutrino_DRbar();\n double calculate_delta_alpha_em(double) const;\n double calculate_delta_alpha_s(double) const;\n void recalculate_mw_pole();\n void update_scale();\n};\n\n} // namespace flexiblesusy\n\n#endif\n", "meta": {"hexsha": "401e269e386d79efb6d95860248cf7ba4d5b6724", "size": 3015, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contrib/MassSpectra/flexiblesusy/models/MSSMatMGUT/MSSMatMGUT_two_scale_low_scale_constraint.hpp", "max_stars_repo_name": "aaronvincent/gambit_aaron", "max_stars_repo_head_hexsha": "a38bd6fc10d781e71f2adafd401c76e1e3476b05", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "contrib/MassSpectra/flexiblesusy/models/MSSMatMGUT/MSSMatMGUT_two_scale_low_scale_constraint.hpp", "max_issues_repo_name": "aaronvincent/gambit_aaron", "max_issues_repo_head_hexsha": "a38bd6fc10d781e71f2adafd401c76e1e3476b05", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "contrib/MassSpectra/flexiblesusy/models/MSSMatMGUT/MSSMatMGUT_two_scale_low_scale_constraint.hpp", "max_forks_repo_name": "aaronvincent/gambit_aaron", "max_forks_repo_head_hexsha": "a38bd6fc10d781e71f2adafd401c76e1e3476b05", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.5, "max_line_length": 93, "alphanum_fraction": 0.7266998342, "num_tokens": 731, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6150878555160665, "lm_q2_score": 0.3451052709578724, "lm_q1q2_score": 0.21227006104076881}} {"text": "//---------------------------------------------------------------------------//\n// Copyright (c) 2018-2020 Mikhail Komarov \n// Copyright (c) 2020 Nikita Kaskov \n//\n// MIT License\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in all\n// copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n// SOFTWARE.\n//---------------------------------------------------------------------------//\n\n#ifndef CRYPTO3_BLOCK_SHACAL2_HPP\n#define CRYPTO3_BLOCK_SHACAL2_HPP\n\n#include \n\n#include \n#include \n\n#include \n\n#ifdef CRYPTO3_BLOCK_SHOW_PROGRESS\n#include \n#endif\n\nnamespace nil {\n namespace crypto3 {\n namespace block {\n\n /*!\n * @brief Shacal2. Merkle-Damgård construction foundation for\n * @ref nil::crypto3::hashes::sha2 \"SHA2\" hashes. Accepts\n * up to a 512-bit key. Fast and seemingly very secure, but obscure.\n * Standardized by NESSIE.\n *\n * @ingroup block\n *\n * Encrypt implemented directly from the SHA standard as found at\n * http://csrc.nist.gov/publications/fips/fips180-2/fips180-2.pdf\n *\n * Decrypt is a straight-forward inverse\n *\n * In SHA terminology:\n * - plaintext = H^(i-1)\n * - ciphertext = H^(i)\n * - key = M^(i)\n * - schedule = W\n *\n * @tparam BlockBits Block cipher block bits. Available values are: 256, 512\n */\n template\n class shacal2 {\n\n typedef detail::shacal2_policy policy_type;\n\n public:\n constexpr static const std::size_t version = BlockBits;\n\n constexpr static const std::size_t word_bits = policy_type::word_bits;\n typedef typename policy_type::word_type word_type;\n\n constexpr static const std::size_t key_bits = policy_type::key_bits;\n constexpr static const std::size_t key_words = policy_type::key_words;\n typedef typename policy_type::key_type key_type;\n\n constexpr static const std::size_t block_bits = policy_type::block_bits;\n constexpr static const std::size_t block_words = policy_type::block_words;\n typedef typename policy_type::block_type block_type;\n\n static const std::size_t rounds = policy_type::rounds;\n typedef typename policy_type::key_schedule_type key_schedule_type;\n\n template\n struct stream_processor {\n struct params_type {\n\n constexpr static const std::size_t value_bits = ValueBits;\n constexpr static const std::size_t length_bits = policy_type::word_bits * 2;\n };\n\n typedef block_stream_processor type;\n };\n\n typedef typename stream_endian::little_octet_big_bit endian_type;\n\n shacal2(const key_type &key) : schedule(build_schedule(key)) {\n }\n\n shacal2(key_schedule_type s) : schedule((prepare_schedule(s), s)) {\n }\n\n block_type encrypt(block_type const &plaintext) const {\n return encrypt_block(plaintext);\n }\n\n inline block_type decrypt(const block_type &ciphertext) const {\n return decrypt_block(ciphertext);\n }\n\n protected:\n const key_schedule_type schedule;\n\n static key_schedule_type build_schedule(const key_type &key) {\n // Copy key into beginning of round_constants_words\n key_schedule_type schedule;\n for (unsigned t = 0; t < key_words; ++t) {\n schedule[t] = key[t];\n }\n prepare_schedule(schedule);\n return schedule;\n }\n\n static void prepare_schedule(key_schedule_type &schedule) {\n for (unsigned t = key_words; t < rounds; ++t) {\n schedule[t] = policy_type::sigma_1(schedule[t - 2]) + schedule[t - 7] +\n policy_type::sigma_0(schedule[t - 15]) + schedule[t - 16];\n }\n }\n\n block_type encrypt_block(const block_type &plaintext) const {\n return encrypt_block(schedule, plaintext);\n }\n\n inline static block_type encrypt_block(const key_schedule_type &schedule, block_type const &plaintext) {\n\n // Initialize working variables with block\n word_type a = plaintext[0], b = plaintext[1], c = plaintext[2], d = plaintext[3], e = plaintext[4],\n f = plaintext[5], g = plaintext[6], h = plaintext[7];\n\n // Encipher block\n#ifdef CRYPTO3_BLOCK_NO_OPTIMIZATION\n\n for (unsigned t = 0; t < rounds; ++t) {\n word_type T1 = h + policy_type::Sigma_1(e) + policy_type::Ch(e, f, g) +\n policy_type::constants[t] + round_constants_words[t];\n word_type T2 = policy_type::Sigma_0(a) + policy_type::Maj(a, b, c);\n\n h = g;\n g = f;\n f = e;\n e = d + T1;\n d = c;\n c = b;\n b = a;\n a = T1 + T2;\n }\n\n#else // CRYPTO3_BLOCK_NO_OPTIMIZATION\n\n BOOST_STATIC_ASSERT(rounds % block_words == 0);\n for (unsigned t = 0; t < rounds;) {\n for (int n = block_words; n--; ++t) {\n word_type T1 = h + policy_type::Sigma_1(e) + policy_type::Ch(e, f, g) +\n policy_type::constants[t] + schedule[t];\n word_type T2 = policy_type::Sigma_0(a) + policy_type::Maj(a, b, c);\n\n h = g;\n g = f;\n f = e;\n e = d + T1;\n d = c;\n c = b;\n b = a;\n a = T1 + T2;\n }\n }\n\n#endif\n\n return {{a, b, c, d, e, f, g, h}};\n }\n\n block_type decrypt_block(const block_type &ciphertext) const {\n return decrypt_block(schedule, ciphertext);\n }\n\n inline static block_type decrypt_block(const key_schedule_type &schedule,\n const block_type &ciphertext) {\n // Initialize working variables with block\n word_type a = ciphertext[0], b = ciphertext[1], c = ciphertext[2], d = ciphertext[3],\n e = ciphertext[4], f = ciphertext[5], g = ciphertext[6], h = ciphertext[7];\n\n // Decipher block\n for (unsigned t = rounds; t--;) {\n word_type T2 = policy_type::Sigma_0(b) + policy_type::Maj(b, c, d);\n word_type T1 = a - T2;\n\n a = b;\n b = c;\n c = d;\n d = e - T1;\n e = f;\n f = g;\n g = h;\n h = T1 - policy_type::Sigma_1(e) - policy_type::Ch(e, f, g) - policy_type::constants[t] -\n schedule[t];\n }\n return {{a, b, c, d, e, f, g, h}};\n }\n };\n } // namespace block\n } // namespace crypto3\n} // namespace nil\n\n#endif // CRYPTO3_BLOCK_CIPHERS_SHACAL2_HPP\n", "meta": {"hexsha": "d4c9c25489d6be9d8e4269523e1533350827a9be", "size": 9307, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/nil/crypto3/block/shacal2.hpp", "max_stars_repo_name": "NilFoundation/crypto3-block", "max_stars_repo_head_hexsha": "94f9cc42ac0fa62c5ee54e7d678abf48ffa9eec5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-09-14T18:09:38.000Z", "max_stars_repo_stars_event_max_datetime": "2021-09-14T18:09:38.000Z", "max_issues_repo_path": "include/nil/crypto3/block/shacal2.hpp", "max_issues_repo_name": "tonlabs/crypto3-block", "max_issues_repo_head_hexsha": "d7eede022f6130797d28bc39eb312bff9afebf07", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 41.0, "max_issues_repo_issues_event_min_datetime": "2019-06-07T23:11:20.000Z", "max_issues_repo_issues_event_max_datetime": "2020-08-16T18:04:20.000Z", "max_forks_repo_path": "include/nil/crypto3/block/shacal2.hpp", "max_forks_repo_name": "tonlabs/crypto3-block", "max_forks_repo_head_hexsha": "d7eede022f6130797d28bc39eb312bff9afebf07", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2020-01-11T15:37:40.000Z", "max_forks_repo_forks_event_max_datetime": "2020-02-26T21:47:05.000Z", "avg_line_length": 42.3045454545, "max_line_length": 120, "alphanum_fraction": 0.5084345117, "num_tokens": 1849, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621765008857981, "lm_q2_score": 0.37754067580180184, "lm_q1q2_score": 0.21224449606431645}} {"text": "// Copyright (c) 2020 CNES\n//\n// All rights reserved. Use of this source code is governed by a\n// BSD-style license that can be found in the LICENSE file.\n#pragma once\n#include \n#include \n#include \n#include \n#include \n#include \"pyinterp/detail/math.hpp\"\n\nnamespace pyinterp::detail::axis::container {\n\n/// Abstraction of a container of values representing a mathematical axis.\n///\n/// @tparam T type of data handled by this container\ntemplate \nclass Abstract {\n public:\n /// Default constructor\n Abstract() = default;\n\n /// Default destructor\n virtual ~Abstract() = default;\n\n /// Copy constructor\n ///\n /// @param rhs right value\n Abstract(const Abstract& rhs) = default;\n\n /// Move constructor\n ///\n /// @param rhs right value\n Abstract(Abstract&& rhs) = default;\n\n /// Copy assignment operator\n ///\n /// @param rhs right value\n auto operator=(const Abstract& rhs) -> Abstract& = default;\n\n /// Move assignment operator\n ///\n /// @param rhs right value\n auto operator=(Abstract&& rhs) -> Abstract& = default;\n\n /// Returns true if the data is arranged in ascending order.\n [[nodiscard]] inline auto is_ascending() const -> bool {\n return is_ascending_;\n }\n\n /// Reverse the order of elements in this axis\n virtual auto flip() -> void = 0;\n\n /// Checks thats axis is monotonic\n [[nodiscard]] virtual auto is_monotonic() const -> bool { return true; }\n\n /// Get the ith coordinate value.\n ///\n /// @param index which coordinate. Between 0 and size()-1 inclusive\n /// @return coordinate value\n [[nodiscard]] virtual auto coordinate_value(size_t index) const -> T = 0;\n\n /// Get the minimum coordinate value.\n ///\n /// @return minimum coordinate value\n [[nodiscard]] virtual auto min_value() const -> T = 0;\n\n /// Get the maximum coordinate value.\n ///\n /// @return maximum coordinate value\n [[nodiscard]] virtual auto max_value() const -> T = 0;\n\n /// Get the number of values for this axis\n ///\n /// @return the number of values\n [[nodiscard]] virtual auto size() const -> int64_t = 0;\n\n /// Gets the first element in the container\n ///\n /// @return the first element\n [[nodiscard]] virtual auto front() const -> T = 0;\n\n /// Gets the last element in the container\n ///\n /// @return the last element\n [[nodiscard]] virtual auto back() const -> T = 0;\n\n /// Search for the index corresponding to the requested value.\n ///\n /// @param coordinate position in this coordinate system\n /// @param bounded if true, returns \"-1\" if the value is located outside this\n /// coordinate system, otherwise the value of the first element if the value\n /// is located before, or the value of the last element of this container if\n /// the requested value is located after.\n /// @return index of the requested value it or -1 if outside this coordinate\n /// system area.\n [[nodiscard]] virtual auto find_index(T coordinate, bool bounded) const\n -> int64_t = 0;\n\n /// compare two variables instances\n ///\n /// @param rhs A variable to compare\n /// @return if variables are equals\n virtual auto operator==(const Abstract& rhs) const -> bool = 0;\n\n protected:\n /// Indicates whether the data is stored in the ascending order.\n bool is_ascending_{true};\n\n /// Calculate if the data is arranged in ascending order.\n [[nodiscard]] inline auto calculate_is_ascending() const -> bool {\n return size() < 2 ? true : coordinate_value(0) < coordinate_value(1);\n }\n};\n\n/// Represents a container for an undefined axis\n///\n/// @tparam T type of data handled by this container\ntemplate \nclass Undefined : public Abstract {\n public:\n /// Default constructor\n Undefined() = default;\n\n /// Default destructor\n ~Undefined() override = default;\n\n /// Copy constructor\n ///\n /// @param rhs right value\n Undefined(const Undefined& rhs) = default;\n\n /// Move constructor\n ///\n /// @param rhs right value\n Undefined(Undefined&& rhs) = default;\n\n /// Copy assignment operator\n ///\n /// @param rhs right value\n auto operator=(const Undefined& rhs) -> Undefined& = default;\n\n /// Move assignment operator\n ///\n /// @param rhs right value\n auto operator=(Undefined&& rhs) -> Undefined& = default;\n\n /// @copydoc Abstract::flip()\n auto flip() -> void override {}\n\n /// @copydoc Abstract::coordinate_value(const size_t) const\n [[nodiscard]] inline auto coordinate_value(const size_t /* index */) const\n noexcept -> T override {\n return math::Fill::value();\n }\n\n /// @copydoc Abstract::min_value() const\n [[nodiscard]] inline auto min_value() const noexcept -> T override {\n return coordinate_value(0);\n }\n\n /// @copydoc Abstract::max_value() const\n [[nodiscard]] inline auto max_value() const noexcept -> T override {\n return coordinate_value(0);\n }\n\n /// @copydoc Abstract::size() const\n [[nodiscard]] inline auto size() const noexcept -> int64_t override {\n return 0;\n }\n\n /// @copydoc Abstract::front() const\n [[nodiscard]] inline auto front() const noexcept -> T override {\n return coordinate_value(0);\n }\n\n /// @copydoc Abstract::back() const\n [[nodiscard]] inline auto back() const noexcept -> T override {\n return coordinate_value(0);\n }\n\n /// @copydoc Abstract::find_index(double,bool) const\n inline int64_t find_index(T /* coordinate */,\n bool /* bounded */) const /// NOLINT\n noexcept override {\n return -1;\n }\n\n /// @copydoc Abstract::operator==(const Abstract&) const\n inline auto operator==(const Abstract& rhs) const noexcept\n -> bool override {\n return dynamic_cast*>(&rhs) != nullptr;\n }\n};\n\n/// Represents a container for an irregularly spaced axis\n///\n/// @tparam T type of data handled by this container\ntemplate \nclass Irregular : public Abstract {\n public:\n /// Creation of a container representing an irregularly spaced coordinate\n /// system.\n ///\n /// @param points axis values\n explicit Irregular(Eigen::Matrix points)\n : points_(std::move(points)) {\n if (points_.size() == 0) {\n throw std::invalid_argument(\"unable to create an empty container.\");\n }\n this->is_ascending_ = this->calculate_is_ascending();\n make_edges();\n }\n\n /// Destructor\n ~Irregular() override = default;\n\n /// Copy constructor\n ///\n /// @param rhs right value\n Irregular(const Irregular& rhs) = default;\n\n /// Move constructor\n ///\n /// @param rhs right value\n Irregular(Irregular&& rhs) = default;\n\n /// Copy assignment operator\n ///\n /// @param rhs right value\n auto operator=(const Irregular& rhs) -> Irregular& = default;\n\n /// Move assignment operator\n ///\n /// @param rhs right value\n auto operator=(Irregular&& rhs) -> Irregular& = default;\n\n /// @copydoc Abstract::flip()\n auto flip() -> void override {\n std::reverse(points_.data(), points_.data() + points_.size());\n this->is_ascending_ = !this->is_ascending_;\n make_edges();\n }\n\n /// @copydoc Abstract::is_monotonic() const\n [[nodiscard]] inline auto is_monotonic() const noexcept -> bool override {\n if (this->is_ascending_) {\n return std::is_sorted(points_.data(), points_.data() + points_.size());\n }\n return std::is_sorted(points_.data(), points_.data() + points_.size(),\n std::greater<>());\n };\n\n /// @copydoc Abstract::coordinate_value(const size_t) const\n [[nodiscard]] inline auto coordinate_value(const size_t index) const\n -> T override {\n return points_[index];\n }\n\n /// @copydoc Abstract::min_value() const\n [[nodiscard]] inline auto min_value() const -> T override {\n return this->is_ascending_ ? front() : back();\n }\n\n /// @copydoc Abstract::max_value() const\n [[nodiscard]] inline auto max_value() const -> T override {\n return this->is_ascending_ ? back() : front();\n }\n\n /// @copydoc Abstract::size() const\n [[nodiscard]] inline auto size() const noexcept -> int64_t override {\n return points_.size();\n }\n\n /// @copydoc Abstract::front() const\n [[nodiscard]] inline auto front() const -> T override { return points_[0]; }\n\n /// @copydoc Abstract::back() const\n [[nodiscard]] inline auto back() const -> T override {\n return points_[points_.size() - 1];\n }\n\n /// @copydoc Abstract::find_index(double,bool) const\n [[nodiscard]] auto find_index(T coordinate, bool bounded) const\n -> int64_t override {\n int64_t low = 0;\n int64_t mid = 0;\n int64_t high = size();\n\n if (this->is_ascending_) {\n if (coordinate < edges_[0]) {\n return bounded ? 0 : -1;\n }\n\n if (coordinate > edges_[edges_.size() - 1]) {\n return bounded ? high - 1 : -1;\n }\n\n while (high > low + 1) {\n // low and high are strictly positive\n mid = (low + high) >> 1; // NOLINT\n auto value = edges_[mid];\n\n if (value == coordinate) {\n return mid;\n }\n value < coordinate ? low = mid : high = mid;\n }\n return low;\n }\n\n if (coordinate < edges_[edges_.size() - 1]) {\n return bounded ? high - 1 : -1;\n }\n\n if (coordinate > edges_[0]) {\n return bounded ? 0 : -1;\n }\n\n while (high > low + 1) {\n // low and high are strictly positive\n mid = (low + high) >> 1; // NOLINT\n auto value = edges_[mid];\n\n if (value == coordinate) {\n return mid;\n }\n value < coordinate ? high = mid : low = mid;\n }\n return low;\n }\n\n /// @copydoc Abstract::operator==(const Abstract&) const\n auto operator==(const Abstract& rhs) const noexcept -> bool override {\n const auto ptr = dynamic_cast*>(&rhs);\n if (ptr != nullptr) {\n return ptr->points_.size() == points_.size() && ptr->points_ == points_;\n }\n return false;\n }\n\n private:\n Eigen::Matrix points_{};\n Eigen::Matrix edges_{};\n\n /// Computes the edges, if the axis data are not spaced regularly.\n void make_edges() {\n auto n = points_.size();\n edges_.resize(n + 1);\n\n for (Eigen::Index ix = 1; ix < n; ++ix) {\n edges_[ix] = (points_[ix - 1] + points_[ix]) / 2;\n }\n\n edges_[0] = 2 * points_[0] - edges_[1];\n edges_[n] = 2 * points_[n - 1] - edges_[n - 1];\n }\n};\n\n/// Represents a container for an regularly spaced axis\n///\n/// @tparam T type of data handled by this container\ntemplate \nclass Regular : public Abstract {\n public:\n /// Create a container from evenly spaced numbers over a specified\n /// interval.\n ///\n /// @param start the starting value of the sequence\n /// @param stop the end value of the sequence\n /// @param num number of samples in the container\n Regular(const T start, const T stop, const T num)\n : size_(static_cast(num)), start_(start) {\n if (num == 0) {\n throw std::invalid_argument(\"unable to create an empty container.\");\n }\n step_ = num == 1 ? stop - start : (stop - start) / (num - 1);\n // The inverse step of this axis is stored in order to optimize the search\n // for an index for a given value by avoiding a division.\n inv_step_ = 1.0 / step_;\n this->is_ascending_ = this->calculate_is_ascending();\n }\n\n /// Destructor\n ~Regular() override = default;\n\n /// Copy constructor\n ///\n /// @param rhs right value\n Regular(const Regular& rhs) = default;\n\n /// Move constructor\n ///\n /// @param rhs right value\n Regular(Regular&& rhs) = default;\n\n /// Copy assignment operator\n ///\n /// @param rhs right value\n auto operator=(const Regular& rhs) -> Regular& = default;\n\n /// Move assignment operator\n ///\n /// @param rhs right value\n auto operator=(Regular&& rhs) -> Regular& = default;\n\n /// Get the step between two successive values.\n ///\n /// @return increment value\n [[nodiscard]] auto step() const -> T { return step_; }\n\n /// @copydoc Abstract::flip()\n auto flip() -> void override {\n start_ = back();\n step_ = -step_;\n inv_step_ = -inv_step_;\n this->is_ascending_ = !this->is_ascending_;\n }\n\n /// @copydoc Abstract::coordinate_value(const size_t) const\n [[nodiscard]] inline auto coordinate_value(const size_t index) const noexcept\n -> T override {\n return start_ + index * step_;\n }\n\n /// @copydoc Abstract::find_index(double,bool) const\n [[nodiscard]] auto find_index(T coordinate, bool bounded) const noexcept\n -> int64_t override {\n auto index =\n static_cast(std::round((coordinate - start_) * inv_step_));\n\n if (index < 0) {\n return bounded ? 0 : -1;\n }\n\n if (index >= size_) {\n return bounded ? size_ - 1 : -1;\n }\n return index;\n }\n\n /// @copydoc Abstract::min_value() const\n [[nodiscard]] inline auto min_value() const noexcept -> T override {\n return coordinate_value(this->is_ascending_ ? 0 : size_ - 1);\n }\n\n /// @copydoc Abstract::max_value() const\n [[nodiscard]] inline auto max_value() const noexcept -> T override {\n return coordinate_value(this->is_ascending_ ? size_ - 1 : 0);\n }\n\n /// @copydoc Abstract::front() const\n [[nodiscard]] inline auto front() const noexcept -> T override {\n return start_;\n }\n\n /// @copydoc Abstract::back() const\n [[nodiscard]] inline auto back() const noexcept -> T override {\n return coordinate_value(size_ - 1);\n }\n\n /// @copydoc Abstract::size() const\n [[nodiscard]] inline auto size() const noexcept -> int64_t override {\n return size_;\n }\n\n /// @copydoc Abstract::operator==(const Abstract&) const\n auto operator==(const Abstract& rhs) const noexcept -> bool override {\n const auto ptr = dynamic_cast*>(&rhs);\n if (ptr != nullptr) {\n return ptr->step_ == step_ && ptr->start_ == start_ &&\n ptr->size_ == size_;\n }\n return false;\n }\n\n private:\n /// Container size.\n int64_t size_{};\n /// Value of the first item in the container.\n T start_{};\n /// The step between two succeeding values.\n T step_{};\n /// The inverse of the step (to avoid a division between real numbers).\n double inv_step_{};\n};\n\n} // namespace pyinterp::detail::axis::container\n", "meta": {"hexsha": "b4958c4fbf039042f37d4e3aa285e3c93191953b", "size": 14067, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/pyinterp/core/include/pyinterp/detail/axis/container.hpp", "max_stars_repo_name": "Geospatial-Data-Science/pangeo-pyinterp", "max_stars_repo_head_hexsha": "aa36a6fdbc4acea4206ebfd97d60aceffe0d98df", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2020-02-19T14:54:23.000Z", "max_stars_repo_stars_event_max_datetime": "2020-02-19T14:54:23.000Z", "max_issues_repo_path": "src/pyinterp/core/include/pyinterp/detail/axis/container.hpp", "max_issues_repo_name": "Geospatial-Data-Science/pangeo-pyinterp", "max_issues_repo_head_hexsha": "aa36a6fdbc4acea4206ebfd97d60aceffe0d98df", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/pyinterp/core/include/pyinterp/detail/axis/container.hpp", "max_forks_repo_name": "Geospatial-Data-Science/pangeo-pyinterp", "max_forks_repo_head_hexsha": "aa36a6fdbc4acea4206ebfd97d60aceffe0d98df", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.7081632653, "max_line_length": 79, "alphanum_fraction": 0.6379469681, "num_tokens": 3534, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073802837478, "lm_q2_score": 0.38491214448393346, "lm_q1q2_score": 0.21193546751369802}} {"text": "// Boost.Geometry Index\n//\n// R-tree R*-tree split algorithm implementation\n//\n// Copyright (c) 2011-2014 Adam Wulkiewicz, Lodz, Poland.\n//\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n#ifndef BOOST_GEOMETRY_INDEX_DETAIL_RTREE_RSTAR_REDISTRIBUTE_ELEMENTS_HPP\n#define BOOST_GEOMETRY_INDEX_DETAIL_RTREE_RSTAR_REDISTRIBUTE_ELEMENTS_HPP\n\n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n\nnamespace geofeatures_boost {} namespace boost = geofeatures_boost; namespace geofeatures_boost { namespace geometry { namespace index {\n\nnamespace detail { namespace rtree {\n\nnamespace rstar {\n\ntemplate \nclass element_axis_corner_less\n{\n typedef typename rtree::element_indexable_type::type indexable_type;\n typedef typename geometry::point_type::type point_type;\n typedef geometry::model::box bounds_type;\n typedef index::detail::bounded_view bounded_view_type;\n\npublic:\n element_axis_corner_less(Translator const& tr)\n : m_tr(tr)\n {}\n\n bool operator()(Element const& e1, Element const& e2) const\n {\n bounded_view_type bounded_ind1(rtree::element_indexable(e1, m_tr));\n bounded_view_type bounded_ind2(rtree::element_indexable(e2, m_tr));\n\n return geometry::get(bounded_ind1)\n < geometry::get(bounded_ind2);\n }\n\nprivate:\n Translator const& m_tr;\n};\n\ntemplate \nclass element_axis_corner_less\n{\npublic:\n element_axis_corner_less(Translator const& tr)\n : m_tr(tr)\n {}\n\n bool operator()(Element const& e1, Element const& e2) const\n {\n return geometry::get(rtree::element_indexable(e1, m_tr))\n < geometry::get(rtree::element_indexable(e2, m_tr));\n }\n\nprivate:\n Translator const& m_tr;\n};\n\ntemplate \nclass element_axis_corner_less\n{\npublic:\n element_axis_corner_less(Translator const& tr)\n : m_tr(tr)\n {}\n\n bool operator()(Element const& e1, Element const& e2) const\n {\n return geometry::get(rtree::element_indexable(e1, m_tr))\n < geometry::get(rtree::element_indexable(e2, m_tr));\n }\n\nprivate:\n Translator const& m_tr;\n};\n\ntemplate \nstruct choose_split_axis_and_index_for_corner\n{\n typedef typename index::detail::default_margin_result::type margin_type;\n typedef typename index::detail::default_content_result::type content_type;\n\n template \n static inline void apply(Elements const& elements,\n size_t & choosen_index,\n margin_type & sum_of_margins,\n content_type & smallest_overlap,\n content_type & smallest_content,\n Parameters const& parameters,\n Translator const& translator)\n {\n typedef typename Elements::value_type element_type;\n typedef typename rtree::element_indexable_type::type indexable_type;\n typedef typename tag::type indexable_tag;\n\n BOOST_GEOMETRY_INDEX_ASSERT(elements.size() == parameters.get_max_elements() + 1, \"wrong number of elements\");\n\n // copy elements\n Elements elements_copy(elements); // MAY THROW, STRONG (alloc, copy)\n \n size_t const index_first = parameters.get_min_elements();\n size_t const index_last = parameters.get_max_elements() - parameters.get_min_elements() + 2;\n\n // sort elements\n element_axis_corner_less elements_less(translator);\n std::sort(elements_copy.begin(), elements_copy.end(), elements_less); // MAY THROW, BASIC (copy)\n// {\n// typename Elements::iterator f = elements_copy.begin() + index_first;\n// typename Elements::iterator l = elements_copy.begin() + index_last;\n// std::nth_element(elements_copy.begin(), f, elements_copy.end(), elements_less); // MAY THROW, BASIC (copy)\n// std::nth_element(f, l, elements_copy.end(), elements_less); // MAY THROW, BASIC (copy)\n// std::sort(f, l, elements_less); // MAY THROW, BASIC (copy)\n// }\n\n // init outputs\n choosen_index = index_first;\n sum_of_margins = 0;\n smallest_overlap = (std::numeric_limits::max)();\n smallest_content = (std::numeric_limits::max)();\n\n // calculate sum of margins for all distributions\n for ( size_t i = index_first ; i < index_last ; ++i )\n {\n // TODO - awulkiew: may be optimized - box of group 1 may be initialized with\n // box of min_elems number of elements and expanded for each iteration by another element\n\n Box box1 = rtree::elements_box(elements_copy.begin(), elements_copy.begin() + i, translator);\n Box box2 = rtree::elements_box(elements_copy.begin() + i, elements_copy.end(), translator);\n \n sum_of_margins += index::detail::comparable_margin(box1) + index::detail::comparable_margin(box2);\n\n content_type ovl = index::detail::intersection_content(box1, box2);\n content_type con = index::detail::content(box1) + index::detail::content(box2);\n\n // TODO - shouldn't here be < instead of <= ?\n if ( ovl < smallest_overlap || (ovl == smallest_overlap && con <= smallest_content) )\n {\n choosen_index = i;\n smallest_overlap = ovl;\n smallest_content = con;\n }\n }\n\n ::geofeatures_boost::ignore_unused_variable_warning(parameters);\n }\n};\n\n//template \n//struct choose_split_axis_and_index_for_axis\n//{\n// BOOST_MPL_ASSERT_MSG(false, NOT_IMPLEMENTED_FOR_THIS_TAG, (ElementIndexableTag));\n//};\n\ntemplate \nstruct choose_split_axis_and_index_for_axis\n{\n typedef typename index::detail::default_margin_result::type margin_type;\n typedef typename index::detail::default_content_result::type content_type;\n\n template \n static inline void apply(Elements const& elements,\n size_t & choosen_corner,\n size_t & choosen_index,\n margin_type & sum_of_margins,\n content_type & smallest_overlap,\n content_type & smallest_content,\n Parameters const& parameters,\n Translator const& translator)\n {\n size_t index1 = 0;\n margin_type som1 = 0;\n content_type ovl1 = (std::numeric_limits::max)();\n content_type con1 = (std::numeric_limits::max)();\n\n choose_split_axis_and_index_for_corner\n ::apply(elements, index1,\n som1, ovl1, con1,\n parameters, translator); // MAY THROW, STRONG\n\n size_t index2 = 0;\n margin_type som2 = 0;\n content_type ovl2 = (std::numeric_limits::max)();\n content_type con2 = (std::numeric_limits::max)();\n\n choose_split_axis_and_index_for_corner\n ::apply(elements, index2,\n som2, ovl2, con2,\n parameters, translator); // MAY THROW, STRONG\n\n sum_of_margins = som1 + som2;\n\n if ( ovl1 < ovl2 || (ovl1 == ovl2 && con1 <= con2) )\n {\n choosen_corner = min_corner;\n choosen_index = index1;\n smallest_overlap = ovl1;\n smallest_content = con1;\n }\n else\n {\n choosen_corner = max_corner;\n choosen_index = index2;\n smallest_overlap = ovl2;\n smallest_content = con2;\n }\n }\n};\n\ntemplate \nstruct choose_split_axis_and_index_for_axis\n{\n typedef typename index::detail::default_margin_result::type margin_type;\n typedef typename index::detail::default_content_result::type content_type;\n\n template \n static inline void apply(Elements const& elements,\n size_t & choosen_corner,\n size_t & choosen_index,\n margin_type & sum_of_margins,\n content_type & smallest_overlap,\n content_type & smallest_content,\n Parameters const& parameters,\n Translator const& translator)\n {\n choose_split_axis_and_index_for_corner\n ::apply(elements, choosen_index,\n sum_of_margins, smallest_overlap, smallest_content,\n parameters, translator); // MAY THROW, STRONG\n\n choosen_corner = min_corner;\n }\n};\n\ntemplate \nstruct choose_split_axis_and_index\n{\n BOOST_STATIC_ASSERT(0 < Dimension);\n\n typedef typename index::detail::default_margin_result::type margin_type;\n typedef typename index::detail::default_content_result::type content_type;\n\n template \n static inline void apply(Elements const& elements,\n size_t & choosen_axis,\n size_t & choosen_corner,\n size_t & choosen_index,\n margin_type & smallest_sum_of_margins,\n content_type & smallest_overlap,\n content_type & smallest_content,\n Parameters const& parameters,\n Translator const& translator)\n {\n typedef typename rtree::element_indexable_type::type element_indexable_type;\n\n choose_split_axis_and_index\n ::apply(elements, choosen_axis, choosen_corner, choosen_index,\n smallest_sum_of_margins, smallest_overlap, smallest_content,\n parameters, translator); // MAY THROW, STRONG\n\n margin_type sum_of_margins = 0;\n\n size_t corner = min_corner;\n size_t index = 0;\n\n content_type overlap_val = (std::numeric_limits::max)();\n content_type content_val = (std::numeric_limits::max)();\n\n choose_split_axis_and_index_for_axis<\n Box,\n Dimension - 1,\n typename tag::type\n >::apply(elements, corner, index, sum_of_margins, overlap_val, content_val, parameters, translator); // MAY THROW, STRONG\n\n if ( sum_of_margins < smallest_sum_of_margins )\n {\n choosen_axis = Dimension - 1;\n choosen_corner = corner;\n choosen_index = index;\n smallest_sum_of_margins = sum_of_margins;\n smallest_overlap = overlap_val;\n smallest_content = content_val;\n }\n }\n};\n\ntemplate \nstruct choose_split_axis_and_index\n{\n typedef typename index::detail::default_margin_result::type margin_type;\n typedef typename index::detail::default_content_result::type content_type;\n\n template \n static inline void apply(Elements const& elements,\n size_t & choosen_axis,\n size_t & choosen_corner,\n size_t & choosen_index,\n margin_type & smallest_sum_of_margins,\n content_type & smallest_overlap,\n content_type & smallest_content,\n Parameters const& parameters,\n Translator const& translator)\n {\n typedef typename rtree::element_indexable_type::type element_indexable_type;\n\n choosen_axis = 0;\n\n choose_split_axis_and_index_for_axis<\n Box,\n 0,\n typename tag::type\n >::apply(elements, choosen_corner, choosen_index, smallest_sum_of_margins, smallest_overlap, smallest_content, parameters, translator); // MAY THROW\n }\n};\n\ntemplate \nstruct nth_element\n{\n BOOST_STATIC_ASSERT(0 < Dimension);\n BOOST_STATIC_ASSERT(I < Dimension);\n\n template \n static inline void apply(Elements & elements, const size_t axis, const size_t index, Translator const& tr)\n {\n //BOOST_GEOMETRY_INDEX_ASSERT(axis < Dimension, \"unexpected axis value\");\n\n if ( axis != I )\n {\n nth_element::apply(elements, axis, index, tr); // MAY THROW, BASIC (copy)\n }\n else\n {\n typedef typename Elements::value_type element_type;\n typedef typename rtree::element_indexable_type::type indexable_type;\n typedef typename tag::type indexable_tag;\n\n element_axis_corner_less less(tr);\n std::nth_element(elements.begin(), elements.begin() + index, elements.end(), less); // MAY THROW, BASIC (copy)\n }\n }\n};\n\ntemplate \nstruct nth_element\n{\n template \n static inline void apply(Elements & /*elements*/, const size_t /*axis*/, const size_t /*index*/, Translator const& /*tr*/)\n {}\n};\n\n} // namespace rstar\n\ntemplate \nstruct redistribute_elements\n{\n typedef typename rtree::node::type node;\n typedef typename rtree::internal_node::type internal_node;\n typedef typename rtree::leaf::type leaf;\n\n typedef typename Options::parameters_type parameters_type;\n\n static const size_t dimension = geometry::dimension::value;\n\n typedef typename index::detail::default_margin_result::type margin_type;\n typedef typename index::detail::default_content_result::type content_type;\n\n template \n static inline void apply(\n Node & n,\n Node & second_node,\n Box & box1,\n Box & box2,\n parameters_type const& parameters,\n Translator const& translator,\n Allocators & allocators)\n {\n typedef typename rtree::elements_type::type elements_type;\n typedef typename elements_type::value_type element_type;\n \n elements_type & elements1 = rtree::elements(n);\n elements_type & elements2 = rtree::elements(second_node);\n\n // copy original elements - use in-memory storage (std::allocator)\n // TODO: move if noexcept\n typedef typename rtree::container_from_elements_type::type\n container_type;\n container_type elements_copy(elements1.begin(), elements1.end()); // MAY THROW, STRONG\n container_type elements_backup(elements1.begin(), elements1.end()); // MAY THROW, STRONG\n\n size_t split_axis = 0;\n size_t split_corner = 0;\n size_t split_index = parameters.get_min_elements();\n margin_type smallest_sum_of_margins = (std::numeric_limits::max)();\n content_type smallest_overlap = (std::numeric_limits::max)();\n content_type smallest_content = (std::numeric_limits::max)();\n\n // NOTE: this function internally copies passed elements\n // why not pass mutable elements and use the same container for all axes/corners\n // and again, the same below calling partial_sort/nth_element\n // It would be even possible to not re-sort/find nth_element if the axis/corner\n // was found for the last sorting - last combination of axis/corner\n rstar::choose_split_axis_and_index\n ::apply(elements_copy,\n split_axis, split_corner, split_index,\n smallest_sum_of_margins, smallest_overlap, smallest_content,\n parameters, translator); // MAY THROW, STRONG\n\n // TODO: awulkiew - get rid of following static_casts?\n BOOST_GEOMETRY_INDEX_ASSERT(split_axis < dimension, \"unexpected value\");\n BOOST_GEOMETRY_INDEX_ASSERT(split_corner == static_cast(min_corner) || split_corner == static_cast(max_corner), \"unexpected value\");\n BOOST_GEOMETRY_INDEX_ASSERT(parameters.get_min_elements() <= split_index && split_index <= parameters.get_max_elements() - parameters.get_min_elements() + 1, \"unexpected value\");\n\n // TODO: consider using nth_element\n if ( split_corner == static_cast(min_corner) )\n {\n rstar::nth_element\n ::apply(elements_copy, split_axis, split_index, translator); // MAY THROW, BASIC (copy)\n }\n else\n {\n rstar::nth_element\n ::apply(elements_copy, split_axis, split_index, translator); // MAY THROW, BASIC (copy)\n }\n\n BOOST_TRY\n {\n // copy elements to nodes\n elements1.assign(elements_copy.begin(), elements_copy.begin() + split_index); // MAY THROW, BASIC\n elements2.assign(elements_copy.begin() + split_index, elements_copy.end()); // MAY THROW, BASIC\n\n // calculate boxes\n box1 = rtree::elements_box(elements1.begin(), elements1.end(), translator);\n box2 = rtree::elements_box(elements2.begin(), elements2.end(), translator);\n }\n BOOST_CATCH(...)\n {\n //elements_copy.clear();\n elements1.clear();\n elements2.clear();\n\n rtree::destroy_elements::apply(elements_backup, allocators);\n //elements_backup.clear();\n\n BOOST_RETHROW // RETHROW, BASIC\n }\n BOOST_CATCH_END\n }\n};\n\n}} // namespace detail::rtree\n\n}}} // namespace geofeatures_boost::geometry::index\n\n#endif // BOOST_GEOMETRY_INDEX_DETAIL_RTREE_RSTAR_REDISTRIBUTE_ELEMENTS_HPP\n", "meta": {"hexsha": "9c9f00fa0993bf4ee172c40f3047ead010df5a09", "size": 20822, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/boost/geometry/index/detail/rtree/rstar/redistribute_elements.hpp", "max_stars_repo_name": "tonystone/geofeatures", "max_stars_repo_head_hexsha": "25aca530a9140b3f259e9ee0833c93522e83a697", "max_stars_repo_licenses": ["BSL-1.0", "Apache-2.0"], "max_stars_count": 24.0, "max_stars_repo_stars_event_min_datetime": "2015-08-25T05:35:37.000Z", "max_stars_repo_stars_event_max_datetime": "2020-10-24T14:21:59.000Z", "max_issues_repo_path": "boost/boost/geometry/index/detail/rtree/rstar/redistribute_elements.hpp", "max_issues_repo_name": "tonystone/geofeatures", "max_issues_repo_head_hexsha": "25aca530a9140b3f259e9ee0833c93522e83a697", "max_issues_repo_licenses": ["BSL-1.0", "Apache-2.0"], "max_issues_count": 97.0, "max_issues_repo_issues_event_min_datetime": "2015-08-25T16:11:16.000Z", "max_issues_repo_issues_event_max_datetime": "2019-03-17T00:54:32.000Z", "max_forks_repo_path": "boost/boost/geometry/index/detail/rtree/rstar/redistribute_elements.hpp", "max_forks_repo_name": "tonystone/geofeatures", "max_forks_repo_head_hexsha": "25aca530a9140b3f259e9ee0833c93522e83a697", "max_forks_repo_licenses": ["BSL-1.0", "Apache-2.0"], "max_forks_count": 9.0, "max_forks_repo_forks_event_min_datetime": "2015-08-26T03:11:38.000Z", "max_forks_repo_forks_event_max_datetime": "2018-03-21T07:16:29.000Z", "avg_line_length": 44.3965884861, "max_line_length": 186, "alphanum_fraction": 0.6303909327, "num_tokens": 4161, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984286266115, "lm_q2_score": 0.3960681662740417, "lm_q1q2_score": 0.2119354534022632}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"qm_model.hpp\"\n#include \"properties.hpp\"\n#include \n#include \n\nConfigBlockReader QM_Model::model_reader() {\n ConfigBlockReader reader{\"qmmodel\"};\n reader.add_entry(\"model\", \"avoided-crossing\");\n return reader;\n}\n\nQM_Model::QM_Model(FileHandle& fh, \n const arma::uvec& in_qmids, \n\t\t arma::mat& in_qm_crd, \n\t\t arma::mat& in_mm_crd, \n\t\t arma::vec& in_mm_chg, \n\t\t const int charge, \n\t\t const int mult,\n\t\t const int excited_states,\n const int min_state):\n QMInterface(in_qmids, in_qm_crd, in_mm_crd, in_mm_chg, charge, mult, excited_states, min_state)\n{\n\n if (NQM > 1){\n throw std::logic_error(\"Model systems only work on the x component of a single atom!\");\n }\n\n if (NMM != 0){\n throw std::logic_error(\"Model systems don't support QM/MM!\");\n }\n \n ConfigBlockReader reader = model_reader();\n reader.parse(fh);\n\n {\n std::string model_in;\n reader.get_data(\"model\", model_in);\n\n if (model_in == \"avoided-crossing\"){\n model= new AvoidedCrossing();\n }\n else if (model_in == \"reflective-avoided-crossing\"){\n model= new ReflectiveAvoidedCrossing();\n }\n else{\n throw std::runtime_error(\"Unknown Model: `\" + model_in + \"`!\");\n }\n }\n}\n\nvoid QM_Model::update(void){ \n QMInterface::update();\n // always take the x component of the first atom\n model->update(crd_qm(0));\n}\n\nvoid QM_Model::get_properties(PropMap &props){\n for (QMProperty p: props.keys()){\n switch(p){\n \n case QMProperty::wfoverlap:{\n arma::mat &U = props.get(QMProperty::wfoverlap);\n U = model->overlaps();\n break;\n }\n\n case QMProperty::nacvector:{\n arma::mat & nac = props.get(QMProperty::nacvector);\n nac.zeros();\n\n const arma::uvec &idx = *props.get_idx(QMProperty::nacvector);\n size_t A = idx[0]; size_t B = idx[1]; \n\n nac(0) = model->nacs()(A,B);\n break;\n }\n \n case QMProperty::qmgradient:{\n arma::mat & g_qm = props.get(QMProperty::qmgradient);\n g_qm.zeros();\n \n arma::uword surface = 0; //assume ground state\n if (props.has_idx(QMProperty::qmgradient)){\n\tsurface = (*props.get_idx(QMProperty::qmgradient))[0];\n }\n g_qm(0) = model->gradients()(surface);\n\n break;\n }\n \n case QMProperty::qmgradient_multi:{\n arma::cube & g_qm = props.get(QMProperty::qmgradient_multi);\n g_qm.zeros();\n \n arma::uvec surfaces = arma::regspace(0,excited_states); //assume all\n if (props.has_idx(QMProperty::qmgradient_multi)){ // specific states\n\tsurfaces = *props.get_idx(QMProperty::qmgradient_multi);\n }\n\n if (g_qm.n_slices != surfaces.n_elem){\n\tthrow std::range_error(\"Insufficient space for requested gradients!\");\n }\n \n arma::uword i=0;\n for (arma::uword surface: surfaces){\n g_qm.slice(i)(0) = model->gradients()(surface);\n i++;\n }\n break;\n }\n \n case QMProperty::energies:{\n arma::vec & energies = props.get(QMProperty::energies);\n energies.zeros();\n \n if (props.has_idx(QMProperty::energies)){\n \tenergies = model->energies();\n }\n else{\n \tenergies = model->energies()(0);\n }\n break;\n }\n\n case QMProperty::mmgradient: //explicit fall-through\n case QMProperty::mmgradient_multi:\n break;\n \n default:\n throw std::invalid_argument(\"Unknown QMProperty!\");\n break;\n }\n }\n}\n\n\n// updates overlap and, phase matches the new eigen vectors to the old ones.\nvoid HamiltonianDynamics::update_overlap(arma::mat &evec){\n static arma::mat evec_last = evec;\n overlap = evec_last.t() * evec;\n\n while (arma::any(overlap.diag() < 0)){ // phase match\n arma::uword c = overlap.diag().index_min();\n evec.col(c) *= -1.0;\n \n overlap = evec_last.t() * evec;\n }\n \n evec_last = evec;\n}\n\nvoid HamiltonianDynamics::save_state(double x){\n std::vector state;\n \n state.push_back( x );\n state.push_back( energy(0) );\n state.push_back( energy(1) );\n state.push_back( gradient(0) );\n state.push_back( gradient(1) );\n state.push_back( overlap(0,1) );\n state.push_back( nac(0,1) );\n \n std::ofstream stream;\n stream.open(state_file, std::ios::out | std::ios::app | std::ios::binary);\n if (!stream){\n throw std::runtime_error(\"Cannot open data file!\");\n }\n else{\n for (auto e: state){\n stream << e << \" \";\n }\n stream << std::endl;\n }\n}\n\nvoid HamiltonianDynamics::scan(double a, double b, int N){\n record = true;\n\n if ((b-a < 0) || (N < 0)){\n throw::std::logic_error(\"Can only scan on [a,b) in N>0 steps!\");\n }\n \n const double dx = (b-a)/N;\n\n for (double x = a; x < b; x += dx){\n update(x);\n }\n \n}\n \nReflectiveAvoidedCrossing::ReflectiveAvoidedCrossing(void){\n energy.set_size(2);\n gradient.set_size(2);\n nac.set_size(2,2);\n overlap.set_size(2,2);\n}\n\nvoid ReflectiveAvoidedCrossing::update(double x){\n double e2 = A*std::tanh(B*(x+7));\n double e1 = e2 + 2*A*std::tanh(B*x) + 2*A;\n \n double v = C * std::exp(-1.0*(x+7)*(x+7));\n\n arma::mat H = {{e1, v },\n {v, -e2}};\n\n arma::vec eval;\n arma::mat evec;\n eig_sym(eval, evec, H);\n\n update_overlap(evec);\n\n energy = eval;\n\n // compute gradients\n {\n double de2 = A*B / std::pow(std::cosh(B*(x+7)),2) ;\n double de1 = A*B / std::pow(std::cosh(B*x) ,2) + de2 ;\n double dv = -C * 2 * (x+7) * std::exp(-1.0*(x+7)*(x+7));\n\n // gradient = 0.5*((de1 - de2) +\n // ((de2-de1)*(e2-e1) + 2*(e1*de2 + e2*de1) + 4*v*dv) /\n // ((e2-e1) + 2 * energy));\n\n \n double gl = 0.5 * (de1 - de2);\n double gr = 0.5 * ((de2-de1)*(e2-e1) + 2*(e1*de2 + e2*de1) + 4*v*dv) /\n std::sqrt((e2-e1)*(e2-e1) + 4*e1*e2+4*v*v);\n\n gradient(0) = gl - gr;\n gradient(1) = gl + gr;\n }\n\n\n // compute NAC\n nac = evec.t() * arma::diagmat(gradient) * evec;\n nac.diag().zeros();\n {\n double gap = energy(1) - energy(0);\n nac(0,1) /= gap;\n nac(1,0) /= -gap;\n }\n \n\n if(record){\n save_state(x);\n }\n}\n\n\n\nAvoidedCrossing::AvoidedCrossing(void){\n energy.set_size(2);\n gradient.set_size(2);\n nac.set_size(2,2);\n overlap.set_size(2,2);\n}\n\nvoid AvoidedCrossing::update(double x){\n double xs = x+7;\n \n double e = A * std::tanh(B*xs);\n double v = C * std::exp(-1.0*xs*xs);\n\n arma::mat H = {{e, v},\n {v, -e}};\n\n arma::vec eval;\n arma::mat evec;\n eig_sym(eval, evec, H);\n\n update_overlap(evec);\n\n energy = eval;\n\n // compute gradients\n double f;\n {\n double sech = 1.0 / std::cosh(B*xs);\n f = e*A*B*sech*sech + -2.0*xs*v*v;\n }\n gradient = f / energy;\n\n\n // compute NAC\n nac = evec.t() * arma::diagmat(gradient) * evec;\n nac.diag().zeros();\n {\n double gap = energy(1) - energy(0);\n nac(0,1) /= gap;\n nac(1,0) /= -gap;\n }\n\n // save a bunch of things\n if(record)\n {\n save_state(x);\n }\n}\n", "meta": {"hexsha": "351117c794f517824513928624394f8bb84d28ac", "size": 7067, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/qm_model.cpp", "max_stars_repo_name": "INAQS/inaqs", "max_stars_repo_head_hexsha": "f85b39aef0cb67cda4b3cfa61017268fe410c362", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": "2022-03-04T18:56:08.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-22T16:49:22.000Z", "max_issues_repo_path": "src/qm_model.cpp", "max_issues_repo_name": "INAQS/inaqs", "max_issues_repo_head_hexsha": "f85b39aef0cb67cda4b3cfa61017268fe410c362", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/qm_model.cpp", "max_forks_repo_name": "INAQS/inaqs", "max_forks_repo_head_hexsha": "f85b39aef0cb67cda4b3cfa61017268fe410c362", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 22.8705501618, "max_line_length": 97, "alphanum_fraction": 0.5839818876, "num_tokens": 2226, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6893056295505783, "lm_q2_score": 0.30735801686526387, "lm_q1q2_score": 0.21186361131272796}} {"text": "// g2o - General Graph Optimization\n// Copyright (C) 2011 R. Kuemmerle, G. Grisetti, W. Burgard\n// All rights reserved.\n//\n// Redistribution and use in source and binary forms, with or without\n// modification, are permitted provided that the following conditions are\n// met:\n//\n// * Redistributions of source code must retain the above copyright notice,\n// this list of conditions and the following disclaimer.\n// * Redistributions in binary form must reproduce the above copyright\n// notice, this list of conditions and the following disclaimer in the\n// documentation and/or other materials provided with the distribution.\n//\n// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS\n// IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED\n// TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\n// PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\n// HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\n// SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\n// TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\n// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\n// LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\n// NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\n// SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n#include \"solver_slam2d_linear.h\"\n\n#include \n\n#include \"g2o/core/sparse_block_matrix.h\"\n#include \"g2o/core/sparse_optimizer.h\"\n#include \"g2o/core/hyper_dijkstra.h\"\n\n#include \"g2o/types/slam2d/edge_se2.h\"\n\n#include \"g2o/stuff/misc.h\"\n#include \"g2o/stuff/scoped_pointer.h\"\n\n#include \"g2o/solvers/csparse/linear_solver_csparse.h\"\n\n#include \"g2o/core/solver.h\"\n\nusing namespace std;\n\nnamespace g2o {\n\n /**\n * \\brief compute the initial guess of theta while travelling along the MST\n */\n class ThetaTreeAction : public HyperDijkstra::TreeAction\n {\n public:\n ThetaTreeAction(double* theta) : HyperDijkstra::TreeAction(), _thetaGuess(theta) {}\n virtual double perform(HyperGraph::Vertex* v, HyperGraph::Vertex* vParent, HyperGraph::Edge* e)\n {\n if (! vParent)\n return 0.;\n EdgeSE2* odom = static_cast(e);\n VertexSE2* from = static_cast(vParent);\n VertexSE2* to = static_cast(v);\n assert(to->hessianIndex() >= 0);\n double fromTheta = from->hessianIndex() < 0 ? 0. : _thetaGuess[from->hessianIndex()];\n bool direct = odom->vertices()[0] == from;\n if (direct) \n _thetaGuess[to->hessianIndex()] = fromTheta + odom->measurement().rotation().angle();\n else\n _thetaGuess[to->hessianIndex()] = fromTheta - odom->measurement().rotation().angle();\n return 1.;\n }\n protected:\n double* _thetaGuess;\n };\n\n SolverSLAM2DLinear::SolverSLAM2DLinear(std::unique_ptr solver)\n : OptimizationAlgorithmGaussNewton(std::move(solver))\n {}\n\n SolverSLAM2DLinear::~SolverSLAM2DLinear()\n {}\n\n OptimizationAlgorithm::SolverResult SolverSLAM2DLinear::solve(int iteration, bool online)\n {\n if (iteration == 0) {\n bool status = solveOrientation();\n if (! status)\n return OptimizationAlgorithm::Fail;\n }\n\n return OptimizationAlgorithmGaussNewton::solve(iteration, online);\n }\n\n bool SolverSLAM2DLinear::solveOrientation()\n {\n assert(_optimizer->indexMapping().size() + 1 == _optimizer->vertices().size() && \"Needs to operate on full graph\");\n assert(_optimizer->vertex(0)->fixed() && \"Graph is not fixed by vertex 0\");\n VectorXD b, x; // will be used for theta and x/y update\n b.setZero(_optimizer->indexMapping().size());\n x.setZero(_optimizer->indexMapping().size());\n\n typedef Eigen::Matrix ScalarMatrix;\n\n ScopedArray blockIndeces(new int[_optimizer->indexMapping().size()]);\n for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i)\n blockIndeces[i] = i+1;\n\n SparseBlockMatrix H(blockIndeces.get(), blockIndeces.get(), _optimizer->indexMapping().size(), _optimizer->indexMapping().size());\n\n // building the structure, diagonal for each active vertex\n for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {\n OptimizableGraph::Vertex* v = _optimizer->indexMapping()[i];\n int poseIdx = v->hessianIndex();\n ScalarMatrix* m = H.block(poseIdx, poseIdx, true);\n m->setZero();\n }\n\n HyperGraph::VertexSet fixedSet;\n\n // off diagonal for each edge\n for (SparseOptimizer::EdgeContainer::const_iterator it = _optimizer->activeEdges().begin(); it != _optimizer->activeEdges().end(); ++it) {\n# ifndef NDEBUG\n EdgeSE2* e = dynamic_cast(*it);\n assert(e && \"Active edges contain non-odometry edge\"); //\n# else\n EdgeSE2* e = static_cast(*it);\n# endif\n OptimizableGraph::Vertex* from = static_cast(e->vertices()[0]);\n OptimizableGraph::Vertex* to = static_cast(e->vertices()[1]);\n\n int ind1 = from->hessianIndex();\n int ind2 = to->hessianIndex();\n if (ind1 == -1 || ind2 == -1) {\n if (ind1 == -1) fixedSet.insert(from); // collect the fixed vertices\n if (ind2 == -1) fixedSet.insert(to);\n continue;\n }\n\n bool transposedBlock = ind1 > ind2;\n if (transposedBlock){ // make sure, we allocate the upper triangle block\n std::swap(ind1, ind2);\n }\n\n ScalarMatrix* m = H.block(ind1, ind2, true);\n m->setZero();\n }\n\n // walk along the Minimal Spanning Tree to compute the guess for the robot orientation\n assert(fixedSet.size() == 1);\n VertexSE2* root = static_cast(*fixedSet.begin());\n VectorXD thetaGuess;\n thetaGuess.setZero(_optimizer->indexMapping().size());\n UniformCostFunction uniformCost;\n HyperDijkstra hyperDijkstra(_optimizer);\n hyperDijkstra.shortestPaths(root, &uniformCost);\n\n HyperDijkstra::computeTree(hyperDijkstra.adjacencyMap());\n ThetaTreeAction thetaTreeAction(thetaGuess.data());\n HyperDijkstra::visitAdjacencyMap(hyperDijkstra.adjacencyMap(), &thetaTreeAction);\n\n // construct for the orientation\n for (SparseOptimizer::EdgeContainer::const_iterator it = _optimizer->activeEdges().begin(); it != _optimizer->activeEdges().end(); ++it) {\n EdgeSE2* e = static_cast(*it);\n VertexSE2* from = static_cast(e->vertices()[0]);\n VertexSE2* to = static_cast(e->vertices()[1]);\n\n double omega = e->information()(2,2);\n\n double fromThetaGuess = from->hessianIndex() < 0 ? 0. : thetaGuess[from->hessianIndex()];\n double toThetaGuess = to->hessianIndex() < 0 ? 0. : thetaGuess[to->hessianIndex()];\n double error = normalize_theta(-e->measurement().rotation().angle() + toThetaGuess - fromThetaGuess);\n\n bool fromNotFixed = !(from->fixed());\n bool toNotFixed = !(to->fixed());\n\n if (fromNotFixed || toNotFixed) {\n double omega_r = - omega * error;\n if (fromNotFixed) {\n b(from->hessianIndex()) -= omega_r;\n (*H.block(from->hessianIndex(), from->hessianIndex()))(0,0) += omega;\n if (toNotFixed) {\n if (from->hessianIndex() > to->hessianIndex())\n (*H.block(to->hessianIndex(), from->hessianIndex()))(0,0) -= omega;\n else\n (*H.block(from->hessianIndex(), to->hessianIndex()))(0,0) -= omega;\n }\n } \n if (toNotFixed ) {\n b(to->hessianIndex()) += omega_r;\n (*H.block(to->hessianIndex(), to->hessianIndex()))(0,0) += omega;\n }\n }\n }\n\n // solve orientation\n typedef LinearSolverCSparse SystemSolver;\n SystemSolver linearSystemSolver;\n linearSystemSolver.init();\n bool ok = linearSystemSolver.solve(H, x.data(), b.data());\n if (!ok) {\n cerr << __PRETTY_FUNCTION__ << \"Failure while solving linear system\" << endl;\n return false;\n }\n\n // update the orientation of the 2D poses and set translation to 0, GN shall solve that\n root->setToOrigin();\n for (size_t i = 0; i < _optimizer->indexMapping().size(); ++i) {\n VertexSE2* v = static_cast(_optimizer->indexMapping()[i]);\n int poseIdx = v->hessianIndex();\n SE2 poseUpdate(0, 0, normalize_theta(thetaGuess(poseIdx) + x(poseIdx)));\n v->setEstimate(poseUpdate);\n }\n\n return true;\n }\n\n} // end namespace\n", "meta": {"hexsha": "9ed9797441063bcbf56ad6c4e5662ec7004d3104", "size": 8560, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "g2opy/g2o/solvers/slam2d_linear/solver_slam2d_linear.cpp", "max_stars_repo_name": "alecone/ROS_project", "max_stars_repo_head_hexsha": "f058fb0bc5c4c9b1a590b7536f75b83af35b7785", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5.0, "max_stars_repo_stars_event_min_datetime": "2018-06-20T08:29:21.000Z", "max_stars_repo_stars_event_max_datetime": "2018-11-12T06:05:52.000Z", "max_issues_repo_path": "thirdparty/g2opy/g2o/solvers/slam2d_linear/solver_slam2d_linear.cpp", "max_issues_repo_name": "rpng/suo_slam", "max_issues_repo_head_hexsha": "5de01433d177fde5cac4423f05fd554e3c00794e", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "thirdparty/g2opy/g2o/solvers/slam2d_linear/solver_slam2d_linear.cpp", "max_forks_repo_name": "rpng/suo_slam", "max_forks_repo_head_hexsha": "5de01433d177fde5cac4423f05fd554e3c00794e", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2019-01-21T13:38:45.000Z", "max_forks_repo_forks_event_max_datetime": "2019-01-21T13:38:45.000Z", "avg_line_length": 39.4470046083, "max_line_length": 148, "alphanum_fraction": 0.6640186916, "num_tokens": 2197, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.39233683016710835, "lm_q1q2_score": 0.211462968317865}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \nusing namespace std;\n\nnamespace ripple {\n\nPrecompiledRegistrar* PrecompiledRegistrar::s_this = nullptr;\n\nPrecompiledExecutor const& PrecompiledRegistrar::executor(std::string const& _name)\n{\n if (!get()->m_execs.count(_name))\n BOOST_THROW_EXCEPTION(ExecutorNotFound());\n return get()->m_execs[_name];\n}\n\nPrecompiledPricer const& PrecompiledRegistrar::pricer(std::string const& _name)\n{\n if (!get()->m_pricers.count(_name))\n BOOST_THROW_EXCEPTION(PricerNotFound());\n return get()->m_pricers[_name];\n}\n\nnamespace\n{\n\n// // Big-endian to/from host endian conversion functions.\n\n// /// Converts a templated integer value to the big-endian byte-stream represented on a templated collection.\n// /// The size of the collection object will be unchanged. If it is too small, it will not represent the\n// /// value properly, if too big then the additional elements will be zeroed out.\n// /// @a Out will typically be either std::string or bytes.\n// /// @a T will typically by unsigned, u160, u256 or bigint.\n// template \n// inline void toBigEndian(T _val, Out& o_out)\n// {\n// \tstatic_assert(std::is_same::value || !std::numeric_limits::is_signed, \"only unsigned types or bigint supported\"); //bigint does not carry sign bit on shift\n// \tfor (auto i = o_out.size(); i != 0; _val >>= 8, i--)\n// \t{\n// \t\tT v = _val & (T)0xff;\n// \t\to_out[i - 1] = (typename Out::value_type)(uint8_t)v;\n// \t}\n// }\n\n// /// Converts a big-endian byte-stream represented on a templated collection to a templated integer value.\n// /// @a _In will typically be either std::string or bytes.\n// /// @a T will typically by unsigned, u160, u256 or bigint.\n// template \n// inline T fromBigEndian(_In const& _bytes)\n// {\n// \tT ret = (T)0;\n// \tfor (auto i: _bytes)\n// \t\tret = (T)((ret << 8) | (eth::byte)(typename std::make_unsigned::type)i);\n// \treturn ret;\n// }\n\nint64_t linearPricer(unsigned _base, unsigned _word, eth::bytesConstRef _in)\n{\n int64_t const s = _in.size();\n int64_t const b = _base;\n int64_t const w = _word;\n return b + (s + 31) / 32 * w;\n}\n\n// ETH_REGISTER_PRECOMPILED_PRICER(ecrecover)\n// (eth::bytesConstRef /*_in*/, ChainOperationParams const& /*_chainParams*/, int64_t const& /*_blockNumber*/)\n// {\n// return 3000;\n// }\n\n// ETH_REGISTER_PRECOMPILED(ecrecover)(eth::bytesConstRef _in)\n// {\n// struct\n// {\n// h256 hash;\n// h256 v;\n// h256 r;\n// h256 s;\n// } in;\n\n// memcpy(&in, _in.data(), min(_in.size(), sizeof(in)));\n\n// h256 ret;\n// u256 v = (u256)in.v;\n// if (v >= 27 && v <= 28)\n// {\n// SignatureStruct sig(in.r, in.s, (byte)((int)v - 27));\n// if (sig.isValid())\n// {\n// try\n// {\n// if (Public rec = recover(sig, in.hash))\n// {\n// ret = dev::sha3(rec);\n// memset(ret.data(), 0, 12);\n// return {true, ret.asBytes()};\n// }\n// }\n// catch (...) {}\n// }\n// }\n// return {true, {}};\n// }\n\nETH_REGISTER_PRECOMPILED_PRICER(sha256)\n(eth::bytesConstRef _in, int64_t const& /*_blockNumber*/)\n{\n return linearPricer(60, 12, _in);\n}\n\nETH_REGISTER_PRECOMPILED(sha256)(eth::bytesConstRef _in)\n{\n auto hashRet = sha512Half(Slice(_in.data(), _in.size()));\n return {true, Blob(hashRet.begin(), hashRet.end())};\n}\n\nETH_REGISTER_PRECOMPILED_PRICER(sm3)\n(eth::bytesConstRef _in, int64_t const& /*_blockNumber*/)\n{\n return linearPricer(60, 12, _in);\n}\n\nETH_REGISTER_PRECOMPILED(sm3)(eth::bytesConstRef _in)\n{\n auto hashRet = sha512Half(Slice(_in.data(), _in.size()));\n return {true, Blob(hashRet.begin(), hashRet.end())};\n}\n\nETH_REGISTER_PRECOMPILED_PRICER(ripemd160)\n(eth::bytesConstRef _in, int64_t const& /*_blockNumber*/)\n{\n return linearPricer(600, 120, _in);\n}\n\nETH_REGISTER_PRECOMPILED(ripemd160)(eth::bytesConstRef _in)\n{\n // return {true, h256(dev::ripemd160(_in), h256::AlignRight).asBytes()};\n ripemd160_hasher rh;\n rh(_in.data(), _in.size());\n auto rhRet = ripemd160_hasher::result_type(rh);\n return {true, Blob(rhRet.begin(), rhRet.end())};\n}\n\nETH_REGISTER_PRECOMPILED_PRICER(identity)\n(eth::bytesConstRef _in, int64_t const& /*_blockNumber*/)\n{\n return linearPricer(15, 3, _in);\n}\n\nETH_REGISTER_PRECOMPILED(identity)(eth::bytesConstRef _in)\n{\n return {true, _in.toBytes()};\n}\n\nETH_REGISTER_PRECOMPILED_PRICER(enbase58)\n(eth::bytesConstRef _in, int64_t const& /*_blockNumber*/)\n{\n return linearPricer(60, 12, _in);\n}\n\nETH_REGISTER_PRECOMPILED(enbase58)(eth::bytesConstRef _in/*, std::uint8_t tokenType = (std::uint8_t)TokenType::AccountID*/)\n{\n auto enRet = base58EncodeToken(TokenType::AccountID, _in.data(), _in.size());\n return { true, Blob(enRet.begin(), enRet.end()) };\n}\n\nETH_REGISTER_PRECOMPILED_PRICER(debase58)\n(eth::bytesConstRef _in, int64_t const& /*_blockNumber*/)\n{\n return linearPricer(60, 12, _in);\n}\n\nETH_REGISTER_PRECOMPILED(debase58)(eth::bytesConstRef _in/*, std::uint8_t tokenType = (std::uint8_t)TokenType::AccountID*/)\n{\n auto enRet = decodeBase58Token(std::string((const char*)_in.data(), _in.size()), TokenType::AccountID);\n return { true, Blob(enRet.begin(), enRet.end()) };\n}\n\n// Parse _count bytes of _in starting with _begin offset as big endian int.\n// If there's not enough bytes in _in, consider it infinitely right-padded with zeroes.\neth::bigint parseBigEndianRightPadded(eth::bytesConstRef _in, eth::bigint const& _begin, eth::bigint const& _count)\n{\n if (_begin > _in.count())\n return 0;\n assert(_count <= numeric_limits::max() / 8); // Otherwise, the return value would not fit in the memory.\n\n size_t const begin{_begin};\n size_t const count{_count};\n\n // crop _in, not going beyond its size\n eth::bytesConstRef cropped = _in.cropped(begin, min(count, _in.count() - begin));\n\n eth::bigint ret = ripple::fromBigEndian(cropped);\n // shift as if we had right-padding zeroes\n assert(count - cropped.count() <= numeric_limits::max() / 8);\n ret <<= 8 * (count - cropped.count());\n\n return ret;\n}\n\n ETH_REGISTER_PRECOMPILED(modexp)(eth::bytesConstRef _in)\n {\n eth::bigint const baseLength(parseBigEndianRightPadded(_in, 0, 32));\n eth::bigint const expLength(parseBigEndianRightPadded(_in, 32, 32));\n eth::bigint const modLength(parseBigEndianRightPadded(_in, 64, 32));\n assert(modLength <= numeric_limits::max() / 8); // Otherwise gas should be too expensive.\n assert(baseLength <= numeric_limits::max() / 8); // Otherwise, gas should be too expensive.\n if (modLength == 0 && baseLength == 0)\n return {true, eth::bytes{}}; // This is a special case where expLength can be very big.\n assert(expLength <= numeric_limits::max() / 8);\n\n eth::bigint const base(parseBigEndianRightPadded(_in, 96, baseLength));\n eth::bigint const exp(parseBigEndianRightPadded(_in, 96 + baseLength, expLength));\n eth::bigint const mod(parseBigEndianRightPadded(_in, 96 + baseLength + expLength, modLength));\n\n eth::bigint const result = mod != 0 ? boost::multiprecision::powm(base, exp, mod) : eth::bigint{0};\n\n size_t const retLength(modLength);\n eth::bytes ret(retLength);\n ripple::toBigEndian(result, ret);\n\n return {true, ret};\n }\n\n namespace\n {\n int64_t expLengthAdjust(int64_t const& _expOffset, int64_t const& _expLength, eth::bytesConstRef _in)\n {\n if (_expLength <= 32)\n {\n eth::bigint const exp(parseBigEndianRightPadded(_in, _expOffset, _expLength));\n return exp ? boost::multiprecision::msb(exp) : 0;\n }\n else\n {\n eth::bigint const expFirstWord(parseBigEndianRightPadded(_in, _expOffset, 32));\n size_t const highestBit(expFirstWord ? boost::multiprecision::msb(expFirstWord) : 0);\n return 8 * (_expLength - 32) + highestBit;\n }\n }\n\n int64_t multComplexity(int64_t const& _x)\n {\n if (_x <= 64)\n return _x * _x;\n if (_x <= 1024)\n return (_x * _x) / 4 + 96 * _x - 3072;\n else\n return (_x * _x) / 16 + 480 * _x - 199680;\n }\n }\n\n ETH_REGISTER_PRECOMPILED_PRICER(modexp)(eth::bytesConstRef _in, int64_t const&)\n {\n int64_t const baseLength(parseBigEndianRightPadded(_in, 0, 32));\n int64_t const expLength(parseBigEndianRightPadded(_in, 32, 32));\n int64_t const modLength(parseBigEndianRightPadded(_in, 64, 32));\n\n int64_t const maxLength(max(modLength, baseLength));\n int64_t const adjustedExpLength(expLengthAdjust(baseLength + 96, expLength, _in));\n\n return multComplexity(maxLength) * max(adjustedExpLength, 1) / 20;\n }\n\n ETH_REGISTER_PRECOMPILED(alt_bn128_G1_add)(eth::bytesConstRef _in)\n {\n return peersafe::alt_bn128_G1_add(_in);\n }\n\n ETH_REGISTER_PRECOMPILED_PRICER(alt_bn128_G1_add)\n (eth::bytesConstRef /*_in*/, int64_t const& _blockNumber)\n {\n return 150;\n // return _blockNumber < _chainParams.istanbulForkBlock ? 500 : 150;\n }\n\n ETH_REGISTER_PRECOMPILED(alt_bn128_G1_mul)(eth::bytesConstRef _in)\n {\n return peersafe::alt_bn128_G1_mul(_in);\n }\n\n ETH_REGISTER_PRECOMPILED_PRICER(alt_bn128_G1_mul)\n (eth::bytesConstRef /*_in*/, int64_t const& _blockNumber)\n {\n return 6000;\n // return _blockNumber < _chainParams.istanbulForkBlock ? 40000 : 6000;\n }\n\n ETH_REGISTER_PRECOMPILED(alt_bn128_pairing_product)(eth::bytesConstRef _in)\n {\n return peersafe::alt_bn128_pairing_product(_in);\n }\n\n ETH_REGISTER_PRECOMPILED_PRICER(alt_bn128_pairing_product)\n (eth::bytesConstRef _in, int64_t const& _blockNumber)\n {\n auto const k = _in.size() / 192;\n return 45000 + k * 34000;\n // return _blockNumber < _chainParams.istanbulForkBlock ? 100000 + k * 80000 : 45000 + k * 34000;\n }\n\n// ETH_REGISTER_PRECOMPILED(blake2_compression)(eth::bytesConstRef _in)\n// {\n// static constexpr size_t roundsSize = 4;\n// static constexpr size_t stateVectorSize = 8 * 8;\n// static constexpr size_t messageBlockSize = 16 * 8;\n// static constexpr size_t offsetCounterSize = 8;\n// static constexpr size_t finalBlockIndicatorSize = 1;\n// static constexpr size_t totalInputSize = roundsSize + stateVectorSize + messageBlockSize +\n// 2 * offsetCounterSize + finalBlockIndicatorSize;\n\n// if (_in.size() != totalInputSize)\n// return {false, {}};\n\n// auto const rounds = fromBigEndian(_in.cropped(0, roundsSize));\n// auto const stateVector = _in.cropped(roundsSize, stateVectorSize);\n// auto const messageBlockVector = _in.cropped(roundsSize + stateVectorSize, messageBlockSize);\n// auto const offsetCounter0 =\n// _in.cropped(roundsSize + stateVectorSize + messageBlockSize, offsetCounterSize);\n// auto const offsetCounter1 = _in.cropped(\n// roundsSize + stateVectorSize + messageBlockSize + offsetCounterSize, offsetCounterSize);\n// uint8_t const finalBlockIndicator =\n// _in[roundsSize + stateVectorSize + messageBlockSize + 2 * offsetCounterSize];\n\n// if (finalBlockIndicator != 0 && finalBlockIndicator != 1)\n// return {false, {}};\n\n// return {true, dev::crypto::blake2FCompression(rounds, stateVector, offsetCounter0,\n// offsetCounter1, finalBlockIndicator, messageBlockVector)};\n// }\n\n// ETH_REGISTER_PRECOMPILED_PRICER(blake2_compression)\n// (eth::bytesConstRef _in, int64_t const&)\n// {\n// auto const rounds = fromBigEndian(_in.cropped(0, 4));\n// return rounds;\n// }\n}\n\n}", "meta": {"hexsha": "c88ef9ee3b77d0b0e599b97b2e2c1c0f35d996f5", "size": 12042, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/peersafe/precompiled/PreContractRegister.cpp", "max_stars_repo_name": "ChainSQL/chainsqld", "max_stars_repo_head_hexsha": "6af7eb0624c67776098250a39eae9f195d8a473a", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 211.0, "max_stars_repo_stars_event_min_datetime": "2017-12-11T03:11:37.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-15T12:38:19.000Z", "max_issues_repo_path": "src/peersafe/precompiled/PreContractRegister.cpp", "max_issues_repo_name": "ChainSQL/chainsqld", "max_issues_repo_head_hexsha": "6af7eb0624c67776098250a39eae9f195d8a473a", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 17.0, "max_issues_repo_issues_event_min_datetime": "2017-12-18T07:22:06.000Z", "max_issues_repo_issues_event_max_datetime": "2022-02-15T10:24:29.000Z", "max_forks_repo_path": "src/peersafe/precompiled/PreContractRegister.cpp", "max_forks_repo_name": "ChainSQL/chainsqld", "max_forks_repo_head_hexsha": "6af7eb0624c67776098250a39eae9f195d8a473a", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 81.0, "max_forks_repo_forks_event_min_datetime": "2017-12-11T03:09:21.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T09:42:42.000Z", "avg_line_length": 34.9043478261, "max_line_length": 178, "alphanum_fraction": 0.6660853679, "num_tokens": 3403, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.523420348936324, "lm_q2_score": 0.40356685373537454, "lm_q1q2_score": 0.2112351034013042}} {"text": "// graph-tool -- a general graph modification and manipulation thingy\n//\n// Copyright (C) 2006-2015 Tiago de Paula Peixoto \n//\n// This program is free software; you can redistribute it and/or\n// modify it under the terms of the GNU General Public License\n// as published by the Free Software Foundation; either version 3\n// of the License, or (at your option) any later version.\n//\n// This program is distributed in the hope that it will be useful,\n// but WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n// GNU General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with this program. If not, see .\n\n#include \"graph.hh\"\n#include \"graph_filtering.hh\"\n#include \"graph_properties.hh\"\n#include \"graph_selectors.hh\"\n\n#include \n\n#include \n#include \n\nusing namespace std;\nusing namespace boost;\nusing namespace graph_tool;\n\nstruct do_all_pairs_search\n{\n template \n void operator()(const Graph& g, VertexIndexMap vertex_index,\n DistMap dist_map, WeightMap weight, bool dense) const\n {\n typedef typename property_traits::value_type::value_type\n dist_t;\n\n int i, N = num_vertices(g);\n #pragma omp parallel for default(shared) private(i) schedule(runtime) if (N > 100)\n for (i = 0; i < N; ++i)\n {\n dist_map[i].clear();\n dist_map[i].resize(num_vertices(g), 0);\n }\n\n if (dense)\n {\n floyd_warshall_all_pairs_shortest_paths\n (g, dist_map,\n weight_map(ConvertedPropertyMap(weight)).\n vertex_index_map(vertex_index));\n }\n else\n {\n johnson_all_pairs_shortest_paths\n (g, dist_map,\n weight_map(ConvertedPropertyMap(weight)).\n vertex_index_map(vertex_index));\n }\n }\n};\n\nvoid get_all_dists(GraphInterface& gi, boost::any dist_map, boost::any weight,\n bool dense)\n{\n typedef ConstantPropertyMap cweight_map_t;\n\n if (weight.empty())\n weight = boost::any(cweight_map_t(1));\n\n run_action<>()\n (gi, std::bind(do_all_pairs_search(), placeholders::_1,\n gi.GetVertexIndex(), placeholders::_2, placeholders::_3,\n dense),\n vertex_scalar_vector_properties(),\n mpl::push_back::type())\n (dist_map, weight);\n}\n\nvoid export_all_dists()\n{\n python::def(\"get_all_dists\", &get_all_dists);\n};\n", "meta": {"hexsha": "e65d96071f44b55443b30b92bdcdada2e969d9b0", "size": 2900, "ext": "cc", "lang": "C++", "max_stars_repo_path": "graph-tool/src/graph/topology/graph_all_distances.cc", "max_stars_repo_name": "johankaito/fufuka", "max_stars_repo_head_hexsha": "32a96ecf98ce305c2206c38443e58fdec88c788d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2015-08-04T19:41:53.000Z", "max_stars_repo_stars_event_max_datetime": "2015-08-04T19:41:53.000Z", "max_issues_repo_path": "graph-tool/src/graph/topology/graph_all_distances.cc", "max_issues_repo_name": "johankaito/fufuka", "max_issues_repo_head_hexsha": "32a96ecf98ce305c2206c38443e58fdec88c788d", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "graph-tool/src/graph/topology/graph_all_distances.cc", "max_forks_repo_name": "johankaito/fufuka", "max_forks_repo_head_hexsha": "32a96ecf98ce305c2206c38443e58fdec88c788d", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.3333333333, "max_line_length": 90, "alphanum_fraction": 0.6589655172, "num_tokens": 652, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5039061854293322, "lm_q2_score": 0.4186969093556867, "lm_q1q2_score": 0.21098396244447495}} {"text": "// Copyright (C) Dimitrios Michail 2019 - 2021.\n// Distributed under the Boost Software License, Version 1.0.\n// (See accompanying file LICENSE_1_0.txt or copy at\n// https://www.boost.org/LICENSE_1_0.txt)\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nusing namespace boost;\nnamespace po = boost::program_options;\n\n#define USAGE \"Computes statistics on FVS and ISO cycle collections given a weighted undirected graph in DIMACS format.\"\n\n\ntemplate\nclass CandidateCycleBuilder {\npublic:\n typedef typename boost::graph_traits::vertex_descriptor Vertex;\n typedef typename boost::graph_traits::edge_descriptor Edge;\n typedef typename boost::property_traits::value_type WeightType;\n\n CandidateCycleBuilder(const Graph &g, const WeightMap &weight_map) :\n g(g), weight_map(weight_map) {\n }\n\n std::tuple, WeightType> operator()(const std::vector> &trees,\n const parmcb::CandidateCycle &c) const {\n\n std::shared_ptr> v = trees[c.tree()].node(boost::source(c.edge(), g));\n std::shared_ptr> u = trees[c.tree()].node(boost::target(c.edge(), g));\n\n Edge e = c.edge();\n bool valid = true;\n WeightType cycle_weight = boost::get(weight_map, e);\n std::set result;\n result.insert(e);\n\n // first part\n Vertex w = boost::source(c.edge(), g);\n std::shared_ptr> ws = trees[c.tree()].node(w);\n while (ws->has_pred()) {\n Edge a = ws->pred();\n if (result.insert(a).second == false) {\n valid = false;\n break;\n }\n cycle_weight += boost::get(weight_map, a);\n w = boost::opposite(a, w, g);\n ws = trees[c.tree()].node(w);\n }\n\n if (!valid) {\n return std::make_tuple(std::set { }, 0.0);\n }\n\n // second part\n w = boost::target(c.edge(), g);\n ws = trees[c.tree()].node(w);\n while (ws->has_pred()) {\n Edge a = ws->pred();\n if (result.insert(a).second == false) {\n valid = false;\n break;\n }\n cycle_weight += boost::get(weight_map, a);\n w = boost::opposite(a, w, g);\n ws = trees[c.tree()].node(w);\n }\n\n if (!valid) {\n return std::make_tuple(std::set { }, 0.0);\n }\n\n return std::make_tuple(result, cycle_weight);\n }\n\nprivate:\n const Graph &g;\n const WeightMap &weight_map;\n};\n\n\n\ntemplate\nvoid print_tree_stats(const char *name, const Graph &g, WeightMap weight_map) {\n std::vector> trees;\n std::vector> cycles;\n CyclesBuilder cycles_builder;\n cycles_builder(g, weight_map, trees, cycles);\n std::cout << name << \" cycles: \" << cycles.size() << std::endl;\n\n CandidateCycleBuilder candidate_cycle_builder(g, weight_map);\n\n typedef typename boost::graph_traits::edge_descriptor Edge;\n typedef typename boost::property_traits::value_type WeightType;\n\n double total_weight = 0.0;\n long total_card = 0;\n for (parmcb::CandidateCycle c : cycles) {\n std::tuple, WeightType> cc = candidate_cycle_builder(trees, c);\n\n total_weight += std::get<1>(cc);\n total_card += std::get<0>(cc).size();\n }\n\n std::cout << name << \" total cycles weight: \" << total_weight << std::endl;\n std::cout << name << \" total cycles cardinality: \" << total_card << std::endl;\n}\n\ntemplate \nvoid print_all_tree_stats(const Graph& g, WeightMap weight_map) {\n print_tree_stats>(\"FVS\", g, weight_map);\n print_tree_stats>(\"ISO\", g, weight_map);\n print_tree_stats>(\"HORTON\", g, weight_map);\n}\n\nint main(int argc, char *argv[]) {\n\n po::variables_map vm;\n try {\n po::options_description desc(USAGE);\n // @formatter:off\n desc.add_options()\n (\"help,h\", \"Help\")\n (\"verbose,v\", po::value()->default_value(false)->implicit_value(true), \"Verbose\")\n (\"input-file,I\",po::value(), \"Input filename\");\n // @formatter:on\n po::positional_options_description pos_desc;\n pos_desc.add(\"input-file\", 1);\n po::command_line_parser parser { argc, argv };\n parser.options(desc).positional(pos_desc).allow_unregistered();\n po::parsed_options parsed_options = parser.run();\n po::store(parsed_options, vm);\n\n if (vm.count(\"help\")) {\n std::cout << desc << std::endl;\n return EXIT_SUCCESS;\n }\n po::notify(vm);\n\n if (!vm.count(\"input-file\")) {\n std::cerr << \"Input file missing. See usage by calling with -h .\" << std::endl;\n exit(EXIT_FAILURE);\n }\n\n } catch (const po::error &ex) {\n std::cerr << \"Invalid arguments:\" << ex.what() << std::endl;\n return EXIT_FAILURE;\n }\n\n typedef adjacency_list > graph_t;\n\n // create graph\n graph_t graph;\n FILE *fp = fopen(vm[\"input-file\"].as().c_str(), \"r\");\n if (fp == NULL) {\n std::cerr << \"Failed to open input file.\" << std::endl;\n exit(EXIT_FAILURE);\n }\n parmcb::read_dimacs_from_file(fp, graph);\n fclose(fp);\n\n if (parmcb::has_loops(graph)) {\n std::cerr << \"Graph has loops, aborting..\" << std::endl;\n return EXIT_FAILURE;\n }\n if (parmcb::has_multiple_edges(graph)) {\n std::cerr << \"Graph has multiple edges, aborting..\" << std::endl;\n return EXIT_FAILURE;\n }\n if (parmcb::has_non_positive_weights(graph, get(boost::edge_weight, graph))) {\n std::cerr << \"Graph has negative or zero weight edges, aborting..\" << std::endl;\n return EXIT_FAILURE;\n }\n\n std::cout << \"graph n: \" << num_vertices(graph) << std::endl;\n std::cout << \"graph m: \" << num_edges(graph) << std::endl;\n std::cout << \"graph n*m: \" << num_vertices(graph)*num_edges(graph) << std::endl;\n std::cout << std::flush;\n\n print_all_tree_stats(graph, get(boost::edge_weight, graph));\n\n return EXIT_SUCCESS;\n}\n\n", "meta": {"hexsha": "cbf0a8523726022816a66cb2fd5baf16f06af597", "size": 7116, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/collection-stats-dimacs.cpp", "max_stars_repo_name": "d-michail/parmcb", "max_stars_repo_head_hexsha": "19d0b7eb01735600c851b969d9e5a87c78b8c711", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/collection-stats-dimacs.cpp", "max_issues_repo_name": "d-michail/parmcb", "max_issues_repo_head_hexsha": "19d0b7eb01735600c851b969d9e5a87c78b8c711", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/collection-stats-dimacs.cpp", "max_forks_repo_name": "d-michail/parmcb", "max_forks_repo_head_hexsha": "19d0b7eb01735600c851b969d9e5a87c78b8c711", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 35.4029850746, "max_line_length": 120, "alphanum_fraction": 0.6187464868, "num_tokens": 1771, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.21096645787755747}} {"text": "/* -*- c++ -*- */\n/*\n * Copyright 2003,2008,2011,2012 Free Software Foundation, Inc.\n *\n * This file is part of GNU Radio\n *\n * GNU Radio is free software; you can redistribute it and/or modify\n * it under the terms of the GNU General Public License as published by\n * the Free Software Foundation; either version 3, or (at your option)\n * any later version.\n *\n * GNU Radio is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with GNU Radio; see the file COPYING. If not, write to\n * the Free Software Foundation, Inc., 51 Franklin Street,\n * Boston, MA 02110-1301, USA.\n */\n\n#include \n#include \n#include \n#include \n#include \n\n#ifdef _WIN32 //http://www.fftw.org/install/windows.html#DLLwisdom\nstatic void my_fftw_write_char(char c, void *f) { fputc(c, (FILE *) f); }\n#define fftw_export_wisdom_to_file(f) fftw_export_wisdom(my_fftw_write_char, (void*) (f))\n#define fftwf_export_wisdom_to_file(f) fftwf_export_wisdom(my_fftw_write_char, (void*) (f))\n#define fftwl_export_wisdom_to_file(f) fftwl_export_wisdom(my_fftw_write_char, (void*) (f))\n\nstatic int my_fftw_read_char(void *f) { return fgetc((FILE *) f); }\n#define fftw_import_wisdom_from_file(f) fftw_import_wisdom(my_fftw_read_char, (void*) (f))\n#define fftwf_import_wisdom_from_file(f) fftwf_import_wisdom(my_fftw_read_char, (void*) (f))\n#define fftwl_import_wisdom_from_file(f) fftwl_import_wisdom(my_fftw_read_char, (void*) (f))\n#include \n#include \n#define O_NOCTTY 0\n#define O_NONBLOCK 0\n#endif //_WIN32\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \nnamespace fs = boost::filesystem;\n\nnamespace gr {\n namespace fft {\n static boost::mutex wisdom_thread_mutex;\n boost::interprocess::file_lock wisdom_lock;\n static bool wisdom_lock_init_done = false; // Modify while holding 'wisdom_thread_mutex'\n\n gr_complex *\n malloc_complex(int size)\n {\n return (gr_complex*) volk_malloc (sizeof (gr_complex)*size, volk_get_alignment ());\n }\n\n float *\n malloc_float(int size)\n {\n return (float*) volk_malloc (sizeof (float)*size, volk_get_alignment ());\n }\n\n double *\n malloc_double(int size)\n {\n return (double*) volk_malloc (sizeof (double)*size, volk_get_alignment ());\n }\n\n void\n free(void *b)\n {\n volk_free(b);\n }\n\n boost::mutex &\n planner::mutex()\n {\n static boost::mutex s_planning_mutex;\n\n return s_planning_mutex;\n }\n\n static std::string\n wisdom_filename()\n {\n static fs::path path;\n path = fs::path(gr::appdata_path()) / \".gr_fftw_wisdom\";\n return path.string();\n }\n\n static void\n wisdom_lock_init()\n {\n if (wisdom_lock_init_done)\n return;\n\n const std::string wisdom_lock_file = wisdom_filename() + \".lock\";\n // std::cerr << \"Creating FFTW wisdom lockfile: \" << wisdom_lock_file << std::endl;\n int fd = open(wisdom_lock_file.c_str(),\n O_WRONLY | O_CREAT | O_NOCTTY | O_NONBLOCK,\n 0666);\n if (fd < 0) {\n throw std::runtime_error(\"Failed to create FFTW wisdom lockfile: \" + wisdom_lock_file);\n }\n close(fd);\n wisdom_lock = boost::interprocess::file_lock(wisdom_lock_file.c_str());\n wisdom_lock_init_done = true;\n }\n\n static void\n lock_wisdom()\n {\n wisdom_thread_mutex.lock();\n wisdom_lock_init();\n wisdom_lock.lock();\n }\n\n static void\n unlock_wisdom()\n {\n // Assumes 'lock_wisdom' has already been called (i.e. this file_lock is valid)\n wisdom_lock.unlock();\n wisdom_thread_mutex.unlock();\n }\n\n static void\n import_wisdom()\n {\n const std::string filename = wisdom_filename ();\n FILE *fp = fopen (filename.c_str(), \"r\");\n if (fp != 0){\n int r = fftwf_import_wisdom_from_file (fp);\n fclose (fp);\n if (!r){\n fprintf (stderr, \"gr::fft: can't import wisdom from %s\\n\", filename.c_str());\n }\n }\n }\n\n static void\n config_threading(int nthreads)\n {\n static int fftw_threads_inited = 0;\n\n#ifdef FFTW3F_THREADS\n if (fftw_threads_inited == 0)\n {\n fftw_threads_inited = 1;\n fftwf_init_threads();\n }\n\n fftwf_plan_with_nthreads(nthreads);\n#endif\n }\n\n static void\n export_wisdom()\n {\n const std::string filename = wisdom_filename ();\n FILE *fp = fopen (filename.c_str(), \"w\");\n if (fp != 0){\n fftwf_export_wisdom_to_file (fp);\n fclose (fp);\n }\n else {\n fprintf (stderr, \"fft_impl_fftw: \");\n perror (filename.c_str());\n }\n }\n\n// ----------------------------------------------------------------\n\n fft_complex::fft_complex(int fft_size, bool forward, int nthreads)\n {\n // Hold global mutex during plan construction and destruction.\n planner::scoped_lock lock(planner::mutex());\n\n assert (sizeof (fftwf_complex) == sizeof (gr_complex));\n\n if (fft_size <= 0){\n throw std::out_of_range (\"fft_impl_fftw: invalid fft_size\");\n }\n\n d_fft_size = fft_size;\n d_inbuf = (gr_complex *) volk_malloc (sizeof (gr_complex) * inbuf_length (), volk_get_alignment ());\n if (d_inbuf == 0) {\n throw std::runtime_error (\"volk_malloc\");\n }\n d_outbuf = (gr_complex *) volk_malloc (sizeof (gr_complex) * outbuf_length (), volk_get_alignment ());\n if (d_outbuf == 0) {\n volk_free (d_inbuf);\n throw std::runtime_error (\"volk_malloc\");\n }\n\n d_nthreads = nthreads;\n config_threading(nthreads);\n lock_wisdom();\n import_wisdom();\t// load prior wisdom from disk\n\n d_plan = fftwf_plan_dft_1d (fft_size,\n reinterpret_cast(d_inbuf),\n reinterpret_cast(d_outbuf),\n forward ? FFTW_FORWARD : FFTW_BACKWARD,\n FFTW_MEASURE);\n\n if (d_plan == NULL) {\n fprintf(stderr, \"gr::fft: error creating plan\\n\");\n throw std::runtime_error (\"fftwf_plan_dft_1d failed\");\n }\n export_wisdom();\t// store new wisdom to disk\n unlock_wisdom();\n }\n\n fft_complex::~fft_complex()\n {\n // Hold global mutex during plan construction and destruction.\n planner::scoped_lock lock(planner::mutex());\n\n fftwf_destroy_plan ((fftwf_plan) d_plan);\n volk_free (d_inbuf);\n volk_free (d_outbuf);\n }\n\n void\n fft_complex::set_nthreads(int n)\n {\n if (n <= 0) {\n throw std::out_of_range (\"gr::fft: invalid number of threads\");\n }\n d_nthreads = n;\n\n#ifdef FFTW3F_THREADS\n fftwf_plan_with_nthreads(d_nthreads);\n#endif\n }\n\n void\n fft_complex::execute()\n {\n fftwf_execute((fftwf_plan) d_plan);\n }\n\n// ----------------------------------------------------------------\n\n fft_real_fwd::fft_real_fwd (int fft_size, int nthreads)\n {\n // Hold global mutex during plan construction and destruction.\n planner::scoped_lock lock(planner::mutex());\n\n assert (sizeof (fftwf_complex) == sizeof (gr_complex));\n\n if (fft_size <= 0) {\n throw std::out_of_range (\"gr::fft: invalid fft_size\");\n }\n\n d_fft_size = fft_size;\n d_inbuf = (float *) volk_malloc (sizeof (float) * inbuf_length (), volk_get_alignment ());\n if (d_inbuf == 0) {\n throw std::runtime_error (\"volk_malloc\");\n }\n\n d_outbuf = (gr_complex *) volk_malloc (sizeof (gr_complex) * outbuf_length (), volk_get_alignment ());\n if (d_outbuf == 0) {\n volk_free (d_inbuf);\n throw std::runtime_error (\"volk_malloc\");\n }\n\n d_nthreads = nthreads;\n config_threading(nthreads);\n lock_wisdom();\n import_wisdom();\t// load prior wisdom from disk\n\n d_plan = fftwf_plan_dft_r2c_1d (fft_size,\n d_inbuf,\n reinterpret_cast(d_outbuf),\n FFTW_MEASURE);\n\n if (d_plan == NULL) {\n fprintf(stderr, \"gr::fft::fft_real_fwd: error creating plan\\n\");\n throw std::runtime_error (\"fftwf_plan_dft_r2c_1d failed\");\n }\n export_wisdom();\t// store new wisdom to disk\n unlock_wisdom();\n }\n\n fft_real_fwd::~fft_real_fwd()\n {\n // Hold global mutex during plan construction and destruction.\n planner::scoped_lock lock(planner::mutex());\n\n fftwf_destroy_plan ((fftwf_plan) d_plan);\n volk_free (d_inbuf);\n volk_free (d_outbuf);\n }\n\n void\n fft_real_fwd::set_nthreads(int n)\n {\n if (n <= 0) {\n throw std::out_of_range (\"gr::fft::fft_real_fwd::set_nthreads: invalid number of threads\");\n }\n d_nthreads = n;\n\n#ifdef FFTW3F_THREADS\n fftwf_plan_with_nthreads(d_nthreads);\n#endif\n }\n\n void\n fft_real_fwd::execute()\n {\n fftwf_execute ((fftwf_plan) d_plan);\n }\n\n // ----------------------------------------------------------------\n\n fft_real_rev::fft_real_rev(int fft_size, int nthreads)\n {\n // Hold global mutex during plan construction and destruction.\n planner::scoped_lock lock(planner::mutex());\n\n assert (sizeof (fftwf_complex) == sizeof (gr_complex));\n\n if (fft_size <= 0) {\n throw std::out_of_range (\"gr::fft::fft_real_rev: invalid fft_size\");\n }\n\n d_fft_size = fft_size;\n d_inbuf = (gr_complex *) volk_malloc (sizeof (gr_complex) * inbuf_length (), volk_get_alignment ());\n if (d_inbuf == 0) {\n throw std::runtime_error (\"volk_malloc\");\n }\n\n d_outbuf = (float *) volk_malloc (sizeof (float) * outbuf_length (), volk_get_alignment ());\n if (d_outbuf == 0) {\n volk_free (d_inbuf);\n throw std::runtime_error (\"volk_malloc\");\n }\n\n d_nthreads = nthreads;\n config_threading(nthreads);\n lock_wisdom();\n import_wisdom();\t// load prior wisdom from disk\n\n // FIXME If there's ever a chance that the planning functions\n // will be called in multiple threads, we've got to ensure single\n // threaded access. They are not thread-safe.\n d_plan = fftwf_plan_dft_c2r_1d (fft_size,\n reinterpret_cast(d_inbuf),\n d_outbuf,\n FFTW_MEASURE);\n\n if (d_plan == NULL) {\n fprintf(stderr, \"gr::fft::fft_real_rev: error creating plan\\n\");\n throw std::runtime_error (\"fftwf_plan_dft_c2r_1d failed\");\n }\n export_wisdom ();\t// store new wisdom to disk\n unlock_wisdom();\n }\n\n fft_real_rev::~fft_real_rev ()\n {\n // Hold global mutex during plan construction and destruction.\n planner::scoped_lock lock(planner::mutex());\n\n fftwf_destroy_plan ((fftwf_plan) d_plan);\n volk_free (d_inbuf);\n volk_free (d_outbuf);\n }\n\n void\n fft_real_rev::set_nthreads(int n)\n {\n if (n <= 0) {\n throw std::out_of_range (\"gr::fft::fft_real_rev::set_nthreads: invalid number of threads\");\n }\n d_nthreads = n;\n\n#ifdef FFTW3F_THREADS\n fftwf_plan_with_nthreads(d_nthreads);\n#endif\n }\n\n void\n fft_real_rev::execute ()\n {\n fftwf_execute ((fftwf_plan) d_plan);\n }\n\n } /* namespace fft */\n} /* namespace gr */\n", "meta": {"hexsha": "718cd0990f666eb9898c096934c6957cb8088faa", "size": 11611, "ext": "cc", "lang": "C++", "max_stars_repo_path": "gnuradio-3.7.13.4/gr-fft/lib/fft.cc", "max_stars_repo_name": "v1259397/cosmic-gnuradio", "max_stars_repo_head_hexsha": "64c149520ac6a7d44179c3f4a38f38add45dd5dc", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2021-03-09T07:32:37.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-09T07:32:37.000Z", "max_issues_repo_path": "gnuradio-3.7.13.4/gr-fft/lib/fft.cc", "max_issues_repo_name": "v1259397/cosmic-gnuradio", "max_issues_repo_head_hexsha": "64c149520ac6a7d44179c3f4a38f38add45dd5dc", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "gnuradio-3.7.13.4/gr-fft/lib/fft.cc", "max_forks_repo_name": "v1259397/cosmic-gnuradio", "max_forks_repo_head_hexsha": "64c149520ac6a7d44179c3f4a38f38add45dd5dc", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.8114143921, "max_line_length": 108, "alphanum_fraction": 0.6206183791, "num_tokens": 2980, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.21096645787755747}} {"text": "// This file is part of LatticeTester.\n//\n// LatticeTester\n// Copyright (C) 2012-2018 Pierre L'Ecuyer and Universite de Montreal\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n// http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\n/*\n * The purpose of this example is to compare the time\n * of each reduction method. We compute several\n * random matrix and apply algorithms. The parameters of this\n * analysis are defined at the beginning of this file,\n * after include part. The output is a graphic generated by R.\n * The User need R distribution and the header RInside.h\n */\n\n\n// Include Header\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n// Include LatticeTester Header\n#include \"latticetester/Util.h\"\n#include \"latticetester/Const.h\"\n#include \"latticetester/IntFactor.h\"\n#include \"latticetester/IntLatticeBasis.h\"\n#include \"latticetester/Reducer.h\"\n#include \"latticetester/ParamReader.h\"\n#include \"latticetester/Config.h\"\n#include \"latticetester/LatticeAnalysis.h\"\n\n// Include NTL Header\n#include \n#include \n#include \n#include \n#include \"NTL/vec_ZZ.h\"\n#include \"NTL/vec_ZZ_p.h\"\n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace LatticeTester;\n\nint ZZRR(int argc, char* argv[]) {\n LatticeAnalysis latAnalysis;\n struct stat buf; // properties of a file or directory\n int status = 0;\n\n for (int j = 2; j < argc; j++) {\n // Do the test for each data file or directory on the command line\n\n stat(argv[j], &buf);\n if (0 != S_ISDIR(buf.st_mode)) // directory\n status |= latAnalysis.doTestFromDirectory (argv[j]);\n else {\n string dataname(argv[j]);\n dataname.append(\".dat\");\n stat(dataname.c_str(), &buf);\n status |= latAnalysis.doTestFromInputFile (argv[j]);\n }\n }\n return 1;\n}\nint ZZDD(int argc, char* argv[]) {\n LatticeAnalysis latAnalysis;\n struct stat buf; // properties of a file or directory\n int status = 0;\n\n for (int j = 2; j < argc; j++) {\n // Do the test for each data file or directory on the command line\n\n stat(argv[j], &buf);\n if (0 != S_ISDIR(buf.st_mode)) // directory\n status |= latAnalysis.doTestFromDirectory (argv[j]);\n else {\n string dataname(argv[j]);\n dataname.append(\".dat\");\n stat(dataname.c_str(), &buf);\n status |= latAnalysis.doTestFromInputFile (argv[j]);\n }\n }\n return 1;\n}\nint LLDD(int argc, char* argv[]) {\n LatticeAnalysis latAnalysis;\n struct stat buf; // properties of a file or directory\n int status = 0;\n\n for (int j = 2; j < argc; j++) {\n // Do the test for each data file or directory on the command line\n\n stat(argv[j], &buf);\n if (0 != S_ISDIR(buf.st_mode)) // directory\n status |= latAnalysis.doTestFromDirectory (argv[j]);\n else {\n string dataname(argv[j]);\n dataname.append(\".dat\");\n stat(dataname.c_str(), &buf);\n status |= latAnalysis.doTestFromInputFile (argv[j]);\n }\n }\n return 1;\n}\n//==============================================================================\n\nint main (int argc, char *argv[])\n{\n std::clock_t start_t = std::clock();\n if (argc < 2) {\n cerr << \"\\n*** Usage:\\n \"\n << argv[0] << \" data_file1 data_file2 ....\" << endl\n << \"or\\n \"\n << argv[0] << \" dir1 dir2 ....\" << endl\n << endl;\n return -1;\n } else if (argc < 3) {\n cerr << \"Must specify types\\n\";\n return -1;\n }\n //cout << \"ARGC \" << argc << endl;\n if (!strcmp(argv[1], \"ZZRR\")) {\n ZZRR(argc, argv);\n std::clock_t end_t = std::clock();\n cout << \"Time: \" << (double)(end_t-start_t)/CLOCKS_PER_SEC << \" sec\\n\";\n } else if (!strcmp(argv[1], \"ZZDD\")) {\n ZZDD(argc, argv);\n std::clock_t end_t = std::clock();\n cout << \"Time: \" << (double)(end_t-start_t)/CLOCKS_PER_SEC << \" sec\\n\";\n } else if (!strcmp(argv[1], \"LLDD\")) {\n LLDD(argc, argv);\n std::clock_t end_t = std::clock();\n cout << \"Time: \" << (double)(end_t-start_t)/CLOCKS_PER_SEC << \" sec\\n\";\n } else {\n cerr << \"No valid type name has been specified\\n\";\n }\n\n return 0;\n}\n\n\n", "meta": {"hexsha": "ae8987f1336f87bcf07d69daa26cd67ab1769b9e", "size": 4845, "ext": "cc", "lang": "C++", "max_stars_repo_path": "progs/TestLattice.cc", "max_stars_repo_name": "edoars/latticetester", "max_stars_repo_head_hexsha": "980179abe2da78b8a13d2b912d2215b97509c528", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "progs/TestLattice.cc", "max_issues_repo_name": "edoars/latticetester", "max_issues_repo_head_hexsha": "980179abe2da78b8a13d2b912d2215b97509c528", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2018-07-27T15:41:06.000Z", "max_issues_repo_issues_event_max_datetime": "2019-06-05T17:41:14.000Z", "max_forks_repo_path": "progs/TestLattice.cc", "max_forks_repo_name": "edoars/latticetester", "max_forks_repo_head_hexsha": "980179abe2da78b8a13d2b912d2215b97509c528", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2021-03-24T01:11:53.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-24T01:11:53.000Z", "avg_line_length": 29.7239263804, "max_line_length": 80, "alphanum_fraction": 0.6392156863, "num_tokens": 1365, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5273165085228825, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.21082727846171376}} {"text": "// Boost.Polygon library detail/voronoi_ctypes.hpp header file\r\n\r\n// Copyright Andrii Sydorchuk 2010-2012.\r\n// Distributed under the Boost Software License, Version 1.0.\r\n// (See accompanying file LICENSE_1_0.txt or copy at\r\n// http://www.boost.org/LICENSE_1_0.txt)\r\n\r\n// See http://www.boost.org for updates, documentation, and revision history.\r\n\r\n#ifndef BOOST_POLYGON_DETAIL_VORONOI_CTYPES\r\n#define BOOST_POLYGON_DETAIL_VORONOI_CTYPES\r\n\r\n#include \r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\nnamespace boost {\r\nnamespace polygon {\r\nnamespace detail {\r\n\r\ntypedef boost::int32_t int32;\r\ntypedef boost::int64_t int64;\r\ntypedef boost::uint32_t uint32;\r\ntypedef boost::uint64_t uint64;\r\ntypedef double fpt64;\r\n\r\n// If two floating-point numbers in the same format are ordered (x < y),\r\n// then they are ordered the same way when their bits are reinterpreted as\r\n// sign-magnitude integers. Values are considered to be almost equal if\r\n// their integer bits reinterpretations differ in not more than maxUlps units.\r\ntemplate \r\nstruct ulp_comparison;\r\n\r\ntemplate <>\r\nstruct ulp_comparison {\r\n enum Result {\r\n LESS = -1,\r\n EQUAL = 0,\r\n MORE = 1\r\n };\r\n\r\n Result operator()(fpt64 a, fpt64 b, unsigned int maxUlps) const {\r\n uint64 ll_a, ll_b;\r\n\r\n // Reinterpret double bits as 64-bit signed integer.\r\n std::memcpy(&ll_a, &a, sizeof(fpt64));\r\n std::memcpy(&ll_b, &b, sizeof(fpt64));\r\n\r\n // Positive 0.0 is integer zero. Negative 0.0 is 0x8000000000000000.\r\n // Map negative zero to an integer zero representation - making it\r\n // identical to positive zero - the smallest negative number is\r\n // represented by negative one, and downwards from there.\r\n if (ll_a < 0x8000000000000000ULL)\r\n ll_a = 0x8000000000000000ULL - ll_a;\r\n if (ll_b < 0x8000000000000000ULL)\r\n ll_b = 0x8000000000000000ULL - ll_b;\r\n\r\n // Compare 64-bit signed integer representations of input values.\r\n // Difference in 1 Ulp is equivalent to a relative error of between\r\n // 1/4,000,000,000,000,000 and 1/8,000,000,000,000,000.\r\n if (ll_a > ll_b)\r\n return (ll_a - ll_b <= maxUlps) ? EQUAL : LESS;\r\n return (ll_b - ll_a <= maxUlps) ? EQUAL : MORE;\r\n }\r\n};\r\n\r\ntemplate \r\nstruct extened_exponent_fpt_traits;\r\n\r\ntemplate <>\r\nstruct extened_exponent_fpt_traits {\r\n public:\r\n typedef int exp_type;\r\n enum {\r\n MAX_SIGNIFICANT_EXP_DIF = 54\r\n };\r\n};\r\n\r\n// Floating point type wrapper. Allows to extend exponent boundaries to the\r\n// integer type range. This class does not handle division by zero, subnormal\r\n// numbers or NaNs.\r\ntemplate >\r\nclass extended_exponent_fpt {\r\n public:\r\n typedef _fpt fpt_type;\r\n typedef typename _traits::exp_type exp_type;\r\n\r\n explicit extended_exponent_fpt(fpt_type val) {\r\n val_ = std::frexp(val, &exp_);\r\n }\r\n\r\n extended_exponent_fpt(fpt_type val, exp_type exp) {\r\n val_ = std::frexp(val, &exp_);\r\n exp_ += exp;\r\n }\r\n\r\n bool is_pos() const {\r\n return val_ > 0;\r\n }\r\n\r\n bool is_neg() const {\r\n return val_ < 0;\r\n }\r\n\r\n bool is_zero() const {\r\n return val_ == 0;\r\n }\r\n\r\n extended_exponent_fpt operator-() const {\r\n return extended_exponent_fpt(-val_, exp_);\r\n }\r\n\r\n extended_exponent_fpt operator+(const extended_exponent_fpt& that) const {\r\n if (this->val_ == 0.0 ||\r\n that.exp_ > this->exp_ + _traits::MAX_SIGNIFICANT_EXP_DIF) {\r\n return that;\r\n }\r\n if (that.val_ == 0.0 ||\r\n this->exp_ > that.exp_ + _traits::MAX_SIGNIFICANT_EXP_DIF) {\r\n return *this;\r\n }\r\n if (this->exp_ >= that.exp_) {\r\n exp_type exp_dif = this->exp_ - that.exp_;\r\n fpt_type val = std::ldexp(this->val_, exp_dif) + that.val_;\r\n return extended_exponent_fpt(val, that.exp_);\r\n } else {\r\n exp_type exp_dif = that.exp_ - this->exp_;\r\n fpt_type val = std::ldexp(that.val_, exp_dif) + this->val_;\r\n return extended_exponent_fpt(val, this->exp_);\r\n }\r\n }\r\n\r\n extended_exponent_fpt operator-(const extended_exponent_fpt& that) const {\r\n if (this->val_ == 0.0 ||\r\n that.exp_ > this->exp_ + _traits::MAX_SIGNIFICANT_EXP_DIF) {\r\n return extended_exponent_fpt(-that.val_, that.exp_);\r\n }\r\n if (that.val_ == 0.0 ||\r\n this->exp_ > that.exp_ + _traits::MAX_SIGNIFICANT_EXP_DIF) {\r\n return *this;\r\n }\r\n if (this->exp_ >= that.exp_) {\r\n exp_type exp_dif = this->exp_ - that.exp_;\r\n fpt_type val = std::ldexp(this->val_, exp_dif) - that.val_;\r\n return extended_exponent_fpt(val, that.exp_);\r\n } else {\r\n exp_type exp_dif = that.exp_ - this->exp_;\r\n fpt_type val = std::ldexp(-that.val_, exp_dif) + this->val_;\r\n return extended_exponent_fpt(val, this->exp_);\r\n }\r\n }\r\n\r\n extended_exponent_fpt operator*(const extended_exponent_fpt& that) const {\r\n fpt_type val = this->val_ * that.val_;\r\n exp_type exp = this->exp_ + that.exp_;\r\n return extended_exponent_fpt(val, exp);\r\n }\r\n\r\n extended_exponent_fpt operator/(const extended_exponent_fpt& that) const {\r\n fpt_type val = this->val_ / that.val_;\r\n exp_type exp = this->exp_ - that.exp_;\r\n return extended_exponent_fpt(val, exp);\r\n }\r\n\r\n extended_exponent_fpt& operator+=(const extended_exponent_fpt& that) {\r\n return *this = *this + that;\r\n }\r\n\r\n extended_exponent_fpt& operator-=(const extended_exponent_fpt& that) {\r\n return *this = *this - that;\r\n }\r\n\r\n extended_exponent_fpt& operator*=(const extended_exponent_fpt& that) {\r\n return *this = *this * that;\r\n }\r\n\r\n extended_exponent_fpt& operator/=(const extended_exponent_fpt& that) {\r\n return *this = *this / that;\r\n }\r\n\r\n extended_exponent_fpt sqrt() const {\r\n fpt_type val = val_;\r\n exp_type exp = exp_;\r\n if (exp & 1) {\r\n val *= 2.0;\r\n --exp;\r\n }\r\n return extended_exponent_fpt(std::sqrt(val), exp >> 1);\r\n }\r\n\r\n fpt_type d() const {\r\n return std::ldexp(val_, exp_);\r\n }\r\n\r\n private:\r\n fpt_type val_;\r\n exp_type exp_;\r\n};\r\ntypedef extended_exponent_fpt efpt64;\r\n\r\ntemplate \r\nextended_exponent_fpt<_fpt> get_sqrt(const extended_exponent_fpt<_fpt>& that) {\r\n return that.sqrt();\r\n}\r\n\r\ntemplate \r\nbool is_pos(const extended_exponent_fpt<_fpt>& that) {\r\n return that.is_pos();\r\n}\r\n\r\ntemplate \r\nbool is_neg(const extended_exponent_fpt<_fpt>& that) {\r\n return that.is_neg();\r\n}\r\n\r\ntemplate \r\nbool is_zero(const extended_exponent_fpt<_fpt>& that) {\r\n return that.is_zero();\r\n}\r\n\r\n// Very efficient stack allocated big integer class.\r\n// Supports next set of arithmetic operations: +, -, *.\r\ntemplate\r\nclass extended_int {\r\n public:\r\n extended_int() {}\r\n\r\n extended_int(int32 that) {\r\n if (that > 0) {\r\n this->chunks_[0] = that;\r\n this->count_ = 1;\r\n } else if (that < 0) {\r\n this->chunks_[0] = -that;\r\n this->count_ = -1;\r\n } else {\r\n this->count_ = 0;\r\n }\r\n }\r\n\r\n extended_int(int64 that) {\r\n if (that > 0) {\r\n this->chunks_[0] = static_cast(that);\r\n this->chunks_[1] = that >> 32;\r\n this->count_ = this->chunks_[1] ? 2 : 1;\r\n } else if (that < 0) {\r\n that = -that;\r\n this->chunks_[0] = static_cast(that);\r\n this->chunks_[1] = that >> 32;\r\n this->count_ = this->chunks_[1] ? -2 : -1;\r\n } else {\r\n this->count_ = 0;\r\n }\r\n }\r\n\r\n extended_int(const std::vector& chunks, bool plus = true) {\r\n this->count_ = static_cast((std::min)(N, chunks.size()));\r\n for (int i = 0; i < this->count_; ++i)\r\n this->chunks_[i] = chunks[chunks.size() - i - 1];\r\n if (!plus)\r\n this->count_ = -this->count_;\r\n }\r\n\r\n template\r\n extended_int(const extended_int& that) {\r\n this->count_ = that.count();\r\n std::memcpy(this->chunks_, that.chunks(), that.size() * sizeof(uint32));\r\n }\r\n\r\n extended_int& operator=(int32 that) {\r\n if (that > 0) {\r\n this->chunks_[0] = that;\r\n this->count_ = 1;\r\n } else if (that < 0) {\r\n this->chunks_[0] = -that;\r\n this->count_ = -1;\r\n } else {\r\n this->count_ = 0;\r\n }\r\n return *this;\r\n }\r\n\r\n extended_int& operator=(int64 that) {\r\n if (that > 0) {\r\n this->chunks_[0] = static_cast(that);\r\n this->chunks_[1] = that >> 32;\r\n this->count_ = this->chunks_[1] ? 2 : 1;\r\n } else if (that < 0) {\r\n that = -that;\r\n this->chunks_[0] = static_cast(that);\r\n this->chunks_[1] = that >> 32;\r\n this->count_ = this->chunks_[1] ? -2 : -1;\r\n } else {\r\n this->count_ = 0;\r\n }\r\n return *this;\r\n }\r\n\r\n template\r\n extended_int& operator=(const extended_int& that) {\r\n this->count_ = that.count();\r\n std::memcpy(this->chunks_, that.chunks(), that.size() * sizeof(uint32));\r\n return *this;\r\n }\r\n\r\n bool is_pos() const {\r\n return this->count_ > 0;\r\n }\r\n\r\n bool is_neg() const {\r\n return this->count_ < 0;\r\n }\r\n\r\n bool is_zero() const {\r\n return this->count_ == 0;\r\n }\r\n\r\n bool operator==(const extended_int& that) const {\r\n if (this->count_ != that.count())\r\n return false;\r\n for (std::size_t i = 0; i < this->size(); ++i)\r\n if (this->chunks_[i] != that.chunks()[i])\r\n return false;\r\n return true;\r\n }\r\n\r\n bool operator!=(const extended_int& that) const {\r\n return !(*this == that);\r\n }\r\n\r\n bool operator<(const extended_int& that) const {\r\n if (this->count_ != that.count())\r\n return this->count_ < that.count();\r\n std::size_t i = this->size();\r\n if (!i)\r\n return false;\r\n do {\r\n --i;\r\n if (this->chunks_[i] != that.chunks()[i])\r\n return (this->chunks_[i] < that.chunks()[i]) ^ (this->count_ < 0);\r\n } while (i);\r\n return false;\r\n }\r\n\r\n bool operator>(const extended_int& that) const {\r\n return that < *this;\r\n }\r\n\r\n bool operator<=(const extended_int& that) const {\r\n return !(that < *this);\r\n }\r\n\r\n bool operator>=(const extended_int& that) const {\r\n return !(*this < that);\r\n }\r\n\r\n extended_int operator-() const {\r\n extended_int ret_val = *this;\r\n ret_val.neg();\r\n return ret_val;\r\n }\r\n\r\n void neg() {\r\n this->count_ = -this->count_;\r\n }\r\n\r\n extended_int operator+(const extended_int& that) const {\r\n extended_int ret_val;\r\n ret_val.add(*this, that);\r\n return ret_val;\r\n }\r\n\r\n void add(const extended_int& e1, const extended_int& e2) {\r\n if (!e1.count()) {\r\n *this = e2;\r\n return;\r\n }\r\n if (!e2.count()) {\r\n *this = e1;\r\n return;\r\n }\r\n if ((e1.count() > 0) ^ (e2.count() > 0)) {\r\n dif(e1.chunks(), e1.size(), e2.chunks(), e2.size());\r\n } else {\r\n add(e1.chunks(), e1.size(), e2.chunks(), e2.size());\r\n }\r\n if (e1.count() < 0)\r\n this->count_ = -this->count_;\r\n }\r\n\r\n extended_int operator-(const extended_int& that) const {\r\n extended_int ret_val;\r\n ret_val.dif(*this, that);\r\n return ret_val;\r\n }\r\n\r\n void dif(const extended_int& e1, const extended_int& e2) {\r\n if (!e1.count()) {\r\n *this = e2;\r\n this->count_ = -this->count_;\r\n return;\r\n }\r\n if (!e2.count()) {\r\n *this = e1;\r\n return;\r\n }\r\n if ((e1.count() > 0) ^ (e2.count() > 0)) {\r\n add(e1.chunks(), e1.size(), e2.chunks(), e2.size());\r\n } else {\r\n dif(e1.chunks(), e1.size(), e2.chunks(), e2.size());\r\n }\r\n if (e1.count() < 0)\r\n this->count_ = -this->count_;\r\n }\r\n\r\n extended_int operator*(int32 that) const {\r\n extended_int temp(that);\r\n return (*this) * temp;\r\n }\r\n\r\n extended_int operator*(int64 that) const {\r\n extended_int temp(that);\r\n return (*this) * temp;\r\n }\r\n\r\n extended_int operator*(const extended_int& that) const {\r\n extended_int ret_val;\r\n ret_val.mul(*this, that);\r\n return ret_val;\r\n }\r\n\r\n void mul(const extended_int& e1, const extended_int& e2) {\r\n if (!e1.count() || !e2.count()) {\r\n this->count_ = 0;\r\n return;\r\n }\r\n mul(e1.chunks(), e1.size(), e2.chunks(), e2.size());\r\n if ((e1.count() > 0) ^ (e2.count() > 0))\r\n this->count_ = -this->count_;\r\n }\r\n\r\n const uint32* chunks() const {\r\n return chunks_;\r\n }\r\n\r\n int32 count() const {\r\n return count_;\r\n }\r\n\r\n std::size_t size() const {\r\n return (std::abs)(count_);\r\n }\r\n\r\n std::pair p() const {\r\n std::pair ret_val(0, 0);\r\n std::size_t sz = this->size();\r\n if (!sz) {\r\n return ret_val;\r\n } else {\r\n if (sz == 1) {\r\n ret_val.first = static_cast(this->chunks_[0]);\r\n } else if (sz == 2) {\r\n ret_val.first = static_cast(this->chunks_[1]) *\r\n static_cast(0x100000000LL) +\r\n static_cast(this->chunks_[0]);\r\n } else {\r\n for (std::size_t i = 1; i <= 3; ++i) {\r\n ret_val.first *= static_cast(0x100000000LL);\r\n ret_val.first += static_cast(this->chunks_[sz - i]);\r\n }\r\n ret_val.second = (sz - 3) << 5;\r\n }\r\n }\r\n if (this->count_ < 0)\r\n ret_val.first = -ret_val.first;\r\n return ret_val;\r\n }\r\n\r\n fpt64 d() const {\r\n std::pair p = this->p();\r\n return std::ldexp(p.first, p.second);\r\n }\r\n\r\n private:\r\n void add(const uint32* c1, std::size_t sz1,\r\n const uint32* c2, std::size_t sz2) {\r\n if (sz1 < sz2) {\r\n add(c2, sz2, c1, sz1);\r\n return;\r\n }\r\n this->count_ = sz1;\r\n uint64 temp = 0;\r\n for (std::size_t i = 0; i < sz2; ++i) {\r\n temp += static_cast(c1[i]) + static_cast(c2[i]);\r\n this->chunks_[i] = static_cast(temp);\r\n temp >>= 32;\r\n }\r\n for (std::size_t i = sz2; i < sz1; ++i) {\r\n temp += static_cast(c1[i]);\r\n this->chunks_[i] = static_cast(temp);\r\n temp >>= 32;\r\n }\r\n if (temp && (this->count_ != N)) {\r\n this->chunks_[this->count_] = static_cast(temp);\r\n ++this->count_;\r\n }\r\n }\r\n\r\n void dif(const uint32* c1, std::size_t sz1,\r\n const uint32* c2, std::size_t sz2,\r\n bool rec = false) {\r\n if (sz1 < sz2) {\r\n dif(c2, sz2, c1, sz1, true);\r\n this->count_ = -this->count_;\r\n return;\r\n } else if ((sz1 == sz2) && !rec) {\r\n do {\r\n --sz1;\r\n if (c1[sz1] < c2[sz1]) {\r\n ++sz1;\r\n dif(c2, sz1, c1, sz1, true);\r\n this->count_ = -this->count_;\r\n return;\r\n } else if (c1[sz1] > c2[sz1]) {\r\n ++sz1;\r\n break;\r\n }\r\n } while (sz1);\r\n if (!sz1) {\r\n this->count_ = 0;\r\n return;\r\n }\r\n sz2 = sz1;\r\n }\r\n this->count_ = sz1-1;\r\n bool flag = false;\r\n for (std::size_t i = 0; i < sz2; ++i) {\r\n this->chunks_[i] = c1[i] - c2[i] - (flag?1:0);\r\n flag = (c1[i] < c2[i]) || ((c1[i] == c2[i]) && flag);\r\n }\r\n for (std::size_t i = sz2; i < sz1; ++i) {\r\n this->chunks_[i] = c1[i] - (flag?1:0);\r\n flag = !c1[i] && flag;\r\n }\r\n if (this->chunks_[this->count_])\r\n ++this->count_;\r\n }\r\n\r\n void mul(const uint32* c1, std::size_t sz1,\r\n const uint32* c2, std::size_t sz2) {\r\n uint64 cur = 0, nxt, tmp;\r\n this->count_ = static_cast((std::min)(N, sz1 + sz2 - 1));\r\n for (std::size_t shift = 0; shift < static_cast(this->count_);\r\n ++shift) {\r\n nxt = 0;\r\n for (std::size_t first = 0; first <= shift; ++first) {\r\n if (first >= sz1)\r\n break;\r\n std::size_t second = shift - first;\r\n if (second >= sz2)\r\n continue;\r\n tmp = static_cast(c1[first]) * static_cast(c2[second]);\r\n cur += static_cast(tmp);\r\n nxt += tmp >> 32;\r\n }\r\n this->chunks_[shift] = static_cast(cur);\r\n cur = nxt + (cur >> 32);\r\n }\r\n if (cur && (this->count_ != N)) {\r\n this->chunks_[this->count_] = static_cast(cur);\r\n ++this->count_;\r\n }\r\n }\r\n\r\n uint32 chunks_[N];\r\n int32 count_;\r\n};\r\n\r\ntemplate \r\nbool is_pos(const extended_int& that) {\r\n return that.count() > 0;\r\n}\r\n\r\ntemplate \r\nbool is_neg(const extended_int& that) {\r\n return that.count() < 0;\r\n}\r\n\r\ntemplate \r\nbool is_zero(const extended_int& that) {\r\n return !that.count();\r\n}\r\n\r\nstruct type_converter_fpt {\r\n template \r\n fpt64 operator()(const T& that) const {\r\n return static_cast(that);\r\n }\r\n\r\n template \r\n fpt64 operator()(const extended_int& that) const {\r\n return that.d();\r\n }\r\n\r\n fpt64 operator()(const extended_exponent_fpt& that) const {\r\n return that.d();\r\n }\r\n};\r\n\r\nstruct type_converter_efpt {\r\n template \r\n extended_exponent_fpt operator()(const extended_int& that) const {\r\n std::pair p = that.p();\r\n return extended_exponent_fpt(p.first, p.second);\r\n }\r\n};\r\n\r\n// Voronoi coordinate type traits make it possible to extend algorithm\r\n// input coordinate range to any user provided integer type and algorithm\r\n// output coordinate range to any ieee-754 like floating point type.\r\ntemplate \r\nstruct voronoi_ctype_traits;\r\n\r\ntemplate <>\r\nstruct voronoi_ctype_traits {\r\n typedef int32 int_type;\r\n typedef int64 int_x2_type;\r\n typedef uint64 uint_x2_type;\r\n typedef extended_int<64> big_int_type;\r\n typedef fpt64 fpt_type;\r\n typedef extended_exponent_fpt efpt_type;\r\n typedef ulp_comparison ulp_cmp_type;\r\n typedef type_converter_fpt to_fpt_converter_type;\r\n typedef type_converter_efpt to_efpt_converter_type;\r\n};\r\n} // detail\r\n} // polygon\r\n} // boost\r\n\r\n#endif // BOOST_POLYGON_DETAIL_VORONOI_CTYPES\r\n", "meta": {"hexsha": "f358355ceef767c41affadbf43f491853fb7ac3b", "size": 17827, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/polygon/detail/voronoi_ctypes.hpp", "max_stars_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_stars_repo_head_hexsha": "4fb2d44945a6305ae3ca87be3872f9432d16f1fb", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 85.0, "max_stars_repo_stars_event_min_datetime": "2015-02-08T20:36:17.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-14T20:38:31.000Z", "max_issues_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/polygon/detail/voronoi_ctypes.hpp", "max_issues_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_issues_repo_head_hexsha": "4fb2d44945a6305ae3ca87be3872f9432d16f1fb", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": 9.0, "max_issues_repo_issues_event_min_datetime": "2015-01-28T16:33:19.000Z", "max_issues_repo_issues_event_max_datetime": "2020-04-12T23:03:28.000Z", "max_forks_repo_path": "ReactAndroid/build/third-party-ndk/boost/boost_1_57_0/boost/polygon/detail/voronoi_ctypes.hpp", "max_forks_repo_name": "kimwoongkyu/react-native-0-36-1-woogie", "max_forks_repo_head_hexsha": "4fb2d44945a6305ae3ca87be3872f9432d16f1fb", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 27.0, "max_forks_repo_forks_event_min_datetime": "2015-01-28T16:33:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-12T05:04:39.000Z", "avg_line_length": 27.7247278383, "max_line_length": 80, "alphanum_fraction": 0.5859090144, "num_tokens": 5215, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5467381519846138, "lm_q2_score": 0.38491213037224875, "lm_q1q2_score": 0.21044614683618404}} {"text": "#ifndef STAN_MATH_TORSTEN_PKMODELONECPT_HPP\n#define STAN_MATH_TORSTEN_PKMODELONECPT_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace torsten {\n\n/**\n * Computes the predicted amounts in each compartment at each event\n * for a one compartment model with first oder absorption.\n *\n * @tparam T0 type of scalar for time of events.\n * @tparam T1 type of scalar for amount at each event.\n * @tparam T2 type of scalar for rate at each event.\n * @tparam T3 type of scalar for inter-dose inteveral at each event.\n * @tparam T4 type of scalars for the model parameters.\n * @tparam T5 type of scalar for bio-variability F.\n * @tparam T6 type of scalar for lag times.\n * @param[in] pMatrix parameters at each event\n * @param[in] time times of events\n * @param[in] amt amount at each event\n * @param[in] rate rate at each event\n * @param[in] ii inter-dose interval at each event\n * @param[in] evid event identity:\n * (0) observation\n * (1) dosing\n * (2) other\n * (3) reset\n * (4) reset AND dosing\n * @param[in] cmt compartment number at each event\n * @param[in] addl additional dosing at each event\n * @param[in] ss steady state approximation at each event (0: no, 1: yes)\n * @return a matrix with predicted amount in each compartment\n * at each event.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nPKModelOneCpt(const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector >& pMatrix,\n const std::vector >& biovar,\n const std::vector >& tlag) {\n using std::vector;\n using Eigen::Dynamic;\n using Eigen::Matrix;\n using boost::math::tools::promote_args;\n using stan::math::check_positive_finite;\n\n int nCmt = 2;\n int nParm = 3;\n\n // Check arguments -- FIX ME: handle the new parameter arguments\n static const char* function(\"PKModelOneCpt\");\n torsten::pmetricsCheck(time, amt, rate, ii, evid, cmt, addl, ss,\n pMatrix, biovar, tlag, function);\n for (size_t i = 0; i < pMatrix.size(); i++) {\n check_positive_finite(function, \"PK parameter CL\", pMatrix[i][0]);\n check_positive_finite(function, \"PK parameter V2\", pMatrix[i][1]);\n }\n\n // FIX ME - we want to check every array of pMatrix, not\n // just the first one (at index 0)\n std::string message4 = \", but must equal the number of parameters in the model: \" // NOLINT\n + boost::lexical_cast(nParm) + \"!\";\n const char* length_error4 = message4.c_str();\n if (!(pMatrix[0].size() == (size_t) nParm))\n stan::math::invalid_argument(function,\n \"The number of parameters per event (length of a vector in the ninth argument) is\", // NOLINT\n pMatrix[0].size(), \"\", length_error4);\n\n std::string message5 = \", but must equal the number of compartments in the model: \" // NOLINT\n + boost::lexical_cast(nCmt) + \"!\";\n const char* length_error5 = message5.c_str();\n if (!(biovar[0].size() == (size_t) nCmt))\n stan::math::invalid_argument(function,\n \"The number of biovariability parameters per event (length of a vector in the tenth argument) is\", // NOLINT\n biovar[0].size(), \"\", length_error5);\n\n if (!(tlag[0].size() == (size_t) nCmt))\n stan::math::invalid_argument(function,\n \"The number of lag times parameters per event (length of a vector in the eleventh argument) is\", // NOLINT\n tlag[0].size(), \"\", length_error5);\n\n // Construct dummy matrix for last argument of pred\n Eigen::Matrix dummy_system;\n std::vector >\n dummy_systems(1, dummy_system);\n\n return Pred(time, amt, rate, ii, evid, cmt, addl, ss,\n pMatrix, biovar, tlag,\n nCmt, dummy_systems,\n Pred1_oneCpt(), PredSS_oneCpt());\n}\n\n/**\n * Overload function to allow user to pass an std::vector for pMatrix.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nPKModelOneCpt(const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector& pMatrix,\n const std::vector >& biovar,\n const std::vector >& tlag) {\n std::vector > vec_pMatrix(1, pMatrix);\n\n return PKModelOneCpt(time, amt, rate, ii, evid, cmt, addl, ss,\n vec_pMatrix, biovar, tlag);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for pMatrix,\n * and biovar.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nPKModelOneCpt(const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector& pMatrix,\n const std::vector& biovar,\n const std::vector >& tlag) {\n std::vector > vec_pMatrix(1, pMatrix);\n std::vector > vec_biovar(1, biovar);\n\n return PKModelOneCpt(time, amt, rate, ii, evid, cmt, addl, ss,\n vec_pMatrix, vec_biovar, tlag);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for pMatrix,\n * and tlag.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nPKModelOneCpt(const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector& pMatrix,\n const std::vector >& biovar,\n const std::vector& tlag) {\n std::vector > vec_pMatrix(1, pMatrix);\n std::vector > vec_tlag(1, tlag);\n\n return PKModelOneCpt(time, amt, rate, ii, evid, cmt, addl, ss,\n vec_pMatrix, biovar, vec_tlag);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for pMatrix,\n * biovar, and tlag.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nPKModelOneCpt(const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector& pMatrix,\n const std::vector& biovar,\n const std::vector& tlag) {\n std::vector > vec_pMatrix(1, pMatrix);\n std::vector > vec_biovar(1, biovar);\n std::vector > vec_tlag(1, tlag);\n\n return PKModelOneCpt(time, amt, rate, ii, evid, cmt, addl, ss,\n vec_pMatrix, vec_biovar, vec_tlag);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for biovar.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nPKModelOneCpt(const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector >& pMatrix,\n const std::vector& biovar,\n const std::vector >& tlag) {\n std::vector > vec_biovar(1, biovar);\n\n return PKModelOneCpt(time, amt, rate, ii, evid, cmt, addl, ss,\n pMatrix, vec_biovar, tlag);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for biovar,\n * and tlag.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nPKModelOneCpt(const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector >& pMatrix,\n const std::vector& biovar,\n const std::vector& tlag) {\n std::vector > vec_biovar(1, biovar);\n std::vector > vec_tlag(1, tlag);\n\n return PKModelOneCpt(time, amt, rate, ii, evid, cmt, addl, ss,\n pMatrix, vec_biovar, vec_tlag);\n}\n\n/**\n * Overload function to allow user to pass an std::vector for tlag.\n */\ntemplate \nEigen::Matrix ::type>::type,\n Eigen::Dynamic, Eigen::Dynamic>\nPKModelOneCpt(const std::vector& time,\n const std::vector& amt,\n const std::vector& rate,\n const std::vector& ii,\n const std::vector& evid,\n const std::vector& cmt,\n const std::vector& addl,\n const std::vector& ss,\n const std::vector >& pMatrix,\n const std::vector >& biovar,\n const std::vector& tlag) {\n std::vector > vec_tlag(1, tlag);\n\n return PKModelOneCpt(time, amt, rate, ii, evid, cmt, addl, ss,\n pMatrix, biovar, vec_tlag);\n}\n\n}\n#endif\n", "meta": {"hexsha": "eeb2b464e98b2e2b2530c24388c3caf3239ba4aa", "size": 12283, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "cmdstan/stan/lib/stan_math/stan/math/torsten/PKModelOneCpt.hpp", "max_stars_repo_name": "yizhang-cae/torsten", "max_stars_repo_head_hexsha": "dc82080ca032325040844cbabe81c9a2b5e046f9", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5851011542032312, "lm_q2_score": 0.35936413829896496, "lm_q1q2_score": 0.210264372097974}} {"text": "#ifndef OFFNFACAG_HPP\n#define OFFNFACAG_HPP\n\n#include \n#include \n#include \n#include \n#include \n\n#include \"arch/ARLAgent.hpp\"\n#include \"bib/Seed.hpp\"\n#include \"bib/Utils.hpp\"\n#include \n#include \n#include \"nn/MLP.hpp\"\n#include \"nn/DODevMLP.hpp\"\n\ntypedef struct _sample {\n std::vector s;\n std::vector pure_a;\n std::vector a;\n std::vector next_s;\n double r;\n bool goal_reached;\n double dpmu;\n\n friend class boost::serialization::access;\n template \n void serialize(Archive& ar, const unsigned int) {\n ar& BOOST_SERIALIZATION_NVP(s);\n ar& BOOST_SERIALIZATION_NVP(pure_a);\n ar& BOOST_SERIALIZATION_NVP(a);\n ar& BOOST_SERIALIZATION_NVP(next_s);\n ar& BOOST_SERIALIZATION_NVP(r);\n ar& BOOST_SERIALIZATION_NVP(goal_reached);\n }\n\n bool operator< (const _sample& b) const {\n for (uint i = 0; i < s.size(); i++) {\n if(s[i] != b.s[i])\n return s[i] < b.s[i];\n }\n\n for (uint i = 0; i < a.size(); i++) {\n if(a[i] != b.a[i])\n return a[i] < b.a[i];\n }\n\n return false;\n }\n\n} sample;\n\ntypedef struct _trajectory {\n std::shared_ptr> transitions;\n double rewards;\n} trajectory;\n\ntemplate\nclass OffNFACAg : public arch::ARLAgent {\n public:\n typedef NN PolicyImpl;\n\n OffNFACAg(unsigned int _nb_motors, unsigned int _nb_sensors)\n : arch::ARLAgent(_nb_motors, _nb_sensors), empty_action(0) {\n\n }\n\n virtual ~OffNFACAg() {\n delete vnn;\n delete ann;\n\n delete ann_testing;\n\n delete hidden_unit_v;\n delete hidden_unit_a;\n }\n\n const std::vector& _run(double reward, const std::vector& sensors,\n bool learning, bool goal_reached, bool) override {\n\n // protect batch norm from testing data and poor data\n vector* next_action = ann_testing->computeOut(sensors);\n \n if (last_action.get() != nullptr && learning) {\n double p0 = 1.f;\n for(uint i=0; i < this->nb_motors; i++) {\n p0 *= bib::Proba::truncatedGaussianDensity(last_action->at(i), last_pure_action->at(i), noise);\n }\n trajectories.back()->transitions->push_back( {last_state, *last_pure_action, *last_action, sensors, reward, goal_reached, p0});\n }\n\n last_pure_action.reset(new vector(*next_action));\n if(learning) {\n if(gaussian_policy) {\n vector* randomized_action = bib::Proba::multidimentionnalTruncatedGaussian(*next_action, noise);\n delete next_action;\n next_action = randomized_action;\n } else if(bib::Utils::rand01() < noise) { //e-greedy\n for (uint i = 0; i < next_action->size(); i++)\n next_action->at(i) = bib::Utils::randin(-1.f, 1.f);\n }\n }\n last_action.reset(next_action);\n\n\n last_state.clear();\n for (uint i = 0; i < sensors.size(); i++)\n last_state.push_back(sensors[i]);\n\n return *next_action;\n }\n\n\n void _unique_invoke(boost::property_tree::ptree* pt, boost::program_options::variables_map*) override {\n// bib::Seed::setFixedSeedUTest();\n hidden_unit_v = bib::to_array(pt->get(\"agent.hidden_unit_v\"));\n hidden_unit_a = bib::to_array(pt->get(\"agent.hidden_unit_a\"));\n noise = pt->get(\"agent.noise\");\n gaussian_policy = pt->get(\"agent.gaussian_policy\");\n update_delta_neg = pt->get(\"agent.update_delta_neg\");\n vnn_from_scratch = pt->get(\"agent.vnn_from_scratch\");\n update_critic_first = pt->get(\"agent.update_critic_first\");\n number_fitted_iteration = pt->get(\"agent.number_fitted_iteration\");\n stoch_iter_actor = pt->get(\"agent.stoch_iter_actor\");\n stoch_iter_critic = pt->get(\"agent.stoch_iter_critic\");\n batch_norm_actor = pt->get(\"agent.batch_norm_actor\");\n batch_norm_critic = pt->get(\"agent.batch_norm_critic\");\n actor_output_layer_type = pt->get(\"agent.actor_output_layer_type\");\n hidden_layer_type = pt->get(\"agent.hidden_layer_type\");\n alpha_a = pt->get(\"agent.alpha_a\");\n alpha_v = pt->get(\"agent.alpha_v\");\n lambda = pt->get(\"agent.lambda\");\n max_trajectory = pt->get(\"agent.max_trajectory\");\n offpolicy_strategy = pt->get(\"agent.offpolicy_strategy\");\n add_v_corrector = pt->get(\"agent.add_v_corrector\");\n offpolicy_actor = pt->get(\"agent.offpolicy_actor\");\n number_global_fitted_iteration = pt->get(\"agent.number_global_fitted_iteration\");\n a3c = pt->get(\"agent.a3c\");\n offpolicy_critic = pt->get(\"agent.offpolicy_critic\");\n shuffle_buffer = pt->get(\"agent.shuffle_buffer\");\n testing_ann_offpolicy = pt->get(\"agent.testing_ann_offpolicy\");\n uint momentum = pt->get(\"agent.momentum\");\n testing_ann_offpolicy = false;\n// testing_vnn = pt->get(\"agent.testing_vnn\");\n gae = false;\n\n if(lambda >= 0.)\n gae = pt->get(\"agent.gae\");\n\n if(lambda < 0. && offpolicy_critic) {\n LOG_DEBUG(\"set lambda please!\");\n exit(1);\n }\n \n if(offpolicy_actor && !gae) {\n LOG_DEBUG(\"to be done? (offpolicy_actor without gae)\");\n exit(1);\n }\n \n if(offpolicy_actor && a3c) {\n LOG_DEBUG(\"a3c is on-policy\");\n exit(1);\n }\n \n if(gae && a3c) {\n LOG_DEBUG(\"choose either gae or a3c\");\n exit(1);\n }\n\n if(offpolicy_actor && gae && stoch_iter_actor > 1) {\n LOG_DEBUG(\"to be done!\");\n exit(1);\n }\n\n ann = new NN(this->get_state_size(), *hidden_unit_a, this->nb_motors, alpha_a, 1, hidden_layer_type, actor_output_layer_type,\n batch_norm_actor, true, momentum);\n// if(std::is_same::value)\n// ann->exploit(pt, nullptr);\n\n vnn = new NN(this->get_state_size(), this->get_state_size(), *hidden_unit_v, alpha_v, 1, -1, hidden_layer_type, batch_norm_critic,\n add_v_corrector && offpolicy_critic && offpolicy_strategy != 0, momentum);\n// if(std::is_same::value)\n// vnn->exploit(pt, ann);\n\n ann_testing = new NN(*ann, false, ::caffe::Phase::TEST);\n ann_testing->increase_batchsize(1);\n\n// if(std::is_same::value) {\n// try {\n// if(pt->get(\"devnn.reset_learning_algo\")) {\n// LOG_ERROR(\"NFAC cannot reset anything with DODevMLP\");\n// exit(1);\n// }\n// } catch(boost::exception const& ) {\n// }\n// }\n \n best_testing_score = std::numeric_limits::lowest();\n }\n\n void _start_episode(const std::vector& sensors, bool learning) override {\n last_state.clear();\n for (uint i = 0; i < sensors.size(); i++)\n last_state.push_back(sensors[i]);\n\n last_action = nullptr;\n last_pure_action = nullptr;\n\n if(learning) {\n if(trajectories.size() >= max_trajectory)\n trajectories.pop_front();\n\n trajectory* t = new trajectory;\n t->transitions.reset(new std::deque);\n t->rewards = 0;\n trajectories.push_back(std::shared_ptr(t));\n }\n\n// if(std::is_same::value && learning) {\n// static_cast(vnn)->inform(episode, last_sum_weighted_reward);\n// static_cast(ann)->inform(episode, last_sum_weighted_reward);\n// static_cast(ann_testing)->inform(episode, last_sum_weighted_reward);\n// }\n\n double* weights = new double[ann->number_of_parameters(false)];\n ann->copyWeightsTo(weights, false);\n ann_testing->copyWeightsFrom(weights, false);\n delete[] weights;\n }\n\n void update_critic() {\n int all_size = alltransitions();\n if (all_size > 0) {\n //remove trace of old policy\n auto iter = [&]() {\n std::vector all_states(all_size * this->get_state_size());\n std::vector all_next_states(all_size * this->get_state_size());\n std::vector v_target(all_size);\n\n int li=0;\n for(auto one_trajectory : trajectories) {\n const std::deque& trajectory = *one_trajectory->transitions;\n\n for (auto it : trajectory) {\n std::copy(it.s.begin(), it.s.end(), all_states.begin() + li * this->get_state_size());\n std::copy(it.next_s.begin(), it.next_s.end(), all_next_states.begin() + li * this->get_state_size());\n li++;\n }\n }\n ASSERT(li == all_size, \"pb\");\n\n decltype(vnn->computeOutVFBatch(all_next_states, empty_action)) all_nextV;\n all_nextV = vnn->computeOutVFBatch(all_next_states, empty_action);\n\n li=0;\n for(auto one_trajectory : trajectories) {\n const std::deque& trajectory = *one_trajectory->transitions;\n for (auto it : trajectory) {\n double target = it.r;\n if (!it.goal_reached) {\n double nextV = all_nextV->at(li);\n target += this->gamma * nextV;\n }\n\n v_target[li] = target;\n li++;\n }\n }\n ASSERT(li == all_size, \"pb\");\n// bib::Logger::PRINT_ELEMENTS(v_target, \"v_target\");\n\n if(vnn_from_scratch) {\n delete vnn;\n vnn = new NN(this->get_state_size(), this->get_state_size(), *hidden_unit_v, alpha_v, all_size, -1, hidden_layer_type,\n batch_norm_critic, add_v_corrector);\n }\n if(lambda < 0.f)\n vnn->learn_batch(all_states, empty_action, v_target, stoch_iter_critic);\n else {\n auto all_V = vnn->computeOutVFBatch(all_states, empty_action);\n std::vector deltas(all_size);\n //\n // Simple computation for lambda return\n //\n for (int i=0; iat(i);\n\n std::vector* all_pi;\n double* ptheta;\n double max_ptheta;\n std::vector* sample_weight;\n if(offpolicy_strategy != 0 && offpolicy_critic) {\n ann->increase_batchsize(all_size);\n if(testing_ann_offpolicy){\n ann_testing->copyParametersFrom(ann);\n ann_testing->increase_batchsize(all_size);\n all_pi = ann_testing->computeOutBatch(all_states);\n ann_testing->increase_batchsize(1);\n }\n else\n all_pi = ann->computeOutBatch(all_states);\n ptheta = new double[all_size];\n\n uint i=0;\n if(offpolicy_strategy >= 1 && offpolicy_strategy <= 3) {\n for(auto one_trajectory : trajectories) {\n const std::deque& trajectory = *one_trajectory->transitions;\n for (auto it : trajectory) {\n double p0 = 1.f;\n for(uint j=0; j < this->nb_motors; j++)\n p0 *= bib::Proba::truncatedGaussianDensity(it.a[j], all_pi->at(i*this->nb_motors+j), noise);\n\n ptheta[i] = p0;\n i++;\n }\n }\n max_ptheta = *std::max_element(ptheta, ptheta+all_size);\n }\n\n if(add_v_corrector)\n sample_weight = new std::vector(all_size);\n }\n\n std::vector diff(all_size);\n int index_shift=0;\n for(auto one_trajectory : trajectories) {\n const std::deque& trajectory = *one_trajectory->transitions;\n li=0;\n for (auto it : trajectory) {\n diff[index_shift+li] = 0;\n// offpolicy_strategy\n// 0 : lambda only\n// 1 : lambda \\pi (TB)\n// 2 : \\pi / \\mu (IS)\n// 3 : lambda min(1,\\pi / \\mu) (Retrace)\n// 4 : lambda * (1-||a_t - \\pi||) (ours)\n// 5 : lambda * (1-min(||a_t - \\pi||, ||u_t - \\pi|| )) (ours)\n if(offpolicy_strategy == 0 || !offpolicy_critic) {\n for (uint n=li; ngamma * lambda, n-li) * deltas[index_shift+n];\n } else if(offpolicy_strategy == 1) {\n// for (int n=trajectory.size()-1; n>=li; n--){\n// double ci = 1.f;\n// for(int subn = 0;subngamma, n-li) * ci * deltas[index_shift+n];\n// }\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda * (ptheta[index_shift+n]/max_ptheta);\n }\n if(add_v_corrector)\n sample_weight->at(index_shift+li) = ptheta[index_shift+li]/max_ptheta;\n } else if(offpolicy_strategy == 2) {\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda * (ptheta[index_shift+n]/trajectory[n].dpmu);\n }\n if(add_v_corrector)\n sample_weight->at(index_shift+li) = ptheta[index_shift+li]/trajectory[li].dpmu;\n } else if(offpolicy_strategy == 3) {\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda * std::min((double)1.f,ptheta[index_shift+n]/trajectory[n].dpmu);\n }\n if(add_v_corrector)\n sample_weight->at(index_shift+li) = std::min((double)1.f,ptheta[index_shift+li]/trajectory[li].dpmu);\n } else if(offpolicy_strategy == 4) {\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda * (1.f - l2dist(trajectory[n].a, *all_pi, index_shift+n));\n }\n if(add_v_corrector)\n sample_weight->at(index_shift+li) = 1.f - l2dist(trajectory[li].a, *all_pi, index_shift+li);\n } else if(offpolicy_strategy == 5) {\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda *\n (1.f - std::min(l2dist(trajectory[n].a, *all_pi, index_shift+n),\n l2dist(trajectory[n].pure_a, *all_pi, index_shift+n)));\n }\n if(add_v_corrector)\n sample_weight->at(index_shift+li) = 1.f - std::min(l2dist(trajectory[li].a, *all_pi, index_shift+li),\n l2dist(trajectory[li].pure_a, *all_pi, index_shift+li));\n }\n\n li++;\n }\n ASSERT(diff[index_shift+trajectory.size() -1] == deltas[index_shift+trajectory.size() -1], \"pb lambda \");\n index_shift += trajectory.size();\n }\n ASSERT(index_shift == all_size, \"index pb\");\n\n // comment following lines to compare with the other formula\n for (int i=0; iat(i);\n\n// bib::Logger::PRINT_ELEMENTS(*all_V, \"all v \");\n// bib::Logger::PRINT_ELEMENTS(diff, \"final diff \");\n if(!offpolicy_critic){\n int size_last_traj = trajectories.back()->transitions->size();\n vnn->increase_batchsize(size_last_traj);\n \n std::vector subdiff(size_last_traj);\n std::vector subsensors(size_last_traj * this->get_state_size());\n \n const std::deque& trajectory = *trajectories.back()->transitions;\n \n li=0;\n for (auto it : trajectory){\n std::copy(it.s.begin(), it.s.end(), subsensors.begin() + li * this->get_state_size());\n li++;\n }\n \n int index=size_last_traj-1;\n for(li=all_size - 1;li>=(all_size-size_last_traj);li--){\n subdiff[index]=diff[li];\n index--;\n }\n vnn->learn_batch(subsensors, empty_action, subdiff, stoch_iter_critic);\n \n vnn->increase_batchsize(all_size);\n } else if(offpolicy_strategy != 0 && add_v_corrector)\n vnn->learn_batch_lw(all_states, empty_action, diff, *sample_weight, stoch_iter_critic);\n else\n vnn->learn_batch(all_states, empty_action, diff, stoch_iter_critic);\n\n if(offpolicy_strategy != 0 && offpolicy_critic) {\n delete[] ptheta;\n delete all_pi;\n if(add_v_corrector)\n delete sample_weight;\n }\n\n delete all_V;\n }\n\n delete all_nextV;\n };\n\n for(uint i=0; ihash());\n if(!learning)\n return;\n \n ann->update_best_param_previous_task(sum_weighted_reward);\n vnn->update_best_param_previous_task(sum_weighted_reward);\n ann_testing->update_best_param_previous_task(sum_weighted_reward);\n\n uint all_size = alltransitions();\n if(all_size > 0) {\n vnn->increase_batchsize(all_size);\n }\n\n for(uint fi = 0 ; fi < number_global_fitted_iteration; fi++){\n if(update_critic_first)\n update_critic();\n\n if(!offpolicy_actor){\n if(a3c)\n actor_update_onpolicy_a3c();\n else\n actor_update_onpolicy();\n } else\n actor_update_offpolicy();\n\n if(!update_critic_first) {\n if(all_size > 0 && !offpolicy_actor) {\n vnn->increase_batchsize(all_size);\n }\n update_critic();\n }\n }\n \n if(shuffle_buffer){\n std::random_shuffle(trajectories.begin(), trajectories.end());\n }\n }\n\n void end_instance(bool learning) override {\n if(learning)\n episode++;\n }\n\n void actor_update_onpolicy() {\n const std::deque& trajectory = *trajectories.back()->transitions;\n vnn->increase_batchsize(trajectory.size());\n if (trajectory.size() > 0) {\n std::vector sensors(trajectory.size() * this->get_state_size());\n std::vector actions(trajectory.size() * this->nb_motors);\n std::vector disable_back(trajectory.size() * this->nb_motors, false);\n const std::vector disable_back_ac(this->nb_motors, true);\n std::vector deltas_blob(trajectory.size() * this->nb_motors);\n std::vector deltas(trajectory.size());\n\n std::vector all_states(trajectory.size() * this->get_state_size());\n std::vector all_next_states(trajectory.size() * this->get_state_size());\n uint li=0;\n for (auto it : trajectory) {\n std::copy(it.s.begin(), it.s.end(), all_states.begin() + li * this->get_state_size());\n std::copy(it.next_s.begin(), it.next_s.end(), all_next_states.begin() + li * this->get_state_size());\n li++;\n }\n\n decltype(vnn->computeOutVFBatch(all_next_states, empty_action)) all_nextV, all_mine;\n all_nextV = vnn->computeOutVFBatch(all_next_states, empty_action);\n all_mine = vnn->computeOutVFBatch(all_states, empty_action);\n\n li=0;\n for (auto it : trajectory) {\n sample sm = it;\n double v_target = sm.r;\n if (!sm.goal_reached) {\n double nextV = all_nextV->at(li);\n v_target += this->gamma * nextV;\n }\n\n deltas[li] = v_target - all_mine->at(li);\n ++li;\n }\n\n if(gae) {\n //\n // Simple computation for lambda return\n //\n std::vector diff(trajectory.size());\n li=0;\n for (auto it : trajectory) {\n diff[li] = 0;\n for (uint n=li; ngamma * lambda, n-li) * deltas[n];\n li++;\n }\n\n ASSERT(diff[trajectory.size() -1] == deltas[trajectory.size() -1], \"pb lambda\");\n li=0;\n for (auto it : trajectory) {\n // diff[li] = diff[li] + all_V->at(li);\n deltas[li] = diff[li];\n ++li;\n }\n }\n\n uint n=0;\n li=0;\n for(auto it = trajectory.begin(); it != trajectory.end() ; ++it) {\n sample sm = *it;\n\n std::copy(it->s.begin(), it->s.end(), sensors.begin() + li * this->get_state_size());\n if(deltas[li] > 0.) {\n std::copy(it->a.begin(), it->a.end(), actions.begin() + li * this->nb_motors);\n n++;\n } else if(update_delta_neg) {\n std::copy(it->pure_a.begin(), it->pure_a.end(), actions.begin() + li * this->nb_motors);\n } else {\n std::copy(it->a.begin(), it->a.end(), actions.begin() + li * this->nb_motors);\n std::copy(disable_back_ac.begin(), disable_back_ac.end(), disable_back.begin() + li * this->nb_motors);\n }\n std::fill(deltas_blob.begin() + li * this->nb_motors, deltas_blob.begin() + (li+1) * this->nb_motors, deltas[li]);\n li++;\n }\n\n ann->increase_batchsize(trajectory.size());\n if(n > 0) {\n for(uint sia = 0; sia < stoch_iter_actor; sia++) {\n //learn BN\n std::vector* ac_out = ann->computeOutBatch(sensors);\n if(batch_norm_actor != 0){\n //re-compute ac_out with BN as testing\n ann_testing->copyParametersFrom(ann);\n ann_testing->increase_batchsize(trajectory.size());\n delete ac_out;\n ac_out = ann_testing->computeOutBatch(sensors);\n if(sia + 1 == stoch_iter_actor )\n ann_testing->increase_batchsize(1);\n }\n\n const auto actor_actions_blob = ann->getNN()->blob_by_name(MLP::actions_blob_name);\n auto ac_diff = actor_actions_blob->mutable_cpu_diff();\n for(int i=0; icount(); i++) {\n if(disable_back[i]) {\n ac_diff[i] = 0.00000000f;\n } else {\n double x = actions[i] - ac_out->at(i);\n ac_diff[i] = -x;\n }\n }\n ann->actor_backward();\n ann->regularize();\n ann->getSolver()->ApplyUpdate();\n ann->getSolver()->set_iter(ann->getSolver()->iter() + 1);\n delete ac_out;\n }\n }\n\n delete all_nextV;\n delete all_mine;\n }\n }\n \n void actor_update_onpolicy_a3c() {\n const std::deque& trajectory = *trajectories.back()->transitions;\n vnn->increase_batchsize(trajectory.size());\n if (trajectory.size() > 0) {\n std::vector sensors(trajectory.size() * this->get_state_size());\n std::vector actions(trajectory.size() * this->nb_motors);\n std::vector deltas_blob(trajectory.size() * this->nb_motors);\n std::vector deltas(trajectory.size());\n \n std::vector all_states(trajectory.size() * this->get_state_size());\n std::vector all_next_states(trajectory.size() * this->get_state_size());\n uint li=0;\n for (auto it : trajectory) {\n std::copy(it.s.begin(), it.s.end(), all_states.begin() + li * this->get_state_size());\n std::copy(it.next_s.begin(), it.next_s.end(), all_next_states.begin() + li * this->get_state_size());\n li++;\n }\n \n decltype(vnn->computeOutVFBatch(all_next_states, empty_action)) all_nextV, all_mine;\n all_nextV = vnn->computeOutVFBatch(all_next_states, empty_action);\n all_mine = vnn->computeOutVFBatch(all_states, empty_action);\n \n li=0;\n double sum_gamma_rt = 0;\n for (auto it : trajectory) {\n sample sm = it;\n sum_gamma_rt += std::pow(gamma, li) * sm.r;\n double v_target = sum_gamma_rt;\n if (!sm.goal_reached) {\n double nextV = all_nextV->at(li);\n v_target += std::pow(this->gamma, li+1) * nextV;\n }\n \n deltas[li] = v_target - all_mine->at(li);\n ++li;\n }\n\n li=0;\n for(auto it = trajectory.begin(); it != trajectory.end() ; ++it) {\n sample sm = *it;\n \n std::copy(it->s.begin(), it->s.end(), sensors.begin() + li * this->get_state_size());\n std::copy(it->a.begin(), it->a.end(), actions.begin() + li * this->nb_motors);\n std::fill(deltas_blob.begin() + li * this->nb_motors, deltas_blob.begin() + (li+1) * this->nb_motors, deltas[li]);\n li++;\n }\n \n for(uint sia = 0; sia < stoch_iter_actor; sia++) {\n ann->increase_batchsize(trajectory.size());\n auto ac_out = ann->computeOutBatch(sensors);\n if(batch_norm_actor != 0){\n //re-compute ac_out with BN as testing\n ann_testing->copyParametersFrom(ann);\n ann_testing->increase_batchsize(trajectory.size());\n delete ac_out;\n ac_out = ann_testing->computeOutBatch(sensors);\n if(sia + 1 == stoch_iter_actor )\n ann_testing->increase_batchsize(1);\n }\n \n const auto actor_actions_blob = ann->getNN()->blob_by_name(MLP::actions_blob_name);\n auto ac_diff = actor_actions_blob->mutable_cpu_diff();\n for(int i=0; icount(); i++) {\n double x = actions[i] - ac_out->at(i);\n ac_diff[i] = -x*deltas_blob[i];\n }\n ann->actor_backward();\n ann->regularize();\n ann->getSolver()->ApplyUpdate();\n ann->getSolver()->set_iter(ann->getSolver()->iter() + 1);\n delete ac_out;\n }\n \n delete all_nextV;\n delete all_mine;\n }\n }\n\n void actor_update_offpolicy() {\n int all_size = alltransitions();\n ann->increase_batchsize(all_size);\n\n std::vector sensors(all_size * this->get_state_size());\n std::vector actions(all_size * this->nb_motors);\n std::vector disable_back(all_size * this->nb_motors, false);\n const std::vector disable_back_ac(this->nb_motors, true);\n std::vector deltas_blob(all_size * this->nb_motors);\n std::vector deltas(all_size);\n\n std::vector all_states(all_size * this->get_state_size());\n std::vector all_next_states(all_size * this->get_state_size());\n\n int li=0;\n for(auto one_trajectory : trajectories) {\n const std::deque& trajectory = *one_trajectory->transitions;\n for (auto it : trajectory) {\n std::copy(it.s.begin(), it.s.end(), all_states.begin() + li * this->get_state_size());\n std::copy(it.next_s.begin(), it.next_s.end(), all_next_states.begin() + li * this->get_state_size());\n li++;\n }\n }\n\n decltype(vnn->computeOutVFBatch(all_next_states, empty_action)) all_nextV, all_mine;\n all_nextV = vnn->computeOutVFBatch(all_next_states, empty_action);\n all_mine = vnn->computeOutVFBatch(all_states, empty_action);\n\n li=0;\n for(auto one_trajectory : trajectories) {\n const std::deque& trajectory = *one_trajectory->transitions;\n for (auto it : trajectory) {\n sample sm = it;\n double v_target = sm.r;\n if (!sm.goal_reached) {\n double nextV = all_nextV->at(li);\n v_target += this->gamma * nextV;\n }\n\n deltas[li] = v_target - all_mine->at(li);\n ++li;\n }\n }\n\n \n if(gae) {\n //\n // Simple computation for lambda return\n //\n std::vector* all_pi;\n std::vector diff(all_size);\n int index_shift=0;\n double* ptheta;\n double max_ptheta;\n if(offpolicy_strategy != 0) {\n if(testing_ann_offpolicy){\n ann_testing->copyParametersFrom(ann);\n ann_testing->increase_batchsize(all_size);\n all_pi = ann_testing->computeOutBatch(all_states);\n ann_testing->increase_batchsize(1);\n } else\n all_pi = ann->computeOutBatch(all_states);\n ptheta = new double[all_size];\n\n uint i=0;\n if(offpolicy_strategy >= 1 && offpolicy_strategy <= 3) {\n for(auto one_trajectory : trajectories) {\n const std::deque& trajectory = *one_trajectory->transitions;\n for (auto it : trajectory) {\n double p0 = 1.f;\n for(uint j=0; j < this->nb_motors; j++)\n p0 *= bib::Proba::truncatedGaussianDensity(it.a[j], all_pi->at(i*this->nb_motors+j), noise);\n\n ptheta[i] = p0;\n i++;\n }\n }\n max_ptheta = *std::max_element(ptheta, ptheta+all_size);\n }\n }\n for(auto one_trajectory : trajectories) {\n li=0;\n const std::deque& trajectory = *one_trajectory->transitions;\n for (auto it : trajectory) {\n diff[index_shift+li] = 0;\n if(offpolicy_strategy == 0) {\n for (uint n=li; ngamma * lambda, n-li) * deltas[index_shift+n];\n } else if(offpolicy_strategy == 1) {\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda * (ptheta[index_shift+n]/max_ptheta);\n }\n if(add_v_corrector)\n diff[index_shift+li] *= ptheta[index_shift+li]/max_ptheta;\n } else if(offpolicy_strategy == 2) {\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda * (ptheta[index_shift+n]/trajectory[n].dpmu);\n }\n if(add_v_corrector)\n diff[index_shift+li] *= ptheta[index_shift+li]/trajectory[li].dpmu;\n } else if(offpolicy_strategy == 3) {\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda * std::min((double)1.f,ptheta[index_shift+n]/trajectory[n].dpmu);\n }\n if(add_v_corrector)\n diff[index_shift+li] *= std::min((double)1.f,ptheta[index_shift+li]/trajectory[li].dpmu);\n } else if(offpolicy_strategy == 4) {\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda * (1.f - l2dist(trajectory[n].a, *all_pi, index_shift+n));\n }\n if(add_v_corrector)\n diff[index_shift+li] *= 1.f - l2dist(trajectory[li].a, *all_pi, index_shift+li);\n } else if(offpolicy_strategy == 5) {\n double target_sum = 0;\n for (int n=trajectory.size()-1; n>=li; n--) {\n target_sum += deltas[index_shift+n];\n diff[index_shift+li] = target_sum;\n target_sum *= this->gamma * lambda *\n (1.f - std::min(l2dist(trajectory[n].a, *all_pi, index_shift+n),\n l2dist(trajectory[n].pure_a, *all_pi, index_shift+n)));\n }\n if(add_v_corrector)\n diff[index_shift+li] *= 1.f - std::min(l2dist(trajectory[li].a, *all_pi, index_shift+li),\n l2dist(trajectory[li].pure_a, *all_pi, index_shift+li));\n }\n li++;\n }\n ASSERT(diff[index_shift+trajectory.size() -1] == deltas[index_shift+trajectory.size() -1], \"pb lambda\");\n index_shift += trajectory.size();\n }\n\n if(offpolicy_strategy != 0) {\n delete all_pi;\n delete [] ptheta;\n }\n\n for (li=0; li < all_size; li++) {\n // diff[li] = diff[li] + all_V->at(li);\n deltas[li] = diff[li];\n }\n }\n\n uint n=0;\n li=0;\n for(auto one_trajectory : trajectories) {\n const std::deque& trajectory = *one_trajectory->transitions;\n for(auto it = trajectory.begin(); it != trajectory.end() ; ++it) {\n std::copy(it->s.begin(), it->s.end(), sensors.begin() + li * this->get_state_size());\n if(deltas[li] > 0.) {\n std::copy(it->a.begin(), it->a.end(), actions.begin() + li * this->nb_motors);\n n++;\n } else if(update_delta_neg) {\n std::copy(it->pure_a.begin(), it->pure_a.end(), actions.begin() + li * this->nb_motors);\n } else {\n std::copy(it->a.begin(), it->a.end(), actions.begin() + li * this->nb_motors);\n std::copy(disable_back_ac.begin(), disable_back_ac.end(), disable_back.begin() + li * this->nb_motors);\n }\n std::fill(deltas_blob.begin() + li * this->nb_motors, deltas_blob.begin() + (li+1) * this->nb_motors, deltas[li]);\n li++;\n }\n }\n\n if(n > 0) {\n for(uint sia = 0; sia < stoch_iter_actor; sia++) {\n std::vector* ac_out = ann->computeOutBatch(sensors);\n if(batch_norm_actor != 0){\n //re-compute ac_out with BN as testing\n ann_testing->copyParametersFrom(ann);\n ann_testing->increase_batchsize(all_size);\n delete ac_out;\n ac_out = ann_testing->computeOutBatch(sensors);\n if(sia + 1 == stoch_iter_actor )\n ann_testing->increase_batchsize(1);\n }\n\n const auto actor_actions_blob = ann->getNN()->blob_by_name(MLP::actions_blob_name);\n auto ac_diff = actor_actions_blob->mutable_cpu_diff();\n for(int i=0; icount(); i++) {\n if(disable_back[i]) {\n ac_diff[i] = 0.00000000f;\n } else {\n double x = actions[i] - ac_out->at(i);\n ac_diff[i] = -x;\n }\n }\n ann->actor_backward();\n ann->regularize();\n ann->getSolver()->ApplyUpdate();\n ann->getSolver()->set_iter(ann->getSolver()->iter() + 1);\n delete ac_out;\n }\n }\n\n delete all_nextV;\n delete all_mine;\n }\n\n void save(const std::string& path, bool save_best, bool learning) override {\n if(save_best && !learning && best_testing_score < this->sum_weighted_reward){\n ann->save(path+\".actor\");\n best_testing_score = this->sum_weighted_reward;\n } else if(!save_best && learning){\n ann->save(path+\".actor\");\n vnn->save(path+\".critic\");\n }\n }\n\n void save_run() override {\n ann->save(\"continue.actor\");\n vnn->save(\"continue.critic\");\n struct algo_state st = {episode};\n bib::XMLEngine::save(st, \"algo_state\", \"continue.algo_state.data\");\n }\n\n void load(const std::string& path) override {\n ann->load(path+\".actor\");\n vnn->load(path+\".critic\");\n }\n\n void load_previous_run() override {\n ann->load(\"continue.actor\");\n vnn->load(\"continue.critic\");\n auto p3 = bib::XMLEngine::load(\"algo_state\", \"continue.algo_state.data\");\n episode = p3->episode;\n delete p3;\n }\n\n protected:\n\n void _display(std::ostream& out) const override {\n out << std::setw(12) << std::fixed << std::setprecision(10) << this->sum_weighted_reward << \" \" << std::setw(\n 8) << std::fixed << std::setprecision(5) << vnn->error() << \" \" << noise << \" \" << alltransitions();\n }\n\n void _dump(std::ostream& out) const override {\n out << std::setw(25) << std::fixed << std::setprecision(22) <<\n this->sum_weighted_reward << \" \" << std::setw(8) << std::fixed <<\n std::setprecision(5) << vnn->error() << \" \" << alltransitions() ;\n }\n\n uint alltransitions() const {\n uint count=0;\n for (auto it : trajectories)\n count += it->transitions->size();\n return count;\n }\n\n double sign(double x) {\n if(x>=0)\n return 1.f;\n return -1.f;\n }\n\n double l2dist(const std::vector& a, const std::vector& b, const uint start) const {\n double r = 0.f;\n for(uint i=0; i> last_action;\n std::shared_ptr> last_pure_action;\n std::vector last_state;\n double alpha_v, alpha_a;\n\n std::deque> trajectories;\n\n NN* ann;\n NN* vnn;\n NN* ann_testing;\n //to make video on determinist env\n double best_testing_score;\n\n std::vector* hidden_unit_v;\n std::vector* hidden_unit_a;\n std::vector empty_action; //dummy action cause c++ cannot accept null reference\n\n struct algo_state {\n uint episode;\n\n friend class boost::serialization::access;\n template \n void serialize(Archive& ar, const unsigned int) {\n ar& BOOST_SERIALIZATION_NVP(episode);\n }\n };\n};\n\n#endif\n\n\n", "meta": {"hexsha": "db4d2403980f8e458d66527b1f99a61654760abe", "size": 38498, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "agent/cacla/include/OffNFACAg.hpp", "max_stars_repo_name": "matthieu637/ddrl", "max_stars_repo_head_hexsha": "a454d09a3ac9be5db960ff180b3d075c2f9e4a70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 27.0, "max_stars_repo_stars_event_min_datetime": "2017-11-27T09:32:41.000Z", "max_stars_repo_stars_event_max_datetime": "2021-03-02T13:50:23.000Z", "max_issues_repo_path": "agent/cacla/include/OffNFACAg.hpp", "max_issues_repo_name": "matthieu637/ddrl", "max_issues_repo_head_hexsha": "a454d09a3ac9be5db960ff180b3d075c2f9e4a70", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 10.0, "max_issues_repo_issues_event_min_datetime": "2018-10-09T14:39:14.000Z", "max_issues_repo_issues_event_max_datetime": "2020-11-10T15:01:00.000Z", "max_forks_repo_path": "agent/cacla/include/OffNFACAg.hpp", "max_forks_repo_name": "matthieu637/ddrl", "max_forks_repo_head_hexsha": "a454d09a3ac9be5db960ff180b3d075c2f9e4a70", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2019-05-16T09:14:15.000Z", "max_forks_repo_forks_event_max_datetime": "2019-08-15T14:35:40.000Z", "avg_line_length": 38.0039486673, "max_line_length": 134, "alphanum_fraction": 0.5737440906, "num_tokens": 9670, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5621764862150634, "lm_q2_score": 0.3738758227716966, "lm_q1q2_score": 0.21018419632655821}} {"text": "#include \n#include \"control.hpp\"\n#include \"defines.hpp\"\n#include \"fad.hpp\"\n#include \"global_residual.hpp\"\n#include \"J2.hpp\"\n#include \"material_params.hpp\"\n\nnamespace calibr8 {\n\nstatic ParameterList get_valid_local_residual_params() {\n ParameterList p;\n p.set(\"type\", \"J2\");\n p.set(\"nonlinear max iters\", 0);\n p.set(\"nonlinear absolute tol\", 0.);\n p.set(\"nonlinear relative tol\", 0.);\n p.sublist(\"materials\");\n return p;\n}\n\nstatic ParameterList get_valid_material_params() {\n ParameterList p;\n p.set(\"E\", 0.);\n p.set(\"nu\", 0.);\n p.set(\"K\", 0.);\n p.set(\"Y\", 0.);\n return p;\n}\n\ntemplate \nJ2::J2(ParameterList const& inputs, int ndims) {\n\n this->m_params_list = inputs;\n this->m_params_list.validateParameters(get_valid_local_residual_params(), 0);\n\n int const num_residuals = 3;\n\n this->m_num_residuals = num_residuals;\n this->m_num_eqs.resize(num_residuals);\n this->m_var_types.resize(num_residuals);\n this->m_resid_names.resize(num_residuals);\n\n\n this->m_resid_names[0] = \"zeta\";\n this->m_var_types[0] = SYM_TENSOR;\n this->m_num_eqs[0] = get_num_eqs(SYM_TENSOR, ndims);\n\n this->m_resid_names[1] = \"Ie\";\n this->m_var_types[1] = SCALAR;\n this->m_num_eqs[1] = get_num_eqs(SCALAR, ndims);\n\n this->m_resid_names[2] = \"alpha\";\n this->m_var_types[2] = SCALAR;\n this->m_num_eqs[2] = get_num_eqs(SCALAR, ndims);\n\n m_max_iters = inputs.get(\"nonlinear max iters\");\n m_abs_tol = inputs.get(\"nonlinear absolute tol\");\n m_rel_tol = inputs.get(\"nonlinear relative tol\");\n\n}\n\ntemplate \nJ2::~J2() {\n}\n\ntemplate \nvoid J2::init_params() {\n\n int const num_params = 4;\n this->m_params.resize(num_params);\n this->m_param_names.resize(num_params);\n\n this->m_param_names.resize(num_params);\n this->m_param_names[0] = \"E\";\n this->m_param_names[1] = \"nu\";\n this->m_param_names[2] = \"K\";\n this->m_param_names[3] = \"Y\";\n\n int const num_elem_sets = this->m_elem_set_names.size();\n resize(this->m_param_values, num_elem_sets, num_params);\n\n ParameterList& all_material_params =\n this->m_params_list.sublist(\"materials\", true);\n\n for (int es = 0; es < num_elem_sets; ++es) {\n std::string const& elem_set_name = this->m_elem_set_names[es];\n ParameterList& material_params =\n all_material_params.sublist(elem_set_name, true);\n material_params.validateParameters(get_valid_material_params(), 0);\n this->m_param_values[es][0] = material_params.get(\"E\");\n this->m_param_values[es][1] = material_params.get(\"nu\");\n this->m_param_values[es][2] = material_params.get(\"K\");\n this->m_param_values[es][3] = material_params.get(\"Y\");\n }\n\n this->m_active_indices.resize(1);\n this->m_active_indices[0].resize(1);\n this->m_active_indices[0][0] = 0;\n}\n\ntemplate \nvoid J2::init_variables_impl() {\n\n int const ndims = this->m_num_dims;\n int const zeta_idx = 0;\n int const Ie_idx = 1;\n int const alpha_idx = 2;\n\n T const Ie = 1.0;\n T const alpha = 0.0;\n Tensor const zeta = minitensor::zero(ndims);\n\n this->set_scalar_xi(Ie_idx, Ie);\n this->set_scalar_xi(alpha_idx, alpha);\n this->set_sym_tensor_xi(zeta_idx, zeta);\n\n}\n\ntemplate \nTensor eval_be_bar(\n RCP> global,\n Tensor const& zeta,\n T const& Ie) {\n int const ndims = global->num_dims();\n Tensor const I = minitensor::eye(ndims);\n Tensor const grad_u = global->grad_vector_x(0);\n Tensor const grad_u_prev = global->grad_vector_x_prev(0);\n Tensor const F = grad_u + I;\n Tensor const F_prev = grad_u_prev + I;\n Tensor const rF = F * minitensor::inverse(F_prev);\n T const det_rF = minitensor::det(rF);\n T const det_rF_13 = cbrt(det_rF);\n Tensor const rF_bar = rF / det_rF_13;\n Tensor const rF_barT = minitensor::transpose(rF_bar);\n Tensor const be_bar = rF_bar * (zeta + Ie * I) * rF_barT;\n return be_bar;\n}\n\ntemplate <>\nint J2::solve_nonlinear(RCP>) {\n return 0;\n}\n\ntemplate <>\nint J2::solve_nonlinear(RCP> global) {\n\n int path;\n\n // pick an initial guess for the local variables\n {\n Tensor const zeta_old = this->sym_tensor_xi_prev(0);\n FADT const Ie_old = this->scalar_xi_prev(1);\n FADT const alpha_old = this->scalar_xi_prev(2);\n Tensor const be_bar_trial = eval_be_bar(global, zeta_old, Ie_old);\n Tensor const zeta = minitensor::dev(be_bar_trial);\n FADT const Ie = minitensor::trace(be_bar_trial) / 3.;\n FADT const alpha = alpha_old;\n this->set_sym_tensor_xi(0, zeta);\n this->set_scalar_xi(1, Ie);\n this->set_scalar_xi(2, alpha);\n path = ELASTIC;\n }\n\n // newton iteration until convergence\n\n int iter = 1;\n double R_norm_0 = 1.;\n bool converged = false;\n\n while ((iter <= m_max_iters) && (!converged)) {\n\n path = this->evaluate(global);\n\n double const R_norm = this->norm_residual();\n if (iter == 1) R_norm_0 = R_norm;\n double const R_norm_rel = R_norm / R_norm_0;\n if ((R_norm_rel < m_rel_tol) || (R_norm < m_abs_tol)) {\n converged = true;\n break;\n }\n\n EMatrix const J = this->eigen_jacobian();\n EVector const R = this->eigen_residual();\n EVector const dxi = J.fullPivLu().solve(-R);\n\n this->add_to_sym_tensor_xi(0, dxi);\n this->add_to_scalar_xi(1, dxi);\n this->add_to_scalar_xi(2, dxi);\n\n iter++;\n\n }\n\n // fail if convergence was not achieved\n if ((iter > m_max_iters) && (!converged)) {\n fail(\"J2:solve_nonlinear failed in %d iterations\", m_max_iters);\n }\n\n return path;\n\n}\n\ntemplate \nint J2::evaluate(\n RCP> global,\n bool force_path,\n int path_in) {\n\n int path = ELASTIC;\n int const ndims = this->m_num_dims;\n double const sqrt_23 = std::sqrt(2./3.);\n double const sqrt_32 = std::sqrt(3./2.);\n\n T const E = this->m_params[0];\n T const nu = this->m_params[1];\n T const K = this->m_params[2];\n T const Y = this->m_params[3];\n T const mu = compute_mu(E, nu);\n\n Tensor const zeta_old = this->sym_tensor_xi_prev(0);\n T const Ie_old = this->scalar_xi_prev(1);\n T const alpha_old = this->scalar_xi_prev(2);\n\n Tensor const zeta = this->sym_tensor_xi(0);\n T const Ie = this->scalar_xi(1);\n T const alpha = this->scalar_xi(2);\n\n Tensor const I = minitensor::eye(ndims);\n Tensor const be_bar_trial = eval_be_bar(global, zeta_old, Ie_old);\n Tensor const s = mu * zeta;\n T const s_mag = minitensor::norm(s);\n Tensor const n = s / s_mag;\n T const sigma_yield = Y + K * alpha;\n T const f = s_mag - sqrt_23 * sigma_yield;\n\n Tensor R_zeta;\n T R_Ie;\n T R_alpha;\n\n if (!force_path) {\n // plastic step\n if (f > m_abs_tol || std::abs(f) < m_abs_tol) {\n T const dgam = sqrt_32 * (alpha - alpha_old);\n R_zeta = zeta - minitensor::dev(be_bar_trial) + 2. * dgam * Ie * n;\n R_Ie = minitensor::det(zeta + Ie * I) - 1.;\n R_alpha = (s_mag - sqrt_23 * sigma_yield) / val(mu);\n path = PLASTIC;\n }\n // elastic step\n else {\n R_zeta = (0. * mu + 1.) * zeta - minitensor::dev(be_bar_trial);\n R_Ie = Ie - minitensor::trace(be_bar_trial) / 3. + 0. * mu;\n R_alpha = alpha - alpha_old + 0. * mu;\n path = ELASTIC;\n }\n }\n\n // force the path\n else {\n path = path_in;\n // plastic step\n if (path == PLASTIC) {\n T const dgam = sqrt_32 * (alpha - alpha_old);\n R_zeta = zeta - minitensor::dev(be_bar_trial) + 2. * dgam * Ie * n;\n R_Ie = minitensor::det(zeta + Ie * I) - 1.;\n R_alpha = (s_mag - sqrt_23 * sigma_yield) / val(mu);\n }\n // elastic step\n else {\n R_zeta = (0. * mu + 1.) * zeta - minitensor::dev(be_bar_trial);\n R_Ie = Ie - minitensor::trace(be_bar_trial) / 3. + 0. * mu;\n R_alpha = alpha - alpha_old + 0. * mu;\n }\n }\n\n this->set_sym_tensor_R(0, R_zeta);\n this->set_scalar_R(1, R_Ie);\n this->set_scalar_R(2, R_alpha);\n\n return path;\n\n}\n\ntemplate \nTensor J2::dev_cauchy(RCP> global) {\n int const ndims = global->num_dims();\n T const E = this->m_params[0];\n T const nu = this->m_params[1];\n T const mu = E / (2. * (1. + nu));\n Tensor const I = minitensor::eye(ndims);\n Tensor const grad_u = global->grad_vector_x(0);\n Tensor const F = grad_u + I;\n Tensor const zeta = this->sym_tensor_xi(0);\n T const J = minitensor::det(F);\n return mu * zeta / J;\n}\n\ntemplate \nTensor J2::cauchy(RCP> global, T p) {\n int const ndims = global->num_dims();\n Tensor const I = minitensor::eye(ndims);\n Tensor const dev_sigma = this->dev_cauchy(global);\n Tensor const sigma = dev_sigma - p * I;\n return sigma;\n}\n\ntemplate class J2;\ntemplate class J2;\n\n}\n", "meta": {"hexsha": "192cdfeec473ad875f6c8dc5a1cad9f2ecac0481", "size": 8800, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/J2.cpp", "max_stars_repo_name": "sandialabs/calibr8", "max_stars_repo_head_hexsha": "a7be9213a49eb2b56f58041a2a88b0184382de4d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5.0, "max_stars_repo_stars_event_min_datetime": "2021-08-31T00:33:36.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-29T17:10:28.000Z", "max_issues_repo_path": "src/J2.cpp", "max_issues_repo_name": "sandialabs/calibr8", "max_issues_repo_head_hexsha": "a7be9213a49eb2b56f58041a2a88b0184382de4d", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/J2.cpp", "max_forks_repo_name": "sandialabs/calibr8", "max_forks_repo_head_hexsha": "a7be9213a49eb2b56f58041a2a88b0184382de4d", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 28.2958199357, "max_line_length": 79, "alphanum_fraction": 0.6573863636, "num_tokens": 2829, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6619228625116081, "lm_q2_score": 0.31742627850202554, "lm_q1q2_score": 0.2101117109024677}} {"text": "// Copyright (C) 2011-2012 by the BEM++ Authors\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n#include \"bempp/common/config_ahmed.hpp\"\n#include \"bempp/common/config_trilinos.hpp\"\n\n#include \"aca_global_assembler.hpp\"\n\n#include \"assembly_options.hpp\"\n#include \"cluster_construction_helper.hpp\"\n#include \"evaluation_options.hpp\"\n#include \"index_permutation.hpp\"\n#include \"discrete_boundary_operator_composition.hpp\"\n#include \"discrete_sparse_boundary_operator.hpp\"\n\n#include \"../common/armadillo_fwd.hpp\"\n#include \"../common/auto_timer.hpp\"\n#include \"../common/boost_shared_array_fwd.hpp\"\n#include \"../common/chunk_statistics.hpp\"\n#include \"../common/to_string.hpp\"\n#include \"../fiber/explicit_instantiation.hpp\"\n#include \"../fiber/local_assembler_for_operators.hpp\"\n#include \"../fiber/local_assembler_for_potential_operators.hpp\"\n#include \"../fiber/serial_blas_region.hpp\"\n#include \"../fiber/scalar_traits.hpp\"\n#include \"../space/space.hpp\"\n\n#include \n#include \n#include \n\n#include \n\n#include \n#include \n#include \n#include \n\n#ifdef WITH_AHMED\n#include \"ahmed_aux.hpp\"\n\n#ifdef __INTEL_COMPILER\n#pragma warning(disable:381)\n#endif\n\n#include \n\n#ifdef __INTEL_COMPILER\n#pragma warning(default:381)\n#endif\n\n#include \"discrete_aca_boundary_operator.hpp\"\n#include \"modified_aca.hpp\"\n#include \"potential_operator_aca_assembly_helper.hpp\"\n#include \"scattered_range.hpp\"\n#include \"weak_form_aca_assembly_helper.hpp\"\n#endif\n\n// #define DUMP_DENSE_BLOCKS // if defined, contents and DOF lists of blocks\n // stored as dense matrices will be printed to the\n // screen\n\nnamespace Bempp\n{\n\n// Body of parallel loop\nnamespace\n{\n\n#ifdef WITH_AHMED\ntemplate \nclass AcaAssemblerLoopBody\n{\n typedef typename Fiber::ScalarTraits::RealType CoordinateType;\n typedef AhmedDofWrapper AhmedDofType;\n typedef bemblcluster AhmedBemBlcluster;\n typedef mblock::Type> AhmedMblock;\npublic:\n typedef tbb::concurrent_queue LeafClusterIndexQueue;\n\n AcaAssemblerLoopBody(\n AcaAssemblyHelper& helper,\n AhmedLeafClusterArray& leafClusters,\n boost::shared_array blocks,\n const AcaOptions& options,\n tbb::atomic& done,\n bool verbose,\n LeafClusterIndexQueue& leafClusterIndexQueue,\n bool symmetric,\n std::vector& stats) :\n m_helper(helper),\n m_leafClusters(leafClusters), m_blocks(blocks),\n m_options(options), m_done(done), m_verbose(verbose),\n m_leafClusterIndexQueue(leafClusterIndexQueue),\n m_symmetric(symmetric),\n m_stats(stats)\n {\n }\n\n template \n void operator() (const Range& r) const {\n const char* TEXT = \"Approximating ... \";\n for (typename Range::const_iterator i = r.begin(); i != r.end(); ++i) {\n size_t leafClusterIndex = 0;\n if (!m_leafClusterIndexQueue.try_pop(leafClusterIndex)) {\n std::cerr << \"AcaWeakFormAssemblerLoopBody::operator(): \"\n \"Warning: try_pop failed; this shouldn't happen!\"\n << std::endl;\n continue;\n }\n m_stats[leafClusterIndex].valid = true;\n m_stats[leafClusterIndex].chunkStart = r.begin();\n m_stats[leafClusterIndex].chunkSize = r.size();\n m_stats[leafClusterIndex].startTime = tbb::tick_count::now();\n\n AhmedBemBlcluster* cluster =\n dynamic_cast(m_leafClusters[leafClusterIndex]);\n if (m_symmetric)\n apprx_sym(m_helper, m_blocks[cluster->getidx()],\n cluster, m_options.eps, m_options.maximumRank,\n true /* complex_sym */);\n else {\n if (m_options.useAhmedAca)\n apprx_unsym(m_helper, m_blocks[cluster->getidx()],\n cluster, m_options.eps, m_options.maximumRank);\n else\n apprx_unsym_shooting(\n m_helper, m_blocks[cluster->getidx()],\n cluster, m_options.eps, m_options.maximumRank);\n }\n m_stats[leafClusterIndex].endTime = tbb::tick_count::now();\n // TODO: recompress\n const int HASH_COUNT = 20;\n if (m_verbose)\n progressbar(std::cout, TEXT, (++m_done) - 1,\n m_leafClusters.size(), HASH_COUNT, true);\n }\n\n }\n\nprivate:\n AcaAssemblyHelper& m_helper;\n AhmedLeafClusterArray& m_leafClusters;\n boost::shared_array m_blocks;\n const AcaOptions& m_options;\n tbb::atomic& m_done;\n bool m_verbose;\n LeafClusterIndexQueue& m_leafClusterIndexQueue;\n bool m_symmetric;\n std::vector& m_stats;\n};\n\nvoid reallyGetClusterIds(const cluster& clusterTree,\n const std::vector& p2oDofs,\n std::vector& clusterIds,\n unsigned int& id)\n{\n if (clusterTree.isleaf())\n for (unsigned int nDof = clusterTree.getnbeg(); nDof < clusterTree.getnend(); ++nDof)\n clusterIds[p2oDofs[nDof]] = id;\n else\n for (unsigned int nSon = 0; nSon < clusterTree.getns(); ++nSon)\n reallyGetClusterIds(*clusterTree.getson(nSon), p2oDofs, clusterIds, ++id);\n}\n\nvoid getClusterIds(const cluster& clusterTree,\n const std::vector& p2oDofs,\n std::vector& clusterIds)\n{\n clusterIds.resize(p2oDofs.size());\n unsigned int id = 0;\n reallyGetClusterIds(clusterTree, p2oDofs, clusterIds, id);\n}\n\ntemplate \nvoid save_arma_matrix(const arma::Mat& a, const std::string& fname)\n{\n std::ofstream out;\n out.precision(17);\n out.open(fname.c_str());\n arma::diskio::save_raw_ascii(a, out);\n out.close();\n}\ntemplate \nvoid save_arma_matrix(const arma::Mat >& a, const std::string& fname)\n{\n std::ofstream out;\n out.precision(17);\n out.open((fname + \"-real.txt\").c_str());\n arma::Mat component = arma::real(a);\n arma::diskio::save_raw_ascii(component, out);\n out.close();\n out.open((fname + \"-imag.txt\").c_str());\n component = arma::imag(a);\n arma::diskio::save_raw_ascii(component, out);\n out.close();\n}\n\ntemplate \nvoid dumpDenseBlocks(\n typename DiscreteAcaBoundaryOperator::AhmedBemBlcluster* clusterTree,\n typename DiscreteAcaBoundaryOperator::AhmedMblockArray& blocks,\n const std::vector& p2oRows,\n const std::vector& p2oCols,\n const std::vector::RealType> >& rowDofs,\n const std::vector::RealType> >& colDofs)\n{\n if (!clusterTree)\n return;\n typedef typename Fiber::ScalarTraits::RealType CoordinateType;\n typedef DiscreteAcaBoundaryOperator AcaOp;\n typedef typename AcaOp::AhmedDofType AhmedDofType;\n typedef typename AcaOp::AhmedMblock AhmedMblock;\n typedef bemcluster Cluster;\n if (clusterTree->isleaf()) {\n unsigned int idx = clusterTree->getidx();\n std::cout << \"LEAF; idx = \" << idx << std::endl;\n if (clusterTree->isGeM(blocks.get()) && clusterTree->isadm()) {\n std::cout << \"Dense block; \" << clusterTree->getb1()\n << \" \" << clusterTree->getb2()\n << \" \" << clusterTree->getn1()\n << \" \" << clusterTree->getn2() << \"\\n\";\n // if (clusterTree->getn1() < 500 || clusterTree->getn2() < 500)\n // return;\n Cluster* clRow = clusterTree->getcl1();\n assert(clRow);\n std::cout << \"Row center of mass: (\"\n << clRow->getcom(0) << \", \"\n << clRow->getcom(1) << \", \"\n << clRow->getcom(2) << \")\\n\";\n std::cout << \"Row icm: \"\n << clRow->geticom() - clusterTree->getb1() << std::endl;\n for (unsigned int nDof = clRow->getnbeg();\n nDof < clRow->getnend(); ++nDof) {\n assert(nDof < p2oRows.size());\n assert(p2oRows[nDof] < rowDofs.size());\n const Point3D dofPos = rowDofs[p2oRows[nDof]];\n std::cout << \" Row dof #\" << p2oRows[nDof] << \" at \"\n << dofPos.x << \", \" << dofPos.y << \", \"\n << dofPos.z << \"\\n\";\n }\n Cluster* clCol = clusterTree->getcl2();\n assert(clCol);\n std::cout << \"Col center of mass: (\"\n << clCol->getcom(0) << \", \"\n << clCol->getcom(1) << \", \"\n << clCol->getcom(2) << \")\\n\";\n std::cout << \"Column icm: \"\n << clCol->geticom() - clusterTree->getb2() << std::endl;\n for (unsigned int nDof = clCol->getnbeg();\n nDof < clCol->getnend(); ++nDof) {\n assert(nDof < p2oCols.size());\n assert(p2oCols[nDof] < colDofs.size());\n const Point3D dofPos = colDofs[p2oCols[nDof]];\n std::cout << \" Col dof #\" << p2oCols[nDof] << \" at \"\n << dofPos.x << \", \" << dofPos.y << \", \"\n << dofPos.z << \"\\n\";\n }\n AhmedMblock* block = blocks[idx];\n arma::Mat ablock(clusterTree->getn1(),\n clusterTree->getn2());\n for (size_t i = 0; i < block->nvals(); ++i)\n ablock[i] = block->getdata()[i];\n save_arma_matrix(ablock, \"block-\" + toString(idx) + \".txt\");\n }\n }\n else\n for (unsigned int nRowSon = 0; nRowSon < clusterTree->getnrs(); ++nRowSon)\n for (unsigned int nColSon = 0; nColSon < clusterTree->getncs(); ++nColSon)\n dumpDenseBlocks(\n dynamic_cast(\n clusterTree->getson(nRowSon, nColSon)),\n blocks,\n p2oRows, p2oCols, rowDofs, colDofs);\n}\n\ntemplate \nstd::auto_ptr >\nassembleAcaOperator(\n AcaAssemblyHelper& helper,\n const shared_ptr::\n AhmedBemBlcluster>& bemBlclusterTree,\n const ParallelizationOptions& parallelOptions,\n const AcaOptions& acaOptions,\n bool verbosityAtLeastDefault,\n bool symmetric,\n const shared_ptr& test_o2pPermutation,\n const shared_ptr& trial_o2pPermutation\n#ifdef DUMP_DENSE_BLOCKS\n ,\n const shared_ptr& test_p2oPermutation,\n const shared_ptr& trial_p2oPermutation,\n const std::vector::RealType> >& testDofCenters,\n const std::vector::RealType> >& trialDofCenters\n#endif // DUMP_DENSE_BLOCKS\n )\n{\n typedef mblock::Type> AhmedMblock;\n boost::shared_array blocks =\n allocateAhmedMblockArray(bemBlclusterTree.get());\n\n const size_t testDofCount = test_o2pPermutation->size();\n const size_t trialDofCount = trial_o2pPermutation->size();\n\n AhmedLeafClusterArray leafClusters(bemBlclusterTree.get());\n leafClusters.sortAccordingToClusterSize();\n const size_t leafClusterCount = leafClusters.size();\n\n int maxThreadCount = 1;\n if (!parallelOptions.isOpenClEnabled())\n {\n if (parallelOptions.maxThreadCount() == ParallelizationOptions::AUTO)\n maxThreadCount = tbb::task_scheduler_init::automatic;\n else\n maxThreadCount = parallelOptions.maxThreadCount();\n }\n tbb::task_scheduler_init scheduler(maxThreadCount);\n tbb::atomic done;\n done = 0;\n\n std::vector chunkStats(leafClusterCount);\n\n typedef AcaAssemblerLoopBody<\n BasisFunctionType, ResultType, AcaAssemblyHelper> Body;\n typename Body::LeafClusterIndexQueue leafClusterIndexQueue;\n for (size_t i = 0; i < leafClusterCount; ++i)\n leafClusterIndexQueue.push(i);\n\n if (verbosityAtLeastDefault)\n std::cout << \"About to start the ACA assembly loop\" << std::endl;\n tbb::tick_count loopStart = tbb::tick_count::now();\n {\n Fiber::SerialBlasRegion region; // if possible, ensure that BLAS is single-threaded\n tbb::parallel_for(tbb::blocked_range(0, leafClusterCount),\n Body(helper, leafClusters, blocks, acaOptions, done,\n verbosityAtLeastDefault,\n leafClusterIndexQueue, symmetric, chunkStats));\n }\n tbb::tick_count loopEnd = tbb::tick_count::now();\n if (verbosityAtLeastDefault) {\n std::cout << \"\\n\"; // the progress bar doesn't print the final \\n\n std::cout << \"ACA loop took \" << (loopEnd - loopStart).seconds() << \" s\"\n << std::endl;\n }\n\n // TODO: parallelise!\n if (acaOptions.recompress) {\n if (verbosityAtLeastDefault)\n std::cout << \"About to start ACA agglomeration\" << std::endl;\n agglH(bemBlclusterTree.get(), blocks.get(),\n acaOptions.eps, acaOptions.maximumRank);\n if (verbosityAtLeastDefault)\n std::cout << \"Agglomeration finished\" << std::endl;\n }\n\n#ifdef DUMP_DENSE_BLOCKS\n dumpDenseBlocks(bemBlclusterTree.get(), blocks,\n test_p2oPermutation->permutedIndices(),\n trial_p2oPermutation->permutedIndices(),\n testDofCenters, trialDofCenters);\n#endif // DUMP_DENSE_BLOCKS\n\n // dumpAhmedMblockArray(blocks, blockCount);\n\n // // Dump timing data of individual chunks\n // std::cout << \"\\nChunks:\\n\";\n // for (int i = 0; i < leafClusterCount; ++i)\n // if (chunkStats[i].valid) {\n // int blockIndex = leafClusters[i]->getidx();\n // std::cout << chunkStats[i].chunkStart << \"\\t\"\n // << chunkStats[i].chunkSize << \"\\t\"\n // << (chunkStats[i].startTime - loopStart).seconds() << \"\\t\"\n // << (chunkStats[i].endTime - loopStart).seconds() << \"\\t\"\n // << (chunkStats[i].endTime - chunkStats[i].startTime).seconds() << \"\\t\"\n // << blocks[blockIndex]->getn1() << \"\\t\"\n // << blocks[blockIndex]->getn2() << \"\\t\"\n // << blocks[blockIndex]->islwr() << \"\\t\"\n // << (blocks[blockIndex]->islwr() ? blocks[blockIndex]->rank() : 0) << \"\\n\";\n // }\n\n if (verbosityAtLeastDefault) {\n#ifdef CHECK_ACA_ERROR // a define from include/AHMED/apprx.h\n std::cout << \"ACA finished. Max error: \" << ACA_error_max << std::endl;\n#endif\n size_t totalEntryCount = testDofCount * trialDofCount;\n size_t origMemory = sizeof(ResultType) * totalEntryCount;\n size_t ahmedMemory = sizeH(bemBlclusterTree.get(), blocks.get());\n int maximumRank = Hmax_rank(bemBlclusterTree.get(), blocks.get());\n size_t accessedEntryCount = helper.accessedEntryCount();\n double accessedFraction = double(accessedEntryCount) / totalEntryCount;\n std::cout << \"\\nNeeded storage: \"\n << ahmedMemory / 1024. / 1024. << \" MB.\\n\"\n << \"Without approximation: \"\n << origMemory / 1024. / 1024. << \" MB.\\n\"\n << \"Compressed to \"\n << (100. * ahmedMemory) / origMemory << \"%.\\n\"\n << \"Maximum rank: \" << maximumRank << \".\\n\"\n << \"Accessed \"\n << 100. * accessedFraction << \"% matrix entries.\\n\"\n << std::endl;\n }\n\n if (acaOptions.outputPostscript) {\n if (verbosityAtLeastDefault)\n std::cout << \"Writing matrix partition ...\" << std::flush;\n std::ofstream os(acaOptions.outputFname.c_str());\n if (symmetric)\n // psoutputHeH() seems to work also for symmetric matrices\n psoutputHeH(os, bemBlclusterTree.get(),\n trialDofCount, blocks.get());\n else\n psoutputGeH(os, bemBlclusterTree.get(),\n std::max(testDofCount, trialDofCount), blocks.get());\n os.close();\n if (verbosityAtLeastDefault)\n std::cout << \" done.\" << std::endl;\n }\n\n int outSymmetry = NO_SYMMETRY;\n if (symmetric) {\n outSymmetry = SYMMETRIC;\n if (!boost::is_complex())\n outSymmetry |= HERMITIAN;\n }\n typedef DiscreteAcaBoundaryOperator DiscreteAcaLinOp;\n std::auto_ptr acaOp(\n new DiscreteAcaLinOp(testDofCount, trialDofCount,\n acaOptions.eps,\n acaOptions.maximumRank,\n outSymmetry,\n bemBlclusterTree, blocks,\n *trial_o2pPermutation, // domain\n *test_o2pPermutation, // range\n parallelOptions));\n return acaOp;\n}\n\n#endif\n\n} // namespace\n\ntemplate \nstd::auto_ptr >\nAcaGlobalAssembler::assembleDetachedWeakForm(\n const Space& testSpace,\n const Space& trialSpace,\n const std::vector& localAssemblers,\n const std::vector& sparseTermsToAdd,\n const std::vector& denseTermMultipliers,\n const std::vector& sparseTermMultipliers,\n const AssemblyOptions& options,\n int symmetry)\n{\n#ifdef WITH_AHMED\n typedef AhmedDofWrapper AhmedDofType;\n typedef ExtendedBemCluster AhmedBemCluster;\n typedef bemblcluster AhmedBemBlcluster;\n typedef DiscreteAcaBoundaryOperator DiscreteAcaLinOp;\n\n const AcaOptions& acaOptions = options.acaOptions();\n const bool indexWithGlobalDofs = acaOptions.globalAssemblyBeforeCompression;\n const bool verbosityAtLeastDefault =\n (options.verbosityLevel() >= VerbosityLevel::DEFAULT);\n const bool verbosityAtLeastHigh =\n (options.verbosityLevel() >= VerbosityLevel::HIGH);\n\n // Currently we don't support Hermitian ACA operators. This is because we\n // don't have the means to really test them -- we would need complex-valued\n // basis functions for that. (Assembly of such a matrix would be very easy\n // -- just change complex_sym from true to false in the call to apprx_sym()\n // in AcaWeakFormAssemblerLoopBody::operator() -- but operations on\n // symmetric/Hermitian matrices are not always trivial and we do need to be\n // able to test them properly.)\n bool symmetric = symmetry & SYMMETRIC;\n if (symmetry & HERMITIAN && !(symmetry & SYMMETRIC) &&\n verbosityAtLeastDefault)\n std::cout << \"Warning: assembly of non-symmetric Hermitian H-matrices \"\n \"is not supported yet. A general H-matrix will be assembled\"\n << std::endl;\n\n#ifndef WITH_TRILINOS\n if (!indexWithGlobalDofs)\n throw std::runtime_error(\"AcaGlobalAssembler::assembleDetachedWeakForm(): \"\n \"ACA assembly with globalAssemblyBeforeCompression \"\n \"set to false requires BEM++ to be linked with \"\n \"Trilinos\");\n#endif // WITH_TRILINOS\n\n const size_t testDofCount = indexWithGlobalDofs ?\n testSpace.globalDofCount() : testSpace.flatLocalDofCount();\n const size_t trialDofCount = indexWithGlobalDofs ?\n trialSpace.globalDofCount() : trialSpace.flatLocalDofCount();\n\n if (symmetric && testDofCount != trialDofCount)\n throw std::invalid_argument(\"AcaGlobalAssembler::assembleDetachedWeakForm(): \"\n \"you cannot generate a symmetric weak form \"\n \"using test and trial spaces with different \"\n \"numbers of DOFs\");\n\n // o2p: map of original indices to permuted indices\n // p2o: map of permuted indices to original indices\n typedef ClusterConstructionHelper CCH;\n shared_ptr testClusterTree;\n shared_ptr test_o2pPermutation, test_p2oPermutation;\n CCH::constructBemCluster(testSpace, indexWithGlobalDofs, acaOptions,\n testClusterTree,\n test_o2pPermutation, test_p2oPermutation);\n shared_ptr trialClusterTree;\n shared_ptr trial_o2pPermutation, trial_p2oPermutation;\n if (symmetric || &testSpace == &trialSpace) {\n trialClusterTree = testClusterTree;\n trial_o2pPermutation = test_o2pPermutation;\n trial_p2oPermutation = test_p2oPermutation;\n } else\n CCH::constructBemCluster(trialSpace, indexWithGlobalDofs, acaOptions,\n trialClusterTree,\n trial_o2pPermutation, trial_p2oPermutation);\n\n// // Export VTK plots showing the disctribution of leaf cluster ids\n// std::vector testClusterIds;\n// getClusterIds(*testClusterTree, test_p2oPermutation->permutedIndices(), testClusterIds);\n// testSpace.dumpClusterIds(\"testClusterIds\", testClusterIds,\n// indexWithGlobalDofs ? GLOBAL_DOFS : FLAT_LOCAL_DOFS);\n// std::vector trialClusterIds;\n// getClusterIds(*trialClusterTree, trial_p2oPermutation->permutedIndices(), trialClusterIds);\n// trialSpace.dumpClusterIds(\"trialClusterIds\", trialClusterIds,\n// indexWithGlobalDofs ? GLOBAL_DOFS : FLAT_LOCAL_DOFS);\n\n if (verbosityAtLeastHigh)\n std::cout << \"Test cluster count: \" << testClusterTree->getncl()\n << \"\\nTrial cluster count: \" << trialClusterTree->getncl()\n << std::endl;\n\n unsigned int blockCount = 0;\n shared_ptr bemBlclusterTree(\n CCH::constructBemBlockCluster(acaOptions, symmetric,\n *testClusterTree, *trialClusterTree,\n blockCount).release());\n\n if (verbosityAtLeastHigh)\n std::cout << \"Mblock count: \" << blockCount << std::endl;\n\n std::vector p2oTestDofs =\n test_p2oPermutation->permutedIndices();\n std::vector p2oTrialDofs =\n trial_p2oPermutation->permutedIndices();\n assert(p2oTestDofs.size() == testDofCount);\n assert(p2oTrialDofs.size() == trialDofCount);\n\n#ifdef DUMP_DENSE_BLOCKS\n std::vector > testDofCenters, trialDofCenters;\n if (indexWithGlobalDofs) {\n testSpace.getGlobalDofPositions(testDofCenters);\n trialSpace.getGlobalDofPositions(trialDofCenters);\n } else {\n testSpace.getFlatLocalDofPositions(testDofCenters);\n trialSpace.getFlatLocalDofPositions(trialDofCenters);\n }\n#endif // DUMP_DENSE_BLOCKS\n\n typedef WeakFormAcaAssemblyHelper\n AcaAssemblyHelper;\n // TODO: It might be better (more efficient and elegant)\n // to pass p2oPermutation than p2oDofs.\n // Also, it might be more logical to rename IndexPermutation to IndexMapping\n // and permute/unpermute to map/unmap.\n AcaAssemblyHelper helper(\n testSpace, trialSpace, p2oTestDofs, p2oTrialDofs,\n localAssemblers, sparseTermsToAdd,\n denseTermMultipliers, sparseTermMultipliers, options);\n\n std::auto_ptr > acaOp =\n assembleAcaOperator(\n helper, bemBlclusterTree,\n options.parallelizationOptions(), options.acaOptions(),\n verbosityAtLeastDefault, symmetric,\n test_o2pPermutation, trial_o2pPermutation\n#ifdef DUMP_DENSE_BLOCKS\n ,\n test_p2oPermutation, trial_p2oPermutation,\n testDofCenters, trialDofCenters\n#endif // DUMP_DENSE_BLOCKS\n );\n\n std::auto_ptr result;\n if (indexWithGlobalDofs)\n result = acaOp;\n else {\n#ifdef WITH_TRILINOS\n // without Trilinos, this code will never be reached -- an exception\n // will be thrown earlier in this function\n typedef DiscreteBoundaryOperatorComposition DiscreteBndOpComp;\n shared_ptr acaOpShared(acaOp.release());\n shared_ptr trialGlobalToLocal =\n constructOperatorMappingGlobalToFlatLocalDofs<\n BasisFunctionType, ResultType>(trialSpace);\n shared_ptr testLocalToGlobal =\n constructOperatorMappingFlatLocalToGlobalDofs<\n BasisFunctionType, ResultType>(testSpace);\n shared_ptr tmp(\n new DiscreteBndOpComp(acaOpShared, trialGlobalToLocal));\n result.reset(new DiscreteBndOpComp(testLocalToGlobal, tmp));\n#endif // WITH_TRILINOS\n }\n return result;\n\n#else // without Ahmed\n throw std::runtime_error(\"AcaGlobalAssembler::assembleDetachedWeakForm(): \"\n \"To enable assembly in ACA mode, recompile BEM++ \"\n \"with the symbol WITH_AHMED defined.\");\n#endif // WITH_AHMED\n}\n\ntemplate \nstd::auto_ptr >\nAcaGlobalAssembler::assembleDetachedWeakForm(\n const Space& testSpace,\n const Space& trialSpace,\n LocalAssemblerForBoundaryOperators& localAssembler,\n const AssemblyOptions& options,\n int symmetry)\n{\n std::vector localAssemblers(\n 1, &localAssembler);\n std::vector sparseTermsToAdd;\n std::vector denseTermsMultipliers(1, 1.0);\n std::vector sparseTermsMultipliers;\n\n return assembleDetachedWeakForm(testSpace, trialSpace, localAssemblers,\n sparseTermsToAdd,\n denseTermsMultipliers,\n sparseTermsMultipliers,\n options, symmetry);\n}\n\ntemplate \nstd::auto_ptr >\nAcaGlobalAssembler::assemblePotentialOperator(\n const arma::Mat& points,\n const Space& trialSpace,\n const std::vector& localAssemblers,\n const std::vector& termMultipliers,\n const EvaluationOptions& options)\n{\n const int symmetric = false;\n\n#ifdef WITH_AHMED\n typedef AhmedDofWrapper AhmedDofType;\n typedef ExtendedBemCluster AhmedBemCluster;\n typedef bemblcluster AhmedBemBlcluster;\n typedef DiscreteAcaBoundaryOperator DiscreteAcaLinOp;\n\n const AcaOptions& acaOptions = options.acaOptions();\n const bool indexWithGlobalDofs = acaOptions.globalAssemblyBeforeCompression;\n const bool verbosityAtLeastDefault =\n (options.verbosityLevel() >= VerbosityLevel::DEFAULT);\n const bool verbosityAtLeastHigh =\n (options.verbosityLevel() >= VerbosityLevel::HIGH);\n\n#ifndef WITH_TRILINOS\n if (!indexWithGlobalDofs)\n throw std::runtime_error(\"AcaGlobalAssembler::assemblePotentialOperator(): \"\n \"ACA assembly with globalAssemblyBeforeCompression \"\n \"set to false requires BEM++ to be linked with \"\n \"Trilinos\");\n#endif // WITH_TRILINOS\n\n if (localAssemblers.empty())\n throw std::runtime_error(\"AcaGlobalAssembler::assemblePotentialOperator(): \"\n \"the 'localAssemblers' vector must not be empty\");\n\n const size_t pointCount = points.n_cols;\n const int componentCount = localAssemblers[0]->resultDimension();\n const size_t testDofCount = pointCount * componentCount;\n const size_t trialDofCount = indexWithGlobalDofs ?\n trialSpace.globalDofCount() : trialSpace.flatLocalDofCount();\n\n // o2p: map of original indices to permuted indices\n // p2o: map of permuted indices to original indices\n typedef ClusterConstructionHelper CCH;\n shared_ptr testClusterTree;\n shared_ptr test_o2pPermutation, test_p2oPermutation;\n CCH::constructBemCluster(points, componentCount, acaOptions,\n testClusterTree,\n test_o2pPermutation, test_p2oPermutation);\n shared_ptr trialClusterTree;\n shared_ptr trial_o2pPermutation, trial_p2oPermutation;\n CCH::constructBemCluster(trialSpace, indexWithGlobalDofs, acaOptions,\n trialClusterTree,\n trial_o2pPermutation, trial_p2oPermutation);\n\n // Print the distribution of cluster ids\n#ifdef DUMP_DENSE_BLOCKS\n std::vector > testDofCenters, trialDofCenters;\n CCH::getComponentDofPositions(points, componentCount, testDofCenters);\n if (indexWithGlobalDofs)\n trialSpace.getGlobalDofPositions(trialDofCenters);\n else\n trialSpace.getFlatLocalDofPositions(trialDofCenters);\n#endif // DUMP_DENSE_BLOCKS\n\n if (verbosityAtLeastHigh)\n std::cout << \"Test cluster count: \" << testClusterTree->getncl()\n << \"\\nTrial cluster count: \" << trialClusterTree->getncl()\n << std::endl;\n\n unsigned int blockCount = 0;\n shared_ptr bemBlclusterTree(\n CCH::constructBemBlockCluster(acaOptions, false /* symmetric */,\n *testClusterTree, *trialClusterTree,\n blockCount).release());\n\n if (verbosityAtLeastHigh)\n std::cout << \"Mblock count: \" << blockCount << std::endl;\n\n std::vector p2oPoints =\n test_p2oPermutation->permutedIndices();\n std::vector p2oTrialDofs =\n trial_p2oPermutation->permutedIndices();\n typedef PotentialOperatorAcaAssemblyHelper\n AcaAssemblyHelper;\n AcaAssemblyHelper helper(points, trialSpace, p2oPoints, p2oTrialDofs,\n localAssemblers, termMultipliers, options);\n\n std::auto_ptr > acaOp =\n assembleAcaOperator(\n helper, bemBlclusterTree,\n options.parallelizationOptions(), options.acaOptions(),\n verbosityAtLeastDefault, symmetric,\n test_o2pPermutation, trial_o2pPermutation\n#ifdef DUMP_DENSE_BLOCKS\n ,\n test_p2oPermutation, trial_p2oPermutation,\n testDofCenters, trialDofCenters\n#endif // DUMP_DENSE_BLOCKS\n );\n\n std::auto_ptr result;\n if (indexWithGlobalDofs)\n result = acaOp;\n else {\n#ifdef WITH_TRILINOS\n // without Trilinos, this code will never be reached -- an exception\n // will be thrown earlier in this function\n typedef DiscreteBoundaryOperatorComposition DiscreteBndOpComp;\n shared_ptr acaOpShared(acaOp.release());\n shared_ptr trialGlobalToLocal =\n constructOperatorMappingGlobalToFlatLocalDofs<\n BasisFunctionType, ResultType>(trialSpace);\n result.reset(new DiscreteBndOpComp(acaOpShared, trialGlobalToLocal));\n#endif // WITH_TRILINOS\n }\n return result;\n\n#else // without Ahmed\n throw std::runtime_error(\"AcaGlobalAssembler::assemblePotentialOperator(): \"\n \"To enable assembly in ACA mode, recompile BEM++ \"\n \"with the symbol WITH_AHMED defined.\");\n#endif // WITH_AHMED\n}\n\ntemplate \nstd::auto_ptr >\nAcaGlobalAssembler::assemblePotentialOperator(\n const arma::Mat& points,\n const Space& trialSpace,\n LocalAssemblerForPotentialOperators& localAssembler,\n const EvaluationOptions& options)\n{\n std::vector localAssemblers(\n 1, &localAssembler);\n std::vector termMultipliers(1, 1.0);\n\n return assemblePotentialOperator(points, trialSpace, localAssemblers,\n termMultipliers, options);\n}\n\nFIBER_INSTANTIATE_CLASS_TEMPLATED_ON_BASIS_AND_RESULT(AcaGlobalAssembler);\n\n} // namespace Bempp\n", "meta": {"hexsha": "3c043e7a720c02afb641db1c8bce1b6de0bbf308", "size": 35399, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/assembly/aca_global_assembler.cpp", "max_stars_repo_name": "UCL/bempp", "max_stars_repo_head_hexsha": "f768ec7d319c02d6e0142512fb61db0607cadf10", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 2.0, "max_stars_repo_stars_event_min_datetime": 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YES\n2. NO", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.4073334000459303, "lm_q1q2_score": 0.21002921340285202}} {"text": "// ====================================================================\n// This file is part of FlexibleSUSY.\n//\n// FlexibleSUSY is free software: you can redistribute it and/or modify\n// it under the terms of the GNU General Public License as published\n// by the Free Software Foundation, either version 3 of the License,\n// or (at your option) any later version.\n//\n// FlexibleSUSY is distributed in the hope that it will be useful, but\n// WITHOUT ANY WARRANTY; without even the implied warranty of\n// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\n// General Public License for more details.\n//\n// You should have received a copy of the GNU General Public License\n// along with FlexibleSUSY. If not, see\n// .\n// ====================================================================\n\n// File generated at Sat 27 Aug 2016 12:43:04\n\n#include \"SingletDM_two_scale_initial_guesser.hpp\"\n#include \"SingletDM_two_scale_model.hpp\"\n#include \"lowe.h\"\n#include \"error.hpp\"\n#include \"ew_input.hpp\"\n#include \"wrappers.hpp\"\n\n#include \n#include \n\nnamespace flexiblesusy {\n\n#define DERIVEDPARAMETER(p) model->p()\n#define INPUTPARAMETER(p) model->get_input().p\n#define MODELPARAMETER(p) model->get_##p()\n#define PHASE(p) model->get_##p()\n#define LowEnergyConstant(p) Electroweak_constants::p\n#define MODEL model\n\nSingletDM_initial_guesser::SingletDM_initial_guesser(\n SingletDM* model_,\n const softsusy::QedQcd& qedqcd_,\n const SingletDM_low_scale_constraint& low_constraint_,\n const SingletDM_susy_scale_constraint& susy_constraint_,\n const SingletDM_high_scale_constraint& high_constraint_\n)\n : Initial_guesser()\n , model(model_)\n , qedqcd(qedqcd_)\n , mu_guess(0.)\n , mc_guess(0.)\n , mt_guess(0.)\n , md_guess(0.)\n , ms_guess(0.)\n , mb_guess(0.)\n , me_guess(0.)\n , mm_guess(0.)\n , mtau_guess(0.)\n , running_precision(1.0e-3)\n , low_constraint(low_constraint_)\n , susy_constraint(susy_constraint_)\n , high_constraint(high_constraint_)\n{\n assert(model && \"SingletDM_initial_guesser: Error: pointer to model\"\n \" SingletDM must not be zero\");\n}\n\nSingletDM_initial_guesser::~SingletDM_initial_guesser()\n{\n}\n\n/**\n * Guesses the DR-bar model parameters by calling\n * guess_susy_parameters() and guess_soft_parameters() .\n */\nvoid SingletDM_initial_guesser::guess()\n{\n guess_susy_parameters();\n guess_soft_parameters();\n}\n\n/**\n * Guesses the SUSY parameters (gauge, Yukawa couplings) at\n * \\f$m_\\text{top}^\\text{pole}\\f$ from the Standard Model gauge\n * couplings and fermion masses. Threshold corrections are ignored.\n * The user-defined initial guess at the low-scale\n * (InitialGuessAtLowScale) is applied here:\n *\n * \\code{.cpp}\n const auto LamSHInput = INPUTPARAMETER(LamSHInput);\n const auto LamSInput = INPUTPARAMETER(LamSInput);\n const auto muSInput = INPUTPARAMETER(muSInput);\n const auto HiggsIN = INPUTPARAMETER(HiggsIN);\n\n MODEL->set_v(Re(LowEnergyConstant(vev)));\n calculate_Yu_DRbar();\n calculate_Yd_DRbar();\n calculate_Ye_DRbar();\n MODEL->set_LamSH(Re(LamSHInput));\n MODEL->set_LamS(Re(LamSInput));\n MODEL->set_muS(Re(muSInput));\n MODEL->set_muH(Re(HiggsIN));\n\n * \\endcode\n */\nvoid SingletDM_initial_guesser::guess_susy_parameters()\n{\n using namespace softsusy;\n\n softsusy::QedQcd leAtMt(qedqcd);\n const double MZ = Electroweak_constants::MZ;\n const double MW = Electroweak_constants::MW;\n const double sinThetaW2 = 1.0 - Sqr(MW / MZ);\n const double mtpole = leAtMt.displayPoleMt();\n\n mu_guess = leAtMt.displayMass(mUp);\n mc_guess = leAtMt.displayMass(mCharm);\n mt_guess = model->get_thresholds() > 0 ?\n leAtMt.displayMass(mTop) - 30.0 :\n leAtMt.displayPoleMt();\n md_guess = leAtMt.displayMass(mDown);\n ms_guess = leAtMt.displayMass(mStrange);\n mb_guess = leAtMt.displayMass(mBottom);\n me_guess = model->get_thresholds() > 0 ?\n leAtMt.displayMass(mElectron) :\n leAtMt.displayPoleMel();\n mm_guess = model->get_thresholds() > 0 ?\n leAtMt.displayMass(mMuon) :\n leAtMt.displayPoleMmuon();\n mtau_guess = leAtMt.displayMass(mTau);\n\n // guess gauge couplings at mt\n const DoubleVector alpha_sm(leAtMt.getGaugeMu(mtpole, sinThetaW2));\n\n MODEL->set_g1(Sqrt(4. * Pi * alpha_sm(1)));\n MODEL->set_g2(Sqrt(4. * Pi * alpha_sm(2)));\n MODEL->set_g3(Sqrt(4. * Pi * alpha_sm(3)));\n\n\n model->set_scale(mtpole);\n\n // apply user-defined initial guess at the low scale\n const auto LamSHInput = INPUTPARAMETER(LamSHInput);\n const auto LamSInput = INPUTPARAMETER(LamSInput);\n const auto muSInput = INPUTPARAMETER(muSInput);\n const auto HiggsIN = INPUTPARAMETER(HiggsIN);\n\n MODEL->set_v(Re(LowEnergyConstant(vev)));\n calculate_Yu_DRbar();\n calculate_Yd_DRbar();\n calculate_Ye_DRbar();\n MODEL->set_LamSH(Re(LamSHInput));\n MODEL->set_LamS(Re(LamSInput));\n MODEL->set_muS(Re(muSInput));\n MODEL->set_muH(Re(HiggsIN));\n\n}\n\nvoid SingletDM_initial_guesser::calculate_DRbar_yukawa_couplings()\n{\n calculate_Yu_DRbar();\n calculate_Yd_DRbar();\n calculate_Ye_DRbar();\n}\n\n/**\n * Calculates the Yukawa couplings Yu of the up-type quarks\n * from the Standard Model up-type quark masses (ignoring threshold\n * corrections).\n */\nvoid SingletDM_initial_guesser::calculate_Yu_DRbar()\n{\n Eigen::Matrix,3,3> upQuarksDRbar(ZEROMATRIXCOMPLEX(3,3));\n upQuarksDRbar(0,0) = mu_guess;\n upQuarksDRbar(1,1) = mc_guess;\n upQuarksDRbar(2,2) = mt_guess;\n\n const auto v = MODELPARAMETER(v);\n MODEL->set_Yu((-((1.4142135623730951*upQuarksDRbar)/v).transpose()).real());\n\n}\n\n/**\n * Calculates the Yukawa couplings Yd of the down-type\n * quarks from the Standard Model down-type quark masses (ignoring\n * threshold corrections).\n */\nvoid SingletDM_initial_guesser::calculate_Yd_DRbar()\n{\n Eigen::Matrix,3,3> downQuarksDRbar(ZEROMATRIXCOMPLEX(3,3));\n downQuarksDRbar(0,0) = md_guess;\n downQuarksDRbar(1,1) = ms_guess;\n downQuarksDRbar(2,2) = mb_guess;\n\n const auto v = MODELPARAMETER(v);\n MODEL->set_Yd((((1.4142135623730951*downQuarksDRbar)/v).transpose()).real())\n ;\n\n}\n\n/**\n * Calculates the Yukawa couplings Ye of the leptons\n * from the Standard Model down-type lepton masses (ignoring threshold\n * corrections).\n */\nvoid SingletDM_initial_guesser::calculate_Ye_DRbar()\n{\n Eigen::Matrix,3,3> downLeptonsDRbar(ZEROMATRIXCOMPLEX(3,3));\n downLeptonsDRbar(0,0) = me_guess;\n downLeptonsDRbar(1,1) = mm_guess;\n downLeptonsDRbar(2,2) = mtau_guess;\n\n const auto v = MODELPARAMETER(v);\n MODEL->set_Ye((((1.4142135623730951*downLeptonsDRbar)/v).transpose()).real()\n );\n\n}\n\n/**\n * Guesses the soft-breaking parameters. At first it runs to the\n * guess of the high-scale (HighScaleFirstGuess) and imposes the\n * high-scale constraint (HighScaleInput):\n *\n * \\code{.cpp}\n\n * \\endcode\n *\n * Afterwards, it runs to the low-scale guess (LowScaleFirstGuess) and\n * solves the EWSB conditions at the tree-level. Finally the DR-bar\n * mass spectrum is calculated.\n */\nvoid SingletDM_initial_guesser::guess_soft_parameters()\n{\n const double low_scale_guess = low_constraint.get_initial_scale_guess();\n const double high_scale_guess = high_constraint.get_initial_scale_guess();\n\n model->run_to(high_scale_guess, running_precision);\n\n // apply high-scale constraint\n high_constraint.set_model(model);\n high_constraint.apply();\n\n // apply user-defined initial guess at the high scale\n\n\n model->run_to(low_scale_guess, running_precision);\n\n // apply EWSB constraint\n model->solve_ewsb_tree_level();\n\n // calculate tree-level spectrum\n model->calculate_DRbar_masses();\n}\n\n} // namespace flexiblesusy\n", "meta": {"hexsha": "28d83bcccd7d6441e18d28795c2dd67c5f221137", "size": 7867, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "contrib/MassSpectra/flexiblesusy/models/SingletDM/SingletDM_two_scale_initial_guesser.cpp", "max_stars_repo_name": "aaronvincent/gambit_aaron", "max_stars_repo_head_hexsha": "a38bd6fc10d781e71f2adafd401c76e1e3476b05", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "contrib/MassSpectra/flexiblesusy/models/SingletDM/SingletDM_two_scale_initial_guesser.cpp", "max_issues_repo_name": "aaronvincent/gambit_aaron", "max_issues_repo_head_hexsha": "a38bd6fc10d781e71f2adafd401c76e1e3476b05", "max_issues_repo_licenses": ["Unlicense"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "contrib/MassSpectra/flexiblesusy/models/SingletDM/SingletDM_two_scale_initial_guesser.cpp", "max_forks_repo_name": "aaronvincent/gambit_aaron", "max_forks_repo_head_hexsha": "a38bd6fc10d781e71f2adafd401c76e1e3476b05", "max_forks_repo_licenses": ["Unlicense"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 30.73046875, "max_line_length": 84, "alphanum_fraction": 0.7150120758, "num_tokens": 2201, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5350984137988772, "lm_q2_score": 0.3923368301671084, "lm_q1q2_score": 0.2099388154972992}} {"text": "#include \"CoverageStats.h\"\r\nusing reseq::CoverageStats;\r\n\r\n//include \r\nusing std::min;\r\nusing std::max;\r\nusing std::sort;\r\n//include \r\nusing std::array;\r\n//include \r\nusing std::ceil;\r\nusing std::exp;\r\nusing std::pow;\r\nusing std::round;\r\n#include \r\nusing std::numeric_limits;\r\n//include \r\nusing std::mutex;\r\nusing std::lock_guard;\r\n//include \r\nusing std::pair;\r\n\r\n#include \r\nusing boost::math::cdf;\r\nusing boost::math::poisson_distribution;\r\n\r\n//include \r\nusing seqan::Dna;\r\nusing seqan::Dna5;\r\nusing seqan::hasFlagRC;\r\nusing seqan::hasFlagLast;\r\n\r\n//include \"utilities.hpp\"\r\nusing reseq::utilities::ConstDna5StringReverseComplement;\r\nusing reseq::utilities::ConstIupacStringReverseComplement;\r\nusing reseq::utilities::ReversedConstCharString;\r\nusing reseq::utilities::ReversedConstCigarString;\r\nusing reseq::utilities::Complement;\r\nusing reseq::utilities::at;\r\nusing reseq::utilities::Divide;\r\nusing reseq::utilities::DominantBase;\r\nusing reseq::utilities::IsN;\r\nusing reseq::utilities::Percent;\r\nusing reseq::utilities::SafePercent;\r\nusing reseq::utilities::SetToMax;\r\nusing reseq::utilities::SetToMin;\r\nusing reseq::utilities::TransformDistanceToStartOfErrorRegion;\r\n\r\n// Definitions so that referencing a const static is valid\r\nconst reseq::uintCovCount reseq::CoverageStats::kMaxCoverage;\r\n\r\nvoid CoverageStats::EvalRead( FullRecord *record, CoverageStats::CoverageBlock *coverage_block, const Reference &reference, QualityStats &qualities, ErrorStats &errors, uintQual phred_quality_offset){\r\n\t// Fragments that did not made the filters have to_ref_pos_=0\r\n\tif(record->to_ref_pos_){\r\n\t\tConstIupacStringReverseComplement reversed_seq(record->record_.seq);\r\n\t\tReversedConstCharString reversed_qual(record->record_.qual);\r\n\t\tReversedConstCigarString rev_cigar(record->record_.cigar);\r\n\r\n\t\tintSeqShift coverage_pos; // Can be before the start of the block, so negative values need to be allowed\r\n\t\tuintSeqLen ref_pos;\r\n\t\tif( hasFlagRC(record->record_) ){\r\n\t\t\tcoverage_pos = record->to_ref_pos_-coverage_block->start_pos_-1;\r\n\t\t\tref_pos = record->to_ref_pos_-1;\r\n\t\t}\r\n\t\telse{\r\n\t\t\tcoverage_pos = record->from_ref_pos_-coverage_block->start_pos_;\r\n\t\t\tref_pos = record->from_ref_pos_;\r\n\t\t}\r\n\r\n\t\tuintReadLen read_pos(0), not_considered_ref_bases(0);\r\n\t\tDna ref_base;\r\n\t\tDna5 base, dom_error;\r\n\t\tuintQual qual(0), last_qual(1);\r\n\t\tuintReadLen num_errors = 0;\r\n\t\tuintPercent error_rate;\r\n\t\tuintReadLenCalc error_rate_sum(0);\r\n\t\tfor( const auto &cigar_element : (hasFlagRC(record->record_)?rev_cigar:record->record_.cigar) ){\r\n\t\t\tswitch( cigar_element.operation ){\r\n\t\t\tcase 'M':\r\n\t\t\tcase '=':\r\n\t\t\tcase 'X':\r\n\t\t\tcase 'S': // Treat soft-clipping as match, so that bwa and bowtie2 behave the same\r\n\t\t\t\tfor(auto i=cigar_element.count; i--; ){\r\n\t\t\t\t\tif(record->from_ref_pos_ <= ref_pos && ref_pos < record->to_ref_pos_){\r\n\t\t\t\t\t\t// We are currently not comparing the adapter to the reference\r\n\t\t\t\t\t\tif( hasFlagRC(record->record_) ){\r\n\t\t\t\t\t\t\tbase = at(reversed_seq, read_pos);\r\n\t\t\t\t\t\t\tqual = at(reversed_qual, read_pos)-phred_quality_offset;\r\n\t\t\t\t\t\t\tref_base = Complement::Dna5(at(reference.ReferenceSequence(record->record_.rID), ref_pos));\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t\telse{\r\n\t\t\t\t\t\t\tbase = at(record->record_.seq, read_pos);\r\n\t\t\t\t\t\t\tqual = at(record->record_.qual, read_pos)-phred_quality_offset;\r\n\t\t\t\t\t\t\tref_base = at(reference.ReferenceSequence(record->record_.rID), ref_pos);\r\n\t\t\t\t\t\t}\r\n\r\n\t\t\t\t\t\tif( !CoveragePosValid(coverage_pos, coverage_block) ){\r\n\t\t\t\t\t\t\t++(not_considered_ref_bases);\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t\telse{\r\n\t\t\t\t\t\t\tif(0 > coverage_pos){\r\n\t\t\t\t\t\t\t\terror_rate = coverage_block->previous_coverage_.at(-1*coverage_pos-1).error_rate_.at(hasFlagRC(record->record_));\r\n\t\t\t\t\t\t\t\tdom_error = coverage_block->previous_coverage_.at(-1*coverage_pos-1).dom_error_.at(hasFlagRC(record->record_));\r\n\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\telse{\r\n\t\t\t\t\t\t\t\terror_rate = coverage_block->coverage_.at(coverage_pos).error_rate_.at(hasFlagRC(record->record_));\r\n\t\t\t\t\t\t\t\tdom_error = coverage_block->coverage_.at(coverage_pos).dom_error_.at(hasFlagRC(record->record_));\r\n\t\t\t\t\t\t\t}\r\n\r\n\t\t\t\t\t\t\tqualities.AddRefBase(hasFlagLast(record->record_), ref_base, dom_error, record->tile_id_, qual, error_rate, last_qual, record->sequence_quality_, read_pos);\r\n\t\t\t\t\t\t\terrors.AddBase(hasFlagLast(record->record_), ref_base, dom_error, record->tile_id_, base, qual, read_pos, num_errors, error_rate);\r\n\r\n\t\t\t\t\t\t\terror_rate_sum += error_rate;\r\n\r\n\t\t\t\t\t\t\tif( ref_base != base ){\r\n\t\t\t\t\t\t\t\t++num_errors;\r\n\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t}\r\n\r\n\t\t\t\t\t\tlast_qual = qual;\r\n\r\n\t\t\t\t\t\t++read_pos;\r\n\r\n\t\t\t\t\t\tif( hasFlagRC(record->record_) ){\r\n\t\t\t\t\t\t\t--coverage_pos;\r\n\t\t\t\t\t\t\t--ref_pos;\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t\telse{\r\n\t\t\t\t\t\t\t++coverage_pos;\r\n\t\t\t\t\t\t\t++ref_pos;\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t\tbreak;\r\n\t\t\tcase 'N':\r\n\t\t\tcase 'D':\r\n\t\t\t\tfor(auto i=cigar_element.count; i--; ){\r\n\t\t\t\t\tif(record->from_ref_pos_ <= ref_pos && ref_pos < record->to_ref_pos_){\r\n\t\t\t\t\t\t// We are currently not comparing the adapter to the reference\r\n\t\t\t\t\t\tif( hasFlagRC(record->record_) ){\r\n\t\t\t\t\t\t\tref_base = Complement::Dna5(at(reference.ReferenceSequence(record->record_.rID), ref_pos));\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t\telse{\r\n\t\t\t\t\t\t\tref_base = at(reference.ReferenceSequence(record->record_.rID), ref_pos);\r\n\t\t\t\t\t\t}\r\n\r\n\t\t\t\t\t\tif( !CoveragePosValid(coverage_pos, coverage_block) ){\r\n\t\t\t\t\t\t\t++(not_considered_ref_bases);\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t\telse{\r\n\t\t\t\t\t\t\tif(0 > coverage_pos){\r\n\t\t\t\t\t\t\t\terror_rate_sum += coverage_block->previous_coverage_.at(-1*coverage_pos-1).error_rate_.at(hasFlagRC(record->record_));\r\n\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\telse{\r\n\t\t\t\t\t\t\t\terror_rate_sum += coverage_block->coverage_.at(coverage_pos).error_rate_.at(hasFlagRC(record->record_));\r\n\t\t\t\t\t\t\t}\r\n\t\t\t\t\t\t\t++num_errors;\r\n\t\t\t\t\t\t}\r\n\r\n\t\t\t\t\t\tif( hasFlagRC(record->record_) ){\r\n\t\t\t\t\t\t\t--coverage_pos;\r\n\t\t\t\t\t\t\t--ref_pos;\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t\telse{\r\n\t\t\t\t\t\t\t++coverage_pos;\r\n\t\t\t\t\t\t\t++ref_pos;\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t\tbreak;\r\n\t\t\tcase 'I':\r\n\t\t\t\tif(record->from_ref_pos_ <= ref_pos && ref_pos < record->to_ref_pos_){\r\n\t\t\t\t\tif( hasFlagRC(record->record_) ){\r\n\t\t\t\t\t\tlast_qual = at(reversed_qual, read_pos+cigar_element.count-1)-phred_quality_offset; // Directly fill last_qual as qual is not needed here\r\n\t\t\t\t\t\tref_base = Complement::Dna5(at(reference.ReferenceSequence(record->record_.rID), ref_pos));\r\n\t\t\t\t\t}\r\n\t\t\t\t\telse{\r\n\t\t\t\t\t\tlast_qual = at(record->record_.qual, read_pos+cigar_element.count-1)-phred_quality_offset; // Directly fill last_qual as qual is not needed here\r\n\t\t\t\t\t\tref_base = at(reference.ReferenceSequence(record->record_.rID), ref_pos);\r\n\t\t\t\t\t}\r\n\r\n\t\t\t\t\tif( CoveragePosValid(coverage_pos, coverage_block) ){\r\n\t\t\t\t\t\tnum_errors += cigar_element.count;\r\n\t\t\t\t\t}\r\n\r\n\t\t\t\t\tread_pos += cigar_element.count;\r\n\t\t\t\t}\r\n\t\t\t\tbreak;\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\tif(not_considered_ref_bases < record->to_ref_pos_-record->from_ref_pos_){ // At least one base was considered\r\n\t\t\tqualities.AddRefRead(hasFlagLast(record->record_), record->tile_id_, record->reference_gc_, record->sequence_quality_, utilities::Divide(error_rate_sum, static_cast(record->to_ref_pos_-record->from_ref_pos_-not_considered_ref_bases)), record->fragment_length_);\r\n\t\t\terrors.AddRead(hasFlagLast(record->record_), num_errors);\r\n\t\t}\r\n\t}\r\n\r\n\tdelete record;\r\n}\r\n\r\ninline void CoverageStats::ApplyZeroCoverageRegion(){\r\n\ttmp_coverage_.at( 0 ) += zero_coverage_region_;\r\n\ttmp_coverage_.at( 0 ) -= excluded_bases_;\r\n\tfor( int strand=2; strand--; ){\r\n\t\ttmp_coverage_stranded_.at(strand).at(0) += zero_coverage_region_;\r\n\t\ttmp_coverage_stranded_.at(strand).at(0) -= excluded_bases_;\r\n\t}\r\n}\r\n\r\ndouble CoverageStats::GetPositionProbabilities(CoverageBlock *block, const Reference &reference, ThreadData &thread){\r\n\t// Get coverage weighted median error rate\r\n\tthread.block_coverage_.clear();\r\n\tthread.block_coverage_.resize(block->coverage_.size(), {0,0});\r\n\tthread.error_rates_sorted_.clear();\r\n\r\n\tuintNucCount total_bases(0), correct_bases(0);\r\n\tfor( uintSeqLen pos = 0; pos < block->coverage_.size(); ++pos ){\r\n\t\tif( block->coverage_.at(pos).valid_ ){\r\n\t\t\tauto ref_base = at(reference.ReferenceSequence(block->sequence_id_), block->start_pos_ + pos);\r\n\t\t\tauto rev_base = Complement::Dna5(ref_base);\r\n\t\t\tfor( int i=5; i--; ){\r\n\t\t\t\tthread.block_coverage_.at(pos).at(0) += block->coverage_.at(pos).coverage_forward_.at(i);\r\n\t\t\t\tif(block->coverage_.at(pos).coverage_forward_.at(i) > block->coverage_.at(pos).coverage_forward_.at( block->coverage_.at(pos).dom_error_.at(0) ) && i != ref_base){\r\n\t\t\t\t\tblock->coverage_.at(pos).dom_error_.at(0) = i;\r\n\t\t\t\t}\r\n\t\t\t\tthread.block_coverage_.at(pos).at(1) += block->coverage_.at(pos).coverage_reverse_.at(i);\r\n\t\t\t\tif(block->coverage_.at(pos).coverage_reverse_.at(i) > block->coverage_.at(pos).coverage_reverse_.at( block->coverage_.at(pos).dom_error_.at(1) ) && i != rev_base){\r\n\t\t\t\t\tblock->coverage_.at(pos).dom_error_.at(1) = i;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\r\n\t\t\ttotal_bases += thread.block_coverage_.at(pos).at(0) + thread.block_coverage_.at(pos).at(1);\r\n\t\t\tcorrect_bases += block->coverage_.at(pos).coverage_forward_.at(ref_base) + block->coverage_.at(pos).coverage_reverse_.at(rev_base);\r\n\r\n\t\t\tthread.error_rates_sorted_.emplace_back(static_cast(thread.block_coverage_.at(pos).at(0)-block->coverage_.at(pos).coverage_forward_.at(ref_base)) / thread.block_coverage_.at(pos).at(0), thread.block_coverage_.at(pos).at(0));\r\n\t\t\tthread.error_rates_sorted_.emplace_back(static_cast(thread.block_coverage_.at(pos).at(1)-block->coverage_.at(pos).coverage_reverse_.at(rev_base)) / thread.block_coverage_.at(pos).at(1), thread.block_coverage_.at(pos).at(1));\r\n\t\t}\r\n\t}\r\n\r\n\tthread.non_sytematic_probability_.clear();\r\n\tthread.non_sytematic_probability_.resize(block->coverage_.size(), {1.0,1.0});\r\n\tthread.non_sytematic_probability_sorted_.clear();\r\n\r\n\tif(correct_bases == total_bases){\r\n\t\treturn 0.0; // Nothing is systematic if we don't have errors\r\n\t}\r\n\r\n\tsort(thread.error_rates_sorted_.begin(), thread.error_rates_sorted_.end());\r\n\tuintNucCount sum_bases(0);\r\n\tuintSeqLen median_error_pos(0);\r\n\twhile(sum_bases < total_bases/2){\r\n\t\tsum_bases += thread.error_rates_sorted_.at(median_error_pos++).second;\r\n\t}\r\n\twhile( median_error_pos < thread.error_rates_sorted_.size() && thread.error_rates_sorted_.at(median_error_pos).first == 0.0 ){ // If the median error is exactly zero, get next non-zero error\r\n\t\t++median_error_pos;\r\n\t}\r\n\tif(median_error_pos == thread.error_rates_sorted_.size()){\r\n\t\treturn 0.0;\r\n\t}\r\n\r\n\tdouble error_rate = thread.error_rates_sorted_.at(median_error_pos).first;\r\n\tSetToMin( error_rate, static_cast(total_bases-correct_bases) / total_bases ); // If the mean is lower than the median use mean (should only happen if we shift median to non-zero value before)\r\n\r\n\t++tmp_block_error_rate_.at(static_cast(round(error_rate*100)));\r\n\terror_rate /= 3.0; // Systematic is only for one base\r\n\r\n\tif(0.0 == error_rate){\r\n\t\treturn 0.0; // Nothing is systematic if we have super low error rates\r\n\t}\r\n\r\n\tfor( uintSeqLen pos = 0; pos < block->coverage_.size(); ++pos ){\r\n\t\tif( block->coverage_.at(pos).valid_ ){\r\n\t\t\t// Handle forward direction\r\n\t\t\tuintCovCount errors = block->coverage_.at(pos).coverage_forward_.at(block->coverage_.at(pos).dom_error_.at(0));\r\n\t\t\tif( 0 < thread.block_coverage_.at(pos).at(0) ){\r\n\t\t\t\tif(0 == errors){\r\n\t\t\t\t\tthread.non_sytematic_probability_sorted_.push_back(1.0);\r\n\t\t\t\t}\r\n\t\t\t\telse{\r\n\t\t\t\t\tpoisson_distribution<> pois_dist( thread.block_coverage_.at(pos).at(0)*error_rate );\r\n\t\t\t\t\tthread.non_sytematic_probability_sorted_.push_back( 1-cdf( pois_dist, errors-1 ) ); // Probability to have at least that many errors (1-probability to have less errors)\r\n\t\t\t\t}\r\n\t\t\t\tthread.non_sytematic_probability_.at(pos).at(0) = thread.non_sytematic_probability_sorted_.back();\r\n\t\t\t\t++tmp_systematic_error_p_values_.at(static_cast(thread.non_sytematic_probability_sorted_.back()*kPValueHistBins));\r\n\t\t\t}\r\n\r\n\t\t\t// Handle reverse direction\r\n\t\t\tif( 0 < thread.block_coverage_.at(pos).at(1) ){\r\n\t\t\t\terrors = block->coverage_.at(pos).coverage_reverse_.at(block->coverage_.at(pos).dom_error_.at(1));\r\n\t\t\t\tif(0 == errors){\r\n\t\t\t\t\tthread.non_sytematic_probability_sorted_.push_back(1.0);\r\n\t\t\t\t}\r\n\t\t\t\telse{\r\n\t\t\t\t\tpoisson_distribution<> pois_dist( thread.block_coverage_.at(pos).at(1)*error_rate );\r\n\t\t\t\t\tthread.non_sytematic_probability_sorted_.push_back( 1-cdf( pois_dist, errors-1 ) ); // Probability to have at least that many errors (1-probability to have less errors)\r\n\t\t\t\t}\r\n\t\t\t\tthread.non_sytematic_probability_.at(pos).at(1) = thread.non_sytematic_probability_sorted_.back();\r\n\t\t\t\t++tmp_systematic_error_p_values_.at(static_cast(thread.non_sytematic_probability_sorted_.back()*kPValueHistBins));\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n\r\n\t// Bejamini-Hochberg\r\n\tsort(thread.non_sytematic_probability_sorted_.begin(), thread.non_sytematic_probability_sorted_.end());\r\n\tsize_t last_systematic(thread.non_sytematic_probability_sorted_.size());\r\n\twhile(--last_systematic && thread.non_sytematic_probability_sorted_.at(last_systematic) > kSystematicErrorFDR*(last_systematic+1)/thread.non_sytematic_probability_sorted_.size() );\r\n\r\n\tif( 0==last_systematic && thread.non_sytematic_probability_sorted_.at(0) > kSystematicErrorFDR/thread.non_sytematic_probability_sorted_.size() ){\r\n\t\t++tmp_block_percent_systematic_.at(0);\r\n\t\treturn 0.0; // No index has a probability lower than expected, so seems like we don't have systematic errors\r\n\t}\r\n\telse if( last_systematic == thread.non_sytematic_probability_sorted_.size()-1 ){\r\n\t\t++tmp_block_percent_systematic_.at(100);\r\n\t\treturn 1.0; // Final probability is lower than expected, so seems like we only have systematic errors;\r\n\t}\r\n\telse{\r\n\t\t++tmp_block_percent_systematic_.at(Percent(last_systematic+1, thread.non_sytematic_probability_sorted_.size()));\r\n\t\treturn thread.non_sytematic_probability_sorted_.at(last_systematic);\r\n\t}\r\n}\r\n\r\nvoid CoverageStats::UpdateCoverageAtSinglePosition(CoveragePosition &nuc_coverage, array &coverage, Dna5 ref_base){\r\n\tDna5 reverse_base = Complement::Dna5(ref_base);\r\n\r\n\tif( coverage_threshold_ > coverage.at(0) ){\r\n\t\tnuc_coverage.coverage_sufficient_.at(0) = false;\r\n\t}\r\n\tif( coverage_threshold_ > coverage.at(1) ){\r\n\t\tnuc_coverage.coverage_sufficient_.at(1) = false;\r\n\t}\r\n\r\n\tauto errors_forward = coverage.at(0) - nuc_coverage.coverage_forward_.at(ref_base);\r\n\tauto errors_reverse = coverage.at(1) - nuc_coverage.coverage_reverse_.at(reverse_base);\r\n\r\n\tauto cov_sum = coverage.at(0) + coverage.at(1);\r\n\tauto error_sum = errors_forward + errors_reverse;\r\n\r\n\t// Account for coverage and error_coverage at that position\r\n\t++tmp_coverage_.at( min(cov_sum, kMaxCoverage) );\r\n\t++tmp_coverage_stranded_.at(0).at( min(coverage.at(0), kMaxCoverage) );\r\n\t++tmp_coverage_stranded_.at(1).at( min(coverage.at(1), kMaxCoverage) );\r\n\tif(cov_sum){\r\n\t\t++tmp_coverage_stranded_percent_.at(0).at( Percent(coverage.at(0), cov_sum) );\r\n\t\t++tmp_coverage_stranded_percent_.at(1).at( Percent(coverage.at(1), cov_sum) );\r\n\r\n\t\tif(nuc_coverage.valid_){\r\n\t\t\t++tmp_error_coverage_.at( min(error_sum, kMaxCoverage) );\r\n\t\t\t++tmp_error_coverage_percent_.at( Percent(error_sum, cov_sum) );\r\n\t\t\tif(coverage.at(0) && coverage.at(1)){\r\n\t\t\t\t++tmp_error_coverage_percent_stranded_.at( Percent(errors_forward, coverage.at(0)) ).at( Percent(errors_reverse, coverage.at(1)) );\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\tif( 10 <= cov_sum ){\r\n\t\t\t++tmp_coverage_stranded_percent_min_cov_10_.at(0).at( Percent(coverage.at(0), cov_sum) );\r\n\t\t\t++tmp_coverage_stranded_percent_min_cov_10_.at(1).at( Percent(coverage.at(1), cov_sum) );\r\n\t\t\tif(nuc_coverage.valid_){\r\n\t\t\t\t++tmp_error_coverage_percent_min_cov_10_.at( Percent(error_sum, cov_sum) );\r\n\t\t\t}\r\n\r\n\t\t\tif( 20 <= cov_sum ){\r\n\t\t\t\t++tmp_coverage_stranded_percent_min_cov_20_.at(0).at( Percent(coverage.at(0), cov_sum) );\r\n\t\t\t\t++tmp_coverage_stranded_percent_min_cov_20_.at(1).at( Percent(coverage.at(1), cov_sum) );\r\n\t\t\t\tif(nuc_coverage.valid_){\r\n\t\t\t\t\t++tmp_error_coverage_percent_min_cov_20_.at( Percent(error_sum, cov_sum) );\r\n\r\n\t\t\t\t\tif(10 <= coverage.at(0) && 10 <= coverage.at(1)){\r\n\t\t\t\t\t\t++tmp_error_coverage_percent_stranded_min_strand_cov_10_.at( Percent(errors_forward, coverage.at(0)) ).at( Percent(errors_reverse, coverage.at(1)) );\r\n\t\t\t\t\t\tif(20 <= coverage.at(0) && 20 <= coverage.at(1)){\r\n\t\t\t\t\t\t\t++tmp_error_coverage_percent_stranded_min_strand_cov_20_.at( Percent(errors_forward, coverage.at(0)) ).at( Percent(errors_reverse, coverage.at(1)) );\r\n\t\t\t\t\t\t}\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t}\r\n\t}\r\n}\r\n\r\nvoid CoverageStats::UpdateDistances(uintSeqLen &distance_to_start_of_error_region, uintPercent &start_rate, uintPercent error_rate) const{\r\n\tif( distance_to_start_of_error_region ){\r\n\t\tif(start_rate < error_rate){\r\n\t\t\tdistance_to_start_of_error_region = 0;\r\n\t\t\tstart_rate = error_rate;\r\n\t\t}\r\n\t\telse if( ++distance_to_start_of_error_region >= reset_distance_ ){\r\n\t\t\tdistance_to_start_of_error_region = 0;\r\n\t\t\tstart_rate = 0;\r\n\t\t}\r\n\t}\r\n\telse{\r\n\t\tif(error_rate){ // If we assign an error rate it is systematic\r\n\t\t\tdistance_to_start_of_error_region = 1;\r\n\t\t\tstart_rate = error_rate;\r\n\t\t}\r\n\t}\r\n}\r\n\r\nCoverageStats::CoverageBlock *CoverageStats::CreateBlock(uintRefSeqId seq_id, uintSeqLen start_pos){\r\n\tCoverageBlock *new_block;\r\n\tif( reuse_mutex_.try_lock() ){\r\n\t\tif( reusable_blocks_.size() ){\r\n\t\t\tnew_block = reusable_blocks_.back();\r\n\t\t\treusable_blocks_.pop_back();\r\n\t\t\treuse_mutex_.unlock();\r\n\r\n\t\t\tnew_block->sequence_id_ = seq_id;\r\n\t\t\tnew_block->start_pos_ = start_pos;\r\n\t\t\tnew_block->previous_block_ = last_block_;\r\n\t\t\tnew_block->next_block_ = NULL;\r\n\t\t\tnew_block->coverage_.clear();\r\n\t\t\tnew_block->previous_coverage_.clear();\r\n\t\t\tnew_block->reads_.clear();\r\n\t\t\tnew_block->scheduled_for_processing_.clear();\r\n\t\t\tnew_block->processed_ = false;\r\n\t\t}\r\n\t\telse{\r\n\t\t\treuse_mutex_.unlock();\r\n\t\t\tnew_block = new CoverageBlock(seq_id, start_pos, last_block_);\r\n\t\t\tnew_block->previous_coverage_.reserve(maximum_read_length_on_reference_);\r\n\t\t}\r\n\t}\r\n\telse{\r\n\t\tnew_block = new CoverageBlock(seq_id, start_pos, last_block_);\r\n\t\tnew_block->previous_coverage_.reserve(maximum_read_length_on_reference_);\r\n\t}\r\n\r\n\tnew_block->reads_.reserve( (*last_block_).reads_.capacity() );\r\n\tnew_block->coverage_.resize( kBlockSize );\r\n\tnew_block->first_variant_id_ = (*last_block_).first_variant_id_;\r\n\t(*last_block_).next_block_ = new_block;\r\n\r\n\treturn new_block;\r\n}\r\n\r\nvoid CoverageStats::UpdateFirstVariant(CoverageBlock &block, const Reference &reference){\r\n\tif( reference.VariantPositionsLoaded() ){\r\n\t\twhile( block.first_variant_id_ < reference.VariantPositions(block.sequence_id_).size() && reference.VariantPositions(block.sequence_id_).at(block.first_variant_id_) < (*last_block_).start_pos_ ){\r\n\t\t\t++block.first_variant_id_;\r\n\t\t}\r\n\t}\r\n}\r\n\r\nbool CoverageStats::IsVariantPosition( intVariantId &cur_var, uintRefSeqId ref_seq_id, uintSeqLen pos, const Reference &reference ) const{\r\n\tif( reference.VariantPositionsLoaded() ){\r\n\t\twhile(cur_var < reference.VariantPositions(ref_seq_id).size() && reference.VariantPositions(ref_seq_id).at(cur_var) < pos){\r\n\t\t\t++cur_var;\r\n\t\t}\r\n\r\n\t\treturn cur_var < reference.VariantPositions(ref_seq_id).size() && reference.VariantPositions(ref_seq_id).at(cur_var) == pos;\r\n\t}\r\n\r\n\treturn false;\r\n}\r\n\r\nvoid CoverageStats::InitBlock(CoverageBlock &block, const Reference &reference){\r\n\t// Ensure that the block is not reaching over the end of the reference sequence\r\n\tif( reference.SequenceLength(block.sequence_id_) < block.start_pos_+kBlockSize){\r\n\t\tblock.coverage_.resize( reference.SequenceLength(block.sequence_id_)-block.start_pos_ );\r\n\t}\r\n\r\n\t// Check for invalid positions\r\n\tauto cur_var = block.first_variant_id_;\r\n\tfor(auto pos = block.start_pos_; pos < block.start_pos_+block.coverage_.size(); ++pos){\r\n\t\tif( IsN( at(reference.ReferenceSequence(block.sequence_id_), pos) ) || IsVariantPosition( cur_var, block.sequence_id_, pos, reference ) ){\r\n\t\t\tblock.coverage_.at(pos-block.start_pos_).valid_ = false;\r\n\t\t}\r\n\t}\r\n}\r\n\r\nvoid CoverageStats::ProcessBlock(CoverageBlock *block, const Reference &reference, ThreadData &thread){\r\n\tauto max_probability_systematic = GetPositionProbabilities(block, reference, thread);\r\n\r\n\tfor( uintSeqLen pos = 0; pos < block->coverage_.size(); ++pos ){\r\n\t\t// Set systematic errors and filter out variants\r\n\t\tarray errors = {block->coverage_.at(pos).coverage_forward_.at(block->coverage_.at(pos).dom_error_.at(0)), block->coverage_.at(pos).coverage_reverse_.at(block->coverage_.at(pos).dom_error_.at(1))};\r\n\t\tarray &coverage = thread.block_coverage_.at(pos);\r\n\t\tif(block->coverage_.at(pos).valid_){\r\n\t\t\tfor(uintTempSeq strand=0; strand < 2; ++strand){\r\n\t\t\t\tif(coverage.at(strand)){\r\n\t\t\t\t\tif( NonSystematicError(coverage.at(strand), errors.at(strand), thread.non_sytematic_probability_.at(pos).at(strand), max_probability_systematic) ){\r\n\t\t\t\t\t\tblock->coverage_.at(pos).dom_error_.at(strand) = 4;\r\n\t\t\t\t\t}\r\n\t\t\t\t\telse{\r\n\t\t\t\t\t\tblock->coverage_.at(pos).error_rate_.at(strand) = Percent(errors.at(strand),coverage.at(strand));\r\n\t\t\t\t\t}\r\n\t\t\t\t}\r\n\t\t\t}\r\n\r\n\t\t\tif( 4 != block->coverage_.at(pos).dom_error_.at(0) && 4 != block->coverage_.at(pos).dom_error_.at(1) && block->coverage_.at(pos).dom_error_.at(0) == Complement::Dna5(block->coverage_.at(pos).dom_error_.at(1)) ){\r\n\t\t\t\t// Variant not in the vcf file\r\n\t\t\t\tblock->coverage_.at(pos).valid_ = false;\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\t// Store coverage and errors\r\n\t\tUpdateCoverageAtSinglePosition( block->coverage_.at(pos), thread.block_coverage_.at(pos), at(reference.ReferenceSequence(block->sequence_id_), block->start_pos_ + pos) );\r\n\t}\r\n\r\n\t// Save information that is still needed in next_block_\r\n\tif( block->next_block_ && (*(block->next_block_)).sequence_id_ == block->sequence_id_ ){\r\n\t\t// Next block is on the same reference sequence\r\n\t\tuintSeqLen overlap(0);\r\n\t\tif( reset_distance_-1 > (*(block->next_block_)).start_pos_ - (block->start_pos_ + block->coverage_.size()) ){\r\n\t\t\toverlap = BasesWithInSysErrorResetDistance(block);\r\n\t\t}\r\n\r\n\t\tuintSeqLen overlap_start(1);\r\n\t\tif(block->start_pos_+kBlockSize == (*block->next_block_).start_pos_){\r\n\t\t\t// Blocks are directly connected, so reads that are shared between both potentially exist\r\n\t\t\tSetToMax(overlap, 2*maximum_read_length_on_reference_);\r\n\t\t}\r\n\t\telse{\r\n\t\t\t// No shared reads exists as blocks are not connected, only systematic errors have to be propagated taking the gap into account\r\n\t\t\toverlap_start += reset_distance_ - overlap;\r\n\t\t}\r\n\r\n\t\tfor(uintReadLen pos_before = 1; pos_before < overlap_start; ++pos_before){\r\n\t\t\t(*block->next_block_).previous_coverage_.emplace_back(); // Insert dummy as we have a short empty region between the two blocks\r\n\t\t}\r\n\t\tfor(uintReadLen pos_overlap = 1; pos_overlap <= overlap; ++pos_overlap){\r\n\t\t\t(*block->next_block_).previous_coverage_.emplace_back( block->coverage_.at(kBlockSize-pos_overlap) );\r\n\t\t}\r\n\t}\r\n\r\n\tblock->processed_ = true;\r\n}\r\n\r\nvoid CoverageStats::CountBlock(CoverageBlock *block, const Reference &reference){\r\n\t// Enter error_rates_ and dominant_errors_ in forward direction\r\n\tDna5 ref_base, prev_ref_base(4);\r\n\tDominantBase dom_ref_base;\r\n\tif(block->start_pos_){\r\n\t\tprev_ref_base = at(reference.ReferenceSequence(block->sequence_id_), block->start_pos_ - 1);\r\n\t\tdom_ref_base.Set( reference.ReferenceSequence(block->sequence_id_), block->start_pos_ );\r\n\t}\r\n\tuintSeqLen gc_bases = min(block->start_pos_, gc_range_);\r\n\tuintSeqLen n_count(0);\r\n\tuintSeqLen gc = reference.GCContentAbsolut( n_count, block->sequence_id_, block->start_pos_-gc_bases, block->start_pos_);\r\n\r\n\t// Initialize distance and start_rate\r\n\tuintSeqLen distance_to_start_of_error_region_forward(0);\r\n\tuintPercent start_rate_forward(0);\r\n\tif( block->previous_coverage_.size() ){\r\n\t\tfor(auto rev_pos = reset_distance_; --rev_pos; ){ // Index 0 is the one directly before this block, so correct order from low to high reference pos is reverse order in previous_coverage_\r\n\t\t\tUpdateDistances(distance_to_start_of_error_region_forward, start_rate_forward, (block->previous_coverage_.at(rev_pos).valid_ && block->previous_coverage_.at(rev_pos).coverage_sufficient_.at(0)?block->previous_coverage_.at(rev_pos).error_rate_.at(0):0));\r\n\t\t}\r\n\t}\r\n\r\n\tfor( uintSeqLen pos = 0; pos < block->coverage_.size(); ++pos ){\r\n\t\tref_base = at(reference.ReferenceSequence(block->sequence_id_), block->start_pos_ + pos);\r\n\r\n\t\t// Enter values\r\n\t\tif(block->coverage_.at(pos).valid_ && block->coverage_.at(pos).coverage_sufficient_.at(0)){\r\n\t\t\tAddSysError(ref_base, prev_ref_base, dom_ref_base.Get(), block->coverage_.at(pos).dom_error_.at(0), block->coverage_.at(pos).error_rate_.at(0), TransformDistanceToStartOfErrorRegion(distance_to_start_of_error_region_forward), SafePercent(gc,gc_bases-n_count), start_rate_forward);\r\n\t\t}\r\n\r\n\t\t// Set values for next iteration\r\n\t\tprev_ref_base = ref_base;\r\n\t\tdom_ref_base.Update( ref_base, reference.ReferenceSequence(block->sequence_id_), block->start_pos_ + pos);\r\n\t\tUpdateDistances(distance_to_start_of_error_region_forward, start_rate_forward, (block->coverage_.at(pos).valid_ && block->coverage_.at(pos).coverage_sufficient_.at(0)?block->coverage_.at(pos).error_rate_.at(0):0));\r\n\t\tauto new_pos = block->start_pos_ + pos;\r\n\t\tif(gc_basessequence_id_, new_pos ) ){\r\n\t\t\t\t++gc;\r\n\t\t\t}\r\n\t\t\telse if( reference.N( block->sequence_id_, new_pos ) ){\r\n\t\t\t\t++n_count;\r\n\t\t\t}\r\n\t\t}\r\n\t\telse{\r\n\t\t\treference.UpdateGC( gc, n_count, block->sequence_id_, new_pos - gc_bases, new_pos);\r\n\t\t}\r\n\t}\r\n\r\n\t// Enter error_rates_ and dominant_errors_ in reverse direction\r\n\tuintSeqLen last_pos = block->coverage_.size();\r\n\tif( NextBlockWithInSysErrorResetDistance(block) ){\r\n\t\t// Next block is at the same reference sequence and closer than reset_distance: Everything that is potentially in an error region starting in the next block cannot be handled yet\r\n\t\tlast_pos -= BasesWithInSysErrorResetDistance(block);\r\n\t}\r\n\t--last_pos; // Shift it from end pos to last valid pos\r\n\r\n\tConstDna5StringReverseComplement reversed_seq(reference.ReferenceSequence(block->sequence_id_));\r\n\tuintSeqLen ref_pos = reference.SequenceLength(block->sequence_id_) - (block->start_pos_ + last_pos) - 1;\r\n\tdom_ref_base.Clear();\r\n\tif(ref_pos){\r\n\t\tprev_ref_base = at(reversed_seq, ref_pos - 1);\r\n\t\tdom_ref_base.Set( reversed_seq, ref_pos );\r\n\t}\r\n\telse{\r\n\t\tprev_ref_base = 4;\r\n\t}\r\n\tgc_bases = min(ref_pos, gc_range_);\r\n\tn_count = 0;\r\n\tgc = reference.GCContentAbsolut( n_count, block->sequence_id_, block->start_pos_+last_pos + 1, block->start_pos_+last_pos + 1 + gc_bases); // + 1 because we want the bases after first one we handle (previous bases in reverse direction)\r\n\r\n\t// Initialize distance and start_rate\r\n\tuintSeqLen distance_to_start_of_error_region_reverse(0);\r\n\tuintPercent start_rate_reverse(0);\r\n\tfor( uintSeqLen pos = block->coverage_.size(); pos-- > last_pos; ){\r\n\t\tUpdateDistances(distance_to_start_of_error_region_reverse, start_rate_reverse, (block->coverage_.at(pos).valid_ && block->coverage_.at(pos).coverage_sufficient_.at(1)?block->coverage_.at(pos).error_rate_.at(1):0));\r\n\t}\r\n\r\n\tfor( uintSeqLen pos = last_pos+1; pos--; ){\r\n\t\tref_base = at(reversed_seq, ref_pos);\r\n\r\n\t\t// Enter values\r\n\t\tif(block->coverage_.at(pos).valid_ && block->coverage_.at(pos).coverage_sufficient_.at(1)){\r\n\t\t\tAddSysError(ref_base, prev_ref_base, dom_ref_base.Get(), block->coverage_.at(pos).dom_error_.at(1), block->coverage_.at(pos).error_rate_.at(1), TransformDistanceToStartOfErrorRegion(distance_to_start_of_error_region_reverse), SafePercent(gc,gc_bases-n_count), start_rate_reverse);\r\n\t\t}\r\n\r\n\t\t// Set values for next iteration\r\n\t\tprev_ref_base = ref_base;\r\n\t\tdom_ref_base.Update( ref_base, reversed_seq, ref_pos);\r\n\t\tUpdateDistances(distance_to_start_of_error_region_reverse, start_rate_reverse, (block->coverage_.at(pos).valid_ && block->coverage_.at(pos).coverage_sufficient_.at(1)?block->coverage_.at(pos).error_rate_.at(1):0));\r\n\t\tauto new_pos = block->start_pos_+pos;\r\n\t\tif(gc_basessequence_id_, new_pos ) ){\r\n\t\t\t\t++gc;\r\n\t\t\t}\r\n\t\t\telse if( reference.N( block->sequence_id_, new_pos ) ){\r\n\t\t\t\t++n_count;\r\n\t\t\t}\r\n\t\t}\r\n\t\telse{\r\n\t\t\treference.UpdateGC( gc, n_count, block->sequence_id_, new_pos + gc_bases, new_pos);\r\n\t\t}\r\n\t\tref_pos++;\r\n\t}\r\n\r\n\t// Handle values from last block that are not added yet due to the overlap based on reset_distance\r\n\tif( block->previous_coverage_.size() ){\r\n\t\tfor(auto rev_pos = 0; rev_pos < reset_distance_; ++rev_pos){ // Index 0 is the one directly before this block, so correct order from high to low reference pos is normal order in previous_coverage_\r\n\t\t\tref_base = at(reversed_seq, ref_pos);\r\n\r\n\t\t\t// Enter values\r\n\t\t\tif(block->previous_coverage_.at(rev_pos).valid_ && block->previous_coverage_.at(rev_pos).coverage_sufficient_.at(1)){\r\n\t\t\t\tAddSysError(ref_base, prev_ref_base, dom_ref_base.Get(), block->previous_coverage_.at(rev_pos).dom_error_.at(1), block->previous_coverage_.at(rev_pos).error_rate_.at(1), TransformDistanceToStartOfErrorRegion(distance_to_start_of_error_region_reverse), SafePercent(gc,gc_bases-n_count), start_rate_reverse);\r\n\t\t\t}\r\n\r\n\t\t\t// Set values for next iteration\r\n\t\t\tprev_ref_base = ref_base;\r\n\t\t\tdom_ref_base.Update( ref_base, reversed_seq, ref_pos);\r\n\t\t\tUpdateDistances(distance_to_start_of_error_region_reverse, start_rate_reverse, (block->previous_coverage_.at(rev_pos).valid_ && block->previous_coverage_.at(rev_pos).coverage_sufficient_.at(1)?block->previous_coverage_.at(rev_pos).error_rate_.at(1):0));\r\n\t\t\tauto new_pos = block->start_pos_-rev_pos-1;\r\n\t\t\tif(gc_basessequence_id_, new_pos ) ){\r\n\t\t\t\t\t++gc;\r\n\t\t\t\t}\r\n\t\t\t\telse if( reference.N( block->sequence_id_, new_pos ) ){\r\n\t\t\t\t\t++n_count;\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t\telse{\r\n\t\t\t\treference.UpdateGC( gc, n_count, block->sequence_id_, new_pos + gc_bases, new_pos);\r\n\t\t\t}\r\n\t\t\tref_pos++;\r\n\t\t}\r\n\t}\r\n}\r\n\r\nCoverageStats::CoverageBlock *CoverageStats::RemoveBlock(CoverageBlock *block){\r\n\treusable_blocks_.push_back(block);\r\n\r\n\treturn block->next_block_;\r\n}\r\n\r\nvoid CoverageStats::Prepare(uintCovCount average_coverage, uintReadLen average_read_length, uintReadLen maximum_read_length_on_reference){\r\n\treusable_blocks_.reserve(100);\r\n\r\n\tif(coverage_threshold_ > average_coverage/4 ){\r\n\t\tif( 1 < average_coverage/4 ){\r\n\t\t\tcoverage_threshold_ = average_coverage/4; // If half the average coverage is lower than the threshold, so we do not just take the statistics from repeat regions (the coverage per strand is already half the total coverage, so in total we have a quarter)\r\n\t\t}\r\n\t\telse{\r\n\t\t\tcoverage_threshold_ = 1; // Do not set anything without having coverage\r\n\t\t}\r\n\t}\r\n\tgc_range_ = average_read_length/2;\r\n\treset_distance_ = average_read_length;\r\n\r\n\tmaximum_read_length_on_reference_ = maximum_read_length_on_reference;\r\n\r\n\tauto max_error_dist = TransformDistanceToStartOfErrorRegion(reset_distance_-1)+1;\r\n\tfor( auto ref_base=4; ref_base--; ){\r\n\t\tfor( auto prev_ref_base=5; prev_ref_base--; ){\r\n\t\t\tfor( auto dom_base=4; dom_base--; ){\r\n\t\t\t\tSetDimensions( tmp_dominant_errors_by_distance_.at(ref_base).at(prev_ref_base).at(dom_base), max_error_dist, 5 );\r\n\t\t\t\tSetDimensions( tmp_dominant_errors_by_gc_.at(ref_base).at(prev_ref_base).at(dom_base), 101, 5 );\r\n\t\t\t\tSetDimensions( tmp_gc_by_distance_de_.at(ref_base).at(prev_ref_base).at(dom_base), max_error_dist, 101 );\r\n\t\t\t\tSetDimensions( tmp_dominant_errors_by_start_rates_.at(ref_base).at(prev_ref_base).at(dom_base), 101, 5 );\r\n\t\t\t\tSetDimensions( tmp_start_rates_by_distance_de_.at(ref_base).at(prev_ref_base).at(dom_base), max_error_dist, 101 );\r\n\t\t\t\tSetDimensions( tmp_start_rates_by_gc_de_.at(ref_base).at(prev_ref_base).at(dom_base), 101, 101 );\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\tfor( auto dom_error=5; dom_error--; ){\r\n\t\t\tSetDimensions( tmp_error_rates_by_distance_.at(ref_base).at(dom_error), max_error_dist, 101 );\r\n\t\t\tSetDimensions( tmp_error_rates_by_gc_.at(ref_base).at(dom_error), 101, 101 );\r\n\t\t\tSetDimensions( tmp_gc_by_distance_er_.at(ref_base).at(dom_error), max_error_dist, 101 );\r\n\t\t\tSetDimensions( tmp_error_rates_by_start_rates_.at(ref_base).at(dom_error), 101, 101 );\r\n\t\t\tSetDimensions( tmp_start_rates_by_distance_er_.at(ref_base).at(dom_error), max_error_dist, 101 );\r\n\t\t\tSetDimensions( tmp_start_rates_by_gc_er_.at(ref_base).at(dom_error), 101, 101 );\r\n\t\t}\r\n\t}\r\n\r\n\t// Setting the size, those aren't atomics\r\n\ttmp_block_error_rate_.resize(101);\r\n\ttmp_block_percent_systematic_.resize(101);\r\n\ttmp_systematic_error_p_values_.resize(kPValueHistBins+1);\r\n\r\n\ttmp_coverage_.resize(kMaxCoverage+1);\r\n\tfor( auto strand=2; strand--; ){\r\n\t\ttmp_coverage_stranded_.at(strand).resize(kMaxCoverage+1);\r\n\t\ttmp_coverage_stranded_percent_.at(strand).resize(101);\r\n\t\ttmp_coverage_stranded_percent_min_cov_10_.at(strand).resize(101);\r\n\t\ttmp_coverage_stranded_percent_min_cov_20_.at(strand).resize(101);\r\n\t}\r\n\ttmp_error_coverage_.resize(kMaxCoverage+1);\r\n\ttmp_error_coverage_percent_.resize(101);\r\n\ttmp_error_coverage_percent_min_cov_10_.resize(101);\r\n\ttmp_error_coverage_percent_min_cov_20_.resize(101);\r\n\tSetDimensions( tmp_error_coverage_percent_stranded_, 101, 101 );\r\n\tSetDimensions( tmp_error_coverage_percent_stranded_min_strand_cov_10_, 101, 101 );\r\n\tSetDimensions( tmp_error_coverage_percent_stranded_min_strand_cov_20_, 101, 101 );\r\n}\r\n\r\n\r\nCoverageStats::CoverageBlock *CoverageStats::FindBlock(uintRefSeqId ref_seq_id, uintSeqLen ref_pos){\r\n\tCoverageBlock *block = last_block_;\r\n\twhile(block->sequence_id_ > ref_seq_id){\r\n\t\tblock = block->previous_block_;\r\n\t}\r\n\twhile(block->start_pos_ > ref_pos){\r\n\t\tblock = block->previous_block_;\r\n\t}\r\n\r\n\treturn block;\r\n}\r\n\r\nbool CoverageStats::EnsureSpace(uintRefSeqId ref_seq_id, uintSeqLen start_pos, uintSeqLen end_pos, FullRecord *record, Reference &reference){\r\n\t// Make sure the variation for the given reference sequence is already loaded\r\n\tif( reference.VariantPositionsLoaded() && !reference.VariantPositionsLoadedForSequence(ref_seq_id) ){\r\n\t\tlock_guard lock(variant_loading_mutex_);\r\n\t\tif(!reference.ReadVariantPositions(ref_seq_id+1)){ // End is specified, so to get the current one +1 is needed\r\n\t\t\treturn false;\r\n\t\t}\r\n\t}\r\n\r\n\tSetToMin(end_pos, reference.SequenceLength(ref_seq_id));\r\n\tif( last_block_ ){\r\n\t\t// As reads are ordered by position, we can jump over not covered regions\r\n\t\tif((*last_block_).sequence_id_ < ref_seq_id){\r\n\t\t\t// Account for the not covered region at the end of the last reference sequence\r\n\t\t\tCountEmptyEndOfSequence(reference);\r\n\r\n\t\t\t// Account for the not covered reference sequences in between\r\n\t\t\tfor( auto seq_id = ref_seq_id; --seq_id > (*last_block_).sequence_id_ ; ){\r\n\t\t\t\tzero_coverage_region_ += reference.SequenceLength(seq_id);\r\n\t\t\t\texcluded_bases_ += reference.SumExcludedBases(seq_id);\r\n\t\t\t\tnum_exclusion_regions_ += reference.NumExcludedRegions(seq_id);\r\n\t\t\t}\r\n\r\n\t\t\t// Account for the not covered region in the current reference sequence before the first position\r\n\t\t\tzero_coverage_region_ += start_pos;\r\n\t\t\texcluded_bases_ += reference.SumExcludedBases(ref_seq_id);\r\n\t\t\tnum_exclusion_regions_ += reference.NumExcludedRegions(ref_seq_id);\r\n\r\n\t\t\t// Create first block of next reference sequence\r\n\t\t\tlast_block_ = CreateBlock(ref_seq_id, start_pos);\r\n\t\t\tInitBlock(*last_block_, reference);\r\n\t\t}\r\n\t\telse if((*last_block_).start_pos_+kBlockSize < start_pos){\r\n\t\t\t// Account for the empty coverage region\r\n\t\t\tzero_coverage_region_ += start_pos - (*last_block_).start_pos_ - kBlockSize;\r\n\r\n\t\t\t// Create first block after the empty coverage region\r\n\t\t\tlast_block_ = CreateBlock(ref_seq_id, start_pos);\r\n\t\t\tUpdateFirstVariant(*last_block_, reference);\r\n\t\t\tInitBlock(*last_block_, reference);\r\n\t\t}\r\n\t}\r\n\telse{\r\n\t\t// Initialize first block\r\n\t\t// Account for the not covered reference sequences before the first position\r\n\t\tfor( auto seq_id = ref_seq_id; seq_id-- ; ){\r\n\t\t\tzero_coverage_region_ += reference.SequenceLength(seq_id);\r\n\t\t\texcluded_bases_ += reference.SumExcludedBases(seq_id);\r\n\t\t\tnum_exclusion_regions_ += reference.NumExcludedRegions(seq_id);\r\n\t\t}\r\n\r\n\t\t// Account for the not covered region in the current reference sequence before the first position\r\n\t\tzero_coverage_region_ += start_pos;\r\n\t\texcluded_bases_ += reference.SumExcludedBases(ref_seq_id);\r\n\t\tnum_exclusion_regions_ += reference.NumExcludedRegions(ref_seq_id);\r\n\r\n\t\t// Create new block\r\n\t\tauto new_block = new CoverageBlock(ref_seq_id, start_pos, NULL);\r\n\t\tnew_block->coverage_.resize( kBlockSize );\r\n\t\tnew_block->previous_coverage_.reserve(maximum_read_length_on_reference_);\r\n\t\tnew_block->reads_.reserve( 2*kBlockSize );\r\n\t\tInitBlock(*new_block, reference);\r\n\r\n\t\tfirst_block_ = new_block;\r\n\t\tlast_block_ = new_block;\r\n\t}\r\n\r\n\t// Create blocks until end_pos is reached\r\n\twhile((*last_block_).start_pos_+kBlockSize < end_pos){\r\n\t\tlast_block_ = CreateBlock((*last_block_).sequence_id_, (*last_block_).start_pos_+kBlockSize);\r\n\t\tUpdateFirstVariant(*last_block_, reference);\r\n\t\tInitBlock(*last_block_, reference);\r\n\t}\r\n\r\n\t// Add read to last block it potentially overlaps (most of the time it is last_block_, but it is not guaranteed so use FindBlock)\r\n\tauto reg_block = FindBlock(ref_seq_id, end_pos);\r\n\treg_block->reads_.push_back(record);\r\n\r\n\treturn true;\r\n}\r\n\r\nvoid CoverageStats::AddFragment( uintRefSeqId ref_seq_id, uintSeqLen ref_pos, CoverageBlock *&block ){\r\n\tif(block){\r\n\t\twhile( block->start_pos_+kBlockSize <= ref_pos || block->sequence_id_ < ref_seq_id ){\r\n\t\t\tblock = block->next_block_;\r\n\t\t}\r\n\t}\r\n\telse{\r\n\t\tblock = FindBlock(ref_seq_id, ref_pos);\r\n\t}\r\n\t++(block->unprocessed_fragments_);\r\n}\r\n\r\nvoid CoverageStats::RemoveFragment( uintRefSeqId ref_seq_id, uintSeqLen ref_pos, CoverageBlock *&block, uintFragCount &processed_fragments ){\r\n\tif(block){\r\n\t\tif( block->start_pos_+kBlockSize <= ref_pos || block->sequence_id_ < ref_seq_id ){\r\n\t\t\tauto old_block = block;\r\n\t\t\tdo{\r\n\t\t\t\tblock = block->next_block_;\r\n\t\t\t} while( block->start_pos_+kBlockSize <= ref_pos || block->sequence_id_ < ref_seq_id );\r\n\r\n\t\t\t// Subtract processed_fragments from old_block only after finding the new block, as it potentially allows other threads to remove old_block\r\n\t\t\told_block->unprocessed_fragments_ -= processed_fragments;\r\n\t\t\tprocessed_fragments = 0;\r\n\t\t}\r\n\t\telse if(block->start_pos_ > ref_pos){ // Here we don't need to compare to sequence_id as pairs with reads on different contigs are not added to unprocessed_fragments_\r\n\t\t\t// Here we don't need to care if we set unprocessed_fragments_ to zero before finding the new block, as it is before this block and must have at least one unprocessed_fragments_\r\n\t\t\tblock->unprocessed_fragments_ -= processed_fragments;\r\n\t\t\tprocessed_fragments = 0;\r\n\r\n\t\t\tdo{\r\n\t\t\t\tblock = block->previous_block_;\r\n\t\t\t} while( block->start_pos_ > ref_pos );\r\n\t\t}\r\n\t}\r\n\telse{\r\n\t\tblock = FindBlock(ref_seq_id, ref_pos);\r\n\t}\r\n\r\n\t++processed_fragments; // Store processed fragments instead of subtracting them directly so that last fragments from this batch can be removed after the CleanUp step, so at least one block remains during CleanUp\r\n}\r\n\r\nreseq::uintRefSeqId CoverageStats::CleanUp(uintSeqLen &still_needed_position, Reference &reference, QualityStats &qualities, ErrorStats &errors, uintQual phred_quality_offset, CoverageBlock *cov_block, uintFragCount &processed_fragments, ThreadData &thread){\r\n\t// Process all blocks that are not needed anymore\r\n\tCoverageBlock *until_block = first_block_;\r\n\twhile(!until_block->unprocessed_fragments_){ // The unprocessed fragments from the current read haven't been removed yet, so end of list cannot be reached\r\n\t\tif( !until_block->scheduled_for_processing_.test_and_set() ){\r\n\t\t\tProcessBlock(until_block, reference, thread);\r\n\t\t}\r\n\r\n\t\tuntil_block = until_block->next_block_;\r\n\t}\r\n\r\n\t// Remove pointers to blocks that are not needed anymore\r\n\tuintRefSeqId still_needed_reference_sequence(0);\r\n\tstill_needed_position = 0;\r\n\t{\r\n\t\tlock_guard lock(clean_up_mutex_);\r\n\r\n\t\tuntil_block = first_block_;\r\n\t\twhile(!until_block->unprocessed_fragments_ && until_block->processed_){ // The unprocessed fragments from the current read haven't been removed yet, so end of list cannot be reached\r\n\t\t\tuntil_block = until_block->next_block_;\r\n\t\t\tfirst_block_ = until_block;\r\n\t\t}\r\n\t\tuntil_block = until_block->previous_block_;\r\n\r\n\t\tif(until_block){\r\n\t\t\t(*first_block_).previous_block_ = NULL;\r\n\t\t\tuntil_block->next_block_ = NULL;\r\n\t\t\tstill_needed_reference_sequence = (*first_block_).sequence_id_;\r\n\t\t\tstill_needed_position = (*first_block_).start_pos_;\r\n\t\t}\r\n\t}\r\n\r\n\t// Subtract processed_fragments from cov_block after until_block is determined, as it potentially allows to remove cov_block\r\n\tcov_block->unprocessed_fragments_ -= processed_fragments;\r\n\tprocessed_fragments = 0;\r\n\r\n\t// Count and remove blocks, where we removed the pointers before\r\n\tif(until_block){\r\n\t\twhile(until_block->previous_block_){\r\n\t\t\tCountBlock(until_block, reference);\r\n\t\t\tfor( auto rec : until_block->reads_){\r\n\t\t\t\tEvalRead(rec, until_block, reference, qualities, errors, phred_quality_offset);\r\n\t\t\t}\r\n\t\t\tuntil_block = until_block->previous_block_;\r\n\t\t}\r\n\r\n\t\tCountBlock(until_block, reference);\r\n\t\tfor( auto rec : until_block->reads_){\r\n\t\t\tEvalRead(rec, until_block, reference, qualities, errors, phred_quality_offset);\r\n\t\t}\r\n\r\n\t\tlock_guard lock(reuse_mutex_);\r\n\t\tdo{\r\n\t\t\tuntil_block = RemoveBlock(until_block);\r\n\t\t} while(until_block);\r\n\t}\r\n\r\n\treference.ClearVariants(still_needed_reference_sequence);\r\n\r\n\treturn still_needed_reference_sequence;\r\n}\r\n\r\nbool CoverageStats::PreLoadVariants(Reference &reference){\r\n\tif(reference.VariantPositionsLoaded() && !reference.VariantPositionsCompletelyLoaded() && !reference.VariantPositionsLoadedForSequence((*last_block_).sequence_id_+2)){\r\n\t\tif( variant_loading_mutex_.try_lock() ){\r\n\t\t\tif( !reference.ReadVariantPositions((*last_block_).sequence_id_+2) ){\r\n\t\t\t\tvariant_loading_mutex_.unlock();\r\n\t\t\t\treturn false;\r\n\t\t\t}\r\n\r\n\t\t\tvariant_loading_mutex_.unlock();\r\n\t\t}\r\n\t}\r\n\r\n\treturn true;\r\n}\r\n\r\nbool CoverageStats::Finalize(const Reference &reference, QualityStats &qualities, ErrorStats &errors, uintQual phred_quality_offset, mutex &print_mutex, ThreadData &thread){\r\n\t// Remove all reusable_blocks_ as they are not needed anymore\r\n\tfor( auto block : reusable_blocks_ ){\r\n\t\tdelete block;\r\n\t}\r\n\tauto final_num_blocks = reusable_blocks_.size();\r\n\treusable_blocks_.clear();\r\n\r\n\tif(first_block_){\r\n\t\tif( last_block_ ){\r\n\t\t\t// Update not covered regions after last block\r\n\t\t\tCountEmptyEndOfSequence(reference);\r\n\r\n\t\t\tfor( auto seq_id = (*last_block_).sequence_id_+1; seq_id < reference.NumberSequences(); ++seq_id ){\r\n\t\t\t\tzero_coverage_region_ += reference.SequenceLength(seq_id);\r\n\t\t\t\texcluded_bases_ += reference.SumExcludedBases(seq_id);\r\n\t\t\t\tnum_exclusion_regions_ += reference.NumExcludedRegions(seq_id);\r\n\t\t\t}\r\n\r\n\t\t\t// Update coverage of remaining blocks and delete them\r\n\t\t\tCoverageBlock *first_block = first_block_; // Use non atomic pointer for the finalization\r\n\t\t\tfirst_block_ = NULL;\r\n\t\t\tlast_block_ = NULL;\r\n\r\n\t\t\twhile(first_block->next_block_){\r\n\t\t\t\t++final_num_blocks;\r\n\t\t\t\tProcessBlock(first_block, reference, thread);\r\n\t\t\t\tCountBlock(first_block, reference);\r\n\t\t\t\tfor( auto rec : first_block->reads_){\r\n\t\t\t\t\tEvalRead(rec, first_block, reference, qualities, errors, phred_quality_offset);\r\n\t\t\t\t}\r\n\r\n\t\t\t\tfirst_block = first_block->next_block_;\r\n\t\t\t\tdelete first_block->previous_block_;\r\n\t\t\t}\r\n\r\n\t\t\t++final_num_blocks;\r\n\t\t\tProcessBlock(first_block, reference, thread);\r\n\t\t\tCountBlock(first_block, reference);\r\n\t\t\tfor( auto rec : first_block->reads_){\r\n\t\t\t\tEvalRead(rec, first_block, reference, qualities, errors, phred_quality_offset);\r\n\t\t\t}\r\n\t\t\tdelete first_block;\r\n\t\t}\r\n\t\telse{\r\n\t\t\tprintErr << \"first_block_ set, but last_block_ not. Something dodgy is going on here\" << std::endl;\r\n\t\t\treturn false;\r\n\t\t}\r\n\t}\r\n\telse{\r\n\t\tif( last_block_ ){\r\n\t\t\tprintErr << \"last_block_ set, but first_block_ not. Something dodgy is going on here\" << std::endl;\r\n\t\t\treturn false;\r\n\t\t}\r\n\t\telse{\r\n\t\t\t// There have been reads in the file (or Finalize wouldn't have been called), but none made the criteria to add coverage\r\n\t\t\tSetAllZero(reference);\r\n\t\t}\r\n\t}\r\n\r\n\t{\r\n\t\tlock_guard lock(print_mutex);\r\n\t\tprintInfo << \"Needed to create \" << final_num_blocks << \" coverage blocks.\" << std::endl;\r\n\t\tauto total_size = reference.TotalSize();\r\n\t\tprintInfo << \"Excluded \" << excluded_bases_ << \" of \" << total_size << \" bases [\" << static_cast(Percent(excluded_bases_, total_size)) << \"%] in the reference due to contig ends and N's over \" << num_exclusion_regions_ << \" regions.\" << std::endl;\r\n\t}\r\n\r\n\tApplyZeroCoverageRegion();\r\n\r\n\tfor( auto ref_base=4; ref_base--; ){\r\n\t\tfor( auto prev_ref_base=5; prev_ref_base--; ){\r\n\t\t\tfor( auto dom_base=4; dom_base--; ){\r\n\t\t\t\tdominant_errors_by_distance_.at(ref_base).at(prev_ref_base).at(dom_base).Acquire( tmp_dominant_errors_by_distance_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tdominant_errors_by_gc_.at(ref_base).at(prev_ref_base).at(dom_base).Acquire( tmp_dominant_errors_by_gc_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tgc_by_distance_de_.at(ref_base).at(prev_ref_base).at(dom_base).Acquire( tmp_gc_by_distance_de_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tdominant_errors_by_start_rates_.at(ref_base).at(prev_ref_base).at(dom_base).Acquire( tmp_dominant_errors_by_start_rates_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tstart_rates_by_distance_de_.at(ref_base).at(prev_ref_base).at(dom_base).Acquire( tmp_start_rates_by_distance_de_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tstart_rates_by_gc_de_.at(ref_base).at(prev_ref_base).at(dom_base).Acquire( tmp_start_rates_by_gc_de_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\tfor( auto dom_error=5; dom_error--; ){\r\n\t\t\terror_rates_by_distance_.at(ref_base).at(dom_error).Acquire( tmp_error_rates_by_distance_.at(ref_base).at(dom_error) );\r\n\t\t\terror_rates_by_gc_.at(ref_base).at(dom_error).Acquire( tmp_error_rates_by_gc_.at(ref_base).at(dom_error) );\r\n\t\t\tgc_by_distance_er_.at(ref_base).at(dom_error).Acquire( tmp_gc_by_distance_er_.at(ref_base).at(dom_error) );\r\n\t\t\terror_rates_by_start_rates_.at(ref_base).at(dom_error).Acquire( tmp_error_rates_by_start_rates_.at(ref_base).at(dom_error) );\r\n\t\t\tstart_rates_by_distance_er_.at(ref_base).at(dom_error).Acquire( tmp_start_rates_by_distance_er_.at(ref_base).at(dom_error) );\r\n\t\t\tstart_rates_by_gc_er_.at(ref_base).at(dom_error).Acquire( tmp_start_rates_by_gc_er_.at(ref_base).at(dom_error) );\r\n\t\t}\r\n\t}\r\n\r\n\tblock_error_rate_.Acquire( tmp_block_error_rate_ );\r\n\tblock_percent_systematic_.Acquire( tmp_block_percent_systematic_ );\r\n\ttmp_systematic_error_p_values_.at(kPValueHistBins-1) += tmp_systematic_error_p_values_.at(kPValueHistBins); // Last bin only contains p-values of exactly 1.0, so merge it with the one before\r\n\ttmp_systematic_error_p_values_.at(kPValueHistBins) = 0;\r\n\tsystematic_error_p_values_.Acquire( tmp_systematic_error_p_values_ );\r\n\r\n\tcoverage_.Acquire( tmp_coverage_ );\r\n\tfor( auto strand=2; strand--; ){\r\n\t\tcoverage_stranded_.at(strand).Acquire( tmp_coverage_stranded_.at(strand) );\r\n\t\tcoverage_stranded_percent_.at(strand).Acquire( tmp_coverage_stranded_percent_.at(strand) );\r\n\t\tcoverage_stranded_percent_min_cov_10_.at(strand).Acquire( tmp_coverage_stranded_percent_min_cov_10_.at(strand) );\r\n\t\tcoverage_stranded_percent_min_cov_20_.at(strand).Acquire( tmp_coverage_stranded_percent_min_cov_20_.at(strand) );\r\n\t}\r\n\terror_coverage_.Acquire( tmp_error_coverage_ );\r\n\terror_coverage_percent_.Acquire( tmp_error_coverage_percent_ );\r\n\terror_coverage_percent_min_cov_10_.Acquire( tmp_error_coverage_percent_min_cov_10_ );\r\n\terror_coverage_percent_min_cov_20_.Acquire( tmp_error_coverage_percent_min_cov_20_ );\r\n\terror_coverage_percent_stranded_.Acquire( tmp_error_coverage_percent_stranded_ );\r\n\terror_coverage_percent_stranded_min_strand_cov_10_.Acquire( tmp_error_coverage_percent_stranded_min_strand_cov_10_ );\r\n\terror_coverage_percent_stranded_min_strand_cov_20_.Acquire( tmp_error_coverage_percent_stranded_min_strand_cov_20_ );\r\n\r\n\treturn true;\r\n}\r\n\r\nvoid CoverageStats::Shrink(){\r\n\tfor( auto ref_base=4; ref_base--; ){\r\n\t\tfor( auto prev_ref_base=5; prev_ref_base--; ){\r\n\t\t\tfor( auto dom_base=4; dom_base--; ){\r\n\t\t\t\tShrinkVect( dominant_errors_by_distance_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tShrinkVect( dominant_errors_by_gc_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tShrinkVect( gc_by_distance_de_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tShrinkVect( dominant_errors_by_start_rates_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tShrinkVect( start_rates_by_distance_de_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t\tShrinkVect( start_rates_by_gc_de_.at(ref_base).at(prev_ref_base).at(dom_base) );\r\n\t\t\t}\r\n\t\t}\r\n\r\n\t\tfor( auto dom_error=5; dom_error--; ){\r\n\t\t\tShrinkVect( error_rates_by_distance_.at(ref_base).at(dom_error) );\r\n\t\t\tShrinkVect( error_rates_by_gc_.at(ref_base).at(dom_error) );\r\n\t\t\tShrinkVect( gc_by_distance_er_.at(ref_base).at(dom_error) );\r\n\t\t\tShrinkVect( error_rates_by_start_rates_.at(ref_base).at(dom_error) );\r\n\t\t\tShrinkVect( start_rates_by_distance_er_.at(ref_base).at(dom_error) );\r\n\t\t\tShrinkVect( start_rates_by_gc_er_.at(ref_base).at(dom_error) );\r\n\t\t}\r\n\t}\r\n\r\n\tShrinkVect( error_coverage_percent_stranded_ );\r\n\tShrinkVect( error_coverage_percent_stranded_min_strand_cov_10_ );\r\n\tShrinkVect( error_coverage_percent_stranded_min_strand_cov_20_ );\r\n}\r\n\r\nvoid CoverageStats::PreparePlotting(){\r\n\terror_rates_by_distance_sum_.Clear();\r\n\terror_rates_by_gc_sum_.Clear();\r\n\tfor( int ref_base=4; ref_base--; ){\r\n\t\tfor( int dom_error=5; dom_error--; ){\r\n\t\t\terror_rates_by_distance_sum_ += error_rates_by_distance_.at(ref_base).at(dom_error);\r\n\t\t\terror_rates_by_gc_sum_ += error_rates_by_gc_.at(ref_base).at(dom_error);\r\n\t\t}\r\n\t}\r\n}\r\n", "meta": {"hexsha": "63d5ade4f63f04922ad151ccf9c30a6b34145089", "size": 49541, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "reseq/CoverageStats.cpp", "max_stars_repo_name": "schmeing/ReSeq", "max_stars_repo_head_hexsha": "053b8d1af635bf0019dc818b3eb0825023abd037", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 29.0, "max_stars_repo_stars_event_min_datetime": "2020-07-18T22:04:45.000Z", "max_stars_repo_stars_event_max_datetime": "2021-12-22T16:48:53.000Z", "max_issues_repo_path": "reseq/CoverageStats.cpp", "max_issues_repo_name": "schmeing/ReSeq", "max_issues_repo_head_hexsha": "053b8d1af635bf0019dc818b3eb0825023abd037", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 15.0, "max_issues_repo_issues_event_min_datetime": "2020-08-03T18:10:44.000Z", "max_issues_repo_issues_event_max_datetime": "2021-12-03T12:52:16.000Z", "max_forks_repo_path": "reseq/CoverageStats.cpp", "max_forks_repo_name": "schmeing/ReSequenceR", "max_forks_repo_head_hexsha": "053b8d1af635bf0019dc818b3eb0825023abd037", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2021-03-08T15:09:21.000Z", "max_forks_repo_forks_event_max_datetime": "2021-03-25T14:14:43.000Z", "avg_line_length": 45.2016423358, "max_line_length": 311, "alphanum_fraction": 0.7365414505, "num_tokens": 12810, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. 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NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.38121956625615, "lm_q1q2_score": 0.20990230106678576}} {"text": "#ifndef VEXCL_MBA_HPP\n#define VEXCL_MBA_HPP\n\n/*\nThe MIT License\n\nCopyright (c) 2012-2018 Denis Demidov \n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n*/\n\n/**\n * \\file vexcl/mba.hpp\n * \\author Denis Demidov \n * \\brief Scattered data interpolation with multilevel B-Splines.\n */\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \n\n// Include boost.preprocessor header if variadic templates are not available.\n// Also include it if we use gcc v4.6.\n// This is required due to bug http://gcc.gnu.org/bugzilla/show_bug.cgi?id=35722\n#if defined(BOOST_NO_VARIADIC_TEMPLATES) || (defined(__GNUC__) && !defined(__clang__) && __GNUC__ == 4 && __GNUC_MINOR__ == 6)\n# include \n# ifndef VEXCL_MAX_ARITY\n# define VEXCL_MAX_ARITY BOOST_PROTO_MAX_ARITY\n# endif\n#endif\nnamespace vex {\n\nstruct mba_terminal {};\n\ntypedef vector_expression<\n typename boost::proto::terminal< mba_terminal >::type\n > mba_terminal_expression;\n\ntemplate \nstruct mba_interp : public mba_terminal_expression {\n typedef typename MBA::value_type value_type;\n\n const MBA &cloud;\n const ExprTuple coord;\n\n mba_interp(const MBA &cloud, const ExprTuple coord)\n : cloud(cloud), coord(coord) {}\n};\n\nnamespace detail {\n // Compile time value of N^M.\n template \n struct power : std::integral_constant::value> {};\n\n template \n struct power : std::integral_constant {};\n\n // Nested loop counter of compile-time size (M loops of size N).\n template \n class scounter {\n public:\n scounter() : idx(0) {\n std::fill(i.begin(), i.end(), static_cast(0));\n }\n\n size_t operator[](size_t d) const {\n return i[d];\n }\n\n scounter& operator++() {\n for(size_t d = M; d--; ) {\n if (++i[d] < N) break;\n i[d] = 0;\n }\n\n ++idx;\n\n return *this;\n }\n\n operator size_t() const {\n return idx;\n }\n\n bool valid() const {\n return idx < power::value;\n }\n private:\n size_t idx;\n std::array i;\n };\n\n // Nested loop counter of run-time size (M loops of given sizes).\n template \n class dcounter {\n public:\n dcounter(const std::array &N)\n : idx(0),\n size(std::accumulate(N.begin(), N.end(),\n static_cast(1), std::multiplies())),\n N(N)\n {\n std::fill(i.begin(), i.end(), static_cast(0));\n }\n\n size_t operator[](size_t d) const {\n return i[d];\n }\n\n dcounter& operator++() {\n for(size_t d = M; d--; ) {\n if (++i[d] < N[d]) break;\n i[d] = 0;\n }\n\n ++idx;\n\n return *this;\n }\n\n operator size_t() const {\n return idx;\n }\n\n bool valid() const {\n return idx < size;\n }\n private:\n size_t idx, size;\n std::array N, i;\n };\n} // namespace detail\n\n/// Scattered data interpolation with multilevel B-Splines.\ntemplate \nclass mba {\n public:\n typedef real value_type;\n typedef std::array point;\n typedef std::array index;\n\n static const size_t ndim = NDIM;\n\n std::vector< backend::command_queue > queue;\n std::vector< backend::device_vector > phi;\n point xmin, hinv;\n index n, stride;\n\n /** Creates the approximation functor.\n * `cmin` and `cmax` specify the domain boundaries, `coo` and `val`\n * contain coordinates and values of the data points. `grid` is the\n * initial control grid size. The approximation hierarchy will have at\n * most `levels` and will stop when the desired approximation precision\n * `tol` will be reached.\n */\n mba(\n const std::vector &queue,\n const point &cmin, const point &cmax,\n const std::vector &coo, std::vector val,\n std::array grid, size_t levels = 8, real tol = 1e-8\n ) : queue(queue)\n {\n init(cmin, cmax, coo.begin(), coo.end(), val.begin(), grid, levels, tol);\n }\n\n /** Creates the approximation functor.\n * `cmin` and `cmax` specify the domain boundaries. Coordinates and\n * values of the data points are passed as iterator ranges. `grid` is\n * the initial control grid size. The approximation hierarchy will have\n * at most `levels` and will stop when the desired approximation\n * precision `tol` will be reached.\n */\n template \n mba(\n const std::vector &queue,\n const point &cmin, const point &cmax,\n CooIter coo_begin, CooIter coo_end, ValIter val_begin,\n std::array grid, size_t levels = 8, real tol = 1e-8\n ) : queue(queue)\n {\n init(cmin, cmax, coo_begin, coo_end, val_begin, grid, levels, tol);\n }\n\n#if !defined(BOOST_NO_VARIADIC_TEMPLATES) && ((!defined(__GNUC__) || (__GNUC__ > 4 || __GNUC__ == 4 && __GNUC_MINOR__ > 6)) || defined(__clang__))\n /// Provide interpolated values at given coordinates.\n template \n auto operator()(const Expr&... expr) const ->\n mba_interp< mba, boost::tuple >\n {\n static_assert(sizeof...(Expr) == NDIM, \"Wrong number of parameters\");\n return mba_interp< mba, boost::tuple >(*this, boost::tie(expr...));\n }\n#else\n\n#define VEXCL_FUNCALL_OPERATOR(z, n, data) \\\n template \\\n mba_interp > \\\n operator()(BOOST_PP_ENUM_BINARY_PARAMS(n, const Expr, &expr)) { \\\n return mba_interp >( \\\n *this, boost::tie(BOOST_PP_ENUM_PARAMS(n, expr))); \\\n }\n\nBOOST_PP_REPEAT_FROM_TO(1, 10, VEXCL_FUNCALL_OPERATOR, ~)\n\n#undef VEXCL_FUNCALL_OPERATOR\n#endif\n private:\n template \n void init(\n const point &cmin, const point &cmax,\n CooIter coo_begin, CooIter coo_end, ValIter val_begin,\n std::array grid, size_t levels = 8, real tol = 1e-8\n )\n {\n for(size_t k = 0; k < NDIM; ++k)\n assert(grid[k] > 1);\n\n double res0 = std::accumulate(\n val_begin, val_begin + (coo_end - coo_begin),\n static_cast(0),\n [](real sum, real v) { return sum + v * v; }\n );\n\n std::unique_ptr psi(\n new lattice(cmin, cmax, grid, coo_begin, coo_end, val_begin)\n );\n double res = psi->update_data(coo_begin, coo_end, val_begin);\n#ifdef VEXCL_MBA_VERBOSE\n std::cout << \"level 0: res = \" << std::scientific << res << std::endl;\n#endif\n\n for (size_t k = 1; (res > res0 * tol) && (k < levels); ++k) {\n for(size_t d = 0; d < NDIM; ++d) grid[d] = 2 * grid[d] - 1;\n\n std::unique_ptr f(\n new lattice(cmin, cmax, grid, coo_begin, coo_end, val_begin)\n );\n res = f->update_data(coo_begin, coo_end, val_begin);\n#ifdef VEXCL_MBA_VERBOSE\n std::cout << \"level \" << k << std::scientific << \": res = \" << res << std::endl;\n#endif\n\n f->append_refined(*psi);\n psi = std::move(f);\n }\n\n xmin = psi->xmin;\n hinv = psi->hinv;\n n = psi->n;\n stride = psi->stride;\n\n phi.reserve(queue.size());\n\n for(auto q = queue.begin(); q != queue.end(); ++q)\n phi.push_back( backend::device_vector(\n *q, psi->phi.size(), psi->phi.data(), backend::MEM_READ_ONLY\n ) );\n }\n\n // Control lattice.\n struct lattice {\n point xmin, hinv;\n index n, stride;\n std::vector phi;\n\n template \n lattice(\n const point &cmin, const point &cmax, std::array grid,\n CooIter coo_begin, CooIter coo_end, ValIter val_begin\n ) : xmin(cmin), n(grid)\n {\n for(size_t d = 0; d < NDIM; ++d) {\n hinv[d] = (grid[d] - 1) / (cmax[d] - cmin[d]);\n xmin[d] -= 1 / hinv[d];\n n[d] += 2;\n }\n\n stride[NDIM - 1] = 1;\n for(size_t d = NDIM - 1; d--; )\n stride[d] = stride[d + 1] * n[d + 1];\n\n std::vector delta(n[0] * stride[0], 0.0);\n std::vector omega(n[0] * stride[0], 0.0);\n\n auto p = coo_begin;\n auto v = val_begin;\n for(; p != coo_end; ++p, ++v) {\n if (!contained(cmin, cmax, *p)) continue;\n\n index i;\n point s;\n\n for(size_t d = 0; d < NDIM; ++d) {\n real u = ((*p)[d] - xmin[d]) * hinv[d];\n i[d] = static_cast(std::floor(u) - 1);\n s[d] = u - std::floor(u);\n }\n\n std::array::value> w;\n real sw2 = 0;\n\n for(detail::scounter<4, NDIM> d; d.valid(); ++d) {\n real buf = 1;\n for(size_t k = 0; k < NDIM; ++k)\n buf *= B(d[k], s[k]);\n\n w[d] = buf;\n sw2 += buf * buf;\n }\n\n for(detail::scounter<4, NDIM> d; d.valid(); ++d) {\n real phi = (*v) * w[d] / sw2;\n\n size_t idx = 0;\n for(size_t k = 0; k < NDIM; ++k) {\n assert(i[k] + d[k] < n[k]);\n\n idx += (i[k] + d[k]) * stride[k];\n }\n\n real w2 = w[d] * w[d];\n\n assert(idx < delta.size());\n\n delta[idx] += w2 * phi;\n omega[idx] += w2;\n }\n }\n\n phi.resize(omega.size());\n\n for(auto w = omega.begin(), d = delta.begin(), f = phi.begin();\n w != omega.end();\n ++w, ++d, ++f\n )\n {\n if (std::fabs(*w) < 1e-32)\n *f = 0;\n else\n *f = (*d) / (*w);\n }\n }\n\n // Get interpolated value at given position.\n real operator()(const point &p) const {\n index i;\n point s;\n\n for(size_t d = 0; d < NDIM; ++d) {\n real u = (p[d] - xmin[d]) * hinv[d];\n i[d] = static_cast(std::floor(u) - 1);\n s[d] = u - std::floor(u);\n }\n\n real f = 0;\n\n for(detail::scounter<4, NDIM> d; d.valid(); ++d) {\n real w = 1;\n for(size_t k = 0; k < NDIM; ++k)\n w *= B(d[k], s[k]);\n\n f += w * get(i, d);\n }\n\n return f;\n }\n\n // Subtract interpolated values from data points.\n template \n real update_data(\n CooIter coo_begin, CooIter coo_end, ValIter val_begin\n ) const\n {\n auto c = coo_begin;\n auto v = val_begin;\n\n real res = 0;\n\n for(; c != coo_end; ++c, ++v) {\n *v -= (*this)(*c);\n\n res += (*v) * (*v);\n }\n\n return res;\n }\n\n // Refine r and append it to the current control lattice.\n void append_refined(const lattice &r) {\n static const std::array s = {{\n 0.125, 0.500, 0.750, 0.500, 0.125\n }};\n\n for(detail::dcounter i(r.n); i.valid(); ++i) {\n real f = r.phi[i];\n for(detail::scounter<5, NDIM> d; d.valid(); ++d) {\n index j;\n bool skip = false;\n size_t idx = 0;\n for(size_t k = 0; k < NDIM; ++k) {\n j[k] = 2 * i[k] + d[k] - 3;\n if (j[k] >= n[k]) { skip = true; break; }\n\n idx += j[k] * stride[k];\n }\n\n if (skip) continue;\n\n real c = 1;\n for(size_t k = 0; k < NDIM; ++k) c *= s[d[k]];\n\n phi[idx] += f * c;\n }\n }\n }\n\n private:\n // Value of k-th B-Spline at t.\n static inline real B(size_t k, real t) {\n assert(0 <= t && t < 1);\n assert(k < 4);\n\n switch (k) {\n case 0:\n return (t * (t * (-t + 3) - 3) + 1) / 6;\n case 1:\n return (t * t * (3 * t - 6) + 4) / 6;\n case 2:\n return (t * (t * (-3 * t + 3) + 3) + 1) / 6;\n case 3:\n default:\n return t * t * t / 6;\n }\n }\n\n // x is within [xmin, xmax].\n static bool contained(\n const point &xmin, const point &xmax, const point &x)\n {\n for(size_t d = 0; d < NDIM; ++d) {\n static const real eps = 1e-12;\n\n if (x[d] - eps < xmin[d]) return false;\n if (x[d] + eps >= xmax[d]) return false;\n }\n\n return true;\n }\n\n // Get value of phi at index (i + d).\n template \n inline real get(const index &i, const Shift &d) const {\n size_t idx = 0;\n\n for(size_t k = 0; k < NDIM; ++k) {\n size_t j = i[k] + d[k];\n\n if (j >= n[k]) return 0;\n idx += j * stride[k];\n }\n\n return phi[idx];\n }\n };\n};\n\nnamespace traits {\n\ntemplate <>\nstruct is_vector_expr_terminal< mba_terminal > : std::true_type {};\n\ntemplate <>\nstruct proto_terminal_is_value< mba_terminal > : std::true_type {};\n\ntemplate \nstruct terminal_preamble< mba_interp > {\n static void get(backend::source_generator &src,\n const mba_interp&,\n const backend::command_queue&, const std::string &prm_name,\n detail::kernel_generator_state_ptr)\n {\n typedef typename MBA::value_type real;\n\n std::string B = prm_name + \"_B\";\n\n src.begin_function(B + \"0\");\n src.begin_function_parameters();\n src.template parameter(\"t\");\n src.end_function_parameters();\n src.new_line() << \"return (t * (t * (-t + 3) - 3) + 1) / 6;\";\n src.end_function();\n\n src.begin_function(B + \"1\");\n src.begin_function_parameters();\n src.template parameter(\"t\");\n src.end_function_parameters();\n src.new_line() << \"return (t * t * (3 * t - 6) + 4) / 6;\";\n src.end_function();\n\n src.begin_function(B + \"2\");\n src.begin_function_parameters();\n src.template parameter(\"t\");\n src.end_function_parameters();\n src.new_line() << \"return (t * (t * (-3 * t + 3) + 3) + 1) / 6;\";\n src.end_function();\n\n src.begin_function(B + \"3\");\n src.begin_function_parameters();\n src.template parameter(\"t\");\n src.end_function_parameters();\n src.new_line() << \"return t * t * t / 6;\";\n src.end_function();\n\n src.begin_function(prm_name + \"_mba\");\n src.begin_function_parameters();\n\n for(size_t k = 0; k < MBA::ndim; ++k)\n src.template parameter(\"x\") << k;\n\n for(size_t k = 0; k < MBA::ndim; ++k) {\n src.template parameter(\"c\" + std::to_string(k));\n src.template parameter(\"h\" + std::to_string(k));\n src.parameter(\"n\" + std::to_string(k));\n src.parameter(\"m\" + std::to_string(k));\n }\n\n src.template parameter< global_ptr >(\"phi\");\n src.end_function_parameters();\n src.new_line() << type_name() << \" u;\";\n for(size_t k = 0; k < MBA::ndim; ++k) {\n src.new_line() << \"u = (x\" << k << \" - c\" << k << \") * h\" << k << \";\";\n src.new_line() << type_name() << \" i\" << k << \" = floor(u) - 1;\";\n src.new_line() << type_name() << \" s\" << k << \" = u - floor(u);\";\n }\n src.new_line() << type_name() << \" f = 0;\";\n src.new_line() << type_name() << \" j, idx;\";\n\n for(detail::scounter<4,MBA::ndim> d; d.valid(); ++d) {\n src.new_line() << \"idx = 0;\";\n for(size_t k = 0; k < MBA::ndim; ++k) {\n src.new_line() << \"j = i\" << k << \" + \" << d[k] << \";\";\n src.new_line() << \"if (j < n\" << k << \")\";\n src.open(\"{\").new_line() << \"idx += j * m\" << k << \";\";\n }\n\n src.new_line() << \"f += \";\n for(size_t k = 0; k < MBA::ndim; ++k) {\n if (k) src << \" * \";\n src << B << d[k] << \"(s\" << k << \")\";\n }\n\n src << \" * phi[idx];\";\n\n for(size_t k = 0; k < MBA::ndim; ++k)\n src.close(\"}\");\n }\n src.new_line() << \"return f;\";\n src.end_function();\n }\n};\n\ntemplate \nstruct kernel_param_declaration< mba_interp > {\n static void get(backend::source_generator &src,\n const mba_interp &term,\n const backend::command_queue &queue, const std::string &prm_name,\n detail::kernel_generator_state_ptr state)\n {\n typedef typename MBA::value_type real;\n boost::fusion::for_each(term.coord, prmdecl(src, queue, prm_name, state));\n\n for(size_t k = 0; k < MBA::ndim; ++k) {\n src.template parameter(prm_name + \"_c\" + std::to_string(k));\n src.template parameter(prm_name + \"_h\" + std::to_string(k));\n src.parameter(prm_name + \"_n\" + std::to_string(k));\n src.parameter(prm_name + \"_m\" + std::to_string(k));\n }\n\n src.parameter< global_ptr >(prm_name + \"_phi\");\n }\n\n struct prmdecl {\n backend::source_generator &s;\n const backend::command_queue &queue;\n const std::string &prm_name;\n detail::kernel_generator_state_ptr state;\n mutable int pos;\n\n prmdecl(backend::source_generator &s,\n const backend::command_queue &queue, const std::string &prm_name,\n detail::kernel_generator_state_ptr state\n ) : s(s), queue(queue), prm_name(prm_name), state(state), pos(0)\n {}\n\n template \n void operator()(const Expr &expr) const {\n std::ostringstream prefix;\n prefix << prm_name << \"_x\" << pos;\n detail::declare_expression_parameter ctx(s, queue, prefix.str(), state);\n detail::extract_terminals()(boost::proto::as_child(expr), ctx);\n\n pos++;\n }\n };\n};\n\ntemplate \nstruct local_terminal_init< mba_interp > {\n static void get(backend::source_generator &src,\n const mba_interp &term,\n const backend::command_queue &queue, const std::string &prm_name,\n detail::kernel_generator_state_ptr state)\n {\n boost::fusion::for_each(term.coord, local_init(src, queue, prm_name, state));\n }\n\n struct local_init {\n backend::source_generator &s;\n const backend::command_queue &queue;\n const std::string &prm_name;\n detail::kernel_generator_state_ptr state;\n mutable int pos;\n\n local_init(backend::source_generator &s,\n const backend::command_queue &queue, const std::string &prm_name,\n detail::kernel_generator_state_ptr state\n ) : s(s), queue(queue), prm_name(prm_name), state(state), pos(0)\n {}\n\n template \n void operator()(const Expr &expr) const {\n std::ostringstream prefix;\n prefix << prm_name << \"_x\" << pos;\n\n detail::output_local_preamble init_ctx(s, queue, prefix.str(), state);\n boost::proto::eval(boost::proto::as_child(expr), init_ctx);\n\n pos++;\n }\n };\n};\n\ntemplate \nstruct partial_vector_expr< mba_interp > {\n static void get(backend::source_generator &src,\n const mba_interp &term,\n const backend::command_queue &queue, const std::string &prm_name,\n detail::kernel_generator_state_ptr state)\n {\n src << prm_name << \"_mba(\";\n\n boost::fusion::for_each(term.coord, buildexpr(src, queue, prm_name, state));\n\n for(size_t k = 0; k < MBA::ndim; ++k) {\n src << \", \" << prm_name << \"_c\" << k\n << \", \" << prm_name << \"_h\" << k\n << \", \" << prm_name << \"_n\" << k\n << \", \" << prm_name << \"_m\" << k;\n }\n\n src << \", \" << prm_name << \"_phi)\";\n }\n\n struct buildexpr {\n backend::source_generator &s;\n const backend::command_queue &queue;\n const std::string &prm_name;\n detail::kernel_generator_state_ptr state;\n mutable int pos;\n\n buildexpr(backend::source_generator &s,\n const backend::command_queue &queue, const std::string &prm_name,\n detail::kernel_generator_state_ptr state\n ) : s(s), queue(queue), prm_name(prm_name), state(state), pos(0)\n {}\n\n template \n void operator()(const Expr &expr) const {\n if(pos) s << \", \";\n\n std::ostringstream prefix;\n prefix << prm_name << \"_x\" << pos;\n\n detail::vector_expr_context ctx(s, queue, prefix.str(), state);\n boost::proto::eval(boost::proto::as_child(expr), ctx);\n\n pos++;\n }\n };\n};\n\ntemplate \nstruct kernel_arg_setter< mba_interp > {\n static void set(const mba_interp &term,\n backend::kernel &kernel, unsigned part, size_t index_offset,\n detail::kernel_generator_state_ptr state)\n {\n\n boost::fusion::for_each(term.coord,\n setargs(kernel, part, index_offset, state));\n\n for(size_t k = 0; k < MBA::ndim; ++k) {\n kernel.push_arg(term.cloud.xmin[k]);\n kernel.push_arg(term.cloud.hinv[k]);\n kernel.push_arg(term.cloud.n[k]);\n kernel.push_arg(term.cloud.stride[k]);\n }\n kernel.push_arg(term.cloud.phi[part]);\n }\n\n struct setargs {\n backend::kernel &kernel;\n unsigned part;\n size_t index_offset;\n detail::kernel_generator_state_ptr state;\n\n setargs(\n backend::kernel &kernel, unsigned part, size_t index_offset,\n detail::kernel_generator_state_ptr state\n )\n : kernel(kernel), part(part), index_offset(index_offset), state(state)\n {}\n\n template \n void operator()(const Expr &expr) const {\n detail::set_expression_argument ctx(kernel, part, index_offset, state);\n detail::extract_terminals()( boost::proto::as_child(expr), ctx);\n }\n };\n};\n\ntemplate \nstruct expression_properties< mba_interp > {\n static void get(const mba_interp &term,\n std::vector &queue_list,\n std::vector &partition,\n size_t &size\n )\n {\n boost::fusion::for_each(term.coord, extrprop(queue_list, partition, size));\n }\n\n struct extrprop {\n std::vector &queue_list;\n std::vector &partition;\n size_t &size;\n\n extrprop(std::vector &queue_list,\n std::vector &partition, size_t &size\n ) : queue_list(queue_list), partition(partition), size(size)\n {}\n\n template \n void operator()(const Expr &expr) const {\n if (queue_list.empty()) {\n detail::get_expression_properties prop;\n detail::extract_terminals()(boost::proto::as_child(expr), prop);\n\n queue_list = prop.queue;\n partition = prop.part;\n size = prop.size;\n }\n }\n };\n};\n\n} //namespace traits\n\n} // namespace vex\n\n\n#endif\n", "meta": {"hexsha": "50be317007f2acbee3f5200b31da369f3a52e7c4", "size": 27908, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external_libraries/vexcl/vexcl/mba.hpp", "max_stars_repo_name": "lkusch/Kratos", "max_stars_repo_head_hexsha": "e8072d8e24ab6f312765185b19d439f01ab7b27b", "max_stars_repo_licenses": ["BSD-4-Clause"], "max_stars_count": 778.0, "max_stars_repo_stars_event_min_datetime": "2017-01-27T16:29:17.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-30T03:01:51.000Z", "max_issues_repo_path": "external_libraries/vexcl/vexcl/mba.hpp", "max_issues_repo_name": "lkusch/Kratos", "max_issues_repo_head_hexsha": "e8072d8e24ab6f312765185b19d439f01ab7b27b", "max_issues_repo_licenses": ["BSD-4-Clause"], "max_issues_count": 6634.0, "max_issues_repo_issues_event_min_datetime": "2017-01-15T22:56:13.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T15:03:36.000Z", "max_forks_repo_path": "external_libraries/vexcl/vexcl/mba.hpp", "max_forks_repo_name": "lkusch/Kratos", "max_forks_repo_head_hexsha": "e8072d8e24ab6f312765185b19d439f01ab7b27b", "max_forks_repo_licenses": ["BSD-4-Clause"], "max_forks_count": 224.0, "max_forks_repo_forks_event_min_datetime": "2017-02-07T14:12:49.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-06T23:09:34.000Z", "avg_line_length": 34.8414481898, "max_line_length": 146, "alphanum_fraction": 0.4960226458, "num_tokens": 6660, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5506073655352404, "lm_q2_score": 0.3812195662561499, "lm_q1q2_score": 0.2099023010667857}} {"text": "/*\n * @file pppBayesTree.cpp\n * @brief Iterative GPS Range/Phase Estimator with collected data\n * @author Ryan Watson & Jason Gross\n */\n\n// GTSAM related includes.\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n// GPSTK\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n\n// BOOST\n#include \n#include \n#include \n#include \n#include \n\n// STD\n#include \n#include \n#include \n#include \n#include \n#include \n\nusing namespace std;\nusing namespace gtsam;\nusing namespace gpstk;\nusing namespace boost;\nusing namespace std::chrono;\nnamespace NM = gtsam::noiseModel;\nnamespace po = boost::program_options;\ntypedef noiseModel::Diagonal diagNoise;\n\n// Intel Threading Building Block\n#ifdef GTSAM_USE_TBB\n #include \n #undef max // TBB seems to include windows.h and we don't want these macros\n #undef min\n#endif\n\nusing symbol_shorthand::X; // nonBiasStates ( dx, dy, dz, trop, cb )\nusing symbol_shorthand::G; // bias states ( Phase Biases )\n\nint main(int argc, char* argv[])\n{\n // define std out print color\n vector prn_vec;\n vector data;\n const string red(\"\\033[0;31m\");\n const string green(\"\\033[0;32m\");\n string confFile, gnssFile, station, sp3File, p1p2File, p1c1File, antennaModel;\n string rnx_file, nav_file, sp3_file, out_file, antexFile;\n double xn, yn, zn, xp, yp, range, phase, rho, minElev, weightFactor;\n double antennaOffSetH, antennaOffSetE, antennaOffSetN;\n int startKey(0), currKey, startEpoch(0), svn, doy;\n int nThreads(-1), phase_break, break_count(0), nextKey, dec_int, itsBelowThree=0, count=0;\n bool printECEF, printENU, printAmb, printUpdateRate, first_ob(true), usingP1(false);\n\n cout.precision(12);\n\n po::options_description desc(\"Available options\");\n desc.add_options()\n (\"help,h\", \"Print help message\")\n (\"confFile,c\", po::value(&confFile)->default_value(\"\"),\n \"Input config file\" )\n (\"out\", po::value(&out_file)->default_value(\"\"),\n \"output file.\")\n (\"usingP1\", \"Are you using P1 instead of C1?\");\n po::variables_map vm;\n po::store(po::command_line_parser(argc, argv).options(desc).run(), vm);\n po::notify(vm);\n\n ConfDataReader confReader;\n confReader.open(confFile);\n\n if (confFile.empty() ) {\n cout << red << \"\\n\\n Currently, you need to provide a conf file \\n\"\n << \"\\n\\n\" << green << desc << endl;\n }\n\n while ( (station = confReader.getEachSection()) != \"\" )\n {\n // Fetch nominal station location [m]\n xn = confReader.fetchListValueAsDouble(\"nominalECEF\",station);\n yn = confReader.fetchListValueAsDouble(\"nominalECEF\",station);\n zn = confReader.fetchListValueAsDouble(\"nominalECEF\",station);\n // day of year ( used for Niell Trop model)\n doy = confReader.getValueAsInt(\"DOY\", station);\n // Elevation cut-off\n minElev = confReader.getValueAsDouble(\"minElev\", station);\n // Code/carrier ratio\n weightFactor = confReader.getValueAsDouble(\"weightFactor\", station);\n // Data file names\n gnssFile = confReader(\"rnxFile\", station);\n sp3File = confReader(\"sp3File\", station);\n p1p2File = confReader(\"p1p2\", station);\n p1c1File = confReader(\"p1c1\", station);\n // Print statements\n printENU = confReader.getValueAsBoolean(\"printENU\", station);\n printAmb = confReader.getValueAsBoolean(\"printAmb\", station);\n printECEF = confReader.getValueAsBoolean(\"printECEF\", station);\n printUpdateRate = confReader.getValueAsBoolean(\"printUpdateRate\", station);\n }\n\n usingP1 = (vm.count(\"usingP1\")>0);\n\n Point3 nomXYZ(xn, yn, zn);\n Point3 prop_xyz = nomXYZ;\n\n\n #ifdef GTSAM_USE_TBB\n std::auto_ptr init;\n if(nThreads > 0) {\n init.reset(new tbb::task_scheduler_init(nThreads));\n }\n else\n cout << green << \" \\n\\n Using threads for all processors\" << endl;\n #else\n if(nThreads > 0) {\n cout << red <<\" \\n\\n GTSAM is not compiled with TBB, so threading is\"\n << \" disabled and the --threads option cannot be used.\"\n << endl;\n exit(1);\n }\n #endif\n\n ISAM2DoglegParams doglegParams;\n ISAM2Params parameters;\n parameters.relinearizeThreshold = 0.1;\n parameters.relinearizeSkip = 100;\n ISAM2 isam(parameters);\n\n double output_time = 0.0;\n double rw = 2.5;\n double rangeWeight = pow(rw,2);\n double phaseWeight = pow(rw*1/weightFactor,2);\n\n string value;\n\n nonBiasStates prior_nonBias = (gtsam::Vector(5) << 0.0, 0.0, 0.0, 0.0, 0.0).finished();\n\n phaseBias bias_state(Z_1x1);\n gnssStateVector phase_arc(Z_34x1);\n gnssStateVector bias_counter(Z_34x1);\n for (int i=1; i<34; i++) {bias_counter(i) = bias_counter(i-1) + 10000; }\n\n nonBiasStates initEst(Z_5x1);\n nonBiasStates between_nonBias_State(Z_5x1);\n\n Values initial_values;\n Values result;\n\n noiseModel::Diagonal::shared_ptr nonBias_InitNoise = noiseModel::Diagonal::Variances((gtsam::Vector(5) << 10.0, 10.0, 10.0, 3e8, 1e-1).finished());\n\n noiseModel::Diagonal::shared_ptr nonBias_ProcessNoise = noiseModel::Diagonal::Variances((gtsam::Vector(5) << 0.1, 0.1, 0.1, 3e6, 3e-5).finished());\n\n noiseModel::Diagonal::shared_ptr initNoise = noiseModel::Diagonal::Variances((gtsam::Vector(1) << 100).finished());\n\n string obs_path = findExampleDataFile(gnssFile);\n string p1p2_path = findExampleDataFile(p1p2File);\n string p1c1_path = findExampleDataFile(p1c1File);\n\n if ( obs_path.empty() )\n {\n cout << \" Must pass in obs file !!! \" << desc << endl;\n exit(1);\n }\n if ( sp3File.empty() )\n {\n cout << \" Must pass in ephemeris file !!! \" << desc << endl;\n exit(1);\n }\n // Declare a \"SP3EphemerisStore\" object to handle precise ephemeris\n SP3EphemerisStore SP3EphList;\n\n size_t pos = 0;\n string path, token;\n string delimiter = \" \";\n while ((pos = sp3File.find(delimiter)) != string::npos) {\n path = findExampleDataFile(sp3File.substr(0,pos));\n SP3EphList.loadFile(path);\n sp3File.erase(0, pos + delimiter.length());\n }\n path = findExampleDataFile(sp3File);\n SP3EphList.loadFile(path);\n\n // Set flags to reject satellites with bad or absent positional\n // values or clocks\n SP3EphList.rejectBadPositions(true);\n SP3EphList.rejectBadClocks(true);\n\n // Create the input observation file stream\n Rinex3ObsStream rin(obs_path);\n\n // station nominal position\n Position nominalPos(xn, yn, zn);\n\n CorrectCodeBiases corrCode;\n corrCode.setDCBFile(p1p2_path, p1c1_path);\n\n if (!usingP1) {\n corrCode.setUsingC1(true);\n }\n\n\n // This is the GNSS data structure that will hold all the\n // GNSS-related information\n gnssRinex gRin;\n\n RequireObservables requireObs;\n requireObs.addRequiredType(TypeID::L1);\n requireObs.addRequiredType(TypeID::L2);\n\n SimpleFilter pObsFilter;\n pObsFilter.setFilteredType(TypeID::C1);\n\n if ( usingP1 )\n {\n requireObs.addRequiredType(TypeID::P1);\n pObsFilter.addFilteredType(TypeID::P1);\n requireObs.addRequiredType(TypeID::P2);\n pObsFilter.addFilteredType(TypeID::P2);\n }\n else\n {\n requireObs.addRequiredType(TypeID::C1);\n pObsFilter.addFilteredType(TypeID::C1);\n requireObs.addRequiredType(TypeID::P2);\n pObsFilter.addFilteredType(TypeID::P2);\n }\n\n // Declare a couple of basic modelers\n BasicModel basic(nominalPos, SP3EphList);\n basic.setMinElev(minElev);\n\n // Object to correct for SP3 Sat Phase-center offset\n ComputeSatPCenter svPcenter(SP3EphList, nominalPos);\n\n // Objects to mark cycle slips\n MWCSDetector markCSMW; // Checks Merbourne-Wubbena cycle slip\n\n // object def several linear combinations\n LinearCombinations comb;\n\n // Object to compute linear combinations for cycle slip detection\n ComputeLinear linear1;\n if ( usingP1 )\n {\n linear1.addLinear(comb.pdeltaCombination);\n linear1.addLinear(comb.mwubbenaCombination);\n }\n else\n {\n linear1.addLinear(comb.pdeltaCombWithC1);\n linear1.addLinear(comb.mwubbenaCombWithC1);\n }\n linear1.addLinear(comb.ldeltaCombination);\n linear1.addLinear(comb.liCombination);\n\n ComputeLinear linear2;\n\n // Read if we should use C1 instead of P1\n if ( usingP1 ) { linear2.addLinear(comb.pcCombination); }\n else { linear2.addLinear(comb.pcCombWithC1); }\n linear2.addLinear(comb.lcCombination);\n\n LICSDetector2 markCSLI2; // Checks LI cycle slips\n\n // Object to keep track of satellite arcs\n SatArcMarker markArc;\n markArc.setDeleteUnstableSats(true);\n\n // Objects to compute gravitational delay effects\n GravitationalDelay grDelay(nominalPos);\n\n // Object to remove eclipsed satellites\n EclipsedSatFilter eclipsedSV;\n\n //Object to compute wind-up effect\n ComputeWindUp windup( SP3EphList, nominalPos );\n\n // Object to compute prefit-residuals\n ComputeLinear linear3(comb.pcPrefit);\n linear3.addLinear(comb.lcPrefit);\n\n TypeIDSet tset;\n tset.insert(TypeID::prefitC);\n tset.insert(TypeID::prefitL);\n\n // Declare a NeillTropModel object, setting the defaults\n NeillTropModel neillTM( nominalPos.getAltitude(),\n nominalPos.getGeodeticLatitude(),\n doy);\n\n // Objects to compute the tropospheric data\n ComputeTropModel computeTropo(neillTM);\n\n // initialize factor graph\n NonlinearFactorGraph *graph = new NonlinearFactorGraph();\n\n auto start = std::chrono::steady_clock::now();\n auto end = std::chrono::steady_clock::now();\n // Loop over all data epochs\n while(rin >> gRin)\n {\n TimeSystem sys;\n sys.fromString(\"GPS\");\n CommonTime time(gRin.header.epoch);\n time.setTimeSystem(sys);\n GPSWeekSecond gpstime( time );\n\n // update nominal ECEF with propogated pos.\n NeillTropModel neillTM( nominalPos.getAltitude(),\n nominalPos.getGeodeticLatitude(),\n doy);\n try\n {\n gRin >> requireObs // Check if required observations are present\n >> pObsFilter // Filter out spurious data\n >> linear1 // Compute linear combinations to detect CS\n >> markCSLI2 // Mark cycle slips\n >> markArc // Keep track of satellite arcs\n >> basic // Compute the basic components of model\n >> eclipsedSV // Remove satellites in eclipse\n >> grDelay // Compute gravitational delay\n >> svPcenter // Computer delta for sat. phase center\n >> corrCode // Correct for differential code biases\n >> windup // phase windup correction\n >> computeTropo // neill trop function\n >> linear2 // Compute ionosphere-free combinations\n >> linear3; // Compute prefit residuals\n }\n catch(Exception& e) {continue; }\n catch(...)\n {\n cerr << \"Unknown exception at epoch: \" << time << endl;\n continue;\n }\n\n // Iterate through the GNSS Data Structure\n satTypeValueMap::const_iterator it;\n typeValueMap::const_iterator itObs;\n if ( itsBelowThree > 0 )\n {\n itsBelowThree = 0;\n continue;\n }\n\n if (gRin.body.size() == 0) { continue; }\n\n // Loop over all observed sats at current epoch\n for (it = gRin.body.begin(); it!= gRin.body.end(); it++)\n {\n\n start = std::chrono::steady_clock::now();\n svn = ((*it).first).id;\n double satX, satY, satZ;\n satX = (*it).second.getValue(TypeID::satX);\n satY = (*it).second.getValue(TypeID::satY);\n satZ = (*it).second.getValue(TypeID::satZ);\n Point3 satXYZ = Point3(satX,satY,satZ);\n double range, rangeRes;\n range = (*it).second.getValue(TypeID::PC);\n rangeRes = (*it).second.getValue(TypeID::prefitC);\n double phase, phaseRes;\n phase = (*it).second.getValue(TypeID::LC);\n phaseRes = (*it).second.getValue(TypeID::prefitL);\n int phase_break;\n phase_break = (*it).second.getValue(TypeID::satArc);\n\n if (first_ob) {\n startKey = count; first_ob=false;\n graph->add(PriorFactor(X(count), initEst, nonBias_InitNoise));\n initial_values.insert(X(count), initEst);\n }\n\n if (phase_arc[svn]!=phase_break)\n {\n bias_state[0] = phase - range;\n if (count > startKey) { bias_counter[svn] = bias_counter[svn] +1; }\n graph->add(PriorFactor(G(bias_counter[svn]), bias_state, initNoise));\n initial_values.insert(G(bias_counter[svn]), bias_state);\n phase_arc[svn] = phase_break;\n }\n // Generate pseudorange factor\n PseudorangeFactor gpsRangeFactor(X(count), rangeRes, satXYZ, nomXYZ, diagNoise::Variances( (gtsam::Vector(1) << elDepWeight(satXYZ, nomXYZ, rangeWeight)).finished()));\n\n graph->add(gpsRangeFactor);\n\n // Generate phase factor\n PhaseFactor gpsPhaseFactor(X(count), G(bias_counter[svn]), phaseRes, satXYZ, nomXYZ, diagNoise::Variances( (gtsam::Vector(1) << elDepWeight(satXYZ, nomXYZ, phaseWeight)).finished() ));\n\n graph->add(gpsPhaseFactor);\n prn_vec.push_back(svn);\n }\n if (count > startKey ) {\n graph->add(BetweenFactor(X(count), X(count-1), between_nonBias_State, nonBias_ProcessNoise));\n }\n isam.update(*graph, initial_values);\n result = isam.calculateEstimate();\n\n end = std::chrono::steady_clock::now();\n\n\n prior_nonBias = result.at(X(count));\n Point3 delta_xyz = (gtsam::Vector(3) << prior_nonBias.x(), prior_nonBias.y(), prior_nonBias.z()).finished();\n Position deltaPos(prior_nonBias.x(), prior_nonBias.y(), prior_nonBias.z());\n prop_xyz = nomXYZ - delta_xyz;\n nominalPos -= deltaPos;\n\n if (printECEF) {\n cout << \"xyz \" << gpstime.week << \" \" << gpstime.sow << \" \" << prop_xyz.x() << \" \" << prop_xyz.y() << \" \" << prop_xyz.z() << endl;\n }\n\n if (printENU) {\n Point3 enu = xyz2enu(prop_xyz, nomXYZ);\n cout << \"enu \" << gpstime.week << \" \" << gpstime.sow << \" \" << enu.x() << \" \" << enu.y() << \" \" << enu.z() << endl;\n }\n\n if (printAmb) {\n for (int k=0; k(G(bias_counter[prn_vec[k]])) << endl;\n }\n }\n\n if (printUpdateRate) {\n cout << \"Elapsed time \"\n << std::chrono::duration_cast(end - start).count()\n << \" µs\" << endl;\n }\n\n output_time = output_time +1;\n graph->resize(0);\n initial_values.clear();\n prn_vec.clear();\n count++;\n initial_values.insert(X(count), prior_nonBias);\n }\n isam.saveGraph(\"gnss.tree\");\n return 0;\n}\n", "meta": {"hexsha": "efcaf0255701a3b34b99166ab9917ae79398c609", "size": 19884, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "trunk/examples/pppBayesTree.cpp", "max_stars_repo_name": "wvu-navLab/PPP-BayesTree", "max_stars_repo_head_hexsha": "6f469775277a1a33447bf4c19603c796c2c63c75", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 40.0, "max_stars_repo_stars_event_min_datetime": "2018-04-23T02:03:40.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-09T14:41:03.000Z", "max_issues_repo_path": "trunk/examples/pppBayesTree.cpp", "max_issues_repo_name": "shaolinbit/PPP-BayesTree", "max_issues_repo_head_hexsha": "6f469775277a1a33447bf4c19603c796c2c63c75", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 2.0, "max_issues_repo_issues_event_min_datetime": "2019-01-02T15:03:54.000Z", "max_issues_repo_issues_event_max_datetime": "2021-11-23T03:04:04.000Z", "max_forks_repo_path": "trunk/examples/pppBayesTree.cpp", "max_forks_repo_name": "shaolinbit/PPP-BayesTree", "max_forks_repo_head_hexsha": "6f469775277a1a33447bf4c19603c796c2c63c75", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 16.0, "max_forks_repo_forks_event_min_datetime": "2018-05-18T05:59:00.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-07T13:51:18.000Z", "avg_line_length": 40.0887096774, "max_line_length": 208, "alphanum_fraction": 0.5701066184, "num_tokens": 4765, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5888891451980403, "lm_q2_score": 0.3557748935136304, "lm_q1q2_score": 0.2095119729241656}} {"text": "#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nstatic int ret;\nstatic int fd;\n\nros::Publisher pubImu;\n#define BAUD 115200 // 115200 for JY61 ,9600 for others\n\ntemplate \nstatic Eigen::Matrix\n\nypr2R(const Eigen::MatrixBase &ypr) {\n typedef typename Derived::Scalar Scalar_t;\n\n Scalar_t y = ypr(0) / 180.0 * M_PI;\n Scalar_t p = ypr(1) / 180.0 * M_PI;\n Scalar_t r = ypr(2) / 180.0 * M_PI;\n\n Eigen::Matrix Rz;\n Rz << cos(y), -sin(y), 0, sin(y), cos(y), 0, 0, 0, 1;\n\n Eigen::Matrix Ry;\n Ry << cos(p), 0., sin(p), 0., 1., 0., -sin(p), 0., cos(p);\n\n Eigen::Matrix Rx;\n Rx << 1., 0., 0., 0., cos(r), -sin(r), 0., sin(r), cos(r);\n\n return Rz * Ry * Rx;\n}\n\nint uart_open(int fd, const char *pathname) {\n fd = open(pathname, O_RDWR | O_NOCTTY);\n if (-1 == fd) {\n perror(\"Can't Open Serial Port\");\n return (-1);\n } else\n printf(\"open %s success!\\n\", pathname);\n if (isatty(STDIN_FILENO) == 0)\n printf(\"standard input is not a terminal device\\n\");\n else\n printf(\"isatty success!\\n\");\n return fd;\n}\n\nint uart_set(int fd, int nSpeed, int nBits, char nEvent, int nStop) {\n struct termios newtio, oldtio;\n if (tcgetattr(fd, &oldtio) != 0) {\n perror(\"SetupSerial 1\");\n printf(\"tcgetattr( fd,&oldtio) -> %d\\n\", tcgetattr(fd, &oldtio));\n return -1;\n }\n bzero(&newtio, sizeof(newtio));\n newtio.c_cflag |= CLOCAL | CREAD;\n newtio.c_cflag &= ~CSIZE;\n switch (nBits) {\n case 7:\n newtio.c_cflag |= CS7;\n break;\n case 8:\n newtio.c_cflag |= CS8;\n break;\n }\n switch (nEvent) {\n case 'o':\n case 'O':\n newtio.c_cflag |= PARENB;\n newtio.c_cflag |= PARODD;\n newtio.c_iflag |= (INPCK | ISTRIP);\n break;\n case 'e':\n case 'E':\n newtio.c_iflag |= (INPCK | ISTRIP);\n newtio.c_cflag |= PARENB;\n newtio.c_cflag &= ~PARODD;\n break;\n case 'n':\n case 'N':\n newtio.c_cflag &= ~PARENB;\n break;\n default:\n break;\n }\n\n /*设置波特率*/\n\n switch (nSpeed) {\n case 2400:\n cfsetispeed(&newtio, B2400);\n cfsetospeed(&newtio, B2400);\n break;\n case 4800:\n cfsetispeed(&newtio, B4800);\n cfsetospeed(&newtio, B4800);\n break;\n case 9600:\n cfsetispeed(&newtio, B9600);\n cfsetospeed(&newtio, B9600);\n break;\n case 115200:\n cfsetispeed(&newtio, B115200);\n cfsetospeed(&newtio, B115200);\n break;\n case 460800:\n cfsetispeed(&newtio, B460800);\n cfsetospeed(&newtio, B460800);\n break;\n default:\n cfsetispeed(&newtio, B9600);\n cfsetospeed(&newtio, B9600);\n break;\n }\n if (nStop == 1)\n newtio.c_cflag &= ~CSTOPB;\n else if (nStop == 2)\n newtio.c_cflag |= CSTOPB;\n newtio.c_cc[VTIME] = 0;\n newtio.c_cc[VMIN] = 0;\n tcflush(fd, TCIFLUSH);\n\n if ((tcsetattr(fd, TCSANOW, &newtio)) != 0) {\n perror(\"com set error\");\n return -1;\n }\n printf(\"set done!\\n\");\n return 0;\n}\n\nint uart_close(int fd) {\n assert(fd);\n close(fd);\n\n return 0;\n}\nint send_data(int fd, char *send_buffer, int length) {\n length = write(fd, send_buffer, length * sizeof(unsigned char));\n return length;\n}\n\nint recv_data(int fd, char *recv_buffer, int length) {\n int has_receive_length = 0;\n while (length - has_receive_length > 0) {\n\n has_receive_length +=\n read(fd, recv_buffer + has_receive_length, length - has_receive_length);\n usleep(1000);\n // std::cout << \"has_receive_length: \" << has_receive_length <<\n // std::endl;\n }\n return length;\n}\n\nfloat a[3], w[3], Angle[3], h[3];\nvoid ParseData(char *buffer) {\n char chrBuf[100];\n signed short sData[4];\n\n for (int i = 0; i < 4; i++) {\n memcpy(&chrBuf[0], buffer + 11 * i, 11);\n\n // std::cout << \"i: \" << i << std::endl;\n // for(int m =0; m< 11; m++)\n // {\n // printf(\"%x \", chrBuf[m]);\n // }\n memcpy(&sData[0], &chrBuf[2], 8);\n char cTemp = 0;\n time_t now;\n for (int j = 0; j < 10; j++) {\n cTemp += chrBuf[j];\n }\n\n if ((chrBuf[0] != 0x55) || ((chrBuf[1] & 0x50) != 0x50) ||\n (cTemp != chrBuf[10])) {\n printf(\"Error:%x %x\\r\\n\", chrBuf[0], chrBuf[1]);\n return;\n }\n switch (chrBuf[1]) {\n case 0x51:\n for (int k = 0; k < 3; k++) {\n a[k] = (float)sData[k] / 32768.0 * 16.0 * 9.81;\n }\n time(&now);\n printf(\"\\r\\nT:%s a:%6.3f %6.3f %6.3f \", asctime(localtime(&now)), a[0],\n a[1], a[2]);\n\n break;\n case 0x52:\n for (int k = 0; k < 3; k++) {\n w[k] = (float)sData[k] / 32768.0 * 2000.0/180*M_PI;\n }\n printf(\"w:%7.3f %7.3f %7.3f \", w[0], w[1], w[2]);\n break;\n case 0x53:\n for (int k = 0; k < 3; k++) {\n Angle[k] = (float)sData[k] / 32768.0 * 180.0;\n }\n printf(\"A:%7.3f %7.3f %7.3f \", Angle[0], Angle[1], Angle[2]);\n\n break;\n\n case 0x54:\n for (int k = 0; k < 3; k++) {\n h[k] = (float)sData[k];\n }\n printf(\"h:%4.0f %4.0f %4.0f \", h[0], h[1], h[2]);\n\n break;\n }\n }\n\n sensor_msgs::Imu imu_msg;\n\n imu_msg.header.stamp = ros::Time::now();\n imu_msg.header.frame_id = \"/imu\";\n imu_msg.angular_velocity.x = w[0];\n imu_msg.angular_velocity.y = w[1];\n imu_msg.angular_velocity.z = w[2];\n\n Eigen::Vector3d ypr;\n ypr << Angle[2], Angle[1], Angle[0];\n Eigen::Matrix R = ypr2R(ypr);\n std::cout << \"R:\\n \" << R << std::endl;\n Eigen::Quaterniond q(R);\n q.normalize();\n\n imu_msg.orientation.w = q.w();\n imu_msg.orientation.x = q.x();\n imu_msg.orientation.y = q.y();\n imu_msg.orientation.z = q.z();\n\n imu_msg.linear_acceleration.x = a[0];\n imu_msg.linear_acceleration.y = a[1];\n imu_msg.linear_acceleration.z = a[2];\n\n pubImu.publish(imu_msg);\n}\n\nint main(int argc, char **argv) {\n ros::init(argc, argv, \"xense_driver\");\n ros::NodeHandle n_private(\"~\");\n\n char r_buf[1024];\n bzero(r_buf, 1024);\n\n fd = uart_open(fd, \"/dev/ttyUSB0\"); /*串口号/dev/ttySn,USB口号/dev/ttyUSBn */\n if (fd == -1) {\n fprintf(stderr, \"uart_open error\\n\");\n exit(EXIT_FAILURE);\n }\n\n if (uart_set(fd, BAUD, 8, 'N', 1) == -1) {\n fprintf(stderr, \"uart set failed!\\n\");\n exit(EXIT_FAILURE);\n }\n\n pubImu = n_private.advertise(\"/imu/data\", 3000);\n\n while (ros::ok()) {\n ret = recv_data(fd, r_buf, 1);\n\n if (r_buf[0] != 0x55)\n continue;\n ret = recv_data(fd, r_buf + 1, 1);\n if (r_buf[1] != 0x51) {\n continue;\n }\n\n ret = recv_data(fd, r_buf + 2, 44 - 2);\n\n // for(int i =0; i< 44; i++)\n // {\n // printf(\"%x \", r_buf[i]);\n // }\n std::cout << std::endl;\n ParseData(r_buf);\n usleep(1000);\n }\n\n ret = uart_close(fd);\n if (ret == -1) {\n fprintf(stderr, \"uart_close error\\n\");\n exit(EXIT_FAILURE);\n }\n\n exit(EXIT_SUCCESS);\n}", "meta": {"hexsha": "410d69715372e96316f491e9a9439fe22271c8a3", "size": 7018, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "nodes/HWT905Device.cpp", "max_stars_repo_name": "minxuanjun/imu_driver", "max_stars_repo_head_hexsha": "ec905390d50f119154d47285e1412ae09c8cc4dc", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "nodes/HWT905Device.cpp", "max_issues_repo_name": "minxuanjun/imu_driver", "max_issues_repo_head_hexsha": "ec905390d50f119154d47285e1412ae09c8cc4dc", "max_issues_repo_licenses": ["BSD-2-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "nodes/HWT905Device.cpp", "max_forks_repo_name": "minxuanjun/imu_driver", "max_forks_repo_head_hexsha": "ec905390d50f119154d47285e1412ae09c8cc4dc", "max_forks_repo_licenses": ["BSD-2-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 23.3156146179, "max_line_length": 80, "alphanum_fraction": 0.5635508692, "num_tokens": 2527, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.588889130767832, "lm_q2_score": 0.35577487985229844, "lm_q1q2_score": 0.2095119597452499}} {"text": "// This file is part of DM-HEOM (https://github.com/noma/dm-heom)\n//\n// Copyright (c) 2015-2019 Matthias Noack, Zuse Institute Berlin\n//\n// Licensed under the 3-clause BSD License, see accompanying LICENSE,\n// CONTRIBUTORS.md, and README.md for further information.\n\n#include \n#include \n#include \n\n#include \n#include \n\n#include \"heom/command_line.hpp\"\n#include \"heom/common.hpp\"\n#include \"heom/dipole_matrix.hpp\"\n#include \"heom/dipole_pseudo_pathway.hpp\"\n#include \"heom/handle_main_exception.hpp\"\n#include \"heom/hierarchy_mask.hpp\"\n#include \"heom/hierarchy_norm.hpp\"\n#include \"heom/instance.hpp\"\n#include \"heom/make_file_observer_list.hpp\"\n#include \"heom/ocl_config.hpp\"\n#include \"heom/ode.hpp\"\n#include \"heom/circular_dichroism_config.hpp\"\n#include \"heom/population_dynamics_solver.hpp\"\n#include \"heom/sites_to_states.hpp\"\n\nnamespace bmt = ::heom::bmt;\nnamespace num = ::heom::num;\nnamespace ocl = ::heom::ocl;\nusing num::int_t;\nusing num::real_t;\n\nint main(int argc, char* argv[])\n{\n\t// start runtime measurement\n\tbmt::timer app_timer;\n\n\t// output compile time configuration\n\tstd::cout << \"-------------------- Compile-Time Configuration ------------\" << std::endl;\n\theom::write_compile_config(std::cout);\n\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\tstd::exception_ptr eptr;\n\ttry {\n\t\t// command line parsing\n\t\theom::command_line command_line { argc, argv };\n\n\t\tstd::cout << \"Parsing OpenCL configuration from file: \" << command_line.ocl_config_filename() << std::endl;\n\t\theom::ocl_config ocl_config(command_line.ocl_config_filename());\n\t\tstd::cout << \"Parsing HEOM configuration from file: \" << command_line.heom_config_filename() << std::endl;\n\t\theom::circular_dichroism_config heom_config(command_line.heom_config_filename());\n\n\t\theom::hierarchy_graph complete_graph(heom_config.baths_number(), heom_config.baths_matsubaras(), heom_config.system_ado_depth());\n\n\t\tusing stepper_t = num::meta_stepper; // uses solver_stepper_type from config\n\t\tusing solver_t = heom::population_dynamics_solver;\n\n\t\t// prepare output file (so we run into possible file errors before starting the computation)\n\t\tauto& observation_filename = heom_config.observations().get().front().get().second; // filename of first specified observation, guaranteed to exist by config checks\n\t\tstd::ofstream observation_file(observation_filename);\n\t\tif (observation_file.fail())\n\t\t\tthrow std::runtime_error(\"Error: could not open output file: \" + observation_filename);\n\n\t\tint_t iterations = heom_config.solver_steps() / heom_config.program_observe_steps();\n\t\tint_t steps_per_iteration = heom_config.program_observe_steps();\n\t\tint_t total_steps = iterations * heom_config.program_observe_steps();\n\n\t\t//output data structure for traces\n\t\tconst size_t num_dipole_matrices = heom_config.dipole_tensor_prefactors().size();\n\t\tconst int_t num_observations = iterations + 1;\n\t\tstd::vector observations(num_observations);\n\n\t\t// circular dichroism pseudo pathway with ground state\n\t\tconst auto& pathway_spec = heom::dipole_pseudo_pathway_wgs_spec;\n\t\tconst auto sts_mode = pathway_spec.sites_to_states_mode();\n\n\t\t// for progress estimate\n\t\tconst size_t solver_runs = num_dipole_matrices;\n\t\tsize_t solver_run = 0;\n\n\t\t// iterate over all dipoles and compute population dynamics\n\t\tfor (size_t tensor_index = 0; tensor_index < heom_config.dipole_tensor_prefactors().size(); ++tensor_index) {\n\t\t\theom::instance heom_instance(heom_config, sts_mode, complete_graph);\n\t\t\theom_instance.set_hierarchy_top(); // NOTE: hierarchy top is initialised to zero with first element being complex_t(1.0,0.0) here\n\n\t\t\t// OpenCL range\n\t\t\tocl::nd_range range {\n\t\t\t\t{}, // offset\n\t\t\t\t{1, static_cast(heom_instance.matrices())}, // global size\n\t\t\t\t{1, 1} // local size\n\t\t\t};\n\n\t\t\t// create a solver from configuration and instance\n\t\t\tsolver_t solver(ocl_config, range, heom_config, heom_instance);\n\t\t\tstd::cout << \"-------------------- OpenCL Runtime Configuration ----------\" << std::endl;\n\t\t\tsolver.write_ocl_runtime_config(std::cout);\n\t\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\t\t\t++solver_run; // for progress estimate\n\n\t\t\t// compute dipole matrices\n\t\t\tauto dipole_matrix_minus = heom::get_dipole_matrix_for_pathway_circular(pathway_spec, 0, heom_config.system_sites(), heom_config, tensor_index);\n\t\t\tauto dipole_matrix_plus = heom::get_dipole_matrix_for_pathway(pathway_spec, 1, heom_config.system_sites(), heom_config, tensor_index);\n\n\t\t\t// pre-scale D- for observer\n\t\t\tdipole_matrix_minus.scale(heom::get_dipole_tensor_prefactor(pathway_spec, heom_config, tensor_index));\n\n\t\t\t// multiply D+ from left\n\t\t\tsolver.hierarchy_mmult_left(dipole_matrix_plus.data());\n\n\t\t\t// setup observer with pre-scaled D-\n\t\t\theom::matrix_trace_observer trace_observer(complete_graph, solver, dipole_matrix_minus.data()); // D- from left via observer\n\n\t\t\t// generate first observation with initial value\n\t\t\tobservations[0] += trace_observer.observe_trace(0.0);\n\n\t\t\t// write header once\n\t\t\tif (tensor_index == 0)\n\t\t\t\ttrace_observer.write_header(observation_file, true);\n\n\t\t\tfor (int_t i = 0; i < iterations; ++i) {\n\t\t\t\t// propagate\n\t\t\t\tsolver.step_forward(steps_per_iteration);\n\n\t\t\t\t// after propagation, we are at:\n\t\t\t\tconst auto current_step = (i + 1) * steps_per_iteration;\n\t\t\t\tconst real_t current_time = current_step * heom_config.solver_step_size();\n\n\t\t\t\t// observe\n\t\t\t\tobservations[i + 1] += trace_observer.observe_trace(current_time);\n\n\t\t\t\t// update status\n\t\t\t\theom::write_progress(current_step - 1, total_steps, solver_run, solver_runs, \"Calculation circular dichroism: \", std::cout);\n\t\t\t}\n\n\t\t\t// write_complex_matrix(result_buffer_top, heom_instance.states(), std::cout);\n\t\t\tstd::cout << \"-------------------- Solver Runtime Summary ----------------\" << std::endl;\n\t\t\tsolver.write_runtime_summary(std::cout);\n\t\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\t\t} // for tensor_index\n\n\t\t// write averaged traces\n\t\tfor (auto& obs : observations) {\n\t\t\tobs.avg(num_dipole_matrices);\n\t\t\tobservation_file << obs << '\\n';\n\t\t}\n\t\tobservation_file << std::flush; // tell the OS to write to disk\n\n\t\tstd::cout << \"-------------------- Memory Summary ------------------------\" << std::endl;\n\t\tstd::cout << \"max. heap via aligned::allocate(..): \"\n\t\t << boost::format(\"%11.2f MiB\") % (heom::memory::aligned::instance().allocated_byte_max() / 1024.0 / 1024.0)\n\t\t << \" (\"\n\t\t << boost::format(\"count: %6i\") % heom::memory::aligned::instance().allocations_max()\n\t\t << ')' << std::endl;\n//\t\tstd::cout << \"max. OpenCL buffer allocations: \"\n//\t\t << boost::format(\"%11.2f MiB\") % (solver.ocl_helper().allocated_byte_max() / 1024.0 / 1024.0)\n//\t\t << \" (\"\n//\t\t << boost::format(\"count: %6i\") % solver.ocl_helper().allocations_max()\n//\t\t << ')' << std::endl;\n\t\tstd::cout << \"------------------------------------------------------------\" << std::endl;\n\n\t} catch (...) {\n\t\teptr = std::current_exception();\n\t}\n\tint ret = heom::handle_main_exception(eptr);\n\n\t// print application runtime\n\tstd::cout << \"time in main():\\t\" << boost::format(\"%11.2f\") % std::chrono::duration_cast(bmt::duration(app_timer.elapsed())).count() << \" s\" << std::endl;\n\n\treturn ret;\n}\n\n\n", "meta": {"hexsha": "b72181d728290d21879dc0e15903b21b0b362ef4", "size": 7502, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "dm-heom/src/app_circular_dichroism.cpp", "max_stars_repo_name": "noma/dm-heom", "max_stars_repo_head_hexsha": "85c94f947190064d21bd38544094731113c15412", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 7.0, "max_stars_repo_stars_event_min_datetime": "2019-07-28T01:02:52.000Z", "max_stars_repo_stars_event_max_datetime": "2021-11-09T04:01:36.000Z", "max_issues_repo_path": "dm-heom/src/app_circular_dichroism.cpp", "max_issues_repo_name": "noma/dm-heom", "max_issues_repo_head_hexsha": "85c94f947190064d21bd38544094731113c15412", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "dm-heom/src/app_circular_dichroism.cpp", "max_forks_repo_name": "noma/dm-heom", "max_forks_repo_head_hexsha": "85c94f947190064d21bd38544094731113c15412", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": 2.0, "max_forks_repo_forks_event_min_datetime": "2020-04-04T15:20:30.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-24T15:16:40.000Z", "avg_line_length": 42.384180791, "max_line_length": 169, "alphanum_fraction": 0.6767528659, "num_tokens": 1910, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5389832206876841, "lm_q2_score": 0.38861802670584894, "lm_q1q2_score": 0.2094585956512109}} {"text": "<<<<<<< HEAD\n/* Copyright (c) 2010-2018, Delft University of Technology\n=======\n/* Copyright (c) 2010-2019, Delft University of Technology\n>>>>>>> origin/master\n * All rigths reserved\n *\n * This file is part of the Tudat. Redistribution and use in source and\n * binary forms, with or without modification, are permitted exclusively\n * under the terms of the Modified BSD license. You should have received\n * a copy of the license with this file. If not, please or visit:\n * http://tudat.tudelft.nl/LICENSE.\n */\n\n#include \n\n#include \"Tudat/Astrodynamics/Propagators/stateTransitionMatrixInterface.h\"\n\nnamespace tudat\n{\n\nnamespace propagators\n{\n\n//! Function to reset the state transition and sensitivity matrix interpolators\nvoid SingleArcCombinedStateTransitionAndSensitivityMatrixInterface::updateMatrixInterpolators(\n const std::shared_ptr< interpolators::OneDimensionalInterpolator< double, Eigen::MatrixXd > >\n stateTransitionMatrixInterpolator,\n const std::shared_ptr< interpolators::OneDimensionalInterpolator< double, Eigen::MatrixXd > >\n<<<<<<< HEAD\n sensitivityMatrixInterpolator )\n=======\n sensitivityMatrixInterpolator,\n const std::vector< std::pair< int, int > >& statePartialAdditionIndices )\n>>>>>>> origin/master\n{\n stateTransitionMatrixInterpolator_ = stateTransitionMatrixInterpolator;\n sensitivityMatrixInterpolator_ = sensitivityMatrixInterpolator;\n statePartialAdditionIndices_ = statePartialAdditionIndices;\n}\n\n//! Function to get the concatenated state transition and sensitivity matrix at a given time.\nEigen::MatrixXd SingleArcCombinedStateTransitionAndSensitivityMatrixInterface::getCombinedStateTransitionAndSensitivityMatrix(\n const double evaluationTime )\n{\n combinedStateTransitionMatrix_.setZero( );\n\n\n // Set Phi and S matrices.\n combinedStateTransitionMatrix_.block( 0, 0, stateTransitionMatrixSize_, stateTransitionMatrixSize_ ) =\n stateTransitionMatrixInterpolator_->interpolate( evaluationTime );\n\n if( sensitivityMatrixSize_ > 0 )\n {\n combinedStateTransitionMatrix_.block( 0, stateTransitionMatrixSize_, stateTransitionMatrixSize_, sensitivityMatrixSize_ ) =\n sensitivityMatrixInterpolator_->interpolate( evaluationTime );\n }\n\n for( unsigned int i = 0; i < statePartialAdditionIndices_.size( ); i++ )\n {\n combinedStateTransitionMatrix_.block( statePartialAdditionIndices_.at( i ).first, 0, 6, stateTransitionMatrixSize_ + sensitivityMatrixSize_ ) +=\n combinedStateTransitionMatrix_.block( statePartialAdditionIndices_.at( i ).second, 0, 6, stateTransitionMatrixSize_ + sensitivityMatrixSize_ );\n }\n\n\n return combinedStateTransitionMatrix_;\n}\n\n//! Constructor\nMultiArcCombinedStateTransitionAndSensitivityMatrixInterface::MultiArcCombinedStateTransitionAndSensitivityMatrixInterface(\n const std::vector< std::shared_ptr< interpolators::OneDimensionalInterpolator< double, Eigen::MatrixXd > > >\n stateTransitionMatrixInterpolators,\n const std::vector< std::shared_ptr< interpolators::OneDimensionalInterpolator< double, Eigen::MatrixXd > > >\n sensitivityMatrixInterpolators,\n const std::vector< double >& arcStartTimes,\n const std::vector< double >& arcEndTimes,\n const int numberOfInitialDynamicalParameters,\n const int numberOfParameters,\n const std::vector< std::vector< std::pair< int, int > > >& statePartialAdditionIndices ):\n CombinedStateTransitionAndSensitivityMatrixInterface( numberOfInitialDynamicalParameters, numberOfParameters ),\n stateTransitionMatrixInterpolators_( stateTransitionMatrixInterpolators ),\n sensitivityMatrixInterpolators_( sensitivityMatrixInterpolators ),\n arcStartTimes_( arcStartTimes ),\n arcEndTimes_( arcEndTimes ),\n statePartialAdditionIndices_( statePartialAdditionIndices )\n{\n if( arcStartTimes_.size( ) != arcEndTimes_.size( ) )\n {\n throw std::runtime_error( \"Error when making MultiArcCombinedStateTransitionAndSensitivityMatrixInterface, incompatible time lists\" );\n }\n numberOfStateArcs_ = arcStartTimes_.size( );\n\n sensitivityMatrixSize_ = numberOfParameters - numberOfStateArcs_ * stateTransitionMatrixSize_;\n\n if( stateTransitionMatrixInterpolators_.size( ) != sensitivityMatrixInterpolators_.size( ) ||\n stateTransitionMatrixInterpolators_.size( ) != static_cast< unsigned int >( numberOfStateArcs_ ) )\n {\n throw std::runtime_error(\n \"Error when making multi arc state transition and sensitivity interface, vector sizes are inconsistent\" );\n }\n\n std::vector< double > arcSplitTimes = arcStartTimes_;\n arcSplitTimes.push_back( std::numeric_limits< double >::max( ));\n lookUpscheme_ = std::make_shared< interpolators::HuntingAlgorithmLookupScheme< double > >(\n arcSplitTimes );\n}\n\n//! Function to reset the state transition and sensitivity matrix interpolators\nvoid MultiArcCombinedStateTransitionAndSensitivityMatrixInterface::updateMatrixInterpolators(\n const std::vector< std::shared_ptr< interpolators::OneDimensionalInterpolator< double, Eigen::MatrixXd > > >\n stateTransitionMatrixInterpolators,\n const std::vector< std::shared_ptr< interpolators::OneDimensionalInterpolator< double, Eigen::MatrixXd > > >\n sensitivityMatrixInterpolators,\n const std::vector< double >& arcStartTimes,\n const std::vector< double >& arcEndTimes,\n const std::vector< std::vector< std::pair< int, int > > >& statePartialAdditionIndices )\n{\n stateTransitionMatrixInterpolators_ = stateTransitionMatrixInterpolators;\n sensitivityMatrixInterpolators_ = sensitivityMatrixInterpolators;\n arcStartTimes_ = arcStartTimes;\n arcEndTimes_ = arcEndTimes;\n statePartialAdditionIndices_ = statePartialAdditionIndices;\n\n if( stateTransitionMatrixInterpolators_.size( ) != sensitivityMatrixInterpolators_.size( ) ||\n stateTransitionMatrixInterpolators_.size( ) != static_cast< unsigned int >( numberOfStateArcs_ ) )\n {\n throw std::runtime_error(\n \"Error when resetting multi arc state transition and sensitivity interface, vector sizes are inconsistent.\" );\n }\n\n std::vector< double > arcSplitTimes = arcStartTimes_;\n arcSplitTimes.push_back( std::numeric_limits< double >::max( ) );\n\n lookUpscheme_ = std::make_shared< interpolators::HuntingAlgorithmLookupScheme< double > >(\n arcSplitTimes );\n}\n\n//! Function to get the concatenated single-arc state transition and sensitivity matrix at a given time.\nEigen::MatrixXd MultiArcCombinedStateTransitionAndSensitivityMatrixInterface::getCombinedStateTransitionAndSensitivityMatrix(\n const double evaluationTime,\n const bool addCentralBodySensitivity )\n{\n Eigen::MatrixXd combinedStateTransitionMatrix = Eigen::MatrixXd::Zero(\n stateTransitionMatrixSize_, stateTransitionMatrixSize_ + sensitivityMatrixSize_ );\n\n int currentArc = getCurrentArc( evaluationTime ).first;\n\n // Set Phi and S matrices.\n if( currentArc >= 0 )\n {\n combinedStateTransitionMatrix.block( 0, 0, stateTransitionMatrixSize_, stateTransitionMatrixSize_ ) =\n stateTransitionMatrixInterpolators_.at( currentArc )->interpolate( evaluationTime );\n combinedStateTransitionMatrix.block( 0, stateTransitionMatrixSize_, stateTransitionMatrixSize_, sensitivityMatrixSize_ ) =\n sensitivityMatrixInterpolators_.at( currentArc )->interpolate( evaluationTime );\n\n for( unsigned int i = 0; i < statePartialAdditionIndices_.at( currentArc ).size( ); i++ )\n {\n int indicesToAdd = addCentralBodySensitivity ?\n ( stateTransitionMatrixSize_ + sensitivityMatrixSize_ ) : stateTransitionMatrixSize_;\n combinedStateTransitionMatrix.block(\n statePartialAdditionIndices_.at( currentArc ).at( i ).first, 0, 6, indicesToAdd ) +=\n combinedStateTransitionMatrix.block(\n statePartialAdditionIndices_.at( currentArc ).at( i ).second, 0, 6, indicesToAdd );\n }\n }\n return combinedStateTransitionMatrix;\n}\n\n//! Function to get the concatenated single-arc state transition and sensitivity matrix at a given time.\nEigen::MatrixXd MultiArcCombinedStateTransitionAndSensitivityMatrixInterface::getCombinedStateTransitionAndSensitivityMatrix(\n const double evaluationTime )\n{\n return getCombinedStateTransitionAndSensitivityMatrix( evaluationTime, true );\n}\n\n//! Function to get the concatenated state transition matrices for each arc and sensitivity matrix at a given time.\nEigen::MatrixXd MultiArcCombinedStateTransitionAndSensitivityMatrixInterface::getFullCombinedStateTransitionAndSensitivityMatrix(\n const double evaluationTime,\n const bool addCentralBodySensitivity )\n{\n Eigen::MatrixXd combinedStateTransitionMatrix = getCombinedStateTransitionAndSensitivityMatrix(\n evaluationTime, addCentralBodySensitivity );\n Eigen::MatrixXd fullCombinedStateTransitionMatrix = Eigen::MatrixXd::Zero(\n stateTransitionMatrixSize_, numberOfStateArcs_ * stateTransitionMatrixSize_ + sensitivityMatrixSize_ );\n\n int currentArc = getCurrentArc( evaluationTime ).first;\n\n // Set Phi and S matrices of current arc.\n<<<<<<< HEAD\n combinedStateTransitionMatrix.block( 0, currentArc * stateTransitionMatrixSize_, stateTransitionMatrixSize_, stateTransitionMatrixSize_ ) =\n stateTransitionMatrixInterpolators_.at( currentArc )->interpolate( evaluationTime );\n\n combinedStateTransitionMatrix.block(\n 0, numberOfStateArcs_ * stateTransitionMatrixSize_, stateTransitionMatrixSize_, sensitivityMatrixSize_ ) =\n sensitivityMatrixInterpolators_.at( currentArc )->interpolate( evaluationTime );\n=======\n if( currentArc >= 0 )\n {\n fullCombinedStateTransitionMatrix.block(\n 0, currentArc * stateTransitionMatrixSize_, stateTransitionMatrixSize_, stateTransitionMatrixSize_ ) =\n stateTransitionMatrixInterpolators_.at( currentArc )->interpolate( evaluationTime );\n fullCombinedStateTransitionMatrix.block(\n 0, numberOfStateArcs_ * stateTransitionMatrixSize_, stateTransitionMatrixSize_, sensitivityMatrixSize_ ) =\n combinedStateTransitionMatrix.block( 0, 0, stateTransitionMatrixSize_, sensitivityMatrixSize_ );\n }\n return fullCombinedStateTransitionMatrix;\n}\n>>>>>>> origin/master\n\n//! Function to get the concatenated state transition matrices for each arc and sensitivity matrix at a given time.\nEigen::MatrixXd MultiArcCombinedStateTransitionAndSensitivityMatrixInterface::getFullCombinedStateTransitionAndSensitivityMatrix(\n const double evaluationTime )\n{\n return getFullCombinedStateTransitionAndSensitivityMatrix( evaluationTime, true );\n}\n\n//! Function to retrieve the current arc for a given time\nstd::pair< int, double > MultiArcCombinedStateTransitionAndSensitivityMatrixInterface::getCurrentArc( const double evaluationTime )\n{\n\n int currentArc = lookUpscheme_->findNearestLowerNeighbour( evaluationTime );\n if( evaluationTime < arcEndTimes_.at( currentArc ) && evaluationTime > arcStartTimes_.at( currentArc ) )\n {\n return std::make_pair( currentArc, arcStartTimes_.at( currentArc ) );\n }\n else\n {\n return std::make_pair( -1, TUDAT_NAN );\n }\n}\n\n//! Function to get the concatenated state transition and sensitivity matrix at a given time.\nEigen::MatrixXd HybridArcCombinedStateTransitionAndSensitivityMatrixInterface::getCombinedStateTransitionAndSensitivityMatrix(\n const double evaluationTime )\n{\n Eigen::MatrixXd combinedStateTransitionMatrix = Eigen::MatrixXd::Zero(\n stateTransitionMatrixSize_, stateTransitionMatrixSize_ + sensitivityMatrixSize_ );\n\n // Get single-arc matrices\n Eigen::MatrixXd singleArcStateTransition = singleArcInterface_->getCombinedStateTransitionAndSensitivityMatrix(\n evaluationTime );\n\n // Get multi-arc matrices\n Eigen::MatrixXd multiArcStateTransition = multiArcInterface_->getCombinedStateTransitionAndSensitivityMatrix(\n evaluationTime, false );\n std::pair< int, double > currentArc = multiArcInterface_->getCurrentArc( evaluationTime );\n\n // Set single-arc block\n combinedStateTransitionMatrix.block( 0, 0, singleArcStateSize_, singleArcStateSize_ ) =\n singleArcStateTransition.block( 0, 0, singleArcStateSize_, singleArcStateSize_ );\n\n // Set single-arc sensitivity block\n combinedStateTransitionMatrix.block( 0, multiArcStateSize_, singleArcStateSize_, sensitivityMatrixSize_ ) =\n singleArcStateTransition.block( 0, singleArcStateSize_, singleArcStateSize_, sensitivityMatrixSize_ );\n\n if( !( currentArc.first < 0 ) )\n {\n // Set multi-arc block\n combinedStateTransitionMatrix.block(\n singleArcStateSize_, singleArcStateSize_, originalMultiArcStateSize_, originalMultiArcStateSize_ ) =\n multiArcStateTransition.block(\n singleArcStateSize_, singleArcStateSize_, originalMultiArcStateSize_, originalMultiArcStateSize_ );\n\n // Get single-arc matrices at current arc start\n Eigen::MatrixXd singleArcStateTransitionAtArcStart = singleArcInterface_->getCombinedStateTransitionAndSensitivityMatrix(\n currentArc.second );\n\n // Set coupled block\n combinedStateTransitionMatrix.block(\n singleArcStateSize_, 0, originalMultiArcStateSize_, singleArcStateSize_ ) =\n multiArcStateTransition.block(\n singleArcStateSize_, 0, originalMultiArcStateSize_, singleArcStateSize_ ) *\n singleArcStateTransitionAtArcStart.block(\n 0, 0, singleArcStateSize_, singleArcStateSize_ );\n\n // Set multi-arc sensitivity block\n combinedStateTransitionMatrix.block(\n singleArcStateSize_, multiArcStateSize_, originalMultiArcStateSize_, sensitivityMatrixSize_ ) =\n multiArcStateTransition.block(\n singleArcStateSize_, multiArcStateSize_, originalMultiArcStateSize_, sensitivityMatrixSize_ );\n\n std::vector< std::pair< int, int > > statePartialAdditionIndices =\n multiArcInterface_->getStatePartialAdditionIndices( currentArc.first );\n\n for( unsigned int i = 0; i < statePartialAdditionIndices.size( ); i++ )\n {\n combinedStateTransitionMatrix.block(\n statePartialAdditionIndices.at( i ).first, multiArcStateSize_,\n 6, sensitivityMatrixSize_ ) +=\n combinedStateTransitionMatrix.block(\n statePartialAdditionIndices.at( i ).second, multiArcStateSize_,\n 6, sensitivityMatrixSize_ );\n\n }\n }\n\n return combinedStateTransitionMatrix;\n\n}\n\n//! Function to get the concatenated state transition matrices for each arc and sensitivity matrix at a given time.\nEigen::MatrixXd HybridArcCombinedStateTransitionAndSensitivityMatrixInterface::getFullCombinedStateTransitionAndSensitivityMatrix(\n const double evaluationTime )\n{\n Eigen::MatrixXd combinedStateTransitionMatrix = getCombinedStateTransitionAndSensitivityMatrix( evaluationTime );\n Eigen::MatrixXd fullCombinedStateTransitionMatrix = Eigen::MatrixXd::Zero(\n stateTransitionMatrixSize_, singleArcStateSize_ + numberOfMultiArcs_ * originalMultiArcStateSize_ + sensitivityMatrixSize_ );\n\n fullCombinedStateTransitionMatrix.block(\n 0, 0, singleArcStateSize_, singleArcStateSize_ ) =\n combinedStateTransitionMatrix.block(\n 0, 0, singleArcStateSize_, singleArcStateSize_ );\n\n fullCombinedStateTransitionMatrix.block(\n 0, singleArcStateSize_ + numberOfMultiArcs_ * originalMultiArcStateSize_, singleArcStateSize_, sensitivityMatrixSize_ ) =\n combinedStateTransitionMatrix.block(\n 0, multiArcStateSize_, singleArcStateSize_, sensitivityMatrixSize_ );\n\n int currentArc = multiArcInterface_->getCurrentArc( evaluationTime ).first;\n\n // Set Phi and S matrices of current arc.\n if( currentArc >= 0 )\n {\n\n // Set multi-arc block\n fullCombinedStateTransitionMatrix.block(\n singleArcStateSize_, singleArcStateSize_ + currentArc * originalMultiArcStateSize_,\n originalMultiArcStateSize_, originalMultiArcStateSize_ ) =\n combinedStateTransitionMatrix.block(\n singleArcStateSize_, singleArcStateSize_, originalMultiArcStateSize_, originalMultiArcStateSize_ );\n\n\n<<<<<<< HEAD\n // Set sensitivity block\n combinedStateTransitionMatrix.block(\n 0, singleArcStateSize_ + numberOfMultiArcs_ * originalMultiArcStateSize_,\n singleArcStateSize_, sensitivityMatrixSize_ ) =\n singleArcStateTransition.block( 0, singleArcStateSize_, singleArcStateSize_, sensitivityMatrixSize_ );\n combinedStateTransitionMatrix.block(\n singleArcStateSize_, singleArcStateSize_ + numberOfMultiArcs_ * originalMultiArcStateSize_,\n originalMultiArcStateSize_, sensitivityMatrixSize_ ) =\n multiArcStateTransition.block(\n singleArcStateSize_, multiArcStateSize_, originalMultiArcStateSize_, sensitivityMatrixSize_ );\n=======\n // Set coupled block\n fullCombinedStateTransitionMatrix.block(\n singleArcStateSize_, 0, originalMultiArcStateSize_, singleArcStateSize_ ) =\n combinedStateTransitionMatrix.block(\n singleArcStateSize_, 0, originalMultiArcStateSize_, singleArcStateSize_ );\n>>>>>>> origin/master\n\n // Set multi-arc sensitivity block\n fullCombinedStateTransitionMatrix.block(\n singleArcStateSize_, singleArcStateSize_ + numberOfMultiArcs_ * originalMultiArcStateSize_,\n originalMultiArcStateSize_, sensitivityMatrixSize_ ) =\n combinedStateTransitionMatrix.block(\n singleArcStateSize_, multiArcStateSize_, originalMultiArcStateSize_, sensitivityMatrixSize_ );\n\n }\n\n return fullCombinedStateTransitionMatrix;\n}\n\n}\n\n}\n\n", "meta": {"hexsha": "33bd454a7d1359f5880bf62d07a7fa4804a75c77", "size": 18429, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Tudat/Astrodynamics/Propagators/stateTransitionMatrixInterface.cpp", "max_stars_repo_name": "ViktorJordanov/tudat", "max_stars_repo_head_hexsha": "069ceeab8f12405c356e19f50d6df037914df85c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "Tudat/Astrodynamics/Propagators/stateTransitionMatrixInterface.cpp", "max_issues_repo_name": "ViktorJordanov/tudat", "max_issues_repo_head_hexsha": "069ceeab8f12405c356e19f50d6df037914df85c", "max_issues_repo_licenses": ["BSD-3-Clause"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "Tudat/Astrodynamics/Propagators/stateTransitionMatrixInterface.cpp", "max_forks_repo_name": "ViktorJordanov/tudat", "max_forks_repo_head_hexsha": "069ceeab8f12405c356e19f50d6df037914df85c", "max_forks_repo_licenses": ["BSD-3-Clause"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 50.0788043478, "max_line_length": 159, "alphanum_fraction": 0.7330837267, "num_tokens": 3821, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.5544704649604273, "lm_q2_score": 0.37754066879814546, "lm_q1q2_score": 0.2093351501699784}} {"text": "/*\n qe2alm.cpp\n\n Copyright (c) 2015 Terumasa Tadano\n\n This file is distributed under the terms of the MIT license.\n Please see the file 'LICENCE.txt' in the root directory \n or http://opensource.org/licenses/mit-license.php for information.\n*/\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \"memory.h\"\n#include \"constants.h\"\n#include \"mathfunctions.h\"\n#include \"qe2alm.h\"\n\nusing namespace std;\n\nint main()\n{\n cout << \" QE2ALM -- a converter of harmonic FCs\" << endl;\n cout << \" Input file (QE fc file generated by q2r.x) : \";\n cin >> file_fc2qe;\n cout << \" Output file (xml file for ALAMODE) : \";\n cin >> file_xmlout;\n cout << \" Impose acoustic sum rule ? [y/n] : \";\n cin >> flag_asr;\n\n ifs_fc2qe.open(file_fc2qe.c_str(), std::ios::in);\n if (!ifs_fc2qe) {\n cout << \" ERROR: Cannot open file \" << file_fc2qe << endl;\n exit(EXIT_FAILURE);\n }\n\n transform(flag_asr.begin(), flag_asr.end(), flag_asr.begin(), ::tolower);\n if (flag_asr[0] == 'y') {\n consider_asr = true;\n } else if (flag_asr[0] == 'n') {\n consider_asr = false;\n } else {\n cout << \" ERROR: Please specify 'y' or 'n' for the acoustic sum rule.\" << endl;\n exit(EXIT_FAILURE);\n }\n\n // Read structural parameters\n\n ifs_fc2qe >> nkd >> natmin >> ibrav\n >> celldm[0] >> celldm[1] >> celldm[2]\n >> celldm[3] >> celldm[4] >> celldm[5];\n\n if (ibrav == 0) {\n for (i = 0; i < 3; ++i) {\n ifs_fc2qe >> lavec[0][i] >> lavec[1][i] >> lavec[2][i];\n }\n\n for (i = 0; i < 3; ++i) {\n for (j = 0; j < 3; ++j) {\n lavec[i][j] *= celldm[0];\n }\n }\n } else {\n calc_lattice_vector(ibrav, celldm, lavec);\n }\n\n recips(lavec, rlavec);\n\n int dummy;\n string str_dummy;\n double tmp;\n\n allocate(kd_symbol, nkd);\n\n for (i = 0; i < nkd; ++i) {\n ifs_fc2qe >> dummy >> kd_symbol[i] >> str_dummy >> tmp;\n kd_symbol[i] = kd_symbol[i].substr(1);\n }\n\n allocate(kd, natmin);\n allocate(xcrd, natmin, 3);\n\n for (i = 0; i < natmin; ++i) {\n ifs_fc2qe >> dummy >> kd[i] >> xcrd[i][0] >> xcrd[i][1] >> xcrd[i][2];\n }\n\n std::string str_na;\n ifs_fc2qe >> str_na;\n\n if (str_na[0] == 'T') {\n include_na = true;\n } else if (str_na[0] == 'F') {\n include_na = false;\n } else {\n cout << \" ERROR: This cannot happen \" << endl;\n exit(EXIT_FAILURE);\n }\n\n if (include_na) {\n cout << \" WARNING: The given force constant file contains information of\" << endl;\n cout << \" dielectric constants and Born effective charges.\" << endl;\n cout << \" The program skips these parts and converts the\" << endl;\n cout << \" short-range terms only.\" << endl;\n }\n\n if (include_na) {\n allocate(born, natmin, 3, 3);\n for (i = 0; i < 3; ++i) {\n for (j = 0; j < 3; ++j) {\n ifs_fc2qe >> dielec[i][j];\n }\n }\n for (i = 0; i < natmin; ++i) {\n ifs_fc2qe >> dummy;\n for (j = 0; j < 3; ++j) {\n for (k = 0; k < 3; ++k) {\n ifs_fc2qe >> born[i][j][k];\n }\n }\n }\n }\n\n // Read force constant info\n\n ifs_fc2qe >> nq[0] >> nq[1] >> nq[2];\n nsize = nq[0] * nq[1] * nq[2];\n\n allocate(fc2, 3 * natmin, 3 * natmin, nsize);\n\n int icell = 0;\n int m1, m2, m3;\n int icrd, jcrd;\n int iat, jat;\n\n for (icrd = 0; icrd < 3; ++icrd) {\n for (jcrd = 0; jcrd < 3; ++jcrd) {\n for (iat = 0; iat < natmin; ++iat) {\n for (jat = 0; jat < natmin; ++jat) {\n ifs_fc2qe >> dummy >> dummy >> dummy >> dummy;\n\n icell = 0;\n for (m3 = 0; m3 < nq[2]; ++m3) {\n for (m2 = 0; m2 < nq[1]; ++m2) {\n for (m1 = 0; m1 < nq[0]; ++m1) {\n ifs_fc2qe >> dummy >> dummy >> dummy;\n\n // Force constant matrix should be transposed here.\n ifs_fc2qe >> fc2[3 * jat + jcrd][3 * iat + icrd][icell];\n\n ++icell;\n }\n }\n }\n }\n }\n }\n }\n\n if (consider_asr) {\n\n double sum_fc;\n\n for (icrd = 0; icrd < 3; ++icrd) {\n for (jcrd = 0; jcrd < 3; ++jcrd) {\n for (iat = 0; iat < natmin; ++iat) {\n sum_fc = 0.0;\n for (jat = 0; jat < natmin; ++jat) {\n for (icell = 0; icell < nsize; ++icell) {\n sum_fc += fc2[3 * iat + icrd][3 * jat + jcrd][icell];\n }\n }\n fc2[3 * iat + icrd][3 * iat + jcrd][0] -= sum_fc;\n }\n }\n }\n }\n\n // Convert positions from alat to crystal basis\n for (i = 0; i < natmin; ++i) {\n rotvec(xcrd[i], xcrd[i], rlavec);\n for (j = 0; j < 3; ++j) {\n xcrd[i][j] *= celldm[0] / (2.0 * pi);\n }\n }\n\n // Extend the system to the supercell\n\n nat = natmin * nsize;\n\n allocate(xcrd_super, nat, 3);\n allocate(kd_super, nsize, natmin);\n allocate(map_p2s, nsize, natmin);\n\n icell = 0;\n int icount = 0;\n\n for (m3 = 0; m3 < nq[2]; ++m3) {\n for (m2 = 0; m2 < nq[1]; ++m2) {\n for (m1 = 0; m1 < nq[0]; ++m1) {\n for (i = 0; i < natmin; ++i) {\n xcrd_super[icount][0] = (xcrd[i][0] + static_cast(m1)) / static_cast(nq[0]);\n xcrd_super[icount][1] = (xcrd[i][1] + static_cast(m2)) / static_cast(nq[1]);\n xcrd_super[icount][2] = (xcrd[i][2] + static_cast(m3)) / static_cast(nq[2]);\n kd_super[icell][i] = kd[i];\n\n map_p2s[icell][i] = icount;\n ++icount;\n }\n ++icell;\n }\n }\n }\n\n for (i = 0; i < 3; ++i) {\n for (j = 0; j < 3; ++j) {\n lavec[i][j] *= static_cast(nq[j]);\n }\n }\n\n allocate(mindist, natmin, nat);\n\n get_pairs_of_minimum_distance(natmin, nat, map_p2s, xcrd_super, mindist);\n\n // Write to XML file\n\n using boost::property_tree::ptree;\n\n ptree pt;\n string str_pos[3], str_tmp;\n\n str_tmp.clear();\n\n // for (i = 0; i < 3; ++i) {\n // str_tmp += \" \" + std::to_string(nq[i]);\n // }\n\n\n pt.put(\"Data.FilenameOfQE\", file_fc2qe);\n // pt.put(\"Data.Qmesh\", str_tmp);\n\n pt.put(\"Data.Structure.NumberOfAtoms\", nat);\n pt.put(\"Data.Structure.NumberOfElements\", nkd);\n\n for (i = 0; i < nkd; ++i) {\n ptree &child = pt.add(\"Data.Structure.AtomicElements.element\", kd_symbol[i]);\n child.put(\".number\", i + 1);\n }\n\n for (i = 0; i < 3; ++i) {\n str_pos[i].clear();\n for (j = 0; j < 3; ++j) {\n str_pos[i] += \" \" + double2string(lavec[j][i]);\n }\n }\n pt.put(\"Data.Structure.LatticeVector\", \"\");\n pt.put(\"Data.Structure.LatticeVector.a1\", str_pos[0]);\n pt.put(\"Data.Structure.LatticeVector.a2\", str_pos[1]);\n pt.put(\"Data.Structure.LatticeVector.a3\", str_pos[2]);\n\n pt.put(\"Data.Structure.Position\", \"\");\n\n icount = 0;\n\n for (i = 0; i < nsize; ++i) {\n\n for (j = 0; j < natmin; ++j) {\n str_tmp.clear();\n for (k = 0; k < 3; ++k) str_tmp += \" \" + double2string(xcrd_super[map_p2s[i][j]][k]);\n ptree &child = pt.add(\"Data.Structure.Position.pos\", str_tmp);\n child.put(\".index\", icount + 1);\n child.put(\".element\", kd_symbol[kd_super[i][j] - 1]);\n\n ++icount;\n }\n }\n\n pt.put(\"Data.Symmetry.NumberOfTranslations\", nsize);\n for (i = 0; i < nsize; ++i) {\n for (j = 0; j < natmin; ++j) {\n ptree &child = pt.add(\"Data.Symmetry.Translations.map\", map_p2s[i][j] + 1);\n child.put(\".tran\", i + 1);\n child.put(\".atom\", j + 1);\n }\n }\n\n\n pt.put(\"Data.ForceConstants\", \"\");\n str_tmp.clear();\n\n int nmulti, kat;\n\n for (iat = 0; iat < natmin; ++iat) {\n for (icrd = 0; icrd < 3; ++icrd) {\n for (icell = 0; icell < nsize; ++icell) {\n for (jat = 0; jat < natmin; ++jat) {\n kat = map_p2s[icell][jat];\n nmulti = mindist[iat][kat].size();\n for (jcrd = 0; jcrd < 3; ++jcrd) {\n if (std::abs(fc2[3 * iat + icrd][3 * jat + jcrd][icell]) < eps15) continue;\n for (i = 0; i < nmulti; ++i) {\n ptree &child = pt.add(\"Data.ForceConstants.HARMONIC.FC2\",\n double2string(fc2[3 * iat + icrd][3 * jat + jcrd][icell] /\n static_cast(nmulti)));\n\n child.put(\".pair1\", boost::lexical_cast(iat + 1)\n + \" \" + boost::lexical_cast(icrd + 1));\n child.put(\".pair2\", boost::lexical_cast(kat + 1)\n + \" \" + boost::lexical_cast(jcrd + 1)\n + \" \" + boost::lexical_cast(\n mindist[iat][kat][i].cell + 1));\n }\n }\n }\n }\n }\n }\n\n\n using namespace boost::property_tree::xml_parser;\n const int indent = 2;\n\n#if BOOST_VERSION >= 105600\n write_xml(file_xmlout, pt, std::locale(),\n xml_writer_make_settings(' ', indent, widen(\"utf-8\")));\n#else\n write_xml(file_xmlout, pt, std::locale(),\n xml_writer_make_settings(' ', indent, widen(\"utf-8\")));\n#endif\n\n\n deallocate(kd);\n deallocate(kd_symbol);\n deallocate(xcrd);\n deallocate(fc2);\n deallocate(xcrd_super);\n deallocate(kd_super);\n deallocate(map_p2s);\n\n if (include_na) {\n deallocate(born);\n }\n}\n\n\nvoid calc_lattice_vector(const int ibrav, double celldm[6], double aa[3][3])\n{\n double a, b, c;\n double alpha, beta, gamma;\n double tx, ty, tz;\n double a2, b2, c2;\n double cosalpha, a_prime, u, v;\n\n switch (ibrav) {\n case 1:\n a = celldm[0];\n aa[0][0] = a;\n aa[0][1] = 0.0;\n aa[0][2] = 0.0;\n aa[1][0] = 0.0;\n aa[1][1] = a;\n aa[1][2] = 0.0;\n aa[2][0] = 0.0;\n aa[2][1] = 0.0;\n aa[2][2] = a;\n\n break;\n\n case 2:\n a = celldm[0] / 2.0;\n aa[0][0] = -a;\n aa[0][1] = 0.0;\n aa[0][2] = a;\n aa[1][0] = 0.0;\n aa[1][1] = a;\n aa[1][2] = a;\n aa[2][0] = -a;\n aa[2][1] = a;\n aa[2][2] = 0.0;\n\n break;\n\n case 3:\n a = celldm[0] / 2.0;\n aa[0][0] = a;\n aa[0][1] = a;\n aa[0][2] = a;\n aa[1][0] = -a;\n aa[1][1] = a;\n aa[1][2] = a;\n aa[2][0] = -a;\n aa[2][1] = -a;\n aa[2][2] = a;\n\n break;\n\n case 4:\n a = celldm[0];\n c = celldm[0] * celldm[2];\n aa[0][0] = a;\n aa[0][1] = 0.0;\n aa[0][2] = 0.0;\n aa[1][0] = -0.5 * a;\n aa[1][1] = sqrt(3.0) / 2.0 * a;\n aa[1][2] = 0.0;\n aa[2][0] = 0.0;\n aa[2][1] = 0.0;\n aa[2][2] = c;\n\n break;\n\n case 5:\n case -5:\n a = celldm[0];\n cosalpha = celldm[3];\n tx = a * sqrt((1.0 - cosalpha) / 2.0);\n ty = a * sqrt((1.0 - cosalpha) / 6.0);\n tz = a * sqrt((1.0 + 2.0 * cosalpha) / 3.0);\n\n if (ibrav == 5) {\n aa[0][0] = tx;\n aa[0][1] = -ty;\n aa[0][2] = tz;\n aa[1][0] = 0.0;\n aa[1][1] = 2.0 * ty;\n aa[1][2] = tz;\n aa[2][0] = -tx;\n aa[2][1] = -ty;\n aa[2][2] = tz;\n } else {\n a_prime = a / sqrt(3.0);\n u = tz - 2.0 * sqrt(2.0) * ty;\n v = tz + sqrt(2.0) * ty;\n aa[0][0] = u;\n aa[0][1] = v;\n aa[0][2] = v;\n aa[1][0] = v;\n aa[1][1] = u;\n aa[1][2] = v;\n aa[2][0] = v;\n aa[2][1] = v;\n aa[2][2] = u;\n }\n\n break;\n\n case 6:\n a = celldm[0];\n c = celldm[0] * celldm[2];\n aa[0][0] = a;\n aa[0][1] = 0.0;\n aa[0][2] = 0.0;\n aa[1][0] = 0.0;\n aa[1][1] = a;\n aa[1][2] = 0.0;\n aa[2][0] = 0.0;\n aa[2][1] = 0.0;\n aa[2][2] = c;\n\n break;\n\n case 7:\n a = celldm[0];\n c = celldm[0] * celldm[2];\n a2 = a * 0.5;\n c2 = c * 0.5;\n aa[0][0] = a2;\n aa[0][1] = -a2;\n aa[0][2] = c2;\n aa[1][0] = a2;\n aa[1][1] = a2;\n aa[1][2] = c2;\n aa[2][0] = -a2;\n aa[2][1] = -a2;\n aa[2][2] = c2;\n\n break;\n\n case 8:\n a = celldm[0];\n b = celldm[0] * celldm[1];\n c = celldm[0] * celldm[2];\n aa[0][0] = a;\n aa[0][1] = 0.0;\n aa[0][2] = 0.0;\n aa[1][0] = 0.0;\n aa[1][1] = b;\n aa[1][2] = 0.0;\n aa[2][0] = 0.0;\n aa[2][1] = 0.0;\n aa[2][2] = c;\n\n break;\n\n case 9:\n case -9:\n a = celldm[0];\n b = celldm[0] * celldm[1];\n c = celldm[0] * celldm[2];\n a2 = a * 0.5;\n b2 = b * 0.5;\n\n if (ibrav == 9) {\n aa[0][0] = a2;\n aa[0][1] = b2;\n aa[0][2] = 0.0;\n aa[1][0] = -a2;\n aa[1][1] = b2;\n aa[1][2] = 0.0;\n aa[2][0] = 0.0;\n aa[2][1] = 0.0;\n aa[2][2] = c;\n } else {\n aa[0][0] = a2;\n aa[0][1] = -b2;\n aa[0][2] = 0.0;\n aa[1][0] = a2;\n aa[1][1] = b2;\n aa[1][2] = 0.0;\n aa[2][0] = 0.0;\n aa[2][1] = 0.0;\n aa[2][2] = c;\n }\n\n break;\n\n case 10:\n a2 = celldm[0] * 0.5;\n b2 = celldm[0] * celldm[1] * 0.5;\n c2 = celldm[0] * celldm[2] * 0.5;\n aa[0][0] = a2;\n aa[0][1] = 0.0;\n aa[0][2] = c2;\n aa[1][0] = a2;\n aa[1][1] = b2;\n aa[1][2] = 0.0;\n aa[2][0] = 0.0;\n aa[2][1] = b2;\n aa[2][2] = c2;\n\n break;\n\n case 11:\n a2 = celldm[0] * 0.5;\n b2 = celldm[0] * celldm[1] * 0.5;\n c2 = celldm[0] * celldm[2] * 0.5;\n aa[0][0] = a2;\n aa[0][1] = b2;\n aa[0][2] = c2;\n aa[1][0] = -a2;\n aa[1][1] = b2;\n aa[1][2] = c2;\n aa[2][0] = -a2;\n aa[2][1] = -b2;\n aa[2][2] = c2;\n\n break;\n\n case 12:\n a = celldm[0];\n b = celldm[0] * celldm[1];\n c = celldm[0] * celldm[2];\n gamma = acos(celldm[3]);\n aa[0][0] = a;\n aa[0][1] = 0.0;\n aa[0][2] = 0.0;\n aa[1][0] = b * cos(gamma);\n aa[1][1] = b * sin(gamma);\n aa[1][2] = 0.0;\n aa[2][0] = 0.0;\n aa[2][1] = 0.0;\n aa[2][2] = c;\n\n break;\n\n case -12:\n a = celldm[0];\n b = celldm[0] * celldm[1];\n c = celldm[0] * celldm[2];\n beta = acos(celldm[4]);\n aa[0][0] = a;\n aa[0][1] = 0.0;\n aa[0][2] = 0.0;\n aa[1][0] = 0.0;\n aa[1][1] = b;\n aa[1][2] = 0.0;\n aa[2][0] = c * cos(beta);\n aa[2][1] = 0.0;\n aa[2][2] = c * sin(beta);\n\n break;\n\n case 13:\n a = celldm[0];\n b = celldm[0] * celldm[1];\n c = celldm[0] * celldm[2];\n gamma = acos(celldm[3]);\n aa[0][0] = 0.5 * a;\n aa[0][1] = 0.0;\n aa[0][2] = -0.5 * c;\n aa[1][0] = b * cos(gamma);\n aa[1][1] = b * sin(gamma);\n aa[1][2] = 0.0;\n aa[2][0] = 0.5 * a;\n aa[2][1] = 0.0;\n aa[2][2] = 0.5 * c;\n\n break;\n\n case 14:\n a = celldm[0];\n b = celldm[0] * celldm[1];\n c = celldm[0] * celldm[2];\n alpha = acos(celldm[3]);\n beta = acos(celldm[4]);\n gamma = acos(celldm[5]);\n aa[0][0] = a;\n aa[0][1] = 0.0;\n aa[0][2] = 0.0;\n aa[1][0] = b * cos(gamma);\n aa[1][1] = b * sin(gamma);\n aa[1][2] = 0.0;\n aa[2][0] = c * cos(beta);\n aa[2][1] = c * (cos(alpha) - cos(beta) * cos(gamma)) / sin(gamma);\n aa[2][2] = c * sqrt(1.0 + 2.0 * cos(alpha) * cos(beta) * cos(gamma)\n - cos(alpha) * cos(alpha) - cos(beta) * cos(beta) - cos(gamma) * cos(gamma)) /\n sin(gamma);\n\n break;\n\n default:\n\n cout << \"ERROR: Invalid ibrav.\" << endl;\n exit(EXIT_FAILURE);\n }\n\n\n // Transpose lavec for later use\n\n double tmp[3][3];\n\n for (i = 0; i < 3; ++i) {\n for (j = 0; j < 3; ++j) {\n tmp[i][j] = aa[i][j];\n }\n }\n\n for (i = 0; i < 3; ++i) {\n for (j = 0; j < 3; ++j) {\n aa[i][j] = tmp[j][i];\n }\n }\n}\n\n\nvoid recips(double aa[3][3], double bb[3][3])\n{\n /*\n Calculate Reciprocal Lattice Vectors\n\n Here, BB is just the inverse matrix of AA (multiplied by factor 2 Pi)\n\n BB = 2 Pi AA^{-1},\n = t(b1, b2, b3)\n\n = (b11 b12 b13)\n (b21 b22 b23)\n (b31 b32 b33),\n\n b1 = t(b11, b12, b13) etc.\n */\n\n double det;\n det = aa[0][0] * aa[1][1] * aa[2][2]\n + aa[1][0] * aa[2][1] * aa[0][2]\n + aa[2][0] * aa[0][1] * aa[1][2]\n - aa[0][0] * aa[2][1] * aa[1][2]\n - aa[2][0] * aa[1][1] * aa[0][2]\n - aa[1][0] * aa[0][1] * aa[2][2];\n\n if (std::abs(det) < eps12) {\n cout << \" ERROR: Lattice vector is singular\" << endl;\n exit(EXIT_FAILURE);\n }\n\n double factor = 2.0 * pi / det;\n\n bb[0][0] = (aa[1][1] * aa[2][2] - aa[1][2] * aa[2][1]) * factor;\n bb[0][1] = (aa[0][2] * aa[2][1] - aa[0][1] * aa[2][2]) * factor;\n bb[0][2] = (aa[0][1] * aa[1][2] - aa[0][2] * aa[1][1]) * factor;\n\n bb[1][0] = (aa[1][2] * aa[2][0] - aa[1][0] * aa[2][2]) * factor;\n bb[1][1] = (aa[0][0] * aa[2][2] - aa[0][2] * aa[2][0]) * factor;\n bb[1][2] = (aa[0][2] * aa[1][0] - aa[0][0] * aa[1][2]) * factor;\n\n bb[2][0] = (aa[1][0] * aa[2][1] - aa[1][1] * aa[2][0]) * factor;\n bb[2][1] = (aa[0][1] * aa[2][0] - aa[0][0] * aa[2][1]) * factor;\n bb[2][2] = (aa[0][0] * aa[1][1] - aa[0][1] * aa[1][0]) * factor;\n}\n\n\nstring double2string(const double d)\n{\n std::string rt;\n std::stringstream ss;\n\n ss << std::scientific << std::setprecision(15) << d;\n ss >> rt;\n return rt;\n}\n\n\nvoid get_pairs_of_minimum_distance(const int natmin, const int nat, int **map_p2s,\n double **xf, std::vector **mindist_pairs)\n{\n int icell = 0;\n int i, j, k;\n int isize, jsize, ksize;\n int iat;\n double dist_tmp;\n double vec[3];\n std::vector **distall;\n int nneib = 27;\n\n double ***xcrd;\n\n allocate(distall, natmin, nat);\n allocate(xcrd, nneib, nat, 3);\n\n for (i = 0; i < nat; ++i) {\n for (j = 0; j < 3; ++j) {\n xcrd[0][i][j] = xf[i][j];\n }\n }\n\n for (isize = -1; isize <= 1; ++isize) {\n for (jsize = -1; jsize <= 1; ++jsize) {\n for (ksize = -1; ksize <= 1; ++ksize) {\n\n if (isize == 0 && jsize == 0 && ksize == 0) continue;\n\n ++icell;\n for (i = 0; i < nat; ++i) {\n xcrd[icell][i][0] = xf[i][0] + static_cast(isize);\n xcrd[icell][i][1] = xf[i][1] + static_cast(jsize);\n xcrd[icell][i][2] = xf[i][2] + static_cast(ksize);\n }\n }\n }\n }\n\n for (icell = 0; icell < nneib; ++icell) {\n for (i = 0; i < nat; ++i) {\n rotvec(xcrd[icell][i], xcrd[icell][i], lavec);\n }\n }\n\n\n for (i = 0; i < natmin; ++i) {\n iat = map_p2s[0][i];\n for (j = 0; j < nat; ++j) {\n for (icell = 0; icell < nneib; ++icell) {\n\n dist_tmp = distance(xcrd[0][iat], xcrd[icell][j]);\n\n for (k = 0; k < 3; ++k) vec[k] = xcrd[icell][j][k] - xcrd[0][iat][k];\n\n distall[i][j].push_back(DistInfo(icell, dist_tmp, vec));\n }\n std::sort(distall[i][j].begin(), distall[i][j].end());\n }\n /*\n for (j = 0; j < nat; ++j) {\n for (k = 0; k < distall[i][j].size(); ++k) {\n std::cout << std::setw(5) << i + 1;\n std::cout << std::setw(5) << j + 1;\n std::cout << std::setw(5) << k + 1;\n std::cout << std::setw(15) << distall[i][j][k].dist << endl;\n }\n }\n */\n }\n\n // Construct pairs of minimum distance.\n\n double dist_min;\n for (i = 0; i < natmin; ++i) {\n for (j = 0; j < nat; ++j) {\n mindist_pairs[i][j].clear();\n\n dist_min = distall[i][j][0].dist;\n for (std::vector::const_iterator it = distall[i][j].begin(); it != distall[i][j].end(); ++it) {\n if (std::abs((*it).dist - dist_min) < 1.e-3) {\n mindist_pairs[i][j].push_back(DistInfo(*it));\n }\n }\n }\n }\n\n deallocate(distall);\n deallocate(xcrd);\n}\n\n\ndouble distance(double *x1, double *x2)\n{\n double dist;\n dist = std::pow(x1[0] - x2[0], 2) + std::pow(x1[1] - x2[1], 2) + std::pow(x1[2] - x2[2], 2);\n dist = std::sqrt(dist);\n\n return dist;\n}\n", "meta": {"hexsha": "70c729aa64a5de2906b24ae2769bc901c509b673", "size": 23028, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tools/qe2alm.cpp", "max_stars_repo_name": "r-masuki/alamode", "max_stars_repo_head_hexsha": "6a75ac277e1e4bdaff4218b28a8d9ab7e5130b3c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 93.0, 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"Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6001883449573377, "lm_q2_score": 0.34864513533394575, "lm_q1q2_score": 0.20925274675350794}} {"text": "#include \n#include \n#include \n#include \n\n#include \n#include \n\n#include \"mimir/algorithm/TestAlgorithm.hpp\"\n#include \"mimir/StateMachineFwd.hpp\"\n#include \"mimir/FkinDds.hpp\"\n\n// ReaderLifespan\n#include \"dds/core/detail/ProprietaryApi.hpp\"\n\nnamespace mimir\n{\n namespace algorithm\n {\n class TestAlgorithm::Impl\n {\n public:\n Impl(\n const YAML::Node& config,\n dds::pub::Publisher publisher,\n dds::sub::Subscriber subscriber) :\n inputReader(dds::core::null),\n outputWriter(dds::core::null)\n {\n\n // Setup input reader\n const auto test_config = config[\"inputs\"][\"signal\"];\n auto test_age = test_config[\"max_age_ms\"].as();\n\n auto readerQos = subscriber.default_datareader_qos();\n //readerQos << dds::core::policy::Durability::TransientLocal()\n // << dds::core::policy::Reliability::Reliable()\n readerQos << org::opensplice::core::policy::ReaderLifespan(\n true,\n dds::core::Duration::from_millisecs(test_age));\n\n const auto topicName = test_config[\"topic\"].as();\n const auto id = test_config[\"id\"].as(); // For filter topic\n auto topic = dds::topic::Topic(\n subscriber.participant(),\n topicName);\n auto filter = dds::topic::Filter(\"id = %0\", {id});\n\n inputReader = dds::sub::DataReader(\n subscriber,\n dds::topic::ContentFilteredTopic(\n topic,\n topicName + id,\n filter),\n readerQos);\n\n // Setup output writer\n auto writerQos = publisher.default_datawriter_qos();\n //writerQos << dds::core::policy::Durability::TransientLocal();\n\n outputWriter = dds::pub::DataWriter(\n publisher,\n dds::topic::Topic(\n publisher.participant(),\n config[\"outputs\"][\"response\"][\"topic\"].as()),\n writerQos);\n\n outputId = config[\"outputs\"][\"response\"][\"id\"].as();\n\n // Setup model description\n casadi::SX x = casadi::SX::sym(\"x\", 1); // state\n casadi::SX x_d = casadi::SX::sym(\"x_d\", 1); // set-point\n casadi::SX T = casadi::SX::sym(\"T\", 1); // time constant\n casadi::SX rhs = -(x-x_d)/T; // prop-ctrl with time constant T\n casadi::SXDict ode = {{\"x\", x}, {\"p\", vertcat(x_d,T)}, {\"ode\", rhs}};\n\n integrator = casadi::integrator(\n \"F\", \"cvodes\", ode,\n {{\"tf\", config[\"time_step_ms\"].as()/1000.}});\n\n p_vec = casadi::DM(std::vector{5.,5.});\n\n }\n ~Impl() {}\n dds::sub::DataReader inputReader;\n dds::pub::DataWriter outputWriter;\n std::string outputId;\n casadi::Function integrator;\n casadi::DM p_vec;\n casadi::DM x_k;\n };\n\n TestAlgorithm::TestAlgorithm(\n const YAML::Node& config,\n boost::statechart::fifo_scheduler<> & scheduler,\n boost::statechart::fifo_scheduler<>::processor_handle machine,\n dds::pub::Publisher publisher,\n dds::sub::Subscriber subscriber) :\n m_impl( new TestAlgorithm::Impl(config, publisher, subscriber)),\n m_scheduler(scheduler),\n m_stateMachine(machine),\n m_time_step(std::chrono::milliseconds(config[\"time_step_ms\"].as())),\n m_next_step(std::chrono::steady_clock::now()),\n m_config(config)\n {}\n void TestAlgorithm::solve(const std::atomic& cancel_token)\n {\n try\n {\n BOOST_LOG_TRIVIAL(trace) << \"Solving \" << name();\n\n // Publish x_k(t_now) (we do not publish future samples)\n m_impl->outputWriter << fkin::IdVec1d(\n m_impl->outputId,\n fkin::Vector1d(m_impl->x_k.get_nonzeros()[0]));\n\n // Fetch input, sample and hold\n auto samples = m_impl->inputReader.read();\n // waitset with short timeout, allowing run in \"open loop\"\n\n // Set new desired value\n if(samples.length() > 0)\n {\n auto sample = (--samples.end());\n if(sample->info().valid())\n {\n m_impl->p_vec(0,0) = sample->data().vec().x();\n BOOST_LOG_TRIVIAL(trace) << \"Got a sample: \" << sample->data().vec().x();\n }\n }\n else\n {\n // TODO: In case of measurements: Disable feedback gain\n BOOST_LOG_TRIVIAL(trace) << \"No valid sample for \" << name() << \", using old\";\n }\n\n if(cancel_token)\n {\n event(new mimir::EvInterrupt());\n return;\n }\n\n // Integrate\n auto result = m_impl->integrator(\n casadi::DMDict( {{\"x0\",m_impl->x_k}, {\"p\", m_impl->p_vec}} ));\n\n if(cancel_token)\n {\n event(new mimir::EvInterrupt());\n return;\n }\n\n // Update output (not published yet)\n m_impl->x_k = result[\"xf\"];\n step_time();\n event(new mimir::EvReady());\n\n }\n catch (...)\n {\n // Need to post event in case of exception so that the state machine\n // knows that the job has finished.\n BOOST_LOG_TRIVIAL(fatal) << name() << \" exception thrown\";\n event(new mimir::EvError());\n throw;\n }\n }\n\n void TestAlgorithm::initialize(const std::atomic&)\n {\n try\n {\n BOOST_LOG_TRIVIAL(debug) << name() << \" is initializing\";\n\n // waitset to get initial measurements that meet requirements.\n // set initial state vector..\n m_impl->x_k = casadi::DM(std::vector{0.});\n // set parameters as well?\n m_next_step = std::chrono::steady_clock::now();\n\n event(new mimir::EvReady());\n }\n catch (...)\n {\n event(new mimir::EvError());\n throw;\n }\n }\n\n void TestAlgorithm::timer(const std::atomic& cancel_token)\n {\n\n // There is no sanity checks in terms of real-timeliness of execution.\n // probably want external time point to wait until.\n typedef std::chrono::milliseconds scm;\n auto now = std::chrono::steady_clock::now();\n auto time_p = m_next_step;\n auto timeleft = std::chrono::duration_cast(time_p - now).count();\n BOOST_LOG_TRIVIAL(trace) << \"Time left: \" << timeleft;\n\n auto fraction = (m_time_step/100 > scm(0) ? m_time_step/100 :\n (m_time_step/50 > scm(0) ? m_time_step/50 :\n (m_time_step/25 > scm(0) ? m_time_step/25 :\n (m_time_step/10 > scm(0) ? m_time_step/10 :\n (m_time_step/2 > scm(0) ? m_time_step/2 : scm(1))))));\n\n while(now < time_p)\n {\n auto diff = time_p - now;\n std::this_thread::sleep_for(diff < fraction ? diff : fraction);\n if(cancel_token)\n break;\n now = std::chrono::steady_clock::now();\n }\n\n if(cancel_token)\n {\n BOOST_LOG_TRIVIAL(trace) << \"Timer Canceled\";\n event(new mimir::EvInterrupt());\n }\n else\n {\n BOOST_LOG_TRIVIAL(trace) << \"Timeout\";\n event(new mimir::EvTimeout());\n }\n }\n\n void TestAlgorithm::event(boost::statechart::event_base * const event)\n {\n // who is responsible for the pointer?\n m_scheduler.queue_event(\n m_stateMachine,\n make_intrusive(event));\n }\n\n TestAlgorithm::~TestAlgorithm() = default;\n }\n}\n", "meta": {"hexsha": "f9de72ca6043969bffdc62ecf65dd84ab501dcc3", "size": 7580, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mimir/algorithm/TestAlgorithm.cpp", "max_stars_repo_name": "sintef-ocean/mimir", "max_stars_repo_head_hexsha": "c1d9671ee61e543e631f04d8b343a8f5e9229f6f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": 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YES\n2. NO", "lm_q1_score": 0.5039061705290805, "lm_q2_score": 0.41489884579676883, "lm_q1q2_score": 0.20907008854238526}} {"text": "///////////////////////////////////////////////////////////////////////////////\n// Orkid\n// Copyrigh 1996-2009, Michael T. Mayers\n// See License at OrkidRoot/license.html or http://www.tweakoz.com/orkid/license.html\n///////////////////////////////////////////////////////////////////////////////\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n//#include \n//#include \n//#include \n//#include \n//#include \n//#include \n\n#if defined(ORK_CONFIG_IX)\n#include \n#endif\n\ns64 giNumRays = 0;\n\nnamespace ork {\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nstatic const int kIW = 2048;\nstatic const int kOS = 0;\nstatic const float kJITTER = 0.5f; // 0.5\n\nstatic int GetNumCores() {\n return OldSchool::GetNumCores();\n}\n\nvoid RgmTri::Compute() {\n mFacePlane.CalcPlaneFromTriangle(mpv0->pos, mpv1->pos, mpv2->pos);\n const fvec3 vn = mFacePlane.n;\n mEdgePlane0.CalcPlaneFromTriangle(mpv0->pos, vn + mpv0->pos, mpv1->pos);\n mEdgePlane1.CalcPlaneFromTriangle(mpv1->pos, vn + mpv1->pos, mpv2->pos);\n mEdgePlane2.CalcPlaneFromTriangle(mpv2->pos, vn + mpv2->pos, mpv0->pos);\n mArea = (mpv0->pos - mpv2->pos).Cross(mpv1->pos - mpv2->pos).Mag() * 0.5f;\n}\n\nJitterer::Jitterer(int ikos, float fradius, const fvec3& dX, const fvec3& dY) {\n miKos = ikos;\n\n int idim = (miKos * 2) + 1;\n miNumSamples = idim * idim;\n\n int isamp = 0;\n\n int idiv = (ikos == 0) ? 1 : ikos;\n\n for (int iy = -ikos; iy <= ikos; iy++) {\n float fy = fradius * float(iy) / float(idiv);\n for (int ix = -ikos; ix <= ikos; ix++) {\n float fx = fradius * float(ix) / float(idiv);\n sample[isamp++] = dX * fx + dY * fy;\n }\n }\n OrkAssert(isamp == miNumSamples);\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nMaterial::Material()\n : mColor(0.2f, 0.2f, 0.2f)\n , m_Refl(0)\n , m_Diff(0.2f)\n , m_Spec(0.8f)\n , m_RIndex(1.5f)\n , m_DRefl(0) {\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid Material::SetParameters(float a_Refl, float a_Refr, const fvec3& a_Col, float a_Diff, float a_Spec) {\n m_Refl = a_Refl;\n m_Refr = a_Refr;\n mColor = a_Col;\n m_Diff = a_Diff;\n m_Spec = a_Spec;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nPrimitive::~Primitive() {\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nfvec3 Primitive::GetColor(const fvec3& a_Pos) const {\n return mMaterial->GetColor();\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\n#define FINDMINMAX(x0, x1, x2, min, max) \\\n min = max = x0; \\\n if (x1 < min) \\\n min = x1; \\\n if (x1 > max) \\\n max = x1; \\\n if (x2 < min) \\\n min = x2; \\\n if (x2 > max) \\\n max = x2;\n// X-tests\n#define AXISTEST_X01(a, b, fa, fb) \\\n p0 = a * v0[1] - b * v0[2], p2 = a * v2[1] - b * v2[2]; \\\n if (p0 < p2) { \\\n min = p0; \\\n max = p2; \\\n } else { \\\n min = p2; \\\n max = p0; \\\n } \\\n rad = fa * a_BoxHalfsize[1] + fb * a_BoxHalfsize[2]; \\\n if (min > rad || max < -rad) \\\n return 0;\n#define AXISTEST_X2(a, b, fa, fb) \\\n p0 = a * v0[1] - b * v0[2], p1 = a * v1[1] - b * v1[2]; \\\n if (p0 < p1) { \\\n min = p0; \\\n max = p1; \\\n } else { \\\n min = p1; \\\n max = p0; \\\n } \\\n rad = fa * a_BoxHalfsize[1] + fb * a_BoxHalfsize[2]; \\\n if (min > rad || max < -rad) \\\n return 0;\n// Y-tests\n#define AXISTEST_Y02(a, b, fa, fb) \\\n p0 = -a * v0[0] + b * v0[2], p2 = -a * v2[0] + b * v2[2]; \\\n if (p0 < p2) { \\\n min = p0; \\\n max = p2; \\\n } else { \\\n min = p2; \\\n max = p0; \\\n } \\\n rad = fa * a_BoxHalfsize[0] + fb * a_BoxHalfsize[2]; \\\n if (min > rad || max < -rad) \\\n return 0;\n#define AXISTEST_Y1(a, b, fa, fb) \\\n p0 = -a * v0[0] + b * v0[2], p1 = -a * v1[0] + b * v1[2]; \\\n if (p0 < p1) { \\\n min = p0; \\\n max = p1; \\\n } else { \\\n min = p1; \\\n max = p0; \\\n } \\\n rad = fa * a_BoxHalfsize[0] + fb * a_BoxHalfsize[2]; \\\n if (min > rad || max < -rad) \\\n return 0;\n// Z-tests\n#define AXISTEST_Z12(a, b, fa, fb) \\\n p1 = a * v1[0] - b * v1[1], p2 = a * v2[0] - b * v2[1]; \\\n if (p2 < p1) { \\\n min = p2; \\\n max = p1; \\\n } else { \\\n min = p1; \\\n max = p2; \\\n } \\\n rad = fa * a_BoxHalfsize[0] + fb * a_BoxHalfsize[1]; \\\n if (min > rad || max < -rad) \\\n return 0;\n#define AXISTEST_Z0(a, b, fa, fb) \\\n p0 = a * v0[0] - b * v0[1], p1 = a * v1[0] - b * v1[1]; \\\n if (p0 < p1) { \\\n min = p0; \\\n max = p1; \\\n } else { \\\n min = p1; \\\n max = p0; \\\n } \\\n rad = fa * a_BoxHalfsize[0] + fb * a_BoxHalfsize[1]; \\\n if (min > rad || max < -rad) \\\n return 0;\n\n///////////////////////////////////////////////////////////////////////////////\n\nbool Primitive::PlaneBoxOverlap(const fvec3& a_Normal, const fvec3& a_Vert, const fvec3& a_MaxBox) {\n fvec3 vmin, vmax;\n for (int q = 0; q < 3; q++) {\n float v = a_Vert[q];\n if (a_Normal[q] > 0.0f) {\n vmin[q] = -a_MaxBox[q] - v;\n vmax[q] = a_MaxBox[q] - v;\n } else {\n vmin[q] = a_MaxBox[q] - v;\n vmax[q] = -a_MaxBox[q] - v;\n }\n }\n if (a_Normal.Dot(vmin) > 0.0f)\n return false;\n if (a_Normal.Dot(vmax) >= 0.0f)\n return true;\n return false;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nbool Primitive::IntersectTriBox(\n const fvec3& a_BoxCentre,\n const fvec3& a_BoxHalfsize,\n const fvec3& a_V0,\n const fvec3& a_V1,\n const fvec3& a_V2) {\n fvec3 v0, v1, v2, normal, e0, e1, e2;\n float min, max, p0, p1, p2, rad, fex, fey, fez;\n v0 = a_V0 - a_BoxCentre;\n v1 = a_V1 - a_BoxCentre;\n v2 = a_V2 - a_BoxCentre;\n e0 = v1 - v0, e1 = v2 - v1, e2 = v0 - v2;\n fex = fabsf(e0[0]);\n fey = fabsf(e0[1]);\n fez = fabsf(e0[2]);\n AXISTEST_X01(e0[2], e0[1], fez, fey);\n AXISTEST_Y02(e0[2], e0[0], fez, fex);\n AXISTEST_Z12(e0[1], e0[0], fey, fex);\n fex = fabsf(e1[0]);\n fey = fabsf(e1[1]);\n fez = fabsf(e1[2]);\n AXISTEST_X01(e1[2], e1[1], fez, fey);\n AXISTEST_Y02(e1[2], e1[0], fez, fex);\n AXISTEST_Z0(e1[1], e1[0], fey, fex);\n fex = fabsf(e2[0]);\n fey = fabsf(e2[1]);\n fez = fabsf(e2[2]);\n AXISTEST_X2(e2[2], e2[1], fez, fey);\n AXISTEST_Y1(e2[2], e2[0], fez, fex);\n AXISTEST_Z12(e2[1], e2[0], fey, fex);\n FINDMINMAX(v0[0], v1[0], v2[0], min, max);\n if (min > a_BoxHalfsize[0] || max < -a_BoxHalfsize[0])\n return false;\n FINDMINMAX(v0[1], v1[1], v2[1], min, max);\n if (min > a_BoxHalfsize[1] || max < -a_BoxHalfsize[1])\n return false;\n FINDMINMAX(v0[2], v1[2], v2[2], min, max);\n if (min > a_BoxHalfsize[2] || max < -a_BoxHalfsize[2])\n return false;\n normal = e0.Cross(e1);\n if (!PlaneBoxOverlap(normal, v0, a_BoxHalfsize))\n return false;\n return true;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nRaytTriangle::RaytTriangle(const RgmVertex* v1, const RgmVertex* v2, const RgmVertex* v3) {\n mMaterial = 0;\n mVertex[0] = v1;\n mVertex[1] = v2;\n mVertex[2] = v3;\n\n //////////////////////////////////////\n // precompute what we can\n //////////////////////////////////////\n\n fvec3 A = mVertex[0]->pos;\n fvec3 B = mVertex[1]->pos;\n fvec3 C = mVertex[2]->pos;\n fvec3 c = B - A;\n fvec3 b = C - A;\n mN = b.Cross(c);\n int u, v;\n if (fabs(mN.x) > fabs(mN.y)) {\n if (fabs(mN.x) > fabs(mN.z))\n k = 0;\n else\n k = 2;\n } else {\n if (fabs(mN.y) > fabs(mN.z))\n k = 1;\n else\n k = 2;\n }\n u = (k + 1) % 3;\n v = (k + 2) % 3;\n // precomp\n float krec = 1.0f / mN[k];\n nu = mN[u] * krec;\n nv = mN[v] * krec;\n nd = mN.Dot(A) * krec;\n // first line equation\n float reci = 1.0f / (b[u] * c[v] - b[v] * c[u]);\n bnu = b[u] * reci;\n bnv = -b[v] * reci;\n // second line equation\n cnu = c[v] * reci;\n cnv = -c[u] * reci;\n // finalize normal\n mN.Normalize();\n // mVertex[0]->SetNormal( m_N );\n // mVertex[1]->SetNormal( m_N );\n // mVertex[2]->SetNormal( m_N );\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nAABox RaytTriangle::GetAABox() const {\n AABox ret;\n ret.BeginGrow();\n ret.Grow(mVertex[0]->pos);\n ret.Grow(mVertex[1]->pos);\n ret.Grow(mVertex[2]->pos);\n ret.EndGrow();\n return ret;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nint RaytTriangle::Intersect(const fray3& a_Ray, fvec3& isect, float& a_Dist) const {\n const RgmTri* tri = this->mRgmPoly;\n const fvec3& v0 = mVertex[0]->pos;\n const fvec3& v1 = mVertex[1]->pos;\n const fvec3& v2 = mVertex[2]->pos;\n const fplane3& fp = tri->mFacePlane;\n const fplane3& ep0 = tri->mEdgePlane0;\n const fplane3& ep1 = tri->mEdgePlane1;\n const fplane3& ep2 = tri->mEdgePlane2;\n float s, t;\n bool bv = CollisionTester::RayTriangleTest(a_Ray, fp, ep0, ep1, ep2, isect, a_Dist);\n return bv;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nfvec3 RaytTriangle::GetNormal(const fvec3& a_Pos) const {\n fvec3 N1 = mVertex[0]->nrm;\n fvec3 N2 = mVertex[1]->nrm;\n fvec3 N3 = mVertex[2]->nrm;\n fvec3 N = N1 + mU * (N2 - N1) + mV * (N3 - N1);\n N.Normalize();\n return N;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nbool RaytTriangle::IntersectBox(const AABox& a_Box) const {\n return IntersectTriBox(\n a_Box.Min() + a_Box.GetSize() * 0.5f, a_Box.GetSize() * 0.5f, mVertex[0]->pos, mVertex[1]->pos, mVertex[2]->pos);\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid RaytTriangle::Rasterize(Engine* peng) const {\n int iw = peng->width();\n int ih = peng->height();\n\n const BakeShader* bshader = GetBakeShader();\n const fvec2& uv0 = mRgmPoly->mpv0->uv;\n const fvec2& uv1 = mRgmPoly->mpv1->uv;\n const fvec2& uv2 = mRgmPoly->mpv2->uv;\n\n fvec3 v0(uv0.x * iw, uv0.y * ih, 0.0f);\n fvec3 v1(uv1.x * iw, uv1.y * ih, 0.0f);\n fvec3 v2(uv2.x * iw, uv2.y * ih, 0.0f);\n\n BakeShadowFragment bv0, bv1, bv2;\n bv0.mPos = mRgmPoly->mpv0->pos;\n bv1.mPos = mRgmPoly->mpv1->pos;\n bv2.mPos = mRgmPoly->mpv2->pos;\n bv0.mNrm = mRgmPoly->mpv0->nrm;\n bv1.mNrm = mRgmPoly->mpv1->nrm;\n bv2.mNrm = mRgmPoly->mpv2->nrm;\n\n peng->RasterizeTriangle(\n *bshader, int(v0.x), int(v0.y), bv0, int(v1.x), int(v1.y), bv1, int(v2.x), int(v2.y), bv2);\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nRaytSphere::RaytSphere(fvec3& center, float radius) {\n mCenter = center;\n mSqRadius = radius * radius;\n mRadius = radius;\n mRRadius = 1.0f / radius;\n // mMaterial = new Material();\n // set vectors for texture mapping\n mVn = fvec3(0, 1, 0);\n mVe = fvec3(1, 0, 0);\n mVc = mVn.Cross(mVe);\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nbool RaytSphere::IntersectSphereBox(const fvec3& a_Centre, const AABox& a_Box) const {\n float dmin = 0;\n fvec3 spos = a_Centre;\n fvec3 bpos = a_Box.Min();\n fvec3 bsize = a_Box.GetSize();\n for (int i = 0; i < 3; i++) {\n if (spos[i] < bpos[i]) {\n dmin = dmin + (spos[i] - bpos[i]) * (spos[i] - bpos[i]);\n } else if (spos[i] > (bpos[i] + bsize[i])) {\n dmin = dmin + (spos[i] - (bpos[i] + bsize[i])) * (spos[i] - (bpos[i] + bsize[i]));\n }\n }\n return (dmin <= mSqRadius);\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nbool RaytSphere::IntersectBox(const AABox& a_Box) const {\n return IntersectSphereBox(mCenter, a_Box);\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid RaytSphere::Rasterize(Engine* peng) const {\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nAABox RaytSphere::GetAABox() const {\n AABox ret;\n return ret;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nint RaytSphere::Intersect(const fray3& a_Ray, fvec3& isect, float& a_Dist) const {\n fvec3 v = a_Ray.mOrigin - mCenter;\n float b = -v.Dot(a_Ray.mDirection);\n float det = (b * b) - v.Dot(v) + mSqRadius;\n int retval = MISS;\n if (det > 0) {\n det = sqrtf(det);\n float i1 = b - det;\n float i2 = b + det;\n if (i2 > 0) {\n if (i1 < 0) {\n if (i2 < a_Dist) {\n a_Dist = i2;\n retval = INPRIM;\n }\n } else {\n if (i1 < a_Dist) {\n a_Dist = i1;\n retval = HIT;\n }\n }\n }\n }\n return retval;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nfvec3 RaytSphere::GetNormal(const fvec3& a_Pos) const {\n return (a_Pos - mCenter) * mRRadius;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nScene::Scene()\n : mExtends()\n , mpFixedGrid(0) {\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nScene::~Scene() {\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid Scene::ExitScene() {\n if (mpFixedGrid)\n delete mpFixedGrid;\n mpFixedGrid = 0;\n}\n\nbool Scene::InitScene(const AABox& scene_box) {\n // Material* mat = new Material();\n // mat->SetParameters( 0.0f, 0.0f, fvec3( 0.4f, 0.3f, 0.3f ), 1.0f, 0.0f );\n // mat = new Material();\n // mat->SetParameters( 0.0f, 0.0f, fvec3( 0.5f, 0.3f, 0.5f ), 0.6f, 0.0f );\n // mat = new Material();\n // mat->SetParameters( 0.0f, 0.0f, fvec3( 0.4f, 0.7f, 0.7f ), 0.5f, 0.0f );\n // mat = new Material();\n // mat->SetParameters( 0.9f, 0, fvec3( 0.9f, 0.9f, 1 ), 0.3f, 0.7f );\n // mat->SetRefrIndex( 1.3f );\n\n mExtends = scene_box;\n\n mpFixedGrid = new FixedGrid;\n\n orkvector Prims;\n\n for (orkmap::const_iterator it = mGeoSets.begin(); it != mGeoSets.end(); it++) {\n const std::string& name = it->first;\n const RgmGeoSet* pset = it->second;\n\n // if( name.find( \"caster_\" ) != std::string::npos )\n {\n int inump = pset->NumPrimitives();\n for (int ip = 0; ip < inump; ip++) {\n const Primitive* prim = pset->GetPrimitive(ip);\n\n Prims.push_back(prim);\n }\n }\n }\n\n mpFixedGrid->BuildGrid(scene_box, Prims);\n\n return true;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nvoid RgmLightContainer::LoadLitFile(const char* pfilename) {\n chunkfile::DefaultLoadAllocator allocator;\n chunkfile::Reader chunkreader(pfilename, \"lit\", allocator);\n if (chunkreader.IsOk()) {\n chunkfile::InputStream* HeaderStream = chunkreader.GetStream(\"header\");\n ///////////////////////////////////////////////////\n int inumlights = 0;\n HeaderStream->GetItem(inumlights);\n for (int il = 0; il < inumlights; il++) {\n int iname, itype;\n fmtx4 mtxW;\n fvec3 clr;\n float intens;\n int icastsshadows;\n HeaderStream->GetItem(iname);\n HeaderStream->GetItem(mtxW);\n HeaderStream->GetItem(clr);\n HeaderStream->GetItem(intens);\n HeaderStream->GetItem(icastsshadows);\n HeaderStream->GetItem(itype);\n const char* pname = chunkreader.GetString(iname);\n const char* ptype = chunkreader.GetString(itype);\n\n if (0 == strcmp(ptype, \"PointLight\")) {\n RgmLight rlite(fvec3::Black(), clr);\n HeaderStream->GetItem(rlite.mPos);\n HeaderStream->GetItem(rlite.mFalloff);\n HeaderStream->GetItem(rlite.mRadius);\n\n rlite.mCastsShadows = bool(icastsshadows);\n mLights[pname] = rlite;\n } else // Dir Light\n {\n RgmLight rlite(mtxW.GetTranslation(), clr);\n\n rlite.mDir = mtxW.GetZNormal();\n rlite.mCastsShadows = bool(icastsshadows);\n mLights[pname] = rlite;\n }\n }\n }\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nRgmModel* LoadRgmFile(const char* pfilename, RgmShaderBuilder& shbuilder) {\n RgmModel* mdl = new RgmModel;\n\n mdl->_aaBox.BeginGrow();\n chunkfile::DefaultLoadAllocator allocator;\n chunkfile::Reader chunkreader(pfilename, \"rgm\", allocator);\n if (chunkreader.IsOk()) {\n chunkfile::InputStream* HeaderStream = chunkreader.GetStream(\"header\");\n chunkfile::InputStream* ModelDataStream = chunkreader.GetStream(\"modeldata\");\n ///////////////////////////////////////////////////\n int inumannos = 0;\n HeaderStream->GetItem(inumannos);\n for (int ia = 0; ia < inumannos; ia++) {\n int ikey, ival;\n HeaderStream->GetItem(ikey);\n HeaderStream->GetItem(ival);\n const char* pkey = chunkreader.GetString(ikey);\n const char* pval = chunkreader.GetString(ival);\n mdl->mAnnos[pkey] = pval;\n }\n ///////////////////////////////////////////////////\n HeaderStream->GetItem(mdl->minumsubs);\n mdl->msubmeshes = new RgmSubMesh[mdl->minumsubs];\n for (int is = 0; is < mdl->minumsubs; is++) {\n RgmSubMesh& sub = mdl->msubmeshes[is];\n sub.mGeoSet = new RgmGeoSet;\n BakeShader* pshader = shbuilder.CreateShader(sub);\n Material* pmaterial = shbuilder.CreateMaterial(sub);\n sub.mpShader = pshader;\n sub.mpMaterial = pmaterial;\n ///////////////////////////////////////////////////\n int inumsubannos = 0;\n HeaderStream->GetItem(inumsubannos);\n for (int ia = 0; ia < inumsubannos; ia++) {\n int ikey, ival;\n HeaderStream->GetItem(ikey);\n HeaderStream->GetItem(ival);\n const char* pkey = chunkreader.GetString(ikey);\n const char* pval = chunkreader.GetString(ival);\n sub.mAnnos[pkey] = pval;\n }\n ///////////////////////////////////////////////////\n int inumtotv;\n int iname;\n HeaderStream->GetItem(iname);\n HeaderStream->GetItem(sub.minumverts);\n HeaderStream->GetItem(inumtotv);\n sub.mpVertices = new RgmVertex[sub.minumverts];\n sub.mname = chunkreader.GetString(iname);\n mdl->mSubMeshMap[sub.mname] = ⊂\n for (int iv = 0; iv < sub.minumverts; iv++) {\n RgmVertex& vtx = sub.mpVertices[iv];\n ModelDataStream->GetItem(vtx.pos);\n ModelDataStream->GetItem(vtx.nrm);\n float fu = fmod((vtx.pos.x) * 0.01f, 1.0f);\n float fv = fmod((vtx.pos.z) * 0.01f, 1.0f);\n ModelDataStream->GetItem(vtx.uv);\n vtx.uv.setX(fu);\n vtx.uv.setY(fv);\n }\n int inumtotp;\n HeaderStream->GetItem(sub.minumtris);\n HeaderStream->GetItem(inumtotp);\n sub.mtriangles = new RgmTri[sub.minumtris];\n for (int ip = 0; ip < sub.minumtris; ip++) {\n RgmTri& tri = sub.mtriangles[ip];\n int inumv;\n HeaderStream->GetItem(inumv);\n OrkAssert(inumv == 3);\n int ivA, ivB, ivC;\n ModelDataStream->GetItem(ivA);\n ModelDataStream->GetItem(ivB);\n ModelDataStream->GetItem(ivC);\n tri.mpv0 = sub.mpVertices + ivA;\n tri.mpv1 = sub.mpVertices + ivB;\n tri.mpv2 = sub.mpVertices + ivC;\n tri.Compute();\n }\n sub.mGeoSet->GetAABox() = AABox();\n sub.mGeoSet->GetAABox().BeginGrow();\n for (int ip = 0; ip < sub.minumtris; ip += 4) // ip++ )\n {\n const RgmTri& tri = sub.mtriangles[ip];\n const RgmVertex* v0 = tri.mpv0;\n const RgmVertex* v1 = tri.mpv1;\n const RgmVertex* v2 = tri.mpv2;\n RaytTriangle* prim = new RaytTriangle(v0, v1, v2);\n prim->mRgmPoly = &tri;\n prim->SetMaterial(pmaterial);\n prim->SetBakeShader(pshader);\n sub.mGeoSet->AddPrimitive(prim);\n sub.mGeoSet->GetAABox().Grow(v0->pos);\n sub.mGeoSet->GetAABox().Grow(v1->pos);\n sub.mGeoSet->GetAABox().Grow(v2->pos);\n mdl->_aaBox.Grow(v0->pos);\n mdl->_aaBox.Grow(v1->pos);\n mdl->_aaBox.Grow(v2->pos);\n }\n sub.mGeoSet->GetAABox().EndGrow();\n }\n }\n mdl->_aaBox.EndGrow();\n return mdl;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nEngine::Engine() {\n mScene = new Scene();\n // KdTree::SetMemoryManager( new MManager() );\n mMod = new int[64];\n mMod = (int*)((((unsigned long)mMod) + 32) & (0xffffffff - 31));\n mMod[0] = 0, mMod[1] = 1, mMod[2] = 2, mMod[3] = 0, mMod[4] = 1;\n // m_Stack = new kdstack[64];\n // m_Stack = (kdstack*)((((unsigned long)m_Stack) + 32) & (0xffffffff - 31));\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nEngine::~Engine() {\n delete mScene;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid Engine::setTarget(RayPixel* dest, int w, int h) {\n // set pixel buffer address & size\n mDest = dest;\n miW = w;\n miH = h;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nstatic float AreaOfTri(const fvec3& A, const fvec3& B, const fvec3& C) {\n float farea = (A - C).Cross(B - C).Mag() * 0.5f;\n return farea;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nconst Primitive* Engine::Raytrace(const fray3& ray, const int irecdepth, const float irindex, fvec3& acc, float& dist) {\n const Primitive* prim = 0;\n\n ////////////////////////////////////////////////////\n // find the nearest intersection\n ////////////////////////////////////////////////////\n\n acc = fvec3(0.1f, 0.1f, 0.2f);\n fvec3 P;\n bool bhit = GetScene()->GetFixedGrid()->FindNearest(ray, dist, P, prim);\n if (false == bhit)\n return 0;\n\n /*\tconst RgmTri& tri = *prim->mRgmPoly;\n\n ////////////////////////////////////////////////////\n // Compute Barycentric\n ////////////////////////////////////////////////////\n\n const fvec3& NA = tri.mpv0->nrm;\n const fvec3& NB = tri.mpv1->nrm;\n const fvec3& NC = tri.mpv2->nrm;\n const fvec3& A = tri.mpv0->pos;\n const fvec3& B = tri.mpv1->pos;\n const fvec3& C = tri.mpv2->pos;\n\n float PBC = ( AreaOfTri(P,B,C) );\n float PCA = ( AreaOfTri(P,C,A) );\n float PAB = ( AreaOfTri(P,A,B) );\n float ABC = tri.mArea;\n\n float a = PBC/ABC;\n float b = PCA/ABC;\n float c = 1.0f - a - b;\n\n //////////////////////////\n // Interpolated Normal\n //////////////////////////\n\n fvec3 N = (NA*a+NB*b+NC*c);\n\n ////////////////////////////////////////////////////\n // lighting\n ////////////////////////////////////////////////////\n\n fvec3 LightPos = fvec3(76,-15,-900);\n fvec3 LMP = (LightPos-P);\n fvec3 LightDir = LMP.Normal();\n\n float fdot = N.Dot(LightDir);\n\n if( fdot<0.0f ) fdot=0.0f;\n\n acc = fvec3(fdot,fdot,fdot);\n\n /////////////////////////////////////////////\n // backfacing to light automatically black\n /////////////////////////////////////////////\n if( fdot <= 0.0f )\n {\n acc = fvec3::Black();\n return prim;\n }\n /////////////////////////////////////////////\n // perform shadowing test\n /////////////////////////////////////////////\n else\n {\n const float shadowbias = 0.1f;\n Ray3 RayToLight( P+LightDir*shadowbias, LightDir );\n const Primitive* primL = 0;\n\n float shadowdist = 100000.0f;\n fvec3 ShadowPos;\n bool bshadowhit = GetScene()->GetFixedGrid()->FindNearest( RayToLight, shadowdist, ShadowPos, primL );\n\n if( bshadowhit )\n {\n float disttolight = LMP.Mag();\n if( shadowdist 0) ? int(a + .5f) : int(a - .5f);\n return iret;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid Engine::DrawSpan(\n const BakeShader& shader,\n int x1,\n int y1,\n const BakeShadowFragment& d1,\n int x2,\n int y2,\n const BakeShadowFragment& d2,\n SpanCtx& ctx,\n orkset* output) {\n OrkAssert(x2 - x1 >= 0 && x2 - x1 >= y2 - y1 && y2 - y1 >= 0);\n\n bool horizontal = y1 == y2;\n bool diagonal = (y2 - y1) == (x2 - x1);\n\n float scale = 1.0 / (x2 - x1);\n\n BakeShadowFragment d = d1;\n BakeShadowFragment d_step = (d2 - d1) * scale * .99999f;\n\n int y = y1;\n float yy = y1;\n float y_step = (y2 - y1) * scale;\n\n for (int x = x1; x <= x2; ++x, d += d_step) {\n int X(x);\n int Y(y);\n if (ctx.swap_xy) {\n std::swap(X, Y);\n }\n if (ctx.flip_y) {\n Y = -Y;\n }\n if (output) {\n output->insert(SpanFragment(X, Y, d));\n } else {\n int ipix = (Y * miW) + X;\n mFragments[ipix] = d;\n shader.Compute(X, Y);\n }\n\n if (!horizontal) {\n y = diagonal ? (y + 1) : int(round(yy += y_step));\n }\n }\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid Engine::DrawSpanE(\n const BakeShader& shader,\n int x1,\n int y1,\n BakeShadowFragment c1,\n int x2,\n int y2,\n BakeShadowFragment c2,\n SpanCtx& ctx,\n orkset* output) {\n if (x1 > x2)\n // force left to right.\n {\n std::swap(x1, x2);\n std::swap(y1, y2);\n std::swap(c1, c2);\n }\n if ((ctx.flip_y = y1 > y2))\n // force down to up.\n {\n y1 = -y1;\n y2 = -y2;\n }\n if ((ctx.swap_xy = y2 - y1 > x2 - x1))\n // force line with <= 45 deg to x-axis.\n {\n std::swap(x1, y1);\n std::swap(x2, y2);\n }\n DrawSpan(shader, x1, y1, c1, x2, y2, c2, ctx, output);\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid Engine::RasterizeTriangle(\n const BakeShader& shader,\n int x1,\n int y1,\n const BakeShadowFragment& d1,\n int x2,\n int y2,\n const BakeShadowFragment& d2,\n int x3,\n int y3,\n const BakeShadowFragment& d3)\n\n{\n SpanCtx ctx;\n\n orkset scanned_pnts;\n // sorted in y val, and in x val if y val the same.\n DrawSpanE(shader, x1, y1, d1, x2, y2, d2, ctx, &scanned_pnts);\n DrawSpanE(shader, x2, y2, d2, x3, y3, d3, ctx, &scanned_pnts);\n DrawSpanE(shader, x3, y3, d3, x1, y1, d1, ctx, &scanned_pnts);\n int cur_yval = miH / 2; // initialize to an invalid value.\n orkset same_yval; // of the scanned result.\n for (orkset::iterator it = scanned_pnts.begin(); it != scanned_pnts.end(); ++it) {\n int y = it->y;\n if (y != cur_yval) {\n if (same_yval.size()) {\n orkset::iterator it1 = same_yval.begin();\n orkset::iterator it2 = --same_yval.end();\n DrawSpanE(shader, it1->x, cur_yval, it1->mData, it2->x, cur_yval, it2->mData, ctx, 0);\n same_yval.clear();\n }\n cur_yval = y;\n }\n same_yval.insert(*it);\n }\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid Engine::InitRender(fvec3& eye, fvec3& target) {\n ///////////////////////////////////////\n // calculate lookat matrix\n ///////////////////////////////////////\n\n fvec3 zdir = (target - eye).Normal();\n fvec3 cross = zdir.Cross(fvec3(0.0f, 1.0f, 0.0f));\n if (cross.Mag() == 0.0f)\n cross = zdir.Cross(fvec3(1.0f, 0.0f, 0.0f));\n if (cross.Mag() == 0.0f)\n cross = zdir.Cross(fvec3(0.0f, 0.0f, 1.0f));\n\n fvec3 up = zdir.Cross(cross);\n\n fmtx4 mtxLookat;\n mtxLookat.LookAt(eye, target, up);\n\n ///////////////////////////////////////\n // set projection matrix\n ///////////////////////////////////////\n\n fmtx4 mtxP;\n\n float faspect = float(width()) / float(height());\n mtxP.Perspective(45, faspect, 1.0f, 10000.0f);\n // mtxP.Ortho( -1, 1, -1, 1, 1, 10000 );\n\n fvec3 vo(0.0f, 0.0f, 0.0f);\n fvec3 vpn(0.0f, 0.0f, 1.0f);\n fvec3 vpf(0.0f, 0.0f, 10000.0f);\n\n fvec3 pvo = vo.Transform(mtxP);\n fvec3 pvn = vpn.Transform(mtxP);\n fvec3 pvf = vpf.Transform(mtxP);\n\n ///////////////////////////////////////\n\n // fmtx4 mtxViewport;\n // float xs = 2.0f/\n // mtxViewport.Scale(\n ///////////////////////////////////////\n\n const fmtx4 matVP = (mtxLookat * mtxP);\n fmtx4 matIVP;\n matIVP.inverseOf(matVP);\n\n // set eye and screen plane position\n mEye = eye;\n\n float x1 = 0.0f; //-m_Width;//*0.5f;\n float x2 = miW;\n float y1 = 0.0f; //-m_Height;\n float y2 = miH;\n\n SRect VP(x1, y1, x2, y2);\n\n fvec4 Vxcyc((x1 + x2) * 0.5f, (y1 + y2) * 0.5f, 0.0f, 1.0f);\n fvec4 Vx0y0(x1, y1, 0.0f, 1.0f);\n fvec4 Vx1y0(x2, y1, 0.0f, 1.0f);\n fvec4 Vx1y1(x2, y2, 0.0f, 1.0f);\n fvec4 Vx0y1(x1, y2, 0.0f, 1.0f);\n\n fvec3 Center, CenterDir;\n\n fmtx4::UnProject(Vxcyc, matIVP, VP, Center);\n fmtx4::UnProject(Vx0y0, matIVP, VP, mCornerTL);\n fmtx4::UnProject(Vx1y0, matIVP, VP, mCornerTR);\n fmtx4::UnProject(Vx1y1, matIVP, VP, mCornerBR);\n fmtx4::UnProject(Vx0y1, matIVP, VP, mCornerBL);\n\n CenterDir = (Center - eye).Normal();\n\n // calculate screen plane interpolation vectors\n mDX = (mCornerTR - mCornerTL) * (1.0f / miW);\n mDY = (mCornerBL - mCornerTL) * (1.0f / miH);\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nconst Primitive* Engine::RenderRay(fvec3 screen_pos, fvec3& acc) {\n AABox e = mScene->GetExtends();\n fvec3 dir = (screen_pos - mEye);\n dir.Normalize();\n Ray3 r(mEye, dir);\n float dist = 100000.0f;\n const Primitive* prim = Raytrace(r, 1, 1.0f, acc, dist);\n return prim;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n\nstruct RenderingJobCtx {\n Engine* mpEngine;\n int miCore;\n int miNumCores;\n int miNumRays;\n};\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid* RenderingJobThread(void* vptr_args) {\n RenderingJobCtx* mpCtx = (RenderingJobCtx*)vptr_args;\n\n mpCtx->miNumRays = 0;\n int ih = mpCtx->mpEngine->height();\n int iw = mpCtx->mpEngine->width();\n\n const fvec3& cTL = mpCtx->mpEngine->CornerTL();\n const fvec3& cTR = mpCtx->mpEngine->CornerTR();\n const fvec3& cBL = mpCtx->mpEngine->CornerBL();\n const fvec3& cBR = mpCtx->mpEngine->CornerBR();\n\n const fvec3& cQX = (cTR - cTL) * 1.0f / float(iw);\n const fvec3& cQY = (cBL - cTL) * 1.0f / float(ih);\n\n const Jitterer my_jitter(kOS, kJITTER, cQX, cQY);\n\n for (int y = mpCtx->miCore; y < ih; y += mpCtx->miNumCores) {\n if (y % 64 == mpCtx->miCore)\n orkprintf(\"core<%d> y<%d>\\n\", mpCtx->miCore, y);\n\n float fy = float(y) / float(ih);\n fvec3 lSC, rSC;\n lSC.lerp(cTL, cBL, fy);\n rSC.lerp(cTR, cBR, fy);\n for (int x = 0; x < iw; x++) {\n float fx = float(x) / float(iw);\n fvec3 screen_pos, jittered_pos;\n screen_pos.lerp(lSC, rSC, fx);\n\n fvec3 acc(0, 0, 0);\n for (int isamp = 0; isamp < my_jitter.miNumSamples; isamp++) {\n jittered_pos = screen_pos + my_jitter.GetSample(isamp);\n\n fvec3 sample(0, 0, 0);\n\n const Primitive* prim = mpCtx->mpEngine->RenderRay(jittered_pos, sample);\n acc += sample;\n mpCtx->miNumRays++;\n }\n\n acc = acc * (1.0f / float(my_jitter.miNumSamples));\n int red, green, blue;\n red = (int)(acc.x * 256);\n green = (int)(acc.y * 256);\n blue = (int)(acc.z * 256);\n if (red > 255)\n red = 255;\n if (green > 255)\n green = 255;\n if (blue > 255)\n blue = 255;\n if (red < 0)\n red = 0;\n if (green < 0)\n green = 0;\n if (blue < 0)\n blue = 0;\n int ipix = (y * iw) + ((iw - 1) - x);\n RayPixel& rp = mpCtx->mpEngine->RefPixel(ipix);\n rp.r = red;\n rp.g = green;\n rp.b = blue;\n }\n }\n return 0;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nbool Engine::Render(const AABox& bbox, const std::string& OutputName) {\n RayPixel* pixels = new RayPixel[kIW * kIW];\n memset((void*)pixels, 0, sizeof(RayPixel) * kIW * kIW);\n\n fvec3 dim = (bbox.Max() - bbox.Min());\n fvec3 ctr = fvec3(0.0f, 275.0f, 0.0f) + (bbox.Min() + bbox.Max()) * 0.5f;\n fvec3 eye = ctr + fvec3(0.0f, 30.0f, 75.0f);\n\n setTarget(pixels, kIW, kIW);\n\n GetScene()->InitScene(bbox);\n InitRender(eye, ctr);\n\n /////////////////////////////////////////////////////////////////\n\n int inumcores = GetNumCores();\n orkvector JobCtxVect;\n#if 0\n\torkvector\t\tThreadVect;\n\tf64 ftimeA = OldSchool::GetRef().GetSystemRelTime( );\n\tfor( int ic=0; icmpEngine = this;\n\t\tctx->miNumCores = inumcores;\n\t\tctx->miCore = ic;\n\n\t\tpthread_t job_thread;\n\t\tif (pthread_create(&job_thread, NULL, RenderingJobThread, (void*)ctx) != 0)\n\t\t{\n\t\t\tOrkAssert(false);\n\t\t}\n\n\t\tThreadVect.push_back(job_thread);\n\t\tJobCtxVect.push_back(ctx);\n\t}\n\tfor( orkvector::iterator it=ThreadVect.begin(); it!=ThreadVect.end(); it++ )\n\t{\n\t\tpthread_t job = (*it);\n\t\tpthread_join(job, NULL);\n\t}\n\tint inumrays = 0;\n\tfor( orkvector::iterator it=JobCtxVect.begin(); it!=JobCtxVect.end(); it++ )\n\t{\n\t\tRenderingJobCtx* ctx = *it;\n\t\tinumrays += ctx->miNumRays;\n\t}\n\tf64 ftimeB = OldSchool::GetRef().GetSystemRelTime( );\n\tf64 ftime = ftimeB-ftimeA;\n\tfloat frayspersec = float(inumrays)/ftime;\n\torkprintf( \"Rays<%d> Time<%f? RaysPerSec<%f>\\n\", inumrays, ftime, frayspersec );\n\n\t/////////////////////////////////////////////////////////////////\n\tGetScene()->ExitScene();\n#endif\n\n /*\tilInit();\n ILuint image;\n ilGenImages(1, &image);\n ilBindImage(image);\n if (!ilTexImage(kIW, kIW, 1, 3, IL_RGB, IL_UNSIGNED_BYTE, pixels))\n {\n ILenum error = ilGetError();\n orkprintf(\"Failed to create image (%04X)\\n\", error);\n }\n\n ilEnable(IL_FILE_OVERWRITE);\n if (!ilSaveImage(OutputName.c_str()))\n {\n ILenum error = ilGetError();\n orkprintf(\"Failed to save to %s (%04X)\\n\", OutputName.c_str(), error);\n }\n */\n return true;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nstruct BakingJobCtx {\n Engine* mpEngine;\n int miCore;\n int miNumCores;\n int miNumRays;\n};\n\n///////////////////////////////////////////////////////////////////////////////\n\nvoid* BakingJobThread(void* pval) {\n BakingJobCtx* mpCtx = (BakingJobCtx*)pval;\n\n const orkmap& geosets = mpCtx->mpEngine->GetScene()->GetGeoSets();\n\n for (orkmap::const_iterator it = geosets.begin(); it != geosets.end(); it++) {\n const std::string& geoname = it->first;\n if (geoname.find(\"raster_\") != std::string::npos) {\n const RgmGeoSet* RasterSet = it->second;\n\n int inumtri = RasterSet->NumPrimitives();\n int iw = mpCtx->mpEngine->width();\n int ih = mpCtx->mpEngine->height();\n int itrictr = inumtri / mpCtx->miNumCores;\n\n int icntdwn = (inumtri >> 8); //\n\n if (0 == icntdwn)\n icntdwn = 1;\n\n for (int ip = mpCtx->miCore; ip < inumtri; ip += mpCtx->miNumCores) {\n if (itrictr % icntdwn == 0)\n orkprintf(\"core<%d> numleft<%d> geoname<%s>\\n\", mpCtx->miCore, itrictr, geoname.c_str());\n itrictr--;\n const Primitive* prim = RasterSet->GetPrimitive(ip);\n prim->Rasterize(mpCtx->mpEngine);\n }\n }\n }\n return 0;\n}\n\nbool Engine::Bake(const AABox& bbox, const std::string& OutputName) {\n int iKKos = kOS;\n\n int idim = (iKKos * 2) + 1;\n int iNumSamples = idim * idim;\n\n int idiv = (kOS == 0) ? 1 : kOS;\n int iKIW = idim * kIW;\n\n RayPixel* pixels = new RayPixel[iKIW * iKIW];\n memset((void*)pixels, 0, sizeof(RayPixel) * iKIW * iKIW);\n mFragments = new BakeShadowFragment[iKIW * iKIW];\n memset((void*)mFragments, 0, sizeof(BakeShadowFragment) * kIW * kIW);\n\n fvec3 dim = (bbox.Max() - bbox.Min());\n fvec3 ctr = (bbox.Min() + bbox.Max()) * 0.5f;\n fvec3 eye = ctr + fvec3(0.0f, 0.0f, 75.0f);\n\n setTarget(pixels, iKIW, iKIW);\n\n orkprintf(\"Initializing Scene\\n\");\n\n GetScene()->InitScene(bbox);\n InitRender(eye, ctr);\n\n /////////////////////////////////////////////////////////////////\n /////////////////////////////////////////////////////////////////\n /////////////////////////////////////////////////////////////////\n // f64 ftimeA = OldSchool::GetRef().GetSystemRelTime( );\n {\n int inumcores = GetNumCores();\n orkvector JobCtxVect;\n#if 0\n\t\torkvector\tThreadVect;\n\t\tfor( int ic=0; icmpEngine = this;\n\t\t\tctx->miNumCores = inumcores;\n\t\t\tctx->miCore = ic;\n\n\t\t\tpthread_t job_thread;\n\t\t\tif (pthread_create(&job_thread, NULL, BakingJobThread, (void*)ctx) != 0)\n\t\t\t{\n\t\t\t\tOrkAssert(false);\n\t\t\t}\n\n\t\t\tThreadVect.push_back(job_thread);\n\n\t\t\tJobCtxVect.push_back(ctx);\n\t\t}\n\t\tfor( orkvector::iterator it=ThreadVect.begin(); it!=ThreadVect.end(); it++ )\n\t\t{\n\t\t\tpthread_t job = (*it);\n\t\t\tpthread_join(job, NULL);\n\t\t}\n\t\tint inumrays = 0;\n\t\t//for( orkvector::iterator it=JobCtxVect.begin(); it!=JobCtxVect.end(); it++ )\n\t\t//{\n\t\t//\tRenderingJobCtx* ctx = *it;\n\t\t//\tinumrays += ctx->miNumRays;\n\t\t//}\n#endif\n }\n // f64 ftimeB = OldSchool::GetRef().GetSystemRelTime( );\n f64 ftime = 1.0; // ftimeB-ftimeA;\n int inrays = int(giNumRays);\n\n float frayspersec = float(inrays) / ftime;\n orkprintf(\"Rays<%d> Time<%f> RaysPerSec<%f>\\n\", inrays, ftime, frayspersec);\n giNumRays = 0;\n /////////////////////////////////////////////////////////////////\n /////////////////////////////////////////////////////////////////\n /////////////////////////////////////////////////////////////////\n GetScene()->ExitScene();\n /////////////////////////////////////////////////////////////////\n /////////////////////////////////////////////////////////////////\n /////////////////////////////////////////////////////////////////\n orkprintf(\"growing uv shells...\\n\");\n static const int kgrowamt = 7;\n for (int iy = 0; iy < iKIW; iy++) {\n for (int ix = 0; ix < iKIW; ix++) {\n int ipix = (iy * iKIW) + ix;\n RayPixel& pixel = pixels[ipix];\n //////////////////////////////\n // we will only ever grow pixels that have not been written to\n if (pixel.a == 0) {\n float faccr = 0.0f;\n float faccg = 0.0f;\n float faccb = 0.0f;\n float fnums = 0.0f;\n for (int oy = -kgrowamt; oy <= kgrowamt; oy++) {\n int iny = iy + oy;\n if (iny < 0)\n continue;\n if (iny >= iKIW)\n continue;\n for (int ox = -kgrowamt; ox <= kgrowamt; ox++) {\n int inx = ix + ox;\n if (ox == 0 && oy == 0)\n continue;\n if (inx < 0)\n continue;\n if (inx >= iKIW)\n continue;\n int ipix2 = (iny * kIW) + inx;\n const RayPixel& opix = pixels[ipix2];\n if (opix.a != 0) {\n faccr += float(opix.r);\n faccg += float(opix.g);\n faccb += float(opix.b);\n fnums += 1.0f;\n }\n }\n }\n if (fnums != 0.0f) {\n pixel.r = u8(faccr / fnums);\n pixel.g = u8(faccg / fnums);\n pixel.b = u8(faccb / fnums);\n }\n }\n }\n }\n /////////////////////////////////////////////////////////////////\n /////////////////////////////////////////////////////////////////\n /////////////////////////////////////////////////////////////////\n\n /*\tboost::gil::rgb8_image_t img(iKIW,iKIW);\n boost::gil::rgb8_view_t myview = boost::gil::view(img);\n\n for( int iy=0; iyGetFixedGrid();\n */\n\n return true;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\nvoid Scene::AddGeoset(const std::string& name, RgmGeoSet* pset) {\n mGeoSets[name] = pset;\n}\nvoid Scene::RemoveGeoset(const std::string& name) {\n orkmap::iterator it = mGeoSets.find(name);\n if (it != mGeoSets.end()) {\n const RgmGeoSet* rval = it->second;\n mGeoSets.erase(it);\n if (rval) {\n delete rval;\n }\n }\n}\n\nconst RgmGeoSet* Scene::FindGeoset(const std::string& name) const {\n const RgmGeoSet* rval = 0;\n orkmap::const_iterator it = mGeoSets.find(name);\n if (it != mGeoSets.end())\n rval = it->second;\n return rval;\n}\n\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n///////////////////////////////////////////////////////////////////////////////\n\n} // namespace ork\n", "meta": {"hexsha": "fb71fc49836aa86ff7d9a5ebaca4b0a9a657e309", "size": 51022, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ork.core/src/math/raytracer.cpp", "max_stars_repo_name": "tweakoz/orkid", "max_stars_repo_head_hexsha": "e3f78dfb3375853fd512a9d0828b009075a18345", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 25.0, "max_stars_repo_stars_event_min_datetime": "2015-02-21T04:21:21.000Z", "max_stars_repo_stars_event_max_datetime": "2022-01-20T05:19:27.000Z", "max_issues_repo_path": "ork.core/src/math/raytracer.cpp", "max_issues_repo_name": "tweakoz/orkid", "max_issues_repo_head_hexsha": "e3f78dfb3375853fd512a9d0828b009075a18345", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": 113.0, "max_issues_repo_issues_event_min_datetime": "2019-08-23T04:52:14.000Z", "max_issues_repo_issues_event_max_datetime": "2021-09-13T04:04:11.000Z", "max_forks_repo_path": "ork.core/src/math/raytracer.cpp", "max_forks_repo_name": "tweakoz/orkid", "max_forks_repo_head_hexsha": "e3f78dfb3375853fd512a9d0828b009075a18345", "max_forks_repo_licenses": ["BSL-1.0"], "max_forks_count": 4.0, "max_forks_repo_forks_event_min_datetime": "2017-02-20T18:17:55.000Z", "max_forks_repo_forks_event_max_datetime": "2020-06-28T03:47:55.000Z", "avg_line_length": 35.6298882682, "max_line_length": 132, "alphanum_fraction": 0.39839677, "num_tokens": 13250, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5428632831725052, "lm_q2_score": 0.3849121444839335, "lm_q1q2_score": 0.20895467048751784}} {"text": "// Boost.Geometry - gis-projections (based on PROJ4)\n\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\n\n// This file was modified by Oracle on 2017, 2018.\n// Modifications copyright (c) 2017-2018, Oracle and/or its affiliates.\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\n\n// Last updated version of proj: 5.0.0\n\n// Original copyright notice:\n\n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#ifndef BOOST_GEOMETRY_PROJECTIONS_LAGRNG_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_LAGRNG_HPP\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n\nnamespace boost { namespace geometry\n{\n\nnamespace projections\n{\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail { namespace lagrng\n {\n\n static const double tolerance = 1e-10;\n\n template \n struct par_lagrng\n {\n T a1;\n T rw;\n T hrw;\n };\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_lagrng_spheroid\n : public base_t_f, T, Parameters>\n {\n par_lagrng m_proj_parm;\n\n inline base_lagrng_spheroid(const Parameters& par)\n : base_t_f, T, Parameters>(*this, par)\n {}\n\n // FORWARD(s_forward) spheroid\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\n inline void fwd(T lp_lon, T lp_lat, T& xy_x, T& xy_y) const\n {\n static const T half_pi = detail::half_pi();\n\n T v, c;\n\n if (fabs(fabs(lp_lat) - half_pi) < tolerance) {\n xy_x = 0;\n xy_y = lp_lat < 0 ? -2. : 2.;\n } else {\n lp_lat = sin(lp_lat);\n v = this->m_proj_parm.a1 * math::pow((T(1) + lp_lat)/(T(1) - lp_lat), this->m_proj_parm.hrw);\n if ((c = 0.5 * (v + 1./v) + cos(lp_lon *= this->m_proj_parm.rw)) < tolerance) {\n BOOST_THROW_EXCEPTION( projection_exception(error_tolerance_condition) );\n }\n xy_x = 2. * sin(lp_lon) / c;\n xy_y = (v - 1./v) / c;\n }\n }\n\n static inline std::string get_name()\n {\n return \"lagrng_spheroid\";\n }\n\n };\n\n // Lagrange\n template \n inline void setup_lagrng(Params const& params, Parameters& par, par_lagrng& proj_parm)\n {\n T phi1;\n\n proj_parm.rw = 0.0;\n bool is_w_set = pj_param_f(params, \"W\", srs::dpar::w, proj_parm.rw);\n \n // Boost.Geometry specific, set default parameters manually\n if (! is_w_set) {\n bool const use_defaults = ! pj_get_param_b(params, \"no_defs\", srs::dpar::no_defs);\n if (use_defaults) {\n proj_parm.rw = 2;\n }\n }\n\n if (proj_parm.rw <= 0)\n BOOST_THROW_EXCEPTION( projection_exception(error_w_or_m_zero_or_less) );\n\n proj_parm.rw = 1. / proj_parm.rw;\n proj_parm.hrw = 0.5 * proj_parm.rw;\n phi1 = pj_get_param_r(params, \"lat_1\", srs::dpar::lat_1);\n if (fabs(fabs(phi1 = sin(phi1)) - 1.) < tolerance)\n BOOST_THROW_EXCEPTION( projection_exception(error_lat_larger_than_90) );\n\n proj_parm.a1 = math::pow((T(1) - phi1)/(T(1) + phi1), proj_parm.hrw);\n\n par.es = 0.;\n }\n\n }} // namespace detail::lagrng\n #endif // doxygen\n\n /*!\n \\brief Lagrange projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Miscellaneous\n - Spheroid\n - no inverse\n \\par Projection parameters\n - W (real)\n - lat_1: Latitude of first standard parallel (degrees)\n \\par Example\n \\image html ex_lagrng.gif\n */\n template \n struct lagrng_spheroid : public detail::lagrng::base_lagrng_spheroid\n {\n template \n inline lagrng_spheroid(Params const& params, Parameters const& par)\n : detail::lagrng::base_lagrng_spheroid(par)\n {\n detail::lagrng::setup_lagrng(params, this->m_par, this->m_proj_parm);\n }\n };\n\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail\n {\n\n // Static projection\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_STATIC_PROJECTION(srs::spar::proj_lagrng, lagrng_spheroid, lagrng_spheroid)\n\n // Factory entry(s)\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_ENTRY_F(lagrng_entry, lagrng_spheroid)\n \n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_BEGIN(lagrng_init)\n {\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_ENTRY(lagrng, lagrng_entry);\n }\n\n } // namespace detail\n #endif // doxygen\n\n} // namespace projections\n\n}} // namespace boost::geometry\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_LAGRNG_HPP\n\n", "meta": {"hexsha": "6df4459624709c27629ee6d99a1d23531c9d92d7", "size": 7406, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/boost/geometry/srs/projections/proj/lagrng.hpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 12278.0, "max_stars_repo_stars_event_min_datetime": "2015-01-29T17:11:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T21:12:00.000Z", "max_issues_repo_path": "3rdParty/boost/1.71.0/boost/geometry/srs/projections/proj/lagrng.hpp", "max_issues_repo_name": "rajeev02101987/arangodb", "max_issues_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 9469.0, "max_issues_repo_issues_event_min_datetime": "2015-01-30T05:33:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T16:17:21.000Z", "max_forks_repo_path": "3rdParty/boost/1.71.0/boost/geometry/srs/projections/proj/lagrng.hpp", "max_forks_repo_name": "rajeev02101987/arangodb", "max_forks_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 892.0, "max_forks_repo_forks_event_min_datetime": "2015-01-29T16:26:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T07:44:30.000Z", "avg_line_length": 37.9794871795, "max_line_length": 122, "alphanum_fraction": 0.61031596, "num_tokens": 1727, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6297746213017459, "lm_q2_score": 0.33111973302838926, "lm_q1q2_score": 0.20853080447348904}} {"text": "/*~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\\\n| Phycas: Python software for phylogenetic analysis |\n| Copyright (C) 2006 Mark T. Holder, Paul O. Lewis and David L. Swofford |\n| |\n| This program is free software; you can redistribute it and/or modify |\n| it under the terms of the GNU General Public License as published by |\n| the Free Software Foundation; either version 2 of the License, or |\n| (at your option) any later version. |\n| |\n| This program is distributed in the hope that it will be useful, |\n| but WITHOUT ANY WARRANTY; without even the implied warranty of |\n| MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |\n| GNU General Public License for more details. |\n| |\n| You should have received a copy of the GNU General Public License along |\n| with this program; if not, write to the Free Software Foundation, Inc., |\n| 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. |\n\\~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~*/\n#include //temporary!\n#include \n#include \n#include \"probability_distribution.hpp\"\n#include \"relative_rate_distribution.hpp\"\n#include \"model.hpp\"\n#include \"basic_tree_node.hpp\"\n#include \"tree_likelihood.hpp\"\n#include \"xlikelihood.hpp\"\n#include \"mcmc_chain_manager.hpp\"\n#include \"dirichlet_move.hpp\"\n#include \"subset_relrates_move.hpp\"\n#include \"basic_tree.hpp\"\n#include \"tree_manip.hpp\"\n#include \"gtr.hpp\"\n#include \"hky.hpp\"\n\nusing namespace phycas;\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tThe constructor simply calls the base class (DirichletMove) constructor.\n*/\nSubsetRelRatesMove::SubsetRelRatesMove()\n : DirichletMove()\n\t{\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tSets the relative rates of the associated partition model to those in the supplied vector `v'. No transformation of\n|\tthe relative rates is done by this function.\n*/\nvoid SubsetRelRatesMove::sendCurrValuesToModel(const double_vect_t & v)\n\t{\n\tPHYCAS_ASSERT(dim == v.size());\n\tpartition_model->setSubsetRelRatesVect(v);\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tObtains the current relative rates from the model, storing them in the supplied vector `v'. No transformation of\n|\tthe relative rates is done by this function.\n*/\nvoid SubsetRelRatesMove::getCurrValuesFromModel(double_vect_t & v) const\n\t{\n\tconst std::vector & rrates = partition_model->getSubsetRelRatesVect();\n\n //std::cerr << \"@#@#@#@#@# SubsetRelRatesMove::getCurrValuesFromModel\" << std::endl;\n //std::copy(rrates.begin(), rrates.end(), std::ostream_iterator(std::cerr, \" \"));\n //std::cerr << std::endl;\n //std::cerr << \"@#@#@#@#@# SubsetRelRatesMove::getCurrValuesFromModel\" << std::endl;\n\n\tPHYCAS_ASSERT(dim == rrates.size());\n\tv.resize(rrates.size());\n\tstd::copy(rrates.begin(), rrates.end(), v.begin());\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tObtains the current relative rates from the model, returning them as an anonymous vector. No transformation of\n|\tthe relative rates is done by this function.\n*/\ndouble_vect_t SubsetRelRatesMove::listCurrValuesFromModel()\n\t{\n\tdouble_vect_t v(dim);\n\tconst double_vect_t & rrates = partition_model->getSubsetRelRatesVect();\n\tPHYCAS_ASSERT(dim == rrates.size());\n\tstd::copy(rrates.begin(), rrates.end(), v.begin());\n\treturn v;\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tSets `partition_model' to the supplied `m'.\n*/\nvoid SubsetRelRatesMove::setPartitionModel(\n PartitionModelShPtr m) /*< is a shared pointer to the new partition model */\n\t{\n\tpartition_model = m;\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tObtains the current relative rates from the model by calling SubsetRelRatesMove::getCurrValuesFromModel.\n*/\nvoid SubsetRelRatesMove::getParams()\n\t{\n\tgetCurrValuesFromModel(orig_relrates);\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tReplaces the relative rates in the model with those supplied in the vector `v'.\n*/\nvoid SubsetRelRatesMove::setParams(\n const double_vect_t & v) /*< is the vector of parameter values to send to the model */\n\t{\n\tPHYCAS_ASSERT(v.size() == dim);\n sendCurrValuesToModel(v);\n\t}\n\n/*--------------------------------------------------------------------------------------------------------------------------\n| Returns current subset relative rates, proportions and transformed relative rates as well as the weighted average of the\n| relative rates, which should always be 1.0.\n*/\nstd::string SubsetRelRatesMove::debugShowTransformedRelativeRates(const std::string msg) const\n\t{\n std::stringstream ss;\n\tss << \">>>>>>>>>> \" << msg << \" <<<<<<<<<<\" << std::endl;\n\tss << \"Relative rates: \";\n\tstd::copy(orig_relrates.begin(), orig_relrates.end(), std::ostream_iterator(ss, \" \"));\n\tss << boost::str(boost::format(\" sum = %.15f\") % std::accumulate(orig_relrates.begin(), orig_relrates.end(), 0.0)) << std::endl;\n\n\tss << \"Subset proportions: \";\n const std::vector & nsites = partition_model->getNumSitesVect();\n double total_sites = (double)partition_model->getTotalNumSites();\n double_vect_t p;\n p.resize(nsites.size());\n std::transform(nsites.begin(), nsites.end(), p.begin(), boost::lambda::_1/total_sites);\n\tstd::copy(p.begin(), p.end(), std::ostream_iterator(ss, \" \"));\n\tss << boost::str(boost::format(\" sum = %.15f\") % std::accumulate(p.begin(), p.end(), 0.0)) << std::endl;\n\n\t// transform relative rates using coefficients so that elements of orig_params sum to 1\n\tss << \"Weighted relative rates: \";\n std::vector tmp(orig_relrates.size(), 0.0);\n\tstd::transform(orig_relrates.begin(), orig_relrates.end(), p.begin(), tmp.begin(), boost::lambda::_1*boost::lambda::_2);\n\tstd::copy(tmp.begin(), tmp.end(), std::ostream_iterator(ss, \" \"));\n\tss << boost::str(boost::format(\" sum = %.15f\") % std::accumulate(tmp.begin(), tmp.end(), 0.0)) << std::endl;\n return ss.str();\n }\n\n/*--------------------------------------------------------------------------------------------------------------------------\n| Chooses a new vector of state frequencies using a sharp Dirichlet distribution centered at the original frequencies.\n*/\nvoid SubsetRelRatesMove::proposeNewState()\n\t{\n\t// transform relative rates using coefficients so that elements of orig_params sum to 1\n\torig_params.resize(orig_relrates.size());\n SubsetProportionsShPtr ptr = partition_model->getSubsetProportions();\n std::vector const & p = ptr->getSubsetProportions();\n //double total_sites = (double)partition_model->getTotalNumSites();\n\tstd::transform(orig_relrates.begin(), orig_relrates.end(), p.begin(), orig_params.begin(), boost::lambda::_1*boost::lambda::_2);\n\n // create vector of Dirichlet parameters for selecting new relative rate values\n\t// The parameter vector for this temporary distribution is obtained by multiplying\n // each of the current relative rates by the value `psi', which is usually very large (e.g. 1000)\n c_forward.resize(orig_params.size());\n\tstd::transform(orig_params.begin(), orig_params.end(), c_forward.begin(), 1.0 + boost::lambda::_1*psi);\n\n\t// create Dirichlet distribution and sample from it\n dir_forward = DirichletShPtr(new DirichletDistribution(c_forward));\n dir_forward->SetLot(getLot().get());\n\tnew_params.resize(orig_params.size());\n new_params = dir_forward->Sample();\n\n // create vector of Dirichlet parameters for selecting old frequencies (needed for Hastings ratio calculation)\n c_reverse.resize(new_params.size());\n\tstd::transform(new_params.begin(), new_params.end(), c_reverse.begin(), 1.0 + boost::lambda::_1*psi);\n dir_reverse = DirichletShPtr(new DirichletDistribution(c_reverse));\n\n\t// transform new_params using coefficients so that elements of new_relrates are equal to proposed new relative rates\n\tnew_relrates.resize(new_params.size());\n\tstd::transform(new_params.begin(), new_params.end(), p.begin(), new_relrates.begin(), boost::lambda::_1/boost::lambda::_2);\n\n#if 0\n std::cerr << \"\\n^^^^^ orig_relrates ^^^^^\" << std::endl;\n std::copy(orig_relrates.begin(), orig_relrates.end(), std::ostream_iterator(std::cerr, \"|\"));\n std::cerr << \"\\n^^^^^ orig_params ^^^^^\" << std::endl;\n std::copy(orig_params.begin(), orig_params.end(), std::ostream_iterator(std::cerr, \"|\"));\n std::cerr << \"\\n^^^^^ c_forward ^^^^^\" << std::endl;\n std::copy(c_forward.begin(), c_forward.end(), std::ostream_iterator(std::cerr, \"|\"));\n std::cerr << \"\\n^^^^^ c_reverse ^^^^^\" << std::endl;\n std::copy(c_reverse.begin(), c_reverse.end(), std::ostream_iterator(std::cerr, \"|\"));\n std::cerr << \"\\n^^^^^ new_params ^^^^^\" << std::endl;\n std::copy(new_params.begin(), new_params.end(), std::ostream_iterator(std::cerr, \"|\"));\n std::cerr << \"\\n^^^^^ new_relrates ^^^^^\" << std::endl;\n std::copy(new_relrates.begin(), new_relrates.end(), std::ostream_iterator(std::cerr, \"|\"));\n std::cerr << std::endl;\n#endif\n\t}\n\n/*--------------------------------------------------------------------------------------------------------------------------\n|\tCalled if the proposed move is accepted. Simply calls the reset() function.\n*/\nvoid SubsetRelRatesMove::accept()\n\t{\n\tMCMCUpdater::accept();\n\treset();\n\t}\n\n/*--------------------------------------------------------------------------------------------------------------------------\n|\tCalled if the proposed move is rejected. Calls the reset() function after reinstating the original relative rates\n| and ensuring that all conditional likelihood arrays will be recalculated when the likelihood is next calculated.\n*/\nvoid SubsetRelRatesMove::revert()\n\t{\n\tMCMCUpdater::revert();\n setParams(orig_relrates);\n\n\tChainManagerShPtr p = chain_mgr.lock();\n\tPHYCAS_ASSERT(p);\n JointPriorManagerShPtr jpm = p->getJointPriorManager();\n jpm->multivariateModified(name, orig_relrates);\n curr_ln_prior = jpm->getLogJointPrior();\n\n // invalidate all CLAs\n likelihood->useAsLikelihoodRoot(NULL);\n likelihood->storeAllCLAs(tree);\n\n\treset();\n\t}\n\n/*----------------------------------------------------------------------------------------------------------------------\n|\tCalls proposeNewState(), then decides whether to accept or reject the proposed new state, calling accept() or\n|\trevert(), whichever is appropriate.\n*/\nbool SubsetRelRatesMove::update()\n\t{\n\tif (is_fixed)\n\t\treturn false;\n\n\tChainManagerShPtr p = chain_mgr.lock();\n\tPHYCAS_ASSERT(p);\n JointPriorManagerShPtr jpm = p->getJointPriorManager();\n\n // copy the current subset relative rates from the model to the data member orig_params\n getParams();\n\n\tproposeNewState();\n\n double prev_ln_prior\t\t= jpm->getLogJointPrior();\n\n\tdouble prev_ln_like\t\t\t= p->getLastLnLike();\n\tPHYCAS_ASSERT(!use_ref_dist || mv_ref_dist);\n\tdouble prev_ln_ref_dist = (use_ref_dist ? mv_ref_dist->GetLnPDF(orig_relrates) : 0.0);\n\n // replace current parameter values with new ones\n setParams(new_relrates);\n\n jpm->multivariateModified(name, new_relrates);\n double curr_ln_prior = jpm->getLogJointPrior();\n\n likelihood->useAsLikelihoodRoot(NULL);\t// invalidates all CLAs\n\tdouble curr_ln_like\t\t\t= (heating_power > 0.0 ? likelihood->calcLnL(tree) : 0.0);\n\tdouble curr_ln_ref_dist = (use_ref_dist ? mv_ref_dist->GetLnPDF(new_relrates) : 0.0);\n\n double prev_posterior = 0.0;\n\tdouble curr_posterior = 0.0;\n\n\tif (is_standard_heating)\n\t\t{\n\t\tprev_posterior = heating_power*(prev_ln_like + prev_ln_prior);\n\t\tcurr_posterior = heating_power*(curr_ln_like + curr_ln_prior);\n\t\tif (use_ref_dist)\n\t\t\t{\n\t\t\tprev_posterior += (1.0 - heating_power)*prev_ln_ref_dist;\n\t\t\tcurr_posterior += (1.0 - heating_power)*curr_ln_ref_dist;\n\t\t\t}\n\t\t}\n\telse\n\t\t{\n\t\tprev_posterior = heating_power*prev_ln_like + prev_ln_prior;\n\t\tcurr_posterior = heating_power*curr_ln_like + curr_ln_prior;\n\t\t}\n\n\tdouble ln_hastings\t\t\t= getLnHastingsRatio();\n\tdouble ln_accept_ratio\t\t= curr_posterior - prev_posterior + ln_hastings;\n\n double lnu = std::log(rng->Uniform());\n\n bool accepted = false;\n\n\tif (ln_accept_ratio >= 0.0 || lnu <= ln_accept_ratio)\n\t\t{\n\t if (save_debug_info)\n \t {\n\t\t\tdebug_info = boost::str(boost::format(\"ACCEPT, prev_ln_like = %.5f, getLastLnLike() = %.5f, curr_ln_like = %.5f, prev_ln_ref_dist = %.5f, curr_ln_ref_dist = %.5f, prev_ln_prior = %.5f, curr_ln_prior = %.5f, lnu = %.5f, ln_accept_ratio = %.5f\\n\") % prev_ln_like % p->getLastLnLike() % curr_ln_like % prev_ln_ref_dist % curr_ln_ref_dist % prev_ln_prior % curr_ln_prior % lnu % ln_accept_ratio);\n debug_info += debugShowTransformedRelativeRates(\"Details\");\n\t\t\t//for (std::vector::const_iterator it = orig_params.begin(); it != orig_params.end(); ++it)\n\t\t\t//\tdebug_info += boost::str(boost::format(\"%15.8f \") % (*it));\n\t\t\t//debug_info += \"\\nnew_params: \";\n\t\t\t//for (std::vector::const_iterator it = new_params.begin(); it != new_params.end(); ++it)\n\t\t\t//\tdebug_info += boost::str(boost::format(\"%15.8f \") % (*it));\n\t\t\t//debug_info += \"\\nc_forward: \";\n\t\t\t//for (std::vector::const_iterator it = c_forward.begin(); it != c_forward.end(); ++it)\n\t\t\t//\tdebug_info += boost::str(boost::format(\"%15.8f \") % (*it));\n\t\t\t//debug_info += \"\\ndir_forward: \";\n\t\t\t//debug_info += dir_forward->GetDistributionDescription();\n\t\t\t}\n\t\tp->setLastLnLike(curr_ln_like);\n\n\t\taccept();\n\t\taccepted = true;\n\t\t}\n\telse\n\t\t{\n\t if (save_debug_info)\n \t {\n\t\t\tdebug_info = boost::str(boost::format(\"REJECT, prev_ln_like = %.5f, getLastLnLike() = %.5f, curr_ln_like = %.5f, prev_ln_ref_dist = %.5f, curr_ln_ref_dist = %.5f, prev_ln_prior = %.5f, curr_ln_prior = %.5f, lnu = %.5f, ln_accept_ratio = %.5f\\n\") % prev_ln_like % p->getLastLnLike() % curr_ln_like % prev_ln_ref_dist % curr_ln_ref_dist % prev_ln_prior % curr_ln_prior % lnu % ln_accept_ratio);\n debug_info += debugShowTransformedRelativeRates(\"Details\");\n //for (std::vector::const_iterator it = orig_params.begin(); it != orig_params.end(); ++it)\n // debug_info += boost::str(boost::format(\"%15.8f \") % (*it));\n //debug_info += \"\\nnew_params: \";\n //for (std::vector::const_iterator it = new_params.begin(); it != new_params.end(); ++it)\n // debug_info += boost::str(boost::format(\"%15.8f \") % (*it));\n //debug_info += \"\\nc_forward: \";\n //for (std::vector::const_iterator it = c_forward.begin(); it != c_forward.end(); ++it)\n // debug_info += boost::str(boost::format(\"%15.8f \") % (*it));\n //debug_info += \"\\ndir_forward: \";\n //debug_info += dir_forward->GetDistributionDescription();\n\t\t\t}\n\t\tcurr_ln_like\t= p->getLastLnLike();\n\t\trevert();\n\t\taccepted = false;\n\t\t}\n\n double inverse_psi = 1.0/psi;\n inverse_psi = p->adaptUpdater(inverse_psi, nattempts, accepted);\n psi = 1.0/inverse_psi;\n\n return accepted;\n\t}\n\n", "meta": {"hexsha": "10ed8dfb054a3ec198914f2e3a0c32647ca6fd0f", "size": 15839, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cpp/subset_relrates_move.cpp", "max_stars_repo_name": "plewis/phycas", "max_stars_repo_head_hexsha": "9f5a4d9b2342dab907d14a46eb91f92ad80a5605", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3.0, "max_stars_repo_stars_event_min_datetime": "2015-09-24T23:12:57.000Z", "max_stars_repo_stars_event_max_datetime": "2021-04-12T07:07:01.000Z", "max_issues_repo_path": "src/cpp/subset_relrates_move.cpp", "max_issues_repo_name": "plewis/phycas", "max_issues_repo_head_hexsha": "9f5a4d9b2342dab907d14a46eb91f92ad80a5605", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/cpp/subset_relrates_move.cpp", "max_forks_repo_name": "plewis/phycas", "max_forks_repo_head_hexsha": "9f5a4d9b2342dab907d14a46eb91f92ad80a5605", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2015-11-23T10:35:43.000Z", "max_forks_repo_forks_event_max_datetime": "2015-11-23T10:35:43.000Z", "avg_line_length": 47.1398809524, "max_line_length": 395, "alphanum_fraction": 0.6056569228, "num_tokens": 3665, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5117166047041654, "lm_q2_score": 0.40733340004593027, "lm_q1q2_score": 0.20843926445410696}} {"text": "/*\n * @file rnxToGtsam.cpp\n * @author Ryan\n * @brief Script to convert Rinex File format to format utilized by GTSAM.\n\n * To do ::\n * Currenlty only works with SP3\n * Need to get nom. pos. from rinex header\n * Get DOY from header for trop estimation\n * need to write to file. Currently only prints to screen.\n */\n\n// GPSTK\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n// GTSAM\n#include \n#include \n\n// BOOST\n#include \n\n// STD\n#include \n#include \n\nusing namespace std;\nusing namespace gpstk;\nusing namespace gtsam;\nusing namespace boost;\n\nnamespace po = boost::program_options;\n\nint main(int argc, char *argv[])\n{\n\n bool usingP1 = false;\n int dec_int, itsBelowThree = 0, count = 0;\n string rnx_file, nav_file, sp3_file, out_file;\n\n cout << fixed << setprecision(12); // Set a proper output format\n\n po::options_description desc(\"Available options\");\n desc.add_options()\n (\"help,h\", \"Print help message\")\n (\"obs\", po::value(&rnx_file)->default_value(\"\"),\n \"Observation file to read\")\n (\"sp3\", po::value(&sp3_file)->default_value(\"\"),\n \"SP3 file to read.\")\n (\"out\", po::value(&out_file)->default_value(\"\"),\n \"output file.\")\n (\"usingP1\", \"Are you using P1 instead of C1?\")\n (\"dec\", po::value(&dec_int)->default_value(0),\n \"decimate input obs file\");\n po::variables_map vm;\n po::store(po::command_line_parser(argc, argv).options(desc).run(), vm);\n po::notify(vm);\n\n usingP1 = (vm.count(\"usingP1\")>0);\n\n if ( rnx_file.empty() )\n {\n cout << \" Must pass in obs file !!! Try --obs \" << desc << endl;\n exit(1);\n }\n if ( nav_file.empty() && sp3_file.empty() )\n {\n cout << \" Must pass in ephemeris file !!! Try --sp3 or --nav\" << desc << endl;\n exit(1);\n }\n\n string obs_path = findExampleDataFile(rnx_file);\n string nav_path = findExampleDataFile(sp3_file);\n\n // Create the input observation file stream\n Rinex3ObsStream rin(obs_path);\n\n // Declare a \"SP3EphemerisStore\" object to handle precise ephemeris\n SP3EphemerisStore SP3EphList;\n\n // Set flags to reject satellites with bad or absent positional\n // values or clocks\n SP3EphList.rejectBadPositions(true);\n SP3EphList.rejectBadClocks(true);\n\n // Load all the SP3 ephemerides files\n SP3EphList.loadFile(nav_path);\n\n // BELL station nominal position\n Position nominalPos(856295.3346, -4843033.4111, 4048017.6649);\n\n CorrectCodeBiases corrCode;\n corrCode.setDCBFile(\"p1p2.DCB\", \"p1c1.DCB\");\n\n if (!usingP1) {\n corrCode.setUsingC1(true);\n }\n\n\n // This is the GNSS data structure that will hold all the\n // GNSS-related information\n gnssRinex gRin;\n\n RequireObservables requireObs;\n requireObs.addRequiredType(TypeID::L1);\n requireObs.addRequiredType(TypeID::L2);\n\n SimpleFilter pObsFilter;\n pObsFilter.setFilteredType(TypeID::C1);\n\n if ( usingP1 )\n {\n requireObs.addRequiredType(TypeID::P1);\n pObsFilter.addFilteredType(TypeID::P1);\n requireObs.addRequiredType(TypeID::P2);\n pObsFilter.addFilteredType(TypeID::P2);\n }\n else\n {\n requireObs.addRequiredType(TypeID::C1);\n pObsFilter.addFilteredType(TypeID::C1);\n requireObs.addRequiredType(TypeID::P2);\n pObsFilter.addFilteredType(TypeID::P2);\n }\n\n // Declare a couple of basic modelers\n BasicModel basic(nominalPos, SP3EphList);\n\n // Object to correct for SP3 Sat Phase-center offset\n AntexReader antexread;\n antexread.open( \"antenna_corr.atx\" );\n ComputeSatPCenter svPcenter(SP3EphList, nominalPos);\n svPcenter.setAntexReader( antexread );\n\n // Objects to mark cycle slips\n MWCSDetector markCSMW; // Checks Merbourne-Wubbena cycle slip\n\n // Declare an object to correct observables\n CorrectObservables corr(SP3EphList);\n\n\n // object def several linear combinations\n LinearCombinations comb;\n\n // Object to compute linear combinations for cycle slip detection\n ComputeLinear linear1;\n if ( usingP1 )\n {\n linear1.addLinear(comb.pdeltaCombination);\n linear1.addLinear(comb.mwubbenaCombination);\n }\n else\n {\n linear1.addLinear(comb.pdeltaCombWithC1);\n linear1.addLinear(comb.mwubbenaCombWithC1);\n }\n linear1.addLinear(comb.ldeltaCombination);\n linear1.addLinear(comb.liCombination);\n\n ComputeLinear linear2;\n\n // Read if we should use C1 instead of P1\n if ( usingP1 )\n {\n linear2.addLinear(comb.pcCombination);\n }\n else\n {\n // WARNING: When using C1 instead of P1 to compute PC combination,\n // be aware that instrumental errors will NOT cancel,\n // introducing a bias that must be taken into account by\n // other means. This won't be taken into account in this\n // example.\n linear2.addLinear(comb.pcCombWithC1);\n }\n linear2.addLinear(comb.lcCombination);\n\n LICSDetector2 markCSLI2; // Checks LI cycle slips\n\n // Object to keep track of satellite arcs\n SatArcMarker markArc;\n markArc.setDeleteUnstableSats(true);\n\n // Objects to compute gravitational delay effects\n GravitationalDelay grDelay(nominalPos);\n\n // Object to align phase with code measurements\n PhaseCodeAlignment phaseAlign;\n\n // Object to remove eclipsed satellites\n EclipsedSatFilter eclipsedSV;\n\n //Object to compute wind-up effect\n ComputeWindUp windup( SP3EphList,\n nominalPos );\n\n\n // Object to compute prefit-residuals\n ComputeLinear linear3(comb.pcPrefit);\n linear3.addLinear(comb.lcPrefit);\n\n CodeSmoother smoothC1;\n\n TypeIDSet tset;\n tset.insert(TypeID::prefitC);\n tset.insert(TypeID::prefitL);\n\n // Declare a NeillTropModel object, setting the defaults\n NeillTropModel neillTM( 310, 39.09, 319);\n\n // Objects to compute the tropospheric data\n ComputeTropModel computeTropo(neillTM);\n\n SimpleFilter pcFilter;\n pcFilter.setFilteredType(TypeID::PC);\n\n // Loop over all data epochs\n while(rin >> gRin)\n {\n TimeSystem sys;\n sys.fromString(\"GPS\");\n CommonTime time(gRin.header.epoch);\n time.setTimeSystem(sys);\n GPSWeekSecond gpstime( time );\n\n try\n {\n gRin >> requireObs // Check if required observations are present\n >> pObsFilter // Filter out spurious data\n >> linear1 // Compute linear combinations to detect CS\n >> markCSLI2 // Mark cycle slips\n >> markCSMW // Mark cycle slips: Melbourne-Wubbena\n >> markArc // Keep track of satellite arcs\n >> basic // Compute the basic components of model\n >> eclipsedSV // Remove satellites in eclipse\n >> svPcenter // Computer delta for sat. phase center\n >> linear2 // Compute ionosphere-free combinations\n >> corr // SP3 Corrections\n >> corrCode // Correct for differential code biases\n >> windup // phase windup correction\n >> grDelay // Compute gravitational delay\n >> computeTropo // neill trop function\n >> pcFilter // screen PC\n >> phaseAlign; // Align phases with codes\n\n }\n catch(Exception& e)\n {\n //cerr << \"Exception at epoch: \" << time << \"; \" << e << endl;\n continue;\n }\n catch(...)\n {\n cerr << \"Unknown exception at epoch: \" << time << endl;\n continue;\n }\n TypeIDSet types;\n types.insert(TypeID::satX);\n types.insert(TypeID::satY);\n types.insert(TypeID::satZ);\n types.insert(TypeID::PC);\n types.insert(TypeID::LC);\n types.insert(TypeID::rho);\n types.insert(TypeID::tropo);\n types.insert(TypeID::tropoSlant);\n types.insert(TypeID::dtSat);\n types.insert(TypeID::rel);\n types.insert(TypeID::gravDelay);\n types.insert(TypeID::instC1);\n types.insert(TypeID::instC2);\n types.insert(TypeID::satArc);\n types.insert(TypeID::satPCenter);\n types.insert(TypeID::windUp);\n gRin.keepOnlyTypeID(types);\n\n // Iterate through the GNSS Data Structure\n satTypeValueMap::const_iterator it;\n typeValueMap::const_iterator itObs;\n if (gRin.numSats() > 4)\n {\n if ( itsBelowThree > 0 )\n {\n itsBelowThree = 0;\n continue;\n }\n for (it = gRin.body.begin(); it!= gRin.body.end(); it++)\n {\n cout << gpstime.week << \" \";\n cout << gpstime.sow << \" \";\n cout << count << \" \";\n cout << (*it).first << \" \";\n\n typeValueMap::const_iterator itObs;\n for( itObs = (*it).second.begin(); itObs != (*it).second.end(); itObs++ )\n {\n //cout << (*itObs).first << \" \";\n cout << (*itObs).second << \" \";\n }\n cout << endl;\n }\n count++;\n }\n else { itsBelowThree++; }\n }\n return 0;\n}\n", "meta": {"hexsha": "e198521742db8fbb9d84f173cd05dbc56f80844a", "size": 12209, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "gtsam/gnssExamples/rnx2Gtsam.cpp", "max_stars_repo_name": "mfkiwl/RobustGNSS", "max_stars_repo_head_hexsha": "f7774af62eb6eaf2198b5a7ca6f2a63dc87a4f7f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 105.0, "max_stars_repo_stars_event_min_datetime": "2017-12-02T14:39:49.000Z", "max_stars_repo_stars_event_max_datetime": "2022-02-19T18:20:25.000Z", "max_issues_repo_path": "gtsam/gnssExamples/rnx2Gtsam.cpp", "max_issues_repo_name": "jianlun37/RobustGNSS", "max_issues_repo_head_hexsha": "5c0ea1861b651a79e880ea8d23b9c2458dc11e10", "max_issues_repo_licenses": ["MIT"], "max_issues_count": 5.0, "max_issues_repo_issues_event_min_datetime": "2018-09-04T15:15:59.000Z", "max_issues_repo_issues_event_max_datetime": "2020-09-08T08:51:02.000Z", "max_forks_repo_path": "gtsam/gnssExamples/rnx2Gtsam.cpp", "max_forks_repo_name": "jianlun37/RobustGNSS", "max_forks_repo_head_hexsha": "5c0ea1861b651a79e880ea8d23b9c2458dc11e10", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 31.0, "max_forks_repo_forks_event_min_datetime": "2018-01-10T03:21:28.000Z", "max_forks_repo_forks_event_max_datetime": "2021-08-06T06:18:35.000Z", "avg_line_length": 36.0147492625, "max_line_length": 106, "alphanum_fraction": 0.5527070194, "num_tokens": 2798, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5312093733737562, "lm_q2_score": 0.39233683016710835, "lm_q1q2_score": 0.20841300170451543}} {"text": "#include \"lingua/chat.hxx\"\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace lingua {\n float operator*(const SemanticVector &lhs, const SemanticVector &rhs) {\n float result = 0;\n auto vl = ChatEngine::instance->getVectorLength();\n for (auto i = 0; i < vl; ++i)\n result += lhs.values[i] * rhs.values[i];\n return result;\n }\n\n float cosine(const SemanticVector &lhs, const SemanticVector &rhs) {\n return (lhs * rhs) / (lhs.magnitude() * rhs.magnitude());\n }\n\n SemanticVector::SemanticVector() : values(new float[ChatEngine::instance->getVectorLength()]) {\n auto vl = ChatEngine::instance->getVectorLength();\n auto seed = std::chrono::system_clock::now().time_since_epoch().count();\n std::default_random_engine gen(seed);\n std::uniform_real_distribution dist(-1.0, 1.0);\n\n for (auto i = 0; i < vl; ++i)\n values[i] = dist(gen);\n }\n\n SemanticVector::SemanticVector(const float *vs) : values(new float[ChatEngine::instance->getVectorLength()]) {\n auto vl = ChatEngine::instance->getVectorLength();\n\n for (auto i = 0; i < vl; ++i)\n values[i] = vs[i];\n }\n\n SemanticVector& SemanticVector::operator=(const SemanticVector &other) {\n values = other.values;\n return *this;\n }\n\n const float* SemanticVector::getValues() const {\n return values;\n }\n\n float SemanticVector::getValue(size_t ix) const {\n return values[ix];\n }\n\n void SemanticVector::setValues(const float *vs) {\n auto vl = ChatEngine::instance->getVectorLength();\n\n for (auto i = 0; i < vl; ++i)\n values[i] = vs[i];\n }\n\n void SemanticVector::setValue(size_t ix, float value) {\n values[ix] = value;\n }\n\n float SemanticVector::magnitude() const {\n auto vl = ChatEngine::instance->getVectorLength();\n float result = 0;\n\n for (auto i = 0; i < vl; ++i)\n result += values[i] * values[i];\n\n return std::sqrt(result);\n }\n\n WordInfo::WordInfo(tag_t t, const std::string &txt) : tag(t), text(txt), semEmb(new SemanticVector), ctxEmb(new SemanticVector) { }\n\n tag_t WordInfo::getTag() const {\n return tag;\n }\n\n std::string WordInfo::getText() const {\n return text;\n }\n\n SemanticVector* WordInfo::getSemanticEmbedding() {\n return semEmb;\n }\n\n SemanticVector* WordInfo::getContextEmbedding() {\n return ctxEmb;\n }\n\n void WordInfo::setSemanticEmbedding(const SemanticVector &sv) {\n if (semEmb)\n *semEmb = sv;\n else {\n semEmb = new SemanticVector(sv);\n }\n }\n\n void WordInfo::setContextEmbedding(const SemanticVector &cv) {\n if (ctxEmb)\n *ctxEmb = cv;\n else {\n ctxEmb = new SemanticVector(cv);\n }\n }\n\n std::string ChatEngine::Tokenizer::currentTitle = \"\";\n\n std::string ChatEngine::Tokenizer::currentBody;\n\n ChatEngine* ChatEngine::instance = nullptr;\n\n ChatEngine::ChatEngine(unsigned pvl, unsigned pcn, unsigned pnr) : sourceFile(\"-\"), docs(), pVectorLength(pvl), pContextNeighborhood(pcn), pNoiseRatio(pnr), learningRate(PARAM_DEFAULT_INITIAL_LEARNING_RATE), dict(), infotbl() { }\n\n ChatEngine::ChatEngine(const std::string &src, unsigned pvl, unsigned pcn, unsigned pnr) : sourceFile(src), docs(), pVectorLength(pvl), pContextNeighborhood(pcn), pNoiseRatio(pnr), learningRate(PARAM_DEFAULT_INITIAL_LEARNING_RATE), dict(), infotbl() { }\n\n void ChatEngine::initialize(unsigned pvl, unsigned pcn, unsigned pnr) {\n instance = new ChatEngine(pvl, pcn, pnr);\n }\n\n void ChatEngine::initialize(const std::string &src, unsigned pvl, unsigned pvn, unsigned pnr) {\n instance = new ChatEngine(src, pvl, pvn, pnr);\n }\n\n void ChatEngine::analyzeSemantics() {\n preprocess();\n learnSemantics();\n }\n\n void ChatEngine::learnSemantics() {\n for (auto it = docs.begin(); it != docs.end(); ++it)\n trainOn(*it);\n }\n\n void ChatEngine::trainOn(const Document &doc) {\n auto toks = doc.getTokens();\n std::size_t targetIndex = pContextNeighborhood;\n std::size_t lastTargetIndex = toks.size() - pContextNeighborhood - 1;\n\n while (targetIndex < lastTargetIndex) {\n auto beginContext = toks.begin() + targetIndex - pContextNeighborhood;\n auto targetPos = toks.begin() + targetIndex;\n auto endContext = toks.begin() + targetIndex + pContextNeighborhood;\n WordInfo targetInfo = infotbl[toks[targetIndex]];\n auto targetEmbedding = targetInfo.getSemanticEmbedding();\n std::vector contextInfo;\n std::vector noiseInfo;\n std::vector contextEmbeddings;\n std::vector noiseEmbeddings;\n std::vector contextTags(beginContext, targetPos);\n std::vector backContextTags(targetPos + 1, endContext);\n std::vector noiseTags;\n\n contextTags.resize(contextTags.size() << 1);\n contextTags.insert(contextTags.end(), backContextTags.begin(), backContextTags.end());\n\n noiseTags = generateNoise(contextTags);\n\n contextInfo.resize(contextTags.size());\n std::transform(contextTags.begin(), contextTags.end(),\n contextInfo.begin(), [&](auto tag) { return this->getInfotbl()[tag]; });\n\n contextEmbeddings.resize(contextInfo.size());\n std::transform(contextInfo.begin(), contextInfo.end(),\n contextEmbeddings.begin(), [&](auto ci) { return ci.getSemanticEmbedding(); });\n\n noiseInfo.resize(noiseTags.size());\n std::transform(noiseTags.begin(), noiseTags.end(),\n noiseInfo.begin(), [&](auto tag) { return this->getInfotbl()[tag]; });\n\n noiseEmbeddings.resize(noiseInfo.size());\n std::transform(noiseInfo.begin(), noiseInfo.end(),\n noiseEmbeddings.begin(), [&](auto ni) { return ni.getSemanticEmbedding(); });\n\n trainTargetEmbedding(targetEmbedding, contextEmbeddings, noiseEmbeddings);\n ++targetIndex;\n break; // DEBUG\n }\n }\n\n void ChatEngine::trainTargetEmbedding(SemanticVector *targetEmbedding, const std::vector &contextEmbeddings, const std::vector &noiseEmbeddings) {\n\n }\n\n std::vector ChatEngine::generateNoise(const std::vector &ctx) {\n auto seed = std::chrono::system_clock::now().time_since_epoch().count();\n std::default_random_engine gen(seed);\n std::uniform_int_distribution dist;\n auto limit = infotbl.size();\n auto needed = ctx.size() * pNoiseRatio;\n std::vector noise;\n\n while (noise.size() < needed) {\n auto candidate = dist(gen) % limit;\n if (std::find(ctx.cbegin(), ctx.cend(), candidate) == ctx.cend())\n noise.push_back(candidate);\n }\n\n return noise;\n }\n\n std::string ChatEngine::getSourceFile() const {\n return sourceFile;\n }\n\n std::vector ChatEngine::getDocs() const {\n return docs;\n }\n\n unsigned ChatEngine::getVectorLength() const {\n return pVectorLength;\n }\n\n unsigned ChatEngine::getContextNeighborhood() const {\n return pContextNeighborhood;\n }\n\n unsigned ChatEngine::getNoiseRatio() const {\n return pNoiseRatio;\n }\n\n float ChatEngine::getLearningRate() const {\n return learningRate;\n }\n\n Dictionary ChatEngine::getDict() const {\n return dict;\n }\n\n Infotbl ChatEngine::getInfotbl() const {\n return infotbl;\n }\n\n void ChatEngine::setSourceFile(const std::string &src) {\n sourceFile = src;\n }\n\n void ChatEngine::addDoc(const Document &doc) {\n docs.push_back(doc);\n }\n\n Document ChatEngine::tokenize(const std::string &text) {\n boost::tokenizer<> tknzr(text);\n std::vector toks;\n\n for (auto it = tknzr.begin(); it != tknzr.end(); ++it) {\n if (dict.find(*it) != dict.end())\n toks.push_back(dict[*it]);\n else {\n tag_t tkn = dict.size();\n dict[*it] = tkn;\n infotbl[tkn] = WordInfo(tkn, *it);\n toks.push_back(tkn);\n }\n }\n\n return Document(toks);\n }\n\n void ChatEngine::printDocuments() const {\n for (auto it = docs.begin(); it != docs.end(); ++it)\n it->printTokens();\n }\n\n void ChatEngine::preprocess() {\n std::ifstream fh(sourceFile);\n std::ofstream nullDev(\"/dev/null\");\n FlexLexer *lexer = new yyFlexLexer(fh, nullDev);\n\n lexer->yylex();\n }\n}\n", "meta": {"hexsha": "e59467d36149f82d21bf7e70e29bf3f3bb7427d6", "size": 9119, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "src/chat.cxx", "max_stars_repo_name": "jamesbolden/lingua", "max_stars_repo_head_hexsha": "3824458338c572c83051031208d660958c085944", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "src/chat.cxx", "max_issues_repo_name": "jamesbolden/lingua", "max_issues_repo_head_hexsha": "3824458338c572c83051031208d660958c085944", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/chat.cxx", "max_forks_repo_name": "jamesbolden/lingua", "max_forks_repo_head_hexsha": "3824458338c572c83051031208d660958c085944", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 32.5678571429, "max_line_length": 257, "alphanum_fraction": 0.6044522426, "num_tokens": 2153, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6926419704455589, "lm_q2_score": 0.3007455852086007, "lm_q1q2_score": 0.2083090147416879}} {"text": "#include \"RayTrace.h\"\n#include \n#include \n#include \n#include \n#include \n\n#include \n\nnamespace RayTrace{\n\t\n\tconst double TraceRecord::noReflection(100.0);\n\t\n\t//========== minimalRayPosition ==========//\n\t\n\tminimalRayPosition::minimalRayPosition():\n\tx(0.0),z(0.0),theta(0.0){}\n\t\n\tminimalRayPosition::minimalRayPosition(double x_, double z_, double theta_):\n\tx(x_),z(z_),theta(theta_){}\n\t\n\tminimalRayPosition& minimalRayPosition::operator +=(const minimalRayPosition& p){\n\t\tx+=p.x;\n\t\tz+=p.z;\n\t\ttheta+=p.theta;\n\t\treturn(*this);\n\t}\n\t\n\tminimalRayPosition& minimalRayPosition::operator -=(const minimalRayPosition& p){\n\t\tx-=p.x;\n\t\tz-=p.z;\n\t\ttheta-=p.theta;\n\t\treturn(*this);\n\t}\n\t\n\tminimalRayPosition& minimalRayPosition::operator *=(double m){\n\t\tx*=m;\n\t\tz*=m;\n\t\ttheta*=m;\n\t\treturn(*this);\n\t}\n\t\n\tconst minimalRayPosition minimalRayPosition::operator +(const minimalRayPosition& p) const{\n\t\tminimalRayPosition res=*this;\n\t\treturn(res+=p);\n\t}\n\t\n\tconst minimalRayPosition minimalRayPosition::operator -(const minimalRayPosition& p) const{\n\t\tminimalRayPosition res=*this;\n\t\treturn(res-=p);\n\t}\n\t\n\tconst minimalRayPosition minimalRayPosition::operator *(double m) const{\n\t\tminimalRayPosition res=*this;\n\t\treturn(res*=m);\n\t}\n\t\n\tconst minimalRayPosition operator *(double m, const minimalRayPosition& p){\n\t\treturn(p*m);\n\t}\n\t\n\tminimalRayPosition abs(const minimalRayPosition& p){\n\t\treturn(minimalRayPosition(std::abs(p.x), std::abs(p.z), std::abs(p.theta)));\n\t}\n\t\n\tvoid minimalRayPosition::makeTiny(double s){\n\t\tx=s;\n\t\tz=s;\n\t\ttheta=s;\n\t}\n\t\n\tvoid minimalRayPosition::giveData(TraceRecord& trace) const{\n\t\ttrace.pathTime=-1.0;\n\t\ttrace.receiptAngle=theta;\n\t\ttrace.attenuation=-1.0;\n\t}\n\t\n\t//========== rayPosition ==========//\n\t\n\trayPosition::rayPosition():\n\tx(0.0),z(0.0),theta(0.0),time(0.0){}\n\t\n\trayPosition::rayPosition(double x_, double z_, double theta_, double time_):\n\tx(x_),z(z_),theta(theta_),time(time_){}\n\t\n\trayPosition& rayPosition::operator +=(const rayPosition& p){\n\t\tx+=p.x;\n\t\tz+=p.z;\n\t\ttheta+=p.theta;\n\t\ttime+=p.time;\n\t\treturn(*this);\n\t}\n\t\n\trayPosition& rayPosition::operator -=(const rayPosition& p){\n\t\tx-=p.x;\n\t\tz-=p.z;\n\t\ttheta-=p.theta;\n\t\ttime-=p.time;\n\t\treturn(*this);\n\t}\n\t\n\trayPosition& rayPosition::operator *=(double m){\n\t\tx*=m;\n\t\tz*=m;\n\t\ttheta*=m;\n\t\ttime*=m;\n\t\treturn(*this);\n\t}\n\t\n\tconst rayPosition rayPosition::operator +(const rayPosition& p) const{\n\t\trayPosition res=*this;\n\t\treturn(res+=p);\n\t}\n\t\n\tconst rayPosition rayPosition::operator -(const rayPosition& p) const{\n\t\trayPosition res=*this;\n\t\treturn(res-=p);\n\t}\n\t\n\tconst rayPosition rayPosition::operator *(double m) const{\n\t\trayPosition res=*this;\n\t\treturn(res*=m);\n\t}\n\t\n\tconst rayPosition operator *(double m, const rayPosition& p){\n\t\treturn(p*m);\n\t}\n\t\n\trayPosition abs(const rayPosition& p){\n\t\treturn(rayPosition(std::abs(p.x), std::abs(p.z), std::abs(p.theta), std::abs(p.time)));\n\t}\n\t\n\tvoid rayPosition::makeTiny(double s){\n\t\tx=s;\n\t\tz=s;\n\t\ttheta=s;\n\t\ttime=s;\n\t}\n\t\n\tvoid rayPosition::giveData(TraceRecord& trace) const{\n\t\ttrace.pathTime=time;\n\t\ttrace.receiptAngle=theta;\n\t\ttrace.attenuation=-1.0;\n\t}\n\n\t//========== fullRayPosition ==========//\n\t\n\tfullRayPosition::fullRayPosition():\n\tx(0.0),z(0.0),theta(0.0),time(0.0),attenuation(1.0){}\n\t\n\tfullRayPosition::fullRayPosition(double x_, double z_, double theta_, double time_, double attenuation_):\n\tx(x_),z(z_),theta(theta_),time(time_),attenuation(attenuation_){}\n\t\n\tfullRayPosition& fullRayPosition::operator +=(const fullRayPosition& p){\n\t\tx+=p.x;\n\t\tz+=p.z;\n\t\ttheta+=p.theta;\n\t\ttime+=p.time;\n\t\tattenuation+=p.attenuation;\n\t\treturn(*this);\n\t}\n\t\n\tfullRayPosition& fullRayPosition::operator -=(const fullRayPosition& p){\n\t\tx-=p.x;\n\t\tz-=p.z;\n\t\ttheta-=p.theta;\n\t\ttime-=p.time;\n\t\tattenuation-=p.attenuation;\n\t\treturn(*this);\n\t}\n\t\n\tfullRayPosition& fullRayPosition::operator *=(double m){\n\t\tx*=m;\n\t\tz*=m;\n\t\ttheta*=m;\n\t\ttime*=m;\n\t\tattenuation*=m;\n\t\treturn(*this);\n\t}\n\t\n\tconst fullRayPosition fullRayPosition::operator +(const fullRayPosition& p) const{\n\t\tfullRayPosition res=*this;\n\t\treturn(res+=p);\n\t}\n\t\n\tconst fullRayPosition fullRayPosition::operator -(const fullRayPosition& p) const{\n\t\tfullRayPosition res=*this;\n\t\treturn(res-=p);\n\t}\n\t\n\tconst fullRayPosition fullRayPosition::operator *(double m) const{\n\t\tfullRayPosition res=*this;\n\t\treturn(res*=m);\n\t}\n\t\n\tconst fullRayPosition operator *(double m, const fullRayPosition& p){\n\t\treturn(p*m);\n\t}\n\t\n\tfullRayPosition abs(const fullRayPosition& p){\n\t\treturn(fullRayPosition(std::abs(p.x), std::abs(p.z), std::abs(p.theta), std::abs(p.time), std::abs(p.attenuation)));\n\t}\n\t\n\tvoid fullRayPosition::makeTiny(double s){\n\t\tx=s;\n\t\tz=s;\n\t\ttheta=s;\n\t\ttime=s;\n\t\tattenuation=s;\n\t}\n\t\n\tvoid fullRayPosition::giveData(TraceRecord& trace) const{\n\t\ttrace.pathTime=time;\n\t\ttrace.receiptAngle=theta;\n\t\ttrace.attenuation=attenuation;\n\t}\n\n\t//========== TraceFinder ==========//\n\n\t//const double TraceFinder::maximum_ice_depth = -2850.0;\n\tconst double TraceFinder::maximum_ice_depth = -3500.0; // changed for testing max ice depth\n\t\n\t///\\brief Computes the derivatives of the position coordinates with respect to path length\n\t///\n\t///This specialization computes the minimum usable set of coordinates: the radial and vertical \n\t///position and the angle. \n\t///\n\t///\\param pos The position at which the derivatives are to be computed\n\t///\\param der The object into which to record the calculated derivatives\n\t///\\param frequency The frequency of the signal being propogated, in GHz\n\ttemplate<>\n\tvoid TraceFinder::computeRayDerivatives(const minimalRayPosition& pos, minimalRayPosition& der, double frequency) const{\n\t\t//put dn/dz into der.theta\n\t\tdouble n;\n\t\trModel->indexOfRefractionWithDerivative(pos.z,n,der.theta);\n\t\tder.x = sin(pos.theta);\n\t\tder.z = cos(pos.theta);\n\t\tder.theta = -(der.x * der.theta)/n;\n\t}\n\t\n\t///\\brief Computes the derivatives of the position coordinates with respect to path length\n\t///\n\t///This specialization computes an intermediate set of coordinates: the radial and \n\t///vertical position, the angle, and the time of flight. \n\t///\n\t///\\param pos The position at which the derivatives are to be computed\n\t///\\param der The object into which to record the calculated derivatives\n\t///\\param frequency The frequency of the signal being propogated, in GHz\n\ttemplate<>\n\tvoid TraceFinder::computeRayDerivatives(const rayPosition& pos, rayPosition& der, double frequency) const{\n\t\t//put n int der.time, put dn/dz into der.theta\n\t\trModel->indexOfRefractionWithDerivative(pos.z,der.time,der.theta);\n\t\tder.x = sin(pos.theta);\n\t\tder.z = cos(pos.theta);\n\t\tder.theta = -(der.x * der.theta)/der.time;\n\t\tder.time /= speedOfLight;\n\t}\n\t\n\t///\\brief Computes the derivatives of the position coordinates with respect to path length\n\t///\n\t///This specialization computes a complete set of coordinates: the radial and vertical \n\t///position, the angle, the time of flight, and the amplitude correction. \n\t///\n\t///\\param pos The position at which the derivatives are to be computed\n\t///\\param der The object into which to record the calculated derivatives\n\t///\\param frequency The frequency of the signal being propogated, in GHz\n\ttemplate<>\n\tvoid TraceFinder::computeRayDerivatives(const fullRayPosition& pos, fullRayPosition& der, double frequency) const{\n\t\t//put n int der.time, put dn/dz into der.theta\n\t\trModel->indexOfRefractionWithDerivative(pos.z,der.time,der.theta);\n\t\tder.x = sin(pos.theta);\n\t\tder.z = cos(pos.theta);\n\t\tder.theta = -(der.x * der.theta)/der.time;\n\t\tder.time /= speedOfLight;\n\t\tder.attenuation=-pos.attenuation/aModel->attenuationLength(pos.z,frequency);\n\t\t//std::cout << \" eval with z=\" << pos.z << \" att=\" << pos.attenuation << \" yields \" << der.attenuation << std::endl;\n\t}\n\n\ttemplate\n\tvoid TraceFinder::rkStep(const positionType& pos, const typename positionType::derivativeType& der, const double h, positionType& newPos, positionType& errors, const double frequency) const{\n\t\t//const double a[5] = {0.2, 0.3, 0.6, 1.0, 0.875};\n\t\tconst double b[15] = {0.2, 0.075, 0.3, -11.0/54.0, 1631.0/55296.0, \n\t\t\t0.225, -0.9, 2.5, 175.0/512.0, \n\t\t\t1.2, -70.0/27.0, 575.0/13824.0, \n\t\t\t35.0/27.0, 44275.0/110592.0, \n\t\t\t253.0/4096.0};\n\t\tconst double c[6] = {37.0/378.0, 0, 250.0/621.0, 125.0/594.0, 0, 512.0/1771.0};\n\t\tconst double cp[6] = {c[0]-2825.0/27648.0, 0, c[2]-18575.0/48384.0, c[3]-13525.0/55296.0, c[4]-277.0/14336.0, c[5]-0.25};\n\t\t\n\t\tpositionType temp;\n\t\ttypename positionType::derivativeType ak2, ak3, ak4, ak5, ak6; //derivatives\n\t\t//first step\n\t\ttemp = pos + h*b[0]*der;\n\t\t//second step\n\t\tcomputeRayDerivatives(temp,ak2,frequency);\n\t\ttemp = pos + h*(b[1]*der + b[5]*ak2);\n\t\t//third step\n\t\tcomputeRayDerivatives(temp,ak3,frequency);\n\t\ttemp = pos + h*(b[2]*der + b[6]*ak2 + b[9]*ak3);\n\t\t//fourth step\n\t\tcomputeRayDerivatives(temp,ak4,frequency);\n\t\ttemp = pos + h*(b[3]*der + b[7]*ak2 + b[10]*ak3 + b[12]*ak4);\n\t\t//fifth step\n\t\tcomputeRayDerivatives(temp,ak5,frequency);\n\t\ttemp = pos + h*(b[4]*der + b[8]*ak2 + b[11]*ak3 + b[13]*ak4 + b[14]*ak5);\n\t\t//sixth step\n\t\tcomputeRayDerivatives(temp,ak6,frequency);\n\t\tnewPos = pos + h*(c[0]*der + c[2]*ak3 + c[3]*ak4 + c[5]*ak6);\n\t\t//estimate error\n\t\terrors = h*(cp[0]*der + cp[2]*ak3 + cp[3]*ak4 + cp[4]*ak5 + cp[5]*ak6);\n\t}\n\t\n\ttemplate\n\tvoid TraceFinder::rkStep(const positionRecordingWrapper& pos, const typename positionRecordingWrapper::derivativeType& der, const double h, positionRecordingWrapper& newPos, positionRecordingWrapper& errors, const double frequency) const{\n\t\t//const double a[5] = {0.2, 0.3, 0.6, 1.0, 0.875};\n\t\tconst double b[15] = {0.2, 0.075, 0.3, -11.0/54.0, 1631.0/55296.0, \n\t\t\t0.225, -0.9, 2.5, 175.0/512.0, \n\t\t\t1.2, -70.0/27.0, 575.0/13824.0, \n\t\t\t35.0/27.0, 44275.0/110592.0, \n\t\t\t253.0/4096.0};\n\t\tconst double c[6] = {37.0/378.0, 0, 250.0/621.0, 125.0/594.0, 0, 512.0/1771.0};\n\t\tconst double cp[6] = {c[0]-2825.0/27648.0, 0, c[2]-18575.0/48384.0, c[3]-13525.0/55296.0, c[4]-277.0/14336.0, c[5]-0.25};\n\t\t\n\t\tpositionRecordingWrapper temp;\n\t\ttypename positionRecordingWrapper::derivativeType ak2, ak3, ak4, ak5, ak6; //derivatives\n\t\t//first step\n\t\ttemp = pos + h*b[0]*der;\n\t\ttemp.recordStep(1);\n\t\t//second step\n\t\tcomputeRayDerivatives(temp,ak2,frequency);\n\t\ttemp = pos + h*(b[1]*der + b[5]*ak2);\n\t\ttemp.recordStep(2);\n\t\t//third step\n\t\tcomputeRayDerivatives(temp,ak3,frequency);\n\t\ttemp = pos + h*(b[2]*der + b[6]*ak2 + b[9]*ak3);\n\t\ttemp.recordStep(3);\n\t\t//fourth step\n\t\tcomputeRayDerivatives(temp,ak4,frequency);\n\t\ttemp = pos + h*(b[3]*der + b[7]*ak2 + b[10]*ak3 + b[12]*ak4);\n\t\ttemp.recordStep(4);\n\t\t//fifth step\n\t\tcomputeRayDerivatives(temp,ak5,frequency);\n\t\ttemp = pos + h*(b[4]*der + b[8]*ak2 + b[11]*ak3 + b[13]*ak4 + b[14]*ak5);\n\t\ttemp.recordStep(5);\n\t\t//sixth step\n\t\tcomputeRayDerivatives(temp,ak6,frequency);\n\t\tnewPos = pos + h*(c[0]*der + c[2]*ak3 + c[3]*ak4 + c[5]*ak6);\n\t\tnewPos.takeStepData(temp);\n\t\t//estimate error\n\t\terrors = h*(cp[0]*der + cp[2]*ak3 + cp[3]*ak4 + cp[4]*ak5 + cp[5]*ak6);\n\t}\n\t\n\tvoid TraceFinder::replayRkStep(const rkStepRecord& step, const double atten, const double& attenDer, double& newAtten, const double frequency) const{\n\t\tconst double b[15] = {0.2, 0.075, 0.3, -11.0/54.0, 1631.0/55296.0, \n\t\t\t0.225, -0.9, 2.5, 175.0/512.0, \n\t\t\t1.2, -70.0/27.0, 575.0/13824.0, \n\t\t\t35.0/27.0, 44275.0/110592.0, \n\t\t\t253.0/4096.0};\n\t\tconst double c[6] = {37.0/378.0, 0, 250.0/621.0, 125.0/594.0, 0, 512.0/1771.0};\n\t\t\n\t\tdouble temp;\n\t\tdouble ak2, ak3, ak4, ak5, ak6; //derivatives\n\t\t\n\t\t//first step\n\t\ttemp = atten + step.length*b[0]*attenDer;\n\t\t//second step\n\t\tak2=-temp/aModel->attenuationLength(step.z[1],frequency);\n\t\t//std::cout << \" eval with z=\" << step.z[1] << \" att=\" << temp << \" yields \" << ak2 << std::endl;\n\t\ttemp = atten + step.length*(b[1]*attenDer + b[5]*ak2);\n\t\t//third step\n\t\tak3=-temp/aModel->attenuationLength(step.z[2],frequency);\n\t\t//std::cout << \" eval with z=\" << step.z[2] << \" att=\" << temp << \" yields \" << ak3 << std::endl;\n\t\ttemp = atten + step.length*(b[2]*attenDer + b[6]*ak2 + b[9]*ak3);\n\t\t//fourth step\n\t\tak4=-temp/aModel->attenuationLength(step.z[3],frequency);\n\t\t//std::cout << \" eval with z=\" << step.z[3] << \" att=\" << temp << \" yields \" << ak4 << std::endl;\n\t\ttemp = atten + step.length*(b[3]*attenDer + b[7]*ak2 + b[10]*ak3 + b[12]*ak4);\n\t\t//fifth step\n\t\tak5=-temp/aModel->attenuationLength(step.z[4],frequency);\n\t\t//std::cout << \" eval with z=\" << step.z[4] << \" att=\" << temp << \" yields \" << ak5 << std::endl;\n\t\ttemp = atten + step.length*(b[4]*attenDer + b[8]*ak2 + b[11]*ak3 + b[13]*ak4 + b[14]*ak5);\n\t\t//sixth step\n\t\tak6=-temp/aModel->attenuationLength(step.z[5],frequency);\n\t\t//std::cout << \" eval with z=\" << step.z[5] << \" att=\" << temp << \" yields \" << ak6 << std::endl;\n\t\tnewAtten = atten + step.length*(c[0]*attenDer + c[2]*ak3 + c[3]*ak4 + c[5]*ak6);\n\t}\n\t\n\t///\\brief Computes the maximum of a set of coordinate errors, scaled by given factors\n\t///\n\t///This specialization computes the minimum usable set of coordinates: the radial and \n\t///vertical position and the angle. \n\t///\n\t///\\param errors The coordinate errors\n\t///\\param scale The scaling factors for the errors\n\t///\\return The maximum error divided by its associated scale\n\ttemplate<>\n\tdouble TraceFinder::maxError(const minimalRayPosition& errors, const minimalRayPosition& scale) const{\n\t\tdouble result=std::abs(errors.x/scale.x);\n\t\tresult=std::max(result,std::abs(errors.z/scale.z));\n\t\tresult=std::max(result,std::abs(errors.theta/scale.theta));\n\t\treturn(result);\n\t}\n\t\n\t///\\brief Computes the maximum of a set of coordinate errors, scaled by given factors\n\t///\n\t///This specialization computes an intermediate set of coordinates: the radial and \n\t///vertical position, the angle, and the time of flight. \n\t///\n\t///\\param errors The coordinate errors\n\t///\\param scale The scaling factors for the errors\n\t///\\return The maximum error divided by its associated scale\n\ttemplate<>\n\tdouble TraceFinder::maxError(const rayPosition& errors, const rayPosition& scale) const{\n\t\tdouble result=std::abs(errors.x/scale.x);\n\t\tresult=std::max(result,std::abs(errors.z/scale.z));\n\t\tresult=std::max(result,std::abs(errors.theta/scale.theta));\n\t\tresult=std::max(result,std::abs(errors.time/scale.time));\n\t\treturn(result);\n\t}\n\t\n\t///\\brief Computes the maximum of a set of coordinate errors, scaled by given factors\n\t///\n\t///This specialization computes a complete set of coordinates: the radial and vertical \n\t///position, the angle, the time of flight, and the amplitude correction. \n\t///\n\t///\\param errors The coordinate errors\n\t///\\param scale The scaling factors for the errors\n\t///\\return The maximum error divided by its associated scale\n\ttemplate<>\n\tdouble TraceFinder::maxError(const fullRayPosition& errors, const fullRayPosition& scale) const{\n\t\tdouble result=std::abs(errors.x/scale.x);\n\t\tresult=std::max(result,std::abs(errors.z/scale.z));\n\t\tresult=std::max(result,std::abs(errors.theta/scale.theta));\n\t\tresult=std::max(result,std::abs(errors.time/scale.time));\n\t\tresult=std::max(result,std::abs(errors.attenuation/scale.attenuation));\n\t\treturn(result);\n\t}\n\t\n\ttemplate\n\tvoid confirmFinalStepPosition(positionType& pos, const positionType& temp, const double& length){\n\t\tpos=temp;\n\t}\n\t\n\ttemplate\n\tvoid confirmFinalStepPosition(positionRecordingWrapper& pos, const positionRecordingWrapper& temp, const double& length){\n\t\tdouble startZ=pos.z;\n\t\tpos=temp;\n\t\tpos.setFirstStep(startZ);\n\t\tpos.setStepLength(length);\n\t\t//pos.dumpData();\n\t}\n\n\t//requires htry>0 !!!\n\ttemplate\n\tvoid TraceFinder::rkStepControl(double& length, double frequency, positionType& pos, typename positionType::derivativeType& der, const positionType& scale, const double htry, const double eps, double& hdid, double& hnext) const{\n\t\tconst double SAFETY=0.9;\n\t\t//const double growthPower=-0.2;\n\t\t//const double shrinkPower=-0.25;\n\t\tconst double growLimit=1.89e-4; //=(5.0/SAFETY)**(1/growthPower)\n\t\tconst double shrinkLimit=6561.0; //=(.1/SAFETY)**(1/shrinkPower)\n\t\t\n\t\t//std::cout << \"\\tTrying RK(CK) step of size \" << htry << std::endl;\n\t\t\n\t\tdouble h=htry, errMax;\n\t\tpositionType errors, temp;\n //std::cerr<<\"begin while loop\"<=shrinkLimit (\"< growLimit){\n\t\t\t//Here, we use a taylor expansion (about 1) for errMax**growthPower. This \n\t\t\t//expansion always underestimates the true function (in the domain [0,1])\n\t\t\t//so it will give conservative advice for the next step's size\n\t\t\terrMax-=1.0;\n\t\t\thnext = SAFETY*h* (1.+errMax*(-.2+errMax*(.12+errMax*(-.088+errMax*.0704))));\n\t\t}\n\t\telse\n\t\t\thnext = 5.0*h;\n\t\t//std::cout << \"\\tError acceptable, plan next step size to be \" << hnext << std::endl;\n\t\tlength+=(hdid=h);\n\t\tconfirmFinalStepPosition(pos,temp,hdid);//pos=temp;\n\t}\n\t\n\tdouble fresnelReflect(double theta, double& polarization, double n1, double n2){\n\t\tdouble s=sin(theta);\n\t\tif(n1*s > n2){ //total internal reflection!\n\t\t\t//std::cout << \"Total internal refraction\" << std::endl;\n\t\t\treturn(1.0);\n\t\t}\n\t\tdouble c=cos(theta);\n\t\tdouble d=sqrt(n2*n2 - n1*n1*s*s);\n\t\tdouble rPerpedicular=(n1*c-d)/(n1*c+d);\n\t\tdouble rParallel=(n2*n2*c-n1*d)/(n2*n2*c+n1*d);\n\t\tdouble cp=cos(polarization);\n\t\tdouble sp=sin(polarization);\n\t\tdouble R=sqrt(rPerpedicular*rPerpedicular*cp*cp + rParallel*rParallel*sp*sp);\n\t\tpolarization=atan((rParallel*sp)/(rPerpedicular*cp));\n\t\treturn(R);\n\t}\n\t\n\tdouble fresnelTransmit(double theta, double& polarization, double n1, double n2){\n\t\tdouble s=sin(theta);\n\t\tif(n1*s > n2){ //total internal reflection!\n\t\t\t//std::cout << \"Total internal refraction\" << std::endl;\n\t\t\treturn(0.0);\n\t\t}\n\t\tdouble c=cos(theta);\n\t\tdouble d=sqrt(n2*n2 - n1*n1*s*s);\n\t\tdouble tPerpedicular=(2.*n1*c)/(n1*c+d);\n\t\tdouble tParallel=(2*n1*n2*c)/(n2*n2*c+n1*d);\n\t\tdouble cp=cos(polarization);\n\t\tdouble sp=sin(polarization);\n\t\tdouble T=sqrt(tPerpedicular*tPerpedicular*cp*cp + tParallel*tParallel*sp*sp);\n\t\tpolarization=atan((tParallel*sp)/(tPerpedicular*cp));\n\t\treturn(T);\n\t}\n\t\n\ttemplate <>\n\tvoid correctAmplitudeReflect(fullRayPosition& pos, double polarization, double n1, double n2){\n\t\tpos.attenuation*=fresnelReflect(pos.theta, polarization, n1, n2);\n\t}\n\t\n\ttemplate <>\n\tvoid correctAmplitudeTransmit(fullRayPosition& pos, double polarization, double n1, double n2){\n\t\tpos.attenuation*=fresnelTransmit(pos.theta, polarization, n1, n2);\n\t}\n\t\n\ttemplate <>\n\tvoid correctAmplitudeReflect >(positionRecordingWrapper& pos, double polarization, double n1, double n2){\n\t\tpos.attenuation*=fresnelReflect(pos.theta, polarization, n1, n2);\n\t}\n\t\n\ttemplate <>\n\tvoid correctAmplitudeTransmit >(positionRecordingWrapper& pos, double polarization, double n1, double n2){\n\t\tpos.attenuation*=fresnelTransmit(pos.theta, polarization, n1, n2);\n\t}\n\t\n\tdouble TraceFinder::recalculateAmplitude(const traceReplayRecord& trace, double frequency, double polarization){\n\t\tdouble attenuation=1.0, attenuationDerivative,nextAttenuation;\n\t\t//std::cout << \" att: \" << attenuation << std::endl;\n\t\tfor(std::vector::const_iterator step=trace.steps.begin(), end=trace.steps.end(); step!=end; ++step){\n\t\t\tswitch(step->stepType){\n\t\t\t\tcase RK_FIRST_STEP:\n\t\t\t\t\t//do nothing; should never occur\n\t\t\t\t\tbreak;\n\t\t\t\tcase RK_AIR_STEP:\n\t\t\t\t\t//TODO: implement this!\n\t\t\t\t\tattenuation*=fresnelTransmit(step->angle, polarization, 1.0, rModel->indexOfRefraction(0.0));\n\t\t\t\t\tbreak;\n\t\t\t\tcase RK_STEP:\n\t\t\t\t\tattenuationDerivative=-attenuation/aModel->attenuationLength(step->rkData.z[0],frequency);\n\t\t\t\t\t//std::cout << \" eval with z=\" << step->rkData.z[0] << \" att=\" << attenuation << \" yields \" << attenuationDerivative << std::endl;\n\t\t\t\t\treplayRkStep(step->rkData, attenuation, attenuationDerivative, nextAttenuation, frequency);\n\t\t\t\t\tattenuation=nextAttenuation;\n\t\t\t\t\tbreak;\n\t\t\t\tcase RK_REFLECT_STEP:\n\t\t\t\t\tif(step->angle<(pi/2.)){ //ray was reflected down from the ice surface\n\t\t\t\t\t\t//use the angle _after_ reflection\n\t\t\t\t\t\tattenuation*=fresnelReflect(step->angle, polarization, rModel->indexOfRefraction(maximum_ice_depth),1.0);\n\t\t\t\t\t}\n\t\t\t\t\telse{ //ray was reflected up from bedrock\n\t\t\t\t\t\t//use the angle _before_ reflection\n\t\t\t\t\t\tattenuation*=fresnelReflect(pi-step->angle, polarization, rModel->indexOfRefraction(0.0),1.0);\n\t\t\t\t\t}\n\t\t\t\t\tbreak;\n\t\t\t}\n\t\t\t//std::cout << \" att: \" << attenuation << std::endl;\n\t\t}\n\t\treturn(attenuation);\n\t}\n\n\tstd::pair TraceFinder::traceMax(double emit_depth, const rayTargetRecord& target, int &sol_error, double left, double right ) const{\n\n // test sol_error\n //int sol_error;\n\n\t\t//std::cout << \"traceMax within domain [\" << left << ',' << right << ']' << std::endl;\n\t\t//unsigned int steps=1;\n\t\tstd::pair result;\n\t\tdouble a=left; //the left boundary of the search interval\n\t\tdouble b=right; //the right boundary of the search interval\n\t\tconst double w=(3.0-sqrt(5.0))/2.0;\n\t\tdouble c=a+w*(b-a); //the best estimate point\n\t\t//frequency and polarization are unimportant\n\t\t//double cy=doTrace(emit_depth,c,target,SurfaceReflection,0.0,0.0).miss;\n\t\tdouble cy=doTrace(emit_depth,c,target,SurfaceReflection,0.0,0.0, sol_error).miss;\n\t\t//std::cout << \"f(\" << c << \")=\" << cy << std::endl;\n\t\tdouble d,dy;\n\t\tconst double tol=1e-3;\n\t\twhile(cy<0.0 && fabs(b-a)>tol*c){\n\t\t\t//std::cout << \"\\tsearch domain is now [\" << a << ',' << b << ']' << std::endl;\n\t\t\tif((c-a)>=(b-c)){ //the left subinterval is larger, so search within it\n\t\t\t\td=c-w*(c-a);\n\t\t\t\tdy=doTrace(emit_depth,d,target,SurfaceReflection,0.0,0.0, sol_error ).miss; //evaluate the miss distance\n\t\t\t\t//std::cout << \"\\t \" << d << ' ' << dy << std::endl;\n\t\t\t\tif(dy>cy){\n\t\t\t\t\tb=c;\n\t\t\t\t\tc=d;\n\t\t\t\t\tcy=dy;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t\ta=d;\n\t\t\t}\n\t\t\telse{ //otherwise seach within the right subinterval\n\t\t\t\td=c+w*(b-c);\n\t\t\t\tdy=doTrace(emit_depth,d,target,SurfaceReflection,0.0,0.0, sol_error ).miss; //evaluate the miss distance\n\t\t\t\t//std::cout << \"\\t \" << d << ' ' << dy << std::endl;\n\t\t\t\tif(dy>cy){\n\t\t\t\t\ta=c;\n\t\t\t\t\tc=d;\n\t\t\t\t\tcy=dy;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t\tb=d;\n\t\t\t}\n\t\t\t//steps++;\n\t\t}\n\t\t//std::cout << \"traceMax stopped at c=\" << c << \" f(c)=\" << cy << std::endl;\n\t\tresult.first = cy>0.0; //whether the best point indicates the existance of roots\n\t\tresult.second=c;\n\t\t//std::cout << \"traceMax took \" << steps << \" steps\" << std::endl;\n\t\treturn(result);\n\t}\n\n\tstd::pair TraceFinder::traceMin(double emit_depth, const rayTargetRecord& target, int &sol_error, double left, double right ) const{\n\n // test sol_error\n //int sol_error;\n\n\t\t//std::cout << \"traceMin within domain [\" << left << ',' << right << ']' << std::endl;\n\t\t//unsigned int steps=1;\n\t\tstd::pair result;\n\t\tdouble a=left; //the left boundary of the search interval\n\t\tdouble b=right; //the right boundary of the search interval\n\t\tconst double w=(3.0-sqrt(5.0))/2.0;\n\t\tdouble c=a+w*(b-a); //the best estimate point\n\t\t//frequency and polarization are unimportant\n\t\tdouble cy=doTrace(emit_depth,c,target,BedrockReflection,0.0,0.0, sol_error ).miss;\n\t\t//std::cout << \"f(\" << c << \")=\" << cy << std::endl;\n\t\tdouble d,dy;\n\t\tconst double tol=1e-3;\n\t\twhile(cy>0.0 && fabs(b-a)>tol*c){\n\t\t\t//std::cout << \"\\tsearch domain is now [\" << a << ',' << b << ']' << std::endl;\n\t\t\tif((c-a)>=(b-c)){ //the left subinterval is larger, so search within it\n\t\t\t\t//std::cout << \"\\t left sub-interval\\n\";\n\t\t\t\td=c-w*(c-a);\n\t\t\t\tdy=doTrace(emit_depth,d,target,BedrockReflection,0.0,0.0, sol_error ).miss; //evaluate the miss distance\n\t\t\t\t//std::cout << \"\\t \" << d << ' ' << dy << std::endl;\n\t\t\t\tif(dy(emit_depth,d,target,BedrockReflection,0.0,0.0, sol_error ).miss; //evaluate the miss distance\n\t\t\t\t//std::cout << \"\\t \" << d << ' ' << dy << std::endl;\n\t\t\t\tif(dy TraceFinder::traceRootImpl(double emit_depth, const rayTargetRecord& target, bool rising, unsigned short allowedReflections, double requiredAccuracy, \n\t\t\t\t\t\t\t\t\t double a, TraceRecord& aTrace, double c, TraceRecord& cTrace, double angle, int &sol_error ) const{\n\n // test sol_error\n //int sol_error;\n\n\t\tconst double miss_eps=requiredAccuracy/100.0;\n\t\tconst double angle_eps=1e-10;\n\t\tdouble lastMiss;\n\t\tTraceRecord trace;\n\t\t\n\t\tdouble e=0.0,ep=0.0,p,q,r,s,t;\n\t\t\n\t\t//initialize these so that the miss change check won't fail accidentally\n\t\ttrace.miss=1.0e3*miss_eps;\n\t\tlastMiss=-1.0e3*miss_eps;\n\t\t\n\t\twhile((c-a)>angle_eps && fabs(trace.miss-lastMiss)>miss_eps){\n // changed\n\t\t\t//std::cout << \"\\tAngular range=\" << (c-a) << \", miss change=\" << fabs(trace.miss-lastMiss) << std::endl;\n\t\t\tlastMiss=trace.miss;\n\t\t\t//std::cout << \" Angular range is now [\" << a << ',' << c << \"]\\n\";\n\t\t\t//std::cout << \" Trying angle=\" << angle << std::endl;\n\t\t\ttrace=doTrace(emit_depth,angle,target,allowedReflections,0.0,0.0, sol_error );\n\t\t\t//steps++;\n\t\t\t//std::cout << \" miss distance was \" << trace.miss << '\\n';\n\t\t\tif(std::abs(trace.miss) < requiredAccuracy){\n\t\t\t\t//std::cout << \"traceRoot took \" << steps << \" steps\" << std::endl;\n\t\t\t\t//doTrace(emit_depth,angle,target,allowedReflections,frequency,polarization)\n\t\t\t\treturn(std::make_pair(angle,trace.miss));\n\t\t\t}\n\t\t\t\n\t\t\t//calculate the quadratic interpolation\n\t\t\tr=trace.miss/cTrace.miss;\n\t\t\ts=trace.miss/aTrace.miss;\n\t\t\tt=aTrace.miss/cTrace.miss;\n\t\t\tp=s*(t*(r-t)*(c-angle)+(r-1)*(angle-a));\n\t\t\tq=(r-1)*(s-1)*(t-1);\n\t\t\t\n\t\t\t//collapse the interval\n\t\t\tif((trace.miss>0.0) != rising){\n\t\t\t\ta=angle;\n\t\t\t\taTrace=trace;\n\t\t\t}\n\t\t\telse{\n\t\t\t\tc=angle;\n\t\t\t\tcTrace=trace;\n\t\t\t}\n\t\t\t\n\t\t\t//accept the interpolation only if it falls within the current boundaries\n\t\t\tif((angle+(p/q))>=a && (angle+(p/q))<=c){\n\t\t\t\t//would like to interpolate, but only do so if recent convergence has been suitably rapid\n\t\t\t\tif(std::abs(p/q) >= 0.5*std::abs(e)){ //it has not; bisect instead\n\t\t\t\t\t//std::cout << \" will bisect\" << std::endl;\n\t\t\t\t\te=ep;\n\t\t\t\t\tep=0.5*(a+c)-angle;\n\t\t\t\t\tangle=0.5*(a+c);\n\t\t\t\t}\n\t\t\t\telse{ //it has; use the interpolation\n\t\t\t\t\t//std::cout << \" will interpolate\" << std::endl;\n\t\t\t\t\tangle+=p/q;\n\t\t\t\t\te=ep;\n\t\t\t\t\tep=p/q;\n\t\t\t\t}\n\t\t\t}\n\t\t\telse{ //otherwise, bisect\n\t\t\t\t//std::cout << \" will bisect\" << std::endl;\n\t\t\t\te=ep;\n\t\t\t\tep=0.5*(a+c)-angle;\n\t\t\t\tangle=0.5*(a+c);\n\t\t\t}\n\t\t}\n\t\t//std::cout << \"traceRoot took \" << steps << \" steps and bailed out\" << std::endl;\n\t\treturn(std::make_pair(angle,trace.miss));\n\t}\n\t\n\t//rough implementation of Brent's method\n\t//This implementation neglects some of the checks made by Brent to ensure \n\t//rapidity of convergence, and does not bother to use secant interpolation. \n\t//In practice it seems not to matter, as it is still capable of the same \n\t//best-case behavior as the full Brent's method. \n\t//This implementation is more conveniently controllable for this purpose \n\t//than NR's zbrent, with regard to the stopping conditions\n\tstd::pair TraceFinder::traceRoot(double emit_depth, const rayTargetRecord& target, double minAngle, double maxAngle, bool rising, unsigned short allowedReflections, double requiredAccuracy, int &sol_error ) const{\n\n // test sol_error\n //int sol_error;\n\n\n\t\tdouble a=minAngle,c=maxAngle;\n\t\tTraceRecord aTrace, cTrace;\n\t\t//frequency and polarization are irrelevant on all traces except the final one\n\t\taTrace = doTrace(emit_depth,a,target,allowedReflections,0.0,0.0, sol_error );\n\t\tcTrace = doTrace(emit_depth,c,target,allowedReflections,0.0,0.0, sol_error );\n\t\tdouble angle=0.5*(minAngle+maxAngle);\n\t\t//std::cout << \"Attempting to find root in range [\" << minAngle << ',' << maxAngle << ']' << std::endl;\n\t\t//std::cout << \" end point miss values are \" << aTrace.miss << ',' << cTrace.miss << std::endl;\n\t\tif((aTrace.miss>0) == (cTrace.miss>0)) //didn't bracket a root. Return a nonsense angle and a huge error\n\t\t\treturn(std::make_pair(-1.,1e6));\n\t\treturn(traceRootImpl(emit_depth, target, rising, allowedReflections, requiredAccuracy, a, aTrace, c, cTrace, angle, sol_error ));\n\t}\n\t\n /*\n\tstd::pair TraceFinder::refineRoot(double emit_depth, const rayTargetRecord& target, const TraceRecord& seed, bool rising, unsigned short allowedReflections, double requiredAccuracy, int &sol_error ) const{\n\n // test sol_error\n //int sol_error;\n\n\n\t\t//bracket the root\n\t\t//std::cout << \"Attempting to refine root near theta=\" << seed.launchAngle << std::endl;\n\t\tdouble a, c;\n\t\tTraceRecord aTrace, cTrace;\n\t\tdouble testDisp=.001; //TODO: this is a problem since there could be a second root arbitrarily close to the one we seek\n\t\t//If we start with testDisp larger than the distance betwee these roots we can bracket them both and incorrectly conclude that we've bracketed nothing\n\t\tconst unsigned int maxTests=(unsigned int)std::ceil(0.5*(sqrt(1.0+(8*pi/testDisp))-1.0));\n\t\tif((seed.miss>0.0)==rising)\n\t\t\ttestDisp*=-1;\n\t\tunsigned int i;\n\t\ta=seed.launchAngle;\n\t\tfor(i=0; ipi)\n\t\t\t\ta=pi;\n\t\t\t//std::cout << \"\\ttesting theta=\" << a << std::endl;\n\t\t\taTrace=doTrace(emit_depth,a,target,allowedReflections,0.0,0.0, sol_error );\n // changed\n\t\t\tstd::cout << \"\\t (miss=\" << aTrace.miss << \")\" << std::endl;\n\t\t\tif(std::abs(aTrace.miss) < requiredAccuracy) {\n // changed\n std::cout << \"miss(emit_depth,a,target,allowedReflections,0.0,0.0));\n\t\t\tif((aTrace.miss*seed.miss)<0.0) {\n // changed\n std::cout << \"trace.miss * seed.miss < 0, break\" << std::endl;\n\t\t\t\tbreak;\n }\n\t\t}\n\t\tif(i==maxTests){\n // changed\n std::cout << \"refineRoot, reached maxTests\" << std::endl;\n\n\t\t\t//std::cerr << \"Last attempt at bracketing was: [\" << seed.launchAngle << \"->\" << seed.miss << ',' << a << \"->\" << aTrace.miss << ']' << std::endl;\n\t\t\t//std::cerr << \"\\trising edge = \" << std::boolalpha << rising << std::endl;\n\t\t\tthrow std::runtime_error(\"TraceFinder::refineRoot: exceeded maximum allowed number of steps for bracketing.\");\n\t\t}\n\t\tif((seed.miss>0.0)==rising){\n\t\t\tc=seed.launchAngle;\n\t\t\tcTrace=seed;\n\t\t}\n\t\telse{\n\t\t\tc=a;\n\t\t\tcTrace=aTrace;\n\t\t\ta=seed.launchAngle;\n\t\t\taTrace=seed;\n\t\t}\n\t\t\n\t\t//std::cout << \"Endpoint miss distances are \" << aTrace.miss << \" and \" << cTrace.miss << std::endl;\n\t\tdouble angle=0.5*(a+c);\n\t\treturn(traceRootImpl(emit_depth, target, rising, allowedReflections, requiredAccuracy, a, aTrace, c, cTrace, angle, sol_error ));\n\t}\n */\n\n\n\n /*\n // changed\n // test obtaining min value case\n\tstd::pair TraceFinder::refineRoot(double emit_depth, const rayTargetRecord& target, const TraceRecord& seed, bool rising, unsigned short allowedReflections, double requiredAccuracy, int &sol_error ) const{\n\n // test sol_error\n //int sol_error;\n\n double min_angle, min_angle_plus, min_angle_minus;\n double min_miss = 1e10; // start with big value\n\n\n\t\t//bracket the root\n\t\t//std::cout << \"Attempting to refine root near theta=\" << seed.launchAngle << std::endl;\n\t\tdouble a, c;\n\t\tTraceRecord aTrace, cTrace;\n\t\tdouble testDisp=.001; //TODO: this is a problem since there could be a second root arbitrarily close to the one we seek\n\t\t//If we start with testDisp larger than the distance betwee these roots we can bracket them both and incorrectly conclude that we've bracketed nothing\n\t\tconst unsigned int maxTests=(unsigned int)std::ceil(0.5*(sqrt(1.0+(8*pi/testDisp))-1.0));\n\t\tif((seed.miss>0.0)==rising)\n\t\t\ttestDisp*=-1;\n\t\tunsigned int i;\n\t\ta=seed.launchAngle;\n\t\tfor(i=0; ipi)\n\t\t\t\ta=pi;\n\t\t\t//std::cout << \"\\ttesting theta=\" << a << std::endl;\n\t\t\taTrace=doTrace(emit_depth,a,target,allowedReflections,0.0,0.0, sol_error );\n\n if ( min_miss > std::abs(aTrace.miss) ) {\n min_miss = std::abs(aTrace.miss);\n min_angle = a;\n\t\t\n if((seed.miss>0.0)==rising) {\n min_angle_plus = a - testDisp;\n min_angle_minus = a + testDisp;\n }\n else {\n min_angle_plus = a + testDisp;\n min_angle_minus = a - testDisp;\n }\n }\n\n // changed\n\t\t\tstd::cout << \"\\t (miss=\" << aTrace.miss << \") at angle: \"<(emit_depth,a,target,allowedReflections,0.0,0.0));\n\t\t\tif((aTrace.miss*seed.miss)<0.0) {\n // changed\n std::cout << \"trace.miss * seed.miss < 0, break\" << std::endl;\n\t\t\t\tbreak;\n }\n\t\t}\n\t\tif(i==maxTests){\n // changed\n std::cout << \"refineRoot, reached maxTests\" << std::endl;\n\n\t\t\t//std::cerr << \"Last attempt at bracketing was: [\" << seed.launchAngle << \"->\" << seed.miss << ',' << a << \"->\" << aTrace.miss << ']' << std::endl;\n\t\t\t//std::cerr << \"\\trising edge = \" << std::boolalpha << rising << std::endl;\n\t\t\tthrow std::runtime_error(\"TraceFinder::refineRoot: exceeded maximum allowed number of steps for bracketing.\");\n\t\t}\n\t\tif((seed.miss>0.0)==rising){\n\t\t\tc=seed.launchAngle;\n\t\t\tcTrace=seed;\n\t\t}\n\t\telse{\n\t\t\tc=a;\n\t\t\tcTrace=aTrace;\n\t\t\ta=seed.launchAngle;\n\t\t\taTrace=seed;\n\t\t}\n\t\t\n // changed\n std::cout<<\"min miss : \"< TraceFinder::evenmore_refineRoot(double emit_depth, const rayTargetRecord& target, const TraceRecord& seed, double angle_min, double angle_max, bool rising, unsigned short allowedReflections, double requiredAccuracy, int &sol_error ) const{\n\n // test sol_error\n //int sol_error;\n\n double min_angle;\n double min_miss = 1e10; // start with big value\n\n\n\t\t//bracket the root\n\t\t//std::cout << \"Attempting to refine root near theta=\" << seed.launchAngle << std::endl;\n\t\tdouble a, c;\n\t\tTraceRecord aTrace, cTrace;\n\t\t//double testDisp=.001; //TODO: this is a problem since there could be a second root arbitrarily close to the one we seek\n\t\tdouble testDisp=.00001; //TODO: this is a problem since there could be a second root arbitrarily close to the one we seek\n\t\t//If we start with testDisp larger than the distance betwee these roots we can bracket them both and incorrectly conclude that we've bracketed nothing\n\t\t//const unsigned int maxTests=(unsigned int)std::ceil(0.5*(sqrt(1.0+(8*pi/testDisp))-1.0));\n\t\tconst unsigned int maxTests=(unsigned int)((angle_max - angle_min)/testDisp);\n\n\t\t//if((seed.miss>0.0)==rising)\n\t\t\t//testDisp*=-1;\n\t\tunsigned int i;\n\t\t//a=seed.launchAngle;\n\t\ta=angle_min;\n\t\tfor(i=0; ipi)\n\t\t\t\ta=pi;\n\t\t\t//std::cout << \"\\ttesting theta=\" << a << std::endl;\n\t\t\taTrace=doTrace(emit_depth,a,target,allowedReflections,0.0,0.0, sol_error );\n\n if ( min_miss > std::abs(aTrace.miss) ) {\n min_miss = std::abs(aTrace.miss);\n min_angle = a;\n }\n\n // changed\n\t\t\t//std::cout << \"\\t second (miss=\" << aTrace.miss << \") at angle: \"<(emit_depth,a,target,allowedReflections,0.0,0.0));\n\t\t\tif((aTrace.miss*seed.miss)<0.0) {\n // changed\n //std::cout << \"second trace.miss * seed.miss < 0, break\" << std::endl;\n\t\t\t\tbreak;\n }\n\t\t}\n\n\n\n\n\n\t\tif(i==maxTests){\n // changed\n //std::cout << \"refineRoot, reached maxTests\" << std::endl;\n\n\t\t\t//std::cerr << \"Last attempt at bracketing was: [\" << seed.launchAngle << \"->\" << seed.miss << ',' << a << \"->\" << aTrace.miss << ']' << std::endl;\n\t\t\t//std::cerr << \"\\trising edge = \" << std::boolalpha << rising << std::endl;\n\t\t\tthrow std::runtime_error(\"TraceFinder::refineRoot: exceeded maximum allowed number of steps for bracketing.\");\n\t\t}\n\t\tif((seed.miss>0.0)==rising){\n\t\t\tc=seed.launchAngle;\n\t\t\tcTrace=seed;\n\t\t}\n\t\telse{\n\t\t\tc=a;\n\t\t\tcTrace=aTrace;\n\t\t\ta=seed.launchAngle;\n\t\t\taTrace=seed;\n\t\t}\n\t\t\n // changed\n //std::cout<<\"min miss : \"< TraceFinder::refineRoot(double emit_depth, const rayTargetRecord& target, const TraceRecord& seed, bool rising, unsigned short allowedReflections, double requiredAccuracy, int &sol_error ) const{\n\n // test sol_error\n //int sol_error;\n\n double min_angle, min_angle_plus, min_angle_minus;\n double min_miss = 1e10; // start with big value\n\n\n\t\t//bracket the root\n\t\t//std::cout << \"Attempting to refine root near theta=\" << seed.launchAngle << std::endl;\n\t\tdouble a, c;\n\t\tTraceRecord aTrace, cTrace, minmissTrace;\n\t\tdouble testDisp=.001; //TODO: this is a problem since there could be a second root arbitrarily close to the one we seek\n\t\t//If we start with testDisp larger than the distance betwee these roots we can bracket them both and incorrectly conclude that we've bracketed nothing\n\t\tconst unsigned int maxTests=(unsigned int)std::ceil(0.5*(sqrt(1.0+(8*pi/testDisp))-1.0));\n\t\tif((seed.miss>0.0)==rising)\n\t\t\ttestDisp*=-1;\n\t\tunsigned int i;\n\t\ta=seed.launchAngle;\n\t\tfor(i=0; ipi)\n\t\t\t\ta=pi;\n\t\t\t//std::cout << \"\\ttesting theta=\" << a << std::endl;\n\t\t\taTrace=doTrace(emit_depth,a,target,allowedReflections,0.0,0.0, sol_error );\n\n if ( min_miss > std::abs(aTrace.miss) ) {\n min_miss = std::abs(aTrace.miss);\n min_angle = a;\n minmissTrace = aTrace;\n\t\t\n if((seed.miss>0.0)==rising) {\n min_angle_plus = a - testDisp;\n min_angle_minus = a + testDisp;\n }\n else {\n min_angle_plus = a + testDisp;\n min_angle_minus = a - testDisp;\n }\n }\n\n // changed\n\t\t\t//std::cout << \"\\t (miss=\" << aTrace.miss << \") at angle: \"<(emit_depth,a,target,allowedReflections,0.0,0.0));\n\t\t\tif((aTrace.miss*seed.miss)<0.0) {\n // changed\n //std::cout << \"trace.miss * seed.miss < 0, break\" << std::endl;\n\t\t\t\tbreak;\n }\n\t\t}\n\t\tif(i==maxTests){\n // changed\n //std::cout << \"refineRoot, reached maxTests\" << std::endl;\n\n\t\t\t//std::cerr << \"Last attempt at bracketing was: [\" << seed.launchAngle << \"->\" << seed.miss << ',' << a << \"->\" << aTrace.miss << ']' << std::endl;\n\t\t\t//std::cerr << \"\\trising edge = \" << std::boolalpha << rising << std::endl;\n\t\t\tthrow std::runtime_error(\"TraceFinder::refineRoot: exceeded maximum allowed number of steps for bracketing.\");\n\t\t}\n\t\tif((seed.miss>0.0)==rising){\n\t\t\tc=seed.launchAngle;\n\t\t\tcTrace=seed;\n\t\t}\n\t\telse{\n\t\t\tc=a;\n\t\t\tcTrace=aTrace;\n\t\t\ta=seed.launchAngle;\n\t\t\taTrace=seed;\n\t\t}\n\t\t\n // changed\n //std::cout<<\"min miss : \"<\n\tvoid dual_insertion_sort(RandomAccessIterator1 begin1, RandomAccessIterator1 end1, RandomAccessIterator2 begin2, Compare comp){\n\t\tRandomAccessIterator2 frontier2=begin2+1;\n\t\tfor(RandomAccessIterator1 frontier1=begin1+1; frontier1!=end1; ++frontier1,++frontier2){\n\t\t\tRandomAccessIterator1 it1=frontier1;\n\t\t\tRandomAccessIterator2 it2=frontier2;\n\t\t\twhile(it1!=begin1){\n\t\t\t\tRandomAccessIterator1 nit=it1-1;\n\t\t\t\tif(comp(*it1,*nit)){\n\t\t\t\t\tstd::iter_swap(it1,nit);\n\t\t\t\t\tstd::iter_swap(it2,it2-1);\n\t\t\t\t\tit1=nit;\n\t\t\t\t\t--it2;\n\t\t\t\t\tcontinue;\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t//std::vector TraceFinder::findPaths(Vector sourcePos, Vector targetPos, double frequency, double polarization, unsigned short allowedReflections, double requiredAccuracy, std::vector* replayBuffer) const{\n\tstd::vector TraceFinder::findPaths(Vector sourcePos, Vector targetPos, double frequency, double polarization, int &sol_cnt, int &sol_error, unsigned short allowedReflections, double requiredAccuracy, std::vector* replayBuffer) const{\n\t//std::vector TraceFinder::findPaths(Vector sourcePos, Vector targetPos, double frequency, double polarization, int &sol_cnt, int &sol_error, int mode, unsigned short allowedReflections, double requiredAccuracy, std::vector* replayBuffer) const{\n\n sol_cnt = 0;\n sol_error = 0;\n \n\n // changed\n //std::cout<<\"begin findPaths\"< results;\n\t\tboost::scoped_ptr > recorder;\n\t\tif(replayBuffer!=NULL)\n\t\t\trecorder.reset(new pathRecorder());\n\t\t//std::cout << \"Finding paths from (\" << sourcePos.GetX() << ',' << sourcePos.GetY() << ',' << sourcePos.GetZ() << \") to (\" << targetPos.GetX() << ',' << targetPos.GetY() << ',' << targetPos.GetZ() << ')' << std::endl;\n\t\tif(sourcePos.GetZ()(sourcePos.GetZ(), (sourcePos.GetZ() >(sourcePos.GetZ(), (sourcePos.GetZ()push_back(recorder->getData());\n\t\t\t\trecorder->clearData();\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 2\\n\"<(sourcePos.GetZ(), 0.0, target, SurfaceReflection, frequency, polarization));\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 3\\n\"< >(sourcePos.GetZ(), 0.0, target, SurfaceReflection, frequency, polarization, recorder.get()));\n\t\t\t\t\treplayBuffer->push_back(recorder->getData());\n\t\t\t\t\trecorder->clearData();\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 4\\n\"<(sourcePos.GetZ(), pi, target, BedrockReflection, frequency, polarization));\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 5\\n\"< >(sourcePos.GetZ(), pi, target, BedrockReflection, frequency, polarization, recorder.get()));\n\t\t\t\t\treplayBuffer->push_back(recorder->getData());\n\t\t\t\t\trecorder->clearData();\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 6\\n\"<push_back(recorder->getData());\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 8\\n\"<estimateRayAngle(sourcePos.GetZ(), targetPos.GetZ(), dist); //TODO: put this back!!!\n\t\t//std::cout << \"Estimate result : \" << est.status << \" refractionmodel solution : \"<< indexOfRefractionModel::SOLUTION << std::endl;\n\n\n\t\tif(est.status==indexOfRefractionModel::SOLUTION){ //got a solution estimate\n\t\t\t\n // changed\n //std::cout << \"Got fast solution, est.angle: \" << est.angle << std::endl;\n\n\t\t\t//std::cout << \"\\t(theta=\" << est.angle << ')' << std::endl;\n\t\t\tif(replayBuffer==NULL)\n {\n //std::cout << \"replayBuffer==NULL\" <(sourcePos.GetZ(), est.angle, target, NoReflection, frequency, polarization, sol_error )); //this is supposed to be a direct ray, so no reflections should be needed\n \n // changed always (if)?\n //std::cout << \"done 1st doTrace (if)\\n\" << std::endl;\n //std::cout << \"results pathLen: \"< >(sourcePos.GetZ(), est.angle, target, NoReflection, frequency, polarization, sol_error, recorder.get())); //this is supposed to be a direct ray, so no reflections should be needed\n\n // changed\n //std::cout << \"done 1st doTrace (else)\\n\" << std::endl;\n\t\t\t\treplayBuffer->push_back(recorder->getData());\n\t\t\t\trecorder->clearData();\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 10\\n\"< requiredAccuracy){\n // changed\n //std::cout << \"Fast Solution not close enough (\" << results.front().miss << \"), pathLen: \"<(sourcePos.GetZ(), est.angle, target, NoReflection, frequency, polarization, sol_error );\n \n // changed\n //std::cout << \"done 2nd doTrace (if)\\n\" << std::endl;\n //std::cout << \"results pathLen: \"< requiredAccuracy ) {\n //std::cout << \"missed too much, pop back!\" << std::endl;\n results.pop_back();\n sol_cnt--;\n }\n else if ( std::isnan(results.front().miss) ) {\n //std::cout << \"nan info! pop back!\" << std::endl;\n results.pop_back();\n sol_cnt--;\n }\n //else\n //sol_cnt++; // sol_cnt already done\n\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 11\\n\"< >(sourcePos.GetZ(), est.angle, target, NoReflection, frequency, polarization, sol_error, recorder.get());\n \n // changed\n //std::cout << \"done 2nd doTrace (else)\\n\" << std::endl;\n //sol_cnt++;\n\n // changed\n // if still don't satisfy accuracy, remove\n if ( std::abs(results.front().miss) > requiredAccuracy ) {\n //std::cout << \"missed too much, pop back!\" << std::endl;\n results.pop_back();\n recorder->clearData();\n replayBuffer->pop_back();\n sol_cnt--;\n }\n else if ( std::isnan(results.front().miss) ) {\n //std::cout << \"nan info! pop back!\" << std::endl;\n results.pop_back();\n recorder->clearData();\n replayBuffer->pop_back();\n sol_cnt--;\n }\n else {\n replayBuffer->front()=recorder->getData();\n recorder->clearData();\n //sol_cnt++; // sol_cnt already done\n }\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 12\\n\"<0) ){ // try reflect solution if there was 1st solution existing\n\t\t\t\t\n // changed\n //std::cout << \"Looking for surface reflected solution, prev est.angle: \" << est.angle << std::endl;\n\n\t\t\t\t//double angle=traceRoot(sourcePos.GetZ(),target,0.0,est.angle-1e-1,true,SurfaceReflection,requiredAccuracy);\n\t\t\t\t\n\t\t\t\t//Here, we want to find another root, potentially near the direct solution, but distinct from it. \n\t\t\t\t//We expect this root to be in the damain [0,est.angle], but we don't want the root finding algorithm\n\t\t\t\t//to get distracted by the root that we know is at est.angle, so we subtract a small angle from the upper \n\t\t\t\t//bound. However, the roots may be arbitrarily close together, so any guess for this safety value may be too large\n\t\t\t\t//and we may end up exlusing the part of the domain which contains the root we want. So, if the root finding\n\t\t\t\t//doesn't manage to find a suitable root we decrease the safety value. \n\t\t\t\tdouble safety=initialSafety;\n\t\t\t\twhile(est.angle-safety < 0)\n\t\t\t\t\tsafety/=10.;\n\t\t\t\tstd::pair root(-1.,10.*requiredAccuracy); //first element is the launch angle, second is miss distance\n\t\t\t\tdo{\n\t\t\t\t\troot=traceRoot(sourcePos.GetZ(),target,\n\t\t\t\t\t\t\t\t 0.0, //minimum angle\n\t\t\t\t\t\t\t\t est.angle-safety, //maximum angle\n\t\t\t\t\t\t\t\t true,SurfaceReflection,requiredAccuracy, sol_error );\n\t\t\t\t\tsafety/=10.;\n\t\t\t\t}while(safety>=minimumSafety && std::abs(root.second)>requiredAccuracy);\n\t\t\t\t//TODO: check here that root.first is within [0.0,est.angle-safety]\n\t\t\t\tif(replayBuffer==NULL)\n {\n // I guess here occurs one kind of error (log_ray_solver), (not occur when NO surface reflection)\n // error which shows\n // errMax>shrinkLimit, h = inf\n //\n // (infinite loop shows above)\n //\n //std::cout << \"Begin 3rd doTrace (replayBuffer==NULL)\" << std::endl;\n\t\t\t\t\tresults.push_back(doTrace(sourcePos.GetZ(),root.first,target,allowedReflections,frequency,polarization, sol_error ));\n // changed\n //std::cout << \"Done 3rd doTrace (if)\\n\" << std::endl;\n //std::cout << \"results pathLen: \"< requiredAccuracy ) {\n //std::cout << \"missed too much, pop back!\" << std::endl;\n results.pop_back();\n }\n else if ( std::isnan(results[1].miss) ) {\n //std::cout << \"nan info! pop back!\" << std::endl;\n results.pop_back();\n }\n else\n sol_cnt++;\n\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 13\\n\"< >(sourcePos.GetZ(),root.first,target,allowedReflections,frequency,polarization,sol_error, recorder.get()));\n // changed\n //std::cout << \"Done 3rd doTrace (else)\\n\" << std::endl;\n\t\t\t\t\t//replayBuffer->push_back(recorder->getData());\n\t\t\t\t\t//recorder->clearData();\n //sol_cnt++;\n\n // changed\n // if still don't satisfy accuracy, remove\n if ( std::abs(results[1].miss) > requiredAccuracy ) {\n //std::cout << \"missed too much, pop back!\" << std::endl;\n results.pop_back();\n recorder->clearData();\n replayBuffer->pop_back();\n }\n else if ( std::isnan(results[1].miss) ) {\n //std::cout << \"nan info! pop back!\" << std::endl;\n results.pop_back();\n recorder->clearData();\n replayBuffer->pop_back();\n }\n else {\n replayBuffer->front()=recorder->getData();\n recorder->clearData();\n sol_cnt++;\n }\n\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 14\\n\"< pi)\n\t\t\t\t\tsafety/=10;\n\t\t\t\tstd::pair root(-1.,10.*requiredAccuracy); //first element is the launch angle, second is miss distance\n\t\t\t\tdo{\n\t\t\t\t\troot=traceRoot(sourcePos.GetZ(),target,\n\t\t\t\t\t\t\t\t est.angle+safety, //minimum angle\n\t\t\t\t\t\t\t\t pi, //maximum angle\n\t\t\t\t\t\t\t\t true,BedrockReflection,requiredAccuracy, sol_error );\n\t\t\t\t\tsafety/=10.;\n\t\t\t\t}while(safety>=minimumSafety && std::abs(root.second)>requiredAccuracy);\n\t\t\t\tif(replayBuffer==NULL)\n {\n //std::cout << \"Begin 4th doTrace (replayBuffer==NULL)\" << std::endl;\n\t\t\t\t\tresults.push_back(doTrace(sourcePos.GetZ(),root.first,target,allowedReflections,frequency,polarization, sol_error ));\n //std::cout << \"Done 4th doTrace\" << std::endl;\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 15\\n\"< >(sourcePos.GetZ(),root.first,target,allowedReflections,frequency,polarization,sol_error, recorder.get()));\n //std::cout << \"Done 4th doTrace\" << std::endl;\n\t\t\t\t\treplayBuffer->push_back(recorder->getData());\n\t\t\t\t\trecorder->clearData();\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 16\\n\"< minRes, maxRes, altMinRes;\n\t\t\tswitch(est.status){\n\t\t\t\tcase indexOfRefractionModel::SOLUTION:\n\t\t\t\t\t//impossible, this case was handled above\n\t\t\t\t\tbreak;\n\t\t\t\tcase indexOfRefractionModel::UPPER_LIMIT:\n\t\t\t\t\t//std::cout << \"UPPER_LIMIT, Got only upper limit of \" << est.angle << std::endl;\n\t\t\t\t\tb=est.angle;//std::min(pi,est.angle+.1); //add a small amount in case the solution is right at the boundary given by the estimate\n\t\t\t\t\tminRes = traceMin(sourcePos.GetZ(),target, sol_error, b,pi );\n\t\t\t\t\tmaxRes = traceMax(sourcePos.GetZ(),target, sol_error, 0.0,b );\n\t\t\t\t\tbreak;\n\t\t\t\tcase indexOfRefractionModel::LOWER_LIMIT:\n\t\t\t\t\t//std::cout << \"LOWER_LIMIT, Got only lower limit of \" << est.angle << std::endl;\n\t\t\t\t\ta=est.angle;//std::max(0.0,est.angle-.1);\n\t\t\t\t\tminRes = traceMin(sourcePos.GetZ(),target, sol_error, a,pi );\n\t\t\t\t\tmaxRes = traceMax(sourcePos.GetZ(),target, sol_error, 0.0,a );\n\t\t\t\t\tbreak;\n\t\t\t\tcase indexOfRefractionModel::NO_SOLUTION:\n\t\t\t\t\t//std::cout << \"NO_SOLUTION, Got no solution\" << std::endl;\n\t\t\t\t\t//Note that getting indexOfRefractionModel::NO_SOLUTION implies no _direct_ solution exists\n\t\t\t\t\t//if we were instructed to find reflected rays, we still need to do the work to look for them\n\t\t\t\t\tif((allowedReflections & SurfaceReflection) || (allowedReflections & BedrockReflection)){\n\t\t\t\t\t\tmaxRes = traceMax(sourcePos.GetZ(),target, sol_error, 0.0,pi );\n\t\t\t\t\t\tminRes = traceMin(sourcePos.GetZ(),target, sol_error, 0.0,pi );\n\t\t\t\t\t}\n\t\t\t\t\tbreak;\n\t\t\t\tcase indexOfRefractionModel::UNKNOWN:\n // I guess here occurs one kind of error (log_ray_solver2), (occur whether or not surface reflection)\n // error which shows\n // terminate called throwing an exceptionAbort trap: 6\n //\n\t\t\t\t\t//std::cout << \"UNKNOWN, Got no information\" << std::endl;\n\t\t\t\t\tmaxRes = traceMax(sourcePos.GetZ(),target, sol_error, 0.0,pi );\n\t\t\t\t\t//std::cout << \"maxRes : \" << maxRes.first << std::endl;\n\t\t\t\t\tminRes = traceMin(sourcePos.GetZ(),target, sol_error, 0.0,pi );\n\t\t\t\t\t//std::cout << \"minRes : \" << minRes.first << std::endl;\n\t\t\t\t\tbreak;\n\t\t\t}\n //std::cout << \"done est.status switch\" << std::endl;\n\n\t\t\tif(minRes.first && maxRes.first){\n //std::cout<<\" minRes & maxRes true\" << std::endl;\n\t\t\t\tif(maxRes.second>minRes.second){\n\t\t\t\t\t//watch out; there must be two direct solutions\n\t\t\t\t\tdualDirect=true;\n\t\t\t\t\taltMinRes=minRes;\n\t\t\t\t\tminRes = traceMin(sourcePos.GetZ(),target, sol_error, maxRes.second,pi ); //look for the other minimum which should exist\n\t\t\t\t}\n\t\t\t\telse{ //we need to waste some time checking for another minimum\n\t\t\t\t\taltMinRes = traceMin(sourcePos.GetZ(),target, sol_error, 0.0,maxRes.second );\n\t\t\t\t\tdualDirect=altMinRes.first; //if a minimum is found, which is negative, then there is second direct solution\n\t\t\t\t}\n\t\t\t}\n\t\t\t\n\t\t\tif(minRes.first && (allowedReflections & BedrockReflection)){\n\t\t\t\t//std::cout << \"Looking for bedrock reflected solution\" << std::endl;\n\t\t\t\tdouble angle=traceRoot(sourcePos.GetZ(),target,minRes.second,pi,true,BedrockReflection,requiredAccuracy, sol_error ).first;\n\t\t\t\tif(replayBuffer==NULL)\n {\n //std::cout << \"Begin 1st doTrace (replayBuffer==NULL)\" << std::endl;\n\t\t\t\t\tresults.push_back(doTrace(sourcePos.GetZ(),angle,target,allowedReflections,frequency,polarization, sol_error ));\n //std::cout << \"Done 1st doTrace\" << std::endl;\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 17\\n\"< >(sourcePos.GetZ(),angle,target,allowedReflections,frequency,polarization,sol_error, recorder.get()));\n //std::cout << \"Done 1st doTrace\" << std::endl;\n\t\t\t\t\treplayBuffer->push_back(recorder->getData());\n\t\t\t\t\trecorder->clearData();\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 18\\n\"<(sourcePos.GetZ(),angle,target,allowedReflections,frequency,polarization, sol_error ));\n //std::cout << \"Done 2nd doTrace\" << std::endl;\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 19\\n\"< >(sourcePos.GetZ(),angle,target,allowedReflections,frequency,polarization,sol_error, recorder.get()));\n //std::cout << \"Done 2nd doTrace\" << std::endl;\n\t\t\t\t\treplayBuffer->push_back(recorder->getData());\n\t\t\t\t\trecorder->clearData();\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 20\\n\"<a)\n\t\t\t\t\ta=maxRes.second;\n\t\t\t\t//std::cout << \"Looking for direct solution\" << std::endl;\n\t\t\t\tdouble angle=traceRoot(sourcePos.GetZ(),target,a,b,false,NoReflection,requiredAccuracy, sol_error ).first;\n\t\t\t\t//std::cout << \"Angle for direct ray is \" << angle << std::endl;\n\t\t\t\tif(replayBuffer==NULL)\n {\n //std::cout << \"Begin 3rd doTrace (replayBuffer==NULL)\" << std::endl;\n\t\t\t\t\tresults.push_back(doTrace(sourcePos.GetZ(),angle,target,allowedReflections,frequency,polarization, sol_error ));\n //std::cout << \"Done 3rd doTrace\" << std::endl;\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 21\\n\"< >(sourcePos.GetZ(),angle,target,allowedReflections,frequency,polarization,sol_error, recorder.get()));\n //std::cout << \"Done 3rd doTrace\" << std::endl;\n\t\t\t\t\treplayBuffer->push_back(recorder->getData());\n\t\t\t\t\trecorder->clearData();\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 22\\n\"< 10.0*requiredAccuracy){ //if it wasn't really a solution, throw it away\n\t\t\t\t\t//std::cout << \"Supposed solution missed by \" << results.back().miss << \" meters, rejecting\" << std::endl;\n\t\t\t\t\tresults.pop_back();\n\t\t\t\t\tif(replayBuffer!=NULL)\n\t\t\t\t\t\treplayBuffer->pop_back();\n\t\t\t\t}\n\t\t\t}\n\t\t\tif(dualDirect){\n\t\t\t\t//std::cout << \"Looking for secondary direct ray\" << std::endl;\n\t\t\t\tdouble angle=traceRoot(sourcePos.GetZ(),target,altMinRes.second,maxRes.second,true,NoReflection,requiredAccuracy, sol_error ).first;\n\t\t\t\t//std::cout << \"Angle for second direct ray is \" << angle << std::endl;\n\t\t\t\tif(replayBuffer==NULL)\n {\n //std::cout << \"Begin 4th doTrace (replayBuffer==NULL)\" << std::endl;\n\t\t\t\t\tresults.push_back(doTrace(sourcePos.GetZ(),angle,target,NoReflection,frequency,polarization, sol_error ));\n //std::cout << \"Done 4th doTrace\" << std::endl;\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 23\\n\"< >(sourcePos.GetZ(),angle,target,NoReflection,frequency,polarization, sol_error, recorder.get()));\n //std::cout << \"Done 4th doTrace\" << std::endl;\n\t\t\t\t\treplayBuffer->push_back(recorder->getData());\n\t\t\t\t\trecorder->clearData();\n sol_cnt++;\n //std::cout<<\"\\n\\tSOL_CNT ADDED! 24\\n\"< 1){\n\t\t\tif(replayBuffer==NULL)\n {\n //std::cout << \"replayBuffer==NULL\" <begin(),&shorterPath);\n }\n\t\t}\n else { // no solution case\n sol_cnt = 0;\n //std::cout<<\"\\n\\tSOL_CNT 0 as no sol\\n\"<* recorder ) const{\n\n // test sol_error\n //int sol_error;\n\n\n\t\tTraceRecord trace;\n\t\t//TODO: if both points are above surface, give obvious answers\n\t\tif(sourcePos.GetZ()indexOfRefraction(0.0);\n\t\tdouble wholeDist = sqrt((targetPos.GetX()-sourcePos.GetX())*(targetPos.GetX()-sourcePos.GetX())+(targetPos.GetY()-sourcePos.GetY())*(targetPos.GetY()-sourcePos.GetY()));\n\t\tdouble upper=std::max(sourcePos.GetZ(),targetPos.GetZ());\n\t\tdouble lower=std::min(sourcePos.GetZ(),targetPos.GetZ());\n\t\trayTargetRecord target(lower,wholeDist);\n\t\tdouble minDist=0.0, maxDist=wholeDist;\n\t\tdouble dist=(minDist+maxDist)/2.; //the distance which will be covered by the ray while in the ice\n\t\tdouble thetaA=0.0,thetaB=0.0;\n\t\twhile((maxDist-minDist)>requiredAccuracy/10.){\n\t\t\t//std::cout << \"Trying dist=\" << dist << std::endl;\n\t\t\t//thetaA is the angle of the straight line ray above the ice\n\t\t\tthetaA=pi-atan((wholeDist-dist)/upper);\n\t\t\t//thetaB is the angle the straight line ray would have below the ice\n\t\t\tthetaB=pi-asin(sin(pi-thetaA)/surfaceN);\n\t\t\t//std::cout << \"Angles are \" << thetaA << ' ' << thetaB << std::endl;\n\t\t\t\n\t\t\tindexOfRefractionModel::RayEstimate est=rModel->estimateRayAngle(0.0, lower, dist);\n\t\t\tif(est.status == indexOfRefractionModel::SOLUTION){\n\t\t\t\t//std::cout << \"Got estimate at angle \" << est.angle << std::endl;\n\t\t\t\tif(est.anglethetaB)\n\t\t\t\t\tminDist=dist;\n\t\t\t\telse //we got lucky\n\t\t\t\t\tbreak;\n\t\t\t}\n\t\t\telse{\n\t\t\t\ttarget.distance = dist;\n\t\t\t\ttrace = doTrace(0.0,thetaB,target,NoReflection,0.0,0.0, sol_error );\n\t\t\t\t//std::cout << \"Trace missed by \" << trace.miss << std::endl;\n\t\t\t\tif(std::abs(trace.miss) <= requiredAccuracy)\n\t\t\t\t\tbreak;\n\t\t\t\tif(trace.miss<0.0)\n\t\t\t\t\tmaxDist=dist;\n\t\t\t\telse if(trace.miss>0.0)\n\t\t\t\t\tminDist=dist;\n\t\t\t}\n\t\t\t\n\t\t\tdist=(minDist+maxDist)/2.;\n\t\t}\n\t\t\n\t\t//compute the path length the above ice segment\n\t\tdouble extraLength=sqrt(upper*upper+(wholeDist-dist)*(wholeDist-dist));\n\t\t//compute attenuation due to passing through the ice surface\n\t\tdouble transmissionAttenuation;\n\t\tif(sourcePos.GetZ() > targetPos.GetZ())\n\t\t\ttransmissionAttenuation=fresnelTransmit(pi-thetaA, polarization, 1.0, surfaceN);\n\t\telse\n\t\t\ttransmissionAttenuation=fresnelTransmit(pi-thetaB, polarization, surfaceN, 1.0);\n\t\t//do the final version trace\n\t\ttarget.distance = dist;\t\n\t\tif(recorder==NULL)\n\t\t\ttrace = doTrace(0.0,thetaB,target,NoReflection,frequency,polarization, sol_error );\n\t\telse{\n\t\t\tcallCallback(recorder,positionRecordingWrapper(fullRayPosition(0.0,upper,((sourcePos.GetZ() > targetPos.GetZ())?thetaA:thetaB)-pi,extraLength/speedOfLight,transmissionAttenuation)),RK_AIR_STEP);\n\t\t\ttrace = doTrace >(0.0,thetaB,target,NoReflection,frequency,polarization, sol_error, recorder);\n\t\t}\n\t\t//include surface crossing attenuation\n\t\ttrace.attenuation*=transmissionAttenuation;\n\t\t//add on path length the above ice segment\n\t\ttrace.pathLen+=extraLength;\n\t\ttrace.pathTime+=extraLength/speedOfLight;\n\t\t//fix up the angles\n\t\tif(sourcePos.GetZ() > targetPos.GetZ()){ //upper is the source depth\n\t\t\ttrace.launchAngle=thetaA;\n\t\t\t//receipt angle is as calculated by doTrace\n\t\t\ttrace.reflectionAngle=-thetaA;\n\t\t}\n\t\telse{ //lower is the source depth\n\t\t\ttrace.launchAngle=pi-trace.receiptAngle;\n\t\t\ttrace.receiptAngle=pi-thetaA;\n\t\t\ttrace.reflectionAngle=thetaB-pi;\n\t\t}\n\t\treturn(trace);\n\t}\n\t\n\tdouble TraceFinder::signalStrength(const TraceRecord& ray, const Vector& src, const Vector& trg, unsigned short allowedReflections, int &sol_error ) const{\n\n // test sol_error\n //int sol_error;\n\n\n\t\tconst double changeThresh=1e-3; //10^(-n) should give about n digits of accuracy\n\t\tconst double startDelta=.002;\n\t\t//std::cout << \"Considering focusing from \" << src << \" to \" << trg << \" at angle \" << ray.launchAngle << std::endl;\n\t\tdouble dist = sqrt((trg.GetX()-src.GetX())*(trg.GetX()-src.GetX())+(trg.GetY()-src.GetY())*(trg.GetY()-src.GetY()));\n\t\tif(ray.launchAngle<.01 || ray.launchAngle>(pi-.01)){\n\t\t\t//std::cout << \"ray too close to vertical\" << std::endl;\n\t\t\treturn(-1.0);\n\t\t}\n\t\t\n\t\t//std::cout << \"Original ray miss distance: \" << ray.miss << std::endl;\n\t\tunsigned int steps=1;\n\t\tdouble scaledChange=1e10;\n\t\tdouble lastChange=0.0;\n\t\tunsigned int scaleIncrease=0;\n\t\tconst double multInc=2.0;\n\t\tdouble ratio,accel;\n\t\tstd::vector terms;\n\t\tstd::queue past;\n\t\tfor(double deltaTheta=startDelta; scaledChange>changeThresh; deltaTheta/=multInc){\n\t\t\t//std::cout << \"\\tdeltaTheta = \" << deltaTheta << std::endl;\n\t\t\t//std::cout << \"\\tlaunch angle will be \" << ray.launchAngle-deltaTheta << std::endl;\n\t\t\tTraceRecord testRay = doTrace(src.GetZ(), ray.launchAngle+deltaTheta, rayTargetRecord(trg.GetZ(),dist), allowedReflections, 0.0, 0.0, sol_error );\n\t\t\t//std::cout << \"\\t\\ttest ray miss distance: \" << testRay.miss << std::endl;\n\t\t\tratio=std::abs(deltaTheta/(ray.miss-testRay.miss));\n\t\t\t//std::cout << \"\\t\\tdeltaTheta/deltaZ = \" << ratio << std::endl;\n\t\t\tterms.push_back(ratio);\n\t\t\tif(!past.empty()){\n\t\t\t\tdouble old=past.back();\n\t\t\t\tdouble mult=multInc;\n\t\t\t\taccel=ratio;\n\t\t\t\tpast.push(ratio);\n\t\t\t\tfor(unsigned int i=0; ilastChange){\n\t\t\t\t\t//std::cout << \"\\t\\tWARNING: SCALE INCREASE!\" << std::endl;\n\t\t\t\t\tscaleIncrease++;\n\t\t\t\t\tif(scaleIncrease>=3){\n\t\t\t\t\t\t//std::cout << \"\\t\\tWARNING: scale increased over \" << scaleIncrease << \" consecutive steps\" << std::endl;\n\t\t\t\t\t\t//scaleIncrease>=n means terms.size()>=n+1\n\t\t\t\t\t\t//accel=(8**(terms.end()-2)-6**(terms.end()-1)+terms.back())/3.0; //n=2 case\n\t\t\t\t\t\taccel=(64.**(terms.end()-3)-56.**(terms.end()-2)+14.**(terms.end()-1)-terms.back())/21.0; //n=3 case\n\t\t\t\t\t\tbreak;\n\t\t\t\t\t}\n\t\t\t\t\t//break;\n\t\t\t\t}\n\t\t\t\telse\n\t\t\t\t\tscaleIncrease=0;\n\t\t\t\tlastChange=scaledChange;\n\t\t\t}\n\t\t\telse{\n\t\t\t\tpast.push(ratio);\n\t\t\t\taccel=ratio;\n\t\t\t}\n\t\t\tsteps++;\n\t\t}\n\t\t\n\t\tdouble ir=rModel->indexOfRefraction(trg.GetZ())/rModel->indexOfRefraction(src.GetZ());\n\t\treturn(sqrt(ir*accel/dist));\n\t}\n\t\n\t//explicitly instantiate\n\ttemplate void TraceFinder::rkStepControl(double&, double, minimalRayPosition&, minimalRayPosition&, const minimalRayPosition&, const double, const double, double&, double&) const;\n\n} //namespace RayTrace\n", "meta": {"hexsha": "4fa67064d735a659d93da84c6c43ac53590cdb09", "size": 84693, "ext": "cc", "lang": "C++", "max_stars_repo_path": "RayTrace.cc", "max_stars_repo_name": "shrishabh/phasedArrayARA", "max_stars_repo_head_hexsha": "bb10fcbf7b0b26c5db34a7aeffbadfef7f1aacf5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, 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YES\n2. NO", "lm_q1_score": 0.5851011397337391, "lm_q2_score": 0.3557748798522984, "lm_q1q2_score": 0.2081642876902139}} {"text": "#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n#include \r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n#define LOOPER_ID_USER 3\r\n\r\nusing namespace std;\r\nusing namespace Eigen;\r\nnamespace bluefin {\r\nnamespace sandshark {\r\n\r\nclass MotionDriver: public DriverBase {\r\n private:\r\n ALooper* _looper;\r\n ASensorManager* _sensorManager;\r\n vector _sensorList;\r\n vector _queueList;\r\n vector _eventList;\r\n\r\n ros::Publisher _orientPub;\r\n sandshark_msgs::OrientationEuler _orientMsg;\r\n\r\n ros::Publisher _orientDebugPub;\r\n sandshark_msgs::OrientationDebug _orientDebugMsg;\r\n\r\n ros::Publisher _orientQuaternionPub;\r\n sandshark_msgs::OrientationQuaternion _orientQuaternionMsg;\r\n\r\n ros::Publisher _rawPub;\r\n sandshark_msgs::MotionRaw _rawMsg;\r\n\r\n ros::Publisher _correctedPub;\r\n sandshark_msgs::MotionCorrected _correctedMsg;\r\n\r\n ros::ServiceServer _accelService;\r\n ros::ServiceServer _magService;\r\n\r\n struct timespec _start;\r\n bool _firstRun;\r\n bool _needSleep;\r\n Vector3d _correctedAccel;\r\n Vector3d _gravityAccel;\r\n Vector3d _linearAccel;\r\n\r\n Vector3d _accelOffset;\r\n Vector3d _accelScaling;\r\n\r\n Vector3d _correctedMag;\r\n\r\n Vector3d _magOffset;\r\n Vector3d _magScaling;\r\n Matrix3d _magAdjust;\r\n\r\n Quaternion _attitudeEstimate;\r\n\r\n Quaternion _rCQuaternion;\r\n\r\n double _alpha;\r\n double _beta;\r\n double _zeta;\r\n\r\n double _gyroBiasX;\r\n double _gyroBiasY;\r\n double _gyroBiasZ;\r\n\r\n double _firstBetaDuration;\r\n double _firstBeta;\r\n double _start_time;\r\n\r\n bool _imuFilterEnabled;\r\n bool _imuFilterAfterBeta;\r\n bool _imuFilterWithBadMag;\r\n double _minGoodMag;\r\n double _maxGoodMag;\r\n\r\n bool _sendUDP;\r\n std::string _sendIPAddress;\r\n int _sendIPPort;\r\n int _sendSocket;\r\n struct sockaddr_in _sendAddrStruct;\r\n bool _sendAndroidQuaternion;\r\n\r\n void convertAndPublishEuler();\r\n\r\n bool ahrsEnabled(double magMagnitude);\r\n void publishEulerAngles();\r\n void publishDebug();\r\n\r\n void reset(bool writeParams);\r\n\r\n void cleanup();\r\n protected:\r\n void startupInitCallback();\r\n bool doInitialize();\r\n bool doRun();\r\n public:\r\n MotionDriver() :\r\n DriverBase(\"MotionDriver\", \"motion\") {\r\n }\r\n\r\n bool handleAccelCalibUpdate(sandshark_msgs::AccelCalib::Request &req, sandshark_msgs::AccelCalib::Response &res);\r\n bool handleMagCalibUpdate(sandshark_msgs::MagCalib::Request &req, sandshark_msgs::MagCalib::Response &res);\r\n\r\n void handleSleep();\r\n void handleShutdown() {\r\n cleanup();\r\n }\r\n};\r\n\r\nvoid MotionDriver::startupInitCallback() {\r\n ROS_INFO(\"Task init! - creating orient node\");\r\n fflush( stdout);\r\n\r\n _orientPub = _publicNode->advertise(\"orientation_euler\", 1);\r\n _orientDebugPub = _publicNode->advertise(\"orientation_debug\", 1);\r\n _orientQuaternionPub = _publicNode->advertise(\"orientation_quaternion\", 1);\r\n _rawPub = _publicNode->advertise(\"raw_sensors\", 1);\r\n _correctedPub = _publicNode->advertise(\"corrected_sensors\", 1);\r\n\r\n _accelService = _publicNode->advertiseService(\"calibrateAccel\", &MotionDriver::handleAccelCalibUpdate, this);\r\n _magService = _publicNode->advertiseService(\"calibrateMag\", &MotionDriver::handleMagCalibUpdate, this);\r\n\r\n _sensorManager = ASensorManager_getInstance();\r\n _looper = ALooper_forThread();\r\n if (_looper == NULL) {\r\n _looper = ALooper_prepare(ALOOPER_PREPARE_ALLOW_NON_CALLBACKS);\r\n ROS_INFO(\"Looper was NULL called prepare!\");\r\n }\r\n ROS_INFO(\"Task init! Param stuff looper is %p\", _looper);\r\n fflush( stdout);\r\n\r\n string key;\r\n vector values;\r\n char comma;\r\n double value;\r\n\r\n _accelOffset << 0.0, 0.0, 0.0;\r\n values.clear();\r\n if (ros::param::search(\"accel_offset\", key)) {\r\n string accel_offset;\r\n ros::param::get(key, accel_offset);\r\n\r\n istringstream iss(accel_offset);\r\n while (iss >> value) {\r\n values.push_back(value);\r\n iss >> comma;\r\n }\r\n\r\n if (values.size() == 3) {\r\n for (unsigned int i = 0; i < values.size(); ++i) {\r\n _accelOffset[i] = values[i];\r\n }\r\n }\r\n }\r\n cout << \"Accel Offset: \" << _accelOffset << std::endl;\r\n\r\n ROS_INFO(\"Task init! Param aOffset\");\r\n fflush( stdout);\r\n\r\n _accelScaling << 9.8, 9.8, 9.8;\r\n values.clear();\r\n if (ros::param::search(\"accel_scaling\", key)) {\r\n string accel_scaling;\r\n ros::param::get(key, accel_scaling);\r\n\r\n istringstream iss(accel_scaling);\r\n while (iss >> value) {\r\n values.push_back(value);\r\n iss >> comma;\r\n }\r\n\r\n if (values.size() == 3) {\r\n for (unsigned int i = 0; i < values.size(); ++i) {\r\n _accelScaling[i] = values[i];\r\n }\r\n }\r\n }\r\n\r\n cout << \"Accel Scaling \" << _accelScaling << std::endl;\r\n\r\n _magOffset << 0.0, 0.0, 0.0;\r\n values.clear();\r\n if (ros::param::search(\"mag_offset\", key)) {\r\n string mag_offset;\r\n ros::param::get(key, mag_offset);\r\n\r\n istringstream iss(mag_offset);\r\n while (iss >> value) {\r\n values.push_back(value);\r\n iss >> comma;\r\n }\r\n\r\n if (values.size() == 3) {\r\n for (unsigned int i = 0; i < values.size(); ++i) {\r\n _magOffset[i] = (values[i]);\r\n }\r\n }\r\n }\r\n cout << \"Mag Offset: \" << _magOffset << std::endl;\r\n\r\n fflush( stdout);\r\n _magAdjust = Matrix3d::Identity();\r\n values.clear();\r\n if (ros::param::search(\"mag_adjust\", key)) {\r\n string mag_adjust;\r\n ros::param::get(key, mag_adjust);\r\n\r\n istringstream iss(mag_adjust);\r\n while (iss >> value) {\r\n values.push_back(value);\r\n iss >> comma;\r\n }\r\n\r\n if (values.size() == 9) {\r\n for (unsigned int i = 0; i < values.size(); ++i) {\r\n int row = i / 3;\r\n int col = i % 3;\r\n ROS_INFO(\"i %d row %d col %d val %f\", i, row, col, values[i]);\r\n fflush( stdout);\r\n _magAdjust(row, col) = values[i];\r\n }\r\n }\r\n }\r\n cout << \"Mag Adjust: \" << _magAdjust << std::endl;\r\n\r\n _magScaling << 1.0, 1.0, 1.0;\r\n values.clear();\r\n if (ros::param::search(\"mag_scaling\", key)) {\r\n string mag_scaling;\r\n ros::param::get(key, mag_scaling);\r\n\r\n istringstream iss(mag_scaling);\r\n while (iss >> value) {\r\n values.push_back(value);\r\n iss >> comma;\r\n }\r\n\r\n if (values.size() == 3) {\r\n for (unsigned int i = 0; i < values.size(); ++i) {\r\n _magScaling[i] = values[i];\r\n }\r\n }\r\n }\r\n\r\n cout << \"Mag Scaling: \" << _magScaling << std::endl;\r\n\r\n ROS_INFO(\"Task init! Private Param stuff\");\r\n fflush( stdout);\r\n _privateNode->param(\"alpha\", _alpha, double(0.8));\r\n _privateNode->param(\"beta\", _beta, double(0.2));\r\n _privateNode->param(\"zeta\", _zeta, double(0.05));\r\n\r\n _privateNode->param(\"firstBetaDuration\", _firstBetaDuration, double(10.0));\r\n _privateNode->param(\"firstBeta\", _firstBeta, double(2.5));\r\n\r\n _privateNode->param(\"sendUDP\", _sendUDP, false);\r\n _privateNode->param(\"sendIPAddress\", _sendIPAddress, std::string(\"127.0.0.1\"));\r\n _privateNode->param(\"sendIPPort\", _sendIPPort, int(5555));\r\n ROS_INFO(\"SendIP %s : %d\", _sendIPAddress.c_str(), _sendIPPort);\r\n _privateNode->param(\"sendAndroidQuaternion\", _sendAndroidQuaternion, false);\r\n\r\n _privateNode->param(\"imuFilterEnabled\", _imuFilterEnabled, false);\r\n _privateNode->param(\"imuFilterAfterBeta\", _imuFilterAfterBeta, false);\r\n _privateNode->param(\"imuFilterWithBadMag\", _imuFilterWithBadMag, false);\r\n _privateNode->param(\"minGoodMag\", _minGoodMag, double(25)); //Units ?!?!?\r\n _privateNode->param(\"maxGoodMag\", _maxGoodMag, double(65));\r\n\r\n _sendSocket = -1;\r\n if (_sendUDP) {\r\n _sendSocket = socket( AF_INET, SOCK_DGRAM, 0);\r\n if (_sendSocket < 0) {\r\n fprintf( stderr, \"Socket is bad!\\n\");\r\n exit(-1);\r\n }\r\n\r\n struct sockaddr_in myaddr;\r\n memset((char *) &myaddr, 0, sizeof(myaddr));\r\n myaddr.sin_family = AF_INET;\r\n myaddr.sin_addr.s_addr = htonl( INADDR_ANY);\r\n myaddr.sin_port = htons(0);\r\n if (::bind(_sendSocket, (struct sockaddr *) &myaddr, sizeof(myaddr)) < 0) {\r\n fprintf( stderr, \"bind failed! %d:%s\\n\", errno, strerror( errno));\r\n exit(-1);\r\n }\r\n\r\n memset(&_sendAddrStruct, 0, sizeof(_sendAddrStruct));\r\n _sendAddrStruct.sin_addr.s_addr = inet_addr(_sendIPAddress.c_str());\r\n _sendAddrStruct.sin_family = AF_INET;\r\n _sendAddrStruct.sin_port = htons(_sendIPPort);\r\n\r\n }\r\n ROS_INFO(\"Param alpha %f beta %f zeta %f\", _alpha, _beta, _zeta);\r\n ROS_INFO(\"Task start! Param stuff complete\");\r\n fflush( stdout);\r\n}\r\n\r\nbool MotionDriver::handleAccelCalibUpdate(sandshark_msgs::AccelCalib::Request &req,\r\n sandshark_msgs::AccelCalib::Response &res) {\r\n std::ostringstream strs;\r\n if ((req.accelOffset.size() < 3) || (req.accelScaling.size() < 3)) {\r\n res.accepted = false;\r\n return true;\r\n }\r\n\r\n _accelOffset << req.accelOffset[0], req.accelOffset[1], req.accelOffset[2];\r\n strs << _accelOffset[0] << \",\" << _accelOffset[1] << \",\" << _accelOffset[2];\r\n _privateNode->setParam(\"accel_offset\", strs.str());\r\n\r\n strs.str(\"\");\r\n strs.clear();\r\n\r\n _accelScaling << req.accelScaling[0], req.accelScaling[1], req.accelScaling[2];\r\n strs << _accelScaling[0] << \",\" << _accelScaling[1] << \",\" << _accelScaling[2];\r\n _privateNode->setParam(\"accel_scaling\", strs.str());\r\n\r\n cout << \"Service UPDATE Accel Offset: \" << _accelOffset << std::endl;\r\n cout << \"Service UPDATE Accel Scaling: \" << _accelScaling << std::endl;\r\n res.accepted = true;\r\n reset(true);\r\n return true;\r\n}\r\n\r\nbool MotionDriver::handleMagCalibUpdate(sandshark_msgs::MagCalib::Request &req,\r\n sandshark_msgs::MagCalib::Response &res) {\r\n std::ostringstream strs;\r\n if ((req.magOffset.size() < 3) || (req.magScaling.size() < 3) || (req.magAdjust.size() < 9)) {\r\n res.accepted = false;\r\n return true;\r\n }\r\n\r\n _magOffset << req.magOffset[0], req.magOffset[1], req.magOffset[2];\r\n strs << _magOffset[0] << \",\" << _magOffset[1] << \",\" << _magOffset[2];\r\n _privateNode->setParam(\"mag_offset\", strs.str());\r\n\r\n strs.str(\"\");\r\n strs.clear();\r\n\r\n _magScaling << req.magScaling[0], req.magScaling[1], req.magScaling[2];\r\n strs << _magScaling[0] << \",\" << _magScaling[1] << \",\" << _magScaling[2];\r\n _privateNode->setParam(\"mag_scaling\", strs.str());\r\n\r\n strs.str(\"\");\r\n strs.clear();\r\n\r\n _magAdjust << req.magAdjust[0], req.magAdjust[1], req.magAdjust[2], req.magAdjust[3], req.magAdjust[4], req.magAdjust[5], req.magAdjust[6], req.magAdjust[7], req.magAdjust[8];\r\n strs << _magAdjust(0, 0) << \",\" << _magAdjust(0, 1) << \",\" << _magAdjust(0, 2) << \",\" << _magAdjust(1, 0) << \",\"\r\n << _magAdjust(1, 1) << \",\" << _magAdjust(1, 2) << \",\" << _magAdjust(2, 0) << \",\" << _magAdjust(2, 1) << \",\"\r\n << _magAdjust(2, 2);\r\n _privateNode->setParam(\"mag_adjust\", strs.str());\r\n\r\n cout << \"Service UPDATE Mag Offset: \" << _magOffset << std::endl;\r\n cout << \"Service UPDATE Mag Scaling: \" << _magScaling << std::endl;\r\n cout << \"Service UPDATE Mag Adjust: \" << _magAdjust << std::endl;\r\n\r\n res.accepted = true;\r\n reset(true);\r\n return true;\r\n}\r\n\r\nbool MotionDriver::doInitialize() {\r\n cleanup();\r\n\r\n _needSleep = false;\r\n ASensorList senList;\r\n int numAllSensors = ASensorManager_getSensorList(_sensorManager, &senList);\r\n\r\n ROS_INFO(\"Task init! Motion numsen %d\", numAllSensors);\r\n fflush( stdout);\r\n int rc;\r\n for (int i = 0; i < numAllSensors; ++i) {\r\n const ASensor *sensor = senList[i];\r\n ROS_INFO(\"Sensor Name %s Vendor %s Res %f Type %d\", ASensor_getName(sensor), ASensor_getVendor(sensor),\r\n ASensor_getResolution(sensor), ASensor_getType(sensor));\r\n if ((std::string(ASensor_getName(sensor)) == \"MPL magnetic field\")\r\n || (std::string(ASensor_getName(sensor)) == \"MPL accel\") || (std::string(ASensor_getName(sensor)) == \"MPL Gyro\")\r\n || (_sendAndroidQuaternion && (std::string(ASensor_getName(sensor)) == \"MPL rotation vector\"))) {\r\n ASensorEventQueue* queue = ASensorManager_createEventQueue(_sensorManager, _looper, LOOPER_ID_USER, NULL, NULL);\r\n if (!queue) {\r\n ROS_INFO(\"error creating sensor event queue\");\r\n return false;\r\n }\r\n rc = ASensorEventQueue_enableSensor(queue, sensor);\r\n if (rc < 0) {\r\n ROS_INFO(\"ASensorEventQueue_enableSensor error: %d\", rc);\r\n return false;\r\n }\r\n rc = ASensorEventQueue_setEventRate(queue, sensor, ASensor_getMinDelay(sensor));\r\n if (rc < 0) {\r\n ROS_INFO(\"ASensorEventQueue_setEventRate error: %d\", rc);\r\n return false;\r\n }\r\n\r\n fprintf( stderr, \"Pushing back Sensor Name %s Vendor %s Res %f\\n\", ASensor_getName(sensor),\r\n ASensor_getVendor(sensor), ASensor_getResolution(sensor));\r\n _sensorList.push_back(sensor);\r\n _queueList.push_back(queue);\r\n _eventList.push_back(ASensorEvent());\r\n }\r\n }\r\n\r\n ROS_INFO(\"Task init! Motion sen complete %d\", (int) _queueList.size());\r\n fflush( stdout);\r\n\r\n reset(false);\r\n\r\n ROS_INFO(\"Task init! Motion init complete event %d\", (int) _eventList.size());\r\n fflush( stdout);\r\n\r\n return true;\r\n}\r\n\r\nvoid MotionDriver::reset(bool writeParams) {\r\n _firstRun = true;\r\n clock_gettime( CLOCK_REALTIME, &_start);\r\n _start_time = ros::WallTime::now().toSec();\r\n\r\n _gyroBiasX = 0.0;\r\n _gyroBiasY = 0.0;\r\n _gyroBiasZ = 0.0;\r\n _attitudeEstimate.w() = 1.0;\r\n _attitudeEstimate.x() = 0.0;\r\n _attitudeEstimate.y() = 0.0;\r\n _attitudeEstimate.z() = 0.0;\r\n\r\n if (writeParams) {\r\n ROS_INFO(\"Writing parameters...\");\r\n //System calls are so dirty, there has to be a better way to do this\r\n system(\"rosparam dump /data/app/bluefin/opt/sandshark/share/sandshark_apps/config/motion.yaml /motion\");\r\n ROS_INFO(\"Done\");\r\n }\r\n}\r\n\r\nvoid MotionDriver::cleanup() {\r\n ROS_INFO(\"Task init! Motion cleanup\");\r\n fflush( stdout);\r\n for (unsigned int i = 0; i < _sensorList.size(); i++) {\r\n ASensorEventQueue_disableSensor(_queueList[i], _sensorList[i]);\r\n ASensorManager_destroyEventQueue(_sensorManager, _queueList[i]);\r\n }\r\n\r\n _sensorList.clear();\r\n _queueList.clear();\r\n ROS_INFO(\"Task init! Cleanup done\");\r\n fflush( stdout);\r\n}\r\n\r\nbool MotionDriver::ahrsEnabled(double magMagnitude) {\r\n //replace AHRS with IMU filter\r\n if (_imuFilterEnabled) {\r\n return false;\r\n }\r\n\r\n //Switch to IMU filter after first Beta\r\n if (_imuFilterAfterBeta) {\r\n double curTime = ros::WallTime::now().toSec();\r\n if ((curTime - _start_time) > _firstBetaDuration) {\r\n return false;\r\n }\r\n }\r\n\r\n if (_imuFilterWithBadMag) {\r\n if ((magMagnitude < _minGoodMag) || (magMagnitude > _maxGoodMag)) {\r\n return false;\r\n }\r\n }\r\n\r\n return true;\r\n}\r\n\r\nbool MotionDriver::doRun() {\r\n static struct timespec current;\r\n static double magX, magY, magZ, accelX, accelY, accelZ, gyroX, gyroY, gyroZ;\r\n //A failure state of the magnetometer is that it will lock up and return\r\n //the same value forever, so we check for that and fail if it does\r\n static double lastMagX = 0, lastMagY = 0, lastMagZ = 0;\r\n static int magLockedCount = 0;\r\n static double o_x, o_y, o_z;\r\n static int magStat = 0, accelStat = 0, gyroStat = 0, count = 0;\r\n static Vector3d accelTemp, magTemp, accelNormalized, magNormalized;\r\n static bool haveMag = false, haveAccel = false, haveGyro = false, haveRotation = false;\r\n static Matrix rotation;\r\n static Quaternion attitudeConj;\r\n static Quaternion magQuaternion;\r\n static Quaternion gyroQuaternion, gyroError;\r\n static Quaternion hQuaternion;\r\n static Quaternion bQuaternion;\r\n static Matrix stepVector;\r\n static Quaternion stepQuaternion;\r\n static Matrix J;\r\n static Matrix F;\r\n static Matrix imuJ;\r\n static Matrix imuF;\r\n static double prev_elapsed = 0.0;\r\n static Vector4d rateOfChange;\r\n static char sendBuf[1400];\r\n static int noEventsCount = 0;\r\n\r\n clock_gettime( CLOCK_REALTIME, ¤t);\r\n //Assume start nsec is zero for simplicity\r\n double elapsed = (current.tv_sec - _start.tv_sec) + ((double) current.tv_nsec) / 1000000000;\r\n if (ASensorEventQueue_hasEvents(_queueList[0]) > 0) { // if the master (first) sensor has new data\r\n for (unsigned int i = 0; i < _queueList.size(); ++i) {\r\n int ret = 0;\r\n int eventCount = 0;\r\n while ((ret = ASensorEventQueue_getEvents(_queueList[i], &_eventList[i], 1)) > 0) { //get all pending events\r\n //WARNING: do NOT put debug messages in here. The latency\r\n //will cause the motion driver to fail to get message\r\n //fast enough and will get stuck in an infinite loop\r\n\r\n clock_gettime( CLOCK_REALTIME, ¤t);\r\n double tempElapsed = (current.tv_sec - _start.tv_sec) + ((double) current.tv_nsec) / 1000000000.0;\r\n double lastEvent = (tempElapsed - elapsed);\r\n if (lastEvent > 2.0) { //No Sensor Data for 2 secs\r\n ROS_WARN(\"Getting current events for more than two seconds, driver failing!\");\r\n return false;\r\n }\r\n\r\n eventCount += ret;\r\n }\r\n\r\n //make sure we got a new value\r\n if (ret < 0) {\r\n ROS_WARN(\"Error from android sensor manager (%d)\", ret);\r\n setErrorMessage(\"Error from android sensor manager\");\r\n noEventsCount = 0;\r\n //TODO: should we actually be failing here?\r\n return false;\r\n } else if (eventCount == 0) {\r\n noEventsCount++;\r\n if (noEventsCount > 5) {\r\n ROS_WARN(\"No events received from sensor manager!\");\r\n setErrorMessage(\"Error from android sensor manager\");\r\n noEventsCount = 0;\r\n //TODO: should we actually be failing here?\r\n return false;\r\n }\r\n } else {\r\n noEventsCount = 0;\r\n struct sysinfo info;\r\n int64_t uptime = 0;\r\n if (sysinfo(&info) == 0) {\r\n uptime = info.uptime;\r\n }\r\n\r\n float *d = _eventList[i].data;\r\n switch (_eventList[i].type) {\r\n case ASENSOR_TYPE_ACCELEROMETER:\r\n if ((_eventList[i].timestamp / 1000000000) - uptime > 2) {\r\n ROS_WARN(\"Processing old accelerometer data from sensor!!\");\r\n }\r\n accelX = d[0];\r\n accelY = d[1];\r\n accelZ = d[2];\r\n accelStat = _eventList[i].acceleration.status;\r\n accelTemp(0) = accelX;\r\n accelTemp(1) = accelY;\r\n accelTemp(2) = accelZ;\r\n if (_firstRun) {\r\n _gravityAccel = accelTemp;\r\n }\r\n haveAccel = true;\r\n accelTemp -= _accelOffset;\r\n _correctedAccel = accelTemp;\r\n accelTemp(0) = _correctedAccel(0) / _accelScaling(0);\r\n accelTemp(1) = _correctedAccel(1) / _accelScaling(1);\r\n accelTemp(2) = _correctedAccel(2) / _accelScaling(2);\r\n _correctedAccel = accelTemp * 9.81;\r\n break;\r\n case ASENSOR_TYPE_MAGNETIC_FIELD:\r\n if ((_eventList[i].timestamp / 1000000000) - uptime > 2) {\r\n ROS_WARN(\"Processing old magnetometer data from sensor!!\");\r\n }\r\n magX = d[0];\r\n magY = d[1];\r\n magZ = d[2];\r\n if (magX == lastMagX && magY == lastMagY && magZ == lastMagZ) {\r\n magLockedCount++;\r\n if (magLockedCount > 10) {\r\n //Something bad is happening! mag has locked up\r\n //Restart so we can get it unlocked\r\n ROS_WARN(\"Magnetometer has locked up!\");\r\n setErrorMessage(\"Magnetometer has locked up!\");\r\n return false;\r\n }\r\n } else {\r\n magLockedCount = 0;\r\n }\r\n lastMagX = magX;\r\n lastMagY = magY;\r\n lastMagZ = magZ;\r\n\r\n magStat = _eventList[i].magnetic.status;\r\n haveMag = true;\r\n magTemp(0) = magX;\r\n magTemp(1) = magY;\r\n magTemp(2) = magZ;\r\n magTemp -= _magOffset;\r\n //_correctedMag = magTemp;\r\n _correctedMag = magTemp.transpose() * _magAdjust;\r\n magTemp(0) = _correctedMag(0) / _magScaling(0);\r\n magTemp(1) = _correctedMag(1) / _magScaling(1);\r\n magTemp(2) = _correctedMag(2) / _magScaling(2);\r\n _correctedMag = magTemp.transpose() * _magAdjust.transpose();\r\n break;\r\n case ASENSOR_TYPE_GYROSCOPE:\r\n if ((_eventList[i].timestamp / 1000000000) - uptime > 2) {\r\n ROS_WARN(\"Processing old gyroscope data from sensor!!\");\r\n }\r\n gyroX = d[0];\r\n gyroY = d[1];\r\n gyroZ = d[2];\r\n gyroStat = _eventList[i].vector.status;\r\n haveGyro = true;\r\n break;\r\n case 11:\r\n o_x = d[0];\r\n o_y = d[1];\r\n o_z = d[2];\r\n // o_w = d[3];\r\n haveRotation = true;\r\n break;\r\n }\r\n }\r\n }\r\n\r\n if (haveMag && haveAccel && haveGyro) {\r\n\r\n _rawMsg.accel_x = accelX;\r\n _rawMsg.accel_y = accelY;\r\n _rawMsg.accel_z = accelZ;\r\n _rawMsg.accel_status = accelStat;\r\n\r\n _rawMsg.mag_x = magX;\r\n _rawMsg.mag_y = magY;\r\n _rawMsg.mag_z = magZ;\r\n _rawMsg.mag_status = magStat;\r\n\r\n _rawMsg.gyro_x = gyroX;\r\n _rawMsg.gyro_y = gyroY;\r\n _rawMsg.gyro_z = gyroZ;\r\n _rawMsg.gyro_status = gyroStat;\r\n\r\n _rawPub.publish(_rawMsg);\r\n\r\n _gravityAccel = _alpha * _gravityAccel + (1 - _alpha) * _correctedAccel;\r\n _linearAccel = _correctedAccel - _gravityAccel;\r\n\r\n _correctedMsg.timestamp = current.tv_sec + ((double) current.tv_nsec) / 1000000000.0;\r\n _correctedMsg.accel_x = _correctedAccel(0);\r\n _correctedMsg.accel_y = _correctedAccel(1);\r\n _correctedMsg.accel_z = _correctedAccel(2);\r\n _correctedMsg.linear_x = _linearAccel(0);\r\n _correctedMsg.linear_y = _linearAccel(1);\r\n _correctedMsg.linear_z = _linearAccel(2);\r\n _correctedMsg.gravity_x = _gravityAccel(0);\r\n _correctedMsg.gravity_y = _gravityAccel(1);\r\n _correctedMsg.gravity_z = _gravityAccel(2);\r\n _correctedMsg.accel_status = accelStat;\r\n\r\n _correctedMsg.mag_x = _correctedMag(0);\r\n _correctedMsg.mag_y = _correctedMag(1);\r\n _correctedMsg.mag_z = _correctedMag(2);\r\n _correctedMsg.mag_status = magStat;\r\n\r\n _correctedMsg.gyro_x = gyroX;\r\n _correctedMsg.gyro_y = gyroY;\r\n _correctedMsg.gyro_z = gyroZ;\r\n _correctedMsg.gyro_status = gyroStat;\r\n\r\n _correctedPub.publish(_correctedMsg);\r\n\r\n accelNormalized = _gravityAccel / _gravityAccel.norm();\r\n magNormalized = _correctedMag / _correctedMag.norm();\r\n\r\n if (ahrsEnabled(_correctedMag.norm())) {\r\n magQuaternion.w() = 0.0;\r\n magQuaternion.x() = magNormalized.x();\r\n magQuaternion.y() = magNormalized.y();\r\n magQuaternion.z() = magNormalized.z();\r\n\r\n hQuaternion = _attitudeEstimate * (magQuaternion * _attitudeEstimate.conjugate());\r\n bQuaternion.w() = 0.0;\r\n bQuaternion.x() = sqrt(hQuaternion.x() * hQuaternion.x() + hQuaternion.y() * hQuaternion.y());\r\n bQuaternion.y() = 0.0;\r\n bQuaternion.z() = hQuaternion.z();\r\n\r\n F[0] = 2.0 * (_attitudeEstimate.x() * _attitudeEstimate.z() - _attitudeEstimate.w() * _attitudeEstimate.y())\r\n - accelNormalized.x();\r\n F[1] = 2.0 * (_attitudeEstimate.w() * _attitudeEstimate.x() + _attitudeEstimate.y() * _attitudeEstimate.z())\r\n - accelNormalized.y();\r\n F[2] = 2.0\r\n * (0.5 - _attitudeEstimate.x() * _attitudeEstimate.x() - _attitudeEstimate.y() * _attitudeEstimate.y())\r\n - accelNormalized.z();\r\n F[3] = 2.0 * bQuaternion.x()\r\n * (0.5 - _attitudeEstimate.y() * _attitudeEstimate.y() - _attitudeEstimate.z() * _attitudeEstimate.z())\r\n + 2.0 * bQuaternion.z()\r\n * (_attitudeEstimate.x() * _attitudeEstimate.z() - _attitudeEstimate.w() * _attitudeEstimate.y())\r\n - magNormalized.x();\r\n F[4] = 2.0 * bQuaternion.x()\r\n * (_attitudeEstimate.x() * _attitudeEstimate.y() - _attitudeEstimate.w() * _attitudeEstimate.z())\r\n + 2.0 * bQuaternion.z()\r\n * (_attitudeEstimate.w() * _attitudeEstimate.x() + _attitudeEstimate.y() * _attitudeEstimate.z())\r\n - magNormalized.y();\r\n F[5] = 2.0 * bQuaternion.x()\r\n * (_attitudeEstimate.w() * _attitudeEstimate.y() + _attitudeEstimate.x() * _attitudeEstimate.z())\r\n + 2.0 * bQuaternion.z()\r\n * (0.5 - _attitudeEstimate.x() * _attitudeEstimate.x() - _attitudeEstimate.y() * _attitudeEstimate.y())\r\n - magNormalized.z();\r\n\r\n J(0, 0) = -2.0 * _attitudeEstimate.y();\r\n J(0, 1) = 2.0 * _attitudeEstimate.z();\r\n J(0, 2) = -2.0 * _attitudeEstimate.w();\r\n J(0, 3) = 2.0 * _attitudeEstimate.x();\r\n J(1, 0) = 2.0 * _attitudeEstimate.x();\r\n J(1, 1) = 2.0 * _attitudeEstimate.w();\r\n J(1, 2) = 2.0 * _attitudeEstimate.z();\r\n J(1, 3) = 2.0 * _attitudeEstimate.y();\r\n J(2, 0) = 0.0;\r\n J(2, 1) = -4.0 * _attitudeEstimate.x();\r\n J(2, 2) = -4.0 * _attitudeEstimate.y();\r\n J(2, 3) = 0.0;\r\n J(3, 0) = -2.0 * bQuaternion.z() * _attitudeEstimate.y();\r\n J(3, 1) = 2.0 * bQuaternion.z() * _attitudeEstimate.z();\r\n J(3, 2) = -4.0 * bQuaternion.x() * _attitudeEstimate.y() - 2.0 * bQuaternion.z() * _attitudeEstimate.w();\r\n J(3, 3) = -4.0 * bQuaternion.x() * _attitudeEstimate.z() + 2.0 * bQuaternion.z() * _attitudeEstimate.x();\r\n J(4, 0) = -2.0 * bQuaternion.x() * _attitudeEstimate.z() + 2.0 * bQuaternion.z() * _attitudeEstimate.x();\r\n J(4, 1) = 2.0 * bQuaternion.x() * _attitudeEstimate.y() + 2.0 * bQuaternion.z() * _attitudeEstimate.w();\r\n J(4, 2) = 2.0 * bQuaternion.x() * _attitudeEstimate.x() + 2.0 * bQuaternion.z() * _attitudeEstimate.z();\r\n J(4, 3) = -2.0 * bQuaternion.x() * _attitudeEstimate.w() + 2.0 * bQuaternion.z() * _attitudeEstimate.y();\r\n J(5, 0) = 2.0 * bQuaternion.x() * _attitudeEstimate.y();\r\n J(5, 1) = 2.0 * bQuaternion.x() * _attitudeEstimate.z() - 4.0 * bQuaternion.z() * _attitudeEstimate.x();\r\n J(5, 2) = 2.0 * bQuaternion.x() * _attitudeEstimate.w() - 4.0 * bQuaternion.z() * _attitudeEstimate.y();\r\n J(5, 3) = 2.0 * bQuaternion.x() * _attitudeEstimate.x();\r\n\r\n stepVector = (J.transpose() * F);\r\n stepVector.normalize();\r\n } else {\r\n imuF[0] = 2.0 * (_attitudeEstimate.x() * _attitudeEstimate.z() - _attitudeEstimate.w() * _attitudeEstimate.y())\r\n - accelNormalized.x();\r\n imuF[1] = 2.0 * (_attitudeEstimate.w() * _attitudeEstimate.x() + _attitudeEstimate.y() * _attitudeEstimate.z())\r\n - accelNormalized.y();\r\n imuF[2] = 2.0\r\n * (0.5 - _attitudeEstimate.x() * _attitudeEstimate.x() - _attitudeEstimate.y() * _attitudeEstimate.y())\r\n - accelNormalized.z();\r\n\r\n imuJ(0, 0) = -2.0 * _attitudeEstimate.y();\r\n imuJ(0, 1) = 2.0 * _attitudeEstimate.z();\r\n imuJ(0, 2) = -2.0 * _attitudeEstimate.w();\r\n imuJ(0, 3) = 2.0 * _attitudeEstimate.x();\r\n imuJ(1, 0) = 2.0 * _attitudeEstimate.x();\r\n imuJ(1, 1) = 2.0 * _attitudeEstimate.w();\r\n imuJ(1, 2) = 2.0 * _attitudeEstimate.z();\r\n imuJ(1, 3) = 2.0 * _attitudeEstimate.y();\r\n imuJ(2, 0) = 0.0;\r\n imuJ(2, 1) = -4.0 * _attitudeEstimate.x();\r\n imuJ(2, 2) = -4.0 * _attitudeEstimate.y();\r\n imuJ(2, 3) = 0.0;\r\n\r\n stepVector = (imuJ.transpose() * imuF);\r\n stepVector.normalize();\r\n }\r\n stepQuaternion.w() = stepVector[0];\r\n stepQuaternion.x() = stepVector[1];\r\n stepQuaternion.y() = stepVector[2];\r\n stepQuaternion.z() = stepVector[3];\r\n gyroError = (_attitudeEstimate.conjugate() * stepQuaternion);\r\n\r\n double timestep = (elapsed - prev_elapsed);\r\n if (timestep < 0.0 || timestep > 2.0) {\r\n ROS_INFO(\"\\ntime %f\\n\", timestep);\r\n }\r\n double curZeta = _zeta;\r\n double curTime = ros::WallTime::now().toSec();\r\n if ((curTime - _start_time) < _firstBetaDuration) {\r\n curZeta = 0.0;\r\n }\r\n\r\n _gyroBiasX += 2.0 * gyroError.x() * timestep * curZeta;\r\n _gyroBiasY += 2.0 * gyroError.y() * timestep * curZeta;\r\n _gyroBiasZ += 2.0 * gyroError.z() * timestep * curZeta;\r\n gyroQuaternion.w() = 0.0;\r\n gyroQuaternion.x() = gyroX - _gyroBiasX;\r\n gyroQuaternion.y() = gyroY - _gyroBiasY;\r\n gyroQuaternion.z() = gyroZ - _gyroBiasZ;\r\n\r\n _rCQuaternion = (_attitudeEstimate * gyroQuaternion);\r\n rateOfChange[0] = _rCQuaternion.w();\r\n rateOfChange[1] = _rCQuaternion.x();\r\n rateOfChange[2] = _rCQuaternion.y();\r\n rateOfChange[3] = _rCQuaternion.z();\r\n\r\n rateOfChange *= 0.5;\r\n if ((curTime - _start_time) < _firstBetaDuration) {\r\n rateOfChange -= _firstBeta * stepVector.transpose();\r\n } else {\r\n rateOfChange -= _beta * stepVector.transpose();\r\n }\r\n\r\n _attitudeEstimate.w() += rateOfChange[0] * timestep;\r\n _attitudeEstimate.x() += rateOfChange[1] * timestep;\r\n _attitudeEstimate.y() += rateOfChange[2] * timestep;\r\n _attitudeEstimate.z() += rateOfChange[3] * timestep;\r\n _attitudeEstimate.normalize();\r\n\r\n _orientQuaternionMsg.w = _attitudeEstimate.w();\r\n _orientQuaternionMsg.x = _attitudeEstimate.x();\r\n _orientQuaternionMsg.y = _attitudeEstimate.y();\r\n _orientQuaternionMsg.z = _attitudeEstimate.z();\r\n\r\n publishEulerAngles();\r\n publishDebug();\r\n _orientQuaternionPub.publish(_orientQuaternionMsg);\r\n\r\n if (_sendUDP && (count % 10 == 0)) {\r\n if (_sendAndroidQuaternion) {\r\n if (haveRotation) {\r\n snprintf(&sendBuf[0], sizeof(sendBuf), \"%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\",\r\n ros::WallTime::now().toSec(), accelX, accelY, accelZ, gyroX, gyroY, gyroZ, magX, magY, magZ,\r\n _attitudeEstimate.w(), _attitudeEstimate.x(), _attitudeEstimate.y(), _attitudeEstimate.z(),\r\n _orientMsg.yaw, _orientMsg.roll, _orientMsg.pitch,\r\n sqrt(1.0 - ((o_x * o_x) + (o_y * o_y) + (o_z * o_z))), o_x, o_y, o_z);\r\n\r\n }\r\n } else {\r\n snprintf(&sendBuf[0], sizeof(sendBuf), \"%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f,%f\",\r\n ros::WallTime::now().toSec(),\r\n //correctedAccel(0), correctedAccel(1), correctedAccel(2),\r\n accelX, accelY, accelZ, gyroX, gyroY, gyroZ,\r\n //correctedMag(0), correctedMag(1), correctedMag(2),\r\n magX, magY, magZ, _attitudeEstimate.w(), _attitudeEstimate.x(), _attitudeEstimate.y(),\r\n _attitudeEstimate.z(),\r\n //SEq_1, SEq_2, SEq_3, SEq_4,\r\n _orientMsg.yaw, _orientMsg.roll, _orientMsg.pitch);\r\n }\r\n sendBuf[sizeof(sendBuf) - 1] = '\\0';\r\n int ret = sendto(_sendSocket, &sendBuf[0], strlen(sendBuf), 0, (struct sockaddr *) &_sendAddrStruct,\r\n sizeof(struct sockaddr));\r\n if (ret < 0) {\r\n fprintf( stderr, \"SendTo failed ret %d - %d:%s\\n\", ret, errno, strerror( errno));\r\n }\r\n }\r\n prev_elapsed = elapsed;\r\n count++;\r\n haveMag = false;\r\n haveAccel = false;\r\n haveGyro = false;\r\n }\r\n } else {\r\n double lastEvent = (elapsed - prev_elapsed);\r\n if (lastEvent > 2.0) { //No Sensor Data for 2 secs\r\n\r\n elapsed = 0.0;\r\n prev_elapsed = 0.0;\r\n return false;\r\n }\r\n _needSleep = true;\r\n }\r\n\r\n _firstRun = false;\r\n return true;\r\n}\r\n\r\nvoid MotionDriver::handleSleep() {\r\n if (_needSleep) {\r\n usleep(10000);\r\n }\r\n _needSleep = false;\r\n}\r\n\r\nvoid MotionDriver::publishDebug() {\r\n //publish gyro bias and the rCQuaternion\r\n\r\n _orientDebugMsg.gyro_bias_x = _gyroBiasX;\r\n _orientDebugMsg.gyro_bias_y = _gyroBiasY;\r\n _orientDebugMsg.gyro_bias_z = _gyroBiasZ;\r\n\r\n _orientDebugMsg.rc_x = _rCQuaternion.x();\r\n _orientDebugMsg.rc_y = _rCQuaternion.y();\r\n _orientDebugMsg.rc_z = _rCQuaternion.z();\r\n _orientDebugMsg.rc_w = _rCQuaternion.w();\r\n\r\n _orientDebugPub.publish(_orientDebugMsg);\r\n}\r\n\r\nvoid MotionDriver::publishEulerAngles() {\r\n\r\n double froll = 0.0;\r\n double fpitch = 0.0;\r\n double fazimuth = 0.0;\r\n\r\n Matrix3d qrotation = _attitudeEstimate.matrix();\r\n //Rotate into vehicle orientation\r\n Matrix3d rotation = qrotation * AngleAxisd(0.5 * M_PI, Vector3d::UnitY());\r\n\r\n //rotation matrix to euler, pitch and azimuth are inverted\r\n froll = atan2(rotation(2, 1), rotation(2, 2));\r\n fpitch = asin(rotation(2, 0));\r\n fazimuth = atan2(rotation(1, 0), rotation(0, 0));\r\n\r\n froll *= 180.0 / M_PI;\r\n fpitch *= 180.0 / M_PI;\r\n fazimuth *= 180.0 / M_PI;\r\n if (fazimuth < 0.0) {\r\n fazimuth += 360.0;\r\n }\r\n\r\n if (fazimuth > 360.0) {\r\n fazimuth -= 360.0;\r\n }\r\n\r\n _orientMsg.roll = froll;\r\n _orientMsg.yaw = fazimuth;\r\n _orientMsg.pitch = fpitch;\r\n\r\n _orientPub.publish(_orientMsg);\r\n\r\n}\r\n\r\n}\r\n}\r\n\r\nint main(int argc, char *argv[]) {\r\n bluefin::sandshark::MotionDriver md;\r\n return bluefin::sandshark::app_main((bluefin::sandshark::TaskBase&) md, argc, argv);\r\n}\r\n", "meta": {"hexsha": "15110aabac771f4b2e3b11e6ab1516963fd6a8c2", "size": 33937, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "vehicle/ros/src/sandshark_drivers/motion/motion_node.cpp", "max_stars_repo_name": "slicht-uri/Sandshark-Beta-Lab-", "max_stars_repo_head_hexsha": "6cff36b227b49b776d13187c307e648d2a52bdae", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "vehicle/ros/src/sandshark_drivers/motion/motion_node.cpp", "max_issues_repo_name": "slicht-uri/Sandshark-Beta-Lab-", "max_issues_repo_head_hexsha": "6cff36b227b49b776d13187c307e648d2a52bdae", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "vehicle/ros/src/sandshark_drivers/motion/motion_node.cpp", "max_forks_repo_name": "slicht-uri/Sandshark-Beta-Lab-", "max_forks_repo_head_hexsha": "6cff36b227b49b776d13187c307e648d2a52bdae", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 1.0, "max_forks_repo_forks_event_min_datetime": "2019-10-18T06:25:14.000Z", "max_forks_repo_forks_event_max_datetime": "2019-10-18T06:25:14.000Z", "avg_line_length": 35.610703043, "max_line_length": 178, "alphanum_fraction": 0.6095412087, "num_tokens": 9841, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230157, "lm_q2_score": 0.4035668537353746, "lm_q1q2_score": 0.20808710711163644}} {"text": "#include \"solver.h\"\n\n#include \n#include \n#include \"../parallel.h\"\n#include \"../std.h\"\n#include \"../time.h\"\n#include \"../wavefunction/wavefunction.h\"\n#include \"davidson.h\"\n\nDet Solver::generate_hf_det() {\n Det det;\n for (size_t i = 0; i < n_up; i++) det.up.set_orb(i, true);\n for (size_t i = 0; i < n_dn; i++) det.dn.set_orb(i, true);\n return det;\n}\n\nvoid Solver::variation(const double eps_var) {\n const double THRESHOLD = 1.0e-6;\n\n // Setup HF or existing wf as initial wf and evaluate energy.\n if (wf.size() == 0) {\n const Det& det_hf = generate_hf_det();\n wf.append_term(det_hf, 1.0);\n energy_hf = energy_var = hamiltonian(det_hf, det_hf);\n if (Parallel::is_master()) printf(\"HF energy: %#.15g Ha\\n\", energy_hf);\n }\n\n std::unordered_set> var_dets_set;\n std::list new_dets;\n for (const auto& term : wf.get_terms()) var_dets_set.insert(term.det.encode());\n double energy_var_new = 0.0; // Ensures the first iteration will run.\n size_t n_iter = 0;\n converged = false;\n while (!converged) {\n Time::start(\"Variation: \" + std::to_string(n_iter));\n\n for (const auto& term : wf.get_terms()) {\n const auto& connected_dets = find_connected_dets(term.det, eps_var / fabs(term.coef));\n for (const auto& new_det : connected_dets) {\n const auto new_det_code = new_det.encode();\n if (var_dets_set.count(new_det_code) == 0) {\n var_dets_set.insert(new_det_code);\n new_dets.push_back(new_det);\n }\n }\n }\n\n if (Parallel::is_master()) {\n printf(\"New / total dets: %'zu / %'zu\\n\", new_dets.size(), var_dets_set.size());\n }\n Time::checkpoint(\"found new dets\");\n\n for (const auto& new_det : new_dets) {\n wf.append_term(new_det, 0.0);\n }\n\n energy_var_new = diagonalize(new_dets.size() > 0);\n if (fabs(energy_var - energy_var_new) < THRESHOLD) converged = true;\n energy_var = energy_var_new;\n if (Parallel::is_master()) {\n printf(\"Variation energy: %#.15g Ha\\n\", energy_var);\n printf(\"Correlation energy (variation): %#.15g Ha\\n\", energy_var - energy_hf);\n }\n\n new_dets.clear();\n\n Time::end();\n n_iter++;\n }\n\n if (Parallel::is_master()) {\n printf(\"Final variation energy: %#.15g Ha\\n\", energy_var);\n printf(\"Correlation energy (variation): %#.15g Ha\\n\", energy_var - energy_hf);\n }\n}\n\ndouble Solver::diagonalize(const bool has_new_dets) {\n std::vector diagonal;\n std::vector initial_vector;\n const size_t max_iterations = has_new_dets ? 5 : 10;\n diagonal.reserve(wf.size());\n initial_vector.reserve(wf.size());\n for (const auto& term : wf.get_terms()) {\n const auto& det = term.det;\n diagonal.push_back(hamiltonian(det, det));\n initial_vector.push_back(term.coef);\n }\n\n std::function hamiltonian_func =\n std::bind(&Solver::hamiltonian, this, std::placeholders::_1, std::placeholders::_2);\n HelperStrings helper_strings(hamiltonian_func);\n helper_strings.setup(wf.get_dets());\n Time::checkpoint(\"helper strings generated\");\n std::function(std::vector)> apply_hamiltonian_func =\n std::bind(&Solver::apply_hamiltonian, this, std::placeholders::_1, helper_strings);\n\n Davidson davidson(diagonal, apply_hamiltonian_func, wf.size());\n if (Parallel::is_master()) davidson.set_verbose(true);\n const size_t n_iter = davidson.diagonalize(initial_vector, max_iterations);\n if (!has_new_dets && n_iter < max_iterations) converged = true;\n\n const double energy_var = davidson.get_lowest_eigenvalue();\n const auto& coefs_new = davidson.get_lowest_eigenvector();\n\n wf.set_coefs(coefs_new);\n wf.sort_by_coefs();\n\n return energy_var;\n}\n\n#pragma omp declare reduction( \\\n vec_double_plus : std::vector < \\\n double > : std::transform( \\\n omp_out \\\n .begin(), omp_out.end(), omp_in.begin(), omp_out.begin(), std::plus < double > ())) initializer(omp_priv = omp_orig)\n\nstd::vector Solver::apply_hamiltonian(\n const std::vector& vec, HelperStrings& helper_strings) {\n const std::size_t n_dets = vec.size();\n const size_t proc_id = Parallel::get_id();\n const size_t n_procs = Parallel::get_n();\n std::vector res(n_dets, 0.0);\n\n const auto& dets = wf.get_dets();\n\n#pragma omp parallel for reduction(vec_double_plus : res) schedule(dynamic, 10)\n for (size_t i = proc_id; i < n_dets; i += n_procs) {\n const auto& connections = helper_strings.find_connections(i);\n for (const auto connection : connections) {\n const size_t j = connection.first;\n const double H_ij = connection.second;\n res[i] += H_ij * vec[j];\n if (i != j) {\n res[j] += H_ij * vec[i];\n }\n }\n }\n\n Parallel::reduce_to_sum_vector(res);\n Time::checkpoint(\"hamiltonian applied\");\n\n return res;\n}\n\n\nvoid Solver::save_variation_result(const std::string& filename) {\n if (Parallel::is_master()) {\n std::ofstream var_file;\n var_file.open(filename);\n var_file << boost::format(\"%.17g %.17g\\n\") % energy_hf % energy_var;\n var_file << boost::format(\"%d %d %d\\n\") % n_up % n_dn % wf.size();\n for (const auto& term : wf.get_terms()) {\n var_file << boost::format(\"%.17g\\n\") % term.coef;\n var_file << term.det.up << std::endl << term.det.dn << std::endl;\n }\n var_file.close();\n printf(\"Variation result saved to: %s\\n\", filename.c_str());\n }\n}\n\n\nbool Solver::load_variation_result(const std::string& filename) {\n std::ifstream var_file;\n size_t n_dets;\n Orbital orb_id;\n double coef;\n var_file.open(filename);\n if (!var_file.is_open()) return false; // Does not exist.\n var_file >> energy_hf >> energy_var;\n var_file >> n_up >> n_dn >> n_dets;\n wf.clear();\n for (std::size_t i = 0; i < n_dets; i++) {\n var_file >> coef;\n Det det;\n for (std::size_t j = 0; j < n_up; j++) {\n var_file >> orb_id;\n det.up.set_orb(orb_id, true);\n }\n for (std::size_t j = 0; j < n_dn; j++) {\n var_file >> orb_id;\n det.dn.set_orb(orb_id, true);\n }\n wf.append_term(det, coef);\n }\n var_file.close();\n if (Parallel::is_master()) {\n printf(\"Loaded %'zu dets from: %s\\n\", n_dets, filename.c_str());\n printf(\"HF energy: %#.15g Ha\\n\", energy_hf);\n printf(\"Variation energy: %#.15g Ha\\n\", energy_var);\n printf(\"Correlation energy (variation): %#.15g Ha\\n\", energy_var - energy_hf);\n }\n return true;\n}", "meta": {"hexsha": "9a7fff93c0b8fc3c7c53301700143fd8bd5cf633", "size": 6466, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/solver/solver.cc", "max_stars_repo_name": "jl2922/hci-17c", "max_stars_repo_head_hexsha": "401a04d67c1d37e83dacc73bebeb8561c13bd4b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2017-08-21T13:55:00.000Z", "max_stars_repo_stars_event_max_datetime": "2017-08-21T13:55:00.000Z", "max_issues_repo_path": "src/solver/solver.cc", "max_issues_repo_name": "jl2922/hci-17c", "max_issues_repo_head_hexsha": "401a04d67c1d37e83dacc73bebeb8561c13bd4b2", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "src/solver/solver.cc", "max_forks_repo_name": "jl2922/hci-17c", "max_forks_repo_head_hexsha": "401a04d67c1d37e83dacc73bebeb8561c13bd4b2", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 33.3298969072, "max_line_length": 137, "alphanum_fraction": 0.649396845, "num_tokens": 1826, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5156199157230156, "lm_q2_score": 0.4035668537353746, "lm_q1q2_score": 0.20808710711163642}} {"text": "// Copyright (C) 2011-2012 by the BEM++ Authors\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n#include \"bempp/common/config_trilinos.hpp\"\n\n#ifdef WITH_TRILINOS\n\n#include \"default_iterative_solver.hpp\"\n\n#include \"belos_solver_wrapper.hpp\"\n#include \"solution.hpp\"\n#include \"blocked_solution.hpp\"\n#include \"../assembly/abstract_boundary_operator.hpp\"\n#include \"../assembly/abstract_boundary_operator_pseudoinverse.hpp\"\n#include \"../assembly/blocked_boundary_operator.hpp\"\n#include \"../assembly/boundary_operator.hpp\"\n#include \"../assembly/context.hpp\"\n#include \"../assembly/discrete_boundary_operator.hpp\"\n#include \"../assembly/discrete_boundary_operator_composition.hpp\"\n#include \"../assembly/identity_operator.hpp\"\n#include \"../assembly/vector.hpp\"\n#include \"../fiber/explicit_instantiation.hpp\"\n#include \"../space/space.hpp\"\n\n#include \n#include \n\n#include \n#include \n\n#include \n\nnamespace Bempp {\n\ntemplate \nTeuchos::RCP>\nwrapInTrilinosVector(arma::Col &col) {\n size_t size = col.n_rows;\n Teuchos::ArrayRCP trilinosArray = Teuchos::arcp(\n col.memptr(), 0 /* lowerOffset */, size, false /* doesn't own memory */);\n typedef Thyra::DefaultSpmdVector TrilinosVector;\n return Teuchos::RCP(\n new TrilinosVector(Thyra::defaultSpmdVectorSpace(size),\n trilinosArray, 1 /* stride */));\n}\n\n/** \\cond HIDDEN_INTERNAL */\n\ntemplate \nstruct DefaultIterativeSolver::Impl {\n\n // Constructor for non-blocked operators\n Impl(const BoundaryOperator &op_,\n ConvergenceTestMode::Mode mode_)\n : op(op_), mode(mode_) {\n typedef BoundaryOperator BoundaryOp;\n typedef Solver Solver_;\n const BoundaryOp &boundaryOp = boost::get(op);\n if (!boundaryOp.isInitialized())\n throw std::invalid_argument(\"DefaultIterativeSolver::Impl::Impl(): \"\n \"boundary operator must be initialized\");\n\n if (mode == ConvergenceTestMode::TEST_CONVERGENCE_IN_DUAL_TO_RANGE) {\n if (boundaryOp.domain()->globalDofCount() !=\n boundaryOp.dualToRange()->globalDofCount())\n throw std::invalid_argument(\"DefaultIterativeSolver::Impl::Impl(): \"\n \"non-square system provided\");\n\n solverWrapper.reset(new BelosSolverWrapper(\n Teuchos::rcp>(\n boundaryOp.weakForm())));\n } else if (mode == ConvergenceTestMode::TEST_CONVERGENCE_IN_RANGE) {\n if (boundaryOp.domain()->globalDofCount() !=\n boundaryOp.range()->globalDofCount())\n throw std::invalid_argument(\"DefaultIterativeSolver::Impl::Impl(): \"\n \"non-square system provided\");\n\n BoundaryOp id =\n identityOperator(boundaryOp.context(), boundaryOp.range(),\n boundaryOp.range(), boundaryOp.dualToRange());\n pinvId = pseudoinverse(id, boundaryOp.dualToRange());\n // dualToRange could be anything here.\n shared_ptr> totalBoundaryOp =\n boost::make_shared>(\n boost::get(pinvId).weakForm(), boundaryOp.weakForm());\n solverWrapper.reset(new BelosSolverWrapper(\n Teuchos::rcp>(\n totalBoundaryOp)));\n } else\n throw std::invalid_argument(\n \"DefaultIterativeSolver::DefaultIterativeSolver(): \"\n \"invalid convergence test mode\");\n }\n\n // Constructor for blocked operators\n Impl(const BlockedBoundaryOperator &op_,\n ConvergenceTestMode::Mode mode_)\n : op(op_), mode(mode_) {\n typedef BlockedBoundaryOperator BoundaryOp;\n typedef Solver Solver_;\n const BoundaryOp &boundaryOp = boost::get(op);\n\n if (mode == ConvergenceTestMode::TEST_CONVERGENCE_IN_DUAL_TO_RANGE) {\n if (boundaryOp.totalGlobalDofCountInDomains() !=\n boundaryOp.totalGlobalDofCountInDualsToRanges())\n throw std::invalid_argument(\"DefaultIterativeSolver::Impl::Impl(): \"\n \"non-square system provided\");\n solverWrapper.reset(new BelosSolverWrapper(\n Teuchos::rcp>(\n boundaryOp.weakForm())));\n } else if (mode == ConvergenceTestMode::TEST_CONVERGENCE_IN_RANGE) {\n if (boundaryOp.totalGlobalDofCountInDomains() !=\n boundaryOp.totalGlobalDofCountInRanges())\n throw std::invalid_argument(\"DefaultIterativeSolver::Impl::Impl(): \"\n \"non-square system provided\");\n\n // Construct a block-diagonal operator composed of pseudoinverses\n // for appropriate spaces\n BlockedOperatorStructure pinvIdStructure;\n size_t rowCount = boundaryOp.rowCount();\n size_t columnCount = boundaryOp.columnCount();\n for (size_t row = 0; row < rowCount; ++row) {\n // Find the first non-zero block in row #row and retrieve its context\n shared_ptr> context;\n for (int col = 0; col < columnCount; ++col)\n if (boundaryOp.block(row, col).context()) {\n context = boundaryOp.block(row, col).context();\n break;\n }\n assert(context);\n\n BoundaryOperator id = identityOperator(\n context, boundaryOp.range(row), boundaryOp.range(row),\n boundaryOp.dualToRange(row));\n pinvIdStructure.setBlock(row, row, pseudoinverse(id));\n }\n pinvId = BlockedBoundaryOperator(\n pinvIdStructure);\n\n shared_ptr> totalBoundaryOp =\n boost::make_shared>(\n boost::get(pinvId).weakForm(), boundaryOp.weakForm());\n solverWrapper.reset(new BelosSolverWrapper(\n Teuchos::rcp>(\n totalBoundaryOp)));\n } else\n throw std::invalid_argument(\n \"DefaultIterativeSolver::DefaultIterativeSolver(): \"\n \"invalid convergence test mode\");\n }\n\n boost::variant,\n BlockedBoundaryOperator> op;\n ConvergenceTestMode::Mode mode;\n boost::scoped_ptr> solverWrapper;\n boost::variant,\n BlockedBoundaryOperator> pinvId;\n};\n\n/** \\endcond */\n\ntemplate \nDefaultIterativeSolver::DefaultIterativeSolver(\n const BoundaryOperator &boundaryOp,\n ConvergenceTestMode::Mode mode)\n : m_impl(new Impl(boundaryOp, mode)) {}\n\ntemplate \nDefaultIterativeSolver::DefaultIterativeSolver(\n const BlockedBoundaryOperator &boundaryOp,\n ConvergenceTestMode::Mode mode)\n : m_impl(new Impl(boundaryOp, mode)) {}\n\ntemplate \nDefaultIterativeSolver::~DefaultIterativeSolver() {}\n\ntemplate \nvoid DefaultIterativeSolver::setPreconditioner(\n const Preconditioner &preconditioner) {\n m_impl->solverWrapper->setPreconditioner(preconditioner.get());\n}\n\ntemplate \nvoid DefaultIterativeSolver::initializeSolver(\n const Teuchos::RCP ¶mList) {\n m_impl->solverWrapper->initializeSolver(paramList);\n}\n\ntemplate \nvoid DefaultIterativeSolver::initializeSolver(\n const Teuchos::RCP ¶mList,\n const Preconditioner &preconditioner) {\n m_impl->solverWrapper->setPreconditioner(preconditioner.get());\n m_impl->solverWrapper->initializeSolver(paramList);\n}\n\ntemplate \nSolution\nDefaultIterativeSolver::solveImplNonblocked(\n const GridFunction &rhs) const {\n typedef BoundaryOperator BoundaryOp;\n typedef typename ScalarTraits::RealType MagnitudeType;\n typedef Thyra::MultiVectorBase TrilinosVector;\n\n const BoundaryOp *boundaryOp = boost::get(&m_impl->op);\n if (!boundaryOp)\n throw std::logic_error(\n \"DefaultIterativeSolver::solve(): for solvers constructed \"\n \"from a BlockedBoundaryOperator the other solve() overload \"\n \"must be used\");\n Solver::checkConsistency(*boundaryOp, rhs,\n m_impl->mode);\n\n // Construct rhs vector\n Vector projectionsVector(\n rhs.projections(boundaryOp->dualToRange()));\n Teuchos::RCP rhsVector;\n if (m_impl->mode == ConvergenceTestMode::TEST_CONVERGENCE_IN_DUAL_TO_RANGE)\n rhsVector = Teuchos::rcpFromRef(projectionsVector);\n else {\n const size_t size = boundaryOp->range()->globalDofCount();\n rhsVector.reset(new Vector(size));\n boost::get(m_impl->pinvId).weakForm()->apply(\n Thyra::NOTRANS, projectionsVector, rhsVector.ptr(), 1., 0.);\n }\n\n // Construct solution vector\n arma::Col armaSolution(rhsVector->range()->dim());\n armaSolution.fill(static_cast(0.));\n Teuchos::RCP solutionVector =\n wrapInTrilinosVector(armaSolution);\n\n // Get number of threads\n Fiber::ParallelizationOptions parallelOptions =\n boundaryOp->context()->assemblyOptions().parallelizationOptions();\n int maxThreadCount = 1;\n if (!parallelOptions.isOpenClEnabled()) {\n if (parallelOptions.maxThreadCount() == ParallelizationOptions::AUTO)\n maxThreadCount = tbb::task_scheduler_init::automatic;\n else\n maxThreadCount = parallelOptions.maxThreadCount();\n }\n\n // Solve\n Thyra::SolveStatus status;\n {\n // Initialize TBB threads here (to prevent their construction and\n // destruction on every matrix-vector multiplication)\n tbb::task_scheduler_init scheduler(maxThreadCount);\n status = m_impl->solverWrapper->solve(Thyra::NOTRANS, *rhsVector,\n solutionVector.ptr());\n }\n\n // Construct grid function and return\n return Solution(\n GridFunction(\n boundaryOp->context(), boundaryOp->domain(), armaSolution),\n status);\n}\n\ntemplate \nBlockedSolution\nDefaultIterativeSolver::solveImplBlocked(\n const std::vector> &rhs) const {\n typedef BlockedBoundaryOperator BoundaryOp;\n typedef typename ScalarTraits::RealType MagnitudeType;\n typedef Thyra::MultiVectorBase TrilinosVector;\n\n const BoundaryOp *boundaryOp = boost::get(&m_impl->op);\n if (!boundaryOp)\n throw std::logic_error(\n \"DefaultIterativeSolver::solve(): for solvers constructed \"\n \"from a (non-blocked) BoundaryOperator the other solve() overload \"\n \"must be used\");\n std::vector> canonicalRhs =\n Solver::canonicalizeBlockedRhs(\n *boundaryOp, rhs, m_impl->mode);\n // Shouldn't be needed, but better safe than sorry...\n Solver::checkConsistency(\n *boundaryOp, canonicalRhs, m_impl->mode);\n\n // Currently we only support convergence testing in space dual to range.\n\n // Construct the right-hand-side vector\n arma::Col armaProjections(\n boundaryOp->totalGlobalDofCountInDualsToRanges());\n for (size_t i = 0, start = 0; i < canonicalRhs.size(); ++i) {\n const arma::Col &chunkProjections =\n canonicalRhs[i].projections(boundaryOp->dualToRange(i));\n size_t chunkSize = chunkProjections.n_rows;\n armaProjections.rows(start, start + chunkSize - 1) = chunkProjections;\n start += chunkSize;\n }\n\n Vector projectionsVector(armaProjections);\n Teuchos::RCP rhsVector;\n if (m_impl->mode == ConvergenceTestMode::TEST_CONVERGENCE_IN_DUAL_TO_RANGE)\n rhsVector = Teuchos::rcpFromRef(projectionsVector);\n else {\n const size_t rhsSize = boundaryOp->totalGlobalDofCountInRanges();\n rhsVector.reset(new Vector(rhsSize));\n boost::get(m_impl->pinvId).weakForm()->apply(\n Thyra::NOTRANS, projectionsVector, rhsVector.ptr(), 1., 0.);\n }\n\n // Initialize the solution vector\n size_t solutionSize = 0;\n for (size_t i = 0; i < canonicalRhs.size(); ++i)\n solutionSize += boundaryOp->domain(i)->globalDofCount();\n arma::Col armaSolution(solutionSize);\n armaSolution.fill(static_cast(0.));\n Teuchos::RCP solutionVector =\n wrapInTrilinosVector(armaSolution);\n\n // Get context of the first non-empty operator\n size_t rowCount = boundaryOp->rowCount();\n shared_ptr> context;\n for (size_t row = 0; row < rowCount; ++row)\n if (boundaryOp->block(row, 0).context()) {\n context = boundaryOp->block(row, 0).context();\n break;\n }\n assert(context);\n\n // Get number of threads\n Fiber::ParallelizationOptions parallelOptions =\n context->assemblyOptions().parallelizationOptions();\n int maxThreadCount = 1;\n if (!parallelOptions.isOpenClEnabled()) {\n if (parallelOptions.maxThreadCount() == ParallelizationOptions::AUTO)\n maxThreadCount = tbb::task_scheduler_init::automatic;\n else\n maxThreadCount = parallelOptions.maxThreadCount();\n }\n\n // Solve\n Thyra::SolveStatus status;\n {\n // Initialize TBB threads here (to prevent their construction and\n // destruction on every matrix-vector multiplication)\n tbb::task_scheduler_init scheduler(maxThreadCount);\n status = m_impl->solverWrapper->solve(Thyra::NOTRANS, *rhsVector,\n solutionVector.ptr());\n }\n\n // Convert chunks of the solution vector into grid functions\n std::vector> solutionFunctions;\n Solver::constructBlockedGridFunction(\n armaSolution, *boundaryOp, solutionFunctions);\n\n // Return solution\n return BlockedSolution(solutionFunctions,\n status);\n}\n\nFIBER_INSTANTIATE_CLASS_TEMPLATED_ON_BASIS_AND_RESULT(DefaultIterativeSolver);\n\n} // namespace Bempp\n\n#endif // WITH_TRILINOS\n", "meta": {"hexsha": "1d6ac5616bfc3f1a7928243165f7932eb123cde6", "size": 16761, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/linalg/default_iterative_solver.cpp", "max_stars_repo_name": "mdavezac/bempp", "max_stars_repo_head_hexsha": "bc573062405bda107d1514e40b6153a8350d5ab5", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "lib/linalg/default_iterative_solver.cpp", "max_issues_repo_name": "mdavezac/bempp", "max_issues_repo_head_hexsha": "bc573062405bda107d1514e40b6153a8350d5ab5", "max_issues_repo_licenses": ["BSL-1.0"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, 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YES\n2. NO", "lm_q1_score": 0.519521321952093, "lm_q2_score": 0.39981164073979497, "lm_q1q2_score": 0.20771067212897357}} {"text": "/* decision_tree.cc -*- C++ -*-\n Jeremy Barnes, 22 March 2004\n Copyright (c) 2004 Jeremy Barnes. All rights reserved.\n $Source$\n\n Implementation of the decision tree.\n*/\n\n#include \"decision_tree.h\"\n#include \"classifier_persist_impl.h\"\n#include \n#include \n#include \n#include \"jml/utils/vector_utils.h\"\n#include \"config_impl.h\"\n#include \"jml/utils/exc_assert.h\"\n#include \"jml/utils/smart_ptr_utils.h\"\n\n\nusing namespace std;\nusing namespace DB;\n\n\n\nnamespace ML {\n\n\n/*****************************************************************************/\n/* DECISION_TREE */\n/*****************************************************************************/\n\nDecision_Tree::Decision_Tree()\n : encoding(OE_PROB), optimized_(false)\n{\n}\n\nDecision_Tree::\nDecision_Tree(DB::Store_Reader & store,\n const std::shared_ptr & fs)\n : optimized_(false)\n{\n throw Exception(\"Decision_Tree constructor(reconst): not implemented\");\n}\n \nDecision_Tree::\nDecision_Tree(std::shared_ptr feature_space,\n const Feature & predicted)\n : Classifier_Impl(feature_space, predicted),\n encoding(OE_PROB),\n optimized_(false)\n{\n}\n \nDecision_Tree::\n~Decision_Tree()\n{\n}\n \nvoid\nDecision_Tree::\nswap(Decision_Tree & other)\n{\n Classifier_Impl::swap(other);\n std::swap(tree, other.tree);\n std::swap(encoding, other.encoding);\n std::swap(optimized_, other.optimized_);\n}\n\nnamespace {\n\nstruct StandardGetFeatures {\n StandardGetFeatures(const Feature_Set & features)\n : features(features)\n {\n }\n\n const Feature_Set & features;\n\n Split::Weights operator () (const Split & split) const\n {\n return split.apply(features);\n }\n};\n\nstruct OptimizedGetFeatures {\n OptimizedGetFeatures(const float * features)\n : features(features)\n {\n }\n\n const float * features;\n\n JML_ALWAYS_INLINE Split::Weights operator () (const Split & split) const\n {\n return split.apply(features);\n }\n \n};\n\nstruct AccumResults {\n explicit AccumResults(double * accum, int nl, double weight)\n : accum(accum), nl(nl), weight(weight)\n {\n }\n\n double * accum;\n int nl;\n double weight;\n\n JML_ALWAYS_INLINE\n void operator () (const Label_Dist & dist, float weight1)\n {\n if (JML_LIKELY(nl == 2)) {\n double factor = weight1 * weight;\n accum[0] += dist[0] * factor;\n accum[1] += dist[1] * factor;\n return;\n }\n\n for (unsigned i = 0; i < nl; ++i)\n accum[i] += dist[i] * weight1 * weight;\n }\n};\n\nstruct DistResults {\n explicit DistResults(double * accum, int nl)\n : accum(accum), nl(nl)\n {\n std::fill(accum, accum + nl, 0.0);\n }\n\n double * accum;\n int nl;\n\n JML_ALWAYS_INLINE\n void operator () (const Label_Dist & dist, float weight)\n {\n for (unsigned i = 0; i < nl; ++i)\n accum[i] += dist[i] * weight;\n }\n \n operator Label_Dist () const { return Label_Dist(accum, accum + nl); }\n};\n\nstruct LabelResults {\n explicit LabelResults(int label)\n : label(label), result(0.0)\n {\n }\n\n JML_ALWAYS_INLINE\n void operator () (const Label_Dist & dist, float weight)\n {\n result += weight * dist[label];\n }\n\n int label;\n double result;\n\n operator double () const { return result; }\n};\n\n} // file scope\n\nfloat\nDecision_Tree::\npredict(int label, const Feature_Set & features,\n PredictionContext * context) const\n{\n StandardGetFeatures get_features(features);\n LabelResults results(label);\n\n predict_recursive_impl(get_features, results, tree.root);\n return results;\n}\n\nLabel_Dist\nDecision_Tree::\npredict(const Feature_Set & features,\n PredictionContext * context) const\n{\n StandardGetFeatures get_features(features);\n int nl = label_count();\n double accum[nl];\n DistResults results(accum, nl);\n\n predict_recursive_impl(get_features, results, tree.root);\n return results;\n}\n\nbool\nDecision_Tree::\noptimization_supported() const\n{\n return true;\n}\n\nbool\nDecision_Tree::\npredict_is_optimized() const\n{\n return optimized_;\n}\n\nbool\nDecision_Tree::\noptimize_impl(Optimization_Info & info)\n{\n optimize_recursive(info, tree.root);\n optimized_ = true;\n return true;\n}\n\nvoid\nDecision_Tree::\noptimize_recursive(Optimization_Info & info,\n const Tree::Ptr & ptr)\n{\n if (!ptr) return;\n if (!ptr.node()) return;\n\n Tree::Node & node = *ptr.node();\n\n node.split.optimize(info);\n optimize_recursive(info, node.child_true);\n optimize_recursive(info, node.child_false);\n optimize_recursive(info, node.child_missing);\n}\n\nLabel_Dist\nDecision_Tree::\noptimized_predict_impl(const float * features,\n const Optimization_Info & info,\n PredictionContext * context) const\n{\n OptimizedGetFeatures get_features(features);\n\n int nl = label_count();\n double accum[nl];\n DistResults results(accum, nl);\n\n predict_recursive_impl(get_features, results, tree.root);\n return results;\n}\n\nvoid\nDecision_Tree::\noptimized_predict_impl(const float * features,\n const Optimization_Info & info,\n double * accum,\n double weight,\n PredictionContext * context) const\n{\n OptimizedGetFeatures get_features(features);\n AccumResults results(accum, label_count(), weight);\n\n predict_recursive_impl(get_features, results, tree.root);\n}\n\nfloat\nDecision_Tree::\noptimized_predict_impl(int label,\n const float * features,\n const Optimization_Info & info,\n PredictionContext * context) const\n{\n OptimizedGetFeatures get_features(features);\n LabelResults results(label);\n\n predict_recursive_impl(get_features, results, tree.root);\n return results;\n}\n\ntemplate\nvoid\nDecision_Tree::\npredict_recursive_impl(const GetFeatures & get_features,\n Results & results,\n const Tree::Ptr & ptr,\n double weight) const\n{\n if (!ptr) return;\n\n if (!ptr.node()) {\n results(ptr.leaf()->pred, weight);\n return;\n }\n\n const Tree::Node & node = *ptr.node();\n \n Split::Weights weights = get_features(node.split);\n \n /* Go down all of the edges that we need to for this example. */\n if (weights[true] > 0.0)\n predict_recursive_impl(get_features, results, node.child_true,\n weights[true]);\n if (weights[false] > 0.0)\n predict_recursive_impl(get_features, results, node.child_false,\n weights[false]);\n if (weights[MISSING] > 0.0)\n predict_recursive_impl(get_features, results, node.child_missing,\n weights[MISSING]);\n}\n\nstd::string\nprintLabels(const distribution & dist)\n{\n string result = \"\";\n for (unsigned i = 0; i < dist.size(); ++i)\n if (dist[i] != 0.0) result += format(\" %d/%.3f\", i, dist[i]);\n return result;\n}\n\nstd::string\nprintLabels(const Tree::Ptr & ptr)\n{\n return printLabels(ptr.pred());\n}\n\nstd::string\nDecision_Tree::\nprint() const\n{\n string result = \"Decision Tree: \";\n float total_weight = tree.root.examples();\n if (tree.root.node()) total_weight = tree.root.examples();\n result = format(\"Decision Tree: (weight = %.2f, cov = %.2f%%) \",\n total_weight, 100.0);\n result += printLabels(tree.root) + \"\\n\";\n result += print_recursive(0, tree.root, total_weight);\n return result;\n}\n\nstd::string\nDecision_Tree::\nsummary() const\n{\n if (!tree.root)\n return \"NULL\";\n \n float total_weight = 0.0;\n if (tree.root.node()) total_weight = tree.root.node()->examples;\n \n if (tree.root.node()) {\n Tree::Node & n = *tree.root.node();\n float cov = n.examples / total_weight;\n float z_adj = n.z / cov;\n return \"Root: \" + n.split.print(*feature_space())\n + format(\" (z = %.4f)\", z_adj);\n }\n else {\n string result = \"leaf: \";\n Tree::Leaf & l = *tree.root.leaf();\n const distribution & dist = l.pred;\n for (unsigned i = 0; i < dist.size(); ++i)\n if (dist[i] != 0.0) result += format(\" %d/%.3f\", i, dist[i]);\n return result;\n }\n}\n\nstring\nDecision_Tree::\nprint_recursive(int level, const Tree::Ptr & ptr,\n float total_weight) const\n{\n string spaces(level * 4, ' ');\n if (ptr.node()) {\n Tree::Node & n = *ptr.node();\n string result;\n float z_cov = n.examples / total_weight;\n float z_adj = n.z / z_cov;\n\n if (n.child_false && n.child_false.examples() > 0) {\n float cov = n.child_false.examples() / total_weight;\n result += spaces \n + format(\" %s (z = %.4f, weight = %.2f, cov = %.2f%%) \",\n n.split.print(*feature_space(), false).c_str(),\n z_adj, n.child_false.examples(), cov * 100.0);\n result += printLabels(n.child_false) + \"\\n\";\n result += print_recursive(level + 1, n.child_false, total_weight);\n }\n\n if (n.child_true && n.child_true.examples() > 0) {\n float cov = n.child_true.examples() / total_weight;\n result += spaces \n + format(\" %s (z = %.4f, weight = %.2f, cov = %.2f%%) \",\n n.split.print(*feature_space(), true).c_str(),\n z_adj, n.child_true.examples(), cov * 100.0);\n result += printLabels(n.child_true) + \"\\n\";\n result += print_recursive(level + 1, n.child_true, total_weight);\n }\n\n if (n.child_missing && n.child_missing.examples() > 0) {\n float cov = n.child_missing.examples() / total_weight;\n result += spaces \n + format(\" %s (z = %.4f, weight = %.2f, cov = %.2f%%) \",\n n.split.print(*feature_space(), MISSING).c_str(),\n z_adj, n.child_missing.examples(), cov * 100.0);\n result += printLabels(n.child_missing) + \"\\n\";\n result += print_recursive(level + 1, n.child_missing, total_weight);\n }\n return result;\n }\n else if (ptr.leaf()) {\n return \"\";\n string result = spaces + \"leaf \";\n Tree::Leaf & l = *ptr.leaf();\n const distribution & dist = l.pred;\n float cov = l.examples / total_weight;\n result += format(\" (weight = %.2f, cov = %.2f%%) \",\n l.examples, cov * 100.0);\n result += printLabels(dist);\n result += \"\\n\";\n return result;\n }\n else return spaces + \"NULL\";\n}\n\nExplanation\nDecision_Tree::\nexplain(const Feature_Set & feature_set,\n int label,\n double weight,\n PredictionContext * context) const\n{\n Explanation result(feature_space(), weight); \n\n explain_recursive(result, feature_set, label, weight, tree.root, 0);\n\n return result;\n}\n\nvoid\nDecision_Tree::\nexplain_recursive(Explanation & explanation,\n const Feature_Set & feature_set,\n int label,\n double weight,\n const Tree::Ptr & ptr,\n const Tree::Node * parent) const\n{\n StandardGetFeatures get_features(feature_set);\n int nl = label_count();\n\n if (label < 0 || label >= nl)\n throw Exception(\"Decision_Tree::explain(): no label\");\n\n if (!ptr) return;\n\n if (!ptr.node()) {\n // It's a leaf; we give the difference to the parent's feature\n if (parent) {\n explanation.feature_weights[parent->split.feature()]\n += weight\n * (ptr.leaf()->pred.at(label) - parent->pred.at(label));\n }\n else {\n // No parent, therefore it's all bias\n explanation.bias += weight * (ptr.leaf()->pred.at(label));\n }\n\n return;\n }\n\n const Tree::Node & node = *ptr.node();\n \n Split::Weights weights = get_features(node.split);\n\n // Accumulate the weight for this split\n if (!parent)\n explanation.bias += weight * node.pred.at(label);\n else\n explanation.feature_weights[parent->split.feature()]\n += weight\n * (node.pred.at(label) - parent->pred.at(label));\n \n /* Go down all of the edges that we need to for this example. */\n if (weights[true] > 0.0)\n explain_recursive(explanation, feature_set, label,\n weight * weights[true],\n node.child_true, &node);\n\n if (weights[false] > 0.0)\n explain_recursive(explanation, feature_set, label,\n weight * weights[false],\n node.child_false, &node);\n\n if (weights[MISSING] > 0.0)\n explain_recursive(explanation, feature_set, label,\n weight * weights[MISSING],\n node.child_missing, &node);\n}\n\nDisjunction\nDecision_Tree::\nto_rules() const\n{\n Disjunction result;\n result.feature_space = feature_space();\n std::vector > path;\n\n to_rules_recursive(result, path, tree.root);\n\n ExcAssert(path.empty());\n\n return result;\n}\n\nvoid\nDecision_Tree::\nto_rules_recursive(Disjunction & result,\n std::vector > & path,\n const Tree::Ptr & ptr) const\n{\n if (ptr.examples() == 0.0) return;\n\n if (ptr.node()) {\n const Tree::Node & node = *ptr.node();\n \n {\n path.push_back(make_sp(new Predicate(node.split, false)));\n to_rules_recursive(result, path, node.child_false);\n path.pop_back();\n }\n\n {\n path.push_back(make_sp(new Predicate(node.split, true)));\n to_rules_recursive(result, path, node.child_true);\n path.pop_back();\n }\n\n {\n path.push_back(make_sp(new Predicate(node.split, MISSING)));\n to_rules_recursive(result, path, node.child_missing);\n path.pop_back();\n }\n }\n else if (ptr.leaf()) {\n std::shared_ptr > c\n (new Conjunction());\n c->predicates = path;\n c->outcome = *ptr.leaf();\n result.predicates.push_back(c);\n }\n}\n\nnamespace {\n\nvoid all_features_recursive(const Tree::Ptr & ptr,\n vector & result)\n{\n if (ptr.node()) {\n Tree::Node & n = *ptr.node();\n result.push_back(n.split.feature());\n\n all_features_recursive(n.child_true, result);\n all_features_recursive(n.child_false, result);\n all_features_recursive(n.child_missing, result);\n }\n}\n\n} // file scope\n\nstd::vector\nDecision_Tree::\nall_features() const\n{\n std::vector result;\n all_features_recursive(tree.root, result);\n make_vector_set(result);\n return result;\n}\n\nOutput_Encoding\nDecision_Tree::\noutput_encoding() const\n{\n return encoding;\n}\n\nvoid\nDecision_Tree::\nserialize(DB::Store_Writer & store) const\n{\n store << string(\"DECISION_TREE\");\n store << compact_size_t(3); // version\n store << compact_size_t(label_count());\n feature_space_->serialize(store, predicted_);\n tree.serialize(store, *feature_space());\n store << encoding;\n store << compact_size_t(12345); // end marker\n}\n\nvoid\nDecision_Tree::\nreconstitute(DB::Store_Reader & store,\n const std::shared_ptr & feature_space)\n{\n string id;\n store >> id;\n\n if (id != \"DECISION_TREE\")\n throw Exception(\"Decision_Tree::reconstitute: read bad ID '\"\n + id + \"'\");\n\n compact_size_t version(store);\n \n switch (version) {\n case 1: {\n compact_size_t label_count(store);\n Classifier_Impl::init(feature_space, MISSING_FEATURE, label_count);\n tree.reconstitute(store, *feature_space);\n break;\n }\n case 2:\n case 3: {\n compact_size_t label_count(store);\n feature_space->reconstitute(store, predicted_);\n Classifier_Impl::init(feature_space, predicted_);\n tree.reconstitute(store, *feature_space);\n if (version >= 3)\n store >> encoding;\n else encoding = OE_PROB;\n break;\n }\n default:\n throw Exception(\"Decision tree: Attempt to reconstitute tree of \"\n \"unknown version \" + ostream_format(version.size_));\n }\n\n compact_size_t marker(store);\n if (marker != 12345)\n throw Exception(\"Decision_Tree::reconstitute: read bad marker at end\");\n\n optimized_ = false;\n}\n \nstd::string\nDecision_Tree::\nclass_id() const\n{\n return \"DECISION_TREE\";\n}\n\nDecision_Tree *\nDecision_Tree::\nmake_copy() const\n{\n return new Decision_Tree(*this);\n}\n\n/*****************************************************************************/\n/* REGISTRATION */\n/*****************************************************************************/\n\nnamespace {\n\nRegister_Factory REGISTER(\"DECISION_TREE\");\n\n} // file scope\n\n} // namespace ML\n\n", "meta": {"hexsha": "299b93492a91de024e746566944161fd04661616", "size": 17513, "ext": "cc", "lang": "C++", "max_stars_repo_path": "jml/boosting/decision_tree.cc", "max_stars_repo_name": "etnrlz/rtbkit", "max_stars_repo_head_hexsha": "0d9cd9e2ee2d7580a27453ad0a2d815410d87091", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 737.0, "max_stars_repo_stars_event_min_datetime": "2015-01-04T01:40:46.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-07T10:09:23.000Z", "max_issues_repo_path": "jml/boosting/decision_tree.cc", "max_issues_repo_name": "TuanTranEngineer/rtbkit", "max_issues_repo_head_hexsha": "502d06acc3f8d90438946b6ae742190f2f4b4fbb", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 56.0, "max_issues_repo_issues_event_min_datetime": "2015-01-05T16:01:03.000Z", "max_issues_repo_issues_event_max_datetime": "2020-06-22T19:02:37.000Z", "max_forks_repo_path": "jml/boosting/decision_tree.cc", "max_forks_repo_name": "TuanTranEngineer/rtbkit", "max_forks_repo_head_hexsha": "502d06acc3f8d90438946b6ae742190f2f4b4fbb", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 329.0, "max_forks_repo_forks_event_min_datetime": "2015-01-01T06:54:27.000Z", "max_forks_repo_forks_event_max_datetime": "2022-02-12T22:21:02.000Z", "avg_line_length": 26.375, "max_line_length": 80, "alphanum_fraction": 0.5756866328, "num_tokens": 3882, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5195213219520929, "lm_q2_score": 0.399811640739795, "lm_q1q2_score": 0.20771067212897354}} {"text": "// Boost.Geometry - gis-projections (based on PROJ4)\n\n// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.\n\n// This file was modified by Oracle on 2017, 2018.\n// Modifications copyright (c) 2017-2018, Oracle and/or its affiliates.\n// Contributed and/or modified by Adam Wulkiewicz, on behalf of Oracle.\n\n// Use, modification and distribution is subject to the Boost Software License,\n// Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at\n// http://www.boost.org/LICENSE_1_0.txt)\n\n// This file is converted from PROJ4, http://trac.osgeo.org/proj\n// PROJ4 is originally written by Gerald Evenden (then of the USGS)\n// PROJ4 is maintained by Frank Warmerdam\n// PROJ4 is converted to Boost.Geometry by Barend Gehrels\n\n// Last updated version of proj: 5.0.0\n\n// Original copyright notice:\n\n// Permission is hereby granted, free of charge, to any person obtaining a\n// copy of this software and associated documentation files (the \"Software\"),\n// to deal in the Software without restriction, including without limitation\n// the rights to use, copy, modify, merge, publish, distribute, sublicense,\n// and/or sell copies of the Software, and to permit persons to whom the\n// Software is furnished to do so, subject to the following conditions:\n\n// The above copyright notice and this permission notice shall be included\n// in all copies or substantial portions of the Software.\n\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS\n// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n// THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n// DEALINGS IN THE SOFTWARE.\n\n#ifndef BOOST_GEOMETRY_PROJECTIONS_FOUC_S_HPP\n#define BOOST_GEOMETRY_PROJECTIONS_FOUC_S_HPP\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n\nnamespace boost { namespace geometry\n{\n\nnamespace projections\n{\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail { namespace fouc_s\n {\n\n static const int max_iter = 10;\n static const double loop_tol = 1e-7;\n\n template \n struct par_fouc_s\n {\n T n, n1;\n };\n\n // template class, using CRTP to implement forward/inverse\n template \n struct base_fouc_s_spheroid\n : public base_t_fi, T, Parameters>\n {\n par_fouc_s m_proj_parm;\n\n inline base_fouc_s_spheroid(const Parameters& par)\n : base_t_fi, T, Parameters>(*this, par)\n {}\n\n // FORWARD(s_forward) spheroid\n // Project coordinates from geographic (lon, lat) to cartesian (x, y)\n inline void fwd(T const& lp_lon, T const& lp_lat, T& xy_x, T& xy_y) const\n {\n T t;\n\n t = cos(lp_lat);\n xy_x = lp_lon * t / (this->m_proj_parm.n + this->m_proj_parm.n1 * t);\n xy_y = this->m_proj_parm.n * lp_lat + this->m_proj_parm.n1 * sin(lp_lat);\n }\n\n // INVERSE(s_inverse) spheroid\n // Project coordinates from cartesian (x, y) to geographic (lon, lat)\n inline void inv(T const& xy_x, T const& xy_y, T& lp_lon, T& lp_lat) const\n {\n static T const half_pi = detail::half_pi();\n\n T V;\n int i;\n\n if (this->m_proj_parm.n != 0.0) {\n lp_lat = xy_y;\n for (i = max_iter; i ; --i) {\n lp_lat -= V = (this->m_proj_parm.n * lp_lat + this->m_proj_parm.n1 * sin(lp_lat) - xy_y ) /\n (this->m_proj_parm.n + this->m_proj_parm.n1 * cos(lp_lat));\n if (fabs(V) < loop_tol)\n break;\n }\n if (!i)\n lp_lat = xy_y < 0. ? -half_pi : half_pi;\n } else\n lp_lat = aasin(xy_y);\n V = cos(lp_lat);\n lp_lon = xy_x * (this->m_proj_parm.n + this->m_proj_parm.n1 * V) / V;\n }\n\n static inline std::string get_name()\n {\n return \"fouc_s_spheroid\";\n }\n\n };\n\n // Foucaut Sinusoidal\n template \n inline void setup_fouc_s(Params const& params, Parameters& par, par_fouc_s& proj_parm)\n {\n proj_parm.n = pj_get_param_f(params, \"n\", srs::dpar::n);\n if ((proj_parm.n < 0.) || (proj_parm.n > 1.))\n BOOST_THROW_EXCEPTION( projection_exception(error_n_out_of_range) );\n\n proj_parm.n1 = 1. - proj_parm.n;\n par.es = 0;\n }\n\n }} // namespace detail::fouc_s\n #endif // doxygen\n\n /*!\n \\brief Foucaut Sinusoidal projection\n \\ingroup projections\n \\tparam Geographic latlong point type\n \\tparam Cartesian xy point type\n \\tparam Parameters parameter type\n \\par Projection characteristics\n - Pseudocylindrical\n - Spheroid\n \\par Projection parameters\n - n (real)\n \\par Example\n \\image html ex_fouc_s.gif\n */\n template \n struct fouc_s_spheroid : public detail::fouc_s::base_fouc_s_spheroid\n {\n template \n inline fouc_s_spheroid(Params const& params, Parameters const& par)\n : detail::fouc_s::base_fouc_s_spheroid(par)\n {\n detail::fouc_s::setup_fouc_s(params, this->m_par, this->m_proj_parm);\n }\n };\n\n #ifndef DOXYGEN_NO_DETAIL\n namespace detail\n {\n\n // Static projection\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_STATIC_PROJECTION(srs::spar::proj_fouc_s, fouc_s_spheroid, fouc_s_spheroid)\n\n // Factory entry(s)\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_ENTRY_FI(fouc_s_entry, fouc_s_spheroid)\n \n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_BEGIN(fouc_s_init)\n {\n BOOST_GEOMETRY_PROJECTIONS_DETAIL_FACTORY_INIT_ENTRY(fouc_s, fouc_s_entry);\n }\n\n } // namespace detail\n #endif // doxygen\n\n} // namespace projections\n\n}} // namespace boost::geometry\n\n#endif // BOOST_GEOMETRY_PROJECTIONS_FOUC_S_HPP\n\n", "meta": {"hexsha": "f6a8c5ed4b51916e50457f88ba22e86629558fb4", "size": 7196, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/boost/geometry/srs/projections/proj/fouc_s.hpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 12278.0, "max_stars_repo_stars_event_min_datetime": "2015-01-29T17:11:33.000Z", "max_stars_repo_stars_event_max_datetime": "2022-03-31T21:12:00.000Z", "max_issues_repo_path": "3rdParty/boost/1.71.0/boost/geometry/srs/projections/proj/fouc_s.hpp", "max_issues_repo_name": "rajeev02101987/arangodb", "max_issues_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max_issues_repo_licenses": ["Apache-2.0"], "max_issues_count": 9469.0, "max_issues_repo_issues_event_min_datetime": "2015-01-30T05:33:07.000Z", "max_issues_repo_issues_event_max_datetime": "2022-03-31T16:17:21.000Z", "max_forks_repo_path": "3rdParty/boost/1.71.0/boost/geometry/srs/projections/proj/fouc_s.hpp", "max_forks_repo_name": "rajeev02101987/arangodb", "max_forks_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max_forks_repo_licenses": ["Apache-2.0"], "max_forks_count": 892.0, "max_forks_repo_forks_event_min_datetime": "2015-01-29T16:26:19.000Z", "max_forks_repo_forks_event_max_datetime": "2022-03-20T07:44:30.000Z", "avg_line_length": 38.2765957447, "max_line_length": 119, "alphanum_fraction": 0.6133963313, "num_tokens": 1710, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO\n\n", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.34158248603300034, "lm_q1q2_score": 0.2075670962222124}} {"text": "/*\n * Copyright (c) 2015 Cryptonomex, Inc., and contributors.\n *\n * The MIT License\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n * THE SOFTWARE.\n */\n#include \n#include \n#include \n\nnamespace graphene { namespace chain {\n typedef boost::multiprecision::uint128_t uint128_t;\n typedef boost::multiprecision::int128_t int128_t;\n\n bool operator == ( const price& a, const price& b )\n {\n if( std::tie( a.base.asset_id, a.quote.asset_id ) != std::tie( b.base.asset_id, b.quote.asset_id ) )\n return false;\n\n const auto amult = uint128_t( b.quote.amount.value ) * a.base.amount.value;\n const auto bmult = uint128_t( a.quote.amount.value ) * b.base.amount.value;\n\n return amult == bmult;\n }\n\n bool operator < ( const price& a, const price& b )\n {\n if( a.base.asset_id < b.base.asset_id ) return true;\n if( a.base.asset_id > b.base.asset_id ) return false;\n if( a.quote.asset_id < b.quote.asset_id ) return true;\n if( a.quote.asset_id > b.quote.asset_id ) return false;\n\n const auto amult = uint128_t( b.quote.amount.value ) * a.base.amount.value;\n const auto bmult = uint128_t( a.quote.amount.value ) * b.base.amount.value;\n\n return amult < bmult;\n }\n\n asset operator * ( const asset& a, const price& b )\n {\n if( a.asset_id == b.base.asset_id )\n {\n FC_ASSERT( b.base.amount.value > 0 );\n uint128_t result = (uint128_t(a.amount.value) * b.quote.amount.value)/b.base.amount.value;\n FC_ASSERT( result <= GRAPHENE_MAX_SHARE_SUPPLY );\n return asset( result.convert_to(), b.quote.asset_id );\n }\n else if( a.asset_id == b.quote.asset_id )\n {\n FC_ASSERT( b.quote.amount.value > 0 );\n uint128_t result = (uint128_t(a.amount.value) * b.base.amount.value)/b.quote.amount.value;\n FC_ASSERT( result <= GRAPHENE_MAX_SHARE_SUPPLY );\n return asset( result.convert_to(), b.base.asset_id );\n }\n FC_THROW_EXCEPTION( fc::assert_exception, \"invalid asset * price\", (\"asset\",a)(\"price\",b) );\n }\n\n asset asset::multiply_and_round_up( const price& b )const\n {\n const asset& a = *this;\n if( a.asset_id == b.base.asset_id )\n {\n FC_ASSERT( b.base.amount.value > 0 );\n uint128_t result = (uint128_t(a.amount.value) * b.quote.amount.value + b.base.amount.value - 1)/b.base.amount.value;\n FC_ASSERT( result <= GRAPHENE_MAX_SHARE_SUPPLY );\n return asset( result.convert_to(), b.quote.asset_id );\n }\n else if( a.asset_id == b.quote.asset_id )\n {\n FC_ASSERT( b.quote.amount.value > 0 );\n uint128_t result = (uint128_t(a.amount.value) * b.base.amount.value + b.quote.amount.value - 1)/b.quote.amount.value;\n FC_ASSERT( result <= GRAPHENE_MAX_SHARE_SUPPLY );\n return asset( result.convert_to(), b.base.asset_id );\n }\n FC_THROW_EXCEPTION( fc::assert_exception, \"invalid asset::multiply_and_round_up(price)\", (\"asset\",a)(\"price\",b) );\n }\n\n price operator / ( const asset& base, const asset& quote )\n { try {\n FC_ASSERT( base.asset_id != quote.asset_id );\n return price{base,quote};\n } FC_CAPTURE_AND_RETHROW( (base)(quote) ) }\n\n price price::max( asset_id_type base, asset_id_type quote ) { return asset( share_type(GRAPHENE_MAX_SHARE_SUPPLY), base ) / asset( share_type(1), quote); }\n price price::min( asset_id_type base, asset_id_type quote ) { return asset( 1, base ) / asset( GRAPHENE_MAX_SHARE_SUPPLY, quote); }\n\n price operator * ( const price& p, const ratio_type& r )\n { try {\n p.validate();\n\n FC_ASSERT( r.numerator() > 0 && r.denominator() > 0 );\n\n if( r.numerator() == r.denominator() ) return p;\n\n boost::rational p128( p.base.amount.value, p.quote.amount.value );\n boost::rational r128( r.numerator(), r.denominator() );\n auto cp = p128 * r128;\n auto ocp = cp;\n\n bool shrinked = false;\n bool using_max = false;\n static const int128_t max( GRAPHENE_MAX_SHARE_SUPPLY );\n while( cp.numerator() > max || cp.denominator() > max )\n {\n if( cp.numerator() == 1 )\n {\n cp = boost::rational( 1, max );\n using_max = true;\n break;\n }\n else if( cp.denominator() == 1 )\n {\n cp = boost::rational( max, 1 );\n using_max = true;\n break;\n }\n else\n {\n cp = boost::rational( cp.numerator() >> 1, cp.denominator() >> 1 );\n shrinked = true;\n }\n }\n if( shrinked ) // maybe not accurate enough due to rounding, do additional checks here\n {\n int128_t num = ocp.numerator();\n int128_t den = ocp.denominator();\n if( num > den )\n {\n num /= den;\n if( num > max )\n num = max;\n den = 1;\n }\n else\n {\n den /= num;\n if( den > max )\n den = max;\n num = 1;\n }\n boost::rational ncp( num, den );\n if( num == max || den == max ) // it's on the edge, we know it's accurate enough\n cp = ncp;\n else\n {\n // from the accurate ocp, now we have ncp and cp. use the one which is closer to ocp.\n // TODO improve performance\n auto diff1 = abs( ncp - ocp );\n auto diff2 = abs( cp - ocp );\n if( diff1 < diff2 ) cp = ncp;\n }\n }\n\n price np = asset( cp.numerator().convert_to(), p.base.asset_id )\n / asset( cp.denominator().convert_to(), p.quote.asset_id );\n\n if( shrinked || using_max )\n {\n if( ( r.numerator() > r.denominator() && np < p )\n || ( r.numerator() < r.denominator() && np > p ) )\n // even with an accurate result, if p is out of valid range, return it\n np = p;\n }\n\n np.validate();\n return np;\n } FC_CAPTURE_AND_RETHROW( (p)(r.numerator())(r.denominator()) ) }\n\n price operator / ( const price& p, const ratio_type& r )\n { try {\n return p * ratio_type( r.denominator(), r.numerator() );\n } FC_CAPTURE_AND_RETHROW( (p)(r.numerator())(r.denominator()) ) }\n\n /**\n * The black swan price is defined as debt/collateral, we want to perform a margin call\n * before debt == collateral. Given a debt/collateral ratio of 1 USD / CORE and\n * a maintenance collateral requirement of 2x we can define the call price to be\n * 2 USD / CORE.\n *\n * This method divides the collateral by the maintenance collateral ratio to derive\n * a call price for the given black swan ratio.\n *\n * There exists some cases where the debt and collateral values are so small that\n * dividing by the collateral ratio will result in a 0 price or really poor\n * rounding errors. No matter what the collateral part of the price ratio can\n * never go to 0 and the debt can never go more than GRAPHENE_MAX_SHARE_SUPPLY\n *\n * CR * DEBT/COLLAT or DEBT/(COLLAT/CR)\n *\n * Note: this function is only used before core-1270 hard fork.\n */\n price price::call_price( const asset& debt, const asset& collateral, uint16_t collateral_ratio)\n { try {\n boost::rational swan(debt.amount.value,collateral.amount.value);\n boost::rational ratio( collateral_ratio, GRAPHENE_COLLATERAL_RATIO_DENOM );\n auto cp = swan * ratio;\n\n while( cp.numerator() > GRAPHENE_MAX_SHARE_SUPPLY || cp.denominator() > GRAPHENE_MAX_SHARE_SUPPLY )\n cp = boost::rational( (cp.numerator() >> 1)+1, (cp.denominator() >> 1)+1 );\n\n return ( asset( cp.denominator().convert_to(), collateral.asset_id )\n / asset( cp.numerator().convert_to(), debt.asset_id ) );\n } FC_CAPTURE_AND_RETHROW( (debt)(collateral)(collateral_ratio) ) }\n\n bool price::is_null() const\n {\n // Effectively same as \"return *this == price();\" but perhaps faster\n return ( base.asset_id == asset_id_type() && quote.asset_id == asset_id_type() );\n }\n\n void price::validate() const\n { try {\n FC_ASSERT( base.amount > share_type(0) );\n FC_ASSERT( quote.amount > share_type(0) );\n FC_ASSERT( base.asset_id != quote.asset_id );\n } FC_CAPTURE_AND_RETHROW( (base)(quote) ) }\n\n void price_feed::validate() const\n { try {\n if( !settlement_price.is_null() )\n settlement_price.validate();\n FC_ASSERT( maximum_short_squeeze_ratio >= GRAPHENE_MIN_COLLATERAL_RATIO );\n FC_ASSERT( maximum_short_squeeze_ratio <= GRAPHENE_MAX_COLLATERAL_RATIO );\n FC_ASSERT( maintenance_collateral_ratio >= GRAPHENE_MIN_COLLATERAL_RATIO );\n FC_ASSERT( maintenance_collateral_ratio <= GRAPHENE_MAX_COLLATERAL_RATIO );\n // Note: there was code here calling `max_short_squeeze_price();` before core-1270 hard fork,\n // in order to make sure that it doesn't overflow,\n // but the code doesn't actually check overflow, and it won't overflow, so the code is removed.\n\n // Note: not checking `maintenance_collateral_ratio >= maximum_short_squeeze_ratio` since launch\n } FC_CAPTURE_AND_RETHROW( (*this) ) }\n\n bool price_feed::is_for( asset_id_type asset_id ) const\n {\n try\n {\n if( !settlement_price.is_null() )\n return (settlement_price.base.asset_id == asset_id);\n if( !core_exchange_rate.is_null() )\n return (core_exchange_rate.base.asset_id == asset_id);\n // (null, null) is valid for any feed\n return true;\n }\n FC_CAPTURE_AND_RETHROW( (*this) )\n }\n\n // This function is kept here due to potential different behavior in edge cases.\n // TODO check after core-1270 hard fork to see if we can safely remove it\n price price_feed::max_short_squeeze_price_before_hf_1270()const\n {\n // settlement price is in debt/collateral\n boost::rational sp( settlement_price.base.amount.value, settlement_price.quote.amount.value );\n boost::rational ratio( GRAPHENE_COLLATERAL_RATIO_DENOM, maximum_short_squeeze_ratio );\n auto cp = sp * ratio;\n\n while( cp.numerator() > GRAPHENE_MAX_SHARE_SUPPLY || cp.denominator() > GRAPHENE_MAX_SHARE_SUPPLY )\n cp = boost::rational( (cp.numerator() >> 1)+(cp.numerator()&1),\n (cp.denominator() >> 1)+(cp.denominator()&1) );\n\n return ( asset( cp.numerator().convert_to(), settlement_price.base.asset_id )\n / asset( cp.denominator().convert_to(), settlement_price.quote.asset_id ) );\n }\n\n price price_feed::max_short_squeeze_price()const\n {\n // settlement price is in debt/collateral\n return settlement_price * ratio_type( GRAPHENE_COLLATERAL_RATIO_DENOM, maximum_short_squeeze_ratio );\n }\n\n price price_feed::maintenance_collateralization()const\n {\n if( settlement_price.is_null() )\n return price();\n return ~settlement_price * ratio_type( maintenance_collateral_ratio, GRAPHENE_COLLATERAL_RATIO_DENOM );\n }\n\n// compile-time table of powers of 10 using template metaprogramming\n\ntemplate< int N >\nstruct p10\n{\n static const int64_t v = 10 * p10::v;\n};\n\ntemplate<>\nstruct p10<0>\n{\n static const int64_t v = 1;\n};\n\nconst int64_t scaled_precision_lut[19] =\n{\n p10< 0 >::v, p10< 1 >::v, p10< 2 >::v, p10< 3 >::v,\n p10< 4 >::v, p10< 5 >::v, p10< 6 >::v, p10< 7 >::v,\n p10< 8 >::v, p10< 9 >::v, p10< 10 >::v, p10< 11 >::v,\n p10< 12 >::v, p10< 13 >::v, p10< 14 >::v, p10< 15 >::v,\n p10< 16 >::v, p10< 17 >::v, p10< 18 >::v\n};\n\n} } // graphene::chain\n", "meta": {"hexsha": "a9c1daf502b1bbaabcb7bd15e829a5a621f017dc", "size": 13544, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libraries/chain/protocol/asset.cpp", "max_stars_repo_name": "cwyyprog/bitshares-core", "max_stars_repo_head_hexsha": "f47c115cd21e62829e242447398b20b40a32cddf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "max_stars_repo_stars_event_min_datetime": "2019-07-05T14:37:30.000Z", "max_stars_repo_stars_event_max_datetime": "2019-07-05T14:37:30.000Z", "max_issues_repo_path": "libraries/chain/protocol/asset.cpp", "max_issues_repo_name": "cwyyprog/bitshares-core", "max_issues_repo_head_hexsha": "f47c115cd21e62829e242447398b20b40a32cddf", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "libraries/chain/protocol/asset.cpp", "max_forks_repo_name": "cwyyprog/bitshares-core", "max_forks_repo_head_hexsha": "f47c115cd21e62829e242447398b20b40a32cddf", "max_forks_repo_licenses": ["MIT"], "max_forks_count": 3.0, "max_forks_repo_forks_event_min_datetime": "2019-08-23T00:18:02.000Z", "max_forks_repo_forks_event_max_datetime": "2019-10-06T21:10:42.000Z", "avg_line_length": 42.5911949686, "max_line_length": 161, "alphanum_fraction": 0.597976964, "num_tokens": 3310, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.6076631698328916, "lm_q2_score": 0.3415824860330003, "lm_q1q2_score": 0.20756709622221237}} {"text": "/* ============================================================================\n * Copyright (c) 2009-2020 BlueQuartz Software, LLC\n *\n * Redistribution and use in source and binary forms, with or without modification,\n * are permitted provided that the following conditions are met:\n *\n * Redistributions of source code must retain the above copyright notice, this\n * list of conditions and the following disclaimer.\n *\n * Redistributions in binary form must reproduce the above copyright notice, this\n * list of conditions and the following disclaimer in the documentation and/or\n * other materials provided with the distribution.\n *\n * Neither the name of BlueQuartz Software, the US Air Force, nor the names of its\n * contributors may be used to endorse or promote products derived from this software\n * without specific prior written permission.\n *\n * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE\n * USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n *\n * The code contained herein was partially funded by the following contracts:\n * United States Air Force Prime Contract FA8650-07-D-5800\n * United States Air Force Prime Contract FA8650-10-D-5210\n * United States Prime Contract Navy N00173-07-C-2068\n *\n * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ */\n#include \"ApplyTransformationToGeometry.h\"\n\n#include \n\n#include \n\n#include \"SIMPLib/Common/Constants.h\"\n#include \"SIMPLib/Common/SIMPLRange.h\"\n#include \"SIMPLib/DataContainers/DataContainerArray.h\"\n#include \"SIMPLib/FilterParameters/AbstractFilterParametersReader.h\"\n#include \"SIMPLib/FilterParameters/DataArraySelectionFilterParameter.h\"\n#include \"SIMPLib/FilterParameters/DataContainerSelectionFilterParameter.h\"\n#include \"SIMPLib/FilterParameters/DynamicTableFilterParameter.h\"\n#include \"SIMPLib/FilterParameters/FloatFilterParameter.h\"\n#include \"SIMPLib/FilterParameters/LinkedChoicesFilterParameter.h\"\n#include \"SIMPLib/Geometry/EdgeGeom.h\"\n#include \"SIMPLib/Geometry/IGeometry2D.h\"\n#include \"SIMPLib/Geometry/IGeometry3D.h\"\n#include \"SIMPLib/Geometry/VertexGeom.h\"\n#include \"SIMPLib/Math/SIMPLibMath.h\"\n#include \"SIMPLib/Utilities/ParallelDataAlgorithm.h\"\n\n#include \"EbsdLib/Core/Orientation.hpp\"\n#include \"EbsdLib/Core/OrientationTransformation.hpp\"\n\n#include \"DREAM3DReview/DREAM3DReviewConstants.h\"\n#include \"DREAM3DReview/DREAM3DReviewVersion.h\"\n\nnamespace ApplyTransformationProgress\n{\nstatic size_t s_InstanceIndex = 0;\nstatic std::map s_ProgressValues;\nstatic std::map s_LastProgressInt;\n} // namespace ApplyTransformationProgress\n\nclass ApplyTransformationToGeometryImpl\n{\n\npublic:\n ApplyTransformationToGeometryImpl(ApplyTransformationToGeometry& filter, float* transformationMatrix, const SharedVertexList::Pointer verticesPtr)\n : m_Filter(filter)\n , m_TransformationMatrix(transformationMatrix)\n , m_Vertices(verticesPtr)\n {\n }\n ~ApplyTransformationToGeometryImpl() = default;\n\n void convert(size_t start, size_t end) const\n {\n using ProjectiveMatrix = Eigen::Matrix;\n Eigen::Map transformation(m_TransformationMatrix);\n\n int64_t progCounter = 0;\n int64_t totalElements = (end - start);\n int64_t progIncrement = static_cast(totalElements / 100);\n\n SharedVertexList& vertices = *(m_Vertices.get());\n for(size_t i = start; i < end; i++)\n {\n if(m_Filter.getCancel())\n {\n return;\n }\n Eigen::Vector4f position(vertices[3 * i + 0], vertices[3 * i + 1], vertices[3 * i + 2], 1);\n Eigen::Vector4f transformedPosition = transformation * position;\n vertices.setTuple(i, transformedPosition.data());\n\n if(progCounter > progIncrement)\n {\n m_Filter.sendThreadSafeProgressMessage(progCounter);\n progCounter = 0;\n }\n progCounter++;\n }\n }\n\n void operator()(const SIMPLRange& range) const\n {\n convert(range.min(), range.max());\n }\n\nprivate:\n ApplyTransformationToGeometry& m_Filter;\n float* m_TransformationMatrix = nullptr;\n SharedVertexList::Pointer m_Vertices;\n};\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nApplyTransformationToGeometry::ApplyTransformationToGeometry()\n{\n m_RotationAngle = 0.0f;\n m_RotationAxis[0] = 0.0f;\n m_RotationAxis[1] = 0.0f;\n m_RotationAxis[2] = 1.0f;\n\n m_Translation[0] = 0.0f;\n m_Translation[1] = 0.0f;\n m_Translation[2] = 0.0f;\n\n m_Scale[0] = 0.0f;\n m_Scale[1] = 0.0f;\n m_Scale[2] = 0.0f;\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nApplyTransformationToGeometry::~ApplyTransformationToGeometry() = default;\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::setupFilterParameters()\n{\n FilterParameterVectorType parameters;\n {\n LinkedChoicesFilterParameter::Pointer parameter = LinkedChoicesFilterParameter::New();\n parameter->setHumanLabel(\"Transformation Type\");\n parameter->setPropertyName(\"TransformationMatrixType\");\n parameter->setSetterCallback(SIMPL_BIND_SETTER(ApplyTransformationToGeometry, this, TransformationMatrixType));\n parameter->setGetterCallback(SIMPL_BIND_GETTER(ApplyTransformationToGeometry, this, TransformationMatrixType));\n std::vector choices;\n choices.push_back(\"No Transformation\");\n choices.push_back(\"Pre-Computed Transformation Matrix\");\n choices.push_back(\"Manual Transformation Matrix\");\n choices.push_back(\"Rotation\");\n choices.push_back(\"Translation\");\n choices.push_back(\"Scale\");\n parameter->setChoices(choices);\n std::vector linkedProps = {\"ComputedTransformationMatrix\", \"ManualTransformationMatrix\", \"RotationAngle\", \"RotationAxis\", \"Translation\", \"Scale\"};\n parameter->setLinkedProperties(linkedProps);\n parameter->setEditable(false);\n parameter->setCategory(FilterParameter::Category::Parameter);\n parameters.push_back(parameter);\n }\n {\n QStringList rHeaders, cHeaders;\n std::vector> defaultTable;\n for(size_t i = 0; i < 4; i++)\n {\n std::vector row(4, 0);\n row[i] = 1.0;\n defaultTable.push_back(row);\n }\n m_ManualTransformationMatrix.setTableData(defaultTable);\n parameters.push_back(SIMPL_NEW_DYN_TABLE_FP(\"Transformation Matrix\", ManualTransformationMatrix, FilterParameter::Category::Parameter, ApplyTransformationToGeometry, 2));\n }\n parameters.push_back(SIMPL_NEW_FLOAT_FP(\"Rotation Angle (Degrees)\", RotationAngle, FilterParameter::Category::Parameter, ApplyTransformationToGeometry, 3));\n parameters.push_back(SIMPL_NEW_FLOAT_VEC3_FP(\"Rotation Axis (ijk)\", RotationAxis, FilterParameter::Category::Parameter, ApplyTransformationToGeometry, 3));\n parameters.push_back(SIMPL_NEW_FLOAT_VEC3_FP(\"Translation\", Translation, FilterParameter::Category::Parameter, ApplyTransformationToGeometry, 4));\n parameters.push_back(SIMPL_NEW_FLOAT_VEC3_FP(\"Scale\", Scale, FilterParameter::Category::Parameter, ApplyTransformationToGeometry, 5));\n DataContainerSelectionFilterParameter::RequirementType dcReq;\n IGeometry::Types geomTypes = {IGeometry::Type::Vertex, IGeometry::Type::Edge, IGeometry::Type::Triangle, IGeometry::Type::Quad, IGeometry::Type::Tetrahedral};\n dcReq.dcGeometryTypes = geomTypes;\n parameters.push_back(SIMPL_NEW_DC_SELECTION_FP(\"Geometry to Transform\", GeometryToTransform, FilterParameter::Category::RequiredArray, ApplyTransformationToGeometry, dcReq));\n {\n DataArraySelectionFilterParameter::RequirementType dasReq =\n DataArraySelectionFilterParameter::CreateRequirement(SIMPL::TypeNames::Float, SIMPL::Defaults::AnyComponentSize, AttributeMatrix::Type::Generic, IGeometry::Type::Any);\n parameters.push_back(SIMPL_NEW_DA_SELECTION_FP(\"Transformation Matrix\", ComputedTransformationMatrix, FilterParameter::Category::RequiredArray, ApplyTransformationToGeometry, dasReq, 1));\n }\n setFilterParameters(parameters);\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::readFilterParameters(AbstractFilterParametersReader* reader, int index)\n{\n reader->openFilterGroup(this, index);\n setManualTransformationMatrix(reader->readDynamicTableData(\"ManualTransformationMatrix\", getManualTransformationMatrix()));\n setComputedTransformationMatrix(reader->readDataArrayPath(\"ComputedTransformationMatrix\", getComputedTransformationMatrix()));\n setGeometryToTransform(reader->readDataArrayPath(\"GeometryToTransform\", getGeometryToTransform()));\n setTransformationMatrixType(reader->readValue(\"TransformationMatrixType\", getTransformationMatrixType()));\n setRotationAxis(reader->readFloatVec3(\"RotationAxis\", getRotationAxis()));\n setRotationAngle(reader->readValue(\"RotationAngle\", getRotationAngle()));\n setTranslation(reader->readFloatVec3(\"Translation\", getTranslation()));\n setScale(reader->readFloatVec3(\"Scale\", getScale()));\n reader->closeFilterGroup();\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::dataCheck()\n{\n clearErrorCode();\n clearWarningCode();\n\n IGeometry::Pointer igeom = getDataContainerArray()->getPrereqGeometryFromDataContainer(this, getGeometryToTransform());\n\n if(getErrorCode() < 0)\n {\n return;\n }\n\n if(!std::dynamic_pointer_cast(igeom) && !std::dynamic_pointer_cast(igeom) && !std::dynamic_pointer_cast(igeom) && !std::dynamic_pointer_cast(igeom))\n {\n QString ss =\n QObject::tr(\"Geometry to transform must be an unstructured geometry (Vertex, Edge, Triangle, Quadrilateral, or Tetrahedral), but the type is %1\").arg(igeom->getGeometryTypeAsString());\n setErrorCondition(-702, ss);\n }\n\n std::vector cDims = {4, 4};\n\n switch(getTransformationMatrixType())\n {\n case 0: // No-Op\n {\n QString ss = QObject::tr(\"No transformation has been selected, so this filter will perform no operations\");\n setWarningCondition(-701, ss);\n }\n case 1: // Transformation matrix from array\n {\n m_TransformationMatrixPtr = getDataContainerArray()->getPrereqArrayFromPath>(this, getComputedTransformationMatrix(), cDims);\n if(m_TransformationMatrixPtr.lock())\n {\n m_TransformationMatrix = m_TransformationMatrixPtr.lock()->getPointer(0);\n }\n break;\n }\n case 2: // Manual transformation matrix\n {\n if(getManualTransformationMatrix().getNumRows() != 4)\n {\n QString ss = QObject::tr(\"Manually entered transformation matrix must have exactly 4 rows\");\n setErrorCondition(-702, ss);\n return;\n }\n if(getManualTransformationMatrix().getNumCols() != 4)\n {\n QString ss = QObject::tr(\"Manually entered transformation matrix must have exactly 4 columns\");\n setErrorCondition(-703, ss);\n return;\n }\n std::vector> tableData = getManualTransformationMatrix().getTableData();\n m_TransformationReference = FloatArrayType::CreateArray(1, cDims, \"_INTERNAL_USE_ONLY_ManualTransformationMatrix\", true);\n m_TransformationReference->initializeWithZeros();\n m_TransformationMatrixPtr = m_TransformationReference;\n if(m_TransformationMatrixPtr.lock())\n {\n m_TransformationMatrix = m_TransformationMatrixPtr.lock()->getPointer(0);\n for(size_t i = 0; i < tableData.size(); i++)\n {\n std::vector row = tableData[i];\n for(size_t j = 0; j < row.size(); j++)\n {\n m_TransformationMatrix[4 * i + j] = static_cast(row[j]);\n }\n }\n }\n break;\n }\n case 3: // Rotation via axis-angle\n {\n float rotAngle = m_RotationAngle * SIMPLib::Constants::k_PiOver180D;\n OrientationF om = OrientationTransformation::ax2om(OrientationF(m_RotationAxis[0], m_RotationAxis[1], m_RotationAxis[2], rotAngle));\n\n m_TransformationReference = FloatArrayType::CreateArray(1, cDims, \"_INTERNAL_USE_ONLY_ManualTransformationMatrix\", true);\n m_TransformationReference->initializeWithZeros();\n m_TransformationMatrixPtr = m_TransformationReference;\n if(m_TransformationMatrixPtr.lock())\n {\n m_TransformationMatrix = m_TransformationMatrixPtr.lock()->getPointer(0);\n for(size_t i = 0; i < 3; i++)\n {\n m_TransformationMatrix[4 * i + 0] = om[3 * i + 0];\n m_TransformationMatrix[4 * i + 1] = om[3 * i + 1];\n m_TransformationMatrix[4 * i + 2] = om[3 * i + 2];\n m_TransformationMatrix[4 * i + 3] = 0.0f;\n }\n m_TransformationMatrix[4 * 3 + 3] = 1.0f;\n }\n break;\n }\n case 4: // Translation\n {\n m_TransformationReference = FloatArrayType::CreateArray(1, cDims, \"_INTERNAL_USE_ONLY_ManualTransformationMatrix\", true);\n m_TransformationReference->initializeWithZeros();\n m_TransformationMatrixPtr = m_TransformationReference;\n if(m_TransformationMatrixPtr.lock())\n {\n m_TransformationMatrix = m_TransformationMatrixPtr.lock()->getPointer(0);\n m_TransformationMatrix[4 * 0 + 0] = 1.0f;\n m_TransformationMatrix[4 * 1 + 1] = 1.0f;\n m_TransformationMatrix[4 * 2 + 2] = 1.0f;\n m_TransformationMatrix[4 * 0 + 3] = m_Translation[0];\n m_TransformationMatrix[4 * 1 + 3] = m_Translation[1];\n m_TransformationMatrix[4 * 2 + 3] = m_Translation[2];\n m_TransformationMatrix[4 * 3 + 3] = 1.0f;\n }\n break;\n }\n case 5: // Scale\n {\n m_TransformationReference = FloatArrayType::CreateArray(1, cDims, \"_INTERNAL_USE_ONLY_ManualTransformationMatrix\", true);\n m_TransformationReference->initializeWithZeros();\n m_TransformationMatrixPtr = m_TransformationReference;\n if(m_TransformationMatrixPtr.lock())\n {\n m_TransformationMatrix = m_TransformationMatrixPtr.lock()->getPointer(0);\n m_TransformationMatrix[4 * 0 + 0] = m_Scale[0];\n m_TransformationMatrix[4 * 1 + 1] = m_Scale[1];\n m_TransformationMatrix[4 * 2 + 2] = m_Scale[2];\n m_TransformationMatrix[4 * 3 + 3] = 1.0f;\n }\n break;\n }\n default: {\n QString ss = QObject::tr(\"Invalid selection for transformation type\");\n setErrorCondition(-701, ss);\n break;\n }\n }\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::applyTransformation()\n{\n\n IGeometry::Pointer igeom = getDataContainerArray()->getDataContainer(m_GeometryToTransform)->getGeometry();\n\n SharedVertexList::Pointer vertexList;\n\n if(IGeometry2D::Pointer igeom2D = std::dynamic_pointer_cast(igeom))\n {\n vertexList = igeom2D->getVertices();\n }\n else if(IGeometry3D::Pointer igeom3D = std::dynamic_pointer_cast(igeom))\n {\n vertexList = igeom3D->getVertices();\n }\n else if(VertexGeom::Pointer vertex = std::dynamic_pointer_cast(igeom))\n {\n vertexList = vertex->getVertices();\n }\n else if(EdgeGeom::Pointer edge = std::dynamic_pointer_cast(igeom))\n {\n vertexList = edge->getVertices();\n }\n else\n {\n return;\n }\n\n using ProjectiveMatrix = Eigen::Matrix;\n Eigen::Map transformation(m_TransformationMatrix);\n m_TotalElements = vertexList->getNumberOfTuples();\n // Allow data-based parallelization\n#if 1\n ParallelDataAlgorithm dataAlg;\n dataAlg.setRange(0, m_TotalElements);\n dataAlg.execute(ApplyTransformationToGeometryImpl(*this, m_TransformationMatrix, vertexList));\n#else\n // THis chunk of code will FORCE single threaded mode. Don't do this unless you really mean it.\n ApplyTransformationToGeometryImpl doThis(*this, m_TransformationMatrix, vertexList);\n doThis({0, vertexList->getNumberOfTuples()});\n#endif\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::execute()\n{\n dataCheck();\n if(getErrorCode() < 0)\n {\n return;\n }\n if(getWarningCode() < 0)\n {\n return;\n }\n\n if(m_TransformationMatrixType == 0)\n {\n return;\n }\n // Needed for Threaded Progress Messages\n m_InstanceIndex = ++ApplyTransformationProgress::s_InstanceIndex;\n ApplyTransformationProgress::s_ProgressValues[m_InstanceIndex] = 0;\n ApplyTransformationProgress::s_LastProgressInt[m_InstanceIndex] = 0;\n\n applyTransformation();\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::sendThreadSafeProgressMessage(int64_t counter)\n{\n std::lock_guard guard(m_ProgressMessage_Mutex);\n\n int64_t& progCounter = ApplyTransformationProgress::s_ProgressValues[m_InstanceIndex];\n progCounter += counter;\n int64_t progressInt = static_cast((static_cast(progCounter) / m_TotalElements) * 100.0f);\n\n int64_t progIncrement = m_TotalElements / 100;\n int64_t prog = 1;\n\n int64_t& lastProgressInt = ApplyTransformationProgress::s_LastProgressInt[m_InstanceIndex];\n\n if(progCounter > prog && lastProgressInt != progressInt)\n {\n QString ss = QObject::tr(\"Transforming || %1% Completed\").arg(progressInt);\n notifyStatusMessage(ss);\n prog += progIncrement;\n }\n\n lastProgressInt = progressInt;\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nAbstractFilter::Pointer ApplyTransformationToGeometry::newFilterInstance(bool copyFilterParameters) const\n{\n ApplyTransformationToGeometry::Pointer filter = ApplyTransformationToGeometry::New();\n if(copyFilterParameters)\n {\n copyFilterParameterInstanceVariables(filter.get());\n }\n return filter;\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nQString ApplyTransformationToGeometry::getCompiledLibraryName() const\n{\n return DREAM3DReviewConstants::DREAM3DReviewBaseName;\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nQString ApplyTransformationToGeometry::getBrandingString() const\n{\n return \"DREAM3DReview\";\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nQString ApplyTransformationToGeometry::getFilterVersion() const\n{\n QString version;\n QTextStream vStream(&version);\n vStream << DREAM3DReview::Version::Major() << \".\" << DREAM3DReview::Version::Minor() << \".\" << DREAM3DReview::Version::Patch();\n return version;\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nQString ApplyTransformationToGeometry::getGroupName() const\n{\n return DREAM3DReviewConstants::FilterGroups::DREAM3DReviewFilters;\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nQUuid ApplyTransformationToGeometry::getUuid() const\n{\n return QUuid(\"{c681caf4-22f2-5885-bbc9-a0476abc72eb}\");\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nQString ApplyTransformationToGeometry::getSubGroupName() const\n{\n return DREAM3DReviewConstants::FilterSubGroups::RotationTransformationFilters;\n}\n\n// -----------------------------------------------------------------------------\n//\n// -----------------------------------------------------------------------------\nQString ApplyTransformationToGeometry::getHumanLabel() const\n{\n return \"Apply Transformation to Geometry\";\n}\n\n// -----------------------------------------------------------------------------\nApplyTransformationToGeometry::Pointer ApplyTransformationToGeometry::NullPointer()\n{\n return Pointer(static_cast(nullptr));\n}\n\n// -----------------------------------------------------------------------------\nstd::shared_ptr ApplyTransformationToGeometry::New()\n{\n struct make_shared_enabler : public ApplyTransformationToGeometry\n {\n };\n std::shared_ptr val = std::make_shared();\n val->setupFilterParameters();\n return val;\n}\n\n// -----------------------------------------------------------------------------\nQString ApplyTransformationToGeometry::getNameOfClass() const\n{\n return QString(\"ApplyTransformationToGeometry\");\n}\n\n// -----------------------------------------------------------------------------\nQString ApplyTransformationToGeometry::ClassName()\n{\n return QString(\"ApplyTransformationToGeometry\");\n}\n\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::setManualTransformationMatrix(const DynamicTableData& value)\n{\n m_ManualTransformationMatrix = value;\n}\n\n// -----------------------------------------------------------------------------\nDynamicTableData ApplyTransformationToGeometry::getManualTransformationMatrix() const\n{\n return m_ManualTransformationMatrix;\n}\n\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::setComputedTransformationMatrix(const DataArrayPath& value)\n{\n m_ComputedTransformationMatrix = value;\n}\n\n// -----------------------------------------------------------------------------\nDataArrayPath ApplyTransformationToGeometry::getComputedTransformationMatrix() const\n{\n return m_ComputedTransformationMatrix;\n}\n\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::setGeometryToTransform(const DataArrayPath& value)\n{\n m_GeometryToTransform = value;\n}\n\n// -----------------------------------------------------------------------------\nDataArrayPath ApplyTransformationToGeometry::getGeometryToTransform() const\n{\n return m_GeometryToTransform;\n}\n\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::setTransformationMatrixType(int value)\n{\n m_TransformationMatrixType = value;\n}\n\n// -----------------------------------------------------------------------------\nint ApplyTransformationToGeometry::getTransformationMatrixType() const\n{\n return m_TransformationMatrixType;\n}\n\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::setRotationAxis(const FloatVec3Type& value)\n{\n m_RotationAxis = value;\n}\n\n// -----------------------------------------------------------------------------\nFloatVec3Type ApplyTransformationToGeometry::getRotationAxis() const\n{\n return m_RotationAxis;\n}\n\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::setRotationAngle(float value)\n{\n m_RotationAngle = value;\n}\n\n// -----------------------------------------------------------------------------\nfloat ApplyTransformationToGeometry::getRotationAngle() const\n{\n return m_RotationAngle;\n}\n\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::setTranslation(const FloatVec3Type& value)\n{\n m_Translation = value;\n}\n\n// -----------------------------------------------------------------------------\nFloatVec3Type ApplyTransformationToGeometry::getTranslation() const\n{\n return m_Translation;\n}\n\n// -----------------------------------------------------------------------------\nvoid ApplyTransformationToGeometry::setScale(const FloatVec3Type& value)\n{\n m_Scale = value;\n}\n\n// -----------------------------------------------------------------------------\nFloatVec3Type ApplyTransformationToGeometry::getScale() const\n{\n return m_Scale;\n}\n", "meta": {"hexsha": "3e4c2bbf4cd4e21d5f99e108ae192ab5e540d30f", "size": 25174, "ext": "cpp", 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NO", "lm_q1_score": 0.5774953651858117, "lm_q2_score": 0.359364152021239, "lm_q1q2_score": 0.20753113220619496}} {"text": "/*******************************************************************************\n*\n* Filename : PlotLimit.cc\n* Description : Macros For plotting confidence level limits\n* Author : Yi-Mu \"Enoch\" Chen [ ensc@hep1.phys.ntu.edu.tw ]\n*\n*******************************************************************************/\n#include \"ManagerUtils/Common/interface/ConfigReader.hpp\"\n#include \"ManagerUtils/Common/interface/STLUtils.hpp\"\n#include \"ManagerUtils/Maths/interface/Intersect.hpp\"\n#include \"ManagerUtils/Maths/interface/Parameter.hpp\"\n#include \"ManagerUtils/PlotUtils/interface/Common.hpp\"\n#include \"ManagerUtils/SampleMgr/interface/SampleMgrLoader.hpp\"\n\n#include \"TstarAnalysis/Common/interface/PlotStyle.hpp\"\n#include \"TstarAnalysis/LimitCalc/interface/Common.hpp\"\n#include \"TstarAnalysis/LimitCalc/interface/Limit.hpp\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"TFile.h\"\n#include \"TTree.h\"\n\nusing namespace std;\nusing namespace mgr;\n\n/*******************************************************************************\n* Defining plotting control flow\n*******************************************************************************/\npair\nMakeLimitPlot( const std::string& additionaltag )\n{\n const map xsec = GetXsectionMap();\n\n TGraph* onesiggraph = MakeCalcGraph( xsec, additionaltag, explim, onesig_up, onesig_down );\n TGraph* twosiggraph = MakeCalcGraph( xsec, additionaltag, explim, twosig_up, twosig_down );\n TGraph* expgraph = MakeCalcGraph( xsec, additionaltag, explim, skip, skip );\n TGraph* obsgraph = MakeCalcGraph( xsec, additionaltag, obslim, skip, skip );\n TGraph* theorygraph = MakeTheoryGraph( xsec );\n\n const double y_max = mgr::GetYmax( twosiggraph, obsgraph );\n const double y_min = mgr::GetYmin( twosiggraph, obsgraph );\n const double x_max = theorygraph->GetX()[theorygraph->GetN()-1];\n const double x_min = theorygraph->GetX()[0];\n\n // ----- Setting Styles --------------------------------------------------------\n tstar::SetOneSigmaStyle( onesiggraph );\n tstar::SetTwoSigmaStyle( twosiggraph );\n tstar::SetExpLimitStyle( expgraph );\n tstar::SetObsLimitStyle( obsgraph );\n tstar::SetTheoryStyle( theorygraph );\n\n // Making object for plotting\n TCanvas* c1 = mgr::NewCanvas();\n mgr::SetSinglePad( c1 );\n\n TMultiGraph* mg = new TMultiGraph();\n\n mg->Add( twosiggraph );\n mg->Add( onesiggraph );\n mg->Add( theorygraph );\n mg->Add( expgraph );\n if( limnamer.CheckInput( \"drawdata\" ) ){\n mg->Add( obsgraph );\n }\n\n mg->Draw( \"AL3\" );\n mgr::SetAxis( mg );\n mg->GetXaxis()->SetTitle( \"t* Mass (GeV/c^{2})\" );// MUST Be after draw!!\n mg->GetYaxis()->SetTitle( \"#sigma(pp#rightarrow t*#bar{t}*) (pb)\" );// https://root.cern.ch/root/roottalk/roottalk09/0078.html\n mg->GetXaxis()->SetLimits( x_min, x_max );\n mg->SetMaximum( y_max*300. );\n mg->SetMinimum( y_min*0.3 );\n\n // Making legend\n const double legend_x_min = 0.55;\n const double legend_y_min = 0.50;\n TLegend* l = mgr::NewLegend( legend_x_min, legend_y_min );\n l->AddEntry( theorygraph, \"R-S Model\", \"lf\" );\n l->AddEntry( (TObject*)NULL, \"\", \"\" );\n l->AddEntry( (TObject*)NULL, \"95% CL_{s} upper limit\", \"\" );\n l->AddEntry( obsgraph, \"Observed\", \"l\" );\n l->AddEntry( expgraph, \"Medium expected\", \"l\" );\n l->AddEntry( onesiggraph, \"68% expected\", \"f\" );\n l->AddEntry( twosiggraph, \"95% expected\", \"f\" );\n l->Draw();\n\n\n // Additional titles settings\n mgr::DrawCMSLabel();\n mgr::DrawLuminosity( limnamer.GetExt( \"era\", \"Lumi\" ) );\n\n // Writing additional text\n const string rootlabel = limnamer.GetChannelEXT( \"Root Name\" );\n // const string fitmethod = limnamer.GetExt( \"fitmethod\", \"Full Name\" );\n // const string funcname = limnamer.GetExt( \"fitfunc\", \"Full Name\" );\n boost::format explimfmt( \"Expected: %s GeV/c^{2}\" );\n boost::format obslimfmt( \"Observed: %s GeV/c^{2}\" );\n\n mgr::LatexMgr latex;\n latex.SetOrigin( PLOT_X_MIN, PLOT_Y_MAX + TEXT_MARGIN/2, BOTTOM_LEFT )\n .WriteLine( rootlabel );\n // .WriteLine( fitmethod )\n // .WriteLine( funcname );\n\n // Cannot have Latex style spacing '\\,'\n const Parameter explim_err = GetInterSect( theorygraph, onesiggraph );\n const Parameter obslim_err = GetInterSect( theorygraph, obsgraph );\n const string explim_str = FloatingPoint( explim_err.CentralValue(), 0, false );\n const string obslim_str = FloatingPoint( obslim_err.CentralValue(), 0, false );\n\n latex.SetOrigin( PLOT_X_TEXT_MAX, legend_y_min-TEXT_MARGIN, TOP_RIGHT );\n latex.WriteLine( boost::str( explimfmt % explim_str ) );\n if( limnamer.CheckInput( \"drawdata\" ) ){\n latex.WriteLine( boost::str( obslimfmt % obslim_str ) );\n }\n\n\n // ----- Saving and cleaning up ------------------------------------------------\n c1->SetLogy( kTRUE );\n c1->Update();\n\n if( limnamer.CheckInput( \"seed\" ) ){\n limnamer.AddCutOptions( \"seed\" );\n }\n\n if( limnamer.CheckInput( \"rMin\" ) ){\n limnamer.AddCutOptions( \"rMin\" );\n }\n\n if( limnamer.CheckInput( \"rMax\" ) ){\n limnamer.AddCutOptions( \"rMax\" );\n }\n\n mgr::SaveToROOT( c1, limnamer.PlotRootFile(), limnamer.OptFileName( \"\", \"limit\", additionaltag ) );\n mgr::SaveToPDF( c1, limnamer.PlotFileName( \"limit\", additionaltag ) );\n delete onesiggraph;\n delete twosiggraph;\n delete obsgraph;\n delete expgraph;\n delete theorygraph;\n delete mg;\n delete l;\n delete c1;\n\n return make_pair( explim_err, obslim_err );\n}\n\n\n/*******************************************************************************\n* Filling out map from master file\n*******************************************************************************/\nmap\nGetXsectionMap()\n{\n const string theoryfile = limnamer.SubPackageDir() / \"data/excitedtoppair13TeV.dat\";\n if( !boost::filesystem::exists( theoryfile ) ){\n cerr << \"Error! File containing theoretical cross section doesn't exists!\" << endl;\n cerr << \"Check file: \" << theoryfile << endl;\n throw std::runtime_error( \"File not found\" );\n }\n\n ifstream xsec_file( theoryfile );\n string line;\n map ans;\n double energy, mass, xsec_value;\n\n while( getline( xsec_file, line ) ){\n istringstream linestream( line );\n linestream >> energy >> mass >> xsec_value;\n ans[mass] = xsec_value * 1000.;\n }\n\n xsec_file.close();\n return ans;\n}\n\n\n/*******************************************************************************\n* Object generation functions\n*******************************************************************************/\nTGraphAsymmErrors*\nMakeTheoryGraph( const map& xsec )\n{\n // Making the PDF/QCD scale error Graph objects for linear interpolation\n const vector siglist = limnamer.MasterConfig().GetStaticStringList( \"Signal List\" );\n TGraph* errorupgraph = new TGraph( siglist.size() );\n TGraph* errordowngraph = new TGraph( siglist.size() );\n\n for( size_t i = 0; i < siglist.size(); ++i ){\n const auto& sig = siglist.at( i );\n const double mass = GetInt( sig );\n\n mgr::SampleGroup sigmgr( sig, limnamer.MasterConfig() );\n\n const mgr::Parameter pdferr = sigmgr.Sample().PDFUncertainty();\n const mgr::Parameter scaleerr = sigmgr.Sample().QCDScaleUncertainty();\n const mgr::Parameter toterr = pdferr * scaleerr;\n\n errorupgraph->SetPoint( i, mass, toterr.RelUpperError() );\n errordowngraph->SetPoint( i, mass, toterr.RelLowerError() );\n }\n\n // Making the main theoretical curve line\n TGraphAsymmErrors* graph = new TGraphAsymmErrors();\n\n for( const auto& point : xsec ){\n const double mass = point.first;\n if( mass < 650 || mass > 1650 ){ continue; }\n const double thisxsec = point.second;\n const double xsecup = thisxsec * errorupgraph->Eval( mass );\n const double xsecdown = thisxsec * errordowngraph->Eval( mass );\n\n const int newbin = graph->GetN();\n graph->SetPoint( newbin, mass, thisxsec );\n graph->SetPointError(\n newbin,\n 0, 0,// No X error for now\n xsecdown,\n xsecup\n );\n }\n\n delete errorupgraph;\n delete errordowngraph;\n\n return graph;\n}\n\n/******************************************************************************/\nextern const int explim = 2;\nextern const int obslim = 5;\nextern const int onesig_up = 3;\nextern const int onesig_down = 1;\nextern const int twosig_up = 4;\nextern const int twosig_down = 0;\nextern const int skip = -1;\n\n\nextern TGraph*\nMakeCalcGraph(\n const std::map& xsec,\n const string& additionaltag,\n const int centralentry,\n const int uperrorentry,\n const int downerrorentry\n )\n{\n const vector siglist = limnamer.MasterConfig().GetStaticStringList( \"Signal List\" );\n TGraphAsymmErrors* graph = new TGraphAsymmErrors( siglist.size() );\n\n unsigned bin = 0;\n\n for( const auto& sig : siglist ){\n const double mass = GetInt( sig );\n const double expxsec = xsec.at( mass );\n const string filename = limnamer.RootFileName( \"combine\", sig, additionaltag );\n\n // Geting contents of higgs combine output file\n TFile* file = TFile::Open( filename.c_str() );\n if( !file ){\n fprintf( stderr, \"Cannot open file (%s), skipping sample for %s\\n\",\n filename.c_str(),\n sig.c_str() );\n continue;\n }\n\n double temp;\n TTree* tree = ( (TTree*)file->Get( \"limit\" ) );\n tree->SetBranchAddress( \"limit\", &temp );\n\n // Getting the entries\n tree->GetEntry( centralentry );\n const double central = temp * expxsec;\n\n if( uperrorentry != skip ){ tree->GetEntry( uperrorentry ); }\n const double errorup = ( uperrorentry != skip ) ?\n temp*expxsec - central : 0;\n\n if( downerrorentry != skip ){ tree->GetEntry( downerrorentry ); }\n const double errordown = ( downerrorentry != skip ) ?\n central - temp*expxsec : 0;\n\n // closing file\n file->Close();\n\n graph->SetPoint( bin, mass, central );\n graph->SetPointError( bin, 50, 50, errordown, errorup );\n cout << boost::format( \"%u %4.0lf %8.6lf[+%8.6lf/-%8.6lf]\" )\n % bin % mass % central % errorup % errordown << endl;\n ++bin;\n\n delete file;\n }\n\n return graph;\n}\n\n\n/*******************************************************************************\n* Function for calculating intersect points\n*******************************************************************************/\n\nParameter\nGetInterSect( const TGraph* graph, const TGraph* errgraph )\n{\n double y = 0;\n double explimit = errgraph->GetX()[0];\n double explimit_up = errgraph->GetX()[0];\n double explimit_down = errgraph->GetX()[0];\n\n for( int i = 0; i < graph->GetN()-1; ++i ){\n for( int j = 0; j < errgraph->GetN()-1; ++j ){\n\n const double graphmass = graph->GetX()[i];\n const double graphmassnext = graph->GetX()[i+1];\n const double graphval = graph->GetY()[i];\n const double graphvalnext = graph->GetY()[i+1];\n const double graphvalup = graph->GetErrorYhigh( i ) + graphval;\n const double graphvalupnext = graph->GetErrorYhigh( i+1 ) + graphvalnext;\n const double graphvaldown = graphval - graph->GetErrorYlow( i );\n const double graphvaldownnext = graphvalnext - graph->GetErrorYlow( i+1 );\n\n const double errgraphmass = errgraph->GetX()[j];\n const double errgraphmassnext = errgraph->GetX()[j+1];\n const double errgraphval = errgraph->GetY()[j];\n const double errgraphvalnext = errgraph->GetY()[j+1];\n const double hierr = errgraph->GetErrorYhigh( j ) + errgraphval;\n const double hierrnext = errgraph->GetErrorYhigh( j+1 ) + errgraphvalnext;\n const double loerr = errgraphval - errgraph->GetErrorYlow( j );\n const double loerrnext = errgraphvalnext - errgraph->GetErrorYlow( j+1 );\n\n // Central value intersect\n Intersect(\n graphmass, graphval,\n graphmassnext, graphvalnext,\n errgraphmass, errgraphval,\n errgraphmassnext, errgraphvalnext,\n explimit, y\n );\n\n // Lower theretical band intersect with upper limit band\n Intersect(\n graphmass, graphvaldown,\n graphmassnext, graphvaldownnext,\n errgraphmass, hierr,\n errgraphmassnext, hierrnext,\n explimit_down, y\n );\n\n // Upper theoretical band intersec with lower limit band\n Intersect(\n graphmass, graphvalup,\n graphmassnext, graphvalupnext,\n errgraphmass, loerr,\n errgraphmassnext, loerrnext,\n explimit_up, y\n );\n }\n }\n\n return Parameter( explimit, explimit_up - explimit, explimit - explimit_down );\n}\n", "meta": {"hexsha": "85cabab05306d1dd4f6d1c46ad12888aeae91878", "size": 12912, "ext": "cc", "lang": "C++", "max_stars_repo_path": "LimitCalc/src/PlotLimit.cc", "max_stars_repo_name": "NTUHEP-Tstar/TstarAnalysis", "max_stars_repo_head_hexsha": "d3bcdf6f0fd19b9a34bbacb1052143856917bea7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_repo_stars_event_min_datetime": null, "max_stars_repo_stars_event_max_datetime": null, "max_issues_repo_path": "LimitCalc/src/PlotLimit.cc", "max_issues_repo_name": "NTUHEP-Tstar/TstarAnalysis", "max_issues_repo_head_hexsha": "d3bcdf6f0fd19b9a34bbacb1052143856917bea7", "max_issues_repo_licenses": ["MIT"], "max_issues_count": null, "max_issues_repo_issues_event_min_datetime": null, "max_issues_repo_issues_event_max_datetime": null, "max_forks_repo_path": "LimitCalc/src/PlotLimit.cc", "max_forks_repo_name": "NTUHEP-Tstar/TstarAnalysis", "max_forks_repo_head_hexsha": "d3bcdf6f0fd19b9a34bbacb1052143856917bea7", "max_forks_repo_licenses": ["MIT"], "max_forks_count": null, "max_forks_repo_forks_event_min_datetime": null, "max_forks_repo_forks_event_max_datetime": null, "avg_line_length": 34.7096774194, "max_line_length": 128, "alphanum_fraction": 0.60494114, "num_tokens": 3410, "lm_name": "Qwen/Qwen-72B", "lm_label": "1. YES\n2. NO", "lm_q1_score": 0.5774953651858118, "lm_q2_score": 0.35936413829896496, "lm_q1q2_score": 0.20753112428164536}} {"text": "/*\n Copyright (C) 2016 Quaternion Risk Management Ltd\n All rights reserved.\n\n This file is part of ORE, a free-software/open-source library\n for transparent pricing and risk analysis - http://opensourcerisk.org\n\n ORE is free software: you can redistribute it and/or modify it\n under the terms of the Modified BSD License. You should have received a\n copy of the license along with this program.\n The license is also available online at \n\n This program is distributed on the basis that it will form a useful\n contribution to risk analytics and model standardisation, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n FITNESS FOR A PARTICULAR PURPOSE. See the license for more details.\n*/\n\n/*! \\file portfolio/builders/vanillaoption.hpp\n \\brief Abstract engine builders for European and American Options\n \\ingroup builders\n*/\n\n#pragma once\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace ore {\nnamespace data {\n\ntemplate class CachingOptionEngineBuilder : public CachingPricingEngineBuilder {\npublic:\n CachingOptionEngineBuilder(const string& model, const string& engine, const set& tradeTypes,\n const AssetClass& assetClass)\n : CachingPricingEngineBuilder(model, engine, tradeTypes), assetClass_(assetClass) {}\n\nprotected:\n boost::shared_ptr getBlackScholesProcess(const string& assetName,\n const Currency& ccy,\n const AssetClass& assetClassUnderlying,\n const std::vector